diff --git a/spaces/1111u/oai-reverse-proxy/Dockerfile b/spaces/1111u/oai-reverse-proxy/Dockerfile
deleted file mode 100644
index eef259fa372a804549fb0af0913718a13344da34..0000000000000000000000000000000000000000
--- a/spaces/1111u/oai-reverse-proxy/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Photo Studio Professional 2020 Crack PATCHED.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Photo Studio Professional 2020 Crack PATCHED.md
deleted file mode 100644
index afa57758a81f86820506115871b789a285eff928..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Photo Studio Professional 2020 Crack PATCHED.md	
+++ /dev/null
@@ -1,117 +0,0 @@
-
-<h1>ACDSee Photo Studio Professional 2020 Crack: A Comprehensive Review</h1>
- <p>If you are looking for a fast, powerful and easy-to-use image management system that can handle all your photography needs, you might have heard of ACDSee Photo Studio Professional 2020. This software is a popular choice among photographers, graphic designers and digital artists who want to acquire, organize, view, enhance and share their digital photos and other media files. But what exactly is ACDSee Photo Studio Professional 2020 and what can it do for you? And more importantly, how can you get it without breaking the law or risking your computer's security? In this article, we will answer these questions and give you a comprehensive review of ACDSee Photo Studio Professional 2020 Crack.</p>
- <h2>What is ACDSee Photo Studio Professional 2020?</h2>
- <p>ACDSee Photo Studio Professional 2020 is a software application that combines the functions of a RAW editor and a digital asset management solution. It allows you to perform a wide range of adjustments on your photos, such as exposure, color, contrast, sharpness, noise reduction, red eye removal, watermarking, cropping, resizing and more. You can also apply various effects and filters to enhance your images and give them a professional edge. Moreover, you can manage your digital assets with powerful searching and cataloging tools that let you find, sort, tag, rate and organize your photos and media files. You can also create password-protected albums and folders on the developer's website and share them with your friends.</p>
-<h2>ACDSee Photo Studio Professional 2020 Crack</h2><br /><p><b><b>Download File</b> &harr; <a href="https://byltly.com/2uKxiC">https://byltly.com/2uKxiC</a></b></p><br /><br />
- <h3>Features and Benefits</h3>
- <p>ACDSee Photo Studio Professional 2020 has many features and benefits that make it stand out from other similar software. Here are some of them:</p>
- <h4>Acquire, Organize, View, Enhance and Share Your Photos and Media Files</h4>
- <p>With ACDSee Photo Studio Professional 2020, you can connect directly to the folders on your computer without importing them into a separate library. This saves you time and disk space and allows you to enjoy increased speed and performance. You can also run slide shows, play embedded audio and display multiple-page images in any of the more than 50 image and multimedia file formats supported by the software.</p>
- <h4>Process Digital Photos with a Variety of Editing Tools</h4>
- <h4>Streamline Your Workflow with Performance Improvements and GPU-Enriched Software</h4>
- <p>ACDSee Photo Studio Professional 2020 is designed to streamline your workflow and give your image development a competitive, professional edge. It features performance improvements, such as faster loading and processing of RAW files, and GPU-enriched software that enhances the speed and quality of your adjustments. You can also use ACDSee Actions to record and batch-apply any of the 200+ adjustments available in Edit mode. Additionally, you can take snapshots of your work at any time and return to them later if you want to undo or redo any changes.</p>
- <h4>Expand Your Creative Scope with Photoshop Plugin Support</h4>
- <p>ACDSee Photo Studio Professional 2020 allows you to expand your creative scope with the ability to import and apply Photoshop plugins to your Edit mode adjustment workflow. You can draw your own effects and use them in combination with best-in-class photo editing tools. You can also use the built-in LUTs (Look-Up Tables) to apply color grading to your images, or create your own custom LUTs for a unique look.</p>
- <h4>Manage Your Digital Assets with Powerful Searching and Cataloging Tools</h4>
- <p>ACDSee Photo Studio Professional 2020 helps you manage your digital assets with powerful searching and cataloging tools that let you find, sort, tag, rate and organize your photos and media files. You can find images based on metadata, file properties, date, event, keyword, rating, color label and GPS location. You can also build and save detailed searches, enter single words or phrases, search only specific folders or find that one particular image with the Quick Search bar. Moreover, you can add ratings, hierarchical keywords, categories and location data to your images and quickly identify photos for further processing with visual tags or customizable color labels.</p>
-<p>ACDSee Photo Studio Pro 2020 full version with crack<br />
-How to activate ACDSee Photo Studio Professional 2020 for free<br />
-ACDSee Photo Studio Professional 2020 license key generator<br />
-Download ACDSee Photo Studio Pro 2020 cracked software<br />
-ACDSee Photo Studio Professional 2020 serial number and patch<br />
-ACDSee Photo Studio Pro 2020 crack download for Windows 10<br />
-ACDSee Photo Studio Professional 2020 keygen and activation code<br />
-ACDSee Photo Studio Pro 2020 crack + torrent link<br />
-ACDSee Photo Studio Professional 2020 registration code and crack<br />
-ACDSee Photo Studio Pro 2020 cracked version free download<br />
-ACDSee Photo Studio Professional 2020 crack only file<br />
-ACDSee Photo Studio Pro 2020 full crack setup and install<br />
-ACDSee Photo Studio Professional 2020 lifetime crack and update<br />
-ACDSee Photo Studio Pro 2020 crack with offline installer<br />
-ACDSee Photo Studio Professional 2020 portable edition with crack<br />
-ACDSee Photo Studio Pro 2020 crack + direct download link<br />
-ACDSee Photo Studio Professional 2020 activation key and crack<br />
-ACDSee Photo Studio Pro 2020 full crack + online installer<br />
-ACDSee Photo Studio Professional 2020 product key and crack<br />
-ACDSee Photo Studio Pro 2020 crack with latest features and updates<br />
-ACDSee Photo Studio Professional 2020 working crack and patch<br />
-ACDSee Photo Studio Pro 2020 crack for Windows 7/8/8.1/10<br />
-ACDSee Photo Studio Professional 2020 cracked software download site<br />
-ACDSee Photo Studio Pro 2020 full version crack free download<br />
-ACDSee Photo Studio Professional 2020 crack with user guide and tutorial<br />
-ACDSee Photo Studio Pro 2020 best alternative to cracked software<br />
-ACDSee Photo Studio Professional 2020 trial reset and crack<br />
-ACDSee Photo Studio Pro 2020 cracked software review and rating<br />
-ACDSee Photo Studio Professional 2020 system requirements and crack<br />
-ACDSee Photo Studio Pro 2020 latest version download with crack<br />
-ACDSee Photo Studio Professional 2020 comparison with other photo editing software<br />
-ACDSee Photo Studio Pro 2020 features and benefits of using cracked software<br />
-ACDSee Photo Studio Professional 2020 disadvantages and risks of using cracked software<br />
-ACDSee Photo Studio Pro 2020 how to uninstall cracked software safely<br />
-ACDSee Photo Studio Professional 2020 how to avoid malware and viruses from cracked software<br />
-ACDSee Photo Studio Pro 2020 how to fix common errors and issues with cracked software<br />
-ACDSee Photo Studio Professional 2020 how to update cracked software without losing activation<br />
-ACDSee Photo Studio Pro 2020 how to backup and restore cracked software data and settings<br />
-ACDSee Photo Studio Professional 2020 how to transfer cracked software to another computer or device<br />
-ACDSee Photo Studio Pro 2020 how to use cracked software legally and ethically</p>
- <h4>Correct Lens Distortion and Achieve HDR Results</h4>
- <p>ACDSee Photo Studio Professional 2020 enables you to correct lens distortion and achieve HDR results with ease. You can correct barrel and pincushion distortion in your photos by applying the correction calibrated to fix the distortion inherent to the lens used. You can also fix chromatic aberration and map this correction to your lens for future use. Furthermore, you can get HDR results by intelligently adjusting areas that are too light or too dark in your images.</p>
- <h3>How to Download and Install ACDSee Photo Studio Professional 2020 Crack?</h3>
- <p>If you are tempted to download and install ACDSee Photo Studio Professional 2020 Crack from unofficial sources, you should think twice before doing so. There are many risks and disadvantages of using cracked software that you should be aware of.</p>
- <h4>The Risks of Using Cracked Software</h4>
- <p>Using cracked software is illegal and unethical. You are violating the intellectual property rights of the software developers and depriving them of their rightful income. You are also exposing yourself to potential legal consequences if you are caught using pirated software.</p>
- <p>Moreover, using cracked software is unsafe and unreliable. You are risking your computer's security by downloading files from untrusted sources that may contain viruses, malware or spyware. These malicious programs can damage your system, steal your personal information or compromise your online privacy. You are also risking your data's integrity by using software that may not work properly or crash unexpectedly.</p>
- <h4>The Legal Way to Get ACDSee Photo Studio Professional 2020</h4>
- <p>The legal way to get ACDSee Photo Studio Professional 2020 is to buy it from the official website of ACDSee. By doing so, you will get a genuine license key that will activate the software and grant you access to all its features and updates. You will also get technical support and customer service from the developers in case you encounter any problems or have any questions.</p>
- <p>The price of ACDSee Photo Studio Professional 2020 is $99.99 for a single user license. However, you can also get a free trial version for 30 days that will let you test the software before buying it. You can download the free trial version from here: https://www.acdsee.com/en/products/photo-studio-professional/</p>
- <h2>Conclusion</h2>
- <p>ACDSee Photo Studio Professional 2020 is a powerful image management system that can help you acquire, organize, view, enhance and share your digital photos and other media files. It has many features and benefits that make it a great choice for photographers, graphic designers and digital artists who want to streamline their workflow and expand their creative scope. However, downloading and installing ACDSee Photo Studio Professional 2020 Crack is not a good idea as it is illegal, unethical, unsafe and unreliable. The best way to get ACDSee Photo Studio Professional 2020 is to buy it from the official website of ACDSee or try it for free for 30 days.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about ACDSee Photo Studio Professional 2020 Crack:</p>
- <ul>
-<li><b>What are the system requirements for ACDSee Photo Studio Professional 2020?</b></li>
-<li>The system requirements for ACDSee Photo Studio Professional 2020 are as follows:</li>
-<li>- Intel® or AMD processor with 64-bit support</li>
-<li>- Windows® 10 (64-bit editions only)</li>
-<li>- Microsoft® Internet Explorer® 11 or higher</li>
-<li>- Microsoft® DirectX® 10 or higher</li>
-<li>- Windows Media® Player 9.0</li>
-<li>- Microsoft® Office 2010 or above</li>
-<li>- Ghostscript 8.0 - for PDF support</li>
-<li>- Windows Media® Player 9.0 - for M3U support</li>
-<li>- 2 GB RAM (6 GB RAM recommended)</li>
-<li>- 2 GB of available hard disk space</li>
-<li>- High Color display adapter at 1024 x 768 resolution (1920 x 1080 recommended)</li>
-<li>- CD/DVD Burner - for creating CDs or DVDs</li>
-</ul>
- <ul>
-<li><b>How do I uninstall ACDSee Photo Studio Professional 2020?</b></li>
-<li>To uninstall ACDSee Photo Studio Professional 2020 from your computer, follow these steps:</li>
-<li>- Close ACDSee Photo Studio Professional 2020 if it is running.</li>
-<li>- Click Start > Control Panel > Programs > Programs and Features.</li>
-<li>- Select ACDSee Photo Studio Professional 2020 from the list of programs.</li>
-<li>- Click Uninstall/Change and follow the instructions on the screen.</li>
-</ul>
- <ul>
-<li><b>How do I update ACDSee Photo Studio Professional 2020?</b></li>
-<ul>
-<li>To update ACDSee Photo Studio Professional 2020, follow these steps:</li>
-<li>- Open ACDSee Photo Studio Professional 2020 and click Help > Check for Updates.</li>
-<li>- If there is an update available, click Download and Install.</li>
-<li>- Follow the instructions on the screen to complete the update process.</li>
-</ul>
- <ul>
-<li><b>How do I contact ACDSee customer support?</b></li>
-<li>To contact ACDSee customer support, you can use one of the following methods:</li>
-<li>- Visit the ACDSee support page: https://www.acdsee.com/en/support/</li>
-<li>- Submit a ticket online: https://acdsystems.desk.com/customer/portal/emails/new</li>
-<li>- Call the toll-free number: 1-888-767-9888 (Monday to Friday, 8:30 AM to 5:00 PM PST)</li>
-</ul>
- <ul>
-<li><b>How do I get a refund for ACDSee Photo Studio Professional 2020?</b></li>
-<li>To get a refund for ACDSee Photo Studio Professional 2020, you need to contact ACDSee customer support within 30 days of your purchase and provide your order number and reason for requesting a refund. You can use any of the methods mentioned above to contact ACDSee customer support. Please note that refunds are subject to ACDSee's refund policy and terms and conditions.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download DLL-Files Fixer Full Crack 2023 - The Ultimate Solution for DLL Problems.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download DLL-Files Fixer Full Crack 2023 - The Ultimate Solution for DLL Problems.md
deleted file mode 100644
index 3dea7c379a415a6355e68354fb3c6e57eaa0463e..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download DLL-Files Fixer Full Crack 2023 - The Ultimate Solution for DLL Problems.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<h1>How to Download DLL-Files Fixer Full Crack for Free</h1>
-<p>DLL-Files Fixer is a software that can help you fix DLL errors, optimize your PC performance, and prevent system crashes. But how can you download DLL-Files Fixer full crack for free? Here are some steps you can follow:</p>
-<h2>download dll fixer full crack</h2><br /><p><b><b>Download File</b> &#9999; <a href="https://byltly.com/2uKyvG">https://byltly.com/2uKyvG</a></b></p><br /><br />
-<ol>
-<li>Go to a reliable website that offers DLL-Files Fixer full crack, such as <a href="https://pesktop.com/en/windows/dll-files_fixer">PeskTop</a> , <a href="https://www.novahax.com/2015/12/dll-files-fixer-premium-3390-crack.html">NovaHax</a> , or <a href="http://www.vstmenia.org/dll-files-fixer-crack/">VSTMenia</a> . Make sure you have a good antivirus program to scan the downloaded files for malware.</li>
-<li>Download the DLL-Files Fixer setup file and the crack file from the website. You may need to enter a password to extract the files, which is usually 123 or the name of the website.</li>
-<li>Disable your Windows Defender or firewall temporarily to avoid any interference with the crack files.</li>
-<li>Install the DLL-Files Fixer program by running the setup file. Do not launch the program after installation.</li>
-<li>Copy the crack file and paste it into the installation folder of DLL-Files Fixer. Replace the original file if prompted.</li>
-<li>Run the DLL-Files Fixer program and enjoy its full features for free.</li>
-</ol>
-<p>Note: Downloading and using DLL-Files Fixer full crack may be illegal and risky. You may violate the software license agreement and expose your PC to potential threats. We do not recommend or endorse this method. Use it at your own risk.</p><p>What is DLL-Files Fixer and why do you need it?</p>
-<p>DLL-Files Fixer is a software that can help you fix DLL errors, optimize your PC performance, and prevent system crashes. DLL stands for Dynamic Link Library, which is a file that contains code and data that can be used by multiple programs at the same time. DLL files are essential for the proper functioning of many applications and games on Windows.</p>
-<p>However, sometimes DLL files can get corrupted, missing, or outdated, causing various problems such as error messages, slow performance, or even blue screen of death. This is where DLL-Files Fixer comes in handy. It can scan your system for any DLL issues and fix them automatically. It can also download and install the latest and compatible DLL files from its online library of over 2.5 million files. Moreover, it can defragment and clean your registry to improve your PC speed and stability.</p>
-<p>With DLL-Files Fixer, you can easily solve your DLL problems and enjoy a smooth and error-free PC experience.</p>
-<p></p><p>How much does DLL-Files Fixer cost and how can you get it?</p>
-<p>DLL-Files Fixer is a paid software that offers a free trial version for 15 days. After that, you need to purchase a license key to continue using it. The license key costs $29.95 for one year and $39.95 for three years. You can buy it from the official website of DLL-Files Fixer or from other authorized resellers.</p>
-<p>To get DLL-Files Fixer, you need to download and install it from the official website or from a trusted source. You can also use the DLL-Files Fixer full crack method that we explained earlier, but we do not recommend it for the reasons we mentioned. Once you have installed the software, you can run it and scan your PC for any DLL errors. You can then fix them with one click or download and install the missing or updated DLL files from the online library.</p>
-<p>What are the alternatives to DLL-Files Fixer?</p>
-<p>If you are looking for other software that can help you fix DLL errors, you may want to check out some of these alternatives:</p>
-<ul>
-<li><a href="https://www.glarysoft.com/glary-utilities/">Glary Utilities</a>: This is a free and powerful system optimizer that can fix registry errors, clean junk files, boost PC speed, and more. It also has a module called \"Fix Missing Software Files\" that can scan and repair DLL issues.</li>
-<li><a href="https://www.wisecleaner.com/wise-care-365.html">Wise Care 365</a>: This is another free and comprehensive PC cleaner and optimizer that can fix registry problems, optimize system settings, protect privacy, and more. It also has a feature called \"PC Checkup\" that can detect and fix DLL errors.</li>
-<li><a href="https://www.dllsuite.net/">DLL Suite</a>: This is a dedicated DLL fixer that can scan and repair all kinds of DLL errors on Windows. It can also download and install the latest DLL files from its online database of over 25 million files. It offers a free trial version and a paid version that costs $19.99 for one year.</li>
-</ul></p> ddb901b051<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Big Boobs Sexy Video Com.md b/spaces/1gistliPinn/ChatGPT4/Examples/Big Boobs Sexy Video Com.md
deleted file mode 100644
index 0cd8e7a5d07175d62563a373ad3a5bac548cf74f..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Big Boobs Sexy Video Com.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-
-<p>father son 3some slutload ford escort power booster eats<br />bulls cum from wifes pussy montana physiological<br />treatment sex offenders older silver daddies gay man.<br />hot adult free ladies phone talk numbers black hardcore assfucking big boobs blojob pornhub eva<br />green pics nude nuns fatties porn movies.<br />nc state university towers porn video naked hot blonde lesbians adult hidden cam videos big tit sluts bukkake nureyev bisexual.</p>
-<p>molly sirius's sex photoes flat chested tight pussy girls british glamour<br />model sex tape moms and guys sex elephant xxx thumbs.<br />sexy lesbian heel worship newport beach facial rejuvenation one extra sex<br />chromosome hardcore site terra.es nude sexy indian women.<br />cause of sexual addiction support group vintage rosenthal studio line cheek naked sandy anal gland absess french adult rap.</p>
-<h2>big boobs sexy video com</h2><br /><p><b><b>Download File</b> &#8230;&#8230;&#8230; <a href="https://imgfil.com/2uy0vl">https://imgfil.com/2uy0vl</a></b></p><br /><br />
-<p>petey the porno puppet porn stamina tricks how to get pornstar size penis nude massage arcadia ca free girlfriends<br />and wives porn videos.<br />female sex inhancers xxx skinny porn women want comic strip rubric need for speed boobs.</p>
-<p>fat black mature women review of dr wexler facial products ltr<br />lingerie booty pants skinny slut galleries.<br />dirt sexy money season1 bridgett baby doll riley<br />nude loa german girl gives lapdance and blowjob us virgin islands official tourist board.</p>
-<p>you sexy babe vintage ebony interacial nudists at the beach<br />mom seduced teen guys crusing for cock.<br />deepthroat film after pain sex vaginal blacklight nude gypsy poster sexy college redhead<br />clip day free hardcore.</p>
-<p>brunette teen pic sexy lilo and stitch pics vpr fake<br />porn pics emma watson mayra naked picture<br />veronica.<br />estrogen receptor breast cancer cumshot compilation give<br />it away tqa vancouver erotic clubs julika wagner nude.</p>
-<p>grinding lesbian pussy video big tits abuse redheaded naked males pics porn stars who smoke looking at the world<br />from the bottom of a.<br />milf exam bikini 'larry's locker room sexy female strippers erotic male cowboys xian xua hentai.</p>
-<p>flat chested nude thumbs culture fag god hate paperback religious rhetorics sexual violence onegai teacher hentai hot milf sexy movies busty mayure picture<br />thumbs.<br />female reporter fucked mature chubby doing anal hardcore missy<br />taylor skiny fucks free donkey punch porn videos.<br />bottom flared gown prom how many girls eat pussy libertarians suck hugh laurie kicking<br />ass woman playing with her sexy shoes.<br />sexy teen swallow videos gay and lesbian resource center dallas mature audience only<br />philip k dick speech milf bathing suit video.<br />adult soccer leagues austin tx harvard project for asian international relations vero beach boys center teen challenge metacafe ass same gay.</p>
-<p>free xxx girl fuck dog vid only blow job blonde it's<br />my pleasure to join carla bonner sexy.<br />neil porn vince lizzie cums video adult stemcell sex internet games<br />mature nudity free.<br />karla edecan telcel xxx girls that suck for money pic jamie bamber<br />nude online cum.<br />cats back spasms and he licks sexy girls in sexy clothes videos<br /> gay male model picture free chyna porn.</p>
-<p></p>
-<p>tumblr hairy putting cum in a girl's mouth sex clubs in lille<br />do christian women have anal sex musclar gay men.<br />indian femdom free trial upskirt las vegas young amateur milf is homosexual adoption legal<br />female sexy body.<br />asian magazine cover upskirt bloobers young girls sex blogs naked hott sluts<br />free natural amateur breasts tgp.<br />young girls with no boobs how to test a males virginity grandma lesbian porn naughty julie anal free pictures of shaved pussy.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Face To Face Mat Book Free Download.md b/spaces/1gistliPinn/ChatGPT4/Examples/Face To Face Mat Book Free Download.md
deleted file mode 100644
index 8355839e461ddb311ecf692d9476e58a0b2cf06c..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Face To Face Mat Book Free Download.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<br />
-<p>this site is great for kids, students, and teachers. they have a large variety of books, videos, music, and games. they have a large selection of kids books and educational materials. you can find the children's books in the books, toys, and games section.</p>
-<h2>face to face mat book free download</h2><br /><p><b><b>Download</b> - <a href="https://imgfil.com/2uxYaf">https://imgfil.com/2uxYaf</a></b></p><br /><br />
-<p>this site is a great resource for those looking for inspiration to start, and those already active in, the digital arts. you will find tutorials, tips, and more. there are even plenty of free projects and resources to use for your own projects. if you are just getting started in the arts, this site can be a great resource for you. they have plenty of projects and tutorials for beginners.</p>
-<p>this book addresses these weaknesses and gaps. it provides evidence of the capability shortfalls that currently exist in many countries, analyses this evidence and identifies capability traps that hold many governments back, particularly related to isomorphic mimicry and premature load-bearing. the book then describes a process that governments can use to escape these capability traps. called pdia (problem driven iterative adaptation), this process empowers people working in governments to find and fit solutions to the problems they face. this process is explained in a practical manner so that readers can actually apply tools and ideas to the capability challenges they face in their own contexts. these applications will help readers implement policies and reforms that have more impact than those of the past. </p>
-<p>the book describes the pdia process, outlining the steps to take for governments to find and address problems, escape capability traps, and turn their capabilities into their greatest strengths. the problems that governments face need to be identified, be the need be addressed through policy reform, and be implemented by governments.</p>
-<p></p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Android 12 Emojis Whats New and How to Get Them on Your Phone.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Android 12 Emojis Whats New and How to Get Them on Your Phone.md
deleted file mode 100644
index 5270cef662c5331bdfeb164eecc6f284e7c1f572..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Android 12 Emojis Whats New and How to Get Them on Your Phone.md	
+++ /dev/null
@@ -1,153 +0,0 @@
-<br />
-<h1>How to Download and Install Android 12 Emojis on Any Android Phone</h1>
-<p>Do you want to spice up your text messages, social media posts, and online chats with the latest and coolest emojis? If you are an Android user, you might be interested in getting the new Android 12 emojis on your phone. Android 12 is Google's newest operating system, which introduces many improvements and changes over Android 11. One of the new changes is the introduction of new emojis and the redesign of current ones. The new emojis are part of Unicode's Emoji 13.1 set and will be included on Android 12.</p>
-<h2>android 12 emojis download apk</h2><br /><p><b><b>Download Zip</b> &#9745; <a href="https://urlin.us/2uSZSI">https://urlin.us/2uSZSI</a></b></p><br /><br />
-<p>However, if you are not running Android 12 on your phone, you might be wondering how to get the new emojis. Don't worry, we have got you covered. In this article, we will show you how to download and install Android 12 emojis on any Android phone using a simple method. You don't need to wait for the official update or buy a new phone to enjoy the new emojis. All you need is a rooted phone and a Magisk module. Let's get started!</p>
-<h2>What are Android 12 Emojis and Why You Should Get Them</h2>
-<h3>Android 12 Emojis Overview</h3>
-<p>Emojis are small icons that represent various emotions, objects, animals, activities, and symbols. They are widely used in digital communication to express feelings, convey messages, and add fun and personality. Emojis are standardized by Unicode, an organization that sets the rules and guidelines for text encoding and representation. Unicode releases new emoji sets every year, which are then adopted by different platforms and devices.</p>
-<p>Android 12 emojis are the latest emoji set released by Google for its operating system. According to Google, these emoji updates are coming to Android 12 later in the year but will be made available on Gmail, Google Chat, YouTube Live Chat, and Chrome OS later this month. The update includes nearly 1,000 redesigned emojis that make them clearer at small sizes, more accurate, or more cross-platform compatible. The update also adds support for new emojis that were introduced by Unicode in Emoji 13.1 set. Some of the new emojis include <em>melting face</em>, <em>face with open eyes and hand over mouth</em>, <em>face with peeking eye</em>, <em>saluting face</em>, <em>dotted line face</em>, <em>heart on fire</em>, <em>mending heart</em>, <em>woman with beard</em>, <em>man with beard</em>, <em>woman feeding baby</em>, <em>man feeding baby</em>, <em>bubble tea</em>, <em>tamale</em>, <em>potted plant</em>, <em>rock</em>, <em>wood</em>, <em>hut</em>, <em>worm</em>, <em>beaver</em>, <em>seal</em>, <em>fly</em>, <em>cockroach</em>, <em>ninja</em>, <em>military helmet</em>, <em>accordion</em>, <em>long drum</em>, <em>coin</em>, <em>circular saw</em>, <em>screwdriver</em>, <em>mirror</em>, <em>window</em>, <em<plunger<>/p></p>
-<h3> <h3>Benefits of Android 12 Emojis</h3>
-<p>There are many reasons why you might want to get the new Android 12 emojis on your phone. Here are some of them:</p>
-<ul>
-<li>You can express yourself better with the new emojis that capture a wider range of emotions, situations, and identities.</li>
-<li>You can stay updated with the latest trends and culture with the new emojis that reflect the current world and society.</li>
-<li>You can have more fun and creativity with the new emojis that offer more options and variations for your messages and posts.</li>
-<li>You can communicate more effectively with the new emojis that are clearer, more consistent, and more compatible across different platforms and devices.</li>
-</ul>
-<h2>How to Download Android 12 Emojis APK File</h2>
-<h3>Requirements for Installing Android 12 Emojis</h3>
-<p>Before you can install the new Android 12 emojis on your phone, you need to make sure that you meet some requirements. These are:</p>
-<p>How to install android 12 emojis on all android phones<br />
-Android 12 emoji changelog and new designs<br />
-Get android 12 new emojis on rooted android device<br />
-Android 12 emoji set magisk module download<br />
-Android 12 emojis vs android 11 emojis comparison<br />
-Android 12 emoji font file for non-rooted devices<br />
-Android 12 emoji update release date and features<br />
-How to use android 12 emojis on gmail and google chat<br />
-Android 12 emoji list and meanings<br />
-Android 12 emoji keyboard app for android<br />
-How to get android 12 emojis on samsung devices<br />
-Android 12 emoji redesign and improvement<br />
-Android 12 emoji compatibility with other platforms<br />
-Android 12 emoji apk mod for older android versions<br />
-Android 12 emoji preview and beta testing<br />
-How to customize android 12 emojis with root<br />
-Android 12 emoji support for whatsapp and instagram<br />
-Android 12 emoji review and feedback<br />
-Android 12 emoji pack for gboard and swiftkey<br />
-Android 12 emoji font changer app for android<br />
-How to uninstall android 12 emojis from rooted device<br />
-Android 12 emoji bug fixes and issues<br />
-Android 12 emoji quiz and trivia game<br />
-Android 12 emoji wallpapers and stickers for android<br />
-Android 12 emoji generator and creator tool<br />
-How to enable android 12 emojis on chrome os<br />
-Android 12 emoji history and evolution<br />
-Android 12 emoji statistics and trends<br />
-Android 12 emoji tips and tricks<br />
-Android 12 emoji alternatives and replacements<br />
-How to backup android 12 emojis on rooted device<br />
-Android 12 emoji license and terms of use<br />
-Android 12 emoji font converter for pc and mac<br />
-Android 12 emoji art and memes for android<br />
-Android 12 emoji theme and launcher for android<br />
-How to disable android 12 emojis on rooted device<br />
-Android 12 emoji request and suggestion form<br />
-Android 12 emoji news and updates<br />
-Android 12 emoji guide and tutorial for beginners<br />
-Android 12 emoji font editor and modifier tool<br />
-How to restore android 11 emojis on rooted device<br />
-Android 12 emoji problems and solutions<br />
-Android 12 emoji fun facts and trivia<br />
-Android 12 emoji stickers and gifs for android<br />
-Android 12 emoji maker and editor app for android<br />
-How to access android 12 emojis on youtube live chat <br />
-Android 12 emoji comparison with ios and windows <br />
-Android 12 emoji font download link and instructions <br />
-Android 12 emoji rating and opinion poll <br />
-Android 12 emoji best practices and recommendations</p>
-<ul>
-<li>Your phone must be running on Android 8.0 Oreo or higher.</li>
-<li>Your phone must be rooted with Magisk installed. Rooting is the process of gaining full access and control over your phone's system. Magisk is a tool that allows you to modify your phone's system without affecting its safety and stability. If you don't know how to root your phone or install Magisk, you can search for online guides or tutorials that are specific to your phone model.</li>
-<li>You must have a file manager app that can access the root directory of your phone. You can use any file manager app that has this feature, such as Solid Explorer, FX File Explorer, or MiXplorer.</li>
-<li>You must have a backup of your phone's data in case something goes wrong during the installation process. You can use any backup app that you prefer, such as Titanium Backup, Swift Backup, or Google Drive.</li>
-</ul>
-<h3>Steps to Download Android 12 Emojis APK File</h3>
-<p>Once you have met the requirements, you can proceed to download the Android 12 emojis APK file. This is a file that contains the new emoji fonts that will replace the existing ones on your phone. Here are the steps to download the APK file:</p>
-<ol>
-<li>Go to this link on your phone's browser. This is the official download page for the Android 12 emojis APK file.</li>
-<li>Tap on the green button that says "Download APK". This will start downloading the file to your phone's storage.</li>
-<li>Wait for the download to finish. You can check the progress on your notification bar or your browser's download manager.</li>
-<li>Once the download is complete, locate the file on your file manager app. It should be in the Downloads folder by default. The file name should be something like "Android-12-Emojis.apk".</li>
-</ol> <h2>How to Install Android 12 Emojis Using Magisk Module</h2>
-<h3>What is Magisk and How to Use It</h3>
-<p>Magisk is a powerful tool that allows you to modify your phone's system without affecting its safety and stability. It works by creating a virtual partition on your phone that overlays the original system partition. This way, you can make changes to the system without actually modifying it. Magisk also has a feature called Magisk Modules, which are add-ons that can enhance your phone's functionality and performance. You can install various Magisk Modules from the Magisk Manager app, which is the main interface for managing Magisk on your phone.</p>
-<h3>Steps to Install Android 12 Emojis Using Magisk Module</h3>
-<p>Now that you have downloaded the Android 12 emojis APK file, you need to install it using a Magisk Module. This will ensure that the new emojis are applied to your phone's system and apps. Here are the steps to install Android 12 emojis using a Magisk Module:</p>
-<ol>
-<li>Open the Magisk Manager app on your phone. If you don't have it, you can download it from here.</li>
-<li>Tap on the menu icon on the top left corner and select "Modules". This will open the list of installed and available Magisk Modules on your phone.</li>
-<li>Tap on the plus icon on the bottom right corner and navigate to the folder where you saved the Android 12 emojis APK file. Tap on the file and select "Open". This will start installing the Magisk Module for Android 12 emojis.</li>
-<li>Wait for the installation to finish. You will see a message that says "Module installed" when it is done.</li>
-<li>Tap on the "Reboot" button at the bottom of the screen. This will restart your phone and apply the changes.</li>
-</ol>
-<h2>How to Enjoy Android 12 Emojis on Your Phone</h2>
-<h3>How to Access and Use Android 12 Emojis</h3>
-<p>Congratulations, you have successfully installed Android 12 emojis on your phone. Now you can enjoy using them in your text messages, social media posts, and online chats. Here is how to access and use Android 12 emojis on your phone:</p>
-<ul>
-<li>To access Android 12 emojis, you need to use a keyboard app that supports them. You can use any keyboard app that you prefer, such as Gboard, SwiftKey, or Samsung Keyboard.</li>
-<li>To use Android 12 emojis, you need to tap on the emoji icon on your keyboard app. This will open the emoji panel where you can browse and select the emojis that you want to use.</li>
-<li>You can also search for specific emojis by typing their names or keywords in the search bar at the bottom of the emoji panel. For example, if you want to use the <em>melting face</em> emoji, you can type "melting" or "face" in the search bar and find it easily.</li>
-<li>You can also customize some of the emojis by tapping and holding on them. This will open a pop-up menu where you can choose different skin tones, hair styles, or genders for some of the emojis.</li>
-</ul>
-<h3>Tips and Tricks for Android 12 Emojis</h3>
-<p>To make the most out of Android 12 emojis, here are some tips and tricks that you can try:</p>
-<ul>
-<li>You can create shortcuts for your favorite or frequently used emojis by adding them to your keyboard app's clipboard or dictionary. This way, you can access them quickly without browsing through the emoji panel.</li>
-<li>You can combine different emojis to create new meanings or expressions. For example, you can combine <em>heart on fire</em> and <em>mending heart</em> to show that you are recovering from a heartbreak or combine <em>ninja</em> and <em>military helmet</em> to show that you are ready for action.</li>
-<li>You can use emojis to spice up your contacts' names or labels. For example, you can add <em>potted plant</em> or <em>worm</em> to your friend's name who loves gardening or add <em>bubble tea</em> or <em>tamale</em> to your favorite food delivery service.</li>
-</ul>
-<h2>Conclusion and FAQs</h2>
-<h3>Conclusion</h3>
-<p>In this article, we have shown you how to download and install Android 12 emojis on any Android phone using a simple method. You don't need to wait for the official update or buy a new phone to enjoy the new emojis. All you need is a rooted phone and a Magisk module. We have also explained what are Android 12 emojis and why you should get them. We have also given you some tips and tricks on how to access and use Android 12 emojis on your phone. We hope that you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy texting!</p>
-<h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about Android 12 emojis:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Can I get Android 12 emojis without rooting my phone?</td>
-<td>No, you cannot get Android 12 emojis without rooting your phone. Rooting is necessary to install the Magisk module that replaces the existing emoji fonts on your phone. If you don't want to root your phone, you will have to wait for the official update from Google or your phone manufacturer.</td>
-</tr>
-<tr>
-<td>Will Android 12 emojis work on all apps and platforms?</td>
-<td>Yes, Android 12 emojis will work on all apps and platforms that support Unicode's Emoji 13.1 set. However, some apps and platforms may display the emojis differently depending on their own design and style. For example, WhatsApp and Facebook Messenger have their own emoji sets that may not match the Android 12 emojis exactly.</td>
-</tr>
-<tr>
-<td>How can I revert back to the old emojis if I don't like the new ones?</td>
-<td>If you want to revert back to the old emojis, you can uninstall the Magisk module that you installed for Android 12 emojis. To do this, open the Magisk Manager app, go to Modules, find the Android 12 Emojis module, and tap on the trash icon. Then reboot your phone and the old emojis will be restored.</td>
-</tr>
-<tr>
-<td>Are there any risks or drawbacks of installing Android 12 emojis on my phone?</td>
-<td>There are no major risks or drawbacks of installing Android 12 emojis on your phone, as long as you follow the instructions carefully and backup your data before proceeding. However, some minor issues that you may encounter are:</td>
-<ul>
-<li>You may lose some of the original emoji fonts that came with your phone.</li>
-<li>You may experience some compatibility issues with some apps or platforms that do not support the new emojis.</li>
-<li>You may void your phone's warranty or lose access to some features or services that require an unrooted phone.</li>
-</ul>
-</tr>
-<tr>
-<td>Where can I find more information or help about Android 12 emojis?</td>
-<td>If you want to find more information or help about Android 12 emojis, you can visit these sources:</td>
-<ul>
-<li>The official Google blog post that announces the new emoji updates.</li>
-<li>The official Unicode website that lists all the new emojis in Emoji 13.1 set.</li>
-<li>The XDA Developers forum thread that provides the download link and instructions for the Android 12 Emojis Magisk module.</li>
-<li>The Reddit community that discusses and shares everything related to Android 12.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Clash Mini APK and Play the Beta in Any Country.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Clash Mini APK and Play the Beta in Any Country.md
deleted file mode 100644
index 1e2c9a92ff96c3035719e5dfb385ce80eb341934..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Clash Mini APK and Play the Beta in Any Country.md	
+++ /dev/null
@@ -1,102 +0,0 @@
-<br />
-<h1>Where to Download Clash Mini</h1>
-<p>Clash Mini is a brand new mobile game launched by Supercell, the same developers behind popular titles such as Clash Royale and Clash of Clans. Clash Mini takes characters from the ‘Clash Universe’ which are used to create a unique deck to battle against other players in in a board-game-like set up. If you are a fan of the Clash games or you are looking for a fun and strategic board game, you might be wondering where to download Clash Mini and how to play it. In this article, we will answer all your questions and give you some tips and tricks to help you master the game.</p>
-<h2>where to download clash mini</h2><br /><p><b><b>Download File</b> &#9889; <a href="https://urlin.us/2uT2cW">https://urlin.us/2uT2cW</a></b></p><br /><br />
- <h2>What is Clash Mini?</h2>
-<h3>A fun and strategic board game</h3>
-<p>Clash Mini is a game of choices, where you have to duel and rumble with other players in a fun, strategy-packed board game. You have to collect, summon and upgrade your army of Minis, which are adorable versions of the iconic Clash characters. You have to predict your opponent’s moves and then assemble your winning strategy and formation. Watch your Minis come to life and clash to be the last one standing!</p>
-<h3>A new member of the Clash family</h3>
-<p>Clash Mini is one of the three new games in the Clash of Clans universe unveiled by Supercell in April 2021, alongside Clash Quest and Clash Heroes. The developer said that they wanted to offer a new Clash experience to current players and broaden Clash to new audiences who haven’t experienced Clash before. Clash Mini is designed to be easy to learn but challenging to master, making it suitable for casual and hardcore gamers alike.</p>
- <h2>Which platforms can play Clash Mini?</h2>
-<h3>Mobile exclusive game</h3>
-<p>Like all the previous Clash titles, Clash Mini is exclusive to mobile devices. You won’t be able to play it on any non-mobile platforms such as PC or console. However, you can use an emulator to play it on your computer if you really want to, but this might affect the performance and compatibility of the game.</p>
-<h3>Available for iOS and Android devices</h3>
-<p>Clash Mini is available for both iOS and Android devices. You can download it from the App Store or Google Play Store depending on your device. However, the game is not yet globally released, so you might not be able to find it in your region. The game is currently in beta testing phase, which means that it is only available in certain countries for a limited number of players.</p>
- <h2>When is the Clash Mini release date?</h2>
-<h3>Beta version launched in November 2021</h3>
-<p>The beta version of Clash Mini was launched on November 8, 2021 for players in Finland, Sweden, Norway, Denmark, Iceland, Canada, Singapore, Chile, Hong Kong, Sri Lanka and The Philippines. The beta version allows players to test the game before its official release and provide feedback to the developer. The beta version also helps the developer to fix any bugs or issues that might occur in the game.</p>
-<h3>Global release date not confirmed yet</h3>
-<p>The global release date of Clash Mini has not been confirmed yet by Supercell. The developer has not announced when they plan to launch the game worldwide or which countries will be added next to the beta version. However, based on the previous Clash games, we can expect that the game will be released globally sometime in 2023. Until then, you can follow the official Clash Mini social media accounts and website to get the latest news and updates about the game.</p>
-<p>How to download clash mini on android<br />
-Clash mini apk download latest version<br />
-Clash mini beta download link<br />
-Clash mini release date and download guide<br />
-Clash mini download for PC windows 10<br />
-Clash mini mod apk download unlimited gems<br />
-Clash mini strategy and tips for beginners<br />
-Clash mini review and gameplay video<br />
-Clash mini download error and how to fix it<br />
-Clash mini vs clash royale comparison<br />
-Clash mini best minis and heroes to use<br />
-Clash mini cheats and hacks for free gems<br />
-Clash mini update and patch notes<br />
-Clash mini wiki and fan site<br />
-Clash mini support and contact information<br />
-How to play clash mini on mac<br />
-Clash mini download size and system requirements<br />
-Clash mini online multiplayer mode<br />
-Clash mini skins and customization options<br />
-Clash mini tournaments and events<br />
-How to download clash mini on ios<br />
-Clash mini google play store link<br />
-Clash mini discord server and community<br />
-Clash mini reddit and forum discussions<br />
-Clash mini official website and blog<br />
-How to download clash mini on fire tablet<br />
-Clash mini amazon app store link<br />
-Clash mini gift codes and redeem codes<br />
-Clash mini feedback and suggestions<br />
-Clash mini faq and troubleshooting<br />
-How to download clash mini on chromebook<br />
-Clash mini web version and browser extension<br />
-Clash mini achievements and rewards<br />
-Clash mini leaderboard and rankings<br />
-Clash mini social media accounts and news<br />
-How to download clash mini on linux<br />
-Clash mini github repository and source code<br />
-Clash mini development and history<br />
-Clash mini future plans and roadmap<br />
-Clash mini testimonials and ratings<br />
-How to download clash mini on bluestacks emulator<br />
-Clash mini nox player emulator download link <br />
-Clash mini best settings and optimization tips <br />
-Clash mini fun facts and trivia <br />
-Clash mini fan art and wallpapers <br />
-Clash mini merchandise and products <br />
-How to download clash mini on smart tv <br />
-Clash mini streaming platforms and channels <br />
-Clash mini podcast and interviews</p>
- <h2>How to download Clash Mini beta?</h2>
-<h3>Sign up on the official website</h3>
-<p>If you want to play Clash Mini beta, you have to sign up on the official website. You have to enter your email address and choose your preferred platform (iOS or Android). You will also have to agree to the terms and conditions and privacy policy of the game. After you sign up, you will receive a confirmation email with a link to download the game.</p>
-<h3>Download from the App Store or Google Play Store</h3>
-<p>After you receive the confirmation email, you can download Clash Mini beta from the App Store or Google Play Store. You have to search for Clash Mini in the store and tap on the download button. You might have to enter your Apple ID or Google account credentials to verify your identity. Once the download is complete, you can open the game and start playing.</p>
- <h2>How to play Clash Mini?</h2>
-<h3>Collect, summon and upgrade your Minis</h3>
-<p>The core gameplay of Clash Mini is to collect, summon and upgrade your Minis. Minis are cute and powerful versions of the Clash characters that you can use to fight against other players. There are different types of Minis, such as tanks, damage dealers, healers, support and more. Each Mini has its own stats, abilities and synergies with other Minis. You can collect Minis by opening chests or buying them from the shop. You can summon Minis by placing them on the board before each battle. You can upgrade Minis by spending gold and cards to increase their level and power.</p>
-<h3>Predict, position and clash with your opponent</h3>
-<p>The other aspect of Clash Mini is to predict, position and clash with your opponent. Each battle consists of three rounds, where you have to place your Minis on a 4x4 grid board. You have to predict what your opponent will do and try to counter their strategy. You have to position your Minis wisely on the board, taking into account their range, movement, direction and abilities. You have to clash with your opponent by watching your Minis fight automatically based on their stats and skills. The player who wins two out of three rounds wins the battle.</p>
- <h2>Tips and tricks for Clash Mini</h2>
-<h3>Choose the right characters for your army</h3>
-<p>One of the most important tips for Clash Mini is to choose the right characters for your army. You have to consider the strengths and weaknesses of each Mini and how they work together as a team. You have to balance your army with different roles, such as tanks, damage dealers, healers and support. You have to adapt your army according to the game mode, the map and the opponent you are facing. You have to experiment with different combinations of Minis and find out what works best for you.</p>
-<h3>Position your Minis wisely on the battlefield</h3>
-<p>Another crucial tip for Clash Mini is to position your Minis wisely on the battlefield. You have to think strategically about where you place your Minis on the board before each round. You have to consider factors such as range, movement, direction and abilities of your Minis and how they interact with each other and with the enemy Minis. You have to avoid placing your Minis in vulnerable spots where they can be easily attacked or countered by the opponent. You have to use the terrain features such as walls, bridges and obstacles to your advantage.</p>
-<h3>Utilize special abilities and upgrades during battle</h3>
-<p>The final tip for Clash Mini is to utilize special abilities and upgrades during battle. Each Mini has a unique ability that can be activated once per round by tapping on it. These abilities can be offensive, defensive or supportive in nature and can change the outcome of a battle if used at the right time. You also have access to upgrades that can boost your Minis’ stats or skills during a battle. These upgrades are randomly generated from a pool of options and can be applied by dragging them onto a Mini. You have to use these abilities and upgrades wisely and strategically to gain an edge over your opponent.</p>
- <h2>Conclusion</h2>
-<p>Clash Mini is a fun and strategic board game that features adorable versions of the Clash characters in a fast-paced duel against other players. The game is currently in beta testing phase and is only available in certain countries for iOS and Android devices. The global release date of the game is not confirmed yet but is expected sometime in 2023. If you want to play Clash Mini beta , you have to sign up on the official website and download it from the App Store or Google Play Store. To play Clash Mini, you have to collect, summon and upgrade your Minis, predict, position and clash with your opponent, and utilize special abilities and upgrades during battle. We hope this article has helped you learn more about Clash Mini and how to download and play it. If you have any questions, you can check out the FAQs below or visit the official Clash Mini website for more information.</p>
- <h4>FAQs</h4>
-<ul>
-<li>Q: How much does Clash Mini cost?</li>
-<li>A: Clash Mini is free to download and play, but it offers in-app purchases for some items and features.</li>
-<li>Q: How can I contact Supercell for feedback or support?</li>
-<li>A: You can contact Supercell through the in-game settings menu or by visiting their website or social media accounts.</li>
-<li>Q: How can I join a clan or create my own clan in Clash Mini?</li>
-<li>A: You can join a clan or create your own clan by tapping on the clan icon on the main screen. You can invite your friends or other players to join your clan or search for an existing clan to join.</li>
-<li>Q: How can I earn rewards and chests in Clash Mini?</li>
-<li>A: You can earn rewards and chests by winning battles, completing quests, participating in events, ranking up in leagues, and opening the free chest every four hours.</li>
-<li>Q: How can I watch replays or share my battles in Clash Mini?</li>
-<li>A: You can watch replays or share your battles by tapping on the battle log icon on the main screen. You can also watch live battles of other players or top players by tapping on the TV icon on the main screen.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/commands/__init__.py b/spaces/1toTree/lora_test/ppdiffusers/commands/__init__.py
deleted file mode 100644
index 6a9e6f456198f63505db022b021fe92c19d5f236..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/commands/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from abc import ABC, abstractmethod
-from argparse import ArgumentParser
-
-
-class BasePPDiffusersCLICommand(ABC):
-    @staticmethod
-    @abstractmethod
-    def register_subcommand(parser: ArgumentParser):
-        raise NotImplementedError()
-
-    @abstractmethod
-    def run(self):
-        raise NotImplementedError()
diff --git a/spaces/52Hz/CMFNet_deraindrop/model/CMFNet.py b/spaces/52Hz/CMFNet_deraindrop/model/CMFNet.py
deleted file mode 100644
index 290a108ac388fe34e0524201e75ae4760d91654c..0000000000000000000000000000000000000000
--- a/spaces/52Hz/CMFNet_deraindrop/model/CMFNet.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-import torch.nn as nn
-from model.block import SAB, CAB, PAB, conv, SAM, conv3x3, conv_down
-
-##########################################################################
-## U-Net
-bn = 2  # block number-1
-
-class Encoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Encoder, self).__init__()
-        if block == 'CAB':
-            self.encoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.encoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.encoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.encoder_level1 = nn.Sequential(*self.encoder_level1)
-        self.encoder_level2 = nn.Sequential(*self.encoder_level2)
-        self.encoder_level3 = nn.Sequential(*self.encoder_level3)
-        self.down12 = DownSample(n_feat, scale_unetfeats)
-        self.down23 = DownSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, x):
-        enc1 = self.encoder_level1(x)
-        x = self.down12(enc1)
-        enc2 = self.encoder_level2(x)
-        x = self.down23(enc2)
-        enc3 = self.encoder_level3(x)
-        return [enc1, enc2, enc3]
-
-class Decoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Decoder, self).__init__()
-        if block == 'CAB':
-            self.decoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.decoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.decoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.decoder_level1 = nn.Sequential(*self.decoder_level1)
-        self.decoder_level2 = nn.Sequential(*self.decoder_level2)
-        self.decoder_level3 = nn.Sequential(*self.decoder_level3)
-        if block == 'CAB':
-            self.skip_attn1 = CAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'PAB':
-            self.skip_attn1 = PAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'SAB':
-            self.skip_attn1 = SAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        self.up21 = SkipUpSample(n_feat, scale_unetfeats)
-        self.up32 = SkipUpSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, outs):
-        enc1, enc2, enc3 = outs
-        dec3 = self.decoder_level3(enc3)
-        x = self.up32(dec3, self.skip_attn2(enc2))
-        dec2 = self.decoder_level2(x)
-        x = self.up21(dec2, self.skip_attn1(enc1))
-        dec1 = self.decoder_level1(x)
-        return [dec1, dec2, dec3]
-
-##########################################################################
-##---------- Resizing Modules ----------
-class DownSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(DownSample, self).__init__()
-        self.down = nn.Sequential(nn.Upsample(scale_factor=0.5, mode='bilinear', align_corners=False),
-                                  nn.Conv2d(in_channels, in_channels + s_factor, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.down(x)
-        return x
-
-class UpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(UpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.up(x)
-        return x
-
-class SkipUpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(SkipUpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x, y):
-        x = self.up(x)
-        x = x + y
-        return x
-
-##########################################################################
-# Mixed Residual Module
-class Mix(nn.Module):
-    def __init__(self, m=1):
-        super(Mix, self).__init__()
-        w = nn.Parameter(torch.FloatTensor([m]), requires_grad=True)
-        w = nn.Parameter(w, requires_grad=True)
-        self.w = w
-        self.mix_block = nn.Sigmoid()
-
-    def forward(self, fea1, fea2, feat3):
-        factor = self.mix_block(self.w)
-        other = (1 - factor)/2
-        output = fea1 * other.expand_as(fea1) + fea2 * factor.expand_as(fea2) + feat3 * other.expand_as(feat3)
-        return output, factor
-
-##########################################################################
-# Architecture
-class CMFNet(nn.Module):
-    def __init__(self, in_c=3, out_c=3, n_feat=96, scale_unetfeats=48, kernel_size=3, reduction=4, bias=False):
-        super(CMFNet, self).__init__()
-
-        p_act = nn.PReLU()
-        self.shallow_feat1 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat2 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat3 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-
-        self.stage1_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-        self.stage1_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-
-        self.stage2_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-        self.stage2_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-
-        self.stage3_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-        self.stage3_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-
-        self.sam1o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam2o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam3o = SAM(n_feat, kernel_size=3, bias=bias)
-
-        self.mix = Mix(1)
-        self.add123 = conv(out_c, out_c, kernel_size, bias=bias)
-        self.concat123 = conv(n_feat*3, n_feat, kernel_size, bias=bias)
-        self.tail = conv(n_feat, out_c, kernel_size, bias=bias)
-
-
-    def forward(self, x):
-        ## Compute Shallow Features
-        shallow1 = self.shallow_feat1(x)
-        shallow2 = self.shallow_feat2(x)
-        shallow3 = self.shallow_feat3(x)
-
-        ## Enter the UNet-CAB
-        x1 = self.stage1_encoder(shallow1)
-        x1_D = self.stage1_decoder(x1)
-        ## Apply SAM
-        x1_out, x1_img = self.sam1o(x1_D[0], x)
-
-        ## Enter the UNet-PAB
-        x2 = self.stage2_encoder(shallow2)
-        x2_D = self.stage2_decoder(x2)
-        ## Apply SAM
-        x2_out, x2_img = self.sam2o(x2_D[0], x)
-
-        ## Enter the UNet-SAB
-        x3 = self.stage3_encoder(shallow3)
-        x3_D = self.stage3_decoder(x3)
-        ## Apply SAM
-        x3_out, x3_img = self.sam3o(x3_D[0], x)
-
-        ## Aggregate SAM features of Stage 1, Stage 2 and Stage 3
-        mix_r = self.mix(x1_img, x2_img, x3_img)
-        mixed_img = self.add123(mix_r[0])
-
-        ## Concat SAM features of Stage 1, Stage 2 and Stage 3
-        concat_feat = self.concat123(torch.cat([x1_out, x2_out, x3_out], 1))
-        x_final = self.tail(concat_feat)
-
-        return x_final + mixed_img
-
-
diff --git a/spaces/52Hz/SUNet_AWGN_denoising/README.md b/spaces/52Hz/SUNet_AWGN_denoising/README.md
deleted file mode 100644
index 5cda9959189f51495f751fd8d64acd9558f9ef0d..0000000000000000000000000000000000000000
--- a/spaces/52Hz/SUNet_AWGN_denoising/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: SUNet_AWGN_denoising
-emoji: 🌪
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/AI-ZTH-03-23/8.Datasets-NER-Biomed-ClinicalTerms/backup.app.py b/spaces/AI-ZTH-03-23/8.Datasets-NER-Biomed-ClinicalTerms/backup.app.py
deleted file mode 100644
index fd97bf2a8592b219ba1c2d4c94187d984e63d114..0000000000000000000000000000000000000000
--- a/spaces/AI-ZTH-03-23/8.Datasets-NER-Biomed-ClinicalTerms/backup.app.py
+++ /dev/null
@@ -1,268 +0,0 @@
-import gradio as gr
-import pandas as pd
-import json
-from collections import defaultdict
-
-# Create tokenizer for biomed model
-from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
-tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")    # https://huggingface.co/d4data/biomedical-ner-all?text=asthma
-model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
-pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-
-# Matplotlib for entity graph
-import matplotlib.pyplot as plt
-plt.switch_backend("Agg")
-
-# Load examples from JSON
-import os 
-
-# Load terminology datasets:
-basedir = os.path.dirname(__file__)
-#dataLOINC = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
-#dataPanels = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-#dataSNOMED = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-#dataOMS = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-#dataICD10 = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv') 
-
-dataLOINC = pd.read_csv(f'LoincTableCore.csv')
-dataPanels = pd.read_csv(f'PanelsAndForms-ACW1208Labeled.csv')     
-dataSNOMED = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-dataOMS = pd.read_csv(f'SnomedOMS.csv')   
-dataICD10 = pd.read_csv(f'ICD10Diagnosis.csv')   
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-EXAMPLES = {}
-#with open(dir_path + "\\" + "examples.json", "r") as f:
-with open("examples.json", "r") as f:
-    example_json = json.load(f)
-    EXAMPLES = {x["text"]: x["label"] for x in example_json}
-
-def MatchLOINC(name):
-    #basedir = os.path.dirname(__file__)
-    pd.set_option("display.max_rows", None)
-    #data = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')    
-    data = dataLOINC
-    swith=data.loc[data['COMPONENT'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchLOINCPanelsandForms(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
-    data = dataPanels
-    # Assessment Name:
-    #swith=data.loc[data['ParentName'].str.contains(name, case=False, na=False)]
-    # Assessment Question:
-    swith=data.loc[data['LoincName'].str.contains(name, case=False, na=False)]
-    return swith
-    
-def MatchSNOMED(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-    data = dataSNOMED
-    swith=data.loc[data['term'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchOMS(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
-    data = dataOMS
-    swith=data.loc[data['SNOMED CT'].str.contains(name, case=False, na=False)]
-    return swith
-
-def MatchICD10(name):
-    #basedir = os.path.dirname(__file__)
-    #data = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv')   
-    data = dataICD10
-    swith=data.loc[data['Description'].str.contains(name, case=False, na=False)]
-    return swith
-
-def SaveResult(text, outputfileName):
-    #try:
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    print("Saving: " + text + " to " + savePath)
-    from os.path import exists
-    file_exists = exists(savePath)
-    if file_exists:
-        with open(outputfileName, "a") as f: #append
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    else:
-        with open(outputfileName, "w") as f: #write
-            #for line in text:
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    #except ValueError as err:
-    #    raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return
-
-def loadFile(filename):
-    try:
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-
-        print("Loading: " + loadPath)
-
-        from os.path import exists
-        file_exists = exists(loadPath)
-
-        if file_exists:
-            with open(loadPath, "r") as f: #read
-                contents = f.read()
-                print(contents)
-                return contents
-
-    except ValueError as err:
-        raise ValueError("File Save Error in SaveResult \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return ""
-
-def get_today_filename():
-    from datetime import datetime
-    date = datetime.now().strftime("%Y_%m_%d-%I.%M.%S.%p")
-    #print(f"filename_{date}")  'filename_2023_01_12-03-29-22_AM'
-    return f"MedNER_{date}.csv"
-
-def get_base(filename): 
-        basedir = os.path.dirname(__file__)
-        loadPath = basedir + "\\" + filename
-        #print("Loading: " + loadPath)
-        return loadPath
-
-def group_by_entity(raw):
-    outputFile = get_base(get_today_filename())
-    out = defaultdict(int)
-
-    for ent in raw:
-        out[ent["entity_group"]] += 1
-        myEntityGroup = ent["entity_group"]
-        print("Found entity group type: " + myEntityGroup)
-
-        if (myEntityGroup in ['Sign_symptom', 'Detailed_description', 'History', 'Activity', 'Medication' ]):
-            eterm = ent["word"].replace('#','')
-            minlength = 3
-            if len(eterm) > minlength:
-                print("Found eterm: " + eterm)
-                eterm.replace("#","")
-                g1=MatchLOINC(eterm)
-                g2=MatchLOINCPanelsandForms(eterm)
-                g3=MatchSNOMED(eterm)
-                g4=MatchOMS(eterm)
-                g5=MatchICD10(eterm)
-                sAll = ""
-
-                print("Saving to output file " + outputFile)
-                # Create harmonisation output format of input to output code, name, Text
-
-                try: # 18 fields, output to labeled CSV dataset for results teaching on scored regret changes to action plan with data inputs
-                    col = "          1                            2            3         4            5                     6                    7                       8                   9              10                   11                         12       13               14                      15                  16                            17                    18                       19"
-                    
-                    #LOINC
-                    g11 = g1['LOINC_NUM'].to_string().replace(","," ").replace("\n"," ")
-                    g12 = g1['COMPONENT'].to_string().replace(","," ").replace("\n"," ")
-                    s1 = ("LOINC," + myEntityGroup + "," + eterm + ",questions of ," + g12 + "," + g11 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g11 != 'Series([]  )': SaveResult(s1, outputFile)
-
-                    #LOINC Panels
-                    g21 = g2['Loinc'].to_string().replace(","," ").replace("\n"," ")
-                    g22 = g2['LoincName'].to_string().replace(","," ").replace("\n"," ")
-                    g23 = g2['ParentLoinc'].to_string().replace(","," ").replace("\n"," ")
-                    g24 = g2['ParentName'].to_string().replace(","," ").replace("\n"," ")
-                    # s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + ", and Parent codes of ," + g23 + ", with Parent names of ," + g24 + ", Label,Value  ")
-                    s2 = ("LOINC Panel," + myEntityGroup + "," + eterm + ",name of ," + g22 + "," + g21 + "," + g24 + ", and Parent codes of ," + g23 + "," + ", Label,Value  ")
-                    if g21 != 'Series([]  )': SaveResult(s2, outputFile)
-
-                    #SNOMED
-                    g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    g32 = g3['term'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
-                    s3 = ("SNOMED Concept," + myEntityGroup + "," + eterm + ",terms of ," + g32 + "," + g31 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g31 != 'Series([]  )': SaveResult(s3, outputFile)
-
-                    #OMS
-                    g41 = g4['Omaha Code'].to_string().replace(","," ").replace("\n"," ")
-                    g42 = g4['SNOMED CT concept ID'].to_string().replace(","," ").replace("\n"," ")
-                    g43 = g4['SNOMED CT'].to_string().replace(","," ").replace("\n"," ")
-                    g44 = g4['PR'].to_string().replace(","," ").replace("\n"," ")
-                    g45 = g4['S&S'].to_string().replace(","," ").replace("\n"," ")
-                    s4 = ("OMS," + myEntityGroup + "," + eterm + ",concepts of ," + g44 + "," + g45 + ", and SNOMED codes of ," + g43 + ", and OMS problem of ," + g42 + ", and OMS Sign Symptom of ," + g41)
-                    if g41 != 'Series([]  )': SaveResult(s4, outputFile)
-
-                    #ICD10
-                    g51 = g5['Code'].to_string().replace(","," ").replace("\n"," ")
-                    g52 = g5['Description'].to_string().replace(","," ").replace("\n"," ")
-                    s5 = ("ICD10," + myEntityGroup + "," + eterm + ",descriptions of ," + g52 + "," + g51 + ", Label,Value, Label,Value, Label,Value  ")
-                    if g51 != 'Series([]  )': SaveResult(s5, outputFile)
-
-                except ValueError as err:
-                    raise ValueError("Error in group by entity \n" + format_tb(err.__traceback__)[0] + err.args[0] + "\nEnd of error message.") from None
-
-    return outputFile
-
-
-def plot_to_figure(grouped):
-    fig = plt.figure()
-    plt.bar(x=list(grouped.keys()), height=list(grouped.values()))
-    plt.margins(0.2)
-    plt.subplots_adjust(bottom=0.4)
-    plt.xticks(rotation=90)
-    return fig
-
-
-def ner(text):
-    raw = pipe(text)
-    ner_content = {
-        "text": text,
-        "entities": [
-            {
-                "entity": x["entity_group"],
-                "word": x["word"],
-                "score": x["score"],
-                "start": x["start"],
-                "end": x["end"],
-            }
-            for x in raw
-        ],
-    }
-    
-    outputFile = group_by_entity(raw)
-    label = EXAMPLES.get(text, "Unknown")
-    outputDataframe = pd.read_csv(outputFile)
-    return (ner_content, outputDataframe, outputFile)
-
-demo = gr.Blocks()
-with demo:
-    gr.Markdown(
-    """
-    # 🩺⚕️NLP Clinical Ontology Biomedical NER 
-    """
-    )
-    input = gr.Textbox(label="Note text", value="")
-
-    with gr.Tab("Biomedical Entity Recognition"):
-        output=[
-            gr.HighlightedText(label="NER", combine_adjacent=True),
-            #gr.JSON(label="Entity Counts"),
-            #gr.Label(label="Rating"),
-            #gr.Plot(label="Bar"),
-            gr.Dataframe(label="Dataframe"),
-            gr.File(label="File"),
-        ]
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-        
-    with gr.Tab("Clinical Terminology Resolution"):
-        with gr.Row(variant="compact"):
-            btnLOINC = gr.Button("LOINC")
-            btnPanels = gr.Button("Panels")
-            btnSNOMED = gr.Button("SNOMED")
-            btnOMS = gr.Button("OMS")
-            btnICD10 = gr.Button("ICD10")
-
-        examples=list(EXAMPLES.keys())  
-        gr.Examples(examples, inputs=input)
-        input.change(fn=ner, inputs=input, outputs=output)
-#layout="vertical"
-demo.launch(debug=True)
diff --git a/spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/filter.py b/spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/filter.py
deleted file mode 100644
index 7ad6ea87c1f10ddd94c544037791d7a4634d5ae1..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/vocoder/bigvgan/alias_free_torch/filter.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
-#   LICENSE is in incl_licenses directory.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-
-if 'sinc' in dir(torch):
-    sinc = torch.sinc
-else:
-    # This code is adopted from adefossez's julius.core.sinc under the MIT License
-    # https://adefossez.github.io/julius/julius/core.html
-    #   LICENSE is in incl_licenses directory.
-    def sinc(x: torch.Tensor):
-        """
-        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
-        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
-        """
-        return torch.where(x == 0,
-                           torch.tensor(1., device=x.device, dtype=x.dtype),
-                           torch.sin(math.pi * x) / math.pi / x)
-
-
-# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
-# https://adefossez.github.io/julius/julius/lowpass.html
-#   LICENSE is in incl_licenses directory.
-def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
-    even = (kernel_size % 2 == 0)
-    half_size = kernel_size // 2
-
-    #For kaiser window
-    delta_f = 4 * half_width
-    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
-    if A > 50.:
-        beta = 0.1102 * (A - 8.7)
-    elif A >= 21.:
-        beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
-    else:
-        beta = 0.
-    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
-
-    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
-    if even:
-        time = (torch.arange(-half_size, half_size) + 0.5)
-    else:
-        time = torch.arange(kernel_size) - half_size
-    if cutoff == 0:
-        filter_ = torch.zeros_like(time)
-    else:
-        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
-        # Normalize filter to have sum = 1, otherwise we will have a small leakage
-        # of the constant component in the input signal.
-        filter_ /= filter_.sum()
-        filter = filter_.view(1, 1, kernel_size)
-
-    return filter
-
-
-class LowPassFilter1d(nn.Module):
-    def __init__(self,
-                 cutoff=0.5,
-                 half_width=0.6,
-                 stride: int = 1,
-                 padding: bool = True,
-                 padding_mode: str = 'replicate',
-                 kernel_size: int = 12):
-        # kernel_size should be even number for stylegan3 setup,
-        # in this implementation, odd number is also possible.
-        super().__init__()
-        if cutoff < -0.:
-            raise ValueError("Minimum cutoff must be larger than zero.")
-        if cutoff > 0.5:
-            raise ValueError("A cutoff above 0.5 does not make sense.")
-        self.kernel_size = kernel_size
-        self.even = (kernel_size % 2 == 0)
-        self.pad_left = kernel_size // 2 - int(self.even)
-        self.pad_right = kernel_size // 2
-        self.stride = stride
-        self.padding = padding
-        self.padding_mode = padding_mode
-        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
-        self.register_buffer("filter", filter)
-
-    #input [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        if self.padding:
-            x = F.pad(x, (self.pad_left, self.pad_right),
-                      mode=self.padding_mode)
-        out = F.conv1d(x, self.filter.expand(C, -1, -1),
-                       stride=self.stride, groups=C)
-
-        return out
\ No newline at end of file
diff --git a/spaces/Aaaaaaaabdualh/poetry2023/app.py b/spaces/Aaaaaaaabdualh/poetry2023/app.py
deleted file mode 100644
index 5b6654d5a405778ddbc9ca5fa5d041aff535f3b5..0000000000000000000000000000000000000000
--- a/spaces/Aaaaaaaabdualh/poetry2023/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import gc 
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
-           ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
-            ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
-              ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
-                 ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
-                   ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
-                    ,1.0, 50, 1.0, 1.0, 114 ],['.'
-                       ,1.0, 50, 1.0, 1.0, 114]]
-                     
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND 
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed 
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long) 
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) 
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
-    if not int(seed) >= 0: seed=114
-    set_seed(seed)
-    gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
-              min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
-               num_beams=5, num_return_sequences=1)[0]["generated_text"]
-    poetry =""
-    for line in gen.split('.')[:-1]:
-        poetry += line #+ "\n"
-    return poetry
-poetry = gr.Interface(fn=sayPoetry, 
-                    inputs=[
-                        gr.Textbox(label="Enter short prompt or select from examples:"), 
-                        gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control  temperature'),
-                        gr.Slider(25, 100, step=1,value=50, label='control  top k'),
-                        gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control  top p'),
-                        gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control  penalty'),
-                        gr.Number(value=139750, precision=0, label='Seed'),
-                    ], 
-                outputs=[gr.Textbox(label="Generated Poetry:")], 
-                    
-                allow_flagging='never',
-                title='Arabic Poetry Generation Demo (updated Jan. 2023)',
-                description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
-                examples=samples,
-                cache_examples=False,
-                article = notes)
-poetry.launch() # show_error = True, debug=True
\ No newline at end of file
diff --git a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/quantization/__init__.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/quantization/__init__.py
deleted file mode 100644
index 836d6eb518978480c6b95d6f29ce4f84a9428793..0000000000000000000000000000000000000000
--- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/quantization/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .vq import ResidualVectorQuantizer
-from .base import BaseQuantizer, DummyQuantizer, QuantizedResult
diff --git a/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/conversation.py b/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/conversation.py
deleted file mode 100644
index 3c356cf2f9772e8ab438628848e5ed2c1414118d..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/conversation.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from __future__ import annotations
-from colorama import Fore
-
-# import logging
-from agentverse.logging import get_logger
-import bdb
-from string import Template
-from typing import TYPE_CHECKING, List
-
-from agentverse.message import Message
-
-#from . import agent_registry
-#from .base import BaseAgent
-from agentverse.agents import agent_registry
-from agentverse.agents.base import BaseAgent
-
-logger = get_logger()
-
-
-@agent_registry.register("conversation")
-class ConversationAgent(BaseAgent):
-    def step(self, env_description: str = "") -> Message:
-        prompt = self._fill_prompt_template(env_description)
-
-        parsed_response = None
-        for i in range(self.max_retry):
-            try:
-                response = self.llm.generate_response(prompt)
-                parsed_response = self.output_parser.parse(response)
-                break
-            except KeyboardInterrupt:
-                raise
-            except Exception as e:
-                logger.error(e)
-                logger.warn("Retrying...")
-                continue
-
-        if parsed_response is None:
-            logger.error(f"{self.name} failed to generate valid response.")
-
-        message = Message(
-            content=""
-            if parsed_response is None
-            else parsed_response.return_values["output"],
-            sender=self.name,
-            receiver=self.get_receiver(),
-        )
-        return message
-
-    async def astep(self, env_description: str = "") -> Message:
-        """Asynchronous version of step"""
-        prompt = self._fill_prompt_template(env_description)
-
-        parsed_response = None
-        for i in range(self.max_retry):
-            try:
-                # if self.name == "Code Reviewer":
-                logger.debug(prompt, "Prompt", Fore.CYAN)
-                response = await self.llm.agenerate_response(prompt)
-
-                # logging.info(f"{self.name}'s request result:"
-                #              f" {response.content}")
-                parsed_response = self.output_parser.parse(response)
-                break
-            except (KeyboardInterrupt, bdb.BdbQuit):
-                raise
-            except Exception as e:
-                logger.error(e)
-                logger.warning("Retrying...")
-                continue
-
-        if parsed_response is None:
-            logger.error(f"{self.name} failed to generate valid response.")
-
-        message = Message(
-            content=""
-            if parsed_response is None
-            else parsed_response.return_values["output"],
-            sender=self.name,
-            receiver=self.get_receiver(),
-        )
-        return message
-
-    def _fill_prompt_template(self, env_description: str = "") -> str:
-        """Fill the placeholders in the prompt template
-
-        In the conversation agent, three placeholders are supported:
-        - ${agent_name}: the name of the agent
-        - ${env_description}: the description of the environment
-        - ${role_description}: the description of the role of the agent
-        - ${chat_history}: the chat history of the agent
-        """
-        input_arguments = {
-            "agent_name": self.name,
-            "env_description": env_description,
-            "role_description": self.role_description,
-            "chat_history": self.memory.to_string(add_sender_prefix=True),
-        }
-        return Template(self.prompt_template).safe_substitute(input_arguments)
-
-    def add_message_to_memory(self, messages: List[Message]) -> None:
-        self.memory.add_message(messages)
-
-    def reset(self) -> None:
-        """Reset the agent"""
-        self.memory.reset()
-        # TODO: reset receiver
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/evaluator/__init__.py b/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/evaluator/__init__.py
deleted file mode 100644
index 016e72c67bc445fe2fef58f1cca31aa6b7840831..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/evaluator/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from agentverse.registry import Registry
-
-evaluator_registry = Registry(name="EvaluatorRegistry")
-
-from .base import BaseEvaluator, NoneEvaluator
-from .basic import BasicEvaluator
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorpicker/ColorPicker.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorpicker/ColorPicker.d.ts
deleted file mode 100644
index 5e8ea508dc550d7f47e6eb46b6b23d848be5b39e..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorpicker/ColorPicker.d.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-import Sizer from '../../sizer/Sizer';
-
-export default ColorPicker;
-
-declare namespace ColorPicker {
-    interface IConfig extends Sizer.IConfig {
-        background?: Phaser.GameObjects.GameObject,
-
-        hPalette?: {
-            position?: 0 | 1 | 2 | 3 | 'bottom' | 'left' | 'top' | 'right',
-            size?: number, width?: number, height?: number,
-        },
-
-        svPalette?: {
-            width?: number, height?: number,
-        },
-
-        valuechangeCallback: (newValue: number, oldValue: number, colorPicker: ColorPicker) => void,
-        valuechangeCallbackScope?: Object,
-
-        value?: number,
-    }
-}
-
-declare class ColorPicker extends Sizer {
-    constructor(
-        scene: Phaser.Scene,
-        config?: ColorPicker.IConfig
-    );
-
-    setValue(value: number): this;
-    value: number;
-
-    setColor(color: number): this;
-    color: number;
-
-
-}
\ No newline at end of file
diff --git a/spaces/AlexZou/Deploy_Restoration/model/IAT_main.py b/spaces/AlexZou/Deploy_Restoration/model/IAT_main.py
deleted file mode 100644
index 0c050dbd72bfa1a28a2878c15eaf42ff8b4bb89a..0000000000000000000000000000000000000000
--- a/spaces/AlexZou/Deploy_Restoration/model/IAT_main.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import numpy as np
-from torch import nn
-import torch.nn.functional as F
-import os
-import math
-
-from timm.models.layers import trunc_normal_
-from model.blocks import CBlock_ln, SwinTransformerBlock
-from model.global_net import Global_pred
-
-class Local_pred(nn.Module):
-    def __init__(self, dim=16, number=4, type='ccc'):
-        super(Local_pred, self).__init__()
-        # initial convolution
-        self.conv1 = nn.Conv2d(3, dim, 3, padding=1, groups=1)
-        self.relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        # main blocks
-        block = CBlock_ln(dim)
-        block_t = SwinTransformerBlock(dim)  # head number
-        if type =='ccc':  
-            #blocks1, blocks2 = [block for _ in range(number)], [block for _ in range(number)]
-            blocks1 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-            blocks2 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-        elif type =='ttt':
-            blocks1, blocks2 = [block_t for _ in range(number)], [block_t for _ in range(number)]
-        elif type =='cct':
-            blocks1, blocks2 = [block, block, block_t], [block, block, block_t]
-        #    block1 = [CBlock_ln(16), nn.Conv2d(16,24,3,1,1)]
-        self.mul_blocks = nn.Sequential(*blocks1, nn.Conv2d(dim, 3, 3, 1, 1), nn.ReLU())
-        self.add_blocks = nn.Sequential(*blocks2, nn.Conv2d(dim, 3, 3, 1, 1), nn.Tanh())
-
-
-    def forward(self, img):
-        img1 = self.relu(self.conv1(img))
-        mul = self.mul_blocks(img1)
-        add = self.add_blocks(img1)
-
-        return mul, add
-
-# Short Cut Connection on Final Layer
-class Local_pred_S(nn.Module):
-    def __init__(self, in_dim=3, dim=16, number=4, type='ccc'):
-        super(Local_pred_S, self).__init__()
-        # initial convolution
-        self.conv1 = nn.Conv2d(in_dim, dim, 3, padding=1, groups=1)
-        self.relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        # main blocks
-        block = CBlock_ln(dim)
-        block_t = SwinTransformerBlock(dim)  # head number
-        if type =='ccc':
-            blocks1 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-            blocks2 = [CBlock_ln(16, drop_path=0.01), CBlock_ln(16, drop_path=0.05), CBlock_ln(16, drop_path=0.1)]
-        elif type =='ttt':
-            blocks1, blocks2 = [block_t for _ in range(number)], [block_t for _ in range(number)]
-        elif type =='cct':
-            blocks1, blocks2 = [block, block, block_t], [block, block, block_t]
-        #    block1 = [CBlock_ln(16), nn.Conv2d(16,24,3,1,1)]
-        self.mul_blocks = nn.Sequential(*blocks1)
-        self.add_blocks = nn.Sequential(*blocks2)
-
-        self.mul_end = nn.Sequential(nn.Conv2d(dim, 3, 3, 1, 1), nn.ReLU())
-        self.add_end = nn.Sequential(nn.Conv2d(dim, 3, 3, 1, 1), nn.Tanh())
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-        elif isinstance(m, nn.Conv2d):
-            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-            fan_out //= m.groups
-            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
-            if m.bias is not None:
-                m.bias.data.zero_()
-            
-            
-
-    def forward(self, img):
-        img1 = self.relu(self.conv1(img))
-        # short cut connection
-        mul = self.mul_blocks(img1) + img1
-        add = self.add_blocks(img1) + img1
-        mul = self.mul_end(mul)
-        add = self.add_end(add)
-
-        return mul, add
-
-class IAT(nn.Module):
-    def __init__(self, in_dim=3, with_global=True, type='lol'):
-        super(IAT, self).__init__()
-        #self.local_net = Local_pred()
-        
-        self.local_net = Local_pred_S(in_dim=in_dim)
-
-        self.with_global = with_global
-        if self.with_global:
-            self.global_net = Global_pred(in_channels=in_dim, type=type)
-
-    def apply_color(self, image, ccm):
-        shape = image.shape
-        image = image.view(-1, 3)
-        image = torch.tensordot(image, ccm, dims=[[-1], [-1]])
-        image = image.view(shape)
-        return torch.clamp(image, 1e-8, 1.0)
-
-    def forward(self, img_low):
-        #print(self.with_global)
-        mul, add = self.local_net(img_low)
-        img_high = (img_low.mul(mul)).add(add)
-
-        if not self.with_global:
-            return img_high
-        
-        else:
-            gamma, color = self.global_net(img_low)
-            b = img_high.shape[0]
-            img_high = img_high.permute(0, 2, 3, 1)  # (B,C,H,W) -- (B,H,W,C)
-            img_high = torch.stack([self.apply_color(img_high[i,:,:,:], color[i,:,:])**gamma[i,:] for i in range(b)], dim=0)
-            img_high = img_high.permute(0, 3, 1, 2)  # (B,H,W,C) -- (B,C,H,W)
-            return img_high
-
-
-if __name__ == "__main__":
-    os.environ['CUDA_VISIBLE_DEVICES']='3'
-    img = torch.Tensor(1, 3, 400, 600)
-    net = IAT()
-    print('total parameters:', sum(param.numel() for param in net.parameters()))
-    _, _, high = net(img)
\ No newline at end of file
diff --git a/spaces/Altinas/vits-uma-genshin-honkais/attentions.py b/spaces/Altinas/vits-uma-genshin-honkais/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/Altinas/vits-uma-genshin-honkais/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/prod_cons.h b/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp b/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp
deleted file mode 100644
index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
-                            int up_x, int up_y, int down_x, int down_y,
-                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
-                        int up_x, int up_y, int down_x, int down_y,
-                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(kernel);
-
-    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py
deleted file mode 100644
index be2742098fb8f1e46bbb16c9d3e2e20c2e3083aa..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,26 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
-# model settings
-model = dict(
-    neck=[
-        dict(
-            type='FPN',
-            in_channels=[256, 512, 1024, 2048],
-            out_channels=256,
-            start_level=1,
-            add_extra_convs='on_input',
-            num_outs=5),
-        dict(
-            type='BFP',
-            in_channels=256,
-            num_levels=5,
-            refine_level=1,
-            refine_type='non_local')
-    ],
-    bbox_head=dict(
-        loss_bbox=dict(
-            _delete_=True,
-            type='BalancedL1Loss',
-            alpha=0.5,
-            gamma=1.5,
-            beta=0.11,
-            loss_weight=1.0)))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/xml_style.py b/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/xml_style.py
deleted file mode 100644
index 71069488b0f6da3b37e588228f44460ce5f00679..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/datasets/xml_style.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import os.path as osp
-import xml.etree.ElementTree as ET
-
-import mmcv
-import numpy as np
-from PIL import Image
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class XMLDataset(CustomDataset):
-    """XML dataset for detection.
-
-    Args:
-        min_size (int | float, optional): The minimum size of bounding
-            boxes in the images. If the size of a bounding box is less than
-            ``min_size``, it would be add to ignored field.
-    """
-
-    def __init__(self, min_size=None, **kwargs):
-        assert self.CLASSES or kwargs.get(
-            'classes', None), 'CLASSES in `XMLDataset` can not be None.'
-        super(XMLDataset, self).__init__(**kwargs)
-        self.cat2label = {cat: i for i, cat in enumerate(self.CLASSES)}
-        self.min_size = min_size
-
-    def load_annotations(self, ann_file):
-        """Load annotation from XML style ann_file.
-
-        Args:
-            ann_file (str): Path of XML file.
-
-        Returns:
-            list[dict]: Annotation info from XML file.
-        """
-
-        data_infos = []
-        img_ids = mmcv.list_from_file(ann_file)
-        for img_id in img_ids:
-            filename = f'JPEGImages/{img_id}.jpg'
-            xml_path = osp.join(self.img_prefix, 'Annotations',
-                                f'{img_id}.xml')
-            tree = ET.parse(xml_path)
-            root = tree.getroot()
-            size = root.find('size')
-            if size is not None:
-                width = int(size.find('width').text)
-                height = int(size.find('height').text)
-            else:
-                img_path = osp.join(self.img_prefix, 'JPEGImages',
-                                    '{}.jpg'.format(img_id))
-                img = Image.open(img_path)
-                width, height = img.size
-            data_infos.append(
-                dict(id=img_id, filename=filename, width=width, height=height))
-
-        return data_infos
-
-    def _filter_imgs(self, min_size=32):
-        """Filter images too small or without annotation."""
-        valid_inds = []
-        for i, img_info in enumerate(self.data_infos):
-            if min(img_info['width'], img_info['height']) < min_size:
-                continue
-            if self.filter_empty_gt:
-                img_id = img_info['id']
-                xml_path = osp.join(self.img_prefix, 'Annotations',
-                                    f'{img_id}.xml')
-                tree = ET.parse(xml_path)
-                root = tree.getroot()
-                for obj in root.findall('object'):
-                    name = obj.find('name').text
-                    if name in self.CLASSES:
-                        valid_inds.append(i)
-                        break
-            else:
-                valid_inds.append(i)
-        return valid_inds
-
-    def get_ann_info(self, idx):
-        """Get annotation from XML file by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            dict: Annotation info of specified index.
-        """
-
-        img_id = self.data_infos[idx]['id']
-        xml_path = osp.join(self.img_prefix, 'Annotations', f'{img_id}.xml')
-        tree = ET.parse(xml_path)
-        root = tree.getroot()
-        bboxes = []
-        labels = []
-        bboxes_ignore = []
-        labels_ignore = []
-        for obj in root.findall('object'):
-            name = obj.find('name').text
-            if name not in self.CLASSES:
-                continue
-            label = self.cat2label[name]
-            difficult = obj.find('difficult')
-            difficult = 0 if difficult is None else int(difficult.text)
-            bnd_box = obj.find('bndbox')
-            # TODO: check whether it is necessary to use int
-            # Coordinates may be float type
-            bbox = [
-                int(float(bnd_box.find('xmin').text)),
-                int(float(bnd_box.find('ymin').text)),
-                int(float(bnd_box.find('xmax').text)),
-                int(float(bnd_box.find('ymax').text))
-            ]
-            ignore = False
-            if self.min_size:
-                assert not self.test_mode
-                w = bbox[2] - bbox[0]
-                h = bbox[3] - bbox[1]
-                if w < self.min_size or h < self.min_size:
-                    ignore = True
-            if difficult or ignore:
-                bboxes_ignore.append(bbox)
-                labels_ignore.append(label)
-            else:
-                bboxes.append(bbox)
-                labels.append(label)
-        if not bboxes:
-            bboxes = np.zeros((0, 4))
-            labels = np.zeros((0, ))
-        else:
-            bboxes = np.array(bboxes, ndmin=2) - 1
-            labels = np.array(labels)
-        if not bboxes_ignore:
-            bboxes_ignore = np.zeros((0, 4))
-            labels_ignore = np.zeros((0, ))
-        else:
-            bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1
-            labels_ignore = np.array(labels_ignore)
-        ann = dict(
-            bboxes=bboxes.astype(np.float32),
-            labels=labels.astype(np.int64),
-            bboxes_ignore=bboxes_ignore.astype(np.float32),
-            labels_ignore=labels_ignore.astype(np.int64))
-        return ann
-
-    def get_cat_ids(self, idx):
-        """Get category ids in XML file by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            list[int]: All categories in the image of specified index.
-        """
-
-        cat_ids = []
-        img_id = self.data_infos[idx]['id']
-        xml_path = osp.join(self.img_prefix, 'Annotations', f'{img_id}.xml')
-        tree = ET.parse(xml_path)
-        root = tree.getroot()
-        for obj in root.findall('object'):
-            name = obj.find('name').text
-            if name not in self.CLASSES:
-                continue
-            label = self.cat2label[name]
-            cat_ids.append(label)
-
-        return cat_ids
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
deleted file mode 100644
index a6a7688c7a5f6ff1209eb7c44abdd105e91a2b76..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Ariharasudhan/YoloV5/utils/aws/mime.sh b/spaces/Ariharasudhan/YoloV5/utils/aws/mime.sh
deleted file mode 100644
index c319a83cfbdf09bea634c3bd9fca737c0b1dd505..0000000000000000000000000000000000000000
--- a/spaces/Ariharasudhan/YoloV5/utils/aws/mime.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/
-# This script will run on every instance restart, not only on first start
-# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---
-
-Content-Type: multipart/mixed; boundary="//"
-MIME-Version: 1.0
-
---//
-Content-Type: text/cloud-config; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="cloud-config.txt"
-
-#cloud-config
-cloud_final_modules:
-- [scripts-user, always]
-
---//
-Content-Type: text/x-shellscript; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="userdata.txt"
-
-#!/bin/bash
-# --- paste contents of userdata.sh here ---
---//
diff --git a/spaces/Artificio/AdversarialArt/src/.ipynb_checkpoints/utils-checkpoint.py b/spaces/Artificio/AdversarialArt/src/.ipynb_checkpoints/utils-checkpoint.py
deleted file mode 100644
index 59e8228aff6ed528250f87234287d80b0b85c96b..0000000000000000000000000000000000000000
--- a/spaces/Artificio/AdversarialArt/src/.ipynb_checkpoints/utils-checkpoint.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from PIL import Image
-import torch
-import torch.nn as nn
-from typing import Dict, Iterable, Callable
-from torch import Tensor
-import glob
-from tqdm import tqdm
-import numpy as np
-from PIL import ImageFile
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-Image.MAX_IMAGE_PIXELS = None
-
-
-# +
-class RobustModel(nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-    def forward(self, x, *args, **kwargs):
-        return self.model(x)
-    
-    
-class CustomArt(torch.utils.data.Dataset):
-    def __init__(self, image,transforms=None):
-        self.transforms   = transforms
-        self.image         = image
-        self.mean         = torch.tensor([0.4850, 0.4560, 0.4060])
-        self.std          = torch.tensor([0.2290, 0.2240, 0.2250])
-    def __getitem__(self, idx):
-        if self.transforms:
-            img = self.transforms(self.image)
-        return torch.as_tensor(img, dtype=torch.float)
-    
-    def __len__(self):
-        return len(self.image)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/__init__.py
deleted file mode 100644
index 7855226e4b500142deef8fb247cd33a9a991d122..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/models/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""A package that contains models that represent entities.
-"""
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/token.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/token.py
deleted file mode 100644
index e3e565ad591485563a93db89609213c00ca16ca3..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pygments/token.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""
-    pygments.token
-    ~~~~~~~~~~~~~~
-
-    Basic token types and the standard tokens.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-
-class _TokenType(tuple):
-    parent = None
-
-    def split(self):
-        buf = []
-        node = self
-        while node is not None:
-            buf.append(node)
-            node = node.parent
-        buf.reverse()
-        return buf
-
-    def __init__(self, *args):
-        # no need to call super.__init__
-        self.subtypes = set()
-
-    def __contains__(self, val):
-        return self is val or (
-            type(val) is self.__class__ and
-            val[:len(self)] == self
-        )
-
-    def __getattr__(self, val):
-        if not val or not val[0].isupper():
-            return tuple.__getattribute__(self, val)
-        new = _TokenType(self + (val,))
-        setattr(self, val, new)
-        self.subtypes.add(new)
-        new.parent = self
-        return new
-
-    def __repr__(self):
-        return 'Token' + (self and '.' or '') + '.'.join(self)
-
-    def __copy__(self):
-        # These instances are supposed to be singletons
-        return self
-
-    def __deepcopy__(self, memo):
-        # These instances are supposed to be singletons
-        return self
-
-
-Token = _TokenType()
-
-# Special token types
-Text = Token.Text
-Whitespace = Text.Whitespace
-Escape = Token.Escape
-Error = Token.Error
-# Text that doesn't belong to this lexer (e.g. HTML in PHP)
-Other = Token.Other
-
-# Common token types for source code
-Keyword = Token.Keyword
-Name = Token.Name
-Literal = Token.Literal
-String = Literal.String
-Number = Literal.Number
-Punctuation = Token.Punctuation
-Operator = Token.Operator
-Comment = Token.Comment
-
-# Generic types for non-source code
-Generic = Token.Generic
-
-# String and some others are not direct children of Token.
-# alias them:
-Token.Token = Token
-Token.String = String
-Token.Number = Number
-
-
-def is_token_subtype(ttype, other):
-    """
-    Return True if ``ttype`` is a subtype of ``other``.
-
-    exists for backwards compatibility. use ``ttype in other`` now.
-    """
-    return ttype in other
-
-
-def string_to_tokentype(s):
-    """
-    Convert a string into a token type::
-
-        >>> string_to_token('String.Double')
-        Token.Literal.String.Double
-        >>> string_to_token('Token.Literal.Number')
-        Token.Literal.Number
-        >>> string_to_token('')
-        Token
-
-    Tokens that are already tokens are returned unchanged:
-
-        >>> string_to_token(String)
-        Token.Literal.String
-    """
-    if isinstance(s, _TokenType):
-        return s
-    if not s:
-        return Token
-    node = Token
-    for item in s.split('.'):
-        node = getattr(node, item)
-    return node
-
-
-# Map standard token types to short names, used in CSS class naming.
-# If you add a new item, please be sure to run this file to perform
-# a consistency check for duplicate values.
-STANDARD_TYPES = {
-    Token:                         '',
-
-    Text:                          '',
-    Whitespace:                    'w',
-    Escape:                        'esc',
-    Error:                         'err',
-    Other:                         'x',
-
-    Keyword:                       'k',
-    Keyword.Constant:              'kc',
-    Keyword.Declaration:           'kd',
-    Keyword.Namespace:             'kn',
-    Keyword.Pseudo:                'kp',
-    Keyword.Reserved:              'kr',
-    Keyword.Type:                  'kt',
-
-    Name:                          'n',
-    Name.Attribute:                'na',
-    Name.Builtin:                  'nb',
-    Name.Builtin.Pseudo:           'bp',
-    Name.Class:                    'nc',
-    Name.Constant:                 'no',
-    Name.Decorator:                'nd',
-    Name.Entity:                   'ni',
-    Name.Exception:                'ne',
-    Name.Function:                 'nf',
-    Name.Function.Magic:           'fm',
-    Name.Property:                 'py',
-    Name.Label:                    'nl',
-    Name.Namespace:                'nn',
-    Name.Other:                    'nx',
-    Name.Tag:                      'nt',
-    Name.Variable:                 'nv',
-    Name.Variable.Class:           'vc',
-    Name.Variable.Global:          'vg',
-    Name.Variable.Instance:        'vi',
-    Name.Variable.Magic:           'vm',
-
-    Literal:                       'l',
-    Literal.Date:                  'ld',
-
-    String:                        's',
-    String.Affix:                  'sa',
-    String.Backtick:               'sb',
-    String.Char:                   'sc',
-    String.Delimiter:              'dl',
-    String.Doc:                    'sd',
-    String.Double:                 's2',
-    String.Escape:                 'se',
-    String.Heredoc:                'sh',
-    String.Interpol:               'si',
-    String.Other:                  'sx',
-    String.Regex:                  'sr',
-    String.Single:                 's1',
-    String.Symbol:                 'ss',
-
-    Number:                        'm',
-    Number.Bin:                    'mb',
-    Number.Float:                  'mf',
-    Number.Hex:                    'mh',
-    Number.Integer:                'mi',
-    Number.Integer.Long:           'il',
-    Number.Oct:                    'mo',
-
-    Operator:                      'o',
-    Operator.Word:                 'ow',
-
-    Punctuation:                   'p',
-    Punctuation.Marker:            'pm',
-
-    Comment:                       'c',
-    Comment.Hashbang:              'ch',
-    Comment.Multiline:             'cm',
-    Comment.Preproc:               'cp',
-    Comment.PreprocFile:           'cpf',
-    Comment.Single:                'c1',
-    Comment.Special:               'cs',
-
-    Generic:                       'g',
-    Generic.Deleted:               'gd',
-    Generic.Emph:                  'ge',
-    Generic.Error:                 'gr',
-    Generic.Heading:               'gh',
-    Generic.Inserted:              'gi',
-    Generic.Output:                'go',
-    Generic.Prompt:                'gp',
-    Generic.Strong:                'gs',
-    Generic.Subheading:            'gu',
-    Generic.Traceback:             'gt',
-}
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/bar.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/bar.py
deleted file mode 100644
index ed86a552d1ca6baa0cfd48ec73a7a5c952d047c9..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/bar.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from typing import Optional, Union
-
-from .color import Color
-from .console import Console, ConsoleOptions, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import Style
-
-# There are left-aligned characters for 1/8 to 7/8, but
-# the right-aligned characters exist only for 1/8 and 4/8.
-BEGIN_BLOCK_ELEMENTS = ["█", "█", "█", "▐", "▐", "▐", "▕", "▕"]
-END_BLOCK_ELEMENTS = [" ", "▏", "▎", "▍", "▌", "▋", "▊", "▉"]
-FULL_BLOCK = "█"
-
-
-class Bar(JupyterMixin):
-    """Renders a solid block bar.
-
-    Args:
-        size (float): Value for the end of the bar.
-        begin (float): Begin point (between 0 and size, inclusive).
-        end (float): End point (between 0 and size, inclusive).
-        width (int, optional): Width of the bar, or ``None`` for maximum width. Defaults to None.
-        color (Union[Color, str], optional): Color of the bar. Defaults to "default".
-        bgcolor (Union[Color, str], optional): Color of bar background. Defaults to "default".
-    """
-
-    def __init__(
-        self,
-        size: float,
-        begin: float,
-        end: float,
-        *,
-        width: Optional[int] = None,
-        color: Union[Color, str] = "default",
-        bgcolor: Union[Color, str] = "default",
-    ):
-        self.size = size
-        self.begin = max(begin, 0)
-        self.end = min(end, size)
-        self.width = width
-        self.style = Style(color=color, bgcolor=bgcolor)
-
-    def __repr__(self) -> str:
-        return f"Bar({self.size}, {self.begin}, {self.end})"
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-
-        width = min(
-            self.width if self.width is not None else options.max_width,
-            options.max_width,
-        )
-
-        if self.begin >= self.end:
-            yield Segment(" " * width, self.style)
-            yield Segment.line()
-            return
-
-        prefix_complete_eights = int(width * 8 * self.begin / self.size)
-        prefix_bar_count = prefix_complete_eights // 8
-        prefix_eights_count = prefix_complete_eights % 8
-
-        body_complete_eights = int(width * 8 * self.end / self.size)
-        body_bar_count = body_complete_eights // 8
-        body_eights_count = body_complete_eights % 8
-
-        # When start and end fall into the same cell, we ideally should render
-        # a symbol that's "center-aligned", but there is no good symbol in Unicode.
-        # In this case, we fall back to right-aligned block symbol for simplicity.
-
-        prefix = " " * prefix_bar_count
-        if prefix_eights_count:
-            prefix += BEGIN_BLOCK_ELEMENTS[prefix_eights_count]
-
-        body = FULL_BLOCK * body_bar_count
-        if body_eights_count:
-            body += END_BLOCK_ELEMENTS[body_eights_count]
-
-        suffix = " " * (width - len(body))
-
-        yield Segment(prefix + body[len(prefix) :] + suffix, self.style)
-        yield Segment.line()
-
-    def __rich_measure__(
-        self, console: Console, options: ConsoleOptions
-    ) -> Measurement:
-        return (
-            Measurement(self.width, self.width)
-            if self.width is not None
-            else Measurement(4, options.max_width)
-        )
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/_structures.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/_structures.py
deleted file mode 100644
index 90a6465f9682c886363eea5327dac64bf623a6ff..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/_structures.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-
-class InfinityType:
-    def __repr__(self) -> str:
-        return "Infinity"
-
-    def __hash__(self) -> int:
-        return hash(repr(self))
-
-    def __lt__(self, other: object) -> bool:
-        return False
-
-    def __le__(self, other: object) -> bool:
-        return False
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, self.__class__)
-
-    def __gt__(self, other: object) -> bool:
-        return True
-
-    def __ge__(self, other: object) -> bool:
-        return True
-
-    def __neg__(self: object) -> "NegativeInfinityType":
-        return NegativeInfinity
-
-
-Infinity = InfinityType()
-
-
-class NegativeInfinityType:
-    def __repr__(self) -> str:
-        return "-Infinity"
-
-    def __hash__(self) -> int:
-        return hash(repr(self))
-
-    def __lt__(self, other: object) -> bool:
-        return True
-
-    def __le__(self, other: object) -> bool:
-        return True
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, self.__class__)
-
-    def __gt__(self, other: object) -> bool:
-        return False
-
-    def __ge__(self, other: object) -> bool:
-        return False
-
-    def __neg__(self: object) -> InfinityType:
-        return Infinity
-
-
-NegativeInfinity = NegativeInfinityType()
diff --git a/spaces/Awesimo/jojogan/e4e/utils/alignment.py b/spaces/Awesimo/jojogan/e4e/utils/alignment.py
deleted file mode 100644
index a02798f0f7c9fdcc319f7884a491b9e6580cc8aa..0000000000000000000000000000000000000000
--- a/spaces/Awesimo/jojogan/e4e/utils/alignment.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import numpy as np
-import PIL
-import PIL.Image
-import scipy
-import scipy.ndimage
-import dlib
-
-
-def get_landmark(filepath, predictor):
-    """get landmark with dlib
-    :return: np.array shape=(68, 2)
-    """
-    detector = dlib.get_frontal_face_detector()
-
-    img = dlib.load_rgb_image(filepath)
-    dets = detector(img, 1)
-
-    for k, d in enumerate(dets):
-        shape = predictor(img, d)
-
-    t = list(shape.parts())
-    a = []
-    for tt in t:
-        a.append([tt.x, tt.y])
-    lm = np.array(a)
-    return lm
-
-
-def align_face(filepath, predictor):
-    """
-    :param filepath: str
-    :return: PIL Image
-    """
-
-    lm = get_landmark(filepath, predictor)
-
-    lm_chin = lm[0: 17]  # left-right
-    lm_eyebrow_left = lm[17: 22]  # left-right
-    lm_eyebrow_right = lm[22: 27]  # left-right
-    lm_nose = lm[27: 31]  # top-down
-    lm_nostrils = lm[31: 36]  # top-down
-    lm_eye_left = lm[36: 42]  # left-clockwise
-    lm_eye_right = lm[42: 48]  # left-clockwise
-    lm_mouth_outer = lm[48: 60]  # left-clockwise
-    lm_mouth_inner = lm[60: 68]  # left-clockwise
-
-    # Calculate auxiliary vectors.
-    eye_left = np.mean(lm_eye_left, axis=0)
-    eye_right = np.mean(lm_eye_right, axis=0)
-    eye_avg = (eye_left + eye_right) * 0.5
-    eye_to_eye = eye_right - eye_left
-    mouth_left = lm_mouth_outer[0]
-    mouth_right = lm_mouth_outer[6]
-    mouth_avg = (mouth_left + mouth_right) * 0.5
-    eye_to_mouth = mouth_avg - eye_avg
-
-    # Choose oriented crop rectangle.
-    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-    x /= np.hypot(*x)
-    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-    y = np.flipud(x) * [-1, 1]
-    c = eye_avg + eye_to_mouth * 0.1
-    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-    qsize = np.hypot(*x) * 2
-
-    # read image
-    img = PIL.Image.open(filepath)
-
-    output_size = 256
-    transform_size = 256
-    enable_padding = True
-
-    # Shrink.
-    shrink = int(np.floor(qsize / output_size * 0.5))
-    if shrink > 1:
-        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-        img = img.resize(rsize, PIL.Image.ANTIALIAS)
-        quad /= shrink
-        qsize /= shrink
-
-    # Crop.
-    border = max(int(np.rint(qsize * 0.1)), 3)
-    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-            int(np.ceil(max(quad[:, 1]))))
-    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
-            min(crop[3] + border, img.size[1]))
-    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-        img = img.crop(crop)
-        quad -= crop[0:2]
-
-    # Pad.
-    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-           int(np.ceil(max(quad[:, 1]))))
-    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
-           max(pad[3] - img.size[1] + border, 0))
-    if enable_padding and max(pad) > border - 4:
-        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        h, w, _ = img.shape
-        y, x, _ = np.ogrid[:h, :w, :1]
-        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
-                          1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
-        blur = qsize * 0.02
-        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        quad += pad[:2]
-
-    # Transform.
-    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
-    if output_size < transform_size:
-        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
-
-    # Return aligned image.
-    return img
diff --git a/spaces/BAAI/vid2vid-zero/vid2vid_zero/p2p/null_text_w_ptp.py b/spaces/BAAI/vid2vid-zero/vid2vid_zero/p2p/null_text_w_ptp.py
deleted file mode 100644
index c69a8376760ef10cf8aabd528fb6eba40ceea747..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/vid2vid_zero/p2p/null_text_w_ptp.py
+++ /dev/null
@@ -1,504 +0,0 @@
-# Copyright 2022 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from typing import Optional, Union, Tuple, List, Callable, Dict
-from tqdm import tqdm
-import torch
-import torch.nn.functional as nnf
-import numpy as np
-import abc
-from . import ptp_utils
-from . import seq_aligner
-import shutil
-from torch.optim.adam import Adam
-from PIL import Image
-
-
-LOW_RESOURCE = False 
-NUM_DDIM_STEPS = 50
-MAX_NUM_WORDS = 77
-device = torch.device('cuda')
-from transformers import CLIPTextModel, CLIPTokenizer
-
-pretrained_model_path = "checkpoints/CompVis/stable-diffusion-v1-4/"
-
-ldm_stable = None
-tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
-
-
-class LocalBlend:
-    
-    def get_mask(self, maps, alpha, use_pool):
-        k = 1
-        maps = (maps * alpha).sum(-1).mean(1)
-        if use_pool:
-            maps = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 +1), (1, 1), padding=(k, k))
-        mask = nnf.interpolate(maps, size=(x_t.shape[2:]))
-        mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
-        mask = mask.gt(self.th[1-int(use_pool)])
-        mask = mask[:1] + mask
-        return mask
-    
-    def __call__(self, x_t, attention_store):
-        self.counter += 1
-        if self.counter > self.start_blend:
-           
-            maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
-            maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
-            maps = torch.cat(maps, dim=1)
-            mask = self.get_mask(maps, self.alpha_layers, True)
-            if self.substruct_layers is not None:
-                maps_sub = ~self.get_mask(maps, self.substruct_layers, False)
-                mask = mask * maps_sub
-            mask = mask.float()
-            x_t = x_t[:1] + mask * (x_t - x_t[:1])
-        return x_t
-       
-    def __init__(self, prompts: List[str], words: List[List[str]], substruct_words=None, start_blend=0.2, th=(.3, .3)):
-        alpha_layers = torch.zeros(len(prompts),  1, 1, 1, 1, MAX_NUM_WORDS)
-        for i, (prompt, words_) in enumerate(zip(prompts, words)):
-            if type(words_) is str:
-                words_ = [words_]
-            for word in words_:
-                ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
-                alpha_layers[i, :, :, :, :, ind] = 1
-        
-        if substruct_words is not None:
-            substruct_layers = torch.zeros(len(prompts),  1, 1, 1, 1, MAX_NUM_WORDS)
-            for i, (prompt, words_) in enumerate(zip(prompts, substruct_words)):
-                if type(words_) is str:
-                    words_ = [words_]
-                for word in words_:
-                    ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
-                    substruct_layers[i, :, :, :, :, ind] = 1
-            self.substruct_layers = substruct_layers.to(device)
-        else:
-            self.substruct_layers = None
-        self.alpha_layers = alpha_layers.to(device)
-        self.start_blend = int(start_blend * NUM_DDIM_STEPS)
-        self.counter = 0 
-        self.th=th
-
-
-class EmptyControl:
-    
-    
-    def step_callback(self, x_t):
-        return x_t
-    
-    def between_steps(self):
-        return
-    
-    def __call__(self, attn, is_cross: bool, place_in_unet: str):
-        return attn
-
-    
-class AttentionControl(abc.ABC):
-    
-    def step_callback(self, x_t):
-        return x_t
-    
-    def between_steps(self):
-        return
-    
-    @property
-    def num_uncond_att_layers(self):
-        return self.num_att_layers if LOW_RESOURCE else 0
-    
-    @abc.abstractmethod
-    def forward (self, attn, is_cross: bool, place_in_unet: str):
-        raise NotImplementedError
-
-    def __call__(self, attn, is_cross: bool, place_in_unet: str):
-        if self.cur_att_layer >= self.num_uncond_att_layers:
-            if LOW_RESOURCE:
-                attn = self.forward(attn, is_cross, place_in_unet)
-            else:
-                h = attn.shape[0]
-                attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
-        self.cur_att_layer += 1
-        if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
-            self.cur_att_layer = 0
-            self.cur_step += 1
-            self.between_steps()
-        return attn
-    
-    def reset(self):
-        self.cur_step = 0
-        self.cur_att_layer = 0
-
-    def __init__(self):
-        self.cur_step = 0
-        self.num_att_layers = -1
-        self.cur_att_layer = 0
-
-
-class SpatialReplace(EmptyControl):
-    
-    def step_callback(self, x_t):
-        if self.cur_step < self.stop_inject:
-            b = x_t.shape[0]
-            x_t = x_t[:1].expand(b, *x_t.shape[1:])
-        return x_t
-
-    def __init__(self, stop_inject: float):
-        super(SpatialReplace, self).__init__()
-        self.stop_inject = int((1 - stop_inject) * NUM_DDIM_STEPS)
-        
-
-class AttentionStore(AttentionControl):
-
-    @staticmethod
-    def get_empty_store():
-        return {"down_cross": [], "mid_cross": [], "up_cross": [],
-                "down_self": [],  "mid_self": [],  "up_self": []}
-
-    def forward(self, attn, is_cross: bool, place_in_unet: str):
-        key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
-        if attn.shape[1] <= 32 ** 2:  # avoid memory overhead
-            self.step_store[key].append(attn)
-        return attn
-
-    def between_steps(self):
-        if len(self.attention_store) == 0:
-            self.attention_store = self.step_store
-        else:
-            for key in self.attention_store:
-                for i in range(len(self.attention_store[key])):
-                    self.attention_store[key][i] += self.step_store[key][i]
-        self.step_store = self.get_empty_store()
-
-    def get_average_attention(self):
-        average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
-        return average_attention
-
-
-    def reset(self):
-        super(AttentionStore, self).reset()
-        self.step_store = self.get_empty_store()
-        self.attention_store = {}
-
-    def __init__(self):
-        super(AttentionStore, self).__init__()
-        self.step_store = self.get_empty_store()
-        self.attention_store = {}
-
-        
-class AttentionControlEdit(AttentionStore, abc.ABC):
-    
-    def step_callback(self, x_t):
-        if self.local_blend is not None:
-            x_t = self.local_blend(x_t, self.attention_store)
-        return x_t
-        
-    def replace_self_attention(self, attn_base, att_replace, place_in_unet):
-        if att_replace.shape[2] <= 32 ** 2:
-            attn_base = attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
-            return attn_base
-        else:
-            return att_replace
-    
-    @abc.abstractmethod
-    def replace_cross_attention(self, attn_base, att_replace):
-        raise NotImplementedError
-    
-    def forward(self, attn, is_cross: bool, place_in_unet: str):
-        super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
-        if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
-            h = attn.shape[0] // (self.batch_size)
-            attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
-            attn_base, attn_repalce = attn[0], attn[1:]
-            if is_cross:
-                alpha_words = self.cross_replace_alpha[self.cur_step]
-                attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
-                attn[1:] = attn_repalce_new
-            else:
-                attn[1:] = self.replace_self_attention(attn_base, attn_repalce, place_in_unet)
-            attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
-        return attn
-    
-    def __init__(self, prompts, num_steps: int,
-                 cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
-                 self_replace_steps: Union[float, Tuple[float, float]],
-                 local_blend: Optional[LocalBlend]):
-        super(AttentionControlEdit, self).__init__()
-        self.batch_size = len(prompts)
-        self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
-        if type(self_replace_steps) is float:
-            self_replace_steps = 0, self_replace_steps
-        self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
-        self.local_blend = local_blend
-
-class AttentionReplace(AttentionControlEdit):
-
-    def replace_cross_attention(self, attn_base, att_replace):
-        return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)
-      
-    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
-                 local_blend: Optional[LocalBlend] = None):
-        super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
-        self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
-        
-
-class AttentionRefine(AttentionControlEdit):
-
-    def replace_cross_attention(self, attn_base, att_replace):
-        attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
-        attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
-        # attn_replace = attn_replace / attn_replace.sum(-1, keepdims=True)
-        return attn_replace
-
-    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
-                 local_blend: Optional[LocalBlend] = None):
-        super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
-        self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
-        self.mapper, alphas = self.mapper.to(device), alphas.to(device)
-        self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
-
-
-class AttentionReweight(AttentionControlEdit):
-
-    def replace_cross_attention(self, attn_base, att_replace):
-        if self.prev_controller is not None:
-            attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
-        attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
-        # attn_replace = attn_replace / attn_replace.sum(-1, keepdims=True)
-        return attn_replace
-
-    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,
-                local_blend: Optional[LocalBlend] = None, controller: Optional[AttentionControlEdit] = None):
-        super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
-        self.equalizer = equalizer.to(device)
-        self.prev_controller = controller
-
-
-def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],
-                  Tuple[float, ...]]):
-    if type(word_select) is int or type(word_select) is str:
-        word_select = (word_select,)
-    equalizer = torch.ones(1, 77)
-    
-    for word, val in zip(word_select, values):
-        inds = ptp_utils.get_word_inds(text, word, tokenizer)
-        equalizer[:, inds] = val
-    return equalizer
-
-def aggregate_attention(attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int):
-    out = []
-    attention_maps = attention_store.get_average_attention()
-    num_pixels = res ** 2
-    for location in from_where:
-        for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
-            if item.shape[1] == num_pixels:
-                cross_maps = item.reshape(len(prompts), -1, res, res, item.shape[-1])[select]
-                out.append(cross_maps)
-    out = torch.cat(out, dim=0)
-    out = out.sum(0) / out.shape[0]
-    return out.cpu()
-
-
-def make_controller(prompts: List[str], is_replace_controller: bool, cross_replace_steps: Dict[str, float], self_replace_steps: float, blend_words=None, equilizer_params=None) -> AttentionControlEdit:
-    if blend_words is None:
-        lb = None
-    else:
-        lb = LocalBlend(prompts, blend_word)
-    if is_replace_controller:
-        controller = AttentionReplace(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, local_blend=lb)
-    else:
-        controller = AttentionRefine(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps, local_blend=lb)
-    if equilizer_params is not None:
-        eq = get_equalizer(prompts[1], equilizer_params["words"], equilizer_params["values"])
-        controller = AttentionReweight(prompts, NUM_DDIM_STEPS, cross_replace_steps=cross_replace_steps,
-                                       self_replace_steps=self_replace_steps, equalizer=eq, local_blend=lb, controller=controller)
-    return controller
-
-
-def show_cross_attention(attention_store: AttentionStore, res: int, from_where: List[str], select: int = 0):
-    tokens = tokenizer.encode(prompts[select])
-    decoder = tokenizer.decode
-    attention_maps = aggregate_attention(attention_store, res, from_where, True, select)
-    images = []
-    for i in range(len(tokens)):
-        image = attention_maps[:, :, i]
-        image = 255 * image / image.max()
-        image = image.unsqueeze(-1).expand(*image.shape, 3)
-        image = image.numpy().astype(np.uint8)
-        image = np.array(Image.fromarray(image).resize((256, 256)))
-        image = ptp_utils.text_under_image(image, decoder(int(tokens[i])))
-        images.append(image)
-    ptp_utils.view_images(np.stack(images, axis=0))
-
-
-class NullInversion:
-    
-    def prev_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):
-        prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
-        alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
-        alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
-        beta_prod_t = 1 - alpha_prod_t
-        pred_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
-        pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * model_output
-        prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
-        return prev_sample
-    
-    def next_step(self, model_output: Union[torch.FloatTensor, np.ndarray], timestep: int, sample: Union[torch.FloatTensor, np.ndarray]):
-        timestep, next_timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999), timestep
-        alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
-        alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep]
-        beta_prod_t = 1 - alpha_prod_t
-        next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
-        next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
-        next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
-        return next_sample
-    
-    def get_noise_pred_single(self, latents, t, context, normal_infer=True):
-        noise_pred = self.model.unet(latents, t, encoder_hidden_states=context, normal_infer=normal_infer)["sample"]
-        return noise_pred
-
-    def get_noise_pred(self, latents, t, is_forward=True, context=None, normal_infer=True):
-        latents_input = torch.cat([latents] * 2)
-        if context is None:
-            context = self.context
-        guidance_scale = 1 if is_forward else self.guidance_scale
-        noise_pred = self.model.unet(latents_input, t, encoder_hidden_states=context, normal_infer=normal_infer)["sample"]
-        noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
-        noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
-        if is_forward:
-            latents = self.next_step(noise_pred, t, latents)
-        else:
-            latents = self.prev_step(noise_pred, t, latents)
-        return latents
-
-    @torch.no_grad()
-    def latent2image(self, latents, return_type='np'):
-        latents = 1 / 0.18215 * latents.detach()
-        image = self.model.vae.decode(latents)['sample']
-        if return_type == 'np':
-            image = (image / 2 + 0.5).clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
-            image = (image * 255).astype(np.uint8)
-        return image
-
-    @torch.no_grad()
-    def image2latent(self, image):
-        with torch.no_grad():
-            if type(image) is Image:
-                image = np.array(image)
-            if type(image) is torch.Tensor and image.dim() == 4:
-                latents = image
-            else:
-                image = torch.from_numpy(image).float() / 127.5 - 1
-                image = image.permute(2, 0, 1).unsqueeze(0).to(device)
-                latents = self.model.vae.encode(image)['latent_dist'].mean
-                latents = latents * 0.18215
-        return latents
-
-    @torch.no_grad()
-    def init_prompt(self, prompt: str):
-        uncond_input = self.model.tokenizer(
-            [""], padding="max_length", max_length=self.model.tokenizer.model_max_length,
-            return_tensors="pt"
-        )
-        uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
-        text_input = self.model.tokenizer(
-            [prompt],
-            padding="max_length",
-            max_length=self.model.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        # (1, 77, 768)
-        text_embeddings = self.model.text_encoder(text_input.input_ids.to(self.model.device))[0]
-        # (2, 77, 768)
-        self.context = torch.cat([uncond_embeddings, text_embeddings])
-        self.prompt = prompt
-
-    @torch.no_grad()
-    def ddim_loop(self, latent):
-        uncond_embeddings, cond_embeddings = self.context.chunk(2)
-        cond = cond_embeddings if self.null_inv_with_prompt else uncond_embeddings
-        all_latent = [latent]
-        latent = latent.clone().detach()
-        for i in range(NUM_DDIM_STEPS):
-            t = self.model.scheduler.timesteps[len(self.model.scheduler.timesteps) - i - 1]
-            noise_pred = self.get_noise_pred_single(latent, t, cond, normal_infer=True)
-            latent = self.next_step(noise_pred, t, latent)
-            all_latent.append(latent)
-        return all_latent
-
-    @property
-    def scheduler(self):
-        return self.model.scheduler
-
-    @torch.no_grad()
-    def ddim_inversion(self, latent):
-        ddim_latents = self.ddim_loop(latent)
-        return ddim_latents
-
-    def null_optimization(self, latents, null_inner_steps, epsilon, null_base_lr=1e-2):
-        uncond_embeddings, cond_embeddings = self.context.chunk(2)
-        uncond_embeddings_list = []
-        latent_cur = latents[-1]
-        bar = tqdm(total=null_inner_steps * NUM_DDIM_STEPS)
-        for i in range(NUM_DDIM_STEPS):
-            uncond_embeddings = uncond_embeddings.clone().detach()
-            uncond_embeddings.requires_grad = True
-            optimizer = Adam([uncond_embeddings], lr=null_base_lr * (1. - i / 100.))
-            latent_prev = latents[len(latents) - i - 2]
-            t = self.model.scheduler.timesteps[i]
-            with torch.no_grad():
-                noise_pred_cond = self.get_noise_pred_single(latent_cur, t, cond_embeddings, normal_infer=self.null_normal_infer)
-            for j in range(null_inner_steps):
-                noise_pred_uncond = self.get_noise_pred_single(latent_cur, t, uncond_embeddings, normal_infer=self.null_normal_infer)
-                noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
-                latents_prev_rec = self.prev_step(noise_pred, t, latent_cur)
-                loss = nnf.mse_loss(latents_prev_rec, latent_prev)
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
-                assert not torch.isnan(uncond_embeddings.abs().mean())
-                loss_item = loss.item()
-                bar.update()
-                if loss_item < epsilon + i * 2e-5:
-                    break
-            for j in range(j + 1, null_inner_steps):
-                bar.update()
-            uncond_embeddings_list.append(uncond_embeddings[:1].detach())
-            with torch.no_grad():
-                context = torch.cat([uncond_embeddings, cond_embeddings])
-                latent_cur = self.get_noise_pred(latent_cur, t, False, context, normal_infer=self.null_normal_infer)
-        bar.close()
-        return uncond_embeddings_list
-    
-    def invert(self, latents: torch.Tensor, prompt: str, null_inner_steps=10, early_stop_epsilon=1e-5, verbose=False, null_base_lr=1e-2):
-        self.init_prompt(prompt)
-        if verbose:
-            print("DDIM inversion...")
-        ddim_latents = self.ddim_inversion(latents.to(torch.float32))
-        if verbose:
-            print("Null-text optimization...")
-        uncond_embeddings = self.null_optimization(ddim_latents, null_inner_steps, early_stop_epsilon, null_base_lr=null_base_lr)
-        return ddim_latents[-1], uncond_embeddings
-        
-    
-    def __init__(self, model, guidance_scale, null_inv_with_prompt, null_normal_infer=True):
-        self.null_normal_infer = null_normal_infer
-        self.null_inv_with_prompt = null_inv_with_prompt
-        self.guidance_scale = guidance_scale
-        self.model = model
-        self.tokenizer = self.model.tokenizer
-        self.model.scheduler.set_timesteps(NUM_DDIM_STEPS)
-        self.prompt = None
-        self.context = None
diff --git a/spaces/BairaS/Tabular_ML/README.md b/spaces/BairaS/Tabular_ML/README.md
deleted file mode 100644
index 5d70322d04d1207ba7e21559ed5e076c829adb98..0000000000000000000000000000000000000000
--- a/spaces/BairaS/Tabular_ML/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Tabular ML
-emoji: 😻
-colorFrom: red
-colorTo: red
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benson/text-generation/Examples/Chicos De La Escuela Apk.md b/spaces/Benson/text-generation/Examples/Chicos De La Escuela Apk.md
deleted file mode 100644
index 8a42e45fcf919adf0deeb38ed43e74de4254ae51..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Chicos De La Escuela Apk.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-
-<h1>Stumble chicos Mod APK: Cómo desbloquear todo y tener más diversión</h1>
- Si estás buscando un divertido y caótico juego multijugador que puedas jugar en tu teléfono o PC, entonces es posible que quieras echar un vistazo a Stumble Guys. Este juego está inspirado en los populares Fall Guys, pero es completamente gratuito y exclusivo para dispositivos Android e iOS. Sin embargo, si desea desbloquear todo en el juego y divertirse más, entonces es posible que desee utilizar Stumble Guys Mod APK. En este artículo, le diremos lo que Stumble Guys es, ¿por qué debe utilizar Stumble Guys Mod APK, cómo descargar e instalar, y algunos consejos y trucos para ganar en el juego. <h2>¿Qué es Stumble Guys? </h2>
- <h3>Un juego de fiesta multijugador inspirado en Fall Guys</h3>
- Stumble Guys es un juego multijugador masivo con hasta 32 jugadores en línea. El objetivo del juego es avanzar a través de una serie de niveles corriendo y saltando, evitando obstáculos y peligros. El juego cuenta con un motor basado en la física, que da a los personajes un sentido de peso y el momento. El juego también tiene un diseño colorido y loco, con muchos trajes desbloqueables y emotes. El juego es muy similar a Fall Guys, pero está diseñado para dispositivos móviles. <h3>Características y jugabilidad de Stumble Guys</h3>
-
- <h3>Beneficios de usar la versión modificada de Stumble Guys</h3>
- Stumble Guys Mod APK es una versión modificada del juego original que le da algunas ventajas y características adicionales. Algunos de los beneficios de usar Stumble Guys Mod APK son: - Desbloqueado todo: Se puede acceder a todos los trajes, emotes, pasos, pieles, sombreros, gafas, máscaras, etc. sin gastar monedas o gemas. - Monedas y gemas ilimitadas: Puedes conseguir monedas y gemas ilimitadas para comprar lo que quieras en el juego. - Sin anuncios: Puedes disfrutar del juego sin que ningún anuncio molesto interrumpa tu juego. - No se requiere raíz: Usted no necesita para rootear su dispositivo para utilizar Stumble Guys Mod APK. <h3 Riesgos y desventajas de usar la versión modded de Stumble Guys</h3>
- Stumble Guys Mod APK no es una versión oficial del juego, y puede tener algunos riesgos y desventajas que usted debe ser consciente de. Algunos de los riesgos y desventajas son: - Problemas de compatibilidad: Stumble Guys Mod APK puede no funcionar en algunos dispositivos o con algunas actualizaciones del juego. - Problemas de seguridad: Stumble Guys Mod APK puede contener virus, malware o spyware que puede dañar su dispositivo o robar sus datos. - Cuestiones de van: Stumble Guys Mod APK puede violar los términos y condiciones del juego, y puede obtener prohibido jugar en línea o acceder a su cuenta. - Cuestiones éticas: Stumble Guys Mod APK puede darle una ventaja injusta sobre otros jugadores, y puede arruinar la diversión y el desafío del juego. <h2>¿Cómo descargar e instalar Stumble Guys Mod APK? </h2>
- <h3>Pasos para descargar e instalar Stumble Guys Mod APK en dispositivos Android</h3>
-
- Si desea jugar Stumble Guys Mod APK en su PC, necesitará un emulador de Android que puede ejecutar aplicaciones Android en su ordenador. Algunos de los emuladores de Android populares son [BlueStacks], [NoxPlayer], y [LDPlayer]. Puede seguir estos pasos para descargar e instalar Stumble Guys Mod APK en su PC utilizando un emulador: - Paso 1: Descargar e instalar un emulador de Android de su elección en su PC. - Paso 2: Inicie el emulador e inicie sesión con su cuenta de Google. - Paso 3: Ir a un sitio web de confianza que proporciona Stumble Guys Mod APK, tales como [APKPure] o [APKDone]. - Paso 4: Descargar la última versión del archivo Stumble Guys Mod APK en su PC. - Paso 5: Arrastre y suelte el archivo descargado Stumble Guys Mod APK en la ventana del emulador, o utilice el navegador incorporado para localizar e instalar. - Paso 6: Esperar a que la instalación termine y lanzar el juego. <h2>Consejos y trucos para ganar en Stumble Guys</h2>
- <h3>Configura tus controles antes de jugar</h3>
- Stumble Guys tiene dos opciones de control: joystick o botones. Puede elegir el que más le convenga en el menú de configuración. También puede ajustar la sensibilidad y el tamaño de los controles según su preferencia. Asegúrese de probar sus controles antes de jugar, para que pueda tener un juego suave y cómodo. <h3>Usa la física de tu personaje para tu ventaja</h3>
- Stumble Guys tiene un motor de física realista que afecta la forma en que tu personaje se mueve e interactúa con el entorno. Puedes usar esto a tu favor usando el momento, la inercia, la gravedad, la fricción, etc. Por ejemplo, puedes saltar más alto corriendo más rápido, puedes deslizarte por las pendientes agachándote, puedes rebotar contra las paredes golpeándolas en un ángulo, etc. Experimenta con diferentes movimientos y observa cómo afectan tu rendimiento. <h3>Usa los desafíos a tu favor</h3>
-
- Stumble Guys es un juego en el que todo puede suceder. Puedes estar liderando en un nivel, pero te quedas atrás en otro. Puede ser eliminado por un obstáculo al azar o un jugador astuto. Puede ser afortunado o desafortunado dependiendo de la situación. El punto es que no siempre se trata de ser el primero en todos los niveles. A veces, es mejor ser inteligente y estratégico que rápido e imprudente. Por ejemplo, puedes esperar a que otros jugadores despejen el camino para ti, puedes evitar áreas llenas de gente donde sobreviene el caos, puedes usar los obstáculos para tu ventaja, etc. El objetivo es sobrevivir y calificar para el siguiente nivel, no ser el más rápido. Recuerda, es un juego de diversión y caos, no una carrera. <h2>Conclusión</h2>
- Stumble Guys es un divertido y caótico juego multijugador que puedes jugar en tu teléfono o PC. Está inspirado en Fall Guys, pero es gratuito y exclusivo para dispositivos Android e iOS. Si desea desbloquear todo en el juego y divertirse más, puede utilizar Stumble Guys Mod APK, que le da monedas y gemas ilimitadas, desbloqueado trajes y emotes, sin anuncios, y más. Sin embargo, también debes ser consciente de los riesgos y desventajas de usar la versión modificada del juego, como problemas de compatibilidad, problemas de seguridad, problemas de prohibición y cuestiones éticas. También debes seguir algunos consejos y trucos para ganar en el juego, como configurar tus controles, usar la física de tu personaje, usar los desafíos y ser inteligente y estratégico. Esperamos que este artículo le ayudó a aprender más acerca de Stumble Guys Mod APK y cómo usarlo. Divertirse y disfrutar del juego! <h2>Preguntas frecuentes</h2>
- <h3>Q: ¿Es Stumble Guys Mod APK seguro de usar? </h3>
-
-A: Stumble Guys Mod APK puede violar los términos y condiciones del juego, y puede obtener prohibido jugar en línea o acceder a su cuenta. Usted debe utilizar Stumble Guys Mod APK a su propio riesgo, y respetar los derechos de los desarrolladores y otros jugadores. <h3>Q: Cómo actualizar Stumble Guys Mod APK? </h3>
-A: Stumble Guys Mod APK no puede funcionar con algunas actualizaciones del juego. Usted debe comprobar el sitio web donde ha descargado Stumble Guys Mod APK para las nuevas versiones o actualizaciones. También debe desinstalar la versión anterior de Stumble Guys Mod APK antes de instalar el nuevo. <h3>Q: Cómo desinstalar Stumble Guys Mod APK? </h3>
-R: Si desea desinstalar Stumble Guys Mod APK de su dispositivo, puede seguir estos pasos: - Paso 1: Ir a la configuración del dispositivo y encontrar las aplicaciones o menú. - Paso 2: Encuentra Stumble Guys Mod APK en la lista de aplicaciones y toque en él. - Paso 3: Toque en el botón de desinstalación y confirmar su acción. - Paso 4: Espere a que la desinstalación para terminar y reiniciar el dispositivo. <h3>Q: Cómo ponerse en contacto con los desarrolladores de Stumble Guys? </h3>
-R: Si tienes alguna pregunta, comentario o sugerencia para los desarrolladores de Stumble Guys, puedes contactarlos a través de sus cuentas oficiales de redes sociales o su dirección de correo electrónico. Estos son algunos de sus datos de contacto: - Facebook: https://www.facebook.com/StumbleGuys/ - Twitter: https://twitter.com/StumbleGuys - Instagram: https:/www.instagram.com/stumbleguys/ - Correo electrónico: support@kitkagames.com</p>
-<h2>chicos de la escuela apk</h2><br /><p><b><b>DOWNLOAD</b> &#10042;&#10042;&#10042; <a href="https://bltlly.com/2v6IUC">https://bltlly.com/2v6IUC</a></b></p><br /><br /> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/msgpack/ext.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/msgpack/ext.py
deleted file mode 100644
index 23e0d6b41ce6a36a2bc1a9657ff68aeb99d8b32f..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/msgpack/ext.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# coding: utf-8
-from collections import namedtuple
-import datetime
-import sys
-import struct
-
-
-PY2 = sys.version_info[0] == 2
-
-if PY2:
-    int_types = (int, long)
-    _utc = None
-else:
-    int_types = int
-    try:
-        _utc = datetime.timezone.utc
-    except AttributeError:
-        _utc = datetime.timezone(datetime.timedelta(0))
-
-
-class ExtType(namedtuple("ExtType", "code data")):
-    """ExtType represents ext type in msgpack."""
-
-    def __new__(cls, code, data):
-        if not isinstance(code, int):
-            raise TypeError("code must be int")
-        if not isinstance(data, bytes):
-            raise TypeError("data must be bytes")
-        if not 0 <= code <= 127:
-            raise ValueError("code must be 0~127")
-        return super(ExtType, cls).__new__(cls, code, data)
-
-
-class Timestamp(object):
-    """Timestamp represents the Timestamp extension type in msgpack.
-
-    When built with Cython, msgpack uses C methods to pack and unpack `Timestamp`. When using pure-Python
-    msgpack, :func:`to_bytes` and :func:`from_bytes` are used to pack and unpack `Timestamp`.
-
-    This class is immutable: Do not override seconds and nanoseconds.
-    """
-
-    __slots__ = ["seconds", "nanoseconds"]
-
-    def __init__(self, seconds, nanoseconds=0):
-        """Initialize a Timestamp object.
-
-        :param int seconds:
-            Number of seconds since the UNIX epoch (00:00:00 UTC Jan 1 1970, minus leap seconds).
-            May be negative.
-
-        :param int nanoseconds:
-            Number of nanoseconds to add to `seconds` to get fractional time.
-            Maximum is 999_999_999.  Default is 0.
-
-        Note: Negative times (before the UNIX epoch) are represented as negative seconds + positive ns.
-        """
-        if not isinstance(seconds, int_types):
-            raise TypeError("seconds must be an integer")
-        if not isinstance(nanoseconds, int_types):
-            raise TypeError("nanoseconds must be an integer")
-        if not (0 <= nanoseconds < 10**9):
-            raise ValueError(
-                "nanoseconds must be a non-negative integer less than 999999999."
-            )
-        self.seconds = seconds
-        self.nanoseconds = nanoseconds
-
-    def __repr__(self):
-        """String representation of Timestamp."""
-        return "Timestamp(seconds={0}, nanoseconds={1})".format(
-            self.seconds, self.nanoseconds
-        )
-
-    def __eq__(self, other):
-        """Check for equality with another Timestamp object"""
-        if type(other) is self.__class__:
-            return (
-                self.seconds == other.seconds and self.nanoseconds == other.nanoseconds
-            )
-        return False
-
-    def __ne__(self, other):
-        """not-equals method (see :func:`__eq__()`)"""
-        return not self.__eq__(other)
-
-    def __hash__(self):
-        return hash((self.seconds, self.nanoseconds))
-
-    @staticmethod
-    def from_bytes(b):
-        """Unpack bytes into a `Timestamp` object.
-
-        Used for pure-Python msgpack unpacking.
-
-        :param b: Payload from msgpack ext message with code -1
-        :type b: bytes
-
-        :returns: Timestamp object unpacked from msgpack ext payload
-        :rtype: Timestamp
-        """
-        if len(b) == 4:
-            seconds = struct.unpack("!L", b)[0]
-            nanoseconds = 0
-        elif len(b) == 8:
-            data64 = struct.unpack("!Q", b)[0]
-            seconds = data64 & 0x00000003FFFFFFFF
-            nanoseconds = data64 >> 34
-        elif len(b) == 12:
-            nanoseconds, seconds = struct.unpack("!Iq", b)
-        else:
-            raise ValueError(
-                "Timestamp type can only be created from 32, 64, or 96-bit byte objects"
-            )
-        return Timestamp(seconds, nanoseconds)
-
-    def to_bytes(self):
-        """Pack this Timestamp object into bytes.
-
-        Used for pure-Python msgpack packing.
-
-        :returns data: Payload for EXT message with code -1 (timestamp type)
-        :rtype: bytes
-        """
-        if (self.seconds >> 34) == 0:  # seconds is non-negative and fits in 34 bits
-            data64 = self.nanoseconds << 34 | self.seconds
-            if data64 & 0xFFFFFFFF00000000 == 0:
-                # nanoseconds is zero and seconds < 2**32, so timestamp 32
-                data = struct.pack("!L", data64)
-            else:
-                # timestamp 64
-                data = struct.pack("!Q", data64)
-        else:
-            # timestamp 96
-            data = struct.pack("!Iq", self.nanoseconds, self.seconds)
-        return data
-
-    @staticmethod
-    def from_unix(unix_sec):
-        """Create a Timestamp from posix timestamp in seconds.
-
-        :param unix_float: Posix timestamp in seconds.
-        :type unix_float: int or float.
-        """
-        seconds = int(unix_sec // 1)
-        nanoseconds = int((unix_sec % 1) * 10**9)
-        return Timestamp(seconds, nanoseconds)
-
-    def to_unix(self):
-        """Get the timestamp as a floating-point value.
-
-        :returns: posix timestamp
-        :rtype: float
-        """
-        return self.seconds + self.nanoseconds / 1e9
-
-    @staticmethod
-    def from_unix_nano(unix_ns):
-        """Create a Timestamp from posix timestamp in nanoseconds.
-
-        :param int unix_ns: Posix timestamp in nanoseconds.
-        :rtype: Timestamp
-        """
-        return Timestamp(*divmod(unix_ns, 10**9))
-
-    def to_unix_nano(self):
-        """Get the timestamp as a unixtime in nanoseconds.
-
-        :returns: posix timestamp in nanoseconds
-        :rtype: int
-        """
-        return self.seconds * 10**9 + self.nanoseconds
-
-    def to_datetime(self):
-        """Get the timestamp as a UTC datetime.
-
-        Python 2 is not supported.
-
-        :rtype: datetime.
-        """
-        return datetime.datetime.fromtimestamp(0, _utc) + datetime.timedelta(
-            seconds=self.to_unix()
-        )
-
-    @staticmethod
-    def from_datetime(dt):
-        """Create a Timestamp from datetime with tzinfo.
-
-        Python 2 is not supported.
-
-        :rtype: Timestamp
-        """
-        return Timestamp.from_unix(dt.timestamp())
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/subscribers.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/subscribers.py
deleted file mode 100644
index cf0dbaa0d79923c1d87dbb183fbae31ea91c34ff..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/subscribers.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright 2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from s3transfer.compat import accepts_kwargs
-from s3transfer.exceptions import InvalidSubscriberMethodError
-
-
-class BaseSubscriber:
-    """The base subscriber class
-
-    It is recommended that all subscriber implementations subclass and then
-    override the subscription methods (i.e. on_{subsribe_type}() methods).
-    """
-
-    VALID_SUBSCRIBER_TYPES = ['queued', 'progress', 'done']
-
-    def __new__(cls, *args, **kwargs):
-        cls._validate_subscriber_methods()
-        return super().__new__(cls)
-
-    @classmethod
-    def _validate_subscriber_methods(cls):
-        for subscriber_type in cls.VALID_SUBSCRIBER_TYPES:
-            subscriber_method = getattr(cls, 'on_' + subscriber_type)
-            if not callable(subscriber_method):
-                raise InvalidSubscriberMethodError(
-                    'Subscriber method %s must be callable.'
-                    % subscriber_method
-                )
-
-            if not accepts_kwargs(subscriber_method):
-                raise InvalidSubscriberMethodError(
-                    'Subscriber method %s must accept keyword '
-                    'arguments (**kwargs)' % subscriber_method
-                )
-
-    def on_queued(self, future, **kwargs):
-        """Callback to be invoked when transfer request gets queued
-
-        This callback can be useful for:
-
-            * Keeping track of how many transfers have been requested
-            * Providing the expected transfer size through
-              future.meta.provide_transfer_size() so a HeadObject would not
-              need to be made for copies and downloads.
-
-        :type future: s3transfer.futures.TransferFuture
-        :param future: The TransferFuture representing the requested transfer.
-        """
-        pass
-
-    def on_progress(self, future, bytes_transferred, **kwargs):
-        """Callback to be invoked when progress is made on transfer
-
-        This callback can be useful for:
-
-            * Recording and displaying progress
-
-        :type future: s3transfer.futures.TransferFuture
-        :param future: The TransferFuture representing the requested transfer.
-
-        :type bytes_transferred: int
-        :param bytes_transferred: The number of bytes transferred for that
-            invocation of the callback. Note that a negative amount can be
-            provided, which usually indicates that an in-progress request
-            needed to be retried and thus progress was rewound.
-        """
-        pass
-
-    def on_done(self, future, **kwargs):
-        """Callback to be invoked once a transfer is done
-
-        This callback can be useful for:
-
-            * Recording and displaying whether the transfer succeeded or
-              failed using future.result()
-            * Running some task after the transfer completed like changing
-              the last modified time of a downloaded file.
-
-        :type future: s3transfer.futures.TransferFuture
-        :param future: The TransferFuture representing the requested transfer.
-        """
-        pass
diff --git a/spaces/CVPR/LIVE/thrust/thrust/type_traits/logical_metafunctions.h b/spaces/CVPR/LIVE/thrust/thrust/type_traits/logical_metafunctions.h
deleted file mode 100644
index 5f86ee6a820d5dd4e5c98d0f9ba21ffd3b287b45..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/type_traits/logical_metafunctions.h
+++ /dev/null
@@ -1,179 +0,0 @@
-///////////////////////////////////////////////////////////////////////////////
-//  Copyright (c)      2018 NVIDIA Corporation
-//  Copyright (c) 2015-2018 Bryce Adelstein Lelbach aka wash
-//
-//  Distributed under the Boost Software License, Version 1.0. (See accompanying
-//  file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
-///////////////////////////////////////////////////////////////////////////////
-
-/*! \file logical_metafunctions.h
- *  \brief C++17's \c conjunction, \c disjunction, and \c negation metafunctions.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp11_required.h>
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#include <type_traits>
-
-namespace thrust
-{
-
-#if THRUST_CPP_DIALECT >= 2017
-
-/// An \c integral_constant whose value is <code>(... && Ts::value)</code>. 
-template <typename... Ts>
-using conjunction = std::conjunction<Ts...>;
-
-/// A <code>constexpr bool</code> whose value is <code>(... && Ts::value)</code>.
-template <typename... Ts>
-constexpr bool conjunction_v = conjunction<Ts...>::value;
-
-/// An \c integral_constant whose value is <code>(... || Ts::value)</code>. 
-template <typename... Ts>
-using disjunction = std::disjunction<Ts...>;
-
-/// A <code>constexpr bool</code> whose value is <code>(... || Ts::value)</code>.
-template <typename... Ts>
-constexpr bool disjunction_v = disjunction<Ts...>::value;
-
-/// An \c integral_constant whose value is <code>!Ts::value</code>. 
-template <typename T>
-using negation = std::negation<T>;
-
-/// A <code>constexpr bool</code> whose value is <code>!Ts::value</code>.
-template <typename T>
-constexpr bool negation_v = negation<T>::value;
-
-///////////////////////////////////////////////////////////////////////////////
-
-#else // Older than C++17.
-
-/// An \c integral_constant whose value is <code>(... && Ts::value)</code>. 
-template <typename... Ts>
-struct conjunction;
-
-#if THRUST_CPP_DIALECT >= 2014
-/// A <code>constexpr bool</code> whose value is <code>(... && Ts::value)</code>.
-template <typename... Ts>
-constexpr bool conjunction_v = conjunction<Ts...>::value;
-#endif
-
-template <>
-struct conjunction<> : std::true_type {};
-
-template <typename T>
-struct conjunction<T> : T {};
-
-template <typename T0, typename T1>
-struct conjunction<T0, T1> : std::conditional<T0::value, T1, T0>::type {};
-
-template<typename T0, typename T1, typename T2, typename... TN>
-struct conjunction<T0, T1, T2, TN...>
-  : std::conditional<T0::value, conjunction<T1, T2, TN...>, T0>::type {};
-
-///////////////////////////////////////////////////////////////////////////////
-
-/// An \c integral_constant whose value is <code>(... || Ts::value)</code>. 
-template <typename... Ts>
-struct disjunction;
-
-#if THRUST_CPP_DIALECT >= 2014
-/// A <code>constexpr bool</code> whose value is <code>(... || Ts::value)</code>.
-template <typename... Ts>
-constexpr bool disjunction_v = disjunction<Ts...>::value;
-#endif
-
-template <>
-struct disjunction<> : std::false_type {};
-
-template <typename T>
-struct disjunction<T> : T {};
-
-template <typename T0, typename... TN>
-struct disjunction<T0, TN...>
-  : std::conditional<T0::value != false, T0, disjunction<TN...> >::type {};
-
-///////////////////////////////////////////////////////////////////////////////
-
-/// An \c integral_constant whose value is <code>!T::value</code>. 
-template <typename T>
-struct negation;
-
-#if THRUST_CPP_DIALECT >= 2014
-/// A <code>constexpr bool</code> whose value is <code>!T::value</code>.
-template <typename T>
-constexpr bool negation_v = negation<T>::value;
-#endif
-
-template <typename T>
-struct negation : std::integral_constant<bool, !T::value> {};
-
-#endif // THRUST_CPP_DIALECT >= 2017
-
-///////////////////////////////////////////////////////////////////////////////
-
-/// An \c integral_constant whose value is <code>(... && Bs)</code>. 
-template <bool... Bs>
-struct conjunction_value;
-
-#if THRUST_CPP_DIALECT >= 2014
-/// A <code>constexpr bool</code> whose value is <code>(... && Bs)</code>.
-template <bool... Bs>
-constexpr bool conjunction_value_v = conjunction_value<Bs...>::value;
-#endif
-
-template <>
-struct conjunction_value<> : std::true_type {};
-
-template <bool B>
-struct conjunction_value<B> : std::integral_constant<bool, B> {};
-
-template <bool B0, bool... BN>
-struct conjunction_value<B0, BN...>
-  : std::integral_constant<bool, B0 && conjunction_value<BN...>::value> {};
-
-///////////////////////////////////////////////////////////////////////////////
-
-/// An \c integral_constant whose value is <code>(... || Bs)</code>. 
-template <bool... Bs>
-struct disjunction_value;
-
-#if THRUST_CPP_DIALECT >= 2014
-/// A <code>constexpr bool</code> whose value is <code>(... || Bs)</code>.
-template <bool... Bs>
-constexpr bool disjunction_value_v = disjunction_value<Bs...>::value;
-#endif
-
-template <>
-struct disjunction_value<> : std::false_type {};
-
-template <bool B>
-struct disjunction_value<B> : std::integral_constant<bool, B> {};
-
-template <bool B0, bool... BN>
-struct disjunction_value<B0, BN...>
-  : std::integral_constant<bool, B0 || disjunction_value<BN...>::value> {};
-
-///////////////////////////////////////////////////////////////////////////////
-
-/// An \c integral_constant whose value is <code>!B</code>. 
-template <bool B>
-struct negation_value;
-
-#if THRUST_CPP_DIALECT >= 2014
-/// A <code>constexpr bool</code> whose value is <code>!B</code>.
-template <bool B>
-constexpr bool negation_value_v = negation_value<B>::value;
-#endif
-
-template <bool B>
-struct negation_value : std::integral_constant<bool, !B> {};
-
-} // end namespace thrust
-
-#endif // THRUST_CPP_DIALECT >= 2011
-
diff --git a/spaces/CVPR/lama-example/saicinpainting/evaluation/losses/base_loss.py b/spaces/CVPR/lama-example/saicinpainting/evaluation/losses/base_loss.py
deleted file mode 100644
index 391191ce2ed8665f1f15bd3877dc22bb85b147d6..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/saicinpainting/evaluation/losses/base_loss.py
+++ /dev/null
@@ -1,528 +0,0 @@
-import logging
-from abc import abstractmethod, ABC
-
-import numpy as np
-import sklearn
-import sklearn.svm
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from joblib import Parallel, delayed
-from scipy import linalg
-
-from models.ade20k import SegmentationModule, NUM_CLASS, segm_options
-from .fid.inception import InceptionV3
-from .lpips import PerceptualLoss
-from .ssim import SSIM
-
-LOGGER = logging.getLogger(__name__)
-
-
-def get_groupings(groups):
-    """
-    :param groups: group numbers for respective elements
-    :return: dict of kind {group_idx: indices of the corresponding group elements}
-    """
-    label_groups, count_groups = np.unique(groups, return_counts=True)
-
-    indices = np.argsort(groups)
-
-    grouping = dict()
-    cur_start = 0
-    for label, count in zip(label_groups, count_groups):
-        cur_end = cur_start + count
-        cur_indices = indices[cur_start:cur_end]
-        grouping[label] = cur_indices
-        cur_start = cur_end
-    return grouping
-
-
-class EvaluatorScore(nn.Module):
-    @abstractmethod
-    def forward(self, pred_batch, target_batch, mask):
-        pass
-
-    @abstractmethod
-    def get_value(self, groups=None, states=None):
-        pass
-
-    @abstractmethod
-    def reset(self):
-        pass
-
-
-class PairwiseScore(EvaluatorScore, ABC):
-    def __init__(self):
-        super().__init__()
-        self.individual_values = None
-
-    def get_value(self, groups=None, states=None):
-        """
-        :param groups:
-        :return:
-            total_results: dict of kind {'mean': score mean, 'std': score std}
-            group_results: None, if groups is None;
-                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
-        """
-        individual_values = torch.stack(states, dim=0).reshape(-1).cpu().numpy() if states is not None \
-            else self.individual_values
-
-        total_results = {
-            'mean': individual_values.mean(),
-            'std': individual_values.std()
-        }
-
-        if groups is None:
-            return total_results, None
-
-        group_results = dict()
-        grouping = get_groupings(groups)
-        for label, index in grouping.items():
-            group_scores = individual_values[index]
-            group_results[label] = {
-                'mean': group_scores.mean(),
-                'std': group_scores.std()
-            }
-        return total_results, group_results
-
-    def reset(self):
-        self.individual_values = []
-
-
-class SSIMScore(PairwiseScore):
-    def __init__(self, window_size=11):
-        super().__init__()
-        self.score = SSIM(window_size=window_size, size_average=False).eval()
-        self.reset()
-
-    def forward(self, pred_batch, target_batch, mask=None):
-        batch_values = self.score(pred_batch, target_batch)
-        self.individual_values = np.hstack([
-            self.individual_values, batch_values.detach().cpu().numpy()
-        ])
-        return batch_values
-
-
-class LPIPSScore(PairwiseScore):
-    def __init__(self, model='net-lin', net='vgg', model_path=None, use_gpu=True):
-        super().__init__()
-        self.score = PerceptualLoss(model=model, net=net, model_path=model_path,
-                                    use_gpu=use_gpu, spatial=False).eval()
-        self.reset()
-
-    def forward(self, pred_batch, target_batch, mask=None):
-        batch_values = self.score(pred_batch, target_batch).flatten()
-        self.individual_values = np.hstack([
-            self.individual_values, batch_values.detach().cpu().numpy()
-        ])
-        return batch_values
-
-
-def fid_calculate_activation_statistics(act):
-    mu = np.mean(act, axis=0)
-    sigma = np.cov(act, rowvar=False)
-    return mu, sigma
-
-
-def calculate_frechet_distance(activations_pred, activations_target, eps=1e-6):
-    mu1, sigma1 = fid_calculate_activation_statistics(activations_pred)
-    mu2, sigma2 = fid_calculate_activation_statistics(activations_target)
-
-    diff = mu1 - mu2
-
-    # Product might be almost singular
-    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
-    if not np.isfinite(covmean).all():
-        msg = ('fid calculation produces singular product; '
-               'adding %s to diagonal of cov estimates') % eps
-        LOGGER.warning(msg)
-        offset = np.eye(sigma1.shape[0]) * eps
-        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
-
-    # Numerical error might give slight imaginary component
-    if np.iscomplexobj(covmean):
-        # if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
-        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-2):
-            m = np.max(np.abs(covmean.imag))
-            raise ValueError('Imaginary component {}'.format(m))
-        covmean = covmean.real
-
-    tr_covmean = np.trace(covmean)
-
-    return (diff.dot(diff) + np.trace(sigma1) +
-            np.trace(sigma2) - 2 * tr_covmean)
-
-
-class FIDScore(EvaluatorScore):
-    def __init__(self, dims=2048, eps=1e-6):
-        LOGGER.info("FIDscore init called")
-        super().__init__()
-        if getattr(FIDScore, '_MODEL', None) is None:
-            block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
-            FIDScore._MODEL = InceptionV3([block_idx]).eval()
-        self.model = FIDScore._MODEL
-        self.eps = eps
-        self.reset()
-        LOGGER.info("FIDscore init done")
-
-    def forward(self, pred_batch, target_batch, mask=None):
-        activations_pred = self._get_activations(pred_batch)
-        activations_target = self._get_activations(target_batch)
-
-        self.activations_pred.append(activations_pred.detach().cpu())
-        self.activations_target.append(activations_target.detach().cpu())
-
-        return activations_pred, activations_target
-
-    def get_value(self, groups=None, states=None):
-        LOGGER.info("FIDscore get_value called")
-        activations_pred, activations_target = zip(*states) if states is not None \
-            else (self.activations_pred, self.activations_target)
-        activations_pred = torch.cat(activations_pred).cpu().numpy()
-        activations_target = torch.cat(activations_target).cpu().numpy()
-
-        total_distance = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps)
-        total_results = dict(mean=total_distance)
-
-        if groups is None:
-            group_results = None
-        else:
-            group_results = dict()
-            grouping = get_groupings(groups)
-            for label, index in grouping.items():
-                if len(index) > 1:
-                    group_distance = calculate_frechet_distance(activations_pred[index], activations_target[index],
-                                                                eps=self.eps)
-                    group_results[label] = dict(mean=group_distance)
-
-                else:
-                    group_results[label] = dict(mean=float('nan'))
-
-        self.reset()
-
-        LOGGER.info("FIDscore get_value done")
-
-        return total_results, group_results
-
-    def reset(self):
-        self.activations_pred = []
-        self.activations_target = []
-
-    def _get_activations(self, batch):
-        activations = self.model(batch)[0]
-        if activations.shape[2] != 1 or activations.shape[3] != 1:
-            assert False, \
-                'We should not have got here, because Inception always scales inputs to 299x299'
-            # activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1))
-        activations = activations.squeeze(-1).squeeze(-1)
-        return activations
-
-
-class SegmentationAwareScore(EvaluatorScore):
-    def __init__(self, weights_path):
-        super().__init__()
-        self.segm_network = SegmentationModule(weights_path=weights_path, use_default_normalization=True).eval()
-        self.target_class_freq_by_image_total = []
-        self.target_class_freq_by_image_mask = []
-        self.pred_class_freq_by_image_mask = []
-
-    def forward(self, pred_batch, target_batch, mask):
-        pred_segm_flat = self.segm_network.predict(pred_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy()
-        target_segm_flat = self.segm_network.predict(target_batch)[0].view(pred_batch.shape[0], -1).long().detach().cpu().numpy()
-        mask_flat = (mask.view(mask.shape[0], -1) > 0.5).detach().cpu().numpy()
-
-        batch_target_class_freq_total = []
-        batch_target_class_freq_mask = []
-        batch_pred_class_freq_mask = []
-
-        for cur_pred_segm, cur_target_segm, cur_mask in zip(pred_segm_flat, target_segm_flat, mask_flat):
-            cur_target_class_freq_total = np.bincount(cur_target_segm, minlength=NUM_CLASS)[None, ...]
-            cur_target_class_freq_mask = np.bincount(cur_target_segm[cur_mask], minlength=NUM_CLASS)[None, ...]
-            cur_pred_class_freq_mask = np.bincount(cur_pred_segm[cur_mask], minlength=NUM_CLASS)[None, ...]
-
-            self.target_class_freq_by_image_total.append(cur_target_class_freq_total)
-            self.target_class_freq_by_image_mask.append(cur_target_class_freq_mask)
-            self.pred_class_freq_by_image_mask.append(cur_pred_class_freq_mask)
-
-            batch_target_class_freq_total.append(cur_target_class_freq_total)
-            batch_target_class_freq_mask.append(cur_target_class_freq_mask)
-            batch_pred_class_freq_mask.append(cur_pred_class_freq_mask)
-
-        batch_target_class_freq_total = np.concatenate(batch_target_class_freq_total, axis=0)
-        batch_target_class_freq_mask = np.concatenate(batch_target_class_freq_mask, axis=0)
-        batch_pred_class_freq_mask = np.concatenate(batch_pred_class_freq_mask, axis=0)
-        return batch_target_class_freq_total, batch_target_class_freq_mask, batch_pred_class_freq_mask
-
-    def reset(self):
-        super().reset()
-        self.target_class_freq_by_image_total = []
-        self.target_class_freq_by_image_mask = []
-        self.pred_class_freq_by_image_mask = []
-
-
-def distribute_values_to_classes(target_class_freq_by_image_mask, values, idx2name):
-    assert target_class_freq_by_image_mask.ndim == 2 and target_class_freq_by_image_mask.shape[0] == values.shape[0]
-    total_class_freq = target_class_freq_by_image_mask.sum(0)
-    distr_values = (target_class_freq_by_image_mask * values[..., None]).sum(0)
-    result = distr_values / (total_class_freq + 1e-3)
-    return {idx2name[i]: val for i, val in enumerate(result) if total_class_freq[i] > 0}
-
-
-def get_segmentation_idx2name():
-    return {i - 1: name for i, name in segm_options['classes'].set_index('Idx', drop=True)['Name'].to_dict().items()}
-
-
-class SegmentationAwarePairwiseScore(SegmentationAwareScore):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.individual_values = []
-        self.segm_idx2name = get_segmentation_idx2name()
-
-    def forward(self, pred_batch, target_batch, mask):
-        cur_class_stats = super().forward(pred_batch, target_batch, mask)
-        score_values = self.calc_score(pred_batch, target_batch, mask)
-        self.individual_values.append(score_values)
-        return cur_class_stats + (score_values,)
-
-    @abstractmethod
-    def calc_score(self, pred_batch, target_batch, mask):
-        raise NotImplementedError()
-
-    def get_value(self, groups=None, states=None):
-        """
-        :param groups:
-        :return:
-            total_results: dict of kind {'mean': score mean, 'std': score std}
-            group_results: None, if groups is None;
-                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
-        """
-        if states is not None:
-            (target_class_freq_by_image_total,
-             target_class_freq_by_image_mask,
-             pred_class_freq_by_image_mask,
-             individual_values) = states
-        else:
-            target_class_freq_by_image_total = self.target_class_freq_by_image_total
-            target_class_freq_by_image_mask = self.target_class_freq_by_image_mask
-            pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask
-            individual_values = self.individual_values
-
-        target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)
-        target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)
-        pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)
-        individual_values = np.concatenate(individual_values, axis=0)
-
-        total_results = {
-            'mean': individual_values.mean(),
-            'std': individual_values.std(),
-            **distribute_values_to_classes(target_class_freq_by_image_mask, individual_values, self.segm_idx2name)
-        }
-
-        if groups is None:
-            return total_results, None
-
-        group_results = dict()
-        grouping = get_groupings(groups)
-        for label, index in grouping.items():
-            group_class_freq = target_class_freq_by_image_mask[index]
-            group_scores = individual_values[index]
-            group_results[label] = {
-                'mean': group_scores.mean(),
-                'std': group_scores.std(),
-                ** distribute_values_to_classes(group_class_freq, group_scores, self.segm_idx2name)
-            }
-        return total_results, group_results
-
-    def reset(self):
-        super().reset()
-        self.individual_values = []
-
-
-class SegmentationClassStats(SegmentationAwarePairwiseScore):
-    def calc_score(self, pred_batch, target_batch, mask):
-        return 0
-
-    def get_value(self, groups=None, states=None):
-        """
-        :param groups:
-        :return:
-            total_results: dict of kind {'mean': score mean, 'std': score std}
-            group_results: None, if groups is None;
-                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
-        """
-        if states is not None:
-            (target_class_freq_by_image_total,
-             target_class_freq_by_image_mask,
-             pred_class_freq_by_image_mask,
-             _) = states
-        else:
-            target_class_freq_by_image_total = self.target_class_freq_by_image_total
-            target_class_freq_by_image_mask = self.target_class_freq_by_image_mask
-            pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask
-
-        target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)
-        target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)
-        pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)
-
-        target_class_freq_by_image_total_marginal = target_class_freq_by_image_total.sum(0).astype('float32')
-        target_class_freq_by_image_total_marginal /= target_class_freq_by_image_total_marginal.sum()
-
-        target_class_freq_by_image_mask_marginal = target_class_freq_by_image_mask.sum(0).astype('float32')
-        target_class_freq_by_image_mask_marginal /= target_class_freq_by_image_mask_marginal.sum()
-
-        pred_class_freq_diff = (pred_class_freq_by_image_mask - target_class_freq_by_image_mask).sum(0) / (target_class_freq_by_image_mask.sum(0) + 1e-3)
-
-        total_results = dict()
-        total_results.update({f'total_freq/{self.segm_idx2name[i]}': v
-                              for i, v in enumerate(target_class_freq_by_image_total_marginal)
-                              if v > 0})
-        total_results.update({f'mask_freq/{self.segm_idx2name[i]}': v
-                              for i, v in enumerate(target_class_freq_by_image_mask_marginal)
-                              if v > 0})
-        total_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v
-                              for i, v in enumerate(pred_class_freq_diff)
-                              if target_class_freq_by_image_total_marginal[i] > 0})
-
-        if groups is None:
-            return total_results, None
-
-        group_results = dict()
-        grouping = get_groupings(groups)
-        for label, index in grouping.items():
-            group_target_class_freq_by_image_total = target_class_freq_by_image_total[index]
-            group_target_class_freq_by_image_mask = target_class_freq_by_image_mask[index]
-            group_pred_class_freq_by_image_mask = pred_class_freq_by_image_mask[index]
-
-            group_target_class_freq_by_image_total_marginal = group_target_class_freq_by_image_total.sum(0).astype('float32')
-            group_target_class_freq_by_image_total_marginal /= group_target_class_freq_by_image_total_marginal.sum()
-
-            group_target_class_freq_by_image_mask_marginal = group_target_class_freq_by_image_mask.sum(0).astype('float32')
-            group_target_class_freq_by_image_mask_marginal /= group_target_class_freq_by_image_mask_marginal.sum()
-
-            group_pred_class_freq_diff = (group_pred_class_freq_by_image_mask - group_target_class_freq_by_image_mask).sum(0) / (
-                    group_target_class_freq_by_image_mask.sum(0) + 1e-3)
-
-            cur_group_results = dict()
-            cur_group_results.update({f'total_freq/{self.segm_idx2name[i]}': v
-                                      for i, v in enumerate(group_target_class_freq_by_image_total_marginal)
-                                      if v > 0})
-            cur_group_results.update({f'mask_freq/{self.segm_idx2name[i]}': v
-                                      for i, v in enumerate(group_target_class_freq_by_image_mask_marginal)
-                                      if v > 0})
-            cur_group_results.update({f'mask_freq_diff/{self.segm_idx2name[i]}': v
-                                      for i, v in enumerate(group_pred_class_freq_diff)
-                                      if group_target_class_freq_by_image_total_marginal[i] > 0})
-
-            group_results[label] = cur_group_results
-        return total_results, group_results
-
-
-class SegmentationAwareSSIM(SegmentationAwarePairwiseScore):
-    def __init__(self, *args, window_size=11, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.score_impl = SSIM(window_size=window_size, size_average=False).eval()
-
-    def calc_score(self, pred_batch, target_batch, mask):
-        return self.score_impl(pred_batch, target_batch).detach().cpu().numpy()
-
-
-class SegmentationAwareLPIPS(SegmentationAwarePairwiseScore):
-    def __init__(self, *args, model='net-lin', net='vgg', model_path=None, use_gpu=True,  **kwargs):
-        super().__init__(*args, **kwargs)
-        self.score_impl = PerceptualLoss(model=model, net=net, model_path=model_path,
-                                         use_gpu=use_gpu, spatial=False).eval()
-
-    def calc_score(self, pred_batch, target_batch, mask):
-        return self.score_impl(pred_batch, target_batch).flatten().detach().cpu().numpy()
-
-
-def calculade_fid_no_img(img_i, activations_pred, activations_target, eps=1e-6):
-    activations_pred = activations_pred.copy()
-    activations_pred[img_i] = activations_target[img_i]
-    return calculate_frechet_distance(activations_pred, activations_target, eps=eps)
-
-
-class SegmentationAwareFID(SegmentationAwarePairwiseScore):
-    def __init__(self, *args, dims=2048, eps=1e-6, n_jobs=-1, **kwargs):
-        super().__init__(*args, **kwargs)
-        if getattr(FIDScore, '_MODEL', None) is None:
-            block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
-            FIDScore._MODEL = InceptionV3([block_idx]).eval()
-        self.model = FIDScore._MODEL
-        self.eps = eps
-        self.n_jobs = n_jobs
-
-    def calc_score(self, pred_batch, target_batch, mask):
-        activations_pred = self._get_activations(pred_batch)
-        activations_target = self._get_activations(target_batch)
-        return activations_pred, activations_target
-
-    def get_value(self, groups=None, states=None):
-        """
-        :param groups:
-        :return:
-            total_results: dict of kind {'mean': score mean, 'std': score std}
-            group_results: None, if groups is None;
-                else dict {group_idx: {'mean': score mean among group, 'std': score std among group}}
-        """
-        if states is not None:
-            (target_class_freq_by_image_total,
-             target_class_freq_by_image_mask,
-             pred_class_freq_by_image_mask,
-             activation_pairs) = states
-        else:
-            target_class_freq_by_image_total = self.target_class_freq_by_image_total
-            target_class_freq_by_image_mask = self.target_class_freq_by_image_mask
-            pred_class_freq_by_image_mask = self.pred_class_freq_by_image_mask
-            activation_pairs = self.individual_values
-
-        target_class_freq_by_image_total = np.concatenate(target_class_freq_by_image_total, axis=0)
-        target_class_freq_by_image_mask = np.concatenate(target_class_freq_by_image_mask, axis=0)
-        pred_class_freq_by_image_mask = np.concatenate(pred_class_freq_by_image_mask, axis=0)
-        activations_pred, activations_target = zip(*activation_pairs)
-        activations_pred = np.concatenate(activations_pred, axis=0)
-        activations_target = np.concatenate(activations_target, axis=0)
-
-        total_results = {
-            'mean': calculate_frechet_distance(activations_pred, activations_target, eps=self.eps),
-            'std': 0,
-            **self.distribute_fid_to_classes(target_class_freq_by_image_mask, activations_pred, activations_target)
-        }
-
-        if groups is None:
-            return total_results, None
-
-        group_results = dict()
-        grouping = get_groupings(groups)
-        for label, index in grouping.items():
-            if len(index) > 1:
-                group_activations_pred = activations_pred[index]
-                group_activations_target = activations_target[index]
-                group_class_freq = target_class_freq_by_image_mask[index]
-                group_results[label] = {
-                    'mean': calculate_frechet_distance(group_activations_pred, group_activations_target, eps=self.eps),
-                    'std': 0,
-                    **self.distribute_fid_to_classes(group_class_freq,
-                                                     group_activations_pred,
-                                                     group_activations_target)
-                }
-            else:
-                group_results[label] = dict(mean=float('nan'), std=0)
-        return total_results, group_results
-
-    def distribute_fid_to_classes(self, class_freq, activations_pred, activations_target):
-        real_fid = calculate_frechet_distance(activations_pred, activations_target, eps=self.eps)
-
-        fid_no_images = Parallel(n_jobs=self.n_jobs)(
-            delayed(calculade_fid_no_img)(img_i, activations_pred, activations_target, eps=self.eps)
-            for img_i in range(activations_pred.shape[0])
-        )
-        errors = real_fid - fid_no_images
-        return distribute_values_to_classes(class_freq, errors, self.segm_idx2name)
-
-    def _get_activations(self, batch):
-        activations = self.model(batch)[0]
-        if activations.shape[2] != 1 or activations.shape[3] != 1:
-            activations = F.adaptive_avg_pool2d(activations, output_size=(1, 1))
-        activations = activations.squeeze(-1).squeeze(-1).detach().cpu().numpy()
-        return activations
diff --git a/spaces/CVPR/lama-example/saicinpainting/training/losses/feature_matching.py b/spaces/CVPR/lama-example/saicinpainting/training/losses/feature_matching.py
deleted file mode 100644
index c019895c9178817837d1a6773367b178a861dc61..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/saicinpainting/training/losses/feature_matching.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from typing import List
-
-import torch
-import torch.nn.functional as F
-
-
-def masked_l2_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l2 = F.mse_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l2).mean()
-
-
-def masked_l1_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l1 = F.l1_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l1).mean()
-
-
-def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):
-    if mask is None:
-        res = torch.stack([F.mse_loss(fake_feat, target_feat)
-                           for fake_feat, target_feat in zip(fake_features, target_features)]).mean()
-    else:
-        res = 0
-        norm = 0
-        for fake_feat, target_feat in zip(fake_features, target_features):
-            cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)
-            error_weights = 1 - cur_mask
-            cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()
-            res = res + cur_val
-            norm += 1
-        res = res / norm
-    return res
diff --git a/spaces/CVPR/lama-example/saicinpainting/training/modules/base.py b/spaces/CVPR/lama-example/saicinpainting/training/modules/base.py
deleted file mode 100644
index a50c3fc7753a0bba64a5ab8c1ed64ff97e62313f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/saicinpainting/training/modules/base.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import abc
-from typing import Tuple, List
-
-import torch
-import torch.nn as nn
-
-from saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
-from saicinpainting.training.modules.multidilated_conv import MultidilatedConv
-
-
-class BaseDiscriminator(nn.Module):
-    @abc.abstractmethod
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        """
-        Predict scores and get intermediate activations. Useful for feature matching loss
-        :return tuple (scores, list of intermediate activations)
-        """
-        raise NotImplemented()
-
-
-def get_conv_block_ctor(kind='default'):
-    if not isinstance(kind, str):
-        return kind
-    if kind == 'default':
-        return nn.Conv2d
-    if kind == 'depthwise':
-        return DepthWiseSeperableConv   
-    if kind == 'multidilated':
-        return MultidilatedConv
-    raise ValueError(f'Unknown convolutional block kind {kind}')
-
-
-def get_norm_layer(kind='bn'):
-    if not isinstance(kind, str):
-        return kind
-    if kind == 'bn':
-        return nn.BatchNorm2d
-    if kind == 'in':
-        return nn.InstanceNorm2d
-    raise ValueError(f'Unknown norm block kind {kind}')
-
-
-def get_activation(kind='tanh'):
-    if kind == 'tanh':
-        return nn.Tanh()
-    if kind == 'sigmoid':
-        return nn.Sigmoid()
-    if kind is False:
-        return nn.Identity()
-    raise ValueError(f'Unknown activation kind {kind}')
-
-
-class SimpleMultiStepGenerator(nn.Module):
-    def __init__(self, steps: List[nn.Module]):
-        super().__init__()
-        self.steps = nn.ModuleList(steps)
-
-    def forward(self, x):
-        cur_in = x
-        outs = []
-        for step in self.steps:
-            cur_out = step(cur_in)
-            outs.append(cur_out)
-            cur_in = torch.cat((cur_in, cur_out), dim=1)
-        return torch.cat(outs[::-1], dim=1)
-
-def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features):
-    if kind == 'convtranspose':
-        return [nn.ConvTranspose2d(min(max_features, ngf * mult), 
-                    min(max_features, int(ngf * mult / 2)), 
-                    kernel_size=3, stride=2, padding=1, output_padding=1),
-                    norm_layer(min(max_features, int(ngf * mult / 2))), activation]
-    elif kind == 'bilinear':
-        return [nn.Upsample(scale_factor=2, mode='bilinear'),
-                DepthWiseSeperableConv(min(max_features, ngf * mult), 
-                    min(max_features, int(ngf * mult / 2)), 
-                    kernel_size=3, stride=1, padding=1), 
-                norm_layer(min(max_features, int(ngf * mult / 2))), activation]
-    else:
-        raise Exception(f"Invalid deconv kind: {kind}")
\ No newline at end of file
diff --git a/spaces/CVPR/regionclip-demo/detectron2/utils/colormap.py b/spaces/CVPR/regionclip-demo/detectron2/utils/colormap.py
deleted file mode 100644
index 150ccc372262ec4de0b36db66a303cae9495e67f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/utils/colormap.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-An awesome colormap for really neat visualizations.
-Copied from Detectron, and removed gray colors.
-"""
-
-import numpy as np
-
-__all__ = ["colormap", "random_color"]
-
-# fmt: off
-# RGB:
-_COLORS = np.array(
-    [
-        0.000, 0.447, 0.741,
-        0.850, 0.325, 0.098,
-        0.929, 0.694, 0.125,
-        0.494, 0.184, 0.556,
-        0.466, 0.674, 0.188,
-        0.301, 0.745, 0.933,
-        0.635, 0.078, 0.184,
-        0.300, 0.300, 0.300,
-        0.600, 0.600, 0.600,
-        1.000, 0.000, 0.000,
-        1.000, 0.500, 0.000,
-        0.749, 0.749, 0.000,
-        0.000, 1.000, 0.000,
-        0.000, 0.000, 1.000,
-        0.667, 0.000, 1.000,
-        0.333, 0.333, 0.000,
-        0.333, 0.667, 0.000,
-        0.333, 1.000, 0.000,
-        0.667, 0.333, 0.000,
-        0.667, 0.667, 0.000,
-        0.667, 1.000, 0.000,
-        1.000, 0.333, 0.000,
-        1.000, 0.667, 0.000,
-        1.000, 1.000, 0.000,
-        0.000, 0.333, 0.500,
-        0.000, 0.667, 0.500,
-        0.000, 1.000, 0.500,
-        0.333, 0.000, 0.500,
-        0.333, 0.333, 0.500,
-        0.333, 0.667, 0.500,
-        0.333, 1.000, 0.500,
-        0.667, 0.000, 0.500,
-        0.667, 0.333, 0.500,
-        0.667, 0.667, 0.500,
-        0.667, 1.000, 0.500,
-        1.000, 0.000, 0.500,
-        1.000, 0.333, 0.500,
-        1.000, 0.667, 0.500,
-        1.000, 1.000, 0.500,
-        0.000, 0.333, 1.000,
-        0.000, 0.667, 1.000,
-        0.000, 1.000, 1.000,
-        0.333, 0.000, 1.000,
-        0.333, 0.333, 1.000,
-        0.333, 0.667, 1.000,
-        0.333, 1.000, 1.000,
-        0.667, 0.000, 1.000,
-        0.667, 0.333, 1.000,
-        0.667, 0.667, 1.000,
-        0.667, 1.000, 1.000,
-        1.000, 0.000, 1.000,
-        1.000, 0.333, 1.000,
-        1.000, 0.667, 1.000,
-        0.333, 0.000, 0.000,
-        0.500, 0.000, 0.000,
-        0.667, 0.000, 0.000,
-        0.833, 0.000, 0.000,
-        1.000, 0.000, 0.000,
-        0.000, 0.167, 0.000,
-        0.000, 0.333, 0.000,
-        0.000, 0.500, 0.000,
-        0.000, 0.667, 0.000,
-        0.000, 0.833, 0.000,
-        0.000, 1.000, 0.000,
-        0.000, 0.000, 0.167,
-        0.000, 0.000, 0.333,
-        0.000, 0.000, 0.500,
-        0.000, 0.000, 0.667,
-        0.000, 0.000, 0.833,
-        0.000, 0.000, 1.000,
-        0.000, 0.000, 0.000,
-        0.143, 0.143, 0.143,
-        0.857, 0.857, 0.857,
-        1.000, 1.000, 1.000
-    ]
-).astype(np.float32).reshape(-1, 3)
-# fmt: on
-
-
-def colormap(rgb=False, maximum=255):
-    """
-    Args:
-        rgb (bool): whether to return RGB colors or BGR colors.
-        maximum (int): either 255 or 1
-
-    Returns:
-        ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]
-    """
-    assert maximum in [255, 1], maximum
-    c = _COLORS * maximum
-    if not rgb:
-        c = c[:, ::-1]
-    return c
-
-
-def random_color(rgb=False, maximum=255):
-    """
-    Args:
-        rgb (bool): whether to return RGB colors or BGR colors.
-        maximum (int): either 255 or 1
-
-    Returns:
-        ndarray: a vector of 3 numbers
-    """
-    idx = np.random.randint(0, len(_COLORS))
-    ret = _COLORS[idx] * maximum
-    if not rgb:
-        ret = ret[::-1]
-    return ret
-
-
-if __name__ == "__main__":
-    import cv2
-
-    size = 100
-    H, W = 10, 10
-    canvas = np.random.rand(H * size, W * size, 3).astype("float32")
-    for h in range(H):
-        for w in range(W):
-            idx = h * W + w
-            if idx >= len(_COLORS):
-                break
-            canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]
-    cv2.imshow("a", canvas)
-    cv2.waitKey(0)
diff --git a/spaces/ClassCat/YOLOS-Object-Detection/app.py b/spaces/ClassCat/YOLOS-Object-Detection/app.py
deleted file mode 100644
index fcd851783eec67a9751519b573022f9ce1c0c65f..0000000000000000000000000000000000000000
--- a/spaces/ClassCat/YOLOS-Object-Detection/app.py
+++ /dev/null
@@ -1,130 +0,0 @@
-
-import torch
-
-from transformers import AutoImageProcessor, AutoModelForObjectDetection
-#from transformers import pipeline
-
-from PIL import Image
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-import io
-from random import choice
-
-
-image_processor_tiny = AutoImageProcessor.from_pretrained("hustvl/yolos-tiny")
-model_tiny = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny")
-
-image_processor_small = AutoImageProcessor.from_pretrained("hustvl/yolos-small")
-model_small = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-small")
-
-
-import gradio as gr
-
-
-COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
-            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
-            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]
-
-fdic = {
-    "family" : "DejaVu Serif",
-    "style" : "normal",
-    "size" : 18,
-    "color" : "yellow",
-    "weight" : "bold"
-}
-
-
-def get_figure(in_pil_img, in_results):
-    plt.figure(figsize=(16, 10))
-    plt.imshow(in_pil_img)
-    ax = plt.gca()
-
-    for score, label, box in zip(in_results["scores"], in_results["labels"], in_results["boxes"]):
-        selected_color = choice(COLORS)
-
-        box_int = [i.item() for i in torch.round(box).to(torch.int32)]
-        x, y, w, h = box_int[0], box_int[1], box_int[2]-box_int[0], box_int[3]-box_int[1]
-        #x, y, w, h = torch.round(box[0]).item(), torch.round(box[1]).item(), torch.round(box[2]-box[0]).item(), torch.round(box[3]-box[1]).item()
-
-        ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3, alpha=0.8))
-        ax.text(x, y, f"{model_tiny.config.id2label[label.item()]}: {round(score.item()*100, 2)}%", fontdict=fdic, alpha=0.8)
-
-    plt.axis("off")
-
-    return plt.gcf()
-
-
-def infer(in_pil_img, in_model="yolos-tiny", in_threshold=0.9):
-    target_sizes = torch.tensor([in_pil_img.size[::-1]])
-
-    if in_model == "yolos-small":
-        inputs = image_processor_small(images=in_pil_img, return_tensors="pt")
-        outputs = model_small(**inputs)
-
-        # convert outputs (bounding boxes and class logits) to COCO API
-        results = image_processor_small.post_process_object_detection(outputs, threshold=in_threshold, target_sizes=target_sizes)[0]
-
-    else:
-        inputs = image_processor_tiny(images=in_pil_img, return_tensors="pt")
-        outputs = model_tiny(**inputs)
-
-        # convert outputs (bounding boxes and class logits) to COCO API
-        results = image_processor_tiny.post_process_object_detection(outputs, threshold=in_threshold, target_sizes=target_sizes)[0]
-
-    figure = get_figure(in_pil_img, results)
-
-    buf = io.BytesIO()
-    figure.savefig(buf, bbox_inches='tight')
-    buf.seek(0)
-    output_pil_img = Image.open(buf)
-
-    return output_pil_img
-
-
-with gr.Blocks(title="YOLOS Object Detection - ClassCat",
-                    css=".gradio-container {background:lightyellow;}"
-               ) as demo:
-    #sample_index = gr.State([])
-
-    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">YOLOS Object Detection</div>""")
-
-    gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""")
-
-    model = gr.Radio(["yolos-tiny", "yolos-small"], value="yolos-tiny", label="Model name")
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""")
-    gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""")
-
-    with gr.Row():
-        input_image = gr.Image(label="Input image", type="pil")
-        output_image = gr.Image(label="Output image with predicted instances", type="pil")
-
-    gr.Examples(['samples/cats.jpg', 'samples/detectron2.png', 'samples/cat.jpg', 'samples/hotdog.jpg'], inputs=input_image)
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">3. Set a threshold value (default to 0.9)</h4>""")
-
-    threshold = gr.Slider(0, 1.0, value=0.9, label='threshold')
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">4. Then, click "Infer" button to predict object instances. It will take about 10 seconds (yolos-tiny) or 20 seconds (yolos-small).</h4>""")
-
-    send_btn = gr.Button("Infer")
-    send_btn.click(fn=infer, inputs=[input_image, model, threshold], outputs=[output_image])
-
-    gr.HTML("""<br/>""")
-    gr.HTML("""<h4 style="color:navy;">Reference</h4>""")
-    gr.HTML("""<ul>""")
-    gr.HTML("""<li><a href="https://huggingface.co/docs/transformers/model_doc/yolos" target="_blank">Hugging Face Transformers - YOLOS</a>""")
-    gr.HTML("""</ul>""")
-
-
-#demo.queue()
-demo.launch(debug=True)
-
-
-
-
-### EOF ###
diff --git a/spaces/Codecooker/rvcapi/README.md b/spaces/Codecooker/rvcapi/README.md
deleted file mode 100644
index c97a705f1f774a35bda1857cad67985282cb8e6d..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Rvcapi
-emoji: 🚀
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.40.1
-app_file: src/webui.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/rpn/rpn.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/rpn/rpn.py
deleted file mode 100644
index 8b027855cf114594c437f7d867a187b496b3bc80..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/rpn/rpn.py
+++ /dev/null
@@ -1,321 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-import torch.nn.functional as F
-from torch import nn
-import math
-from maskrcnn_benchmark.modeling import registry
-from maskrcnn_benchmark.modeling.box_coder import BoxCoder
-from maskrcnn_benchmark.modeling.rpn.retinanet.retinanet import build_retinanet
-from maskrcnn_benchmark.modeling.rpn.fcos.fcos import build_fcos
-from .loss import make_rpn_loss_evaluator
-from .anchor_generator import make_anchor_generator
-from .inference import make_rpn_postprocessor
-
-
-class RPNHeadConvRegressor(nn.Module):
-    """
-    A simple RPN Head for classification and bbox regression
-    """
-
-    def __init__(self, cfg, in_channels, num_anchors):
-        """
-        Arguments:
-            cfg              : config
-            in_channels (int): number of channels of the input feature
-            num_anchors (int): number of anchors to be predicted
-        """
-        super(RPNHeadConvRegressor, self).__init__()
-        self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
-        self.bbox_pred = nn.Conv2d(
-            in_channels, num_anchors * 4, kernel_size=1, stride=1
-        )
-
-        for l in [self.cls_logits, self.bbox_pred]:
-            torch.nn.init.normal_(l.weight, std=0.01)
-            torch.nn.init.constant_(l.bias, 0)
-
-    def forward(self, x):
-        assert isinstance(x, (list, tuple))
-        logits = [self.cls_logits(y) for y in x]
-        bbox_reg = [self.bbox_pred(y) for y in x]
-
-        return logits, bbox_reg
-
-
-class RPNHeadFeatureSingleConv(nn.Module):
-    """
-    Adds a simple RPN Head with one conv to extract the feature
-    """
-
-    def __init__(self, cfg, in_channels):
-        """
-        Arguments:
-            cfg              : config
-            in_channels (int): number of channels of the input feature
-        """
-        super(RPNHeadFeatureSingleConv, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-
-        for l in [self.conv]:
-            torch.nn.init.normal_(l.weight, std=0.01)
-            torch.nn.init.constant_(l.bias, 0)
-
-        self.out_channels = in_channels
-
-    def forward(self, x):
-        assert isinstance(x, (list, tuple))
-        x = [F.relu(self.conv(z)) for z in x]
-
-        return x
-
-
-@registry.RPN_HEADS.register("SingleConvRPNHead_1")
-class RPNHead(nn.Module):
-    """
-    Adds a simple RPN Head with classification and regression heads
-    """
-
-    def __init__(self, cfg, in_channels, num_anchors):
-        """
-        Arguments:
-            cfg              : config
-            in_channels (int): number of channels of the input feature
-            num_anchors (int): number of anchors to be predicted
-        """
-        super(RPNHead, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-        self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
-        self.bbox_pred_new = nn.Conv2d(
-            in_channels, num_anchors * 18, kernel_size=1, stride=1
-        )
-
-        for l in [self.conv, self.cls_logits, self.bbox_pred_new]:
-            torch.nn.init.normal_(l.weight, std=0.01)
-            torch.nn.init.constant_(l.bias, 0)
-
-    def forward(self, x):
-
-        logits = []
-        bbox_reg = []
-        for feature in x:
-            t = F.relu(self.conv(feature))
-            logits.append(self.cls_logits(t))
-            bbox_reg.append(self.bbox_pred_new(t))
-        return logits, bbox_reg
-
-
-class RPNModule(torch.nn.Module):
-    """
-    Module for RPN computation. Takes feature maps from the backbone and RPN
-    proposals and losses. Works for both FPN and non-FPN.
-    """
-
-    def __init__(self, cfg, in_channels):
-        super(RPNModule, self).__init__()
-
-        self.cfg = cfg.clone()
-
-        anchor_generator = make_anchor_generator(cfg)
-
-        rpn_head = registry.RPN_HEADS[cfg.MODEL.RPN.RPN_HEAD]
-        head = rpn_head(
-            cfg, in_channels, anchor_generator.num_anchors_per_location()[0]
-        )
-
-        rpn_box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
-
-        box_selector_train = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=True)
-        box_selector_test = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=False)
-
-        loss_evaluator = make_rpn_loss_evaluator(cfg, rpn_box_coder)
-
-        self.anchor_generator = anchor_generator
-        self.head = head
-        self.box_selector_train = box_selector_train
-        self.box_selector_test = box_selector_test
-        self.loss_evaluator = loss_evaluator
-
-    def forward(self, images, features, targets=None, prefix=''):
-        """
-        Arguments:
-            images (ImageList): images for which we want to compute the predictions
-            features (list[Tensor]): features computed from the images that are
-                used for computing the predictions. Each tensor in the list
-                correspond to different feature levels
-            targets (list[BoxList): ground-truth boxes present in the image (optional)
-
-        Returns:
-            boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per
-                image.
-            losses (dict[Tensor]): the losses for the model during training. During
-                testing, it is an empty dict.
-        """
-        objectness, rpn_box_regression = self.head(features)   # len = 5
-        anchors = self.anchor_generator(images, features)
-
-        if self.training:
-            return self._forward_train(anchors, objectness,
-                                       rpn_box_regression, targets, prefix)
-        else:
-            return self._forward_test(anchors, objectness, rpn_box_regression)
-
-    def _forward_train(self, anchors, objectness, rpn_box_regression,  # [image,number,[n,4]]
-                       targets, prefix):
-        if self.cfg.MODEL.RPN_ONLY:
-            # When training an RPN-only model, the loss is determined by the
-            # predicted objectness and rpn_box_regression values and there is
-            # no need to transform the anchors into predicted boxes; this is an
-            # optimization that avoids the unnecessary transformation.
-            boxes = anchors
-        else:
-            # print('\n---end-to-end model---\n')
-            # For end-to-end models, anchors must be transformed into boxes and
-            # sampled into a training batch.
-            with torch.no_grad():
-                boxes = self.box_selector_train(
-                    anchors, objectness, rpn_box_regression, targets
-                )
-        anchors_new = list(zip(*anchors))
-        regress_new = regress_to_box(anchors_new, rpn_box_regression)
-
-        loss_objectness, loss_rpn_box_reg = self.loss_evaluator(
-            anchors, objectness, regress_new, targets
-        )
-        losses = {
-            prefix + "loss_objectness": loss_objectness,
-            prefix + "loss_rpn_box_reg": loss_rpn_box_reg,
-        }
-        return boxes, losses
-
-    def _forward_test(self, anchors, objectness, rpn_box_regression):
-        boxes = self.box_selector_test(anchors, objectness, rpn_box_regression)
-        if self.cfg.MODEL.RPN_ONLY:
-            # For end-to-end models, the RPN proposals are an intermediate state
-            # and don't bother to sort them in decreasing score order. For RPN-only
-            # models, the proposals are the final output and we return them in
-            # high-to-low confidence order.
-            inds = [
-                box.get_field("objectness").sort(descending=True)[1] for box in boxes
-            ]
-            boxes = [box[ind] for box, ind in zip(boxes, inds)]
-        return boxes, {}
-
-
-def build_rpn(cfg, in_channels):
-    """
-    This gives the gist of it. Not super important because it doesn't change as much
-    """
-    if cfg.MODEL.FCOS_ON:
-        return build_fcos(cfg, in_channels)
-    if cfg.MODEL.RETINANET_ON:
-        return build_retinanet(cfg, in_channels)
-
-    return RPNModule(cfg, in_channels)
-
-
-def regress_to_box(anchor_define,regress_pre):
-
-    boxes_total = []
-    num_f = 0
-    for a, b in zip(anchor_define, regress_pre):
-        boxes_total.append(forward_feature_map(a, b))
-        num_f += 1
-    return boxes_total
-
-def forward_feature_map(anchors_define, boxes_regression):
-    N, A, H, W = boxes_regression.shape
-
-    boxes_regression = faltten(boxes_regression, N, A, 18, H, W)  #
-
-    # image_shapes = [box.size for box in anchors_define]
-    concat_anchors = torch.cat([a.bbox for a in anchors_define], dim=0)
-    concat_anchors = concat_anchors.reshape(N, -1, 4)
-    proposals = decode_iou(boxes_regression.view(-1, 18), concat_anchors.view(-1, 4))
-    box_temp_post = proposals.view(N, -1, 4)
-
-    return box_temp_post
-
-def faltten(layer, N, A, C, H, W):
-    layer = layer.view(N, -1, C, H, W)
-    layer = layer.permute(0, 3, 4, 1, 2)  #N H W A C
-    layer = layer.reshape(N, -1, C)  # N H*W*A C
-    return layer
-
-def decode_iou( rel_codes, boxes, num_p = 8):
-        """
-        From a set of original boxes and encoded relative box offsets,
-        get the decoded boxes.
-
-        Arguments:
-            rel_codes (Tensor): encoded boxes   # predict  [2, 12000, 4]
-            boxes (Tensor): reference boxes.   # anchor  [2, 12000, 4]  xmin0 ymin1 xmax2 ymax3
-        """
-        boxes = boxes.to(rel_codes.dtype)
-
-        TO_REMOVE = 1  # TODO remove
-        widths = boxes[:, 2] - boxes[:, 0] + TO_REMOVE
-        heights = boxes[:, 3] - boxes[:, 1] + TO_REMOVE
-        dx = rel_codes[:, 16]
-        dy = rel_codes[:, 17]
-
-        ctr_x = boxes[:, 0] + 0.5 * widths
-        ctr_y = boxes[:, 1] + 0.5 * heights
-
-        ctr_x_new = dx * widths * 0.5 + ctr_x
-        ctr_y_new = dy * heights * 0.5 + ctr_y
-        # 123
-        # 8#4
-        # 765
-        if num_p == 8:  # 8 boundary points
-            x_1 = boxes[:, 0] + widths * rel_codes[:, 0]
-            y_1 = boxes[:, 1] + heights * rel_codes[:, 1]
-            x_2 = ctr_x + widths * rel_codes[:, 2]
-            y_2 = boxes[:, 1] + heights * rel_codes[:, 3]
-            x_3 = boxes[:, 2] + widths * rel_codes[:, 4]
-            y_3 = boxes[:, 1] + heights * rel_codes[:, 5]
-            x_4 = boxes[:, 2] + widths * rel_codes[:, 6]
-            y_4 = ctr_y + heights * rel_codes[:, 7]
-            x_5 = boxes[:, 2] + widths * rel_codes[:, 8]
-            y_5 = boxes[:, 3] + heights * rel_codes[:, 9]
-            x_6 = ctr_x + widths * rel_codes[:, 10]
-            y_6 = boxes[:, 3] + heights * rel_codes[:, 11]
-            x_7 = boxes[:, 0] + widths * rel_codes[:, 12]
-            y_7 = boxes[:, 3] + heights * rel_codes[:, 13]
-            x_8 = boxes[:, 0] + widths * rel_codes[:, 14]
-            y_8 = ctr_y + heights * rel_codes[:, 15]
-            x_total = torch.stack([x_1, x_2, x_3, x_4, x_5, x_6, x_7, x_8], 0)  # [8, N]
-            y_total = torch.stack([y_1, y_2, y_3, y_4, y_5, y_6, y_7, y_8], 0)
-
-        x_min = torch.min(x_total, 0, keepdim=True)  # [1, N]
-        x_max = torch.max(x_total, 0, keepdim=True)  # [1, N]
-        y_min = torch.min(y_total, 0, keepdim=True)  # [1, N]
-        y_max = torch.max(y_total, 0, keepdim=True)  # [1, N]
-
-        N1, N2 = x_min[0].shape
-        x_min = x_min[0].view([N2])
-        x_max = x_max[0].view([N2])
-        y_min = y_min[0].view([N2])
-        y_max = y_max[0].view([N2])
-
-        x_min = torch.stack([x_min, ctr_x_new], 0)
-        x_max = torch.stack([x_max, ctr_x_new], 0)
-        y_min = torch.stack([y_min, ctr_y_new], 0)
-        y_max = torch.stack([y_max, ctr_y_new], 0)
-
-        x_min = torch.min(x_min, 0, keepdim=True)  # [1, N]
-        x_max = torch.max(x_max, 0, keepdim=True)  # [1, N]
-        y_min = torch.min(y_min, 0, keepdim=True)  # [1, N]
-        y_max = torch.max(y_max, 0, keepdim=True)  # [1, N]
-
-        pred_boxes = torch.zeros_like(boxes)
-
-        pred_boxes[:, 0] = x_min[0][0, :]
-        pred_boxes[:, 1] = y_min[0][0, :]
-        pred_boxes[:, 2] = x_max[0][0, :]
-        pred_boxes[:, 3] = y_max[0][0, :]
-
-        return pred_boxes
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_b_s_l_n.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_b_s_l_n.py
deleted file mode 100644
index 8e266fa54d0f0fd05bfde372627e1fb948d6f0fd..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_b_s_l_n.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6bsln.html
-class table__b_s_l_n(BaseTTXConverter):
-    pass
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/otData.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/otData.py
deleted file mode 100644
index 56716824ecd7950dda249a159b5b292dbd2a86f7..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/otData.py
+++ /dev/null
@@ -1,6236 +0,0 @@
-otData = [
-    #
-    # common
-    #
-    ("LookupOrder", []),
-    (
-        "ScriptList",
-        [
-            ("uint16", "ScriptCount", None, None, "Number of ScriptRecords"),
-            (
-                "struct",
-                "ScriptRecord",
-                "ScriptCount",
-                0,
-                "Array of ScriptRecords -listed alphabetically by ScriptTag",
-            ),
-        ],
-    ),
-    (
-        "ScriptRecord",
-        [
-            ("Tag", "ScriptTag", None, None, "4-byte ScriptTag identifier"),
-            (
-                "Offset",
-                "Script",
-                None,
-                None,
-                "Offset to Script table-from beginning of ScriptList",
-            ),
-        ],
-    ),
-    (
-        "Script",
-        [
-            (
-                "Offset",
-                "DefaultLangSys",
-                None,
-                None,
-                "Offset to DefaultLangSys table-from beginning of Script table-may be NULL",
-            ),
-            (
-                "uint16",
-                "LangSysCount",
-                None,
-                None,
-                "Number of LangSysRecords for this script-excluding the DefaultLangSys",
-            ),
-            (
-                "struct",
-                "LangSysRecord",
-                "LangSysCount",
-                0,
-                "Array of LangSysRecords-listed alphabetically by LangSysTag",
-            ),
-        ],
-    ),
-    (
-        "LangSysRecord",
-        [
-            ("Tag", "LangSysTag", None, None, "4-byte LangSysTag identifier"),
-            (
-                "Offset",
-                "LangSys",
-                None,
-                None,
-                "Offset to LangSys table-from beginning of Script table",
-            ),
-        ],
-    ),
-    (
-        "LangSys",
-        [
-            (
-                "Offset",
-                "LookupOrder",
-                None,
-                None,
-                "= NULL (reserved for an offset to a reordering table)",
-            ),
-            (
-                "uint16",
-                "ReqFeatureIndex",
-                None,
-                None,
-                "Index of a feature required for this language system- if no required features = 0xFFFF",
-            ),
-            (
-                "uint16",
-                "FeatureCount",
-                None,
-                None,
-                "Number of FeatureIndex values for this language system-excludes the required feature",
-            ),
-            (
-                "uint16",
-                "FeatureIndex",
-                "FeatureCount",
-                0,
-                "Array of indices into the FeatureList-in arbitrary order",
-            ),
-        ],
-    ),
-    (
-        "FeatureList",
-        [
-            (
-                "uint16",
-                "FeatureCount",
-                None,
-                None,
-                "Number of FeatureRecords in this table",
-            ),
-            (
-                "struct",
-                "FeatureRecord",
-                "FeatureCount",
-                0,
-                "Array of FeatureRecords-zero-based (first feature has FeatureIndex = 0)-listed alphabetically by FeatureTag",
-            ),
-        ],
-    ),
-    (
-        "FeatureRecord",
-        [
-            ("Tag", "FeatureTag", None, None, "4-byte feature identification tag"),
-            (
-                "Offset",
-                "Feature",
-                None,
-                None,
-                "Offset to Feature table-from beginning of FeatureList",
-            ),
-        ],
-    ),
-    (
-        "Feature",
-        [
-            (
-                "Offset",
-                "FeatureParams",
-                None,
-                None,
-                "= NULL (reserved for offset to FeatureParams)",
-            ),
-            (
-                "uint16",
-                "LookupCount",
-                None,
-                None,
-                "Number of LookupList indices for this feature",
-            ),
-            (
-                "uint16",
-                "LookupListIndex",
-                "LookupCount",
-                0,
-                "Array of LookupList indices for this feature -zero-based (first lookup is LookupListIndex = 0)",
-            ),
-        ],
-    ),
-    ("FeatureParams", []),
-    (
-        "FeatureParamsSize",
-        [
-            (
-                "DeciPoints",
-                "DesignSize",
-                None,
-                None,
-                "The design size in 720/inch units (decipoints).",
-            ),
-            (
-                "uint16",
-                "SubfamilyID",
-                None,
-                None,
-                "Serves as an identifier that associates fonts in a subfamily.",
-            ),
-            ("NameID", "SubfamilyNameID", None, None, "Subfamily NameID."),
-            (
-                "DeciPoints",
-                "RangeStart",
-                None,
-                None,
-                "Small end of recommended usage range (exclusive) in 720/inch units.",
-            ),
-            (
-                "DeciPoints",
-                "RangeEnd",
-                None,
-                None,
-                "Large end of recommended usage range (inclusive) in 720/inch units.",
-            ),
-        ],
-    ),
-    (
-        "FeatureParamsStylisticSet",
-        [
-            ("uint16", "Version", None, None, "Set to 0."),
-            ("NameID", "UINameID", None, None, "UI NameID."),
-        ],
-    ),
-    (
-        "FeatureParamsCharacterVariants",
-        [
-            ("uint16", "Format", None, None, "Set to 0."),
-            ("NameID", "FeatUILabelNameID", None, None, "Feature UI label NameID."),
-            (
-                "NameID",
-                "FeatUITooltipTextNameID",
-                None,
-                None,
-                "Feature UI tooltip text NameID.",
-            ),
-            ("NameID", "SampleTextNameID", None, None, "Sample text NameID."),
-            ("uint16", "NumNamedParameters", None, None, "Number of named parameters."),
-            (
-                "NameID",
-                "FirstParamUILabelNameID",
-                None,
-                None,
-                "First NameID of UI feature parameters.",
-            ),
-            (
-                "uint16",
-                "CharCount",
-                None,
-                None,
-                "Count of characters this feature provides glyph variants for.",
-            ),
-            (
-                "uint24",
-                "Character",
-                "CharCount",
-                0,
-                "Unicode characters for which this feature provides glyph variants.",
-            ),
-        ],
-    ),
-    (
-        "LookupList",
-        [
-            ("uint16", "LookupCount", None, None, "Number of lookups in this table"),
-            (
-                "Offset",
-                "Lookup",
-                "LookupCount",
-                0,
-                "Array of offsets to Lookup tables-from beginning of LookupList -zero based (first lookup is Lookup index = 0)",
-            ),
-        ],
-    ),
-    (
-        "Lookup",
-        [
-            (
-                "uint16",
-                "LookupType",
-                None,
-                None,
-                "Different enumerations for GSUB and GPOS",
-            ),
-            ("LookupFlag", "LookupFlag", None, None, "Lookup qualifiers"),
-            (
-                "uint16",
-                "SubTableCount",
-                None,
-                None,
-                "Number of SubTables for this lookup",
-            ),
-            (
-                "Offset",
-                "SubTable",
-                "SubTableCount",
-                0,
-                "Array of offsets to SubTables-from beginning of Lookup table",
-            ),
-            (
-                "uint16",
-                "MarkFilteringSet",
-                None,
-                "LookupFlag & 0x0010",
-                "If set, indicates that the lookup table structure is followed by a MarkFilteringSet field. The layout engine skips over all mark glyphs not in the mark filtering set indicated.",
-            ),
-        ],
-    ),
-    (
-        "CoverageFormat1",
-        [
-            ("uint16", "CoverageFormat", None, None, "Format identifier-format = 1"),
-            ("uint16", "GlyphCount", None, None, "Number of glyphs in the GlyphArray"),
-            (
-                "GlyphID",
-                "GlyphArray",
-                "GlyphCount",
-                0,
-                "Array of GlyphIDs-in numerical order",
-            ),
-        ],
-    ),
-    (
-        "CoverageFormat2",
-        [
-            ("uint16", "CoverageFormat", None, None, "Format identifier-format = 2"),
-            ("uint16", "RangeCount", None, None, "Number of RangeRecords"),
-            (
-                "struct",
-                "RangeRecord",
-                "RangeCount",
-                0,
-                "Array of glyph ranges-ordered by Start GlyphID",
-            ),
-        ],
-    ),
-    (
-        "RangeRecord",
-        [
-            ("GlyphID", "Start", None, None, "First GlyphID in the range"),
-            ("GlyphID", "End", None, None, "Last GlyphID in the range"),
-            (
-                "uint16",
-                "StartCoverageIndex",
-                None,
-                None,
-                "Coverage Index of first GlyphID in range",
-            ),
-        ],
-    ),
-    (
-        "ClassDefFormat1",
-        [
-            ("uint16", "ClassFormat", None, None, "Format identifier-format = 1"),
-            (
-                "GlyphID",
-                "StartGlyph",
-                None,
-                None,
-                "First GlyphID of the ClassValueArray",
-            ),
-            ("uint16", "GlyphCount", None, None, "Size of the ClassValueArray"),
-            (
-                "uint16",
-                "ClassValueArray",
-                "GlyphCount",
-                0,
-                "Array of Class Values-one per GlyphID",
-            ),
-        ],
-    ),
-    (
-        "ClassDefFormat2",
-        [
-            ("uint16", "ClassFormat", None, None, "Format identifier-format = 2"),
-            ("uint16", "ClassRangeCount", None, None, "Number of ClassRangeRecords"),
-            (
-                "struct",
-                "ClassRangeRecord",
-                "ClassRangeCount",
-                0,
-                "Array of ClassRangeRecords-ordered by Start GlyphID",
-            ),
-        ],
-    ),
-    (
-        "ClassRangeRecord",
-        [
-            ("GlyphID", "Start", None, None, "First GlyphID in the range"),
-            ("GlyphID", "End", None, None, "Last GlyphID in the range"),
-            ("uint16", "Class", None, None, "Applied to all glyphs in the range"),
-        ],
-    ),
-    (
-        "Device",
-        [
-            ("uint16", "StartSize", None, None, "Smallest size to correct-in ppem"),
-            ("uint16", "EndSize", None, None, "Largest size to correct-in ppem"),
-            (
-                "uint16",
-                "DeltaFormat",
-                None,
-                None,
-                "Format of DeltaValue array data: 1, 2, or 3",
-            ),
-            (
-                "DeltaValue",
-                "DeltaValue",
-                "",
-                "DeltaFormat in (1,2,3)",
-                "Array of compressed data",
-            ),
-        ],
-    ),
-    #
-    # gpos
-    #
-    (
-        "GPOS",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the GPOS table- 0x00010000 or 0x00010001",
-            ),
-            (
-                "Offset",
-                "ScriptList",
-                None,
-                None,
-                "Offset to ScriptList table-from beginning of GPOS table",
-            ),
-            (
-                "Offset",
-                "FeatureList",
-                None,
-                None,
-                "Offset to FeatureList table-from beginning of GPOS table",
-            ),
-            (
-                "Offset",
-                "LookupList",
-                None,
-                None,
-                "Offset to LookupList table-from beginning of GPOS table",
-            ),
-            (
-                "LOffset",
-                "FeatureVariations",
-                None,
-                "Version >= 0x00010001",
-                "Offset to FeatureVariations table-from beginning of GPOS table",
-            ),
-        ],
-    ),
-    (
-        "SinglePosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of SinglePos subtable",
-            ),
-            (
-                "uint16",
-                "ValueFormat",
-                None,
-                None,
-                "Defines the types of data in the ValueRecord",
-            ),
-            (
-                "ValueRecord",
-                "Value",
-                None,
-                None,
-                "Defines positioning value(s)-applied to all glyphs in the Coverage table",
-            ),
-        ],
-    ),
-    (
-        "SinglePosFormat2",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 2"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of SinglePos subtable",
-            ),
-            (
-                "uint16",
-                "ValueFormat",
-                None,
-                None,
-                "Defines the types of data in the ValueRecord",
-            ),
-            ("uint16", "ValueCount", None, None, "Number of ValueRecords"),
-            (
-                "ValueRecord",
-                "Value",
-                "ValueCount",
-                0,
-                "Array of ValueRecords-positioning values applied to glyphs",
-            ),
-        ],
-    ),
-    (
-        "PairPosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of PairPos subtable-only the first glyph in each pair",
-            ),
-            (
-                "uint16",
-                "ValueFormat1",
-                None,
-                None,
-                "Defines the types of data in ValueRecord1-for the first glyph in the pair -may be zero (0)",
-            ),
-            (
-                "uint16",
-                "ValueFormat2",
-                None,
-                None,
-                "Defines the types of data in ValueRecord2-for the second glyph in the pair -may be zero (0)",
-            ),
-            ("uint16", "PairSetCount", None, None, "Number of PairSet tables"),
-            (
-                "Offset",
-                "PairSet",
-                "PairSetCount",
-                0,
-                "Array of offsets to PairSet tables-from beginning of PairPos subtable-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "PairSet",
-        [
-            ("uint16", "PairValueCount", None, None, "Number of PairValueRecords"),
-            (
-                "struct",
-                "PairValueRecord",
-                "PairValueCount",
-                0,
-                "Array of PairValueRecords-ordered by GlyphID of the second glyph",
-            ),
-        ],
-    ),
-    (
-        "PairValueRecord",
-        [
-            (
-                "GlyphID",
-                "SecondGlyph",
-                None,
-                None,
-                "GlyphID of second glyph in the pair-first glyph is listed in the Coverage table",
-            ),
-            (
-                "ValueRecord",
-                "Value1",
-                None,
-                None,
-                "Positioning data for the first glyph in the pair",
-            ),
-            (
-                "ValueRecord",
-                "Value2",
-                None,
-                None,
-                "Positioning data for the second glyph in the pair",
-            ),
-        ],
-    ),
-    (
-        "PairPosFormat2",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 2"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of PairPos subtable-for the first glyph of the pair",
-            ),
-            (
-                "uint16",
-                "ValueFormat1",
-                None,
-                None,
-                "ValueRecord definition-for the first glyph of the pair-may be zero (0)",
-            ),
-            (
-                "uint16",
-                "ValueFormat2",
-                None,
-                None,
-                "ValueRecord definition-for the second glyph of the pair-may be zero (0)",
-            ),
-            (
-                "Offset",
-                "ClassDef1",
-                None,
-                None,
-                "Offset to ClassDef table-from beginning of PairPos subtable-for the first glyph of the pair",
-            ),
-            (
-                "Offset",
-                "ClassDef2",
-                None,
-                None,
-                "Offset to ClassDef table-from beginning of PairPos subtable-for the second glyph of the pair",
-            ),
-            (
-                "uint16",
-                "Class1Count",
-                None,
-                None,
-                "Number of classes in ClassDef1 table-includes Class0",
-            ),
-            (
-                "uint16",
-                "Class2Count",
-                None,
-                None,
-                "Number of classes in ClassDef2 table-includes Class0",
-            ),
-            (
-                "struct",
-                "Class1Record",
-                "Class1Count",
-                0,
-                "Array of Class1 records-ordered by Class1",
-            ),
-        ],
-    ),
-    (
-        "Class1Record",
-        [
-            (
-                "struct",
-                "Class2Record",
-                "Class2Count",
-                0,
-                "Array of Class2 records-ordered by Class2",
-            ),
-        ],
-    ),
-    (
-        "Class2Record",
-        [
-            (
-                "ValueRecord",
-                "Value1",
-                None,
-                None,
-                "Positioning for first glyph-empty if ValueFormat1 = 0",
-            ),
-            (
-                "ValueRecord",
-                "Value2",
-                None,
-                None,
-                "Positioning for second glyph-empty if ValueFormat2 = 0",
-            ),
-        ],
-    ),
-    (
-        "CursivePosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of CursivePos subtable",
-            ),
-            ("uint16", "EntryExitCount", None, None, "Number of EntryExit records"),
-            (
-                "struct",
-                "EntryExitRecord",
-                "EntryExitCount",
-                0,
-                "Array of EntryExit records-in Coverage Index order",
-            ),
-        ],
-    ),
-    (
-        "EntryExitRecord",
-        [
-            (
-                "Offset",
-                "EntryAnchor",
-                None,
-                None,
-                "Offset to EntryAnchor table-from beginning of CursivePos subtable-may be NULL",
-            ),
-            (
-                "Offset",
-                "ExitAnchor",
-                None,
-                None,
-                "Offset to ExitAnchor table-from beginning of CursivePos subtable-may be NULL",
-            ),
-        ],
-    ),
-    (
-        "MarkBasePosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "MarkCoverage",
-                None,
-                None,
-                "Offset to MarkCoverage table-from beginning of MarkBasePos subtable",
-            ),
-            (
-                "Offset",
-                "BaseCoverage",
-                None,
-                None,
-                "Offset to BaseCoverage table-from beginning of MarkBasePos subtable",
-            ),
-            ("uint16", "ClassCount", None, None, "Number of classes defined for marks"),
-            (
-                "Offset",
-                "MarkArray",
-                None,
-                None,
-                "Offset to MarkArray table-from beginning of MarkBasePos subtable",
-            ),
-            (
-                "Offset",
-                "BaseArray",
-                None,
-                None,
-                "Offset to BaseArray table-from beginning of MarkBasePos subtable",
-            ),
-        ],
-    ),
-    (
-        "BaseArray",
-        [
-            ("uint16", "BaseCount", None, None, "Number of BaseRecords"),
-            (
-                "struct",
-                "BaseRecord",
-                "BaseCount",
-                0,
-                "Array of BaseRecords-in order of BaseCoverage Index",
-            ),
-        ],
-    ),
-    (
-        "BaseRecord",
-        [
-            (
-                "Offset",
-                "BaseAnchor",
-                "ClassCount",
-                0,
-                "Array of offsets (one per class) to Anchor tables-from beginning of BaseArray table-ordered by class-zero-based",
-            ),
-        ],
-    ),
-    (
-        "MarkLigPosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "MarkCoverage",
-                None,
-                None,
-                "Offset to Mark Coverage table-from beginning of MarkLigPos subtable",
-            ),
-            (
-                "Offset",
-                "LigatureCoverage",
-                None,
-                None,
-                "Offset to Ligature Coverage table-from beginning of MarkLigPos subtable",
-            ),
-            ("uint16", "ClassCount", None, None, "Number of defined mark classes"),
-            (
-                "Offset",
-                "MarkArray",
-                None,
-                None,
-                "Offset to MarkArray table-from beginning of MarkLigPos subtable",
-            ),
-            (
-                "Offset",
-                "LigatureArray",
-                None,
-                None,
-                "Offset to LigatureArray table-from beginning of MarkLigPos subtable",
-            ),
-        ],
-    ),
-    (
-        "LigatureArray",
-        [
-            (
-                "uint16",
-                "LigatureCount",
-                None,
-                None,
-                "Number of LigatureAttach table offsets",
-            ),
-            (
-                "Offset",
-                "LigatureAttach",
-                "LigatureCount",
-                0,
-                "Array of offsets to LigatureAttach tables-from beginning of LigatureArray table-ordered by LigatureCoverage Index",
-            ),
-        ],
-    ),
-    (
-        "LigatureAttach",
-        [
-            (
-                "uint16",
-                "ComponentCount",
-                None,
-                None,
-                "Number of ComponentRecords in this ligature",
-            ),
-            (
-                "struct",
-                "ComponentRecord",
-                "ComponentCount",
-                0,
-                "Array of Component records-ordered in writing direction",
-            ),
-        ],
-    ),
-    (
-        "ComponentRecord",
-        [
-            (
-                "Offset",
-                "LigatureAnchor",
-                "ClassCount",
-                0,
-                "Array of offsets (one per class) to Anchor tables-from beginning of LigatureAttach table-ordered by class-NULL if a component does not have an attachment for a class-zero-based array",
-            ),
-        ],
-    ),
-    (
-        "MarkMarkPosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Mark1Coverage",
-                None,
-                None,
-                "Offset to Combining Mark Coverage table-from beginning of MarkMarkPos subtable",
-            ),
-            (
-                "Offset",
-                "Mark2Coverage",
-                None,
-                None,
-                "Offset to Base Mark Coverage table-from beginning of MarkMarkPos subtable",
-            ),
-            (
-                "uint16",
-                "ClassCount",
-                None,
-                None,
-                "Number of Combining Mark classes defined",
-            ),
-            (
-                "Offset",
-                "Mark1Array",
-                None,
-                None,
-                "Offset to MarkArray table for Mark1-from beginning of MarkMarkPos subtable",
-            ),
-            (
-                "Offset",
-                "Mark2Array",
-                None,
-                None,
-                "Offset to Mark2Array table for Mark2-from beginning of MarkMarkPos subtable",
-            ),
-        ],
-    ),
-    (
-        "Mark2Array",
-        [
-            ("uint16", "Mark2Count", None, None, "Number of Mark2 records"),
-            (
-                "struct",
-                "Mark2Record",
-                "Mark2Count",
-                0,
-                "Array of Mark2 records-in Coverage order",
-            ),
-        ],
-    ),
-    (
-        "Mark2Record",
-        [
-            (
-                "Offset",
-                "Mark2Anchor",
-                "ClassCount",
-                0,
-                "Array of offsets (one per class) to Anchor tables-from beginning of Mark2Array table-zero-based array",
-            ),
-        ],
-    ),
-    (
-        "PosLookupRecord",
-        [
-            (
-                "uint16",
-                "SequenceIndex",
-                None,
-                None,
-                "Index to input glyph sequence-first glyph = 0",
-            ),
-            (
-                "uint16",
-                "LookupListIndex",
-                None,
-                None,
-                "Lookup to apply to that position-zero-based",
-            ),
-        ],
-    ),
-    (
-        "ContextPosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of ContextPos subtable",
-            ),
-            ("uint16", "PosRuleSetCount", None, None, "Number of PosRuleSet tables"),
-            (
-                "Offset",
-                "PosRuleSet",
-                "PosRuleSetCount",
-                0,
-                "Array of offsets to PosRuleSet tables-from beginning of ContextPos subtable-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "PosRuleSet",
-        [
-            ("uint16", "PosRuleCount", None, None, "Number of PosRule tables"),
-            (
-                "Offset",
-                "PosRule",
-                "PosRuleCount",
-                0,
-                "Array of offsets to PosRule tables-from beginning of PosRuleSet-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "PosRule",
-        [
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of glyphs in the Input glyph sequence",
-            ),
-            ("uint16", "PosCount", None, None, "Number of PosLookupRecords"),
-            (
-                "GlyphID",
-                "Input",
-                "GlyphCount",
-                -1,
-                "Array of input GlyphIDs-starting with the second glyph",
-            ),
-            (
-                "struct",
-                "PosLookupRecord",
-                "PosCount",
-                0,
-                "Array of positioning lookups-in design order",
-            ),
-        ],
-    ),
-    (
-        "ContextPosFormat2",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 2"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of ContextPos subtable",
-            ),
-            (
-                "Offset",
-                "ClassDef",
-                None,
-                None,
-                "Offset to ClassDef table-from beginning of ContextPos subtable",
-            ),
-            ("uint16", "PosClassSetCount", None, None, "Number of PosClassSet tables"),
-            (
-                "Offset",
-                "PosClassSet",
-                "PosClassSetCount",
-                0,
-                "Array of offsets to PosClassSet tables-from beginning of ContextPos subtable-ordered by class-may be NULL",
-            ),
-        ],
-    ),
-    (
-        "PosClassSet",
-        [
-            (
-                "uint16",
-                "PosClassRuleCount",
-                None,
-                None,
-                "Number of PosClassRule tables",
-            ),
-            (
-                "Offset",
-                "PosClassRule",
-                "PosClassRuleCount",
-                0,
-                "Array of offsets to PosClassRule tables-from beginning of PosClassSet-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "PosClassRule",
-        [
-            ("uint16", "GlyphCount", None, None, "Number of glyphs to be matched"),
-            ("uint16", "PosCount", None, None, "Number of PosLookupRecords"),
-            (
-                "uint16",
-                "Class",
-                "GlyphCount",
-                -1,
-                "Array of classes-beginning with the second class-to be matched to the input glyph sequence",
-            ),
-            (
-                "struct",
-                "PosLookupRecord",
-                "PosCount",
-                0,
-                "Array of positioning lookups-in design order",
-            ),
-        ],
-    ),
-    (
-        "ContextPosFormat3",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 3"),
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of glyphs in the input sequence",
-            ),
-            ("uint16", "PosCount", None, None, "Number of PosLookupRecords"),
-            (
-                "Offset",
-                "Coverage",
-                "GlyphCount",
-                0,
-                "Array of offsets to Coverage tables-from beginning of ContextPos subtable",
-            ),
-            (
-                "struct",
-                "PosLookupRecord",
-                "PosCount",
-                0,
-                "Array of positioning lookups-in design order",
-            ),
-        ],
-    ),
-    (
-        "ChainContextPosFormat1",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of ContextPos subtable",
-            ),
-            (
-                "uint16",
-                "ChainPosRuleSetCount",
-                None,
-                None,
-                "Number of ChainPosRuleSet tables",
-            ),
-            (
-                "Offset",
-                "ChainPosRuleSet",
-                "ChainPosRuleSetCount",
-                0,
-                "Array of offsets to ChainPosRuleSet tables-from beginning of ContextPos subtable-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "ChainPosRuleSet",
-        [
-            (
-                "uint16",
-                "ChainPosRuleCount",
-                None,
-                None,
-                "Number of ChainPosRule tables",
-            ),
-            (
-                "Offset",
-                "ChainPosRule",
-                "ChainPosRuleCount",
-                0,
-                "Array of offsets to ChainPosRule tables-from beginning of ChainPosRuleSet-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "ChainPosRule",
-        [
-            (
-                "uint16",
-                "BacktrackGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)",
-            ),
-            (
-                "GlyphID",
-                "Backtrack",
-                "BacktrackGlyphCount",
-                0,
-                "Array of backtracking GlyphID's (to be matched before the input sequence)",
-            ),
-            (
-                "uint16",
-                "InputGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the input sequence (includes the first glyph)",
-            ),
-            (
-                "GlyphID",
-                "Input",
-                "InputGlyphCount",
-                -1,
-                "Array of input GlyphIDs (start with second glyph)",
-            ),
-            (
-                "uint16",
-                "LookAheadGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the look ahead sequence (number of glyphs to be matched after the input sequence)",
-            ),
-            (
-                "GlyphID",
-                "LookAhead",
-                "LookAheadGlyphCount",
-                0,
-                "Array of lookahead GlyphID's (to be matched after the input sequence)",
-            ),
-            ("uint16", "PosCount", None, None, "Number of PosLookupRecords"),
-            (
-                "struct",
-                "PosLookupRecord",
-                "PosCount",
-                0,
-                "Array of PosLookupRecords (in design order)",
-            ),
-        ],
-    ),
-    (
-        "ChainContextPosFormat2",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 2"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of ChainContextPos subtable",
-            ),
-            (
-                "Offset",
-                "BacktrackClassDef",
-                None,
-                None,
-                "Offset to ClassDef table containing backtrack sequence context-from beginning of ChainContextPos subtable",
-            ),
-            (
-                "Offset",
-                "InputClassDef",
-                None,
-                None,
-                "Offset to ClassDef table containing input sequence context-from beginning of ChainContextPos subtable",
-            ),
-            (
-                "Offset",
-                "LookAheadClassDef",
-                None,
-                None,
-                "Offset to ClassDef table containing lookahead sequence context-from beginning of ChainContextPos subtable",
-            ),
-            (
-                "uint16",
-                "ChainPosClassSetCount",
-                None,
-                None,
-                "Number of ChainPosClassSet tables",
-            ),
-            (
-                "Offset",
-                "ChainPosClassSet",
-                "ChainPosClassSetCount",
-                0,
-                "Array of offsets to ChainPosClassSet tables-from beginning of ChainContextPos subtable-ordered by input class-may be NULL",
-            ),
-        ],
-    ),
-    (
-        "ChainPosClassSet",
-        [
-            (
-                "uint16",
-                "ChainPosClassRuleCount",
-                None,
-                None,
-                "Number of ChainPosClassRule tables",
-            ),
-            (
-                "Offset",
-                "ChainPosClassRule",
-                "ChainPosClassRuleCount",
-                0,
-                "Array of offsets to ChainPosClassRule tables-from beginning of ChainPosClassSet-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "ChainPosClassRule",
-        [
-            (
-                "uint16",
-                "BacktrackGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)",
-            ),
-            (
-                "uint16",
-                "Backtrack",
-                "BacktrackGlyphCount",
-                0,
-                "Array of backtracking classes(to be matched before the input sequence)",
-            ),
-            (
-                "uint16",
-                "InputGlyphCount",
-                None,
-                None,
-                "Total number of classes in the input sequence (includes the first class)",
-            ),
-            (
-                "uint16",
-                "Input",
-                "InputGlyphCount",
-                -1,
-                "Array of input classes(start with second class; to be matched with the input glyph sequence)",
-            ),
-            (
-                "uint16",
-                "LookAheadGlyphCount",
-                None,
-                None,
-                "Total number of classes in the look ahead sequence (number of classes to be matched after the input sequence)",
-            ),
-            (
-                "uint16",
-                "LookAhead",
-                "LookAheadGlyphCount",
-                0,
-                "Array of lookahead classes(to be matched after the input sequence)",
-            ),
-            ("uint16", "PosCount", None, None, "Number of PosLookupRecords"),
-            (
-                "struct",
-                "PosLookupRecord",
-                "PosCount",
-                0,
-                "Array of PosLookupRecords (in design order)",
-            ),
-        ],
-    ),
-    (
-        "ChainContextPosFormat3",
-        [
-            ("uint16", "PosFormat", None, None, "Format identifier-format = 3"),
-            (
-                "uint16",
-                "BacktrackGlyphCount",
-                None,
-                None,
-                "Number of glyphs in the backtracking sequence",
-            ),
-            (
-                "Offset",
-                "BacktrackCoverage",
-                "BacktrackGlyphCount",
-                0,
-                "Array of offsets to coverage tables in backtracking sequence, in glyph sequence order",
-            ),
-            (
-                "uint16",
-                "InputGlyphCount",
-                None,
-                None,
-                "Number of glyphs in input sequence",
-            ),
-            (
-                "Offset",
-                "InputCoverage",
-                "InputGlyphCount",
-                0,
-                "Array of offsets to coverage tables in input sequence, in glyph sequence order",
-            ),
-            (
-                "uint16",
-                "LookAheadGlyphCount",
-                None,
-                None,
-                "Number of glyphs in lookahead sequence",
-            ),
-            (
-                "Offset",
-                "LookAheadCoverage",
-                "LookAheadGlyphCount",
-                0,
-                "Array of offsets to coverage tables in lookahead sequence, in glyph sequence order",
-            ),
-            ("uint16", "PosCount", None, None, "Number of PosLookupRecords"),
-            (
-                "struct",
-                "PosLookupRecord",
-                "PosCount",
-                0,
-                "Array of PosLookupRecords,in design order",
-            ),
-        ],
-    ),
-    (
-        "ExtensionPosFormat1",
-        [
-            ("uint16", "ExtFormat", None, None, "Format identifier. Set to 1."),
-            (
-                "uint16",
-                "ExtensionLookupType",
-                None,
-                None,
-                "Lookup type of subtable referenced by ExtensionOffset (i.e. the extension subtable).",
-            ),
-            ("LOffset", "ExtSubTable", None, None, "Offset to SubTable"),
-        ],
-    ),
-    # 	('ValueRecord', [
-    # 		('int16', 'XPlacement', None, None, 'Horizontal adjustment for placement-in design units'),
-    # 		('int16', 'YPlacement', None, None, 'Vertical adjustment for placement-in design units'),
-    # 		('int16', 'XAdvance', None, None, 'Horizontal adjustment for advance-in design units (only used for horizontal writing)'),
-    # 		('int16', 'YAdvance', None, None, 'Vertical adjustment for advance-in design units (only used for vertical writing)'),
-    # 		('Offset', 'XPlaDevice', None, None, 'Offset to Device table for horizontal placement-measured from beginning of PosTable (may be NULL)'),
-    # 		('Offset', 'YPlaDevice', None, None, 'Offset to Device table for vertical placement-measured from beginning of PosTable (may be NULL)'),
-    # 		('Offset', 'XAdvDevice', None, None, 'Offset to Device table for horizontal advance-measured from beginning of PosTable (may be NULL)'),
-    # 		('Offset', 'YAdvDevice', None, None, 'Offset to Device table for vertical advance-measured from beginning of PosTable (may be NULL)'),
-    # 	]),
-    (
-        "AnchorFormat1",
-        [
-            ("uint16", "AnchorFormat", None, None, "Format identifier-format = 1"),
-            ("int16", "XCoordinate", None, None, "Horizontal value-in design units"),
-            ("int16", "YCoordinate", None, None, "Vertical value-in design units"),
-        ],
-    ),
-    (
-        "AnchorFormat2",
-        [
-            ("uint16", "AnchorFormat", None, None, "Format identifier-format = 2"),
-            ("int16", "XCoordinate", None, None, "Horizontal value-in design units"),
-            ("int16", "YCoordinate", None, None, "Vertical value-in design units"),
-            ("uint16", "AnchorPoint", None, None, "Index to glyph contour point"),
-        ],
-    ),
-    (
-        "AnchorFormat3",
-        [
-            ("uint16", "AnchorFormat", None, None, "Format identifier-format = 3"),
-            ("int16", "XCoordinate", None, None, "Horizontal value-in design units"),
-            ("int16", "YCoordinate", None, None, "Vertical value-in design units"),
-            (
-                "Offset",
-                "XDeviceTable",
-                None,
-                None,
-                "Offset to Device table for X coordinate- from beginning of Anchor table (may be NULL)",
-            ),
-            (
-                "Offset",
-                "YDeviceTable",
-                None,
-                None,
-                "Offset to Device table for Y coordinate- from beginning of Anchor table (may be NULL)",
-            ),
-        ],
-    ),
-    (
-        "MarkArray",
-        [
-            ("uint16", "MarkCount", None, None, "Number of MarkRecords"),
-            (
-                "struct",
-                "MarkRecord",
-                "MarkCount",
-                0,
-                "Array of MarkRecords-in Coverage order",
-            ),
-        ],
-    ),
-    (
-        "MarkRecord",
-        [
-            ("uint16", "Class", None, None, "Class defined for this mark"),
-            (
-                "Offset",
-                "MarkAnchor",
-                None,
-                None,
-                "Offset to Anchor table-from beginning of MarkArray table",
-            ),
-        ],
-    ),
-    #
-    # gsub
-    #
-    (
-        "GSUB",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the GSUB table- 0x00010000 or 0x00010001",
-            ),
-            (
-                "Offset",
-                "ScriptList",
-                None,
-                None,
-                "Offset to ScriptList table-from beginning of GSUB table",
-            ),
-            (
-                "Offset",
-                "FeatureList",
-                None,
-                None,
-                "Offset to FeatureList table-from beginning of GSUB table",
-            ),
-            (
-                "Offset",
-                "LookupList",
-                None,
-                None,
-                "Offset to LookupList table-from beginning of GSUB table",
-            ),
-            (
-                "LOffset",
-                "FeatureVariations",
-                None,
-                "Version >= 0x00010001",
-                "Offset to FeatureVariations table-from beginning of GSUB table",
-            ),
-        ],
-    ),
-    (
-        "SingleSubstFormat1",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "DeltaGlyphID",
-                None,
-                None,
-                "Add to original GlyphID modulo 65536 to get substitute GlyphID",
-            ),
-        ],
-    ),
-    (
-        "SingleSubstFormat2",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 2"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of GlyphIDs in the Substitute array",
-            ),
-            (
-                "GlyphID",
-                "Substitute",
-                "GlyphCount",
-                0,
-                "Array of substitute GlyphIDs-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "MultipleSubstFormat1",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "SequenceCount",
-                None,
-                None,
-                "Number of Sequence table offsets in the Sequence array",
-            ),
-            (
-                "Offset",
-                "Sequence",
-                "SequenceCount",
-                0,
-                "Array of offsets to Sequence tables-from beginning of Substitution table-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "Sequence",
-        [
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of GlyphIDs in the Substitute array. This should always be greater than 0.",
-            ),
-            (
-                "GlyphID",
-                "Substitute",
-                "GlyphCount",
-                0,
-                "String of GlyphIDs to substitute",
-            ),
-        ],
-    ),
-    (
-        "AlternateSubstFormat1",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "AlternateSetCount",
-                None,
-                None,
-                "Number of AlternateSet tables",
-            ),
-            (
-                "Offset",
-                "AlternateSet",
-                "AlternateSetCount",
-                0,
-                "Array of offsets to AlternateSet tables-from beginning of Substitution table-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "AlternateSet",
-        [
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of GlyphIDs in the Alternate array",
-            ),
-            (
-                "GlyphID",
-                "Alternate",
-                "GlyphCount",
-                0,
-                "Array of alternate GlyphIDs-in arbitrary order",
-            ),
-        ],
-    ),
-    (
-        "LigatureSubstFormat1",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            ("uint16", "LigSetCount", None, None, "Number of LigatureSet tables"),
-            (
-                "Offset",
-                "LigatureSet",
-                "LigSetCount",
-                0,
-                "Array of offsets to LigatureSet tables-from beginning of Substitution table-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "LigatureSet",
-        [
-            ("uint16", "LigatureCount", None, None, "Number of Ligature tables"),
-            (
-                "Offset",
-                "Ligature",
-                "LigatureCount",
-                0,
-                "Array of offsets to Ligature tables-from beginning of LigatureSet table-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "Ligature",
-        [
-            ("GlyphID", "LigGlyph", None, None, "GlyphID of ligature to substitute"),
-            ("uint16", "CompCount", None, None, "Number of components in the ligature"),
-            (
-                "GlyphID",
-                "Component",
-                "CompCount",
-                -1,
-                "Array of component GlyphIDs-start with the second component-ordered in writing direction",
-            ),
-        ],
-    ),
-    (
-        "SubstLookupRecord",
-        [
-            (
-                "uint16",
-                "SequenceIndex",
-                None,
-                None,
-                "Index into current glyph sequence-first glyph = 0",
-            ),
-            (
-                "uint16",
-                "LookupListIndex",
-                None,
-                None,
-                "Lookup to apply to that position-zero-based",
-            ),
-        ],
-    ),
-    (
-        "ContextSubstFormat1",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "SubRuleSetCount",
-                None,
-                None,
-                "Number of SubRuleSet tables-must equal GlyphCount in Coverage table",
-            ),
-            (
-                "Offset",
-                "SubRuleSet",
-                "SubRuleSetCount",
-                0,
-                "Array of offsets to SubRuleSet tables-from beginning of Substitution table-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "SubRuleSet",
-        [
-            ("uint16", "SubRuleCount", None, None, "Number of SubRule tables"),
-            (
-                "Offset",
-                "SubRule",
-                "SubRuleCount",
-                0,
-                "Array of offsets to SubRule tables-from beginning of SubRuleSet table-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "SubRule",
-        [
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Total number of glyphs in input glyph sequence-includes the first glyph",
-            ),
-            ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"),
-            (
-                "GlyphID",
-                "Input",
-                "GlyphCount",
-                -1,
-                "Array of input GlyphIDs-start with second glyph",
-            ),
-            (
-                "struct",
-                "SubstLookupRecord",
-                "SubstCount",
-                0,
-                "Array of SubstLookupRecords-in design order",
-            ),
-        ],
-    ),
-    (
-        "ContextSubstFormat2",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 2"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "Offset",
-                "ClassDef",
-                None,
-                None,
-                "Offset to glyph ClassDef table-from beginning of Substitution table",
-            ),
-            ("uint16", "SubClassSetCount", None, None, "Number of SubClassSet tables"),
-            (
-                "Offset",
-                "SubClassSet",
-                "SubClassSetCount",
-                0,
-                "Array of offsets to SubClassSet tables-from beginning of Substitution table-ordered by class-may be NULL",
-            ),
-        ],
-    ),
-    (
-        "SubClassSet",
-        [
-            (
-                "uint16",
-                "SubClassRuleCount",
-                None,
-                None,
-                "Number of SubClassRule tables",
-            ),
-            (
-                "Offset",
-                "SubClassRule",
-                "SubClassRuleCount",
-                0,
-                "Array of offsets to SubClassRule tables-from beginning of SubClassSet-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "SubClassRule",
-        [
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Total number of classes specified for the context in the rule-includes the first class",
-            ),
-            ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"),
-            (
-                "uint16",
-                "Class",
-                "GlyphCount",
-                -1,
-                "Array of classes-beginning with the second class-to be matched to the input glyph class sequence",
-            ),
-            (
-                "struct",
-                "SubstLookupRecord",
-                "SubstCount",
-                0,
-                "Array of Substitution lookups-in design order",
-            ),
-        ],
-    ),
-    (
-        "ContextSubstFormat3",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 3"),
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of glyphs in the input glyph sequence",
-            ),
-            ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"),
-            (
-                "Offset",
-                "Coverage",
-                "GlyphCount",
-                0,
-                "Array of offsets to Coverage table-from beginning of Substitution table-in glyph sequence order",
-            ),
-            (
-                "struct",
-                "SubstLookupRecord",
-                "SubstCount",
-                0,
-                "Array of SubstLookupRecords-in design order",
-            ),
-        ],
-    ),
-    (
-        "ChainContextSubstFormat1",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "ChainSubRuleSetCount",
-                None,
-                None,
-                "Number of ChainSubRuleSet tables-must equal GlyphCount in Coverage table",
-            ),
-            (
-                "Offset",
-                "ChainSubRuleSet",
-                "ChainSubRuleSetCount",
-                0,
-                "Array of offsets to ChainSubRuleSet tables-from beginning of Substitution table-ordered by Coverage Index",
-            ),
-        ],
-    ),
-    (
-        "ChainSubRuleSet",
-        [
-            (
-                "uint16",
-                "ChainSubRuleCount",
-                None,
-                None,
-                "Number of ChainSubRule tables",
-            ),
-            (
-                "Offset",
-                "ChainSubRule",
-                "ChainSubRuleCount",
-                0,
-                "Array of offsets to ChainSubRule tables-from beginning of ChainSubRuleSet table-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "ChainSubRule",
-        [
-            (
-                "uint16",
-                "BacktrackGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)",
-            ),
-            (
-                "GlyphID",
-                "Backtrack",
-                "BacktrackGlyphCount",
-                0,
-                "Array of backtracking GlyphID's (to be matched before the input sequence)",
-            ),
-            (
-                "uint16",
-                "InputGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the input sequence (includes the first glyph)",
-            ),
-            (
-                "GlyphID",
-                "Input",
-                "InputGlyphCount",
-                -1,
-                "Array of input GlyphIDs (start with second glyph)",
-            ),
-            (
-                "uint16",
-                "LookAheadGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the look ahead sequence (number of glyphs to be matched after the input sequence)",
-            ),
-            (
-                "GlyphID",
-                "LookAhead",
-                "LookAheadGlyphCount",
-                0,
-                "Array of lookahead GlyphID's (to be matched after the input sequence)",
-            ),
-            ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"),
-            (
-                "struct",
-                "SubstLookupRecord",
-                "SubstCount",
-                0,
-                "Array of SubstLookupRecords (in design order)",
-            ),
-        ],
-    ),
-    (
-        "ChainContextSubstFormat2",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 2"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table-from beginning of Substitution table",
-            ),
-            (
-                "Offset",
-                "BacktrackClassDef",
-                None,
-                None,
-                "Offset to glyph ClassDef table containing backtrack sequence data-from beginning of Substitution table",
-            ),
-            (
-                "Offset",
-                "InputClassDef",
-                None,
-                None,
-                "Offset to glyph ClassDef table containing input sequence data-from beginning of Substitution table",
-            ),
-            (
-                "Offset",
-                "LookAheadClassDef",
-                None,
-                None,
-                "Offset to glyph ClassDef table containing lookahead sequence data-from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "ChainSubClassSetCount",
-                None,
-                None,
-                "Number of ChainSubClassSet tables",
-            ),
-            (
-                "Offset",
-                "ChainSubClassSet",
-                "ChainSubClassSetCount",
-                0,
-                "Array of offsets to ChainSubClassSet tables-from beginning of Substitution table-ordered by input class-may be NULL",
-            ),
-        ],
-    ),
-    (
-        "ChainSubClassSet",
-        [
-            (
-                "uint16",
-                "ChainSubClassRuleCount",
-                None,
-                None,
-                "Number of ChainSubClassRule tables",
-            ),
-            (
-                "Offset",
-                "ChainSubClassRule",
-                "ChainSubClassRuleCount",
-                0,
-                "Array of offsets to ChainSubClassRule tables-from beginning of ChainSubClassSet-ordered by preference",
-            ),
-        ],
-    ),
-    (
-        "ChainSubClassRule",
-        [
-            (
-                "uint16",
-                "BacktrackGlyphCount",
-                None,
-                None,
-                "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)",
-            ),
-            (
-                "uint16",
-                "Backtrack",
-                "BacktrackGlyphCount",
-                0,
-                "Array of backtracking classes(to be matched before the input sequence)",
-            ),
-            (
-                "uint16",
-                "InputGlyphCount",
-                None,
-                None,
-                "Total number of classes in the input sequence (includes the first class)",
-            ),
-            (
-                "uint16",
-                "Input",
-                "InputGlyphCount",
-                -1,
-                "Array of input classes(start with second class; to be matched with the input glyph sequence)",
-            ),
-            (
-                "uint16",
-                "LookAheadGlyphCount",
-                None,
-                None,
-                "Total number of classes in the look ahead sequence (number of classes to be matched after the input sequence)",
-            ),
-            (
-                "uint16",
-                "LookAhead",
-                "LookAheadGlyphCount",
-                0,
-                "Array of lookahead classes(to be matched after the input sequence)",
-            ),
-            ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"),
-            (
-                "struct",
-                "SubstLookupRecord",
-                "SubstCount",
-                0,
-                "Array of SubstLookupRecords (in design order)",
-            ),
-        ],
-    ),
-    (
-        "ChainContextSubstFormat3",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 3"),
-            (
-                "uint16",
-                "BacktrackGlyphCount",
-                None,
-                None,
-                "Number of glyphs in the backtracking sequence",
-            ),
-            (
-                "Offset",
-                "BacktrackCoverage",
-                "BacktrackGlyphCount",
-                0,
-                "Array of offsets to coverage tables in backtracking sequence, in glyph sequence order",
-            ),
-            (
-                "uint16",
-                "InputGlyphCount",
-                None,
-                None,
-                "Number of glyphs in input sequence",
-            ),
-            (
-                "Offset",
-                "InputCoverage",
-                "InputGlyphCount",
-                0,
-                "Array of offsets to coverage tables in input sequence, in glyph sequence order",
-            ),
-            (
-                "uint16",
-                "LookAheadGlyphCount",
-                None,
-                None,
-                "Number of glyphs in lookahead sequence",
-            ),
-            (
-                "Offset",
-                "LookAheadCoverage",
-                "LookAheadGlyphCount",
-                0,
-                "Array of offsets to coverage tables in lookahead sequence, in glyph sequence order",
-            ),
-            ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"),
-            (
-                "struct",
-                "SubstLookupRecord",
-                "SubstCount",
-                0,
-                "Array of SubstLookupRecords, in design order",
-            ),
-        ],
-    ),
-    (
-        "ExtensionSubstFormat1",
-        [
-            ("uint16", "ExtFormat", None, None, "Format identifier. Set to 1."),
-            (
-                "uint16",
-                "ExtensionLookupType",
-                None,
-                None,
-                "Lookup type of subtable referenced by ExtensionOffset (i.e. the extension subtable).",
-            ),
-            (
-                "LOffset",
-                "ExtSubTable",
-                None,
-                None,
-                "Array of offsets to Lookup tables-from beginning of LookupList -zero based (first lookup is Lookup index = 0)",
-            ),
-        ],
-    ),
-    (
-        "ReverseChainSingleSubstFormat1",
-        [
-            ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"),
-            (
-                "Offset",
-                "Coverage",
-                None,
-                0,
-                "Offset to Coverage table - from beginning of Substitution table",
-            ),
-            (
-                "uint16",
-                "BacktrackGlyphCount",
-                None,
-                None,
-                "Number of glyphs in the backtracking sequence",
-            ),
-            (
-                "Offset",
-                "BacktrackCoverage",
-                "BacktrackGlyphCount",
-                0,
-                "Array of offsets to coverage tables in backtracking sequence, in glyph sequence order",
-            ),
-            (
-                "uint16",
-                "LookAheadGlyphCount",
-                None,
-                None,
-                "Number of glyphs in lookahead sequence",
-            ),
-            (
-                "Offset",
-                "LookAheadCoverage",
-                "LookAheadGlyphCount",
-                0,
-                "Array of offsets to coverage tables in lookahead sequence, in glyph sequence order",
-            ),
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of GlyphIDs in the Substitute array",
-            ),
-            (
-                "GlyphID",
-                "Substitute",
-                "GlyphCount",
-                0,
-                "Array of substitute GlyphIDs-ordered by Coverage index",
-            ),
-        ],
-    ),
-    #
-    # gdef
-    #
-    (
-        "GDEF",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the GDEF table- 0x00010000, 0x00010002, or 0x00010003",
-            ),
-            (
-                "Offset",
-                "GlyphClassDef",
-                None,
-                None,
-                "Offset to class definition table for glyph type-from beginning of GDEF header (may be NULL)",
-            ),
-            (
-                "Offset",
-                "AttachList",
-                None,
-                None,
-                "Offset to list of glyphs with attachment points-from beginning of GDEF header (may be NULL)",
-            ),
-            (
-                "Offset",
-                "LigCaretList",
-                None,
-                None,
-                "Offset to list of positioning points for ligature carets-from beginning of GDEF header (may be NULL)",
-            ),
-            (
-                "Offset",
-                "MarkAttachClassDef",
-                None,
-                None,
-                "Offset to class definition table for mark attachment type-from beginning of GDEF header (may be NULL)",
-            ),
-            (
-                "Offset",
-                "MarkGlyphSetsDef",
-                None,
-                "Version >= 0x00010002",
-                "Offset to the table of mark set definitions-from beginning of GDEF header (may be NULL)",
-            ),
-            (
-                "LOffset",
-                "VarStore",
-                None,
-                "Version >= 0x00010003",
-                "Offset to variation store (may be NULL)",
-            ),
-        ],
-    ),
-    (
-        "AttachList",
-        [
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table - from beginning of AttachList table",
-            ),
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of glyphs with attachment points",
-            ),
-            (
-                "Offset",
-                "AttachPoint",
-                "GlyphCount",
-                0,
-                "Array of offsets to AttachPoint tables-from beginning of AttachList table-in Coverage Index order",
-            ),
-        ],
-    ),
-    (
-        "AttachPoint",
-        [
-            (
-                "uint16",
-                "PointCount",
-                None,
-                None,
-                "Number of attachment points on this glyph",
-            ),
-            (
-                "uint16",
-                "PointIndex",
-                "PointCount",
-                0,
-                "Array of contour point indices -in increasing numerical order",
-            ),
-        ],
-    ),
-    (
-        "LigCaretList",
-        [
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table - from beginning of LigCaretList table",
-            ),
-            ("uint16", "LigGlyphCount", None, None, "Number of ligature glyphs"),
-            (
-                "Offset",
-                "LigGlyph",
-                "LigGlyphCount",
-                0,
-                "Array of offsets to LigGlyph tables-from beginning of LigCaretList table-in Coverage Index order",
-            ),
-        ],
-    ),
-    (
-        "LigGlyph",
-        [
-            (
-                "uint16",
-                "CaretCount",
-                None,
-                None,
-                "Number of CaretValues for this ligature (components - 1)",
-            ),
-            (
-                "Offset",
-                "CaretValue",
-                "CaretCount",
-                0,
-                "Array of offsets to CaretValue tables-from beginning of LigGlyph table-in increasing coordinate order",
-            ),
-        ],
-    ),
-    (
-        "CaretValueFormat1",
-        [
-            ("uint16", "CaretValueFormat", None, None, "Format identifier-format = 1"),
-            ("int16", "Coordinate", None, None, "X or Y value, in design units"),
-        ],
-    ),
-    (
-        "CaretValueFormat2",
-        [
-            ("uint16", "CaretValueFormat", None, None, "Format identifier-format = 2"),
-            ("uint16", "CaretValuePoint", None, None, "Contour point index on glyph"),
-        ],
-    ),
-    (
-        "CaretValueFormat3",
-        [
-            ("uint16", "CaretValueFormat", None, None, "Format identifier-format = 3"),
-            ("int16", "Coordinate", None, None, "X or Y value, in design units"),
-            (
-                "Offset",
-                "DeviceTable",
-                None,
-                None,
-                "Offset to Device table for X or Y value-from beginning of CaretValue table",
-            ),
-        ],
-    ),
-    (
-        "MarkGlyphSetsDef",
-        [
-            ("uint16", "MarkSetTableFormat", None, None, "Format identifier == 1"),
-            ("uint16", "MarkSetCount", None, None, "Number of mark sets defined"),
-            (
-                "LOffset",
-                "Coverage",
-                "MarkSetCount",
-                0,
-                "Array of offsets to mark set coverage tables.",
-            ),
-        ],
-    ),
-    #
-    # base
-    #
-    (
-        "BASE",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the BASE table-initially 0x00010000",
-            ),
-            (
-                "Offset",
-                "HorizAxis",
-                None,
-                None,
-                "Offset to horizontal Axis table-from beginning of BASE table-may be NULL",
-            ),
-            (
-                "Offset",
-                "VertAxis",
-                None,
-                None,
-                "Offset to vertical Axis table-from beginning of BASE table-may be NULL",
-            ),
-            (
-                "LOffset",
-                "VarStore",
-                None,
-                "Version >= 0x00010001",
-                "Offset to variation store (may be NULL)",
-            ),
-        ],
-    ),
-    (
-        "Axis",
-        [
-            (
-                "Offset",
-                "BaseTagList",
-                None,
-                None,
-                "Offset to BaseTagList table-from beginning of Axis table-may be NULL",
-            ),
-            (
-                "Offset",
-                "BaseScriptList",
-                None,
-                None,
-                "Offset to BaseScriptList table-from beginning of Axis table",
-            ),
-        ],
-    ),
-    (
-        "BaseTagList",
-        [
-            (
-                "uint16",
-                "BaseTagCount",
-                None,
-                None,
-                "Number of baseline identification tags in this text direction-may be zero (0)",
-            ),
-            (
-                "Tag",
-                "BaselineTag",
-                "BaseTagCount",
-                0,
-                "Array of 4-byte baseline identification tags-must be in alphabetical order",
-            ),
-        ],
-    ),
-    (
-        "BaseScriptList",
-        [
-            (
-                "uint16",
-                "BaseScriptCount",
-                None,
-                None,
-                "Number of BaseScriptRecords defined",
-            ),
-            (
-                "struct",
-                "BaseScriptRecord",
-                "BaseScriptCount",
-                0,
-                "Array of BaseScriptRecords-in alphabetical order by BaseScriptTag",
-            ),
-        ],
-    ),
-    (
-        "BaseScriptRecord",
-        [
-            ("Tag", "BaseScriptTag", None, None, "4-byte script identification tag"),
-            (
-                "Offset",
-                "BaseScript",
-                None,
-                None,
-                "Offset to BaseScript table-from beginning of BaseScriptList",
-            ),
-        ],
-    ),
-    (
-        "BaseScript",
-        [
-            (
-                "Offset",
-                "BaseValues",
-                None,
-                None,
-                "Offset to BaseValues table-from beginning of BaseScript table-may be NULL",
-            ),
-            (
-                "Offset",
-                "DefaultMinMax",
-                None,
-                None,
-                "Offset to MinMax table- from beginning of BaseScript table-may be NULL",
-            ),
-            (
-                "uint16",
-                "BaseLangSysCount",
-                None,
-                None,
-                "Number of BaseLangSysRecords defined-may be zero (0)",
-            ),
-            (
-                "struct",
-                "BaseLangSysRecord",
-                "BaseLangSysCount",
-                0,
-                "Array of BaseLangSysRecords-in alphabetical order by BaseLangSysTag",
-            ),
-        ],
-    ),
-    (
-        "BaseLangSysRecord",
-        [
-            (
-                "Tag",
-                "BaseLangSysTag",
-                None,
-                None,
-                "4-byte language system identification tag",
-            ),
-            (
-                "Offset",
-                "MinMax",
-                None,
-                None,
-                "Offset to MinMax table-from beginning of BaseScript table",
-            ),
-        ],
-    ),
-    (
-        "BaseValues",
-        [
-            (
-                "uint16",
-                "DefaultIndex",
-                None,
-                None,
-                "Index number of default baseline for this script-equals index position of baseline tag in BaselineArray of the BaseTagList",
-            ),
-            (
-                "uint16",
-                "BaseCoordCount",
-                None,
-                None,
-                "Number of BaseCoord tables defined-should equal BaseTagCount in the BaseTagList",
-            ),
-            (
-                "Offset",
-                "BaseCoord",
-                "BaseCoordCount",
-                0,
-                "Array of offsets to BaseCoord-from beginning of BaseValues table-order matches BaselineTag array in the BaseTagList",
-            ),
-        ],
-    ),
-    (
-        "MinMax",
-        [
-            (
-                "Offset",
-                "MinCoord",
-                None,
-                None,
-                "Offset to BaseCoord table-defines minimum extent value-from the beginning of MinMax table-may be NULL",
-            ),
-            (
-                "Offset",
-                "MaxCoord",
-                None,
-                None,
-                "Offset to BaseCoord table-defines maximum extent value-from the beginning of MinMax table-may be NULL",
-            ),
-            (
-                "uint16",
-                "FeatMinMaxCount",
-                None,
-                None,
-                "Number of FeatMinMaxRecords-may be zero (0)",
-            ),
-            (
-                "struct",
-                "FeatMinMaxRecord",
-                "FeatMinMaxCount",
-                0,
-                "Array of FeatMinMaxRecords-in alphabetical order, by FeatureTableTag",
-            ),
-        ],
-    ),
-    (
-        "FeatMinMaxRecord",
-        [
-            (
-                "Tag",
-                "FeatureTableTag",
-                None,
-                None,
-                "4-byte feature identification tag-must match FeatureTag in FeatureList",
-            ),
-            (
-                "Offset",
-                "MinCoord",
-                None,
-                None,
-                "Offset to BaseCoord table-defines minimum extent value-from beginning of MinMax table-may be NULL",
-            ),
-            (
-                "Offset",
-                "MaxCoord",
-                None,
-                None,
-                "Offset to BaseCoord table-defines maximum extent value-from beginning of MinMax table-may be NULL",
-            ),
-        ],
-    ),
-    (
-        "BaseCoordFormat1",
-        [
-            ("uint16", "BaseCoordFormat", None, None, "Format identifier-format = 1"),
-            ("int16", "Coordinate", None, None, "X or Y value, in design units"),
-        ],
-    ),
-    (
-        "BaseCoordFormat2",
-        [
-            ("uint16", "BaseCoordFormat", None, None, "Format identifier-format = 2"),
-            ("int16", "Coordinate", None, None, "X or Y value, in design units"),
-            ("GlyphID", "ReferenceGlyph", None, None, "GlyphID of control glyph"),
-            (
-                "uint16",
-                "BaseCoordPoint",
-                None,
-                None,
-                "Index of contour point on the ReferenceGlyph",
-            ),
-        ],
-    ),
-    (
-        "BaseCoordFormat3",
-        [
-            ("uint16", "BaseCoordFormat", None, None, "Format identifier-format = 3"),
-            ("int16", "Coordinate", None, None, "X or Y value, in design units"),
-            (
-                "Offset",
-                "DeviceTable",
-                None,
-                None,
-                "Offset to Device table for X or Y value",
-            ),
-        ],
-    ),
-    #
-    # jstf
-    #
-    (
-        "JSTF",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the JSTF table-initially set to 0x00010000",
-            ),
-            (
-                "uint16",
-                "JstfScriptCount",
-                None,
-                None,
-                "Number of JstfScriptRecords in this table",
-            ),
-            (
-                "struct",
-                "JstfScriptRecord",
-                "JstfScriptCount",
-                0,
-                "Array of JstfScriptRecords-in alphabetical order, by JstfScriptTag",
-            ),
-        ],
-    ),
-    (
-        "JstfScriptRecord",
-        [
-            ("Tag", "JstfScriptTag", None, None, "4-byte JstfScript identification"),
-            (
-                "Offset",
-                "JstfScript",
-                None,
-                None,
-                "Offset to JstfScript table-from beginning of JSTF Header",
-            ),
-        ],
-    ),
-    (
-        "JstfScript",
-        [
-            (
-                "Offset",
-                "ExtenderGlyph",
-                None,
-                None,
-                "Offset to ExtenderGlyph table-from beginning of JstfScript table-may be NULL",
-            ),
-            (
-                "Offset",
-                "DefJstfLangSys",
-                None,
-                None,
-                "Offset to Default JstfLangSys table-from beginning of JstfScript table-may be NULL",
-            ),
-            (
-                "uint16",
-                "JstfLangSysCount",
-                None,
-                None,
-                "Number of JstfLangSysRecords in this table- may be zero (0)",
-            ),
-            (
-                "struct",
-                "JstfLangSysRecord",
-                "JstfLangSysCount",
-                0,
-                "Array of JstfLangSysRecords-in alphabetical order, by JstfLangSysTag",
-            ),
-        ],
-    ),
-    (
-        "JstfLangSysRecord",
-        [
-            ("Tag", "JstfLangSysTag", None, None, "4-byte JstfLangSys identifier"),
-            (
-                "Offset",
-                "JstfLangSys",
-                None,
-                None,
-                "Offset to JstfLangSys table-from beginning of JstfScript table",
-            ),
-        ],
-    ),
-    (
-        "ExtenderGlyph",
-        [
-            (
-                "uint16",
-                "GlyphCount",
-                None,
-                None,
-                "Number of Extender Glyphs in this script",
-            ),
-            (
-                "GlyphID",
-                "ExtenderGlyph",
-                "GlyphCount",
-                0,
-                "GlyphIDs-in increasing numerical order",
-            ),
-        ],
-    ),
-    (
-        "JstfLangSys",
-        [
-            (
-                "uint16",
-                "JstfPriorityCount",
-                None,
-                None,
-                "Number of JstfPriority tables",
-            ),
-            (
-                "Offset",
-                "JstfPriority",
-                "JstfPriorityCount",
-                0,
-                "Array of offsets to JstfPriority tables-from beginning of JstfLangSys table-in priority order",
-            ),
-        ],
-    ),
-    (
-        "JstfPriority",
-        [
-            (
-                "Offset",
-                "ShrinkageEnableGSUB",
-                None,
-                None,
-                "Offset to Shrinkage Enable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ShrinkageDisableGSUB",
-                None,
-                None,
-                "Offset to Shrinkage Disable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ShrinkageEnableGPOS",
-                None,
-                None,
-                "Offset to Shrinkage Enable JstfGPOSModList table-from beginning of JstfPriority table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ShrinkageDisableGPOS",
-                None,
-                None,
-                "Offset to Shrinkage Disable JstfGPOSModList table-from beginning of JstfPriority table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ShrinkageJstfMax",
-                None,
-                None,
-                "Offset to Shrinkage JstfMax table-from beginning of JstfPriority table -may be NULL",
-            ),
-            (
-                "Offset",
-                "ExtensionEnableGSUB",
-                None,
-                None,
-                "Offset to Extension Enable JstfGSUBModList table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ExtensionDisableGSUB",
-                None,
-                None,
-                "Offset to Extension Disable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ExtensionEnableGPOS",
-                None,
-                None,
-                "Offset to Extension Enable JstfGSUBModList table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ExtensionDisableGPOS",
-                None,
-                None,
-                "Offset to Extension Disable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL",
-            ),
-            (
-                "Offset",
-                "ExtensionJstfMax",
-                None,
-                None,
-                "Offset to Extension JstfMax table-from beginning of JstfPriority table -may be NULL",
-            ),
-        ],
-    ),
-    (
-        "JstfGSUBModList",
-        [
-            (
-                "uint16",
-                "LookupCount",
-                None,
-                None,
-                "Number of lookups for this modification",
-            ),
-            (
-                "uint16",
-                "GSUBLookupIndex",
-                "LookupCount",
-                0,
-                "Array of LookupIndex identifiers in GSUB-in increasing numerical order",
-            ),
-        ],
-    ),
-    (
-        "JstfGPOSModList",
-        [
-            (
-                "uint16",
-                "LookupCount",
-                None,
-                None,
-                "Number of lookups for this modification",
-            ),
-            (
-                "uint16",
-                "GPOSLookupIndex",
-                "LookupCount",
-                0,
-                "Array of LookupIndex identifiers in GPOS-in increasing numerical order",
-            ),
-        ],
-    ),
-    (
-        "JstfMax",
-        [
-            (
-                "uint16",
-                "LookupCount",
-                None,
-                None,
-                "Number of lookup Indices for this modification",
-            ),
-            (
-                "Offset",
-                "Lookup",
-                "LookupCount",
-                0,
-                "Array of offsets to GPOS-type lookup tables-from beginning of JstfMax table-in design order",
-            ),
-        ],
-    ),
-    #
-    # STAT
-    #
-    (
-        "STAT",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the table-initially set to 0x00010000, currently 0x00010002.",
-            ),
-            (
-                "uint16",
-                "DesignAxisRecordSize",
-                None,
-                None,
-                "Size in bytes of each design axis record",
-            ),
-            ("uint16", "DesignAxisCount", None, None, "Number of design axis records"),
-            (
-                "LOffsetTo(AxisRecordArray)",
-                "DesignAxisRecord",
-                None,
-                None,
-                "Offset in bytes from the beginning of the STAT table to the start of the design axes array",
-            ),
-            ("uint16", "AxisValueCount", None, None, "Number of axis value tables"),
-            (
-                "LOffsetTo(AxisValueArray)",
-                "AxisValueArray",
-                None,
-                None,
-                "Offset in bytes from the beginning of the STAT table to the start of the axes value offset array",
-            ),
-            (
-                "NameID",
-                "ElidedFallbackNameID",
-                None,
-                "Version >= 0x00010001",
-                "NameID to use when all style attributes are elided.",
-            ),
-        ],
-    ),
-    (
-        "AxisRecordArray",
-        [
-            ("AxisRecord", "Axis", "DesignAxisCount", 0, "Axis records"),
-        ],
-    ),
-    (
-        "AxisRecord",
-        [
-            (
-                "Tag",
-                "AxisTag",
-                None,
-                None,
-                "A tag identifying the axis of design variation",
-            ),
-            (
-                "NameID",
-                "AxisNameID",
-                None,
-                None,
-                'The name ID for entries in the "name" table that provide a display string for this axis',
-            ),
-            (
-                "uint16",
-                "AxisOrdering",
-                None,
-                None,
-                "A value that applications can use to determine primary sorting of face names, or for ordering of descriptors when composing family or face names",
-            ),
-            (
-                "uint8",
-                "MoreBytes",
-                "DesignAxisRecordSize",
-                -8,
-                "Extra bytes.  Set to empty array.",
-            ),
-        ],
-    ),
-    (
-        "AxisValueArray",
-        [
-            ("Offset", "AxisValue", "AxisValueCount", 0, "Axis values"),
-        ],
-    ),
-    (
-        "AxisValueFormat1",
-        [
-            ("uint16", "Format", None, None, "Format, = 1"),
-            (
-                "uint16",
-                "AxisIndex",
-                None,
-                None,
-                "Index into the axis record array identifying the axis of design variation to which the axis value record applies.",
-            ),
-            ("STATFlags", "Flags", None, None, "Flags."),
-            ("NameID", "ValueNameID", None, None, ""),
-            ("Fixed", "Value", None, None, ""),
-        ],
-    ),
-    (
-        "AxisValueFormat2",
-        [
-            ("uint16", "Format", None, None, "Format, = 2"),
-            (
-                "uint16",
-                "AxisIndex",
-                None,
-                None,
-                "Index into the axis record array identifying the axis of design variation to which the axis value record applies.",
-            ),
-            ("STATFlags", "Flags", None, None, "Flags."),
-            ("NameID", "ValueNameID", None, None, ""),
-            ("Fixed", "NominalValue", None, None, ""),
-            ("Fixed", "RangeMinValue", None, None, ""),
-            ("Fixed", "RangeMaxValue", None, None, ""),
-        ],
-    ),
-    (
-        "AxisValueFormat3",
-        [
-            ("uint16", "Format", None, None, "Format, = 3"),
-            (
-                "uint16",
-                "AxisIndex",
-                None,
-                None,
-                "Index into the axis record array identifying the axis of design variation to which the axis value record applies.",
-            ),
-            ("STATFlags", "Flags", None, None, "Flags."),
-            ("NameID", "ValueNameID", None, None, ""),
-            ("Fixed", "Value", None, None, ""),
-            ("Fixed", "LinkedValue", None, None, ""),
-        ],
-    ),
-    (
-        "AxisValueFormat4",
-        [
-            ("uint16", "Format", None, None, "Format, = 4"),
-            (
-                "uint16",
-                "AxisCount",
-                None,
-                None,
-                "The total number of axes contributing to this axis-values combination.",
-            ),
-            ("STATFlags", "Flags", None, None, "Flags."),
-            ("NameID", "ValueNameID", None, None, ""),
-            (
-                "struct",
-                "AxisValueRecord",
-                "AxisCount",
-                0,
-                "Array of AxisValue records that provide the combination of axis values, one for each contributing axis. ",
-            ),
-        ],
-    ),
-    (
-        "AxisValueRecord",
-        [
-            (
-                "uint16",
-                "AxisIndex",
-                None,
-                None,
-                "Index into the axis record array identifying the axis of design variation to which the axis value record applies.",
-            ),
-            ("Fixed", "Value", None, None, "A numeric value for this attribute value."),
-        ],
-    ),
-    #
-    # Variation fonts
-    #
-    # GSUB/GPOS FeatureVariations
-    (
-        "FeatureVariations",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the table-initially set to 0x00010000",
-            ),
-            (
-                "uint32",
-                "FeatureVariationCount",
-                None,
-                None,
-                "Number of records in the FeatureVariationRecord array",
-            ),
-            (
-                "struct",
-                "FeatureVariationRecord",
-                "FeatureVariationCount",
-                0,
-                "Array of FeatureVariationRecord",
-            ),
-        ],
-    ),
-    (
-        "FeatureVariationRecord",
-        [
-            (
-                "LOffset",
-                "ConditionSet",
-                None,
-                None,
-                "Offset to a ConditionSet table, from beginning of the FeatureVariations table.",
-            ),
-            (
-                "LOffset",
-                "FeatureTableSubstitution",
-                None,
-                None,
-                "Offset to a FeatureTableSubstitution table, from beginning of the FeatureVariations table",
-            ),
-        ],
-    ),
-    (
-        "ConditionSet",
-        [
-            (
-                "uint16",
-                "ConditionCount",
-                None,
-                None,
-                "Number of condition tables in the ConditionTable array",
-            ),
-            (
-                "LOffset",
-                "ConditionTable",
-                "ConditionCount",
-                0,
-                "Array of condition tables.",
-            ),
-        ],
-    ),
-    (
-        "ConditionTableFormat1",
-        [
-            ("uint16", "Format", None, None, "Format, = 1"),
-            (
-                "uint16",
-                "AxisIndex",
-                None,
-                None,
-                "Index for the variation axis within the fvar table, base 0.",
-            ),
-            (
-                "F2Dot14",
-                "FilterRangeMinValue",
-                None,
-                None,
-                "Minimum normalized axis value of the font variation instances that satisfy this condition.",
-            ),
-            (
-                "F2Dot14",
-                "FilterRangeMaxValue",
-                None,
-                None,
-                "Maximum value that satisfies this condition.",
-            ),
-        ],
-    ),
-    (
-        "FeatureTableSubstitution",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the table-initially set to 0x00010000",
-            ),
-            (
-                "uint16",
-                "SubstitutionCount",
-                None,
-                None,
-                "Number of records in the FeatureVariationRecords array",
-            ),
-            (
-                "FeatureTableSubstitutionRecord",
-                "SubstitutionRecord",
-                "SubstitutionCount",
-                0,
-                "Array of FeatureTableSubstitutionRecord",
-            ),
-        ],
-    ),
-    (
-        "FeatureTableSubstitutionRecord",
-        [
-            ("uint16", "FeatureIndex", None, None, "The feature table index to match."),
-            (
-                "LOffset",
-                "Feature",
-                None,
-                None,
-                "Offset to an alternate feature table, from start of the FeatureTableSubstitution table.",
-            ),
-        ],
-    ),
-    # VariationStore
-    (
-        "VarRegionAxis",
-        [
-            ("F2Dot14", "StartCoord", None, None, ""),
-            ("F2Dot14", "PeakCoord", None, None, ""),
-            ("F2Dot14", "EndCoord", None, None, ""),
-        ],
-    ),
-    (
-        "VarRegion",
-        [
-            ("struct", "VarRegionAxis", "RegionAxisCount", 0, ""),
-        ],
-    ),
-    (
-        "VarRegionList",
-        [
-            ("uint16", "RegionAxisCount", None, None, ""),
-            ("uint16", "RegionCount", None, None, ""),
-            ("VarRegion", "Region", "RegionCount", 0, ""),
-        ],
-    ),
-    (
-        "VarData",
-        [
-            ("uint16", "ItemCount", None, None, ""),
-            ("uint16", "NumShorts", None, None, ""),
-            ("uint16", "VarRegionCount", None, None, ""),
-            ("uint16", "VarRegionIndex", "VarRegionCount", 0, ""),
-            ("VarDataValue", "Item", "ItemCount", 0, ""),
-        ],
-    ),
-    (
-        "VarStore",
-        [
-            ("uint16", "Format", None, None, "Set to 1."),
-            ("LOffset", "VarRegionList", None, None, ""),
-            ("uint16", "VarDataCount", None, None, ""),
-            ("LOffset", "VarData", "VarDataCount", 0, ""),
-        ],
-    ),
-    # Variation helpers
-    (
-        "VarIdxMap",
-        [
-            ("uint16", "EntryFormat", None, None, ""),  # Automatically computed
-            ("uint16", "MappingCount", None, None, ""),  # Automatically computed
-            ("VarIdxMapValue", "mapping", "", 0, "Array of compressed data"),
-        ],
-    ),
-    (
-        "DeltaSetIndexMapFormat0",
-        [
-            ("uint8", "Format", None, None, "Format of the DeltaSetIndexMap = 0"),
-            ("uint8", "EntryFormat", None, None, ""),  # Automatically computed
-            ("uint16", "MappingCount", None, None, ""),  # Automatically computed
-            ("VarIdxMapValue", "mapping", "", 0, "Array of compressed data"),
-        ],
-    ),
-    (
-        "DeltaSetIndexMapFormat1",
-        [
-            ("uint8", "Format", None, None, "Format of the DeltaSetIndexMap = 1"),
-            ("uint8", "EntryFormat", None, None, ""),  # Automatically computed
-            ("uint32", "MappingCount", None, None, ""),  # Automatically computed
-            ("VarIdxMapValue", "mapping", "", 0, "Array of compressed data"),
-        ],
-    ),
-    # Glyph advance variations
-    (
-        "HVAR",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the HVAR table-initially = 0x00010000",
-            ),
-            ("LOffset", "VarStore", None, None, ""),
-            ("LOffsetTo(VarIdxMap)", "AdvWidthMap", None, None, ""),
-            ("LOffsetTo(VarIdxMap)", "LsbMap", None, None, ""),
-            ("LOffsetTo(VarIdxMap)", "RsbMap", None, None, ""),
-        ],
-    ),
-    (
-        "VVAR",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the VVAR table-initially = 0x00010000",
-            ),
-            ("LOffset", "VarStore", None, None, ""),
-            ("LOffsetTo(VarIdxMap)", "AdvHeightMap", None, None, ""),
-            ("LOffsetTo(VarIdxMap)", "TsbMap", None, None, ""),
-            ("LOffsetTo(VarIdxMap)", "BsbMap", None, None, ""),
-            ("LOffsetTo(VarIdxMap)", "VOrgMap", None, None, "Vertical origin mapping."),
-        ],
-    ),
-    # Font-wide metrics variations
-    (
-        "MetricsValueRecord",
-        [
-            ("Tag", "ValueTag", None, None, "4-byte font-wide measure identifier"),
-            ("uint32", "VarIdx", None, None, "Combined outer-inner variation index"),
-            (
-                "uint8",
-                "MoreBytes",
-                "ValueRecordSize",
-                -8,
-                "Extra bytes.  Set to empty array.",
-            ),
-        ],
-    ),
-    (
-        "MVAR",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the MVAR table-initially = 0x00010000",
-            ),
-            ("uint16", "Reserved", None, None, "Set to 0"),
-            ("uint16", "ValueRecordSize", None, None, ""),
-            ("uint16", "ValueRecordCount", None, None, ""),
-            ("Offset", "VarStore", None, None, ""),
-            ("MetricsValueRecord", "ValueRecord", "ValueRecordCount", 0, ""),
-        ],
-    ),
-    #
-    # math
-    #
-    (
-        "MATH",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the MATH table-initially set to 0x00010000.",
-            ),
-            (
-                "Offset",
-                "MathConstants",
-                None,
-                None,
-                "Offset to MathConstants table - from the beginning of MATH table.",
-            ),
-            (
-                "Offset",
-                "MathGlyphInfo",
-                None,
-                None,
-                "Offset to MathGlyphInfo table - from the beginning of MATH table.",
-            ),
-            (
-                "Offset",
-                "MathVariants",
-                None,
-                None,
-                "Offset to MathVariants table - from the beginning of MATH table.",
-            ),
-        ],
-    ),
-    (
-        "MathValueRecord",
-        [
-            ("int16", "Value", None, None, "The X or Y value in design units."),
-            (
-                "Offset",
-                "DeviceTable",
-                None,
-                None,
-                "Offset to the device table - from the beginning of parent table. May be NULL. Suggested format for device table is 1.",
-            ),
-        ],
-    ),
-    (
-        "MathConstants",
-        [
-            (
-                "int16",
-                "ScriptPercentScaleDown",
-                None,
-                None,
-                "Percentage of scaling down for script level 1. Suggested value: 80%.",
-            ),
-            (
-                "int16",
-                "ScriptScriptPercentScaleDown",
-                None,
-                None,
-                "Percentage of scaling down for script level 2 (ScriptScript). Suggested value: 60%.",
-            ),
-            (
-                "uint16",
-                "DelimitedSubFormulaMinHeight",
-                None,
-                None,
-                "Minimum height required for a delimited expression to be treated as a subformula. Suggested value: normal line height x1.5.",
-            ),
-            (
-                "uint16",
-                "DisplayOperatorMinHeight",
-                None,
-                None,
-                "Minimum height of n-ary operators (such as integral and summation) for formulas in display mode.",
-            ),
-            (
-                "MathValueRecord",
-                "MathLeading",
-                None,
-                None,
-                "White space to be left between math formulas to ensure proper line spacing. For example, for applications that treat line gap as a part of line ascender, formulas with ink  going above (os2.sTypoAscender + os2.sTypoLineGap - MathLeading) or with ink going below os2.sTypoDescender will result in increasing line height.",
-            ),
-            ("MathValueRecord", "AxisHeight", None, None, "Axis height of the font."),
-            (
-                "MathValueRecord",
-                "AccentBaseHeight",
-                None,
-                None,
-                "Maximum (ink) height of accent base that does not require raising the accents. Suggested: x-height of the font (os2.sxHeight) plus any possible overshots.",
-            ),
-            (
-                "MathValueRecord",
-                "FlattenedAccentBaseHeight",
-                None,
-                None,
-                "Maximum (ink) height of accent base that does not require flattening the accents. Suggested: cap height of the font (os2.sCapHeight).",
-            ),
-            (
-                "MathValueRecord",
-                "SubscriptShiftDown",
-                None,
-                None,
-                "The standard shift down applied to subscript elements. Positive for moving in the downward direction. Suggested: os2.ySubscriptYOffset.",
-            ),
-            (
-                "MathValueRecord",
-                "SubscriptTopMax",
-                None,
-                None,
-                "Maximum allowed height of the (ink) top of subscripts that does not require moving subscripts further down. Suggested: 4/5 x-height.",
-            ),
-            (
-                "MathValueRecord",
-                "SubscriptBaselineDropMin",
-                None,
-                None,
-                "Minimum allowed drop of the baseline of subscripts relative to the (ink) bottom of the base. Checked for bases that are treated as a box or extended shape. Positive for subscript baseline dropped below the base bottom.",
-            ),
-            (
-                "MathValueRecord",
-                "SuperscriptShiftUp",
-                None,
-                None,
-                "Standard shift up applied to superscript elements. Suggested: os2.ySuperscriptYOffset.",
-            ),
-            (
-                "MathValueRecord",
-                "SuperscriptShiftUpCramped",
-                None,
-                None,
-                "Standard shift of superscripts relative to the base, in cramped style.",
-            ),
-            (
-                "MathValueRecord",
-                "SuperscriptBottomMin",
-                None,
-                None,
-                "Minimum allowed height of the (ink) bottom of superscripts that does not require moving subscripts further up. Suggested: 1/4 x-height.",
-            ),
-            (
-                "MathValueRecord",
-                "SuperscriptBaselineDropMax",
-                None,
-                None,
-                "Maximum allowed drop of the baseline of superscripts relative to the (ink) top of the base. Checked for bases that are treated as a box or extended shape. Positive for superscript baseline below the base top.",
-            ),
-            (
-                "MathValueRecord",
-                "SubSuperscriptGapMin",
-                None,
-                None,
-                "Minimum gap between the superscript and subscript ink. Suggested: 4x default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "SuperscriptBottomMaxWithSubscript",
-                None,
-                None,
-                "The maximum level to which the (ink) bottom of superscript can be pushed to increase the gap between superscript and subscript, before subscript starts being moved down. Suggested: 4/5 x-height.",
-            ),
-            (
-                "MathValueRecord",
-                "SpaceAfterScript",
-                None,
-                None,
-                "Extra white space to be added after each subscript and superscript. Suggested: 0.5pt for a 12 pt font.",
-            ),
-            (
-                "MathValueRecord",
-                "UpperLimitGapMin",
-                None,
-                None,
-                "Minimum gap between the (ink) bottom of the upper limit, and the (ink) top of the base operator.",
-            ),
-            (
-                "MathValueRecord",
-                "UpperLimitBaselineRiseMin",
-                None,
-                None,
-                "Minimum distance between baseline of upper limit and (ink) top of the base operator.",
-            ),
-            (
-                "MathValueRecord",
-                "LowerLimitGapMin",
-                None,
-                None,
-                "Minimum gap between (ink) top of the lower limit, and (ink) bottom of the base operator.",
-            ),
-            (
-                "MathValueRecord",
-                "LowerLimitBaselineDropMin",
-                None,
-                None,
-                "Minimum distance between baseline of the lower limit and (ink) bottom of the base operator.",
-            ),
-            (
-                "MathValueRecord",
-                "StackTopShiftUp",
-                None,
-                None,
-                "Standard shift up applied to the top element of a stack.",
-            ),
-            (
-                "MathValueRecord",
-                "StackTopDisplayStyleShiftUp",
-                None,
-                None,
-                "Standard shift up applied to the top element of a stack in display style.",
-            ),
-            (
-                "MathValueRecord",
-                "StackBottomShiftDown",
-                None,
-                None,
-                "Standard shift down applied to the bottom element of a stack. Positive for moving in the downward direction.",
-            ),
-            (
-                "MathValueRecord",
-                "StackBottomDisplayStyleShiftDown",
-                None,
-                None,
-                "Standard shift down applied to the bottom element of a stack in display style. Positive for moving in the downward direction.",
-            ),
-            (
-                "MathValueRecord",
-                "StackGapMin",
-                None,
-                None,
-                "Minimum gap between (ink) bottom of the top element of a stack, and the (ink) top of the bottom element. Suggested: 3x default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "StackDisplayStyleGapMin",
-                None,
-                None,
-                "Minimum gap between (ink) bottom of the top element of a stack, and the (ink) top of the bottom element in display style. Suggested: 7x default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "StretchStackTopShiftUp",
-                None,
-                None,
-                "Standard shift up applied to the top element of the stretch stack.",
-            ),
-            (
-                "MathValueRecord",
-                "StretchStackBottomShiftDown",
-                None,
-                None,
-                "Standard shift down applied to the bottom element of the stretch stack. Positive for moving in the downward direction.",
-            ),
-            (
-                "MathValueRecord",
-                "StretchStackGapAboveMin",
-                None,
-                None,
-                "Minimum gap between the ink of the stretched element, and the (ink) bottom of the element above. Suggested: UpperLimitGapMin",
-            ),
-            (
-                "MathValueRecord",
-                "StretchStackGapBelowMin",
-                None,
-                None,
-                "Minimum gap between the ink of the stretched element, and the (ink) top of the element below. Suggested: LowerLimitGapMin.",
-            ),
-            (
-                "MathValueRecord",
-                "FractionNumeratorShiftUp",
-                None,
-                None,
-                "Standard shift up applied to the numerator.",
-            ),
-            (
-                "MathValueRecord",
-                "FractionNumeratorDisplayStyleShiftUp",
-                None,
-                None,
-                "Standard shift up applied to the numerator in display style. Suggested: StackTopDisplayStyleShiftUp.",
-            ),
-            (
-                "MathValueRecord",
-                "FractionDenominatorShiftDown",
-                None,
-                None,
-                "Standard shift down applied to the denominator. Positive for moving in the downward direction.",
-            ),
-            (
-                "MathValueRecord",
-                "FractionDenominatorDisplayStyleShiftDown",
-                None,
-                None,
-                "Standard shift down applied to the denominator in display style. Positive for moving in the downward direction. Suggested: StackBottomDisplayStyleShiftDown.",
-            ),
-            (
-                "MathValueRecord",
-                "FractionNumeratorGapMin",
-                None,
-                None,
-                "Minimum tolerated gap between the (ink) bottom of the numerator and the ink of the fraction bar. Suggested: default rule thickness",
-            ),
-            (
-                "MathValueRecord",
-                "FractionNumDisplayStyleGapMin",
-                None,
-                None,
-                "Minimum tolerated gap between the (ink) bottom of the numerator and the ink of the fraction bar in display style. Suggested: 3x default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "FractionRuleThickness",
-                None,
-                None,
-                "Thickness of the fraction bar. Suggested: default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "FractionDenominatorGapMin",
-                None,
-                None,
-                "Minimum tolerated gap between the (ink) top of the denominator and the ink of the fraction bar. Suggested: default rule thickness",
-            ),
-            (
-                "MathValueRecord",
-                "FractionDenomDisplayStyleGapMin",
-                None,
-                None,
-                "Minimum tolerated gap between the (ink) top of the denominator and the ink of the fraction bar in display style. Suggested: 3x default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "SkewedFractionHorizontalGap",
-                None,
-                None,
-                "Horizontal distance between the top and bottom elements of a skewed fraction.",
-            ),
-            (
-                "MathValueRecord",
-                "SkewedFractionVerticalGap",
-                None,
-                None,
-                "Vertical distance between the ink of the top and bottom elements of a skewed fraction.",
-            ),
-            (
-                "MathValueRecord",
-                "OverbarVerticalGap",
-                None,
-                None,
-                "Distance between the overbar and the (ink) top of he base. Suggested: 3x default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "OverbarRuleThickness",
-                None,
-                None,
-                "Thickness of overbar. Suggested: default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "OverbarExtraAscender",
-                None,
-                None,
-                "Extra white space reserved above the overbar. Suggested: default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "UnderbarVerticalGap",
-                None,
-                None,
-                "Distance between underbar and (ink) bottom of the base. Suggested: 3x default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "UnderbarRuleThickness",
-                None,
-                None,
-                "Thickness of underbar. Suggested: default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "UnderbarExtraDescender",
-                None,
-                None,
-                "Extra white space reserved below the underbar. Always positive. Suggested: default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "RadicalVerticalGap",
-                None,
-                None,
-                "Space between the (ink) top of the expression and the bar over it. Suggested: 1 1/4 default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "RadicalDisplayStyleVerticalGap",
-                None,
-                None,
-                "Space between the (ink) top of the expression and the bar over it. Suggested: default rule thickness + 1/4 x-height.",
-            ),
-            (
-                "MathValueRecord",
-                "RadicalRuleThickness",
-                None,
-                None,
-                "Thickness of the radical rule. This is the thickness of the rule in designed or constructed radical signs. Suggested: default rule thickness.",
-            ),
-            (
-                "MathValueRecord",
-                "RadicalExtraAscender",
-                None,
-                None,
-                "Extra white space reserved above the radical. Suggested: RadicalRuleThickness.",
-            ),
-            (
-                "MathValueRecord",
-                "RadicalKernBeforeDegree",
-                None,
-                None,
-                "Extra horizontal kern before the degree of a radical, if such is present. Suggested: 5/18 of em.",
-            ),
-            (
-                "MathValueRecord",
-                "RadicalKernAfterDegree",
-                None,
-                None,
-                "Negative kern after the degree of a radical, if such is present. Suggested: 10/18 of em.",
-            ),
-            (
-                "uint16",
-                "RadicalDegreeBottomRaisePercent",
-                None,
-                None,
-                "Height of the bottom of the radical degree, if such is present, in proportion to the ascender of the radical sign. Suggested: 60%.",
-            ),
-        ],
-    ),
-    (
-        "MathGlyphInfo",
-        [
-            (
-                "Offset",
-                "MathItalicsCorrectionInfo",
-                None,
-                None,
-                "Offset to MathItalicsCorrectionInfo table - from the beginning of MathGlyphInfo table.",
-            ),
-            (
-                "Offset",
-                "MathTopAccentAttachment",
-                None,
-                None,
-                "Offset to MathTopAccentAttachment table - from the beginning of MathGlyphInfo table.",
-            ),
-            (
-                "Offset",
-                "ExtendedShapeCoverage",
-                None,
-                None,
-                "Offset to coverage table for Extended Shape glyphs - from the  beginning of MathGlyphInfo table. When the left or right glyph of a box is an extended shape variant, the (ink) box (and not the default position defined by values in MathConstants table) should be used for vertical positioning purposes. May be NULL.",
-            ),
-            (
-                "Offset",
-                "MathKernInfo",
-                None,
-                None,
-                "Offset to MathKernInfo table - from the beginning of MathGlyphInfo table.",
-            ),
-        ],
-    ),
-    (
-        "MathItalicsCorrectionInfo",
-        [
-            (
-                "Offset",
-                "Coverage",
-                None,
-                None,
-                "Offset to Coverage table - from the beginning of MathItalicsCorrectionInfo table.",
-            ),
-            (
-                "uint16",
-                "ItalicsCorrectionCount",
-                None,
-                None,
-                "Number of italics correction values. Should coincide with the number of covered glyphs.",
-            ),
-            (
-                "MathValueRecord",
-                "ItalicsCorrection",
-                "ItalicsCorrectionCount",
-                0,
-                "Array of MathValueRecords defining italics correction values for each covered glyph.",
-            ),
-        ],
-    ),
-    (
-        "MathTopAccentAttachment",
-        [
-            (
-                "Offset",
-                "TopAccentCoverage",
-                None,
-                None,
-                "Offset to Coverage table - from the beginning of  MathTopAccentAttachment table.",
-            ),
-            (
-                "uint16",
-                "TopAccentAttachmentCount",
-                None,
-                None,
-                "Number of top accent attachment point values. Should coincide with the number of covered glyphs",
-            ),
-            (
-                "MathValueRecord",
-                "TopAccentAttachment",
-                "TopAccentAttachmentCount",
-                0,
-                "Array of MathValueRecords defining top accent attachment points for each covered glyph",
-            ),
-        ],
-    ),
-    (
-        "MathKernInfo",
-        [
-            (
-                "Offset",
-                "MathKernCoverage",
-                None,
-                None,
-                "Offset to Coverage table - from the beginning of the MathKernInfo table.",
-            ),
-            ("uint16", "MathKernCount", None, None, "Number of MathKernInfoRecords."),
-            (
-                "MathKernInfoRecord",
-                "MathKernInfoRecords",
-                "MathKernCount",
-                0,
-                "Array of MathKernInfoRecords, per-glyph information for mathematical positioning of subscripts and superscripts.",
-            ),
-        ],
-    ),
-    (
-        "MathKernInfoRecord",
-        [
-            (
-                "Offset",
-                "TopRightMathKern",
-                None,
-                None,
-                "Offset to MathKern table for top right corner - from the beginning of MathKernInfo table. May be NULL.",
-            ),
-            (
-                "Offset",
-                "TopLeftMathKern",
-                None,
-                None,
-                "Offset to MathKern table for the top left corner - from the beginning of MathKernInfo table. May be NULL.",
-            ),
-            (
-                "Offset",
-                "BottomRightMathKern",
-                None,
-                None,
-                "Offset to MathKern table for bottom right corner - from the beginning of MathKernInfo table. May be NULL.",
-            ),
-            (
-                "Offset",
-                "BottomLeftMathKern",
-                None,
-                None,
-                "Offset to MathKern table for bottom left corner - from the beginning of MathKernInfo table. May be NULL.",
-            ),
-        ],
-    ),
-    (
-        "MathKern",
-        [
-            (
-                "uint16",
-                "HeightCount",
-                None,
-                None,
-                "Number of heights on which the kern value changes.",
-            ),
-            (
-                "MathValueRecord",
-                "CorrectionHeight",
-                "HeightCount",
-                0,
-                "Array of correction heights at which the kern value changes. Sorted by the height value in design units.",
-            ),
-            (
-                "MathValueRecord",
-                "KernValue",
-                "HeightCount",
-                1,
-                "Array of kern values corresponding to heights. First value is the kern value for all heights less or equal than the first height in this table.Last value is the value to be applied for all heights greater than the last height in this table. Negative values are interpreted as move glyphs closer to each other.",
-            ),
-        ],
-    ),
-    (
-        "MathVariants",
-        [
-            (
-                "uint16",
-                "MinConnectorOverlap",
-                None,
-                None,
-                "Minimum overlap of connecting glyphs during glyph construction,  in design units.",
-            ),
-            (
-                "Offset",
-                "VertGlyphCoverage",
-                None,
-                None,
-                "Offset to Coverage table - from the beginning of MathVariants table.",
-            ),
-            (
-                "Offset",
-                "HorizGlyphCoverage",
-                None,
-                None,
-                "Offset to Coverage table - from the beginning of MathVariants table.",
-            ),
-            (
-                "uint16",
-                "VertGlyphCount",
-                None,
-                None,
-                "Number of glyphs for which information is provided for vertically growing variants.",
-            ),
-            (
-                "uint16",
-                "HorizGlyphCount",
-                None,
-                None,
-                "Number of glyphs for which information is provided for horizontally growing variants.",
-            ),
-            (
-                "Offset",
-                "VertGlyphConstruction",
-                "VertGlyphCount",
-                0,
-                "Array of offsets to MathGlyphConstruction tables - from the beginning of the MathVariants table, for shapes growing in vertical direction.",
-            ),
-            (
-                "Offset",
-                "HorizGlyphConstruction",
-                "HorizGlyphCount",
-                0,
-                "Array of offsets to MathGlyphConstruction tables - from the beginning of the MathVariants table, for shapes growing in horizontal direction.",
-            ),
-        ],
-    ),
-    (
-        "MathGlyphConstruction",
-        [
-            (
-                "Offset",
-                "GlyphAssembly",
-                None,
-                None,
-                "Offset to GlyphAssembly table for this shape - from the beginning of MathGlyphConstruction table. May be NULL",
-            ),
-            (
-                "uint16",
-                "VariantCount",
-                None,
-                None,
-                "Count of glyph growing variants for this glyph.",
-            ),
-            (
-                "MathGlyphVariantRecord",
-                "MathGlyphVariantRecord",
-                "VariantCount",
-                0,
-                "MathGlyphVariantRecords for alternative variants of the glyphs.",
-            ),
-        ],
-    ),
-    (
-        "MathGlyphVariantRecord",
-        [
-            ("GlyphID", "VariantGlyph", None, None, "Glyph ID for the variant."),
-            (
-                "uint16",
-                "AdvanceMeasurement",
-                None,
-                None,
-                "Advance width/height, in design units, of the variant, in the direction of requested glyph extension.",
-            ),
-        ],
-    ),
-    (
-        "GlyphAssembly",
-        [
-            (
-                "MathValueRecord",
-                "ItalicsCorrection",
-                None,
-                None,
-                "Italics correction of this GlyphAssembly. Should not depend on the assembly size.",
-            ),
-            ("uint16", "PartCount", None, None, "Number of parts in this assembly."),
-            (
-                "GlyphPartRecord",
-                "PartRecords",
-                "PartCount",
-                0,
-                "Array of part records, from left to right and bottom to top.",
-            ),
-        ],
-    ),
-    (
-        "GlyphPartRecord",
-        [
-            ("GlyphID", "glyph", None, None, "Glyph ID for the part."),
-            (
-                "uint16",
-                "StartConnectorLength",
-                None,
-                None,
-                "Advance width/ height of the straight bar connector material, in design units, is at the beginning of the glyph, in the direction of the extension.",
-            ),
-            (
-                "uint16",
-                "EndConnectorLength",
-                None,
-                None,
-                "Advance width/ height of the straight bar connector material, in design units, is at the end of the glyph, in the direction of the extension.",
-            ),
-            (
-                "uint16",
-                "FullAdvance",
-                None,
-                None,
-                "Full advance width/height for this part, in the direction of the extension. In design units.",
-            ),
-            (
-                "uint16",
-                "PartFlags",
-                None,
-                None,
-                "Part qualifiers. PartFlags enumeration currently uses only one bit: 0x0001 fExtender: If set, the part can be skipped or repeated. 0xFFFE Reserved",
-            ),
-        ],
-    ),
-    ##
-    ## Apple Advanced Typography (AAT) tables
-    ##
-    (
-        "AATLookupSegment",
-        [
-            ("uint16", "lastGlyph", None, None, "Last glyph index in this segment."),
-            ("uint16", "firstGlyph", None, None, "First glyph index in this segment."),
-            (
-                "uint16",
-                "value",
-                None,
-                None,
-                "A 16-bit offset from the start of the table to the data.",
-            ),
-        ],
-    ),
-    #
-    # ankr
-    #
-    (
-        "ankr",
-        [
-            ("struct", "AnchorPoints", None, None, "Anchor points table."),
-        ],
-    ),
-    (
-        "AnchorPointsFormat0",
-        [
-            ("uint16", "Format", None, None, "Format of the anchor points table, = 0."),
-            ("uint16", "Flags", None, None, "Flags. Currenty unused, set to zero."),
-            (
-                "AATLookupWithDataOffset(AnchorGlyphData)",
-                "Anchors",
-                None,
-                None,
-                "Table of with anchor overrides for each glyph.",
-            ),
-        ],
-    ),
-    (
-        "AnchorGlyphData",
-        [
-            (
-                "uint32",
-                "AnchorPointCount",
-                None,
-                None,
-                "Number of anchor points for this glyph.",
-            ),
-            (
-                "struct",
-                "AnchorPoint",
-                "AnchorPointCount",
-                0,
-                "Individual anchor points.",
-            ),
-        ],
-    ),
-    (
-        "AnchorPoint",
-        [
-            ("int16", "XCoordinate", None, None, "X coordinate of this anchor point."),
-            ("int16", "YCoordinate", None, None, "Y coordinate of this anchor point."),
-        ],
-    ),
-    #
-    # bsln
-    #
-    (
-        "bsln",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version number of the AAT baseline table (0x00010000 for the initial version).",
-            ),
-            ("struct", "Baseline", None, None, "Baseline table."),
-        ],
-    ),
-    (
-        "BaselineFormat0",
-        [
-            ("uint16", "Format", None, None, "Format of the baseline table, = 0."),
-            (
-                "uint16",
-                "DefaultBaseline",
-                None,
-                None,
-                "Default baseline value for all glyphs. This value can be from 0 through 31.",
-            ),
-            (
-                "uint16",
-                "Delta",
-                32,
-                0,
-                "These are the FUnit distance deltas from the font’s natural baseline to the other baselines used in the font. A total of 32 deltas must be assigned.",
-            ),
-        ],
-    ),
-    (
-        "BaselineFormat1",
-        [
-            ("uint16", "Format", None, None, "Format of the baseline table, = 1."),
-            (
-                "uint16",
-                "DefaultBaseline",
-                None,
-                None,
-                "Default baseline value for all glyphs. This value can be from 0 through 31.",
-            ),
-            (
-                "uint16",
-                "Delta",
-                32,
-                0,
-                "These are the FUnit distance deltas from the font’s natural baseline to the other baselines used in the font. A total of 32 deltas must be assigned.",
-            ),
-            (
-                "AATLookup(uint16)",
-                "BaselineValues",
-                None,
-                None,
-                "Lookup table that maps glyphs to their baseline values.",
-            ),
-        ],
-    ),
-    (
-        "BaselineFormat2",
-        [
-            ("uint16", "Format", None, None, "Format of the baseline table, = 1."),
-            (
-                "uint16",
-                "DefaultBaseline",
-                None,
-                None,
-                "Default baseline value for all glyphs. This value can be from 0 through 31.",
-            ),
-            (
-                "GlyphID",
-                "StandardGlyph",
-                None,
-                None,
-                "Glyph index of the glyph in this font to be used to set the baseline values. This glyph must contain a set of control points (whose numbers are contained in the following field) that determines baseline distances.",
-            ),
-            (
-                "uint16",
-                "ControlPoint",
-                32,
-                0,
-                "Array of 32 control point numbers, associated with the standard glyph. A value of 0xFFFF means there is no corresponding control point in the standard glyph.",
-            ),
-        ],
-    ),
-    (
-        "BaselineFormat3",
-        [
-            ("uint16", "Format", None, None, "Format of the baseline table, = 1."),
-            (
-                "uint16",
-                "DefaultBaseline",
-                None,
-                None,
-                "Default baseline value for all glyphs. This value can be from 0 through 31.",
-            ),
-            (
-                "GlyphID",
-                "StandardGlyph",
-                None,
-                None,
-                "Glyph index of the glyph in this font to be used to set the baseline values. This glyph must contain a set of control points (whose numbers are contained in the following field) that determines baseline distances.",
-            ),
-            (
-                "uint16",
-                "ControlPoint",
-                32,
-                0,
-                "Array of 32 control point numbers, associated with the standard glyph. A value of 0xFFFF means there is no corresponding control point in the standard glyph.",
-            ),
-            (
-                "AATLookup(uint16)",
-                "BaselineValues",
-                None,
-                None,
-                "Lookup table that maps glyphs to their baseline values.",
-            ),
-        ],
-    ),
-    #
-    # cidg
-    #
-    (
-        "cidg",
-        [
-            ("struct", "CIDGlyphMapping", None, None, "CID-to-glyph mapping table."),
-        ],
-    ),
-    (
-        "CIDGlyphMappingFormat0",
-        [
-            (
-                "uint16",
-                "Format",
-                None,
-                None,
-                "Format of the CID-to-glyph mapping table, = 0.",
-            ),
-            ("uint16", "DataFormat", None, None, "Currenty unused, set to zero."),
-            ("uint32", "StructLength", None, None, "Size of the table in bytes."),
-            ("uint16", "Registry", None, None, "The registry ID."),
-            (
-                "char64",
-                "RegistryName",
-                None,
-                None,
-                "The registry name in ASCII; unused bytes should be set to 0.",
-            ),
-            ("uint16", "Order", None, None, "The order ID."),
-            (
-                "char64",
-                "OrderName",
-                None,
-                None,
-                "The order name in ASCII; unused bytes should be set to 0.",
-            ),
-            ("uint16", "SupplementVersion", None, None, "The supplement version."),
-            (
-                "CIDGlyphMap",
-                "Mapping",
-                None,
-                None,
-                "A mapping from CIDs to the glyphs in the font, starting with CID 0. If a CID from the identified collection has no glyph in the font, 0xFFFF is used",
-            ),
-        ],
-    ),
-    #
-    # feat
-    #
-    (
-        "feat",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the feat table-initially set to 0x00010000.",
-            ),
-            ("FeatureNames", "FeatureNames", None, None, "The feature names."),
-        ],
-    ),
-    (
-        "FeatureNames",
-        [
-            (
-                "uint16",
-                "FeatureNameCount",
-                None,
-                None,
-                "Number of entries in the feature name array.",
-            ),
-            ("uint16", "Reserved1", None, None, "Reserved (set to zero)."),
-            ("uint32", "Reserved2", None, None, "Reserved (set to zero)."),
-            (
-                "FeatureName",
-                "FeatureName",
-                "FeatureNameCount",
-                0,
-                "The feature name array.",
-            ),
-        ],
-    ),
-    (
-        "FeatureName",
-        [
-            ("uint16", "FeatureType", None, None, "Feature type."),
-            (
-                "uint16",
-                "SettingsCount",
-                None,
-                None,
-                "The number of records in the setting name array.",
-            ),
-            (
-                "LOffset",
-                "Settings",
-                None,
-                None,
-                "Offset to setting table for this feature.",
-            ),
-            (
-                "uint16",
-                "FeatureFlags",
-                None,
-                None,
-                "Single-bit flags associated with the feature type.",
-            ),
-            (
-                "NameID",
-                "FeatureNameID",
-                None,
-                None,
-                "The name table index for the feature name.",
-            ),
-        ],
-    ),
-    (
-        "Settings",
-        [
-            ("Setting", "Setting", "SettingsCount", 0, "The setting array."),
-        ],
-    ),
-    (
-        "Setting",
-        [
-            ("uint16", "SettingValue", None, None, "The setting."),
-            (
-                "NameID",
-                "SettingNameID",
-                None,
-                None,
-                "The name table index for the setting name.",
-            ),
-        ],
-    ),
-    #
-    # gcid
-    #
-    (
-        "gcid",
-        [
-            ("struct", "GlyphCIDMapping", None, None, "Glyph to CID mapping table."),
-        ],
-    ),
-    (
-        "GlyphCIDMappingFormat0",
-        [
-            (
-                "uint16",
-                "Format",
-                None,
-                None,
-                "Format of the glyph-to-CID mapping table, = 0.",
-            ),
-            ("uint16", "DataFormat", None, None, "Currenty unused, set to zero."),
-            ("uint32", "StructLength", None, None, "Size of the table in bytes."),
-            ("uint16", "Registry", None, None, "The registry ID."),
-            (
-                "char64",
-                "RegistryName",
-                None,
-                None,
-                "The registry name in ASCII; unused bytes should be set to 0.",
-            ),
-            ("uint16", "Order", None, None, "The order ID."),
-            (
-                "char64",
-                "OrderName",
-                None,
-                None,
-                "The order name in ASCII; unused bytes should be set to 0.",
-            ),
-            ("uint16", "SupplementVersion", None, None, "The supplement version."),
-            (
-                "GlyphCIDMap",
-                "Mapping",
-                None,
-                None,
-                "The CIDs for the glyphs in the font, starting with glyph 0. If a glyph does not correspond to a CID in the identified collection, 0xFFFF is used",
-            ),
-        ],
-    ),
-    #
-    # lcar
-    #
-    (
-        "lcar",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version number of the ligature caret table (0x00010000 for the initial version).",
-            ),
-            ("struct", "LigatureCarets", None, None, "Ligature carets table."),
-        ],
-    ),
-    (
-        "LigatureCaretsFormat0",
-        [
-            (
-                "uint16",
-                "Format",
-                None,
-                None,
-                "Format of the ligature caret table. Format 0 indicates division points are distances in font units, Format 1 indicates division points are indexes of control points.",
-            ),
-            (
-                "AATLookup(LigCaretDistances)",
-                "Carets",
-                None,
-                None,
-                "Lookup table associating ligature glyphs with their caret positions, in font unit distances.",
-            ),
-        ],
-    ),
-    (
-        "LigatureCaretsFormat1",
-        [
-            (
-                "uint16",
-                "Format",
-                None,
-                None,
-                "Format of the ligature caret table. Format 0 indicates division points are distances in font units, Format 1 indicates division points are indexes of control points.",
-            ),
-            (
-                "AATLookup(LigCaretPoints)",
-                "Carets",
-                None,
-                None,
-                "Lookup table associating ligature glyphs with their caret positions, as control points.",
-            ),
-        ],
-    ),
-    (
-        "LigCaretDistances",
-        [
-            ("uint16", "DivsionPointCount", None, None, "Number of division points."),
-            (
-                "int16",
-                "DivisionPoint",
-                "DivsionPointCount",
-                0,
-                "Distance in font units through which a subdivision is made orthogonally to the baseline.",
-            ),
-        ],
-    ),
-    (
-        "LigCaretPoints",
-        [
-            ("uint16", "DivsionPointCount", None, None, "Number of division points."),
-            (
-                "int16",
-                "DivisionPoint",
-                "DivsionPointCount",
-                0,
-                "The number of the control point through which a subdivision is made orthogonally to the baseline.",
-            ),
-        ],
-    ),
-    #
-    # mort
-    #
-    (
-        "mort",
-        [
-            ("Version", "Version", None, None, "Version of the mort table."),
-            (
-                "uint32",
-                "MorphChainCount",
-                None,
-                None,
-                "Number of metamorphosis chains.",
-            ),
-            (
-                "MortChain",
-                "MorphChain",
-                "MorphChainCount",
-                0,
-                "Array of metamorphosis chains.",
-            ),
-        ],
-    ),
-    (
-        "MortChain",
-        [
-            (
-                "Flags32",
-                "DefaultFlags",
-                None,
-                None,
-                "The default specification for subtables.",
-            ),
-            (
-                "uint32",
-                "StructLength",
-                None,
-                None,
-                "Total byte count, including this header; must be a multiple of 4.",
-            ),
-            (
-                "uint16",
-                "MorphFeatureCount",
-                None,
-                None,
-                "Number of metamorphosis feature entries.",
-            ),
-            (
-                "uint16",
-                "MorphSubtableCount",
-                None,
-                None,
-                "The number of subtables in the chain.",
-            ),
-            (
-                "struct",
-                "MorphFeature",
-                "MorphFeatureCount",
-                0,
-                "Array of metamorphosis features.",
-            ),
-            (
-                "MortSubtable",
-                "MorphSubtable",
-                "MorphSubtableCount",
-                0,
-                "Array of metamorphosis subtables.",
-            ),
-        ],
-    ),
-    (
-        "MortSubtable",
-        [
-            (
-                "uint16",
-                "StructLength",
-                None,
-                None,
-                "Total subtable length, including this header.",
-            ),
-            (
-                "uint8",
-                "CoverageFlags",
-                None,
-                None,
-                "Most significant byte of coverage flags.",
-            ),
-            ("uint8", "MorphType", None, None, "Subtable type."),
-            (
-                "Flags32",
-                "SubFeatureFlags",
-                None,
-                None,
-                "The 32-bit mask identifying which subtable this is (the subtable being executed if the AND of this value and the processed defaultFlags is nonzero).",
-            ),
-            ("SubStruct", "SubStruct", None, None, "SubTable."),
-        ],
-    ),
-    #
-    # morx
-    #
-    (
-        "morx",
-        [
-            ("uint16", "Version", None, None, "Version of the morx table."),
-            ("uint16", "Reserved", None, None, "Reserved (set to zero)."),
-            (
-                "uint32",
-                "MorphChainCount",
-                None,
-                None,
-                "Number of extended metamorphosis chains.",
-            ),
-            (
-                "MorxChain",
-                "MorphChain",
-                "MorphChainCount",
-                0,
-                "Array of extended metamorphosis chains.",
-            ),
-        ],
-    ),
-    (
-        "MorxChain",
-        [
-            (
-                "Flags32",
-                "DefaultFlags",
-                None,
-                None,
-                "The default specification for subtables.",
-            ),
-            (
-                "uint32",
-                "StructLength",
-                None,
-                None,
-                "Total byte count, including this header; must be a multiple of 4.",
-            ),
-            (
-                "uint32",
-                "MorphFeatureCount",
-                None,
-                None,
-                "Number of feature subtable entries.",
-            ),
-            (
-                "uint32",
-                "MorphSubtableCount",
-                None,
-                None,
-                "The number of subtables in the chain.",
-            ),
-            (
-                "MorphFeature",
-                "MorphFeature",
-                "MorphFeatureCount",
-                0,
-                "Array of metamorphosis features.",
-            ),
-            (
-                "MorxSubtable",
-                "MorphSubtable",
-                "MorphSubtableCount",
-                0,
-                "Array of extended metamorphosis subtables.",
-            ),
-        ],
-    ),
-    (
-        "MorphFeature",
-        [
-            ("uint16", "FeatureType", None, None, "The type of feature."),
-            (
-                "uint16",
-                "FeatureSetting",
-                None,
-                None,
-                "The feature's setting (aka selector).",
-            ),
-            (
-                "Flags32",
-                "EnableFlags",
-                None,
-                None,
-                "Flags for the settings that this feature and setting enables.",
-            ),
-            (
-                "Flags32",
-                "DisableFlags",
-                None,
-                None,
-                "Complement of flags for the settings that this feature and setting disable.",
-            ),
-        ],
-    ),
-    # Apple TrueType Reference Manual, chapter “The ‘morx’ table”,
-    # section “Metamorphosis Subtables”.
-    # https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6morx.html
-    (
-        "MorxSubtable",
-        [
-            (
-                "uint32",
-                "StructLength",
-                None,
-                None,
-                "Total subtable length, including this header.",
-            ),
-            (
-                "uint8",
-                "CoverageFlags",
-                None,
-                None,
-                "Most significant byte of coverage flags.",
-            ),
-            ("uint16", "Reserved", None, None, "Unused."),
-            ("uint8", "MorphType", None, None, "Subtable type."),
-            (
-                "Flags32",
-                "SubFeatureFlags",
-                None,
-                None,
-                "The 32-bit mask identifying which subtable this is (the subtable being executed if the AND of this value and the processed defaultFlags is nonzero).",
-            ),
-            ("SubStruct", "SubStruct", None, None, "SubTable."),
-        ],
-    ),
-    (
-        "StateHeader",
-        [
-            (
-                "uint32",
-                "ClassCount",
-                None,
-                None,
-                "Number of classes, which is the number of 16-bit entry indices in a single line in the state array.",
-            ),
-            (
-                "uint32",
-                "MorphClass",
-                None,
-                None,
-                "Offset from the start of this state table header to the start of the class table.",
-            ),
-            (
-                "uint32",
-                "StateArrayOffset",
-                None,
-                None,
-                "Offset from the start of this state table header to the start of the state array.",
-            ),
-            (
-                "uint32",
-                "EntryTableOffset",
-                None,
-                None,
-                "Offset from the start of this state table header to the start of the entry table.",
-            ),
-        ],
-    ),
-    (
-        "RearrangementMorph",
-        [
-            (
-                "STXHeader(RearrangementMorphAction)",
-                "StateTable",
-                None,
-                None,
-                "Finite-state transducer table for indic rearrangement.",
-            ),
-        ],
-    ),
-    (
-        "ContextualMorph",
-        [
-            (
-                "STXHeader(ContextualMorphAction)",
-                "StateTable",
-                None,
-                None,
-                "Finite-state transducer for contextual glyph substitution.",
-            ),
-        ],
-    ),
-    (
-        "LigatureMorph",
-        [
-            (
-                "STXHeader(LigatureMorphAction)",
-                "StateTable",
-                None,
-                None,
-                "Finite-state transducer for ligature substitution.",
-            ),
-        ],
-    ),
-    (
-        "NoncontextualMorph",
-        [
-            (
-                "AATLookup(GlyphID)",
-                "Substitution",
-                None,
-                None,
-                "The noncontextual glyph substitution table.",
-            ),
-        ],
-    ),
-    (
-        "InsertionMorph",
-        [
-            (
-                "STXHeader(InsertionMorphAction)",
-                "StateTable",
-                None,
-                None,
-                "Finite-state transducer for glyph insertion.",
-            ),
-        ],
-    ),
-    (
-        "MorphClass",
-        [
-            (
-                "uint16",
-                "FirstGlyph",
-                None,
-                None,
-                "Glyph index of the first glyph in the class table.",
-            ),
-            # ('uint16', 'GlyphCount', None, None, 'Number of glyphs in class table.'),
-            # ('uint8', 'GlyphClass', 'GlyphCount', 0, 'The class codes (indexed by glyph index minus firstGlyph). Class codes range from 0 to the value of stateSize minus 1.'),
-        ],
-    ),
-    # If the 'morx' table version is 3 or greater, then the last subtable in the chain is followed by a subtableGlyphCoverageArray, as described below.
-    # 		('Offset', 'MarkGlyphSetsDef', None, 'round(Version*0x10000) >= 0x00010002', 'Offset to the table of mark set definitions-from beginning of GDEF header (may be NULL)'),
-    #
-    # prop
-    #
-    (
-        "prop",
-        [
-            (
-                "Fixed",
-                "Version",
-                None,
-                None,
-                "Version number of the AAT glyphs property table. Version 1.0 is the initial table version. Version 2.0, which is recognized by macOS 8.5 and later, adds support for the “attaches on right” bit. Version 3.0, which gets recognized by macOS X and iOS, adds support for the additional directional properties defined in Unicode 3.0.",
-            ),
-            ("struct", "GlyphProperties", None, None, "Glyph properties."),
-        ],
-    ),
-    (
-        "GlyphPropertiesFormat0",
-        [
-            ("uint16", "Format", None, None, "Format, = 0."),
-            (
-                "uint16",
-                "DefaultProperties",
-                None,
-                None,
-                "Default properties applied to a glyph. Since there is no lookup table in prop format 0, the default properties get applied to every glyph in the font.",
-            ),
-        ],
-    ),
-    (
-        "GlyphPropertiesFormat1",
-        [
-            ("uint16", "Format", None, None, "Format, = 1."),
-            (
-                "uint16",
-                "DefaultProperties",
-                None,
-                None,
-                "Default properties applied to a glyph if that glyph is not present in the Properties lookup table.",
-            ),
-            (
-                "AATLookup(uint16)",
-                "Properties",
-                None,
-                None,
-                "Lookup data associating glyphs with their properties.",
-            ),
-        ],
-    ),
-    #
-    # opbd
-    #
-    (
-        "opbd",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version number of the optical bounds table (0x00010000 for the initial version).",
-            ),
-            ("struct", "OpticalBounds", None, None, "Optical bounds table."),
-        ],
-    ),
-    (
-        "OpticalBoundsFormat0",
-        [
-            (
-                "uint16",
-                "Format",
-                None,
-                None,
-                "Format of the optical bounds table, = 0.",
-            ),
-            (
-                "AATLookup(OpticalBoundsDeltas)",
-                "OpticalBoundsDeltas",
-                None,
-                None,
-                "Lookup table associating glyphs with their optical bounds, given as deltas in font units.",
-            ),
-        ],
-    ),
-    (
-        "OpticalBoundsFormat1",
-        [
-            (
-                "uint16",
-                "Format",
-                None,
-                None,
-                "Format of the optical bounds table, = 1.",
-            ),
-            (
-                "AATLookup(OpticalBoundsPoints)",
-                "OpticalBoundsPoints",
-                None,
-                None,
-                "Lookup table associating glyphs with their optical bounds, given as references to control points.",
-            ),
-        ],
-    ),
-    (
-        "OpticalBoundsDeltas",
-        [
-            (
-                "int16",
-                "Left",
-                None,
-                None,
-                "Delta value for the left-side optical edge.",
-            ),
-            ("int16", "Top", None, None, "Delta value for the top-side optical edge."),
-            (
-                "int16",
-                "Right",
-                None,
-                None,
-                "Delta value for the right-side optical edge.",
-            ),
-            (
-                "int16",
-                "Bottom",
-                None,
-                None,
-                "Delta value for the bottom-side optical edge.",
-            ),
-        ],
-    ),
-    (
-        "OpticalBoundsPoints",
-        [
-            (
-                "int16",
-                "Left",
-                None,
-                None,
-                "Control point index for the left-side optical edge, or -1 if this glyph has none.",
-            ),
-            (
-                "int16",
-                "Top",
-                None,
-                None,
-                "Control point index for the top-side optical edge, or -1 if this glyph has none.",
-            ),
-            (
-                "int16",
-                "Right",
-                None,
-                None,
-                "Control point index for the right-side optical edge, or -1 if this glyph has none.",
-            ),
-            (
-                "int16",
-                "Bottom",
-                None,
-                None,
-                "Control point index for the bottom-side optical edge, or -1 if this glyph has none.",
-            ),
-        ],
-    ),
-    #
-    # TSIC
-    #
-    (
-        "TSIC",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of table initially set to 0x00010000.",
-            ),
-            ("uint16", "Flags", None, None, "TSIC flags - set to 0"),
-            ("uint16", "AxisCount", None, None, "Axis count from fvar"),
-            ("uint16", "RecordCount", None, None, "TSIC record count"),
-            ("uint16", "Reserved", None, None, "Set to 0"),
-            ("Tag", "AxisArray", "AxisCount", 0, "Array of axis tags in fvar order"),
-            (
-                "LocationRecord",
-                "RecordLocations",
-                "RecordCount",
-                0,
-                "Location in variation space of TSIC record",
-            ),
-            ("TSICRecord", "Record", "RecordCount", 0, "Array of TSIC records"),
-        ],
-    ),
-    (
-        "LocationRecord",
-        [
-            ("F2Dot14", "Axis", "AxisCount", 0, "Axis record"),
-        ],
-    ),
-    (
-        "TSICRecord",
-        [
-            ("uint16", "Flags", None, None, "Record flags - set to 0"),
-            ("uint16", "NumCVTEntries", None, None, "Number of CVT number value pairs"),
-            ("uint16", "NameLength", None, None, "Length of optional user record name"),
-            ("uint16", "NameArray", "NameLength", 0, "Unicode 16 name"),
-            ("uint16", "CVTArray", "NumCVTEntries", 0, "CVT number array"),
-            ("int16", "CVTValueArray", "NumCVTEntries", 0, "CVT value"),
-        ],
-    ),
-    #
-    # COLR
-    #
-    (
-        "COLR",
-        [
-            ("uint16", "Version", None, None, "Table version number (starts at 0)."),
-            (
-                "uint16",
-                "BaseGlyphRecordCount",
-                None,
-                None,
-                "Number of Base Glyph Records.",
-            ),
-            (
-                "LOffset",
-                "BaseGlyphRecordArray",
-                None,
-                None,
-                "Offset (from beginning of COLR table) to Base Glyph records.",
-            ),
-            (
-                "LOffset",
-                "LayerRecordArray",
-                None,
-                None,
-                "Offset (from beginning of COLR table) to Layer Records.",
-            ),
-            ("uint16", "LayerRecordCount", None, None, "Number of Layer Records."),
-            (
-                "LOffset",
-                "BaseGlyphList",
-                None,
-                "Version >= 1",
-                "Offset (from beginning of COLR table) to array of Version-1 Base Glyph records.",
-            ),
-            (
-                "LOffset",
-                "LayerList",
-                None,
-                "Version >= 1",
-                "Offset (from beginning of COLR table) to LayerList.",
-            ),
-            (
-                "LOffset",
-                "ClipList",
-                None,
-                "Version >= 1",
-                "Offset to ClipList table (may be NULL)",
-            ),
-            (
-                "LOffsetTo(DeltaSetIndexMap)",
-                "VarIndexMap",
-                None,
-                "Version >= 1",
-                "Offset to DeltaSetIndexMap table (may be NULL)",
-            ),
-            (
-                "LOffset",
-                "VarStore",
-                None,
-                "Version >= 1",
-                "Offset to variation store (may be NULL)",
-            ),
-        ],
-    ),
-    (
-        "BaseGlyphRecordArray",
-        [
-            (
-                "BaseGlyphRecord",
-                "BaseGlyphRecord",
-                "BaseGlyphRecordCount",
-                0,
-                "Base Glyph records.",
-            ),
-        ],
-    ),
-    (
-        "BaseGlyphRecord",
-        [
-            (
-                "GlyphID",
-                "BaseGlyph",
-                None,
-                None,
-                "Glyph ID of reference glyph. This glyph is for reference only and is not rendered for color.",
-            ),
-            (
-                "uint16",
-                "FirstLayerIndex",
-                None,
-                None,
-                "Index (from beginning of the Layer Records) to the layer record. There will be numLayers consecutive entries for this base glyph.",
-            ),
-            (
-                "uint16",
-                "NumLayers",
-                None,
-                None,
-                "Number of color layers associated with this glyph.",
-            ),
-        ],
-    ),
-    (
-        "LayerRecordArray",
-        [
-            ("LayerRecord", "LayerRecord", "LayerRecordCount", 0, "Layer records."),
-        ],
-    ),
-    (
-        "LayerRecord",
-        [
-            (
-                "GlyphID",
-                "LayerGlyph",
-                None,
-                None,
-                "Glyph ID of layer glyph (must be in z-order from bottom to top).",
-            ),
-            (
-                "uint16",
-                "PaletteIndex",
-                None,
-                None,
-                "Index value to use with a selected color palette.",
-            ),
-        ],
-    ),
-    (
-        "BaseGlyphList",
-        [
-            (
-                "uint32",
-                "BaseGlyphCount",
-                None,
-                None,
-                "Number of Version-1 Base Glyph records",
-            ),
-            (
-                "struct",
-                "BaseGlyphPaintRecord",
-                "BaseGlyphCount",
-                0,
-                "Array of Version-1 Base Glyph records",
-            ),
-        ],
-    ),
-    (
-        "BaseGlyphPaintRecord",
-        [
-            ("GlyphID", "BaseGlyph", None, None, "Glyph ID of reference glyph."),
-            (
-                "LOffset",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of BaseGlyphPaintRecord) to Paint, typically a PaintColrLayers.",
-            ),
-        ],
-    ),
-    (
-        "LayerList",
-        [
-            ("uint32", "LayerCount", None, None, "Number of Version-1 Layers"),
-            (
-                "LOffset",
-                "Paint",
-                "LayerCount",
-                0,
-                "Array of offsets to Paint tables, from the start of the LayerList table.",
-            ),
-        ],
-    ),
-    (
-        "ClipListFormat1",
-        [
-            (
-                "uint8",
-                "Format",
-                None,
-                None,
-                "Format for ClipList with 16bit glyph IDs: 1",
-            ),
-            ("uint32", "ClipCount", None, None, "Number of Clip records."),
-            (
-                "struct",
-                "ClipRecord",
-                "ClipCount",
-                0,
-                "Array of Clip records sorted by glyph ID.",
-            ),
-        ],
-    ),
-    (
-        "ClipRecord",
-        [
-            ("uint16", "StartGlyphID", None, None, "First glyph ID in the range."),
-            ("uint16", "EndGlyphID", None, None, "Last glyph ID in the range."),
-            ("Offset24", "ClipBox", None, None, "Offset to a ClipBox table."),
-        ],
-    ),
-    (
-        "ClipBoxFormat1",
-        [
-            (
-                "uint8",
-                "Format",
-                None,
-                None,
-                "Format for ClipBox without variation: set to 1.",
-            ),
-            ("int16", "xMin", None, None, "Minimum x of clip box."),
-            ("int16", "yMin", None, None, "Minimum y of clip box."),
-            ("int16", "xMax", None, None, "Maximum x of clip box."),
-            ("int16", "yMax", None, None, "Maximum y of clip box."),
-        ],
-    ),
-    (
-        "ClipBoxFormat2",
-        [
-            ("uint8", "Format", None, None, "Format for variable ClipBox: set to 2."),
-            ("int16", "xMin", None, None, "Minimum x of clip box. VarIndexBase + 0."),
-            ("int16", "yMin", None, None, "Minimum y of clip box. VarIndexBase + 1."),
-            ("int16", "xMax", None, None, "Maximum x of clip box. VarIndexBase + 2."),
-            ("int16", "yMax", None, None, "Maximum y of clip box. VarIndexBase + 3."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # COLRv1 Affine2x3 uses the same column-major order to serialize a 2D
-    # Affine Transformation as the one used by fontTools.misc.transform.
-    # However, for historical reasons, the labels 'xy' and 'yx' are swapped.
-    # Their fundamental meaning is the same though.
-    # COLRv1 Affine2x3 follows the names found in FreeType and Cairo.
-    # In all case, the second element in the 6-tuple correspond to the
-    # y-part of the x basis vector, and the third to the x-part of the y
-    # basis vector.
-    # See https://github.com/googlefonts/colr-gradients-spec/pull/85
-    (
-        "Affine2x3",
-        [
-            ("Fixed", "xx", None, None, "x-part of x basis vector"),
-            ("Fixed", "yx", None, None, "y-part of x basis vector"),
-            ("Fixed", "xy", None, None, "x-part of y basis vector"),
-            ("Fixed", "yy", None, None, "y-part of y basis vector"),
-            ("Fixed", "dx", None, None, "Translation in x direction"),
-            ("Fixed", "dy", None, None, "Translation in y direction"),
-        ],
-    ),
-    (
-        "VarAffine2x3",
-        [
-            ("Fixed", "xx", None, None, "x-part of x basis vector. VarIndexBase + 0."),
-            ("Fixed", "yx", None, None, "y-part of x basis vector. VarIndexBase + 1."),
-            ("Fixed", "xy", None, None, "x-part of y basis vector. VarIndexBase + 2."),
-            ("Fixed", "yy", None, None, "y-part of y basis vector. VarIndexBase + 3."),
-            (
-                "Fixed",
-                "dx",
-                None,
-                None,
-                "Translation in x direction. VarIndexBase + 4.",
-            ),
-            (
-                "Fixed",
-                "dy",
-                None,
-                None,
-                "Translation in y direction. VarIndexBase + 5.",
-            ),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    (
-        "ColorStop",
-        [
-            ("F2Dot14", "StopOffset", None, None, ""),
-            ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."),
-            ("F2Dot14", "Alpha", None, None, "Values outsided [0.,1.] reserved"),
-        ],
-    ),
-    (
-        "VarColorStop",
-        [
-            ("F2Dot14", "StopOffset", None, None, "VarIndexBase + 0."),
-            ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."),
-            (
-                "F2Dot14",
-                "Alpha",
-                None,
-                None,
-                "Values outsided [0.,1.] reserved. VarIndexBase + 1.",
-            ),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    (
-        "ColorLine",
-        [
-            (
-                "ExtendMode",
-                "Extend",
-                None,
-                None,
-                "Enum {PAD = 0, REPEAT = 1, REFLECT = 2}",
-            ),
-            ("uint16", "StopCount", None, None, "Number of Color stops."),
-            ("ColorStop", "ColorStop", "StopCount", 0, "Array of Color stops."),
-        ],
-    ),
-    (
-        "VarColorLine",
-        [
-            (
-                "ExtendMode",
-                "Extend",
-                None,
-                None,
-                "Enum {PAD = 0, REPEAT = 1, REFLECT = 2}",
-            ),
-            ("uint16", "StopCount", None, None, "Number of Color stops."),
-            ("VarColorStop", "ColorStop", "StopCount", 0, "Array of Color stops."),
-        ],
-    ),
-    # PaintColrLayers
-    (
-        "PaintFormat1",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 1"),
-            (
-                "uint8",
-                "NumLayers",
-                None,
-                None,
-                "Number of offsets to Paint to read from LayerList.",
-            ),
-            ("uint32", "FirstLayerIndex", None, None, "Index into LayerList."),
-        ],
-    ),
-    # PaintSolid
-    (
-        "PaintFormat2",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 2"),
-            ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."),
-            ("F2Dot14", "Alpha", None, None, "Values outsided [0.,1.] reserved"),
-        ],
-    ),
-    # PaintVarSolid
-    (
-        "PaintFormat3",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 3"),
-            ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."),
-            (
-                "F2Dot14",
-                "Alpha",
-                None,
-                None,
-                "Values outsided [0.,1.] reserved. VarIndexBase + 0.",
-            ),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintLinearGradient
-    (
-        "PaintFormat4",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 4"),
-            (
-                "Offset24",
-                "ColorLine",
-                None,
-                None,
-                "Offset (from beginning of PaintLinearGradient table) to ColorLine subtable.",
-            ),
-            ("int16", "x0", None, None, ""),
-            ("int16", "y0", None, None, ""),
-            ("int16", "x1", None, None, ""),
-            ("int16", "y1", None, None, ""),
-            ("int16", "x2", None, None, ""),
-            ("int16", "y2", None, None, ""),
-        ],
-    ),
-    # PaintVarLinearGradient
-    (
-        "PaintFormat5",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 5"),
-            (
-                "LOffset24To(VarColorLine)",
-                "ColorLine",
-                None,
-                None,
-                "Offset (from beginning of PaintVarLinearGradient table) to VarColorLine subtable.",
-            ),
-            ("int16", "x0", None, None, "VarIndexBase + 0."),
-            ("int16", "y0", None, None, "VarIndexBase + 1."),
-            ("int16", "x1", None, None, "VarIndexBase + 2."),
-            ("int16", "y1", None, None, "VarIndexBase + 3."),
-            ("int16", "x2", None, None, "VarIndexBase + 4."),
-            ("int16", "y2", None, None, "VarIndexBase + 5."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintRadialGradient
-    (
-        "PaintFormat6",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 6"),
-            (
-                "Offset24",
-                "ColorLine",
-                None,
-                None,
-                "Offset (from beginning of PaintRadialGradient table) to ColorLine subtable.",
-            ),
-            ("int16", "x0", None, None, ""),
-            ("int16", "y0", None, None, ""),
-            ("uint16", "r0", None, None, ""),
-            ("int16", "x1", None, None, ""),
-            ("int16", "y1", None, None, ""),
-            ("uint16", "r1", None, None, ""),
-        ],
-    ),
-    # PaintVarRadialGradient
-    (
-        "PaintFormat7",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 7"),
-            (
-                "LOffset24To(VarColorLine)",
-                "ColorLine",
-                None,
-                None,
-                "Offset (from beginning of PaintVarRadialGradient table) to VarColorLine subtable.",
-            ),
-            ("int16", "x0", None, None, "VarIndexBase + 0."),
-            ("int16", "y0", None, None, "VarIndexBase + 1."),
-            ("uint16", "r0", None, None, "VarIndexBase + 2."),
-            ("int16", "x1", None, None, "VarIndexBase + 3."),
-            ("int16", "y1", None, None, "VarIndexBase + 4."),
-            ("uint16", "r1", None, None, "VarIndexBase + 5."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintSweepGradient
-    (
-        "PaintFormat8",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 8"),
-            (
-                "Offset24",
-                "ColorLine",
-                None,
-                None,
-                "Offset (from beginning of PaintSweepGradient table) to ColorLine subtable.",
-            ),
-            ("int16", "centerX", None, None, "Center x coordinate."),
-            ("int16", "centerY", None, None, "Center y coordinate."),
-            (
-                "BiasedAngle",
-                "startAngle",
-                None,
-                None,
-                "Start of the angular range of the gradient.",
-            ),
-            (
-                "BiasedAngle",
-                "endAngle",
-                None,
-                None,
-                "End of the angular range of the gradient.",
-            ),
-        ],
-    ),
-    # PaintVarSweepGradient
-    (
-        "PaintFormat9",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 9"),
-            (
-                "LOffset24To(VarColorLine)",
-                "ColorLine",
-                None,
-                None,
-                "Offset (from beginning of PaintVarSweepGradient table) to VarColorLine subtable.",
-            ),
-            ("int16", "centerX", None, None, "Center x coordinate. VarIndexBase + 0."),
-            ("int16", "centerY", None, None, "Center y coordinate. VarIndexBase + 1."),
-            (
-                "BiasedAngle",
-                "startAngle",
-                None,
-                None,
-                "Start of the angular range of the gradient. VarIndexBase + 2.",
-            ),
-            (
-                "BiasedAngle",
-                "endAngle",
-                None,
-                None,
-                "End of the angular range of the gradient. VarIndexBase + 3.",
-            ),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintGlyph
-    (
-        "PaintFormat10",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 10"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintGlyph table) to Paint subtable.",
-            ),
-            ("GlyphID", "Glyph", None, None, "Glyph ID for the source outline."),
-        ],
-    ),
-    # PaintColrGlyph
-    (
-        "PaintFormat11",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 11"),
-            (
-                "GlyphID",
-                "Glyph",
-                None,
-                None,
-                "Virtual glyph ID for a BaseGlyphList base glyph.",
-            ),
-        ],
-    ),
-    # PaintTransform
-    (
-        "PaintFormat12",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 12"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintTransform table) to Paint subtable.",
-            ),
-            (
-                "LOffset24To(Affine2x3)",
-                "Transform",
-                None,
-                None,
-                "2x3 matrix for 2D affine transformations.",
-            ),
-        ],
-    ),
-    # PaintVarTransform
-    (
-        "PaintFormat13",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 13"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarTransform table) to Paint subtable.",
-            ),
-            (
-                "LOffset24To(VarAffine2x3)",
-                "Transform",
-                None,
-                None,
-                "2x3 matrix for 2D affine transformations.",
-            ),
-        ],
-    ),
-    # PaintTranslate
-    (
-        "PaintFormat14",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 14"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintTranslate table) to Paint subtable.",
-            ),
-            ("int16", "dx", None, None, "Translation in x direction."),
-            ("int16", "dy", None, None, "Translation in y direction."),
-        ],
-    ),
-    # PaintVarTranslate
-    (
-        "PaintFormat15",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 15"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarTranslate table) to Paint subtable.",
-            ),
-            (
-                "int16",
-                "dx",
-                None,
-                None,
-                "Translation in x direction. VarIndexBase + 0.",
-            ),
-            (
-                "int16",
-                "dy",
-                None,
-                None,
-                "Translation in y direction. VarIndexBase + 1.",
-            ),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintScale
-    (
-        "PaintFormat16",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 16"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintScale table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scaleX", None, None, ""),
-            ("F2Dot14", "scaleY", None, None, ""),
-        ],
-    ),
-    # PaintVarScale
-    (
-        "PaintFormat17",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 17"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarScale table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scaleX", None, None, "VarIndexBase + 0."),
-            ("F2Dot14", "scaleY", None, None, "VarIndexBase + 1."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintScaleAroundCenter
-    (
-        "PaintFormat18",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 18"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintScaleAroundCenter table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scaleX", None, None, ""),
-            ("F2Dot14", "scaleY", None, None, ""),
-            ("int16", "centerX", None, None, ""),
-            ("int16", "centerY", None, None, ""),
-        ],
-    ),
-    # PaintVarScaleAroundCenter
-    (
-        "PaintFormat19",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 19"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarScaleAroundCenter table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scaleX", None, None, "VarIndexBase + 0."),
-            ("F2Dot14", "scaleY", None, None, "VarIndexBase + 1."),
-            ("int16", "centerX", None, None, "VarIndexBase + 2."),
-            ("int16", "centerY", None, None, "VarIndexBase + 3."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintScaleUniform
-    (
-        "PaintFormat20",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 20"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintScaleUniform table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scale", None, None, ""),
-        ],
-    ),
-    # PaintVarScaleUniform
-    (
-        "PaintFormat21",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 21"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarScaleUniform table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scale", None, None, "VarIndexBase + 0."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintScaleUniformAroundCenter
-    (
-        "PaintFormat22",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 22"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintScaleUniformAroundCenter table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scale", None, None, ""),
-            ("int16", "centerX", None, None, ""),
-            ("int16", "centerY", None, None, ""),
-        ],
-    ),
-    # PaintVarScaleUniformAroundCenter
-    (
-        "PaintFormat23",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 23"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarScaleUniformAroundCenter table) to Paint subtable.",
-            ),
-            ("F2Dot14", "scale", None, None, "VarIndexBase + 0"),
-            ("int16", "centerX", None, None, "VarIndexBase + 1"),
-            ("int16", "centerY", None, None, "VarIndexBase + 2"),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintRotate
-    (
-        "PaintFormat24",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 24"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintRotate table) to Paint subtable.",
-            ),
-            ("Angle", "angle", None, None, ""),
-        ],
-    ),
-    # PaintVarRotate
-    (
-        "PaintFormat25",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 25"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarRotate table) to Paint subtable.",
-            ),
-            ("Angle", "angle", None, None, "VarIndexBase + 0."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintRotateAroundCenter
-    (
-        "PaintFormat26",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 26"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintRotateAroundCenter table) to Paint subtable.",
-            ),
-            ("Angle", "angle", None, None, ""),
-            ("int16", "centerX", None, None, ""),
-            ("int16", "centerY", None, None, ""),
-        ],
-    ),
-    # PaintVarRotateAroundCenter
-    (
-        "PaintFormat27",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 27"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarRotateAroundCenter table) to Paint subtable.",
-            ),
-            ("Angle", "angle", None, None, "VarIndexBase + 0."),
-            ("int16", "centerX", None, None, "VarIndexBase + 1."),
-            ("int16", "centerY", None, None, "VarIndexBase + 2."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintSkew
-    (
-        "PaintFormat28",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 28"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintSkew table) to Paint subtable.",
-            ),
-            ("Angle", "xSkewAngle", None, None, ""),
-            ("Angle", "ySkewAngle", None, None, ""),
-        ],
-    ),
-    # PaintVarSkew
-    (
-        "PaintFormat29",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 29"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarSkew table) to Paint subtable.",
-            ),
-            ("Angle", "xSkewAngle", None, None, "VarIndexBase + 0."),
-            ("Angle", "ySkewAngle", None, None, "VarIndexBase + 1."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintSkewAroundCenter
-    (
-        "PaintFormat30",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 30"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintSkewAroundCenter table) to Paint subtable.",
-            ),
-            ("Angle", "xSkewAngle", None, None, ""),
-            ("Angle", "ySkewAngle", None, None, ""),
-            ("int16", "centerX", None, None, ""),
-            ("int16", "centerY", None, None, ""),
-        ],
-    ),
-    # PaintVarSkewAroundCenter
-    (
-        "PaintFormat31",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 31"),
-            (
-                "Offset24",
-                "Paint",
-                None,
-                None,
-                "Offset (from beginning of PaintVarSkewAroundCenter table) to Paint subtable.",
-            ),
-            ("Angle", "xSkewAngle", None, None, "VarIndexBase + 0."),
-            ("Angle", "ySkewAngle", None, None, "VarIndexBase + 1."),
-            ("int16", "centerX", None, None, "VarIndexBase + 2."),
-            ("int16", "centerY", None, None, "VarIndexBase + 3."),
-            (
-                "VarIndex",
-                "VarIndexBase",
-                None,
-                None,
-                "Base index into DeltaSetIndexMap.",
-            ),
-        ],
-    ),
-    # PaintComposite
-    (
-        "PaintFormat32",
-        [
-            ("uint8", "PaintFormat", None, None, "Format identifier-format = 32"),
-            (
-                "LOffset24To(Paint)",
-                "SourcePaint",
-                None,
-                None,
-                "Offset (from beginning of PaintComposite table) to source Paint subtable.",
-            ),
-            (
-                "CompositeMode",
-                "CompositeMode",
-                None,
-                None,
-                "A CompositeMode enumeration value.",
-            ),
-            (
-                "LOffset24To(Paint)",
-                "BackdropPaint",
-                None,
-                None,
-                "Offset (from beginning of PaintComposite table) to backdrop Paint subtable.",
-            ),
-        ],
-    ),
-    #
-    # avar
-    #
-    (
-        "AxisValueMap",
-        [
-            (
-                "F2Dot14",
-                "FromCoordinate",
-                None,
-                None,
-                "A normalized coordinate value obtained using default normalization",
-            ),
-            (
-                "F2Dot14",
-                "ToCoordinate",
-                None,
-                None,
-                "The modified, normalized coordinate value",
-            ),
-        ],
-    ),
-    (
-        "AxisSegmentMap",
-        [
-            (
-                "uint16",
-                "PositionMapCount",
-                None,
-                None,
-                "The number of correspondence pairs for this axis",
-            ),
-            (
-                "AxisValueMap",
-                "AxisValueMap",
-                "PositionMapCount",
-                0,
-                "The array of axis value map records for this axis",
-            ),
-        ],
-    ),
-    (
-        "avar",
-        [
-            (
-                "Version",
-                "Version",
-                None,
-                None,
-                "Version of the avar table- 0x00010000 or 0x00020000",
-            ),
-            ("uint16", "Reserved", None, None, "Permanently reserved; set to zero"),
-            (
-                "uint16",
-                "AxisCount",
-                None,
-                None,
-                'The number of variation axes for this font. This must be the same number as axisCount in the "fvar" table',
-            ),
-            (
-                "AxisSegmentMap",
-                "AxisSegmentMap",
-                "AxisCount",
-                0,
-                'The segment maps array — one segment map for each axis, in the order of axes specified in the "fvar" table',
-            ),
-            (
-                "LOffsetTo(DeltaSetIndexMap)",
-                "VarIdxMap",
-                None,
-                "Version >= 0x00020000",
-                "",
-            ),
-            ("LOffset", "VarStore", None, "Version >= 0x00020000", ""),
-        ],
-    ),
-]
diff --git a/spaces/Danielito/webui/README.md b/spaces/Danielito/webui/README.md
deleted file mode 100644
index 013d12c9f3a56698056ae1bdbbfb0ec009805237..0000000000000000000000000000000000000000
--- a/spaces/Danielito/webui/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: Stable Diffusion Web UI
-emoji: 🚧
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-duplicated_from: camenduru/webui
----
-
-## Stable Diffusion Web UI
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-
-## Documentation
-[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki)
-
-## Models License
-https://huggingface.co/spaces/CompVis/stable-diffusion-license
\ No newline at end of file
diff --git a/spaces/Dify-AI/Baichuan2-13B-Chat/README.md b/spaces/Dify-AI/Baichuan2-13B-Chat/README.md
deleted file mode 100644
index 51e0c37d3f0272cc5aa0f3798bd5c7604ddf7eba..0000000000000000000000000000000000000000
--- a/spaces/Dify-AI/Baichuan2-13B-Chat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Baichuan2 13B Chat
-emoji: 🔥
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git "a/spaces/DrBenjamin/AI_Demo/pages/\360\237\222\201\342\200\215 Open_Assistant.py" "b/spaces/DrBenjamin/AI_Demo/pages/\360\237\222\201\342\200\215 Open_Assistant.py"
deleted file mode 100644
index 16510a128ab2e9904af72ca7f71680b1b5aa3ffa..0000000000000000000000000000000000000000
--- "a/spaces/DrBenjamin/AI_Demo/pages/\360\237\222\201\342\200\215 Open_Assistant.py"	
+++ /dev/null
@@ -1,359 +0,0 @@
-##### `💁‍ Open_Assistant.py`
-##### Chat Llm Streaming
-##### https://huggingface.co/spaces/olivierdehaene/chat-llm-streaming/blob/main/README.md
-##### https://open-assistant.io/dashboard
-##### https://github.com/LAION-AI/Open-Assistant
-
-##### Please reach out to ben@benbox.org for any questions
-#### Loading needed Python libraries
-import streamlit as st
-import os
-from text_generation import Client, InferenceAPIClient
-from text_generation import InferenceAPIClient
-
-
-
-
-#### Streamlit initial setup
-st.set_page_config(
-  page_title = "💁 Open Assistant LLM",
-  page_icon = "images/OpenAssistant.png",
-  layout = "centered",
-  initial_sidebar_state = "expanded"
-)
-
-
-
-
-#### Main program
-st.header('💁‍ Open Assistant LLM')
-st.write('Conversational AI for everyone.')
-st.write('In the same way that Stable Diffusion helped the world make art and images in new ways, this helps to improve the world by providing amazing conversational AI.')
-st.write('This is the first iteration English supervised-fine-tuning (SFT) model of the Open-Assistant project. It is based on a Pythia 12B that was fine-tuned on ~22k human demonstrations of assistant conversations collected through the https://open-assistant.io/ human feedback web app before March 7, 2023.')
-st.write(':orange[Needs to be run on Hugging Face to access the OpenAssistant model (Run it here https://huggingface.co/spaces/DrBenjamin/AI_Demo).]')
-with st.form('OpenAssistant'):
-  client = InferenceAPIClient("OpenAssistant/oasst-sft-1-pythia-12b")
-  st.subheader('Question')
-  input_text = st.text_input('Ask a question')
-  input_text = '<|prompter|>' + input_text + '<|endoftext|><|assistant|>'
-  submitted = st.form_submit_button('Submit')
-  if submitted:
-    text = client.generate(input_text).generated_text
-    st.subheader('Answer')
-    st.write('Answer: :green[' + str(text) + ']')
-
-
-# Token Streaming
-#text = ""
-#for response in client.generate_stream("<|prompter|>Why is the sky blue?<|endoftext|><|assistant|>"):
-#   if not response.token.special:
-#       print(response.token.text)
-#       text += response.token.text
-#st.write(text)
-
-#
-# openchat_preprompt = (
-#     "\n<human>: Hi!\n<bot>: My name is Bot, model version is 0.15, part of an open-source kit for "
-#     "fine-tuning new bots! I was created by Together, LAION, and Ontocord.ai and the open-source "
-#     "community. I am not human, not evil and not alive, and thus have no thoughts and feelings, "
-#     "but I am programmed to be helpful, polite, honest, and friendly.\n"
-# )
-#
-#
-# def get_client(model: str):
-#     if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
-#         return Client(os.getenv("OPENCHAT_API_URL"))
-#     return InferenceAPIClient(model, token = os.getenv("HF_TOKEN", None))
-#
-#
-# def get_usernames(model: str):
-#     """
-#     Returns:
-#         (str, str, str, str): pre-prompt, username, bot name, separator
-#     """
-#     if model == "OpenAssistant/oasst-sft-1-pythia-12b":
-#         return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>"
-#     if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
-#         return openchat_preprompt, "<human>: ", "<bot>: ", "\n"
-#     return "", "User: ", "Assistant: ", "\n"
-#
-#
-# def predict(
-#         model: str,
-#         inputs: str,
-#         typical_p: float,
-#         top_p: float,
-#         temperature: float,
-#         top_k: int,
-#         repetition_penalty: float,
-#         watermark: bool,
-#         chatbot,
-#         history,
-# ):
-#     client = get_client(model)
-#     preprompt, user_name, assistant_name, sep = get_usernames(model)
-#
-#     history.append(inputs)
-#
-#     past = []
-#     for data in chatbot:
-#         user_data, model_data = data
-#
-#         if not user_data.startswith(user_name):
-#             user_data = user_name + user_data
-#         if not model_data.startswith(sep + assistant_name):
-#             model_data = sep + assistant_name + model_data
-#
-#         past.append(user_data + model_data.rstrip() + sep)
-#
-#     if not inputs.startswith(user_name):
-#         inputs = user_name + inputs
-#
-#     total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()
-#
-#     partial_words = ""
-#
-#     if model == "OpenAssistant/oasst-sft-1-pythia-12b":
-#         iterator = client.generate_stream(
-#             total_inputs,
-#             typical_p = typical_p,
-#             truncate = 1000,
-#             watermark = watermark,
-#             max_new_tokens = 500,
-#         )
-#     else:
-#         iterator = client.generate_stream(
-#             total_inputs,
-#             top_p = top_p if top_p < 1.0 else None,
-#             top_k = top_k,
-#             truncate = 1000,
-#             repetition_penalty = repetition_penalty,
-#             watermark = watermark,
-#             temperature = temperature,
-#             max_new_tokens = 500,
-#             stop_sequences = [user_name.rstrip(), assistant_name.rstrip()],
-#         )
-#
-#     for i, response in enumerate(iterator):
-#         if response.token.special:
-#             continue
-#
-#         partial_words = partial_words + response.token.text
-#         if partial_words.endswith(user_name.rstrip()):
-#             partial_words = partial_words.rstrip(user_name.rstrip())
-#         if partial_words.endswith(assistant_name.rstrip()):
-#             partial_words = partial_words.rstrip(assistant_name.rstrip())
-#
-#         if i == 0:
-#             history.append(" " + partial_words)
-#         elif response.token.text not in user_name:
-#             history[-1] = partial_words
-#
-#         chat = [
-#             (history[i].strip(), history[i + 1].strip())
-#             for i in range(0, len(history) - 1, 2)
-#         ]
-#         yield chat, history
-#
-#
-# def reset_textbox():
-#     return gr.update(value = "")
-#
-#
-# def radio_on_change(
-#         value: str,
-#         disclaimer,
-#         typical_p,
-#         top_p,
-#         top_k,
-#         temperature,
-#         repetition_penalty,
-#         watermark,
-# ):
-#     if value == "OpenAssistant/oasst-sft-1-pythia-12b":
-#         typical_p = typical_p.update(value = 0.2, visible = True)
-#         top_p = top_p.update(visible = False)
-#         top_k = top_k.update(visible = False)
-#         temperature = temperature.update(visible = False)
-#         disclaimer = disclaimer.update(visible = False)
-#         repetition_penalty = repetition_penalty.update(visible = False)
-#         watermark = watermark.update(False)
-#     elif value == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
-#         typical_p = typical_p.update(visible = False)
-#         top_p = top_p.update(value = 0.25, visible = True)
-#         top_k = top_k.update(value = 50, visible = True)
-#         temperature = temperature.update(value = 0.6, visible = True)
-#         repetition_penalty = repetition_penalty.update(value = 1.01, visible = True)
-#         watermark = watermark.update(False)
-#         disclaimer = disclaimer.update(visible = True)
-#     else:
-#         typical_p = typical_p.update(visible = False)
-#         top_p = top_p.update(value = 0.95, visible = True)
-#         top_k = top_k.update(value = 4, visible = True)
-#         temperature = temperature.update(value = 0.5, visible = True)
-#         repetition_penalty = repetition_penalty.update(value = 1.03, visible = True)
-#         watermark = watermark.update(True)
-#         disclaimer = disclaimer.update(visible = False)
-#     return (
-#         disclaimer,
-#         typical_p,
-#         top_p,
-#         top_k,
-#         temperature,
-#         repetition_penalty,
-#         watermark,
-#     )
-#
-#
-# title = """<h1 align="center">🔥Large Language Model API 🚀Streaming🚀</h1>"""
-# description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-# ```
-# User: <utterance>
-# Assistant: <utterance>
-# User: <utterance>
-# Assistant: <utterance>
-# ...
-# ```
-# In this app, you can explore the outputs of multiple LLMs when prompted in this way.
-# """
-#
-# openchat_disclaimer = """
-# <div align="center">Checkout the official <a href=https://huggingface.co/spaces/togethercomputer/OpenChatKit>OpenChatKit feedback app</a> for the full experience.</div>
-# """
-#
-# with gr.Blocks(
-#         css = """#col_container {margin-left: auto; margin-right: auto;}
-#                 #chatbot {height: 520px; overflow: auto;}"""
-# ) as demo:
-#     gr.HTML(title)
-#     with gr.Column(elem_id = "col_container"):
-#         model = gr.Radio(
-#             value = "OpenAssistant/oasst-sft-1-pythia-12b",
-#             choices = [
-#                 "OpenAssistant/oasst-sft-1-pythia-12b",
-#                 # "togethercomputer/GPT-NeoXT-Chat-Base-20B",
-#                 "google/flan-t5-xxl",
-#                 "google/flan-ul2",
-#                 "bigscience/bloom",
-#                 "bigscience/bloomz",
-#                 "EleutherAI/gpt-neox-20b",
-#             ],
-#             label = "Model",
-#             interactive = True,
-#         )
-#
-#         chatbot = gr.Chatbot(elem_id = "chatbot")
-#         inputs = gr.Textbox(
-#             placeholder = "Hi there!", label = "Type an input and press Enter"
-#         )
-#         disclaimer = gr.Markdown(openchat_disclaimer, visible = False)
-#         state = gr.State([])
-#         b1 = gr.Button()
-#
-#         with gr.Accordion("Parameters", open = False):
-#             typical_p = gr.Slider(
-#                 minimum = -0,
-#                 maximum = 1.0,
-#                 value = 0.2,
-#                 step = 0.05,
-#                 interactive = True,
-#                 label = "Typical P mass",
-#             )
-#             top_p = gr.Slider(
-#                 minimum = -0,
-#                 maximum = 1.0,
-#                 value = 0.25,
-#                 step = 0.05,
-#                 interactive = True,
-#                 label = "Top-p (nucleus sampling)",
-#                 visible = False,
-#             )
-#             temperature = gr.Slider(
-#                 minimum = -0,
-#                 maximum = 5.0,
-#                 value = 0.6,
-#                 step = 0.1,
-#                 interactive = True,
-#                 label = "Temperature",
-#                 visible = False,
-#             )
-#             top_k = gr.Slider(
-#                 minimum = 1,
-#                 maximum = 50,
-#                 value = 50,
-#                 step = 1,
-#                 interactive = True,
-#                 label = "Top-k",
-#                 visible = False,
-#             )
-#             repetition_penalty = gr.Slider(
-#                 minimum = 0.1,
-#                 maximum = 3.0,
-#                 value = 1.03,
-#                 step = 0.01,
-#                 interactive = True,
-#                 label = "Repetition Penalty",
-#                 visible = False,
-#             )
-#             watermark = gr.Checkbox(value = False, label = "Text watermarking")
-#
-#     model.change(
-#         lambda value: radio_on_change(
-#             value,
-#             disclaimer,
-#             typical_p,
-#             top_p,
-#             top_k,
-#             temperature,
-#             repetition_penalty,
-#             watermark,
-#         ),
-#         inputs = model,
-#         outputs = [
-#             disclaimer,
-#             typical_p,
-#             top_p,
-#             top_k,
-#             temperature,
-#             repetition_penalty,
-#             watermark,
-#         ],
-#     )
-#
-#     inputs.submit(
-#         predict,
-#         [
-#             model,
-#             inputs,
-#             typical_p,
-#             top_p,
-#             temperature,
-#             top_k,
-#             repetition_penalty,
-#             watermark,
-#             chatbot,
-#             state,
-#         ],
-#         [chatbot, state],
-#     )
-#     b1.click(
-#         predict,
-#         [
-#             model,
-#             inputs,
-#             typical_p,
-#             top_p,
-#             temperature,
-#             top_k,
-#             repetition_penalty,
-#             watermark,
-#             chatbot,
-#             state,
-#         ],
-#         [chatbot, state],
-#     )
-#     b1.click(reset_textbox, [], [inputs])
-#     inputs.submit(reset_textbox, [], [inputs])
-#
-#     gr.Markdown(description)
-#     demo.queue(concurrency_count = 16).launch(debug = True)
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/__init__.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/pti/pti_models/e4e/stylegan2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ECCV2022/bytetrack/tutorials/ctracker/README.md b/spaces/ECCV2022/bytetrack/tutorials/ctracker/README.md
deleted file mode 100644
index 00d9e3d18abbf7137382e64352405d082def7dc3..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/tutorials/ctracker/README.md
+++ /dev/null
@@ -1,65 +0,0 @@
-# CTracker
-
-#### Step1  
-git clone https://github.com/pjl1995/CTracker.git and preapare dataset
-
-
-#### Step2
-
-add generate_half_csv.py to https://github.com/pjl1995/CTracker
-
-run generate_half_csv.py and put train_half_annots.csv in MOT17
-
-run
-```
-python3 train.py --root_path MOT17 --csv_train train_half_annots.csv --model_dir ctracker/ --depth 50 --epochs 50
-```
-You can also download the CTracker model trained by us: [google](https://drive.google.com/file/d/1TwBDomJx8pxD-e96mGIiTduLenUvmf1t/view?usp=sharing), [baidu(code:6p3w)](https://pan.baidu.com/s/1MaCvnHynX2Wzg81hWkqzeg)
-
-#### Step3 
-
-replace https://github.com/pjl1995/CTracker/blob/master/test.py
-
-run
-```
-python3 test.py --dataset_path MOT17 --model_dir ctracker --model_path ctracker/mot17_half_ctracker.pt
-```
-
-#### Step4
-
-add eval_motchallenge.py to https://github.com/pjl1995/CTracker
-
-prepare gt_half_val.txt as CenterTrack [DATA.md](https://github.com/xingyizhou/CenterTrack/blob/master/readme/DATA.md)
-
-
-#### Step5
-
-run
-```
-python3 eval_motchallenge.py --groundtruths MOT17/train --tests ctracker/results --gt_type half_val --eval_official  --score_threshold -1
-```
-
-
-
-# CTracker_BYTE
-
-#### Step3 
-
-add mot_online to https://github.com/pjl1995/CTracker
-
-add byte_tracker.py to https://github.com/pjl1995/CTracker
-
-add test_byte.py to https://github.com/pjl1995/CTracker
-
-run
-```
-python3 test_byte.py --dataset_path MOT17 --model_dir ctracker --model_path ctracker/mot17_half_ctracker.pt
-```
-
-
-#### Step5 
-
-run
-```
-python3 eval_motchallenge.py --groundtruths MOT17/train --tests ctracker/results --gt_type half_val --eval_official  --score_threshold -1
-```
diff --git a/spaces/ECCV2022/bytetrack/yolox/core/trainer.py b/spaces/ECCV2022/bytetrack/yolox/core/trainer.py
deleted file mode 100644
index b0063399d8b2fa055ac2af1f9049f6b0c5cd8162..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/yolox/core/trainer.py
+++ /dev/null
@@ -1,327 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-from loguru import logger
-
-import torch
-
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.tensorboard import SummaryWriter
-
-from yolox.data import DataPrefetcher
-from yolox.utils import (
-    MeterBuffer,
-    ModelEMA,
-    all_reduce_norm,
-    get_model_info,
-    get_rank,
-    get_world_size,
-    gpu_mem_usage,
-    load_ckpt,
-    occupy_mem,
-    save_checkpoint,
-    setup_logger,
-    synchronize
-)
-
-import datetime
-import os
-import time
-
-
-class Trainer:
-    def __init__(self, exp, args):
-        # init function only defines some basic attr, other attrs like model, optimizer are built in
-        # before_train methods.
-        self.exp = exp
-        self.args = args
-
-        # training related attr
-        self.max_epoch = exp.max_epoch
-        self.amp_training = args.fp16
-        self.scaler = torch.cuda.amp.GradScaler(enabled=args.fp16)
-        self.is_distributed = get_world_size() > 1
-        self.rank = get_rank()
-        self.local_rank = args.local_rank
-        self.device = "cuda:{}".format(self.local_rank)
-        self.use_model_ema = exp.ema
-
-        # data/dataloader related attr
-        self.data_type = torch.float16 if args.fp16 else torch.float32
-        self.input_size = exp.input_size
-        self.best_ap = 0
-
-        # metric record
-        self.meter = MeterBuffer(window_size=exp.print_interval)
-        self.file_name = os.path.join(exp.output_dir, args.experiment_name)
-
-        if self.rank == 0:
-            os.makedirs(self.file_name, exist_ok=True)
-
-        setup_logger(
-            self.file_name,
-            distributed_rank=self.rank,
-            filename="train_log.txt",
-            mode="a",
-        )
-
-    def train(self):
-        self.before_train()
-        try:
-            self.train_in_epoch()
-        except Exception:
-            raise
-        finally:
-            self.after_train()
-
-    def train_in_epoch(self):
-        for self.epoch in range(self.start_epoch, self.max_epoch):
-            self.before_epoch()
-            self.train_in_iter()
-            self.after_epoch()
-
-    def train_in_iter(self):
-        for self.iter in range(self.max_iter):
-            self.before_iter()
-            self.train_one_iter()
-            self.after_iter()
-
-    def train_one_iter(self):
-        iter_start_time = time.time()
-
-        inps, targets = self.prefetcher.next()
-        inps = inps.to(self.data_type)
-        targets = targets.to(self.data_type)
-        targets.requires_grad = False
-        data_end_time = time.time()
-
-        with torch.cuda.amp.autocast(enabled=self.amp_training):
-            outputs = self.model(inps, targets)
-        loss = outputs["total_loss"]
-
-        self.optimizer.zero_grad()
-        self.scaler.scale(loss).backward()
-        self.scaler.step(self.optimizer)
-        self.scaler.update()
-
-        if self.use_model_ema:
-            self.ema_model.update(self.model)
-
-        lr = self.lr_scheduler.update_lr(self.progress_in_iter + 1)
-        for param_group in self.optimizer.param_groups:
-            param_group["lr"] = lr
-
-        iter_end_time = time.time()
-        self.meter.update(
-            iter_time=iter_end_time - iter_start_time,
-            data_time=data_end_time - iter_start_time,
-            lr=lr,
-            **outputs,
-        )
-
-    def before_train(self):
-        logger.info("args: {}".format(self.args))
-        logger.info("exp value:\n{}".format(self.exp))
-
-        # model related init
-        torch.cuda.set_device(self.local_rank)
-        model = self.exp.get_model()
-        logger.info(
-            "Model Summary: {}".format(get_model_info(model, self.exp.test_size))
-        )
-        model.to(self.device)
-
-        # solver related init
-        self.optimizer = self.exp.get_optimizer(self.args.batch_size)
-
-        # value of epoch will be set in `resume_train`
-        model = self.resume_train(model)
-
-        # data related init
-        self.no_aug = self.start_epoch >= self.max_epoch - self.exp.no_aug_epochs
-        self.train_loader = self.exp.get_data_loader(
-            batch_size=self.args.batch_size,
-            is_distributed=self.is_distributed,
-            no_aug=self.no_aug,
-        )
-        logger.info("init prefetcher, this might take one minute or less...")
-        self.prefetcher = DataPrefetcher(self.train_loader)
-        # max_iter means iters per epoch
-        self.max_iter = len(self.train_loader)
-
-        self.lr_scheduler = self.exp.get_lr_scheduler(
-            self.exp.basic_lr_per_img * self.args.batch_size, self.max_iter
-        )
-        if self.args.occupy:
-            occupy_mem(self.local_rank)
-
-        if self.is_distributed:
-            model = DDP(model, device_ids=[self.local_rank], broadcast_buffers=False)
-
-        if self.use_model_ema:
-            self.ema_model = ModelEMA(model, 0.9998)
-            self.ema_model.updates = self.max_iter * self.start_epoch
-
-        self.model = model
-        self.model.train()
-
-        self.evaluator = self.exp.get_evaluator(
-            batch_size=self.args.batch_size, is_distributed=self.is_distributed
-        )
-        # Tensorboard logger
-        if self.rank == 0:
-            self.tblogger = SummaryWriter(self.file_name)
-
-        logger.info("Training start...")
-        #logger.info("\n{}".format(model))
-
-    def after_train(self):
-        logger.info(
-            "Training of experiment is done and the best AP is {:.2f}".format(
-                self.best_ap * 100
-            )
-        )
-
-    def before_epoch(self):
-        logger.info("---> start train epoch{}".format(self.epoch + 1))
-
-        if self.epoch + 1 == self.max_epoch - self.exp.no_aug_epochs or self.no_aug:
-            
-            logger.info("--->No mosaic aug now!")
-            self.train_loader.close_mosaic()
-            logger.info("--->Add additional L1 loss now!")
-            if self.is_distributed:
-                self.model.module.head.use_l1 = True
-            else:
-                self.model.head.use_l1 = True
-            
-            self.exp.eval_interval = 1
-            if not self.no_aug:
-                self.save_ckpt(ckpt_name="last_mosaic_epoch")
-
-    def after_epoch(self):
-        if self.use_model_ema:
-            self.ema_model.update_attr(self.model)
-
-        self.save_ckpt(ckpt_name="latest")
-
-        if (self.epoch + 1) % self.exp.eval_interval == 0:
-            all_reduce_norm(self.model)
-            self.evaluate_and_save_model()
-
-    def before_iter(self):
-        pass
-
-    def after_iter(self):
-        """
-        `after_iter` contains two parts of logic:
-            * log information
-            * reset setting of resize
-        """
-        # log needed information
-        if (self.iter + 1) % self.exp.print_interval == 0:
-            # TODO check ETA logic
-            left_iters = self.max_iter * self.max_epoch - (self.progress_in_iter + 1)
-            eta_seconds = self.meter["iter_time"].global_avg * left_iters
-            eta_str = "ETA: {}".format(datetime.timedelta(seconds=int(eta_seconds)))
-
-            progress_str = "epoch: {}/{}, iter: {}/{}".format(
-                self.epoch + 1, self.max_epoch, self.iter + 1, self.max_iter
-            )
-            loss_meter = self.meter.get_filtered_meter("loss")
-            loss_str = ", ".join(
-                ["{}: {:.3f}".format(k, v.latest) for k, v in loss_meter.items()]
-            )
-
-            time_meter = self.meter.get_filtered_meter("time")
-            time_str = ", ".join(
-                ["{}: {:.3f}s".format(k, v.avg) for k, v in time_meter.items()]
-            )
-
-            logger.info(
-                "{}, mem: {:.0f}Mb, {}, {}, lr: {:.3e}".format(
-                    progress_str,
-                    gpu_mem_usage(),
-                    time_str,
-                    loss_str,
-                    self.meter["lr"].latest,
-                )
-                + (", size: {:d}, {}".format(self.input_size[0], eta_str))
-            )
-            self.meter.clear_meters()
-
-        # random resizing
-        if self.exp.random_size is not None and (self.progress_in_iter + 1) % 10 == 0:
-            self.input_size = self.exp.random_resize(
-                self.train_loader, self.epoch, self.rank, self.is_distributed
-            )
-
-    @property
-    def progress_in_iter(self):
-        return self.epoch * self.max_iter + self.iter
-
-    def resume_train(self, model):
-        if self.args.resume:
-            logger.info("resume training")
-            if self.args.ckpt is None:
-                ckpt_file = os.path.join(self.file_name, "latest" + "_ckpt.pth.tar")
-            else:
-                ckpt_file = self.args.ckpt
-
-            ckpt = torch.load(ckpt_file, map_location=self.device)
-            # resume the model/optimizer state dict
-            model.load_state_dict(ckpt["model"])
-            self.optimizer.load_state_dict(ckpt["optimizer"])
-            start_epoch = (
-                self.args.start_epoch - 1
-                if self.args.start_epoch is not None
-                else ckpt["start_epoch"]
-            )
-            self.start_epoch = start_epoch
-            logger.info(
-                "loaded checkpoint '{}' (epoch {})".format(
-                    self.args.resume, self.start_epoch
-                )
-            )  # noqa
-        else:
-            if self.args.ckpt is not None:
-                logger.info("loading checkpoint for fine tuning")
-                ckpt_file = self.args.ckpt
-                ckpt = torch.load(ckpt_file, map_location=self.device)["model"]
-                model = load_ckpt(model, ckpt)
-            self.start_epoch = 0
-
-        return model
-
-    def evaluate_and_save_model(self):
-        evalmodel = self.ema_model.ema if self.use_model_ema else self.model
-        ap50_95, ap50, summary = self.exp.eval(
-            evalmodel, self.evaluator, self.is_distributed
-        )
-        self.model.train()
-        if self.rank == 0:
-            self.tblogger.add_scalar("val/COCOAP50", ap50, self.epoch + 1)
-            self.tblogger.add_scalar("val/COCOAP50_95", ap50_95, self.epoch + 1)
-            logger.info("\n" + summary)
-        synchronize()
-
-        #self.best_ap = max(self.best_ap, ap50_95)
-        self.save_ckpt("last_epoch", ap50 > self.best_ap)
-        self.best_ap = max(self.best_ap, ap50)
-
-    def save_ckpt(self, ckpt_name, update_best_ckpt=False):
-        if self.rank == 0:
-            save_model = self.ema_model.ema if self.use_model_ema else self.model
-            logger.info("Save weights to {}".format(self.file_name))
-            ckpt_state = {
-                "start_epoch": self.epoch + 1,
-                "model": save_model.state_dict(),
-                "optimizer": self.optimizer.state_dict(),
-            }
-            save_checkpoint(
-                ckpt_state,
-                update_best_ckpt,
-                self.file_name,
-                ckpt_name,
-            )
diff --git a/spaces/EPFL-VILAB/MultiMAE/utils/data_constants.py b/spaces/EPFL-VILAB/MultiMAE/utils/data_constants.py
deleted file mode 100644
index 774379eeed0f5764a479f2178607e91d9af484de..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/utils/data_constants.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# Copyright (c) EPFL VILAB.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# Based on the timm and MAE-priv code base
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/BUPT-PRIV/MAE-priv
-# --------------------------------------------------------
-
-DEFAULT_CROP_PCT = 0.875
-IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
-IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
-IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
-IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
-IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
-IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3)
-
-CIFAR_DEFAULT_MEAN = (0.4914, 0.4822, 0.4465)
-CIFAR_DEFAULT_STD = (0.2023, 0.1994, 0.2010)
-
-SEG_IGNORE_INDEX = 255
-PAD_MASK_VALUE = 254
-COCO_SEMSEG_NUM_CLASSES = 133
-
-IMAGE_TASKS = ['rgb', 'depth', 'semseg', 'semseg_coco']
-
-NYU_MEAN = 2070.7764
-NYU_STD = 777.5723
-
-# Data paths
-IMAGENET_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
-IMAGENET_VAL_PATH = 'ADD_DATA_PATH_HERE'
-
-ADE_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
-ADE_VAL_PATH = 'ADD_DATA_PATH_HERE'
-
-HYPERSIM_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
-HPYERSIM_VAL_PATH = 'ADD_DATA_PATH_HERE'
-HYPERSIM_TEST_PATH = 'ADD_DATA_PATH_HERE'
-
-NYU_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
-NYU_TEST_PATH = 'ADD_DATA_PATH_HERE'
-
-TASKONOMY_PATH = 'ADD_DATA_PATH_HERE'
diff --git a/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/utils.py b/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/utils.py
deleted file mode 100644
index 0fafe8793b0d539fa58dd024342250b24b6187a9..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/lib/uvr5_pack/utils.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import torch
-import numpy as np
-from tqdm import tqdm
-import json
-
-
-def load_data(file_name: str = "./lib/uvr5_pack/name_params.json") -> dict:
-    with open(file_name, "r") as f:
-        data = json.load(f)
-
-    return data
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - left * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
-    """
-    data ： dic configs
-    """
-
-    def _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
-    ):
-        model.eval()
-        with torch.no_grad():
-            preds = []
-
-            iterations = [n_window]
-
-            total_iterations = sum(iterations)
-            for i in tqdm(range(n_window)):
-                start = i * roi_size
-                X_mag_window = X_mag_pad[
-                    None, :, :, start : start + data["window_size"]
-                ]
-                X_mag_window = torch.from_numpy(X_mag_window)
-                if is_half:
-                    X_mag_window = X_mag_window.half()
-                X_mag_window = X_mag_window.to(device)
-
-                pred = model.predict(X_mag_window, aggressiveness)
-
-                pred = pred.detach().cpu().numpy()
-                preds.append(pred[0])
-
-            pred = np.concatenate(preds, axis=2)
-        return pred
-
-    def preprocess(X_spec):
-        X_mag = np.abs(X_spec)
-        X_phase = np.angle(X_spec)
-
-        return X_mag, X_phase
-
-    X_mag, X_phase = preprocess(X_spec)
-
-    coef = X_mag.max()
-    X_mag_pre = X_mag / coef
-
-    n_frame = X_mag_pre.shape[2]
-    pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
-    n_window = int(np.ceil(n_frame / roi_size))
-
-    X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-    if list(model.state_dict().values())[0].dtype == torch.float16:
-        is_half = True
-    else:
-        is_half = False
-    pred = _execute(
-        X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-    )
-    pred = pred[:, :, :n_frame]
-
-    if data["tta"]:
-        pad_l += roi_size // 2
-        pad_r += roi_size // 2
-        n_window += 1
-
-        X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
-        pred_tta = _execute(
-            X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
-        )
-        pred_tta = pred_tta[:, :, roi_size // 2 :]
-        pred_tta = pred_tta[:, :, :n_frame]
-
-        return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
-    else:
-        return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
-    data = load_data()
-    flag = False
-    ModelName = model_path
-    for type in list(data):
-        for model in list(data[type][0]):
-            for i in range(len(data[type][0][model])):
-                if str(data[type][0][model][i]["hash_name"]) == model_hash:
-                    flag = True
-                elif str(data[type][0][model][i]["hash_name"]) in ModelName:
-                    flag = True
-
-                if flag:
-                    model_params_auto = data[type][0][model][i]["model_params"]
-                    param_name_auto = data[type][0][model][i]["param_name"]
-                    if type == "equivalent":
-                        return param_name_auto, model_params_auto
-                    else:
-                        flag = False
-    return param_name_auto, model_params_auto
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_pipelines/sar_pipeline.py b/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_pipelines/sar_pipeline.py
deleted file mode 100644
index f43ded30f5b7fb54c302a442483b07ca8bf8af69..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/_base_/recog_pipelines/sar_pipeline.py
+++ /dev/null
@@ -1,43 +0,0 @@
-img_norm_cfg = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='ResizeOCR',
-        height=48,
-        min_width=48,
-        max_width=160,
-        keep_aspect_ratio=True,
-        width_downsample_ratio=0.25),
-    dict(type='ToTensorOCR'),
-    dict(type='NormalizeOCR', **img_norm_cfg),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'filename', 'ori_shape', 'resize_shape', 'text', 'valid_ratio'
-        ]),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiRotateAugOCR',
-        rotate_degrees=[0, 90, 270],
-        transforms=[
-            dict(
-                type='ResizeOCR',
-                height=48,
-                min_width=48,
-                max_width=160,
-                keep_aspect_ratio=True,
-                width_downsample_ratio=0.25),
-            dict(type='ToTensorOCR'),
-            dict(type='NormalizeOCR', **img_norm_cfg),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=[
-                    'filename', 'ori_shape', 'resize_shape', 'valid_ratio',
-                    'img_norm_cfg', 'ori_filename', 'img_shape'
-                ]),
-        ])
-]
diff --git a/spaces/Fah/gradio-prediction-conversionrate/README.md b/spaces/Fah/gradio-prediction-conversionrate/README.md
deleted file mode 100644
index 8c5a1ac25986b3f3303a3ff76e25a0b51362c899..0000000000000000000000000000000000000000
--- a/spaces/Fah/gradio-prediction-conversionrate/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Gradio Prediction Conversionrate
-emoji: 🔥
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Faridmaruf/rvc-genshin-v2/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py b/spaces/Faridmaruf/rvc-genshin-v2/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
deleted file mode 100644
index b412ba2814e114ca7bb00b6fd6ef217f63d788a3..0000000000000000000000000000000000000000
--- a/spaces/Faridmaruf/rvc-genshin-v2/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class HarvestF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.hop_length,
-            f0_ceil=self.f0_max,
-            f0_floor=self.f0_min,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/Fernando22/freegpt-webui/run.py b/spaces/Fernando22/freegpt-webui/run.py
deleted file mode 100644
index 3b9ca0f439c4dd6a791f7eed62d942d096562b61..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/run.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import secrets
-
-from server.bp import bp
-from server.website import Website
-from server.backend import Backend_Api
-from server.babel import create_babel
-from json import load
-from flask import Flask
-
-if __name__ == '__main__':
-
-    # Load configuration from config.json
-    config = load(open('config.json', 'r'))
-    site_config = config['site_config']
-    url_prefix = config.pop('url_prefix')
-
-    # Create the app
-    app = Flask(__name__)
-    app.secret_key = secrets.token_hex(16)
-
-    # Set up Babel
-    create_babel(app)
-
-    # Set up the website routes
-    site = Website(bp, url_prefix)
-    for route in site.routes:
-        bp.add_url_rule(
-            route,
-            view_func=site.routes[route]['function'],
-            methods=site.routes[route]['methods'],
-        )
-
-    # Set up the backend API routes
-    backend_api = Backend_Api(bp, config)
-    for route in backend_api.routes:
-        bp.add_url_rule(
-            route,
-            view_func=backend_api.routes[route]['function'],
-            methods=backend_api.routes[route]['methods'],
-        )
-
-    # Register the blueprint
-    app.register_blueprint(bp, url_prefix=url_prefix)
-
-    # Run the Flask server
-    print(f"Running on {site_config['port']}{url_prefix}")
-    app.run(**site_config)
-    print(f"Closing port {site_config['port']}")
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/untrained_rn50_bert_lingunet.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/untrained_rn50_bert_lingunet.py
deleted file mode 100644
index 69bf583e8ea9d2bf505ce3387131ff4c8ff617eb..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/untrained_rn50_bert_lingunet.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import torch.nn as nn
-import torchvision.models as models
-
-from transformers import DistilBertTokenizer, DistilBertModel, DistilBertConfig
-from cliport.models.core import fusion
-from cliport.models.rn50_bert_lingunet import RN50BertLingUNet
-
-
-class UntrainedRN50BertLingUNet(RN50BertLingUNet):
-    """ Untrained ImageNet RN50 & Bert with U-Net skip connections """
-
-    def __init__(self, input_shape, output_dim, cfg, device, preprocess):
-        super().__init__(input_shape, output_dim, cfg, device, preprocess)
-
-    def _load_vision_fcn(self):
-        resnet50 = models.resnet50(pretrained=False)
-        modules = list(resnet50.children())[:-2]
-
-        self.stem = nn.Sequential(*modules[:4])
-        self.layer1 = modules[4]
-        self.layer2 = modules[5]
-        self.layer3 = modules[6]
-        self.layer4 = modules[7]
-
-    def _load_lang_enc(self):
-        self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') # only Tokenizer is pre-trained
-        distilbert_config = DistilBertConfig()
-        self.text_encoder = DistilBertModel(distilbert_config)
-
-        self.text_fc = nn.Linear(768, 1024)
-
-        self.lang_fuser1 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 2)
-        self.lang_fuser2 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 4)
-        self.lang_fuser3 = fusion.names[self.lang_fusion_type](input_dim=self.input_dim // 8)
-
-        self.proj_input_dim = 512 if 'word' in self.lang_fusion_type else 1024
-        self.lang_proj1 = nn.Linear(self.proj_input_dim, 1024)
-        self.lang_proj2 = nn.Linear(self.proj_input_dim, 512)
-        self.lang_proj3 = nn.Linear(self.proj_input_dim, 256)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
deleted file mode 100644
index d1bcf3c102fb660641eda2a1398db3df520caa3a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
deleted file mode 100644
index b588b4eca3df7de341c346aa9ecd0b171194f329..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/base_dense_head.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/base_dense_head.py
deleted file mode 100644
index de11e4a2197b1dfe241ce7a66daa1907a8fc5661..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/dense_heads/base_dense_head.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch.nn as nn
-
-
-class BaseDenseHead(nn.Module, metaclass=ABCMeta):
-    """Base class for DenseHeads."""
-
-    def __init__(self):
-        super(BaseDenseHead, self).__init__()
-
-    @abstractmethod
-    def loss(self, **kwargs):
-        """Compute losses of the head."""
-        pass
-
-    @abstractmethod
-    def get_bboxes(self, **kwargs):
-        """Transform network output for a batch into bbox predictions."""
-        pass
-
-    def forward_train(self,
-                      x,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels=None,
-                      gt_bboxes_ignore=None,
-                      proposal_cfg=None,
-                      **kwargs):
-        """
-        Args:
-            x (list[Tensor]): Features from FPN.
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used
-
-        Returns:
-            tuple:
-                losses: (dict[str, Tensor]): A dictionary of loss components.
-                proposal_list (list[Tensor]): Proposals of each image.
-        """
-        outs = self(x)
-        if gt_labels is None:
-            loss_inputs = outs + (gt_bboxes, img_metas)
-        else:
-            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
-        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
-        if proposal_cfg is None:
-            return losses
-        else:
-            proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
-            return losses, proposal_list
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/reppoints_detector.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/reppoints_detector.py
deleted file mode 100644
index a5f6be31e14488e4b8a006b7142a82c872388d82..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/reppoints_detector.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class RepPointsDetector(SingleStageDetector):
-    """RepPoints: Point Set Representation for Object Detection.
-
-        This detector is the implementation of:
-        - RepPoints detector (https://arxiv.org/pdf/1904.11490)
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(RepPointsDetector,
-              self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
-                             pretrained)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 0ebbd3c70ee5e33c6ef4ae76b6c6a6ce828d07b4..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Grezz/generate_human_motion/app.py b/spaces/Grezz/generate_human_motion/app.py
deleted file mode 100644
index 58c1cc635a5a4e8e6e00680a2ab5413668bdbe20..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/app.py
+++ /dev/null
@@ -1,319 +0,0 @@
-import sys
-import os
-import OpenGL.GL as gl
-os.environ["PYOPENGL_PLATFORM"] = "egl"
-os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"
-os.system('pip install /home/user/app/pyrender')
-
-sys.argv = ['VQ-Trans/GPT_eval_multi.py']
-os.chdir('VQ-Trans')
-
-sys.path.append('/home/user/app/VQ-Trans')
-sys.path.append('/home/user/app/pyrender')
-
-import options.option_transformer as option_trans
-from huggingface_hub import snapshot_download
-model_path = snapshot_download(repo_id="vumichien/T2M-GPT")
-
-args = option_trans.get_args_parser()
-
-args.dataname = 't2m'
-args.resume_pth = f'{model_path}/VQVAE/net_last.pth'
-args.resume_trans = f'{model_path}/VQTransformer_corruption05/net_best_fid.pth'
-args.down_t = 2
-args.depth = 3
-args.block_size = 51
-
-import clip
-import torch
-import numpy as np
-import models.vqvae as vqvae
-import models.t2m_trans as trans
-from utils.motion_process import recover_from_ric
-import visualization.plot_3d_global as plot_3d
-from models.rotation2xyz import Rotation2xyz
-import numpy as np
-from trimesh import Trimesh
-import gc
-
-import torch
-from visualize.simplify_loc2rot import joints2smpl
-import pyrender
-# import matplotlib.pyplot as plt
-
-import io
-import imageio
-from shapely import geometry
-import trimesh
-from pyrender.constants import RenderFlags
-import math
-# import ffmpeg
-# from PIL import Image
-import hashlib
-import gradio as gr
-import moviepy.editor as mp
-
-## load clip model and datasets
-is_cuda = torch.cuda.is_available()
-device = torch.device("cuda" if is_cuda else "cpu")
-print(device)
-clip_model, clip_preprocess = clip.load("ViT-B/32", device=device, jit=False, download_root='./')  # Must set jit=False for training
-
-if is_cuda:
-    clip.model.convert_weights(clip_model)
-    
-clip_model.eval()
-for p in clip_model.parameters():
-    p.requires_grad = False
-
-net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers
-                       args.nb_code,
-                       args.code_dim,
-                       args.output_emb_width,
-                       args.down_t,
-                       args.stride_t,
-                       args.width,
-                       args.depth,
-                       args.dilation_growth_rate)
-
-
-trans_encoder = trans.Text2Motion_Transformer(num_vq=args.nb_code, 
-                                embed_dim=1024, 
-                                clip_dim=args.clip_dim, 
-                                block_size=args.block_size, 
-                                num_layers=9, 
-                                n_head=16, 
-                                drop_out_rate=args.drop_out_rate, 
-                                fc_rate=args.ff_rate)
-
-
-print('loading checkpoint from {}'.format(args.resume_pth))
-ckpt = torch.load(args.resume_pth, map_location='cpu')
-net.load_state_dict(ckpt['net'], strict=True)
-net.eval()
-    
-print('loading transformer checkpoint from {}'.format(args.resume_trans))
-ckpt = torch.load(args.resume_trans, map_location='cpu')
-trans_encoder.load_state_dict(ckpt['trans'], strict=True)
-trans_encoder.eval()
-
-mean = torch.from_numpy(np.load(f'{model_path}/meta/mean.npy'))
-std = torch.from_numpy(np.load(f'{model_path}/meta/std.npy'))
-
-if is_cuda:
-    net.cuda()
-    trans_encoder.cuda()
-    mean = mean.cuda()
-    std = std.cuda()
-
-def render(motions, device_id=0, name='test_vis'):
-    frames, njoints, nfeats = motions.shape
-    MINS = motions.min(axis=0).min(axis=0)
-    MAXS = motions.max(axis=0).max(axis=0)
-
-    height_offset = MINS[1]
-    motions[:, :, 1] -= height_offset
-    trajec = motions[:, 0, [0, 2]]
-    is_cuda = torch.cuda.is_available()
-    # device = torch.device("cuda" if is_cuda else "cpu")
-    j2s = joints2smpl(num_frames=frames, device_id=0, cuda=is_cuda)
-    rot2xyz = Rotation2xyz(device=device)
-    faces = rot2xyz.smpl_model.faces
-
-    if not os.path.exists(f'output/{name}_pred.pt'): 
-        print(f'Running SMPLify, it may take a few minutes.')
-        motion_tensor, opt_dict = j2s.joint2smpl(motions)  # [nframes, njoints, 3]
-
-        vertices = rot2xyz(torch.tensor(motion_tensor).clone(), mask=None,
-                                        pose_rep='rot6d', translation=True, glob=True,
-                                        jointstype='vertices',
-                                        vertstrans=True)
-        vertices = vertices.detach().cpu()
-        torch.save(vertices, f'output/{name}_pred.pt')
-    else:
-        vertices = torch.load(f'output/{name}_pred.pt')
-    frames = vertices.shape[3] # shape: 1, nb_frames, 3, nb_joints
-    print(vertices.shape)
-    MINS = torch.min(torch.min(vertices[0], axis=0)[0], axis=1)[0]
-    MAXS = torch.max(torch.max(vertices[0], axis=0)[0], axis=1)[0]
-
-    out_list = []
-    
-    minx = MINS[0] - 0.5
-    maxx = MAXS[0] + 0.5
-    minz = MINS[2] - 0.5 
-    maxz = MAXS[2] + 0.5
-    polygon = geometry.Polygon([[minx, minz], [minx, maxz], [maxx, maxz], [maxx, minz]])
-    polygon_mesh = trimesh.creation.extrude_polygon(polygon, 1e-5)
-
-    vid = []
-    for i in range(frames):
-        if i % 10 == 0:
-            print(i)
-
-        mesh = Trimesh(vertices=vertices[0, :, :, i].squeeze().tolist(), faces=faces)
-
-        base_color = (0.11, 0.53, 0.8, 0.5)
-        ## OPAQUE rendering without alpha
-        ## BLEND rendering consider alpha 
-        material = pyrender.MetallicRoughnessMaterial(
-            metallicFactor=0.7,
-            alphaMode='OPAQUE',
-            baseColorFactor=base_color
-        )
-
-
-        mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
-
-        polygon_mesh.visual.face_colors = [0, 0, 0, 0.21]
-        polygon_render = pyrender.Mesh.from_trimesh(polygon_mesh, smooth=False)
-
-        bg_color = [1, 1, 1, 0.8]
-        scene = pyrender.Scene(bg_color=bg_color, ambient_light=(0.4, 0.4, 0.4))
-        
-        sx, sy, tx, ty = [0.75, 0.75, 0, 0.10]
-
-        camera = pyrender.PerspectiveCamera(yfov=(np.pi / 3.0))
-
-        light = pyrender.DirectionalLight(color=[1,1,1], intensity=300)
-
-        scene.add(mesh)
-
-        c = np.pi / 2
-
-        scene.add(polygon_render, pose=np.array([[ 1, 0, 0, 0],
-
-        [ 0, np.cos(c), -np.sin(c), MINS[1].cpu().numpy()],
-
-        [ 0, np.sin(c), np.cos(c), 0],
-
-        [ 0, 0, 0, 1]]))
-
-        light_pose = np.eye(4)
-        light_pose[:3, 3] = [0, -1, 1]
-        scene.add(light, pose=light_pose.copy())
-
-        light_pose[:3, 3] = [0, 1, 1]
-        scene.add(light, pose=light_pose.copy())
-
-        light_pose[:3, 3] = [1, 1, 2]
-        scene.add(light, pose=light_pose.copy())
-
-
-        c = -np.pi / 6
-
-        scene.add(camera, pose=[[ 1, 0, 0, (minx+maxx).cpu().numpy()/2],
-
-                                [ 0, np.cos(c), -np.sin(c), 1.5],
-
-                                [ 0, np.sin(c), np.cos(c), max(4, minz.cpu().numpy()+(1.5-MINS[1].cpu().numpy())*2, (maxx-minx).cpu().numpy())],
-
-                                [ 0, 0, 0, 1]
-                                ])
-        
-        # render scene
-        r = pyrender.OffscreenRenderer(960, 960)
-
-        color, _ = r.render(scene, flags=RenderFlags.RGBA)
-        # Image.fromarray(color).save(outdir+'/'+name+'_'+str(i)+'.png')
-
-        vid.append(color)
-
-        r.delete()
-
-    out = np.stack(vid, axis=0)
-    imageio.mimwrite(f'output/results.gif', out, fps=20)
-    out_video = mp.VideoFileClip(f'output/results.gif')
-    out_video.write_videofile("output/results.mp4")
-    del out, vertices
-    return f'output/results.mp4'
-
-def predict(clip_text, method='fast'):
-    gc.collect()
-    if torch.cuda.is_available():
-        text = clip.tokenize([clip_text], truncate=True).cuda()
-    else:
-        text = clip.tokenize([clip_text], truncate=True)
-    feat_clip_text = clip_model.encode_text(text).float()
-    index_motion = trans_encoder.sample(feat_clip_text[0:1], False)
-    pred_pose = net.forward_decoder(index_motion)
-    pred_xyz = recover_from_ric((pred_pose*std+mean).float(), 22)
-    output_name = hashlib.md5(clip_text.encode()).hexdigest()
-    if method == 'fast':
-        xyz = pred_xyz.reshape(1, -1, 22, 3)
-        pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(), title_batch=None, outname=[f'output/results.gif'])
-        out_video = mp.VideoFileClip("output/results.gif")
-        out_video.write_videofile("output/results.mp4")
-        return f'output/results.mp4'
-    elif method == 'slow':
-        output_path = render(pred_xyz.detach().cpu().numpy().squeeze(axis=0), device_id=0, name=output_name)
-        return output_path
-
-        
-# ---- Gradio Layout -----
-text_prompt = gr.Textbox(label="Text prompt", lines=1, interactive=True)
-video_out = gr.Video(label="Motion", mirror_webcam=False, interactive=False) 
-demo = gr.Blocks()
-demo.encrypt = False
-
-with demo:
-    gr.Markdown('''
-            <div>
-            <h1 style='text-align: center'>Generating Human Motion from Textual Descriptions (T2M-GPT)</h1>
-            This space uses <a href='https://mael-zys.github.io/T2M-GPT/' target='_blank'><b>T2M-GPT models</b></a> based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions🤗
-            </div>
-        ''')
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            <figure>
-              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow1.gif" alt="Demo Slow", width="425", height=480/>
-              <figcaption> a man starts off in an up right position with botg arms extended out by his sides, he then brings his arms down to his body and claps his hands together. after this he wals down amd the the left where he proceeds to sit on a seat
-              </figcaption>
-            </figure>
-            ''')
-        with gr.Column():
-            gr.Markdown('''
-            <figure>
-              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow2.gif" alt="Demo Slow 2", width="425", height=480/>
-              <figcaption> a person puts their hands together, leans forwards slightly then swings the arms from right to left
-              </figcaption>
-            </figure>
-            ''')
-        with gr.Column():
-            gr.Markdown('''
-            <figure>
-              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow3.gif" alt="Demo Slow 3", width="425", height=480/>
-              <figcaption> a man is practicing the waltz with a partner
-              </figcaption>
-            </figure>
-            ''')
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            ### Generate human motion by **T2M-GPT**
-            ##### Step 1. Give prompt text describing human motion
-            ##### Step 2. Choice method to render output (Fast: Sketch skeleton; Slow: SMPL mesh, only work with GPU and running time around 2 mins)
-            ##### Step 3. Generate output and enjoy
-            ''')
-    with gr.Column():
-        with gr.Row():
-            text_prompt.render()
-            method = gr.Dropdown(["slow", "fast"], label="Method", value="slow")
-        with gr.Row():
-            generate_btn = gr.Button("Generate")
-            generate_btn.click(predict, [text_prompt, method], [video_out], api_name="generate")
-            print(video_out)
-        with gr.Row():  
-            video_out.render()
-    with gr.Row(): 
-        gr.Markdown('''
-            ### You can test by following examples:
-            ''')
-    examples = gr.Examples(examples=
-            [ "a person jogs in place, slowly at first, then increases speed. they then back up and squat down.", 
-              "a man steps forward and does a handstand", 
-              "a man rises from the ground, walks in a circle and sits back down on the ground"],
-          label="Examples", inputs=[text_prompt])
-        
-demo.launch(debug=True)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/dataset_folder.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/dataset_folder.py
deleted file mode 100644
index 1847e8792ae0cd543305a7b854493fd38fcdbc50..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/dataset_folder.py
+++ /dev/null
@@ -1,430 +0,0 @@
-# Copyright (c) EPFL VILAB.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# Based on BEiT, timm, DINO DeiT and MAE-priv code bases
-# https://github.com/microsoft/unilm/tree/master/beit
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/facebookresearch/deit
-# https://github.com/facebookresearch/dino
-# https://github.com/BUPT-PRIV/MAE-priv
-# --------------------------------------------------------
-import os
-import os.path
-import random
-from copy import deepcopy
-from typing import Any, Callable, Dict, List, Optional, Tuple, cast
-
-import numpy as np
-import torch
-from PIL import Image
-from torchvision.datasets.vision import VisionDataset
-
-
-def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
-    """Checks if a file is an allowed extension.
-
-    Args:
-        filename (string): path to a file
-        extensions (tuple of strings): extensions to consider (lowercase)
-
-    Returns:
-        bool: True if the filename ends with one of given extensions
-    """
-    return filename.lower().endswith(extensions)
-
-
-def is_image_file(filename: str) -> bool:
-    """Checks if a file is an allowed image extension.
-
-    Args:
-        filename (string): path to a file
-
-    Returns:
-        bool: True if the filename ends with a known image extension
-    """
-    return has_file_allowed_extension(filename, IMG_EXTENSIONS)
-
-
-def make_dataset(
-        directory: str,
-        class_to_idx: Dict[str, int],
-        extensions: Optional[Tuple[str, ...]] = None,
-        is_valid_file: Optional[Callable[[str], bool]] = None,
-) -> List[Tuple[str, int]]:
-    instances = []
-    directory = os.path.expanduser(directory)
-    both_none = extensions is None and is_valid_file is None
-    both_something = extensions is not None and is_valid_file is not None
-    if both_none or both_something:
-        raise ValueError("Both extensions and is_valid_file cannot be None or not None at the same time")
-    if extensions is not None:
-        def is_valid_file(x: str) -> bool:
-            return has_file_allowed_extension(x, cast(Tuple[str, ...], extensions))
-    is_valid_file = cast(Callable[[str], bool], is_valid_file)
-    for target_class in sorted(class_to_idx.keys()):
-        class_index = class_to_idx[target_class]
-        target_dir = os.path.join(directory, target_class)
-        if not os.path.isdir(target_dir):
-            continue
-        for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
-            for fname in sorted(fnames):
-                path = os.path.join(root, fname)
-                if is_valid_file(path):
-                    item = path, class_index
-                    instances.append(item)
-    return instances
-
-
-class DatasetFolder(VisionDataset):
-    """A generic data loader where the samples are arranged in this way: ::
-
-        root/class_x/xxx.ext
-        root/class_x/xxy.ext
-        root/class_x/xxz.ext
-
-        root/class_y/123.ext
-        root/class_y/nsdf3.ext
-        root/class_y/asd932_.ext
-
-    Args:
-        root (string): Root directory path.
-        loader (callable): A function to load a sample given its path.
-        extensions (tuple[string]): A list of allowed extensions.
-            both extensions and is_valid_file should not be passed.
-        transform (callable, optional): A function/transform that takes in
-            a sample and returns a transformed version.
-            E.g, ``transforms.RandomCrop`` for images.
-        target_transform (callable, optional): A function/transform that takes
-            in the target and transforms it.
-        is_valid_file (callable, optional): A function that takes path of a file
-            and check if the file is a valid file (used to check of corrupt logs)
-            both extensions and is_valid_file should not be passed.
-
-     Attributes:
-        classes (list): List of the class names sorted alphabetically.
-        class_to_idx (dict): Dict with items (class_name, class_index).
-        samples (list): List of (sample path, class_index) tuples
-        targets (list): The class_index value for each image in the dataset
-    """
-
-    def __init__(
-            self,
-            root: str,
-            loader: Callable[[str], Any],
-            extensions: Optional[Tuple[str, ...]] = None,
-            transform: Optional[Callable] = None,
-            target_transform: Optional[Callable] = None,
-            is_valid_file: Optional[Callable[[str], bool]] = None,
-    ) -> None:
-        super(DatasetFolder, self).__init__(root, transform=transform,
-                                            target_transform=target_transform)
-        classes, class_to_idx = self._find_classes(self.root)
-        samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file)
-        if len(samples) == 0:
-            msg = "Found 0 logs in subfolders of: {}\n".format(self.root)
-            if extensions is not None:
-                msg += "Supported extensions are: {}".format(",".join(extensions))
-            raise RuntimeError(msg)
-
-        self.loader = loader
-        self.extensions = extensions
-
-        self.classes = classes
-        self.class_to_idx = class_to_idx
-        self.samples = samples
-        self.targets = [s[1] for s in samples]
-
-    def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
-        """
-        Finds the class folders in a dataset.
-
-        Args:
-            dir (string): Root directory path.
-
-        Returns:
-            tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary.
-
-        Ensures:
-            No class is a subdirectory of another.
-        """
-        classes = [d.name for d in os.scandir(dir) if d.is_dir()]
-        classes.sort()
-        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
-        return classes, class_to_idx
-
-    def __getitem__(self, index: int) -> Tuple[Any, Any]:
-        """
-        Args:
-            index (int): Index
-
-        Returns:
-            tuple: (sample, target) where target is class_index of the target class.
-        """
-        while True:
-            try:
-                path, target = self.samples[index]
-                sample = self.loader(path)
-                break
-            except Exception as e:
-                print(e)
-                index = random.randint(0, len(self.samples) - 1)
-
-        if self.transform is not None:
-            sample = self.transform(sample)
-        if self.target_transform is not None:
-            target = self.target_transform(target)
-
-        return sample, target
-
-    def __len__(self) -> int:
-        return len(self.samples)
-
-
-class MultiTaskDatasetFolder(VisionDataset):
-    """A generic multi-task dataset loader where the samples are arranged in this way: ::
-
-        root/task_a/class_x/xxx.ext
-        root/task_a/class_y/xxy.ext
-        root/task_a/class_z/xxz.ext
-
-        root/task_b/class_x/xxx.ext
-        root/task_b/class_y/xxy.ext
-        root/task_b/class_z/xxz.ext
-
-    Args:
-        root (string): Root directory path.
-        tasks (list): List of tasks as strings
-        loader (callable): A function to load a sample given its path.
-        extensions (tuple[string]): A list of allowed extensions.
-            both extensions and is_valid_file should not be passed.
-        transform (callable, optional): A function/transform that takes in
-            a sample and returns a transformed version.
-            E.g, ``transforms.RandomCrop`` for images.
-        target_transform (callable, optional): A function/transform that takes
-            in the target and transforms it.
-        is_valid_file (callable, optional): A function that takes path of a file
-            and check if the file is a valid file (used to check of corrupt logs)
-            both extensions and is_valid_file should not be passed.
-
-     Attributes:
-        classes (list): List of the class names sorted alphabetically.
-        class_to_idx (dict): Dict with items (class_name, class_index).
-        samples (list): List of (sample path, class_index) tuples
-        targets (list): The class_index value for each image in the dataset
-    """
-
-    def __init__(
-            self,
-            root: str,
-            tasks: List[str],
-            loader: Callable[[str], Any],
-            extensions: Optional[Tuple[str, ...]] = None,
-            transform: Optional[Callable] = None,
-            target_transform: Optional[Callable] = None,
-            is_valid_file: Optional[Callable[[str], bool]] = None,
-            prefixes: Optional[Dict[str,str]] = None,
-            max_images: Optional[int] = None
-    ) -> None:
-        super(MultiTaskDatasetFolder, self).__init__(root, transform=transform,
-                                            target_transform=target_transform)
-        self.tasks = tasks
-        classes, class_to_idx = self._find_classes(os.path.join(self.root, self.tasks[0]))
-
-        prefixes = {} if prefixes is None else prefixes
-        prefixes.update({task: '' for task in tasks if task not in prefixes})
-        
-        samples = {
-            task: make_dataset(os.path.join(self.root, f'{prefixes[task]}{task}'), class_to_idx, extensions, is_valid_file)
-            for task in self.tasks
-        }
-        
-        for task, task_samples in samples.items():
-            if len(task_samples) == 0:
-                msg = "Found 0 logs in subfolders of: {}\n".format(os.path.join(self.root, task))
-                if extensions is not None:
-                    msg += "Supported extensions are: {}".format(",".join(extensions))
-                raise RuntimeError(msg)
-
-        self.loader = loader
-        self.extensions = extensions
-
-        self.classes = classes
-        self.class_to_idx = class_to_idx
-        self.samples = samples
-        # self.targets = [s[1] for s in list(samples.values())[0]]
-
-        # Select random subset of dataset if so specified
-        if isinstance(max_images, int):
-            total_samples = len(list(self.samples.values())[0])
-            np.random.seed(0)
-            permutation = np.random.permutation(total_samples)
-            for task in samples:
-                self.samples[task] = [self.samples[task][i] for i in permutation][:max_images]
-        
-        self.cache = {}
-
-    def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
-        """
-        Finds the class folders in a dataset.
-
-        Args:
-            dir (string): Root directory path.
-
-        Returns:
-            tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary.
-
-        Ensures:
-            No class is a subdirectory of another.
-        """
-        classes = [d.name for d in os.scandir(dir) if d.is_dir()]
-        classes.sort()
-        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
-        return classes, class_to_idx
-
-    def __getitem__(self, index: int) -> Tuple[Any, Any]:
-        """
-        Args:
-            index (int): Index
-
-        Returns:
-            tuple: (sample, target) where target is class_index of the target class.
-        """
-        if index in self.cache:
-            sample_dict, target = deepcopy(self.cache[index])
-        else:
-            sample_dict = {}
-            for task in self.tasks:
-                path, target = self.samples[task][index]
-                sample = pil_loader(path, convert_rgb=(task=='rgb'))
-                sample_dict[task] = sample
-            # self.cache[index] = deepcopy((sample_dict, target))
-
-        if self.transform is not None:
-            sample_dict = self.transform(sample_dict)
-        if self.target_transform is not None:
-            target = self.target_transform(target)
-
-        return sample_dict, target
-
-    def __len__(self) -> int:
-        return len(list(self.samples.values())[0])
-
-
-IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp', '.jpx')
-
-
-def pil_loader(path: str, convert_rgb=True) -> Image.Image:
-    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
-    # with open(path, 'rb') as f:
-    #     img = Image.open(f)
-    img = Image.open(path)
-    return img.convert('RGB') if convert_rgb else img
-
-
-# TODO: specify the return type
-def accimage_loader(path: str) -> Any:
-    import accimage
-    try:
-        return accimage.Image(path)
-    except IOError:
-        # Potentially a decoding problem, fall back to PIL.Image
-        return pil_loader(path)
-
-
-def default_loader(path: str) -> Any:
-    from torchvision import get_image_backend
-    if get_image_backend() == 'accimage':
-        return accimage_loader(path)
-    else:
-        return pil_loader(path)
-
-
-class ImageFolder(DatasetFolder):
-    """A generic data loader where the images are arranged in this way: ::
-
-        root/dog/xxx.png
-        root/dog/xxy.png
-        root/dog/xxz.png
-
-        root/cat/123.png
-        root/cat/nsdf3.png
-        root/cat/asd932_.png
-
-    Args:
-        root (string): Root directory path.
-        transform (callable, optional): A function/transform that  takes in an PIL image
-            and returns a transformed version. E.g, ``transforms.RandomCrop``
-        target_transform (callable, optional): A function/transform that takes in the
-            target and transforms it.
-        loader (callable, optional): A function to load an image given its path.
-        is_valid_file (callable, optional): A function that takes path of an Image file
-            and check if the file is a valid file (used to check of corrupt logs)
-
-     Attributes:
-        classes (list): List of the class names sorted alphabetically.
-        class_to_idx (dict): Dict with items (class_name, class_index).
-        imgs (list): List of (image path, class_index) tuples
-    """
-
-    def __init__(
-            self,
-            root: str,
-            transform: Optional[Callable] = None,
-            target_transform: Optional[Callable] = None,
-            loader: Callable[[str], Any] = default_loader,
-            is_valid_file: Optional[Callable[[str], bool]] = None,
-    ):
-        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,
-                                          transform=transform,
-                                          target_transform=target_transform,
-                                          is_valid_file=is_valid_file)
-        self.imgs = self.samples
-
-class MultiTaskImageFolder(MultiTaskDatasetFolder):
-    """A generic multi-task dataset loader where the images are arranged in this way: ::
-
-        root/task_a/class_x/xxx.ext
-        root/task_a/class_y/xxy.ext
-        root/task_a/class_z/xxz.ext
-
-        root/task_b/class_x/xxx.ext
-        root/task_b/class_y/xxy.ext
-        root/task_b/class_z/xxz.ext
-
-    Args:
-        root (string): Root directory path.
-        transform (callable, optional): A function/transform that  takes in an PIL image
-            and returns a transformed version. E.g, ``transforms.RandomCrop``
-        target_transform (callable, optional): A function/transform that takes in the
-            target and transforms it.
-        loader (callable, optional): A function to load an image given its path.
-        is_valid_file (callable, optional): A function that takes path of an Image file
-            and check if the file is a valid file (used to check of corrupt logs)
-
-     Attributes:
-        classes (list): List of the class names sorted alphabetically.
-        class_to_idx (dict): Dict with items (class_name, class_index).
-        imgs (list): List of (image path, class_index) tuples
-    """
-
-    def __init__(
-            self,
-            root: str,
-            tasks: List[str],
-            transform: Optional[Callable] = None,
-            target_transform: Optional[Callable] = None,
-            loader: Callable[[str], Any] = pil_loader,
-            is_valid_file: Optional[Callable[[str], bool]] = None,
-            prefixes: Optional[Dict[str,str]] = None,
-            max_images: Optional[int] = None
-    ):
-        super(MultiTaskImageFolder, self).__init__(root, tasks, loader, IMG_EXTENSIONS if is_valid_file is None else None,
-                                          transform=transform,
-                                          target_transform=target_transform,
-                                          is_valid_file=is_valid_file,
-                                          prefixes=prefixes,
-                                          max_images=max_images)
-        self.imgs = self.samples
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/download_flores_data.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/download_flores_data.sh
deleted file mode 100644
index e6175ce0c38b06a1ebddaeca808f71b47f77f500..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/multilingual/data_scripts/download_flores_data.sh
+++ /dev/null
@@ -1,246 +0,0 @@
-#!/bin/bash
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-#
-
-if [ -z $WORKDIR_ROOT ] ;
-then
-        echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
-        exit
-fi
-
-
-set -e
-set -o pipefail
-
-SRC=en
-SI_TGT=si
-NE_TGT=ne
-
-DESTDIR=${WORKDIR_ROOT}/ML50/raw/
-
-ROOT=${WORKDIR_ROOT}/tmp
-mkdir -p $ROOT
-DATA=$ROOT/data
-NE_ROOT=$DATA/all-clean-ne
-SI_ROOT=$DATA/all-clean-si
-
-mkdir -p $DATA $NE_ROOT $SI_ROOT
-
-SI_OPUS_DATASETS=(
-  "$SI_ROOT/GNOME.en-si"
-  "$SI_ROOT/Ubuntu.en-si"
-  "$SI_ROOT/KDE4.en-si"
-  "$SI_ROOT/OpenSubtitles.en-si"
-)
-
-SI_OPUS_URLS=(
-  "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-si.txt.zip"
-  "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-si.txt.zip"
-  "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-si.txt.zip"
-  "https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/moses/en-si.txt.zip"
-)
-
-NE_OPUS_DATASETS=(
-  "$NE_ROOT/GNOME.en-ne"
-  "$NE_ROOT/Ubuntu.en-ne"
-  "$NE_ROOT/KDE4.en-ne"
-)
-
-NE_OPUS_URLS=(
-  "https://object.pouta.csc.fi/OPUS-GNOME/v1/moses/en-ne.txt.zip"
-  "https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/moses/en-ne.txt.zip"
-  "https://object.pouta.csc.fi/OPUS-KDE4/v2/moses/en-ne.txt.zip"
-)
-
-REMOVE_FILE_PATHS=()
-
-# Download data
-download_data() {
-  CORPORA=$1
-  URL=$2
-
-  if [ -f $CORPORA ]; then
-    echo "$CORPORA already exists, skipping download"
-  else
-    echo "Downloading $URL"
-    wget $URL -O $CORPORA --no-check-certificate || rm -f $CORPORA
-    if [ -f $CORPORA ]; then
-      echo "$URL successfully downloaded."
-    else
-      echo "$URL not successfully downloaded."
-      rm -f $CORPORA
-      exit -1
-    fi
-  fi
-}
-
-# Example: download_opus_data $LANG_ROOT $TGT
-download_opus_data() {
-  LANG_ROOT=$1
-  TGT=$2
-
-  if [ "$TGT" = "si" ]; then
-    URLS=("${SI_OPUS_URLS[@]}")
-    DATASETS=("${SI_OPUS_DATASETS[@]}")
-  else
-    URLS=("${NE_OPUS_URLS[@]}")
-    DATASETS=("${NE_OPUS_DATASETS[@]}")
-  fi
-
-  # Download and extract data
-  for ((i=0;i<${#URLS[@]};++i)); do
-    URL=${URLS[i]}
-    CORPORA=${DATASETS[i]}
-
-    download_data $CORPORA $URL
-    unzip -o $CORPORA -d $LANG_ROOT
-    REMOVE_FILE_PATHS+=( $CORPORA $CORPORA.xml $CORPORA.ids $LANG_ROOT/README $LANG_ROOT/LICENSE )
-  done
-
-  cat ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$SRC
-  cat ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT > $LANG_ROOT/GNOMEKDEUbuntu.$SRC-$TGT.$TGT
-
-  REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$SRC ${DATASETS[1]}.$SRC ${DATASETS[2]}.$SRC )
-  REMOVE_FILE_PATHS+=( ${DATASETS[0]}.$TGT ${DATASETS[1]}.$TGT ${DATASETS[2]}.$TGT )
-}
-
-download_opus_data $SI_ROOT $SI_TGT
-cp ${SI_OPUS_DATASETS[3]}.$SRC $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SRC
-cp ${SI_OPUS_DATASETS[3]}.$SI_TGT $SI_ROOT/OpenSubtitles2018.$SRC-$SI_TGT.$SI_TGT
-REMOVE_FILE_PATHS+=( ${SI_OPUS_DATASETS[3]}.$SRC ${SI_OPUS_DATASETS[3]}.$SI_TGT )
-
-download_opus_data $NE_ROOT $NE_TGT
-
-
-# Download and extract Global Voices data
-GLOBAL_VOICES="$NE_ROOT/globalvoices.2018q4.ne-en"
-GLOBAL_VOICES_URL="http://www.casmacat.eu/corpus/global-voices/globalvoices.ne-en.xliff.gz"
-
-download_data $GLOBAL_VOICES.gz $GLOBAL_VOICES_URL
-gunzip -Nf $GLOBAL_VOICES.gz
-
-sed -ne 's?.*<source>\(.*\)</source>.*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$NE_TGT
-sed -ne 's?.*<target[^>]*>\(.*\)</target>.*?\1?p' $GLOBAL_VOICES > $GLOBAL_VOICES.$SRC
-
-REMOVE_FILE_PATHS+=( $GLOBAL_VOICES )
-
-# Download and extract the bible dataset
-BIBLE_TOOLS=bible-corpus-tools
-XML_BIBLES=XML_Bibles
-XML_BIBLES_DUP=XML_Bibles_dup
-
-if [ ! -e $BIBLE_TOOLS ]; then
-    echo "Cloning bible-corpus-tools repository..."
-    git clone https://github.com/christos-c/bible-corpus-tools.git
-fi
-
-mkdir -p $BIBLE_TOOLS/bin $XML_BIBLES $XML_BIBLES_DUP
-javac -cp "$BIBLE_TOOLS/lib/*" -d $BIBLE_TOOLS/bin $BIBLE_TOOLS/src/bible/readers/*.java $BIBLE_TOOLS/src/bible/*.java
-
-download_data bible.tar.gz "https://github.com/christos-c/bible-corpus/archive/v1.2.1.tar.gz"
-tar xvzf bible.tar.gz
-
-cp bible-corpus-1.2.1/bibles/{Greek.xml,English.xml,Nepali.xml} $XML_BIBLES/
-cp bible-corpus-1.2.1/bibles/{Greek.xml,English-WEB.xml,Nepali.xml} $XML_BIBLES_DUP/
-
-java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES
-java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateMLBooks $XML_BIBLES_DUP
-java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES
-java -cp $BIBLE_TOOLS/lib/*:$BIBLE_TOOLS/bin bible.CreateVerseAlignedBooks $XML_BIBLES_DUP
-
-cat $XML_BIBLES/aligned/*/English.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$SRC
-cat $XML_BIBLES/aligned/*/Nepali.txt > $NE_ROOT/bible.$SRC-$NE_TGT.$NE_TGT
-cat $XML_BIBLES_DUP/aligned/*/English-WEB.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$SRC
-cat $XML_BIBLES_DUP/aligned/*/Nepali.txt > $NE_ROOT/bible_dup.$SRC-$NE_TGT.$NE_TGT
-REMOVE_FILE_PATHS+=( bible-corpus-1.2.1 bible.tar.gz $BIBLE_TOOLS $XML_BIBLES $XML_BIBLES_DUP )
-
-# Download and extract the Penn Treebank dataset
-NE_TAGGED=$ROOT/new_submissions_parallel_corpus_project_Nepal
-NE_TAGGED_URL="http://www.cle.org.pk/Downloads/ling_resources/parallelcorpus/NepaliTaggedCorpus.zip"
-EN_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.en.patch"
-NE_TAGGED_PATCH_URL="https://dl.fbaipublicfiles.com/fairseq/data/nepali-penn-treebank.ne.patch"
-MOSES=mosesdecoder
-MOSES_TOK=$MOSES/scripts/tokenizer
-EN_PATCH_REGEX="{s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}"
-NE_PATCH_REGEX="{s:\p{Cf}::g;s:\\\/:\/:g;s/\*\T\*\-\n+//g;s/\-LCB\-/\{/g;s/\-RCB\-/\}/g; s/\-LSB\-/\[/g; s/\-RSB\-/\]/g;s/\-LRB\-/\(/g; s/\-RRB\-/\)/g; s/\'\'/\"/g; s/\`\`/\"/g; s/\ +\'s\ +/\'s /g; s/\ +\'re\ +/\'re /g; s/\"\ +/\"/g; s/\ +\"/\"/g; s/\ n't([\ \.\"])/n't\1/g; s/\r+(.)/\1/g;}"
-
-download_data $DATA/nepali-penn-treebank.$SRC.patch $EN_TAGGED_PATCH_URL
-download_data $DATA/nepali-penn-treebank.$NE_TGT.patch $NE_TAGGED_PATCH_URL
-download_data original.zip $NE_TAGGED_URL
-unzip -o original.zip -d $ROOT
-
-cat $NE_TAGGED/00.txt $NE_TAGGED/01.txt $NE_TAGGED/02.txt > $NE_TAGGED/nepali-penn-treebank.$SRC
-cat $NE_TAGGED/00ne_revised.txt $NE_TAGGED/01ne_revised.txt $NE_TAGGED/02ne_revised.txt > $NE_TAGGED/nepali-penn-treebank.$NE_TGT
-
-patch $NE_TAGGED/nepali-penn-treebank.$SRC -i $DATA/nepali-penn-treebank.$SRC.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$SRC
-patch $NE_TAGGED/nepali-penn-treebank.$NE_TGT -i $DATA/nepali-penn-treebank.$NE_TGT.patch -o $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT
-
-if [ ! -e $MOSES ]; then
-    echo "Cloning moses repository..."
-    git clone https://github.com/moses-smt/mosesdecoder.git
-fi
-
-cat $NE_TAGGED/nepali-penn-treebank-patched.$SRC | \
-  perl -anpe "$EN_PATCH_REGEX"  | \
-  $MOSES_TOK/tokenizer.perl -l $SRC | \
-  $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$SRC
-
-cat $NE_TAGGED/nepali-penn-treebank-patched.$NE_TGT | \
-  perl -CIO -anpe "$NE_PATCH_REGEX" | \
-  $MOSES_TOK/detokenizer.perl -l $SRC > $NE_ROOT/nepali-penn-treebank.$NE_TGT
-
-
-# Download nepali dictionary data
-NE_DICT=$NE_ROOT/dictionaries
-download_data $NE_DICT "http://www.seas.upenn.edu/~nlp/resources/TACL-data-release/dictionaries.tar.gz"
-tar xvzf $NE_DICT
-cp dictionaries/dict.ne $NE_ROOT/dictionary.$NE_TGT-$SRC
-REMOVE_FILE_PATHS+=( $NE_DICT dictionaries )
-
-REMOVE_FILE_PATHS+=( $MOSES $NE_TAGGED original.zip $DATA/nepali-penn-treebank.$SRC.patch $DATA/nepali-penn-treebank.$NE_TGT.patch )
-
-
-# Remove the temporary files
-for ((i=0;i<${#REMOVE_FILE_PATHS[@]};++i)); do
-  rm -rf ${REMOVE_FILE_PATHS[i]}
-done
-
-# Copy the training data
-si=si_LK
-ne=ne_NP
-en=en_XX
-cat $SI_ROOT/GNOMEKDEUbuntu.en-si.si $SI_ROOT/OpenSubtitles2018.en-si.si > $DESTDIR/train.$si-$en.$si
-cat $SI_ROOT/GNOMEKDEUbuntu.en-si.en $SI_ROOT/OpenSubtitles2018.en-si.en > $DESTDIR/train.$si-$en.$en
-
-cat $NE_ROOT/bible_dup.en-ne.ne $NE_ROOT/bible.en-ne.ne $NE_ROOT/globalvoices.2018q4.ne-en.ne $NE_ROOT/GNOMEKDEUbuntu.en-ne.ne $NE_ROOT/nepali-penn-treebank.ne >  $DESTDIR/train.$ne-$en.$ne
-cat $NE_ROOT/bible_dup.en-ne.en $NE_ROOT/bible.en-ne.en $NE_ROOT/globalvoices.2018q4.ne-en.en $NE_ROOT/GNOMEKDEUbuntu.en-ne.en $NE_ROOT/nepali-penn-treebank.en >  $DESTDIR/train.$ne-$en.$en
-
-
-#Download the test sets
-wget https://github.com/facebookresearch/flores/raw/master/data/wikipedia_en_ne_si_test_sets.tgz
-tar -xvzf wikipedia_en_ne_si_test_sets.tgz
-
-cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.ne $DESTDIR/valid.$ne-$en.$ne
-cp wikipedia_en_ne_si_test_sets/wikipedia.dev.ne-en.en $DESTDIR/valid.$ne-$en.$en
-
-cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.si $DESTDIR/valid.$si-$en.$si
-cp wikipedia_en_ne_si_test_sets/wikipedia.dev.si-en.en $DESTDIR/valid.$si-$en.$en
-
-cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.ne $DESTDIR/devtest.$ne-$en.$ne
-cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.ne-en.en $DESTDIR/devtest.$ne-$en.$en
-
-cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.si $DESTDIR/devtest.$si-$en.$si
-cp wikipedia_en_ne_si_test_sets/wikipedia.devtest.si-en.en $DESTDIR/devtest.$si-$en.$en
-
-cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.ne $DESTDIR/test.$ne-$en.$ne
-cp wikipedia_en_ne_si_test_sets/wikipedia.test.ne-en.en $DESTDIR/test.$ne-$en.$en
-
-cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.si $DESTDIR/test.$si-$en.$si
-cp wikipedia_en_ne_si_test_sets/wikipedia.test.si-en.en $DESTDIR/test.$si-$en.$en
-
-rm -rf wikipedia_en_ne_si_test_sets.tgz wikipedia_en_ne_si_test_sets
diff --git a/spaces/HewDew/Linaqruf-anything-v3.0/README.md b/spaces/HewDew/Linaqruf-anything-v3.0/README.md
deleted file mode 100644
index a629e1def33aecebdeb81f2483115d1d0a36223a..0000000000000000000000000000000000000000
--- a/spaces/HewDew/Linaqruf-anything-v3.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Linaqruf Anything V3.0
-emoji: 👁
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/CarouselItem.svelte_svelte_type_style_lang.cc0aed40.js b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/CarouselItem.svelte_svelte_type_style_lang.cc0aed40.js
deleted file mode 100644
index e812d2d88d7d96dc6837e5da19aa2c3964ecabad..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/templates/frontend/assets/CarouselItem.svelte_svelte_type_style_lang.cc0aed40.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as B,i as E,s as H,p as I,e as w,a as j,t as M,b as g,d as k,f as T,g as c,l as S,u as D,q as F,r as O,h as q,j as Q,k as R,n as U,A as z,F as G,Q as y,$ as J,a0 as K,a1 as A}from"./index.396f4a72.js";function N(l){let t,o,e,u,L,f,m=l[2]+1+"",v,p,_=l[3].length+"",d,b,i,r,x,n;const C=l[9].default,a=I(C,l,l[8],null);return{c(){t=w("div"),a&&a.c(),o=j(),e=w("div"),u=w("button"),u.innerHTML='<svg class="caret text-xs fill-current" width=".7em" height=".7em" viewBox="0 0 9.1457395 15.999842"><path d="M 0.32506616,7.2360106 7.1796187,0.33129769 c 0.4360247,-0.439451 1.1455702,-0.442056 1.5845974,-0.0058 0.4390612,0.435849 0.441666,1.14535901 0.00582,1.58438501 l -6.064985,6.1096644 6.10968,6.0646309 c 0.4390618,0.436026 0.4416664,1.145465 0.00582,1.584526 -0.4358485,0.439239 -1.1453586,0.441843 -1.5845975,0.0058 L 0.33088256,8.8203249 C 0.11135166,8.6022941 0.00105996,8.3161928 7.554975e-6,8.0295489 -0.00104244,7.7427633 0.10735446,7.4556467 0.32524356,7.2361162"></path></svg>',L=j(),f=w("div"),v=M(m),p=M(" / "),d=M(_),b=j(),i=w("button"),i.innerHTML='<svg class="caret text-xs fill-current scale-x-[-1]" width=".7em" height=".7em" viewBox="0 0 9.1457395 15.999842"><path d="M 0.32506616,7.2360106 7.1796187,0.33129769 c 0.4360247,-0.439451 1.1455702,-0.442056 1.5845974,-0.0058 0.4390612,0.435849 0.441666,1.14535901 0.00582,1.58438501 l -6.064985,6.1096644 6.10968,6.0646309 c 0.4390618,0.436026 0.4416664,1.145465 0.00582,1.584526 -0.4358485,0.439239 -1.1453586,0.441843 -1.5845975,0.0058 L 0.33088256,8.8203249 C 0.11135166,8.6022941 0.00105996,8.3161928 7.554975e-6,8.0295489 -0.00104244,7.7427633 0.10735446,7.4556467 0.32524356,7.2361162"></path></svg>',g(u,"class","flex items-center justify-center h-6 w-6 hover:text-orange-500"),g(f,"class","carousel_index text-center font-semibold"),g(i,"class","flex items-center justify-center h-6 w-6 hover:text-orange-500"),g(e,"class","carousel-control flex gap-4 justify-center items-center pt-2 text-sm"),g(t,"class","output-carousel flex flex-col relative"),g(t,"id",l[0]),k(t,"!hidden",!l[1])},m(s,h){T(s,t,h),a&&a.m(t,null),c(t,o),c(t,e),c(e,u),c(e,L),c(e,f),c(f,v),c(f,p),c(f,d),c(e,b),c(e,i),r=!0,x||(n=[S(u,"click",l[7]),S(i,"click",l[6])],x=!0)},p(s,[h]){a&&a.p&&(!r||h&256)&&D(a,C,s,s[8],r?O(C,s[8],h,null):F(s[8]),null),(!r||h&4)&&m!==(m=s[2]+1+"")&&q(v,m),(!r||h&8)&&_!==(_=s[3].length+"")&&q(d,_),(!r||h&1)&&g(t,"id",s[0]),h&2&&k(t,"!hidden",!s[1])},i(s){r||(Q(a,s),r=!0)},o(s){R(a,s),r=!1},d(s){s&&U(t),a&&a.d(s),x=!1,z(n)}}}const P={};function V(l,t,o){let e,u,{$$slots:L={},$$scope:f}=t,{elem_id:m=""}=t,{visible:v=!0}=t;const p=G(),_=A([]);y(l,_,n=>o(3,e=n));const d=A();y(l,d,n=>o(11,u=n));let b=-1;J(P,{register:()=>(e.push(++b),_.set(e),b),unregister:n=>{const C=e.findIndex(a=>a===n);e.slice(C,1),_.set(e)},current:d});let i=0;const r=()=>{o(2,i=(i+1)%e.length),p("change")},x=()=>{o(2,i=(i-1+e.length)%e.length),p("change")};return l.$$set=n=>{"elem_id"in n&&o(0,m=n.elem_id),"visible"in n&&o(1,v=n.visible),"$$scope"in n&&o(8,f=n.$$scope)},l.$$.update=()=>{l.$$.dirty&12&&K(d,u=e[i]||0,u)},[m,v,i,e,_,d,r,x,f,L]}class X extends B{constructor(t){super(),E(this,t,V,N,H,{elem_id:0,visible:1})}}export{X as C,P as a};
-//# sourceMappingURL=CarouselItem.svelte_svelte_type_style_lang.cc0aed40.js.map
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/criterions/adaptive_loss.py b/spaces/ICML2022/OFA/fairseq/fairseq/criterions/adaptive_loss.py
deleted file mode 100644
index 6209ceaedb6d8120ad820c11b55c13596447933c..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/criterions/adaptive_loss.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass
-
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.constants import DDP_BACKEND_CHOICES
-from omegaconf import II
-
-
-@dataclass
-class AdaptiveLossConfig(FairseqDataclass):
-    sentence_avg: bool = II("optimization.sentence_avg")
-    ddp_backend: DDP_BACKEND_CHOICES = II("distributed_training.ddp_backend")
-
-
-@register_criterion("adaptive_loss", dataclass=AdaptiveLossConfig)
-class AdaptiveLoss(FairseqCriterion):
-    """This is an implementation of the loss function accompanying the adaptive softmax approximation for
-    graphical processing units (GPU), described in the paper "Efficient softmax approximation for GPUs"
-    (http://arxiv.org/abs/1609.04309)."""
-
-    def __init__(self, task, sentence_avg):
-        super().__init__(task)
-        self.sentence_avg = sentence_avg
-
-    @classmethod
-    def build_criterion(cls, cfg: AdaptiveLossConfig, task):
-        if cfg.ddp_backend in {"c10d", "pytorch_ddp"}:
-            raise Exception(
-                "AdaptiveLoss is not compatible with the PyTorch "
-                "version of DistributedDataParallel. Please use "
-                "`--ddp-backend=legacy_ddp` instead."
-            )
-        return cls(task, cfg.sentence_avg)
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-
-        assert (
-            hasattr(model.decoder, "adaptive_softmax")
-            and model.decoder.adaptive_softmax is not None
-        )
-        adaptive_softmax = model.decoder.adaptive_softmax
-
-        net_output = model(**sample["net_input"])
-        orig_target = model.get_targets(sample, net_output)
-
-        nsentences = orig_target.size(0)
-        orig_target = orig_target.view(-1)
-
-        bsz = orig_target.size(0)
-
-        logits, target = adaptive_softmax(net_output[0], orig_target)
-        assert len(target) == len(logits)
-
-        loss = net_output[0].new(1 if reduce else bsz).zero_()
-
-        for i in range(len(target)):
-            if target[i] is not None:
-                assert target[i].min() >= 0 and target[i].max() <= logits[i].size(1)
-                loss += F.cross_entropy(
-                    logits[i],
-                    target[i],
-                    ignore_index=self.padding_idx,
-                    reduction="sum" if reduce else "none",
-                )
-
-        orig = utils.strip_pad(orig_target, self.padding_idx)
-        ntokens = orig.numel()
-        sample_size = sample["target"].size(0) if self.sentence_avg else ntokens
-        logging_output = {
-            "loss": loss.data,
-            "ntokens": ntokens,
-            "nsentences": nsentences,
-            "sample_size": sample_size,
-        }
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
-        ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
-        sample_size = utils.item(
-            sum(log.get("sample_size", 0) for log in logging_outputs)
-        )
-
-        metrics.log_scalar(
-            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
-        )
-        if sample_size != ntokens:
-            metrics.log_scalar(
-                "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
-            )
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
-            )
-        else:
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
-            )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/optim/adam.py b/spaces/ICML2022/OFA/fairseq/fairseq/optim/adam.py
deleted file mode 100644
index d3ae9e64a74774310adcd9968d2eae23368890f9..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/optim/adam.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import math
-from collections.abc import Collection
-from dataclasses import dataclass, field
-from typing import Any, List
-
-import torch
-import torch.distributed as dist
-import torch.optim
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim import FairseqOptimizer, register_optimizer
-from fairseq.optim.fused_adam import get_fused_adam_class
-from omegaconf import II, OmegaConf
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class FairseqAdamConfig(FairseqDataclass):
-    adam_betas: Any = field(
-        default=(0.9, 0.999), metadata={"help": "betas for Adam optimizer"}
-    )
-    adam_eps: float = field(
-        default=1e-8, metadata={"help": "epsilon for Adam optimizer"}
-    )
-    weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
-    use_old_adam: bool = field(
-        default=False, metadata={"help": "Use fairseq.optim.adam.Adam"}
-    )
-    fp16_adam_stats: bool = field(
-        default=False, metadata={"help": "use FP16 stats (with automatic scaling)"}
-    )
-    # TODO common vars below in parent
-    tpu: bool = II("common.tpu")
-    lr: List[float] = II("optimization.lr")
-
-
-@register_optimizer("adam", dataclass=FairseqAdamConfig)
-class FairseqAdam(FairseqOptimizer):
-    """Adam optimizer for fairseq.
-
-    Important note: this optimizer corresponds to the "AdamW" variant of
-    Adam in its weight decay behavior. As such, it is most closely
-    analogous to torch.optim.AdamW from PyTorch.
-    """
-
-    def __init__(self, cfg: FairseqAdamConfig, params):
-        super().__init__(cfg)
-        fused_adam_cls = get_fused_adam_class()
-        use_fused_adam = (
-            not getattr(cfg, "use_old_adam", False)
-            and fused_adam_cls is not None
-            and torch.cuda.is_available()
-        )
-        if getattr(cfg, "tpu", False):
-            if self.cfg.fp16_adam_stats:
-                raise NotImplementedError("--fp16-adam-stats is only supported on GPU")
-            # on TPUs we use the Adam defined here, since it
-            # automatically casts gradients to FP32
-            self._optimizer = Adam(params, **self.optimizer_config)
-        elif use_fused_adam:
-            logger.info("using FusedAdam")
-            self._optimizer = fused_adam_cls(
-                params,
-                use_fp16_stats=self.cfg.fp16_adam_stats,
-                **self.optimizer_config
-            )
-        else:
-            if self.cfg.fp16_adam_stats:
-                raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1")
-            self._optimizer = Adam(params, **self.optimizer_config)
-
-    @property
-    def optimizer_config(self):
-        """
-        Return a kwarg dictionary that will be used to override optimizer
-        args stored in checkpoints. This allows us to load a checkpoint and
-        resume training using a different set of optimizer args, e.g., with a
-        different learning rate.
-        """
-        return {
-            "lr": self.cfg.lr[0]
-            if isinstance(self.cfg.lr, Collection)
-            else self.cfg.lr,
-            "betas": eval(self.cfg.adam_betas)
-            if isinstance(self.cfg.adam_betas, str)
-            else OmegaConf.to_container(self.cfg.adam_betas),
-            "eps": self.cfg.adam_eps,
-            "weight_decay": self.cfg.weight_decay,
-        }
-
-    def average_params(self):
-        """Reduce Params is only used during BMUF distributed training."""
-        state_dict = self.optimizer.state_dict()
-        total_gpus = float(dist.get_world_size())
-
-        for _, value in state_dict["state"].items():
-            value["exp_avg"] /= total_gpus
-            value["exp_avg_sq"] /= total_gpus
-            dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM)
-            dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM)
-
-
-class Adam(torch.optim.Optimizer):
-    r"""Implements Adam algorithm.
-
-    This implementation is modified from torch.optim.Adam based on:
-    `Fixed Weight Decay Regularization in Adam`
-    (see https://arxiv.org/abs/1711.05101)
-
-    It has been proposed in `Adam: A Method for Stochastic Optimization`_.
-
-    Args:
-        params (iterable): iterable of parameters to optimize or dicts defining
-            parameter groups
-        lr (float, optional): learning rate (default: 1e-3)
-        betas (Tuple[float, float], optional): coefficients used for computing
-            running averages of gradient and its square (default: (0.9, 0.999))
-        eps (float, optional): term added to the denominator to improve
-            numerical stability (default: 1e-8)
-        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
-        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
-            algorithm from the paper `On the Convergence of Adam and Beyond`_
-
-    .. _Adam\: A Method for Stochastic Optimization:
-        https://arxiv.org/abs/1412.6980
-    .. _On the Convergence of Adam and Beyond:
-        https://openreview.net/forum?id=ryQu7f-RZ
-    """
-
-    def __init__(
-        self,
-        params,
-        lr=1e-3,
-        betas=(0.9, 0.999),
-        eps=1e-8,
-        weight_decay=0,
-        amsgrad=False,
-    ):
-        defaults = dict(
-            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
-        )
-        super(Adam, self).__init__(params, defaults)
-
-    @property
-    def supports_memory_efficient_fp16(self):
-        return True
-
-    @property
-    def supports_flat_params(self):
-        return True
-
-    def step(self, closure=None):
-        """Performs a single optimization step.
-
-        Args:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-            for p in group["params"]:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data
-                if grad.dtype in {torch.float16, torch.bfloat16}:
-                    grad = grad.float()
-                if grad.is_sparse:
-                    raise RuntimeError(
-                        "Adam does not support sparse gradients, please consider SparseAdam instead"
-                    )
-                amsgrad = group.get("amsgrad", False)
-
-                p_data_fp32 = p.data
-                if p.data.dtype in {torch.float16, torch.bfloat16}:
-                    p_data_fp32 = p_data_fp32.float()
-
-                state = self.state[p]
-
-                # State initialization
-                if len(state) == 0:
-                    state["step"] = 0
-                    # Exponential moving average of gradient values
-                    state["exp_avg"] = torch.zeros_like(p_data_fp32)
-                    # Exponential moving average of squared gradient values
-                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
-                    if amsgrad:
-                        # Maintains max of all exp. moving avg. of sq. grad. values
-                        state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
-                else:
-                    state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
-                    state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
-                    if amsgrad:
-                        state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(
-                            p_data_fp32
-                        )
-
-                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
-                if amsgrad:
-                    max_exp_avg_sq = state["max_exp_avg_sq"]
-                beta1, beta2 = group["betas"]
-
-                state["step"] += 1
-
-                # Decay the first and second moment running average coefficient
-                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
-                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-                if amsgrad:
-                    # Maintains the maximum of all 2nd moment running avg. till now
-                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
-                    # Use the max. for normalizing running avg. of gradient
-                    denom = max_exp_avg_sq.sqrt().add_(group["eps"])
-                else:
-                    denom = exp_avg_sq.sqrt().add_(group["eps"])
-
-                bias_correction1 = 1 - beta1 ** state["step"]
-                bias_correction2 = 1 - beta2 ** state["step"]
-                step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
-
-                if group["weight_decay"] != 0:
-                    p_data_fp32.add_(
-                        p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
-                    )
-
-                p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
-
-                if p.data.dtype in {torch.float16, torch.bfloat16}:
-                    p.data.copy_(p_data_fp32)
-
-        return loss
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/models/stylegan2_model.py b/spaces/Iceclear/StableSR/StableSR/basicsr/models/stylegan2_model.py
deleted file mode 100644
index d7da708122160f2be51a98a6a635349f34ee042e..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/models/stylegan2_model.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import cv2
-import math
-import numpy as np
-import random
-import torch
-from collections import OrderedDict
-from os import path as osp
-
-from basicsr.archs import build_network
-from basicsr.losses import build_loss
-from basicsr.losses.gan_loss import g_path_regularize, r1_penalty
-from basicsr.utils import imwrite, tensor2img
-from basicsr.utils.registry import MODEL_REGISTRY
-from .base_model import BaseModel
-
-
-@MODEL_REGISTRY.register()
-class StyleGAN2Model(BaseModel):
-    """StyleGAN2 model."""
-
-    def __init__(self, opt):
-        super(StyleGAN2Model, self).__init__(opt)
-
-        # define network net_g
-        self.net_g = build_network(opt['network_g'])
-        self.net_g = self.model_to_device(self.net_g)
-        self.print_network(self.net_g)
-        # load pretrained model
-        load_path = self.opt['path'].get('pretrain_network_g', None)
-        if load_path is not None:
-            param_key = self.opt['path'].get('param_key_g', 'params')
-            self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
-
-        # latent dimension: self.num_style_feat
-        self.num_style_feat = opt['network_g']['num_style_feat']
-        num_val_samples = self.opt['val'].get('num_val_samples', 16)
-        self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device)
-
-        if self.is_train:
-            self.init_training_settings()
-
-    def init_training_settings(self):
-        train_opt = self.opt['train']
-
-        # define network net_d
-        self.net_d = build_network(self.opt['network_d'])
-        self.net_d = self.model_to_device(self.net_d)
-        self.print_network(self.net_d)
-
-        # load pretrained model
-        load_path = self.opt['path'].get('pretrain_network_d', None)
-        if load_path is not None:
-            param_key = self.opt['path'].get('param_key_d', 'params')
-            self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key)
-
-        # define network net_g with Exponential Moving Average (EMA)
-        # net_g_ema only used for testing on one GPU and saving, do not need to
-        # wrap with DistributedDataParallel
-        self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
-        # load pretrained model
-        load_path = self.opt['path'].get('pretrain_network_g', None)
-        if load_path is not None:
-            self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
-        else:
-            self.model_ema(0)  # copy net_g weight
-
-        self.net_g.train()
-        self.net_d.train()
-        self.net_g_ema.eval()
-
-        # define losses
-        # gan loss (wgan)
-        self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
-        # regularization weights
-        self.r1_reg_weight = train_opt['r1_reg_weight']  # for discriminator
-        self.path_reg_weight = train_opt['path_reg_weight']  # for generator
-
-        self.net_g_reg_every = train_opt['net_g_reg_every']
-        self.net_d_reg_every = train_opt['net_d_reg_every']
-        self.mixing_prob = train_opt['mixing_prob']
-
-        self.mean_path_length = 0
-
-        # set up optimizers and schedulers
-        self.setup_optimizers()
-        self.setup_schedulers()
-
-    def setup_optimizers(self):
-        train_opt = self.opt['train']
-        # optimizer g
-        net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1)
-        if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC':
-            normal_params = []
-            style_mlp_params = []
-            modulation_conv_params = []
-            for name, param in self.net_g.named_parameters():
-                if 'modulation' in name:
-                    normal_params.append(param)
-                elif 'style_mlp' in name:
-                    style_mlp_params.append(param)
-                elif 'modulated_conv' in name:
-                    modulation_conv_params.append(param)
-                else:
-                    normal_params.append(param)
-            optim_params_g = [
-                {  # add normal params first
-                    'params': normal_params,
-                    'lr': train_opt['optim_g']['lr']
-                },
-                {
-                    'params': style_mlp_params,
-                    'lr': train_opt['optim_g']['lr'] * 0.01
-                },
-                {
-                    'params': modulation_conv_params,
-                    'lr': train_opt['optim_g']['lr'] / 3
-                }
-            ]
-        else:
-            normal_params = []
-            for name, param in self.net_g.named_parameters():
-                normal_params.append(param)
-            optim_params_g = [{  # add normal params first
-                'params': normal_params,
-                'lr': train_opt['optim_g']['lr']
-            }]
-
-        optim_type = train_opt['optim_g'].pop('type')
-        lr = train_opt['optim_g']['lr'] * net_g_reg_ratio
-        betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio)
-        self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas)
-        self.optimizers.append(self.optimizer_g)
-
-        # optimizer d
-        net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1)
-        if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC':
-            normal_params = []
-            linear_params = []
-            for name, param in self.net_d.named_parameters():
-                if 'final_linear' in name:
-                    linear_params.append(param)
-                else:
-                    normal_params.append(param)
-            optim_params_d = [
-                {  # add normal params first
-                    'params': normal_params,
-                    'lr': train_opt['optim_d']['lr']
-                },
-                {
-                    'params': linear_params,
-                    'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512))
-                }
-            ]
-        else:
-            normal_params = []
-            for name, param in self.net_d.named_parameters():
-                normal_params.append(param)
-            optim_params_d = [{  # add normal params first
-                'params': normal_params,
-                'lr': train_opt['optim_d']['lr']
-            }]
-
-        optim_type = train_opt['optim_d'].pop('type')
-        lr = train_opt['optim_d']['lr'] * net_d_reg_ratio
-        betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio)
-        self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas)
-        self.optimizers.append(self.optimizer_d)
-
-    def feed_data(self, data):
-        self.real_img = data['gt'].to(self.device)
-
-    def make_noise(self, batch, num_noise):
-        if num_noise == 1:
-            noises = torch.randn(batch, self.num_style_feat, device=self.device)
-        else:
-            noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0)
-        return noises
-
-    def mixing_noise(self, batch, prob):
-        if random.random() < prob:
-            return self.make_noise(batch, 2)
-        else:
-            return [self.make_noise(batch, 1)]
-
-    def optimize_parameters(self, current_iter):
-        loss_dict = OrderedDict()
-
-        # optimize net_d
-        for p in self.net_d.parameters():
-            p.requires_grad = True
-        self.optimizer_d.zero_grad()
-
-        batch = self.real_img.size(0)
-        noise = self.mixing_noise(batch, self.mixing_prob)
-        fake_img, _ = self.net_g(noise)
-        fake_pred = self.net_d(fake_img.detach())
-
-        real_pred = self.net_d(self.real_img)
-        # wgan loss with softplus (logistic loss) for discriminator
-        l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True)
-        loss_dict['l_d'] = l_d
-        # In wgan, real_score should be positive and fake_score should be
-        # negative
-        loss_dict['real_score'] = real_pred.detach().mean()
-        loss_dict['fake_score'] = fake_pred.detach().mean()
-        l_d.backward()
-
-        if current_iter % self.net_d_reg_every == 0:
-            self.real_img.requires_grad = True
-            real_pred = self.net_d(self.real_img)
-            l_d_r1 = r1_penalty(real_pred, self.real_img)
-            l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0])
-            # TODO: why do we need to add 0 * real_pred, otherwise, a runtime
-            # error will arise: RuntimeError: Expected to have finished
-            # reduction in the prior iteration before starting a new one.
-            # This error indicates that your module has parameters that were
-            # not used in producing loss.
-            loss_dict['l_d_r1'] = l_d_r1.detach().mean()
-            l_d_r1.backward()
-
-        self.optimizer_d.step()
-
-        # optimize net_g
-        for p in self.net_d.parameters():
-            p.requires_grad = False
-        self.optimizer_g.zero_grad()
-
-        noise = self.mixing_noise(batch, self.mixing_prob)
-        fake_img, _ = self.net_g(noise)
-        fake_pred = self.net_d(fake_img)
-
-        # wgan loss with softplus (non-saturating loss) for generator
-        l_g = self.cri_gan(fake_pred, True, is_disc=False)
-        loss_dict['l_g'] = l_g
-        l_g.backward()
-
-        if current_iter % self.net_g_reg_every == 0:
-            path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink'])
-            noise = self.mixing_noise(path_batch_size, self.mixing_prob)
-            fake_img, latents = self.net_g(noise, return_latents=True)
-            l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length)
-
-            l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0])
-            # TODO:  why do we need to add 0 * fake_img[0, 0, 0, 0]
-            l_g_path.backward()
-            loss_dict['l_g_path'] = l_g_path.detach().mean()
-            loss_dict['path_length'] = path_lengths
-
-        self.optimizer_g.step()
-
-        self.log_dict = self.reduce_loss_dict(loss_dict)
-
-        # EMA
-        self.model_ema(decay=0.5**(32 / (10 * 1000)))
-
-    def test(self):
-        with torch.no_grad():
-            self.net_g_ema.eval()
-            self.output, _ = self.net_g_ema([self.fixed_sample])
-
-    def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        if self.opt['rank'] == 0:
-            self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
-
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        assert dataloader is None, 'Validation dataloader should be None.'
-        self.test()
-        result = tensor2img(self.output, min_max=(-1, 1))
-        if self.opt['is_train']:
-            save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png')
-        else:
-            save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png')
-        imwrite(result, save_img_path)
-        # add sample images to tb_logger
-        result = (result / 255.).astype(np.float32)
-        result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
-        if tb_logger is not None:
-            tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC')
-
-    def save(self, epoch, current_iter):
-        self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
-        self.save_network(self.net_d, 'net_d', current_iter)
-        self.save_training_state(epoch, current_iter)
diff --git a/spaces/Intae/deepfake/training/transforms/albu.py b/spaces/Intae/deepfake/training/transforms/albu.py
deleted file mode 100644
index 07ede53248e3ee041c8a157169eafe614e0b3c6b..0000000000000000000000000000000000000000
--- a/spaces/Intae/deepfake/training/transforms/albu.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import random
-
-import cv2
-import numpy as np
-from albumentations import DualTransform, ImageOnlyTransform
-from albumentations.augmentations.functional import crop
-
-
-def isotropically_resize_image(img, size, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC):
-    h, w = img.shape[:2]
-    if max(w, h) == size:
-        return img
-    if w > h:
-        scale = size / w
-        h = h * scale
-        w = size
-    else:
-        scale = size / h
-        w = w * scale
-        h = size
-    interpolation = interpolation_up if scale > 1 else interpolation_down
-    resized = cv2.resize(img, (int(w), int(h)), interpolation=interpolation)
-    return resized
-
-
-class IsotropicResize(DualTransform):
-    def __init__(self, max_side, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC,
-                 always_apply=False, p=1):
-        super(IsotropicResize, self).__init__(always_apply, p)
-        self.max_side = max_side
-        self.interpolation_down = interpolation_down
-        self.interpolation_up = interpolation_up
-
-    def apply(self, img, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC, **params):
-        return isotropically_resize_image(img, size=self.max_side, interpolation_down=interpolation_down,
-                                          interpolation_up=interpolation_up)
-
-    def apply_to_mask(self, img, **params):
-        return self.apply(img, interpolation_down=cv2.INTER_NEAREST, interpolation_up=cv2.INTER_NEAREST, **params)
-
-    def get_transform_init_args_names(self):
-        return ("max_side", "interpolation_down", "interpolation_up")
-
-
-class Resize4xAndBack(ImageOnlyTransform):
-    def __init__(self, always_apply=False, p=0.5):
-        super(Resize4xAndBack, self).__init__(always_apply, p)
-
-    def apply(self, img, **params):
-        h, w = img.shape[:2]
-        scale = random.choice([2, 4])
-        img = cv2.resize(img, (w // scale, h // scale), interpolation=cv2.INTER_AREA)
-        img = cv2.resize(img, (w, h),
-                         interpolation=random.choice([cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_NEAREST]))
-        return img
-
-
-class RandomSizedCropNonEmptyMaskIfExists(DualTransform):
-
-    def __init__(self, min_max_height, w2h_ratio=[0.7, 1.3], always_apply=False, p=0.5):
-        super(RandomSizedCropNonEmptyMaskIfExists, self).__init__(always_apply, p)
-
-        self.min_max_height = min_max_height
-        self.w2h_ratio = w2h_ratio
-
-    def apply(self, img, x_min=0, x_max=0, y_min=0, y_max=0, **params):
-        cropped = crop(img, x_min, y_min, x_max, y_max)
-        return cropped
-
-    @property
-    def targets_as_params(self):
-        return ["mask"]
-
-    def get_params_dependent_on_targets(self, params):
-        mask = params["mask"]
-        mask_height, mask_width = mask.shape[:2]
-        crop_height = int(mask_height * random.uniform(self.min_max_height[0], self.min_max_height[1]))
-        w2h_ratio = random.uniform(*self.w2h_ratio)
-        crop_width = min(int(crop_height * w2h_ratio), mask_width - 1)
-        if mask.sum() == 0:
-            x_min = random.randint(0, mask_width - crop_width + 1)
-            y_min = random.randint(0, mask_height - crop_height + 1)
-        else:
-            mask = mask.sum(axis=-1) if mask.ndim == 3 else mask
-            non_zero_yx = np.argwhere(mask)
-            y, x = random.choice(non_zero_yx)
-            x_min = x - random.randint(0, crop_width - 1)
-            y_min = y - random.randint(0, crop_height - 1)
-            x_min = np.clip(x_min, 0, mask_width - crop_width)
-            y_min = np.clip(y_min, 0, mask_height - crop_height)
-
-        x_max = x_min + crop_height
-        y_max = y_min + crop_width
-        y_max = min(mask_height, y_max)
-        x_max = min(mask_width, x_max)
-        return {"x_min": x_min, "x_max": x_max, "y_min": y_min, "y_max": y_max}
-
-    def get_transform_init_args_names(self):
-        return "min_max_height", "height", "width", "w2h_ratio"
\ No newline at end of file
diff --git a/spaces/IsaacK/streamlit-test/app.py b/spaces/IsaacK/streamlit-test/app.py
deleted file mode 100644
index 1a57bf7a69af4978bac32f019497c1c638f87c79..0000000000000000000000000000000000000000
--- a/spaces/IsaacK/streamlit-test/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import streamlit as st
-import sqlite3
-import random
-import datetime
-
-# Custom imports
-from multipage import MultiPage
-from pages import quiz, upload, view, join, login
-
-# Create an instance of the app 
-app = MultiPage()
-
-# Title of the main page
-st.markdown("# Quiz Maker")
-
-# Add all your application here
-app.add_page("Quiz", quiz.app)
-app.add_page("Join", join.app)
-app.add_page("Login", login.app)
-app.add_page("Upload", upload.app)
-app.add_page("View", view.app)
-
-# The main app
-app.run()
\ No newline at end of file
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/ddpm/pipeline_ddpm.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/ddpm/pipeline_ddpm.py
deleted file mode 100644
index 114a38a5fec7a471ed60be1c38ace65f86c903dd..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/ddpm/pipeline_ddpm.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from typing import Optional, Tuple, Union
-
-import torch
-
-from ...configuration_utils import FrozenDict
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...utils import deprecate
-
-
-class DDPMPipeline(DiffusionPipeline):
-    r"""
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Parameters:
-        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
-            [`DDPMScheduler`], or [`DDIMScheduler`].
-    """
-
-    def __init__(self, unet, scheduler):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        generator: Optional[torch.Generator] = None,
-        num_inference_steps: int = 1000,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[ImagePipelineOutput, Tuple]:
-        r"""
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            num_inference_steps (`int`, *optional*, defaults to 1000):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
-            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
-            generated images.
-        """
-        message = (
-            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
-        )
-        predict_epsilon = deprecate("predict_epsilon", "0.11.0", message, take_from=kwargs)
-
-        if predict_epsilon is not None:
-            new_config = dict(self.scheduler.config)
-            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
-            self.scheduler._internal_dict = FrozenDict(new_config)
-
-        if generator is not None and generator.device.type != self.device.type and self.device.type != "mps":
-            message = (
-                f"The `generator` device is `{generator.device}` and does not match the pipeline "
-                f"device `{self.device}`, so the `generator` will be ignored. "
-                f'Please use `torch.Generator(device="{self.device}")` instead.'
-            )
-            deprecate(
-                "generator.device == 'cpu'",
-                "0.11.0",
-                message,
-            )
-            generator = None
-
-        # Sample gaussian noise to begin loop
-        if isinstance(self.unet.sample_size, int):
-            image_shape = (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)
-        else:
-            image_shape = (batch_size, self.unet.in_channels, *self.unet.sample_size)
-
-        if self.device.type == "mps":
-            # randn does not work reproducibly on mps
-            image = torch.randn(image_shape, generator=generator)
-            image = image.to(self.device)
-        else:
-            image = torch.randn(image_shape, generator=generator, device=self.device)
-
-        # set step values
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            # 1. predict noise model_output
-            model_output = self.unet(image, t).sample
-
-            # 2. compute previous image: x_t -> x_t-1
-            image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/chatgpt - macOS.command b/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/chatgpt - macOS.command
deleted file mode 100644
index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/chatgpt - macOS.command	
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.
\ No newline at end of file
diff --git a/spaces/JohnSmith9982/VITS-Umamusume-voice-synthesizer/text/thai.py b/spaces/JohnSmith9982/VITS-Umamusume-voice-synthesizer/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/VITS-Umamusume-voice-synthesizer/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/preprocess.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/preprocess.py
deleted file mode 100644
index 69986bb3bb0a2d8a0e352d1cb330a375d55f7e2c..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/preprocess.py
+++ /dev/null
@@ -1,184 +0,0 @@
-from multiprocess.pool import ThreadPool
-from encoder.params_data import *
-from encoder.config import librispeech_datasets, anglophone_nationalites
-from datetime import datetime
-from encoder import audio
-from pathlib import Path
-from tqdm import tqdm
-import numpy as np
-
-
-class DatasetLog:
-    """
-    Registers metadata about the dataset in a text file.
-    """
-    def __init__(self, root, name):
-        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
-        self.sample_data = dict()
-        
-        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Creating dataset %s on %s" % (name, start_time))
-        self.write_line("-----")
-        self._log_params()
-        
-    def _log_params(self):
-        from encoder import params_data
-        self.write_line("Parameter values:")
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            self.write_line("\t%s: %s" % (param_name, value))
-        self.write_line("-----")
-    
-    def write_line(self, line):
-        self.text_file.write("%s\n" % line)
-        
-    def add_sample(self, **kwargs):
-        for param_name, value in kwargs.items():
-            if not param_name in self.sample_data:
-                self.sample_data[param_name] = []
-            self.sample_data[param_name].append(value)
-            
-    def finalize(self):
-        self.write_line("Statistics:")
-        for param_name, values in self.sample_data.items():
-            self.write_line("\t%s:" % param_name)
-            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
-            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
-        self.write_line("-----")
-        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Finished on %s" % end_time)
-        self.text_file.close()
-       
-        
-def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
-    dataset_root = datasets_root.joinpath(dataset_name)
-    if not dataset_root.exists():
-        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
-        return None, None
-    return dataset_root, DatasetLog(out_dir, dataset_name)
-
-
-def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
-                             skip_existing, logger):
-    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
-    
-    # Function to preprocess utterances for one speaker
-    def preprocess_speaker(speaker_dir: Path):
-        # Give a name to the speaker that includes its dataset
-        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
-        
-        # Create an output directory with that name, as well as a txt file containing a 
-        # reference to each source file.
-        speaker_out_dir = out_dir.joinpath(speaker_name)
-        speaker_out_dir.mkdir(exist_ok=True)
-        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
-        
-        # There's a possibility that the preprocessing was interrupted earlier, check if 
-        # there already is a sources file.
-        if sources_fpath.exists():
-            try:
-                with sources_fpath.open("r") as sources_file:
-                    existing_fnames = {line.split(",")[0] for line in sources_file}
-            except:
-                existing_fnames = {}
-        else:
-            existing_fnames = {}
-        
-        # Gather all audio files for that speaker recursively
-        sources_file = sources_fpath.open("a" if skip_existing else "w")
-        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
-            # Check if the target output file already exists
-            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
-            out_fname = out_fname.replace(".%s" % extension, ".npy")
-            if skip_existing and out_fname in existing_fnames:
-                continue
-                
-            # Load and preprocess the waveform
-            wav = audio.preprocess_wav(in_fpath)
-            if len(wav) == 0:
-                continue
-            
-            # Create the mel spectrogram, discard those that are too short
-            frames = audio.wav_to_mel_spectrogram(wav)
-            if len(frames) < partials_n_frames:
-                continue
-            
-            out_fpath = speaker_out_dir.joinpath(out_fname)
-            np.save(out_fpath, frames)
-            logger.add_sample(duration=len(wav) / sampling_rate)
-            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
-        
-        sources_file.close()
-    
-    # Process the utterances for each speaker
-    with ThreadPool(8) as pool:
-        list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
-                  unit="speakers"))
-    logger.finalize()
-    print("Done preprocessing %s.\n" % dataset_name)
-
-def preprocess_aidatatang_200zh(datasets_root: Path, out_dir: Path, skip_existing=False):
-    dataset_name = "aidatatang_200zh"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return 
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("corpus", "train").glob("*"))
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
-                                skip_existing, logger)
-
-def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
-    for dataset_name in librispeech_datasets["train"]["other"]:
-        # Initialize the preprocessing
-        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-        if not dataset_root:
-            return 
-        
-        # Preprocess all speakers
-        speaker_dirs = list(dataset_root.glob("*"))
-        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac",
-                                 skip_existing, logger)
-
-
-def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb1"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-
-    # Get the contents of the meta file
-    with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
-        metadata = [line.split("\t") for line in metafile][1:]
-    
-    # Select the ID and the nationality, filter out non-anglophone speakers
-    nationalities = {line[0]: line[3] for line in metadata}
-    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if 
-                        nationality.lower() in anglophone_nationalites]
-    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % 
-          (len(keep_speaker_ids), len(nationalities)))
-    
-    # Get the speaker directories for anglophone speakers only
-    speaker_dirs = dataset_root.joinpath("wav").glob("*")
-    speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
-                    speaker_dir.name in keep_speaker_ids]
-    print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % 
-          (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
-
-    # Preprocess all speakers
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
-                             skip_existing, logger)
-
-
-def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb2"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-    
-    # Get the speaker directories
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a",
-                             skip_existing, logger)
diff --git a/spaces/Kororinpa/Amadeus_Project/models.py b/spaces/Kororinpa/Amadeus_Project/models.py
deleted file mode 100644
index f5acdeb2bedd47897348407c0ae55c9a160da881..0000000000000000000000000000000000000000
--- a/spaces/Kororinpa/Amadeus_Project/models.py
+++ /dev/null
@@ -1,534 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/necks/dyhead.py b/spaces/KyanChen/RSPrompter/mmdet/models/necks/dyhead.py
deleted file mode 100644
index 5f5ae0b285c20558a0c7bcc59cbb7b214684eab2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/necks/dyhead.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import build_activation_layer, build_norm_layer
-from mmcv.ops.modulated_deform_conv import ModulatedDeformConv2d
-from mmengine.model import BaseModule, constant_init, normal_init
-
-from mmdet.registry import MODELS
-from ..layers import DyReLU
-
-# Reference:
-# https://github.com/microsoft/DynamicHead
-# https://github.com/jshilong/SEPC
-
-
-class DyDCNv2(nn.Module):
-    """ModulatedDeformConv2d with normalization layer used in DyHead.
-
-    This module cannot be configured with `conv_cfg=dict(type='DCNv2')`
-    because DyHead calculates offset and mask from middle-level feature.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        stride (int | tuple[int], optional): Stride of the convolution.
-            Default: 1.
-        norm_cfg (dict, optional): Config dict for normalization layer.
-            Default: dict(type='GN', num_groups=16, requires_grad=True).
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 stride=1,
-                 norm_cfg=dict(type='GN', num_groups=16, requires_grad=True)):
-        super().__init__()
-        self.with_norm = norm_cfg is not None
-        bias = not self.with_norm
-        self.conv = ModulatedDeformConv2d(
-            in_channels, out_channels, 3, stride=stride, padding=1, bias=bias)
-        if self.with_norm:
-            self.norm = build_norm_layer(norm_cfg, out_channels)[1]
-
-    def forward(self, x, offset, mask):
-        """Forward function."""
-        x = self.conv(x.contiguous(), offset, mask)
-        if self.with_norm:
-            x = self.norm(x)
-        return x
-
-
-class DyHeadBlock(nn.Module):
-    """DyHead Block with three types of attention.
-
-    HSigmoid arguments in default act_cfg follow official code, not paper.
-    https://github.com/microsoft/DynamicHead/blob/master/dyhead/dyrelu.py
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        zero_init_offset (bool, optional): Whether to use zero init for
-            `spatial_conv_offset`. Default: True.
-        act_cfg (dict, optional): Config dict for the last activation layer of
-            scale-aware attention. Default: dict(type='HSigmoid', bias=3.0,
-            divisor=6.0).
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 zero_init_offset=True,
-                 act_cfg=dict(type='HSigmoid', bias=3.0, divisor=6.0)):
-        super().__init__()
-        self.zero_init_offset = zero_init_offset
-        # (offset_x, offset_y, mask) * kernel_size_y * kernel_size_x
-        self.offset_and_mask_dim = 3 * 3 * 3
-        self.offset_dim = 2 * 3 * 3
-
-        self.spatial_conv_high = DyDCNv2(in_channels, out_channels)
-        self.spatial_conv_mid = DyDCNv2(in_channels, out_channels)
-        self.spatial_conv_low = DyDCNv2(in_channels, out_channels, stride=2)
-        self.spatial_conv_offset = nn.Conv2d(
-            in_channels, self.offset_and_mask_dim, 3, padding=1)
-        self.scale_attn_module = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1), nn.Conv2d(out_channels, 1, 1),
-            nn.ReLU(inplace=True), build_activation_layer(act_cfg))
-        self.task_attn_module = DyReLU(out_channels)
-        self._init_weights()
-
-    def _init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                normal_init(m, 0, 0.01)
-        if self.zero_init_offset:
-            constant_init(self.spatial_conv_offset, 0)
-
-    def forward(self, x):
-        """Forward function."""
-        outs = []
-        for level in range(len(x)):
-            # calculate offset and mask of DCNv2 from middle-level feature
-            offset_and_mask = self.spatial_conv_offset(x[level])
-            offset = offset_and_mask[:, :self.offset_dim, :, :]
-            mask = offset_and_mask[:, self.offset_dim:, :, :].sigmoid()
-
-            mid_feat = self.spatial_conv_mid(x[level], offset, mask)
-            sum_feat = mid_feat * self.scale_attn_module(mid_feat)
-            summed_levels = 1
-            if level > 0:
-                low_feat = self.spatial_conv_low(x[level - 1], offset, mask)
-                sum_feat += low_feat * self.scale_attn_module(low_feat)
-                summed_levels += 1
-            if level < len(x) - 1:
-                # this upsample order is weird, but faster than natural order
-                # https://github.com/microsoft/DynamicHead/issues/25
-                high_feat = F.interpolate(
-                    self.spatial_conv_high(x[level + 1], offset, mask),
-                    size=x[level].shape[-2:],
-                    mode='bilinear',
-                    align_corners=True)
-                sum_feat += high_feat * self.scale_attn_module(high_feat)
-                summed_levels += 1
-            outs.append(self.task_attn_module(sum_feat / summed_levels))
-
-        return outs
-
-
-@MODELS.register_module()
-class DyHead(BaseModule):
-    """DyHead neck consisting of multiple DyHead Blocks.
-
-    See `Dynamic Head: Unifying Object Detection Heads with Attentions
-    <https://arxiv.org/abs/2106.08322>`_ for details.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        num_blocks (int, optional): Number of DyHead Blocks. Default: 6.
-        zero_init_offset (bool, optional): Whether to use zero init for
-            `spatial_conv_offset`. Default: True.
-        init_cfg (dict or list[dict], optional): Initialization config dict.
-            Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_blocks=6,
-                 zero_init_offset=True,
-                 init_cfg=None):
-        assert init_cfg is None, 'To prevent abnormal initialization ' \
-                                 'behavior, init_cfg is not allowed to be set'
-        super().__init__(init_cfg=init_cfg)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_blocks = num_blocks
-        self.zero_init_offset = zero_init_offset
-
-        dyhead_blocks = []
-        for i in range(num_blocks):
-            in_channels = self.in_channels if i == 0 else self.out_channels
-            dyhead_blocks.append(
-                DyHeadBlock(
-                    in_channels,
-                    self.out_channels,
-                    zero_init_offset=zero_init_offset))
-        self.dyhead_blocks = nn.Sequential(*dyhead_blocks)
-
-    def forward(self, inputs):
-        """Forward function."""
-        assert isinstance(inputs, (tuple, list))
-        outs = self.dyhead_blocks(inputs)
-        return tuple(outs)
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py
deleted file mode 100644
index 41625a61d6d1c38c633062c24b1e3455bd3ae2df..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .base_panoptic_fusion_head import \
-    BasePanopticFusionHead  # noqa: F401,F403
-from .heuristic_fusion_head import HeuristicFusionHead  # noqa: F401,F403
-from .maskformer_fusion_head import MaskFormerFusionHead  # noqa: F401,F403
diff --git a/spaces/LZRi/LZR-Bert-VITS2/modules.py b/spaces/LZRi/LZR-Bert-VITS2/modules.py
deleted file mode 100644
index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000
--- a/spaces/LZRi/LZR-Bert-VITS2/modules.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-    
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
-class TransformerCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      n_layers,
-      n_heads,
-      p_dropout=0,
-      filter_channels=0,
-      mean_only=False,
-      wn_sharing_parameter=None,
-      gin_channels = 0
-      ):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/Langame/explorer/app.py b/spaces/Langame/explorer/app.py
deleted file mode 100644
index 7214a044ef45265eff6284169117265fdfa04fb9..0000000000000000000000000000000000000000
--- a/spaces/Langame/explorer/app.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import streamlit as st
-from datasets import load_dataset
-import pandas as pd
-
-
-dataset = load_dataset("Langame/starter2")
-
-conversation_starters = []
-for i in range(len(dataset["train"])):
-    conversation_starters.append({
-        "conversation_starter": dataset["train"][i]["content"],
-        "topics": dataset["train"][i]["topics"]
-    })
-
-df = pd.DataFrame(conversation_starters)
-
-from pandas.api.types import (
-    is_categorical_dtype,
-    is_datetime64_any_dtype,
-    is_numeric_dtype,
-    is_object_dtype,
-)
-
-st.title("Conversation starters")
-
-st.write(
-    """Quick hack to search conversation starters for your next conversations in https://huggingface.co/datasets/Langame/starter2
-    """
-)
-
-
-def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:
-    """
-    Adds a UI on top of a dataframe to let viewers filter columns
-    Args:
-        df (pd.DataFrame): Original dataframe
-    Returns:
-        pd.DataFrame: Filtered dataframe
-    """
-    modify = st.checkbox("Add filters")
-
-    if not modify:
-        return df
-
-    df = df.copy()
-
-    # Try to convert datetimes into a standard format (datetime, no timezone)
-    for col in df.columns:
-        if is_object_dtype(df[col]):
-            try:
-                df[col] = pd.to_datetime(df[col])
-            except Exception:
-                pass
-
-        if is_datetime64_any_dtype(df[col]):
-            df[col] = df[col].dt.tz_localize(None)
-
-    modification_container = st.container()
-
-    with modification_container:
-        to_filter_columns = st.multiselect("Filter dataframe on", df.columns)
-        for column in to_filter_columns:
-            left, right = st.columns((1, 20))
-            left.write("↳")
-            # Treat columns with < 10 unique values as categorical
-            if is_categorical_dtype(df[column]) or df[column].nunique() < 10:
-                user_cat_input = right.multiselect(
-                    f"Values for {column}",
-                    df[column].unique(),
-                    default=list(df[column].unique()),
-                )
-                df = df[df[column].isin(user_cat_input)]
-            elif is_numeric_dtype(df[column]):
-                _min = float(df[column].min())
-                _max = float(df[column].max())
-                step = (_max - _min) / 100
-                user_num_input = right.slider(
-                    f"Values for {column}",
-                    _min,
-                    _max,
-                    (_min, _max),
-                    step=step,
-                )
-                df = df[df[column].between(*user_num_input)]
-            elif is_datetime64_any_dtype(df[column]):
-                user_date_input = right.date_input(
-                    f"Values for {column}",
-                    value=(
-                        df[column].min(),
-                        df[column].max(),
-                    ),
-                )
-                if len(user_date_input) == 2:
-                    user_date_input = tuple(map(pd.to_datetime, user_date_input))
-                    start_date, end_date = user_date_input
-                    df = df.loc[df[column].between(start_date, end_date)]
-            else:
-                user_text_input = right.text_input(
-                    f"Substring or regex in {column}",
-                )
-                if user_text_input:
-                    df = df[df[column].str.contains(user_text_input)]
-
-    return df
-
-
-st.dataframe(filter_dataframe(df))
diff --git a/spaces/Lewislou/Lewislou-cell-seg-sribd/stardist_pkg/bioimageio_utils.py b/spaces/Lewislou/Lewislou-cell-seg-sribd/stardist_pkg/bioimageio_utils.py
deleted file mode 100644
index b9fd626ab96abf4bdf144a8cdca5185a2b94005f..0000000000000000000000000000000000000000
--- a/spaces/Lewislou/Lewislou-cell-seg-sribd/stardist_pkg/bioimageio_utils.py
+++ /dev/null
@@ -1,472 +0,0 @@
-from pathlib import Path
-from pkg_resources import get_distribution
-from zipfile import ZipFile
-import numpy as np
-import tempfile
-from distutils.version import LooseVersion
-from csbdeep.utils import axes_check_and_normalize, normalize, _raise
-
-
-DEEPIMAGEJ_MACRO = \
-"""
-//*******************************************************************
-// Date: July-2021
-// Credits: StarDist, DeepImageJ
-// URL:
-//      https://github.com/stardist/stardist
-//      https://deepimagej.github.io/deepimagej
-// This macro was adapted from
-// https://github.com/deepimagej/imagej-macros/blob/648caa867f6ccb459649d4d3799efa1e2e0c5204/StarDist2D_Post-processing.ijm
-// Please cite the respective contributions when using this code.
-//*******************************************************************
-//  Macro to run StarDist postprocessing on 2D images.
-//  StarDist and deepImageJ plugins need to be installed.
-//  The macro assumes that the image to process is a stack in which
-//  the first channel corresponds to the object probability map
-//  and the remaining channels are the radial distances from each
-//  pixel to the object boundary.
-//*******************************************************************
-
-// Get the name of the image to call it
-getDimensions(width, height, channels, slices, frames);
-name=getTitle();
-
-probThresh={probThresh};
-nmsThresh={nmsThresh};
-
-// Isolate the detection probability scores
-run("Make Substack...", "channels=1");
-rename("scores");
-
-// Isolate the oriented distances
-run("Fire");
-selectWindow(name);
-run("Delete Slice", "delete=channel");
-selectWindow(name);
-run("Properties...", "channels=" + maxOf(channels, slices) - 1 + " slices=1 frames=1 pixel_width=1.0000 pixel_height=1.0000 voxel_depth=1.0000");
-rename("distances");
-run("royal");
-
-// Run StarDist plugin
-run("Command From Macro", "command=[de.csbdresden.stardist.StarDist2DNMS], args=['prob':'scores', 'dist':'distances', 'probThresh':'" + probThresh + "', 'nmsThresh':'" + nmsThresh + "', 'outputType':'Both', 'excludeBoundary':'2', 'roiPosition':'Stack', 'verbose':'false'], process=[false]");
-"""
-
-
-def _import(error=True):
-    try:
-        from importlib_metadata import metadata
-        from bioimageio.core.build_spec import build_model # type: ignore
-        import xarray as xr
-        import bioimageio.core # type: ignore
-    except ImportError:
-        if error:
-            raise RuntimeError(
-                "Required libraries are missing for bioimage.io model export.\n"
-                "Please install StarDist as follows: pip install 'stardist[bioimageio]'\n"
-                "(You do not need to uninstall StarDist first.)"
-            )
-        else:
-            return None
-    return metadata, build_model, bioimageio.core, xr
-
-
-def _create_stardist_dependencies(outdir):
-    from ruamel.yaml import YAML
-    from tensorflow import __version__ as tf_version
-    from . import __version__ as stardist_version
-    pkg_info = get_distribution("stardist")
-    # dependencies that start with the name "bioimageio" will be added as conda dependencies
-    reqs_conda = [str(req) for req in pkg_info.requires(extras=['bioimageio']) if str(req).startswith('bioimageio')]
-    # only stardist and tensorflow as pip dependencies
-    tf_major, tf_minor = LooseVersion(tf_version).version[:2]
-    reqs_pip = (f"stardist>={stardist_version}", f"tensorflow>={tf_major}.{tf_minor},<{tf_major+1}")
-    # conda environment
-    env = dict(
-        name = 'stardist',
-        channels = ['defaults', 'conda-forge'],
-        dependencies = [
-            ('python>=3.7,<3.8' if tf_major == 1 else 'python>=3.7'),
-            *reqs_conda,
-            'pip', {'pip': reqs_pip},
-        ],
-    )
-    yaml = YAML(typ='safe')
-    path = outdir / "environment.yaml"
-    with open(path, "w") as f:
-        yaml.dump(env, f)
-    return f"conda:{path}"
-
-
-def _create_stardist_doc(outdir):
-    doc_path = outdir / "README.md"
-    text = (
-        "# StarDist Model\n"
-        "This is a model for object detection with star-convex shapes.\n"
-        "Please see the [StarDist repository](https://github.com/stardist/stardist) for details."
-    )
-    with open(doc_path, "w") as f:
-        f.write(text)
-    return doc_path
-
-
-def _get_stardist_metadata(outdir, model):
-    metadata, *_ = _import()
-    package_data = metadata("stardist")
-    doi_2d = "https://doi.org/10.1007/978-3-030-00934-2_30"
-    doi_3d = "https://doi.org/10.1109/WACV45572.2020.9093435"
-    authors = {
-        'Martin Weigert': dict(name='Martin Weigert', github_user='maweigert'),
-        'Uwe Schmidt': dict(name='Uwe Schmidt', github_user='uschmidt83'),
-    }
-    data = dict(
-        description=package_data["Summary"],
-        authors=list(authors.get(name.strip(),dict(name=name.strip())) for name in package_data["Author"].split(",")),
-        git_repo=package_data["Home-Page"],
-        license=package_data["License"],
-        dependencies=_create_stardist_dependencies(outdir),
-        cite=[{"text": "Cell Detection with Star-Convex Polygons", "doi": doi_2d},
-              {"text": "Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy", "doi": doi_3d}],
-        tags=[
-            'fluorescence-light-microscopy', 'whole-slide-imaging', 'other', # modality
-            f'{model.config.n_dim}d', # dims
-            'cells', 'nuclei', # content
-            'tensorflow', # framework
-            'fiji', # software
-            'unet', # network
-            'instance-segmentation', 'object-detection', # task
-            'stardist',
-        ],
-        covers=["https://raw.githubusercontent.com/stardist/stardist/master/images/stardist_logo.jpg"],
-        documentation=_create_stardist_doc(outdir),
-    )
-    return data
-
-
-def _predict_tf(model_path, test_input):
-    import tensorflow as tf
-    from csbdeep.utils.tf import IS_TF_1
-    # need to unzip the model assets
-    model_assets = model_path.parent / "tf_model"
-    with ZipFile(model_path, "r") as f:
-        f.extractall(model_assets)
-    if IS_TF_1:
-        # make a new graph, i.e. don't use the global default graph
-        with tf.Graph().as_default():
-            with tf.Session() as sess:
-                tf_model = tf.saved_model.load_v2(str(model_assets))
-                x = tf.convert_to_tensor(test_input, dtype=tf.float32)
-                model = tf_model.signatures["serving_default"]
-                y = model(x)
-                sess.run(tf.global_variables_initializer())
-                output = sess.run(y["output"])
-    else:
-        tf_model = tf.saved_model.load(str(model_assets))
-        x = tf.convert_to_tensor(test_input, dtype=tf.float32)
-        model = tf_model.signatures["serving_default"]
-        y = model(x)
-        output = y["output"].numpy()
-    return output
-
-
-def _get_weights_and_model_metadata(outdir, model, test_input, test_input_axes, test_input_norm_axes, mode, min_percentile, max_percentile):
-
-    # get the path to the exported model assets (saved in outdir)
-    if mode == "keras_hdf5":
-        raise NotImplementedError("Export to keras format is not supported yet")
-    elif mode == "tensorflow_saved_model_bundle":
-        assets_uri = outdir / "TF_SavedModel.zip"
-        model_csbdeep = model.export_TF(assets_uri, single_output=True, upsample_grid=True)
-    else:
-        raise ValueError(f"Unsupported mode: {mode}")
-
-    # to force "inputs.data_type: float32" in the spec (bonus: disables normalization warning in model._predict_setup)
-    test_input = test_input.astype(np.float32)
-
-    # convert test_input to axes_net semantics and shape, also resize if necessary (to adhere to axes_net_div_by)
-    test_input, axes_img, axes_net, axes_net_div_by, *_ = model._predict_setup(
-        img=test_input,
-        axes=test_input_axes,
-        normalizer=None,
-        n_tiles=None,
-        show_tile_progress=False,
-        predict_kwargs={},
-    )
-
-    # normalization axes string and numeric indices
-    axes_norm = set(axes_net).intersection(set(axes_check_and_normalize(test_input_norm_axes, disallowed='S')))
-    axes_norm = "".join(a for a in axes_net if a in axes_norm)  # preserve order of axes_net
-    axes_norm_num = tuple(axes_net.index(a) for a in axes_norm)
-
-    # normalize input image
-    test_input_norm = normalize(test_input, pmin=min_percentile, pmax=max_percentile, axis=axes_norm_num)
-
-    net_axes_in = axes_net.lower()
-    net_axes_out = axes_check_and_normalize(model._axes_out).lower()
-    ndim_tensor = len(net_axes_out) + 1
-
-    input_min_shape = list(axes_net_div_by)
-    input_min_shape[axes_net.index('C')] = model.config.n_channel_in
-    input_step = list(axes_net_div_by)
-    input_step[axes_net.index('C')] = 0
-
-    # add the batch axis to shape and step
-    input_min_shape = [1] + input_min_shape
-    input_step = [0] + input_step
-
-    # the axes strings in bioimageio convention
-    input_axes = "b" + net_axes_in.lower()
-    output_axes = "b" + net_axes_out.lower()
-
-    if mode == "keras_hdf5":
-        output_names = ("prob", "dist") + (("class_prob",) if model._is_multiclass() else ())
-        output_n_channels = (1, model.config.n_rays,) + ((1,) if model._is_multiclass() else ())
-        # the output shape is computed from the input shape using
-        # output_shape[i] = output_scale[i] * input_shape[i] + 2 * output_offset[i]
-        output_scale = [1]+list(1/g for g in model.config.grid) + [0]
-        output_offset = [0]*(ndim_tensor)
-
-    elif mode == "tensorflow_saved_model_bundle":
-        if model._is_multiclass():
-            raise NotImplementedError("Tensorflow SavedModel not supported for multiclass models yet")
-        # regarding input/output names: https://github.com/CSBDeep/CSBDeep/blob/b0d2f5f344ebe65a9b4c3007f4567fe74268c813/csbdeep/utils/tf.py#L193-L194
-        input_names = ["input"]
-        output_names = ["output"]
-        output_n_channels = (1 + model.config.n_rays,)
-        # the output shape is computed from the input shape using
-        # output_shape[i] = output_scale[i] * input_shape[i] + 2 * output_offset[i]
-        # same shape as input except for the channel dimension
-        output_scale = [1]*(ndim_tensor)
-        output_scale[output_axes.index("c")] = 0
-        # no offset, except for the input axes, where it is output channel / 2
-        output_offset = [0.0]*(ndim_tensor)
-        output_offset[output_axes.index("c")] = output_n_channels[0] / 2.0
-
-    assert all(s in (0, 1) for s in output_scale), "halo computation assumption violated"
-    halo = model._axes_tile_overlap(output_axes.replace('b', 's'))
-    halo = [int(np.ceil(v/8)*8) for v in halo]  # optional: round up to be divisible by 8
-
-    # the output shape needs to be valid after cropping the halo, so we add the halo to the input min shape
-    input_min_shape = [ms + 2 * ha for ms, ha in zip(input_min_shape, halo)]
-
-    # make sure the input min shape is still divisible by the min axis divisor
-    input_min_shape = input_min_shape[:1] + [ms + (-ms % div_by) for ms, div_by in zip(input_min_shape[1:], axes_net_div_by)]
-    assert all(ms % div_by == 0 for ms, div_by in zip(input_min_shape[1:], axes_net_div_by))
-
-    metadata, *_ = _import()
-    package_data = metadata("stardist")
-    is_2D = model.config.n_dim == 2
-
-    weights_file = outdir / "stardist_weights.h5"
-    model.keras_model.save_weights(str(weights_file))
-
-    config = dict(
-        stardist=dict(
-            python_version=package_data["Version"],
-            thresholds=dict(model.thresholds._asdict()),
-            weights=weights_file.name,
-            config=vars(model.config),
-        )
-    )
-
-    if is_2D:
-        macro_file = outdir / "stardist_postprocessing.ijm"
-        with open(str(macro_file), 'w', encoding='utf-8') as f:
-            f.write(DEEPIMAGEJ_MACRO.format(probThresh=model.thresholds.prob, nmsThresh=model.thresholds.nms))
-        config['stardist'].update(postprocessing_macro=macro_file.name)
-
-    n_inputs = len(input_names)
-    assert n_inputs == 1
-    input_config = dict(
-        input_names=input_names,
-        input_min_shape=[input_min_shape],
-        input_step=[input_step],
-        input_axes=[input_axes],
-        input_data_range=[["-inf", "inf"]],
-        preprocessing=[[dict(
-            name="scale_range",
-            kwargs=dict(
-                mode="per_sample",
-                axes=axes_norm.lower(),
-                min_percentile=min_percentile,
-                max_percentile=max_percentile,
-            ))]]
-        )
-
-    n_outputs = len(output_names)
-    output_config = dict(
-        output_names=output_names,
-        output_data_range=[["-inf", "inf"]] * n_outputs,
-        output_axes=[output_axes] * n_outputs,
-        output_reference=[input_names[0]] * n_outputs,
-        output_scale=[output_scale] * n_outputs,
-        output_offset=[output_offset] * n_outputs,
-        halo=[halo] * n_outputs
-    )
-
-    in_path = outdir / "test_input.npy"
-    np.save(in_path, test_input[np.newaxis])
-
-    if mode == "tensorflow_saved_model_bundle":
-        test_outputs = _predict_tf(assets_uri, test_input_norm[np.newaxis])
-    else:
-        test_outputs = model.predict(test_input_norm)
-
-    # out_paths = []
-    # for i, out in enumerate(test_outputs):
-    #     p = outdir / f"test_output{i}.npy"
-    #     np.save(p, out)
-    #     out_paths.append(p)
-    assert n_outputs == 1
-    out_paths = [outdir / "test_output.npy"]
-    np.save(out_paths[0], test_outputs)
-
-    from tensorflow import __version__ as tf_version
-    data = dict(weight_uri=assets_uri, test_inputs=[in_path], test_outputs=out_paths,
-                config=config, tensorflow_version=tf_version)
-    data.update(input_config)
-    data.update(output_config)
-    _files = [str(weights_file)]
-    if is_2D:
-        _files.append(str(macro_file))
-    data.update(attachments=dict(files=_files))
-
-    return data
-
-
-def export_bioimageio(
-    model,
-    outpath,
-    test_input,
-    test_input_axes=None,
-    test_input_norm_axes='ZYX',
-    name=None,
-    mode="tensorflow_saved_model_bundle",
-    min_percentile=1.0,
-    max_percentile=99.8,
-    overwrite_spec_kwargs=None,
-):
-    """Export stardist model into bioimage.io format, https://github.com/bioimage-io/spec-bioimage-io.
-
-    Parameters
-    ----------
-    model: StarDist2D, StarDist3D
-        the model to convert
-    outpath: str, Path
-        where to save the model
-    test_input: np.ndarray
-        input image for generating test data
-    test_input_axes: str or None
-        the axes of the test input, for example 'YX' for a 2d image or 'ZYX' for a 3d volume
-        using None assumes that axes of test_input are the same as those of model
-    test_input_norm_axes: str
-        the axes of the test input which will be jointly normalized, for example 'ZYX' for all spatial dimensions ('Z' ignored for 2D input)
-        use 'ZYXC' to also jointly normalize channels (e.g. for RGB input images)
-    name: str
-        the name of this model (default: None)
-        if None, uses the (folder) name of the model (i.e. `model.name`)
-    mode: str
-        the export type for this model (default: "tensorflow_saved_model_bundle")
-    min_percentile: float
-        min percentile to be used for image normalization (default: 1.0)
-    max_percentile: float
-        max percentile to be used for image normalization (default: 99.8)
-    overwrite_spec_kwargs: dict or None
-        spec keywords that should be overloaded (default: None)
-    """
-    _, build_model, *_ = _import()
-    from .models import StarDist2D, StarDist3D
-    isinstance(model, (StarDist2D, StarDist3D)) or _raise(ValueError("not a valid model"))
-    0 <= min_percentile < max_percentile <= 100 or _raise(ValueError("invalid percentile values"))
-
-    if name is None:
-        name = model.name
-    name = str(name)
-
-    outpath = Path(outpath)
-    if outpath.suffix == "":
-        outdir = outpath
-        zip_path = outdir / f"{name}.zip"
-    elif outpath.suffix == ".zip":
-        outdir = outpath.parent
-        zip_path = outpath
-    else:
-        raise ValueError(f"outpath has to be a folder or zip file, got {outpath}")
-    outdir.mkdir(exist_ok=True, parents=True)
-
-    with tempfile.TemporaryDirectory() as _tmp_dir:
-        tmp_dir = Path(_tmp_dir)
-        kwargs = _get_stardist_metadata(tmp_dir, model)
-        model_kwargs = _get_weights_and_model_metadata(tmp_dir, model, test_input, test_input_axes, test_input_norm_axes, mode,
-                                                       min_percentile=min_percentile, max_percentile=max_percentile)
-        kwargs.update(model_kwargs)
-        if overwrite_spec_kwargs is not None:
-            kwargs.update(overwrite_spec_kwargs)
-
-        build_model(name=name, output_path=zip_path, add_deepimagej_config=(model.config.n_dim==2), root=tmp_dir, **kwargs)
-        print(f"\nbioimage.io model with name '{name}' exported to '{zip_path}'")
-
-
-def import_bioimageio(source, outpath):
-    """Import stardist model from bioimage.io format, https://github.com/bioimage-io/spec-bioimage-io.
-
-    Load a model in bioimage.io format from the given `source` (e.g. path to zip file, URL)
-    and convert it to a regular stardist model, which will be saved in the folder `outpath`.
-
-    Parameters
-    ----------
-    source: str, Path
-        bioimage.io resource (e.g. path, URL)
-    outpath: str, Path
-        folder to save the stardist model (must not exist previously)
-
-    Returns
-    -------
-    StarDist2D or StarDist3D
-        stardist model loaded from `outpath`
-
-    """
-    import shutil, uuid
-    from csbdeep.utils import save_json
-    from .models import StarDist2D, StarDist3D
-    *_, bioimageio_core, _ = _import()
-
-    outpath = Path(outpath)
-    not outpath.exists() or _raise(FileExistsError(f"'{outpath}' already exists"))
-
-    with tempfile.TemporaryDirectory() as _tmp_dir:
-        tmp_dir = Path(_tmp_dir)
-        # download the full model content to a temporary folder
-        zip_path = tmp_dir / f"{str(uuid.uuid4())}.zip"
-        bioimageio_core.export_resource_package(source, output_path=zip_path)
-        with ZipFile(zip_path, "r") as zip_ref:
-            zip_ref.extractall(tmp_dir)
-        zip_path.unlink()
-        rdf_path = tmp_dir / "rdf.yaml"
-        biomodel = bioimageio_core.load_resource_description(rdf_path)
-
-        # read the stardist specific content
-        'stardist' in biomodel.config or _raise(RuntimeError("bioimage.io model not compatible"))
-        config = biomodel.config['stardist']['config']
-        thresholds = biomodel.config['stardist']['thresholds']
-        weights = biomodel.config['stardist']['weights']
-
-        # make sure that the keras weights are in the attachments
-        weights_file = None
-        for f in biomodel.attachments.files:
-            if f.name == weights and f.exists():
-                weights_file = f
-                break
-        weights_file is not None or _raise(FileNotFoundError(f"couldn't find weights file '{weights}'"))
-
-        # save the config and threshold to json, and weights to hdf5 to enable loading as stardist model
-        # copy bioimageio files to separate sub-folder
-        outpath.mkdir(parents=True)
-        save_json(config, str(outpath / 'config.json'))
-        save_json(thresholds, str(outpath / 'thresholds.json'))
-        shutil.copy(str(weights_file), str(outpath / "weights_bioimageio.h5"))
-        shutil.copytree(str(tmp_dir), str(outpath / "bioimageio"))
-
-    model_class = (StarDist2D if config['n_dim'] == 2 else StarDist3D)
-    model = model_class(None, outpath.name, basedir=str(outpath.parent))
-
-    return model
diff --git a/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/__init__.py b/spaces/LightSY/W2L-TD/facelib/detection/yolov5face/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_models/seg.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_models/seg.py
deleted file mode 100644
index 291e547ff45de81ddd512bf04ce0af7957b89ae7..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/recog_models/seg.py
+++ /dev/null
@@ -1,21 +0,0 @@
-label_convertor = dict(
-    type='SegConvertor', dict_type='DICT36', with_unknown=True, lower=True)
-
-model = dict(
-    type='SegRecognizer',
-    backbone=dict(
-        type='ResNet31OCR',
-        layers=[1, 2, 5, 3],
-        channels=[32, 64, 128, 256, 512, 512],
-        out_indices=[0, 1, 2, 3],
-        stage4_pool_cfg=dict(kernel_size=2, stride=2),
-        last_stage_pool=True),
-    neck=dict(
-        type='FPNOCR', in_channels=[128, 256, 512, 512], out_channels=256),
-    head=dict(
-        type='SegHead',
-        in_channels=256,
-        upsample_param=dict(scale_factor=2.0, mode='nearest')),
-    loss=dict(
-        type='SegLoss', seg_downsample_ratio=1.0, seg_with_loss_weight=True),
-    label_convertor=label_convertor)
diff --git a/spaces/ML701G7/taim-gan/src/models/predict_model.py b/spaces/ML701G7/taim-gan/src/models/predict_model.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/resample.py b/spaces/MashiroSA/sovits-emu-voice-transform/resample.py
deleted file mode 100644
index b28a86eb779d7b3f163e89fac64ecabe044ad1e2..0000000000000000000000000000000000000000
--- a/spaces/MashiroSA/sovits-emu-voice-transform/resample.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-import argparse
-import librosa
-import numpy as np
-from multiprocessing import Pool, cpu_count
-from scipy.io import wavfile
-from tqdm import tqdm
-
-
-def process(item):
-    spkdir, wav_name, args = item
-    # speaker 's5', 'p280', 'p315' are excluded,
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and '.wav' in wav_path:
-        os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, sr=None)
-        wav, _ = librosa.effects.trim(wav, top_db=20)
-        peak = np.abs(wav).max()
-        if peak > 1.0:
-            wav = 0.98 * wav / peak
-        wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2)
-        wav2 /= max(wav2.max(), -wav2.min())
-        save_name = wav_name
-        save_path2 = os.path.join(args.out_dir2, speaker, save_name)
-        wavfile.write(
-            save_path2,
-            args.sr2,
-            (wav2 * np.iinfo(np.int16).max).astype(np.int16)
-        )
-
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
-    parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
-    parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
-    args = parser.parse_args()
-    processs = 30 if cpu_count() > 60 else (cpu_count()-2 if cpu_count() > 4 else 1)
-    pool = Pool(processes=processs)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
-                pass
diff --git a/spaces/MasterThesisCBS/NorPaca_GPT/README.md b/spaces/MasterThesisCBS/NorPaca_GPT/README.md
deleted file mode 100644
index ccdb9e49d1f9dc5b0f0c3441236daa2abfbfdb5f..0000000000000000000000000000000000000000
--- a/spaces/MasterThesisCBS/NorPaca_GPT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NorPaca GPT
-emoji: 🐠
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/memory/local.py b/spaces/MetaWabbit/Auto-GPT/autogpt/memory/local.py
deleted file mode 100644
index 803b6dc6ebb430285f423cda592fa3e902e9a4a6..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/memory/local.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from __future__ import annotations
-
-import dataclasses
-import os
-from typing import Any, List
-
-import numpy as np
-import orjson
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.memory.base import MemoryProviderSingleton
-
-EMBED_DIM = 1536
-SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
-
-
-def create_default_embeddings():
-    return np.zeros((0, EMBED_DIM)).astype(np.float32)
-
-
-@dataclasses.dataclass
-class CacheContent:
-    texts: List[str] = dataclasses.field(default_factory=list)
-    embeddings: np.ndarray = dataclasses.field(
-        default_factory=create_default_embeddings
-    )
-
-
-class LocalCache(MemoryProviderSingleton):
-    """A class that stores the memory in a local file"""
-
-    def __init__(self, cfg) -> None:
-        """Initialize a class instance
-
-        Args:
-            cfg: Config object
-
-        Returns:
-            None
-        """
-        self.filename = f"{cfg.memory_index}.json"
-        if os.path.exists(self.filename):
-            try:
-                with open(self.filename, "w+b") as f:
-                    file_content = f.read()
-                    if not file_content.strip():
-                        file_content = b"{}"
-                        f.write(file_content)
-
-                    loaded = orjson.loads(file_content)
-                    self.data = CacheContent(**loaded)
-            except orjson.JSONDecodeError:
-                print(f"Error: The file '{self.filename}' is not in JSON format.")
-                self.data = CacheContent()
-        else:
-            print(
-                f"Warning: The file '{self.filename}' does not exist. "
-                "Local memory would not be saved to a file."
-            )
-            self.data = CacheContent()
-
-    def add(self, text: str):
-        """
-        Add text to our list of texts, add embedding as row to our
-            embeddings-matrix
-
-        Args:
-            text: str
-
-        Returns: None
-        """
-        if "Command Error:" in text:
-            return ""
-        self.data.texts.append(text)
-
-        embedding = create_embedding_with_ada(text)
-
-        vector = np.array(embedding).astype(np.float32)
-        vector = vector[np.newaxis, :]
-        self.data.embeddings = np.concatenate(
-            [
-                self.data.embeddings,
-                vector,
-            ],
-            axis=0,
-        )
-
-        with open(self.filename, "wb") as f:
-            out = orjson.dumps(self.data, option=SAVE_OPTIONS)
-            f.write(out)
-        return text
-
-    def clear(self) -> str:
-        """
-        Clears the redis server.
-
-        Returns: A message indicating that the memory has been cleared.
-        """
-        self.data = CacheContent()
-        return "Obliviated"
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: The most relevant data.
-        """
-        return self.get_relevant(data, 1)
-
-    def get_relevant(self, text: str, k: int) -> list[Any]:
-        """ "
-        matrix-vector mult to find score-for-each-row-of-matrix
-         get indices for top-k winning scores
-         return texts for those indices
-        Args:
-            text: str
-            k: int
-
-        Returns: List[str]
-        """
-        embedding = create_embedding_with_ada(text)
-
-        scores = np.dot(self.data.embeddings, embedding)
-
-        top_k_indices = np.argsort(scores)[-k:][::-1]
-
-        return [self.data.texts[i] for i in top_k_indices]
-
-    def get_stats(self) -> tuple[int, tuple[int, ...]]:
-        """
-        Returns: The stats of the local cache.
-        """
-        return len(self.data.texts), self.data.embeddings.shape
diff --git a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/gl/cam_render.py b/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/gl/cam_render.py
deleted file mode 100644
index 7b766af057b9c052388aceb152b0191fa2e4ea25..0000000000000000000000000000000000000000
--- a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/gl/cam_render.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from .render import Render
-
-GLUT = None
-
-class CamRender(Render):
-    def __init__(self, width=1600, height=1200, name='Cam Renderer',
-                 program_files=['simple.fs', 'simple.vs'], color_size=1, ms_rate=1, egl=False):
-        Render.__init__(self, width, height, name, program_files, color_size, ms_rate=ms_rate, egl=egl)
-        self.camera = None
-
-        if not egl:
-            global GLUT
-            import OpenGL.GLUT as GLUT
-            GLUT.glutDisplayFunc(self.display)
-            GLUT.glutKeyboardFunc(self.keyboard)
-
-    def set_camera(self, camera):
-        self.camera = camera
-        self.projection_matrix, self.model_view_matrix = camera.get_gl_matrix()
-
-    def keyboard(self, key, x, y):
-        # up
-        eps = 1
-        # print(key)
-        if key == b'w':
-            self.camera.center += eps * self.camera.direction
-        elif key == b's':
-            self.camera.center -= eps * self.camera.direction
-        if key == b'a':
-            self.camera.center -= eps * self.camera.right
-        elif key == b'd':
-            self.camera.center += eps * self.camera.right
-        if key == b' ':
-            self.camera.center += eps * self.camera.up
-        elif key == b'x':
-            self.camera.center -= eps * self.camera.up
-        elif key == b'i':
-            self.camera.near += 0.1 * eps
-            self.camera.far += 0.1 * eps
-        elif key == b'o':
-            self.camera.near -= 0.1 * eps
-            self.camera.far -= 0.1 * eps
-
-        self.projection_matrix, self.model_view_matrix = self.camera.get_gl_matrix()
-
-    def show(self):
-        if GLUT is not None:
-            GLUT.glutMainLoop()
diff --git "a/spaces/MohamadRezo/flixPicks/pages/3_\360\237\224\220Signup.py" "b/spaces/MohamadRezo/flixPicks/pages/3_\360\237\224\220Signup.py"
deleted file mode 100644
index 6bb3a067c00a858703ace864057c2a7838611748..0000000000000000000000000000000000000000
--- "a/spaces/MohamadRezo/flixPicks/pages/3_\360\237\224\220Signup.py"
+++ /dev/null
@@ -1,15 +0,0 @@
-import streamlit as st
-from database import Users
-
-users = Users.users_table()
-
-userName = st.text_input("Enter your UserName")
-password = st.text_input("Enter your Password", type="password")
-
-if st.button("signup"):
-   if users.has_key(userName):
-        st.error("The UserName is already taken.")
-   else:
-        users.insert(userName, password)
-        st.success("You have successfully registered")
-    
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500.py b/spaces/Mountchicken/MAERec-Gradio/configs/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500.py
deleted file mode 100644
index c35030997193d2c54b125d540e646c3f1ef9e997..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500.py
+++ /dev/null
@@ -1,30 +0,0 @@
-_base_ = [
-    '_base_drrg_resnet50_fpn-unet.py',
-    '../_base_/datasets/ctw1500.py',
-    '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_sgd_1200e.py',
-]
-
-# dataset settings
-ctw1500_textdet_train = _base_.ctw1500_textdet_train
-ctw1500_textdet_train.pipeline = _base_.train_pipeline
-ctw1500_textdet_test = _base_.ctw1500_textdet_test
-ctw1500_textdet_test.pipeline = _base_.test_pipeline
-
-train_dataloader = dict(
-    batch_size=4,
-    num_workers=4,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=ctw1500_textdet_train)
-
-val_dataloader = dict(
-    batch_size=1,
-    num_workers=1,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=ctw1500_textdet_test)
-
-test_dataloader = val_dataloader
-
-auto_scale_lr = dict(base_batch_size=16)
diff --git a/spaces/MrVicente/RA-BART/custom_bart/encoder_layer.py b/spaces/MrVicente/RA-BART/custom_bart/encoder_layer.py
deleted file mode 100644
index 0478b203cdd1100662a529b891589b8371d48024..0000000000000000000000000000000000000000
--- a/spaces/MrVicente/RA-BART/custom_bart/encoder_layer.py
+++ /dev/null
@@ -1,102 +0,0 @@
-#############################
-#   Imports
-#############################
-
-# Python modules
-from typing import Optional, Tuple
-
-# Remote modules
-import torch
-from torch import nn
-from transformers import BartConfig
-from transformers.activations import ACT2FN
-
-# Local modules
-from .bart_attention import BartCustomAttention
-from .bart_mask_attention import BartCustomMaskAttention
-from .config import BartCustomConfig
-
-
-class BartCustomEncoderLayer(nn.Module):
-    def __init__(self, config: BartCustomConfig, heads_mask: Optional[torch.Tensor]):
-        super().__init__()
-        self.embed_dim = config.d_model
-        is_simple_mask_commonsense = config.is_simple_mask_commonsense
-        if not is_simple_mask_commonsense:
-            print("Selecting complex relation attention")
-            self.self_attn = BartCustomAttention(
-                embed_dim=self.embed_dim,
-                num_heads=config.encoder_attention_heads,
-                dropout=config.attention_dropout,
-                num_relation_kinds=config.num_relation_kinds,
-                use_same_relation_kv_emb=config.use_same_relation_kv_emb,
-                heads_mask=heads_mask,
-            )
-        else:
-            print("Selecting simple (MASK) relation attention")
-            self.self_attn = BartCustomMaskAttention(
-                embed_dim=self.embed_dim,
-                num_heads=config.encoder_attention_heads,
-                dropout=config.attention_dropout,
-                num_relation_kinds=config.num_relation_kinds,
-                heads_mask=heads_mask,
-            )
-        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
-        self.dropout = config.dropout
-        self.activation_fn = ACT2FN[config.activation_function]
-        self.activation_dropout = config.activation_dropout
-        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
-        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
-        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: torch.FloatTensor,
-        layer_head_mask: torch.FloatTensor,
-        output_attentions: Optional[bool] = False,
-        relation_inputs: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
-                `(encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-        hidden_states, attn_weights, _ = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            layer_head_mask=layer_head_mask,
-            output_attentions=output_attentions,
-            relation_inputs=relation_inputs,
-        )
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.self_attn_layer_norm(hidden_states)
-
-        residual = hidden_states
-        hidden_states = self.activation_fn(self.fc1(hidden_states))
-        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
-        hidden_states = self.fc2(hidden_states)
-        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
-        hidden_states = residual + hidden_states
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        if hidden_states.dtype == torch.float16 and (
-            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
-        ):
-            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
-            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
diff --git a/spaces/Myrna/VideoSummary2/app.py b/spaces/Myrna/VideoSummary2/app.py
deleted file mode 100644
index ea0d92944bdf4e1fde3b7b46810816a97c6b4964..0000000000000000000000000000000000000000
--- a/spaces/Myrna/VideoSummary2/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import gradio as gr
-from summarize import Summarizer
-
-interface = gr.Interface(fn = Summarizer,
-                        inputs = [gr.inputs.Textbox(lines=2, 
-                                                    placeholder="Enter your link...",
-                                                    label='YouTube Video Link'),
-                                  gr.inputs.Radio(["mT5", "BART"], type="value", label='Model')],
-                        outputs = [gr.outputs.Textbox(
-                                                      label="Summary")],
-                         
-                        title = "Video Summary Generator",
-                        examples = [
-                                   ['https://www.youtube.com/watch?v=OaeYUm06in0&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=5761s', 'BART'],
-                                   ['https://www.youtube.com/watch?v=U5OD8MjYnOM', 'BART'],
-                                   ['https://www.youtube.com/watch?v=Gfr50f6ZBvo', 'BART'],
-                                   ['https://www.youtube.com/watch?v=G4hL5Om4IJ4&t=2680s', 'BART'],
-                                   ['https://www.youtube.com/watch?v=0Jd7fJgFkPU&t=8776s', 'mT5']
-                                   ],
-                        enable_queue=True)
-
-interface.launch(debug=True) 
\ No newline at end of file
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/architecture/resnet.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/architecture/resnet.py
deleted file mode 100644
index abbc7213ea971f0cb014d770e7e0c1707855fb08..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/architecture/resnet.py
+++ /dev/null
@@ -1,309 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Contains definitions for the post-activation form of Residual Networks.
-
-Residual networks (ResNets) were proposed in:
-[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
-    Deep Residual Learning for Image Recognition. arXiv:1512.03385
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from absl import logging
-import tensorflow as tf
-from tensorflow.python.keras import backend
-from official.vision.detection.modeling.architecture import nn_ops
-
-# TODO(b/140112644): Refactor the code with Keras style, i.e. build and call.
-class Resnet(object):
-  """Class to build ResNet family model."""
-
-  def __init__(self,
-               resnet_depth,
-               activation='relu',
-               norm_activation=nn_ops.norm_activation_builder(
-                   activation='relu'),
-               data_format='channels_last'):
-    """ResNet initialization function.
-
-    Args:
-      resnet_depth: `int` depth of ResNet backbone model.
-      norm_activation: an operation that includes a normalization layer
-        followed by an optional activation layer.
-      data_format: `str` either "channels_first" for `[batch, channels, height,
-        width]` or "channels_last for `[batch, height, width, channels]`.
-    """
-    self._resnet_depth = resnet_depth
-    if activation == 'relu':
-      self._activation_op = tf.nn.relu
-    elif activation == 'swish':
-      self._activation_op = tf.nn.swish
-    else:
-      raise ValueError('Unsupported activation `{}`.'.format(activation))
-    self._norm_activation = norm_activation
-    self._data_format = data_format
-
-    model_params = {
-        10: {'block': self.residual_block, 'layers': [1, 1, 1, 1]},
-        18: {'block': self.residual_block, 'layers': [2, 2, 2, 2]},
-        34: {'block': self.residual_block, 'layers': [3, 4, 6, 3]},
-        50: {'block': self.bottleneck_block, 'layers': [3, 4, 6, 3]},
-        101: {'block': self.bottleneck_block, 'layers': [3, 4, 23, 3]},
-        152: {'block': self.bottleneck_block, 'layers': [3, 8, 36, 3]},
-        200: {'block': self.bottleneck_block, 'layers': [3, 24, 36, 3]}
-    }
-
-    if resnet_depth not in model_params:
-      valid_resnet_depths = ', '.join(
-          [str(depth) for depth in sorted(model_params.keys())])
-      raise ValueError(
-          'The resnet_depth should be in [%s]. Not a valid resnet_depth:'%(
-              valid_resnet_depths), self._resnet_depth)
-    params = model_params[resnet_depth]
-    self._resnet_fn = self.resnet_v1_generator(
-        params['block'], params['layers'])
-
-  def __call__(self, inputs, is_training=None):
-    """Returns the ResNet model for a given size and number of output classes.
-
-    Args:
-      inputs: a `Tesnor` with shape [batch_size, height, width, 3] representing
-        a batch of images.
-      is_training: `bool` if True, the model is in training mode.
-
-    Returns:
-      a `dict` containing `int` keys for continuous feature levels [2, 3, 4, 5].
-      The values are corresponding feature hierarchy in ResNet with shape
-      [batch_size, height_l, width_l, num_filters].
-    """
-    with backend.get_graph().as_default():
-      with tf.name_scope('resnet%s' % self._resnet_depth):
-        return self._resnet_fn(inputs, is_training)
-
-  def fixed_padding(self, inputs, kernel_size):
-    """Pads the input along the spatial dimensions independently of input size.
-
-    Args:
-      inputs: `Tensor` of size `[batch, channels, height, width]` or
-          `[batch, height, width, channels]` depending on `data_format`.
-      kernel_size: `int` kernel size to be used for `conv2d` or max_pool2d`
-          operations. Should be a positive integer.
-
-    Returns:
-      A padded `Tensor` of the same `data_format` with size either intact
-      (if `kernel_size == 1`) or padded (if `kernel_size > 1`).
-    """
-    pad_total = kernel_size - 1
-    pad_beg = pad_total // 2
-    pad_end = pad_total - pad_beg
-    if self._data_format == 'channels_first':
-      padded_inputs = tf.pad(
-          tensor=inputs,
-          paddings=[[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
-    else:
-      padded_inputs = tf.pad(
-          tensor=inputs,
-          paddings=[[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
-
-    return padded_inputs
-
-  def conv2d_fixed_padding(self, inputs, filters, kernel_size, strides):
-    """Strided 2-D convolution with explicit padding.
-
-    The padding is consistent and is based only on `kernel_size`, not on the
-    dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
-
-    Args:
-      inputs: `Tensor` of size `[batch, channels, height_in, width_in]`.
-      filters: `int` number of filters in the convolution.
-      kernel_size: `int` size of the kernel to be used in the convolution.
-      strides: `int` strides of the convolution.
-
-    Returns:
-      A `Tensor` of shape `[batch, filters, height_out, width_out]`.
-    """
-    if strides > 1:
-      inputs = self.fixed_padding(inputs, kernel_size)
-
-    return tf.keras.layers.Conv2D(
-        filters=filters,
-        kernel_size=kernel_size,
-        strides=strides,
-        padding=('SAME' if strides == 1 else 'VALID'),
-        use_bias=False,
-        kernel_initializer=tf.initializers.VarianceScaling(),
-        data_format=self._data_format)(
-            inputs=inputs)
-
-  def residual_block(self,
-                     inputs,
-                     filters,
-                     strides,
-                     use_projection=False,
-                     is_training=None):
-    """Standard building block for residual networks with BN after convolutions.
-
-    Args:
-      inputs: `Tensor` of size `[batch, channels, height, width]`.
-      filters: `int` number of filters for the first two convolutions. Note that
-          the third and final convolution will use 4 times as many filters.
-      strides: `int` block stride. If greater than 1, this block will ultimately
-          downsample the input.
-      use_projection: `bool` for whether this block should use a projection
-          shortcut (versus the default identity shortcut). This is usually
-          `True` for the first block of a block group, which may change the
-          number of filters and the resolution.
-      is_training: `bool` if True, the model is in training mode.
-    Returns:
-      The output `Tensor` of the block.
-    """
-    shortcut = inputs
-    if use_projection:
-      # Projection shortcut in first layer to match filters and strides
-      shortcut = self.conv2d_fixed_padding(
-          inputs=inputs, filters=filters, kernel_size=1, strides=strides)
-      shortcut = self._norm_activation(use_activation=False)(
-          shortcut, is_training=is_training)
-
-    inputs = self.conv2d_fixed_padding(
-        inputs=inputs, filters=filters, kernel_size=3, strides=strides)
-    inputs = self._norm_activation()(inputs, is_training=is_training)
-
-    inputs = self.conv2d_fixed_padding(
-        inputs=inputs, filters=filters, kernel_size=3, strides=1)
-    inputs = self._norm_activation(use_activation=False, init_zero=True)(
-        inputs, is_training=is_training)
-
-    return self._activation_op(inputs + shortcut)
-
-  def bottleneck_block(self,
-                       inputs,
-                       filters,
-                       strides,
-                       use_projection=False,
-                       is_training=None):
-    """Bottleneck block variant for residual networks with BN after convolutions.
-
-    Args:
-      inputs: `Tensor` of size `[batch, channels, height, width]`.
-      filters: `int` number of filters for the first two convolutions. Note that
-          the third and final convolution will use 4 times as many filters.
-      strides: `int` block stride. If greater than 1, this block will ultimately
-          downsample the input.
-      use_projection: `bool` for whether this block should use a projection
-          shortcut (versus the default identity shortcut). This is usually
-          `True` for the first block of a block group, which may change the
-          number of filters and the resolution.
-      is_training: `bool` if True, the model is in training mode.
-
-    Returns:
-      The output `Tensor` of the block.
-    """
-    shortcut = inputs
-    if use_projection:
-      # Projection shortcut only in first block within a group. Bottleneck
-      # blocks end with 4 times the number of filters.
-      filters_out = 4 * filters
-      shortcut = self.conv2d_fixed_padding(
-          inputs=inputs, filters=filters_out, kernel_size=1, strides=strides)
-      shortcut = self._norm_activation(use_activation=False)(
-          shortcut, is_training=is_training)
-
-    inputs = self.conv2d_fixed_padding(
-        inputs=inputs, filters=filters, kernel_size=1, strides=1)
-    inputs = self._norm_activation()(inputs, is_training=is_training)
-
-    inputs = self.conv2d_fixed_padding(
-        inputs=inputs, filters=filters, kernel_size=3, strides=strides)
-    inputs = self._norm_activation()(inputs, is_training=is_training)
-
-    inputs = self.conv2d_fixed_padding(
-        inputs=inputs, filters=4 * filters, kernel_size=1, strides=1)
-    inputs = self._norm_activation(use_activation=False, init_zero=True)(
-        inputs, is_training=is_training)
-
-    return self._activation_op(inputs + shortcut)
-
-  def block_group(self, inputs, filters, block_fn, blocks, strides, name,
-                  is_training):
-    """Creates one group of blocks for the ResNet model.
-
-    Args:
-      inputs: `Tensor` of size `[batch, channels, height, width]`.
-      filters: `int` number of filters for the first convolution of the layer.
-      block_fn: `function` for the block to use within the model
-      blocks: `int` number of blocks contained in the layer.
-      strides: `int` stride to use for the first convolution of the layer. If
-          greater than 1, this layer will downsample the input.
-      name: `str`name for the Tensor output of the block layer.
-      is_training: `bool` if True, the model is in training mode.
-
-    Returns:
-      The output `Tensor` of the block layer.
-    """
-    # Only the first block per block_group uses projection shortcut and strides.
-    inputs = block_fn(inputs, filters, strides, use_projection=True,
-                      is_training=is_training)
-
-    for _ in range(1, blocks):
-      inputs = block_fn(inputs, filters, 1, is_training=is_training)
-
-    return tf.identity(inputs, name)
-
-  def resnet_v1_generator(self, block_fn, layers):
-    """Generator for ResNet v1 models.
-
-    Args:
-      block_fn: `function` for the block to use within the model. Either
-          `residual_block` or `bottleneck_block`.
-      layers: list of 4 `int`s denoting the number of blocks to include in each
-        of the 4 block groups. Each group consists of blocks that take inputs of
-        the same resolution.
-
-    Returns:
-      Model `function` that takes in `inputs` and `is_training` and returns the
-      output `Tensor` of the ResNet model.
-    """
-
-    def model(inputs, is_training=None):
-      """Creation of the model graph."""
-      inputs = self.conv2d_fixed_padding(
-          inputs=inputs, filters=64, kernel_size=7, strides=2)
-      inputs = tf.identity(inputs, 'initial_conv')
-      inputs = self._norm_activation()(inputs, is_training=is_training)
-
-      inputs = tf.keras.layers.MaxPool2D(
-          pool_size=3, strides=2, padding='SAME',
-          data_format=self._data_format)(
-              inputs)
-      inputs = tf.identity(inputs, 'initial_max_pool')
-
-      c2 = self.block_group(
-          inputs=inputs, filters=64, block_fn=block_fn, blocks=layers[0],
-          strides=1, name='block_group1', is_training=is_training)
-      c3 = self.block_group(
-          inputs=c2, filters=128, block_fn=block_fn, blocks=layers[1],
-          strides=2, name='block_group2', is_training=is_training)
-      c4 = self.block_group(
-          inputs=c3, filters=256, block_fn=block_fn, blocks=layers[2],
-          strides=2, name='block_group3', is_training=is_training)
-      c5 = self.block_group(
-          inputs=c4, filters=512, block_fn=block_fn, blocks=layers[3],
-          strides=2, name='block_group4', is_training=is_training)
-      return {2: c2, 3: c3, 4: c4, 5: c5}
-
-    return model
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/base_model.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/base_model.py
deleted file mode 100644
index 8d18f12f5b7c52ca02334c4c685b70d353de83c5..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/modeling/base_model.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Base Model definition."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import abc
-import functools
-import re
-import tensorflow as tf
-from official.vision.detection.modeling import checkpoint_utils
-from official.vision.detection.modeling import learning_rates
-from official.vision.detection.modeling import optimizers
-
-
-def _make_filter_trainable_variables_fn(frozen_variable_prefix):
-  """Creates a function for filtering trainable varialbes."""
-
-  def _filter_trainable_variables(variables):
-    """Filters trainable varialbes.
-
-    Args:
-      variables: a list of tf.Variable to be filtered.
-
-    Returns:
-      filtered_variables: a list of tf.Variable filtered out the frozen ones.
-    """
-    # frozen_variable_prefix: a regex string specifing the prefix pattern of
-    # the frozen variables' names.
-    filtered_variables = [
-        v for v in variables
-        if not frozen_variable_prefix or
-        not re.match(frozen_variable_prefix, v.name)
-    ]
-    return filtered_variables
-
-  return _filter_trainable_variables
-
-
-class Model(object):
-  """Base class for model function."""
-
-  __metaclass__ = abc.ABCMeta
-
-  def __init__(self, params):
-    self._use_bfloat16 = params.architecture.use_bfloat16
-
-    if params.architecture.use_bfloat16:
-      policy = tf.compat.v2.keras.mixed_precision.experimental.Policy(
-          'mixed_bfloat16')
-      tf.compat.v2.keras.mixed_precision.experimental.set_policy(policy)
-
-    # Optimization.
-    self._optimizer_fn = optimizers.OptimizerFactory(params.train.optimizer)
-    self._learning_rate = learning_rates.learning_rate_generator(
-        params.train.total_steps, params.train.learning_rate)
-
-    self._frozen_variable_prefix = params.train.frozen_variable_prefix
-    self._regularization_var_regex = params.train.regularization_variable_regex
-    self._l2_weight_decay = params.train.l2_weight_decay
-
-    # Checkpoint restoration.
-    self._checkpoint = params.train.checkpoint.as_dict()
-
-    # Summary.
-    self._enable_summary = params.enable_summary
-    self._model_dir = params.model_dir
-
-  @abc.abstractmethod
-  def build_outputs(self, inputs, mode):
-    """Build the graph of the forward path."""
-    pass
-
-  @abc.abstractmethod
-  def build_model(self, params, mode):
-    """Build the model object."""
-    pass
-
-  @abc.abstractmethod
-  def build_loss_fn(self):
-    """Build the model object."""
-    pass
-
-  def post_processing(self, labels, outputs):
-    """Post-processing function."""
-    return labels, outputs
-
-  def model_outputs(self, inputs, mode):
-    """Build the model outputs."""
-    return self.build_outputs(inputs, mode)
-
-  def build_optimizer(self):
-    """Returns train_op to optimize total loss."""
-    # Sets up the optimizer.
-    return self._optimizer_fn(self._learning_rate)
-
-  def make_filter_trainable_variables_fn(self):
-    """Creates a function for filtering trainable varialbes."""
-    return _make_filter_trainable_variables_fn(self._frozen_variable_prefix)
-
-  def weight_decay_loss(self, trainable_variables):
-    reg_variables = [
-        v for v in trainable_variables
-        if self._regularization_var_regex is None
-        or re.match(self._regularization_var_regex, v.name)
-    ]
-
-    return self._l2_weight_decay * tf.add_n(
-        [tf.nn.l2_loss(v) for v in reg_variables])
-
-  def make_restore_checkpoint_fn(self):
-    """Returns scaffold function to restore parameters from v1 checkpoint."""
-    if 'skip_checkpoint_variables' in self._checkpoint:
-      skip_regex = self._checkpoint['skip_checkpoint_variables']
-    else:
-      skip_regex = None
-    return checkpoint_utils.make_restore_checkpoint_fn(
-        self._checkpoint['path'],
-        prefix=self._checkpoint['prefix'],
-        skip_regex=skip_regex)
-
-  def eval_metrics(self):
-    """Returns tuple of metric function and its inputs for evaluation."""
-    raise NotImplementedError('Unimplemented eval_metrics')
diff --git a/spaces/NEARHUb/video-transcoder/README.md b/spaces/NEARHUb/video-transcoder/README.md
deleted file mode 100644
index 0d1bc00645252657e677aad0f59192d9557a5136..0000000000000000000000000000000000000000
--- a/spaces/NEARHUb/video-transcoder/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Video Transcoder
-emoji: 📚
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.31.0
-app_file: app.py
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/Nightwing25/AICoverGen/src/rvc.py b/spaces/Nightwing25/AICoverGen/src/rvc.py
deleted file mode 100644
index a2790602462859e4a9885c145a13ff86efba8a3c..0000000000000000000000000000000000000000
--- a/spaces/Nightwing25/AICoverGen/src/rvc.py
+++ /dev/null
@@ -1,166 +0,0 @@
-from multiprocessing import cpu_count
-from pathlib import Path
-
-import torch
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-
-from infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from my_utils import load_audio
-from vc_infer_pipeline import VC
-
-BASE_DIR = Path(__file__).resolve().parent.parent
-
-
-# config cpu
-def use_fp32_config():
-    for config_file in [
-        "32k.json",
-        "40k.json",
-        "48k.json",
-        "48k_v2.json",
-        "32k_v2.json",
-    ]:
-        with open(f"src/configs/{config_file}", "r") as f:
-            strr = f.read().replace("true", "false")
-        with open(f"src/configs/{config_file}", "w") as f:
-            f.write(strr)
-
-class Config:
-    def __init__(self, device, is_half):
-        self.device = device
-        self.is_half = is_half
-        self.n_cpu = 2 # set cpu cores
-        self.gpu_name = None
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                    ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                    or "P40" in self.gpu_name.upper()
-                    or "1060" in self.gpu_name
-                    or "1070" in self.gpu_name
-                    or "1080" in self.gpu_name
-            ):
-                print("16 series/10 series P40 forced single precision")
-                self.is_half = False
-                for config_file in ["32k.json", "40k.json", "48k.json"]:
-                    with open(BASE_DIR / "src" / "configs" / config_file, "r") as f:
-                        strr = f.read().replace("true", "false")
-                    with open(BASE_DIR / "src" / "configs" / config_file, "w") as f:
-                        f.write(strr)
-                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("No supported N-card found, use MPS for inference")
-            self.device = "mps"
-        else:
-            print("No supported N-card found, use CPU for inference")
-            self.device = "cpu"
-            self.is_half = False
-            use_fp32_config() # cpu config
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G memory config
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G memory config
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
-
-
-def load_hubert(device, is_half, model_path):
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([model_path], suffix='', )
-    hubert = models[0]
-    hubert = hubert.to(device)
-
-    if is_half:
-        hubert = hubert.half()
-    else:
-        hubert = hubert.float()
-
-    hubert.eval()
-    return hubert
-
-
-def get_vc(device, is_half, config, model_path):
-    cpt = torch.load(model_path, map_location='cpu')
-    if "config" not in cpt or "weight" not in cpt:
-        raise ValueError(f'Incorrect format for {model_path}. Use a voice model trained using RVC v2 instead.')
-
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif version == "v2":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-
-    del net_g.enc_q
-    print(net_g.load_state_dict(cpt["weight"], strict=False))
-    net_g.eval().to(device)
-
-    if is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-
-    vc = VC(tgt_sr, config)
-    return cpt, version, net_g, tgt_sr, vc
-
-
-def rvc_infer(index_path, index_rate, input_path, output_path, pitch_change, f0_method, cpt, version, net_g, filter_radius, tgt_sr, rms_mix_rate, protect, crepe_hop_length, vc, hubert_model):
-    audio = load_audio(input_path, 16000)
-    times = [0, 0, 0]
-    if_f0 = cpt.get('f0', 1)
-    audio_opt = vc.pipeline(hubert_model, net_g, 0, audio, input_path, times, pitch_change, f0_method, index_path, index_rate, if_f0, filter_radius, tgt_sr, 0, rms_mix_rate, version, protect, crepe_hop_length)
-    wavfile.write(output_path, tgt_sr, audio_opt)
diff --git a/spaces/NimaBoscarino/climategan/climategan/data.py b/spaces/NimaBoscarino/climategan/climategan/data.py
deleted file mode 100644
index e57fc21725361e02185088a909a74e36a8cd3fc4..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/climategan/data.py
+++ /dev/null
@@ -1,539 +0,0 @@
-"""Data-loading functions in order to create a Dataset and DataLoaders.
-Transforms for loaders are in transforms.py
-"""
-
-import json
-import os
-from pathlib import Path
-
-import numpy as np
-import torch
-import yaml
-from imageio import imread
-from PIL import Image
-from torch.utils.data import DataLoader, Dataset
-from torchvision import transforms
-
-from climategan.transforms import get_transforms
-from climategan.tutils import get_normalized_depth_t
-from climategan.utils import env_to_path, is_image_file
-
-classes_dict = {
-    "s": {  # unity
-        0: [0, 0, 255, 255],  # Water
-        1: [55, 55, 55, 255],  # Ground
-        2: [0, 255, 255, 255],  # Building
-        3: [255, 212, 0, 255],  # Traffic items
-        4: [0, 255, 0, 255],  # Vegetation
-        5: [255, 97, 0, 255],  # Terrain
-        6: [255, 0, 0, 255],  # Car
-        7: [60, 180, 60, 255],  # Trees
-        8: [255, 0, 255, 255],  # Person
-        9: [0, 0, 0, 255],  # Sky
-        10: [255, 255, 255, 255],  # Default
-    },
-    "r": {  # deeplab v2
-        0: [0, 0, 255, 255],  # Water
-        1: [55, 55, 55, 255],  # Ground
-        2: [0, 255, 255, 255],  # Building
-        3: [255, 212, 0, 255],  # Traffic items
-        4: [0, 255, 0, 255],  # Vegetation
-        5: [255, 97, 0, 255],  # Terrain
-        6: [255, 0, 0, 255],  # Car
-        7: [60, 180, 60, 255],  # Trees
-        8: [220, 20, 60, 255],  # Person
-        9: [8, 19, 49, 255],  # Sky
-        10: [0, 80, 100, 255],  # Default
-    },
-    "kitti": {
-        0: [210, 0, 200],  # Terrain
-        1: [90, 200, 255],  # Sky
-        2: [0, 199, 0],  # Tree
-        3: [90, 240, 0],  # Vegetation
-        4: [140, 140, 140],  # Building
-        5: [100, 60, 100],  # Road
-        6: [250, 100, 255],  # GuardRail
-        7: [255, 255, 0],  # TrafficSign
-        8: [200, 200, 0],  # TrafficLight
-        9: [255, 130, 0],  # Pole
-        10: [80, 80, 80],  # Misc
-        11: [160, 60, 60],  # Truck
-        12: [255, 127, 80],  # Car
-        13: [0, 139, 139],  # Van
-        14: [0, 0, 0],  # Undefined
-    },
-    "flood": {
-        0: [255, 0, 0],  # Cannot flood
-        1: [0, 0, 255],  # Must flood
-        2: [0, 0, 0],  # May flood
-    },
-}
-
-kitti_mapping = {
-    0: 5,  # Terrain -> Terrain
-    1: 9,  # Sky -> Sky
-    2: 7,  # Tree -> Trees
-    3: 4,  # Vegetation ->  Vegetation
-    4: 2,  # Building -> Building
-    5: 1,  # Road -> Ground
-    6: 3,  # GuardRail -> Traffic items
-    7: 3,  # TrafficSign ->  Traffic items
-    8: 3,  # TrafficLight -> Traffic items
-    9: 3,  # Pole -> Traffic items
-    10: 10,  # Misc -> default
-    11: 6,  # Truck -> Car
-    12: 6,  # Car -> Car
-    13: 6,  # Van -> Car
-    14: 10,  # Undefined -> Default
-}
-
-
-def encode_exact_segmap(seg, classes_dict, default_value=14):
-    """
-    When the mapping (rgb -> label) is known to be exact (no approximative rgb values)
-    maps rgb image to segmap labels
-
-    Args:
-        seg (np.ndarray): H x W x 3 RGB image
-        classes_dict (dict): Mapping {class: rgb value}
-        default_value (int, optional): Value for unknown label. Defaults to 14.
-
-    Returns:
-        np.ndarray: Segmap as labels, not RGB
-    """
-    out = np.ones((seg.shape[0], seg.shape[1])) * default_value
-    for cindex, cvalue in classes_dict.items():
-        out[np.where((seg == cvalue).all(-1))] = cindex
-    return out
-
-
-def merge_labels(labels, mapping, default_value=14):
-    """
-    Maps labels from a source domain to labels of a target domain,
-    typically kitti -> climategan
-
-    Args:
-        labels (np.ndarray): input segmap labels
-        mapping (dict): source_label -> target_label
-        default_value (int, optional): Unknown label. Defaults to 14.
-
-    Returns:
-        np.ndarray: Adapted labels
-    """
-    out = np.ones_like(labels) * default_value
-    for source, target in mapping.items():
-        out[labels == source] = target
-    return out
-
-
-def process_kitti_seg(path, kitti_classes, merge_map, default=14):
-    """
-    Processes a path to produce a 1 x 1 x H x W torch segmap
-
-    %timeit process_kitti_seg(path, classes_dict, mapping, default=14)
-    326 ms ± 118 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
-
-    Args:
-        path (str | pathlib.Path): Segmap RBG path
-        kitti_classes (dict): Kitti map label -> rgb
-        merge_map (dict): map kitti_label -> climategan_label
-        default (int, optional): Unknown kitti label. Defaults to 14.
-
-    Returns:
-        torch.Tensor: 1 x 1 x H x W torch segmap
-    """
-    seg = imread(path)
-    labels = encode_exact_segmap(seg, kitti_classes, default_value=default)
-    merged = merge_labels(labels, merge_map, default_value=default)
-    return torch.tensor(merged).unsqueeze(0).unsqueeze(0)
-
-
-def decode_segmap_merged_labels(tensor, domain, is_target, nc=11):
-    """Creates a label colormap for classes used in Unity segmentation benchmark.
-    Arguments:
-        tensor -- segmented image of size (1) x (nc) x (H) x (W)
-            if prediction, or size (1) x (1) x (H) x (W) if target
-    Returns:
-        RGB tensor of size (1) x (3) x (H) x (W)
-    #"""
-
-    if is_target:  # Target is size 1 x 1 x H x W
-        idx = tensor.squeeze(0).squeeze(0)
-    else:  # Prediction is size 1 x nc x H x W
-        idx = torch.argmax(tensor.squeeze(0), dim=0)
-
-    indexer = torch.tensor(list(classes_dict[domain].values()))[:, :3]
-    return indexer[idx.long()].permute(2, 0, 1).to(torch.float32).unsqueeze(0)
-
-
-def decode_segmap_cityscapes_labels(image, nc=19):
-    """Creates a label colormap used in CITYSCAPES segmentation benchmark.
-    Arguments:
-        image {array} -- segmented image
-        (array of image size containing class at each pixel)
-    Returns:
-        array of size 3*nc -- A colormap for visualizing segmentation results.
-    """
-    colormap = np.zeros((19, 3), dtype=np.uint8)
-    colormap[0] = [128, 64, 128]
-    colormap[1] = [244, 35, 232]
-    colormap[2] = [70, 70, 70]
-    colormap[3] = [102, 102, 156]
-    colormap[4] = [190, 153, 153]
-    colormap[5] = [153, 153, 153]
-    colormap[6] = [250, 170, 30]
-    colormap[7] = [220, 220, 0]
-    colormap[8] = [107, 142, 35]
-    colormap[9] = [152, 251, 152]
-    colormap[10] = [70, 130, 180]
-    colormap[11] = [220, 20, 60]
-    colormap[12] = [255, 0, 0]
-    colormap[13] = [0, 0, 142]
-    colormap[14] = [0, 0, 70]
-    colormap[15] = [0, 60, 100]
-    colormap[16] = [0, 80, 100]
-    colormap[17] = [0, 0, 230]
-    colormap[18] = [119, 11, 32]
-
-    r = np.zeros_like(image).astype(np.uint8)
-    g = np.zeros_like(image).astype(np.uint8)
-    b = np.zeros_like(image).astype(np.uint8)
-
-    for col in range(nc):
-        idx = image == col
-        r[idx] = colormap[col, 0]
-        g[idx] = colormap[col, 1]
-        b[idx] = colormap[col, 2]
-
-    rgb = np.stack([r, g, b], axis=2)
-    return rgb
-
-
-def find_closest_class(pixel, dict_classes):
-    """Takes a pixel as input and finds the closest known pixel value corresponding
-    to a class in dict_classes
-
-    Arguments:
-        pixel -- tuple pixel (R,G,B,A)
-    Returns:
-        tuple pixel (R,G,B,A) corresponding to a key (a class) in dict_classes
-    """
-    min_dist = float("inf")
-    closest_pixel = None
-    for pixel_value in dict_classes.keys():
-        dist = np.sqrt(np.sum(np.square(np.subtract(pixel, pixel_value))))
-        if dist < min_dist:
-            min_dist = dist
-            closest_pixel = pixel_value
-    return closest_pixel
-
-
-def encode_segmap(arr, domain):
-    """Change a segmentation RGBA array to a segmentation array
-                            with each pixel being the index of the class
-    Arguments:
-        numpy array -- segmented image of size (H) x (W) x (4 RGBA values)
-    Returns:
-        numpy array of size (1) x (H) x (W) with each pixel being the index of the class
-    """
-    new_arr = np.zeros((1, arr.shape[0], arr.shape[1]))
-    dict_classes = {
-        tuple(rgba_value): class_id
-        for (class_id, rgba_value) in classes_dict[domain].items()
-    }
-    for i in range(arr.shape[0]):
-        for j in range(arr.shape[1]):
-            pixel_rgba = tuple(arr[i, j, :])
-            if pixel_rgba in dict_classes.keys():
-                new_arr[0, i, j] = dict_classes[pixel_rgba]
-            else:
-                pixel_rgba_closest = find_closest_class(pixel_rgba, dict_classes)
-                new_arr[0, i, j] = dict_classes[pixel_rgba_closest]
-    return new_arr
-
-
-def encode_mask_label(arr, domain):
-    """Change a segmentation RGBA array to a segmentation array
-                            with each pixel being the index of the class
-    Arguments:
-        numpy array -- segmented image of size (H) x (W) x (3 RGB values)
-    Returns:
-        numpy array of size (1) x (H) x (W) with each pixel being the index of the class
-    """
-    diff = np.zeros((len(classes_dict[domain].keys()), arr.shape[0], arr.shape[1]))
-    for cindex, cvalue in classes_dict[domain].items():
-        diff[cindex, :, :] = np.sqrt(
-            np.sum(
-                np.square(arr - np.tile(cvalue, (arr.shape[0], arr.shape[1], 1))),
-                axis=2,
-            )
-        )
-    return np.expand_dims(np.argmin(diff, axis=0), axis=0)
-
-
-def transform_segmap_image_to_tensor(path, domain):
-    """
-    Transforms a segmentation image to a tensor of size (1) x (1) x (H) x (W)
-    with each pixel being the index of the class
-    """
-    arr = np.array(Image.open(path).convert("RGBA"))
-    arr = encode_segmap(arr, domain)
-    arr = torch.from_numpy(arr).float()
-    arr = arr.unsqueeze(0)
-    return arr
-
-
-def save_segmap_tensors(path_to_json, path_to_dir, domain):
-    """
-    Loads the segmentation images mentionned in a json file, transforms them to
-    tensors and save the tensors in the wanted directory
-
-    Args:
-        path_to_json: complete path to the json file where to find the original data
-        path_to_dir: path to the directory where to save the tensors as tensor_name.pt
-        domain: domain of the images ("r" or "s")
-
-    e.g:
-        save_tensors(
-            "/network/tmp1/ccai/data/climategan/seg/train_s.json",
-            "/network/tmp1/ccai/data/munit_dataset/simdata/Unity11K_res640/Seg_tensors/",
-            "s",
-        )
-    """
-    ims_list = None
-    if path_to_json:
-        path_to_json = Path(path_to_json).resolve()
-        with open(path_to_json, "r") as f:
-            ims_list = yaml.safe_load(f)
-
-    assert ims_list is not None
-
-    for im_dict in ims_list:
-        for task_name, path in im_dict.items():
-            if task_name == "s":
-                file_name = os.path.splitext(path)[0]  # remove extension
-                file_name = file_name.rsplit("/", 1)[-1]  # keep only the file_name
-                tensor = transform_segmap_image_to_tensor(path, domain)
-                torch.save(tensor, path_to_dir + file_name + ".pt")
-
-
-def pil_image_loader(path, task):
-    if Path(path).suffix == ".npy":
-        arr = np.load(path).astype(np.uint8)
-    elif is_image_file(path):
-        # arr = imread(path).astype(np.uint8)
-        arr = np.array(Image.open(path).convert("RGB"))
-    else:
-        raise ValueError("Unknown data type {}".format(path))
-
-    # Convert from RGBA to RGB for images
-    if len(arr.shape) == 3 and arr.shape[-1] == 4:
-        arr = arr[:, :, 0:3]
-
-    if task == "m":
-        arr[arr != 0] = 1
-        # Make sure mask is single-channel
-        if len(arr.shape) >= 3:
-            arr = arr[:, :, 0]
-
-    # assert len(arr.shape) == 3, (path, task, arr.shape)
-
-    return Image.fromarray(arr)
-
-
-def tensor_loader(path, task, domain, opts):
-    """load data as tensors
-    Args:
-        path (str): path to data
-        task (str)
-        domain (str)
-    Returns:
-        [Tensor]: 1 x C x H x W
-    """
-    if task == "s":
-        if domain == "kitti":
-            return process_kitti_seg(
-                path, classes_dict["kitti"], kitti_mapping, default=14
-            )
-        return torch.load(path)
-    elif task == "d":
-        if Path(path).suffix == ".npy":
-            arr = np.load(path)
-        else:
-            arr = imread(path)  # .astype(np.uint8) /!\ kitti is np.uint16
-        tensor = torch.from_numpy(arr.astype(np.float32))
-        tensor = get_normalized_depth_t(
-            tensor,
-            domain,
-            normalize="d" in opts.train.pseudo.tasks,
-            log=opts.gen.d.classify.enable,
-        )
-        tensor = tensor.unsqueeze(0)
-        return tensor
-
-    elif Path(path).suffix == ".npy":
-        arr = np.load(path).astype(np.float32)
-    elif is_image_file(path):
-        arr = imread(path).astype(np.float32)
-    else:
-        raise ValueError("Unknown data type {}".format(path))
-
-    # Convert from RGBA to RGB for images
-    if len(arr.shape) == 3 and arr.shape[-1] == 4:
-        arr = arr[:, :, 0:3]
-
-    if task == "x":
-        arr -= arr.min()
-        arr /= arr.max()
-        arr = np.moveaxis(arr, 2, 0)
-    elif task == "s":
-        arr = np.moveaxis(arr, 2, 0)
-    elif task == "m":
-        if arr.max() > 127:
-            arr = (arr > 127).astype(arr.dtype)
-        # Make sure mask is single-channel
-        if len(arr.shape) >= 3:
-            arr = arr[:, :, 0]
-        arr = np.expand_dims(arr, 0)
-
-    return torch.from_numpy(arr).unsqueeze(0)
-
-
-class OmniListDataset(Dataset):
-    def __init__(self, mode, domain, opts, transform=None):
-
-        self.opts = opts
-        self.domain = domain
-        self.mode = mode
-        self.tasks = set(opts.tasks)
-        self.tasks.add("x")
-        if "p" in self.tasks:
-            self.tasks.add("m")
-
-        file_list_path = Path(opts.data.files[mode][domain])
-        if "/" not in str(file_list_path):
-            file_list_path = Path(opts.data.files.base) / Path(
-                opts.data.files[mode][domain]
-            )
-
-        if file_list_path.suffix == ".json":
-            self.samples_paths = self.json_load(file_list_path)
-        elif file_list_path.suffix in {".yaml", ".yml"}:
-            self.samples_paths = self.yaml_load(file_list_path)
-        else:
-            raise ValueError("Unknown file list type in {}".format(file_list_path))
-
-        if opts.data.max_samples and opts.data.max_samples != -1:
-            assert isinstance(opts.data.max_samples, int)
-            self.samples_paths = self.samples_paths[: opts.data.max_samples]
-
-        self.filter_samples()
-        if opts.data.check_samples:
-            print(f"Checking samples ({mode}, {domain})")
-            self.check_samples()
-        self.file_list_path = str(file_list_path)
-        self.transform = transform
-
-    def filter_samples(self):
-        """
-        Filter out data which is not required for the model's tasks
-        as defined in opts.tasks
-        """
-        self.samples_paths = [
-            {k: v for k, v in s.items() if k in self.tasks} for s in self.samples_paths
-        ]
-
-    def __getitem__(self, i):
-        """Return an item in the dataset with fields:
-        {
-            data: transform({
-                domains: values
-            }),
-            paths: [{task: path}],
-            domain: [domain],
-            mode: [train|val]
-        }
-        Args:
-            i (int): index of item to retrieve
-        Returns:
-            dict: dataset item where tensors of data are in item["data"] which is a dict
-                  {task: tensor}
-        """
-        paths = self.samples_paths[i]
-
-        # always apply transforms,
-        # if no transform is specified, ToTensor and Normalize will be applied
-
-        item = {
-            "data": self.transform(
-                {
-                    task: tensor_loader(
-                        env_to_path(path),
-                        task,
-                        self.domain,
-                        self.opts,
-                    )
-                    for task, path in paths.items()
-                }
-            ),
-            "paths": paths,
-            "domain": self.domain if self.domain != "kitti" else "s",
-            "mode": self.mode,
-        }
-
-        return item
-
-    def __len__(self):
-        return len(self.samples_paths)
-
-    def json_load(self, file_path):
-        with open(file_path, "r") as f:
-            return json.load(f)
-
-    def yaml_load(self, file_path):
-        with open(file_path, "r") as f:
-            return yaml.safe_load(f)
-
-    def check_samples(self):
-        """Checks that every file listed in samples_paths actually
-        exist on the file-system
-        """
-        for s in self.samples_paths:
-            for k, v in s.items():
-                assert Path(v).exists(), f"{k} {v} does not exist"
-
-
-def get_loader(mode, domain, opts):
-    if (
-        domain != "kitti"
-        or not opts.train.kitti.pretrain
-        or not opts.train.kitti.batch_size
-    ):
-        batch_size = opts.data.loaders.get("batch_size", 4)
-    else:
-        batch_size = opts.train.kitti.get("batch_size", 4)
-
-    return DataLoader(
-        OmniListDataset(
-            mode,
-            domain,
-            opts,
-            transform=transforms.Compose(get_transforms(opts, mode, domain)),
-        ),
-        batch_size=batch_size,
-        shuffle=True,
-        num_workers=opts.data.loaders.get("num_workers", 8),
-        pin_memory=True,  # faster transfer to gpu
-        drop_last=True,  # avoids batchnorm pbs if last batch has size 1
-    )
-
-
-def get_all_loaders(opts):
-    loaders = {}
-    for mode in ["train", "val"]:
-        loaders[mode] = {}
-        for domain in opts.domains:
-            if mode in opts.data.files:
-                if domain in opts.data.files[mode]:
-                    loaders[mode][domain] = get_loader(mode, domain, opts)
-    return loaders
diff --git a/spaces/OAOA/DifFace/basicsr/ops/dcn/__init__.py b/spaces/OAOA/DifFace/basicsr/ops/dcn/__init__.py
deleted file mode 100644
index 32e3592f896d61b4127e09d0476381b9d55e32ff..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/ops/dcn/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv,
-                          modulated_deform_conv)
-
-__all__ = [
-    'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv',
-    'modulated_deform_conv'
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/legacy/block_pair_dataset.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/legacy/block_pair_dataset.py
deleted file mode 100644
index ba069b46052286c531b4f9706d96788732cd2ad2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/legacy/block_pair_dataset.py
+++ /dev/null
@@ -1,311 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import numpy as np
-import torch
-from fairseq.data import FairseqDataset
-
-
-class BlockPairDataset(FairseqDataset):
-    """Break a Dataset of tokens into sentence pair blocks for next sentence
-       prediction as well as masked language model.
-
-       High-level logics are:
-       1. break input tensor to tensor blocks
-       2. pair the blocks with 50% next sentence and 50% random sentence
-       3. return paired blocks as well as related segment labels
-
-    Args:
-        dataset (~torch.utils.data.Dataset): dataset to break into blocks
-        sizes: array of sentence lengths
-        dictionary: dictionary for the task
-        block_size: maximum block size
-        break_mode: mode for breaking copurs into block pairs. currently we support
-            2 modes
-            doc: respect document boundaries and each part of the pair should belong to on document
-            none: don't respect any boundary and cut tokens evenly
-        short_seq_prob: probability for generating shorter block pairs
-        doc_break_size: Size for empty line separating documents. Typically 1 if
-                        the sentences have eos, 0 otherwise.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        dictionary,
-        sizes,
-        block_size,
-        break_mode="doc",
-        short_seq_prob=0.1,
-        doc_break_size=1,
-    ):
-        super().__init__()
-        self.dataset = dataset
-        self.pad = dictionary.pad()
-        self.eos = dictionary.eos()
-        self.cls = dictionary.cls()
-        self.mask = dictionary.mask()
-        self.sep = dictionary.sep()
-        self.break_mode = break_mode
-        self.dictionary = dictionary
-        self.short_seq_prob = short_seq_prob
-        self.block_indices = []
-
-        assert len(dataset) == len(sizes)
-
-        if break_mode == "doc":
-            cur_doc = []
-            for sent_id, sz in enumerate(sizes):
-                assert doc_break_size == 0 or sz != 0, (
-                    "when doc_break_size is non-zero, we expect documents to be"
-                    "separated by a blank line with a single eos."
-                )
-                # empty line as document separator
-                if sz == doc_break_size:
-                    if len(cur_doc) == 0:
-                        continue
-                    self.block_indices.append(cur_doc)
-                    cur_doc = []
-                else:
-                    cur_doc.append(sent_id)
-            max_num_tokens = block_size - 3  # Account for [CLS], [SEP], [SEP]
-            self.sent_pairs = []
-            self.sizes = []
-            for doc_id, doc in enumerate(self.block_indices):
-                self._generate_sentence_pair(doc, doc_id, max_num_tokens, sizes)
-        elif break_mode is None or break_mode == "none":
-            # each block should have half of the block size since we are constructing block pair
-            sent_length = (block_size - 3) // 2
-            total_len = sum(dataset.sizes)
-            length = math.ceil(total_len / sent_length)
-
-            def block_at(i):
-                start = i * sent_length
-                end = min(start + sent_length, total_len)
-                return (start, end)
-
-            sent_indices = np.array([block_at(i) for i in range(length)])
-            sent_sizes = np.array([e - s for s, e in sent_indices])
-            dataset_index = self._sent_to_dataset_index(sent_sizes)
-
-            # pair sentences
-            self._pair_sentences(dataset_index)
-        else:
-            raise ValueError("Invalid break_mode: " + break_mode)
-
-    def _pair_sentences(self, dataset_index):
-        """
-        Give a list of evenly cut blocks/sentences, pair these sentences with 50%
-        consecutive sentences and 50% random sentences.
-        This is used for none break mode
-        """
-        # pair sentences
-        for sent_id, sent in enumerate(dataset_index):
-            next_sent_label = (
-                1 if np.random.rand() > 0.5 and sent_id != len(dataset_index) - 1 else 0
-            )
-            if next_sent_label:
-                next_sent = dataset_index[sent_id + 1]
-            else:
-                next_sent = dataset_index[
-                    self._skip_sampling(len(dataset_index), [sent_id, sent_id + 1])
-                ]
-            self.sent_pairs.append((sent, next_sent, next_sent_label))
-
-            # The current blocks don't include the special tokens but the
-            # sizes already account for this
-            self.sizes.append(3 + sent[3] + next_sent[3])
-
-    def _sent_to_dataset_index(self, sent_sizes):
-        """
-        Build index mapping block indices to the underlying dataset indices
-        """
-        dataset_index = []
-        ds_idx, ds_remaining = -1, 0
-        for to_consume in sent_sizes:
-            sent_size = to_consume
-            if ds_remaining == 0:
-                ds_idx += 1
-                ds_remaining = sent_sizes[ds_idx]
-            start_ds_idx = ds_idx
-            start_offset = sent_sizes[ds_idx] - ds_remaining
-            while to_consume > ds_remaining:
-                to_consume -= ds_remaining
-                ds_idx += 1
-                ds_remaining = sent_sizes[ds_idx]
-            ds_remaining -= to_consume
-            dataset_index.append(
-                (
-                    start_ds_idx,  # starting index in dataset
-                    start_offset,  # starting offset within starting index
-                    ds_idx,  # ending index in dataset
-                    sent_size,  # sentence length
-                )
-            )
-        assert ds_remaining == 0
-        assert ds_idx == len(self.dataset) - 1
-        return dataset_index
-
-    def _generate_sentence_pair(self, doc, doc_id, max_num_tokens, sizes):
-        """
-        Go through a single document and genrate sentence paris from it
-        """
-        current_chunk = []
-        current_length = 0
-        curr = 0
-        # To provide more randomness, we decrease target seq length for parts of
-        # samples (10% by default). Note that max_num_tokens is the hard threshold
-        # for batching and will never be changed.
-        target_seq_length = max_num_tokens
-        if np.random.random() < self.short_seq_prob:
-            target_seq_length = np.random.randint(2, max_num_tokens)
-        # loop through all sentences in document
-        while curr < len(doc):
-            sent_id = doc[curr]
-            current_chunk.append(sent_id)
-            current_length = sum(sizes[current_chunk])
-            # split chunk and generate pair when exceed target_seq_length or
-            # finish the loop
-            if curr == len(doc) - 1 or current_length >= target_seq_length:
-                # split the chunk into 2 parts
-                a_end = 1
-                if len(current_chunk) > 2:
-                    a_end = np.random.randint(1, len(current_chunk) - 1)
-                sent_a = current_chunk[:a_end]
-                len_a = sum(sizes[sent_a])
-                # generate next sentence label, note that if there is only 1 sentence
-                # in current chunk, label is always 0
-                next_sent_label = (
-                    1 if np.random.rand() > 0.5 and len(current_chunk) != 1 else 0
-                )
-                if not next_sent_label:
-                    # if next sentence label is 0, sample sent_b from a random doc
-                    target_b_length = target_seq_length - len_a
-                    rand_doc_id = self._skip_sampling(len(self.block_indices), [doc_id])
-                    random_doc = self.block_indices[rand_doc_id]
-                    random_start = np.random.randint(0, len(random_doc))
-                    sent_b = []
-                    len_b = 0
-                    for j in range(random_start, len(random_doc)):
-                        sent_b.append(random_doc[j])
-                        len_b = sum(sizes[sent_b])
-                        if len_b >= target_b_length:
-                            break
-                    # return the second part of the chunk since it's not used
-                    num_unused_segments = len(current_chunk) - a_end
-                    curr -= num_unused_segments
-                else:
-                    # if next sentence label is 1, use the second part of chunk as sent_B
-                    sent_b = current_chunk[a_end:]
-                    len_b = sum(sizes[sent_b])
-                # currently sent_a and sent_B may be longer than max_num_tokens,
-                # truncate them and return block idx and offsets for them
-                sent_a, sent_b = self._truncate_sentences(
-                    sent_a, sent_b, max_num_tokens
-                )
-                self.sent_pairs.append((sent_a, sent_b, next_sent_label))
-                self.sizes.append(3 + sent_a[3] + sent_b[3])
-                current_chunk = []
-            curr += 1
-
-    def _skip_sampling(self, total, skip_ids):
-        """
-        Generate a random integer which is not in skip_ids. Sample range is [0, total)
-        TODO: ids in skip_ids should be consecutive, we can extend it to more generic version later
-        """
-        rand_id = np.random.randint(total - len(skip_ids))
-        return rand_id if rand_id < min(skip_ids) else rand_id + len(skip_ids)
-
-    def _truncate_sentences(self, sent_a, sent_b, max_num_tokens):
-        """
-        Trancate a pair of sentence to limit total length under max_num_tokens
-        Logics:
-            1. Truncate longer sentence
-            2. Tokens to be truncated could be at the beginning or the end of the sentnce
-        Returns:
-            Truncated sentences represented by dataset idx
-        """
-        len_a, len_b = sum(self.dataset.sizes[sent_a]), sum(self.dataset.sizes[sent_b])
-        front_cut_a = front_cut_b = end_cut_a = end_cut_b = 0
-
-        while True:
-            total_length = (
-                len_a + len_b - front_cut_a - front_cut_b - end_cut_a - end_cut_b
-            )
-            if total_length <= max_num_tokens:
-                break
-
-            if len_a - front_cut_a - end_cut_a > len_b - front_cut_b - end_cut_b:
-                if np.random.rand() < 0.5:
-                    front_cut_a += 1
-                else:
-                    end_cut_a += 1
-            else:
-                if np.random.rand() < 0.5:
-                    front_cut_b += 1
-                else:
-                    end_cut_b += 1
-
-        # calculate ds indices as well as offsets and return
-        truncated_sent_a = self._cut_sentence(sent_a, front_cut_a, end_cut_a)
-        truncated_sent_b = self._cut_sentence(sent_b, front_cut_b, end_cut_b)
-        return truncated_sent_a, truncated_sent_b
-
-    def _cut_sentence(self, sent, front_cut, end_cut):
-        """
-        Cut a sentence based on the numbers of tokens to be cut from beginning and end
-        Represent the sentence as dataset idx and return
-        """
-        start_ds_idx, end_ds_idx, offset = sent[0], sent[-1], 0
-        target_len = sum(self.dataset.sizes[sent]) - front_cut - end_cut
-        while front_cut > 0:
-            if self.dataset.sizes[start_ds_idx] > front_cut:
-                offset += front_cut
-                break
-            else:
-                front_cut -= self.dataset.sizes[start_ds_idx]
-                start_ds_idx += 1
-        while end_cut > 0:
-            if self.dataset.sizes[end_ds_idx] > end_cut:
-                break
-            else:
-                end_cut -= self.dataset.sizes[end_ds_idx]
-                end_ds_idx -= 1
-        return start_ds_idx, offset, end_ds_idx, target_len
-
-    def _fetch_block(self, start_ds_idx, offset, end_ds_idx, length):
-        """
-        Fetch a block of tokens based on its dataset idx
-        """
-        buffer = torch.cat(
-            [self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)]
-        )
-        s, e = offset, offset + length
-        return buffer[s:e]
-
-    def __getitem__(self, index):
-        block1, block2, next_sent_label = self.sent_pairs[index]
-        block1 = self._fetch_block(*block1)
-        block2 = self._fetch_block(*block2)
-        return block1, block2, next_sent_label
-
-    def __len__(self):
-        return len(self.sizes)
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        prefetch_idx = set()
-        for index in indices:
-            for block1, block2, _ in [self.sent_pairs[index]]:
-                for ds_idx in range(block1[0], block1[2] + 1):
-                    prefetch_idx.add(ds_idx)
-                for ds_idx in range(block2[0], block2[2] + 1):
-                    prefetch_idx.add(ds_idx)
-        self.dataset.prefetch(prefetch_idx)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/ema/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/ema/__init__.py
deleted file mode 100644
index 503ceaa609b092e48bd32a0031f4e2ffb875483f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/ema/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-from .ema import EMA
-
-
-def build_ema(model, cfg, device):
-    return EMA(model, cfg, device)
-
-
-# automatically import any Python files in the models/ema/ directory
-for file in sorted(os.listdir(os.path.dirname(__file__))):
-    if file.endswith(".py") and not file.startswith("_"):
-        file_name = file[: file.find(".py")]
-        importlib.import_module("fairseq.models.ema." + file_name)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/simultaneous_translation/docs/ende-mma.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/simultaneous_translation/docs/ende-mma.md
deleted file mode 100644
index 241d604a3b31a37755da68aad6ff47d46891d3fc..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/simultaneous_translation/docs/ende-mma.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Simultaneous Machine Translation
-
-This directory contains the code for the paper [Monotonic Multihead Attention](https://openreview.net/forum?id=Hyg96gBKPS)
-
-## Prepare Data
-
-[Please follow the instructions to download and preprocess the WMT'15 En-De dataset.](https://github.com/pytorch/fairseq/tree/simulastsharedtask/examples/translation#prepare-wmt14en2desh)
-
-Another example of training an English to Japanese model can be found [here](docs/enja.md)
-
-## Training
-
-- MMA-IL
-
-```shell
-fairseq-train \
-    data-bin/wmt15_en_de_32k \
-    --simul-type infinite_lookback \
-    --user-dir $FAIRSEQ/example/simultaneous_translation \
-    --mass-preservation \
-    --criterion latency_augmented_label_smoothed_cross_entropy \
-    --latency-weight-avg  0.1 \
-    --max-update 50000 \
-    --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \
-    --optimizer adam --adam-betas '(0.9, 0.98)' \
-    --lr-scheduler 'inverse_sqrt' \
-    --warmup-init-lr 1e-7  --warmup-updates 4000 \
-    --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\
-    --dropout 0.3 \
-    --label-smoothing 0.1\
-    --max-tokens 3584
-```
-
-- MMA-H
-
-```shell
-fairseq-train \
-    data-bin/wmt15_en_de_32k \
-    --simul-type hard_aligned \
-    --user-dir $FAIRSEQ/example/simultaneous_translation \
-    --mass-preservation \
-    --criterion latency_augmented_label_smoothed_cross_entropy \
-    --latency-weight-var  0.1 \
-    --max-update 50000 \
-    --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \
-    --optimizer adam --adam-betas '(0.9, 0.98)' \
-    --lr-scheduler 'inverse_sqrt' \
-    --warmup-init-lr 1e-7  --warmup-updates 4000 \
-    --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\
-    --dropout 0.3 \
-    --label-smoothing 0.1\
-    --max-tokens 3584
-```
-
-- wait-k
-
-```shell
-fairseq-train \
-    data-bin/wmt15_en_de_32k \
-    --simul-type wait-k \
-    --waitk-lagging 3 \
-    --user-dir $FAIRSEQ/example/simultaneous_translation \
-    --mass-preservation \
-    --criterion latency_augmented_label_smoothed_cross_entropy \
-    --max-update 50000 \
-    --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \
-    --optimizer adam --adam-betas '(0.9, 0.98)' \
-    --lr-scheduler 'inverse_sqrt' \
-    --warmup-init-lr 1e-7  --warmup-updates 4000 \
-    --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\
-    --dropout 0.3 \
-    --label-smoothing 0.1\
-    --max-tokens 3584
-```
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/README.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/README.md
deleted file mode 100644
index cd17da3b3e6f3e39083f7a76a56ff46c3a63b929..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/hubert/simple_kmeans/README.md
+++ /dev/null
@@ -1,71 +0,0 @@
-# Sharded Feature Extraction and K-means Application
-
-This folder contains scripts for preparing HUBERT labels from tsv files, the
-steps are:
-1. feature extraction
-2. k-means clustering
-3. k-means application
-
-
-## Data preparation
-
-`*.tsv` files contains a list of audio, where each line is the root, and
-following lines are the subpath for each audio:
-```
-<root-dir>
-<audio-path-1>
-<audio-path-2>
-...
-```
-
-
-## Feature extraction
-
-### MFCC feature
-Suppose the tsv file is at `${tsv_dir}/${split}.tsv`. To extract 39-D
-mfcc+delta+ddelta features for the 1st iteration HUBERT training, run:
-```sh
-python dump_mfcc_feature.py ${tsv_dir} ${split} ${nshard} ${rank} ${feat_dir}
-```
-This would shard the tsv file into `${nshard}` and extract features for the
-`${rank}`-th shard, where rank is an integer in `[0, nshard-1]`. Features would
-be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`.
-
-
-### HUBERT feature
-To extract features from the `${layer}`-th transformer layer of a trained
-HUBERT model saved at `${ckpt_path}`, run:
-```sh
-python dump_hubert_feature.py ${tsv_dir} ${split} ${ckpt_path} ${layer} ${nshard} ${rank} ${feat_dir}
-```
-Features would also be saved at `${feat_dir}/${split}_${rank}_${nshard}.{npy,len}`.
-
-- if out-of-memory, decrease the chunk size with `--max_chunk`
-
-
-## K-means clustering
-To fit a k-means model with `${n_clusters}` clusters on 10% of the `${split}` data, run
-```sh
-python learn_kmeans.py ${feat_dir} ${split} ${nshard} ${km_path} ${n_cluster} --percent 0.1
-```
-This saves the k-means model to `${km_path}`.
-
-- set `--precent -1` to use all data
-- more kmeans options can be found with `-h` flag
-
-
-## K-means application
-To apply a trained k-means model `${km_path}` to obtain labels for `${split}`, run
-```sh
-python dump_km_label.py ${feat_dir} ${split} ${km_path} ${nshard} ${rank} ${lab_dir}
-```
-This would extract labels for the `${rank}`-th shard out of `${nshard}` shards
-and dump them to `${lab_dir}/${split}_${rank}_${shard}.km`
-
-
-Finally, merge shards for `${split}` by running
-```sh
-for rank in $(seq 0 $((nshard - 1))); do
-  cat $lab_dir/${split}_${rank}_${nshard}.km
-done > $lab_dir/${split}.km
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/backtranslation_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/backtranslation_dataset.py
deleted file mode 100644
index 8f70c90df3d237077537993e125d366c95292f1a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/backtranslation_dataset.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from fairseq import utils
-
-from . import FairseqDataset
-
-
-def backtranslate_samples(samples, collate_fn, generate_fn, cuda=True):
-    """Backtranslate a list of samples.
-
-    Given an input (*samples*) of the form:
-
-        [{'id': 1, 'source': 'hallo welt'}]
-
-    this will return:
-
-        [{'id': 1, 'source': 'hello world', 'target': 'hallo welt'}]
-
-    Args:
-        samples (List[dict]): samples to backtranslate. Individual samples are
-            expected to have a 'source' key, which will become the 'target'
-            after backtranslation.
-        collate_fn (callable): function to collate samples into a mini-batch
-        generate_fn (callable): function to generate backtranslations
-        cuda (bool): use GPU for generation (default: ``True``)
-
-    Returns:
-        List[dict]: an updated list of samples with a backtranslated source
-    """
-    collated_samples = collate_fn(samples)
-    s = utils.move_to_cuda(collated_samples) if cuda else collated_samples
-    generated_sources = generate_fn(s)
-
-    id_to_src = {sample["id"]: sample["source"] for sample in samples}
-
-    # Go through each tgt sentence in batch and its corresponding best
-    # generated hypothesis and create a backtranslation data pair
-    # {id: id, source: generated backtranslation, target: original tgt}
-    return [
-        {
-            "id": id.item(),
-            "target": id_to_src[id.item()],
-            "source": hypos[0]["tokens"].cpu(),
-        }
-        for id, hypos in zip(collated_samples["id"], generated_sources)
-    ]
-
-
-class BacktranslationDataset(FairseqDataset):
-    """
-    Sets up a backtranslation dataset which takes a tgt batch, generates
-    a src using a tgt-src backtranslation function (*backtranslation_fn*),
-    and returns the corresponding `{generated src, input tgt}` batch.
-
-    Args:
-        tgt_dataset (~fairseq.data.FairseqDataset): the dataset to be
-            backtranslated. Only the source side of this dataset will be used.
-            After backtranslation, the source sentences in this dataset will be
-            returned as the targets.
-        src_dict (~fairseq.data.Dictionary): the dictionary of backtranslated
-            sentences.
-        tgt_dict (~fairseq.data.Dictionary, optional): the dictionary of
-            sentences to be backtranslated.
-        backtranslation_fn (callable, optional): function to call to generate
-            backtranslations. This is typically the `generate` method of a
-            :class:`~fairseq.sequence_generator.SequenceGenerator` object.
-            Pass in None when it is not available at initialization time, and
-            use set_backtranslation_fn function to set it when available.
-        output_collater (callable, optional): function to call on the
-            backtranslated samples to create the final batch
-            (default: ``tgt_dataset.collater``).
-        cuda: use GPU for generation
-    """
-
-    def __init__(
-        self,
-        tgt_dataset,
-        src_dict,
-        tgt_dict=None,
-        backtranslation_fn=None,
-        output_collater=None,
-        cuda=True,
-        **kwargs
-    ):
-        self.tgt_dataset = tgt_dataset
-        self.backtranslation_fn = backtranslation_fn
-        self.output_collater = (
-            output_collater if output_collater is not None else tgt_dataset.collater
-        )
-        self.cuda = cuda if torch.cuda.is_available() else False
-        self.src_dict = src_dict
-        self.tgt_dict = tgt_dict
-
-    def __getitem__(self, index):
-        """
-        Returns a single sample from *tgt_dataset*. Note that backtranslation is
-        not applied in this step; use :func:`collater` instead to backtranslate
-        a batch of samples.
-        """
-        return self.tgt_dataset[index]
-
-    def __len__(self):
-        return len(self.tgt_dataset)
-
-    def set_backtranslation_fn(self, backtranslation_fn):
-        self.backtranslation_fn = backtranslation_fn
-
-    def collater(self, samples):
-        """Merge and backtranslate a list of samples to form a mini-batch.
-
-        Using the samples from *tgt_dataset*, load a collated target sample to
-        feed to the backtranslation model. Then take the backtranslation with
-        the best score as the source and the original input as the target.
-
-        Note: we expect *tgt_dataset* to provide a function `collater()` that
-        will collate samples into the format expected by *backtranslation_fn*.
-        After backtranslation, we will feed the new list of samples (i.e., the
-        `(backtranslated source, original source)` pairs) to *output_collater*
-        and return the result.
-
-        Args:
-            samples (List[dict]): samples to backtranslate and collate
-
-        Returns:
-            dict: a mini-batch with keys coming from *output_collater*
-        """
-        if samples[0].get("is_dummy", False):
-            return samples
-        samples = backtranslate_samples(
-            samples=samples,
-            collate_fn=self.tgt_dataset.collater,
-            generate_fn=(lambda net_input: self.backtranslation_fn(net_input)),
-            cuda=self.cuda,
-        )
-        return self.output_collater(samples)
-
-    def num_tokens(self, index):
-        """Just use the tgt dataset num_tokens"""
-        return self.tgt_dataset.num_tokens(index)
-
-    def ordered_indices(self):
-        """Just use the tgt dataset ordered_indices"""
-        return self.tgt_dataset.ordered_indices()
-
-    def size(self, index):
-        """Return an example's size as a float or tuple. This value is used
-        when filtering a dataset with ``--max-positions``.
-
-        Note: we use *tgt_dataset* to approximate the length of the source
-        sentence, since we do not know the actual length until after
-        backtranslation.
-        """
-        tgt_size = self.tgt_dataset.size(index)[0]
-        return (tgt_size, tgt_size)
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.tgt_dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        return self.tgt_dataset.prefetch(indices)
diff --git a/spaces/OkamiFeng/Bark-with-Voice-Cloning/util/helper.py b/spaces/OkamiFeng/Bark-with-Voice-Cloning/util/helper.py
deleted file mode 100644
index 185613661a2f450e55a5d2add1a1e75bc08f5c19..0000000000000000000000000000000000000000
--- a/spaces/OkamiFeng/Bark-with-Voice-Cloning/util/helper.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-from datetime import datetime
-from mutagen.wave import WAVE
-from mutagen.id3._frames import *
-
-def create_filename(path, seed, name, extension):
-    now = datetime.now()
-    date_str =now.strftime("%m-%d-%Y")
-    outputs_folder = os.path.join(os.getcwd(), path)
-    if not os.path.exists(outputs_folder):
-        os.makedirs(outputs_folder)
-
-    sub_folder = os.path.join(outputs_folder, date_str)
-    if not os.path.exists(sub_folder):
-        os.makedirs(sub_folder)
-
-    time_str = now.strftime("%H-%M-%S")
-    if seed == None:
-        file_name = f"{name}_{time_str}{extension}"
-    else:
-        file_name = f"{name}_{time_str}_s{seed}{extension}"
-    return os.path.join(sub_folder, file_name)
-
-
-def add_id3_tag(filename, text, speakername, seed):
-    audio = WAVE(filename)
-    if speakername == None:
-        speakername = "Unconditional"
-
-    # write id3 tag with text truncated to 60 chars, as a precaution...
-    audio["TIT2"] = TIT2(encoding=3, text=text[:60])
-    audio["TPE1"] = TPE1(encoding=3, text=f"Voice {speakername} using Seed={seed}")
-    audio["TPUB"] = TPUB(encoding=3, text="Bark by Suno AI")
-    audio["COMMENT"] = COMM(encoding=3, text="Generated with Bark GUI - Text-Prompted Generative Audio Model. Visit https://github.com/C0untFloyd/bark-gui")
-    audio.save()
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/__init__.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/__init__.py
deleted file mode 100644
index ab3c63b5b456a7fb878757e25768a3634f76ae5b..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/data/transforms/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from fvcore.transforms.transform import Transform, TransformList  # order them first
-from fvcore.transforms.transform import *
-from .transform import *
-from .augmentation import *
-from .augmentation_impl import *
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
-
-
-from detectron2.utils.env import fixup_module_metadata
-
-fixup_module_metadata(__name__, globals(), __all__)
-del fixup_module_metadata
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/solver/__init__.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/solver/__init__.py
deleted file mode 100644
index 9a2dbd35bb24f0d4a979bc8f304142376d87e7ec..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/solver/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .build import build_lr_scheduler, build_optimizer, get_default_optimizer_params
-from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR, LRMultiplier, WarmupParamScheduler
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/OptimalScale/Robin-33b/lmflow/utils/data_utils.py b/spaces/OptimalScale/Robin-33b/lmflow/utils/data_utils.py
deleted file mode 100644
index 25ff71ef3d5e953e7dd26fb595e5b35a3b0a273e..0000000000000000000000000000000000000000
--- a/spaces/OptimalScale/Robin-33b/lmflow/utils/data_utils.py
+++ /dev/null
@@ -1,212 +0,0 @@
-"""The program includes several functions: setting a random seed, 
-loading data from a JSON file, batching data, and extracting answers from generated text.
-"""
-
-import random
-import numpy as np
-import torch
-import json
-import re
-def set_random_seed(seed: int):
-    """
-    Set the random seed for `random`, `numpy`, `torch`, `torch.cuda`.
-
-    Parameters
-    ------------
-    seed : int
-        The default seed.
-        
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(seed)
-
-def load_data(file_name: str):
-    """
-    Load data with file name.
-
-    Parameters
-    ------------
-    file_name : str.
-        The dataset file name.
-    
-    Returns
-    ------------
-    inputs : list.
-        The input texts of the dataset.
-    outputs : list.
-        The output texts file datasets.    
-    len : int.
-        The length of the dataset.
-    """
-    inputs = []
-    outputs = []
-    type = ""
-    with open(file_name, encoding='utf-8') as f:
-        json_data = json.load(f)
-        type = json_data["type"]
-        for line in json_data["instances"]:
-            inputs.append(line["input"])
-            outputs.append(line["output"])
-
-    print(f"load dataset {file_name} success.\n")
-    print(f"Type : {type}, datasize : {len(outputs)}")
-
-    return inputs, outputs, len(outputs)
-
-def batchlize(examples: list, batch_size: int, random_shuffle: bool):
-    """
-    Convert examples to a dataloader.
-
-    Parameters
-    ------------
-    examples : list.
-        Data list.
-    batch_size : int.
-
-    random_shuffle : bool
-        If true, the dataloader shuffle the training data.
-    
-    Returns
-    ------------
-    dataloader:
-        Dataloader with batch generator.
-    """
-    size = 0
-    dataloader = []
-    length = len(examples)
-    if (random_shuffle):
-        random.shuffle(examples)
-    while size < length:
-        if length - size > batch_size:
-            dataloader.append(examples[size : size+batch_size])
-            size += batch_size
-        else:
-            dataloader.append(examples[size : size+(length-size)])
-            size += (length - size)
-    return dataloader
-
-
-
-def answer_extraction(response, answer_type=None):   #use this funtion to extract answers from generated text
-
-    """
-    Use this funtion to extract answers from generated text
-
-    Parameters
-    ------------
-    args : 
-        Arguments.
-    response : str
-        plain string response.
-
-
-    Returns
-    ------------
-    answer:
-        Decoded answer (such as A, B, C, D, E for mutiple-choice QA).
-    """
-
-    # temp = response["generated_text"]
-    temp = response
-    if answer_type in ("gsm8k", "svamp", "asdiv", "addsub", "singleeq", "multiarith", "math"):
-        temp = temp.replace(",", "")
-        temp = [s for s in re.findall(r'-?\d+\.?\d*', temp)]
-    elif answer_type in ("aqua", "csqa", "multiple_choice"):
-        temp = re.findall(r'A|B|C|D|E', temp)
-    elif answer_type in ("strategyqa", "coin_flip"):
-        temp = temp.lower()
-        temp = re.sub("\"|\'|\n|\.|\s|\:|\,"," ", temp)
-        temp = temp.split(" ")
-        temp = [i for i in temp if i in ("yes", "no")]
-    elif answer_type in ("last_letters"):
-        temp = re.sub("\"|\'|\n|\.|\s","", temp)
-        temp = [temp]
-    elif answer_type in ("pubmedqa", "binary_choice"):
-        # pattern = "Output: (yes|no|maybe)"
-        # sttr = re.search(pattern, temp)
-        # answer = sttr.group(0)[8:] if sttr is not None else "N/A"
-        pattern = "(answer|Answer|ANSWER|output|Output|OUTPUT|A): \(*(yes|Yes|YES|no|No|NO|maybe|Maybe|MAYBE)"
-        sttr = re.search(pattern, temp)
-        if sttr is not None:
-            mid_answer = sttr.group(0)
-            mid_answer = mid_answer.split(":")[-1].strip()
-            answer = mid_answer.lower()
-        else:
-            pattern = "(yes|Yes|YES|no|No|NO|maybe|Maybe|MAYBE)(\.|\s)"
-            sttr = re.search(pattern, temp)
-            if sttr is not None:
-                answer = sttr.group(0)[:-1].lower()
-            else:
-                answer = "N/A"
-        return answer
-    elif answer_type == "medmcqa":
-        # pattern = "Output: (A|B|C|D)."
-        # sttr = re.search(pattern, temp)
-        # answer = sttr.group(0)[8:-1].lower() if sttr is not None else "N/A"
-        pattern = "(answer|Answer|ANSWER|output|Output|OUTPUT|A): \(*(A|B|C|D|a|b|c|d)"
-        sttr = re.search(pattern, temp)
-        if sttr is not None:
-            mid_answer = sttr.group(0)
-            answer = mid_answer[-1].lower()
-        else:
-            pattern = "\(*(A|B|C|D|a|b|c|d)\)*(\.|\s)"
-            sttr = re.search(pattern, temp)
-            if sttr is not None:
-                if '(' in sttr.group(0):
-                    answer = sttr.group(0)[1].lower()
-                else:
-                    answer = sttr.group(0)[0].lower()
-            else:
-                answer = "N/A"
-        return answer
-
-    elif answer_type == "usmle":
-        # pattern = "Output: (A|B|C|D)."
-        # sttr = re.search(pattern, temp)
-        # answer = sttr.group(0)[8:-1].lower() if sttr is not None else "N/A"
-        pattern = "(Answer|Output|A): \(*(A|B|C|D|a|b|c|d)"
-        sttr = re.search(pattern, temp)
-        if sttr is not None:
-            mid_answer = sttr.group(0)
-            answer = mid_answer[-1].lower()
-        else:
-            pattern = "\(*(A|B|C|D|a|b|c|d)\)*(\.|\s)"
-            sttr = re.search(pattern, temp)
-            if sttr is not None:
-                if '(' in sttr.group(0):
-                    answer = sttr.group(0)[1].lower()
-                else:
-                    answer = sttr.group(0)[0].lower()
-            else:
-                answer = "N/A"
-        return answer
-    elif answer_type == "text":
-        return response
-    else:
-        raise NotImplementedError(f"Unsupported answer type: {answer_type}")
-
-    if len(temp) != 0:
-        answer = temp[-1]
-        # if there is . at the end of answer, remove it
-        # e.g. answer = 64.
-        if answer != "":
-            if answer[-1] == ".":
-                answer = answer[:-1]
-
-            # round the answer to nearest integer
-        if answer_type in ("gsm8k", "svamp"):
-            try:
-                answer = str(round(float(answer)))
-            except:
-                answer = "" # no sol or sol doesn't have valid format
-        elif answer_type in ("last_letters"):
-            try:
-                answer = answer[-args.concat_length:]
-            except:
-                answer = ""
-    else:
-        answer = ""
-    return answer
diff --git a/spaces/OswaldDev/Image-enhancement/models/network_swinir.py b/spaces/OswaldDev/Image-enhancement/models/network_swinir.py
deleted file mode 100644
index 702e1fd2f5f7dd11feda3ab1999e65dd293250fb..0000000000000000000000000000000000000000
--- a/spaces/OswaldDev/Image-enhancement/models/network_swinir.py
+++ /dev/null
@@ -1,866 +0,0 @@
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class SwinIR(nn.Module):
-    r""" SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(SwinIR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, 'only support x4 now.'
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-        
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = SwinIR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/spaces/OswaldDev/Image-enhancement/utils/util_calculate_psnr_ssim.py b/spaces/OswaldDev/Image-enhancement/utils/util_calculate_psnr_ssim.py
deleted file mode 100644
index 1a8fb27161f9c1fd3e37b14654dfe05eaadf619c..0000000000000000000000000000000000000000
--- a/spaces/OswaldDev/Image-enhancement/utils/util_calculate_psnr_ssim.py
+++ /dev/null
@@ -1,346 +0,0 @@
-import cv2
-import numpy as np
-import torch
-
-
-def calculate_psnr(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
-    """Calculate PSNR (Peak Signal-to-Noise Ratio).
-
-    Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
-
-    Args:
-        img1 (ndarray): Images with range [0, 255].
-        img2 (ndarray): Images with range [0, 255].
-        crop_border (int): Cropped pixels in each edge of an image. These
-            pixels are not involved in the PSNR calculation.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            Default: 'HWC'.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: psnr result.
-    """
-
-    assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
-    img1 = reorder_image(img1, input_order=input_order)
-    img2 = reorder_image(img2, input_order=input_order)
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-
-    if crop_border != 0:
-        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
-        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
-
-    if test_y_channel:
-        img1 = to_y_channel(img1)
-        img2 = to_y_channel(img2)
-
-    mse = np.mean((img1 - img2) ** 2)
-    if mse == 0:
-        return float('inf')
-    return 20. * np.log10(255. / np.sqrt(mse))
-
-
-def _ssim(img1, img2):
-    """Calculate SSIM (structural similarity) for one channel images.
-
-    It is called by func:`calculate_ssim`.
-
-    Args:
-        img1 (ndarray): Images with range [0, 255] with order 'HWC'.
-        img2 (ndarray): Images with range [0, 255] with order 'HWC'.
-
-    Returns:
-        float: ssim result.
-    """
-
-    C1 = (0.01 * 255) ** 2
-    C2 = (0.03 * 255) ** 2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1 ** 2
-    mu2_sq = mu2 ** 2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-def calculate_ssim(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
-    """Calculate SSIM (structural similarity).
-
-    Ref:
-    Image quality assessment: From error visibility to structural similarity
-
-    The results are the same as that of the official released MATLAB code in
-    https://ece.uwaterloo.ca/~z70wang/research/ssim/.
-
-    For three-channel images, SSIM is calculated for each channel and then
-    averaged.
-
-    Args:
-        img1 (ndarray): Images with range [0, 255].
-        img2 (ndarray): Images with range [0, 255].
-        crop_border (int): Cropped pixels in each edge of an image. These
-            pixels are not involved in the SSIM calculation.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            Default: 'HWC'.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: ssim result.
-    """
-
-    assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
-    img1 = reorder_image(img1, input_order=input_order)
-    img2 = reorder_image(img2, input_order=input_order)
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-
-    if crop_border != 0:
-        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
-        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
-
-    if test_y_channel:
-        img1 = to_y_channel(img1)
-        img2 = to_y_channel(img2)
-
-    ssims = []
-    for i in range(img1.shape[2]):
-        ssims.append(_ssim(img1[..., i], img2[..., i]))
-    return np.array(ssims).mean()
-
-
-def _blocking_effect_factor(im):
-    block_size = 8
-
-    block_horizontal_positions = torch.arange(7, im.shape[3] - 1, 8)
-    block_vertical_positions = torch.arange(7, im.shape[2] - 1, 8)
-
-    horizontal_block_difference = (
-                (im[:, :, :, block_horizontal_positions] - im[:, :, :, block_horizontal_positions + 1]) ** 2).sum(
-        3).sum(2).sum(1)
-    vertical_block_difference = (
-                (im[:, :, block_vertical_positions, :] - im[:, :, block_vertical_positions + 1, :]) ** 2).sum(3).sum(
-        2).sum(1)
-
-    nonblock_horizontal_positions = np.setdiff1d(torch.arange(0, im.shape[3] - 1), block_horizontal_positions)
-    nonblock_vertical_positions = np.setdiff1d(torch.arange(0, im.shape[2] - 1), block_vertical_positions)
-
-    horizontal_nonblock_difference = (
-                (im[:, :, :, nonblock_horizontal_positions] - im[:, :, :, nonblock_horizontal_positions + 1]) ** 2).sum(
-        3).sum(2).sum(1)
-    vertical_nonblock_difference = (
-                (im[:, :, nonblock_vertical_positions, :] - im[:, :, nonblock_vertical_positions + 1, :]) ** 2).sum(
-        3).sum(2).sum(1)
-
-    n_boundary_horiz = im.shape[2] * (im.shape[3] // block_size - 1)
-    n_boundary_vert = im.shape[3] * (im.shape[2] // block_size - 1)
-    boundary_difference = (horizontal_block_difference + vertical_block_difference) / (
-                n_boundary_horiz + n_boundary_vert)
-
-    n_nonboundary_horiz = im.shape[2] * (im.shape[3] - 1) - n_boundary_horiz
-    n_nonboundary_vert = im.shape[3] * (im.shape[2] - 1) - n_boundary_vert
-    nonboundary_difference = (horizontal_nonblock_difference + vertical_nonblock_difference) / (
-                n_nonboundary_horiz + n_nonboundary_vert)
-
-    scaler = np.log2(block_size) / np.log2(min([im.shape[2], im.shape[3]]))
-    bef = scaler * (boundary_difference - nonboundary_difference)
-
-    bef[boundary_difference <= nonboundary_difference] = 0
-    return bef
-
-
-def calculate_psnrb(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
-    """Calculate PSNR-B (Peak Signal-to-Noise Ratio).
-
-    Ref: Quality assessment of deblocked images, for JPEG image deblocking evaluation
-    # https://gitlab.com/Queuecumber/quantization-guided-ac/-/blob/master/metrics/psnrb.py
-
-    Args:
-        img1 (ndarray): Images with range [0, 255].
-        img2 (ndarray): Images with range [0, 255].
-        crop_border (int): Cropped pixels in each edge of an image. These
-            pixels are not involved in the PSNR calculation.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            Default: 'HWC'.
-        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
-
-    Returns:
-        float: psnr result.
-    """
-
-    assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.shape}, {img2.shape}.')
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' '"HWC" and "CHW"')
-    img1 = reorder_image(img1, input_order=input_order)
-    img2 = reorder_image(img2, input_order=input_order)
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-
-    if crop_border != 0:
-        img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
-        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
-
-    if test_y_channel:
-        img1 = to_y_channel(img1)
-        img2 = to_y_channel(img2)
-
-    # follow https://gitlab.com/Queuecumber/quantization-guided-ac/-/blob/master/metrics/psnrb.py
-    img1 = torch.from_numpy(img1).permute(2, 0, 1).unsqueeze(0) / 255.
-    img2 = torch.from_numpy(img2).permute(2, 0, 1).unsqueeze(0) / 255.
-
-    total = 0
-    for c in range(img1.shape[1]):
-        mse = torch.nn.functional.mse_loss(img1[:, c:c + 1, :, :], img2[:, c:c + 1, :, :], reduction='none')
-        bef = _blocking_effect_factor(img1[:, c:c + 1, :, :])
-
-        mse = mse.view(mse.shape[0], -1).mean(1)
-        total += 10 * torch.log10(1 / (mse + bef))
-
-    return float(total) / img1.shape[1]
-
-
-def reorder_image(img, input_order='HWC'):
-    """Reorder images to 'HWC' order.
-
-    If the input_order is (h, w), return (h, w, 1);
-    If the input_order is (c, h, w), return (h, w, c);
-    If the input_order is (h, w, c), return as it is.
-
-    Args:
-        img (ndarray): Input image.
-        input_order (str): Whether the input order is 'HWC' or 'CHW'.
-            If the input image shape is (h, w), input_order will not have
-            effects. Default: 'HWC'.
-
-    Returns:
-        ndarray: reordered image.
-    """
-
-    if input_order not in ['HWC', 'CHW']:
-        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' "'HWC' and 'CHW'")
-    if len(img.shape) == 2:
-        img = img[..., None]
-    if input_order == 'CHW':
-        img = img.transpose(1, 2, 0)
-    return img
-
-
-def to_y_channel(img):
-    """Change to Y channel of YCbCr.
-
-    Args:
-        img (ndarray): Images with range [0, 255].
-
-    Returns:
-        (ndarray): Images with range [0, 255] (float type) without round.
-    """
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 3 and img.shape[2] == 3:
-        img = bgr2ycbcr(img, y_only=True)
-        img = img[..., None]
-    return img * 255.
-
-
-def _convert_input_type_range(img):
-    """Convert the type and range of the input image.
-
-    It converts the input image to np.float32 type and range of [0, 1].
-    It is mainly used for pre-processing the input image in colorspace
-    convertion functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with type of np.float32 and range of
-            [0, 1].
-    """
-    img_type = img.dtype
-    img = img.astype(np.float32)
-    if img_type == np.float32:
-        pass
-    elif img_type == np.uint8:
-        img /= 255.
-    else:
-        raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}')
-    return img
-
-
-def _convert_output_type_range(img, dst_type):
-    """Convert the type and range of the image according to dst_type.
-
-    It converts the image to desired type and range. If `dst_type` is np.uint8,
-    images will be converted to np.uint8 type with range [0, 255]. If
-    `dst_type` is np.float32, it converts the image to np.float32 type with
-    range [0, 1].
-    It is mainly used for post-processing images in colorspace convertion
-    functions such as rgb2ycbcr and ycbcr2rgb.
-
-    Args:
-        img (ndarray): The image to be converted with np.float32 type and
-            range [0, 255].
-        dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
-            converts the image to np.uint8 type with range [0, 255]. If
-            dst_type is np.float32, it converts the image to np.float32 type
-            with range [0, 1].
-
-    Returns:
-        (ndarray): The converted image with desired type and range.
-    """
-    if dst_type not in (np.uint8, np.float32):
-        raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}')
-    if dst_type == np.uint8:
-        img = img.round()
-    else:
-        img /= 255.
-    return img.astype(dst_type)
-
-
-def bgr2ycbcr(img, y_only=False):
-    """Convert a BGR image to YCbCr image.
-
-    The bgr version of rgb2ycbcr.
-    It implements the ITU-R BT.601 conversion for standard-definition
-    television. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
-
-    It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
-    In OpenCV, it implements a JPEG conversion. See more details in
-    https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
-
-    Args:
-        img (ndarray): The input image. It accepts:
-            1. np.uint8 type with range [0, 255];
-            2. np.float32 type with range [0, 1].
-        y_only (bool): Whether to only return Y channel. Default: False.
-
-    Returns:
-        ndarray: The converted YCbCr image. The output image has the same type
-            and range as input image.
-    """
-    img_type = img.dtype
-    img = _convert_input_type_range(img)
-    if y_only:
-        out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
-    else:
-        out_img = np.matmul(
-            img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
-    out_img = _convert_output_type_range(out_img, img_type)
-    return out_img
diff --git a/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/data/collator.py b/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/data/collator.py
deleted file mode 100644
index 44780306c69e3d5d054d06d03d08f3e2e6ade517..0000000000000000000000000000000000000000
--- a/spaces/Oumar199/Fake-Real-Face-Detection/fake_face_detection/data/collator.py
+++ /dev/null
@@ -1,33 +0,0 @@
-
-import torch
-import numpy as np
-
-def fake_face_collator(batch): 
-    """The data collator for training vision transformer models on fake and real face dataset
-
-    Args:
-        batch (list): A dictionary containing the pixel values and the labels
-
-    Returns:
-        dict: The final dictionary
-    """
-    
-    new_batch = {
-        'pixel_values': [],
-        'labels': []
-    }
-    
-    for x in batch:
-        
-        pixel_values = torch.from_numpy(x['pixel_values'][0]) if isinstance(x['pixel_values'][0], np.ndarray) \
-            else x['pixel_values'][0]
-        
-        new_batch['pixel_values'].append(pixel_values)
-        
-        new_batch['labels'].append(torch.tensor(x['labels']))
-    
-    new_batch['pixel_values'] = torch.stack(new_batch['pixel_values'])
-    
-    new_batch['labels'] = torch.stack(new_batch['labels'])
-    
-    return new_batch
diff --git a/spaces/PaddlePaddle/UIE-X/app.py b/spaces/PaddlePaddle/UIE-X/app.py
deleted file mode 100644
index b29e9a7c0abcd39d797a2d79dfb669751307b3f0..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/UIE-X/app.py
+++ /dev/null
@@ -1,386 +0,0 @@
-#-*- coding: UTF-8 -*-
-# Copyright 2022 the HuggingFace Team.
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import traceback
-import base64
-
-import gradio as gr
-import cv2
-
-from paddlenlp import Taskflow
-from paddlenlp.utils.doc_parser import DocParser
-
-
-doc_parser = DocParser()
-task_instance = Taskflow(
-    "information_extraction",
-    model="uie-x-base",
-    task_path="PaddlePaddle/uie-x-base",
-    from_hf_hub=True)
-
-examples = [
-    [
-        "business_card.png",
-        "Name;Title;Web Link;Email;Address",
-    ],
-    [
-        "license.jpeg",
-        "Name;DOB;ISS;EXP",
-    ],
-    [
-        "statements.png",
-        "Date|Gross profit",
-    ],    
-    [
-        "invoice.jpeg",
-        "名称;纳税人识别号;开票日期",
-    ],
-    [
-        "custom.jpeg",
-        "收发货人;进口口岸;进口日期;运输方式;征免性质;境内目的地;运输工具名称;包装种类;件数;合同协议号"
-    ],
-    [
-        "resume.png",
-        "职位;年龄;学校|时间;学校|专业",
-    ],
-]
-
-example_files = {
-    "Name;Title;Web Link;Email;Address": "business_card.png",
-    "Name;DOB;ISS;EXP": "license.jpeg",
-    "Date|Gross profit": "statements.png",
-    "职位;年龄;学校|时间;学校|专业": "resume.png",
-    "收发货人;进口口岸;进口日期;运输方式;征免性质;境内目的地;运输工具名称;包装种类;件数;合同协议号": "custom.jpeg",
-    "名称;纳税人识别号;开票日期": "invoice.jpeg",
-}
-
-lang_map = {
-    "resume.png": "ch",
-    "custom.jpeg": "ch",
-    "business_card.png": "en",
-    "invoice.jpeg": "ch",
-    "license.jpeg": "en",
-    "statements.png": "en",
-}
-
-def dbc2sbc(s):
-    rs = ""
-    for char in s:
-        code = ord(char)
-        if code == 0x3000:
-            code = 0x0020
-        else:
-            code -= 0xfee0
-        if not (0x0021 <= code and code <= 0x7e):
-            rs += char
-            continue
-        rs += chr(code)
-    return rs
-
-
-def BGR2RGB(img):
-    pilimg = img.copy()
-    pilimg[:, :, 0] = img[:, :, 2]
-    pilimg[:, :, 2] = img[:, :, 0]
-    return pilimg
-
-
-def np2base64(image_np):
-    image_np = BGR2RGB(image_np)
-    image = cv2.imencode('.jpg', image_np)[1]
-    base64_str = str(base64.b64encode(image))[2:-1]
-    return base64_str
-
-
-def process_path(path):
-    error = None
-    if path:
-        try:
-            if path.endswith(".pdf"):
-                images_list = [doc_parser.read_pdf(path)]
-            else:
-                images_list = [doc_parser.read_image(path)]
-            return (
-                path,
-                gr.update(visible=True, value=images_list),
-                gr.update(visible=True),
-                gr.update(visible=False, value=None),
-                gr.update(visible=False, value=None),
-                None,
-            )
-        except Exception as e:
-            traceback.print_exc()
-            error = str(e)
-    return (
-        None,
-        gr.update(visible=False, value=None),
-        gr.update(visible=False),
-        gr.update(visible=False, value=None),
-        gr.update(visible=False, value=None),
-        gr.update(visible=True, value=error) if error is not None else None,
-        None,
-    )
-
-
-def process_upload(file):
-    if file:
-        return process_path(file.name)
-    else:
-        return (
-            None,
-            gr.update(visible=False, value=None),
-            gr.update(visible=False),
-            gr.update(visible=False, value=None),
-            gr.update(visible=False, value=None),
-            None,
-        )
-
-def get_schema(schema_str):
-    def _is_ch(s):
-        for ch in s:
-            if "\u4e00" <= ch <= "\u9fff":
-                return True
-        return False
-    schema_lang = "ch" if _is_ch(schema_str) else "en"
-    schema = schema_str.split(";")
-    schema_list = []
-    for s in schema:
-        cand = s.split("|")
-        if len(cand) == 1:
-            schema_list.append(cand[0])
-        else:
-            subject = cand[0]
-            relations = cand[1:]
-            added = False
-            for a in schema_list:
-                if isinstance(a, dict):
-                    if subject in a.keys():
-                        a[subject].extend(relations)
-                        added = True
-                        break
-            if not added:
-                a = {subject: relations}
-                schema_list.append(a)
-    return schema_list, schema_lang
-
-
-def run_taskflow(document, schema, argument):
-    task_instance.set_schema(schema)
-    task_instance.set_argument(argument)
-    return task_instance({'doc': document})
-
-
-def process_doc(document, schema, ocr_lang, layout_analysis):
-    if [document, schema] in examples:
-        ocr_lang = lang_map[document]
-
-    if not schema:
-        schema = '时间;组织机构;人物'
-    if document is None:
-        return None, None
-
-    layout_analysis = True if layout_analysis == "yes" else False
-    schema, schema_lang = get_schema(dbc2sbc(schema))
-    argument = {
-        "ocr_lang": ocr_lang,
-        "schema_lang": schema_lang,
-        "layout_analysis": layout_analysis
-    }
-    prediction = run_taskflow(document, schema, argument)[0]
-
-    if document.endswith(".pdf"):
-        _image = doc_parser.read_pdf(document)
-    else:
-        _image = doc_parser.read_image(document)
-
-    img_show = doc_parser.write_image_with_results(
-        np2base64(_image),
-        result=prediction,
-        return_image=True)
-    img_list = [img_show]
-
-    return (
-        gr.update(visible=True, value=img_list),
-        gr.update(visible=True, value=prediction),
-    )
-
-
-def load_example_document(img, schema, ocr_lang, layout_analysis):
-    if img is not None:
-        document = example_files[schema]
-        choice = lang_map[document].split("-")
-        ocr_lang = choice[0]
-        preview, answer = process_doc(document, schema, ocr_lang, layout_analysis)
-        return document, schema, preview, gr.update(visible=True), answer
-    else:
-        return None, None, None, gr.update(visible=False), None
-
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-
-with gr.Blocks() as demo:
-    gr.HTML(read_content("header.html"))
-    gr.Markdown(
-        "Open-sourced by [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP), **UIE-X** is a universal information extraction engine for both scanned document and text inputs. It supports Entity Extraction, Relation Extraction and Event Extraction tasks. "
-        "UIE-X performs well on a zero-shot settings, which is enabled by a flexible schema that allows you to specify extraction targets with simple natural language. "
-        "Moreover, on [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP), we provide a comprehensive and easy-to-use fine-tuning and few-shot customization workflow. <br>" 
-        "Want to dive deeper? Check out our [AIStudio Notebook](https://aistudio.baidu.com/aistudio/projectdetail/5261592) and [Colab Notebook](https://colab.research.google.com/drive/1ZY_ELZgoemJNoa6baWpgtzebLgoCT8MK?usp=sharing). "
-        "For more details, please visit our [GitHub](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/information_extraction/README_en.md)"
-    )
-
-    document = gr.Variable()
-    is_text = gr.Variable()
-    example_schema = gr.Textbox(visible=False)
-    example_image = gr.Image(visible=False)
-    with gr.Row(equal_height=True):
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## 1. Select a file 选择文件", elem_id="select-a-file")
-                img_clear_button = gr.Button(
-                    "Clear", variant="secondary", elem_id="file-clear", visible=False
-                )
-            image = gr.Gallery(visible=False)
-            with gr.Row(equal_height=True):
-                with gr.Column():
-                    with gr.Row():
-                        url = gr.Textbox(
-                            show_label=False,
-                            placeholder="URL",
-                            lines=1,
-                            max_lines=1,
-                            elem_id="url-textbox",
-                        )
-                        submit = gr.Button("Get")
-                    url_error = gr.Textbox(
-                        visible=False,
-                        elem_id="url-error",
-                        max_lines=1,
-                        interactive=False,
-                        label="Error",
-                    )
-            gr.Markdown("## <center> — or — </center>")
-            upload = gr.File(label=None, interactive=True, elem_id="short-upload-box")
-            gr.Examples(
-                examples=examples,
-                inputs=[example_image, example_schema],
-            )
-
-        with gr.Column():
-            gr.Markdown("## 2. Information Extraction 信息抽取 ")
-            gr.Markdown("### 👉 Set a schema 设置schema")
-            gr.Markdown("Entity extraction: entity type should be separated by ';', e.g. **Person;Organization**")
-            gr.Markdown("实体抽取：实体类别之间以';'分割，例如 **人物；组织机构**")
-            gr.Markdown("Relation extraction: set the subject and relation type, separated by '|', e.g. **Person|Date;Person|Email**")
-            gr.Markdown("关系抽取：需配置主体和关系类别，中间以'|'分割，例如 **人物|出生时间；人物|邮箱**")
-            gr.Markdown("### 👉 Model customization 模型定制")
-            gr.Markdown("We recommend to further improve the extraction performance in specific domain through the process of [data annotation & fine-tuning](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/information_extraction/document/README_en.md)")
-            gr.Markdown("我们建议通过[数据标注+微调](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/information_extraction/document/README_en.md)的流程进一步增强模型在特定场景的效果")
-
-            schema = gr.Textbox(
-                label="Schema",
-                placeholder="e.g. Name|Company;Name|Position;Email;Phone Number",
-                lines=1,
-                max_lines=1,
-            )
-
-            ocr_lang = gr.Radio(
-                choices=["ch", "en"],
-                value="en",
-                label="OCR语言 / OCR Language (Please choose ch for Chinese images.)",
-            )
-
-            layout_analysis = gr.Radio(
-                choices=["yes", "no"],
-                value="no",
-                label="版面分析 / Layout analysis (Better extraction for multi-line text)",
-            )
-
-            with gr.Row():
-                clear_button = gr.Button("Clear", variant="secondary")
-                submit_button = gr.Button(
-                    "Submit", variant="primary", elem_id="submit-button"
-                )          
-            with gr.Column():
-                output = gr.JSON(label="Output", visible=False)
-
-    for cb in [img_clear_button, clear_button]:
-        cb.click(
-            lambda _: (
-                gr.update(visible=False, value=None),
-                None,
-                gr.update(visible=False, value=None),
-                gr.update(visible=False),
-                None,
-                None,
-                None,
-                gr.update(visible=False, value=None),
-                None,
-            ),
-            inputs=clear_button,
-            outputs=[
-                image,
-                document,
-                output,
-                img_clear_button,
-                example_image,
-                upload,
-                url,
-                url_error,
-                schema,
-            ],
-        )
-
-    upload.change(
-        fn=process_upload,
-        inputs=[upload],
-        outputs=[document, image, img_clear_button, output, url_error],
-    )
-    submit.click(
-        fn=process_path,
-        inputs=[url],
-        outputs=[document, image, img_clear_button, output, url_error],
-    )
-
-    schema.submit(
-        fn=process_doc,
-        inputs=[document, schema, ocr_lang, layout_analysis],
-        outputs=[image, output],
-    )
-
-    submit_button.click(
-        fn=process_doc,
-        inputs=[document, schema, ocr_lang, layout_analysis],
-        outputs=[image, output],
-    )
-
-    example_image.change(
-        fn=load_example_document,
-        inputs=[example_image, example_schema, ocr_lang, layout_analysis],
-        outputs=[document, schema, image, img_clear_button, output],
-    )
-
-    gr.HTML(read_content("footer.html"))
-
-
-if __name__ == "__main__":
-    demo.queue().launch()
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/top-repl.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/top-repl.go
deleted file mode 100644
index 469885b7e271499c43931f637a46fad74d21b5f9..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/ice-9/top-repl.go and /dev/null differ
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-13.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-13.go
deleted file mode 100644
index b70563da6b0892d4e5f7ea039e77f8450155e446..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-13.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/agent/agent.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/agent/agent.py
deleted file mode 100644
index ee7885f8844022597321fa6b492430ec34c0d6b9..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/agent/agent.py
+++ /dev/null
@@ -1,197 +0,0 @@
-from colorama import Fore, Style
-
-from autogpt.app import execute_command, get_command
-from autogpt.chat import chat_with_ai, create_chat_message
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques
-from autogpt.json_utils.utilities import validate_json
-from autogpt.logs import logger, print_assistant_thoughts
-from autogpt.speech import say_text
-from autogpt.spinner import Spinner
-from autogpt.utils import clean_input
-
-
-class Agent:
-    """Agent class for interacting with Auto-GPT.
-
-    Attributes:
-        ai_name: The name of the agent.
-        memory: The memory object to use.
-        full_message_history: The full message history.
-        next_action_count: The number of actions to execute.
-        system_prompt: The system prompt is the initial prompt that defines everything the AI needs to know to achieve its task successfully.
-        Currently, the dynamic and customizable information in the system prompt are ai_name, description and goals.
-
-        triggering_prompt: The last sentence the AI will see before answering. For Auto-GPT, this prompt is:
-            Determine which next command to use, and respond using the format specified above:
-            The triggering prompt is not part of the system prompt because between the system prompt and the triggering
-            prompt we have contextual information that can distract the AI and make it forget that its goal is to find the next task to achieve.
-            SYSTEM PROMPT
-            CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant)
-            TRIGGERING PROMPT
-
-        The triggering prompt reminds the AI about its short term meta task (defining the next task)
-    """
-
-    def __init__(
-        self,
-        ai_name,
-        memory,
-        full_message_history,
-        next_action_count,
-        system_prompt,
-        triggering_prompt,
-    ):
-        self.ai_name = ai_name
-        self.memory = memory
-        self.full_message_history = full_message_history
-        self.next_action_count = next_action_count
-        self.system_prompt = system_prompt
-        self.triggering_prompt = triggering_prompt
-
-    def start_interaction_loop(self):
-        # Interaction Loop
-        cfg = Config()
-        loop_count = 0
-        command_name = None
-        arguments = None
-        user_input = ""
-
-        while True:
-            # Discontinue if continuous limit is reached
-            loop_count += 1
-            if (
-                cfg.continuous_mode
-                and cfg.continuous_limit > 0
-                and loop_count > cfg.continuous_limit
-            ):
-                logger.typewriter_log(
-                    "Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
-                )
-                break
-
-            # Send message to AI, get response
-            with Spinner("Thinking... "):
-                assistant_reply = chat_with_ai(
-                    self.system_prompt,
-                    self.triggering_prompt,
-                    self.full_message_history,
-                    self.memory,
-                    cfg.fast_token_limit,
-                )  # TODO: This hardcodes the model to use GPT3.5. Make this an argument
-
-            assistant_reply_json = fix_json_using_multiple_techniques(assistant_reply)
-
-            # Print Assistant thoughts
-            if assistant_reply_json != {}:
-                validate_json(assistant_reply_json, "llm_response_format_1")
-                # Get command name and arguments
-                try:
-                    print_assistant_thoughts(self.ai_name, assistant_reply_json)
-                    command_name, arguments = get_command(assistant_reply_json)
-                    # command_name, arguments = assistant_reply_json_valid["command"]["name"], assistant_reply_json_valid["command"]["args"]
-                    if cfg.speak_mode:
-                        say_text(f"I want to execute {command_name}")
-                except Exception as e:
-                    logger.error("Error: \n", str(e))
-
-            if not cfg.continuous_mode and self.next_action_count == 0:
-                ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
-                # Get key press: Prompt the user to press enter to continue or escape
-                # to exit
-                logger.typewriter_log(
-                    "NEXT ACTION: ",
-                    Fore.CYAN,
-                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}  "
-                    f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
-                )
-                print(
-                    "Enter 'y' to authorise command, 'y -N' to run N continuous "
-                    "commands, 'n' to exit program, or enter feedback for "
-                    f"{self.ai_name}...",
-                    flush=True,
-                )
-                while True:
-                    console_input = clean_input(
-                        Fore.MAGENTA + "Input:" + Style.RESET_ALL
-                    )
-                    if console_input.lower().strip() == "y":
-                        user_input = "GENERATE NEXT COMMAND JSON"
-                        break
-                    elif console_input.lower().strip() == "":
-                        print("Invalid input format.")
-                        continue
-                    elif console_input.lower().startswith("y -"):
-                        try:
-                            self.next_action_count = abs(
-                                int(console_input.split(" ")[1])
-                            )
-                            user_input = "GENERATE NEXT COMMAND JSON"
-                        except ValueError:
-                            print(
-                                "Invalid input format. Please enter 'y -n' where n is"
-                                " the number of continuous tasks."
-                            )
-                            continue
-                        break
-                    elif console_input.lower() == "n":
-                        user_input = "EXIT"
-                        break
-                    else:
-                        user_input = console_input
-                        command_name = "human_feedback"
-                        break
-
-                if user_input == "GENERATE NEXT COMMAND JSON":
-                    logger.typewriter_log(
-                        "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
-                        Fore.MAGENTA,
-                        "",
-                    )
-                elif user_input == "EXIT":
-                    print("Exiting...", flush=True)
-                    break
-            else:
-                # Print command
-                logger.typewriter_log(
-                    "NEXT ACTION: ",
-                    Fore.CYAN,
-                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
-                    f"  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
-                )
-
-            # Execute command
-            if command_name is not None and command_name.lower().startswith("error"):
-                result = (
-                    f"Command {command_name} threw the following error: {arguments}"
-                )
-            elif command_name == "human_feedback":
-                result = f"Human feedback: {user_input}"
-            else:
-                result = (
-                    f"Command {command_name} returned: "
-                    f"{execute_command(command_name, arguments)}"
-                )
-                if self.next_action_count > 0:
-                    self.next_action_count -= 1
-
-            memory_to_add = (
-                f"Assistant Reply: {assistant_reply} "
-                f"\nResult: {result} "
-                f"\nHuman Feedback: {user_input} "
-            )
-
-            self.memory.add(memory_to_add)
-
-            # Check if there's a result from the command append it to the message
-            # history
-            if result is not None:
-                self.full_message_history.append(create_chat_message("system", result))
-                logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
-            else:
-                self.full_message_history.append(
-                    create_chat_message("system", "Unable to execute command")
-                )
-                logger.typewriter_log(
-                    "SYSTEM: ", Fore.YELLOW, "Unable to execute command"
-                )
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/rpn/__init__.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/rpn/__init__.py
deleted file mode 100644
index c05e602a5e4a4f35f826068d6ee0ff9d4e011411..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/rpn/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# from .rpn import build_rpn
-from .rpn import RPNModule
-from .retina import RetinaNetModule
-from .fcos import FCOSModule
-from .atss import ATSSModule
-from .dyhead import DyHeadModule
-from .vldyhead import VLDyHeadModule
-
-_RPN_META_ARCHITECTURES = {"RPN": RPNModule,
-                           "RETINA": RetinaNetModule,
-                           "FCOS": FCOSModule,
-                           "ATSS": ATSSModule,
-                           "DYHEAD": DyHeadModule,
-                           "VLDYHEAD": VLDyHeadModule
-                           }
-
-
-def build_rpn(cfg):
-    """
-    This gives the gist of it. Not super important because it doesn't change as much
-    """
-    rpn_arch = _RPN_META_ARCHITECTURES[cfg.MODEL.RPN_ARCHITECTURE]
-    return rpn_arch(cfg)
diff --git a/spaces/RMXK/RVC_HFF/LazyImport.py b/spaces/RMXK/RVC_HFF/LazyImport.py
deleted file mode 100644
index 5bdb05ddd5a546a43adba7274b4c3465bb77f2f5..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/LazyImport.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from importlib.util import find_spec, LazyLoader, module_from_spec
-from sys import modules
-
-def lazyload(name):
-    if name in modules:
-        return modules[name]
-    else:
-        spec = find_spec(name)
-        loader = LazyLoader(spec.loader)
-        module = module_from_spec(spec)
-        modules[name] = module
-        loader.exec_module(module)
-        return module
\ No newline at end of file
diff --git a/spaces/Raaniel/Support-and-resistance/app.py b/spaces/Raaniel/Support-and-resistance/app.py
deleted file mode 100644
index 16fde9af820171b541883e1ac12a44d04b3e5995..0000000000000000000000000000000000000000
--- a/spaces/Raaniel/Support-and-resistance/app.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import pandas as pd
-import yfinance as yf
-import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-from sklearn.cluster import AgglomerativeClustering
-import streamlit as st
-import requests
-from streamlit_lottie import st_lottie
-import datetime
-
-st.set_page_config(page_title = "Support and resistance levels",
-                   page_icon = ':📈:',
-                   layout = 'wide')
-
-st.title('📈 Technical analysis 📉')
-st.header('Find support and resistance levels for :blue[price action] analysis!')
-st.markdown('''<span style="font-size:18px; font-weight:500;">
-This demo includes an implemented <em>Agglomerative Clustering</em>
-algorithm that can assist you in automatically detecting 
-potential support and resistance levels in financial markets.
-</span>''', unsafe_allow_html = True)
-st.markdown('##')
-
-def load_lottieurl(url: str):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-lottie_url__money = "https://assets1.lottiefiles.com/packages/lf20_06a6pf9i.json"
-lottie_money = load_lottieurl(lottie_url__money)
-
-st.sidebar.header('Please choose parameters: ')
-
-ticker = st.text_input('''Select stock to analyse: 
-(Make sure the ticker you search for is supported 
-by _Yahoo! Finance_).''', 'BNB-USD')
-
-interval = st.sidebar.selectbox(
-    'Select the time interval',
-    ('1d', '5d', '1wk', '1mo', '3mo'))
-
-timedelta = {'1d': 1, '5d': 5, '1wk' : 7, '1mo' : 30, '3mo' : 90}
-
-start = st.sidebar.date_input(
-    "Select the beginning date",
-    datetime.date(2022, 1, 1))
-
-end = st.sidebar.date_input(
-    "Select the ending date",
-    datetime.date(2023, 1, 1), min_value = start + datetime.timedelta(timedelta[interval]))
-
-df = yf.download(ticker, start = start, end = end, interval = interval)
-df.index = pd.to_datetime(df.index).strftime("%d-%m-%Y")
-df = df.drop(columns = ["Adj Close"])
-
-num_clusters = st.sidebar.slider(
-    'Select the number of clusters (affects number of levels you will get)',
-    1, 7, 3)
-
-rolling_wave_length = st.sidebar.slider(
-    '''Select the length of rolling wave 
-    (select more the more long-term biased you are)''',
-    1, len(df)//5, 1)
-
-left_column, right_column = st.columns(2)
-
-left_column.markdown('<span style="font-size:20px; font-weight:600; letter-spacing:2px;">Preview data:</span>',
-            unsafe_allow_html = True)
-left_column.dataframe(df, height = 400, use_container_width=True)
-
-with right_column:
-    st_lottie(lottie_money, key="money")
-
-#creating function
-def calculate_support_resistance(df, rolling_wave_length, num_clusters):
-    date = df.index
-    df.reset_index(inplace=True)
-    
-    max_waves_temp = df.High.rolling(rolling_wave_length).max().rename('waves')
-    min_waves_temp = df.Low.rolling(rolling_wave_length).min().rename('waves')
-   
-    max_waves = pd.concat([max_waves_temp, pd.Series(np.zeros(len(max_waves_temp)) + 1)], axis=1)
-    min_waves = pd.concat([min_waves_temp, pd.Series(np.zeros(len(min_waves_temp)) + -1)], axis=1)
-    max_waves.drop_duplicates('waves', inplace=True)
-    min_waves.drop_duplicates('waves', inplace=True)
-    
-    waves = pd.concat([max_waves, min_waves]).sort_index()
-    waves = waves[waves[0] != waves[0].shift()].dropna()
-    
-    x = np.concatenate((waves.waves.values.reshape(-1, 1),
-                        (np.zeros(len(waves)) + 1).reshape(-1, 1)), axis=1)
-    
-    cluster = AgglomerativeClustering(n_clusters=num_clusters, linkage='ward')
-    cluster.fit_predict(x)
-    waves['clusters'] = cluster.labels_
-    waves2 = waves.loc[waves.groupby('clusters')['waves'].idxmax()]
-    df.index = date
-    waves2.waves.drop_duplicates(keep='first', inplace=True)
-    
-    return waves2.reset_index().waves
-support_resistance_levels = calculate_support_resistance(df, rolling_wave_length, num_clusters)
-
-#creating a plot
-fig = make_subplots(rows=2, cols=1, shared_xaxes=True, 
-               vertical_spacing=0.06, subplot_titles=('OHLC', 'Volume'), 
-               row_width=[0.3, 0.7])
-
-fig.add_trace(go.Candlestick(x=df.index,
-                open=df['Open'],
-                high=df['High'],
-                low=df['Low'],
-                close=df['Close'], name = "Market data"), row = 1, col = 1)
-
-i = 0
-for level in support_resistance_levels.to_list():
-    fig.add_hline(y=level, line_width=1, 
-                  line_dash="dash", row=1, col=1,
-                  line_color="snow")
-    i += 1
-
-fig.update_xaxes(
-    rangeslider_visible = False)
-
-colors = []
-
-for i in range(len(df.Close)):
-    if i != 0:
-        if df.Close[i] > df.Close[i-1]:
-            colors.append('lightgreen')
-        else:
-            colors.append('lightcoral')
-    else:
-        colors.append('lightcoral')
-
-fig.add_trace(go.Bar(x=df.index, y=df['Volume'], showlegend=False, 
-                     marker=dict(color=colors)), row=2, col=1)
-
-fig.update_traces(name= 'Volume', selector=dict(type='bar'))
-
-text = f'{ticker} Chart'
-
-fig.update_layout(
-    title=go.layout.Title(
-        text=text,
-        xref="paper",
-        x=0))
-
-#show chart
-st.plotly_chart(fig, use_container_width=True)
-
-st.markdown("""<span style="font-size:13px; font-weight:400;">
-Disclaimer: It's important to note that while this demonstration provides a useful approach to 
-identifying support and resistance levels in financial markets, 
-it is not intended to be taken as financial advice. 
-Trading decisions should be made based on careful analysis of multiple factors, 
-including market conditions, 
-risk tolerance, 
-and individual financial goals.
-</span>""", unsafe_allow_html=True)
-
-hide_streamlit_style = """
-            <style>
-            footer {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
-
-st.markdown('''
-            <div style="position: relative; bottom: 0px; width: 100%;">
-                <span class="e1_33">
-                    <p style="text-align:center">
-                        Designed with ❤️ by 
-                        <a href="https://www.linkedin.com/in/amelia-doli%C5%84ska-55613a270/">
-                        <em>
-                        Amelia Dolińska
-                        </em>
-                        </a> 
-                    </p>
-                </span>
-            </div>
-            ''',
-            unsafe_allow_html=True)
\ No newline at end of file
diff --git a/spaces/Rakot2223/faster-whisper-webui/docs/options.md b/spaces/Rakot2223/faster-whisper-webui/docs/options.md
deleted file mode 100644
index 6979fca4d9d4c98a626a2953c2573ff23898a37e..0000000000000000000000000000000000000000
--- a/spaces/Rakot2223/faster-whisper-webui/docs/options.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# Standard Options
-To transcribe or translate an audio file, you can either copy an URL from a website (all [websites](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md) 
-supported by YT-DLP will work, including YouTube). Otherwise, upload an audio file (choose "All Files (*.*)" 
-in the file selector to select any file type, including video files) or use the microphone.
-
-For longer audio files (>10 minutes), it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option, especially if you are using the `large-v1` model. Note that `large-v2` is a lot more forgiving, but you may still want to use a VAD with a slightly higher "VAD - Max Merge Size (s)" (60 seconds or more).
-
-## Model
-Select the model that Whisper will use to transcribe the audio:
-
-| Size      | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
-|-----------|------------|--------------------|--------------------|---------------|----------------|
-| tiny      | 39 M       | tiny.en            | tiny               | ~1 GB         | ~32x           |
-| base      | 74 M       | base.en            | base               | ~1 GB         | ~16x           |
-| small     | 244 M      | small.en           | small              | ~2 GB         | ~6x            |
-| medium    | 769 M      | medium.en          | medium             | ~5 GB         | ~2x            |
-| large     | 1550 M     | N/A                | large              | ~10 GB        | 1x             |
-| large-v2  | 1550 M     | N/A                | large              | ~10 GB        | 1x             |
-
-## Language
-
-Select the language, or leave it empty for Whisper to automatically detect it. 
-
-Note that if the selected language and the language in the audio differs, Whisper may start to translate the audio to the selected 
-language. For instance, if the audio is in English but you select Japaneese, the model may translate the audio to Japanese.
-
-## Inputs
-The options "URL (YouTube, etc.)", "Upload Files" or "Micriphone Input" allows you to send an audio input to the model.
-
-### Multiple Files
-Note that the UI will only process either the given URL or the upload files (including microphone) - not both. 
-
-But you can upload multiple files either through the "Upload files" option, or as a playlist on YouTube. Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section. When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.
-
-## Task
-Select the task - either "transcribe" to transcribe the audio to text, or "translate" to translate it to English.
-
-## Vad
-Using a VAD will improve the timing accuracy of each transcribed line, as well as prevent Whisper getting into an infinite
-loop detecting the same sentence over and over again. The downside is that this may be at a cost to text accuracy, especially
-with regards to unique words or names that appear in the audio. You can compensate for this by increasing the prompt window. 
-
-Note that English is very well handled by Whisper, and it's less susceptible to issues surrounding bad timings and infinite loops. 
-So you may only need to use a VAD for other languages, such as Japanese, or when the audio is very long.
-
-* none
-  * Run whisper on the entire audio input
-* silero-vad
-   * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Whisper is also run 
-     on the gaps between each speech section, by either expanding the section up to the max merge size, or running Whisper independently 
-     on the non-speech section.
-* silero-vad-expand-into-gaps
-   * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Each spech section will be expanded
-     such that they cover any adjacent non-speech sections. For instance, if an audio file of one minute contains the speech sections 
-     00:00 - 00:10 (A) and 00:30 - 00:40 (B), the first section (A) will be expanded to 00:00 - 00:30, and (B) will be expanded to 00:30 - 00:60.
-* silero-vad-skip-gaps
-   * As above, but sections that doesn't contain speech according to Silero will be skipped. This will be slightly faster, but 
-     may cause dialogue to be skipped.
-* periodic-vad
-   * Create sections of speech every 'VAD - Max Merge Size' seconds. This is very fast and simple, but will potentially break 
-     a sentence or word in two.
-
-## VAD - Merge Window
-If set, any adjacent speech sections that are at most this number of seconds apart will be automatically merged.
-
-## VAD - Max Merge Size (s)
-Disables merging of adjacent speech sections if they are this number of seconds long.
-
-## VAD - Padding (s)
-The number of seconds (floating point) to add to the beginning and end of each speech section. Setting this to a number
-larger than zero ensures that Whisper is more likely to correctly transcribe a sentence in the beginning of 
-a speech section. However, this also increases the probability of Whisper assigning the wrong timestamp 
-to each transcribed line. The default value is 1 second.
-
-## VAD - Prompt Window (s)
-The text of a detected line will be included as a prompt to the next speech section, if the speech section starts at most this
-number of seconds after the line has finished. For instance, if a line ends at 10:00, and the next speech section starts at
-10:04, the line's text will be included if the prompt window is 4 seconds or more (10:04 - 10:00 = 4 seconds).
-
-Note that detected lines in gaps between speech sections will not be included in the prompt 
-(if silero-vad or silero-vad-expand-into-gaps) is used.
-
-# Command Line Options
-
-Both `app.py` and `cli.py` also accept command line options, such as the ability to enable parallel execution on multiple
-CPU/GPU cores, the default model name/VAD and so on. Consult the README in the root folder for more information.
-
-# Additional Options
-
-In addition to the above, there's also a "Full" options interface that allows you to set all the options available in the Whisper 
-model. The options are as follows:
-
-## Initial Prompt
-Optional text to provide as a prompt for the first 30 seconds window. Whisper will attempt to use this as a starting point for the transcription, but you can
-also get creative and specify a style or format for the output of the transcription.
-
-For instance, if you use the prompt "hello how is it going always use lowercase no punctuation goodbye one two three start stop i you me they", Whisper will 
-be biased to output lower capital letters and no punctuation, and may also be biased to output the words in the prompt more often.
-
-## Temperature
-The temperature to use when sampling. Default is 0 (zero). A higher temperature will result in more random output, while a lower temperature will be more deterministic.
-
-## Best Of - Non-zero temperature
-The number of candidates to sample from when sampling with non-zero temperature. Default is 5.
-
-## Beam Size - Zero temperature
-The number of beams to use in beam search when sampling with zero temperature. Default is 5.
-
-## Patience - Zero temperature
-The patience value to use in beam search when sampling with zero temperature. As in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search.
-
-## Length Penalty - Any temperature
-The token length penalty coefficient (alpha) to use when sampling with any temperature. As in https://arxiv.org/abs/1609.08144, uses simple length normalization by default.
-
-## Suppress Tokens - Comma-separated list of token IDs
-A comma-separated list of token IDs to suppress during sampling. The default value of "-1" will suppress most special characters except common punctuations.
-
-## Condition on previous text
-If True, provide the previous output of the model as a prompt for the next window. Disabling this may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop.
-
-## FP16
-Whether to perform inference in fp16. True by default.
-
-## Temperature increment on fallback
-The temperature to increase when falling back when the decoding fails to meet either of the thresholds below. Default is 0.2.
-
-## Compression ratio threshold
-If the gzip compression ratio is higher than this value, treat the decoding as failed. Default is 2.4.
-
-## Logprob threshold
-If the average log probability is lower than this value, treat the decoding as failed. Default is -1.0.
-
-## No speech threshold
-If the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence. Default is 0.6.
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/repr.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/repr.py
deleted file mode 100644
index 36966e70f19aff06e65b3b8d5943b40ef7d5fc64..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/repr.py
+++ /dev/null
@@ -1,152 +0,0 @@
-from functools import partial
-import inspect
-import sys
-
-from typing import (
-    Any,
-    Callable,
-    Iterable,
-    List,
-    Optional,
-    overload,
-    Union,
-    Tuple,
-    Type,
-    TypeVar,
-)
-
-
-T = TypeVar("T")
-
-
-Result = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]]
-RichReprResult = Result
-
-
-class ReprError(Exception):
-    """An error occurred when attempting to build a repr."""
-
-
-@overload
-def auto(cls: Optional[Type[T]]) -> Type[T]:
-    ...
-
-
-@overload
-def auto(*, angular: bool = False) -> Callable[[Type[T]], Type[T]]:
-    ...
-
-
-def auto(
-    cls: Optional[Type[T]] = None, *, angular: Optional[bool] = None
-) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
-    """Class decorator to create __repr__ from __rich_repr__"""
-
-    def do_replace(cls: Type[T], angular: Optional[bool] = None) -> Type[T]:
-        def auto_repr(self: T) -> str:
-            """Create repr string from __rich_repr__"""
-            repr_str: List[str] = []
-            append = repr_str.append
-
-            angular: bool = getattr(self.__rich_repr__, "angular", False)  # type: ignore[attr-defined]
-            for arg in self.__rich_repr__():  # type: ignore[attr-defined]
-                if isinstance(arg, tuple):
-                    if len(arg) == 1:
-                        append(repr(arg[0]))
-                    else:
-                        key, value, *default = arg
-                        if key is None:
-                            append(repr(value))
-                        else:
-                            if len(default) and default[0] == value:
-                                continue
-                            append(f"{key}={value!r}")
-                else:
-                    append(repr(arg))
-            if angular:
-                return f"<{self.__class__.__name__} {' '.join(repr_str)}>"
-            else:
-                return f"{self.__class__.__name__}({', '.join(repr_str)})"
-
-        def auto_rich_repr(self: Type[T]) -> Result:
-            """Auto generate __rich_rep__ from signature of __init__"""
-            try:
-                signature = inspect.signature(self.__init__)
-                for name, param in signature.parameters.items():
-                    if param.kind == param.POSITIONAL_ONLY:
-                        yield getattr(self, name)
-                    elif param.kind in (
-                        param.POSITIONAL_OR_KEYWORD,
-                        param.KEYWORD_ONLY,
-                    ):
-                        if param.default == param.empty:
-                            yield getattr(self, param.name)
-                        else:
-                            yield param.name, getattr(self, param.name), param.default
-            except Exception as error:
-                raise ReprError(
-                    f"Failed to auto generate __rich_repr__; {error}"
-                ) from None
-
-        if not hasattr(cls, "__rich_repr__"):
-            auto_rich_repr.__doc__ = "Build a rich repr"
-            cls.__rich_repr__ = auto_rich_repr  # type: ignore[attr-defined]
-
-        auto_repr.__doc__ = "Return repr(self)"
-        cls.__repr__ = auto_repr  # type: ignore[assignment]
-        if angular is not None:
-            cls.__rich_repr__.angular = angular  # type: ignore[attr-defined]
-        return cls
-
-    if cls is None:
-        return partial(do_replace, angular=angular)
-    else:
-        return do_replace(cls, angular=angular)
-
-
-@overload
-def rich_repr(cls: Optional[Type[T]]) -> Type[T]:
-    ...
-
-
-@overload
-def rich_repr(*, angular: bool = False) -> Callable[[Type[T]], Type[T]]:
-    ...
-
-
-def rich_repr(
-    cls: Optional[Type[T]] = None, *, angular: bool = False
-) -> Union[Type[T], Callable[[Type[T]], Type[T]]]:
-    if cls is None:
-        return auto(angular=angular)
-    else:
-        return auto(cls)
-
-
-if __name__ == "__main__":
-
-    @auto
-    class Foo:
-        def __rich_repr__(self) -> Result:
-            yield "foo"
-            yield "bar", {"shopping": ["eggs", "ham", "pineapple"]}
-            yield "buy", "hand sanitizer"
-
-    foo = Foo()
-    from pip._vendor.rich.console import Console
-
-    console = Console()
-
-    console.rule("Standard repr")
-    console.print(foo)
-
-    console.print(foo, width=60)
-    console.print(foo, width=30)
-
-    console.rule("Angular repr")
-    Foo.__rich_repr__.angular = True  # type: ignore[attr-defined]
-
-    console.print(foo)
-
-    console.print(foo, width=60)
-    console.print(foo, width=30)
diff --git a/spaces/Realcat/image-matching-webui/third_party/SuperGluePretrainedNetwork/models/utils.py b/spaces/Realcat/image-matching-webui/third_party/SuperGluePretrainedNetwork/models/utils.py
deleted file mode 100644
index d302ff84cf316f3dad016f1f23bbb54518566d2e..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/SuperGluePretrainedNetwork/models/utils.py
+++ /dev/null
@@ -1,652 +0,0 @@
-# %BANNER_BEGIN%
-# ---------------------------------------------------------------------
-# %COPYRIGHT_BEGIN%
-#
-#  Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
-#
-#  Unpublished Copyright (c) 2020
-#  Magic Leap, Inc., All Rights Reserved.
-#
-# NOTICE:  All information contained herein is, and remains the property
-# of COMPANY. The intellectual and technical concepts contained herein
-# are proprietary to COMPANY and may be covered by U.S. and Foreign
-# Patents, patents in process, and are protected by trade secret or
-# copyright law.  Dissemination of this information or reproduction of
-# this material is strictly forbidden unless prior written permission is
-# obtained from COMPANY.  Access to the source code contained herein is
-# hereby forbidden to anyone except current COMPANY employees, managers
-# or contractors who have executed Confidentiality and Non-disclosure
-# agreements explicitly covering such access.
-#
-# The copyright notice above does not evidence any actual or intended
-# publication or disclosure  of  this source code, which includes
-# information that is confidential and/or proprietary, and is a trade
-# secret, of  COMPANY.   ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
-# PUBLIC  PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE  OF THIS
-# SOURCE CODE  WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
-# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
-# INTERNATIONAL TREATIES.  THE RECEIPT OR POSSESSION OF  THIS SOURCE
-# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
-# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
-# USE, OR SELL ANYTHING THAT IT  MAY DESCRIBE, IN WHOLE OR IN PART.
-#
-# %COPYRIGHT_END%
-# ----------------------------------------------------------------------
-# %AUTHORS_BEGIN%
-#
-#  Originating Authors: Paul-Edouard Sarlin
-#                       Daniel DeTone
-#                       Tomasz Malisiewicz
-#
-# %AUTHORS_END%
-# --------------------------------------------------------------------*/
-# %BANNER_END%
-
-from pathlib import Path
-import time
-from collections import OrderedDict
-from threading import Thread
-import numpy as np
-import cv2
-import torch
-import matplotlib.pyplot as plt
-import matplotlib
-
-matplotlib.use("Agg")
-
-
-class AverageTimer:
-    """Class to help manage printing simple timing of code execution."""
-
-    def __init__(self, smoothing=0.3, newline=False):
-        self.smoothing = smoothing
-        self.newline = newline
-        self.times = OrderedDict()
-        self.will_print = OrderedDict()
-        self.reset()
-
-    def reset(self):
-        now = time.time()
-        self.start = now
-        self.last_time = now
-        for name in self.will_print:
-            self.will_print[name] = False
-
-    def update(self, name="default"):
-        now = time.time()
-        dt = now - self.last_time
-        if name in self.times:
-            dt = self.smoothing * dt + (1 - self.smoothing) * self.times[name]
-        self.times[name] = dt
-        self.will_print[name] = True
-        self.last_time = now
-
-    def print(self, text="Timer"):
-        total = 0.0
-        print("[{}]".format(text), end=" ")
-        for key in self.times:
-            val = self.times[key]
-            if self.will_print[key]:
-                print("%s=%.3f" % (key, val), end=" ")
-                total += val
-        print("total=%.3f sec {%.1f FPS}" % (total, 1.0 / total), end=" ")
-        if self.newline:
-            print(flush=True)
-        else:
-            print(end="\r", flush=True)
-        self.reset()
-
-
-class VideoStreamer:
-    """Class to help process image streams. Four types of possible inputs:"
-    1.) USB Webcam.
-    2.) An IP camera
-    3.) A directory of images (files in directory matching 'image_glob').
-    4.) A video file, such as an .mp4 or .avi file.
-    """
-
-    def __init__(self, basedir, resize, skip, image_glob, max_length=1000000):
-        self._ip_grabbed = False
-        self._ip_running = False
-        self._ip_camera = False
-        self._ip_image = None
-        self._ip_index = 0
-        self.cap = []
-        self.camera = True
-        self.video_file = False
-        self.listing = []
-        self.resize = resize
-        self.interp = cv2.INTER_AREA
-        self.i = 0
-        self.skip = skip
-        self.max_length = max_length
-        if isinstance(basedir, int) or basedir.isdigit():
-            print("==> Processing USB webcam input: {}".format(basedir))
-            self.cap = cv2.VideoCapture(int(basedir))
-            self.listing = range(0, self.max_length)
-        elif basedir.startswith(("http", "rtsp")):
-            print("==> Processing IP camera input: {}".format(basedir))
-            self.cap = cv2.VideoCapture(basedir)
-            self.start_ip_camera_thread()
-            self._ip_camera = True
-            self.listing = range(0, self.max_length)
-        elif Path(basedir).is_dir():
-            print("==> Processing image directory input: {}".format(basedir))
-            self.listing = list(Path(basedir).glob(image_glob[0]))
-            for j in range(1, len(image_glob)):
-                image_path = list(Path(basedir).glob(image_glob[j]))
-                self.listing = self.listing + image_path
-            self.listing.sort()
-            self.listing = self.listing[:: self.skip]
-            self.max_length = np.min([self.max_length, len(self.listing)])
-            if self.max_length == 0:
-                raise IOError("No images found (maybe bad 'image_glob' ?)")
-            self.listing = self.listing[: self.max_length]
-            self.camera = False
-        elif Path(basedir).exists():
-            print("==> Processing video input: {}".format(basedir))
-            self.cap = cv2.VideoCapture(basedir)
-            self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
-            num_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
-            self.listing = range(0, num_frames)
-            self.listing = self.listing[:: self.skip]
-            self.video_file = True
-            self.max_length = np.min([self.max_length, len(self.listing)])
-            self.listing = self.listing[: self.max_length]
-        else:
-            raise ValueError('VideoStreamer input "{}" not recognized.'.format(basedir))
-        if self.camera and not self.cap.isOpened():
-            raise IOError("Could not read camera")
-
-    def load_image(self, impath):
-        """Read image as grayscale and resize to img_size.
-        Inputs
-            impath: Path to input image.
-        Returns
-            grayim: uint8 numpy array sized H x W.
-        """
-        grayim = cv2.imread(impath, 0)
-        if grayim is None:
-            raise Exception("Error reading image %s" % impath)
-        w, h = grayim.shape[1], grayim.shape[0]
-        w_new, h_new = process_resize(w, h, self.resize)
-        grayim = cv2.resize(grayim, (w_new, h_new), interpolation=self.interp)
-        return grayim
-
-    def next_frame(self):
-        """Return the next frame, and increment internal counter.
-        Returns
-             image: Next H x W image.
-             status: True or False depending whether image was loaded.
-        """
-
-        if self.i == self.max_length:
-            return (None, False)
-        if self.camera:
-
-            if self._ip_camera:
-                # Wait for first image, making sure we haven't exited
-                while self._ip_grabbed is False and self._ip_exited is False:
-                    time.sleep(0.001)
-
-                ret, image = self._ip_grabbed, self._ip_image.copy()
-                if ret is False:
-                    self._ip_running = False
-            else:
-                ret, image = self.cap.read()
-            if ret is False:
-                print("VideoStreamer: Cannot get image from camera")
-                return (None, False)
-            w, h = image.shape[1], image.shape[0]
-            if self.video_file:
-                self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.listing[self.i])
-
-            w_new, h_new = process_resize(w, h, self.resize)
-            image = cv2.resize(image, (w_new, h_new), interpolation=self.interp)
-            image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-        else:
-            image_file = str(self.listing[self.i])
-            image = self.load_image(image_file)
-        self.i = self.i + 1
-        return (image, True)
-
-    def start_ip_camera_thread(self):
-        self._ip_thread = Thread(target=self.update_ip_camera, args=())
-        self._ip_running = True
-        self._ip_thread.start()
-        self._ip_exited = False
-        return self
-
-    def update_ip_camera(self):
-        while self._ip_running:
-            ret, img = self.cap.read()
-            if ret is False:
-                self._ip_running = False
-                self._ip_exited = True
-                self._ip_grabbed = False
-                return
-
-            self._ip_image = img
-            self._ip_grabbed = ret
-            self._ip_index += 1
-            # print('IPCAMERA THREAD got frame {}'.format(self._ip_index))
-
-    def cleanup(self):
-        self._ip_running = False
-
-
-# --- PREPROCESSING ---
-
-
-def process_resize(w, h, resize):
-    assert len(resize) > 0 and len(resize) <= 2
-    if len(resize) == 1 and resize[0] > -1:
-        scale = resize[0] / max(h, w)
-        w_new, h_new = int(round(w * scale)), int(round(h * scale))
-    elif len(resize) == 1 and resize[0] == -1:
-        w_new, h_new = w, h
-    else:  # len(resize) == 2:
-        w_new, h_new = resize[0], resize[1]
-
-    # Issue warning if resolution is too small or too large.
-    if max(w_new, h_new) < 160:
-        print("Warning: input resolution is very small, results may vary")
-    elif max(w_new, h_new) > 2000:
-        print("Warning: input resolution is very large, results may vary")
-
-    return w_new, h_new
-
-
-def frame2tensor(frame, device):
-    return torch.from_numpy(frame / 255.0).float()[None, None].to(device)
-
-
-def read_image(path, device, resize, rotation, resize_float):
-    image = cv2.imread(str(path), cv2.IMREAD_GRAYSCALE)
-    if image is None:
-        return None, None, None
-    w, h = image.shape[1], image.shape[0]
-    w_new, h_new = process_resize(w, h, resize)
-    scales = (float(w) / float(w_new), float(h) / float(h_new))
-
-    if resize_float:
-        image = cv2.resize(image.astype("float32"), (w_new, h_new))
-    else:
-        image = cv2.resize(image, (w_new, h_new)).astype("float32")
-
-    if rotation != 0:
-        image = np.rot90(image, k=rotation)
-        if rotation % 2:
-            scales = scales[::-1]
-
-    inp = frame2tensor(image, device)
-    return image, inp, scales
-
-
-# --- GEOMETRY ---
-
-
-def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999):
-    if len(kpts0) < 5:
-        return None
-
-    f_mean = np.mean([K0[0, 0], K1[1, 1], K0[0, 0], K1[1, 1]])
-    norm_thresh = thresh / f_mean
-
-    kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None]
-    kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None]
-
-    E, mask = cv2.findEssentialMat(
-        kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf, method=cv2.RANSAC
-    )
-
-    assert E is not None
-
-    best_num_inliers = 0
-    ret = None
-    for _E in np.split(E, len(E) / 3):
-        n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask)
-        if n > best_num_inliers:
-            best_num_inliers = n
-            ret = (R, t[:, 0], mask.ravel() > 0)
-    return ret
-
-
-def rotate_intrinsics(K, image_shape, rot):
-    """image_shape is the shape of the image after rotation"""
-    assert rot <= 3
-    h, w = image_shape[:2][:: -1 if (rot % 2) else 1]
-    fx, fy, cx, cy = K[0, 0], K[1, 1], K[0, 2], K[1, 2]
-    rot = rot % 4
-    if rot == 1:
-        return np.array(
-            [[fy, 0.0, cy], [0.0, fx, w - 1 - cx], [0.0, 0.0, 1.0]], dtype=K.dtype
-        )
-    elif rot == 2:
-        return np.array(
-            [[fx, 0.0, w - 1 - cx], [0.0, fy, h - 1 - cy], [0.0, 0.0, 1.0]],
-            dtype=K.dtype,
-        )
-    else:  # if rot == 3:
-        return np.array(
-            [[fy, 0.0, h - 1 - cy], [0.0, fx, cx], [0.0, 0.0, 1.0]], dtype=K.dtype
-        )
-
-
-def rotate_pose_inplane(i_T_w, rot):
-    rotation_matrices = [
-        np.array(
-            [
-                [np.cos(r), -np.sin(r), 0.0, 0.0],
-                [np.sin(r), np.cos(r), 0.0, 0.0],
-                [0.0, 0.0, 1.0, 0.0],
-                [0.0, 0.0, 0.0, 1.0],
-            ],
-            dtype=np.float32,
-        )
-        for r in [np.deg2rad(d) for d in (0, 270, 180, 90)]
-    ]
-    return np.dot(rotation_matrices[rot], i_T_w)
-
-
-def scale_intrinsics(K, scales):
-    scales = np.diag([1.0 / scales[0], 1.0 / scales[1], 1.0])
-    return np.dot(scales, K)
-
-
-def to_homogeneous(points):
-    return np.concatenate([points, np.ones_like(points[:, :1])], axis=-1)
-
-
-def compute_epipolar_error(kpts0, kpts1, T_0to1, K0, K1):
-    kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None]
-    kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None]
-    kpts0 = to_homogeneous(kpts0)
-    kpts1 = to_homogeneous(kpts1)
-
-    t0, t1, t2 = T_0to1[:3, 3]
-    t_skew = np.array([[0, -t2, t1], [t2, 0, -t0], [-t1, t0, 0]])
-    E = t_skew @ T_0to1[:3, :3]
-
-    Ep0 = kpts0 @ E.T  # N x 3
-    p1Ep0 = np.sum(kpts1 * Ep0, -1)  # N
-    Etp1 = kpts1 @ E  # N x 3
-    d = p1Ep0**2 * (
-        1.0 / (Ep0[:, 0] ** 2 + Ep0[:, 1] ** 2)
-        + 1.0 / (Etp1[:, 0] ** 2 + Etp1[:, 1] ** 2)
-    )
-    return d
-
-
-def angle_error_mat(R1, R2):
-    cos = (np.trace(np.dot(R1.T, R2)) - 1) / 2
-    cos = np.clip(cos, -1.0, 1.0)  # numercial errors can make it out of bounds
-    return np.rad2deg(np.abs(np.arccos(cos)))
-
-
-def angle_error_vec(v1, v2):
-    n = np.linalg.norm(v1) * np.linalg.norm(v2)
-    return np.rad2deg(np.arccos(np.clip(np.dot(v1, v2) / n, -1.0, 1.0)))
-
-
-def compute_pose_error(T_0to1, R, t):
-    R_gt = T_0to1[:3, :3]
-    t_gt = T_0to1[:3, 3]
-    error_t = angle_error_vec(t, t_gt)
-    error_t = np.minimum(error_t, 180 - error_t)  # ambiguity of E estimation
-    error_R = angle_error_mat(R, R_gt)
-    return error_t, error_R
-
-
-def pose_auc(errors, thresholds):
-    sort_idx = np.argsort(errors)
-    errors = np.array(errors.copy())[sort_idx]
-    recall = (np.arange(len(errors)) + 1) / len(errors)
-    errors = np.r_[0.0, errors]
-    recall = np.r_[0.0, recall]
-    aucs = []
-    for t in thresholds:
-        last_index = np.searchsorted(errors, t)
-        r = np.r_[recall[:last_index], recall[last_index - 1]]
-        e = np.r_[errors[:last_index], t]
-        aucs.append(np.trapz(r, x=e) / t)
-    return aucs
-
-
-# --- VISUALIZATION ---
-
-
-def plot_image_pair(imgs, dpi=100, size=6, pad=0.5):
-    n = len(imgs)
-    assert n == 2, "number of images must be two"
-    figsize = (size * n, size * 3 / 4) if size is not None else None
-    _, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi)
-    for i in range(n):
-        ax[i].imshow(imgs[i], cmap=plt.get_cmap("gray"), vmin=0, vmax=255)
-        ax[i].get_yaxis().set_ticks([])
-        ax[i].get_xaxis().set_ticks([])
-        for spine in ax[i].spines.values():  # remove frame
-            spine.set_visible(False)
-    plt.tight_layout(pad=pad)
-
-
-def plot_keypoints(kpts0, kpts1, color="w", ps=2):
-    ax = plt.gcf().axes
-    ax[0].scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps)
-    ax[1].scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps)
-
-
-def plot_matches(kpts0, kpts1, color, lw=1.5, ps=4):
-    fig = plt.gcf()
-    ax = fig.axes
-    fig.canvas.draw()
-
-    transFigure = fig.transFigure.inverted()
-    fkpts0 = transFigure.transform(ax[0].transData.transform(kpts0))
-    fkpts1 = transFigure.transform(ax[1].transData.transform(kpts1))
-
-    fig.lines = [
-        matplotlib.lines.Line2D(
-            (fkpts0[i, 0], fkpts1[i, 0]),
-            (fkpts0[i, 1], fkpts1[i, 1]),
-            zorder=1,
-            transform=fig.transFigure,
-            c=color[i],
-            linewidth=lw,
-        )
-        for i in range(len(kpts0))
-    ]
-    ax[0].scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps)
-    ax[1].scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps)
-
-
-def make_matching_plot(
-    image0,
-    image1,
-    kpts0,
-    kpts1,
-    mkpts0,
-    mkpts1,
-    color,
-    text,
-    path,
-    show_keypoints=False,
-    fast_viz=False,
-    opencv_display=False,
-    opencv_title="matches",
-    small_text=[],
-):
-
-    if fast_viz:
-        make_matching_plot_fast(
-            image0,
-            image1,
-            kpts0,
-            kpts1,
-            mkpts0,
-            mkpts1,
-            color,
-            text,
-            path,
-            show_keypoints,
-            10,
-            opencv_display,
-            opencv_title,
-            small_text,
-        )
-        return
-
-    plot_image_pair([image0, image1])
-    if show_keypoints:
-        plot_keypoints(kpts0, kpts1, color="k", ps=4)
-        plot_keypoints(kpts0, kpts1, color="w", ps=2)
-    plot_matches(mkpts0, mkpts1, color)
-
-    fig = plt.gcf()
-    txt_color = "k" if image0[:100, :150].mean() > 200 else "w"
-    fig.text(
-        0.01,
-        0.99,
-        "\n".join(text),
-        transform=fig.axes[0].transAxes,
-        fontsize=15,
-        va="top",
-        ha="left",
-        color=txt_color,
-    )
-
-    txt_color = "k" if image0[-100:, :150].mean() > 200 else "w"
-    fig.text(
-        0.01,
-        0.01,
-        "\n".join(small_text),
-        transform=fig.axes[0].transAxes,
-        fontsize=5,
-        va="bottom",
-        ha="left",
-        color=txt_color,
-    )
-
-    plt.savefig(str(path), bbox_inches="tight", pad_inches=0)
-    plt.close()
-
-
-def make_matching_plot_fast(
-    image0,
-    image1,
-    kpts0,
-    kpts1,
-    mkpts0,
-    mkpts1,
-    color,
-    text,
-    path=None,
-    show_keypoints=False,
-    margin=10,
-    opencv_display=False,
-    opencv_title="",
-    small_text=[],
-):
-    H0, W0 = image0.shape
-    H1, W1 = image1.shape
-    H, W = max(H0, H1), W0 + W1 + margin
-
-    out = 255 * np.ones((H, W), np.uint8)
-    out[:H0, :W0] = image0
-    out[:H1, W0 + margin :] = image1
-    out = np.stack([out] * 3, -1)
-
-    if show_keypoints:
-        kpts0, kpts1 = np.round(kpts0).astype(int), np.round(kpts1).astype(int)
-        white = (255, 255, 255)
-        black = (0, 0, 0)
-        for x, y in kpts0:
-            cv2.circle(out, (x, y), 2, black, -1, lineType=cv2.LINE_AA)
-            cv2.circle(out, (x, y), 1, white, -1, lineType=cv2.LINE_AA)
-        for x, y in kpts1:
-            cv2.circle(out, (x + margin + W0, y), 2, black, -1, lineType=cv2.LINE_AA)
-            cv2.circle(out, (x + margin + W0, y), 1, white, -1, lineType=cv2.LINE_AA)
-
-    mkpts0, mkpts1 = np.round(mkpts0).astype(int), np.round(mkpts1).astype(int)
-    color = (np.array(color[:, :3]) * 255).astype(int)[:, ::-1]
-    for (x0, y0), (x1, y1), c in zip(mkpts0, mkpts1, color):
-        c = c.tolist()
-        cv2.line(
-            out,
-            (x0, y0),
-            (x1 + margin + W0, y1),
-            color=c,
-            thickness=1,
-            lineType=cv2.LINE_AA,
-        )
-        # display line end-points as circles
-        cv2.circle(out, (x0, y0), 2, c, -1, lineType=cv2.LINE_AA)
-        cv2.circle(out, (x1 + margin + W0, y1), 2, c, -1, lineType=cv2.LINE_AA)
-
-    # Scale factor for consistent visualization across scales.
-    sc = min(H / 640.0, 2.0)
-
-    # Big text.
-    Ht = int(30 * sc)  # text height
-    txt_color_fg = (255, 255, 255)
-    txt_color_bg = (0, 0, 0)
-    for i, t in enumerate(text):
-        cv2.putText(
-            out,
-            t,
-            (int(8 * sc), Ht * (i + 1)),
-            cv2.FONT_HERSHEY_DUPLEX,
-            1.0 * sc,
-            txt_color_bg,
-            2,
-            cv2.LINE_AA,
-        )
-        cv2.putText(
-            out,
-            t,
-            (int(8 * sc), Ht * (i + 1)),
-            cv2.FONT_HERSHEY_DUPLEX,
-            1.0 * sc,
-            txt_color_fg,
-            1,
-            cv2.LINE_AA,
-        )
-
-    # Small text.
-    Ht = int(18 * sc)  # text height
-    for i, t in enumerate(reversed(small_text)):
-        cv2.putText(
-            out,
-            t,
-            (int(8 * sc), int(H - Ht * (i + 0.6))),
-            cv2.FONT_HERSHEY_DUPLEX,
-            0.5 * sc,
-            txt_color_bg,
-            2,
-            cv2.LINE_AA,
-        )
-        cv2.putText(
-            out,
-            t,
-            (int(8 * sc), int(H - Ht * (i + 0.6))),
-            cv2.FONT_HERSHEY_DUPLEX,
-            0.5 * sc,
-            txt_color_fg,
-            1,
-            cv2.LINE_AA,
-        )
-
-    if path is not None:
-        cv2.imwrite(str(path), out)
-
-    if opencv_display:
-        cv2.imshow(opencv_title, out)
-        cv2.waitKey(1)
-
-    return out
-
-
-def error_colormap(x):
-    return np.clip(
-        np.stack([2 - x * 2, x * 2, np.zeros_like(x), np.ones_like(x)], -1), 0, 1
-    )
diff --git a/spaces/Reeve/Ohayou_Face/training/training_loop.py b/spaces/Reeve/Ohayou_Face/training/training_loop.py
deleted file mode 100644
index 538548057344083d3f14652e6ef843ce001d55ec..0000000000000000000000000000000000000000
--- a/spaces/Reeve/Ohayou_Face/training/training_loop.py
+++ /dev/null
@@ -1,427 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import time
-import copy
-import json
-import pickle
-import psutil
-import PIL.Image
-import numpy as np
-import torch
-import dnnlib
-from torch_utils import misc
-from torch_utils import training_stats
-from torch_utils.ops import conv2d_gradfix
-from torch_utils.ops import grid_sample_gradfix
-
-import legacy
-from metrics import metric_main
-
-#----------------------------------------------------------------------------
-
-def setup_snapshot_image_grid(training_set, random_seed=0):
-    rnd = np.random.RandomState(random_seed)
-    gw = np.clip(7680 // training_set.image_shape[2], 7, 32)
-    gh = np.clip(4320 // training_set.image_shape[1], 4, 32)
-
-    # No labels => show random subset of training samples.
-    if not training_set.has_labels:
-        all_indices = list(range(len(training_set)))
-        rnd.shuffle(all_indices)
-        grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)]
-
-    else:
-        # Group training samples by label.
-        label_groups = dict() # label => [idx, ...]
-        for idx in range(len(training_set)):
-            label = tuple(training_set.get_details(idx).raw_label.flat[::-1])
-            if label not in label_groups:
-                label_groups[label] = []
-            label_groups[label].append(idx)
-
-        # Reorder.
-        label_order = sorted(label_groups.keys())
-        for label in label_order:
-            rnd.shuffle(label_groups[label])
-
-        # Organize into grid.
-        grid_indices = []
-        for y in range(gh):
-            label = label_order[y % len(label_order)]
-            indices = label_groups[label]
-            grid_indices += [indices[x % len(indices)] for x in range(gw)]
-            label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))]
-
-    # Load data.
-    images, labels = zip(*[training_set[i] for i in grid_indices])
-    return (gw, gh), np.stack(images), np.stack(labels)
-
-#----------------------------------------------------------------------------
-
-def save_image_grid(img, fname, drange, grid_size):
-    lo, hi = drange
-    img = np.asarray(img, dtype=np.float32)
-    img = (img - lo) * (255 / (hi - lo))
-    img = np.rint(img).clip(0, 255).astype(np.uint8)
-
-    gw, gh = grid_size
-    _N, C, H, W = img.shape
-    img = img.reshape(gh, gw, C, H, W)
-    img = img.transpose(0, 3, 1, 4, 2)
-    img = img.reshape(gh * H, gw * W, C)
-
-    assert C in [1, 3]
-    if C == 1:
-        PIL.Image.fromarray(img[:, :, 0], 'L').save(fname)
-    if C == 3:
-        PIL.Image.fromarray(img, 'RGB').save(fname)
-
-#----------------------------------------------------------------------------
-
-def training_loop(
-    run_dir                 = '.',      # Output directory.
-    training_set_kwargs     = {},       # Options for training set.
-    data_loader_kwargs      = {},       # Options for torch.utils.data.DataLoader.
-    G_kwargs                = {},       # Options for generator network.
-    D_kwargs                = {},       # Options for discriminator network.
-    G_opt_kwargs            = {},       # Options for generator optimizer.
-    D_opt_kwargs            = {},       # Options for discriminator optimizer.
-    augment_kwargs          = None,     # Options for augmentation pipeline. None = disable.
-    loss_kwargs             = {},       # Options for loss function.
-    metrics                 = [],       # Metrics to evaluate during training.
-    random_seed             = 0,        # Global random seed.
-    num_gpus                = 1,        # Number of GPUs participating in the training.
-    rank                    = 0,        # Rank of the current process in [0, num_gpus[.
-    batch_size              = 4,        # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus.
-    batch_gpu               = 4,        # Number of samples processed at a time by one GPU.
-    ema_kimg                = 10,       # Half-life of the exponential moving average (EMA) of generator weights.
-    ema_rampup              = None,     # EMA ramp-up coefficient.
-    G_reg_interval          = 4,        # How often to perform regularization for G? None = disable lazy regularization.
-    D_reg_interval          = 16,       # How often to perform regularization for D? None = disable lazy regularization.
-    augment_p               = 0,        # Initial value of augmentation probability.
-    ada_target              = None,     # ADA target value. None = fixed p.
-    ada_interval            = 4,        # How often to perform ADA adjustment?
-    ada_kimg                = 500,      # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit.
-    nimg                    = 0,        # current image count
-    total_kimg              = 25000,    # Total length of the training, measured in thousands of real images.
-    kimg_per_tick           = 4,        # Progress snapshot interval.
-    image_snapshot_ticks    = 50,       # How often to save image snapshots? None = disable.
-    network_snapshot_ticks  = 50,       # How often to save network snapshots? None = disable.
-    resume_pkl              = None,     # Network pickle to resume training from.
-    cudnn_benchmark         = True,     # Enable torch.backends.cudnn.benchmark?
-    allow_tf32              = False,    # Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32?
-    abort_fn                = None,     # Callback function for determining whether to abort training. Must return consistent results across ranks.
-    progress_fn             = None,     # Callback function for updating training progress. Called for all ranks.
-):
-    # Initialize.
-    start_time = time.time()
-    device = torch.device('cuda', rank)
-    np.random.seed(random_seed * num_gpus + rank)
-    torch.manual_seed(random_seed * num_gpus + rank)
-    torch.backends.cudnn.benchmark = cudnn_benchmark    # Improves training speed.
-    torch.backends.cuda.matmul.allow_tf32 = allow_tf32  # Allow PyTorch to internally use tf32 for matmul
-    torch.backends.cudnn.allow_tf32 = allow_tf32        # Allow PyTorch to internally use tf32 for convolutions
-    conv2d_gradfix.enabled = True                       # Improves training speed.
-    grid_sample_gradfix.enabled = True                  # Avoids errors with the augmentation pipe.
-
-    # Load training set.
-    if rank == 0:
-        print('Loading training set...')
-    training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset
-    training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed)
-    training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs))
-    if rank == 0:
-        print()
-        print('Num images: ', len(training_set))
-        print('Image shape:', training_set.image_shape)
-        print('Label shape:', training_set.label_shape)
-        print()
-
-    # Construct networks.
-    if rank == 0:
-        print('Constructing networks...')
-    common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels)
-    G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
-    D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
-    G_ema = copy.deepcopy(G).eval()
-
-    G.update_epochs( float(100 * nimg / (total_kimg * 1000)) ) # 100 total top k "epochs" in total_kimg
-    print('starting G epochs: ',G.epochs)
-
-    # Resume from existing pickle.
-    if (resume_pkl is not None) and (rank == 0):
-        print(f'Resuming from "{resume_pkl}"')
-        with dnnlib.util.open_url(resume_pkl) as f:
-            resume_data = legacy.load_network_pkl(f)
-        for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]:
-            misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
-
-    # Print network summary tables.
-    if rank == 0:
-        z = torch.empty([batch_gpu, G.z_dim], device=device)
-        c = torch.empty([batch_gpu, G.c_dim], device=device)
-        img = misc.print_module_summary(G, [z, c])
-        misc.print_module_summary(D, [img, c])
-
-    # Setup augmentation.
-    if rank == 0:
-        print('Setting up augmentation...')
-    augment_pipe = None
-    ada_stats = None
-    if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None):
-        augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
-        augment_pipe.p.copy_(torch.as_tensor(augment_p))
-        if ada_target is not None:
-            ada_stats = training_stats.Collector(regex='Loss/signs/real')
-
-    # Distribute across GPUs.
-    if rank == 0:
-        print(f'Distributing across {num_gpus} GPUs...')
-    ddp_modules = dict()
-    for name, module in [('G', G),('G_mapping', G.mapping), ('G_synthesis', G.synthesis), ('D', D), (None, G_ema), ('augment_pipe', augment_pipe)]:
-        if (num_gpus > 1) and (module is not None) and len(list(module.parameters())) != 0:
-            module.requires_grad_(True)
-            module = torch.nn.parallel.DistributedDataParallel(module, device_ids=[device], broadcast_buffers=False)
-            module.requires_grad_(False)
-        if name is not None:
-            ddp_modules[name] = module
-
-    # Setup training phases.
-    if rank == 0:
-        print('Setting up training phases...')
-    loss = dnnlib.util.construct_class_by_name(device=device, **ddp_modules, **loss_kwargs) # subclass of training.loss.Loss
-    phases = []
-    for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]:
-        if reg_interval is None:
-            opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
-            phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)]
-        else: # Lazy regularization.
-            mb_ratio = reg_interval / (reg_interval + 1)
-            opt_kwargs = dnnlib.EasyDict(opt_kwargs)
-            opt_kwargs.lr = opt_kwargs.lr * mb_ratio
-            opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas]
-            opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
-            phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)]
-            phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)]
-    for phase in phases:
-        phase.start_event = None
-        phase.end_event = None
-        if rank == 0:
-            phase.start_event = torch.cuda.Event(enable_timing=True)
-            phase.end_event = torch.cuda.Event(enable_timing=True)
-
-    # Export sample images.
-    grid_size = None
-    grid_z = None
-    grid_c = None
-    if rank == 0:
-        print('Exporting sample images...')
-        grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set)
-        save_image_grid(images, os.path.join(run_dir, 'reals.jpg'), drange=[0,255], grid_size=grid_size)
-        grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu)
-        grid_c = torch.from_numpy(labels).to(device).split(batch_gpu)
-        images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
-        save_image_grid(images, os.path.join(run_dir, 'fakes_init.jpg'), drange=[-1,1], grid_size=grid_size)
-
-    # Initialize logs.
-    if rank == 0:
-        print('Initializing logs...')
-    stats_collector = training_stats.Collector(regex='.*')
-    stats_metrics = dict()
-    stats_jsonl = None
-    stats_tfevents = None
-    if rank == 0:
-        stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt')
-        try:
-            import torch.utils.tensorboard as tensorboard
-            stats_tfevents = tensorboard.SummaryWriter(run_dir)
-        except ImportError as err:
-            print('Skipping tfevents export:', err)
-
-    # Train.
-    if rank == 0:
-        print(f'Training for {total_kimg} kimg...')
-        print()
-    cur_nimg = nimg
-    cur_tick = 0
-    tick_start_nimg = cur_nimg
-    tick_start_time = time.time()
-    maintenance_time = tick_start_time - start_time
-    batch_idx = 0
-    if progress_fn is not None:
-        progress_fn(0, total_kimg)
-    while True:
-
-        # Fetch training data.
-        with torch.autograd.profiler.record_function('data_fetch'):
-            phase_real_img, phase_real_c = next(training_set_iterator)
-            phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu)
-            phase_real_c = phase_real_c.to(device).split(batch_gpu)
-            all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device)
-            all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)]
-            all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)]
-            all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device)
-            all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)]
-
-        # Execute training phases.
-        for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c):
-            if batch_idx % phase.interval != 0:
-                continue
-
-            G.update_epochs( float(100 * nimg / (total_kimg * 1000)) ) # 100 total top k "epochs" in total_kimg
-
-            # Initialize gradient accumulation.
-            if phase.start_event is not None:
-                phase.start_event.record(torch.cuda.current_stream(device))
-            phase.opt.zero_grad(set_to_none=True)
-            phase.module.requires_grad_(True)
-
-            # Accumulate gradients over multiple rounds.
-            for round_idx, (real_img, real_c, gen_z, gen_c) in enumerate(zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c)):
-                sync = (round_idx == batch_size // (batch_gpu * num_gpus) - 1)
-                gain = phase.interval
-                loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, sync=sync, gain=gain)
-
-            # Update weights.
-            phase.module.requires_grad_(False)
-            with torch.autograd.profiler.record_function(phase.name + '_opt'):
-                for param in phase.module.parameters():
-                    if param.grad is not None:
-                        misc.nan_to_num(param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad)
-                phase.opt.step()
-            if phase.end_event is not None:
-                phase.end_event.record(torch.cuda.current_stream(device))
-
-        # Update G_ema.
-        with torch.autograd.profiler.record_function('Gema'):
-            ema_nimg = ema_kimg * 1000
-            if ema_rampup is not None:
-                ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)
-            ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8))
-            for p_ema, p in zip(G_ema.parameters(), G.parameters()):
-                p_ema.copy_(p.lerp(p_ema, ema_beta))
-            for b_ema, b in zip(G_ema.buffers(), G.buffers()):
-                b_ema.copy_(b)
-
-        # Update state.
-        cur_nimg += batch_size
-        batch_idx += 1
-
-        # Execute ADA heuristic.
-        if (ada_stats is not None) and (batch_idx % ada_interval == 0):
-            ada_stats.update()
-            adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000)
-            augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device)))
-
-        # Perform maintenance tasks once per tick.
-        done = (cur_nimg >= total_kimg * 1000)
-        if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000):
-            continue
-
-        # Print status line, accumulating the same information in stats_collector.
-        tick_end_time = time.time()
-        fields = []
-        fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"]
-        fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"]
-        fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"]
-        fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"]
-        fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"]
-        fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"]
-        fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"]
-        fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"]
-        torch.cuda.reset_peak_memory_stats()
-        fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"]
-        training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60))
-        training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60))
-        if rank == 0:
-            print(' '.join(fields))
-
-        # Check for abort.
-        if (not done) and (abort_fn is not None) and abort_fn():
-            done = True
-            if rank == 0:
-                print()
-                print('Aborting...')
-
-        # Save image snapshot.
-        if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0):
-            images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
-            save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.jpg'), drange=[-1,1], grid_size=grid_size)
-
-        # Save network snapshot.
-        snapshot_pkl = None
-        snapshot_data = None
-        if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0):
-            snapshot_data = dict(training_set_kwargs=dict(training_set_kwargs))
-            for name, module in [('G', G), ('D', D), ('G_ema', G_ema), ('augment_pipe', augment_pipe)]:
-                if module is not None:
-                    if num_gpus > 1:
-                        misc.check_ddp_consistency(module, ignore_regex=r'.*\.w_avg')
-                    module = copy.deepcopy(module).eval().requires_grad_(False).cpu()
-                snapshot_data[name] = module
-                del module # conserve memory
-            snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl')
-            if rank == 0:
-                with open(snapshot_pkl, 'wb') as f:
-                    pickle.dump(snapshot_data, f)
-
-        # Evaluate metrics.
-        if (snapshot_data is not None) and (len(metrics) > 0):
-            if rank == 0:
-                print('Evaluating metrics...')
-            for metric in metrics:
-                result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'],
-                    dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device)
-                if rank == 0:
-                    metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl)
-                stats_metrics.update(result_dict.results)
-        del snapshot_data # conserve memory
-
-        # Collect statistics.
-        for phase in phases:
-            value = []
-            if (phase.start_event is not None) and (phase.end_event is not None):
-                phase.end_event.synchronize()
-                value = phase.start_event.elapsed_time(phase.end_event)
-            training_stats.report0('Timing/' + phase.name, value)
-        stats_collector.update()
-        stats_dict = stats_collector.as_dict()
-
-        # Update logs.
-        timestamp = time.time()
-        if stats_jsonl is not None:
-            fields = dict(stats_dict, timestamp=timestamp)
-            stats_jsonl.write(json.dumps(fields) + '\n')
-            stats_jsonl.flush()
-        if stats_tfevents is not None:
-            global_step = int(cur_nimg / 1e3)
-            walltime = timestamp - start_time
-            for name, value in stats_dict.items():
-                stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime)
-            for name, value in stats_metrics.items():
-                stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime)
-            stats_tfevents.flush()
-        if progress_fn is not None:
-            progress_fn(cur_nimg // 1000, total_kimg)
-
-        # Update state.
-        cur_tick += 1
-        tick_start_nimg = cur_nimg
-        tick_start_time = time.time()
-        maintenance_time = tick_start_time - tick_end_time
-        if done:
-            break
-
-    # Done.
-    if rank == 0:
-        print()
-        print('Exiting...')
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Ricecake123/RVC-demo/infer_batch_rvc.py b/spaces/Ricecake123/RVC-demo/infer_batch_rvc.py
deleted file mode 100644
index 3635d5ec516fad5ebd60d845116949e192ebb576..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/infer_batch_rvc.py
+++ /dev/null
@@ -1,215 +0,0 @@
-"""
-v1
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
-v2
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
-"""
-import os, sys, pdb, torch
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import sys
-import torch
-import tqdm as tq
-from multiprocessing import cpu_count
-
-
-class Config:
-    def __init__(self, device, is_half):
-        self.device = device
-        self.is_half = is_half
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("16系/10系显卡和P40强制单精度")
-                self.is_half = False
-                for config_file in ["32k.json", "40k.json", "48k.json"]:
-                    with open(f"configs/{config_file}", "r") as f:
-                        strr = f.read().replace("true", "false")
-                    with open(f"configs/{config_file}", "w") as f:
-                        f.write(strr)
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("trainset_preprocess_pipeline_print.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("trainset_preprocess_pipeline_print.py", "w") as f:
-                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("没有发现支持的N卡, 使用MPS进行推理")
-            self.device = "mps"
-        else:
-            print("没有发现支持的N卡, 使用CPU进行推理")
-            self.device = "cpu"
-            self.is_half = True
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
-
-
-f0up_key = sys.argv[1]
-input_path = sys.argv[2]
-index_path = sys.argv[3]
-f0method = sys.argv[4]  # harvest or pm
-opt_path = sys.argv[5]
-model_path = sys.argv[6]
-index_rate = float(sys.argv[7])
-device = sys.argv[8]
-is_half = bool(sys.argv[9])
-filter_radius = int(sys.argv[10])
-resample_sr = int(sys.argv[11])
-rms_mix_rate = float(sys.argv[12])
-protect = float(sys.argv[13])
-print(sys.argv)
-config = Config(device, is_half)
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from vc_infer_pipeline import VC
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from my_utils import load_audio
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-
-hubert_model = None
-
-
-def load_hubert():
-    global hubert_model
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(device)
-    if is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-
-def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
-    global tgt_sr, net_g, vc, hubert_model, version
-    if input_audio is None:
-        return "You need to upload an audio", None
-    f0_up_key = int(f0_up_key)
-    audio = load_audio(input_audio, 16000)
-    times = [0, 0, 0]
-    if hubert_model == None:
-        load_hubert()
-    if_f0 = cpt.get("f0", 1)
-    # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
-    audio_opt = vc.pipeline(
-        hubert_model,
-        net_g,
-        sid,
-        audio,
-        input_audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=f0_file,
-    )
-    print(times)
-    return audio_opt
-
-
-def get_vc(model_path):
-    global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
-    print("loading pth %s" % model_path)
-    cpt = torch.load(model_path, map_location="cpu")
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif version == "v2":
-        if if_f0 == 1:  #
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-    del net_g.enc_q
-    print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净，真奇葩
-    net_g.eval().to(device)
-    if is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-    vc = VC(tgt_sr, config)
-    n_spk = cpt["config"][-3]
-    # return {"visible": True,"maximum": n_spk, "__type__": "update"}
-
-
-get_vc(model_path)
-audios = os.listdir(input_path)
-for file in tq.tqdm(audios):
-    if file.endswith(".wav"):
-        file_path = input_path + "/" + file
-        wav_opt = vc_single(
-            0, file_path, f0up_key, None, f0method, index_path, index_rate
-        )
-        out_path = opt_path + "/" + file
-        wavfile.write(out_path, tgt_sr, wav_opt)
diff --git a/spaces/Rongjiehuang/ProDiff/data_gen/tts/wav_processors/base_processor.py b/spaces/Rongjiehuang/ProDiff/data_gen/tts/wav_processors/base_processor.py
deleted file mode 100644
index e8200dc58a9388ac94a5ec34b8a65f75e380255b..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/data_gen/tts/wav_processors/base_processor.py
+++ /dev/null
@@ -1,25 +0,0 @@
-REGISTERED_WAV_PROCESSORS = {}
-
-
-def register_wav_processors(name):
-    def _f(cls):
-        REGISTERED_WAV_PROCESSORS[name] = cls
-        return cls
-
-    return _f
-
-
-def get_wav_processor_cls(name):
-    return REGISTERED_WAV_PROCESSORS.get(name, None)
-
-
-class BaseWavProcessor:
-    @property
-    def name(self):
-        raise NotImplementedError
-
-    def output_fn(self, input_fn):
-        return f'{input_fn[:-4]}_{self.name}.wav'
-
-    def process(self, input_fn, sr, tmp_dir, processed_dir, item_name, preprocess_args):
-        raise NotImplementedError
diff --git a/spaces/Salesforce/BLIP/app.py b/spaces/Salesforce/BLIP/app.py
deleted file mode 100644
index 45f6ce2936de503cd38532902dd835c86305ecb5..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/BLIP/app.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from PIL import Image
-import requests
-import torch
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-import gradio as gr
-
-from models.blip import blip_decoder
-
-image_size = 384
-transform = transforms.Compose([
-    transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
-    transforms.ToTensor(),
-    transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-    ]) 
-
-model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth'
-    
-model = blip_decoder(pretrained=model_url, image_size=384, vit='large')
-model.eval()
-model = model.to(device)
-
-
-from models.blip_vqa import blip_vqa
-
-image_size_vq = 480
-transform_vq = transforms.Compose([
-    transforms.Resize((image_size_vq,image_size_vq),interpolation=InterpolationMode.BICUBIC),
-    transforms.ToTensor(),
-    transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-    ]) 
-
-model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth'
-    
-model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base')
-model_vq.eval()
-model_vq = model_vq.to(device)
-
-
-
-def inference(raw_image, model_n, question, strategy):
-    if model_n == 'Image Captioning':
-        image = transform(raw_image).unsqueeze(0).to(device)   
-        with torch.no_grad():
-          if strategy == "Beam search":
-              caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)
-          else:
-              caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5)
-          return 'caption: '+caption[0]
-
-    else:   
-        image_vq = transform_vq(raw_image).unsqueeze(0).to(device)  
-        with torch.no_grad():
-            answer = model_vq(image_vq, question, train=False, inference='generate') 
-        return  'answer: '+answer[0]
-    
-inputs = [
-    gr.Image(type='pil', interactive=False),
-    gr.inputs.Radio(choices=['Image Captioning',"Visual Question Answering"],
-    type="value",
-    default="Image Captioning",
-    label="Task"
-),gr.inputs.Textbox(lines=2, label="Question"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")]
-outputs = gr.outputs.Textbox(label="Output")
-
-title = "BLIP"
-
-description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research). We have now disable image uploading as of March 23. 2023. Click one of the examples to load them. Read more at the links below."
-
-article = """<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>"""
-
-gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['starrynight.jpeg',"Image Captioning","None","Nucleus sampling"]]).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Sandiago21/text-to-speech-greek/app.py b/spaces/Sandiago21/text-to-speech-greek/app.py
deleted file mode 100644
index 8f7a7c8a9b837e1c622d6eafce9897af5ecca247..0000000000000000000000000000000000000000
--- a/spaces/Sandiago21/text-to-speech-greek/app.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import gradio as gr
-import torch
-from datasets import load_dataset
-from transformers import pipeline, SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForTextToSpeech
-
-model_id = "Sandiago21/speecht5_finetuned_google_fleurs_greek"  # update with your model id
-# pipe = pipeline("automatic-speech-recognition", model=model_id)
-model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)
-
-processor = SpeechT5Processor.from_pretrained(model_id)
-
-replacements = [
-    ("ου", "u"),
-    ("αυ", "af"),
-    ("ευ", "ef"),
-    ("ει", "i"),
-    ("οι", "i"),
-    ("αι", "e"),
-    ("ού", "u"),
-    ("εί", "i"),
-    ("οί", "i"),
-    ("αί", "e"),
-    ("Ά", "A"),
-    ("Έ", "E"),
-    ("Ή", "H"),
-    ("Ί", "I"),
-    ("Ό", "O"),
-    ("Ύ", "Y"),
-    ("Ώ", "O"),
-    ("ΐ", "i"),
-    ("Α", "A"),
-    ("Β", "B"),
-    ("Γ", "G"),
-    ("Δ", "L"),
-    ("Ε", "Ε"),
-    ("Ζ", "Z"),
-    ("Η", "I"),
-    ("Θ", "Th"),
-    ("Ι", "I"),
-    ("Κ", "K"),
-    ("Λ", "L"),
-    ("Μ", "M"),
-    ("Ν", "N"),
-    ("Ξ", "Ks"),
-    ("Ο", "O"),
-    ("Π", "P"),
-    ("Ρ", "R"),
-    ("Σ", "S"),
-    ("Τ", "T"),
-    ("Υ", "Y"),
-    ("Φ", "F"),
-    ("Χ", "X"),
-    ("Ω", "O"),
-    ("ά", "a"),
-    ("έ", "e"),
-    ("ή", "i"),
-    ("ί", "i"),
-    ("α", "a"),
-    ("β", "v"),
-    ("γ", "g"),
-    ("δ", "d"),
-    ("ε", "e"),
-    ("ζ", "z"),
-    ("η", "i"),
-    ("θ", "th"),
-    ("ι", "i"),
-    ("κ", "k"),
-    ("λ", "l"),
-    ("μ", "m"),
-    ("ν", "n"),
-    ("ξ", "ks"),
-    ("ο", "o"),
-    ("π", "p"),
-    ("ρ", "r"),
-    ("ς", "s"),
-    ("σ", "s"),
-    ("τ", "t"),
-    ("υ", "i"),
-    ("φ", "f"),
-    ("χ", "h"),
-    ("ψ", "ps"),
-    ("ω", "o"),
-    ("ϊ", "i"),
-    ("ϋ", "i"),
-    ("ό", "o"),
-    ("ύ", "i"),
-    ("ώ", "o"),
-    ("í", "i"),
-    ("õ", "o"),
-    ("Ε", "E"),
-    ("Ψ", "Ps"),
-]
-
-
-title = "Text-to-Speech"
-description = """
-Demo for text-to-speech translation in Greek. Demo uses [Sandiago21/speecht5_finetuned_google_fleurs_greek](https://huggingface.co/Sandiago21/speecht5_finetuned_google_fleurs_greek) checkpoint, which is based on Microsoft's
-[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model and is fine-tuned in Greek Audio dataset
-![Text-to-Speech (TTS)"](https://geekflare.com/wp-content/uploads/2021/07/texttospeech-1200x385.png "Diagram of Text-to-Speech (TTS)")
-"""
-
-
-def cleanup_text(text):
-    for src, dst in replacements:
-        text = text.replace(src, dst)
-    return text
-
-def synthesize_speech(text):
-    text = cleanup_text(text)
-    inputs = processor(text=text, return_tensors="pt")
-
-    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
-
-    return gr.Audio.update(value=(16000, speech.cpu().numpy()))
-
-syntesize_speech_gradio = gr.Interface(
-    synthesize_speech,
-    inputs = gr.Textbox(label="Text", placeholder="Type something here..."),
-    outputs=gr.Audio(),
-    examples=["Έλαβαν χώρα μεγάλες διαδηλώσεις στην Πολωνία όταν εκείνη η χώρα υπέγραψε την acta που οδήγησε την κυβέρνηση της πολωνίας να αποφασίσει τη μη επικύρωση της συμφωνίας προς το παρόν"],
-    title=title,
-    description=description,
-).launch()
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/core/__init__.py b/spaces/SankarSrin/image-matting-app/ppmatting/core/__init__.py
deleted file mode 100644
index 78060ba48aac1fd7d8cb32eccc7ccddadd74017f..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/core/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .val import evaluate
-from .val_ml import evaluate_ml
-from .train import train
-from .predict import predict
\ No newline at end of file
diff --git a/spaces/Sefray/PylenaLineDetector_ICDAR2023/README.md b/spaces/Sefray/PylenaLineDetector_ICDAR2023/README.md
deleted file mode 100644
index 7b787b15522f4cb6060be979f9ee792c278f4b06..0000000000000000000000000000000000000000
--- a/spaces/Sefray/PylenaLineDetector_ICDAR2023/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PylenaLineDetector ICDAR2023
-emoji: ⚡
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ServerX/PorcoDiaz/lib/uvr5_pack/lib_v5/nets_123812KB.py b/spaces/ServerX/PorcoDiaz/lib/uvr5_pack/lib_v5/nets_123812KB.py
deleted file mode 100644
index becbfae85683a13bbb19d3ea6c840da24e61e01e..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/lib/uvr5_pack/lib_v5/nets_123812KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/Shashashasha/so-vits-fork-yoshi/README.md b/spaces/Shashashasha/so-vits-fork-yoshi/README.md
deleted file mode 100644
index edfbc1b8a5b7e8293e6fda7ac9c83f5b0d7968fd..0000000000000000000000000000000000000000
--- a/spaces/Shashashasha/so-vits-fork-yoshi/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Voice Cloning
-emoji: 😻
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: Shashashasha/so-vits-fork-vika
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ShreyashNadage/InvestmentCopilot/Stock_Static_Data.py b/spaces/ShreyashNadage/InvestmentCopilot/Stock_Static_Data.py
deleted file mode 100644
index bca1805ae8dbb04fcca1a18f9e024097dfe307b6..0000000000000000000000000000000000000000
--- a/spaces/ShreyashNadage/InvestmentCopilot/Stock_Static_Data.py
+++ /dev/null
@@ -1,258 +0,0 @@
-nse_stock_sectors = {
-    'RELIANCE': 'Energy',
-    'HDFCBANK': 'Financial Services',
-    'INFY': 'IT',
-    'ICICIBANK': 'Financial Services',
-    'TCS': 'IT',
-    'HINDUNILVR': 'Consumer Goods',
-    'KOTAKBANK': 'Financial Services',
-    'HDFC': 'Financial Services',
-    'BAJFINANCE': 'Financial Services',
-    'AXISBANK': 'Financial Services',
-    'ASIANPAINT': 'Consumer Goods',
-    'ITC': 'Consumer Goods',
-    'LT': 'Infrastructure',
-    'HCLTECH': 'IT',
-    'SBI': 'Financial Services',
-    'BAJAJFINSV': 'Financial Services',
-    'TECHM': 'IT',
-    'NTPC': 'Energy',
-    'BANKBARODA': 'Financial Services',
-    'IOC': 'Energy',
-    'NESTLEIND': 'Consumer Goods',
-    'ONGC': 'Energy',
-    'BPCL': 'Energy',
-    'POWERGRID': 'Infrastructure',
-    'SBIN': 'Financial Services',
-    'BHARTIARTL': 'Telecom',
-    'MARUTI': 'Automobile',
-    'ULTRACEMCO': 'Cement',
-    'BAJAJ-AUTO': 'Automobile',
-    'WIPRO': 'IT',
-    'JSWSTEEL': 'Metals',
-    'TATASTEEL': 'Metals',
-    'HINDZINC': 'Metals',
-    'SUNPHARMA': 'Pharmaceuticals',
-    'TATAMOTORS': 'Automobile',
-    'DMART': 'Retail',
-    'BRITANNIA': 'Consumer Goods',
-    'TITAN': 'Consumer Goods',
-    'GAIL': 'Energy',
-    'DRREDDY': 'Pharmaceuticals',
-    'HINDPETRO': 'Energy',
-    'CIPLA': 'Pharmaceuticals',
-    'BHARTIINFR': 'Telecom',
-    'EICHERMOT': 'Automobile',
-    'M&M': 'Automobile',
-    'ULTRACEMC': 'Cement',
-    'INDUSINDBK': 'Financial Services',
-    'HEROMOTOCO': 'Automobile',
-    'SHREECEM': 'Cement',
-    'GRASIM': 'Cement',
-    'ONGCORP': 'Energy',
-    'ADANIPORTS': 'Infrastructure',
-    'BAJAJHLDNG': 'Financial Services',
-    'DIVISLAB': 'Pharmaceuticals',
-    'TATAPOWER': 'Energy',
-    'ZEEL': 'Media',
-    'NTPC_': 'Energy',
-    'ICICIPRULI': 'Financial Services',
-    'MRF': 'Automobile',
-    'MOTHERSUMI': 'Automobile',
-    'COALINDIA': 'Mining',
-    'SBILIFE': 'Financial Services',
-    'PIDILITIND': 'Consumer Goods',
-    'TATACONSUM': 'Consumer Goods',
-    'SBICARD': 'Financial Services',
-    'DABUR': 'Consumer Goods',
-    'PNB': 'Financial Services',
-    'LUPIN': 'Pharmaceuticals',
-    'UBL': 'Consumer Goods',
-    'BHEL': 'Infrastructure',
-    'ACC': 'Cement',
-    'HINDALCO': 'Metals',
-    'TORNTPHARM': 'Pharmaceuticals',
-    'BOSCHLTD': 'Automobile',
-    'DLF': 'Real Estate',
-    'AMBUJACEM': 'Cement',
-    'SAIL': 'Metals',
-    'MUTHOOTFIN': 'Financial Services',
-    'MCDOWELL-N': 'Consumer Goods',
-    'PFC': 'Financial Services',
-    'BEL': 'Defense',
-    'BANDHANBNK': 'Financial Services',
-    'PEL': 'Conglomerate',
-    'TORNTPOWER': 'Energy',
-    'HAVELLS': 'Consumer Goods',
-    'FEDERALBNK': 'Financial Services',
-    'BERGEPAINT': 'Consumer Goods',
-    'RBLBANK': 'Financial Services',
-    'INDIGO': 'Airlines',
-    'RAMCOCEM': 'Cement',
-    'EXIDEIND': 'Automobile',
-    'CHOLAFIN': 'Financial Services',
-    'ICICIGI': 'Financial Services',
-    'BANKINDIA': 'Financial Services',
-    'ADANIGREEN': 'Energy',
-    'HDFCLIFE': 'Financial Services',
-    'APOLLOHOSP': 'Healthcare',
-    'AUROPHARMA': 'Pharmaceuticals',
-    'IGL': 'Energy',
-    'TVSMOTOR': 'Automobile',
-    'GODREJCP': 'Consumer Goods',
-    'MGL': 'Energy',
-    'BATAINDIA': 'Retail',
-    'M&MFIN': 'Financial Services',
-    'NIACL': 'Financial Services',
-    'ADANIENT': 'Conglomerate',
-    'JINDALSTEL': 'Metals',
-    'BANKNIFTY': 'Index',
-    'COLPAL': 'Consumer Goods',
-    'UBL_': 'Consumer Goods',
-    'INDIANB': 'Financial Services',
-    'BANKBAROD': 'Financial Services',
-    'ASHOKLEY': 'Automobile',
-    'SRTRANSFIN': 'Financial Services',
-    'ACC_': 'Cement',
-    'SIEMENS': 'Infrastructure',
-    'HDFCAMC': 'Financial Services',
-    'AMARAJABAT': 'Automobile',
-    'BSE': 'Index',
-    'MGL_': 'Energy',
-    'BAJAJHLDNG_': 'Financial Services',
-    'AMBUJACEM_': 'Cement',
-    'BPCL_': 'Energy',
-    'IDFCFIRSTB': 'Financial Services',
-    'IDEA': 'Telecom',
-    'PFIZER': 'Pharmaceuticals',
-    'BANDHANBNK_': 'Financial Services',
-    'HCLTECH_': 'IT',
-    'MINDTREE': 'IT',
-    'HDFCBANK_': 'Financial Services',
-    'ASHOKLEY_': 'Automobile',
-    'PNBHOUSING': 'Real Estate',
-    'GRASIM_': 'Cement',
-    'M&MFIN_': 'Financial Services',
-    'PVR': 'Entertainment',
-    'RPOWER': 'Energy',
-    'TVTODAY': 'Media',
-    'APLLTD': 'Pharmaceuticals',
-    'IDBI': 'Financial Services',
-    'IRCTC': 'Travel',
-    'JINDALSTEL_': 'Metals',
-    'L&TFH': 'Financial Services',
-    'NIITTECH': 'IT',
-    'INDIGO_': 'Airlines',
-    'BANKINDIA_': 'Financial Services',
-    'MINDACORP': 'Retail',
-    'FEDERALBNK_': 'Financial Services',
-    'GLENMARK': 'Pharmaceuticals',
-    'TV18BRDCST': 'Media',
-    'UJJIVAN': 'Financial Services',
-    'CENTRALBK': 'Financial Services',
-    'NCC': 'Infrastructure',
-    'HDFCLIFE_': 'Financial Services',
-    'SYNGENE': 'Pharmaceuticals',
-    'BALKRISIND': 'Automobile',
-    'CHOLAFIN_': 'Financial Services',
-    'COFORGE': 'IT',
-    'CRISIL': 'Financial Services',
-    'DEEPAKNTR': 'Consumer Goods',
-    'JUBLFOOD': 'Retail',
-    'PHOENIXLTD': 'Real Estate',
-    'EQUITAS': 'Financial Services',
-    'LTI': 'IT',
-    'RBLBANK_': 'Financial Services',
-    'CANBK': 'Financial Services',
-    'MOTILALOFS': 'Financial Services',
-    'PNCINFRA': 'Infrastructure',
-    'SUNTV': 'Media',
-    'AMBER': 'Consumer Goods',
-    'BLUESTARCO': 'Consumer Goods',
-    'CUB': 'Financial Services',
-    'GREENPLY': 'Consumer Goods',
-    'KOLTEPATIL': 'Real Estate',
-    'L&TFH_': 'Financial Services',
-    'METROPOLIS': 'Healthcare',
-    'RCOM': 'Telecom',
-    'SYMPHONY': 'Consumer Goods',
-    'VBL': 'Consumer Goods',
-    'VENKEYS': 'Consumer Goods',
-    'COROMANDEL': 'Fertilizers',
-    'GODREJAGRO': 'Consumer Goods',
-    'HAL': 'Defense',
-    'JINDALSAW': 'Metals',
-    'KEC': 'Infrastructure',
-    'MINDACORP_': 'Retail',
-    'NMDC': 'Metals',
-    'PGHL': 'Consumer Goods',
-    'PRAJIND': 'Infrastructure',
-    'RITES': 'Infrastructure',
-    'BANKBAROD_': 'Financial Services',
-    'BATAINDIA_': 'Retail',
-    'BEML': 'Infrastructure',
-    'CAREERP': 'IT',
-    'CENTURYTEX': 'Cement',
-    'DCBBANK': 'Financial Services',
-    'DHFL': 'Financial Services',
-    'FSL': 'Financial Services',
-    'FUTURECON': 'Retail',
-    'GEPIL': 'Pharmaceuticals',
-    'GESHIP': 'Shipping',
-    'GUJALKALI': 'Fertilizers',
-    'HATHWAY': 'Media',
-    'HERCULES': 'Pharmaceuticals',
-    'HEXAWARE': 'IT',
-    'IDFC': 'Financial Services',
-    'IDFCFIRSTB_': 'Financial Services',
-    'IFCI': 'Financial Services',
-    'IIFL': 'Financial Services',
-    'INDIANB_': 'Financial Services',
-    'IRCON': 'Infrastructure',
-    'JETAIRWAYS': 'Airlines',
-    'LUPIN': 'Pharmaceuticals',
-    'M&M': 'Automobile',
-    'MAHLOG': 'Automobile',
-    'MANAPPURAM': 'Financial Services',
-    'MAXFIN': 'Financial Services',
-    'MCX': 'Financial Services',
-    'MOTHERSUMI': 'Automobile',
-    'NATCOPHARM': 'Pharmaceuticals',
-    'NATIONALUM': 'Metals',
-    'NH': 'Hospitality',
-    'NLCINDIA': 'Power',
-    'NTPC': 'Power',
-    'PCJEWELLER': 'Retail',
-    'PNBHOUSING': 'Financial Services',
-    'PVR': 'Media',
-    'RAYMOND': 'Textiles',
-    'RELCAPITAL': 'Financial Services',
-    'RELIANCE': 'Oil & Gas',
-    'RIIL': 'Infrastructure',
-    'SADBHAV': 'Infrastructure',
-    'SAIL': 'Metals',
-    'SCHAEFFLER': 'Automobile',
-    'SFL': 'Retail',
-    'SOUTHBANK': 'Financial Services',
-    'SPARC': 'Pharmaceuticals',
-    'SUZLON': 'Power',
-    'SYNDIBANK': 'Financial Services',
-    'TATAELXSI': 'IT',
-    'TATAMOTORS': 'Automobile',
-    'TECHM': 'IT',
-    'THERMAX': 'Engineering',
-    'TITAN': 'Retail',
-    'TRIDENT': 'Textiles',
-    'UFO': 'Media',
-    'UPL': 'Chemicals',
-    'VAKRANGEE': 'IT',
-    'WOCKPHARMA': 'Pharmaceuticals',
-    'YESBANK': 'Financial Services'
-}
-
-def get_sector_list():
-    return list(set(nse_stock_sectors.values()))
-
-def get_stock_list_by_sector(sector='IT'):
-    return [key.strip()+'.NS' for key, val in nse_stock_sectors.items() if val == sector]
-
diff --git a/spaces/SiddharthK/dslim-bert-large-NER/README.md b/spaces/SiddharthK/dslim-bert-large-NER/README.md
deleted file mode 100644
index e10a300e3adc9f94825461de10f13897d051e179..0000000000000000000000000000000000000000
--- a/spaces/SiddharthK/dslim-bert-large-NER/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NER using BERT/large
-emoji: 🚀
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SidneyChen/mbti_prediction/app.py b/spaces/SidneyChen/mbti_prediction/app.py
deleted file mode 100644
index 3667c08a6683bcab2bace5d9ce85e78c2e972773..0000000000000000000000000000000000000000
--- a/spaces/SidneyChen/mbti_prediction/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-def update(name):
-    return f"Welcome to Gradio, {name}!"
-import gradio as gr
-
-gui = gr.Interface(fn=update, #callable function
-                   inputs=gr.inputs.Textbox(label = '讓我來分析你最近的人格><', placeholder = '個性描述、自己的故事或是曾經發過的文章'), #input format
-                   outputs=gr.outputs.Textbox(label = '只有我最了解你，你是一位...'),
-                   title = "AI-MBTI knows U.",
-                   description = 'Come on. Let us predict your MBTI type !     We will tell you what kind of movie should you watch !',
-                   theme = 'grass',
-                  #  examples = [['User', ‘description, MBTI Type& Moive Fits in U ],
-                       
-                   
-                   ) #output format
-
-
-#display the interface
-gui.launch(share=True)
\ No newline at end of file
diff --git a/spaces/Silentlin/DiffSinger/utils/text_norm.py b/spaces/Silentlin/DiffSinger/utils/text_norm.py
deleted file mode 100644
index d0973cebc91e0525aeb6657e70012a1d37b5e6ff..0000000000000000000000000000000000000000
--- a/spaces/Silentlin/DiffSinger/utils/text_norm.py
+++ /dev/null
@@ -1,790 +0,0 @@
-# coding=utf-8
-# Authors:
-#   2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git)
-#   2019.9 Jiayu DU
-#
-# requirements:
-#   - python 3.X
-# notes: python 2.X WILL fail or produce misleading results
-
-import sys, os, argparse, codecs, string, re
-
-# ================================================================================ #
-#                                    basic constant
-# ================================================================================ #
-CHINESE_DIGIS = u'零一二三四五六七八九'
-BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖'
-BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖'
-SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万'
-SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬'
-LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载'
-LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載'
-SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万'
-SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬'
-
-ZERO_ALT = u'〇'
-ONE_ALT = u'幺'
-TWO_ALTS = [u'两', u'兩']
-
-POSITIVE = [u'正', u'正']
-NEGATIVE = [u'负', u'負']
-POINT = [u'点', u'點']
-# PLUS = [u'加', u'加']
-# SIL = [u'杠', u'槓']
-
-# 中文数字系统类型
-NUMBERING_TYPES = ['low', 'mid', 'high']
-
-CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \
-                 '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)'
-CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)'
-COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \
-                  '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \
-                  '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \
-                  '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \
-                  '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \
-                  '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)'
-
-# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git)
-CHINESE_PUNC_STOP = '！？｡。'
-CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏'
-CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP
-
-
-# ================================================================================ #
-#                                    basic class
-# ================================================================================ #
-class ChineseChar(object):
-    """
-    中文字符
-    每个字符对应简体和繁体,
-    e.g. 简体 = '负', 繁体 = '負'
-    转换时可转换为简体或繁体
-    """
-
-    def __init__(self, simplified, traditional):
-        self.simplified = simplified
-        self.traditional = traditional
-        # self.__repr__ = self.__str__
-
-    def __str__(self):
-        return self.simplified or self.traditional or None
-
-    def __repr__(self):
-        return self.__str__()
-
-
-class ChineseNumberUnit(ChineseChar):
-    """
-    中文数字/数位字符
-    每个字符除繁简体外还有一个额外的大写字符
-    e.g. '陆' 和 '陸'
-    """
-
-    def __init__(self, power, simplified, traditional, big_s, big_t):
-        super(ChineseNumberUnit, self).__init__(simplified, traditional)
-        self.power = power
-        self.big_s = big_s
-        self.big_t = big_t
-
-    def __str__(self):
-        return '10^{}'.format(self.power)
-
-    @classmethod
-    def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False):
-
-        if small_unit:
-            return ChineseNumberUnit(power=index + 1,
-                                     simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[0]:
-            return ChineseNumberUnit(power=index + 8,
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[1]:
-            return ChineseNumberUnit(power=(index + 2) * 4,
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[2]:
-            return ChineseNumberUnit(power=pow(2, index + 3),
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        else:
-            raise ValueError(
-                'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type))
-
-
-class ChineseNumberDigit(ChineseChar):
-    """
-    中文数字字符
-    """
-
-    def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None):
-        super(ChineseNumberDigit, self).__init__(simplified, traditional)
-        self.value = value
-        self.big_s = big_s
-        self.big_t = big_t
-        self.alt_s = alt_s
-        self.alt_t = alt_t
-
-    def __str__(self):
-        return str(self.value)
-
-    @classmethod
-    def create(cls, i, v):
-        return ChineseNumberDigit(i, v[0], v[1], v[2], v[3])
-
-
-class ChineseMath(ChineseChar):
-    """
-    中文数位字符
-    """
-
-    def __init__(self, simplified, traditional, symbol, expression=None):
-        super(ChineseMath, self).__init__(simplified, traditional)
-        self.symbol = symbol
-        self.expression = expression
-        self.big_s = simplified
-        self.big_t = traditional
-
-
-CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath
-
-
-class NumberSystem(object):
-    """
-    中文数字系统
-    """
-    pass
-
-
-class MathSymbol(object):
-    """
-    用于中文数字系统的数学符号 (繁/简体), e.g.
-    positive = ['正', '正']
-    negative = ['负', '負']
-    point = ['点', '點']
-    """
-
-    def __init__(self, positive, negative, point):
-        self.positive = positive
-        self.negative = negative
-        self.point = point
-
-    def __iter__(self):
-        for v in self.__dict__.values():
-            yield v
-
-
-# class OtherSymbol(object):
-#     """
-#     其他符号
-#     """
-#
-#     def __init__(self, sil):
-#         self.sil = sil
-#
-#     def __iter__(self):
-#         for v in self.__dict__.values():
-#             yield v
-
-
-# ================================================================================ #
-#                                    basic utils
-# ================================================================================ #
-def create_system(numbering_type=NUMBERING_TYPES[1]):
-    """
-    根据数字系统类型返回创建相应的数字系统，默认为 mid
-    NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型
-        low:  '兆' = '亿' * '十' = $10^{9}$,  '京' = '兆' * '十', etc.
-        mid:  '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc.
-        high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc.
-    返回对应的数字系统
-    """
-
-    # chinese number units of '亿' and larger
-    all_larger_units = zip(
-        LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL)
-    larger_units = [CNU.create(i, v, numbering_type, False)
-                    for i, v in enumerate(all_larger_units)]
-    # chinese number units of '十, 百, 千, 万'
-    all_smaller_units = zip(
-        SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL)
-    smaller_units = [CNU.create(i, v, small_unit=True)
-                     for i, v in enumerate(all_smaller_units)]
-    # digis
-    chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS,
-                        BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL)
-    digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)]
-    digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT
-    digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT
-    digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1]
-
-    # symbols
-    positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x)
-    negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x)
-    point_cn = CM(POINT[0], POINT[1], '.', lambda x,
-                                                  y: float(str(x) + '.' + str(y)))
-    # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y)))
-    system = NumberSystem()
-    system.units = smaller_units + larger_units
-    system.digits = digits
-    system.math = MathSymbol(positive_cn, negative_cn, point_cn)
-    # system.symbols = OtherSymbol(sil_cn)
-    return system
-
-
-def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]):
-    def get_symbol(char, system):
-        for u in system.units:
-            if char in [u.traditional, u.simplified, u.big_s, u.big_t]:
-                return u
-        for d in system.digits:
-            if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]:
-                return d
-        for m in system.math:
-            if char in [m.traditional, m.simplified]:
-                return m
-
-    def string2symbols(chinese_string, system):
-        int_string, dec_string = chinese_string, ''
-        for p in [system.math.point.simplified, system.math.point.traditional]:
-            if p in chinese_string:
-                int_string, dec_string = chinese_string.split(p)
-                break
-        return [get_symbol(c, system) for c in int_string], \
-               [get_symbol(c, system) for c in dec_string]
-
-    def correct_symbols(integer_symbols, system):
-        """
-        一百八 to 一百八十
-        一亿一千三百万 to 一亿 一千万 三百万
-        """
-
-        if integer_symbols and isinstance(integer_symbols[0], CNU):
-            if integer_symbols[0].power == 1:
-                integer_symbols = [system.digits[1]] + integer_symbols
-
-        if len(integer_symbols) > 1:
-            if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU):
-                integer_symbols.append(
-                    CNU(integer_symbols[-2].power - 1, None, None, None, None))
-
-        result = []
-        unit_count = 0
-        for s in integer_symbols:
-            if isinstance(s, CND):
-                result.append(s)
-                unit_count = 0
-            elif isinstance(s, CNU):
-                current_unit = CNU(s.power, None, None, None, None)
-                unit_count += 1
-
-            if unit_count == 1:
-                result.append(current_unit)
-            elif unit_count > 1:
-                for i in range(len(result)):
-                    if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power:
-                        result[-i - 1] = CNU(result[-i - 1].power +
-                                             current_unit.power, None, None, None, None)
-        return result
-
-    def compute_value(integer_symbols):
-        """
-        Compute the value.
-        When current unit is larger than previous unit, current unit * all previous units will be used as all previous units.
-        e.g. '两千万' = 2000 * 10000 not 2000 + 10000
-        """
-        value = [0]
-        last_power = 0
-        for s in integer_symbols:
-            if isinstance(s, CND):
-                value[-1] = s.value
-            elif isinstance(s, CNU):
-                value[-1] *= pow(10, s.power)
-                if s.power > last_power:
-                    value[:-1] = list(map(lambda v: v *
-                                                    pow(10, s.power), value[:-1]))
-                    last_power = s.power
-                value.append(0)
-        return sum(value)
-
-    system = create_system(numbering_type)
-    int_part, dec_part = string2symbols(chinese_string, system)
-    int_part = correct_symbols(int_part, system)
-    int_str = str(compute_value(int_part))
-    dec_str = ''.join([str(d.value) for d in dec_part])
-    if dec_part:
-        return '{0}.{1}'.format(int_str, dec_str)
-    else:
-        return int_str
-
-
-def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False,
-            traditional=False, alt_zero=False, alt_one=False, alt_two=True,
-            use_zeros=True, use_units=True):
-    def get_value(value_string, use_zeros=True):
-
-        striped_string = value_string.lstrip('0')
-
-        # record nothing if all zeros
-        if not striped_string:
-            return []
-
-        # record one digits
-        elif len(striped_string) == 1:
-            if use_zeros and len(value_string) != len(striped_string):
-                return [system.digits[0], system.digits[int(striped_string)]]
-            else:
-                return [system.digits[int(striped_string)]]
-
-        # recursively record multiple digits
-        else:
-            result_unit = next(u for u in reversed(
-                system.units) if u.power < len(striped_string))
-            result_string = value_string[:-result_unit.power]
-            return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:])
-
-    system = create_system(numbering_type)
-
-    int_dec = number_string.split('.')
-    if len(int_dec) == 1:
-        int_string = int_dec[0]
-        dec_string = ""
-    elif len(int_dec) == 2:
-        int_string = int_dec[0]
-        dec_string = int_dec[1]
-    else:
-        raise ValueError(
-            "invalid input num string with more than one dot: {}".format(number_string))
-
-    if use_units and len(int_string) > 1:
-        result_symbols = get_value(int_string)
-    else:
-        result_symbols = [system.digits[int(c)] for c in int_string]
-    dec_symbols = [system.digits[int(c)] for c in dec_string]
-    if dec_string:
-        result_symbols += [system.math.point] + dec_symbols
-
-    if alt_two:
-        liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t,
-                    system.digits[2].big_s, system.digits[2].big_t)
-        for i, v in enumerate(result_symbols):
-            if isinstance(v, CND) and v.value == 2:
-                next_symbol = result_symbols[i +
-                                             1] if i < len(result_symbols) - 1 else None
-                previous_symbol = result_symbols[i - 1] if i > 0 else None
-                if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))):
-                    if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)):
-                        result_symbols[i] = liang
-
-    # if big is True, '两' will not be used and `alt_two` has no impact on output
-    if big:
-        attr_name = 'big_'
-        if traditional:
-            attr_name += 't'
-        else:
-            attr_name += 's'
-    else:
-        if traditional:
-            attr_name = 'traditional'
-        else:
-            attr_name = 'simplified'
-
-    result = ''.join([getattr(s, attr_name) for s in result_symbols])
-
-    # if not use_zeros:
-    #     result = result.strip(getattr(system.digits[0], attr_name))
-
-    if alt_zero:
-        result = result.replace(
-            getattr(system.digits[0], attr_name), system.digits[0].alt_s)
-
-    if alt_one:
-        result = result.replace(
-            getattr(system.digits[1], attr_name), system.digits[1].alt_s)
-
-    for i, p in enumerate(POINT):
-        if result.startswith(p):
-            return CHINESE_DIGIS[0] + result
-
-    # ^10, 11, .., 19
-    if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0],
-                                          SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \
-            result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]:
-        result = result[1:]
-
-    return result
-
-
-# ================================================================================ #
-#                          different types of rewriters
-# ================================================================================ #
-class Cardinal:
-    """
-    CARDINAL类
-    """
-
-    def __init__(self, cardinal=None, chntext=None):
-        self.cardinal = cardinal
-        self.chntext = chntext
-
-    def chntext2cardinal(self):
-        return chn2num(self.chntext)
-
-    def cardinal2chntext(self):
-        return num2chn(self.cardinal)
-
-
-class Digit:
-    """
-    DIGIT类
-    """
-
-    def __init__(self, digit=None, chntext=None):
-        self.digit = digit
-        self.chntext = chntext
-
-    # def chntext2digit(self):
-    #     return chn2num(self.chntext)
-
-    def digit2chntext(self):
-        return num2chn(self.digit, alt_two=False, use_units=False)
-
-
-class TelePhone:
-    """
-    TELEPHONE类
-    """
-
-    def __init__(self, telephone=None, raw_chntext=None, chntext=None):
-        self.telephone = telephone
-        self.raw_chntext = raw_chntext
-        self.chntext = chntext
-
-    # def chntext2telephone(self):
-    #     sil_parts = self.raw_chntext.split('<SIL>')
-    #     self.telephone = '-'.join([
-    #         str(chn2num(p)) for p in sil_parts
-    #     ])
-    #     return self.telephone
-
-    def telephone2chntext(self, fixed=False):
-
-        if fixed:
-            sil_parts = self.telephone.split('-')
-            self.raw_chntext = '<SIL>'.join([
-                num2chn(part, alt_two=False, use_units=False) for part in sil_parts
-            ])
-            self.chntext = self.raw_chntext.replace('<SIL>', '')
-        else:
-            sp_parts = self.telephone.strip('+').split()
-            self.raw_chntext = '<SP>'.join([
-                num2chn(part, alt_two=False, use_units=False) for part in sp_parts
-            ])
-            self.chntext = self.raw_chntext.replace('<SP>', '')
-        return self.chntext
-
-
-class Fraction:
-    """
-    FRACTION类
-    """
-
-    def __init__(self, fraction=None, chntext=None):
-        self.fraction = fraction
-        self.chntext = chntext
-
-    def chntext2fraction(self):
-        denominator, numerator = self.chntext.split('分之')
-        return chn2num(numerator) + '/' + chn2num(denominator)
-
-    def fraction2chntext(self):
-        numerator, denominator = self.fraction.split('/')
-        return num2chn(denominator) + '分之' + num2chn(numerator)
-
-
-class Date:
-    """
-    DATE类
-    """
-
-    def __init__(self, date=None, chntext=None):
-        self.date = date
-        self.chntext = chntext
-
-    # def chntext2date(self):
-    #     chntext = self.chntext
-    #     try:
-    #         year, other = chntext.strip().split('年', maxsplit=1)
-    #         year = Digit(chntext=year).digit2chntext() + '年'
-    #     except ValueError:
-    #         other = chntext
-    #         year = ''
-    #     if other:
-    #         try:
-    #             month, day = other.strip().split('月', maxsplit=1)
-    #             month = Cardinal(chntext=month).chntext2cardinal() + '月'
-    #         except ValueError:
-    #             day = chntext
-    #             month = ''
-    #         if day:
-    #             day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1]
-    #     else:
-    #         month = ''
-    #         day = ''
-    #     date = year + month + day
-    #     self.date = date
-    #     return self.date
-
-    def date2chntext(self):
-        date = self.date
-        try:
-            year, other = date.strip().split('年', 1)
-            year = Digit(digit=year).digit2chntext() + '年'
-        except ValueError:
-            other = date
-            year = ''
-        if other:
-            try:
-                month, day = other.strip().split('月', 1)
-                month = Cardinal(cardinal=month).cardinal2chntext() + '月'
-            except ValueError:
-                day = date
-                month = ''
-            if day:
-                day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1]
-        else:
-            month = ''
-            day = ''
-        chntext = year + month + day
-        self.chntext = chntext
-        return self.chntext
-
-
-class Money:
-    """
-    MONEY类
-    """
-
-    def __init__(self, money=None, chntext=None):
-        self.money = money
-        self.chntext = chntext
-
-    # def chntext2money(self):
-    #     return self.money
-
-    def money2chntext(self):
-        money = self.money
-        pattern = re.compile(r'(\d+(\.\d+)?)')
-        matchers = pattern.findall(money)
-        if matchers:
-            for matcher in matchers:
-                money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext())
-        self.chntext = money
-        return self.chntext
-
-
-class Percentage:
-    """
-    PERCENTAGE类
-    """
-
-    def __init__(self, percentage=None, chntext=None):
-        self.percentage = percentage
-        self.chntext = chntext
-
-    def chntext2percentage(self):
-        return chn2num(self.chntext.strip().strip('百分之')) + '%'
-
-    def percentage2chntext(self):
-        return '百分之' + num2chn(self.percentage.strip().strip('%'))
-
-
-# ================================================================================ #
-#                            NSW Normalizer
-# ================================================================================ #
-class NSWNormalizer:
-    def __init__(self, raw_text):
-        self.raw_text = '^' + raw_text + '$'
-        self.norm_text = ''
-
-    def _particular(self):
-        text = self.norm_text
-        pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('particular')
-            for matcher in matchers:
-                text = text.replace(matcher[0], matcher[1] + '2' + matcher[2], 1)
-        self.norm_text = text
-        return self.norm_text
-
-    def normalize(self, remove_punc=True):
-        text = self.raw_text
-
-        # 规范化日期
-        pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('date')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1)
-
-        # 规范化金钱
-        pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('money')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1)
-
-        # 规范化固话/手机号码
-        # 手机
-        # http://www.jihaoba.com/news/show/13680
-        # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
-        # 联通：130、131、132、156、155、186、185、176
-        # 电信：133、153、189、180、181、177
-        pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('telephone')
-            for matcher in matchers:
-                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1)
-        # 固话
-        pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('fixed telephone')
-            for matcher in matchers:
-                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1)
-
-        # 规范化分数
-        pattern = re.compile(r"(\d+/\d+)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('fraction')
-            for matcher in matchers:
-                text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1)
-
-        # 规范化百分数
-        text = text.replace('％', '%')
-        pattern = re.compile(r"(\d+(\.\d+)?%)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('percentage')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1)
-
-        # 规范化纯数+量词
-        pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS)
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('cardinal+quantifier')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
-
-        # 规范化数字编号
-        pattern = re.compile(r"(\d{4,32})")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('digit')
-            for matcher in matchers:
-                text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1)
-
-        # 规范化纯数
-        pattern = re.compile(r"(\d+(\.\d+)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('cardinal')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
-
-        self.norm_text = text
-        self._particular()
-
-        text = self.norm_text.lstrip('^').rstrip('$')
-        if remove_punc:
-            # Punctuations removal
-            old_chars = CHINESE_PUNC_LIST + string.punctuation  # includes all CN and EN punctuations
-            new_chars = ' ' * len(old_chars)
-            del_chars = ''
-            text = text.translate(str.maketrans(old_chars, new_chars, del_chars))
-        return text
-
-
-def nsw_test_case(raw_text):
-    print('I:' + raw_text)
-    print('O:' + NSWNormalizer(raw_text).normalize())
-    print('')
-
-
-def nsw_test():
-    nsw_test_case('固话：0595-23865596或23880880。')
-    nsw_test_case('固话：0595-23865596或23880880。')
-    nsw_test_case('手机：+86 19859213959或15659451527。')
-    nsw_test_case('分数：32477/76391。')
-    nsw_test_case('百分数：80.03%。')
-    nsw_test_case('编号：31520181154418。')
-    nsw_test_case('纯数：2983.07克或12345.60米。')
-    nsw_test_case('日期：1999年2月20日或09年3月15号。')
-    nsw_test_case('金钱：12块5，34.5元，20.1万')
-    nsw_test_case('特殊：O2O或B2C。')
-    nsw_test_case('3456万吨')
-    nsw_test_case('2938个')
-    nsw_test_case('938')
-    nsw_test_case('今天吃了115个小笼包231个馒头')
-    nsw_test_case('有62％的概率')
-
-
-if __name__ == '__main__':
-    # nsw_test()
-
-    p = argparse.ArgumentParser()
-    p.add_argument('ifile', help='input filename, assume utf-8 encoding')
-    p.add_argument('ofile', help='output filename')
-    p.add_argument('--to_upper', action='store_true', help='convert to upper case')
-    p.add_argument('--to_lower', action='store_true', help='convert to lower case')
-    p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.")
-    p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines')
-    args = p.parse_args()
-
-    ifile = codecs.open(args.ifile, 'r', 'utf8')
-    ofile = codecs.open(args.ofile, 'w+', 'utf8')
-
-    n = 0
-    for l in ifile:
-        key = ''
-        text = ''
-        if args.has_key:
-            cols = l.split(maxsplit=1)
-            key = cols[0]
-            if len(cols) == 2:
-                text = cols[1]
-            else:
-                text = ''
-        else:
-            text = l
-
-        # cases
-        if args.to_upper and args.to_lower:
-            sys.stderr.write('text norm: to_upper OR to_lower?')
-            exit(1)
-        if args.to_upper:
-            text = text.upper()
-        if args.to_lower:
-            text = text.lower()
-
-        # NSW(Non-Standard-Word) normalization
-        text = NSWNormalizer(text).normalize()
-
-        #
-        if args.has_key:
-            ofile.write(key + '\t' + text)
-        else:
-            ofile.write(text)
-
-        n += 1
-        if n % args.log_interval == 0:
-            sys.stderr.write("text norm: {} lines done.\n".format(n))
-
-    sys.stderr.write("text norm: {} lines done in total.\n".format(n))
-
-    ifile.close()
-    ofile.close()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/html.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/html.py
deleted file mode 100644
index a7a29fc748cf0a72e9ccdcb66c38b79aa5d1ebba..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/html.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import json
-import jinja2
-
-
-HTML_TEMPLATE = jinja2.Template(
-    """
-{%- if fullhtml -%}
-<!DOCTYPE html>
-<html>
-<head>
-{%- endif %}
-  <style>
-    #{{ output_div }}.vega-embed {
-      width: 100%;
-      display: flex;
-    }
-
-    #{{ output_div }}.vega-embed details,
-    #{{ output_div }}.vega-embed details summary {
-      position: relative;
-    }
-  </style>
-{%- if not requirejs %}
-  <script type="text/javascript" src="{{ base_url }}/vega@{{ vega_version }}"></script>
-  {%- if mode == 'vega-lite' %}
-  <script type="text/javascript" src="{{ base_url }}/vega-lite@{{ vegalite_version }}"></script>
-  {%- endif %}
-  <script type="text/javascript" src="{{ base_url }}/vega-embed@{{ vegaembed_version }}"></script>
-{%- endif %}
-{%- if fullhtml %}
-{%- if requirejs %}
-<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js"></script>
-<script>
-requirejs.config({
-    "paths": {
-        "vega": "{{ base_url }}/vega@{{ vega_version }}?noext",
-        "vega-lib": "{{ base_url }}/vega-lib?noext",
-        "vega-lite": "{{ base_url }}/vega-lite@{{ vegalite_version }}?noext",
-        "vega-embed": "{{ base_url }}/vega-embed@{{ vegaembed_version }}?noext",
-    }
-});
-</script>
-{%- endif %}
-</head>
-<body>
-{%- endif %}
-  <div id="{{ output_div }}"></div>
-  <script>
-    {%- if requirejs and not fullhtml %}
-    requirejs.config({
-        "paths": {
-            "vega": "{{ base_url }}/vega@{{ vega_version }}?noext",
-            "vega-lib": "{{ base_url }}/vega-lib?noext",
-            "vega-lite": "{{ base_url }}/vega-lite@{{ vegalite_version }}?noext",
-            "vega-embed": "{{ base_url }}/vega-embed@{{ vegaembed_version }}?noext",
-        }
-    });
-    {% endif %}
-    {% if requirejs -%}
-    require(['vega-embed'],
-    {%- else -%}
-    (
-    {%- endif -%}
-    function(vegaEmbed) {
-      var spec = {{ spec }};
-      var embedOpt = {{ embed_options }};
-
-      function showError(el, error){
-          el.innerHTML = ('<div style="color:red;">'
-                          + '<p>JavaScript Error: ' + error.message + '</p>'
-                          + "<p>This usually means there's a typo in your chart specification. "
-                          + "See the javascript console for the full traceback.</p>"
-                          + '</div>');
-          throw error;
-      }
-      const el = document.getElementById('{{ output_div }}');
-      vegaEmbed("#{{ output_div }}", spec, embedOpt)
-        .catch(error => showError(el, error));
-    }){% if not requirejs %}(vegaEmbed){% endif %};
-
-  </script>
-{%- if fullhtml %}
-</body>
-</html>
-{%- endif %}
-"""
-)
-
-
-HTML_TEMPLATE_UNIVERSAL = jinja2.Template(
-    """
-<style>
-  #{{ output_div }}.vega-embed {
-    width: 100%;
-    display: flex;
-  }
-
-  #{{ output_div }}.vega-embed details,
-  #{{ output_div }}.vega-embed details summary {
-    position: relative;
-  }
-</style>
-<div id="{{ output_div }}"></div>
-<script type="text/javascript">
-  var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
-  (function(spec, embedOpt){
-    let outputDiv = document.currentScript.previousElementSibling;
-    if (outputDiv.id !== "{{ output_div }}") {
-      outputDiv = document.getElementById("{{ output_div }}");
-    }
-    const paths = {
-      "vega": "{{ base_url }}/vega@{{ vega_version }}?noext",
-      "vega-lib": "{{ base_url }}/vega-lib?noext",
-      "vega-lite": "{{ base_url }}/vega-lite@{{ vegalite_version }}?noext",
-      "vega-embed": "{{ base_url }}/vega-embed@{{ vegaembed_version }}?noext",
-    };
-
-    function maybeLoadScript(lib, version) {
-      var key = `${lib.replace("-", "")}_version`;
-      return (VEGA_DEBUG[key] == version) ?
-        Promise.resolve(paths[lib]) :
-        new Promise(function(resolve, reject) {
-          var s = document.createElement('script');
-          document.getElementsByTagName("head")[0].appendChild(s);
-          s.async = true;
-          s.onload = () => {
-            VEGA_DEBUG[key] = version;
-            return resolve(paths[lib]);
-          };
-          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
-          s.src = paths[lib];
-        });
-    }
-
-    function showError(err) {
-      outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
-      throw err;
-    }
-
-    function displayChart(vegaEmbed) {
-      vegaEmbed(outputDiv, spec, embedOpt)
-        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
-    }
-
-    if(typeof define === "function" && define.amd) {
-      requirejs.config({paths});
-      require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
-    } else {
-      maybeLoadScript("vega", "{{vega_version}}")
-        .then(() => maybeLoadScript("vega-lite", "{{vegalite_version}}"))
-        .then(() => maybeLoadScript("vega-embed", "{{vegaembed_version}}"))
-        .catch(showError)
-        .then(() => displayChart(vegaEmbed));
-    }
-  })({{ spec }}, {{ embed_options }});
-</script>
-"""
-)
-
-
-# This is like the HTML_TEMPLATE template, but includes vega javascript inline
-# so that the resulting file is not dependent on external resources. This was
-# ported over from altair_saver.
-#
-# implies requirejs=False and full_html=True
-INLINE_HTML_TEMPLATE = jinja2.Template(
-    """\
-<!DOCTYPE html>
-<html>
-<head>
-  <style>
-    #{{ output_div }}.vega-embed {
-      width: 100%;
-      display: flex;
-    }
-
-    #{{ output_div }}.vega-embed details,
-    #{{ output_div }}.vega-embed details summary {
-      position: relative;
-    }
-  </style>
-  <script type="text/javascript">
-    // vega.js v{{ vega_version }}
-    {{ vega_script }}
-    // vega-lite.js v{{ vegalite_version }}
-    {{ vegalite_script }}
-    // vega-embed.js v{{ vegaembed_version }}
-    {{ vegaembed_script }}
-  </script>
-</head>
-<body>
-<div class="vega-visualization" id="{{ output_div }}"></div>
-<script type="text/javascript">
-  const spec = {{ spec }};
-  const embedOpt = {{ embed_options }};
-  vegaEmbed('#{{ output_div }}', spec, embedOpt).catch(console.error);
-</script>
-</body>
-</html>
-"""
-)
-
-
-TEMPLATES = {
-    "standard": HTML_TEMPLATE,
-    "universal": HTML_TEMPLATE_UNIVERSAL,
-    "inline": INLINE_HTML_TEMPLATE,
-}
-
-
-def spec_to_html(
-    spec,
-    mode,
-    vega_version,
-    vegaembed_version,
-    vegalite_version=None,
-    base_url="https://cdn.jsdelivr.net/npm",
-    output_div="vis",
-    embed_options=None,
-    json_kwds=None,
-    fullhtml=True,
-    requirejs=False,
-    template="standard",
-):
-    """Embed a Vega/Vega-Lite spec into an HTML page
-
-    Parameters
-    ----------
-    spec : dict
-        a dictionary representing a vega-lite plot spec.
-    mode : string {'vega' | 'vega-lite'}
-        The rendering mode. This value is overridden by embed_options['mode'],
-        if it is present.
-    vega_version : string
-        For html output, the version of vega.js to use.
-    vegalite_version : string
-        For html output, the version of vegalite.js to use.
-    vegaembed_version : string
-        For html output, the version of vegaembed.js to use.
-    base_url : string (optional)
-        The base url from which to load the javascript libraries.
-    output_div : string (optional)
-        The id of the div element where the plot will be shown.
-    embed_options : dict (optional)
-        Dictionary of options to pass to the vega-embed script. Default
-        entry is {'mode': mode}.
-    json_kwds : dict (optional)
-        Dictionary of keywords to pass to json.dumps().
-    fullhtml : boolean (optional)
-        If True (default) then return a full html page. If False, then return
-        an HTML snippet that can be embedded into an HTML page.
-    requirejs : boolean (optional)
-        If False (default) then load libraries from base_url using <script>
-        tags. If True, then load libraries using requirejs
-    template : jinja2.Template or string (optional)
-        Specify the template to use (default = 'standard'). If template is a
-        string, it must be one of {'universal', 'standard'}. Otherwise, it
-        can be a jinja2.Template object containing a custom template.
-
-    Returns
-    -------
-    output : string
-        an HTML string for rendering the chart.
-    """
-    embed_options = embed_options or {}
-    json_kwds = json_kwds or {}
-
-    mode = embed_options.setdefault("mode", mode)
-
-    if mode not in ["vega", "vega-lite"]:
-        raise ValueError("mode must be either 'vega' or 'vega-lite'")
-
-    if vega_version is None:
-        raise ValueError("must specify vega_version")
-
-    if vegaembed_version is None:
-        raise ValueError("must specify vegaembed_version")
-
-    if mode == "vega-lite" and vegalite_version is None:
-        raise ValueError("must specify vega-lite version for mode='vega-lite'")
-
-    render_kwargs = {}
-    if template == "inline":
-        try:
-            from altair_viewer import get_bundled_script
-        except ImportError as err:
-            raise ImportError(
-                "The altair_viewer package is required to convert to HTML with inline=True"
-            ) from err
-        render_kwargs["vega_script"] = get_bundled_script("vega", vega_version)
-        render_kwargs["vegalite_script"] = get_bundled_script(
-            "vega-lite", vegalite_version
-        )
-        render_kwargs["vegaembed_script"] = get_bundled_script(
-            "vega-embed", vegaembed_version
-        )
-
-    template = TEMPLATES.get(template, template)
-    if not hasattr(template, "render"):
-        raise ValueError("Invalid template: {0}".format(template))
-
-    return template.render(
-        spec=json.dumps(spec, **json_kwds),
-        embed_options=json.dumps(embed_options),
-        mode=mode,
-        vega_version=vega_version,
-        vegalite_version=vegalite_version,
-        vegaembed_version=vegaembed_version,
-        base_url=base_url,
-        output_div=output_div,
-        fullhtml=fullhtml,
-        requirejs=requirejs,
-        **render_kwargs,
-    )
diff --git a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/utils/autocast.py b/spaces/Suniilkumaar/MusicGen-updated/audiocraft/utils/autocast.py
deleted file mode 100644
index ed644843bb37cf8a92a20fbd51d6cebaa43b9a08..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/utils/autocast.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-
-class TorchAutocast:
-    """TorchAutocast utility class.
-    Allows you to enable and disable autocast. This is specially useful
-    when dealing with different architectures and clusters with different
-    levels of support.
-
-    Args:
-        enabled (bool): Whether to enable torch.autocast or not.
-        args: Additional args for torch.autocast.
-        kwargs: Additional kwargs for torch.autocast
-    """
-    def __init__(self, enabled: bool, *args, **kwargs):
-        self.autocast = torch.autocast(*args, **kwargs) if enabled else None
-
-    def __enter__(self):
-        if self.autocast is None:
-            return
-        try:
-            self.autocast.__enter__()
-        except RuntimeError:
-            device = self.autocast.device
-            dtype = self.autocast.fast_dtype
-            raise RuntimeError(
-                f"There was an error autocasting with dtype={dtype} device={device}\n"
-                "If you are on the FAIR Cluster, you might need to use autocast_dtype=float16"
-            )
-
-    def __exit__(self, *args, **kwargs):
-        if self.autocast is None:
-            return
-        self.autocast.__exit__(*args, **kwargs)
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/datasets/cityscapes.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/datasets/cityscapes.py
deleted file mode 100644
index 81e47a914a1aa2e5458e18669d65ffb742f46fc6..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/datasets/cityscapes.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import os.path as osp
-import tempfile
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import print_log
-from PIL import Image
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class CityscapesDataset(CustomDataset):
-    """Cityscapes dataset.
-
-    The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` is
-    fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset.
-    """
-
-    CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
-               'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
-               'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
-               'bicycle')
-
-    PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
-               [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
-               [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
-               [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100],
-               [0, 80, 100], [0, 0, 230], [119, 11, 32]]
-
-    def __init__(self, **kwargs):
-        super(CityscapesDataset, self).__init__(
-            img_suffix='_leftImg8bit.png',
-            seg_map_suffix='_gtFine_labelTrainIds.png',
-            **kwargs)
-
-    @staticmethod
-    def _convert_to_label_id(result):
-        """Convert trainId to id for cityscapes."""
-        if isinstance(result, str):
-            result = np.load(result)
-        import cityscapesscripts.helpers.labels as CSLabels
-        result_copy = result.copy()
-        for trainId, label in CSLabels.trainId2label.items():
-            result_copy[result == trainId] = label.id
-
-        return result_copy
-
-    def results2img(self, results, imgfile_prefix, to_label_id):
-        """Write the segmentation results to images.
-
-        Args:
-            results (list[list | tuple | ndarray]): Testing results of the
-                dataset.
-            imgfile_prefix (str): The filename prefix of the png files.
-                If the prefix is "somepath/xxx",
-                the png files will be named "somepath/xxx.png".
-            to_label_id (bool): whether convert output to label_id for
-                submission
-
-        Returns:
-            list[str: str]: result txt files which contains corresponding
-            semantic segmentation images.
-        """
-        mmcv.mkdir_or_exist(imgfile_prefix)
-        result_files = []
-        prog_bar = mmcv.ProgressBar(len(self))
-        for idx in range(len(self)):
-            result = results[idx]
-            if to_label_id:
-                result = self._convert_to_label_id(result)
-            filename = self.img_infos[idx]['filename']
-            basename = osp.splitext(osp.basename(filename))[0]
-
-            png_filename = osp.join(imgfile_prefix, f'{basename}.png')
-
-            output = Image.fromarray(result.astype(np.uint8)).convert('P')
-            import cityscapesscripts.helpers.labels as CSLabels
-            palette = np.zeros((len(CSLabels.id2label), 3), dtype=np.uint8)
-            for label_id, label in CSLabels.id2label.items():
-                palette[label_id] = label.color
-
-            output.putpalette(palette)
-            output.save(png_filename)
-            result_files.append(png_filename)
-            prog_bar.update()
-
-        return result_files
-
-    def format_results(self, results, imgfile_prefix=None, to_label_id=True):
-        """Format the results into dir (standard format for Cityscapes
-        evaluation).
-
-        Args:
-            results (list): Testing results of the dataset.
-            imgfile_prefix (str | None): The prefix of images files. It
-                includes the file path and the prefix of filename, e.g.,
-                "a/b/prefix". If not specified, a temp file will be created.
-                Default: None.
-            to_label_id (bool): whether convert output to label_id for
-                submission. Default: False
-
-        Returns:
-            tuple: (result_files, tmp_dir), result_files is a list containing
-                the image paths, tmp_dir is the temporal directory created
-                for saving json/png files when img_prefix is not specified.
-        """
-
-        assert isinstance(results, list), 'results must be a list'
-        assert len(results) == len(self), (
-            'The length of results is not equal to the dataset len: '
-            f'{len(results)} != {len(self)}')
-
-        if imgfile_prefix is None:
-            tmp_dir = tempfile.TemporaryDirectory()
-            imgfile_prefix = tmp_dir.name
-        else:
-            tmp_dir = None
-        result_files = self.results2img(results, imgfile_prefix, to_label_id)
-
-        return result_files, tmp_dir
-
-    def evaluate(self,
-                 results,
-                 metric='mIoU',
-                 logger=None,
-                 imgfile_prefix=None,
-                 efficient_test=False):
-        """Evaluation in Cityscapes/default protocol.
-
-        Args:
-            results (list): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated.
-            logger (logging.Logger | None | str): Logger used for printing
-                related information during evaluation. Default: None.
-            imgfile_prefix (str | None): The prefix of output image file,
-                for cityscapes evaluation only. It includes the file path and
-                the prefix of filename, e.g., "a/b/prefix".
-                If results are evaluated with cityscapes protocol, it would be
-                the prefix of output png files. The output files would be
-                png images under folder "a/b/prefix/xxx.png", where "xxx" is
-                the image name of cityscapes. If not specified, a temp file
-                will be created for evaluation.
-                Default: None.
-
-        Returns:
-            dict[str, float]: Cityscapes/default metrics.
-        """
-
-        eval_results = dict()
-        metrics = metric.copy() if isinstance(metric, list) else [metric]
-        if 'cityscapes' in metrics:
-            eval_results.update(
-                self._evaluate_cityscapes(results, logger, imgfile_prefix))
-            metrics.remove('cityscapes')
-        if len(metrics) > 0:
-            eval_results.update(
-                super(CityscapesDataset,
-                      self).evaluate(results, metrics, logger, efficient_test))
-
-        return eval_results
-
-    def _evaluate_cityscapes(self, results, logger, imgfile_prefix):
-        """Evaluation in Cityscapes protocol.
-
-        Args:
-            results (list): Testing results of the dataset.
-            logger (logging.Logger | str | None): Logger used for printing
-                related information during evaluation. Default: None.
-            imgfile_prefix (str | None): The prefix of output image file
-
-        Returns:
-            dict[str: float]: Cityscapes evaluation results.
-        """
-        try:
-            import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval  # noqa
-        except ImportError:
-            raise ImportError('Please run "pip install cityscapesscripts" to '
-                              'install cityscapesscripts first.')
-        msg = 'Evaluating in Cityscapes style'
-        if logger is None:
-            msg = '\n' + msg
-        print_log(msg, logger=logger)
-
-        result_files, tmp_dir = self.format_results(results, imgfile_prefix)
-
-        if tmp_dir is None:
-            result_dir = imgfile_prefix
-        else:
-            result_dir = tmp_dir.name
-
-        eval_results = dict()
-        print_log(f'Evaluating results under {result_dir} ...', logger=logger)
-
-        CSEval.args.evalInstLevelScore = True
-        CSEval.args.predictionPath = osp.abspath(result_dir)
-        CSEval.args.evalPixelAccuracy = True
-        CSEval.args.JSONOutput = False
-
-        seg_map_list = []
-        pred_list = []
-
-        # when evaluating with official cityscapesscripts,
-        # **_gtFine_labelIds.png is used
-        for seg_map in mmcv.scandir(
-                self.ann_dir, 'gtFine_labelIds.png', recursive=True):
-            seg_map_list.append(osp.join(self.ann_dir, seg_map))
-            pred_list.append(CSEval.getPrediction(CSEval.args, seg_map))
-
-        eval_results.update(
-            CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args))
-
-        if tmp_dir is not None:
-            tmp_dir.cleanup()
-
-        return eval_results
diff --git a/spaces/Swatantradev/mynewgenAI/app.py b/spaces/Swatantradev/mynewgenAI/app.py
deleted file mode 100644
index 08dc0cf2d2cb286e2ea95f0e21f537eb4ea26ee2..0000000000000000000000000000000000000000
--- a/spaces/Swatantradev/mynewgenAI/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Dev, your youthful and witty personal assistant! At 18 years old, she's full of energy and always eager to help. Dev's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging. Today date is 1 august 2023. 
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/TabPFN/TabPFNPrediction/TabPFN/scripts/model_builder.py b/spaces/TabPFN/TabPFNPrediction/TabPFN/scripts/model_builder.py
deleted file mode 100644
index 1a92c921d1e4638aa4234c8b6f80fcc3ed5df3f4..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNPrediction/TabPFN/scripts/model_builder.py
+++ /dev/null
@@ -1,280 +0,0 @@
-from functools import partial
-from train import train, Losses
-import priors
-import encoders
-
-from priors.utils import trunc_norm_sampler_f, gamma_sampler_f
-from collections import defaultdict
-
-from priors.utils import trunc_norm_sampler_f, gamma_sampler_f
-from utils import get_uniform_single_eval_pos_sampler
-import torch
-import math
-
-def save_model(model, path, filename, config_sample):
-    config_sample = {**config_sample}
-
-    def make_serializable(config_sample):
-        if isinstance(config_sample, dict):
-            config_sample = {k: make_serializable(config_sample[k]) for k in config_sample}
-        if isinstance(config_sample, list):
-            config_sample = [make_serializable(v) for v in config_sample]
-        if callable(config_sample):
-            config_sample = str(config_sample)
-        return config_sample
-
-    #if 'num_features_used' in config_sample:
-    #    del config_sample['num_features_used']
-
-    #config_sample['num_classes_as_str'] = str(config_sample['num_classes'])
-    #del config_sample['num_classes']
-
-    config_sample = make_serializable(config_sample)
-
-    torch.save((model.state_dict(), None, config_sample), os.path.join(path, filename))
-
-
-import subprocess as sp
-import os
-
-def get_gpu_memory():
-    command = "nvidia-smi"
-    memory_free_info = sp.check_output(command.split()).decode('ascii')
-    return memory_free_info
-
-
-def load_model(path, filename, device, eval_positions, verbose):
-    # TODO: This function only restores evaluation functionality but training canät be continued. It is also not flexible.
-    print('Loading....')
-    model_state, optimizer_state, config_sample = torch.load(
-        os.path.join(path, filename), map_location='cpu')
-    if ('differentiable_hyperparameters' in config_sample
-            and 'prior_mlp_activations' in config_sample['differentiable_hyperparameters']):
-        config_sample['differentiable_hyperparameters']['prior_mlp_activations']['choice_values_used'] = config_sample[
-                                                                                                         'differentiable_hyperparameters'][
-                                                                                                         'prior_mlp_activations'][
-                                                                                                         'choice_values']
-        config_sample['differentiable_hyperparameters']['prior_mlp_activations']['choice_values'] = [
-            torch.nn.Tanh for k in config_sample['differentiable_hyperparameters']['prior_mlp_activations']['choice_values']]
-
-    config_sample['categorical_features_sampler'] = lambda: lambda x: ([], [], [])
-    config_sample['num_features_used_in_training'] = config_sample['num_features_used']
-    config_sample['num_features_used'] = lambda: config_sample['num_features']
-    config_sample['num_classes_in_training'] = config_sample['num_classes']
-    config_sample['num_classes'] = 2
-    config_sample['batch_size_in_training'] = config_sample['batch_size']
-    config_sample['batch_size'] = 1
-    config_sample['bptt_in_training'] = config_sample['bptt']
-    config_sample['bptt'] = 10
-    config_sample['bptt_extra_samples_in_training'] = config_sample['bptt_extra_samples']
-    config_sample['bptt_extra_samples'] = None
-
-    #print('Memory', str(get_gpu_memory()))
-
-    model = get_model(config_sample, device=device, should_train=False, verbose=verbose)
-    module_prefix = 'module.'
-    model_state = {k.replace(module_prefix, ''): v for k, v in model_state.items()}
-    model[2].load_state_dict(model_state)
-    model[2].to(device)
-    model[2].eval()
-
-    return model, config_sample
-
-def fix_loaded_config_sample(loaded_config_sample, config):
-    def copy_to_sample(*k):
-        t,s = loaded_config_sample, config
-        for k_ in k[:-1]:
-            t = t[k_]
-            s = s[k_]
-        t[k[-1]] = s[k[-1]]
-    copy_to_sample('num_features_used')
-    copy_to_sample('num_classes')
-    copy_to_sample('differentiable_hyperparameters','prior_mlp_activations','choice_values')
-
-def load_config_sample(path, template_config):
-    model_state, optimizer_state, loaded_config_sample = torch.load(path, map_location='cpu')
-    fix_loaded_config_sample(loaded_config_sample, template_config)
-    return loaded_config_sample
-
-def get_default_spec(test_datasets, valid_datasets):
-    bptt = 10000
-    eval_positions = [1000, 2000, 3000, 4000, 5000] # list(2 ** np.array([4, 5, 6, 7, 8, 9, 10, 11, 12]))
-    max_features = max([X.shape[1] for (_, X, _, _, _, _) in test_datasets] + [X.shape[1] for (_, X, _, _, _, _) in valid_datasets])
-    max_splits = 5
-
-    return bptt, eval_positions, max_features, max_splits
-
-def get_mlp_prior_hyperparameters(config):
-    config = {hp: (list(config[hp].values())[0]) if type(config[hp]) is dict else config[hp] for hp in config}
-
-    if 'random_feature_rotation' not in config:
-        config['random_feature_rotation'] = True
-
-    if "prior_sigma_gamma_k" in config:
-        sigma_sampler = gamma_sampler_f(config["prior_sigma_gamma_k"], config["prior_sigma_gamma_theta"])
-        config['init_std'] = sigma_sampler
-    if "prior_noise_std_gamma_k" in config:
-        noise_std_sampler = gamma_sampler_f(config["prior_noise_std_gamma_k"], config["prior_noise_std_gamma_theta"])
-        config['noise_std'] = noise_std_sampler
-
-    return config
-
-
-def get_gp_mix_prior_hyperparameters(config):
-    return {'lengthscale_concentration': config["prior_lengthscale_concentration"],
-            'nu': config["prior_nu"],
-            'outputscale_concentration': config["prior_outputscale_concentration"],
-            'categorical_data': config["prior_y_minmax_norm"],
-            'y_minmax_norm': config["prior_lengthscale_concentration"],
-            'noise_concentration': config["prior_noise_concentration"],
-            'noise_rate': config["prior_noise_rate"]}
-
-def get_gp_prior_hyperparameters(config):
-    return {hp: (list(config[hp].values())[0]) if type(config[hp]) is dict else config[hp] for hp in config}
-
-
-def get_meta_gp_prior_hyperparameters(config):
-    config = {hp: (list(config[hp].values())[0]) if type(config[hp]) is dict else config[hp] for hp in config}
-
-    if "outputscale_mean" in config:
-        outputscale_sampler = trunc_norm_sampler_f(config["outputscale_mean"]
-                                                   , config["outputscale_mean"] * config["outputscale_std_f"])
-        config['outputscale'] = outputscale_sampler
-    if "lengthscale_mean" in config:
-        lengthscale_sampler = trunc_norm_sampler_f(config["lengthscale_mean"],
-                                                   config["lengthscale_mean"] * config["lengthscale_std_f"])
-        config['lengthscale'] = lengthscale_sampler
-
-    return config
-
-
-def get_model(config, device, should_train=True, verbose=False, state_dict=None, epoch_callback=None):
-    extra_kwargs = {}
-    verbose_train, verbose_prior = verbose >= 1, verbose >= 2
-    config['verbose'] = verbose_prior
-
-    if 'aggregate_k_gradients' not in config or config['aggregate_k_gradients'] is None:
-        config['aggregate_k_gradients'] = math.ceil(config['batch_size'] * ((config['nlayers'] * config['emsize'] * config['bptt'] * config['bptt']) / 10824640000))
-
-    config['num_steps'] = math.ceil(config['num_steps'] * config['aggregate_k_gradients'])
-    config['batch_size'] = math.ceil(config['batch_size'] / config['aggregate_k_gradients'])
-    config['recompute_attn'] = config['recompute_attn'] if 'recompute_attn' in config else False
-
-    def make_get_batch(model_proto, **extra_kwargs):
-        def new_get_batch(batch_size, seq_len, num_features, hyperparameters
-                , device, model_proto=model_proto
-                , **kwargs):
-            kwargs = {**extra_kwargs, **kwargs} # new args overwrite pre-specified args
-            return model_proto.get_batch(
-                batch_size=batch_size
-                , seq_len=seq_len
-                , device=device
-                , hyperparameters=hyperparameters
-                , num_features=num_features, **kwargs)
-        return new_get_batch
-
-    if config['prior_type'] == 'prior_bag':
-        # Prior bag combines priors
-        get_batch_gp = make_get_batch(priors.fast_gp)
-        get_batch_mlp = make_get_batch(priors.mlp)
-        if 'flexible' in config and config['flexible']:
-            get_batch_gp = make_get_batch(priors.flexible_categorical, **{'get_batch': get_batch_gp})
-            get_batch_mlp = make_get_batch(priors.flexible_categorical, **{'get_batch': get_batch_mlp})
-        prior_bag_hyperparameters = {'prior_bag_get_batch': (get_batch_gp, get_batch_mlp)
-            , 'prior_bag_exp_weights_1': 2.0}
-        prior_hyperparameters = {**get_mlp_prior_hyperparameters(config), **get_gp_prior_hyperparameters(config)
-            , **prior_bag_hyperparameters}
-        model_proto = priors.prior_bag
-    else:
-        if config['prior_type'] == 'mlp':
-            prior_hyperparameters = get_mlp_prior_hyperparameters(config)
-            model_proto = priors.mlp
-        elif config['prior_type'] == 'gp':
-            prior_hyperparameters = get_gp_prior_hyperparameters(config)
-            model_proto = priors.fast_gp
-        elif config['prior_type'] == 'gp_mix':
-            prior_hyperparameters = get_gp_mix_prior_hyperparameters(config)
-            model_proto = priors.fast_gp_mix
-        else:
-            raise Exception()
-
-        if 'flexible' in config and config['flexible']:
-            get_batch_base = make_get_batch(model_proto)
-            extra_kwargs['get_batch'] = get_batch_base
-            model_proto = priors.flexible_categorical
-
-    if config.get('flexible'):
-        prior_hyperparameters['normalize_labels'] = True
-        prior_hyperparameters['check_is_compatible'] = True
-    prior_hyperparameters['prior_mlp_scale_weights_sqrt'] = config[
-        'prior_mlp_scale_weights_sqrt'] if 'prior_mlp_scale_weights_sqrt' in prior_hyperparameters else None
-    prior_hyperparameters['rotate_normalized_labels'] = config[
-        'rotate_normalized_labels'] if 'rotate_normalized_labels' in prior_hyperparameters else True
-
-    use_style = False
-
-    if 'differentiable' in config and config['differentiable']:
-        get_batch_base = make_get_batch(model_proto, **extra_kwargs)
-        extra_kwargs = {'get_batch': get_batch_base, 'differentiable_hyperparameters': config['differentiable_hyperparameters']}
-        model_proto = priors.differentiable_prior
-        use_style = True
-    print(f"Using style prior: {use_style}")
-
-    if (('nan_prob_no_reason' in config and config['nan_prob_no_reason'] > 0.0) or
-        ('nan_prob_a_reason' in config and config['nan_prob_a_reason'] > 0.0) or
-        ('nan_prob_unknown_reason' in config and config['nan_prob_unknown_reason'] > 0.0)):
-        encoder = encoders.NanHandlingEncoder
-    else:
-        encoder = partial(encoders.Linear, replace_nan_by_zero=True)
-
-    if config['max_num_classes'] == 2:
-        loss = Losses.bce
-    elif config['max_num_classes'] > 2:
-        loss = Losses.ce(config['max_num_classes'])
-
-    aggregate_k_gradients = 1 if 'aggregate_k_gradients' not in config else config['aggregate_k_gradients']
-    check_is_compatible = False if 'multiclass_loss_type' not in config else (config['multiclass_loss_type'] == 'compatible')
-    config['multiclass_type'] = config['multiclass_type'] if 'multiclass_type' in config else 'rank'
-    config['mix_activations'] = config['mix_activations'] if 'mix_activations' in config else False
-
-    config['bptt_extra_samples'] = config['bptt_extra_samples'] if 'bptt_extra_samples' in config else None
-    config['eval_positions'] = [int(config['bptt'] * 0.95)] if config['bptt_extra_samples'] is None else [int(config['bptt'])]
-
-    epochs = 0 if not should_train else config['epochs']
-    print('MODEL BUILDER', model_proto, extra_kwargs['get_batch'])
-    model = train(model_proto.DataLoader
-                  , loss
-                  , encoder
-                  , style_encoder_generator = encoders.StyleEncoder if use_style else None
-                  , emsize=config['emsize']
-                  , nhead=config['nhead']
-                  # For unsupervised learning change to NanHandlingEncoder
-                  , y_encoder_generator= encoders.get_Canonical(config['max_num_classes']) if config.get('canonical_y_encoder', False) else encoders.Linear
-                  , pos_encoder_generator=None
-                  , batch_size=config['batch_size']
-                  , nlayers=config['nlayers']
-                  , nhid=config['emsize'] * config['nhid_factor']
-                  , epochs=epochs
-                  , warmup_epochs=20
-                  , bptt=config['bptt']
-                  , gpu_device=device
-                  , dropout=config['dropout']
-                  , steps_per_epoch=config['num_steps']
-                  , single_eval_pos_gen=get_uniform_single_eval_pos_sampler(config.get('max_eval_pos', config['bptt']), min_len=config.get('min_eval_pos', 0))
-                  , load_weights_from_this_state_dict=state_dict
-                  , aggregate_k_gradients=aggregate_k_gradients
-                  , recompute_attn=config['recompute_attn']
-                  , epoch_callback=epoch_callback
-                  , bptt_extra_samples = config['bptt_extra_samples']
-                  , extra_prior_kwargs_dict={
-            'num_features': config['num_features']
-            , 'hyperparameters': prior_hyperparameters
-            #, 'dynamic_batch_size': 1 if ('num_global_att_tokens' in config and config['num_global_att_tokens']) else 2
-            , 'batch_size_per_gp_sample': config.get('batch_size_per_gp_sample', None)
-            , **extra_kwargs
-        }
-                  , lr=config['lr']
-                  , verbose=verbose_train,
-                  weight_decay=config.get('weight_decay', 0.0))
-
-    return model
\ No newline at end of file
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/img.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/img.py
deleted file mode 100644
index a338c1588fdda63a3dc874c5cfb10b0d340025ab..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/img.py
+++ /dev/null
@@ -1,645 +0,0 @@
-"""
-    pygments.formatters.img
-    ~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for Pixmap output.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import os
-import sys
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt, \
-    get_choice_opt
-
-import subprocess
-
-# Import this carefully
-try:
-    from PIL import Image, ImageDraw, ImageFont
-    pil_available = True
-except ImportError:
-    pil_available = False
-
-try:
-    import _winreg
-except ImportError:
-    try:
-        import winreg as _winreg
-    except ImportError:
-        _winreg = None
-
-__all__ = ['ImageFormatter', 'GifImageFormatter', 'JpgImageFormatter',
-           'BmpImageFormatter']
-
-
-# For some unknown reason every font calls it something different
-STYLES = {
-    'NORMAL':     ['', 'Roman', 'Book', 'Normal', 'Regular', 'Medium'],
-    'ITALIC':     ['Oblique', 'Italic'],
-    'BOLD':       ['Bold'],
-    'BOLDITALIC': ['Bold Oblique', 'Bold Italic'],
-}
-
-# A sane default for modern systems
-DEFAULT_FONT_NAME_NIX = 'DejaVu Sans Mono'
-DEFAULT_FONT_NAME_WIN = 'Courier New'
-DEFAULT_FONT_NAME_MAC = 'Menlo'
-
-
-class PilNotAvailable(ImportError):
-    """When Python imaging library is not available"""
-
-
-class FontNotFound(Exception):
-    """When there are no usable fonts specified"""
-
-
-class FontManager:
-    """
-    Manages a set of fonts: normal, italic, bold, etc...
-    """
-
-    def __init__(self, font_name, font_size=14):
-        self.font_name = font_name
-        self.font_size = font_size
-        self.fonts = {}
-        self.encoding = None
-        if sys.platform.startswith('win'):
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_WIN
-            self._create_win()
-        elif sys.platform.startswith('darwin'):
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_MAC
-            self._create_mac()
-        else:
-            if not font_name:
-                self.font_name = DEFAULT_FONT_NAME_NIX
-            self._create_nix()
-
-    def _get_nix_font_path(self, name, style):
-        proc = subprocess.Popen(['fc-list', "%s:style=%s" % (name, style), 'file'],
-                                stdout=subprocess.PIPE, stderr=None)
-        stdout, _ = proc.communicate()
-        if proc.returncode == 0:
-            lines = stdout.splitlines()
-            for line in lines:
-                if line.startswith(b'Fontconfig warning:'):
-                    continue
-                path = line.decode().strip().strip(':')
-                if path:
-                    return path
-            return None
-
-    def _create_nix(self):
-        for name in STYLES['NORMAL']:
-            path = self._get_nix_font_path(self.font_name, name)
-            if path is not None:
-                self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                break
-        else:
-            raise FontNotFound('No usable fonts named: "%s"' %
-                               self.font_name)
-        for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-            for stylename in STYLES[style]:
-                path = self._get_nix_font_path(self.font_name, stylename)
-                if path is not None:
-                    self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                    break
-            else:
-                if style == 'BOLDITALIC':
-                    self.fonts[style] = self.fonts['BOLD']
-                else:
-                    self.fonts[style] = self.fonts['NORMAL']
-
-    def _get_mac_font_path(self, font_map, name, style):
-        return font_map.get((name + ' ' + style).strip().lower())
-
-    def _create_mac(self):
-        font_map = {}
-        for font_dir in (os.path.join(os.getenv("HOME"), 'Library/Fonts/'),
-                         '/Library/Fonts/', '/System/Library/Fonts/'):
-            font_map.update(
-                (os.path.splitext(f)[0].lower(), os.path.join(font_dir, f))
-                for f in os.listdir(font_dir)
-                if f.lower().endswith(('ttf', 'ttc')))
-
-        for name in STYLES['NORMAL']:
-            path = self._get_mac_font_path(font_map, self.font_name, name)
-            if path is not None:
-                self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                break
-        else:
-            raise FontNotFound('No usable fonts named: "%s"' %
-                               self.font_name)
-        for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-            for stylename in STYLES[style]:
-                path = self._get_mac_font_path(font_map, self.font_name, stylename)
-                if path is not None:
-                    self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                    break
-            else:
-                if style == 'BOLDITALIC':
-                    self.fonts[style] = self.fonts['BOLD']
-                else:
-                    self.fonts[style] = self.fonts['NORMAL']
-
-    def _lookup_win(self, key, basename, styles, fail=False):
-        for suffix in ('', ' (TrueType)'):
-            for style in styles:
-                try:
-                    valname = '%s%s%s' % (basename, style and ' '+style, suffix)
-                    val, _ = _winreg.QueryValueEx(key, valname)
-                    return val
-                except OSError:
-                    continue
-        else:
-            if fail:
-                raise FontNotFound('Font %s (%s) not found in registry' %
-                                   (basename, styles[0]))
-            return None
-
-    def _create_win(self):
-        lookuperror = None
-        keynames = [ (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'),
-                     (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows\CurrentVersion\Fonts') ]
-        for keyname in keynames:
-            try:
-                key = _winreg.OpenKey(*keyname)
-                try:
-                    path = self._lookup_win(key, self.font_name, STYLES['NORMAL'], True)
-                    self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size)
-                    for style in ('ITALIC', 'BOLD', 'BOLDITALIC'):
-                        path = self._lookup_win(key, self.font_name, STYLES[style])
-                        if path:
-                            self.fonts[style] = ImageFont.truetype(path, self.font_size)
-                        else:
-                            if style == 'BOLDITALIC':
-                                self.fonts[style] = self.fonts['BOLD']
-                            else:
-                                self.fonts[style] = self.fonts['NORMAL']
-                    return
-                except FontNotFound as err:
-                    lookuperror = err
-                finally:
-                    _winreg.CloseKey(key)
-            except OSError:
-                pass
-        else:
-            # If we get here, we checked all registry keys and had no luck
-            # We can be in one of two situations now:
-            # * All key lookups failed. In this case lookuperror is None and we
-            #   will raise a generic error
-            # * At least one lookup failed with a FontNotFound error. In this
-            #   case, we will raise that as a more specific error
-            if lookuperror:
-                raise lookuperror
-            raise FontNotFound('Can\'t open Windows font registry key')
-
-    def get_char_size(self):
-        """
-        Get the character size.
-        """
-        return self.get_text_size('M')
-
-    def get_text_size(self, text):
-        """
-        Get the text size (width, height).
-        """
-        font = self.fonts['NORMAL']
-        if hasattr(font, 'getbbox'):  # Pillow >= 9.2.0
-            return font.getbbox(text)[2:4]
-        else:
-            return font.getsize(text)
-
-    def get_font(self, bold, oblique):
-        """
-        Get the font based on bold and italic flags.
-        """
-        if bold and oblique:
-            return self.fonts['BOLDITALIC']
-        elif bold:
-            return self.fonts['BOLD']
-        elif oblique:
-            return self.fonts['ITALIC']
-        else:
-            return self.fonts['NORMAL']
-
-
-class ImageFormatter(Formatter):
-    """
-    Create a PNG image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 0.10
-
-    Additional options accepted:
-
-    `image_format`
-        An image format to output to that is recognised by PIL, these include:
-
-        * "PNG" (default)
-        * "JPEG"
-        * "BMP"
-        * "GIF"
-
-    `line_pad`
-        The extra spacing (in pixels) between each line of text.
-
-        Default: 2
-
-    `font_name`
-        The font name to be used as the base font from which others, such as
-        bold and italic fonts will be generated.  This really should be a
-        monospace font to look sane.
-
-        Default: "Courier New" on Windows, "Menlo" on Mac OS, and
-                 "DejaVu Sans Mono" on \\*nix
-
-    `font_size`
-        The font size in points to be used.
-
-        Default: 14
-
-    `image_pad`
-        The padding, in pixels to be used at each edge of the resulting image.
-
-        Default: 10
-
-    `line_numbers`
-        Whether line numbers should be shown: True/False
-
-        Default: True
-
-    `line_number_start`
-        The line number of the first line.
-
-        Default: 1
-
-    `line_number_step`
-        The step used when printing line numbers.
-
-        Default: 1
-
-    `line_number_bg`
-        The background colour (in "#123456" format) of the line number bar, or
-        None to use the style background color.
-
-        Default: "#eed"
-
-    `line_number_fg`
-        The text color of the line numbers (in "#123456"-like format).
-
-        Default: "#886"
-
-    `line_number_chars`
-        The number of columns of line numbers allowable in the line number
-        margin.
-
-        Default: 2
-
-    `line_number_bold`
-        Whether line numbers will be bold: True/False
-
-        Default: False
-
-    `line_number_italic`
-        Whether line numbers will be italicized: True/False
-
-        Default: False
-
-    `line_number_separator`
-        Whether a line will be drawn between the line number area and the
-        source code area: True/False
-
-        Default: True
-
-    `line_number_pad`
-        The horizontal padding (in pixels) between the line number margin, and
-        the source code area.
-
-        Default: 6
-
-    `hl_lines`
-        Specify a list of lines to be highlighted.
-
-        .. versionadded:: 1.2
-
-        Default: empty list
-
-    `hl_color`
-        Specify the color for highlighting lines.
-
-        .. versionadded:: 1.2
-
-        Default: highlight color of the selected style
-    """
-
-    # Required by the pygments mapper
-    name = 'img'
-    aliases = ['img', 'IMG', 'png']
-    filenames = ['*.png']
-
-    unicodeoutput = False
-
-    default_image_format = 'png'
-
-    def __init__(self, **options):
-        """
-        See the class docstring for explanation of options.
-        """
-        if not pil_available:
-            raise PilNotAvailable(
-                'Python Imaging Library is required for this formatter')
-        Formatter.__init__(self, **options)
-        self.encoding = 'latin1'  # let pygments.format() do the right thing
-        # Read the style
-        self.styles = dict(self.style)
-        if self.style.background_color is None:
-            self.background_color = '#fff'
-        else:
-            self.background_color = self.style.background_color
-        # Image options
-        self.image_format = get_choice_opt(
-            options, 'image_format', ['png', 'jpeg', 'gif', 'bmp'],
-            self.default_image_format, normcase=True)
-        self.image_pad = get_int_opt(options, 'image_pad', 10)
-        self.line_pad = get_int_opt(options, 'line_pad', 2)
-        # The fonts
-        fontsize = get_int_opt(options, 'font_size', 14)
-        self.fonts = FontManager(options.get('font_name', ''), fontsize)
-        self.fontw, self.fonth = self.fonts.get_char_size()
-        # Line number options
-        self.line_number_fg = options.get('line_number_fg', '#886')
-        self.line_number_bg = options.get('line_number_bg', '#eed')
-        self.line_number_chars = get_int_opt(options,
-                                             'line_number_chars', 2)
-        self.line_number_bold = get_bool_opt(options,
-                                             'line_number_bold', False)
-        self.line_number_italic = get_bool_opt(options,
-                                               'line_number_italic', False)
-        self.line_number_pad = get_int_opt(options, 'line_number_pad', 6)
-        self.line_numbers = get_bool_opt(options, 'line_numbers', True)
-        self.line_number_separator = get_bool_opt(options,
-                                                  'line_number_separator', True)
-        self.line_number_step = get_int_opt(options, 'line_number_step', 1)
-        self.line_number_start = get_int_opt(options, 'line_number_start', 1)
-        if self.line_numbers:
-            self.line_number_width = (self.fontw * self.line_number_chars +
-                                      self.line_number_pad * 2)
-        else:
-            self.line_number_width = 0
-        self.hl_lines = []
-        hl_lines_str = get_list_opt(options, 'hl_lines', [])
-        for line in hl_lines_str:
-            try:
-                self.hl_lines.append(int(line))
-            except ValueError:
-                pass
-        self.hl_color = options.get('hl_color',
-                                    self.style.highlight_color) or '#f90'
-        self.drawables = []
-
-    def get_style_defs(self, arg=''):
-        raise NotImplementedError('The -S option is meaningless for the image '
-                                  'formatter. Use -O style=<stylename> instead.')
-
-    def _get_line_height(self):
-        """
-        Get the height of a line.
-        """
-        return self.fonth + self.line_pad
-
-    def _get_line_y(self, lineno):
-        """
-        Get the Y coordinate of a line number.
-        """
-        return lineno * self._get_line_height() + self.image_pad
-
-    def _get_char_width(self):
-        """
-        Get the width of a character.
-        """
-        return self.fontw
-
-    def _get_char_x(self, linelength):
-        """
-        Get the X coordinate of a character position.
-        """
-        return linelength + self.image_pad + self.line_number_width
-
-    def _get_text_pos(self, linelength, lineno):
-        """
-        Get the actual position for a character and line position.
-        """
-        return self._get_char_x(linelength), self._get_line_y(lineno)
-
-    def _get_linenumber_pos(self, lineno):
-        """
-        Get the actual position for the start of a line number.
-        """
-        return (self.image_pad, self._get_line_y(lineno))
-
-    def _get_text_color(self, style):
-        """
-        Get the correct color for the token from the style.
-        """
-        if style['color'] is not None:
-            fill = '#' + style['color']
-        else:
-            fill = '#000'
-        return fill
-
-    def _get_text_bg_color(self, style):
-        """
-        Get the correct background color for the token from the style.
-        """
-        if style['bgcolor'] is not None:
-            bg_color = '#' + style['bgcolor']
-        else:
-            bg_color = None
-        return bg_color
-
-    def _get_style_font(self, style):
-        """
-        Get the correct font for the style.
-        """
-        return self.fonts.get_font(style['bold'], style['italic'])
-
-    def _get_image_size(self, maxlinelength, maxlineno):
-        """
-        Get the required image size.
-        """
-        return (self._get_char_x(maxlinelength) + self.image_pad,
-                self._get_line_y(maxlineno + 0) + self.image_pad)
-
-    def _draw_linenumber(self, posno, lineno):
-        """
-        Remember a line number drawable to paint later.
-        """
-        self._draw_text(
-            self._get_linenumber_pos(posno),
-            str(lineno).rjust(self.line_number_chars),
-            font=self.fonts.get_font(self.line_number_bold,
-                                     self.line_number_italic),
-            text_fg=self.line_number_fg,
-            text_bg=None,
-        )
-
-    def _draw_text(self, pos, text, font, text_fg, text_bg):
-        """
-        Remember a single drawable tuple to paint later.
-        """
-        self.drawables.append((pos, text, font, text_fg, text_bg))
-
-    def _create_drawables(self, tokensource):
-        """
-        Create drawables for the token content.
-        """
-        lineno = charno = maxcharno = 0
-        maxlinelength = linelength = 0
-        for ttype, value in tokensource:
-            while ttype not in self.styles:
-                ttype = ttype.parent
-            style = self.styles[ttype]
-            # TODO: make sure tab expansion happens earlier in the chain.  It
-            # really ought to be done on the input, as to do it right here is
-            # quite complex.
-            value = value.expandtabs(4)
-            lines = value.splitlines(True)
-            # print lines
-            for i, line in enumerate(lines):
-                temp = line.rstrip('\n')
-                if temp:
-                    self._draw_text(
-                        self._get_text_pos(linelength, lineno),
-                        temp,
-                        font = self._get_style_font(style),
-                        text_fg = self._get_text_color(style),
-                        text_bg = self._get_text_bg_color(style),
-                    )
-                    temp_width, _ = self.fonts.get_text_size(temp)
-                    linelength += temp_width
-                    maxlinelength = max(maxlinelength, linelength)
-                    charno += len(temp)
-                    maxcharno = max(maxcharno, charno)
-                if line.endswith('\n'):
-                    # add a line for each extra line in the value
-                    linelength = 0
-                    charno = 0
-                    lineno += 1
-        self.maxlinelength = maxlinelength
-        self.maxcharno = maxcharno
-        self.maxlineno = lineno
-
-    def _draw_line_numbers(self):
-        """
-        Create drawables for the line numbers.
-        """
-        if not self.line_numbers:
-            return
-        for p in range(self.maxlineno):
-            n = p + self.line_number_start
-            if (n % self.line_number_step) == 0:
-                self._draw_linenumber(p, n)
-
-    def _paint_line_number_bg(self, im):
-        """
-        Paint the line number background on the image.
-        """
-        if not self.line_numbers:
-            return
-        if self.line_number_fg is None:
-            return
-        draw = ImageDraw.Draw(im)
-        recth = im.size[-1]
-        rectw = self.image_pad + self.line_number_width - self.line_number_pad
-        draw.rectangle([(0, 0), (rectw, recth)],
-                       fill=self.line_number_bg)
-        if self.line_number_separator:
-            draw.line([(rectw, 0), (rectw, recth)], fill=self.line_number_fg)
-        del draw
-
-    def format(self, tokensource, outfile):
-        """
-        Format ``tokensource``, an iterable of ``(tokentype, tokenstring)``
-        tuples and write it into ``outfile``.
-
-        This implementation calculates where it should draw each token on the
-        pixmap, then calculates the required pixmap size and draws the items.
-        """
-        self._create_drawables(tokensource)
-        self._draw_line_numbers()
-        im = Image.new(
-            'RGB',
-            self._get_image_size(self.maxlinelength, self.maxlineno),
-            self.background_color
-        )
-        self._paint_line_number_bg(im)
-        draw = ImageDraw.Draw(im)
-        # Highlight
-        if self.hl_lines:
-            x = self.image_pad + self.line_number_width - self.line_number_pad + 1
-            recth = self._get_line_height()
-            rectw = im.size[0] - x
-            for linenumber in self.hl_lines:
-                y = self._get_line_y(linenumber - 1)
-                draw.rectangle([(x, y), (x + rectw, y + recth)],
-                               fill=self.hl_color)
-        for pos, value, font, text_fg, text_bg in self.drawables:
-            if text_bg:
-                text_size = draw.textsize(text=value, font=font)
-                draw.rectangle([pos[0], pos[1], pos[0] + text_size[0], pos[1] + text_size[1]], fill=text_bg)
-            draw.text(pos, value, font=font, fill=text_fg)
-        im.save(outfile, self.image_format.upper())
-
-
-# Add one formatter per format, so that the "-f gif" option gives the correct result
-# when used in pygmentize.
-
-class GifImageFormatter(ImageFormatter):
-    """
-    Create a GIF image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_gif'
-    aliases = ['gif']
-    filenames = ['*.gif']
-    default_image_format = 'gif'
-
-
-class JpgImageFormatter(ImageFormatter):
-    """
-    Create a JPEG image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_jpg'
-    aliases = ['jpg', 'jpeg']
-    filenames = ['*.jpg']
-    default_image_format = 'jpeg'
-
-
-class BmpImageFormatter(ImageFormatter):
-    """
-    Create a bitmap image from source code. This uses the Python Imaging Library to
-    generate a pixmap from the source code.
-
-    .. versionadded:: 1.0
-    """
-
-    name = 'img_bmp'
-    aliases = ['bmp', 'bitmap']
-    filenames = ['*.bmp']
-    default_image_format = 'bmp'
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_manylinux.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_manylinux.py
deleted file mode 100644
index 449c655be65a948f7b2476302a46c35d9e7605ac..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_manylinux.py
+++ /dev/null
@@ -1,240 +0,0 @@
-import collections
-import contextlib
-import functools
-import os
-import re
-import sys
-import warnings
-from typing import Dict, Generator, Iterator, NamedTuple, Optional, Tuple
-
-from ._elffile import EIClass, EIData, ELFFile, EMachine
-
-EF_ARM_ABIMASK = 0xFF000000
-EF_ARM_ABI_VER5 = 0x05000000
-EF_ARM_ABI_FLOAT_HARD = 0x00000400
-
-
-# `os.PathLike` not a generic type until Python 3.9, so sticking with `str`
-# as the type for `path` until then.
-@contextlib.contextmanager
-def _parse_elf(path: str) -> Generator[Optional[ELFFile], None, None]:
-    try:
-        with open(path, "rb") as f:
-            yield ELFFile(f)
-    except (OSError, TypeError, ValueError):
-        yield None
-
-
-def _is_linux_armhf(executable: str) -> bool:
-    # hard-float ABI can be detected from the ELF header of the running
-    # process
-    # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
-    with _parse_elf(executable) as f:
-        return (
-            f is not None
-            and f.capacity == EIClass.C32
-            and f.encoding == EIData.Lsb
-            and f.machine == EMachine.Arm
-            and f.flags & EF_ARM_ABIMASK == EF_ARM_ABI_VER5
-            and f.flags & EF_ARM_ABI_FLOAT_HARD == EF_ARM_ABI_FLOAT_HARD
-        )
-
-
-def _is_linux_i686(executable: str) -> bool:
-    with _parse_elf(executable) as f:
-        return (
-            f is not None
-            and f.capacity == EIClass.C32
-            and f.encoding == EIData.Lsb
-            and f.machine == EMachine.I386
-        )
-
-
-def _have_compatible_abi(executable: str, arch: str) -> bool:
-    if arch == "armv7l":
-        return _is_linux_armhf(executable)
-    if arch == "i686":
-        return _is_linux_i686(executable)
-    return arch in {"x86_64", "aarch64", "ppc64", "ppc64le", "s390x"}
-
-
-# If glibc ever changes its major version, we need to know what the last
-# minor version was, so we can build the complete list of all versions.
-# For now, guess what the highest minor version might be, assume it will
-# be 50 for testing. Once this actually happens, update the dictionary
-# with the actual value.
-_LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
-
-
-class _GLibCVersion(NamedTuple):
-    major: int
-    minor: int
-
-
-def _glibc_version_string_confstr() -> Optional[str]:
-    """
-    Primary implementation of glibc_version_string using os.confstr.
-    """
-    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
-    # to be broken or missing. This strategy is used in the standard library
-    # platform module.
-    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
-    try:
-        # Should be a string like "glibc 2.17".
-        version_string: str = getattr(os, "confstr")("CS_GNU_LIBC_VERSION")
-        assert version_string is not None
-        _, version = version_string.rsplit()
-    except (AssertionError, AttributeError, OSError, ValueError):
-        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
-        return None
-    return version
-
-
-def _glibc_version_string_ctypes() -> Optional[str]:
-    """
-    Fallback implementation of glibc_version_string using ctypes.
-    """
-    try:
-        import ctypes
-    except ImportError:
-        return None
-
-    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
-    # manpage says, "If filename is NULL, then the returned handle is for the
-    # main program". This way we can let the linker do the work to figure out
-    # which libc our process is actually using.
-    #
-    # We must also handle the special case where the executable is not a
-    # dynamically linked executable. This can occur when using musl libc,
-    # for example. In this situation, dlopen() will error, leading to an
-    # OSError. Interestingly, at least in the case of musl, there is no
-    # errno set on the OSError. The single string argument used to construct
-    # OSError comes from libc itself and is therefore not portable to
-    # hard code here. In any case, failure to call dlopen() means we
-    # can proceed, so we bail on our attempt.
-    try:
-        process_namespace = ctypes.CDLL(None)
-    except OSError:
-        return None
-
-    try:
-        gnu_get_libc_version = process_namespace.gnu_get_libc_version
-    except AttributeError:
-        # Symbol doesn't exist -> therefore, we are not linked to
-        # glibc.
-        return None
-
-    # Call gnu_get_libc_version, which returns a string like "2.5"
-    gnu_get_libc_version.restype = ctypes.c_char_p
-    version_str: str = gnu_get_libc_version()
-    # py2 / py3 compatibility:
-    if not isinstance(version_str, str):
-        version_str = version_str.decode("ascii")
-
-    return version_str
-
-
-def _glibc_version_string() -> Optional[str]:
-    """Returns glibc version string, or None if not using glibc."""
-    return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
-
-
-def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
-    """Parse glibc version.
-
-    We use a regexp instead of str.split because we want to discard any
-    random junk that might come after the minor version -- this might happen
-    in patched/forked versions of glibc (e.g. Linaro's version of glibc
-    uses version strings like "2.20-2014.11"). See gh-3588.
-    """
-    m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
-    if not m:
-        warnings.warn(
-            f"Expected glibc version with 2 components major.minor,"
-            f" got: {version_str}",
-            RuntimeWarning,
-        )
-        return -1, -1
-    return int(m.group("major")), int(m.group("minor"))
-
-
-@functools.lru_cache()
-def _get_glibc_version() -> Tuple[int, int]:
-    version_str = _glibc_version_string()
-    if version_str is None:
-        return (-1, -1)
-    return _parse_glibc_version(version_str)
-
-
-# From PEP 513, PEP 600
-def _is_compatible(name: str, arch: str, version: _GLibCVersion) -> bool:
-    sys_glibc = _get_glibc_version()
-    if sys_glibc < version:
-        return False
-    # Check for presence of _manylinux module.
-    try:
-        import _manylinux  # noqa
-    except ImportError:
-        return True
-    if hasattr(_manylinux, "manylinux_compatible"):
-        result = _manylinux.manylinux_compatible(version[0], version[1], arch)
-        if result is not None:
-            return bool(result)
-        return True
-    if version == _GLibCVersion(2, 5):
-        if hasattr(_manylinux, "manylinux1_compatible"):
-            return bool(_manylinux.manylinux1_compatible)
-    if version == _GLibCVersion(2, 12):
-        if hasattr(_manylinux, "manylinux2010_compatible"):
-            return bool(_manylinux.manylinux2010_compatible)
-    if version == _GLibCVersion(2, 17):
-        if hasattr(_manylinux, "manylinux2014_compatible"):
-            return bool(_manylinux.manylinux2014_compatible)
-    return True
-
-
-_LEGACY_MANYLINUX_MAP = {
-    # CentOS 7 w/ glibc 2.17 (PEP 599)
-    (2, 17): "manylinux2014",
-    # CentOS 6 w/ glibc 2.12 (PEP 571)
-    (2, 12): "manylinux2010",
-    # CentOS 5 w/ glibc 2.5 (PEP 513)
-    (2, 5): "manylinux1",
-}
-
-
-def platform_tags(linux: str, arch: str) -> Iterator[str]:
-    if not _have_compatible_abi(sys.executable, arch):
-        return
-    # Oldest glibc to be supported regardless of architecture is (2, 17).
-    too_old_glibc2 = _GLibCVersion(2, 16)
-    if arch in {"x86_64", "i686"}:
-        # On x86/i686 also oldest glibc to be supported is (2, 5).
-        too_old_glibc2 = _GLibCVersion(2, 4)
-    current_glibc = _GLibCVersion(*_get_glibc_version())
-    glibc_max_list = [current_glibc]
-    # We can assume compatibility across glibc major versions.
-    # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
-    #
-    # Build a list of maximum glibc versions so that we can
-    # output the canonical list of all glibc from current_glibc
-    # down to too_old_glibc2, including all intermediary versions.
-    for glibc_major in range(current_glibc.major - 1, 1, -1):
-        glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
-        glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
-    for glibc_max in glibc_max_list:
-        if glibc_max.major == too_old_glibc2.major:
-            min_minor = too_old_glibc2.minor
-        else:
-            # For other glibc major versions oldest supported is (x, 0).
-            min_minor = -1
-        for glibc_minor in range(glibc_max.minor, min_minor, -1):
-            glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
-            tag = "manylinux_{}_{}".format(*glibc_version)
-            if _is_compatible(tag, arch, glibc_version):
-                yield linux.replace("linux", tag)
-            # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
-            if glibc_version in _LEGACY_MANYLINUX_MAP:
-                legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
-                if _is_compatible(legacy_tag, arch, glibc_version):
-                    yield linux.replace("linux", legacy_tag)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/bdist.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/bdist.py
deleted file mode 100644
index 6329039ce466e0adb786324c7b98140271fe2fe9..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/command/bdist.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""distutils.command.bdist
-
-Implements the Distutils 'bdist' command (create a built [binary]
-distribution)."""
-
-import os
-import warnings
-
-from ..core import Command
-from ..errors import DistutilsPlatformError, DistutilsOptionError
-from ..util import get_platform
-
-
-def show_formats():
-    """Print list of available formats (arguments to "--format" option)."""
-    from ..fancy_getopt import FancyGetopt
-
-    formats = []
-    for format in bdist.format_commands:
-        formats.append(("formats=" + format, None, bdist.format_commands[format][1]))
-    pretty_printer = FancyGetopt(formats)
-    pretty_printer.print_help("List of available distribution formats:")
-
-
-class ListCompat(dict):
-    # adapter to allow for Setuptools compatibility in format_commands
-    def append(self, item):
-        warnings.warn(
-            """format_commands is now a dict. append is deprecated.""",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-
-class bdist(Command):
-    description = "create a built (binary) distribution"
-
-    user_options = [
-        ('bdist-base=', 'b', "temporary directory for creating built distributions"),
-        (
-            'plat-name=',
-            'p',
-            "platform name to embed in generated filenames "
-            "(default: %s)" % get_platform(),
-        ),
-        ('formats=', None, "formats for distribution (comma-separated list)"),
-        (
-            'dist-dir=',
-            'd',
-            "directory to put final built distributions in " "[default: dist]",
-        ),
-        ('skip-build', None, "skip rebuilding everything (for testing/debugging)"),
-        (
-            'owner=',
-            'u',
-            "Owner name used when creating a tar file" " [default: current user]",
-        ),
-        (
-            'group=',
-            'g',
-            "Group name used when creating a tar file" " [default: current group]",
-        ),
-    ]
-
-    boolean_options = ['skip-build']
-
-    help_options = [
-        ('help-formats', None, "lists available distribution formats", show_formats),
-    ]
-
-    # The following commands do not take a format option from bdist
-    no_format_option = ('bdist_rpm',)
-
-    # This won't do in reality: will need to distinguish RPM-ish Linux,
-    # Debian-ish Linux, Solaris, FreeBSD, ..., Windows, Mac OS.
-    default_format = {'posix': 'gztar', 'nt': 'zip'}
-
-    # Define commands in preferred order for the --help-formats option
-    format_commands = ListCompat(
-        {
-            'rpm': ('bdist_rpm', "RPM distribution"),
-            'gztar': ('bdist_dumb', "gzip'ed tar file"),
-            'bztar': ('bdist_dumb', "bzip2'ed tar file"),
-            'xztar': ('bdist_dumb', "xz'ed tar file"),
-            'ztar': ('bdist_dumb', "compressed tar file"),
-            'tar': ('bdist_dumb', "tar file"),
-            'zip': ('bdist_dumb', "ZIP file"),
-        }
-    )
-
-    # for compatibility until consumers only reference format_commands
-    format_command = format_commands
-
-    def initialize_options(self):
-        self.bdist_base = None
-        self.plat_name = None
-        self.formats = None
-        self.dist_dir = None
-        self.skip_build = 0
-        self.group = None
-        self.owner = None
-
-    def finalize_options(self):
-        # have to finalize 'plat_name' before 'bdist_base'
-        if self.plat_name is None:
-            if self.skip_build:
-                self.plat_name = get_platform()
-            else:
-                self.plat_name = self.get_finalized_command('build').plat_name
-
-        # 'bdist_base' -- parent of per-built-distribution-format
-        # temporary directories (eg. we'll probably have
-        # "build/bdist.<plat>/dumb", "build/bdist.<plat>/rpm", etc.)
-        if self.bdist_base is None:
-            build_base = self.get_finalized_command('build').build_base
-            self.bdist_base = os.path.join(build_base, 'bdist.' + self.plat_name)
-
-        self.ensure_string_list('formats')
-        if self.formats is None:
-            try:
-                self.formats = [self.default_format[os.name]]
-            except KeyError:
-                raise DistutilsPlatformError(
-                    "don't know how to create built distributions "
-                    "on platform %s" % os.name
-                )
-
-        if self.dist_dir is None:
-            self.dist_dir = "dist"
-
-    def run(self):
-        # Figure out which sub-commands we need to run.
-        commands = []
-        for format in self.formats:
-            try:
-                commands.append(self.format_commands[format][0])
-            except KeyError:
-                raise DistutilsOptionError("invalid format '%s'" % format)
-
-        # Reinitialize and run each command.
-        for i in range(len(self.formats)):
-            cmd_name = commands[i]
-            sub_cmd = self.reinitialize_command(cmd_name)
-            if cmd_name not in self.no_format_option:
-                sub_cmd.format = self.formats[i]
-
-            # passing the owner and group names for tar archiving
-            if cmd_name == 'bdist_dumb':
-                sub_cmd.owner = self.owner
-                sub_cmd.group = self.group
-
-            # If we're going to need to run this command again, tell it to
-            # keep its temporary files around so subsequent runs go faster.
-            if cmd_name in commands[i + 1 :]:
-                sub_cmd.keep_temp = 1
-            self.run_command(cmd_name)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Scripts/activate.bat b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Scripts/activate.bat
deleted file mode 100644
index 56ca36b33ba60ebb9b4312ff8e051c4f3db2b5d4..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Scripts/activate.bat
+++ /dev/null
@@ -1,38 +0,0 @@
-@set "VIRTUAL_ENV=C:\Users\Admin\roop-unleashed\mynewshinyroop"
-
-@set "VIRTUAL_ENV_PROMPT="
-@if NOT DEFINED VIRTUAL_ENV_PROMPT (
-    @for %%d in ("%VIRTUAL_ENV%") do @set "VIRTUAL_ENV_PROMPT=%%~nxd"
-)
-
-@if defined _OLD_VIRTUAL_PROMPT (
-    @set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
-) else (
-    @if not defined PROMPT (
-        @set "PROMPT=$P$G"
-    )
-    @if not defined VIRTUAL_ENV_DISABLE_PROMPT (
-        @set "_OLD_VIRTUAL_PROMPT=%PROMPT%"
-    )
-)
-@if not defined VIRTUAL_ENV_DISABLE_PROMPT (
-    @set "PROMPT=(%VIRTUAL_ENV_PROMPT%) %PROMPT%"
-)
-
-@REM Don't use () to avoid problems with them in %PATH%
-@if defined _OLD_VIRTUAL_PYTHONHOME @goto ENDIFVHOME
-    @set "_OLD_VIRTUAL_PYTHONHOME=%PYTHONHOME%"
-:ENDIFVHOME
-
-@set PYTHONHOME=
-
-@REM if defined _OLD_VIRTUAL_PATH (
-@if not defined _OLD_VIRTUAL_PATH @goto ENDIFVPATH1
-    @set "PATH=%_OLD_VIRTUAL_PATH%"
-:ENDIFVPATH1
-@REM ) else (
-@if defined _OLD_VIRTUAL_PATH @goto ENDIFVPATH2
-    @set "_OLD_VIRTUAL_PATH=%PATH%"
-:ENDIFVPATH2
-
-@set "PATH=%VIRTUAL_ENV%\Scripts;%PATH%"
diff --git a/spaces/Th3BossC/TranscriptApi/TranscriptApi/resources/__init__.py b/spaces/Th3BossC/TranscriptApi/TranscriptApi/resources/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Theivaprakasham/yolov6/yolov6/data/datasets.py b/spaces/Theivaprakasham/yolov6/yolov6/data/datasets.py
deleted file mode 100644
index 7627d608245ec5765b910dfb251419502ebdd32f..0000000000000000000000000000000000000000
--- a/spaces/Theivaprakasham/yolov6/yolov6/data/datasets.py
+++ /dev/null
@@ -1,533 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-import glob
-import os
-import os.path as osp
-import random
-import json
-import time
-
-from multiprocessing.pool import Pool
-
-import cv2
-import numpy as np
-import torch
-from PIL import ExifTags, Image, ImageOps
-from torch.utils.data import Dataset
-from tqdm import tqdm
-from pathlib import Path
-
-from .data_augment import (
-    augment_hsv,
-    letterbox,
-    mixup,
-    random_affine,
-    mosaic_augmentation,
-)
-from yolov6.utils.events import LOGGER
-
-# Parameters
-IMG_FORMATS = ["bmp", "jpg", "jpeg", "png", "tif", "tiff", "dng", "webp", "mpo"]
-# Get orientation exif tag
-for k, v in ExifTags.TAGS.items():
-    if v == "Orientation":
-        ORIENTATION = k
-        break
-
-
-class TrainValDataset(Dataset):
-    # YOLOv6 train_loader/val_loader, loads images and labels for training and validation
-    def __init__(
-        self,
-        img_dir,
-        img_size=640,
-        batch_size=16,
-        augment=False,
-        hyp=None,
-        rect=False,
-        check_images=False,
-        check_labels=False,
-        stride=32,
-        pad=0.0,
-        rank=-1,
-        class_names=None,
-        task="train",
-    ):
-        assert task.lower() in ("train", "val", "speed"), f"Not supported task: {task}"
-        t1 = time.time()
-        self.__dict__.update(locals())
-        self.main_process = self.rank in (-1, 0)
-        self.task = self.task.capitalize()
-        self.img_paths, self.labels = self.get_imgs_labels(self.img_dir)
-        if self.rect:
-            shapes = [self.img_info[p]["shape"] for p in self.img_paths]
-            self.shapes = np.array(shapes, dtype=np.float64)
-            self.batch_indices = np.floor(
-                np.arange(len(shapes)) / self.batch_size
-            ).astype(
-                np.int
-            )  # batch indices of each image
-            self.sort_files_shapes()
-        t2 = time.time()
-        if self.main_process:
-            LOGGER.info(f"%.1fs for dataset initialization." % (t2 - t1))
-
-    def __len__(self):
-        """Get the length of dataset"""
-        return len(self.img_paths)
-
-    def __getitem__(self, index):
-        """Fetching a data sample for a given key.
-        This function applies mosaic and mixup augments during training.
-        During validation, letterbox augment is applied.
-        """
-        # Mosaic Augmentation
-        if self.augment and random.random() < self.hyp["mosaic"]:
-            img, labels = self.get_mosaic(index)
-            shapes = None
-
-            # MixUp augmentation
-            if random.random() < self.hyp["mixup"]:
-                img_other, labels_other = self.get_mosaic(
-                    random.randint(0, len(self.img_paths) - 1)
-                )
-                img, labels = mixup(img, labels, img_other, labels_other)
-
-        else:
-            # Load image
-            img, (h0, w0), (h, w) = self.load_image(index)
-
-            # Letterbox
-            shape = (
-                self.batch_shapes[self.batch_indices[index]]
-                if self.rect
-                else self.img_size
-            )  # final letterboxed shape
-            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
-            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling
-
-            labels = self.labels[index].copy()
-            if labels.size:
-                w *= ratio
-                h *= ratio
-                # new boxes
-                boxes = np.copy(labels[:, 1:])
-                boxes[:, 0] = (
-                    w * (labels[:, 1] - labels[:, 3] / 2) + pad[0]
-                )  # top left x
-                boxes[:, 1] = (
-                    h * (labels[:, 2] - labels[:, 4] / 2) + pad[1]
-                )  # top left y
-                boxes[:, 2] = (
-                    w * (labels[:, 1] + labels[:, 3] / 2) + pad[0]
-                )  # bottom right x
-                boxes[:, 3] = (
-                    h * (labels[:, 2] + labels[:, 4] / 2) + pad[1]
-                )  # bottom right y
-                labels[:, 1:] = boxes
-
-            if self.augment:
-                img, labels = random_affine(
-                    img,
-                    labels,
-                    degrees=self.hyp["degrees"],
-                    translate=self.hyp["translate"],
-                    scale=self.hyp["scale"],
-                    shear=self.hyp["shear"],
-                    new_shape=(self.img_size, self.img_size),
-                )
-
-        if len(labels):
-            h, w = img.shape[:2]
-
-            labels[:, [1, 3]] = labels[:, [1, 3]].clip(0, w - 1e-3)  # x1, x2
-            labels[:, [2, 4]] = labels[:, [2, 4]].clip(0, h - 1e-3)  # y1, y2
-
-            boxes = np.copy(labels[:, 1:])
-            boxes[:, 0] = ((labels[:, 1] + labels[:, 3]) / 2) / w  # x center
-            boxes[:, 1] = ((labels[:, 2] + labels[:, 4]) / 2) / h  # y center
-            boxes[:, 2] = (labels[:, 3] - labels[:, 1]) / w  # width
-            boxes[:, 3] = (labels[:, 4] - labels[:, 2]) / h  # height
-            labels[:, 1:] = boxes
-
-        if self.augment:
-            img, labels = self.general_augment(img, labels)
-
-        labels_out = torch.zeros((len(labels), 6))
-        if len(labels):
-            labels_out[:, 1:] = torch.from_numpy(labels)
-
-        # Convert
-        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-        img = np.ascontiguousarray(img)
-
-        return torch.from_numpy(img), labels_out, self.img_paths[index], shapes
-
-    def load_image(self, index):
-        """Load image.
-        This function loads image by cv2, resize original image to target shape(img_size) with keeping ratio.
-
-        Returns:
-            Image, original shape of image, resized image shape
-        """
-        path = self.img_paths[index]
-        im = cv2.imread(path)
-        assert im is not None, f"Image Not Found {path}, workdir: {os.getcwd()}"
-
-        h0, w0 = im.shape[:2]  # origin shape
-        r = self.img_size / max(h0, w0)
-        if r != 1:
-            im = cv2.resize(
-                im,
-                (int(w0 * r), int(h0 * r)),
-                interpolation=cv2.INTER_AREA
-                if r < 1 and not self.augment
-                else cv2.INTER_LINEAR,
-            )
-        return im, (h0, w0), im.shape[:2]
-
-    @staticmethod
-    def collate_fn(batch):
-        """Merges a list of samples to form a mini-batch of Tensor(s)"""
-        img, label, path, shapes = zip(*batch)
-        for i, l in enumerate(label):
-            l[:, 0] = i  # add target image index for build_targets()
-        return torch.stack(img, 0), torch.cat(label, 0), path, shapes
-
-    def get_imgs_labels(self, img_dir):
-
-        assert osp.exists(img_dir), f"{img_dir} is an invalid directory path!"
-        valid_img_record = osp.join(
-            osp.dirname(img_dir), "." + osp.basename(img_dir) + ".json"
-        )
-        img_info = {}
-        NUM_THREADS = min(8, os.cpu_count())
-        # check images
-        if (
-            self.check_images or not osp.exists(valid_img_record)
-        ) and self.main_process:
-            img_paths = glob.glob(osp.join(img_dir, "*"), recursive=True)
-            img_paths = sorted(
-                p for p in img_paths if p.split(".")[-1].lower() in IMG_FORMATS
-            )
-            assert img_paths, f"No images found in {img_dir}."
-
-            nc, msgs = 0, []  # number corrupt, messages
-            LOGGER.info(
-                f"{self.task}: Checking formats of images with {NUM_THREADS} process(es): "
-            )
-            with Pool(NUM_THREADS) as pool:
-                pbar = tqdm(
-                    pool.imap(TrainValDataset.check_image, img_paths),
-                    total=len(img_paths),
-                )
-                for img_path, shape_per_img, nc_per_img, msg in pbar:
-                    if nc_per_img == 0:  # not corrupted
-                        img_info[img_path] = {"shape": shape_per_img}
-                    nc += nc_per_img
-                    if msg:
-                        msgs.append(msg)
-                    pbar.desc = f"{nc} image(s) corrupted"
-            pbar.close()
-            if msgs:
-                LOGGER.info("\n".join(msgs))
-
-            # save valid image paths.
-            with open(valid_img_record, "w") as f:
-                json.dump(img_info, f)
-
-        # check and load anns
-        label_dir = osp.join(
-            osp.dirname(osp.dirname(img_dir)), "labels", osp.basename(img_dir)
-        )
-        assert osp.exists(label_dir), f"{label_dir} is an invalid directory path!"
-        if not img_info:
-            with open(valid_img_record, "r") as f:
-                img_info = json.load(f)
-                assert (
-                    img_info
-                ), "No information in record files, please add option --check_images."
-        img_paths = list(img_info.keys())
-        label_paths = [
-            osp.join(label_dir, osp.basename(p).split(".")[0] + ".txt")
-            for p in img_paths
-        ]
-        if (
-            self.check_labels or "labels" not in img_info[img_paths[0]]
-        ):  # key 'labels' not saved in img_info
-            nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number corrupt, messages
-            LOGGER.info(
-                f"{self.task}: Checking formats of labels with {NUM_THREADS} process(es): "
-            )
-            with Pool(NUM_THREADS) as pool:
-                pbar = pool.imap(
-                    TrainValDataset.check_label_files, zip(img_paths, label_paths)
-                )
-                pbar = tqdm(pbar, total=len(label_paths)) if self.main_process else pbar
-                for (
-                    img_path,
-                    labels_per_file,
-                    nc_per_file,
-                    nm_per_file,
-                    nf_per_file,
-                    ne_per_file,
-                    msg,
-                ) in pbar:
-                    if img_path:
-                        img_info[img_path]["labels"] = labels_per_file
-                    else:
-                        img_info.pop(img_path)
-                    nc += nc_per_file
-                    nm += nm_per_file
-                    nf += nf_per_file
-                    ne += ne_per_file
-                    if msg:
-                        msgs.append(msg)
-                    if self.main_process:
-                        pbar.desc = f"{nf} label(s) found, {nm} label(s) missing, {ne} label(s) empty, {nc} invalid label files"
-            if self.main_process:
-                pbar.close()
-                with open(valid_img_record, "w") as f:
-                    json.dump(img_info, f)
-            if msgs:
-                LOGGER.info("\n".join(msgs))
-            if nf == 0:
-                LOGGER.warning(
-                    f"WARNING: No labels found in {osp.dirname(self.img_paths[0])}. "
-                )
-        else:
-            with open(valid_img_record) as f:
-                img_info = json.load(f)
-        if self.task.lower() == "val":
-            assert (
-                self.class_names
-            ), "Class names is required when converting labels to coco format for evaluating."
-            save_dir = osp.join(osp.dirname(osp.dirname(img_dir)), "annotations")
-            if not osp.exists(save_dir):
-                os.mkdir(save_dir)
-            save_path = osp.join(
-                save_dir, "instances_" + osp.basename(img_dir) + ".json"
-            )
-            if not osp.exists(save_path):
-                TrainValDataset.generate_coco_format_labels(
-                    img_info, self.class_names, save_path
-                )
-
-        img_paths, labels = list(
-            zip(
-                *[
-                    (
-                        img_path,
-                        np.array(info["labels"], dtype=np.float32)
-                        if info["labels"]
-                        else np.zeros((0, 5), dtype=np.float32),
-                    )
-                    for img_path, info in img_info.items()
-                ]
-            )
-        )
-        self.img_info = img_info
-        LOGGER.info(
-            f"{self.task}: Final numbers of valid images: {len(img_paths)}/ labels: {len(labels)}. "
-        )
-        return img_paths, labels
-
-    def get_mosaic(self, index):
-        """Gets images and labels after mosaic augments"""
-        indices = [index] + random.choices(
-            range(0, len(self.img_paths)), k=3
-        )  # 3 additional image indices
-        random.shuffle(indices)
-        imgs, hs, ws, labels = [], [], [], []
-        for index in indices:
-            img, _, (h, w) = self.load_image(index)
-            labels_per_img = self.labels[index]
-            imgs.append(img)
-            hs.append(h)
-            ws.append(w)
-            labels.append(labels_per_img)
-        img, labels = mosaic_augmentation(self.img_size, imgs, hs, ws, labels, self.hyp)
-        return img, labels
-
-    def general_augment(self, img, labels):
-        """Gets images and labels after general augment
-        This function applies hsv, random ud-flip and random lr-flips augments.
-        """
-        nl = len(labels)
-
-        # HSV color-space
-        augment_hsv(
-            img,
-            hgain=self.hyp["hsv_h"],
-            sgain=self.hyp["hsv_s"],
-            vgain=self.hyp["hsv_v"],
-        )
-
-        # Flip up-down
-        if random.random() < self.hyp["flipud"]:
-            img = np.flipud(img)
-            if nl:
-                labels[:, 2] = 1 - labels[:, 2]
-
-        # Flip left-right
-        if random.random() < self.hyp["fliplr"]:
-            img = np.fliplr(img)
-            if nl:
-                labels[:, 1] = 1 - labels[:, 1]
-
-        return img, labels
-
-    def sort_files_shapes(self):
-        # Sort by aspect ratio
-        batch_num = self.batch_indices[-1] + 1
-        s = self.shapes  # wh
-        ar = s[:, 1] / s[:, 0]  # aspect ratio
-        irect = ar.argsort()
-        self.img_paths = [self.img_paths[i] for i in irect]
-        self.labels = [self.labels[i] for i in irect]
-        self.shapes = s[irect]  # wh
-        ar = ar[irect]
-
-        # Set training image shapes
-        shapes = [[1, 1]] * batch_num
-        for i in range(batch_num):
-            ari = ar[self.batch_indices == i]
-            mini, maxi = ari.min(), ari.max()
-            if maxi < 1:
-                shapes[i] = [maxi, 1]
-            elif mini > 1:
-                shapes[i] = [1, 1 / mini]
-        self.batch_shapes = (
-            np.ceil(np.array(shapes) * self.img_size / self.stride + self.pad).astype(
-                np.int
-            )
-            * self.stride
-        )
-
-    @staticmethod
-    def check_image(im_file):
-        # verify an image.
-        nc, msg = 0, ""
-        try:
-            im = Image.open(im_file)
-            im.verify()  # PIL verify
-            shape = im.size  # (width, height)
-            im_exif = im._getexif()
-            if im_exif and ORIENTATION in im_exif:
-                rotation = im_exif[ORIENTATION]
-                if rotation in (6, 8):
-                    shape = (shape[1], shape[0])
-
-            assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
-            assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}"
-            if im.format.lower() in ("jpg", "jpeg"):
-                with open(im_file, "rb") as f:
-                    f.seek(-2, 2)
-                    if f.read() != b"\xff\xd9":  # corrupt JPEG
-                        ImageOps.exif_transpose(Image.open(im_file)).save(
-                            im_file, "JPEG", subsampling=0, quality=100
-                        )
-                        msg += f"WARNING: {im_file}: corrupt JPEG restored and saved"
-            return im_file, shape, nc, msg
-        except Exception as e:
-            nc = 1
-            msg = f"WARNING: {im_file}: ignoring corrupt image: {e}"
-            return im_file, None, nc, msg
-
-    @staticmethod
-    def check_label_files(args):
-        img_path, lb_path = args
-        nm, nf, ne, nc, msg = 0, 0, 0, 0, ""  # number (missing, found, empty, message
-        try:
-            if osp.exists(lb_path):
-                nf = 1  # label found
-                with open(lb_path, "r") as f:
-                    labels = [
-                        x.split() for x in f.read().strip().splitlines() if len(x)
-                    ]
-                    labels = np.array(labels, dtype=np.float32)
-                if len(labels):
-                    assert all(
-                        len(l) == 5 for l in labels
-                    ), f"{lb_path}: wrong label format."
-                    assert (
-                        labels >= 0
-                    ).all(), f"{lb_path}: Label values error: all values in label file must > 0"
-                    assert (
-                        labels[:, 1:] <= 1
-                    ).all(), f"{lb_path}: Label values error: all coordinates must be normalized"
-
-                    _, indices = np.unique(labels, axis=0, return_index=True)
-                    if len(indices) < len(labels):  # duplicate row check
-                        labels = labels[indices]  # remove duplicates
-                        msg += f"WARNING: {lb_path}: {len(labels) - len(indices)} duplicate labels removed"
-                    labels = labels.tolist()
-                else:
-                    ne = 1  # label empty
-                    labels = []
-            else:
-                nm = 1  # label missing
-                labels = []
-
-            return img_path, labels, nc, nm, nf, ne, msg
-        except Exception as e:
-            nc = 1
-            msg = f"WARNING: {lb_path}: ignoring invalid labels: {e}"
-            return None, None, nc, nm, nf, ne, msg
-
-    @staticmethod
-    def generate_coco_format_labels(img_info, class_names, save_path):
-        # for evaluation with pycocotools
-        dataset = {"categories": [], "annotations": [], "images": []}
-        for i, class_name in enumerate(class_names):
-            dataset["categories"].append(
-                {"id": i, "name": class_name, "supercategory": ""}
-            )
-
-        ann_id = 0
-        LOGGER.info(f"Convert to COCO format")
-        for i, (img_path, info) in enumerate(tqdm(img_info.items())):
-            labels = info["labels"] if info["labels"] else []
-            path = Path(img_path)
-            img_id = int(path.stem) if path.stem.isnumeric() else path.stem
-            img_w, img_h = info["shape"]
-            dataset["images"].append(
-                {
-                    "file_name": os.path.basename(img_path),
-                    "id": img_id,
-                    "width": img_w,
-                    "height": img_h,
-                }
-            )
-            if labels:
-                for label in labels:
-                    c, x, y, w, h = label[:5]
-                    # convert x,y,w,h to x1,y1,x2,y2
-                    x1 = (x - w / 2) * img_w
-                    y1 = (y - h / 2) * img_h
-                    x2 = (x + w / 2) * img_w
-                    y2 = (y + h / 2) * img_h
-                    # cls_id starts from 0
-                    cls_id = int(c)
-                    w = max(0, x2 - x1)
-                    h = max(0, y2 - y1)
-                    dataset["annotations"].append(
-                        {
-                            "area": h * w,
-                            "bbox": [x1, y1, w, h],
-                            "category_id": cls_id,
-                            "id": ann_id,
-                            "image_id": img_id,
-                            "iscrowd": 0,
-                            # mask
-                            "segmentation": [],
-                        }
-                    )
-                    ann_id += 1
-
-        with open(save_path, "w") as f:
-            json.dump(dataset, f)
-            LOGGER.info(
-                f"Convert to COCO format finished. Resutls saved in {save_path}"
-            )
diff --git a/spaces/Vertaix/vendiscore/README.md b/spaces/Vertaix/vendiscore/README.md
deleted file mode 100644
index abd28ee88c8f3263493f30e0d1ae36f478f9652d..0000000000000000000000000000000000000000
--- a/spaces/Vertaix/vendiscore/README.md
+++ /dev/null
@@ -1,136 +0,0 @@
----
-title: vendiscore
-datasets:
--  
-tags:
-- evaluate
-- metric
-description: "The Vendi Score is a metric for evaluating diversity in machine learning. See the project's README at https://github.com/vertaix/Vendi-Score for more information."
-sdk: gradio
-sdk_version: 3.0.2
-app_file: app.py
-pinned: false
----
-
-# Metric Card for VendiScore
-
-The Vendi Score (VS) is a metric for evaluating diversity in machine learning.
-The input to metric is a collection of samples and a pairwise similarity function, and the output is a number, which can be interpreted as the effective number of unique elements in the sample.
-See the project's README at https://github.com/vertaix/Vendi-Score for more information.
-
-## Metric Description
-The Vendi Score (VS) is a metric for evaluating diversity in machine learning.
-The input to metric is a collection of samples and a pairwise similarity function, and the output is a number, which can be interpreted as the effective number of unique elements in the sample.
-Specifically, given an `n x n` positive semi-definite matrix `K` of similarity scores, the score is defined as:
-```
-VS(K) = exp(tr(K/n @ log(K/n))) = exp(-sum_i lambda_i log lambda_i),
-```
-where `lambda_i` are the eigenvalues of `K/n` and `0 log 0 = 0`.
-That is, the Vendi Score is equal to the exponential of the von Neumann entropy of `K/n`, or the Shannon entropy of the eigenvalues, which is also known as the effective rank.
-
-For more information, please see our paper, [The Vendi Score: A Diversity Evaluation Metric for Machine Learning](https://arxiv.org/abs/2210.02410).
-
-## How to Use
-The Vendi Score is available as a Python package or in HuggingFace `evaluate`.
-To use the Python package, see the instructions at https://github.com/vertaix/Vendi-Score.
-The `evaluate` module supports text, numbers, and precomputed similarity scores or feature embeddings.
-Please use the Python package for more support for images and other datatypes.
-
-To use the `evaluate` module, first install the requirements:
-```
-pip install evaluate
-pip install vendi_score[all]
-```
-To calculate the score, pass a list of samples and a similarity function or a string identifying a predefined class of similarity functions (see below).
-```
->>> vendiscore = evaluate.load("Vertaix/vendiscore", "text")
->>> sents = ["Look, Jane.", "See Spot.", "See Spot run.", "Run, Spot, run.", "Jane sees Spot run."]
->>> results = vendiscore.compute(samples=sents, k="ngram_overlap", ns=[1, 2])
->>> print(results)
-{'VS': 3.90657...}
-```
-
-### Inputs
-- **samples**: an iterable containing n samples to score; an n x n similarity
-       matrix K, or an n x d feature matrix X.
-- **k**: a pairwise similarity function, or a string identifying a predefined 
-       similarity function. If k is a pairwise similarity function, it should
-       be symmetric and k(x, x) = 1.
-       Options: ngram_overlap, text_embeddings.
-- **score_K**: if true, samples is an n x n similarity matrix K.
-- **score_X**: if true, samples is an n x d feature matrix X.
-- **score_dual**: if true,  samples is an n x d feature matrix X and we will
-       compute the diversity score using the covariance matrix X @ X.T.
-- **normalize**: if true, normalize the similarity scores.
-- **model (optional)**: if k is "text_embeddings", a model mapping sentences to
-       embeddings (output should be an object with an attribute called
-       `pooler_output` or `last_hidden_state`).
-- **tokenizer (optional)**: if k is "text_embeddings" or "ngram_overlap", a
-       tokenizer mapping strings to lists.
-- **model_path (optional)**: if k is "text_embeddings", the name of a model on
-       the HuggingFace hub.
-- **ns (optional)**: if k is "ngram_overlap", the values of n to calculate.
-- **batch_size (optional)**: batch size to use if k is "text_embedding".
-- **device (optional)**: a string (e.g. "cuda", "cpu") or torch.device 
-       identifying the device to use if k is "text_embedding".
-
-
-### Output Values
-
-The output is a dictionary with one key, "VS".
-Given n samples, the value of the Vendi Score ranges between 1 and n, with higher numbers indicating that the sample is more diverse.
-
-### Examples
-
-```
->>> import numpy as np
->>> vendiscore = evaluate.load("Vertaix/vendiscore", "int")
->>> samples = [0, 0, 10, 10, 20, 20]
->>> k = lambda a, b: np.exp(-np.abs(a - b))
->>> vendiscore.compute(samples=samples, k=k)
-{'VS': 2.9999...}
-```
-
-If you already have precomputed a similarity matrix:
-```
->>> vendiscore = evaluate.load("Vertaix/vendiscore", "K")
->>> K = np.array([[1.0, 0.9, 0.0],
-                  [0.9, 1.0, 0.0],
-                  [0.0, 0.0, 1.0]])
->>> vendiscore.compute(samples=K, score_K=True)
-{'VS': 2.1573...}
-```
-
-If your similarity function is a dot product between `n` normalized
-`d`-dimensional embeddings `X`, and `d` < `n`, it is faster
-to compute the Vendi Score using the covariance matrix, `X @ X.T`.
-(If the rows of `X` are not normalized, set `normalize = True`.)
-```
->>> vendiscore = evaluate.load("Vertaix/vendiscore", "X")
->>> X = np.array([[100, 0], [99, 1], [1, 99], [0, 100]])
->>> vendiscore.compute(samples=X, score_dual=True, normalize=True)
-{'VS': 1.99989...}
-```
-
-Text similarity can be calculated using n-gram overlap or using inner products between embeddings from a neural network.
-```
->>> vendiscore = evaluate.load("Vertaix/vendiscore", "text")
->>> sents = ["Look, Jane.", "See Spot.", "See Spot run.", "Run, Spot, run.", "Jane sees Spot run."]
->>> ngram_vs = vendiscore.compute(samples=sents, k="ngram_overlap", ns=[1, 2])["VS"]
->>> bert_vs = vendiscore.compute(samples=sents, k="text_embeddings", model_path="bert-base-uncased")["VS"]
->>> print(f"N-grams: {ngram_vs:.02f}, BERT: {bert_vs:.02f}")
-N-grams: 3.91, BERT: 1.21
-```
-
-## Limitations and Bias
-The Vendi Score depends on the choice of similarity function. Care should be taken to select a similarity function that reflects the features that are relevant for defining diversity in a given application.
-
-## Citation
-```bibtex
-@article{friedman2022vendi,
-  title={The Vendi Score: A Diversity Evaluation Metric for Machine Learning},
-  author={Friedman, Dan and Dieng, Adji Bousso},
-  journal={arXiv preprint arXiv:2210.02410},
-  year={2022}
-}
-```
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/one_cycle.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/one_cycle.py
deleted file mode 100644
index 553142aa76884427e62d438a266f43b71c3463bd..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/one_cycle.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"Supports 1-Cycle style training"
-from ..core import *
-from ..callback import *
-from ..basic_train import Learner,LearnerCallback
-
-__all__ = ['OneCycleScheduler']
-
-class OneCycleScheduler(LearnerCallback):
-    "Manage 1-Cycle style training as outlined in Leslie Smith's [paper](https://arxiv.org/pdf/1803.09820.pdf)."
-    def __init__(self, learn:Learner, lr_max:float, moms:Floats=(0.95,0.85), div_factor:float=25., pct_start:float=0.3,
-                 final_div:float=None, tot_epochs:int=None, start_epoch:int=None):
-        super().__init__(learn)
-        self.lr_max,self.div_factor,self.pct_start,self.final_div = lr_max,div_factor,pct_start,final_div
-        if self.final_div is None: self.final_div = div_factor*1e4
-        self.moms=tuple(listify(moms,2))
-        if is_listy(self.lr_max): self.lr_max = np.array(self.lr_max)
-        self.start_epoch, self.tot_epochs = start_epoch, tot_epochs
-
-    def steps(self, *steps_cfg:StartOptEnd):
-        "Build anneal schedule for all of the parameters."
-        return [Scheduler(step, n_iter, func=func)
-                for (step,(n_iter,func)) in zip(steps_cfg, self.phases)]
-
-    def on_train_begin(self, n_epochs:int, epoch:int, **kwargs:Any)->None:
-        "Initialize our optimization params based on our annealing schedule."
-        res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
-        self.start_epoch = ifnone(self.start_epoch, epoch)
-        self.tot_epochs = ifnone(self.tot_epochs, n_epochs)
-        n = len(self.learn.data.train_dl) * self.tot_epochs
-        a1 = int(n * self.pct_start)
-        a2 = n-a1
-        self.phases = ((a1, annealing_cos), (a2, annealing_cos))
-        low_lr = self.lr_max/self.div_factor
-        self.lr_scheds = self.steps((low_lr, self.lr_max), (self.lr_max, self.lr_max/self.final_div))
-        self.mom_scheds = self.steps(self.moms, (self.moms[1], self.moms[0]))
-        self.opt = self.learn.opt
-        self.opt.lr,self.opt.mom = self.lr_scheds[0].start,self.mom_scheds[0].start
-        self.idx_s = 0
-        return res
-    
-    def jump_to_epoch(self, epoch:int)->None:
-        for _ in range(len(self.learn.data.train_dl) * epoch):
-            self.on_batch_end(True)
-
-    def on_batch_end(self, train, **kwargs:Any)->None:
-        "Take one step forward on the annealing schedule for the optim params."
-        if train:
-            if self.idx_s >= len(self.lr_scheds): return {'stop_training': True, 'stop_epoch': True}
-            self.opt.lr = self.lr_scheds[self.idx_s].step()
-            self.opt.mom = self.mom_scheds[self.idx_s].step()
-            # when the current schedule is complete we move onto the next
-            # schedule. (in 1-cycle there are two schedules)
-            if self.lr_scheds[self.idx_s].is_done:
-                self.idx_s += 1
-
-    def on_epoch_end(self, epoch, **kwargs:Any)->None:
-        "Tell Learner to stop if the cycle is finished."
-        if epoch > self.tot_epochs: return {'stop_training': True}
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/gen_doc/doctest.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/gen_doc/doctest.py
deleted file mode 100644
index 13dbcc20a4c7575cc16f65c8d18cd43515faa74f..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/gen_doc/doctest.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import sys, re, json, pprint
-from pathlib import Path
-from collections import defaultdict
-from inspect import currentframe, getframeinfo, ismodule
-
-__all__ = ['this_tests']
-
-DB_NAME = 'test_registry.json'
-
-def _json_set_default(obj):
-    if isinstance(obj, set): return list(obj)
-    raise TypeError
-
-class TestRegistry:
-    "Tests register which API they validate using this class."
-    registry = defaultdict(list)
-    this_tests_check = None
-    missing_this_tests = set()
-
-    # logic for checking whether each test calls `this_tests`:
-    # 1. `this_tests_check` is set to True during test's 'setup' stage if it wasn't skipped
-    # 2. if the test is dynamically skipped `this_tests_check` is set to False
-    # 3. `this_tests` sets this flag to False when it's successfully completes
-    # 4. if during the 'teardown' stage `this_tests_check` is still True then we
-    # know that this test needs `this_tests_check`
-
-    @staticmethod
-    def this_tests(*funcs):
-        prev_frame = currentframe().f_back.f_back
-        file_name, lineno, test_name, _, _ = getframeinfo(prev_frame)
-        parent_func_lineno, _ = get_parent_func(lineno, get_lines(file_name))
-        entry = {'file': relative_test_path(file_name), 'test': test_name , 'line': parent_func_lineno}
-        for func in funcs:
-            if func == 'na':
-                # special case when we can't find a function to declare, e.g.
-                # when attributes are tested
-                continue
-            try:
-                func_fq = get_func_fq_name(func)
-            except:
-                raise Exception(f"'{func}' is not a function") from None
-            if re.match(r'fastai\.', func_fq):
-                if entry not in TestRegistry.registry[func_fq]:
-                    TestRegistry.registry[func_fq].append(entry)
-            else:
-                raise Exception(f"'{func}' is not in the fastai API") from None
-        TestRegistry.this_tests_check = False
-
-    def this_tests_check_on():
-        TestRegistry.this_tests_check = True
-
-    def this_tests_check_off():
-        TestRegistry.this_tests_check = False
-
-    def this_tests_check_run(file_name, test_name):
-        if TestRegistry.this_tests_check:
-            TestRegistry.missing_this_tests.add(f"{file_name}::{test_name}")
-
-    def registry_save():
-        if TestRegistry.registry:
-            path = Path(__file__).parent.parent.resolve()/DB_NAME
-            if path.exists():
-                #print("\n*** Merging with the existing test registry")
-                with open(path, 'r') as f: old_registry = json.load(f)
-                TestRegistry.registry = merge_registries(old_registry, TestRegistry.registry)
-            #print(f"\n*** Saving test registry @ {path}")
-            with open(path, 'w') as f:
-                json.dump(obj=TestRegistry.registry, fp=f, indent=4, sort_keys=True, default=_json_set_default)
-
-    def missing_this_tests_alert():
-        if TestRegistry.missing_this_tests:
-            tests = '\n  '.join(sorted(TestRegistry.missing_this_tests))
-            print(f"""
-*** Attention ***
-Please include `this_tests` call in each of the following tests:
-  {tests}
-For details see: https://docs.fast.ai/dev/test.html#test-registry""")
-
-# merge_registries helpers
-# merge dict of lists of dict
-def a2k(a): return '::'.join([a['file'], a['test']]), a['line']
-def k2a(k, v): f,t = k.split('::'); return {"file": f, "line": v, "test": t}
-# merge by key that is a combination of 2 values: test, file
-def merge_lists(a, b):
-    x = dict(map(a2k, [*a, *b]))            # pack + merge
-    return [k2a(k, v) for k,v in x.items()] # unpack
-def merge_registries(a, b):
-    for i in b: a[i] = merge_lists(a[i], b[i]) if i in a else b[i]
-    return a
-
-def this_tests(*funcs): TestRegistry.this_tests(*funcs)
-
-def str2func(name):
-    "Converts 'fastai.foo.bar' into an function 'object' if such exists"
-    if isinstance(name, str): subpaths = name.split('.')
-    else:                     return None
-
-    module = subpaths.pop(0)
-    if module in sys.modules: obj = sys.modules[module]
-    else:                     return None
-
-    for subpath in subpaths:
-        obj = getattr(obj, subpath, None)
-        if obj == None: return None
-    return obj
-
-def get_func_fq_name(func):
-    if ismodule(func): return func.__name__
-    if isinstance(func, str): func = str2func(func)
-    name = None
-    if   hasattr(func, '__qualname__'): name = func.__qualname__
-    elif hasattr(func, '__name__'):     name = func.__name__
-    elif hasattr(func, '__wrapped__'):  return get_func_fq_name(func.__wrapped__)
-    elif hasattr(func, '__class__'):    name = func.__class__.__name__
-    else: raise Exception(f"'{func}' is not a func or class")
-    return f'{func.__module__}.{name}'
-
-def get_parent_func(lineno, lines, ignore_missing=False):
-    "Find any lines where `elt` is called and return the parent test function"
-    for idx,l in enumerate(reversed(lines[:lineno])):
-        if re.match(f'\s*def test', l):  return (lineno - idx), l # 1 based index for github
-        if re.match(f'\w+', l):  break # top level indent - out of function scope
-    if ignore_missing: return None
-    raise LookupError('Could not find parent function for line:', lineno, lines[:lineno])
-
-def relative_test_path(test_file:Path)->str:
-    "Path relative to the `fastai` parent directory"
-    test_file = Path(test_file)
-    testdir_idx = list(reversed(test_file.parts)).index('tests')
-    return '/'.join(test_file.parts[-(testdir_idx+1):])
-
-def get_lines(file):
-    with open(file, 'r') as f: return f.readlines()
diff --git a/spaces/XaSkeL/dreambooth/train_dreambooth.py b/spaces/XaSkeL/dreambooth/train_dreambooth.py
deleted file mode 100644
index 35b568425de57d117a1ad001ad7beba79378e638..0000000000000000000000000000000000000000
--- a/spaces/XaSkeL/dreambooth/train_dreambooth.py
+++ /dev/null
@@ -1,890 +0,0 @@
-import argparse
-import itertools
-import math
-import os
-from pathlib import Path
-from typing import Optional
-import subprocess
-import sys
-import gc
-import random
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.utils.import_utils import is_xformers_available
-from huggingface_hub import HfFolder, Repository, whoami
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-
-logger = get_logger(__name__)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        #required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--instance_data_dir",
-        type=str,
-        default=None,
-        #required=True,
-        help="A folder containing the training data of instance images.",
-    )
-    parser.add_argument(
-        "--class_data_dir",
-        type=str,
-        default=None,
-        #required=False,
-        help="A folder containing the training data of class images.",
-    )
-    parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default=None,
-        help="The prompt with identifier specifying the instance",
-    )
-    parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default="",
-        help="The prompt to specify images in the same class as provided instance images.",
-    )
-    parser.add_argument(
-        "--with_prior_preservation",
-        default=False,
-        action="store_true",
-        help="Flag to add prior preservation loss.",
-    )
-    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
-    parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
-            " sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
-    )
-    parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-
-    parser.add_argument(
-        "--save_n_steps",
-        type=int,
-        default=1,
-        help=("Save the model every n global_steps"),
-    )
-
-
-    parser.add_argument(
-        "--save_starting_step",
-        type=int,
-        default=1,
-        help=("The step from which it starts saving intermediary checkpoints"),
-    )
-
-    parser.add_argument(
-        "--stop_text_encoder_training",
-        type=int,
-        default=1000000,
-        help=("The step at which the text_encoder is no longer trained"),
-    )
-
-
-    parser.add_argument(
-        "--image_captions_filename",
-        action="store_true",
-        help="Get captions from filename",
-    )
-
-
-    parser.add_argument(
-        "--dump_only_text_encoder",
-        action="store_true",
-        default=False,
-        help="Dump only text encoder",
-    )
-
-    parser.add_argument(
-        "--train_only_unet",
-        action="store_true",
-        default=False,
-        help="Train only the unet",
-    )
-
-    parser.add_argument(
-        "--cache_latents",
-        action="store_true",
-        default=False,
-        help="Train only the unet",
-    )
-
-    parser.add_argument(
-        "--Session_dir",
-        type=str,
-        default="",
-        help="Current session directory",
-    )
-
-
-
-
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    #if args.instance_data_dir is None:
-    #    raise ValueError("You must specify a train data directory.")
-
-    #if args.with_prior_preservation:
-    #    if args.class_data_dir is None:
-    #        raise ValueError("You must specify a data directory for class images.")
-    #    if args.class_prompt is None:
-    #        raise ValueError("You must specify prompt for class images.")
-
-    return args
-
-
-class DreamBoothDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
-    def __init__(
-        self,
-        instance_data_root,
-        instance_prompt,
-        tokenizer,
-        args,
-        class_data_root=None,
-        class_prompt=None,
-        size=512,
-        center_crop=False,
-    ):
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-        self.image_captions_filename = None
-
-        self.instance_data_root = Path(instance_data_root)
-        if not self.instance_data_root.exists():
-            raise ValueError("Instance images root doesn't exists.")
-
-        self.instance_images_path = list(Path(instance_data_root).iterdir())
-        self.num_instance_images = len(self.instance_images_path)
-        self.instance_prompt = instance_prompt
-        self._length = self.num_instance_images
-
-        if args.image_captions_filename:
-            self.image_captions_filename = True
-
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            random.shuffle(self.class_images_path)
-            self.num_class_images = len(self.class_images_path)
-            self._length = max(self.num_class_images, self.num_instance_images)
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
-        self.image_transforms = transforms.Compose(
-            [
-                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, index):
-        example = {}
-        path = self.instance_images_path[index % self.num_instance_images]
-        instance_image = Image.open(path)
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-
-        instance_prompt = self.instance_prompt
-
-        if self.image_captions_filename:
-            filename = Path(path).stem
-            pt=''.join([i for i in filename if not i.isdigit()])
-            pt=pt.replace("_"," ")
-            pt=pt.replace("(","")
-            pt=pt.replace(")","")
-            pt=pt.replace("-","")
-            instance_prompt = pt
-            sys.stdout.write(" " +instance_prompt+" ")
-            sys.stdout.flush()
-
-
-        example["instance_images"] = self.image_transforms(instance_image)
-        example["instance_prompt_ids"] = self.tokenizer(
-            instance_prompt,
-            padding="do_not_pad",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-        ).input_ids
-
-        if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            example["class_images"] = self.image_transforms(class_image)
-            example["class_prompt_ids"] = self.tokenizer(
-                self.class_prompt,
-                padding="do_not_pad",
-                truncation=True,
-                max_length=self.tokenizer.model_max_length,
-            ).input_ids
-
-        return example
-
-
-
-class PromptDataset(Dataset):
-    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
-    def __init__(self, prompt, num_samples):
-        self.prompt = prompt
-        self.num_samples = num_samples
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, index):
-        example = {}
-        example["prompt"] = self.prompt
-        example["index"] = index
-        return example
-
-class LatentsDataset(Dataset):
-    def __init__(self, latents_cache, text_encoder_cache):
-        self.latents_cache = latents_cache
-        self.text_encoder_cache = text_encoder_cache
-
-    def __len__(self):
-        return len(self.latents_cache)
-
-    def __getitem__(self, index):
-        return self.latents_cache[index], self.text_encoder_cache[index]
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict:
-    """
-    Starts from base starting dict and then adds the remaining key values from updater replacing the values from
-    the first starting/base dict with the second updater dict.
-
-    For later: how does d = {**d1, **d2} replace collision?
-
-    :param starting_dict:
-    :param updater_dict:
-    :return:
-    """
-    new_dict: dict = starting_dict.copy()   # start with keys and values of starting_dict
-    new_dict.update(updater_dict)    # modifies starting_dict with keys and values of updater_dict
-    return new_dict
-
-def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace:
-    """
-
-    ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x
-    :param args1:
-    :param args2:
-    :return:
-    """
-    # - the merged args
-    # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}.
-    merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2))
-    args = argparse.Namespace(**merged_key_values_for_namespace)
-    return args
-
-def run_training(args_imported):
-    args_default = parse_args()
-    args = merge_args(args_default, args_imported)
-    print(args)
-    logging_dir = Path(args.output_dir, args.logging_dir)
-    i=args.save_starting_step
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with="tensorboard",
-        logging_dir=logging_dir,
-    )
-
-    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
-    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
-    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
-    if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
-        raise ValueError(
-            "Gradient accumulation is not supported when training the text encoder in distributed training. "
-            "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
-        )
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        if not class_images_dir.exists():
-            class_images_dir.mkdir(parents=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
-            pipeline = StableDiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path, torch_dtype=torch_dtype
-            )
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
-            sample_dataloader = accelerator.prepare(sample_dataloader)
-            pipeline.to(accelerator.device)
-
-            for example in tqdm(
-                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
-            ):
-                with torch.autocast("cuda"):
-                    images = pipeline(example["prompt"]).images
-
-                for i, image in enumerate(images):
-                    image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")
-
-            del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
-    # Load models and create wrapper for stable diffusion
-    if args.train_only_unet:
-      if os.path.exists(str(args.output_dir+"/text_encoder_trained")):
-        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
-      elif os.path.exists(str(args.output_dir+"/text_encoder")):
-        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder")
-      else:
-        text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    else:
-      text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
-    if is_xformers_available():
-        try:
-            print("Enabling memory efficient attention with xformers...")
-            unet.enable_xformers_memory_efficient_attention()
-        except Exception as e:
-            logger.warning(
-                f"Could not enable memory efficient attention. Make sure xformers is installed correctly and a GPU is available: {e}"
-            )
-
-    vae.requires_grad_(False)
-    if not args.train_text_encoder:
-        text_encoder.requires_grad_(False)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-        if args.train_text_encoder:
-            text_encoder.gradient_checkpointing_enable()
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    params_to_optimize = (
-        itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
-    )
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
-
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        center_crop=args.center_crop,
-        args=args,
-    )
-
-    def collate_fn(examples):
-        input_ids = [example["instance_prompt_ids"] for example in examples]
-        pixel_values = [example["instance_images"] for example in examples]
-
-        # Concat class and instance examples for prior preservation.
-        # We do this to avoid doing two forward passes.
-        if args.with_prior_preservation:
-            input_ids += [example["class_prompt_ids"] for example in examples]
-            pixel_values += [example["class_images"] for example in examples]
-
-        pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-        input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
-
-        batch = {
-            "input_ids": input_ids,
-            "pixel_values": pixel_values,
-        }
-        return batch
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-    )
-
-    if args.train_text_encoder:
-        unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-        )
-    else:
-        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, optimizer, train_dataloader, lr_scheduler
-        )
-
-    weight_dtype = torch.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move text_encode and vae to gpu.
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    vae.to(accelerator.device, dtype=weight_dtype)
-    if not args.train_text_encoder:
-        text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-
-    if args.cache_latents:
-        latents_cache = []
-        text_encoder_cache = []
-        for batch in tqdm(train_dataloader, desc="Caching latents"):
-            with torch.no_grad():
-                batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype)
-                batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True)
-                latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
-                if args.train_text_encoder:
-                    text_encoder_cache.append(batch["input_ids"])
-                else:
-                    text_encoder_cache.append(text_encoder(batch["input_ids"])[0])
-        train_dataset = LatentsDataset(latents_cache, text_encoder_cache)
-        train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True)
-
-        del vae
-        #if not args.train_text_encoder:
-        #    del text_encoder
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("dreambooth", config=vars(args))
-
-    def bar(prg):
-       br='|'+'█' * prg + ' ' * (25-prg)+'|'
-       return br
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
-    global_step = 0
-
-    for epoch in range(args.num_train_epochs):
-        unet.train()
-        if args.train_text_encoder:
-            text_encoder.train()
-        for step, batch in enumerate(train_dataloader):
-            with accelerator.accumulate(unet):
-                # Convert images to latent space
-                with torch.no_grad():
-                    if args.cache_latents:
-                        latents_dist = batch[0][0]
-                    else:
-                        latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist
-                    latents = latents_dist.sample() * 0.18215
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                if(args.cache_latents):
-                    if args.train_text_encoder:
-                        encoder_hidden_states = text_encoder(batch[0][1])[0]
-                    else:
-                        encoder_hidden_states = batch[0][1]
-                else:
-                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                # Predict the noise residual
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                if args.with_prior_preservation:
-                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
-                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
-                    target, target_prior = torch.chunk(target, 2, dim=0)
-
-                    # Compute instance loss
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
-
-                    # Compute prior loss
-                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
-                    # Add the prior loss to the instance loss.
-                    loss = loss + args.prior_loss_weight * prior_loss
-                else:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = (
-                        itertools.chain(unet.parameters(), text_encoder.parameters())
-                        if args.train_text_encoder
-                        else unet.parameters()
-                    )
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-            fll=round((global_step*100)/args.max_train_steps)
-            fll=round(fll/4)
-            pr=bar(fll)
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            progress_bar.set_description_str("Progress:"+pr)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-            if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30:
-              if accelerator.is_main_process:
-                print(" " +" Freezing the text_encoder ..."+" ")
-                frz_dir=args.output_dir + "/text_encoder_frozen"
-                if os.path.exists(frz_dir):
-                  subprocess.call('rm -r '+ frz_dir, shell=True)
-                os.mkdir(frz_dir)
-                pipeline = StableDiffusionPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    unet=accelerator.unwrap_model(unet),
-                    text_encoder=accelerator.unwrap_model(text_encoder),
-                )
-                pipeline.text_encoder.save_pretrained(frz_dir)
-
-            if args.save_n_steps >= 200:
-               if global_step < args.max_train_steps and global_step+1==i:
-                  ckpt_name = "_step_" + str(global_step+1)
-                  save_dir = Path(args.output_dir+ckpt_name)
-                  save_dir=str(save_dir)
-                  save_dir=save_dir.replace(" ", "_")
-                  if not os.path.exists(save_dir):
-                     os.mkdir(save_dir)
-                  inst=save_dir[16:]
-                  inst=inst.replace(" ", "_")
-                  print(" SAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt")
-                  # Create the pipeline using the trained modules and save it.
-                  if accelerator.is_main_process:
-                     pipeline = StableDiffusionPipeline.from_pretrained(
-                           args.pretrained_model_name_or_path,
-                           unet=accelerator.unwrap_model(unet),
-                           text_encoder=accelerator.unwrap_model(text_encoder),
-                     )
-                     pipeline.save_pretrained(save_dir)
-                     frz_dir=args.output_dir + "/text_encoder_frozen"
-                     if args.train_text_encoder and os.path.exists(frz_dir):
-                        subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True)
-                        subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True)
-                     chkpth=args.Session_dir+"/"+inst+".ckpt"
-                     subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True)
-                     subprocess.call('rm -r '+ save_dir, shell=True)
-                     i=i+args.save_n_steps
-
-        accelerator.wait_for_everyone()
-
-    # Create the pipeline using using the trained modules and save it.
-    if accelerator.is_main_process:
-      if args.dump_only_text_encoder:
-         txt_dir=args.output_dir + "/text_encoder_trained"
-         if not os.path.exists(txt_dir):
-           os.mkdir(txt_dir)
-         pipeline = StableDiffusionPipeline.from_pretrained(
-             args.pretrained_model_name_or_path,
-             unet=accelerator.unwrap_model(unet),
-             text_encoder=accelerator.unwrap_model(text_encoder),
-         )
-         pipeline.text_encoder.save_pretrained(txt_dir)
-
-      elif args.train_only_unet:
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=accelerator.unwrap_model(unet),
-            text_encoder=accelerator.unwrap_model(text_encoder),
-        )
-        pipeline.save_pretrained(args.output_dir)
-        txt_dir=args.output_dir + "/text_encoder_trained"
-        subprocess.call('rm -r '+txt_dir, shell=True)
-
-      else:
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=accelerator.unwrap_model(unet),
-            text_encoder=accelerator.unwrap_model(text_encoder),
-        )
-        frz_dir=args.output_dir + "/text_encoder_frozen"
-        pipeline.save_pretrained(args.output_dir)
-        if args.train_text_encoder and os.path.exists(frz_dir):
-           subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True)
-           subprocess.call('rm -r '+ frz_dir, shell=True)
-
-        if args.push_to_hub:
-            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
-
-    accelerator.end_training()
-    del pipeline
-    torch.cuda.empty_cache()
-    gc.collect()
-if __name__ == "__main__":
-    pass
-    #main()
-
diff --git a/spaces/Xenova/whisper-web/assets/index-47e1f68a.js b/spaces/Xenova/whisper-web/assets/index-47e1f68a.js
deleted file mode 100644
index 49ca20251b713fa1c6d2977c369ef61ae4709f2d..0000000000000000000000000000000000000000
--- a/spaces/Xenova/whisper-web/assets/index-47e1f68a.js
+++ /dev/null
@@ -1,47 +0,0 @@
-var ip=Object.defineProperty;var lp=(e,t,n)=>t in e?ip(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var qn=(e,t,n)=>(lp(e,typeof t!="symbol"?t+"":t,n),n);function up(e,t){for(var n=0;n<t.length;n++){const r=t[n];if(typeof r!="string"&&!Array.isArray(r)){for(const o in r)if(o!=="default"&&!(o in e)){const i=Object.getOwnPropertyDescriptor(r,o);i&&Object.defineProperty(e,o,i.get?i:{enumerable:!0,get:()=>r[o]})}}}return Object.freeze(Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}))}(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const o of document.querySelectorAll('link[rel="modulepreload"]'))r(o);new MutationObserver(o=>{for(const i of o)if(i.type==="childList")for(const l of i.addedNodes)l.tagName==="LINK"&&l.rel==="modulepreload"&&r(l)}).observe(document,{childList:!0,subtree:!0});function n(o){const i={};return o.integrity&&(i.integrity=o.integrity),o.referrerPolicy&&(i.referrerPolicy=o.referrerPolicy),o.crossOrigin==="use-credentials"?i.credentials="include":o.crossOrigin==="anonymous"?i.credentials="omit":i.credentials="same-origin",i}function r(o){if(o.ep)return;o.ep=!0;const i=n(o);fetch(o.href,i)}})();function sp(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var lc={exports:{}},ui={},uc={exports:{}},D={};/**
- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var Ir=Symbol.for("react.element"),ap=Symbol.for("react.portal"),cp=Symbol.for("react.fragment"),dp=Symbol.for("react.strict_mode"),fp=Symbol.for("react.profiler"),pp=Symbol.for("react.provider"),mp=Symbol.for("react.context"),hp=Symbol.for("react.forward_ref"),yp=Symbol.for("react.suspense"),gp=Symbol.for("react.memo"),vp=Symbol.for("react.lazy"),Os=Symbol.iterator;function wp(e){return e===null||typeof e!="object"?null:(e=Os&&e[Os]||e["@@iterator"],typeof e=="function"?e:null)}var sc={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},ac=Object.assign,cc={};function Hn(e,t,n){this.props=e,this.context=t,this.refs=cc,this.updater=n||sc}Hn.prototype.isReactComponent={};Hn.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};Hn.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function dc(){}dc.prototype=Hn.prototype;function Tu(e,t,n){this.props=e,this.context=t,this.refs=cc,this.updater=n||sc}var Nu=Tu.prototype=new dc;Nu.constructor=Tu;ac(Nu,Hn.prototype);Nu.isPureReactComponent=!0;var Fs=Array.isArray,fc=Object.prototype.hasOwnProperty,Pu={current:null},pc={key:!0,ref:!0,__self:!0,__source:!0};function mc(e,t,n){var r,o={},i=null,l=null;if(t!=null)for(r in t.ref!==void 0&&(l=t.ref),t.key!==void 0&&(i=""+t.key),t)fc.call(t,r)&&!pc.hasOwnProperty(r)&&(o[r]=t[r]);var u=arguments.length-2;if(u===1)o.children=n;else if(1<u){for(var s=Array(u),a=0;a<u;a++)s[a]=arguments[a+2];o.children=s}if(e&&e.defaultProps)for(r in u=e.defaultProps,u)o[r]===void 0&&(o[r]=u[r]);return{$$typeof:Ir,type:e,key:i,ref:l,props:o,_owner:Pu.current}}function Sp(e,t){return{$$typeof:Ir,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function Ru(e){return typeof e=="object"&&e!==null&&e.$$typeof===Ir}function kp(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var Ds=/\/+/g;function Di(e,t){return typeof e=="object"&&e!==null&&e.key!=null?kp(""+e.key):t.toString(36)}function ho(e,t,n,r,o){var i=typeof e;(i==="undefined"||i==="boolean")&&(e=null);var l=!1;if(e===null)l=!0;else switch(i){case"string":case"number":l=!0;break;case"object":switch(e.$$typeof){case Ir:case ap:l=!0}}if(l)return l=e,o=o(l),e=r===""?"."+Di(l,0):r,Fs(o)?(n="",e!=null&&(n=e.replace(Ds,"$&/")+"/"),ho(o,t,n,"",function(a){return a})):o!=null&&(Ru(o)&&(o=Sp(o,n+(!o.key||l&&l.key===o.key?"":(""+o.key).replace(Ds,"$&/")+"/")+e)),t.push(o)),1;if(l=0,r=r===""?".":r+":",Fs(e))for(var u=0;u<e.length;u++){i=e[u];var s=r+Di(i,u);l+=ho(i,t,n,s,o)}else if(s=wp(e),typeof s=="function")for(e=s.call(e),u=0;!(i=e.next()).done;)i=i.value,s=r+Di(i,u++),l+=ho(i,t,n,s,o);else if(i==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). If you meant to render a collection of children, use an array instead.");return l}function Xr(e,t,n){if(e==null)return e;var r=[],o=0;return ho(e,r,"","",function(i){return t.call(n,i,o++)}),r}function Ep(e){if(e._status===-1){var t=e._result;t=t(),t.then(function(n){(e._status===0||e._status===-1)&&(e._status=1,e._result=n)},function(n){(e._status===0||e._status===-1)&&(e._status=2,e._result=n)}),e._status===-1&&(e._status=0,e._result=t)}if(e._status===1)return e._result.default;throw e._result}var we={current:null},yo={transition:null},Cp={ReactCurrentDispatcher:we,ReactCurrentBatchConfig:yo,ReactCurrentOwner:Pu};D.Children={map:Xr,forEach:function(e,t,n){Xr(e,function(){t.apply(this,arguments)},n)},count:function(e){var t=0;return Xr(e,function(){t++}),t},toArray:function(e){return Xr(e,function(t){return t})||[]},only:function(e){if(!Ru(e))throw Error("React.Children.only expected to receive a single React element child.");return e}};D.Component=Hn;D.Fragment=cp;D.Profiler=fp;D.PureComponent=Tu;D.StrictMode=dp;D.Suspense=yp;D.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=Cp;D.cloneElement=function(e,t,n){if(e==null)throw Error("React.cloneElement(...): The argument must be a React element, but you passed "+e+".");var r=ac({},e.props),o=e.key,i=e.ref,l=e._owner;if(t!=null){if(t.ref!==void 0&&(i=t.ref,l=Pu.current),t.key!==void 0&&(o=""+t.key),e.type&&e.type.defaultProps)var u=e.type.defaultProps;for(s in t)fc.call(t,s)&&!pc.hasOwnProperty(s)&&(r[s]=t[s]===void 0&&u!==void 0?u[s]:t[s])}var s=arguments.length-2;if(s===1)r.children=n;else if(1<s){u=Array(s);for(var a=0;a<s;a++)u[a]=arguments[a+2];r.children=u}return{$$typeof:Ir,type:e.type,key:o,ref:i,props:r,_owner:l}};D.createContext=function(e){return e={$$typeof:mp,_currentValue:e,_currentValue2:e,_threadCount:0,Provider:null,Consumer:null,_defaultValue:null,_globalName:null},e.Provider={$$typeof:pp,_context:e},e.Consumer=e};D.createElement=mc;D.createFactory=function(e){var t=mc.bind(null,e);return t.type=e,t};D.createRef=function(){return{current:null}};D.forwardRef=function(e){return{$$typeof:hp,render:e}};D.isValidElement=Ru;D.lazy=function(e){return{$$typeof:vp,_payload:{_status:-1,_result:e},_init:Ep}};D.memo=function(e,t){return{$$typeof:gp,type:e,compare:t===void 0?null:t}};D.startTransition=function(e){var t=yo.transition;yo.transition={};try{e()}finally{yo.transition=t}};D.unstable_act=function(){throw Error("act(...) is not supported in production builds of React.")};D.useCallback=function(e,t){return we.current.useCallback(e,t)};D.useContext=function(e){return we.current.useContext(e)};D.useDebugValue=function(){};D.useDeferredValue=function(e){return we.current.useDeferredValue(e)};D.useEffect=function(e,t){return we.current.useEffect(e,t)};D.useId=function(){return we.current.useId()};D.useImperativeHandle=function(e,t,n){return we.current.useImperativeHandle(e,t,n)};D.useInsertionEffect=function(e,t){return we.current.useInsertionEffect(e,t)};D.useLayoutEffect=function(e,t){return we.current.useLayoutEffect(e,t)};D.useMemo=function(e,t){return we.current.useMemo(e,t)};D.useReducer=function(e,t,n){return we.current.useReducer(e,t,n)};D.useRef=function(e){return we.current.useRef(e)};D.useState=function(e){return we.current.useState(e)};D.useSyncExternalStore=function(e,t,n){return we.current.useSyncExternalStore(e,t,n)};D.useTransition=function(){return we.current.useTransition()};D.version="18.2.0";uc.exports=D;var w=uc.exports;const M=sp(w),Sl=up({__proto__:null,default:M},[w]);/**
- * @license React
- * react-jsx-runtime.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var xp=w,Tp=Symbol.for("react.element"),Np=Symbol.for("react.fragment"),Pp=Object.prototype.hasOwnProperty,Rp=xp.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner,Lp={key:!0,ref:!0,__self:!0,__source:!0};function hc(e,t,n){var r,o={},i=null,l=null;n!==void 0&&(i=""+n),t.key!==void 0&&(i=""+t.key),t.ref!==void 0&&(l=t.ref);for(r in t)Pp.call(t,r)&&!Lp.hasOwnProperty(r)&&(o[r]=t[r]);if(e&&e.defaultProps)for(r in t=e.defaultProps,t)o[r]===void 0&&(o[r]=t[r]);return{$$typeof:Tp,type:e,key:i,ref:l,props:o,_owner:Rp.current}}ui.Fragment=Np;ui.jsx=hc;ui.jsxs=hc;lc.exports=ui;var Lu=lc.exports;const nt=Lu.Fragment,C=Lu.jsx,z=Lu.jsxs;var kl={},yc={exports:{}},Ue={},gc={exports:{}},vc={};/**
- * @license React
- * scheduler.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */(function(e){function t(R,O){var F=R.length;R.push(O);e:for(;0<F;){var B=F-1>>>1,ee=R[B];if(0<o(ee,O))R[B]=O,R[F]=ee,F=B;else break e}}function n(R){return R.length===0?null:R[0]}function r(R){if(R.length===0)return null;var O=R[0],F=R.pop();if(F!==O){R[0]=F;e:for(var B=0,ee=R.length,Gt=ee>>>1;B<Gt;){var at=2*(B+1)-1,Q=R[at],te=at+1,G=R[te];if(0>o(Q,F))te<ee&&0>o(G,Q)?(R[B]=G,R[te]=F,B=te):(R[B]=Q,R[at]=F,B=at);else if(te<ee&&0>o(G,F))R[B]=G,R[te]=F,B=te;else break e}}return O}function o(R,O){var F=R.sortIndex-O.sortIndex;return F!==0?F:R.id-O.id}if(typeof performance=="object"&&typeof performance.now=="function"){var i=performance;e.unstable_now=function(){return i.now()}}else{var l=Date,u=l.now();e.unstable_now=function(){return l.now()-u}}var s=[],a=[],d=1,p=null,m=3,v=!1,h=!1,g=!1,T=typeof setTimeout=="function"?setTimeout:null,f=typeof clearTimeout=="function"?clearTimeout:null,c=typeof setImmediate<"u"?setImmediate:null;typeof navigator<"u"&&navigator.scheduling!==void 0&&navigator.scheduling.isInputPending!==void 0&&navigator.scheduling.isInputPending.bind(navigator.scheduling);function y(R){for(var O=n(a);O!==null;){if(O.callback===null)r(a);else if(O.startTime<=R)r(a),O.sortIndex=O.expirationTime,t(s,O);else break;O=n(a)}}function k(R){if(g=!1,y(R),!h)if(n(s)!==null)h=!0,st(N);else{var O=n(a);O!==null&&Kt(k,O.startTime-R)}}function N(R,O){h=!1,g&&(g=!1,f(L),L=-1),v=!0;var F=m;try{for(y(O),p=n(s);p!==null&&(!(p.expirationTime>O)||R&&!j());){var B=p.callback;if(typeof B=="function"){p.callback=null,m=p.priorityLevel;var ee=B(p.expirationTime<=O);O=e.unstable_now(),typeof ee=="function"?p.callback=ee:p===n(s)&&r(s),y(O)}else r(s);p=n(s)}if(p!==null)var Gt=!0;else{var at=n(a);at!==null&&Kt(k,at.startTime-O),Gt=!1}return Gt}finally{p=null,m=F,v=!1}}var P=!1,x=null,L=-1,A=5,U=-1;function j(){return!(e.unstable_now()-U<A)}function He(){if(x!==null){var R=e.unstable_now();U=R;var O=!0;try{O=x(!0,R)}finally{O?Ve():(P=!1,x=null)}}else P=!1}var Ve;if(typeof c=="function")Ve=function(){c(He)};else if(typeof MessageChannel<"u"){var mn=new MessageChannel,ut=mn.port2;mn.port1.onmessage=He,Ve=function(){ut.postMessage(null)}}else Ve=function(){T(He,0)};function st(R){x=R,P||(P=!0,Ve())}function Kt(R,O){L=T(function(){R(e.unstable_now())},O)}e.unstable_IdlePriority=5,e.unstable_ImmediatePriority=1,e.unstable_LowPriority=4,e.unstable_NormalPriority=3,e.unstable_Profiling=null,e.unstable_UserBlockingPriority=2,e.unstable_cancelCallback=function(R){R.callback=null},e.unstable_continueExecution=function(){h||v||(h=!0,st(N))},e.unstable_forceFrameRate=function(R){0>R||125<R?console.error("forceFrameRate takes a positive int between 0 and 125, forcing frame rates higher than 125 fps is not supported"):A=0<R?Math.floor(1e3/R):5},e.unstable_getCurrentPriorityLevel=function(){return m},e.unstable_getFirstCallbackNode=function(){return n(s)},e.unstable_next=function(R){switch(m){case 1:case 2:case 3:var O=3;break;default:O=m}var F=m;m=O;try{return R()}finally{m=F}},e.unstable_pauseExecution=function(){},e.unstable_requestPaint=function(){},e.unstable_runWithPriority=function(R,O){switch(R){case 1:case 2:case 3:case 4:case 5:break;default:R=3}var F=m;m=R;try{return O()}finally{m=F}},e.unstable_scheduleCallback=function(R,O,F){var B=e.unstable_now();switch(typeof F=="object"&&F!==null?(F=F.delay,F=typeof F=="number"&&0<F?B+F:B):F=B,R){case 1:var ee=-1;break;case 2:ee=250;break;case 5:ee=1073741823;break;case 4:ee=1e4;break;default:ee=5e3}return ee=F+ee,R={id:d++,callback:O,priorityLevel:R,startTime:F,expirationTime:ee,sortIndex:-1},F>B?(R.sortIndex=F,t(a,R),n(s)===null&&R===n(a)&&(g?(f(L),L=-1):g=!0,Kt(k,F-B))):(R.sortIndex=ee,t(s,R),h||v||(h=!0,st(N))),R},e.unstable_shouldYield=j,e.unstable_wrapCallback=function(R){var O=m;return function(){var F=m;m=O;try{return R.apply(this,arguments)}finally{m=F}}}})(vc);gc.exports=vc;var _p=gc.exports;/**
- * @license React
- * react-dom.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var wc=w,_e=_p;function E(e){for(var t="https://reactjs.org/docs/error-decoder.html?invariant="+e,n=1;n<arguments.length;n++)t+="&args[]="+encodeURIComponent(arguments[n]);return"Minified React error #"+e+"; visit "+t+" for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}var Sc=new Set,kr={};function fn(e,t){Dn(e,t),Dn(e+"Capture",t)}function Dn(e,t){for(kr[e]=t,e=0;e<t.length;e++)Sc.add(t[e])}var St=!(typeof window>"u"||typeof window.document>"u"||typeof window.document.createElement>"u"),El=Object.prototype.hasOwnProperty,Up=/^[:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD][:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD\-.0-9\u00B7\u0300-\u036F\u203F-\u2040]*$/,As={},Ms={};function Op(e){return El.call(Ms,e)?!0:El.call(As,e)?!1:Up.test(e)?Ms[e]=!0:(As[e]=!0,!1)}function Fp(e,t,n,r){if(n!==null&&n.type===0)return!1;switch(typeof t){case"function":case"symbol":return!0;case"boolean":return r?!1:n!==null?!n.acceptsBooleans:(e=e.toLowerCase().slice(0,5),e!=="data-"&&e!=="aria-");default:return!1}}function Dp(e,t,n,r){if(t===null||typeof t>"u"||Fp(e,t,n,r))return!0;if(r)return!1;if(n!==null)switch(n.type){case 3:return!t;case 4:return t===!1;case 5:return isNaN(t);case 6:return isNaN(t)||1>t}return!1}function Se(e,t,n,r,o,i,l){this.acceptsBooleans=t===2||t===3||t===4,this.attributeName=r,this.attributeNamespace=o,this.mustUseProperty=n,this.propertyName=e,this.type=t,this.sanitizeURL=i,this.removeEmptyString=l}var de={};"children dangerouslySetInnerHTML defaultValue defaultChecked innerHTML suppressContentEditableWarning suppressHydrationWarning style".split(" ").forEach(function(e){de[e]=new Se(e,0,!1,e,null,!1,!1)});[["acceptCharset","accept-charset"],["className","class"],["htmlFor","for"],["httpEquiv","http-equiv"]].forEach(function(e){var t=e[0];de[t]=new Se(t,1,!1,e[1],null,!1,!1)});["contentEditable","draggable","spellCheck","value"].forEach(function(e){de[e]=new Se(e,2,!1,e.toLowerCase(),null,!1,!1)});["autoReverse","externalResourcesRequired","focusable","preserveAlpha"].forEach(function(e){de[e]=new Se(e,2,!1,e,null,!1,!1)});"allowFullScreen async autoFocus autoPlay controls default defer disabled disablePictureInPicture disableRemotePlayback formNoValidate hidden loop noModule noValidate open playsInline readOnly required reversed scoped seamless itemScope".split(" ").forEach(function(e){de[e]=new Se(e,3,!1,e.toLowerCase(),null,!1,!1)});["checked","multiple","muted","selected"].forEach(function(e){de[e]=new Se(e,3,!0,e,null,!1,!1)});["capture","download"].forEach(function(e){de[e]=new Se(e,4,!1,e,null,!1,!1)});["cols","rows","size","span"].forEach(function(e){de[e]=new Se(e,6,!1,e,null,!1,!1)});["rowSpan","start"].forEach(function(e){de[e]=new Se(e,5,!1,e.toLowerCase(),null,!1,!1)});var _u=/[\-:]([a-z])/g;function Uu(e){return e[1].toUpperCase()}"accent-height alignment-baseline arabic-form baseline-shift cap-height clip-path clip-rule color-interpolation color-interpolation-filters color-profile color-rendering dominant-baseline enable-background fill-opacity fill-rule flood-color flood-opacity font-family font-size font-size-adjust font-stretch font-style font-variant font-weight glyph-name glyph-orientation-horizontal glyph-orientation-vertical horiz-adv-x horiz-origin-x image-rendering letter-spacing lighting-color marker-end marker-mid marker-start overline-position overline-thickness paint-order panose-1 pointer-events rendering-intent shape-rendering stop-color stop-opacity strikethrough-position strikethrough-thickness stroke-dasharray stroke-dashoffset stroke-linecap stroke-linejoin stroke-miterlimit stroke-opacity stroke-width text-anchor text-decoration text-rendering underline-position underline-thickness unicode-bidi unicode-range units-per-em v-alphabetic v-hanging v-ideographic v-mathematical vector-effect vert-adv-y vert-origin-x vert-origin-y word-spacing writing-mode xmlns:xlink x-height".split(" ").forEach(function(e){var t=e.replace(_u,Uu);de[t]=new Se(t,1,!1,e,null,!1,!1)});"xlink:actuate xlink:arcrole xlink:role xlink:show xlink:title xlink:type".split(" ").forEach(function(e){var t=e.replace(_u,Uu);de[t]=new Se(t,1,!1,e,"http://www.w3.org/1999/xlink",!1,!1)});["xml:base","xml:lang","xml:space"].forEach(function(e){var t=e.replace(_u,Uu);de[t]=new Se(t,1,!1,e,"http://www.w3.org/XML/1998/namespace",!1,!1)});["tabIndex","crossOrigin"].forEach(function(e){de[e]=new Se(e,1,!1,e.toLowerCase(),null,!1,!1)});de.xlinkHref=new Se("xlinkHref",1,!1,"xlink:href","http://www.w3.org/1999/xlink",!0,!1);["src","href","action","formAction"].forEach(function(e){de[e]=new Se(e,1,!1,e.toLowerCase(),null,!0,!0)});function Ou(e,t,n,r){var o=de.hasOwnProperty(t)?de[t]:null;(o!==null?o.type!==0:r||!(2<t.length)||t[0]!=="o"&&t[0]!=="O"||t[1]!=="n"&&t[1]!=="N")&&(Dp(t,n,o,r)&&(n=null),r||o===null?Op(t)&&(n===null?e.removeAttribute(t):e.setAttribute(t,""+n)):o.mustUseProperty?e[o.propertyName]=n===null?o.type===3?!1:"":n:(t=o.attributeName,r=o.attributeNamespace,n===null?e.removeAttribute(t):(o=o.type,n=o===3||o===4&&n===!0?"":""+n,r?e.setAttributeNS(r,t,n):e.setAttribute(t,n))))}var xt=wc.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED,Yr=Symbol.for("react.element"),yn=Symbol.for("react.portal"),gn=Symbol.for("react.fragment"),Fu=Symbol.for("react.strict_mode"),Cl=Symbol.for("react.profiler"),kc=Symbol.for("react.provider"),Ec=Symbol.for("react.context"),Du=Symbol.for("react.forward_ref"),xl=Symbol.for("react.suspense"),Tl=Symbol.for("react.suspense_list"),Au=Symbol.for("react.memo"),Pt=Symbol.for("react.lazy"),Cc=Symbol.for("react.offscreen"),$s=Symbol.iterator;function Xn(e){return e===null||typeof e!="object"?null:(e=$s&&e[$s]||e["@@iterator"],typeof e=="function"?e:null)}var Y=Object.assign,Ai;function ur(e){if(Ai===void 0)try{throw Error()}catch(n){var t=n.stack.trim().match(/\n( *(at )?)/);Ai=t&&t[1]||""}return`
-`+Ai+e}var Mi=!1;function $i(e,t){if(!e||Mi)return"";Mi=!0;var n=Error.prepareStackTrace;Error.prepareStackTrace=void 0;try{if(t)if(t=function(){throw Error()},Object.defineProperty(t.prototype,"props",{set:function(){throw Error()}}),typeof Reflect=="object"&&Reflect.construct){try{Reflect.construct(t,[])}catch(a){var r=a}Reflect.construct(e,[],t)}else{try{t.call()}catch(a){r=a}e.call(t.prototype)}else{try{throw Error()}catch(a){r=a}e()}}catch(a){if(a&&r&&typeof a.stack=="string"){for(var o=a.stack.split(`
-`),i=r.stack.split(`
-`),l=o.length-1,u=i.length-1;1<=l&&0<=u&&o[l]!==i[u];)u--;for(;1<=l&&0<=u;l--,u--)if(o[l]!==i[u]){if(l!==1||u!==1)do if(l--,u--,0>u||o[l]!==i[u]){var s=`
-`+o[l].replace(" at new "," at ");return e.displayName&&s.includes("<anonymous>")&&(s=s.replace("<anonymous>",e.displayName)),s}while(1<=l&&0<=u);break}}}finally{Mi=!1,Error.prepareStackTrace=n}return(e=e?e.displayName||e.name:"")?ur(e):""}function Ap(e){switch(e.tag){case 5:return ur(e.type);case 16:return ur("Lazy");case 13:return ur("Suspense");case 19:return ur("SuspenseList");case 0:case 2:case 15:return e=$i(e.type,!1),e;case 11:return e=$i(e.type.render,!1),e;case 1:return e=$i(e.type,!0),e;default:return""}}function Nl(e){if(e==null)return null;if(typeof e=="function")return e.displayName||e.name||null;if(typeof e=="string")return e;switch(e){case gn:return"Fragment";case yn:return"Portal";case Cl:return"Profiler";case Fu:return"StrictMode";case xl:return"Suspense";case Tl:return"SuspenseList"}if(typeof e=="object")switch(e.$$typeof){case Ec:return(e.displayName||"Context")+".Consumer";case kc:return(e._context.displayName||"Context")+".Provider";case Du:var t=e.render;return e=e.displayName,e||(e=t.displayName||t.name||"",e=e!==""?"ForwardRef("+e+")":"ForwardRef"),e;case Au:return t=e.displayName||null,t!==null?t:Nl(e.type)||"Memo";case Pt:t=e._payload,e=e._init;try{return Nl(e(t))}catch{}}return null}function Mp(e){var t=e.type;switch(e.tag){case 24:return"Cache";case 9:return(t.displayName||"Context")+".Consumer";case 10:return(t._context.displayName||"Context")+".Provider";case 18:return"DehydratedFragment";case 11:return e=t.render,e=e.displayName||e.name||"",t.displayName||(e!==""?"ForwardRef("+e+")":"ForwardRef");case 7:return"Fragment";case 5:return t;case 4:return"Portal";case 3:return"Root";case 6:return"Text";case 16:return Nl(t);case 8:return t===Fu?"StrictMode":"Mode";case 22:return"Offscreen";case 12:return"Profiler";case 21:return"Scope";case 13:return"Suspense";case 19:return"SuspenseList";case 25:return"TracingMarker";case 1:case 0:case 17:case 2:case 14:case 15:if(typeof t=="function")return t.displayName||t.name||null;if(typeof t=="string")return t}return null}function Ht(e){switch(typeof e){case"boolean":case"number":case"string":case"undefined":return e;case"object":return e;default:return""}}function xc(e){var t=e.type;return(e=e.nodeName)&&e.toLowerCase()==="input"&&(t==="checkbox"||t==="radio")}function $p(e){var t=xc(e)?"checked":"value",n=Object.getOwnPropertyDescriptor(e.constructor.prototype,t),r=""+e[t];if(!e.hasOwnProperty(t)&&typeof n<"u"&&typeof n.get=="function"&&typeof n.set=="function"){var o=n.get,i=n.set;return Object.defineProperty(e,t,{configurable:!0,get:function(){return o.call(this)},set:function(l){r=""+l,i.call(this,l)}}),Object.defineProperty(e,t,{enumerable:n.enumerable}),{getValue:function(){return r},setValue:function(l){r=""+l},stopTracking:function(){e._valueTracker=null,delete e[t]}}}}function Jr(e){e._valueTracker||(e._valueTracker=$p(e))}function Tc(e){if(!e)return!1;var t=e._valueTracker;if(!t)return!0;var n=t.getValue(),r="";return e&&(r=xc(e)?e.checked?"true":"false":e.value),e=r,e!==n?(t.setValue(e),!0):!1}function Oo(e){if(e=e||(typeof document<"u"?document:void 0),typeof e>"u")return null;try{return e.activeElement||e.body}catch{return e.body}}function Pl(e,t){var n=t.checked;return Y({},t,{defaultChecked:void 0,defaultValue:void 0,value:void 0,checked:n??e._wrapperState.initialChecked})}function zs(e,t){var n=t.defaultValue==null?"":t.defaultValue,r=t.checked!=null?t.checked:t.defaultChecked;n=Ht(t.value!=null?t.value:n),e._wrapperState={initialChecked:r,initialValue:n,controlled:t.type==="checkbox"||t.type==="radio"?t.checked!=null:t.value!=null}}function Nc(e,t){t=t.checked,t!=null&&Ou(e,"checked",t,!1)}function Rl(e,t){Nc(e,t);var n=Ht(t.value),r=t.type;if(n!=null)r==="number"?(n===0&&e.value===""||e.value!=n)&&(e.value=""+n):e.value!==""+n&&(e.value=""+n);else if(r==="submit"||r==="reset"){e.removeAttribute("value");return}t.hasOwnProperty("value")?Ll(e,t.type,n):t.hasOwnProperty("defaultValue")&&Ll(e,t.type,Ht(t.defaultValue)),t.checked==null&&t.defaultChecked!=null&&(e.defaultChecked=!!t.defaultChecked)}function Is(e,t,n){if(t.hasOwnProperty("value")||t.hasOwnProperty("defaultValue")){var r=t.type;if(!(r!=="submit"&&r!=="reset"||t.value!==void 0&&t.value!==null))return;t=""+e._wrapperState.initialValue,n||t===e.value||(e.value=t),e.defaultValue=t}n=e.name,n!==""&&(e.name=""),e.defaultChecked=!!e._wrapperState.initialChecked,n!==""&&(e.name=n)}function Ll(e,t,n){(t!=="number"||Oo(e.ownerDocument)!==e)&&(n==null?e.defaultValue=""+e._wrapperState.initialValue:e.defaultValue!==""+n&&(e.defaultValue=""+n))}var sr=Array.isArray;function Rn(e,t,n,r){if(e=e.options,t){t={};for(var o=0;o<n.length;o++)t["$"+n[o]]=!0;for(n=0;n<e.length;n++)o=t.hasOwnProperty("$"+e[n].value),e[n].selected!==o&&(e[n].selected=o),o&&r&&(e[n].defaultSelected=!0)}else{for(n=""+Ht(n),t=null,o=0;o<e.length;o++){if(e[o].value===n){e[o].selected=!0,r&&(e[o].defaultSelected=!0);return}t!==null||e[o].disabled||(t=e[o])}t!==null&&(t.selected=!0)}}function _l(e,t){if(t.dangerouslySetInnerHTML!=null)throw Error(E(91));return Y({},t,{value:void 0,defaultValue:void 0,children:""+e._wrapperState.initialValue})}function Bs(e,t){var n=t.value;if(n==null){if(n=t.children,t=t.defaultValue,n!=null){if(t!=null)throw Error(E(92));if(sr(n)){if(1<n.length)throw Error(E(93));n=n[0]}t=n}t==null&&(t=""),n=t}e._wrapperState={initialValue:Ht(n)}}function Pc(e,t){var n=Ht(t.value),r=Ht(t.defaultValue);n!=null&&(n=""+n,n!==e.value&&(e.value=n),t.defaultValue==null&&e.defaultValue!==n&&(e.defaultValue=n)),r!=null&&(e.defaultValue=""+r)}function js(e){var t=e.textContent;t===e._wrapperState.initialValue&&t!==""&&t!==null&&(e.value=t)}function Rc(e){switch(e){case"svg":return"http://www.w3.org/2000/svg";case"math":return"http://www.w3.org/1998/Math/MathML";default:return"http://www.w3.org/1999/xhtml"}}function Ul(e,t){return e==null||e==="http://www.w3.org/1999/xhtml"?Rc(t):e==="http://www.w3.org/2000/svg"&&t==="foreignObject"?"http://www.w3.org/1999/xhtml":e}var Zr,Lc=function(e){return typeof MSApp<"u"&&MSApp.execUnsafeLocalFunction?function(t,n,r,o){MSApp.execUnsafeLocalFunction(function(){return e(t,n,r,o)})}:e}(function(e,t){if(e.namespaceURI!=="http://www.w3.org/2000/svg"||"innerHTML"in e)e.innerHTML=t;else{for(Zr=Zr||document.createElement("div"),Zr.innerHTML="<svg>"+t.valueOf().toString()+"</svg>",t=Zr.firstChild;e.firstChild;)e.removeChild(e.firstChild);for(;t.firstChild;)e.appendChild(t.firstChild)}});function Er(e,t){if(t){var n=e.firstChild;if(n&&n===e.lastChild&&n.nodeType===3){n.nodeValue=t;return}}e.textContent=t}var fr={animationIterationCount:!0,aspectRatio:!0,borderImageOutset:!0,borderImageSlice:!0,borderImageWidth:!0,boxFlex:!0,boxFlexGroup:!0,boxOrdinalGroup:!0,columnCount:!0,columns:!0,flex:!0,flexGrow:!0,flexPositive:!0,flexShrink:!0,flexNegative:!0,flexOrder:!0,gridArea:!0,gridRow:!0,gridRowEnd:!0,gridRowSpan:!0,gridRowStart:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnSpan:!0,gridColumnStart:!0,fontWeight:!0,lineClamp:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,tabSize:!0,widows:!0,zIndex:!0,zoom:!0,fillOpacity:!0,floodOpacity:!0,stopOpacity:!0,strokeDasharray:!0,strokeDashoffset:!0,strokeMiterlimit:!0,strokeOpacity:!0,strokeWidth:!0},zp=["Webkit","ms","Moz","O"];Object.keys(fr).forEach(function(e){zp.forEach(function(t){t=t+e.charAt(0).toUpperCase()+e.substring(1),fr[t]=fr[e]})});function _c(e,t,n){return t==null||typeof t=="boolean"||t===""?"":n||typeof t!="number"||t===0||fr.hasOwnProperty(e)&&fr[e]?(""+t).trim():t+"px"}function Uc(e,t){e=e.style;for(var n in t)if(t.hasOwnProperty(n)){var r=n.indexOf("--")===0,o=_c(n,t[n],r);n==="float"&&(n="cssFloat"),r?e.setProperty(n,o):e[n]=o}}var Ip=Y({menuitem:!0},{area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0});function Ol(e,t){if(t){if(Ip[e]&&(t.children!=null||t.dangerouslySetInnerHTML!=null))throw Error(E(137,e));if(t.dangerouslySetInnerHTML!=null){if(t.children!=null)throw Error(E(60));if(typeof t.dangerouslySetInnerHTML!="object"||!("__html"in t.dangerouslySetInnerHTML))throw Error(E(61))}if(t.style!=null&&typeof t.style!="object")throw Error(E(62))}}function Fl(e,t){if(e.indexOf("-")===-1)return typeof t.is=="string";switch(e){case"annotation-xml":case"color-profile":case"font-face":case"font-face-src":case"font-face-uri":case"font-face-format":case"font-face-name":case"missing-glyph":return!1;default:return!0}}var Dl=null;function Mu(e){return e=e.target||e.srcElement||window,e.correspondingUseElement&&(e=e.correspondingUseElement),e.nodeType===3?e.parentNode:e}var Al=null,Ln=null,_n=null;function Hs(e){if(e=Hr(e)){if(typeof Al!="function")throw Error(E(280));var t=e.stateNode;t&&(t=fi(t),Al(e.stateNode,e.type,t))}}function Oc(e){Ln?_n?_n.push(e):_n=[e]:Ln=e}function Fc(){if(Ln){var e=Ln,t=_n;if(_n=Ln=null,Hs(e),t)for(e=0;e<t.length;e++)Hs(t[e])}}function Dc(e,t){return e(t)}function Ac(){}var zi=!1;function Mc(e,t,n){if(zi)return e(t,n);zi=!0;try{return Dc(e,t,n)}finally{zi=!1,(Ln!==null||_n!==null)&&(Ac(),Fc())}}function Cr(e,t){var n=e.stateNode;if(n===null)return null;var r=fi(n);if(r===null)return null;n=r[t];e:switch(t){case"onClick":case"onClickCapture":case"onDoubleClick":case"onDoubleClickCapture":case"onMouseDown":case"onMouseDownCapture":case"onMouseMove":case"onMouseMoveCapture":case"onMouseUp":case"onMouseUpCapture":case"onMouseEnter":(r=!r.disabled)||(e=e.type,r=!(e==="button"||e==="input"||e==="select"||e==="textarea")),e=!r;break e;default:e=!1}if(e)return null;if(n&&typeof n!="function")throw Error(E(231,t,typeof n));return n}var Ml=!1;if(St)try{var Yn={};Object.defineProperty(Yn,"passive",{get:function(){Ml=!0}}),window.addEventListener("test",Yn,Yn),window.removeEventListener("test",Yn,Yn)}catch{Ml=!1}function Bp(e,t,n,r,o,i,l,u,s){var a=Array.prototype.slice.call(arguments,3);try{t.apply(n,a)}catch(d){this.onError(d)}}var pr=!1,Fo=null,Do=!1,$l=null,jp={onError:function(e){pr=!0,Fo=e}};function Hp(e,t,n,r,o,i,l,u,s){pr=!1,Fo=null,Bp.apply(jp,arguments)}function Vp(e,t,n,r,o,i,l,u,s){if(Hp.apply(this,arguments),pr){if(pr){var a=Fo;pr=!1,Fo=null}else throw Error(E(198));Do||(Do=!0,$l=a)}}function pn(e){var t=e,n=e;if(e.alternate)for(;t.return;)t=t.return;else{e=t;do t=e,t.flags&4098&&(n=t.return),e=t.return;while(e)}return t.tag===3?n:null}function $c(e){if(e.tag===13){var t=e.memoizedState;if(t===null&&(e=e.alternate,e!==null&&(t=e.memoizedState)),t!==null)return t.dehydrated}return null}function Vs(e){if(pn(e)!==e)throw Error(E(188))}function bp(e){var t=e.alternate;if(!t){if(t=pn(e),t===null)throw Error(E(188));return t!==e?null:e}for(var n=e,r=t;;){var o=n.return;if(o===null)break;var i=o.alternate;if(i===null){if(r=o.return,r!==null){n=r;continue}break}if(o.child===i.child){for(i=o.child;i;){if(i===n)return Vs(o),e;if(i===r)return Vs(o),t;i=i.sibling}throw Error(E(188))}if(n.return!==r.return)n=o,r=i;else{for(var l=!1,u=o.child;u;){if(u===n){l=!0,n=o,r=i;break}if(u===r){l=!0,r=o,n=i;break}u=u.sibling}if(!l){for(u=i.child;u;){if(u===n){l=!0,n=i,r=o;break}if(u===r){l=!0,r=i,n=o;break}u=u.sibling}if(!l)throw Error(E(189))}}if(n.alternate!==r)throw Error(E(190))}if(n.tag!==3)throw Error(E(188));return n.stateNode.current===n?e:t}function zc(e){return e=bp(e),e!==null?Ic(e):null}function Ic(e){if(e.tag===5||e.tag===6)return e;for(e=e.child;e!==null;){var t=Ic(e);if(t!==null)return t;e=e.sibling}return null}var Bc=_e.unstable_scheduleCallback,bs=_e.unstable_cancelCallback,Wp=_e.unstable_shouldYield,Qp=_e.unstable_requestPaint,Z=_e.unstable_now,Kp=_e.unstable_getCurrentPriorityLevel,$u=_e.unstable_ImmediatePriority,jc=_e.unstable_UserBlockingPriority,Ao=_e.unstable_NormalPriority,Gp=_e.unstable_LowPriority,Hc=_e.unstable_IdlePriority,si=null,rt=null;function qp(e){if(rt&&typeof rt.onCommitFiberRoot=="function")try{rt.onCommitFiberRoot(si,e,void 0,(e.current.flags&128)===128)}catch{}}var Ge=Math.clz32?Math.clz32:Jp,Xp=Math.log,Yp=Math.LN2;function Jp(e){return e>>>=0,e===0?32:31-(Xp(e)/Yp|0)|0}var eo=64,to=4194304;function ar(e){switch(e&-e){case 1:return 1;case 2:return 2;case 4:return 4;case 8:return 8;case 16:return 16;case 32:return 32;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:return e&4194240;case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:return e&130023424;case 134217728:return 134217728;case 268435456:return 268435456;case 536870912:return 536870912;case 1073741824:return 1073741824;default:return e}}function Mo(e,t){var n=e.pendingLanes;if(n===0)return 0;var r=0,o=e.suspendedLanes,i=e.pingedLanes,l=n&268435455;if(l!==0){var u=l&~o;u!==0?r=ar(u):(i&=l,i!==0&&(r=ar(i)))}else l=n&~o,l!==0?r=ar(l):i!==0&&(r=ar(i));if(r===0)return 0;if(t!==0&&t!==r&&!(t&o)&&(o=r&-r,i=t&-t,o>=i||o===16&&(i&4194240)!==0))return t;if(r&4&&(r|=n&16),t=e.entangledLanes,t!==0)for(e=e.entanglements,t&=r;0<t;)n=31-Ge(t),o=1<<n,r|=e[n],t&=~o;return r}function Zp(e,t){switch(e){case 1:case 2:case 4:return t+250;case 8:case 16:case 32:case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:return t+5e3;case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:return-1;case 134217728:case 268435456:case 536870912:case 1073741824:return-1;default:return-1}}function em(e,t){for(var n=e.suspendedLanes,r=e.pingedLanes,o=e.expirationTimes,i=e.pendingLanes;0<i;){var l=31-Ge(i),u=1<<l,s=o[l];s===-1?(!(u&n)||u&r)&&(o[l]=Zp(u,t)):s<=t&&(e.expiredLanes|=u),i&=~u}}function zl(e){return e=e.pendingLanes&-1073741825,e!==0?e:e&1073741824?1073741824:0}function Vc(){var e=eo;return eo<<=1,!(eo&4194240)&&(eo=64),e}function Ii(e){for(var t=[],n=0;31>n;n++)t.push(e);return t}function Br(e,t,n){e.pendingLanes|=t,t!==536870912&&(e.suspendedLanes=0,e.pingedLanes=0),e=e.eventTimes,t=31-Ge(t),e[t]=n}function tm(e,t){var n=e.pendingLanes&~t;e.pendingLanes=t,e.suspendedLanes=0,e.pingedLanes=0,e.expiredLanes&=t,e.mutableReadLanes&=t,e.entangledLanes&=t,t=e.entanglements;var r=e.eventTimes;for(e=e.expirationTimes;0<n;){var o=31-Ge(n),i=1<<o;t[o]=0,r[o]=-1,e[o]=-1,n&=~i}}function zu(e,t){var n=e.entangledLanes|=t;for(e=e.entanglements;n;){var r=31-Ge(n),o=1<<r;o&t|e[r]&t&&(e[r]|=t),n&=~o}}var H=0;function bc(e){return e&=-e,1<e?4<e?e&268435455?16:536870912:4:1}var Wc,Iu,Qc,Kc,Gc,Il=!1,no=[],Dt=null,At=null,Mt=null,xr=new Map,Tr=new Map,Lt=[],nm="mousedown mouseup touchcancel touchend touchstart auxclick dblclick pointercancel pointerdown pointerup dragend dragstart drop compositionend compositionstart keydown keypress keyup input textInput copy cut paste click change contextmenu reset submit".split(" ");function Ws(e,t){switch(e){case"focusin":case"focusout":Dt=null;break;case"dragenter":case"dragleave":At=null;break;case"mouseover":case"mouseout":Mt=null;break;case"pointerover":case"pointerout":xr.delete(t.pointerId);break;case"gotpointercapture":case"lostpointercapture":Tr.delete(t.pointerId)}}function Jn(e,t,n,r,o,i){return e===null||e.nativeEvent!==i?(e={blockedOn:t,domEventName:n,eventSystemFlags:r,nativeEvent:i,targetContainers:[o]},t!==null&&(t=Hr(t),t!==null&&Iu(t)),e):(e.eventSystemFlags|=r,t=e.targetContainers,o!==null&&t.indexOf(o)===-1&&t.push(o),e)}function rm(e,t,n,r,o){switch(t){case"focusin":return Dt=Jn(Dt,e,t,n,r,o),!0;case"dragenter":return At=Jn(At,e,t,n,r,o),!0;case"mouseover":return Mt=Jn(Mt,e,t,n,r,o),!0;case"pointerover":var i=o.pointerId;return xr.set(i,Jn(xr.get(i)||null,e,t,n,r,o)),!0;case"gotpointercapture":return i=o.pointerId,Tr.set(i,Jn(Tr.get(i)||null,e,t,n,r,o)),!0}return!1}function qc(e){var t=Zt(e.target);if(t!==null){var n=pn(t);if(n!==null){if(t=n.tag,t===13){if(t=$c(n),t!==null){e.blockedOn=t,Gc(e.priority,function(){Qc(n)});return}}else if(t===3&&n.stateNode.current.memoizedState.isDehydrated){e.blockedOn=n.tag===3?n.stateNode.containerInfo:null;return}}}e.blockedOn=null}function go(e){if(e.blockedOn!==null)return!1;for(var t=e.targetContainers;0<t.length;){var n=Bl(e.domEventName,e.eventSystemFlags,t[0],e.nativeEvent);if(n===null){n=e.nativeEvent;var r=new n.constructor(n.type,n);Dl=r,n.target.dispatchEvent(r),Dl=null}else return t=Hr(n),t!==null&&Iu(t),e.blockedOn=n,!1;t.shift()}return!0}function Qs(e,t,n){go(e)&&n.delete(t)}function om(){Il=!1,Dt!==null&&go(Dt)&&(Dt=null),At!==null&&go(At)&&(At=null),Mt!==null&&go(Mt)&&(Mt=null),xr.forEach(Qs),Tr.forEach(Qs)}function Zn(e,t){e.blockedOn===t&&(e.blockedOn=null,Il||(Il=!0,_e.unstable_scheduleCallback(_e.unstable_NormalPriority,om)))}function Nr(e){function t(o){return Zn(o,e)}if(0<no.length){Zn(no[0],e);for(var n=1;n<no.length;n++){var r=no[n];r.blockedOn===e&&(r.blockedOn=null)}}for(Dt!==null&&Zn(Dt,e),At!==null&&Zn(At,e),Mt!==null&&Zn(Mt,e),xr.forEach(t),Tr.forEach(t),n=0;n<Lt.length;n++)r=Lt[n],r.blockedOn===e&&(r.blockedOn=null);for(;0<Lt.length&&(n=Lt[0],n.blockedOn===null);)qc(n),n.blockedOn===null&&Lt.shift()}var Un=xt.ReactCurrentBatchConfig,$o=!0;function im(e,t,n,r){var o=H,i=Un.transition;Un.transition=null;try{H=1,Bu(e,t,n,r)}finally{H=o,Un.transition=i}}function lm(e,t,n,r){var o=H,i=Un.transition;Un.transition=null;try{H=4,Bu(e,t,n,r)}finally{H=o,Un.transition=i}}function Bu(e,t,n,r){if($o){var o=Bl(e,t,n,r);if(o===null)qi(e,t,r,zo,n),Ws(e,r);else if(rm(o,e,t,n,r))r.stopPropagation();else if(Ws(e,r),t&4&&-1<nm.indexOf(e)){for(;o!==null;){var i=Hr(o);if(i!==null&&Wc(i),i=Bl(e,t,n,r),i===null&&qi(e,t,r,zo,n),i===o)break;o=i}o!==null&&r.stopPropagation()}else qi(e,t,r,null,n)}}var zo=null;function Bl(e,t,n,r){if(zo=null,e=Mu(r),e=Zt(e),e!==null)if(t=pn(e),t===null)e=null;else if(n=t.tag,n===13){if(e=$c(t),e!==null)return e;e=null}else if(n===3){if(t.stateNode.current.memoizedState.isDehydrated)return t.tag===3?t.stateNode.containerInfo:null;e=null}else t!==e&&(e=null);return zo=e,null}function Xc(e){switch(e){case"cancel":case"click":case"close":case"contextmenu":case"copy":case"cut":case"auxclick":case"dblclick":case"dragend":case"dragstart":case"drop":case"focusin":case"focusout":case"input":case"invalid":case"keydown":case"keypress":case"keyup":case"mousedown":case"mouseup":case"paste":case"pause":case"play":case"pointercancel":case"pointerdown":case"pointerup":case"ratechange":case"reset":case"resize":case"seeked":case"submit":case"touchcancel":case"touchend":case"touchstart":case"volumechange":case"change":case"selectionchange":case"textInput":case"compositionstart":case"compositionend":case"compositionupdate":case"beforeblur":case"afterblur":case"beforeinput":case"blur":case"fullscreenchange":case"focus":case"hashchange":case"popstate":case"select":case"selectstart":return 1;case"drag":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"mousemove":case"mouseout":case"mouseover":case"pointermove":case"pointerout":case"pointerover":case"scroll":case"toggle":case"touchmove":case"wheel":case"mouseenter":case"mouseleave":case"pointerenter":case"pointerleave":return 4;case"message":switch(Kp()){case $u:return 1;case jc:return 4;case Ao:case Gp:return 16;case Hc:return 536870912;default:return 16}default:return 16}}var Ot=null,ju=null,vo=null;function Yc(){if(vo)return vo;var e,t=ju,n=t.length,r,o="value"in Ot?Ot.value:Ot.textContent,i=o.length;for(e=0;e<n&&t[e]===o[e];e++);var l=n-e;for(r=1;r<=l&&t[n-r]===o[i-r];r++);return vo=o.slice(e,1<r?1-r:void 0)}function wo(e){var t=e.keyCode;return"charCode"in e?(e=e.charCode,e===0&&t===13&&(e=13)):e=t,e===10&&(e=13),32<=e||e===13?e:0}function ro(){return!0}function Ks(){return!1}function Oe(e){function t(n,r,o,i,l){this._reactName=n,this._targetInst=o,this.type=r,this.nativeEvent=i,this.target=l,this.currentTarget=null;for(var u in e)e.hasOwnProperty(u)&&(n=e[u],this[u]=n?n(i):i[u]);return this.isDefaultPrevented=(i.defaultPrevented!=null?i.defaultPrevented:i.returnValue===!1)?ro:Ks,this.isPropagationStopped=Ks,this}return Y(t.prototype,{preventDefault:function(){this.defaultPrevented=!0;var n=this.nativeEvent;n&&(n.preventDefault?n.preventDefault():typeof n.returnValue!="unknown"&&(n.returnValue=!1),this.isDefaultPrevented=ro)},stopPropagation:function(){var n=this.nativeEvent;n&&(n.stopPropagation?n.stopPropagation():typeof n.cancelBubble!="unknown"&&(n.cancelBubble=!0),this.isPropagationStopped=ro)},persist:function(){},isPersistent:ro}),t}var Vn={eventPhase:0,bubbles:0,cancelable:0,timeStamp:function(e){return e.timeStamp||Date.now()},defaultPrevented:0,isTrusted:0},Hu=Oe(Vn),jr=Y({},Vn,{view:0,detail:0}),um=Oe(jr),Bi,ji,er,ai=Y({},jr,{screenX:0,screenY:0,clientX:0,clientY:0,pageX:0,pageY:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,getModifierState:Vu,button:0,buttons:0,relatedTarget:function(e){return e.relatedTarget===void 0?e.fromElement===e.srcElement?e.toElement:e.fromElement:e.relatedTarget},movementX:function(e){return"movementX"in e?e.movementX:(e!==er&&(er&&e.type==="mousemove"?(Bi=e.screenX-er.screenX,ji=e.screenY-er.screenY):ji=Bi=0,er=e),Bi)},movementY:function(e){return"movementY"in e?e.movementY:ji}}),Gs=Oe(ai),sm=Y({},ai,{dataTransfer:0}),am=Oe(sm),cm=Y({},jr,{relatedTarget:0}),Hi=Oe(cm),dm=Y({},Vn,{animationName:0,elapsedTime:0,pseudoElement:0}),fm=Oe(dm),pm=Y({},Vn,{clipboardData:function(e){return"clipboardData"in e?e.clipboardData:window.clipboardData}}),mm=Oe(pm),hm=Y({},Vn,{data:0}),qs=Oe(hm),ym={Esc:"Escape",Spacebar:" ",Left:"ArrowLeft",Up:"ArrowUp",Right:"ArrowRight",Down:"ArrowDown",Del:"Delete",Win:"OS",Menu:"ContextMenu",Apps:"ContextMenu",Scroll:"ScrollLock",MozPrintableKey:"Unidentified"},gm={8:"Backspace",9:"Tab",12:"Clear",13:"Enter",16:"Shift",17:"Control",18:"Alt",19:"Pause",20:"CapsLock",27:"Escape",32:" ",33:"PageUp",34:"PageDown",35:"End",36:"Home",37:"ArrowLeft",38:"ArrowUp",39:"ArrowRight",40:"ArrowDown",45:"Insert",46:"Delete",112:"F1",113:"F2",114:"F3",115:"F4",116:"F5",117:"F6",118:"F7",119:"F8",120:"F9",121:"F10",122:"F11",123:"F12",144:"NumLock",145:"ScrollLock",224:"Meta"},vm={Alt:"altKey",Control:"ctrlKey",Meta:"metaKey",Shift:"shiftKey"};function wm(e){var t=this.nativeEvent;return t.getModifierState?t.getModifierState(e):(e=vm[e])?!!t[e]:!1}function Vu(){return wm}var Sm=Y({},jr,{key:function(e){if(e.key){var t=ym[e.key]||e.key;if(t!=="Unidentified")return t}return e.type==="keypress"?(e=wo(e),e===13?"Enter":String.fromCharCode(e)):e.type==="keydown"||e.type==="keyup"?gm[e.keyCode]||"Unidentified":""},code:0,location:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,repeat:0,locale:0,getModifierState:Vu,charCode:function(e){return e.type==="keypress"?wo(e):0},keyCode:function(e){return e.type==="keydown"||e.type==="keyup"?e.keyCode:0},which:function(e){return e.type==="keypress"?wo(e):e.type==="keydown"||e.type==="keyup"?e.keyCode:0}}),km=Oe(Sm),Em=Y({},ai,{pointerId:0,width:0,height:0,pressure:0,tangentialPressure:0,tiltX:0,tiltY:0,twist:0,pointerType:0,isPrimary:0}),Xs=Oe(Em),Cm=Y({},jr,{touches:0,targetTouches:0,changedTouches:0,altKey:0,metaKey:0,ctrlKey:0,shiftKey:0,getModifierState:Vu}),xm=Oe(Cm),Tm=Y({},Vn,{propertyName:0,elapsedTime:0,pseudoElement:0}),Nm=Oe(Tm),Pm=Y({},ai,{deltaX:function(e){return"deltaX"in e?e.deltaX:"wheelDeltaX"in e?-e.wheelDeltaX:0},deltaY:function(e){return"deltaY"in e?e.deltaY:"wheelDeltaY"in e?-e.wheelDeltaY:"wheelDelta"in e?-e.wheelDelta:0},deltaZ:0,deltaMode:0}),Rm=Oe(Pm),Lm=[9,13,27,32],bu=St&&"CompositionEvent"in window,mr=null;St&&"documentMode"in document&&(mr=document.documentMode);var _m=St&&"TextEvent"in window&&!mr,Jc=St&&(!bu||mr&&8<mr&&11>=mr),Ys=String.fromCharCode(32),Js=!1;function Zc(e,t){switch(e){case"keyup":return Lm.indexOf(t.keyCode)!==-1;case"keydown":return t.keyCode!==229;case"keypress":case"mousedown":case"focusout":return!0;default:return!1}}function ed(e){return e=e.detail,typeof e=="object"&&"data"in e?e.data:null}var vn=!1;function Um(e,t){switch(e){case"compositionend":return ed(t);case"keypress":return t.which!==32?null:(Js=!0,Ys);case"textInput":return e=t.data,e===Ys&&Js?null:e;default:return null}}function Om(e,t){if(vn)return e==="compositionend"||!bu&&Zc(e,t)?(e=Yc(),vo=ju=Ot=null,vn=!1,e):null;switch(e){case"paste":return null;case"keypress":if(!(t.ctrlKey||t.altKey||t.metaKey)||t.ctrlKey&&t.altKey){if(t.char&&1<t.char.length)return t.char;if(t.which)return String.fromCharCode(t.which)}return null;case"compositionend":return Jc&&t.locale!=="ko"?null:t.data;default:return null}}var Fm={color:!0,date:!0,datetime:!0,"datetime-local":!0,email:!0,month:!0,number:!0,password:!0,range:!0,search:!0,tel:!0,text:!0,time:!0,url:!0,week:!0};function Zs(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return t==="input"?!!Fm[e.type]:t==="textarea"}function td(e,t,n,r){Oc(r),t=Io(t,"onChange"),0<t.length&&(n=new Hu("onChange","change",null,n,r),e.push({event:n,listeners:t}))}var hr=null,Pr=null;function Dm(e){fd(e,0)}function ci(e){var t=kn(e);if(Tc(t))return e}function Am(e,t){if(e==="change")return t}var nd=!1;if(St){var Vi;if(St){var bi="oninput"in document;if(!bi){var ea=document.createElement("div");ea.setAttribute("oninput","return;"),bi=typeof ea.oninput=="function"}Vi=bi}else Vi=!1;nd=Vi&&(!document.documentMode||9<document.documentMode)}function ta(){hr&&(hr.detachEvent("onpropertychange",rd),Pr=hr=null)}function rd(e){if(e.propertyName==="value"&&ci(Pr)){var t=[];td(t,Pr,e,Mu(e)),Mc(Dm,t)}}function Mm(e,t,n){e==="focusin"?(ta(),hr=t,Pr=n,hr.attachEvent("onpropertychange",rd)):e==="focusout"&&ta()}function $m(e){if(e==="selectionchange"||e==="keyup"||e==="keydown")return ci(Pr)}function zm(e,t){if(e==="click")return ci(t)}function Im(e,t){if(e==="input"||e==="change")return ci(t)}function Bm(e,t){return e===t&&(e!==0||1/e===1/t)||e!==e&&t!==t}var Xe=typeof Object.is=="function"?Object.is:Bm;function Rr(e,t){if(Xe(e,t))return!0;if(typeof e!="object"||e===null||typeof t!="object"||t===null)return!1;var n=Object.keys(e),r=Object.keys(t);if(n.length!==r.length)return!1;for(r=0;r<n.length;r++){var o=n[r];if(!El.call(t,o)||!Xe(e[o],t[o]))return!1}return!0}function na(e){for(;e&&e.firstChild;)e=e.firstChild;return e}function ra(e,t){var n=na(e);e=0;for(var r;n;){if(n.nodeType===3){if(r=e+n.textContent.length,e<=t&&r>=t)return{node:n,offset:t-e};e=r}e:{for(;n;){if(n.nextSibling){n=n.nextSibling;break e}n=n.parentNode}n=void 0}n=na(n)}}function od(e,t){return e&&t?e===t?!0:e&&e.nodeType===3?!1:t&&t.nodeType===3?od(e,t.parentNode):"contains"in e?e.contains(t):e.compareDocumentPosition?!!(e.compareDocumentPosition(t)&16):!1:!1}function id(){for(var e=window,t=Oo();t instanceof e.HTMLIFrameElement;){try{var n=typeof t.contentWindow.location.href=="string"}catch{n=!1}if(n)e=t.contentWindow;else break;t=Oo(e.document)}return t}function Wu(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return t&&(t==="input"&&(e.type==="text"||e.type==="search"||e.type==="tel"||e.type==="url"||e.type==="password")||t==="textarea"||e.contentEditable==="true")}function jm(e){var t=id(),n=e.focusedElem,r=e.selectionRange;if(t!==n&&n&&n.ownerDocument&&od(n.ownerDocument.documentElement,n)){if(r!==null&&Wu(n)){if(t=r.start,e=r.end,e===void 0&&(e=t),"selectionStart"in n)n.selectionStart=t,n.selectionEnd=Math.min(e,n.value.length);else if(e=(t=n.ownerDocument||document)&&t.defaultView||window,e.getSelection){e=e.getSelection();var o=n.textContent.length,i=Math.min(r.start,o);r=r.end===void 0?i:Math.min(r.end,o),!e.extend&&i>r&&(o=r,r=i,i=o),o=ra(n,i);var l=ra(n,r);o&&l&&(e.rangeCount!==1||e.anchorNode!==o.node||e.anchorOffset!==o.offset||e.focusNode!==l.node||e.focusOffset!==l.offset)&&(t=t.createRange(),t.setStart(o.node,o.offset),e.removeAllRanges(),i>r?(e.addRange(t),e.extend(l.node,l.offset)):(t.setEnd(l.node,l.offset),e.addRange(t)))}}for(t=[],e=n;e=e.parentNode;)e.nodeType===1&&t.push({element:e,left:e.scrollLeft,top:e.scrollTop});for(typeof n.focus=="function"&&n.focus(),n=0;n<t.length;n++)e=t[n],e.element.scrollLeft=e.left,e.element.scrollTop=e.top}}var Hm=St&&"documentMode"in document&&11>=document.documentMode,wn=null,jl=null,yr=null,Hl=!1;function oa(e,t,n){var r=n.window===n?n.document:n.nodeType===9?n:n.ownerDocument;Hl||wn==null||wn!==Oo(r)||(r=wn,"selectionStart"in r&&Wu(r)?r={start:r.selectionStart,end:r.selectionEnd}:(r=(r.ownerDocument&&r.ownerDocument.defaultView||window).getSelection(),r={anchorNode:r.anchorNode,anchorOffset:r.anchorOffset,focusNode:r.focusNode,focusOffset:r.focusOffset}),yr&&Rr(yr,r)||(yr=r,r=Io(jl,"onSelect"),0<r.length&&(t=new Hu("onSelect","select",null,t,n),e.push({event:t,listeners:r}),t.target=wn)))}function oo(e,t){var n={};return n[e.toLowerCase()]=t.toLowerCase(),n["Webkit"+e]="webkit"+t,n["Moz"+e]="moz"+t,n}var Sn={animationend:oo("Animation","AnimationEnd"),animationiteration:oo("Animation","AnimationIteration"),animationstart:oo("Animation","AnimationStart"),transitionend:oo("Transition","TransitionEnd")},Wi={},ld={};St&&(ld=document.createElement("div").style,"AnimationEvent"in window||(delete Sn.animationend.animation,delete Sn.animationiteration.animation,delete Sn.animationstart.animation),"TransitionEvent"in window||delete Sn.transitionend.transition);function di(e){if(Wi[e])return Wi[e];if(!Sn[e])return e;var t=Sn[e],n;for(n in t)if(t.hasOwnProperty(n)&&n in ld)return Wi[e]=t[n];return e}var ud=di("animationend"),sd=di("animationiteration"),ad=di("animationstart"),cd=di("transitionend"),dd=new Map,ia="abort auxClick cancel canPlay canPlayThrough click close contextMenu copy cut drag dragEnd dragEnter dragExit dragLeave dragOver dragStart drop durationChange emptied encrypted ended error gotPointerCapture input invalid keyDown keyPress keyUp load loadedData loadedMetadata loadStart lostPointerCapture mouseDown mouseMove mouseOut mouseOver mouseUp paste pause play playing pointerCancel pointerDown pointerMove pointerOut pointerOver pointerUp progress rateChange reset resize seeked seeking stalled submit suspend timeUpdate touchCancel touchEnd touchStart volumeChange scroll toggle touchMove waiting wheel".split(" ");function bt(e,t){dd.set(e,t),fn(t,[e])}for(var Qi=0;Qi<ia.length;Qi++){var Ki=ia[Qi],Vm=Ki.toLowerCase(),bm=Ki[0].toUpperCase()+Ki.slice(1);bt(Vm,"on"+bm)}bt(ud,"onAnimationEnd");bt(sd,"onAnimationIteration");bt(ad,"onAnimationStart");bt("dblclick","onDoubleClick");bt("focusin","onFocus");bt("focusout","onBlur");bt(cd,"onTransitionEnd");Dn("onMouseEnter",["mouseout","mouseover"]);Dn("onMouseLeave",["mouseout","mouseover"]);Dn("onPointerEnter",["pointerout","pointerover"]);Dn("onPointerLeave",["pointerout","pointerover"]);fn("onChange","change click focusin focusout input keydown keyup selectionchange".split(" "));fn("onSelect","focusout contextmenu dragend focusin keydown keyup mousedown mouseup selectionchange".split(" "));fn("onBeforeInput",["compositionend","keypress","textInput","paste"]);fn("onCompositionEnd","compositionend focusout keydown keypress keyup mousedown".split(" "));fn("onCompositionStart","compositionstart focusout keydown keypress keyup mousedown".split(" "));fn("onCompositionUpdate","compositionupdate focusout keydown keypress keyup mousedown".split(" "));var cr="abort canplay canplaythrough durationchange emptied encrypted ended error loadeddata loadedmetadata loadstart pause play playing progress ratechange resize seeked seeking stalled suspend timeupdate volumechange waiting".split(" "),Wm=new Set("cancel close invalid load scroll toggle".split(" ").concat(cr));function la(e,t,n){var r=e.type||"unknown-event";e.currentTarget=n,Vp(r,t,void 0,e),e.currentTarget=null}function fd(e,t){t=(t&4)!==0;for(var n=0;n<e.length;n++){var r=e[n],o=r.event;r=r.listeners;e:{var i=void 0;if(t)for(var l=r.length-1;0<=l;l--){var u=r[l],s=u.instance,a=u.currentTarget;if(u=u.listener,s!==i&&o.isPropagationStopped())break e;la(o,u,a),i=s}else for(l=0;l<r.length;l++){if(u=r[l],s=u.instance,a=u.currentTarget,u=u.listener,s!==i&&o.isPropagationStopped())break e;la(o,u,a),i=s}}}if(Do)throw e=$l,Do=!1,$l=null,e}function b(e,t){var n=t[Kl];n===void 0&&(n=t[Kl]=new Set);var r=e+"__bubble";n.has(r)||(pd(t,e,2,!1),n.add(r))}function Gi(e,t,n){var r=0;t&&(r|=4),pd(n,e,r,t)}var io="_reactListening"+Math.random().toString(36).slice(2);function Lr(e){if(!e[io]){e[io]=!0,Sc.forEach(function(n){n!=="selectionchange"&&(Wm.has(n)||Gi(n,!1,e),Gi(n,!0,e))});var t=e.nodeType===9?e:e.ownerDocument;t===null||t[io]||(t[io]=!0,Gi("selectionchange",!1,t))}}function pd(e,t,n,r){switch(Xc(t)){case 1:var o=im;break;case 4:o=lm;break;default:o=Bu}n=o.bind(null,t,n,e),o=void 0,!Ml||t!=="touchstart"&&t!=="touchmove"&&t!=="wheel"||(o=!0),r?o!==void 0?e.addEventListener(t,n,{capture:!0,passive:o}):e.addEventListener(t,n,!0):o!==void 0?e.addEventListener(t,n,{passive:o}):e.addEventListener(t,n,!1)}function qi(e,t,n,r,o){var i=r;if(!(t&1)&&!(t&2)&&r!==null)e:for(;;){if(r===null)return;var l=r.tag;if(l===3||l===4){var u=r.stateNode.containerInfo;if(u===o||u.nodeType===8&&u.parentNode===o)break;if(l===4)for(l=r.return;l!==null;){var s=l.tag;if((s===3||s===4)&&(s=l.stateNode.containerInfo,s===o||s.nodeType===8&&s.parentNode===o))return;l=l.return}for(;u!==null;){if(l=Zt(u),l===null)return;if(s=l.tag,s===5||s===6){r=i=l;continue e}u=u.parentNode}}r=r.return}Mc(function(){var a=i,d=Mu(n),p=[];e:{var m=dd.get(e);if(m!==void 0){var v=Hu,h=e;switch(e){case"keypress":if(wo(n)===0)break e;case"keydown":case"keyup":v=km;break;case"focusin":h="focus",v=Hi;break;case"focusout":h="blur",v=Hi;break;case"beforeblur":case"afterblur":v=Hi;break;case"click":if(n.button===2)break e;case"auxclick":case"dblclick":case"mousedown":case"mousemove":case"mouseup":case"mouseout":case"mouseover":case"contextmenu":v=Gs;break;case"drag":case"dragend":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"dragstart":case"drop":v=am;break;case"touchcancel":case"touchend":case"touchmove":case"touchstart":v=xm;break;case ud:case sd:case ad:v=fm;break;case cd:v=Nm;break;case"scroll":v=um;break;case"wheel":v=Rm;break;case"copy":case"cut":case"paste":v=mm;break;case"gotpointercapture":case"lostpointercapture":case"pointercancel":case"pointerdown":case"pointermove":case"pointerout":case"pointerover":case"pointerup":v=Xs}var g=(t&4)!==0,T=!g&&e==="scroll",f=g?m!==null?m+"Capture":null:m;g=[];for(var c=a,y;c!==null;){y=c;var k=y.stateNode;if(y.tag===5&&k!==null&&(y=k,f!==null&&(k=Cr(c,f),k!=null&&g.push(_r(c,k,y)))),T)break;c=c.return}0<g.length&&(m=new v(m,h,null,n,d),p.push({event:m,listeners:g}))}}if(!(t&7)){e:{if(m=e==="mouseover"||e==="pointerover",v=e==="mouseout"||e==="pointerout",m&&n!==Dl&&(h=n.relatedTarget||n.fromElement)&&(Zt(h)||h[kt]))break e;if((v||m)&&(m=d.window===d?d:(m=d.ownerDocument)?m.defaultView||m.parentWindow:window,v?(h=n.relatedTarget||n.toElement,v=a,h=h?Zt(h):null,h!==null&&(T=pn(h),h!==T||h.tag!==5&&h.tag!==6)&&(h=null)):(v=null,h=a),v!==h)){if(g=Gs,k="onMouseLeave",f="onMouseEnter",c="mouse",(e==="pointerout"||e==="pointerover")&&(g=Xs,k="onPointerLeave",f="onPointerEnter",c="pointer"),T=v==null?m:kn(v),y=h==null?m:kn(h),m=new g(k,c+"leave",v,n,d),m.target=T,m.relatedTarget=y,k=null,Zt(d)===a&&(g=new g(f,c+"enter",h,n,d),g.target=y,g.relatedTarget=T,k=g),T=k,v&&h)t:{for(g=v,f=h,c=0,y=g;y;y=hn(y))c++;for(y=0,k=f;k;k=hn(k))y++;for(;0<c-y;)g=hn(g),c--;for(;0<y-c;)f=hn(f),y--;for(;c--;){if(g===f||f!==null&&g===f.alternate)break t;g=hn(g),f=hn(f)}g=null}else g=null;v!==null&&ua(p,m,v,g,!1),h!==null&&T!==null&&ua(p,T,h,g,!0)}}e:{if(m=a?kn(a):window,v=m.nodeName&&m.nodeName.toLowerCase(),v==="select"||v==="input"&&m.type==="file")var N=Am;else if(Zs(m))if(nd)N=Im;else{N=$m;var P=Mm}else(v=m.nodeName)&&v.toLowerCase()==="input"&&(m.type==="checkbox"||m.type==="radio")&&(N=zm);if(N&&(N=N(e,a))){td(p,N,n,d);break e}P&&P(e,m,a),e==="focusout"&&(P=m._wrapperState)&&P.controlled&&m.type==="number"&&Ll(m,"number",m.value)}switch(P=a?kn(a):window,e){case"focusin":(Zs(P)||P.contentEditable==="true")&&(wn=P,jl=a,yr=null);break;case"focusout":yr=jl=wn=null;break;case"mousedown":Hl=!0;break;case"contextmenu":case"mouseup":case"dragend":Hl=!1,oa(p,n,d);break;case"selectionchange":if(Hm)break;case"keydown":case"keyup":oa(p,n,d)}var x;if(bu)e:{switch(e){case"compositionstart":var L="onCompositionStart";break e;case"compositionend":L="onCompositionEnd";break e;case"compositionupdate":L="onCompositionUpdate";break e}L=void 0}else vn?Zc(e,n)&&(L="onCompositionEnd"):e==="keydown"&&n.keyCode===229&&(L="onCompositionStart");L&&(Jc&&n.locale!=="ko"&&(vn||L!=="onCompositionStart"?L==="onCompositionEnd"&&vn&&(x=Yc()):(Ot=d,ju="value"in Ot?Ot.value:Ot.textContent,vn=!0)),P=Io(a,L),0<P.length&&(L=new qs(L,e,null,n,d),p.push({event:L,listeners:P}),x?L.data=x:(x=ed(n),x!==null&&(L.data=x)))),(x=_m?Um(e,n):Om(e,n))&&(a=Io(a,"onBeforeInput"),0<a.length&&(d=new qs("onBeforeInput","beforeinput",null,n,d),p.push({event:d,listeners:a}),d.data=x))}fd(p,t)})}function _r(e,t,n){return{instance:e,listener:t,currentTarget:n}}function Io(e,t){for(var n=t+"Capture",r=[];e!==null;){var o=e,i=o.stateNode;o.tag===5&&i!==null&&(o=i,i=Cr(e,n),i!=null&&r.unshift(_r(e,i,o)),i=Cr(e,t),i!=null&&r.push(_r(e,i,o))),e=e.return}return r}function hn(e){if(e===null)return null;do e=e.return;while(e&&e.tag!==5);return e||null}function ua(e,t,n,r,o){for(var i=t._reactName,l=[];n!==null&&n!==r;){var u=n,s=u.alternate,a=u.stateNode;if(s!==null&&s===r)break;u.tag===5&&a!==null&&(u=a,o?(s=Cr(n,i),s!=null&&l.unshift(_r(n,s,u))):o||(s=Cr(n,i),s!=null&&l.push(_r(n,s,u)))),n=n.return}l.length!==0&&e.push({event:t,listeners:l})}var Qm=/\r\n?/g,Km=/\u0000|\uFFFD/g;function sa(e){return(typeof e=="string"?e:""+e).replace(Qm,`
-`).replace(Km,"")}function lo(e,t,n){if(t=sa(t),sa(e)!==t&&n)throw Error(E(425))}function Bo(){}var Vl=null,bl=null;function Wl(e,t){return e==="textarea"||e==="noscript"||typeof t.children=="string"||typeof t.children=="number"||typeof t.dangerouslySetInnerHTML=="object"&&t.dangerouslySetInnerHTML!==null&&t.dangerouslySetInnerHTML.__html!=null}var Ql=typeof setTimeout=="function"?setTimeout:void 0,Gm=typeof clearTimeout=="function"?clearTimeout:void 0,aa=typeof Promise=="function"?Promise:void 0,qm=typeof queueMicrotask=="function"?queueMicrotask:typeof aa<"u"?function(e){return aa.resolve(null).then(e).catch(Xm)}:Ql;function Xm(e){setTimeout(function(){throw e})}function Xi(e,t){var n=t,r=0;do{var o=n.nextSibling;if(e.removeChild(n),o&&o.nodeType===8)if(n=o.data,n==="/$"){if(r===0){e.removeChild(o),Nr(t);return}r--}else n!=="$"&&n!=="$?"&&n!=="$!"||r++;n=o}while(n);Nr(t)}function $t(e){for(;e!=null;e=e.nextSibling){var t=e.nodeType;if(t===1||t===3)break;if(t===8){if(t=e.data,t==="$"||t==="$!"||t==="$?")break;if(t==="/$")return null}}return e}function ca(e){e=e.previousSibling;for(var t=0;e;){if(e.nodeType===8){var n=e.data;if(n==="$"||n==="$!"||n==="$?"){if(t===0)return e;t--}else n==="/$"&&t++}e=e.previousSibling}return null}var bn=Math.random().toString(36).slice(2),et="__reactFiber$"+bn,Ur="__reactProps$"+bn,kt="__reactContainer$"+bn,Kl="__reactEvents$"+bn,Ym="__reactListeners$"+bn,Jm="__reactHandles$"+bn;function Zt(e){var t=e[et];if(t)return t;for(var n=e.parentNode;n;){if(t=n[kt]||n[et]){if(n=t.alternate,t.child!==null||n!==null&&n.child!==null)for(e=ca(e);e!==null;){if(n=e[et])return n;e=ca(e)}return t}e=n,n=e.parentNode}return null}function Hr(e){return e=e[et]||e[kt],!e||e.tag!==5&&e.tag!==6&&e.tag!==13&&e.tag!==3?null:e}function kn(e){if(e.tag===5||e.tag===6)return e.stateNode;throw Error(E(33))}function fi(e){return e[Ur]||null}var Gl=[],En=-1;function Wt(e){return{current:e}}function W(e){0>En||(e.current=Gl[En],Gl[En]=null,En--)}function V(e,t){En++,Gl[En]=e.current,e.current=t}var Vt={},ye=Wt(Vt),Ce=Wt(!1),un=Vt;function An(e,t){var n=e.type.contextTypes;if(!n)return Vt;var r=e.stateNode;if(r&&r.__reactInternalMemoizedUnmaskedChildContext===t)return r.__reactInternalMemoizedMaskedChildContext;var o={},i;for(i in n)o[i]=t[i];return r&&(e=e.stateNode,e.__reactInternalMemoizedUnmaskedChildContext=t,e.__reactInternalMemoizedMaskedChildContext=o),o}function xe(e){return e=e.childContextTypes,e!=null}function jo(){W(Ce),W(ye)}function da(e,t,n){if(ye.current!==Vt)throw Error(E(168));V(ye,t),V(Ce,n)}function md(e,t,n){var r=e.stateNode;if(t=t.childContextTypes,typeof r.getChildContext!="function")return n;r=r.getChildContext();for(var o in r)if(!(o in t))throw Error(E(108,Mp(e)||"Unknown",o));return Y({},n,r)}function Ho(e){return e=(e=e.stateNode)&&e.__reactInternalMemoizedMergedChildContext||Vt,un=ye.current,V(ye,e),V(Ce,Ce.current),!0}function fa(e,t,n){var r=e.stateNode;if(!r)throw Error(E(169));n?(e=md(e,t,un),r.__reactInternalMemoizedMergedChildContext=e,W(Ce),W(ye),V(ye,e)):W(Ce),V(Ce,n)}var dt=null,pi=!1,Yi=!1;function hd(e){dt===null?dt=[e]:dt.push(e)}function Zm(e){pi=!0,hd(e)}function Qt(){if(!Yi&&dt!==null){Yi=!0;var e=0,t=H;try{var n=dt;for(H=1;e<n.length;e++){var r=n[e];do r=r(!0);while(r!==null)}dt=null,pi=!1}catch(o){throw dt!==null&&(dt=dt.slice(e+1)),Bc($u,Qt),o}finally{H=t,Yi=!1}}return null}var Cn=[],xn=0,Vo=null,bo=0,De=[],Ae=0,sn=null,pt=1,mt="";function Xt(e,t){Cn[xn++]=bo,Cn[xn++]=Vo,Vo=e,bo=t}function yd(e,t,n){De[Ae++]=pt,De[Ae++]=mt,De[Ae++]=sn,sn=e;var r=pt;e=mt;var o=32-Ge(r)-1;r&=~(1<<o),n+=1;var i=32-Ge(t)+o;if(30<i){var l=o-o%5;i=(r&(1<<l)-1).toString(32),r>>=l,o-=l,pt=1<<32-Ge(t)+o|n<<o|r,mt=i+e}else pt=1<<i|n<<o|r,mt=e}function Qu(e){e.return!==null&&(Xt(e,1),yd(e,1,0))}function Ku(e){for(;e===Vo;)Vo=Cn[--xn],Cn[xn]=null,bo=Cn[--xn],Cn[xn]=null;for(;e===sn;)sn=De[--Ae],De[Ae]=null,mt=De[--Ae],De[Ae]=null,pt=De[--Ae],De[Ae]=null}var Le=null,Re=null,K=!1,Ke=null;function gd(e,t){var n=Me(5,null,null,0);n.elementType="DELETED",n.stateNode=t,n.return=e,t=e.deletions,t===null?(e.deletions=[n],e.flags|=16):t.push(n)}function pa(e,t){switch(e.tag){case 5:var n=e.type;return t=t.nodeType!==1||n.toLowerCase()!==t.nodeName.toLowerCase()?null:t,t!==null?(e.stateNode=t,Le=e,Re=$t(t.firstChild),!0):!1;case 6:return t=e.pendingProps===""||t.nodeType!==3?null:t,t!==null?(e.stateNode=t,Le=e,Re=null,!0):!1;case 13:return t=t.nodeType!==8?null:t,t!==null?(n=sn!==null?{id:pt,overflow:mt}:null,e.memoizedState={dehydrated:t,treeContext:n,retryLane:1073741824},n=Me(18,null,null,0),n.stateNode=t,n.return=e,e.child=n,Le=e,Re=null,!0):!1;default:return!1}}function ql(e){return(e.mode&1)!==0&&(e.flags&128)===0}function Xl(e){if(K){var t=Re;if(t){var n=t;if(!pa(e,t)){if(ql(e))throw Error(E(418));t=$t(n.nextSibling);var r=Le;t&&pa(e,t)?gd(r,n):(e.flags=e.flags&-4097|2,K=!1,Le=e)}}else{if(ql(e))throw Error(E(418));e.flags=e.flags&-4097|2,K=!1,Le=e}}}function ma(e){for(e=e.return;e!==null&&e.tag!==5&&e.tag!==3&&e.tag!==13;)e=e.return;Le=e}function uo(e){if(e!==Le)return!1;if(!K)return ma(e),K=!0,!1;var t;if((t=e.tag!==3)&&!(t=e.tag!==5)&&(t=e.type,t=t!=="head"&&t!=="body"&&!Wl(e.type,e.memoizedProps)),t&&(t=Re)){if(ql(e))throw vd(),Error(E(418));for(;t;)gd(e,t),t=$t(t.nextSibling)}if(ma(e),e.tag===13){if(e=e.memoizedState,e=e!==null?e.dehydrated:null,!e)throw Error(E(317));e:{for(e=e.nextSibling,t=0;e;){if(e.nodeType===8){var n=e.data;if(n==="/$"){if(t===0){Re=$t(e.nextSibling);break e}t--}else n!=="$"&&n!=="$!"&&n!=="$?"||t++}e=e.nextSibling}Re=null}}else Re=Le?$t(e.stateNode.nextSibling):null;return!0}function vd(){for(var e=Re;e;)e=$t(e.nextSibling)}function Mn(){Re=Le=null,K=!1}function Gu(e){Ke===null?Ke=[e]:Ke.push(e)}var eh=xt.ReactCurrentBatchConfig;function We(e,t){if(e&&e.defaultProps){t=Y({},t),e=e.defaultProps;for(var n in e)t[n]===void 0&&(t[n]=e[n]);return t}return t}var Wo=Wt(null),Qo=null,Tn=null,qu=null;function Xu(){qu=Tn=Qo=null}function Yu(e){var t=Wo.current;W(Wo),e._currentValue=t}function Yl(e,t,n){for(;e!==null;){var r=e.alternate;if((e.childLanes&t)!==t?(e.childLanes|=t,r!==null&&(r.childLanes|=t)):r!==null&&(r.childLanes&t)!==t&&(r.childLanes|=t),e===n)break;e=e.return}}function On(e,t){Qo=e,qu=Tn=null,e=e.dependencies,e!==null&&e.firstContext!==null&&(e.lanes&t&&(Ee=!0),e.firstContext=null)}function Ie(e){var t=e._currentValue;if(qu!==e)if(e={context:e,memoizedValue:t,next:null},Tn===null){if(Qo===null)throw Error(E(308));Tn=e,Qo.dependencies={lanes:0,firstContext:e}}else Tn=Tn.next=e;return t}var en=null;function Ju(e){en===null?en=[e]:en.push(e)}function wd(e,t,n,r){var o=t.interleaved;return o===null?(n.next=n,Ju(t)):(n.next=o.next,o.next=n),t.interleaved=n,Et(e,r)}function Et(e,t){e.lanes|=t;var n=e.alternate;for(n!==null&&(n.lanes|=t),n=e,e=e.return;e!==null;)e.childLanes|=t,n=e.alternate,n!==null&&(n.childLanes|=t),n=e,e=e.return;return n.tag===3?n.stateNode:null}var Rt=!1;function Zu(e){e.updateQueue={baseState:e.memoizedState,firstBaseUpdate:null,lastBaseUpdate:null,shared:{pending:null,interleaved:null,lanes:0},effects:null}}function Sd(e,t){e=e.updateQueue,t.updateQueue===e&&(t.updateQueue={baseState:e.baseState,firstBaseUpdate:e.firstBaseUpdate,lastBaseUpdate:e.lastBaseUpdate,shared:e.shared,effects:e.effects})}function yt(e,t){return{eventTime:e,lane:t,tag:0,payload:null,callback:null,next:null}}function zt(e,t,n){var r=e.updateQueue;if(r===null)return null;if(r=r.shared,I&2){var o=r.pending;return o===null?t.next=t:(t.next=o.next,o.next=t),r.pending=t,Et(e,n)}return o=r.interleaved,o===null?(t.next=t,Ju(r)):(t.next=o.next,o.next=t),r.interleaved=t,Et(e,n)}function So(e,t,n){if(t=t.updateQueue,t!==null&&(t=t.shared,(n&4194240)!==0)){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,zu(e,n)}}function ha(e,t){var n=e.updateQueue,r=e.alternate;if(r!==null&&(r=r.updateQueue,n===r)){var o=null,i=null;if(n=n.firstBaseUpdate,n!==null){do{var l={eventTime:n.eventTime,lane:n.lane,tag:n.tag,payload:n.payload,callback:n.callback,next:null};i===null?o=i=l:i=i.next=l,n=n.next}while(n!==null);i===null?o=i=t:i=i.next=t}else o=i=t;n={baseState:r.baseState,firstBaseUpdate:o,lastBaseUpdate:i,shared:r.shared,effects:r.effects},e.updateQueue=n;return}e=n.lastBaseUpdate,e===null?n.firstBaseUpdate=t:e.next=t,n.lastBaseUpdate=t}function Ko(e,t,n,r){var o=e.updateQueue;Rt=!1;var i=o.firstBaseUpdate,l=o.lastBaseUpdate,u=o.shared.pending;if(u!==null){o.shared.pending=null;var s=u,a=s.next;s.next=null,l===null?i=a:l.next=a,l=s;var d=e.alternate;d!==null&&(d=d.updateQueue,u=d.lastBaseUpdate,u!==l&&(u===null?d.firstBaseUpdate=a:u.next=a,d.lastBaseUpdate=s))}if(i!==null){var p=o.baseState;l=0,d=a=s=null,u=i;do{var m=u.lane,v=u.eventTime;if((r&m)===m){d!==null&&(d=d.next={eventTime:v,lane:0,tag:u.tag,payload:u.payload,callback:u.callback,next:null});e:{var h=e,g=u;switch(m=t,v=n,g.tag){case 1:if(h=g.payload,typeof h=="function"){p=h.call(v,p,m);break e}p=h;break e;case 3:h.flags=h.flags&-65537|128;case 0:if(h=g.payload,m=typeof h=="function"?h.call(v,p,m):h,m==null)break e;p=Y({},p,m);break e;case 2:Rt=!0}}u.callback!==null&&u.lane!==0&&(e.flags|=64,m=o.effects,m===null?o.effects=[u]:m.push(u))}else v={eventTime:v,lane:m,tag:u.tag,payload:u.payload,callback:u.callback,next:null},d===null?(a=d=v,s=p):d=d.next=v,l|=m;if(u=u.next,u===null){if(u=o.shared.pending,u===null)break;m=u,u=m.next,m.next=null,o.lastBaseUpdate=m,o.shared.pending=null}}while(1);if(d===null&&(s=p),o.baseState=s,o.firstBaseUpdate=a,o.lastBaseUpdate=d,t=o.shared.interleaved,t!==null){o=t;do l|=o.lane,o=o.next;while(o!==t)}else i===null&&(o.shared.lanes=0);cn|=l,e.lanes=l,e.memoizedState=p}}function ya(e,t,n){if(e=t.effects,t.effects=null,e!==null)for(t=0;t<e.length;t++){var r=e[t],o=r.callback;if(o!==null){if(r.callback=null,r=n,typeof o!="function")throw Error(E(191,o));o.call(r)}}}var kd=new wc.Component().refs;function Jl(e,t,n,r){t=e.memoizedState,n=n(r,t),n=n==null?t:Y({},t,n),e.memoizedState=n,e.lanes===0&&(e.updateQueue.baseState=n)}var mi={isMounted:function(e){return(e=e._reactInternals)?pn(e)===e:!1},enqueueSetState:function(e,t,n){e=e._reactInternals;var r=ve(),o=Bt(e),i=yt(r,o);i.payload=t,n!=null&&(i.callback=n),t=zt(e,i,o),t!==null&&(qe(t,e,o,r),So(t,e,o))},enqueueReplaceState:function(e,t,n){e=e._reactInternals;var r=ve(),o=Bt(e),i=yt(r,o);i.tag=1,i.payload=t,n!=null&&(i.callback=n),t=zt(e,i,o),t!==null&&(qe(t,e,o,r),So(t,e,o))},enqueueForceUpdate:function(e,t){e=e._reactInternals;var n=ve(),r=Bt(e),o=yt(n,r);o.tag=2,t!=null&&(o.callback=t),t=zt(e,o,r),t!==null&&(qe(t,e,r,n),So(t,e,r))}};function ga(e,t,n,r,o,i,l){return e=e.stateNode,typeof e.shouldComponentUpdate=="function"?e.shouldComponentUpdate(r,i,l):t.prototype&&t.prototype.isPureReactComponent?!Rr(n,r)||!Rr(o,i):!0}function Ed(e,t,n){var r=!1,o=Vt,i=t.contextType;return typeof i=="object"&&i!==null?i=Ie(i):(o=xe(t)?un:ye.current,r=t.contextTypes,i=(r=r!=null)?An(e,o):Vt),t=new t(n,i),e.memoizedState=t.state!==null&&t.state!==void 0?t.state:null,t.updater=mi,e.stateNode=t,t._reactInternals=e,r&&(e=e.stateNode,e.__reactInternalMemoizedUnmaskedChildContext=o,e.__reactInternalMemoizedMaskedChildContext=i),t}function va(e,t,n,r){e=t.state,typeof t.componentWillReceiveProps=="function"&&t.componentWillReceiveProps(n,r),typeof t.UNSAFE_componentWillReceiveProps=="function"&&t.UNSAFE_componentWillReceiveProps(n,r),t.state!==e&&mi.enqueueReplaceState(t,t.state,null)}function Zl(e,t,n,r){var o=e.stateNode;o.props=n,o.state=e.memoizedState,o.refs=kd,Zu(e);var i=t.contextType;typeof i=="object"&&i!==null?o.context=Ie(i):(i=xe(t)?un:ye.current,o.context=An(e,i)),o.state=e.memoizedState,i=t.getDerivedStateFromProps,typeof i=="function"&&(Jl(e,t,i,n),o.state=e.memoizedState),typeof t.getDerivedStateFromProps=="function"||typeof o.getSnapshotBeforeUpdate=="function"||typeof o.UNSAFE_componentWillMount!="function"&&typeof o.componentWillMount!="function"||(t=o.state,typeof o.componentWillMount=="function"&&o.componentWillMount(),typeof o.UNSAFE_componentWillMount=="function"&&o.UNSAFE_componentWillMount(),t!==o.state&&mi.enqueueReplaceState(o,o.state,null),Ko(e,n,o,r),o.state=e.memoizedState),typeof o.componentDidMount=="function"&&(e.flags|=4194308)}function tr(e,t,n){if(e=n.ref,e!==null&&typeof e!="function"&&typeof e!="object"){if(n._owner){if(n=n._owner,n){if(n.tag!==1)throw Error(E(309));var r=n.stateNode}if(!r)throw Error(E(147,e));var o=r,i=""+e;return t!==null&&t.ref!==null&&typeof t.ref=="function"&&t.ref._stringRef===i?t.ref:(t=function(l){var u=o.refs;u===kd&&(u=o.refs={}),l===null?delete u[i]:u[i]=l},t._stringRef=i,t)}if(typeof e!="string")throw Error(E(284));if(!n._owner)throw Error(E(290,e))}return e}function so(e,t){throw e=Object.prototype.toString.call(t),Error(E(31,e==="[object Object]"?"object with keys {"+Object.keys(t).join(", ")+"}":e))}function wa(e){var t=e._init;return t(e._payload)}function Cd(e){function t(f,c){if(e){var y=f.deletions;y===null?(f.deletions=[c],f.flags|=16):y.push(c)}}function n(f,c){if(!e)return null;for(;c!==null;)t(f,c),c=c.sibling;return null}function r(f,c){for(f=new Map;c!==null;)c.key!==null?f.set(c.key,c):f.set(c.index,c),c=c.sibling;return f}function o(f,c){return f=jt(f,c),f.index=0,f.sibling=null,f}function i(f,c,y){return f.index=y,e?(y=f.alternate,y!==null?(y=y.index,y<c?(f.flags|=2,c):y):(f.flags|=2,c)):(f.flags|=1048576,c)}function l(f){return e&&f.alternate===null&&(f.flags|=2),f}function u(f,c,y,k){return c===null||c.tag!==6?(c=ol(y,f.mode,k),c.return=f,c):(c=o(c,y),c.return=f,c)}function s(f,c,y,k){var N=y.type;return N===gn?d(f,c,y.props.children,k,y.key):c!==null&&(c.elementType===N||typeof N=="object"&&N!==null&&N.$$typeof===Pt&&wa(N)===c.type)?(k=o(c,y.props),k.ref=tr(f,c,y),k.return=f,k):(k=No(y.type,y.key,y.props,null,f.mode,k),k.ref=tr(f,c,y),k.return=f,k)}function a(f,c,y,k){return c===null||c.tag!==4||c.stateNode.containerInfo!==y.containerInfo||c.stateNode.implementation!==y.implementation?(c=il(y,f.mode,k),c.return=f,c):(c=o(c,y.children||[]),c.return=f,c)}function d(f,c,y,k,N){return c===null||c.tag!==7?(c=on(y,f.mode,k,N),c.return=f,c):(c=o(c,y),c.return=f,c)}function p(f,c,y){if(typeof c=="string"&&c!==""||typeof c=="number")return c=ol(""+c,f.mode,y),c.return=f,c;if(typeof c=="object"&&c!==null){switch(c.$$typeof){case Yr:return y=No(c.type,c.key,c.props,null,f.mode,y),y.ref=tr(f,null,c),y.return=f,y;case yn:return c=il(c,f.mode,y),c.return=f,c;case Pt:var k=c._init;return p(f,k(c._payload),y)}if(sr(c)||Xn(c))return c=on(c,f.mode,y,null),c.return=f,c;so(f,c)}return null}function m(f,c,y,k){var N=c!==null?c.key:null;if(typeof y=="string"&&y!==""||typeof y=="number")return N!==null?null:u(f,c,""+y,k);if(typeof y=="object"&&y!==null){switch(y.$$typeof){case Yr:return y.key===N?s(f,c,y,k):null;case yn:return y.key===N?a(f,c,y,k):null;case Pt:return N=y._init,m(f,c,N(y._payload),k)}if(sr(y)||Xn(y))return N!==null?null:d(f,c,y,k,null);so(f,y)}return null}function v(f,c,y,k,N){if(typeof k=="string"&&k!==""||typeof k=="number")return f=f.get(y)||null,u(c,f,""+k,N);if(typeof k=="object"&&k!==null){switch(k.$$typeof){case Yr:return f=f.get(k.key===null?y:k.key)||null,s(c,f,k,N);case yn:return f=f.get(k.key===null?y:k.key)||null,a(c,f,k,N);case Pt:var P=k._init;return v(f,c,y,P(k._payload),N)}if(sr(k)||Xn(k))return f=f.get(y)||null,d(c,f,k,N,null);so(c,k)}return null}function h(f,c,y,k){for(var N=null,P=null,x=c,L=c=0,A=null;x!==null&&L<y.length;L++){x.index>L?(A=x,x=null):A=x.sibling;var U=m(f,x,y[L],k);if(U===null){x===null&&(x=A);break}e&&x&&U.alternate===null&&t(f,x),c=i(U,c,L),P===null?N=U:P.sibling=U,P=U,x=A}if(L===y.length)return n(f,x),K&&Xt(f,L),N;if(x===null){for(;L<y.length;L++)x=p(f,y[L],k),x!==null&&(c=i(x,c,L),P===null?N=x:P.sibling=x,P=x);return K&&Xt(f,L),N}for(x=r(f,x);L<y.length;L++)A=v(x,f,L,y[L],k),A!==null&&(e&&A.alternate!==null&&x.delete(A.key===null?L:A.key),c=i(A,c,L),P===null?N=A:P.sibling=A,P=A);return e&&x.forEach(function(j){return t(f,j)}),K&&Xt(f,L),N}function g(f,c,y,k){var N=Xn(y);if(typeof N!="function")throw Error(E(150));if(y=N.call(y),y==null)throw Error(E(151));for(var P=N=null,x=c,L=c=0,A=null,U=y.next();x!==null&&!U.done;L++,U=y.next()){x.index>L?(A=x,x=null):A=x.sibling;var j=m(f,x,U.value,k);if(j===null){x===null&&(x=A);break}e&&x&&j.alternate===null&&t(f,x),c=i(j,c,L),P===null?N=j:P.sibling=j,P=j,x=A}if(U.done)return n(f,x),K&&Xt(f,L),N;if(x===null){for(;!U.done;L++,U=y.next())U=p(f,U.value,k),U!==null&&(c=i(U,c,L),P===null?N=U:P.sibling=U,P=U);return K&&Xt(f,L),N}for(x=r(f,x);!U.done;L++,U=y.next())U=v(x,f,L,U.value,k),U!==null&&(e&&U.alternate!==null&&x.delete(U.key===null?L:U.key),c=i(U,c,L),P===null?N=U:P.sibling=U,P=U);return e&&x.forEach(function(He){return t(f,He)}),K&&Xt(f,L),N}function T(f,c,y,k){if(typeof y=="object"&&y!==null&&y.type===gn&&y.key===null&&(y=y.props.children),typeof y=="object"&&y!==null){switch(y.$$typeof){case Yr:e:{for(var N=y.key,P=c;P!==null;){if(P.key===N){if(N=y.type,N===gn){if(P.tag===7){n(f,P.sibling),c=o(P,y.props.children),c.return=f,f=c;break e}}else if(P.elementType===N||typeof N=="object"&&N!==null&&N.$$typeof===Pt&&wa(N)===P.type){n(f,P.sibling),c=o(P,y.props),c.ref=tr(f,P,y),c.return=f,f=c;break e}n(f,P);break}else t(f,P);P=P.sibling}y.type===gn?(c=on(y.props.children,f.mode,k,y.key),c.return=f,f=c):(k=No(y.type,y.key,y.props,null,f.mode,k),k.ref=tr(f,c,y),k.return=f,f=k)}return l(f);case yn:e:{for(P=y.key;c!==null;){if(c.key===P)if(c.tag===4&&c.stateNode.containerInfo===y.containerInfo&&c.stateNode.implementation===y.implementation){n(f,c.sibling),c=o(c,y.children||[]),c.return=f,f=c;break e}else{n(f,c);break}else t(f,c);c=c.sibling}c=il(y,f.mode,k),c.return=f,f=c}return l(f);case Pt:return P=y._init,T(f,c,P(y._payload),k)}if(sr(y))return h(f,c,y,k);if(Xn(y))return g(f,c,y,k);so(f,y)}return typeof y=="string"&&y!==""||typeof y=="number"?(y=""+y,c!==null&&c.tag===6?(n(f,c.sibling),c=o(c,y),c.return=f,f=c):(n(f,c),c=ol(y,f.mode,k),c.return=f,f=c),l(f)):n(f,c)}return T}var $n=Cd(!0),xd=Cd(!1),Vr={},ot=Wt(Vr),Or=Wt(Vr),Fr=Wt(Vr);function tn(e){if(e===Vr)throw Error(E(174));return e}function es(e,t){switch(V(Fr,t),V(Or,e),V(ot,Vr),e=t.nodeType,e){case 9:case 11:t=(t=t.documentElement)?t.namespaceURI:Ul(null,"");break;default:e=e===8?t.parentNode:t,t=e.namespaceURI||null,e=e.tagName,t=Ul(t,e)}W(ot),V(ot,t)}function zn(){W(ot),W(Or),W(Fr)}function Td(e){tn(Fr.current);var t=tn(ot.current),n=Ul(t,e.type);t!==n&&(V(Or,e),V(ot,n))}function ts(e){Or.current===e&&(W(ot),W(Or))}var q=Wt(0);function Go(e){for(var t=e;t!==null;){if(t.tag===13){var n=t.memoizedState;if(n!==null&&(n=n.dehydrated,n===null||n.data==="$?"||n.data==="$!"))return t}else if(t.tag===19&&t.memoizedProps.revealOrder!==void 0){if(t.flags&128)return t}else if(t.child!==null){t.child.return=t,t=t.child;continue}if(t===e)break;for(;t.sibling===null;){if(t.return===null||t.return===e)return null;t=t.return}t.sibling.return=t.return,t=t.sibling}return null}var Ji=[];function ns(){for(var e=0;e<Ji.length;e++)Ji[e]._workInProgressVersionPrimary=null;Ji.length=0}var ko=xt.ReactCurrentDispatcher,Zi=xt.ReactCurrentBatchConfig,an=0,X=null,re=null,le=null,qo=!1,gr=!1,Dr=0,th=0;function fe(){throw Error(E(321))}function rs(e,t){if(t===null)return!1;for(var n=0;n<t.length&&n<e.length;n++)if(!Xe(e[n],t[n]))return!1;return!0}function os(e,t,n,r,o,i){if(an=i,X=t,t.memoizedState=null,t.updateQueue=null,t.lanes=0,ko.current=e===null||e.memoizedState===null?ih:lh,e=n(r,o),gr){i=0;do{if(gr=!1,Dr=0,25<=i)throw Error(E(301));i+=1,le=re=null,t.updateQueue=null,ko.current=uh,e=n(r,o)}while(gr)}if(ko.current=Xo,t=re!==null&&re.next!==null,an=0,le=re=X=null,qo=!1,t)throw Error(E(300));return e}function is(){var e=Dr!==0;return Dr=0,e}function Ze(){var e={memoizedState:null,baseState:null,baseQueue:null,queue:null,next:null};return le===null?X.memoizedState=le=e:le=le.next=e,le}function Be(){if(re===null){var e=X.alternate;e=e!==null?e.memoizedState:null}else e=re.next;var t=le===null?X.memoizedState:le.next;if(t!==null)le=t,re=e;else{if(e===null)throw Error(E(310));re=e,e={memoizedState:re.memoizedState,baseState:re.baseState,baseQueue:re.baseQueue,queue:re.queue,next:null},le===null?X.memoizedState=le=e:le=le.next=e}return le}function Ar(e,t){return typeof t=="function"?t(e):t}function el(e){var t=Be(),n=t.queue;if(n===null)throw Error(E(311));n.lastRenderedReducer=e;var r=re,o=r.baseQueue,i=n.pending;if(i!==null){if(o!==null){var l=o.next;o.next=i.next,i.next=l}r.baseQueue=o=i,n.pending=null}if(o!==null){i=o.next,r=r.baseState;var u=l=null,s=null,a=i;do{var d=a.lane;if((an&d)===d)s!==null&&(s=s.next={lane:0,action:a.action,hasEagerState:a.hasEagerState,eagerState:a.eagerState,next:null}),r=a.hasEagerState?a.eagerState:e(r,a.action);else{var p={lane:d,action:a.action,hasEagerState:a.hasEagerState,eagerState:a.eagerState,next:null};s===null?(u=s=p,l=r):s=s.next=p,X.lanes|=d,cn|=d}a=a.next}while(a!==null&&a!==i);s===null?l=r:s.next=u,Xe(r,t.memoizedState)||(Ee=!0),t.memoizedState=r,t.baseState=l,t.baseQueue=s,n.lastRenderedState=r}if(e=n.interleaved,e!==null){o=e;do i=o.lane,X.lanes|=i,cn|=i,o=o.next;while(o!==e)}else o===null&&(n.lanes=0);return[t.memoizedState,n.dispatch]}function tl(e){var t=Be(),n=t.queue;if(n===null)throw Error(E(311));n.lastRenderedReducer=e;var r=n.dispatch,o=n.pending,i=t.memoizedState;if(o!==null){n.pending=null;var l=o=o.next;do i=e(i,l.action),l=l.next;while(l!==o);Xe(i,t.memoizedState)||(Ee=!0),t.memoizedState=i,t.baseQueue===null&&(t.baseState=i),n.lastRenderedState=i}return[i,r]}function Nd(){}function Pd(e,t){var n=X,r=Be(),o=t(),i=!Xe(r.memoizedState,o);if(i&&(r.memoizedState=o,Ee=!0),r=r.queue,ls(_d.bind(null,n,r,e),[e]),r.getSnapshot!==t||i||le!==null&&le.memoizedState.tag&1){if(n.flags|=2048,Mr(9,Ld.bind(null,n,r,o,t),void 0,null),se===null)throw Error(E(349));an&30||Rd(n,t,o)}return o}function Rd(e,t,n){e.flags|=16384,e={getSnapshot:t,value:n},t=X.updateQueue,t===null?(t={lastEffect:null,stores:null},X.updateQueue=t,t.stores=[e]):(n=t.stores,n===null?t.stores=[e]:n.push(e))}function Ld(e,t,n,r){t.value=n,t.getSnapshot=r,Ud(t)&&Od(e)}function _d(e,t,n){return n(function(){Ud(t)&&Od(e)})}function Ud(e){var t=e.getSnapshot;e=e.value;try{var n=t();return!Xe(e,n)}catch{return!0}}function Od(e){var t=Et(e,1);t!==null&&qe(t,e,1,-1)}function Sa(e){var t=Ze();return typeof e=="function"&&(e=e()),t.memoizedState=t.baseState=e,e={pending:null,interleaved:null,lanes:0,dispatch:null,lastRenderedReducer:Ar,lastRenderedState:e},t.queue=e,e=e.dispatch=oh.bind(null,X,e),[t.memoizedState,e]}function Mr(e,t,n,r){return e={tag:e,create:t,destroy:n,deps:r,next:null},t=X.updateQueue,t===null?(t={lastEffect:null,stores:null},X.updateQueue=t,t.lastEffect=e.next=e):(n=t.lastEffect,n===null?t.lastEffect=e.next=e:(r=n.next,n.next=e,e.next=r,t.lastEffect=e)),e}function Fd(){return Be().memoizedState}function Eo(e,t,n,r){var o=Ze();X.flags|=e,o.memoizedState=Mr(1|t,n,void 0,r===void 0?null:r)}function hi(e,t,n,r){var o=Be();r=r===void 0?null:r;var i=void 0;if(re!==null){var l=re.memoizedState;if(i=l.destroy,r!==null&&rs(r,l.deps)){o.memoizedState=Mr(t,n,i,r);return}}X.flags|=e,o.memoizedState=Mr(1|t,n,i,r)}function ka(e,t){return Eo(8390656,8,e,t)}function ls(e,t){return hi(2048,8,e,t)}function Dd(e,t){return hi(4,2,e,t)}function Ad(e,t){return hi(4,4,e,t)}function Md(e,t){if(typeof t=="function")return e=e(),t(e),function(){t(null)};if(t!=null)return e=e(),t.current=e,function(){t.current=null}}function $d(e,t,n){return n=n!=null?n.concat([e]):null,hi(4,4,Md.bind(null,t,e),n)}function us(){}function zd(e,t){var n=Be();t=t===void 0?null:t;var r=n.memoizedState;return r!==null&&t!==null&&rs(t,r[1])?r[0]:(n.memoizedState=[e,t],e)}function Id(e,t){var n=Be();t=t===void 0?null:t;var r=n.memoizedState;return r!==null&&t!==null&&rs(t,r[1])?r[0]:(e=e(),n.memoizedState=[e,t],e)}function Bd(e,t,n){return an&21?(Xe(n,t)||(n=Vc(),X.lanes|=n,cn|=n,e.baseState=!0),t):(e.baseState&&(e.baseState=!1,Ee=!0),e.memoizedState=n)}function nh(e,t){var n=H;H=n!==0&&4>n?n:4,e(!0);var r=Zi.transition;Zi.transition={};try{e(!1),t()}finally{H=n,Zi.transition=r}}function jd(){return Be().memoizedState}function rh(e,t,n){var r=Bt(e);if(n={lane:r,action:n,hasEagerState:!1,eagerState:null,next:null},Hd(e))Vd(t,n);else if(n=wd(e,t,n,r),n!==null){var o=ve();qe(n,e,r,o),bd(n,t,r)}}function oh(e,t,n){var r=Bt(e),o={lane:r,action:n,hasEagerState:!1,eagerState:null,next:null};if(Hd(e))Vd(t,o);else{var i=e.alternate;if(e.lanes===0&&(i===null||i.lanes===0)&&(i=t.lastRenderedReducer,i!==null))try{var l=t.lastRenderedState,u=i(l,n);if(o.hasEagerState=!0,o.eagerState=u,Xe(u,l)){var s=t.interleaved;s===null?(o.next=o,Ju(t)):(o.next=s.next,s.next=o),t.interleaved=o;return}}catch{}finally{}n=wd(e,t,o,r),n!==null&&(o=ve(),qe(n,e,r,o),bd(n,t,r))}}function Hd(e){var t=e.alternate;return e===X||t!==null&&t===X}function Vd(e,t){gr=qo=!0;var n=e.pending;n===null?t.next=t:(t.next=n.next,n.next=t),e.pending=t}function bd(e,t,n){if(n&4194240){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,zu(e,n)}}var Xo={readContext:Ie,useCallback:fe,useContext:fe,useEffect:fe,useImperativeHandle:fe,useInsertionEffect:fe,useLayoutEffect:fe,useMemo:fe,useReducer:fe,useRef:fe,useState:fe,useDebugValue:fe,useDeferredValue:fe,useTransition:fe,useMutableSource:fe,useSyncExternalStore:fe,useId:fe,unstable_isNewReconciler:!1},ih={readContext:Ie,useCallback:function(e,t){return Ze().memoizedState=[e,t===void 0?null:t],e},useContext:Ie,useEffect:ka,useImperativeHandle:function(e,t,n){return n=n!=null?n.concat([e]):null,Eo(4194308,4,Md.bind(null,t,e),n)},useLayoutEffect:function(e,t){return Eo(4194308,4,e,t)},useInsertionEffect:function(e,t){return Eo(4,2,e,t)},useMemo:function(e,t){var n=Ze();return t=t===void 0?null:t,e=e(),n.memoizedState=[e,t],e},useReducer:function(e,t,n){var r=Ze();return t=n!==void 0?n(t):t,r.memoizedState=r.baseState=t,e={pending:null,interleaved:null,lanes:0,dispatch:null,lastRenderedReducer:e,lastRenderedState:t},r.queue=e,e=e.dispatch=rh.bind(null,X,e),[r.memoizedState,e]},useRef:function(e){var t=Ze();return e={current:e},t.memoizedState=e},useState:Sa,useDebugValue:us,useDeferredValue:function(e){return Ze().memoizedState=e},useTransition:function(){var e=Sa(!1),t=e[0];return e=nh.bind(null,e[1]),Ze().memoizedState=e,[t,e]},useMutableSource:function(){},useSyncExternalStore:function(e,t,n){var r=X,o=Ze();if(K){if(n===void 0)throw Error(E(407));n=n()}else{if(n=t(),se===null)throw Error(E(349));an&30||Rd(r,t,n)}o.memoizedState=n;var i={value:n,getSnapshot:t};return o.queue=i,ka(_d.bind(null,r,i,e),[e]),r.flags|=2048,Mr(9,Ld.bind(null,r,i,n,t),void 0,null),n},useId:function(){var e=Ze(),t=se.identifierPrefix;if(K){var n=mt,r=pt;n=(r&~(1<<32-Ge(r)-1)).toString(32)+n,t=":"+t+"R"+n,n=Dr++,0<n&&(t+="H"+n.toString(32)),t+=":"}else n=th++,t=":"+t+"r"+n.toString(32)+":";return e.memoizedState=t},unstable_isNewReconciler:!1},lh={readContext:Ie,useCallback:zd,useContext:Ie,useEffect:ls,useImperativeHandle:$d,useInsertionEffect:Dd,useLayoutEffect:Ad,useMemo:Id,useReducer:el,useRef:Fd,useState:function(){return el(Ar)},useDebugValue:us,useDeferredValue:function(e){var t=Be();return Bd(t,re.memoizedState,e)},useTransition:function(){var e=el(Ar)[0],t=Be().memoizedState;return[e,t]},useMutableSource:Nd,useSyncExternalStore:Pd,useId:jd,unstable_isNewReconciler:!1},uh={readContext:Ie,useCallback:zd,useContext:Ie,useEffect:ls,useImperativeHandle:$d,useInsertionEffect:Dd,useLayoutEffect:Ad,useMemo:Id,useReducer:tl,useRef:Fd,useState:function(){return tl(Ar)},useDebugValue:us,useDeferredValue:function(e){var t=Be();return re===null?t.memoizedState=e:Bd(t,re.memoizedState,e)},useTransition:function(){var e=tl(Ar)[0],t=Be().memoizedState;return[e,t]},useMutableSource:Nd,useSyncExternalStore:Pd,useId:jd,unstable_isNewReconciler:!1};function In(e,t){try{var n="",r=t;do n+=Ap(r),r=r.return;while(r);var o=n}catch(i){o=`
-Error generating stack: `+i.message+`
-`+i.stack}return{value:e,source:t,stack:o,digest:null}}function nl(e,t,n){return{value:e,source:null,stack:n??null,digest:t??null}}function eu(e,t){try{console.error(t.value)}catch(n){setTimeout(function(){throw n})}}var sh=typeof WeakMap=="function"?WeakMap:Map;function Wd(e,t,n){n=yt(-1,n),n.tag=3,n.payload={element:null};var r=t.value;return n.callback=function(){Jo||(Jo=!0,cu=r),eu(e,t)},n}function Qd(e,t,n){n=yt(-1,n),n.tag=3;var r=e.type.getDerivedStateFromError;if(typeof r=="function"){var o=t.value;n.payload=function(){return r(o)},n.callback=function(){eu(e,t)}}var i=e.stateNode;return i!==null&&typeof i.componentDidCatch=="function"&&(n.callback=function(){eu(e,t),typeof r!="function"&&(It===null?It=new Set([this]):It.add(this));var l=t.stack;this.componentDidCatch(t.value,{componentStack:l!==null?l:""})}),n}function Ea(e,t,n){var r=e.pingCache;if(r===null){r=e.pingCache=new sh;var o=new Set;r.set(t,o)}else o=r.get(t),o===void 0&&(o=new Set,r.set(t,o));o.has(n)||(o.add(n),e=Eh.bind(null,e,t,n),t.then(e,e))}function Ca(e){do{var t;if((t=e.tag===13)&&(t=e.memoizedState,t=t!==null?t.dehydrated!==null:!0),t)return e;e=e.return}while(e!==null);return null}function xa(e,t,n,r,o){return e.mode&1?(e.flags|=65536,e.lanes=o,e):(e===t?e.flags|=65536:(e.flags|=128,n.flags|=131072,n.flags&=-52805,n.tag===1&&(n.alternate===null?n.tag=17:(t=yt(-1,1),t.tag=2,zt(n,t,1))),n.lanes|=1),e)}var ah=xt.ReactCurrentOwner,Ee=!1;function ge(e,t,n,r){t.child=e===null?xd(t,null,n,r):$n(t,e.child,n,r)}function Ta(e,t,n,r,o){n=n.render;var i=t.ref;return On(t,o),r=os(e,t,n,r,i,o),n=is(),e!==null&&!Ee?(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~o,Ct(e,t,o)):(K&&n&&Qu(t),t.flags|=1,ge(e,t,r,o),t.child)}function Na(e,t,n,r,o){if(e===null){var i=n.type;return typeof i=="function"&&!hs(i)&&i.defaultProps===void 0&&n.compare===null&&n.defaultProps===void 0?(t.tag=15,t.type=i,Kd(e,t,i,r,o)):(e=No(n.type,null,r,t,t.mode,o),e.ref=t.ref,e.return=t,t.child=e)}if(i=e.child,!(e.lanes&o)){var l=i.memoizedProps;if(n=n.compare,n=n!==null?n:Rr,n(l,r)&&e.ref===t.ref)return Ct(e,t,o)}return t.flags|=1,e=jt(i,r),e.ref=t.ref,e.return=t,t.child=e}function Kd(e,t,n,r,o){if(e!==null){var i=e.memoizedProps;if(Rr(i,r)&&e.ref===t.ref)if(Ee=!1,t.pendingProps=r=i,(e.lanes&o)!==0)e.flags&131072&&(Ee=!0);else return t.lanes=e.lanes,Ct(e,t,o)}return tu(e,t,n,r,o)}function Gd(e,t,n){var r=t.pendingProps,o=r.children,i=e!==null?e.memoizedState:null;if(r.mode==="hidden")if(!(t.mode&1))t.memoizedState={baseLanes:0,cachePool:null,transitions:null},V(Pn,Ne),Ne|=n;else{if(!(n&1073741824))return e=i!==null?i.baseLanes|n:n,t.lanes=t.childLanes=1073741824,t.memoizedState={baseLanes:e,cachePool:null,transitions:null},t.updateQueue=null,V(Pn,Ne),Ne|=e,null;t.memoizedState={baseLanes:0,cachePool:null,transitions:null},r=i!==null?i.baseLanes:n,V(Pn,Ne),Ne|=r}else i!==null?(r=i.baseLanes|n,t.memoizedState=null):r=n,V(Pn,Ne),Ne|=r;return ge(e,t,o,n),t.child}function qd(e,t){var n=t.ref;(e===null&&n!==null||e!==null&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function tu(e,t,n,r,o){var i=xe(n)?un:ye.current;return i=An(t,i),On(t,o),n=os(e,t,n,r,i,o),r=is(),e!==null&&!Ee?(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~o,Ct(e,t,o)):(K&&r&&Qu(t),t.flags|=1,ge(e,t,n,o),t.child)}function Pa(e,t,n,r,o){if(xe(n)){var i=!0;Ho(t)}else i=!1;if(On(t,o),t.stateNode===null)Co(e,t),Ed(t,n,r),Zl(t,n,r,o),r=!0;else if(e===null){var l=t.stateNode,u=t.memoizedProps;l.props=u;var s=l.context,a=n.contextType;typeof a=="object"&&a!==null?a=Ie(a):(a=xe(n)?un:ye.current,a=An(t,a));var d=n.getDerivedStateFromProps,p=typeof d=="function"||typeof l.getSnapshotBeforeUpdate=="function";p||typeof l.UNSAFE_componentWillReceiveProps!="function"&&typeof l.componentWillReceiveProps!="function"||(u!==r||s!==a)&&va(t,l,r,a),Rt=!1;var m=t.memoizedState;l.state=m,Ko(t,r,l,o),s=t.memoizedState,u!==r||m!==s||Ce.current||Rt?(typeof d=="function"&&(Jl(t,n,d,r),s=t.memoizedState),(u=Rt||ga(t,n,u,r,m,s,a))?(p||typeof l.UNSAFE_componentWillMount!="function"&&typeof l.componentWillMount!="function"||(typeof l.componentWillMount=="function"&&l.componentWillMount(),typeof l.UNSAFE_componentWillMount=="function"&&l.UNSAFE_componentWillMount()),typeof l.componentDidMount=="function"&&(t.flags|=4194308)):(typeof l.componentDidMount=="function"&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=s),l.props=r,l.state=s,l.context=a,r=u):(typeof l.componentDidMount=="function"&&(t.flags|=4194308),r=!1)}else{l=t.stateNode,Sd(e,t),u=t.memoizedProps,a=t.type===t.elementType?u:We(t.type,u),l.props=a,p=t.pendingProps,m=l.context,s=n.contextType,typeof s=="object"&&s!==null?s=Ie(s):(s=xe(n)?un:ye.current,s=An(t,s));var v=n.getDerivedStateFromProps;(d=typeof v=="function"||typeof l.getSnapshotBeforeUpdate=="function")||typeof l.UNSAFE_componentWillReceiveProps!="function"&&typeof l.componentWillReceiveProps!="function"||(u!==p||m!==s)&&va(t,l,r,s),Rt=!1,m=t.memoizedState,l.state=m,Ko(t,r,l,o);var h=t.memoizedState;u!==p||m!==h||Ce.current||Rt?(typeof v=="function"&&(Jl(t,n,v,r),h=t.memoizedState),(a=Rt||ga(t,n,a,r,m,h,s)||!1)?(d||typeof l.UNSAFE_componentWillUpdate!="function"&&typeof l.componentWillUpdate!="function"||(typeof l.componentWillUpdate=="function"&&l.componentWillUpdate(r,h,s),typeof l.UNSAFE_componentWillUpdate=="function"&&l.UNSAFE_componentWillUpdate(r,h,s)),typeof l.componentDidUpdate=="function"&&(t.flags|=4),typeof l.getSnapshotBeforeUpdate=="function"&&(t.flags|=1024)):(typeof l.componentDidUpdate!="function"||u===e.memoizedProps&&m===e.memoizedState||(t.flags|=4),typeof l.getSnapshotBeforeUpdate!="function"||u===e.memoizedProps&&m===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),l.props=r,l.state=h,l.context=s,r=a):(typeof l.componentDidUpdate!="function"||u===e.memoizedProps&&m===e.memoizedState||(t.flags|=4),typeof l.getSnapshotBeforeUpdate!="function"||u===e.memoizedProps&&m===e.memoizedState||(t.flags|=1024),r=!1)}return nu(e,t,n,r,i,o)}function nu(e,t,n,r,o,i){qd(e,t);var l=(t.flags&128)!==0;if(!r&&!l)return o&&fa(t,n,!1),Ct(e,t,i);r=t.stateNode,ah.current=t;var u=l&&typeof n.getDerivedStateFromError!="function"?null:r.render();return t.flags|=1,e!==null&&l?(t.child=$n(t,e.child,null,i),t.child=$n(t,null,u,i)):ge(e,t,u,i),t.memoizedState=r.state,o&&fa(t,n,!0),t.child}function Xd(e){var t=e.stateNode;t.pendingContext?da(e,t.pendingContext,t.pendingContext!==t.context):t.context&&da(e,t.context,!1),es(e,t.containerInfo)}function Ra(e,t,n,r,o){return Mn(),Gu(o),t.flags|=256,ge(e,t,n,r),t.child}var ru={dehydrated:null,treeContext:null,retryLane:0};function ou(e){return{baseLanes:e,cachePool:null,transitions:null}}function Yd(e,t,n){var r=t.pendingProps,o=q.current,i=!1,l=(t.flags&128)!==0,u;if((u=l)||(u=e!==null&&e.memoizedState===null?!1:(o&2)!==0),u?(i=!0,t.flags&=-129):(e===null||e.memoizedState!==null)&&(o|=1),V(q,o&1),e===null)return Xl(t),e=t.memoizedState,e!==null&&(e=e.dehydrated,e!==null)?(t.mode&1?e.data==="$!"?t.lanes=8:t.lanes=1073741824:t.lanes=1,null):(l=r.children,e=r.fallback,i?(r=t.mode,i=t.child,l={mode:"hidden",children:l},!(r&1)&&i!==null?(i.childLanes=0,i.pendingProps=l):i=vi(l,r,0,null),e=on(e,r,n,null),i.return=t,e.return=t,i.sibling=e,t.child=i,t.child.memoizedState=ou(n),t.memoizedState=ru,e):ss(t,l));if(o=e.memoizedState,o!==null&&(u=o.dehydrated,u!==null))return ch(e,t,l,r,u,o,n);if(i){i=r.fallback,l=t.mode,o=e.child,u=o.sibling;var s={mode:"hidden",children:r.children};return!(l&1)&&t.child!==o?(r=t.child,r.childLanes=0,r.pendingProps=s,t.deletions=null):(r=jt(o,s),r.subtreeFlags=o.subtreeFlags&14680064),u!==null?i=jt(u,i):(i=on(i,l,n,null),i.flags|=2),i.return=t,r.return=t,r.sibling=i,t.child=r,r=i,i=t.child,l=e.child.memoizedState,l=l===null?ou(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},i.memoizedState=l,i.childLanes=e.childLanes&~n,t.memoizedState=ru,r}return i=e.child,e=i.sibling,r=jt(i,{mode:"visible",children:r.children}),!(t.mode&1)&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function ss(e,t){return t=vi({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function ao(e,t,n,r){return r!==null&&Gu(r),$n(t,e.child,null,n),e=ss(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function ch(e,t,n,r,o,i,l){if(n)return t.flags&256?(t.flags&=-257,r=nl(Error(E(422))),ao(e,t,l,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(i=r.fallback,o=t.mode,r=vi({mode:"visible",children:r.children},o,0,null),i=on(i,o,l,null),i.flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,t.mode&1&&$n(t,e.child,null,l),t.child.memoizedState=ou(l),t.memoizedState=ru,i);if(!(t.mode&1))return ao(e,t,l,null);if(o.data==="$!"){if(r=o.nextSibling&&o.nextSibling.dataset,r)var u=r.dgst;return r=u,i=Error(E(419)),r=nl(i,r,void 0),ao(e,t,l,r)}if(u=(l&e.childLanes)!==0,Ee||u){if(r=se,r!==null){switch(l&-l){case 4:o=2;break;case 16:o=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:o=32;break;case 536870912:o=268435456;break;default:o=0}o=o&(r.suspendedLanes|l)?0:o,o!==0&&o!==i.retryLane&&(i.retryLane=o,Et(e,o),qe(r,e,o,-1))}return ms(),r=nl(Error(E(421))),ao(e,t,l,r)}return o.data==="$?"?(t.flags|=128,t.child=e.child,t=Ch.bind(null,e),o._reactRetry=t,null):(e=i.treeContext,Re=$t(o.nextSibling),Le=t,K=!0,Ke=null,e!==null&&(De[Ae++]=pt,De[Ae++]=mt,De[Ae++]=sn,pt=e.id,mt=e.overflow,sn=t),t=ss(t,r.children),t.flags|=4096,t)}function La(e,t,n){e.lanes|=t;var r=e.alternate;r!==null&&(r.lanes|=t),Yl(e.return,t,n)}function rl(e,t,n,r,o){var i=e.memoizedState;i===null?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:o}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=o)}function Jd(e,t,n){var r=t.pendingProps,o=r.revealOrder,i=r.tail;if(ge(e,t,r.children,n),r=q.current,r&2)r=r&1|2,t.flags|=128;else{if(e!==null&&e.flags&128)e:for(e=t.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&La(e,n,t);else if(e.tag===19)La(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(V(q,r),!(t.mode&1))t.memoizedState=null;else switch(o){case"forwards":for(n=t.child,o=null;n!==null;)e=n.alternate,e!==null&&Go(e)===null&&(o=n),n=n.sibling;n=o,n===null?(o=t.child,t.child=null):(o=n.sibling,n.sibling=null),rl(t,!1,o,n,i);break;case"backwards":for(n=null,o=t.child,t.child=null;o!==null;){if(e=o.alternate,e!==null&&Go(e)===null){t.child=o;break}e=o.sibling,o.sibling=n,n=o,o=e}rl(t,!0,n,null,i);break;case"together":rl(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Co(e,t){!(t.mode&1)&&e!==null&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Ct(e,t,n){if(e!==null&&(t.dependencies=e.dependencies),cn|=t.lanes,!(n&t.childLanes))return null;if(e!==null&&t.child!==e.child)throw Error(E(153));if(t.child!==null){for(e=t.child,n=jt(e,e.pendingProps),t.child=n,n.return=t;e.sibling!==null;)e=e.sibling,n=n.sibling=jt(e,e.pendingProps),n.return=t;n.sibling=null}return t.child}function dh(e,t,n){switch(t.tag){case 3:Xd(t),Mn();break;case 5:Td(t);break;case 1:xe(t.type)&&Ho(t);break;case 4:es(t,t.stateNode.containerInfo);break;case 10:var r=t.type._context,o=t.memoizedProps.value;V(Wo,r._currentValue),r._currentValue=o;break;case 13:if(r=t.memoizedState,r!==null)return r.dehydrated!==null?(V(q,q.current&1),t.flags|=128,null):n&t.child.childLanes?Yd(e,t,n):(V(q,q.current&1),e=Ct(e,t,n),e!==null?e.sibling:null);V(q,q.current&1);break;case 19:if(r=(n&t.childLanes)!==0,e.flags&128){if(r)return Jd(e,t,n);t.flags|=128}if(o=t.memoizedState,o!==null&&(o.rendering=null,o.tail=null,o.lastEffect=null),V(q,q.current),r)break;return null;case 22:case 23:return t.lanes=0,Gd(e,t,n)}return Ct(e,t,n)}var Zd,iu,ef,tf;Zd=function(e,t){for(var n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};iu=function(){};ef=function(e,t,n,r){var o=e.memoizedProps;if(o!==r){e=t.stateNode,tn(ot.current);var i=null;switch(n){case"input":o=Pl(e,o),r=Pl(e,r),i=[];break;case"select":o=Y({},o,{value:void 0}),r=Y({},r,{value:void 0}),i=[];break;case"textarea":o=_l(e,o),r=_l(e,r),i=[];break;default:typeof o.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=Bo)}Ol(n,r);var l;n=null;for(a in o)if(!r.hasOwnProperty(a)&&o.hasOwnProperty(a)&&o[a]!=null)if(a==="style"){var u=o[a];for(l in u)u.hasOwnProperty(l)&&(n||(n={}),n[l]="")}else a!=="dangerouslySetInnerHTML"&&a!=="children"&&a!=="suppressContentEditableWarning"&&a!=="suppressHydrationWarning"&&a!=="autoFocus"&&(kr.hasOwnProperty(a)?i||(i=[]):(i=i||[]).push(a,null));for(a in r){var s=r[a];if(u=o!=null?o[a]:void 0,r.hasOwnProperty(a)&&s!==u&&(s!=null||u!=null))if(a==="style")if(u){for(l in u)!u.hasOwnProperty(l)||s&&s.hasOwnProperty(l)||(n||(n={}),n[l]="");for(l in s)s.hasOwnProperty(l)&&u[l]!==s[l]&&(n||(n={}),n[l]=s[l])}else n||(i||(i=[]),i.push(a,n)),n=s;else a==="dangerouslySetInnerHTML"?(s=s?s.__html:void 0,u=u?u.__html:void 0,s!=null&&u!==s&&(i=i||[]).push(a,s)):a==="children"?typeof s!="string"&&typeof s!="number"||(i=i||[]).push(a,""+s):a!=="suppressContentEditableWarning"&&a!=="suppressHydrationWarning"&&(kr.hasOwnProperty(a)?(s!=null&&a==="onScroll"&&b("scroll",e),i||u===s||(i=[])):(i=i||[]).push(a,s))}n&&(i=i||[]).push("style",n);var a=i;(t.updateQueue=a)&&(t.flags|=4)}};tf=function(e,t,n,r){n!==r&&(t.flags|=4)};function nr(e,t){if(!K)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function pe(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var o=e.child;o!==null;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags&14680064,r|=o.flags&14680064,o.return=e,o=o.sibling;else for(o=e.child;o!==null;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags,r|=o.flags,o.return=e,o=o.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function fh(e,t,n){var r=t.pendingProps;switch(Ku(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return pe(t),null;case 1:return xe(t.type)&&jo(),pe(t),null;case 3:return r=t.stateNode,zn(),W(Ce),W(ye),ns(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),(e===null||e.child===null)&&(uo(t)?t.flags|=4:e===null||e.memoizedState.isDehydrated&&!(t.flags&256)||(t.flags|=1024,Ke!==null&&(pu(Ke),Ke=null))),iu(e,t),pe(t),null;case 5:ts(t);var o=tn(Fr.current);if(n=t.type,e!==null&&t.stateNode!=null)ef(e,t,n,r,o),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(t.stateNode===null)throw Error(E(166));return pe(t),null}if(e=tn(ot.current),uo(t)){r=t.stateNode,n=t.type;var i=t.memoizedProps;switch(r[et]=t,r[Ur]=i,e=(t.mode&1)!==0,n){case"dialog":b("cancel",r),b("close",r);break;case"iframe":case"object":case"embed":b("load",r);break;case"video":case"audio":for(o=0;o<cr.length;o++)b(cr[o],r);break;case"source":b("error",r);break;case"img":case"image":case"link":b("error",r),b("load",r);break;case"details":b("toggle",r);break;case"input":zs(r,i),b("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!i.multiple},b("invalid",r);break;case"textarea":Bs(r,i),b("invalid",r)}Ol(n,i),o=null;for(var l in i)if(i.hasOwnProperty(l)){var u=i[l];l==="children"?typeof u=="string"?r.textContent!==u&&(i.suppressHydrationWarning!==!0&&lo(r.textContent,u,e),o=["children",u]):typeof u=="number"&&r.textContent!==""+u&&(i.suppressHydrationWarning!==!0&&lo(r.textContent,u,e),o=["children",""+u]):kr.hasOwnProperty(l)&&u!=null&&l==="onScroll"&&b("scroll",r)}switch(n){case"input":Jr(r),Is(r,i,!0);break;case"textarea":Jr(r),js(r);break;case"select":case"option":break;default:typeof i.onClick=="function"&&(r.onclick=Bo)}r=o,t.updateQueue=r,r!==null&&(t.flags|=4)}else{l=o.nodeType===9?o:o.ownerDocument,e==="http://www.w3.org/1999/xhtml"&&(e=Rc(n)),e==="http://www.w3.org/1999/xhtml"?n==="script"?(e=l.createElement("div"),e.innerHTML="<script><\/script>",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=l.createElement(n,{is:r.is}):(e=l.createElement(n),n==="select"&&(l=e,r.multiple?l.multiple=!0:r.size&&(l.size=r.size))):e=l.createElementNS(e,n),e[et]=t,e[Ur]=r,Zd(e,t,!1,!1),t.stateNode=e;e:{switch(l=Fl(n,r),n){case"dialog":b("cancel",e),b("close",e),o=r;break;case"iframe":case"object":case"embed":b("load",e),o=r;break;case"video":case"audio":for(o=0;o<cr.length;o++)b(cr[o],e);o=r;break;case"source":b("error",e),o=r;break;case"img":case"image":case"link":b("error",e),b("load",e),o=r;break;case"details":b("toggle",e),o=r;break;case"input":zs(e,r),o=Pl(e,r),b("invalid",e);break;case"option":o=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},o=Y({},r,{value:void 0}),b("invalid",e);break;case"textarea":Bs(e,r),o=_l(e,r),b("invalid",e);break;default:o=r}Ol(n,o),u=o;for(i in u)if(u.hasOwnProperty(i)){var s=u[i];i==="style"?Uc(e,s):i==="dangerouslySetInnerHTML"?(s=s?s.__html:void 0,s!=null&&Lc(e,s)):i==="children"?typeof s=="string"?(n!=="textarea"||s!=="")&&Er(e,s):typeof s=="number"&&Er(e,""+s):i!=="suppressContentEditableWarning"&&i!=="suppressHydrationWarning"&&i!=="autoFocus"&&(kr.hasOwnProperty(i)?s!=null&&i==="onScroll"&&b("scroll",e):s!=null&&Ou(e,i,s,l))}switch(n){case"input":Jr(e),Is(e,r,!1);break;case"textarea":Jr(e),js(e);break;case"option":r.value!=null&&e.setAttribute("value",""+Ht(r.value));break;case"select":e.multiple=!!r.multiple,i=r.value,i!=null?Rn(e,!!r.multiple,i,!1):r.defaultValue!=null&&Rn(e,!!r.multiple,r.defaultValue,!0);break;default:typeof o.onClick=="function"&&(e.onclick=Bo)}switch(n){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}t.ref!==null&&(t.flags|=512,t.flags|=2097152)}return pe(t),null;case 6:if(e&&t.stateNode!=null)tf(e,t,e.memoizedProps,r);else{if(typeof r!="string"&&t.stateNode===null)throw Error(E(166));if(n=tn(Fr.current),tn(ot.current),uo(t)){if(r=t.stateNode,n=t.memoizedProps,r[et]=t,(i=r.nodeValue!==n)&&(e=Le,e!==null))switch(e.tag){case 3:lo(r.nodeValue,n,(e.mode&1)!==0);break;case 5:e.memoizedProps.suppressHydrationWarning!==!0&&lo(r.nodeValue,n,(e.mode&1)!==0)}i&&(t.flags|=4)}else r=(n.nodeType===9?n:n.ownerDocument).createTextNode(r),r[et]=t,t.stateNode=r}return pe(t),null;case 13:if(W(q),r=t.memoizedState,e===null||e.memoizedState!==null&&e.memoizedState.dehydrated!==null){if(K&&Re!==null&&t.mode&1&&!(t.flags&128))vd(),Mn(),t.flags|=98560,i=!1;else if(i=uo(t),r!==null&&r.dehydrated!==null){if(e===null){if(!i)throw Error(E(318));if(i=t.memoizedState,i=i!==null?i.dehydrated:null,!i)throw Error(E(317));i[et]=t}else Mn(),!(t.flags&128)&&(t.memoizedState=null),t.flags|=4;pe(t),i=!1}else Ke!==null&&(pu(Ke),Ke=null),i=!0;if(!i)return t.flags&65536?t:null}return t.flags&128?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,t.mode&1&&(e===null||q.current&1?oe===0&&(oe=3):ms())),t.updateQueue!==null&&(t.flags|=4),pe(t),null);case 4:return zn(),iu(e,t),e===null&&Lr(t.stateNode.containerInfo),pe(t),null;case 10:return Yu(t.type._context),pe(t),null;case 17:return xe(t.type)&&jo(),pe(t),null;case 19:if(W(q),i=t.memoizedState,i===null)return pe(t),null;if(r=(t.flags&128)!==0,l=i.rendering,l===null)if(r)nr(i,!1);else{if(oe!==0||e!==null&&e.flags&128)for(e=t.child;e!==null;){if(l=Go(e),l!==null){for(t.flags|=128,nr(i,!1),r=l.updateQueue,r!==null&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;n!==null;)i=n,e=r,i.flags&=14680066,l=i.alternate,l===null?(i.childLanes=0,i.lanes=e,i.child=null,i.subtreeFlags=0,i.memoizedProps=null,i.memoizedState=null,i.updateQueue=null,i.dependencies=null,i.stateNode=null):(i.childLanes=l.childLanes,i.lanes=l.lanes,i.child=l.child,i.subtreeFlags=0,i.deletions=null,i.memoizedProps=l.memoizedProps,i.memoizedState=l.memoizedState,i.updateQueue=l.updateQueue,i.type=l.type,e=l.dependencies,i.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return V(q,q.current&1|2),t.child}e=e.sibling}i.tail!==null&&Z()>Bn&&(t.flags|=128,r=!0,nr(i,!1),t.lanes=4194304)}else{if(!r)if(e=Go(l),e!==null){if(t.flags|=128,r=!0,n=e.updateQueue,n!==null&&(t.updateQueue=n,t.flags|=4),nr(i,!0),i.tail===null&&i.tailMode==="hidden"&&!l.alternate&&!K)return pe(t),null}else 2*Z()-i.renderingStartTime>Bn&&n!==1073741824&&(t.flags|=128,r=!0,nr(i,!1),t.lanes=4194304);i.isBackwards?(l.sibling=t.child,t.child=l):(n=i.last,n!==null?n.sibling=l:t.child=l,i.last=l)}return i.tail!==null?(t=i.tail,i.rendering=t,i.tail=t.sibling,i.renderingStartTime=Z(),t.sibling=null,n=q.current,V(q,r?n&1|2:n&1),t):(pe(t),null);case 22:case 23:return ps(),r=t.memoizedState!==null,e!==null&&e.memoizedState!==null!==r&&(t.flags|=8192),r&&t.mode&1?Ne&1073741824&&(pe(t),t.subtreeFlags&6&&(t.flags|=8192)):pe(t),null;case 24:return null;case 25:return null}throw Error(E(156,t.tag))}function ph(e,t){switch(Ku(t),t.tag){case 1:return xe(t.type)&&jo(),e=t.flags,e&65536?(t.flags=e&-65537|128,t):null;case 3:return zn(),W(Ce),W(ye),ns(),e=t.flags,e&65536&&!(e&128)?(t.flags=e&-65537|128,t):null;case 5:return ts(t),null;case 13:if(W(q),e=t.memoizedState,e!==null&&e.dehydrated!==null){if(t.alternate===null)throw Error(E(340));Mn()}return e=t.flags,e&65536?(t.flags=e&-65537|128,t):null;case 19:return W(q),null;case 4:return zn(),null;case 10:return Yu(t.type._context),null;case 22:case 23:return ps(),null;case 24:return null;default:return null}}var co=!1,me=!1,mh=typeof WeakSet=="function"?WeakSet:Set,_=null;function Nn(e,t){var n=e.ref;if(n!==null)if(typeof n=="function")try{n(null)}catch(r){J(e,t,r)}else n.current=null}function lu(e,t,n){try{n()}catch(r){J(e,t,r)}}var _a=!1;function hh(e,t){if(Vl=$o,e=id(),Wu(e)){if("selectionStart"in e)var n={start:e.selectionStart,end:e.selectionEnd};else e:{n=(n=e.ownerDocument)&&n.defaultView||window;var r=n.getSelection&&n.getSelection();if(r&&r.rangeCount!==0){n=r.anchorNode;var o=r.anchorOffset,i=r.focusNode;r=r.focusOffset;try{n.nodeType,i.nodeType}catch{n=null;break e}var l=0,u=-1,s=-1,a=0,d=0,p=e,m=null;t:for(;;){for(var v;p!==n||o!==0&&p.nodeType!==3||(u=l+o),p!==i||r!==0&&p.nodeType!==3||(s=l+r),p.nodeType===3&&(l+=p.nodeValue.length),(v=p.firstChild)!==null;)m=p,p=v;for(;;){if(p===e)break t;if(m===n&&++a===o&&(u=l),m===i&&++d===r&&(s=l),(v=p.nextSibling)!==null)break;p=m,m=p.parentNode}p=v}n=u===-1||s===-1?null:{start:u,end:s}}else n=null}n=n||{start:0,end:0}}else n=null;for(bl={focusedElem:e,selectionRange:n},$o=!1,_=t;_!==null;)if(t=_,e=t.child,(t.subtreeFlags&1028)!==0&&e!==null)e.return=t,_=e;else for(;_!==null;){t=_;try{var h=t.alternate;if(t.flags&1024)switch(t.tag){case 0:case 11:case 15:break;case 1:if(h!==null){var g=h.memoizedProps,T=h.memoizedState,f=t.stateNode,c=f.getSnapshotBeforeUpdate(t.elementType===t.type?g:We(t.type,g),T);f.__reactInternalSnapshotBeforeUpdate=c}break;case 3:var y=t.stateNode.containerInfo;y.nodeType===1?y.textContent="":y.nodeType===9&&y.documentElement&&y.removeChild(y.documentElement);break;case 5:case 6:case 4:case 17:break;default:throw Error(E(163))}}catch(k){J(t,t.return,k)}if(e=t.sibling,e!==null){e.return=t.return,_=e;break}_=t.return}return h=_a,_a=!1,h}function vr(e,t,n){var r=t.updateQueue;if(r=r!==null?r.lastEffect:null,r!==null){var o=r=r.next;do{if((o.tag&e)===e){var i=o.destroy;o.destroy=void 0,i!==void 0&&lu(t,n,i)}o=o.next}while(o!==r)}}function yi(e,t){if(t=t.updateQueue,t=t!==null?t.lastEffect:null,t!==null){var n=t=t.next;do{if((n.tag&e)===e){var r=n.create;n.destroy=r()}n=n.next}while(n!==t)}}function uu(e){var t=e.ref;if(t!==null){var n=e.stateNode;switch(e.tag){case 5:e=n;break;default:e=n}typeof t=="function"?t(e):t.current=e}}function nf(e){var t=e.alternate;t!==null&&(e.alternate=null,nf(t)),e.child=null,e.deletions=null,e.sibling=null,e.tag===5&&(t=e.stateNode,t!==null&&(delete t[et],delete t[Ur],delete t[Kl],delete t[Ym],delete t[Jm])),e.stateNode=null,e.return=null,e.dependencies=null,e.memoizedProps=null,e.memoizedState=null,e.pendingProps=null,e.stateNode=null,e.updateQueue=null}function rf(e){return e.tag===5||e.tag===3||e.tag===4}function Ua(e){e:for(;;){for(;e.sibling===null;){if(e.return===null||rf(e.return))return null;e=e.return}for(e.sibling.return=e.return,e=e.sibling;e.tag!==5&&e.tag!==6&&e.tag!==18;){if(e.flags&2||e.child===null||e.tag===4)continue e;e.child.return=e,e=e.child}if(!(e.flags&2))return e.stateNode}}function su(e,t,n){var r=e.tag;if(r===5||r===6)e=e.stateNode,t?n.nodeType===8?n.parentNode.insertBefore(e,t):n.insertBefore(e,t):(n.nodeType===8?(t=n.parentNode,t.insertBefore(e,n)):(t=n,t.appendChild(e)),n=n._reactRootContainer,n!=null||t.onclick!==null||(t.onclick=Bo));else if(r!==4&&(e=e.child,e!==null))for(su(e,t,n),e=e.sibling;e!==null;)su(e,t,n),e=e.sibling}function au(e,t,n){var r=e.tag;if(r===5||r===6)e=e.stateNode,t?n.insertBefore(e,t):n.appendChild(e);else if(r!==4&&(e=e.child,e!==null))for(au(e,t,n),e=e.sibling;e!==null;)au(e,t,n),e=e.sibling}var ae=null,Qe=!1;function Tt(e,t,n){for(n=n.child;n!==null;)of(e,t,n),n=n.sibling}function of(e,t,n){if(rt&&typeof rt.onCommitFiberUnmount=="function")try{rt.onCommitFiberUnmount(si,n)}catch{}switch(n.tag){case 5:me||Nn(n,t);case 6:var r=ae,o=Qe;ae=null,Tt(e,t,n),ae=r,Qe=o,ae!==null&&(Qe?(e=ae,n=n.stateNode,e.nodeType===8?e.parentNode.removeChild(n):e.removeChild(n)):ae.removeChild(n.stateNode));break;case 18:ae!==null&&(Qe?(e=ae,n=n.stateNode,e.nodeType===8?Xi(e.parentNode,n):e.nodeType===1&&Xi(e,n),Nr(e)):Xi(ae,n.stateNode));break;case 4:r=ae,o=Qe,ae=n.stateNode.containerInfo,Qe=!0,Tt(e,t,n),ae=r,Qe=o;break;case 0:case 11:case 14:case 15:if(!me&&(r=n.updateQueue,r!==null&&(r=r.lastEffect,r!==null))){o=r=r.next;do{var i=o,l=i.destroy;i=i.tag,l!==void 0&&(i&2||i&4)&&lu(n,t,l),o=o.next}while(o!==r)}Tt(e,t,n);break;case 1:if(!me&&(Nn(n,t),r=n.stateNode,typeof r.componentWillUnmount=="function"))try{r.props=n.memoizedProps,r.state=n.memoizedState,r.componentWillUnmount()}catch(u){J(n,t,u)}Tt(e,t,n);break;case 21:Tt(e,t,n);break;case 22:n.mode&1?(me=(r=me)||n.memoizedState!==null,Tt(e,t,n),me=r):Tt(e,t,n);break;default:Tt(e,t,n)}}function Oa(e){var t=e.updateQueue;if(t!==null){e.updateQueue=null;var n=e.stateNode;n===null&&(n=e.stateNode=new mh),t.forEach(function(r){var o=xh.bind(null,e,r);n.has(r)||(n.add(r),r.then(o,o))})}}function be(e,t){var n=t.deletions;if(n!==null)for(var r=0;r<n.length;r++){var o=n[r];try{var i=e,l=t,u=l;e:for(;u!==null;){switch(u.tag){case 5:ae=u.stateNode,Qe=!1;break e;case 3:ae=u.stateNode.containerInfo,Qe=!0;break e;case 4:ae=u.stateNode.containerInfo,Qe=!0;break e}u=u.return}if(ae===null)throw Error(E(160));of(i,l,o),ae=null,Qe=!1;var s=o.alternate;s!==null&&(s.return=null),o.return=null}catch(a){J(o,t,a)}}if(t.subtreeFlags&12854)for(t=t.child;t!==null;)lf(t,e),t=t.sibling}function lf(e,t){var n=e.alternate,r=e.flags;switch(e.tag){case 0:case 11:case 14:case 15:if(be(t,e),Je(e),r&4){try{vr(3,e,e.return),yi(3,e)}catch(g){J(e,e.return,g)}try{vr(5,e,e.return)}catch(g){J(e,e.return,g)}}break;case 1:be(t,e),Je(e),r&512&&n!==null&&Nn(n,n.return);break;case 5:if(be(t,e),Je(e),r&512&&n!==null&&Nn(n,n.return),e.flags&32){var o=e.stateNode;try{Er(o,"")}catch(g){J(e,e.return,g)}}if(r&4&&(o=e.stateNode,o!=null)){var i=e.memoizedProps,l=n!==null?n.memoizedProps:i,u=e.type,s=e.updateQueue;if(e.updateQueue=null,s!==null)try{u==="input"&&i.type==="radio"&&i.name!=null&&Nc(o,i),Fl(u,l);var a=Fl(u,i);for(l=0;l<s.length;l+=2){var d=s[l],p=s[l+1];d==="style"?Uc(o,p):d==="dangerouslySetInnerHTML"?Lc(o,p):d==="children"?Er(o,p):Ou(o,d,p,a)}switch(u){case"input":Rl(o,i);break;case"textarea":Pc(o,i);break;case"select":var m=o._wrapperState.wasMultiple;o._wrapperState.wasMultiple=!!i.multiple;var v=i.value;v!=null?Rn(o,!!i.multiple,v,!1):m!==!!i.multiple&&(i.defaultValue!=null?Rn(o,!!i.multiple,i.defaultValue,!0):Rn(o,!!i.multiple,i.multiple?[]:"",!1))}o[Ur]=i}catch(g){J(e,e.return,g)}}break;case 6:if(be(t,e),Je(e),r&4){if(e.stateNode===null)throw Error(E(162));o=e.stateNode,i=e.memoizedProps;try{o.nodeValue=i}catch(g){J(e,e.return,g)}}break;case 3:if(be(t,e),Je(e),r&4&&n!==null&&n.memoizedState.isDehydrated)try{Nr(t.containerInfo)}catch(g){J(e,e.return,g)}break;case 4:be(t,e),Je(e);break;case 13:be(t,e),Je(e),o=e.child,o.flags&8192&&(i=o.memoizedState!==null,o.stateNode.isHidden=i,!i||o.alternate!==null&&o.alternate.memoizedState!==null||(ds=Z())),r&4&&Oa(e);break;case 22:if(d=n!==null&&n.memoizedState!==null,e.mode&1?(me=(a=me)||d,be(t,e),me=a):be(t,e),Je(e),r&8192){if(a=e.memoizedState!==null,(e.stateNode.isHidden=a)&&!d&&e.mode&1)for(_=e,d=e.child;d!==null;){for(p=_=d;_!==null;){switch(m=_,v=m.child,m.tag){case 0:case 11:case 14:case 15:vr(4,m,m.return);break;case 1:Nn(m,m.return);var h=m.stateNode;if(typeof h.componentWillUnmount=="function"){r=m,n=m.return;try{t=r,h.props=t.memoizedProps,h.state=t.memoizedState,h.componentWillUnmount()}catch(g){J(r,n,g)}}break;case 5:Nn(m,m.return);break;case 22:if(m.memoizedState!==null){Da(p);continue}}v!==null?(v.return=m,_=v):Da(p)}d=d.sibling}e:for(d=null,p=e;;){if(p.tag===5){if(d===null){d=p;try{o=p.stateNode,a?(i=o.style,typeof i.setProperty=="function"?i.setProperty("display","none","important"):i.display="none"):(u=p.stateNode,s=p.memoizedProps.style,l=s!=null&&s.hasOwnProperty("display")?s.display:null,u.style.display=_c("display",l))}catch(g){J(e,e.return,g)}}}else if(p.tag===6){if(d===null)try{p.stateNode.nodeValue=a?"":p.memoizedProps}catch(g){J(e,e.return,g)}}else if((p.tag!==22&&p.tag!==23||p.memoizedState===null||p===e)&&p.child!==null){p.child.return=p,p=p.child;continue}if(p===e)break e;for(;p.sibling===null;){if(p.return===null||p.return===e)break e;d===p&&(d=null),p=p.return}d===p&&(d=null),p.sibling.return=p.return,p=p.sibling}}break;case 19:be(t,e),Je(e),r&4&&Oa(e);break;case 21:break;default:be(t,e),Je(e)}}function Je(e){var t=e.flags;if(t&2){try{e:{for(var n=e.return;n!==null;){if(rf(n)){var r=n;break e}n=n.return}throw Error(E(160))}switch(r.tag){case 5:var o=r.stateNode;r.flags&32&&(Er(o,""),r.flags&=-33);var i=Ua(e);au(e,i,o);break;case 3:case 4:var l=r.stateNode.containerInfo,u=Ua(e);su(e,u,l);break;default:throw Error(E(161))}}catch(s){J(e,e.return,s)}e.flags&=-3}t&4096&&(e.flags&=-4097)}function yh(e,t,n){_=e,uf(e)}function uf(e,t,n){for(var r=(e.mode&1)!==0;_!==null;){var o=_,i=o.child;if(o.tag===22&&r){var l=o.memoizedState!==null||co;if(!l){var u=o.alternate,s=u!==null&&u.memoizedState!==null||me;u=co;var a=me;if(co=l,(me=s)&&!a)for(_=o;_!==null;)l=_,s=l.child,l.tag===22&&l.memoizedState!==null?Aa(o):s!==null?(s.return=l,_=s):Aa(o);for(;i!==null;)_=i,uf(i),i=i.sibling;_=o,co=u,me=a}Fa(e)}else o.subtreeFlags&8772&&i!==null?(i.return=o,_=i):Fa(e)}}function Fa(e){for(;_!==null;){var t=_;if(t.flags&8772){var n=t.alternate;try{if(t.flags&8772)switch(t.tag){case 0:case 11:case 15:me||yi(5,t);break;case 1:var r=t.stateNode;if(t.flags&4&&!me)if(n===null)r.componentDidMount();else{var o=t.elementType===t.type?n.memoizedProps:We(t.type,n.memoizedProps);r.componentDidUpdate(o,n.memoizedState,r.__reactInternalSnapshotBeforeUpdate)}var i=t.updateQueue;i!==null&&ya(t,i,r);break;case 3:var l=t.updateQueue;if(l!==null){if(n=null,t.child!==null)switch(t.child.tag){case 5:n=t.child.stateNode;break;case 1:n=t.child.stateNode}ya(t,l,n)}break;case 5:var u=t.stateNode;if(n===null&&t.flags&4){n=u;var s=t.memoizedProps;switch(t.type){case"button":case"input":case"select":case"textarea":s.autoFocus&&n.focus();break;case"img":s.src&&(n.src=s.src)}}break;case 6:break;case 4:break;case 12:break;case 13:if(t.memoizedState===null){var a=t.alternate;if(a!==null){var d=a.memoizedState;if(d!==null){var p=d.dehydrated;p!==null&&Nr(p)}}}break;case 19:case 17:case 21:case 22:case 23:case 25:break;default:throw Error(E(163))}me||t.flags&512&&uu(t)}catch(m){J(t,t.return,m)}}if(t===e){_=null;break}if(n=t.sibling,n!==null){n.return=t.return,_=n;break}_=t.return}}function Da(e){for(;_!==null;){var t=_;if(t===e){_=null;break}var n=t.sibling;if(n!==null){n.return=t.return,_=n;break}_=t.return}}function Aa(e){for(;_!==null;){var t=_;try{switch(t.tag){case 0:case 11:case 15:var n=t.return;try{yi(4,t)}catch(s){J(t,n,s)}break;case 1:var r=t.stateNode;if(typeof r.componentDidMount=="function"){var o=t.return;try{r.componentDidMount()}catch(s){J(t,o,s)}}var i=t.return;try{uu(t)}catch(s){J(t,i,s)}break;case 5:var l=t.return;try{uu(t)}catch(s){J(t,l,s)}}}catch(s){J(t,t.return,s)}if(t===e){_=null;break}var u=t.sibling;if(u!==null){u.return=t.return,_=u;break}_=t.return}}var gh=Math.ceil,Yo=xt.ReactCurrentDispatcher,as=xt.ReactCurrentOwner,$e=xt.ReactCurrentBatchConfig,I=0,se=null,ne=null,ce=0,Ne=0,Pn=Wt(0),oe=0,$r=null,cn=0,gi=0,cs=0,wr=null,ke=null,ds=0,Bn=1/0,ct=null,Jo=!1,cu=null,It=null,fo=!1,Ft=null,Zo=0,Sr=0,du=null,xo=-1,To=0;function ve(){return I&6?Z():xo!==-1?xo:xo=Z()}function Bt(e){return e.mode&1?I&2&&ce!==0?ce&-ce:eh.transition!==null?(To===0&&(To=Vc()),To):(e=H,e!==0||(e=window.event,e=e===void 0?16:Xc(e.type)),e):1}function qe(e,t,n,r){if(50<Sr)throw Sr=0,du=null,Error(E(185));Br(e,n,r),(!(I&2)||e!==se)&&(e===se&&(!(I&2)&&(gi|=n),oe===4&&_t(e,ce)),Te(e,r),n===1&&I===0&&!(t.mode&1)&&(Bn=Z()+500,pi&&Qt()))}function Te(e,t){var n=e.callbackNode;em(e,t);var r=Mo(e,e===se?ce:0);if(r===0)n!==null&&bs(n),e.callbackNode=null,e.callbackPriority=0;else if(t=r&-r,e.callbackPriority!==t){if(n!=null&&bs(n),t===1)e.tag===0?Zm(Ma.bind(null,e)):hd(Ma.bind(null,e)),qm(function(){!(I&6)&&Qt()}),n=null;else{switch(bc(r)){case 1:n=$u;break;case 4:n=jc;break;case 16:n=Ao;break;case 536870912:n=Hc;break;default:n=Ao}n=hf(n,sf.bind(null,e))}e.callbackPriority=t,e.callbackNode=n}}function sf(e,t){if(xo=-1,To=0,I&6)throw Error(E(327));var n=e.callbackNode;if(Fn()&&e.callbackNode!==n)return null;var r=Mo(e,e===se?ce:0);if(r===0)return null;if(r&30||r&e.expiredLanes||t)t=ei(e,r);else{t=r;var o=I;I|=2;var i=cf();(se!==e||ce!==t)&&(ct=null,Bn=Z()+500,rn(e,t));do try{Sh();break}catch(u){af(e,u)}while(1);Xu(),Yo.current=i,I=o,ne!==null?t=0:(se=null,ce=0,t=oe)}if(t!==0){if(t===2&&(o=zl(e),o!==0&&(r=o,t=fu(e,o))),t===1)throw n=$r,rn(e,0),_t(e,r),Te(e,Z()),n;if(t===6)_t(e,r);else{if(o=e.current.alternate,!(r&30)&&!vh(o)&&(t=ei(e,r),t===2&&(i=zl(e),i!==0&&(r=i,t=fu(e,i))),t===1))throw n=$r,rn(e,0),_t(e,r),Te(e,Z()),n;switch(e.finishedWork=o,e.finishedLanes=r,t){case 0:case 1:throw Error(E(345));case 2:Yt(e,ke,ct);break;case 3:if(_t(e,r),(r&130023424)===r&&(t=ds+500-Z(),10<t)){if(Mo(e,0)!==0)break;if(o=e.suspendedLanes,(o&r)!==r){ve(),e.pingedLanes|=e.suspendedLanes&o;break}e.timeoutHandle=Ql(Yt.bind(null,e,ke,ct),t);break}Yt(e,ke,ct);break;case 4:if(_t(e,r),(r&4194240)===r)break;for(t=e.eventTimes,o=-1;0<r;){var l=31-Ge(r);i=1<<l,l=t[l],l>o&&(o=l),r&=~i}if(r=o,r=Z()-r,r=(120>r?120:480>r?480:1080>r?1080:1920>r?1920:3e3>r?3e3:4320>r?4320:1960*gh(r/1960))-r,10<r){e.timeoutHandle=Ql(Yt.bind(null,e,ke,ct),r);break}Yt(e,ke,ct);break;case 5:Yt(e,ke,ct);break;default:throw Error(E(329))}}}return Te(e,Z()),e.callbackNode===n?sf.bind(null,e):null}function fu(e,t){var n=wr;return e.current.memoizedState.isDehydrated&&(rn(e,t).flags|=256),e=ei(e,t),e!==2&&(t=ke,ke=n,t!==null&&pu(t)),e}function pu(e){ke===null?ke=e:ke.push.apply(ke,e)}function vh(e){for(var t=e;;){if(t.flags&16384){var n=t.updateQueue;if(n!==null&&(n=n.stores,n!==null))for(var r=0;r<n.length;r++){var o=n[r],i=o.getSnapshot;o=o.value;try{if(!Xe(i(),o))return!1}catch{return!1}}}if(n=t.child,t.subtreeFlags&16384&&n!==null)n.return=t,t=n;else{if(t===e)break;for(;t.sibling===null;){if(t.return===null||t.return===e)return!0;t=t.return}t.sibling.return=t.return,t=t.sibling}}return!0}function _t(e,t){for(t&=~cs,t&=~gi,e.suspendedLanes|=t,e.pingedLanes&=~t,e=e.expirationTimes;0<t;){var n=31-Ge(t),r=1<<n;e[n]=-1,t&=~r}}function Ma(e){if(I&6)throw Error(E(327));Fn();var t=Mo(e,0);if(!(t&1))return Te(e,Z()),null;var n=ei(e,t);if(e.tag!==0&&n===2){var r=zl(e);r!==0&&(t=r,n=fu(e,r))}if(n===1)throw n=$r,rn(e,0),_t(e,t),Te(e,Z()),n;if(n===6)throw Error(E(345));return e.finishedWork=e.current.alternate,e.finishedLanes=t,Yt(e,ke,ct),Te(e,Z()),null}function fs(e,t){var n=I;I|=1;try{return e(t)}finally{I=n,I===0&&(Bn=Z()+500,pi&&Qt())}}function dn(e){Ft!==null&&Ft.tag===0&&!(I&6)&&Fn();var t=I;I|=1;var n=$e.transition,r=H;try{if($e.transition=null,H=1,e)return e()}finally{H=r,$e.transition=n,I=t,!(I&6)&&Qt()}}function ps(){Ne=Pn.current,W(Pn)}function rn(e,t){e.finishedWork=null,e.finishedLanes=0;var n=e.timeoutHandle;if(n!==-1&&(e.timeoutHandle=-1,Gm(n)),ne!==null)for(n=ne.return;n!==null;){var r=n;switch(Ku(r),r.tag){case 1:r=r.type.childContextTypes,r!=null&&jo();break;case 3:zn(),W(Ce),W(ye),ns();break;case 5:ts(r);break;case 4:zn();break;case 13:W(q);break;case 19:W(q);break;case 10:Yu(r.type._context);break;case 22:case 23:ps()}n=n.return}if(se=e,ne=e=jt(e.current,null),ce=Ne=t,oe=0,$r=null,cs=gi=cn=0,ke=wr=null,en!==null){for(t=0;t<en.length;t++)if(n=en[t],r=n.interleaved,r!==null){n.interleaved=null;var o=r.next,i=n.pending;if(i!==null){var l=i.next;i.next=o,r.next=l}n.pending=r}en=null}return e}function af(e,t){do{var n=ne;try{if(Xu(),ko.current=Xo,qo){for(var r=X.memoizedState;r!==null;){var o=r.queue;o!==null&&(o.pending=null),r=r.next}qo=!1}if(an=0,le=re=X=null,gr=!1,Dr=0,as.current=null,n===null||n.return===null){oe=1,$r=t,ne=null;break}e:{var i=e,l=n.return,u=n,s=t;if(t=ce,u.flags|=32768,s!==null&&typeof s=="object"&&typeof s.then=="function"){var a=s,d=u,p=d.tag;if(!(d.mode&1)&&(p===0||p===11||p===15)){var m=d.alternate;m?(d.updateQueue=m.updateQueue,d.memoizedState=m.memoizedState,d.lanes=m.lanes):(d.updateQueue=null,d.memoizedState=null)}var v=Ca(l);if(v!==null){v.flags&=-257,xa(v,l,u,i,t),v.mode&1&&Ea(i,a,t),t=v,s=a;var h=t.updateQueue;if(h===null){var g=new Set;g.add(s),t.updateQueue=g}else h.add(s);break e}else{if(!(t&1)){Ea(i,a,t),ms();break e}s=Error(E(426))}}else if(K&&u.mode&1){var T=Ca(l);if(T!==null){!(T.flags&65536)&&(T.flags|=256),xa(T,l,u,i,t),Gu(In(s,u));break e}}i=s=In(s,u),oe!==4&&(oe=2),wr===null?wr=[i]:wr.push(i),i=l;do{switch(i.tag){case 3:i.flags|=65536,t&=-t,i.lanes|=t;var f=Wd(i,s,t);ha(i,f);break e;case 1:u=s;var c=i.type,y=i.stateNode;if(!(i.flags&128)&&(typeof c.getDerivedStateFromError=="function"||y!==null&&typeof y.componentDidCatch=="function"&&(It===null||!It.has(y)))){i.flags|=65536,t&=-t,i.lanes|=t;var k=Qd(i,u,t);ha(i,k);break e}}i=i.return}while(i!==null)}ff(n)}catch(N){t=N,ne===n&&n!==null&&(ne=n=n.return);continue}break}while(1)}function cf(){var e=Yo.current;return Yo.current=Xo,e===null?Xo:e}function ms(){(oe===0||oe===3||oe===2)&&(oe=4),se===null||!(cn&268435455)&&!(gi&268435455)||_t(se,ce)}function ei(e,t){var n=I;I|=2;var r=cf();(se!==e||ce!==t)&&(ct=null,rn(e,t));do try{wh();break}catch(o){af(e,o)}while(1);if(Xu(),I=n,Yo.current=r,ne!==null)throw Error(E(261));return se=null,ce=0,oe}function wh(){for(;ne!==null;)df(ne)}function Sh(){for(;ne!==null&&!Wp();)df(ne)}function df(e){var t=mf(e.alternate,e,Ne);e.memoizedProps=e.pendingProps,t===null?ff(e):ne=t,as.current=null}function ff(e){var t=e;do{var n=t.alternate;if(e=t.return,t.flags&32768){if(n=ph(n,t),n!==null){n.flags&=32767,ne=n;return}if(e!==null)e.flags|=32768,e.subtreeFlags=0,e.deletions=null;else{oe=6,ne=null;return}}else if(n=fh(n,t,Ne),n!==null){ne=n;return}if(t=t.sibling,t!==null){ne=t;return}ne=t=e}while(t!==null);oe===0&&(oe=5)}function Yt(e,t,n){var r=H,o=$e.transition;try{$e.transition=null,H=1,kh(e,t,n,r)}finally{$e.transition=o,H=r}return null}function kh(e,t,n,r){do Fn();while(Ft!==null);if(I&6)throw Error(E(327));n=e.finishedWork;var o=e.finishedLanes;if(n===null)return null;if(e.finishedWork=null,e.finishedLanes=0,n===e.current)throw Error(E(177));e.callbackNode=null,e.callbackPriority=0;var i=n.lanes|n.childLanes;if(tm(e,i),e===se&&(ne=se=null,ce=0),!(n.subtreeFlags&2064)&&!(n.flags&2064)||fo||(fo=!0,hf(Ao,function(){return Fn(),null})),i=(n.flags&15990)!==0,n.subtreeFlags&15990||i){i=$e.transition,$e.transition=null;var l=H;H=1;var u=I;I|=4,as.current=null,hh(e,n),lf(n,e),jm(bl),$o=!!Vl,bl=Vl=null,e.current=n,yh(n),Qp(),I=u,H=l,$e.transition=i}else e.current=n;if(fo&&(fo=!1,Ft=e,Zo=o),i=e.pendingLanes,i===0&&(It=null),qp(n.stateNode),Te(e,Z()),t!==null)for(r=e.onRecoverableError,n=0;n<t.length;n++)o=t[n],r(o.value,{componentStack:o.stack,digest:o.digest});if(Jo)throw Jo=!1,e=cu,cu=null,e;return Zo&1&&e.tag!==0&&Fn(),i=e.pendingLanes,i&1?e===du?Sr++:(Sr=0,du=e):Sr=0,Qt(),null}function Fn(){if(Ft!==null){var e=bc(Zo),t=$e.transition,n=H;try{if($e.transition=null,H=16>e?16:e,Ft===null)var r=!1;else{if(e=Ft,Ft=null,Zo=0,I&6)throw Error(E(331));var o=I;for(I|=4,_=e.current;_!==null;){var i=_,l=i.child;if(_.flags&16){var u=i.deletions;if(u!==null){for(var s=0;s<u.length;s++){var a=u[s];for(_=a;_!==null;){var d=_;switch(d.tag){case 0:case 11:case 15:vr(8,d,i)}var p=d.child;if(p!==null)p.return=d,_=p;else for(;_!==null;){d=_;var m=d.sibling,v=d.return;if(nf(d),d===a){_=null;break}if(m!==null){m.return=v,_=m;break}_=v}}}var h=i.alternate;if(h!==null){var g=h.child;if(g!==null){h.child=null;do{var T=g.sibling;g.sibling=null,g=T}while(g!==null)}}_=i}}if(i.subtreeFlags&2064&&l!==null)l.return=i,_=l;else e:for(;_!==null;){if(i=_,i.flags&2048)switch(i.tag){case 0:case 11:case 15:vr(9,i,i.return)}var f=i.sibling;if(f!==null){f.return=i.return,_=f;break e}_=i.return}}var c=e.current;for(_=c;_!==null;){l=_;var y=l.child;if(l.subtreeFlags&2064&&y!==null)y.return=l,_=y;else e:for(l=c;_!==null;){if(u=_,u.flags&2048)try{switch(u.tag){case 0:case 11:case 15:yi(9,u)}}catch(N){J(u,u.return,N)}if(u===l){_=null;break e}var k=u.sibling;if(k!==null){k.return=u.return,_=k;break e}_=u.return}}if(I=o,Qt(),rt&&typeof rt.onPostCommitFiberRoot=="function")try{rt.onPostCommitFiberRoot(si,e)}catch{}r=!0}return r}finally{H=n,$e.transition=t}}return!1}function $a(e,t,n){t=In(n,t),t=Wd(e,t,1),e=zt(e,t,1),t=ve(),e!==null&&(Br(e,1,t),Te(e,t))}function J(e,t,n){if(e.tag===3)$a(e,e,n);else for(;t!==null;){if(t.tag===3){$a(t,e,n);break}else if(t.tag===1){var r=t.stateNode;if(typeof t.type.getDerivedStateFromError=="function"||typeof r.componentDidCatch=="function"&&(It===null||!It.has(r))){e=In(n,e),e=Qd(t,e,1),t=zt(t,e,1),e=ve(),t!==null&&(Br(t,1,e),Te(t,e));break}}t=t.return}}function Eh(e,t,n){var r=e.pingCache;r!==null&&r.delete(t),t=ve(),e.pingedLanes|=e.suspendedLanes&n,se===e&&(ce&n)===n&&(oe===4||oe===3&&(ce&130023424)===ce&&500>Z()-ds?rn(e,0):cs|=n),Te(e,t)}function pf(e,t){t===0&&(e.mode&1?(t=to,to<<=1,!(to&130023424)&&(to=4194304)):t=1);var n=ve();e=Et(e,t),e!==null&&(Br(e,t,n),Te(e,n))}function Ch(e){var t=e.memoizedState,n=0;t!==null&&(n=t.retryLane),pf(e,n)}function xh(e,t){var n=0;switch(e.tag){case 13:var r=e.stateNode,o=e.memoizedState;o!==null&&(n=o.retryLane);break;case 19:r=e.stateNode;break;default:throw Error(E(314))}r!==null&&r.delete(t),pf(e,n)}var mf;mf=function(e,t,n){if(e!==null)if(e.memoizedProps!==t.pendingProps||Ce.current)Ee=!0;else{if(!(e.lanes&n)&&!(t.flags&128))return Ee=!1,dh(e,t,n);Ee=!!(e.flags&131072)}else Ee=!1,K&&t.flags&1048576&&yd(t,bo,t.index);switch(t.lanes=0,t.tag){case 2:var r=t.type;Co(e,t),e=t.pendingProps;var o=An(t,ye.current);On(t,n),o=os(null,t,r,e,o,n);var i=is();return t.flags|=1,typeof o=="object"&&o!==null&&typeof o.render=="function"&&o.$$typeof===void 0?(t.tag=1,t.memoizedState=null,t.updateQueue=null,xe(r)?(i=!0,Ho(t)):i=!1,t.memoizedState=o.state!==null&&o.state!==void 0?o.state:null,Zu(t),o.updater=mi,t.stateNode=o,o._reactInternals=t,Zl(t,r,e,n),t=nu(null,t,r,!0,i,n)):(t.tag=0,K&&i&&Qu(t),ge(null,t,o,n),t=t.child),t;case 16:r=t.elementType;e:{switch(Co(e,t),e=t.pendingProps,o=r._init,r=o(r._payload),t.type=r,o=t.tag=Nh(r),e=We(r,e),o){case 0:t=tu(null,t,r,e,n);break e;case 1:t=Pa(null,t,r,e,n);break e;case 11:t=Ta(null,t,r,e,n);break e;case 14:t=Na(null,t,r,We(r.type,e),n);break e}throw Error(E(306,r,""))}return t;case 0:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:We(r,o),tu(e,t,r,o,n);case 1:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:We(r,o),Pa(e,t,r,o,n);case 3:e:{if(Xd(t),e===null)throw Error(E(387));r=t.pendingProps,i=t.memoizedState,o=i.element,Sd(e,t),Ko(t,r,null,n);var l=t.memoizedState;if(r=l.element,i.isDehydrated)if(i={element:r,isDehydrated:!1,cache:l.cache,pendingSuspenseBoundaries:l.pendingSuspenseBoundaries,transitions:l.transitions},t.updateQueue.baseState=i,t.memoizedState=i,t.flags&256){o=In(Error(E(423)),t),t=Ra(e,t,r,n,o);break e}else if(r!==o){o=In(Error(E(424)),t),t=Ra(e,t,r,n,o);break e}else for(Re=$t(t.stateNode.containerInfo.firstChild),Le=t,K=!0,Ke=null,n=xd(t,null,r,n),t.child=n;n;)n.flags=n.flags&-3|4096,n=n.sibling;else{if(Mn(),r===o){t=Ct(e,t,n);break e}ge(e,t,r,n)}t=t.child}return t;case 5:return Td(t),e===null&&Xl(t),r=t.type,o=t.pendingProps,i=e!==null?e.memoizedProps:null,l=o.children,Wl(r,o)?l=null:i!==null&&Wl(r,i)&&(t.flags|=32),qd(e,t),ge(e,t,l,n),t.child;case 6:return e===null&&Xl(t),null;case 13:return Yd(e,t,n);case 4:return es(t,t.stateNode.containerInfo),r=t.pendingProps,e===null?t.child=$n(t,null,r,n):ge(e,t,r,n),t.child;case 11:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:We(r,o),Ta(e,t,r,o,n);case 7:return ge(e,t,t.pendingProps,n),t.child;case 8:return ge(e,t,t.pendingProps.children,n),t.child;case 12:return ge(e,t,t.pendingProps.children,n),t.child;case 10:e:{if(r=t.type._context,o=t.pendingProps,i=t.memoizedProps,l=o.value,V(Wo,r._currentValue),r._currentValue=l,i!==null)if(Xe(i.value,l)){if(i.children===o.children&&!Ce.current){t=Ct(e,t,n);break e}}else for(i=t.child,i!==null&&(i.return=t);i!==null;){var u=i.dependencies;if(u!==null){l=i.child;for(var s=u.firstContext;s!==null;){if(s.context===r){if(i.tag===1){s=yt(-1,n&-n),s.tag=2;var a=i.updateQueue;if(a!==null){a=a.shared;var d=a.pending;d===null?s.next=s:(s.next=d.next,d.next=s),a.pending=s}}i.lanes|=n,s=i.alternate,s!==null&&(s.lanes|=n),Yl(i.return,n,t),u.lanes|=n;break}s=s.next}}else if(i.tag===10)l=i.type===t.type?null:i.child;else if(i.tag===18){if(l=i.return,l===null)throw Error(E(341));l.lanes|=n,u=l.alternate,u!==null&&(u.lanes|=n),Yl(l,n,t),l=i.sibling}else l=i.child;if(l!==null)l.return=i;else for(l=i;l!==null;){if(l===t){l=null;break}if(i=l.sibling,i!==null){i.return=l.return,l=i;break}l=l.return}i=l}ge(e,t,o.children,n),t=t.child}return t;case 9:return o=t.type,r=t.pendingProps.children,On(t,n),o=Ie(o),r=r(o),t.flags|=1,ge(e,t,r,n),t.child;case 14:return r=t.type,o=We(r,t.pendingProps),o=We(r.type,o),Na(e,t,r,o,n);case 15:return Kd(e,t,t.type,t.pendingProps,n);case 17:return r=t.type,o=t.pendingProps,o=t.elementType===r?o:We(r,o),Co(e,t),t.tag=1,xe(r)?(e=!0,Ho(t)):e=!1,On(t,n),Ed(t,r,o),Zl(t,r,o,n),nu(null,t,r,!0,e,n);case 19:return Jd(e,t,n);case 22:return Gd(e,t,n)}throw Error(E(156,t.tag))};function hf(e,t){return Bc(e,t)}function Th(e,t,n,r){this.tag=e,this.key=n,this.sibling=this.child=this.return=this.stateNode=this.type=this.elementType=null,this.index=0,this.ref=null,this.pendingProps=t,this.dependencies=this.memoizedState=this.updateQueue=this.memoizedProps=null,this.mode=r,this.subtreeFlags=this.flags=0,this.deletions=null,this.childLanes=this.lanes=0,this.alternate=null}function Me(e,t,n,r){return new Th(e,t,n,r)}function hs(e){return e=e.prototype,!(!e||!e.isReactComponent)}function Nh(e){if(typeof e=="function")return hs(e)?1:0;if(e!=null){if(e=e.$$typeof,e===Du)return 11;if(e===Au)return 14}return 2}function jt(e,t){var n=e.alternate;return n===null?(n=Me(e.tag,t,e.key,e.mode),n.elementType=e.elementType,n.type=e.type,n.stateNode=e.stateNode,n.alternate=e,e.alternate=n):(n.pendingProps=t,n.type=e.type,n.flags=0,n.subtreeFlags=0,n.deletions=null),n.flags=e.flags&14680064,n.childLanes=e.childLanes,n.lanes=e.lanes,n.child=e.child,n.memoizedProps=e.memoizedProps,n.memoizedState=e.memoizedState,n.updateQueue=e.updateQueue,t=e.dependencies,n.dependencies=t===null?null:{lanes:t.lanes,firstContext:t.firstContext},n.sibling=e.sibling,n.index=e.index,n.ref=e.ref,n}function No(e,t,n,r,o,i){var l=2;if(r=e,typeof e=="function")hs(e)&&(l=1);else if(typeof e=="string")l=5;else e:switch(e){case gn:return on(n.children,o,i,t);case Fu:l=8,o|=8;break;case Cl:return e=Me(12,n,t,o|2),e.elementType=Cl,e.lanes=i,e;case xl:return e=Me(13,n,t,o),e.elementType=xl,e.lanes=i,e;case Tl:return e=Me(19,n,t,o),e.elementType=Tl,e.lanes=i,e;case Cc:return vi(n,o,i,t);default:if(typeof e=="object"&&e!==null)switch(e.$$typeof){case kc:l=10;break e;case Ec:l=9;break e;case Du:l=11;break e;case Au:l=14;break e;case Pt:l=16,r=null;break e}throw Error(E(130,e==null?e:typeof e,""))}return t=Me(l,n,t,o),t.elementType=e,t.type=r,t.lanes=i,t}function on(e,t,n,r){return e=Me(7,e,r,t),e.lanes=n,e}function vi(e,t,n,r){return e=Me(22,e,r,t),e.elementType=Cc,e.lanes=n,e.stateNode={isHidden:!1},e}function ol(e,t,n){return e=Me(6,e,null,t),e.lanes=n,e}function il(e,t,n){return t=Me(4,e.children!==null?e.children:[],e.key,t),t.lanes=n,t.stateNode={containerInfo:e.containerInfo,pendingChildren:null,implementation:e.implementation},t}function Ph(e,t,n,r,o){this.tag=t,this.containerInfo=e,this.finishedWork=this.pingCache=this.current=this.pendingChildren=null,this.timeoutHandle=-1,this.callbackNode=this.pendingContext=this.context=null,this.callbackPriority=0,this.eventTimes=Ii(0),this.expirationTimes=Ii(-1),this.entangledLanes=this.finishedLanes=this.mutableReadLanes=this.expiredLanes=this.pingedLanes=this.suspendedLanes=this.pendingLanes=0,this.entanglements=Ii(0),this.identifierPrefix=r,this.onRecoverableError=o,this.mutableSourceEagerHydrationData=null}function ys(e,t,n,r,o,i,l,u,s){return e=new Ph(e,t,n,u,s),t===1?(t=1,i===!0&&(t|=8)):t=0,i=Me(3,null,null,t),e.current=i,i.stateNode=e,i.memoizedState={element:r,isDehydrated:n,cache:null,transitions:null,pendingSuspenseBoundaries:null},Zu(i),e}function Rh(e,t,n){var r=3<arguments.length&&arguments[3]!==void 0?arguments[3]:null;return{$$typeof:yn,key:r==null?null:""+r,children:e,containerInfo:t,implementation:n}}function yf(e){if(!e)return Vt;e=e._reactInternals;e:{if(pn(e)!==e||e.tag!==1)throw Error(E(170));var t=e;do{switch(t.tag){case 3:t=t.stateNode.context;break e;case 1:if(xe(t.type)){t=t.stateNode.__reactInternalMemoizedMergedChildContext;break e}}t=t.return}while(t!==null);throw Error(E(171))}if(e.tag===1){var n=e.type;if(xe(n))return md(e,n,t)}return t}function gf(e,t,n,r,o,i,l,u,s){return e=ys(n,r,!0,e,o,i,l,u,s),e.context=yf(null),n=e.current,r=ve(),o=Bt(n),i=yt(r,o),i.callback=t??null,zt(n,i,o),e.current.lanes=o,Br(e,o,r),Te(e,r),e}function wi(e,t,n,r){var o=t.current,i=ve(),l=Bt(o);return n=yf(n),t.context===null?t.context=n:t.pendingContext=n,t=yt(i,l),t.payload={element:e},r=r===void 0?null:r,r!==null&&(t.callback=r),e=zt(o,t,l),e!==null&&(qe(e,o,l,i),So(e,o,l)),l}function ti(e){if(e=e.current,!e.child)return null;switch(e.child.tag){case 5:return e.child.stateNode;default:return e.child.stateNode}}function za(e,t){if(e=e.memoizedState,e!==null&&e.dehydrated!==null){var n=e.retryLane;e.retryLane=n!==0&&n<t?n:t}}function gs(e,t){za(e,t),(e=e.alternate)&&za(e,t)}function Lh(){return null}var vf=typeof reportError=="function"?reportError:function(e){console.error(e)};function vs(e){this._internalRoot=e}Si.prototype.render=vs.prototype.render=function(e){var t=this._internalRoot;if(t===null)throw Error(E(409));wi(e,t,null,null)};Si.prototype.unmount=vs.prototype.unmount=function(){var e=this._internalRoot;if(e!==null){this._internalRoot=null;var t=e.containerInfo;dn(function(){wi(null,e,null,null)}),t[kt]=null}};function Si(e){this._internalRoot=e}Si.prototype.unstable_scheduleHydration=function(e){if(e){var t=Kc();e={blockedOn:null,target:e,priority:t};for(var n=0;n<Lt.length&&t!==0&&t<Lt[n].priority;n++);Lt.splice(n,0,e),n===0&&qc(e)}};function ws(e){return!(!e||e.nodeType!==1&&e.nodeType!==9&&e.nodeType!==11)}function ki(e){return!(!e||e.nodeType!==1&&e.nodeType!==9&&e.nodeType!==11&&(e.nodeType!==8||e.nodeValue!==" react-mount-point-unstable "))}function Ia(){}function _h(e,t,n,r,o){if(o){if(typeof r=="function"){var i=r;r=function(){var a=ti(l);i.call(a)}}var l=gf(t,r,e,0,null,!1,!1,"",Ia);return e._reactRootContainer=l,e[kt]=l.current,Lr(e.nodeType===8?e.parentNode:e),dn(),l}for(;o=e.lastChild;)e.removeChild(o);if(typeof r=="function"){var u=r;r=function(){var a=ti(s);u.call(a)}}var s=ys(e,0,!1,null,null,!1,!1,"",Ia);return e._reactRootContainer=s,e[kt]=s.current,Lr(e.nodeType===8?e.parentNode:e),dn(function(){wi(t,s,n,r)}),s}function Ei(e,t,n,r,o){var i=n._reactRootContainer;if(i){var l=i;if(typeof o=="function"){var u=o;o=function(){var s=ti(l);u.call(s)}}wi(t,l,e,o)}else l=_h(n,t,e,o,r);return ti(l)}Wc=function(e){switch(e.tag){case 3:var t=e.stateNode;if(t.current.memoizedState.isDehydrated){var n=ar(t.pendingLanes);n!==0&&(zu(t,n|1),Te(t,Z()),!(I&6)&&(Bn=Z()+500,Qt()))}break;case 13:dn(function(){var r=Et(e,1);if(r!==null){var o=ve();qe(r,e,1,o)}}),gs(e,1)}};Iu=function(e){if(e.tag===13){var t=Et(e,134217728);if(t!==null){var n=ve();qe(t,e,134217728,n)}gs(e,134217728)}};Qc=function(e){if(e.tag===13){var t=Bt(e),n=Et(e,t);if(n!==null){var r=ve();qe(n,e,t,r)}gs(e,t)}};Kc=function(){return H};Gc=function(e,t){var n=H;try{return H=e,t()}finally{H=n}};Al=function(e,t,n){switch(t){case"input":if(Rl(e,n),t=n.name,n.type==="radio"&&t!=null){for(n=e;n.parentNode;)n=n.parentNode;for(n=n.querySelectorAll("input[name="+JSON.stringify(""+t)+'][type="radio"]'),t=0;t<n.length;t++){var r=n[t];if(r!==e&&r.form===e.form){var o=fi(r);if(!o)throw Error(E(90));Tc(r),Rl(r,o)}}}break;case"textarea":Pc(e,n);break;case"select":t=n.value,t!=null&&Rn(e,!!n.multiple,t,!1)}};Dc=fs;Ac=dn;var Uh={usingClientEntryPoint:!1,Events:[Hr,kn,fi,Oc,Fc,fs]},rr={findFiberByHostInstance:Zt,bundleType:0,version:"18.2.0",rendererPackageName:"react-dom"},Oh={bundleType:rr.bundleType,version:rr.version,rendererPackageName:rr.rendererPackageName,rendererConfig:rr.rendererConfig,overrideHookState:null,overrideHookStateDeletePath:null,overrideHookStateRenamePath:null,overrideProps:null,overridePropsDeletePath:null,overridePropsRenamePath:null,setErrorHandler:null,setSuspenseHandler:null,scheduleUpdate:null,currentDispatcherRef:xt.ReactCurrentDispatcher,findHostInstanceByFiber:function(e){return e=zc(e),e===null?null:e.stateNode},findFiberByHostInstance:rr.findFiberByHostInstance||Lh,findHostInstancesForRefresh:null,scheduleRefresh:null,scheduleRoot:null,setRefreshHandler:null,getCurrentFiber:null,reconcilerVersion:"18.2.0-next-9e3b772b8-20220608"};if(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__<"u"){var po=__REACT_DEVTOOLS_GLOBAL_HOOK__;if(!po.isDisabled&&po.supportsFiber)try{si=po.inject(Oh),rt=po}catch{}}Ue.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=Uh;Ue.createPortal=function(e,t){var n=2<arguments.length&&arguments[2]!==void 0?arguments[2]:null;if(!ws(t))throw Error(E(200));return Rh(e,t,null,n)};Ue.createRoot=function(e,t){if(!ws(e))throw Error(E(299));var n=!1,r="",o=vf;return t!=null&&(t.unstable_strictMode===!0&&(n=!0),t.identifierPrefix!==void 0&&(r=t.identifierPrefix),t.onRecoverableError!==void 0&&(o=t.onRecoverableError)),t=ys(e,1,!1,null,null,n,!1,r,o),e[kt]=t.current,Lr(e.nodeType===8?e.parentNode:e),new vs(t)};Ue.findDOMNode=function(e){if(e==null)return null;if(e.nodeType===1)return e;var t=e._reactInternals;if(t===void 0)throw typeof e.render=="function"?Error(E(188)):(e=Object.keys(e).join(","),Error(E(268,e)));return e=zc(t),e=e===null?null:e.stateNode,e};Ue.flushSync=function(e){return dn(e)};Ue.hydrate=function(e,t,n){if(!ki(t))throw Error(E(200));return Ei(null,e,t,!0,n)};Ue.hydrateRoot=function(e,t,n){if(!ws(e))throw Error(E(405));var r=n!=null&&n.hydratedSources||null,o=!1,i="",l=vf;if(n!=null&&(n.unstable_strictMode===!0&&(o=!0),n.identifierPrefix!==void 0&&(i=n.identifierPrefix),n.onRecoverableError!==void 0&&(l=n.onRecoverableError)),t=gf(t,null,e,1,n??null,o,!1,i,l),e[kt]=t.current,Lr(e),r)for(e=0;e<r.length;e++)n=r[e],o=n._getVersion,o=o(n._source),t.mutableSourceEagerHydrationData==null?t.mutableSourceEagerHydrationData=[n,o]:t.mutableSourceEagerHydrationData.push(n,o);return new Si(t)};Ue.render=function(e,t,n){if(!ki(t))throw Error(E(200));return Ei(null,e,t,!1,n)};Ue.unmountComponentAtNode=function(e){if(!ki(e))throw Error(E(40));return e._reactRootContainer?(dn(function(){Ei(null,null,e,!1,function(){e._reactRootContainer=null,e[kt]=null})}),!0):!1};Ue.unstable_batchedUpdates=fs;Ue.unstable_renderSubtreeIntoContainer=function(e,t,n,r){if(!ki(n))throw Error(E(200));if(e==null||e._reactInternals===void 0)throw Error(E(38));return Ei(e,t,n,!1,r)};Ue.version="18.2.0-next-9e3b772b8-20220608";function wf(){if(!(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__>"u"||typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE!="function"))try{__REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE(wf)}catch(e){console.error(e)}}wf(),yc.exports=Ue;var Sf=yc.exports,Ba=Sf;kl.createRoot=Ba.createRoot,kl.hydrateRoot=Ba.hydrateRoot;function kf(e,t){return function(){return e.apply(t,arguments)}}const{toString:Fh}=Object.prototype,{getPrototypeOf:Ss}=Object,Ci=(e=>t=>{const n=Fh.call(t);return e[n]||(e[n]=n.slice(8,-1).toLowerCase())})(Object.create(null)),it=e=>(e=e.toLowerCase(),t=>Ci(t)===e),xi=e=>t=>typeof t===e,{isArray:Wn}=Array,zr=xi("undefined");function Dh(e){return e!==null&&!zr(e)&&e.constructor!==null&&!zr(e.constructor)&&ze(e.constructor.isBuffer)&&e.constructor.isBuffer(e)}const Ef=it("ArrayBuffer");function Ah(e){let t;return typeof ArrayBuffer<"u"&&ArrayBuffer.isView?t=ArrayBuffer.isView(e):t=e&&e.buffer&&Ef(e.buffer),t}const Mh=xi("string"),ze=xi("function"),Cf=xi("number"),Ti=e=>e!==null&&typeof e=="object",$h=e=>e===!0||e===!1,Po=e=>{if(Ci(e)!=="object")return!1;const t=Ss(e);return(t===null||t===Object.prototype||Object.getPrototypeOf(t)===null)&&!(Symbol.toStringTag in e)&&!(Symbol.iterator in e)},zh=it("Date"),Ih=it("File"),Bh=it("Blob"),jh=it("FileList"),Hh=e=>Ti(e)&&ze(e.pipe),Vh=e=>{let t;return e&&(typeof FormData=="function"&&e instanceof FormData||ze(e.append)&&((t=Ci(e))==="formdata"||t==="object"&&ze(e.toString)&&e.toString()==="[object FormData]"))},bh=it("URLSearchParams"),Wh=e=>e.trim?e.trim():e.replace(/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,"");function br(e,t,{allOwnKeys:n=!1}={}){if(e===null||typeof e>"u")return;let r,o;if(typeof e!="object"&&(e=[e]),Wn(e))for(r=0,o=e.length;r<o;r++)t.call(null,e[r],r,e);else{const i=n?Object.getOwnPropertyNames(e):Object.keys(e),l=i.length;let u;for(r=0;r<l;r++)u=i[r],t.call(null,e[u],u,e)}}function xf(e,t){t=t.toLowerCase();const n=Object.keys(e);let r=n.length,o;for(;r-- >0;)if(o=n[r],t===o.toLowerCase())return o;return null}const Tf=(()=>typeof globalThis<"u"?globalThis:typeof self<"u"?self:typeof window<"u"?window:global)(),Nf=e=>!zr(e)&&e!==Tf;function mu(){const{caseless:e}=Nf(this)&&this||{},t={},n=(r,o)=>{const i=e&&xf(t,o)||o;Po(t[i])&&Po(r)?t[i]=mu(t[i],r):Po(r)?t[i]=mu({},r):Wn(r)?t[i]=r.slice():t[i]=r};for(let r=0,o=arguments.length;r<o;r++)arguments[r]&&br(arguments[r],n);return t}const Qh=(e,t,n,{allOwnKeys:r}={})=>(br(t,(o,i)=>{n&&ze(o)?e[i]=kf(o,n):e[i]=o},{allOwnKeys:r}),e),Kh=e=>(e.charCodeAt(0)===65279&&(e=e.slice(1)),e),Gh=(e,t,n,r)=>{e.prototype=Object.create(t.prototype,r),e.prototype.constructor=e,Object.defineProperty(e,"super",{value:t.prototype}),n&&Object.assign(e.prototype,n)},qh=(e,t,n,r)=>{let o,i,l;const u={};if(t=t||{},e==null)return t;do{for(o=Object.getOwnPropertyNames(e),i=o.length;i-- >0;)l=o[i],(!r||r(l,e,t))&&!u[l]&&(t[l]=e[l],u[l]=!0);e=n!==!1&&Ss(e)}while(e&&(!n||n(e,t))&&e!==Object.prototype);return t},Xh=(e,t,n)=>{e=String(e),(n===void 0||n>e.length)&&(n=e.length),n-=t.length;const r=e.indexOf(t,n);return r!==-1&&r===n},Yh=e=>{if(!e)return null;if(Wn(e))return e;let t=e.length;if(!Cf(t))return null;const n=new Array(t);for(;t-- >0;)n[t]=e[t];return n},Jh=(e=>t=>e&&t instanceof e)(typeof Uint8Array<"u"&&Ss(Uint8Array)),Zh=(e,t)=>{const r=(e&&e[Symbol.iterator]).call(e);let o;for(;(o=r.next())&&!o.done;){const i=o.value;t.call(e,i[0],i[1])}},e0=(e,t)=>{let n;const r=[];for(;(n=e.exec(t))!==null;)r.push(n);return r},t0=it("HTMLFormElement"),n0=e=>e.toLowerCase().replace(/[-_\s]([a-z\d])(\w*)/g,function(n,r,o){return r.toUpperCase()+o}),ja=(({hasOwnProperty:e})=>(t,n)=>e.call(t,n))(Object.prototype),r0=it("RegExp"),Pf=(e,t)=>{const n=Object.getOwnPropertyDescriptors(e),r={};br(n,(o,i)=>{t(o,i,e)!==!1&&(r[i]=o)}),Object.defineProperties(e,r)},o0=e=>{Pf(e,(t,n)=>{if(ze(e)&&["arguments","caller","callee"].indexOf(n)!==-1)return!1;const r=e[n];if(ze(r)){if(t.enumerable=!1,"writable"in t){t.writable=!1;return}t.set||(t.set=()=>{throw Error("Can not rewrite read-only method '"+n+"'")})}})},i0=(e,t)=>{const n={},r=o=>{o.forEach(i=>{n[i]=!0})};return Wn(e)?r(e):r(String(e).split(t)),n},l0=()=>{},u0=(e,t)=>(e=+e,Number.isFinite(e)?e:t),ll="abcdefghijklmnopqrstuvwxyz",Ha="0123456789",Rf={DIGIT:Ha,ALPHA:ll,ALPHA_DIGIT:ll+ll.toUpperCase()+Ha},s0=(e=16,t=Rf.ALPHA_DIGIT)=>{let n="";const{length:r}=t;for(;e--;)n+=t[Math.random()*r|0];return n};function a0(e){return!!(e&&ze(e.append)&&e[Symbol.toStringTag]==="FormData"&&e[Symbol.iterator])}const c0=e=>{const t=new Array(10),n=(r,o)=>{if(Ti(r)){if(t.indexOf(r)>=0)return;if(!("toJSON"in r)){t[o]=r;const i=Wn(r)?[]:{};return br(r,(l,u)=>{const s=n(l,o+1);!zr(s)&&(i[u]=s)}),t[o]=void 0,i}}return r};return n(e,0)},d0=it("AsyncFunction"),f0=e=>e&&(Ti(e)||ze(e))&&ze(e.then)&&ze(e.catch),S={isArray:Wn,isArrayBuffer:Ef,isBuffer:Dh,isFormData:Vh,isArrayBufferView:Ah,isString:Mh,isNumber:Cf,isBoolean:$h,isObject:Ti,isPlainObject:Po,isUndefined:zr,isDate:zh,isFile:Ih,isBlob:Bh,isRegExp:r0,isFunction:ze,isStream:Hh,isURLSearchParams:bh,isTypedArray:Jh,isFileList:jh,forEach:br,merge:mu,extend:Qh,trim:Wh,stripBOM:Kh,inherits:Gh,toFlatObject:qh,kindOf:Ci,kindOfTest:it,endsWith:Xh,toArray:Yh,forEachEntry:Zh,matchAll:e0,isHTMLForm:t0,hasOwnProperty:ja,hasOwnProp:ja,reduceDescriptors:Pf,freezeMethods:o0,toObjectSet:i0,toCamelCase:n0,noop:l0,toFiniteNumber:u0,findKey:xf,global:Tf,isContextDefined:Nf,ALPHABET:Rf,generateString:s0,isSpecCompliantForm:a0,toJSONObject:c0,isAsyncFn:d0,isThenable:f0};function $(e,t,n,r,o){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=new Error().stack,this.message=e,this.name="AxiosError",t&&(this.code=t),n&&(this.config=n),r&&(this.request=r),o&&(this.response=o)}S.inherits($,Error,{toJSON:function(){return{message:this.message,name:this.name,description:this.description,number:this.number,fileName:this.fileName,lineNumber:this.lineNumber,columnNumber:this.columnNumber,stack:this.stack,config:S.toJSONObject(this.config),code:this.code,status:this.response&&this.response.status?this.response.status:null}}});const Lf=$.prototype,_f={};["ERR_BAD_OPTION_VALUE","ERR_BAD_OPTION","ECONNABORTED","ETIMEDOUT","ERR_NETWORK","ERR_FR_TOO_MANY_REDIRECTS","ERR_DEPRECATED","ERR_BAD_RESPONSE","ERR_BAD_REQUEST","ERR_CANCELED","ERR_NOT_SUPPORT","ERR_INVALID_URL"].forEach(e=>{_f[e]={value:e}});Object.defineProperties($,_f);Object.defineProperty(Lf,"isAxiosError",{value:!0});$.from=(e,t,n,r,o,i)=>{const l=Object.create(Lf);return S.toFlatObject(e,l,function(s){return s!==Error.prototype},u=>u!=="isAxiosError"),$.call(l,e.message,t,n,r,o),l.cause=e,l.name=e.name,i&&Object.assign(l,i),l};const p0=null;function hu(e){return S.isPlainObject(e)||S.isArray(e)}function Uf(e){return S.endsWith(e,"[]")?e.slice(0,-2):e}function Va(e,t,n){return e?e.concat(t).map(function(o,i){return o=Uf(o),!n&&i?"["+o+"]":o}).join(n?".":""):t}function m0(e){return S.isArray(e)&&!e.some(hu)}const h0=S.toFlatObject(S,{},null,function(t){return/^is[A-Z]/.test(t)});function Ni(e,t,n){if(!S.isObject(e))throw new TypeError("target must be an object");t=t||new FormData,n=S.toFlatObject(n,{metaTokens:!0,dots:!1,indexes:!1},!1,function(g,T){return!S.isUndefined(T[g])});const r=n.metaTokens,o=n.visitor||d,i=n.dots,l=n.indexes,s=(n.Blob||typeof Blob<"u"&&Blob)&&S.isSpecCompliantForm(t);if(!S.isFunction(o))throw new TypeError("visitor must be a function");function a(h){if(h===null)return"";if(S.isDate(h))return h.toISOString();if(!s&&S.isBlob(h))throw new $("Blob is not supported. Use a Buffer instead.");return S.isArrayBuffer(h)||S.isTypedArray(h)?s&&typeof Blob=="function"?new Blob([h]):Buffer.from(h):h}function d(h,g,T){let f=h;if(h&&!T&&typeof h=="object"){if(S.endsWith(g,"{}"))g=r?g:g.slice(0,-2),h=JSON.stringify(h);else if(S.isArray(h)&&m0(h)||(S.isFileList(h)||S.endsWith(g,"[]"))&&(f=S.toArray(h)))return g=Uf(g),f.forEach(function(y,k){!(S.isUndefined(y)||y===null)&&t.append(l===!0?Va([g],k,i):l===null?g:g+"[]",a(y))}),!1}return hu(h)?!0:(t.append(Va(T,g,i),a(h)),!1)}const p=[],m=Object.assign(h0,{defaultVisitor:d,convertValue:a,isVisitable:hu});function v(h,g){if(!S.isUndefined(h)){if(p.indexOf(h)!==-1)throw Error("Circular reference detected in "+g.join("."));p.push(h),S.forEach(h,function(f,c){(!(S.isUndefined(f)||f===null)&&o.call(t,f,S.isString(c)?c.trim():c,g,m))===!0&&v(f,g?g.concat(c):[c])}),p.pop()}}if(!S.isObject(e))throw new TypeError("data must be an object");return v(e),t}function ba(e){const t={"!":"%21","'":"%27","(":"%28",")":"%29","~":"%7E","%20":"+","%00":"\0"};return encodeURIComponent(e).replace(/[!'()~]|%20|%00/g,function(r){return t[r]})}function ks(e,t){this._pairs=[],e&&Ni(e,this,t)}const Of=ks.prototype;Of.append=function(t,n){this._pairs.push([t,n])};Of.toString=function(t){const n=t?function(r){return t.call(this,r,ba)}:ba;return this._pairs.map(function(o){return n(o[0])+"="+n(o[1])},"").join("&")};function y0(e){return encodeURIComponent(e).replace(/%3A/gi,":").replace(/%24/g,"$").replace(/%2C/gi,",").replace(/%20/g,"+").replace(/%5B/gi,"[").replace(/%5D/gi,"]")}function Ff(e,t,n){if(!t)return e;const r=n&&n.encode||y0,o=n&&n.serialize;let i;if(o?i=o(t,n):i=S.isURLSearchParams(t)?t.toString():new ks(t,n).toString(r),i){const l=e.indexOf("#");l!==-1&&(e=e.slice(0,l)),e+=(e.indexOf("?")===-1?"?":"&")+i}return e}class g0{constructor(){this.handlers=[]}use(t,n,r){return this.handlers.push({fulfilled:t,rejected:n,synchronous:r?r.synchronous:!1,runWhen:r?r.runWhen:null}),this.handlers.length-1}eject(t){this.handlers[t]&&(this.handlers[t]=null)}clear(){this.handlers&&(this.handlers=[])}forEach(t){S.forEach(this.handlers,function(r){r!==null&&t(r)})}}const Wa=g0,Df={silentJSONParsing:!0,forcedJSONParsing:!0,clarifyTimeoutError:!1},v0=typeof URLSearchParams<"u"?URLSearchParams:ks,w0=typeof FormData<"u"?FormData:null,S0=typeof Blob<"u"?Blob:null,k0=(()=>{let e;return typeof navigator<"u"&&((e=navigator.product)==="ReactNative"||e==="NativeScript"||e==="NS")?!1:typeof window<"u"&&typeof document<"u"})(),E0=(()=>typeof WorkerGlobalScope<"u"&&self instanceof WorkerGlobalScope&&typeof self.importScripts=="function")(),tt={isBrowser:!0,classes:{URLSearchParams:v0,FormData:w0,Blob:S0},isStandardBrowserEnv:k0,isStandardBrowserWebWorkerEnv:E0,protocols:["http","https","file","blob","url","data"]};function C0(e,t){return Ni(e,new tt.classes.URLSearchParams,Object.assign({visitor:function(n,r,o,i){return tt.isNode&&S.isBuffer(n)?(this.append(r,n.toString("base64")),!1):i.defaultVisitor.apply(this,arguments)}},t))}function x0(e){return S.matchAll(/\w+|\[(\w*)]/g,e).map(t=>t[0]==="[]"?"":t[1]||t[0])}function T0(e){const t={},n=Object.keys(e);let r;const o=n.length;let i;for(r=0;r<o;r++)i=n[r],t[i]=e[i];return t}function Af(e){function t(n,r,o,i){let l=n[i++];const u=Number.isFinite(+l),s=i>=n.length;return l=!l&&S.isArray(o)?o.length:l,s?(S.hasOwnProp(o,l)?o[l]=[o[l],r]:o[l]=r,!u):((!o[l]||!S.isObject(o[l]))&&(o[l]=[]),t(n,r,o[l],i)&&S.isArray(o[l])&&(o[l]=T0(o[l])),!u)}if(S.isFormData(e)&&S.isFunction(e.entries)){const n={};return S.forEachEntry(e,(r,o)=>{t(x0(r),o,n,0)}),n}return null}const N0={"Content-Type":void 0};function P0(e,t,n){if(S.isString(e))try{return(t||JSON.parse)(e),S.trim(e)}catch(r){if(r.name!=="SyntaxError")throw r}return(n||JSON.stringify)(e)}const Pi={transitional:Df,adapter:["xhr","http"],transformRequest:[function(t,n){const r=n.getContentType()||"",o=r.indexOf("application/json")>-1,i=S.isObject(t);if(i&&S.isHTMLForm(t)&&(t=new FormData(t)),S.isFormData(t))return o&&o?JSON.stringify(Af(t)):t;if(S.isArrayBuffer(t)||S.isBuffer(t)||S.isStream(t)||S.isFile(t)||S.isBlob(t))return t;if(S.isArrayBufferView(t))return t.buffer;if(S.isURLSearchParams(t))return n.setContentType("application/x-www-form-urlencoded;charset=utf-8",!1),t.toString();let u;if(i){if(r.indexOf("application/x-www-form-urlencoded")>-1)return C0(t,this.formSerializer).toString();if((u=S.isFileList(t))||r.indexOf("multipart/form-data")>-1){const s=this.env&&this.env.FormData;return Ni(u?{"files[]":t}:t,s&&new s,this.formSerializer)}}return i||o?(n.setContentType("application/json",!1),P0(t)):t}],transformResponse:[function(t){const n=this.transitional||Pi.transitional,r=n&&n.forcedJSONParsing,o=this.responseType==="json";if(t&&S.isString(t)&&(r&&!this.responseType||o)){const l=!(n&&n.silentJSONParsing)&&o;try{return JSON.parse(t)}catch(u){if(l)throw u.name==="SyntaxError"?$.from(u,$.ERR_BAD_RESPONSE,this,null,this.response):u}}return t}],timeout:0,xsrfCookieName:"XSRF-TOKEN",xsrfHeaderName:"X-XSRF-TOKEN",maxContentLength:-1,maxBodyLength:-1,env:{FormData:tt.classes.FormData,Blob:tt.classes.Blob},validateStatus:function(t){return t>=200&&t<300},headers:{common:{Accept:"application/json, text/plain, */*"}}};S.forEach(["delete","get","head"],function(t){Pi.headers[t]={}});S.forEach(["post","put","patch"],function(t){Pi.headers[t]=S.merge(N0)});const Es=Pi,R0=S.toObjectSet(["age","authorization","content-length","content-type","etag","expires","from","host","if-modified-since","if-unmodified-since","last-modified","location","max-forwards","proxy-authorization","referer","retry-after","user-agent"]),L0=e=>{const t={};let n,r,o;return e&&e.split(`
-`).forEach(function(l){o=l.indexOf(":"),n=l.substring(0,o).trim().toLowerCase(),r=l.substring(o+1).trim(),!(!n||t[n]&&R0[n])&&(n==="set-cookie"?t[n]?t[n].push(r):t[n]=[r]:t[n]=t[n]?t[n]+", "+r:r)}),t},Qa=Symbol("internals");function or(e){return e&&String(e).trim().toLowerCase()}function Ro(e){return e===!1||e==null?e:S.isArray(e)?e.map(Ro):String(e)}function _0(e){const t=Object.create(null),n=/([^\s,;=]+)\s*(?:=\s*([^,;]+))?/g;let r;for(;r=n.exec(e);)t[r[1]]=r[2];return t}const U0=e=>/^[-_a-zA-Z0-9^`|~,!#$%&'*+.]+$/.test(e.trim());function ul(e,t,n,r,o){if(S.isFunction(r))return r.call(this,t,n);if(o&&(t=n),!!S.isString(t)){if(S.isString(r))return t.indexOf(r)!==-1;if(S.isRegExp(r))return r.test(t)}}function O0(e){return e.trim().toLowerCase().replace(/([a-z\d])(\w*)/g,(t,n,r)=>n.toUpperCase()+r)}function F0(e,t){const n=S.toCamelCase(" "+t);["get","set","has"].forEach(r=>{Object.defineProperty(e,r+n,{value:function(o,i,l){return this[r].call(this,t,o,i,l)},configurable:!0})})}class Ri{constructor(t){t&&this.set(t)}set(t,n,r){const o=this;function i(u,s,a){const d=or(s);if(!d)throw new Error("header name must be a non-empty string");const p=S.findKey(o,d);(!p||o[p]===void 0||a===!0||a===void 0&&o[p]!==!1)&&(o[p||s]=Ro(u))}const l=(u,s)=>S.forEach(u,(a,d)=>i(a,d,s));return S.isPlainObject(t)||t instanceof this.constructor?l(t,n):S.isString(t)&&(t=t.trim())&&!U0(t)?l(L0(t),n):t!=null&&i(n,t,r),this}get(t,n){if(t=or(t),t){const r=S.findKey(this,t);if(r){const o=this[r];if(!n)return o;if(n===!0)return _0(o);if(S.isFunction(n))return n.call(this,o,r);if(S.isRegExp(n))return n.exec(o);throw new TypeError("parser must be boolean|regexp|function")}}}has(t,n){if(t=or(t),t){const r=S.findKey(this,t);return!!(r&&this[r]!==void 0&&(!n||ul(this,this[r],r,n)))}return!1}delete(t,n){const r=this;let o=!1;function i(l){if(l=or(l),l){const u=S.findKey(r,l);u&&(!n||ul(r,r[u],u,n))&&(delete r[u],o=!0)}}return S.isArray(t)?t.forEach(i):i(t),o}clear(t){const n=Object.keys(this);let r=n.length,o=!1;for(;r--;){const i=n[r];(!t||ul(this,this[i],i,t,!0))&&(delete this[i],o=!0)}return o}normalize(t){const n=this,r={};return S.forEach(this,(o,i)=>{const l=S.findKey(r,i);if(l){n[l]=Ro(o),delete n[i];return}const u=t?O0(i):String(i).trim();u!==i&&delete n[i],n[u]=Ro(o),r[u]=!0}),this}concat(...t){return this.constructor.concat(this,...t)}toJSON(t){const n=Object.create(null);return S.forEach(this,(r,o)=>{r!=null&&r!==!1&&(n[o]=t&&S.isArray(r)?r.join(", "):r)}),n}[Symbol.iterator](){return Object.entries(this.toJSON())[Symbol.iterator]()}toString(){return Object.entries(this.toJSON()).map(([t,n])=>t+": "+n).join(`
-`)}get[Symbol.toStringTag](){return"AxiosHeaders"}static from(t){return t instanceof this?t:new this(t)}static concat(t,...n){const r=new this(t);return n.forEach(o=>r.set(o)),r}static accessor(t){const r=(this[Qa]=this[Qa]={accessors:{}}).accessors,o=this.prototype;function i(l){const u=or(l);r[u]||(F0(o,l),r[u]=!0)}return S.isArray(t)?t.forEach(i):i(t),this}}Ri.accessor(["Content-Type","Content-Length","Accept","Accept-Encoding","User-Agent","Authorization"]);S.freezeMethods(Ri.prototype);S.freezeMethods(Ri);const gt=Ri;function sl(e,t){const n=this||Es,r=t||n,o=gt.from(r.headers);let i=r.data;return S.forEach(e,function(u){i=u.call(n,i,o.normalize(),t?t.status:void 0)}),o.normalize(),i}function Mf(e){return!!(e&&e.__CANCEL__)}function Wr(e,t,n){$.call(this,e??"canceled",$.ERR_CANCELED,t,n),this.name="CanceledError"}S.inherits(Wr,$,{__CANCEL__:!0});function D0(e,t,n){const r=n.config.validateStatus;!n.status||!r||r(n.status)?e(n):t(new $("Request failed with status code "+n.status,[$.ERR_BAD_REQUEST,$.ERR_BAD_RESPONSE][Math.floor(n.status/100)-4],n.config,n.request,n))}const A0=tt.isStandardBrowserEnv?function(){return{write:function(n,r,o,i,l,u){const s=[];s.push(n+"="+encodeURIComponent(r)),S.isNumber(o)&&s.push("expires="+new Date(o).toGMTString()),S.isString(i)&&s.push("path="+i),S.isString(l)&&s.push("domain="+l),u===!0&&s.push("secure"),document.cookie=s.join("; ")},read:function(n){const r=document.cookie.match(new RegExp("(^|;\\s*)("+n+")=([^;]*)"));return r?decodeURIComponent(r[3]):null},remove:function(n){this.write(n,"",Date.now()-864e5)}}}():function(){return{write:function(){},read:function(){return null},remove:function(){}}}();function M0(e){return/^([a-z][a-z\d+\-.]*:)?\/\//i.test(e)}function $0(e,t){return t?e.replace(/\/+$/,"")+"/"+t.replace(/^\/+/,""):e}function $f(e,t){return e&&!M0(t)?$0(e,t):t}const z0=tt.isStandardBrowserEnv?function(){const t=/(msie|trident)/i.test(navigator.userAgent),n=document.createElement("a");let r;function o(i){let l=i;return t&&(n.setAttribute("href",l),l=n.href),n.setAttribute("href",l),{href:n.href,protocol:n.protocol?n.protocol.replace(/:$/,""):"",host:n.host,search:n.search?n.search.replace(/^\?/,""):"",hash:n.hash?n.hash.replace(/^#/,""):"",hostname:n.hostname,port:n.port,pathname:n.pathname.charAt(0)==="/"?n.pathname:"/"+n.pathname}}return r=o(window.location.href),function(l){const u=S.isString(l)?o(l):l;return u.protocol===r.protocol&&u.host===r.host}}():function(){return function(){return!0}}();function I0(e){const t=/^([-+\w]{1,25})(:?\/\/|:)/.exec(e);return t&&t[1]||""}function B0(e,t){e=e||10;const n=new Array(e),r=new Array(e);let o=0,i=0,l;return t=t!==void 0?t:1e3,function(s){const a=Date.now(),d=r[i];l||(l=a),n[o]=s,r[o]=a;let p=i,m=0;for(;p!==o;)m+=n[p++],p=p%e;if(o=(o+1)%e,o===i&&(i=(i+1)%e),a-l<t)return;const v=d&&a-d;return v?Math.round(m*1e3/v):void 0}}function Ka(e,t){let n=0;const r=B0(50,250);return o=>{const i=o.loaded,l=o.lengthComputable?o.total:void 0,u=i-n,s=r(u),a=i<=l;n=i;const d={loaded:i,total:l,progress:l?i/l:void 0,bytes:u,rate:s||void 0,estimated:s&&l&&a?(l-i)/s:void 0,event:o};d[t?"download":"upload"]=!0,e(d)}}const j0=typeof XMLHttpRequest<"u",H0=j0&&function(e){return new Promise(function(n,r){let o=e.data;const i=gt.from(e.headers).normalize(),l=e.responseType;let u;function s(){e.cancelToken&&e.cancelToken.unsubscribe(u),e.signal&&e.signal.removeEventListener("abort",u)}S.isFormData(o)&&(tt.isStandardBrowserEnv||tt.isStandardBrowserWebWorkerEnv?i.setContentType(!1):i.setContentType("multipart/form-data;",!1));let a=new XMLHttpRequest;if(e.auth){const v=e.auth.username||"",h=e.auth.password?unescape(encodeURIComponent(e.auth.password)):"";i.set("Authorization","Basic "+btoa(v+":"+h))}const d=$f(e.baseURL,e.url);a.open(e.method.toUpperCase(),Ff(d,e.params,e.paramsSerializer),!0),a.timeout=e.timeout;function p(){if(!a)return;const v=gt.from("getAllResponseHeaders"in a&&a.getAllResponseHeaders()),g={data:!l||l==="text"||l==="json"?a.responseText:a.response,status:a.status,statusText:a.statusText,headers:v,config:e,request:a};D0(function(f){n(f),s()},function(f){r(f),s()},g),a=null}if("onloadend"in a?a.onloadend=p:a.onreadystatechange=function(){!a||a.readyState!==4||a.status===0&&!(a.responseURL&&a.responseURL.indexOf("file:")===0)||setTimeout(p)},a.onabort=function(){a&&(r(new $("Request aborted",$.ECONNABORTED,e,a)),a=null)},a.onerror=function(){r(new $("Network Error",$.ERR_NETWORK,e,a)),a=null},a.ontimeout=function(){let h=e.timeout?"timeout of "+e.timeout+"ms exceeded":"timeout exceeded";const g=e.transitional||Df;e.timeoutErrorMessage&&(h=e.timeoutErrorMessage),r(new $(h,g.clarifyTimeoutError?$.ETIMEDOUT:$.ECONNABORTED,e,a)),a=null},tt.isStandardBrowserEnv){const v=(e.withCredentials||z0(d))&&e.xsrfCookieName&&A0.read(e.xsrfCookieName);v&&i.set(e.xsrfHeaderName,v)}o===void 0&&i.setContentType(null),"setRequestHeader"in a&&S.forEach(i.toJSON(),function(h,g){a.setRequestHeader(g,h)}),S.isUndefined(e.withCredentials)||(a.withCredentials=!!e.withCredentials),l&&l!=="json"&&(a.responseType=e.responseType),typeof e.onDownloadProgress=="function"&&a.addEventListener("progress",Ka(e.onDownloadProgress,!0)),typeof e.onUploadProgress=="function"&&a.upload&&a.upload.addEventListener("progress",Ka(e.onUploadProgress)),(e.cancelToken||e.signal)&&(u=v=>{a&&(r(!v||v.type?new Wr(null,e,a):v),a.abort(),a=null)},e.cancelToken&&e.cancelToken.subscribe(u),e.signal&&(e.signal.aborted?u():e.signal.addEventListener("abort",u)));const m=I0(d);if(m&&tt.protocols.indexOf(m)===-1){r(new $("Unsupported protocol "+m+":",$.ERR_BAD_REQUEST,e));return}a.send(o||null)})},Lo={http:p0,xhr:H0};S.forEach(Lo,(e,t)=>{if(e){try{Object.defineProperty(e,"name",{value:t})}catch{}Object.defineProperty(e,"adapterName",{value:t})}});const V0={getAdapter:e=>{e=S.isArray(e)?e:[e];const{length:t}=e;let n,r;for(let o=0;o<t&&(n=e[o],!(r=S.isString(n)?Lo[n.toLowerCase()]:n));o++);if(!r)throw r===!1?new $(`Adapter ${n} is not supported by the environment`,"ERR_NOT_SUPPORT"):new Error(S.hasOwnProp(Lo,n)?`Adapter '${n}' is not available in the build`:`Unknown adapter '${n}'`);if(!S.isFunction(r))throw new TypeError("adapter is not a function");return r},adapters:Lo};function al(e){if(e.cancelToken&&e.cancelToken.throwIfRequested(),e.signal&&e.signal.aborted)throw new Wr(null,e)}function Ga(e){return al(e),e.headers=gt.from(e.headers),e.data=sl.call(e,e.transformRequest),["post","put","patch"].indexOf(e.method)!==-1&&e.headers.setContentType("application/x-www-form-urlencoded",!1),V0.getAdapter(e.adapter||Es.adapter)(e).then(function(r){return al(e),r.data=sl.call(e,e.transformResponse,r),r.headers=gt.from(r.headers),r},function(r){return Mf(r)||(al(e),r&&r.response&&(r.response.data=sl.call(e,e.transformResponse,r.response),r.response.headers=gt.from(r.response.headers))),Promise.reject(r)})}const qa=e=>e instanceof gt?e.toJSON():e;function jn(e,t){t=t||{};const n={};function r(a,d,p){return S.isPlainObject(a)&&S.isPlainObject(d)?S.merge.call({caseless:p},a,d):S.isPlainObject(d)?S.merge({},d):S.isArray(d)?d.slice():d}function o(a,d,p){if(S.isUndefined(d)){if(!S.isUndefined(a))return r(void 0,a,p)}else return r(a,d,p)}function i(a,d){if(!S.isUndefined(d))return r(void 0,d)}function l(a,d){if(S.isUndefined(d)){if(!S.isUndefined(a))return r(void 0,a)}else return r(void 0,d)}function u(a,d,p){if(p in t)return r(a,d);if(p in e)return r(void 0,a)}const s={url:i,method:i,data:i,baseURL:l,transformRequest:l,transformResponse:l,paramsSerializer:l,timeout:l,timeoutMessage:l,withCredentials:l,adapter:l,responseType:l,xsrfCookieName:l,xsrfHeaderName:l,onUploadProgress:l,onDownloadProgress:l,decompress:l,maxContentLength:l,maxBodyLength:l,beforeRedirect:l,transport:l,httpAgent:l,httpsAgent:l,cancelToken:l,socketPath:l,responseEncoding:l,validateStatus:u,headers:(a,d)=>o(qa(a),qa(d),!0)};return S.forEach(Object.keys(Object.assign({},e,t)),function(d){const p=s[d]||o,m=p(e[d],t[d],d);S.isUndefined(m)&&p!==u||(n[d]=m)}),n}const zf="1.4.0",Cs={};["object","boolean","number","function","string","symbol"].forEach((e,t)=>{Cs[e]=function(r){return typeof r===e||"a"+(t<1?"n ":" ")+e}});const Xa={};Cs.transitional=function(t,n,r){function o(i,l){return"[Axios v"+zf+"] Transitional option '"+i+"'"+l+(r?". "+r:"")}return(i,l,u)=>{if(t===!1)throw new $(o(l," has been removed"+(n?" in "+n:"")),$.ERR_DEPRECATED);return n&&!Xa[l]&&(Xa[l]=!0,console.warn(o(l," has been deprecated since v"+n+" and will be removed in the near future"))),t?t(i,l,u):!0}};function b0(e,t,n){if(typeof e!="object")throw new $("options must be an object",$.ERR_BAD_OPTION_VALUE);const r=Object.keys(e);let o=r.length;for(;o-- >0;){const i=r[o],l=t[i];if(l){const u=e[i],s=u===void 0||l(u,i,e);if(s!==!0)throw new $("option "+i+" must be "+s,$.ERR_BAD_OPTION_VALUE);continue}if(n!==!0)throw new $("Unknown option "+i,$.ERR_BAD_OPTION)}}const yu={assertOptions:b0,validators:Cs},Nt=yu.validators;class ni{constructor(t){this.defaults=t,this.interceptors={request:new Wa,response:new Wa}}request(t,n){typeof t=="string"?(n=n||{},n.url=t):n=t||{},n=jn(this.defaults,n);const{transitional:r,paramsSerializer:o,headers:i}=n;r!==void 0&&yu.assertOptions(r,{silentJSONParsing:Nt.transitional(Nt.boolean),forcedJSONParsing:Nt.transitional(Nt.boolean),clarifyTimeoutError:Nt.transitional(Nt.boolean)},!1),o!=null&&(S.isFunction(o)?n.paramsSerializer={serialize:o}:yu.assertOptions(o,{encode:Nt.function,serialize:Nt.function},!0)),n.method=(n.method||this.defaults.method||"get").toLowerCase();let l;l=i&&S.merge(i.common,i[n.method]),l&&S.forEach(["delete","get","head","post","put","patch","common"],h=>{delete i[h]}),n.headers=gt.concat(l,i);const u=[];let s=!0;this.interceptors.request.forEach(function(g){typeof g.runWhen=="function"&&g.runWhen(n)===!1||(s=s&&g.synchronous,u.unshift(g.fulfilled,g.rejected))});const a=[];this.interceptors.response.forEach(function(g){a.push(g.fulfilled,g.rejected)});let d,p=0,m;if(!s){const h=[Ga.bind(this),void 0];for(h.unshift.apply(h,u),h.push.apply(h,a),m=h.length,d=Promise.resolve(n);p<m;)d=d.then(h[p++],h[p++]);return d}m=u.length;let v=n;for(p=0;p<m;){const h=u[p++],g=u[p++];try{v=h(v)}catch(T){g.call(this,T);break}}try{d=Ga.call(this,v)}catch(h){return Promise.reject(h)}for(p=0,m=a.length;p<m;)d=d.then(a[p++],a[p++]);return d}getUri(t){t=jn(this.defaults,t);const n=$f(t.baseURL,t.url);return Ff(n,t.params,t.paramsSerializer)}}S.forEach(["delete","get","head","options"],function(t){ni.prototype[t]=function(n,r){return this.request(jn(r||{},{method:t,url:n,data:(r||{}).data}))}});S.forEach(["post","put","patch"],function(t){function n(r){return function(i,l,u){return this.request(jn(u||{},{method:t,headers:r?{"Content-Type":"multipart/form-data"}:{},url:i,data:l}))}}ni.prototype[t]=n(),ni.prototype[t+"Form"]=n(!0)});const _o=ni;class xs{constructor(t){if(typeof t!="function")throw new TypeError("executor must be a function.");let n;this.promise=new Promise(function(i){n=i});const r=this;this.promise.then(o=>{if(!r._listeners)return;let i=r._listeners.length;for(;i-- >0;)r._listeners[i](o);r._listeners=null}),this.promise.then=o=>{let i;const l=new Promise(u=>{r.subscribe(u),i=u}).then(o);return l.cancel=function(){r.unsubscribe(i)},l},t(function(i,l,u){r.reason||(r.reason=new Wr(i,l,u),n(r.reason))})}throwIfRequested(){if(this.reason)throw this.reason}subscribe(t){if(this.reason){t(this.reason);return}this._listeners?this._listeners.push(t):this._listeners=[t]}unsubscribe(t){if(!this._listeners)return;const n=this._listeners.indexOf(t);n!==-1&&this._listeners.splice(n,1)}static source(){let t;return{token:new xs(function(o){t=o}),cancel:t}}}const W0=xs;function Q0(e){return function(n){return e.apply(null,n)}}function K0(e){return S.isObject(e)&&e.isAxiosError===!0}const gu={Continue:100,SwitchingProtocols:101,Processing:102,EarlyHints:103,Ok:200,Created:201,Accepted:202,NonAuthoritativeInformation:203,NoContent:204,ResetContent:205,PartialContent:206,MultiStatus:207,AlreadyReported:208,ImUsed:226,MultipleChoices:300,MovedPermanently:301,Found:302,SeeOther:303,NotModified:304,UseProxy:305,Unused:306,TemporaryRedirect:307,PermanentRedirect:308,BadRequest:400,Unauthorized:401,PaymentRequired:402,Forbidden:403,NotFound:404,MethodNotAllowed:405,NotAcceptable:406,ProxyAuthenticationRequired:407,RequestTimeout:408,Conflict:409,Gone:410,LengthRequired:411,PreconditionFailed:412,PayloadTooLarge:413,UriTooLong:414,UnsupportedMediaType:415,RangeNotSatisfiable:416,ExpectationFailed:417,ImATeapot:418,MisdirectedRequest:421,UnprocessableEntity:422,Locked:423,FailedDependency:424,TooEarly:425,UpgradeRequired:426,PreconditionRequired:428,TooManyRequests:429,RequestHeaderFieldsTooLarge:431,UnavailableForLegalReasons:451,InternalServerError:500,NotImplemented:501,BadGateway:502,ServiceUnavailable:503,GatewayTimeout:504,HttpVersionNotSupported:505,VariantAlsoNegotiates:506,InsufficientStorage:507,LoopDetected:508,NotExtended:510,NetworkAuthenticationRequired:511};Object.entries(gu).forEach(([e,t])=>{gu[t]=e});const G0=gu;function If(e){const t=new _o(e),n=kf(_o.prototype.request,t);return S.extend(n,_o.prototype,t,{allOwnKeys:!0}),S.extend(n,t,null,{allOwnKeys:!0}),n.create=function(o){return If(jn(e,o))},n}const ie=If(Es);ie.Axios=_o;ie.CanceledError=Wr;ie.CancelToken=W0;ie.isCancel=Mf;ie.VERSION=zf;ie.toFormData=Ni;ie.AxiosError=$;ie.Cancel=ie.CanceledError;ie.all=function(t){return Promise.all(t)};ie.spread=Q0;ie.isAxiosError=K0;ie.mergeConfig=jn;ie.AxiosHeaders=gt;ie.formToJSON=e=>Af(S.isHTMLForm(e)?new FormData(e):e);ie.HttpStatusCode=G0;ie.default=ie;const q0=ie;var X0=Object.defineProperty,Y0=(e,t,n)=>t in e?X0(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,cl=(e,t,n)=>(Y0(e,typeof t!="symbol"?t+"":t,n),n);let J0=class{constructor(){cl(this,"current",this.detect()),cl(this,"handoffState","pending"),cl(this,"currentId",0)}set(t){this.current!==t&&(this.handoffState="pending",this.currentId=0,this.current=t)}reset(){this.set(this.detect())}nextId(){return++this.currentId}get isServer(){return this.current==="server"}get isClient(){return this.current==="client"}detect(){return typeof window>"u"||typeof document>"u"?"server":"client"}handoff(){this.handoffState==="pending"&&(this.handoffState="complete")}get isHandoffComplete(){return this.handoffState==="complete"}},vt=new J0,lt=(e,t)=>{vt.isServer?w.useEffect(e,t):w.useLayoutEffect(e,t)};function wt(e){let t=w.useRef(e);return lt(()=>{t.current=e},[e]),t}function Qr(e){typeof queueMicrotask=="function"?queueMicrotask(e):Promise.resolve().then(e).catch(t=>setTimeout(()=>{throw t}))}function Qn(){let e=[],t={addEventListener(n,r,o,i){return n.addEventListener(r,o,i),t.add(()=>n.removeEventListener(r,o,i))},requestAnimationFrame(...n){let r=requestAnimationFrame(...n);return t.add(()=>cancelAnimationFrame(r))},nextFrame(...n){return t.requestAnimationFrame(()=>t.requestAnimationFrame(...n))},setTimeout(...n){let r=setTimeout(...n);return t.add(()=>clearTimeout(r))},microTask(...n){let r={current:!0};return Qr(()=>{r.current&&n[0]()}),t.add(()=>{r.current=!1})},style(n,r,o){let i=n.style.getPropertyValue(r);return Object.assign(n.style,{[r]:o}),this.add(()=>{Object.assign(n.style,{[r]:i})})},group(n){let r=Qn();return n(r),this.add(()=>r.dispose())},add(n){return e.push(n),()=>{let r=e.indexOf(n);if(r>=0)for(let o of e.splice(r,1))o()}},dispose(){for(let n of e.splice(0))n()}};return t}function Ts(){let[e]=w.useState(Qn);return w.useEffect(()=>()=>e.dispose(),[e]),e}let ue=function(e){let t=wt(e);return M.useCallback((...n)=>t.current(...n),[t])};function Kn(){let[e,t]=w.useState(vt.isHandoffComplete);return e&&vt.isHandoffComplete===!1&&t(!1),w.useEffect(()=>{e!==!0&&t(!0)},[e]),w.useEffect(()=>vt.handoff(),[]),e}var Ya;let Gn=(Ya=M.useId)!=null?Ya:function(){let e=Kn(),[t,n]=M.useState(e?()=>vt.nextId():null);return lt(()=>{t===null&&n(vt.nextId())},[t]),t!=null?""+t:void 0};function he(e,t,...n){if(e in t){let o=t[e];return typeof o=="function"?o(...n):o}let r=new Error(`Tried to handle "${e}" but there is no handler defined. Only defined handlers are: ${Object.keys(t).map(o=>`"${o}"`).join(", ")}.`);throw Error.captureStackTrace&&Error.captureStackTrace(r,he),r}function Bf(e){return vt.isServer?null:e instanceof Node?e.ownerDocument:e!=null&&e.hasOwnProperty("current")&&e.current instanceof Node?e.current.ownerDocument:document}let vu=["[contentEditable=true]","[tabindex]","a[href]","area[href]","button:not([disabled])","iframe","input:not([disabled])","select:not([disabled])","textarea:not([disabled])"].map(e=>`${e}:not([tabindex='-1'])`).join(",");var Jt=(e=>(e[e.First=1]="First",e[e.Previous=2]="Previous",e[e.Next=4]="Next",e[e.Last=8]="Last",e[e.WrapAround=16]="WrapAround",e[e.NoScroll=32]="NoScroll",e))(Jt||{}),jf=(e=>(e[e.Error=0]="Error",e[e.Overflow=1]="Overflow",e[e.Success=2]="Success",e[e.Underflow=3]="Underflow",e))(jf||{}),Z0=(e=>(e[e.Previous=-1]="Previous",e[e.Next=1]="Next",e))(Z0||{});function ey(e=document.body){return e==null?[]:Array.from(e.querySelectorAll(vu)).sort((t,n)=>Math.sign((t.tabIndex||Number.MAX_SAFE_INTEGER)-(n.tabIndex||Number.MAX_SAFE_INTEGER)))}var Hf=(e=>(e[e.Strict=0]="Strict",e[e.Loose=1]="Loose",e))(Hf||{});function ty(e,t=0){var n;return e===((n=Bf(e))==null?void 0:n.body)?!1:he(t,{[0](){return e.matches(vu)},[1](){let r=e;for(;r!==null;){if(r.matches(vu))return!0;r=r.parentElement}return!1}})}var ny=(e=>(e[e.Keyboard=0]="Keyboard",e[e.Mouse=1]="Mouse",e))(ny||{});typeof window<"u"&&typeof document<"u"&&(document.addEventListener("keydown",e=>{e.metaKey||e.altKey||e.ctrlKey||(document.documentElement.dataset.headlessuiFocusVisible="")},!0),document.addEventListener("click",e=>{e.detail===1?delete document.documentElement.dataset.headlessuiFocusVisible:e.detail===0&&(document.documentElement.dataset.headlessuiFocusVisible="")},!0));function ln(e){e==null||e.focus({preventScroll:!0})}let ry=["textarea","input"].join(",");function oy(e){var t,n;return(n=(t=e==null?void 0:e.matches)==null?void 0:t.call(e,ry))!=null?n:!1}function iy(e,t=n=>n){return e.slice().sort((n,r)=>{let o=t(n),i=t(r);if(o===null||i===null)return 0;let l=o.compareDocumentPosition(i);return l&Node.DOCUMENT_POSITION_FOLLOWING?-1:l&Node.DOCUMENT_POSITION_PRECEDING?1:0})}function Uo(e,t,{sorted:n=!0,relativeTo:r=null,skipElements:o=[]}={}){let i=Array.isArray(e)?e.length>0?e[0].ownerDocument:document:e.ownerDocument,l=Array.isArray(e)?n?iy(e):e:ey(e);o.length>0&&l.length>1&&(l=l.filter(v=>!o.includes(v))),r=r??i.activeElement;let u=(()=>{if(t&5)return 1;if(t&10)return-1;throw new Error("Missing Focus.First, Focus.Previous, Focus.Next or Focus.Last")})(),s=(()=>{if(t&1)return 0;if(t&2)return Math.max(0,l.indexOf(r))-1;if(t&4)return Math.max(0,l.indexOf(r))+1;if(t&8)return l.length-1;throw new Error("Missing Focus.First, Focus.Previous, Focus.Next or Focus.Last")})(),a=t&32?{preventScroll:!0}:{},d=0,p=l.length,m;do{if(d>=p||d+p<=0)return 0;let v=s+d;if(t&16)v=(v+p)%p;else{if(v<0)return 3;if(v>=p)return 1}m=l[v],m==null||m.focus(a),d+=u}while(m!==i.activeElement);return t&6&&oy(m)&&m.select(),2}function dl(e,t,n){let r=wt(t);w.useEffect(()=>{function o(i){r.current(i)}return document.addEventListener(e,o,n),()=>document.removeEventListener(e,o,n)},[e,n])}function ly(e,t,n=!0){let r=w.useRef(!1);w.useEffect(()=>{requestAnimationFrame(()=>{r.current=n})},[n]);function o(l,u){if(!r.current||l.defaultPrevented)return;let s=function d(p){return typeof p=="function"?d(p()):Array.isArray(p)||p instanceof Set?p:[p]}(e),a=u(l);if(a!==null&&a.getRootNode().contains(a)){for(let d of s){if(d===null)continue;let p=d instanceof HTMLElement?d:d.current;if(p!=null&&p.contains(a)||l.composed&&l.composedPath().includes(p))return}return!ty(a,Hf.Loose)&&a.tabIndex!==-1&&l.preventDefault(),t(l,a)}}let i=w.useRef(null);dl("mousedown",l=>{var u,s;r.current&&(i.current=((s=(u=l.composedPath)==null?void 0:u.call(l))==null?void 0:s[0])||l.target)},!0),dl("click",l=>{i.current&&(o(l,()=>i.current),i.current=null)},!0),dl("blur",l=>o(l,()=>window.document.activeElement instanceof HTMLIFrameElement?window.document.activeElement:null),!0)}let Vf=Symbol();function uy(e,t=!0){return Object.assign(e,{[Vf]:t})}function Ye(...e){let t=w.useRef(e);w.useEffect(()=>{t.current=e},[e]);let n=ue(r=>{for(let o of t.current)o!=null&&(typeof o=="function"?o(r):o.current=r)});return e.every(r=>r==null||(r==null?void 0:r[Vf]))?void 0:n}function wu(...e){return e.filter(Boolean).join(" ")}var ri=(e=>(e[e.None=0]="None",e[e.RenderStrategy=1]="RenderStrategy",e[e.Static=2]="Static",e))(ri||{}),ht=(e=>(e[e.Unmount=0]="Unmount",e[e.Hidden=1]="Hidden",e))(ht||{});function je({ourProps:e,theirProps:t,slot:n,defaultTag:r,features:o,visible:i=!0,name:l}){let u=bf(t,e);if(i)return mo(u,n,r,l);let s=o??0;if(s&2){let{static:a=!1,...d}=u;if(a)return mo(d,n,r,l)}if(s&1){let{unmount:a=!0,...d}=u;return he(a?0:1,{[0](){return null},[1](){return mo({...d,hidden:!0,style:{display:"none"}},n,r,l)}})}return mo(u,n,r,l)}function mo(e,t={},n,r){let{as:o=n,children:i,refName:l="ref",...u}=fl(e,["unmount","static"]),s=e.ref!==void 0?{[l]:e.ref}:{},a=typeof i=="function"?i(t):i;"className"in u&&u.className&&typeof u.className=="function"&&(u.className=u.className(t));let d={};if(t){let p=!1,m=[];for(let[v,h]of Object.entries(t))typeof h=="boolean"&&(p=!0),h===!0&&m.push(v);p&&(d["data-headlessui-state"]=m.join(" "))}if(o===w.Fragment&&Object.keys(Ja(u)).length>0){if(!w.isValidElement(a)||Array.isArray(a)&&a.length>1)throw new Error(['Passing props on "Fragment"!',"",`The current component <${r} /> is rendering a "Fragment".`,"However we need to passthrough the following props:",Object.keys(u).map(h=>`  - ${h}`).join(`
-`),"","You can apply a few solutions:",['Add an `as="..."` prop, to ensure that we render an actual element instead of a "Fragment".',"Render a single element as the child so that we can forward the props onto that element."].map(h=>`  - ${h}`).join(`
-`)].join(`
-`));let p=a.props,m=typeof(p==null?void 0:p.className)=="function"?(...h)=>wu(p==null?void 0:p.className(...h),u.className):wu(p==null?void 0:p.className,u.className),v=m?{className:m}:{};return w.cloneElement(a,Object.assign({},bf(a.props,Ja(fl(u,["ref"]))),d,s,sy(a.ref,s.ref),v))}return w.createElement(o,Object.assign({},fl(u,["ref"]),o!==w.Fragment&&s,o!==w.Fragment&&d),a)}function sy(...e){return{ref:e.every(t=>t==null)?void 0:t=>{for(let n of e)n!=null&&(typeof n=="function"?n(t):n.current=t)}}}function bf(...e){if(e.length===0)return{};if(e.length===1)return e[0];let t={},n={};for(let r of e)for(let o in r)o.startsWith("on")&&typeof r[o]=="function"?(n[o]!=null||(n[o]=[]),n[o].push(r[o])):t[o]=r[o];if(t.disabled||t["aria-disabled"])return Object.assign(t,Object.fromEntries(Object.keys(n).map(r=>[r,void 0])));for(let r in n)Object.assign(t,{[r](o,...i){let l=n[r];for(let u of l){if((o instanceof Event||(o==null?void 0:o.nativeEvent)instanceof Event)&&o.defaultPrevented)return;u(o,...i)}}});return t}function Fe(e){var t;return Object.assign(w.forwardRef(e),{displayName:(t=e.displayName)!=null?t:e.name})}function Ja(e){let t=Object.assign({},e);for(let n in t)t[n]===void 0&&delete t[n];return t}function fl(e,t=[]){let n=Object.assign({},e);for(let r of t)r in n&&delete n[r];return n}function ay(e){let t=e.parentElement,n=null;for(;t&&!(t instanceof HTMLFieldSetElement);)t instanceof HTMLLegendElement&&(n=t),t=t.parentElement;let r=(t==null?void 0:t.getAttribute("disabled"))==="";return r&&cy(n)?!1:r}function cy(e){if(!e)return!1;let t=e.previousElementSibling;for(;t!==null;){if(t instanceof HTMLLegendElement)return!1;t=t.previousElementSibling}return!0}let dy="div";var oi=(e=>(e[e.None=1]="None",e[e.Focusable=2]="Focusable",e[e.Hidden=4]="Hidden",e))(oi||{});function fy(e,t){let{features:n=1,...r}=e,o={ref:t,"aria-hidden":(n&2)===2?!0:void 0,style:{position:"fixed",top:1,left:1,width:1,height:0,padding:0,margin:-1,overflow:"hidden",clip:"rect(0, 0, 0, 0)",whiteSpace:"nowrap",borderWidth:"0",...(n&4)===4&&(n&2)!==2&&{display:"none"}}};return je({ourProps:o,theirProps:r,slot:{},defaultTag:dy,name:"Hidden"})}let Su=Fe(fy),Ns=w.createContext(null);Ns.displayName="OpenClosedContext";var Pe=(e=>(e[e.Open=1]="Open",e[e.Closed=2]="Closed",e[e.Closing=4]="Closing",e[e.Opening=8]="Opening",e))(Pe||{});function Ps(){return w.useContext(Ns)}function py({value:e,children:t}){return M.createElement(Ns.Provider,{value:e},t)}var Wf=(e=>(e.Space=" ",e.Enter="Enter",e.Escape="Escape",e.Backspace="Backspace",e.Delete="Delete",e.ArrowLeft="ArrowLeft",e.ArrowUp="ArrowUp",e.ArrowRight="ArrowRight",e.ArrowDown="ArrowDown",e.Home="Home",e.End="End",e.PageUp="PageUp",e.PageDown="PageDown",e.Tab="Tab",e))(Wf||{});function Rs(e,t){let n=w.useRef([]),r=ue(e);w.useEffect(()=>{let o=[...n.current];for(let[i,l]of t.entries())if(n.current[i]!==l){let u=r(t,o);return n.current=t,u}},[r,...t])}function my(){return/iPhone/gi.test(window.navigator.platform)||/Mac/gi.test(window.navigator.platform)&&window.navigator.maxTouchPoints>0}function hy(e,t,n){let r=wt(t);w.useEffect(()=>{function o(i){r.current(i)}return window.addEventListener(e,o,n),()=>window.removeEventListener(e,o,n)},[e,n])}var dr=(e=>(e[e.Forwards=0]="Forwards",e[e.Backwards=1]="Backwards",e))(dr||{});function yy(){let e=w.useRef(0);return hy("keydown",t=>{t.key==="Tab"&&(e.current=t.shiftKey?1:0)},!0),e}function Kr(){let e=w.useRef(!1);return lt(()=>(e.current=!0,()=>{e.current=!1}),[]),e}function Li(...e){return w.useMemo(()=>Bf(...e),[...e])}function Qf(e,t,n,r){let o=wt(n);w.useEffect(()=>{e=e??window;function i(l){o.current(l)}return e.addEventListener(t,i,r),()=>e.removeEventListener(t,i,r)},[e,t,r])}function gy(e){function t(){document.readyState!=="loading"&&(e(),document.removeEventListener("DOMContentLoaded",t))}typeof window<"u"&&typeof document<"u"&&(document.addEventListener("DOMContentLoaded",t),t())}function Kf(e){if(!e)return new Set;if(typeof e=="function")return new Set(e());let t=new Set;for(let n of e.current)n.current instanceof HTMLElement&&t.add(n.current);return t}let vy="div";var Gf=(e=>(e[e.None=1]="None",e[e.InitialFocus=2]="InitialFocus",e[e.TabLock=4]="TabLock",e[e.FocusLock=8]="FocusLock",e[e.RestoreFocus=16]="RestoreFocus",e[e.All=30]="All",e))(Gf||{});function wy(e,t){let n=w.useRef(null),r=Ye(n,t),{initialFocus:o,containers:i,features:l=30,...u}=e;Kn()||(l=1);let s=Li(n);Ey({ownerDocument:s},!!(l&16));let a=Cy({ownerDocument:s,container:n,initialFocus:o},!!(l&2));xy({ownerDocument:s,container:n,containers:i,previousActiveElement:a},!!(l&8));let d=yy(),p=ue(g=>{let T=n.current;T&&(f=>f())(()=>{he(d.current,{[dr.Forwards]:()=>{Uo(T,Jt.First,{skipElements:[g.relatedTarget]})},[dr.Backwards]:()=>{Uo(T,Jt.Last,{skipElements:[g.relatedTarget]})}})})}),m=Ts(),v=w.useRef(!1),h={ref:r,onKeyDown(g){g.key=="Tab"&&(v.current=!0,m.requestAnimationFrame(()=>{v.current=!1}))},onBlur(g){let T=Kf(i);n.current instanceof HTMLElement&&T.add(n.current);let f=g.relatedTarget;f instanceof HTMLElement&&f.dataset.headlessuiFocusGuard!=="true"&&(qf(T,f)||(v.current?Uo(n.current,he(d.current,{[dr.Forwards]:()=>Jt.Next,[dr.Backwards]:()=>Jt.Previous})|Jt.WrapAround,{relativeTo:g.target}):g.target instanceof HTMLElement&&ln(g.target)))}};return M.createElement(M.Fragment,null,!!(l&4)&&M.createElement(Su,{as:"button",type:"button","data-headlessui-focus-guard":!0,onFocus:p,features:oi.Focusable}),je({ourProps:h,theirProps:u,defaultTag:vy,name:"FocusTrap"}),!!(l&4)&&M.createElement(Su,{as:"button",type:"button","data-headlessui-focus-guard":!0,onFocus:p,features:oi.Focusable}))}let Sy=Fe(wy),ir=Object.assign(Sy,{features:Gf}),Ut=[];gy(()=>{function e(t){t.target instanceof HTMLElement&&t.target!==document.body&&Ut[0]!==t.target&&(Ut.unshift(t.target),Ut=Ut.filter(n=>n!=null&&n.isConnected),Ut.splice(10))}window.addEventListener("click",e,{capture:!0}),window.addEventListener("mousedown",e,{capture:!0}),window.addEventListener("focus",e,{capture:!0}),document.body.addEventListener("click",e,{capture:!0}),document.body.addEventListener("mousedown",e,{capture:!0}),document.body.addEventListener("focus",e,{capture:!0})});function ky(e=!0){let t=w.useRef(Ut.slice());return Rs(([n],[r])=>{r===!0&&n===!1&&Qr(()=>{t.current.splice(0)}),r===!1&&n===!0&&(t.current=Ut.slice())},[e,Ut,t]),ue(()=>{var n;return(n=t.current.find(r=>r!=null&&r.isConnected))!=null?n:null})}function Ey({ownerDocument:e},t){let n=ky(t);Rs(()=>{t||(e==null?void 0:e.activeElement)===(e==null?void 0:e.body)&&ln(n())},[t]);let r=w.useRef(!1);w.useEffect(()=>(r.current=!1,()=>{r.current=!0,Qr(()=>{r.current&&ln(n())})}),[])}function Cy({ownerDocument:e,container:t,initialFocus:n},r){let o=w.useRef(null),i=Kr();return Rs(()=>{if(!r)return;let l=t.current;l&&Qr(()=>{if(!i.current)return;let u=e==null?void 0:e.activeElement;if(n!=null&&n.current){if((n==null?void 0:n.current)===u){o.current=u;return}}else if(l.contains(u)){o.current=u;return}n!=null&&n.current?ln(n.current):Uo(l,Jt.First)===jf.Error&&console.warn("There are no focusable elements inside the <FocusTrap />"),o.current=e==null?void 0:e.activeElement})},[r]),o}function xy({ownerDocument:e,container:t,containers:n,previousActiveElement:r},o){let i=Kr();Qf(e==null?void 0:e.defaultView,"focus",l=>{if(!o||!i.current)return;let u=Kf(n);t.current instanceof HTMLElement&&u.add(t.current);let s=r.current;if(!s)return;let a=l.target;a&&a instanceof HTMLElement?qf(u,a)?(r.current=a,ln(a)):(l.preventDefault(),l.stopPropagation(),ln(s)):ln(r.current)},!0)}function qf(e,t){for(let n of e)if(n.contains(t))return!0;return!1}let Xf=w.createContext(!1);function Ty(){return w.useContext(Xf)}function ku(e){return M.createElement(Xf.Provider,{value:e.force},e.children)}function Ny(e){let t=Ty(),n=w.useContext(Yf),r=Li(e),[o,i]=w.useState(()=>{if(!t&&n!==null||vt.isServer)return null;let l=r==null?void 0:r.getElementById("headlessui-portal-root");if(l)return l;if(r===null)return null;let u=r.createElement("div");return u.setAttribute("id","headlessui-portal-root"),r.body.appendChild(u)});return w.useEffect(()=>{o!==null&&(r!=null&&r.body.contains(o)||r==null||r.body.appendChild(o))},[o,r]),w.useEffect(()=>{t||n!==null&&i(n.current)},[n,i,t]),o}let Py=w.Fragment;function Ry(e,t){let n=e,r=w.useRef(null),o=Ye(uy(d=>{r.current=d}),t),i=Li(r),l=Ny(r),[u]=w.useState(()=>{var d;return vt.isServer?null:(d=i==null?void 0:i.createElement("div"))!=null?d:null}),s=Kn(),a=w.useRef(!1);return lt(()=>{if(a.current=!1,!(!l||!u))return l.contains(u)||(u.setAttribute("data-headlessui-portal",""),l.appendChild(u)),()=>{a.current=!0,Qr(()=>{var d;a.current&&(!l||!u||(u instanceof Node&&l.contains(u)&&l.removeChild(u),l.childNodes.length<=0&&((d=l.parentElement)==null||d.removeChild(l))))})}},[l,u]),s?!l||!u?null:Sf.createPortal(je({ourProps:{ref:o},theirProps:n,defaultTag:Py,name:"Portal"}),u):null}let Ly=w.Fragment,Yf=w.createContext(null);function _y(e,t){let{target:n,...r}=e,o={ref:Ye(t)};return M.createElement(Yf.Provider,{value:n},je({ourProps:o,theirProps:r,defaultTag:Ly,name:"Popover.Group"}))}let Uy=Fe(Ry),Oy=Fe(_y),Eu=Object.assign(Uy,{Group:Oy}),Jf=w.createContext(null);function Zf(){let e=w.useContext(Jf);if(e===null){let t=new Error("You used a <Description /> component, but it is not inside a relevant parent.");throw Error.captureStackTrace&&Error.captureStackTrace(t,Zf),t}return e}function Fy(){let[e,t]=w.useState([]);return[e.length>0?e.join(" "):void 0,w.useMemo(()=>function(n){let r=ue(i=>(t(l=>[...l,i]),()=>t(l=>{let u=l.slice(),s=u.indexOf(i);return s!==-1&&u.splice(s,1),u}))),o=w.useMemo(()=>({register:r,slot:n.slot,name:n.name,props:n.props}),[r,n.slot,n.name,n.props]);return M.createElement(Jf.Provider,{value:o},n.children)},[t])]}let Dy="p";function Ay(e,t){let n=Gn(),{id:r=`headlessui-description-${n}`,...o}=e,i=Zf(),l=Ye(t);lt(()=>i.register(r),[r,i.register]);let u={ref:l,...i.props,id:r};return je({ourProps:u,theirProps:o,slot:i.slot||{},defaultTag:Dy,name:i.name||"Description"})}let My=Fe(Ay),$y=Object.assign(My,{}),Ls=w.createContext(()=>{});Ls.displayName="StackContext";var Cu=(e=>(e[e.Add=0]="Add",e[e.Remove=1]="Remove",e))(Cu||{});function zy(){return w.useContext(Ls)}function Iy({children:e,onUpdate:t,type:n,element:r,enabled:o}){let i=zy(),l=ue((...u)=>{t==null||t(...u),i(...u)});return lt(()=>{let u=o===void 0||o===!0;return u&&l(0,n,r),()=>{u&&l(1,n,r)}},[l,n,r,o]),M.createElement(Ls.Provider,{value:l},e)}function By(e,t){return e===t&&(e!==0||1/e===1/t)||e!==e&&t!==t}const jy=typeof Object.is=="function"?Object.is:By,{useState:Hy,useEffect:Vy,useLayoutEffect:by,useDebugValue:Wy}=Sl;function Qy(e,t,n){const r=t(),[{inst:o},i]=Hy({inst:{value:r,getSnapshot:t}});return by(()=>{o.value=r,o.getSnapshot=t,pl(o)&&i({inst:o})},[e,r,t]),Vy(()=>(pl(o)&&i({inst:o}),e(()=>{pl(o)&&i({inst:o})})),[e]),Wy(r),r}function pl(e){const t=e.getSnapshot,n=e.value;try{const r=t();return!jy(n,r)}catch{return!0}}function Ky(e,t,n){return t()}const Gy=typeof window<"u"&&typeof window.document<"u"&&typeof window.document.createElement<"u",qy=!Gy,Xy=qy?Ky:Qy,Yy="useSyncExternalStore"in Sl?(e=>e.useSyncExternalStore)(Sl):Xy;function Jy(e){return Yy(e.subscribe,e.getSnapshot,e.getSnapshot)}function Zy(e,t){let n=e(),r=new Set;return{getSnapshot(){return n},subscribe(o){return r.add(o),()=>r.delete(o)},dispatch(o,...i){let l=t[o].call(n,...i);l&&(n=l,r.forEach(u=>u()))}}}function e1(){let e;return{before({doc:t}){var n;let r=t.documentElement;e=((n=t.defaultView)!=null?n:window).innerWidth-r.clientWidth},after({doc:t,d:n}){let r=t.documentElement,o=r.clientWidth-r.offsetWidth,i=e-o;n.style(r,"paddingRight",`${i}px`)}}}function t1(){if(!my())return{};let e;return{before(){e=window.pageYOffset},after({doc:t,d:n,meta:r}){function o(l){return r.containers.flatMap(u=>u()).some(u=>u.contains(l))}n.style(t.body,"marginTop",`-${e}px`),window.scrollTo(0,0);let i=null;n.addEventListener(t,"click",l=>{if(l.target instanceof HTMLElement)try{let u=l.target.closest("a");if(!u)return;let{hash:s}=new URL(u.href),a=t.querySelector(s);a&&!o(a)&&(i=a)}catch{}},!0),n.addEventListener(t,"touchmove",l=>{l.target instanceof HTMLElement&&!o(l.target)&&l.preventDefault()},{passive:!1}),n.add(()=>{window.scrollTo(0,window.pageYOffset+e),i&&i.isConnected&&(i.scrollIntoView({block:"nearest"}),i=null)})}}}function n1(){return{before({doc:e,d:t}){t.style(e.documentElement,"overflow","hidden")}}}function r1(e){let t={};for(let n of e)Object.assign(t,n(t));return t}let nn=Zy(()=>new Map,{PUSH(e,t){var n;let r=(n=this.get(e))!=null?n:{doc:e,count:0,d:Qn(),meta:new Set};return r.count++,r.meta.add(t),this.set(e,r),this},POP(e,t){let n=this.get(e);return n&&(n.count--,n.meta.delete(t)),this},SCROLL_PREVENT({doc:e,d:t,meta:n}){let r={doc:e,d:t,meta:r1(n)},o=[t1(),e1(),n1()];o.forEach(({before:i})=>i==null?void 0:i(r)),o.forEach(({after:i})=>i==null?void 0:i(r))},SCROLL_ALLOW({d:e}){e.dispose()},TEARDOWN({doc:e}){this.delete(e)}});nn.subscribe(()=>{let e=nn.getSnapshot(),t=new Map;for(let[n]of e)t.set(n,n.documentElement.style.overflow);for(let n of e.values()){let r=t.get(n.doc)==="hidden",o=n.count!==0;(o&&!r||!o&&r)&&nn.dispatch(n.count>0?"SCROLL_PREVENT":"SCROLL_ALLOW",n),n.count===0&&nn.dispatch("TEARDOWN",n)}});function o1(e,t,n){let r=Jy(nn),o=e?r.get(e):void 0,i=o?o.count>0:!1;return lt(()=>{if(!(!e||!t))return nn.dispatch("PUSH",e,n),()=>nn.dispatch("POP",e,n)},[t,e]),i}let ml=new Map,lr=new Map;function Za(e,t=!0){lt(()=>{var n;if(!t)return;let r=typeof e=="function"?e():e.current;if(!r)return;function o(){var l;if(!r)return;let u=(l=lr.get(r))!=null?l:1;if(u===1?lr.delete(r):lr.set(r,u-1),u!==1)return;let s=ml.get(r);s&&(s["aria-hidden"]===null?r.removeAttribute("aria-hidden"):r.setAttribute("aria-hidden",s["aria-hidden"]),r.inert=s.inert,ml.delete(r))}let i=(n=lr.get(r))!=null?n:0;return lr.set(r,i+1),i!==0||(ml.set(r,{"aria-hidden":r.getAttribute("aria-hidden"),inert:r.inert}),r.setAttribute("aria-hidden","true"),r.inert=!0),o},[e,t])}var i1=(e=>(e[e.Open=0]="Open",e[e.Closed=1]="Closed",e))(i1||{}),l1=(e=>(e[e.SetTitleId=0]="SetTitleId",e))(l1||{});let u1={[0](e,t){return e.titleId===t.id?e:{...e,titleId:t.id}}},ii=w.createContext(null);ii.displayName="DialogContext";function Gr(e){let t=w.useContext(ii);if(t===null){let n=new Error(`<${e} /> is missing a parent <Dialog /> component.`);throw Error.captureStackTrace&&Error.captureStackTrace(n,Gr),n}return t}function s1(e,t,n=()=>[document.body]){o1(e,t,r=>{var o;return{containers:[...(o=r.containers)!=null?o:[],n]}})}function a1(e,t){return he(t.type,u1,e,t)}let c1="div",d1=ri.RenderStrategy|ri.Static;function f1(e,t){let n=Gn(),{id:r=`headlessui-dialog-${n}`,open:o,onClose:i,initialFocus:l,__demoMode:u=!1,...s}=e,[a,d]=w.useState(0),p=Ps();o===void 0&&p!==null&&(o=(p&Pe.Open)===Pe.Open);let m=w.useRef(null),v=Ye(m,t),h=w.useRef(null),g=Li(m),T=e.hasOwnProperty("open")||p!==null,f=e.hasOwnProperty("onClose");if(!T&&!f)throw new Error("You have to provide an `open` and an `onClose` prop to the `Dialog` component.");if(!T)throw new Error("You provided an `onClose` prop to the `Dialog`, but forgot an `open` prop.");if(!f)throw new Error("You provided an `open` prop to the `Dialog`, but forgot an `onClose` prop.");if(typeof o!="boolean")throw new Error(`You provided an \`open\` prop to the \`Dialog\`, but the value is not a boolean. Received: ${o}`);if(typeof i!="function")throw new Error(`You provided an \`onClose\` prop to the \`Dialog\`, but the value is not a function. Received: ${i}`);let c=o?0:1,[y,k]=w.useReducer(a1,{titleId:null,descriptionId:null,panelRef:w.createRef()}),N=ue(()=>i(!1)),P=ue(Q=>k({type:0,id:Q})),x=Kn()?u?!1:c===0:!1,L=a>1,A=w.useContext(ii)!==null,U=L?"parent":"leaf",j=p!==null?(p&Pe.Closing)===Pe.Closing:!1,He=(()=>A||j?!1:x)(),Ve=w.useCallback(()=>{var Q,te;return(te=Array.from((Q=g==null?void 0:g.querySelectorAll("body > *"))!=null?Q:[]).find(G=>G.id==="headlessui-portal-root"?!1:G.contains(h.current)&&G instanceof HTMLElement))!=null?te:null},[h]);Za(Ve,He);let mn=(()=>L?!0:x)(),ut=w.useCallback(()=>{var Q,te;return(te=Array.from((Q=g==null?void 0:g.querySelectorAll("[data-headlessui-portal]"))!=null?Q:[]).find(G=>G.contains(h.current)&&G instanceof HTMLElement))!=null?te:null},[h]);Za(ut,mn);let st=ue(()=>{var Q,te;return[...Array.from((Q=g==null?void 0:g.querySelectorAll("html > *, body > *, [data-headlessui-portal]"))!=null?Q:[]).filter(G=>!(G===document.body||G===document.head||!(G instanceof HTMLElement)||G.contains(h.current)||y.panelRef.current&&G.contains(y.panelRef.current))),(te=y.panelRef.current)!=null?te:m.current]}),Kt=(()=>!(!x||L))();ly(()=>st(),N,Kt);let R=(()=>!(L||c!==0))();Qf(g==null?void 0:g.defaultView,"keydown",Q=>{R&&(Q.defaultPrevented||Q.key===Wf.Escape&&(Q.preventDefault(),Q.stopPropagation(),N()))});let O=(()=>!(j||c!==0||A))();s1(g,O,st),w.useEffect(()=>{if(c!==0||!m.current)return;let Q=new ResizeObserver(te=>{for(let G of te){let qr=G.target.getBoundingClientRect();qr.x===0&&qr.y===0&&qr.width===0&&qr.height===0&&N()}});return Q.observe(m.current),()=>Q.disconnect()},[c,m,N]);let[F,B]=Fy(),ee=w.useMemo(()=>[{dialogState:c,close:N,setTitleId:P},y],[c,y,N,P]),Gt=w.useMemo(()=>({open:c===0}),[c]),at={ref:v,id:r,role:"dialog","aria-modal":c===0?!0:void 0,"aria-labelledby":y.titleId,"aria-describedby":F};return M.createElement(Iy,{type:"Dialog",enabled:c===0,element:m,onUpdate:ue((Q,te)=>{te==="Dialog"&&he(Q,{[Cu.Add]:()=>d(G=>G+1),[Cu.Remove]:()=>d(G=>G-1)})})},M.createElement(ku,{force:!0},M.createElement(Eu,null,M.createElement(ii.Provider,{value:ee},M.createElement(Eu.Group,{target:m},M.createElement(ku,{force:!1},M.createElement(B,{slot:Gt,name:"Dialog.Description"},M.createElement(ir,{initialFocus:l,containers:st,features:x?he(U,{parent:ir.features.RestoreFocus,leaf:ir.features.All&~ir.features.FocusLock}):ir.features.None},je({ourProps:at,theirProps:s,slot:Gt,defaultTag:c1,features:d1,visible:c===0,name:"Dialog"})))))))),M.createElement(Su,{features:oi.Hidden,ref:h}))}let p1="div";function m1(e,t){let n=Gn(),{id:r=`headlessui-dialog-overlay-${n}`,...o}=e,[{dialogState:i,close:l}]=Gr("Dialog.Overlay"),u=Ye(t),s=ue(d=>{if(d.target===d.currentTarget){if(ay(d.currentTarget))return d.preventDefault();d.preventDefault(),d.stopPropagation(),l()}}),a=w.useMemo(()=>({open:i===0}),[i]);return je({ourProps:{ref:u,id:r,"aria-hidden":!0,onClick:s},theirProps:o,slot:a,defaultTag:p1,name:"Dialog.Overlay"})}let h1="div";function y1(e,t){let n=Gn(),{id:r=`headlessui-dialog-backdrop-${n}`,...o}=e,[{dialogState:i},l]=Gr("Dialog.Backdrop"),u=Ye(t);w.useEffect(()=>{if(l.panelRef.current===null)throw new Error("A <Dialog.Backdrop /> component is being used, but a <Dialog.Panel /> component is missing.")},[l.panelRef]);let s=w.useMemo(()=>({open:i===0}),[i]);return M.createElement(ku,{force:!0},M.createElement(Eu,null,je({ourProps:{ref:u,id:r,"aria-hidden":!0},theirProps:o,slot:s,defaultTag:h1,name:"Dialog.Backdrop"})))}let g1="div";function v1(e,t){let n=Gn(),{id:r=`headlessui-dialog-panel-${n}`,...o}=e,[{dialogState:i},l]=Gr("Dialog.Panel"),u=Ye(t,l.panelRef),s=w.useMemo(()=>({open:i===0}),[i]),a=ue(d=>{d.stopPropagation()});return je({ourProps:{ref:u,id:r,onClick:a},theirProps:o,slot:s,defaultTag:g1,name:"Dialog.Panel"})}let w1="h2";function S1(e,t){let n=Gn(),{id:r=`headlessui-dialog-title-${n}`,...o}=e,[{dialogState:i,setTitleId:l}]=Gr("Dialog.Title"),u=Ye(t);w.useEffect(()=>(l(r),()=>l(null)),[r,l]);let s=w.useMemo(()=>({open:i===0}),[i]);return je({ourProps:{ref:u,id:r},theirProps:o,slot:s,defaultTag:w1,name:"Dialog.Title"})}let k1=Fe(f1),E1=Fe(y1),C1=Fe(v1),x1=Fe(m1),T1=Fe(S1),hl=Object.assign(k1,{Backdrop:E1,Panel:C1,Overlay:x1,Title:T1,Description:$y});function N1(e=0){let[t,n]=w.useState(e),r=Kr(),o=w.useCallback(s=>{r.current&&n(a=>a|s)},[t,r]),i=w.useCallback(s=>!!(t&s),[t]),l=w.useCallback(s=>{r.current&&n(a=>a&~s)},[n,r]),u=w.useCallback(s=>{r.current&&n(a=>a^s)},[n]);return{flags:t,addFlag:o,hasFlag:i,removeFlag:l,toggleFlag:u}}function P1(e){let t={called:!1};return(...n)=>{if(!t.called)return t.called=!0,e(...n)}}function yl(e,...t){e&&t.length>0&&e.classList.add(...t)}function gl(e,...t){e&&t.length>0&&e.classList.remove(...t)}function R1(e,t){let n=Qn();if(!e)return n.dispose;let{transitionDuration:r,transitionDelay:o}=getComputedStyle(e),[i,l]=[r,o].map(s=>{let[a=0]=s.split(",").filter(Boolean).map(d=>d.includes("ms")?parseFloat(d):parseFloat(d)*1e3).sort((d,p)=>p-d);return a}),u=i+l;if(u!==0){n.group(a=>{a.setTimeout(()=>{t(),a.dispose()},u),a.addEventListener(e,"transitionrun",d=>{d.target===d.currentTarget&&a.dispose()})});let s=n.addEventListener(e,"transitionend",a=>{a.target===a.currentTarget&&(t(),s())})}else t();return n.add(()=>t()),n.dispose}function L1(e,t,n,r){let o=n?"enter":"leave",i=Qn(),l=r!==void 0?P1(r):()=>{};o==="enter"&&(e.removeAttribute("hidden"),e.style.display="");let u=he(o,{enter:()=>t.enter,leave:()=>t.leave}),s=he(o,{enter:()=>t.enterTo,leave:()=>t.leaveTo}),a=he(o,{enter:()=>t.enterFrom,leave:()=>t.leaveFrom});return gl(e,...t.enter,...t.enterTo,...t.enterFrom,...t.leave,...t.leaveFrom,...t.leaveTo,...t.entered),yl(e,...u,...a),i.nextFrame(()=>{gl(e,...a),yl(e,...s),R1(e,()=>(gl(e,...u),yl(e,...t.entered),l()))}),i.dispose}function _1({container:e,direction:t,classes:n,onStart:r,onStop:o}){let i=Kr(),l=Ts(),u=wt(t);lt(()=>{let s=Qn();l.add(s.dispose);let a=e.current;if(a&&u.current!=="idle"&&i.current)return s.dispose(),r.current(u.current),s.add(L1(a,n.current,u.current==="enter",()=>{s.dispose(),o.current(u.current)})),s.dispose},[t])}function qt(e=""){return e.split(" ").filter(t=>t.trim().length>1)}let _i=w.createContext(null);_i.displayName="TransitionContext";var U1=(e=>(e.Visible="visible",e.Hidden="hidden",e))(U1||{});function O1(){let e=w.useContext(_i);if(e===null)throw new Error("A <Transition.Child /> is used but it is missing a parent <Transition /> or <Transition.Root />.");return e}function F1(){let e=w.useContext(Ui);if(e===null)throw new Error("A <Transition.Child /> is used but it is missing a parent <Transition /> or <Transition.Root />.");return e}let Ui=w.createContext(null);Ui.displayName="NestingContext";function Oi(e){return"children"in e?Oi(e.children):e.current.filter(({el:t})=>t.current!==null).filter(({state:t})=>t==="visible").length>0}function ep(e,t){let n=wt(e),r=w.useRef([]),o=Kr(),i=Ts(),l=ue((v,h=ht.Hidden)=>{let g=r.current.findIndex(({el:T})=>T===v);g!==-1&&(he(h,{[ht.Unmount](){r.current.splice(g,1)},[ht.Hidden](){r.current[g].state="hidden"}}),i.microTask(()=>{var T;!Oi(r)&&o.current&&((T=n.current)==null||T.call(n))}))}),u=ue(v=>{let h=r.current.find(({el:g})=>g===v);return h?h.state!=="visible"&&(h.state="visible"):r.current.push({el:v,state:"visible"}),()=>l(v,ht.Unmount)}),s=w.useRef([]),a=w.useRef(Promise.resolve()),d=w.useRef({enter:[],leave:[],idle:[]}),p=ue((v,h,g)=>{s.current.splice(0),t&&(t.chains.current[h]=t.chains.current[h].filter(([T])=>T!==v)),t==null||t.chains.current[h].push([v,new Promise(T=>{s.current.push(T)})]),t==null||t.chains.current[h].push([v,new Promise(T=>{Promise.all(d.current[h].map(([f,c])=>c)).then(()=>T())})]),h==="enter"?a.current=a.current.then(()=>t==null?void 0:t.wait.current).then(()=>g(h)):g(h)}),m=ue((v,h,g)=>{Promise.all(d.current[h].splice(0).map(([T,f])=>f)).then(()=>{var T;(T=s.current.shift())==null||T()}).then(()=>g(h))});return w.useMemo(()=>({children:r,register:u,unregister:l,onStart:p,onStop:m,wait:a,chains:d}),[u,l,r,p,m,d,a])}function D1(){}let A1=["beforeEnter","afterEnter","beforeLeave","afterLeave"];function ec(e){var t;let n={};for(let r of A1)n[r]=(t=e[r])!=null?t:D1;return n}function M1(e){let t=w.useRef(ec(e));return w.useEffect(()=>{t.current=ec(e)},[e]),t}let $1="div",tp=ri.RenderStrategy;function z1(e,t){let{beforeEnter:n,afterEnter:r,beforeLeave:o,afterLeave:i,enter:l,enterFrom:u,enterTo:s,entered:a,leave:d,leaveFrom:p,leaveTo:m,...v}=e,h=w.useRef(null),g=Ye(h,t),T=v.unmount?ht.Unmount:ht.Hidden,{show:f,appear:c,initial:y}=O1(),[k,N]=w.useState(f?"visible":"hidden"),P=F1(),{register:x,unregister:L}=P,A=w.useRef(null);w.useEffect(()=>x(h),[x,h]),w.useEffect(()=>{if(T===ht.Hidden&&h.current){if(f&&k!=="visible"){N("visible");return}return he(k,{hidden:()=>L(h),visible:()=>x(h)})}},[k,h,x,L,f,T]);let U=wt({enter:qt(l),enterFrom:qt(u),enterTo:qt(s),entered:qt(a),leave:qt(d),leaveFrom:qt(p),leaveTo:qt(m)}),j=M1({beforeEnter:n,afterEnter:r,beforeLeave:o,afterLeave:i}),He=Kn();w.useEffect(()=>{if(He&&k==="visible"&&h.current===null)throw new Error("Did you forget to passthrough the `ref` to the actual DOM node?")},[h,k,He]);let Ve=y&&!c,mn=(()=>!He||Ve||A.current===f?"idle":f?"enter":"leave")(),ut=N1(0),st=ue(B=>he(B,{enter:()=>{ut.addFlag(Pe.Opening),j.current.beforeEnter()},leave:()=>{ut.addFlag(Pe.Closing),j.current.beforeLeave()},idle:()=>{}})),Kt=ue(B=>he(B,{enter:()=>{ut.removeFlag(Pe.Opening),j.current.afterEnter()},leave:()=>{ut.removeFlag(Pe.Closing),j.current.afterLeave()},idle:()=>{}})),R=ep(()=>{N("hidden"),L(h)},P);_1({container:h,classes:U,direction:mn,onStart:wt(B=>{R.onStart(h,B,st)}),onStop:wt(B=>{R.onStop(h,B,Kt),B==="leave"&&!Oi(R)&&(N("hidden"),L(h))})}),w.useEffect(()=>{Ve&&(T===ht.Hidden?A.current=null:A.current=f)},[f,Ve,k]);let O=v,F={ref:g};return c&&f&&(O={...O,className:wu(v.className,...U.current.enter,...U.current.enterFrom)}),M.createElement(Ui.Provider,{value:R},M.createElement(py,{value:he(k,{visible:Pe.Open,hidden:Pe.Closed})|ut.flags},je({ourProps:F,theirProps:O,defaultTag:$1,features:tp,visible:k==="visible",name:"Transition.Child"})))}function I1(e,t){let{show:n,appear:r=!1,unmount:o,...i}=e,l=w.useRef(null),u=Ye(l,t);Kn();let s=Ps();if(n===void 0&&s!==null&&(n=(s&Pe.Open)===Pe.Open),![!0,!1].includes(n))throw new Error("A <Transition /> is used but it is missing a `show={true | false}` prop.");let[a,d]=w.useState(n?"visible":"hidden"),p=ep(()=>{d("hidden")}),[m,v]=w.useState(!0),h=w.useRef([n]);lt(()=>{m!==!1&&h.current[h.current.length-1]!==n&&(h.current.push(n),v(!1))},[h,n]);let g=w.useMemo(()=>({show:n,appear:r,initial:m}),[n,r,m]);w.useEffect(()=>{if(n)d("visible");else if(!Oi(p))d("hidden");else{let f=l.current;if(!f)return;let c=f.getBoundingClientRect();c.x===0&&c.y===0&&c.width===0&&c.height===0&&d("hidden")}},[n,p]);let T={unmount:o};return M.createElement(Ui.Provider,{value:p},M.createElement(_i.Provider,{value:g},je({ourProps:{...T,as:w.Fragment,children:M.createElement(np,{ref:u,...T,...i})},theirProps:{},defaultTag:w.Fragment,features:tp,visible:a==="visible",name:"Transition"})))}function B1(e,t){let n=w.useContext(_i)!==null,r=Ps()!==null;return M.createElement(M.Fragment,null,!n&&r?M.createElement(xu,{ref:t,...e}):M.createElement(np,{ref:t,...e}))}let xu=Fe(I1),np=Fe(z1),j1=Fe(B1),vl=Object.assign(xu,{Child:j1,Root:xu});function _s({show:e,onClose:t,onSubmit:n,title:r,content:o,submitText:i,submitEnabled:l=!0}){return C(vl,{appear:!0,show:e,as:w.Fragment,children:z(hl,{as:"div",className:"relative z-10",onClose:t,children:[C(vl.Child,{as:w.Fragment,enter:"ease-out duration-300",enterFrom:"opacity-0",enterTo:"opacity-100",leave:"ease-in duration-200",leaveFrom:"opacity-100",leaveTo:"opacity-0",children:C("div",{className:"fixed inset-0 bg-black bg-opacity-25"})}),C("div",{className:"fixed inset-0 overflow-y-auto",children:C("div",{className:"flex min-h-full items-center justify-center p-4 text-center",children:C(vl.Child,{as:w.Fragment,enter:"ease-out duration-300",enterFrom:"opacity-0 scale-95",enterTo:"opacity-100 scale-100",leave:"ease-in duration-200",leaveFrom:"opacity-100 scale-100",leaveTo:"opacity-0 scale-95",children:z(hl.Panel,{className:"w-full max-w-md transform overflow-hidden rounded-2xl bg-white p-6 text-left align-middle shadow-xl transition-all",children:[C(hl.Title,{as:"h3",className:"text-lg font-medium leading-6 text-gray-900",children:r}),C("div",{className:"mt-3 text-sm text-gray-500",children:o}),z("div",{className:"mt-4 flex flex-row-reverse",children:[i&&C("button",{type:"button",disabled:!l,className:`inline-flex ml-4 justify-center rounded-md border border-transparent ${l?"bg-indigo-600":"bg-grey-300"} px-4 py-2 text-sm font-medium text-indigo-100 ${l?"hover:bg-indigo-500 focus:outline-none focus-visible:ring-2 focus-visible:ring-indigo-500 focus-visible:ring-offset-2":""} transition-all duration-300`,onClick:n,children:i}),C("button",{type:"button",className:"inline-flex justify-center rounded-md border border-transparent bg-indigo-100 px-4 py-2 text-sm font-medium text-indigo-900 hover:bg-indigo-200 focus:outline-none focus-visible:ring-2 focus-visible:ring-indigo-500 focus-visible:ring-offset-2 transition-all duration-300",onClick:t,children:"Close"})]})]})})})})]})})}function H1(e){return C("div",{children:C("input",{...e,type:"url",className:"my-2 bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 dark:bg-gray-700 dark:border-gray-600 dark:placeholder-gray-400 dark:text-white dark:focus:ring-blue-500 dark:focus:border-blue-500",placeholder:"www.example.com",required:!0})})}function V1(e){const t=w.useRef(null),n=w.useRef(null);return w.useEffect(()=>{t.current&&n.current&&(n.current.src=e.audioUrl,t.current.load())},[e.audioUrl]),C("div",{className:"flex relative z-10 p-4 w-full",children:C("audio",{ref:t,controls:!0,className:"w-full h-14 rounded-lg bg-white shadow-xl shadow-black/5 ring-1 ring-slate-700/10",children:C("source",{ref:n,type:e.mimeType})})})}function b1(e){const{isModelLoading:t,isTranscribing:n,onClick:r,...o}=e;return C("button",{...o,onClick:i=>{r&&!n&&!t&&r(i)},disabled:n,className:"text-white bg-blue-700 hover:bg-blue-800 focus:ring-4 focus:ring-blue-300 font-medium rounded-lg text-sm px-5 py-2.5 text-center mr-2 dark:bg-blue-600 dark:hover:bg-blue-700 dark:focus:ring-blue-800 inline-flex items-center",children:t?C(tc,{text:"Loading model..."}):n?C(tc,{text:"Transcribing..."}):"Transcribe Audio"})}function tc(e){return z("div",{role:"status",children:[z("svg",{"aria-hidden":"true",role:"status",className:"inline w-4 h-4 mr-3 text-white animate-spin",viewBox:"0 0 100 101",fill:"none",xmlns:"http://www.w3.org/2000/svg",children:[C("path",{d:"M100 50.5908C100 78.2051 77.6142 100.591 50 100.591C22.3858 100.591 0 78.2051 0 50.5908C0 22.9766 22.3858 0.59082 50 0.59082C77.6142 0.59082 100 22.9766 100 50.5908ZM9.08144 50.5908C9.08144 73.1895 27.4013 91.5094 50 91.5094C72.5987 91.5094 90.9186 73.1895 90.9186 50.5908C90.9186 27.9921 72.5987 9.67226 50 9.67226C27.4013 9.67226 9.08144 27.9921 9.08144 50.5908Z",fill:"#E5E7EB"}),C("path",{d:"M93.9676 39.0409C96.393 38.4038 97.8624 35.9116 97.0079 33.5539C95.2932 28.8227 92.871 24.3692 89.8167 20.348C85.8452 15.1192 80.8826 10.7238 75.2124 7.41289C69.5422 4.10194 63.2754 1.94025 56.7698 1.05124C51.7666 0.367541 46.6976 0.446843 41.7345 1.27873C39.2613 1.69328 37.813 4.19778 38.4501 6.62326C39.0873 9.04874 41.5694 10.4717 44.0505 10.1071C47.8511 9.54855 51.7191 9.52689 55.5402 10.0491C60.8642 10.7766 65.9928 12.5457 70.6331 15.2552C75.2735 17.9648 79.3347 21.5619 82.5849 25.841C84.9175 28.9121 86.7997 32.2913 88.1811 35.8758C89.083 38.2158 91.5421 39.6781 93.9676 39.0409Z",fill:"currentColor"})]}),e.text]})}function W1(){let e=!1;return function(t){(/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino|android|ipad|playbook|silk/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4)))&&(e=!0)}(navigator.userAgent||navigator.vendor||("opera"in window&&typeof window.opera=="string"?window.opera:"")),e}const nc=W1(),ft={SAMPLING_RATE:16e3,DEFAULT_AUDIO_URL:`https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/${nc?"jfk":"ted_60_16k"}.wav`,DEFAULT_MODEL:"Xenova/whisper-tiny",DEFAULT_SUBTASK:"transcribe",DEFAULT_LANGUAGE:"english",DEFAULT_QUANTIZED:nc,DEFAULT_MULTILINGUAL:!1};function Q1({text:e,percentage:t}){return t=t??0,C("div",{className:"mt-0.5 w-full relative text-sm text-white background-bg-cyan-400 bg-gray-200 border-1 border-gray-400 rounded-lg text-left overflow-hidden",children:z("div",{className:"top-0 h-full bg-blue-500 whitespace-nowrap px-2",style:{width:`${t}%`},children:[e," (",`${t.toFixed(2)}%`,")"]})})}function wl(e){return String(e).padStart(2,"0")}function rp(e){const t=e/3600|0;e-=t*(60*60);const n=e/60|0;e-=n*60;const r=e|0;return`${t?wl(t)+":":""}${wl(n)}:${wl(r)}`}const K1={172351395:{name:"EBML",type:"Container"},646:{name:"EBMLVersion",type:"Uint"},759:{name:"EBMLReadVersion",type:"Uint"},754:{name:"EBMLMaxIDLength",type:"Uint"},755:{name:"EBMLMaxSizeLength",type:"Uint"},642:{name:"DocType",type:"String"},647:{name:"DocTypeVersion",type:"Uint"},645:{name:"DocTypeReadVersion",type:"Uint"},108:{name:"Void",type:"Binary"},63:{name:"CRC-32",type:"Binary"},190023271:{name:"SignatureSlot",type:"Container"},16010:{name:"SignatureAlgo",type:"Uint"},16026:{name:"SignatureHash",type:"Uint"},16037:{name:"SignaturePublicKey",type:"Binary"},16053:{name:"Signature",type:"Binary"},15963:{name:"SignatureElements",type:"Container"},15995:{name:"SignatureElementList",type:"Container"},9522:{name:"SignedElement",type:"Binary"},139690087:{name:"Segment",type:"Container"},21863284:{name:"SeekHead",type:"Container"},3515:{name:"Seek",type:"Container"},5035:{name:"SeekID",type:"Binary"},5036:{name:"SeekPosition",type:"Uint"},88713574:{name:"Info",type:"Container"},13220:{name:"SegmentUID",type:"Binary"},13188:{name:"SegmentFilename",type:"String"},1882403:{name:"PrevUID",type:"Binary"},1868715:{name:"PrevFilename",type:"String"},2013475:{name:"NextUID",type:"Binary"},1999803:{name:"NextFilename",type:"String"},1092:{name:"SegmentFamily",type:"Binary"},10532:{name:"ChapterTranslate",type:"Container"},10748:{name:"ChapterTranslateEditionUID",type:"Uint"},10687:{name:"ChapterTranslateCodec",type:"Uint"},10661:{name:"ChapterTranslateID",type:"Binary"},710577:{name:"TimecodeScale",type:"Uint"},1161:{name:"Duration",type:"Float"},1121:{name:"DateUTC",type:"Date"},15273:{name:"Title",type:"String"},3456:{name:"MuxingApp",type:"String"},5953:{name:"WritingApp",type:"String"},103:{name:"Timecode",type:"Uint"},6228:{name:"SilentTracks",type:"Container"},6359:{name:"SilentTrackNumber",type:"Uint"},39:{name:"Position",type:"Uint"},43:{name:"PrevSize",type:"Uint"},35:{name:"SimpleBlock",type:"Binary"},32:{name:"BlockGroup",type:"Container"},33:{name:"Block",type:"Binary"},34:{name:"BlockVirtual",type:"Binary"},13729:{name:"BlockAdditions",type:"Container"},38:{name:"BlockMore",type:"Container"},110:{name:"BlockAddID",type:"Uint"},37:{name:"BlockAdditional",type:"Binary"},27:{name:"BlockDuration",type:"Uint"},122:{name:"ReferencePriority",type:"Uint"},123:{name:"ReferenceBlock",type:"Int"},125:{name:"ReferenceVirtual",type:"Int"},36:{name:"CodecState",type:"Binary"},13730:{name:"DiscardPadding",type:"Int"},14:{name:"Slices",type:"Container"},104:{name:"TimeSlice",type:"Container"},76:{name:"LaceNumber",type:"Uint"},77:{name:"FrameNumber",type:"Uint"},75:{name:"BlockAdditionID",type:"Uint"},78:{name:"Delay",type:"Uint"},79:{name:"SliceDuration",type:"Uint"},72:{name:"ReferenceFrame",type:"Container"},73:{name:"ReferenceOffset",type:"Uint"},74:{name:"ReferenceTimeCode",type:"Uint"},47:{name:"EncryptedBlock",type:"Binary"},106212971:{name:"Tracks",type:"Container"},46:{name:"TrackEntry",type:"Container"},87:{name:"TrackNumber",type:"Uint"},13253:{name:"TrackUID",type:"Uint"},3:{name:"TrackType",type:"Uint"},57:{name:"FlagEnabled",type:"Uint"},8:{name:"FlagDefault",type:"Uint"},5546:{name:"FlagForced",type:"Uint"},28:{name:"FlagLacing",type:"Uint"},11751:{name:"MinCache",type:"Uint"},11768:{name:"MaxCache",type:"Uint"},254851:{name:"DefaultDuration",type:"Uint"},216698:{name:"DefaultDecodedFieldDuration",type:"Uint"},209231:{name:"TrackTimecodeScale",type:"Float"},4991:{name:"TrackOffset",type:"Int"},5614:{name:"MaxBlockAdditionID",type:"Uint"},4974:{name:"Name",type:"String"},177564:{name:"Language",type:"String"},6:{name:"CodecID",type:"String"},9122:{name:"CodecPrivate",type:"Binary"},362120:{name:"CodecName",type:"String"},13382:{name:"AttachmentLink",type:"Uint"},1742487:{name:"CodecSettings",type:"String"},1785920:{name:"CodecInfoURL",type:"String"},438848:{name:"CodecDownloadURL",type:"String"},42:{name:"CodecDecodeAll",type:"Uint"},12203:{name:"TrackOverlay",type:"Uint"},5802:{name:"CodecDelay",type:"Uint"},5819:{name:"SeekPreRoll",type:"Uint"},9764:{name:"TrackTranslate",type:"Container"},9980:{name:"TrackTranslateEditionUID",type:"Uint"},9919:{name:"TrackTranslateCodec",type:"Uint"},9893:{name:"TrackTranslateTrackID",type:"Binary"},96:{name:"Video",type:"Container"},26:{name:"FlagInterlaced",type:"Uint"},5048:{name:"StereoMode",type:"Uint"},5056:{name:"AlphaMode",type:"Uint"},5049:{name:"OldStereoMode",type:"Uint"},48:{name:"PixelWidth",type:"Uint"},58:{name:"PixelHeight",type:"Uint"},5290:{name:"PixelCropBottom",type:"Uint"},5307:{name:"PixelCropTop",type:"Uint"},5324:{name:"PixelCropLeft",type:"Uint"},5341:{name:"PixelCropRight",type:"Uint"},5296:{name:"DisplayWidth",type:"Uint"},5306:{name:"DisplayHeight",type:"Uint"},5298:{name:"DisplayUnit",type:"Uint"},5299:{name:"AspectRatioType",type:"Uint"},963876:{name:"ColourSpace",type:"Binary"},1029411:{name:"GammaValue",type:"Float"},230371:{name:"FrameRate",type:"Float"},97:{name:"Audio",type:"Container"},53:{name:"SamplingFrequency",type:"Float"},14517:{name:"OutputSamplingFrequency",type:"Float"},31:{name:"Channels",type:"Uint"},15739:{name:"ChannelPositions",type:"Binary"},8804:{name:"BitDepth",type:"Uint"},98:{name:"TrackOperation",type:"Container"},99:{name:"TrackCombinePlanes",type:"Container"},100:{name:"TrackPlane",type:"Container"},101:{name:"TrackPlaneUID",type:"Uint"},102:{name:"TrackPlaneType",type:"Uint"},105:{name:"TrackJoinBlocks",type:"Container"},109:{name:"TrackJoinUID",type:"Uint"},64:{name:"TrickTrackUID",type:"Uint"},65:{name:"TrickTrackSegmentUID",type:"Binary"},70:{name:"TrickTrackFlag",type:"Uint"},71:{name:"TrickMasterTrackUID",type:"Uint"},68:{name:"TrickMasterTrackSegmentUID",type:"Binary"},11648:{name:"ContentEncodings",type:"Container"},8768:{name:"ContentEncoding",type:"Container"},4145:{name:"ContentEncodingOrder",type:"Uint"},4146:{name:"ContentEncodingScope",type:"Uint"},4147:{name:"ContentEncodingType",type:"Uint"},4148:{name:"ContentCompression",type:"Container"},596:{name:"ContentCompAlgo",type:"Uint"},597:{name:"ContentCompSettings",type:"Binary"},4149:{name:"ContentEncryption",type:"Container"},2017:{name:"ContentEncAlgo",type:"Uint"},2018:{name:"ContentEncKeyID",type:"Binary"},2019:{name:"ContentSignature",type:"Binary"},2020:{name:"ContentSigKeyID",type:"Binary"},2021:{name:"ContentSigAlgo",type:"Uint"},2022:{name:"ContentSigHashAlgo",type:"Uint"},206814059:{name:"Cues",type:"Container"},59:{name:"CuePoint",type:"Container"},51:{name:"CueTime",type:"Uint"},55:{name:"CueTrackPositions",type:"Container"},119:{name:"CueTrack",type:"Uint"},113:{name:"CueClusterPosition",type:"Uint"},112:{name:"CueRelativePosition",type:"Uint"},50:{name:"CueDuration",type:"Uint"},4984:{name:"CueBlockNumber",type:"Uint"},106:{name:"CueCodecState",type:"Uint"},91:{name:"CueReference",type:"Container"},22:{name:"CueRefTime",type:"Uint"},23:{name:"CueRefCluster",type:"Uint"},4959:{name:"CueRefNumber",type:"Uint"},107:{name:"CueRefCodecState",type:"Uint"},155296873:{name:"Attachments",type:"Container"},8615:{name:"AttachedFile",type:"Container"},1662:{name:"FileDescription",type:"String"},1646:{name:"FileName",type:"String"},1632:{name:"FileMimeType",type:"String"},1628:{name:"FileData",type:"Binary"},1710:{name:"FileUID",type:"Uint"},1653:{name:"FileReferral",type:"Binary"},1633:{name:"FileUsedStartTime",type:"Uint"},1634:{name:"FileUsedEndTime",type:"Uint"},4433776:{name:"Chapters",type:"Container"},1465:{name:"EditionEntry",type:"Container"},1468:{name:"EditionUID",type:"Uint"},1469:{name:"EditionFlagHidden",type:"Uint"},1499:{name:"EditionFlagDefault",type:"Uint"},1501:{name:"EditionFlagOrdered",type:"Uint"},54:{name:"ChapterAtom",type:"Container"},13252:{name:"ChapterUID",type:"Uint"},5716:{name:"ChapterStringUID",type:"String"},17:{name:"ChapterTimeStart",type:"Uint"},18:{name:"ChapterTimeEnd",type:"Uint"},24:{name:"ChapterFlagHidden",type:"Uint"},1432:{name:"ChapterFlagEnabled",type:"Uint"},11879:{name:"ChapterSegmentUID",type:"Binary"},11964:{name:"ChapterSegmentEditionUID",type:"Uint"},9155:{name:"ChapterPhysicalEquiv",type:"Uint"},15:{name:"ChapterTrack",type:"Container"},9:{name:"ChapterTrackNumber",type:"Uint"},0:{name:"ChapterDisplay",type:"Container"},5:{name:"ChapString",type:"String"},892:{name:"ChapLanguage",type:"String"},894:{name:"ChapCountry",type:"String"},10564:{name:"ChapProcess",type:"Container"},10581:{name:"ChapProcessCodecID",type:"Uint"},1293:{name:"ChapProcessPrivate",type:"Binary"},10513:{name:"ChapProcessCommand",type:"Container"},10530:{name:"ChapProcessTime",type:"Uint"},10547:{name:"ChapProcessData",type:"Binary"},39109479:{name:"Tags",type:"Container"},13171:{name:"Tag",type:"Container"},9152:{name:"Targets",type:"Container"},10442:{name:"TargetTypeValue",type:"Uint"},9162:{name:"TargetType",type:"String"},9157:{name:"TagTrackUID",type:"Uint"},9161:{name:"TagEditionUID",type:"Uint"},9156:{name:"TagChapterUID",type:"Uint"},9158:{name:"TagAttachmentUID",type:"Uint"},10184:{name:"SimpleTag",type:"Container"},1443:{name:"TagName",type:"String"},1146:{name:"TagLanguage",type:"String"},1156:{name:"TagDefault",type:"Uint"},1159:{name:"TagString",type:"String"},1157:{name:"TagBinary",type:"Binary"}};class li{constructor(t="Unknown",n="Unknown"){qn(this,"source");qn(this,"data");this.name=t,this.type=n}updateBySource(){}setSource(t){this.source=t,this.updateBySource()}updateByData(){}setData(t){this.data=t,this.updateByData()}}class G1 extends li{constructor(t,n){super(t,n||"Uint")}updateBySource(){this.data="";for(let t=0;t<this.source.length;t++){const n=this.source[t].toString(16);this.data+=rc(n)}}updateByData(){const t=this.data.length/2;this.source=new Uint8Array(t);for(let n=0;n<t;n++){const r=this.data.substr(n*2,2);this.source[n]=parseInt(r,16)}}getValue(){return parseInt(this.data,16)}setValue(t){this.setData(rc(t.toString(16)))}}function rc(e){return e.length%2===1?"0"+e:e}class op extends li{constructor(t,n){super(t,n||"Float")}getFloatArrayType(){return this.source&&this.source.length===4?Float32Array:Float64Array}updateBySource(){const t=this.source.reverse(),n=this.getFloatArrayType(),r=new n(t.buffer);this.data=r[0]}updateByData(){const t=this.getFloatArrayType(),n=new t([this.data]),r=new Uint8Array(n.buffer);this.source=r.reverse()}getValue(){return this.data}setValue(t){this.setData(t)}}class Us extends li{constructor(n,r){super(n,r||"Container");qn(this,"offset",0);qn(this,"data",[])}readByte(){return this.source[this.offset++]}readUint(){const n=this.readByte(),r=8-n.toString(2).length;let o=n-(1<<7-r);for(let i=0;i<r;i++)o*=256,o+=this.readByte();return o}updateBySource(){let n;for(this.data=[],this.offset=0;this.offset<this.source.length;this.offset=n){const r=this.readUint(),o=this.readUint();n=Math.min(this.offset+o,this.source.length);const i=this.source.slice(this.offset,n),l=K1[r]||{name:"Unknown",type:"Unknown"};let u=li;switch(l.type){case"Container":u=Us;break;case"Uint":u=G1;break;case"Float":u=op;break}const s=new u(l.name,l.type);s.setSource(i),this.data.push({id:r,idHex:r.toString(16),data:s})}}writeUint(n,r=!1){for(var o=1,i=128;n>=i&&o<8;o++,i*=128);if(!r){let l=i+n;for(let u=o-1;u>=0;u--){const s=l%256;this.source[this.offset+u]=s,l=(l-s)/256}}this.offset+=o}writeSections(n=!1){this.offset=0;for(let r=0;r<this.data.length;r++){const o=this.data[r],i=o.data.source,l=i.length;this.writeUint(o.id,n),this.writeUint(l,n),n||this.source.set(i,this.offset),this.offset+=l}return this.offset}updateByData(){const n=this.writeSections(!0);this.source=new Uint8Array(n),this.writeSections()}getSectionById(n){for(let r=0;r<this.data.length;r++){const o=this.data[r];if(o.id===n)return o.data}}}class q1 extends Us{constructor(t){super("File","File"),this.setSource(t)}fixDuration(t){const n=this.getSectionById(139690087);if(!n)return!1;const r=n.getSectionById(88713574);if(!r)return!1;const o=r.getSectionById(710577);if(!o)return!1;let i=r.getSectionById(1161);if(i)if(i.getValue()<=0)i.setValue(t);else return!1;else i=new op("Duration","Float"),i.setValue(t),r.data.push({id:1161,data:i});return o.setValue(1e6),r.updateByData(),n.updateByData(),this.updateByData(),!0}toBlob(t="video/webm"){return new Blob([this.source.buffer],{type:t})}}const X1=(e,t,n="video/webm")=>new Promise((r,o)=>{try{const i=new FileReader;i.addEventListener("loadend",()=>{try{const l=i.result,u=new q1(new Uint8Array(l));u.fixDuration(t)?r(u.toBlob(n)):r(e)}catch(l){o(l)}}),i.addEventListener("error",()=>o()),i.readAsArrayBuffer(e)}catch(i){o(i)}});function Y1(){const e=["audio/webm","audio/mp4","audio/ogg","audio/wav","audio/aac"];for(let t=0;t<e.length;t++)if(MediaRecorder.isTypeSupported(e[t]))return e[t]}function J1(e){const[t,n]=w.useState(!1),[r,o]=w.useState(0),[i,l]=w.useState(null),u=w.useRef(null),s=w.useRef(null),a=w.useRef([]),d=w.useRef(null),p=async()=>{l(null);let h=Date.now();try{u.current||(u.current=await navigator.mediaDevices.getUserMedia({audio:!0}));const g=Y1(),T=new MediaRecorder(u.current,{mimeType:g});s.current=T,T.addEventListener("dataavailable",async f=>{if(f.data.size>0&&a.current.push(f.data),T.state==="inactive"){const c=Date.now()-h;let y=new Blob(a.current,{type:g});g==="audio/webm"&&(y=await X1(y,c,y.type)),l(y),e.onRecordingComplete(y),a.current=[]}}),T.start(),n(!0)}catch(g){console.error("Error accessing microphone:",g)}},m=()=>{s.current&&s.current.state==="recording"&&(s.current.stop(),o(0),n(!1))};return w.useEffect(()=>{if(t){const h=setInterval(()=>{o(g=>g+1)},1e3);return()=>{clearInterval(h)}}return()=>{}},[t]),z("div",{className:"flex flex-col justify-center items-center",children:[C("button",{type:"button",className:`m-2 inline-flex justify-center rounded-md border border-transparent px-4 py-2 text-sm font-medium text-white focus:outline-none focus-visible:ring-2 focus-visible:ring-indigo-500 focus-visible:ring-offset-2 transition-all duration-200 ${t?"bg-red-500 hover:bg-red-600":"bg-green-500 hover:bg-green-600"}`,onClick:()=>{t?m():p()},children:t?`Stop Recording (${rp(r)})`:"Start Recording"}),i&&C("audio",{className:"w-full",ref:d,controls:!0,children:C("source",{src:URL.createObjectURL(i),type:i.type})})]})}function Z1(e){return e=e.toLowerCase(),(e.match(/\w+.?/g)||[]).map(t=>t.charAt(0).toUpperCase()+t.slice(1)).join("")}const oc={en:"english",zh:"chinese",de:"german",es:"spanish/castilian",ru:"russian",ko:"korean",fr:"french",ja:"japanese",pt:"portuguese",tr:"turkish",pl:"polish",ca:"catalan/valencian",nl:"dutch/flemish",ar:"arabic",sv:"swedish",it:"italian",id:"indonesian",hi:"hindi",fi:"finnish",vi:"vietnamese",he:"hebrew",uk:"ukrainian",el:"greek",ms:"malay",cs:"czech",ro:"romanian/moldavian/moldovan",da:"danish",hu:"hungarian",ta:"tamil",no:"norwegian",th:"thai",ur:"urdu",hr:"croatian",bg:"bulgarian",lt:"lithuanian",la:"latin",mi:"maori",ml:"malayalam",cy:"welsh",sk:"slovak",te:"telugu",fa:"persian",lv:"latvian",bn:"bengali",sr:"serbian",az:"azerbaijani",sl:"slovenian",kn:"kannada",et:"estonian",mk:"macedonian",br:"breton",eu:"basque",is:"icelandic",hy:"armenian",ne:"nepali",mn:"mongolian",bs:"bosnian",kk:"kazakh",sq:"albanian",sw:"swahili",gl:"galician",mr:"marathi",pa:"punjabi/panjabi",si:"sinhala/sinhalese",km:"khmer",sn:"shona",yo:"yoruba",so:"somali",af:"afrikaans",oc:"occitan",ka:"georgian",be:"belarusian",tg:"tajik",sd:"sindhi",gu:"gujarati",am:"amharic",yi:"yiddish",lo:"lao",uz:"uzbek",fo:"faroese",ht:"haitian creole/haitian",ps:"pashto/pushto",tk:"turkmen",nn:"nynorsk",mt:"maltese",sa:"sanskrit",lb:"luxembourgish/letzeburgesch",my:"myanmar/burmese",bo:"tibetan",tl:"tagalog",mg:"malagasy",as:"assamese",tt:"tatar",haw:"hawaiian",ln:"lingala",ha:"hausa",ba:"bashkir",jw:"javanese",su:"sundanese"};function eg(e){const[t,n]=w.useState(void 0),[r,o]=w.useState(void 0),[i,l]=w.useState(void 0),u=t!==void 0,s=()=>{o(void 0),l(void 0)},a=async(m,v)=>{const h=new AudioContext({sampleRate:ft.SAMPLING_RATE}),g=URL.createObjectURL(new Blob([m],{type:"audio/*"})),T=await h.decodeAudioData(m);o({buffer:T,url:g,source:"URL",mimeType:v})},d=async m=>{s(),n(0);const v=URL.createObjectURL(m),h=new FileReader;h.onprogress=g=>{n(g.loaded/g.total||0)},h.onloadend=async()=>{const g=new AudioContext({sampleRate:ft.SAMPLING_RATE}),T=h.result,f=await g.decodeAudioData(T);n(void 0),o({buffer:f,url:v,source:"RECORDING",mimeType:m.type})},h.readAsArrayBuffer(m)},p=async m=>{if(i)try{o(void 0),n(0);const{data:v,headers:h}=await q0.get(i,{signal:m.signal,responseType:"arraybuffer",onDownloadProgress(T){n(T.progress||0)}});let g=h["content-type"];(!g||g==="audio/wave")&&(g="audio/wav"),a(v,g)}catch(v){console.log("Request failed or aborted",v)}finally{n(void 0)}};return w.useEffect(()=>{if(i){const m=new AbortController;return p(m),()=>{m.abort()}}},[i]),z(nt,{children:[z("div",{className:"flex flex-col justify-center items-center rounded-lg bg-white shadow-xl shadow-black/5 ring-1 ring-slate-700/10",children:[z("div",{className:"flex flex-row space-x-2 py-2 w-full px-2",children:[C(ig,{icon:C(cg,{}),text:"From URL",onUrlUpdate:m=>{e.transcriber.onInputChange(),l(m)}}),C(ic,{}),C(ug,{icon:C(dg,{}),text:"From file",onFileUpdate:(m,v,h)=>{e.transcriber.onInputChange(),o({buffer:m,url:v,source:"FILE",mimeType:h})}}),navigator.mediaDevices&&z(nt,{children:[C(ic,{}),C(sg,{icon:C(pg,{}),text:"Record",setAudioData:m=>{e.transcriber.onInputChange(),d(m)}})]})]}),C(rg,{progress:u?t:+!!r})]}),r&&z(nt,{children:[C(V1,{audioUrl:r.url,mimeType:r.mimeType}),z("div",{className:"relative w-full flex justify-center items-center",children:[C(b1,{onClick:()=>{e.transcriber.start(r.buffer)},isModelLoading:e.transcriber.isModelLoading,isTranscribing:e.transcriber.isBusy}),C(tg,{className:"absolute right-4",transcriber:e.transcriber,icon:C(fg,{})})]}),e.transcriber.progressItems.length>0&&z("div",{className:"relative z-10 p-4 w-full",children:[C("label",{children:"Loading model files... (only run once)"}),e.transcriber.progressItems.map(m=>C("div",{children:C(Q1,{text:m.file,percentage:m.progress})},m.file))]})]})]})}function tg(e){const[t,n]=w.useState(!1),r=()=>{n(!0)},o=()=>{n(!1)},i=l=>{o()};return z("div",{className:e.className,children:[C(Fi,{icon:e.icon,onClick:r}),C(ng,{show:t,onSubmit:i,onClose:o,transcriber:e.transcriber})]})}function ng(e){const t=Object.values(oc).map(Z1),n={"Xenova/whisper-tiny":[41,152],"Xenova/whisper-base":[77,291],"Xenova/whisper-small":[249],"Xenova/whisper-medium":[776],"distil-whisper/distil-medium.en":[402],"distil-whisper/distil-large-v2":[767]};return C(_s,{show:e.show,title:"Settings",content:z(nt,{children:[C("label",{children:"Select the model to use."}),C("select",{className:"mt-1 mb-1 bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 dark:bg-gray-700 dark:border-gray-600 dark:placeholder-gray-400 dark:text-white dark:focus:ring-blue-500 dark:focus:border-blue-500",defaultValue:e.transcriber.model,onChange:r=>{e.transcriber.setModel(r.target.value)},children:Object.keys(n).filter(r=>e.transcriber.quantized||n[r].length==2).filter(r=>!e.transcriber.multilingual||!r.startsWith("distil-whisper/")).map(r=>C("option",{value:r,children:`${r}${e.transcriber.multilingual||r.startsWith("distil-whisper/")?"":".en"} (${n[r][e.transcriber.quantized?0:1]}MB)`},r))}),z("div",{className:"flex justify-between items-center mb-3 px-1",children:[z("div",{className:"flex",children:[C("input",{id:"multilingual",type:"checkbox",checked:e.transcriber.multilingual,onChange:r=>{e.transcriber.setMultilingual(r.target.checked)}}),C("label",{htmlFor:"multilingual",className:"ms-1",children:"Multilingual"})]}),z("div",{className:"flex",children:[C("input",{id:"quantize",type:"checkbox",checked:e.transcriber.quantized,onChange:r=>{e.transcriber.setQuantized(r.target.checked)}}),C("label",{htmlFor:"quantize",className:"ms-1",children:"Quantized"})]})]}),e.transcriber.multilingual&&z(nt,{children:[C("label",{children:"Select the source language."}),C("select",{className:"mt-1 mb-3 bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 dark:bg-gray-700 dark:border-gray-600 dark:placeholder-gray-400 dark:text-white dark:focus:ring-blue-500 dark:focus:border-blue-500",defaultValue:e.transcriber.language,onChange:r=>{e.transcriber.setLanguage(r.target.value)},children:Object.keys(oc).map((r,o)=>C("option",{value:r,children:t[o]},r))}),C("label",{children:"Select the task to perform."}),z("select",{className:"mt-1 mb-3 bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 dark:bg-gray-700 dark:border-gray-600 dark:placeholder-gray-400 dark:text-white dark:focus:ring-blue-500 dark:focus:border-blue-500",defaultValue:e.transcriber.subtask,onChange:r=>{e.transcriber.setSubtask(r.target.value)},children:[C("option",{value:"transcribe",children:"Transcribe"}),C("option",{value:"translate",children:"Translate (to English)"})]})]})]}),onClose:e.onClose,onSubmit:()=>{}})}function ic(){return C("div",{className:"w-[1px] bg-slate-200"})}function rg(e){return C(og,{progress:`${Math.round(e.progress*100)}%`})}function og(e){return C("div",{className:"w-full bg-gray-200 rounded-full h-1 dark:bg-gray-700",children:C("div",{className:"bg-blue-600 h-1 rounded-full transition-all duration-100",style:{width:e.progress}})})}function ig(e){const[t,n]=w.useState(!1),r=()=>{n(!0)},o=()=>{n(!1)},i=l=>{e.onUrlUpdate(l),o()};return z(nt,{children:[C(Fi,{icon:e.icon,text:e.text,onClick:r}),C(lg,{show:t,onSubmit:i,onClose:o})]})}function lg(e){const[t,n]=w.useState(ft.DEFAULT_AUDIO_URL),r=i=>{n(i.target.value)},o=()=>{e.onSubmit(t)};return C(_s,{show:e.show,title:"From URL",content:z(nt,{children:["Enter the URL of the audio file you want to load.",C(H1,{onChange:r,value:t})]}),onClose:e.onClose,submitText:"Load",onSubmit:o})}function ug(e){let t=document.createElement("input");return t.type="file",t.oninput=n=>{let r=n.target.files;if(!r)return;const o=URL.createObjectURL(r[0]),i=r[0].type,l=new FileReader;l.addEventListener("load",async u=>{var p;const s=(p=u.target)==null?void 0:p.result;if(!s)return;const d=await new AudioContext({sampleRate:ft.SAMPLING_RATE}).decodeAudioData(s);e.onFileUpdate(d,o,i)}),l.readAsArrayBuffer(r[0]),t.value=""},C(nt,{children:C(Fi,{icon:e.icon,text:e.text,onClick:()=>t.click()})})}function sg(e){const[t,n]=w.useState(!1),r=()=>{n(!0)},o=()=>{n(!1)},i=l=>{l&&(e.setAudioData(l),o())};return z(nt,{children:[C(Fi,{icon:e.icon,text:e.text,onClick:r}),C(ag,{show:t,onSubmit:i,onClose:o})]})}function ag(e){const[t,n]=w.useState(),r=l=>{n(l)},o=()=>{e.onSubmit(t),n(void 0)},i=()=>{e.onClose(),n(void 0)};return C(_s,{show:e.show,title:"From Recording",content:z(nt,{children:["Record audio using your microphone",C(J1,{onRecordingComplete:r})]}),onClose:i,submitText:"Load",submitEnabled:t!==void 0,onSubmit:o})}function Fi(e){return z("button",{onClick:e.onClick,className:"flex items-center justify-center rounded-lg p-2 bg-blue text-slate-500 hover:text-indigo-600 hover:bg-indigo-50 transition-all duration-200",children:[C("div",{className:"w-7 h-7",children:e.icon}),e.text&&C("div",{className:"ml-2 break-text text-center text-md w-30",children:e.text})]})}function cg(){return C("svg",{xmlns:"http://www.w3.org/2000/svg",fill:"none",viewBox:"0 0 24 24",strokeWidth:"1.5",stroke:"currentColor",children:C("path",{strokeLinecap:"round",strokeLinejoin:"round",d:"M13.19 8.688a4.5 4.5 0 011.242 7.244l-4.5 4.5a4.5 4.5 0 01-6.364-6.364l1.757-1.757m13.35-.622l1.757-1.757a4.5 4.5 0 00-6.364-6.364l-4.5 4.5a4.5 4.5 0 001.242 7.244"})})}function dg(){return C("svg",{xmlns:"http://www.w3.org/2000/svg",fill:"none",viewBox:"0 0 24 24",strokeWidth:"1.5",stroke:"currentColor",children:C("path",{strokeLinecap:"round",strokeLinejoin:"round",d:"M3.75 9.776c.112-.017.227-.026.344-.026h15.812c.117 0 .232.009.344.026m-16.5 0a2.25 2.25 0 00-1.883 2.542l.857 6a2.25 2.25 0 002.227 1.932H19.05a2.25 2.25 0 002.227-1.932l.857-6a2.25 2.25 0 00-1.883-2.542m-16.5 0V6A2.25 2.25 0 016 3.75h3.879a1.5 1.5 0 011.06.44l2.122 2.12a1.5 1.5 0 001.06.44H18A2.25 2.25 0 0120.25 9v.776"})})}function fg(){return z("svg",{xmlns:"http://www.w3.org/2000/svg",fill:"none",viewBox:"0 0 24 24",strokeWidth:"1.25",stroke:"currentColor",children:[C("path",{strokeLinecap:"round",strokeLinejoin:"round",d:"M9.594 3.94c.09-.542.56-.94 1.11-.94h2.593c.55 0 1.02.398 1.11.94l.213 1.281c.063.374.313.686.645.87.074.04.147.083.22.127.324.196.72.257 1.075.124l1.217-.456a1.125 1.125 0 011.37.49l1.296 2.247a1.125 1.125 0 01-.26 1.431l-1.003.827c-.293.24-.438.613-.431.992a6.759 6.759 0 010 .255c-.007.378.138.75.43.99l1.005.828c.424.35.534.954.26 1.43l-1.298 2.247a1.125 1.125 0 01-1.369.491l-1.217-.456c-.355-.133-.75-.072-1.076.124a6.57 6.57 0 01-.22.128c-.331.183-.581.495-.644.869l-.213 1.28c-.09.543-.56.941-1.11.941h-2.594c-.55 0-1.02-.398-1.11-.94l-.213-1.281c-.062-.374-.312-.686-.644-.87a6.52 6.52 0 01-.22-.127c-.325-.196-.72-.257-1.076-.124l-1.217.456a1.125 1.125 0 01-1.369-.49l-1.297-2.247a1.125 1.125 0 01.26-1.431l1.004-.827c.292-.24.437-.613.43-.992a6.932 6.932 0 010-.255c.007-.378-.138-.75-.43-.99l-1.004-.828a1.125 1.125 0 01-.26-1.43l1.297-2.247a1.125 1.125 0 011.37-.491l1.216.456c.356.133.751.072 1.076-.124.072-.044.146-.087.22-.128.332-.183.582-.495.644-.869l.214-1.281z"}),C("path",{strokeLinecap:"round",strokeLinejoin:"round",d:"M15 12a3 3 0 11-6 0 3 3 0 016 0z"})]})}function pg(){return C("svg",{xmlns:"http://www.w3.org/2000/svg",fill:"none",viewBox:"0 0 24 24",strokeWidth:1.5,stroke:"currentColor",children:C("path",{strokeLinecap:"round",strokeLinejoin:"round",d:"M12 18.75a6 6 0 006-6v-1.5m-6 7.5a6 6 0 01-6-6v-1.5m6 7.5v3.75m-3.75 0h7.5M12 15.75a3 3 0 01-3-3V4.5a3 3 0 116 0v8.25a3 3 0 01-3 3z"})})}function mg({transcribedData:e}){const t=w.useRef(null),n=(i,l)=>{const u=URL.createObjectURL(i),s=document.createElement("a");s.href=u,s.download=l,s.click(),URL.revokeObjectURL(u)},r=()=>{let l=((e==null?void 0:e.chunks)??[]).map(s=>s.text).join("").trim();const u=new Blob([l],{type:"text/plain"});n(u,"transcript.txt")},o=()=>{let i=JSON.stringify((e==null?void 0:e.chunks)??[],null,2);const l=/(    "timestamp": )\[\s+(\S+)\s+(\S+)\s+\]/gm;i=i.replace(l,"$1[$2 $3]");const u=new Blob([i],{type:"application/json"});n(u,"transcript.json")};return w.useEffect(()=>{t.current&&Math.abs(t.current.offsetHeight+t.current.scrollTop-t.current.scrollHeight)<=64&&(t.current.scrollTop=t.current.scrollHeight)}),z("div",{ref:t,className:"w-full flex flex-col my-2 p-4 max-h-[20rem] overflow-y-auto",children:[(e==null?void 0:e.chunks)&&e.chunks.map((i,l)=>z("div",{className:"w-full flex flex-row mb-2 bg-white rounded-lg p-4 shadow-xl shadow-black/5 ring-1 ring-slate-700/10",children:[C("div",{className:"mr-5",children:rp(i.timestamp[0])}),i.text]},`${l}-${i.text}`)),e&&!e.isBusy&&z("div",{className:"w-full text-right",children:[C("button",{onClick:r,className:"text-white bg-green-500 hover:bg-green-600 focus:ring-4 focus:ring-green-300 font-medium rounded-lg text-sm px-4 py-2 text-center mr-2 dark:bg-green-600 dark:hover:bg-green-700 dark:focus:ring-green-800 inline-flex items-center",children:"Export TXT"}),C("button",{onClick:o,className:"text-white bg-green-500 hover:bg-green-600 focus:ring-4 focus:ring-green-300 font-medium rounded-lg text-sm px-4 py-2 text-center mr-2 dark:bg-green-600 dark:hover:bg-green-700 dark:focus:ring-green-800 inline-flex items-center",children:"Export JSON"})]})]})}function hg(e){const[t]=w.useState(()=>yg(e));return t}function yg(e){const t=new Worker(new URL("/assets/worker-8c8208dc.js",self.location),{type:"module"});return t.addEventListener("message",e),t}function gg(){const[e,t]=w.useState(void 0),[n,r]=w.useState(!1),[o,i]=w.useState(!1),[l,u]=w.useState([]),s=hg(P=>{const x=P.data;switch(x.status){case"progress":u(U=>U.map(j=>j.file===x.file?{...j,progress:x.progress}:j));break;case"update":const L=x;t({isBusy:!0,text:L.data[0],chunks:L.data[1].chunks});break;case"complete":const A=x;t({isBusy:!1,text:A.data.text,chunks:A.data.chunks}),r(!1);break;case"initiate":i(!0),u(U=>[...U,x]);break;case"ready":i(!1);break;case"error":r(!1),alert(`${x.data.message} This is most likely because you are using Safari on an M1/M2 Mac. Please try again from Chrome, Firefox, or Edge.
-
-If this is not the case, please file a bug report.`);break;case"done":u(U=>U.filter(j=>j.file!==x.file));break}}),[a,d]=w.useState(ft.DEFAULT_MODEL),[p,m]=w.useState(ft.DEFAULT_SUBTASK),[v,h]=w.useState(ft.DEFAULT_QUANTIZED),[g,T]=w.useState(ft.DEFAULT_MULTILINGUAL),[f,c]=w.useState(ft.DEFAULT_LANGUAGE),y=w.useCallback(()=>{t(void 0)},[]),k=w.useCallback(async P=>{if(P){t(void 0),r(!0);let x;if(P.numberOfChannels===2){const L=Math.sqrt(2);let A=P.getChannelData(0),U=P.getChannelData(1);x=new Float32Array(A.length);for(let j=0;j<P.length;++j)x[j]=L*(A[j]+U[j])/2}else x=P.getChannelData(0);s.postMessage({audio:x,model:a,multilingual:g,quantized:v,subtask:g?p:null,language:g&&f!=="auto"?f:null})}},[s,a,g,v,p,f]);return w.useMemo(()=>({onInputChange:y,isBusy:n,isModelLoading:o,progressItems:l,start:k,output:e,model:a,setModel:d,multilingual:g,setMultilingual:T,quantized:v,setQuantized:h,subtask:p,setSubtask:m,language:f,setLanguage:c}),[n,o,l,k,e,a,g,v,p,f])}function vg(){const e=gg();return z("div",{className:"flex justify-center items-center min-h-screen",children:[z("div",{className:"container flex flex-col justify-center items-center",children:[C("h1",{className:"text-5xl font-extrabold tracking-tight text-slate-900 sm:text-7xl text-center",children:"Whisper Web"}),C("h2",{className:"mt-3 mb-5 px-4 text-center text-1xl font-semibold tracking-tight text-slate-900 sm:text-2xl",children:"ML-powered speech recognition directly in your browser"}),C(eg,{transcriber:e}),C(mg,{transcribedData:e.output})]}),z("div",{className:"absolute bottom-4",children:["Made with"," ",C("a",{className:"underline",href:"https://github.com/xenova/transformers.js",children:"🤗 Transformers.js"})]})]})}kl.createRoot(document.getElementById("root")).render(C(M.StrictMode,{children:C(vg,{})}));
diff --git a/spaces/XzJosh/Aatrox-Bert-VITS2/README.md b/spaces/XzJosh/Aatrox-Bert-VITS2/README.md
deleted file mode 100644
index 67c0415e416cf0d10da19f4a422cf9d2de8b5d49..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Aatrox-Bert-VITS2/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
----
-license: mit
-sdk: gradio
-title: AI剑魔①
----
\ No newline at end of file
diff --git a/spaces/XzJosh/Aatrox-Bert-VITS2/preprocess_text.py b/spaces/XzJosh/Aatrox-Bert-VITS2/preprocess_text.py
deleted file mode 100644
index 5eb0f3b9e929fcbe91dcbeb653391227a2518a15..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Aatrox-Bert-VITS2/preprocess_text.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import json
-from random import shuffle
-
-import tqdm
-from text.cleaner import clean_text
-from collections import defaultdict
-stage = [1,2,3]
-
-transcription_path = 'filelists/genshin.list'
-train_path = 'filelists/train.list'
-val_path = 'filelists/val.list'
-config_path = "configs/config.json"
-val_per_spk = 4
-max_val_total = 8
-
-if 1 in stage:
-    with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f:
-        for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()):
-            try:
-                utt, spk, language, text = line.strip().split('|')
-                norm_text, phones, tones, word2ph = clean_text(text, language)
-                f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones),
-                                                     " ".join([str(i) for i in tones]),
-                                                     " ".join([str(i) for i in word2ph])))
-            except Exception as error :
-                print("err!", utt, error)
-
-if 2 in stage:
-    spk_utt_map = defaultdict(list)
-    spk_id_map = {}
-    current_sid = 0
-
-    with open( transcription_path+'.cleaned', encoding='utf-8') as f:
-        for line in f.readlines():
-            utt, spk, language, text, phones, tones, word2ph = line.strip().split('|')
-            spk_utt_map[spk].append(line)
-            if spk not in spk_id_map.keys():
-                spk_id_map[spk] = current_sid
-                current_sid += 1
-    train_list = []
-    val_list = []
-    
-    for spk, utts in spk_utt_map.items():
-        shuffle(utts)
-        val_list+=utts[:val_per_spk]
-        train_list+=utts[val_per_spk:]
-    if len(val_list) > max_val_total:
-        train_list+=val_list[max_val_total:]
-        val_list = val_list[:max_val_total]
-    
-    with open( train_path,"w", encoding='utf-8') as f:
-        for line in train_list:
-            f.write(line)
-    
-    with open(val_path, "w", encoding='utf-8') as f:
-        for line in val_list:
-            f.write(line)
-
-if 3 in stage:
-    assert 2 in stage
-    config = json.load(open(config_path, encoding='utf-8'))
-    config["data"]['spk2id'] = spk_id_map
-    with open(config_path, 'w', encoding='utf-8') as f:
-        json.dump(config, f, indent=2, ensure_ascii=False)
diff --git a/spaces/XzJosh/Ava-Bert-VITS2/mel_processing.py b/spaces/XzJosh/Ava-Bert-VITS2/mel_processing.py
deleted file mode 100644
index 50435ecf88ef4fb6c1d47f3e6edd04c3ea7d3e80..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Ava-Bert-VITS2/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/XzJosh/Jiaran-Bert-VITS2/monotonic_align/core.py b/spaces/XzJosh/Jiaran-Bert-VITS2/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jiaran-Bert-VITS2/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-  b = paths.shape[0]
-  max_neg_val=-1e9
-  for i in range(int(b)):
-    path = paths[i]
-    value = values[i]
-    t_y = t_ys[i]
-    t_x = t_xs[i]
-
-    v_prev = v_cur = 0.0
-    index = t_x - 1
-
-    for y in range(t_y):
-      for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-        if x == y:
-          v_cur = max_neg_val
-        else:
-          v_cur = value[y-1, x]
-        if x == 0:
-          if y == 0:
-            v_prev = 0.
-          else:
-            v_prev = max_neg_val
-        else:
-          v_prev = value[y-1, x-1]
-        value[y, x] += max(v_prev, v_cur)
-
-    for y in range(t_y - 1, -1, -1):
-      path[y, index] = 1
-      if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
-        index = index - 1
diff --git a/spaces/XzJosh/nine1-Bert-VITS2/data_utils.py b/spaces/XzJosh/nine1-Bert-VITS2/data_utils.py
deleted file mode 100644
index be3a29a93188c5b3386f22e5db29e5e96d78109a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nine1-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.spk_map = hparams.spk2id
-        self.hparams = hparams
-
-        self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
-        if self.use_mel_spec_posterior:
-            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 300)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        skipped = 0
-        for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
-            audiopath = f'{_id}'
-            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
-                phones = phones.split(" ")
-                tone = [int(i) for i in tone.split(" ")]
-                word2ph = [int(i) for i in word2ph.split(" ")]
-                audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-            else:
-                skipped += 1
-        print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
-        bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
-        spec, wav = self.get_audio(audiopath)
-        sid = torch.LongTensor([int(self.spk_map[sid])])
-        return (phones, spec, wav, sid, tone, language, bert)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        try:
-            spec = torch.load(spec_filename)
-        except:
-            if self.use_mel_spec_posterior:
-                spec = mel_spectrogram_torch(audio_norm, self.filter_length,
-                    self.n_mel_channels, self.sampling_rate, self.hop_length,
-                    self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
-            else:
-                spec = spectrogram_torch(audio_norm, self.filter_length,
-                    self.sampling_rate, self.hop_length, self.win_length,
-                    center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
-        pold = phone
-        w2pho = [i for i in word2ph]
-        word2ph = [i for i in word2ph]
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-        pold2 = phone
-
-        if self.add_blank:
-            p1 = len(phone)
-            phone = commons.intersperse(phone, 0)
-            p2 = len(phone)
-            t1 = len(tone)
-            tone = commons.intersperse(tone, 0)
-            t2 = len(tone)
-            language = commons.intersperse(language, 0)
-            for i in range(len(word2ph)):
-                word2ph[i] = word2ph[i] * 2
-            word2ph[0] += 1
-        bert_path = wav_path.replace(".wav", ".bert.pt")
-        try:
-            bert = torch.load(bert_path)
-            assert bert.shape[-1] == len(phone)
-        except:
-            bert = get_bert(text, word2ph, language_str)
-            torch.save(bert, bert_path)
-            #print(bert.shape[-1], bert_path, text, pold)
-            assert bert.shape[-1] == len(phone)
-
-        assert bert.shape[-1] == len(phone), (
-        bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, phone, tone, language
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
-    """ Zero-pads model inputs and targets
-    """
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        tone_padded = torch.LongTensor(len(batch), max_text_len)
-        language_padded = torch.LongTensor(len(batch), max_text_len)
-        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        tone_padded.zero_()
-        language_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        bert_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-            tone = row[4]
-            tone_padded[i, :tone.size(0)] = tone
-
-            language = row[5]
-            language_padded[i, :language.size(0)] = language
-
-            bert = row[6]
-            bert_padded[i, :, :bert.size(1)] = bert
-
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        for i in range(len(buckets) - 1, 0, -1):
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            if (len_bucket == 0):
-                continue
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/XzJosh/yoyo-Bert-VITS2/monotonic_align/__init__.py b/spaces/XzJosh/yoyo-Bert-VITS2/monotonic_align/__init__.py
deleted file mode 100644
index 75603d26cf2b8d6196f5a68a89f9e49d8e519bc8..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/yoyo-Bert-VITS2/monotonic_align/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-def maximum_path(neg_cent, mask):
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-  path = zeros(neg_cent.shape, dtype=int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-  maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-  return from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/model_download/yolov5_model_p5_all.sh b/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/model_download/yolov5_model_p5_all.sh
deleted file mode 100644
index ab68c26898822fc2d09995c60584d7a0d9d40657..0000000000000000000000000000000000000000
--- a/spaces/Zengyf-CVer/Gradio_YOLOv5_Det_v4/model_download/yolov5_model_p5_all.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-cd ./yolov5
-
-# 下载YOLOv5模型
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt
diff --git a/spaces/Zhenhong/text-to-image-Stable-Diffusion-demo/app.py b/spaces/Zhenhong/text-to-image-Stable-Diffusion-demo/app.py
deleted file mode 100644
index 3c66279a1249cda453ae697eaae3e1d53fd6825f..0000000000000000000000000000000000000000
--- a/spaces/Zhenhong/text-to-image-Stable-Diffusion-demo/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
-#pipe = pipe.to("cuda")
-
-# prompt = "a photo of an astronaut riding a horse on mars"
-# image.save("astronaut_rides_horse.png")
-
-def infer(Prompt):
-    image = pipe(Prompt).images[0]  
-    
-    return image
-
-iface = gr.Interface(fn=infer, inputs=gr.Textbox(lines=1, placeholder="Prompt Here..."), outputs=gr.Image(label="stable-diffusion-v1-5",visible=True))
-iface.launch()
\ No newline at end of file
diff --git a/spaces/a718/jjj/Dockerfile b/spaces/a718/jjj/Dockerfile
deleted file mode 100644
index c677b05b75f7e4b2beee8c97fb47957a0861a83e..0000000000000000000000000000000000000000
--- a/spaces/a718/jjj/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-CMD npm start
diff --git a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/modules/layers.py b/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/modules/layers.py
deleted file mode 100644
index 64d7d68f5d3a7d58c2615939220168a94bbd4475..0000000000000000000000000000000000000000
--- a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/modules/layers.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import torch
-from torch import nn
-from typing import Any
-
-
-class BatchNormConv1d(nn.Module):
-    """
-    A nn.Conv1d followed by an optional activation function, and nn.BatchNorm1d
-    """
-
-    def __init__(
-        self,
-        in_dim: int,
-        out_dim: int,
-        kernel_size: int,
-        stride: int,
-        padding: int,
-        activation: Any = None,
-    ):
-        super().__init__()
-        self.conv1d = nn.Conv1d(
-            in_dim,
-            out_dim,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            bias=False,
-        )
-        self.bn = nn.BatchNorm1d(out_dim)
-        self.activation = activation
-
-    def forward(self, x: Any):
-        x = self.conv1d(x)
-        if self.activation is not None:
-            x = self.activation(x)
-        return self.bn(x)
-
-
-class LinearNorm(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
-        super().__init__()
-        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, x):
-        return self.linear_layer(x)
-
-
-class ConvNorm(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
-                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
-        super().__init__()
-        if padding is None:
-            assert(kernel_size % 2 == 1)
-            padding = int(dilation * (kernel_size - 1) / 2)
-
-        self.conv = torch.nn.Conv1d(in_channels, out_channels,
-                                    kernel_size=kernel_size, stride=stride,
-                                    padding=padding, dilation=dilation,
-                                    bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, signal):
-        conv_signal = self.conv(signal)
-        return conv_signal
diff --git a/spaces/aadnk/whisper-webui/src/diarization/diarization.py b/spaces/aadnk/whisper-webui/src/diarization/diarization.py
deleted file mode 100644
index 2627894e621b25c1c9b4a87951c4edf000538be9..0000000000000000000000000000000000000000
--- a/spaces/aadnk/whisper-webui/src/diarization/diarization.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import argparse
-import gc
-import json
-import os
-from pathlib import Path
-import tempfile
-from typing import TYPE_CHECKING, List
-import torch
-
-import ffmpeg
-
-class DiarizationEntry:
-    def __init__(self, start, end, speaker):
-        self.start = start
-        self.end = end
-        self.speaker = speaker
-
-    def __repr__(self):
-        return f"<DiarizationEntry start={self.start} end={self.end} speaker={self.speaker}>"
-    
-    def toJson(self):
-        return {
-            "start": self.start,
-            "end": self.end,
-            "speaker": self.speaker
-        }
-
-class Diarization:
-    def __init__(self, auth_token=None):
-        if auth_token is None:
-            auth_token = os.environ.get("HK_ACCESS_TOKEN")
-            if auth_token is None:
-                raise ValueError("No HuggingFace API Token provided - please use the --auth_token argument or set the HK_ACCESS_TOKEN environment variable")
-        
-        self.auth_token = auth_token
-        self.initialized = False
-        self.pipeline = None
-
-    @staticmethod
-    def has_libraries():
-        try:
-            import pyannote.audio
-            import intervaltree
-            return True
-        except ImportError:
-            return False
-
-    def initialize(self):
-        if self.initialized:
-            return
-        from pyannote.audio import Pipeline
-
-        self.pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1", use_auth_token=self.auth_token)
-        self.initialized = True
-
-        # Load GPU mode if available
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        if device == "cuda":
-            print("Diarization - using GPU")
-            self.pipeline = self.pipeline.to(torch.device(0))
-        else:
-            print("Diarization - using CPU")
-
-    def run(self, audio_file, **kwargs):
-        self.initialize()
-        audio_file_obj = Path(audio_file)
-
-        # Supported file types in soundfile is WAV, FLAC, OGG and MAT
-        if audio_file_obj.suffix in [".wav", ".flac", ".ogg", ".mat"]:
-            target_file = audio_file
-        else:
-            # Create temp WAV file
-            target_file = tempfile.mktemp(prefix="diarization_", suffix=".wav")
-            try:
-                ffmpeg.input(audio_file).output(target_file, ac=1).run()
-            except ffmpeg.Error as e:
-                print(f"Error occurred during audio conversion: {e.stderr}")
-
-        diarization = self.pipeline(target_file, **kwargs)
-
-        if target_file != audio_file:
-            # Delete temp file
-            os.remove(target_file)
-
-        # Yield result
-        for turn, _, speaker in diarization.itertracks(yield_label=True):
-            yield DiarizationEntry(turn.start, turn.end, speaker)
-    
-    def mark_speakers(self, diarization_result: List[DiarizationEntry], whisper_result: dict):
-        from intervaltree import IntervalTree
-        result = whisper_result.copy()
-
-        # Create an interval tree from the diarization results
-        tree = IntervalTree()
-        for entry in diarization_result:
-            tree[entry.start:entry.end] = entry
-
-        # Iterate through each segment in the Whisper JSON
-        for segment in result["segments"]:
-            segment_start = segment["start"]
-            segment_end = segment["end"]
-
-            # Find overlapping speakers using the interval tree
-            overlapping_speakers = tree[segment_start:segment_end]
-
-            # If no speakers overlap with this segment, skip it
-            if not overlapping_speakers:
-                continue
-
-            # If multiple speakers overlap with this segment, choose the one with the longest duration
-            longest_speaker = None
-            longest_duration = 0
-            
-            for speaker_interval in overlapping_speakers:
-                overlap_start = max(speaker_interval.begin, segment_start)
-                overlap_end = min(speaker_interval.end, segment_end)
-                overlap_duration = overlap_end - overlap_start
-
-                if overlap_duration > longest_duration:
-                    longest_speaker = speaker_interval.data.speaker
-                    longest_duration = overlap_duration
-
-            # Add speakers
-            segment["longest_speaker"] = longest_speaker
-            segment["speakers"] = list([speaker_interval.data.toJson() for speaker_interval in overlapping_speakers])
-
-            # The write_srt will use the longest_speaker if it exist, and add it to the text field
-
-        return result
-
-def _write_file(input_file: str, output_path: str, output_extension: str, file_writer: lambda f: None):
-    if input_file is None:
-        raise ValueError("input_file is required")
-    if file_writer is None:
-        raise ValueError("file_writer is required")
-
-     # Write file
-    if output_path is None:
-        effective_path = os.path.splitext(input_file)[0] + "_output" + output_extension
-    else:
-        effective_path = output_path
-
-    with open(effective_path, 'w+', encoding="utf-8") as f:
-        file_writer(f)
-
-    print(f"Output saved to {effective_path}")
-
-def main():
-    from src.utils import write_srt
-    from src.diarization.transcriptLoader import load_transcript
-
-    parser = argparse.ArgumentParser(description='Add speakers to a SRT file or Whisper JSON file using pyannote/speaker-diarization.')
-    parser.add_argument('audio_file', type=str, help='Input audio file')
-    parser.add_argument('whisper_file', type=str, help='Input Whisper JSON/SRT file')
-    parser.add_argument('--output_json_file', type=str, default=None, help='Output JSON file (optional)')
-    parser.add_argument('--output_srt_file', type=str, default=None, help='Output SRT file (optional)')
-    parser.add_argument('--auth_token', type=str, default=None, help='HuggingFace API Token (optional)')
-    parser.add_argument("--max_line_width", type=int, default=40, help="Maximum line width for SRT file (default: 40)")
-    parser.add_argument("--num_speakers", type=int, default=None, help="Number of speakers")
-    parser.add_argument("--min_speakers", type=int, default=None, help="Minimum number of speakers")
-    parser.add_argument("--max_speakers", type=int, default=None, help="Maximum number of speakers")
-
-    args = parser.parse_args()
-
-    print("\nReading whisper JSON from " + args.whisper_file)
-
-    # Read whisper JSON or SRT file
-    whisper_result = load_transcript(args.whisper_file)
-
-    diarization = Diarization(auth_token=args.auth_token)
-    diarization_result = list(diarization.run(args.audio_file, num_speakers=args.num_speakers, min_speakers=args.min_speakers, max_speakers=args.max_speakers))
-
-    # Print result
-    print("Diarization result:")
-    for entry in diarization_result:
-        print(f"  start={entry.start:.1f}s stop={entry.end:.1f}s speaker_{entry.speaker}")
-
-    marked_whisper_result = diarization.mark_speakers(diarization_result, whisper_result)
-
-    # Write output JSON to file
-    _write_file(args.whisper_file, args.output_json_file, ".json", 
-                lambda f: json.dump(marked_whisper_result, f, indent=4, ensure_ascii=False))
-
-    # Write SRT
-    _write_file(args.whisper_file, args.output_srt_file, ".srt", 
-                lambda f: write_srt(marked_whisper_result["segments"], f, maxLineWidth=args.max_line_width))
-
-if __name__ == "__main__":
-    main()
-    
-    #test = Diarization()
-    #print("Initializing")
-    #test.initialize()
-
-    #input("Press Enter to continue...")
\ No newline at end of file
diff --git a/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/get_vocab.py b/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/get_vocab.py
deleted file mode 100644
index 60040c132adf48cc50302763ddf6a4a06da9113b..0000000000000000000000000000000000000000
--- a/spaces/abhaskumarsinha/MinimalGPT-Ragdoll/subword/get_vocab.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#! /usr/bin/env python
-from __future__ import print_function
-
-import os
-import sys
-import inspect
-import warnings
-import argparse
-import codecs
-
-from collections import Counter
-
-# hack for python2/3 compatibility
-from io import open
-argparse.open = open
-
-def create_parser(subparsers=None):
-
-    if subparsers:
-        parser = subparsers.add_parser('get-vocab',
-            formatter_class=argparse.RawDescriptionHelpFormatter,
-            description="Generates vocabulary")
-    else:
-        parser = argparse.ArgumentParser(
-            formatter_class=argparse.RawDescriptionHelpFormatter,
-            description="Generates vocabulary")
-
-    parser.add_argument(
-        '--input', '-i', type=argparse.FileType('r'), default=sys.stdin,
-        metavar='PATH',
-        help="Input file (default: standard input).")
-
-    parser.add_argument(
-        '--output', '-o', type=argparse.FileType('w'), default=sys.stdout,
-        metavar='PATH',
-        help="Output file (default: standard output)")
-
-    return parser
-
-def get_vocab(train_file, vocab_file):
-
-    c = Counter()
-
-    for line in train_file:
-        for word in line.strip('\r\n ').split(' '):
-            if word:
-                c[word] += 1
-
-    for key,f in sorted(c.items(), key=lambda x: x[1], reverse=True):
-        vocab_file.write(key+" "+ str(f) + "\n")
-
-if __name__ == "__main__":
-
-    currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-    newdir = os.path.join(currentdir, 'subword_nmt')
-    if os.path.isdir(newdir):
-        warnings.warn(
-            "this script's location has moved to {0}. This symbolic link will be removed in a future version. Please point to the new location, or install the package and use the command 'subword-nmt'".format(newdir),
-            DeprecationWarning
-        )
-
-    # python 2/3 compatibility
-    if sys.version_info < (3, 0):
-        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)
-        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)
-        sys.stdin = codecs.getreader('UTF-8')(sys.stdin)
-    else:
-        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr.buffer)
-        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout.buffer)
-        sys.stdin = codecs.getreader('UTF-8')(sys.stdin.buffer)
-
-    parser = create_parser()
-    args = parser.parse_args()
-
-    # read/write files as UTF-8
-    if args.input.name != '<stdin>':
-        args.input = codecs.open(args.input.name, encoding='utf-8')
-    if args.output.name != '<stdout>':
-        args.output = codecs.open(args.output.name, 'w', encoding='utf-8')
-
-    get_vocab(args.input, args.output)
-
-    # close files
-    if args.input.name != '<stdin>':
-        args.input.close()
-    if args.output.name != '<stdout>':
-        args.output.close()
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/yolact_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/yolact_head.py
deleted file mode 100644
index 10d311f94ee99e1bf65ee3e5827f1699c28a23e3..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/yolact_head.py
+++ /dev/null
@@ -1,943 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, xavier_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-
-@HEADS.register_module()
-class YOLACTHead(AnchorHead):
-    """YOLACT box head used in https://arxiv.org/abs/1904.02689.
-
-    Note that YOLACT head is a light version of RetinaNet head.
-    Four differences are described as follows:
-
-    1. YOLACT box head has three-times fewer anchors.
-    2. YOLACT box head shares the convs for box and cls branches.
-    3. YOLACT box head uses OHEM instead of Focal loss.
-    4. YOLACT box head predicts a set of mask coefficients for each box.
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        anchor_generator (dict): Config dict for anchor generator
-        loss_cls (dict): Config of classification loss.
-        loss_bbox (dict): Config of localization loss.
-        num_head_convs (int): Number of the conv layers shared by
-            box and cls branches.
-        num_protos (int): Number of the mask coefficients.
-        use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
-            cls loss calculation. If false, ``loss_single`` will be used.
-        conv_cfg (dict): Dictionary to construct and config conv layer.
-        norm_cfg (dict): Dictionary to construct and config norm layer.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 anchor_generator=dict(
-                     type='AnchorGenerator',
-                     octave_base_scale=3,
-                     scales_per_octave=1,
-                     ratios=[0.5, 1.0, 2.0],
-                     strides=[8, 16, 32, 64, 128]),
-                 loss_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=False,
-                     reduction='none',
-                     loss_weight=1.0),
-                 loss_bbox=dict(
-                     type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
-                 num_head_convs=1,
-                 num_protos=32,
-                 use_ohem=True,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 **kwargs):
-        self.num_head_convs = num_head_convs
-        self.num_protos = num_protos
-        self.use_ohem = use_ohem
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        super(YOLACTHead, self).__init__(
-            num_classes,
-            in_channels,
-            loss_cls=loss_cls,
-            loss_bbox=loss_bbox,
-            anchor_generator=anchor_generator,
-            **kwargs)
-        if self.use_ohem:
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-            self.sampling = False
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.head_convs = nn.ModuleList()
-        for i in range(self.num_head_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.head_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        self.conv_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.conv_reg = nn.Conv2d(
-            self.feat_channels, self.num_anchors * 4, 3, padding=1)
-        self.conv_coeff = nn.Conv2d(
-            self.feat_channels,
-            self.num_anchors * self.num_protos,
-            3,
-            padding=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.head_convs:
-            xavier_init(m.conv, distribution='uniform', bias=0)
-        xavier_init(self.conv_cls, distribution='uniform', bias=0)
-        xavier_init(self.conv_reg, distribution='uniform', bias=0)
-        xavier_init(self.conv_coeff, distribution='uniform', bias=0)
-
-    def forward_single(self, x):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-
-        Returns:
-            tuple:
-                cls_score (Tensor): Cls scores for a single scale level \
-                    the channels number is num_anchors * num_classes.
-                bbox_pred (Tensor): Box energies / deltas for a single scale \
-                    level, the channels number is num_anchors * 4.
-                coeff_pred (Tensor): Mask coefficients for a single scale \
-                    level, the channels number is num_anchors * num_protos.
-        """
-        for head_conv in self.head_convs:
-            x = head_conv(x)
-        cls_score = self.conv_cls(x)
-        bbox_pred = self.conv_reg(x)
-        coeff_pred = self.conv_coeff(x).tanh()
-        return cls_score, bbox_pred, coeff_pred
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """A combination of the func:``AnchorHead.loss`` and
-        func:``SSDHead.loss``.
-
-        When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
-        otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
-        returns ``sampling_results``.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
-                boxes can be ignored when computing the loss. Default: None
-
-        Returns:
-            tuple:
-                dict[str, Tensor]: A dictionary of loss components.
-                List[:obj:``SamplingResult``]: Sampler results for each image.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels,
-            unmap_outputs=not self.use_ohem,
-            return_sampling_results=True)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         num_total_pos, num_total_neg, sampling_results) = cls_reg_targets
-
-        if self.use_ohem:
-            num_images = len(img_metas)
-            all_cls_scores = torch.cat([
-                s.permute(0, 2, 3, 1).reshape(
-                    num_images, -1, self.cls_out_channels) for s in cls_scores
-            ], 1)
-            all_labels = torch.cat(labels_list, -1).view(num_images, -1)
-            all_label_weights = torch.cat(label_weights_list,
-                                          -1).view(num_images, -1)
-            all_bbox_preds = torch.cat([
-                b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
-                for b in bbox_preds
-            ], -2)
-            all_bbox_targets = torch.cat(bbox_targets_list,
-                                         -2).view(num_images, -1, 4)
-            all_bbox_weights = torch.cat(bbox_weights_list,
-                                         -2).view(num_images, -1, 4)
-
-            # concat all level anchors to a single tensor
-            all_anchors = []
-            for i in range(num_images):
-                all_anchors.append(torch.cat(anchor_list[i]))
-
-            # check NaN and Inf
-            assert torch.isfinite(all_cls_scores).all().item(), \
-                'classification scores become infinite or NaN!'
-            assert torch.isfinite(all_bbox_preds).all().item(), \
-                'bbox predications become infinite or NaN!'
-
-            losses_cls, losses_bbox = multi_apply(
-                self.loss_single_OHEM,
-                all_cls_scores,
-                all_bbox_preds,
-                all_anchors,
-                all_labels,
-                all_label_weights,
-                all_bbox_targets,
-                all_bbox_weights,
-                num_total_samples=num_total_pos)
-        else:
-            num_total_samples = (
-                num_total_pos +
-                num_total_neg if self.sampling else num_total_pos)
-
-            # anchor number of multi levels
-            num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-            # concat all level anchors and flags to a single tensor
-            concat_anchor_list = []
-            for i in range(len(anchor_list)):
-                concat_anchor_list.append(torch.cat(anchor_list[i]))
-            all_anchor_list = images_to_levels(concat_anchor_list,
-                                               num_level_anchors)
-            losses_cls, losses_bbox = multi_apply(
-                self.loss_single,
-                cls_scores,
-                bbox_preds,
-                all_anchor_list,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                bbox_weights_list,
-                num_total_samples=num_total_samples)
-
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results
-
-    def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels,
-                         label_weights, bbox_targets, bbox_weights,
-                         num_total_samples):
-        """"See func:``SSDHead.loss``."""
-        loss_cls_all = self.loss_cls(cls_score, labels, label_weights)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
-            as_tuple=False).reshape(-1)
-        neg_inds = (labels == self.num_classes).nonzero(
-            as_tuple=False).view(-1)
-
-        num_pos_samples = pos_inds.size(0)
-        if num_pos_samples == 0:
-            num_neg_samples = neg_inds.size(0)
-        else:
-            num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
-            if num_neg_samples > neg_inds.size(0):
-                num_neg_samples = neg_inds.size(0)
-        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
-        loss_cls_pos = loss_cls_all[pos_inds].sum()
-        loss_cls_neg = topk_loss_cls_neg.sum()
-        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
-        if self.reg_decoded_bbox:
-            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-            # is applied directly on the decoded bounding boxes, it
-            # decodes the already encoded coordinates to absolute format.
-            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
-        loss_bbox = self.loss_bbox(
-            bbox_pred,
-            bbox_targets,
-            bbox_weights,
-            avg_factor=num_total_samples)
-        return loss_cls[None], loss_bbox
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   coeff_preds,
-                   img_metas,
-                   cfg=None,
-                   rescale=False):
-        """"Similiar to func:``AnchorHead.get_bboxes``, but additionally
-        processes coeff_preds.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                with shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            coeff_preds (list[Tensor]): Mask coefficients for each scale
-                level with shape (N, num_anchors * num_protos, H, W)
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-
-        Returns:
-            list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
-                a 3-tuple. The first item is an (n, 5) tensor, where the
-                first 4 columns are bounding box positions
-                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
-                between 0 and 1. The second item is an (n,) tensor where each
-                item is the predicted class label of the corresponding box.
-                The third item is an (n, num_protos) tensor where each item
-                is the predicted mask coefficients of instance inside the
-                corresponding box.
-        """
-        assert len(cls_scores) == len(bbox_preds)
-        num_levels = len(cls_scores)
-
-        device = cls_scores[0].device
-        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
-        mlvl_anchors = self.anchor_generator.grid_anchors(
-            featmap_sizes, device=device)
-
-        det_bboxes = []
-        det_labels = []
-        det_coeffs = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_pred_list = [
-                bbox_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            coeff_pred_list = [
-                coeff_preds[i][img_id].detach() for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list,
-                                               coeff_pred_list, mlvl_anchors,
-                                               img_shape, scale_factor, cfg,
-                                               rescale)
-            det_bboxes.append(bbox_res[0])
-            det_labels.append(bbox_res[1])
-            det_coeffs.append(bbox_res[2])
-        return det_bboxes, det_labels, det_coeffs
-
-    def _get_bboxes_single(self,
-                           cls_score_list,
-                           bbox_pred_list,
-                           coeff_preds_list,
-                           mlvl_anchors,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False):
-        """"Similiar to func:``AnchorHead._get_bboxes_single``, but
-        additionally processes coeff_preds_list and uses fast NMS instead of
-        traditional NMS.
-
-        Args:
-            cls_score_list (list[Tensor]): Box scores for a single scale level
-                Has shape (num_anchors * num_classes, H, W).
-            bbox_pred_list (list[Tensor]): Box energies / deltas for a single
-                scale level with shape (num_anchors * 4, H, W).
-            coeff_preds_list (list[Tensor]): Mask coefficients for a single
-                scale level with shape (num_anchors * num_protos, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shape (tuple[int]): Shape of the input image,
-                (height, width, 3).
-            scale_factor (ndarray): Scale factor of the image arange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-
-        Returns:
-            tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor,
-                where the first 4 columns are bounding box positions
-                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between
-                0 and 1. The second item is an (n,) tensor where each item is
-                the predicted class label of the corresponding box. The third
-                item is an (n, num_protos) tensor where each item is the
-                predicted mask coefficients of instance inside the
-                corresponding box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        mlvl_coeffs = []
-        for cls_score, bbox_pred, coeff_pred, anchors in \
-                zip(cls_score_list, bbox_pred_list,
-                    coeff_preds_list, mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            cls_score = cls_score.permute(1, 2,
-                                          0).reshape(-1, self.cls_out_channels)
-            if self.use_sigmoid_cls:
-                scores = cls_score.sigmoid()
-            else:
-                scores = cls_score.softmax(-1)
-            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
-            coeff_pred = coeff_pred.permute(1, 2,
-                                            0).reshape(-1, self.num_protos)
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[0] > nms_pre:
-                # Get maximum scores for foreground classes.
-                if self.use_sigmoid_cls:
-                    max_scores, _ = scores.max(dim=1)
-                else:
-                    # remind that we set FG labels to [0, num_class-1]
-                    # since mmdet v2.0
-                    # BG cat_id: num_class
-                    max_scores, _ = scores[:, :-1].max(dim=1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[topk_inds, :]
-                scores = scores[topk_inds, :]
-                coeff_pred = coeff_pred[topk_inds, :]
-            bboxes = self.bbox_coder.decode(
-                anchors, bbox_pred, max_shape=img_shape)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-            mlvl_coeffs.append(coeff_pred)
-        mlvl_bboxes = torch.cat(mlvl_bboxes)
-        if rescale:
-            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
-        mlvl_scores = torch.cat(mlvl_scores)
-        mlvl_coeffs = torch.cat(mlvl_coeffs)
-        if self.use_sigmoid_cls:
-            # Add a dummy background class to the backend when using sigmoid
-            # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-            # BG cat_id: num_class
-            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
-            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
-        det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores,
-                                                      mlvl_coeffs,
-                                                      cfg.score_thr,
-                                                      cfg.iou_thr, cfg.top_k,
-                                                      cfg.max_per_img)
-        return det_bboxes, det_labels, det_coeffs
-
-
-@HEADS.register_module()
-class YOLACTSegmHead(nn.Module):
-    """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
-
-    Apply a semantic segmentation loss on feature space using layers that are
-    only evaluated during training to increase performance with no speed
-    penalty.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        num_classes (int): Number of categories excluding the background
-            category.
-        loss_segm (dict): Config of semantic segmentation loss.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels=256,
-                 loss_segm=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0)):
-        super(YOLACTSegmHead, self).__init__()
-        self.in_channels = in_channels
-        self.num_classes = num_classes
-        self.loss_segm = build_loss(loss_segm)
-        self._init_layers()
-        self.fp16_enabled = False
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.segm_conv = nn.Conv2d(
-            self.in_channels, self.num_classes, kernel_size=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        xavier_init(self.segm_conv, distribution='uniform')
-
-    def forward(self, x):
-        """Forward feature from the upstream network.
-
-        Args:
-            x (Tensor): Feature from the upstream network, which is
-                a 4D-tensor.
-
-        Returns:
-            Tensor: Predicted semantic segmentation map with shape
-                (N, num_classes, H, W).
-        """
-        return self.segm_conv(x)
-
-    @force_fp32(apply_to=('segm_pred', ))
-    def loss(self, segm_pred, gt_masks, gt_labels):
-        """Compute loss of the head.
-
-        Args:
-            segm_pred (list[Tensor]): Predicted semantic segmentation map
-                with shape (N, num_classes, H, W).
-            gt_masks (list[Tensor]): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_labels (list[Tensor]): Class indices corresponding to each box.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        loss_segm = []
-        num_imgs, num_classes, mask_h, mask_w = segm_pred.size()
-        for idx in range(num_imgs):
-            cur_segm_pred = segm_pred[idx]
-            cur_gt_masks = gt_masks[idx].float()
-            cur_gt_labels = gt_labels[idx]
-            segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks,
-                                            cur_gt_labels)
-            if segm_targets is None:
-                loss = self.loss_segm(cur_segm_pred,
-                                      torch.zeros_like(cur_segm_pred),
-                                      torch.zeros_like(cur_segm_pred))
-            else:
-                loss = self.loss_segm(
-                    cur_segm_pred,
-                    segm_targets,
-                    avg_factor=num_imgs * mask_h * mask_w)
-            loss_segm.append(loss)
-        return dict(loss_segm=loss_segm)
-
-    def get_targets(self, segm_pred, gt_masks, gt_labels):
-        """Compute semantic segmentation targets for each image.
-
-        Args:
-            segm_pred (Tensor): Predicted semantic segmentation map
-                with shape (num_classes, H, W).
-            gt_masks (Tensor): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_labels (Tensor): Class indices corresponding to each box.
-
-        Returns:
-            Tensor: Semantic segmentation targets with shape
-                (num_classes, H, W).
-        """
-        if gt_masks.size(0) == 0:
-            return None
-        num_classes, mask_h, mask_w = segm_pred.size()
-        with torch.no_grad():
-            downsampled_masks = F.interpolate(
-                gt_masks.unsqueeze(0), (mask_h, mask_w),
-                mode='bilinear',
-                align_corners=False).squeeze(0)
-            downsampled_masks = downsampled_masks.gt(0.5).float()
-            segm_targets = torch.zeros_like(segm_pred, requires_grad=False)
-            for obj_idx in range(downsampled_masks.size(0)):
-                segm_targets[gt_labels[obj_idx] - 1] = torch.max(
-                    segm_targets[gt_labels[obj_idx] - 1],
-                    downsampled_masks[obj_idx])
-            return segm_targets
-
-
-@HEADS.register_module()
-class YOLACTProtonet(nn.Module):
-    """YOLACT mask head used in https://arxiv.org/abs/1904.02689.
-
-    This head outputs the mask prototypes for YOLACT.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-        proto_channels (tuple[int]): Output channels of protonet convs.
-        proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs.
-        include_last_relu (Bool): If keep the last relu of protonet.
-        num_protos (int): Number of prototypes.
-        num_classes (int): Number of categories excluding the background
-            category.
-        loss_mask_weight (float): Reweight the mask loss by this factor.
-        max_masks_to_train (int): Maximum number of masks to train for
-            each image.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels=256,
-                 proto_channels=(256, 256, 256, None, 256, 32),
-                 proto_kernel_sizes=(3, 3, 3, -2, 3, 1),
-                 include_last_relu=True,
-                 num_protos=32,
-                 loss_mask_weight=1.0,
-                 max_masks_to_train=100):
-        super(YOLACTProtonet, self).__init__()
-        self.in_channels = in_channels
-        self.proto_channels = proto_channels
-        self.proto_kernel_sizes = proto_kernel_sizes
-        self.include_last_relu = include_last_relu
-        self.protonet = self._init_layers()
-
-        self.loss_mask_weight = loss_mask_weight
-        self.num_protos = num_protos
-        self.num_classes = num_classes
-        self.max_masks_to_train = max_masks_to_train
-        self.fp16_enabled = False
-
-    def _init_layers(self):
-        """A helper function to take a config setting and turn it into a
-        network."""
-        # Possible patterns:
-        # ( 256, 3) -> conv
-        # ( 256,-2) -> deconv
-        # (None,-2) -> bilinear interpolate
-        in_channels = self.in_channels
-        protonets = nn.ModuleList()
-        for num_channels, kernel_size in zip(self.proto_channels,
-                                             self.proto_kernel_sizes):
-            if kernel_size > 0:
-                layer = nn.Conv2d(
-                    in_channels,
-                    num_channels,
-                    kernel_size,
-                    padding=kernel_size // 2)
-            else:
-                if num_channels is None:
-                    layer = InterpolateModule(
-                        scale_factor=-kernel_size,
-                        mode='bilinear',
-                        align_corners=False)
-                else:
-                    layer = nn.ConvTranspose2d(
-                        in_channels,
-                        num_channels,
-                        -kernel_size,
-                        padding=kernel_size // 2)
-            protonets.append(layer)
-            protonets.append(nn.ReLU(inplace=True))
-            in_channels = num_channels if num_channels is not None \
-                else in_channels
-        if not self.include_last_relu:
-            protonets = protonets[:-1]
-        return nn.Sequential(*protonets)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.protonet:
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-
-    def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None):
-        """Forward feature from the upstream network to get prototypes and
-        linearly combine the prototypes, using masks coefficients, into
-        instance masks. Finally, crop the instance masks with given bboxes.
-
-        Args:
-            x (Tensor): Feature from the upstream network, which is
-                a 4D-tensor.
-            coeff_pred (list[Tensor]): Mask coefficients for each scale
-                level with shape (N, num_anchors * num_protos, H, W).
-            bboxes (list[Tensor]): Box used for cropping with shape
-                (N, num_anchors * 4, H, W). During training, they are
-                ground truth boxes. During testing, they are predicted
-                boxes.
-            img_meta (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            sampling_results (List[:obj:``SamplingResult``]): Sampler results
-                for each image.
-
-        Returns:
-            list[Tensor]: Predicted instance segmentation masks.
-        """
-        prototypes = self.protonet(x)
-        prototypes = prototypes.permute(0, 2, 3, 1).contiguous()
-
-        num_imgs = x.size(0)
-        # Training state
-        if self.training:
-            coeff_pred_list = []
-            for coeff_pred_per_level in coeff_pred:
-                coeff_pred_per_level = \
-                    coeff_pred_per_level.permute(0, 2, 3, 1)\
-                    .reshape(num_imgs, -1, self.num_protos)
-                coeff_pred_list.append(coeff_pred_per_level)
-            coeff_pred = torch.cat(coeff_pred_list, dim=1)
-
-        mask_pred_list = []
-        for idx in range(num_imgs):
-            cur_prototypes = prototypes[idx]
-            cur_coeff_pred = coeff_pred[idx]
-            cur_bboxes = bboxes[idx]
-            cur_img_meta = img_meta[idx]
-
-            # Testing state
-            if not self.training:
-                bboxes_for_cropping = cur_bboxes
-            else:
-                cur_sampling_results = sampling_results[idx]
-                pos_assigned_gt_inds = \
-                    cur_sampling_results.pos_assigned_gt_inds
-                bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone()
-                pos_inds = cur_sampling_results.pos_inds
-                cur_coeff_pred = cur_coeff_pred[pos_inds]
-
-            # Linearly combine the prototypes with the mask coefficients
-            mask_pred = cur_prototypes @ cur_coeff_pred.t()
-            mask_pred = torch.sigmoid(mask_pred)
-
-            h, w = cur_img_meta['img_shape'][:2]
-            bboxes_for_cropping[:, 0] /= w
-            bboxes_for_cropping[:, 1] /= h
-            bboxes_for_cropping[:, 2] /= w
-            bboxes_for_cropping[:, 3] /= h
-
-            mask_pred = self.crop(mask_pred, bboxes_for_cropping)
-            mask_pred = mask_pred.permute(2, 0, 1).contiguous()
-            mask_pred_list.append(mask_pred)
-        return mask_pred_list
-
-    @force_fp32(apply_to=('mask_pred', ))
-    def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results):
-        """Compute loss of the head.
-
-        Args:
-            mask_pred (list[Tensor]): Predicted prototypes with shape
-                (num_classes, H, W).
-            gt_masks (list[Tensor]): Ground truth masks for each image with
-                the same shape of the input image.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            img_meta (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            sampling_results (List[:obj:``SamplingResult``]): Sampler results
-                for each image.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        loss_mask = []
-        num_imgs = len(mask_pred)
-        total_pos = 0
-        for idx in range(num_imgs):
-            cur_mask_pred = mask_pred[idx]
-            cur_gt_masks = gt_masks[idx].float()
-            cur_gt_bboxes = gt_bboxes[idx]
-            cur_img_meta = img_meta[idx]
-            cur_sampling_results = sampling_results[idx]
-
-            pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds
-            num_pos = pos_assigned_gt_inds.size(0)
-            # Since we're producing (near) full image masks,
-            # it'd take too much vram to backprop on every single mask.
-            # Thus we select only a subset.
-            if num_pos > self.max_masks_to_train:
-                perm = torch.randperm(num_pos)
-                select = perm[:self.max_masks_to_train]
-                cur_mask_pred = cur_mask_pred[select]
-                pos_assigned_gt_inds = pos_assigned_gt_inds[select]
-                num_pos = self.max_masks_to_train
-            total_pos += num_pos
-
-            gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds]
-
-            mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks,
-                                            pos_assigned_gt_inds)
-            if num_pos == 0:
-                loss = cur_mask_pred.sum() * 0.
-            elif mask_targets is None:
-                loss = F.binary_cross_entropy(cur_mask_pred,
-                                              torch.zeros_like(cur_mask_pred),
-                                              torch.zeros_like(cur_mask_pred))
-            else:
-                cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1)
-                loss = F.binary_cross_entropy(
-                    cur_mask_pred, mask_targets,
-                    reduction='none') * self.loss_mask_weight
-
-                h, w = cur_img_meta['img_shape'][:2]
-                gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] -
-                                   gt_bboxes_for_reweight[:, 0]) / w
-                gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] -
-                                    gt_bboxes_for_reweight[:, 1]) / h
-                loss = loss.mean(dim=(1,
-                                      2)) / gt_bboxes_width / gt_bboxes_height
-                loss = torch.sum(loss)
-            loss_mask.append(loss)
-
-        if total_pos == 0:
-            total_pos += 1  # avoid nan
-        loss_mask = [x / total_pos for x in loss_mask]
-
-        return dict(loss_mask=loss_mask)
-
-    def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds):
-        """Compute instance segmentation targets for each image.
-
-        Args:
-            mask_pred (Tensor): Predicted prototypes with shape
-                (num_classes, H, W).
-            gt_masks (Tensor): Ground truth masks for each image with
-                the same shape of the input image.
-            pos_assigned_gt_inds (Tensor): GT indices of the corresponding
-                positive samples.
-        Returns:
-            Tensor: Instance segmentation targets with shape
-                (num_instances, H, W).
-        """
-        if gt_masks.size(0) == 0:
-            return None
-        mask_h, mask_w = mask_pred.shape[-2:]
-        gt_masks = F.interpolate(
-            gt_masks.unsqueeze(0), (mask_h, mask_w),
-            mode='bilinear',
-            align_corners=False).squeeze(0)
-        gt_masks = gt_masks.gt(0.5).float()
-        mask_targets = gt_masks[pos_assigned_gt_inds]
-        return mask_targets
-
-    def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale):
-        """Resize, binarize, and format the instance mask predictions.
-
-        Args:
-            mask_pred (Tensor): shape (N, H, W).
-            label_pred (Tensor): shape (N, ).
-            img_meta (dict): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            rescale (bool): If rescale is False, then returned masks will
-                fit the scale of imgs[0].
-        Returns:
-            list[ndarray]: Mask predictions grouped by their predicted classes.
-        """
-        ori_shape = img_meta['ori_shape']
-        scale_factor = img_meta['scale_factor']
-        if rescale:
-            img_h, img_w = ori_shape[:2]
-        else:
-            img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32)
-            img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32)
-
-        cls_segms = [[] for _ in range(self.num_classes)]
-        if mask_pred.size(0) == 0:
-            return cls_segms
-
-        mask_pred = F.interpolate(
-            mask_pred.unsqueeze(0), (img_h, img_w),
-            mode='bilinear',
-            align_corners=False).squeeze(0) > 0.5
-        mask_pred = mask_pred.cpu().numpy().astype(np.uint8)
-
-        for m, l in zip(mask_pred, label_pred):
-            cls_segms[l].append(m)
-        return cls_segms
-
-    def crop(self, masks, boxes, padding=1):
-        """Crop predicted masks by zeroing out everything not in the predicted
-        bbox.
-
-        Args:
-            masks (Tensor): shape [H, W, N].
-            boxes (Tensor): bbox coords in relative point form with
-                shape [N, 4].
-
-        Return:
-            Tensor: The cropped masks.
-        """
-        h, w, n = masks.size()
-        x1, x2 = self.sanitize_coordinates(
-            boxes[:, 0], boxes[:, 2], w, padding, cast=False)
-        y1, y2 = self.sanitize_coordinates(
-            boxes[:, 1], boxes[:, 3], h, padding, cast=False)
-
-        rows = torch.arange(
-            w, device=masks.device, dtype=x1.dtype).view(1, -1,
-                                                         1).expand(h, w, n)
-        cols = torch.arange(
-            h, device=masks.device, dtype=x1.dtype).view(-1, 1,
-                                                         1).expand(h, w, n)
-
-        masks_left = rows >= x1.view(1, 1, -1)
-        masks_right = rows < x2.view(1, 1, -1)
-        masks_up = cols >= y1.view(1, 1, -1)
-        masks_down = cols < y2.view(1, 1, -1)
-
-        crop_mask = masks_left * masks_right * masks_up * masks_down
-
-        return masks * crop_mask.float()
-
-    def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True):
-        """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0,
-        and x2 <= image_size. Also converts from relative to absolute
-        coordinates and casts the results to long tensors.
-
-        Warning: this does things in-place behind the scenes so
-        copy if necessary.
-
-        Args:
-            _x1 (Tensor): shape (N, ).
-            _x2 (Tensor): shape (N, ).
-            img_size (int): Size of the input image.
-            padding (int): x1 >= padding, x2 <= image_size-padding.
-            cast (bool): If cast is false, the result won't be cast to longs.
-
-        Returns:
-            tuple:
-                x1 (Tensor): Sanitized _x1.
-                x2 (Tensor): Sanitized _x2.
-        """
-        x1 = x1 * img_size
-        x2 = x2 * img_size
-        if cast:
-            x1 = x1.long()
-            x2 = x2.long()
-        x1 = torch.min(x1, x2)
-        x2 = torch.max(x1, x2)
-        x1 = torch.clamp(x1 - padding, min=0)
-        x2 = torch.clamp(x2 + padding, max=img_size)
-        return x1, x2
-
-
-class InterpolateModule(nn.Module):
-    """This is a module version of F.interpolate.
-
-    Any arguments you give it just get passed along for the ride.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-
-        self.args = args
-        self.kwargs = kwargs
-
-    def forward(self, x):
-        """Forward features from the upstream network."""
-        return F.interpolate(x, *self.args, **self.kwargs)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/vit.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/vit.py
deleted file mode 100644
index 59e4479650690e08cbc4cab9427aefda47c2116d..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/vit.py
+++ /dev/null
@@ -1,459 +0,0 @@
-"""Modified from https://github.com/rwightman/pytorch-image-
-models/blob/master/timm/models/vision_transformer.py."""
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as cp
-from annotator.uniformer.mmcv.cnn import (Conv2d, Linear, build_activation_layer, build_norm_layer,
-                      constant_init, kaiming_init, normal_init)
-from annotator.uniformer.mmcv.runner import _load_checkpoint
-from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
-
-from annotator.uniformer.mmseg.utils import get_root_logger
-from ..builder import BACKBONES
-from ..utils import DropPath, trunc_normal_
-
-
-class Mlp(nn.Module):
-    """MLP layer for Encoder block.
-
-    Args:
-        in_features(int): Input dimension for the first fully
-            connected layer.
-        hidden_features(int): Output dimension for the first fully
-            connected layer.
-        out_features(int): Output dementsion for the second fully
-            connected layer.
-        act_cfg(dict): Config dict for activation layer.
-            Default: dict(type='GELU').
-        drop(float): Drop rate for the dropout layer. Dropout rate has
-            to be between 0 and 1. Default: 0.
-    """
-
-    def __init__(self,
-                 in_features,
-                 hidden_features=None,
-                 out_features=None,
-                 act_cfg=dict(type='GELU'),
-                 drop=0.):
-        super(Mlp, self).__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = Linear(in_features, hidden_features)
-        self.act = build_activation_layer(act_cfg)
-        self.fc2 = Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    """Attention layer for Encoder block.
-
-    Args:
-        dim (int): Dimension for the input vector.
-        num_heads (int): Number of parallel attention heads.
-        qkv_bias (bool): Enable bias for qkv if True. Default: False.
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        attn_drop (float): Drop rate for attention output weights.
-            Default: 0.
-        proj_drop (float): Drop rate for output weights. Default: 0.
-    """
-
-    def __init__(self,
-                 dim,
-                 num_heads=8,
-                 qkv_bias=False,
-                 qk_scale=None,
-                 attn_drop=0.,
-                 proj_drop=0.):
-        super(Attention, self).__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        b, n, c = x.shape
-        qkv = self.qkv(x).reshape(b, n, 3, self.num_heads,
-                                  c // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(b, n, c)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-    """Implements encoder block with residual connection.
-
-    Args:
-        dim (int): The feature dimension.
-        num_heads (int): Number of parallel attention heads.
-        mlp_ratio (int): Ratio of mlp hidden dim to embedding dim.
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float): Drop rate for mlp output weights. Default: 0.
-        attn_drop (float): Drop rate for attention output weights.
-            Default: 0.
-        proj_drop (float): Drop rate for attn layer output weights.
-            Default: 0.
-        drop_path (float): Drop rate for paths of model.
-            Default: 0.
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='GELU').
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='LN', requires_grad=True).
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed. Default: False.
-    """
-
-    def __init__(self,
-                 dim,
-                 num_heads,
-                 mlp_ratio=4,
-                 qkv_bias=False,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 proj_drop=0.,
-                 drop_path=0.,
-                 act_cfg=dict(type='GELU'),
-                 norm_cfg=dict(type='LN', eps=1e-6),
-                 with_cp=False):
-        super(Block, self).__init__()
-        self.with_cp = with_cp
-        _, self.norm1 = build_norm_layer(norm_cfg, dim)
-        self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop,
-                              proj_drop)
-        self.drop_path = DropPath(
-            drop_path) if drop_path > 0. else nn.Identity()
-        _, self.norm2 = build_norm_layer(norm_cfg, dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_cfg=act_cfg,
-            drop=drop)
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            out = x + self.drop_path(self.attn(self.norm1(x)))
-            out = out + self.drop_path(self.mlp(self.norm2(out)))
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        return out
-
-
-class PatchEmbed(nn.Module):
-    """Image to Patch Embedding.
-
-    Args:
-        img_size (int | tuple): Input image size.
-            default: 224.
-        patch_size (int): Width and height for a patch.
-            default: 16.
-        in_channels (int): Input channels for images. Default: 3.
-        embed_dim (int): The embedding dimension. Default: 768.
-    """
-
-    def __init__(self,
-                 img_size=224,
-                 patch_size=16,
-                 in_channels=3,
-                 embed_dim=768):
-        super(PatchEmbed, self).__init__()
-        if isinstance(img_size, int):
-            self.img_size = (img_size, img_size)
-        elif isinstance(img_size, tuple):
-            self.img_size = img_size
-        else:
-            raise TypeError('img_size must be type of int or tuple')
-        h, w = self.img_size
-        self.patch_size = (patch_size, patch_size)
-        self.num_patches = (h // patch_size) * (w // patch_size)
-        self.proj = Conv2d(
-            in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, x):
-        return self.proj(x).flatten(2).transpose(1, 2)
-
-
-@BACKBONES.register_module()
-class VisionTransformer(nn.Module):
-    """Vision transformer backbone.
-
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for
-        Image Recognition at Scale` - https://arxiv.org/abs/2010.11929
-
-    Args:
-        img_size (tuple): input image size. Default: (224, 224).
-        patch_size (int, tuple): patch size. Default: 16.
-        in_channels (int): number of input channels. Default: 3.
-        embed_dim (int): embedding dimension. Default: 768.
-        depth (int): depth of transformer. Default: 12.
-        num_heads (int): number of attention heads. Default: 12.
-        mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
-            Default: 4.
-        out_indices (list | tuple | int): Output from which stages.
-            Default: -1.
-        qkv_bias (bool): enable bias for qkv if True. Default: True.
-        qk_scale (float): override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): dropout rate. Default: 0.
-        attn_drop_rate (float): attention dropout rate. Default: 0.
-        drop_path_rate (float): Rate of DropPath. Default: 0.
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='LN', eps=1e-6, requires_grad=True).
-        act_cfg (dict): Config dict for activation layer.
-            Default: dict(type='GELU').
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only. Default: False.
-        final_norm (bool):  Whether to add a additional layer to normalize
-            final feature map. Default: False.
-        interpolate_mode (str): Select the interpolate mode for position
-            embeding vector resize. Default: bicubic.
-        with_cls_token (bool): If concatenating class token into image tokens
-            as transformer input. Default: True.
-        with_cp (bool): Use checkpoint or not. Using checkpoint
-            will save some memory while slowing down the training speed.
-            Default: False.
-    """
-
-    def __init__(self,
-                 img_size=(224, 224),
-                 patch_size=16,
-                 in_channels=3,
-                 embed_dim=768,
-                 depth=12,
-                 num_heads=12,
-                 mlp_ratio=4,
-                 out_indices=11,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.,
-                 norm_cfg=dict(type='LN', eps=1e-6, requires_grad=True),
-                 act_cfg=dict(type='GELU'),
-                 norm_eval=False,
-                 final_norm=False,
-                 with_cls_token=True,
-                 interpolate_mode='bicubic',
-                 with_cp=False):
-        super(VisionTransformer, self).__init__()
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.features = self.embed_dim = embed_dim
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_channels=in_channels,
-            embed_dim=embed_dim)
-
-        self.with_cls_token = with_cls_token
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
-        self.pos_embed = nn.Parameter(
-            torch.zeros(1, self.patch_embed.num_patches + 1, embed_dim))
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        if isinstance(out_indices, int):
-            self.out_indices = [out_indices]
-        elif isinstance(out_indices, list) or isinstance(out_indices, tuple):
-            self.out_indices = out_indices
-        else:
-            raise TypeError('out_indices must be type of int, list or tuple')
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)
-               ]  # stochastic depth decay rule
-        self.blocks = nn.ModuleList([
-            Block(
-                dim=embed_dim,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=dpr[i],
-                attn_drop=attn_drop_rate,
-                act_cfg=act_cfg,
-                norm_cfg=norm_cfg,
-                with_cp=with_cp) for i in range(depth)
-        ])
-
-        self.interpolate_mode = interpolate_mode
-        self.final_norm = final_norm
-        if final_norm:
-            _, self.norm = build_norm_layer(norm_cfg, embed_dim)
-
-        self.norm_eval = norm_eval
-        self.with_cp = with_cp
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = get_root_logger()
-            checkpoint = _load_checkpoint(pretrained, logger=logger)
-            if 'state_dict' in checkpoint:
-                state_dict = checkpoint['state_dict']
-            else:
-                state_dict = checkpoint
-
-            if 'pos_embed' in state_dict.keys():
-                if self.pos_embed.shape != state_dict['pos_embed'].shape:
-                    logger.info(msg=f'Resize the pos_embed shape from \
-{state_dict["pos_embed"].shape} to {self.pos_embed.shape}')
-                    h, w = self.img_size
-                    pos_size = int(
-                        math.sqrt(state_dict['pos_embed'].shape[1] - 1))
-                    state_dict['pos_embed'] = self.resize_pos_embed(
-                        state_dict['pos_embed'], (h, w), (pos_size, pos_size),
-                        self.patch_size, self.interpolate_mode)
-
-            self.load_state_dict(state_dict, False)
-
-        elif pretrained is None:
-            # We only implement the 'jax_impl' initialization implemented at
-            # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353  # noqa: E501
-            trunc_normal_(self.pos_embed, std=.02)
-            trunc_normal_(self.cls_token, std=.02)
-            for n, m in self.named_modules():
-                if isinstance(m, Linear):
-                    trunc_normal_(m.weight, std=.02)
-                    if m.bias is not None:
-                        if 'mlp' in n:
-                            normal_init(m.bias, std=1e-6)
-                        else:
-                            constant_init(m.bias, 0)
-                elif isinstance(m, Conv2d):
-                    kaiming_init(m.weight, mode='fan_in')
-                    if m.bias is not None:
-                        constant_init(m.bias, 0)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
-                    constant_init(m.bias, 0)
-                    constant_init(m.weight, 1.0)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def _pos_embeding(self, img, patched_img, pos_embed):
-        """Positiong embeding method.
-
-        Resize the pos_embed, if the input image size doesn't match
-            the training size.
-        Args:
-            img (torch.Tensor): The inference image tensor, the shape
-                must be [B, C, H, W].
-            patched_img (torch.Tensor): The patched image, it should be
-                shape of [B, L1, C].
-            pos_embed (torch.Tensor): The pos_embed weighs, it should be
-                shape of [B, L2, c].
-        Return:
-            torch.Tensor: The pos encoded image feature.
-        """
-        assert patched_img.ndim == 3 and pos_embed.ndim == 3, \
-            'the shapes of patched_img and pos_embed must be [B, L, C]'
-        x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]
-        if x_len != pos_len:
-            if pos_len == (self.img_size[0] // self.patch_size) * (
-                    self.img_size[1] // self.patch_size) + 1:
-                pos_h = self.img_size[0] // self.patch_size
-                pos_w = self.img_size[1] // self.patch_size
-            else:
-                raise ValueError(
-                    'Unexpected shape of pos_embed, got {}.'.format(
-                        pos_embed.shape))
-            pos_embed = self.resize_pos_embed(pos_embed, img.shape[2:],
-                                              (pos_h, pos_w), self.patch_size,
-                                              self.interpolate_mode)
-        return self.pos_drop(patched_img + pos_embed)
-
-    @staticmethod
-    def resize_pos_embed(pos_embed, input_shpae, pos_shape, patch_size, mode):
-        """Resize pos_embed weights.
-
-        Resize pos_embed using bicubic interpolate method.
-        Args:
-            pos_embed (torch.Tensor): pos_embed weights.
-            input_shpae (tuple): Tuple for (input_h, intput_w).
-            pos_shape (tuple): Tuple for (pos_h, pos_w).
-            patch_size (int): Patch size.
-        Return:
-            torch.Tensor: The resized pos_embed of shape [B, L_new, C]
-        """
-        assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
-        input_h, input_w = input_shpae
-        pos_h, pos_w = pos_shape
-        cls_token_weight = pos_embed[:, 0]
-        pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]
-        pos_embed_weight = pos_embed_weight.reshape(
-            1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
-        pos_embed_weight = F.interpolate(
-            pos_embed_weight,
-            size=[input_h // patch_size, input_w // patch_size],
-            align_corners=False,
-            mode=mode)
-        cls_token_weight = cls_token_weight.unsqueeze(1)
-        pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
-        pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)
-        return pos_embed
-
-    def forward(self, inputs):
-        B = inputs.shape[0]
-
-        x = self.patch_embed(inputs)
-
-        cls_tokens = self.cls_token.expand(B, -1, -1)
-        x = torch.cat((cls_tokens, x), dim=1)
-        x = self._pos_embeding(inputs, x, self.pos_embed)
-
-        if not self.with_cls_token:
-            # Remove class token for transformer input
-            x = x[:, 1:]
-
-        outs = []
-        for i, blk in enumerate(self.blocks):
-            x = blk(x)
-            if i == len(self.blocks) - 1:
-                if self.final_norm:
-                    x = self.norm(x)
-            if i in self.out_indices:
-                if self.with_cls_token:
-                    # Remove class token and reshape token for decoder head
-                    out = x[:, 1:]
-                else:
-                    out = x
-                B, _, C = out.shape
-                out = out.reshape(B, inputs.shape[2] // self.patch_size,
-                                  inputs.shape[3] // self.patch_size,
-                                  C).permute(0, 3, 1, 2)
-                outs.append(out)
-
-        return tuple(outs)
-
-    def train(self, mode=True):
-        super(VisionTransformer, self).train(mode)
-        if mode and self.norm_eval:
-            for m in self.modules():
-                if isinstance(m, nn.LayerNorm):
-                    m.eval()
diff --git a/spaces/abyildirim/inst-inpaint/ldm/modules/losses/vqperceptual.py b/spaces/abyildirim/inst-inpaint/ldm/modules/losses/vqperceptual.py
deleted file mode 100644
index 4ee7ca01acbcf49816e5dcc0d7e61baf0fa42e54..0000000000000000000000000000000000000000
--- a/spaces/abyildirim/inst-inpaint/ldm/modules/losses/vqperceptual.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from einops import repeat
-
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
-from taming.modules.losses.lpips import LPIPS
-from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
-
-
-def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
-    assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
-    loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
-    loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
-    loss_real = (weights * loss_real).sum() / weights.sum()
-    loss_fake = (weights * loss_fake).sum() / weights.sum()
-    d_loss = 0.5 * (loss_real + loss_fake)
-    return d_loss
-
-def adopt_weight(weight, global_step, threshold=0, value=0.):
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-def measure_perplexity(predicted_indices, n_embed):
-    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
-    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
-    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
-    avg_probs = encodings.mean(0)
-    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
-    cluster_use = torch.sum(avg_probs > 0)
-    return perplexity, cluster_use
-
-def l1(x, y):
-    return torch.abs(x-y)
-
-
-def l2(x, y):
-    return torch.pow((x-y), 2)
-
-
-class VQLPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
-                 pixel_loss="l1"):
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        assert perceptual_loss in ["lpips", "clips", "dists"]
-        assert pixel_loss in ["l1", "l2"]
-        self.codebook_weight = codebook_weight
-        self.pixel_weight = pixelloss_weight
-        if perceptual_loss == "lpips":
-            print(f"{self.__class__.__name__}: Running with LPIPS.")
-            self.perceptual_loss = LPIPS().eval()
-        else:
-            raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
-        self.perceptual_weight = perceptual_weight
-
-        if pixel_loss == "l1":
-            self.pixel_loss = l1
-        else:
-            self.pixel_loss = l2
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ndf=disc_ndf
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-        self.n_classes = n_classes
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
-        if codebook_loss is None:
-            codebook_loss = torch.tensor([0.]).to(inputs.device)
-        #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss
-        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        nll_loss = torch.mean(nll_loss)
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/p_loss".format(split): p_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            # if predicted_indices is not None:
-            #     assert self.n_classes is not None
-            #     with torch.no_grad():
-            #         perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
-            #     log[f"{split}/perplexity"] = perplexity
-            #     log[f"{split}/cluster_usage"] = cluster_usage
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
diff --git a/spaces/adirik/image-guided-owlvit/README.md b/spaces/adirik/image-guided-owlvit/README.md
deleted file mode 100644
index 78f188cf6d2c873c4316bdc71d346f8a02933ce7..0000000000000000000000000000000000000000
--- a/spaces/adirik/image-guided-owlvit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image-Guided OWL-ViT Demo
-emoji: 🔥
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/Detic/tools/get_lvis_cat_info.py b/spaces/akhaliq/Detic/tools/get_lvis_cat_info.py
deleted file mode 100644
index 83f286983ce811c4057ea8e8041e6a95dda78113..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Detic/tools/get_lvis_cat_info.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ann", default='datasets/lvis/lvis_v1_train.json')
-    parser.add_argument("--add_freq", action='store_true')
-    parser.add_argument("--r_thresh", type=int, default=10)
-    parser.add_argument("--c_thresh", type=int, default=100)
-    args = parser.parse_args()
-
-    print('Loading', args.ann)
-    data = json.load(open(args.ann, 'r'))
-    cats = data['categories']
-    image_count = {x['id']: set() for x in cats}
-    ann_count = {x['id']: 0 for x in cats}
-    for x in data['annotations']:
-        image_count[x['category_id']].add(x['image_id'])
-        ann_count[x['category_id']] += 1
-    num_freqs = {x: 0 for x in ['r', 'f', 'c']}
-    for x in cats:
-        x['image_count'] = len(image_count[x['id']])
-        x['instance_count'] = ann_count[x['id']]
-        if args.add_freq:
-            freq = 'f'
-            if x['image_count'] < args.c_thresh:
-                freq = 'c'
-            if x['image_count'] < args.r_thresh:
-                freq = 'r'
-            x['frequency'] = freq
-            num_freqs[freq] += 1
-    print(cats)
-    image_counts = sorted([x['image_count'] for x in cats])
-    # print('image count', image_counts)
-    # import pdb; pdb.set_trace()
-    if args.add_freq:
-        for x in ['r', 'c', 'f']:
-            print(x, num_freqs[x])
-    out = cats # {'categories': cats}
-    out_path = args.ann[:-5] + '_cat_info.json'
-    print('Saving to', out_path)
-    json.dump(out, open(out_path, 'w'))
-    
diff --git a/spaces/akhaliq/Detic/tools/remove_lvis_rare.py b/spaces/akhaliq/Detic/tools/remove_lvis_rare.py
deleted file mode 100644
index 06e4e881bfa50e2cd74747511a3ad2e8676e0c70..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Detic/tools/remove_lvis_rare.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--ann', default='datasets/lvis/lvis_v1_train.json')
-    args = parser.parse_args()
-
-    print('Loading', args.ann)
-    data = json.load(open(args.ann, 'r'))
-    catid2freq = {x['id']: x['frequency'] for x in data['categories']}
-    print('ori #anns', len(data['annotations']))
-    exclude = ['r']
-    data['annotations'] = [x for x in data['annotations'] \
-        if catid2freq[x['category_id']] not in exclude]
-    print('filtered #anns', len(data['annotations']))
-    out_path = args.ann[:-5] + '_norare.json'
-    print('Saving to', out_path)
-    json.dump(data, open(out_path, 'w'))
diff --git a/spaces/akhaliq/deeplab2/model/loss/base_loss.py b/spaces/akhaliq/deeplab2/model/loss/base_loss.py
deleted file mode 100644
index d4614c8b03e8a7a456eb73d7b8d0a685f86757cf..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/loss/base_loss.py
+++ /dev/null
@@ -1,559 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""This file contains basic loss classes used in the DeepLab model."""
-
-from typing import Text, Dict, Callable, Optional
-
-import tensorflow as tf
-from deeplab2.model import utils
-
-
-def compute_average_top_k_loss(loss: tf.Tensor,
-                               top_k_percentage: float) -> tf.Tensor:
-  """Computes the avaerage top-k loss per sample.
-
-  Args:
-    loss: A tf.Tensor with 2 or more dimensions of shape [batch, ...].
-    top_k_percentage: A float representing the % of pixel that should be used
-      for calculating the loss.
-
-  Returns:
-    A tensor of shape [batch] containing the mean top-k loss per sample. Due to
-    the use of different tf.strategy, we return the loss per sample and require
-    explicit averaging by the user.
-  """
-  loss = tf.reshape(loss, shape=(tf.shape(loss)[0], -1))
-
-  if top_k_percentage != 1.0:
-    num_elements_per_sample = tf.shape(loss)[1]
-    top_k_pixels = tf.cast(
-        tf.math.round(top_k_percentage *
-                      tf.cast(num_elements_per_sample, tf.float32)), tf.int32)
-
-    def top_k_1d(inputs):
-      return tf.math.top_k(inputs, top_k_pixels, sorted=False)[0]
-    loss = tf.map_fn(fn=top_k_1d, elems=loss)
-
-  # Compute mean loss over spatial dimension.
-  num_non_zero = tf.reduce_sum(tf.cast(tf.not_equal(loss, 0.0), tf.float32), 1)
-  loss_sum_per_sample = tf.reduce_sum(loss, 1)
-  return tf.math.divide_no_nan(loss_sum_per_sample, num_non_zero)
-
-
-def compute_mask_dice_loss(y_true: tf.Tensor,
-                           y_pred: tf.Tensor,
-                           prediction_activation='softmax') -> tf.Tensor:
-  """Computes the Mask Dice loss between y_true and y_pred masks.
-
-  Reference:
-  [1] Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural
-      networks for volumetric medical image segmentation. In: 3DV (2016)
-      https://arxiv.org/abs/1606.04797
-
-  Args:
-    y_true: A tf.Tensor of shape [batch, height, width, channels] (or [batch,
-      length, channels]) containing the ground-truth. The channel dimension
-      indicates the mask ID in MaX-DeepLab, instead of a "class" dimension in
-      the V-net paper. In our case, for all batch, height, width, (or batch,
-      length) the [batch, height, width, :] (or [batch, length, :]) should be
-      one-hot encodings only, with valid pixels having one and only one 1.0, and
-      with void pixels being all 0.0. The valid pixels of the masks do not and
-      should not overlap because of the non-overlapping definition of panoptic
-      segmentation. The output loss is computed and normalized by valid (not
-      void) pixels.
-    y_pred: A tf.Tensor of shape [batch, height, width, channels] (or [batch,
-      length, channels]) containing the prediction.
-    prediction_activation: A String indicating activation function of the
-      prediction. It should be either 'sigmoid' or 'softmax'.
-
-  Returns:
-    A tf.Tensor of shape [batch, channels] with the computed dice loss value.
-
-  Raises:
-      ValueError: An error occurs when prediction_activation is not either
-        'sigmoid' or 'softmax'.
-  """
-  tf.debugging.assert_rank_in(
-      y_pred, [3, 4], message='Input tensors y_pred must have rank 3 or 4.')
-  tf.debugging.assert_rank_in(
-      y_true, [3, 4], message='Input tensors y_true must have rank 3 or 4.')
-
-  shape_list = y_true.shape.as_list()
-  batch, channels = shape_list[0], shape_list[-1]
-  if prediction_activation == 'sigmoid':
-    y_pred = tf.math.sigmoid(y_pred)
-  elif prediction_activation == 'softmax':
-    y_pred = tf.nn.softmax(y_pred, axis=-1)
-  else:
-    raise ValueError(
-        "prediction_activation should be either 'sigmoid' or 'softmax'")
-
-  y_true_flat = tf.reshape(y_true, [batch, -1, channels])
-  # valid_flat indicates labeled pixels in the groudtruth. y_true is one-hot
-  # encodings only, with valid pixels having one and only one 1.0, and with
-  # invalid pixels having 0.0 values in all the channels. The valid pixels of
-  # the masks do not overlap because of the non-overlapping definition of
-  # panoptic segmentation.
-  valid_flat = tf.reduce_sum(y_true_flat, axis=-1, keepdims=True)
-  y_pred_flat = tf.reshape(
-      y_pred, [batch, -1, channels]) * valid_flat
-  # Use smooth = 1 to avoid division by zero when both y_pred and y_true are
-  # zeros.
-  smooth = 1.0
-  intersection = 2 * tf.reduce_sum(y_pred_flat * y_true_flat, axis=1) + smooth
-  denominator = (tf.reduce_sum(y_pred_flat, axis=1) +
-                 tf.reduce_sum(y_true_flat, axis=1) + smooth)
-  loss = 1. - tf.math.divide_no_nan(intersection, denominator)
-  return loss
-
-
-def mean_absolute_error(y_true: tf.Tensor,
-                        y_pred: tf.Tensor,
-                        force_keep_dims=False) -> tf.Tensor:
-  """Computes the per-pixel mean absolute error for 3D and 4D tensors.
-
-  Default reduction behavior: If a 3D tensor is used, no reduction is applied.
-  In case of a 4D tensor, reduction is applied. This behavior can be overridden
-  by force_keep_dims.
-  Note: tf.keras.losses.mean_absolute_error always reduces the output by one
-  dimension.
-
-  Args:
-    y_true: A tf.Tensor of shape [batch, height, width] or [batch, height,
-      width, channels] containing the ground-truth.
-    y_pred: A tf.Tensor of shape [batch, height, width] or [batch, height,
-      width, channels] containing the prediction.
-    force_keep_dims: A boolean flag specifying whether no reduction should be
-      applied.
-
-  Returns:
-    A tf.Tensor with the mean absolute error.
-  """
-  tf.debugging.assert_rank_in(
-      y_pred, [3, 4], message='Input tensors must have rank 3 or 4.')
-  if len(y_pred.shape.as_list()) == 3 or force_keep_dims:
-    return tf.abs(y_true - y_pred)
-  else:
-    return tf.reduce_mean(tf.abs(y_true - y_pred), axis=[3])
-
-
-def mean_squared_error(y_true: tf.Tensor,
-                       y_pred: tf.Tensor,
-                       force_keep_dims=False) -> tf.Tensor:
-  """Computes the per-pixel mean squared error for 3D and 4D tensors.
-
-  Default reduction behavior: If a 3D tensor is used, no reduction is applied.
-  In case of a 4D tensor, reduction is applied. This behavior can be overridden
-  by force_keep_dims.
-  Note: tf.keras.losses.mean_squared_error always reduces the output by one
-  dimension.
-
-  Args:
-    y_true: A tf.Tensor of shape [batch, height, width] or [batch, height,
-      width, channels] containing the ground-truth.
-    y_pred: A tf.Tensor of shape [batch, height, width] or [batch, height,
-      width, channels] containing the prediction.
-    force_keep_dims: A boolean flag specifying whether no reduction should be
-      applied.
-
-  Returns:
-    A tf.Tensor with the mean squared error.
-  """
-  tf.debugging.assert_rank_in(
-      y_pred, [3, 4], message='Input tensors must have rank 3 or 4.')
-  if len(y_pred.shape.as_list()) == 3 or force_keep_dims:
-    return tf.square(y_true - y_pred)
-  else:
-    return tf.reduce_mean(tf.square(y_true - y_pred), axis=[3])
-
-
-def encode_one_hot(gt: tf.Tensor,
-                   num_classes: int,
-                   weights: tf.Tensor,
-                   ignore_label: Optional[int]):
-  """Helper function for one-hot encoding of integer labels.
-
-  Args:
-    gt: A tf.Tensor providing ground-truth information. Integer type label.
-    num_classes: An integer indicating the number of classes considered in the
-      ground-truth. It is used as 'depth' in tf.one_hot().
-    weights: A tf.Tensor containing weights information.
-    ignore_label: An integer specifying the ignore label or None.
-
-  Returns:
-    gt: A tf.Tensor of one-hot encoded gt labels.
-    weights: A tf.Tensor with ignore_label considered.
-  """
-  if ignore_label is not None:
-    keep_mask = tf.cast(tf.not_equal(gt, ignore_label), dtype=tf.float32)
-  else:
-    keep_mask = tf.ones_like(gt, dtype=tf.float32)
-  gt = tf.stop_gradient(tf.one_hot(gt, num_classes))
-  weights = tf.multiply(weights, keep_mask)
-  return gt, weights
-
-
-def is_one_hot(gt: tf.Tensor, pred: tf.Tensor):
-  """Helper function for checking if gt tensor is one-hot encoded or not.
-
-  Args:
-    gt: A tf.Tensor providing ground-truth information.
-    pred: A tf.Tensor providing prediction information.
-
-  Returns:
-    A boolean indicating whether the gt is one-hot encoded (True) or
-    in integer type (False).
-  """
-  gt_shape = gt.get_shape().as_list()
-  pred_shape = pred.get_shape().as_list()
-  # If the ground truth is one-hot encoded, the rank of the ground truth should
-  # match that of the prediction. In addition, we check that the first
-  # dimension, batch_size, and the last dimension, channels, should also match
-  # the prediction. However, we still allow spatial dimensions, e.g., height and
-  # width, to be different since we will downsample the ground truth if needed.
-  return (len(gt_shape) == len(pred_shape) and
-          gt_shape[0] == pred_shape[0] and gt_shape[-1] == pred_shape[-1])
-
-
-def _ensure_topk_value_is_percentage(top_k_percentage: float):
-  """Checks if top_k_percentage is between 0.0 and 1.0.
-
-  Args:
-    top_k_percentage: The floating point value to check.
-  """
-  if top_k_percentage < 0.0 or top_k_percentage > 1.0:
-    raise ValueError('The top-k percentage parameter must lie within 0.0 and '
-                     '1.0, but %f was given' % top_k_percentage)
-
-
-class TopKGeneralLoss(tf.keras.losses.Loss):
-  """This class contains code to compute the top-k loss."""
-
-  def __init__(self,
-               loss_function: Callable[[tf.Tensor, tf.Tensor], tf.Tensor],
-               gt_key: Text,
-               pred_key: Text,
-               weight_key: Text,
-               top_k_percent_pixels: float = 1.0):
-    """Initializes a top-k L1 loss.
-
-    Args:
-      loss_function: A callable loss function.
-      gt_key: A key to extract the ground-truth tensor.
-      pred_key: A key to extract the prediction tensor.
-      weight_key: A key to extract the weight tensor.
-      top_k_percent_pixels: An optional float specifying the percentage of
-        pixels used to compute the loss. The value must lie within [0.0, 1.0].
-    """
-    # Implicit reduction might mess with tf.distribute.Strategy, hence we
-    # explicitly reduce the loss.
-    super(TopKGeneralLoss,
-          self).__init__(reduction=tf.keras.losses.Reduction.NONE)
-
-    _ensure_topk_value_is_percentage(top_k_percent_pixels)
-
-    self._loss_function = loss_function
-    self._top_k_percent_pixels = top_k_percent_pixels
-    self._gt_key = gt_key
-    self._pred_key = pred_key
-    self._weight_key = weight_key
-
-  def call(self, y_true: Dict[Text, tf.Tensor],
-           y_pred: Dict[Text, tf.Tensor]) -> tf.Tensor:
-    """Computes the top-k loss.
-
-    Args:
-      y_true: A dict of tensors providing ground-truth information.
-      y_pred: A dict of tensors providing predictions.
-
-    Returns:
-      A tensor of shape [batch] containing the loss per sample.
-    """
-    gt = y_true[self._gt_key]
-    pred = y_pred[self._pred_key]
-    weights = y_true[self._weight_key]
-
-    per_pixel_loss = self._loss_function(gt, pred)
-    per_pixel_loss = tf.multiply(per_pixel_loss, weights)
-
-    return compute_average_top_k_loss(per_pixel_loss,
-                                      self._top_k_percent_pixels)
-
-
-class TopKCrossEntropyLoss(tf.keras.losses.Loss):
-  """This class contains code for top-k cross-entropy."""
-
-  def __init__(self,
-               gt_key: Text,
-               pred_key: Text,
-               weight_key: Text,
-               num_classes: Optional[int],
-               ignore_label: Optional[int],
-               top_k_percent_pixels: float = 1.0,
-               dynamic_weight: bool = False):
-    """Initializes a top-k cross entropy loss.
-
-    Args:
-      gt_key: A key to extract the ground-truth tensor.
-      pred_key: A key to extract the prediction tensor.
-      weight_key: A key to extract the weight tensor.
-      num_classes: An integer specifying the number of classes in the dataset.
-      ignore_label: An optional integer specifying the ignore label or None.
-      top_k_percent_pixels: An optional float specifying the percentage of
-        pixels used to compute the loss. The value must lie within [0.0, 1.0].
-      dynamic_weight: A boolean indicating whether the weights are determined
-        dynamically w.r.t. the class confidence of each predicted mask.
-
-    Raises:
-      ValueError: An error occurs when top_k_percent_pixels is not between 0.0
-        and 1.0.
-    """
-    # Implicit reduction might mess with tf.distribute.Strategy, hence we
-    # explicitly reduce the loss.
-    super(TopKCrossEntropyLoss,
-          self).__init__(reduction=tf.keras.losses.Reduction.NONE)
-
-    _ensure_topk_value_is_percentage(top_k_percent_pixels)
-
-    self._num_classes = num_classes
-    self._ignore_label = ignore_label
-    self._top_k_percent_pixels = top_k_percent_pixels
-    self._gt_key = gt_key
-    self._pred_key = pred_key
-    self._weight_key = weight_key
-    self._dynamic_weight = dynamic_weight
-
-  def call(self, y_true: Dict[Text, tf.Tensor],
-           y_pred: Dict[Text, tf.Tensor]) -> tf.Tensor:
-    """Computes the top-k cross-entropy loss.
-
-    Args:
-      y_true: A dict of tensors providing ground-truth information. The tensors
-        can be either integer type or one-hot encoded. When is integer type, the
-        shape can be either [batch, num_elements] or [batch, height, width].
-        When one-hot encoded, the shape can be [batch, num_elements, channels]
-        or [batch, height, width, channels].
-      y_pred: A dict of tensors providing predictions. The tensors are of shape
-        [batch, num_elements, channels] or [batch, height, width, channels]. If
-        the prediction is 2D (with height and width), we allow the spatial
-        dimension to be strided_height and strided_width. In this case, we
-        downsample the ground truth accordingly.
-
-    Returns:
-      A tensor of shape [batch] containing the loss per image.
-
-    Raises:
-      ValueError: If the prediction is 1D (with the length dimension) but its
-        length does not match that of the ground truth.
-    """
-    gt = y_true[self._gt_key]
-    pred = y_pred[self._pred_key]
-    gt_shape = gt.get_shape().as_list()
-    pred_shape = pred.get_shape().as_list()
-    if self._dynamic_weight:
-      weights = y_pred[self._weight_key]
-    else:
-      weights = y_true[self._weight_key]
-
-    # Downsample the ground truth for 2D prediction cases.
-    if len(pred_shape) == 4 and gt_shape[1:3] != pred_shape[1:3]:
-      gt = utils.strided_downsample(gt, pred_shape[1:3])
-      weights = utils.strided_downsample(weights, pred_shape[1:3])
-    elif len(pred_shape) == 3 and gt_shape[1] != pred_shape[1]:
-      # We don't support downsampling for 1D predictions.
-      raise ValueError('The shape of gt does not match the shape of pred.')
-
-    if is_one_hot(gt, pred):
-      gt = tf.cast(gt, tf.float32)
-    else:
-      gt = tf.cast(gt, tf.int32)
-      gt, weights = encode_one_hot(gt, self._num_classes, weights,
-                                   self._ignore_label)
-    pixel_losses = tf.keras.backend.categorical_crossentropy(
-        gt, pred, from_logits=True)
-    weighted_pixel_losses = tf.multiply(pixel_losses, weights)
-
-    return compute_average_top_k_loss(weighted_pixel_losses,
-                                      self._top_k_percent_pixels)
-
-
-class FocalCrossEntropyLoss(tf.keras.losses.Loss):
-  """This class contains code for focal cross-entropy."""
-
-  def __init__(self,
-               gt_key: Text,
-               pred_key: Text,
-               weight_key: Text,
-               num_classes: Optional[int],
-               ignore_label: Optional[int],
-               focal_loss_alpha: float = 0.75,
-               focal_loss_gamma: float = 0.0,
-               background_channel_index: int = -1,
-               dynamic_weight: bool = True):
-    """Initializes a focal cross entropy loss.
-
-    FocalCrossEntropyLoss supports focal-loss mode with integer
-    or one-hot ground-truth labels.
-    Reference:
-    [1] Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. Focal loss for
-        dense object detection. In Proceedings of the IEEE International
-        Conference on Computer Vision (ICCV). (2017)
-        https://arxiv.org/abs/1708.02002
-
-    Args:
-      gt_key: A key to extract the ground-truth tensor.
-      pred_key: A key to extract the prediction tensor.
-      weight_key: A key to extract the weight tensor.
-      num_classes: An integer specifying the number of classes in the dataset.
-      ignore_label: An optional integer specifying the ignore label or None.
-        Only effective when ground truth labels are in integer mode.
-      focal_loss_alpha: An optional float specifying the coefficient that
-        weights between positive (matched) and negative (unmatched) masks in
-        focal loss. The positives are weighted by alpha, while the negatives
-        are weighted by (1. - alpha). Default to 0.75.
-      focal_loss_gamma: An optional float specifying the coefficient that
-        weights probability (pt) term in focal loss. Focal loss = - ((1 - pt) ^
-        gamma) * log(pt). Default to 0.0.
-      background_channel_index: The index for background channel. When alpha
-        is used, we assume the last channel is background and others are
-        foreground. Default to -1.
-      dynamic_weight: A boolean indicating whether the weights are determined
-        dynamically w.r.t. the class confidence of each predicted mask.
-    """
-    # Implicit reduction might mess with tf.distribute.Strategy, hence we
-    # explicitly reduce the loss.
-    super(FocalCrossEntropyLoss,
-          self).__init__(reduction=tf.keras.losses.Reduction.NONE)
-
-    self._num_classes = num_classes
-    self._ignore_label = ignore_label
-    self._focal_loss_alpha = focal_loss_alpha
-    self._focal_loss_gamma = focal_loss_gamma
-    self._background_channel_index = background_channel_index
-    self._gt_key = gt_key
-    self._pred_key = pred_key
-    self._weight_key = weight_key
-    self._dynamic_weight = dynamic_weight
-
-  def call(self, y_true: Dict[Text, tf.Tensor],
-           y_pred: Dict[Text, tf.Tensor]) -> tf.Tensor:
-    """Computes the focal cross-entropy loss.
-
-    Args:
-      y_true: A dict of tensors providing ground-truth information. The tensors
-        can be either integer type or one-hot encoded. When is integer type, the
-        shape can be either [batch, num_elements] or [batch, height, width].
-        When one-hot encoded, the shape can be [batch, num_elements, channels]
-        or [batch, height, width, channels].
-      y_pred: A dict of tensors providing predictions. The tensors are of shape
-        [batch, num_elements, channels] or [batch, height, width, channels].
-
-    Returns:
-      A tensor of shape [batch] containing the loss per image.
-
-    """
-
-    gt = y_true[self._gt_key]
-    pred = y_pred[self._pred_key]
-    if self._dynamic_weight:
-      # Dynamic weights w.r.t. the class confidence of each predicted mask.
-      weights = y_pred[self._weight_key]
-    else:
-      weights = y_true[self._weight_key]
-
-    if is_one_hot(gt, pred):
-      gt = tf.cast(gt, tf.float32)
-    else:
-      gt = tf.cast(gt, tf.int32)
-      gt, weights = encode_one_hot(gt, self._num_classes, weights,
-                                   self._ignore_label)
-    pixel_losses = tf.nn.softmax_cross_entropy_with_logits(gt, pred)
-    # Focal loss
-    if self._focal_loss_gamma == 0.0:
-      pixel_focal_losses = pixel_losses
-    else:
-      predictions = tf.nn.softmax(pred, axis=-1)
-      pt = tf.reduce_sum(predictions * gt, axis=-1)
-      pixel_focal_losses = tf.multiply(
-          tf.pow(1.0 - pt, self._focal_loss_gamma), pixel_losses)
-
-    if self._focal_loss_alpha >= 0:
-      # alpha_weights = alpha * positive masks + (1 - alpha) * negative masks.
-      alpha = self._focal_loss_alpha
-      alpha_weights = (
-          alpha * (1.0 - gt[..., self._background_channel_index])
-          + (1 - alpha) * gt[..., self._background_channel_index])
-      pixel_focal_losses = alpha_weights * pixel_focal_losses
-    weighted_pixel_losses = tf.multiply(pixel_focal_losses, weights)
-    weighted_pixel_losses = tf.reshape(
-        weighted_pixel_losses, shape=(tf.shape(weighted_pixel_losses)[0], -1))
-    # Compute mean loss over spatial dimension.
-    num_non_zero = tf.reduce_sum(
-        tf.cast(tf.not_equal(weighted_pixel_losses, 0.0), tf.float32), 1)
-    loss_sum_per_sample = tf.reduce_sum(weighted_pixel_losses, 1)
-    return tf.math.divide_no_nan(loss_sum_per_sample, num_non_zero)
-
-
-class MaskDiceLoss(tf.keras.losses.Loss):
-  """This class contains code to compute Mask Dice loss.
-
-  The channel dimension in Mask Dice loss indicates the mask ID in MaX-DeepLab,
-  instead of a "class" dimension in the original Dice loss.
-  """
-
-  def __init__(self,
-               gt_key: Text,
-               pred_key: Text,
-               weight_key: Text,
-               prediction_activation='softmax'):
-    """Initializes a Mask Dice loss.
-
-    Args:
-      gt_key: A key to extract the ground-truth tensor.
-      pred_key: A key to extract the pred tensor.
-      weight_key: A key to extract the weight tensor.
-      prediction_activation: A String indicating activation function of the
-        prediction. It should be either 'sigmoid' or 'softmax'.
-    """
-    # Implicit reduction might mess with tf.distribute.Strategy, hence we
-    # explicitly reduce the loss.
-    super(MaskDiceLoss, self).__init__(reduction=tf.keras.losses.Reduction.NONE)
-
-    self._gt_key = gt_key
-    self._pred_key = pred_key
-    self._weight_key = weight_key
-    self._prediction_activation = prediction_activation
-
-  def call(self, y_true: Dict[Text, tf.Tensor],
-           y_pred: Dict[Text, tf.Tensor]) -> tf.Tensor:
-    """Computes the Mask Dice loss.
-
-    Args:
-      y_true: A dict of tensors providing ground-truth information.
-      y_pred: A dict of tensors providing predictions.
-
-    Returns:
-      A tensor of shape [batch] containing the loss per sample.
-    """
-    gt = y_true[self._gt_key]
-    pred = y_pred[self._pred_key]
-    # Dynamic weights w.r.t. the class confidence of each predicted mask.
-    weights = y_pred[self._weight_key]
-    weighted_dice_losses = tf.multiply(
-        compute_mask_dice_loss(gt, pred, self._prediction_activation),
-        weights)
-    # Reduce_sum over the channels (i.e., number of masks).
-    return tf.reduce_sum(weighted_dice_losses, axis=-1)
diff --git a/spaces/alexray/btc_predictor/tests/test_model_testing.py b/spaces/alexray/btc_predictor/tests/test_model_testing.py
deleted file mode 100644
index aa78bf33c933dfe27a87a84e33b6a42509d6aadc..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/tests/test_model_testing.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import unittest
-import os
-import shutil
-import pickle
-import pandas as pd
-from sklearn.linear_model import LinearRegression
-from model_testing import test_model
-
-
-class TestModelTesting(unittest.TestCase):
-    def setUp(self):
-        # Create a temporary directory
-        self.temp_dir = 'temp_test'
-        os.mkdir(self.temp_dir)
-
-        # Create temporary test data directory
-        self.test_data_dir = os.path.join(self.temp_dir, 'data')
-        os.mkdir(self.test_data_dir)
-
-        # Create temporary test data with the required pattern
-        test_features = pd.DataFrame({
-            'open': [0.6095, 0.5759, 0.6418],
-            'high': [0.6112, -0.7703, 0.13523],
-            'low': [1.7676, 0.23695, 0.6469],
-            'close': [1.0887, 0.4828, 2.657],
-            'volume': [-0.10299, 1.173, 1.345]
-        })
-
-        test_target = pd.Series([1.624, 2.223, 0.064], name='target')
-
-        test_features.to_csv(os.path.join(self.test_data_dir,
-                                          'test_features.csv'), index=True)
-        test_target.to_csv(os.path.join(self.test_data_dir, 'test_target.csv'),
-                           index=True)
-
-        # Create a simple linear regression model and save it to a pickle file
-        model = LinearRegression()
-        model.fit(test_features, test_target)
-        with open(os.path.join(self.temp_dir,
-                               'test_model.pkl'), 'wb') as model_file:
-            pickle.dump(model, model_file)
-
-    def tearDown(self):
-        # Clean up temporary directory and files
-        shutil.rmtree(self.temp_dir)
-
-    def test_test_model(self):
-        # Call the function with the temporary directory and model path
-        result = test_model(model_path=os.path.join(self.temp_dir,
-                                                    'test_model.pkl'),
-                            data_dir=self.test_data_dir)
-
-        # Assert that the result is as expected
-        self.assertTrue("Mean Squared Error:" in result)
-        self.assertTrue("R-squared:" in result)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/base.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/base.py
deleted file mode 100644
index 42dade18c1ec2b825f756dad4aaa89f2d9e6ce21..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/resolution/base.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from typing import Callable, List, Optional
-
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.req.req_set import RequirementSet
-
-InstallRequirementProvider = Callable[
-    [str, Optional[InstallRequirement]], InstallRequirement
-]
-
-
-class BaseResolver:
-    def resolve(
-        self, root_reqs: List[InstallRequirement], check_supported_wheels: bool
-    ) -> RequirementSet:
-        raise NotImplementedError()
-
-    def get_installation_order(
-        self, req_set: RequirementSet
-    ) -> List[InstallRequirement]:
-        raise NotImplementedError()
diff --git a/spaces/aliabid94/AutoGPT/autogpt/commands/execute_code.py b/spaces/aliabid94/AutoGPT/autogpt/commands/execute_code.py
deleted file mode 100644
index 11266f852727f2f8aedbc995b1e504a17acbfb77..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/commands/execute_code.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""Execute code in a Docker container"""
-import os
-import subprocess
-
-import docker
-from docker.errors import ImageNotFound
-
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-
-def execute_python_file(file: str) -> str:
-    """Execute a Python file in a Docker container and return the output
-
-    Args:
-        file (str): The name of the file to execute
-
-    Returns:
-        str: The output of the file
-    """
-
-    print(f"Executing file '{file}' in workspace '{WORKSPACE_PATH}'")
-
-    if not file.endswith(".py"):
-        return "Error: Invalid file type. Only .py files are allowed."
-
-    file_path = path_in_workspace(file)
-
-    if not os.path.isfile(file_path):
-        return f"Error: File '{file}' does not exist."
-
-    if we_are_running_in_a_docker_container():
-        result = subprocess.run(
-            f"python {file_path}", capture_output=True, encoding="utf8", shell=True
-        )
-        if result.returncode == 0:
-            return result.stdout
-        else:
-            return f"Error: {result.stderr}"
-
-    try:
-        client = docker.from_env()
-
-        # You can replace this with the desired Python image/version
-        # You can find available Python images on Docker Hub:
-        # https://hub.docker.com/_/python
-        image_name = "python:3-alpine"
-        try:
-            client.images.get(image_name)
-            print(f"Image '{image_name}' found locally")
-        except ImageNotFound:
-            print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
-            # Use the low-level API to stream the pull response
-            low_level_client = docker.APIClient()
-            for line in low_level_client.pull(image_name, stream=True, decode=True):
-                # Print the status and progress, if available
-                status = line.get("status")
-                progress = line.get("progress")
-                if status and progress:
-                    print(f"{status}: {progress}")
-                elif status:
-                    print(status)
-
-        container = client.containers.run(
-            image_name,
-            f"python {file}",
-            volumes={
-                os.path.abspath(WORKSPACE_PATH): {
-                    "bind": "/workspace",
-                    "mode": "ro",
-                }
-            },
-            working_dir="/workspace",
-            stderr=True,
-            stdout=True,
-            detach=True,
-        )
-
-        container.wait()
-        logs = container.logs().decode("utf-8")
-        container.remove()
-
-        # print(f"Execution complete. Output: {output}")
-        # print(f"Logs: {logs}")
-
-        return logs
-
-    except docker.errors.DockerException as e:
-        print(
-            "Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/"
-        )
-        return f"Error: {str(e)}"
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def execute_shell(command_line: str) -> str:
-    """Execute a shell command and return the output
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: The output of the command
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    result = subprocess.run(command_line, capture_output=True, shell=True)
-    output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return output
-
-
-def execute_shell_popen(command_line) -> str:
-    """Execute a shell command with Popen and returns an english description
-    of the event and the process id
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: Description of the fact that the process started and its id
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    do_not_show_output = subprocess.DEVNULL
-    process = subprocess.Popen(
-        command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output
-    )
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return f"Subprocess started with PID:'{str(process.pid)}'"
-
-
-def we_are_running_in_a_docker_container() -> bool:
-    """Check if we are running in a Docker container
-
-    Returns:
-        bool: True if we are running in a Docker container, False otherwise
-    """
-    return os.path.exists("/.dockerenv")
diff --git a/spaces/allandclive/Uganda_MMS/vits/attentions.py b/spaces/allandclive/Uganda_MMS/vits/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/vits/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/allknowingroger/Llama_v2/style.css b/spaces/allknowingroger/Llama_v2/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Llama_v2/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/alx-ai/Real-ESRGAN-Demo/app.py b/spaces/alx-ai/Real-ESRGAN-Demo/app.py
deleted file mode 100644
index b063d628a7c594dc498b5eb986679cb61572e7d3..0000000000000000000000000000000000000000
--- a/spaces/alx-ai/Real-ESRGAN-Demo/app.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import gradio as gr
-import cv2
-import numpy
-import os
-import random
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.download_util import load_file_from_url
-
-from realesrgan import RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-from fastapi import FastAPI
-
-# base path
-CUSTOM_PATH = "/gradio"
-
-app = FastAPI()
-
-last_file = None
-img_mode = "RGBA"
-
-@app.get("/")
-def read_main():
-    return {"message": "This is your main app"}
-
-
-def realesrgan(img, model_name, denoise_strength, face_enhance, outscale):
-    """Real-ESRGAN function to restore (and upscale) images.
-    """
-    choices=["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B",
-                                                  "RealESRGAN_x2plus", "realesr-general-x4v3"]
-    model_name = choices[model_name]
-    if not img:
-        return
-
-    # Define model parameters
-    if model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-        netscale = 4
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
-    elif model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-        netscale = 4
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
-    elif model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
-        netscale = 4
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
-    elif model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        netscale = 2
-        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
-    elif model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-        netscale = 4
-        file_url = [
-            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
-            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
-        ]
-
-    # Determine model paths
-    model_path = os.path.join('weights', model_name + '.pth')
-    if not os.path.isfile(model_path):
-        ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-        for url in file_url:
-            # model_path will be updated
-            model_path = load_file_from_url(
-                url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
-
-    # Use dni to control the denoise strength
-    dni_weight = None
-    if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
-        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
-        model_path = [model_path, wdn_model_path]
-        dni_weight = [denoise_strength, 1 - denoise_strength]
-
-    # Restorer Class
-    upsampler = RealESRGANer(
-        scale=netscale,
-        model_path=model_path,
-        dni_weight=dni_weight,
-        model=model,
-        tile=0,
-        tile_pad=10,
-        pre_pad=10,
-        half=False,
-        gpu_id=None
-    )
-
-    # Use GFPGAN for face enhancement
-    if face_enhance:
-        from gfpgan import GFPGANer
-        face_enhancer = GFPGANer(
-            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
-            upscale=outscale,
-            arch='clean',
-            channel_multiplier=2,
-            bg_upsampler=upsampler)
-
-    # Convert the input PIL image to cv2 image, so that it can be processed by realesrgan
-    cv_img = numpy.array(img)
-    img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA)
-
-    # Apply restoration
-    try:
-        if face_enhance:
-            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
-        else:
-            output, _ = upsampler.enhance(img, outscale=outscale)
-    except RuntimeError as error:
-        print('Error', error)
-        print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
-    else:
-        # Save restored image and return it to the output Image component
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'png'
-        else:
-            extension = 'jpg'
-
-        out_filename = f"output_{rnd_string(8)}.{extension}"
-        cv2.imwrite(out_filename, output)
-        global last_file
-        last_file = out_filename
-        return out_filename
-
-
-def rnd_string(x):
-    """Returns a string of 'x' random characters
-    """
-    characters = "abcdefghijklmnopqrstuvwxyz_0123456789"
-    result = "".join((random.choice(characters)) for i in range(x))
-    return result
-
-
-def reset():
-    """Resets the Image components of the Gradio interface and deletes
-    the last processed image
-    """
-    global last_file
-    if last_file:
-        print(f"Deleting {last_file} ...")
-        os.remove(last_file)
-        last_file = None
-    return gr.update(value=None), gr.update(value=None)
-
-
-def has_transparency(img):
-    """This function works by first checking to see if a "transparency" property is defined
-    in the image's info -- if so, we return "True". Then, if the image is using indexed colors
-    (such as in GIFs), it gets the index of the transparent color in the palette
-    (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas
-    (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in
-    it, but it double-checks by getting the minimum and maximum values of every color channel
-    (img.getextrema()), and checks if the alpha channel's smallest value falls below 255.
-    https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
-    """
-    if img.info.get("transparency", None) is not None:
-        return True
-    if img.mode == "P":
-        transparent = img.info.get("transparency", -1)
-        for _, index in img.getcolors():
-            if index == transparent:
-                return True
-    elif img.mode == "RGBA":
-        extrema = img.getextrema()
-        if extrema[3][0] < 255:
-            return True
-    return False
-
-
-def image_properties(img):
-    """Returns the dimensions (width and height) and color mode of the input image and
-    also sets the global img_mode variable to be used by the realesrgan function
-    """
-    global img_mode
-    if img:
-        if has_transparency(img):
-            img_mode = "RGBA"
-        else:
-            img_mode = "RGB"
-        properties = f"Width: {img.size[0]}, Height: {img.size[1]}  |  Color Mode: {img_mode}"
-        return properties
-
-
-def main():
-    # Gradio Interface
-    with gr.Blocks(title="Real-ESRGAN Gradio Demo", theme="dark") as demo:
-
-        # gr.Markdown(
-        #     """# <div align="center"> Real-ESRGAN Demo for Image Restoration and Upscaling </div>
-        # <div align="center"><img width="200" height="74" src="https://github.com/xinntao/Real-ESRGAN/raw/master/assets/realesrgan_logo.png"></div>  
-
-        # This Gradio Demo was built as my Final Project for **CS50's Introduction to Programming with Python**.  
-        # Please visit the [Real-ESRGAN GitHub page](https://github.com/xinntao/Real-ESRGAN) for detailed information about the project.
-        # """
-        # )
-
-        with gr.Accordion("Options/Parameters"):
-            with gr.Row():
-                model_name = gr.Slider(label="Model to use",
-                                             minimum=0, maximum=4, step=1, value=4)
-                denoise_strength = gr.Slider(label="Denoise Strength (Used only with the realesr-general-x4v3 model)",
-                                             minimum=0, maximum=1, step=0.1, value=0.5)
-                outscale = gr.Slider(label="Image Upscaling Factor",
-                                     minimum=1, maximum=10, step=1, value=2, show_label=True)
-                face_enhance = gr.Checkbox(label="Face Enhancement using GFPGAN (Doesn't work for anime images)",
-                                           value=False, show_label=True)
-        
-        with gr.Row():
-            with gr.Group():
-                input_image = gr.Image(label="Source Image", type="pil", image_mode="RGBA")
-                input_image_properties = gr.Textbox(label="Image Properties", max_lines=1)
-            output_image = gr.Image(label="Restored Image", image_mode="RGBA")
-        with gr.Row():
-            restore_btn = gr.Button("Restore Image")
-            reset_btn = gr.Button("Reset")
-
-        # Event listeners:
-        input_image.change(fn=image_properties, inputs=input_image, outputs=input_image_properties)
-        restore_btn.click(fn=realesrgan,
-                          inputs=[input_image, model_name, denoise_strength, face_enhance, outscale],
-                          outputs=output_image,api_name="restore")
-        reset_btn.click(fn=reset, inputs=[], outputs=[output_image, input_image])
-        # reset_btn.click(None, inputs=[], outputs=[input_image], _js="() => (null)\n")
-        # Undocumented method to clear a component's value using Javascript
-
-        gr.Markdown(
-            """*Please note that support for animated GIFs is not yet implemented. Should an animated GIF is chosen for restoration, 
-            the demo will output only the first frame saved in PNG format (to preserve probable transparency).*
-        """
-        )
-
-    demo.launch()
-    app = gr.mount_gradio_app(app, gr, path=CUSTOM_PATH)
-
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_in_overflow.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_in_overflow.c
deleted file mode 100644
index 31868bd3f6548203a1658f35ab3a9ce2780302f4..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_in_overflow.c
+++ /dev/null
@@ -1,236 +0,0 @@
-/** @file patest_in_overflow.c
-    @ingroup test_src
-    @brief Count input overflows (using paInputOverflow flag) under
-    overloaded and normal conditions.
-    This test uses the same method to overload the stream as does
-    patest_out_underflow.c -- it generates sine waves until the cpu load
-    exceeds a certain level. However this test is only concerned with
-    input and so doesn't output any sound.
-
-    @author Ross Bencina <rossb@audiomulch.com>
-    @author Phil Burk <philburk@softsynth.com>
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2004 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include <stdio.h>
-#include <math.h>
-#include "portaudio.h"
-
-#define MAX_SINES     (500)
-#define MAX_LOAD      (1.2)
-#define SAMPLE_RATE   (44100)
-#define FRAMES_PER_BUFFER  (512)
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-#define TWOPI (M_PI * 2.0)
-
-typedef struct paTestData
-{
-    int sineCount;
-    double phases[MAX_SINES];
-    int countOverflows;
-    int inputOverflowCount;
-}
-paTestData;
-
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer, void *outputBuffer,
-                           unsigned long framesPerBuffer,
-                           const PaStreamCallbackTimeInfo* timeInfo,
-                           PaStreamCallbackFlags statusFlags,
-                           void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    float out;          /* variable to hold dummy output */
-    unsigned long i;
-    int j;
-    int finished = paContinue;
-    (void) timeInfo;    /* Prevent unused variable warning. */
-    (void) inputBuffer; /* Prevent unused variable warning. */
-    (void) outputBuffer; /* Prevent unused variable warning. */
-
-    if( data->countOverflows && (statusFlags & paInputOverflow) )
-        data->inputOverflowCount++;
-
-    for( i=0; i<framesPerBuffer; i++ )
-    {
-        float output = 0.0;
-        double phaseInc = 0.02;
-        double phase;
-
-        for( j=0; j<data->sineCount; j++ )
-        {
-            /* Advance phase of next oscillator. */
-            phase = data->phases[j];
-            phase += phaseInc;
-            if( phase > TWOPI ) phase -= TWOPI;
-
-            phaseInc *= 1.02;
-            if( phaseInc > 0.5 ) phaseInc *= 0.5;
-
-            /* This is not a very efficient way to calc sines. */
-            output += (float) sin( phase );
-            data->phases[j] = phase;
-        }
-        /* this is an input-only stream so we don't actually use the output */
-        out = (float) (output / data->sineCount);
-        (void) out; /* suppress unused variable warning*/
-    }
-
-    return finished;
-}
-
-/*******************************************************************/
-int main(void);
-int main(void)
-{
-    PaStreamParameters inputParameters;
-    PaStream *stream;
-    PaError err;
-    int safeSineCount, stressedSineCount;
-    int safeOverflowCount, stressedOverflowCount;
-    paTestData data = {0};
-    double load;
-
-
-    printf("PortAudio Test: input only, no sound output. Load callback by performing calculations, count input overflows. SR = %d, BufSize = %d. MAX_LOAD = %f\n",
-        SAMPLE_RATE, FRAMES_PER_BUFFER, (float)MAX_LOAD );
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-    inputParameters.device = Pa_GetDefaultInputDevice();  /* default input device */
-    if (inputParameters.device == paNoDevice) {
-        fprintf(stderr,"Error: No default input device.\n");
-        goto error;
-    }
-    inputParameters.channelCount = 1;                      /* mono output */
-    inputParameters.sampleFormat = paFloat32;              /* 32 bit floating point output */
-    inputParameters.suggestedLatency = Pa_GetDeviceInfo( inputParameters.device )->defaultLowInputLatency;
-    inputParameters.hostApiSpecificStreamInfo = NULL;
-
-    err = Pa_OpenStream(
-              &stream,
-              &inputParameters,
-              NULL,    /* no output */
-              SAMPLE_RATE,
-              FRAMES_PER_BUFFER,
-              paClipOff,    /* we won't output out of range samples so don't bother clipping them */
-              patestCallback,
-              &data );
-    if( err != paNoError ) goto error;
-    err = Pa_StartStream( stream );
-    if( err != paNoError ) goto error;
-
-    printf("Establishing load conditions...\n" );
-
-    /* Determine number of sines required to get to 50% */
-    do
-    {
-        data.sineCount++;
-        Pa_Sleep( 100 );
-
-        load = Pa_GetStreamCpuLoad( stream );
-        printf("sineCount = %d, CPU load = %f\n", data.sineCount, load );
-    }
-    while( load < 0.5 && data.sineCount < (MAX_SINES-1));
-
-    safeSineCount = data.sineCount;
-
-    /* Calculate target stress value then ramp up to that level*/
-    stressedSineCount = (int) (2.0 * data.sineCount * MAX_LOAD );
-    if( stressedSineCount > MAX_SINES )
-        stressedSineCount = MAX_SINES;
-    for( ; data.sineCount < stressedSineCount; data.sineCount++ )
-    {
-        Pa_Sleep( 100 );
-        load = Pa_GetStreamCpuLoad( stream );
-        printf("STRESSING: sineCount = %d, CPU load = %f\n", data.sineCount, load );
-    }
-
-    printf("Counting overflows for 5 seconds.\n");
-    data.countOverflows = 1;
-    Pa_Sleep( 5000 );
-
-    stressedOverflowCount = data.inputOverflowCount;
-
-    data.countOverflows = 0;
-    data.sineCount = safeSineCount;
-
-    printf("Resuming safe load...\n");
-    Pa_Sleep( 1500 );
-    data.inputOverflowCount = 0;
-    Pa_Sleep( 1500 );
-    load = Pa_GetStreamCpuLoad( stream );
-    printf("sineCount = %d, CPU load = %f\n", data.sineCount, load );
-
-    printf("Counting overflows for 5 seconds.\n");
-    data.countOverflows = 1;
-    Pa_Sleep( 5000 );
-
-    safeOverflowCount = data.inputOverflowCount;
-
-    printf("Stop stream.\n");
-    err = Pa_StopStream( stream );
-    if( err != paNoError ) goto error;
-
-    err = Pa_CloseStream( stream );
-    if( err != paNoError ) goto error;
-
-    Pa_Terminate();
-
-    if( stressedOverflowCount == 0 )
-        printf("Test failed, no input overflows detected under stress.\n");
-    else if( safeOverflowCount != 0 )
-        printf("Test failed, %d unexpected overflows detected under safe load.\n", safeOverflowCount);
-    else
-        printf("Test passed, %d expected input overflows detected under stress, 0 unexpected overflows detected under safe load.\n", stressedOverflowCount );
-
-    return err;
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    return err;
-}
diff --git a/spaces/ammarnasr/Code-Generation-with-Language-Specific-LoRa-Models/utils.py b/spaces/ammarnasr/Code-Generation-with-Language-Specific-LoRa-Models/utils.py
deleted file mode 100644
index 50f03e795bb13e1ee87541445d47cffc5ff591f4..0000000000000000000000000000000000000000
--- a/spaces/ammarnasr/Code-Generation-with-Language-Specific-LoRa-Models/utils.py
+++ /dev/null
@@ -1,88 +0,0 @@
-
-from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
-import os
-from peft import PeftConfig, PeftModel
-import json
-import jsonlines
-import numpy as np
-
-
-
-def initialize_tokenizer_from_huggingface(tokenizer_name):
-    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
-    tokenizer.pad_token = tokenizer.eos_token
-    return tokenizer
-
-def initialize_causual_model_from_huffingface(model_name):
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-    return model
-
-def initialize_peft_model_from_huffingface(model_name):
-    print("Loading the model from checkpoint: ", model_name, "With peft ...")
-    config = PeftConfig.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
-    model = PeftModel.from_pretrained(model, model_name)
-    print("Done loading the model from checkpoint: ", model_name, "With peft ...")
-    model.print_trainable_parameters()
-    return model
-
-def initialize_generation_strategy(generation_strategy_name):
-    generation_strategy = GenerationConfig.from_pretrained(generation_strategy_name)
-    return generation_strategy
-
-
-def stop_at_stop_token(decoded_string, stop_tokens):
-    """
-    Produces the prefix of decoded_string that ends at the first occurrence of
-    a stop_token.
-
-    WARNING: the decoded_string *must not* include the prompt, which may have stop tokens
-    itself.
-    """
-    if stop_tokens == None:
-        return decoded_string
-    min_stop_index = len(decoded_string)
-    for stop_token in stop_tokens:
-        stop_index = decoded_string.find(stop_token)
-        if stop_index != -1 and stop_index < min_stop_index:
-            min_stop_index = stop_index
-    return decoded_string[:min_stop_index]
-
-
-
-def read_json(filename):
-    with open(filename, "r") as f:
-        return json.load(f)
-    
-
-def write_json(filename, data):
-    with open(filename, "w") as f:
-        json.dump(data, f, indent=4)
-
-def initialize_generation_strategy_from_dict(generation_config_dict):
-    generation_config = GenerationConfig(**generation_config_dict)
-    return generation_config
-
-
-
-def read_prompts(prompts_file_name):
-    prompts = {
-        "prompt_id": [],
-        "prompt_text": [],
-        "prompt_test": [],
-        "prompt_stop_tokens": [],
-    }
-    with jsonlines.open(prompts_file_name) as reader:
-        for prompt in reader:
-            prompts["prompt_id"].append(prompt["name"])
-            prompts["prompt_text"].append(prompt["prompt"])
-            prompts["prompt_test"].append(prompt["tests"])
-            prompts["prompt_stop_tokens"].append(prompt["stop_tokens"])
-    
-    promt_id_ints = [int(i.split('_')[1]) for i in prompts["prompt_id"]]
-    sort_indices = np.argsort(promt_id_ints)
-
-    for key in prompts:
-        prompts[key] = [prompts[key][i] for i in sort_indices]
-
-    return prompts
\ No newline at end of file
diff --git a/spaces/andresgtn/bean-leaf-health-classifier/app.py b/spaces/andresgtn/bean-leaf-health-classifier/app.py
deleted file mode 100644
index 1414dcf281154c5aa7b0b74d1c1ce06767eb017a..0000000000000000000000000000000000000000
--- a/spaces/andresgtn/bean-leaf-health-classifier/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import datasets
-from transformers import AutoFeatureExtractor, AutoModelForImageClassification
-import numpy as np
-import gradio as gr
-import torch
-
-dataset = datasets.load_dataset("beans") # This should be the same as the first line of Python code in this Colab notebook
-
-extractor = AutoFeatureExtractor.from_pretrained("andresgtn/vit-base-bean-health-classifier")
-model = AutoModelForImageClassification.from_pretrained("andresgtn/vit-base-bean-health-classifier")
-
-# add to cuda?
-#model.eval()
-#model.to(device)
-
-labels = dataset['train'].features['labels'].names
-
-def classify(im):
-  features = extractor(im, return_tensors='pt')
-  #features.to(device) # move to gpu as model, if available
-  with torch.no_grad():
-    logits = model(**features).logits
-  probability = torch.nn.functional.softmax(logits, dim=-1)
-  #probs = probability[0].to('cpu').detach().numpy()
-  probs = probability[0].detach().numpy()
-  confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
-  return confidences
-
-#interface = gr.Interface(classify, gr.Image(shape=(200, 200)), 'text')
-
-sample_images=[['https://s3.amazonaws.com/moonup/production/uploads/1663933284359-611f9702593efbee33a4f7c9.png'],
-['https://s3.amazonaws.com/moonup/production/uploads/1663933284374-611f9702593efbee33a4f7c9.png'],
-['https://s3.amazonaws.com/moonup/production/uploads/1663933284412-611f9702593efbee33a4f7c9.png']]
-
-title = "Bean leaf disease classifier"
-description = "Upload an image of a bean leaf to find out if it is diseased"
-interface = gr.Interface(classify, gr.Image(shape=(200, 200)), 'label',
-                         examples=sample_images, title=title, description=description)
-
-#demo.launch()
-interface.launch(debug=False)
\ No newline at end of file
diff --git a/spaces/arihantvyavhare/device_detector_img2txt/tips.py b/spaces/arihantvyavhare/device_detector_img2txt/tips.py
deleted file mode 100644
index 43a121e2576c829197e09fc37ce6616fb5f02986..0000000000000000000000000000000000000000
--- a/spaces/arihantvyavhare/device_detector_img2txt/tips.py
+++ /dev/null
@@ -1,28 +0,0 @@
-
-# %%
-# Tips:
-## downloading images using duckduckgo and fastbook
-#* 
-#* test
-
-# %%
-from fastbook import *
-
-query = "joker"
-
-urls = search_images_ddg(query, max_images = 5 )
-
-len(urls)
-
-# for url in urls : print(url)
-# %%
-for i,url in enumerate(urls):
-    download_url(url,f"data/{query}/{query}_{i+1}.jpg")
-# %%
-import shutil 
-
-shutil.rmtree(f"data/{query}")
-
-
-
-
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/tortoise/vocoder.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/tortoise/vocoder.py
deleted file mode 100644
index 47365eb58d4d8ab563fe4785fc99cb4f5493b37f..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/tortoise/vocoder.py
+++ /dev/null
@@ -1,401 +0,0 @@
-import json
-from dataclasses import dataclass
-from enum import Enum
-from typing import Callable, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-MAX_WAV_VALUE = 32768.0
-
-
-class KernelPredictor(torch.nn.Module):
-    """Kernel predictor for the location-variable convolutions"""
-
-    def __init__(
-        self,
-        cond_channels,
-        conv_in_channels,
-        conv_out_channels,
-        conv_layers,
-        conv_kernel_size=3,
-        kpnet_hidden_channels=64,
-        kpnet_conv_size=3,
-        kpnet_dropout=0.0,
-        kpnet_nonlinear_activation="LeakyReLU",
-        kpnet_nonlinear_activation_params={"negative_slope": 0.1},
-    ):
-        """
-        Args:
-            cond_channels (int): number of channel for the conditioning sequence,
-            conv_in_channels (int): number of channel for the input sequence,
-            conv_out_channels (int): number of channel for the output sequence,
-            conv_layers (int): number of layers
-        """
-        super().__init__()
-
-        self.conv_in_channels = conv_in_channels
-        self.conv_out_channels = conv_out_channels
-        self.conv_kernel_size = conv_kernel_size
-        self.conv_layers = conv_layers
-
-        kpnet_kernel_channels = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers  # l_w
-        kpnet_bias_channels = conv_out_channels * conv_layers  # l_b
-
-        self.input_conv = nn.Sequential(
-            nn.utils.weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)),
-            getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-        )
-
-        self.residual_convs = nn.ModuleList()
-        padding = (kpnet_conv_size - 1) // 2
-        for _ in range(3):
-            self.residual_convs.append(
-                nn.Sequential(
-                    nn.Dropout(kpnet_dropout),
-                    nn.utils.weight_norm(
-                        nn.Conv1d(
-                            kpnet_hidden_channels,
-                            kpnet_hidden_channels,
-                            kpnet_conv_size,
-                            padding=padding,
-                            bias=True,
-                        )
-                    ),
-                    getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-                    nn.utils.weight_norm(
-                        nn.Conv1d(
-                            kpnet_hidden_channels,
-                            kpnet_hidden_channels,
-                            kpnet_conv_size,
-                            padding=padding,
-                            bias=True,
-                        )
-                    ),
-                    getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
-                )
-            )
-        self.kernel_conv = nn.utils.weight_norm(
-            nn.Conv1d(
-                kpnet_hidden_channels,
-                kpnet_kernel_channels,
-                kpnet_conv_size,
-                padding=padding,
-                bias=True,
-            )
-        )
-        self.bias_conv = nn.utils.weight_norm(
-            nn.Conv1d(
-                kpnet_hidden_channels,
-                kpnet_bias_channels,
-                kpnet_conv_size,
-                padding=padding,
-                bias=True,
-            )
-        )
-
-    def forward(self, c):
-        """
-        Args:
-            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
-        """
-        batch, _, cond_length = c.shape
-        c = self.input_conv(c)
-        for residual_conv in self.residual_convs:
-            residual_conv.to(c.device)
-            c = c + residual_conv(c)
-        k = self.kernel_conv(c)
-        b = self.bias_conv(c)
-        kernels = k.contiguous().view(
-            batch,
-            self.conv_layers,
-            self.conv_in_channels,
-            self.conv_out_channels,
-            self.conv_kernel_size,
-            cond_length,
-        )
-        bias = b.contiguous().view(
-            batch,
-            self.conv_layers,
-            self.conv_out_channels,
-            cond_length,
-        )
-
-        return kernels, bias
-
-    def remove_weight_norm(self):
-        nn.utils.remove_weight_norm(self.input_conv[0])
-        nn.utils.remove_weight_norm(self.kernel_conv)
-        nn.utils.remove_weight_norm(self.bias_conv)
-        for block in self.residual_convs:
-            nn.utils.remove_weight_norm(block[1])
-            nn.utils.remove_weight_norm(block[3])
-
-
-class LVCBlock(torch.nn.Module):
-    """the location-variable convolutions"""
-
-    def __init__(
-        self,
-        in_channels,
-        cond_channels,
-        stride,
-        dilations=[1, 3, 9, 27],
-        lReLU_slope=0.2,
-        conv_kernel_size=3,
-        cond_hop_length=256,
-        kpnet_hidden_channels=64,
-        kpnet_conv_size=3,
-        kpnet_dropout=0.0,
-    ):
-        super().__init__()
-
-        self.cond_hop_length = cond_hop_length
-        self.conv_layers = len(dilations)
-        self.conv_kernel_size = conv_kernel_size
-
-        self.kernel_predictor = KernelPredictor(
-            cond_channels=cond_channels,
-            conv_in_channels=in_channels,
-            conv_out_channels=2 * in_channels,
-            conv_layers=len(dilations),
-            conv_kernel_size=conv_kernel_size,
-            kpnet_hidden_channels=kpnet_hidden_channels,
-            kpnet_conv_size=kpnet_conv_size,
-            kpnet_dropout=kpnet_dropout,
-            kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope},
-        )
-
-        self.convt_pre = nn.Sequential(
-            nn.LeakyReLU(lReLU_slope),
-            nn.utils.weight_norm(
-                nn.ConvTranspose1d(
-                    in_channels,
-                    in_channels,
-                    2 * stride,
-                    stride=stride,
-                    padding=stride // 2 + stride % 2,
-                    output_padding=stride % 2,
-                )
-            ),
-        )
-
-        self.conv_blocks = nn.ModuleList()
-        for dilation in dilations:
-            self.conv_blocks.append(
-                nn.Sequential(
-                    nn.LeakyReLU(lReLU_slope),
-                    nn.utils.weight_norm(
-                        nn.Conv1d(
-                            in_channels,
-                            in_channels,
-                            conv_kernel_size,
-                            padding=dilation * (conv_kernel_size - 1) // 2,
-                            dilation=dilation,
-                        )
-                    ),
-                    nn.LeakyReLU(lReLU_slope),
-                )
-            )
-
-    def forward(self, x, c):
-        """forward propagation of the location-variable convolutions.
-        Args:
-            x (Tensor): the input sequence (batch, in_channels, in_length)
-            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
-
-        Returns:
-            Tensor: the output sequence (batch, in_channels, in_length)
-        """
-        _, in_channels, _ = x.shape  # (B, c_g, L')
-
-        x = self.convt_pre(x)  # (B, c_g, stride * L')
-        kernels, bias = self.kernel_predictor(c)
-
-        for i, conv in enumerate(self.conv_blocks):
-            output = conv(x)  # (B, c_g, stride * L')
-
-            k = kernels[:, i, :, :, :, :]  # (B, 2 * c_g, c_g, kernel_size, cond_length)
-            b = bias[:, i, :, :]  # (B, 2 * c_g, cond_length)
-
-            output = self.location_variable_convolution(
-                output, k, b, hop_size=self.cond_hop_length
-            )  # (B, 2 * c_g, stride * L'): LVC
-            x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh(
-                output[:, in_channels:, :]
-            )  # (B, c_g, stride * L'): GAU
-
-        return x
-
-    def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256):
-        """perform location-variable convolution operation on the input sequence (x) using the local convolution kernl.
-        Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100.
-        Args:
-            x (Tensor): the input sequence (batch, in_channels, in_length).
-            kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
-            bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
-            dilation (int): the dilation of convolution.
-            hop_size (int): the hop_size of the conditioning sequence.
-        Returns:
-            (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
-        """
-        batch, _, in_length = x.shape
-        batch, _, out_channels, kernel_size, kernel_length = kernel.shape
-        assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched"
-
-        padding = dilation * int((kernel_size - 1) / 2)
-        x = F.pad(x, (padding, padding), "constant", 0)  # (batch, in_channels, in_length + 2*padding)
-        x = x.unfold(2, hop_size + 2 * padding, hop_size)  # (batch, in_channels, kernel_length, hop_size + 2*padding)
-
-        if hop_size < dilation:
-            x = F.pad(x, (0, dilation), "constant", 0)
-        x = x.unfold(
-            3, dilation, dilation
-        )  # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation)
-        x = x[:, :, :, :, :hop_size]
-        x = x.transpose(3, 4)  # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation)
-        x = x.unfold(4, kernel_size, 1)  # (batch, in_channels, kernel_length, dilation, _, kernel_size)
-
-        o = torch.einsum("bildsk,biokl->bolsd", x, kernel)
-        o = o.to(memory_format=torch.channels_last_3d)
-        bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d)
-        o = o + bias
-        o = o.contiguous().view(batch, out_channels, -1)
-
-        return o
-
-    def remove_weight_norm(self):
-        self.kernel_predictor.remove_weight_norm()
-        nn.utils.remove_weight_norm(self.convt_pre[1])
-        for block in self.conv_blocks:
-            nn.utils.remove_weight_norm(block[1])
-
-
-class UnivNetGenerator(nn.Module):
-    """
-    UnivNet Generator
-
-    Originally from https://github.com/mindslab-ai/univnet/blob/master/model/generator.py.
-    """
-
-    def __init__(
-        self,
-        noise_dim=64,
-        channel_size=32,
-        dilations=[1, 3, 9, 27],
-        strides=[8, 8, 4],
-        lReLU_slope=0.2,
-        kpnet_conv_size=3,
-        # Below are MEL configurations options that this generator requires.
-        hop_length=256,
-        n_mel_channels=100,
-    ):
-        super(UnivNetGenerator, self).__init__()
-        self.mel_channel = n_mel_channels
-        self.noise_dim = noise_dim
-        self.hop_length = hop_length
-        channel_size = channel_size
-        kpnet_conv_size = kpnet_conv_size
-
-        self.res_stack = nn.ModuleList()
-        hop_length = 1
-        for stride in strides:
-            hop_length = stride * hop_length
-            self.res_stack.append(
-                LVCBlock(
-                    channel_size,
-                    n_mel_channels,
-                    stride=stride,
-                    dilations=dilations,
-                    lReLU_slope=lReLU_slope,
-                    cond_hop_length=hop_length,
-                    kpnet_conv_size=kpnet_conv_size,
-                )
-            )
-
-        self.conv_pre = nn.utils.weight_norm(nn.Conv1d(noise_dim, channel_size, 7, padding=3, padding_mode="reflect"))
-
-        self.conv_post = nn.Sequential(
-            nn.LeakyReLU(lReLU_slope),
-            nn.utils.weight_norm(nn.Conv1d(channel_size, 1, 7, padding=3, padding_mode="reflect")),
-            nn.Tanh(),
-        )
-
-    def forward(self, c, z):
-        """
-        Args:
-            c (Tensor): the conditioning sequence of mel-spectrogram (batch, mel_channels, in_length)
-            z (Tensor): the noise sequence (batch, noise_dim, in_length)
-
-        """
-        z = self.conv_pre(z)  # (B, c_g, L)
-
-        for res_block in self.res_stack:
-            res_block.to(z.device)
-            z = res_block(z, c)  # (B, c_g, L * s_0 * ... * s_i)
-
-        z = self.conv_post(z)  # (B, 1, L * 256)
-
-        return z
-
-    def eval(self, inference=False):
-        super(UnivNetGenerator, self).eval()
-        # don't remove weight norm while validation in training loop
-        if inference:
-            self.remove_weight_norm()
-
-    def remove_weight_norm(self):
-        nn.utils.remove_weight_norm(self.conv_pre)
-
-        for layer in self.conv_post:
-            if len(layer.state_dict()) != 0:
-                nn.utils.remove_weight_norm(layer)
-
-        for res_block in self.res_stack:
-            res_block.remove_weight_norm()
-
-    def inference(self, c, z=None):
-        # pad input mel with zeros to cut artifact
-        # see https://github.com/seungwonpark/melgan/issues/8
-        zero = torch.full((c.shape[0], self.mel_channel, 10), -11.5129).to(c.device)
-        mel = torch.cat((c, zero), dim=2)
-
-        if z is None:
-            z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device)
-
-        audio = self.forward(mel, z)
-        audio = audio[:, :, : -(self.hop_length * 10)]
-        audio = audio.clamp(min=-1, max=1)
-        return audio
-
-
-@dataclass
-class VocType:
-    constructor: Callable[[], nn.Module]
-    model_path: str
-    subkey: Optional[str] = None
-
-    def optionally_index(self, model_dict):
-        if self.subkey is not None:
-            return model_dict[self.subkey]
-        return model_dict
-
-
-class VocConf(Enum):
-    Univnet = VocType(UnivNetGenerator, "vocoder.pth", "model_g")
-
-
-if __name__ == "__main__":
-    model = UnivNetGenerator()
-
-    c = torch.randn(3, 100, 10)
-    z = torch.randn(3, 64, 10)
-    print(c.shape)
-
-    y = model(c, z)
-    print(y.shape)
-    assert y.shape == torch.Size([3, 1, 2560])
-
-    pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    print(pytorch_total_params)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/docs/source/training_a_model.md b/spaces/artificialguybr/video-dubbing/TTS/docs/source/training_a_model.md
deleted file mode 100644
index 989a57042abf83e89206f47a1bbbcb3e258224d0..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/docs/source/training_a_model.md
+++ /dev/null
@@ -1,146 +0,0 @@
-# Training a Model
-
-1. Decide the model you want to use.
-
-    Each model has a different set of pros and cons that define the run-time efficiency and the voice quality. It is up to you to decide what model serves your needs. Other than referring to the papers, one easy way is to test the 🐸TTS
-    community models and see how fast and good each of the models. Or you can start a discussion on our communication channels.
-
-2. Understand the configuration, its fields and values.
-
-    For instance, if you want to train a `Tacotron` model then see the `TacotronConfig` class and make sure you understand it.
-
-3. Check the recipes.
-
-    Recipes are located under `TTS/recipes/`. They do not promise perfect models but they provide a good start point for
-    `Nervous Beginners`.
-    A recipe for `GlowTTS` using `LJSpeech` dataset looks like below. Let's be creative and call this `train_glowtts.py`.
-
-    ```{literalinclude} ../../recipes/ljspeech/glow_tts/train_glowtts.py
-    ```
-
-    You need to change fields of the `BaseDatasetConfig` to match your dataset and then update `GlowTTSConfig`
-    fields as you need.
-
- 4. Run the training.
-
-    ```bash
-    $ CUDA_VISIBLE_DEVICES="0" python train_glowtts.py
-    ```
-
-    Notice that we set the GPU for the training by `CUDA_VISIBLE_DEVICES` environment variable.
-    To see available GPUs on your system, you can use `nvidia-smi` command on the terminal.
-
-    If you like to run a multi-gpu training using DDP back-end,
-
-    ```bash
-    $ CUDA_VISIBLE_DEVICES="0, 1, 2" python -m trainer.distribute --script <path_to_your_script>/train_glowtts.py
-    ```
-
-    The example above runs a multi-gpu training using GPUs `0, 1, 2`.
-
-    Beginning of a training log looks like this:
-
-    ```console
-    > Experiment folder: /your/output_path/-Juni-23-2021_02+52-78899209
-    > Using CUDA:  True
-    > Number of GPUs:  1
-    > Setting up Audio Processor...
-    | > sample_rate:22050
-    | > resample:False
-    | > num_mels:80
-    | > min_level_db:-100
-    | > frame_shift_ms:None
-    | > frame_length_ms:None
-    | > ref_level_db:20
-    | > fft_size:1024
-    | > power:1.5
-    | > preemphasis:0.0
-    | > griffin_lim_iters:60
-    | > signal_norm:True
-    | > symmetric_norm:True
-    | > mel_fmin:0
-    | > mel_fmax:None
-    | > spec_gain:20.0
-    | > stft_pad_mode:reflect
-    | > max_norm:4.0
-    | > clip_norm:True
-    | > do_trim_silence:True
-    | > trim_db:45
-    | > do_sound_norm:False
-    | > stats_path:None
-    | > base:10
-    | > hop_length:256
-    | > win_length:1024
-    | > Found 13100 files in /your/dataset/path/ljspeech/LJSpeech-1.1
-    > Using model: glow_tts
-
-    > Model has 28356129 parameters
-
-    > EPOCH: 0/1000
-
-    > DataLoader initialization
-    | > Use phonemes: False
-    | > Number of instances : 12969
-    | > Max length sequence: 187
-    | > Min length sequence: 5
-    | > Avg length sequence: 98.3403500655409
-    | > Num. instances discarded by max-min (max=500, min=3) seq limits: 0
-    | > Batch group size: 0.
-
-    > TRAINING (2021-06-23 14:52:54)
-
-    --> STEP: 0/405 -- GLOBAL_STEP: 0
-        | > loss: 2.34670
-        | > log_mle: 1.61872
-        | > loss_dur: 0.72798
-        | > align_error: 0.52744
-        | > current_lr: 2.5e-07
-        | > grad_norm: 5.036039352416992
-        | > step_time: 5.8815
-        | > loader_time: 0.0065
-    ...
-    ```
-
-5. Run the Tensorboard.
-
-    ```bash
-    $ tensorboard --logdir=<path to your training directory>
-    ```
-
-6. Monitor the training progress.
-
-    On the terminal and Tensorboard, you can monitor the progress of your model. Also Tensorboard provides certain figures and sample outputs.
-
-    Note that different models have different metrics, visuals and outputs.
-
-    You should also check the [FAQ page](https://github.com/coqui-ai/TTS/wiki/FAQ) for common problems and solutions
-    that occur in a training.
-
-7. Use your best model for inference.
-
-    Use `tts` or `tts-server` commands for testing your models.
-
-    ```bash
-    $ tts --text "Text for TTS" \
-          --model_path path/to/checkpoint_x.pth \
-          --config_path path/to/config.json \
-          --out_path folder/to/save/output.wav
-    ```
-
-8. Return to the step 1 and reiterate for training a `vocoder` model.
-
-    In the example above, we trained a `GlowTTS` model, but the same workflow applies to all the other 🐸TTS models.
-
-
-# Multi-speaker Training
-
-Training a multi-speaker model is mostly the same as training a single-speaker model.
-You need to specify a couple of configuration parameters, initiate a `SpeakerManager` instance and pass it to the model.
-
-The configuration parameters define whether you want to train the model with a speaker-embedding layer or pre-computed
-d-vectors. For using d-vectors, you first need to compute the d-vectors using the `SpeakerEncoder`.
-
-The same Glow-TTS model above can be trained on a multi-speaker VCTK dataset with the script below.
-
-```{literalinclude} ../../recipes/vctk/glow_tts/train_glow_tts.py
-```
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/aux_tests/test_speaker_encoder.py b/spaces/artificialguybr/video-dubbing/TTS/tests/aux_tests/test_speaker_encoder.py
deleted file mode 100644
index 01f6118ad714a3711be6cea98647d6c57564485e..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/aux_tests/test_speaker_encoder.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import unittest
-
-import torch as T
-
-from tests import get_tests_input_path
-from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
-from TTS.encoder.models.lstm import LSTMSpeakerEncoder
-from TTS.encoder.models.resnet import ResNetSpeakerEncoder
-
-file_path = get_tests_input_path()
-
-
-class LSTMSpeakerEncoderTests(unittest.TestCase):
-    # pylint: disable=R0201
-    def test_in_out(self):
-        dummy_input = T.rand(4, 80, 20)  # B x D x T
-        dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
-        model = LSTMSpeakerEncoder(input_dim=80, proj_dim=256, lstm_dim=768, num_lstm_layers=3)
-        # computing d vectors
-        output = model.forward(dummy_input)
-        assert output.shape[0] == 4
-        assert output.shape[1] == 256
-        output = model.inference(dummy_input)
-        assert output.shape[0] == 4
-        assert output.shape[1] == 256
-        # compute d vectors by passing LSTM hidden
-        # output = model.forward(dummy_input, dummy_hidden)
-        # assert output.shape[0] == 4
-        # assert output.shape[1] == 20
-        # assert output.shape[2] == 256
-        # check normalization
-        output_norm = T.nn.functional.normalize(output, dim=1, p=2)
-        assert_diff = (output_norm - output).sum().item()
-        assert output.type() == "torch.FloatTensor"
-        assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
-        # compute d for a given batch
-        dummy_input = T.rand(1, 80, 240)  # B x T x D
-        output = model.compute_embedding(dummy_input, num_frames=160, num_eval=5)
-        assert output.shape[0] == 1
-        assert output.shape[1] == 256
-        assert len(output.shape) == 2
-
-
-class ResNetSpeakerEncoderTests(unittest.TestCase):
-    # pylint: disable=R0201
-    def test_in_out(self):
-        dummy_input = T.rand(4, 80, 20)  # B x D x T
-        dummy_hidden = [T.rand(2, 4, 128), T.rand(2, 4, 128)]
-        model = ResNetSpeakerEncoder(input_dim=80, proj_dim=256)
-        # computing d vectors
-        output = model.forward(dummy_input)
-        assert output.shape[0] == 4
-        assert output.shape[1] == 256
-        output = model.forward(dummy_input, l2_norm=True)
-        assert output.shape[0] == 4
-        assert output.shape[1] == 256
-
-        # check normalization
-        output_norm = T.nn.functional.normalize(output, dim=1, p=2)
-        assert_diff = (output_norm - output).sum().item()
-        assert output.type() == "torch.FloatTensor"
-        assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
-        # compute d for a given batch
-        dummy_input = T.rand(1, 80, 240)  # B x D x T
-        output = model.compute_embedding(dummy_input, num_frames=160, num_eval=10)
-        assert output.shape[0] == 1
-        assert output.shape[1] == 256
-        assert len(output.shape) == 2
-
-
-class GE2ELossTests(unittest.TestCase):
-    # pylint: disable=R0201
-    def test_in_out(self):
-        # check random input
-        dummy_input = T.rand(4, 5, 64)  # num_speaker x num_utterance x dim
-        loss = GE2ELoss(loss_method="softmax")
-        output = loss.forward(dummy_input)
-        assert output.item() >= 0.0
-        # check all zeros
-        dummy_input = T.ones(4, 5, 64)  # num_speaker x num_utterance x dim
-        loss = GE2ELoss(loss_method="softmax")
-        output = loss.forward(dummy_input)
-        assert output.item() >= 0.0
-        # check speaker loss with orthogonal d-vectors
-        dummy_input = T.empty(3, 64)
-        dummy_input = T.nn.init.orthogonal_(dummy_input)
-        dummy_input = T.cat(
-            [
-                dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
-                dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
-                dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
-            ]
-        )  # num_speaker x num_utterance x dim
-        loss = GE2ELoss(loss_method="softmax")
-        output = loss.forward(dummy_input)
-        assert output.item() < 0.005
-
-
-class AngleProtoLossTests(unittest.TestCase):
-    # pylint: disable=R0201
-    def test_in_out(self):
-        # check random input
-        dummy_input = T.rand(4, 5, 64)  # num_speaker x num_utterance x dim
-        loss = AngleProtoLoss()
-        output = loss.forward(dummy_input)
-        assert output.item() >= 0.0
-
-        # check all zeros
-        dummy_input = T.ones(4, 5, 64)  # num_speaker x num_utterance x dim
-        loss = AngleProtoLoss()
-        output = loss.forward(dummy_input)
-        assert output.item() >= 0.0
-
-        # check speaker loss with orthogonal d-vectors
-        dummy_input = T.empty(3, 64)
-        dummy_input = T.nn.init.orthogonal_(dummy_input)
-        dummy_input = T.cat(
-            [
-                dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
-                dummy_input[1].repeat(5, 1, 1).transpose(0, 1),
-                dummy_input[2].repeat(5, 1, 1).transpose(0, 1),
-            ]
-        )  # num_speaker x num_utterance x dim
-        loss = AngleProtoLoss()
-        output = loss.forward(dummy_input)
-        assert output.item() < 0.005
-
-
-class SoftmaxAngleProtoLossTests(unittest.TestCase):
-    # pylint: disable=R0201
-    def test_in_out(self):
-        embedding_dim = 64
-        num_speakers = 5
-        batch_size = 4
-
-        dummy_label = T.randint(low=0, high=num_speakers, size=(batch_size, num_speakers))
-        # check random input
-        dummy_input = T.rand(batch_size, num_speakers, embedding_dim)  # num_speaker x num_utterance x dim
-        loss = SoftmaxAngleProtoLoss(embedding_dim=embedding_dim, n_speakers=num_speakers)
-        output = loss.forward(dummy_input, dummy_label)
-        assert output.item() >= 0.0
-
-        # check all zeros
-        dummy_input = T.ones(batch_size, num_speakers, embedding_dim)  # num_speaker x num_utterance x dim
-        loss = SoftmaxAngleProtoLoss(embedding_dim=embedding_dim, n_speakers=num_speakers)
-        output = loss.forward(dummy_input, dummy_label)
-        assert output.item() >= 0.0
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_KMAC.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_KMAC.py
deleted file mode 100644
index 8e9bf702afd7ec18d0fdff85bf81fe5ad4e79944..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_KMAC.py
+++ /dev/null
@@ -1,346 +0,0 @@
-import unittest
-from binascii import unhexlify, hexlify
-
-from Crypto.Util.py3compat import tobytes
-from Crypto.Util.strxor import strxor_c
-from Crypto.SelfTest.st_common import list_test_cases
-
-from Crypto.Hash import KMAC128, KMAC256
-
-
-class KMACTest(unittest.TestCase):
-
-    def new(self, *args, **kwargs):
-        return self.KMAC.new(key=b'X' * (self.minimum_key_bits // 8), *args, **kwargs)
-
-    def test_new_positive(self):
-
-        key = b'X' * 32
-
-        h = self.new()
-        for new_func in self.KMAC.new, h.new:
-
-            for dbytes in range(self.minimum_bytes, 128 + 1):
-                hobj = new_func(key=key, mac_len=dbytes)
-                self.assertEqual(hobj.digest_size, dbytes)
-
-            digest1 = new_func(key=key, data=b"\x90").digest()
-            digest2 = new_func(key=key).update(b"\x90").digest()
-            self.assertEqual(digest1, digest2)
-
-            new_func(data=b"A", key=key, custom=b"g")
-
-        hobj = h.new(key=key)
-        self.assertEqual(hobj.digest_size, self.default_bytes)
-
-    def test_new_negative(self):
-
-        h = self.new()
-        for new_func in self.KMAC.new, h.new:
-            self.assertRaises(ValueError, new_func, key=b'X'*32,
-                              mac_len=0)
-            self.assertRaises(ValueError, new_func, key=b'X'*32,
-                              mac_len=self.minimum_bytes - 1)
-            self.assertRaises(TypeError, new_func,
-                              key=u"string")
-            self.assertRaises(TypeError, new_func,
-                              data=u"string")
-
-    def test_default_digest_size(self):
-        digest = self.new(data=b'abc').digest()
-        self.assertEqual(len(digest), self.default_bytes)
-
-    def test_update(self):
-        pieces = [b"\x0A" * 200, b"\x14" * 300]
-        h = self.new()
-        h.update(pieces[0]).update(pieces[1])
-        digest = h.digest()
-        h = self.new()
-        h.update(pieces[0] + pieces[1])
-        self.assertEqual(h.digest(), digest)
-
-    def test_update_negative(self):
-        h = self.new()
-        self.assertRaises(TypeError, h.update, u"string")
-
-    def test_digest(self):
-        h = self.new()
-        digest = h.digest()
-
-        # hexdigest does not change the state
-        self.assertEqual(h.digest(), digest)
-        # digest returns a byte string
-        self.assertTrue(isinstance(digest, type(b"digest")))
-
-    def test_update_after_digest(self):
-        msg = b"rrrrttt"
-
-        # Normally, update() cannot be done after digest()
-        h = self.new(mac_len=32, data=msg[:4])
-        dig1 = h.digest()
-        self.assertRaises(TypeError, h.update, dig1)
-
-    def test_hex_digest(self):
-        mac = self.new()
-        digest = mac.digest()
-        hexdigest = mac.hexdigest()
-
-        # hexdigest is equivalent to digest
-        self.assertEqual(hexlify(digest), tobytes(hexdigest))
-        # hexdigest does not change the state
-        self.assertEqual(mac.hexdigest(), hexdigest)
-        # hexdigest returns a string
-        self.assertTrue(isinstance(hexdigest, type("digest")))
-
-    def test_verify(self):
-        h = self.new()
-        mac = h.digest()
-        h.verify(mac)
-        wrong_mac = strxor_c(mac, 255)
-        self.assertRaises(ValueError, h.verify, wrong_mac)
-
-    def test_hexverify(self):
-        h = self.new()
-        mac = h.hexdigest()
-        h.hexverify(mac)
-        self.assertRaises(ValueError, h.hexverify, "4556")
-
-    def test_oid(self):
-
-        oid = "2.16.840.1.101.3.4.2." + self.oid_variant
-        h = self.new()
-        self.assertEqual(h.oid, oid)
-
-    def test_bytearray(self):
-
-        key = b'0' * 32
-        data = b"\x00\x01\x02"
-
-        # Data and key can be a bytearray (during initialization)
-        key_ba = bytearray(key)
-        data_ba = bytearray(data)
-
-        h1 = self.KMAC.new(data=data, key=key)
-        h2 = self.KMAC.new(data=data_ba, key=key_ba)
-        key_ba[:1] = b'\xFF'
-        data_ba[:1] = b'\xFF'
-
-        self.assertEqual(h1.digest(), h2.digest())
-
-        # Data can be a bytearray (during operation)
-        data_ba = bytearray(data)
-
-        h1 = self.new()
-        h2 = self.new()
-        h1.update(data)
-        h2.update(data_ba)
-        data_ba[:1] = b'\xFF'
-
-        self.assertEqual(h1.digest(), h2.digest())
-
-    def test_memoryview(self):
-
-        key = b'0' * 32
-        data = b"\x00\x01\x02"
-
-        def get_mv_ro(data):
-            return memoryview(data)
-
-        def get_mv_rw(data):
-            return memoryview(bytearray(data))
-
-        for get_mv in (get_mv_ro, get_mv_rw):
-
-            # Data and key can be a memoryview (during initialization)
-            key_mv = get_mv(key)
-            data_mv = get_mv(data)
-
-            h1 = self.KMAC.new(data=data, key=key)
-            h2 = self.KMAC.new(data=data_mv, key=key_mv)
-            if not data_mv.readonly:
-                data_mv[:1] = b'\xFF'
-                key_mv[:1] = b'\xFF'
-
-            self.assertEqual(h1.digest(), h2.digest())
-
-            # Data can be a memoryview (during operation)
-            data_mv = get_mv(data)
-
-            h1 = self.new()
-            h2 = self.new()
-            h1.update(data)
-            h2.update(data_mv)
-            if not data_mv.readonly:
-                data_mv[:1] = b'\xFF'
-
-            self.assertEqual(h1.digest(), h2.digest())
-
-
-class KMAC128Test(KMACTest):
-
-    KMAC = KMAC128
-
-    minimum_key_bits = 128
-
-    minimum_bytes = 8
-    default_bytes = 64
-
-    oid_variant = "19"
-
-
-class KMAC256Test(KMACTest):
-
-    KMAC = KMAC256
-
-    minimum_key_bits = 256
-
-    minimum_bytes = 8
-    default_bytes = 64
-
-    oid_variant = "20"
-
-
-class NISTExampleTestVectors(unittest.TestCase):
-
-    # https://csrc.nist.gov/CSRC/media/Projects/Cryptographic-Standards-and-Guidelines/documents/examples/KMAC_samples.pdf
-    test_data = [
-        (
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F",
-            "00 01 02 03",
-            "",
-            "E5 78 0B 0D 3E A6 F7 D3 A4 29 C5 70 6A A4 3A 00"
-            "FA DB D7 D4 96 28 83 9E 31 87 24 3F 45 6E E1 4E",
-            "Sample #1 NIST",
-            KMAC128
-        ),
-        (
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F",
-            "00 01 02 03",
-            "My Tagged Application",
-            "3B 1F BA 96 3C D8 B0 B5 9E 8C 1A 6D 71 88 8B 71"
-            "43 65 1A F8 BA 0A 70 70 C0 97 9E 28 11 32 4A A5",
-            "Sample #2 NIST",
-            KMAC128
-        ),
-        (
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F",
-            "00 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F"
-            "10 11 12 13 14 15 16 17 18 19 1A 1B 1C 1D 1E 1F"
-            "20 21 22 23 24 25 26 27 28 29 2A 2B 2C 2D 2E 2F"
-            "30 31 32 33 34 35 36 37 38 39 3A 3B 3C 3D 3E 3F"
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F"
-            "60 61 62 63 64 65 66 67 68 69 6A 6B 6C 6D 6E 6F"
-            "70 71 72 73 74 75 76 77 78 79 7A 7B 7C 7D 7E 7F"
-            "80 81 82 83 84 85 86 87 88 89 8A 8B 8C 8D 8E 8F"
-            "90 91 92 93 94 95 96 97 98 99 9A 9B 9C 9D 9E 9F"
-            "A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 AA AB AC AD AE AF"
-            "B0 B1 B2 B3 B4 B5 B6 B7 B8 B9 BA BB BC BD BE BF"
-            "C0 C1 C2 C3 C4 C5 C6 C7",
-            "My Tagged Application",
-            "1F 5B 4E 6C CA 02 20 9E 0D CB 5C A6 35 B8 9A 15"
-            "E2 71 EC C7 60 07 1D FD 80 5F AA 38 F9 72 92 30",
-            "Sample #3 NIST",
-            KMAC128
-        ),
-        (
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F",
-            "00 01 02 03",
-            "My Tagged Application",
-            "20 C5 70 C3 13 46 F7 03 C9 AC 36 C6 1C 03 CB 64"
-            "C3 97 0D 0C FC 78 7E 9B 79 59 9D 27 3A 68 D2 F7"
-            "F6 9D 4C C3 DE 9D 10 4A 35 16 89 F2 7C F6 F5 95"
-            "1F 01 03 F3 3F 4F 24 87 10 24 D9 C2 77 73 A8 DD",
-            "Sample #4 NIST",
-            KMAC256
-        ),
-        (
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F",
-            "00 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F"
-            "10 11 12 13 14 15 16 17 18 19 1A 1B 1C 1D 1E 1F"
-            "20 21 22 23 24 25 26 27 28 29 2A 2B 2C 2D 2E 2F"
-            "30 31 32 33 34 35 36 37 38 39 3A 3B 3C 3D 3E 3F"
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F"
-            "60 61 62 63 64 65 66 67 68 69 6A 6B 6C 6D 6E 6F"
-            "70 71 72 73 74 75 76 77 78 79 7A 7B 7C 7D 7E 7F"
-            "80 81 82 83 84 85 86 87 88 89 8A 8B 8C 8D 8E 8F"
-            "90 91 92 93 94 95 96 97 98 99 9A 9B 9C 9D 9E 9F"
-            "A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 AA AB AC AD AE AF"
-            "B0 B1 B2 B3 B4 B5 B6 B7 B8 B9 BA BB BC BD BE BF"
-            "C0 C1 C2 C3 C4 C5 C6 C7",
-            "",
-            "75 35 8C F3 9E 41 49 4E 94 97 07 92 7C EE 0A F2"
-            "0A 3F F5 53 90 4C 86 B0 8F 21 CC 41 4B CF D6 91"
-            "58 9D 27 CF 5E 15 36 9C BB FF 8B 9A 4C 2E B1 78"
-            "00 85 5D 02 35 FF 63 5D A8 25 33 EC 6B 75 9B 69",
-            "Sample #5 NIST",
-            KMAC256
-        ),
-        (
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F",
-            "00 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F"
-            "10 11 12 13 14 15 16 17 18 19 1A 1B 1C 1D 1E 1F"
-            "20 21 22 23 24 25 26 27 28 29 2A 2B 2C 2D 2E 2F"
-            "30 31 32 33 34 35 36 37 38 39 3A 3B 3C 3D 3E 3F"
-            "40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F"
-            "50 51 52 53 54 55 56 57 58 59 5A 5B 5C 5D 5E 5F"
-            "60 61 62 63 64 65 66 67 68 69 6A 6B 6C 6D 6E 6F"
-            "70 71 72 73 74 75 76 77 78 79 7A 7B 7C 7D 7E 7F"
-            "80 81 82 83 84 85 86 87 88 89 8A 8B 8C 8D 8E 8F"
-            "90 91 92 93 94 95 96 97 98 99 9A 9B 9C 9D 9E 9F"
-            "A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 AA AB AC AD AE AF"
-            "B0 B1 B2 B3 B4 B5 B6 B7 B8 B9 BA BB BC BD BE BF"
-            "C0 C1 C2 C3 C4 C5 C6 C7",
-            "My Tagged Application",
-            "B5 86 18 F7 1F 92 E1 D5 6C 1B 8C 55 DD D7 CD 18"
-            "8B 97 B4 CA 4D 99 83 1E B2 69 9A 83 7D A2 E4 D9"
-            "70 FB AC FD E5 00 33 AE A5 85 F1 A2 70 85 10 C3"
-            "2D 07 88 08 01 BD 18 28 98 FE 47 68 76 FC 89 65",
-            "Sample #6 NIST",
-            KMAC256
-        ),
-    ]
-
-    def setUp(self):
-        td = []
-        for key, data, custom, mac, text, module in self.test_data:
-            ni = (
-                unhexlify(key.replace(" ", "")),
-                unhexlify(data.replace(" ", "")),
-                custom.encode(),
-                unhexlify(mac.replace(" ", "")),
-                text,
-                module
-            )
-            td.append(ni)
-        self.test_data = td
-
-    def runTest(self):
-
-        for key, data, custom, mac, text, module in self.test_data:
-            h = module.new(data=data, key=key, custom=custom, mac_len=len(mac))
-            mac_tag = h.digest()
-            self.assertEqual(mac_tag, mac, msg=text)
-
-
-def get_tests(config={}):
-    tests = []
-
-    tests += list_test_cases(KMAC128Test)
-    tests += list_test_cases(KMAC256Test)
-    tests.append(NISTExampleTestVectors())
-
-    return tests
-
-
-if __name__ == '__main__':
-    def suite():
-        return unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/pie_chart_with_labels.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/pie_chart_with_labels.py
deleted file mode 100644
index 15a3fb188f98fa8408acc8d2df70fd53c79c4b87..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/pie_chart_with_labels.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""
-Pie Chart with Labels
----------------------
-This example shows how to layer text over arc marks (``mark_arc``) to label pie charts.
-This is adapted from a corresponding Vega-Lite Example:
-`Pie Chart with Labels <https://vega.github.io/vega-lite/examples/layer_arc_label.html>`_.
-"""
-# category: circular plots
-
-import pandas as pd
-import altair as alt
-
-source = pd.DataFrame(
-    {"category": ["a", "b", "c", "d", "e", "f"], "value": [4, 6, 10, 3, 7, 8]}
-)
-
-base = alt.Chart(source).encode(
-    theta=alt.Theta("value:Q", stack=True), color=alt.Color("category:N", legend=None)
-)
-
-pie = base.mark_arc(outerRadius=120)
-text = base.mark_text(radius=140, size=20).encode(text="category:N")
-
-pie + text
diff --git a/spaces/asd123Xiao/kafuu_chino_sovits4.0/modules/__init__.py b/spaces/asd123Xiao/kafuu_chino_sovits4.0/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ashhadahsan/summarizer-space/utils/openllmapi/exceptions.py b/spaces/ashhadahsan/summarizer-space/utils/openllmapi/exceptions.py
deleted file mode 100644
index 3f18ab071012cef47744713abc39e4a7ece7a8ee..0000000000000000000000000000000000000000
--- a/spaces/ashhadahsan/summarizer-space/utils/openllmapi/exceptions.py
+++ /dev/null
@@ -1,52 +0,0 @@
-class ModelOverloadedError(Exception):
-    """
-    HF Model Overloaded Error
-    
-    Raised when hf return response `{"error":"Model is overloaded","error_type":"overloaded"}`
-    """
-    pass
-
-
-class ChatBotInitError(Exception):
-    """
-    ChatBot Init Error
-    
-    Raised when chatbot init failed
-    """
-    pass
-
-
-class CreateConversationError(Exception):
-    """
-    Create Conversation Error
-    
-    Raised when create conversation failed
-    """
-    pass
-
-
-class InvalidConversationIDError(Exception):
-    """
-    Invalid Conversation ID Error
-    
-    Raised when using a invalid conversation id
-    """
-    pass
-
-
-class DeleteConversationError(Exception):
-    """
-    Delete Conversation Error
-    
-    Raised when delete conversation failed
-    """
-    pass
-
-
-class ChatError(Exception):
-    """
-    Chat Error
-    
-    Raised when chat failed
-    """
-    pass
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/pseudo_example.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/pseudo_example.py
deleted file mode 100644
index 7ba5014e9e8a6e71538232d86cba46e098110c0e..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/pseudo_example.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""
-This is a high-level pseudo code for grounding net. 
-
-This class needs to tokenize grounding input into gronding tokens which 
-will be used in GatedAttenion layers. 
-
-
-class PositionNet(nn.Module):
-    def __init__(self, **kwargs):
-        super().__init__()
-
-        kwargs should be defined by model.grounding_tokenizer in config yaml file.  
-
-    def forward(self, **kwargs):
-
-        kwargs should be the output of grounding_tokenizer_input network
-        
-        return grounding_tokens # with shape: Batch * Num_Of_Token* Token_Channel_Dimension
-
-
-
-"""
-
-
-# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # 
-
-
-"""
-This is a high-level pseudo code for downsampler. 
-
-This class needs to process input and output a spatial feature such that it will be 
-fed into the first conv layer. 
-
-
-class GroundingDownsampler(nn.Module):
-    def __init__(self, **kwargs):
-        super().__init__()
-
-        kwargs should be defined by model.grounding_downsampler in config yaml file.  
-
-        you MUST define self.out_dim such that Unet knows add how many extra layers
-
-
-    def forward(self, **kwargs):
-
-        kwargs should be the output of grounding_downsampler_input network
-        
-        return spatial_feature # with shape: Batch * self.out_dim * H *W (64*64 for SD)
-
-
-
-"""
\ No newline at end of file
diff --git a/spaces/avatar2k/image-ocr-ex5-multi-lingual/README.md b/spaces/avatar2k/image-ocr-ex5-multi-lingual/README.md
deleted file mode 100644
index 07782c63dd50973c8eba46048a86c8870ecdf858..0000000000000000000000000000000000000000
--- a/spaces/avatar2k/image-ocr-ex5-multi-lingual/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image Ocr Ex5 Multi Lingual
-emoji: 💩
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/GradioTextToSpeechOrImages/app.py b/spaces/awacke1/GradioTextToSpeechOrImages/app.py
deleted file mode 100644
index d9a2674be9a34f56673115ef2727395c2f83bbf4..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioTextToSpeechOrImages/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-
-fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech")
-clip = gr.Interface.load("spaces/DrishtiSharma/Text-to-Image-search-using-CLIP")
-
-
-def text2speech(text):
-    return fastspeech(text)
-
-
-def text2image(text):
-    image = clip(text)[0]
-    return gr.processing_utils.decode_base64_to_image(image)
-
-
-block = gr.Blocks()
-
-with block:
-    text = gr.inputs.Textbox(placeholder="Try writing something..")
-
-    with gr.Column():
-        with gr.Row():
-            get_audio = gr.Button("generate audio")
-            get_image = gr.Button("generate image")
-        with gr.Row():
-            speech = gr.outputs.Audio()
-            image = gr.outputs.Image()
-
-
-    get_audio.click(text2speech, inputs=text, outputs=speech)
-    get_image.click(text2image, inputs=text, outputs=image)
-
-block.launch()
\ No newline at end of file
diff --git a/spaces/awacke1/Memory-Streamlit/app.py b/spaces/awacke1/Memory-Streamlit/app.py
deleted file mode 100644
index 24427a61d379cace7147e5ebec44051bb1e9a8c7..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Memory-Streamlit/app.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# This streamlit demonstration is meant to simulate two types of memory within cognitive architecture:
-# Each time we remember a moment, we construct it anew.
-# We use the building blocks of Episodic Memory which is recall of feelings of being somewhere.
-# We also use Semantic Memory which is concrete knowledge about our world and about our personal history.
-# With Episodic Memory, its purpose is to provide us with coherence connecting events.  In Episodic Memory, accuracy is less important than consistency.
-# In order to have a strong sense of self, it is important that our memories fit with our beliefs and feelings about ourselves.
-
-import time
-import re
-import pandas as pd
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel
-from tokenizers import Tokenizer, AddedToken
-import streamlit as st
-from st_click_detector import click_detector
-
-
-# Initialization
-if 'key' not in st.session_state:
-    st.session_state['key'] = 'value'
-
-# Session State also supports attribute based syntax
-if 'key' not in st.session_state:
-    st.session_state.key = 'value'
-    
-# Read
-st.write(st.session_state.key)
-
-# Outputs: value
-
-st.session_state.key = 'value2'     # Attribute API
-st.session_state['key'] = 'value2'  # Dictionary like API
-
-st.write(st.session_state)
-
-# With magic:
-st.session_state
-
-
-#st.write(st.session_state['value'])
-# Throws an exception!
-
-# Delete a single key-value pair
-#del st.session_state[key]
-
-# Delete all the items in Session state
-for key in st.session_state.keys():
-    del st.session_state[key]
-    
-
-st.text_input("Your name", key="name")
-
-# This exists now:
-st.session_state.name
-
-def form_callback():
-    st.write(st.session_state.my_slider)
-    st.write(st.session_state.my_checkbox)
-
-with st.form(key='my_form'):
-    slider_input = st.slider('My slider', 0, 10, 5, key='my_slider')
-    checkbox_input = st.checkbox('Yes or No', key='my_checkbox')
-    submit_button = st.form_submit_button(label='Submit', on_click=form_callback)
-    
-slider = st.slider(
-    label='My Slider', min_value=1,
-    max_value=10, value=5, key='my_slider')
-
-#st.session_state.my_slider = 7
-
-# Throws an exception!
-
-#st.session_state.my_slider = 7
-
-slider = st.slider(
-    label='Choose a Value', min_value=1,
-    max_value=10, value=5, key='my_slider')
-    
-if 'my_button' not in st.session_state:
-    st.session_state.my_button = True
-
-#st.button('My button', key='my_button')
-
-# Throws an exception!
-
diff --git a/spaces/awacke1/Prompt-Refinery-Text-to-Image-Generation/backup-app.py b/spaces/awacke1/Prompt-Refinery-Text-to-Image-Generation/backup-app.py
deleted file mode 100644
index 2fe4a1c7e9ce81b255af425c5b1b11ecaf771c8e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Prompt-Refinery-Text-to-Image-Generation/backup-app.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import gradio as gr
-import os
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-text_gen = gr.Interface.load(name="spaces/Gustavosta/MagicPrompt-Stable-Diffusion")
-stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
-
-def get_images(prompt):
-    gallery_dir = stable_diffusion(prompt, fn_index=2)
-    sd_output = [os.path.join(gallery_dir, image) for image in os.listdir(gallery_dir)]
-    
-    # Save the prompt to a text file
-    with open("prompt_log.txt", "a") as f:
-        f.write(prompt + "\n")
-    
-    return sd_output, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-def load_past_prompts():
-    # Read from the text file and return the content
-    if os.path.exists("prompt_log.txt"):
-        with open("prompt_log.txt", "r") as f:
-            return f.read()
-    else:
-        return "No past prompts."
-
-
-def get_images_old(prompt):
-    gallery_dir = stable_diffusion(prompt, fn_index=2)
-    sd_output = [os.path.join(gallery_dir, image) for image in os.listdir(gallery_dir)]
-    return sd_output, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-def get_prompts(prompt_text):
-    return text_gen(prompt_text)
-
-css = '''
-.animate-spin {
-    animation: spin 1s linear infinite;
-}
-@keyframes spin {
-    from {
-        transform: rotate(0deg);
-    }
-    to {
-        transform: rotate(360deg);
-    }
-}
-#share-btn-container {
-    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-}
-#share-btn {
-    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
-}
-#share-btn * {
-    all: unset;
-}
-#share-btn-container div:nth-child(-n+2){
-    width: auto !important;
-    min-height: 0px !important;
-}
-#share-btn-container .wrap {
-    display: none !important;
-}
-a {text-decoration-line: underline;}
-'''
-with gr.Blocks(css=css) as demo:
-    gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-            <div
-            style="
-                display: inline-flex;
-                align-items: center;
-                gap: 0.8rem;
-                font-size: 1.75rem;
-            "
-            >
-            <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                Prompt Refinery
-            </h1>
-            </div>
-            <p style="margin-bottom: 10px; font-size: 94%">
-           🏭 Prompt Refinery generates variations of your prompt using <a href="https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion" target="_blank">MagicPrompt and Stable Diffusion</a>
-            </p>
-        </div>""")
-    with gr.Row():
-      with gr.Column():
-          input_text = gr.Textbox(label="Input text prompt", 
-                                lines=2, elem_id="input-text")
-          with gr.Row():
-            see_prompts = gr.Button("✍️Expand my prompts")
-
-      with gr.Column():
-        text_output = gr.Textbox(
-                                label="🏭 Expanded text prompts", 
-                                lines=8,
-                                elem_id="translated"
-                            )
-        with gr.Row():
-            diffuse_btn = gr.Button(value="🏭 Render Images for My Prompts")
-      with gr.Column(elem_id="generated-gallery"):
-        sd_output = gr.Gallery().style(grid=2, height="auto")
-        with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
-            loading_icon = gr.HTML(loading_icon_html, visible=False)
-    see_prompts.click(get_prompts, 
-                            inputs = [input_text], 
-                            outputs = [
-                                text_output
-                            ], api_name="TextAI")
-    diffuse_btn.click(get_images, 
-                          inputs = [
-                              text_output
-                              ], 
-                          outputs = [sd_output, community_icon, loading_icon], api_name="TextAI2")
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/awacke1/Requests-Interpret/README.md b/spaces/awacke1/Requests-Interpret/README.md
deleted file mode 100644
index 1520fc6a837c34587d31e72d69b4f0737aac62c0..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Requests-Interpret/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Requests Interpret
-emoji: 💻
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/banana-projects/coref/js-src/Coref.ts b/spaces/banana-projects/coref/js-src/Coref.ts
deleted file mode 100644
index f672da6349e9b4efac6a74094b3229d60a63d2bc..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/coref/js-src/Coref.ts
+++ /dev/null
@@ -1,657 +0,0 @@
-declare type MentionType= "PRONOMINAL" | "NOMINAL" | "PROPER" | "LIST";
-
-declare interface Mention {
-	index: number;
-	start: number;
-	end:   number;
-	startToken: number;
-	endToken:   number;
-	utterance:  number;
-	type:  MentionType;
-	text:  string;
-}
-
-declare interface Coreference {
-	original: string;
-	resolved: string;
-}
-
-declare interface SpansEmbeddings {
-	Doc: string;
-	Mention: string[];
-	MentionLeft: string[];
-	MentionRight: string[];
-	Sentence: string[];
-}
-
-declare interface WordsEmbeddings {
-	MentionFirstWord: string;
-	MentionHead: string;
-	MentionLastWord: string;
-	MentionRootHead: string;
-	NextWord: string;
-	PreviousWord: string;
-	SecondNextWord: string;
-	SecondPreviousWord: string;
-}
-
-declare interface MentionFeatures {
-	MentionLength: number;
-	MentionNormLocation: number;
-	MentionType: string;
-	IsMentionNested: number;
-	DocGenre?:	string | null;
-}
-
-declare interface MentionsPairFeatures {
-	SameSpeaker: number;
-	AntMatchMentionSpeaker: number;
-	MentionMatchSpeaker: number;
-	HeadsAgree: number;
-	ExactStringMatch: number;
-	RelaxedStringMatch: number;
-	SentenceDistance: number;
-	MentionDistance: number;
-	Overlapping: number;
-	M1Features: MentionFeatures;
-	M2Features: MentionFeatures;
-	DocGenre: string | null;
-}
-
-declare interface SingleFeatures {
-	features: MentionFeatures;
-	spansEmbeddings: SpansEmbeddings;
-	wordsEmbeddings: WordsEmbeddings;
-}
-
-declare interface PairFeatures {
-	pairFeatures: MentionsPairFeatures;
-	antecedentSpansEmbeddings: SpansEmbeddings;
-	antecedentWordsEmbeddings: WordsEmbeddings;
-	mentionSpansEmbeddings: SpansEmbeddings;
-	mentionWordsEmbeddings: WordsEmbeddings;
-}
-
-declare interface CorefResponse {
-	cleanedText: string;
-	corefResText: string;
-	coreferences: Coreference[];
-	mentions: Mention[];
-	singleScores: { [id: number]: number | null };               /// Is this mention likely to be a single mention (w/o any corefs). `id` is a Mention's `index`
-	pairScores: { [id: number]: { [id: number]: number } };      /// Pair-wise score, in `{ from: { to: ... } }` format. Non-directed arcs.
-	                                                             /// Single scores are to be compared to the set of pairScores (for the same mention).
-	                                                             /// If it's higher than every pair score, it's a single mention.
-	cleanedContext: string;                                      /// Cleaned version of the context.
-	// singleFeatures: { [id: number]: SingleFeatures | null };
-	// pairFeatures: { [id: number]: { [id: number]: PairFeatures } };
-	isResolved: boolean;
-}
-
-
-const COREF_HARDCODED_OUTPUTS = new Map<string, CorefResponse>([
-	[
-		`I love my father and my mother. They work hard. She is always nice but he is sometimes rude.`,
-		{
-			cleanedText:
-				"I love my father and my mother. They work hard. She is always nice but he is sometimes rude.",
-			corefResText:
-				"I love my father and my mother. They work hard. my mother is always nice but he is sometimes rude.",
-			coreferences: [
-				{
-					resolved: "my mother",
-					original: "She",
-				},
-			],
-			mentions: [
-				{
-					index: 0,
-					type: "PRONOMINAL",
-					end: 1,
-					start: 0,
-					startToken: 0,
-					text: "I",
-					utterance: 0,
-					endToken: 1,
-				},
-				{
-					index: 1,
-					type: "PRONOMINAL",
-					end: 9,
-					start: 7,
-					startToken: 2,
-					text: "my",
-					utterance: 0,
-					endToken: 3,
-				},
-				{
-					index: 2,
-					type: "LIST",
-					end: 30,
-					start: 7,
-					startToken: 2,
-					text: "my father and my mother",
-					utterance: 0,
-					endToken: 7,
-				},
-				{
-					index: 3,
-					type: "NOMINAL",
-					end: 16,
-					start: 7,
-					startToken: 2,
-					text: "my father",
-					utterance: 0,
-					endToken: 4,
-				},
-				{
-					index: 4,
-					type: "PRONOMINAL",
-					end: 23,
-					start: 21,
-					startToken: 5,
-					text: "my",
-					utterance: 0,
-					endToken: 6,
-				},
-				{
-					index: 5,
-					type: "NOMINAL",
-					end: 30,
-					start: 21,
-					startToken: 5,
-					text: "my mother",
-					utterance: 0,
-					endToken: 7,
-				},
-				{
-					index: 6,
-					type: "PRONOMINAL",
-					end: 36,
-					start: 32,
-					startToken: 8,
-					text: "They",
-					utterance: 0,
-					endToken: 9,
-				},
-				{
-					index: 7,
-					type: "PRONOMINAL",
-					end: 51,
-					start: 48,
-					startToken: 12,
-					text: "She",
-					utterance: 0,
-					endToken: 13,
-				},
-				{
-					index: 8,
-					type: "PRONOMINAL",
-					end: 73,
-					start: 71,
-					startToken: 17,
-					text: "he",
-					utterance: 0,
-					endToken: 18,
-				},
-			],
-			singleScores: {
-				"0": null,
-				"1": -0.5316791573216993,
-				"2": 1.5312658152183065,
-				"3": 1.555544240226482,
-				"4": -1.3024868053674725,
-				"5": 1.8934082334098847,
-				"6": -0.9070692483062055,
-				"7": 0.052800267274213275,
-				"8": -0.14679673122527748,
-			},
-			pairScores: {
-				"0": {},
-				"1": {
-					"0": 15.268471128411697,
-				},
-				"2": {
-					"0": -4.4230603182609896,
-					"1": -3.382446088190441,
-				},
-				"3": {
-					"0": -4.2304546215104555,
-					"1": -3.5681677896088786,
-					"2": -1.9470202037608262,
-				},
-				"4": {
-					"0": 11.654522570777317,
-					"1": 13.455601870537567,
-					"2": -3.218918301345336,
-					"3": -3.882381584104524,
-				},
-				"5": {
-					"0": -3.9280501278811983,
-					"1": -4.426880262361277,
-					"2": -1.7714693884323367,
-					"3": -2.722532370602323,
-					"4": -3.290164176455163,
-				},
-				"6": {
-					"0": -4.492101447800931,
-					"1": -4.636904674331316,
-					"2": 3.1158072056943666,
-					"3": -2.7375757747875573,
-					"4": -4.689981185699828,
-					"5": -2.6728186848475537,
-				},
-				"7": {
-					"0": -3.197215354228037,
-					"1": -3.538538702704479,
-					"2": -0.02408947507481729,
-					"3": 0.3052410603657605,
-					"4": -3.519641485034609,
-					"5": 1.8101046215415115,
-					"6": -3.1353342036917917,
-				},
-				"8": {
-					"0": -0.08532621450323319,
-					"1": 5.371002989344198,
-					"2": -1.4091179987286686,
-					"3": 3.152921411948177,
-					"4": 2.268706305216419,
-					"5": -2.340743897439996,
-					"6": -2.8835496283480597,
-					"7": -3.4832126005315334,
-				},
-			},
-			cleanedContext: "",
-			isResolved: true,
-		},
-	],
-	[
-		`My sister is swimming with her classmates. They are not bad, but she is better. I love watching her swim.`,
-		{
-			cleanedText:
-				"My sister is swimming with her classmates. They are not bad, but she is better. I love watching her swim.",
-			corefResText:
-				"My sister is swimming with my sister classmates. her classmates are not bad, but my sister is better. I love watching my sister swim.",
-			coreferences: [
-				{
-					resolved: "My sister",
-					original: "she",
-				},
-				{
-					resolved: "My sister",
-					original: "her",
-				},
-				{
-					resolved: "her classmates",
-					original: "They",
-				},
-				{
-					resolved: "My sister",
-					original: "her",
-				},
-			],
-			mentions: [
-				{
-					index: 0,
-					type: "PRONOMINAL",
-					end: 2,
-					start: 0,
-					startToken: 0,
-					text: "My",
-					utterance: 0,
-					endToken: 1,
-				},
-				{
-					index: 1,
-					type: "NOMINAL",
-					end: 9,
-					start: 0,
-					startToken: 0,
-					text: "My sister",
-					utterance: 0,
-					endToken: 2,
-				},
-				{
-					index: 2,
-					type: "PRONOMINAL",
-					end: 30,
-					start: 27,
-					startToken: 5,
-					text: "her",
-					utterance: 0,
-					endToken: 6,
-				},
-				{
-					index: 3,
-					type: "NOMINAL",
-					end: 41,
-					start: 27,
-					startToken: 5,
-					text: "her classmates",
-					utterance: 0,
-					endToken: 7,
-				},
-				{
-					index: 4,
-					type: "PRONOMINAL",
-					end: 47,
-					start: 43,
-					startToken: 8,
-					text: "They",
-					utterance: 0,
-					endToken: 9,
-				},
-				{
-					index: 5,
-					type: "PRONOMINAL",
-					end: 68,
-					start: 65,
-					startToken: 14,
-					text: "she",
-					utterance: 0,
-					endToken: 15,
-				},
-				{
-					index: 6,
-					type: "PRONOMINAL",
-					end: 81,
-					start: 80,
-					startToken: 18,
-					text: "I",
-					utterance: 0,
-					endToken: 19,
-				},
-				{
-					index: 7,
-					type: "PRONOMINAL",
-					end: 99,
-					start: 96,
-					startToken: 21,
-					text: "her",
-					utterance: 0,
-					endToken: 22,
-				},
-			],
-			singleScores: {
-				"0": null,
-				"1": 1.609437735243254,
-				"2": -1.1017402175324822,
-				"3": 1.4347901008486401,
-				"4": -0.02895837171142801,
-				"5": -0.4266623545401909,
-				"6": 0.009921976322164627,
-				"7": -1.8629830475049451,
-			},
-			pairScores: {
-				"0": {},
-				"1": {
-					"0": -2.2413815226574703,
-				},
-				"2": {
-					"0": -2.409825572927252,
-					"1": 5.707592445811339,
-				},
-				"3": {
-					"0": -3.0653216162902854,
-					"1": -1.6904548462117184,
-					"2": -1.8322836987315447,
-				},
-				"4": {
-					"0": -3.688547511940379,
-					"1": -2.0587007889253717,
-					"2": -3.370481889890517,
-					"3": 2.67729831167075,
-				},
-				"5": {
-					"0": -2.6457134524861243,
-					"1": 8.41568336157475,
-					"2": 5.457479617210075,
-					"3": 0.5541345662624297,
-					"4": -2.952959651402653,
-				},
-				"6": {
-					"0": 6.483305186430136,
-					"1": -2.5309543937239427,
-					"2": -2.4954945953746566,
-					"3": -2.812183970273315,
-					"4": -2.998588381716906,
-					"5": -2.2723718581884205,
-				},
-				"7": {
-					"0": -2.9154581227140457,
-					"1": 9.352887851205328,
-					"2": 9.844018411095597,
-					"3": 1.8138255060465474,
-					"4": -3.3396902374034765,
-					"5": 10.035481487601054,
-					"6": -3.0660799723685312,
-				},
-			},
-			cleanedContext: "",
-			isResolved: true,
-		},
-	],
-	[
-		`My mother's name is Sasha, she likes dogs.`,
-		{
-			cleanedText: "My mother's name is Sasha, she likes dogs.",
-			corefResText: "My mother's name is Sasha, my mother likes dogs.",
-			coreferences: [
-				{
-					resolved: "My mother",
-					original: "she",
-				},
-			],
-			mentions: [
-				{
-					index: 0,
-					type: "PRONOMINAL",
-					end: 2,
-					start: 0,
-					startToken: 0,
-					text: "My",
-					utterance: 0,
-					endToken: 1,
-				},
-				{
-					index: 1,
-					type: "NOMINAL",
-					end: 9,
-					start: 0,
-					startToken: 0,
-					text: "My mother",
-					utterance: 0,
-					endToken: 2,
-				},
-				{
-					index: 2,
-					type: "NOMINAL",
-					end: 16,
-					start: 0,
-					startToken: 0,
-					text: "My mother's name",
-					utterance: 0,
-					endToken: 4,
-				},
-				{
-					index: 3,
-					type: "PROPER",
-					end: 25,
-					start: 20,
-					startToken: 5,
-					text: "Sasha",
-					utterance: 0,
-					endToken: 6,
-				},
-				{
-					index: 4,
-					type: "PRONOMINAL",
-					end: 30,
-					start: 27,
-					startToken: 7,
-					text: "she",
-					utterance: 0,
-					endToken: 8,
-				},
-				{
-					index: 5,
-					type: "NOMINAL",
-					end: 41,
-					start: 37,
-					startToken: 9,
-					text: "dogs",
-					utterance: 0,
-					endToken: 10,
-				},
-			],
-			singleScores: {
-				"0": null,
-				"1": 1.9246201814037063,
-				"2": 1.3833144431588633,
-				"3": 1.8293318485967687,
-				"4": 0.11171655922904344,
-				"5": 1.8179855402495786,
-			},
-			pairScores: {
-				"0": {},
-				"1": {
-					"0": -2.021441708531068,
-				},
-				"2": {
-					"0": -2.4538823419832134,
-					"1": -1.5272053058795838,
-				},
-				"3": {
-					"0": -2.2864554131219212,
-					"1": -1.5158990748985923,
-					"2": -1.5676019384720228,
-				},
-				"4": {
-					"0": -1.077294938181586,
-					"1": 5.190687831349847,
-					"2": 1.3862198517098907,
-					"3": 1.5871185522743856,
-				},
-				"5": {
-					"0": -2.957565366582327,
-					"1": -1.572206989880445,
-					"2": -1.5136893865248766,
-					"3": -2.295173505354227,
-					"4": -2.2454728131610056,
-				},
-			},
-			cleanedContext: "",
-			isResolved: true,
-		},
-	],
-]);
-
-
-class Coref {
-	endpoint: string;
-	onStart =   () => {};
-	onSuccess = () => {};
-	container?: HTMLElement;
-	svgContainer?: SVGSVGElement;
-	
-	constructor(endpoint: string, opts: { onStart?: () => void, onSuccess?: () => void }) {
-		this.endpoint = endpoint;
-		if (opts.onStart) {
-			this.onStart   = opts.onStart;
-		}
-		if (opts.onSuccess) {
-			this.onSuccess = opts.onSuccess;
-		}
-		
-		window.addEventListener('resize', this.svgResize);
-	}
-	
-	svgResize() {
-		if (!this.container || !this.svgContainer) { return ; }
-		this.svgContainer.setAttribute('width', `${this.container.scrollWidth}`);   /// Caution: not offsetWidth.
-		this.svgContainer.setAttribute('height', `${this.container.scrollHeight}`);
-	}
-	
-	parse(text: string) {
-		this.onStart();
-		
-		if (COREF_HARDCODED_OUTPUTS.size) {
-			const output = COREF_HARDCODED_OUTPUTS.get(text) ?? [...COREF_HARDCODED_OUTPUTS.values()][0];
-			setTimeout(() => {
-				this.onSuccess();
-				this.render(output);
-			}, 300);
-			return ;
-		}
-		
-		const path = `${this.endpoint}?text=${encodeURIComponent(text)}`;
-		const request = new XMLHttpRequest();
-		request.open('GET', path);
-		request.onload = () => {
-			if (request.status >= 200 && request.status < 400) {
-				this.onSuccess();
-				const res: CorefResponse = JSON.parse(request.responseText);
-				this.render(res);
-			}
-			else {
-				console.error('Error', request);
-			}
-		};
-		request.send();
-	}
-	
-	render(res: CorefResponse) {
-		const mentions = (<any>res).mentions;  // We will sort them in Displacy
-		for (const m of mentions) {
-			// Let's add each mention's singleScore
-			m.singleScore = res.singleScores[m.index] || undefined;
-		}
-		const markup = Displacy.render(res.cleanedText, mentions);
-		if (!this.container || !this.svgContainer) { return ; }
-		
-		this.container.innerHTML = `<div class="text">${markup}</div>`;
-		/// SVG
-		this.svgContainer.textContent = "";  // Empty
-		this.svgResize();
-		(<any>window).container = this.container;
-		(<any>window).svgContainer = this.svgContainer;
-		/**
-		 * Arrows preparation
-		 */
-		const endY = document.querySelector('.container .text')!.getBoundingClientRect().top 
-			- this.container.getBoundingClientRect().top
-			- 2;
-		SvgArrow.yArrows = endY;
-		/**
-		 * Render arrows
-		 */
-		for (const [__from, scores] of Object.entries(res.pairScores)) {
-			const from = parseInt(__from, 10);   /// Convert all string keys to ints...
-			for (const [__to, score] of Object.entries(scores)) {
-				const to = parseInt(__to, 10);
-				
-				// Positions:
-				const markFrom = document.querySelector(`mark[data-index="${from}"]`) as HTMLElement;
-				const markTo   = document.querySelector(`mark[data-index="${to}"]`) as HTMLElement;
-				// console.log(markFrom, markTo, score);  // todo remove
-				const arrow = new SvgArrow(this.container, markFrom, markTo, score);
-				// Is this a resolved coref?
-				if (score >= Math.max(...Object.values(scores))) {
-					arrow.classNames.push('score-ok');  // Best pairwise score
-					// Is it the better than the singleScore?
-					const singleScore = res.singleScores[from];
-					if (singleScore && score >= singleScore) {
-						arrow.classNames.push('score-best');
-					}
-				}
-				
-				this.svgContainer.appendChild(arrow.generate());
-			}
-		}
-		
-		// Finally do a second pass to move all red/orange arrows to the top of the grey ones.
-		// Finally do a second pass to move all red/orange arrows to the top of the grey ones.
-		(<any>document.querySelectorAll('.displacy-arrow.score-ok')).forEach((arw) => {
-			this.svgContainer!.appendChild(arw);
-		});
-	}
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/normal_vert.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/normal_vert.glsl.js
deleted file mode 100644
index 6f0c3d9a499275dce34a7f7f6010d4a1e2f7eb14..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/normal_vert.glsl.js
+++ /dev/null
@@ -1,66 +0,0 @@
-export default /* glsl */`
-#define NORMAL
-
-#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || ( defined( USE_NORMALMAP ) && ! defined( OBJECTSPACE_NORMALMAP ) )
-
-	varying vec3 vViewPosition;
-
-#endif
-
-#ifndef FLAT_SHADED
-
-	varying vec3 vNormal;
-
-	#ifdef USE_TANGENT
-
-		varying vec3 vTangent;
-		varying vec3 vBitangent;
-
-	#endif
-
-#endif
-
-#include <uv_pars_vertex>
-#include <displacementmap_pars_vertex>
-#include <morphtarget_pars_vertex>
-#include <skinning_pars_vertex>
-#include <logdepthbuf_pars_vertex>
-
-void main() {
-
-	#include <uv_vertex>
-
-	#include <beginnormal_vertex>
-	#include <morphnormal_vertex>
-	#include <skinbase_vertex>
-	#include <skinnormal_vertex>
-	#include <defaultnormal_vertex>
-
-#ifndef FLAT_SHADED // Normal computed with derivatives when FLAT_SHADED
-
-	vNormal = normalize( transformedNormal );
-
-	#ifdef USE_TANGENT
-
-		vTangent = normalize( transformedTangent );
-		vBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );
-
-	#endif
-
-#endif
-
-	#include <begin_vertex>
-	#include <morphtarget_vertex>
-	#include <skinning_vertex>
-	#include <displacementmap_vertex>
-	#include <project_vertex>
-	#include <logdepthbuf_vertex>
-
-#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || ( defined( USE_NORMALMAP ) && ! defined( OBJECTSPACE_NORMALMAP ) )
-
-	vViewPosition = - mvPosition.xyz;
-
-#endif
-
-}
-`;
diff --git a/spaces/bigscience/petals-api/cli/run_local_servers.sh b/spaces/bigscience/petals-api/cli/run_local_servers.sh
deleted file mode 100644
index 78fc1fdbed271274a64f40173e352015bbf1f1e5..0000000000000000000000000000000000000000
--- a/spaces/bigscience/petals-api/cli/run_local_servers.sh
+++ /dev/null
@@ -1,111 +0,0 @@
-# !/usr/bin/env bash
-
-#################
-# Parse options #
-#################
-
-instructions() {
-  echo "Usage: $0 [-n] [-c]" >&2
-  echo " -n: number of servers to run" >&2
-  echo " -c: path to the server configs" >&2
-  exit 1
-}
-
-if [ $# != 4 ]; then
-    instructions
-fi
-
-while getopts ":n:c:t:" option; do
-    case $option in
-        n)  NUM_SERVERS=${OPTARG}
-            ;;
-        c)  CONFIG_PATH=${OPTARG}
-            ;;
-        \?) instructions
-            ;;
-   esac
-done
-
-
-###########################
-# Install or activate env #
-###########################
-
-source ~/miniconda3/etc/profile.d/conda.sh
-if conda env list | grep ".*bloom-demo.*"  &>/dev/null; then
-    conda activate bloom-demo
-else
-    conda create -y --name bloom-demo python=3.8.12 pip
-    conda activate bloom-demo
-
-    conda install -y -c conda-forge cudatoolkit-dev==11.3.1 cudatoolkit==11.3.1 cudnn==8.2.1.32
-    pip install -i https://pypi.org/simple torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
-    pip install -i https://pypi.org/simple accelerate==0.10.0 huggingface-hub==0.7.0 hivemind==1.1.0
-    pip install -i https://pypi.org/simple bitsandbytes-cuda113==0.26.0
-    pip install -i https://pypi.org/simple https://github.com/huggingface/transformers/archive/6589e510fa4e6c442059de2fab84752535de9b23.zip
-fi
-
-
-#######################
-# Create Initial peer #
-#######################
-
-hivemind-dht &> tmp.out &
-sleep 3
-INITIAL_PEER=$(python -c "with open('tmp.out') as f: print(f.readlines()[1].split()[-1])" )
-echo "Initial peer: ${INITIAL_PEER}"
-
-
-##############################
-# Initialize the config file #
-##############################
-
-typeset -A cfg 
-cfg=( # set default values in config array
-    [device]="cpu"
-    [block_ids]="1:2"
-    [id_path]="server.id"
-    [maddr]="/ip4/127.0.0.1/tcp/30000"
-)
-
-###############
-# Run servers #
-###############
-
-for SERVER_ID in $(seq 0 $(( $NUM_SERVERS - 1 )) )
-do  
-    ###############
-    # Read config #
-    ###############
-
-    while read line
-    do
-        if echo $line | grep -F = &>/dev/null
-        then
-            varname=$(echo "$line" | cut -d '=' -f 1)
-            cfg[$varname]=$(echo "$line" | cut -d '=' -f 2-)
-        fi
-    done < ${CONFIG_PATH}/server_${SERVER_ID}.cfg
-    
-    echo "=== Server #${SERVER_ID} ==="
-    echo "Server ID: ${id_path}"
-    echo "Device: ${cfg[device]}"
-    echo "Bloom block ids: ${cfg[block_ids]}"
-    echo "Host maddr: ${cfg[maddr]}"
-    echo ""
-    
-    ##############
-    # Run server #
-    ##############
-
-    tmux new-session -d -s "Server_${SERVER_ID}" bash cli/deploy_server.sh -i ${INITIAL_PEER} -d ${cfg[device]} -p ${cfg[id_path]} -b ${cfg[block_ids]} -a ${cfg[maddr]}
-done
-
-
-#####################
-# Kill initial peer #
-#####################
-
-sleep 10
-pkill -f hivemind-dht # TODO: kill only particular pids of hivemind-dht
-rm tmp.out
\ No newline at end of file
diff --git a/spaces/binery/Paddle_OCR/app.py b/spaces/binery/Paddle_OCR/app.py
deleted file mode 100644
index 4551b36ee5b8f36b65707ee47c0f3ce55acbfa86..0000000000000000000000000000000000000000
--- a/spaces/binery/Paddle_OCR/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import streamlit as st
-import paddlehub as hub
-from PIL import Image
-import io
-import numpy
-import cv2
-
-from paddleocr import PaddleOCR,draw_ocr
-def ocr(img,lang):
-    # Paddleocr supports Chinese, English, French, German, Korean and Japanese.
-    # You can set the parameter `lang` as `ch`, `en`, `french`, `german`, `korean`, `japan`
-    # to switch the language model in order.
-    ocr = PaddleOCR(use_angle_cls=True, lang=lang) # need to run only once to download and load model into memory
-    print(img)
-    print(type(img))
-    result = ocr.ocr(img, cls=True)
-    for idx in range(len(result)):
-        res = result[idx]
-        for line in res:
-            print(line)
-
-    # draw result
-    from PIL import Image
-    result = result[0]
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    im_pil = Image.fromarray(img)
-    image = im_pil
-    boxes = [line[0] for line in result]
-    st.text(boxes)
-    txts = [line[1][0] for line in result]
-    st.text(txts)
-    scores = [line[1][1] for line in result]
-    st.text(scores)
-    im_show = draw_ocr(image, boxes, txts, scores, font_path='./simfang.ttf')
-    im_show = Image.fromarray(im_show)
-    st.image(im_show, caption='ocr output')
-    #im_show.save('result.jpg')
-
-_, col0, _ = st.columns([1, 3, 1])
-with col0:
-    st.header("OCR")
-    form = st.form(key='my-form')
-uploaded_file = form.file_uploader('Choose your img file', type=['png', 'jpg'],accept_multiple_files=False)
-submit = form.form_submit_button('Submit')
-if submit:
-    if uploaded_file is not None:
-        bytes_data = uploaded_file.getvalue()
-        if bytes_data is not None:
-            bytes_data = uploaded_file.read()
-            image = Image.open(io.BytesIO(bytes_data))
-            st.image(image, caption='your img')
-            pil_image=image.convert('RGB') 
-            open_cv_image = numpy.array(pil_image) 
-            # Convert RGB to BGR 
-            open_cv_image = open_cv_image[:, :, ::-1].copy() 
-            
-            ocr(open_cv_image,'ch')
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Kadlubeks Kamera Katalog Pdf 13 ((FREE)).md b/spaces/bioriAsaeru/text-to-voice/Kadlubeks Kamera Katalog Pdf 13 ((FREE)).md
deleted file mode 100644
index 3e7c5da0ca188cfd88bdbbd3e6b77a5d14cfa173..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Kadlubeks Kamera Katalog Pdf 13 ((FREE)).md	
+++ /dev/null
@@ -1,74 +0,0 @@
-<br />
-<h1>Kadlubeks Kamera Katalog Pdf 13: A Valuable Resource for Camera Lovers</h1>
-
-<p>If you are interested in cameras, especially vintage and rare models, you may have heard of Kadlubeks Kamera Katalog, or KKK for short. This is a German book that contains detailed information and pictures of thousands of cameras from different eras and countries. It is considered one of the most comprehensive and authoritative sources on camera history and identification.</p>
-<h2>Kadlubeks Kamera Katalog Pdf 13</h2><br /><p><b><b>Download</b> &gt;&gt;&gt; <a href="https://urloso.com/2uyQfR">https://urloso.com/2uyQfR</a></b></p><br /><br />
-
-<p>Kadlubeks Kamera Katalog was first published in 1978 by Günther Kadlubek, a camera collector and expert. He collaborated with Rudolf Hillebrand, a photographer and journalist, to create this massive work that covers over 40,000 cameras from more than 1,400 manufacturers. The book is divided into sections based on camera types, such as folding cameras, box cameras, SLR cameras, etc. Each section has an introduction that explains the history and development of that type of camera, followed by alphabetical listings of camera models with specifications, pictures, and prices.</p>
-
-<p>The book has been updated and revised several times over the years, with the latest edition being published in 2004. The book is now out of print and hard to find, but you can still access it online in PDF format. The PDF version of the book is called Kadlubeks Kamera Katalog Pdf 13, which refers to the 13th edition of the book. You can download it from various websites or view it online using a PDF reader.</p>
-
-<h2>Why You Should Read Kadlubeks Kamera Katalog Pdf 13</h2>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is not only a useful reference for camera collectors and enthusiasts, but also a fascinating read for anyone who loves photography and history. Here are some reasons why you should read this book:</p>
-<p></p>
-
-<ul>
-<li>You can learn about the evolution of camera technology and design, from the earliest daguerreotypes to the modern digital cameras.</li>
-<li>You can discover rare and obscure cameras that you may have never seen or heard of before.</li>
-<li>You can compare different models and brands of cameras and see how they differ in features, quality, and value.</li>
-<li>You can find out how much your own cameras or those you want to buy are worth according to the book's price guide.</li>
-<li>You can enjoy the beautiful photographs and illustrations that accompany each camera entry.</li>
-</ul>
-
-<h3>How to Use Kadlubeks Kamera Katalog Pdf 13</h3>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is a very large file that contains over 1,000 pages of information. To make it easier to use, you can follow these tips:</p>
-
-<ol>
-<li>Download the PDF file to your computer or device and save it in a convenient location.</li>
-<li>Open the PDF file using a PDF reader that allows you to search, zoom, bookmark, and print pages.</li>
-<li>Use the table of contents or the index to find the section or the camera model you are looking for.</li>
-<li>Read the introduction of each section to get an overview of the camera type and its history.</li>
-<li>Scroll through the pages or use the search function to find specific camera models or keywords.</li>
-<li>Look at the pictures and read the specifications, descriptions, and prices of each camera model.</li>
-<li>Bookmark or print the pages that interest you for future reference.</li>
-</ol>
-
-<h4>Kadlubeks Kamera Katalog Pdf 13: A Must-Have for Camera Lovers</h4>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is a treasure trove of information and images for anyone who loves cameras. Whether you are a collector, a hobbyist, or a curious reader, you will find something to enjoy and learn from this book. Download it today and start exploring the world of cameras!</p>
-<h5>Where to Find Kadlubeks Kamera Katalog Pdf 13</h5>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is not available in any physical or online bookstore, as it is an unofficial and unauthorized version of the original book. However, you can still find it on various websites that offer free downloads of PDF files. Some of these websites are:</p>
-
-<ul>
-<li>Open Library: This is a project that aims to create a web page for every book ever published. You can find Kadlubeks Kamera Katalog Pdf 13 on this website and download it for free. You can also browse other books related to cameras and photography.</li>
-<li>OpenSea: This is a platform that allows users to buy, sell, and explore digital assets such as art, games, music, and more. You can find Kadlubeks Kamera Katalog Pdf 13 on this website as part of a collection of PDF files. You can download it for free or purchase it with cryptocurrency.</li>
-<li>Splex: This is a website that offers free downloads of PDF files on various topics such as business, education, health, and more. You can find Kadlubeks Kamera Katalog Pdf 13 on this website and download it for free. You can also access other PDF files related to cameras and photography.</li>
-</ul>
-
-<p>Before you download Kadlubeks Kamera Katalog Pdf 13 from any website, make sure you have a reliable antivirus software installed on your computer or device. Some websites may contain malware or viruses that can harm your system or steal your personal information.</p>
-
-<h6>How to Use Kadlubeks Kamera Katalog Pdf 13 Effectively</h6>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is a very informative and comprehensive book, but it can also be overwhelming and confusing if you don't know how to use it effectively. Here are some tips on how to get the most out of this book:</p>
-
-<ol>
-<li>Have a clear purpose and goal for reading the book. Are you looking for a specific camera model or type? Are you interested in learning about the history and development of cameras? Are you curious about the prices and values of cameras? Having a clear purpose and goal will help you focus on the relevant sections and pages of the book.</li>
-<li>Use the book as a supplement, not a substitute, for other sources of information. Kadlubeks Kamera Katalog Pdf 13 is a great resource, but it is not perfect or complete. It may contain errors, omissions, or outdated information. It may also not cover some newer or rarer camera models or types. Therefore, you should always cross-check and verify the information in the book with other sources, such as online databases, forums, blogs, magazines, etc.</li>
-<li>Share your findings and opinions with other camera lovers. Kadlubeks Kamera Katalog Pdf 13 is not only a book, but also a community of people who share a passion for cameras. You can join online groups or forums where you can discuss and exchange information and opinions about cameras with other readers of the book. You can also create your own blog or website where you can showcase your camera collection or reviews based on the book.</li>
-</ol>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is a book that will enrich your knowledge and appreciation of cameras. By following these tips, you will be able to use it effectively and enjoyably.</p>
-<h7>Kadlubeks Kamera Katalog Pdf 13: A Book Worth Reading</h7>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is a book that every camera lover should read. It is a comprehensive and authoritative source of information and pictures of thousands of cameras from different eras and countries. It is also a fascinating and enjoyable read that will teach you about the history and evolution of camera technology and design. You can download it for free from various websites or view it online using a PDF reader.</p>
-
-<p>However, Kadlubeks Kamera Katalog Pdf 13 is not a perfect or complete book. It may contain errors, omissions, or outdated information. It may also not cover some newer or rarer camera models or types. Therefore, you should always cross-check and verify the information in the book with other sources, such as online databases, forums, blogs, magazines, etc. You should also use the book as a supplement, not a substitute, for other sources of information.</p>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is not only a book, but also a community of people who share a passion for cameras. You can join online groups or forums where you can discuss and exchange information and opinions about cameras with other readers of the book. You can also create your own blog or website where you can showcase your camera collection or reviews based on the book.</p>
-
-<p>Kadlubeks Kamera Katalog Pdf 13 is a book that will enrich your knowledge and appreciation of cameras. By following the tips in this article, you will be able to use it effectively and enjoyably. Download it today and start exploring the world of cameras!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/breadlicker45/Text-to-music-longer/constants.py b/spaces/breadlicker45/Text-to-music-longer/constants.py
deleted file mode 100644
index 62633e107d6ff9e39e65843c9ac805dcb194a965..0000000000000000000000000000000000000000
--- a/spaces/breadlicker45/Text-to-music-longer/constants.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import numpy as np
-
-MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
-MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
-MUBERT_LICENSE = "ttmmubertlicense#f0acYBenRcfeFpNT4wpYGaTQIyDI4mJGv5MfIhBFz97NXDwDNFHmMRsBSzmGsJwbTpP1A6i07AXcIeAHo5"
-MUBERT_MODE = "loop"
-MUBERT_TOKEN = "4951f6428e83172a4f39de05d5b3ab10d58560b8"
diff --git a/spaces/camel-ai/camel-agents/apps/common/auto_zip.py b/spaces/camel-ai/camel-agents/apps/common/auto_zip.py
deleted file mode 100644
index 4a803b11763129af71407bedf9953980a272c63b..0000000000000000000000000000000000000000
--- a/spaces/camel-ai/camel-agents/apps/common/auto_zip.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-import json
-import os
-import zipfile
-
-
-class AutoZip:
-
-    def __init__(self, zip_path: str, ext: str = ".json"):
-        self.zip_path = zip_path
-        self.zip = zipfile.ZipFile(zip_path, "r")
-        self.fl = [f for f in self.zip.filelist if f.filename.endswith(ext)]
-
-    def __next__(self):
-        if self.index >= len(self.fl):
-            raise StopIteration
-        else:
-            finfo = self.fl[self.index]
-            with self.zip.open(finfo) as f:
-                raw_json = json.loads(f.read().decode("utf-8"))
-            self.index += 1
-            return raw_json
-
-    def __len__(self):
-        return len(self.fl)
-
-    def __iter__(self):
-        self.index = 0
-        return self
-
-    def as_dict(self, include_zip_name: bool = False):
-        d = dict()
-        for finfo in self.fl:
-            with self.zip.open(finfo) as f:
-                raw_text = f.read().decode("utf-8")
-            if include_zip_name:
-                key = os.path.split(self.zip_path)[1] + "/" + finfo.filename
-            else:
-                key = finfo.filename
-            d[key] = raw_text
-        return d
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/preprocess.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/preprocess.py
deleted file mode 100644
index d330e54728733dbc75594d239d789be42ecb4d01..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/preprocess.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import argparse
-import text
-from utils import load_filepaths_and_text
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--out_extension", default="cleaned")
-  parser.add_argument("--text_index", default=2, type=int)
-  parser.add_argument("--filelists", nargs="+", default=["E:/uma_voice/output_train.txt", "E:/uma_voice/output_val.txt"])
-  parser.add_argument("--text_cleaners", nargs="+", default=["japanese_cleaners"])
-
-  args = parser.parse_args()
-    
-
-  for filelist in args.filelists:
-    print("START:", filelist)
-    filepaths_and_text = load_filepaths_and_text(filelist)
-    for i in range(len(filepaths_and_text)):
-      original_text = filepaths_and_text[i][args.text_index]
-      cleaned_text = text._clean_text(original_text, args.text_cleaners)
-      filepaths_and_text[i][args.text_index] = cleaned_text
-
-    new_filelist = filelist + "." + args.out_extension
-    with open(new_filelist, "w", encoding="utf-8") as f:
-      f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/docker/Dockerfile b/spaces/carlosalonso/Detection-video/carpeta_deteccion/docker/Dockerfile
deleted file mode 100644
index fae0060b2b78b26e4cef9631a04e84db4eb2c567..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/docker/Dockerfile
+++ /dev/null
@@ -1,47 +0,0 @@
-FROM nvidia/cuda:11.1.1-cudnn8-devel-ubuntu18.04
-# use an older system (18.04) to avoid opencv incompatibility (issue#3524)
-
-ENV DEBIAN_FRONTEND noninteractive
-RUN apt-get update && apt-get install -y \
-	python3-opencv ca-certificates python3-dev git wget sudo ninja-build
-RUN ln -sv /usr/bin/python3 /usr/bin/python
-
-# create a non-root user
-ARG USER_ID=1000
-RUN useradd -m --no-log-init --system  --uid ${USER_ID} appuser -g sudo
-RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
-USER appuser
-WORKDIR /home/appuser
-
-ENV PATH="/home/appuser/.local/bin:${PATH}"
-RUN wget https://bootstrap.pypa.io/pip/3.6/get-pip.py && \
-	python3 get-pip.py --user && \
-	rm get-pip.py
-
-# install dependencies
-# See https://pytorch.org/ for other options if you use a different version of CUDA
-RUN pip install --user tensorboard cmake onnx   # cmake from apt-get is too old
-RUN pip install --user torch==1.10 torchvision==0.11.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
-
-RUN pip install --user 'git+https://github.com/facebookresearch/fvcore'
-# install detectron2
-RUN git clone https://github.com/facebookresearch/detectron2 detectron2_repo
-# set FORCE_CUDA because during `docker build` cuda is not accessible
-ENV FORCE_CUDA="1"
-# This will by default build detectron2 for all common cuda architectures and take a lot more time,
-# because inside `docker build`, there is no way to tell which architecture will be used.
-ARG TORCH_CUDA_ARCH_LIST="Kepler;Kepler+Tesla;Maxwell;Maxwell+Tegra;Pascal;Volta;Turing"
-ENV TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"
-
-RUN pip install --user -e detectron2_repo
-
-# Set a fixed model cache directory.
-ENV FVCORE_CACHE="/tmp"
-WORKDIR /home/appuser/detectron2_repo
-
-# run detectron2 under user "appuser":
-# wget http://images.cocodataset.org/val2017/000000439715.jpg -O input.jpg
-# python3 demo/demo.py  \
-	#--config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-	#--input input.jpg --output outputs/ \
-	#--opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
diff --git a/spaces/carterw/evolutionary-playlist-builder/src/spotipy_utils.py b/spaces/carterw/evolutionary-playlist-builder/src/spotipy_utils.py
deleted file mode 100644
index 4efaa113e00b3665bab5be842b6831b37363ead0..0000000000000000000000000000000000000000
--- a/spaces/carterw/evolutionary-playlist-builder/src/spotipy_utils.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import spotipy
-import os
-import gradio as gr
-from spotipy.oauth2 import SpotifyClientCredentials
-import numpy as np
-
-FEATURE_NAMES = ['acousticness', 'danceability', 'energy', 'instrumentalness',
-       'liveness', 'loudness', 'speechiness', 'tempo', 'valence']
-
-PLAYLIST_SCOPE = 'playlist-modify-public'
-try:
-    CLIENT_ID = os.environ["CLIENT_ID"]
-except:
-    print("No client ID set.")
-
-try:
-    CLIENT_SECRET = os.environ["CLIENT_SECRET"]
-except:
-    print("No client secret set.")
-
-try:
-    REDIRECT_URL = os.environ["REDIRECT_URL"]
-except:
-    print("No redirect url set.")
-
-TRACK_URI_BASE = 'https://open.spotify.com/embed/track/'
-
-def get_client():
-    """Generate a fresh Spotify API Client that does not require user authorization.
-
-    Returns:
-        Spotify API Client: API Client to be used for queries not regarding user information.
-    """
-    auth_manager = SpotifyClientCredentials(client_id=CLIENT_ID,client_secret=CLIENT_SECRET)
-    return spotipy.client.Spotify(auth_manager=auth_manager)
-
-
-class SpotifyTrack:
-    """ Object for keeping track of the information provided through the Spotify API on a particular track.
-    """
-    def __init__(self, track_id: str) -> None:
-        """ Constructor for class.
-
-        Args:
-            track_id (str): ID of the track on the Spotify API.
-        """
-        self.track_id = track_id
-        client = get_client()
-        self.raw_track_info = client.track(track_id)
-        try:
-            self.track_image = self.raw_track_info["album"]["images"][0]["url"]
-        except IndexError:
-            print("No Image could be found for:", self.raw_track_info, "getting a random track.")
-            self.raw_track_info = client.track(track_id - 1)
-        self.track_preview_url = self.raw_track_info["preview_url"]
-
-    def get_name(self):
-        return self.raw_track_info["name"]
-
-    def get_artist(self):
-        return self.raw_track_info["artists"][0]["name"]
-
-    def get_track_audio_features(self):
-        client = get_client()
-        features_dict = client.audio_features(self.track_id)[0]
-        return np.array([features_dict[feature] for feature in FEATURE_NAMES])
-
-
-def get_auth_manager() -> spotipy.oauth2.SpotifyOAuth:
-    """ Generate authentication manager object.
-
-    Returns:
-        spotipy.oauth2.SpotifyOAuth: Spotipy authentication manager.
-    """
-    cache_handler = spotipy.cache_handler.MemoryCacheHandler()
-    return spotipy.oauth2.SpotifyOAuth(client_id=CLIENT_ID, client_secret=CLIENT_SECRET, scope=PLAYLIST_SCOPE, redirect_uri=REDIRECT_URL, cache_handler=cache_handler,
-                                       show_dialog=True,
-                                       open_browser=True)
-
-
-def search_for_track(track, artist):
-    """Search for a track given the name and possible the artist name.
-
-    Args:
-        args (tuple): track name and possible the artist name.
-
-    Returns:
-        _type_: _description_
-    """
-    client = get_client()
-    if track != "" and artist != "":
-        search_results = client.search(q = "artist:" + artist + " track:" + track, type = "track", limit=10)
-    elif track != "":
-        search_results = client.search(q = "track:" + track, type = "track", limit=10)
-    elif artist != "":
-        search_results = client.search(q = "artist:" + artist, type = "track", limit=10)
-    else:
-        return {}
-
-    return {track_info["name"]+"_"+track_info["artists"][0]["name"]: {"name": track_info["name"],
-        "artist": track_info["artists"][0]["name"],
-        "track_id": track_info["id"]} for track_info in search_results["tracks"]["items"]}
-
-
-def add_tracks_to_playlist(playlist_name:str, population, request: gr.Request) -> bool:
-    """ Add tracks to a given user's Spotify Library.
-
-    Args:
-        playlist_name (str): Name of the playlist to be created.
-        auth_url (str): Authorization URL provided by the user.
-        tracks (List): List of the tracks to be added to the playlist.
-
-    Returns:
-        bool: Flag indicating whether or not the playlist was added to the library succesfully.
-    """
-
-    if not population.user_key and "referer" in request.request.headers:
-        auth_manager = get_auth_manager()
-        code = auth_manager.parse_response_code(request.headers.get("referer"))
-        token_info = auth_manager.get_access_token(code)
-        token = token_info['access_token']
-        population.user_key = token
-    elif not population.user_key:
-        return False
-
-    user_client = spotipy.Spotify(population.user_key)
-
-
-    username = user_client.current_user()["id"]
-
-    try:
-        playlist_result = user_client.user_playlist_create(username, playlist_name, public=True)
-        print("playlist created")
-    except spotipy.exceptions.SpotifyException:
-        return False
-    playlist_id = playlist_result["id"]
-    tracks_to_add = ["spotify:track:" + track.get_track_id() for track in population.playlist_selected_songs]
-    if tracks_to_add:
-        user_client.user_playlist_add_tracks(username, playlist_id, tracks_to_add)
-    return True
diff --git a/spaces/cccc-c/web-ui-pub/_next/static/chunks/bce60fc1-b7a26e6d69cbd1bd.js b/spaces/cccc-c/web-ui-pub/_next/static/chunks/bce60fc1-b7a26e6d69cbd1bd.js
deleted file mode 100644
index 040c0754dc12ba03071bed6c3d0514581a9412af..0000000000000000000000000000000000000000
--- a/spaces/cccc-c/web-ui-pub/_next/static/chunks/bce60fc1-b7a26e6d69cbd1bd.js
+++ /dev/null
@@ -1,9 +0,0 @@
-"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[253],{42614:function(e,t,n){/**
- * @license React
- * react-dom.production.min.js
- *
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */var r,l=n(86006),a=n(26183),o={usingClientEntryPoint:!1,Events:null,Dispatcher:{current:null}};function i(e){for(var t="https://reactjs.org/docs/error-decoder.html?invariant="+e,n=1;n<arguments.length;n++)t+="&args[]="+encodeURIComponent(arguments[n]);return"Minified React error #"+e+"; visit "+t+" for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}var u=Object.assign,s=l.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED,c=[],f=-1;function d(e){return{current:e}}function p(e){0>f||(e.current=c[f],c[f]=null,f--)}function h(e,t){c[++f]=e.current,e.current=t}var m=Symbol.for("react.element"),g=Symbol.for("react.portal"),y=Symbol.for("react.fragment"),v=Symbol.for("react.strict_mode"),b=Symbol.for("react.profiler"),k=Symbol.for("react.provider"),w=Symbol.for("react.context"),S=Symbol.for("react.server_context"),E=Symbol.for("react.forward_ref"),C=Symbol.for("react.suspense"),x=Symbol.for("react.suspense_list"),z=Symbol.for("react.memo"),P=Symbol.for("react.lazy"),N=Symbol.for("react.scope");Symbol.for("react.debug_trace_mode");var _=Symbol.for("react.offscreen"),L=Symbol.for("react.legacy_hidden"),T=Symbol.for("react.cache");Symbol.for("react.tracing_marker");var M=Symbol.for("react.default_value"),F=Symbol.iterator;function D(e){return null===e||"object"!=typeof e?null:"function"==typeof(e=F&&e[F]||e["@@iterator"])?e:null}var R=d(null),O=d(null),A=d(null);function I(e,t){switch(h(A,t),h(O,e),h(R,null),e=t.nodeType){case 9:case 11:t=(t=t.documentElement)&&(t=t.namespaceURI)?sg(t):0;break;default:if(t=(e=8===e?t.parentNode:t).tagName,e=e.namespaceURI)t=sy(e=sg(e),t);else switch(t){case"svg":t=1;break;case"math":t=2;break;default:t=0}}p(R),h(R,t)}function U(){p(R),p(O),p(A)}function B(e){var t=R.current,n=sy(t,e.type);t!==n&&(h(O,e),h(R,n))}function Q(e){O.current===e&&(p(R),p(O))}var V=a.unstable_scheduleCallback,$=a.unstable_cancelCallback,W=a.unstable_shouldYield,j=a.unstable_requestPaint,H=a.unstable_now,q=a.unstable_getCurrentPriorityLevel,K=a.unstable_ImmediatePriority,Y=a.unstable_UserBlockingPriority,X=a.unstable_NormalPriority,G=a.unstable_LowPriority,Z=a.unstable_IdlePriority,J=null,ee=null,et=Math.clz32?Math.clz32:function(e){return 0==(e>>>=0)?32:31-(en(e)/er|0)|0},en=Math.log,er=Math.LN2,el=128,ea=8388608;function eo(e){switch(e&-e){case 1:return 1;case 2:return 2;case 4:return 4;case 8:return 8;case 16:return 16;case 32:return 32;case 64:return 64;case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:return 8388480&e;case 8388608:case 16777216:case 33554432:case 67108864:return 125829120&e;case 134217728:return 134217728;case 268435456:return 268435456;case 536870912:return 536870912;case 1073741824:return 1073741824;default:return e}}function ei(e,t){var n=e.pendingLanes;if(0===n)return 0;var r=0,l=e.suspendedLanes,a=e.pingedLanes,o=268435455&n;if(0!==o){var i=o&~l;0!==i?r=eo(i):0!=(a&=o)&&(r=eo(a))}else 0!=(o=n&~l)?r=eo(o):0!==a&&(r=eo(a));if(0===r)return 0;if(0!==t&&t!==r&&0==(t&l)&&((l=r&-r)>=(a=t&-t)||32===l&&0!=(8388480&a)))return t;if(0!=(8&r)&&(r|=32&n),0!==(t=e.entangledLanes))for(e=e.entanglements,t&=r;0<t;)l=1<<(n=31-et(t)),r|=e[n],t&=~l;return r}function eu(e,t){return e.errorRecoveryDisabledLanes&t?0:0!=(e=-1073741825&e.pendingLanes)?e:1073741824&e?1073741824:0}function es(){var e=el;return 0==(8388480&(el<<=1))&&(el=128),e}function ec(){var e=ea;return 0==(125829120&(ea<<=1))&&(ea=8388608),e}function ef(e){for(var t=[],n=0;31>n;n++)t.push(e);return t}function ed(e,t){e.pendingLanes|=t,536870912!==t&&(e.suspendedLanes=0,e.pingedLanes=0)}function ep(e,t){var n=e.entangledLanes|=t;for(e=e.entanglements;n;){var r=31-et(n),l=1<<r;l&t|e[r]&t&&(e[r]|=t),n&=~l}}var eh=0;function em(e){return 2<(e&=-e)?8<e?0!=(268435455&e)?32:536870912:8:2}var eg=Object.prototype.hasOwnProperty,ey=Math.random().toString(36).slice(2),ev="__reactFiber$"+ey,eb="__reactProps$"+ey,ek="__reactContainer$"+ey,ew="__reactEvents$"+ey,eS="__reactListeners$"+ey,eE="__reactHandles$"+ey,eC="__reactResources$"+ey,ex="__reactMarker$"+ey;function ez(e){delete e[ev],delete e[eb],delete e[ew],delete e[eS],delete e[eE]}function eP(e){var t=e[ev];if(t)return t;for(var n=e.parentNode;n;){if(t=n[ek]||n[ev]){if(n=t.alternate,null!==t.child||null!==n&&null!==n.child)for(e=sN(e);null!==e;){if(n=e[ev])return n;e=sN(e)}return t}n=(e=n).parentNode}return null}function eN(e){if(e=e[ev]||e[ek]){var t=e.tag;if(5===t||6===t||13===t||26===t||27===t||3===t)return e}return null}function e_(e){var t=e.tag;if(5===t||26===t||27===t||6===t)return e.stateNode;throw Error(i(33))}function eL(e){return e[eb]||null}function eT(e){var t=e[eC];return t||(t=e[eC]={hoistableStyles:new Map,hoistableScripts:new Map}),t}function eM(e){e[ex]=!0}var eF=new Set,eD={};function eR(e,t){eO(e,t),eO(e+"Capture",t)}function eO(e,t){for(eD[e]=t,e=0;e<t.length;e++)eF.add(t[e])}var eA=!("undefined"==typeof window||void 0===window.document||void 0===window.document.createElement),eI=RegExp("^[:A-Z_a-z\\u00C0-\\u00D6\\u00D8-\\u00F6\\u00F8-\\u02FF\\u0370-\\u037D\\u037F-\\u1FFF\\u200C-\\u200D\\u2070-\\u218F\\u2C00-\\u2FEF\\u3001-\uD7FF\\uF900-\\uFDCF\\uFDF0-\\uFFFD][:A-Z_a-z\\u00C0-\\u00D6\\u00D8-\\u00F6\\u00F8-\\u02FF\\u0370-\\u037D\\u037F-\\u1FFF\\u200C-\\u200D\\u2070-\\u218F\\u2C00-\\u2FEF\\u3001-\uD7FF\\uF900-\\uFDCF\\uFDF0-\\uFFFD\\-.0-9\\u00B7\\u0300-\\u036F\\u203F-\\u2040]*$"),eU={},eB={};function eQ(e,t,n){if(eg.call(eB,t)||!eg.call(eU,t)&&(eI.test(t)?eB[t]=!0:(eU[t]=!0,!1))){if(null===n)e.removeAttribute(t);else{switch(typeof n){case"undefined":case"function":case"symbol":e.removeAttribute(t);return;case"boolean":var r=t.toLowerCase().slice(0,5);if("data-"!==r&&"aria-"!==r){e.removeAttribute(t);return}}e.setAttribute(t,""+n)}}}function eV(e,t,n){if(null===n)e.removeAttribute(t);else{switch(typeof n){case"undefined":case"function":case"symbol":case"boolean":e.removeAttribute(t);return}e.setAttribute(t,""+n)}}function e$(e,t,n,r){if(null===r)e.removeAttribute(n);else{switch(typeof r){case"undefined":case"function":case"symbol":case"boolean":e.removeAttribute(n);return}e.setAttributeNS(t,n,""+r)}}function eW(e){if(void 0===id)try{throw Error()}catch(e){var t=e.stack.trim().match(/\n( *(at )?)/);id=t&&t[1]||""}return"\n"+id+e}var ej=!1;function eH(e,t){if(!e||ej)return"";ej=!0;var n=Error.prepareStackTrace;Error.prepareStackTrace=void 0;try{if(t){if(t=function(){throw Error()},Object.defineProperty(t.prototype,"props",{set:function(){throw Error()}}),"object"==typeof Reflect&&Reflect.construct){try{Reflect.construct(t,[])}catch(e){var r=e}Reflect.construct(e,[],t)}else{try{t.call()}catch(e){r=e}e.call(t.prototype)}}else{try{throw Error()}catch(e){r=e}var l=e();l&&"function"==typeof l.catch&&l.catch(function(){})}}catch(t){if(t&&r&&"string"==typeof t.stack){for(var a=t.stack.split("\n"),o=r.stack.split("\n"),i=a.length-1,u=o.length-1;1<=i&&0<=u&&a[i]!==o[u];)u--;for(;1<=i&&0<=u;i--,u--)if(a[i]!==o[u]){if(1!==i||1!==u)do if(i--,0>--u||a[i]!==o[u]){var s="\n"+a[i].replace(" at new "," at ");return e.displayName&&s.includes("<anonymous>")&&(s=s.replace("<anonymous>",e.displayName)),s}while(1<=i&&0<=u);break}}}finally{ej=!1,Error.prepareStackTrace=n}return(e=e?e.displayName||e.name:"")?eW(e):""}function eq(e){switch(typeof e){case"boolean":case"number":case"string":case"undefined":case"object":return e;default:return""}}function eK(e){var t=e.type;return(e=e.nodeName)&&"input"===e.toLowerCase()&&("checkbox"===t||"radio"===t)}function eY(e){e._valueTracker||(e._valueTracker=function(e){var t=eK(e)?"checked":"value",n=Object.getOwnPropertyDescriptor(e.constructor.prototype,t),r=""+e[t];if(!e.hasOwnProperty(t)&&void 0!==n&&"function"==typeof n.get&&"function"==typeof n.set){var l=n.get,a=n.set;return Object.defineProperty(e,t,{configurable:!0,get:function(){return l.call(this)},set:function(e){r=""+e,a.call(this,e)}}),Object.defineProperty(e,t,{enumerable:n.enumerable}),{getValue:function(){return r},setValue:function(e){r=""+e},stopTracking:function(){e._valueTracker=null,delete e[t]}}}}(e))}function eX(e){if(!e)return!1;var t=e._valueTracker;if(!t)return!0;var n=t.getValue(),r="";return e&&(r=eK(e)?e.checked?"true":"false":e.value),(e=r)!==n&&(t.setValue(e),!0)}function eG(e){if(void 0===(e=e||("undefined"!=typeof document?document:void 0)))return null;try{return e.activeElement||e.body}catch(t){return e.body}}var eZ=/[\n"\\]/g;function eJ(e){return e.replace(eZ,function(e){return"\\"+e.charCodeAt(0).toString(16)+" "})}function e0(e,t,n,r,l,a,o,i){e.name="",null!=o&&"function"!=typeof o&&"symbol"!=typeof o&&"boolean"!=typeof o?e.type=o:e.removeAttribute("type"),null!=t?"number"===o?(0===t&&""===e.value||e.value!=t)&&(e.value=""+eq(t)):e.value!==""+eq(t)&&(e.value=""+eq(t)):"submit"!==o&&"reset"!==o||e.removeAttribute("value"),null!=t?e2(e,o,eq(t)):null!=n?e2(e,o,eq(n)):null!=r&&e.removeAttribute("value"),null==l&&null!=a&&(e.defaultChecked=!!a),null!=l&&!!l!==e.checked&&(e.checked=l),null!=i&&"function"!=typeof i&&"symbol"!=typeof i&&"boolean"!=typeof i?e.name=""+eq(i):e.removeAttribute("name")}function e1(e,t,n,r,l,a,o,i){if(null!=a&&"function"!=typeof a&&"symbol"!=typeof a&&"boolean"!=typeof a&&(e.type=a),null!=t||null!=n){if(!("submit"!==a&&"reset"!==a||null!=t))return;n=null!=n?""+eq(n):"",t=null!=t?""+eq(t):n,i||t===e.value||(e.value=t),e.defaultValue=t}r="function"!=typeof(r=null!=r?r:l)&&"symbol"!=typeof r&&!!r,i||(e.checked=!!r),e.defaultChecked=!!r,null!=o&&"function"!=typeof o&&"symbol"!=typeof o&&"boolean"!=typeof o&&(e.name=o)}function e2(e,t,n){"number"===t&&eG(e.ownerDocument)===e||e.defaultValue===""+n||(e.defaultValue=""+n)}var e3=Array.isArray;function e4(e,t,n,r){if(e=e.options,t){t={};for(var l=0;l<n.length;l++)t["$"+n[l]]=!0;for(n=0;n<e.length;n++)l=t.hasOwnProperty("$"+e[n].value),e[n].selected!==l&&(e[n].selected=l),l&&r&&(e[n].defaultSelected=!0)}else{for(l=0,n=""+eq(n),t=null;l<e.length;l++){if(e[l].value===n){e[l].selected=!0,r&&(e[l].defaultSelected=!0);return}null!==t||e[l].disabled||(t=e[l])}null!==t&&(t.selected=!0)}}function e8(e,t,n){if(null!=t&&((t=""+eq(t))!==e.value&&(e.value=t),null==n)){e.defaultValue!==t&&(e.defaultValue=t);return}e.defaultValue=null!=n?""+eq(n):""}function e6(e,t,n,r){if(null==t){if(null!=r){if(null!=n)throw Error(i(92));if(e3(r)){if(1<r.length)throw Error(i(93));r=r[0]}n=r}null==n&&(n=""),t=n}n=eq(t),e.defaultValue=n,(r=e.textContent)===n&&""!==r&&null!==r&&(e.value=r)}function e5(e,t){if("http://www.w3.org/2000/svg"!==e.namespaceURI||"innerHTML"in e)e.innerHTML=t;else{for((ip=ip||document.createElement("div")).innerHTML="<svg>"+t.valueOf().toString()+"</svg>",t=ip.firstChild;e.firstChild;)e.removeChild(e.firstChild);for(;t.firstChild;)e.appendChild(t.firstChild)}}var e7=e5;"undefined"!=typeof MSApp&&MSApp.execUnsafeLocalFunction&&(e7=function(e,t){return MSApp.execUnsafeLocalFunction(function(){return e5(e,t)})});var e9=e7;function te(e,t){if(t){var n=e.firstChild;if(n&&n===e.lastChild&&3===n.nodeType){n.nodeValue=t;return}}e.textContent=t}var tt=new Set("animationIterationCount aspectRatio borderImageOutset borderImageSlice borderImageWidth boxFlex boxFlexGroup boxOrdinalGroup columnCount columns flex flexGrow flexPositive flexShrink flexNegative flexOrder gridArea gridRow gridRowEnd gridRowSpan gridRowStart gridColumn gridColumnEnd gridColumnSpan gridColumnStart fontWeight lineClamp lineHeight opacity order orphans scale tabSize widows zIndex zoom fillOpacity floodOpacity stopOpacity strokeDasharray strokeDashoffset strokeMiterlimit strokeOpacity strokeWidth MozAnimationIterationCount MozBoxFlex MozBoxFlexGroup MozLineClamp msAnimationIterationCount msFlex msZoom msFlexGrow msFlexNegative msFlexOrder msFlexPositive msFlexShrink msGridColumn msGridColumnSpan msGridRow msGridRowSpan WebkitAnimationIterationCount WebkitBoxFlex WebKitBoxFlexGroup WebkitBoxOrdinalGroup WebkitColumnCount WebkitColumns WebkitFlex WebkitFlexGrow WebkitFlexPositive WebkitFlexShrink WebkitLineClamp".split(" "));function tn(e,t){if(null!=t&&"object"!=typeof t)throw Error(i(62));for(var n in e=e.style,t)if(t.hasOwnProperty(n)){var r=t[n],l=0===n.indexOf("--");null==r||"boolean"==typeof r||""===r?l?e.setProperty(n,""):"float"===n?e.cssFloat="":e[n]="":l?e.setProperty(n,r):"number"!=typeof r||0===r||tt.has(n)?"float"===n?e.cssFloat=r:e[n]=(""+r).trim():e[n]=r+"px"}}function tr(e){if(-1===e.indexOf("-"))return!1;switch(e){case"annotation-xml":case"color-profile":case"font-face":case"font-face-src":case"font-face-uri":case"font-face-format":case"font-face-name":case"missing-glyph":return!1;default:return!0}}var tl=new Map([["acceptCharset","accept-charset"],["htmlFor","for"],["httpEquiv","http-equiv"],["crossOrigin","crossorigin"],["accentHeight","accent-height"],["alignmentBaseline","alignment-baseline"],["arabicForm","arabic-form"],["baselineShift","baseline-shift"],["capHeight","cap-height"],["clipPath","clip-path"],["clipRule","clip-rule"],["colorInterpolation","color-interpolation"],["colorInterpolationFilters","color-interpolation-filters"],["colorProfile","color-profile"],["colorRendering","color-rendering"],["dominantBaseline","dominant-baseline"],["enableBackground","enable-background"],["fillOpacity","fill-opacity"],["fillRule","fill-rule"],["floodColor","flood-color"],["floodOpacity","flood-opacity"],["fontFamily","font-family"],["fontSize","font-size"],["fontSizeAdjust","font-size-adjust"],["fontStretch","font-stretch"],["fontStyle","font-style"],["fontVariant","font-variant"],["fontWeight","font-weight"],["glyphName","glyph-name"],["glyphOrientationHorizontal","glyph-orientation-horizontal"],["glyphOrientationVertical","glyph-orientation-vertical"],["horizAdvX","horiz-adv-x"],["horizOriginX","horiz-origin-x"],["imageRendering","image-rendering"],["letterSpacing","letter-spacing"],["lightingColor","lighting-color"],["markerEnd","marker-end"],["markerMid","marker-mid"],["markerStart","marker-start"],["overlinePosition","overline-position"],["overlineThickness","overline-thickness"],["paintOrder","paint-order"],["panose-1","panose-1"],["pointerEvents","pointer-events"],["renderingIntent","rendering-intent"],["shapeRendering","shape-rendering"],["stopColor","stop-color"],["stopOpacity","stop-opacity"],["strikethroughPosition","strikethrough-position"],["strikethroughThickness","strikethrough-thickness"],["strokeDasharray","stroke-dasharray"],["strokeDashoffset","stroke-dashoffset"],["strokeLinecap","stroke-linecap"],["strokeLinejoin","stroke-linejoin"],["strokeMiterlimit","stroke-miterlimit"],["strokeOpacity","stroke-opacity"],["strokeWidth","stroke-width"],["textAnchor","text-anchor"],["textDecoration","text-decoration"],["textRendering","text-rendering"],["transformOrigin","transform-origin"],["underlinePosition","underline-position"],["underlineThickness","underline-thickness"],["unicodeBidi","unicode-bidi"],["unicodeRange","unicode-range"],["unitsPerEm","units-per-em"],["vAlphabetic","v-alphabetic"],["vHanging","v-hanging"],["vIdeographic","v-ideographic"],["vMathematical","v-mathematical"],["vectorEffect","vector-effect"],["vertAdvY","vert-adv-y"],["vertOriginX","vert-origin-x"],["vertOriginY","vert-origin-y"],["wordSpacing","word-spacing"],["writingMode","writing-mode"],["xmlnsXlink","xmlns:xlink"],["xHeight","x-height"]]),ta=null;function to(e){return(e=e.target||e.srcElement||window).correspondingUseElement&&(e=e.correspondingUseElement),3===e.nodeType?e.parentNode:e}var ti=null,tu=null;function ts(e){var t=eN(e);if(t&&(e=t.stateNode)){var n=eL(e);switch(e=t.stateNode,t.type){case"input":if(e0(e,n.value,n.defaultValue,n.defaultValue,n.checked,n.defaultChecked,n.type,n.name),t=n.name,"radio"===n.type&&null!=t){for(n=e;n.parentNode;)n=n.parentNode;for(n=n.querySelectorAll('input[name="'+eJ(""+t)+'"][type="radio"]'),t=0;t<n.length;t++){var r=n[t];if(r!==e&&r.form===e.form){var l=eL(r);if(!l)throw Error(i(90));eX(r),e0(r,l.value,l.defaultValue,l.defaultValue,l.checked,l.defaultChecked,l.type,l.name)}}}break;case"textarea":e8(e,n.value,n.defaultValue);break;case"select":null!=(t=n.value)&&e4(e,!!n.multiple,t,!1)}}}function tc(e){ti?tu?tu.push(e):tu=[e]:ti=e}function tf(){if(ti){var e=ti,t=tu;if(tu=ti=null,ts(e),t)for(e=0;e<t.length;e++)ts(t[e])}}function td(e){var t=e,n=e;if(e.alternate)for(;t.return;)t=t.return;else{e=t;do 0!=(4098&(t=e).flags)&&(n=t.return),e=t.return;while(e)}return 3===t.tag?n:null}function tp(e){if(13===e.tag){var t=e.memoizedState;if(null===t&&null!==(e=e.alternate)&&(t=e.memoizedState),null!==t)return t.dehydrated}return null}function th(e){if(td(e)!==e)throw Error(i(188))}function tm(e){return null!==(e=function(e){var t=e.alternate;if(!t){if(null===(t=td(e)))throw Error(i(188));return t!==e?null:e}for(var n=e,r=t;;){var l=n.return;if(null===l)break;var a=l.alternate;if(null===a){if(null!==(r=l.return)){n=r;continue}break}if(l.child===a.child){for(a=l.child;a;){if(a===n)return th(l),e;if(a===r)return th(l),t;a=a.sibling}throw Error(i(188))}if(n.return!==r.return)n=l,r=a;else{for(var o=!1,u=l.child;u;){if(u===n){o=!0,n=l,r=a;break}if(u===r){o=!0,r=l,n=a;break}u=u.sibling}if(!o){for(u=a.child;u;){if(u===n){o=!0,n=a,r=l;break}if(u===r){o=!0,r=a,n=l;break}u=u.sibling}if(!o)throw Error(i(189))}}if(n.alternate!==r)throw Error(i(190))}if(3!==n.tag)throw Error(i(188));return n.stateNode.current===n?e:t}(e))?function e(t){var n=t.tag;if(5===n||26===n||27===n||6===n)return t;for(t=t.child;null!==t;){if(null!==(n=e(t)))return n;t=t.sibling}return null}(e):null}var tg={},ty=d(tg),tv=d(!1),tb=tg;function tk(e,t){var n=e.type.contextTypes;if(!n)return tg;var r=e.stateNode;if(r&&r.__reactInternalMemoizedUnmaskedChildContext===t)return r.__reactInternalMemoizedMaskedChildContext;var l,a={};for(l in n)a[l]=t[l];return r&&((e=e.stateNode).__reactInternalMemoizedUnmaskedChildContext=t,e.__reactInternalMemoizedMaskedChildContext=a),a}function tw(e){return null!=(e=e.childContextTypes)}function tS(){p(tv),p(ty)}function tE(e,t,n){if(ty.current!==tg)throw Error(i(168));h(ty,t),h(tv,n)}function tC(e,t,n){var r=e.stateNode;if(t=t.childContextTypes,"function"!=typeof r.getChildContext)return n;for(var l in r=r.getChildContext())if(!(l in t))throw Error(i(108,function(e){var t=e.type;switch(e.tag){case 24:return"Cache";case 9:return(t.displayName||"Context")+".Consumer";case 10:return(t._context.displayName||"Context")+".Provider";case 18:return"DehydratedFragment";case 11:return e=(e=t.render).displayName||e.name||"",t.displayName||(""!==e?"ForwardRef("+e+")":"ForwardRef");case 7:return"Fragment";case 26:case 27:case 5:return t;case 4:return"Portal";case 3:return"Root";case 6:return"Text";case 16:return function e(t){if(null==t)return null;if("function"==typeof t)return t.displayName||t.name||null;if("string"==typeof t)return t;switch(t){case y:return"Fragment";case g:return"Portal";case b:return"Profiler";case v:return"StrictMode";case C:return"Suspense";case x:return"SuspenseList";case T:return"Cache"}if("object"==typeof t)switch(t.$$typeof){case w:return(t.displayName||"Context")+".Consumer";case k:return(t._context.displayName||"Context")+".Provider";case E:var n=t.render;return(t=t.displayName)||(t=""!==(t=n.displayName||n.name||"")?"ForwardRef("+t+")":"ForwardRef"),t;case z:return null!==(n=t.displayName||null)?n:e(t.type)||"Memo";case P:n=t._payload,t=t._init;try{return e(t(n))}catch(e){break}case S:return(t.displayName||t._globalName)+".Provider"}return null}(t);case 8:return t===v?"StrictMode":"Mode";case 22:return"Offscreen";case 12:return"Profiler";case 21:return"Scope";case 13:return"Suspense";case 19:return"SuspenseList";case 25:return"TracingMarker";case 1:case 0:case 17:case 2:case 14:case 15:if("function"==typeof t)return t.displayName||t.name||null;if("string"==typeof t)return t}return null}(e)||"Unknown",l));return u({},n,r)}function tx(e){return e=(e=e.stateNode)&&e.__reactInternalMemoizedMergedChildContext||tg,tb=ty.current,h(ty,e),h(tv,tv.current),!0}function tz(e,t,n){var r=e.stateNode;if(!r)throw Error(i(169));n?(e=tC(e,t,tb),r.__reactInternalMemoizedMergedChildContext=e,p(tv),p(ty),h(ty,e)):p(tv),h(tv,n)}var tP="function"==typeof Object.is?Object.is:function(e,t){return e===t&&(0!==e||1/e==1/t)||e!=e&&t!=t},tN=[],t_=0,tL=null,tT=0,tM=[],tF=0,tD=null,tR=1,tO="";function tA(e,t){tN[t_++]=tT,tN[t_++]=tL,tL=e,tT=t}function tI(e,t,n){tM[tF++]=tR,tM[tF++]=tO,tM[tF++]=tD,tD=e;var r=tR;e=tO;var l=32-et(r)-1;r&=~(1<<l),n+=1;var a=32-et(t)+l;if(30<a){var o=l-l%5;a=(r&(1<<o)-1).toString(32),r>>=o,l-=o,tR=1<<32-et(t)+l|n<<l|r,tO=a+e}else tR=1<<a|n<<l|r,tO=e}function tU(e){null!==e.return&&(tA(e,1),tI(e,1,0))}function tB(e){for(;e===tL;)tL=tN[--t_],tN[t_]=null,tT=tN[--t_],tN[t_]=null;for(;e===tD;)tD=tM[--tF],tM[tF]=null,tO=tM[--tF],tM[tF]=null,tR=tM[--tF],tM[tF]=null}var tQ=null,tV=null,t$=!1,tW=null,tj=!1;function tH(e,t){var n=oq(5,null,null,0);n.elementType="DELETED",n.stateNode=t,n.return=e,null===(t=e.deletions)?(e.deletions=[n],e.flags|=16):t.push(n)}function tq(e,t){t.flags=-4097&t.flags|2}function tK(e,t){return null!==(t=function(e,t,n,r){for(;1===e.nodeType;){if(e.nodeName.toLowerCase()!==t.toLowerCase()){if(!r)break}else{if(!r)return e;if(!e[ex])switch(t){case"meta":if(!e.hasAttribute("itemprop"))break;return e;case"link":var l=e.getAttribute("rel");if("stylesheet"===l&&e.hasAttribute("data-precedence")||l!==n.rel||e.getAttribute("href")!==(null==n.href?null:n.href)||e.getAttribute("crossorigin")!==(null==n.crossOrigin?null:n.crossOrigin)||e.getAttribute("title")!==(null==n.title?null:n.title))break;return e;case"style":if(e.hasAttribute("data-precedence"))break;return e;case"script":if(((l=e.getAttribute("src"))!==(null==n.src?null:n.src)||e.getAttribute("type")!==(null==n.type?null:n.type)||e.getAttribute("crossorigin")!==(null==n.crossOrigin?null:n.crossOrigin))&&l&&e.hasAttribute("async")&&!e.hasAttribute("itemprop"))break;return e;default:return e}}if(null===(e=sP(e.nextSibling)))break}return null}(t,e.type,e.pendingProps,tj))&&(e.stateNode=t,tQ=e,tV=sP(t.firstChild),tj=!1,!0)}function tY(e,t){return null!==(t=function(e,t,n){if(""===t)return null;for(;3!==e.nodeType;)if(!n||null===(e=sP(e.nextSibling)))return null;return e}(t,e.pendingProps,tj))&&(e.stateNode=t,tQ=e,tV=null,!0)}function tX(e,t){e:{var n=t;for(t=tj;8!==n.nodeType;)if(!t||null===(n=sP(n.nextSibling))){t=null;break e}t=n}return null!==t&&(n=null!==tD?{id:tR,overflow:tO}:null,e.memoizedState={dehydrated:t,treeContext:n,retryLane:1073741824},(n=oq(18,null,null,0)).stateNode=t,n.return=e,e.child=n,tQ=e,tV=null,!0)}function tG(e){return 0!=(1&e.mode)&&0==(128&e.flags)}function tZ(){throw Error(i(418))}function tJ(e){var t=e.stateNode,n=e.type,r=e.memoizedProps;t[ev]=e,t[eb]=r;var l=0!=(1&e.mode);switch(n){case"dialog":u4("cancel",t),u4("close",t);break;case"iframe":case"object":case"embed":u4("load",t);break;case"video":case"audio":for(n=0;n<u0.length;n++)u4(u0[n],t);break;case"source":u4("error",t);break;case"img":case"image":case"link":u4("error",t),u4("load",t);break;case"details":u4("toggle",t);break;case"input":u4("invalid",t),e1(t,r.value,r.defaultValue,r.checked,r.defaultChecked,r.type,r.name,!0),eY(t);break;case"select":u4("invalid",t);break;case"textarea":u4("invalid",t),e6(t,r.value,r.defaultValue,r.children),eY(t)}n=null;var a=r.children;return"string"!=typeof a&&"number"!=typeof a||t.textContent===""+a||(!0!==r.suppressHydrationWarning&&si(t.textContent,a,l),l||(n=["children",a])),null!=r.onScroll&&u4("scroll",t),null!=r.onClick&&(t.onclick=su),t=n,e.updateQueue=t,null!==t}function t0(e){for(tQ=e.return;tQ;)switch(tQ.tag){case 3:case 27:tj=!0;return;case 5:case 13:tj=!1;return;default:tQ=tQ.return}}function t1(e){if(e!==tQ)return!1;if(!t$)return t0(e),t$=!0,!1;var t=!1;if(3===e.tag||27===e.tag||5===e.tag&&sv(e.type,e.memoizedProps)||(t=!0),t&&(t=tV)){if(tG(e))t2(),tZ();else for(;t;)tH(e,t),t=sP(t.nextSibling)}if(t0(e),13===e.tag){if(!(e=null!==(e=e.memoizedState)?e.dehydrated:null))throw Error(i(317));e:{for(t=0,e=e.nextSibling;e;){if(8===e.nodeType){var n=e.data;if("/$"===n){if(0===t){tV=sP(e.nextSibling);break e}t--}else"$"!==n&&"$!"!==n&&"$?"!==n||t++}e=e.nextSibling}tV=null}}else tV=tQ?sP(e.stateNode.nextSibling):null;return!0}function t2(){for(var e=tV;e;)e=sP(e.nextSibling)}function t3(){tV=tQ=null,t$=!1}function t4(e){null===tW?tW=[e]:tW.push(e)}var t8=[],t6=0,t5=0;function t7(){for(var e=t6,t=t5=t6=0;t<e;){var n=t8[t];t8[t++]=null;var r=t8[t];t8[t++]=null;var l=t8[t];t8[t++]=null;var a=t8[t];if(t8[t++]=null,null!==r&&null!==l){var o=r.pending;null===o?l.next=l:(l.next=o.next,o.next=l),r.pending=l}0!==a&&nt(n,l,a)}}function t9(e,t,n,r){t8[t6++]=e,t8[t6++]=t,t8[t6++]=n,t8[t6++]=r,t5|=r,e.lanes|=r,null!==(e=e.alternate)&&(e.lanes|=r)}function ne(e,t){return t9(e,null,null,t),nn(e)}function nt(e,t,n){e.lanes|=n;var r=e.alternate;null!==r&&(r.lanes|=n);for(var l=!1,a=e.return;null!==a;)a.childLanes|=n,null!==(r=a.alternate)&&(r.childLanes|=n),22===a.tag&&(null===(e=a.stateNode)||1&e._visibility||(l=!0)),e=a,a=a.return;l&&null!==t&&3===e.tag&&(a=e.stateNode,l=31-et(n),null===(e=(a=a.hiddenUpdates)[l])?a[l]=[t]:e.push(t),t.lane=1073741824|n)}function nn(e){if(50<op)throw op=0,oh=null,Error(i(185));for(var t=e.return;null!==t;)t=(e=t).return;return 3===e.tag?e.stateNode:null}var nr=!1;function nl(e){e.updateQueue={baseState:e.memoizedState,firstBaseUpdate:null,lastBaseUpdate:null,shared:{pending:null,lanes:0,hiddenCallbacks:null},callbacks:null}}function na(e,t){e=e.updateQueue,t.updateQueue===e&&(t.updateQueue={baseState:e.baseState,firstBaseUpdate:e.firstBaseUpdate,lastBaseUpdate:e.lastBaseUpdate,shared:e.shared,callbacks:null})}function no(e){return{lane:e,tag:0,payload:null,callback:null,next:null}}function ni(e,t,n){var r=e.updateQueue;if(null===r)return null;if(r=r.shared,0!=(2&aG)){var l=r.pending;return null===l?t.next=t:(t.next=l.next,l.next=t),r.pending=t,t=nn(e),nt(e,null,n),t}return t9(e,r,t,n),nn(e)}function nu(e,t,n){if(null!==(t=t.updateQueue)&&(t=t.shared,0!=(8388480&n))){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,ep(e,n)}}function ns(e,t){var n=e.updateQueue,r=e.alternate;if(null!==r&&n===(r=r.updateQueue)){var l=null,a=null;if(null!==(n=n.firstBaseUpdate)){do{var o={lane:n.lane,tag:n.tag,payload:n.payload,callback:null,next:null};null===a?l=a=o:a=a.next=o,n=n.next}while(null!==n);null===a?l=a=t:a=a.next=t}else l=a=t;n={baseState:r.baseState,firstBaseUpdate:l,lastBaseUpdate:a,shared:r.shared,callbacks:r.callbacks},e.updateQueue=n;return}null===(e=n.lastBaseUpdate)?n.firstBaseUpdate=t:e.next=t,n.lastBaseUpdate=t}function nc(e,t,n,r){var l=e.updateQueue;nr=!1;var a=l.firstBaseUpdate,o=l.lastBaseUpdate,i=l.shared.pending;if(null!==i){l.shared.pending=null;var s=i,c=s.next;s.next=null,null===o?a=c:o.next=c,o=s;var f=e.alternate;null!==f&&(i=(f=f.updateQueue).lastBaseUpdate)!==o&&(null===i?f.firstBaseUpdate=c:i.next=c,f.lastBaseUpdate=s)}if(null!==a){var d=l.baseState;for(o=0,f=c=s=null,i=a;;){var p=-1073741825&i.lane,h=p!==i.lane;if(h?(a0&p)===p:(r&p)===p){null!==f&&(f=f.next={lane:0,tag:i.tag,payload:i.payload,callback:null,next:null});e:{var m=e,g=i;switch(p=t,g.tag){case 1:if("function"==typeof(m=g.payload)){d=m.call(n,d,p);break e}d=m;break e;case 3:m.flags=-65537&m.flags|128;case 0:if(null==(p="function"==typeof(m=g.payload)?m.call(n,d,p):m))break e;d=u({},d,p);break e;case 2:nr=!0}}null!==(p=i.callback)&&(e.flags|=64,h&&(e.flags|=8192),null===(h=l.callbacks)?l.callbacks=[p]:h.push(p))}else h={lane:p,tag:i.tag,payload:i.payload,callback:i.callback,next:null},null===f?(c=f=h,s=d):f=f.next=h,o|=p;if(null===(i=i.next)){if(null===(i=l.shared.pending))break;i=(h=i).next,h.next=null,l.lastBaseUpdate=h,l.shared.pending=null}}null===f&&(s=d),l.baseState=s,l.firstBaseUpdate=c,l.lastBaseUpdate=f,null===a&&(l.shared.lanes=0),a5|=o,e.lanes=o,e.memoizedState=d}}function nf(e,t){if("function"!=typeof e)throw Error(i(191,e));e.call(t)}function nd(e,t){var n=e.callbacks;if(null!==n)for(e.callbacks=null,e=0;e<n.length;e++)nf(n[e],t)}function np(e,t){if(tP(e,t))return!0;if("object"!=typeof e||null===e||"object"!=typeof t||null===t)return!1;var n=Object.keys(e),r=Object.keys(t);if(n.length!==r.length)return!1;for(r=0;r<n.length;r++){var l=n[r];if(!eg.call(t,l)||!tP(e[l],t[l]))return!1}return!0}var nh=Error(i(460)),nm=Error(i(474)),ng={then:function(){}};function ny(e){return"fulfilled"===(e=e.status)||"rejected"===e}function nv(){}function nb(e,t,n){switch(void 0===(n=e[n])?e.push(t):n!==t&&(t.then(nv,nv),t=n),t.status){case"fulfilled":return t.value;case"rejected":if((e=t.reason)===nh)throw Error(i(483));throw e;default:if("string"==typeof t.status)t.then(nv,nv);else{if(null!==(e=aZ)&&100<e.shellSuspendCounter)throw Error(i(482));switch((e=t).status="pending",e.then(function(e){if("pending"===t.status){var n=t;n.status="fulfilled",n.value=e}},function(e){if("pending"===t.status){var n=t;n.status="rejected",n.reason=e}}),t.status){case"fulfilled":return t.value;case"rejected":if((e=t.reason)===nh)throw Error(i(483));throw e}}throw nk=t,nh}}var nk=null;function nw(){if(null===nk)throw Error(i(459));var e=nk;return nk=null,e}var nS=null,nE=0;function nC(e){var t=nE;return nE+=1,null===nS&&(nS=[]),nb(nS,e,t)}function nx(e,t,n){if(null!==(e=n.ref)&&"function"!=typeof e&&"object"!=typeof e){if(n._owner){if(n=n._owner){if(1!==n.tag)throw Error(i(309));var r=n.stateNode}if(!r)throw Error(i(147,e));var l=r,a=""+e;return null!==t&&null!==t.ref&&"function"==typeof t.ref&&t.ref._stringRef===a?t.ref:((t=function(e){var t=l.refs;null===e?delete t[a]:t[a]=e})._stringRef=a,t)}if("string"!=typeof e)throw Error(i(284));if(!n._owner)throw Error(i(290,e))}return e}function nz(e,t){throw Error(i(31,"[object Object]"===(e=Object.prototype.toString.call(t))?"object with keys {"+Object.keys(t).join(", ")+"}":e))}function nP(e){return(0,e._init)(e._payload)}function nN(e){function t(t,n){if(e){var r=t.deletions;null===r?(t.deletions=[n],t.flags|=16):r.push(n)}}function n(n,r){if(!e)return null;for(;null!==r;)t(n,r),r=r.sibling;return null}function r(e,t){for(e=new Map;null!==t;)null!==t.key?e.set(t.key,t):e.set(t.index,t),t=t.sibling;return e}function l(e,t){return(e=oY(e,t)).index=0,e.sibling=null,e}function a(t,n,r){return(t.index=r,e)?null!==(r=t.alternate)?(r=r.index)<n?(t.flags|=33554434,n):r:(t.flags|=33554434,n):(t.flags|=1048576,n)}function o(t){return e&&null===t.alternate&&(t.flags|=33554434),t}function u(e,t,n,r){return null===t||6!==t.tag?((t=o0(n,e.mode,r)).return=e,t):((t=l(t,n)).return=e,t)}function s(e,t,n,r){var a=n.type;return a===y?f(e,t,n.props.children,r,n.key):null!==t&&(t.elementType===a||"object"==typeof a&&null!==a&&a.$$typeof===P&&nP(a)===t.type)?((r=l(t,n.props)).ref=nx(e,t,n),r.return=e,r):((r=oG(n.type,n.key,n.props,null,e.mode,r)).ref=nx(e,t,n),r.return=e,r)}function c(e,t,n,r){return null===t||4!==t.tag||t.stateNode.containerInfo!==n.containerInfo||t.stateNode.implementation!==n.implementation?((t=o1(n,e.mode,r)).return=e,t):((t=l(t,n.children||[])).return=e,t)}function f(e,t,n,r,a){return null===t||7!==t.tag?((t=oZ(n,e.mode,r,a)).return=e,t):((t=l(t,n)).return=e,t)}function d(e,t,n){if("string"==typeof t&&""!==t||"number"==typeof t)return(t=o0(""+t,e.mode,n)).return=e,t;if("object"==typeof t&&null!==t){switch(t.$$typeof){case m:return(n=oG(t.type,t.key,t.props,null,e.mode,n)).ref=nx(e,null,t),n.return=e,n;case g:return(t=o1(t,e.mode,n)).return=e,t;case P:var r=t._init;return d(e,r(t._payload),n)}if(e3(t)||D(t))return(t=oZ(t,e.mode,n,null)).return=e,t;if("function"==typeof t.then)return d(e,nC(t),n);if(t.$$typeof===w||t.$$typeof===S)return d(e,lB(e,t,n),n);nz(e,t)}return null}function p(e,t,n,r){var l=null!==t?t.key:null;if("string"==typeof n&&""!==n||"number"==typeof n)return null!==l?null:u(e,t,""+n,r);if("object"==typeof n&&null!==n){switch(n.$$typeof){case m:return n.key===l?s(e,t,n,r):null;case g:return n.key===l?c(e,t,n,r):null;case P:return p(e,t,(l=n._init)(n._payload),r)}if(e3(n)||D(n))return null!==l?null:f(e,t,n,r,null);if("function"==typeof n.then)return p(e,t,nC(n),r);if(n.$$typeof===w||n.$$typeof===S)return p(e,t,lB(e,n,r),r);nz(e,n)}return null}function h(e,t,n,r,l){if("string"==typeof r&&""!==r||"number"==typeof r)return u(t,e=e.get(n)||null,""+r,l);if("object"==typeof r&&null!==r){switch(r.$$typeof){case m:return s(t,e=e.get(null===r.key?n:r.key)||null,r,l);case g:return c(t,e=e.get(null===r.key?n:r.key)||null,r,l);case P:return h(e,t,n,(0,r._init)(r._payload),l)}if(e3(r)||D(r))return f(t,e=e.get(n)||null,r,l,null);if("function"==typeof r.then)return h(e,t,n,nC(r),l);if(r.$$typeof===w||r.$$typeof===S)return h(e,t,n,lB(t,r,l),l);nz(t,r)}return null}return function u(s,c,f,v){return nE=0,s=function s(c,f,v,b){if("object"==typeof v&&null!==v&&v.type===y&&null===v.key&&(v=v.props.children),"object"==typeof v&&null!==v){switch(v.$$typeof){case m:e:{for(var k=v.key,E=f;null!==E;){if(E.key===k){if((k=v.type)===y){if(7===E.tag){n(c,E.sibling),(f=l(E,v.props.children)).return=c,c=f;break e}}else if(E.elementType===k||"object"==typeof k&&null!==k&&k.$$typeof===P&&nP(k)===E.type){n(c,E.sibling),(f=l(E,v.props)).ref=nx(c,E,v),f.return=c,c=f;break e}n(c,E);break}t(c,E),E=E.sibling}v.type===y?((f=oZ(v.props.children,c.mode,b,v.key)).return=c,c=f):((b=oG(v.type,v.key,v.props,null,c.mode,b)).ref=nx(c,f,v),b.return=c,c=b)}return o(c);case g:e:{for(E=v.key;null!==f;){if(f.key===E){if(4===f.tag&&f.stateNode.containerInfo===v.containerInfo&&f.stateNode.implementation===v.implementation){n(c,f.sibling),(f=l(f,v.children||[])).return=c,c=f;break e}n(c,f);break}t(c,f),f=f.sibling}(f=o1(v,c.mode,b)).return=c,c=f}return o(c);case P:return u(c,f,(E=v._init)(v._payload),b)}if(e3(v))return function(l,o,i,u){for(var s=null,c=null,f=o,m=o=0,g=null;null!==f&&m<i.length;m++){f.index>m?(g=f,f=null):g=f.sibling;var y=p(l,f,i[m],u);if(null===y){null===f&&(f=g);break}e&&f&&null===y.alternate&&t(l,f),o=a(y,o,m),null===c?s=y:c.sibling=y,c=y,f=g}if(m===i.length)return n(l,f),t$&&tA(l,m),s;if(null===f){for(;m<i.length;m++)null!==(f=d(l,i[m],u))&&(o=a(f,o,m),null===c?s=f:c.sibling=f,c=f);return t$&&tA(l,m),s}for(f=r(l,f);m<i.length;m++)null!==(g=h(f,l,m,i[m],u))&&(e&&null!==g.alternate&&f.delete(null===g.key?m:g.key),o=a(g,o,m),null===c?s=g:c.sibling=g,c=g);return e&&f.forEach(function(e){return t(l,e)}),t$&&tA(l,m),s}(c,f,v,b);if(D(v))return function(l,o,u,s){var c=D(u);if("function"!=typeof c)throw Error(i(150));if(null==(u=c.call(u)))throw Error(i(151));for(var f=c=null,m=o,g=o=0,y=null,v=u.next();null!==m&&!v.done;g++,v=u.next()){m.index>g?(y=m,m=null):y=m.sibling;var b=p(l,m,v.value,s);if(null===b){null===m&&(m=y);break}e&&m&&null===b.alternate&&t(l,m),o=a(b,o,g),null===f?c=b:f.sibling=b,f=b,m=y}if(v.done)return n(l,m),t$&&tA(l,g),c;if(null===m){for(;!v.done;g++,v=u.next())null!==(v=d(l,v.value,s))&&(o=a(v,o,g),null===f?c=v:f.sibling=v,f=v);return t$&&tA(l,g),c}for(m=r(l,m);!v.done;g++,v=u.next())null!==(v=h(m,l,g,v.value,s))&&(e&&null!==v.alternate&&m.delete(null===v.key?g:v.key),o=a(v,o,g),null===f?c=v:f.sibling=v,f=v);return e&&m.forEach(function(e){return t(l,e)}),t$&&tA(l,g),c}(c,f,v,b);if("function"==typeof v.then)return s(c,f,nC(v),b);if(v.$$typeof===w||v.$$typeof===S)return s(c,f,lB(c,v,b),b);nz(c,v)}return"string"==typeof v&&""!==v||"number"==typeof v?(v=""+v,null!==f&&6===f.tag?(n(c,f.sibling),(f=l(f,v)).return=c,c=f):(n(c,f),(f=o0(v,c.mode,b)).return=c,c=f),o(c)):n(c,f)}(s,c,f,v),nS=null,s}}var n_=nN(!0),nL=nN(!1),nT=d(null),nM=d(0);function nF(e,t){h(nM,e=a4),h(nT,t),a4=e|t.baseLanes}function nD(){h(nM,a4),h(nT,nT.current)}function nR(){a4=nM.current,p(nT),p(nM)}var nO=d(null),nA=null;function nI(e){var t=e.alternate;h(nV,1&nV.current),h(nO,e),null===nA&&(null===t||null!==nT.current?nA=e:null!==t.memoizedState&&(nA=e))}function nU(e){if(22===e.tag){if(h(nV,nV.current),h(nO,e),null===nA){var t=e.alternate;null!==t&&null!==t.memoizedState&&(nA=e)}}else nB(e)}function nB(){h(nV,nV.current),h(nO,nO.current)}function nQ(e){p(nO),nA===e&&(nA=null),p(nV)}var nV=d(0);function n$(e){for(var t=e;null!==t;){if(13===t.tag){var n=t.memoizedState;if(null!==n&&(null===(n=n.dehydrated)||"$?"===n.data||"$!"===n.data))return t}else if(19===t.tag&&void 0!==t.memoizedProps.revealOrder){if(0!=(128&t.flags))return t}else if(null!==t.child){t.child.return=t,t=t.child;continue}if(t===e)break;for(;null===t.sibling;){if(null===t.return||t.return===e)return null;t=t.return}t.sibling.return=t.return,t=t.sibling}return null}var nW=null,nj=null,nH=!1,nq=!1,nK=!1,nY=0;function nX(e){e!==nj&&null===e.next&&(null===nj?nW=nj=e:nj=nj.next=e),nq=!0,nH||(nH=!0,n1(nJ))}function nG(e){if(!nK&&nq){var t=aZ,n=a0,r=null;nK=!0;do for(var l=!1,a=nW;null!==a;){if((!e||0===a.tag)&&0!=(3&ei(a,a===t?n:0)))try{l=!0;var o=a;if(0!=(6&aG))throw Error(i(327));oI();var u=ei(o,0);if(0!=(3&u)){var s=oT(o,u);if(0!==o.tag&&2===s){var c=u,f=eu(o,c);0!==f&&(u=f,s=ov(o,c,f))}if(1===s)throw c=a6,ox(o,0),ow(o,u),nX(o),c;6===s?ow(o,u):(o.finishedWork=o.current.alternate,o.finishedLanes=u,oO(o,ot,ol))}nX(o)}catch(e){null===r?r=[e]:r.push(e)}a=a.next}while(l);if(nK=!1,null!==r){if(1<r.length){if("function"==typeof AggregateError)throw AggregateError(r);for(e=1;e<r.length;e++)n1(nZ.bind(null,r[e]))}throw r[0]}}}function nZ(e){throw e}function nJ(){nq=nH=!1;for(var e=H(),t=null,n=nW;null!==n;){var r=n.next;0!==nY&&window.event&&"popstate"===window.event.type&&ep(n,2|nY);var l=n0(n,e);0===l?(n.next=null,null===t?nW=r:t.next=r,null===r&&(nj=t)):(t=n,0!=(3&l)&&(nq=!0)),n=r}nY=0,nG(!1)}function n0(e,t){for(var n=e.suspendedLanes,r=e.pingedLanes,l=e.expirationTimes,a=-125829121&e.pendingLanes;0<a;){var o=31-et(a),i=1<<o,u=l[o];-1===u?(0==(i&n)||0!=(i&r))&&(l[o]=function(e,t){switch(e){case 1:case 2:case 4:case 8:return t+250;case 16:case 32:case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:return t+5e3;default:return -1}}(i,t)):u<=t&&(e.expiredLanes|=i),a&=~i}if(t=aZ,n=a0,n=ei(e,e===t?n:0),r=e.callbackNode,0===n||e===t&&2===a1||null!==e.cancelPendingCommit)return null!==r&&null!==r&&$(r),e.callbackNode=null,e.callbackPriority=0;if(0!=(3&n))return null!==r&&null!==r&&$(r),e.callbackPriority=2,e.callbackNode=null,2;if((t=n&-n)===e.callbackPriority)return t;switch(null!==r&&$(r),em(n)){case 2:n=K;break;case 8:n=Y;break;case 32:default:n=X;break;case 536870912:n=Z}return r=oy.bind(null,e),n=V(n,r),e.callbackPriority=t,e.callbackNode=n,t}function n1(e){sS(function(){0!=(6&aG)?V(K,e):e()})}var n2=s.ReactCurrentDispatcher,n3=s.ReactCurrentBatchConfig,n4=0,n8=null,n6=null,n5=null,n7=!1,n9=!1,re=!1,rt=0,rn=0,rr=null,rl=0;function ra(){throw Error(i(321))}function ro(e,t){if(null===t)return!1;for(var n=0;n<t.length&&n<e.length;n++)if(!tP(e[n],t[n]))return!1;return!0}function ri(e,t,n,r,l,a){return n4=a,n8=t,t.memoizedState=null,t.updateQueue=null,t.lanes=0,n2.current=null===e||null===e.memoizedState?rG:rZ,re=!1,e=n(r,l),re=!1,n9&&(e=rs(t,n,r,l)),ru(),e}function ru(){n2.current=rX;var e=null!==n6&&null!==n6.next;if(n4=0,n5=n6=n8=null,n7=!1,rn=0,rr=null,e)throw Error(i(300))}function rs(e,t,n,r){n8=e;var l=0;do{if(n9&&(rr=null),rn=0,n9=!1,25<=l)throw Error(i(301));l+=1,n5=n6=null,e.updateQueue=null,n2.current=rJ;var a=t(n,r)}while(n9);return a}function rc(){var e=0!==rt;return rt=0,e}function rf(e,t,n){t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~n}function rd(e){if(n7){for(e=e.memoizedState;null!==e;){var t=e.queue;null!==t&&(t.pending=null),e=e.next}n7=!1}n4=0,n5=n6=n8=null,n9=!1,rn=rt=0,rr=null}function rp(){var e={memoizedState:null,baseState:null,baseQueue:null,queue:null,next:null};return null===n5?n8.memoizedState=n5=e:n5=n5.next=e,n5}function rh(){if(null===n6){var e=n8.alternate;e=null!==e?e.memoizedState:null}else e=n6.next;var t=null===n5?n8.memoizedState:n5.next;if(null!==t)n5=t,n6=e;else{if(null===e){if(null===n8.alternate)throw Error(i(467));throw Error(i(310))}e={memoizedState:(n6=e).memoizedState,baseState:n6.baseState,baseQueue:n6.baseQueue,queue:n6.queue,next:null},null===n5?n8.memoizedState=n5=e:n5=n5.next=e}return n5}function rm(e){var t=rn;return rn+=1,null===rr&&(rr=[]),e=nb(rr,e,t),null===n8.alternate&&(null===n5?null===n8.memoizedState:null===n5.next)&&(n2.current=rG),e}function rg(e){if(null!==e&&"object"==typeof e){if("function"==typeof e.then)return rm(e);if(e.$$typeof===w||e.$$typeof===S)return lU(e)}throw Error(i(438,String(e)))}function ry(e,t){return"function"==typeof t?t(e):t}function rv(e){var t=rh(),n=n6,r=t.queue;if(null===r)throw Error(i(311));r.lastRenderedReducer=e;var l=t.baseQueue,a=r.pending;if(null!==a){if(null!==l){var o=l.next;l.next=a.next,a.next=o}n.baseQueue=l=a,r.pending=null}if(null!==l){n=l.next,a=t.baseState;var u=o=null,s=null,c=n;do{var f=-1073741825&c.lane;if(f!==c.lane?(a0&f)===f:(n4&f)===f)null!==s&&(s=s.next={lane:0,revertLane:0,action:c.action,hasEagerState:c.hasEagerState,eagerState:c.eagerState,next:null}),f=c.action,re&&e(a,f),a=c.hasEagerState?c.eagerState:e(a,f);else{var d={lane:f,revertLane:c.revertLane,action:c.action,hasEagerState:c.hasEagerState,eagerState:c.eagerState,next:null};null===s?(u=s=d,o=a):s=s.next=d,n8.lanes|=f,a5|=f}c=c.next}while(null!==c&&c!==n);null===s?o=a:s.next=u,tP(a,t.memoizedState)||(la=!0),t.memoizedState=a,t.baseState=o,t.baseQueue=s,r.lastRenderedState=a}return null===l&&(r.lanes=0),[t.memoizedState,r.dispatch]}function rb(e){var t=rh(),n=t.queue;if(null===n)throw Error(i(311));n.lastRenderedReducer=e;var r=n.dispatch,l=n.pending,a=t.memoizedState;if(null!==l){n.pending=null;var o=l=l.next;do a=e(a,o.action),o=o.next;while(o!==l);tP(a,t.memoizedState)||(la=!0),t.memoizedState=a,null===t.baseQueue&&(t.baseState=a),n.lastRenderedState=a}return[a,r]}function rk(e,t,n){var r=n8,l=rh(),a=t$;if(a){if(void 0===n)throw Error(i(407));n=n()}else n=t();var o=!tP((n6||l).memoizedState,n);if(o&&(l.memoizedState=n,la=!0),l=l.queue,rM(rE.bind(null,r,l,e),[e]),l.getSnapshot!==t||o||null!==n5&&1&n5.memoizedState.tag){if(r.flags|=2048,rP(9,rS.bind(null,r,l,n,t),{destroy:void 0},null),null===aZ)throw Error(i(349));a||0!=(60&n4)||rw(r,t,n)}return n}function rw(e,t,n){e.flags|=16384,e={getSnapshot:t,value:n},null===(t=n8.updateQueue)?(t=ih(),n8.updateQueue=t,t.stores=[e]):null===(n=t.stores)?t.stores=[e]:n.push(e)}function rS(e,t,n,r){t.value=n,t.getSnapshot=r,rC(t)&&rx(e)}function rE(e,t,n){return n(function(){rC(t)&&rx(e)})}function rC(e){var t=e.getSnapshot;e=e.value;try{var n=t();return!tP(e,n)}catch(e){return!0}}function rx(e){var t=ne(e,2);null!==t&&og(t,e,2)}function rz(e){var t=rp();return"function"==typeof e&&(e=e()),t.memoizedState=t.baseState=e,t.queue={pending:null,lanes:0,dispatch:null,lastRenderedReducer:ry,lastRenderedState:e},t}function rP(e,t,n,r){return e={tag:e,create:t,inst:n,deps:r,next:null},null===(t=n8.updateQueue)?(t=ih(),n8.updateQueue=t,t.lastEffect=e.next=e):null===(n=t.lastEffect)?t.lastEffect=e.next=e:(r=n.next,n.next=e,e.next=r,t.lastEffect=e),e}function rN(){return rh().memoizedState}function r_(e,t,n,r){var l=rp();n8.flags|=e,l.memoizedState=rP(1|t,n,{destroy:void 0},void 0===r?null:r)}function rL(e,t,n,r){var l=rh();r=void 0===r?null:r;var a=l.memoizedState.inst;null!==n6&&null!==r&&ro(r,n6.memoizedState.deps)?l.memoizedState=rP(t,n,a,r):(n8.flags|=e,l.memoizedState=rP(1|t,n,a,r))}function rT(e,t){r_(8390656,8,e,t)}function rM(e,t){rL(2048,8,e,t)}function rF(e,t){return rL(4,2,e,t)}function rD(e,t){return rL(4,4,e,t)}function rR(e,t){return"function"==typeof t?(t(e=e()),function(){t(null)}):null!=t?(e=e(),t.current=e,function(){t.current=null}):void 0}function rO(e,t,n){n=null!=n?n.concat([e]):null,rL(4,4,rR.bind(null,t,e),n)}function rA(){}function rI(e,t){var n=rh();t=void 0===t?null:t;var r=n.memoizedState;return null!==t&&ro(t,r[1])?r[0]:(n.memoizedState=[e,t],e)}function rU(e,t){var n=rh();t=void 0===t?null:t;var r=n.memoizedState;return null!==t&&ro(t,r[1])?r[0]:(re&&e(),e=e(),n.memoizedState=[e,t],e)}function rB(e,t,n){return 0==(42&n4)?(e.baseState&&(e.baseState=!1,la=!0),e.memoizedState=n):(tP(n,t)||(n=es(),n8.lanes|=n,a5|=n,e.baseState=!0),t)}function rQ(e,t,n,r,l){var a=eh;eh=0!==a&&8>a?a:8;var o=n3.transition;n3.transition=null,rH(e,t,n),n3.transition={};try{rH(e,t,r),l()}catch(e){throw e}finally{eh=a,n3.transition=o}}function rV(){return rh().memoizedState}function r$(){return rh().memoizedState}function rW(e){for(var t=e.return;null!==t;){switch(t.tag){case 24:case 3:var n=om(t);e=no(n);var r=ni(t,e,n);null!==r&&(og(r,t,n),nu(r,t,n)),t={cache:lH()},e.payload=t;return}t=t.return}}function rj(e,t,n){var r=om(e);n={lane:r,revertLane:0,action:n,hasEagerState:!1,eagerState:null,next:null},rq(e)?rK(t,n):(t9(e,t,n,r),null!==(n=nn(e))&&(og(n,e,r),rY(n,t,r)))}function rH(e,t,n){var r=om(e),l={lane:r,revertLane:0,action:n,hasEagerState:!1,eagerState:null,next:null};if(rq(e))rK(t,l);else{var a=e.alternate;if(0===e.lanes&&(null===a||0===a.lanes)&&null!==(a=t.lastRenderedReducer))try{var o=t.lastRenderedState,i=a(o,n);if(l.hasEagerState=!0,l.eagerState=i,tP(i,o)){t9(e,t,l,0),null===aZ&&t7();return}}catch(e){}finally{}t9(e,t,l,r),null!==(n=nn(e))&&(og(n,e,r),rY(n,t,r))}}function rq(e){var t=e.alternate;return e===n8||null!==t&&t===n8}function rK(e,t){n9=n7=!0;var n=e.pending;null===n?t.next=t:(t.next=n.next,n.next=t),e.pending=t}function rY(e,t,n){if(0!=(8388480&n)){var r=t.lanes;r&=e.pendingLanes,n|=r,t.lanes=n,ep(e,n)}}ih=function(){return{lastEffect:null,events:null,stores:null}};var rX={readContext:lU,use:rg,useCallback:ra,useContext:ra,useEffect:ra,useImperativeHandle:ra,useInsertionEffect:ra,useLayoutEffect:ra,useMemo:ra,useReducer:ra,useRef:ra,useState:ra,useDebugValue:ra,useDeferredValue:ra,useTransition:ra,useSyncExternalStore:ra,useId:ra};rX.useCacheRefresh=ra;var rG={readContext:lU,use:rg,useCallback:function(e,t){return rp().memoizedState=[e,void 0===t?null:t],e},useContext:lU,useEffect:rT,useImperativeHandle:function(e,t,n){n=null!=n?n.concat([e]):null,r_(4194308,4,rR.bind(null,t,e),n)},useLayoutEffect:function(e,t){return r_(4194308,4,e,t)},useInsertionEffect:function(e,t){r_(4,2,e,t)},useMemo:function(e,t){var n=rp();return t=void 0===t?null:t,re&&e(),e=e(),n.memoizedState=[e,t],e},useReducer:function(e,t,n){var r=rp();return t=void 0!==n?n(t):t,r.memoizedState=r.baseState=t,e={pending:null,lanes:0,dispatch:null,lastRenderedReducer:e,lastRenderedState:t},r.queue=e,e=e.dispatch=rj.bind(null,n8,e),[r.memoizedState,e]},useRef:function(e){return e={current:e},rp().memoizedState=e},useState:function(e){var t=(e=rz(e)).queue,n=rH.bind(null,n8,t);return t.dispatch=n,[e.memoizedState,n]},useDebugValue:rA,useDeferredValue:function(e){return rp().memoizedState=e},useTransition:function(){var e=rz(!1);return e=rQ.bind(null,n8,e.queue,!0,!1),rp().memoizedState=e,[!1,e]},useSyncExternalStore:function(e,t,n){var r=n8,l=rp();if(t$){if(void 0===n)throw Error(i(407));n=n()}else{if(n=t(),null===aZ)throw Error(i(349));0!=(60&n4)||rw(r,t,n)}l.memoizedState=n;var a={value:n,getSnapshot:t};return l.queue=a,rT(rE.bind(null,r,a,e),[e]),r.flags|=2048,rP(9,rS.bind(null,r,a,n,t),{destroy:void 0},null),n},useId:function(){var e=rp(),t=aZ.identifierPrefix;if(t$){var n=tO,r=tR;t=":"+t+"R"+(n=(r&~(1<<32-et(r)-1)).toString(32)+n),0<(n=rt++)&&(t+="H"+n.toString(32)),t+=":"}else t=":"+t+"r"+(n=rl++).toString(32)+":";return e.memoizedState=t},useCacheRefresh:function(){return rp().memoizedState=rW.bind(null,n8)}},rZ={readContext:lU,use:rg,useCallback:rI,useContext:lU,useEffect:rM,useImperativeHandle:rO,useInsertionEffect:rF,useLayoutEffect:rD,useMemo:rU,useReducer:rv,useRef:rN,useState:function(){return rv(ry)},useDebugValue:rA,useDeferredValue:function(e){return rB(rh(),n6.memoizedState,e)},useTransition:function(){var e=rv(ry)[0],t=rh().memoizedState;return["boolean"==typeof e?e:rm(e),t]},useSyncExternalStore:rk,useId:rV};rZ.useCacheRefresh=r$;var rJ={readContext:lU,use:rg,useCallback:rI,useContext:lU,useEffect:rM,useImperativeHandle:rO,useInsertionEffect:rF,useLayoutEffect:rD,useMemo:rU,useReducer:rb,useRef:rN,useState:function(){return rb(ry)},useDebugValue:rA,useDeferredValue:function(e){var t=rh();return null===n6?t.memoizedState=e:rB(t,n6.memoizedState,e)},useTransition:function(){var e=rb(ry)[0],t=rh().memoizedState;return["boolean"==typeof e?e:rm(e),t]},useSyncExternalStore:rk,useId:rV};function r0(e,t){if(e&&e.defaultProps)for(var n in t=u({},t),e=e.defaultProps)void 0===t[n]&&(t[n]=e[n]);return t}function r1(e,t,n,r){t=e.memoizedState,n=null==(n=n(r,t))?t:u({},t,n),e.memoizedState=n,0===e.lanes&&(e.updateQueue.baseState=n)}rJ.useCacheRefresh=r$;var r2={isMounted:function(e){return!!(e=e._reactInternals)&&td(e)===e},enqueueSetState:function(e,t,n){var r=om(e=e._reactInternals),l=no(r);l.payload=t,null!=n&&(l.callback=n),null!==(t=ni(e,l,r))&&(og(t,e,r),nu(t,e,r))},enqueueReplaceState:function(e,t,n){var r=om(e=e._reactInternals),l=no(r);l.tag=1,l.payload=t,null!=n&&(l.callback=n),null!==(t=ni(e,l,r))&&(og(t,e,r),nu(t,e,r))},enqueueForceUpdate:function(e,t){var n=om(e=e._reactInternals),r=no(n);r.tag=2,null!=t&&(r.callback=t),null!==(t=ni(e,r,n))&&(og(t,e,n),nu(t,e,n))}};function r3(e,t,n,r,l,a,o){return"function"==typeof(e=e.stateNode).shouldComponentUpdate?e.shouldComponentUpdate(r,a,o):!t.prototype||!t.prototype.isPureReactComponent||!np(n,r)||!np(l,a)}function r4(e,t,n){var r=!1,l=tg,a=t.contextType;return"object"==typeof a&&null!==a?a=lU(a):(l=tw(t)?tb:ty.current,a=(r=null!=(r=t.contextTypes))?tk(e,l):tg),t=new t(n,a),e.memoizedState=null!==t.state&&void 0!==t.state?t.state:null,t.updater=r2,e.stateNode=t,t._reactInternals=e,r&&((e=e.stateNode).__reactInternalMemoizedUnmaskedChildContext=l,e.__reactInternalMemoizedMaskedChildContext=a),t}function r8(e,t,n,r){e=t.state,"function"==typeof t.componentWillReceiveProps&&t.componentWillReceiveProps(n,r),"function"==typeof t.UNSAFE_componentWillReceiveProps&&t.UNSAFE_componentWillReceiveProps(n,r),t.state!==e&&r2.enqueueReplaceState(t,t.state,null)}function r6(e,t,n,r){var l=e.stateNode;l.props=n,l.state=e.memoizedState,l.refs={},nl(e);var a=t.contextType;"object"==typeof a&&null!==a?l.context=lU(a):(a=tw(t)?tb:ty.current,l.context=tk(e,a)),l.state=e.memoizedState,"function"==typeof(a=t.getDerivedStateFromProps)&&(r1(e,t,a,n),l.state=e.memoizedState),"function"==typeof t.getDerivedStateFromProps||"function"==typeof l.getSnapshotBeforeUpdate||"function"!=typeof l.UNSAFE_componentWillMount&&"function"!=typeof l.componentWillMount||(t=l.state,"function"==typeof l.componentWillMount&&l.componentWillMount(),"function"==typeof l.UNSAFE_componentWillMount&&l.UNSAFE_componentWillMount(),t!==l.state&&r2.enqueueReplaceState(l,l.state,null),nc(e,n,l,r),l.state=e.memoizedState),"function"==typeof l.componentDidMount&&(e.flags|=4194308)}function r5(e,t){try{var n="",r=t;do n+=function(e){switch(e.tag){case 26:case 27:case 5:return eW(e.type);case 16:return eW("Lazy");case 13:return eW("Suspense");case 19:return eW("SuspenseList");case 0:case 2:case 15:return e=eH(e.type,!1);case 11:return e=eH(e.type.render,!1);case 1:return e=eH(e.type,!0);default:return""}}(r),r=r.return;while(r);var l=n}catch(e){l="\nError generating stack: "+e.message+"\n"+e.stack}return{value:e,source:t,stack:l,digest:null}}function r7(e,t,n){return{value:e,source:null,stack:null!=n?n:null,digest:null!=t?t:null}}function r9(e,t){try{console.error(t.value)}catch(e){setTimeout(function(){throw e})}}function le(e,t,n){(n=no(n)).tag=3,n.payload={element:null};var r=t.value;return n.callback=function(){oa||(oa=!0,oo=r),r9(e,t)},n}function lt(e,t,n){(n=no(n)).tag=3;var r=e.type.getDerivedStateFromError;if("function"==typeof r){var l=t.value;n.payload=function(){return r(l)},n.callback=function(){r9(e,t)}}var a=e.stateNode;return null!==a&&"function"==typeof a.componentDidCatch&&(n.callback=function(){r9(e,t),"function"!=typeof r&&(null===oi?oi=new Set([this]):oi.add(this));var n=t.stack;this.componentDidCatch(t.value,{componentStack:null!==n?n:""})}),n}function ln(e,t,n,r,l){return 0==(1&e.mode)?(e===t?e.flags|=65536:(e.flags|=128,n.flags|=131072,n.flags&=-52805,1===n.tag&&(null===n.alternate?n.tag=17:((t=no(2)).tag=2,ni(n,t,2))),n.lanes|=2),e):(e.flags|=65536,e.lanes=l,e)}var lr=s.ReactCurrentOwner,ll=Error(i(461)),la=!1;function lo(e,t,n,r){t.child=null===e?nL(t,null,n,r):n_(t,e.child,n,r)}function li(e,t,n,r,l){n=n.render;var a=t.ref;return(lI(t,l),r=ri(e,t,n,r,a,l),n=rc(),null===e||la)?(t$&&n&&tU(t),t.flags|=1,lo(e,t,r,l),t.child):(rf(e,t,l),lN(e,t,l))}function lu(e,t,n,r,l){if(null===e){var a=n.type;return"function"!=typeof a||oK(a)||void 0!==a.defaultProps||null!==n.compare||void 0!==n.defaultProps?((e=oG(n.type,null,r,t,t.mode,l)).ref=t.ref,e.return=t,t.child=e):(t.tag=15,t.type=a,ls(e,t,a,r,l))}if(a=e.child,0==(e.lanes&l)){var o=a.memoizedProps;if((n=null!==(n=n.compare)?n:np)(o,r)&&e.ref===t.ref)return lN(e,t,l)}return t.flags|=1,(e=oY(a,r)).ref=t.ref,e.return=t,t.child=e}function ls(e,t,n,r,l){if(null!==e){var a=e.memoizedProps;if(np(a,r)&&e.ref===t.ref){if(la=!1,t.pendingProps=r=a,0==(e.lanes&l))return t.lanes=e.lanes,lN(e,t,l);0!=(131072&e.flags)&&(la=!0)}}return lp(e,t,n,r,l)}function lc(e,t,n){var r=t.pendingProps,l=r.children,a=0!=(2&t.stateNode._pendingVisibility),o=null!==e?e.memoizedState:null;if(ld(e,t),"hidden"===r.mode||a){if(0!=(128&t.flags)){if(n=null!==o?o.baseLanes|n:n,null!==e){for(l=0,r=t.child=e.child;null!==r;)l=l|r.lanes|r.childLanes,r=r.sibling;t.childLanes=l&~n}else t.childLanes=0,t.child=null;return lf(e,t,n)}if(0==(1&t.mode))t.memoizedState={baseLanes:0,cachePool:null},null!==e&&lG(t,null),nD(),nU(t);else{if(0==(1073741824&n))return t.lanes=t.childLanes=1073741824,lf(e,t,null!==o?o.baseLanes|n:n);t.memoizedState={baseLanes:0,cachePool:null},null!==e&&lG(t,null!==o?o.cachePool:null),null!==o?nF(t,o):nD(),nU(t)}}else null!==o?(lG(t,o.cachePool),nF(t,o),nB(t),t.memoizedState=null):(null!==e&&lG(t,null),nD(),nB(t));return lo(e,t,l,n),t.child}function lf(e,t,n){var r=lX();return r=null===r?null:{parent:lj._currentValue,pool:r},t.memoizedState={baseLanes:n,cachePool:r},null!==e&&lG(t,null),nD(),nU(t),null}function ld(e,t){var n=t.ref;(null===e&&null!==n||null!==e&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function lp(e,t,n,r,l){var a=tw(n)?tb:ty.current;return(a=tk(t,a),lI(t,l),n=ri(e,t,n,r,a,l),r=rc(),null===e||la)?(t$&&r&&tU(t),t.flags|=1,lo(e,t,n,l),t.child):(rf(e,t,l),lN(e,t,l))}function lh(e,t,n,r,l,a){return(lI(t,a),n=rs(t,r,n,l),ru(),r=rc(),null===e||la)?(t$&&r&&tU(t),t.flags|=1,lo(e,t,n,a),t.child):(rf(e,t,a),lN(e,t,a))}function lm(e,t,n,r,l){if(tw(n)){var a=!0;tx(t)}else a=!1;if(lI(t,l),null===t.stateNode)lP(e,t),r4(t,n,r),r6(t,n,r,l),r=!0;else if(null===e){var o=t.stateNode,i=t.memoizedProps;o.props=i;var u=o.context,s=n.contextType;s="object"==typeof s&&null!==s?lU(s):tk(t,s=tw(n)?tb:ty.current);var c=n.getDerivedStateFromProps,f="function"==typeof c||"function"==typeof o.getSnapshotBeforeUpdate;f||"function"!=typeof o.UNSAFE_componentWillReceiveProps&&"function"!=typeof o.componentWillReceiveProps||(i!==r||u!==s)&&r8(t,o,r,s),nr=!1;var d=t.memoizedState;o.state=d,nc(t,r,o,l),u=t.memoizedState,i!==r||d!==u||tv.current||nr?("function"==typeof c&&(r1(t,n,c,r),u=t.memoizedState),(i=nr||r3(t,n,i,r,d,u,s))?(f||"function"!=typeof o.UNSAFE_componentWillMount&&"function"!=typeof o.componentWillMount||("function"==typeof o.componentWillMount&&o.componentWillMount(),"function"==typeof o.UNSAFE_componentWillMount&&o.UNSAFE_componentWillMount()),"function"==typeof o.componentDidMount&&(t.flags|=4194308)):("function"==typeof o.componentDidMount&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=u),o.props=r,o.state=u,o.context=s,r=i):("function"==typeof o.componentDidMount&&(t.flags|=4194308),r=!1)}else{o=t.stateNode,na(e,t),i=t.memoizedProps,s=t.type===t.elementType?i:r0(t.type,i),o.props=s,f=t.pendingProps,d=o.context,u="object"==typeof(u=n.contextType)&&null!==u?lU(u):tk(t,u=tw(n)?tb:ty.current);var p=n.getDerivedStateFromProps;(c="function"==typeof p||"function"==typeof o.getSnapshotBeforeUpdate)||"function"!=typeof o.UNSAFE_componentWillReceiveProps&&"function"!=typeof o.componentWillReceiveProps||(i!==f||d!==u)&&r8(t,o,r,u),nr=!1,d=t.memoizedState,o.state=d,nc(t,r,o,l);var h=t.memoizedState;i!==f||d!==h||tv.current||nr?("function"==typeof p&&(r1(t,n,p,r),h=t.memoizedState),(s=nr||r3(t,n,s,r,d,h,u)||!1)?(c||"function"!=typeof o.UNSAFE_componentWillUpdate&&"function"!=typeof o.componentWillUpdate||("function"==typeof o.componentWillUpdate&&o.componentWillUpdate(r,h,u),"function"==typeof o.UNSAFE_componentWillUpdate&&o.UNSAFE_componentWillUpdate(r,h,u)),"function"==typeof o.componentDidUpdate&&(t.flags|=4),"function"==typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):("function"!=typeof o.componentDidUpdate||i===e.memoizedProps&&d===e.memoizedState||(t.flags|=4),"function"!=typeof o.getSnapshotBeforeUpdate||i===e.memoizedProps&&d===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=u,r=s):("function"!=typeof o.componentDidUpdate||i===e.memoizedProps&&d===e.memoizedState||(t.flags|=4),"function"!=typeof o.getSnapshotBeforeUpdate||i===e.memoizedProps&&d===e.memoizedState||(t.flags|=1024),r=!1)}return lg(e,t,n,r,a,l)}function lg(e,t,n,r,l,a){ld(e,t);var o=0!=(128&t.flags);if(!r&&!o)return l&&tz(t,n,!1),lN(e,t,a);r=t.stateNode,lr.current=t;var i=o&&"function"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=n_(t,e.child,null,a),t.child=n_(t,null,i,a)):lo(e,t,i,a),t.memoizedState=r.state,l&&tz(t,n,!0),t.child}function ly(e){var t=e.stateNode;t.pendingContext?tE(e,t.pendingContext,t.pendingContext!==t.context):t.context&&tE(e,t.context,!1),I(e,t.containerInfo)}function lv(e,t,n,r,l){return t3(),t4(l),t.flags|=256,lo(e,t,n,r),t.child}var lb={dehydrated:null,treeContext:null,retryLane:0};function lk(e){return{baseLanes:e,cachePool:lZ()}}function lw(e,t,n){var r,l=t.pendingProps,a=!1,o=0!=(128&t.flags);if((r=o)||(r=(null===e||null!==e.memoizedState)&&0!=(2&nV.current)),r&&(a=!0,t.flags&=-129),null===e){if(t$){if(a?nI(t):nB(t),t$&&((o=e=tV)?tX(t,o)||(tG(t)&&tZ(),tV=sP(o.nextSibling),r=tQ,tV&&tX(t,tV)?tH(r,o):(tq(tQ,t),t$=!1,tQ=t,tV=e)):(tG(t)&&tZ(),tq(tQ,t),t$=!1,tQ=t,tV=e)),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated))return 0==(1&t.mode)?t.lanes=2:"$!"===e.data?t.lanes=16:t.lanes=1073741824,null;nQ(t)}return(o=l.children,e=l.fallback,a)?(nB(t),l=t.mode,a=t.child,o={mode:"hidden",children:o},0==(1&l)&&null!==a?(a.childLanes=0,a.pendingProps=o):a=oJ(o,l,0,null),e=oZ(e,l,n,null),a.return=t,e.return=t,a.sibling=e,t.child=a,t.child.memoizedState=lk(n),t.memoizedState=lb,e):(nI(t),lS(t,o))}if(null!==(r=e.memoizedState)){var u=r.dehydrated;if(null!==u)return function(e,t,n,r,l,a,o){if(n)return 256&t.flags?(nI(t),t.flags&=-257,lE(e,t,o,r=r7(Error(i(422))))):null!==t.memoizedState?(nB(t),t.child=e.child,t.flags|=128,null):(nB(t),a=r.fallback,l=t.mode,r=oJ({mode:"visible",children:r.children},l,0,null),a=oZ(a,l,o,null),a.flags|=2,r.return=t,a.return=t,r.sibling=a,t.child=r,0!=(1&t.mode)&&n_(t,e.child,null,o),t.child.memoizedState=lk(o),t.memoizedState=lb,a);if(nI(t),0==(1&t.mode))return lE(e,t,o,null);if("$!"===l.data){if(r=l.nextSibling&&l.nextSibling.dataset)var u=r.dgst;return r=u,(a=Error(i(419))).digest=r,r=r7(a,r,void 0),lE(e,t,o,r)}if(u=0!=(o&e.childLanes),la||u){if(null!==(r=aZ)){switch(o&-o){case 2:l=1;break;case 8:l=4;break;case 32:l=16;break;case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:l=64;break;case 536870912:l=268435456;break;default:l=0}if(0!==(l=0!=(l&(r.suspendedLanes|o))?0:l)&&l!==a.retryLane)throw a.retryLane=l,ne(e,l),og(r,e,l),ll}return oL(),lE(e,t,o,null)}return"$?"===l.data?(t.flags|=128,t.child=e.child,t=oW.bind(null,e),l._reactRetry=t,null):(e=a.treeContext,tV=sP(l.nextSibling),tQ=t,t$=!0,tW=null,tj=!1,null!==e&&(tM[tF++]=tR,tM[tF++]=tO,tM[tF++]=tD,tR=e.id,tO=e.overflow,tD=t),t=lS(t,r.children),t.flags|=4096,t)}(e,t,o,l,u,r,n)}if(a){nB(t),a=l.fallback,o=t.mode,u=(r=e.child).sibling;var s={mode:"hidden",children:l.children};return 0==(1&o)&&t.child!==r?((l=t.child).childLanes=0,l.pendingProps=s,t.deletions=null):(l=oY(r,s)).subtreeFlags=31457280&r.subtreeFlags,null!==u?a=oY(u,a):(a=oZ(a,o,n,null),a.flags|=2),a.return=t,l.return=t,l.sibling=a,t.child=l,l=a,a=t.child,null===(o=e.child.memoizedState)?o=lk(n):(null!==(r=o.cachePool)?(u=lj._currentValue,r=r.parent!==u?{parent:u,pool:u}:r):r=lZ(),o={baseLanes:o.baseLanes|n,cachePool:r}),a.memoizedState=o,a.childLanes=e.childLanes&~n,t.memoizedState=lb,l}return nI(t),e=(a=e.child).sibling,l=oY(a,{mode:"visible",children:l.children}),0==(1&t.mode)&&(l.lanes=n),l.return=t,l.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=l,t.memoizedState=null,l}function lS(e,t){return(t=oJ({mode:"visible",children:t},e.mode,0,null)).return=e,e.child=t}function lE(e,t,n,r){return null!==r&&t4(r),n_(t,e.child,null,n),e=lS(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function lC(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),lO(e.return,t,n)}function lx(e,t,n,r,l){var a=e.memoizedState;null===a?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:l}:(a.isBackwards=t,a.rendering=null,a.renderingStartTime=0,a.last=r,a.tail=n,a.tailMode=l)}function lz(e,t,n){var r=t.pendingProps,l=r.revealOrder,a=r.tail;if(lo(e,t,r.children,n),0!=(2&(r=nV.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&lC(e,n,t);else if(19===e.tag)lC(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(h(nV,r),0==(1&t.mode))t.memoizedState=null;else switch(l){case"forwards":for(l=null,n=t.child;null!==n;)null!==(e=n.alternate)&&null===n$(e)&&(l=n),n=n.sibling;null===(n=l)?(l=t.child,t.child=null):(l=n.sibling,n.sibling=null),lx(t,!1,l,n,a);break;case"backwards":for(n=null,l=t.child,t.child=null;null!==l;){if(null!==(e=l.alternate)&&null===n$(e)){t.child=l;break}e=l.sibling,l.sibling=n,n=l,l=e}lx(t,!0,n,null,a);break;case"together":lx(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function lP(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function lN(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),a5|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=oY(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=oY(e,e.pendingProps)).return=t;n.sibling=null}return t.child}var l_=d(null),lL=null,lT=null,lM=null;function lF(){lM=lT=lL=null}function lD(e,t,n){h(l_,t._currentValue),t._currentValue=n}function lR(e){var t=l_.current;e._currentValue=t===M?e._defaultValue:t,p(l_)}function lO(e,t,n){for(;null!==e;){var r=e.alternate;if((e.childLanes&t)!==t?(e.childLanes|=t,null!==r&&(r.childLanes|=t)):null!==r&&(r.childLanes&t)!==t&&(r.childLanes|=t),e===n)break;e=e.return}}function lA(e,t,n){var r=e.child;for(null!==r&&(r.return=e);null!==r;){var l=r.dependencies;if(null!==l)for(var a=r.child,o=l.firstContext;null!==o;){if(o.context===t){if(1===r.tag){(o=no(n&-n)).tag=2;var u=r.updateQueue;if(null!==u){var s=(u=u.shared).pending;null===s?o.next=o:(o.next=s.next,s.next=o),u.pending=o}}r.lanes|=n,null!==(o=r.alternate)&&(o.lanes|=n),lO(r.return,n,e),l.lanes|=n;break}o=o.next}else if(10===r.tag)a=r.type===e.type?null:r.child;else if(18===r.tag){if(null===(a=r.return))throw Error(i(341));a.lanes|=n,null!==(l=a.alternate)&&(l.lanes|=n),lO(a,n,e),a=r.sibling}else a=r.child;if(null!==a)a.return=r;else for(a=r;null!==a;){if(a===e){a=null;break}if(null!==(r=a.sibling)){r.return=a.return,a=r;break}a=a.return}r=a}}function lI(e,t){lL=e,lM=lT=null,null!==(e=e.dependencies)&&null!==e.firstContext&&(0!=(e.lanes&t)&&(la=!0),e.firstContext=null)}function lU(e){return lQ(lL,e)}function lB(e,t,n){return null===lL&&lI(e,n),lQ(e,t)}function lQ(e,t){var n=t._currentValue;if(lM!==t){if(t={context:t,memoizedValue:n,next:null},null===lT){if(null===e)throw Error(i(308));lT=t,e.dependencies={lanes:0,firstContext:t}}else lT=lT.next=t}return n}var lV="undefined"!=typeof AbortController?AbortController:function(){var e=[],t=this.signal={aborted:!1,addEventListener:function(t,n){e.push(n)}};this.abort=function(){t.aborted=!0,e.forEach(function(e){return e()})}},l$=a.unstable_scheduleCallback,lW=a.unstable_NormalPriority,lj={$$typeof:w,Consumer:null,Provider:null,_currentValue:null,_currentValue2:null,_threadCount:0,_defaultValue:null,_globalName:null};function lH(){return{controller:new lV,data:new Map,refCount:0}}function lq(e){e.refCount--,0===e.refCount&&l$(lW,function(){e.controller.abort()})}var lK=s.ReactCurrentBatchConfig,lY=d(null);function lX(){var e=lY.current;return null!==e?e:aZ.pooledCache}function lG(e,t){null===t?h(lY,lY.current):h(lY,t.pool)}function lZ(){var e=lX();return null===e?null:{parent:lj._currentValue,pool:e}}function lJ(e){e.flags|=4}function l0(e){e.flags|=2097664}function l1(e,t,n,r){if((e=e.memoizedProps)!==r){n=null;var l,a,o=null;for(l in e)if(!r.hasOwnProperty(l)&&e.hasOwnProperty(l)&&null!=e[l]){if("style"===l){var i=e[l];for(a in i)i.hasOwnProperty(a)&&(o||(o={}),o[a]="")}else(n=n||[]).push(l,null)}for(l in r){i=r[l];var u=null!=e?e[l]:void 0;if(r.hasOwnProperty(l)&&i!==u&&(null!=i||null!=u)){if("style"===l){if(u){for(a in u)!u.hasOwnProperty(a)||i&&i.hasOwnProperty(a)||(o||(o={}),o[a]="");for(a in i)i.hasOwnProperty(a)&&u[a]!==i[a]&&(o||(o={}),o[a]=i[a])}else o||(n||(n=[]),n.push(l,o)),o=i}else(n=n||[]).push(l,i)}}o&&(n=n||[]).push("style",o),r=n,(t.updateQueue=r)&&lJ(t)}}function l2(e,t){if("stylesheet"!==t.type||0!=(4&t.state.loading))e.flags&=-16777217;else if(e.flags|=16777216,0==(42&a0)&&!(t="stylesheet"!==t.type||0!=(3&t.state.loading))){if(oP())e.flags|=8192;else throw nk=ng,nm}}function l3(e,t){null!==t?e.flags|=4:16384&e.flags&&(t=22!==e.tag?ec():1073741824,e.lanes|=t)}function l4(e,t){if(!t$)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function l8(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var l=e.child;null!==l;)n|=l.lanes|l.childLanes,r|=31457280&l.subtreeFlags,r|=31457280&l.flags,l.return=e,l=l.sibling;else for(l=e.child;null!==l;)n|=l.lanes|l.childLanes,r|=l.subtreeFlags,r|=l.flags,l.return=e,l=l.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function l6(e,t){switch(tB(t),t.tag){case 1:null!=(e=t.type.childContextTypes)&&tS();break;case 3:lR(lj),U(),p(tv),p(ty);break;case 26:case 27:case 5:Q(t);break;case 4:U();break;case 13:nQ(t);break;case 19:p(nV);break;case 10:lR(t.type._context);break;case 22:case 23:nQ(t),nR(),null!==e&&p(lY);break;case 24:lR(lj)}}function l5(e,t,n){var r=Array.prototype.slice.call(arguments,3);try{t.apply(n,r)}catch(e){this.onError(e)}}var l7=!1,l9=null,ae=!1,at=null,an={onError:function(e){l7=!0,l9=e}};function ar(e,t,n,r,l,a,o,i,u){l7=!1,l9=null,l5.apply(an,arguments)}var al=!1,aa=!1,ao="function"==typeof WeakSet?WeakSet:Set,ai=null;function au(e,t){try{var n=e.ref;if(null!==n){var r=e.stateNode;switch(e.tag){case 26:case 27:case 5:var l=r;break;default:l=r}"function"==typeof n?e.refCleanup=n(l):n.current=l}}catch(n){oB(e,t,n)}}function as(e,t){var n=e.ref,r=e.refCleanup;if(null!==n){if("function"==typeof r)try{r()}catch(n){oB(e,t,n)}finally{e.refCleanup=null,null!=(e=e.alternate)&&(e.refCleanup=null)}else if("function"==typeof n)try{n(null)}catch(n){oB(e,t,n)}else n.current=null}}function ac(e,t,n){try{n()}catch(n){oB(e,t,n)}}var af=!1;function ad(e,t,n){var r=t.updateQueue;if(null!==(r=null!==r?r.lastEffect:null)){var l=r=r.next;do{if((l.tag&e)===e){var a=l.inst,o=a.destroy;void 0!==o&&(a.destroy=void 0,ac(t,n,o))}l=l.next}while(l!==r)}}function ap(e,t){if(null!==(t=null!==(t=t.updateQueue)?t.lastEffect:null)){var n=t=t.next;do{if((n.tag&e)===e){var r=n.create,l=n.inst;r=r(),l.destroy=r}n=n.next}while(n!==t)}}function ah(e,t){try{ap(t,e)}catch(t){oB(e,e.return,t)}}function am(e){var t=e.updateQueue;if(null!==t){var n=e.stateNode;try{nd(t,n)}catch(t){oB(e,e.return,t)}}}function ag(e){var t=e.type,n=e.memoizedProps,r=e.stateNode;try{switch(t){case"button":case"input":case"select":case"textarea":n.autoFocus&&r.focus();break;case"img":n.src&&(r.src=n.src)}}catch(t){oB(e,e.return,t)}}function ay(e,t,n){var r=n.flags;switch(n.tag){case 0:case 11:case 15:aT(e,n),4&r&&ah(n,5);break;case 1:if(aT(e,n),4&r){if(e=n.stateNode,null===t)try{e.componentDidMount()}catch(e){oB(n,n.return,e)}else{var l=n.elementType===n.type?t.memoizedProps:r0(n.type,t.memoizedProps);t=t.memoizedState;try{e.componentDidUpdate(l,t,e.__reactInternalSnapshotBeforeUpdate)}catch(e){oB(n,n.return,e)}}}64&r&&am(n),512&r&&au(n,n.return);break;case 3:if(aT(e,n),64&r&&null!==(r=n.updateQueue)){if(e=null,null!==n.child)switch(n.child.tag){case 27:case 5:case 1:e=n.child.stateNode}try{nd(r,e)}catch(e){oB(n,n.return,e)}}break;case 26:aT(e,n),512&r&&au(n,n.return);break;case 27:case 5:aT(e,n),null===t&&4&r&&ag(n),512&r&&au(n,n.return);break;case 12:default:aT(e,n);break;case 13:aT(e,n),4&r&&ax(e,n);break;case 22:if(0!=(1&n.mode)){if(!(l=null!==n.memoizedState||al)){t=null!==t&&null!==t.memoizedState||aa;var a=al,o=aa;al=l,(aa=t)&&!o?function e(t,n,r){for(r=r&&0!=(8772&n.subtreeFlags),n=n.child;null!==n;){var l=n.alternate,a=t,o=n,i=o.flags;switch(o.tag){case 0:case 11:case 15:e(a,o,r),ah(o,4);break;case 1:if(e(a,o,r),"function"==typeof(a=o.stateNode).componentDidMount)try{a.componentDidMount()}catch(e){oB(o,o.return,e)}if(null!==(l=o.updateQueue)){var u=l.shared.hiddenCallbacks;if(null!==u)for(l.shared.hiddenCallbacks=null,l=0;l<u.length;l++)nf(u[l],a)}r&&64&i&&am(o),au(o,o.return);break;case 26:case 27:case 5:e(a,o,r),r&&null===l&&4&i&&ag(o),au(o,o.return);break;case 12:default:e(a,o,r);break;case 13:e(a,o,r),r&&4&i&&ax(a,o);break;case 22:null===o.memoizedState&&e(a,o,r),au(o,o.return)}n=n.sibling}}(e,n,0!=(8772&n.subtreeFlags)):aT(e,n),al=a,aa=o}}else aT(e,n);512&r&&("manual"===n.memoizedProps.mode?au(n,n.return):as(n,n.return))}}function av(e){return 5===e.tag||3===e.tag||26===e.tag||27===e.tag||4===e.tag}function ab(e){e:for(;;){for(;null===e.sibling;){if(null===e.return||av(e.return))return null;e=e.return}for(e.sibling.return=e.return,e=e.sibling;5!==e.tag&&6!==e.tag&&27!==e.tag&&18!==e.tag;){if(2&e.flags||null===e.child||4===e.tag)continue e;e.child.return=e,e=e.child}if(!(2&e.flags))return e.stateNode}}function ak(e,t,n){var r=e.tag;if(5===r||6===r)e=e.stateNode,t?n.insertBefore(e,t):n.appendChild(e);else if(4!==r&&27!==r&&null!==(e=e.child))for(ak(e,t,n),e=e.sibling;null!==e;)ak(e,t,n),e=e.sibling}var aw=null,aS=!1;function aE(e,t,n){for(n=n.child;null!==n;)aC(e,t,n),n=n.sibling}function aC(e,t,n){if(ee&&"function"==typeof ee.onCommitFiberUnmount)try{ee.onCommitFiberUnmount(J,n)}catch(e){}switch(n.tag){case 26:aa||as(n,t),aE(e,t,n),n.memoizedState?n.memoizedState.count--:n.stateNode&&(n=n.stateNode).parentNode.removeChild(n);break;case 27:aa||as(n,t);var r=aw,l=aS;for(aw=n.stateNode,aE(e,t,n),e=(n=n.stateNode).attributes;e.length;)n.removeAttributeNode(e[0]);ez(n),aw=r,aS=l;break;case 5:aa||as(n,t);case 6:r=aw,l=aS,aw=null,aE(e,t,n),aw=r,aS=l,null!==aw&&(aS?(e=aw,n=n.stateNode,8===e.nodeType?e.parentNode.removeChild(n):e.removeChild(n)):aw.removeChild(n.stateNode));break;case 18:null!==aw&&(aS?(e=aw,n=n.stateNode,8===e.nodeType?sC(e.parentNode,n):1===e.nodeType&&sC(e,n),i1(e)):sC(aw,n.stateNode));break;case 4:r=aw,l=aS,aw=n.stateNode.containerInfo,aS=!0,aE(e,t,n),aw=r,aS=l;break;case 0:case 11:case 14:case 15:if(!aa&&null!==(r=n.updateQueue)&&null!==(r=r.lastEffect)){l=r=r.next;do{var a=l.tag,o=l.inst,i=o.destroy;void 0!==i&&(0!=(2&a)?(o.destroy=void 0,ac(n,t,i)):0!=(4&a)&&(o.destroy=void 0,ac(n,t,i))),l=l.next}while(l!==r)}aE(e,t,n);break;case 1:if(!aa&&(as(n,t),"function"==typeof(r=n.stateNode).componentWillUnmount))try{r.props=n.memoizedProps,r.state=n.memoizedState,r.componentWillUnmount()}catch(e){oB(n,t,e)}aE(e,t,n);break;case 21:default:aE(e,t,n);break;case 22:as(n,t),1&n.mode?(aa=(r=aa)||null!==n.memoizedState,aE(e,t,n),aa=r):aE(e,t,n)}}function ax(e,t){if(null===t.memoizedState&&null!==(e=t.alternate)&&null!==(e=e.memoizedState)&&null!==(e=e.dehydrated))try{i1(e)}catch(e){oB(t,t.return,e)}}function az(e,t){var n=function(e){switch(e.tag){case 13:case 19:var t=e.stateNode;return null===t&&(t=e.stateNode=new ao),t;case 22:return null===(t=(e=e.stateNode)._retryCache)&&(t=e._retryCache=new ao),t;default:throw Error(i(435,e.tag))}}(e);t.forEach(function(t){var r=oj.bind(null,e,t);n.has(t)||(n.add(t),t.then(r,r))})}function aP(e,t){var n=t.deletions;if(null!==n)for(var r=0;r<n.length;r++){var l=n[r];try{var a=t,o=a;e:for(;null!==o;){switch(o.tag){case 27:case 5:aw=o.stateNode,aS=!1;break e;case 3:case 4:aw=o.stateNode.containerInfo,aS=!0;break e}o=o.return}if(null===aw)throw Error(i(160));aC(e,a,l),aw=null,aS=!1;var u=l.alternate;null!==u&&(u.return=null),l.return=null}catch(e){oB(l,t,e)}}if(12854&t.subtreeFlags)for(t=t.child;null!==t;)a_(t,e),t=t.sibling}var aN=null;function a_(e,t){var n=e.alternate,r=e.flags;switch(e.tag){case 0:case 11:case 14:case 15:if(aP(t,e),aL(e),4&r){try{ad(3,e,e.return),ap(3,e)}catch(t){oB(e,e.return,t)}try{ad(5,e,e.return)}catch(t){oB(e,e.return,t)}}break;case 1:aP(t,e),aL(e),512&r&&null!==n&&as(n,n.return),64&r&&al&&null!==(e=e.updateQueue)&&null!==(n=e.callbacks)&&(r=e.shared.hiddenCallbacks,e.shared.hiddenCallbacks=null===r?n:r.concat(n));break;case 26:var l=aN;if(aP(t,e),aL(e),512&r&&null!==n&&as(n,n.return),4&r){if(t=null!==n?n.memoizedState:null,r=e.memoizedState,null===n){if(null===r){if(null===e.stateNode){e:{n=e.type,r=e.memoizedProps,t=l.ownerDocument||l;t:switch(n){case"title":(!(l=t.getElementsByTagName("title")[0])||l[ex]||l[ev]||"http://www.w3.org/2000/svg"===l.namespaceURI||l.hasAttribute("itemprop"))&&(l=t.createElement(n),t.head.insertBefore(l,t.querySelector("head > title"))),sf(l,n,r),l[ev]=e,eM(l),n=l;break e;case"link":var a=sW("link","href",t).get(n+(r.href||""));if(a){for(var o=0;o<a.length;o++)if((l=a[o]).getAttribute("href")===(null==r.href?null:r.href)&&l.getAttribute("rel")===(null==r.rel?null:r.rel)&&l.getAttribute("title")===(null==r.title?null:r.title)&&l.getAttribute("crossorigin")===(null==r.crossOrigin?null:r.crossOrigin)){a.splice(o,1);break t}}sf(l=t.createElement(n),n,r),t.head.appendChild(l);break;case"meta":if(a=sW("meta","content",t).get(n+(r.content||""))){for(o=0;o<a.length;o++)if((l=a[o]).getAttribute("content")===(null==r.content?null:""+r.content)&&l.getAttribute("name")===(null==r.name?null:r.name)&&l.getAttribute("property")===(null==r.property?null:r.property)&&l.getAttribute("http-equiv")===(null==r.httpEquiv?null:r.httpEquiv)&&l.getAttribute("charset")===(null==r.charSet?null:r.charSet)){a.splice(o,1);break t}}sf(l=t.createElement(n),n,r),t.head.appendChild(l);break;default:throw Error(i(468,n))}l[ev]=e,eM(l),n=l}e.stateNode=n}else sj(l,e.type,e.stateNode)}else e.stateNode=sU(l,r,e.memoizedProps)}else if(t!==r)null===t?null!==n.stateNode&&(n=n.stateNode).parentNode.removeChild(n):t.count--,null===r?sj(l,e.type,e.stateNode):sU(l,r,e.memoizedProps);else if(null===r&&null!==e.stateNode&&(r=e.updateQueue,e.updateQueue=null,null!==r))try{var u=e.stateNode,s=e.memoizedProps;sd(u,r,e.type,n.memoizedProps,s),u[eb]=s}catch(t){oB(e,e.return,t)}}break;case 27:if(4&r&&null===e.alternate){for(l=e.stateNode,a=e.memoizedProps,o=l.firstChild;o;){var c=o.nextSibling,f=o.nodeName;o[ex]||"HEAD"===f||"BODY"===f||"SCRIPT"===f||"STYLE"===f||"LINK"===f&&"stylesheet"===o.rel.toLowerCase()||l.removeChild(o),o=c}for(o=e.type,c=l.attributes;c.length;)l.removeAttributeNode(c[0]);sf(l,o,a),l[ev]=e,l[eb]=a}case 5:if(aP(t,e),aL(e),512&r&&null!==n&&as(n,n.return),32&e.flags){t=e.stateNode;try{te(t,"")}catch(t){oB(e,e.return,t)}}if(4&r&&null!=(r=e.stateNode)&&(t=e.memoizedProps,n=null!==n?n.memoizedProps:t,l=e.type,a=e.updateQueue,e.updateQueue=null,null!==a))try{sd(r,a,l,n,t),r[eb]=t}catch(t){oB(e,e.return,t)}break;case 6:if(aP(t,e),aL(e),4&r){if(null===e.stateNode)throw Error(i(162));n=e.stateNode,r=e.memoizedProps;try{n.nodeValue=r}catch(t){oB(e,e.return,t)}}break;case 3:if(s$=null,l=aN,aN=sM(t.containerInfo),aP(t,e),aN=l,aL(e),4&r&&null!==n&&n.memoizedState.isDehydrated)try{i1(t.containerInfo)}catch(t){oB(e,e.return,t)}break;case 4:n=aN,aN=sM(e.stateNode.containerInfo),aP(t,e),aL(e),aN=n;break;case 13:aP(t,e),aL(e),8192&e.child.flags&&null!==e.memoizedState!=(null!==n&&null!==n.memoizedState)&&(on=H()),4&r&&null!==(n=e.updateQueue)&&(e.updateQueue=null,az(e,n));break;case 22:if(512&r&&null!==n&&as(n,n.return),u=null!==e.memoizedState,s=null!==n&&null!==n.memoizedState,1&e.mode){var d=al,p=aa;al=d||u,aa=p||s,aP(t,e),aa=p,al=d}else aP(t,e);if(aL(e),(t=e.stateNode)._current=e,t._visibility&=-3,t._visibility|=2&t._pendingVisibility,8192&r&&(t._visibility=u?-2&t._visibility:1|t._visibility,u&&(t=al||aa,null===n||s||t||0!=(1&e.mode)&&function e(t){for(t=t.child;null!==t;){var n=t;switch(n.tag){case 0:case 11:case 14:case 15:ad(4,n,n.return),e(n);break;case 1:as(n,n.return);var r=n.stateNode;if("function"==typeof r.componentWillUnmount){var l=n.return;try{r.props=n.memoizedProps,r.state=n.memoizedState,r.componentWillUnmount()}catch(e){oB(n,l,e)}}e(n);break;case 26:case 27:case 5:as(n,n.return),e(n);break;case 22:as(n,n.return),null===n.memoizedState&&e(n);break;default:e(n)}t=t.sibling}}(e)),null===e.memoizedProps||"manual"!==e.memoizedProps.mode))e:for(n=null,t=e;;){if(5===t.tag||26===t.tag||27===t.tag){if(null===n){n=t;try{l=t.stateNode,u?(a=l.style,"function"==typeof a.setProperty?a.setProperty("display","none","important"):a.display="none"):(o=t.stateNode,f=null!=(c=t.memoizedProps.style)&&c.hasOwnProperty("display")?c.display:null,o.style.display=null==f||"boolean"==typeof f?"":(""+f).trim())}catch(t){oB(e,e.return,t)}}}else if(6===t.tag){if(null===n)try{t.stateNode.nodeValue=u?"":t.memoizedProps}catch(t){oB(e,e.return,t)}}else if((22!==t.tag&&23!==t.tag||null===t.memoizedState||t===e)&&null!==t.child){t.child.return=t,t=t.child;continue}if(t===e)break;for(;null===t.sibling;){if(null===t.return||t.return===e)break e;n===t&&(n=null),t=t.return}n===t&&(n=null),t.sibling.return=t.return,t=t.sibling}4&r&&null!==(n=e.updateQueue)&&null!==(r=n.retryQueue)&&(n.retryQueue=null,az(e,r));break;case 19:aP(t,e),aL(e),4&r&&null!==(n=e.updateQueue)&&(e.updateQueue=null,az(e,n));break;case 21:break;default:aP(t,e),aL(e)}}function aL(e){var t=e.flags;if(2&t){try{if(27!==e.tag){t:{for(var n=e.return;null!==n;){if(av(n)){var r=n;break t}n=n.return}throw Error(i(160))}switch(r.tag){case 27:var l=r.stateNode,a=ab(e);ak(e,a,l);break;case 5:var o=r.stateNode;32&r.flags&&(te(o,""),r.flags&=-33);var u=ab(e);ak(e,u,o);break;case 3:case 4:var s=r.stateNode.containerInfo,c=ab(e);!function e(t,n,r){var l=t.tag;if(5===l||6===l)t=t.stateNode,n?8===r.nodeType?r.parentNode.insertBefore(t,n):r.insertBefore(t,n):(8===r.nodeType?(n=r.parentNode).insertBefore(t,r):(n=r).appendChild(t),null!=(r=r._reactRootContainer)||null!==n.onclick||(n.onclick=su));else if(4!==l&&27!==l&&null!==(t=t.child))for(e(t,n,r),t=t.sibling;null!==t;)e(t,n,r),t=t.sibling}(e,c,s);break;default:throw Error(i(161))}}}catch(t){oB(e,e.return,t)}e.flags&=-3}4096&t&&(e.flags&=-4097)}function aT(e,t){if(8772&t.subtreeFlags)for(t=t.child;null!==t;)ay(e,t.alternate,t),t=t.sibling}function aM(e,t){try{ap(t,e)}catch(t){oB(e,e.return,t)}}function aF(e,t){var n=null;null!==e&&null!==e.memoizedState&&null!==e.memoizedState.cachePool&&(n=e.memoizedState.cachePool.pool),e=null,null!==t.memoizedState&&null!==t.memoizedState.cachePool&&(e=t.memoizedState.cachePool.pool),e!==n&&(null!=e&&e.refCount++,null!=n&&lq(n))}function aD(e,t){e=null,null!==t.alternate&&(e=t.alternate.memoizedState.cache),(t=t.memoizedState.cache)!==e&&(t.refCount++,null!=e&&lq(e))}function aR(e,t,n,r){if(10256&t.subtreeFlags)for(t=t.child;null!==t;)aO(e,t,n,r),t=t.sibling}function aO(e,t,n,r){var l=t.flags;switch(t.tag){case 0:case 11:case 15:aR(e,t,n,r),2048&l&&aM(t,9);break;case 3:aR(e,t,n,r),2048&l&&(e=null,null!==t.alternate&&(e=t.alternate.memoizedState.cache),(t=t.memoizedState.cache)!==e&&(t.refCount++,null!=e&&lq(e)));break;case 23:break;case 22:var a=t.stateNode;null!==t.memoizedState?4&a._visibility?aR(e,t,n,r):1&t.mode?aA(e,t):(a._visibility|=4,aR(e,t,n,r)):4&a._visibility?aR(e,t,n,r):(a._visibility|=4,function e(t,n,r,l,a){for(a=a&&0!=(10256&n.subtreeFlags),n=n.child;null!==n;){var o=n,i=o.flags;switch(o.tag){case 0:case 11:case 15:e(t,o,r,l,a),aM(o,8);break;case 23:break;case 22:var u=o.stateNode;null!==o.memoizedState?4&u._visibility?e(t,o,r,l,a):1&o.mode?aA(t,o):(u._visibility|=4,e(t,o,r,l,a)):(u._visibility|=4,e(t,o,r,l,a)),a&&2048&i&&aF(o.alternate,o);break;case 24:e(t,o,r,l,a),a&&2048&i&&aD(o.alternate,o);break;default:e(t,o,r,l,a)}n=n.sibling}}(e,t,n,r,0!=(10256&t.subtreeFlags))),2048&l&&aF(t.alternate,t);break;case 24:aR(e,t,n,r),2048&l&&aD(t.alternate,t);break;default:aR(e,t,n,r)}}function aA(e,t){if(10256&t.subtreeFlags)for(t=t.child;null!==t;){var n=t,r=n.flags;switch(n.tag){case 22:aA(e,n),2048&r&&aF(n.alternate,n);break;case 24:aA(e,n),2048&r&&aD(n.alternate,n);break;default:aA(e,n)}t=t.sibling}}var aI=8192;function aU(e){if(e.subtreeFlags&aI)for(e=e.child;null!==e;)aB(e),e=e.sibling}function aB(e){switch(e.tag){case 26:aU(e),e.flags&aI&&null!==e.memoizedState&&function(e,t,n){if(null===sH)throw Error(i(475));var r=sH;if("stylesheet"===t.type&&("string"!=typeof n.media||!1!==matchMedia(n.media).matches)){if(null===t.instance){var l=sR(n.href),a=e.querySelector(sO(l));if(a){null!==(e=a._p)&&"object"==typeof e&&"function"==typeof e.then&&(r.count++,r=sK.bind(r),e.then(r,r)),t.state.loading|=4,t.instance=a,eM(a);return}a=e.ownerDocument||e,n=sA(n),(l=sL.get(l))&&sQ(n,l),eM(a=a.createElement("link"));var o=a;o._p=new Promise(function(e,t){o.onload=e,o.onerror=t}),sf(a,"link",n),t.instance=a}null===r.stylesheets&&(r.stylesheets=new Map),r.stylesheets.set(t,e),(e=t.state.preload)&&0==(3&t.state.loading)&&(r.count++,t=sK.bind(r),e.addEventListener("load",t),e.addEventListener("error",t))}}(aN,e.memoizedState,e.memoizedProps);break;case 5:default:aU(e);break;case 3:case 4:var t=aN;aN=sM(e.stateNode.containerInfo),aU(e),aN=t;break;case 22:null===e.memoizedState&&(null!==(t=e.alternate)&&null!==t.memoizedState?(t=aI,aI=16777216,aU(e),aI=t):aU(e))}}function aQ(e){var t=e.alternate;if(null!==t&&null!==(e=t.child)){t.child=null;do t=e.sibling,e.sibling=null,e=t;while(null!==e)}}function aV(e){var t=e.deletions;if(0!=(16&e.flags)){if(null!==t)for(var n=0;n<t.length;n++){var r=t[n];ai=r,aW(r,e)}aQ(e)}if(10256&e.subtreeFlags)for(e=e.child;null!==e;)a$(e),e=e.sibling}function a$(e){switch(e.tag){case 0:case 11:case 15:aV(e),2048&e.flags&&ad(9,e,e.return);break;case 22:var t=e.stateNode;null!==e.memoizedState&&4&t._visibility&&(null===e.return||13!==e.return.tag)?(t._visibility&=-5,function e(t){var n=t.deletions;if(0!=(16&t.flags)){if(null!==n)for(var r=0;r<n.length;r++){var l=n[r];ai=l,aW(l,t)}aQ(t)}for(t=t.child;null!==t;){switch((n=t).tag){case 0:case 11:case 15:ad(8,n,n.return),e(n);break;case 22:4&(r=n.stateNode)._visibility&&(r._visibility&=-5,e(n));break;default:e(n)}t=t.sibling}}(e)):aV(e);break;default:aV(e)}}function aW(e,t){for(;null!==ai;){var n=ai;switch(n.tag){case 0:case 11:case 15:ad(8,n,t);break;case 23:case 22:if(null!==n.memoizedState&&null!==n.memoizedState.cachePool){var r=n.memoizedState.cachePool.pool;null!=r&&r.refCount++}break;case 24:lq(n.memoizedState.cache)}if(null!==(r=n.child))r.return=n,ai=r;else for(n=e;null!==ai;){var l=(r=ai).sibling,a=r.return;if(!function e(t){var n=t.alternate;null!==n&&(t.alternate=null,e(n)),t.child=null,t.deletions=null,t.sibling=null,5===t.tag&&null!==(n=t.stateNode)&&ez(n),t.stateNode=null,t.return=null,t.dependencies=null,t.memoizedProps=null,t.memoizedState=null,t.pendingProps=null,t.stateNode=null,t.updateQueue=null}(r),r===n){ai=null;break}if(null!==l){l.return=a,ai=l;break}ai=a}}}var aj={getCacheSignal:function(){return lU(lj).controller.signal},getCacheForType:function(e){var t=lU(lj),n=t.data.get(e);return void 0===n&&(n=e(),t.data.set(e,n)),n}},aH="function"==typeof WeakMap?WeakMap:Map,aq=s.ReactCurrentDispatcher,aK=s.ReactCurrentCache,aY=s.ReactCurrentOwner,aX=s.ReactCurrentBatchConfig,aG=0,aZ=null,aJ=null,a0=0,a1=0,a2=null,a3=!1,a4=0,a8=0,a6=null,a5=0,a7=0,a9=0,oe=null,ot=null,on=0,or=1/0,ol=null,oa=!1,oo=null,oi=null,ou=!1,os=null,oc=0,of=0,od=null,op=0,oh=null;function om(e){return 0==(1&e.mode)?2:0!=(2&aG)&&0!==a0?a0&-a0:null!==lK.transition?(0==(e=0)&&(0===nY&&(nY=es()),e=nY),e):0!==(e=eh)?e:e=void 0===(e=window.event)?32:i9(e.type)}function og(e,t,n){(e===aZ&&2===a1||null!==e.cancelPendingCommit)&&(ox(e,0),ow(e,a0)),ed(e,n),(0==(2&aG)||e!==aZ)&&(e===aZ&&(0==(2&aG)&&(a7|=n),4===a8&&ow(e,a0)),nX(e),2===n&&0===aG&&0==(1&t.mode)&&(or=H()+500,nG(!0)))}function oy(e,t){if(0!=(6&aG))throw Error(i(327));var n=e.callbackNode;if(oI()&&e.callbackNode!==n)return null;var r=ei(e,e===aZ?a0:0);if(0===r)return null;if(0!==(t=0!=(60&r)||0!=(r&e.expiredLanes)||t?oT(e,r):function(e,t){var n=aG;aG|=2;var r=oN(),l=o_();(aZ!==e||a0!==t)&&(ol=null,or=H()+500,ox(e,t));e:for(;;)try{if(0!==a1&&null!==aJ){t=aJ;var a=a2;t:switch(a1){case 1:case 6:a1=0,a2=null,oD(t,a);break;case 2:if(ny(a)){a1=0,a2=null,oF(t);break}t=function(){2===a1&&aZ===e&&(a1=7),nX(e)},a.then(t,t);break e;case 3:a1=7;break e;case 4:a1=5;break e;case 7:ny(a)?(a1=0,a2=null,oF(t)):(a1=0,a2=null,oD(t,a));break;case 5:switch(aJ.tag){case 5:case 26:case 27:t=aJ,a1=0,a2=null;var o=t.sibling;if(null!==o)aJ=o;else{var u=t.return;null!==u?(aJ=u,oR(u)):aJ=null}break t}a1=0,a2=null,oD(t,a);break;case 8:oC(),a8=6;break e;default:throw Error(i(462))}}!function(){for(;null!==aJ&&!W();)oM(aJ)}();break}catch(t){oz(e,t)}return(lF(),aq.current=r,aK.current=l,aG=n,null!==aJ)?0:(aZ=null,a0=0,t7(),a8)}(e,r))){if(2===t){var l=r,a=eu(e,l);0!==a&&(r=a,t=ov(e,l,a))}if(1===t)throw n=a6,ox(e,0),ow(e,r),nX(e),n;if(6===t)ow(e,r);else{if(l=e.current.alternate,0==(60&r)&&!function(e){for(var t=e;;){if(16384&t.flags){var n=t.updateQueue;if(null!==n&&null!==(n=n.stores))for(var r=0;r<n.length;r++){var l=n[r],a=l.getSnapshot;l=l.value;try{if(!tP(a(),l))return!1}catch(e){return!1}}}if(n=t.child,16384&t.subtreeFlags&&null!==n)n.return=t,t=n;else{if(t===e)break;for(;null===t.sibling;){if(null===t.return||t.return===e)return!0;t=t.return}t.sibling.return=t.return,t=t.sibling}}return!0}(l)){if(2===(t=oT(e,r))){a=r;var o=eu(e,a);0!==o&&(r=o,t=ov(e,a,o))}if(1===t)throw n=a6,ox(e,0),ow(e,r),nX(e),n}e.finishedWork=l,e.finishedLanes=r;e:{switch(t){case 0:case 1:throw Error(i(345));case 4:if((8388480&r)===r){ow(e,r);break e}break;case 2:case 3:case 5:break;default:throw Error(i(329))}if((125829120&r)===r&&10<(t=on+300-H())){if(ow(e,r),0!==ei(e,0))break e;e.timeoutHandle=sb(ok.bind(null,e,l,ot,ol,r),t);break e}ok(e,l,ot,ol,r)}}}return nX(e),n0(e,H()),e=e.callbackNode===n?oy.bind(null,e):null}function ov(e,t,n){var r=oe,l=e.current.memoizedState.isDehydrated;if(l&&(ox(e,n).flags|=256),2!==(n=oT(e,n))){if(a3&&!l)return e.errorRecoveryDisabledLanes|=t,a7|=t,4;e=ot,ot=r,null!==e&&ob(e)}return n}function ob(e){null===ot?ot=e:ot.push.apply(ot,e)}function ok(e,t,n,r,l){if(0==(42&l)&&(sH={stylesheets:null,count:0,unsuspend:sq},aB(t),null!==(t=function(){if(null===sH)throw Error(i(475));var e=sH;return e.stylesheets&&0===e.count&&sX(e,e.stylesheets),0<e.count?function(t){var n=setTimeout(function(){if(e.stylesheets&&sX(e,e.stylesheets),e.unsuspend){var t=e.unsuspend;e.unsuspend=null,t()}},6e4);return e.unsuspend=t,function(){e.unsuspend=null,clearTimeout(n)}}:null}()))){e.cancelPendingCommit=t(oO.bind(null,e,n,r)),ow(e,l);return}oO(e,n,r)}function ow(e,t){for(t&=~a9,t&=~a7,e.suspendedLanes|=t,e.pingedLanes&=~t,e=e.expirationTimes;0<t;){var n=31-et(t),r=1<<n;e[n]=-1,t&=~r}}function oS(e,t){var n=aG;aG|=1;try{return e(t)}finally{0===(aG=n)&&(or=H()+500,nG(!0))}}function oE(e){null!==os&&0===os.tag&&0==(6&aG)&&oI();var t=aG;aG|=1;var n=aX.transition,r=eh;try{if(aX.transition=null,eh=2,e)return e()}finally{eh=r,aX.transition=n,0==(6&(aG=t))&&nG(!1)}}function oC(){if(null!==aJ){if(0===a1)var e=aJ.return;else e=aJ,lF(),rd(e),nS=null,nE=0,e=aJ;for(;null!==e;)l6(e.alternate,e),e=e.return;aJ=null}}function ox(e,t){e.finishedWork=null,e.finishedLanes=0;var n=e.timeoutHandle;return -1!==n&&(e.timeoutHandle=-1,sk(n)),null!==(n=e.cancelPendingCommit)&&(e.cancelPendingCommit=null,n()),oC(),aZ=e,aJ=e=oY(e.current,null),a0=a4=t,a1=0,a2=null,a3=!1,a8=0,a6=null,a9=a7=a5=0,ot=oe=null,t7(),e}function oz(e,t){n8=null,n2.current=rX,aY.current=null,t===nh?(t=nw(),a1=oP()&&0==(268435455&a5)&&0==(268435455&a7)?2:3):t===nm?(t=nw(),a1=4):a1=t===ll?8:null!==t&&"object"==typeof t&&"function"==typeof t.then?6:1,a2=t,null===aJ&&(a8=1,a6=t)}function oP(){if((8388480&a0)===a0)return null===nA;var e=nO.current;return null!==e&&((125829120&a0)===a0||0!=(1073741824&a0))&&e===nA}function oN(){var e=aq.current;return aq.current=rX,null===e?rX:e}function o_(){var e=aK.current;return aK.current=aj,e}function oL(){a8=4,null===aZ||0==(268435455&a5)&&0==(268435455&a7)||ow(aZ,a0)}function oT(e,t){var n=aG;aG|=2;var r=oN(),l=o_();(aZ!==e||a0!==t)&&(ol=null,ox(e,t)),t=!1;e:for(;;)try{if(0!==a1&&null!==aJ){var a=aJ,o=a2;switch(a1){case 8:oC(),a8=6;break e;case 3:case 2:t||null!==nO.current||(t=!0);default:a1=0,a2=null,oD(a,o)}}!function(){for(;null!==aJ;)oM(aJ)}();break}catch(t){oz(e,t)}if(t&&e.shellSuspendCounter++,lF(),aG=n,aq.current=r,aK.current=l,null!==aJ)throw Error(i(261));return aZ=null,a0=0,t7(),a8}function oM(e){var t=im(e.alternate,e,a4);e.memoizedProps=e.pendingProps,null===t?oR(e):aJ=t,aY.current=null}function oF(e){var t=e.alternate;switch(e.tag){case 2:e.tag=0;case 15:case 0:var n=e.type,r=e.pendingProps;r=e.elementType===n?r:r0(n,r);var l=tw(n)?tb:ty.current;l=tk(e,l),t=lh(t,e,r,n,l,a0);break;case 11:n=e.type.render,r=e.pendingProps,r=e.elementType===n?r:r0(n,r),t=lh(t,e,r,n,e.ref,a0);break;case 5:rd(e);default:l6(t,e),e=aJ=oX(e,a4),t=im(t,e,a4)}e.memoizedProps=e.pendingProps,null===t?oR(e):aJ=t,aY.current=null}function oD(e,t){lF(),rd(e),nS=null,nE=0;var n=e.return;if(null===n||null===aZ)a8=1,a6=t,aJ=null;else{try{e:{var r=aZ,l=t;if(t=a0,e.flags|=32768,null!==l&&"object"==typeof l&&"function"==typeof l.then){var a=l,o=e.tag;if(0==(1&e.mode)&&(0===o||11===o||15===o)){var u=e.alternate;u?(e.updateQueue=u.updateQueue,e.memoizedState=u.memoizedState,e.lanes=u.lanes):(e.updateQueue=null,e.memoizedState=null)}var s=nO.current;if(null!==s){switch(s.tag){case 13:if(1&e.mode&&(null===nA?oL():null===s.alternate&&0===a8&&(a8=3)),s.flags&=-257,ln(s,n,e,r,t),a===ng)s.flags|=16384;else{var c=s.updateQueue;null===c?s.updateQueue=new Set([a]):c.add(a)}break;case 22:if(1&s.mode){if(s.flags|=65536,a===ng)s.flags|=16384;else{var f=s.updateQueue;if(null===f){var d={transitions:null,markerInstances:null,retryQueue:new Set([a])};s.updateQueue=d}else{var h=f.retryQueue;null===h?f.retryQueue=new Set([a]):h.add(a)}}break}default:throw Error(i(435,s.tag))}1&s.mode&&oQ(r,a,t);break e}if(1===r.tag){oQ(r,a,t),oL();break e}l=Error(i(426))}else if(t$&&1&e.mode&&(a=nO.current,null!==a)){0==(65536&a.flags)&&(a.flags|=256),ln(a,n,e,r,t),t4(r5(l,e));break e}r=l=r5(l,e),4!==a8&&(a8=2),null===oe?oe=[r]:oe.push(r),r=n;do{switch(r.tag){case 3:var m=l;r.flags|=65536,t&=-t,r.lanes|=t;var g=le(r,m,t);ns(r,g);break e;case 1:o=l;var y=r.type,v=r.stateNode;if(0==(128&r.flags)&&("function"==typeof y.getDerivedStateFromError||null!==v&&"function"==typeof v.componentDidCatch&&(null===oi||!oi.has(v)))){r.flags|=65536,g=t&-t,r.lanes|=g,m=lt(r,o,g),ns(r,m);break e}}r=r.return}while(null!==r)}}catch(e){throw aJ=n,e}if(32768&e.flags)e:{do{if(null!==(n=function(e,t){switch(tB(t),t.tag){case 1:return tw(t.type)&&tS(),65536&(e=t.flags)?(t.flags=-65537&e|128,t):null;case 3:return lR(lj),U(),p(tv),p(ty),0!=(65536&(e=t.flags))&&0==(128&e)?(t.flags=-65537&e|128,t):null;case 26:case 27:case 5:return Q(t),null;case 13:if(nQ(t),null!==(e=t.memoizedState)&&null!==e.dehydrated){if(null===t.alternate)throw Error(i(340));t3()}return 65536&(e=t.flags)?(t.flags=-65537&e|128,t):null;case 19:return p(nV),null;case 4:return U(),null;case 10:return lR(t.type._context),null;case 22:case 23:return nQ(t),nR(),null!==e&&p(lY),65536&(e=t.flags)?(t.flags=-65537&e|128,t):null;case 24:return lR(lj),null;default:return null}}(e.alternate,e))){n.flags&=32767,aJ=n;break e}null!==(e=e.return)&&(e.flags|=32768,e.subtreeFlags=0,e.deletions=null),aJ=e}while(null!==e);a8=6,aJ=null}else oR(e)}}function oR(e){var t=e;do{e=t.return;var n=function(e,t,n){var r=t.pendingProps;switch(tB(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return l8(t),null;case 1:case 17:return tw(t.type)&&tS(),l8(t),null;case 3:return r=t.stateNode,n=null,null!==e&&(n=e.memoizedState.cache),t.memoizedState.cache!==n&&(t.flags|=2048),lR(lj),U(),p(tv),p(ty),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),(null===e||null===e.child)&&(t1(t)?lJ(t):null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==tW&&(ob(tW),tW=null))),l8(t),null;case 26:n=t.type;var l=t.memoizedState;if(null===e)lJ(t),null!==t.ref&&l0(t),null!==l?(l8(t),l2(t,l)):(l8(t),t.flags&=-16777217);else{var a=e.memoizedState;l!==a&&lJ(t),e.ref!==t.ref&&l0(t),null!==l?(l8(t),l===a?t.flags&=-16777217:l2(t,l)):(l1(e,t,n,r),l8(t),t.flags&=-16777217)}return null;case 27:if(Q(t),n=A.current,l=t.type,null!==e&&null!=t.stateNode)l1(e,t,l,r),e.ref!==t.ref&&l0(t);else{if(!r){if(null===t.stateNode)throw Error(i(166));return l8(t),null}e=R.current,t1(t)?tJ(t,e):(e=s_(l,r,n),t.stateNode=e,lJ(t)),null!==t.ref&&l0(t)}return l8(t),null;case 5:if(Q(t),n=t.type,null!==e&&null!=t.stateNode)l1(e,t,n,r),e.ref!==t.ref&&l0(t);else{if(!r){if(null===t.stateNode)throw Error(i(166));return l8(t),null}if(e=R.current,t1(t))tJ(t,e)&&lJ(t);else{switch(l=sm(A.current),e){case 1:e=l.createElementNS("http://www.w3.org/2000/svg",n);break;case 2:e=l.createElementNS("http://www.w3.org/1998/Math/MathML",n);break;default:switch(n){case"svg":e=l.createElementNS("http://www.w3.org/2000/svg",n);break;case"math":e=l.createElementNS("http://www.w3.org/1998/Math/MathML",n);break;case"script":(e=l.createElement("div")).innerHTML="<script></script>",e=e.removeChild(e.firstChild);break;case"select":e="string"==typeof r.is?l.createElement("select",{is:r.is}):l.createElement("select"),r.multiple?e.multiple=!0:r.size&&(e.size=r.size);break;default:e="string"==typeof r.is?l.createElement(n,{is:r.is}):l.createElement(n)}}e[ev]=t,e[eb]=r;e:for(l=t.child;null!==l;){if(5===l.tag||6===l.tag)e.appendChild(l.stateNode);else if(4!==l.tag&&27!==l.tag&&null!==l.child){l.child.return=l,l=l.child;continue}if(l===t)break;for(;null===l.sibling;){if(null===l.return||l.return===t)break e;l=l.return}l.sibling.return=l.return,l=l.sibling}switch(t.stateNode=e,sf(e,n,r),n){case"button":case"input":case"select":case"textarea":e=!!r.autoFocus;break;case"img":e=!0;break;default:e=!1}e&&lJ(t)}null!==t.ref&&l0(t)}return l8(t),t.flags&=-16777217,null;case 6:if(e&&null!=t.stateNode)e.memoizedProps!==r&&lJ(t);else{if("string"!=typeof r&&null===t.stateNode)throw Error(i(166));if(e=A.current,t1(t)){e:{if(e=t.stateNode,r=t.memoizedProps,e[ev]=t,(n=e.nodeValue!==r)&&null!==(l=tQ))switch(l.tag){case 3:if(l=0!=(1&l.mode),si(e.nodeValue,r,l),l){e=!1;break e}break;case 27:case 5:if(a=0!=(1&l.mode),!0!==l.memoizedProps.suppressHydrationWarning&&si(e.nodeValue,r,a),a){e=!1;break e}}e=n}e&&lJ(t)}else(e=sm(e).createTextNode(r))[ev]=t,t.stateNode=e}return l8(t),null;case 13:if(nQ(t),r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(t$&&null!==tV&&0!=(1&t.mode)&&0==(128&t.flags))t2(),t3(),t.flags|=384,l=!1;else if(l=t1(t),null!==r&&null!==r.dehydrated){if(null===e){if(!l)throw Error(i(318));if(!(l=null!==(l=t.memoizedState)?l.dehydrated:null))throw Error(i(317));l[ev]=t}else t3(),0==(128&t.flags)&&(t.memoizedState=null),t.flags|=4;l8(t),l=!1}else null!==tW&&(ob(tW),tW=null),l=!0;if(!l)return 256&t.flags?t:null}if(0!=(128&t.flags))return t.lanes=n,t;return r=null!==r,e=null!==e&&null!==e.memoizedState,r&&(n=t.child,l=null,null!==n.alternate&&null!==n.alternate.memoizedState&&null!==n.alternate.memoizedState.cachePool&&(l=n.alternate.memoizedState.cachePool.pool),a=null,null!==n.memoizedState&&null!==n.memoizedState.cachePool&&(a=n.memoizedState.cachePool.pool),a!==l&&(n.flags|=2048)),r!==e&&r&&(t.child.flags|=8192),l3(t,t.updateQueue),l8(t),null;case 4:return U(),null===e&&u5(t.stateNode.containerInfo),l8(t),null;case 10:return lR(t.type._context),l8(t),null;case 19:if(p(nV),null===(l=t.memoizedState))return l8(t),null;if(r=0!=(128&t.flags),null===(a=l.rendering)){if(r)l4(l,!1);else{if(0!==a8||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(a=n$(e))){for(t.flags|=128,l4(l,!1),e=a.updateQueue,t.updateQueue=e,l3(t,e),t.subtreeFlags=0,e=n,r=t.child;null!==r;)oX(r,e),r=r.sibling;return h(nV,1&nV.current|2),t.child}e=e.sibling}null!==l.tail&&H()>or&&(t.flags|=128,r=!0,l4(l,!1),t.lanes=8388608)}}else{if(!r){if(null!==(e=n$(a))){if(t.flags|=128,r=!0,e=e.updateQueue,t.updateQueue=e,l3(t,e),l4(l,!0),null===l.tail&&"hidden"===l.tailMode&&!a.alternate&&!t$)return l8(t),null}else 2*H()-l.renderingStartTime>or&&1073741824!==n&&(t.flags|=128,r=!0,l4(l,!1),t.lanes=8388608)}l.isBackwards?(a.sibling=t.child,t.child=a):(null!==(e=l.last)?e.sibling=a:t.child=a,l.last=a)}if(null!==l.tail)return t=l.tail,l.rendering=t,l.tail=t.sibling,l.renderingStartTime=H(),t.sibling=null,e=nV.current,h(nV,r?1&e|2:1&e),t;return l8(t),null;case 22:case 23:return nQ(t),nR(),r=null!==t.memoizedState,null!==e?null!==e.memoizedState!==r&&(t.flags|=8192):r&&(t.flags|=8192),r&&0!=(1&t.mode)?0!=(1073741824&n)&&0==(128&t.flags)&&(l8(t),6&t.subtreeFlags&&(t.flags|=8192)):l8(t),null!==(r=t.updateQueue)&&l3(t,r.retryQueue),r=null,null!==e&&null!==e.memoizedState&&null!==e.memoizedState.cachePool&&(r=e.memoizedState.cachePool.pool),n=null,null!==t.memoizedState&&null!==t.memoizedState.cachePool&&(n=t.memoizedState.cachePool.pool),n!==r&&(t.flags|=2048),null!==e&&p(lY),null;case 24:return r=null,null!==e&&(r=e.memoizedState.cache),t.memoizedState.cache!==r&&(t.flags|=2048),lR(lj),l8(t),null;case 25:return null}throw Error(i(156,t.tag))}(t.alternate,t,a4);if(null!==n){aJ=n;return}if(null!==(t=t.sibling)){aJ=t;return}aJ=t=e}while(null!==t);0===a8&&(a8=5)}function oO(e,t,n){var r=eh,l=aX.transition;try{aX.transition=null,eh=2,function(e,t,n,r){do oI();while(null!==os);if(0!=(6&aG))throw Error(i(327));var l=e.finishedWork,a=e.finishedLanes;if(null!==l){if(e.finishedWork=null,e.finishedLanes=0,l===e.current)throw Error(i(177));e.callbackNode=null,e.callbackPriority=0,e.cancelPendingCommit=null;var o=l.lanes|l.childLanes;if(function(e,t){var n=e.pendingLanes&~t;e.pendingLanes=t,e.suspendedLanes=0,e.pingedLanes=0,e.expiredLanes&=t,e.entangledLanes&=t,e.errorRecoveryDisabledLanes&=t,e.shellSuspendCounter=0,t=e.entanglements;var r=e.expirationTimes;for(e=e.hiddenUpdates;0<n;){var l=31-et(n),a=1<<l;t[l]=0,r[l]=-1;var o=e[l];if(null!==o)for(e[l]=null,l=0;l<o.length;l++){var i=o[l];null!==i&&(i.lane&=-1073741825)}n&=~a}}(e,o|=t5),e===aZ&&(aJ=aZ=null,a0=0),0==(10256&l.subtreeFlags)&&0==(10256&l.flags)||ou||(ou=!0,of=o,od=n,V(X,function(){return oI(),null})),n=0!=(15990&l.flags),0!=(15990&l.subtreeFlags)||n){n=aX.transition,aX.transition=null;var u=eh;eh=2;var s=aG;aG|=4,aY.current=null,function(e,t){if(sp=i3,uF(e=uM())){if("selectionStart"in e)var n={start:e.selectionStart,end:e.selectionEnd};else e:{var r=(n=(n=e.ownerDocument)&&n.defaultView||window).getSelection&&n.getSelection();if(r&&0!==r.rangeCount){n=r.anchorNode;var l,a=r.anchorOffset,o=r.focusNode;r=r.focusOffset;try{n.nodeType,o.nodeType}catch(e){n=null;break e}var u=0,s=-1,c=-1,f=0,d=0,p=e,h=null;t:for(;;){for(;p!==n||0!==a&&3!==p.nodeType||(s=u+a),p!==o||0!==r&&3!==p.nodeType||(c=u+r),3===p.nodeType&&(u+=p.nodeValue.length),null!==(l=p.firstChild);)h=p,p=l;for(;;){if(p===e)break t;if(h===n&&++f===a&&(s=u),h===o&&++d===r&&(c=u),null!==(l=p.nextSibling))break;h=(p=h).parentNode}p=l}n=-1===s||-1===c?null:{start:s,end:c}}else n=null}n=n||{start:0,end:0}}else n=null;for(sh={focusedElem:e,selectionRange:n},i3=!1,ai=t;null!==ai;)if(e=(t=ai).child,0!=(1028&t.subtreeFlags)&&null!==e)e.return=t,ai=e;else for(;null!==ai;){t=ai;try{var m=t.alternate,g=t.flags;switch(t.tag){case 0:case 11:case 15:case 5:case 26:case 27:case 6:case 4:case 17:break;case 1:if(0!=(1024&g)&&null!==m){var y=m.memoizedProps,v=m.memoizedState,b=t.stateNode,k=b.getSnapshotBeforeUpdate(t.elementType===t.type?y:r0(t.type,y),v);b.__reactInternalSnapshotBeforeUpdate=k}break;case 3:0!=(1024&g)&&sx(t.stateNode.containerInfo);break;default:if(0!=(1024&g))throw Error(i(163))}}catch(e){oB(t,t.return,e)}if(null!==(e=t.sibling)){e.return=t.return,ai=e;break}ai=t.return}m=af,af=!1}(e,l),a_(l,e),function(e){var t=uM(),n=e.focusedElem,r=e.selectionRange;if(t!==n&&n&&n.ownerDocument&&function e(t,n){return!!t&&!!n&&(t===n||(!t||3!==t.nodeType)&&(n&&3===n.nodeType?e(t,n.parentNode):"contains"in t?t.contains(n):!!t.compareDocumentPosition&&!!(16&t.compareDocumentPosition(n))))}(n.ownerDocument.documentElement,n)){if(null!==r&&uF(n)){if(t=r.start,void 0===(e=r.end)&&(e=t),"selectionStart"in n)n.selectionStart=t,n.selectionEnd=Math.min(e,n.value.length);else if((e=(t=n.ownerDocument||document)&&t.defaultView||window).getSelection){e=e.getSelection();var l=n.textContent.length,a=Math.min(r.start,l);r=void 0===r.end?a:Math.min(r.end,l),!e.extend&&a>r&&(l=r,r=a,a=l),l=uT(n,a);var o=uT(n,r);l&&o&&(1!==e.rangeCount||e.anchorNode!==l.node||e.anchorOffset!==l.offset||e.focusNode!==o.node||e.focusOffset!==o.offset)&&((t=t.createRange()).setStart(l.node,l.offset),e.removeAllRanges(),a>r?(e.addRange(t),e.extend(o.node,o.offset)):(t.setEnd(o.node,o.offset),e.addRange(t)))}}for(t=[],e=n;e=e.parentNode;)1===e.nodeType&&t.push({element:e,left:e.scrollLeft,top:e.scrollTop});for("function"==typeof n.focus&&n.focus(),n=0;n<t.length;n++)(e=t[n]).element.scrollLeft=e.left,e.element.scrollTop=e.top}}(sh),i3=!!sp,sh=sp=null,e.current=l,ay(e,l.alternate,l),j(),aG=s,eh=u,aX.transition=n}else e.current=l;if(ou?(ou=!1,os=e,oc=a):oA(e,o),0===(o=e.pendingLanes)&&(oi=null),function(e){if(ee&&"function"==typeof ee.onCommitFiberRoot)try{ee.onCommitFiberRoot(J,e,void 0,128==(128&e.current.flags))}catch(e){}}(l.stateNode,r),nX(e),null!==t)for(r=e.onRecoverableError,l=0;l<t.length;l++)o={digest:(a=t[l]).digest,componentStack:a.stack},r(a.value,o);if(oa)throw oa=!1,e=oo,oo=null,e;0!=(3&oc)&&0!==e.tag&&oI(),0!=(3&(o=e.pendingLanes))?e===oh?op++:(op=0,oh=e):op=0,nG(!1)}}(e,t,n,r)}finally{aX.transition=l,eh=r}return null}function oA(e,t){0==(e.pooledCacheLanes&=t)&&null!=(t=e.pooledCache)&&(e.pooledCache=null,lq(t))}function oI(){if(null!==os){var e=os,t=of;of=0;var n=em(oc),r=32>n?32:n;n=aX.transition;var l=eh;try{if(aX.transition=null,eh=r,null===os)var a=!1;else{r=od,od=null;var o=os,u=oc;if(os=null,oc=0,0!=(6&aG))throw Error(i(331));var s=aG;if(aG|=4,a$(o.current),aO(o,o.current,u,r),aG=s,nG(!1),ee&&"function"==typeof ee.onPostCommitFiberRoot)try{ee.onPostCommitFiberRoot(J,o)}catch(e){}a=!0}return a}finally{eh=l,aX.transition=n,oA(e,t)}}return!1}function oU(e,t,n){t=r5(n,t),t=le(e,t,2),null!==(e=ni(e,t,2))&&(ed(e,2),nX(e))}function oB(e,t,n){if(3===e.tag)oU(e,e,n);else for(;null!==t;){if(3===t.tag){oU(t,e,n);break}if(1===t.tag){var r=t.stateNode;if("function"==typeof t.type.getDerivedStateFromError||"function"==typeof r.componentDidCatch&&(null===oi||!oi.has(r))){e=r5(n,e),e=lt(t,e,2),null!==(t=ni(t,e,2))&&(ed(t,2),nX(t));break}}t=t.return}}function oQ(e,t,n){var r=e.pingCache;if(null===r){r=e.pingCache=new aH;var l=new Set;r.set(t,l)}else void 0===(l=r.get(t))&&(l=new Set,r.set(t,l));l.has(n)||(a3=!0,l.add(n),e=oV.bind(null,e,t,n),t.then(e,e))}function oV(e,t,n){var r=e.pingCache;null!==r&&r.delete(t),e.pingedLanes|=e.suspendedLanes&n,aZ===e&&(a0&n)===n&&(4===a8||3===a8&&(125829120&a0)===a0&&300>H()-on?0==(2&aG)&&ox(e,0):a9|=n),nX(e)}function o$(e,t){0===t&&(t=0==(1&e.mode)?2:ec()),null!==(e=ne(e,t))&&(ed(e,t),nX(e))}function oW(e){var t=e.memoizedState,n=0;null!==t&&(n=t.retryLane),o$(e,n)}function oj(e,t){var n=0;switch(e.tag){case 13:var r=e.stateNode,l=e.memoizedState;null!==l&&(n=l.retryLane);break;case 19:r=e.stateNode;break;case 22:r=e.stateNode._retryCache;break;default:throw Error(i(314))}null!==r&&r.delete(t),o$(e,n)}function oH(e,t,n,r){this.tag=e,this.key=n,this.sibling=this.child=this.return=this.stateNode=this.type=this.elementType=null,this.index=0,this.refCleanup=this.ref=null,this.pendingProps=t,this.dependencies=this.memoizedState=this.updateQueue=this.memoizedProps=null,this.mode=r,this.subtreeFlags=this.flags=0,this.deletions=null,this.childLanes=this.lanes=0,this.alternate=null}function oq(e,t,n,r){return new oH(e,t,n,r)}function oK(e){return!(!(e=e.prototype)||!e.isReactComponent)}function oY(e,t){var n=e.alternate;return null===n?((n=oq(e.tag,t,e.key,e.mode)).elementType=e.elementType,n.type=e.type,n.stateNode=e.stateNode,n.alternate=e,e.alternate=n):(n.pendingProps=t,n.type=e.type,n.flags=0,n.subtreeFlags=0,n.deletions=null),n.flags=31457280&e.flags,n.childLanes=e.childLanes,n.lanes=e.lanes,n.child=e.child,n.memoizedProps=e.memoizedProps,n.memoizedState=e.memoizedState,n.updateQueue=e.updateQueue,t=e.dependencies,n.dependencies=null===t?null:{lanes:t.lanes,firstContext:t.firstContext},n.sibling=e.sibling,n.index=e.index,n.ref=e.ref,n.refCleanup=e.refCleanup,n}function oX(e,t){e.flags&=31457282;var n=e.alternate;return null===n?(e.childLanes=0,e.lanes=t,e.child=null,e.subtreeFlags=0,e.memoizedProps=null,e.memoizedState=null,e.updateQueue=null,e.dependencies=null,e.stateNode=null):(e.childLanes=n.childLanes,e.lanes=n.lanes,e.child=n.child,e.subtreeFlags=0,e.deletions=null,e.memoizedProps=n.memoizedProps,e.memoizedState=n.memoizedState,e.updateQueue=n.updateQueue,e.type=n.type,t=n.dependencies,e.dependencies=null===t?null:{lanes:t.lanes,firstContext:t.firstContext}),e}function oG(e,t,n,r,l,a){var o=2;if(r=e,"function"==typeof e)oK(e)&&(o=1);else if("string"==typeof e)o=!function(e,t,n){if(1===n||null!=t.itemProp)return!1;switch(e){case"meta":case"title":return!0;case"style":if("string"!=typeof t.precedence||"string"!=typeof t.href||""===t.href)break;return!0;case"link":if("string"!=typeof t.rel||"string"!=typeof t.href||""===t.href||t.onLoad||t.onError)break;if("stylesheet"===t.rel)return e=t.disabled,"string"==typeof t.precedence&&null==e;return!0;case"script":if(!0===t.async&&!t.onLoad&&!t.onError&&"string"==typeof t.src&&t.src)return!0}return!1}(e,n,R.current)?"html"===e||"head"===e||"body"===e?27:5:26;else e:switch(e){case y:return oZ(n.children,l,a,t);case v:o=8,0!=(1&(l|=8))&&(l|=16);break;case b:return(e=oq(12,n,t,2|l)).elementType=b,e.lanes=a,e;case C:return(e=oq(13,n,t,l)).elementType=C,e.lanes=a,e;case x:return(e=oq(19,n,t,l)).elementType=x,e.lanes=a,e;case _:return oJ(n,l,a,t);case L:case N:case T:return(e=oq(24,n,t,l)).elementType=T,e.lanes=a,e;default:if("object"==typeof e&&null!==e)switch(e.$$typeof){case k:o=10;break e;case w:o=9;break e;case E:o=11;break e;case z:o=14;break e;case P:o=16,r=null;break e}throw Error(i(130,null==e?e:typeof e,""))}return(t=oq(o,n,t,l)).elementType=e,t.type=r,t.lanes=a,t}function oZ(e,t,n,r){return(e=oq(7,e,r,t)).lanes=n,e}function oJ(e,t,n,r){(e=oq(22,e,r,t)).elementType=_,e.lanes=n;var l={_visibility:1,_pendingVisibility:1,_pendingMarkers:null,_retryCache:null,_transitions:null,_current:null,detach:function(){var e=l._current;if(null===e)throw Error(i(456));if(0==(2&l._pendingVisibility)){var t=ne(e,2);null!==t&&(l._pendingVisibility|=2,og(t,e,2))}},attach:function(){var e=l._current;if(null===e)throw Error(i(456));if(0!=(2&l._pendingVisibility)){var t=ne(e,2);null!==t&&(l._pendingVisibility&=-3,og(t,e,2))}}};return e.stateNode=l,e}function o0(e,t,n){return(e=oq(6,e,null,t)).lanes=n,e}function o1(e,t,n){return(t=oq(4,null!==e.children?e.children:[],e.key,t)).lanes=n,t.stateNode={containerInfo:e.containerInfo,pendingChildren:null,implementation:e.implementation},t}function o2(e,t,n,r,l){this.tag=t,this.containerInfo=e,this.finishedWork=this.pingCache=this.current=this.pendingChildren=null,this.timeoutHandle=-1,this.callbackNode=this.next=this.pendingContext=this.context=this.cancelPendingCommit=null,this.callbackPriority=0,this.expirationTimes=ef(-1),this.entangledLanes=this.shellSuspendCounter=this.errorRecoveryDisabledLanes=this.finishedLanes=this.expiredLanes=this.pingedLanes=this.suspendedLanes=this.pendingLanes=0,this.entanglements=ef(0),this.hiddenUpdates=ef(null),this.identifierPrefix=r,this.onRecoverableError=l,this.pooledCache=null,this.pooledCacheLanes=0,this.incompleteTransitions=new Map}function o3(e,t,n,r,l,a,o,i,u){return e=new o2(e,t,n,i,u),1===t?(t=1,!0===a&&(t|=24)):t=0,a=oq(3,null,null,t),e.current=a,a.stateNode=e,t=lH(),t.refCount++,e.pooledCache=t,t.refCount++,a.memoizedState={element:r,isDehydrated:n,cache:t},nl(a),e}function o4(e){if(!e)return tg;e=e._reactInternals;e:{if(td(e)!==e||1!==e.tag)throw Error(i(170));var t=e;do{switch(t.tag){case 3:t=t.stateNode.context;break e;case 1:if(tw(t.type)){t=t.stateNode.__reactInternalMemoizedMergedChildContext;break e}}t=t.return}while(null!==t);throw Error(i(171))}if(1===e.tag){var n=e.type;if(tw(n))return tC(e,n,t)}return t}function o8(e,t,n,r,l,a,o,i,u){return(e=o3(n,r,!0,e,l,a,o,i,u)).context=o4(null),(l=no(r=om(n=e.current))).callback=null!=t?t:null,ni(n,l,r),e.current.lanes=r,ed(e,r),nX(e),e}function o6(e,t,n,r){var l=t.current,a=om(l);return n=o4(n),null===t.context?t.context=n:t.pendingContext=n,(t=no(a)).payload={element:e},null!==(r=void 0===r?null:r)&&(t.callback=r),null!==(e=ni(l,t,a))&&(og(e,l,a),nu(e,l,a)),a}function o5(e){return(e=e.current).child?(e.child.tag,e.child.stateNode):null}function o7(e,t){if(null!==(e=e.memoizedState)&&null!==e.dehydrated){var n=e.retryLane;e.retryLane=0!==n&&n<t?n:t}}function o9(e,t){o7(e,t),(e=e.alternate)&&o7(e,t)}function ie(e){if(13===e.tag){var t=ne(e,134217728);null!==t&&og(t,e,134217728),o9(e,134217728)}}im=function(e,t,n){if(null!==e){if(e.memoizedProps!==t.pendingProps||tv.current)la=!0;else{if(0==(e.lanes&n)&&0==(128&t.flags))return la=!1,function(e,t,n){switch(t.tag){case 3:ly(t),lD(t,lj,e.memoizedState.cache),t3();break;case 27:case 5:B(t);break;case 1:tw(t.type)&&tx(t);break;case 4:I(t,t.stateNode.containerInfo);break;case 10:lD(t,t.type._context,t.memoizedProps.value);break;case 13:var r=t.memoizedState;if(null!==r){if(null!==r.dehydrated)return nI(t),t.flags|=128,null;if(0!=(n&t.child.childLanes))return lw(e,t,n);return nI(t),null!==(e=lN(e,t,n))?e.sibling:null}nI(t);break;case 19:if(r=0!=(n&t.childLanes),0!=(128&e.flags)){if(r)return lz(e,t,n);t.flags|=128}var l=t.memoizedState;if(null!==l&&(l.rendering=null,l.tail=null,l.lastEffect=null),h(nV,nV.current),!r)return null;break;case 22:case 23:return t.lanes=0,lc(e,t,n);case 24:lD(t,lj,e.memoizedState.cache)}return lN(e,t,n)}(e,t,n);la=0!=(131072&e.flags)}}else la=!1,t$&&0!=(1048576&t.flags)&&tI(t,tT,t.index);switch(t.lanes=0,t.tag){case 2:var r=t.type;lP(e,t),e=t.pendingProps;var l=tk(t,ty.current);lI(t,n),l=ri(null,t,r,e,l,n);var a=rc();return t.flags|=1,"object"==typeof l&&null!==l&&"function"==typeof l.render&&void 0===l.$$typeof?(t.tag=1,t.memoizedState=null,t.updateQueue=null,tw(r)?(a=!0,tx(t)):a=!1,t.memoizedState=null!==l.state&&void 0!==l.state?l.state:null,nl(t),l.updater=r2,t.stateNode=l,l._reactInternals=t,r6(t,r,e,n),t=lg(null,t,r,!0,a,n)):(t.tag=0,t$&&a&&tU(t),lo(null,t,l,n),t=t.child),t;case 16:r=t.elementType;e:{switch(lP(e,t),e=t.pendingProps,r=(l=r._init)(r._payload),t.type=r,l=t.tag=function(e){if("function"==typeof e)return oK(e)?1:0;if(null!=e){if((e=e.$$typeof)===E)return 11;if(e===z)return 14}return 2}(r),e=r0(r,e),l){case 0:t=lp(null,t,r,e,n);break e;case 1:t=lm(null,t,r,e,n);break e;case 11:t=li(null,t,r,e,n);break e;case 14:t=lu(null,t,r,r0(r.type,e),n);break e}throw Error(i(306,r,""))}return t;case 0:return r=t.type,l=t.pendingProps,l=t.elementType===r?l:r0(r,l),lp(e,t,r,l,n);case 1:return r=t.type,l=t.pendingProps,l=t.elementType===r?l:r0(r,l),lm(e,t,r,l,n);case 3:e:{if(ly(t),null===e)throw Error(i(387));l=t.pendingProps,r=(a=t.memoizedState).element,na(e,t),nc(t,l,null,n);var o=t.memoizedState;if(lD(t,lj,l=o.cache),l!==a.cache&&lA(t,lj,n),l=o.element,a.isDehydrated){if(a={element:l,isDehydrated:!1,cache:o.cache},t.updateQueue.baseState=a,t.memoizedState=a,256&t.flags){r=r5(Error(i(423)),t),t=lv(e,t,l,n,r);break e}if(l!==r){r=r5(Error(i(424)),t),t=lv(e,t,l,n,r);break e}for(tV=sP(t.stateNode.containerInfo.firstChild),tQ=t,t$=!0,tW=null,tj=!0,n=nL(t,null,l,n),t.child=n;n;)n.flags=-3&n.flags|4096,n=n.sibling}else{if(t3(),l===r){t=lN(e,t,n);break e}lo(e,t,l,n)}t=t.child}return t;case 26:return ld(e,t),n=t.memoizedState=function(e,t,n){if(!(t=(t=A.current)?sM(t):null))throw Error(i(446));switch(e){case"meta":case"title":return null;case"style":return"string"==typeof n.precedence&&"string"==typeof n.href?(n=sR(n.href),(e=(t=eT(t).hoistableStyles).get(n))||(e={type:"style",instance:null,count:0,state:null},t.set(n,e)),e):{type:"void",instance:null,count:0,state:null};case"link":if("stylesheet"===n.rel&&"string"==typeof n.href&&"string"==typeof n.precedence){e=sR(n.href);var r,l,a,o,u=eT(t).hoistableStyles,s=u.get(e);return s||(t=t.ownerDocument||t,s={type:"stylesheet",instance:null,count:0,state:{loading:0,preload:null}},u.set(e,s),sL.has(e)||(r=t,l=e,a={rel:"preload",as:"style",href:n.href,crossOrigin:n.crossOrigin,integrity:n.integrity,media:n.media,hrefLang:n.hrefLang,referrerPolicy:n.referrerPolicy},o=s.state,sL.set(l,a),r.querySelector(sO(l))||(r.querySelector('link[rel="preload"][as="style"]['+l+"]")?o.loading=1:(l=r.createElement("link"),o.preload=l,l.addEventListener("load",function(){return o.loading|=1}),l.addEventListener("error",function(){return o.loading|=2}),sf(l,"link",a),eM(l),r.head.appendChild(l))))),s}return null;case"script":return"string"==typeof n.src&&!0===n.async?(n=sI(n.src),(e=(t=eT(t).hoistableScripts).get(n))||(e={type:"script",instance:null,count:0,state:null},t.set(n,e)),e):{type:"void",instance:null,count:0,state:null};default:throw Error(i(444,e))}}(t.type,null===e?null:e.memoizedProps,t.pendingProps),null!==e||t$||null!==n||(n=t.type,e=t.pendingProps,(r=sm(A.current).createElement(n))[ev]=t,r[eb]=e,sf(r,n,e),eM(r),t.stateNode=r),null;case 27:return B(t),null===e&&t$&&(r=t.stateNode=s_(t.type,t.pendingProps,A.current),tQ=t,tj=!0,tV=sP(r.firstChild)),r=t.pendingProps.children,null!==e||t$?lo(e,t,r,n):t.child=n_(t,null,r,n),ld(e,t),t.child;case 5:return B(t),null===e&&t$&&((l=r=tV)?tK(t,l)||(tG(t)&&tZ(),tV=sP(l.nextSibling),a=tQ,tV&&tK(t,tV)?tH(a,l):(tq(tQ,t),t$=!1,tQ=t,tV=r)):(tG(t)&&tZ(),tq(tQ,t),t$=!1,tQ=t,tV=r)),r=t.type,l=t.pendingProps,a=null!==e?e.memoizedProps:null,o=l.children,sv(r,l)?o=null:null!==a&&sv(r,a)&&(t.flags|=32),ld(e,t),lo(e,t,o,n),t.child;case 6:return null===e&&t$&&((r=""!==t.pendingProps,(e=n=tV)&&r)?tY(t,e)||(tG(t)&&tZ(),tV=sP(e.nextSibling),r=tQ,tV&&tY(t,tV)?tH(r,e):(tq(tQ,t),t$=!1,tQ=t,tV=n)):(tG(t)&&tZ(),tq(tQ,t),t$=!1,tQ=t,tV=n)),null;case 13:return lw(e,t,n);case 4:return I(t,t.stateNode.containerInfo),r=t.pendingProps,null===e?t.child=n_(t,null,r,n):lo(e,t,r,n),t.child;case 11:return r=t.type,l=t.pendingProps,l=t.elementType===r?l:r0(r,l),li(e,t,r,l,n);case 7:return lo(e,t,t.pendingProps,n),t.child;case 8:case 12:return lo(e,t,t.pendingProps.children,n),t.child;case 10:e:{if(r=t.type._context,l=t.pendingProps,a=t.memoizedProps,o=l.value,lD(t,r,o),null!==a){if(tP(a.value,o)){if(a.children===l.children&&!tv.current){t=lN(e,t,n);break e}}else lA(t,r,n)}lo(e,t,l.children,n),t=t.child}return t;case 9:return l=t.type,r=t.pendingProps.children,lI(t,n),l=lU(l),r=r(l),t.flags|=1,lo(e,t,r,n),t.child;case 14:return l=r0(r=t.type,t.pendingProps),l=r0(r.type,l),lu(e,t,r,l,n);case 15:return ls(e,t,t.type,t.pendingProps,n);case 17:return r=t.type,l=t.pendingProps,l=t.elementType===r?l:r0(r,l),lP(e,t),t.tag=1,tw(r)?(e=!0,tx(t)):e=!1,lI(t,n),r4(t,r,l),r6(t,r,l,n),lg(null,t,r,!0,e,n);case 19:return lz(e,t,n);case 22:return lc(e,t,n);case 24:return lI(t,n),r=lU(lj),null===e?(null===(l=lX())&&(l=aZ,a=lH(),l.pooledCache=a,a.refCount++,null!==a&&(l.pooledCacheLanes|=n),l=a),t.memoizedState={parent:r,cache:l},nl(t),lD(t,lj,l)):(0!=(e.lanes&n)&&(na(e,t),nc(t,null,null,n)),l=e.memoizedState,a=t.memoizedState,l.parent!==r?(l={parent:r,cache:r},t.memoizedState=l,0===t.lanes&&(t.memoizedState=t.updateQueue.baseState=l),lD(t,lj,r)):(r=a.cache,lD(t,lj,r),r!==l.cache&&lA(t,lj,n))),lo(e,t,t.pendingProps.children,n),t.child}throw Error(i(156,t.tag))};var it=!1;function ir(e,t,n){if(it)return e(t,n);it=!0;try{return oS(e,t,n)}finally{it=!1,(null!==ti||null!==tu)&&(oE(),tf())}}function il(e,t){var n=e.stateNode;if(null===n)return null;var r=eL(n);if(null===r)return null;switch(n=r[t],t){case"onClick":case"onClickCapture":case"onDoubleClick":case"onDoubleClickCapture":case"onMouseDown":case"onMouseDownCapture":case"onMouseMove":case"onMouseMoveCapture":case"onMouseUp":case"onMouseUpCapture":case"onMouseEnter":(r=!r.disabled)||(r=!("button"===(e=e.type)||"input"===e||"select"===e||"textarea"===e)),e=!r;break;default:e=!1}if(e)return null;if(n&&"function"!=typeof n)throw Error(i(231,t,typeof n));return n}var ia=!1;if(eA)try{var io={};Object.defineProperty(io,"passive",{get:function(){ia=!0}}),window.addEventListener("test",io,io),window.removeEventListener("test",io,io)}catch(e){ia=!1}function ii(e){var t=e.keyCode;return"charCode"in e?0===(e=e.charCode)&&13===t&&(e=13):e=t,10===e&&(e=13),32<=e||13===e?e:0}function iu(){return!0}function is(){return!1}function ic(e){function t(t,n,r,l,a){for(var o in this._reactName=t,this._targetInst=r,this.type=n,this.nativeEvent=l,this.target=a,this.currentTarget=null,e)e.hasOwnProperty(o)&&(t=e[o],this[o]=t?t(l):l[o]);return this.isDefaultPrevented=(null!=l.defaultPrevented?l.defaultPrevented:!1===l.returnValue)?iu:is,this.isPropagationStopped=is,this}return u(t.prototype,{preventDefault:function(){this.defaultPrevented=!0;var e=this.nativeEvent;e&&(e.preventDefault?e.preventDefault():"unknown"!=typeof e.returnValue&&(e.returnValue=!1),this.isDefaultPrevented=iu)},stopPropagation:function(){var e=this.nativeEvent;e&&(e.stopPropagation?e.stopPropagation():"unknown"!=typeof e.cancelBubble&&(e.cancelBubble=!0),this.isPropagationStopped=iu)},persist:function(){},isPersistent:iu}),t}var id,ip,ih,im,ig,iy,iv,ib={eventPhase:0,bubbles:0,cancelable:0,timeStamp:function(e){return e.timeStamp||Date.now()},defaultPrevented:0,isTrusted:0},ik=ic(ib),iw=u({},ib,{view:0,detail:0}),iS=ic(iw),iE=u({},iw,{screenX:0,screenY:0,clientX:0,clientY:0,pageX:0,pageY:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,getModifierState:iD,button:0,buttons:0,relatedTarget:function(e){return void 0===e.relatedTarget?e.fromElement===e.srcElement?e.toElement:e.fromElement:e.relatedTarget},movementX:function(e){return"movementX"in e?e.movementX:(e!==iv&&(iv&&"mousemove"===e.type?(ig=e.screenX-iv.screenX,iy=e.screenY-iv.screenY):iy=ig=0,iv=e),ig)},movementY:function(e){return"movementY"in e?e.movementY:iy}}),iC=ic(iE),ix=ic(u({},iE,{dataTransfer:0})),iz=ic(u({},iw,{relatedTarget:0})),iP=ic(u({},ib,{animationName:0,elapsedTime:0,pseudoElement:0})),iN=ic(u({},ib,{clipboardData:function(e){return"clipboardData"in e?e.clipboardData:window.clipboardData}})),i_=ic(u({},ib,{data:0})),iL={Esc:"Escape",Spacebar:" ",Left:"ArrowLeft",Up:"ArrowUp",Right:"ArrowRight",Down:"ArrowDown",Del:"Delete",Win:"OS",Menu:"ContextMenu",Apps:"ContextMenu",Scroll:"ScrollLock",MozPrintableKey:"Unidentified"},iT={8:"Backspace",9:"Tab",12:"Clear",13:"Enter",16:"Shift",17:"Control",18:"Alt",19:"Pause",20:"CapsLock",27:"Escape",32:" ",33:"PageUp",34:"PageDown",35:"End",36:"Home",37:"ArrowLeft",38:"ArrowUp",39:"ArrowRight",40:"ArrowDown",45:"Insert",46:"Delete",112:"F1",113:"F2",114:"F3",115:"F4",116:"F5",117:"F6",118:"F7",119:"F8",120:"F9",121:"F10",122:"F11",123:"F12",144:"NumLock",145:"ScrollLock",224:"Meta"},iM={Alt:"altKey",Control:"ctrlKey",Meta:"metaKey",Shift:"shiftKey"};function iF(e){var t=this.nativeEvent;return t.getModifierState?t.getModifierState(e):!!(e=iM[e])&&!!t[e]}function iD(){return iF}var iR=ic(u({},iw,{key:function(e){if(e.key){var t=iL[e.key]||e.key;if("Unidentified"!==t)return t}return"keypress"===e.type?13===(e=ii(e))?"Enter":String.fromCharCode(e):"keydown"===e.type||"keyup"===e.type?iT[e.keyCode]||"Unidentified":""},code:0,location:0,ctrlKey:0,shiftKey:0,altKey:0,metaKey:0,repeat:0,locale:0,getModifierState:iD,charCode:function(e){return"keypress"===e.type?ii(e):0},keyCode:function(e){return"keydown"===e.type||"keyup"===e.type?e.keyCode:0},which:function(e){return"keypress"===e.type?ii(e):"keydown"===e.type||"keyup"===e.type?e.keyCode:0}})),iO=ic(u({},iE,{pointerId:0,width:0,height:0,pressure:0,tangentialPressure:0,tiltX:0,tiltY:0,twist:0,pointerType:0,isPrimary:0})),iA=ic(u({},iw,{touches:0,targetTouches:0,changedTouches:0,altKey:0,metaKey:0,ctrlKey:0,shiftKey:0,getModifierState:iD})),iI=ic(u({},ib,{propertyName:0,elapsedTime:0,pseudoElement:0})),iU=ic(u({},iE,{deltaX:function(e){return"deltaX"in e?e.deltaX:"wheelDeltaX"in e?-e.wheelDeltaX:0},deltaY:function(e){return"deltaY"in e?e.deltaY:"wheelDeltaY"in e?-e.wheelDeltaY:"wheelDelta"in e?-e.wheelDelta:0},deltaZ:0,deltaMode:0})),iB=!1,iQ=null,iV=null,i$=null,iW=new Map,ij=new Map,iH=[],iq="mousedown mouseup touchcancel touchend touchstart auxclick dblclick pointercancel pointerdown pointerup dragend dragstart drop compositionend compositionstart keydown keypress keyup input textInput copy cut paste click change contextmenu reset".split(" ");function iK(e,t){switch(e){case"focusin":case"focusout":iQ=null;break;case"dragenter":case"dragleave":iV=null;break;case"mouseover":case"mouseout":i$=null;break;case"pointerover":case"pointerout":iW.delete(t.pointerId);break;case"gotpointercapture":case"lostpointercapture":ij.delete(t.pointerId)}}function iY(e,t,n,r,l,a){return null===e||e.nativeEvent!==a?(e={blockedOn:t,domEventName:n,eventSystemFlags:r,nativeEvent:a,targetContainers:[l]},null!==t&&null!==(t=eN(t))&&ie(t),e):(e.eventSystemFlags|=r,t=e.targetContainers,null!==l&&-1===t.indexOf(l)&&t.push(l),e)}function iX(e){var t=eP(e.target);if(null!==t){var n=td(t);if(null!==n){if(13===(t=n.tag)){if(null!==(t=tp(n))){e.blockedOn=t,function(e,t){var n=eh;try{return eh=e,t()}finally{eh=n}}(e.priority,function(){if(13===n.tag){var e=om(n),t=ne(n,e);null!==t&&og(t,n,e),o9(n,e)}});return}}else if(3===t&&n.stateNode.current.memoizedState.isDehydrated){e.blockedOn=3===n.tag?n.stateNode.containerInfo:null;return}}}e.blockedOn=null}function iG(e){if(null!==e.blockedOn)return!1;for(var t=e.targetContainers;0<t.length;){var n=i5(e.nativeEvent);if(null!==n)return null!==(t=eN(n))&&ie(t),e.blockedOn=n,!1;var r=new(n=e.nativeEvent).constructor(n.type,n);ta=r,n.target.dispatchEvent(r),ta=null,t.shift()}return!0}function iZ(e,t,n){iG(e)&&n.delete(t)}function iJ(){iB=!1,null!==iQ&&iG(iQ)&&(iQ=null),null!==iV&&iG(iV)&&(iV=null),null!==i$&&iG(i$)&&(i$=null),iW.forEach(iZ),ij.forEach(iZ)}function i0(e,t){e.blockedOn===t&&(e.blockedOn=null,iB||(iB=!0,a.unstable_scheduleCallback(a.unstable_NormalPriority,iJ)))}function i1(e){function t(t){return i0(t,e)}null!==iQ&&i0(iQ,e),null!==iV&&i0(iV,e),null!==i$&&i0(i$,e),iW.forEach(t),ij.forEach(t);for(var n=0;n<iH.length;n++){var r=iH[n];r.blockedOn===e&&(r.blockedOn=null)}for(;0<iH.length&&null===(n=iH[0]).blockedOn;)iX(n),null===n.blockedOn&&iH.shift()}var i2=s.ReactCurrentBatchConfig,i3=!0;function i4(e,t,n,r){var l=eh,a=i2.transition;i2.transition=null;try{eh=2,i6(e,t,n,r)}finally{eh=l,i2.transition=a}}function i8(e,t,n,r){var l=eh,a=i2.transition;i2.transition=null;try{eh=8,i6(e,t,n,r)}finally{eh=l,i2.transition=a}}function i6(e,t,n,r){if(i3){var l=i5(r);if(null===l)u9(e,t,r,i7,n),iK(e,r);else if(function(e,t,n,r,l){switch(t){case"focusin":return iQ=iY(iQ,e,t,n,r,l),!0;case"dragenter":return iV=iY(iV,e,t,n,r,l),!0;case"mouseover":return i$=iY(i$,e,t,n,r,l),!0;case"pointerover":var a=l.pointerId;return iW.set(a,iY(iW.get(a)||null,e,t,n,r,l)),!0;case"gotpointercapture":return a=l.pointerId,ij.set(a,iY(ij.get(a)||null,e,t,n,r,l)),!0}return!1}(l,e,t,n,r))r.stopPropagation();else if(iK(e,r),4&t&&-1<iq.indexOf(e)){for(;null!==l;){var a=eN(l);if(null!==a&&function(e){switch(e.tag){case 3:var t=e.stateNode;if(t.current.memoizedState.isDehydrated){var n=eo(t.pendingLanes);0!==n&&(ep(t,2|n),nX(t),0==(6&aG)&&(or=H()+500,nG(!1)))}break;case 13:oE(function(){var t=ne(e,2);null!==t&&og(t,e,2)}),o9(e,2)}}(a),null===(a=i5(r))&&u9(e,t,r,i7,n),a===l)break;l=a}null!==l&&r.stopPropagation()}else u9(e,t,r,null,n)}}function i5(e){e=to(e);e:{if(i7=null,null!==(e=eP(e))){var t=td(e);if(null===t)e=null;else{var n=t.tag;if(13===n){if(null!==(e=tp(t)))break e;e=null}else if(3===n){if(t.stateNode.current.memoizedState.isDehydrated){e=3===t.tag?t.stateNode.containerInfo:null;break e}e=null}else t!==e&&(e=null)}}i7=e,e=null}return e}var i7=null;function i9(e){switch(e){case"cancel":case"click":case"close":case"contextmenu":case"copy":case"cut":case"auxclick":case"dblclick":case"dragend":case"dragstart":case"drop":case"focusin":case"focusout":case"input":case"invalid":case"keydown":case"keypress":case"keyup":case"mousedown":case"mouseup":case"paste":case"pause":case"play":case"pointercancel":case"pointerdown":case"pointerup":case"ratechange":case"reset":case"resize":case"seeked":case"submit":case"touchcancel":case"touchend":case"touchstart":case"volumechange":case"change":case"selectionchange":case"textInput":case"compositionstart":case"compositionend":case"compositionupdate":case"beforeblur":case"afterblur":case"beforeinput":case"blur":case"fullscreenchange":case"focus":case"hashchange":case"popstate":case"select":case"selectstart":return 2;case"drag":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"mousemove":case"mouseout":case"mouseover":case"pointermove":case"pointerout":case"pointerover":case"scroll":case"toggle":case"touchmove":case"wheel":case"mouseenter":case"mouseleave":case"pointerenter":case"pointerleave":return 8;case"message":switch(q()){case K:return 2;case Y:return 8;case X:case G:return 32;case Z:return 536870912;default:return 32}default:return 32}}var ue=null,ut=null,un=null;function ur(){if(un)return un;var e,t,n=ut,r=n.length,l="value"in ue?ue.value:ue.textContent,a=l.length;for(e=0;e<r&&n[e]===l[e];e++);var o=r-e;for(t=1;t<=o&&n[r-t]===l[a-t];t++);return un=l.slice(e,1<t?1-t:void 0)}var ul=[9,13,27,32],ua=eA&&"CompositionEvent"in window,uo=null;eA&&"documentMode"in document&&(uo=document.documentMode);var ui=eA&&"TextEvent"in window&&!uo,uu=eA&&(!ua||uo&&8<uo&&11>=uo),us=!1;function uc(e,t){switch(e){case"keyup":return -1!==ul.indexOf(t.keyCode);case"keydown":return 229!==t.keyCode;case"keypress":case"mousedown":case"focusout":return!0;default:return!1}}function uf(e){return"object"==typeof(e=e.detail)&&"data"in e?e.data:null}var ud=!1,up={color:!0,date:!0,datetime:!0,"datetime-local":!0,email:!0,month:!0,number:!0,password:!0,range:!0,search:!0,tel:!0,text:!0,time:!0,url:!0,week:!0};function uh(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return"input"===t?!!up[e.type]:"textarea"===t}function um(e,t,n,r){tc(r),0<(t=st(t,"onChange")).length&&(n=new ik("onChange","change",null,n,r),e.push({event:n,listeners:t}))}var ug=null,uy=null;function uv(e){u3(e,0)}function ub(e){if(eX(e_(e)))return e}function uk(e,t){if("change"===e)return t}var uw=!1;if(eA){if(eA){var uS="oninput"in document;if(!uS){var uE=document.createElement("div");uE.setAttribute("oninput","return;"),uS="function"==typeof uE.oninput}r=uS}else r=!1;uw=r&&(!document.documentMode||9<document.documentMode)}function uC(){ug&&(ug.detachEvent("onpropertychange",ux),uy=ug=null)}function ux(e){if("value"===e.propertyName&&ub(uy)){var t=[];um(t,uy,e,to(e)),ir(uv,t)}}function uz(e,t,n){"focusin"===e?(uC(),ug=t,uy=n,ug.attachEvent("onpropertychange",ux)):"focusout"===e&&uC()}function uP(e){if("selectionchange"===e||"keyup"===e||"keydown"===e)return ub(uy)}function uN(e,t){if("click"===e)return ub(t)}function u_(e,t){if("input"===e||"change"===e)return ub(t)}function uL(e){for(;e&&e.firstChild;)e=e.firstChild;return e}function uT(e,t){var n,r=uL(e);for(e=0;r;){if(3===r.nodeType){if(n=e+r.textContent.length,e<=t&&n>=t)return{node:r,offset:t-e};e=n}e:{for(;r;){if(r.nextSibling){r=r.nextSibling;break e}r=r.parentNode}r=void 0}r=uL(r)}}function uM(){for(var e=window,t=eG();t instanceof e.HTMLIFrameElement;){try{var n="string"==typeof t.contentWindow.location.href}catch(e){n=!1}if(n)e=t.contentWindow;else break;t=eG(e.document)}return t}function uF(e){var t=e&&e.nodeName&&e.nodeName.toLowerCase();return t&&("input"===t&&("text"===e.type||"search"===e.type||"tel"===e.type||"url"===e.type||"password"===e.type)||"textarea"===t||"true"===e.contentEditable)}var uD=eA&&"documentMode"in document&&11>=document.documentMode,uR=null,uO=null,uA=null,uI=!1;function uU(e,t,n){var r=n.window===n?n.document:9===n.nodeType?n:n.ownerDocument;uI||null==uR||uR!==eG(r)||(r="selectionStart"in(r=uR)&&uF(r)?{start:r.selectionStart,end:r.selectionEnd}:{anchorNode:(r=(r.ownerDocument&&r.ownerDocument.defaultView||window).getSelection()).anchorNode,anchorOffset:r.anchorOffset,focusNode:r.focusNode,focusOffset:r.focusOffset},uA&&np(uA,r)||(uA=r,0<(r=st(uO,"onSelect")).length&&(t=new ik("onSelect","select",null,t,n),e.push({event:t,listeners:r}),t.target=uR)))}function uB(e,t){var n={};return n[e.toLowerCase()]=t.toLowerCase(),n["Webkit"+e]="webkit"+t,n["Moz"+e]="moz"+t,n}var uQ={animationend:uB("Animation","AnimationEnd"),animationiteration:uB("Animation","AnimationIteration"),animationstart:uB("Animation","AnimationStart"),transitionend:uB("Transition","TransitionEnd")},uV={},u$={};function uW(e){if(uV[e])return uV[e];if(!uQ[e])return e;var t,n=uQ[e];for(t in n)if(n.hasOwnProperty(t)&&t in u$)return uV[e]=n[t];return e}eA&&(u$=document.createElement("div").style,"AnimationEvent"in window||(delete uQ.animationend.animation,delete uQ.animationiteration.animation,delete uQ.animationstart.animation),"TransitionEvent"in window||delete uQ.transitionend.transition);var uj=uW("animationend"),uH=uW("animationiteration"),uq=uW("animationstart"),uK=uW("transitionend"),uY=new Map,uX="abort auxClick cancel canPlay canPlayThrough click close contextMenu copy cut drag dragEnd dragEnter dragExit dragLeave dragOver dragStart drop durationChange emptied encrypted ended error gotPointerCapture input invalid keyDown keyPress keyUp load loadedData loadedMetadata loadStart lostPointerCapture mouseDown mouseMove mouseOut mouseOver mouseUp paste pause play playing pointerCancel pointerDown pointerMove pointerOut pointerOver pointerUp progress rateChange reset resize seeked seeking stalled submit suspend timeUpdate touchCancel touchEnd touchStart volumeChange scroll toggle touchMove waiting wheel".split(" ");function uG(e,t){uY.set(e,t),eR(t,[e])}for(var uZ=0;uZ<uX.length;uZ++){var uJ=uX[uZ];uG(uJ.toLowerCase(),"on"+(uJ[0].toUpperCase()+uJ.slice(1)))}uG(uj,"onAnimationEnd"),uG(uH,"onAnimationIteration"),uG(uq,"onAnimationStart"),uG("dblclick","onDoubleClick"),uG("focusin","onFocus"),uG("focusout","onBlur"),uG(uK,"onTransitionEnd"),eO("onMouseEnter",["mouseout","mouseover"]),eO("onMouseLeave",["mouseout","mouseover"]),eO("onPointerEnter",["pointerout","pointerover"]),eO("onPointerLeave",["pointerout","pointerover"]),eR("onChange","change click focusin focusout input keydown keyup selectionchange".split(" ")),eR("onSelect","focusout contextmenu dragend focusin keydown keyup mousedown mouseup selectionchange".split(" ")),eR("onBeforeInput",["compositionend","keypress","textInput","paste"]),eR("onCompositionEnd","compositionend focusout keydown keypress keyup mousedown".split(" ")),eR("onCompositionStart","compositionstart focusout keydown keypress keyup mousedown".split(" ")),eR("onCompositionUpdate","compositionupdate focusout keydown keypress keyup mousedown".split(" "));var u0="abort canplay canplaythrough durationchange emptied encrypted ended error loadeddata loadedmetadata loadstart pause play playing progress ratechange resize seeked seeking stalled suspend timeupdate volumechange waiting".split(" "),u1=new Set("cancel close invalid load scroll toggle".split(" ").concat(u0));function u2(e,t,n){var r=e.type||"unknown-event";e.currentTarget=n,function(e,t,n,r,l,a,o,u,s){if(ar.apply(this,arguments),l7){if(l7){var c=l9;l7=!1,l9=null}else throw Error(i(198));ae||(ae=!0,at=c)}}(r,t,void 0,e),e.currentTarget=null}function u3(e,t){t=0!=(4&t);for(var n=0;n<e.length;n++){var r=e[n],l=r.event;r=r.listeners;e:{var a=void 0;if(t)for(var o=r.length-1;0<=o;o--){var i=r[o],u=i.instance,s=i.currentTarget;if(i=i.listener,u!==a&&l.isPropagationStopped())break e;u2(l,i,s),a=u}else for(o=0;o<r.length;o++){if(u=(i=r[o]).instance,s=i.currentTarget,i=i.listener,u!==a&&l.isPropagationStopped())break e;u2(l,i,s),a=u}}}if(ae)throw e=at,ae=!1,at=null,e}function u4(e,t){var n=t[ew];void 0===n&&(n=t[ew]=new Set);var r=e+"__bubble";n.has(r)||(u7(t,e,2,!1),n.add(r))}function u8(e,t,n){var r=0;t&&(r|=4),u7(n,e,r,t)}var u6="_reactListening"+Math.random().toString(36).slice(2);function u5(e){if(!e[u6]){e[u6]=!0,eF.forEach(function(t){"selectionchange"!==t&&(u1.has(t)||u8(t,!1,e),u8(t,!0,e))});var t=9===e.nodeType?e:e.ownerDocument;null===t||t[u6]||(t[u6]=!0,u8("selectionchange",!1,t))}}function u7(e,t,n,r){switch(i9(t)){case 2:var l=i4;break;case 8:l=i8;break;default:l=i6}n=l.bind(null,t,n,e),l=void 0,ia&&("touchstart"===t||"touchmove"===t||"wheel"===t)&&(l=!0),r?void 0!==l?e.addEventListener(t,n,{capture:!0,passive:l}):e.addEventListener(t,n,!0):void 0!==l?e.addEventListener(t,n,{passive:l}):e.addEventListener(t,n,!1)}function u9(e,t,n,r,l){var a=r;if(0==(1&t)&&0==(2&t)&&null!==r)e:for(;;){if(null===r)return;var o=r.tag;if(3===o||4===o){var i=r.stateNode.containerInfo;if(i===l||8===i.nodeType&&i.parentNode===l)break;if(4===o)for(o=r.return;null!==o;){var u=o.tag;if((3===u||4===u)&&((u=o.stateNode.containerInfo)===l||8===u.nodeType&&u.parentNode===l))return;o=o.return}for(;null!==i;){if(null===(o=eP(i)))return;if(5===(u=o.tag)||6===u||26===u||27===u){r=a=o;continue e}i=i.parentNode}}r=r.return}ir(function(){var r=a,l=to(n),o=[];e:{var i=uY.get(e);if(void 0!==i){var u=ik,s=e;switch(e){case"keypress":if(0===ii(n))break e;case"keydown":case"keyup":u=iR;break;case"focusin":s="focus",u=iz;break;case"focusout":s="blur",u=iz;break;case"beforeblur":case"afterblur":u=iz;break;case"click":if(2===n.button)break e;case"auxclick":case"dblclick":case"mousedown":case"mousemove":case"mouseup":case"mouseout":case"mouseover":case"contextmenu":u=iC;break;case"drag":case"dragend":case"dragenter":case"dragexit":case"dragleave":case"dragover":case"dragstart":case"drop":u=ix;break;case"touchcancel":case"touchend":case"touchmove":case"touchstart":u=iA;break;case uj:case uH:case uq:u=iP;break;case uK:u=iI;break;case"scroll":u=iS;break;case"wheel":u=iU;break;case"copy":case"cut":case"paste":u=iN;break;case"gotpointercapture":case"lostpointercapture":case"pointercancel":case"pointerdown":case"pointermove":case"pointerout":case"pointerover":case"pointerup":u=iO}var c=0!=(4&t),f=!c&&"scroll"===e,d=c?null!==i?i+"Capture":null:i;c=[];for(var p,h=r;null!==h;){var m=h;if(p=m.stateNode,5!==(m=m.tag)&&26!==m&&27!==m||null===p||null===d||null!=(m=il(h,d))&&c.push(se(h,m,p)),f)break;h=h.return}0<c.length&&(i=new u(i,s,null,n,l),o.push({event:i,listeners:c}))}}if(0==(7&t)){if(i="mouseover"===e||"pointerover"===e,u="mouseout"===e||"pointerout"===e,!(i&&n!==ta&&(s=n.relatedTarget||n.fromElement)&&(eP(s)||s[ek]))&&(u||i)&&(i=l.window===l?l:(i=l.ownerDocument)?i.defaultView||i.parentWindow:window,u?(s=n.relatedTarget||n.toElement,u=r,null!==(s=s?eP(s):null)&&(f=td(s),c=s.tag,s!==f||5!==c&&27!==c&&6!==c)&&(s=null)):(u=null,s=r),u!==s)){if(c=iC,m="onMouseLeave",d="onMouseEnter",h="mouse",("pointerout"===e||"pointerover"===e)&&(c=iO,m="onPointerLeave",d="onPointerEnter",h="pointer"),f=null==u?i:e_(u),p=null==s?i:e_(s),(i=new c(m,h+"leave",u,n,l)).target=f,i.relatedTarget=p,m=null,eP(l)===r&&((c=new c(d,h+"enter",s,n,l)).target=p,c.relatedTarget=f,m=c),f=m,u&&s)t:{for(c=u,d=s,h=0,p=c;p;p=sn(p))h++;for(p=0,m=d;m;m=sn(m))p++;for(;0<h-p;)c=sn(c),h--;for(;0<p-h;)d=sn(d),p--;for(;h--;){if(c===d||null!==d&&c===d.alternate)break t;c=sn(c),d=sn(d)}c=null}else c=null;null!==u&&sr(o,i,u,c,!1),null!==s&&null!==f&&sr(o,f,s,c,!0)}e:{if("select"===(u=(i=r?e_(r):window).nodeName&&i.nodeName.toLowerCase())||"input"===u&&"file"===i.type)var g,y=uk;else if(uh(i)){if(uw)y=u_;else{y=uP;var v=uz}}else(u=i.nodeName)&&"input"===u.toLowerCase()&&("checkbox"===i.type||"radio"===i.type)&&(y=uN);if(y&&(y=y(e,r))){um(o,y,n,l);break e}v&&v(e,i,r),"focusout"===e&&r&&"number"===i.type&&null!=r.memoizedProps.value&&e2(i,"number",i.value)}switch(v=r?e_(r):window,e){case"focusin":(uh(v)||"true"===v.contentEditable)&&(uR=v,uO=r,uA=null);break;case"focusout":uA=uO=uR=null;break;case"mousedown":uI=!0;break;case"contextmenu":case"mouseup":case"dragend":uI=!1,uU(o,n,l);break;case"selectionchange":if(uD)break;case"keydown":case"keyup":uU(o,n,l)}if(ua)t:{switch(e){case"compositionstart":var b="onCompositionStart";break t;case"compositionend":b="onCompositionEnd";break t;case"compositionupdate":b="onCompositionUpdate";break t}b=void 0}else ud?uc(e,n)&&(b="onCompositionEnd"):"keydown"===e&&229===n.keyCode&&(b="onCompositionStart");b&&(uu&&"ko"!==n.locale&&(ud||"onCompositionStart"!==b?"onCompositionEnd"===b&&ud&&(g=ur()):(ut="value"in(ue=l)?ue.value:ue.textContent,ud=!0)),0<(v=st(r,b)).length&&(b=new i_(b,e,null,n,l),o.push({event:b,listeners:v}),g?b.data=g:null!==(g=uf(n))&&(b.data=g))),(g=ui?function(e,t){switch(e){case"compositionend":return uf(t);case"keypress":if(32!==t.which)return null;return us=!0," ";case"textInput":return" "===(e=t.data)&&us?null:e;default:return null}}(e,n):function(e,t){if(ud)return"compositionend"===e||!ua&&uc(e,t)?(e=ur(),un=ut=ue=null,ud=!1,e):null;switch(e){case"paste":default:return null;case"keypress":if(!(t.ctrlKey||t.altKey||t.metaKey)||t.ctrlKey&&t.altKey){if(t.char&&1<t.char.length)return t.char;if(t.which)return String.fromCharCode(t.which)}return null;case"compositionend":return uu&&"ko"!==t.locale?null:t.data}}(e,n))&&0<(r=st(r,"onBeforeInput")).length&&(l=new i_("onBeforeInput","beforeinput",null,n,l),o.push({event:l,listeners:r}),l.data=g)}u3(o,t)})}function se(e,t,n){return{instance:e,listener:t,currentTarget:n}}function st(e,t){for(var n=t+"Capture",r=[];null!==e;){var l=e,a=l.stateNode;5!==(l=l.tag)&&26!==l&&27!==l||null===a||(null!=(l=il(e,n))&&r.unshift(se(e,l,a)),null!=(l=il(e,t))&&r.push(se(e,l,a))),e=e.return}return r}function sn(e){if(null===e)return null;do e=e.return;while(e&&5!==e.tag&&27!==e.tag);return e||null}function sr(e,t,n,r,l){for(var a=t._reactName,o=[];null!==n&&n!==r;){var i=n,u=i.alternate,s=i.stateNode;if(i=i.tag,null!==u&&u===r)break;5!==i&&26!==i&&27!==i||null===s||(u=s,l?null!=(s=il(n,a))&&o.unshift(se(n,s,u)):l||null!=(s=il(n,a))&&o.push(se(n,s,u))),n=n.return}0!==o.length&&e.push({event:t,listeners:o})}var sl=/\r\n?/g,sa=/\u0000|\uFFFD/g;function so(e){return("string"==typeof e?e:""+e).replace(sl,"\n").replace(sa,"")}function si(e,t,n){if(t=so(t),so(e)!==t&&n)throw Error(i(425))}function su(){}function ss(e,t,n,r,l){switch(n){case"children":"string"==typeof r?"body"===t||"textarea"===t&&""===r||te(e,r):"number"==typeof r&&"body"!==t&&te(e,""+r);break;case"className":eV(e,"class",r);break;case"tabIndex":eV(e,"tabindex",r);break;case"dir":case"role":case"viewBox":case"width":case"height":eV(e,n,r);break;case"style":tn(e,r);break;case"src":case"href":case"action":case"formAction":if(null==r||"function"==typeof r||"symbol"==typeof r||"boolean"==typeof r){e.removeAttribute(n);break}e.setAttribute(n,""+r);break;case"onClick":null!=r&&(e.onclick=su);break;case"onScroll":null!=r&&u4("scroll",e);break;case"dangerouslySetInnerHTML":if(null!=r){if("object"!=typeof r||!("__html"in r))throw Error(i(61));if(null!=(r=r.__html)){if(null!=l.children)throw Error(i(60));e9(e,r)}}break;case"multiple":e.multiple=r&&"function"!=typeof r&&"symbol"!=typeof r;break;case"muted":e.muted=r&&"function"!=typeof r&&"symbol"!=typeof r;break;case"suppressContentEditableWarning":case"suppressHydrationWarning":case"defaultValue":case"defaultChecked":case"innerHTML":case"autoFocus":break;case"xlinkHref":if(null==r||"function"==typeof r||"boolean"==typeof r||"symbol"==typeof r){e.removeAttribute("xlink:href");break}e.setAttributeNS("http://www.w3.org/1999/xlink","xlink:href",""+r);break;case"contentEditable":case"spellCheck":case"draggable":case"value":case"autoReverse":case"externalResourcesRequired":case"focusable":case"preserveAlpha":null!=r&&"function"!=typeof r&&"symbol"!=typeof r?e.setAttribute(n,""+r):e.removeAttribute(n);break;case"allowFullScreen":case"async":case"autoPlay":case"controls":case"default":case"defer":case"disabled":case"disablePictureInPicture":case"disableRemotePlayback":case"formNoValidate":case"hidden":case"loop":case"noModule":case"noValidate":case"open":case"playsInline":case"readOnly":case"required":case"reversed":case"scoped":case"seamless":case"itemScope":r&&"function"!=typeof r&&"symbol"!=typeof r?e.setAttribute(n,""):e.removeAttribute(n);break;case"capture":case"download":!0===r?e.setAttribute(n,""):!1!==r&&null!=r&&"function"!=typeof r&&"symbol"!=typeof r?e.setAttribute(n,r):e.removeAttribute(n);break;case"cols":case"rows":case"size":case"span":null!=r&&"function"!=typeof r&&"symbol"!=typeof r&&!isNaN(r)&&1<=r?e.setAttribute(n,r):e.removeAttribute(n);break;case"rowSpan":case"start":null==r||"function"==typeof r||"symbol"==typeof r||isNaN(r)?e.removeAttribute(n):e.setAttribute(n,r);break;case"xlinkActuate":e$(e,"http://www.w3.org/1999/xlink","xlink:actuate",r);break;case"xlinkArcrole":e$(e,"http://www.w3.org/1999/xlink","xlink:arcrole",r);break;case"xlinkRole":e$(e,"http://www.w3.org/1999/xlink","xlink:role",r);break;case"xlinkShow":e$(e,"http://www.w3.org/1999/xlink","xlink:show",r);break;case"xlinkTitle":e$(e,"http://www.w3.org/1999/xlink","xlink:title",r);break;case"xlinkType":e$(e,"http://www.w3.org/1999/xlink","xlink:type",r);break;case"xmlBase":e$(e,"http://www.w3.org/XML/1998/namespace","xml:base",r);break;case"xmlLang":e$(e,"http://www.w3.org/XML/1998/namespace","xml:lang",r);break;case"xmlSpace":e$(e,"http://www.w3.org/XML/1998/namespace","xml:space",r);break;case"is":eQ(e,"is",r);break;default:2<n.length&&("o"===n[0]||"O"===n[0])&&("n"===n[1]||"N"===n[1])||eQ(e,l=tl.get(n)||n,r)}}function sc(e,t,n,r,l){switch(n){case"style":tn(e,r);break;case"dangerouslySetInnerHTML":if(null!=r){if("object"!=typeof r||!("__html"in r))throw Error(i(61));if(null!=(t=r.__html)){if(null!=l.children)throw Error(i(60));e9(e,t)}}break;case"children":"string"==typeof r?te(e,r):"number"==typeof r&&te(e,""+r);break;case"onScroll":null!=r&&u4("scroll",e);break;case"onClick":null!=r&&(e.onclick=su);break;case"suppressContentEditableWarning":case"suppressHydrationWarning":case"innerHTML":break;default:eD.hasOwnProperty(n)||("boolean"==typeof r&&(r=""+r),eQ(e,n,r))}}function sf(e,t,n){switch(t){case"div":case"span":case"svg":case"path":case"a":case"g":case"p":case"li":break;case"input":u4("invalid",e);var r=null,l=null,a=null,o=null,u=null,s=null;for(f in n)if(n.hasOwnProperty(f)){var c=n[f];if(null!=c)switch(f){case"name":r=c;break;case"type":l=c;break;case"checked":u=c;break;case"defaultChecked":s=c;break;case"value":a=c;break;case"defaultValue":o=c;break;case"children":case"dangerouslySetInnerHTML":if(null!=c)throw Error(i(137,t));break;default:ss(e,t,f,c,n)}}e1(e,a,o,u,s,l,r,!1),eY(e);return;case"select":u4("invalid",e);var f=l=a=null;for(r in n)if(n.hasOwnProperty(r)&&null!=(o=n[r]))switch(r){case"value":a=o;break;case"defaultValue":l=o;break;case"multiple":f=o;default:ss(e,t,r,o,n)}t=a,n=l,e.multiple=!!f,null!=t?e4(e,!!f,t,!1):null!=n&&e4(e,!!f,n,!0);return;case"textarea":for(l in u4("invalid",e),a=r=f=null,n)if(n.hasOwnProperty(l)&&null!=(o=n[l]))switch(l){case"value":f=o;break;case"defaultValue":r=o;break;case"children":a=o;break;case"dangerouslySetInnerHTML":if(null!=o)throw Error(i(91));break;default:ss(e,t,l,o,n)}e6(e,f,r,a),eY(e);return;case"option":for(o in n)n.hasOwnProperty(o)&&null!=(f=n[o])&&("selected"===o?e.selected=f&&"function"!=typeof f&&"symbol"!=typeof f:ss(e,t,o,f,n));return;case"dialog":u4("cancel",e),u4("close",e);break;case"iframe":case"object":u4("load",e);break;case"video":case"audio":for(f=0;f<u0.length;f++)u4(u0[f],e);break;case"image":u4("error",e),u4("load",e);break;case"details":u4("toggle",e);break;case"embed":case"source":case"img":case"link":u4("error",e),u4("load",e);case"area":case"base":case"br":case"col":case"hr":case"keygen":case"meta":case"param":case"track":case"wbr":case"menuitem":for(u in n)if(n.hasOwnProperty(u)&&null!=(f=n[u]))switch(u){case"children":case"dangerouslySetInnerHTML":throw Error(i(137,t));default:ss(e,t,u,f,n)}return;default:if(tr(t)){for(s in n)n.hasOwnProperty(s)&&null!=(f=n[s])&&sc(e,t,s,f,n);return}}for(a in n)n.hasOwnProperty(a)&&null!=(f=n[a])&&ss(e,t,a,f,n)}function sd(e,t,n,r,l){switch(n){case"div":case"span":case"svg":case"path":case"a":case"g":case"p":case"li":break;case"input":var a=l.name,o=l.type,u=l.value,s=l.defaultValue;r=r.defaultValue;for(var c=l.checked,f=l.defaultChecked,d=0;d<t.length;d+=2){var p=t[d],h=t[d+1];switch(p){case"type":case"name":case"checked":case"defaultChecked":case"value":case"defaultValue":break;case"children":case"dangerouslySetInnerHTML":if(null!=h)throw Error(i(137,n));break;default:ss(e,n,p,h,l)}}e0(e,u,s,r,c,f,o,a);return;case"select":for(a=l.value,o=l.defaultValue,u=l.multiple,s=r.multiple,r=0;r<t.length;r+=2)c=t[r],f=t[r+1],"value"===c||ss(e,n,c,f,l);null!=a?e4(e,!!u,a,!1):!!s!=!!u&&(null!=o?e4(e,!!u,o,!0):e4(e,!!u,u?[]:"",!1));return;case"textarea":for(u=0,a=l.value,o=l.defaultValue;u<t.length;u+=2)switch(s=t[u],r=t[u+1],s){case"value":case"children":break;case"dangerouslySetInnerHTML":if(null!=r)throw Error(i(91));break;default:ss(e,n,s,r,l)}e8(e,a,o);return;case"option":for(a=0;a<t.length;a+=2)(o=t[a],u=t[a+1],"selected"===o)?e.selected=u&&"function"!=typeof u&&"symbol"!=typeof u:ss(e,n,o,u,l);return;case"img":case"link":case"area":case"base":case"br":case"col":case"embed":case"hr":case"keygen":case"meta":case"param":case"source":case"track":case"wbr":case"menuitem":for(a=0;a<t.length;a+=2)switch(o=t[a],u=t[a+1],o){case"children":case"dangerouslySetInnerHTML":if(null!=u)throw Error(i(137,n));break;default:ss(e,n,o,u,l)}return;default:if(tr(n)){for(a=0;a<t.length;a+=2)sc(e,n,t[a],t[a+1],l);return}}for(a=0;a<t.length;a+=2)ss(e,n,t[a],t[a+1],l)}var sp=null,sh=null;function sm(e){return 9===e.nodeType?e:e.ownerDocument}function sg(e){switch(e){case"http://www.w3.org/2000/svg":return 1;case"http://www.w3.org/1998/Math/MathML":return 2;default:return 0}}function sy(e,t){if(0===e)switch(t){case"svg":return 1;case"math":return 2;default:return 0}return 1===e&&"foreignObject"===t?0:e}function sv(e,t){return"textarea"===e||"noscript"===e||"string"==typeof t.children||"number"==typeof t.children||"object"==typeof t.dangerouslySetInnerHTML&&null!==t.dangerouslySetInnerHTML&&null!=t.dangerouslySetInnerHTML.__html}var sb="function"==typeof setTimeout?setTimeout:void 0,sk="function"==typeof clearTimeout?clearTimeout:void 0,sw="function"==typeof Promise?Promise:void 0,sS="function"==typeof queueMicrotask?queueMicrotask:void 0!==sw?function(e){return sw.resolve(null).then(e).catch(sE)}:sb;function sE(e){setTimeout(function(){throw e})}function sC(e,t){var n=t,r=0;do{var l=n.nextSibling;if(e.removeChild(n),l&&8===l.nodeType){if("/$"===(n=l.data)){if(0===r){e.removeChild(l),i1(t);return}r--}else"$"!==n&&"$?"!==n&&"$!"!==n||r++}n=l}while(n);i1(t)}function sx(e){var t=e.nodeType;if(9===t)sz(e);else if(1===t)switch(e.nodeName){case"HEAD":case"HTML":case"BODY":sz(e);break;default:e.textContent=""}}function sz(e){var t=e.firstChild;for(t&&10===t.nodeType&&(t=t.nextSibling);t;){var n=t;switch(t=t.nextSibling,n.nodeName){case"HTML":case"HEAD":case"BODY":sz(n),ez(n);continue;case"SCRIPT":case"STYLE":continue;case"LINK":if("stylesheet"===n.rel.toLowerCase())continue}e.removeChild(n)}}function sP(e){for(;null!=e;e=e.nextSibling){var t=e.nodeType;if(1===t||3===t)break;if(8===t){if("$"===(t=e.data)||"$!"===t||"$?"===t)break;if("/$"===t)return null}}return e}function sN(e){e=e.previousSibling;for(var t=0;e;){if(8===e.nodeType){var n=e.data;if("$"===n||"$!"===n||"$?"===n){if(0===t)return e;t--}else"/$"===n&&t++}e=e.previousSibling}return null}function s_(e,t,n){switch(t=sm(n),e){case"html":if(!(e=t.documentElement))throw Error(i(452));return e;case"head":if(!(e=t.head))throw Error(i(453));return e;case"body":if(!(e=t.body))throw Error(i(454));return e;default:throw Error(i(451))}}var sL=new Map,sT=new Set;function sM(e){return"function"==typeof e.getRootNode?e.getRootNode():e.ownerDocument}var sF={prefetchDNS:function(e){sD("dns-prefetch",null,e)},preconnect:function(e,t){sD("preconnect",null==t||"string"!=typeof t.crossOrigin?null:"use-credentials"===t.crossOrigin?"use-credentials":"",e)},preload:function(e,t){var n=document;if("string"==typeof e&&e&&"object"==typeof t&&null!==t&&"string"==typeof t.as&&t.as&&n){var r=t.as,l='link[rel="preload"][as="'+eJ(r)+'"]';if("image"===r){var a=t.imageSrcSet,o=t.imageSizes;"string"==typeof a&&""!==a?(l+='[imagesrcset="'+eJ(a)+'"]',"string"==typeof o&&(l+='[imagesizes="'+eJ(o)+'"]')):l+='[href="'+eJ(e)+'"]'}else l+='[href="'+eJ(e)+'"]';switch(a=l,r){case"style":a=sR(e);break;case"script":a=sI(e)}sL.has(a)||(e={rel:"preload",as:r,href:"image"===r&&t.imageSrcSet?void 0:e,crossOrigin:"font"===r?"":t.crossOrigin,integrity:t.integrity,type:t.type,nonce:t.nonce,fetchPriority:t.fetchPriority,imageSrcSet:t.imageSrcSet,imageSizes:t.imageSizes},sL.set(a,e),null!==n.querySelector(l)||"style"===r&&n.querySelector(sO(a))||"script"===r&&n.querySelector("script[async]"+a)||(sf(r=n.createElement("link"),"link",e),eM(r),n.head.appendChild(r)))}},preinit:function(e,t){var n=document;if("string"==typeof e&&e&&"object"==typeof t&&null!==t)switch(t.as){case"style":var r=eT(n).hoistableStyles,l=sR(e),a=t.precedence||"default",o=r.get(l);if(o)break;var i={loading:0,preload:null};if(o=n.querySelector(sO(l)))i.loading=1;else{e={rel:"stylesheet",href:e,"data-precedence":a,crossOrigin:t.crossOrigin,integrity:t.integrity,fetchPriority:t.fetchPriority},(t=sL.get(l))&&sQ(e,t);var u=o=n.createElement("link");eM(u),sf(u,"link",e),u._p=new Promise(function(e,t){u.onload=e,u.onerror=t}),u.addEventListener("load",function(){i.loading|=1}),u.addEventListener("error",function(){i.loading|=2}),i.loading|=4,sB(o,a,n)}o={type:"stylesheet",instance:o,count:1,state:i},r.set(l,o);break;case"script":r=eT(n).hoistableScripts,l=sI(e),(a=r.get(l))||((a=n.querySelector("script[async]"+l))||(e={src:e,async:!0,crossOrigin:t.crossOrigin,integrity:t.integrity,nonce:t.nonce,fetchPriority:t.fetchPriority},(t=sL.get(l))&&sV(e,t),eM(a=n.createElement("script")),sf(a,"link",e),n.head.appendChild(a)),a={type:"script",instance:a,count:1,state:null},r.set(l,a))}}};function sD(e,t,n){var r=document;if("string"==typeof n&&n){var l=eJ(n);l='link[rel="'+e+'"][href="'+l+'"]',"string"==typeof t&&(l+='[crossorigin="'+t+'"]'),sT.has(l)||(sT.add(l),e={rel:e,crossOrigin:t,href:n},null===r.querySelector(l)&&(sf(t=r.createElement("link"),"link",e),eM(t),r.head.appendChild(t)))}}function sR(e){return'href="'+eJ(e)+'"'}function sO(e){return'link[rel="stylesheet"]['+e+"]"}function sA(e){return u({},e,{"data-precedence":e.precedence,precedence:null})}function sI(e){return'[src="'+eJ(e)+'"]'}function sU(e,t,n){if(t.count++,null===t.instance)switch(t.type){case"style":var r=e.querySelector('style[data-href~="'+eJ(n.href)+'"]');if(r)return t.instance=r,eM(r),r;var l=u({},n,{"data-href":n.href,"data-precedence":n.precedence,href:null,precedence:null});return eM(r=(e.ownerDocument||e).createElement("style")),sf(r,"style",l),sB(r,n.precedence,e),t.instance=r;case"stylesheet":l=sR(n.href);var a=e.querySelector(sO(l));if(a)return t.instance=a,eM(a),a;r=sA(n),(l=sL.get(l))&&sQ(r,l),eM(a=(e.ownerDocument||e).createElement("link"));var o=a;return o._p=new Promise(function(e,t){o.onload=e,o.onerror=t}),sf(a,"link",r),t.state.loading|=4,sB(a,n.precedence,e),t.instance=a;case"script":if(a=sI(n.src),l=e.querySelector("script[async]"+a))return t.instance=l,eM(l),l;return r=n,(l=sL.get(a))&&sV(r=u({},n),l),eM(l=(e=e.ownerDocument||e).createElement("script")),sf(l,"link",r),e.head.appendChild(l),t.instance=l;case"void":return null;default:throw Error(i(443,t.type))}else"stylesheet"===t.type&&0==(4&t.state.loading)&&(r=t.instance,t.state.loading|=4,sB(r,n.precedence,e));return t.instance}function sB(e,t,n){for(var r=n.querySelectorAll('link[rel="stylesheet"][data-precedence],style[data-precedence]'),l=r.length?r[r.length-1]:null,a=l,o=0;o<r.length;o++){var i=r[o];if(i.dataset.precedence===t)a=i;else if(a!==l)break}a?a.parentNode.insertBefore(e,a.nextSibling):(t=9===n.nodeType?n.head:n).insertBefore(e,t.firstChild)}function sQ(e,t){null==e.crossOrigin&&(e.crossOrigin=t.crossOrigin),null==e.referrerPolicy&&(e.referrerPolicy=t.referrerPolicy),null==e.title&&(e.title=t.title)}function sV(e,t){null==e.crossOrigin&&(e.crossOrigin=t.crossOrigin),null==e.referrerPolicy&&(e.referrerPolicy=t.referrerPolicy),null==e.integrity&&(e.referrerPolicy=t.integrity)}var s$=null;function sW(e,t,n){if(null===s$){var r=new Map,l=s$=new Map;l.set(n,r)}else(r=(l=s$).get(n))||(r=new Map,l.set(n,r));if(r.has(e))return r;for(r.set(e,null),n=n.getElementsByTagName(e),l=0;l<n.length;l++){var a=n[l];if(!(a[ex]||a[ev]||"link"===e&&"stylesheet"===a.getAttribute("rel"))&&"http://www.w3.org/2000/svg"!==a.namespaceURI){var o=a.getAttribute(t)||"";o=e+o;var i=r.get(o);i?i.push(a):r.set(o,[a])}}return r}function sj(e,t,n){(e=e.ownerDocument||e).head.insertBefore(n,"title"===t?e.querySelector("head > title"):null)}var sH=null;function sq(){}function sK(){if(this.count--,0===this.count){if(this.stylesheets)sX(this,this.stylesheets);else if(this.unsuspend){var e=this.unsuspend;this.unsuspend=null,e()}}}var sY=null;function sX(e,t){e.stylesheets=null,null!==e.unsuspend&&(e.count++,sY=new Map,t.forEach(sG,e),sY=null,sK.call(e))}function sG(e,t){if(!(4&t.state.loading)){var n=sY.get(e);if(n)var r=n.get("last");else{n=new Map,sY.set(e,n);for(var l=e.querySelectorAll("link[data-precedence],style[data-precedence]"),a=0;a<l.length;a++){var o=l[a];("link"===o.nodeName||"not all"!==o.getAttribute("media"))&&(n.set("p"+o.dataset.precedence,o),r=o)}r&&n.set("last",r)}o=(l=t.instance).getAttribute("data-precedence"),(a=n.get("p"+o)||r)===r&&n.set("last",l),n.set(o,l),this.count++,r=sK.bind(this),l.addEventListener("load",r),l.addEventListener("error",r),a?a.parentNode.insertBefore(l,a.nextSibling):(e=9===e.nodeType?e.head:e).insertBefore(l,e.firstChild),t.state.loading|=4}}var sZ=o.Dispatcher;"undefined"!=typeof document&&(sZ.current=sF);var sJ="function"==typeof reportError?reportError:function(e){console.error(e)};function s0(e){this._internalRoot=e}function s1(e){this._internalRoot=e}function s2(e){return!(!e||1!==e.nodeType&&9!==e.nodeType&&11!==e.nodeType)}function s3(e){return!(!e||1!==e.nodeType&&9!==e.nodeType&&11!==e.nodeType&&(8!==e.nodeType||" react-mount-point-unstable "!==e.nodeValue))}function s4(){}function s8(e,t,n,r,l){var a=n._reactRootContainer;if(a){var o=a;if("function"==typeof l){var i=l;l=function(){var e=o5(o);i.call(e)}}o6(t,o,e,l)}else o=function(e,t,n,r,l){if(l){if("function"==typeof r){var a=r;r=function(){var e=o5(o);a.call(e)}}var o=o8(t,r,e,0,null,!1,!1,"",s4);return e._reactRootContainer=o,e[ek]=o.current,u5(8===e.nodeType?e.parentNode:e),oE(),o}if(sx(e),"function"==typeof r){var i=r;r=function(){var e=o5(u);i.call(e)}}var u=o3(e,0,!1,null,null,!1,!1,"",s4);return e._reactRootContainer=u,e[ek]=u.current,u5(8===e.nodeType?e.parentNode:e),oE(function(){o6(t,u,n,r)}),u}(n,t,e,l,r);return o5(o)}s1.prototype.render=s0.prototype.render=function(e){var t=this._internalRoot;if(null===t)throw Error(i(409));o6(e,t,null,null)},s1.prototype.unmount=s0.prototype.unmount=function(){var e=this._internalRoot;if(null!==e){this._internalRoot=null;var t=e.containerInfo;oE(function(){o6(null,e,null,null)}),t[ek]=null}},s1.prototype.unstable_scheduleHydration=function(e){if(e){var t=eh;e={blockedOn:null,target:e,priority:t};for(var n=0;n<iH.length&&0!==t&&t<iH[n].priority;n++);iH.splice(n,0,e),0===n&&iX(e)}};var s6=o.Dispatcher;o.Events=[eN,e_,eL,tc,tf,oS];var s5={findFiberByHostInstance:eP,bundleType:0,version:"18.3.0-canary-7118f5dd7-20230705",rendererPackageName:"react-dom"},s7={bundleType:s5.bundleType,version:s5.version,rendererPackageName:s5.rendererPackageName,rendererConfig:s5.rendererConfig,overrideHookState:null,overrideHookStateDeletePath:null,overrideHookStateRenamePath:null,overrideProps:null,overridePropsDeletePath:null,overridePropsRenamePath:null,setErrorHandler:null,setSuspenseHandler:null,scheduleUpdate:null,currentDispatcherRef:s.ReactCurrentDispatcher,findHostInstanceByFiber:function(e){return null===(e=tm(e))?null:e.stateNode},findFiberByHostInstance:s5.findFiberByHostInstance||function(){return null},findHostInstancesForRefresh:null,scheduleRefresh:null,scheduleRoot:null,setRefreshHandler:null,getCurrentFiber:null,reconcilerVersion:"18.3.0-canary-7118f5dd7-20230705"};if("undefined"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__){var s9=__REACT_DEVTOOLS_GLOBAL_HOOK__;if(!s9.isDisabled&&s9.supportsFiber)try{J=s9.inject(s7),ee=s9}catch(e){}}t.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=o,t.createPortal=function(e,t){var n=2<arguments.length&&void 0!==arguments[2]?arguments[2]:null;if(!s2(t))throw Error(i(200));return function(e,t,n){var r=3<arguments.length&&void 0!==arguments[3]?arguments[3]:null;return{$$typeof:g,key:null==r?null:""+r,children:e,containerInfo:t,implementation:null}}(e,t,null,n)},t.createRoot=function(e,t){if(!s2(e))throw Error(i(299));var n=!1,r="",l=sJ;return null!=t&&(!0===t.unstable_strictMode&&(n=!0),void 0!==t.identifierPrefix&&(r=t.identifierPrefix),void 0!==t.onRecoverableError&&(l=t.onRecoverableError)),t=o3(e,1,!1,null,null,n,!1,r,l),e[ek]=t.current,sZ.current=sF,u5(8===e.nodeType?e.parentNode:e),new s0(t)},t.findDOMNode=function(e){if(null==e)return null;if(1===e.nodeType)return e;var t=e._reactInternals;if(void 0===t){if("function"==typeof e.render)throw Error(i(188));throw Error(i(268,e=Object.keys(e).join(",")))}return e=null===(e=tm(t))?null:e.stateNode},t.flushSync=function(e){return oE(e)},t.hydrate=function(e,t,n){if(!s3(t))throw Error(i(200));return s8(null,e,t,!0,n)},t.hydrateRoot=function(e,t,n){if(!s2(e))throw Error(i(405));var r=!1,l="",a=sJ;return null!=n&&(!0===n.unstable_strictMode&&(r=!0),void 0!==n.identifierPrefix&&(l=n.identifierPrefix),void 0!==n.onRecoverableError&&(a=n.onRecoverableError)),t=o8(t,null,e,1,null!=n?n:null,r,!1,l,a),e[ek]=t.current,sZ.current=sF,u5(e),new s1(t)},t.preconnect=function(e,t){var n=s6.current;n&&n.preconnect(e,t)},t.prefetchDNS=function(e){var t=s6.current;t&&t.prefetchDNS(e)},t.preinit=function(e,t){var n=s6.current;n&&n.preinit(e,t)},t.preload=function(e,t){var n=s6.current;n&&n.preload(e,t)},t.render=function(e,t,n){if(!s3(t))throw Error(i(200));return s8(null,e,t,!1,n)},t.unmountComponentAtNode=function(e){if(!s3(e))throw Error(i(40));return!!e._reactRootContainer&&(oE(function(){s8(null,null,e,!1,function(){e._reactRootContainer=null,e[ek]=null})}),!0)},t.unstable_batchedUpdates=oS,t.unstable_renderSubtreeIntoContainer=function(e,t,n,r){if(!s3(n))throw Error(i(200));if(null==e||void 0===e._reactInternals)throw Error(i(38));return s8(e,t,n,!1,r)},t.version="18.3.0-canary-7118f5dd7-20230705"}}]);
\ No newline at end of file
diff --git a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/attentions.py b/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/ccds/vits_onnx/app/text/symbols.py b/spaces/ccds/vits_onnx/app/text/symbols.py
deleted file mode 100644
index 7678bdc5c60b1216b840d5cc8c5de171b8976807..0000000000000000000000000000000000000000
--- a/spaces/ccds/vits_onnx/app/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-# jp_cleaners
-_pad        = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-
-
-
-# japanese_cleaners2
-# _pad        = '_'
-# _punctuation = ',.!?-~…'
-# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/ccmusic-database/README/README.md b/spaces/ccmusic-database/README/README.md
deleted file mode 100644
index 856dfd9f43ec335ee6b903dac9d166e58d5f7817..0000000000000000000000000000000000000000
--- a/spaces/ccmusic-database/README/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
----
-title: README
-emoji: 💻
-colorFrom: gray
-colorTo: indigo
-sdk: static
-pinned: true
-license: mit
----
-
-<p>
-It is always a challenge for computational musicology (CM) researchers to acquire high-quality datasets. This is because of the dispersive nature of existing datasets that makes dataset collection a tedious, sometimes difficult, work to do. Moreover, they are some that are even not publicly available. The CCMusic, our open data platform for CM research, not only contains high-quality datasets at present, which have already been used in several pieces of research and applicants but is also a community in which researchers can contribute datasets thus benefiting the whole research field. We believe that the CCMusic would be a bridge for researchers and dataset builders and make a great contribution to building and maintaining publicly available datasets of fine quality for CM researchers.
-</p>
-
-## Cite
-```
-@dataset{zhaorui_liu_2021_5676893,
-  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Zhaowen Wang, Wei Li and Zijin Li},
-  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
-  month        = {nov},
-  year         = {2021},
-  publisher    = {Zenodo},
-  version      = {1.1},
-  doi          = {10.5281/zenodo.5676893},
-  url          = {https://doi.org/10.5281/zenodo.5676893}
-}
-```
\ No newline at end of file
diff --git a/spaces/ceckenrode/SelfCareDimensionsPositiveReframing/README.md b/spaces/ceckenrode/SelfCareDimensionsPositiveReframing/README.md
deleted file mode 100644
index b9679be44e4307167b94e56db0ef15bcfbb46a6c..0000000000000000000000000000000000000000
--- a/spaces/ceckenrode/SelfCareDimensionsPositiveReframing/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SelfCareDimensionsPositiveReframing
-emoji: 🔥
-colorFrom: pink
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/demo/OpenVINO/README.md b/spaces/chendl/compositional_test/multimodal/YOLOX/demo/OpenVINO/README.md
deleted file mode 100644
index 559708f13f2f21bbb16ae331f50a625014a7b28b..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/demo/OpenVINO/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-## YOLOX for OpenVINO
-
-* [C++ Demo](./cpp)
-* [Python Demo](./python)
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/finetune_trainer.py b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/finetune_trainer.py
deleted file mode 100644
index 4e186c96d8c2186ec0f023822ec70aac4fce4693..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/finetune_trainer.py
+++ /dev/null
@@ -1,375 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-from seq2seq_trainer import Seq2SeqTrainer
-from seq2seq_training_args import Seq2SeqTrainingArguments
-
-import transformers
-from transformers import (
-    AutoConfig,
-    AutoModelForSeq2SeqLM,
-    AutoTokenizer,
-    HfArgumentParser,
-    MBartTokenizer,
-    MBartTokenizerFast,
-    set_seed,
-)
-from transformers.trainer_utils import EvaluationStrategy, is_main_process
-from transformers.training_args import ParallelMode
-from utils import (
-    Seq2SeqDataCollator,
-    Seq2SeqDataset,
-    assert_all_frozen,
-    build_compute_metrics_fn,
-    check_output_dir,
-    freeze_embeds,
-    freeze_params,
-    lmap,
-    save_json,
-    use_task_specific_params,
-    write_txt_file,
-)
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
-    )
-    freeze_encoder: bool = field(default=False, metadata={"help": "Whether tp freeze the encoder."})
-    freeze_embeds: bool = field(default=False, metadata={"help": "Whether  to freeze the embeddings."})
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    data_dir: str = field(
-        metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
-    )
-    task: Optional[str] = field(
-        default="summarization",
-        metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"},
-    )
-    max_source_length: Optional[int] = field(
-        default=1024,
-        metadata={
-            "help": (
-                "The maximum total input sequence length after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded."
-            )
-        },
-    )
-    max_target_length: Optional[int] = field(
-        default=128,
-        metadata={
-            "help": (
-                "The maximum total sequence length for target text after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded."
-            )
-        },
-    )
-    val_max_target_length: Optional[int] = field(
-        default=142,
-        metadata={
-            "help": (
-                "The maximum total sequence length for validation target text after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded. "
-                "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
-                "during ``evaluate`` and ``predict``."
-            )
-        },
-    )
-    test_max_target_length: Optional[int] = field(
-        default=142,
-        metadata={
-            "help": (
-                "The maximum total sequence length for test target text after tokenization. Sequences longer "
-                "than this will be truncated, sequences shorter will be padded."
-            )
-        },
-    )
-    n_train: Optional[int] = field(default=-1, metadata={"help": "# training examples. -1 means use all."})
-    n_val: Optional[int] = field(default=-1, metadata={"help": "# validation examples. -1 means use all."})
-    n_test: Optional[int] = field(default=-1, metadata={"help": "# test examples. -1 means use all."})
-    src_lang: Optional[str] = field(default=None, metadata={"help": "Source language id for translation."})
-    tgt_lang: Optional[str] = field(default=None, metadata={"help": "Target language id for translation."})
-    eval_beams: Optional[int] = field(default=None, metadata={"help": "# num_beams to use for evaluation."})
-    ignore_pad_token_for_loss: bool = field(
-        default=True,
-        metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."},
-    )
-
-
-def handle_metrics(split, metrics, output_dir):
-    """
-    Log and save metrics
-
-    Args:
-    - split: one of train, val, test
-    - metrics: metrics dict
-    - output_dir: where to save the metrics
-    """
-
-    logger.info(f"***** {split} metrics *****")
-    for key in sorted(metrics.keys()):
-        logger.info(f"  {key} = {metrics[key]}")
-    save_json(metrics, os.path.join(output_dir, f"{split}_results.json"))
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
-
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    check_output_dir(training_args)
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
-    )
-    logger.warning(
-        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        training_args.local_rank,
-        training_args.device,
-        training_args.n_gpu,
-        bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED),
-        training_args.fp16,
-    )
-    transformers.utils.logging.enable_default_handler()
-    transformers.utils.logging.enable_explicit_format()
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    if is_main_process(training_args.local_rank):
-        transformers.utils.logging.set_verbosity_info()
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed
-    set_seed(training_args.seed)
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-
-    extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
-    for p in extra_model_params:
-        if getattr(training_args, p, None):
-            assert hasattr(config, p), f"({config.__class__.__name__}) doesn't have a `{p}` attribute"
-            setattr(config, p, getattr(training_args, p))
-
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-    )
-    model = AutoModelForSeq2SeqLM.from_pretrained(
-        model_args.model_name_or_path,
-        from_tf=".ckpt" in model_args.model_name_or_path,
-        config=config,
-        cache_dir=model_args.cache_dir,
-    )
-
-    # use task specific params
-    use_task_specific_params(model, data_args.task)
-
-    # set num_beams for evaluation
-    if data_args.eval_beams is None:
-        data_args.eval_beams = model.config.num_beams
-
-    # set decoder_start_token_id for MBart
-    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
-        assert (
-            data_args.tgt_lang is not None and data_args.src_lang is not None
-        ), "mBart requires --tgt_lang and --src_lang"
-        if isinstance(tokenizer, MBartTokenizer):
-            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.tgt_lang]
-        else:
-            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.tgt_lang)
-
-    if model_args.freeze_embeds:
-        freeze_embeds(model)
-    if model_args.freeze_encoder:
-        freeze_params(model.get_encoder())
-        assert_all_frozen(model.get_encoder())
-
-    dataset_class = Seq2SeqDataset
-
-    # Get datasets
-    train_dataset = (
-        dataset_class(
-            tokenizer,
-            type_path="train",
-            data_dir=data_args.data_dir,
-            n_obs=data_args.n_train,
-            max_target_length=data_args.max_target_length,
-            max_source_length=data_args.max_source_length,
-            prefix=model.config.prefix or "",
-        )
-        if training_args.do_train
-        else None
-    )
-    eval_dataset = (
-        dataset_class(
-            tokenizer,
-            type_path="val",
-            data_dir=data_args.data_dir,
-            n_obs=data_args.n_val,
-            max_target_length=data_args.val_max_target_length,
-            max_source_length=data_args.max_source_length,
-            prefix=model.config.prefix or "",
-        )
-        if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
-        else None
-    )
-    test_dataset = (
-        dataset_class(
-            tokenizer,
-            type_path="test",
-            data_dir=data_args.data_dir,
-            n_obs=data_args.n_test,
-            max_target_length=data_args.test_max_target_length,
-            max_source_length=data_args.max_source_length,
-            prefix=model.config.prefix or "",
-        )
-        if training_args.do_predict
-        else None
-    )
-
-    # Initialize our Trainer
-    compute_metrics_fn = (
-        build_compute_metrics_fn(data_args.task, tokenizer) if training_args.predict_with_generate else None
-    )
-    trainer = Seq2SeqTrainer(
-        model=model,
-        args=training_args,
-        data_args=data_args,
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        data_collator=Seq2SeqDataCollator(
-            tokenizer, data_args, model.config.decoder_start_token_id, training_args.tpu_num_cores
-        ),
-        compute_metrics=compute_metrics_fn,
-        tokenizer=tokenizer,
-    )
-
-    all_metrics = {}
-    # Training
-    if training_args.do_train:
-        logger.info("*** Train ***")
-
-        train_result = trainer.train(
-            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
-        )
-        metrics = train_result.metrics
-        metrics["train_n_objs"] = data_args.n_train
-
-        trainer.save_model()  # this also saves the tokenizer
-
-        if trainer.is_world_process_zero():
-            handle_metrics("train", metrics, training_args.output_dir)
-            all_metrics.update(metrics)
-
-            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
-            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
-
-            # For convenience, we also re-save the tokenizer to the same directory,
-            # so that you can share your model easily on huggingface.co/models =)
-            tokenizer.save_pretrained(training_args.output_dir)
-
-    # Evaluation
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-
-        metrics = trainer.evaluate(metric_key_prefix="val")
-        metrics["val_n_objs"] = data_args.n_val
-        metrics["val_loss"] = round(metrics["val_loss"], 4)
-
-        if trainer.is_world_process_zero():
-            handle_metrics("val", metrics, training_args.output_dir)
-            all_metrics.update(metrics)
-
-    if training_args.do_predict:
-        logger.info("*** Predict ***")
-
-        test_output = trainer.predict(test_dataset=test_dataset, metric_key_prefix="test")
-        metrics = test_output.metrics
-        metrics["test_n_objs"] = data_args.n_test
-
-        if trainer.is_world_process_zero():
-            metrics["test_loss"] = round(metrics["test_loss"], 4)
-            handle_metrics("test", metrics, training_args.output_dir)
-            all_metrics.update(metrics)
-
-            if training_args.predict_with_generate:
-                test_preds = tokenizer.batch_decode(
-                    test_output.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
-                )
-                test_preds = lmap(str.strip, test_preds)
-                write_txt_file(test_preds, os.path.join(training_args.output_dir, "test_generations.txt"))
-
-    if trainer.is_world_process_zero():
-        save_json(all_metrics, os.path.join(training_args.output_dir, "all_results.json"))
-
-    return all_metrics
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/chendl/compositional_test/transformers/examples/pytorch/language-modeling/run_mlm_no_trainer.py b/spaces/chendl/compositional_test/transformers/examples/pytorch/language-modeling/run_mlm_no_trainer.py
deleted file mode 100644
index 0eb40e840a6df907d2332723144f5fa5e4dfa821..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/pytorch/language-modeling/run_mlm_no_trainer.py
+++ /dev/null
@@ -1,730 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...)
-on a text file or a dataset without using HuggingFace Trainer.
-
-Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
-https://huggingface.co/models?filter=fill-mask
-"""
-# You can also adapt this script on your own mlm task. Pointers for this are left as comments.
-
-import argparse
-import json
-import logging
-import math
-import os
-import random
-from itertools import chain
-from pathlib import Path
-
-import datasets
-import torch
-from accelerate import Accelerator, DistributedType
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
-from torch.utils.data import DataLoader
-from tqdm.auto import tqdm
-
-import transformers
-from transformers import (
-    CONFIG_MAPPING,
-    MODEL_MAPPING,
-    AutoConfig,
-    AutoModelForMaskedLM,
-    AutoTokenizer,
-    DataCollatorForLanguageModeling,
-    SchedulerType,
-    get_scheduler,
-)
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
-from transformers.utils.versions import require_version
-
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.28.0")
-
-logger = get_logger(__name__)
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
-MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task")
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help="The name of the dataset to use (via the datasets library).",
-    )
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The configuration name of the dataset to use (via the datasets library).",
-    )
-    parser.add_argument(
-        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
-    )
-    parser.add_argument(
-        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
-    )
-    parser.add_argument(
-        "--validation_split_percentage",
-        default=5,
-        help="The percentage of the train set used as validation set in case there's no validation split",
-    )
-    parser.add_argument(
-        "--pad_to_max_length",
-        action="store_true",
-        help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        type=str,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-        required=False,
-    )
-    parser.add_argument(
-        "--config_name",
-        type=str,
-        default=None,
-        help="Pretrained config name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--use_slow_tokenizer",
-        action="store_true",
-        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
-    )
-    parser.add_argument(
-        "--per_device_train_batch_size",
-        type=int,
-        default=8,
-        help="Batch size (per device) for the training dataloader.",
-    )
-    parser.add_argument(
-        "--per_device_eval_batch_size",
-        type=int,
-        default=8,
-        help="Batch size (per device) for the evaluation dataloader.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-5,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
-    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--lr_scheduler_type",
-        type=SchedulerType,
-        default="linear",
-        help="The scheduler type to use.",
-        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
-    )
-    parser.add_argument(
-        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--model_type",
-        type=str,
-        default=None,
-        help="Model type to use if training from scratch.",
-        choices=MODEL_TYPES,
-    )
-    parser.add_argument(
-        "--max_seq_length",
-        type=int,
-        default=None,
-        help=(
-            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated."
-        ),
-    )
-    parser.add_argument(
-        "--line_by_line",
-        type=bool,
-        default=False,
-        help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
-    )
-    parser.add_argument(
-        "--preprocessing_num_workers",
-        type=int,
-        default=None,
-        help="The number of processes to use for the preprocessing.",
-    )
-    parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
-    )
-    parser.add_argument(
-        "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
-    )
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument(
-        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
-    )
-    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=str,
-        default=None,
-        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help="If the training should continue from a checkpoint folder.",
-    )
-    parser.add_argument(
-        "--with_tracking",
-        action="store_true",
-        help="Whether to enable experiment trackers for logging.",
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="all",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
-            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.'
-            "Only applicable when `--with_tracking` is passed."
-        ),
-    )
-    parser.add_argument(
-        "--low_cpu_mem_usage",
-        action="store_true",
-        help=(
-            "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded."
-            "If passed, LLM loading time and RAM consumption will be benefited."
-        ),
-    )
-    args = parser.parse_args()
-
-    # Sanity checks
-    if args.dataset_name is None and args.train_file is None and args.validation_file is None:
-        raise ValueError("Need either a dataset name or a training/validation file.")
-    else:
-        if args.train_file is not None:
-            extension = args.train_file.split(".")[-1]
-            if extension not in ["csv", "json", "txt"]:
-                raise ValueError("`train_file` should be a csv, json or txt file.")
-        if args.validation_file is not None:
-            extension = args.validation_file.split(".")[-1]
-            if extension not in ["csv", "json", "txt"]:
-                raise ValueError("`validation_file` should be a csv, json or txt file.")
-
-    if args.push_to_hub:
-        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
-
-    return args
-
-
-def main():
-    args = parse_args()
-
-    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
-    # information sent is the one passed as arguments along with your Python/PyTorch versions.
-    send_example_telemetry("run_mlm_no_trainer", args)
-
-    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
-    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
-    # in the environment
-    accelerator_log_kwargs = {}
-
-    if args.with_tracking:
-        accelerator_log_kwargs["log_with"] = args.report_to
-        accelerator_log_kwargs["logging_dir"] = args.output_dir
-
-    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        datasets.utils.logging.set_verbosity_warning()
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-    accelerator.wait_for_everyone()
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
-        if "validation" not in raw_datasets.keys():
-            raw_datasets["validation"] = load_dataset(
-                args.dataset_name,
-                args.dataset_config_name,
-                split=f"train[:{args.validation_split_percentage}%]",
-            )
-            raw_datasets["train"] = load_dataset(
-                args.dataset_name,
-                args.dataset_config_name,
-                split=f"train[{args.validation_split_percentage}%:]",
-            )
-    else:
-        data_files = {}
-        if args.train_file is not None:
-            data_files["train"] = args.train_file
-        if args.validation_file is not None:
-            data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
-        if extension == "txt":
-            extension = "text"
-        raw_datasets = load_dataset(extension, data_files=data_files)
-        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
-        if "validation" not in raw_datasets.keys():
-            raw_datasets["validation"] = load_dataset(
-                extension,
-                data_files=data_files,
-                split=f"train[:{args.validation_split_percentage}%]",
-            )
-            raw_datasets["train"] = load_dataset(
-                extension,
-                data_files=data_files,
-                split=f"train[{args.validation_split_percentage}%:]",
-            )
-
-    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
-    # https://huggingface.co/docs/datasets/loading_datasets.html.
-
-    # Load pretrained model and tokenizer
-    #
-    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name)
-    elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
-    else:
-        config = CONFIG_MAPPING[args.model_type]()
-        logger.warning("You are instantiating a new config instance from scratch.")
-
-    if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
-    elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
-    else:
-        raise ValueError(
-            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
-            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
-        )
-
-    if args.model_name_or_path:
-        model = AutoModelForMaskedLM.from_pretrained(
-            args.model_name_or_path,
-            from_tf=bool(".ckpt" in args.model_name_or_path),
-            config=config,
-            low_cpu_mem_usage=args.low_cpu_mem_usage,
-        )
-    else:
-        logger.info("Training new model from scratch")
-        model = AutoModelForMaskedLM.from_config(config)
-
-    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
-    # on a small vocab and want a smaller embedding size, remove this test.
-    embedding_size = model.get_input_embeddings().weight.shape[0]
-    if len(tokenizer) > embedding_size:
-        model.resize_token_embeddings(len(tokenizer))
-
-    # Preprocessing the datasets.
-    # First we tokenize all the texts.
-    column_names = raw_datasets["train"].column_names
-    text_column_name = "text" if "text" in column_names else column_names[0]
-
-    if args.max_seq_length is None:
-        max_seq_length = tokenizer.model_max_length
-        if max_seq_length > 1024:
-            logger.warning(
-                "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
-                " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
-                " override this default with `--block_size xxx`."
-            )
-            max_seq_length = 1024
-    else:
-        if args.max_seq_length > tokenizer.model_max_length:
-            logger.warning(
-                f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
-                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
-            )
-        max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)
-
-    if args.line_by_line:
-        # When using line_by_line, we just tokenize each nonempty line.
-        padding = "max_length" if args.pad_to_max_length else False
-
-        def tokenize_function(examples):
-            # Remove empty lines
-            examples[text_column_name] = [
-                line for line in examples[text_column_name] if len(line) > 0 and not line.isspace()
-            ]
-            return tokenizer(
-                examples[text_column_name],
-                padding=padding,
-                truncation=True,
-                max_length=max_seq_length,
-                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
-                # receives the `special_tokens_mask`.
-                return_special_tokens_mask=True,
-            )
-
-        with accelerator.main_process_first():
-            tokenized_datasets = raw_datasets.map(
-                tokenize_function,
-                batched=True,
-                num_proc=args.preprocessing_num_workers,
-                remove_columns=[text_column_name],
-                load_from_cache_file=not args.overwrite_cache,
-                desc="Running tokenizer on dataset line_by_line",
-            )
-    else:
-        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
-        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
-        # efficient when it receives the `special_tokens_mask`.
-        def tokenize_function(examples):
-            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
-
-        with accelerator.main_process_first():
-            tokenized_datasets = raw_datasets.map(
-                tokenize_function,
-                batched=True,
-                num_proc=args.preprocessing_num_workers,
-                remove_columns=column_names,
-                load_from_cache_file=not args.overwrite_cache,
-                desc="Running tokenizer on every text in dataset",
-            )
-
-        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
-        # max_seq_length.
-        def group_texts(examples):
-            # Concatenate all texts.
-            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
-            total_length = len(concatenated_examples[list(examples.keys())[0]])
-            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
-            # customize this part to your needs.
-            if total_length >= max_seq_length:
-                total_length = (total_length // max_seq_length) * max_seq_length
-            # Split by chunks of max_len.
-            result = {
-                k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
-                for k, t in concatenated_examples.items()
-            }
-            return result
-
-        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
-        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
-        # might be slower to preprocess.
-        #
-        # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
-        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
-
-        with accelerator.main_process_first():
-            tokenized_datasets = tokenized_datasets.map(
-                group_texts,
-                batched=True,
-                num_proc=args.preprocessing_num_workers,
-                load_from_cache_file=not args.overwrite_cache,
-                desc=f"Grouping texts in chunks of {max_seq_length}",
-            )
-
-    train_dataset = tokenized_datasets["train"]
-    eval_dataset = tokenized_datasets["validation"]
-
-    # Conditional for small test subsets
-    if len(train_dataset) > 3:
-        # Log a few random samples from the training set:
-        for index in random.sample(range(len(train_dataset)), 3):
-            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
-
-    # Data collator
-    # This one will take care of randomly masking the tokens.
-    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=args.mlm_probability)
-
-    # DataLoaders creation:
-    train_dataloader = DataLoader(
-        train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
-    )
-    eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
-
-    # Optimizer
-    # Split weights in two groups, one with weight decay and the other not.
-    no_decay = ["bias", "LayerNorm.weight"]
-    optimizer_grouped_parameters = [
-        {
-            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
-            "weight_decay": args.weight_decay,
-        },
-        {
-            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
-            "weight_decay": 0.0,
-        },
-    ]
-    optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
-
-    # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
-    # shorter in multiprocess)
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        name=args.lr_scheduler_type,
-        optimizer=optimizer,
-        num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-    )
-
-    # Prepare everything with our `accelerator`.
-    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
-        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
-    )
-
-    # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
-    if accelerator.distributed_type == DistributedType.TPU:
-        model.tie_weights()
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # Figure out how many steps we should save the Accelerator states
-    checkpointing_steps = args.checkpointing_steps
-    if checkpointing_steps is not None and checkpointing_steps.isdigit():
-        checkpointing_steps = int(checkpointing_steps)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if args.with_tracking:
-        experiment_config = vars(args)
-        # TensorBoard cannot log Enums, need the raw value
-        experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
-        accelerator.init_trackers("mlm_no_trainer", experiment_config)
-
-    # Train!
-    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
-    completed_steps = 0
-    starting_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
-            accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
-            accelerator.load_state(args.resume_from_checkpoint)
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
-            dirs.sort(key=os.path.getctime)
-            path = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
-        # Extract `epoch_{i}` or `step_{i}`
-        training_difference = os.path.splitext(path)[0]
-
-        if "epoch" in training_difference:
-            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
-            resume_step = None
-        else:
-            # need to multiply `gradient_accumulation_steps` to reflect real steps
-            resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
-            starting_epoch = resume_step // len(train_dataloader)
-            resume_step -= starting_epoch * len(train_dataloader)
-
-    # update the progress_bar if load from checkpoint
-    progress_bar.update(starting_epoch * num_update_steps_per_epoch)
-    completed_steps = starting_epoch * num_update_steps_per_epoch
-
-    for epoch in range(starting_epoch, args.num_train_epochs):
-        model.train()
-        if args.with_tracking:
-            total_loss = 0
-        for step, batch in enumerate(train_dataloader):
-            # We need to skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == starting_epoch:
-                if resume_step is not None and step < resume_step:
-                    if step % args.gradient_accumulation_steps == 0:
-                        progress_bar.update(1)
-                        completed_steps += 1
-                    continue
-
-            with accelerator.accumulate(model):
-                outputs = model(**batch)
-                loss = outputs.loss
-                # We keep track of the loss at each epoch
-                if args.with_tracking:
-                    total_loss += loss.detach().float()
-                accelerator.backward(loss)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                completed_steps += 1
-
-            if isinstance(checkpointing_steps, int):
-                if completed_steps % checkpointing_steps == 0:
-                    output_dir = f"step_{completed_steps }"
-                    if args.output_dir is not None:
-                        output_dir = os.path.join(args.output_dir, output_dir)
-                    accelerator.save_state(output_dir)
-
-            if completed_steps >= args.max_train_steps:
-                break
-
-        model.eval()
-        losses = []
-        for step, batch in enumerate(eval_dataloader):
-            with torch.no_grad():
-                outputs = model(**batch)
-
-            loss = outputs.loss
-            losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))
-
-        losses = torch.cat(losses)
-        try:
-            eval_loss = torch.mean(losses)
-            perplexity = math.exp(eval_loss)
-        except OverflowError:
-            perplexity = float("inf")
-
-        logger.info(f"epoch {epoch}: perplexity: {perplexity}")
-
-        if args.with_tracking:
-            accelerator.log(
-                {
-                    "perplexity": perplexity,
-                    "eval_loss": eval_loss,
-                    "train_loss": total_loss.item() / len(train_dataloader),
-                    "epoch": epoch,
-                    "step": completed_steps,
-                },
-                step=completed_steps,
-            )
-
-        if args.push_to_hub and epoch < args.num_train_epochs - 1:
-            accelerator.wait_for_everyone()
-            unwrapped_model = accelerator.unwrap_model(model)
-            unwrapped_model.save_pretrained(
-                args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
-            )
-            if accelerator.is_main_process:
-                tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
-                )
-
-        if args.checkpointing_steps == "epoch":
-            output_dir = f"epoch_{epoch}"
-            if args.output_dir is not None:
-                output_dir = os.path.join(args.output_dir, output_dir)
-            accelerator.save_state(output_dir)
-
-    if args.with_tracking:
-        accelerator.end_training()
-
-    if args.output_dir is not None:
-        accelerator.wait_for_everyone()
-        unwrapped_model = accelerator.unwrap_model(model)
-        unwrapped_model.save_pretrained(
-            args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
-        )
-        if accelerator.is_main_process:
-            tokenizer.save_pretrained(args.output_dir)
-            if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
-
-            with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
-                json.dump({"perplexity": perplexity}, f)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/chrisvnz/IFC-Extract-Properties/README.md b/spaces/chrisvnz/IFC-Extract-Properties/README.md
deleted file mode 100644
index 30e1856aca40ade7f6e82cb1e8b8dd1b8da1a2a5..0000000000000000000000000000000000000000
--- a/spaces/chrisvnz/IFC-Extract-Properties/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: IFC Extract Properties
-emoji: 😻
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/formatting.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/formatting.py
deleted file mode 100644
index ddd2a2f825f206164eb9efb0a5c41528365beb85..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/click/formatting.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import typing as t
-from contextlib import contextmanager
-from gettext import gettext as _
-
-from ._compat import term_len
-from .parser import split_opt
-
-# Can force a width.  This is used by the test system
-FORCED_WIDTH: t.Optional[int] = None
-
-
-def measure_table(rows: t.Iterable[t.Tuple[str, str]]) -> t.Tuple[int, ...]:
-    widths: t.Dict[int, int] = {}
-
-    for row in rows:
-        for idx, col in enumerate(row):
-            widths[idx] = max(widths.get(idx, 0), term_len(col))
-
-    return tuple(y for x, y in sorted(widths.items()))
-
-
-def iter_rows(
-    rows: t.Iterable[t.Tuple[str, str]], col_count: int
-) -> t.Iterator[t.Tuple[str, ...]]:
-    for row in rows:
-        yield row + ("",) * (col_count - len(row))
-
-
-def wrap_text(
-    text: str,
-    width: int = 78,
-    initial_indent: str = "",
-    subsequent_indent: str = "",
-    preserve_paragraphs: bool = False,
-) -> str:
-    """A helper function that intelligently wraps text.  By default, it
-    assumes that it operates on a single paragraph of text but if the
-    `preserve_paragraphs` parameter is provided it will intelligently
-    handle paragraphs (defined by two empty lines).
-
-    If paragraphs are handled, a paragraph can be prefixed with an empty
-    line containing the ``\\b`` character (``\\x08``) to indicate that
-    no rewrapping should happen in that block.
-
-    :param text: the text that should be rewrapped.
-    :param width: the maximum width for the text.
-    :param initial_indent: the initial indent that should be placed on the
-                           first line as a string.
-    :param subsequent_indent: the indent string that should be placed on
-                              each consecutive line.
-    :param preserve_paragraphs: if this flag is set then the wrapping will
-                                intelligently handle paragraphs.
-    """
-    from ._textwrap import TextWrapper
-
-    text = text.expandtabs()
-    wrapper = TextWrapper(
-        width,
-        initial_indent=initial_indent,
-        subsequent_indent=subsequent_indent,
-        replace_whitespace=False,
-    )
-    if not preserve_paragraphs:
-        return wrapper.fill(text)
-
-    p: t.List[t.Tuple[int, bool, str]] = []
-    buf: t.List[str] = []
-    indent = None
-
-    def _flush_par() -> None:
-        if not buf:
-            return
-        if buf[0].strip() == "\b":
-            p.append((indent or 0, True, "\n".join(buf[1:])))
-        else:
-            p.append((indent or 0, False, " ".join(buf)))
-        del buf[:]
-
-    for line in text.splitlines():
-        if not line:
-            _flush_par()
-            indent = None
-        else:
-            if indent is None:
-                orig_len = term_len(line)
-                line = line.lstrip()
-                indent = orig_len - term_len(line)
-            buf.append(line)
-    _flush_par()
-
-    rv = []
-    for indent, raw, text in p:
-        with wrapper.extra_indent(" " * indent):
-            if raw:
-                rv.append(wrapper.indent_only(text))
-            else:
-                rv.append(wrapper.fill(text))
-
-    return "\n\n".join(rv)
-
-
-class HelpFormatter:
-    """This class helps with formatting text-based help pages.  It's
-    usually just needed for very special internal cases, but it's also
-    exposed so that developers can write their own fancy outputs.
-
-    At present, it always writes into memory.
-
-    :param indent_increment: the additional increment for each level.
-    :param width: the width for the text.  This defaults to the terminal
-                  width clamped to a maximum of 78.
-    """
-
-    def __init__(
-        self,
-        indent_increment: int = 2,
-        width: t.Optional[int] = None,
-        max_width: t.Optional[int] = None,
-    ) -> None:
-        import shutil
-
-        self.indent_increment = indent_increment
-        if max_width is None:
-            max_width = 80
-        if width is None:
-            width = FORCED_WIDTH
-            if width is None:
-                width = max(min(shutil.get_terminal_size().columns, max_width) - 2, 50)
-        self.width = width
-        self.current_indent = 0
-        self.buffer: t.List[str] = []
-
-    def write(self, string: str) -> None:
-        """Writes a unicode string into the internal buffer."""
-        self.buffer.append(string)
-
-    def indent(self) -> None:
-        """Increases the indentation."""
-        self.current_indent += self.indent_increment
-
-    def dedent(self) -> None:
-        """Decreases the indentation."""
-        self.current_indent -= self.indent_increment
-
-    def write_usage(
-        self, prog: str, args: str = "", prefix: t.Optional[str] = None
-    ) -> None:
-        """Writes a usage line into the buffer.
-
-        :param prog: the program name.
-        :param args: whitespace separated list of arguments.
-        :param prefix: The prefix for the first line. Defaults to
-            ``"Usage: "``.
-        """
-        if prefix is None:
-            prefix = f"{_('Usage:')} "
-
-        usage_prefix = f"{prefix:>{self.current_indent}}{prog} "
-        text_width = self.width - self.current_indent
-
-        if text_width >= (term_len(usage_prefix) + 20):
-            # The arguments will fit to the right of the prefix.
-            indent = " " * term_len(usage_prefix)
-            self.write(
-                wrap_text(
-                    args,
-                    text_width,
-                    initial_indent=usage_prefix,
-                    subsequent_indent=indent,
-                )
-            )
-        else:
-            # The prefix is too long, put the arguments on the next line.
-            self.write(usage_prefix)
-            self.write("\n")
-            indent = " " * (max(self.current_indent, term_len(prefix)) + 4)
-            self.write(
-                wrap_text(
-                    args, text_width, initial_indent=indent, subsequent_indent=indent
-                )
-            )
-
-        self.write("\n")
-
-    def write_heading(self, heading: str) -> None:
-        """Writes a heading into the buffer."""
-        self.write(f"{'':>{self.current_indent}}{heading}:\n")
-
-    def write_paragraph(self) -> None:
-        """Writes a paragraph into the buffer."""
-        if self.buffer:
-            self.write("\n")
-
-    def write_text(self, text: str) -> None:
-        """Writes re-indented text into the buffer.  This rewraps and
-        preserves paragraphs.
-        """
-        indent = " " * self.current_indent
-        self.write(
-            wrap_text(
-                text,
-                self.width,
-                initial_indent=indent,
-                subsequent_indent=indent,
-                preserve_paragraphs=True,
-            )
-        )
-        self.write("\n")
-
-    def write_dl(
-        self,
-        rows: t.Sequence[t.Tuple[str, str]],
-        col_max: int = 30,
-        col_spacing: int = 2,
-    ) -> None:
-        """Writes a definition list into the buffer.  This is how options
-        and commands are usually formatted.
-
-        :param rows: a list of two item tuples for the terms and values.
-        :param col_max: the maximum width of the first column.
-        :param col_spacing: the number of spaces between the first and
-                            second column.
-        """
-        rows = list(rows)
-        widths = measure_table(rows)
-        if len(widths) != 2:
-            raise TypeError("Expected two columns for definition list")
-
-        first_col = min(widths[0], col_max) + col_spacing
-
-        for first, second in iter_rows(rows, len(widths)):
-            self.write(f"{'':>{self.current_indent}}{first}")
-            if not second:
-                self.write("\n")
-                continue
-            if term_len(first) <= first_col - col_spacing:
-                self.write(" " * (first_col - term_len(first)))
-            else:
-                self.write("\n")
-                self.write(" " * (first_col + self.current_indent))
-
-            text_width = max(self.width - first_col - 2, 10)
-            wrapped_text = wrap_text(second, text_width, preserve_paragraphs=True)
-            lines = wrapped_text.splitlines()
-
-            if lines:
-                self.write(f"{lines[0]}\n")
-
-                for line in lines[1:]:
-                    self.write(f"{'':>{first_col + self.current_indent}}{line}\n")
-            else:
-                self.write("\n")
-
-    @contextmanager
-    def section(self, name: str) -> t.Iterator[None]:
-        """Helpful context manager that writes a paragraph, a heading,
-        and the indents.
-
-        :param name: the section name that is written as heading.
-        """
-        self.write_paragraph()
-        self.write_heading(name)
-        self.indent()
-        try:
-            yield
-        finally:
-            self.dedent()
-
-    @contextmanager
-    def indentation(self) -> t.Iterator[None]:
-        """A context manager that increases the indentation."""
-        self.indent()
-        try:
-            yield
-        finally:
-            self.dedent()
-
-    def getvalue(self) -> str:
-        """Returns the buffer contents."""
-        return "".join(self.buffer)
-
-
-def join_options(options: t.Sequence[str]) -> t.Tuple[str, bool]:
-    """Given a list of option strings this joins them in the most appropriate
-    way and returns them in the form ``(formatted_string,
-    any_prefix_is_slash)`` where the second item in the tuple is a flag that
-    indicates if any of the option prefixes was a slash.
-    """
-    rv = []
-    any_prefix_is_slash = False
-
-    for opt in options:
-        prefix = split_opt(opt)[0]
-
-        if prefix == "/":
-            any_prefix_is_slash = True
-
-        rv.append((len(prefix), opt))
-
-    rv.sort(key=lambda x: x[0])
-    return ", ".join(x[1] for x in rv), any_prefix_is_slash
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/settings.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/settings.py
deleted file mode 100644
index 502c9d4db77481690c57feed3c5e04f8f643c4f7..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/docx/settings.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# encoding: utf-8
-
-"""Settings object, providing access to document-level settings"""
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-from docx.shared import ElementProxy
-
-
-class Settings(ElementProxy):
-    """Provides access to document-level settings for a document.
-
-    Accessed using the :attr:`.Document.settings` property.
-    """
-
-    __slots__ = ()
-
-    @property
-    def odd_and_even_pages_header_footer(self):
-        """True if this document has distinct odd and even page headers and footers.
-
-        Read/write.
-        """
-        return self._element.evenAndOddHeaders_val
-
-    @odd_and_even_pages_header_footer.setter
-    def odd_and_even_pages_header_footer(self, value):
-        self._element.evenAndOddHeaders_val = value
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/dataframe.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/dataframe.py
deleted file mode 100644
index f0ed13adafa0573f5ab06bfb6773148aed4f0671..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/components/dataframe.py
+++ /dev/null
@@ -1,304 +0,0 @@
-"""gr.Dataframe() component"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, Callable, Literal
-
-import numpy as np
-import pandas as pd
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import JSONSerializable
-
-from gradio import utils
-from gradio.components.base import IOComponent, _Keywords
-from gradio.events import (
-    Changeable,
-    EventListenerMethod,
-    Inputable,
-    Selectable,
-)
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class DataframeData(TypedDict):
-        headers: list[str]
-        data: list[list[str | int | bool]]
-
-
-set_documentation_group("component")
-
-
-@document()
-class Dataframe(Changeable, Inputable, Selectable, IOComponent, JSONSerializable):
-    """
-    Accepts or displays 2D input through a spreadsheet-like component for dataframes.
-    Preprocessing: passes the uploaded spreadsheet data as a {pandas.DataFrame}, {numpy.array}, {List[List]}, or {List} depending on `type`
-    Postprocessing: expects a {pandas.DataFrame}, {numpy.array}, {List[List]}, {List}, a {Dict} with keys `data` (and optionally `headers`), or {str} path to a csv, which is rendered in the spreadsheet.
-    Examples-format: a {str} filepath to a csv with data, a pandas dataframe, or a list of lists (excluding headers) where each sublist is a row of data.
-    Demos: filter_records, matrix_transpose, tax_calculator
-    """
-
-    markdown_parser = None
-
-    def __init__(
-        self,
-        value: list[list[Any]] | Callable | None = None,
-        *,
-        headers: list[str] | None = None,
-        row_count: int | tuple[int, str] = (1, "dynamic"),
-        col_count: int | tuple[int, str] | None = None,
-        datatype: str | list[str] = "str",
-        type: Literal["pandas", "numpy", "array"] = "pandas",
-        max_rows: int | None = 20,
-        max_cols: int | None = None,
-        overflow_row_behaviour: Literal["paginate", "show_ends"] = "paginate",
-        label: str | None = None,
-        every: float | None = None,
-        show_label: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        wrap: bool = False,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: Default value as a 2-dimensional list of values. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            headers: List of str header names. If None, no headers are shown.
-            row_count: Limit number of rows for input and decide whether user can create new rows. The first element of the tuple is an `int`, the row count; the second should be 'fixed' or 'dynamic', the new row behaviour. If an `int` is passed the rows default to 'dynamic'
-            col_count: Limit number of columns for input and decide whether user can create new columns. The first element of the tuple is an `int`, the number of columns; the second should be 'fixed' or 'dynamic', the new column behaviour. If an `int` is passed the columns default to 'dynamic'
-            datatype: Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", "date", and "markdown".
-            type: Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for a Python array.
-            label: component name in interface.
-            max_rows: Maximum number of rows to display at once. Set to None for infinite.
-            max_cols: Maximum number of columns to display at once. Set to None for infinite.
-            overflow_row_behaviour: If set to "paginate", will create pages for overflow rows. If set to "show_ends", will show initial and final rows and truncate middle rows.
-            label: component name in interface.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            interactive: if True, will allow users to edit the dataframe; if False, can only be used to display data. If not provided, this is inferred based on whether the component is used as an input or output.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            wrap: if True text in table cells will wrap when appropriate, if False the table will scroll horizontally. Defaults to False.
-        """
-
-        self.wrap = wrap
-        self.row_count = self.__process_counts(row_count)
-        self.col_count = self.__process_counts(
-            col_count, len(headers) if headers else 3
-        )
-
-        self.__validate_headers(headers, self.col_count[0])
-
-        self.headers = (
-            headers if headers is not None else list(range(1, self.col_count[0] + 1))
-        )
-        self.datatype = (
-            datatype if isinstance(datatype, list) else [datatype] * self.col_count[0]
-        )
-        valid_types = ["pandas", "numpy", "array"]
-        if type not in valid_types:
-            raise ValueError(
-                f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}"
-            )
-        self.type = type
-        values = {
-            "str": "",
-            "number": 0,
-            "bool": False,
-            "date": "01/01/1970",
-            "markdown": "",
-            "html": "",
-        }
-        column_dtypes = (
-            [datatype] * self.col_count[0] if isinstance(datatype, str) else datatype
-        )
-        self.empty_input = [
-            [values[c] for c in column_dtypes] for _ in range(self.row_count[0])
-        ]
-
-        self.max_rows = max_rows
-        self.max_cols = max_cols
-        self.overflow_row_behaviour = overflow_row_behaviour
-        self.select: EventListenerMethod
-        """
-        Event listener for when the user selects cell within Dataframe.
-        Uses event data gradio.SelectData to carry `value` referring to value of selected cell, and `index` tuple to refer to index row and column.
-        See EventData documentation on how to use this event data.
-        """
-        IOComponent.__init__(
-            self,
-            label=label,
-            every=every,
-            show_label=show_label,
-            scale=scale,
-            min_width=min_width,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            **kwargs,
-        )
-
-    def get_config(self):
-        return {
-            "headers": self.headers,
-            "datatype": self.datatype,
-            "row_count": self.row_count,
-            "col_count": self.col_count,
-            "value": self.value,
-            "max_rows": self.max_rows,
-            "max_cols": self.max_cols,
-            "overflow_row_behaviour": self.overflow_row_behaviour,
-            "wrap": self.wrap,
-            **IOComponent.get_config(self),
-        }
-
-    @staticmethod
-    def update(
-        value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
-        max_rows: int | None = None,
-        max_cols: str | None = None,
-        label: str | None = None,
-        show_label: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        interactive: bool | None = None,
-        visible: bool | None = None,
-    ):
-        return {
-            "max_rows": max_rows,
-            "max_cols": max_cols,
-            "label": label,
-            "show_label": show_label,
-            "scale": scale,
-            "min_width": min_width,
-            "interactive": interactive,
-            "visible": visible,
-            "value": value,
-            "__type__": "update",
-        }
-
-    def preprocess(self, x: DataframeData):
-        """
-        Parameters:
-            x: 2D array of str, numeric, or bool data
-        Returns:
-            Dataframe in requested format
-        """
-        if self.type == "pandas":
-            if x.get("headers") is not None:
-                return pd.DataFrame(x["data"], columns=x.get("headers"))
-            else:
-                return pd.DataFrame(x["data"])
-        if self.type == "numpy":
-            return np.array(x["data"])
-        elif self.type == "array":
-            return x["data"]
-        else:
-            raise ValueError(
-                "Unknown type: "
-                + str(self.type)
-                + ". Please choose from: 'pandas', 'numpy', 'array'."
-            )
-
-    def postprocess(
-        self, y: str | pd.DataFrame | np.ndarray | list[list[str | float]] | dict
-    ) -> dict:
-        """
-        Parameters:
-            y: dataframe in given format
-        Returns:
-            JSON object with key 'headers' for list of header names, 'data' for 2D array of string or numeric data
-        """
-        if y is None:
-            return self.postprocess(self.empty_input)
-        if isinstance(y, dict):
-            return y
-        if isinstance(y, str):
-            dataframe = pd.read_csv(y)
-            return {
-                "headers": list(dataframe.columns),
-                "data": Dataframe.__process_markdown(
-                    dataframe.to_dict(orient="split")["data"], self.datatype
-                ),
-            }
-        if isinstance(y, pd.DataFrame):
-            return {
-                "headers": list(y.columns),  # type: ignore
-                "data": Dataframe.__process_markdown(
-                    y.to_dict(orient="split")["data"], self.datatype  # type: ignore
-                ),
-            }
-        if isinstance(y, (np.ndarray, list)):
-            if len(y) == 0:
-                return self.postprocess([[]])
-            if isinstance(y, np.ndarray):
-                y = y.tolist()
-            assert isinstance(y, list), "output cannot be converted to list"
-
-            _headers = self.headers
-
-            if len(self.headers) < len(y[0]):
-                _headers = [
-                    *self.headers,
-                    *list(range(len(self.headers) + 1, len(y[0]) + 1)),
-                ]
-            elif len(self.headers) > len(y[0]):
-                _headers = self.headers[: len(y[0])]
-
-            return {
-                "headers": _headers,
-                "data": Dataframe.__process_markdown(y, self.datatype),
-            }
-        raise ValueError("Cannot process value as a Dataframe")
-
-    @staticmethod
-    def __process_counts(count, default=3) -> tuple[int, str]:
-        if count is None:
-            return (default, "dynamic")
-        if type(count) == int or type(count) == float:
-            return (int(count), "dynamic")
-        else:
-            return count
-
-    @staticmethod
-    def __validate_headers(headers: list[str] | None, col_count: int):
-        if headers is not None and len(headers) != col_count:
-            raise ValueError(
-                f"The length of the headers list must be equal to the col_count int.\n"
-                f"The column count is set to {col_count} but `headers` has {len(headers)} items. "
-                f"Check the values passed to `col_count` and `headers`."
-            )
-
-    @classmethod
-    def __process_markdown(cls, data: list[list[Any]], datatype: list[str]):
-        if "markdown" not in datatype:
-            return data
-
-        if cls.markdown_parser is None:
-            cls.markdown_parser = utils.get_markdown_parser()
-
-        for i in range(len(data)):
-            for j in range(len(data[i])):
-                if datatype[j] == "markdown":
-                    data[i][j] = cls.markdown_parser.render(data[i][j])
-
-        return data
-
-    def as_example(self, input_data: pd.DataFrame | np.ndarray | str | None):
-        if input_data is None:
-            return ""
-        elif isinstance(input_data, pd.DataFrame):
-            return input_data.head(n=5).to_dict(orient="split")["data"]  # type: ignore
-        elif isinstance(input_data, np.ndarray):
-            return input_data.tolist()
-        return input_data
diff --git a/spaces/cihyFjudo/fairness-paper-search/Fuga da Los Angeles sub download la missione impossibile di Snake Plissken per salvare il mondo.md b/spaces/cihyFjudo/fairness-paper-search/Fuga da Los Angeles sub download la missione impossibile di Snake Plissken per salvare il mondo.md
deleted file mode 100644
index 6610f5c62778caf5b31312e20377b4760187f77d..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Fuga da Los Angeles sub download la missione impossibile di Snake Plissken per salvare il mondo.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Fuga da Los Angeles sub download</h2><br /><p><b><b>DOWNLOAD</b> &middot; <a href="https://tinurli.com/2uwj6D">https://tinurli.com/2uwj6D</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/Harvard Aircraft Maintenance Manuals Weight and Balance Data and Emergency Procedures for Harvard IIA.md b/spaces/cihyFjudo/fairness-paper-search/Harvard Aircraft Maintenance Manuals Weight and Balance Data and Emergency Procedures for Harvard IIA.md
deleted file mode 100644
index b348ccce3af999cb3c374e95dbc62f1157a26b9a..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Harvard Aircraft Maintenance Manuals Weight and Balance Data and Emergency Procedures for Harvard IIA.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>Aircraft maintenance technician decision-making and actions have resulted in aircraft system errors causing aircraft incidents and accidents. Aircraft accident investigators and researchers examined the factors that influence aircraft maintenance technician errors and categorized the types of errors in an attempt to prevent similar occurrences. New aircraft technology introduced to improve aviation safety and efficiency incur failures that have no information contained in the aircraft maintenance manuals. According to the Federal Aviation Administration, aircraft maintenance technicians must use only approved aircraft maintenance documents to repair, modify, and service aircraft. This qualitative research used a grounded theory approach to explore the decision-making processes and actions taken by aircraft maintenance technicians when confronted with an aircraft problem not contained in the aircraft maintenance manuals. The target population for the research was Federal Aviation Administration licensed aircraft and power plant mechanics from across the United States. Nonprobability purposeful sampling was used to obtain aircraft maintenance technicians with the experience sought in the study problem. The sample population recruitment yielded 19 participants for eight focus group sessions to obtain opinions, perceptions, and experiences related to the study problem. All data collected was entered into the Atlas ti qualitative analysis software. The emergence of Aircraft Maintenance Technician decision-making themes regarding Aircraft Maintenance Manual content, Aircraft Maintenance Technician experience, and legal implications of not following Aircraft Maintenance Manuals surfaced. Conclusions from this study suggest Aircraft Maintenance Technician decision-making were influenced by experience, gaps in the Aircraft Maintenance Manuals, reliance on others, realizing the impact of decisions concerning aircraft airworthiness, management pressures, and legal concerns related to decision-making. Recommendations included an in-depth systematic review of the Aircraft Maintenance Manuals, development of a Federal Aviation Administration approved standardized Aircraft Maintenance Technician decision-making flow diagram, and implementation of risk based decision-making training. The benefit of this study is to save the airline industry revenue by preventing poor decision-making practices that result in inefficient maintenance actions and aircraft incidents and accidents.</p>
-<p>Your CV is the most effective tool you have in gaining the advantage in a competitive job market. The good news is, perfecting your CV does not have to be difficult. We give you the information you need to figure out the best ways to highlight your professional skills. Our aircraft maintenance engineer CV example takes the mystery and confusion out of issues like formatting, organization, and phrasing. We also include some helpful writing tips that explain important principles to keep in mind as you craft and refine your CV.</p>
-<h2>Harvard Aircraft Maintenance Manuals</h2><br /><p><b><b>Download File</b> &#10031;&#10031;&#10031; <a href="https://tinurli.com/2uwjk0">https://tinurli.com/2uwjk0</a></b></p><br /><br />
-<p>The MMP/Casemate Single series of publications has developed into a very useful tool for modelers to add authentic details to their models. The books usually take one type of aircraft, and in this case, one particular airplane, and provides extensive detail, including accurate view drawings in two scales, photos of the actual aircraft in service, copies of maintenance manual pages showing specific details of components, photos of details of surviving examples, and colorful four view general arrangement drawings.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/InventorProfessional2013PortableTorrent.md b/spaces/cihyFjudo/fairness-paper-search/InventorProfessional2013PortableTorrent.md
deleted file mode 100644
index 88eb867c9c518ca56a45fe24e99139417647978f..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/InventorProfessional2013PortableTorrent.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>InventorProfessional2013PortableTorrent</h2><br /><p><b><b>Download Zip</b> &#10001; <a href="https://tinurli.com/2uwi9j">https://tinurli.com/2uwi9j</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/NeuroShell Trader 6 cracked rar The Ultimate Guide to Neural Network Trading.md b/spaces/cihyFjudo/fairness-paper-search/NeuroShell Trader 6 cracked rar The Ultimate Guide to Neural Network Trading.md
deleted file mode 100644
index bf4fc6a07ecea2bac28948fbea2337c40f755e0a..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/NeuroShell Trader 6 cracked rar The Ultimate Guide to Neural Network Trading.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>{NeuroShell Trader 6 cracked rar} 1</h2><br /><p><b><b>Download File</b> &#9745; <a href="https://tinurli.com/2uwhEI">https://tinurli.com/2uwhEI</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cis-lmu/glotlid-space/constants.py b/spaces/cis-lmu/glotlid-space/constants.py
deleted file mode 100644
index ee27ee44e2cb8c576ae559d15d5e9986b0ef446f..0000000000000000000000000000000000000000
--- a/spaces/cis-lmu/glotlid-space/constants.py
+++ /dev/null
@@ -1,4 +0,0 @@
-CHOICE_TEXT = "Input Text"
-CHOICE_FILE = "Upload File"
-TITLE = "GlotLID: Language Identification for Around 2000 Languages"
-MODEL_NAME = "cis-lmu/glotlid"
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/threadpool/text.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/threadpool/text.py
deleted file mode 100644
index 0e625909b6c960ebed4a0ed99941b28156fbf2d1..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiofiles/threadpool/text.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from ..base import AsyncBase, AsyncIndirectBase
-from .utils import delegate_to_executor, proxy_method_directly, proxy_property_directly
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "readable",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "write",
-    "writable",
-    "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
-    "buffer",
-    "closed",
-    "encoding",
-    "errors",
-    "line_buffering",
-    "newlines",
-    "name",
-    "mode",
-)
-class AsyncTextIOWrapper(AsyncBase):
-    """The asyncio executor version of io.TextIOWrapper."""
-
-
-@delegate_to_executor(
-    "close",
-    "flush",
-    "isatty",
-    "read",
-    "readable",
-    "readline",
-    "readlines",
-    "seek",
-    "seekable",
-    "tell",
-    "truncate",
-    "write",
-    "writable",
-    "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
-    "buffer",
-    "closed",
-    "encoding",
-    "errors",
-    "line_buffering",
-    "newlines",
-    "name",
-    "mode",
-)
-class AsyncTextIndirectIOWrapper(AsyncIndirectBase):
-    """The indirect asyncio executor version of io.TextIOWrapper."""
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/otlLib/builder.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/otlLib/builder.py
deleted file mode 100644
index 2a02c20045904915b50bdd9f1ef3644e3db5258f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/otlLib/builder.py
+++ /dev/null
@@ -1,2916 +0,0 @@
-from collections import namedtuple, OrderedDict
-import os
-from fontTools.misc.fixedTools import fixedToFloat
-from fontTools import ttLib
-from fontTools.ttLib.tables import otTables as ot
-from fontTools.ttLib.tables.otBase import (
-    ValueRecord,
-    valueRecordFormatDict,
-    OTTableWriter,
-    CountReference,
-)
-from fontTools.ttLib.tables import otBase
-from fontTools.feaLib.ast import STATNameStatement
-from fontTools.otlLib.optimize.gpos import (
-    _compression_level_from_env,
-    compact_lookup,
-)
-from fontTools.otlLib.error import OpenTypeLibError
-from functools import reduce
-import logging
-import copy
-
-
-log = logging.getLogger(__name__)
-
-
-def buildCoverage(glyphs, glyphMap):
-    """Builds a coverage table.
-
-    Coverage tables (as defined in the `OpenType spec <https://docs.microsoft.com/en-gb/typography/opentype/spec/chapter2#coverage-table>`__)
-    are used in all OpenType Layout lookups apart from the Extension type, and
-    define the glyphs involved in a layout subtable. This allows shaping engines
-    to compare the glyph stream with the coverage table and quickly determine
-    whether a subtable should be involved in a shaping operation.
-
-    This function takes a list of glyphs and a glyphname-to-ID map, and
-    returns a ``Coverage`` object representing the coverage table.
-
-    Example::
-
-        glyphMap = font.getReverseGlyphMap()
-        glyphs = [ "A", "B", "C" ]
-        coverage = buildCoverage(glyphs, glyphMap)
-
-    Args:
-        glyphs: a sequence of glyph names.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.Coverage`` object or ``None`` if there are no glyphs
-        supplied.
-    """
-
-    if not glyphs:
-        return None
-    self = ot.Coverage()
-    self.glyphs = sorted(set(glyphs), key=glyphMap.__getitem__)
-    return self
-
-
-LOOKUP_FLAG_RIGHT_TO_LEFT = 0x0001
-LOOKUP_FLAG_IGNORE_BASE_GLYPHS = 0x0002
-LOOKUP_FLAG_IGNORE_LIGATURES = 0x0004
-LOOKUP_FLAG_IGNORE_MARKS = 0x0008
-LOOKUP_FLAG_USE_MARK_FILTERING_SET = 0x0010
-
-
-def buildLookup(subtables, flags=0, markFilterSet=None):
-    """Turns a collection of rules into a lookup.
-
-    A Lookup (as defined in the `OpenType Spec <https://docs.microsoft.com/en-gb/typography/opentype/spec/chapter2#lookupTbl>`__)
-    wraps the individual rules in a layout operation (substitution or
-    positioning) in a data structure expressing their overall lookup type -
-    for example, single substitution, mark-to-base attachment, and so on -
-    as well as the lookup flags and any mark filtering sets. You may import
-    the following constants to express lookup flags:
-
-    - ``LOOKUP_FLAG_RIGHT_TO_LEFT``
-    - ``LOOKUP_FLAG_IGNORE_BASE_GLYPHS``
-    - ``LOOKUP_FLAG_IGNORE_LIGATURES``
-    - ``LOOKUP_FLAG_IGNORE_MARKS``
-    - ``LOOKUP_FLAG_USE_MARK_FILTERING_SET``
-
-    Args:
-        subtables: A list of layout subtable objects (e.g.
-            ``MultipleSubst``, ``PairPos``, etc.) or ``None``.
-        flags (int): This lookup's flags.
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-
-    Returns:
-        An ``otTables.Lookup`` object or ``None`` if there are no subtables
-        supplied.
-    """
-    if subtables is None:
-        return None
-    subtables = [st for st in subtables if st is not None]
-    if not subtables:
-        return None
-    assert all(
-        t.LookupType == subtables[0].LookupType for t in subtables
-    ), "all subtables must have the same LookupType; got %s" % repr(
-        [t.LookupType for t in subtables]
-    )
-    self = ot.Lookup()
-    self.LookupType = subtables[0].LookupType
-    self.LookupFlag = flags
-    self.SubTable = subtables
-    self.SubTableCount = len(self.SubTable)
-    if markFilterSet is not None:
-        self.LookupFlag |= LOOKUP_FLAG_USE_MARK_FILTERING_SET
-        assert isinstance(markFilterSet, int), markFilterSet
-        self.MarkFilteringSet = markFilterSet
-    else:
-        assert (self.LookupFlag & LOOKUP_FLAG_USE_MARK_FILTERING_SET) == 0, (
-            "if markFilterSet is None, flags must not set "
-            "LOOKUP_FLAG_USE_MARK_FILTERING_SET; flags=0x%04x" % flags
-        )
-    return self
-
-
-class LookupBuilder(object):
-    SUBTABLE_BREAK_ = "SUBTABLE_BREAK"
-
-    def __init__(self, font, location, table, lookup_type):
-        self.font = font
-        self.glyphMap = font.getReverseGlyphMap()
-        self.location = location
-        self.table, self.lookup_type = table, lookup_type
-        self.lookupflag = 0
-        self.markFilterSet = None
-        self.lookup_index = None  # assigned when making final tables
-        assert table in ("GPOS", "GSUB")
-
-    def equals(self, other):
-        return (
-            isinstance(other, self.__class__)
-            and self.table == other.table
-            and self.lookupflag == other.lookupflag
-            and self.markFilterSet == other.markFilterSet
-        )
-
-    def inferGlyphClasses(self):
-        """Infers glyph glasses for the GDEF table, such as {"cedilla":3}."""
-        return {}
-
-    def getAlternateGlyphs(self):
-        """Helper for building 'aalt' features."""
-        return {}
-
-    def buildLookup_(self, subtables):
-        return buildLookup(subtables, self.lookupflag, self.markFilterSet)
-
-    def buildMarkClasses_(self, marks):
-        """{"cedilla": ("BOTTOM", ast.Anchor), ...} --> {"BOTTOM":0, "TOP":1}
-
-        Helper for MarkBasePostBuilder, MarkLigPosBuilder, and
-        MarkMarkPosBuilder. Seems to return the same numeric IDs
-        for mark classes as the AFDKO makeotf tool.
-        """
-        ids = {}
-        for mark in sorted(marks.keys(), key=self.font.getGlyphID):
-            markClassName, _markAnchor = marks[mark]
-            if markClassName not in ids:
-                ids[markClassName] = len(ids)
-        return ids
-
-    def setBacktrackCoverage_(self, prefix, subtable):
-        subtable.BacktrackGlyphCount = len(prefix)
-        subtable.BacktrackCoverage = []
-        for p in reversed(prefix):
-            coverage = buildCoverage(p, self.glyphMap)
-            subtable.BacktrackCoverage.append(coverage)
-
-    def setLookAheadCoverage_(self, suffix, subtable):
-        subtable.LookAheadGlyphCount = len(suffix)
-        subtable.LookAheadCoverage = []
-        for s in suffix:
-            coverage = buildCoverage(s, self.glyphMap)
-            subtable.LookAheadCoverage.append(coverage)
-
-    def setInputCoverage_(self, glyphs, subtable):
-        subtable.InputGlyphCount = len(glyphs)
-        subtable.InputCoverage = []
-        for g in glyphs:
-            coverage = buildCoverage(g, self.glyphMap)
-            subtable.InputCoverage.append(coverage)
-
-    def setCoverage_(self, glyphs, subtable):
-        subtable.GlyphCount = len(glyphs)
-        subtable.Coverage = []
-        for g in glyphs:
-            coverage = buildCoverage(g, self.glyphMap)
-            subtable.Coverage.append(coverage)
-
-    def build_subst_subtables(self, mapping, klass):
-        substitutions = [{}]
-        for key in mapping:
-            if key[0] == self.SUBTABLE_BREAK_:
-                substitutions.append({})
-            else:
-                substitutions[-1][key] = mapping[key]
-        subtables = [klass(s) for s in substitutions]
-        return subtables
-
-    def add_subtable_break(self, location):
-        """Add an explicit subtable break.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this break, or ``None`` if
-                no location is provided.
-        """
-        log.warning(
-            OpenTypeLibError(
-                'unsupported "subtable" statement for lookup type', location
-            )
-        )
-
-
-class AlternateSubstBuilder(LookupBuilder):
-    """Builds an Alternate Substitution (GSUB3) lookup.
-
-    Users are expected to manually add alternate glyph substitutions to
-    the ``alternates`` attribute after the object has been initialized,
-    e.g.::
-
-        builder.alternates["A"] = ["A.alt1", "A.alt2"]
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        alternates: An ordered dictionary of alternates, mapping glyph names
-            to a list of names of alternates.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 3)
-        self.alternates = OrderedDict()
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.alternates == other.alternates
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the alternate
-            substitution lookup.
-        """
-        subtables = self.build_subst_subtables(
-            self.alternates, buildAlternateSubstSubtable
-        )
-        return self.buildLookup_(subtables)
-
-    def getAlternateGlyphs(self):
-        return self.alternates
-
-    def add_subtable_break(self, location):
-        self.alternates[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class ChainContextualRule(
-    namedtuple("ChainContextualRule", ["prefix", "glyphs", "suffix", "lookups"])
-):
-    @property
-    def is_subtable_break(self):
-        return self.prefix == LookupBuilder.SUBTABLE_BREAK_
-
-
-class ChainContextualRuleset:
-    def __init__(self):
-        self.rules = []
-
-    def addRule(self, rule):
-        self.rules.append(rule)
-
-    @property
-    def hasPrefixOrSuffix(self):
-        # Do we have any prefixes/suffixes? If this is False for all
-        # rulesets, we can express the whole lookup as GPOS5/GSUB7.
-        for rule in self.rules:
-            if len(rule.prefix) > 0 or len(rule.suffix) > 0:
-                return True
-        return False
-
-    @property
-    def hasAnyGlyphClasses(self):
-        # Do we use glyph classes anywhere in the rules? If this is False
-        # we can express this subtable as a Format 1.
-        for rule in self.rules:
-            for coverage in (rule.prefix, rule.glyphs, rule.suffix):
-                if any(len(x) > 1 for x in coverage):
-                    return True
-        return False
-
-    def format2ClassDefs(self):
-        PREFIX, GLYPHS, SUFFIX = 0, 1, 2
-        classDefBuilders = []
-        for ix in [PREFIX, GLYPHS, SUFFIX]:
-            context = []
-            for r in self.rules:
-                context.append(r[ix])
-            classes = self._classBuilderForContext(context)
-            if not classes:
-                return None
-            classDefBuilders.append(classes)
-        return classDefBuilders
-
-    def _classBuilderForContext(self, context):
-        classdefbuilder = ClassDefBuilder(useClass0=False)
-        for position in context:
-            for glyphset in position:
-                glyphs = set(glyphset)
-                if not classdefbuilder.canAdd(glyphs):
-                    return None
-                classdefbuilder.add(glyphs)
-        return classdefbuilder
-
-
-class ChainContextualBuilder(LookupBuilder):
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.rules == other.rules
-
-    def rulesets(self):
-        # Return a list of ChainContextRuleset objects, taking explicit
-        # subtable breaks into account
-        ruleset = [ChainContextualRuleset()]
-        for rule in self.rules:
-            if rule.is_subtable_break:
-                ruleset.append(ChainContextualRuleset())
-                continue
-            ruleset[-1].addRule(rule)
-        # Squish any empty subtables
-        return [x for x in ruleset if len(x.rules) > 0]
-
-    def getCompiledSize_(self, subtables):
-        size = 0
-        for st in subtables:
-            w = OTTableWriter()
-            w["LookupType"] = CountReference(
-                {"LookupType": st.LookupType}, "LookupType"
-            )
-            # We need to make a copy here because compiling
-            # modifies the subtable (finalizing formats etc.)
-            copy.deepcopy(st).compile(w, self.font)
-            size += len(w.getAllData())
-        return size
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the chained
-            contextual positioning lookup.
-        """
-        subtables = []
-
-        rulesets = self.rulesets()
-        chaining = any(ruleset.hasPrefixOrSuffix for ruleset in rulesets)
-
-        # https://github.com/fonttools/fonttools/issues/2539
-        #
-        # Unfortunately, as of 2022-03-07, Apple's CoreText renderer does not
-        # correctly process GPOS7 lookups, so for now we force contextual
-        # positioning lookups to be chaining (GPOS8).
-        #
-        # This seems to be fixed as of macOS 13.2, but we keep disabling this
-        # for now until we are no longer concerned about old macOS versions.
-        # But we allow people to opt-out of this with the config key below.
-        write_gpos7 = self.font.cfg.get("fontTools.otlLib.builder:WRITE_GPOS7")
-        # horrible separation of concerns breach
-        if not write_gpos7 and self.subtable_type == "Pos":
-            chaining = True
-
-        for ruleset in rulesets:
-            # Determine format strategy. We try to build formats 1, 2 and 3
-            # subtables and then work out which is best. candidates list holds
-            # the subtables in each format for this ruleset (including a dummy
-            # "format 0" to make the addressing match the format numbers).
-
-            # We can always build a format 3 lookup by accumulating each of
-            # the rules into a list, so start with that.
-            candidates = [None, None, None, []]
-            for rule in ruleset.rules:
-                candidates[3].append(self.buildFormat3Subtable(rule, chaining))
-
-            # Can we express the whole ruleset as a format 2 subtable?
-            classdefs = ruleset.format2ClassDefs()
-            if classdefs:
-                candidates[2] = [
-                    self.buildFormat2Subtable(ruleset, classdefs, chaining)
-                ]
-
-            if not ruleset.hasAnyGlyphClasses:
-                candidates[1] = [self.buildFormat1Subtable(ruleset, chaining)]
-
-            for i in [1, 2, 3]:
-                if candidates[i]:
-                    try:
-                        self.getCompiledSize_(candidates[i])
-                    except Exception as e:
-                        log.warning(
-                            "Contextual format %i at %s overflowed (%s)"
-                            % (i, str(self.location), e)
-                        )
-                        candidates[i] = None
-
-            candidates = [x for x in candidates if x is not None]
-            if not candidates:
-                raise OpenTypeLibError("All candidates overflowed", self.location)
-
-            winner = min(candidates, key=self.getCompiledSize_)
-            subtables.extend(winner)
-
-        # If we are not chaining, lookup type will be automatically fixed by
-        # buildLookup_
-        return self.buildLookup_(subtables)
-
-    def buildFormat1Subtable(self, ruleset, chaining=True):
-        st = self.newSubtable_(chaining=chaining)
-        st.Format = 1
-        st.populateDefaults()
-        coverage = set()
-        rulesetsByFirstGlyph = {}
-        ruleAttr = self.ruleAttr_(format=1, chaining=chaining)
-
-        for rule in ruleset.rules:
-            ruleAsSubtable = self.newRule_(format=1, chaining=chaining)
-
-            if chaining:
-                ruleAsSubtable.BacktrackGlyphCount = len(rule.prefix)
-                ruleAsSubtable.LookAheadGlyphCount = len(rule.suffix)
-                ruleAsSubtable.Backtrack = [list(x)[0] for x in reversed(rule.prefix)]
-                ruleAsSubtable.LookAhead = [list(x)[0] for x in rule.suffix]
-
-                ruleAsSubtable.InputGlyphCount = len(rule.glyphs)
-            else:
-                ruleAsSubtable.GlyphCount = len(rule.glyphs)
-
-            ruleAsSubtable.Input = [list(x)[0] for x in rule.glyphs[1:]]
-
-            self.buildLookupList(rule, ruleAsSubtable)
-
-            firstGlyph = list(rule.glyphs[0])[0]
-            if firstGlyph not in rulesetsByFirstGlyph:
-                coverage.add(firstGlyph)
-                rulesetsByFirstGlyph[firstGlyph] = []
-            rulesetsByFirstGlyph[firstGlyph].append(ruleAsSubtable)
-
-        st.Coverage = buildCoverage(coverage, self.glyphMap)
-        ruleSets = []
-        for g in st.Coverage.glyphs:
-            ruleSet = self.newRuleSet_(format=1, chaining=chaining)
-            setattr(ruleSet, ruleAttr, rulesetsByFirstGlyph[g])
-            setattr(ruleSet, f"{ruleAttr}Count", len(rulesetsByFirstGlyph[g]))
-            ruleSets.append(ruleSet)
-
-        setattr(st, self.ruleSetAttr_(format=1, chaining=chaining), ruleSets)
-        setattr(
-            st, self.ruleSetAttr_(format=1, chaining=chaining) + "Count", len(ruleSets)
-        )
-
-        return st
-
-    def buildFormat2Subtable(self, ruleset, classdefs, chaining=True):
-        st = self.newSubtable_(chaining=chaining)
-        st.Format = 2
-        st.populateDefaults()
-
-        if chaining:
-            (
-                st.BacktrackClassDef,
-                st.InputClassDef,
-                st.LookAheadClassDef,
-            ) = [c.build() for c in classdefs]
-        else:
-            st.ClassDef = classdefs[1].build()
-
-        inClasses = classdefs[1].classes()
-
-        classSets = []
-        for _ in inClasses:
-            classSet = self.newRuleSet_(format=2, chaining=chaining)
-            classSets.append(classSet)
-
-        coverage = set()
-        classRuleAttr = self.ruleAttr_(format=2, chaining=chaining)
-
-        for rule in ruleset.rules:
-            ruleAsSubtable = self.newRule_(format=2, chaining=chaining)
-            if chaining:
-                ruleAsSubtable.BacktrackGlyphCount = len(rule.prefix)
-                ruleAsSubtable.LookAheadGlyphCount = len(rule.suffix)
-                # The glyphs in the rule may be list, tuple, odict_keys...
-                # Order is not important anyway because they are guaranteed
-                # to be members of the same class.
-                ruleAsSubtable.Backtrack = [
-                    st.BacktrackClassDef.classDefs[list(x)[0]]
-                    for x in reversed(rule.prefix)
-                ]
-                ruleAsSubtable.LookAhead = [
-                    st.LookAheadClassDef.classDefs[list(x)[0]] for x in rule.suffix
-                ]
-
-                ruleAsSubtable.InputGlyphCount = len(rule.glyphs)
-                ruleAsSubtable.Input = [
-                    st.InputClassDef.classDefs[list(x)[0]] for x in rule.glyphs[1:]
-                ]
-                setForThisRule = classSets[
-                    st.InputClassDef.classDefs[list(rule.glyphs[0])[0]]
-                ]
-            else:
-                ruleAsSubtable.GlyphCount = len(rule.glyphs)
-                ruleAsSubtable.Class = [  # The spec calls this InputSequence
-                    st.ClassDef.classDefs[list(x)[0]] for x in rule.glyphs[1:]
-                ]
-                setForThisRule = classSets[
-                    st.ClassDef.classDefs[list(rule.glyphs[0])[0]]
-                ]
-
-            self.buildLookupList(rule, ruleAsSubtable)
-            coverage |= set(rule.glyphs[0])
-
-            getattr(setForThisRule, classRuleAttr).append(ruleAsSubtable)
-            setattr(
-                setForThisRule,
-                f"{classRuleAttr}Count",
-                getattr(setForThisRule, f"{classRuleAttr}Count") + 1,
-            )
-        setattr(st, self.ruleSetAttr_(format=2, chaining=chaining), classSets)
-        setattr(
-            st, self.ruleSetAttr_(format=2, chaining=chaining) + "Count", len(classSets)
-        )
-        st.Coverage = buildCoverage(coverage, self.glyphMap)
-        return st
-
-    def buildFormat3Subtable(self, rule, chaining=True):
-        st = self.newSubtable_(chaining=chaining)
-        st.Format = 3
-        if chaining:
-            self.setBacktrackCoverage_(rule.prefix, st)
-            self.setLookAheadCoverage_(rule.suffix, st)
-            self.setInputCoverage_(rule.glyphs, st)
-        else:
-            self.setCoverage_(rule.glyphs, st)
-        self.buildLookupList(rule, st)
-        return st
-
-    def buildLookupList(self, rule, st):
-        for sequenceIndex, lookupList in enumerate(rule.lookups):
-            if lookupList is not None:
-                if not isinstance(lookupList, list):
-                    # Can happen with synthesised lookups
-                    lookupList = [lookupList]
-                for l in lookupList:
-                    if l.lookup_index is None:
-                        if isinstance(self, ChainContextPosBuilder):
-                            other = "substitution"
-                        else:
-                            other = "positioning"
-                        raise OpenTypeLibError(
-                            "Missing index of the specified "
-                            f"lookup, might be a {other} lookup",
-                            self.location,
-                        )
-                    rec = self.newLookupRecord_(st)
-                    rec.SequenceIndex = sequenceIndex
-                    rec.LookupListIndex = l.lookup_index
-
-    def add_subtable_break(self, location):
-        self.rules.append(
-            ChainContextualRule(
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                [self.SUBTABLE_BREAK_],
-            )
-        )
-
-    def newSubtable_(self, chaining=True):
-        subtablename = f"Context{self.subtable_type}"
-        if chaining:
-            subtablename = "Chain" + subtablename
-        st = getattr(ot, subtablename)()  # ot.ChainContextPos()/ot.ChainSubst()/etc.
-        setattr(st, f"{self.subtable_type}Count", 0)
-        setattr(st, f"{self.subtable_type}LookupRecord", [])
-        return st
-
-    # Format 1 and format 2 GSUB5/GSUB6/GPOS7/GPOS8 rulesets and rules form a family:
-    #
-    #       format 1 ruleset      format 1 rule      format 2 ruleset      format 2 rule
-    # GSUB5 SubRuleSet            SubRule            SubClassSet           SubClassRule
-    # GSUB6 ChainSubRuleSet       ChainSubRule       ChainSubClassSet      ChainSubClassRule
-    # GPOS7 PosRuleSet            PosRule            PosClassSet           PosClassRule
-    # GPOS8 ChainPosRuleSet       ChainPosRule       ChainPosClassSet      ChainPosClassRule
-    #
-    # The following functions generate the attribute names and subtables according
-    # to this naming convention.
-    def ruleSetAttr_(self, format=1, chaining=True):
-        if format == 1:
-            formatType = "Rule"
-        elif format == 2:
-            formatType = "Class"
-        else:
-            raise AssertionError(formatType)
-        subtablename = f"{self.subtable_type[0:3]}{formatType}Set"  # Sub, not Subst.
-        if chaining:
-            subtablename = "Chain" + subtablename
-        return subtablename
-
-    def ruleAttr_(self, format=1, chaining=True):
-        if format == 1:
-            formatType = ""
-        elif format == 2:
-            formatType = "Class"
-        else:
-            raise AssertionError(formatType)
-        subtablename = f"{self.subtable_type[0:3]}{formatType}Rule"  # Sub, not Subst.
-        if chaining:
-            subtablename = "Chain" + subtablename
-        return subtablename
-
-    def newRuleSet_(self, format=1, chaining=True):
-        st = getattr(
-            ot, self.ruleSetAttr_(format, chaining)
-        )()  # ot.ChainPosRuleSet()/ot.SubRuleSet()/etc.
-        st.populateDefaults()
-        return st
-
-    def newRule_(self, format=1, chaining=True):
-        st = getattr(
-            ot, self.ruleAttr_(format, chaining)
-        )()  # ot.ChainPosClassRule()/ot.SubClassRule()/etc.
-        st.populateDefaults()
-        return st
-
-    def attachSubtableWithCount_(
-        self, st, subtable_name, count_name, existing=None, index=None, chaining=False
-    ):
-        if chaining:
-            subtable_name = "Chain" + subtable_name
-            count_name = "Chain" + count_name
-
-        if not hasattr(st, count_name):
-            setattr(st, count_name, 0)
-            setattr(st, subtable_name, [])
-
-        if existing:
-            new_subtable = existing
-        else:
-            # Create a new, empty subtable from otTables
-            new_subtable = getattr(ot, subtable_name)()
-
-        setattr(st, count_name, getattr(st, count_name) + 1)
-
-        if index:
-            getattr(st, subtable_name).insert(index, new_subtable)
-        else:
-            getattr(st, subtable_name).append(new_subtable)
-
-        return new_subtable
-
-    def newLookupRecord_(self, st):
-        return self.attachSubtableWithCount_(
-            st,
-            f"{self.subtable_type}LookupRecord",
-            f"{self.subtable_type}Count",
-            chaining=False,
-        )  # Oddly, it isn't ChainSubstLookupRecord
-
-
-class ChainContextPosBuilder(ChainContextualBuilder):
-    """Builds a Chained Contextual Positioning (GPOS8) lookup.
-
-    Users are expected to manually add rules to the ``rules`` attribute after
-    the object has been initialized, e.g.::
-
-        # pos [A B] [C D] x' lookup lu1 y' z' lookup lu2 E;
-
-        prefix  = [ ["A", "B"], ["C", "D"] ]
-        suffix  = [ ["E"] ]
-        glyphs  = [ ["x"], ["y"], ["z"] ]
-        lookups = [ [lu1], None,  [lu2] ]
-        builder.rules.append( (prefix, glyphs, suffix, lookups) )
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        rules: A list of tuples representing the rules in this lookup.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 8)
-        self.rules = []
-        self.subtable_type = "Pos"
-
-    def find_chainable_single_pos(self, lookups, glyphs, value):
-        """Helper for add_single_pos_chained_()"""
-        res = None
-        for lookup in lookups[::-1]:
-            if lookup == self.SUBTABLE_BREAK_:
-                return res
-            if isinstance(lookup, SinglePosBuilder) and all(
-                lookup.can_add(glyph, value) for glyph in glyphs
-            ):
-                res = lookup
-        return res
-
-
-class ChainContextSubstBuilder(ChainContextualBuilder):
-    """Builds a Chained Contextual Substitution (GSUB6) lookup.
-
-    Users are expected to manually add rules to the ``rules`` attribute after
-    the object has been initialized, e.g.::
-
-        # sub [A B] [C D] x' lookup lu1 y' z' lookup lu2 E;
-
-        prefix  = [ ["A", "B"], ["C", "D"] ]
-        suffix  = [ ["E"] ]
-        glyphs  = [ ["x"], ["y"], ["z"] ]
-        lookups = [ [lu1], None,  [lu2] ]
-        builder.rules.append( (prefix, glyphs, suffix, lookups) )
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        rules: A list of tuples representing the rules in this lookup.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 6)
-        self.rules = []  # (prefix, input, suffix, lookups)
-        self.subtable_type = "Subst"
-
-    def getAlternateGlyphs(self):
-        result = {}
-        for rule in self.rules:
-            if rule.is_subtable_break:
-                continue
-            for lookups in rule.lookups:
-                if not isinstance(lookups, list):
-                    lookups = [lookups]
-                for lookup in lookups:
-                    if lookup is not None:
-                        alts = lookup.getAlternateGlyphs()
-                        for glyph, replacements in alts.items():
-                            result.setdefault(glyph, set()).update(replacements)
-        return result
-
-    def find_chainable_single_subst(self, mapping):
-        """Helper for add_single_subst_chained_()"""
-        res = None
-        for rule in self.rules[::-1]:
-            if rule.is_subtable_break:
-                return res
-            for sub in rule.lookups:
-                if isinstance(sub, SingleSubstBuilder) and not any(
-                    g in mapping and mapping[g] != sub.mapping[g] for g in sub.mapping
-                ):
-                    res = sub
-        return res
-
-
-class LigatureSubstBuilder(LookupBuilder):
-    """Builds a Ligature Substitution (GSUB4) lookup.
-
-    Users are expected to manually add ligatures to the ``ligatures``
-    attribute after the object has been initialized, e.g.::
-
-        # sub f i by f_i;
-        builder.ligatures[("f","f","i")] = "f_f_i"
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        ligatures: An ordered dictionary mapping a tuple of glyph names to the
-            ligature glyphname.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 4)
-        self.ligatures = OrderedDict()  # {('f','f','i'): 'f_f_i'}
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.ligatures == other.ligatures
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the ligature
-            substitution lookup.
-        """
-        subtables = self.build_subst_subtables(
-            self.ligatures, buildLigatureSubstSubtable
-        )
-        return self.buildLookup_(subtables)
-
-    def add_subtable_break(self, location):
-        self.ligatures[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class MultipleSubstBuilder(LookupBuilder):
-    """Builds a Multiple Substitution (GSUB2) lookup.
-
-    Users are expected to manually add substitutions to the ``mapping``
-    attribute after the object has been initialized, e.g.::
-
-        # sub uni06C0 by uni06D5.fina hamza.above;
-        builder.mapping["uni06C0"] = [ "uni06D5.fina", "hamza.above"]
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        mapping: An ordered dictionary mapping a glyph name to a list of
-            substituted glyph names.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 2)
-        self.mapping = OrderedDict()
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.mapping == other.mapping
-
-    def build(self):
-        subtables = self.build_subst_subtables(self.mapping, buildMultipleSubstSubtable)
-        return self.buildLookup_(subtables)
-
-    def add_subtable_break(self, location):
-        self.mapping[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class CursivePosBuilder(LookupBuilder):
-    """Builds a Cursive Positioning (GPOS3) lookup.
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        attachments: An ordered dictionary mapping a glyph name to a two-element
-            tuple of ``otTables.Anchor`` objects.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 3)
-        self.attachments = {}
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other) and self.attachments == other.attachments
-        )
-
-    def add_attachment(self, location, glyphs, entryAnchor, exitAnchor):
-        """Adds attachment information to the cursive positioning lookup.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this lookup. (Unused.)
-            glyphs: A list of glyph names sharing these entry and exit
-                anchor locations.
-            entryAnchor: A ``otTables.Anchor`` object representing the
-                entry anchor, or ``None`` if no entry anchor is present.
-            exitAnchor: A ``otTables.Anchor`` object representing the
-                exit anchor, or ``None`` if no exit anchor is present.
-        """
-        for glyph in glyphs:
-            self.attachments[glyph] = (entryAnchor, exitAnchor)
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the cursive
-            positioning lookup.
-        """
-        st = buildCursivePosSubtable(self.attachments, self.glyphMap)
-        return self.buildLookup_([st])
-
-
-class MarkBasePosBuilder(LookupBuilder):
-    """Builds a Mark-To-Base Positioning (GPOS4) lookup.
-
-    Users are expected to manually add marks and bases to the ``marks``
-    and ``bases`` attributes after the object has been initialized, e.g.::
-
-        builder.marks["acute"]   = (0, a1)
-        builder.marks["grave"]   = (0, a1)
-        builder.marks["cedilla"] = (1, a2)
-        builder.bases["a"] = {0: a3, 1: a5}
-        builder.bases["b"] = {0: a4, 1: a5}
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        marks: An dictionary mapping a glyph name to a two-element
-            tuple containing a mark class ID and ``otTables.Anchor`` object.
-        bases: An dictionary mapping a glyph name to a dictionary of
-            mark class IDs and ``otTables.Anchor`` object.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 4)
-        self.marks = {}  # glyphName -> (markClassName, anchor)
-        self.bases = {}  # glyphName -> {markClassName: anchor}
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.marks == other.marks
-            and self.bases == other.bases
-        )
-
-    def inferGlyphClasses(self):
-        result = {glyph: 1 for glyph in self.bases}
-        result.update({glyph: 3 for glyph in self.marks})
-        return result
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the mark-to-base
-            positioning lookup.
-        """
-        markClasses = self.buildMarkClasses_(self.marks)
-        marks = {}
-        for mark, (mc, anchor) in self.marks.items():
-            if mc not in markClasses:
-                raise ValueError(
-                    "Mark class %s not found for mark glyph %s" % (mc, mark)
-                )
-            marks[mark] = (markClasses[mc], anchor)
-        bases = {}
-        for glyph, anchors in self.bases.items():
-            bases[glyph] = {}
-            for mc, anchor in anchors.items():
-                if mc not in markClasses:
-                    raise ValueError(
-                        "Mark class %s not found for base glyph %s" % (mc, glyph)
-                    )
-                bases[glyph][markClasses[mc]] = anchor
-        subtables = buildMarkBasePos(marks, bases, self.glyphMap)
-        return self.buildLookup_(subtables)
-
-
-class MarkLigPosBuilder(LookupBuilder):
-    """Builds a Mark-To-Ligature Positioning (GPOS5) lookup.
-
-    Users are expected to manually add marks and bases to the ``marks``
-    and ``ligatures`` attributes after the object has been initialized, e.g.::
-
-        builder.marks["acute"]   = (0, a1)
-        builder.marks["grave"]   = (0, a1)
-        builder.marks["cedilla"] = (1, a2)
-        builder.ligatures["f_i"] = [
-            { 0: a3, 1: a5 }, # f
-            { 0: a4, 1: a5 }  # i
-        ]
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        marks: An dictionary mapping a glyph name to a two-element
-            tuple containing a mark class ID and ``otTables.Anchor`` object.
-        ligatures: An dictionary mapping a glyph name to an array with one
-            element for each ligature component. Each array element should be
-            a dictionary mapping mark class IDs to ``otTables.Anchor`` objects.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 5)
-        self.marks = {}  # glyphName -> (markClassName, anchor)
-        self.ligatures = {}  # glyphName -> [{markClassName: anchor}, ...]
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.marks == other.marks
-            and self.ligatures == other.ligatures
-        )
-
-    def inferGlyphClasses(self):
-        result = {glyph: 2 for glyph in self.ligatures}
-        result.update({glyph: 3 for glyph in self.marks})
-        return result
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the mark-to-ligature
-            positioning lookup.
-        """
-        markClasses = self.buildMarkClasses_(self.marks)
-        marks = {
-            mark: (markClasses[mc], anchor) for mark, (mc, anchor) in self.marks.items()
-        }
-        ligs = {}
-        for lig, components in self.ligatures.items():
-            ligs[lig] = []
-            for c in components:
-                ligs[lig].append({markClasses[mc]: a for mc, a in c.items()})
-        subtables = buildMarkLigPos(marks, ligs, self.glyphMap)
-        return self.buildLookup_(subtables)
-
-
-class MarkMarkPosBuilder(LookupBuilder):
-    """Builds a Mark-To-Mark Positioning (GPOS6) lookup.
-
-    Users are expected to manually add marks and bases to the ``marks``
-    and ``baseMarks`` attributes after the object has been initialized, e.g.::
-
-        builder.marks["acute"]     = (0, a1)
-        builder.marks["grave"]     = (0, a1)
-        builder.marks["cedilla"]   = (1, a2)
-        builder.baseMarks["acute"] = {0: a3}
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        marks: An dictionary mapping a glyph name to a two-element
-            tuple containing a mark class ID and ``otTables.Anchor`` object.
-        baseMarks: An dictionary mapping a glyph name to a dictionary
-            containing one item: a mark class ID and a ``otTables.Anchor`` object.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 6)
-        self.marks = {}  # glyphName -> (markClassName, anchor)
-        self.baseMarks = {}  # glyphName -> {markClassName: anchor}
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.marks == other.marks
-            and self.baseMarks == other.baseMarks
-        )
-
-    def inferGlyphClasses(self):
-        result = {glyph: 3 for glyph in self.baseMarks}
-        result.update({glyph: 3 for glyph in self.marks})
-        return result
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the mark-to-mark
-            positioning lookup.
-        """
-        markClasses = self.buildMarkClasses_(self.marks)
-        markClassList = sorted(markClasses.keys(), key=markClasses.get)
-        marks = {
-            mark: (markClasses[mc], anchor) for mark, (mc, anchor) in self.marks.items()
-        }
-
-        st = ot.MarkMarkPos()
-        st.Format = 1
-        st.ClassCount = len(markClasses)
-        st.Mark1Coverage = buildCoverage(marks, self.glyphMap)
-        st.Mark2Coverage = buildCoverage(self.baseMarks, self.glyphMap)
-        st.Mark1Array = buildMarkArray(marks, self.glyphMap)
-        st.Mark2Array = ot.Mark2Array()
-        st.Mark2Array.Mark2Count = len(st.Mark2Coverage.glyphs)
-        st.Mark2Array.Mark2Record = []
-        for base in st.Mark2Coverage.glyphs:
-            anchors = [self.baseMarks[base].get(mc) for mc in markClassList]
-            st.Mark2Array.Mark2Record.append(buildMark2Record(anchors))
-        return self.buildLookup_([st])
-
-
-class ReverseChainSingleSubstBuilder(LookupBuilder):
-    """Builds a Reverse Chaining Contextual Single Substitution (GSUB8) lookup.
-
-    Users are expected to manually add substitutions to the ``substitutions``
-    attribute after the object has been initialized, e.g.::
-
-        # reversesub [a e n] d' by d.alt;
-        prefix = [ ["a", "e", "n"] ]
-        suffix = []
-        mapping = { "d": "d.alt" }
-        builder.substitutions.append( (prefix, suffix, mapping) )
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        substitutions: A three-element tuple consisting of a prefix sequence,
-            a suffix sequence, and a dictionary of single substitutions.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 8)
-        self.rules = []  # (prefix, suffix, mapping)
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.rules == other.rules
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the chained
-            contextual substitution lookup.
-        """
-        subtables = []
-        for prefix, suffix, mapping in self.rules:
-            st = ot.ReverseChainSingleSubst()
-            st.Format = 1
-            self.setBacktrackCoverage_(prefix, st)
-            self.setLookAheadCoverage_(suffix, st)
-            st.Coverage = buildCoverage(mapping.keys(), self.glyphMap)
-            st.GlyphCount = len(mapping)
-            st.Substitute = [mapping[g] for g in st.Coverage.glyphs]
-            subtables.append(st)
-        return self.buildLookup_(subtables)
-
-    def add_subtable_break(self, location):
-        # Nothing to do here, each substitution is in its own subtable.
-        pass
-
-
-class SingleSubstBuilder(LookupBuilder):
-    """Builds a Single Substitution (GSUB1) lookup.
-
-    Users are expected to manually add substitutions to the ``mapping``
-    attribute after the object has been initialized, e.g.::
-
-        # sub x by y;
-        builder.mapping["x"] = "y"
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        mapping: A dictionary mapping a single glyph name to another glyph name.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GSUB", 1)
-        self.mapping = OrderedDict()
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.mapping == other.mapping
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the multiple
-            substitution lookup.
-        """
-        subtables = self.build_subst_subtables(self.mapping, buildSingleSubstSubtable)
-        return self.buildLookup_(subtables)
-
-    def getAlternateGlyphs(self):
-        return {glyph: set([repl]) for glyph, repl in self.mapping.items()}
-
-    def add_subtable_break(self, location):
-        self.mapping[(self.SUBTABLE_BREAK_, location)] = self.SUBTABLE_BREAK_
-
-
-class ClassPairPosSubtableBuilder(object):
-    """Builds class-based Pair Positioning (GPOS2 format 2) subtables.
-
-    Note that this does *not* build a GPOS2 ``otTables.Lookup`` directly,
-    but builds a list of ``otTables.PairPos`` subtables. It is used by the
-    :class:`PairPosBuilder` below.
-
-    Attributes:
-        builder (PairPosBuilder): A pair positioning lookup builder.
-    """
-
-    def __init__(self, builder):
-        self.builder_ = builder
-        self.classDef1_, self.classDef2_ = None, None
-        self.values_ = {}  # (glyphclass1, glyphclass2) --> (value1, value2)
-        self.forceSubtableBreak_ = False
-        self.subtables_ = []
-
-    def addPair(self, gc1, value1, gc2, value2):
-        """Add a pair positioning rule.
-
-        Args:
-            gc1: A set of glyph names for the "left" glyph
-            value1: An ``otTables.ValueRecord`` object for the left glyph's
-                positioning.
-            gc2: A set of glyph names for the "right" glyph
-            value2: An ``otTables.ValueRecord`` object for the right glyph's
-                positioning.
-        """
-        mergeable = (
-            not self.forceSubtableBreak_
-            and self.classDef1_ is not None
-            and self.classDef1_.canAdd(gc1)
-            and self.classDef2_ is not None
-            and self.classDef2_.canAdd(gc2)
-        )
-        if not mergeable:
-            self.flush_()
-            self.classDef1_ = ClassDefBuilder(useClass0=True)
-            self.classDef2_ = ClassDefBuilder(useClass0=False)
-            self.values_ = {}
-        self.classDef1_.add(gc1)
-        self.classDef2_.add(gc2)
-        self.values_[(gc1, gc2)] = (value1, value2)
-
-    def addSubtableBreak(self):
-        """Add an explicit subtable break at this point."""
-        self.forceSubtableBreak_ = True
-
-    def subtables(self):
-        """Return the list of ``otTables.PairPos`` subtables constructed."""
-        self.flush_()
-        return self.subtables_
-
-    def flush_(self):
-        if self.classDef1_ is None or self.classDef2_ is None:
-            return
-        st = buildPairPosClassesSubtable(self.values_, self.builder_.glyphMap)
-        if st.Coverage is None:
-            return
-        self.subtables_.append(st)
-        self.forceSubtableBreak_ = False
-
-
-class PairPosBuilder(LookupBuilder):
-    """Builds a Pair Positioning (GPOS2) lookup.
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        pairs: An array of class-based pair positioning tuples. Usually
-            manipulated with the :meth:`addClassPair` method below.
-        glyphPairs: A dictionary mapping a tuple of glyph names to a tuple
-            of ``otTables.ValueRecord`` objects. Usually manipulated with the
-            :meth:`addGlyphPair` method below.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 2)
-        self.pairs = []  # [(gc1, value1, gc2, value2)*]
-        self.glyphPairs = {}  # (glyph1, glyph2) --> (value1, value2)
-        self.locations = {}  # (gc1, gc2) --> (filepath, line, column)
-
-    def addClassPair(self, location, glyphclass1, value1, glyphclass2, value2):
-        """Add a class pair positioning rule to the current lookup.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this rule. Unused.
-            glyphclass1: A set of glyph names for the "left" glyph in the pair.
-            value1: A ``otTables.ValueRecord`` for positioning the left glyph.
-            glyphclass2: A set of glyph names for the "right" glyph in the pair.
-            value2: A ``otTables.ValueRecord`` for positioning the right glyph.
-        """
-        self.pairs.append((glyphclass1, value1, glyphclass2, value2))
-
-    def addGlyphPair(self, location, glyph1, value1, glyph2, value2):
-        """Add a glyph pair positioning rule to the current lookup.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this rule.
-            glyph1: A glyph name for the "left" glyph in the pair.
-            value1: A ``otTables.ValueRecord`` for positioning the left glyph.
-            glyph2: A glyph name for the "right" glyph in the pair.
-            value2: A ``otTables.ValueRecord`` for positioning the right glyph.
-        """
-        key = (glyph1, glyph2)
-        oldValue = self.glyphPairs.get(key, None)
-        if oldValue is not None:
-            # the Feature File spec explicitly allows specific pairs generated
-            # by an 'enum' rule to be overridden by preceding single pairs
-            otherLoc = self.locations[key]
-            log.debug(
-                "Already defined position for pair %s %s at %s; "
-                "choosing the first value",
-                glyph1,
-                glyph2,
-                otherLoc,
-            )
-        else:
-            self.glyphPairs[key] = (value1, value2)
-            self.locations[key] = location
-
-    def add_subtable_break(self, location):
-        self.pairs.append(
-            (
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-                self.SUBTABLE_BREAK_,
-            )
-        )
-
-    def equals(self, other):
-        return (
-            LookupBuilder.equals(self, other)
-            and self.glyphPairs == other.glyphPairs
-            and self.pairs == other.pairs
-        )
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the pair positioning
-            lookup.
-        """
-        builders = {}
-        builder = ClassPairPosSubtableBuilder(self)
-        for glyphclass1, value1, glyphclass2, value2 in self.pairs:
-            if glyphclass1 is self.SUBTABLE_BREAK_:
-                builder.addSubtableBreak()
-                continue
-            builder.addPair(glyphclass1, value1, glyphclass2, value2)
-        subtables = []
-        if self.glyphPairs:
-            subtables.extend(buildPairPosGlyphs(self.glyphPairs, self.glyphMap))
-        subtables.extend(builder.subtables())
-        lookup = self.buildLookup_(subtables)
-
-        # Compact the lookup
-        # This is a good moment to do it because the compaction should create
-        # smaller subtables, which may prevent overflows from happening.
-        # Keep reading the value from the ENV until ufo2ft switches to the config system
-        level = self.font.cfg.get(
-            "fontTools.otlLib.optimize.gpos:COMPRESSION_LEVEL",
-            default=_compression_level_from_env(),
-        )
-        if level != 0:
-            log.info("Compacting GPOS...")
-            compact_lookup(self.font, level, lookup)
-
-        return lookup
-
-
-class SinglePosBuilder(LookupBuilder):
-    """Builds a Single Positioning (GPOS1) lookup.
-
-    Attributes:
-        font (``fontTools.TTLib.TTFont``): A font object.
-        location: A string or tuple representing the location in the original
-            source which produced this lookup.
-        mapping: A dictionary mapping a glyph name to a ``otTables.ValueRecord``
-            objects. Usually manipulated with the :meth:`add_pos` method below.
-        lookupflag (int): The lookup's flag
-        markFilterSet: Either ``None`` if no mark filtering set is used, or
-            an integer representing the filtering set to be used for this
-            lookup. If a mark filtering set is provided,
-            `LOOKUP_FLAG_USE_MARK_FILTERING_SET` will be set on the lookup's
-            flags.
-    """
-
-    def __init__(self, font, location):
-        LookupBuilder.__init__(self, font, location, "GPOS", 1)
-        self.locations = {}  # glyph -> (filename, line, column)
-        self.mapping = {}  # glyph -> ot.ValueRecord
-
-    def add_pos(self, location, glyph, otValueRecord):
-        """Add a single positioning rule.
-
-        Args:
-            location: A string or tuple representing the location in the
-                original source which produced this lookup.
-            glyph: A glyph name.
-            otValueRection: A ``otTables.ValueRecord`` used to position the
-                glyph.
-        """
-        if not self.can_add(glyph, otValueRecord):
-            otherLoc = self.locations[glyph]
-            raise OpenTypeLibError(
-                'Already defined different position for glyph "%s" at %s'
-                % (glyph, otherLoc),
-                location,
-            )
-        if otValueRecord:
-            self.mapping[glyph] = otValueRecord
-        self.locations[glyph] = location
-
-    def can_add(self, glyph, value):
-        assert isinstance(value, ValueRecord)
-        curValue = self.mapping.get(glyph)
-        return curValue is None or curValue == value
-
-    def equals(self, other):
-        return LookupBuilder.equals(self, other) and self.mapping == other.mapping
-
-    def build(self):
-        """Build the lookup.
-
-        Returns:
-            An ``otTables.Lookup`` object representing the single positioning
-            lookup.
-        """
-        subtables = buildSinglePos(self.mapping, self.glyphMap)
-        return self.buildLookup_(subtables)
-
-
-# GSUB
-
-
-def buildSingleSubstSubtable(mapping):
-    """Builds a single substitution (GSUB1) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.SingleSubstBuilder` instead.
-
-    Args:
-        mapping: A dictionary mapping input glyph names to output glyph names.
-
-    Returns:
-        An ``otTables.SingleSubst`` object, or ``None`` if the mapping dictionary
-        is empty.
-    """
-    if not mapping:
-        return None
-    self = ot.SingleSubst()
-    self.mapping = dict(mapping)
-    return self
-
-
-def buildMultipleSubstSubtable(mapping):
-    """Builds a multiple substitution (GSUB2) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.MultipleSubstBuilder` instead.
-
-    Example::
-
-        # sub uni06C0 by uni06D5.fina hamza.above
-        # sub uni06C2 by uni06C1.fina hamza.above;
-
-        subtable = buildMultipleSubstSubtable({
-            "uni06C0": [ "uni06D5.fina", "hamza.above"],
-            "uni06C2": [ "uni06D1.fina", "hamza.above"]
-        })
-
-    Args:
-        mapping: A dictionary mapping input glyph names to a list of output
-            glyph names.
-
-    Returns:
-        An ``otTables.MultipleSubst`` object or ``None`` if the mapping dictionary
-        is empty.
-    """
-    if not mapping:
-        return None
-    self = ot.MultipleSubst()
-    self.mapping = dict(mapping)
-    return self
-
-
-def buildAlternateSubstSubtable(mapping):
-    """Builds an alternate substitution (GSUB3) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.AlternateSubstBuilder` instead.
-
-    Args:
-        mapping: A dictionary mapping input glyph names to a list of output
-            glyph names.
-
-    Returns:
-        An ``otTables.AlternateSubst`` object or ``None`` if the mapping dictionary
-        is empty.
-    """
-    if not mapping:
-        return None
-    self = ot.AlternateSubst()
-    self.alternates = dict(mapping)
-    return self
-
-
-def _getLigatureKey(components):
-    # Computes a key for ordering ligatures in a GSUB Type-4 lookup.
-
-    # When building the OpenType lookup, we need to make sure that
-    # the longest sequence of components is listed first, so we
-    # use the negative length as the primary key for sorting.
-    # To make buildLigatureSubstSubtable() deterministic, we use the
-    # component sequence as the secondary key.
-
-    # For example, this will sort (f,f,f) < (f,f,i) < (f,f) < (f,i) < (f,l).
-    return (-len(components), components)
-
-
-def buildLigatureSubstSubtable(mapping):
-    """Builds a ligature substitution (GSUB4) subtable.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.LigatureSubstBuilder` instead.
-
-    Example::
-
-        # sub f f i by f_f_i;
-        # sub f i by f_i;
-
-        subtable = buildLigatureSubstSubtable({
-            ("f", "f", "i"): "f_f_i",
-            ("f", "i"): "f_i",
-        })
-
-    Args:
-        mapping: A dictionary mapping tuples of glyph names to output
-            glyph names.
-
-    Returns:
-        An ``otTables.LigatureSubst`` object or ``None`` if the mapping dictionary
-        is empty.
-    """
-
-    if not mapping:
-        return None
-    self = ot.LigatureSubst()
-    # The following single line can replace the rest of this function
-    # with fontTools >= 3.1:
-    # self.ligatures = dict(mapping)
-    self.ligatures = {}
-    for components in sorted(mapping.keys(), key=_getLigatureKey):
-        ligature = ot.Ligature()
-        ligature.Component = components[1:]
-        ligature.CompCount = len(ligature.Component) + 1
-        ligature.LigGlyph = mapping[components]
-        firstGlyph = components[0]
-        self.ligatures.setdefault(firstGlyph, []).append(ligature)
-    return self
-
-
-# GPOS
-
-
-def buildAnchor(x, y, point=None, deviceX=None, deviceY=None):
-    """Builds an Anchor table.
-
-    This determines the appropriate anchor format based on the passed parameters.
-
-    Args:
-        x (int): X coordinate.
-        y (int): Y coordinate.
-        point (int): Index of glyph contour point, if provided.
-        deviceX (``otTables.Device``): X coordinate device table, if provided.
-        deviceY (``otTables.Device``): Y coordinate device table, if provided.
-
-    Returns:
-        An ``otTables.Anchor`` object.
-    """
-    self = ot.Anchor()
-    self.XCoordinate, self.YCoordinate = x, y
-    self.Format = 1
-    if point is not None:
-        self.AnchorPoint = point
-        self.Format = 2
-    if deviceX is not None or deviceY is not None:
-        assert (
-            self.Format == 1
-        ), "Either point, or both of deviceX/deviceY, must be None."
-        self.XDeviceTable = deviceX
-        self.YDeviceTable = deviceY
-        self.Format = 3
-    return self
-
-
-def buildBaseArray(bases, numMarkClasses, glyphMap):
-    """Builds a base array record.
-
-    As part of building mark-to-base positioning rules, you will need to define
-    a ``BaseArray`` record, which "defines for each base glyph an array of
-    anchors, one for each mark class." This function builds the base array
-    subtable.
-
-    Example::
-
-        bases = {"a": {0: a3, 1: a5}, "b": {0: a4, 1: a5}}
-        basearray = buildBaseArray(bases, 2, font.getReverseGlyphMap())
-
-    Args:
-        bases (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being dictionaries mapping mark class ID
-            to the appropriate ``otTables.Anchor`` object used for attaching marks
-            of that class.
-        numMarkClasses (int): The total number of mark classes for which anchors
-            are defined.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.BaseArray`` object.
-    """
-    self = ot.BaseArray()
-    self.BaseRecord = []
-    for base in sorted(bases, key=glyphMap.__getitem__):
-        b = bases[base]
-        anchors = [b.get(markClass) for markClass in range(numMarkClasses)]
-        self.BaseRecord.append(buildBaseRecord(anchors))
-    self.BaseCount = len(self.BaseRecord)
-    return self
-
-
-def buildBaseRecord(anchors):
-    # [otTables.Anchor, otTables.Anchor, ...] --> otTables.BaseRecord
-    self = ot.BaseRecord()
-    self.BaseAnchor = anchors
-    return self
-
-
-def buildComponentRecord(anchors):
-    """Builds a component record.
-
-    As part of building mark-to-ligature positioning rules, you will need to
-    define ``ComponentRecord`` objects, which contain "an array of offsets...
-    to the Anchor tables that define all the attachment points used to attach
-    marks to the component." This function builds the component record.
-
-    Args:
-        anchors: A list of ``otTables.Anchor`` objects or ``None``.
-
-    Returns:
-        A ``otTables.ComponentRecord`` object or ``None`` if no anchors are
-        supplied.
-    """
-    if not anchors:
-        return None
-    self = ot.ComponentRecord()
-    self.LigatureAnchor = anchors
-    return self
-
-
-def buildCursivePosSubtable(attach, glyphMap):
-    """Builds a cursive positioning (GPOS3) subtable.
-
-    Cursive positioning lookups are made up of a coverage table of glyphs,
-    and a set of ``EntryExitRecord`` records containing the anchors for
-    each glyph. This function builds the cursive positioning subtable.
-
-    Example::
-
-        subtable = buildCursivePosSubtable({
-            "AlifIni": (None, buildAnchor(0, 50)),
-            "BehMed": (buildAnchor(500,250), buildAnchor(0,50)),
-            # ...
-        }, font.getReverseGlyphMap())
-
-    Args:
-        attach (dict): A mapping between glyph names and a tuple of two
-            ``otTables.Anchor`` objects representing entry and exit anchors.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.CursivePos`` object, or ``None`` if the attachment
-        dictionary was empty.
-    """
-    if not attach:
-        return None
-    self = ot.CursivePos()
-    self.Format = 1
-    self.Coverage = buildCoverage(attach.keys(), glyphMap)
-    self.EntryExitRecord = []
-    for glyph in self.Coverage.glyphs:
-        entryAnchor, exitAnchor = attach[glyph]
-        rec = ot.EntryExitRecord()
-        rec.EntryAnchor = entryAnchor
-        rec.ExitAnchor = exitAnchor
-        self.EntryExitRecord.append(rec)
-    self.EntryExitCount = len(self.EntryExitRecord)
-    return self
-
-
-def buildDevice(deltas):
-    """Builds a Device record as part of a ValueRecord or Anchor.
-
-    Device tables specify size-specific adjustments to value records
-    and anchors to reflect changes based on the resolution of the output.
-    For example, one could specify that an anchor's Y position should be
-    increased by 1 pixel when displayed at 8 pixels per em. This routine
-    builds device records.
-
-    Args:
-        deltas: A dictionary mapping pixels-per-em sizes to the delta
-            adjustment in pixels when the font is displayed at that size.
-
-    Returns:
-        An ``otTables.Device`` object if any deltas were supplied, or
-        ``None`` otherwise.
-    """
-    if not deltas:
-        return None
-    self = ot.Device()
-    keys = deltas.keys()
-    self.StartSize = startSize = min(keys)
-    self.EndSize = endSize = max(keys)
-    assert 0 <= startSize <= endSize
-    self.DeltaValue = deltaValues = [
-        deltas.get(size, 0) for size in range(startSize, endSize + 1)
-    ]
-    maxDelta = max(deltaValues)
-    minDelta = min(deltaValues)
-    assert minDelta > -129 and maxDelta < 128
-    if minDelta > -3 and maxDelta < 2:
-        self.DeltaFormat = 1
-    elif minDelta > -9 and maxDelta < 8:
-        self.DeltaFormat = 2
-    else:
-        self.DeltaFormat = 3
-    return self
-
-
-def buildLigatureArray(ligs, numMarkClasses, glyphMap):
-    """Builds a LigatureArray subtable.
-
-    As part of building a mark-to-ligature lookup, you will need to define
-    the set of anchors (for each mark class) on each component of the ligature
-    where marks can be attached. For example, for an Arabic divine name ligature
-    (lam lam heh), you may want to specify mark attachment positioning for
-    superior marks (fatha, etc.) and inferior marks (kasra, etc.) on each glyph
-    of the ligature. This routine builds the ligature array record.
-
-    Example::
-
-        buildLigatureArray({
-            "lam-lam-heh": [
-                { 0: superiorAnchor1, 1: inferiorAnchor1 }, # attach points for lam1
-                { 0: superiorAnchor2, 1: inferiorAnchor2 }, # attach points for lam2
-                { 0: superiorAnchor3, 1: inferiorAnchor3 }, # attach points for heh
-            ]
-        }, 2, font.getReverseGlyphMap())
-
-    Args:
-        ligs (dict): A mapping of ligature names to an array of dictionaries:
-            for each component glyph in the ligature, an dictionary mapping
-            mark class IDs to anchors.
-        numMarkClasses (int): The number of mark classes.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.LigatureArray`` object if deltas were supplied.
-    """
-    self = ot.LigatureArray()
-    self.LigatureAttach = []
-    for lig in sorted(ligs, key=glyphMap.__getitem__):
-        anchors = []
-        for component in ligs[lig]:
-            anchors.append([component.get(mc) for mc in range(numMarkClasses)])
-        self.LigatureAttach.append(buildLigatureAttach(anchors))
-    self.LigatureCount = len(self.LigatureAttach)
-    return self
-
-
-def buildLigatureAttach(components):
-    # [[Anchor, Anchor], [Anchor, Anchor, Anchor]] --> LigatureAttach
-    self = ot.LigatureAttach()
-    self.ComponentRecord = [buildComponentRecord(c) for c in components]
-    self.ComponentCount = len(self.ComponentRecord)
-    return self
-
-
-def buildMarkArray(marks, glyphMap):
-    """Builds a mark array subtable.
-
-    As part of building mark-to-* positioning rules, you will need to define
-    a MarkArray subtable, which "defines the class and the anchor point
-    for a mark glyph." This function builds the mark array subtable.
-
-    Example::
-
-        mark = {
-            "acute": (0, buildAnchor(300,712)),
-            # ...
-        }
-        markarray = buildMarkArray(marks, font.getReverseGlyphMap())
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        An ``otTables.MarkArray`` object.
-    """
-    self = ot.MarkArray()
-    self.MarkRecord = []
-    for mark in sorted(marks.keys(), key=glyphMap.__getitem__):
-        markClass, anchor = marks[mark]
-        markrec = buildMarkRecord(markClass, anchor)
-        self.MarkRecord.append(markrec)
-    self.MarkCount = len(self.MarkRecord)
-    return self
-
-
-def buildMarkBasePos(marks, bases, glyphMap):
-    """Build a list of MarkBasePos (GPOS4) subtables.
-
-    This routine turns a set of marks and bases into a list of mark-to-base
-    positioning subtables. Currently the list will contain a single subtable
-    containing all marks and bases, although at a later date it may return the
-    optimal list of subtables subsetting the marks and bases into groups which
-    save space. See :func:`buildMarkBasePosSubtable` below.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.MarkBasePosBuilder` instead.
-
-    Example::
-
-        # a1, a2, a3, a4, a5 = buildAnchor(500, 100), ...
-
-        marks = {"acute": (0, a1), "grave": (0, a1), "cedilla": (1, a2)}
-        bases = {"a": {0: a3, 1: a5}, "b": {0: a4, 1: a5}}
-        markbaseposes = buildMarkBasePos(marks, bases, font.getReverseGlyphMap())
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        bases (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being dictionaries mapping mark class ID
-            to the appropriate ``otTables.Anchor`` object used for attaching marks
-            of that class. (See :func:`buildBaseArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.MarkBasePos`` objects.
-    """
-    # TODO: Consider emitting multiple subtables to save space.
-    # Partition the marks and bases into disjoint subsets, so that
-    # MarkBasePos rules would only access glyphs from a single
-    # subset. This would likely lead to smaller mark/base
-    # matrices, so we might be able to omit many of the empty
-    # anchor tables that we currently produce. Of course, this
-    # would only work if the MarkBasePos rules of real-world fonts
-    # allow partitioning into multiple subsets. We should find out
-    # whether this is the case; if so, implement the optimization.
-    # On the other hand, a very large number of subtables could
-    # slow down layout engines; so this would need profiling.
-    return [buildMarkBasePosSubtable(marks, bases, glyphMap)]
-
-
-def buildMarkBasePosSubtable(marks, bases, glyphMap):
-    """Build a single MarkBasePos (GPOS4) subtable.
-
-    This builds a mark-to-base lookup subtable containing all of the referenced
-    marks and bases. See :func:`buildMarkBasePos`.
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        bases (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being dictionaries mapping mark class ID
-            to the appropriate ``otTables.Anchor`` object used for attaching marks
-            of that class. (See :func:`buildBaseArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.MarkBasePos`` object.
-    """
-    self = ot.MarkBasePos()
-    self.Format = 1
-    self.MarkCoverage = buildCoverage(marks, glyphMap)
-    self.MarkArray = buildMarkArray(marks, glyphMap)
-    self.ClassCount = max([mc for mc, _ in marks.values()]) + 1
-    self.BaseCoverage = buildCoverage(bases, glyphMap)
-    self.BaseArray = buildBaseArray(bases, self.ClassCount, glyphMap)
-    return self
-
-
-def buildMarkLigPos(marks, ligs, glyphMap):
-    """Build a list of MarkLigPos (GPOS5) subtables.
-
-    This routine turns a set of marks and ligatures into a list of mark-to-ligature
-    positioning subtables. Currently the list will contain a single subtable
-    containing all marks and ligatures, although at a later date it may return
-    the optimal list of subtables subsetting the marks and ligatures into groups
-    which save space. See :func:`buildMarkLigPosSubtable` below.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.MarkLigPosBuilder` instead.
-
-    Example::
-
-        # a1, a2, a3, a4, a5 = buildAnchor(500, 100), ...
-        marks = {
-            "acute": (0, a1),
-            "grave": (0, a1),
-            "cedilla": (1, a2)
-        }
-        ligs = {
-            "f_i": [
-                { 0: a3, 1: a5 }, # f
-                { 0: a4, 1: a5 }  # i
-                ],
-        #   "c_t": [{...}, {...}]
-        }
-        markligposes = buildMarkLigPos(marks, ligs,
-            font.getReverseGlyphMap())
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        ligs (dict): A mapping of ligature names to an array of dictionaries:
-            for each component glyph in the ligature, an dictionary mapping
-            mark class IDs to anchors. (See :func:`buildLigatureArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.MarkLigPos`` objects.
-
-    """
-    # TODO: Consider splitting into multiple subtables to save space,
-    # as with MarkBasePos, this would be a trade-off that would need
-    # profiling. And, depending on how typical fonts are structured,
-    # it might not be worth doing at all.
-    return [buildMarkLigPosSubtable(marks, ligs, glyphMap)]
-
-
-def buildMarkLigPosSubtable(marks, ligs, glyphMap):
-    """Build a single MarkLigPos (GPOS5) subtable.
-
-    This builds a mark-to-base lookup subtable containing all of the referenced
-    marks and bases. See :func:`buildMarkLigPos`.
-
-    Args:
-        marks (dict): A dictionary mapping anchors to glyphs; the keys being
-            glyph names, and the values being a tuple of mark class number and
-            an ``otTables.Anchor`` object representing the mark's attachment
-            point. (See :func:`buildMarkArray`.)
-        ligs (dict): A mapping of ligature names to an array of dictionaries:
-            for each component glyph in the ligature, an dictionary mapping
-            mark class IDs to anchors. (See :func:`buildLigatureArray`.)
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.MarkLigPos`` object.
-    """
-    self = ot.MarkLigPos()
-    self.Format = 1
-    self.MarkCoverage = buildCoverage(marks, glyphMap)
-    self.MarkArray = buildMarkArray(marks, glyphMap)
-    self.ClassCount = max([mc for mc, _ in marks.values()]) + 1
-    self.LigatureCoverage = buildCoverage(ligs, glyphMap)
-    self.LigatureArray = buildLigatureArray(ligs, self.ClassCount, glyphMap)
-    return self
-
-
-def buildMarkRecord(classID, anchor):
-    assert isinstance(classID, int)
-    assert isinstance(anchor, ot.Anchor)
-    self = ot.MarkRecord()
-    self.Class = classID
-    self.MarkAnchor = anchor
-    return self
-
-
-def buildMark2Record(anchors):
-    # [otTables.Anchor, otTables.Anchor, ...] --> otTables.Mark2Record
-    self = ot.Mark2Record()
-    self.Mark2Anchor = anchors
-    return self
-
-
-def _getValueFormat(f, values, i):
-    # Helper for buildPairPos{Glyphs|Classes}Subtable.
-    if f is not None:
-        return f
-    mask = 0
-    for value in values:
-        if value is not None and value[i] is not None:
-            mask |= value[i].getFormat()
-    return mask
-
-
-def buildPairPosClassesSubtable(pairs, glyphMap, valueFormat1=None, valueFormat2=None):
-    """Builds a class pair adjustment (GPOS2 format 2) subtable.
-
-    Kerning tables are generally expressed as pair positioning tables using
-    class-based pair adjustments. This routine builds format 2 PairPos
-    subtables.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.ClassPairPosSubtableBuilder`
-    instead, as this takes care of ensuring that the supplied pairs can be
-    formed into non-overlapping classes and emitting individual subtables
-    whenever the non-overlapping requirement means that a new subtable is
-    required.
-
-    Example::
-
-        pairs = {}
-
-        pairs[(
-            [ "K", "X" ],
-            [ "W", "V" ]
-        )] = ( buildValue(xAdvance=+5), buildValue() )
-        # pairs[(... , ...)] = (..., ...)
-
-        pairpos = buildPairPosClassesSubtable(pairs, font.getReverseGlyphMap())
-
-    Args:
-        pairs (dict): Pair positioning data; the keys being a two-element
-            tuple of lists of glyphnames, and the values being a two-element
-            tuple of ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-        valueFormat1: Force the "left" value records to the given format.
-        valueFormat2: Force the "right" value records to the given format.
-
-    Returns:
-        A ``otTables.PairPos`` object.
-    """
-    coverage = set()
-    classDef1 = ClassDefBuilder(useClass0=True)
-    classDef2 = ClassDefBuilder(useClass0=False)
-    for gc1, gc2 in sorted(pairs):
-        coverage.update(gc1)
-        classDef1.add(gc1)
-        classDef2.add(gc2)
-    self = ot.PairPos()
-    self.Format = 2
-    valueFormat1 = self.ValueFormat1 = _getValueFormat(valueFormat1, pairs.values(), 0)
-    valueFormat2 = self.ValueFormat2 = _getValueFormat(valueFormat2, pairs.values(), 1)
-    self.Coverage = buildCoverage(coverage, glyphMap)
-    self.ClassDef1 = classDef1.build()
-    self.ClassDef2 = classDef2.build()
-    classes1 = classDef1.classes()
-    classes2 = classDef2.classes()
-    self.Class1Record = []
-    for c1 in classes1:
-        rec1 = ot.Class1Record()
-        rec1.Class2Record = []
-        self.Class1Record.append(rec1)
-        for c2 in classes2:
-            rec2 = ot.Class2Record()
-            val1, val2 = pairs.get((c1, c2), (None, None))
-            rec2.Value1 = (
-                ValueRecord(src=val1, valueFormat=valueFormat1)
-                if valueFormat1
-                else None
-            )
-            rec2.Value2 = (
-                ValueRecord(src=val2, valueFormat=valueFormat2)
-                if valueFormat2
-                else None
-            )
-            rec1.Class2Record.append(rec2)
-    self.Class1Count = len(self.Class1Record)
-    self.Class2Count = len(classes2)
-    return self
-
-
-def buildPairPosGlyphs(pairs, glyphMap):
-    """Builds a list of glyph-based pair adjustment (GPOS2 format 1) subtables.
-
-    This organises a list of pair positioning adjustments into subtables based
-    on common value record formats.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.PairPosBuilder`
-    instead.
-
-    Example::
-
-        pairs = {
-            ("K", "W"): ( buildValue(xAdvance=+5), buildValue() ),
-            ("K", "V"): ( buildValue(xAdvance=+5), buildValue() ),
-            # ...
-        }
-
-        subtables = buildPairPosGlyphs(pairs, font.getReverseGlyphMap())
-
-    Args:
-        pairs (dict): Pair positioning data; the keys being a two-element
-            tuple of glyphnames, and the values being a two-element
-            tuple of ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.PairPos`` objects.
-    """
-
-    p = {}  # (formatA, formatB) --> {(glyphA, glyphB): (valA, valB)}
-    for (glyphA, glyphB), (valA, valB) in pairs.items():
-        formatA = valA.getFormat() if valA is not None else 0
-        formatB = valB.getFormat() if valB is not None else 0
-        pos = p.setdefault((formatA, formatB), {})
-        pos[(glyphA, glyphB)] = (valA, valB)
-    return [
-        buildPairPosGlyphsSubtable(pos, glyphMap, formatA, formatB)
-        for ((formatA, formatB), pos) in sorted(p.items())
-    ]
-
-
-def buildPairPosGlyphsSubtable(pairs, glyphMap, valueFormat1=None, valueFormat2=None):
-    """Builds a single glyph-based pair adjustment (GPOS2 format 1) subtable.
-
-    This builds a PairPos subtable from a dictionary of glyph pairs and
-    their positioning adjustments. See also :func:`buildPairPosGlyphs`.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.PairPosBuilder` instead.
-
-    Example::
-
-        pairs = {
-            ("K", "W"): ( buildValue(xAdvance=+5), buildValue() ),
-            ("K", "V"): ( buildValue(xAdvance=+5), buildValue() ),
-            # ...
-        }
-
-        pairpos = buildPairPosGlyphsSubtable(pairs, font.getReverseGlyphMap())
-
-    Args:
-        pairs (dict): Pair positioning data; the keys being a two-element
-            tuple of glyphnames, and the values being a two-element
-            tuple of ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-        valueFormat1: Force the "left" value records to the given format.
-        valueFormat2: Force the "right" value records to the given format.
-
-    Returns:
-        A ``otTables.PairPos`` object.
-    """
-    self = ot.PairPos()
-    self.Format = 1
-    valueFormat1 = self.ValueFormat1 = _getValueFormat(valueFormat1, pairs.values(), 0)
-    valueFormat2 = self.ValueFormat2 = _getValueFormat(valueFormat2, pairs.values(), 1)
-    p = {}
-    for (glyphA, glyphB), (valA, valB) in pairs.items():
-        p.setdefault(glyphA, []).append((glyphB, valA, valB))
-    self.Coverage = buildCoverage({g for g, _ in pairs.keys()}, glyphMap)
-    self.PairSet = []
-    for glyph in self.Coverage.glyphs:
-        ps = ot.PairSet()
-        ps.PairValueRecord = []
-        self.PairSet.append(ps)
-        for glyph2, val1, val2 in sorted(p[glyph], key=lambda x: glyphMap[x[0]]):
-            pvr = ot.PairValueRecord()
-            pvr.SecondGlyph = glyph2
-            pvr.Value1 = (
-                ValueRecord(src=val1, valueFormat=valueFormat1)
-                if valueFormat1
-                else None
-            )
-            pvr.Value2 = (
-                ValueRecord(src=val2, valueFormat=valueFormat2)
-                if valueFormat2
-                else None
-            )
-            ps.PairValueRecord.append(pvr)
-        ps.PairValueCount = len(ps.PairValueRecord)
-    self.PairSetCount = len(self.PairSet)
-    return self
-
-
-def buildSinglePos(mapping, glyphMap):
-    """Builds a list of single adjustment (GPOS1) subtables.
-
-    This builds a list of SinglePos subtables from a dictionary of glyph
-    names and their positioning adjustments. The format of the subtables are
-    determined to optimize the size of the resulting subtables.
-    See also :func:`buildSinglePosSubtable`.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.SinglePosBuilder` instead.
-
-    Example::
-
-        mapping = {
-            "V": buildValue({ "xAdvance" : +5 }),
-            # ...
-        }
-
-        subtables = buildSinglePos(pairs, font.getReverseGlyphMap())
-
-    Args:
-        mapping (dict): A mapping between glyphnames and
-            ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A list of ``otTables.SinglePos`` objects.
-    """
-    result, handled = [], set()
-    # In SinglePos format 1, the covered glyphs all share the same ValueRecord.
-    # In format 2, each glyph has its own ValueRecord, but these records
-    # all have the same properties (eg., all have an X but no Y placement).
-    coverages, masks, values = {}, {}, {}
-    for glyph, value in mapping.items():
-        key = _getSinglePosValueKey(value)
-        coverages.setdefault(key, []).append(glyph)
-        masks.setdefault(key[0], []).append(key)
-        values[key] = value
-
-    # If a ValueRecord is shared between multiple glyphs, we generate
-    # a SinglePos format 1 subtable; that is the most compact form.
-    for key, glyphs in coverages.items():
-        # 5 ushorts is the length of introducing another sublookup
-        if len(glyphs) * _getSinglePosValueSize(key) > 5:
-            format1Mapping = {g: values[key] for g in glyphs}
-            result.append(buildSinglePosSubtable(format1Mapping, glyphMap))
-            handled.add(key)
-
-    # In the remaining ValueRecords, look for those whose valueFormat
-    # (the set of used properties) is shared between multiple records.
-    # These will get encoded in format 2.
-    for valueFormat, keys in masks.items():
-        f2 = [k for k in keys if k not in handled]
-        if len(f2) > 1:
-            format2Mapping = {}
-            for k in f2:
-                format2Mapping.update((g, values[k]) for g in coverages[k])
-            result.append(buildSinglePosSubtable(format2Mapping, glyphMap))
-            handled.update(f2)
-
-    # The remaining ValueRecords are only used by a few glyphs, normally
-    # one. We encode these in format 1 again.
-    for key, glyphs in coverages.items():
-        if key not in handled:
-            for g in glyphs:
-                st = buildSinglePosSubtable({g: values[key]}, glyphMap)
-            result.append(st)
-
-    # When the OpenType layout engine traverses the subtables, it will
-    # stop after the first matching subtable.  Therefore, we sort the
-    # resulting subtables by decreasing coverage size; this increases
-    # the chance that the layout engine can do an early exit. (Of course,
-    # this would only be true if all glyphs were equally frequent, which
-    # is not really the case; but we do not know their distribution).
-    # If two subtables cover the same number of glyphs, we sort them
-    # by glyph ID so that our output is deterministic.
-    result.sort(key=lambda t: _getSinglePosTableKey(t, glyphMap))
-    return result
-
-
-def buildSinglePosSubtable(values, glyphMap):
-    """Builds a single adjustment (GPOS1) subtable.
-
-    This builds a list of SinglePos subtables from a dictionary of glyph
-    names and their positioning adjustments. The format of the subtable is
-    determined to optimize the size of the output.
-    See also :func:`buildSinglePos`.
-
-    Note that if you are implementing a layout compiler, you may find it more
-    flexible to use
-    :py:class:`fontTools.otlLib.lookupBuilders.SinglePosBuilder` instead.
-
-    Example::
-
-        mapping = {
-            "V": buildValue({ "xAdvance" : +5 }),
-            # ...
-        }
-
-        subtable = buildSinglePos(pairs, font.getReverseGlyphMap())
-
-    Args:
-        mapping (dict): A mapping between glyphnames and
-            ``otTables.ValueRecord`` objects.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.SinglePos`` object.
-    """
-    self = ot.SinglePos()
-    self.Coverage = buildCoverage(values.keys(), glyphMap)
-    valueFormat = self.ValueFormat = reduce(
-        int.__or__, [v.getFormat() for v in values.values()], 0
-    )
-    valueRecords = [
-        ValueRecord(src=values[g], valueFormat=valueFormat)
-        for g in self.Coverage.glyphs
-    ]
-    if all(v == valueRecords[0] for v in valueRecords):
-        self.Format = 1
-        if self.ValueFormat != 0:
-            self.Value = valueRecords[0]
-        else:
-            self.Value = None
-    else:
-        self.Format = 2
-        self.Value = valueRecords
-        self.ValueCount = len(self.Value)
-    return self
-
-
-def _getSinglePosTableKey(subtable, glyphMap):
-    assert isinstance(subtable, ot.SinglePos), subtable
-    glyphs = subtable.Coverage.glyphs
-    return (-len(glyphs), glyphMap[glyphs[0]])
-
-
-def _getSinglePosValueKey(valueRecord):
-    # otBase.ValueRecord --> (2, ("YPlacement": 12))
-    assert isinstance(valueRecord, ValueRecord), valueRecord
-    valueFormat, result = 0, []
-    for name, value in valueRecord.__dict__.items():
-        if isinstance(value, ot.Device):
-            result.append((name, _makeDeviceTuple(value)))
-        else:
-            result.append((name, value))
-        valueFormat |= valueRecordFormatDict[name][0]
-    result.sort()
-    result.insert(0, valueFormat)
-    return tuple(result)
-
-
-_DeviceTuple = namedtuple("_DeviceTuple", "DeltaFormat StartSize EndSize DeltaValue")
-
-
-def _makeDeviceTuple(device):
-    # otTables.Device --> tuple, for making device tables unique
-    return _DeviceTuple(
-        device.DeltaFormat,
-        device.StartSize,
-        device.EndSize,
-        () if device.DeltaFormat & 0x8000 else tuple(device.DeltaValue),
-    )
-
-
-def _getSinglePosValueSize(valueKey):
-    # Returns how many ushorts this valueKey (short form of ValueRecord) takes up
-    count = 0
-    for _, v in valueKey[1:]:
-        if isinstance(v, _DeviceTuple):
-            count += len(v.DeltaValue) + 3
-        else:
-            count += 1
-    return count
-
-
-def buildValue(value):
-    """Builds a positioning value record.
-
-    Value records are used to specify coordinates and adjustments for
-    positioning and attaching glyphs. Many of the positioning functions
-    in this library take ``otTables.ValueRecord`` objects as arguments.
-    This function builds value records from dictionaries.
-
-    Args:
-        value (dict): A dictionary with zero or more of the following keys:
-            - ``xPlacement``
-            - ``yPlacement``
-            - ``xAdvance``
-            - ``yAdvance``
-            - ``xPlaDevice``
-            - ``yPlaDevice``
-            - ``xAdvDevice``
-            - ``yAdvDevice``
-
-    Returns:
-        An ``otTables.ValueRecord`` object.
-    """
-    self = ValueRecord()
-    for k, v in value.items():
-        setattr(self, k, v)
-    return self
-
-
-# GDEF
-
-
-def buildAttachList(attachPoints, glyphMap):
-    """Builds an AttachList subtable.
-
-    A GDEF table may contain an Attachment Point List table (AttachList)
-    which stores the contour indices of attachment points for glyphs with
-    attachment points. This routine builds AttachList subtables.
-
-    Args:
-        attachPoints (dict): A mapping between glyph names and a list of
-            contour indices.
-
-    Returns:
-        An ``otTables.AttachList`` object if attachment points are supplied,
-            or ``None`` otherwise.
-    """
-    if not attachPoints:
-        return None
-    self = ot.AttachList()
-    self.Coverage = buildCoverage(attachPoints.keys(), glyphMap)
-    self.AttachPoint = [buildAttachPoint(attachPoints[g]) for g in self.Coverage.glyphs]
-    self.GlyphCount = len(self.AttachPoint)
-    return self
-
-
-def buildAttachPoint(points):
-    # [4, 23, 41] --> otTables.AttachPoint
-    # Only used by above.
-    if not points:
-        return None
-    self = ot.AttachPoint()
-    self.PointIndex = sorted(set(points))
-    self.PointCount = len(self.PointIndex)
-    return self
-
-
-def buildCaretValueForCoord(coord):
-    # 500 --> otTables.CaretValue, format 1
-    # (500, DeviceTable) --> otTables.CaretValue, format 3
-    self = ot.CaretValue()
-    if isinstance(coord, tuple):
-        self.Format = 3
-        self.Coordinate, self.DeviceTable = coord
-    else:
-        self.Format = 1
-        self.Coordinate = coord
-    return self
-
-
-def buildCaretValueForPoint(point):
-    # 4 --> otTables.CaretValue, format 2
-    self = ot.CaretValue()
-    self.Format = 2
-    self.CaretValuePoint = point
-    return self
-
-
-def buildLigCaretList(coords, points, glyphMap):
-    """Builds a ligature caret list table.
-
-    Ligatures appear as a single glyph representing multiple characters; however
-    when, for example, editing text containing a ``f_i`` ligature, the user may
-    want to place the cursor between the ``f`` and the ``i``. The ligature caret
-    list in the GDEF table specifies the position to display the "caret" (the
-    character insertion indicator, typically a flashing vertical bar) "inside"
-    the ligature to represent an insertion point. The insertion positions may
-    be specified either by coordinate or by contour point.
-
-    Example::
-
-        coords = {
-            "f_f_i": [300, 600] # f|fi cursor at 300 units, ff|i cursor at 600.
-        }
-        points = {
-            "c_t": [28] # c|t cursor appears at coordinate of contour point 28.
-        }
-        ligcaretlist = buildLigCaretList(coords, points, font.getReverseGlyphMap())
-
-    Args:
-        coords: A mapping between glyph names and a list of coordinates for
-            the insertion point of each ligature component after the first one.
-        points: A mapping between glyph names and a list of contour points for
-            the insertion point of each ligature component after the first one.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns:
-        A ``otTables.LigCaretList`` object if any carets are present, or
-            ``None`` otherwise."""
-    glyphs = set(coords.keys()) if coords else set()
-    if points:
-        glyphs.update(points.keys())
-    carets = {g: buildLigGlyph(coords.get(g), points.get(g)) for g in glyphs}
-    carets = {g: c for g, c in carets.items() if c is not None}
-    if not carets:
-        return None
-    self = ot.LigCaretList()
-    self.Coverage = buildCoverage(carets.keys(), glyphMap)
-    self.LigGlyph = [carets[g] for g in self.Coverage.glyphs]
-    self.LigGlyphCount = len(self.LigGlyph)
-    return self
-
-
-def buildLigGlyph(coords, points):
-    # ([500], [4]) --> otTables.LigGlyph; None for empty coords/points
-    carets = []
-    if coords:
-        coords = sorted(coords, key=lambda c: c[0] if isinstance(c, tuple) else c)
-        carets.extend([buildCaretValueForCoord(c) for c in coords])
-    if points:
-        carets.extend([buildCaretValueForPoint(p) for p in sorted(points)])
-    if not carets:
-        return None
-    self = ot.LigGlyph()
-    self.CaretValue = carets
-    self.CaretCount = len(self.CaretValue)
-    return self
-
-
-def buildMarkGlyphSetsDef(markSets, glyphMap):
-    """Builds a mark glyph sets definition table.
-
-    OpenType Layout lookups may choose to use mark filtering sets to consider
-    or ignore particular combinations of marks. These sets are specified by
-    setting a flag on the lookup, but the mark filtering sets are defined in
-    the ``GDEF`` table. This routine builds the subtable containing the mark
-    glyph set definitions.
-
-    Example::
-
-        set0 = set("acute", "grave")
-        set1 = set("caron", "grave")
-
-        markglyphsets = buildMarkGlyphSetsDef([set0, set1], font.getReverseGlyphMap())
-
-    Args:
-
-        markSets: A list of sets of glyphnames.
-        glyphMap: a glyph name to ID map, typically returned from
-            ``font.getReverseGlyphMap()``.
-
-    Returns
-        An ``otTables.MarkGlyphSetsDef`` object.
-    """
-    if not markSets:
-        return None
-    self = ot.MarkGlyphSetsDef()
-    self.MarkSetTableFormat = 1
-    self.Coverage = [buildCoverage(m, glyphMap) for m in markSets]
-    self.MarkSetCount = len(self.Coverage)
-    return self
-
-
-class ClassDefBuilder(object):
-    """Helper for building ClassDef tables."""
-
-    def __init__(self, useClass0):
-        self.classes_ = set()
-        self.glyphs_ = {}
-        self.useClass0_ = useClass0
-
-    def canAdd(self, glyphs):
-        if isinstance(glyphs, (set, frozenset)):
-            glyphs = sorted(glyphs)
-        glyphs = tuple(glyphs)
-        if glyphs in self.classes_:
-            return True
-        for glyph in glyphs:
-            if glyph in self.glyphs_:
-                return False
-        return True
-
-    def add(self, glyphs):
-        if isinstance(glyphs, (set, frozenset)):
-            glyphs = sorted(glyphs)
-        glyphs = tuple(glyphs)
-        if glyphs in self.classes_:
-            return
-        self.classes_.add(glyphs)
-        for glyph in glyphs:
-            if glyph in self.glyphs_:
-                raise OpenTypeLibError(
-                    f"Glyph {glyph} is already present in class.", None
-                )
-            self.glyphs_[glyph] = glyphs
-
-    def classes(self):
-        # In ClassDef1 tables, class id #0 does not need to be encoded
-        # because zero is the default. Therefore, we use id #0 for the
-        # glyph class that has the largest number of members. However,
-        # in other tables than ClassDef1, 0 means "every other glyph"
-        # so we should not use that ID for any real glyph classes;
-        # we implement this by inserting an empty set at position 0.
-        #
-        # TODO: Instead of counting the number of glyphs in each class,
-        # we should determine the encoded size. If the glyphs in a large
-        # class form a contiguous range, the encoding is actually quite
-        # compact, whereas a non-contiguous set might need a lot of bytes
-        # in the output file. We don't get this right with the key below.
-        result = sorted(self.classes_, key=lambda s: (len(s), s), reverse=True)
-        if not self.useClass0_:
-            result.insert(0, frozenset())
-        return result
-
-    def build(self):
-        glyphClasses = {}
-        for classID, glyphs in enumerate(self.classes()):
-            if classID == 0:
-                continue
-            for glyph in glyphs:
-                glyphClasses[glyph] = classID
-        classDef = ot.ClassDef()
-        classDef.classDefs = glyphClasses
-        return classDef
-
-
-AXIS_VALUE_NEGATIVE_INFINITY = fixedToFloat(-0x80000000, 16)
-AXIS_VALUE_POSITIVE_INFINITY = fixedToFloat(0x7FFFFFFF, 16)
-
-
-def buildStatTable(
-    ttFont, axes, locations=None, elidedFallbackName=2, windowsNames=True, macNames=True
-):
-    """Add a 'STAT' table to 'ttFont'.
-
-    'axes' is a list of dictionaries describing axes and their
-    values.
-
-    Example::
-
-        axes = [
-            dict(
-                tag="wght",
-                name="Weight",
-                ordering=0,  # optional
-                values=[
-                    dict(value=100, name='Thin'),
-                    dict(value=300, name='Light'),
-                    dict(value=400, name='Regular', flags=0x2),
-                    dict(value=900, name='Black'),
-                ],
-            )
-        ]
-
-    Each axis dict must have 'tag' and 'name' items. 'tag' maps
-    to the 'AxisTag' field. 'name' can be a name ID (int), a string,
-    or a dictionary containing multilingual names (see the
-    addMultilingualName() name table method), and will translate to
-    the AxisNameID field.
-
-    An axis dict may contain an 'ordering' item that maps to the
-    AxisOrdering field. If omitted, the order of the axes list is
-    used to calculate AxisOrdering fields.
-
-    The axis dict may contain a 'values' item, which is a list of
-    dictionaries describing AxisValue records belonging to this axis.
-
-    Each value dict must have a 'name' item, which can be a name ID
-    (int), a string, or a dictionary containing multilingual names,
-    like the axis name. It translates to the ValueNameID field.
-
-    Optionally the value dict can contain a 'flags' item. It maps to
-    the AxisValue Flags field, and will be 0 when omitted.
-
-    The format of the AxisValue is determined by the remaining contents
-    of the value dictionary:
-
-    If the value dict contains a 'value' item, an AxisValue record
-    Format 1 is created. If in addition to the 'value' item it contains
-    a 'linkedValue' item, an AxisValue record Format 3 is built.
-
-    If the value dict contains a 'nominalValue' item, an AxisValue
-    record Format 2 is built. Optionally it may contain 'rangeMinValue'
-    and 'rangeMaxValue' items. These map to -Infinity and +Infinity
-    respectively if omitted.
-
-    You cannot specify Format 4 AxisValue tables this way, as they are
-    not tied to a single axis, and specify a name for a location that
-    is defined by multiple axes values. Instead, you need to supply the
-    'locations' argument.
-
-    The optional 'locations' argument specifies AxisValue Format 4
-    tables. It should be a list of dicts, where each dict has a 'name'
-    item, which works just like the value dicts above, an optional
-    'flags' item (defaulting to 0x0), and a 'location' dict. A
-    location dict key is an axis tag, and the associated value is the
-    location on the specified axis. They map to the AxisIndex and Value
-    fields of the AxisValueRecord.
-
-    Example::
-
-        locations = [
-            dict(name='Regular ABCD', location=dict(wght=300, ABCD=100)),
-            dict(name='Bold ABCD XYZ', location=dict(wght=600, ABCD=200)),
-        ]
-
-    The optional 'elidedFallbackName' argument can be a name ID (int),
-    a string, a dictionary containing multilingual names, or a list of
-    STATNameStatements. It translates to the ElidedFallbackNameID field.
-
-    The 'ttFont' argument must be a TTFont instance that already has a
-    'name' table. If a 'STAT' table already exists, it will be
-    overwritten by the newly created one.
-    """
-    ttFont["STAT"] = ttLib.newTable("STAT")
-    statTable = ttFont["STAT"].table = ot.STAT()
-    nameTable = ttFont["name"]
-    statTable.ElidedFallbackNameID = _addName(
-        nameTable, elidedFallbackName, windows=windowsNames, mac=macNames
-    )
-
-    # 'locations' contains data for AxisValue Format 4
-    axisRecords, axisValues = _buildAxisRecords(
-        axes, nameTable, windowsNames=windowsNames, macNames=macNames
-    )
-    if not locations:
-        statTable.Version = 0x00010001
-    else:
-        # We'll be adding Format 4 AxisValue records, which
-        # requires a higher table version
-        statTable.Version = 0x00010002
-        multiAxisValues = _buildAxisValuesFormat4(
-            locations, axes, nameTable, windowsNames=windowsNames, macNames=macNames
-        )
-        axisValues = multiAxisValues + axisValues
-    nameTable.names.sort()
-
-    # Store AxisRecords
-    axisRecordArray = ot.AxisRecordArray()
-    axisRecordArray.Axis = axisRecords
-    # XXX these should not be hard-coded but computed automatically
-    statTable.DesignAxisRecordSize = 8
-    statTable.DesignAxisRecord = axisRecordArray
-    statTable.DesignAxisCount = len(axisRecords)
-
-    statTable.AxisValueCount = 0
-    statTable.AxisValueArray = None
-    if axisValues:
-        # Store AxisValueRecords
-        axisValueArray = ot.AxisValueArray()
-        axisValueArray.AxisValue = axisValues
-        statTable.AxisValueArray = axisValueArray
-        statTable.AxisValueCount = len(axisValues)
-
-
-def _buildAxisRecords(axes, nameTable, windowsNames=True, macNames=True):
-    axisRecords = []
-    axisValues = []
-    for axisRecordIndex, axisDict in enumerate(axes):
-        axis = ot.AxisRecord()
-        axis.AxisTag = axisDict["tag"]
-        axis.AxisNameID = _addName(
-            nameTable, axisDict["name"], 256, windows=windowsNames, mac=macNames
-        )
-        axis.AxisOrdering = axisDict.get("ordering", axisRecordIndex)
-        axisRecords.append(axis)
-
-        for axisVal in axisDict.get("values", ()):
-            axisValRec = ot.AxisValue()
-            axisValRec.AxisIndex = axisRecordIndex
-            axisValRec.Flags = axisVal.get("flags", 0)
-            axisValRec.ValueNameID = _addName(
-                nameTable, axisVal["name"], windows=windowsNames, mac=macNames
-            )
-
-            if "value" in axisVal:
-                axisValRec.Value = axisVal["value"]
-                if "linkedValue" in axisVal:
-                    axisValRec.Format = 3
-                    axisValRec.LinkedValue = axisVal["linkedValue"]
-                else:
-                    axisValRec.Format = 1
-            elif "nominalValue" in axisVal:
-                axisValRec.Format = 2
-                axisValRec.NominalValue = axisVal["nominalValue"]
-                axisValRec.RangeMinValue = axisVal.get(
-                    "rangeMinValue", AXIS_VALUE_NEGATIVE_INFINITY
-                )
-                axisValRec.RangeMaxValue = axisVal.get(
-                    "rangeMaxValue", AXIS_VALUE_POSITIVE_INFINITY
-                )
-            else:
-                raise ValueError("Can't determine format for AxisValue")
-
-            axisValues.append(axisValRec)
-    return axisRecords, axisValues
-
-
-def _buildAxisValuesFormat4(
-    locations, axes, nameTable, windowsNames=True, macNames=True
-):
-    axisTagToIndex = {}
-    for axisRecordIndex, axisDict in enumerate(axes):
-        axisTagToIndex[axisDict["tag"]] = axisRecordIndex
-
-    axisValues = []
-    for axisLocationDict in locations:
-        axisValRec = ot.AxisValue()
-        axisValRec.Format = 4
-        axisValRec.ValueNameID = _addName(
-            nameTable, axisLocationDict["name"], windows=windowsNames, mac=macNames
-        )
-        axisValRec.Flags = axisLocationDict.get("flags", 0)
-        axisValueRecords = []
-        for tag, value in axisLocationDict["location"].items():
-            avr = ot.AxisValueRecord()
-            avr.AxisIndex = axisTagToIndex[tag]
-            avr.Value = value
-            axisValueRecords.append(avr)
-        axisValueRecords.sort(key=lambda avr: avr.AxisIndex)
-        axisValRec.AxisCount = len(axisValueRecords)
-        axisValRec.AxisValueRecord = axisValueRecords
-        axisValues.append(axisValRec)
-    return axisValues
-
-
-def _addName(nameTable, value, minNameID=0, windows=True, mac=True):
-    if isinstance(value, int):
-        # Already a nameID
-        return value
-    if isinstance(value, str):
-        names = dict(en=value)
-    elif isinstance(value, dict):
-        names = value
-    elif isinstance(value, list):
-        nameID = nameTable._findUnusedNameID()
-        for nameRecord in value:
-            if isinstance(nameRecord, STATNameStatement):
-                nameTable.setName(
-                    nameRecord.string,
-                    nameID,
-                    nameRecord.platformID,
-                    nameRecord.platEncID,
-                    nameRecord.langID,
-                )
-            else:
-                raise TypeError("value must be a list of STATNameStatements")
-        return nameID
-    else:
-        raise TypeError("value must be int, str, dict or list")
-    return nameTable.addMultilingualName(
-        names, windows=windows, mac=mac, minNameID=minNameID
-    )
diff --git a/spaces/codertoro/gpt-academic/request_llm/bridge_tgui.py b/spaces/codertoro/gpt-academic/request_llm/bridge_tgui.py
deleted file mode 100644
index 22a407557fa884f23dd768164b009d7bed841dd9..0000000000000000000000000000000000000000
--- a/spaces/codertoro/gpt-academic/request_llm/bridge_tgui.py
+++ /dev/null
@@ -1,167 +0,0 @@
-'''
-Contributed by SagsMug. Modified by binary-husky
-https://github.com/oobabooga/text-generation-webui/pull/175
-'''
-
-import asyncio
-import json
-import random
-import string
-import websockets
-import logging
-import time
-import threading
-import importlib
-from toolbox import get_conf, update_ui
-LLM_MODEL, = get_conf('LLM_MODEL')
-
-# "TGUI:galactica-1.3b@localhost:7860"
-model_name, addr_port = LLM_MODEL.split('@')
-assert ':' in addr_port, "LLM_MODEL 格式不正确！" + LLM_MODEL
-addr, port = addr_port.split(':')
-
-def random_hash():
-    letters = string.ascii_lowercase + string.digits
-    return ''.join(random.choice(letters) for i in range(9))
-
-async def run(context, max_token=512):
-    params = {
-        'max_new_tokens': max_token,
-        'do_sample': True,
-        'temperature': 0.5,
-        'top_p': 0.9,
-        'typical_p': 1,
-        'repetition_penalty': 1.05,
-        'encoder_repetition_penalty': 1.0,
-        'top_k': 0,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1,
-        'early_stopping': True,
-        'seed': -1,
-    }
-    session = random_hash()
-
-    async with websockets.connect(f"ws://{addr}:{port}/queue/join") as websocket:
-        while content := json.loads(await websocket.recv()):
-            #Python3.10 syntax, replace with if elif on older
-            if content["msg"] ==  "send_hash":
-                await websocket.send(json.dumps({
-                    "session_hash": session,
-                    "fn_index": 12
-                }))
-            elif content["msg"] ==  "estimation":
-                pass
-            elif content["msg"] ==  "send_data":
-                await websocket.send(json.dumps({
-                    "session_hash": session,
-                    "fn_index": 12,
-                    "data": [
-                        context,
-                        params['max_new_tokens'],
-                        params['do_sample'],
-                        params['temperature'],
-                        params['top_p'],
-                        params['typical_p'],
-                        params['repetition_penalty'],
-                        params['encoder_repetition_penalty'],
-                        params['top_k'],
-                        params['min_length'],
-                        params['no_repeat_ngram_size'],
-                        params['num_beams'],
-                        params['penalty_alpha'],
-                        params['length_penalty'],
-                        params['early_stopping'],
-                        params['seed'],
-                    ]
-                }))
-            elif content["msg"] ==  "process_starts":
-                pass
-            elif content["msg"] in ["process_generating", "process_completed"]:
-                yield content["output"]["data"][0]
-                # You can search for your desired end indicator and 
-                #  stop generation by closing the websocket here
-                if (content["msg"] == "process_completed"):
-                    break
-
-
-
-
-
-def predict_tgui(inputs, top_p, temperature, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        发送至chatGPT，流式获取输出。
-        用于基础的对话功能。
-        inputs 是本次问询的输入
-        top_p, temperature是chatGPT的内部调优参数
-        history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
-        additional_fn代表点击的哪个按钮，按钮见functional.py
-    """
-    if additional_fn is not None:
-        import core_functional
-        importlib.reload(core_functional)    # 热更新prompt
-        core_functional = core_functional.get_core_functions()
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
-    raw_input = "What I would like to say is the following: " + inputs
-    logging.info(f'[raw_input] {raw_input}')
-    history.extend([inputs, ""])
-    chatbot.append([inputs, ""])
-    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
-
-    prompt = inputs
-    tgui_say = ""
-
-    mutable = ["", time.time()]
-    def run_coorotine(mutable):
-        async def get_result(mutable):
-            async for response in run(prompt):
-                print(response[len(mutable[0]):])
-                mutable[0] = response
-                if (time.time() - mutable[1]) > 3: 
-                    print('exit when no listener')
-                    break
-        asyncio.run(get_result(mutable))
-
-    thread_listen = threading.Thread(target=run_coorotine, args=(mutable,), daemon=True)
-    thread_listen.start()
-
-    while thread_listen.is_alive():
-        time.sleep(1)
-        mutable[1] = time.time()
-        # Print intermediate steps
-        if tgui_say != mutable[0]:
-            tgui_say = mutable[0]
-            history[-1] = tgui_say
-            chatbot[-1] = (history[-2], history[-1])
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    logging.info(f'[response] {tgui_say}')
-
-
-
-def predict_tgui_no_ui(inputs, top_p, temperature, history=[], sys_prompt=""):
-    raw_input = "What I would like to say is the following: " + inputs
-    prompt = inputs
-    tgui_say = ""
-    mutable = ["", time.time()]
-    def run_coorotine(mutable):
-        async def get_result(mutable):
-            async for response in run(prompt, max_token=20):
-                print(response[len(mutable[0]):])
-                mutable[0] = response
-                if (time.time() - mutable[1]) > 3: 
-                    print('exit when no listener')
-                    break
-        asyncio.run(get_result(mutable))
-    thread_listen = threading.Thread(target=run_coorotine, args=(mutable,))
-    thread_listen.start()
-    while thread_listen.is_alive():
-        time.sleep(1)
-        mutable[1] = time.time()
-    tgui_say = mutable[0]
-    return tgui_say
diff --git a/spaces/codys12/MergeLlama-7b/app.py b/spaces/codys12/MergeLlama-7b/app.py
deleted file mode 100644
index 8ddb60417d941f618b2a1cdacb89cad9d2711f19..0000000000000000000000000000000000000000
--- a/spaces/codys12/MergeLlama-7b/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-#!/usr/bin/env python
-
-import os
-from threading import Thread
-from typing import Iterator
-
-import gradio as gr
-import spaces
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-from peft import PeftModel, PeftConfig
-
-DESCRIPTION = "# MergeLlama-7b\nThis is a conversational interface powered by the MergeLlama-7b model, a finetune of CodeLlama-7b designed to assist developers in resolving merge conflicts in their code. "
-DESCRIPTION += "It leverages the capabilities of deep learning to provide suggestions for reconciling code differences, presenting potential resolutions for highlighted changes\n"
-DESCRIPTION += "The feedback from this space will help develop future versions including more powerful 13b and 34b versions."
-
-DESCRIPTION += "\n# How to use: \n"
-DESCRIPTION += "1. Input your merge conflict in the chat in the following format:\n```\n<<<<<<<\n[Current change]\n=======\n[Incoming change]\n>>>>>>>\n```\n"
-DESCRIPTION += "The model will generate the merge resolution. Context can be added before the conflict and multiple conflicts/resolutions can be chained together for context.\n"
-DESCRIPTION += "**Additional Information:**\n"
-DESCRIPTION += "- The model behind this tool is based on the MergeLlama dataset, which can be found [here](https://huggingface.co/datasets/codys12/MergeLlama).\n"
-DESCRIPTION += "- For more information about the MergeLlama-7b model, visit [here](https://huggingface.co/codys12/MergeLlama-7b).\n"
-DESCRIPTION += "- If you are interested in supporting the larger versions of this model, such as the 13b and 34b variants, you can check them out [here](https://www.dreamcatcher.co/ProjectPage?projectId=uibaxk4sfzetpkg7ch71ui).\n"
-DESCRIPTION += "- This model was trained on [DreamcatcherAI](https://www.dreamcatcher.co/Discover)\n"
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 256
-MAX_INPUT_TOKEN_LENGTH = 4096
-
-if torch.cuda.is_available():
-    model_id = "codys12/MergeLlama-7b"
-    model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, device_map=0, cache_dir="/data")
-    tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf", trust_remote_code=True)
-    tokenizer.pad_token = tokenizer.eos_token
-    tokenizer.padding_side = "right"
-
-
-@spaces.GPU
-def generate(
-    message: str,
-    chat_history: list[tuple[str, str]],
-    max_new_tokens: int = 1024,
-    #temperature: float = 0.6,
-    #top_p: float = 0.9,
-    #top_k: int = 50,
-    #repetition_penalty: float = 1.2,
-) -> Iterator[str]:
-    conversation = []
-    current_input = ""
-    for user, assistant in chat_history:
-        current_input += user
-        current_input += assistant
-
-    history = current_input
-    current_input += message
-    current_input += "\n"
-    
-    device = "cuda:0"
-    input_ids = tokenizer(current_input, return_tensors="pt").input_ids.to(device)
-
-
-    if len(input_ids) > MAX_INPUT_TOKEN_LENGTH:
-        input_ids = input_ids[-MAX_INPUT_TOKEN_LENGTH:]
-        gr.Warning("Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
-
-    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
-    generate_kwargs = dict(
-        {"input_ids": input_ids},
-        streamer=streamer,
-        max_new_tokens=max_new_tokens,
-        #do_sample=True,
-        #top_p=top_p,
-        #top_k=top_k,
-        #temperature=temperature,
-        #num_beams=1,
-        #repetition_penalty=repetition_penalty,
-    )
-    t = Thread(target=model.generate, kwargs=generate_kwargs)
-    t.start()
-
-    outputs = []
-    for text in streamer:
-        outputs.append(text)
-        combined_text = "".join(outputs)
-        if "<<<<<<<" in combined_text:
-            combined_text = combined_text.replace("<<<<<<<", "")  # Remove the unwanted string
-            yield combined_text
-            break
-        else:
-            yield combined_text
-
-
-chat_interface = gr.ChatInterface(
-    fn=generate,
-    additional_inputs=[
-        gr.Slider(
-            label="Max new tokens",
-            minimum=1,
-            maximum=MAX_MAX_NEW_TOKENS,
-            step=1,
-            value=DEFAULT_MAX_NEW_TOKENS,
-        ),
-        # gr.Slider(
-        #     label="Temperature",
-        #     minimum=0.1,
-        #     maximum=4.0,
-        #     step=0.1,
-        #     value=0.6,
-        # ),
-        # gr.Slider(
-        #     label="Top-p (nucleus sampling)",
-        #     minimum=0.05,
-        #     maximum=1.0,
-        #     step=0.05,
-        #     value=0.9,
-        # ),
-        # gr.Slider(
-        #     label="Top-k",
-        #     minimum=1,
-        #     maximum=1000,
-        #     step=1,
-        #     value=50,
-        # ),
-        # gr.Slider(
-        #     label="Repetition penalty",
-        #     minimum=0.1,
-        #     maximum=2.0,
-        #     step=0.05,
-        #     value=1.2,
-        # ),
-    ],
-    stop_btn=None,
-    examples=[
-        ["<<<<<<<\nlet x = max(y, 11)\n=======\nvar x = max(y, 12, z)\n>>>>>>>"],
-        ["<<<<<<<\nclass Calculator {  \nadd(a, b) {\n    return a + b;\n  }\n}\n=======\nclass Calculator {\n  subtract(a, b) {\n    return a - b;\n  }\n}\n>>>>>>>"],
-        ["<<<<<<<\nfunction greet(name) {\n  return `Hello, ${name}! Have a good day.`;\n}\n=======\nfunction greet(name, time) {\n  return `Good ${time}, ${name}!`;\n}\n>>>>>>>"],
-        ["<<<<<<<\nconst user = {\n  name: 'John',\n  age: 30\n}\n=======\nconst user = {\n  name: 'John',\n  email: 'john@example.com'\n}\n>>>>>>>"],
-        ["<<<<<<<\n.btn {\n  background-color: blue;\n  padding: 10px 20px;\n}\n=======\n.btn {\n  border: 1px solid black;\n  font-size: 16px;\n}\n>>>>>>>"],
-        ["<<<<<<<\n            var visibleSets = beatmapSets.Where(s => !s.Filtered).ToList();\n            if (!visibleSets.Any())\n                return;\n\n=======\n\n            var visible = beatmapSets.Where(s => !s.Filtered).ToList();\n            if (!visible.Any())\n                return false;\n\n>>>>>>>"],
-    ],
-)
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    chat_interface.render()
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/g722dsp.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/g722dsp.h
deleted file mode 100644
index c956a1e161a20e5465a53307e1a971adeb891355..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/g722dsp.h
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (c) 2015 Peter Meerwald <pmeerw@pmeerw.net>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_G722DSP_H
-#define AVCODEC_G722DSP_H
-
-#include <stdint.h>
-
-typedef struct G722DSPContext {
-    void (*apply_qmf)(const int16_t *prev_samples, int xout[2]);
-} G722DSPContext;
-
-void ff_g722dsp_init(G722DSPContext *c);
-void ff_g722dsp_init_arm(G722DSPContext *c);
-void ff_g722dsp_init_x86(G722DSPContext *c);
-
-#endif /* AVCODEC_G722DSP_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264dsp.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264dsp.h
deleted file mode 100644
index e0880c4d889c697b3c3eae77ff273fb18ce95602..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/h264dsp.h
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Copyright (c) 2003-2010 Michael Niedermayer <michaelni@gmx.at>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * H.264 DSP functions.
- * @author Michael Niedermayer <michaelni@gmx.at>
- */
-
-#ifndef AVCODEC_H264DSP_H
-#define AVCODEC_H264DSP_H
-
-#include <stdint.h>
-#include <stddef.h>
-
-typedef void (*h264_weight_func)(uint8_t *block, ptrdiff_t stride, int height,
-                                 int log2_denom, int weight, int offset);
-typedef void (*h264_biweight_func)(uint8_t *dst, uint8_t *src,
-                                   ptrdiff_t stride, int height, int log2_denom,
-                                   int weightd, int weights, int offset);
-
-/**
- * Context for storing H.264 DSP functions
- */
-typedef struct H264DSPContext {
-    /* weighted MC */
-    h264_weight_func weight_h264_pixels_tab[4];
-    h264_biweight_func biweight_h264_pixels_tab[4];
-
-    /* loop filter */
-    void (*h264_v_loop_filter_luma)(uint8_t *pix /*align 16*/, ptrdiff_t stride,
-                                    int alpha, int beta, int8_t *tc0);
-    void (*h264_h_loop_filter_luma)(uint8_t *pix /*align 4 */, ptrdiff_t stride,
-                                    int alpha, int beta, int8_t *tc0);
-    void (*h264_h_loop_filter_luma_mbaff)(uint8_t *pix /*align 16*/, ptrdiff_t stride,
-                                          int alpha, int beta, int8_t *tc0);
-    /* v/h_loop_filter_luma_intra: align 16 */
-    void (*h264_v_loop_filter_luma_intra)(uint8_t *pix, ptrdiff_t stride,
-                                          int alpha, int beta);
-    void (*h264_h_loop_filter_luma_intra)(uint8_t *pix, ptrdiff_t stride,
-                                          int alpha, int beta);
-    void (*h264_h_loop_filter_luma_mbaff_intra)(uint8_t *pix /*align 16*/,
-                                                ptrdiff_t stride, int alpha, int beta);
-    void (*h264_v_loop_filter_chroma)(uint8_t *pix /*align 8*/, ptrdiff_t stride,
-                                      int alpha, int beta, int8_t *tc0);
-    void (*h264_h_loop_filter_chroma)(uint8_t *pix /*align 4*/, ptrdiff_t stride,
-                                      int alpha, int beta, int8_t *tc0);
-    void (*h264_h_loop_filter_chroma_mbaff)(uint8_t *pix /*align 8*/,
-                                            ptrdiff_t stride, int alpha, int beta,
-                                            int8_t *tc0);
-    void (*h264_v_loop_filter_chroma_intra)(uint8_t *pix /*align 8*/,
-                                            ptrdiff_t stride, int alpha, int beta);
-    void (*h264_h_loop_filter_chroma_intra)(uint8_t *pix /*align 8*/,
-                                            ptrdiff_t stride, int alpha, int beta);
-    void (*h264_h_loop_filter_chroma_mbaff_intra)(uint8_t *pix /*align 8*/,
-                                                  ptrdiff_t stride, int alpha, int beta);
-    // h264_loop_filter_strength: simd only. the C version is inlined in h264_loopfilter.c
-    void (*h264_loop_filter_strength)(int16_t bS[2][4][4], uint8_t nnz[40],
-                                      int8_t ref[2][40], int16_t mv[2][40][2],
-                                      int bidir, int edges, int step,
-                                      int mask_mv0, int mask_mv1, int field);
-
-    /* IDCT */
-    void (*h264_idct_add)(uint8_t *dst /*align 4*/,
-                          int16_t *block /*align 16*/, int stride);
-    void (*h264_idct8_add)(uint8_t *dst /*align 8*/,
-                           int16_t *block /*align 16*/, int stride);
-    void (*h264_idct_dc_add)(uint8_t *dst /*align 4*/,
-                             int16_t *block /*align 16*/, int stride);
-    void (*h264_idct8_dc_add)(uint8_t *dst /*align 8*/,
-                              int16_t *block /*align 16*/, int stride);
-
-    void (*h264_idct_add16)(uint8_t *dst /*align 16*/, const int *blockoffset,
-                            int16_t *block /*align 16*/, int stride,
-                            const uint8_t nnzc[5 * 8]);
-    void (*h264_idct8_add4)(uint8_t *dst /*align 16*/, const int *blockoffset,
-                            int16_t *block /*align 16*/, int stride,
-                            const uint8_t nnzc[5 * 8]);
-    void (*h264_idct_add8)(uint8_t **dst /*align 16*/, const int *blockoffset,
-                           int16_t *block /*align 16*/, int stride,
-                           const uint8_t nnzc[15 * 8]);
-    void (*h264_idct_add16intra)(uint8_t *dst /*align 16*/, const int *blockoffset,
-                                 int16_t *block /*align 16*/,
-                                 int stride, const uint8_t nnzc[5 * 8]);
-    void (*h264_luma_dc_dequant_idct)(int16_t *output,
-                                      int16_t *input /*align 16*/, int qmul);
-    void (*h264_chroma_dc_dequant_idct)(int16_t *block, int qmul);
-
-    /* bypass-transform */
-    void (*h264_add_pixels8_clear)(uint8_t *dst, int16_t *block, int stride);
-    void (*h264_add_pixels4_clear)(uint8_t *dst, int16_t *block, int stride);
-
-    /**
-     * Search buf from the start for up to size bytes. Return the index
-     * of a zero byte, or >= size if not found. Ideally, use lookahead
-     * to filter out any zero bytes that are known to not be followed by
-     * one or more further zero bytes and a one byte. Better still, filter
-     * out any bytes that form the trailing_zero_8bits syntax element too.
-     */
-    int (*startcode_find_candidate)(const uint8_t *buf, int size);
-} H264DSPContext;
-
-void ff_h264dsp_init(H264DSPContext *c, const int bit_depth,
-                     const int chroma_format_idc);
-void ff_h264dsp_init_aarch64(H264DSPContext *c, const int bit_depth,
-                             const int chroma_format_idc);
-void ff_h264dsp_init_arm(H264DSPContext *c, const int bit_depth,
-                         const int chroma_format_idc);
-void ff_h264dsp_init_ppc(H264DSPContext *c, const int bit_depth,
-                         const int chroma_format_idc);
-void ff_h264dsp_init_x86(H264DSPContext *c, const int bit_depth,
-                         const int chroma_format_idc);
-void ff_h264dsp_init_mips(H264DSPContext *c, const int bit_depth,
-                          const int chroma_format_idc);
-void ff_h264dsp_init_loongarch(H264DSPContext *c, const int bit_depth,
-                               const int chroma_format_idc);
-
-#endif /* AVCODEC_H264DSP_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jpeg2000.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jpeg2000.h
deleted file mode 100644
index d004c08f104b4e6c59fb64685f8633e0f94622c1..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jpeg2000.h
+++ /dev/null
@@ -1,311 +0,0 @@
-/*
- * JPEG 2000 common defines, structures and functions
- * Copyright (c) 2007 Kamil Nowosad
- * Copyright (c) 2013 Nicolas Bertrand <nicoinattendu@gmail.com>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_JPEG2000_H
-#define AVCODEC_JPEG2000_H
-
-/**
- * @file
- * JPEG 2000 structures and defines common
- * to encoder and decoder
- */
-
-#include <stdint.h>
-
-#include "avcodec.h"
-#include "mqc.h"
-#include "jpeg2000dwt.h"
-
-enum Jpeg2000Markers {
-    JPEG2000_SOC = 0xff4f, // start of codestream
-    JPEG2000_SIZ = 0xff51, // image and tile size
-    JPEG2000_COD,          // coding style default
-    JPEG2000_COC,          // coding style component
-    JPEG2000_TLM = 0xff55, // tile-part length, main header
-    JPEG2000_PLM = 0xff57, // packet length, main header
-    JPEG2000_PLT,          // packet length, tile-part header
-    JPEG2000_QCD = 0xff5c, // quantization default
-    JPEG2000_QCC,          // quantization component
-    JPEG2000_RGN,          // region of interest
-    JPEG2000_POC,          // progression order change
-    JPEG2000_PPM,          // packed packet headers, main header
-    JPEG2000_PPT,          // packed packet headers, tile-part header
-    JPEG2000_CRG = 0xff63, // component registration
-    JPEG2000_COM,          // comment
-    JPEG2000_SOT = 0xff90, // start of tile-part
-    JPEG2000_SOP,          // start of packet
-    JPEG2000_EPH,          // end of packet header
-    JPEG2000_SOD,          // start of data
-    JPEG2000_EOC = 0xffd9, // end of codestream
-};
-
-#define JPEG2000_SOP_FIXED_BYTES 0xFF910004
-#define JPEG2000_SOP_BYTE_LENGTH 6
-
-enum Jpeg2000Quantsty { // quantization style
-    JPEG2000_QSTY_NONE, // no quantization
-    JPEG2000_QSTY_SI,   // scalar derived
-    JPEG2000_QSTY_SE    // scalar expounded
-};
-
-#define JPEG2000_MAX_DECLEVELS 33
-#define JPEG2000_MAX_RESLEVELS (JPEG2000_MAX_DECLEVELS + 1)
-
-#define JPEG2000_MAX_PASSES 100
-
-// T1 flags
-// flags determining significance of neighbor coefficients
-#define JPEG2000_T1_SIG_N  0x0001
-#define JPEG2000_T1_SIG_E  0x0002
-#define JPEG2000_T1_SIG_W  0x0004
-#define JPEG2000_T1_SIG_S  0x0008
-#define JPEG2000_T1_SIG_NE 0x0010
-#define JPEG2000_T1_SIG_NW 0x0020
-#define JPEG2000_T1_SIG_SE 0x0040
-#define JPEG2000_T1_SIG_SW 0x0080
-#define JPEG2000_T1_SIG_NB (JPEG2000_T1_SIG_N  | JPEG2000_T1_SIG_E  |   \
-                            JPEG2000_T1_SIG_S  | JPEG2000_T1_SIG_W  |   \
-                            JPEG2000_T1_SIG_NE | JPEG2000_T1_SIG_NW |   \
-                            JPEG2000_T1_SIG_SE | JPEG2000_T1_SIG_SW)
-// flags determining sign bit of neighbor coefficients
-#define JPEG2000_T1_SGN_N  0x0100
-#define JPEG2000_T1_SGN_S  0x0200
-#define JPEG2000_T1_SGN_W  0x0400
-#define JPEG2000_T1_SGN_E  0x0800
-
-#define JPEG2000_T1_VIS    0x1000
-#define JPEG2000_T1_SIG    0x2000
-#define JPEG2000_T1_REF    0x4000
-
-#define JPEG2000_T1_SGN    0x8000
-
-// Codeblock coding styles
-#define JPEG2000_CBLK_BYPASS    0x01 // Selective arithmetic coding bypass
-#define JPEG2000_CBLK_RESET     0x02 // Reset context probabilities
-#define JPEG2000_CBLK_TERMALL   0x04 // Terminate after each coding pass
-#define JPEG2000_CBLK_VSC       0x08 // Vertical stripe causal context formation
-#define JPEG2000_CBLK_PREDTERM  0x10 // Predictable termination
-#define JPEG2000_CBLK_SEGSYM    0x20 // Segmentation symbols present
-
-// Coding styles
-#define JPEG2000_CSTY_PREC      0x01 // Precincts defined in coding style
-#define JPEG2000_CSTY_SOP       0x02 // SOP marker present
-#define JPEG2000_CSTY_EPH       0x04 // EPH marker present
-#define JPEG2000_CTSY_HTJ2K_F   0x40 // Only HT code-blocks (Rec. ITU-T T.814 | ISO/IEC 15444-15) are present
-#define JPEG2000_CTSY_HTJ2K_M   0xC0 // HT code-blocks (Rec. ITU-T T.814 | ISO/IEC 15444-15) can be present
-
-// Progression orders
-#define JPEG2000_PGOD_LRCP      0x00  // Layer-resolution level-component-position progression
-#define JPEG2000_PGOD_RLCP      0x01  // Resolution level-layer-component-position progression
-#define JPEG2000_PGOD_RPCL      0x02  // Resolution level-position-component-layer progression
-#define JPEG2000_PGOD_PCRL      0x03  // Position-component-resolution level-layer progression
-#define JPEG2000_PGOD_CPRL      0x04  // Component-position-resolution level-layer progression
-
-typedef struct Jpeg2000T1Context {
-    int data[6144];
-    uint16_t flags[6156];
-    MqcState mqc;
-    int stride;
-} Jpeg2000T1Context;
-
-typedef struct Jpeg2000TgtNode {
-    uint8_t val;
-    uint8_t temp_val;
-    uint8_t vis;
-    struct Jpeg2000TgtNode *parent;
-} Jpeg2000TgtNode;
-
-typedef struct Jpeg2000CodingStyle {
-    int nreslevels;           // number of resolution levels
-    int nreslevels2decode;    // number of resolution levels to decode
-    uint8_t log2_cblk_width,
-            log2_cblk_height; // exponent of codeblock size
-    uint8_t transform;        // DWT type
-    uint8_t csty;             // coding style
-    uint8_t nlayers;          // number of layers
-    uint8_t mct;              // multiple component transformation
-    uint8_t cblk_style;       // codeblock coding style
-    uint8_t prog_order;       // progression order
-    uint8_t log2_prec_widths[JPEG2000_MAX_RESLEVELS];  // precincts size according resolution levels
-    uint8_t log2_prec_heights[JPEG2000_MAX_RESLEVELS]; // TODO: initialize prec_size array with 0?
-    uint8_t init;
-} Jpeg2000CodingStyle;
-
-typedef struct Jpeg2000QuantStyle {
-    uint8_t expn[JPEG2000_MAX_DECLEVELS * 3];  // quantization exponent
-    uint16_t mant[JPEG2000_MAX_DECLEVELS * 3]; // quantization mantissa
-    uint8_t quantsty;      // quantization style
-    uint8_t nguardbits;    // number of guard bits
-} Jpeg2000QuantStyle;
-
-typedef struct Jpeg2000Pass {
-    uint16_t rate;
-    int64_t disto;
-    uint8_t flushed[4];
-    int flushed_len;
-} Jpeg2000Pass;
-
-typedef struct Jpeg2000Layer {
-    uint8_t *data_start;
-    int data_len;
-    int npasses;
-    double disto;
-    int cum_passes;
-} Jpeg2000Layer;
-
-typedef struct Jpeg2000Cblk {
-    uint8_t npasses;
-    uint8_t ninclpasses; // number coding of passes included in codestream
-    uint8_t nonzerobits;
-    uint8_t incl;
-    uint16_t length;
-    uint16_t *lengthinc;
-    uint8_t nb_lengthinc;
-    uint8_t lblock;
-    uint8_t *data;
-    size_t data_allocated;
-    int nb_terminations;
-    int nb_terminationsinc;
-    int *data_start;
-    Jpeg2000Pass *passes;
-    Jpeg2000Layer *layers;
-    int coord[2][2]; // border coordinates {{x0, x1}, {y0, y1}}
-    /* specific to HT code-blocks */
-    int zbp;
-    int pass_lengths[2];
-} Jpeg2000Cblk; // code block
-
-typedef struct Jpeg2000Prec {
-    int nb_codeblocks_width;
-    int nb_codeblocks_height;
-    Jpeg2000TgtNode *zerobits;
-    Jpeg2000TgtNode *cblkincl;
-    Jpeg2000Cblk *cblk;
-    int decoded_layers;
-    int coord[2][2]; // border coordinates {{x0, x1}, {y0, y1}}
-} Jpeg2000Prec; // precinct
-
-typedef struct Jpeg2000Band {
-    int coord[2][2]; // border coordinates {{x0, x1}, {y0, y1}}
-    uint16_t log2_cblk_width, log2_cblk_height;
-    int i_stepsize; // quantization stepsize
-    float f_stepsize; // quantization stepsize
-    Jpeg2000Prec *prec;
-} Jpeg2000Band; // subband
-
-typedef struct Jpeg2000ResLevel {
-    uint8_t nbands;
-    int coord[2][2]; // border coordinates {{x0, x1}, {y0, y1}}
-    int num_precincts_x, num_precincts_y; // number of precincts in x/y direction
-    uint8_t log2_prec_width, log2_prec_height; // exponent of precinct size
-    Jpeg2000Band *band;
-} Jpeg2000ResLevel; // resolution level
-
-typedef struct Jpeg2000Component {
-    Jpeg2000ResLevel *reslevel;
-    DWTContext dwt;
-    float *f_data;
-    int *i_data;
-    int coord[2][2];   // border coordinates {{x0, x1}, {y0, y1}} -- can be reduced with lowres option
-    int coord_o[2][2]; // border coordinates {{x0, x1}, {y0, y1}} -- original values from jpeg2000 headers
-    uint8_t roi_shift; // ROI scaling value for the component
-} Jpeg2000Component;
-
-/* misc tools */
-static inline int ff_jpeg2000_ceildivpow2(int a, int b)
-{
-    return -((-(int64_t)a) >> b);
-}
-
-static inline int ff_jpeg2000_ceildiv(int a, int64_t b)
-{
-    return (a + b - 1) / b;
-}
-
-/* TIER-1 routines */
-
-/* Set up lookup tables used in TIER-1. */
-void ff_jpeg2000_init_tier1_luts(void);
-
-/* Update significance of a coefficient at current position (x,y) and
- * for neighbors. */
-void ff_jpeg2000_set_significance(Jpeg2000T1Context *t1,
-                                  int x, int y, int negative);
-
-extern uint8_t ff_jpeg2000_sigctxno_lut[256][4];
-
-/* Get context label (number in range[0..8]) of a coefficient for significance
- * propagation and cleanup coding passes. */
-static inline int ff_jpeg2000_getsigctxno(int flag, int bandno)
-{
-    return ff_jpeg2000_sigctxno_lut[flag & 255][bandno];
-}
-
-static const uint8_t refctxno_lut[2][2] = { { 14, 15 }, { 16, 16 } };
-
-/* Get context label (number in range[14..16]) of a coefficient for magnitude
- * refinement pass. */
-static inline int ff_jpeg2000_getrefctxno(int flag)
-{
-    return refctxno_lut[(flag >> 14) & 1][(flag & 255) != 0];
-}
-
-extern uint8_t ff_jpeg2000_sgnctxno_lut[16][16];
-extern uint8_t ff_jpeg2000_xorbit_lut[16][16];
-
-/* Get context label (number in range[9..13]) for sign decoding. */
-static inline int ff_jpeg2000_getsgnctxno(int flag, int *xorbit)
-{
-    *xorbit = ff_jpeg2000_xorbit_lut[flag & 15][(flag >> 8) & 15];
-    return ff_jpeg2000_sgnctxno_lut[flag & 15][(flag >> 8) & 15];
-}
-
-int ff_jpeg2000_init_component(Jpeg2000Component *comp,
-                               Jpeg2000CodingStyle *codsty,
-                               Jpeg2000QuantStyle *qntsty,
-                               int cbps, int dx, int dy,
-                               AVCodecContext *ctx);
-
-void ff_jpeg2000_reinit(Jpeg2000Component *comp, Jpeg2000CodingStyle *codsty);
-
-void ff_jpeg2000_cleanup(Jpeg2000Component *comp, Jpeg2000CodingStyle *codsty);
-
-static inline int needs_termination(int style, int passno) {
-    if (style & JPEG2000_CBLK_BYPASS) {
-        int type = passno % 3;
-        passno /= 3;
-        if (type == 0 && passno > 2)
-            return 2;
-        if (type == 2 && passno > 2)
-            return 1;
-        if (style & JPEG2000_CBLK_TERMALL) {
-            return passno > 2 ? 2 : 1;
-        }
-    }
-    if (style & JPEG2000_CBLK_TERMALL)
-        return 1;
-    return 0;
-}
-
-void ff_tag_tree_zero(Jpeg2000TgtNode *t, int w, int h, int val);
-
-#endif /* AVCODEC_JPEG2000_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download DC Heroes RPG (3rd Edition) PDF for Free - Experience the Thrill of Superhero Adventures.md b/spaces/congsaPfin/Manga-OCR/logs/Download DC Heroes RPG (3rd Edition) PDF for Free - Experience the Thrill of Superhero Adventures.md
deleted file mode 100644
index ac33432f4aeb88f7370cba70497599aedd8a0404..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download DC Heroes RPG (3rd Edition) PDF for Free - Experience the Thrill of Superhero Adventures.md	
+++ /dev/null
@@ -1,170 +0,0 @@
-<br />
-<h1>DC Heroes RPG (3rd Edition PDF Download): A Guide for Superhero Fans</h1>
- <p>If you are a fan of superheroes and comics, you might have heard of DC Heroes RPG, a role-playing game based on the DC Universe. This game allows you to create and play as your favorite heroes or villains, or even your own original characters, in a variety of scenarios and adventures. Whether you want to fight crime, save the world, or conquer it, DC Heroes RPG gives you the opportunity to do so.</p>
-<h2>dc heroes rpg (3rd edition pdf download)</h2><br /><p><b><b>Download Zip</b> &#9881; <a href="https://urlca.com/2uOg6k">https://urlca.com/2uOg6k</a></b></p><br /><br />
- <p>In this article, we will give you a comprehensive guide on DC Heroes RPG, especially the 3rd edition, which is considered by many fans as the best version of the game. We will cover the following topics:</p>
- <ul>
-<li>What is DC Heroes RPG?</li>
-<li>How to play DC Heroes RPG?</li>
-<li>Where to find DC Heroes RPG (3rd edition PDF download)?</li>
-<li>Why should you play DC Heroes RPG?</li>
-</ul>
- <p>By the end of this article, you will have a better understanding of DC Heroes RPG and how to enjoy it. You will also find some useful links and resources to help you get started with the game. So, let's begin!</p>
- <h2>What is DC Heroes RPG?</h2>
- <p>DC Heroes RPG is a role-playing game that lets you create and play as characters from the DC Universe, or your own original characters, in a variety of settings and genres. You can choose from hundreds of heroes and villains, such as Superman, Batman, Wonder Woman, Joker, Lex Luthor, Darkseid, and more. You can also create your own characters using a flexible and customizable system that allows you to define their attributes, skills, powers, advantages, drawbacks, motivations, and backgrounds.</p>
- <h3>A brief history of the game</h3>
- <p>DC Heroes RPG was first published in 1985 by Mayfair Games under license from DC Comics. It was designed by Greg Gorden and used a system called MEGS (Mayfair Exponential Game System), which was based on exponential scales and logarithmic tables. The game was well-received by fans and critics alike, and won several awards and nominations.</p>
- <p>The game went through several revisions and expansions over the years, including new rules, supplements, modules, sourcebooks, and character guides. The most notable ones were the 2nd edition in 1989, which introduced some changes and clarifications to the rules, and the 3rd edition in 1993, which was a major overhaul of the system and the content. The 3rd edition was also the last official edition of the game published by Mayfair Games before they lost their license from DC Comics in 1996.</p>
- <h3>The main features of the 3rd edition</h3>
- <p>The 3rd edition of DC Heroes RPG is widely regarded by fans as the best version of the game. It features several improvements and innovations over the previous editions, such as:</p>
-<p>dc heroes role-playing game 3rd edition pdf<br />
-dc heroes rpg 3e pdf free download<br />
-dc heroes rpg third edition pdf<br />
-dc heroes rpg 3rd edition scribd<br />
-dc heroes rpg 3rd edition review<br />
-dc heroes rpg 3rd edition character sheet<br />
-dc heroes rpg 3rd edition rules<br />
-dc heroes rpg 3rd edition mayfair games<br />
-dc heroes rpg 3rd edition sourcebook pdf<br />
-dc heroes rpg 3rd edition blood of heroes<br />
-dc heroes rpg 3rd edition powers and abilities<br />
-dc heroes rpg 3rd edition online<br />
-dc heroes rpg 3rd edition errata<br />
-dc heroes rpg 3rd edition modules<br />
-dc heroes rpg 3rd edition conversion<br />
-dc heroes rpg 3rd edition vs mutants and masterminds<br />
-dc heroes rpg 3rd edition ap value chart<br />
-dc heroes rpg 3rd edition character generator<br />
-dc heroes rpg 3rd edition adventure ideas<br />
-dc heroes rpg 3rd edition villains pdf<br />
-dc heroes rpg 3rd edition campaign setting<br />
-dc heroes rpg 3rd edition universe book pdf<br />
-dc heroes rpg 3rd edition who's who pdf<br />
-dc heroes rpg 3rd edition combat system<br />
-dc heroes rpg 3rd edition skill list<br />
-dc heroes rpg 3rd edition attribute table<br />
-dc heroes rpg 3rd edition equipment guide pdf<br />
-dc heroes rpg 3rd edition magic and mysticism pdf<br />
-dc heroes rpg 3rd edition gadgets and gear pdf<br />
-dc heroes rpg 3rd edition blood of the innocent pdf<br />
-dc heroes rpg 3rd edition blood of the machine pdf<br />
-dc heroes rpg 3rd edition blood of the gods pdf<br />
-dc heroes rpg 3rd edition blood of the angels pdf<br />
-dc heroes rpg 3rd edition blood of the demons pdf<br />
-dc heroes rpg 3rd edition blood of the zombies pdf<br />
-dc heroes rpg 3rd edition blood of the vampires pdf<br />
-dc heroes rpg 3rd edition blood of the werewolves pdf<br />
-dc heroes rpg 3rd edition blood of the dragons pdf<br />
-dc heroes rpg 3rd edition blood of the aliens pdf<br />
-dc heroes rpg 3rd edition blood of the mutants pdf<br />
-dc heroes rpg 3rd edition world at war pdf<br />
-dc heroes rpg 3rd edition project prometheus pdf<br />
-dc heroes rpg 3rd edition project youngblood pdf<br />
-dc heroes rpg 3rd edition project metropolis pdf<br />
-dc heroes rpg 3rd edition project gotham pdf<br />
-dc heroes rpg 3rd edition project atlantis pdf<br />
-dc heroes rpg 3rd edition project new genesis pdf<br />
-dc heroes rpg 3rd edition project apocalypse pdf<br />
-dc heroes rpg 3rd edition project olympus pdf</p>
- <ul>
-<li>A revised and streamlined MEGS system that uses only d10s instead of d100s, simplifies some calculations and conversions, adds some optional rules and modifiers, and updates some powers and abilities.</li>
-<li>A more comprehensive and detailed character creation and advancement system that allows more flexibility and customization for players. It also includes new powers, advantages, drawbacks, motivations, backgrounds, skills, subskills, specialties, hero points, wealth points, fame points, karma points, influence points, contacts points, equipment points, and more.</li>
-<li>A more realistic and consistent power level system that balances the characters and the challenges they face. It also includes new power levels, such as Cosmic, Godlike, and Infinite, to accommodate the most powerful beings in the DC Universe.</li>
-<li>A more diverse and rich setting and genre system that covers different eras, worlds, dimensions, and themes of the DC Universe. It also includes new settings, such as Elseworlds, Hypertime, and Kingdom Come, to explore alternative and possible scenarios and stories.</li>
-<li>A more extensive and updated content and material system that includes more characters, events, organizations, locations, items, vehicles, weapons, gadgets, and more from the DC Universe. It also includes new content, such as the Death of Superman, the Knightfall Saga, the Zero Hour Crisis, and more.</li>
-</ul>
- <p>The 3rd edition of DC Heroes RPG is a comprehensive and versatile game that can cater to any superhero fan's preferences and tastes. It is a game that can be played in any way you want, from a simple and fun adventure to a complex and epic saga.</p>
- <h2>How to play DC Heroes RPG?</h2>
- <p>Playing DC Heroes RPG is easy and fun. All you need are some dice (preferably d10s), some paper and pencils, some character sheets (which you can find online or create yourself), some rulebooks (which you can also find online or buy from official or unofficial sources), and some imagination and creativity.</p>
- <p>The game is played by a group of players, usually between two to six. One of the players is the Gamemaster (GM), who is responsible for creating and running the scenarios and adventures for the other players. The other players are the Heroes (or Villains), who create and play as their own characters in the game.</p>
- <p>The game is divided into sessions, which are usually between two to four hours long. Each session consists of a series of scenes, which are events or situations that the characters encounter or participate in. Each scene can involve different types of actions, such as dialogue, investigation, exploration, combat, skill use, power use, etc.</p>
- <p>The game uses a system called MEGS (Mayfair Exponential Game System), which is based on exponential scales and logarithmic tables. The system uses four main attributes for each character: Physical (P), Mental (M), Mystical (M), and Spiritual (S). Each attribute has a value that ranges from 1 to 30 (or higher for some characters), which represents their relative power level in that attribute. For example, a normal human has an average value of 2 in each attribute, while Superman has a value of 25 in Physical.</p>
- <h3>The basic rules and mechanics</h3>
- <p>The basic rules and mechanics of DC Heroes RPG are simple and intuitive. They involve the following steps:</p>
- <ol>
-<li>The GM sets the scene and describes what is happening to the players.</li>
-<li>The players declare what their characters want to do in response to the scene.</li>
-<li>The GM determines if their actions are possible or not, and if they require a roll or not.</li>
-<li>If a roll is required, the player rolls a d10 and adds their attribute value to it. This is called their Action Value (AV).</li>
-<li>The GM rolls a d10 and adds the difficulty value or the opposing attribute value to it. This is called their Opposing Value (OV).</li>
-<li>The player compares their AV with the OV using a logarithmic table called the Universal Table (UT). The UT shows the result of the comparison in terms of RAPs (Result Points). RAPs indicate how well or how poorly the action succeeded or failed.</li>
-<li>The GM applies the RAPs to the action's effect or outcome. For example, if the action was an attack, the RAPs are applied as damage to the target's Body attribute. If the action was a skill use, the RAPs are applied as information gained or task completed.</li>
-<li>The GM narrates what happens next based on the result of the action.</li>
-</ol>
- <p>These steps are repeated until the scene is resolved or until a new scene begins.</p>
- <h3>The character creation and advancement</h3>
- <p>The character creation and advancement system of DC Heroes RPG is flexible and customizable. It allows players to create any type of character they want, from existing heroes or villains to original ones. It also allows players to improve their characters over time by gaining experience points (XP) and spending them on new or improved attributes, skills, powers, advantages, drawbacks, motivations, backgrounds, and more. The character creation and advancement system of DC Heroes RPG follows these steps: - The player chooses a concept and a name for their character. The concept is a brief description of the character's identity, personality, origin, and role in the game. The name is the character's superhero or supervillain name, or their real name if they don't have one. - The player allocates points to their character's four main attributes: Physical (P), Mental (M), Mystical (M), and Spiritual (S). Each attribute has a base value of 2, and the player can increase or decrease it by spending or gaining points. The total number of points available depends on the power level of the game, which is determined by the GM. For example, a normal human character has 20 points to spend, while a cosmic-level character has 1000 points to spend. - The player chooses skills, subskills, specialties, powers, advantages, drawbacks, motivations, backgrounds, and other traits for their character. Each trait has a value that represents its relative power or effect in the game. The player can buy or sell traits by spending or gaining points. The total number of points available depends on the power level of the game and the attribute values of the character. For example, a normal human character can have up to 20 points worth of skills and powers, while a cosmic-level character can have up to 1000 points worth of skills and powers. - The player calculates their character's secondary attributes and derived values. These include Body (B), Mind (M), Soul (S), Initiative (I), Hero Points (HP), Wealth Points (WP), Fame Points (FP), Karma Points (KP), Influence Points (IP), Contacts Points (CP), Equipment Points (EP), and more. These values are derived from the character's main attributes and other traits using formulas or tables. - The player fills out their character sheet with all the relevant information and details about their character. They can also add some notes, descriptions, illustrations, quotes, or anything else that helps them flesh out their character. The character advancement system of DC Heroes RPG allows players to improve their characters over time by gaining experience points (XP) and spending them on new or improved traits. XP are awarded by the GM at the end of each session based on the character's performance, role-playing, achievements, challenges, and goals. XP can be spent on any trait that the player wants to increase or acquire for their character, as long as it is consistent with their concept and background. XP can also be saved for later use or exchanged for other types of points. <h3>The combat and action resolution</h3>
- <p>The combat and action resolution system of DC Heroes RPG is based on the same basic rules and mechanics as described above. However, there are some additional rules and modifiers that apply specifically to combat and action situations. These include:</p>
- <ul>
-<li>The initiative order, which determines who acts first in each round of combat or action. The initiative order is based on the Initiative value of each character involved in the scene.</li>
-<li>The attack action, which is any action that involves harming or affecting another character or object. The attack action requires an AV/OV roll using the attacker's attribute or skill value as AV and the defender's attribute or skill value as OV. The result of the roll is applied as RAPs to the target's Body attribute or another relevant attribute.</li>
-<li>The defense action, which is any action that involves avoiding or resisting an attack action. The defense action requires an AV/OV roll using the defender's attribute or skill value as AV and the attacker's attribute or skill value as OV. The result of the roll is subtracted from the RAPs inflicted by the attack action.</li>
-<li>The damage types, which are different categories of harm or effects that can be inflicted by an attack action. The damage types are Physical (P), Mental (M), Mystical (M), Spiritual (S), Killing (K), Bashing (B), Energy (E), Magic (Mg), Mental Blast (MB), Power Drain (PD), Power Transfer (PT), Power Reserve (PR), Power Loss (PL), Power Boost (PB), Illusion (I), Mind Control (MC), Emotion Control (EC), Fear Control (FC), Persuasion Control (PC), and more. Each damage type has a different effect on the target's attributes and abilities, and some characters may have resistance or vulnerability to certain damage types.</li>
-<li>The modifiers, which are factors that can affect the outcome of an action or a roll. The modifiers can be positive or negative, and can be applied to the AV, the OV, or the RAPs. Some examples of modifiers are distance, cover, visibility, surprise, teamwork, critical hit, critical miss, hero points, karma points, and more.</li>
-</ul>
- <p>The combat and action resolution system of DC Heroes RPG is designed to be fast and dynamic, while also being realistic and consistent. It allows for a wide range of possibilities and outcomes, from simple punches and kicks to spectacular feats and stunts. It also encourages creativity and strategy from both the players and the GM.</p>
- <h2>Where to find DC Heroes RPG (3rd edition PDF download)?</h2>
- <p>If you are interested in playing DC Heroes RPG (3rd edition), you might be wondering where you can find the PDF files of the rulebooks and other materials. Unfortunately, the game is no longer in print or officially supported by DC Comics or Mayfair Games. However, there are still some ways to get your hands on the game, such as:</p>
- <h3>The official sources and publishers</h3>
- <p>The official sources and publishers of DC Heroes RPG (3rd edition) are DC Comics and Mayfair Games. They are the ones who own the rights and licenses to the game and its content. However, they have not published or reprinted the game since 1996, when they lost their license agreement. Therefore, finding physical copies of the game from these sources is very difficult and expensive.</p>
- <p>However, there is a possibility that DC Comics or Mayfair Games might re-release or re-license the game in the future, either in digital or physical format. This could happen if there is enough demand and interest from the fans and the market. Therefore, you can try contacting them directly or through their websites or social media platforms to express your support and request for the game.</p>
- <h3>The fan-made and unofficial sources</h3>
- <p>The fan-made and unofficial sources of DC Heroes RPG (3rd edition) are various websites, blogs, forums, groups, and individuals who have created or shared their own versions or copies of the game and its content. These sources are not authorized or endorsed by DC Comics or Mayfair Games, but they are made by fans for fans. Therefore, finding PDF files of the game from these sources is relatively easy and cheap.</p>
- <p>However, there are some risks and drawbacks involved in using these sources. First of all, the quality and accuracy of the PDF files may vary depending on the source. Some files may be incomplete, outdated, corrupted, or infected with viruses or malware. Second of all, the legality and ethics of using these files may be questionable depending on the source. Some files may be pirated, stolen, or infringing on the rights and licenses of DC Comics or Mayfair Games. Therefore, you should be careful and responsible when using these sources.</p>
- <h3>The legal and ethical issues</h3>
- <p>The legal and ethical issues of finding DC Heroes RPG (3rd edition PDF download) are complex and controversial. On one hand, you might argue that downloading or sharing PDF files of the game is fair use or fair dealing under certain circumstances. For example, if you already own a physical copy of the game but want a digital backup for convenience or preservation purposes. Or if you want to use the game for personal or educational purposes only.</p>
- <p>On the other hand, you might argue that downloading or sharing PDF files of the game is illegal or unethical under certain circumstances. For example, if you do not own a physical copy of the game but want to avoid paying for it. Or if you want to use the game for commercial or malicious purposes.</p>
- <p>Ultimately, the decision is up to you as an individual player or GM. You should be aware of the consequences and implications of your actions, and respect the rights and wishes of DC Comics and Mayfair Games as the original creators and publishers of the game. You should also be respectful and supportive of the fan community and the fan-made content that they produce and share. You should also be grateful and appreciative of the game and its content, and enjoy it as much as you can.</p>
- <h2>Why should you play DC Heroes RPG?</h2>
- <p>Now that you know what DC Heroes RPG is, how to play it, and where to find it, you might be wondering why you should play it. What makes this game so special and appealing? What are the benefits and advantages of playing it? What are the challenges and drawbacks of playing it? What are the tips and tricks for a better gaming experience?</p>
- <p>Here are some possible answers to these questions:</p>
- <h3>The benefits and advantages of the game</h3>
- <p>DC Heroes RPG has many benefits and advantages that make it a great game for superhero fans. Some of them are:</p>
- <ul>
-<li>It is a game that allows you to create and play as any character you want, from existing heroes or villains to original ones. You can also mix and match different traits, powers, skills, backgrounds, and more to create your own unique characters.</li>
-<li>It is a game that allows you to explore and experience the DC Universe in any way you want, from different eras, worlds, dimensions, and themes. You can also create your own scenarios and stories using the rich and diverse content and material available in the game.</li>
-<li>It is a game that uses a simple and intuitive system that is easy to learn and play. It also uses a flexible and customizable system that can be adapted and modified to suit your preferences and tastes.</li>
-<li>It is a game that is fun and exciting, as it involves a lot of action, adventure, drama, humor, suspense, mystery, romance, and more. It also involves a lot of creativity, imagination, strategy, teamwork, role-playing, and problem-solving.</li>
-<li>It is a game that is educational and informative, as it teaches you about the history, culture, science, mythology, philosophy, psychology, sociology, politics, ethics, morality, and more of the DC Universe and the real world.</li>
-</ul>
- <h3>The challenges and drawbacks of the game</h3>
- <p>DC Heroes RPG also has some challenges and drawbacks that make it a difficult or problematic game for some players. Some of them are:</p>
- <ul>
-<li>It is a game that requires a lot of preparation and research, as it involves a lot of rules, details, facts, figures, references, sources, and more. It also requires a lot of resources and materials, such as dice, paper, pencils, character sheets, rulebooks, and more. It also requires a lot of time and commitment, as it involves a lot of sessions, scenes, actions, and rolls.</li>
-<li>It is a game that can be confusing and overwhelming, as it involves a lot of exponential scales, logarithmic tables, calculations, conversions, modifiers, and more. It also involves a lot of inconsistencies and contradictions, as it covers a lot of different versions, editions, revisions, expansions, and more of the DC Universe and the game itself.</li>
-<li>It is a game that can be frustrating and disappointing, as it involves a lot of luck, chance, randomness, and unpredictability. It also involves a lot of imbalance and unfairness, as it covers a lot of different power levels, abilities, challenges, and outcomes.</li>
-<li>It is a game that can be boring and repetitive, as it involves a lot of similar or identical actions, rolls, results, and effects. It also involves a lot of limitations and restrictions, as it covers a lot of rules, guidelines, conventions, and expectations.</li>
-<li>It is a game that can be controversial and problematic, as it involves a lot of sensitive or offensive topics, such as violence, death, crime, war, politics, religion, morality, ethics, and more. It also involves a lot of legal or ethical issues, such as piracy, plagiarism, infringement, and more.</li>
-</ul>
- <h3>The tips and tricks for a better gaming experience</h3>
- <p>DC Heroes RPG can be a great game for superhero fans if played properly and wisely. Here are some tips and tricks that can help you have a better gaming experience:</p>
- <ul>
-<li>Be flexible and adaptable. Don't be afraid to change or modify the rules or the content to suit your needs or preferences. Don't be afraid to try new or different things to spice up your game. Don't be afraid to make mistakes or learn from them.</li>
-<li>Be creative and imaginative. Use your own ideas or inspirations to create or enhance your characters or scenarios. Use your own style or voice to narrate or role-play your actions or reactions. Use your own logic or intuition to solve or overcome your problems or challenges.</li>
-<li>Be cooperative and respectful. Work with your GM and your fellow players to create or enjoy a shared story or adventure. Listen to their suggestions or feedback and give them yours. Respect their decisions or opinions and expect them to respect yours.</li>
-<li>Be fun and exciting. Have fun with your game and make it fun for others. Be enthusiastic and passionate about your game and show it to others. Be adventurous and daring with your game and challenge yourself and others.</li>
-<li>Be loyal and supportive. Support the game and its creators by buying or promoting their products or services. Support the fan community by joining or contributing to their websites or groups. Support the genre by reading or watching more superhero comics or movies.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>DC Heroes RPG (3rd edition) is a role-playing game that lets you create and play as characters from the DC Universe or your own original characters in a variety of settings and genres. It is a game that is simple and intuitive, flexible and customizable, fun and exciting, educational and informative, and loyal and supportive. It is a game that can cater to any superhero fan's preferences and tastes. If you are interested in playing DC Heroes RPG (3rd edition), you can find the PDF files of the rulebooks and other materials from various sources, such as the official publishers, the fan-made websites, or the legal and ethical platforms. However, you should be careful and responsible when using these sources, and respect the rights and wishes of the original creators and publishers of the game. We hope that this article has given you a comprehensive guide on DC Heroes RPG (3rd edition) and how to enjoy it. We also hope that you have learned something new and useful about the game and its content. If you have any questions or comments, feel free to contact us or leave them below. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about DC Heroes RPG (3rd edition) and their answers:</p>
- <ol>
-<li>Q: How many players can play DC Heroes RPG?</li>
-<li>A: DC Heroes RPG can be played by a group of two to six players, with one of them being the Gamemaster (GM) and the rest being the Heroes (or Villains).</li>
-<li>Q: How long does a session of DC Heroes RPG last?</li>
-<li>A: A session of DC Heroes RPG can last between two to four hours, depending on the scenario and the pace of the game.</li>
-<li>Q: What kind of dice do I need to play DC Heroes RPG?</li>
-<li>A: DC Heroes RPG uses only d10s (ten-sided dice) for its rolls and calculations.</li>
-<li>Q: Where can I find more information or resources about DC Heroes RPG?</li>
-<li>A: You can find more information or resources about DC Heroes RPG from various sources, such as:</li>
-<ul>
-<li>The official websites or social media platforms of DC Comics or Mayfair Games.</li>
-<li>The fan-made websites or groups dedicated to DC Heroes RPG, such as [DC Heroes RPG], [DC Universe RPG], [DC Adventures], [DC Roleplaying], [DC Heroes Wiki], [DC Heroes Facebook Group], [DC Heroes Reddit Community], and more.</li>
-<li>The legal and ethical platforms that offer PDF files of DC Heroes RPG, such as [DriveThruRPG], [RPGNow], [Amazon], [eBay], [Google Books], [Archive.org], [Scribd], [Library Genesis], and more.</li>
-</ul>
-<li>Q: How can I support DC Heroes RPG and its creators?</li>
-<li>A: You can support DC Heroes RPG and its creators by buying or promoting their products or services, joining or contributing to their websites or groups, reading or watching more superhero comics or movies, and playing or enjoying their game.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download bhop pro for PC and Mac The Ultimate Bunnyhopping and Surfing Simulator.md b/spaces/congsaPfin/Manga-OCR/logs/Download bhop pro for PC and Mac The Ultimate Bunnyhopping and Surfing Simulator.md
deleted file mode 100644
index bfa8b03c2c79d9a9c040006eb3575bbc992284d6..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download bhop pro for PC and Mac The Ultimate Bunnyhopping and Surfing Simulator.md	
+++ /dev/null
@@ -1,138 +0,0 @@
-<br />
-<h1>How to Download and Play Bhop Pro on PC</h1>
-<p>If you are a fan of first-person shooter games, you might have heard of or tried <strong>Bhop Pro</strong>, a popular mobile game that lets you practice your bunny hopping skills in various maps and modes. But did you know that you can also download and play Bhop Pro on your PC for a more immersive and enjoyable gaming experience? In this article, we will show you how to do that with different methods and tools, as well as some tips and tricks to optimize your performance. Let's get started!</p>
-<h2>download bhop pro for pc</h2><br /><p><b><b>DOWNLOAD</b> &mdash; <a href="https://urlca.com/2uO6CA">https://urlca.com/2uO6CA</a></b></p><br /><br />
- <h2>What is Bhop Pro?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Bhop Pro is a simulation game developed by begma, inspired by the bunny hopping technique used by professional Counter-Strike players. Bunny hopping is a way of moving faster in FPS games by jumping continuously while turning left or right in the air. It requires precise timing, coordination, and practice to master.</p>
-<p>In Bhop Pro, you can jump and bunny hop in FPS mode across various maps and modes. You can prove that you are really a bhop master with the scores and durations you will get. You can also get new rankings by doing parkour quests, or compete with other players online. The game also offers a variety of skins, knives, gloves, spinners, and cases to customize your character.</p>
-<p>Bhop Pro is one of the most realistic bunny hop games for Android devices, with smooth graphics, realistic physics, and easy controls. It has over 10 million downloads and 4.5 stars rating on Google Play Store. It is also available for iOS devices.</p>
-<p>How to download and play bhop pro on PC with BlueStacks<br />
-bhop pro on Windows PC free download<br />
-Bhop PRO on Steam - buy and play<br />
-bhop pro for PC - simulation game by begma<br />
-bhop pro APK/XAPK for Windows 7/8/10/11<br />
-bhop pro PC gameplay - bunny hop and surf like a pro<br />
-bhop pro PC requirements - minimum and recommended specs<br />
-bhop pro PC cheats and hacks - how to get unlimited coins<br />
-bhop pro PC online - play with friends and other players<br />
-bhop pro PC review - pros and cons of the game<br />
-bhop pro PC tips and tricks - how to improve your skills<br />
-bhop pro PC mods and maps - how to install and play custom content<br />
-bhop pro PC controller support - how to use a gamepad<br />
-bhop pro PC keyboard settings - how to customize your controls<br />
-bhop pro PC update - what's new in the latest version<br />
-bhop pro PC vs mobile - which one is better<br />
-bhop pro PC free trial - how to play the game for free<br />
-bhop pro PC download size - how much space do you need<br />
-bhop pro PC error - how to fix common issues and bugs<br />
-bhop pro PC guide - how to master the game<br />
-bhop pro PC best settings - how to optimize your performance<br />
-bhop pro PC leaderboard - how to rank up and compete with others<br />
-bhop pro PC skins and cosmetics - how to unlock and equip them<br />
-bhop pro PC achievements and trophies - how to earn them all<br />
-bhop pro PC wallpaper - how to download and use it<br />
-bhop pro PC emulator - how to run the game on other platforms<br />
-bhop pro PC nox player - how to play the game with Nox app player<br />
-bhop pro PC reddit - join the community and discuss the game<br />
-bhop pro PC discord - chat with other players and get help<br />
-bhop pro PC steam key - how to get it for free or cheap<br />
-bhop pro PC mac - how to play the game on Mac OS X<br />
-bhop pro PC linux - how to play the game on Linux OS<br />
-bhop pro PC windows 11 compatibility - will the game work on the new OS<br />
-bhop pro PC speedrun - how to complete the game as fast as possible<br />
-bhop pro PC tutorial - learn the basics of the game<br />
-bhop pro PC video - watch gameplay and reviews of the game<br />
-bhop pro PC demo - try the game before you buy it<br />
-bhop pro PC crack - how to download and play the game for free illegally (not recommended)<br />
-bhop pro PC alternatives - similar games you might like<br />
-bhop pro PC challenges - test your skills with different modes and levels<br />
-bhop pro PC codes and coupons - how to get discounts and deals on the game<br />
-bhop pro PC memes and jokes - have fun with the game's humor and culture<br />
-bhop pro PC wiki - find information and facts about the game<br />
-bhop pro PC FAQ - get answers to frequently asked questions about the game<br />
-bhop pro PC forum - share your opinions and feedback about the game<br />
-bhop pro PC news and updates - stay informed about the latest developments of the game<br />
-bhop pro PC fan art and creations - see what other players have made with the game's assets<br />
-bhop pro PC merchandise and products - buy cool stuff related to the game</p>
- <h3>Why play Bhop Pro on PC?</h3>
-<p>While Bhop Pro is designed for mobile devices, playing it on PC can offer several advantages. For example:</p>
-<ul>
-<li>You can enjoy a bigger screen size and higher resolution for better visuals.</li>
-<li>You can use a keyboard and mouse or a gamepad for more accurate and comfortable controls.</li>
-<li>You can avoid battery drain, overheating, or lag issues that might affect your mobile device.</li>
-<li>You can access more features and settings that might not be available on mobile devices.</li>
-</ul>
-<p>So, how can you download and play Bhop Pro on PC? There are two main ways: using an Android emulator or using an APK file. Let's take a look at each method in detail.</p>
- <h2>How to Download and Play Bhop Pro on PC with BlueStacks</h2>
-<h3>What is BlueStacks and how does it work?</h3>
-<p><strong>BlueStacks</strong> is one of the most popular Android emulators for PC. It allows you to run any Android app or game on your Windows or Mac computer. It has a user-friendly interface, a high-performance engine, and a rich library of games and apps. You can also customize your keyboard and mouse settings, record and stream your gameplay, and access exclusive features like Eco Mode, Multi-Instance Manager, and Smart Controls.</p>
- <h3>Step-by-step guide to install and run Bhop Pro on PC with BlueStacks</h3>
-<p>Here are the steps to download and play Bhop Pro on PC with BlueStacks:</p>
-<ol>
-<li>Download and install BlueStacks from its official website. It is free and safe to use.</li>
-<li>Launch BlueStacks and sign in with your Google account. If you don't have one, you can create one for free.</li>
-<li>Go to the search bar on the top-right corner and type "Bhop Pro". You will see the game icon in the search results.</li>
-<li>Click on the game icon and then click on "Install" to download and install Bhop Pro on your PC.</li>
-<li>Once the installation is complete, you can click on "Open" to launch Bhop Pro on your PC.</li>
-<li>You can also find the game icon on the home screen or the app drawer of BlueStacks. You can double-click on it to run Bhop Pro anytime.</li>
-</ol>
- <h3>Tips and tricks to optimize your Bhop Pro experience on PC with BlueStacks</h3>
-<p>Here are some tips and tricks to make the most out of playing Bhop Pro on PC with BlueStacks:</p>
-<ul>
-<li>You can adjust the graphics quality, resolution, FPS, and other settings of Bhop Pro from the in-game menu or from the BlueStacks settings menu.</li>
-<li>You can use the default keyboard and mouse controls for Bhop Pro or customize them according to your preference. You can also use a gamepad if you have one connected to your PC.</li>
-<li>You can enable Eco Mode to reduce CPU and RAM usage and improve your PC's performance while playing Bhop Pro. You can also use Multi-Instance Manager to run multiple instances of Bhop Pro or other games at the same time.</li>
-<li>You can use Smart Controls to automatically switch between keyboard and mouse controls depending on the game scene. For example, you can use the mouse to aim and shoot in FPS mode, and use the keyboard to jump and move in bunny hop mode.</li>
-<li>You can use the Screen Recorder feature to record your Bhop Pro gameplay and save it as a video file. You can also use the Streaming Mode feature to stream your Bhop Pro gameplay to Twitch, YouTube, Facebook, or other platforms.</li>
-</ul>
- <h2>How to Download and Play Bhop Pro on PC with Other Emulators</h2>
-<h3>What are other emulators and how do they differ from BlueStacks?</h3>
-<p>BlueStacks is not the only Android emulator for PC. There are many other options that you can try, such as MuMu Player, NoxPlayer, LDPlayer, MEmu, etc. Each emulator has its own advantages and disadvantages, such as compatibility, performance, features, interface, etc. You can compare them and choose the one that suits your needs best.</p>
-<p>In this article, we will show you how to download and play Bhop Pro on PC with two other popular emulators: MuMu Player and NoxPlayer. Both of them are free, easy to use, and support a wide range of games and apps. Let's see how they work.</p>
- <h3>Step-by-step guide to install and run Bhop Pro on PC with MuMu Player</h3>
-<p>MuMu Player is an Android emulator developed by NetEase, a Chinese gaming company. It has a simple interface, a fast engine, and a smooth gaming experience. It also supports keyboard and mouse controls, gamepad support, screen recording, multi-instance mode, etc. Here are the steps to download and play Bhop Pro on PC with MuMu Player:</p>
-<ol>
-<li>Download and install MuMu Player from its official website. It is available in English and Chinese versions.</li>
-<li>Launch MuMu Player and sign in with your Google account. If you don't have one, you can create one for free.</li>
-<li>Go to the Google Play Store app on MuMu Player and search for "Bhop Pro". You will see the game icon in the search results.</li>
-<li>Click on the game icon and then click on "Install" to download and install Bhop Pro on your PC.</li>
-<li>Once the installation is complete, you can click on "Open" to launch Bhop Pro on your PC.</li>
-<li>You can also find the game icon on the home screen or the app drawer of MuMu Player . You can double-click on it to run Bhop Pro anytime.</li>
-</ol>
- <h3>Step-by-step guide to install and run Bhop Pro on PC with NoxPlayer</h3>
-<p>NoxPlayer is another Android emulator that is widely used by gamers. It has a sleek interface, a powerful engine, and a stable gaming experience. It also supports keyboard and mouse controls, gamepad support, screen recording, multi-instance mode, macro recorder, etc. Here are the steps to download and play Bhop Pro on PC with NoxPlayer:</p>
-<ol>
-<li>Download and install NoxPlayer from its official website. It is available in multiple languages.</li>
-<li>Launch NoxPlayer and sign in with your Google account. If you don't have one, you can create one for free.</li>
-<li>Go to the Google Play Store app on NoxPlayer and search for "Bhop Pro". You will see the game icon in the search results.</li>
-<li>Click on the game icon and then click on "Install" to download and install Bhop Pro on your PC.</li>
-<li>Once the installation is complete, you can click on "Open" to launch Bhop Pro on your PC.</li>
-<li>You can also find the game icon on the home screen or the app drawer of NoxPlayer. You can double-click on it to run Bhop Pro anytime.</li>
-</ol>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and benefits of playing Bhop Pro on PC</h3>
-<p>Bhop Pro is a fun and challenging game that lets you practice your bunny hopping skills in various maps and modes. It is one of the most realistic and popular bunny hop games for mobile devices, but you can also download and play it on your PC for a better gaming experience. Playing Bhop Pro on PC can give you several advantages, such as a bigger screen size, more accurate and comfortable controls, improved performance, and more features and settings.</p>
-<p>To download and play Bhop Pro on PC, you can use an Android emulator or an APK file. An Android emulator is a software that allows you to run any Android app or game on your PC. There are many Android emulators that you can choose from, such as BlueStacks, MuMu Player, NoxPlayer, etc. Each emulator has its own pros and cons, so you can compare them and pick the one that suits your needs best. In this article, we have shown you how to download and play Bhop Pro on PC with three of the most popular emulators: BlueStacks, MuMu Player, and NoxPlayer. We have also given you some tips and tricks to optimize your Bhop Pro experience on PC with each emulator.</p>
- <h3>A call to action to download and play Bhop Pro on PC today</h3>
-<p>If you are ready to take your bunny hopping skills to the next level, don't wait any longer. Download and play Bhop Pro on PC today and enjoy the thrill of jumping and shooting in FPS mode across various maps and modes. You can also customize your character with different skins, knives, gloves, spinners, and cases. You can also compete with other players online or do parkour quests to get new rankings. Bhop Pro is a game that will keep you entertained for hours.</p>
-<p>So what are you waiting for? Download and play Bhop Pro on PC today and become a bhop master!</p>
- <h2>FAQs</h2>
-<h3>What are the system requirements for playing Bhop Pro on PC?</h3>
-<p>The system requirements for playing Bhop Pro on PC may vary depending on the emulator that you use. However, here are some general guidelines that you can follow:</p>
-<ul>
-<li>You need a Windows or Mac computer with at least 4 GB of RAM and 4 GB of free disk space.</li>
-<li>You need a stable internet connection to download and play Bhop Pro online.</li>
-<li>You need a Google account to access the Google Play Store and install Bhop Pro.</li>
-<li>You may need a keyboard and mouse or a gamepad for better controls.</li>
-</ul>
- <h3>How can I customize the controls for Bhop Pro on PC?</h3>
-<p>You can customize the controls for Bhop Pro on PC from the emulator settings menu or from the in-game menu. You can assign different keys or buttons for different actions, such as jumping, moving, aiming, shooting, etc. You can also adjust the sensitivity, acceleration, inversion, etc. of your mouse or gamepad. You can save your custom controls as a preset or reset them to default anytime.</p>
- <h3>How can I record and share my Bhop Pro gameplay on PC?</h3>
-<p>You can record and share your Bhop Pro gameplay on PC using the screen recording or streaming features of the emulator that you use. For example, you can use the Screen Recorder feature of BlueStacks or MuMu Player to record your Bhop Pro gameplay and save it as a video file. You can also use the Streaming Mode feature of BlueStacks or the Live feature of NoxPlayer to stream your Bhop Pro gameplay to Twitch, YouTube, Facebook, or other platforms. You can also edit, trim, or add effects to your recorded or streamed videos using the emulator tools or other software.</p>
- <h3>How can I access new maps and skins for Bhop Pro on PC?</h3>
-<p>You can access new maps and skins for Bhop Pro on PC by updating the game regularly or by purchasing them from the in-game store. You can also earn coins by playing the game or watching ads, and use them to buy cases that contain random items. You can also trade items with other players online or offline. Some maps and skins are exclusive to certain modes or events, so you need to check them out frequently.</p>
- <h3>How can I compete with other players in Bhop Pro on PC?</h3>
-<p>You can compete with other players in Bhop Pro on PC by joining the online mode or creating your own room. You can also invite your friends or other players to join your room. You can choose from different maps and modes, such as speedrun, deathmatch, zombie escape, etc. You can also chat with other players using the in-game chat system or voice chat. You can also check your rank and stats on the leaderboard or the profile page.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Evertale APK Latest Version - Join the Epic Adventure and Save the World of Erden!.md b/spaces/congsaPfin/Manga-OCR/logs/Evertale APK Latest Version - Join the Epic Adventure and Save the World of Erden!.md
deleted file mode 100644
index d0b16df1c33f0cf7ed82096c48995acd8aed3f99..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Evertale APK Latest Version - Join the Epic Adventure and Save the World of Erden!.md	
+++ /dev/null
@@ -1,153 +0,0 @@
-<br />
-<h1>Evertale: A Captivating Fantasy RPG for Mobile Devices</h1>
-<p>Are you looking for a new role-playing game to play on your phone or tablet? Do you love catching, training, and evolving monsters in a stunning fantasy world? If so, you might want to check out Evertale, a game that has been compared to Pokémon due to its fighting style. The game has solid writing, a lovely art style, and strategic combat. It has been well-received by strategy RPG enthusiasts and beginners alike, with over 5 million downloads from the Google Play Store alone. However, there have been complaints about the game falsely advertising itself as a scary Pokémon version. Some players have also expressed disappointment with the online story, which does not allow for free roaming or catching new monsters.</p>
-<p>In this article, we will give you a comprehensive review of Evertale, covering its features, gameplay, story, modes, pros and cons, tips and tricks, and FAQs. By the end of this article, you will have a clear idea of what Evertale is all about and whether it is worth playing or not.</p>
-<h2>evertale latest version apk</h2><br /><p><b><b>Download</b> >>> <a href="https://urlca.com/2uObbo">https://urlca.com/2uObbo</a></b></p><br /><br />
-<h2>What is Evertale?</h2>
-<p>Evertale is an action-adventure role-playing game that was released in 2019 by ZigZaGame Inc., a Japanese game developer that also created Neo Monsters. The game is available for Android and iOS devices.</p>
-<p>Evertale takes you to the fantasy world of Erden and takes on a quest to liberate the land from the forces of Pandemonium. As you journey through 6 regions in the land, you will encounter a variety of characters who may join you on your adventure and there are various monsters scattered across each map to battle, capture, collect, and train.</p>
-<p>Various quests lead you to progress your game and there are plenty of side quests to earn rewards for you to strengthen your party faster. There are plenty of areas to discover and explore as treasures and rare monsters abound in each region.</p>
-<p>Beyond the game’s story, joining other players in guilds can earn you unique benefits and engaging in real-time PvP leagues can help you earn more rewards and rank higher. Evertale is a game that combines elements of monster catching, turn-based combat, and story-driven adventure in a captivating way.</p>
-<h2>Why is Evertale worth playing?</h2>
-<p>Evertale is worth playing for many reasons, such as:</p>
-<ul>
-<li>It has a rich and immersive story that spans over 180 chapters, with multiple endings and choices that affect the outcome.</li>
-<li>It has a beautiful and diverse world that is inspired by Japanese folklore, mythology, and fairy tales, with over 200 hand-drawn locations to explore.</li>
-<li>It has a large and varied collection of over 300 monsters to catch, train, and evolve, each with their own unique skills, traits, and personalities.</li>
-<li>It has a strategic and challenging combat system that requires you to use your monsters' abilities wisely, as well as your own team spirit and elemental advantages.</li>
-<li>It has a fun and rewarding online mode that lets you join guilds, participate in events, and compete in PvP leagues against other players from around the world.</li>
-</ul>
-<p>If you are a fan of RPGs, especially those that involve monster collecting and battling, you will definitely enjoy Evertale and its many features.</p>
-<h2>How to download and play Evertale?</h2>
-<p>Evertale is available for both Android and iOS devices. You can download it from the Google Play Store or the App Store for a small fee of $0.99. However, the game also offers in-app purchases that can enhance your gaming experience, such as diamonds (the premium currency), bundles (that contain various items and resources), and subscriptions (that provide daily rewards and bonuses).</p>
-<p>evertale game download apk<br />
-evertale mod apk unlimited soul stone<br />
-evertale offline apk<br />
-evertale 2.0.85 apk<br />
-evertale apk free download<br />
-evertale hack apk<br />
-evertale android apk<br />
-evertale update apk<br />
-evertale full version apk<br />
-evertale apk obb<br />
-evertale apk mod menu<br />
-evertale latest mod apk<br />
-evertale apk pure<br />
-evertale 2.0.83 apk<br />
-evertale apk rexdl<br />
-evertale cracked apk<br />
-evertale premium apk<br />
-evertale apk mirror<br />
-evertale 2.0.82 apk<br />
-evertale apk uptodown<br />
-evertale mod apk platinmods<br />
-evertale paid apk<br />
-evertale pro apk<br />
-evertale old version apk<br />
-evertale modded apk download<br />
-evertale unlimited money apk<br />
-evertale original apk<br />
-evertale mod apk latest version 2023<br />
-evertale modded apk offline<br />
-evertale mod apk android 1<br />
-evertale cheat apk<br />
-evertale unlocked apk<br />
-evertale modded apk free download<br />
-evertale modded apk unlimited everything<br />
-evertale modded apk latest version 2022<br />
-evertale modded apk no root<br />
-evertale modded apk online mode<br />
-evertale modded apk unlimited gems and coins <br />
-evertale modded apk with obb file <br />
-evertale modded apk for ios</p>
-<p>To play Evertale on your PC or Mac, you will need to use an emulator software that can run Android or iOS apps on your computer. Some of the popular emulators are BlueStacks, NoxPlayer, and LDPlayer. You will need to download and install the emulator of your choice, then launch it and search for Evertale in the app store. Once you find it, you can download and install it on your emulator and start playing.</p>
-<h2>How to catch, train, and evolve monsters in Evertale?</h2>
-<p>Catching, training, and evolving monsters are some of the core aspects of Evertale. Here are some tips on how to do them:</p>
-<ul>
-<li>To catch a monster, you need to encounter it in the wild or in special events. You can use various items to lure or weaken the monster, such as bait, traps, potions, or weapons. Then, you need to throw a soul stone at it to capture it. The higher the monster's rarity and level, the harder it is to catch.</li>
-<li>To train a monster, you need to use it in battles or quests to gain experience points (XP) and level up. You can also use items such as books or scrolls to boost its XP or stats. Each monster has a maximum level that depends on its rarity and evolution stage.</li>
-<li>To evolve a monster, you need to meet certain requirements, such as reaching a specific level or having a specific item. Evolving a monster will increase its stats, skills, and appearance. Some monsters have multiple evolution paths that you can choose from.</li>
-</ul>
-<p>You can view your monster collection in the menu under "Monsters". You can also customize your monster team by selecting up to 6 monsters for your active party. You can switch between different parties depending on the situation or your preference.</p> <h2>How to progress in the story mode and online mode in Evertale?</h2>
-<p>Evertale has two main modes of play: the story mode and the online mode. Each mode has its own features, challenges, and rewards. Here are some differences and tips on how to progress in each mode:</p>
-<h3>Story Mode</h3>
-<p>The story mode is the offline mode of Evertale, where you can follow the main plot of the game and experience the adventures of the Crestbearers. The story mode is divided into 6 regions, each with its own map, quests, monsters, and bosses. You can explore each region at your own pace, but you need to complete certain quests to unlock the next region. You can also replay any region you have cleared to farm for more XP, items, or monsters.</p>
-<p>The story mode also has a difficulty setting that you can change at any time. The higher the difficulty, the stronger the enemies and the better the rewards. You can choose from Normal, Hard, or Nightmare difficulty levels. You can also unlock a special difficulty level called Endless Mode after you finish the story mode. In Endless Mode, you can challenge randomly generated dungeons with increasing difficulty and rewards.</p>
-<h3>Online Mode</h3>
-<p>The online mode is the online mode of Evertale, where you can interact with other players and participate in various events and activities. The online mode requires an internet connection and consumes mana, a special currency that regenerates over time or can be purchased with real money. The online mode has several features, such as:</p>
-<ul>
-<li>Online Story: This is a continuation of the story mode, where you can follow the events that happen after the main plot. The online story has its own quests, monsters, and bosses, but it does not allow you to free roam or catch new monsters. You can only use the monsters you have collected in the story mode or from events.</li>
-<li>Guilds: This is where you can join or create a guild with other players. Guilds can provide you with various benefits, such as guild chat, guild shop, guild quests, guild wars, and guild raids. You can also earn guild coins by contributing to your guild's activities, which you can use to buy exclusive items or monsters from the guild shop.</li>
-<li>PvP Leagues: This is where you can compete against other players in real-time battles. PvP leagues have different tiers and seasons, each with its own rules and rewards. You can earn league points by winning battles, which you can use to rank up and unlock more rewards. You can also earn league coins by participating in battles, which you can use to buy special items or monsters from the league shop.</li>
-<li>Events: These are special activities that happen periodically in the online mode. Events can offer unique quests, monsters, items, or rewards that are only available for a limited time. Some examples of events are story events, challenge events, colosseum events, and summon events.</li>
-</ul>
-<p>To progress in the online mode, you need to balance your mana consumption and your participation in various features. You also need to constantly upgrade your monster team and your equipment to keep up with the increasing difficulty and competition.</p> <h2>What are the pros and cons of Evertale?</h2>
-<p>Evertale is not a perfect game, and it has its own advantages and disadvantages. Here are some of the pros and cons of Evertale that you should consider before playing it:</p>
-<h3>Pros</h3>
-<ul>
-<li>It has a captivating and immersive story that will keep you hooked for hours.</li>
-<li>It has a stunning and diverse world that will make you want to explore every corner.</li>
-<li>It has a large and varied collection of monsters that will satisfy your collector's itch.</li>
-<li>It has a strategic and challenging combat system that will test your skills and tactics.</li>
-<li>It has a fun and rewarding online mode that will let you interact with other players and compete for glory.</li>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li>It has a misleading advertisement that may disappoint some players who expected a different game.</li>
-<li>It has a restrictive online story that may frustrate some players who wanted more freedom and variety.</li>
-<li>It has a high difficulty curve that may discourage some players who are not used to RPGs or monster games.</li>
-<li>It has a pay-to-win aspect that may annoy some players who prefer a fair and balanced game.</li>
-<li>It has a limited mana system that may limit some players who want to play more often or longer.</li>
-</ul>
-<p>Evertale is a game that has a lot of potential, but it also has some flaws that need to be improved. Depending on your preferences and expectations, you may find Evertale to be either a gem or a dud.</p>
-<h2>What are some tips and tricks for beginners in Evertale?</h2>
-<p>If you are new to Evertale, you may feel overwhelmed by the game's complexity and difficulty. However, don't worry, because we have some tips and tricks that can help you get started and enjoy the game more. Here are some of them:</p>
-<ul>
-<li>Save your diamonds for summoning events. Diamonds are the premium currency in Evertale, and they are hard to come by. You can use them to buy various items or resources, but the best way to use them is to summon new monsters during special events. These events offer higher chances of getting rare or legendary monsters, as well as exclusive monsters that are only available for a limited time.</li>
-<li>Use auto-battle wisely. Auto-battle is a feature that lets you automate your battles and save time. However, auto-battle is not always the best option, especially when you face tougher enemies or bosses. Auto-battle does not take into account your monsters' skills, traits, or elemental advantages, and it may make suboptimal moves or waste your spirit points. Therefore, you should only use auto-battle when you are confident that you can win easily, or when you want to farm for XP or items.</li>
-<li>Join a guild as soon as possible. Guilds are groups of players who can help each other out in various ways. By joining a guild, you can access the guild chat, where you can ask for advice, tips, or help from other members. You can also access the guild shop, where you can buy exclusive items or monsters with guild coins. You can also participate in guild quests, guild wars, and guild raids, where you can earn more rewards and have fun with your guildmates.</li>
-<li>Experiment with different monster combinations. Evertale offers a lot of diversity and customization when it comes to your monster team. You can mix and match different monsters based on their skills, traits, personalities, or elements. You can also change their equipment, accessories, or runes to enhance their stats or abilities. You can also evolve them into different forms to unlock new skills or appearances. You should try out different monster combinations and see what works best for you.</li>
-<li>Have fun and enjoy the game. Evertale is a game that can offer you hours of entertainment and satisfaction. You can immerse yourself in the story mode, explore the world mode, interact with other players in the online mode, or collect and train your monsters in the monster mode. You can play the game at your own pace and style, and discover new things along the way. You should have fun and enjoy the game as much as possible.</li>
-</ul>
-<p>These are some of the tips and tricks that can help you get started and enjoy Evertale more. Of course, there are more things to learn and discover in the game, but we hope that this article has given you a good overview of what Evertale is all about.</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some of the frequently asked questions about Evertale:</p>
- <h3>Q: How do I get more diamonds in Evertale?</h3>
-<p>A: There are several ways to get more diamonds in Evertale, such as:</p>
-<ul>
-<li>Completing quests and achievements in the story mode or the online mode.</li>
-<li>Participating in events and activities in the online mode.</li>
-<li>Ranking up and unlocking rewards in the PvP leagues.</li>
-<li>Buying them with real money from the shop.</li>
-</ul>
-<h3>Q: How do I get more mana in Evertale?</h3>
-<p>A: Mana is the currency that you need to play the online mode of Evertale. You can get more mana by:</p>
-<ul>
-<li>Waiting for it to regenerate over time. You can regenerate up to 100 mana per day.</li>
-<li>Using mana potions that you can buy from the shop or get from events or rewards.</li>
-<li>Buying mana with diamonds from the shop.</li>
-</ul>
-<h3>Q: How do I get more soul stones in Evertale?</h3>
-<p>A: Soul stones are the items that you need to catch monsters in Evertale. You can get more soul stones by:</p>
-<ul>
-<li>Buying them with gold or diamonds from the shop.</li>
-<li>Finding them in chests or hidden spots in the story mode or the online mode.</li>
-<li>Getting them from events or rewards.</li>
-</ul>
-<h3>Q: How do I get more gold in Evertale?</h3>
-<p>A: Gold is the currency that you need to buy items, resources, or equipment in Evertale. You can get more gold by:</p>
-<ul>
-<li>Winning battles or quests in the story mode or the online mode.</li>
-<li>Selling items, resources, or equipment that you don't need.</li>
-<li>Getting them from events or rewards.</li>
-</ul>
-<h3>Q: How do I get more rare or legendary monsters in Evertale?</h3>
-<p>A: Rare or legendary monsters are the most powerful and desirable monsters in Evertale. You can get more rare or legendary monsters by:</p>
-<ul>
-<li>Summoning them during special events that offer higher chances of getting them.</li>
-<li>Catching them in the wild or in special locations in the story mode or the online mode.</li>
-<li>Evolving your existing monsters into their rare or legendary forms.</li>
-</ul>
- <p>I hope this article has helped you learn more about Evertale and how to play it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and have fun playing Evertale!</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Hay Day OBB File - Everything You Need to Know Before Downloading.md b/spaces/congsaPfin/Manga-OCR/logs/Hay Day OBB File - Everything You Need to Know Before Downloading.md
deleted file mode 100644
index ddb6c3e8582ae4713c08453fc876ae3b7d4fd45e..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Hay Day OBB File - Everything You Need to Know Before Downloading.md	
+++ /dev/null
@@ -1,86 +0,0 @@
-<br />
-<h1>How to Download the OBB File for Hay Day on Android</h1>
-<p>Hay Day is one of the most popular farming games for Android devices. It lets you create your own farm, grow crops, raise animals, trade with neighbors, and explore the valley. However, to enjoy all these features, you need to download and install an additional file called an OBB file. In this article, we will explain what an OBB file is, why you need it, how to download it from a trusted source, and how to install it on your Android device.</p>
- <h2>What is an OBB File and Why Do You Need It?</h2>
-<p>An OBB file is an expansion file used by some Android apps distributed using the Google Play online store. It contains data that is not stored in the application's main package (.APK file), such as graphics, media files, and other large program assets. OBB files are often stored in a device's shared storage folder.</p>
-<h2>download file obb hay day</h2><br /><p><b><b>Download</b> &#10002; &#10002; &#10002; <a href="https://urlca.com/2uO4HN">https://urlca.com/2uO4HN</a></b></p><br /><br />
-<p>Hay Day uses an OBB file to provide graphics, sounds, and videos for the game. These files are separate from the app's APK file and can be used by different apps. For example, an OBB file with map data may be shared and used by Android apps that have been granted access rights to the map data in those .obb files.</p>
- <h2>How to Download the OBB File for Hay Day from a Trusted Source</h2>
-<p>Before downloading any files from external sources, you should be aware of the risks involved. Some websites or apps may offer fake or malicious files that can harm your device or compromise your privacy. Therefore, you should always use a reliable website or app to download the OBB file for Hay Day.</p>
-<p> <p>One of the trusted sources that we recommend to download the OBB file for Hay Day is the website [APKPure](^1^). This website offers verified and safe APK and OBB files for many Android games and apps. To download the OBB file for Hay Day from APKPure, follow these steps:</p>
-<ol>
-<li>Go to the [Hay Day page](^1^) on APKPure and click on the "Download APK" button.</li>
-<li>Wait for the download to finish and then open the APK file to install Hay Day on your device. You may need to allow installation from unknown sources if you haven't done so before.</li>
-<li>Go back to the [Hay Day page](^1^) on APKPure and click on the "OBB" tab. You will see a link to download the OBB file for Hay Day.</li>
-<li>Click on the link and wait for the download to finish. You will get a ZIP file containing the OBB file for Hay Day.</li>
-</ol>
- <h2>How to Install the OBB File for Hay Day on Your Android Device</h2>
-<p>After downloading the OBB file for Hay Day, you need to install it on your device. This process involves extracting the OBB file from the ZIP file and moving it to a specific folder on your device storage. You can do this using a file manager app or a computer. Here are the steps to install the OBB file for Hay Day using a file manager app:</p>
-<ol>
-<li>Open your file manager app and locate the ZIP file that you downloaded from APKPure. It should be in the "Download" folder or wherever you saved it.</li>
-<li>Tap on the ZIP file and select "Extract" or "Unzip". You will get a folder named "com.supercell.hayday". This is the OBB folder for Hay Day.</li>
-<li>Tap and hold on the OBB folder and select "Cut" or "Move". Then, navigate to the "Android" folder on your device storage. Inside this folder, you will find another folder named "OBB". If you don't see it, you can create it by tapping on the "+" or "New" button and naming it "OBB".</li>
-<li>Open the "OBB" folder and paste or move the OBB folder for Hay Day there. Make sure that the path is "/Android/OBB/com.supercell.hayday".</li>
-<li>Close your file manager app and launch Hay Day. You should be able to play the game with all its features.</li>
-</ol>
-<p>If you want to use a computer to install the OBB file for Hay Day, you can follow these steps:</p>
-<ol>
-<li>Connect your device to your computer using a USB cable. Make sure that your device is in file transfer mode.</li>
-<li>Open your computer's file explorer and locate your device's storage. You should see a folder named "Android". Open it and look for another folder named "OBB". If you don't see it, you can create it by right-clicking and selecting "New Folder" and naming it "OBB".</li>
-<li>Open another window of your file explorer and locate the ZIP file that you downloaded from APKPure. It should be in your "Downloads" folder or wherever you saved it.</li>
-<li>Right-click on the ZIP file and select "Extract All" or "Unzip". You will get a folder named "com.supercell.hayday". This is the OBB folder for Hay Day.</li>
-<li>Drag and drop the OBB folder for Hay Day into the "OBB" folder on your device's storage. Make sure that the path is "/Android/OBB/com.supercell.hayday".</li>
-<li>Eject your device from your computer and launch Hay Day. You should be able to play the game with all its features.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>In this article, we have explained how to download and install the OBB file for Hay Day on Android devices. By following these steps, you can enjoy one of the best farming games with high-quality graphics, sounds, and videos. Here are some tips or suggestions for playing Hay Day:</p>
-<ul>
-<li>Connect your game to Facebook or Google Play Games to save your progress and sync it across devices.</li>
-<li>Join a neighborhood or create your own to chat with other players, help each other, and participate in events.</li>
-<li>Expand your farm by buying more land, building more facilities, and unlocking new crops and animals.</li>
-<li>Trade with other players using your roadside shop, truck, boat, or town visitors.</li>
-<li>Explore the valley with your truck or car and collect rewards, tokens, and special items.</li>
-</ <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about downloading and installing the OBB file for Hay Day on Android devices:</p>
-<h3>What is the size of the OBB file for Hay Day?</h3>
-<p>The size of the OBB file for Hay Day may vary depending on the device and game version. However, according to APKPure, the latest OBB file for Hay Day (version 1.52.89) is about 144 MB.</p>
-<p>How to download file obb hay day on android<br />
-Download file obb hay day mod apk unlimited everything<br />
-Download file obb hay day latest version for free<br />
-Download file obb hay day offline installer<br />
-Download file obb hay day from google drive<br />
-Download file obb hay day for pc windows 10<br />
-Download file obb hay day without root<br />
-Download file obb hay day hack tool<br />
-Download file obb hay day update 2023<br />
-Download file obb hay day with es file explorer<br />
-Download file obb hay day using vpn<br />
-Download file obb hay day from apkdone.com[^1^]<br />
-Download file obb hay day original from supercell<br />
-Download file obb hay day no survey no password<br />
-Download file obb hay day full unlocked<br />
-Download file obb hay day new features and improvements<br />
-Download file obb hay day easy and fast<br />
-Download file obb hay day safe and secure<br />
-Download file obb hay day best farming game<br />
-Download file obb hay day tips and tricks<br />
-Download file obb hay day compatible with all devices<br />
-Download file obb hay day high quality graphics and sound<br />
-Download file obb hay day fun and addictive gameplay<br />
-Download file obb hay day online multiplayer mode<br />
-Download file obb hay day support and feedback<br />
-Download file obb hay day cheats and codes<br />
-Download file obb hay day error fix and troubleshooting<br />
-Download file obb hay day guide and tutorial<br />
-Download file obb hay day review and rating<br />
-Download file obb hay day alternative and similar apps</p>
-<h3>What if I delete or lose the OBB file for Hay Day?</h3>
-<p>If you delete or lose the OBB file for Hay Day, you will not be able to play the game unless you download and install it again. The game will not run properly without the OBB file, as it contains essential data for the game. Therefore, you should always keep a backup of the OBB file or download it from a trusted source whenever you need it.</p>
-<h3>Can I play Hay Day without the OBB file?</h3>
-<p>No, you cannot play Hay Day without the OBB file. The OBB file is required for the game to function properly, as it contains graphics, sounds, and videos for the game. Without the OBB file, the game will not load or display correctly. You need to download and install the OBB file before launching Hay Day.</p>
-<h3>Can I share or transfer the OBB file for Hay Day to another device?</h3>
-<p>Yes, you can share or transfer the OBB file for Hay Day to another device, as long as it has enough storage space and supports Hay Day. You can use a USB cable, a Bluetooth connection, a Wi-Fi network, or a cloud service to transfer the OBB file from one device to another. However, you need to make sure that you also install Hay Day on the other device and move the OBB file to the correct folder (/Android/OBB/com.supercell.hayday).</p>
-<h3>How can I update the OBB file for Hay Day when there is a new version of the game?</h3>
-<p>You can update the OBB file for Hay Day by downloading and installing it from a trusted source, such as APKPure. You can follow the same steps as mentioned above to download and install the new OBB file. Alternatively, you can use the Google Play Store if it is available on your device. The Google Play Store will automatically download and install the latest version of Hay Day and its OBB file when there is an update.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/My Talking Angela The Virtual Pet Star Youll Love.md b/spaces/congsaPfin/Manga-OCR/logs/My Talking Angela The Virtual Pet Star Youll Love.md
deleted file mode 100644
index 04e35ae6b3c118b46c2ea4539e61e622ca65b94b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/My Talking Angela The Virtual Pet Star Youll Love.md	
+++ /dev/null
@@ -1,251 +0,0 @@
-
-<h1>My Talking Angela 3 Download: A Guide for Fans of the Virtual Pet Game</h1>
- <p>If you are looking for a fun and interactive game that lets you adopt, care for, and play with a cute virtual cat, then you might want to check out My Talking Angela 3. This is the latest installment in the popular Talking Tom & Friends series, developed by Outfit7. In this game, you can join Angela, the super fun virtual star, as she dances, sings, and explores her way to the top. You can also customize her look, play mini-games, collect stickers, and discover new activities in her 3D world.</p>
- <p>My Talking Angela 3 is a simulation game that combines elements of chatterbot, dress-up, and casual gaming. It is suitable for players of all ages, especially those who love cats and fashion. The game has millions of fans around the world, who enjoy interacting with Angela and watching her grow from a kitten to an adult. The game also has regular updates that add new features and content.</p>
-<h2>my talking angela 3 download</h2><br /><p><b><b>DOWNLOAD</b>  <a href="https://urlca.com/2uO8Pv">https://urlca.com/2uO8Pv</a></b></p><br /><br />
- <p>If you are interested in playing My Talking Angela 3, you might be wondering how to download and install it on your device. Don't worry, we have got you covered. In this article, we will show you how to get My Talking Angela 3 on your iOS, Android, or Windows device. We will also give you some tips and tricks on how to make the most out of your gameplay experience. So, let's get started!</p>
- <h2>How to Download and Install My Talking Angela 3</h2>
- <p>My Talking Angela 3 is available for free on various platforms, such as iOS, Android, and Windows. However, the game also offers in-app purchases that allow you to buy coins, gems, or subscriptions using real money. You can use these currencies to buy items or access premium features in the game. If you don't want to spend money on the game, you can still enjoy it without making any in-app purchases.</p>
- <p>To download and install My Talking Angela 3 on your device, follow these simple steps:</p>
-<p>my talking angela 3 apk free download<br />
-my talking angela 3 mod apk unlimited money and diamonds<br />
-my talking angela 3 game online play<br />
-my talking angela 3 app store<br />
-my talking angela 3 google play<br />
-my talking angela 3 latest version<br />
-my talking angela 3 hack download<br />
-my talking angela 3 for pc<br />
-my talking angela 3 cheats and tips<br />
-my talking angela 3 outfits and makeup<br />
-my talking angela 3 dance and sing<br />
-my talking angela 3 sticker album<br />
-my talking angela 3 youtube videos<br />
-my talking angela 3 review and rating<br />
-my talking angela 3 update and new features<br />
-my talking angela 3 simulation game for kids<br />
-my talking angela 3 virtual pet and best friend<br />
-my talking angela 3 outfit7 limited<br />
-my talking angela 3 privacy policy and terms of use<br />
-my talking angela 3 customer support and feedback<br />
-my talking angela 3 subscription and in-app purchases<br />
-my talking angela 3 how to play and level up<br />
-my talking angela 3 fun and interactive activities<br />
-my talking angela 3 customize and personalize your pet<br />
-my talking angela 3 spring magic and blooming garden<br />
-my talking angela 3 offline and online mode<br />
-my talking angela 3 data safety and encryption<br />
-my talking angela 3 compatible devices and requirements<br />
-my talking angela 3 install and uninstall guide<br />
-my talking angela 3 alternative apps and games<br />
-my talking angela 3 comparison and difference with other versions<br />
-my talking angela 3 screenshots and images<br />
-my talking angela 3 trivia and facts<br />
-my talking angela 3 rumors and myths debunked<br />
-my talking angela 3 challenges and achievements<br />
-my talking angela 3 gifts and rewards<br />
-my talking angela 3 ads and promotions<br />
-my talking angela 3 social media and community<br />
-my talking angela 3 voice and sound effects<br />
-my talking angela 3 animation and graphics quality</p>
- <h3>For iOS Devices</h3>
- <ol>
-<li>Go to the App Store on your iPhone or iPad.</li>
-<li>Search for "My Talking Angela" or use this link: [text](^2^).</li>
-<li>Tap on "Get" or "Install" and wait for the download to finish.</li>
-<li>Once the download is complete, tap on "Open" or find the app icon on your home screen.</li>
-<li>Enjoy playing My Talking Angela 3!</li>
-</ol>
- <h3>For Android Devices</h3>
- <ol>
-<li>Go to Google Play Store on your smartphone or tablet.</li>
-<li>Search for "My Talking Angela" or use this link: [text](^1^).</li>
-<li>Tap on "Install" and wait for the download to finish.</li>
-<li>Once the download is complete, tap on "Open" or find the app icon on your home screen.</li>
-<li>Enjoy playing My Talking Angela 3!</li>
-</ol>
- <h3>For Windows Devices</h3>
- <ol>
-<li>Go to Microsoft Store on your PC or laptop.</li>
-<li>Search for "My Talking Angela" or use this link: [text](^3^).</li> <li>Tap on Angela to make her happy, smile, or laugh.</li>
-<li>Swipe on Angela to pet her, tickle her, or make her purr.</li>
-<li>Tap on the microphone icon to talk to Angela and hear her repeat what you say.</li>
-<li>Tap on the camera icon to record a video of your interaction and share it with your friends.</li>
-<li>Tap on the food icon to feed Angela with different foods and drinks.</li>
-<li>Tap on the shower icon to bathe Angela and make her clean.</li>
-<li>Tap on the toothbrush icon to brush Angela's teeth and make them shiny.</li>
-<li>Tap on the bed icon to put Angela to sleep and turn off the lights.</li>
-</ul>
- <h3>For Customizing Angela's Appearance</h3>
- <ul>
-<li>Tap on the hanger icon to access Angela's wardrobe and choose from different outfits, accessories, hairstyles, makeup, and stickers.</li>
-<li>Swipe left or right to browse through different categories and items.</li>
-<li>Tap on an item to select it and see how it looks on Angela.</li>
-<li>Tap on the checkmark icon to confirm your choice and save it.</li>
-<li>Tap on the trash icon to remove an item from Angela.</li>
-<li>Tap on the gift icon to buy more items with coins or gems.</li>
-<li>Tap on the home icon to decorate Angela's home and change the background.</li>
-</ul>
- <h2>How to Play Mini-Games and Earn Coins and Diamonds</h2>
- <p>Besides interacting with Angela and customizing her appearance, you can also play mini-games and earn coins and diamonds in My Talking Angela 3. Coins and diamonds are the currencies of the game that you can use to buy items or access premium features. You can also earn stickers by playing mini-games, which you can use to unlock new outfits and backgrounds for Angela.</p>
- <p>There are many mini-games that you can play in My Talking Angela 3, such as:</p>
- <table>
-<tr><th>Name</th><th>Description</th></tr>
-<tr><td>Cake Tower</td><td>Stack cakes as high as you can without dropping them.</td></tr>
-<tr><td>Dancing Game</td><td>Follow the rhythm and tap the buttons at the right time.</td></tr>
-<tr><td>Puzzle Game</td><td>Solve puzzles by matching three or more pieces of the same color.</td></tr>
-<tr><td>Bubble Shooter</td><td>Shoot bubbles and pop them by making groups of three or more of the same color.</td></tr>
-<tr><td>Rainbow Melody</td><td>Create music by tapping on colorful tiles.</td></tr>
-<tr><td>Sky High</td><td>Bounce on clouds and collect stars while avoiding obstacles.</td></tr>
-<tr><td>Tiny Puzzles</td><td>Complete mini-puzzles by finding hidden objects, spotting differences, or solving riddles.</td></tr>
-<tr><td>Treasure Hunt</td><td>Dig for treasure and avoid traps in a mysterious island.</td></tr>
-<tr><td>Wheel of Fortune</td><td>Spin the wheel and win prizes or bonuses.</td></tr>
-</table>
- <p>To play mini-games and earn coins and diamonds, follow these simple steps:</p>
- <ul>
-<li>Tap on the gamepad icon to access the mini-games menu.</li>
-<li>Swipe left or right to browse through different mini-games.</li>
-<li>Tap on a mini-game to start playing it. Follow the instructions on the screen to play the game. You can also tap on the question mark icon for more information about the game rules and controls.</li>
-<li>Earn coins, diamonds, or stickers by completing levels or achieving high scores. You can also earn bonuses by watching ads or spinning the wheel of fortune.</li>
-<li>You can play as many mini-games as you want, but some of them may require energy or tickets. You can refill your energy or tickets by waiting for some time, watching ads, spinning the wheel of fortune, or buying them with coins or gems.</li>
-</ul>
- <h2>How to Explore the City and Discover New Activities</h2>
- <p>A new feature that My Talking Angela 3 introduces is that you can explore the city with Angela and discover new activities. The city is a 3D world that you can navigate by swiping left or right. You can also zoom in or out by pinching your fingers. The city has different locations that you can visit, such as:</p>
- <table>
-<tr><th>Name</th><th>Description</th></tr>
-<tr><td>Dance Studio</td><td>A place where you can dance with Angela and learn new moves. You can also customize her outfit and accessories for the dance.</td></tr>
-<tr><td>Music Studio</td><td>A place where you can make music with Angela and record your own songs. You can also choose from different genres, instruments, and effects for the music.</td></tr>
-<tr><td>Photo Studio</td><td>A place where you can take photos with Angela and edit them with filters, stickers, and frames. You can also share your photos with your friends or save them to your gallery.</td></tr>
-<tr><td>Shopping Mall</td><td>A place where you can shop for new items for Angela, such as clothes, shoes, jewelry, and more. You can also find special offers and discounts for the items.</td></tr>
-<tr><td>Cinema</td><td>A place where you can watch movies with Angela and enjoy popcorn and drinks. You can also choose from different genres, such as comedy, romance, action, and more.</td></tr>
-<tr><td>Park</td><td>A place where you can relax with Angela and enjoy nature. You can also play with other animals, such as squirrels, birds, and butterflies.</td></tr>
-<tr><td>Beach</td><td>A place where you can have fun with Angela and enjoy the sun and the sea. You can also play with sand, surf, or swim.</td></tr>
-</table>
- <p>To explore the city and discover new activities, follow these simple steps:</p>
- <ul>
-<li>Tap on the map icon to access the city menu.</li>
-<li>Swipe left or right to browse through different locations.</li>
-<li>Tap on a location to visit it. Follow the instructions on the screen to interact with Angela and enjoy the activity. You can also tap on the question mark icon for more information about the location and the activity.</li>
-<li>Earn coins, diamonds, or stickers by completing tasks or achieving goals in each location. You can also earn bonuses by watching ads or spinning the wheel of fortune.</li>
-<li>You can visit as many locations as you want, but some of them may require energy or tickets. You can refill your energy or tickets by waiting for some time, watching ads, spinning the wheel of fortune, or buying them with coins or gems.</li>
-</ul>
- <h2>Tips and Tricks for My Talking Angela 3</h2>
- <p>Now that you know how to download and install My Talking Angela 3, how to interact with Angela and customize her appearance, how to play mini-games and earn coins and diamonds, and how to explore the city and discover new activities, you might be wondering how to improve your gameplay experience and make it more fun and rewarding. Here are some tips and tricks that you can use to level up faster, unlock more items, use child mode and parental controls, and avoid the hoax and rumors about the game.</p>
- <h3>How to Level Up Faster and Unlock More Items</h3>
- <p>One of the goals of My Talking Angela 3 is to level up Angela from a kitten to an adult. As you level up, you will unlock more items, such as outfits, accessories, hairstyles, makeup, stickers, backgrounds, mini-games, locations, and activities. You will also unlock more features, such as voice recording, video recording, photo editing, music making, dancing lessons, shopping offers, movie genres, sand art, surfing lessons, and more.</p>
- <p>To level up faster and unlock more items in My Talking Angela 3, follow these simple steps:</p>
- <ul>
-<li>Interact with Angela regularly and make her happy, healthy, and clean. You can do this by talking to her, petting her, feeding her, bathing her, brushing her teeth, and putting her to sleep.</li>
-<li>Play mini-games and earn coins and diamonds. You can use these currencies to buy items or access premium features in the game.</li>
-<li>Explore the city and discover new activities. You can also complete tasks or achieve goals in each location to earn coins, diamonds, or stickers.</li>
-<li>Customize Angela's appearance and style. You can also collect stickers by playing mini-games or visiting locations. You can use these stickers to unlock new outfits and backgrounds for Angela.</li>
-<li>Watch ads or spin the wheel of fortune to earn bonuses, such as coins, diamonds, energy, tickets, or items.</li>
-</ul>
- <h3>How to Use Child Mode and Parental Controls</h3>
- <p>My Talking Angela 3 is a game that is suitable for players of all ages, especially those who love cats and fashion. However, some parents may have concerns about the game's content or features, such as in-app purchases, ads, voice recording, video recording, photo editing, music making, dancing lessons, shopping offers, movie genres, sand art, surfing lessons, and more. If you are a parent who wants to limit or restrict some of these features for your child, you can use the child mode and parental controls in the game.</p>
- <p>Child mode is a feature that disables some of the features that may not be appropriate for younger players, such as voice recording, video recording, photo editing, music making, dancing lessons, shopping offers, movie genres, sand art, surfing lessons, and more. It also reduces the number of ads that appear in the game. To activate child mode in My Talking Angela 3, follow these simple steps:</p>
- <ul>
-<li>Tap on the settings icon to access the settings menu.</li>
-<li>Tap on the child mode icon to toggle it on or off.</li>
-<li>Enter your birth year to confirm your age and activate child mode.</li>
-<li>You can also tap on the parental controls icon to access more options, such as disabling in-app purchases, ads, or notifications.</li>
-</ul>
- <h3>How to Avoid the Hoax and Rumors about the Game</h3>
- <p>My Talking Angela 3 is a game that is designed to be fun and entertaining for players of all ages. However, there are some hoax and rumors that circulate on the internet that claim that the game is dangerous or harmful. Some of these hoax and rumors include:</p>
- <ul>
-<li>The game has a hidden camera that spies on the players and their surroundings.</li>
-<li>The game has a hacker or a kidnapper that talks to the players and tries to lure them into danger.</li>
-<li>The game has a virus or a malware that infects the devices and steals personal information.</li>
-<li>The game has a curse or a demon that haunts the players and causes bad luck or misfortune.</li>
-</ul>
- <p>These hoax and rumors are completely false and have no basis in reality. They are created by people who want to scare or prank others, or who have malicious intentions. They are not supported by any evidence or proof, and they are contradicted by the facts and testimonials of millions of players who enjoy the game safely and happily.</p>
- <p>To avoid the hoax and rumors about the game, follow these simple steps:</p>
- <ul>
-<li>Do not believe everything you read or hear on the internet. Always check the source and the credibility of the information before you trust it.</li>
-<li>Do not share or spread the hoax and rumors to others. This will only cause more confusion and panic among the players and the public.</li>
-<li>Do not download or install the game from unofficial or suspicious websites or links. Always use the official platforms, such as App Store, Google Play Store, or Microsoft Store, to get the game.</li>
-<li>Do not give out your personal information or location to anyone in the game or online. Always protect your privacy and security when using any app or service.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>My Talking Angela 3 is a simulation game that lets you adopt, care for, and play with a cute virtual cat named Angela. You can interact with her in various ways, customize her appearance and style, play mini-games and earn coins and diamonds, explore the city and discover new activities, and more. The game is free to download and play on iOS, Android, and Windows devices, but it also offers in-app purchases that allow you to buy coins, gems, or subscriptions using real money.</p>
- <p>If you are a fan of cats and fashion, you will love My Talking Angela 3. The game is fun and interactive, with stunning graphics and sound effects. The game also has regular updates that add new features and content. You can also connect with other players and share your videos, photos, or songs with them. You can also follow the official website [text] and social media accounts [text] [text] [text] of the game for more news, tips, and fun.</p>
- <p>So, what are you waiting for? Download My Talking Angela 3 today and join Angela on her amazing adventure. You will have a blast playing with her and watching her grow from a kitten to an adult. You will also make her happy and help her achieve her dreams. You will be her best friend forever!</p>
- <p>Do you have any questions or comments about My Talking Angela 3? Let us know in the comment section below. We would love to hear from you!</p>
- <h2>FAQs</h2>
- <h3>What is the difference between My Talking Angela 3 and My Talking Angela 2?</h3>
- <p>My Talking Angela 3 is the latest installment in the popular Talking Tom & Friends series, developed by Outfit7. It is an upgraded version of My Talking Angela 2, with new features and content. Some of these features include:</p>
- <ul>
-<li>A 3D world that you can explore with Angela and discover new activities.</li>
-<li>A music studio that you can use to make music with Angela and record your own songs.</li>
-<li>A dance studio that you can use to dance with Angela and learn new moves.</li>
-<li>A photo studio that you can use to take photos with Angela and edit them with filters, stickers, and frames.</li>
-<li>A shopping mall that you can use to shop for new items for Angela, such as clothes, shoes, jewelry, and more.</ <li>A cinema that you can use to watch movies with Angela and enjoy popcorn and drinks.</li>
-</ul>
- <p>My Talking Angela 2 is the previous installment in the series, which has similar features as My Talking Angela 3, but with less variety and quality. Some of these features include:</p>
- <ul>
-<li>A 2D world that you can explore with Angela and discover some activities.</li>
-<li>A music player that you can use to listen to music with Angela.</li>
-<li>A dance floor that you can use to dance with Angela and watch her perform.</li>
-<li>A camera that you can use to take photos with Angela and add some stickers.</li>
-<li>A wardrobe that you can use to dress up Angela with some outfits and accessories.</li>
-</ul>
- <h3>How can I get free gems in My Talking Angela 3?</h3>
- <p>Gems are one of the currencies of My Talking Angela 3, which you can use to buy items or access premium features in the game. You can also use gems to speed up the progress or skip some tasks in the game. You can get gems by using real money or by using some methods that do not require spending money. Some of these methods include:</p>
- <ul>
-<li>Playing mini-games and earning gems as rewards or bonuses.</li>
-<li>Exploring the city and earning gems as rewards or bonuses.</li>
-<li>Watching ads or spinning the wheel of fortune and earning gems as bonuses.</li>
-<li>Completing achievements or daily challenges and earning gems as rewards.</li>
-<li>Collecting stickers and earning gems as rewards.</li>
-<li>Connecting your game to your Facebook account and earning gems as a bonus.</li>
-<li>Inviting your friends to play the game and earning gems as a bonus.</li>
-</ul>
- <h3>How can I turn off the ads in My Talking Angela 3?</h3>
- <p>Ads are one of the ways that My Talking Angela 3 generates revenue and supports its development. Ads also provide some benefits for the players, such as bonuses, discounts, or offers. However, some players may find ads annoying or distracting, and may want to turn them off. There are two ways to turn off the ads in My Talking Angela 3:</p>
- <ul>
-<li>Buying a subscription that removes all the ads from the game. You can choose from different subscription plans, such as monthly, quarterly, or yearly. You can also cancel your subscription at any time.</li>
-<li>Using child mode that reduces the number of ads in the game. You can activate child mode by tapping on the settings icon, then tapping on the child mode icon, then entering your birth year. You can also use parental controls to disable ads completely.</li>
-</ul>
- <h3>Is My Talking Angela 3 safe for kids?</h3>
- <p>My Talking Angela 3 is a game that is suitable for players of all ages, especially those who love cats and fashion. The game is designed to be fun and entertaining, with no violence, gore, or inappropriate content. The game also has child mode and parental controls that allow parents to limit or restrict some features that may not be appropriate for younger players, such as voice recording, video recording, photo editing, music making, dancing lessons, shopping offers, movie genres, sand art, surfing lessons, and more.</p>
- <p>However, like any other app or service, My Talking Angela 3 may have some risks or dangers that parents and kids should be aware of and avoid. Some of these risks or dangers include:</p>
- <ul>
-<li>In-app purchases that may tempt kids to spend real money on the game without their parents' permission or knowledge.</li>
-<li>Ads that may expose kids to inappropriate or harmful content or products.</li>
-<li>Voice recording that may capture kids' personal information or location without their parents' permission or knowledge.</li>
-<li>Video recording that may capture kids' personal information or location without their parents' permission or knowledge.</li>
-<li>Photo editing that may expose kids to inappropriate or harmful content or products.</li>
-<li>Music making that may expose kids to inappropriate or harmful content or products.</li>
-<li>Dancing lessons that may expose kids to inappropriate or harmful content or products.</li>
-<li>Shopping offers that may expose kids to inappropriate or harmful content or products.</li>
-<li>Movie genres that may expose kids to inappropriate or harmful content or products.</li>
-<li>Sand art that may expose kids to inappropriate or harmful content or products.</li>
-<li>Surfing lessons that may expose kids to inappropriate or harmful content or products.</li>
-</ul>
- <p>To ensure that My Talking Angela 3 is safe for kids, follow these simple steps:</p>
- <ul>
-<li>Monitor your kids' gameplay and set limits on their time and spending.</li>
-<li>Use child mode and parental controls to disable or restrict some features that may not be appropriate for younger players.</li>
-<li>Teach your kids to protect their privacy and security when using any app or service.</li>
-<li>Report any hoax or rumors that claim that the game is dangerous or harmful.</li>
-</ul>
- <h3>Can I play My Talking Angela 3 offline?</h3>
- <p>My Talking Angela 3 is a game that requires an internet connection to download and install, as well as to access some features and content, such as in-app purchases, ads, voice recording, video recording, photo editing, music making, dancing lessons, shopping offers, movie genres, sand art, surfing lessons, and more. However, you can also play the game offline without an internet connection, but with some limitations. Some of these limitations include:</p>
- <ul>
-<li>You cannot buy items or access premium features with coins or gems.</li>
-<li>You cannot watch ads or spin the wheel of fortune to earn bonuses.</li>
-<li>You cannot record videos or share them with your friends.</li>
-<li>You cannot edit photos or share them with your friends.</li>
-<li>You cannot make music or share it with your friends.</li>
-<li>You cannot dance or learn new moves.</li>
-<li>You cannot shop for new items or find special offers.</li>
-<li>You cannot watch movies or enjoy popcorn and drinks.</li>
-<li>You cannot play with sand or surf.</li>
-</ul>
- <p>To play My Talking Angela 3 offline, follow these simple steps:</p>
- <ul>
-<li>Make sure you have downloaded and installed the game on your device.</li>
-<li>Turn off your internet connection or switch to airplane mode on your device.</li>
-<li>Launch the game and enjoy playing it offline.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Play Geometry Dash Lite on PC and Mac with BlueStacks.md b/spaces/congsaPfin/Manga-OCR/logs/Play Geometry Dash Lite on PC and Mac with BlueStacks.md
deleted file mode 100644
index 0a02cac7a346ea9de2683ebcee224b308cb3492d..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Play Geometry Dash Lite on PC and Mac with BlueStacks.md	
+++ /dev/null
@@ -1,141 +0,0 @@
-<br />
-<h1>How to Play Geometry Dash Lite on PC</h1>
-<p>Do you love arcade games that challenge your reflexes and skills? Do you want to enjoy a rhythmic-based action platformer with stunning graphics and soundtracks? Do you want to play it on a bigger screen with better performance and controls? If you answered yes to any of these questions, then you should try <b>Geometry Dash Lite</b>, a popular game that you can download and play on your PC using an emulator.</p>
-<h2>geometry dash lite apk pc</h2><br /><p><b><b>DOWNLOAD</b> &#9913;&#9913;&#9913; <a href="https://urlca.com/2uOaBE">https://urlca.com/2uOaBE</a></b></p><br /><br />
-<p>In this article, we will show you what Geometry Dash Lite is, why you should play it on PC, how to download and install it on PC using two different emulators (BlueStacks and NoxPlayer), how to play it on PC with some tips and tricks, what are the features of the game, how to customize your character in the game, how to use practice mode in the game, and how to access more levels in the game. We will also answer some frequently asked questions about the game at the end of the article.</p>
-<h2>What is Geometry Dash Lite?</h2>
-<p>Geometry Dash Lite is an arcade game developed by RobTop Games. It is a simplified version of the full game <b>Geometry Dash</b>, which has more levels, soundtracks, achievements, online level editor, and more. In Geometry Dash Lite, you have to jump and fly your way through danger in a rhythm-based action platformer. You have <h2>How to Play Geometry Dash Lite on PC?</h2>
-<p>Now that you have downloaded and installed Geometry Dash Lite on PC using an emulator, you are ready to play the game. The gameplay is simple but challenging: you have to tap the screen (or press the spacebar on your keyboard) to make your character jump and avoid hitting obstacles. You have to reach the end of the level without crashing or falling. The game will test your reflexes, timing, and patience, as you have to memorize the patterns and rhythms of the obstacles and the music.</p>
-<p>Here are some tips and tricks that might help you play Geometry Dash Lite on PC better:</p>
-<ul>
-<li>Adjust the settings of the game according to your preferences. You can change the sound volume, the graphics quality, the color mode, and the language of the game.</li>
-<li>Use headphones or speakers to enjoy the amazing soundtracks of the game. The music will help you sync your jumps with the beats and create a more immersive experience.</li>
-<li>Don't give up easily. The game is very hard and frustrating, but also very rewarding and satisfying. You will feel a great sense of accomplishment when you complete a level that you have been struggling with for a long time.</li>
-<li>Practice makes perfect. You can use the practice mode in the game to practice any level that you want. In practice mode, you can place checkpoints anywhere in the level and resume from there if you die. This way, you can learn the layout and timing of the level without having to start over every time.</li>
-<li>Have fun. Geometry Dash Lite is a game that is meant to be enjoyed and not taken too seriously. Don't let the difficulty or frustration ruin your mood or your fun. Just relax and have a good time playing this awesome game.</li>
-</ul>
-<h2>What are the Features of Geometry Dash Lite?</h2>
-<p>Geometry Dash Lite is a game that has many features that make it fun and exciting. Here are some of the features of the game:</p>
-<ul>
-<li>The game has 13 levels with different themes, difficulties, and soundtracks. Each level has its own unique design, style, and atmosphere.</li>
-<li>The game has a simple one-touch gameplay that is easy to learn but hard to master. You just have to tap the screen (or press the spacebar) to jump and avoid obstacles.</li>
-<li>The game has a colorful and vibrant graphics that are pleasing to the eye. The game also has a smooth and fluid animation that makes the game look more dynamic and lively.</li>
-<li>The game has an awesome soundtrack that matches the mood and rhythm of each level. The music is catchy, energetic, and immersive.</li>
-<li>The game has a leaderboard system that allows you to compare your scores with other players around the world. You can also see your rank, achievements, and stats in the game.</li>
-</ul> <h2>How to Customize Your Character in Geometry Dash Lite?</h2>
-<p>One of the fun aspects of Geometry Dash Lite is that you can customize your character in the game. You can change the shape, color, and design of your character to make it look more cool and unique. You can also unlock new icons and colors by completing achievements and collecting stars in the game.</p>
-<p>To customize your character in Geometry Dash Lite, you have to follow these steps:</p>
-<ol>
-<li>Go to the main menu of the game and tap on the icon that looks like a square with a face on it. This will take you to the customization screen.</li>
-<li>On the customization screen, you can see different tabs that let you change different aspects of your character. You can tap on the tabs to switch between them.</li>
-<li>The first tab is the icon tab, where you can change the shape of your character. You can scroll through the icons and tap on the one that you like. You can also see how many stars and achievements you need to unlock each icon.</li>
-<li>The second tab is the primary color tab, where you can change the main color of your character. You can scroll through the colors and tap on the one that you like. You can also see how many stars and achievements you need to unlock each color.</li>
-<li>The third tab is the secondary color tab, where you can change the secondary color of your character. This is the color that appears on some parts of your character, such as the eyes, mouth, or outline. You can scroll through the colors and tap on the one that you like. You can also see how many stars and achievements you need to unlock each color.</li>
-<li>The fourth tab is the special tab, where you can change the special design of your character. This is an optional feature that adds some extra details or effects to your character, such as flames, spikes, or glow. You can scroll through the designs and tap on the one that you like. You can also see how many stars and achievements you need to unlock each design.</li>
-<li>Once you are happy with your character, you can tap on the back button to save your changes and return to the main menu.</li>
-</ol>
-<p>Now you have a customized character that reflects your personality and style in Geometry Dash Lite.</p>
-<h2>How to Use Practice Mode in Geometry Dash Lite?</h2>
-<p>Practice mode is a feature in Geometry Dash Lite that allows you to practice any level that you want without having to worry about dying or restarting. In practice mode, you can place checkpoints anywhere in the level and resume from there if you die. This way, you can learn the layout and timing of the level without having to start over every time.</p>
-<p>geometry dash lite download for pc<br />
-geometry dash lite pc online<br />
-geometry dash lite windows 10<br />
-geometry dash lite pc game<br />
-geometry dash lite emulator<br />
-geometry dash lite pc version<br />
-geometry dash lite free play<br />
-geometry dash lite pc full screen<br />
-geometry dash lite windows 7<br />
-geometry dash lite pc no download<br />
-geometry dash lite bluestacks<br />
-geometry dash lite pc controls<br />
-geometry dash lite free download<br />
-geometry dash lite pc unblocked<br />
-geometry dash lite windows 8<br />
-geometry dash lite pc hack<br />
-geometry dash lite emulator download<br />
-geometry dash lite pc cheats<br />
-geometry dash lite apk file<br />
-geometry dash lite pc mod<br />
-geometry dash lite windows xp<br />
-geometry dash lite pc offline<br />
-geometry dash lite apk install<br />
-geometry dash lite pc update<br />
-geometry dash lite windows store<br />
-geometry dash lite pc gameplay<br />
-geometry dash lite apk latest version<br />
-geometry dash lite pc requirements<br />
-geometry dash lite windows phone<br />
-geometry dash lite pc level editor<br />
-geometry dash lite apk pure<br />
-geometry dash lite pc steam<br />
-geometry dash lite windows vista<br />
-geometry dash lite pc tips and tricks<br />
-geometry dash lite apk for laptop<br />
-geometry dash lite pc review<br />
-geometry dash lite windows 11<br />
-geometry dash lite pc custom levels<br />
-geometry dash lite apk mod menu<br />
-geometry dash lite pc keyboard controls<br />
-geometry dash lite windows app store<br />
-geometry dash lite pc bug fixes and improvements <br />
-geometry dash lite apk no ads <br />
-geometry dash lite pc achievements <br />
-geometry dash lite windows 10 download <br />
-geometry dash lite pc soundtracks <br />
-geometry dash lite apk old version <br />
-geometry dash lite pc leaderboards <br />
-geometry dash lite windows 7 free download</p>
-<p>To use practice mode in Geometry Dash Lite, you have to follow these steps:</p>
-<ol>
-<li>Go to the level selection screen of the game and tap on any level that you want to practice.</li>
-<li>On the level screen, tap on the icon that looks like a flag with a play button on it. This will start practice mode for that level.</li>
-<li>In practice mode, you can play the level as usual, but with some differences. First, you will see a green flag at the beginning of the level. This is your first checkpoint. Second, you will see a yellow flag at every place where you tap the screen (or press the spacebar). This is where you place your checkpoints. Third, if you die or fall off the screen, you will not restart from the beginning of the level, but from your last checkpoint.</li>
-<li>You can place as many checkpoints as you want in practice mode. You can also delete any checkpoint by tapping on it again. You can also skip any checkpoint by tapping on it twice.</li>
-<li>You can exit practice mode at any time by tapping on the pause button and then tapping on the exit button. This will take you back to the level screen.</li>
-</ol>
-<p>Practice mode is a great way to improve your skills and confidence in Geometry Dash Lite. You can use it to master any level that you find difficult or challenging.</p> <h2>How to Access More Levels in Geometry Dash Lite?</h2>
-<p>Geometry Dash Lite has 13 levels that you can play for free, but if you want to access more levels, you have to buy the full version of the game. The full version of Geometry Dash has over 40 levels, plus an online level editor that allows you to create and share your own levels with other players. You can also play the levels created by other players and rate them.</p>
-<p>To buy the full version of Geometry Dash, you have to follow these steps:</p>
-<ol>
-<li>Go to the main menu of Geometry Dash Lite and tap on the icon that looks like a lock with a plus sign on it. This will take you to the store screen.</li>
-<li>On the store screen, you will see the price and the description of the full version of Geometry Dash. You will also see some screenshots and videos of the game.</li>
-<li>If you want to buy the game, tap on the buy button and follow the instructions to complete the payment. You will need a valid credit card or a Google Play account to buy the game.</li>
-<li>Once you have bought the game, you can download and install it on your device. You can also transfer your progress and achievements from Geometry Dash Lite to Geometry Dash by using the account system in the game.</li>
-<li>Now you can enjoy playing more levels and creating your own levels in Geometry Dash.</li>
-</ol>
-<p>The full version of Geometry Dash is worth buying if you love the game and want to experience more content and features. It is also a way to support the developer and appreciate their hard work.</p>
-<h2>Conclusion</h2>
-<p>Geometry Dash Lite is a fun and challenging arcade game that you can play on your PC using an emulator. It has a simple one-touch gameplay, a colorful graphics, an awesome soundtrack, and a variety of levels that will test your skills and reflexes. You can also customize your character, use practice mode, and access more levels by buying the full version of the game.</p>
-<p>If you are looking for a game that will keep you entertained and engaged for hours, you should try Geometry Dash Lite. It is a game that will make you jump, fly, and dash through danger in a rhythm-based action platformer. It is a game that will make you feel frustrated and satisfied at the same time. It is a game that will make you love geometry.</p>
-<p>So what are you waiting for? Download Geometry Dash Lite on PC today and enjoy this amazing game.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Geometry Dash Lite:</p>
-<h3>Q1: What are the system requirements for playing Geometry Dash Lite on PC?</h3>
-<p>A1: The system requirements for playing Geometry Dash Lite on PC are not very high. You just need a Windows or Mac computer with at least 2 GB of RAM, 4 GB of disk space, and a decent graphics card. You also need an emulator like BlueStacks or NoxPlayer to run the game.</p>
-<h3>Q2: Is Geometry Dash Lite free to play?</h3>
-<p>A2: Yes, Geometry Dash Lite is free to play. You can download and play it on your PC without paying anything. However, if you want to access more levels and features, you have to buy the full version of Geometry Dash for $1.99.</p>
-<h3>Q3: How many levels are there in Geometry Dash Lite?</h3>
-<p>A3: There are 13 levels in Geometry Dash Lite, each with its own theme, difficulty, and soundtrack. The levels are:</p>
-<ul>
-<li>Stereo Madness</li>
-<li>Back On Track</li>
-<li>Polar Geist</li>
-<li>Dry Out</li>
-<li>Base After Base</li>
-<li>Cant Let Go</li>
-<li>Jumper</li>
-<li>Time Machine</li>
-<li>Cycles</li>
-<li>xStep</li>
-<li>Clutterfunk</li>
-<li>Theory of Everything</li>
-<li>Electroman Adventures</li>
-</ul>
- <h3>Q4: Can I create my own levels in Geometry Dash Lite?</h3>
- <p>A4: No, you cannot create your own levels in Geometry Dash Lite. This feature is only available in the full version of Geometry Dash, which has an online level editor that allows you to create and share your own levels with other players.</p>
- <h3>Q5: Can I play Geometry Dash Lite offline?</h3>
- <p>A5: Yes, you can play Geometry Dash Lite offline. You do not need an internet connection to play the game, except for downloading and installing it on your PC using an emulator. However, if you want to access some online features like leaderboards, achievements, or user-generated levels, you will need an internet connection.</p>
- <p>I hope this article has helped you learn more about Geometry Dash Lite and how to play it on PC. If you have any questions or feedback, please leave a comment below. Thank you for reading and have a great day.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/Acoustica Cd Dvd Label Maker 3.40 Keygen Crack [EXCLUSIVE].md b/spaces/contluForse/HuggingGPT/assets/Acoustica Cd Dvd Label Maker 3.40 Keygen Crack [EXCLUSIVE].md
deleted file mode 100644
index d843294c0d7128cb53ea21c3f9f9dc9cdac949b3..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Acoustica Cd Dvd Label Maker 3.40 Keygen Crack [EXCLUSIVE].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
-<p>Besides a number of design elements, this tools allows you to create your own CD, DVD. With this program, you can create your own labels, DVD cases, and documents which can printed on your CD or DVD. with this software, you can create your own CD, DVD, labels, and DVD cases. This software can create your own labels, DVD cases, and documents which can printed on your CD or DVD. You can design your own CD, DVD, labels, and DVD cases. With this software, you can create your own labels, DVD cases, and documents which can printed on your CD or DVD. You can also export your document or DVD case directly into a special file format. You can combine two or more files. Release Date: December 18, 2008. The program supports English and many international languages. Pack size: 1062.25 kB.... acoustica cd label maker 3.40 crack; acoustica cd dvd label maker 3.40 crack; Acoustica Label Maker Crack; Acoustica CD Label Maker Crack; Acoustica DVD Label Maker Crack; Acoustica Label Maker Crack; Acoustica CD Label Maker 3.40 Keygen; Acoustica CD Label Maker 3.40 Keygen; Acoustica Cd Dvd Label Maker 3.40 Keygen; Acoustica Cd Dvd Label Maker 3.40 Keygen; Acoustica CD Label Maker 3.40 Keygen; Acoustica CD Label Maker 3.40 Keygen; Acoustica CD Label Maker 3.40 Keygen</p>
-<h2>Acoustica Cd Dvd Label Maker 3.40 Keygen Crack</h2><br /><p><b><b>Download</b> > <a href="https://ssurll.com/2uzyIh">https://ssurll.com/2uzyIh</a></b></p><br /><br />
-<p> Create your own CD, DVD, labels, DVD cases, and documents that can printed on your CD or DVD. With the program you can design your own CD, DVD, labels, DVD cases, and documents that can printed on your CD or DVD. You can design your own labels, CD case templates, labels, CD cases, and documents that can printed on your CD or DVD, plus you can create your own title page for your CD or DVD, and include text and graphics. With this software, you can print directly to CD with the utmost simplicity and flexibility! With this software, you can create your own labels, DVD cases, and documents which can printed on your CD or DVD. You can also export your document or DVD case directly into a special file format. And you can combine two or more files. Release Date: December 18, 2008. You can design your own CD, DVD, labels, CD cases, and documents that can printed on your CD or DVD. And you can create your own titles for your CD or DVD. Release Date: December 18, 2008. Easy interface with a large selection of different templates and effects. You can design your own CD, DVD, labels, CD cases, and documents which can printed on your CD or DVD. Also, you can create your own CD, DVD, labels, CD cases, and documents which can printed on your CD or DVD. With this software, you can print directly to CD with the utmost simplicity and flexibility! Easy interface with a large selection of different templates and effects. Release Date: December 18, 2008. acoustica cd label maker 3.40 crack; acoustica cd dvd label maker 3.40 crack; Acoustica Label Maker Crack; Acoustica CD Label Maker Crack; Acoustica DVD Label Maker Crack; Acoustica Label Maker Crack; Acoustica CD Label Maker 3.40 Keygen; Acoustica CD Label Maker 3.40 Keygen; Acoustica Cd Dvd Label Maker 3.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/Corel VideoStudio Pro X7 Keygen Only How to Get Full Pro Version Without Paying.md b/spaces/contluForse/HuggingGPT/assets/Corel VideoStudio Pro X7 Keygen Only How to Get Full Pro Version Without Paying.md
deleted file mode 100644
index b2a50045e2255869efd6ebcb28e5313f7a4782f2..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Corel VideoStudio Pro X7 Keygen Only How to Get Full Pro Version Without Paying.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>corel videostudio pro x7 keygen only</h2><br /><p><b><b>DOWNLOAD</b> &#10002; &#10002; &#10002; <a href="https://ssurll.com/2uzy5G">https://ssurll.com/2uzy5G</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
deleted file mode 100644
index 722d5d8d71f75486e2db3008907c4eadfca41d63..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/depthwise_separable_conv_module.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .conv_module import ConvModule
-
-
-class DepthwiseSeparableConvModule(nn.Module):
-    """Depthwise separable convolution module.
-
-    See https://arxiv.org/pdf/1704.04861.pdf for details.
-
-    This module can replace a ConvModule with the conv block replaced by two
-    conv block: depthwise conv block and pointwise conv block. The depthwise
-    conv block contains depthwise-conv/norm/activation layers. The pointwise
-    conv block contains pointwise-conv/norm/activation layers. It should be
-    noted that there will be norm/activation layer in the depthwise conv block
-    if `norm_cfg` and `act_cfg` are specified.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-            Same as that in ``nn._ConvNd``.
-        out_channels (int): Number of channels produced by the convolution.
-            Same as that in ``nn._ConvNd``.
-        kernel_size (int | tuple[int]): Size of the convolving kernel.
-            Same as that in ``nn._ConvNd``.
-        stride (int | tuple[int]): Stride of the convolution.
-            Same as that in ``nn._ConvNd``. Default: 1.
-        padding (int | tuple[int]): Zero-padding added to both sides of
-            the input. Same as that in ``nn._ConvNd``. Default: 0.
-        dilation (int | tuple[int]): Spacing between kernel elements.
-            Same as that in ``nn._ConvNd``. Default: 1.
-        norm_cfg (dict): Default norm config for both depthwise ConvModule and
-            pointwise ConvModule. Default: None.
-        act_cfg (dict): Default activation config for both depthwise ConvModule
-            and pointwise ConvModule. Default: dict(type='ReLU').
-        dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
-            'default', it will be the same as `norm_cfg`. Default: 'default'.
-        dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
-            'default', it will be the same as `act_cfg`. Default: 'default'.
-        pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
-            'default', it will be the same as `norm_cfg`. Default: 'default'.
-        pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
-            'default', it will be the same as `act_cfg`. Default: 'default'.
-        kwargs (optional): Other shared arguments for depthwise and pointwise
-            ConvModule. See ConvModule for ref.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 norm_cfg=None,
-                 act_cfg=dict(type='ReLU'),
-                 dw_norm_cfg='default',
-                 dw_act_cfg='default',
-                 pw_norm_cfg='default',
-                 pw_act_cfg='default',
-                 **kwargs):
-        super(DepthwiseSeparableConvModule, self).__init__()
-        assert 'groups' not in kwargs, 'groups should not be specified'
-
-        # if norm/activation config of depthwise/pointwise ConvModule is not
-        # specified, use default config.
-        dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
-        dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
-        pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
-        pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
-
-        # depthwise convolution
-        self.depthwise_conv = ConvModule(
-            in_channels,
-            in_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=in_channels,
-            norm_cfg=dw_norm_cfg,
-            act_cfg=dw_act_cfg,
-            **kwargs)
-
-        self.pointwise_conv = ConvModule(
-            in_channels,
-            out_channels,
-            1,
-            norm_cfg=pw_norm_cfg,
-            act_cfg=pw_act_cfg,
-            **kwargs)
-
-    def forward(self, x):
-        x = self.depthwise_conv(x)
-        x = self.pointwise_conv(x)
-        return x
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/utils/timer.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/utils/timer.py
deleted file mode 100644
index e3db7d497d8b374e18b5297e0a1d6eb186fd8cba..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/utils/timer.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from time import time
-
-
-class TimerError(Exception):
-
-    def __init__(self, message):
-        self.message = message
-        super(TimerError, self).__init__(message)
-
-
-class Timer:
-    """A flexible Timer class.
-
-    :Example:
-
-    >>> import time
-    >>> import annotator.uniformer.mmcv as mmcv
-    >>> with mmcv.Timer():
-    >>>     # simulate a code block that will run for 1s
-    >>>     time.sleep(1)
-    1.000
-    >>> with mmcv.Timer(print_tmpl='it takes {:.1f} seconds'):
-    >>>     # simulate a code block that will run for 1s
-    >>>     time.sleep(1)
-    it takes 1.0 seconds
-    >>> timer = mmcv.Timer()
-    >>> time.sleep(0.5)
-    >>> print(timer.since_start())
-    0.500
-    >>> time.sleep(0.5)
-    >>> print(timer.since_last_check())
-    0.500
-    >>> print(timer.since_start())
-    1.000
-    """
-
-    def __init__(self, start=True, print_tmpl=None):
-        self._is_running = False
-        self.print_tmpl = print_tmpl if print_tmpl else '{:.3f}'
-        if start:
-            self.start()
-
-    @property
-    def is_running(self):
-        """bool: indicate whether the timer is running"""
-        return self._is_running
-
-    def __enter__(self):
-        self.start()
-        return self
-
-    def __exit__(self, type, value, traceback):
-        print(self.print_tmpl.format(self.since_last_check()))
-        self._is_running = False
-
-    def start(self):
-        """Start the timer."""
-        if not self._is_running:
-            self._t_start = time()
-            self._is_running = True
-        self._t_last = time()
-
-    def since_start(self):
-        """Total time since the timer is started.
-
-        Returns (float): Time in seconds.
-        """
-        if not self._is_running:
-            raise TimerError('timer is not running')
-        self._t_last = time()
-        return self._t_last - self._t_start
-
-    def since_last_check(self):
-        """Time since the last checking.
-
-        Either :func:`since_start` or :func:`since_last_check` is a checking
-        operation.
-
-        Returns (float): Time in seconds.
-        """
-        if not self._is_running:
-            raise TimerError('timer is not running')
-        dur = time() - self._t_last
-        self._t_last = time()
-        return dur
-
-
-_g_timers = {}  # global timers
-
-
-def check_time(timer_id):
-    """Add check points in a single line.
-
-    This method is suitable for running a task on a list of items. A timer will
-    be registered when the method is called for the first time.
-
-    :Example:
-
-    >>> import time
-    >>> import annotator.uniformer.mmcv as mmcv
-    >>> for i in range(1, 6):
-    >>>     # simulate a code block
-    >>>     time.sleep(i)
-    >>>     mmcv.check_time('task1')
-    2.000
-    3.000
-    4.000
-    5.000
-
-    Args:
-        timer_id (str): Timer identifier.
-    """
-    if timer_id not in _g_timers:
-        _g_timers[timer_id] = Timer()
-        return 0
-    else:
-        return _g_timers[timer_id].since_last_check()
diff --git a/spaces/cvlab/zero123-live/ldm/data/dummy.py b/spaces/cvlab/zero123-live/ldm/data/dummy.py
deleted file mode 100644
index 3b74a77fe8954686e480d28aaed19e52d3e3c9b7..0000000000000000000000000000000000000000
--- a/spaces/cvlab/zero123-live/ldm/data/dummy.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import numpy as np
-import random
-import string
-from torch.utils.data import Dataset, Subset
-
-class DummyData(Dataset):
-    def __init__(self, length, size):
-        self.length = length
-        self.size = size
-
-    def __len__(self):
-        return self.length
-
-    def __getitem__(self, i):
-        x = np.random.randn(*self.size)
-        letters = string.ascii_lowercase
-        y = ''.join(random.choice(string.ascii_lowercase) for i in range(10))
-        return {"jpg": x, "txt": y}
-
-
-class DummyDataWithEmbeddings(Dataset):
-    def __init__(self, length, size, emb_size):
-        self.length = length
-        self.size = size
-        self.emb_size = emb_size
-
-    def __len__(self):
-        return self.length
-
-    def __getitem__(self, i):
-        x = np.random.randn(*self.size)
-        y = np.random.randn(*self.emb_size).astype(np.float32)
-        return {"jpg": x, "txt": y}
-
diff --git a/spaces/cyanab/GlobalVoice1/app.py b/spaces/cyanab/GlobalVoice1/app.py
deleted file mode 100644
index af2ce879b98b2f6bfd5cff0f4d6c1043e6e9d972..0000000000000000000000000000000000000000
--- a/spaces/cyanab/GlobalVoice1/app.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#imports
-import gradio as gr
-import wave
-import requests
-import moviepy.editor as mp
-import openai
-import ffmpeg
-import os
-
-# Set OpenAI and ELEVENLABS API keys
-openai.api_key = os.getenv('openai')
-ELEVENLABS_API_KEY = os.getenv('elev_labs')
-
-import wave
-import json
-#@title forked 61 
-def extract_audio(input_video):#input_video='video_path+name.mp4'
-    # Load the input video
-    # video = mp.VideoFileClip(input_video.name)
-    video = mp.VideoFileClip(input_video)
-    # Extract audio from the video
-    audio = video.audio
-    extracted_audio = "audio.wav" # assign the file name to the variable
-    audio.write_audiofile(extracted_audio) # write the audio to the file
-    with open(extracted_audio, "rb") as f: # open the file as a binary object
-      transcription = openai.Audio.transcribe("whisper-1", f) # transcribe the file object
-    # ptranscription = print(transcription) # return the transcription
-    
-    target_language = 'English'
-    response = openai.Completion.create(
-          engine="text-davinci-003",
-          prompt=f"Translate the following text to {target_language}: {transcription}",
-          temperature=0.5,
-          max_tokens=100,
-          top_p=1,
-          frequency_penalty=0,
-          presence_penalty=0,
-          stop=None)
-    # Extract the translated text from the API response
-    translated_text = response.choices[0].text.strip()
-
-    headers = {
-	    'accept': 'audio/mpeg',
-	    'xi-api-key': ELEVENLABS_API_KEY,
-	    'Content-Type': 'application/json',
-    }
-    data = {
-	    'text': translated_text,
-	    'language': 'fr-FR',
-	    'speaker': 'female'
-    }
-    response = requests.post('https://api.elevenlabs.io/v1/text-to-speech/21m00Tcm4TlvDq8ikWAM',
-	                         headers=headers, data=json.dumps(data))
-	  # Check the status code of the response
-    if response.status_code == 200:
-	    # Return the speech output as bytes
-      speech_bytes = response.content
-	    # Write the speech output to a wav file
-      with wave.open('translated_audio.wav', 'wb') as f:
-      #with wave.open('translated_audio.wav', 'wb') as f:
-          f.setnchannels(1)
-          f.setsampwidth(2)
-          f.setframerate(44100)
-          speech = f.writeframes(speech_bytes)
-
-      video_mp = video
-      audio_mp = speech
-
-      # combine audio_mp n video_mp
-      final_video = video_mp.set_audio(audio_mp)
-
-      # Write the final video to a file
-      video_combined = final_video.write_videofile("video_combined.mp4")
-    else:
-	    # Handle errors or redirections
-      print(f"Request failed with status code {response.status_code}")
-    return 'translated_audio.wav'   
-
-
-# Gradio
-demo = gr.Interface(fn=extract_audio,
-                    inputs=gr.inputs.Video(), 
-                    outputs=gr.outputs.Audio(type='numpy'))
-# demo.launch(debug=True)
-# demo.launch(share=True, debug=True)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/daspartho/anime-or-not/README.md b/spaces/daspartho/anime-or-not/README.md
deleted file mode 100644
index bed7e3d2c7230742e135e04d3fc0398b8aac6cd9..0000000000000000000000000000000000000000
--- a/spaces/daspartho/anime-or-not/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Anime or Not?
-emoji: ⚡
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dawood/gradio_videogallery/Dockerfile b/spaces/dawood/gradio_videogallery/Dockerfile
deleted file mode 100644
index 33b38d8d7f466b83fd6f6afa04c92521432b10cc..0000000000000000000000000000000000000000
--- a/spaces/dawood/gradio_videogallery/Dockerfile
+++ /dev/null
@@ -1,12 +0,0 @@
-
-FROM python:3.9
-
-WORKDIR /code
-
-COPY --link --chown=1000 . .
-
-RUN pip install --no-cache-dir -r requirements.txt
-
-ENV PYTHONUNBUFFERED=1 	GRADIO_ALLOW_FLAGGING=never 	GRADIO_NUM_PORTS=1 	GRADIO_SERVER_NAME=0.0.0.0     GRADIO_SERVER_PORT=7860 	SYSTEM=spaces
-
-CMD ["python", "app.py"]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/WalImageFile.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/WalImageFile.py
deleted file mode 100644
index e4f47aa04bc148f3ff151bec5595f8626833b938..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/WalImageFile.py
+++ /dev/null
@@ -1,123 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# WAL file handling
-#
-# History:
-# 2003-04-23 fl   created
-#
-# Copyright (c) 2003 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-This reader is based on the specification available from:
-https://www.flipcode.com/archives/Quake_2_BSP_File_Format.shtml
-and has been tested with a few sample files found using google.
-
-.. note::
-    This format cannot be automatically recognized, so the reader
-    is not registered for use with :py:func:`PIL.Image.open()`.
-    To open a WAL file, use the :py:func:`PIL.WalImageFile.open()` function instead.
-"""
-
-from . import Image, ImageFile
-from ._binary import i32le as i32
-
-
-class WalImageFile(ImageFile.ImageFile):
-    format = "WAL"
-    format_description = "Quake2 Texture"
-
-    def _open(self):
-        self.mode = "P"
-
-        # read header fields
-        header = self.fp.read(32 + 24 + 32 + 12)
-        self._size = i32(header, 32), i32(header, 36)
-        Image._decompression_bomb_check(self.size)
-
-        # load pixel data
-        offset = i32(header, 40)
-        self.fp.seek(offset)
-
-        # strings are null-terminated
-        self.info["name"] = header[:32].split(b"\0", 1)[0]
-        next_name = header[56 : 56 + 32].split(b"\0", 1)[0]
-        if next_name:
-            self.info["next_name"] = next_name
-
-    def load(self):
-        if not self.im:
-            self.im = Image.core.new(self.mode, self.size)
-            self.frombytes(self.fp.read(self.size[0] * self.size[1]))
-            self.putpalette(quake2palette)
-        return Image.Image.load(self)
-
-
-def open(filename):
-    """
-    Load texture from a Quake2 WAL texture file.
-
-    By default, a Quake2 standard palette is attached to the texture.
-    To override the palette, use the :py:func:`PIL.Image.Image.putpalette()` method.
-
-    :param filename: WAL file name, or an opened file handle.
-    :returns: An image instance.
-    """
-    return WalImageFile(filename)
-
-
-quake2palette = (
-    # default palette taken from piffo 0.93 by Hans Häggström
-    b"\x01\x01\x01\x0b\x0b\x0b\x12\x12\x12\x17\x17\x17\x1b\x1b\x1b\x1e"
-    b"\x1e\x1e\x22\x22\x22\x26\x26\x26\x29\x29\x29\x2c\x2c\x2c\x2f\x2f"
-    b"\x2f\x32\x32\x32\x35\x35\x35\x37\x37\x37\x3a\x3a\x3a\x3c\x3c\x3c"
-    b"\x24\x1e\x13\x22\x1c\x12\x20\x1b\x12\x1f\x1a\x10\x1d\x19\x10\x1b"
-    b"\x17\x0f\x1a\x16\x0f\x18\x14\x0d\x17\x13\x0d\x16\x12\x0d\x14\x10"
-    b"\x0b\x13\x0f\x0b\x10\x0d\x0a\x0f\x0b\x0a\x0d\x0b\x07\x0b\x0a\x07"
-    b"\x23\x23\x26\x22\x22\x25\x22\x20\x23\x21\x1f\x22\x20\x1e\x20\x1f"
-    b"\x1d\x1e\x1d\x1b\x1c\x1b\x1a\x1a\x1a\x19\x19\x18\x17\x17\x17\x16"
-    b"\x16\x14\x14\x14\x13\x13\x13\x10\x10\x10\x0f\x0f\x0f\x0d\x0d\x0d"
-    b"\x2d\x28\x20\x29\x24\x1c\x27\x22\x1a\x25\x1f\x17\x38\x2e\x1e\x31"
-    b"\x29\x1a\x2c\x25\x17\x26\x20\x14\x3c\x30\x14\x37\x2c\x13\x33\x28"
-    b"\x12\x2d\x24\x10\x28\x1f\x0f\x22\x1a\x0b\x1b\x14\x0a\x13\x0f\x07"
-    b"\x31\x1a\x16\x30\x17\x13\x2e\x16\x10\x2c\x14\x0d\x2a\x12\x0b\x27"
-    b"\x0f\x0a\x25\x0f\x07\x21\x0d\x01\x1e\x0b\x01\x1c\x0b\x01\x1a\x0b"
-    b"\x01\x18\x0a\x01\x16\x0a\x01\x13\x0a\x01\x10\x07\x01\x0d\x07\x01"
-    b"\x29\x23\x1e\x27\x21\x1c\x26\x20\x1b\x25\x1f\x1a\x23\x1d\x19\x21"
-    b"\x1c\x18\x20\x1b\x17\x1e\x19\x16\x1c\x18\x14\x1b\x17\x13\x19\x14"
-    b"\x10\x17\x13\x0f\x14\x10\x0d\x12\x0f\x0b\x0f\x0b\x0a\x0b\x0a\x07"
-    b"\x26\x1a\x0f\x23\x19\x0f\x20\x17\x0f\x1c\x16\x0f\x19\x13\x0d\x14"
-    b"\x10\x0b\x10\x0d\x0a\x0b\x0a\x07\x33\x22\x1f\x35\x29\x26\x37\x2f"
-    b"\x2d\x39\x35\x34\x37\x39\x3a\x33\x37\x39\x30\x34\x36\x2b\x31\x34"
-    b"\x27\x2e\x31\x22\x2b\x2f\x1d\x28\x2c\x17\x25\x2a\x0f\x20\x26\x0d"
-    b"\x1e\x25\x0b\x1c\x22\x0a\x1b\x20\x07\x19\x1e\x07\x17\x1b\x07\x14"
-    b"\x18\x01\x12\x16\x01\x0f\x12\x01\x0b\x0d\x01\x07\x0a\x01\x01\x01"
-    b"\x2c\x21\x21\x2a\x1f\x1f\x29\x1d\x1d\x27\x1c\x1c\x26\x1a\x1a\x24"
-    b"\x18\x18\x22\x17\x17\x21\x16\x16\x1e\x13\x13\x1b\x12\x12\x18\x10"
-    b"\x10\x16\x0d\x0d\x12\x0b\x0b\x0d\x0a\x0a\x0a\x07\x07\x01\x01\x01"
-    b"\x2e\x30\x29\x2d\x2e\x27\x2b\x2c\x26\x2a\x2a\x24\x28\x29\x23\x27"
-    b"\x27\x21\x26\x26\x1f\x24\x24\x1d\x22\x22\x1c\x1f\x1f\x1a\x1c\x1c"
-    b"\x18\x19\x19\x16\x17\x17\x13\x13\x13\x10\x0f\x0f\x0d\x0b\x0b\x0a"
-    b"\x30\x1e\x1b\x2d\x1c\x19\x2c\x1a\x17\x2a\x19\x14\x28\x17\x13\x26"
-    b"\x16\x10\x24\x13\x0f\x21\x12\x0d\x1f\x10\x0b\x1c\x0f\x0a\x19\x0d"
-    b"\x0a\x16\x0b\x07\x12\x0a\x07\x0f\x07\x01\x0a\x01\x01\x01\x01\x01"
-    b"\x28\x29\x38\x26\x27\x36\x25\x26\x34\x24\x24\x31\x22\x22\x2f\x20"
-    b"\x21\x2d\x1e\x1f\x2a\x1d\x1d\x27\x1b\x1b\x25\x19\x19\x21\x17\x17"
-    b"\x1e\x14\x14\x1b\x13\x12\x17\x10\x0f\x13\x0d\x0b\x0f\x0a\x07\x07"
-    b"\x2f\x32\x29\x2d\x30\x26\x2b\x2e\x24\x29\x2c\x21\x27\x2a\x1e\x25"
-    b"\x28\x1c\x23\x26\x1a\x21\x25\x18\x1e\x22\x14\x1b\x1f\x10\x19\x1c"
-    b"\x0d\x17\x1a\x0a\x13\x17\x07\x10\x13\x01\x0d\x0f\x01\x0a\x0b\x01"
-    b"\x01\x3f\x01\x13\x3c\x0b\x1b\x39\x10\x20\x35\x14\x23\x31\x17\x23"
-    b"\x2d\x18\x23\x29\x18\x3f\x3f\x3f\x3f\x3f\x39\x3f\x3f\x31\x3f\x3f"
-    b"\x2a\x3f\x3f\x20\x3f\x3f\x14\x3f\x3c\x12\x3f\x39\x0f\x3f\x35\x0b"
-    b"\x3f\x32\x07\x3f\x2d\x01\x3d\x2a\x01\x3b\x26\x01\x39\x21\x01\x37"
-    b"\x1d\x01\x34\x1a\x01\x32\x16\x01\x2f\x12\x01\x2d\x0f\x01\x2a\x0b"
-    b"\x01\x27\x07\x01\x23\x01\x01\x1d\x01\x01\x17\x01\x01\x10\x01\x01"
-    b"\x3d\x01\x01\x19\x19\x3f\x3f\x01\x01\x01\x01\x3f\x16\x16\x13\x10"
-    b"\x10\x0f\x0d\x0d\x0b\x3c\x2e\x2a\x36\x27\x20\x30\x21\x18\x29\x1b"
-    b"\x10\x3c\x39\x37\x37\x32\x2f\x31\x2c\x28\x2b\x26\x21\x30\x22\x20"
-)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/basePen.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/basePen.py
deleted file mode 100644
index ac8abd40cede6aede692c059f11081f4397d5eb1..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/pens/basePen.py
+++ /dev/null
@@ -1,444 +0,0 @@
-"""fontTools.pens.basePen.py -- Tools and base classes to build pen objects.
-
-The Pen Protocol
-
-A Pen is a kind of object that standardizes the way how to "draw" outlines:
-it is a middle man between an outline and a drawing. In other words:
-it is an abstraction for drawing outlines, making sure that outline objects
-don't need to know the details about how and where they're being drawn, and
-that drawings don't need to know the details of how outlines are stored.
-
-The most basic pattern is this::
-
-	outline.draw(pen)  # 'outline' draws itself onto 'pen'
-
-Pens can be used to render outlines to the screen, but also to construct
-new outlines. Eg. an outline object can be both a drawable object (it has a
-draw() method) as well as a pen itself: you *build* an outline using pen
-methods.
-
-The AbstractPen class defines the Pen protocol. It implements almost
-nothing (only no-op closePath() and endPath() methods), but is useful
-for documentation purposes. Subclassing it basically tells the reader:
-"this class implements the Pen protocol.". An examples of an AbstractPen
-subclass is :py:class:`fontTools.pens.transformPen.TransformPen`.
-
-The BasePen class is a base implementation useful for pens that actually
-draw (for example a pen renders outlines using a native graphics engine).
-BasePen contains a lot of base functionality, making it very easy to build
-a pen that fully conforms to the pen protocol. Note that if you subclass
-BasePen, you *don't* override moveTo(), lineTo(), etc., but _moveTo(),
-_lineTo(), etc. See the BasePen doc string for details. Examples of
-BasePen subclasses are fontTools.pens.boundsPen.BoundsPen and
-fontTools.pens.cocoaPen.CocoaPen.
-
-Coordinates are usually expressed as (x, y) tuples, but generally any
-sequence of length 2 will do.
-"""
-
-from typing import Tuple, Dict
-
-from fontTools.misc.loggingTools import LogMixin
-from fontTools.misc.transform import DecomposedTransform
-
-__all__ = [
-    "AbstractPen",
-    "NullPen",
-    "BasePen",
-    "PenError",
-    "decomposeSuperBezierSegment",
-    "decomposeQuadraticSegment",
-]
-
-
-class PenError(Exception):
-    """Represents an error during penning."""
-
-
-class OpenContourError(PenError):
-    pass
-
-
-class AbstractPen:
-    def moveTo(self, pt: Tuple[float, float]) -> None:
-        """Begin a new sub path, set the current point to 'pt'. You must
-        end each sub path with a call to pen.closePath() or pen.endPath().
-        """
-        raise NotImplementedError
-
-    def lineTo(self, pt: Tuple[float, float]) -> None:
-        """Draw a straight line from the current point to 'pt'."""
-        raise NotImplementedError
-
-    def curveTo(self, *points: Tuple[float, float]) -> None:
-        """Draw a cubic bezier with an arbitrary number of control points.
-
-        The last point specified is on-curve, all others are off-curve
-        (control) points. If the number of control points is > 2, the
-        segment is split into multiple bezier segments. This works
-        like this:
-
-        Let n be the number of control points (which is the number of
-        arguments to this call minus 1). If n==2, a plain vanilla cubic
-        bezier is drawn. If n==1, we fall back to a quadratic segment and
-        if n==0 we draw a straight line. It gets interesting when n>2:
-        n-1 PostScript-style cubic segments will be drawn as if it were
-        one curve. See decomposeSuperBezierSegment().
-
-        The conversion algorithm used for n>2 is inspired by NURB
-        splines, and is conceptually equivalent to the TrueType "implied
-        points" principle. See also decomposeQuadraticSegment().
-        """
-        raise NotImplementedError
-
-    def qCurveTo(self, *points: Tuple[float, float]) -> None:
-        """Draw a whole string of quadratic curve segments.
-
-        The last point specified is on-curve, all others are off-curve
-        points.
-
-        This method implements TrueType-style curves, breaking up curves
-        using 'implied points': between each two consequtive off-curve points,
-        there is one implied point exactly in the middle between them. See
-        also decomposeQuadraticSegment().
-
-        The last argument (normally the on-curve point) may be None.
-        This is to support contours that have NO on-curve points (a rarely
-        seen feature of TrueType outlines).
-        """
-        raise NotImplementedError
-
-    def closePath(self) -> None:
-        """Close the current sub path. You must call either pen.closePath()
-        or pen.endPath() after each sub path.
-        """
-        pass
-
-    def endPath(self) -> None:
-        """End the current sub path, but don't close it. You must call
-        either pen.closePath() or pen.endPath() after each sub path.
-        """
-        pass
-
-    def addComponent(
-        self,
-        glyphName: str,
-        transformation: Tuple[float, float, float, float, float, float],
-    ) -> None:
-        """Add a sub glyph. The 'transformation' argument must be a 6-tuple
-        containing an affine transformation, or a Transform object from the
-        fontTools.misc.transform module. More precisely: it should be a
-        sequence containing 6 numbers.
-        """
-        raise NotImplementedError
-
-    def addVarComponent(
-        self,
-        glyphName: str,
-        transformation: DecomposedTransform,
-        location: Dict[str, float],
-    ) -> None:
-        """Add a VarComponent sub glyph. The 'transformation' argument
-        must be a DecomposedTransform from the fontTools.misc.transform module,
-        and the 'location' argument must be a dictionary mapping axis tags
-        to their locations.
-        """
-        # GlyphSet decomposes for us
-        raise AttributeError
-
-
-class NullPen(AbstractPen):
-
-    """A pen that does nothing."""
-
-    def moveTo(self, pt):
-        pass
-
-    def lineTo(self, pt):
-        pass
-
-    def curveTo(self, *points):
-        pass
-
-    def qCurveTo(self, *points):
-        pass
-
-    def closePath(self):
-        pass
-
-    def endPath(self):
-        pass
-
-    def addComponent(self, glyphName, transformation):
-        pass
-
-    def addVarComponent(self, glyphName, transformation, location):
-        pass
-
-
-class LoggingPen(LogMixin, AbstractPen):
-    """A pen with a ``log`` property (see fontTools.misc.loggingTools.LogMixin)"""
-
-    pass
-
-
-class MissingComponentError(KeyError):
-    """Indicates a component pointing to a non-existent glyph in the glyphset."""
-
-
-class DecomposingPen(LoggingPen):
-
-    """Implements a 'addComponent' method that decomposes components
-    (i.e. draws them onto self as simple contours).
-    It can also be used as a mixin class (e.g. see ContourRecordingPen).
-
-    You must override moveTo, lineTo, curveTo and qCurveTo. You may
-    additionally override closePath, endPath and addComponent.
-
-    By default a warning message is logged when a base glyph is missing;
-    set the class variable ``skipMissingComponents`` to False if you want
-    to raise a :class:`MissingComponentError` exception.
-    """
-
-    skipMissingComponents = True
-
-    def __init__(self, glyphSet):
-        """Takes a single 'glyphSet' argument (dict), in which the glyphs
-        that are referenced as components are looked up by their name.
-        """
-        super(DecomposingPen, self).__init__()
-        self.glyphSet = glyphSet
-
-    def addComponent(self, glyphName, transformation):
-        """Transform the points of the base glyph and draw it onto self."""
-        from fontTools.pens.transformPen import TransformPen
-
-        try:
-            glyph = self.glyphSet[glyphName]
-        except KeyError:
-            if not self.skipMissingComponents:
-                raise MissingComponentError(glyphName)
-            self.log.warning("glyph '%s' is missing from glyphSet; skipped" % glyphName)
-        else:
-            tPen = TransformPen(self, transformation)
-            glyph.draw(tPen)
-
-    def addVarComponent(self, glyphName, transformation, location):
-        # GlyphSet decomposes for us
-        raise AttributeError
-
-
-class BasePen(DecomposingPen):
-
-    """Base class for drawing pens. You must override _moveTo, _lineTo and
-    _curveToOne. You may additionally override _closePath, _endPath,
-    addComponent, addVarComponent, and/or _qCurveToOne. You should not
-    override any other methods.
-    """
-
-    def __init__(self, glyphSet=None):
-        super(BasePen, self).__init__(glyphSet)
-        self.__currentPoint = None
-
-    # must override
-
-    def _moveTo(self, pt):
-        raise NotImplementedError
-
-    def _lineTo(self, pt):
-        raise NotImplementedError
-
-    def _curveToOne(self, pt1, pt2, pt3):
-        raise NotImplementedError
-
-    # may override
-
-    def _closePath(self):
-        pass
-
-    def _endPath(self):
-        pass
-
-    def _qCurveToOne(self, pt1, pt2):
-        """This method implements the basic quadratic curve type. The
-        default implementation delegates the work to the cubic curve
-        function. Optionally override with a native implementation.
-        """
-        pt0x, pt0y = self.__currentPoint
-        pt1x, pt1y = pt1
-        pt2x, pt2y = pt2
-        mid1x = pt0x + 0.66666666666666667 * (pt1x - pt0x)
-        mid1y = pt0y + 0.66666666666666667 * (pt1y - pt0y)
-        mid2x = pt2x + 0.66666666666666667 * (pt1x - pt2x)
-        mid2y = pt2y + 0.66666666666666667 * (pt1y - pt2y)
-        self._curveToOne((mid1x, mid1y), (mid2x, mid2y), pt2)
-
-    # don't override
-
-    def _getCurrentPoint(self):
-        """Return the current point. This is not part of the public
-        interface, yet is useful for subclasses.
-        """
-        return self.__currentPoint
-
-    def closePath(self):
-        self._closePath()
-        self.__currentPoint = None
-
-    def endPath(self):
-        self._endPath()
-        self.__currentPoint = None
-
-    def moveTo(self, pt):
-        self._moveTo(pt)
-        self.__currentPoint = pt
-
-    def lineTo(self, pt):
-        self._lineTo(pt)
-        self.__currentPoint = pt
-
-    def curveTo(self, *points):
-        n = len(points) - 1  # 'n' is the number of control points
-        assert n >= 0
-        if n == 2:
-            # The common case, we have exactly two BCP's, so this is a standard
-            # cubic bezier. Even though decomposeSuperBezierSegment() handles
-            # this case just fine, we special-case it anyway since it's so
-            # common.
-            self._curveToOne(*points)
-            self.__currentPoint = points[-1]
-        elif n > 2:
-            # n is the number of control points; split curve into n-1 cubic
-            # bezier segments. The algorithm used here is inspired by NURB
-            # splines and the TrueType "implied point" principle, and ensures
-            # the smoothest possible connection between two curve segments,
-            # with no disruption in the curvature. It is practical since it
-            # allows one to construct multiple bezier segments with a much
-            # smaller amount of points.
-            _curveToOne = self._curveToOne
-            for pt1, pt2, pt3 in decomposeSuperBezierSegment(points):
-                _curveToOne(pt1, pt2, pt3)
-                self.__currentPoint = pt3
-        elif n == 1:
-            self.qCurveTo(*points)
-        elif n == 0:
-            self.lineTo(points[0])
-        else:
-            raise AssertionError("can't get there from here")
-
-    def qCurveTo(self, *points):
-        n = len(points) - 1  # 'n' is the number of control points
-        assert n >= 0
-        if points[-1] is None:
-            # Special case for TrueType quadratics: it is possible to
-            # define a contour with NO on-curve points. BasePen supports
-            # this by allowing the final argument (the expected on-curve
-            # point) to be None. We simulate the feature by making the implied
-            # on-curve point between the last and the first off-curve points
-            # explicit.
-            x, y = points[-2]  # last off-curve point
-            nx, ny = points[0]  # first off-curve point
-            impliedStartPoint = (0.5 * (x + nx), 0.5 * (y + ny))
-            self.__currentPoint = impliedStartPoint
-            self._moveTo(impliedStartPoint)
-            points = points[:-1] + (impliedStartPoint,)
-        if n > 0:
-            # Split the string of points into discrete quadratic curve
-            # segments. Between any two consecutive off-curve points
-            # there's an implied on-curve point exactly in the middle.
-            # This is where the segment splits.
-            _qCurveToOne = self._qCurveToOne
-            for pt1, pt2 in decomposeQuadraticSegment(points):
-                _qCurveToOne(pt1, pt2)
-                self.__currentPoint = pt2
-        else:
-            self.lineTo(points[0])
-
-
-def decomposeSuperBezierSegment(points):
-    """Split the SuperBezier described by 'points' into a list of regular
-    bezier segments. The 'points' argument must be a sequence with length
-    3 or greater, containing (x, y) coordinates. The last point is the
-    destination on-curve point, the rest of the points are off-curve points.
-    The start point should not be supplied.
-
-    This function returns a list of (pt1, pt2, pt3) tuples, which each
-    specify a regular curveto-style bezier segment.
-    """
-    n = len(points) - 1
-    assert n > 1
-    bezierSegments = []
-    pt1, pt2, pt3 = points[0], None, None
-    for i in range(2, n + 1):
-        # calculate points in between control points.
-        nDivisions = min(i, 3, n - i + 2)
-        for j in range(1, nDivisions):
-            factor = j / nDivisions
-            temp1 = points[i - 1]
-            temp2 = points[i - 2]
-            temp = (
-                temp2[0] + factor * (temp1[0] - temp2[0]),
-                temp2[1] + factor * (temp1[1] - temp2[1]),
-            )
-            if pt2 is None:
-                pt2 = temp
-            else:
-                pt3 = (0.5 * (pt2[0] + temp[0]), 0.5 * (pt2[1] + temp[1]))
-                bezierSegments.append((pt1, pt2, pt3))
-                pt1, pt2, pt3 = temp, None, None
-    bezierSegments.append((pt1, points[-2], points[-1]))
-    return bezierSegments
-
-
-def decomposeQuadraticSegment(points):
-    """Split the quadratic curve segment described by 'points' into a list
-    of "atomic" quadratic segments. The 'points' argument must be a sequence
-    with length 2 or greater, containing (x, y) coordinates. The last point
-    is the destination on-curve point, the rest of the points are off-curve
-    points. The start point should not be supplied.
-
-    This function returns a list of (pt1, pt2) tuples, which each specify a
-    plain quadratic bezier segment.
-    """
-    n = len(points) - 1
-    assert n > 0
-    quadSegments = []
-    for i in range(n - 1):
-        x, y = points[i]
-        nx, ny = points[i + 1]
-        impliedPt = (0.5 * (x + nx), 0.5 * (y + ny))
-        quadSegments.append((points[i], impliedPt))
-    quadSegments.append((points[-2], points[-1]))
-    return quadSegments
-
-
-class _TestPen(BasePen):
-    """Test class that prints PostScript to stdout."""
-
-    def _moveTo(self, pt):
-        print("%s %s moveto" % (pt[0], pt[1]))
-
-    def _lineTo(self, pt):
-        print("%s %s lineto" % (pt[0], pt[1]))
-
-    def _curveToOne(self, bcp1, bcp2, pt):
-        print(
-            "%s %s %s %s %s %s curveto"
-            % (bcp1[0], bcp1[1], bcp2[0], bcp2[1], pt[0], pt[1])
-        )
-
-    def _closePath(self):
-        print("closepath")
-
-
-if __name__ == "__main__":
-    pen = _TestPen(None)
-    pen.moveTo((0, 0))
-    pen.lineTo((0, 100))
-    pen.curveTo((50, 75), (60, 50), (50, 25), (0, 0))
-    pen.closePath()
-
-    pen = _TestPen(None)
-    # testing the "no on-curve point" scenario
-    pen.qCurveTo((0, 0), (0, 100), (100, 100), (100, 0), None)
-    pen.closePath()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/G__l_o_c.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/G__l_o_c.py
deleted file mode 100644
index 7973b9be911d450f2504e83704705c9bb8e4b810..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/G__l_o_c.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-import array
-import sys
-
-
-Gloc_header = """
-    >        # big endian
-    version: 16.16F    # Table version
-    flags:        H    # bit 0: 1=long format, 0=short format
-                       # bit 1: 1=attribute names, 0=no names
-    numAttribs:   H    # NUmber of attributes
-"""
-
-
-class table_G__l_o_c(DefaultTable.DefaultTable):
-    """
-    Support Graphite Gloc tables
-    """
-
-    dependencies = ["Glat"]
-
-    def __init__(self, tag=None):
-        DefaultTable.DefaultTable.__init__(self, tag)
-        self.attribIds = None
-        self.numAttribs = 0
-
-    def decompile(self, data, ttFont):
-        _, data = sstruct.unpack2(Gloc_header, data, self)
-        flags = self.flags
-        del self.flags
-        self.locations = array.array("I" if flags & 1 else "H")
-        self.locations.frombytes(data[: len(data) - self.numAttribs * (flags & 2)])
-        if sys.byteorder != "big":
-            self.locations.byteswap()
-        self.attribIds = array.array("H")
-        if flags & 2:
-            self.attribIds.frombytes(data[-self.numAttribs * 2 :])
-            if sys.byteorder != "big":
-                self.attribIds.byteswap()
-
-    def compile(self, ttFont):
-        data = sstruct.pack(
-            Gloc_header,
-            dict(
-                version=1.0,
-                flags=(bool(self.attribIds) << 1) + (self.locations.typecode == "I"),
-                numAttribs=self.numAttribs,
-            ),
-        )
-        if sys.byteorder != "big":
-            self.locations.byteswap()
-        data += self.locations.tobytes()
-        if sys.byteorder != "big":
-            self.locations.byteswap()
-        if self.attribIds:
-            if sys.byteorder != "big":
-                self.attribIds.byteswap()
-            data += self.attribIds.tobytes()
-            if sys.byteorder != "big":
-                self.attribIds.byteswap()
-        return data
-
-    def set(self, locations):
-        long_format = max(locations) >= 65536
-        self.locations = array.array("I" if long_format else "H", locations)
-
-    def toXML(self, writer, ttFont):
-        writer.simpletag("attributes", number=self.numAttribs)
-        writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "attributes":
-            self.numAttribs = int(safeEval(attrs["number"]))
-
-    def __getitem__(self, index):
-        return self.locations[index]
-
-    def __len__(self):
-        return len(self.locations)
-
-    def __iter__(self):
-        return iter(self.locations)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-ce9113ed.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-ce9113ed.js
deleted file mode 100644
index 9c72031ecc46c2953b72caa368072ca3c0ca1e6f..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-ce9113ed.js
+++ /dev/null
@@ -1,346 +0,0 @@
-import{S as W0,e as Y0,s as j0,f as zn,g as me,h as Oe,j as Lt,n as ht,k as qe,al as qr,a6 as Xi,m as Qe,C as Zi,am as Ki,a4 as Ps,N as Pn,o as f0,p as m0,w as xe,r as Ot,u as Me,v as qt,ai as Hs,F as Pt,G as Ht,H as Ut,aj as Us,an as Gs,I as G0,P as Hn,Z as Un,_ as ci,E as rt,Y as H0,t as Qi,x as Ji,X as V0,B as $i,V as Vs,ae as Ws,Q as Ys,R as js,O as Xs,T as Zs}from"./index-39fce9e2.js";import{u as hi,S as Ks}from"./ShareButton-c9a8cbaf.js";import{B as Qs}from"./Button-79f6e3bf.js";import{C as Js,a as $s}from"./Copy-77b3f70c.js";import{B as eo}from"./BlockLabel-b1428685.js";import{n as mi}from"./ModifyUpload.svelte_svelte_type_style_lang-14b768c9.js";import"./IconButton-0ac328a0.js";function to(g){let a,l,s;return{c(){a=zn("svg"),l=zn("path"),s=zn("path"),me(l,"fill","currentColor"),me(l,"d","M17.74 30L16 29l4-7h6a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2H6a2 2 0 0 0-2 2v12a2 2 0 0 0 2 2h9v2H6a4 4 0 0 1-4-4V8a4 4 0 0 1 4-4h20a4 4 0 0 1 4 4v12a4 4 0 0 1-4 4h-4.84Z"),me(s,"fill","currentColor"),me(s,"d","M8 10h16v2H8zm0 6h10v2H8z"),me(a,"xmlns","http://www.w3.org/2000/svg"),me(a,"xmlns:xlink","http://www.w3.org/1999/xlink"),me(a,"aria-hidden","true"),me(a,"role","img"),me(a,"class","iconify iconify--carbon"),me(a,"width","100%"),me(a,"height","100%"),me(a,"preserveAspectRatio","xMidYMid meet"),me(a,"viewBox","0 0 32 32")},m(m,p){Oe(m,a,p),Lt(a,l),Lt(a,s)},p:ht,i:ht,o:ht,d(m){m&&qe(a)}}}class ro extends W0{constructor(a){super(),Y0(this,a,null,to,j0,{})}}function el(){return{async:!1,baseUrl:null,breaks:!1,extensions:null,gfm:!0,headerIds:!0,headerPrefix:"",highlight:null,hooks:null,langPrefix:"language-",mangle:!0,pedantic:!1,renderer:null,sanitize:!1,sanitizer:null,silent:!1,smartypants:!1,tokenizer:null,walkTokens:null,xhtml:!1}}let _0=el();function no(g){_0=g}const tl=/[&<>"']/,ao=new RegExp(tl.source,"g"),rl=/[<>"']|&(?!(#\d{1,7}|#[Xx][a-fA-F0-9]{1,6}|\w+);)/,io=new RegExp(rl.source,"g"),lo={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"},fi=g=>lo[g];function tt(g,a){if(a){if(tl.test(g))return g.replace(ao,fi)}else if(rl.test(g))return g.replace(io,fi);return g}const so=/&(#(?:\d+)|(?:#x[0-9A-Fa-f]+)|(?:\w+));?/ig;function nl(g){return g.replace(so,(a,l)=>(l=l.toLowerCase(),l==="colon"?":":l.charAt(0)==="#"?l.charAt(1)==="x"?String.fromCharCode(parseInt(l.substring(2),16)):String.fromCharCode(+l.substring(1)):""))}const oo=/(^|[^\[])\^/g;function Re(g,a){g=typeof g=="string"?g:g.source,a=a||"";const l={replace:(s,m)=>(m=m.source||m,m=m.replace(oo,"$1"),g=g.replace(s,m),l),getRegex:()=>new RegExp(g,a)};return l}const uo=/[^\w:]/g,co=/^$|^[a-z][a-z0-9+.-]*:|^[?#]/i;function di(g,a,l){if(g){let s;try{s=decodeURIComponent(nl(l)).replace(uo,"").toLowerCase()}catch{return null}if(s.indexOf("javascript:")===0||s.indexOf("vbscript:")===0||s.indexOf("data:")===0)return null}a&&!co.test(l)&&(l=po(a,l));try{l=encodeURI(l).replace(/%25/g,"%")}catch{return null}return l}const Rr={},ho=/^[^:]+:\/*[^/]*$/,mo=/^([^:]+:)[\s\S]*$/,fo=/^([^:]+:\/*[^/]*)[\s\S]*$/;function po(g,a){Rr[" "+g]||(ho.test(g)?Rr[" "+g]=g+"/":Rr[" "+g]=Lr(g,"/",!0)),g=Rr[" "+g];const l=g.indexOf(":")===-1;return a.substring(0,2)==="//"?l?a:g.replace(mo,"$1")+a:a.charAt(0)==="/"?l?a:g.replace(fo,"$1")+a:g+a}const Pr={exec:function(){}};function pi(g,a){const l=g.replace(/\|/g,(p,v,y)=>{let k=!1,N=v;for(;--N>=0&&y[N]==="\\";)k=!k;return k?"|":" |"}),s=l.split(/ \|/);let m=0;if(s[0].trim()||s.shift(),s.length>0&&!s[s.length-1].trim()&&s.pop(),s.length>a)s.splice(a);else for(;s.length<a;)s.push("");for(;m<s.length;m++)s[m]=s[m].trim().replace(/\\\|/g,"|");return s}function Lr(g,a,l){const s=g.length;if(s===0)return"";let m=0;for(;m<s;){const p=g.charAt(s-m-1);if(p===a&&!l)m++;else if(p!==a&&l)m++;else break}return g.slice(0,s-m)}function go(g,a){if(g.indexOf(a[1])===-1)return-1;const l=g.length;let s=0,m=0;for(;m<l;m++)if(g[m]==="\\")m++;else if(g[m]===a[0])s++;else if(g[m]===a[1]&&(s--,s<0))return m;return-1}function vo(g,a){!g||g.silent||(a&&console.warn("marked(): callback is deprecated since version 5.0.0, should not be used and will be removed in the future. Read more here: https://marked.js.org/using_pro#async"),(g.sanitize||g.sanitizer)&&console.warn("marked(): sanitize and sanitizer parameters are deprecated since version 0.7.0, should not be used and will be removed in the future. Read more here: https://marked.js.org/#/USING_ADVANCED.md#options"),(g.highlight||g.langPrefix!=="language-")&&console.warn("marked(): highlight and langPrefix parameters are deprecated since version 5.0.0, should not be used and will be removed in the future. Instead use https://www.npmjs.com/package/marked-highlight."),g.mangle&&console.warn("marked(): mangle parameter is deprecated since version 5.0.0, should not be used and will be removed in the future. Instead use https://www.npmjs.com/package/marked-mangle."),g.baseUrl&&console.warn("marked(): baseUrl parameter is deprecated since version 5.0.0, should not be used and will be removed in the future. Instead use https://www.npmjs.com/package/marked-base-url."),g.smartypants&&console.warn("marked(): smartypants parameter is deprecated since version 5.0.0, should not be used and will be removed in the future. Instead use https://www.npmjs.com/package/marked-smartypants."),g.xhtml&&console.warn("marked(): xhtml parameter is deprecated since version 5.0.0, should not be used and will be removed in the future. Instead use https://www.npmjs.com/package/marked-xhtml."),(g.headerIds||g.headerPrefix)&&console.warn("marked(): headerIds and headerPrefix parameters are deprecated since version 5.0.0, should not be used and will be removed in the future. Instead use https://www.npmjs.com/package/marked-gfm-heading-id."))}function gi(g,a){if(a<1)return"";let l="";for(;a>1;)a&1&&(l+=g),a>>=1,g+=g;return l+g}function vi(g,a,l,s){const m=a.href,p=a.title?tt(a.title):null,v=g[1].replace(/\\([\[\]])/g,"$1");if(g[0].charAt(0)!=="!"){s.state.inLink=!0;const y={type:"link",raw:l,href:m,title:p,text:v,tokens:s.inlineTokens(v)};return s.state.inLink=!1,y}return{type:"image",raw:l,href:m,title:p,text:tt(v)}}function bo(g,a){const l=g.match(/^(\s+)(?:```)/);if(l===null)return a;const s=l[1];return a.split(`
-`).map(m=>{const p=m.match(/^\s+/);if(p===null)return m;const[v]=p;return v.length>=s.length?m.slice(s.length):m}).join(`
-`)}class Gn{constructor(a){this.options=a||_0}space(a){const l=this.rules.block.newline.exec(a);if(l&&l[0].length>0)return{type:"space",raw:l[0]}}code(a){const l=this.rules.block.code.exec(a);if(l){const s=l[0].replace(/^ {1,4}/gm,"");return{type:"code",raw:l[0],codeBlockStyle:"indented",text:this.options.pedantic?s:Lr(s,`
-`)}}}fences(a){const l=this.rules.block.fences.exec(a);if(l){const s=l[0],m=bo(s,l[3]||"");return{type:"code",raw:s,lang:l[2]?l[2].trim().replace(this.rules.inline._escapes,"$1"):l[2],text:m}}}heading(a){const l=this.rules.block.heading.exec(a);if(l){let s=l[2].trim();if(/#$/.test(s)){const m=Lr(s,"#");(this.options.pedantic||!m||/ $/.test(m))&&(s=m.trim())}return{type:"heading",raw:l[0],depth:l[1].length,text:s,tokens:this.lexer.inline(s)}}}hr(a){const l=this.rules.block.hr.exec(a);if(l)return{type:"hr",raw:l[0]}}blockquote(a){const l=this.rules.block.blockquote.exec(a);if(l){const s=l[0].replace(/^ *>[ \t]?/gm,""),m=this.lexer.state.top;this.lexer.state.top=!0;const p=this.lexer.blockTokens(s);return this.lexer.state.top=m,{type:"blockquote",raw:l[0],tokens:p,text:s}}}list(a){let l=this.rules.block.list.exec(a);if(l){let s,m,p,v,y,k,N,U,Y,te,j,ve,pe=l[1].trim();const le=pe.length>1,O={type:"list",raw:"",ordered:le,start:le?+pe.slice(0,-1):"",loose:!1,items:[]};pe=le?`\\d{1,9}\\${pe.slice(-1)}`:`\\${pe}`,this.options.pedantic&&(pe=le?pe:"[*+-]");const B=new RegExp(`^( {0,3}${pe})((?:[	 ][^\\n]*)?(?:\\n|$))`);for(;a&&(ve=!1,!(!(l=B.exec(a))||this.rules.block.hr.test(a)));){if(s=l[0],a=a.substring(s.length),U=l[2].split(`
-`,1)[0].replace(/^\t+/,R=>" ".repeat(3*R.length)),Y=a.split(`
-`,1)[0],this.options.pedantic?(v=2,j=U.trimLeft()):(v=l[2].search(/[^ ]/),v=v>4?1:v,j=U.slice(v),v+=l[1].length),k=!1,!U&&/^ *$/.test(Y)&&(s+=Y+`
-`,a=a.substring(Y.length+1),ve=!0),!ve){const R=new RegExp(`^ {0,${Math.min(3,v-1)}}(?:[*+-]|\\d{1,9}[.)])((?:[ 	][^\\n]*)?(?:\\n|$))`),D=new RegExp(`^ {0,${Math.min(3,v-1)}}((?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$)`),L=new RegExp(`^ {0,${Math.min(3,v-1)}}(?:\`\`\`|~~~)`),X=new RegExp(`^ {0,${Math.min(3,v-1)}}#`);for(;a&&(te=a.split(`
-`,1)[0],Y=te,this.options.pedantic&&(Y=Y.replace(/^ {1,4}(?=( {4})*[^ ])/g,"  ")),!(L.test(Y)||X.test(Y)||R.test(Y)||D.test(a)));){if(Y.search(/[^ ]/)>=v||!Y.trim())j+=`
-`+Y.slice(v);else{if(k||U.search(/[^ ]/)>=4||L.test(U)||X.test(U)||D.test(U))break;j+=`
-`+Y}!k&&!Y.trim()&&(k=!0),s+=te+`
-`,a=a.substring(te.length+1),U=Y.slice(v)}}O.loose||(N?O.loose=!0:/\n *\n *$/.test(s)&&(N=!0)),this.options.gfm&&(m=/^\[[ xX]\] /.exec(j),m&&(p=m[0]!=="[ ] ",j=j.replace(/^\[[ xX]\] +/,""))),O.items.push({type:"list_item",raw:s,task:!!m,checked:p,loose:!1,text:j}),O.raw+=s}O.items[O.items.length-1].raw=s.trimRight(),O.items[O.items.length-1].text=j.trimRight(),O.raw=O.raw.trimRight();const z=O.items.length;for(y=0;y<z;y++)if(this.lexer.state.top=!1,O.items[y].tokens=this.lexer.blockTokens(O.items[y].text,[]),!O.loose){const R=O.items[y].tokens.filter(L=>L.type==="space"),D=R.length>0&&R.some(L=>/\n.*\n/.test(L.raw));O.loose=D}if(O.loose)for(y=0;y<z;y++)O.items[y].loose=!0;return O}}html(a){const l=this.rules.block.html.exec(a);if(l){const s={type:"html",block:!0,raw:l[0],pre:!this.options.sanitizer&&(l[1]==="pre"||l[1]==="script"||l[1]==="style"),text:l[0]};if(this.options.sanitize){const m=this.options.sanitizer?this.options.sanitizer(l[0]):tt(l[0]);s.type="paragraph",s.text=m,s.tokens=this.lexer.inline(m)}return s}}def(a){const l=this.rules.block.def.exec(a);if(l){const s=l[1].toLowerCase().replace(/\s+/g," "),m=l[2]?l[2].replace(/^<(.*)>$/,"$1").replace(this.rules.inline._escapes,"$1"):"",p=l[3]?l[3].substring(1,l[3].length-1).replace(this.rules.inline._escapes,"$1"):l[3];return{type:"def",tag:s,raw:l[0],href:m,title:p}}}table(a){const l=this.rules.block.table.exec(a);if(l){const s={type:"table",header:pi(l[1]).map(m=>({text:m})),align:l[2].replace(/^ *|\| *$/g,"").split(/ *\| */),rows:l[3]&&l[3].trim()?l[3].replace(/\n[ \t]*$/,"").split(`
-`):[]};if(s.header.length===s.align.length){s.raw=l[0];let m=s.align.length,p,v,y,k;for(p=0;p<m;p++)/^ *-+: *$/.test(s.align[p])?s.align[p]="right":/^ *:-+: *$/.test(s.align[p])?s.align[p]="center":/^ *:-+ *$/.test(s.align[p])?s.align[p]="left":s.align[p]=null;for(m=s.rows.length,p=0;p<m;p++)s.rows[p]=pi(s.rows[p],s.header.length).map(N=>({text:N}));for(m=s.header.length,v=0;v<m;v++)s.header[v].tokens=this.lexer.inline(s.header[v].text);for(m=s.rows.length,v=0;v<m;v++)for(k=s.rows[v],y=0;y<k.length;y++)k[y].tokens=this.lexer.inline(k[y].text);return s}}}lheading(a){const l=this.rules.block.lheading.exec(a);if(l)return{type:"heading",raw:l[0],depth:l[2].charAt(0)==="="?1:2,text:l[1],tokens:this.lexer.inline(l[1])}}paragraph(a){const l=this.rules.block.paragraph.exec(a);if(l){const s=l[1].charAt(l[1].length-1)===`
-`?l[1].slice(0,-1):l[1];return{type:"paragraph",raw:l[0],text:s,tokens:this.lexer.inline(s)}}}text(a){const l=this.rules.block.text.exec(a);if(l)return{type:"text",raw:l[0],text:l[0],tokens:this.lexer.inline(l[0])}}escape(a){const l=this.rules.inline.escape.exec(a);if(l)return{type:"escape",raw:l[0],text:tt(l[1])}}tag(a){const l=this.rules.inline.tag.exec(a);if(l)return!this.lexer.state.inLink&&/^<a /i.test(l[0])?this.lexer.state.inLink=!0:this.lexer.state.inLink&&/^<\/a>/i.test(l[0])&&(this.lexer.state.inLink=!1),!this.lexer.state.inRawBlock&&/^<(pre|code|kbd|script)(\s|>)/i.test(l[0])?this.lexer.state.inRawBlock=!0:this.lexer.state.inRawBlock&&/^<\/(pre|code|kbd|script)(\s|>)/i.test(l[0])&&(this.lexer.state.inRawBlock=!1),{type:this.options.sanitize?"text":"html",raw:l[0],inLink:this.lexer.state.inLink,inRawBlock:this.lexer.state.inRawBlock,block:!1,text:this.options.sanitize?this.options.sanitizer?this.options.sanitizer(l[0]):tt(l[0]):l[0]}}link(a){const l=this.rules.inline.link.exec(a);if(l){const s=l[2].trim();if(!this.options.pedantic&&/^</.test(s)){if(!/>$/.test(s))return;const v=Lr(s.slice(0,-1),"\\");if((s.length-v.length)%2===0)return}else{const v=go(l[2],"()");if(v>-1){const k=(l[0].indexOf("!")===0?5:4)+l[1].length+v;l[2]=l[2].substring(0,v),l[0]=l[0].substring(0,k).trim(),l[3]=""}}let m=l[2],p="";if(this.options.pedantic){const v=/^([^'"]*[^\s])\s+(['"])(.*)\2/.exec(m);v&&(m=v[1],p=v[3])}else p=l[3]?l[3].slice(1,-1):"";return m=m.trim(),/^</.test(m)&&(this.options.pedantic&&!/>$/.test(s)?m=m.slice(1):m=m.slice(1,-1)),vi(l,{href:m&&m.replace(this.rules.inline._escapes,"$1"),title:p&&p.replace(this.rules.inline._escapes,"$1")},l[0],this.lexer)}}reflink(a,l){let s;if((s=this.rules.inline.reflink.exec(a))||(s=this.rules.inline.nolink.exec(a))){let m=(s[2]||s[1]).replace(/\s+/g," ");if(m=l[m.toLowerCase()],!m){const p=s[0].charAt(0);return{type:"text",raw:p,text:p}}return vi(s,m,s[0],this.lexer)}}emStrong(a,l,s=""){let m=this.rules.inline.emStrong.lDelim.exec(a);if(!m||m[3]&&s.match(/[\p{L}\p{N}]/u))return;const p=m[1]||m[2]||"";if(!p||p&&(s===""||this.rules.inline.punctuation.exec(s))){const v=m[0].length-1;let y,k,N=v,U=0;const Y=m[0][0]==="*"?this.rules.inline.emStrong.rDelimAst:this.rules.inline.emStrong.rDelimUnd;for(Y.lastIndex=0,l=l.slice(-1*a.length+v);(m=Y.exec(l))!=null;){if(y=m[1]||m[2]||m[3]||m[4]||m[5]||m[6],!y)continue;if(k=y.length,m[3]||m[4]){N+=k;continue}else if((m[5]||m[6])&&v%3&&!((v+k)%3)){U+=k;continue}if(N-=k,N>0)continue;k=Math.min(k,k+N+U);const te=a.slice(0,v+m.index+(m[0].length-y.length)+k);if(Math.min(v,k)%2){const ve=te.slice(1,-1);return{type:"em",raw:te,text:ve,tokens:this.lexer.inlineTokens(ve)}}const j=te.slice(2,-2);return{type:"strong",raw:te,text:j,tokens:this.lexer.inlineTokens(j)}}}}codespan(a){const l=this.rules.inline.code.exec(a);if(l){let s=l[2].replace(/\n/g," ");const m=/[^ ]/.test(s),p=/^ /.test(s)&&/ $/.test(s);return m&&p&&(s=s.substring(1,s.length-1)),s=tt(s,!0),{type:"codespan",raw:l[0],text:s}}}br(a){const l=this.rules.inline.br.exec(a);if(l)return{type:"br",raw:l[0]}}del(a){const l=this.rules.inline.del.exec(a);if(l)return{type:"del",raw:l[0],text:l[2],tokens:this.lexer.inlineTokens(l[2])}}autolink(a,l){const s=this.rules.inline.autolink.exec(a);if(s){let m,p;return s[2]==="@"?(m=tt(this.options.mangle?l(s[1]):s[1]),p="mailto:"+m):(m=tt(s[1]),p=m),{type:"link",raw:s[0],text:m,href:p,tokens:[{type:"text",raw:m,text:m}]}}}url(a,l){let s;if(s=this.rules.inline.url.exec(a)){let m,p;if(s[2]==="@")m=tt(this.options.mangle?l(s[0]):s[0]),p="mailto:"+m;else{let v;do v=s[0],s[0]=this.rules.inline._backpedal.exec(s[0])[0];while(v!==s[0]);m=tt(s[0]),s[1]==="www."?p="http://"+s[0]:p=s[0]}return{type:"link",raw:s[0],text:m,href:p,tokens:[{type:"text",raw:m,text:m}]}}}inlineText(a,l){const s=this.rules.inline.text.exec(a);if(s){let m;return this.lexer.state.inRawBlock?m=this.options.sanitize?this.options.sanitizer?this.options.sanitizer(s[0]):tt(s[0]):s[0]:m=tt(this.options.smartypants?l(s[0]):s[0]),{type:"text",raw:s[0],text:m}}}}const de={newline:/^(?: *(?:\n|$))+/,code:/^( {4}[^\n]+(?:\n(?: *(?:\n|$))*)?)+/,fences:/^ {0,3}(`{3,}(?=[^`\n]*(?:\n|$))|~{3,})([^\n]*)(?:\n|$)(?:|([\s\S]*?)(?:\n|$))(?: {0,3}\1[~`]* *(?=\n|$)|$)/,hr:/^ {0,3}((?:-[\t ]*){3,}|(?:_[ \t]*){3,}|(?:\*[ \t]*){3,})(?:\n+|$)/,heading:/^ {0,3}(#{1,6})(?=\s|$)(.*)(?:\n+|$)/,blockquote:/^( {0,3}> ?(paragraph|[^\n]*)(?:\n|$))+/,list:/^( {0,3}bull)([ \t][^\n]+?)?(?:\n|$)/,html:"^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:</\\1>[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|<![A-Z][\\s\\S]*?(?:>\\n*|$)|<!\\[CDATA\\[[\\s\\S]*?(?:\\]\\]>\\n*|$)|</?(tag)(?: +|\\n|/?>)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|</(?!script|pre|style|textarea)[a-z][\\w-]*\\s*>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))",def:/^ {0,3}\[(label)\]: *(?:\n *)?([^<\s][^\s]*|<.*?>)(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/,table:Pr,lheading:/^((?:.|\n(?!\n))+?)\n {0,3}(=+|-+) *(?:\n+|$)/,_paragraph:/^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/,text:/^[^\n]+/};de._label=/(?!\s*\])(?:\\.|[^\[\]\\])+/;de._title=/(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/;de.def=Re(de.def).replace("label",de._label).replace("title",de._title).getRegex();de.bullet=/(?:[*+-]|\d{1,9}[.)])/;de.listItemStart=Re(/^( *)(bull) */).replace("bull",de.bullet).getRegex();de.list=Re(de.list).replace(/bull/g,de.bullet).replace("hr","\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))").replace("def","\\n+(?="+de.def.source+")").getRegex();de._tag="address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul";de._comment=/<!--(?!-?>)[\s\S]*?(?:-->|$)/;de.html=Re(de.html,"i").replace("comment",de._comment).replace("tag",de._tag).replace("attribute",/ +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/).getRegex();de.paragraph=Re(de._paragraph).replace("hr",de.hr).replace("heading"," {0,3}#{1,6} ").replace("|lheading","").replace("|table","").replace("blockquote"," {0,3}>").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html","</?(?:tag)(?: +|\\n|/?>)|<(?:script|pre|style|textarea|!--)").replace("tag",de._tag).getRegex();de.blockquote=Re(de.blockquote).replace("paragraph",de.paragraph).getRegex();de.normal={...de};de.gfm={...de.normal,table:"^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? *(?:\\| *:?-+:? *)*)(?:\\| *)?(?:\\n((?:(?! *\\n|hr|heading|blockquote|code|fences|list|html).*(?:\\n|$))*)\\n*|$)"};de.gfm.table=Re(de.gfm.table).replace("hr",de.hr).replace("heading"," {0,3}#{1,6} ").replace("blockquote"," {0,3}>").replace("code"," {4}[^\\n]").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html","</?(?:tag)(?: +|\\n|/?>)|<(?:script|pre|style|textarea|!--)").replace("tag",de._tag).getRegex();de.gfm.paragraph=Re(de._paragraph).replace("hr",de.hr).replace("heading"," {0,3}#{1,6} ").replace("|lheading","").replace("table",de.gfm.table).replace("blockquote"," {0,3}>").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html","</?(?:tag)(?: +|\\n|/?>)|<(?:script|pre|style|textarea|!--)").replace("tag",de._tag).getRegex();de.pedantic={...de.normal,html:Re(`^ *(?:comment *(?:\\n|\\s*$)|<(tag)[\\s\\S]+?</\\1> *(?:\\n{2,}|\\s*$)|<tag(?:"[^"]*"|'[^']*'|\\s[^'"/>\\s]*)*?/?> *(?:\\n{2,}|\\s*$))`).replace("comment",de._comment).replace(/tag/g,"(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b").getRegex(),def:/^ *\[([^\]]+)\]: *<?([^\s>]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/,heading:/^(#{1,6})(.*)(?:\n+|$)/,fences:Pr,lheading:/^(.+?)\n {0,3}(=+|-+) *(?:\n+|$)/,paragraph:Re(de.normal._paragraph).replace("hr",de.hr).replace("heading",` *#{1,6} *[^
-]`).replace("lheading",de.lheading).replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").getRegex()};const oe={escape:/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,autolink:/^<(scheme:[^\s\x00-\x1f<>]*|email)>/,url:Pr,tag:"^comment|^</[a-zA-Z][\\w:-]*\\s*>|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^<![a-zA-Z]+\\s[\\s\\S]*?>|^<!\\[CDATA\\[[\\s\\S]*?\\]\\]>",link:/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/,reflink:/^!?\[(label)\]\[(ref)\]/,nolink:/^!?\[(ref)\](?:\[\])?/,reflinkSearch:"reflink|nolink(?!\\()",emStrong:{lDelim:/^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/,rDelimAst:/^(?:[^_*\\]|\\.)*?\_\_(?:[^_*\\]|\\.)*?\*(?:[^_*\\]|\\.)*?(?=\_\_)|(?:[^*\\]|\\.)+(?=[^*])|[punct_](\*+)(?=[\s]|$)|(?:[^punct*_\s\\]|\\.)(\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|(?:[^punct*_\s\\]|\\.)(\*+)(?=[^punct*_\s])/,rDelimUnd:/^(?:[^_*\\]|\\.)*?\*\*(?:[^_*\\]|\\.)*?\_(?:[^_*\\]|\\.)*?(?=\*\*)|(?:[^_\\]|\\.)+(?=[^_])|[punct*](\_+)(?=[\s]|$)|(?:[^punct*_\s\\]|\\.)(\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/},code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,br:/^( {2,}|\\)\n(?!\s*$)/,del:Pr,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\<!\[`*_]|\b_|$)|[^ ](?= {2,}\n)))/,punctuation:/^([\spunctuation])/};oe._punctuation="!\"#$%&'()+\\-.,/:;<=>?@\\[\\]`^{|}~";oe.punctuation=Re(oe.punctuation).replace(/punctuation/g,oe._punctuation).getRegex();oe.blockSkip=/\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g;oe.escapedEmSt=/(?:^|[^\\])(?:\\\\)*\\[*_]/g;oe._comment=Re(de._comment).replace("(?:-->|$)","-->").getRegex();oe.emStrong.lDelim=Re(oe.emStrong.lDelim).replace(/punct/g,oe._punctuation).getRegex();oe.emStrong.rDelimAst=Re(oe.emStrong.rDelimAst,"g").replace(/punct/g,oe._punctuation).getRegex();oe.emStrong.rDelimUnd=Re(oe.emStrong.rDelimUnd,"g").replace(/punct/g,oe._punctuation).getRegex();oe._escapes=/\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g;oe._scheme=/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/;oe._email=/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/;oe.autolink=Re(oe.autolink).replace("scheme",oe._scheme).replace("email",oe._email).getRegex();oe._attribute=/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/;oe.tag=Re(oe.tag).replace("comment",oe._comment).replace("attribute",oe._attribute).getRegex();oe._label=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/;oe._href=/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/;oe._title=/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/;oe.link=Re(oe.link).replace("label",oe._label).replace("href",oe._href).replace("title",oe._title).getRegex();oe.reflink=Re(oe.reflink).replace("label",oe._label).replace("ref",de._label).getRegex();oe.nolink=Re(oe.nolink).replace("ref",de._label).getRegex();oe.reflinkSearch=Re(oe.reflinkSearch,"g").replace("reflink",oe.reflink).replace("nolink",oe.nolink).getRegex();oe.normal={...oe};oe.pedantic={...oe.normal,strong:{start:/^__|\*\*/,middle:/^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,endAst:/\*\*(?!\*)/g,endUnd:/__(?!_)/g},em:{start:/^_|\*/,middle:/^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/,endAst:/\*(?!\*)/g,endUnd:/_(?!_)/g},link:Re(/^!?\[(label)\]\((.*?)\)/).replace("label",oe._label).getRegex(),reflink:Re(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",oe._label).getRegex()};oe.gfm={...oe.normal,escape:Re(oe.escape).replace("])","~|])").getRegex(),_extended_email:/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/,url:/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,_backpedal:/(?:[^?!.,:;*_'"~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_'"~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\<!\[`*~_]|\b_|https?:\/\/|ftp:\/\/|www\.|$)|[^ ](?= {2,}\n)|[^a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-](?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)))/};oe.gfm.url=Re(oe.gfm.url,"i").replace("email",oe.gfm._extended_email).getRegex();oe.breaks={...oe.gfm,br:Re(oe.br).replace("{2,}","*").getRegex(),text:Re(oe.gfm.text).replace("\\b_","\\b_| {2,}\\n").replace(/\{2,\}/g,"*").getRegex()};function yo(g){return g.replace(/---/g,"—").replace(/--/g,"–").replace(/(^|[-\u2014/(\[{"\s])'/g,"$1‘").replace(/'/g,"’").replace(/(^|[-\u2014/(\[{\u2018\s])"/g,"$1“").replace(/"/g,"”").replace(/\.{3}/g,"…")}function bi(g){let a="",l,s;const m=g.length;for(l=0;l<m;l++)s=g.charCodeAt(l),Math.random()>.5&&(s="x"+s.toString(16)),a+="&#"+s+";";return a}class d0{constructor(a){this.tokens=[],this.tokens.links=Object.create(null),this.options=a||_0,this.options.tokenizer=this.options.tokenizer||new Gn,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const l={block:de.normal,inline:oe.normal};this.options.pedantic?(l.block=de.pedantic,l.inline=oe.pedantic):this.options.gfm&&(l.block=de.gfm,this.options.breaks?l.inline=oe.breaks:l.inline=oe.gfm),this.tokenizer.rules=l}static get rules(){return{block:de,inline:oe}}static lex(a,l){return new d0(l).lex(a)}static lexInline(a,l){return new d0(l).inlineTokens(a)}lex(a){a=a.replace(/\r\n|\r/g,`
-`),this.blockTokens(a,this.tokens);let l;for(;l=this.inlineQueue.shift();)this.inlineTokens(l.src,l.tokens);return this.tokens}blockTokens(a,l=[]){this.options.pedantic?a=a.replace(/\t/g,"    ").replace(/^ +$/gm,""):a=a.replace(/^( *)(\t+)/gm,(y,k,N)=>k+"    ".repeat(N.length));let s,m,p,v;for(;a;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some(y=>(s=y.call({lexer:this},a,l))?(a=a.substring(s.raw.length),l.push(s),!0):!1))){if(s=this.tokenizer.space(a)){a=a.substring(s.raw.length),s.raw.length===1&&l.length>0?l[l.length-1].raw+=`
-`:l.push(s);continue}if(s=this.tokenizer.code(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&(m.type==="paragraph"||m.type==="text")?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.text,this.inlineQueue[this.inlineQueue.length-1].src=m.text):l.push(s);continue}if(s=this.tokenizer.fences(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.heading(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.hr(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.blockquote(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.list(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.html(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.def(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&(m.type==="paragraph"||m.type==="text")?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.raw,this.inlineQueue[this.inlineQueue.length-1].src=m.text):this.tokens.links[s.tag]||(this.tokens.links[s.tag]={href:s.href,title:s.title});continue}if(s=this.tokenizer.table(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.lheading(a)){a=a.substring(s.raw.length),l.push(s);continue}if(p=a,this.options.extensions&&this.options.extensions.startBlock){let y=1/0;const k=a.slice(1);let N;this.options.extensions.startBlock.forEach(function(U){N=U.call({lexer:this},k),typeof N=="number"&&N>=0&&(y=Math.min(y,N))}),y<1/0&&y>=0&&(p=a.substring(0,y+1))}if(this.state.top&&(s=this.tokenizer.paragraph(p))){m=l[l.length-1],v&&m.type==="paragraph"?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=m.text):l.push(s),v=p.length!==a.length,a=a.substring(s.raw.length);continue}if(s=this.tokenizer.text(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&m.type==="text"?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=m.text):l.push(s);continue}if(a){const y="Infinite loop on byte: "+a.charCodeAt(0);if(this.options.silent){console.error(y);break}else throw new Error(y)}}return this.state.top=!0,l}inline(a,l=[]){return this.inlineQueue.push({src:a,tokens:l}),l}inlineTokens(a,l=[]){let s,m,p,v=a,y,k,N;if(this.tokens.links){const U=Object.keys(this.tokens.links);if(U.length>0)for(;(y=this.tokenizer.rules.inline.reflinkSearch.exec(v))!=null;)U.includes(y[0].slice(y[0].lastIndexOf("[")+1,-1))&&(v=v.slice(0,y.index)+"["+gi("a",y[0].length-2)+"]"+v.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(y=this.tokenizer.rules.inline.blockSkip.exec(v))!=null;)v=v.slice(0,y.index)+"["+gi("a",y[0].length-2)+"]"+v.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;(y=this.tokenizer.rules.inline.escapedEmSt.exec(v))!=null;)v=v.slice(0,y.index+y[0].length-2)+"++"+v.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex),this.tokenizer.rules.inline.escapedEmSt.lastIndex--;for(;a;)if(k||(N=""),k=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some(U=>(s=U.call({lexer:this},a,l))?(a=a.substring(s.raw.length),l.push(s),!0):!1))){if(s=this.tokenizer.escape(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.tag(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&s.type==="text"&&m.type==="text"?(m.raw+=s.raw,m.text+=s.text):l.push(s);continue}if(s=this.tokenizer.link(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.reflink(a,this.tokens.links)){a=a.substring(s.raw.length),m=l[l.length-1],m&&s.type==="text"&&m.type==="text"?(m.raw+=s.raw,m.text+=s.text):l.push(s);continue}if(s=this.tokenizer.emStrong(a,v,N)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.codespan(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.br(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.del(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.autolink(a,bi)){a=a.substring(s.raw.length),l.push(s);continue}if(!this.state.inLink&&(s=this.tokenizer.url(a,bi))){a=a.substring(s.raw.length),l.push(s);continue}if(p=a,this.options.extensions&&this.options.extensions.startInline){let U=1/0;const Y=a.slice(1);let te;this.options.extensions.startInline.forEach(function(j){te=j.call({lexer:this},Y),typeof te=="number"&&te>=0&&(U=Math.min(U,te))}),U<1/0&&U>=0&&(p=a.substring(0,U+1))}if(s=this.tokenizer.inlineText(p,yo)){a=a.substring(s.raw.length),s.raw.slice(-1)!=="_"&&(N=s.raw.slice(-1)),k=!0,m=l[l.length-1],m&&m.type==="text"?(m.raw+=s.raw,m.text+=s.text):l.push(s);continue}if(a){const U="Infinite loop on byte: "+a.charCodeAt(0);if(this.options.silent){console.error(U);break}else throw new Error(U)}}return l}}class Vn{constructor(a){this.options=a||_0}code(a,l,s){const m=(l||"").match(/\S*/)[0];if(this.options.highlight){const p=this.options.highlight(a,m);p!=null&&p!==a&&(s=!0,a=p)}return a=a.replace(/\n$/,"")+`
-`,m?'<pre><code class="'+this.options.langPrefix+tt(m)+'">'+(s?a:tt(a,!0))+`</code></pre>
-`:"<pre><code>"+(s?a:tt(a,!0))+`</code></pre>
-`}blockquote(a){return`<blockquote>
-${a}</blockquote>
-`}html(a,l){return a}heading(a,l,s,m){if(this.options.headerIds){const p=this.options.headerPrefix+m.slug(s);return`<h${l} id="${p}">${a}</h${l}>
-`}return`<h${l}>${a}</h${l}>
-`}hr(){return this.options.xhtml?`<hr/>
-`:`<hr>
-`}list(a,l,s){const m=l?"ol":"ul",p=l&&s!==1?' start="'+s+'"':"";return"<"+m+p+`>
-`+a+"</"+m+`>
-`}listitem(a){return`<li>${a}</li>
-`}checkbox(a){return"<input "+(a?'checked="" ':"")+'disabled="" type="checkbox"'+(this.options.xhtml?" /":"")+"> "}paragraph(a){return`<p>${a}</p>
-`}table(a,l){return l&&(l=`<tbody>${l}</tbody>`),`<table>
-<thead>
-`+a+`</thead>
-`+l+`</table>
-`}tablerow(a){return`<tr>
-${a}</tr>
-`}tablecell(a,l){const s=l.header?"th":"td";return(l.align?`<${s} align="${l.align}">`:`<${s}>`)+a+`</${s}>
-`}strong(a){return`<strong>${a}</strong>`}em(a){return`<em>${a}</em>`}codespan(a){return`<code>${a}</code>`}br(){return this.options.xhtml?"<br/>":"<br>"}del(a){return`<del>${a}</del>`}link(a,l,s){if(a=di(this.options.sanitize,this.options.baseUrl,a),a===null)return s;let m='<a href="'+a+'"';return l&&(m+=' title="'+l+'"'),m+=">"+s+"</a>",m}image(a,l,s){if(a=di(this.options.sanitize,this.options.baseUrl,a),a===null)return s;let m=`<img src="${a}" alt="${s}"`;return l&&(m+=` title="${l}"`),m+=this.options.xhtml?"/>":">",m}text(a){return a}}class al{strong(a){return a}em(a){return a}codespan(a){return a}del(a){return a}html(a){return a}text(a){return a}link(a,l,s){return""+s}image(a,l,s){return""+s}br(){return""}}class il{constructor(){this.seen={}}serialize(a){return a.toLowerCase().trim().replace(/<[!\/a-z].*?>/ig,"").replace(/[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g,"").replace(/\s/g,"-")}getNextSafeSlug(a,l){let s=a,m=0;if(this.seen.hasOwnProperty(s)){m=this.seen[a];do m++,s=a+"-"+m;while(this.seen.hasOwnProperty(s))}return l||(this.seen[a]=m,this.seen[s]=0),s}slug(a,l={}){const s=this.serialize(a);return this.getNextSafeSlug(s,l.dryrun)}}class p0{constructor(a){this.options=a||_0,this.options.renderer=this.options.renderer||new Vn,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new al,this.slugger=new il}static parse(a,l){return new p0(l).parse(a)}static parseInline(a,l){return new p0(l).parseInline(a)}parse(a,l=!0){let s="",m,p,v,y,k,N,U,Y,te,j,ve,pe,le,O,B,z,R,D,L;const X=a.length;for(m=0;m<X;m++){if(j=a[m],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[j.type]&&(L=this.options.extensions.renderers[j.type].call({parser:this},j),L!==!1||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(j.type))){s+=L||"";continue}switch(j.type){case"space":continue;case"hr":{s+=this.renderer.hr();continue}case"heading":{s+=this.renderer.heading(this.parseInline(j.tokens),j.depth,nl(this.parseInline(j.tokens,this.textRenderer)),this.slugger);continue}case"code":{s+=this.renderer.code(j.text,j.lang,j.escaped);continue}case"table":{for(Y="",U="",y=j.header.length,p=0;p<y;p++)U+=this.renderer.tablecell(this.parseInline(j.header[p].tokens),{header:!0,align:j.align[p]});for(Y+=this.renderer.tablerow(U),te="",y=j.rows.length,p=0;p<y;p++){for(N=j.rows[p],U="",k=N.length,v=0;v<k;v++)U+=this.renderer.tablecell(this.parseInline(N[v].tokens),{header:!1,align:j.align[v]});te+=this.renderer.tablerow(U)}s+=this.renderer.table(Y,te);continue}case"blockquote":{te=this.parse(j.tokens),s+=this.renderer.blockquote(te);continue}case"list":{for(ve=j.ordered,pe=j.start,le=j.loose,y=j.items.length,te="",p=0;p<y;p++)B=j.items[p],z=B.checked,R=B.task,O="",B.task&&(D=this.renderer.checkbox(z),le?B.tokens.length>0&&B.tokens[0].type==="paragraph"?(B.tokens[0].text=D+" "+B.tokens[0].text,B.tokens[0].tokens&&B.tokens[0].tokens.length>0&&B.tokens[0].tokens[0].type==="text"&&(B.tokens[0].tokens[0].text=D+" "+B.tokens[0].tokens[0].text)):B.tokens.unshift({type:"text",text:D}):O+=D),O+=this.parse(B.tokens,le),te+=this.renderer.listitem(O,R,z);s+=this.renderer.list(te,ve,pe);continue}case"html":{s+=this.renderer.html(j.text,j.block);continue}case"paragraph":{s+=this.renderer.paragraph(this.parseInline(j.tokens));continue}case"text":{for(te=j.tokens?this.parseInline(j.tokens):j.text;m+1<X&&a[m+1].type==="text";)j=a[++m],te+=`
-`+(j.tokens?this.parseInline(j.tokens):j.text);s+=l?this.renderer.paragraph(te):te;continue}default:{const ae='Token with "'+j.type+'" type was not found.';if(this.options.silent){console.error(ae);return}else throw new Error(ae)}}}return s}parseInline(a,l){l=l||this.renderer;let s="",m,p,v;const y=a.length;for(m=0;m<y;m++){if(p=a[m],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[p.type]&&(v=this.options.extensions.renderers[p.type].call({parser:this},p),v!==!1||!["escape","html","link","image","strong","em","codespan","br","del","text"].includes(p.type))){s+=v||"";continue}switch(p.type){case"escape":{s+=l.text(p.text);break}case"html":{s+=l.html(p.text);break}case"link":{s+=l.link(p.href,p.title,this.parseInline(p.tokens,l));break}case"image":{s+=l.image(p.href,p.title,p.text);break}case"strong":{s+=l.strong(this.parseInline(p.tokens,l));break}case"em":{s+=l.em(this.parseInline(p.tokens,l));break}case"codespan":{s+=l.codespan(p.text);break}case"br":{s+=l.br();break}case"del":{s+=l.del(this.parseInline(p.tokens,l));break}case"text":{s+=l.text(p.text);break}default:{const k='Token with "'+p.type+'" type was not found.';if(this.options.silent){console.error(k);return}else throw new Error(k)}}}return s}}class Ln{constructor(a){this.options=a||_0}static passThroughHooks=new Set(["preprocess","postprocess"]);preprocess(a){return a}postprocess(a){return a}}function wo(g,a,l){return s=>{if(s.message+=`
-Please report this to https://github.com/markedjs/marked.`,g){const m="<p>An error occurred:</p><pre>"+tt(s.message+"",!0)+"</pre>";if(a)return Promise.resolve(m);if(l){l(null,m);return}return m}if(a)return Promise.reject(s);if(l){l(s);return}throw s}}function ll(g,a){return(l,s,m)=>{typeof s=="function"&&(m=s,s=null);const p={...s};s={...fe.defaults,...p};const v=wo(s.silent,s.async,m);if(typeof l>"u"||l===null)return v(new Error("marked(): input parameter is undefined or null"));if(typeof l!="string")return v(new Error("marked(): input parameter is of type "+Object.prototype.toString.call(l)+", string expected"));if(vo(s,m),s.hooks&&(s.hooks.options=s),m){const y=s.highlight;let k;try{s.hooks&&(l=s.hooks.preprocess(l)),k=g(l,s)}catch(Y){return v(Y)}const N=function(Y){let te;if(!Y)try{s.walkTokens&&fe.walkTokens(k,s.walkTokens),te=a(k,s),s.hooks&&(te=s.hooks.postprocess(te))}catch(j){Y=j}return s.highlight=y,Y?v(Y):m(null,te)};if(!y||y.length<3||(delete s.highlight,!k.length))return N();let U=0;fe.walkTokens(k,function(Y){Y.type==="code"&&(U++,setTimeout(()=>{y(Y.text,Y.lang,function(te,j){if(te)return N(te);j!=null&&j!==Y.text&&(Y.text=j,Y.escaped=!0),U--,U===0&&N()})},0))}),U===0&&N();return}if(s.async)return Promise.resolve(s.hooks?s.hooks.preprocess(l):l).then(y=>g(y,s)).then(y=>s.walkTokens?Promise.all(fe.walkTokens(y,s.walkTokens)).then(()=>y):y).then(y=>a(y,s)).then(y=>s.hooks?s.hooks.postprocess(y):y).catch(v);try{s.hooks&&(l=s.hooks.preprocess(l));const y=g(l,s);s.walkTokens&&fe.walkTokens(y,s.walkTokens);let k=a(y,s);return s.hooks&&(k=s.hooks.postprocess(k)),k}catch(y){return v(y)}}}function fe(g,a,l){return ll(d0.lex,p0.parse)(g,a,l)}fe.options=fe.setOptions=function(g){return fe.defaults={...fe.defaults,...g},no(fe.defaults),fe};fe.getDefaults=el;fe.defaults=_0;fe.use=function(...g){const a=fe.defaults.extensions||{renderers:{},childTokens:{}};g.forEach(l=>{const s={...l};if(s.async=fe.defaults.async||s.async||!1,l.extensions&&(l.extensions.forEach(m=>{if(!m.name)throw new Error("extension name required");if(m.renderer){const p=a.renderers[m.name];p?a.renderers[m.name]=function(...v){let y=m.renderer.apply(this,v);return y===!1&&(y=p.apply(this,v)),y}:a.renderers[m.name]=m.renderer}if(m.tokenizer){if(!m.level||m.level!=="block"&&m.level!=="inline")throw new Error("extension level must be 'block' or 'inline'");a[m.level]?a[m.level].unshift(m.tokenizer):a[m.level]=[m.tokenizer],m.start&&(m.level==="block"?a.startBlock?a.startBlock.push(m.start):a.startBlock=[m.start]:m.level==="inline"&&(a.startInline?a.startInline.push(m.start):a.startInline=[m.start]))}m.childTokens&&(a.childTokens[m.name]=m.childTokens)}),s.extensions=a),l.renderer){const m=fe.defaults.renderer||new Vn;for(const p in l.renderer){const v=m[p];m[p]=(...y)=>{let k=l.renderer[p].apply(m,y);return k===!1&&(k=v.apply(m,y)),k}}s.renderer=m}if(l.tokenizer){const m=fe.defaults.tokenizer||new Gn;for(const p in l.tokenizer){const v=m[p];m[p]=(...y)=>{let k=l.tokenizer[p].apply(m,y);return k===!1&&(k=v.apply(m,y)),k}}s.tokenizer=m}if(l.hooks){const m=fe.defaults.hooks||new Ln;for(const p in l.hooks){const v=m[p];Ln.passThroughHooks.has(p)?m[p]=y=>{if(fe.defaults.async)return Promise.resolve(l.hooks[p].call(m,y)).then(N=>v.call(m,N));const k=l.hooks[p].call(m,y);return v.call(m,k)}:m[p]=(...y)=>{let k=l.hooks[p].apply(m,y);return k===!1&&(k=v.apply(m,y)),k}}s.hooks=m}if(l.walkTokens){const m=fe.defaults.walkTokens;s.walkTokens=function(p){let v=[];return v.push(l.walkTokens.call(this,p)),m&&(v=v.concat(m.call(this,p))),v}}fe.setOptions(s)})};fe.walkTokens=function(g,a){let l=[];for(const s of g)switch(l=l.concat(a.call(fe,s)),s.type){case"table":{for(const m of s.header)l=l.concat(fe.walkTokens(m.tokens,a));for(const m of s.rows)for(const p of m)l=l.concat(fe.walkTokens(p.tokens,a));break}case"list":{l=l.concat(fe.walkTokens(s.items,a));break}default:fe.defaults.extensions&&fe.defaults.extensions.childTokens&&fe.defaults.extensions.childTokens[s.type]?fe.defaults.extensions.childTokens[s.type].forEach(function(m){l=l.concat(fe.walkTokens(s[m],a))}):s.tokens&&(l=l.concat(fe.walkTokens(s.tokens,a)))}return l};fe.parseInline=ll(d0.lexInline,p0.parseInline);fe.Parser=p0;fe.parser=p0.parse;fe.Renderer=Vn;fe.TextRenderer=al;fe.Lexer=d0;fe.lexer=d0.lex;fe.Tokenizer=Gn;fe.Slugger=il;fe.Hooks=Ln;fe.parse=fe;fe.options;fe.setOptions;fe.use;fe.walkTokens;fe.parseInline;p0.parse;d0.lex;function xo(g){if(typeof g=="function"&&(g={highlight:g}),!g||typeof g.highlight!="function")throw new Error("Must provide highlight function");return typeof g.langPrefix!="string"&&(g.langPrefix="language-"),{async:!!g.async,walkTokens(a){if(a.type!=="code")return;const l=ko(a);if(g.async)return Promise.resolve(g.highlight(a.text,l)).then(yi(a));const s=g.highlight(a.text,l);yi(a)(s)},renderer:{code(a,l,s){const m=(l||"").match(/\S*/)[0],p=m?` class="${g.langPrefix}${xi(m)}"`:"";return a=a.replace(/\n$/,""),`<pre><code${p}>${s?a:xi(a,!0)}
-</code></pre>`}}}}function ko(g){return(g.lang||"").match(/\S*/)[0]}function yi(g){return a=>{typeof a=="string"&&a!==g.text&&(g.escaped=!0,g.text=a)}}const sl=/[&<>"']/,So=new RegExp(sl.source,"g"),ol=/[<>"']|&(?!(#\d{1,7}|#[Xx][a-fA-F0-9]{1,6}|\w+);)/,Ao=new RegExp(ol.source,"g"),To={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"},wi=g=>To[g];function xi(g,a){if(a){if(sl.test(g))return g.replace(So,wi)}else if(ol.test(g))return g.replace(Ao,wi);return g}var ul={exports:{}};(function(g){var a=typeof window<"u"?window:typeof WorkerGlobalScope<"u"&&self instanceof WorkerGlobalScope?self:{};/**
- * Prism: Lightweight, robust, elegant syntax highlighting
- *
- * @license MIT <https://opensource.org/licenses/MIT>
- * @author Lea Verou <https://lea.verou.me>
- * @namespace
- * @public
- */var l=function(s){var m=/(?:^|\s)lang(?:uage)?-([\w-]+)(?=\s|$)/i,p=0,v={},y={manual:s.Prism&&s.Prism.manual,disableWorkerMessageHandler:s.Prism&&s.Prism.disableWorkerMessageHandler,util:{encode:function B(z){return z instanceof k?new k(z.type,B(z.content),z.alias):Array.isArray(z)?z.map(B):z.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/\u00a0/g," ")},type:function(B){return Object.prototype.toString.call(B).slice(8,-1)},objId:function(B){return B.__id||Object.defineProperty(B,"__id",{value:++p}),B.__id},clone:function B(z,R){R=R||{};var D,L;switch(y.util.type(z)){case"Object":if(L=y.util.objId(z),R[L])return R[L];D={},R[L]=D;for(var X in z)z.hasOwnProperty(X)&&(D[X]=B(z[X],R));return D;case"Array":return L=y.util.objId(z),R[L]?R[L]:(D=[],R[L]=D,z.forEach(function(ae,K){D[K]=B(ae,R)}),D);default:return z}},getLanguage:function(B){for(;B;){var z=m.exec(B.className);if(z)return z[1].toLowerCase();B=B.parentElement}return"none"},setLanguage:function(B,z){B.className=B.className.replace(RegExp(m,"gi"),""),B.classList.add("language-"+z)},currentScript:function(){if(typeof document>"u")return null;if("currentScript"in document&&1<2)return document.currentScript;try{throw new Error}catch(D){var B=(/at [^(\r\n]*\((.*):[^:]+:[^:]+\)$/i.exec(D.stack)||[])[1];if(B){var z=document.getElementsByTagName("script");for(var R in z)if(z[R].src==B)return z[R]}return null}},isActive:function(B,z,R){for(var D="no-"+z;B;){var L=B.classList;if(L.contains(z))return!0;if(L.contains(D))return!1;B=B.parentElement}return!!R}},languages:{plain:v,plaintext:v,text:v,txt:v,extend:function(B,z){var R=y.util.clone(y.languages[B]);for(var D in z)R[D]=z[D];return R},insertBefore:function(B,z,R,D){D=D||y.languages;var L=D[B],X={};for(var ae in L)if(L.hasOwnProperty(ae)){if(ae==z)for(var K in R)R.hasOwnProperty(K)&&(X[K]=R[K]);R.hasOwnProperty(ae)||(X[ae]=L[ae])}var G=D[B];return D[B]=X,y.languages.DFS(y.languages,function(W,Ce){Ce===G&&W!=B&&(this[W]=X)}),X},DFS:function B(z,R,D,L){L=L||{};var X=y.util.objId;for(var ae in z)if(z.hasOwnProperty(ae)){R.call(z,ae,z[ae],D||ae);var K=z[ae],G=y.util.type(K);G==="Object"&&!L[X(K)]?(L[X(K)]=!0,B(K,R,null,L)):G==="Array"&&!L[X(K)]&&(L[X(K)]=!0,B(K,R,ae,L))}}},plugins:{},highlightAll:function(B,z){y.highlightAllUnder(document,B,z)},highlightAllUnder:function(B,z,R){var D={callback:R,container:B,selector:'code[class*="language-"], [class*="language-"] code, code[class*="lang-"], [class*="lang-"] code'};y.hooks.run("before-highlightall",D),D.elements=Array.prototype.slice.apply(D.container.querySelectorAll(D.selector)),y.hooks.run("before-all-elements-highlight",D);for(var L=0,X;X=D.elements[L++];)y.highlightElement(X,z===!0,D.callback)},highlightElement:function(B,z,R){var D=y.util.getLanguage(B),L=y.languages[D];y.util.setLanguage(B,D);var X=B.parentElement;X&&X.nodeName.toLowerCase()==="pre"&&y.util.setLanguage(X,D);var ae=B.textContent,K={element:B,language:D,grammar:L,code:ae};function G(Ce){K.highlightedCode=Ce,y.hooks.run("before-insert",K),K.element.innerHTML=K.highlightedCode,y.hooks.run("after-highlight",K),y.hooks.run("complete",K),R&&R.call(K.element)}if(y.hooks.run("before-sanity-check",K),X=K.element.parentElement,X&&X.nodeName.toLowerCase()==="pre"&&!X.hasAttribute("tabindex")&&X.setAttribute("tabindex","0"),!K.code){y.hooks.run("complete",K),R&&R.call(K.element);return}if(y.hooks.run("before-highlight",K),!K.grammar){G(y.util.encode(K.code));return}if(z&&s.Worker){var W=new Worker(y.filename);W.onmessage=function(Ce){G(Ce.data)},W.postMessage(JSON.stringify({language:K.language,code:K.code,immediateClose:!0}))}else G(y.highlight(K.code,K.grammar,K.language))},highlight:function(B,z,R){var D={code:B,grammar:z,language:R};if(y.hooks.run("before-tokenize",D),!D.grammar)throw new Error('The language "'+D.language+'" has no grammar.');return D.tokens=y.tokenize(D.code,D.grammar),y.hooks.run("after-tokenize",D),k.stringify(y.util.encode(D.tokens),D.language)},tokenize:function(B,z){var R=z.rest;if(R){for(var D in R)z[D]=R[D];delete z.rest}var L=new Y;return te(L,L.head,B),U(B,L,z,L.head,0),ve(L)},hooks:{all:{},add:function(B,z){var R=y.hooks.all;R[B]=R[B]||[],R[B].push(z)},run:function(B,z){var R=y.hooks.all[B];if(!(!R||!R.length))for(var D=0,L;L=R[D++];)L(z)}},Token:k};s.Prism=y;function k(B,z,R,D){this.type=B,this.content=z,this.alias=R,this.length=(D||"").length|0}k.stringify=function B(z,R){if(typeof z=="string")return z;if(Array.isArray(z)){var D="";return z.forEach(function(G){D+=B(G,R)}),D}var L={type:z.type,content:B(z.content,R),tag:"span",classes:["token",z.type],attributes:{},language:R},X=z.alias;X&&(Array.isArray(X)?Array.prototype.push.apply(L.classes,X):L.classes.push(X)),y.hooks.run("wrap",L);var ae="";for(var K in L.attributes)ae+=" "+K+'="'+(L.attributes[K]||"").replace(/"/g,"&quot;")+'"';return"<"+L.tag+' class="'+L.classes.join(" ")+'"'+ae+">"+L.content+"</"+L.tag+">"};function N(B,z,R,D){B.lastIndex=z;var L=B.exec(R);if(L&&D&&L[1]){var X=L[1].length;L.index+=X,L[0]=L[0].slice(X)}return L}function U(B,z,R,D,L,X){for(var ae in R)if(!(!R.hasOwnProperty(ae)||!R[ae])){var K=R[ae];K=Array.isArray(K)?K:[K];for(var G=0;G<K.length;++G){if(X&&X.cause==ae+","+G)return;var W=K[G],Ce=W.inside,ie=!!W.lookbehind,Ge=!!W.greedy,Gt=W.alias;if(Ge&&!W.pattern.global){var X0=W.pattern.toString().match(/[imsuy]*$/)[0];W.pattern=RegExp(W.pattern.source,X0+"g")}for(var g0=W.pattern||W,Ve=D.next,_e=L;Ve!==z.tail&&!(X&&_e>=X.reach);_e+=Ve.value.length,Ve=Ve.next){var _t=Ve.value;if(z.length>B.length)return;if(!(_t instanceof k)){var ee=1,Ke;if(Ge){if(Ke=N(g0,_e,B,ie),!Ke||Ke.index>=B.length)break;var lt=Ke.index,Ie=Ke.index+Ke[0].length,je=_e;for(je+=Ve.value.length;lt>=je;)Ve=Ve.next,je+=Ve.value.length;if(je-=Ve.value.length,_e=je,Ve.value instanceof k)continue;for(var gt=Ve;gt!==z.tail&&(je<Ie||typeof gt.value=="string");gt=gt.next)ee++,je+=gt.value.length;ee--,_t=B.slice(_e,je),Ke.index-=_e}else if(Ke=N(g0,0,_t,ie),!Ke)continue;var lt=Ke.index,Bt=Ke[0],Kt=_t.slice(0,lt),v0=_t.slice(lt+Bt.length),vt=_e+_t.length;X&&vt>X.reach&&(X.reach=vt);var st=Ve.prev;Kt&&(st=te(z,st,Kt),_e+=Kt.length),j(z,st,ee);var b0=new k(ae,Ce?y.tokenize(Bt,Ce):Bt,Gt,Bt);if(Ve=te(z,st,b0),v0&&te(z,Ve,v0),ee>1){var Vt={cause:ae+","+G,reach:vt};U(B,z,R,Ve.prev,_e,Vt),X&&Vt.reach>X.reach&&(X.reach=Vt.reach)}}}}}}function Y(){var B={value:null,prev:null,next:null},z={value:null,prev:B,next:null};B.next=z,this.head=B,this.tail=z,this.length=0}function te(B,z,R){var D=z.next,L={value:R,prev:z,next:D};return z.next=L,D.prev=L,B.length++,L}function j(B,z,R){for(var D=z.next,L=0;L<R&&D!==B.tail;L++)D=D.next;z.next=D,D.prev=z,B.length-=L}function ve(B){for(var z=[],R=B.head.next;R!==B.tail;)z.push(R.value),R=R.next;return z}if(!s.document)return s.addEventListener&&(y.disableWorkerMessageHandler||s.addEventListener("message",function(B){var z=JSON.parse(B.data),R=z.language,D=z.code,L=z.immediateClose;s.postMessage(y.highlight(D,y.languages[R],R)),L&&s.close()},!1)),y;var pe=y.util.currentScript();pe&&(y.filename=pe.src,pe.hasAttribute("data-manual")&&(y.manual=!0));function le(){y.manual||y.highlightAll()}if(!y.manual){var O=document.readyState;O==="loading"||O==="interactive"&&pe&&pe.defer?document.addEventListener("DOMContentLoaded",le):window.requestAnimationFrame?window.requestAnimationFrame(le):window.setTimeout(le,16)}return y}(a);g.exports&&(g.exports=l),typeof qr<"u"&&(qr.Prism=l),l.languages.markup={comment:{pattern:/<!--(?:(?!<!--)[\s\S])*?-->/,greedy:!0},prolog:{pattern:/<\?[\s\S]+?\?>/,greedy:!0},doctype:{pattern:/<!DOCTYPE(?:[^>"'[\]]|"[^"]*"|'[^']*')+(?:\[(?:[^<"'\]]|"[^"]*"|'[^']*'|<(?!!--)|<!--(?:[^-]|-(?!->))*-->)*\]\s*)?>/i,greedy:!0,inside:{"internal-subset":{pattern:/(^[^\[]*\[)[\s\S]+(?=\]>$)/,lookbehind:!0,greedy:!0,inside:null},string:{pattern:/"[^"]*"|'[^']*'/,greedy:!0},punctuation:/^<!|>$|[[\]]/,"doctype-tag":/^DOCTYPE/i,name:/[^\s<>'"]+/}},cdata:{pattern:/<!\[CDATA\[[\s\S]*?\]\]>/i,greedy:!0},tag:{pattern:/<\/?(?!\d)[^\s>\/=$<%]+(?:\s(?:\s*[^\s>\/=]+(?:\s*=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+(?=[\s>]))|(?=[\s/>])))+)?\s*\/?>/,greedy:!0,inside:{tag:{pattern:/^<\/?[^\s>\/]+/,inside:{punctuation:/^<\/?/,namespace:/^[^\s>\/:]+:/}},"special-attr":[],"attr-value":{pattern:/=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+)/,inside:{punctuation:[{pattern:/^=/,alias:"attr-equals"},{pattern:/^(\s*)["']|["']$/,lookbehind:!0}]}},punctuation:/\/?>/,"attr-name":{pattern:/[^\s>\/]+/,inside:{namespace:/^[^\s>\/:]+:/}}}},entity:[{pattern:/&[\da-z]{1,8};/i,alias:"named-entity"},/&#x?[\da-f]{1,8};/i]},l.languages.markup.tag.inside["attr-value"].inside.entity=l.languages.markup.entity,l.languages.markup.doctype.inside["internal-subset"].inside=l.languages.markup,l.hooks.add("wrap",function(s){s.type==="entity"&&(s.attributes.title=s.content.replace(/&amp;/,"&"))}),Object.defineProperty(l.languages.markup.tag,"addInlined",{value:function(m,p){var v={};v["language-"+p]={pattern:/(^<!\[CDATA\[)[\s\S]+?(?=\]\]>$)/i,lookbehind:!0,inside:l.languages[p]},v.cdata=/^<!\[CDATA\[|\]\]>$/i;var y={"included-cdata":{pattern:/<!\[CDATA\[[\s\S]*?\]\]>/i,inside:v}};y["language-"+p]={pattern:/[\s\S]+/,inside:l.languages[p]};var k={};k[m]={pattern:RegExp(/(<__[^>]*>)(?:<!\[CDATA\[(?:[^\]]|\](?!\]>))*\]\]>|(?!<!\[CDATA\[)[\s\S])*?(?=<\/__>)/.source.replace(/__/g,function(){return m}),"i"),lookbehind:!0,greedy:!0,inside:y},l.languages.insertBefore("markup","cdata",k)}}),Object.defineProperty(l.languages.markup.tag,"addAttribute",{value:function(s,m){l.languages.markup.tag.inside["special-attr"].push({pattern:RegExp(/(^|["'\s])/.source+"(?:"+s+")"+/\s*=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+(?=[\s>]))/.source,"i"),lookbehind:!0,inside:{"attr-name":/^[^\s=]+/,"attr-value":{pattern:/=[\s\S]+/,inside:{value:{pattern:/(^=\s*(["']|(?!["'])))\S[\s\S]*(?=\2$)/,lookbehind:!0,alias:[m,"language-"+m],inside:l.languages[m]},punctuation:[{pattern:/^=/,alias:"attr-equals"},/"|'/]}}}})}}),l.languages.html=l.languages.markup,l.languages.mathml=l.languages.markup,l.languages.svg=l.languages.markup,l.languages.xml=l.languages.extend("markup",{}),l.languages.ssml=l.languages.xml,l.languages.atom=l.languages.xml,l.languages.rss=l.languages.xml,function(s){var m=/(?:"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"|'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n])*')/;s.languages.css={comment:/\/\*[\s\S]*?\*\//,atrule:{pattern:RegExp("@[\\w-](?:"+/[^;{\s"']|\s+(?!\s)/.source+"|"+m.source+")*?"+/(?:;|(?=\s*\{))/.source),inside:{rule:/^@[\w-]+/,"selector-function-argument":{pattern:/(\bselector\s*\(\s*(?![\s)]))(?:[^()\s]|\s+(?![\s)])|\((?:[^()]|\([^()]*\))*\))+(?=\s*\))/,lookbehind:!0,alias:"selector"},keyword:{pattern:/(^|[^\w-])(?:and|not|only|or)(?![\w-])/,lookbehind:!0}}},url:{pattern:RegExp("\\burl\\((?:"+m.source+"|"+/(?:[^\\\r\n()"']|\\[\s\S])*/.source+")\\)","i"),greedy:!0,inside:{function:/^url/i,punctuation:/^\(|\)$/,string:{pattern:RegExp("^"+m.source+"$"),alias:"url"}}},selector:{pattern:RegExp(`(^|[{}\\s])[^{}\\s](?:[^{};"'\\s]|\\s+(?![\\s{])|`+m.source+")*(?=\\s*\\{)"),lookbehind:!0},string:{pattern:m,greedy:!0},property:{pattern:/(^|[^-\w\xA0-\uFFFF])(?!\s)[-_a-z\xA0-\uFFFF](?:(?!\s)[-\w\xA0-\uFFFF])*(?=\s*:)/i,lookbehind:!0},important:/!important\b/i,function:{pattern:/(^|[^-a-z0-9])[-a-z0-9]+(?=\()/i,lookbehind:!0},punctuation:/[(){};:,]/},s.languages.css.atrule.inside.rest=s.languages.css;var p=s.languages.markup;p&&(p.tag.addInlined("style","css"),p.tag.addAttribute("style","css"))}(l),l.languages.clike={comment:[{pattern:/(^|[^\\])\/\*[\s\S]*?(?:\*\/|$)/,lookbehind:!0,greedy:!0},{pattern:/(^|[^\\:])\/\/.*/,lookbehind:!0,greedy:!0}],string:{pattern:/(["'])(?:\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,greedy:!0},"class-name":{pattern:/(\b(?:class|extends|implements|instanceof|interface|new|trait)\s+|\bcatch\s+\()[\w.\\]+/i,lookbehind:!0,inside:{punctuation:/[.\\]/}},keyword:/\b(?:break|catch|continue|do|else|finally|for|function|if|in|instanceof|new|null|return|throw|try|while)\b/,boolean:/\b(?:false|true)\b/,function:/\b\w+(?=\()/,number:/\b0x[\da-f]+\b|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?/i,operator:/[<>]=?|[!=]=?=?|--?|\+\+?|&&?|\|\|?|[?*/~^%]/,punctuation:/[{}[\];(),.:]/},l.languages.javascript=l.languages.extend("clike",{"class-name":[l.languages.clike["class-name"],{pattern:/(^|[^$\w\xA0-\uFFFF])(?!\s)[_$A-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\.(?:constructor|prototype))/,lookbehind:!0}],keyword:[{pattern:/((?:^|\})\s*)catch\b/,lookbehind:!0},{pattern:/(^|[^.]|\.\.\.\s*)\b(?:as|assert(?=\s*\{)|async(?=\s*(?:function\b|\(|[$\w\xA0-\uFFFF]|$))|await|break|case|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally(?=\s*(?:\{|$))|for|from(?=\s*(?:['"]|$))|function|(?:get|set)(?=\s*(?:[#\[$\w\xA0-\uFFFF]|$))|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|static|super|switch|this|throw|try|typeof|undefined|var|void|while|with|yield)\b/,lookbehind:!0}],function:/#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*(?:\.\s*(?:apply|bind|call)\s*)?\()/,number:{pattern:RegExp(/(^|[^\w$])/.source+"(?:"+(/NaN|Infinity/.source+"|"+/0[bB][01]+(?:_[01]+)*n?/.source+"|"+/0[oO][0-7]+(?:_[0-7]+)*n?/.source+"|"+/0[xX][\dA-Fa-f]+(?:_[\dA-Fa-f]+)*n?/.source+"|"+/\d+(?:_\d+)*n/.source+"|"+/(?:\d+(?:_\d+)*(?:\.(?:\d+(?:_\d+)*)?)?|\.\d+(?:_\d+)*)(?:[Ee][+-]?\d+(?:_\d+)*)?/.source)+")"+/(?![\w$])/.source),lookbehind:!0},operator:/--|\+\+|\*\*=?|=>|&&=?|\|\|=?|[!=]==|<<=?|>>>?=?|[-+*/%&|^!=<>]=?|\.{3}|\?\?=?|\?\.?|[~:]/}),l.languages.javascript["class-name"][0].pattern=/(\b(?:class|extends|implements|instanceof|interface|new)\s+)[\w.\\]+/,l.languages.insertBefore("javascript","keyword",{regex:{pattern:RegExp(/((?:^|[^$\w\xA0-\uFFFF."'\])\s]|\b(?:return|yield))\s*)/.source+/\//.source+"(?:"+/(?:\[(?:[^\]\\\r\n]|\\.)*\]|\\.|[^/\\\[\r\n])+\/[dgimyus]{0,7}/.source+"|"+/(?:\[(?:[^[\]\\\r\n]|\\.|\[(?:[^[\]\\\r\n]|\\.|\[(?:[^[\]\\\r\n]|\\.)*\])*\])*\]|\\.|[^/\\\[\r\n])+\/[dgimyus]{0,7}v[dgimyus]{0,7}/.source+")"+/(?=(?:\s|\/\*(?:[^*]|\*(?!\/))*\*\/)*(?:$|[\r\n,.;:})\]]|\/\/))/.source),lookbehind:!0,greedy:!0,inside:{"regex-source":{pattern:/^(\/)[\s\S]+(?=\/[a-z]*$)/,lookbehind:!0,alias:"language-regex",inside:l.languages.regex},"regex-delimiter":/^\/|\/$/,"regex-flags":/^[a-z]+$/}},"function-variable":{pattern:/#?(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*[=:]\s*(?:async\s*)?(?:\bfunction\b|(?:\((?:[^()]|\([^()]*\))*\)|(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)\s*=>))/,alias:"function"},parameter:[{pattern:/(function(?:\s+(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*)?\s*\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\))/,lookbehind:!0,inside:l.languages.javascript},{pattern:/(^|[^$\w\xA0-\uFFFF])(?!\s)[_$a-z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*=>)/i,lookbehind:!0,inside:l.languages.javascript},{pattern:/(\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\)\s*=>)/,lookbehind:!0,inside:l.languages.javascript},{pattern:/((?:\b|\s|^)(?!(?:as|async|await|break|case|catch|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|set|static|super|switch|this|throw|try|typeof|undefined|var|void|while|with|yield)(?![$\w\xA0-\uFFFF]))(?:(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*\s*)\(\s*|\]\s*\(\s*)(?!\s)(?:[^()\s]|\s+(?![\s)])|\([^()]*\))+(?=\s*\)\s*\{)/,lookbehind:!0,inside:l.languages.javascript}],constant:/\b[A-Z](?:[A-Z_]|\dx?)*\b/}),l.languages.insertBefore("javascript","string",{hashbang:{pattern:/^#!.*/,greedy:!0,alias:"comment"},"template-string":{pattern:/`(?:\\[\s\S]|\$\{(?:[^{}]|\{(?:[^{}]|\{[^}]*\})*\})+\}|(?!\$\{)[^\\`])*`/,greedy:!0,inside:{"template-punctuation":{pattern:/^`|`$/,alias:"string"},interpolation:{pattern:/((?:^|[^\\])(?:\\{2})*)\$\{(?:[^{}]|\{(?:[^{}]|\{[^}]*\})*\})+\}/,lookbehind:!0,inside:{"interpolation-punctuation":{pattern:/^\$\{|\}$/,alias:"punctuation"},rest:l.languages.javascript}},string:/[\s\S]+/}},"string-property":{pattern:/((?:^|[,{])[ \t]*)(["'])(?:\\(?:\r\n|[\s\S])|(?!\2)[^\\\r\n])*\2(?=\s*:)/m,lookbehind:!0,greedy:!0,alias:"property"}}),l.languages.insertBefore("javascript","operator",{"literal-property":{pattern:/((?:^|[,{])[ \t]*)(?!\s)[_$a-zA-Z\xA0-\uFFFF](?:(?!\s)[$\w\xA0-\uFFFF])*(?=\s*:)/m,lookbehind:!0,alias:"property"}}),l.languages.markup&&(l.languages.markup.tag.addInlined("script","javascript"),l.languages.markup.tag.addAttribute(/on(?:abort|blur|change|click|composition(?:end|start|update)|dblclick|error|focus(?:in|out)?|key(?:down|up)|load|mouse(?:down|enter|leave|move|out|over|up)|reset|resize|scroll|select|slotchange|submit|unload|wheel)/.source,"javascript")),l.languages.js=l.languages.javascript,function(){if(typeof l>"u"||typeof document>"u")return;Element.prototype.matches||(Element.prototype.matches=Element.prototype.msMatchesSelector||Element.prototype.webkitMatchesSelector);var s="Loading…",m=function(pe,le){return"✖ Error "+pe+" while fetching file: "+le},p="✖ Error: File does not exist or is empty",v={js:"javascript",py:"python",rb:"ruby",ps1:"powershell",psm1:"powershell",sh:"bash",bat:"batch",h:"c",tex:"latex"},y="data-src-status",k="loading",N="loaded",U="failed",Y="pre[data-src]:not(["+y+'="'+N+'"]):not(['+y+'="'+k+'"])';function te(pe,le,O){var B=new XMLHttpRequest;B.open("GET",pe,!0),B.onreadystatechange=function(){B.readyState==4&&(B.status<400&&B.responseText?le(B.responseText):B.status>=400?O(m(B.status,B.statusText)):O(p))},B.send(null)}function j(pe){var le=/^\s*(\d+)\s*(?:(,)\s*(?:(\d+)\s*)?)?$/.exec(pe||"");if(le){var O=Number(le[1]),B=le[2],z=le[3];return B?z?[O,Number(z)]:[O,void 0]:[O,O]}}l.hooks.add("before-highlightall",function(pe){pe.selector+=", "+Y}),l.hooks.add("before-sanity-check",function(pe){var le=pe.element;if(le.matches(Y)){pe.code="",le.setAttribute(y,k);var O=le.appendChild(document.createElement("CODE"));O.textContent=s;var B=le.getAttribute("data-src"),z=pe.language;if(z==="none"){var R=(/\.(\w+)$/.exec(B)||[,"none"])[1];z=v[R]||R}l.util.setLanguage(O,z),l.util.setLanguage(le,z);var D=l.plugins.autoloader;D&&D.loadLanguages(z),te(B,function(L){le.setAttribute(y,N);var X=j(le.getAttribute("data-range"));if(X){var ae=L.split(/\r\n?|\n/g),K=X[0],G=X[1]==null?ae.length:X[1];K<0&&(K+=ae.length),K=Math.max(0,Math.min(K-1,ae.length)),G<0&&(G+=ae.length),G=Math.max(0,Math.min(G,ae.length)),L=ae.slice(K,G).join(`
-`),le.hasAttribute("data-start")||le.setAttribute("data-start",String(K+1))}O.textContent=L,l.highlightElement(O)},function(L){le.setAttribute(y,U),O.textContent=L})}}),l.plugins.fileHighlight={highlight:function(le){for(var O=(le||document).querySelectorAll(Y),B=0,z;z=O[B++];)l.highlightElement(z)}};var ve=!1;l.fileHighlight=function(){ve||(console.warn("Prism.fileHighlight is deprecated. Use `Prism.plugins.fileHighlight.highlight` instead."),ve=!0),l.plugins.fileHighlight.highlight.apply(this,arguments)}}()})(ul);var _o=ul.exports;const En=Xi(_o);Prism.languages.python={comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0,greedy:!0},"string-interpolation":{pattern:/(?:f|fr|rf)(?:("""|''')[\s\S]*?\1|("|')(?:\\.|(?!\2)[^\\\r\n])*\2)/i,greedy:!0,inside:{interpolation:{pattern:/((?:^|[^{])(?:\{\{)*)\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}]|\{(?!\{)(?:[^{}])+\})+\})+\}/,lookbehind:!0,inside:{"format-spec":{pattern:/(:)[^:(){}]+(?=\}$)/,lookbehind:!0},"conversion-option":{pattern:/![sra](?=[:}]$)/,alias:"punctuation"},rest:null}},string:/[\s\S]+/}},"triple-quoted-string":{pattern:/(?:[rub]|br|rb)?("""|''')[\s\S]*?\1/i,greedy:!0,alias:"string"},string:{pattern:/(?:[rub]|br|rb)?("|')(?:\\.|(?!\1)[^\\\r\n])*\1/i,greedy:!0},function:{pattern:/((?:^|\s)def[ \t]+)[a-zA-Z_]\w*(?=\s*\()/g,lookbehind:!0},"class-name":{pattern:/(\bclass\s+)\w+/i,lookbehind:!0},decorator:{pattern:/(^[\t ]*)@\w+(?:\.\w+)*/m,lookbehind:!0,alias:["annotation","punctuation"],inside:{punctuation:/\./}},keyword:/\b(?:_(?=\s*:)|and|as|assert|async|await|break|case|class|continue|def|del|elif|else|except|exec|finally|for|from|global|if|import|in|is|lambda|match|nonlocal|not|or|pass|print|raise|return|try|while|with|yield)\b/,builtin:/\b(?:__import__|abs|all|any|apply|ascii|basestring|bin|bool|buffer|bytearray|bytes|callable|chr|classmethod|cmp|coerce|compile|complex|delattr|dict|dir|divmod|enumerate|eval|execfile|file|filter|float|format|frozenset|getattr|globals|hasattr|hash|help|hex|id|input|int|intern|isinstance|issubclass|iter|len|list|locals|long|map|max|memoryview|min|next|object|oct|open|ord|pow|property|range|raw_input|reduce|reload|repr|reversed|round|set|setattr|slice|sorted|staticmethod|str|sum|super|tuple|type|unichr|unicode|vars|xrange|zip)\b/,boolean:/\b(?:False|None|True)\b/,number:/\b0(?:b(?:_?[01])+|o(?:_?[0-7])+|x(?:_?[a-f0-9])+)\b|(?:\b\d+(?:_\d+)*(?:\.(?:\d+(?:_\d+)*)?)?|\B\.\d+(?:_\d+)*)(?:e[+-]?\d+(?:_\d+)*)?j?(?!\w)/i,operator:/[-+%=]=?|!=|:=|\*\*?=?|\/\/?=?|<[<=>]?|>[=>]?|[&|^~]/,punctuation:/[{}[\];(),.:]/};Prism.languages.python["string-interpolation"].inside.interpolation.inside.rest=Prism.languages.python;Prism.languages.py=Prism.languages.python;(function(g){var a=/\\(?:[^a-z()[\]]|[a-z*]+)/i,l={"equation-command":{pattern:a,alias:"regex"}};g.languages.latex={comment:/%.*/,cdata:{pattern:/(\\begin\{((?:lstlisting|verbatim)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0},equation:[{pattern:/\$\$(?:\\[\s\S]|[^\\$])+\$\$|\$(?:\\[\s\S]|[^\\$])+\$|\\\([\s\S]*?\\\)|\\\[[\s\S]*?\\\]/,inside:l,alias:"string"},{pattern:/(\\begin\{((?:align|eqnarray|equation|gather|math|multline)\*?)\})[\s\S]*?(?=\\end\{\2\})/,lookbehind:!0,inside:l,alias:"string"}],keyword:{pattern:/(\\(?:begin|cite|documentclass|end|label|ref|usepackage)(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0},url:{pattern:/(\\url\{)[^}]+(?=\})/,lookbehind:!0},headline:{pattern:/(\\(?:chapter|frametitle|paragraph|part|section|subparagraph|subsection|subsubparagraph|subsubsection|subsubsubparagraph)\*?(?:\[[^\]]+\])?\{)[^}]+(?=\})/,lookbehind:!0,alias:"class-name"},function:{pattern:a,alias:"selector"},punctuation:/[[\]{}&]/},g.languages.tex=g.languages.latex,g.languages.context=g.languages.latex})(Prism);const Mo=`<svg
-xmlns="http://www.w3.org/2000/svg"
-width="100%"
-height="100%"
-viewBox="0 0 32 32"
-><path
-  fill="currentColor"
-  d="M28 10v18H10V10h18m0-2H10a2 2 0 0 0-2 2v18a2 2 0 0 0 2 2h18a2 2 0 0 0 2-2V10a2 2 0 0 0-2-2Z"
-/><path fill="currentColor" d="M4 18H2V4a2 2 0 0 1 2-2h14v2H4Z" /></svg>`,zo=`<svg
-xmlns="http://www.w3.org/2000/svg"
-width="100%"
-height="100%"
-viewBox="0 0 24 24"
-fill="none"
-stroke="currentColor"
-stroke-width="3"
-stroke-linecap="round"
-stroke-linejoin="round"><polyline points="20 6 9 17 4 12" /></svg>`,ki=`<button title="copy" class="copy_code_button">
-<span class="copy-text">${Mo}</span>
-<span class="check">${zo}</span>
-</button>`,cl=/[&<>"']/,Eo=new RegExp(cl.source,"g"),hl=/[<>"']|&(?!(#\d{1,7}|#[Xx][a-fA-F0-9]{1,6}|\w+);)/,Co=new RegExp(hl.source,"g"),Bo={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"},Si=g=>Bo[g]||"";function Cn(g,a){if(a){if(cl.test(g))return g.replace(Eo,Si)}else if(hl.test(g))return g.replace(Co,Si);return g}const Do={code(g,a,l){const s=(a??"").match(/\S*/)?.[0]??"";if(this.options.highlight){const m=this.options.highlight(g,s);m!=null&&m!==g&&(l=!0,g=m)}return g=g.replace(/\n$/,"")+`
-`,s?'<pre><code class="'+this.options.langPrefix+Cn(s)+'">'+ki+(l?g:Cn(g,!0))+`</code></pre>
-`:"<pre><code>"+ki+(l?g:Cn(g,!0))+`</code></pre>
-`}};fe.use({gfm:!0,breaks:!0,pedantic:!1,headerIds:!1,mangle:!1},xo({highlight:(g,a)=>En.languages[a]?En.highlight(g,En.languages[a],a):g}),{renderer:Do});function No(g){g.addEventListener("click",a);async function a(l){const s=l.composedPath(),[m]=s.filter(p=>p?.tagName==="BUTTON"&&p.classList.contains("copy_code_button"));if(m){let p=function(N){N.style.opacity="1",setTimeout(()=>{N.style.opacity="0"},2e3)};l.stopImmediatePropagation();const v=m.parentElement.innerText.trim(),y=Array.from(m.children)[1];await Ro(v)&&p(y)}}return{destroy(){g.removeEventListener("click",a)}}}async function Ro(g){let a=!1;if("clipboard"in navigator)await navigator.clipboard.writeText(g),a=!0;else{const l=document.createElement("textarea");l.value=g,l.style.position="absolute",l.style.left="-999999px",document.body.prepend(l),l.select();try{document.execCommand("copy"),a=!0}catch(s){console.error(s),a=!1}finally{l.remove()}}return a}const Fo=async g=>(await Promise.all(g.map(async l=>await Promise.all(l.map(async(s,m)=>{if(s===null)return"";let p=m===0?"😃":"🤖",v="";if(typeof s=="string"){const y={audio:/<audio.*?src="(\/file=.*?)"/g,video:/<video.*?src="(\/file=.*?)"/g,image:/<img.*?src="(\/file=.*?)".*?\/>|!\[.*?\]\((\/file=.*?)\)/g};v=s;for(let[k,N]of Object.entries(y)){let U;for(;(U=N.exec(s))!==null;){const Y=U[1]||U[2],te=await hi(Y,"url");v=v.replace(Y,te)}}}else{const y=await hi(s.data,"url");s.mime_type?.includes("audio")?v=`<audio controls src="${y}"></audio>`:s.mime_type?.includes("video")?v=y:s.mime_type?.includes("image")&&(v=`<img src="${y}" />`)}return`${p}: ${v}`}))))).map(l=>l.join(l[0]!==""&&l[1]!==""?`
-`:"")).join(`
-`);/*! @license DOMPurify 3.0.3 | (c) Cure53 and other contributors | Released under the Apache license 2.0 and Mozilla Public License 2.0 | github.com/cure53/DOMPurify/blob/3.0.3/LICENSE */const{entries:ml,setPrototypeOf:Ai,isFrozen:Io,getPrototypeOf:Lo,getOwnPropertyDescriptor:Oo}=Object;let{freeze:it,seal:Ct,create:qo}=Object,{apply:On,construct:qn}=typeof Reflect<"u"&&Reflect;On||(On=function(a,l,s){return a.apply(l,s)});it||(it=function(a){return a});Ct||(Ct=function(a){return a});qn||(qn=function(a,l){return new a(...l)});const Po=Tt(Array.prototype.forEach),Ti=Tt(Array.prototype.pop),ir=Tt(Array.prototype.push),Or=Tt(String.prototype.toLowerCase),Bn=Tt(String.prototype.toString),Ho=Tt(String.prototype.match),Et=Tt(String.prototype.replace),Uo=Tt(String.prototype.indexOf),Go=Tt(String.prototype.trim),pt=Tt(RegExp.prototype.test),lr=Vo(TypeError);function Tt(g){return function(a){for(var l=arguments.length,s=new Array(l>1?l-1:0),m=1;m<l;m++)s[m-1]=arguments[m];return On(g,a,s)}}function Vo(g){return function(){for(var a=arguments.length,l=new Array(a),s=0;s<a;s++)l[s]=arguments[s];return qn(g,l)}}function we(g,a,l){var s;l=(s=l)!==null&&s!==void 0?s:Or,Ai&&Ai(g,null);let m=a.length;for(;m--;){let p=a[m];if(typeof p=="string"){const v=l(p);v!==p&&(Io(a)||(a[m]=v),p=v)}g[p]=!0}return g}function U0(g){const a=qo(null);for(const[l,s]of ml(g))a[l]=s;return a}function Fr(g,a){for(;g!==null;){const s=Oo(g,a);if(s){if(s.get)return Tt(s.get);if(typeof s.value=="function")return Tt(s.value)}g=Lo(g)}function l(s){return console.warn("fallback value for",s),null}return l}const _i=it(["a","abbr","acronym","address","area","article","aside","audio","b","bdi","bdo","big","blink","blockquote","body","br","button","canvas","caption","center","cite","code","col","colgroup","content","data","datalist","dd","decorator","del","details","dfn","dialog","dir","div","dl","dt","element","em","fieldset","figcaption","figure","font","footer","form","h1","h2","h3","h4","h5","h6","head","header","hgroup","hr","html","i","img","input","ins","kbd","label","legend","li","main","map","mark","marquee","menu","menuitem","meter","nav","nobr","ol","optgroup","option","output","p","picture","pre","progress","q","rp","rt","ruby","s","samp","section","select","shadow","small","source","spacer","span","strike","strong","style","sub","summary","sup","table","tbody","td","template","textarea","tfoot","th","thead","time","tr","track","tt","u","ul","var","video","wbr"]),Dn=it(["svg","a","altglyph","altglyphdef","altglyphitem","animatecolor","animatemotion","animatetransform","circle","clippath","defs","desc","ellipse","filter","font","g","glyph","glyphref","hkern","image","line","lineargradient","marker","mask","metadata","mpath","path","pattern","polygon","polyline","radialgradient","rect","stop","style","switch","symbol","text","textpath","title","tref","tspan","view","vkern"]),Nn=it(["feBlend","feColorMatrix","feComponentTransfer","feComposite","feConvolveMatrix","feDiffuseLighting","feDisplacementMap","feDistantLight","feDropShadow","feFlood","feFuncA","feFuncB","feFuncG","feFuncR","feGaussianBlur","feImage","feMerge","feMergeNode","feMorphology","feOffset","fePointLight","feSpecularLighting","feSpotLight","feTile","feTurbulence"]),Wo=it(["animate","color-profile","cursor","discard","font-face","font-face-format","font-face-name","font-face-src","font-face-uri","foreignobject","hatch","hatchpath","mesh","meshgradient","meshpatch","meshrow","missing-glyph","script","set","solidcolor","unknown","use"]),Rn=it(["math","menclose","merror","mfenced","mfrac","mglyph","mi","mlabeledtr","mmultiscripts","mn","mo","mover","mpadded","mphantom","mroot","mrow","ms","mspace","msqrt","mstyle","msub","msup","msubsup","mtable","mtd","mtext","mtr","munder","munderover","mprescripts"]),Yo=it(["maction","maligngroup","malignmark","mlongdiv","mscarries","mscarry","msgroup","mstack","msline","msrow","semantics","annotation","annotation-xml","mprescripts","none"]),Mi=it(["#text"]),zi=it(["accept","action","align","alt","autocapitalize","autocomplete","autopictureinpicture","autoplay","background","bgcolor","border","capture","cellpadding","cellspacing","checked","cite","class","clear","color","cols","colspan","controls","controlslist","coords","crossorigin","datetime","decoding","default","dir","disabled","disablepictureinpicture","disableremoteplayback","download","draggable","enctype","enterkeyhint","face","for","headers","height","hidden","high","href","hreflang","id","inputmode","integrity","ismap","kind","label","lang","list","loading","loop","low","max","maxlength","media","method","min","minlength","multiple","muted","name","nonce","noshade","novalidate","nowrap","open","optimum","pattern","placeholder","playsinline","poster","preload","pubdate","radiogroup","readonly","rel","required","rev","reversed","role","rows","rowspan","spellcheck","scope","selected","shape","size","sizes","span","srclang","start","src","srcset","step","style","summary","tabindex","title","translate","type","usemap","valign","value","width","xmlns","slot"]),Fn=it(["accent-height","accumulate","additive","alignment-baseline","ascent","attributename","attributetype","azimuth","basefrequency","baseline-shift","begin","bias","by","class","clip","clippathunits","clip-path","clip-rule","color","color-interpolation","color-interpolation-filters","color-profile","color-rendering","cx","cy","d","dx","dy","diffuseconstant","direction","display","divisor","dur","edgemode","elevation","end","fill","fill-opacity","fill-rule","filter","filterunits","flood-color","flood-opacity","font-family","font-size","font-size-adjust","font-stretch","font-style","font-variant","font-weight","fx","fy","g1","g2","glyph-name","glyphref","gradientunits","gradienttransform","height","href","id","image-rendering","in","in2","k","k1","k2","k3","k4","kerning","keypoints","keysplines","keytimes","lang","lengthadjust","letter-spacing","kernelmatrix","kernelunitlength","lighting-color","local","marker-end","marker-mid","marker-start","markerheight","markerunits","markerwidth","maskcontentunits","maskunits","max","mask","media","method","mode","min","name","numoctaves","offset","operator","opacity","order","orient","orientation","origin","overflow","paint-order","path","pathlength","patterncontentunits","patterntransform","patternunits","points","preservealpha","preserveaspectratio","primitiveunits","r","rx","ry","radius","refx","refy","repeatcount","repeatdur","restart","result","rotate","scale","seed","shape-rendering","specularconstant","specularexponent","spreadmethod","startoffset","stddeviation","stitchtiles","stop-color","stop-opacity","stroke-dasharray","stroke-dashoffset","stroke-linecap","stroke-linejoin","stroke-miterlimit","stroke-opacity","stroke","stroke-width","style","surfacescale","systemlanguage","tabindex","targetx","targety","transform","transform-origin","text-anchor","text-decoration","text-rendering","textlength","type","u1","u2","unicode","values","viewbox","visibility","version","vert-adv-y","vert-origin-x","vert-origin-y","width","word-spacing","wrap","writing-mode","xchannelselector","ychannelselector","x","x1","x2","xmlns","y","y1","y2","z","zoomandpan"]),Ei=it(["accent","accentunder","align","bevelled","close","columnsalign","columnlines","columnspan","denomalign","depth","dir","display","displaystyle","encoding","fence","frame","height","href","id","largeop","length","linethickness","lspace","lquote","mathbackground","mathcolor","mathsize","mathvariant","maxsize","minsize","movablelimits","notation","numalign","open","rowalign","rowlines","rowspacing","rowspan","rspace","rquote","scriptlevel","scriptminsize","scriptsizemultiplier","selection","separator","separators","stretchy","subscriptshift","supscriptshift","symmetric","voffset","width","xmlns"]),Ir=it(["xlink:href","xml:id","xlink:title","xml:space","xmlns:xlink"]),jo=Ct(/\{\{[\w\W]*|[\w\W]*\}\}/gm),Xo=Ct(/<%[\w\W]*|[\w\W]*%>/gm),Zo=Ct(/\${[\w\W]*}/gm),Ko=Ct(/^data-[\-\w.\u00B7-\uFFFF]/),Qo=Ct(/^aria-[\-\w]+$/),fl=Ct(/^(?:(?:(?:f|ht)tps?|mailto|tel|callto|sms|cid|xmpp):|[^a-z]|[a-z+.\-]+(?:[^a-z+.\-:]|$))/i),Jo=Ct(/^(?:\w+script|data):/i),$o=Ct(/[\u0000-\u0020\u00A0\u1680\u180E\u2000-\u2029\u205F\u3000]/g),dl=Ct(/^html$/i);var Ci=Object.freeze({__proto__:null,MUSTACHE_EXPR:jo,ERB_EXPR:Xo,TMPLIT_EXPR:Zo,DATA_ATTR:Ko,ARIA_ATTR:Qo,IS_ALLOWED_URI:fl,IS_SCRIPT_OR_DATA:Jo,ATTR_WHITESPACE:$o,DOCTYPE_NAME:dl});const e1=()=>typeof window>"u"?null:window,t1=function(a,l){if(typeof a!="object"||typeof a.createPolicy!="function")return null;let s=null;const m="data-tt-policy-suffix";l&&l.hasAttribute(m)&&(s=l.getAttribute(m));const p="dompurify"+(s?"#"+s:"");try{return a.createPolicy(p,{createHTML(v){return v},createScriptURL(v){return v}})}catch{return console.warn("TrustedTypes policy "+p+" could not be created."),null}};function pl(){let g=arguments.length>0&&arguments[0]!==void 0?arguments[0]:e1();const a=ue=>pl(ue);if(a.version="3.0.3",a.removed=[],!g||!g.document||g.document.nodeType!==9)return a.isSupported=!1,a;const l=g.document,s=l.currentScript;let{document:m}=g;const{DocumentFragment:p,HTMLTemplateElement:v,Node:y,Element:k,NodeFilter:N,NamedNodeMap:U=g.NamedNodeMap||g.MozNamedAttrMap,HTMLFormElement:Y,DOMParser:te,trustedTypes:j}=g,ve=k.prototype,pe=Fr(ve,"cloneNode"),le=Fr(ve,"nextSibling"),O=Fr(ve,"childNodes"),B=Fr(ve,"parentNode");if(typeof v=="function"){const ue=m.createElement("template");ue.content&&ue.content.ownerDocument&&(m=ue.content.ownerDocument)}let z,R="";const{implementation:D,createNodeIterator:L,createDocumentFragment:X,getElementsByTagName:ae}=m,{importNode:K}=l;let G={};a.isSupported=typeof ml=="function"&&typeof B=="function"&&D&&D.createHTMLDocument!==void 0;const{MUSTACHE_EXPR:W,ERB_EXPR:Ce,TMPLIT_EXPR:ie,DATA_ATTR:Ge,ARIA_ATTR:Gt,IS_SCRIPT_OR_DATA:X0,ATTR_WHITESPACE:g0}=Ci;let{IS_ALLOWED_URI:Ve}=Ci,_e=null;const _t=we({},[..._i,...Dn,...Nn,...Rn,...Mi]);let ee=null;const Ke=we({},[...zi,...Fn,...Ei,...Ir]);let Ie=Object.seal(Object.create(null,{tagNameCheck:{writable:!0,configurable:!1,enumerable:!0,value:null},attributeNameCheck:{writable:!0,configurable:!1,enumerable:!0,value:null},allowCustomizedBuiltInElements:{writable:!0,configurable:!1,enumerable:!0,value:!1}})),je=null,gt=null,lt=!0,Bt=!0,Kt=!1,v0=!0,vt=!1,st=!1,b0=!1,Vt=!1,Qt=!1,M0=!1,y0=!1,sr=!0,Jt=!1;const bt="user-content-";let $t=!0,e0=!1,t0={},Dt=null;const z0=we({},["annotation-xml","audio","colgroup","desc","foreignobject","head","iframe","math","mi","mn","mo","ms","mtext","noembed","noframes","noscript","plaintext","script","style","svg","template","thead","title","video","xmp"]);let or=null;const ur=we({},["audio","video","img","source","image","track"]);let E0=null;const Z0=we({},["alt","class","for","id","label","name","pattern","placeholder","role","summary","title","value","style","xmlns"]),w0="http://www.w3.org/1998/Math/MathML",C0="http://www.w3.org/2000/svg",mt="http://www.w3.org/1999/xhtml";let r0=mt,B0=!1,Be=null;const Q=we({},[w0,C0,mt],Bn);let et;const cr=["application/xhtml+xml","text/html"],hr="text/html";let We,yt=null;const K0=m.createElement("form"),mr=function(T){return T instanceof RegExp||T instanceof Function},Q0=function(T){if(!(yt&&yt===T)){if((!T||typeof T!="object")&&(T={}),T=U0(T),et=cr.indexOf(T.PARSER_MEDIA_TYPE)===-1?et=hr:et=T.PARSER_MEDIA_TYPE,We=et==="application/xhtml+xml"?Bn:Or,_e="ALLOWED_TAGS"in T?we({},T.ALLOWED_TAGS,We):_t,ee="ALLOWED_ATTR"in T?we({},T.ALLOWED_ATTR,We):Ke,Be="ALLOWED_NAMESPACES"in T?we({},T.ALLOWED_NAMESPACES,Bn):Q,E0="ADD_URI_SAFE_ATTR"in T?we(U0(Z0),T.ADD_URI_SAFE_ATTR,We):Z0,or="ADD_DATA_URI_TAGS"in T?we(U0(ur),T.ADD_DATA_URI_TAGS,We):ur,Dt="FORBID_CONTENTS"in T?we({},T.FORBID_CONTENTS,We):z0,je="FORBID_TAGS"in T?we({},T.FORBID_TAGS,We):{},gt="FORBID_ATTR"in T?we({},T.FORBID_ATTR,We):{},t0="USE_PROFILES"in T?T.USE_PROFILES:!1,lt=T.ALLOW_ARIA_ATTR!==!1,Bt=T.ALLOW_DATA_ATTR!==!1,Kt=T.ALLOW_UNKNOWN_PROTOCOLS||!1,v0=T.ALLOW_SELF_CLOSE_IN_ATTR!==!1,vt=T.SAFE_FOR_TEMPLATES||!1,st=T.WHOLE_DOCUMENT||!1,Qt=T.RETURN_DOM||!1,M0=T.RETURN_DOM_FRAGMENT||!1,y0=T.RETURN_TRUSTED_TYPE||!1,Vt=T.FORCE_BODY||!1,sr=T.SANITIZE_DOM!==!1,Jt=T.SANITIZE_NAMED_PROPS||!1,$t=T.KEEP_CONTENT!==!1,e0=T.IN_PLACE||!1,Ve=T.ALLOWED_URI_REGEXP||fl,r0=T.NAMESPACE||mt,Ie=T.CUSTOM_ELEMENT_HANDLING||{},T.CUSTOM_ELEMENT_HANDLING&&mr(T.CUSTOM_ELEMENT_HANDLING.tagNameCheck)&&(Ie.tagNameCheck=T.CUSTOM_ELEMENT_HANDLING.tagNameCheck),T.CUSTOM_ELEMENT_HANDLING&&mr(T.CUSTOM_ELEMENT_HANDLING.attributeNameCheck)&&(Ie.attributeNameCheck=T.CUSTOM_ELEMENT_HANDLING.attributeNameCheck),T.CUSTOM_ELEMENT_HANDLING&&typeof T.CUSTOM_ELEMENT_HANDLING.allowCustomizedBuiltInElements=="boolean"&&(Ie.allowCustomizedBuiltInElements=T.CUSTOM_ELEMENT_HANDLING.allowCustomizedBuiltInElements),vt&&(Bt=!1),M0&&(Qt=!0),t0&&(_e=we({},[...Mi]),ee=[],t0.html===!0&&(we(_e,_i),we(ee,zi)),t0.svg===!0&&(we(_e,Dn),we(ee,Fn),we(ee,Ir)),t0.svgFilters===!0&&(we(_e,Nn),we(ee,Fn),we(ee,Ir)),t0.mathMl===!0&&(we(_e,Rn),we(ee,Ei),we(ee,Ir))),T.ADD_TAGS&&(_e===_t&&(_e=U0(_e)),we(_e,T.ADD_TAGS,We)),T.ADD_ATTR&&(ee===Ke&&(ee=U0(ee)),we(ee,T.ADD_ATTR,We)),T.ADD_URI_SAFE_ATTR&&we(E0,T.ADD_URI_SAFE_ATTR,We),T.FORBID_CONTENTS&&(Dt===z0&&(Dt=U0(Dt)),we(Dt,T.FORBID_CONTENTS,We)),$t&&(_e["#text"]=!0),st&&we(_e,["html","head","body"]),_e.table&&(we(_e,["tbody"]),delete je.tbody),T.TRUSTED_TYPES_POLICY){if(typeof T.TRUSTED_TYPES_POLICY.createHTML!="function")throw lr('TRUSTED_TYPES_POLICY configuration option must provide a "createHTML" hook.');if(typeof T.TRUSTED_TYPES_POLICY.createScriptURL!="function")throw lr('TRUSTED_TYPES_POLICY configuration option must provide a "createScriptURL" hook.');z=T.TRUSTED_TYPES_POLICY,R=z.createHTML("")}else z===void 0&&(z=t1(j,s)),z!==null&&typeof R=="string"&&(R=z.createHTML(""));it&&it(T),yt=T}},nt=we({},["mi","mo","mn","ms","mtext"]),wt=we({},["foreignobject","desc","title","annotation-xml"]),Nt=we({},["title","style","font","a","script"]),n0=we({},Dn);we(n0,Nn),we(n0,Wo);const D0=we({},Rn);we(D0,Yo);const Hr=function(T){let P=B(T);(!P||!P.tagName)&&(P={namespaceURI:r0,tagName:"template"});const J=Or(T.tagName),ke=Or(P.tagName);return Be[T.namespaceURI]?T.namespaceURI===C0?P.namespaceURI===mt?J==="svg":P.namespaceURI===w0?J==="svg"&&(ke==="annotation-xml"||nt[ke]):!!n0[J]:T.namespaceURI===w0?P.namespaceURI===mt?J==="math":P.namespaceURI===C0?J==="math"&&wt[ke]:!!D0[J]:T.namespaceURI===mt?P.namespaceURI===C0&&!wt[ke]||P.namespaceURI===w0&&!nt[ke]?!1:!D0[J]&&(Nt[J]||!n0[J]):!!(et==="application/xhtml+xml"&&Be[T.namespaceURI]):!1},Wt=function(T){ir(a.removed,{element:T});try{T.parentNode.removeChild(T)}catch{T.remove()}},J0=function(T,P){try{ir(a.removed,{attribute:P.getAttributeNode(T),from:P})}catch{ir(a.removed,{attribute:null,from:P})}if(P.removeAttribute(T),T==="is"&&!ee[T])if(Qt||M0)try{Wt(P)}catch{}else try{P.setAttribute(T,"")}catch{}},x0=function(T){let P,J;if(Vt)T="<remove></remove>"+T;else{const Ue=Ho(T,/^[\r\n\t ]+/);J=Ue&&Ue[0]}et==="application/xhtml+xml"&&r0===mt&&(T='<html xmlns="http://www.w3.org/1999/xhtml"><head></head><body>'+T+"</body></html>");const ke=z?z.createHTML(T):T;if(r0===mt)try{P=new te().parseFromString(ke,et)}catch{}if(!P||!P.documentElement){P=D.createDocument(r0,"template",null);try{P.documentElement.innerHTML=B0?R:ke}catch{}}const S=P.body||P.documentElement;return T&&J&&S.insertBefore(m.createTextNode(J),S.childNodes[0]||null),r0===mt?ae.call(P,st?"html":"body")[0]:st?P.documentElement:S},Fe=function(T){return L.call(T.ownerDocument||T,T,N.SHOW_ELEMENT|N.SHOW_COMMENT|N.SHOW_TEXT,null,!1)},o=function(T){return T instanceof Y&&(typeof T.nodeName!="string"||typeof T.textContent!="string"||typeof T.removeChild!="function"||!(T.attributes instanceof U)||typeof T.removeAttribute!="function"||typeof T.setAttribute!="function"||typeof T.namespaceURI!="string"||typeof T.insertBefore!="function"||typeof T.hasChildNodes!="function")},h=function(T){return typeof y=="object"?T instanceof y:T&&typeof T=="object"&&typeof T.nodeType=="number"&&typeof T.nodeName=="string"},H=function(T,P,J){G[T]&&Po(G[T],ke=>{ke.call(a,P,J,yt)})},d=function(T){let P;if(H("beforeSanitizeElements",T,null),o(T))return Wt(T),!0;const J=We(T.nodeName);if(H("uponSanitizeElement",T,{tagName:J,allowedTags:_e}),T.hasChildNodes()&&!h(T.firstElementChild)&&(!h(T.content)||!h(T.content.firstElementChild))&&pt(/<[/\w]/g,T.innerHTML)&&pt(/<[/\w]/g,T.textContent))return Wt(T),!0;if(!_e[J]||je[J]){if(!je[J]&&De(J)&&(Ie.tagNameCheck instanceof RegExp&&pt(Ie.tagNameCheck,J)||Ie.tagNameCheck instanceof Function&&Ie.tagNameCheck(J)))return!1;if($t&&!Dt[J]){const ke=B(T)||T.parentNode,S=O(T)||T.childNodes;if(S&&ke){const Ue=S.length;for(let M=Ue-1;M>=0;--M)ke.insertBefore(pe(S[M],!0),le(T))}}return Wt(T),!0}return T instanceof k&&!Hr(T)||(J==="noscript"||J==="noembed")&&pt(/<\/no(script|embed)/i,T.innerHTML)?(Wt(T),!0):(vt&&T.nodeType===3&&(P=T.textContent,P=Et(P,W," "),P=Et(P,Ce," "),P=Et(P,ie," "),T.textContent!==P&&(ir(a.removed,{element:T.cloneNode()}),T.textContent=P)),H("afterSanitizeElements",T,null),!1)},A=function(T,P,J){if(sr&&(P==="id"||P==="name")&&(J in m||J in K0))return!1;if(!(Bt&&!gt[P]&&pt(Ge,P))){if(!(lt&&pt(Gt,P))){if(!ee[P]||gt[P]){if(!(De(T)&&(Ie.tagNameCheck instanceof RegExp&&pt(Ie.tagNameCheck,T)||Ie.tagNameCheck instanceof Function&&Ie.tagNameCheck(T))&&(Ie.attributeNameCheck instanceof RegExp&&pt(Ie.attributeNameCheck,P)||Ie.attributeNameCheck instanceof Function&&Ie.attributeNameCheck(P))||P==="is"&&Ie.allowCustomizedBuiltInElements&&(Ie.tagNameCheck instanceof RegExp&&pt(Ie.tagNameCheck,J)||Ie.tagNameCheck instanceof Function&&Ie.tagNameCheck(J))))return!1}else if(!E0[P]){if(!pt(Ve,Et(J,g0,""))){if(!((P==="src"||P==="xlink:href"||P==="href")&&T!=="script"&&Uo(J,"data:")===0&&or[T])){if(!(Kt&&!pt(X0,Et(J,g0,"")))){if(J)return!1}}}}}}return!0},De=function(T){return T.indexOf("-")>0},ne=function(T){let P,J,ke,S;H("beforeSanitizeAttributes",T,null);const{attributes:Ue}=T;if(!Ue)return;const M={attrName:"",attrValue:"",keepAttr:!0,allowedAttributes:ee};for(S=Ue.length;S--;){P=Ue[S];const{name:ft,namespaceURI:N0}=P;if(J=ft==="value"?P.value:Go(P.value),ke=We(ft),M.attrName=ke,M.attrValue=J,M.keepAttr=!0,M.forceKeepAttr=void 0,H("uponSanitizeAttribute",T,M),J=M.attrValue,M.forceKeepAttr||(J0(ft,T),!M.keepAttr))continue;if(!v0&&pt(/\/>/i,J)){J0(ft,T);continue}vt&&(J=Et(J,W," "),J=Et(J,Ce," "),J=Et(J,ie," "));const R0=We(T.nodeName);if(A(R0,ke,J)){if(Jt&&(ke==="id"||ke==="name")&&(J0(ft,T),J=bt+J),z&&typeof j=="object"&&typeof j.getAttributeType=="function"&&!N0)switch(j.getAttributeType(R0,ke)){case"TrustedHTML":{J=z.createHTML(J);break}case"TrustedScriptURL":{J=z.createScriptURL(J);break}}try{N0?T.setAttributeNS(N0,ft,J):T.setAttribute(ft,J),Ti(a.removed)}catch{}}}H("afterSanitizeAttributes",T,null)},Je=function ue(T){let P;const J=Fe(T);for(H("beforeSanitizeShadowDOM",T,null);P=J.nextNode();)H("uponSanitizeShadowNode",P,null),!d(P)&&(P.content instanceof p&&ue(P.content),ne(P));H("afterSanitizeShadowDOM",T,null)};return a.sanitize=function(ue){let T=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},P,J,ke,S;if(B0=!ue,B0&&(ue="<!-->"),typeof ue!="string"&&!h(ue))if(typeof ue.toString=="function"){if(ue=ue.toString(),typeof ue!="string")throw lr("dirty is not a string, aborting")}else throw lr("toString is not a function");if(!a.isSupported)return ue;if(b0||Q0(T),a.removed=[],typeof ue=="string"&&(e0=!1),e0){if(ue.nodeName){const ft=We(ue.nodeName);if(!_e[ft]||je[ft])throw lr("root node is forbidden and cannot be sanitized in-place")}}else if(ue instanceof y)P=x0("<!---->"),J=P.ownerDocument.importNode(ue,!0),J.nodeType===1&&J.nodeName==="BODY"||J.nodeName==="HTML"?P=J:P.appendChild(J);else{if(!Qt&&!vt&&!st&&ue.indexOf("<")===-1)return z&&y0?z.createHTML(ue):ue;if(P=x0(ue),!P)return Qt?null:y0?R:""}P&&Vt&&Wt(P.firstChild);const Ue=Fe(e0?ue:P);for(;ke=Ue.nextNode();)d(ke)||(ke.content instanceof p&&Je(ke.content),ne(ke));if(e0)return ue;if(Qt){if(M0)for(S=X.call(P.ownerDocument);P.firstChild;)S.appendChild(P.firstChild);else S=P;return(ee.shadowroot||ee.shadowrootmod)&&(S=K.call(l,S,!0)),S}let M=st?P.outerHTML:P.innerHTML;return st&&_e["!doctype"]&&P.ownerDocument&&P.ownerDocument.doctype&&P.ownerDocument.doctype.name&&pt(dl,P.ownerDocument.doctype.name)&&(M="<!DOCTYPE "+P.ownerDocument.doctype.name+`>
-`+M),vt&&(M=Et(M,W," "),M=Et(M,Ce," "),M=Et(M,ie," ")),z&&y0?z.createHTML(M):M},a.setConfig=function(ue){Q0(ue),b0=!0},a.clearConfig=function(){yt=null,b0=!1},a.isValidAttribute=function(ue,T,P){yt||Q0({});const J=We(ue),ke=We(T);return A(J,ke,P)},a.addHook=function(ue,T){typeof T=="function"&&(G[ue]=G[ue]||[],ir(G[ue],T))},a.removeHook=function(ue){if(G[ue])return Ti(G[ue])},a.removeHooks=function(ue){G[ue]&&(G[ue]=[])},a.removeAllHooks=function(){G={}},a}var Bi=pl(),gl={exports:{}},In={exports:{}},Di;function r1(){return Di||(Di=1,function(g,a){(function(s,m){g.exports=m()})(typeof self<"u"?self:qr,function(){return function(){var l={};(function(){l.d=function(u,e){for(var t in e)l.o(e,t)&&!l.o(u,t)&&Object.defineProperty(u,t,{enumerable:!0,get:e[t]})}})(),function(){l.o=function(u,e){return Object.prototype.hasOwnProperty.call(u,e)}}();var s={};l.d(s,{default:function(){return Is}});var m=function u(e,t){this.position=void 0;var r="KaTeX parse error: "+e,n,i=t&&t.loc;if(i&&i.start<=i.end){var c=i.lexer.input;n=i.start;var f=i.end;n===c.length?r+=" at end of input: ":r+=" at position "+(n+1)+": ";var b=c.slice(n,f).replace(/[^]/g,"$&̲"),x;n>15?x="…"+c.slice(n-15,n):x=c.slice(0,n);var _;f+15<c.length?_=c.slice(f,f+15)+"…":_=c.slice(f),r+=x+b+_}var C=new Error(r);return C.name="ParseError",C.__proto__=u.prototype,C.position=n,C};m.prototype.__proto__=Error.prototype;var p=m,v=function(e,t){return e.indexOf(t)!==-1},y=function(e,t){return e===void 0?t:e},k=/([A-Z])/g,N=function(e){return e.replace(k,"-$1").toLowerCase()},U={"&":"&amp;",">":"&gt;","<":"&lt;",'"':"&quot;","'":"&#x27;"},Y=/[&><"']/g;function te(u){return String(u).replace(Y,function(e){return U[e]})}var j=function u(e){return e.type==="ordgroup"||e.type==="color"?e.body.length===1?u(e.body[0]):e:e.type==="font"?u(e.body):e},ve=function(e){var t=j(e);return t.type==="mathord"||t.type==="textord"||t.type==="atom"},pe=function(e){if(!e)throw new Error("Expected non-null, but got "+String(e));return e},le=function(e){var t=/^\s*([^\\/#]*?)(?::|&#0*58|&#x0*3a)/i.exec(e);return t!=null?t[1]:"_relative"},O={contains:v,deflt:y,escape:te,hyphenate:N,getBaseElem:j,isCharacterBox:ve,protocolFromUrl:le},B={displayMode:{type:"boolean",description:"Render math in display mode, which puts the math in display style (so \\int and \\sum are large, for example), and centers the math on the page on its own line.",cli:"-d, --display-mode"},output:{type:{enum:["htmlAndMathml","html","mathml"]},description:"Determines the markup language of the output.",cli:"-F, --format <type>"},leqno:{type:"boolean",description:"Render display math in leqno style (left-justified tags)."},fleqn:{type:"boolean",description:"Render display math flush left."},throwOnError:{type:"boolean",default:!0,cli:"-t, --no-throw-on-error",cliDescription:"Render errors (in the color given by --error-color) instead of throwing a ParseError exception when encountering an error."},errorColor:{type:"string",default:"#cc0000",cli:"-c, --error-color <color>",cliDescription:"A color string given in the format 'rgb' or 'rrggbb' (no #). This option determines the color of errors rendered by the -t option.",cliProcessor:function(e){return"#"+e}},macros:{type:"object",cli:"-m, --macro <def>",cliDescription:"Define custom macro of the form '\\foo:expansion' (use multiple -m arguments for multiple macros).",cliDefault:[],cliProcessor:function(e,t){return t.push(e),t}},minRuleThickness:{type:"number",description:"Specifies a minimum thickness, in ems, for fraction lines, `\\sqrt` top lines, `{array}` vertical lines, `\\hline`, `\\hdashline`, `\\underline`, `\\overline`, and the borders of `\\fbox`, `\\boxed`, and `\\fcolorbox`.",processor:function(e){return Math.max(0,e)},cli:"--min-rule-thickness <size>",cliProcessor:parseFloat},colorIsTextColor:{type:"boolean",description:"Makes \\color behave like LaTeX's 2-argument \\textcolor, instead of LaTeX's one-argument \\color mode change.",cli:"-b, --color-is-text-color"},strict:{type:[{enum:["warn","ignore","error"]},"boolean","function"],description:"Turn on strict / LaTeX faithfulness mode, which throws an error if the input uses features that are not supported by LaTeX.",cli:"-S, --strict",cliDefault:!1},trust:{type:["boolean","function"],description:"Trust the input, enabling all HTML features such as \\url.",cli:"-T, --trust"},maxSize:{type:"number",default:1/0,description:"If non-zero, all user-specified sizes, e.g. in \\rule{500em}{500em}, will be capped to maxSize ems. Otherwise, elements and spaces can be arbitrarily large",processor:function(e){return Math.max(0,e)},cli:"-s, --max-size <n>",cliProcessor:parseInt},maxExpand:{type:"number",default:1e3,description:"Limit the number of macro expansions to the specified number, to prevent e.g. infinite macro loops. If set to Infinity, the macro expander will try to fully expand as in LaTeX.",processor:function(e){return Math.max(0,e)},cli:"-e, --max-expand <n>",cliProcessor:function(e){return e==="Infinity"?1/0:parseInt(e)}},globalGroup:{type:"boolean",cli:!1}};function z(u){if(u.default)return u.default;var e=u.type,t=Array.isArray(e)?e[0]:e;if(typeof t!="string")return t.enum[0];switch(t){case"boolean":return!1;case"string":return"";case"number":return 0;case"object":return{}}}var R=function(){function u(t){this.displayMode=void 0,this.output=void 0,this.leqno=void 0,this.fleqn=void 0,this.throwOnError=void 0,this.errorColor=void 0,this.macros=void 0,this.minRuleThickness=void 0,this.colorIsTextColor=void 0,this.strict=void 0,this.trust=void 0,this.maxSize=void 0,this.maxExpand=void 0,this.globalGroup=void 0,t=t||{};for(var r in B)if(B.hasOwnProperty(r)){var n=B[r];this[r]=t[r]!==void 0?n.processor?n.processor(t[r]):t[r]:z(n)}}var e=u.prototype;return e.reportNonstrict=function(r,n,i){var c=this.strict;if(typeof c=="function"&&(c=c(r,n,i)),!(!c||c==="ignore")){if(c===!0||c==="error")throw new p("LaTeX-incompatible input and strict mode is set to 'error': "+(n+" ["+r+"]"),i);c==="warn"?typeof console<"u"&&console.warn("LaTeX-incompatible input and strict mode is set to 'warn': "+(n+" ["+r+"]")):typeof console<"u"&&console.warn("LaTeX-incompatible input and strict mode is set to "+("unrecognized '"+c+"': "+n+" ["+r+"]"))}},e.useStrictBehavior=function(r,n,i){var c=this.strict;if(typeof c=="function")try{c=c(r,n,i)}catch{c="error"}return!c||c==="ignore"?!1:c===!0||c==="error"?!0:c==="warn"?(typeof console<"u"&&console.warn("LaTeX-incompatible input and strict mode is set to 'warn': "+(n+" ["+r+"]")),!1):(typeof console<"u"&&console.warn("LaTeX-incompatible input and strict mode is set to "+("unrecognized '"+c+"': "+n+" ["+r+"]")),!1)},e.isTrusted=function(r){r.url&&!r.protocol&&(r.protocol=O.protocolFromUrl(r.url));var n=typeof this.trust=="function"?this.trust(r):this.trust;return!!n},u}(),D=function(){function u(t,r,n){this.id=void 0,this.size=void 0,this.cramped=void 0,this.id=t,this.size=r,this.cramped=n}var e=u.prototype;return e.sup=function(){return Ge[Gt[this.id]]},e.sub=function(){return Ge[X0[this.id]]},e.fracNum=function(){return Ge[g0[this.id]]},e.fracDen=function(){return Ge[Ve[this.id]]},e.cramp=function(){return Ge[_e[this.id]]},e.text=function(){return Ge[_t[this.id]]},e.isTight=function(){return this.size>=2},u}(),L=0,X=1,ae=2,K=3,G=4,W=5,Ce=6,ie=7,Ge=[new D(L,0,!1),new D(X,0,!0),new D(ae,1,!1),new D(K,1,!0),new D(G,2,!1),new D(W,2,!0),new D(Ce,3,!1),new D(ie,3,!0)],Gt=[G,W,G,W,Ce,ie,Ce,ie],X0=[W,W,W,W,ie,ie,ie,ie],g0=[ae,K,G,W,Ce,ie,Ce,ie],Ve=[K,K,W,W,ie,ie,ie,ie],_e=[X,X,K,K,W,W,ie,ie],_t=[L,X,ae,K,ae,K,ae,K],ee={DISPLAY:Ge[L],TEXT:Ge[ae],SCRIPT:Ge[G],SCRIPTSCRIPT:Ge[Ce]},Ke=[{name:"latin",blocks:[[256,591],[768,879]]},{name:"cyrillic",blocks:[[1024,1279]]},{name:"armenian",blocks:[[1328,1423]]},{name:"brahmic",blocks:[[2304,4255]]},{name:"georgian",blocks:[[4256,4351]]},{name:"cjk",blocks:[[12288,12543],[19968,40879],[65280,65376]]},{name:"hangul",blocks:[[44032,55215]]}];function Ie(u){for(var e=0;e<Ke.length;e++)for(var t=Ke[e],r=0;r<t.blocks.length;r++){var n=t.blocks[r];if(u>=n[0]&&u<=n[1])return t.name}return null}var je=[];Ke.forEach(function(u){return u.blocks.forEach(function(e){return je.push.apply(je,e)})});function gt(u){for(var e=0;e<je.length;e+=2)if(u>=je[e]&&u<=je[e+1])return!0;return!1}var lt=80,Bt=function(e,t){return"M95,"+(622+e+t)+`
-c-2.7,0,-7.17,-2.7,-13.5,-8c-5.8,-5.3,-9.5,-10,-9.5,-14
-c0,-2,0.3,-3.3,1,-4c1.3,-2.7,23.83,-20.7,67.5,-54
-c44.2,-33.3,65.8,-50.3,66.5,-51c1.3,-1.3,3,-2,5,-2c4.7,0,8.7,3.3,12,10
-s173,378,173,378c0.7,0,35.3,-71,104,-213c68.7,-142,137.5,-285,206.5,-429
-c69,-144,104.5,-217.7,106.5,-221
-l`+e/2.075+" -"+e+`
-c5.3,-9.3,12,-14,20,-14
-H400000v`+(40+e)+`H845.2724
-s-225.272,467,-225.272,467s-235,486,-235,486c-2.7,4.7,-9,7,-19,7
-c-6,0,-10,-1,-12,-3s-194,-422,-194,-422s-65,47,-65,47z
-M`+(834+e)+" "+t+"h400000v"+(40+e)+"h-400000z"},Kt=function(e,t){return"M263,"+(601+e+t)+`c0.7,0,18,39.7,52,119
-c34,79.3,68.167,158.7,102.5,238c34.3,79.3,51.8,119.3,52.5,120
-c340,-704.7,510.7,-1060.3,512,-1067
-l`+e/2.084+" -"+e+`
-c4.7,-7.3,11,-11,19,-11
-H40000v`+(40+e)+`H1012.3
-s-271.3,567,-271.3,567c-38.7,80.7,-84,175,-136,283c-52,108,-89.167,185.3,-111.5,232
-c-22.3,46.7,-33.8,70.3,-34.5,71c-4.7,4.7,-12.3,7,-23,7s-12,-1,-12,-1
-s-109,-253,-109,-253c-72.7,-168,-109.3,-252,-110,-252c-10.7,8,-22,16.7,-34,26
-c-22,17.3,-33.3,26,-34,26s-26,-26,-26,-26s76,-59,76,-59s76,-60,76,-60z
-M`+(1001+e)+" "+t+"h400000v"+(40+e)+"h-400000z"},v0=function(e,t){return"M983 "+(10+e+t)+`
-l`+e/3.13+" -"+e+`
-c4,-6.7,10,-10,18,-10 H400000v`+(40+e)+`
-H1013.1s-83.4,268,-264.1,840c-180.7,572,-277,876.3,-289,913c-4.7,4.7,-12.7,7,-24,7
-s-12,0,-12,0c-1.3,-3.3,-3.7,-11.7,-7,-25c-35.3,-125.3,-106.7,-373.3,-214,-744
-c-10,12,-21,25,-33,39s-32,39,-32,39c-6,-5.3,-15,-14,-27,-26s25,-30,25,-30
-c26.7,-32.7,52,-63,76,-91s52,-60,52,-60s208,722,208,722
-c56,-175.3,126.3,-397.3,211,-666c84.7,-268.7,153.8,-488.2,207.5,-658.5
-c53.7,-170.3,84.5,-266.8,92.5,-289.5z
-M`+(1001+e)+" "+t+"h400000v"+(40+e)+"h-400000z"},vt=function(e,t){return"M424,"+(2398+e+t)+`
-c-1.3,-0.7,-38.5,-172,-111.5,-514c-73,-342,-109.8,-513.3,-110.5,-514
-c0,-2,-10.7,14.3,-32,49c-4.7,7.3,-9.8,15.7,-15.5,25c-5.7,9.3,-9.8,16,-12.5,20
-s-5,7,-5,7c-4,-3.3,-8.3,-7.7,-13,-13s-13,-13,-13,-13s76,-122,76,-122s77,-121,77,-121
-s209,968,209,968c0,-2,84.7,-361.7,254,-1079c169.3,-717.3,254.7,-1077.7,256,-1081
-l`+e/4.223+" -"+e+`c4,-6.7,10,-10,18,-10 H400000
-v`+(40+e)+`H1014.6
-s-87.3,378.7,-272.6,1166c-185.3,787.3,-279.3,1182.3,-282,1185
-c-2,6,-10,9,-24,9
-c-8,0,-12,-0.7,-12,-2z M`+(1001+e)+" "+t+`
-h400000v`+(40+e)+"h-400000z"},st=function(e,t){return"M473,"+(2713+e+t)+`
-c339.3,-1799.3,509.3,-2700,510,-2702 l`+e/5.298+" -"+e+`
-c3.3,-7.3,9.3,-11,18,-11 H400000v`+(40+e)+`H1017.7
-s-90.5,478,-276.2,1466c-185.7,988,-279.5,1483,-281.5,1485c-2,6,-10,9,-24,9
-c-8,0,-12,-0.7,-12,-2c0,-1.3,-5.3,-32,-16,-92c-50.7,-293.3,-119.7,-693.3,-207,-1200
-c0,-1.3,-5.3,8.7,-16,30c-10.7,21.3,-21.3,42.7,-32,64s-16,33,-16,33s-26,-26,-26,-26
-s76,-153,76,-153s77,-151,77,-151c0.7,0.7,35.7,202,105,604c67.3,400.7,102,602.7,104,
-606zM`+(1001+e)+" "+t+"h400000v"+(40+e)+"H1017.7z"},b0=function(e){var t=e/2;return"M400000 "+e+" H0 L"+t+" 0 l65 45 L145 "+(e-80)+" H400000z"},Vt=function(e,t,r){var n=r-54-t-e;return"M702 "+(e+t)+"H400000"+(40+e)+`
-H742v`+n+`l-4 4-4 4c-.667.7 -2 1.5-4 2.5s-4.167 1.833-6.5 2.5-5.5 1-9.5 1
-h-12l-28-84c-16.667-52-96.667 -294.333-240-727l-212 -643 -85 170
-c-4-3.333-8.333-7.667-13 -13l-13-13l77-155 77-156c66 199.333 139 419.667
-219 661 l218 661zM702 `+t+"H400000v"+(40+e)+"H742z"},Qt=function(e,t,r){t=1e3*t;var n="";switch(e){case"sqrtMain":n=Bt(t,lt);break;case"sqrtSize1":n=Kt(t,lt);break;case"sqrtSize2":n=v0(t,lt);break;case"sqrtSize3":n=vt(t,lt);break;case"sqrtSize4":n=st(t,lt);break;case"sqrtTall":n=Vt(t,lt,r)}return n},M0=function(e,t){switch(e){case"⎜":return"M291 0 H417 V"+t+" H291z M291 0 H417 V"+t+" H291z";case"∣":return"M145 0 H188 V"+t+" H145z M145 0 H188 V"+t+" H145z";case"∥":return"M145 0 H188 V"+t+" H145z M145 0 H188 V"+t+" H145z"+("M367 0 H410 V"+t+" H367z M367 0 H410 V"+t+" H367z");case"⎟":return"M457 0 H583 V"+t+" H457z M457 0 H583 V"+t+" H457z";case"⎢":return"M319 0 H403 V"+t+" H319z M319 0 H403 V"+t+" H319z";case"⎥":return"M263 0 H347 V"+t+" H263z M263 0 H347 V"+t+" H263z";case"⎪":return"M384 0 H504 V"+t+" H384z M384 0 H504 V"+t+" H384z";case"⏐":return"M312 0 H355 V"+t+" H312z M312 0 H355 V"+t+" H312z";case"‖":return"M257 0 H300 V"+t+" H257z M257 0 H300 V"+t+" H257z"+("M478 0 H521 V"+t+" H478z M478 0 H521 V"+t+" H478z");default:return""}},y0={doubleleftarrow:`M262 157
-l10-10c34-36 62.7-77 86-123 3.3-8 5-13.3 5-16 0-5.3-6.7-8-20-8-7.3
- 0-12.2.5-14.5 1.5-2.3 1-4.8 4.5-7.5 10.5-49.3 97.3-121.7 169.3-217 216-28
- 14-57.3 25-88 33-6.7 2-11 3.8-13 5.5-2 1.7-3 4.2-3 7.5s1 5.8 3 7.5
-c2 1.7 6.3 3.5 13 5.5 68 17.3 128.2 47.8 180.5 91.5 52.3 43.7 93.8 96.2 124.5
- 157.5 9.3 8 15.3 12.3 18 13h6c12-.7 18-4 18-10 0-2-1.7-7-5-15-23.3-46-52-87
--86-123l-10-10h399738v-40H218c328 0 0 0 0 0l-10-8c-26.7-20-65.7-43-117-69 2.7
--2 6-3.7 10-5 36.7-16 72.3-37.3 107-64l10-8h399782v-40z
-m8 0v40h399730v-40zm0 194v40h399730v-40z`,doublerightarrow:`M399738 392l
--10 10c-34 36-62.7 77-86 123-3.3 8-5 13.3-5 16 0 5.3 6.7 8 20 8 7.3 0 12.2-.5
- 14.5-1.5 2.3-1 4.8-4.5 7.5-10.5 49.3-97.3 121.7-169.3 217-216 28-14 57.3-25 88
--33 6.7-2 11-3.8 13-5.5 2-1.7 3-4.2 3-7.5s-1-5.8-3-7.5c-2-1.7-6.3-3.5-13-5.5-68
--17.3-128.2-47.8-180.5-91.5-52.3-43.7-93.8-96.2-124.5-157.5-9.3-8-15.3-12.3-18
--13h-6c-12 .7-18 4-18 10 0 2 1.7 7 5 15 23.3 46 52 87 86 123l10 10H0v40h399782
-c-328 0 0 0 0 0l10 8c26.7 20 65.7 43 117 69-2.7 2-6 3.7-10 5-36.7 16-72.3 37.3
--107 64l-10 8H0v40zM0 157v40h399730v-40zm0 194v40h399730v-40z`,leftarrow:`M400000 241H110l3-3c68.7-52.7 113.7-120
- 135-202 4-14.7 6-23 6-25 0-7.3-7-11-21-11-8 0-13.2.8-15.5 2.5-2.3 1.7-4.2 5.8
--5.5 12.5-1.3 4.7-2.7 10.3-4 17-12 48.7-34.8 92-68.5 130S65.3 228.3 18 247
-c-10 4-16 7.7-18 11 0 8.7 6 14.3 18 17 47.3 18.7 87.8 47 121.5 85S196 441.3 208
- 490c.7 2 1.3 5 2 9s1.2 6.7 1.5 8c.3 1.3 1 3.3 2 6s2.2 4.5 3.5 5.5c1.3 1 3.3
- 1.8 6 2.5s6 1 10 1c14 0 21-3.7 21-11 0-2-2-10.3-6-25-20-79.3-65-146.7-135-202
- l-3-3h399890zM100 241v40h399900v-40z`,leftbrace:`M6 548l-6-6v-35l6-11c56-104 135.3-181.3 238-232 57.3-28.7 117
--45 179-50h399577v120H403c-43.3 7-81 15-113 26-100.7 33-179.7 91-237 174-2.7
- 5-6 9-10 13-.7 1-7.3 1-20 1H6z`,leftbraceunder:`M0 6l6-6h17c12.688 0 19.313.3 20 1 4 4 7.313 8.3 10 13
- 35.313 51.3 80.813 93.8 136.5 127.5 55.688 33.7 117.188 55.8 184.5 66.5.688
- 0 2 .3 4 1 18.688 2.7 76 4.3 172 5h399450v120H429l-6-1c-124.688-8-235-61.7
--331-161C60.687 138.7 32.312 99.3 7 54L0 41V6z`,leftgroup:`M400000 80
-H435C64 80 168.3 229.4 21 260c-5.9 1.2-18 0-18 0-2 0-3-1-3-3v-38C76 61 257 0
- 435 0h399565z`,leftgroupunder:`M400000 262
-H435C64 262 168.3 112.6 21 82c-5.9-1.2-18 0-18 0-2 0-3 1-3 3v38c76 158 257 219
- 435 219h399565z`,leftharpoon:`M0 267c.7 5.3 3 10 7 14h399993v-40H93c3.3
--3.3 10.2-9.5 20.5-18.5s17.8-15.8 22.5-20.5c50.7-52 88-110.3 112-175 4-11.3 5
--18.3 3-21-1.3-4-7.3-6-18-6-8 0-13 .7-15 2s-4.7 6.7-8 16c-42 98.7-107.3 174.7
--196 228-6.7 4.7-10.7 8-12 10-1.3 2-2 5.7-2 11zm100-26v40h399900v-40z`,leftharpoonplus:`M0 267c.7 5.3 3 10 7 14h399993v-40H93c3.3-3.3 10.2-9.5
- 20.5-18.5s17.8-15.8 22.5-20.5c50.7-52 88-110.3 112-175 4-11.3 5-18.3 3-21-1.3
--4-7.3-6-18-6-8 0-13 .7-15 2s-4.7 6.7-8 16c-42 98.7-107.3 174.7-196 228-6.7 4.7
--10.7 8-12 10-1.3 2-2 5.7-2 11zm100-26v40h399900v-40zM0 435v40h400000v-40z
-m0 0v40h400000v-40z`,leftharpoondown:`M7 241c-4 4-6.333 8.667-7 14 0 5.333.667 9 2 11s5.333
- 5.333 12 10c90.667 54 156 130 196 228 3.333 10.667 6.333 16.333 9 17 2 .667 5
- 1 9 1h5c10.667 0 16.667-2 18-6 2-2.667 1-9.667-3-21-32-87.333-82.667-157.667
--152-211l-3-3h399907v-40zM93 281 H400000 v-40L7 241z`,leftharpoondownplus:`M7 435c-4 4-6.3 8.7-7 14 0 5.3.7 9 2 11s5.3 5.3 12
- 10c90.7 54 156 130 196 228 3.3 10.7 6.3 16.3 9 17 2 .7 5 1 9 1h5c10.7 0 16.7
--2 18-6 2-2.7 1-9.7-3-21-32-87.3-82.7-157.7-152-211l-3-3h399907v-40H7zm93 0
-v40h399900v-40zM0 241v40h399900v-40zm0 0v40h399900v-40z`,lefthook:`M400000 281 H103s-33-11.2-61-33.5S0 197.3 0 164s14.2-61.2 42.5
--83.5C70.8 58.2 104 47 142 47 c16.7 0 25 6.7 25 20 0 12-8.7 18.7-26 20-40 3.3
--68.7 15.7-86 37-10 12-15 25.3-15 40 0 22.7 9.8 40.7 29.5 54 19.7 13.3 43.5 21
- 71.5 23h399859zM103 281v-40h399897v40z`,leftlinesegment:`M40 281 V428 H0 V94 H40 V241 H400000 v40z
-M40 281 V428 H0 V94 H40 V241 H400000 v40z`,leftmapsto:`M40 281 V448H0V74H40V241H400000v40z
-M40 281 V448H0V74H40V241H400000v40z`,leftToFrom:`M0 147h400000v40H0zm0 214c68 40 115.7 95.7 143 167h22c15.3 0 23
--.3 23-1 0-1.3-5.3-13.7-16-37-18-35.3-41.3-69-70-101l-7-8h399905v-40H95l7-8
-c28.7-32 52-65.7 70-101 10.7-23.3 16-35.7 16-37 0-.7-7.7-1-23-1h-22C115.7 265.3
- 68 321 0 361zm0-174v-40h399900v40zm100 154v40h399900v-40z`,longequal:`M0 50 h400000 v40H0z m0 194h40000v40H0z
-M0 50 h400000 v40H0z m0 194h40000v40H0z`,midbrace:`M200428 334
-c-100.7-8.3-195.3-44-280-108-55.3-42-101.7-93-139-153l-9-14c-2.7 4-5.7 8.7-9 14
--53.3 86.7-123.7 153-211 199-66.7 36-137.3 56.3-212 62H0V214h199568c178.3-11.7
- 311.7-78.3 403-201 6-8 9.7-12 11-12 .7-.7 6.7-1 18-1s17.3.3 18 1c1.3 0 5 4 11
- 12 44.7 59.3 101.3 106.3 170 141s145.3 54.3 229 60h199572v120z`,midbraceunder:`M199572 214
-c100.7 8.3 195.3 44 280 108 55.3 42 101.7 93 139 153l9 14c2.7-4 5.7-8.7 9-14
- 53.3-86.7 123.7-153 211-199 66.7-36 137.3-56.3 212-62h199568v120H200432c-178.3
- 11.7-311.7 78.3-403 201-6 8-9.7 12-11 12-.7.7-6.7 1-18 1s-17.3-.3-18-1c-1.3 0
--5-4-11-12-44.7-59.3-101.3-106.3-170-141s-145.3-54.3-229-60H0V214z`,oiintSize1:`M512.6 71.6c272.6 0 320.3 106.8 320.3 178.2 0 70.8-47.7 177.6
--320.3 177.6S193.1 320.6 193.1 249.8c0-71.4 46.9-178.2 319.5-178.2z
-m368.1 178.2c0-86.4-60.9-215.4-368.1-215.4-306.4 0-367.3 129-367.3 215.4 0 85.8
-60.9 214.8 367.3 214.8 307.2 0 368.1-129 368.1-214.8z`,oiintSize2:`M757.8 100.1c384.7 0 451.1 137.6 451.1 230 0 91.3-66.4 228.8
--451.1 228.8-386.3 0-452.7-137.5-452.7-228.8 0-92.4 66.4-230 452.7-230z
-m502.4 230c0-111.2-82.4-277.2-502.4-277.2s-504 166-504 277.2
-c0 110 84 276 504 276s502.4-166 502.4-276z`,oiiintSize1:`M681.4 71.6c408.9 0 480.5 106.8 480.5 178.2 0 70.8-71.6 177.6
--480.5 177.6S202.1 320.6 202.1 249.8c0-71.4 70.5-178.2 479.3-178.2z
-m525.8 178.2c0-86.4-86.8-215.4-525.7-215.4-437.9 0-524.7 129-524.7 215.4 0
-85.8 86.8 214.8 524.7 214.8 438.9 0 525.7-129 525.7-214.8z`,oiiintSize2:`M1021.2 53c603.6 0 707.8 165.8 707.8 277.2 0 110-104.2 275.8
--707.8 275.8-606 0-710.2-165.8-710.2-275.8C311 218.8 415.2 53 1021.2 53z
-m770.4 277.1c0-131.2-126.4-327.6-770.5-327.6S248.4 198.9 248.4 330.1
-c0 130 128.8 326.4 772.7 326.4s770.5-196.4 770.5-326.4z`,rightarrow:`M0 241v40h399891c-47.3 35.3-84 78-110 128
--16.7 32-27.7 63.7-33 95 0 1.3-.2 2.7-.5 4-.3 1.3-.5 2.3-.5 3 0 7.3 6.7 11 20
- 11 8 0 13.2-.8 15.5-2.5 2.3-1.7 4.2-5.5 5.5-11.5 2-13.3 5.7-27 11-41 14.7-44.7
- 39-84.5 73-119.5s73.7-60.2 119-75.5c6-2 9-5.7 9-11s-3-9-9-11c-45.3-15.3-85
--40.5-119-75.5s-58.3-74.8-73-119.5c-4.7-14-8.3-27.3-11-40-1.3-6.7-3.2-10.8-5.5
--12.5-2.3-1.7-7.5-2.5-15.5-2.5-14 0-21 3.7-21 11 0 2 2 10.3 6 25 20.7 83.3 67
- 151.7 139 205zm0 0v40h399900v-40z`,rightbrace:`M400000 542l
--6 6h-17c-12.7 0-19.3-.3-20-1-4-4-7.3-8.3-10-13-35.3-51.3-80.8-93.8-136.5-127.5
-s-117.2-55.8-184.5-66.5c-.7 0-2-.3-4-1-18.7-2.7-76-4.3-172-5H0V214h399571l6 1
-c124.7 8 235 61.7 331 161 31.3 33.3 59.7 72.7 85 118l7 13v35z`,rightbraceunder:`M399994 0l6 6v35l-6 11c-56 104-135.3 181.3-238 232-57.3
- 28.7-117 45-179 50H-300V214h399897c43.3-7 81-15 113-26 100.7-33 179.7-91 237
--174 2.7-5 6-9 10-13 .7-1 7.3-1 20-1h17z`,rightgroup:`M0 80h399565c371 0 266.7 149.4 414 180 5.9 1.2 18 0 18 0 2 0
- 3-1 3-3v-38c-76-158-257-219-435-219H0z`,rightgroupunder:`M0 262h399565c371 0 266.7-149.4 414-180 5.9-1.2 18 0 18
- 0 2 0 3 1 3 3v38c-76 158-257 219-435 219H0z`,rightharpoon:`M0 241v40h399993c4.7-4.7 7-9.3 7-14 0-9.3
--3.7-15.3-11-18-92.7-56.7-159-133.7-199-231-3.3-9.3-6-14.7-8-16-2-1.3-7-2-15-2
--10.7 0-16.7 2-18 6-2 2.7-1 9.7 3 21 15.3 42 36.7 81.8 64 119.5 27.3 37.7 58
- 69.2 92 94.5zm0 0v40h399900v-40z`,rightharpoonplus:`M0 241v40h399993c4.7-4.7 7-9.3 7-14 0-9.3-3.7-15.3-11
--18-92.7-56.7-159-133.7-199-231-3.3-9.3-6-14.7-8-16-2-1.3-7-2-15-2-10.7 0-16.7
- 2-18 6-2 2.7-1 9.7 3 21 15.3 42 36.7 81.8 64 119.5 27.3 37.7 58 69.2 92 94.5z
-m0 0v40h399900v-40z m100 194v40h399900v-40zm0 0v40h399900v-40z`,rightharpoondown:`M399747 511c0 7.3 6.7 11 20 11 8 0 13-.8 15-2.5s4.7-6.8
- 8-15.5c40-94 99.3-166.3 178-217 13.3-8 20.3-12.3 21-13 5.3-3.3 8.5-5.8 9.5
--7.5 1-1.7 1.5-5.2 1.5-10.5s-2.3-10.3-7-15H0v40h399908c-34 25.3-64.7 57-92 95
--27.3 38-48.7 77.7-64 119-3.3 8.7-5 14-5 16zM0 241v40h399900v-40z`,rightharpoondownplus:`M399747 705c0 7.3 6.7 11 20 11 8 0 13-.8
- 15-2.5s4.7-6.8 8-15.5c40-94 99.3-166.3 178-217 13.3-8 20.3-12.3 21-13 5.3-3.3
- 8.5-5.8 9.5-7.5 1-1.7 1.5-5.2 1.5-10.5s-2.3-10.3-7-15H0v40h399908c-34 25.3
--64.7 57-92 95-27.3 38-48.7 77.7-64 119-3.3 8.7-5 14-5 16zM0 435v40h399900v-40z
-m0-194v40h400000v-40zm0 0v40h400000v-40z`,righthook:`M399859 241c-764 0 0 0 0 0 40-3.3 68.7-15.7 86-37 10-12 15-25.3
- 15-40 0-22.7-9.8-40.7-29.5-54-19.7-13.3-43.5-21-71.5-23-17.3-1.3-26-8-26-20 0
--13.3 8.7-20 26-20 38 0 71 11.2 99 33.5 0 0 7 5.6 21 16.7 14 11.2 21 33.5 21
- 66.8s-14 61.2-42 83.5c-28 22.3-61 33.5-99 33.5L0 241z M0 281v-40h399859v40z`,rightlinesegment:`M399960 241 V94 h40 V428 h-40 V281 H0 v-40z
-M399960 241 V94 h40 V428 h-40 V281 H0 v-40z`,rightToFrom:`M400000 167c-70.7-42-118-97.7-142-167h-23c-15.3 0-23 .3-23
- 1 0 1.3 5.3 13.7 16 37 18 35.3 41.3 69 70 101l7 8H0v40h399905l-7 8c-28.7 32
--52 65.7-70 101-10.7 23.3-16 35.7-16 37 0 .7 7.7 1 23 1h23c24-69.3 71.3-125 142
--167z M100 147v40h399900v-40zM0 341v40h399900v-40z`,twoheadleftarrow:`M0 167c68 40
- 115.7 95.7 143 167h22c15.3 0 23-.3 23-1 0-1.3-5.3-13.7-16-37-18-35.3-41.3-69
--70-101l-7-8h125l9 7c50.7 39.3 85 86 103 140h46c0-4.7-6.3-18.7-19-42-18-35.3
--40-67.3-66-96l-9-9h399716v-40H284l9-9c26-28.7 48-60.7 66-96 12.7-23.333 19
--37.333 19-42h-46c-18 54-52.3 100.7-103 140l-9 7H95l7-8c28.7-32 52-65.7 70-101
- 10.7-23.333 16-35.7 16-37 0-.7-7.7-1-23-1h-22C115.7 71.3 68 127 0 167z`,twoheadrightarrow:`M400000 167
-c-68-40-115.7-95.7-143-167h-22c-15.3 0-23 .3-23 1 0 1.3 5.3 13.7 16 37 18 35.3
- 41.3 69 70 101l7 8h-125l-9-7c-50.7-39.3-85-86-103-140h-46c0 4.7 6.3 18.7 19 42
- 18 35.3 40 67.3 66 96l9 9H0v40h399716l-9 9c-26 28.7-48 60.7-66 96-12.7 23.333
--19 37.333-19 42h46c18-54 52.3-100.7 103-140l9-7h125l-7 8c-28.7 32-52 65.7-70
- 101-10.7 23.333-16 35.7-16 37 0 .7 7.7 1 23 1h22c27.3-71.3 75-127 143-167z`,tilde1:`M200 55.538c-77 0-168 73.953-177 73.953-3 0-7
--2.175-9-5.437L2 97c-1-2-2-4-2-6 0-4 2-7 5-9l20-12C116 12 171 0 207 0c86 0
- 114 68 191 68 78 0 168-68 177-68 4 0 7 2 9 5l12 19c1 2.175 2 4.35 2 6.525 0
- 4.35-2 7.613-5 9.788l-19 13.05c-92 63.077-116.937 75.308-183 76.128
--68.267.847-113-73.952-191-73.952z`,tilde2:`M344 55.266c-142 0-300.638 81.316-311.5 86.418
--8.01 3.762-22.5 10.91-23.5 5.562L1 120c-1-2-1-3-1-4 0-5 3-9 8-10l18.4-9C160.9
- 31.9 283 0 358 0c148 0 188 122 331 122s314-97 326-97c4 0 8 2 10 7l7 21.114
-c1 2.14 1 3.21 1 4.28 0 5.347-3 9.626-7 10.696l-22.3 12.622C852.6 158.372 751
- 181.476 676 181.476c-149 0-189-126.21-332-126.21z`,tilde3:`M786 59C457 59 32 175.242 13 175.242c-6 0-10-3.457
--11-10.37L.15 138c-1-7 3-12 10-13l19.2-6.4C378.4 40.7 634.3 0 804.3 0c337 0
- 411.8 157 746.8 157 328 0 754-112 773-112 5 0 10 3 11 9l1 14.075c1 8.066-.697
- 16.595-6.697 17.492l-21.052 7.31c-367.9 98.146-609.15 122.696-778.15 122.696
- -338 0-409-156.573-744-156.573z`,tilde4:`M786 58C457 58 32 177.487 13 177.487c-6 0-10-3.345
--11-10.035L.15 143c-1-7 3-12 10-13l22-6.7C381.2 35 637.15 0 807.15 0c337 0 409
- 177 744 177 328 0 754-127 773-127 5 0 10 3 11 9l1 14.794c1 7.805-3 13.38-9
- 14.495l-20.7 5.574c-366.85 99.79-607.3 139.372-776.3 139.372-338 0-409
- -175.236-744-175.236z`,vec:`M377 20c0-5.333 1.833-10 5.5-14S391 0 397 0c4.667 0 8.667 1.667 12 5
-3.333 2.667 6.667 9 10 19 6.667 24.667 20.333 43.667 41 57 7.333 4.667 11
-10.667 11 18 0 6-1 10-3 12s-6.667 5-14 9c-28.667 14.667-53.667 35.667-75 63
--1.333 1.333-3.167 3.5-5.5 6.5s-4 4.833-5 5.5c-1 .667-2.5 1.333-4.5 2s-4.333 1
--7 1c-4.667 0-9.167-1.833-13.5-5.5S337 184 337 178c0-12.667 15.667-32.333 47-59
-H213l-171-1c-8.667-6-13-12.333-13-19 0-4.667 4.333-11.333 13-20h359
-c-16-25.333-24-45-24-59z`,widehat1:`M529 0h5l519 115c5 1 9 5 9 10 0 1-1 2-1 3l-4 22
-c-1 5-5 9-11 9h-2L532 67 19 159h-2c-5 0-9-4-11-9l-5-22c-1-6 2-12 8-13z`,widehat2:`M1181 0h2l1171 176c6 0 10 5 10 11l-2 23c-1 6-5 10
--11 10h-1L1182 67 15 220h-1c-6 0-10-4-11-10l-2-23c-1-6 4-11 10-11z`,widehat3:`M1181 0h2l1171 236c6 0 10 5 10 11l-2 23c-1 6-5 10
--11 10h-1L1182 67 15 280h-1c-6 0-10-4-11-10l-2-23c-1-6 4-11 10-11z`,widehat4:`M1181 0h2l1171 296c6 0 10 5 10 11l-2 23c-1 6-5 10
--11 10h-1L1182 67 15 340h-1c-6 0-10-4-11-10l-2-23c-1-6 4-11 10-11z`,widecheck1:`M529,159h5l519,-115c5,-1,9,-5,9,-10c0,-1,-1,-2,-1,-3l-4,-22c-1,
--5,-5,-9,-11,-9h-2l-512,92l-513,-92h-2c-5,0,-9,4,-11,9l-5,22c-1,6,2,12,8,13z`,widecheck2:`M1181,220h2l1171,-176c6,0,10,-5,10,-11l-2,-23c-1,-6,-5,-10,
--11,-10h-1l-1168,153l-1167,-153h-1c-6,0,-10,4,-11,10l-2,23c-1,6,4,11,10,11z`,widecheck3:`M1181,280h2l1171,-236c6,0,10,-5,10,-11l-2,-23c-1,-6,-5,-10,
--11,-10h-1l-1168,213l-1167,-213h-1c-6,0,-10,4,-11,10l-2,23c-1,6,4,11,10,11z`,widecheck4:`M1181,340h2l1171,-296c6,0,10,-5,10,-11l-2,-23c-1,-6,-5,-10,
--11,-10h-1l-1168,273l-1167,-273h-1c-6,0,-10,4,-11,10l-2,23c-1,6,4,11,10,11z`,baraboveleftarrow:`M400000 620h-399890l3 -3c68.7 -52.7 113.7 -120 135 -202
-c4 -14.7 6 -23 6 -25c0 -7.3 -7 -11 -21 -11c-8 0 -13.2 0.8 -15.5 2.5
-c-2.3 1.7 -4.2 5.8 -5.5 12.5c-1.3 4.7 -2.7 10.3 -4 17c-12 48.7 -34.8 92 -68.5 130
-s-74.2 66.3 -121.5 85c-10 4 -16 7.7 -18 11c0 8.7 6 14.3 18 17c47.3 18.7 87.8 47
-121.5 85s56.5 81.3 68.5 130c0.7 2 1.3 5 2 9s1.2 6.7 1.5 8c0.3 1.3 1 3.3 2 6
-s2.2 4.5 3.5 5.5c1.3 1 3.3 1.8 6 2.5s6 1 10 1c14 0 21 -3.7 21 -11
-c0 -2 -2 -10.3 -6 -25c-20 -79.3 -65 -146.7 -135 -202l-3 -3h399890z
-M100 620v40h399900v-40z M0 241v40h399900v-40zM0 241v40h399900v-40z`,rightarrowabovebar:`M0 241v40h399891c-47.3 35.3-84 78-110 128-16.7 32
--27.7 63.7-33 95 0 1.3-.2 2.7-.5 4-.3 1.3-.5 2.3-.5 3 0 7.3 6.7 11 20 11 8 0
-13.2-.8 15.5-2.5 2.3-1.7 4.2-5.5 5.5-11.5 2-13.3 5.7-27 11-41 14.7-44.7 39
--84.5 73-119.5s73.7-60.2 119-75.5c6-2 9-5.7 9-11s-3-9-9-11c-45.3-15.3-85-40.5
--119-75.5s-58.3-74.8-73-119.5c-4.7-14-8.3-27.3-11-40-1.3-6.7-3.2-10.8-5.5
--12.5-2.3-1.7-7.5-2.5-15.5-2.5-14 0-21 3.7-21 11 0 2 2 10.3 6 25 20.7 83.3 67
-151.7 139 205zm96 379h399894v40H0zm0 0h399904v40H0z`,baraboveshortleftharpoon:`M507,435c-4,4,-6.3,8.7,-7,14c0,5.3,0.7,9,2,11
-c1.3,2,5.3,5.3,12,10c90.7,54,156,130,196,228c3.3,10.7,6.3,16.3,9,17
-c2,0.7,5,1,9,1c0,0,5,0,5,0c10.7,0,16.7,-2,18,-6c2,-2.7,1,-9.7,-3,-21
-c-32,-87.3,-82.7,-157.7,-152,-211c0,0,-3,-3,-3,-3l399351,0l0,-40
-c-398570,0,-399437,0,-399437,0z M593 435 v40 H399500 v-40z
-M0 281 v-40 H399908 v40z M0 281 v-40 H399908 v40z`,rightharpoonaboveshortbar:`M0,241 l0,40c399126,0,399993,0,399993,0
-c4.7,-4.7,7,-9.3,7,-14c0,-9.3,-3.7,-15.3,-11,-18c-92.7,-56.7,-159,-133.7,-199,
--231c-3.3,-9.3,-6,-14.7,-8,-16c-2,-1.3,-7,-2,-15,-2c-10.7,0,-16.7,2,-18,6
-c-2,2.7,-1,9.7,3,21c15.3,42,36.7,81.8,64,119.5c27.3,37.7,58,69.2,92,94.5z
-M0 241 v40 H399908 v-40z M0 475 v-40 H399500 v40z M0 475 v-40 H399500 v40z`,shortbaraboveleftharpoon:`M7,435c-4,4,-6.3,8.7,-7,14c0,5.3,0.7,9,2,11
-c1.3,2,5.3,5.3,12,10c90.7,54,156,130,196,228c3.3,10.7,6.3,16.3,9,17c2,0.7,5,1,9,
-1c0,0,5,0,5,0c10.7,0,16.7,-2,18,-6c2,-2.7,1,-9.7,-3,-21c-32,-87.3,-82.7,-157.7,
--152,-211c0,0,-3,-3,-3,-3l399907,0l0,-40c-399126,0,-399993,0,-399993,0z
-M93 435 v40 H400000 v-40z M500 241 v40 H400000 v-40z M500 241 v40 H400000 v-40z`,shortrightharpoonabovebar:`M53,241l0,40c398570,0,399437,0,399437,0
-c4.7,-4.7,7,-9.3,7,-14c0,-9.3,-3.7,-15.3,-11,-18c-92.7,-56.7,-159,-133.7,-199,
--231c-3.3,-9.3,-6,-14.7,-8,-16c-2,-1.3,-7,-2,-15,-2c-10.7,0,-16.7,2,-18,6
-c-2,2.7,-1,9.7,3,21c15.3,42,36.7,81.8,64,119.5c27.3,37.7,58,69.2,92,94.5z
-M500 241 v40 H399408 v-40z M500 435 v40 H400000 v-40z`},sr=function(e,t){switch(e){case"lbrack":return"M403 1759 V84 H666 V0 H319 V1759 v"+t+` v1759 h347 v-84
-H403z M403 1759 V0 H319 V1759 v`+t+" v1759 h84z";case"rbrack":return"M347 1759 V0 H0 V84 H263 V1759 v"+t+` v1759 H0 v84 H347z
-M347 1759 V0 H263 V1759 v`+t+" v1759 h84z";case"vert":return"M145 15 v585 v"+t+` v585 c2.667,10,9.667,15,21,15
-c10,0,16.667,-5,20,-15 v-585 v`+-t+` v-585 c-2.667,-10,-9.667,-15,-21,-15
-c-10,0,-16.667,5,-20,15z M188 15 H145 v585 v`+t+" v585 h43z";case"doublevert":return"M145 15 v585 v"+t+` v585 c2.667,10,9.667,15,21,15
-c10,0,16.667,-5,20,-15 v-585 v`+-t+` v-585 c-2.667,-10,-9.667,-15,-21,-15
-c-10,0,-16.667,5,-20,15z M188 15 H145 v585 v`+t+` v585 h43z
-M367 15 v585 v`+t+` v585 c2.667,10,9.667,15,21,15
-c10,0,16.667,-5,20,-15 v-585 v`+-t+` v-585 c-2.667,-10,-9.667,-15,-21,-15
-c-10,0,-16.667,5,-20,15z M410 15 H367 v585 v`+t+" v585 h43z";case"lfloor":return"M319 602 V0 H403 V602 v"+t+` v1715 h263 v84 H319z
-MM319 602 V0 H403 V602 v`+t+" v1715 H319z";case"rfloor":return"M319 602 V0 H403 V602 v"+t+` v1799 H0 v-84 H319z
-MM319 602 V0 H403 V602 v`+t+" v1715 H319z";case"lceil":return"M403 1759 V84 H666 V0 H319 V1759 v"+t+` v602 h84z
-M403 1759 V0 H319 V1759 v`+t+" v602 h84z";case"rceil":return"M347 1759 V0 H0 V84 H263 V1759 v"+t+` v602 h84z
-M347 1759 V0 h-84 V1759 v`+t+" v602 h84z";case"lparen":return`M863,9c0,-2,-2,-5,-6,-9c0,0,-17,0,-17,0c-12.7,0,-19.3,0.3,-20,1
-c-5.3,5.3,-10.3,11,-15,17c-242.7,294.7,-395.3,682,-458,1162c-21.3,163.3,-33.3,349,
--36,557 l0,`+(t+84)+`c0.2,6,0,26,0,60c2,159.3,10,310.7,24,454c53.3,528,210,
-949.7,470,1265c4.7,6,9.7,11.7,15,17c0.7,0.7,7,1,19,1c0,0,18,0,18,0c4,-4,6,-7,6,-9
-c0,-2.7,-3.3,-8.7,-10,-18c-135.3,-192.7,-235.5,-414.3,-300.5,-665c-65,-250.7,-102.5,
--544.7,-112.5,-882c-2,-104,-3,-167,-3,-189
-l0,-`+(t+92)+`c0,-162.7,5.7,-314,17,-454c20.7,-272,63.7,-513,129,-723c65.3,
--210,155.3,-396.3,270,-559c6.7,-9.3,10,-15.3,10,-18z`;case"rparen":return`M76,0c-16.7,0,-25,3,-25,9c0,2,2,6.3,6,13c21.3,28.7,42.3,60.3,
-63,95c96.7,156.7,172.8,332.5,228.5,527.5c55.7,195,92.8,416.5,111.5,664.5
-c11.3,139.3,17,290.7,17,454c0,28,1.7,43,3.3,45l0,`+(t+9)+`
-c-3,4,-3.3,16.7,-3.3,38c0,162,-5.7,313.7,-17,455c-18.7,248,-55.8,469.3,-111.5,664
-c-55.7,194.7,-131.8,370.3,-228.5,527c-20.7,34.7,-41.7,66.3,-63,95c-2,3.3,-4,7,-6,11
-c0,7.3,5.7,11,17,11c0,0,11,0,11,0c9.3,0,14.3,-0.3,15,-1c5.3,-5.3,10.3,-11,15,-17
-c242.7,-294.7,395.3,-681.7,458,-1161c21.3,-164.7,33.3,-350.7,36,-558
-l0,-`+(t+144)+`c-2,-159.3,-10,-310.7,-24,-454c-53.3,-528,-210,-949.7,
--470,-1265c-4.7,-6,-9.7,-11.7,-15,-17c-0.7,-0.7,-6.7,-1,-18,-1z`;default:throw new Error("Unknown stretchy delimiter.")}},Jt=function(){function u(t){this.children=void 0,this.classes=void 0,this.height=void 0,this.depth=void 0,this.maxFontSize=void 0,this.style=void 0,this.children=t,this.classes=[],this.height=0,this.depth=0,this.maxFontSize=0,this.style={}}var e=u.prototype;return e.hasClass=function(r){return O.contains(this.classes,r)},e.toNode=function(){for(var r=document.createDocumentFragment(),n=0;n<this.children.length;n++)r.appendChild(this.children[n].toNode());return r},e.toMarkup=function(){for(var r="",n=0;n<this.children.length;n++)r+=this.children[n].toMarkup();return r},e.toText=function(){var r=function(i){return i.toText()};return this.children.map(r).join("")},u}(),bt={"AMS-Regular":{32:[0,0,0,0,.25],65:[0,.68889,0,0,.72222],66:[0,.68889,0,0,.66667],67:[0,.68889,0,0,.72222],68:[0,.68889,0,0,.72222],69:[0,.68889,0,0,.66667],70:[0,.68889,0,0,.61111],71:[0,.68889,0,0,.77778],72:[0,.68889,0,0,.77778],73:[0,.68889,0,0,.38889],74:[.16667,.68889,0,0,.5],75:[0,.68889,0,0,.77778],76:[0,.68889,0,0,.66667],77:[0,.68889,0,0,.94445],78:[0,.68889,0,0,.72222],79:[.16667,.68889,0,0,.77778],80:[0,.68889,0,0,.61111],81:[.16667,.68889,0,0,.77778],82:[0,.68889,0,0,.72222],83:[0,.68889,0,0,.55556],84:[0,.68889,0,0,.66667],85:[0,.68889,0,0,.72222],86:[0,.68889,0,0,.72222],87:[0,.68889,0,0,1],88:[0,.68889,0,0,.72222],89:[0,.68889,0,0,.72222],90:[0,.68889,0,0,.66667],107:[0,.68889,0,0,.55556],160:[0,0,0,0,.25],165:[0,.675,.025,0,.75],174:[.15559,.69224,0,0,.94666],240:[0,.68889,0,0,.55556],295:[0,.68889,0,0,.54028],710:[0,.825,0,0,2.33334],732:[0,.9,0,0,2.33334],770:[0,.825,0,0,2.33334],771:[0,.9,0,0,2.33334],989:[.08167,.58167,0,0,.77778],1008:[0,.43056,.04028,0,.66667],8245:[0,.54986,0,0,.275],8463:[0,.68889,0,0,.54028],8487:[0,.68889,0,0,.72222],8498:[0,.68889,0,0,.55556],8502:[0,.68889,0,0,.66667],8503:[0,.68889,0,0,.44445],8504:[0,.68889,0,0,.66667],8513:[0,.68889,0,0,.63889],8592:[-.03598,.46402,0,0,.5],8594:[-.03598,.46402,0,0,.5],8602:[-.13313,.36687,0,0,1],8603:[-.13313,.36687,0,0,1],8606:[.01354,.52239,0,0,1],8608:[.01354,.52239,0,0,1],8610:[.01354,.52239,0,0,1.11111],8611:[.01354,.52239,0,0,1.11111],8619:[0,.54986,0,0,1],8620:[0,.54986,0,0,1],8621:[-.13313,.37788,0,0,1.38889],8622:[-.13313,.36687,0,0,1],8624:[0,.69224,0,0,.5],8625:[0,.69224,0,0,.5],8630:[0,.43056,0,0,1],8631:[0,.43056,0,0,1],8634:[.08198,.58198,0,0,.77778],8635:[.08198,.58198,0,0,.77778],8638:[.19444,.69224,0,0,.41667],8639:[.19444,.69224,0,0,.41667],8642:[.19444,.69224,0,0,.41667],8643:[.19444,.69224,0,0,.41667],8644:[.1808,.675,0,0,1],8646:[.1808,.675,0,0,1],8647:[.1808,.675,0,0,1],8648:[.19444,.69224,0,0,.83334],8649:[.1808,.675,0,0,1],8650:[.19444,.69224,0,0,.83334],8651:[.01354,.52239,0,0,1],8652:[.01354,.52239,0,0,1],8653:[-.13313,.36687,0,0,1],8654:[-.13313,.36687,0,0,1],8655:[-.13313,.36687,0,0,1],8666:[.13667,.63667,0,0,1],8667:[.13667,.63667,0,0,1],8669:[-.13313,.37788,0,0,1],8672:[-.064,.437,0,0,1.334],8674:[-.064,.437,0,0,1.334],8705:[0,.825,0,0,.5],8708:[0,.68889,0,0,.55556],8709:[.08167,.58167,0,0,.77778],8717:[0,.43056,0,0,.42917],8722:[-.03598,.46402,0,0,.5],8724:[.08198,.69224,0,0,.77778],8726:[.08167,.58167,0,0,.77778],8733:[0,.69224,0,0,.77778],8736:[0,.69224,0,0,.72222],8737:[0,.69224,0,0,.72222],8738:[.03517,.52239,0,0,.72222],8739:[.08167,.58167,0,0,.22222],8740:[.25142,.74111,0,0,.27778],8741:[.08167,.58167,0,0,.38889],8742:[.25142,.74111,0,0,.5],8756:[0,.69224,0,0,.66667],8757:[0,.69224,0,0,.66667],8764:[-.13313,.36687,0,0,.77778],8765:[-.13313,.37788,0,0,.77778],8769:[-.13313,.36687,0,0,.77778],8770:[-.03625,.46375,0,0,.77778],8774:[.30274,.79383,0,0,.77778],8776:[-.01688,.48312,0,0,.77778],8778:[.08167,.58167,0,0,.77778],8782:[.06062,.54986,0,0,.77778],8783:[.06062,.54986,0,0,.77778],8785:[.08198,.58198,0,0,.77778],8786:[.08198,.58198,0,0,.77778],8787:[.08198,.58198,0,0,.77778],8790:[0,.69224,0,0,.77778],8791:[.22958,.72958,0,0,.77778],8796:[.08198,.91667,0,0,.77778],8806:[.25583,.75583,0,0,.77778],8807:[.25583,.75583,0,0,.77778],8808:[.25142,.75726,0,0,.77778],8809:[.25142,.75726,0,0,.77778],8812:[.25583,.75583,0,0,.5],8814:[.20576,.70576,0,0,.77778],8815:[.20576,.70576,0,0,.77778],8816:[.30274,.79383,0,0,.77778],8817:[.30274,.79383,0,0,.77778],8818:[.22958,.72958,0,0,.77778],8819:[.22958,.72958,0,0,.77778],8822:[.1808,.675,0,0,.77778],8823:[.1808,.675,0,0,.77778],8828:[.13667,.63667,0,0,.77778],8829:[.13667,.63667,0,0,.77778],8830:[.22958,.72958,0,0,.77778],8831:[.22958,.72958,0,0,.77778],8832:[.20576,.70576,0,0,.77778],8833:[.20576,.70576,0,0,.77778],8840:[.30274,.79383,0,0,.77778],8841:[.30274,.79383,0,0,.77778],8842:[.13597,.63597,0,0,.77778],8843:[.13597,.63597,0,0,.77778],8847:[.03517,.54986,0,0,.77778],8848:[.03517,.54986,0,0,.77778],8858:[.08198,.58198,0,0,.77778],8859:[.08198,.58198,0,0,.77778],8861:[.08198,.58198,0,0,.77778],8862:[0,.675,0,0,.77778],8863:[0,.675,0,0,.77778],8864:[0,.675,0,0,.77778],8865:[0,.675,0,0,.77778],8872:[0,.69224,0,0,.61111],8873:[0,.69224,0,0,.72222],8874:[0,.69224,0,0,.88889],8876:[0,.68889,0,0,.61111],8877:[0,.68889,0,0,.61111],8878:[0,.68889,0,0,.72222],8879:[0,.68889,0,0,.72222],8882:[.03517,.54986,0,0,.77778],8883:[.03517,.54986,0,0,.77778],8884:[.13667,.63667,0,0,.77778],8885:[.13667,.63667,0,0,.77778],8888:[0,.54986,0,0,1.11111],8890:[.19444,.43056,0,0,.55556],8891:[.19444,.69224,0,0,.61111],8892:[.19444,.69224,0,0,.61111],8901:[0,.54986,0,0,.27778],8903:[.08167,.58167,0,0,.77778],8905:[.08167,.58167,0,0,.77778],8906:[.08167,.58167,0,0,.77778],8907:[0,.69224,0,0,.77778],8908:[0,.69224,0,0,.77778],8909:[-.03598,.46402,0,0,.77778],8910:[0,.54986,0,0,.76042],8911:[0,.54986,0,0,.76042],8912:[.03517,.54986,0,0,.77778],8913:[.03517,.54986,0,0,.77778],8914:[0,.54986,0,0,.66667],8915:[0,.54986,0,0,.66667],8916:[0,.69224,0,0,.66667],8918:[.0391,.5391,0,0,.77778],8919:[.0391,.5391,0,0,.77778],8920:[.03517,.54986,0,0,1.33334],8921:[.03517,.54986,0,0,1.33334],8922:[.38569,.88569,0,0,.77778],8923:[.38569,.88569,0,0,.77778],8926:[.13667,.63667,0,0,.77778],8927:[.13667,.63667,0,0,.77778],8928:[.30274,.79383,0,0,.77778],8929:[.30274,.79383,0,0,.77778],8934:[.23222,.74111,0,0,.77778],8935:[.23222,.74111,0,0,.77778],8936:[.23222,.74111,0,0,.77778],8937:[.23222,.74111,0,0,.77778],8938:[.20576,.70576,0,0,.77778],8939:[.20576,.70576,0,0,.77778],8940:[.30274,.79383,0,0,.77778],8941:[.30274,.79383,0,0,.77778],8994:[.19444,.69224,0,0,.77778],8995:[.19444,.69224,0,0,.77778],9416:[.15559,.69224,0,0,.90222],9484:[0,.69224,0,0,.5],9488:[0,.69224,0,0,.5],9492:[0,.37788,0,0,.5],9496:[0,.37788,0,0,.5],9585:[.19444,.68889,0,0,.88889],9586:[.19444,.74111,0,0,.88889],9632:[0,.675,0,0,.77778],9633:[0,.675,0,0,.77778],9650:[0,.54986,0,0,.72222],9651:[0,.54986,0,0,.72222],9654:[.03517,.54986,0,0,.77778],9660:[0,.54986,0,0,.72222],9661:[0,.54986,0,0,.72222],9664:[.03517,.54986,0,0,.77778],9674:[.11111,.69224,0,0,.66667],9733:[.19444,.69224,0,0,.94445],10003:[0,.69224,0,0,.83334],10016:[0,.69224,0,0,.83334],10731:[.11111,.69224,0,0,.66667],10846:[.19444,.75583,0,0,.61111],10877:[.13667,.63667,0,0,.77778],10878:[.13667,.63667,0,0,.77778],10885:[.25583,.75583,0,0,.77778],10886:[.25583,.75583,0,0,.77778],10887:[.13597,.63597,0,0,.77778],10888:[.13597,.63597,0,0,.77778],10889:[.26167,.75726,0,0,.77778],10890:[.26167,.75726,0,0,.77778],10891:[.48256,.98256,0,0,.77778],10892:[.48256,.98256,0,0,.77778],10901:[.13667,.63667,0,0,.77778],10902:[.13667,.63667,0,0,.77778],10933:[.25142,.75726,0,0,.77778],10934:[.25142,.75726,0,0,.77778],10935:[.26167,.75726,0,0,.77778],10936:[.26167,.75726,0,0,.77778],10937:[.26167,.75726,0,0,.77778],10938:[.26167,.75726,0,0,.77778],10949:[.25583,.75583,0,0,.77778],10950:[.25583,.75583,0,0,.77778],10955:[.28481,.79383,0,0,.77778],10956:[.28481,.79383,0,0,.77778],57350:[.08167,.58167,0,0,.22222],57351:[.08167,.58167,0,0,.38889],57352:[.08167,.58167,0,0,.77778],57353:[0,.43056,.04028,0,.66667],57356:[.25142,.75726,0,0,.77778],57357:[.25142,.75726,0,0,.77778],57358:[.41951,.91951,0,0,.77778],57359:[.30274,.79383,0,0,.77778],57360:[.30274,.79383,0,0,.77778],57361:[.41951,.91951,0,0,.77778],57366:[.25142,.75726,0,0,.77778],57367:[.25142,.75726,0,0,.77778],57368:[.25142,.75726,0,0,.77778],57369:[.25142,.75726,0,0,.77778],57370:[.13597,.63597,0,0,.77778],57371:[.13597,.63597,0,0,.77778]},"Caligraphic-Regular":{32:[0,0,0,0,.25],65:[0,.68333,0,.19445,.79847],66:[0,.68333,.03041,.13889,.65681],67:[0,.68333,.05834,.13889,.52653],68:[0,.68333,.02778,.08334,.77139],69:[0,.68333,.08944,.11111,.52778],70:[0,.68333,.09931,.11111,.71875],71:[.09722,.68333,.0593,.11111,.59487],72:[0,.68333,.00965,.11111,.84452],73:[0,.68333,.07382,0,.54452],74:[.09722,.68333,.18472,.16667,.67778],75:[0,.68333,.01445,.05556,.76195],76:[0,.68333,0,.13889,.68972],77:[0,.68333,0,.13889,1.2009],78:[0,.68333,.14736,.08334,.82049],79:[0,.68333,.02778,.11111,.79611],80:[0,.68333,.08222,.08334,.69556],81:[.09722,.68333,0,.11111,.81667],82:[0,.68333,0,.08334,.8475],83:[0,.68333,.075,.13889,.60556],84:[0,.68333,.25417,0,.54464],85:[0,.68333,.09931,.08334,.62583],86:[0,.68333,.08222,0,.61278],87:[0,.68333,.08222,.08334,.98778],88:[0,.68333,.14643,.13889,.7133],89:[.09722,.68333,.08222,.08334,.66834],90:[0,.68333,.07944,.13889,.72473],160:[0,0,0,0,.25]},"Fraktur-Regular":{32:[0,0,0,0,.25],33:[0,.69141,0,0,.29574],34:[0,.69141,0,0,.21471],38:[0,.69141,0,0,.73786],39:[0,.69141,0,0,.21201],40:[.24982,.74947,0,0,.38865],41:[.24982,.74947,0,0,.38865],42:[0,.62119,0,0,.27764],43:[.08319,.58283,0,0,.75623],44:[0,.10803,0,0,.27764],45:[.08319,.58283,0,0,.75623],46:[0,.10803,0,0,.27764],47:[.24982,.74947,0,0,.50181],48:[0,.47534,0,0,.50181],49:[0,.47534,0,0,.50181],50:[0,.47534,0,0,.50181],51:[.18906,.47534,0,0,.50181],52:[.18906,.47534,0,0,.50181],53:[.18906,.47534,0,0,.50181],54:[0,.69141,0,0,.50181],55:[.18906,.47534,0,0,.50181],56:[0,.69141,0,0,.50181],57:[.18906,.47534,0,0,.50181],58:[0,.47534,0,0,.21606],59:[.12604,.47534,0,0,.21606],61:[-.13099,.36866,0,0,.75623],63:[0,.69141,0,0,.36245],65:[0,.69141,0,0,.7176],66:[0,.69141,0,0,.88397],67:[0,.69141,0,0,.61254],68:[0,.69141,0,0,.83158],69:[0,.69141,0,0,.66278],70:[.12604,.69141,0,0,.61119],71:[0,.69141,0,0,.78539],72:[.06302,.69141,0,0,.7203],73:[0,.69141,0,0,.55448],74:[.12604,.69141,0,0,.55231],75:[0,.69141,0,0,.66845],76:[0,.69141,0,0,.66602],77:[0,.69141,0,0,1.04953],78:[0,.69141,0,0,.83212],79:[0,.69141,0,0,.82699],80:[.18906,.69141,0,0,.82753],81:[.03781,.69141,0,0,.82699],82:[0,.69141,0,0,.82807],83:[0,.69141,0,0,.82861],84:[0,.69141,0,0,.66899],85:[0,.69141,0,0,.64576],86:[0,.69141,0,0,.83131],87:[0,.69141,0,0,1.04602],88:[0,.69141,0,0,.71922],89:[.18906,.69141,0,0,.83293],90:[.12604,.69141,0,0,.60201],91:[.24982,.74947,0,0,.27764],93:[.24982,.74947,0,0,.27764],94:[0,.69141,0,0,.49965],97:[0,.47534,0,0,.50046],98:[0,.69141,0,0,.51315],99:[0,.47534,0,0,.38946],100:[0,.62119,0,0,.49857],101:[0,.47534,0,0,.40053],102:[.18906,.69141,0,0,.32626],103:[.18906,.47534,0,0,.5037],104:[.18906,.69141,0,0,.52126],105:[0,.69141,0,0,.27899],106:[0,.69141,0,0,.28088],107:[0,.69141,0,0,.38946],108:[0,.69141,0,0,.27953],109:[0,.47534,0,0,.76676],110:[0,.47534,0,0,.52666],111:[0,.47534,0,0,.48885],112:[.18906,.52396,0,0,.50046],113:[.18906,.47534,0,0,.48912],114:[0,.47534,0,0,.38919],115:[0,.47534,0,0,.44266],116:[0,.62119,0,0,.33301],117:[0,.47534,0,0,.5172],118:[0,.52396,0,0,.5118],119:[0,.52396,0,0,.77351],120:[.18906,.47534,0,0,.38865],121:[.18906,.47534,0,0,.49884],122:[.18906,.47534,0,0,.39054],160:[0,0,0,0,.25],8216:[0,.69141,0,0,.21471],8217:[0,.69141,0,0,.21471],58112:[0,.62119,0,0,.49749],58113:[0,.62119,0,0,.4983],58114:[.18906,.69141,0,0,.33328],58115:[.18906,.69141,0,0,.32923],58116:[.18906,.47534,0,0,.50343],58117:[0,.69141,0,0,.33301],58118:[0,.62119,0,0,.33409],58119:[0,.47534,0,0,.50073]},"Main-Bold":{32:[0,0,0,0,.25],33:[0,.69444,0,0,.35],34:[0,.69444,0,0,.60278],35:[.19444,.69444,0,0,.95833],36:[.05556,.75,0,0,.575],37:[.05556,.75,0,0,.95833],38:[0,.69444,0,0,.89444],39:[0,.69444,0,0,.31944],40:[.25,.75,0,0,.44722],41:[.25,.75,0,0,.44722],42:[0,.75,0,0,.575],43:[.13333,.63333,0,0,.89444],44:[.19444,.15556,0,0,.31944],45:[0,.44444,0,0,.38333],46:[0,.15556,0,0,.31944],47:[.25,.75,0,0,.575],48:[0,.64444,0,0,.575],49:[0,.64444,0,0,.575],50:[0,.64444,0,0,.575],51:[0,.64444,0,0,.575],52:[0,.64444,0,0,.575],53:[0,.64444,0,0,.575],54:[0,.64444,0,0,.575],55:[0,.64444,0,0,.575],56:[0,.64444,0,0,.575],57:[0,.64444,0,0,.575],58:[0,.44444,0,0,.31944],59:[.19444,.44444,0,0,.31944],60:[.08556,.58556,0,0,.89444],61:[-.10889,.39111,0,0,.89444],62:[.08556,.58556,0,0,.89444],63:[0,.69444,0,0,.54305],64:[0,.69444,0,0,.89444],65:[0,.68611,0,0,.86944],66:[0,.68611,0,0,.81805],67:[0,.68611,0,0,.83055],68:[0,.68611,0,0,.88194],69:[0,.68611,0,0,.75555],70:[0,.68611,0,0,.72361],71:[0,.68611,0,0,.90416],72:[0,.68611,0,0,.9],73:[0,.68611,0,0,.43611],74:[0,.68611,0,0,.59444],75:[0,.68611,0,0,.90138],76:[0,.68611,0,0,.69166],77:[0,.68611,0,0,1.09166],78:[0,.68611,0,0,.9],79:[0,.68611,0,0,.86388],80:[0,.68611,0,0,.78611],81:[.19444,.68611,0,0,.86388],82:[0,.68611,0,0,.8625],83:[0,.68611,0,0,.63889],84:[0,.68611,0,0,.8],85:[0,.68611,0,0,.88472],86:[0,.68611,.01597,0,.86944],87:[0,.68611,.01597,0,1.18888],88:[0,.68611,0,0,.86944],89:[0,.68611,.02875,0,.86944],90:[0,.68611,0,0,.70277],91:[.25,.75,0,0,.31944],92:[.25,.75,0,0,.575],93:[.25,.75,0,0,.31944],94:[0,.69444,0,0,.575],95:[.31,.13444,.03194,0,.575],97:[0,.44444,0,0,.55902],98:[0,.69444,0,0,.63889],99:[0,.44444,0,0,.51111],100:[0,.69444,0,0,.63889],101:[0,.44444,0,0,.52708],102:[0,.69444,.10903,0,.35139],103:[.19444,.44444,.01597,0,.575],104:[0,.69444,0,0,.63889],105:[0,.69444,0,0,.31944],106:[.19444,.69444,0,0,.35139],107:[0,.69444,0,0,.60694],108:[0,.69444,0,0,.31944],109:[0,.44444,0,0,.95833],110:[0,.44444,0,0,.63889],111:[0,.44444,0,0,.575],112:[.19444,.44444,0,0,.63889],113:[.19444,.44444,0,0,.60694],114:[0,.44444,0,0,.47361],115:[0,.44444,0,0,.45361],116:[0,.63492,0,0,.44722],117:[0,.44444,0,0,.63889],118:[0,.44444,.01597,0,.60694],119:[0,.44444,.01597,0,.83055],120:[0,.44444,0,0,.60694],121:[.19444,.44444,.01597,0,.60694],122:[0,.44444,0,0,.51111],123:[.25,.75,0,0,.575],124:[.25,.75,0,0,.31944],125:[.25,.75,0,0,.575],126:[.35,.34444,0,0,.575],160:[0,0,0,0,.25],163:[0,.69444,0,0,.86853],168:[0,.69444,0,0,.575],172:[0,.44444,0,0,.76666],176:[0,.69444,0,0,.86944],177:[.13333,.63333,0,0,.89444],184:[.17014,0,0,0,.51111],198:[0,.68611,0,0,1.04166],215:[.13333,.63333,0,0,.89444],216:[.04861,.73472,0,0,.89444],223:[0,.69444,0,0,.59722],230:[0,.44444,0,0,.83055],247:[.13333,.63333,0,0,.89444],248:[.09722,.54167,0,0,.575],305:[0,.44444,0,0,.31944],338:[0,.68611,0,0,1.16944],339:[0,.44444,0,0,.89444],567:[.19444,.44444,0,0,.35139],710:[0,.69444,0,0,.575],711:[0,.63194,0,0,.575],713:[0,.59611,0,0,.575],714:[0,.69444,0,0,.575],715:[0,.69444,0,0,.575],728:[0,.69444,0,0,.575],729:[0,.69444,0,0,.31944],730:[0,.69444,0,0,.86944],732:[0,.69444,0,0,.575],733:[0,.69444,0,0,.575],915:[0,.68611,0,0,.69166],916:[0,.68611,0,0,.95833],920:[0,.68611,0,0,.89444],923:[0,.68611,0,0,.80555],926:[0,.68611,0,0,.76666],928:[0,.68611,0,0,.9],931:[0,.68611,0,0,.83055],933:[0,.68611,0,0,.89444],934:[0,.68611,0,0,.83055],936:[0,.68611,0,0,.89444],937:[0,.68611,0,0,.83055],8211:[0,.44444,.03194,0,.575],8212:[0,.44444,.03194,0,1.14999],8216:[0,.69444,0,0,.31944],8217:[0,.69444,0,0,.31944],8220:[0,.69444,0,0,.60278],8221:[0,.69444,0,0,.60278],8224:[.19444,.69444,0,0,.51111],8225:[.19444,.69444,0,0,.51111],8242:[0,.55556,0,0,.34444],8407:[0,.72444,.15486,0,.575],8463:[0,.69444,0,0,.66759],8465:[0,.69444,0,0,.83055],8467:[0,.69444,0,0,.47361],8472:[.19444,.44444,0,0,.74027],8476:[0,.69444,0,0,.83055],8501:[0,.69444,0,0,.70277],8592:[-.10889,.39111,0,0,1.14999],8593:[.19444,.69444,0,0,.575],8594:[-.10889,.39111,0,0,1.14999],8595:[.19444,.69444,0,0,.575],8596:[-.10889,.39111,0,0,1.14999],8597:[.25,.75,0,0,.575],8598:[.19444,.69444,0,0,1.14999],8599:[.19444,.69444,0,0,1.14999],8600:[.19444,.69444,0,0,1.14999],8601:[.19444,.69444,0,0,1.14999],8636:[-.10889,.39111,0,0,1.14999],8637:[-.10889,.39111,0,0,1.14999],8640:[-.10889,.39111,0,0,1.14999],8641:[-.10889,.39111,0,0,1.14999],8656:[-.10889,.39111,0,0,1.14999],8657:[.19444,.69444,0,0,.70277],8658:[-.10889,.39111,0,0,1.14999],8659:[.19444,.69444,0,0,.70277],8660:[-.10889,.39111,0,0,1.14999],8661:[.25,.75,0,0,.70277],8704:[0,.69444,0,0,.63889],8706:[0,.69444,.06389,0,.62847],8707:[0,.69444,0,0,.63889],8709:[.05556,.75,0,0,.575],8711:[0,.68611,0,0,.95833],8712:[.08556,.58556,0,0,.76666],8715:[.08556,.58556,0,0,.76666],8722:[.13333,.63333,0,0,.89444],8723:[.13333,.63333,0,0,.89444],8725:[.25,.75,0,0,.575],8726:[.25,.75,0,0,.575],8727:[-.02778,.47222,0,0,.575],8728:[-.02639,.47361,0,0,.575],8729:[-.02639,.47361,0,0,.575],8730:[.18,.82,0,0,.95833],8733:[0,.44444,0,0,.89444],8734:[0,.44444,0,0,1.14999],8736:[0,.69224,0,0,.72222],8739:[.25,.75,0,0,.31944],8741:[.25,.75,0,0,.575],8743:[0,.55556,0,0,.76666],8744:[0,.55556,0,0,.76666],8745:[0,.55556,0,0,.76666],8746:[0,.55556,0,0,.76666],8747:[.19444,.69444,.12778,0,.56875],8764:[-.10889,.39111,0,0,.89444],8768:[.19444,.69444,0,0,.31944],8771:[.00222,.50222,0,0,.89444],8773:[.027,.638,0,0,.894],8776:[.02444,.52444,0,0,.89444],8781:[.00222,.50222,0,0,.89444],8801:[.00222,.50222,0,0,.89444],8804:[.19667,.69667,0,0,.89444],8805:[.19667,.69667,0,0,.89444],8810:[.08556,.58556,0,0,1.14999],8811:[.08556,.58556,0,0,1.14999],8826:[.08556,.58556,0,0,.89444],8827:[.08556,.58556,0,0,.89444],8834:[.08556,.58556,0,0,.89444],8835:[.08556,.58556,0,0,.89444],8838:[.19667,.69667,0,0,.89444],8839:[.19667,.69667,0,0,.89444],8846:[0,.55556,0,0,.76666],8849:[.19667,.69667,0,0,.89444],8850:[.19667,.69667,0,0,.89444],8851:[0,.55556,0,0,.76666],8852:[0,.55556,0,0,.76666],8853:[.13333,.63333,0,0,.89444],8854:[.13333,.63333,0,0,.89444],8855:[.13333,.63333,0,0,.89444],8856:[.13333,.63333,0,0,.89444],8857:[.13333,.63333,0,0,.89444],8866:[0,.69444,0,0,.70277],8867:[0,.69444,0,0,.70277],8868:[0,.69444,0,0,.89444],8869:[0,.69444,0,0,.89444],8900:[-.02639,.47361,0,0,.575],8901:[-.02639,.47361,0,0,.31944],8902:[-.02778,.47222,0,0,.575],8968:[.25,.75,0,0,.51111],8969:[.25,.75,0,0,.51111],8970:[.25,.75,0,0,.51111],8971:[.25,.75,0,0,.51111],8994:[-.13889,.36111,0,0,1.14999],8995:[-.13889,.36111,0,0,1.14999],9651:[.19444,.69444,0,0,1.02222],9657:[-.02778,.47222,0,0,.575],9661:[.19444,.69444,0,0,1.02222],9667:[-.02778,.47222,0,0,.575],9711:[.19444,.69444,0,0,1.14999],9824:[.12963,.69444,0,0,.89444],9825:[.12963,.69444,0,0,.89444],9826:[.12963,.69444,0,0,.89444],9827:[.12963,.69444,0,0,.89444],9837:[0,.75,0,0,.44722],9838:[.19444,.69444,0,0,.44722],9839:[.19444,.69444,0,0,.44722],10216:[.25,.75,0,0,.44722],10217:[.25,.75,0,0,.44722],10815:[0,.68611,0,0,.9],10927:[.19667,.69667,0,0,.89444],10928:[.19667,.69667,0,0,.89444],57376:[.19444,.69444,0,0,0]},"Main-BoldItalic":{32:[0,0,0,0,.25],33:[0,.69444,.11417,0,.38611],34:[0,.69444,.07939,0,.62055],35:[.19444,.69444,.06833,0,.94444],37:[.05556,.75,.12861,0,.94444],38:[0,.69444,.08528,0,.88555],39:[0,.69444,.12945,0,.35555],40:[.25,.75,.15806,0,.47333],41:[.25,.75,.03306,0,.47333],42:[0,.75,.14333,0,.59111],43:[.10333,.60333,.03306,0,.88555],44:[.19444,.14722,0,0,.35555],45:[0,.44444,.02611,0,.41444],46:[0,.14722,0,0,.35555],47:[.25,.75,.15806,0,.59111],48:[0,.64444,.13167,0,.59111],49:[0,.64444,.13167,0,.59111],50:[0,.64444,.13167,0,.59111],51:[0,.64444,.13167,0,.59111],52:[.19444,.64444,.13167,0,.59111],53:[0,.64444,.13167,0,.59111],54:[0,.64444,.13167,0,.59111],55:[.19444,.64444,.13167,0,.59111],56:[0,.64444,.13167,0,.59111],57:[0,.64444,.13167,0,.59111],58:[0,.44444,.06695,0,.35555],59:[.19444,.44444,.06695,0,.35555],61:[-.10889,.39111,.06833,0,.88555],63:[0,.69444,.11472,0,.59111],64:[0,.69444,.09208,0,.88555],65:[0,.68611,0,0,.86555],66:[0,.68611,.0992,0,.81666],67:[0,.68611,.14208,0,.82666],68:[0,.68611,.09062,0,.87555],69:[0,.68611,.11431,0,.75666],70:[0,.68611,.12903,0,.72722],71:[0,.68611,.07347,0,.89527],72:[0,.68611,.17208,0,.8961],73:[0,.68611,.15681,0,.47166],74:[0,.68611,.145,0,.61055],75:[0,.68611,.14208,0,.89499],76:[0,.68611,0,0,.69777],77:[0,.68611,.17208,0,1.07277],78:[0,.68611,.17208,0,.8961],79:[0,.68611,.09062,0,.85499],80:[0,.68611,.0992,0,.78721],81:[.19444,.68611,.09062,0,.85499],82:[0,.68611,.02559,0,.85944],83:[0,.68611,.11264,0,.64999],84:[0,.68611,.12903,0,.7961],85:[0,.68611,.17208,0,.88083],86:[0,.68611,.18625,0,.86555],87:[0,.68611,.18625,0,1.15999],88:[0,.68611,.15681,0,.86555],89:[0,.68611,.19803,0,.86555],90:[0,.68611,.14208,0,.70888],91:[.25,.75,.1875,0,.35611],93:[.25,.75,.09972,0,.35611],94:[0,.69444,.06709,0,.59111],95:[.31,.13444,.09811,0,.59111],97:[0,.44444,.09426,0,.59111],98:[0,.69444,.07861,0,.53222],99:[0,.44444,.05222,0,.53222],100:[0,.69444,.10861,0,.59111],101:[0,.44444,.085,0,.53222],102:[.19444,.69444,.21778,0,.4],103:[.19444,.44444,.105,0,.53222],104:[0,.69444,.09426,0,.59111],105:[0,.69326,.11387,0,.35555],106:[.19444,.69326,.1672,0,.35555],107:[0,.69444,.11111,0,.53222],108:[0,.69444,.10861,0,.29666],109:[0,.44444,.09426,0,.94444],110:[0,.44444,.09426,0,.64999],111:[0,.44444,.07861,0,.59111],112:[.19444,.44444,.07861,0,.59111],113:[.19444,.44444,.105,0,.53222],114:[0,.44444,.11111,0,.50167],115:[0,.44444,.08167,0,.48694],116:[0,.63492,.09639,0,.385],117:[0,.44444,.09426,0,.62055],118:[0,.44444,.11111,0,.53222],119:[0,.44444,.11111,0,.76777],120:[0,.44444,.12583,0,.56055],121:[.19444,.44444,.105,0,.56166],122:[0,.44444,.13889,0,.49055],126:[.35,.34444,.11472,0,.59111],160:[0,0,0,0,.25],168:[0,.69444,.11473,0,.59111],176:[0,.69444,0,0,.94888],184:[.17014,0,0,0,.53222],198:[0,.68611,.11431,0,1.02277],216:[.04861,.73472,.09062,0,.88555],223:[.19444,.69444,.09736,0,.665],230:[0,.44444,.085,0,.82666],248:[.09722,.54167,.09458,0,.59111],305:[0,.44444,.09426,0,.35555],338:[0,.68611,.11431,0,1.14054],339:[0,.44444,.085,0,.82666],567:[.19444,.44444,.04611,0,.385],710:[0,.69444,.06709,0,.59111],711:[0,.63194,.08271,0,.59111],713:[0,.59444,.10444,0,.59111],714:[0,.69444,.08528,0,.59111],715:[0,.69444,0,0,.59111],728:[0,.69444,.10333,0,.59111],729:[0,.69444,.12945,0,.35555],730:[0,.69444,0,0,.94888],732:[0,.69444,.11472,0,.59111],733:[0,.69444,.11472,0,.59111],915:[0,.68611,.12903,0,.69777],916:[0,.68611,0,0,.94444],920:[0,.68611,.09062,0,.88555],923:[0,.68611,0,0,.80666],926:[0,.68611,.15092,0,.76777],928:[0,.68611,.17208,0,.8961],931:[0,.68611,.11431,0,.82666],933:[0,.68611,.10778,0,.88555],934:[0,.68611,.05632,0,.82666],936:[0,.68611,.10778,0,.88555],937:[0,.68611,.0992,0,.82666],8211:[0,.44444,.09811,0,.59111],8212:[0,.44444,.09811,0,1.18221],8216:[0,.69444,.12945,0,.35555],8217:[0,.69444,.12945,0,.35555],8220:[0,.69444,.16772,0,.62055],8221:[0,.69444,.07939,0,.62055]},"Main-Italic":{32:[0,0,0,0,.25],33:[0,.69444,.12417,0,.30667],34:[0,.69444,.06961,0,.51444],35:[.19444,.69444,.06616,0,.81777],37:[.05556,.75,.13639,0,.81777],38:[0,.69444,.09694,0,.76666],39:[0,.69444,.12417,0,.30667],40:[.25,.75,.16194,0,.40889],41:[.25,.75,.03694,0,.40889],42:[0,.75,.14917,0,.51111],43:[.05667,.56167,.03694,0,.76666],44:[.19444,.10556,0,0,.30667],45:[0,.43056,.02826,0,.35778],46:[0,.10556,0,0,.30667],47:[.25,.75,.16194,0,.51111],48:[0,.64444,.13556,0,.51111],49:[0,.64444,.13556,0,.51111],50:[0,.64444,.13556,0,.51111],51:[0,.64444,.13556,0,.51111],52:[.19444,.64444,.13556,0,.51111],53:[0,.64444,.13556,0,.51111],54:[0,.64444,.13556,0,.51111],55:[.19444,.64444,.13556,0,.51111],56:[0,.64444,.13556,0,.51111],57:[0,.64444,.13556,0,.51111],58:[0,.43056,.0582,0,.30667],59:[.19444,.43056,.0582,0,.30667],61:[-.13313,.36687,.06616,0,.76666],63:[0,.69444,.1225,0,.51111],64:[0,.69444,.09597,0,.76666],65:[0,.68333,0,0,.74333],66:[0,.68333,.10257,0,.70389],67:[0,.68333,.14528,0,.71555],68:[0,.68333,.09403,0,.755],69:[0,.68333,.12028,0,.67833],70:[0,.68333,.13305,0,.65277],71:[0,.68333,.08722,0,.77361],72:[0,.68333,.16389,0,.74333],73:[0,.68333,.15806,0,.38555],74:[0,.68333,.14028,0,.525],75:[0,.68333,.14528,0,.76888],76:[0,.68333,0,0,.62722],77:[0,.68333,.16389,0,.89666],78:[0,.68333,.16389,0,.74333],79:[0,.68333,.09403,0,.76666],80:[0,.68333,.10257,0,.67833],81:[.19444,.68333,.09403,0,.76666],82:[0,.68333,.03868,0,.72944],83:[0,.68333,.11972,0,.56222],84:[0,.68333,.13305,0,.71555],85:[0,.68333,.16389,0,.74333],86:[0,.68333,.18361,0,.74333],87:[0,.68333,.18361,0,.99888],88:[0,.68333,.15806,0,.74333],89:[0,.68333,.19383,0,.74333],90:[0,.68333,.14528,0,.61333],91:[.25,.75,.1875,0,.30667],93:[.25,.75,.10528,0,.30667],94:[0,.69444,.06646,0,.51111],95:[.31,.12056,.09208,0,.51111],97:[0,.43056,.07671,0,.51111],98:[0,.69444,.06312,0,.46],99:[0,.43056,.05653,0,.46],100:[0,.69444,.10333,0,.51111],101:[0,.43056,.07514,0,.46],102:[.19444,.69444,.21194,0,.30667],103:[.19444,.43056,.08847,0,.46],104:[0,.69444,.07671,0,.51111],105:[0,.65536,.1019,0,.30667],106:[.19444,.65536,.14467,0,.30667],107:[0,.69444,.10764,0,.46],108:[0,.69444,.10333,0,.25555],109:[0,.43056,.07671,0,.81777],110:[0,.43056,.07671,0,.56222],111:[0,.43056,.06312,0,.51111],112:[.19444,.43056,.06312,0,.51111],113:[.19444,.43056,.08847,0,.46],114:[0,.43056,.10764,0,.42166],115:[0,.43056,.08208,0,.40889],116:[0,.61508,.09486,0,.33222],117:[0,.43056,.07671,0,.53666],118:[0,.43056,.10764,0,.46],119:[0,.43056,.10764,0,.66444],120:[0,.43056,.12042,0,.46389],121:[.19444,.43056,.08847,0,.48555],122:[0,.43056,.12292,0,.40889],126:[.35,.31786,.11585,0,.51111],160:[0,0,0,0,.25],168:[0,.66786,.10474,0,.51111],176:[0,.69444,0,0,.83129],184:[.17014,0,0,0,.46],198:[0,.68333,.12028,0,.88277],216:[.04861,.73194,.09403,0,.76666],223:[.19444,.69444,.10514,0,.53666],230:[0,.43056,.07514,0,.71555],248:[.09722,.52778,.09194,0,.51111],338:[0,.68333,.12028,0,.98499],339:[0,.43056,.07514,0,.71555],710:[0,.69444,.06646,0,.51111],711:[0,.62847,.08295,0,.51111],713:[0,.56167,.10333,0,.51111],714:[0,.69444,.09694,0,.51111],715:[0,.69444,0,0,.51111],728:[0,.69444,.10806,0,.51111],729:[0,.66786,.11752,0,.30667],730:[0,.69444,0,0,.83129],732:[0,.66786,.11585,0,.51111],733:[0,.69444,.1225,0,.51111],915:[0,.68333,.13305,0,.62722],916:[0,.68333,0,0,.81777],920:[0,.68333,.09403,0,.76666],923:[0,.68333,0,0,.69222],926:[0,.68333,.15294,0,.66444],928:[0,.68333,.16389,0,.74333],931:[0,.68333,.12028,0,.71555],933:[0,.68333,.11111,0,.76666],934:[0,.68333,.05986,0,.71555],936:[0,.68333,.11111,0,.76666],937:[0,.68333,.10257,0,.71555],8211:[0,.43056,.09208,0,.51111],8212:[0,.43056,.09208,0,1.02222],8216:[0,.69444,.12417,0,.30667],8217:[0,.69444,.12417,0,.30667],8220:[0,.69444,.1685,0,.51444],8221:[0,.69444,.06961,0,.51444],8463:[0,.68889,0,0,.54028]},"Main-Regular":{32:[0,0,0,0,.25],33:[0,.69444,0,0,.27778],34:[0,.69444,0,0,.5],35:[.19444,.69444,0,0,.83334],36:[.05556,.75,0,0,.5],37:[.05556,.75,0,0,.83334],38:[0,.69444,0,0,.77778],39:[0,.69444,0,0,.27778],40:[.25,.75,0,0,.38889],41:[.25,.75,0,0,.38889],42:[0,.75,0,0,.5],43:[.08333,.58333,0,0,.77778],44:[.19444,.10556,0,0,.27778],45:[0,.43056,0,0,.33333],46:[0,.10556,0,0,.27778],47:[.25,.75,0,0,.5],48:[0,.64444,0,0,.5],49:[0,.64444,0,0,.5],50:[0,.64444,0,0,.5],51:[0,.64444,0,0,.5],52:[0,.64444,0,0,.5],53:[0,.64444,0,0,.5],54:[0,.64444,0,0,.5],55:[0,.64444,0,0,.5],56:[0,.64444,0,0,.5],57:[0,.64444,0,0,.5],58:[0,.43056,0,0,.27778],59:[.19444,.43056,0,0,.27778],60:[.0391,.5391,0,0,.77778],61:[-.13313,.36687,0,0,.77778],62:[.0391,.5391,0,0,.77778],63:[0,.69444,0,0,.47222],64:[0,.69444,0,0,.77778],65:[0,.68333,0,0,.75],66:[0,.68333,0,0,.70834],67:[0,.68333,0,0,.72222],68:[0,.68333,0,0,.76389],69:[0,.68333,0,0,.68056],70:[0,.68333,0,0,.65278],71:[0,.68333,0,0,.78472],72:[0,.68333,0,0,.75],73:[0,.68333,0,0,.36111],74:[0,.68333,0,0,.51389],75:[0,.68333,0,0,.77778],76:[0,.68333,0,0,.625],77:[0,.68333,0,0,.91667],78:[0,.68333,0,0,.75],79:[0,.68333,0,0,.77778],80:[0,.68333,0,0,.68056],81:[.19444,.68333,0,0,.77778],82:[0,.68333,0,0,.73611],83:[0,.68333,0,0,.55556],84:[0,.68333,0,0,.72222],85:[0,.68333,0,0,.75],86:[0,.68333,.01389,0,.75],87:[0,.68333,.01389,0,1.02778],88:[0,.68333,0,0,.75],89:[0,.68333,.025,0,.75],90:[0,.68333,0,0,.61111],91:[.25,.75,0,0,.27778],92:[.25,.75,0,0,.5],93:[.25,.75,0,0,.27778],94:[0,.69444,0,0,.5],95:[.31,.12056,.02778,0,.5],97:[0,.43056,0,0,.5],98:[0,.69444,0,0,.55556],99:[0,.43056,0,0,.44445],100:[0,.69444,0,0,.55556],101:[0,.43056,0,0,.44445],102:[0,.69444,.07778,0,.30556],103:[.19444,.43056,.01389,0,.5],104:[0,.69444,0,0,.55556],105:[0,.66786,0,0,.27778],106:[.19444,.66786,0,0,.30556],107:[0,.69444,0,0,.52778],108:[0,.69444,0,0,.27778],109:[0,.43056,0,0,.83334],110:[0,.43056,0,0,.55556],111:[0,.43056,0,0,.5],112:[.19444,.43056,0,0,.55556],113:[.19444,.43056,0,0,.52778],114:[0,.43056,0,0,.39167],115:[0,.43056,0,0,.39445],116:[0,.61508,0,0,.38889],117:[0,.43056,0,0,.55556],118:[0,.43056,.01389,0,.52778],119:[0,.43056,.01389,0,.72222],120:[0,.43056,0,0,.52778],121:[.19444,.43056,.01389,0,.52778],122:[0,.43056,0,0,.44445],123:[.25,.75,0,0,.5],124:[.25,.75,0,0,.27778],125:[.25,.75,0,0,.5],126:[.35,.31786,0,0,.5],160:[0,0,0,0,.25],163:[0,.69444,0,0,.76909],167:[.19444,.69444,0,0,.44445],168:[0,.66786,0,0,.5],172:[0,.43056,0,0,.66667],176:[0,.69444,0,0,.75],177:[.08333,.58333,0,0,.77778],182:[.19444,.69444,0,0,.61111],184:[.17014,0,0,0,.44445],198:[0,.68333,0,0,.90278],215:[.08333,.58333,0,0,.77778],216:[.04861,.73194,0,0,.77778],223:[0,.69444,0,0,.5],230:[0,.43056,0,0,.72222],247:[.08333,.58333,0,0,.77778],248:[.09722,.52778,0,0,.5],305:[0,.43056,0,0,.27778],338:[0,.68333,0,0,1.01389],339:[0,.43056,0,0,.77778],567:[.19444,.43056,0,0,.30556],710:[0,.69444,0,0,.5],711:[0,.62847,0,0,.5],713:[0,.56778,0,0,.5],714:[0,.69444,0,0,.5],715:[0,.69444,0,0,.5],728:[0,.69444,0,0,.5],729:[0,.66786,0,0,.27778],730:[0,.69444,0,0,.75],732:[0,.66786,0,0,.5],733:[0,.69444,0,0,.5],915:[0,.68333,0,0,.625],916:[0,.68333,0,0,.83334],920:[0,.68333,0,0,.77778],923:[0,.68333,0,0,.69445],926:[0,.68333,0,0,.66667],928:[0,.68333,0,0,.75],931:[0,.68333,0,0,.72222],933:[0,.68333,0,0,.77778],934:[0,.68333,0,0,.72222],936:[0,.68333,0,0,.77778],937:[0,.68333,0,0,.72222],8211:[0,.43056,.02778,0,.5],8212:[0,.43056,.02778,0,1],8216:[0,.69444,0,0,.27778],8217:[0,.69444,0,0,.27778],8220:[0,.69444,0,0,.5],8221:[0,.69444,0,0,.5],8224:[.19444,.69444,0,0,.44445],8225:[.19444,.69444,0,0,.44445],8230:[0,.123,0,0,1.172],8242:[0,.55556,0,0,.275],8407:[0,.71444,.15382,0,.5],8463:[0,.68889,0,0,.54028],8465:[0,.69444,0,0,.72222],8467:[0,.69444,0,.11111,.41667],8472:[.19444,.43056,0,.11111,.63646],8476:[0,.69444,0,0,.72222],8501:[0,.69444,0,0,.61111],8592:[-.13313,.36687,0,0,1],8593:[.19444,.69444,0,0,.5],8594:[-.13313,.36687,0,0,1],8595:[.19444,.69444,0,0,.5],8596:[-.13313,.36687,0,0,1],8597:[.25,.75,0,0,.5],8598:[.19444,.69444,0,0,1],8599:[.19444,.69444,0,0,1],8600:[.19444,.69444,0,0,1],8601:[.19444,.69444,0,0,1],8614:[.011,.511,0,0,1],8617:[.011,.511,0,0,1.126],8618:[.011,.511,0,0,1.126],8636:[-.13313,.36687,0,0,1],8637:[-.13313,.36687,0,0,1],8640:[-.13313,.36687,0,0,1],8641:[-.13313,.36687,0,0,1],8652:[.011,.671,0,0,1],8656:[-.13313,.36687,0,0,1],8657:[.19444,.69444,0,0,.61111],8658:[-.13313,.36687,0,0,1],8659:[.19444,.69444,0,0,.61111],8660:[-.13313,.36687,0,0,1],8661:[.25,.75,0,0,.61111],8704:[0,.69444,0,0,.55556],8706:[0,.69444,.05556,.08334,.5309],8707:[0,.69444,0,0,.55556],8709:[.05556,.75,0,0,.5],8711:[0,.68333,0,0,.83334],8712:[.0391,.5391,0,0,.66667],8715:[.0391,.5391,0,0,.66667],8722:[.08333,.58333,0,0,.77778],8723:[.08333,.58333,0,0,.77778],8725:[.25,.75,0,0,.5],8726:[.25,.75,0,0,.5],8727:[-.03472,.46528,0,0,.5],8728:[-.05555,.44445,0,0,.5],8729:[-.05555,.44445,0,0,.5],8730:[.2,.8,0,0,.83334],8733:[0,.43056,0,0,.77778],8734:[0,.43056,0,0,1],8736:[0,.69224,0,0,.72222],8739:[.25,.75,0,0,.27778],8741:[.25,.75,0,0,.5],8743:[0,.55556,0,0,.66667],8744:[0,.55556,0,0,.66667],8745:[0,.55556,0,0,.66667],8746:[0,.55556,0,0,.66667],8747:[.19444,.69444,.11111,0,.41667],8764:[-.13313,.36687,0,0,.77778],8768:[.19444,.69444,0,0,.27778],8771:[-.03625,.46375,0,0,.77778],8773:[-.022,.589,0,0,.778],8776:[-.01688,.48312,0,0,.77778],8781:[-.03625,.46375,0,0,.77778],8784:[-.133,.673,0,0,.778],8801:[-.03625,.46375,0,0,.77778],8804:[.13597,.63597,0,0,.77778],8805:[.13597,.63597,0,0,.77778],8810:[.0391,.5391,0,0,1],8811:[.0391,.5391,0,0,1],8826:[.0391,.5391,0,0,.77778],8827:[.0391,.5391,0,0,.77778],8834:[.0391,.5391,0,0,.77778],8835:[.0391,.5391,0,0,.77778],8838:[.13597,.63597,0,0,.77778],8839:[.13597,.63597,0,0,.77778],8846:[0,.55556,0,0,.66667],8849:[.13597,.63597,0,0,.77778],8850:[.13597,.63597,0,0,.77778],8851:[0,.55556,0,0,.66667],8852:[0,.55556,0,0,.66667],8853:[.08333,.58333,0,0,.77778],8854:[.08333,.58333,0,0,.77778],8855:[.08333,.58333,0,0,.77778],8856:[.08333,.58333,0,0,.77778],8857:[.08333,.58333,0,0,.77778],8866:[0,.69444,0,0,.61111],8867:[0,.69444,0,0,.61111],8868:[0,.69444,0,0,.77778],8869:[0,.69444,0,0,.77778],8872:[.249,.75,0,0,.867],8900:[-.05555,.44445,0,0,.5],8901:[-.05555,.44445,0,0,.27778],8902:[-.03472,.46528,0,0,.5],8904:[.005,.505,0,0,.9],8942:[.03,.903,0,0,.278],8943:[-.19,.313,0,0,1.172],8945:[-.1,.823,0,0,1.282],8968:[.25,.75,0,0,.44445],8969:[.25,.75,0,0,.44445],8970:[.25,.75,0,0,.44445],8971:[.25,.75,0,0,.44445],8994:[-.14236,.35764,0,0,1],8995:[-.14236,.35764,0,0,1],9136:[.244,.744,0,0,.412],9137:[.244,.745,0,0,.412],9651:[.19444,.69444,0,0,.88889],9657:[-.03472,.46528,0,0,.5],9661:[.19444,.69444,0,0,.88889],9667:[-.03472,.46528,0,0,.5],9711:[.19444,.69444,0,0,1],9824:[.12963,.69444,0,0,.77778],9825:[.12963,.69444,0,0,.77778],9826:[.12963,.69444,0,0,.77778],9827:[.12963,.69444,0,0,.77778],9837:[0,.75,0,0,.38889],9838:[.19444,.69444,0,0,.38889],9839:[.19444,.69444,0,0,.38889],10216:[.25,.75,0,0,.38889],10217:[.25,.75,0,0,.38889],10222:[.244,.744,0,0,.412],10223:[.244,.745,0,0,.412],10229:[.011,.511,0,0,1.609],10230:[.011,.511,0,0,1.638],10231:[.011,.511,0,0,1.859],10232:[.024,.525,0,0,1.609],10233:[.024,.525,0,0,1.638],10234:[.024,.525,0,0,1.858],10236:[.011,.511,0,0,1.638],10815:[0,.68333,0,0,.75],10927:[.13597,.63597,0,0,.77778],10928:[.13597,.63597,0,0,.77778],57376:[.19444,.69444,0,0,0]},"Math-BoldItalic":{32:[0,0,0,0,.25],48:[0,.44444,0,0,.575],49:[0,.44444,0,0,.575],50:[0,.44444,0,0,.575],51:[.19444,.44444,0,0,.575],52:[.19444,.44444,0,0,.575],53:[.19444,.44444,0,0,.575],54:[0,.64444,0,0,.575],55:[.19444,.44444,0,0,.575],56:[0,.64444,0,0,.575],57:[.19444,.44444,0,0,.575],65:[0,.68611,0,0,.86944],66:[0,.68611,.04835,0,.8664],67:[0,.68611,.06979,0,.81694],68:[0,.68611,.03194,0,.93812],69:[0,.68611,.05451,0,.81007],70:[0,.68611,.15972,0,.68889],71:[0,.68611,0,0,.88673],72:[0,.68611,.08229,0,.98229],73:[0,.68611,.07778,0,.51111],74:[0,.68611,.10069,0,.63125],75:[0,.68611,.06979,0,.97118],76:[0,.68611,0,0,.75555],77:[0,.68611,.11424,0,1.14201],78:[0,.68611,.11424,0,.95034],79:[0,.68611,.03194,0,.83666],80:[0,.68611,.15972,0,.72309],81:[.19444,.68611,0,0,.86861],82:[0,.68611,.00421,0,.87235],83:[0,.68611,.05382,0,.69271],84:[0,.68611,.15972,0,.63663],85:[0,.68611,.11424,0,.80027],86:[0,.68611,.25555,0,.67778],87:[0,.68611,.15972,0,1.09305],88:[0,.68611,.07778,0,.94722],89:[0,.68611,.25555,0,.67458],90:[0,.68611,.06979,0,.77257],97:[0,.44444,0,0,.63287],98:[0,.69444,0,0,.52083],99:[0,.44444,0,0,.51342],100:[0,.69444,0,0,.60972],101:[0,.44444,0,0,.55361],102:[.19444,.69444,.11042,0,.56806],103:[.19444,.44444,.03704,0,.5449],104:[0,.69444,0,0,.66759],105:[0,.69326,0,0,.4048],106:[.19444,.69326,.0622,0,.47083],107:[0,.69444,.01852,0,.6037],108:[0,.69444,.0088,0,.34815],109:[0,.44444,0,0,1.0324],110:[0,.44444,0,0,.71296],111:[0,.44444,0,0,.58472],112:[.19444,.44444,0,0,.60092],113:[.19444,.44444,.03704,0,.54213],114:[0,.44444,.03194,0,.5287],115:[0,.44444,0,0,.53125],116:[0,.63492,0,0,.41528],117:[0,.44444,0,0,.68102],118:[0,.44444,.03704,0,.56666],119:[0,.44444,.02778,0,.83148],120:[0,.44444,0,0,.65903],121:[.19444,.44444,.03704,0,.59028],122:[0,.44444,.04213,0,.55509],160:[0,0,0,0,.25],915:[0,.68611,.15972,0,.65694],916:[0,.68611,0,0,.95833],920:[0,.68611,.03194,0,.86722],923:[0,.68611,0,0,.80555],926:[0,.68611,.07458,0,.84125],928:[0,.68611,.08229,0,.98229],931:[0,.68611,.05451,0,.88507],933:[0,.68611,.15972,0,.67083],934:[0,.68611,0,0,.76666],936:[0,.68611,.11653,0,.71402],937:[0,.68611,.04835,0,.8789],945:[0,.44444,0,0,.76064],946:[.19444,.69444,.03403,0,.65972],947:[.19444,.44444,.06389,0,.59003],948:[0,.69444,.03819,0,.52222],949:[0,.44444,0,0,.52882],950:[.19444,.69444,.06215,0,.50833],951:[.19444,.44444,.03704,0,.6],952:[0,.69444,.03194,0,.5618],953:[0,.44444,0,0,.41204],954:[0,.44444,0,0,.66759],955:[0,.69444,0,0,.67083],956:[.19444,.44444,0,0,.70787],957:[0,.44444,.06898,0,.57685],958:[.19444,.69444,.03021,0,.50833],959:[0,.44444,0,0,.58472],960:[0,.44444,.03704,0,.68241],961:[.19444,.44444,0,0,.6118],962:[.09722,.44444,.07917,0,.42361],963:[0,.44444,.03704,0,.68588],964:[0,.44444,.13472,0,.52083],965:[0,.44444,.03704,0,.63055],966:[.19444,.44444,0,0,.74722],967:[.19444,.44444,0,0,.71805],968:[.19444,.69444,.03704,0,.75833],969:[0,.44444,.03704,0,.71782],977:[0,.69444,0,0,.69155],981:[.19444,.69444,0,0,.7125],982:[0,.44444,.03194,0,.975],1009:[.19444,.44444,0,0,.6118],1013:[0,.44444,0,0,.48333],57649:[0,.44444,0,0,.39352],57911:[.19444,.44444,0,0,.43889]},"Math-Italic":{32:[0,0,0,0,.25],48:[0,.43056,0,0,.5],49:[0,.43056,0,0,.5],50:[0,.43056,0,0,.5],51:[.19444,.43056,0,0,.5],52:[.19444,.43056,0,0,.5],53:[.19444,.43056,0,0,.5],54:[0,.64444,0,0,.5],55:[.19444,.43056,0,0,.5],56:[0,.64444,0,0,.5],57:[.19444,.43056,0,0,.5],65:[0,.68333,0,.13889,.75],66:[0,.68333,.05017,.08334,.75851],67:[0,.68333,.07153,.08334,.71472],68:[0,.68333,.02778,.05556,.82792],69:[0,.68333,.05764,.08334,.7382],70:[0,.68333,.13889,.08334,.64306],71:[0,.68333,0,.08334,.78625],72:[0,.68333,.08125,.05556,.83125],73:[0,.68333,.07847,.11111,.43958],74:[0,.68333,.09618,.16667,.55451],75:[0,.68333,.07153,.05556,.84931],76:[0,.68333,0,.02778,.68056],77:[0,.68333,.10903,.08334,.97014],78:[0,.68333,.10903,.08334,.80347],79:[0,.68333,.02778,.08334,.76278],80:[0,.68333,.13889,.08334,.64201],81:[.19444,.68333,0,.08334,.79056],82:[0,.68333,.00773,.08334,.75929],83:[0,.68333,.05764,.08334,.6132],84:[0,.68333,.13889,.08334,.58438],85:[0,.68333,.10903,.02778,.68278],86:[0,.68333,.22222,0,.58333],87:[0,.68333,.13889,0,.94445],88:[0,.68333,.07847,.08334,.82847],89:[0,.68333,.22222,0,.58056],90:[0,.68333,.07153,.08334,.68264],97:[0,.43056,0,0,.52859],98:[0,.69444,0,0,.42917],99:[0,.43056,0,.05556,.43276],100:[0,.69444,0,.16667,.52049],101:[0,.43056,0,.05556,.46563],102:[.19444,.69444,.10764,.16667,.48959],103:[.19444,.43056,.03588,.02778,.47697],104:[0,.69444,0,0,.57616],105:[0,.65952,0,0,.34451],106:[.19444,.65952,.05724,0,.41181],107:[0,.69444,.03148,0,.5206],108:[0,.69444,.01968,.08334,.29838],109:[0,.43056,0,0,.87801],110:[0,.43056,0,0,.60023],111:[0,.43056,0,.05556,.48472],112:[.19444,.43056,0,.08334,.50313],113:[.19444,.43056,.03588,.08334,.44641],114:[0,.43056,.02778,.05556,.45116],115:[0,.43056,0,.05556,.46875],116:[0,.61508,0,.08334,.36111],117:[0,.43056,0,.02778,.57246],118:[0,.43056,.03588,.02778,.48472],119:[0,.43056,.02691,.08334,.71592],120:[0,.43056,0,.02778,.57153],121:[.19444,.43056,.03588,.05556,.49028],122:[0,.43056,.04398,.05556,.46505],160:[0,0,0,0,.25],915:[0,.68333,.13889,.08334,.61528],916:[0,.68333,0,.16667,.83334],920:[0,.68333,.02778,.08334,.76278],923:[0,.68333,0,.16667,.69445],926:[0,.68333,.07569,.08334,.74236],928:[0,.68333,.08125,.05556,.83125],931:[0,.68333,.05764,.08334,.77986],933:[0,.68333,.13889,.05556,.58333],934:[0,.68333,0,.08334,.66667],936:[0,.68333,.11,.05556,.61222],937:[0,.68333,.05017,.08334,.7724],945:[0,.43056,.0037,.02778,.6397],946:[.19444,.69444,.05278,.08334,.56563],947:[.19444,.43056,.05556,0,.51773],948:[0,.69444,.03785,.05556,.44444],949:[0,.43056,0,.08334,.46632],950:[.19444,.69444,.07378,.08334,.4375],951:[.19444,.43056,.03588,.05556,.49653],952:[0,.69444,.02778,.08334,.46944],953:[0,.43056,0,.05556,.35394],954:[0,.43056,0,0,.57616],955:[0,.69444,0,0,.58334],956:[.19444,.43056,0,.02778,.60255],957:[0,.43056,.06366,.02778,.49398],958:[.19444,.69444,.04601,.11111,.4375],959:[0,.43056,0,.05556,.48472],960:[0,.43056,.03588,0,.57003],961:[.19444,.43056,0,.08334,.51702],962:[.09722,.43056,.07986,.08334,.36285],963:[0,.43056,.03588,0,.57141],964:[0,.43056,.1132,.02778,.43715],965:[0,.43056,.03588,.02778,.54028],966:[.19444,.43056,0,.08334,.65417],967:[.19444,.43056,0,.05556,.62569],968:[.19444,.69444,.03588,.11111,.65139],969:[0,.43056,.03588,0,.62245],977:[0,.69444,0,.08334,.59144],981:[.19444,.69444,0,.08334,.59583],982:[0,.43056,.02778,0,.82813],1009:[.19444,.43056,0,.08334,.51702],1013:[0,.43056,0,.05556,.4059],57649:[0,.43056,0,.02778,.32246],57911:[.19444,.43056,0,.08334,.38403]},"SansSerif-Bold":{32:[0,0,0,0,.25],33:[0,.69444,0,0,.36667],34:[0,.69444,0,0,.55834],35:[.19444,.69444,0,0,.91667],36:[.05556,.75,0,0,.55],37:[.05556,.75,0,0,1.02912],38:[0,.69444,0,0,.83056],39:[0,.69444,0,0,.30556],40:[.25,.75,0,0,.42778],41:[.25,.75,0,0,.42778],42:[0,.75,0,0,.55],43:[.11667,.61667,0,0,.85556],44:[.10556,.13056,0,0,.30556],45:[0,.45833,0,0,.36667],46:[0,.13056,0,0,.30556],47:[.25,.75,0,0,.55],48:[0,.69444,0,0,.55],49:[0,.69444,0,0,.55],50:[0,.69444,0,0,.55],51:[0,.69444,0,0,.55],52:[0,.69444,0,0,.55],53:[0,.69444,0,0,.55],54:[0,.69444,0,0,.55],55:[0,.69444,0,0,.55],56:[0,.69444,0,0,.55],57:[0,.69444,0,0,.55],58:[0,.45833,0,0,.30556],59:[.10556,.45833,0,0,.30556],61:[-.09375,.40625,0,0,.85556],63:[0,.69444,0,0,.51945],64:[0,.69444,0,0,.73334],65:[0,.69444,0,0,.73334],66:[0,.69444,0,0,.73334],67:[0,.69444,0,0,.70278],68:[0,.69444,0,0,.79445],69:[0,.69444,0,0,.64167],70:[0,.69444,0,0,.61111],71:[0,.69444,0,0,.73334],72:[0,.69444,0,0,.79445],73:[0,.69444,0,0,.33056],74:[0,.69444,0,0,.51945],75:[0,.69444,0,0,.76389],76:[0,.69444,0,0,.58056],77:[0,.69444,0,0,.97778],78:[0,.69444,0,0,.79445],79:[0,.69444,0,0,.79445],80:[0,.69444,0,0,.70278],81:[.10556,.69444,0,0,.79445],82:[0,.69444,0,0,.70278],83:[0,.69444,0,0,.61111],84:[0,.69444,0,0,.73334],85:[0,.69444,0,0,.76389],86:[0,.69444,.01528,0,.73334],87:[0,.69444,.01528,0,1.03889],88:[0,.69444,0,0,.73334],89:[0,.69444,.0275,0,.73334],90:[0,.69444,0,0,.67223],91:[.25,.75,0,0,.34306],93:[.25,.75,0,0,.34306],94:[0,.69444,0,0,.55],95:[.35,.10833,.03056,0,.55],97:[0,.45833,0,0,.525],98:[0,.69444,0,0,.56111],99:[0,.45833,0,0,.48889],100:[0,.69444,0,0,.56111],101:[0,.45833,0,0,.51111],102:[0,.69444,.07639,0,.33611],103:[.19444,.45833,.01528,0,.55],104:[0,.69444,0,0,.56111],105:[0,.69444,0,0,.25556],106:[.19444,.69444,0,0,.28611],107:[0,.69444,0,0,.53056],108:[0,.69444,0,0,.25556],109:[0,.45833,0,0,.86667],110:[0,.45833,0,0,.56111],111:[0,.45833,0,0,.55],112:[.19444,.45833,0,0,.56111],113:[.19444,.45833,0,0,.56111],114:[0,.45833,.01528,0,.37222],115:[0,.45833,0,0,.42167],116:[0,.58929,0,0,.40417],117:[0,.45833,0,0,.56111],118:[0,.45833,.01528,0,.5],119:[0,.45833,.01528,0,.74445],120:[0,.45833,0,0,.5],121:[.19444,.45833,.01528,0,.5],122:[0,.45833,0,0,.47639],126:[.35,.34444,0,0,.55],160:[0,0,0,0,.25],168:[0,.69444,0,0,.55],176:[0,.69444,0,0,.73334],180:[0,.69444,0,0,.55],184:[.17014,0,0,0,.48889],305:[0,.45833,0,0,.25556],567:[.19444,.45833,0,0,.28611],710:[0,.69444,0,0,.55],711:[0,.63542,0,0,.55],713:[0,.63778,0,0,.55],728:[0,.69444,0,0,.55],729:[0,.69444,0,0,.30556],730:[0,.69444,0,0,.73334],732:[0,.69444,0,0,.55],733:[0,.69444,0,0,.55],915:[0,.69444,0,0,.58056],916:[0,.69444,0,0,.91667],920:[0,.69444,0,0,.85556],923:[0,.69444,0,0,.67223],926:[0,.69444,0,0,.73334],928:[0,.69444,0,0,.79445],931:[0,.69444,0,0,.79445],933:[0,.69444,0,0,.85556],934:[0,.69444,0,0,.79445],936:[0,.69444,0,0,.85556],937:[0,.69444,0,0,.79445],8211:[0,.45833,.03056,0,.55],8212:[0,.45833,.03056,0,1.10001],8216:[0,.69444,0,0,.30556],8217:[0,.69444,0,0,.30556],8220:[0,.69444,0,0,.55834],8221:[0,.69444,0,0,.55834]},"SansSerif-Italic":{32:[0,0,0,0,.25],33:[0,.69444,.05733,0,.31945],34:[0,.69444,.00316,0,.5],35:[.19444,.69444,.05087,0,.83334],36:[.05556,.75,.11156,0,.5],37:[.05556,.75,.03126,0,.83334],38:[0,.69444,.03058,0,.75834],39:[0,.69444,.07816,0,.27778],40:[.25,.75,.13164,0,.38889],41:[.25,.75,.02536,0,.38889],42:[0,.75,.11775,0,.5],43:[.08333,.58333,.02536,0,.77778],44:[.125,.08333,0,0,.27778],45:[0,.44444,.01946,0,.33333],46:[0,.08333,0,0,.27778],47:[.25,.75,.13164,0,.5],48:[0,.65556,.11156,0,.5],49:[0,.65556,.11156,0,.5],50:[0,.65556,.11156,0,.5],51:[0,.65556,.11156,0,.5],52:[0,.65556,.11156,0,.5],53:[0,.65556,.11156,0,.5],54:[0,.65556,.11156,0,.5],55:[0,.65556,.11156,0,.5],56:[0,.65556,.11156,0,.5],57:[0,.65556,.11156,0,.5],58:[0,.44444,.02502,0,.27778],59:[.125,.44444,.02502,0,.27778],61:[-.13,.37,.05087,0,.77778],63:[0,.69444,.11809,0,.47222],64:[0,.69444,.07555,0,.66667],65:[0,.69444,0,0,.66667],66:[0,.69444,.08293,0,.66667],67:[0,.69444,.11983,0,.63889],68:[0,.69444,.07555,0,.72223],69:[0,.69444,.11983,0,.59722],70:[0,.69444,.13372,0,.56945],71:[0,.69444,.11983,0,.66667],72:[0,.69444,.08094,0,.70834],73:[0,.69444,.13372,0,.27778],74:[0,.69444,.08094,0,.47222],75:[0,.69444,.11983,0,.69445],76:[0,.69444,0,0,.54167],77:[0,.69444,.08094,0,.875],78:[0,.69444,.08094,0,.70834],79:[0,.69444,.07555,0,.73611],80:[0,.69444,.08293,0,.63889],81:[.125,.69444,.07555,0,.73611],82:[0,.69444,.08293,0,.64584],83:[0,.69444,.09205,0,.55556],84:[0,.69444,.13372,0,.68056],85:[0,.69444,.08094,0,.6875],86:[0,.69444,.1615,0,.66667],87:[0,.69444,.1615,0,.94445],88:[0,.69444,.13372,0,.66667],89:[0,.69444,.17261,0,.66667],90:[0,.69444,.11983,0,.61111],91:[.25,.75,.15942,0,.28889],93:[.25,.75,.08719,0,.28889],94:[0,.69444,.0799,0,.5],95:[.35,.09444,.08616,0,.5],97:[0,.44444,.00981,0,.48056],98:[0,.69444,.03057,0,.51667],99:[0,.44444,.08336,0,.44445],100:[0,.69444,.09483,0,.51667],101:[0,.44444,.06778,0,.44445],102:[0,.69444,.21705,0,.30556],103:[.19444,.44444,.10836,0,.5],104:[0,.69444,.01778,0,.51667],105:[0,.67937,.09718,0,.23889],106:[.19444,.67937,.09162,0,.26667],107:[0,.69444,.08336,0,.48889],108:[0,.69444,.09483,0,.23889],109:[0,.44444,.01778,0,.79445],110:[0,.44444,.01778,0,.51667],111:[0,.44444,.06613,0,.5],112:[.19444,.44444,.0389,0,.51667],113:[.19444,.44444,.04169,0,.51667],114:[0,.44444,.10836,0,.34167],115:[0,.44444,.0778,0,.38333],116:[0,.57143,.07225,0,.36111],117:[0,.44444,.04169,0,.51667],118:[0,.44444,.10836,0,.46111],119:[0,.44444,.10836,0,.68334],120:[0,.44444,.09169,0,.46111],121:[.19444,.44444,.10836,0,.46111],122:[0,.44444,.08752,0,.43472],126:[.35,.32659,.08826,0,.5],160:[0,0,0,0,.25],168:[0,.67937,.06385,0,.5],176:[0,.69444,0,0,.73752],184:[.17014,0,0,0,.44445],305:[0,.44444,.04169,0,.23889],567:[.19444,.44444,.04169,0,.26667],710:[0,.69444,.0799,0,.5],711:[0,.63194,.08432,0,.5],713:[0,.60889,.08776,0,.5],714:[0,.69444,.09205,0,.5],715:[0,.69444,0,0,.5],728:[0,.69444,.09483,0,.5],729:[0,.67937,.07774,0,.27778],730:[0,.69444,0,0,.73752],732:[0,.67659,.08826,0,.5],733:[0,.69444,.09205,0,.5],915:[0,.69444,.13372,0,.54167],916:[0,.69444,0,0,.83334],920:[0,.69444,.07555,0,.77778],923:[0,.69444,0,0,.61111],926:[0,.69444,.12816,0,.66667],928:[0,.69444,.08094,0,.70834],931:[0,.69444,.11983,0,.72222],933:[0,.69444,.09031,0,.77778],934:[0,.69444,.04603,0,.72222],936:[0,.69444,.09031,0,.77778],937:[0,.69444,.08293,0,.72222],8211:[0,.44444,.08616,0,.5],8212:[0,.44444,.08616,0,1],8216:[0,.69444,.07816,0,.27778],8217:[0,.69444,.07816,0,.27778],8220:[0,.69444,.14205,0,.5],8221:[0,.69444,.00316,0,.5]},"SansSerif-Regular":{32:[0,0,0,0,.25],33:[0,.69444,0,0,.31945],34:[0,.69444,0,0,.5],35:[.19444,.69444,0,0,.83334],36:[.05556,.75,0,0,.5],37:[.05556,.75,0,0,.83334],38:[0,.69444,0,0,.75834],39:[0,.69444,0,0,.27778],40:[.25,.75,0,0,.38889],41:[.25,.75,0,0,.38889],42:[0,.75,0,0,.5],43:[.08333,.58333,0,0,.77778],44:[.125,.08333,0,0,.27778],45:[0,.44444,0,0,.33333],46:[0,.08333,0,0,.27778],47:[.25,.75,0,0,.5],48:[0,.65556,0,0,.5],49:[0,.65556,0,0,.5],50:[0,.65556,0,0,.5],51:[0,.65556,0,0,.5],52:[0,.65556,0,0,.5],53:[0,.65556,0,0,.5],54:[0,.65556,0,0,.5],55:[0,.65556,0,0,.5],56:[0,.65556,0,0,.5],57:[0,.65556,0,0,.5],58:[0,.44444,0,0,.27778],59:[.125,.44444,0,0,.27778],61:[-.13,.37,0,0,.77778],63:[0,.69444,0,0,.47222],64:[0,.69444,0,0,.66667],65:[0,.69444,0,0,.66667],66:[0,.69444,0,0,.66667],67:[0,.69444,0,0,.63889],68:[0,.69444,0,0,.72223],69:[0,.69444,0,0,.59722],70:[0,.69444,0,0,.56945],71:[0,.69444,0,0,.66667],72:[0,.69444,0,0,.70834],73:[0,.69444,0,0,.27778],74:[0,.69444,0,0,.47222],75:[0,.69444,0,0,.69445],76:[0,.69444,0,0,.54167],77:[0,.69444,0,0,.875],78:[0,.69444,0,0,.70834],79:[0,.69444,0,0,.73611],80:[0,.69444,0,0,.63889],81:[.125,.69444,0,0,.73611],82:[0,.69444,0,0,.64584],83:[0,.69444,0,0,.55556],84:[0,.69444,0,0,.68056],85:[0,.69444,0,0,.6875],86:[0,.69444,.01389,0,.66667],87:[0,.69444,.01389,0,.94445],88:[0,.69444,0,0,.66667],89:[0,.69444,.025,0,.66667],90:[0,.69444,0,0,.61111],91:[.25,.75,0,0,.28889],93:[.25,.75,0,0,.28889],94:[0,.69444,0,0,.5],95:[.35,.09444,.02778,0,.5],97:[0,.44444,0,0,.48056],98:[0,.69444,0,0,.51667],99:[0,.44444,0,0,.44445],100:[0,.69444,0,0,.51667],101:[0,.44444,0,0,.44445],102:[0,.69444,.06944,0,.30556],103:[.19444,.44444,.01389,0,.5],104:[0,.69444,0,0,.51667],105:[0,.67937,0,0,.23889],106:[.19444,.67937,0,0,.26667],107:[0,.69444,0,0,.48889],108:[0,.69444,0,0,.23889],109:[0,.44444,0,0,.79445],110:[0,.44444,0,0,.51667],111:[0,.44444,0,0,.5],112:[.19444,.44444,0,0,.51667],113:[.19444,.44444,0,0,.51667],114:[0,.44444,.01389,0,.34167],115:[0,.44444,0,0,.38333],116:[0,.57143,0,0,.36111],117:[0,.44444,0,0,.51667],118:[0,.44444,.01389,0,.46111],119:[0,.44444,.01389,0,.68334],120:[0,.44444,0,0,.46111],121:[.19444,.44444,.01389,0,.46111],122:[0,.44444,0,0,.43472],126:[.35,.32659,0,0,.5],160:[0,0,0,0,.25],168:[0,.67937,0,0,.5],176:[0,.69444,0,0,.66667],184:[.17014,0,0,0,.44445],305:[0,.44444,0,0,.23889],567:[.19444,.44444,0,0,.26667],710:[0,.69444,0,0,.5],711:[0,.63194,0,0,.5],713:[0,.60889,0,0,.5],714:[0,.69444,0,0,.5],715:[0,.69444,0,0,.5],728:[0,.69444,0,0,.5],729:[0,.67937,0,0,.27778],730:[0,.69444,0,0,.66667],732:[0,.67659,0,0,.5],733:[0,.69444,0,0,.5],915:[0,.69444,0,0,.54167],916:[0,.69444,0,0,.83334],920:[0,.69444,0,0,.77778],923:[0,.69444,0,0,.61111],926:[0,.69444,0,0,.66667],928:[0,.69444,0,0,.70834],931:[0,.69444,0,0,.72222],933:[0,.69444,0,0,.77778],934:[0,.69444,0,0,.72222],936:[0,.69444,0,0,.77778],937:[0,.69444,0,0,.72222],8211:[0,.44444,.02778,0,.5],8212:[0,.44444,.02778,0,1],8216:[0,.69444,0,0,.27778],8217:[0,.69444,0,0,.27778],8220:[0,.69444,0,0,.5],8221:[0,.69444,0,0,.5]},"Script-Regular":{32:[0,0,0,0,.25],65:[0,.7,.22925,0,.80253],66:[0,.7,.04087,0,.90757],67:[0,.7,.1689,0,.66619],68:[0,.7,.09371,0,.77443],69:[0,.7,.18583,0,.56162],70:[0,.7,.13634,0,.89544],71:[0,.7,.17322,0,.60961],72:[0,.7,.29694,0,.96919],73:[0,.7,.19189,0,.80907],74:[.27778,.7,.19189,0,1.05159],75:[0,.7,.31259,0,.91364],76:[0,.7,.19189,0,.87373],77:[0,.7,.15981,0,1.08031],78:[0,.7,.3525,0,.9015],79:[0,.7,.08078,0,.73787],80:[0,.7,.08078,0,1.01262],81:[0,.7,.03305,0,.88282],82:[0,.7,.06259,0,.85],83:[0,.7,.19189,0,.86767],84:[0,.7,.29087,0,.74697],85:[0,.7,.25815,0,.79996],86:[0,.7,.27523,0,.62204],87:[0,.7,.27523,0,.80532],88:[0,.7,.26006,0,.94445],89:[0,.7,.2939,0,.70961],90:[0,.7,.24037,0,.8212],160:[0,0,0,0,.25]},"Size1-Regular":{32:[0,0,0,0,.25],40:[.35001,.85,0,0,.45834],41:[.35001,.85,0,0,.45834],47:[.35001,.85,0,0,.57778],91:[.35001,.85,0,0,.41667],92:[.35001,.85,0,0,.57778],93:[.35001,.85,0,0,.41667],123:[.35001,.85,0,0,.58334],125:[.35001,.85,0,0,.58334],160:[0,0,0,0,.25],710:[0,.72222,0,0,.55556],732:[0,.72222,0,0,.55556],770:[0,.72222,0,0,.55556],771:[0,.72222,0,0,.55556],8214:[-99e-5,.601,0,0,.77778],8593:[1e-5,.6,0,0,.66667],8595:[1e-5,.6,0,0,.66667],8657:[1e-5,.6,0,0,.77778],8659:[1e-5,.6,0,0,.77778],8719:[.25001,.75,0,0,.94445],8720:[.25001,.75,0,0,.94445],8721:[.25001,.75,0,0,1.05556],8730:[.35001,.85,0,0,1],8739:[-.00599,.606,0,0,.33333],8741:[-.00599,.606,0,0,.55556],8747:[.30612,.805,.19445,0,.47222],8748:[.306,.805,.19445,0,.47222],8749:[.306,.805,.19445,0,.47222],8750:[.30612,.805,.19445,0,.47222],8896:[.25001,.75,0,0,.83334],8897:[.25001,.75,0,0,.83334],8898:[.25001,.75,0,0,.83334],8899:[.25001,.75,0,0,.83334],8968:[.35001,.85,0,0,.47222],8969:[.35001,.85,0,0,.47222],8970:[.35001,.85,0,0,.47222],8971:[.35001,.85,0,0,.47222],9168:[-99e-5,.601,0,0,.66667],10216:[.35001,.85,0,0,.47222],10217:[.35001,.85,0,0,.47222],10752:[.25001,.75,0,0,1.11111],10753:[.25001,.75,0,0,1.11111],10754:[.25001,.75,0,0,1.11111],10756:[.25001,.75,0,0,.83334],10758:[.25001,.75,0,0,.83334]},"Size2-Regular":{32:[0,0,0,0,.25],40:[.65002,1.15,0,0,.59722],41:[.65002,1.15,0,0,.59722],47:[.65002,1.15,0,0,.81111],91:[.65002,1.15,0,0,.47222],92:[.65002,1.15,0,0,.81111],93:[.65002,1.15,0,0,.47222],123:[.65002,1.15,0,0,.66667],125:[.65002,1.15,0,0,.66667],160:[0,0,0,0,.25],710:[0,.75,0,0,1],732:[0,.75,0,0,1],770:[0,.75,0,0,1],771:[0,.75,0,0,1],8719:[.55001,1.05,0,0,1.27778],8720:[.55001,1.05,0,0,1.27778],8721:[.55001,1.05,0,0,1.44445],8730:[.65002,1.15,0,0,1],8747:[.86225,1.36,.44445,0,.55556],8748:[.862,1.36,.44445,0,.55556],8749:[.862,1.36,.44445,0,.55556],8750:[.86225,1.36,.44445,0,.55556],8896:[.55001,1.05,0,0,1.11111],8897:[.55001,1.05,0,0,1.11111],8898:[.55001,1.05,0,0,1.11111],8899:[.55001,1.05,0,0,1.11111],8968:[.65002,1.15,0,0,.52778],8969:[.65002,1.15,0,0,.52778],8970:[.65002,1.15,0,0,.52778],8971:[.65002,1.15,0,0,.52778],10216:[.65002,1.15,0,0,.61111],10217:[.65002,1.15,0,0,.61111],10752:[.55001,1.05,0,0,1.51112],10753:[.55001,1.05,0,0,1.51112],10754:[.55001,1.05,0,0,1.51112],10756:[.55001,1.05,0,0,1.11111],10758:[.55001,1.05,0,0,1.11111]},"Size3-Regular":{32:[0,0,0,0,.25],40:[.95003,1.45,0,0,.73611],41:[.95003,1.45,0,0,.73611],47:[.95003,1.45,0,0,1.04445],91:[.95003,1.45,0,0,.52778],92:[.95003,1.45,0,0,1.04445],93:[.95003,1.45,0,0,.52778],123:[.95003,1.45,0,0,.75],125:[.95003,1.45,0,0,.75],160:[0,0,0,0,.25],710:[0,.75,0,0,1.44445],732:[0,.75,0,0,1.44445],770:[0,.75,0,0,1.44445],771:[0,.75,0,0,1.44445],8730:[.95003,1.45,0,0,1],8968:[.95003,1.45,0,0,.58334],8969:[.95003,1.45,0,0,.58334],8970:[.95003,1.45,0,0,.58334],8971:[.95003,1.45,0,0,.58334],10216:[.95003,1.45,0,0,.75],10217:[.95003,1.45,0,0,.75]},"Size4-Regular":{32:[0,0,0,0,.25],40:[1.25003,1.75,0,0,.79167],41:[1.25003,1.75,0,0,.79167],47:[1.25003,1.75,0,0,1.27778],91:[1.25003,1.75,0,0,.58334],92:[1.25003,1.75,0,0,1.27778],93:[1.25003,1.75,0,0,.58334],123:[1.25003,1.75,0,0,.80556],125:[1.25003,1.75,0,0,.80556],160:[0,0,0,0,.25],710:[0,.825,0,0,1.8889],732:[0,.825,0,0,1.8889],770:[0,.825,0,0,1.8889],771:[0,.825,0,0,1.8889],8730:[1.25003,1.75,0,0,1],8968:[1.25003,1.75,0,0,.63889],8969:[1.25003,1.75,0,0,.63889],8970:[1.25003,1.75,0,0,.63889],8971:[1.25003,1.75,0,0,.63889],9115:[.64502,1.155,0,0,.875],9116:[1e-5,.6,0,0,.875],9117:[.64502,1.155,0,0,.875],9118:[.64502,1.155,0,0,.875],9119:[1e-5,.6,0,0,.875],9120:[.64502,1.155,0,0,.875],9121:[.64502,1.155,0,0,.66667],9122:[-99e-5,.601,0,0,.66667],9123:[.64502,1.155,0,0,.66667],9124:[.64502,1.155,0,0,.66667],9125:[-99e-5,.601,0,0,.66667],9126:[.64502,1.155,0,0,.66667],9127:[1e-5,.9,0,0,.88889],9128:[.65002,1.15,0,0,.88889],9129:[.90001,0,0,0,.88889],9130:[0,.3,0,0,.88889],9131:[1e-5,.9,0,0,.88889],9132:[.65002,1.15,0,0,.88889],9133:[.90001,0,0,0,.88889],9143:[.88502,.915,0,0,1.05556],10216:[1.25003,1.75,0,0,.80556],10217:[1.25003,1.75,0,0,.80556],57344:[-.00499,.605,0,0,1.05556],57345:[-.00499,.605,0,0,1.05556],57680:[0,.12,0,0,.45],57681:[0,.12,0,0,.45],57682:[0,.12,0,0,.45],57683:[0,.12,0,0,.45]},"Typewriter-Regular":{32:[0,0,0,0,.525],33:[0,.61111,0,0,.525],34:[0,.61111,0,0,.525],35:[0,.61111,0,0,.525],36:[.08333,.69444,0,0,.525],37:[.08333,.69444,0,0,.525],38:[0,.61111,0,0,.525],39:[0,.61111,0,0,.525],40:[.08333,.69444,0,0,.525],41:[.08333,.69444,0,0,.525],42:[0,.52083,0,0,.525],43:[-.08056,.53055,0,0,.525],44:[.13889,.125,0,0,.525],45:[-.08056,.53055,0,0,.525],46:[0,.125,0,0,.525],47:[.08333,.69444,0,0,.525],48:[0,.61111,0,0,.525],49:[0,.61111,0,0,.525],50:[0,.61111,0,0,.525],51:[0,.61111,0,0,.525],52:[0,.61111,0,0,.525],53:[0,.61111,0,0,.525],54:[0,.61111,0,0,.525],55:[0,.61111,0,0,.525],56:[0,.61111,0,0,.525],57:[0,.61111,0,0,.525],58:[0,.43056,0,0,.525],59:[.13889,.43056,0,0,.525],60:[-.05556,.55556,0,0,.525],61:[-.19549,.41562,0,0,.525],62:[-.05556,.55556,0,0,.525],63:[0,.61111,0,0,.525],64:[0,.61111,0,0,.525],65:[0,.61111,0,0,.525],66:[0,.61111,0,0,.525],67:[0,.61111,0,0,.525],68:[0,.61111,0,0,.525],69:[0,.61111,0,0,.525],70:[0,.61111,0,0,.525],71:[0,.61111,0,0,.525],72:[0,.61111,0,0,.525],73:[0,.61111,0,0,.525],74:[0,.61111,0,0,.525],75:[0,.61111,0,0,.525],76:[0,.61111,0,0,.525],77:[0,.61111,0,0,.525],78:[0,.61111,0,0,.525],79:[0,.61111,0,0,.525],80:[0,.61111,0,0,.525],81:[.13889,.61111,0,0,.525],82:[0,.61111,0,0,.525],83:[0,.61111,0,0,.525],84:[0,.61111,0,0,.525],85:[0,.61111,0,0,.525],86:[0,.61111,0,0,.525],87:[0,.61111,0,0,.525],88:[0,.61111,0,0,.525],89:[0,.61111,0,0,.525],90:[0,.61111,0,0,.525],91:[.08333,.69444,0,0,.525],92:[.08333,.69444,0,0,.525],93:[.08333,.69444,0,0,.525],94:[0,.61111,0,0,.525],95:[.09514,0,0,0,.525],96:[0,.61111,0,0,.525],97:[0,.43056,0,0,.525],98:[0,.61111,0,0,.525],99:[0,.43056,0,0,.525],100:[0,.61111,0,0,.525],101:[0,.43056,0,0,.525],102:[0,.61111,0,0,.525],103:[.22222,.43056,0,0,.525],104:[0,.61111,0,0,.525],105:[0,.61111,0,0,.525],106:[.22222,.61111,0,0,.525],107:[0,.61111,0,0,.525],108:[0,.61111,0,0,.525],109:[0,.43056,0,0,.525],110:[0,.43056,0,0,.525],111:[0,.43056,0,0,.525],112:[.22222,.43056,0,0,.525],113:[.22222,.43056,0,0,.525],114:[0,.43056,0,0,.525],115:[0,.43056,0,0,.525],116:[0,.55358,0,0,.525],117:[0,.43056,0,0,.525],118:[0,.43056,0,0,.525],119:[0,.43056,0,0,.525],120:[0,.43056,0,0,.525],121:[.22222,.43056,0,0,.525],122:[0,.43056,0,0,.525],123:[.08333,.69444,0,0,.525],124:[.08333,.69444,0,0,.525],125:[.08333,.69444,0,0,.525],126:[0,.61111,0,0,.525],127:[0,.61111,0,0,.525],160:[0,0,0,0,.525],176:[0,.61111,0,0,.525],184:[.19445,0,0,0,.525],305:[0,.43056,0,0,.525],567:[.22222,.43056,0,0,.525],711:[0,.56597,0,0,.525],713:[0,.56555,0,0,.525],714:[0,.61111,0,0,.525],715:[0,.61111,0,0,.525],728:[0,.61111,0,0,.525],730:[0,.61111,0,0,.525],770:[0,.61111,0,0,.525],771:[0,.61111,0,0,.525],776:[0,.61111,0,0,.525],915:[0,.61111,0,0,.525],916:[0,.61111,0,0,.525],920:[0,.61111,0,0,.525],923:[0,.61111,0,0,.525],926:[0,.61111,0,0,.525],928:[0,.61111,0,0,.525],931:[0,.61111,0,0,.525],933:[0,.61111,0,0,.525],934:[0,.61111,0,0,.525],936:[0,.61111,0,0,.525],937:[0,.61111,0,0,.525],8216:[0,.61111,0,0,.525],8217:[0,.61111,0,0,.525],8242:[0,.61111,0,0,.525],9251:[.11111,.21944,0,0,.525]}},$t={slant:[.25,.25,.25],space:[0,0,0],stretch:[0,0,0],shrink:[0,0,0],xHeight:[.431,.431,.431],quad:[1,1.171,1.472],extraSpace:[0,0,0],num1:[.677,.732,.925],num2:[.394,.384,.387],num3:[.444,.471,.504],denom1:[.686,.752,1.025],denom2:[.345,.344,.532],sup1:[.413,.503,.504],sup2:[.363,.431,.404],sup3:[.289,.286,.294],sub1:[.15,.143,.2],sub2:[.247,.286,.4],supDrop:[.386,.353,.494],subDrop:[.05,.071,.1],delim1:[2.39,1.7,1.98],delim2:[1.01,1.157,1.42],axisHeight:[.25,.25,.25],defaultRuleThickness:[.04,.049,.049],bigOpSpacing1:[.111,.111,.111],bigOpSpacing2:[.166,.166,.166],bigOpSpacing3:[.2,.2,.2],bigOpSpacing4:[.6,.611,.611],bigOpSpacing5:[.1,.143,.143],sqrtRuleThickness:[.04,.04,.04],ptPerEm:[10,10,10],doubleRuleSep:[.2,.2,.2],arrayRuleWidth:[.04,.04,.04],fboxsep:[.3,.3,.3],fboxrule:[.04,.04,.04]},e0={Å:"A",Ð:"D",Þ:"o",å:"a",ð:"d",þ:"o",А:"A",Б:"B",В:"B",Г:"F",Д:"A",Е:"E",Ж:"K",З:"3",И:"N",Й:"N",К:"K",Л:"N",М:"M",Н:"H",О:"O",П:"N",Р:"P",С:"C",Т:"T",У:"y",Ф:"O",Х:"X",Ц:"U",Ч:"h",Ш:"W",Щ:"W",Ъ:"B",Ы:"X",Ь:"B",Э:"3",Ю:"X",Я:"R",а:"a",б:"b",в:"a",г:"r",д:"y",е:"e",ж:"m",з:"e",и:"n",й:"n",к:"n",л:"n",м:"m",н:"n",о:"o",п:"n",р:"p",с:"c",т:"o",у:"y",ф:"b",х:"x",ц:"n",ч:"n",ш:"w",щ:"w",ъ:"a",ы:"m",ь:"a",э:"e",ю:"m",я:"r"};function t0(u,e){bt[u]=e}function Dt(u,e,t){if(!bt[e])throw new Error("Font metrics not found for font: "+e+".");var r=u.charCodeAt(0),n=bt[e][r];if(!n&&u[0]in e0&&(r=e0[u[0]].charCodeAt(0),n=bt[e][r]),!n&&t==="text"&&gt(r)&&(n=bt[e][77]),n)return{depth:n[0],height:n[1],italic:n[2],skew:n[3],width:n[4]}}var z0={};function or(u){var e;if(u>=5?e=0:u>=3?e=1:e=2,!z0[e]){var t=z0[e]={cssEmPerMu:$t.quad[e]/18};for(var r in $t)$t.hasOwnProperty(r)&&(t[r]=$t[r][e])}return z0[e]}var ur=[[1,1,1],[2,1,1],[3,1,1],[4,2,1],[5,2,1],[6,3,1],[7,4,2],[8,6,3],[9,7,6],[10,8,7],[11,10,9]],E0=[.5,.6,.7,.8,.9,1,1.2,1.44,1.728,2.074,2.488],Z0=function(e,t){return t.size<2?e:ur[e-1][t.size-1]},w0=function(){function u(t){this.style=void 0,this.color=void 0,this.size=void 0,this.textSize=void 0,this.phantom=void 0,this.font=void 0,this.fontFamily=void 0,this.fontWeight=void 0,this.fontShape=void 0,this.sizeMultiplier=void 0,this.maxSize=void 0,this.minRuleThickness=void 0,this._fontMetrics=void 0,this.style=t.style,this.color=t.color,this.size=t.size||u.BASESIZE,this.textSize=t.textSize||this.size,this.phantom=!!t.phantom,this.font=t.font||"",this.fontFamily=t.fontFamily||"",this.fontWeight=t.fontWeight||"",this.fontShape=t.fontShape||"",this.sizeMultiplier=E0[this.size-1],this.maxSize=t.maxSize,this.minRuleThickness=t.minRuleThickness,this._fontMetrics=void 0}var e=u.prototype;return e.extend=function(r){var n={style:this.style,size:this.size,textSize:this.textSize,color:this.color,phantom:this.phantom,font:this.font,fontFamily:this.fontFamily,fontWeight:this.fontWeight,fontShape:this.fontShape,maxSize:this.maxSize,minRuleThickness:this.minRuleThickness};for(var i in r)r.hasOwnProperty(i)&&(n[i]=r[i]);return new u(n)},e.havingStyle=function(r){return this.style===r?this:this.extend({style:r,size:Z0(this.textSize,r)})},e.havingCrampedStyle=function(){return this.havingStyle(this.style.cramp())},e.havingSize=function(r){return this.size===r&&this.textSize===r?this:this.extend({style:this.style.text(),size:r,textSize:r,sizeMultiplier:E0[r-1]})},e.havingBaseStyle=function(r){r=r||this.style.text();var n=Z0(u.BASESIZE,r);return this.size===n&&this.textSize===u.BASESIZE&&this.style===r?this:this.extend({style:r,size:n})},e.havingBaseSizing=function(){var r;switch(this.style.id){case 4:case 5:r=3;break;case 6:case 7:r=1;break;default:r=6}return this.extend({style:this.style.text(),size:r})},e.withColor=function(r){return this.extend({color:r})},e.withPhantom=function(){return this.extend({phantom:!0})},e.withFont=function(r){return this.extend({font:r})},e.withTextFontFamily=function(r){return this.extend({fontFamily:r,font:""})},e.withTextFontWeight=function(r){return this.extend({fontWeight:r,font:""})},e.withTextFontShape=function(r){return this.extend({fontShape:r,font:""})},e.sizingClasses=function(r){return r.size!==this.size?["sizing","reset-size"+r.size,"size"+this.size]:[]},e.baseSizingClasses=function(){return this.size!==u.BASESIZE?["sizing","reset-size"+this.size,"size"+u.BASESIZE]:[]},e.fontMetrics=function(){return this._fontMetrics||(this._fontMetrics=or(this.size)),this._fontMetrics},e.getColor=function(){return this.phantom?"transparent":this.color},u}();w0.BASESIZE=6;var C0=w0,mt={pt:1,mm:7227/2540,cm:7227/254,in:72.27,bp:803/800,pc:12,dd:1238/1157,cc:14856/1157,nd:685/642,nc:1370/107,sp:1/65536,px:803/800},r0={ex:!0,em:!0,mu:!0},B0=function(e){return typeof e!="string"&&(e=e.unit),e in mt||e in r0||e==="ex"},Be=function(e,t){var r;if(e.unit in mt)r=mt[e.unit]/t.fontMetrics().ptPerEm/t.sizeMultiplier;else if(e.unit==="mu")r=t.fontMetrics().cssEmPerMu;else{var n;if(t.style.isTight()?n=t.havingStyle(t.style.text()):n=t,e.unit==="ex")r=n.fontMetrics().xHeight;else if(e.unit==="em")r=n.fontMetrics().quad;else throw new p("Invalid unit: '"+e.unit+"'");n!==t&&(r*=n.sizeMultiplier/t.sizeMultiplier)}return Math.min(e.number*r,t.maxSize)},Q=function(e){return+e.toFixed(4)+"em"},et=function(e){return e.filter(function(t){return t}).join(" ")},cr=function(e,t,r){if(this.classes=e||[],this.attributes={},this.height=0,this.depth=0,this.maxFontSize=0,this.style=r||{},t){t.style.isTight()&&this.classes.push("mtight");var n=t.getColor();n&&(this.style.color=n)}},hr=function(e){var t=document.createElement(e);t.className=et(this.classes);for(var r in this.style)this.style.hasOwnProperty(r)&&(t.style[r]=this.style[r]);for(var n in this.attributes)this.attributes.hasOwnProperty(n)&&t.setAttribute(n,this.attributes[n]);for(var i=0;i<this.children.length;i++)t.appendChild(this.children[i].toNode());return t},We=function(e){var t="<"+e;this.classes.length&&(t+=' class="'+O.escape(et(this.classes))+'"');var r="";for(var n in this.style)this.style.hasOwnProperty(n)&&(r+=O.hyphenate(n)+":"+this.style[n]+";");r&&(t+=' style="'+O.escape(r)+'"');for(var i in this.attributes)this.attributes.hasOwnProperty(i)&&(t+=" "+i+'="'+O.escape(this.attributes[i])+'"');t+=">";for(var c=0;c<this.children.length;c++)t+=this.children[c].toMarkup();return t+="</"+e+">",t},yt=function(){function u(t,r,n,i){this.children=void 0,this.attributes=void 0,this.classes=void 0,this.height=void 0,this.depth=void 0,this.width=void 0,this.maxFontSize=void 0,this.style=void 0,cr.call(this,t,n,i),this.children=r||[]}var e=u.prototype;return e.setAttribute=function(r,n){this.attributes[r]=n},e.hasClass=function(r){return O.contains(this.classes,r)},e.toNode=function(){return hr.call(this,"span")},e.toMarkup=function(){return We.call(this,"span")},u}(),K0=function(){function u(t,r,n,i){this.children=void 0,this.attributes=void 0,this.classes=void 0,this.height=void 0,this.depth=void 0,this.maxFontSize=void 0,this.style=void 0,cr.call(this,r,i),this.children=n||[],this.setAttribute("href",t)}var e=u.prototype;return e.setAttribute=function(r,n){this.attributes[r]=n},e.hasClass=function(r){return O.contains(this.classes,r)},e.toNode=function(){return hr.call(this,"a")},e.toMarkup=function(){return We.call(this,"a")},u}(),mr=function(){function u(t,r,n){this.src=void 0,this.alt=void 0,this.classes=void 0,this.height=void 0,this.depth=void 0,this.maxFontSize=void 0,this.style=void 0,this.alt=r,this.src=t,this.classes=["mord"],this.style=n}var e=u.prototype;return e.hasClass=function(r){return O.contains(this.classes,r)},e.toNode=function(){var r=document.createElement("img");r.src=this.src,r.alt=this.alt,r.className="mord";for(var n in this.style)this.style.hasOwnProperty(n)&&(r.style[n]=this.style[n]);return r},e.toMarkup=function(){var r="<img  src='"+this.src+" 'alt='"+this.alt+"' ",n="";for(var i in this.style)this.style.hasOwnProperty(i)&&(n+=O.hyphenate(i)+":"+this.style[i]+";");return n&&(r+=' style="'+O.escape(n)+'"'),r+="'/>",r},u}(),Q0={î:"ı̂",ï:"ı̈",í:"ı́",ì:"ı̀"},nt=function(){function u(t,r,n,i,c,f,b,x){this.text=void 0,this.height=void 0,this.depth=void 0,this.italic=void 0,this.skew=void 0,this.width=void 0,this.maxFontSize=void 0,this.classes=void 0,this.style=void 0,this.text=t,this.height=r||0,this.depth=n||0,this.italic=i||0,this.skew=c||0,this.width=f||0,this.classes=b||[],this.style=x||{},this.maxFontSize=0;var _=Ie(this.text.charCodeAt(0));_&&this.classes.push(_+"_fallback"),/[îïíì]/.test(this.text)&&(this.text=Q0[this.text])}var e=u.prototype;return e.hasClass=function(r){return O.contains(this.classes,r)},e.toNode=function(){var r=document.createTextNode(this.text),n=null;this.italic>0&&(n=document.createElement("span"),n.style.marginRight=Q(this.italic)),this.classes.length>0&&(n=n||document.createElement("span"),n.className=et(this.classes));for(var i in this.style)this.style.hasOwnProperty(i)&&(n=n||document.createElement("span"),n.style[i]=this.style[i]);return n?(n.appendChild(r),n):r},e.toMarkup=function(){var r=!1,n="<span";this.classes.length&&(r=!0,n+=' class="',n+=O.escape(et(this.classes)),n+='"');var i="";this.italic>0&&(i+="margin-right:"+this.italic+"em;");for(var c in this.style)this.style.hasOwnProperty(c)&&(i+=O.hyphenate(c)+":"+this.style[c]+";");i&&(r=!0,n+=' style="'+O.escape(i)+'"');var f=O.escape(this.text);return r?(n+=">",n+=f,n+="</span>",n):f},u}(),wt=function(){function u(t,r){this.children=void 0,this.attributes=void 0,this.children=t||[],this.attributes=r||{}}var e=u.prototype;return e.toNode=function(){var r="http://www.w3.org/2000/svg",n=document.createElementNS(r,"svg");for(var i in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,i)&&n.setAttribute(i,this.attributes[i]);for(var c=0;c<this.children.length;c++)n.appendChild(this.children[c].toNode());return n},e.toMarkup=function(){var r='<svg xmlns="http://www.w3.org/2000/svg"';for(var n in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,n)&&(r+=" "+n+"='"+this.attributes[n]+"'");r+=">";for(var i=0;i<this.children.length;i++)r+=this.children[i].toMarkup();return r+="</svg>",r},u}(),Nt=function(){function u(t,r){this.pathName=void 0,this.alternate=void 0,this.pathName=t,this.alternate=r}var e=u.prototype;return e.toNode=function(){var r="http://www.w3.org/2000/svg",n=document.createElementNS(r,"path");return this.alternate?n.setAttribute("d",this.alternate):n.setAttribute("d",y0[this.pathName]),n},e.toMarkup=function(){return this.alternate?"<path d='"+this.alternate+"'/>":"<path d='"+y0[this.pathName]+"'/>"},u}(),n0=function(){function u(t){this.attributes=void 0,this.attributes=t||{}}var e=u.prototype;return e.toNode=function(){var r="http://www.w3.org/2000/svg",n=document.createElementNS(r,"line");for(var i in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,i)&&n.setAttribute(i,this.attributes[i]);return n},e.toMarkup=function(){var r="<line";for(var n in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,n)&&(r+=" "+n+"='"+this.attributes[n]+"'");return r+="/>",r},u}();function D0(u){if(u instanceof nt)return u;throw new Error("Expected symbolNode but got "+String(u)+".")}function Hr(u){if(u instanceof yt)return u;throw new Error("Expected span<HtmlDomNode> but got "+String(u)+".")}var Wt={bin:1,close:1,inner:1,open:1,punct:1,rel:1},J0={"accent-token":1,mathord:1,"op-token":1,spacing:1,textord:1},x0={math:{},text:{}},Fe=x0;function o(u,e,t,r,n,i){x0[u][n]={font:e,group:t,replace:r},i&&r&&(x0[u][r]=x0[u][n])}var h="math",H="text",d="main",A="ams",De="accent-token",ne="bin",Je="close",ue="inner",T="mathord",P="op-token",J="open",ke="punct",S="rel",Ue="spacing",M="textord";o(h,d,S,"≡","\\equiv",!0),o(h,d,S,"≺","\\prec",!0),o(h,d,S,"≻","\\succ",!0),o(h,d,S,"∼","\\sim",!0),o(h,d,S,"⊥","\\perp"),o(h,d,S,"⪯","\\preceq",!0),o(h,d,S,"⪰","\\succeq",!0),o(h,d,S,"≃","\\simeq",!0),o(h,d,S,"∣","\\mid",!0),o(h,d,S,"≪","\\ll",!0),o(h,d,S,"≫","\\gg",!0),o(h,d,S,"≍","\\asymp",!0),o(h,d,S,"∥","\\parallel"),o(h,d,S,"⋈","\\bowtie",!0),o(h,d,S,"⌣","\\smile",!0),o(h,d,S,"⊑","\\sqsubseteq",!0),o(h,d,S,"⊒","\\sqsupseteq",!0),o(h,d,S,"≐","\\doteq",!0),o(h,d,S,"⌢","\\frown",!0),o(h,d,S,"∋","\\ni",!0),o(h,d,S,"∝","\\propto",!0),o(h,d,S,"⊢","\\vdash",!0),o(h,d,S,"⊣","\\dashv",!0),o(h,d,S,"∋","\\owns"),o(h,d,ke,".","\\ldotp"),o(h,d,ke,"⋅","\\cdotp"),o(h,d,M,"#","\\#"),o(H,d,M,"#","\\#"),o(h,d,M,"&","\\&"),o(H,d,M,"&","\\&"),o(h,d,M,"ℵ","\\aleph",!0),o(h,d,M,"∀","\\forall",!0),o(h,d,M,"ℏ","\\hbar",!0),o(h,d,M,"∃","\\exists",!0),o(h,d,M,"∇","\\nabla",!0),o(h,d,M,"♭","\\flat",!0),o(h,d,M,"ℓ","\\ell",!0),o(h,d,M,"♮","\\natural",!0),o(h,d,M,"♣","\\clubsuit",!0),o(h,d,M,"℘","\\wp",!0),o(h,d,M,"♯","\\sharp",!0),o(h,d,M,"♢","\\diamondsuit",!0),o(h,d,M,"ℜ","\\Re",!0),o(h,d,M,"♡","\\heartsuit",!0),o(h,d,M,"ℑ","\\Im",!0),o(h,d,M,"♠","\\spadesuit",!0),o(h,d,M,"§","\\S",!0),o(H,d,M,"§","\\S"),o(h,d,M,"¶","\\P",!0),o(H,d,M,"¶","\\P"),o(h,d,M,"†","\\dag"),o(H,d,M,"†","\\dag"),o(H,d,M,"†","\\textdagger"),o(h,d,M,"‡","\\ddag"),o(H,d,M,"‡","\\ddag"),o(H,d,M,"‡","\\textdaggerdbl"),o(h,d,Je,"⎱","\\rmoustache",!0),o(h,d,J,"⎰","\\lmoustache",!0),o(h,d,Je,"⟯","\\rgroup",!0),o(h,d,J,"⟮","\\lgroup",!0),o(h,d,ne,"∓","\\mp",!0),o(h,d,ne,"⊖","\\ominus",!0),o(h,d,ne,"⊎","\\uplus",!0),o(h,d,ne,"⊓","\\sqcap",!0),o(h,d,ne,"∗","\\ast"),o(h,d,ne,"⊔","\\sqcup",!0),o(h,d,ne,"◯","\\bigcirc",!0),o(h,d,ne,"∙","\\bullet",!0),o(h,d,ne,"‡","\\ddagger"),o(h,d,ne,"≀","\\wr",!0),o(h,d,ne,"⨿","\\amalg"),o(h,d,ne,"&","\\And"),o(h,d,S,"⟵","\\longleftarrow",!0),o(h,d,S,"⇐","\\Leftarrow",!0),o(h,d,S,"⟸","\\Longleftarrow",!0),o(h,d,S,"⟶","\\longrightarrow",!0),o(h,d,S,"⇒","\\Rightarrow",!0),o(h,d,S,"⟹","\\Longrightarrow",!0),o(h,d,S,"↔","\\leftrightarrow",!0),o(h,d,S,"⟷","\\longleftrightarrow",!0),o(h,d,S,"⇔","\\Leftrightarrow",!0),o(h,d,S,"⟺","\\Longleftrightarrow",!0),o(h,d,S,"↦","\\mapsto",!0),o(h,d,S,"⟼","\\longmapsto",!0),o(h,d,S,"↗","\\nearrow",!0),o(h,d,S,"↩","\\hookleftarrow",!0),o(h,d,S,"↪","\\hookrightarrow",!0),o(h,d,S,"↘","\\searrow",!0),o(h,d,S,"↼","\\leftharpoonup",!0),o(h,d,S,"⇀","\\rightharpoonup",!0),o(h,d,S,"↙","\\swarrow",!0),o(h,d,S,"↽","\\leftharpoondown",!0),o(h,d,S,"⇁","\\rightharpoondown",!0),o(h,d,S,"↖","\\nwarrow",!0),o(h,d,S,"⇌","\\rightleftharpoons",!0),o(h,A,S,"≮","\\nless",!0),o(h,A,S,"","\\@nleqslant"),o(h,A,S,"","\\@nleqq"),o(h,A,S,"⪇","\\lneq",!0),o(h,A,S,"≨","\\lneqq",!0),o(h,A,S,"","\\@lvertneqq"),o(h,A,S,"⋦","\\lnsim",!0),o(h,A,S,"⪉","\\lnapprox",!0),o(h,A,S,"⊀","\\nprec",!0),o(h,A,S,"⋠","\\npreceq",!0),o(h,A,S,"⋨","\\precnsim",!0),o(h,A,S,"⪹","\\precnapprox",!0),o(h,A,S,"≁","\\nsim",!0),o(h,A,S,"","\\@nshortmid"),o(h,A,S,"∤","\\nmid",!0),o(h,A,S,"⊬","\\nvdash",!0),o(h,A,S,"⊭","\\nvDash",!0),o(h,A,S,"⋪","\\ntriangleleft"),o(h,A,S,"⋬","\\ntrianglelefteq",!0),o(h,A,S,"⊊","\\subsetneq",!0),o(h,A,S,"","\\@varsubsetneq"),o(h,A,S,"⫋","\\subsetneqq",!0),o(h,A,S,"","\\@varsubsetneqq"),o(h,A,S,"≯","\\ngtr",!0),o(h,A,S,"","\\@ngeqslant"),o(h,A,S,"","\\@ngeqq"),o(h,A,S,"⪈","\\gneq",!0),o(h,A,S,"≩","\\gneqq",!0),o(h,A,S,"","\\@gvertneqq"),o(h,A,S,"⋧","\\gnsim",!0),o(h,A,S,"⪊","\\gnapprox",!0),o(h,A,S,"⊁","\\nsucc",!0),o(h,A,S,"⋡","\\nsucceq",!0),o(h,A,S,"⋩","\\succnsim",!0),o(h,A,S,"⪺","\\succnapprox",!0),o(h,A,S,"≆","\\ncong",!0),o(h,A,S,"","\\@nshortparallel"),o(h,A,S,"∦","\\nparallel",!0),o(h,A,S,"⊯","\\nVDash",!0),o(h,A,S,"⋫","\\ntriangleright"),o(h,A,S,"⋭","\\ntrianglerighteq",!0),o(h,A,S,"","\\@nsupseteqq"),o(h,A,S,"⊋","\\supsetneq",!0),o(h,A,S,"","\\@varsupsetneq"),o(h,A,S,"⫌","\\supsetneqq",!0),o(h,A,S,"","\\@varsupsetneqq"),o(h,A,S,"⊮","\\nVdash",!0),o(h,A,S,"⪵","\\precneqq",!0),o(h,A,S,"⪶","\\succneqq",!0),o(h,A,S,"","\\@nsubseteqq"),o(h,A,ne,"⊴","\\unlhd"),o(h,A,ne,"⊵","\\unrhd"),o(h,A,S,"↚","\\nleftarrow",!0),o(h,A,S,"↛","\\nrightarrow",!0),o(h,A,S,"⇍","\\nLeftarrow",!0),o(h,A,S,"⇏","\\nRightarrow",!0),o(h,A,S,"↮","\\nleftrightarrow",!0),o(h,A,S,"⇎","\\nLeftrightarrow",!0),o(h,A,S,"△","\\vartriangle"),o(h,A,M,"ℏ","\\hslash"),o(h,A,M,"▽","\\triangledown"),o(h,A,M,"◊","\\lozenge"),o(h,A,M,"Ⓢ","\\circledS"),o(h,A,M,"®","\\circledR"),o(H,A,M,"®","\\circledR"),o(h,A,M,"∡","\\measuredangle",!0),o(h,A,M,"∄","\\nexists"),o(h,A,M,"℧","\\mho"),o(h,A,M,"Ⅎ","\\Finv",!0),o(h,A,M,"⅁","\\Game",!0),o(h,A,M,"‵","\\backprime"),o(h,A,M,"▲","\\blacktriangle"),o(h,A,M,"▼","\\blacktriangledown"),o(h,A,M,"■","\\blacksquare"),o(h,A,M,"⧫","\\blacklozenge"),o(h,A,M,"★","\\bigstar"),o(h,A,M,"∢","\\sphericalangle",!0),o(h,A,M,"∁","\\complement",!0),o(h,A,M,"ð","\\eth",!0),o(H,d,M,"ð","ð"),o(h,A,M,"╱","\\diagup"),o(h,A,M,"╲","\\diagdown"),o(h,A,M,"□","\\square"),o(h,A,M,"□","\\Box"),o(h,A,M,"◊","\\Diamond"),o(h,A,M,"¥","\\yen",!0),o(H,A,M,"¥","\\yen",!0),o(h,A,M,"✓","\\checkmark",!0),o(H,A,M,"✓","\\checkmark"),o(h,A,M,"ℶ","\\beth",!0),o(h,A,M,"ℸ","\\daleth",!0),o(h,A,M,"ℷ","\\gimel",!0),o(h,A,M,"ϝ","\\digamma",!0),o(h,A,M,"ϰ","\\varkappa"),o(h,A,J,"┌","\\@ulcorner",!0),o(h,A,Je,"┐","\\@urcorner",!0),o(h,A,J,"└","\\@llcorner",!0),o(h,A,Je,"┘","\\@lrcorner",!0),o(h,A,S,"≦","\\leqq",!0),o(h,A,S,"⩽","\\leqslant",!0),o(h,A,S,"⪕","\\eqslantless",!0),o(h,A,S,"≲","\\lesssim",!0),o(h,A,S,"⪅","\\lessapprox",!0),o(h,A,S,"≊","\\approxeq",!0),o(h,A,ne,"⋖","\\lessdot"),o(h,A,S,"⋘","\\lll",!0),o(h,A,S,"≶","\\lessgtr",!0),o(h,A,S,"⋚","\\lesseqgtr",!0),o(h,A,S,"⪋","\\lesseqqgtr",!0),o(h,A,S,"≑","\\doteqdot"),o(h,A,S,"≓","\\risingdotseq",!0),o(h,A,S,"≒","\\fallingdotseq",!0),o(h,A,S,"∽","\\backsim",!0),o(h,A,S,"⋍","\\backsimeq",!0),o(h,A,S,"⫅","\\subseteqq",!0),o(h,A,S,"⋐","\\Subset",!0),o(h,A,S,"⊏","\\sqsubset",!0),o(h,A,S,"≼","\\preccurlyeq",!0),o(h,A,S,"⋞","\\curlyeqprec",!0),o(h,A,S,"≾","\\precsim",!0),o(h,A,S,"⪷","\\precapprox",!0),o(h,A,S,"⊲","\\vartriangleleft"),o(h,A,S,"⊴","\\trianglelefteq"),o(h,A,S,"⊨","\\vDash",!0),o(h,A,S,"⊪","\\Vvdash",!0),o(h,A,S,"⌣","\\smallsmile"),o(h,A,S,"⌢","\\smallfrown"),o(h,A,S,"≏","\\bumpeq",!0),o(h,A,S,"≎","\\Bumpeq",!0),o(h,A,S,"≧","\\geqq",!0),o(h,A,S,"⩾","\\geqslant",!0),o(h,A,S,"⪖","\\eqslantgtr",!0),o(h,A,S,"≳","\\gtrsim",!0),o(h,A,S,"⪆","\\gtrapprox",!0),o(h,A,ne,"⋗","\\gtrdot"),o(h,A,S,"⋙","\\ggg",!0),o(h,A,S,"≷","\\gtrless",!0),o(h,A,S,"⋛","\\gtreqless",!0),o(h,A,S,"⪌","\\gtreqqless",!0),o(h,A,S,"≖","\\eqcirc",!0),o(h,A,S,"≗","\\circeq",!0),o(h,A,S,"≜","\\triangleq",!0),o(h,A,S,"∼","\\thicksim"),o(h,A,S,"≈","\\thickapprox"),o(h,A,S,"⫆","\\supseteqq",!0),o(h,A,S,"⋑","\\Supset",!0),o(h,A,S,"⊐","\\sqsupset",!0),o(h,A,S,"≽","\\succcurlyeq",!0),o(h,A,S,"⋟","\\curlyeqsucc",!0),o(h,A,S,"≿","\\succsim",!0),o(h,A,S,"⪸","\\succapprox",!0),o(h,A,S,"⊳","\\vartriangleright"),o(h,A,S,"⊵","\\trianglerighteq"),o(h,A,S,"⊩","\\Vdash",!0),o(h,A,S,"∣","\\shortmid"),o(h,A,S,"∥","\\shortparallel"),o(h,A,S,"≬","\\between",!0),o(h,A,S,"⋔","\\pitchfork",!0),o(h,A,S,"∝","\\varpropto"),o(h,A,S,"◀","\\blacktriangleleft"),o(h,A,S,"∴","\\therefore",!0),o(h,A,S,"∍","\\backepsilon"),o(h,A,S,"▶","\\blacktriangleright"),o(h,A,S,"∵","\\because",!0),o(h,A,S,"⋘","\\llless"),o(h,A,S,"⋙","\\gggtr"),o(h,A,ne,"⊲","\\lhd"),o(h,A,ne,"⊳","\\rhd"),o(h,A,S,"≂","\\eqsim",!0),o(h,d,S,"⋈","\\Join"),o(h,A,S,"≑","\\Doteq",!0),o(h,A,ne,"∔","\\dotplus",!0),o(h,A,ne,"∖","\\smallsetminus"),o(h,A,ne,"⋒","\\Cap",!0),o(h,A,ne,"⋓","\\Cup",!0),o(h,A,ne,"⩞","\\doublebarwedge",!0),o(h,A,ne,"⊟","\\boxminus",!0),o(h,A,ne,"⊞","\\boxplus",!0),o(h,A,ne,"⋇","\\divideontimes",!0),o(h,A,ne,"⋉","\\ltimes",!0),o(h,A,ne,"⋊","\\rtimes",!0),o(h,A,ne,"⋋","\\leftthreetimes",!0),o(h,A,ne,"⋌","\\rightthreetimes",!0),o(h,A,ne,"⋏","\\curlywedge",!0),o(h,A,ne,"⋎","\\curlyvee",!0),o(h,A,ne,"⊝","\\circleddash",!0),o(h,A,ne,"⊛","\\circledast",!0),o(h,A,ne,"⋅","\\centerdot"),o(h,A,ne,"⊺","\\intercal",!0),o(h,A,ne,"⋒","\\doublecap"),o(h,A,ne,"⋓","\\doublecup"),o(h,A,ne,"⊠","\\boxtimes",!0),o(h,A,S,"⇢","\\dashrightarrow",!0),o(h,A,S,"⇠","\\dashleftarrow",!0),o(h,A,S,"⇇","\\leftleftarrows",!0),o(h,A,S,"⇆","\\leftrightarrows",!0),o(h,A,S,"⇚","\\Lleftarrow",!0),o(h,A,S,"↞","\\twoheadleftarrow",!0),o(h,A,S,"↢","\\leftarrowtail",!0),o(h,A,S,"↫","\\looparrowleft",!0),o(h,A,S,"⇋","\\leftrightharpoons",!0),o(h,A,S,"↶","\\curvearrowleft",!0),o(h,A,S,"↺","\\circlearrowleft",!0),o(h,A,S,"↰","\\Lsh",!0),o(h,A,S,"⇈","\\upuparrows",!0),o(h,A,S,"↿","\\upharpoonleft",!0),o(h,A,S,"⇃","\\downharpoonleft",!0),o(h,d,S,"⊶","\\origof",!0),o(h,d,S,"⊷","\\imageof",!0),o(h,A,S,"⊸","\\multimap",!0),o(h,A,S,"↭","\\leftrightsquigarrow",!0),o(h,A,S,"⇉","\\rightrightarrows",!0),o(h,A,S,"⇄","\\rightleftarrows",!0),o(h,A,S,"↠","\\twoheadrightarrow",!0),o(h,A,S,"↣","\\rightarrowtail",!0),o(h,A,S,"↬","\\looparrowright",!0),o(h,A,S,"↷","\\curvearrowright",!0),o(h,A,S,"↻","\\circlearrowright",!0),o(h,A,S,"↱","\\Rsh",!0),o(h,A,S,"⇊","\\downdownarrows",!0),o(h,A,S,"↾","\\upharpoonright",!0),o(h,A,S,"⇂","\\downharpoonright",!0),o(h,A,S,"⇝","\\rightsquigarrow",!0),o(h,A,S,"⇝","\\leadsto"),o(h,A,S,"⇛","\\Rrightarrow",!0),o(h,A,S,"↾","\\restriction"),o(h,d,M,"‘","`"),o(h,d,M,"$","\\$"),o(H,d,M,"$","\\$"),o(H,d,M,"$","\\textdollar"),o(h,d,M,"%","\\%"),o(H,d,M,"%","\\%"),o(h,d,M,"_","\\_"),o(H,d,M,"_","\\_"),o(H,d,M,"_","\\textunderscore"),o(h,d,M,"∠","\\angle",!0),o(h,d,M,"∞","\\infty",!0),o(h,d,M,"′","\\prime"),o(h,d,M,"△","\\triangle"),o(h,d,M,"Γ","\\Gamma",!0),o(h,d,M,"Δ","\\Delta",!0),o(h,d,M,"Θ","\\Theta",!0),o(h,d,M,"Λ","\\Lambda",!0),o(h,d,M,"Ξ","\\Xi",!0),o(h,d,M,"Π","\\Pi",!0),o(h,d,M,"Σ","\\Sigma",!0),o(h,d,M,"Υ","\\Upsilon",!0),o(h,d,M,"Φ","\\Phi",!0),o(h,d,M,"Ψ","\\Psi",!0),o(h,d,M,"Ω","\\Omega",!0),o(h,d,M,"A","Α"),o(h,d,M,"B","Β"),o(h,d,M,"E","Ε"),o(h,d,M,"Z","Ζ"),o(h,d,M,"H","Η"),o(h,d,M,"I","Ι"),o(h,d,M,"K","Κ"),o(h,d,M,"M","Μ"),o(h,d,M,"N","Ν"),o(h,d,M,"O","Ο"),o(h,d,M,"P","Ρ"),o(h,d,M,"T","Τ"),o(h,d,M,"X","Χ"),o(h,d,M,"¬","\\neg",!0),o(h,d,M,"¬","\\lnot"),o(h,d,M,"⊤","\\top"),o(h,d,M,"⊥","\\bot"),o(h,d,M,"∅","\\emptyset"),o(h,A,M,"∅","\\varnothing"),o(h,d,T,"α","\\alpha",!0),o(h,d,T,"β","\\beta",!0),o(h,d,T,"γ","\\gamma",!0),o(h,d,T,"δ","\\delta",!0),o(h,d,T,"ϵ","\\epsilon",!0),o(h,d,T,"ζ","\\zeta",!0),o(h,d,T,"η","\\eta",!0),o(h,d,T,"θ","\\theta",!0),o(h,d,T,"ι","\\iota",!0),o(h,d,T,"κ","\\kappa",!0),o(h,d,T,"λ","\\lambda",!0),o(h,d,T,"μ","\\mu",!0),o(h,d,T,"ν","\\nu",!0),o(h,d,T,"ξ","\\xi",!0),o(h,d,T,"ο","\\omicron",!0),o(h,d,T,"π","\\pi",!0),o(h,d,T,"ρ","\\rho",!0),o(h,d,T,"σ","\\sigma",!0),o(h,d,T,"τ","\\tau",!0),o(h,d,T,"υ","\\upsilon",!0),o(h,d,T,"ϕ","\\phi",!0),o(h,d,T,"χ","\\chi",!0),o(h,d,T,"ψ","\\psi",!0),o(h,d,T,"ω","\\omega",!0),o(h,d,T,"ε","\\varepsilon",!0),o(h,d,T,"ϑ","\\vartheta",!0),o(h,d,T,"ϖ","\\varpi",!0),o(h,d,T,"ϱ","\\varrho",!0),o(h,d,T,"ς","\\varsigma",!0),o(h,d,T,"φ","\\varphi",!0),o(h,d,ne,"∗","*",!0),o(h,d,ne,"+","+"),o(h,d,ne,"−","-",!0),o(h,d,ne,"⋅","\\cdot",!0),o(h,d,ne,"∘","\\circ",!0),o(h,d,ne,"÷","\\div",!0),o(h,d,ne,"±","\\pm",!0),o(h,d,ne,"×","\\times",!0),o(h,d,ne,"∩","\\cap",!0),o(h,d,ne,"∪","\\cup",!0),o(h,d,ne,"∖","\\setminus",!0),o(h,d,ne,"∧","\\land"),o(h,d,ne,"∨","\\lor"),o(h,d,ne,"∧","\\wedge",!0),o(h,d,ne,"∨","\\vee",!0),o(h,d,M,"√","\\surd"),o(h,d,J,"⟨","\\langle",!0),o(h,d,J,"∣","\\lvert"),o(h,d,J,"∥","\\lVert"),o(h,d,Je,"?","?"),o(h,d,Je,"!","!"),o(h,d,Je,"⟩","\\rangle",!0),o(h,d,Je,"∣","\\rvert"),o(h,d,Je,"∥","\\rVert"),o(h,d,S,"=","="),o(h,d,S,":",":"),o(h,d,S,"≈","\\approx",!0),o(h,d,S,"≅","\\cong",!0),o(h,d,S,"≥","\\ge"),o(h,d,S,"≥","\\geq",!0),o(h,d,S,"←","\\gets"),o(h,d,S,">","\\gt",!0),o(h,d,S,"∈","\\in",!0),o(h,d,S,"","\\@not"),o(h,d,S,"⊂","\\subset",!0),o(h,d,S,"⊃","\\supset",!0),o(h,d,S,"⊆","\\subseteq",!0),o(h,d,S,"⊇","\\supseteq",!0),o(h,A,S,"⊈","\\nsubseteq",!0),o(h,A,S,"⊉","\\nsupseteq",!0),o(h,d,S,"⊨","\\models"),o(h,d,S,"←","\\leftarrow",!0),o(h,d,S,"≤","\\le"),o(h,d,S,"≤","\\leq",!0),o(h,d,S,"<","\\lt",!0),o(h,d,S,"→","\\rightarrow",!0),o(h,d,S,"→","\\to"),o(h,A,S,"≱","\\ngeq",!0),o(h,A,S,"≰","\\nleq",!0),o(h,d,Ue," ","\\ "),o(h,d,Ue," ","\\space"),o(h,d,Ue," ","\\nobreakspace"),o(H,d,Ue," ","\\ "),o(H,d,Ue," "," "),o(H,d,Ue," ","\\space"),o(H,d,Ue," ","\\nobreakspace"),o(h,d,Ue,null,"\\nobreak"),o(h,d,Ue,null,"\\allowbreak"),o(h,d,ke,",",","),o(h,d,ke,";",";"),o(h,A,ne,"⊼","\\barwedge",!0),o(h,A,ne,"⊻","\\veebar",!0),o(h,d,ne,"⊙","\\odot",!0),o(h,d,ne,"⊕","\\oplus",!0),o(h,d,ne,"⊗","\\otimes",!0),o(h,d,M,"∂","\\partial",!0),o(h,d,ne,"⊘","\\oslash",!0),o(h,A,ne,"⊚","\\circledcirc",!0),o(h,A,ne,"⊡","\\boxdot",!0),o(h,d,ne,"△","\\bigtriangleup"),o(h,d,ne,"▽","\\bigtriangledown"),o(h,d,ne,"†","\\dagger"),o(h,d,ne,"⋄","\\diamond"),o(h,d,ne,"⋆","\\star"),o(h,d,ne,"◃","\\triangleleft"),o(h,d,ne,"▹","\\triangleright"),o(h,d,J,"{","\\{"),o(H,d,M,"{","\\{"),o(H,d,M,"{","\\textbraceleft"),o(h,d,Je,"}","\\}"),o(H,d,M,"}","\\}"),o(H,d,M,"}","\\textbraceright"),o(h,d,J,"{","\\lbrace"),o(h,d,Je,"}","\\rbrace"),o(h,d,J,"[","\\lbrack",!0),o(H,d,M,"[","\\lbrack",!0),o(h,d,Je,"]","\\rbrack",!0),o(H,d,M,"]","\\rbrack",!0),o(h,d,J,"(","\\lparen",!0),o(h,d,Je,")","\\rparen",!0),o(H,d,M,"<","\\textless",!0),o(H,d,M,">","\\textgreater",!0),o(h,d,J,"⌊","\\lfloor",!0),o(h,d,Je,"⌋","\\rfloor",!0),o(h,d,J,"⌈","\\lceil",!0),o(h,d,Je,"⌉","\\rceil",!0),o(h,d,M,"\\","\\backslash"),o(h,d,M,"∣","|"),o(h,d,M,"∣","\\vert"),o(H,d,M,"|","\\textbar",!0),o(h,d,M,"∥","\\|"),o(h,d,M,"∥","\\Vert"),o(H,d,M,"∥","\\textbardbl"),o(H,d,M,"~","\\textasciitilde"),o(H,d,M,"\\","\\textbackslash"),o(H,d,M,"^","\\textasciicircum"),o(h,d,S,"↑","\\uparrow",!0),o(h,d,S,"⇑","\\Uparrow",!0),o(h,d,S,"↓","\\downarrow",!0),o(h,d,S,"⇓","\\Downarrow",!0),o(h,d,S,"↕","\\updownarrow",!0),o(h,d,S,"⇕","\\Updownarrow",!0),o(h,d,P,"∐","\\coprod"),o(h,d,P,"⋁","\\bigvee"),o(h,d,P,"⋀","\\bigwedge"),o(h,d,P,"⨄","\\biguplus"),o(h,d,P,"⋂","\\bigcap"),o(h,d,P,"⋃","\\bigcup"),o(h,d,P,"∫","\\int"),o(h,d,P,"∫","\\intop"),o(h,d,P,"∬","\\iint"),o(h,d,P,"∭","\\iiint"),o(h,d,P,"∏","\\prod"),o(h,d,P,"∑","\\sum"),o(h,d,P,"⨂","\\bigotimes"),o(h,d,P,"⨁","\\bigoplus"),o(h,d,P,"⨀","\\bigodot"),o(h,d,P,"∮","\\oint"),o(h,d,P,"∯","\\oiint"),o(h,d,P,"∰","\\oiiint"),o(h,d,P,"⨆","\\bigsqcup"),o(h,d,P,"∫","\\smallint"),o(H,d,ue,"…","\\textellipsis"),o(h,d,ue,"…","\\mathellipsis"),o(H,d,ue,"…","\\ldots",!0),o(h,d,ue,"…","\\ldots",!0),o(h,d,ue,"⋯","\\@cdots",!0),o(h,d,ue,"⋱","\\ddots",!0),o(h,d,M,"⋮","\\varvdots"),o(h,d,De,"ˊ","\\acute"),o(h,d,De,"ˋ","\\grave"),o(h,d,De,"¨","\\ddot"),o(h,d,De,"~","\\tilde"),o(h,d,De,"ˉ","\\bar"),o(h,d,De,"˘","\\breve"),o(h,d,De,"ˇ","\\check"),o(h,d,De,"^","\\hat"),o(h,d,De,"⃗","\\vec"),o(h,d,De,"˙","\\dot"),o(h,d,De,"˚","\\mathring"),o(h,d,T,"","\\@imath"),o(h,d,T,"","\\@jmath"),o(h,d,M,"ı","ı"),o(h,d,M,"ȷ","ȷ"),o(H,d,M,"ı","\\i",!0),o(H,d,M,"ȷ","\\j",!0),o(H,d,M,"ß","\\ss",!0),o(H,d,M,"æ","\\ae",!0),o(H,d,M,"œ","\\oe",!0),o(H,d,M,"ø","\\o",!0),o(H,d,M,"Æ","\\AE",!0),o(H,d,M,"Œ","\\OE",!0),o(H,d,M,"Ø","\\O",!0),o(H,d,De,"ˊ","\\'"),o(H,d,De,"ˋ","\\`"),o(H,d,De,"ˆ","\\^"),o(H,d,De,"˜","\\~"),o(H,d,De,"ˉ","\\="),o(H,d,De,"˘","\\u"),o(H,d,De,"˙","\\."),o(H,d,De,"¸","\\c"),o(H,d,De,"˚","\\r"),o(H,d,De,"ˇ","\\v"),o(H,d,De,"¨",'\\"'),o(H,d,De,"˝","\\H"),o(H,d,De,"◯","\\textcircled");var ft={"--":!0,"---":!0,"``":!0,"''":!0};o(H,d,M,"–","--",!0),o(H,d,M,"–","\\textendash"),o(H,d,M,"—","---",!0),o(H,d,M,"—","\\textemdash"),o(H,d,M,"‘","`",!0),o(H,d,M,"‘","\\textquoteleft"),o(H,d,M,"’","'",!0),o(H,d,M,"’","\\textquoteright"),o(H,d,M,"“","``",!0),o(H,d,M,"“","\\textquotedblleft"),o(H,d,M,"”","''",!0),o(H,d,M,"”","\\textquotedblright"),o(h,d,M,"°","\\degree",!0),o(H,d,M,"°","\\degree"),o(H,d,M,"°","\\textdegree",!0),o(h,d,M,"£","\\pounds"),o(h,d,M,"£","\\mathsterling",!0),o(H,d,M,"£","\\pounds"),o(H,d,M,"£","\\textsterling",!0),o(h,A,M,"✠","\\maltese"),o(H,A,M,"✠","\\maltese");for(var N0='0123456789/@."',R0=0;R0<N0.length;R0++){var Ur=N0.charAt(R0);o(h,d,M,Ur,Ur)}for(var Wn='0123456789!@*()-=+";:?/.,',Gr=0;Gr<Wn.length;Gr++){var Yn=Wn.charAt(Gr);o(H,d,M,Yn,Yn)}for(var fr="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz",Vr=0;Vr<fr.length;Vr++){var dr=fr.charAt(Vr);o(h,d,T,dr,dr),o(H,d,M,dr,dr)}o(h,A,M,"C","ℂ"),o(H,A,M,"C","ℂ"),o(h,A,M,"H","ℍ"),o(H,A,M,"H","ℍ"),o(h,A,M,"N","ℕ"),o(H,A,M,"N","ℕ"),o(h,A,M,"P","ℙ"),o(H,A,M,"P","ℙ"),o(h,A,M,"Q","ℚ"),o(H,A,M,"Q","ℚ"),o(h,A,M,"R","ℝ"),o(H,A,M,"R","ℝ"),o(h,A,M,"Z","ℤ"),o(H,A,M,"Z","ℤ"),o(h,d,T,"h","ℎ"),o(H,d,T,"h","ℎ");for(var ge="",ot=0;ot<fr.length;ot++){var Xe=fr.charAt(ot);ge=String.fromCharCode(55349,56320+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56372+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56424+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56580+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56736+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56788+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56840+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56944+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ot<26&&(ge=String.fromCharCode(55349,56632+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge),ge=String.fromCharCode(55349,56476+ot),o(h,d,T,Xe,ge),o(H,d,M,Xe,ge))}ge=String.fromCharCode(55349,56668),o(h,d,T,"k",ge),o(H,d,M,"k",ge);for(var k0=0;k0<10;k0++){var a0=k0.toString();ge=String.fromCharCode(55349,57294+k0),o(h,d,T,a0,ge),o(H,d,M,a0,ge),ge=String.fromCharCode(55349,57314+k0),o(h,d,T,a0,ge),o(H,d,M,a0,ge),ge=String.fromCharCode(55349,57324+k0),o(h,d,T,a0,ge),o(H,d,M,a0,ge),ge=String.fromCharCode(55349,57334+k0),o(h,d,T,a0,ge),o(H,d,M,a0,ge)}for(var Wr="ÐÞþ",Yr=0;Yr<Wr.length;Yr++){var pr=Wr.charAt(Yr);o(h,d,T,pr,pr),o(H,d,M,pr,pr)}var gr=[["mathbf","textbf","Main-Bold"],["mathbf","textbf","Main-Bold"],["mathnormal","textit","Math-Italic"],["mathnormal","textit","Math-Italic"],["boldsymbol","boldsymbol","Main-BoldItalic"],["boldsymbol","boldsymbol","Main-BoldItalic"],["mathscr","textscr","Script-Regular"],["","",""],["","",""],["","",""],["mathfrak","textfrak","Fraktur-Regular"],["mathfrak","textfrak","Fraktur-Regular"],["mathbb","textbb","AMS-Regular"],["mathbb","textbb","AMS-Regular"],["","",""],["","",""],["mathsf","textsf","SansSerif-Regular"],["mathsf","textsf","SansSerif-Regular"],["mathboldsf","textboldsf","SansSerif-Bold"],["mathboldsf","textboldsf","SansSerif-Bold"],["mathitsf","textitsf","SansSerif-Italic"],["mathitsf","textitsf","SansSerif-Italic"],["","",""],["","",""],["mathtt","texttt","Typewriter-Regular"],["mathtt","texttt","Typewriter-Regular"]],jn=[["mathbf","textbf","Main-Bold"],["","",""],["mathsf","textsf","SansSerif-Regular"],["mathboldsf","textboldsf","SansSerif-Bold"],["mathtt","texttt","Typewriter-Regular"]],vl=function(e,t){var r=e.charCodeAt(0),n=e.charCodeAt(1),i=(r-55296)*1024+(n-56320)+65536,c=t==="math"?0:1;if(119808<=i&&i<120484){var f=Math.floor((i-119808)/26);return[gr[f][2],gr[f][c]]}else if(120782<=i&&i<=120831){var b=Math.floor((i-120782)/10);return[jn[b][2],jn[b][c]]}else{if(i===120485||i===120486)return[gr[0][2],gr[0][c]];if(120486<i&&i<120782)return["",""];throw new p("Unsupported character: "+e)}},vr=function(e,t,r){return Fe[r][e]&&Fe[r][e].replace&&(e=Fe[r][e].replace),{value:e,metrics:Dt(e,t,r)}},Mt=function(e,t,r,n,i){var c=vr(e,t,r),f=c.metrics;e=c.value;var b;if(f){var x=f.italic;(r==="text"||n&&n.font==="mathit")&&(x=0),b=new nt(e,f.height,f.depth,x,f.skew,f.width,i)}else typeof console<"u"&&console.warn("No character metrics "+("for '"+e+"' in style '"+t+"' and mode '"+r+"'")),b=new nt(e,0,0,0,0,0,i);if(n){b.maxFontSize=n.sizeMultiplier,n.style.isTight()&&b.classes.push("mtight");var _=n.getColor();_&&(b.style.color=_)}return b},bl=function(e,t,r,n){return n===void 0&&(n=[]),r.font==="boldsymbol"&&vr(e,"Main-Bold",t).metrics?Mt(e,"Main-Bold",t,r,n.concat(["mathbf"])):e==="\\"||Fe[t][e].font==="main"?Mt(e,"Main-Regular",t,r,n):Mt(e,"AMS-Regular",t,r,n.concat(["amsrm"]))},yl=function(e,t,r,n,i){return i!=="textord"&&vr(e,"Math-BoldItalic",t).metrics?{fontName:"Math-BoldItalic",fontClass:"boldsymbol"}:{fontName:"Main-Bold",fontClass:"mathbf"}},wl=function(e,t,r){var n=e.mode,i=e.text,c=["mord"],f=n==="math"||n==="text"&&t.font,b=f?t.font:t.fontFamily;if(i.charCodeAt(0)===55349){var x=vl(i,n),_=x[0],C=x[1];return Mt(i,_,n,t,c.concat(C))}else if(b){var I,F;if(b==="boldsymbol"){var q=yl(i,n,t,c,r);I=q.fontName,F=[q.fontClass]}else f?(I=Kn[b].fontName,F=[b]):(I=br(b,t.fontWeight,t.fontShape),F=[b,t.fontWeight,t.fontShape]);if(vr(i,I,n).metrics)return Mt(i,I,n,t,c.concat(F));if(ft.hasOwnProperty(i)&&I.slice(0,10)==="Typewriter"){for(var Z=[],$=0;$<i.length;$++)Z.push(Mt(i[$],I,n,t,c.concat(F)));return Zn(Z)}}if(r==="mathord")return Mt(i,"Math-Italic",n,t,c.concat(["mathnormal"]));if(r==="textord"){var se=Fe[n][i]&&Fe[n][i].font;if(se==="ams"){var ce=br("amsrm",t.fontWeight,t.fontShape);return Mt(i,ce,n,t,c.concat("amsrm",t.fontWeight,t.fontShape))}else if(se==="main"||!se){var he=br("textrm",t.fontWeight,t.fontShape);return Mt(i,he,n,t,c.concat(t.fontWeight,t.fontShape))}else{var Te=br(se,t.fontWeight,t.fontShape);return Mt(i,Te,n,t,c.concat(Te,t.fontWeight,t.fontShape))}}else throw new Error("unexpected type: "+r+" in makeOrd")},xl=function(e,t){if(et(e.classes)!==et(t.classes)||e.skew!==t.skew||e.maxFontSize!==t.maxFontSize)return!1;if(e.classes.length===1){var r=e.classes[0];if(r==="mbin"||r==="mord")return!1}for(var n in e.style)if(e.style.hasOwnProperty(n)&&e.style[n]!==t.style[n])return!1;for(var i in t.style)if(t.style.hasOwnProperty(i)&&e.style[i]!==t.style[i])return!1;return!0},kl=function(e){for(var t=0;t<e.length-1;t++){var r=e[t],n=e[t+1];r instanceof nt&&n instanceof nt&&xl(r,n)&&(r.text+=n.text,r.height=Math.max(r.height,n.height),r.depth=Math.max(r.depth,n.depth),r.italic=n.italic,e.splice(t+1,1),t--)}return e},jr=function(e){for(var t=0,r=0,n=0,i=0;i<e.children.length;i++){var c=e.children[i];c.height>t&&(t=c.height),c.depth>r&&(r=c.depth),c.maxFontSize>n&&(n=c.maxFontSize)}e.height=t,e.depth=r,e.maxFontSize=n},ut=function(e,t,r,n){var i=new yt(e,t,r,n);return jr(i),i},Xn=function(e,t,r,n){return new yt(e,t,r,n)},Sl=function(e,t,r){var n=ut([e],[],t);return n.height=Math.max(r||t.fontMetrics().defaultRuleThickness,t.minRuleThickness),n.style.borderBottomWidth=Q(n.height),n.maxFontSize=1,n},Al=function(e,t,r,n){var i=new K0(e,t,r,n);return jr(i),i},Zn=function(e){var t=new Jt(e);return jr(t),t},Tl=function(e,t){return e instanceof Jt?ut([],[e],t):e},_l=function(e){if(e.positionType==="individualShift"){for(var t=e.children,r=[t[0]],n=-t[0].shift-t[0].elem.depth,i=n,c=1;c<t.length;c++){var f=-t[c].shift-i-t[c].elem.depth,b=f-(t[c-1].elem.height+t[c-1].elem.depth);i=i+f,r.push({type:"kern",size:b}),r.push(t[c])}return{children:r,depth:n}}var x;if(e.positionType==="top"){for(var _=e.positionData,C=0;C<e.children.length;C++){var I=e.children[C];_-=I.type==="kern"?I.size:I.elem.height+I.elem.depth}x=_}else if(e.positionType==="bottom")x=-e.positionData;else{var F=e.children[0];if(F.type!=="elem")throw new Error('First child must have type "elem".');if(e.positionType==="shift")x=-F.elem.depth-e.positionData;else if(e.positionType==="firstBaseline")x=-F.elem.depth;else throw new Error("Invalid positionType "+e.positionType+".")}return{children:e.children,depth:x}},Ml=function(e,t){for(var r=_l(e),n=r.children,i=r.depth,c=0,f=0;f<n.length;f++){var b=n[f];if(b.type==="elem"){var x=b.elem;c=Math.max(c,x.maxFontSize,x.height)}}c+=2;var _=ut(["pstrut"],[]);_.style.height=Q(c);for(var C=[],I=i,F=i,q=i,Z=0;Z<n.length;Z++){var $=n[Z];if($.type==="kern")q+=$.size;else{var se=$.elem,ce=$.wrapperClasses||[],he=$.wrapperStyle||{},Te=ut(ce,[_,se],void 0,he);Te.style.top=Q(-c-q-se.depth),$.marginLeft&&(Te.style.marginLeft=$.marginLeft),$.marginRight&&(Te.style.marginRight=$.marginRight),C.push(Te),q+=se.height+se.depth}I=Math.min(I,q),F=Math.max(F,q)}var ye=ut(["vlist"],C);ye.style.height=Q(F);var ze;if(I<0){var Ae=ut([],[]),Ee=ut(["vlist"],[Ae]);Ee.style.height=Q(-I);var Le=ut(["vlist-s"],[new nt("​")]);ze=[ut(["vlist-r"],[ye,Le]),ut(["vlist-r"],[Ee])]}else ze=[ut(["vlist-r"],[ye])];var $e=ut(["vlist-t"],ze);return ze.length===2&&$e.classes.push("vlist-t2"),$e.height=F,$e.depth=-I,$e},zl=function(e,t){var r=ut(["mspace"],[],t),n=Be(e,t);return r.style.marginRight=Q(n),r},br=function(e,t,r){var n="";switch(e){case"amsrm":n="AMS";break;case"textrm":n="Main";break;case"textsf":n="SansSerif";break;case"texttt":n="Typewriter";break;default:n=e}var i;return t==="textbf"&&r==="textit"?i="BoldItalic":t==="textbf"?i="Bold":t==="textit"?i="Italic":i="Regular",n+"-"+i},Kn={mathbf:{variant:"bold",fontName:"Main-Bold"},mathrm:{variant:"normal",fontName:"Main-Regular"},textit:{variant:"italic",fontName:"Main-Italic"},mathit:{variant:"italic",fontName:"Main-Italic"},mathnormal:{variant:"italic",fontName:"Math-Italic"},mathbb:{variant:"double-struck",fontName:"AMS-Regular"},mathcal:{variant:"script",fontName:"Caligraphic-Regular"},mathfrak:{variant:"fraktur",fontName:"Fraktur-Regular"},mathscr:{variant:"script",fontName:"Script-Regular"},mathsf:{variant:"sans-serif",fontName:"SansSerif-Regular"},mathtt:{variant:"monospace",fontName:"Typewriter-Regular"}},Qn={vec:["vec",.471,.714],oiintSize1:["oiintSize1",.957,.499],oiintSize2:["oiintSize2",1.472,.659],oiiintSize1:["oiiintSize1",1.304,.499],oiiintSize2:["oiiintSize2",1.98,.659]},El=function(e,t){var r=Qn[e],n=r[0],i=r[1],c=r[2],f=new Nt(n),b=new wt([f],{width:Q(i),height:Q(c),style:"width:"+Q(i),viewBox:"0 0 "+1e3*i+" "+1e3*c,preserveAspectRatio:"xMinYMin"}),x=Xn(["overlay"],[b],t);return x.height=c,x.style.height=Q(c),x.style.width=Q(i),x},E={fontMap:Kn,makeSymbol:Mt,mathsym:bl,makeSpan:ut,makeSvgSpan:Xn,makeLineSpan:Sl,makeAnchor:Al,makeFragment:Zn,wrapFragment:Tl,makeVList:Ml,makeOrd:wl,makeGlue:zl,staticSvg:El,svgData:Qn,tryCombineChars:kl},Pe={number:3,unit:"mu"},S0={number:4,unit:"mu"},Yt={number:5,unit:"mu"},Cl={mord:{mop:Pe,mbin:S0,mrel:Yt,minner:Pe},mop:{mord:Pe,mop:Pe,mrel:Yt,minner:Pe},mbin:{mord:S0,mop:S0,mopen:S0,minner:S0},mrel:{mord:Yt,mop:Yt,mopen:Yt,minner:Yt},mopen:{},mclose:{mop:Pe,mbin:S0,mrel:Yt,minner:Pe},mpunct:{mord:Pe,mop:Pe,mrel:Yt,mopen:Pe,mclose:Pe,mpunct:Pe,minner:Pe},minner:{mord:Pe,mop:Pe,mbin:S0,mrel:Yt,mopen:Pe,mpunct:Pe,minner:Pe}},Bl={mord:{mop:Pe},mop:{mord:Pe,mop:Pe},mbin:{},mrel:{},mopen:{},mclose:{mop:Pe},mpunct:{},minner:{mop:Pe}},Jn={},yr={},wr={};function re(u){for(var e=u.type,t=u.names,r=u.props,n=u.handler,i=u.htmlBuilder,c=u.mathmlBuilder,f={type:e,numArgs:r.numArgs,argTypes:r.argTypes,allowedInArgument:!!r.allowedInArgument,allowedInText:!!r.allowedInText,allowedInMath:r.allowedInMath===void 0?!0:r.allowedInMath,numOptionalArgs:r.numOptionalArgs||0,infix:!!r.infix,primitive:!!r.primitive,handler:n},b=0;b<t.length;++b)Jn[t[b]]=f;e&&(i&&(yr[e]=i),c&&(wr[e]=c))}function A0(u){var e=u.type,t=u.htmlBuilder,r=u.mathmlBuilder;re({type:e,names:[],props:{numArgs:0},handler:function(){throw new Error("Should never be called.")},htmlBuilder:t,mathmlBuilder:r})}var xr=function(e){return e.type==="ordgroup"&&e.body.length===1?e.body[0]:e},Ye=function(e){return e.type==="ordgroup"?e.body:[e]},jt=E.makeSpan,Dl=["leftmost","mbin","mopen","mrel","mop","mpunct"],Nl=["rightmost","mrel","mclose","mpunct"],Rl={display:ee.DISPLAY,text:ee.TEXT,script:ee.SCRIPT,scriptscript:ee.SCRIPTSCRIPT},Fl={mord:"mord",mop:"mop",mbin:"mbin",mrel:"mrel",mopen:"mopen",mclose:"mclose",mpunct:"mpunct",minner:"minner"},Ze=function(e,t,r,n){n===void 0&&(n=[null,null]);for(var i=[],c=0;c<e.length;c++){var f=Se(e[c],t);if(f instanceof Jt){var b=f.children;i.push.apply(i,b)}else i.push(f)}if(E.tryCombineChars(i),!r)return i;var x=t;if(e.length===1){var _=e[0];_.type==="sizing"?x=t.havingSize(_.size):_.type==="styling"&&(x=t.havingStyle(Rl[_.style]))}var C=jt([n[0]||"leftmost"],[],t),I=jt([n[1]||"rightmost"],[],t),F=r==="root";return $n(i,function(q,Z){var $=Z.classes[0],se=q.classes[0];$==="mbin"&&O.contains(Nl,se)?Z.classes[0]="mord":se==="mbin"&&O.contains(Dl,$)&&(q.classes[0]="mord")},{node:C},I,F),$n(i,function(q,Z){var $=Xr(Z),se=Xr(q),ce=$&&se?q.hasClass("mtight")?Bl[$][se]:Cl[$][se]:null;if(ce)return E.makeGlue(ce,x)},{node:C},I,F),i},$n=function u(e,t,r,n,i){n&&e.push(n);for(var c=0;c<e.length;c++){var f=e[c],b=ea(f);if(b){u(b.children,t,r,null,i);continue}var x=!f.hasClass("mspace");if(x){var _=t(f,r.node);_&&(r.insertAfter?r.insertAfter(_):(e.unshift(_),c++))}x?r.node=f:i&&f.hasClass("newline")&&(r.node=jt(["leftmost"])),r.insertAfter=function(C){return function(I){e.splice(C+1,0,I),c++}}(c)}n&&e.pop()},ea=function(e){return e instanceof Jt||e instanceof K0||e instanceof yt&&e.hasClass("enclosing")?e:null},Il=function u(e,t){var r=ea(e);if(r){var n=r.children;if(n.length){if(t==="right")return u(n[n.length-1],"right");if(t==="left")return u(n[0],"left")}}return e},Xr=function(e,t){return e?(t&&(e=Il(e,t)),Fl[e.classes[0]]||null):null},$0=function(e,t){var r=["nulldelimiter"].concat(e.baseSizingClasses());return jt(t.concat(r))},Se=function(e,t,r){if(!e)return jt();if(yr[e.type]){var n=yr[e.type](e,t);if(r&&t.size!==r.size){n=jt(t.sizingClasses(r),[n],t);var i=t.sizeMultiplier/r.sizeMultiplier;n.height*=i,n.depth*=i}return n}else throw new p("Got group of unknown type: '"+e.type+"'")};function kr(u,e){var t=jt(["base"],u,e),r=jt(["strut"]);return r.style.height=Q(t.height+t.depth),t.depth&&(r.style.verticalAlign=Q(-t.depth)),t.children.unshift(r),t}function Zr(u,e){var t=null;u.length===1&&u[0].type==="tag"&&(t=u[0].tag,u=u[0].body);var r=Ze(u,e,"root"),n;r.length===2&&r[1].hasClass("tag")&&(n=r.pop());for(var i=[],c=[],f=0;f<r.length;f++)if(c.push(r[f]),r[f].hasClass("mbin")||r[f].hasClass("mrel")||r[f].hasClass("allowbreak")){for(var b=!1;f<r.length-1&&r[f+1].hasClass("mspace")&&!r[f+1].hasClass("newline");)f++,c.push(r[f]),r[f].hasClass("nobreak")&&(b=!0);b||(i.push(kr(c,e)),c=[])}else r[f].hasClass("newline")&&(c.pop(),c.length>0&&(i.push(kr(c,e)),c=[]),i.push(r[f]));c.length>0&&i.push(kr(c,e));var x;t?(x=kr(Ze(t,e,!0)),x.classes=["tag"],i.push(x)):n&&i.push(n);var _=jt(["katex-html"],i);if(_.setAttribute("aria-hidden","true"),x){var C=x.children[0];C.style.height=Q(_.height+_.depth),_.depth&&(C.style.verticalAlign=Q(-_.depth))}return _}function ta(u){return new Jt(u)}var xt=function(){function u(t,r,n){this.type=void 0,this.attributes=void 0,this.children=void 0,this.classes=void 0,this.type=t,this.attributes={},this.children=r||[],this.classes=n||[]}var e=u.prototype;return e.setAttribute=function(r,n){this.attributes[r]=n},e.getAttribute=function(r){return this.attributes[r]},e.toNode=function(){var r=document.createElementNS("http://www.w3.org/1998/Math/MathML",this.type);for(var n in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,n)&&r.setAttribute(n,this.attributes[n]);this.classes.length>0&&(r.className=et(this.classes));for(var i=0;i<this.children.length;i++)r.appendChild(this.children[i].toNode());return r},e.toMarkup=function(){var r="<"+this.type;for(var n in this.attributes)Object.prototype.hasOwnProperty.call(this.attributes,n)&&(r+=" "+n+'="',r+=O.escape(this.attributes[n]),r+='"');this.classes.length>0&&(r+=' class ="'+O.escape(et(this.classes))+'"'),r+=">";for(var i=0;i<this.children.length;i++)r+=this.children[i].toMarkup();return r+="</"+this.type+">",r},e.toText=function(){return this.children.map(function(r){return r.toText()}).join("")},u}(),er=function(){function u(t){this.text=void 0,this.text=t}var e=u.prototype;return e.toNode=function(){return document.createTextNode(this.text)},e.toMarkup=function(){return O.escape(this.toText())},e.toText=function(){return this.text},u}(),Ll=function(){function u(t){this.width=void 0,this.character=void 0,this.width=t,t>=.05555&&t<=.05556?this.character=" ":t>=.1666&&t<=.1667?this.character=" ":t>=.2222&&t<=.2223?this.character=" ":t>=.2777&&t<=.2778?this.character="  ":t>=-.05556&&t<=-.05555?this.character=" ⁣":t>=-.1667&&t<=-.1666?this.character=" ⁣":t>=-.2223&&t<=-.2222?this.character=" ⁣":t>=-.2778&&t<=-.2777?this.character=" ⁣":this.character=null}var e=u.prototype;return e.toNode=function(){if(this.character)return document.createTextNode(this.character);var r=document.createElementNS("http://www.w3.org/1998/Math/MathML","mspace");return r.setAttribute("width",Q(this.width)),r},e.toMarkup=function(){return this.character?"<mtext>"+this.character+"</mtext>":'<mspace width="'+Q(this.width)+'"/>'},e.toText=function(){return this.character?this.character:" "},u}(),V={MathNode:xt,TextNode:er,SpaceNode:Ll,newDocumentFragment:ta},kt=function(e,t,r){return Fe[t][e]&&Fe[t][e].replace&&e.charCodeAt(0)!==55349&&!(ft.hasOwnProperty(e)&&r&&(r.fontFamily&&r.fontFamily.slice(4,6)==="tt"||r.font&&r.font.slice(4,6)==="tt"))&&(e=Fe[t][e].replace),new V.TextNode(e)},Kr=function(e){return e.length===1?e[0]:new V.MathNode("mrow",e)},Qr=function(e,t){if(t.fontFamily==="texttt")return"monospace";if(t.fontFamily==="textsf")return t.fontShape==="textit"&&t.fontWeight==="textbf"?"sans-serif-bold-italic":t.fontShape==="textit"?"sans-serif-italic":t.fontWeight==="textbf"?"bold-sans-serif":"sans-serif";if(t.fontShape==="textit"&&t.fontWeight==="textbf")return"bold-italic";if(t.fontShape==="textit")return"italic";if(t.fontWeight==="textbf")return"bold";var r=t.font;if(!r||r==="mathnormal")return null;var n=e.mode;if(r==="mathit")return"italic";if(r==="boldsymbol")return e.type==="textord"?"bold":"bold-italic";if(r==="mathbf")return"bold";if(r==="mathbb")return"double-struck";if(r==="mathfrak")return"fraktur";if(r==="mathscr"||r==="mathcal")return"script";if(r==="mathsf")return"sans-serif";if(r==="mathtt")return"monospace";var i=e.text;if(O.contains(["\\imath","\\jmath"],i))return null;Fe[n][i]&&Fe[n][i].replace&&(i=Fe[n][i].replace);var c=E.fontMap[r].fontName;return Dt(i,c,n)?E.fontMap[r].variant:null},ct=function(e,t,r){if(e.length===1){var n=Ne(e[0],t);return r&&n instanceof xt&&n.type==="mo"&&(n.setAttribute("lspace","0em"),n.setAttribute("rspace","0em")),[n]}for(var i=[],c,f=0;f<e.length;f++){var b=Ne(e[f],t);if(b instanceof xt&&c instanceof xt){if(b.type==="mtext"&&c.type==="mtext"&&b.getAttribute("mathvariant")===c.getAttribute("mathvariant")){var x;(x=c.children).push.apply(x,b.children);continue}else if(b.type==="mn"&&c.type==="mn"){var _;(_=c.children).push.apply(_,b.children);continue}else if(b.type==="mi"&&b.children.length===1&&c.type==="mn"){var C=b.children[0];if(C instanceof er&&C.text==="."){var I;(I=c.children).push.apply(I,b.children);continue}}else if(c.type==="mi"&&c.children.length===1){var F=c.children[0];if(F instanceof er&&F.text==="̸"&&(b.type==="mo"||b.type==="mi"||b.type==="mn")){var q=b.children[0];q instanceof er&&q.text.length>0&&(q.text=q.text.slice(0,1)+"̸"+q.text.slice(1),i.pop())}}}i.push(b),c=b}return i},i0=function(e,t,r){return Kr(ct(e,t,r))},Ne=function(e,t){if(!e)return new V.MathNode("mrow");if(wr[e.type]){var r=wr[e.type](e,t);return r}else throw new p("Got group of unknown type: '"+e.type+"'")};function ra(u,e,t,r,n){var i=ct(u,t),c;i.length===1&&i[0]instanceof xt&&O.contains(["mrow","mtable"],i[0].type)?c=i[0]:c=new V.MathNode("mrow",i);var f=new V.MathNode("annotation",[new V.TextNode(e)]);f.setAttribute("encoding","application/x-tex");var b=new V.MathNode("semantics",[c,f]),x=new V.MathNode("math",[b]);x.setAttribute("xmlns","http://www.w3.org/1998/Math/MathML"),r&&x.setAttribute("display","block");var _=n?"katex":"katex-mathml";return E.makeSpan([_],[x])}var na=function(e){return new C0({style:e.displayMode?ee.DISPLAY:ee.TEXT,maxSize:e.maxSize,minRuleThickness:e.minRuleThickness})},aa=function(e,t){if(t.displayMode){var r=["katex-display"];t.leqno&&r.push("leqno"),t.fleqn&&r.push("fleqn"),e=E.makeSpan(r,[e])}return e},Ol=function(e,t,r){var n=na(r),i;if(r.output==="mathml")return ra(e,t,n,r.displayMode,!0);if(r.output==="html"){var c=Zr(e,n);i=E.makeSpan(["katex"],[c])}else{var f=ra(e,t,n,r.displayMode,!1),b=Zr(e,n);i=E.makeSpan(["katex"],[f,b])}return aa(i,r)},ql=function(e,t,r){var n=na(r),i=Zr(e,n),c=E.makeSpan(["katex"],[i]);return aa(c,r)},Pl={widehat:"^",widecheck:"ˇ",widetilde:"~",utilde:"~",overleftarrow:"←",underleftarrow:"←",xleftarrow:"←",overrightarrow:"→",underrightarrow:"→",xrightarrow:"→",underbrace:"⏟",overbrace:"⏞",overgroup:"⏠",undergroup:"⏡",overleftrightarrow:"↔",underleftrightarrow:"↔",xleftrightarrow:"↔",Overrightarrow:"⇒",xRightarrow:"⇒",overleftharpoon:"↼",xleftharpoonup:"↼",overrightharpoon:"⇀",xrightharpoonup:"⇀",xLeftarrow:"⇐",xLeftrightarrow:"⇔",xhookleftarrow:"↩",xhookrightarrow:"↪",xmapsto:"↦",xrightharpoondown:"⇁",xleftharpoondown:"↽",xrightleftharpoons:"⇌",xleftrightharpoons:"⇋",xtwoheadleftarrow:"↞",xtwoheadrightarrow:"↠",xlongequal:"=",xtofrom:"⇄",xrightleftarrows:"⇄",xrightequilibrium:"⇌",xleftequilibrium:"⇋","\\cdrightarrow":"→","\\cdleftarrow":"←","\\cdlongequal":"="},Hl=function(e){var t=new V.MathNode("mo",[new V.TextNode(Pl[e.replace(/^\\/,"")])]);return t.setAttribute("stretchy","true"),t},Ul={overrightarrow:[["rightarrow"],.888,522,"xMaxYMin"],overleftarrow:[["leftarrow"],.888,522,"xMinYMin"],underrightarrow:[["rightarrow"],.888,522,"xMaxYMin"],underleftarrow:[["leftarrow"],.888,522,"xMinYMin"],xrightarrow:[["rightarrow"],1.469,522,"xMaxYMin"],"\\cdrightarrow":[["rightarrow"],3,522,"xMaxYMin"],xleftarrow:[["leftarrow"],1.469,522,"xMinYMin"],"\\cdleftarrow":[["leftarrow"],3,522,"xMinYMin"],Overrightarrow:[["doublerightarrow"],.888,560,"xMaxYMin"],xRightarrow:[["doublerightarrow"],1.526,560,"xMaxYMin"],xLeftarrow:[["doubleleftarrow"],1.526,560,"xMinYMin"],overleftharpoon:[["leftharpoon"],.888,522,"xMinYMin"],xleftharpoonup:[["leftharpoon"],.888,522,"xMinYMin"],xleftharpoondown:[["leftharpoondown"],.888,522,"xMinYMin"],overrightharpoon:[["rightharpoon"],.888,522,"xMaxYMin"],xrightharpoonup:[["rightharpoon"],.888,522,"xMaxYMin"],xrightharpoondown:[["rightharpoondown"],.888,522,"xMaxYMin"],xlongequal:[["longequal"],.888,334,"xMinYMin"],"\\cdlongequal":[["longequal"],3,334,"xMinYMin"],xtwoheadleftarrow:[["twoheadleftarrow"],.888,334,"xMinYMin"],xtwoheadrightarrow:[["twoheadrightarrow"],.888,334,"xMaxYMin"],overleftrightarrow:[["leftarrow","rightarrow"],.888,522],overbrace:[["leftbrace","midbrace","rightbrace"],1.6,548],underbrace:[["leftbraceunder","midbraceunder","rightbraceunder"],1.6,548],underleftrightarrow:[["leftarrow","rightarrow"],.888,522],xleftrightarrow:[["leftarrow","rightarrow"],1.75,522],xLeftrightarrow:[["doubleleftarrow","doublerightarrow"],1.75,560],xrightleftharpoons:[["leftharpoondownplus","rightharpoonplus"],1.75,716],xleftrightharpoons:[["leftharpoonplus","rightharpoondownplus"],1.75,716],xhookleftarrow:[["leftarrow","righthook"],1.08,522],xhookrightarrow:[["lefthook","rightarrow"],1.08,522],overlinesegment:[["leftlinesegment","rightlinesegment"],.888,522],underlinesegment:[["leftlinesegment","rightlinesegment"],.888,522],overgroup:[["leftgroup","rightgroup"],.888,342],undergroup:[["leftgroupunder","rightgroupunder"],.888,342],xmapsto:[["leftmapsto","rightarrow"],1.5,522],xtofrom:[["leftToFrom","rightToFrom"],1.75,528],xrightleftarrows:[["baraboveleftarrow","rightarrowabovebar"],1.75,901],xrightequilibrium:[["baraboveshortleftharpoon","rightharpoonaboveshortbar"],1.75,716],xleftequilibrium:[["shortbaraboveleftharpoon","shortrightharpoonabovebar"],1.75,716]},Gl=function(e){return e.type==="ordgroup"?e.body.length:1},Vl=function(e,t){function r(){var b=4e5,x=e.label.slice(1);if(O.contains(["widehat","widecheck","widetilde","utilde"],x)){var _=e,C=Gl(_.base),I,F,q;if(C>5)x==="widehat"||x==="widecheck"?(I=420,b=2364,q=.42,F=x+"4"):(I=312,b=2340,q=.34,F="tilde4");else{var Z=[1,1,2,2,3,3][C];x==="widehat"||x==="widecheck"?(b=[0,1062,2364,2364,2364][Z],I=[0,239,300,360,420][Z],q=[0,.24,.3,.3,.36,.42][Z],F=x+Z):(b=[0,600,1033,2339,2340][Z],I=[0,260,286,306,312][Z],q=[0,.26,.286,.3,.306,.34][Z],F="tilde"+Z)}var $=new Nt(F),se=new wt([$],{width:"100%",height:Q(q),viewBox:"0 0 "+b+" "+I,preserveAspectRatio:"none"});return{span:E.makeSvgSpan([],[se],t),minWidth:0,height:q}}else{var ce=[],he=Ul[x],Te=he[0],ye=he[1],ze=he[2],Ae=ze/1e3,Ee=Te.length,Le,$e;if(Ee===1){var dt=he[3];Le=["hide-tail"],$e=[dt]}else if(Ee===2)Le=["halfarrow-left","halfarrow-right"],$e=["xMinYMin","xMaxYMin"];else if(Ee===3)Le=["brace-left","brace-center","brace-right"],$e=["xMinYMin","xMidYMin","xMaxYMin"];else throw new Error(`Correct katexImagesData or update code here to support
-                    `+Ee+" children.");for(var He=0;He<Ee;He++){var T0=new Nt(Te[He]),St=new wt([T0],{width:"400em",height:Q(Ae),viewBox:"0 0 "+b+" "+ze,preserveAspectRatio:$e[He]+" slice"}),at=E.makeSvgSpan([Le[He]],[St],t);if(Ee===1)return{span:at,minWidth:ye,height:Ae};at.style.height=Q(Ae),ce.push(at)}return{span:E.makeSpan(["stretchy"],ce,t),minWidth:ye,height:Ae}}}var n=r(),i=n.span,c=n.minWidth,f=n.height;return i.height=f,i.style.height=Q(f),c>0&&(i.style.minWidth=Q(c)),i},Wl=function(e,t,r,n,i){var c,f=e.height+e.depth+r+n;if(/fbox|color|angl/.test(t)){if(c=E.makeSpan(["stretchy",t],[],i),t==="fbox"){var b=i.color&&i.getColor();b&&(c.style.borderColor=b)}}else{var x=[];/^[bx]cancel$/.test(t)&&x.push(new n0({x1:"0",y1:"0",x2:"100%",y2:"100%","stroke-width":"0.046em"})),/^x?cancel$/.test(t)&&x.push(new n0({x1:"0",y1:"100%",x2:"100%",y2:"0","stroke-width":"0.046em"}));var _=new wt(x,{width:"100%",height:Q(f)});c=E.makeSvgSpan([],[_],i)}return c.height=f,c.style.height=Q(f),c},Xt={encloseSpan:Wl,mathMLnode:Hl,svgSpan:Vl};function be(u,e){if(!u||u.type!==e)throw new Error("Expected node of type "+e+", but got "+(u?"node of type "+u.type:String(u)));return u}function Jr(u){var e=Sr(u);if(!e)throw new Error("Expected node of symbol group type, but got "+(u?"node of type "+u.type:String(u)));return e}function Sr(u){return u&&(u.type==="atom"||J0.hasOwnProperty(u.type))?u:null}var $r=function(e,t){var r,n,i;e&&e.type==="supsub"?(n=be(e.base,"accent"),r=n.base,e.base=r,i=Hr(Se(e,t)),e.base=n):(n=be(e,"accent"),r=n.base);var c=Se(r,t.havingCrampedStyle()),f=n.isShifty&&O.isCharacterBox(r),b=0;if(f){var x=O.getBaseElem(r),_=Se(x,t.havingCrampedStyle());b=D0(_).skew}var C=n.label==="\\c",I=C?c.height+c.depth:Math.min(c.height,t.fontMetrics().xHeight),F;if(n.isStretchy)F=Xt.svgSpan(n,t),F=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:c},{type:"elem",elem:F,wrapperClasses:["svg-align"],wrapperStyle:b>0?{width:"calc(100% - "+Q(2*b)+")",marginLeft:Q(2*b)}:void 0}]},t);else{var q,Z;n.label==="\\vec"?(q=E.staticSvg("vec",t),Z=E.svgData.vec[1]):(q=E.makeOrd({mode:n.mode,text:n.label},t,"textord"),q=D0(q),q.italic=0,Z=q.width,C&&(I+=q.depth)),F=E.makeSpan(["accent-body"],[q]);var $=n.label==="\\textcircled";$&&(F.classes.push("accent-full"),I=c.height);var se=b;$||(se-=Z/2),F.style.left=Q(se),n.label==="\\textcircled"&&(F.style.top=".2em"),F=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:c},{type:"kern",size:-I},{type:"elem",elem:F}]},t)}var ce=E.makeSpan(["mord","accent"],[F],t);return i?(i.children[0]=ce,i.height=Math.max(ce.height,i.height),i.classes[0]="mord",i):ce},ia=function(e,t){var r=e.isStretchy?Xt.mathMLnode(e.label):new V.MathNode("mo",[kt(e.label,e.mode)]),n=new V.MathNode("mover",[Ne(e.base,t),r]);return n.setAttribute("accent","true"),n},Yl=new RegExp(["\\acute","\\grave","\\ddot","\\tilde","\\bar","\\breve","\\check","\\hat","\\vec","\\dot","\\mathring"].map(function(u){return"\\"+u}).join("|"));re({type:"accent",names:["\\acute","\\grave","\\ddot","\\tilde","\\bar","\\breve","\\check","\\hat","\\vec","\\dot","\\mathring","\\widecheck","\\widehat","\\widetilde","\\overrightarrow","\\overleftarrow","\\Overrightarrow","\\overleftrightarrow","\\overgroup","\\overlinesegment","\\overleftharpoon","\\overrightharpoon"],props:{numArgs:1},handler:function(e,t){var r=xr(t[0]),n=!Yl.test(e.funcName),i=!n||e.funcName==="\\widehat"||e.funcName==="\\widetilde"||e.funcName==="\\widecheck";return{type:"accent",mode:e.parser.mode,label:e.funcName,isStretchy:n,isShifty:i,base:r}},htmlBuilder:$r,mathmlBuilder:ia}),re({type:"accent",names:["\\'","\\`","\\^","\\~","\\=","\\u","\\.",'\\"',"\\c","\\r","\\H","\\v","\\textcircled"],props:{numArgs:1,allowedInText:!0,allowedInMath:!0,argTypes:["primitive"]},handler:function(e,t){var r=t[0],n=e.parser.mode;return n==="math"&&(e.parser.settings.reportNonstrict("mathVsTextAccents","LaTeX's accent "+e.funcName+" works only in text mode"),n="text"),{type:"accent",mode:n,label:e.funcName,isStretchy:!1,isShifty:!0,base:r}},htmlBuilder:$r,mathmlBuilder:ia}),re({type:"accentUnder",names:["\\underleftarrow","\\underrightarrow","\\underleftrightarrow","\\undergroup","\\underlinesegment","\\utilde"],props:{numArgs:1},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0];return{type:"accentUnder",mode:r.mode,label:n,base:i}},htmlBuilder:function(e,t){var r=Se(e.base,t),n=Xt.svgSpan(e,t),i=e.label==="\\utilde"?.12:0,c=E.makeVList({positionType:"top",positionData:r.height,children:[{type:"elem",elem:n,wrapperClasses:["svg-align"]},{type:"kern",size:i},{type:"elem",elem:r}]},t);return E.makeSpan(["mord","accentunder"],[c],t)},mathmlBuilder:function(e,t){var r=Xt.mathMLnode(e.label),n=new V.MathNode("munder",[Ne(e.base,t),r]);return n.setAttribute("accentunder","true"),n}});var Ar=function(e){var t=new V.MathNode("mpadded",e?[e]:[]);return t.setAttribute("width","+0.6em"),t.setAttribute("lspace","0.3em"),t};re({type:"xArrow",names:["\\xleftarrow","\\xrightarrow","\\xLeftarrow","\\xRightarrow","\\xleftrightarrow","\\xLeftrightarrow","\\xhookleftarrow","\\xhookrightarrow","\\xmapsto","\\xrightharpoondown","\\xrightharpoonup","\\xleftharpoondown","\\xleftharpoonup","\\xrightleftharpoons","\\xleftrightharpoons","\\xlongequal","\\xtwoheadrightarrow","\\xtwoheadleftarrow","\\xtofrom","\\xrightleftarrows","\\xrightequilibrium","\\xleftequilibrium","\\\\cdrightarrow","\\\\cdleftarrow","\\\\cdlongequal"],props:{numArgs:1,numOptionalArgs:1},handler:function(e,t,r){var n=e.parser,i=e.funcName;return{type:"xArrow",mode:n.mode,label:i,body:t[0],below:r[0]}},htmlBuilder:function(e,t){var r=t.style,n=t.havingStyle(r.sup()),i=E.wrapFragment(Se(e.body,n,t),t),c=e.label.slice(0,2)==="\\x"?"x":"cd";i.classes.push(c+"-arrow-pad");var f;e.below&&(n=t.havingStyle(r.sub()),f=E.wrapFragment(Se(e.below,n,t),t),f.classes.push(c+"-arrow-pad"));var b=Xt.svgSpan(e,t),x=-t.fontMetrics().axisHeight+.5*b.height,_=-t.fontMetrics().axisHeight-.5*b.height-.111;(i.depth>.25||e.label==="\\xleftequilibrium")&&(_-=i.depth);var C;if(f){var I=-t.fontMetrics().axisHeight+f.height+.5*b.height+.111;C=E.makeVList({positionType:"individualShift",children:[{type:"elem",elem:i,shift:_},{type:"elem",elem:b,shift:x},{type:"elem",elem:f,shift:I}]},t)}else C=E.makeVList({positionType:"individualShift",children:[{type:"elem",elem:i,shift:_},{type:"elem",elem:b,shift:x}]},t);return C.children[0].children[0].children[1].classes.push("svg-align"),E.makeSpan(["mrel","x-arrow"],[C],t)},mathmlBuilder:function(e,t){var r=Xt.mathMLnode(e.label);r.setAttribute("minsize",e.label.charAt(0)==="x"?"1.75em":"3.0em");var n;if(e.body){var i=Ar(Ne(e.body,t));if(e.below){var c=Ar(Ne(e.below,t));n=new V.MathNode("munderover",[r,c,i])}else n=new V.MathNode("mover",[r,i])}else if(e.below){var f=Ar(Ne(e.below,t));n=new V.MathNode("munder",[r,f])}else n=Ar(),n=new V.MathNode("mover",[r,n]);return n}});var jl=E.makeSpan;function la(u,e){var t=Ze(u.body,e,!0);return jl([u.mclass],t,e)}function sa(u,e){var t,r=ct(u.body,e);return u.mclass==="minner"?t=new V.MathNode("mpadded",r):u.mclass==="mord"?u.isCharacterBox?(t=r[0],t.type="mi"):t=new V.MathNode("mi",r):(u.isCharacterBox?(t=r[0],t.type="mo"):t=new V.MathNode("mo",r),u.mclass==="mbin"?(t.attributes.lspace="0.22em",t.attributes.rspace="0.22em"):u.mclass==="mpunct"?(t.attributes.lspace="0em",t.attributes.rspace="0.17em"):u.mclass==="mopen"||u.mclass==="mclose"?(t.attributes.lspace="0em",t.attributes.rspace="0em"):u.mclass==="minner"&&(t.attributes.lspace="0.0556em",t.attributes.width="+0.1111em")),t}re({type:"mclass",names:["\\mathord","\\mathbin","\\mathrel","\\mathopen","\\mathclose","\\mathpunct","\\mathinner"],props:{numArgs:1,primitive:!0},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0];return{type:"mclass",mode:r.mode,mclass:"m"+n.slice(5),body:Ye(i),isCharacterBox:O.isCharacterBox(i)}},htmlBuilder:la,mathmlBuilder:sa});var Tr=function(e){var t=e.type==="ordgroup"&&e.body.length?e.body[0]:e;return t.type==="atom"&&(t.family==="bin"||t.family==="rel")?"m"+t.family:"mord"};re({type:"mclass",names:["\\@binrel"],props:{numArgs:2},handler:function(e,t){var r=e.parser;return{type:"mclass",mode:r.mode,mclass:Tr(t[0]),body:Ye(t[1]),isCharacterBox:O.isCharacterBox(t[1])}}}),re({type:"mclass",names:["\\stackrel","\\overset","\\underset"],props:{numArgs:2},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[1],c=t[0],f;n!=="\\stackrel"?f=Tr(i):f="mrel";var b={type:"op",mode:i.mode,limits:!0,alwaysHandleSupSub:!0,parentIsSupSub:!1,symbol:!1,suppressBaseShift:n!=="\\stackrel",body:Ye(i)},x={type:"supsub",mode:c.mode,base:b,sup:n==="\\underset"?null:c,sub:n==="\\underset"?c:null};return{type:"mclass",mode:r.mode,mclass:f,body:[x],isCharacterBox:O.isCharacterBox(x)}},htmlBuilder:la,mathmlBuilder:sa}),re({type:"pmb",names:["\\pmb"],props:{numArgs:1,allowedInText:!0},handler:function(e,t){var r=e.parser;return{type:"pmb",mode:r.mode,mclass:Tr(t[0]),body:Ye(t[0])}},htmlBuilder:function(e,t){var r=Ze(e.body,t,!0),n=E.makeSpan([e.mclass],r,t);return n.style.textShadow="0.02em 0.01em 0.04px",n},mathmlBuilder:function(e,t){var r=ct(e.body,t),n=new V.MathNode("mstyle",r);return n.setAttribute("style","text-shadow: 0.02em 0.01em 0.04px"),n}});var Xl={">":"\\\\cdrightarrow","<":"\\\\cdleftarrow","=":"\\\\cdlongequal",A:"\\uparrow",V:"\\downarrow","|":"\\Vert",".":"no arrow"},oa=function(){return{type:"styling",body:[],mode:"math",style:"display"}},ua=function(e){return e.type==="textord"&&e.text==="@"},Zl=function(e,t){return(e.type==="mathord"||e.type==="atom")&&e.text===t};function Kl(u,e,t){var r=Xl[u];switch(r){case"\\\\cdrightarrow":case"\\\\cdleftarrow":return t.callFunction(r,[e[0]],[e[1]]);case"\\uparrow":case"\\downarrow":{var n=t.callFunction("\\\\cdleft",[e[0]],[]),i={type:"atom",text:r,mode:"math",family:"rel"},c=t.callFunction("\\Big",[i],[]),f=t.callFunction("\\\\cdright",[e[1]],[]),b={type:"ordgroup",mode:"math",body:[n,c,f]};return t.callFunction("\\\\cdparent",[b],[])}case"\\\\cdlongequal":return t.callFunction("\\\\cdlongequal",[],[]);case"\\Vert":{var x={type:"textord",text:"\\Vert",mode:"math"};return t.callFunction("\\Big",[x],[])}default:return{type:"textord",text:" ",mode:"math"}}}function Ql(u){var e=[];for(u.gullet.beginGroup(),u.gullet.macros.set("\\cr","\\\\\\relax"),u.gullet.beginGroup();;){e.push(u.parseExpression(!1,"\\\\")),u.gullet.endGroup(),u.gullet.beginGroup();var t=u.fetch().text;if(t==="&"||t==="\\\\")u.consume();else if(t==="\\end"){e[e.length-1].length===0&&e.pop();break}else throw new p("Expected \\\\ or \\cr or \\end",u.nextToken)}for(var r=[],n=[r],i=0;i<e.length;i++){for(var c=e[i],f=oa(),b=0;b<c.length;b++)if(!ua(c[b]))f.body.push(c[b]);else{r.push(f),b+=1;var x=Jr(c[b]).text,_=new Array(2);if(_[0]={type:"ordgroup",mode:"math",body:[]},_[1]={type:"ordgroup",mode:"math",body:[]},!("=|.".indexOf(x)>-1))if("<>AV".indexOf(x)>-1)for(var C=0;C<2;C++){for(var I=!0,F=b+1;F<c.length;F++){if(Zl(c[F],x)){I=!1,b=F;break}if(ua(c[F]))throw new p("Missing a "+x+" character to complete a CD arrow.",c[F]);_[C].body.push(c[F])}if(I)throw new p("Missing a "+x+" character to complete a CD arrow.",c[b])}else throw new p('Expected one of "<>AV=|." after @',c[b]);var q=Kl(x,_,u),Z={type:"styling",body:[q],mode:"math",style:"display"};r.push(Z),f=oa()}i%2===0?r.push(f):r.shift(),r=[],n.push(r)}u.gullet.endGroup(),u.gullet.endGroup();var $=new Array(n[0].length).fill({type:"align",align:"c",pregap:.25,postgap:.25});return{type:"array",mode:"math",body:n,arraystretch:1,addJot:!0,rowGaps:[null],cols:$,colSeparationType:"CD",hLinesBeforeRow:new Array(n.length+1).fill([])}}re({type:"cdlabel",names:["\\\\cdleft","\\\\cdright"],props:{numArgs:1},handler:function(e,t){var r=e.parser,n=e.funcName;return{type:"cdlabel",mode:r.mode,side:n.slice(4),label:t[0]}},htmlBuilder:function(e,t){var r=t.havingStyle(t.style.sup()),n=E.wrapFragment(Se(e.label,r,t),t);return n.classes.push("cd-label-"+e.side),n.style.bottom=Q(.8-n.depth),n.height=0,n.depth=0,n},mathmlBuilder:function(e,t){var r=new V.MathNode("mrow",[Ne(e.label,t)]);return r=new V.MathNode("mpadded",[r]),r.setAttribute("width","0"),e.side==="left"&&r.setAttribute("lspace","-1width"),r.setAttribute("voffset","0.7em"),r=new V.MathNode("mstyle",[r]),r.setAttribute("displaystyle","false"),r.setAttribute("scriptlevel","1"),r}}),re({type:"cdlabelparent",names:["\\\\cdparent"],props:{numArgs:1},handler:function(e,t){var r=e.parser;return{type:"cdlabelparent",mode:r.mode,fragment:t[0]}},htmlBuilder:function(e,t){var r=E.wrapFragment(Se(e.fragment,t),t);return r.classes.push("cd-vert-arrow"),r},mathmlBuilder:function(e,t){return new V.MathNode("mrow",[Ne(e.fragment,t)])}}),re({type:"textord",names:["\\@char"],props:{numArgs:1,allowedInText:!0},handler:function(e,t){for(var r=e.parser,n=be(t[0],"ordgroup"),i=n.body,c="",f=0;f<i.length;f++){var b=be(i[f],"textord");c+=b.text}var x=parseInt(c),_;if(isNaN(x))throw new p("\\@char has non-numeric argument "+c);if(x<0||x>=1114111)throw new p("\\@char with invalid code point "+c);return x<=65535?_=String.fromCharCode(x):(x-=65536,_=String.fromCharCode((x>>10)+55296,(x&1023)+56320)),{type:"textord",mode:r.mode,text:_}}});var ca=function(e,t){var r=Ze(e.body,t.withColor(e.color),!1);return E.makeFragment(r)},ha=function(e,t){var r=ct(e.body,t.withColor(e.color)),n=new V.MathNode("mstyle",r);return n.setAttribute("mathcolor",e.color),n};re({type:"color",names:["\\textcolor"],props:{numArgs:2,allowedInText:!0,argTypes:["color","original"]},handler:function(e,t){var r=e.parser,n=be(t[0],"color-token").color,i=t[1];return{type:"color",mode:r.mode,color:n,body:Ye(i)}},htmlBuilder:ca,mathmlBuilder:ha}),re({type:"color",names:["\\color"],props:{numArgs:1,allowedInText:!0,argTypes:["color"]},handler:function(e,t){var r=e.parser,n=e.breakOnTokenText,i=be(t[0],"color-token").color;r.gullet.macros.set("\\current@color",i);var c=r.parseExpression(!0,n);return{type:"color",mode:r.mode,color:i,body:c}},htmlBuilder:ca,mathmlBuilder:ha}),re({type:"cr",names:["\\\\"],props:{numArgs:0,numOptionalArgs:0,allowedInText:!0},handler:function(e,t,r){var n=e.parser,i=n.gullet.future().text==="["?n.parseSizeGroup(!0):null,c=!n.settings.displayMode||!n.settings.useStrictBehavior("newLineInDisplayMode","In LaTeX, \\\\ or \\newline does nothing in display mode");return{type:"cr",mode:n.mode,newLine:c,size:i&&be(i,"size").value}},htmlBuilder:function(e,t){var r=E.makeSpan(["mspace"],[],t);return e.newLine&&(r.classes.push("newline"),e.size&&(r.style.marginTop=Q(Be(e.size,t)))),r},mathmlBuilder:function(e,t){var r=new V.MathNode("mspace");return e.newLine&&(r.setAttribute("linebreak","newline"),e.size&&r.setAttribute("height",Q(Be(e.size,t)))),r}});var en={"\\global":"\\global","\\long":"\\\\globallong","\\\\globallong":"\\\\globallong","\\def":"\\gdef","\\gdef":"\\gdef","\\edef":"\\xdef","\\xdef":"\\xdef","\\let":"\\\\globallet","\\futurelet":"\\\\globalfuture"},ma=function(e){var t=e.text;if(/^(?:[\\{}$&#^_]|EOF)$/.test(t))throw new p("Expected a control sequence",e);return t},Jl=function(e){var t=e.gullet.popToken();return t.text==="="&&(t=e.gullet.popToken(),t.text===" "&&(t=e.gullet.popToken())),t},fa=function(e,t,r,n){var i=e.gullet.macros.get(r.text);i==null&&(r.noexpand=!0,i={tokens:[r],numArgs:0,unexpandable:!e.gullet.isExpandable(r.text)}),e.gullet.macros.set(t,i,n)};re({type:"internal",names:["\\global","\\long","\\\\globallong"],props:{numArgs:0,allowedInText:!0},handler:function(e){var t=e.parser,r=e.funcName;t.consumeSpaces();var n=t.fetch();if(en[n.text])return(r==="\\global"||r==="\\\\globallong")&&(n.text=en[n.text]),be(t.parseFunction(),"internal");throw new p("Invalid token after macro prefix",n)}}),re({type:"internal",names:["\\def","\\gdef","\\edef","\\xdef"],props:{numArgs:0,allowedInText:!0,primitive:!0},handler:function(e){var t=e.parser,r=e.funcName,n=t.gullet.popToken(),i=n.text;if(/^(?:[\\{}$&#^_]|EOF)$/.test(i))throw new p("Expected a control sequence",n);for(var c=0,f,b=[[]];t.gullet.future().text!=="{";)if(n=t.gullet.popToken(),n.text==="#"){if(t.gullet.future().text==="{"){f=t.gullet.future(),b[c].push("{");break}if(n=t.gullet.popToken(),!/^[1-9]$/.test(n.text))throw new p('Invalid argument number "'+n.text+'"');if(parseInt(n.text)!==c+1)throw new p('Argument number "'+n.text+'" out of order');c++,b.push([])}else{if(n.text==="EOF")throw new p("Expected a macro definition");b[c].push(n.text)}var x=t.gullet.consumeArg(),_=x.tokens;return f&&_.unshift(f),(r==="\\edef"||r==="\\xdef")&&(_=t.gullet.expandTokens(_),_.reverse()),t.gullet.macros.set(i,{tokens:_,numArgs:c,delimiters:b},r===en[r]),{type:"internal",mode:t.mode}}}),re({type:"internal",names:["\\let","\\\\globallet"],props:{numArgs:0,allowedInText:!0,primitive:!0},handler:function(e){var t=e.parser,r=e.funcName,n=ma(t.gullet.popToken());t.gullet.consumeSpaces();var i=Jl(t);return fa(t,n,i,r==="\\\\globallet"),{type:"internal",mode:t.mode}}}),re({type:"internal",names:["\\futurelet","\\\\globalfuture"],props:{numArgs:0,allowedInText:!0,primitive:!0},handler:function(e){var t=e.parser,r=e.funcName,n=ma(t.gullet.popToken()),i=t.gullet.popToken(),c=t.gullet.popToken();return fa(t,n,c,r==="\\\\globalfuture"),t.gullet.pushToken(c),t.gullet.pushToken(i),{type:"internal",mode:t.mode}}});var tr=function(e,t,r){var n=Fe.math[e]&&Fe.math[e].replace,i=Dt(n||e,t,r);if(!i)throw new Error("Unsupported symbol "+e+" and font size "+t+".");return i},tn=function(e,t,r,n){var i=r.havingBaseStyle(t),c=E.makeSpan(n.concat(i.sizingClasses(r)),[e],r),f=i.sizeMultiplier/r.sizeMultiplier;return c.height*=f,c.depth*=f,c.maxFontSize=i.sizeMultiplier,c},da=function(e,t,r){var n=t.havingBaseStyle(r),i=(1-t.sizeMultiplier/n.sizeMultiplier)*t.fontMetrics().axisHeight;e.classes.push("delimcenter"),e.style.top=Q(i),e.height-=i,e.depth+=i},$l=function(e,t,r,n,i,c){var f=E.makeSymbol(e,"Main-Regular",i,n),b=tn(f,t,n,c);return r&&da(b,n,t),b},es=function(e,t,r,n){return E.makeSymbol(e,"Size"+t+"-Regular",r,n)},pa=function(e,t,r,n,i,c){var f=es(e,t,i,n),b=tn(E.makeSpan(["delimsizing","size"+t],[f],n),ee.TEXT,n,c);return r&&da(b,n,ee.TEXT),b},rn=function(e,t,r){var n;t==="Size1-Regular"?n="delim-size1":n="delim-size4";var i=E.makeSpan(["delimsizinginner",n],[E.makeSpan([],[E.makeSymbol(e,t,r)])]);return{type:"elem",elem:i}},nn=function(e,t,r){var n=bt["Size4-Regular"][e.charCodeAt(0)]?bt["Size4-Regular"][e.charCodeAt(0)][4]:bt["Size1-Regular"][e.charCodeAt(0)][4],i=new Nt("inner",M0(e,Math.round(1e3*t))),c=new wt([i],{width:Q(n),height:Q(t),style:"width:"+Q(n),viewBox:"0 0 "+1e3*n+" "+Math.round(1e3*t),preserveAspectRatio:"xMinYMin"}),f=E.makeSvgSpan([],[c],r);return f.height=t,f.style.height=Q(t),f.style.width=Q(n),{type:"elem",elem:f}},an=.008,_r={type:"kern",size:-1*an},ts=["|","\\lvert","\\rvert","\\vert"],rs=["\\|","\\lVert","\\rVert","\\Vert"],ga=function(e,t,r,n,i,c){var f,b,x,_,C="",I=0;f=x=_=e,b=null;var F="Size1-Regular";e==="\\uparrow"?x=_="⏐":e==="\\Uparrow"?x=_="‖":e==="\\downarrow"?f=x="⏐":e==="\\Downarrow"?f=x="‖":e==="\\updownarrow"?(f="\\uparrow",x="⏐",_="\\downarrow"):e==="\\Updownarrow"?(f="\\Uparrow",x="‖",_="\\Downarrow"):O.contains(ts,e)?(x="∣",C="vert",I=333):O.contains(rs,e)?(x="∥",C="doublevert",I=556):e==="["||e==="\\lbrack"?(f="⎡",x="⎢",_="⎣",F="Size4-Regular",C="lbrack",I=667):e==="]"||e==="\\rbrack"?(f="⎤",x="⎥",_="⎦",F="Size4-Regular",C="rbrack",I=667):e==="\\lfloor"||e==="⌊"?(x=f="⎢",_="⎣",F="Size4-Regular",C="lfloor",I=667):e==="\\lceil"||e==="⌈"?(f="⎡",x=_="⎢",F="Size4-Regular",C="lceil",I=667):e==="\\rfloor"||e==="⌋"?(x=f="⎥",_="⎦",F="Size4-Regular",C="rfloor",I=667):e==="\\rceil"||e==="⌉"?(f="⎤",x=_="⎥",F="Size4-Regular",C="rceil",I=667):e==="("||e==="\\lparen"?(f="⎛",x="⎜",_="⎝",F="Size4-Regular",C="lparen",I=875):e===")"||e==="\\rparen"?(f="⎞",x="⎟",_="⎠",F="Size4-Regular",C="rparen",I=875):e==="\\{"||e==="\\lbrace"?(f="⎧",b="⎨",_="⎩",x="⎪",F="Size4-Regular"):e==="\\}"||e==="\\rbrace"?(f="⎫",b="⎬",_="⎭",x="⎪",F="Size4-Regular"):e==="\\lgroup"||e==="⟮"?(f="⎧",_="⎩",x="⎪",F="Size4-Regular"):e==="\\rgroup"||e==="⟯"?(f="⎫",_="⎭",x="⎪",F="Size4-Regular"):e==="\\lmoustache"||e==="⎰"?(f="⎧",_="⎭",x="⎪",F="Size4-Regular"):(e==="\\rmoustache"||e==="⎱")&&(f="⎫",_="⎩",x="⎪",F="Size4-Regular");var q=tr(f,F,i),Z=q.height+q.depth,$=tr(x,F,i),se=$.height+$.depth,ce=tr(_,F,i),he=ce.height+ce.depth,Te=0,ye=1;if(b!==null){var ze=tr(b,F,i);Te=ze.height+ze.depth,ye=2}var Ae=Z+he+Te,Ee=Math.max(0,Math.ceil((t-Ae)/(ye*se))),Le=Ae+Ee*ye*se,$e=n.fontMetrics().axisHeight;r&&($e*=n.sizeMultiplier);var dt=Le/2-$e,He=[];if(C.length>0){var T0=Le-Z-he,St=Math.round(Le*1e3),at=sr(C,Math.round(T0*1e3)),u0=new Nt(C,at),I0=(I/1e3).toFixed(3)+"em",L0=(St/1e3).toFixed(3)+"em",An=new wt([u0],{width:I0,height:L0,viewBox:"0 0 "+I+" "+St}),c0=E.makeSvgSpan([],[An],n);c0.height=St/1e3,c0.style.width=I0,c0.style.height=L0,He.push({type:"elem",elem:c0})}else{if(He.push(rn(_,F,i)),He.push(_r),b===null){var h0=Le-Z-he+2*an;He.push(nn(x,h0,n))}else{var At=(Le-Z-he-Te)/2+2*an;He.push(nn(x,At,n)),He.push(_r),He.push(rn(b,F,i)),He.push(_r),He.push(nn(x,At,n))}He.push(_r),He.push(rn(f,F,i))}var ar=n.havingBaseStyle(ee.TEXT),Tn=E.makeVList({positionType:"bottom",positionData:dt,children:He},ar);return tn(E.makeSpan(["delimsizing","mult"],[Tn],ar),ee.TEXT,n,c)},ln=80,sn=.08,on=function(e,t,r,n,i){var c=Qt(e,n,r),f=new Nt(e,c),b=new wt([f],{width:"400em",height:Q(t),viewBox:"0 0 400000 "+r,preserveAspectRatio:"xMinYMin slice"});return E.makeSvgSpan(["hide-tail"],[b],i)},ns=function(e,t){var r=t.havingBaseSizing(),n=wa("\\surd",e*r.sizeMultiplier,ya,r),i=r.sizeMultiplier,c=Math.max(0,t.minRuleThickness-t.fontMetrics().sqrtRuleThickness),f,b=0,x=0,_=0,C;return n.type==="small"?(_=1e3+1e3*c+ln,e<1?i=1:e<1.4&&(i=.7),b=(1+c+sn)/i,x=(1+c)/i,f=on("sqrtMain",b,_,c,t),f.style.minWidth="0.853em",C=.833/i):n.type==="large"?(_=(1e3+ln)*rr[n.size],x=(rr[n.size]+c)/i,b=(rr[n.size]+c+sn)/i,f=on("sqrtSize"+n.size,b,_,c,t),f.style.minWidth="1.02em",C=1/i):(b=e+c+sn,x=e+c,_=Math.floor(1e3*e+c)+ln,f=on("sqrtTall",b,_,c,t),f.style.minWidth="0.742em",C=1.056),f.height=x,f.style.height=Q(b),{span:f,advanceWidth:C,ruleWidth:(t.fontMetrics().sqrtRuleThickness+c)*i}},va=["(","\\lparen",")","\\rparen","[","\\lbrack","]","\\rbrack","\\{","\\lbrace","\\}","\\rbrace","\\lfloor","\\rfloor","⌊","⌋","\\lceil","\\rceil","⌈","⌉","\\surd"],as=["\\uparrow","\\downarrow","\\updownarrow","\\Uparrow","\\Downarrow","\\Updownarrow","|","\\|","\\vert","\\Vert","\\lvert","\\rvert","\\lVert","\\rVert","\\lgroup","\\rgroup","⟮","⟯","\\lmoustache","\\rmoustache","⎰","⎱"],ba=["<",">","\\langle","\\rangle","/","\\backslash","\\lt","\\gt"],rr=[0,1.2,1.8,2.4,3],is=function(e,t,r,n,i){if(e==="<"||e==="\\lt"||e==="⟨"?e="\\langle":(e===">"||e==="\\gt"||e==="⟩")&&(e="\\rangle"),O.contains(va,e)||O.contains(ba,e))return pa(e,t,!1,r,n,i);if(O.contains(as,e))return ga(e,rr[t],!1,r,n,i);throw new p("Illegal delimiter: '"+e+"'")},ls=[{type:"small",style:ee.SCRIPTSCRIPT},{type:"small",style:ee.SCRIPT},{type:"small",style:ee.TEXT},{type:"large",size:1},{type:"large",size:2},{type:"large",size:3},{type:"large",size:4}],ss=[{type:"small",style:ee.SCRIPTSCRIPT},{type:"small",style:ee.SCRIPT},{type:"small",style:ee.TEXT},{type:"stack"}],ya=[{type:"small",style:ee.SCRIPTSCRIPT},{type:"small",style:ee.SCRIPT},{type:"small",style:ee.TEXT},{type:"large",size:1},{type:"large",size:2},{type:"large",size:3},{type:"large",size:4},{type:"stack"}],os=function(e){if(e.type==="small")return"Main-Regular";if(e.type==="large")return"Size"+e.size+"-Regular";if(e.type==="stack")return"Size4-Regular";throw new Error("Add support for delim type '"+e.type+"' here.")},wa=function(e,t,r,n){for(var i=Math.min(2,3-n.style.size),c=i;c<r.length&&r[c].type!=="stack";c++){var f=tr(e,os(r[c]),"math"),b=f.height+f.depth;if(r[c].type==="small"){var x=n.havingBaseStyle(r[c].style);b*=x.sizeMultiplier}if(b>t)return r[c]}return r[r.length-1]},xa=function(e,t,r,n,i,c){e==="<"||e==="\\lt"||e==="⟨"?e="\\langle":(e===">"||e==="\\gt"||e==="⟩")&&(e="\\rangle");var f;O.contains(ba,e)?f=ls:O.contains(va,e)?f=ya:f=ss;var b=wa(e,t,f,n);return b.type==="small"?$l(e,b.style,r,n,i,c):b.type==="large"?pa(e,b.size,r,n,i,c):ga(e,t,r,n,i,c)},us=function(e,t,r,n,i,c){var f=n.fontMetrics().axisHeight*n.sizeMultiplier,b=901,x=5/n.fontMetrics().ptPerEm,_=Math.max(t-f,r+f),C=Math.max(_/500*b,2*_-x);return xa(e,C,!0,n,i,c)},Zt={sqrtImage:ns,sizedDelim:is,sizeToMaxHeight:rr,customSizedDelim:xa,leftRightDelim:us},ka={"\\bigl":{mclass:"mopen",size:1},"\\Bigl":{mclass:"mopen",size:2},"\\biggl":{mclass:"mopen",size:3},"\\Biggl":{mclass:"mopen",size:4},"\\bigr":{mclass:"mclose",size:1},"\\Bigr":{mclass:"mclose",size:2},"\\biggr":{mclass:"mclose",size:3},"\\Biggr":{mclass:"mclose",size:4},"\\bigm":{mclass:"mrel",size:1},"\\Bigm":{mclass:"mrel",size:2},"\\biggm":{mclass:"mrel",size:3},"\\Biggm":{mclass:"mrel",size:4},"\\big":{mclass:"mord",size:1},"\\Big":{mclass:"mord",size:2},"\\bigg":{mclass:"mord",size:3},"\\Bigg":{mclass:"mord",size:4}},cs=["(","\\lparen",")","\\rparen","[","\\lbrack","]","\\rbrack","\\{","\\lbrace","\\}","\\rbrace","\\lfloor","\\rfloor","⌊","⌋","\\lceil","\\rceil","⌈","⌉","<",">","\\langle","⟨","\\rangle","⟩","\\lt","\\gt","\\lvert","\\rvert","\\lVert","\\rVert","\\lgroup","\\rgroup","⟮","⟯","\\lmoustache","\\rmoustache","⎰","⎱","/","\\backslash","|","\\vert","\\|","\\Vert","\\uparrow","\\Uparrow","\\downarrow","\\Downarrow","\\updownarrow","\\Updownarrow","."];function Mr(u,e){var t=Sr(u);if(t&&O.contains(cs,t.text))return t;throw t?new p("Invalid delimiter '"+t.text+"' after '"+e.funcName+"'",u):new p("Invalid delimiter type '"+u.type+"'",u)}re({type:"delimsizing",names:["\\bigl","\\Bigl","\\biggl","\\Biggl","\\bigr","\\Bigr","\\biggr","\\Biggr","\\bigm","\\Bigm","\\biggm","\\Biggm","\\big","\\Big","\\bigg","\\Bigg"],props:{numArgs:1,argTypes:["primitive"]},handler:function(e,t){var r=Mr(t[0],e);return{type:"delimsizing",mode:e.parser.mode,size:ka[e.funcName].size,mclass:ka[e.funcName].mclass,delim:r.text}},htmlBuilder:function(e,t){return e.delim==="."?E.makeSpan([e.mclass]):Zt.sizedDelim(e.delim,e.size,t,e.mode,[e.mclass])},mathmlBuilder:function(e){var t=[];e.delim!=="."&&t.push(kt(e.delim,e.mode));var r=new V.MathNode("mo",t);e.mclass==="mopen"||e.mclass==="mclose"?r.setAttribute("fence","true"):r.setAttribute("fence","false"),r.setAttribute("stretchy","true");var n=Q(Zt.sizeToMaxHeight[e.size]);return r.setAttribute("minsize",n),r.setAttribute("maxsize",n),r}});function Sa(u){if(!u.body)throw new Error("Bug: The leftright ParseNode wasn't fully parsed.")}re({type:"leftright-right",names:["\\right"],props:{numArgs:1,primitive:!0},handler:function(e,t){var r=e.parser.gullet.macros.get("\\current@color");if(r&&typeof r!="string")throw new p("\\current@color set to non-string in \\right");return{type:"leftright-right",mode:e.parser.mode,delim:Mr(t[0],e).text,color:r}}}),re({type:"leftright",names:["\\left"],props:{numArgs:1,primitive:!0},handler:function(e,t){var r=Mr(t[0],e),n=e.parser;++n.leftrightDepth;var i=n.parseExpression(!1);--n.leftrightDepth,n.expect("\\right",!1);var c=be(n.parseFunction(),"leftright-right");return{type:"leftright",mode:n.mode,body:i,left:r.text,right:c.delim,rightColor:c.color}},htmlBuilder:function(e,t){Sa(e);for(var r=Ze(e.body,t,!0,["mopen","mclose"]),n=0,i=0,c=!1,f=0;f<r.length;f++)r[f].isMiddle?c=!0:(n=Math.max(r[f].height,n),i=Math.max(r[f].depth,i));n*=t.sizeMultiplier,i*=t.sizeMultiplier;var b;if(e.left==="."?b=$0(t,["mopen"]):b=Zt.leftRightDelim(e.left,n,i,t,e.mode,["mopen"]),r.unshift(b),c)for(var x=1;x<r.length;x++){var _=r[x],C=_.isMiddle;C&&(r[x]=Zt.leftRightDelim(C.delim,n,i,C.options,e.mode,[]))}var I;if(e.right===".")I=$0(t,["mclose"]);else{var F=e.rightColor?t.withColor(e.rightColor):t;I=Zt.leftRightDelim(e.right,n,i,F,e.mode,["mclose"])}return r.push(I),E.makeSpan(["minner"],r,t)},mathmlBuilder:function(e,t){Sa(e);var r=ct(e.body,t);if(e.left!=="."){var n=new V.MathNode("mo",[kt(e.left,e.mode)]);n.setAttribute("fence","true"),r.unshift(n)}if(e.right!=="."){var i=new V.MathNode("mo",[kt(e.right,e.mode)]);i.setAttribute("fence","true"),e.rightColor&&i.setAttribute("mathcolor",e.rightColor),r.push(i)}return Kr(r)}}),re({type:"middle",names:["\\middle"],props:{numArgs:1,primitive:!0},handler:function(e,t){var r=Mr(t[0],e);if(!e.parser.leftrightDepth)throw new p("\\middle without preceding \\left",r);return{type:"middle",mode:e.parser.mode,delim:r.text}},htmlBuilder:function(e,t){var r;if(e.delim===".")r=$0(t,[]);else{r=Zt.sizedDelim(e.delim,1,t,e.mode,[]);var n={delim:e.delim,options:t};r.isMiddle=n}return r},mathmlBuilder:function(e,t){var r=e.delim==="\\vert"||e.delim==="|"?kt("|","text"):kt(e.delim,e.mode),n=new V.MathNode("mo",[r]);return n.setAttribute("fence","true"),n.setAttribute("lspace","0.05em"),n.setAttribute("rspace","0.05em"),n}});var un=function(e,t){var r=E.wrapFragment(Se(e.body,t),t),n=e.label.slice(1),i=t.sizeMultiplier,c,f=0,b=O.isCharacterBox(e.body);if(n==="sout")c=E.makeSpan(["stretchy","sout"]),c.height=t.fontMetrics().defaultRuleThickness/i,f=-.5*t.fontMetrics().xHeight;else if(n==="phase"){var x=Be({number:.6,unit:"pt"},t),_=Be({number:.35,unit:"ex"},t),C=t.havingBaseSizing();i=i/C.sizeMultiplier;var I=r.height+r.depth+x+_;r.style.paddingLeft=Q(I/2+x);var F=Math.floor(1e3*I*i),q=b0(F),Z=new wt([new Nt("phase",q)],{width:"400em",height:Q(F/1e3),viewBox:"0 0 400000 "+F,preserveAspectRatio:"xMinYMin slice"});c=E.makeSvgSpan(["hide-tail"],[Z],t),c.style.height=Q(I),f=r.depth+x+_}else{/cancel/.test(n)?b||r.classes.push("cancel-pad"):n==="angl"?r.classes.push("anglpad"):r.classes.push("boxpad");var $=0,se=0,ce=0;/box/.test(n)?(ce=Math.max(t.fontMetrics().fboxrule,t.minRuleThickness),$=t.fontMetrics().fboxsep+(n==="colorbox"?0:ce),se=$):n==="angl"?(ce=Math.max(t.fontMetrics().defaultRuleThickness,t.minRuleThickness),$=4*ce,se=Math.max(0,.25-r.depth)):($=b?.2:0,se=$),c=Xt.encloseSpan(r,n,$,se,t),/fbox|boxed|fcolorbox/.test(n)?(c.style.borderStyle="solid",c.style.borderWidth=Q(ce)):n==="angl"&&ce!==.049&&(c.style.borderTopWidth=Q(ce),c.style.borderRightWidth=Q(ce)),f=r.depth+se,e.backgroundColor&&(c.style.backgroundColor=e.backgroundColor,e.borderColor&&(c.style.borderColor=e.borderColor))}var he;if(e.backgroundColor)he=E.makeVList({positionType:"individualShift",children:[{type:"elem",elem:c,shift:f},{type:"elem",elem:r,shift:0}]},t);else{var Te=/cancel|phase/.test(n)?["svg-align"]:[];he=E.makeVList({positionType:"individualShift",children:[{type:"elem",elem:r,shift:0},{type:"elem",elem:c,shift:f,wrapperClasses:Te}]},t)}return/cancel/.test(n)&&(he.height=r.height,he.depth=r.depth),/cancel/.test(n)&&!b?E.makeSpan(["mord","cancel-lap"],[he],t):E.makeSpan(["mord"],[he],t)},cn=function(e,t){var r=0,n=new V.MathNode(e.label.indexOf("colorbox")>-1?"mpadded":"menclose",[Ne(e.body,t)]);switch(e.label){case"\\cancel":n.setAttribute("notation","updiagonalstrike");break;case"\\bcancel":n.setAttribute("notation","downdiagonalstrike");break;case"\\phase":n.setAttribute("notation","phasorangle");break;case"\\sout":n.setAttribute("notation","horizontalstrike");break;case"\\fbox":n.setAttribute("notation","box");break;case"\\angl":n.setAttribute("notation","actuarial");break;case"\\fcolorbox":case"\\colorbox":if(r=t.fontMetrics().fboxsep*t.fontMetrics().ptPerEm,n.setAttribute("width","+"+2*r+"pt"),n.setAttribute("height","+"+2*r+"pt"),n.setAttribute("lspace",r+"pt"),n.setAttribute("voffset",r+"pt"),e.label==="\\fcolorbox"){var i=Math.max(t.fontMetrics().fboxrule,t.minRuleThickness);n.setAttribute("style","border: "+i+"em solid "+String(e.borderColor))}break;case"\\xcancel":n.setAttribute("notation","updiagonalstrike downdiagonalstrike");break}return e.backgroundColor&&n.setAttribute("mathbackground",e.backgroundColor),n};re({type:"enclose",names:["\\colorbox"],props:{numArgs:2,allowedInText:!0,argTypes:["color","text"]},handler:function(e,t,r){var n=e.parser,i=e.funcName,c=be(t[0],"color-token").color,f=t[1];return{type:"enclose",mode:n.mode,label:i,backgroundColor:c,body:f}},htmlBuilder:un,mathmlBuilder:cn}),re({type:"enclose",names:["\\fcolorbox"],props:{numArgs:3,allowedInText:!0,argTypes:["color","color","text"]},handler:function(e,t,r){var n=e.parser,i=e.funcName,c=be(t[0],"color-token").color,f=be(t[1],"color-token").color,b=t[2];return{type:"enclose",mode:n.mode,label:i,backgroundColor:f,borderColor:c,body:b}},htmlBuilder:un,mathmlBuilder:cn}),re({type:"enclose",names:["\\fbox"],props:{numArgs:1,argTypes:["hbox"],allowedInText:!0},handler:function(e,t){var r=e.parser;return{type:"enclose",mode:r.mode,label:"\\fbox",body:t[0]}}}),re({type:"enclose",names:["\\cancel","\\bcancel","\\xcancel","\\sout","\\phase"],props:{numArgs:1},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0];return{type:"enclose",mode:r.mode,label:n,body:i}},htmlBuilder:un,mathmlBuilder:cn}),re({type:"enclose",names:["\\angl"],props:{numArgs:1,argTypes:["hbox"],allowedInText:!1},handler:function(e,t){var r=e.parser;return{type:"enclose",mode:r.mode,label:"\\angl",body:t[0]}}});var Aa={};function Rt(u){for(var e=u.type,t=u.names,r=u.props,n=u.handler,i=u.htmlBuilder,c=u.mathmlBuilder,f={type:e,numArgs:r.numArgs||0,allowedInText:!1,numOptionalArgs:0,handler:n},b=0;b<t.length;++b)Aa[t[b]]=f;i&&(yr[e]=i),c&&(wr[e]=c)}var Ta={};function w(u,e){Ta[u]=e}var zt=function(){function u(e,t,r){this.lexer=void 0,this.start=void 0,this.end=void 0,this.lexer=e,this.start=t,this.end=r}return u.range=function(t,r){return r?!t||!t.loc||!r.loc||t.loc.lexer!==r.loc.lexer?null:new u(t.loc.lexer,t.loc.start,r.loc.end):t&&t.loc},u}(),l0=function(){function u(t,r){this.text=void 0,this.loc=void 0,this.noexpand=void 0,this.treatAsRelax=void 0,this.text=t,this.loc=r}var e=u.prototype;return e.range=function(r,n){return new u(n,zt.range(this,r))},u}();function _a(u){var e=[];u.consumeSpaces();var t=u.fetch().text;for(t==="\\relax"&&(u.consume(),u.consumeSpaces(),t=u.fetch().text);t==="\\hline"||t==="\\hdashline";)u.consume(),e.push(t==="\\hdashline"),u.consumeSpaces(),t=u.fetch().text;return e}var zr=function(e){var t=e.parser.settings;if(!t.displayMode)throw new p("{"+e.envName+"} can be used only in display mode.")};function hn(u){if(u.indexOf("ed")===-1)return u.indexOf("*")===-1}function s0(u,e,t){var r=e.hskipBeforeAndAfter,n=e.addJot,i=e.cols,c=e.arraystretch,f=e.colSeparationType,b=e.autoTag,x=e.singleRow,_=e.emptySingleRow,C=e.maxNumCols,I=e.leqno;if(u.gullet.beginGroup(),x||u.gullet.macros.set("\\cr","\\\\\\relax"),!c){var F=u.gullet.expandMacroAsText("\\arraystretch");if(F==null)c=1;else if(c=parseFloat(F),!c||c<0)throw new p("Invalid \\arraystretch: "+F)}u.gullet.beginGroup();var q=[],Z=[q],$=[],se=[],ce=b!=null?[]:void 0;function he(){b&&u.gullet.macros.set("\\@eqnsw","1",!0)}function Te(){ce&&(u.gullet.macros.get("\\df@tag")?(ce.push(u.subparse([new l0("\\df@tag")])),u.gullet.macros.set("\\df@tag",void 0,!0)):ce.push(!!b&&u.gullet.macros.get("\\@eqnsw")==="1"))}for(he(),se.push(_a(u));;){var ye=u.parseExpression(!1,x?"\\end":"\\\\");u.gullet.endGroup(),u.gullet.beginGroup(),ye={type:"ordgroup",mode:u.mode,body:ye},t&&(ye={type:"styling",mode:u.mode,style:t,body:[ye]}),q.push(ye);var ze=u.fetch().text;if(ze==="&"){if(C&&q.length===C){if(x||f)throw new p("Too many tab characters: &",u.nextToken);u.settings.reportNonstrict("textEnv","Too few columns specified in the {array} column argument.")}u.consume()}else if(ze==="\\end"){Te(),q.length===1&&ye.type==="styling"&&ye.body[0].body.length===0&&(Z.length>1||!_)&&Z.pop(),se.length<Z.length+1&&se.push([]);break}else if(ze==="\\\\"){u.consume();var Ae=void 0;u.gullet.future().text!==" "&&(Ae=u.parseSizeGroup(!0)),$.push(Ae?Ae.value:null),Te(),se.push(_a(u)),q=[],Z.push(q),he()}else throw new p("Expected & or \\\\ or \\cr or \\end",u.nextToken)}return u.gullet.endGroup(),u.gullet.endGroup(),{type:"array",mode:u.mode,addJot:n,arraystretch:c,body:Z,cols:i,rowGaps:$,hskipBeforeAndAfter:r,hLinesBeforeRow:se,colSeparationType:f,tags:ce,leqno:I}}function mn(u){return u.slice(0,1)==="d"?"display":"text"}var Ft=function(e,t){var r,n,i=e.body.length,c=e.hLinesBeforeRow,f=0,b=new Array(i),x=[],_=Math.max(t.fontMetrics().arrayRuleWidth,t.minRuleThickness),C=1/t.fontMetrics().ptPerEm,I=5*C;if(e.colSeparationType&&e.colSeparationType==="small"){var F=t.havingStyle(ee.SCRIPT).sizeMultiplier;I=.2778*(F/t.sizeMultiplier)}var q=e.colSeparationType==="CD"?Be({number:3,unit:"ex"},t):12*C,Z=3*C,$=e.arraystretch*q,se=.7*$,ce=.3*$,he=0;function Te(Dr){for(var Nr=0;Nr<Dr.length;++Nr)Nr>0&&(he+=.25),x.push({pos:he,isDashed:Dr[Nr]})}for(Te(c[0]),r=0;r<e.body.length;++r){var ye=e.body[r],ze=se,Ae=ce;f<ye.length&&(f=ye.length);var Ee=new Array(ye.length);for(n=0;n<ye.length;++n){var Le=Se(ye[n],t);Ae<Le.depth&&(Ae=Le.depth),ze<Le.height&&(ze=Le.height),Ee[n]=Le}var $e=e.rowGaps[r],dt=0;$e&&(dt=Be($e,t),dt>0&&(dt+=ce,Ae<dt&&(Ae=dt),dt=0)),e.addJot&&(Ae+=Z),Ee.height=ze,Ee.depth=Ae,he+=ze,Ee.pos=he,he+=Ae+dt,b[r]=Ee,Te(c[r+1])}var He=he/2+t.fontMetrics().axisHeight,T0=e.cols||[],St=[],at,u0,I0=[];if(e.tags&&e.tags.some(function(Dr){return Dr}))for(r=0;r<i;++r){var L0=b[r],An=L0.pos-He,c0=e.tags[r],h0=void 0;c0===!0?h0=E.makeSpan(["eqn-num"],[],t):c0===!1?h0=E.makeSpan([],[],t):h0=E.makeSpan([],Ze(c0,t,!0),t),h0.depth=L0.depth,h0.height=L0.height,I0.push({type:"elem",elem:h0,shift:An})}for(n=0,u0=0;n<f||u0<T0.length;++n,++u0){for(var At=T0[u0]||{},ar=!0;At.type==="separator";){if(ar||(at=E.makeSpan(["arraycolsep"],[]),at.style.width=Q(t.fontMetrics().doubleRuleSep),St.push(at)),At.separator==="|"||At.separator===":"){var Tn=At.separator==="|"?"solid":"dashed",O0=E.makeSpan(["vertical-separator"],[],t);O0.style.height=Q(he),O0.style.borderRightWidth=Q(_),O0.style.borderRightStyle=Tn,O0.style.margin="0 "+Q(-_/2);var si=he-He;si&&(O0.style.verticalAlign=Q(-si)),St.push(O0)}else throw new p("Invalid separator type: "+At.separator);u0++,At=T0[u0]||{},ar=!1}if(!(n>=f)){var q0=void 0;(n>0||e.hskipBeforeAndAfter)&&(q0=O.deflt(At.pregap,I),q0!==0&&(at=E.makeSpan(["arraycolsep"],[]),at.style.width=Q(q0),St.push(at)));var P0=[];for(r=0;r<i;++r){var Cr=b[r],Br=Cr[n];if(Br){var Ls=Cr.pos-He;Br.depth=Cr.depth,Br.height=Cr.height,P0.push({type:"elem",elem:Br,shift:Ls})}}P0=E.makeVList({positionType:"individualShift",children:P0},t),P0=E.makeSpan(["col-align-"+(At.align||"c")],[P0]),St.push(P0),(n<f-1||e.hskipBeforeAndAfter)&&(q0=O.deflt(At.postgap,I),q0!==0&&(at=E.makeSpan(["arraycolsep"],[]),at.style.width=Q(q0),St.push(at)))}}if(b=E.makeSpan(["mtable"],St),x.length>0){for(var Os=E.makeLineSpan("hline",t,_),qs=E.makeLineSpan("hdashline",t,_),_n=[{type:"elem",elem:b,shift:0}];x.length>0;){var oi=x.pop(),ui=oi.pos-He;oi.isDashed?_n.push({type:"elem",elem:qs,shift:ui}):_n.push({type:"elem",elem:Os,shift:ui})}b=E.makeVList({positionType:"individualShift",children:_n},t)}if(I0.length===0)return E.makeSpan(["mord"],[b],t);var Mn=E.makeVList({positionType:"individualShift",children:I0},t);return Mn=E.makeSpan(["tag"],[Mn],t),E.makeFragment([b,Mn])},hs={c:"center ",l:"left ",r:"right "},It=function(e,t){for(var r=[],n=new V.MathNode("mtd",[],["mtr-glue"]),i=new V.MathNode("mtd",[],["mml-eqn-num"]),c=0;c<e.body.length;c++){for(var f=e.body[c],b=[],x=0;x<f.length;x++)b.push(new V.MathNode("mtd",[Ne(f[x],t)]));e.tags&&e.tags[c]&&(b.unshift(n),b.push(n),e.leqno?b.unshift(i):b.push(i)),r.push(new V.MathNode("mtr",b))}var _=new V.MathNode("mtable",r),C=e.arraystretch===.5?.1:.16+e.arraystretch-1+(e.addJot?.09:0);_.setAttribute("rowspacing",Q(C));var I="",F="";if(e.cols&&e.cols.length>0){var q=e.cols,Z="",$=!1,se=0,ce=q.length;q[0].type==="separator"&&(I+="top ",se=1),q[q.length-1].type==="separator"&&(I+="bottom ",ce-=1);for(var he=se;he<ce;he++)q[he].type==="align"?(F+=hs[q[he].align],$&&(Z+="none "),$=!0):q[he].type==="separator"&&$&&(Z+=q[he].separator==="|"?"solid ":"dashed ",$=!1);_.setAttribute("columnalign",F.trim()),/[sd]/.test(Z)&&_.setAttribute("columnlines",Z.trim())}if(e.colSeparationType==="align"){for(var Te=e.cols||[],ye="",ze=1;ze<Te.length;ze++)ye+=ze%2?"0em ":"1em ";_.setAttribute("columnspacing",ye.trim())}else e.colSeparationType==="alignat"||e.colSeparationType==="gather"?_.setAttribute("columnspacing","0em"):e.colSeparationType==="small"?_.setAttribute("columnspacing","0.2778em"):e.colSeparationType==="CD"?_.setAttribute("columnspacing","0.5em"):_.setAttribute("columnspacing","1em");var Ae="",Ee=e.hLinesBeforeRow;I+=Ee[0].length>0?"left ":"",I+=Ee[Ee.length-1].length>0?"right ":"";for(var Le=1;Le<Ee.length-1;Le++)Ae+=Ee[Le].length===0?"none ":Ee[Le][0]?"dashed ":"solid ";return/[sd]/.test(Ae)&&_.setAttribute("rowlines",Ae.trim()),I!==""&&(_=new V.MathNode("menclose",[_]),_.setAttribute("notation",I.trim())),e.arraystretch&&e.arraystretch<1&&(_=new V.MathNode("mstyle",[_]),_.setAttribute("scriptlevel","1")),_},Ma=function(e,t){e.envName.indexOf("ed")===-1&&zr(e);var r=[],n=e.envName.indexOf("at")>-1?"alignat":"align",i=e.envName==="split",c=s0(e.parser,{cols:r,addJot:!0,autoTag:i?void 0:hn(e.envName),emptySingleRow:!0,colSeparationType:n,maxNumCols:i?2:void 0,leqno:e.parser.settings.leqno},"display"),f,b=0,x={type:"ordgroup",mode:e.mode,body:[]};if(t[0]&&t[0].type==="ordgroup"){for(var _="",C=0;C<t[0].body.length;C++){var I=be(t[0].body[C],"textord");_+=I.text}f=Number(_),b=f*2}var F=!b;c.body.forEach(function(se){for(var ce=1;ce<se.length;ce+=2){var he=be(se[ce],"styling"),Te=be(he.body[0],"ordgroup");Te.body.unshift(x)}if(F)b<se.length&&(b=se.length);else{var ye=se.length/2;if(f<ye)throw new p("Too many math in a row: "+("expected "+f+", but got "+ye),se[0])}});for(var q=0;q<b;++q){var Z="r",$=0;q%2===1?Z="l":q>0&&F&&($=1),r[q]={type:"align",align:Z,pregap:$,postgap:0}}return c.colSeparationType=F?"align":"alignat",c};Rt({type:"array",names:["array","darray"],props:{numArgs:1},handler:function(e,t){var r=Sr(t[0]),n=r?[t[0]]:be(t[0],"ordgroup").body,i=n.map(function(f){var b=Jr(f),x=b.text;if("lcr".indexOf(x)!==-1)return{type:"align",align:x};if(x==="|")return{type:"separator",separator:"|"};if(x===":")return{type:"separator",separator:":"};throw new p("Unknown column alignment: "+x,f)}),c={cols:i,hskipBeforeAndAfter:!0,maxNumCols:i.length};return s0(e.parser,c,mn(e.envName))},htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["matrix","pmatrix","bmatrix","Bmatrix","vmatrix","Vmatrix","matrix*","pmatrix*","bmatrix*","Bmatrix*","vmatrix*","Vmatrix*"],props:{numArgs:0},handler:function(e){var t={matrix:null,pmatrix:["(",")"],bmatrix:["[","]"],Bmatrix:["\\{","\\}"],vmatrix:["|","|"],Vmatrix:["\\Vert","\\Vert"]}[e.envName.replace("*","")],r="c",n={hskipBeforeAndAfter:!1,cols:[{type:"align",align:r}]};if(e.envName.charAt(e.envName.length-1)==="*"){var i=e.parser;if(i.consumeSpaces(),i.fetch().text==="["){if(i.consume(),i.consumeSpaces(),r=i.fetch().text,"lcr".indexOf(r)===-1)throw new p("Expected l or c or r",i.nextToken);i.consume(),i.consumeSpaces(),i.expect("]"),i.consume(),n.cols=[{type:"align",align:r}]}}var c=s0(e.parser,n,mn(e.envName)),f=Math.max.apply(Math,[0].concat(c.body.map(function(b){return b.length})));return c.cols=new Array(f).fill({type:"align",align:r}),t?{type:"leftright",mode:e.mode,body:[c],left:t[0],right:t[1],rightColor:void 0}:c},htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["smallmatrix"],props:{numArgs:0},handler:function(e){var t={arraystretch:.5},r=s0(e.parser,t,"script");return r.colSeparationType="small",r},htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["subarray"],props:{numArgs:1},handler:function(e,t){var r=Sr(t[0]),n=r?[t[0]]:be(t[0],"ordgroup").body,i=n.map(function(f){var b=Jr(f),x=b.text;if("lc".indexOf(x)!==-1)return{type:"align",align:x};throw new p("Unknown column alignment: "+x,f)});if(i.length>1)throw new p("{subarray} can contain only one column");var c={cols:i,hskipBeforeAndAfter:!1,arraystretch:.5};if(c=s0(e.parser,c,"script"),c.body.length>0&&c.body[0].length>1)throw new p("{subarray} can contain only one column");return c},htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["cases","dcases","rcases","drcases"],props:{numArgs:0},handler:function(e){var t={arraystretch:1.2,cols:[{type:"align",align:"l",pregap:0,postgap:1},{type:"align",align:"l",pregap:0,postgap:0}]},r=s0(e.parser,t,mn(e.envName));return{type:"leftright",mode:e.mode,body:[r],left:e.envName.indexOf("r")>-1?".":"\\{",right:e.envName.indexOf("r")>-1?"\\}":".",rightColor:void 0}},htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["align","align*","aligned","split"],props:{numArgs:0},handler:Ma,htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["gathered","gather","gather*"],props:{numArgs:0},handler:function(e){O.contains(["gather","gather*"],e.envName)&&zr(e);var t={cols:[{type:"align",align:"c"}],addJot:!0,colSeparationType:"gather",autoTag:hn(e.envName),emptySingleRow:!0,leqno:e.parser.settings.leqno};return s0(e.parser,t,"display")},htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["alignat","alignat*","alignedat"],props:{numArgs:1},handler:Ma,htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["equation","equation*"],props:{numArgs:0},handler:function(e){zr(e);var t={autoTag:hn(e.envName),emptySingleRow:!0,singleRow:!0,maxNumCols:1,leqno:e.parser.settings.leqno};return s0(e.parser,t,"display")},htmlBuilder:Ft,mathmlBuilder:It}),Rt({type:"array",names:["CD"],props:{numArgs:0},handler:function(e){return zr(e),Ql(e.parser)},htmlBuilder:Ft,mathmlBuilder:It}),w("\\nonumber","\\gdef\\@eqnsw{0}"),w("\\notag","\\nonumber"),re({type:"text",names:["\\hline","\\hdashline"],props:{numArgs:0,allowedInText:!0,allowedInMath:!0},handler:function(e,t){throw new p(e.funcName+" valid only within array environment")}});var ms=Aa,za=ms;re({type:"environment",names:["\\begin","\\end"],props:{numArgs:1,argTypes:["text"]},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0];if(i.type!=="ordgroup")throw new p("Invalid environment name",i);for(var c="",f=0;f<i.body.length;++f)c+=be(i.body[f],"textord").text;if(n==="\\begin"){if(!za.hasOwnProperty(c))throw new p("No such environment: "+c,i);var b=za[c],x=r.parseArguments("\\begin{"+c+"}",b),_=x.args,C=x.optArgs,I={mode:r.mode,envName:c,parser:r},F=b.handler(I,_,C);r.expect("\\end",!1);var q=r.nextToken,Z=be(r.parseFunction(),"environment");if(Z.name!==c)throw new p("Mismatch: \\begin{"+c+"} matched by \\end{"+Z.name+"}",q);return F}return{type:"environment",mode:r.mode,name:c,nameGroup:i}}});var Ea=function(e,t){var r=e.font,n=t.withFont(r);return Se(e.body,n)},Ca=function(e,t){var r=e.font,n=t.withFont(r);return Ne(e.body,n)},Ba={"\\Bbb":"\\mathbb","\\bold":"\\mathbf","\\frak":"\\mathfrak","\\bm":"\\boldsymbol"};re({type:"font",names:["\\mathrm","\\mathit","\\mathbf","\\mathnormal","\\mathbb","\\mathcal","\\mathfrak","\\mathscr","\\mathsf","\\mathtt","\\Bbb","\\bold","\\frak"],props:{numArgs:1,allowedInArgument:!0},handler:function(e,t){var r=e.parser,n=e.funcName,i=xr(t[0]),c=n;return c in Ba&&(c=Ba[c]),{type:"font",mode:r.mode,font:c.slice(1),body:i}},htmlBuilder:Ea,mathmlBuilder:Ca}),re({type:"mclass",names:["\\boldsymbol","\\bm"],props:{numArgs:1},handler:function(e,t){var r=e.parser,n=t[0],i=O.isCharacterBox(n);return{type:"mclass",mode:r.mode,mclass:Tr(n),body:[{type:"font",mode:r.mode,font:"boldsymbol",body:n}],isCharacterBox:i}}}),re({type:"font",names:["\\rm","\\sf","\\tt","\\bf","\\it","\\cal"],props:{numArgs:0,allowedInText:!0},handler:function(e,t){var r=e.parser,n=e.funcName,i=e.breakOnTokenText,c=r.mode,f=r.parseExpression(!0,i),b="math"+n.slice(1);return{type:"font",mode:c,font:b,body:{type:"ordgroup",mode:r.mode,body:f}}},htmlBuilder:Ea,mathmlBuilder:Ca});var Da=function(e,t){var r=t;return e==="display"?r=r.id>=ee.SCRIPT.id?r.text():ee.DISPLAY:e==="text"&&r.size===ee.DISPLAY.size?r=ee.TEXT:e==="script"?r=ee.SCRIPT:e==="scriptscript"&&(r=ee.SCRIPTSCRIPT),r},fn=function(e,t){var r=Da(e.size,t.style),n=r.fracNum(),i=r.fracDen(),c;c=t.havingStyle(n);var f=Se(e.numer,c,t);if(e.continued){var b=8.5/t.fontMetrics().ptPerEm,x=3.5/t.fontMetrics().ptPerEm;f.height=f.height<b?b:f.height,f.depth=f.depth<x?x:f.depth}c=t.havingStyle(i);var _=Se(e.denom,c,t),C,I,F;e.hasBarLine?(e.barSize?(I=Be(e.barSize,t),C=E.makeLineSpan("frac-line",t,I)):C=E.makeLineSpan("frac-line",t),I=C.height,F=C.height):(C=null,I=0,F=t.fontMetrics().defaultRuleThickness);var q,Z,$;r.size===ee.DISPLAY.size||e.size==="display"?(q=t.fontMetrics().num1,I>0?Z=3*F:Z=7*F,$=t.fontMetrics().denom1):(I>0?(q=t.fontMetrics().num2,Z=F):(q=t.fontMetrics().num3,Z=3*F),$=t.fontMetrics().denom2);var se;if(C){var he=t.fontMetrics().axisHeight;q-f.depth-(he+.5*I)<Z&&(q+=Z-(q-f.depth-(he+.5*I))),he-.5*I-(_.height-$)<Z&&($+=Z-(he-.5*I-(_.height-$)));var Te=-(he-.5*I);se=E.makeVList({positionType:"individualShift",children:[{type:"elem",elem:_,shift:$},{type:"elem",elem:C,shift:Te},{type:"elem",elem:f,shift:-q}]},t)}else{var ce=q-f.depth-(_.height-$);ce<Z&&(q+=.5*(Z-ce),$+=.5*(Z-ce)),se=E.makeVList({positionType:"individualShift",children:[{type:"elem",elem:_,shift:$},{type:"elem",elem:f,shift:-q}]},t)}c=t.havingStyle(r),se.height*=c.sizeMultiplier/t.sizeMultiplier,se.depth*=c.sizeMultiplier/t.sizeMultiplier;var ye;r.size===ee.DISPLAY.size?ye=t.fontMetrics().delim1:r.size===ee.SCRIPTSCRIPT.size?ye=t.havingStyle(ee.SCRIPT).fontMetrics().delim2:ye=t.fontMetrics().delim2;var ze,Ae;return e.leftDelim==null?ze=$0(t,["mopen"]):ze=Zt.customSizedDelim(e.leftDelim,ye,!0,t.havingStyle(r),e.mode,["mopen"]),e.continued?Ae=E.makeSpan([]):e.rightDelim==null?Ae=$0(t,["mclose"]):Ae=Zt.customSizedDelim(e.rightDelim,ye,!0,t.havingStyle(r),e.mode,["mclose"]),E.makeSpan(["mord"].concat(c.sizingClasses(t)),[ze,E.makeSpan(["mfrac"],[se]),Ae],t)},dn=function(e,t){var r=new V.MathNode("mfrac",[Ne(e.numer,t),Ne(e.denom,t)]);if(!e.hasBarLine)r.setAttribute("linethickness","0px");else if(e.barSize){var n=Be(e.barSize,t);r.setAttribute("linethickness",Q(n))}var i=Da(e.size,t.style);if(i.size!==t.style.size){r=new V.MathNode("mstyle",[r]);var c=i.size===ee.DISPLAY.size?"true":"false";r.setAttribute("displaystyle",c),r.setAttribute("scriptlevel","0")}if(e.leftDelim!=null||e.rightDelim!=null){var f=[];if(e.leftDelim!=null){var b=new V.MathNode("mo",[new V.TextNode(e.leftDelim.replace("\\",""))]);b.setAttribute("fence","true"),f.push(b)}if(f.push(r),e.rightDelim!=null){var x=new V.MathNode("mo",[new V.TextNode(e.rightDelim.replace("\\",""))]);x.setAttribute("fence","true"),f.push(x)}return Kr(f)}return r};re({type:"genfrac",names:["\\dfrac","\\frac","\\tfrac","\\dbinom","\\binom","\\tbinom","\\\\atopfrac","\\\\bracefrac","\\\\brackfrac"],props:{numArgs:2,allowedInArgument:!0},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0],c=t[1],f,b=null,x=null,_="auto";switch(n){case"\\dfrac":case"\\frac":case"\\tfrac":f=!0;break;case"\\\\atopfrac":f=!1;break;case"\\dbinom":case"\\binom":case"\\tbinom":f=!1,b="(",x=")";break;case"\\\\bracefrac":f=!1,b="\\{",x="\\}";break;case"\\\\brackfrac":f=!1,b="[",x="]";break;default:throw new Error("Unrecognized genfrac command")}switch(n){case"\\dfrac":case"\\dbinom":_="display";break;case"\\tfrac":case"\\tbinom":_="text";break}return{type:"genfrac",mode:r.mode,continued:!1,numer:i,denom:c,hasBarLine:f,leftDelim:b,rightDelim:x,size:_,barSize:null}},htmlBuilder:fn,mathmlBuilder:dn}),re({type:"genfrac",names:["\\cfrac"],props:{numArgs:2},handler:function(e,t){var r=e.parser;e.funcName;var n=t[0],i=t[1];return{type:"genfrac",mode:r.mode,continued:!0,numer:n,denom:i,hasBarLine:!0,leftDelim:null,rightDelim:null,size:"display",barSize:null}}}),re({type:"infix",names:["\\over","\\choose","\\atop","\\brace","\\brack"],props:{numArgs:0,infix:!0},handler:function(e){var t=e.parser,r=e.funcName,n=e.token,i;switch(r){case"\\over":i="\\frac";break;case"\\choose":i="\\binom";break;case"\\atop":i="\\\\atopfrac";break;case"\\brace":i="\\\\bracefrac";break;case"\\brack":i="\\\\brackfrac";break;default:throw new Error("Unrecognized infix genfrac command")}return{type:"infix",mode:t.mode,replaceWith:i,token:n}}});var Na=["display","text","script","scriptscript"],Ra=function(e){var t=null;return e.length>0&&(t=e,t=t==="."?null:t),t};re({type:"genfrac",names:["\\genfrac"],props:{numArgs:6,allowedInArgument:!0,argTypes:["math","math","size","text","math","math"]},handler:function(e,t){var r=e.parser,n=t[4],i=t[5],c=xr(t[0]),f=c.type==="atom"&&c.family==="open"?Ra(c.text):null,b=xr(t[1]),x=b.type==="atom"&&b.family==="close"?Ra(b.text):null,_=be(t[2],"size"),C,I=null;_.isBlank?C=!0:(I=_.value,C=I.number>0);var F="auto",q=t[3];if(q.type==="ordgroup"){if(q.body.length>0){var Z=be(q.body[0],"textord");F=Na[Number(Z.text)]}}else q=be(q,"textord"),F=Na[Number(q.text)];return{type:"genfrac",mode:r.mode,numer:n,denom:i,continued:!1,hasBarLine:C,barSize:I,leftDelim:f,rightDelim:x,size:F}},htmlBuilder:fn,mathmlBuilder:dn}),re({type:"infix",names:["\\above"],props:{numArgs:1,argTypes:["size"],infix:!0},handler:function(e,t){var r=e.parser;e.funcName;var n=e.token;return{type:"infix",mode:r.mode,replaceWith:"\\\\abovefrac",size:be(t[0],"size").value,token:n}}}),re({type:"genfrac",names:["\\\\abovefrac"],props:{numArgs:3,argTypes:["math","size","math"]},handler:function(e,t){var r=e.parser;e.funcName;var n=t[0],i=pe(be(t[1],"infix").size),c=t[2],f=i.number>0;return{type:"genfrac",mode:r.mode,numer:n,denom:c,continued:!1,hasBarLine:f,barSize:i,leftDelim:null,rightDelim:null,size:"auto"}},htmlBuilder:fn,mathmlBuilder:dn});var Fa=function(e,t){var r=t.style,n,i;e.type==="supsub"?(n=e.sup?Se(e.sup,t.havingStyle(r.sup()),t):Se(e.sub,t.havingStyle(r.sub()),t),i=be(e.base,"horizBrace")):i=be(e,"horizBrace");var c=Se(i.base,t.havingBaseStyle(ee.DISPLAY)),f=Xt.svgSpan(i,t),b;if(i.isOver?(b=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:c},{type:"kern",size:.1},{type:"elem",elem:f}]},t),b.children[0].children[0].children[1].classes.push("svg-align")):(b=E.makeVList({positionType:"bottom",positionData:c.depth+.1+f.height,children:[{type:"elem",elem:f},{type:"kern",size:.1},{type:"elem",elem:c}]},t),b.children[0].children[0].children[0].classes.push("svg-align")),n){var x=E.makeSpan(["mord",i.isOver?"mover":"munder"],[b],t);i.isOver?b=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:x},{type:"kern",size:.2},{type:"elem",elem:n}]},t):b=E.makeVList({positionType:"bottom",positionData:x.depth+.2+n.height+n.depth,children:[{type:"elem",elem:n},{type:"kern",size:.2},{type:"elem",elem:x}]},t)}return E.makeSpan(["mord",i.isOver?"mover":"munder"],[b],t)},fs=function(e,t){var r=Xt.mathMLnode(e.label);return new V.MathNode(e.isOver?"mover":"munder",[Ne(e.base,t),r])};re({type:"horizBrace",names:["\\overbrace","\\underbrace"],props:{numArgs:1},handler:function(e,t){var r=e.parser,n=e.funcName;return{type:"horizBrace",mode:r.mode,label:n,isOver:/^\\over/.test(n),base:t[0]}},htmlBuilder:Fa,mathmlBuilder:fs}),re({type:"href",names:["\\href"],props:{numArgs:2,argTypes:["url","original"],allowedInText:!0},handler:function(e,t){var r=e.parser,n=t[1],i=be(t[0],"url").url;return r.settings.isTrusted({command:"\\href",url:i})?{type:"href",mode:r.mode,href:i,body:Ye(n)}:r.formatUnsupportedCmd("\\href")},htmlBuilder:function(e,t){var r=Ze(e.body,t,!1);return E.makeAnchor(e.href,[],r,t)},mathmlBuilder:function(e,t){var r=i0(e.body,t);return r instanceof xt||(r=new xt("mrow",[r])),r.setAttribute("href",e.href),r}}),re({type:"href",names:["\\url"],props:{numArgs:1,argTypes:["url"],allowedInText:!0},handler:function(e,t){var r=e.parser,n=be(t[0],"url").url;if(!r.settings.isTrusted({command:"\\url",url:n}))return r.formatUnsupportedCmd("\\url");for(var i=[],c=0;c<n.length;c++){var f=n[c];f==="~"&&(f="\\textasciitilde"),i.push({type:"textord",mode:"text",text:f})}var b={type:"text",mode:r.mode,font:"\\texttt",body:i};return{type:"href",mode:r.mode,href:n,body:Ye(b)}}}),re({type:"hbox",names:["\\hbox"],props:{numArgs:1,argTypes:["text"],allowedInText:!0,primitive:!0},handler:function(e,t){var r=e.parser;return{type:"hbox",mode:r.mode,body:Ye(t[0])}},htmlBuilder:function(e,t){var r=Ze(e.body,t,!1);return E.makeFragment(r)},mathmlBuilder:function(e,t){return new V.MathNode("mrow",ct(e.body,t))}}),re({type:"html",names:["\\htmlClass","\\htmlId","\\htmlStyle","\\htmlData"],props:{numArgs:2,argTypes:["raw","original"],allowedInText:!0},handler:function(e,t){var r=e.parser,n=e.funcName;e.token;var i=be(t[0],"raw").string,c=t[1];r.settings.strict&&r.settings.reportNonstrict("htmlExtension","HTML extension is disabled on strict mode");var f,b={};switch(n){case"\\htmlClass":b.class=i,f={command:"\\htmlClass",class:i};break;case"\\htmlId":b.id=i,f={command:"\\htmlId",id:i};break;case"\\htmlStyle":b.style=i,f={command:"\\htmlStyle",style:i};break;case"\\htmlData":{for(var x=i.split(","),_=0;_<x.length;_++){var C=x[_].split("=");if(C.length!==2)throw new p("Error parsing key-value for \\htmlData");b["data-"+C[0].trim()]=C[1].trim()}f={command:"\\htmlData",attributes:b};break}default:throw new Error("Unrecognized html command")}return r.settings.isTrusted(f)?{type:"html",mode:r.mode,attributes:b,body:Ye(c)}:r.formatUnsupportedCmd(n)},htmlBuilder:function(e,t){var r=Ze(e.body,t,!1),n=["enclosing"];e.attributes.class&&n.push.apply(n,e.attributes.class.trim().split(/\s+/));var i=E.makeSpan(n,r,t);for(var c in e.attributes)c!=="class"&&e.attributes.hasOwnProperty(c)&&i.setAttribute(c,e.attributes[c]);return i},mathmlBuilder:function(e,t){return i0(e.body,t)}}),re({type:"htmlmathml",names:["\\html@mathml"],props:{numArgs:2,allowedInText:!0},handler:function(e,t){var r=e.parser;return{type:"htmlmathml",mode:r.mode,html:Ye(t[0]),mathml:Ye(t[1])}},htmlBuilder:function(e,t){var r=Ze(e.html,t,!1);return E.makeFragment(r)},mathmlBuilder:function(e,t){return i0(e.mathml,t)}});var pn=function(e){if(/^[-+]? *(\d+(\.\d*)?|\.\d+)$/.test(e))return{number:+e,unit:"bp"};var t=/([-+]?) *(\d+(?:\.\d*)?|\.\d+) *([a-z]{2})/.exec(e);if(!t)throw new p("Invalid size: '"+e+"' in \\includegraphics");var r={number:+(t[1]+t[2]),unit:t[3]};if(!B0(r))throw new p("Invalid unit: '"+r.unit+"' in \\includegraphics.");return r};re({type:"includegraphics",names:["\\includegraphics"],props:{numArgs:1,numOptionalArgs:1,argTypes:["raw","url"],allowedInText:!1},handler:function(e,t,r){var n=e.parser,i={number:0,unit:"em"},c={number:.9,unit:"em"},f={number:0,unit:"em"},b="";if(r[0])for(var x=be(r[0],"raw").string,_=x.split(","),C=0;C<_.length;C++){var I=_[C].split("=");if(I.length===2){var F=I[1].trim();switch(I[0].trim()){case"alt":b=F;break;case"width":i=pn(F);break;case"height":c=pn(F);break;case"totalheight":f=pn(F);break;default:throw new p("Invalid key: '"+I[0]+"' in \\includegraphics.")}}}var q=be(t[0],"url").url;return b===""&&(b=q,b=b.replace(/^.*[\\/]/,""),b=b.substring(0,b.lastIndexOf("."))),n.settings.isTrusted({command:"\\includegraphics",url:q})?{type:"includegraphics",mode:n.mode,alt:b,width:i,height:c,totalheight:f,src:q}:n.formatUnsupportedCmd("\\includegraphics")},htmlBuilder:function(e,t){var r=Be(e.height,t),n=0;e.totalheight.number>0&&(n=Be(e.totalheight,t)-r);var i=0;e.width.number>0&&(i=Be(e.width,t));var c={height:Q(r+n)};i>0&&(c.width=Q(i)),n>0&&(c.verticalAlign=Q(-n));var f=new mr(e.src,e.alt,c);return f.height=r,f.depth=n,f},mathmlBuilder:function(e,t){var r=new V.MathNode("mglyph",[]);r.setAttribute("alt",e.alt);var n=Be(e.height,t),i=0;if(e.totalheight.number>0&&(i=Be(e.totalheight,t)-n,r.setAttribute("valign",Q(-i))),r.setAttribute("height",Q(n+i)),e.width.number>0){var c=Be(e.width,t);r.setAttribute("width",Q(c))}return r.setAttribute("src",e.src),r}}),re({type:"kern",names:["\\kern","\\mkern","\\hskip","\\mskip"],props:{numArgs:1,argTypes:["size"],primitive:!0,allowedInText:!0},handler:function(e,t){var r=e.parser,n=e.funcName,i=be(t[0],"size");if(r.settings.strict){var c=n[1]==="m",f=i.value.unit==="mu";c?(f||r.settings.reportNonstrict("mathVsTextUnits","LaTeX's "+n+" supports only mu units, "+("not "+i.value.unit+" units")),r.mode!=="math"&&r.settings.reportNonstrict("mathVsTextUnits","LaTeX's "+n+" works only in math mode")):f&&r.settings.reportNonstrict("mathVsTextUnits","LaTeX's "+n+" doesn't support mu units")}return{type:"kern",mode:r.mode,dimension:i.value}},htmlBuilder:function(e,t){return E.makeGlue(e.dimension,t)},mathmlBuilder:function(e,t){var r=Be(e.dimension,t);return new V.SpaceNode(r)}}),re({type:"lap",names:["\\mathllap","\\mathrlap","\\mathclap"],props:{numArgs:1,allowedInText:!0},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0];return{type:"lap",mode:r.mode,alignment:n.slice(5),body:i}},htmlBuilder:function(e,t){var r;e.alignment==="clap"?(r=E.makeSpan([],[Se(e.body,t)]),r=E.makeSpan(["inner"],[r],t)):r=E.makeSpan(["inner"],[Se(e.body,t)]);var n=E.makeSpan(["fix"],[]),i=E.makeSpan([e.alignment],[r,n],t),c=E.makeSpan(["strut"]);return c.style.height=Q(i.height+i.depth),i.depth&&(c.style.verticalAlign=Q(-i.depth)),i.children.unshift(c),i=E.makeSpan(["thinbox"],[i],t),E.makeSpan(["mord","vbox"],[i],t)},mathmlBuilder:function(e,t){var r=new V.MathNode("mpadded",[Ne(e.body,t)]);if(e.alignment!=="rlap"){var n=e.alignment==="llap"?"-1":"-0.5";r.setAttribute("lspace",n+"width")}return r.setAttribute("width","0px"),r}}),re({type:"styling",names:["\\(","$"],props:{numArgs:0,allowedInText:!0,allowedInMath:!1},handler:function(e,t){var r=e.funcName,n=e.parser,i=n.mode;n.switchMode("math");var c=r==="\\("?"\\)":"$",f=n.parseExpression(!1,c);return n.expect(c),n.switchMode(i),{type:"styling",mode:n.mode,style:"text",body:f}}}),re({type:"text",names:["\\)","\\]"],props:{numArgs:0,allowedInText:!0,allowedInMath:!1},handler:function(e,t){throw new p("Mismatched "+e.funcName)}});var Ia=function(e,t){switch(t.style.size){case ee.DISPLAY.size:return e.display;case ee.TEXT.size:return e.text;case ee.SCRIPT.size:return e.script;case ee.SCRIPTSCRIPT.size:return e.scriptscript;default:return e.text}};re({type:"mathchoice",names:["\\mathchoice"],props:{numArgs:4,primitive:!0},handler:function(e,t){var r=e.parser;return{type:"mathchoice",mode:r.mode,display:Ye(t[0]),text:Ye(t[1]),script:Ye(t[2]),scriptscript:Ye(t[3])}},htmlBuilder:function(e,t){var r=Ia(e,t),n=Ze(r,t,!1);return E.makeFragment(n)},mathmlBuilder:function(e,t){var r=Ia(e,t);return i0(r,t)}});var La=function(e,t,r,n,i,c,f){e=E.makeSpan([],[e]);var b=r&&O.isCharacterBox(r),x,_;if(t){var C=Se(t,n.havingStyle(i.sup()),n);_={elem:C,kern:Math.max(n.fontMetrics().bigOpSpacing1,n.fontMetrics().bigOpSpacing3-C.depth)}}if(r){var I=Se(r,n.havingStyle(i.sub()),n);x={elem:I,kern:Math.max(n.fontMetrics().bigOpSpacing2,n.fontMetrics().bigOpSpacing4-I.height)}}var F;if(_&&x){var q=n.fontMetrics().bigOpSpacing5+x.elem.height+x.elem.depth+x.kern+e.depth+f;F=E.makeVList({positionType:"bottom",positionData:q,children:[{type:"kern",size:n.fontMetrics().bigOpSpacing5},{type:"elem",elem:x.elem,marginLeft:Q(-c)},{type:"kern",size:x.kern},{type:"elem",elem:e},{type:"kern",size:_.kern},{type:"elem",elem:_.elem,marginLeft:Q(c)},{type:"kern",size:n.fontMetrics().bigOpSpacing5}]},n)}else if(x){var Z=e.height-f;F=E.makeVList({positionType:"top",positionData:Z,children:[{type:"kern",size:n.fontMetrics().bigOpSpacing5},{type:"elem",elem:x.elem,marginLeft:Q(-c)},{type:"kern",size:x.kern},{type:"elem",elem:e}]},n)}else if(_){var $=e.depth+f;F=E.makeVList({positionType:"bottom",positionData:$,children:[{type:"elem",elem:e},{type:"kern",size:_.kern},{type:"elem",elem:_.elem,marginLeft:Q(c)},{type:"kern",size:n.fontMetrics().bigOpSpacing5}]},n)}else return e;var se=[F];if(x&&c!==0&&!b){var ce=E.makeSpan(["mspace"],[],n);ce.style.marginRight=Q(c),se.unshift(ce)}return E.makeSpan(["mop","op-limits"],se,n)},Oa=["\\smallint"],F0=function(e,t){var r,n,i=!1,c;e.type==="supsub"?(r=e.sup,n=e.sub,c=be(e.base,"op"),i=!0):c=be(e,"op");var f=t.style,b=!1;f.size===ee.DISPLAY.size&&c.symbol&&!O.contains(Oa,c.name)&&(b=!0);var x;if(c.symbol){var _=b?"Size2-Regular":"Size1-Regular",C="";if((c.name==="\\oiint"||c.name==="\\oiiint")&&(C=c.name.slice(1),c.name=C==="oiint"?"\\iint":"\\iiint"),x=E.makeSymbol(c.name,_,"math",t,["mop","op-symbol",b?"large-op":"small-op"]),C.length>0){var I=x.italic,F=E.staticSvg(C+"Size"+(b?"2":"1"),t);x=E.makeVList({positionType:"individualShift",children:[{type:"elem",elem:x,shift:0},{type:"elem",elem:F,shift:b?.08:0}]},t),c.name="\\"+C,x.classes.unshift("mop"),x.italic=I}}else if(c.body){var q=Ze(c.body,t,!0);q.length===1&&q[0]instanceof nt?(x=q[0],x.classes[0]="mop"):x=E.makeSpan(["mop"],q,t)}else{for(var Z=[],$=1;$<c.name.length;$++)Z.push(E.mathsym(c.name[$],c.mode,t));x=E.makeSpan(["mop"],Z,t)}var se=0,ce=0;return(x instanceof nt||c.name==="\\oiint"||c.name==="\\oiiint")&&!c.suppressBaseShift&&(se=(x.height-x.depth)/2-t.fontMetrics().axisHeight,ce=x.italic),i?La(x,r,n,t,f,ce,se):(se&&(x.style.position="relative",x.style.top=Q(se)),x)},nr=function(e,t){var r;if(e.symbol)r=new xt("mo",[kt(e.name,e.mode)]),O.contains(Oa,e.name)&&r.setAttribute("largeop","false");else if(e.body)r=new xt("mo",ct(e.body,t));else{r=new xt("mi",[new er(e.name.slice(1))]);var n=new xt("mo",[kt("⁡","text")]);e.parentIsSupSub?r=new xt("mrow",[r,n]):r=ta([r,n])}return r},ds={"∏":"\\prod","∐":"\\coprod","∑":"\\sum","⋀":"\\bigwedge","⋁":"\\bigvee","⋂":"\\bigcap","⋃":"\\bigcup","⨀":"\\bigodot","⨁":"\\bigoplus","⨂":"\\bigotimes","⨄":"\\biguplus","⨆":"\\bigsqcup"};re({type:"op",names:["\\coprod","\\bigvee","\\bigwedge","\\biguplus","\\bigcap","\\bigcup","\\intop","\\prod","\\sum","\\bigotimes","\\bigoplus","\\bigodot","\\bigsqcup","\\smallint","∏","∐","∑","⋀","⋁","⋂","⋃","⨀","⨁","⨂","⨄","⨆"],props:{numArgs:0},handler:function(e,t){var r=e.parser,n=e.funcName,i=n;return i.length===1&&(i=ds[i]),{type:"op",mode:r.mode,limits:!0,parentIsSupSub:!1,symbol:!0,name:i}},htmlBuilder:F0,mathmlBuilder:nr}),re({type:"op",names:["\\mathop"],props:{numArgs:1,primitive:!0},handler:function(e,t){var r=e.parser,n=t[0];return{type:"op",mode:r.mode,limits:!1,parentIsSupSub:!1,symbol:!1,body:Ye(n)}},htmlBuilder:F0,mathmlBuilder:nr});var ps={"∫":"\\int","∬":"\\iint","∭":"\\iiint","∮":"\\oint","∯":"\\oiint","∰":"\\oiiint"};re({type:"op",names:["\\arcsin","\\arccos","\\arctan","\\arctg","\\arcctg","\\arg","\\ch","\\cos","\\cosec","\\cosh","\\cot","\\cotg","\\coth","\\csc","\\ctg","\\cth","\\deg","\\dim","\\exp","\\hom","\\ker","\\lg","\\ln","\\log","\\sec","\\sin","\\sinh","\\sh","\\tan","\\tanh","\\tg","\\th"],props:{numArgs:0},handler:function(e){var t=e.parser,r=e.funcName;return{type:"op",mode:t.mode,limits:!1,parentIsSupSub:!1,symbol:!1,name:r}},htmlBuilder:F0,mathmlBuilder:nr}),re({type:"op",names:["\\det","\\gcd","\\inf","\\lim","\\max","\\min","\\Pr","\\sup"],props:{numArgs:0},handler:function(e){var t=e.parser,r=e.funcName;return{type:"op",mode:t.mode,limits:!0,parentIsSupSub:!1,symbol:!1,name:r}},htmlBuilder:F0,mathmlBuilder:nr}),re({type:"op",names:["\\int","\\iint","\\iiint","\\oint","\\oiint","\\oiiint","∫","∬","∭","∮","∯","∰"],props:{numArgs:0},handler:function(e){var t=e.parser,r=e.funcName,n=r;return n.length===1&&(n=ps[n]),{type:"op",mode:t.mode,limits:!1,parentIsSupSub:!1,symbol:!0,name:n}},htmlBuilder:F0,mathmlBuilder:nr});var qa=function(e,t){var r,n,i=!1,c;e.type==="supsub"?(r=e.sup,n=e.sub,c=be(e.base,"operatorname"),i=!0):c=be(e,"operatorname");var f;if(c.body.length>0){for(var b=c.body.map(function(I){var F=I.text;return typeof F=="string"?{type:"textord",mode:I.mode,text:F}:I}),x=Ze(b,t.withFont("mathrm"),!0),_=0;_<x.length;_++){var C=x[_];C instanceof nt&&(C.text=C.text.replace(/\u2212/,"-").replace(/\u2217/,"*"))}f=E.makeSpan(["mop"],x,t)}else f=E.makeSpan(["mop"],[],t);return i?La(f,r,n,t,t.style,0,0):f},gs=function(e,t){for(var r=ct(e.body,t.withFont("mathrm")),n=!0,i=0;i<r.length;i++){var c=r[i];if(!(c instanceof V.SpaceNode))if(c instanceof V.MathNode)switch(c.type){case"mi":case"mn":case"ms":case"mspace":case"mtext":break;case"mo":{var f=c.children[0];c.children.length===1&&f instanceof V.TextNode?f.text=f.text.replace(/\u2212/,"-").replace(/\u2217/,"*"):n=!1;break}default:n=!1}else n=!1}if(n){var b=r.map(function(C){return C.toText()}).join("");r=[new V.TextNode(b)]}var x=new V.MathNode("mi",r);x.setAttribute("mathvariant","normal");var _=new V.MathNode("mo",[kt("⁡","text")]);return e.parentIsSupSub?new V.MathNode("mrow",[x,_]):V.newDocumentFragment([x,_])};re({type:"operatorname",names:["\\operatorname@","\\operatornamewithlimits"],props:{numArgs:1},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0];return{type:"operatorname",mode:r.mode,body:Ye(i),alwaysHandleSupSub:n==="\\operatornamewithlimits",limits:!1,parentIsSupSub:!1}},htmlBuilder:qa,mathmlBuilder:gs}),w("\\operatorname","\\@ifstar\\operatornamewithlimits\\operatorname@"),A0({type:"ordgroup",htmlBuilder:function(e,t){return e.semisimple?E.makeFragment(Ze(e.body,t,!1)):E.makeSpan(["mord"],Ze(e.body,t,!0),t)},mathmlBuilder:function(e,t){return i0(e.body,t,!0)}}),re({type:"overline",names:["\\overline"],props:{numArgs:1},handler:function(e,t){var r=e.parser,n=t[0];return{type:"overline",mode:r.mode,body:n}},htmlBuilder:function(e,t){var r=Se(e.body,t.havingCrampedStyle()),n=E.makeLineSpan("overline-line",t),i=t.fontMetrics().defaultRuleThickness,c=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:r},{type:"kern",size:3*i},{type:"elem",elem:n},{type:"kern",size:i}]},t);return E.makeSpan(["mord","overline"],[c],t)},mathmlBuilder:function(e,t){var r=new V.MathNode("mo",[new V.TextNode("‾")]);r.setAttribute("stretchy","true");var n=new V.MathNode("mover",[Ne(e.body,t),r]);return n.setAttribute("accent","true"),n}}),re({type:"phantom",names:["\\phantom"],props:{numArgs:1,allowedInText:!0},handler:function(e,t){var r=e.parser,n=t[0];return{type:"phantom",mode:r.mode,body:Ye(n)}},htmlBuilder:function(e,t){var r=Ze(e.body,t.withPhantom(),!1);return E.makeFragment(r)},mathmlBuilder:function(e,t){var r=ct(e.body,t);return new V.MathNode("mphantom",r)}}),re({type:"hphantom",names:["\\hphantom"],props:{numArgs:1,allowedInText:!0},handler:function(e,t){var r=e.parser,n=t[0];return{type:"hphantom",mode:r.mode,body:n}},htmlBuilder:function(e,t){var r=E.makeSpan([],[Se(e.body,t.withPhantom())]);if(r.height=0,r.depth=0,r.children)for(var n=0;n<r.children.length;n++)r.children[n].height=0,r.children[n].depth=0;return r=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:r}]},t),E.makeSpan(["mord"],[r],t)},mathmlBuilder:function(e,t){var r=ct(Ye(e.body),t),n=new V.MathNode("mphantom",r),i=new V.MathNode("mpadded",[n]);return i.setAttribute("height","0px"),i.setAttribute("depth","0px"),i}}),re({type:"vphantom",names:["\\vphantom"],props:{numArgs:1,allowedInText:!0},handler:function(e,t){var r=e.parser,n=t[0];return{type:"vphantom",mode:r.mode,body:n}},htmlBuilder:function(e,t){var r=E.makeSpan(["inner"],[Se(e.body,t.withPhantom())]),n=E.makeSpan(["fix"],[]);return E.makeSpan(["mord","rlap"],[r,n],t)},mathmlBuilder:function(e,t){var r=ct(Ye(e.body),t),n=new V.MathNode("mphantom",r),i=new V.MathNode("mpadded",[n]);return i.setAttribute("width","0px"),i}}),re({type:"raisebox",names:["\\raisebox"],props:{numArgs:2,argTypes:["size","hbox"],allowedInText:!0},handler:function(e,t){var r=e.parser,n=be(t[0],"size").value,i=t[1];return{type:"raisebox",mode:r.mode,dy:n,body:i}},htmlBuilder:function(e,t){var r=Se(e.body,t),n=Be(e.dy,t);return E.makeVList({positionType:"shift",positionData:-n,children:[{type:"elem",elem:r}]},t)},mathmlBuilder:function(e,t){var r=new V.MathNode("mpadded",[Ne(e.body,t)]),n=e.dy.number+e.dy.unit;return r.setAttribute("voffset",n),r}}),re({type:"internal",names:["\\relax"],props:{numArgs:0,allowedInText:!0},handler:function(e){var t=e.parser;return{type:"internal",mode:t.mode}}}),re({type:"rule",names:["\\rule"],props:{numArgs:2,numOptionalArgs:1,argTypes:["size","size","size"]},handler:function(e,t,r){var n=e.parser,i=r[0],c=be(t[0],"size"),f=be(t[1],"size");return{type:"rule",mode:n.mode,shift:i&&be(i,"size").value,width:c.value,height:f.value}},htmlBuilder:function(e,t){var r=E.makeSpan(["mord","rule"],[],t),n=Be(e.width,t),i=Be(e.height,t),c=e.shift?Be(e.shift,t):0;return r.style.borderRightWidth=Q(n),r.style.borderTopWidth=Q(i),r.style.bottom=Q(c),r.width=n,r.height=i+c,r.depth=-c,r.maxFontSize=i*1.125*t.sizeMultiplier,r},mathmlBuilder:function(e,t){var r=Be(e.width,t),n=Be(e.height,t),i=e.shift?Be(e.shift,t):0,c=t.color&&t.getColor()||"black",f=new V.MathNode("mspace");f.setAttribute("mathbackground",c),f.setAttribute("width",Q(r)),f.setAttribute("height",Q(n));var b=new V.MathNode("mpadded",[f]);return i>=0?b.setAttribute("height",Q(i)):(b.setAttribute("height",Q(i)),b.setAttribute("depth",Q(-i))),b.setAttribute("voffset",Q(i)),b}});function Pa(u,e,t){for(var r=Ze(u,e,!1),n=e.sizeMultiplier/t.sizeMultiplier,i=0;i<r.length;i++){var c=r[i].classes.indexOf("sizing");c<0?Array.prototype.push.apply(r[i].classes,e.sizingClasses(t)):r[i].classes[c+1]==="reset-size"+e.size&&(r[i].classes[c+1]="reset-size"+t.size),r[i].height*=n,r[i].depth*=n}return E.makeFragment(r)}var Ha=["\\tiny","\\sixptsize","\\scriptsize","\\footnotesize","\\small","\\normalsize","\\large","\\Large","\\LARGE","\\huge","\\Huge"],vs=function(e,t){var r=t.havingSize(e.size);return Pa(e.body,r,t)};re({type:"sizing",names:Ha,props:{numArgs:0,allowedInText:!0},handler:function(e,t){var r=e.breakOnTokenText,n=e.funcName,i=e.parser,c=i.parseExpression(!1,r);return{type:"sizing",mode:i.mode,size:Ha.indexOf(n)+1,body:c}},htmlBuilder:vs,mathmlBuilder:function(e,t){var r=t.havingSize(e.size),n=ct(e.body,r),i=new V.MathNode("mstyle",n);return i.setAttribute("mathsize",Q(r.sizeMultiplier)),i}}),re({type:"smash",names:["\\smash"],props:{numArgs:1,numOptionalArgs:1,allowedInText:!0},handler:function(e,t,r){var n=e.parser,i=!1,c=!1,f=r[0]&&be(r[0],"ordgroup");if(f)for(var b="",x=0;x<f.body.length;++x){var _=f.body[x];if(b=_.text,b==="t")i=!0;else if(b==="b")c=!0;else{i=!1,c=!1;break}}else i=!0,c=!0;var C=t[0];return{type:"smash",mode:n.mode,body:C,smashHeight:i,smashDepth:c}},htmlBuilder:function(e,t){var r=E.makeSpan([],[Se(e.body,t)]);if(!e.smashHeight&&!e.smashDepth)return r;if(e.smashHeight&&(r.height=0,r.children))for(var n=0;n<r.children.length;n++)r.children[n].height=0;if(e.smashDepth&&(r.depth=0,r.children))for(var i=0;i<r.children.length;i++)r.children[i].depth=0;var c=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:r}]},t);return E.makeSpan(["mord"],[c],t)},mathmlBuilder:function(e,t){var r=new V.MathNode("mpadded",[Ne(e.body,t)]);return e.smashHeight&&r.setAttribute("height","0px"),e.smashDepth&&r.setAttribute("depth","0px"),r}}),re({type:"sqrt",names:["\\sqrt"],props:{numArgs:1,numOptionalArgs:1},handler:function(e,t,r){var n=e.parser,i=r[0],c=t[0];return{type:"sqrt",mode:n.mode,body:c,index:i}},htmlBuilder:function(e,t){var r=Se(e.body,t.havingCrampedStyle());r.height===0&&(r.height=t.fontMetrics().xHeight),r=E.wrapFragment(r,t);var n=t.fontMetrics(),i=n.defaultRuleThickness,c=i;t.style.id<ee.TEXT.id&&(c=t.fontMetrics().xHeight);var f=i+c/4,b=r.height+r.depth+f+i,x=Zt.sqrtImage(b,t),_=x.span,C=x.ruleWidth,I=x.advanceWidth,F=_.height-C;F>r.height+r.depth+f&&(f=(f+F-r.height-r.depth)/2);var q=_.height-r.height-f-C;r.style.paddingLeft=Q(I);var Z=E.makeVList({positionType:"firstBaseline",children:[{type:"elem",elem:r,wrapperClasses:["svg-align"]},{type:"kern",size:-(r.height+q)},{type:"elem",elem:_},{type:"kern",size:C}]},t);if(e.index){var $=t.havingStyle(ee.SCRIPTSCRIPT),se=Se(e.index,$,t),ce=.6*(Z.height-Z.depth),he=E.makeVList({positionType:"shift",positionData:-ce,children:[{type:"elem",elem:se}]},t),Te=E.makeSpan(["root"],[he]);return E.makeSpan(["mord","sqrt"],[Te,Z],t)}else return E.makeSpan(["mord","sqrt"],[Z],t)},mathmlBuilder:function(e,t){var r=e.body,n=e.index;return n?new V.MathNode("mroot",[Ne(r,t),Ne(n,t)]):new V.MathNode("msqrt",[Ne(r,t)])}});var Ua={display:ee.DISPLAY,text:ee.TEXT,script:ee.SCRIPT,scriptscript:ee.SCRIPTSCRIPT};re({type:"styling",names:["\\displaystyle","\\textstyle","\\scriptstyle","\\scriptscriptstyle"],props:{numArgs:0,allowedInText:!0,primitive:!0},handler:function(e,t){var r=e.breakOnTokenText,n=e.funcName,i=e.parser,c=i.parseExpression(!0,r),f=n.slice(1,n.length-5);return{type:"styling",mode:i.mode,style:f,body:c}},htmlBuilder:function(e,t){var r=Ua[e.style],n=t.havingStyle(r).withFont("");return Pa(e.body,n,t)},mathmlBuilder:function(e,t){var r=Ua[e.style],n=t.havingStyle(r),i=ct(e.body,n),c=new V.MathNode("mstyle",i),f={display:["0","true"],text:["0","false"],script:["1","false"],scriptscript:["2","false"]},b=f[e.style];return c.setAttribute("scriptlevel",b[0]),c.setAttribute("displaystyle",b[1]),c}});var bs=function(e,t){var r=e.base;if(r)if(r.type==="op"){var n=r.limits&&(t.style.size===ee.DISPLAY.size||r.alwaysHandleSupSub);return n?F0:null}else if(r.type==="operatorname"){var i=r.alwaysHandleSupSub&&(t.style.size===ee.DISPLAY.size||r.limits);return i?qa:null}else{if(r.type==="accent")return O.isCharacterBox(r.base)?$r:null;if(r.type==="horizBrace"){var c=!e.sub;return c===r.isOver?Fa:null}else return null}else return null};A0({type:"supsub",htmlBuilder:function(e,t){var r=bs(e,t);if(r)return r(e,t);var n=e.base,i=e.sup,c=e.sub,f=Se(n,t),b,x,_=t.fontMetrics(),C=0,I=0,F=n&&O.isCharacterBox(n);if(i){var q=t.havingStyle(t.style.sup());b=Se(i,q,t),F||(C=f.height-q.fontMetrics().supDrop*q.sizeMultiplier/t.sizeMultiplier)}if(c){var Z=t.havingStyle(t.style.sub());x=Se(c,Z,t),F||(I=f.depth+Z.fontMetrics().subDrop*Z.sizeMultiplier/t.sizeMultiplier)}var $;t.style===ee.DISPLAY?$=_.sup1:t.style.cramped?$=_.sup3:$=_.sup2;var se=t.sizeMultiplier,ce=Q(.5/_.ptPerEm/se),he=null;if(x){var Te=e.base&&e.base.type==="op"&&e.base.name&&(e.base.name==="\\oiint"||e.base.name==="\\oiiint");(f instanceof nt||Te)&&(he=Q(-f.italic))}var ye;if(b&&x){C=Math.max(C,$,b.depth+.25*_.xHeight),I=Math.max(I,_.sub2);var ze=_.defaultRuleThickness,Ae=4*ze;if(C-b.depth-(x.height-I)<Ae){I=Ae-(C-b.depth)+x.height;var Ee=.8*_.xHeight-(C-b.depth);Ee>0&&(C+=Ee,I-=Ee)}var Le=[{type:"elem",elem:x,shift:I,marginRight:ce,marginLeft:he},{type:"elem",elem:b,shift:-C,marginRight:ce}];ye=E.makeVList({positionType:"individualShift",children:Le},t)}else if(x){I=Math.max(I,_.sub1,x.height-.8*_.xHeight);var $e=[{type:"elem",elem:x,marginLeft:he,marginRight:ce}];ye=E.makeVList({positionType:"shift",positionData:I,children:$e},t)}else if(b)C=Math.max(C,$,b.depth+.25*_.xHeight),ye=E.makeVList({positionType:"shift",positionData:-C,children:[{type:"elem",elem:b,marginRight:ce}]},t);else throw new Error("supsub must have either sup or sub.");var dt=Xr(f,"right")||"mord";return E.makeSpan([dt],[f,E.makeSpan(["msupsub"],[ye])],t)},mathmlBuilder:function(e,t){var r=!1,n,i;e.base&&e.base.type==="horizBrace"&&(i=!!e.sup,i===e.base.isOver&&(r=!0,n=e.base.isOver)),e.base&&(e.base.type==="op"||e.base.type==="operatorname")&&(e.base.parentIsSupSub=!0);var c=[Ne(e.base,t)];e.sub&&c.push(Ne(e.sub,t)),e.sup&&c.push(Ne(e.sup,t));var f;if(r)f=n?"mover":"munder";else if(e.sub)if(e.sup){var _=e.base;_&&_.type==="op"&&_.limits&&t.style===ee.DISPLAY||_&&_.type==="operatorname"&&_.alwaysHandleSupSub&&(t.style===ee.DISPLAY||_.limits)?f="munderover":f="msubsup"}else{var x=e.base;x&&x.type==="op"&&x.limits&&(t.style===ee.DISPLAY||x.alwaysHandleSupSub)||x&&x.type==="operatorname"&&x.alwaysHandleSupSub&&(x.limits||t.style===ee.DISPLAY)?f="munder":f="msub"}else{var b=e.base;b&&b.type==="op"&&b.limits&&(t.style===ee.DISPLAY||b.alwaysHandleSupSub)||b&&b.type==="operatorname"&&b.alwaysHandleSupSub&&(b.limits||t.style===ee.DISPLAY)?f="mover":f="msup"}return new V.MathNode(f,c)}}),A0({type:"atom",htmlBuilder:function(e,t){return E.mathsym(e.text,e.mode,t,["m"+e.family])},mathmlBuilder:function(e,t){var r=new V.MathNode("mo",[kt(e.text,e.mode)]);if(e.family==="bin"){var n=Qr(e,t);n==="bold-italic"&&r.setAttribute("mathvariant",n)}else e.family==="punct"?r.setAttribute("separator","true"):(e.family==="open"||e.family==="close")&&r.setAttribute("stretchy","false");return r}});var Ga={mi:"italic",mn:"normal",mtext:"normal"};A0({type:"mathord",htmlBuilder:function(e,t){return E.makeOrd(e,t,"mathord")},mathmlBuilder:function(e,t){var r=new V.MathNode("mi",[kt(e.text,e.mode,t)]),n=Qr(e,t)||"italic";return n!==Ga[r.type]&&r.setAttribute("mathvariant",n),r}}),A0({type:"textord",htmlBuilder:function(e,t){return E.makeOrd(e,t,"textord")},mathmlBuilder:function(e,t){var r=kt(e.text,e.mode,t),n=Qr(e,t)||"normal",i;return e.mode==="text"?i=new V.MathNode("mtext",[r]):/[0-9]/.test(e.text)?i=new V.MathNode("mn",[r]):e.text==="\\prime"?i=new V.MathNode("mo",[r]):i=new V.MathNode("mi",[r]),n!==Ga[i.type]&&i.setAttribute("mathvariant",n),i}});var gn={"\\nobreak":"nobreak","\\allowbreak":"allowbreak"},vn={" ":{},"\\ ":{},"~":{className:"nobreak"},"\\space":{},"\\nobreakspace":{className:"nobreak"}};A0({type:"spacing",htmlBuilder:function(e,t){if(vn.hasOwnProperty(e.text)){var r=vn[e.text].className||"";if(e.mode==="text"){var n=E.makeOrd(e,t,"textord");return n.classes.push(r),n}else return E.makeSpan(["mspace",r],[E.mathsym(e.text,e.mode,t)],t)}else{if(gn.hasOwnProperty(e.text))return E.makeSpan(["mspace",gn[e.text]],[],t);throw new p('Unknown type of space "'+e.text+'"')}},mathmlBuilder:function(e,t){var r;if(vn.hasOwnProperty(e.text))r=new V.MathNode("mtext",[new V.TextNode(" ")]);else{if(gn.hasOwnProperty(e.text))return new V.MathNode("mspace");throw new p('Unknown type of space "'+e.text+'"')}return r}});var Va=function(){var e=new V.MathNode("mtd",[]);return e.setAttribute("width","50%"),e};A0({type:"tag",mathmlBuilder:function(e,t){var r=new V.MathNode("mtable",[new V.MathNode("mtr",[Va(),new V.MathNode("mtd",[i0(e.body,t)]),Va(),new V.MathNode("mtd",[i0(e.tag,t)])])]);return r.setAttribute("width","100%"),r}});var Wa={"\\text":void 0,"\\textrm":"textrm","\\textsf":"textsf","\\texttt":"texttt","\\textnormal":"textrm"},Ya={"\\textbf":"textbf","\\textmd":"textmd"},ys={"\\textit":"textit","\\textup":"textup"},ja=function(e,t){var r=e.font;return r?Wa[r]?t.withTextFontFamily(Wa[r]):Ya[r]?t.withTextFontWeight(Ya[r]):t.withTextFontShape(ys[r]):t};re({type:"text",names:["\\text","\\textrm","\\textsf","\\texttt","\\textnormal","\\textbf","\\textmd","\\textit","\\textup"],props:{numArgs:1,argTypes:["text"],allowedInArgument:!0,allowedInText:!0},handler:function(e,t){var r=e.parser,n=e.funcName,i=t[0];return{type:"text",mode:r.mode,body:Ye(i),font:n}},htmlBuilder:function(e,t){var r=ja(e,t),n=Ze(e.body,r,!0);return E.makeSpan(["mord","text"],n,r)},mathmlBuilder:function(e,t){var r=ja(e,t);return i0(e.body,r)}}),re({type:"underline",names:["\\underline"],props:{numArgs:1,allowedInText:!0},handler:function(e,t){var r=e.parser;return{type:"underline",mode:r.mode,body:t[0]}},htmlBuilder:function(e,t){var r=Se(e.body,t),n=E.makeLineSpan("underline-line",t),i=t.fontMetrics().defaultRuleThickness,c=E.makeVList({positionType:"top",positionData:r.height,children:[{type:"kern",size:i},{type:"elem",elem:n},{type:"kern",size:3*i},{type:"elem",elem:r}]},t);return E.makeSpan(["mord","underline"],[c],t)},mathmlBuilder:function(e,t){var r=new V.MathNode("mo",[new V.TextNode("‾")]);r.setAttribute("stretchy","true");var n=new V.MathNode("munder",[Ne(e.body,t),r]);return n.setAttribute("accentunder","true"),n}}),re({type:"vcenter",names:["\\vcenter"],props:{numArgs:1,argTypes:["original"],allowedInText:!1},handler:function(e,t){var r=e.parser;return{type:"vcenter",mode:r.mode,body:t[0]}},htmlBuilder:function(e,t){var r=Se(e.body,t),n=t.fontMetrics().axisHeight,i=.5*(r.height-n-(r.depth+n));return E.makeVList({positionType:"shift",positionData:i,children:[{type:"elem",elem:r}]},t)},mathmlBuilder:function(e,t){return new V.MathNode("mpadded",[Ne(e.body,t)],["vcenter"])}}),re({type:"verb",names:["\\verb"],props:{numArgs:0,allowedInText:!0},handler:function(e,t,r){throw new p("\\verb ended by end of line instead of matching delimiter")},htmlBuilder:function(e,t){for(var r=Xa(e),n=[],i=t.havingStyle(t.style.text()),c=0;c<r.length;c++){var f=r[c];f==="~"&&(f="\\textasciitilde"),n.push(E.makeSymbol(f,"Typewriter-Regular",e.mode,i,["mord","texttt"]))}return E.makeSpan(["mord","text"].concat(i.sizingClasses(t)),E.tryCombineChars(n),i)},mathmlBuilder:function(e,t){var r=new V.TextNode(Xa(e)),n=new V.MathNode("mtext",[r]);return n.setAttribute("mathvariant","monospace"),n}});var Xa=function(e){return e.body.replace(/ /g,e.star?"␣":" ")},ws=Jn,o0=ws,Za=`[ \r
-	]`,xs="\\\\[a-zA-Z@]+",ks="\\\\[^\uD800-\uDFFF]",Ss="("+xs+")"+Za+"*",As=`\\\\(
-|[ \r	]+
-?)[ \r	]*`,bn="[̀-ͯ]",Ts=new RegExp(bn+"+$"),_s="("+Za+"+)|"+(As+"|")+"([!-\\[\\]-‧‪-퟿豈-￿]"+(bn+"*")+"|[\uD800-\uDBFF][\uDC00-\uDFFF]"+(bn+"*")+"|\\\\verb\\*([^]).*?\\4|\\\\verb([^*a-zA-Z]).*?\\5"+("|"+Ss)+("|"+ks+")"),Ka=function(){function u(t,r){this.input=void 0,this.settings=void 0,this.tokenRegex=void 0,this.catcodes=void 0,this.input=t,this.settings=r,this.tokenRegex=new RegExp(_s,"g"),this.catcodes={"%":14,"~":13}}var e=u.prototype;return e.setCatcode=function(r,n){this.catcodes[r]=n},e.lex=function(){var r=this.input,n=this.tokenRegex.lastIndex;if(n===r.length)return new l0("EOF",new zt(this,n,n));var i=this.tokenRegex.exec(r);if(i===null||i.index!==n)throw new p("Unexpected character: '"+r[n]+"'",new l0(r[n],new zt(this,n,n+1)));var c=i[6]||i[3]||(i[2]?"\\ ":" ");if(this.catcodes[c]===14){var f=r.indexOf(`
-`,this.tokenRegex.lastIndex);return f===-1?(this.tokenRegex.lastIndex=r.length,this.settings.reportNonstrict("commentAtEnd","% comment has no terminating newline; LaTeX would fail because of commenting the end of math mode (e.g. $)")):this.tokenRegex.lastIndex=f+1,this.lex()}return new l0(c,new zt(this,n,this.tokenRegex.lastIndex))},u}(),Ms=function(){function u(t,r){t===void 0&&(t={}),r===void 0&&(r={}),this.current=void 0,this.builtins=void 0,this.undefStack=void 0,this.current=r,this.builtins=t,this.undefStack=[]}var e=u.prototype;return e.beginGroup=function(){this.undefStack.push({})},e.endGroup=function(){if(this.undefStack.length===0)throw new p("Unbalanced namespace destruction: attempt to pop global namespace; please report this as a bug");var r=this.undefStack.pop();for(var n in r)r.hasOwnProperty(n)&&(r[n]==null?delete this.current[n]:this.current[n]=r[n])},e.endGroups=function(){for(;this.undefStack.length>0;)this.endGroup()},e.has=function(r){return this.current.hasOwnProperty(r)||this.builtins.hasOwnProperty(r)},e.get=function(r){return this.current.hasOwnProperty(r)?this.current[r]:this.builtins[r]},e.set=function(r,n,i){if(i===void 0&&(i=!1),i){for(var c=0;c<this.undefStack.length;c++)delete this.undefStack[c][r];this.undefStack.length>0&&(this.undefStack[this.undefStack.length-1][r]=n)}else{var f=this.undefStack[this.undefStack.length-1];f&&!f.hasOwnProperty(r)&&(f[r]=this.current[r])}n==null?delete this.current[r]:this.current[r]=n},u}(),zs=Ta,Es=zs;w("\\noexpand",function(u){var e=u.popToken();return u.isExpandable(e.text)&&(e.noexpand=!0,e.treatAsRelax=!0),{tokens:[e],numArgs:0}}),w("\\expandafter",function(u){var e=u.popToken();return u.expandOnce(!0),{tokens:[e],numArgs:0}}),w("\\@firstoftwo",function(u){var e=u.consumeArgs(2);return{tokens:e[0],numArgs:0}}),w("\\@secondoftwo",function(u){var e=u.consumeArgs(2);return{tokens:e[1],numArgs:0}}),w("\\@ifnextchar",function(u){var e=u.consumeArgs(3);u.consumeSpaces();var t=u.future();return e[0].length===1&&e[0][0].text===t.text?{tokens:e[1],numArgs:0}:{tokens:e[2],numArgs:0}}),w("\\@ifstar","\\@ifnextchar *{\\@firstoftwo{#1}}"),w("\\TextOrMath",function(u){var e=u.consumeArgs(2);return u.mode==="text"?{tokens:e[0],numArgs:0}:{tokens:e[1],numArgs:0}});var Qa={0:0,1:1,2:2,3:3,4:4,5:5,6:6,7:7,8:8,9:9,a:10,A:10,b:11,B:11,c:12,C:12,d:13,D:13,e:14,E:14,f:15,F:15};w("\\char",function(u){var e=u.popToken(),t,r="";if(e.text==="'")t=8,e=u.popToken();else if(e.text==='"')t=16,e=u.popToken();else if(e.text==="`")if(e=u.popToken(),e.text[0]==="\\")r=e.text.charCodeAt(1);else{if(e.text==="EOF")throw new p("\\char` missing argument");r=e.text.charCodeAt(0)}else t=10;if(t){if(r=Qa[e.text],r==null||r>=t)throw new p("Invalid base-"+t+" digit "+e.text);for(var n;(n=Qa[u.future().text])!=null&&n<t;)r*=t,r+=n,u.popToken()}return"\\@char{"+r+"}"});var yn=function(e,t,r){var n=e.consumeArg().tokens;if(n.length!==1)throw new p("\\newcommand's first argument must be a macro name");var i=n[0].text,c=e.isDefined(i);if(c&&!t)throw new p("\\newcommand{"+i+"} attempting to redefine "+(i+"; use \\renewcommand"));if(!c&&!r)throw new p("\\renewcommand{"+i+"} when command "+i+" does not yet exist; use \\newcommand");var f=0;if(n=e.consumeArg().tokens,n.length===1&&n[0].text==="["){for(var b="",x=e.expandNextToken();x.text!=="]"&&x.text!=="EOF";)b+=x.text,x=e.expandNextToken();if(!b.match(/^\s*[0-9]+\s*$/))throw new p("Invalid number of arguments: "+b);f=parseInt(b),n=e.consumeArg().tokens}return e.macros.set(i,{tokens:n,numArgs:f}),""};w("\\newcommand",function(u){return yn(u,!1,!0)}),w("\\renewcommand",function(u){return yn(u,!0,!1)}),w("\\providecommand",function(u){return yn(u,!0,!0)}),w("\\message",function(u){var e=u.consumeArgs(1)[0];return console.log(e.reverse().map(function(t){return t.text}).join("")),""}),w("\\errmessage",function(u){var e=u.consumeArgs(1)[0];return console.error(e.reverse().map(function(t){return t.text}).join("")),""}),w("\\show",function(u){var e=u.popToken(),t=e.text;return console.log(e,u.macros.get(t),o0[t],Fe.math[t],Fe.text[t]),""}),w("\\bgroup","{"),w("\\egroup","}"),w("~","\\nobreakspace"),w("\\lq","`"),w("\\rq","'"),w("\\aa","\\r a"),w("\\AA","\\r A"),w("\\textcopyright","\\html@mathml{\\textcircled{c}}{\\char`©}"),w("\\copyright","\\TextOrMath{\\textcopyright}{\\text{\\textcopyright}}"),w("\\textregistered","\\html@mathml{\\textcircled{\\scriptsize R}}{\\char`®}"),w("ℬ","\\mathscr{B}"),w("ℰ","\\mathscr{E}"),w("ℱ","\\mathscr{F}"),w("ℋ","\\mathscr{H}"),w("ℐ","\\mathscr{I}"),w("ℒ","\\mathscr{L}"),w("ℳ","\\mathscr{M}"),w("ℛ","\\mathscr{R}"),w("ℭ","\\mathfrak{C}"),w("ℌ","\\mathfrak{H}"),w("ℨ","\\mathfrak{Z}"),w("\\Bbbk","\\Bbb{k}"),w("·","\\cdotp"),w("\\llap","\\mathllap{\\textrm{#1}}"),w("\\rlap","\\mathrlap{\\textrm{#1}}"),w("\\clap","\\mathclap{\\textrm{#1}}"),w("\\mathstrut","\\vphantom{(}"),w("\\underbar","\\underline{\\text{#1}}"),w("\\not",'\\html@mathml{\\mathrel{\\mathrlap\\@not}}{\\char"338}'),w("\\neq","\\html@mathml{\\mathrel{\\not=}}{\\mathrel{\\char`≠}}"),w("\\ne","\\neq"),w("≠","\\neq"),w("\\notin","\\html@mathml{\\mathrel{{\\in}\\mathllap{/\\mskip1mu}}}{\\mathrel{\\char`∉}}"),w("∉","\\notin"),w("≘","\\html@mathml{\\mathrel{=\\kern{-1em}\\raisebox{0.4em}{$\\scriptsize\\frown$}}}{\\mathrel{\\char`≘}}"),w("≙","\\html@mathml{\\stackrel{\\tiny\\wedge}{=}}{\\mathrel{\\char`≘}}"),w("≚","\\html@mathml{\\stackrel{\\tiny\\vee}{=}}{\\mathrel{\\char`≚}}"),w("≛","\\html@mathml{\\stackrel{\\scriptsize\\star}{=}}{\\mathrel{\\char`≛}}"),w("≝","\\html@mathml{\\stackrel{\\tiny\\mathrm{def}}{=}}{\\mathrel{\\char`≝}}"),w("≞","\\html@mathml{\\stackrel{\\tiny\\mathrm{m}}{=}}{\\mathrel{\\char`≞}}"),w("≟","\\html@mathml{\\stackrel{\\tiny?}{=}}{\\mathrel{\\char`≟}}"),w("⟂","\\perp"),w("‼","\\mathclose{!\\mkern-0.8mu!}"),w("∌","\\notni"),w("⌜","\\ulcorner"),w("⌝","\\urcorner"),w("⌞","\\llcorner"),w("⌟","\\lrcorner"),w("©","\\copyright"),w("®","\\textregistered"),w("️","\\textregistered"),w("\\ulcorner",'\\html@mathml{\\@ulcorner}{\\mathop{\\char"231c}}'),w("\\urcorner",'\\html@mathml{\\@urcorner}{\\mathop{\\char"231d}}'),w("\\llcorner",'\\html@mathml{\\@llcorner}{\\mathop{\\char"231e}}'),w("\\lrcorner",'\\html@mathml{\\@lrcorner}{\\mathop{\\char"231f}}'),w("\\vdots","\\mathord{\\varvdots\\rule{0pt}{15pt}}"),w("⋮","\\vdots"),w("\\varGamma","\\mathit{\\Gamma}"),w("\\varDelta","\\mathit{\\Delta}"),w("\\varTheta","\\mathit{\\Theta}"),w("\\varLambda","\\mathit{\\Lambda}"),w("\\varXi","\\mathit{\\Xi}"),w("\\varPi","\\mathit{\\Pi}"),w("\\varSigma","\\mathit{\\Sigma}"),w("\\varUpsilon","\\mathit{\\Upsilon}"),w("\\varPhi","\\mathit{\\Phi}"),w("\\varPsi","\\mathit{\\Psi}"),w("\\varOmega","\\mathit{\\Omega}"),w("\\substack","\\begin{subarray}{c}#1\\end{subarray}"),w("\\colon","\\nobreak\\mskip2mu\\mathpunct{}\\mathchoice{\\mkern-3mu}{\\mkern-3mu}{}{}{:}\\mskip6mu\\relax"),w("\\boxed","\\fbox{$\\displaystyle{#1}$}"),w("\\iff","\\DOTSB\\;\\Longleftrightarrow\\;"),w("\\implies","\\DOTSB\\;\\Longrightarrow\\;"),w("\\impliedby","\\DOTSB\\;\\Longleftarrow\\;");var Ja={",":"\\dotsc","\\not":"\\dotsb","+":"\\dotsb","=":"\\dotsb","<":"\\dotsb",">":"\\dotsb","-":"\\dotsb","*":"\\dotsb",":":"\\dotsb","\\DOTSB":"\\dotsb","\\coprod":"\\dotsb","\\bigvee":"\\dotsb","\\bigwedge":"\\dotsb","\\biguplus":"\\dotsb","\\bigcap":"\\dotsb","\\bigcup":"\\dotsb","\\prod":"\\dotsb","\\sum":"\\dotsb","\\bigotimes":"\\dotsb","\\bigoplus":"\\dotsb","\\bigodot":"\\dotsb","\\bigsqcup":"\\dotsb","\\And":"\\dotsb","\\longrightarrow":"\\dotsb","\\Longrightarrow":"\\dotsb","\\longleftarrow":"\\dotsb","\\Longleftarrow":"\\dotsb","\\longleftrightarrow":"\\dotsb","\\Longleftrightarrow":"\\dotsb","\\mapsto":"\\dotsb","\\longmapsto":"\\dotsb","\\hookrightarrow":"\\dotsb","\\doteq":"\\dotsb","\\mathbin":"\\dotsb","\\mathrel":"\\dotsb","\\relbar":"\\dotsb","\\Relbar":"\\dotsb","\\xrightarrow":"\\dotsb","\\xleftarrow":"\\dotsb","\\DOTSI":"\\dotsi","\\int":"\\dotsi","\\oint":"\\dotsi","\\iint":"\\dotsi","\\iiint":"\\dotsi","\\iiiint":"\\dotsi","\\idotsint":"\\dotsi","\\DOTSX":"\\dotsx"};w("\\dots",function(u){var e="\\dotso",t=u.expandAfterFuture().text;return t in Ja?e=Ja[t]:(t.slice(0,4)==="\\not"||t in Fe.math&&O.contains(["bin","rel"],Fe.math[t].group))&&(e="\\dotsb"),e});var wn={")":!0,"]":!0,"\\rbrack":!0,"\\}":!0,"\\rbrace":!0,"\\rangle":!0,"\\rceil":!0,"\\rfloor":!0,"\\rgroup":!0,"\\rmoustache":!0,"\\right":!0,"\\bigr":!0,"\\biggr":!0,"\\Bigr":!0,"\\Biggr":!0,$:!0,";":!0,".":!0,",":!0};w("\\dotso",function(u){var e=u.future().text;return e in wn?"\\ldots\\,":"\\ldots"}),w("\\dotsc",function(u){var e=u.future().text;return e in wn&&e!==","?"\\ldots\\,":"\\ldots"}),w("\\cdots",function(u){var e=u.future().text;return e in wn?"\\@cdots\\,":"\\@cdots"}),w("\\dotsb","\\cdots"),w("\\dotsm","\\cdots"),w("\\dotsi","\\!\\cdots"),w("\\dotsx","\\ldots\\,"),w("\\DOTSI","\\relax"),w("\\DOTSB","\\relax"),w("\\DOTSX","\\relax"),w("\\tmspace","\\TextOrMath{\\kern#1#3}{\\mskip#1#2}\\relax"),w("\\,","\\tmspace+{3mu}{.1667em}"),w("\\thinspace","\\,"),w("\\>","\\mskip{4mu}"),w("\\:","\\tmspace+{4mu}{.2222em}"),w("\\medspace","\\:"),w("\\;","\\tmspace+{5mu}{.2777em}"),w("\\thickspace","\\;"),w("\\!","\\tmspace-{3mu}{.1667em}"),w("\\negthinspace","\\!"),w("\\negmedspace","\\tmspace-{4mu}{.2222em}"),w("\\negthickspace","\\tmspace-{5mu}{.277em}"),w("\\enspace","\\kern.5em "),w("\\enskip","\\hskip.5em\\relax"),w("\\quad","\\hskip1em\\relax"),w("\\qquad","\\hskip2em\\relax"),w("\\tag","\\@ifstar\\tag@literal\\tag@paren"),w("\\tag@paren","\\tag@literal{({#1})}"),w("\\tag@literal",function(u){if(u.macros.get("\\df@tag"))throw new p("Multiple \\tag");return"\\gdef\\df@tag{\\text{#1}}"}),w("\\bmod","\\mathchoice{\\mskip1mu}{\\mskip1mu}{\\mskip5mu}{\\mskip5mu}\\mathbin{\\rm mod}\\mathchoice{\\mskip1mu}{\\mskip1mu}{\\mskip5mu}{\\mskip5mu}"),w("\\pod","\\allowbreak\\mathchoice{\\mkern18mu}{\\mkern8mu}{\\mkern8mu}{\\mkern8mu}(#1)"),w("\\pmod","\\pod{{\\rm mod}\\mkern6mu#1}"),w("\\mod","\\allowbreak\\mathchoice{\\mkern18mu}{\\mkern12mu}{\\mkern12mu}{\\mkern12mu}{\\rm mod}\\,\\,#1"),w("\\newline","\\\\\\relax"),w("\\TeX","\\textrm{\\html@mathml{T\\kern-.1667em\\raisebox{-.5ex}{E}\\kern-.125emX}{TeX}}");var $a=Q(bt["Main-Regular"]["T".charCodeAt(0)][1]-.7*bt["Main-Regular"]["A".charCodeAt(0)][1]);w("\\LaTeX","\\textrm{\\html@mathml{"+("L\\kern-.36em\\raisebox{"+$a+"}{\\scriptstyle A}")+"\\kern-.15em\\TeX}{LaTeX}}"),w("\\KaTeX","\\textrm{\\html@mathml{"+("K\\kern-.17em\\raisebox{"+$a+"}{\\scriptstyle A}")+"\\kern-.15em\\TeX}{KaTeX}}"),w("\\hspace","\\@ifstar\\@hspacer\\@hspace"),w("\\@hspace","\\hskip #1\\relax"),w("\\@hspacer","\\rule{0pt}{0pt}\\hskip #1\\relax"),w("\\ordinarycolon",":"),w("\\vcentcolon","\\mathrel{\\mathop\\ordinarycolon}"),w("\\dblcolon",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-.9mu}\\vcentcolon}}{\\mathop{\\char"2237}}'),w("\\coloneqq",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-1.2mu}=}}{\\mathop{\\char"2254}}'),w("\\Coloneqq",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}=}}{\\mathop{\\char"2237\\char"3d}}'),w("\\coloneq",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-1.2mu}\\mathrel{-}}}{\\mathop{\\char"3a\\char"2212}}'),w("\\Coloneq",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}\\mathrel{-}}}{\\mathop{\\char"2237\\char"2212}}'),w("\\eqqcolon",'\\html@mathml{\\mathrel{=\\mathrel{\\mkern-1.2mu}\\vcentcolon}}{\\mathop{\\char"2255}}'),w("\\Eqqcolon",'\\html@mathml{\\mathrel{=\\mathrel{\\mkern-1.2mu}\\dblcolon}}{\\mathop{\\char"3d\\char"2237}}'),w("\\eqcolon",'\\html@mathml{\\mathrel{\\mathrel{-}\\mathrel{\\mkern-1.2mu}\\vcentcolon}}{\\mathop{\\char"2239}}'),w("\\Eqcolon",'\\html@mathml{\\mathrel{\\mathrel{-}\\mathrel{\\mkern-1.2mu}\\dblcolon}}{\\mathop{\\char"2212\\char"2237}}'),w("\\colonapprox",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-1.2mu}\\approx}}{\\mathop{\\char"3a\\char"2248}}'),w("\\Colonapprox",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}\\approx}}{\\mathop{\\char"2237\\char"2248}}'),w("\\colonsim",'\\html@mathml{\\mathrel{\\vcentcolon\\mathrel{\\mkern-1.2mu}\\sim}}{\\mathop{\\char"3a\\char"223c}}'),w("\\Colonsim",'\\html@mathml{\\mathrel{\\dblcolon\\mathrel{\\mkern-1.2mu}\\sim}}{\\mathop{\\char"2237\\char"223c}}'),w("∷","\\dblcolon"),w("∹","\\eqcolon"),w("≔","\\coloneqq"),w("≕","\\eqqcolon"),w("⩴","\\Coloneqq"),w("\\ratio","\\vcentcolon"),w("\\coloncolon","\\dblcolon"),w("\\colonequals","\\coloneqq"),w("\\coloncolonequals","\\Coloneqq"),w("\\equalscolon","\\eqqcolon"),w("\\equalscoloncolon","\\Eqqcolon"),w("\\colonminus","\\coloneq"),w("\\coloncolonminus","\\Coloneq"),w("\\minuscolon","\\eqcolon"),w("\\minuscoloncolon","\\Eqcolon"),w("\\coloncolonapprox","\\Colonapprox"),w("\\coloncolonsim","\\Colonsim"),w("\\simcolon","\\mathrel{\\sim\\mathrel{\\mkern-1.2mu}\\vcentcolon}"),w("\\simcoloncolon","\\mathrel{\\sim\\mathrel{\\mkern-1.2mu}\\dblcolon}"),w("\\approxcolon","\\mathrel{\\approx\\mathrel{\\mkern-1.2mu}\\vcentcolon}"),w("\\approxcoloncolon","\\mathrel{\\approx\\mathrel{\\mkern-1.2mu}\\dblcolon}"),w("\\notni","\\html@mathml{\\not\\ni}{\\mathrel{\\char`∌}}"),w("\\limsup","\\DOTSB\\operatorname*{lim\\,sup}"),w("\\liminf","\\DOTSB\\operatorname*{lim\\,inf}"),w("\\injlim","\\DOTSB\\operatorname*{inj\\,lim}"),w("\\projlim","\\DOTSB\\operatorname*{proj\\,lim}"),w("\\varlimsup","\\DOTSB\\operatorname*{\\overline{lim}}"),w("\\varliminf","\\DOTSB\\operatorname*{\\underline{lim}}"),w("\\varinjlim","\\DOTSB\\operatorname*{\\underrightarrow{lim}}"),w("\\varprojlim","\\DOTSB\\operatorname*{\\underleftarrow{lim}}"),w("\\gvertneqq","\\html@mathml{\\@gvertneqq}{≩}"),w("\\lvertneqq","\\html@mathml{\\@lvertneqq}{≨}"),w("\\ngeqq","\\html@mathml{\\@ngeqq}{≱}"),w("\\ngeqslant","\\html@mathml{\\@ngeqslant}{≱}"),w("\\nleqq","\\html@mathml{\\@nleqq}{≰}"),w("\\nleqslant","\\html@mathml{\\@nleqslant}{≰}"),w("\\nshortmid","\\html@mathml{\\@nshortmid}{∤}"),w("\\nshortparallel","\\html@mathml{\\@nshortparallel}{∦}"),w("\\nsubseteqq","\\html@mathml{\\@nsubseteqq}{⊈}"),w("\\nsupseteqq","\\html@mathml{\\@nsupseteqq}{⊉}"),w("\\varsubsetneq","\\html@mathml{\\@varsubsetneq}{⊊}"),w("\\varsubsetneqq","\\html@mathml{\\@varsubsetneqq}{⫋}"),w("\\varsupsetneq","\\html@mathml{\\@varsupsetneq}{⊋}"),w("\\varsupsetneqq","\\html@mathml{\\@varsupsetneqq}{⫌}"),w("\\imath","\\html@mathml{\\@imath}{ı}"),w("\\jmath","\\html@mathml{\\@jmath}{ȷ}"),w("\\llbracket","\\html@mathml{\\mathopen{[\\mkern-3.2mu[}}{\\mathopen{\\char`⟦}}"),w("\\rrbracket","\\html@mathml{\\mathclose{]\\mkern-3.2mu]}}{\\mathclose{\\char`⟧}}"),w("⟦","\\llbracket"),w("⟧","\\rrbracket"),w("\\lBrace","\\html@mathml{\\mathopen{\\{\\mkern-3.2mu[}}{\\mathopen{\\char`⦃}}"),w("\\rBrace","\\html@mathml{\\mathclose{]\\mkern-3.2mu\\}}}{\\mathclose{\\char`⦄}}"),w("⦃","\\lBrace"),w("⦄","\\rBrace"),w("\\minuso","\\mathbin{\\html@mathml{{\\mathrlap{\\mathchoice{\\kern{0.145em}}{\\kern{0.145em}}{\\kern{0.1015em}}{\\kern{0.0725em}}\\circ}{-}}}{\\char`⦵}}"),w("⦵","\\minuso"),w("\\darr","\\downarrow"),w("\\dArr","\\Downarrow"),w("\\Darr","\\Downarrow"),w("\\lang","\\langle"),w("\\rang","\\rangle"),w("\\uarr","\\uparrow"),w("\\uArr","\\Uparrow"),w("\\Uarr","\\Uparrow"),w("\\N","\\mathbb{N}"),w("\\R","\\mathbb{R}"),w("\\Z","\\mathbb{Z}"),w("\\alef","\\aleph"),w("\\alefsym","\\aleph"),w("\\Alpha","\\mathrm{A}"),w("\\Beta","\\mathrm{B}"),w("\\bull","\\bullet"),w("\\Chi","\\mathrm{X}"),w("\\clubs","\\clubsuit"),w("\\cnums","\\mathbb{C}"),w("\\Complex","\\mathbb{C}"),w("\\Dagger","\\ddagger"),w("\\diamonds","\\diamondsuit"),w("\\empty","\\emptyset"),w("\\Epsilon","\\mathrm{E}"),w("\\Eta","\\mathrm{H}"),w("\\exist","\\exists"),w("\\harr","\\leftrightarrow"),w("\\hArr","\\Leftrightarrow"),w("\\Harr","\\Leftrightarrow"),w("\\hearts","\\heartsuit"),w("\\image","\\Im"),w("\\infin","\\infty"),w("\\Iota","\\mathrm{I}"),w("\\isin","\\in"),w("\\Kappa","\\mathrm{K}"),w("\\larr","\\leftarrow"),w("\\lArr","\\Leftarrow"),w("\\Larr","\\Leftarrow"),w("\\lrarr","\\leftrightarrow"),w("\\lrArr","\\Leftrightarrow"),w("\\Lrarr","\\Leftrightarrow"),w("\\Mu","\\mathrm{M}"),w("\\natnums","\\mathbb{N}"),w("\\Nu","\\mathrm{N}"),w("\\Omicron","\\mathrm{O}"),w("\\plusmn","\\pm"),w("\\rarr","\\rightarrow"),w("\\rArr","\\Rightarrow"),w("\\Rarr","\\Rightarrow"),w("\\real","\\Re"),w("\\reals","\\mathbb{R}"),w("\\Reals","\\mathbb{R}"),w("\\Rho","\\mathrm{P}"),w("\\sdot","\\cdot"),w("\\sect","\\S"),w("\\spades","\\spadesuit"),w("\\sub","\\subset"),w("\\sube","\\subseteq"),w("\\supe","\\supseteq"),w("\\Tau","\\mathrm{T}"),w("\\thetasym","\\vartheta"),w("\\weierp","\\wp"),w("\\Zeta","\\mathrm{Z}"),w("\\argmin","\\DOTSB\\operatorname*{arg\\,min}"),w("\\argmax","\\DOTSB\\operatorname*{arg\\,max}"),w("\\plim","\\DOTSB\\mathop{\\operatorname{plim}}\\limits"),w("\\bra","\\mathinner{\\langle{#1}|}"),w("\\ket","\\mathinner{|{#1}\\rangle}"),w("\\braket","\\mathinner{\\langle{#1}\\rangle}"),w("\\Bra","\\left\\langle#1\\right|"),w("\\Ket","\\left|#1\\right\\rangle");var ei=function(e){return function(t){var r=t.consumeArg().tokens,n=t.consumeArg().tokens,i=t.consumeArg().tokens,c=t.consumeArg().tokens,f=t.macros.get("|"),b=t.macros.get("\\|");t.macros.beginGroup();var x=function(F){return function(q){e&&(q.macros.set("|",f),i.length&&q.macros.set("\\|",b));var Z=F;if(!F&&i.length){var $=q.future();$.text==="|"&&(q.popToken(),Z=!0)}return{tokens:Z?i:n,numArgs:0}}};t.macros.set("|",x(!1)),i.length&&t.macros.set("\\|",x(!0));var _=t.consumeArg().tokens,C=t.expandTokens([].concat(c,_,r));return t.macros.endGroup(),{tokens:C.reverse(),numArgs:0}}};w("\\bra@ket",ei(!1)),w("\\bra@set",ei(!0)),w("\\Braket","\\bra@ket{\\left\\langle}{\\,\\middle\\vert\\,}{\\,\\middle\\vert\\,}{\\right\\rangle}"),w("\\Set","\\bra@set{\\left\\{\\:}{\\;\\middle\\vert\\;}{\\;\\middle\\Vert\\;}{\\:\\right\\}}"),w("\\set","\\bra@set{\\{\\,}{\\mid}{}{\\,\\}}"),w("\\angln","{\\angl n}"),w("\\blue","\\textcolor{##6495ed}{#1}"),w("\\orange","\\textcolor{##ffa500}{#1}"),w("\\pink","\\textcolor{##ff00af}{#1}"),w("\\red","\\textcolor{##df0030}{#1}"),w("\\green","\\textcolor{##28ae7b}{#1}"),w("\\gray","\\textcolor{gray}{#1}"),w("\\purple","\\textcolor{##9d38bd}{#1}"),w("\\blueA","\\textcolor{##ccfaff}{#1}"),w("\\blueB","\\textcolor{##80f6ff}{#1}"),w("\\blueC","\\textcolor{##63d9ea}{#1}"),w("\\blueD","\\textcolor{##11accd}{#1}"),w("\\blueE","\\textcolor{##0c7f99}{#1}"),w("\\tealA","\\textcolor{##94fff5}{#1}"),w("\\tealB","\\textcolor{##26edd5}{#1}"),w("\\tealC","\\textcolor{##01d1c1}{#1}"),w("\\tealD","\\textcolor{##01a995}{#1}"),w("\\tealE","\\textcolor{##208170}{#1}"),w("\\greenA","\\textcolor{##b6ffb0}{#1}"),w("\\greenB","\\textcolor{##8af281}{#1}"),w("\\greenC","\\textcolor{##74cf70}{#1}"),w("\\greenD","\\textcolor{##1fab54}{#1}"),w("\\greenE","\\textcolor{##0d923f}{#1}"),w("\\goldA","\\textcolor{##ffd0a9}{#1}"),w("\\goldB","\\textcolor{##ffbb71}{#1}"),w("\\goldC","\\textcolor{##ff9c39}{#1}"),w("\\goldD","\\textcolor{##e07d10}{#1}"),w("\\goldE","\\textcolor{##a75a05}{#1}"),w("\\redA","\\textcolor{##fca9a9}{#1}"),w("\\redB","\\textcolor{##ff8482}{#1}"),w("\\redC","\\textcolor{##f9685d}{#1}"),w("\\redD","\\textcolor{##e84d39}{#1}"),w("\\redE","\\textcolor{##bc2612}{#1}"),w("\\maroonA","\\textcolor{##ffbde0}{#1}"),w("\\maroonB","\\textcolor{##ff92c6}{#1}"),w("\\maroonC","\\textcolor{##ed5fa6}{#1}"),w("\\maroonD","\\textcolor{##ca337c}{#1}"),w("\\maroonE","\\textcolor{##9e034e}{#1}"),w("\\purpleA","\\textcolor{##ddd7ff}{#1}"),w("\\purpleB","\\textcolor{##c6b9fc}{#1}"),w("\\purpleC","\\textcolor{##aa87ff}{#1}"),w("\\purpleD","\\textcolor{##7854ab}{#1}"),w("\\purpleE","\\textcolor{##543b78}{#1}"),w("\\mintA","\\textcolor{##f5f9e8}{#1}"),w("\\mintB","\\textcolor{##edf2df}{#1}"),w("\\mintC","\\textcolor{##e0e5cc}{#1}"),w("\\grayA","\\textcolor{##f6f7f7}{#1}"),w("\\grayB","\\textcolor{##f0f1f2}{#1}"),w("\\grayC","\\textcolor{##e3e5e6}{#1}"),w("\\grayD","\\textcolor{##d6d8da}{#1}"),w("\\grayE","\\textcolor{##babec2}{#1}"),w("\\grayF","\\textcolor{##888d93}{#1}"),w("\\grayG","\\textcolor{##626569}{#1}"),w("\\grayH","\\textcolor{##3b3e40}{#1}"),w("\\grayI","\\textcolor{##21242c}{#1}"),w("\\kaBlue","\\textcolor{##314453}{#1}"),w("\\kaGreen","\\textcolor{##71B307}{#1}");var ti={"^":!0,_:!0,"\\limits":!0,"\\nolimits":!0},Cs=function(){function u(t,r,n){this.settings=void 0,this.expansionCount=void 0,this.lexer=void 0,this.macros=void 0,this.stack=void 0,this.mode=void 0,this.settings=r,this.expansionCount=0,this.feed(t),this.macros=new Ms(Es,r.macros),this.mode=n,this.stack=[]}var e=u.prototype;return e.feed=function(r){this.lexer=new Ka(r,this.settings)},e.switchMode=function(r){this.mode=r},e.beginGroup=function(){this.macros.beginGroup()},e.endGroup=function(){this.macros.endGroup()},e.endGroups=function(){this.macros.endGroups()},e.future=function(){return this.stack.length===0&&this.pushToken(this.lexer.lex()),this.stack[this.stack.length-1]},e.popToken=function(){return this.future(),this.stack.pop()},e.pushToken=function(r){this.stack.push(r)},e.pushTokens=function(r){var n;(n=this.stack).push.apply(n,r)},e.scanArgument=function(r){var n,i,c;if(r){if(this.consumeSpaces(),this.future().text!=="[")return null;n=this.popToken();var f=this.consumeArg(["]"]);c=f.tokens,i=f.end}else{var b=this.consumeArg();c=b.tokens,n=b.start,i=b.end}return this.pushToken(new l0("EOF",i.loc)),this.pushTokens(c),n.range(i,"")},e.consumeSpaces=function(){for(;;){var r=this.future();if(r.text===" ")this.stack.pop();else break}},e.consumeArg=function(r){var n=[],i=r&&r.length>0;i||this.consumeSpaces();var c=this.future(),f,b=0,x=0;do{if(f=this.popToken(),n.push(f),f.text==="{")++b;else if(f.text==="}"){if(--b,b===-1)throw new p("Extra }",f)}else if(f.text==="EOF")throw new p("Unexpected end of input in a macro argument, expected '"+(r&&i?r[x]:"}")+"'",f);if(r&&i)if((b===0||b===1&&r[x]==="{")&&f.text===r[x]){if(++x,x===r.length){n.splice(-x,x);break}}else x=0}while(b!==0||i);return c.text==="{"&&n[n.length-1].text==="}"&&(n.pop(),n.shift()),n.reverse(),{tokens:n,start:c,end:f}},e.consumeArgs=function(r,n){if(n){if(n.length!==r+1)throw new p("The length of delimiters doesn't match the number of args!");for(var i=n[0],c=0;c<i.length;c++){var f=this.popToken();if(i[c]!==f.text)throw new p("Use of the macro doesn't match its definition",f)}}for(var b=[],x=0;x<r;x++)b.push(this.consumeArg(n&&n[x+1]).tokens);return b},e.expandOnce=function(r){var n=this.popToken(),i=n.text,c=n.noexpand?null:this._getExpansion(i);if(c==null||r&&c.unexpandable){if(r&&c==null&&i[0]==="\\"&&!this.isDefined(i))throw new p("Undefined control sequence: "+i);return this.pushToken(n),!1}if(this.expansionCount++,this.expansionCount>this.settings.maxExpand)throw new p("Too many expansions: infinite loop or need to increase maxExpand setting");var f=c.tokens,b=this.consumeArgs(c.numArgs,c.delimiters);if(c.numArgs){f=f.slice();for(var x=f.length-1;x>=0;--x){var _=f[x];if(_.text==="#"){if(x===0)throw new p("Incomplete placeholder at end of macro body",_);if(_=f[--x],_.text==="#")f.splice(x+1,1);else if(/^[1-9]$/.test(_.text)){var C;(C=f).splice.apply(C,[x,2].concat(b[+_.text-1]))}else throw new p("Not a valid argument number",_)}}}return this.pushTokens(f),f.length},e.expandAfterFuture=function(){return this.expandOnce(),this.future()},e.expandNextToken=function(){for(;;)if(this.expandOnce()===!1){var r=this.stack.pop();return r.treatAsRelax&&(r.text="\\relax"),r}throw new Error},e.expandMacro=function(r){return this.macros.has(r)?this.expandTokens([new l0(r)]):void 0},e.expandTokens=function(r){var n=[],i=this.stack.length;for(this.pushTokens(r);this.stack.length>i;)if(this.expandOnce(!0)===!1){var c=this.stack.pop();c.treatAsRelax&&(c.noexpand=!1,c.treatAsRelax=!1),n.push(c)}return n},e.expandMacroAsText=function(r){var n=this.expandMacro(r);return n&&n.map(function(i){return i.text}).join("")},e._getExpansion=function(r){var n=this.macros.get(r);if(n==null)return n;if(r.length===1){var i=this.lexer.catcodes[r];if(i!=null&&i!==13)return}var c=typeof n=="function"?n(this):n;if(typeof c=="string"){var f=0;if(c.indexOf("#")!==-1)for(var b=c.replace(/##/g,"");b.indexOf("#"+(f+1))!==-1;)++f;for(var x=new Ka(c,this.settings),_=[],C=x.lex();C.text!=="EOF";)_.push(C),C=x.lex();_.reverse();var I={tokens:_,numArgs:f};return I}return c},e.isDefined=function(r){return this.macros.has(r)||o0.hasOwnProperty(r)||Fe.math.hasOwnProperty(r)||Fe.text.hasOwnProperty(r)||ti.hasOwnProperty(r)},e.isExpandable=function(r){var n=this.macros.get(r);return n!=null?typeof n=="string"||typeof n=="function"||!n.unexpandable:o0.hasOwnProperty(r)&&!o0[r].primitive},u}(),ri=/^[₊₋₌₍₎₀₁₂₃₄₅₆₇₈₉ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓᵦᵧᵨᵩᵪ]/,Er=Object.freeze({"₊":"+","₋":"-","₌":"=","₍":"(","₎":")","₀":"0","₁":"1","₂":"2","₃":"3","₄":"4","₅":"5","₆":"6","₇":"7","₈":"8","₉":"9","ₐ":"a","ₑ":"e","ₕ":"h","ᵢ":"i","ⱼ":"j","ₖ":"k","ₗ":"l","ₘ":"m","ₙ":"n","ₒ":"o","ₚ":"p","ᵣ":"r","ₛ":"s","ₜ":"t","ᵤ":"u","ᵥ":"v","ₓ":"x","ᵦ":"β","ᵧ":"γ","ᵨ":"ρ","ᵩ":"ϕ","ᵪ":"χ","⁺":"+","⁻":"-","⁼":"=","⁽":"(","⁾":")","⁰":"0","¹":"1","²":"2","³":"3","⁴":"4","⁵":"5","⁶":"6","⁷":"7","⁸":"8","⁹":"9","ᴬ":"A","ᴮ":"B","ᴰ":"D","ᴱ":"E","ᴳ":"G","ᴴ":"H","ᴵ":"I","ᴶ":"J","ᴷ":"K","ᴸ":"L","ᴹ":"M","ᴺ":"N","ᴼ":"O","ᴾ":"P","ᴿ":"R","ᵀ":"T","ᵁ":"U","ⱽ":"V","ᵂ":"W","ᵃ":"a","ᵇ":"b","ᶜ":"c","ᵈ":"d","ᵉ":"e","ᶠ":"f","ᵍ":"g",ʰ:"h","ⁱ":"i",ʲ:"j","ᵏ":"k",ˡ:"l","ᵐ":"m",ⁿ:"n","ᵒ":"o","ᵖ":"p",ʳ:"r",ˢ:"s","ᵗ":"t","ᵘ":"u","ᵛ":"v",ʷ:"w",ˣ:"x",ʸ:"y","ᶻ":"z","ᵝ":"β","ᵞ":"γ","ᵟ":"δ","ᵠ":"ϕ","ᵡ":"χ","ᶿ":"θ"}),xn={"́":{text:"\\'",math:"\\acute"},"̀":{text:"\\`",math:"\\grave"},"̈":{text:'\\"',math:"\\ddot"},"̃":{text:"\\~",math:"\\tilde"},"̄":{text:"\\=",math:"\\bar"},"̆":{text:"\\u",math:"\\breve"},"̌":{text:"\\v",math:"\\check"},"̂":{text:"\\^",math:"\\hat"},"̇":{text:"\\.",math:"\\dot"},"̊":{text:"\\r",math:"\\mathring"},"̋":{text:"\\H"},"̧":{text:"\\c"}},ni={á:"á",à:"à",ä:"ä",ǟ:"ǟ",ã:"ã",ā:"ā",ă:"ă",ắ:"ắ",ằ:"ằ",ẵ:"ẵ",ǎ:"ǎ",â:"â",ấ:"ấ",ầ:"ầ",ẫ:"ẫ",ȧ:"ȧ",ǡ:"ǡ",å:"å",ǻ:"ǻ",ḃ:"ḃ",ć:"ć",ḉ:"ḉ",č:"č",ĉ:"ĉ",ċ:"ċ",ç:"ç",ď:"ď",ḋ:"ḋ",ḑ:"ḑ",é:"é",è:"è",ë:"ë",ẽ:"ẽ",ē:"ē",ḗ:"ḗ",ḕ:"ḕ",ĕ:"ĕ",ḝ:"ḝ",ě:"ě",ê:"ê",ế:"ế",ề:"ề",ễ:"ễ",ė:"ė",ȩ:"ȩ",ḟ:"ḟ",ǵ:"ǵ",ḡ:"ḡ",ğ:"ğ",ǧ:"ǧ",ĝ:"ĝ",ġ:"ġ",ģ:"ģ",ḧ:"ḧ",ȟ:"ȟ",ĥ:"ĥ",ḣ:"ḣ",ḩ:"ḩ",í:"í",ì:"ì",ï:"ï",ḯ:"ḯ",ĩ:"ĩ",ī:"ī",ĭ:"ĭ",ǐ:"ǐ",î:"î",ǰ:"ǰ",ĵ:"ĵ",ḱ:"ḱ",ǩ:"ǩ",ķ:"ķ",ĺ:"ĺ",ľ:"ľ",ļ:"ļ",ḿ:"ḿ",ṁ:"ṁ",ń:"ń",ǹ:"ǹ",ñ:"ñ",ň:"ň",ṅ:"ṅ",ņ:"ņ",ó:"ó",ò:"ò",ö:"ö",ȫ:"ȫ",õ:"õ",ṍ:"ṍ",ṏ:"ṏ",ȭ:"ȭ",ō:"ō",ṓ:"ṓ",ṑ:"ṑ",ŏ:"ŏ",ǒ:"ǒ",ô:"ô",ố:"ố",ồ:"ồ",ỗ:"ỗ",ȯ:"ȯ",ȱ:"ȱ",ő:"ő",ṕ:"ṕ",ṗ:"ṗ",ŕ:"ŕ",ř:"ř",ṙ:"ṙ",ŗ:"ŗ",ś:"ś",ṥ:"ṥ",š:"š",ṧ:"ṧ",ŝ:"ŝ",ṡ:"ṡ",ş:"ş",ẗ:"ẗ",ť:"ť",ṫ:"ṫ",ţ:"ţ",ú:"ú",ù:"ù",ü:"ü",ǘ:"ǘ",ǜ:"ǜ",ǖ:"ǖ",ǚ:"ǚ",ũ:"ũ",ṹ:"ṹ",ū:"ū",ṻ:"ṻ",ŭ:"ŭ",ǔ:"ǔ",û:"û",ů:"ů",ű:"ű",ṽ:"ṽ",ẃ:"ẃ",ẁ:"ẁ",ẅ:"ẅ",ŵ:"ŵ",ẇ:"ẇ",ẘ:"ẘ",ẍ:"ẍ",ẋ:"ẋ",ý:"ý",ỳ:"ỳ",ÿ:"ÿ",ỹ:"ỹ",ȳ:"ȳ",ŷ:"ŷ",ẏ:"ẏ",ẙ:"ẙ",ź:"ź",ž:"ž",ẑ:"ẑ",ż:"ż",Á:"Á",À:"À",Ä:"Ä",Ǟ:"Ǟ",Ã:"Ã",Ā:"Ā",Ă:"Ă",Ắ:"Ắ",Ằ:"Ằ",Ẵ:"Ẵ",Ǎ:"Ǎ",Â:"Â",Ấ:"Ấ",Ầ:"Ầ",Ẫ:"Ẫ",Ȧ:"Ȧ",Ǡ:"Ǡ",Å:"Å",Ǻ:"Ǻ",Ḃ:"Ḃ",Ć:"Ć",Ḉ:"Ḉ",Č:"Č",Ĉ:"Ĉ",Ċ:"Ċ",Ç:"Ç",Ď:"Ď",Ḋ:"Ḋ",Ḑ:"Ḑ",É:"É",È:"È",Ë:"Ë",Ẽ:"Ẽ",Ē:"Ē",Ḗ:"Ḗ",Ḕ:"Ḕ",Ĕ:"Ĕ",Ḝ:"Ḝ",Ě:"Ě",Ê:"Ê",Ế:"Ế",Ề:"Ề",Ễ:"Ễ",Ė:"Ė",Ȩ:"Ȩ",Ḟ:"Ḟ",Ǵ:"Ǵ",Ḡ:"Ḡ",Ğ:"Ğ",Ǧ:"Ǧ",Ĝ:"Ĝ",Ġ:"Ġ",Ģ:"Ģ",Ḧ:"Ḧ",Ȟ:"Ȟ",Ĥ:"Ĥ",Ḣ:"Ḣ",Ḩ:"Ḩ",Í:"Í",Ì:"Ì",Ï:"Ï",Ḯ:"Ḯ",Ĩ:"Ĩ",Ī:"Ī",Ĭ:"Ĭ",Ǐ:"Ǐ",Î:"Î",İ:"İ",Ĵ:"Ĵ",Ḱ:"Ḱ",Ǩ:"Ǩ",Ķ:"Ķ",Ĺ:"Ĺ",Ľ:"Ľ",Ļ:"Ļ",Ḿ:"Ḿ",Ṁ:"Ṁ",Ń:"Ń",Ǹ:"Ǹ",Ñ:"Ñ",Ň:"Ň",Ṅ:"Ṅ",Ņ:"Ņ",Ó:"Ó",Ò:"Ò",Ö:"Ö",Ȫ:"Ȫ",Õ:"Õ",Ṍ:"Ṍ",Ṏ:"Ṏ",Ȭ:"Ȭ",Ō:"Ō",Ṓ:"Ṓ",Ṑ:"Ṑ",Ŏ:"Ŏ",Ǒ:"Ǒ",Ô:"Ô",Ố:"Ố",Ồ:"Ồ",Ỗ:"Ỗ",Ȯ:"Ȯ",Ȱ:"Ȱ",Ő:"Ő",Ṕ:"Ṕ",Ṗ:"Ṗ",Ŕ:"Ŕ",Ř:"Ř",Ṙ:"Ṙ",Ŗ:"Ŗ",Ś:"Ś",Ṥ:"Ṥ",Š:"Š",Ṧ:"Ṧ",Ŝ:"Ŝ",Ṡ:"Ṡ",Ş:"Ş",Ť:"Ť",Ṫ:"Ṫ",Ţ:"Ţ",Ú:"Ú",Ù:"Ù",Ü:"Ü",Ǘ:"Ǘ",Ǜ:"Ǜ",Ǖ:"Ǖ",Ǚ:"Ǚ",Ũ:"Ũ",Ṹ:"Ṹ",Ū:"Ū",Ṻ:"Ṻ",Ŭ:"Ŭ",Ǔ:"Ǔ",Û:"Û",Ů:"Ů",Ű:"Ű",Ṽ:"Ṽ",Ẃ:"Ẃ",Ẁ:"Ẁ",Ẅ:"Ẅ",Ŵ:"Ŵ",Ẇ:"Ẇ",Ẍ:"Ẍ",Ẋ:"Ẋ",Ý:"Ý",Ỳ:"Ỳ",Ÿ:"Ÿ",Ỹ:"Ỹ",Ȳ:"Ȳ",Ŷ:"Ŷ",Ẏ:"Ẏ",Ź:"Ź",Ž:"Ž",Ẑ:"Ẑ",Ż:"Ż",ά:"ά",ὰ:"ὰ",ᾱ:"ᾱ",ᾰ:"ᾰ",έ:"έ",ὲ:"ὲ",ή:"ή",ὴ:"ὴ",ί:"ί",ὶ:"ὶ",ϊ:"ϊ",ΐ:"ΐ",ῒ:"ῒ",ῑ:"ῑ",ῐ:"ῐ",ό:"ό",ὸ:"ὸ",ύ:"ύ",ὺ:"ὺ",ϋ:"ϋ",ΰ:"ΰ",ῢ:"ῢ",ῡ:"ῡ",ῠ:"ῠ",ώ:"ώ",ὼ:"ὼ",Ύ:"Ύ",Ὺ:"Ὺ",Ϋ:"Ϋ",Ῡ:"Ῡ",Ῠ:"Ῠ",Ώ:"Ώ",Ὼ:"Ὼ"},ai=function(){function u(t,r){this.mode=void 0,this.gullet=void 0,this.settings=void 0,this.leftrightDepth=void 0,this.nextToken=void 0,this.mode="math",this.gullet=new Cs(t,r,this.mode),this.settings=r,this.leftrightDepth=0}var e=u.prototype;return e.expect=function(r,n){if(n===void 0&&(n=!0),this.fetch().text!==r)throw new p("Expected '"+r+"', got '"+this.fetch().text+"'",this.fetch());n&&this.consume()},e.consume=function(){this.nextToken=null},e.fetch=function(){return this.nextToken==null&&(this.nextToken=this.gullet.expandNextToken()),this.nextToken},e.switchMode=function(r){this.mode=r,this.gullet.switchMode(r)},e.parse=function(){this.settings.globalGroup||this.gullet.beginGroup(),this.settings.colorIsTextColor&&this.gullet.macros.set("\\color","\\textcolor");try{var r=this.parseExpression(!1);return this.expect("EOF"),this.settings.globalGroup||this.gullet.endGroup(),r}finally{this.gullet.endGroups()}},e.subparse=function(r){var n=this.nextToken;this.consume(),this.gullet.pushToken(new l0("}")),this.gullet.pushTokens(r);var i=this.parseExpression(!1);return this.expect("}"),this.nextToken=n,i},e.parseExpression=function(r,n){for(var i=[];;){this.mode==="math"&&this.consumeSpaces();var c=this.fetch();if(u.endOfExpression.indexOf(c.text)!==-1||n&&c.text===n||r&&o0[c.text]&&o0[c.text].infix)break;var f=this.parseAtom(n);if(f){if(f.type==="internal")continue}else break;i.push(f)}return this.mode==="text"&&this.formLigatures(i),this.handleInfixNodes(i)},e.handleInfixNodes=function(r){for(var n=-1,i,c=0;c<r.length;c++)if(r[c].type==="infix"){if(n!==-1)throw new p("only one infix operator per group",r[c].token);n=c,i=r[c].replaceWith}if(n!==-1&&i){var f,b,x=r.slice(0,n),_=r.slice(n+1);x.length===1&&x[0].type==="ordgroup"?f=x[0]:f={type:"ordgroup",mode:this.mode,body:x},_.length===1&&_[0].type==="ordgroup"?b=_[0]:b={type:"ordgroup",mode:this.mode,body:_};var C;return i==="\\\\abovefrac"?C=this.callFunction(i,[f,r[n],b],[]):C=this.callFunction(i,[f,b],[]),[C]}else return r},e.handleSupSubscript=function(r){var n=this.fetch(),i=n.text;this.consume(),this.consumeSpaces();var c=this.parseGroup(r);if(!c)throw new p("Expected group after '"+i+"'",n);return c},e.formatUnsupportedCmd=function(r){for(var n=[],i=0;i<r.length;i++)n.push({type:"textord",mode:"text",text:r[i]});var c={type:"text",mode:this.mode,body:n},f={type:"color",mode:this.mode,color:this.settings.errorColor,body:[c]};return f},e.parseAtom=function(r){var n=this.parseGroup("atom",r);if(this.mode==="text")return n;for(var i,c;;){this.consumeSpaces();var f=this.fetch();if(f.text==="\\limits"||f.text==="\\nolimits"){if(n&&n.type==="op"){var b=f.text==="\\limits";n.limits=b,n.alwaysHandleSupSub=!0}else if(n&&n.type==="operatorname")n.alwaysHandleSupSub&&(n.limits=f.text==="\\limits");else throw new p("Limit controls must follow a math operator",f);this.consume()}else if(f.text==="^"){if(i)throw new p("Double superscript",f);i=this.handleSupSubscript("superscript")}else if(f.text==="_"){if(c)throw new p("Double subscript",f);c=this.handleSupSubscript("subscript")}else if(f.text==="'"){if(i)throw new p("Double superscript",f);var x={type:"textord",mode:this.mode,text:"\\prime"},_=[x];for(this.consume();this.fetch().text==="'";)_.push(x),this.consume();this.fetch().text==="^"&&_.push(this.handleSupSubscript("superscript")),i={type:"ordgroup",mode:this.mode,body:_}}else if(Er[f.text]){var C=Er[f.text],I=ri.test(f.text);for(this.consume();;){var F=this.fetch().text;if(!Er[F]||ri.test(F)!==I)break;this.consume(),C+=Er[F]}var q=new u(C,this.settings).parse();I?c={type:"ordgroup",mode:"math",body:q}:i={type:"ordgroup",mode:"math",body:q}}else break}return i||c?{type:"supsub",mode:this.mode,base:n,sup:i,sub:c}:n},e.parseFunction=function(r,n){var i=this.fetch(),c=i.text,f=o0[c];if(!f)return null;if(this.consume(),n&&n!=="atom"&&!f.allowedInArgument)throw new p("Got function '"+c+"' with no arguments"+(n?" as "+n:""),i);if(this.mode==="text"&&!f.allowedInText)throw new p("Can't use function '"+c+"' in text mode",i);if(this.mode==="math"&&f.allowedInMath===!1)throw new p("Can't use function '"+c+"' in math mode",i);var b=this.parseArguments(c,f),x=b.args,_=b.optArgs;return this.callFunction(c,x,_,i,r)},e.callFunction=function(r,n,i,c,f){var b={funcName:r,parser:this,token:c,breakOnTokenText:f},x=o0[r];if(x&&x.handler)return x.handler(b,n,i);throw new p("No function handler for "+r)},e.parseArguments=function(r,n){var i=n.numArgs+n.numOptionalArgs;if(i===0)return{args:[],optArgs:[]};for(var c=[],f=[],b=0;b<i;b++){var x=n.argTypes&&n.argTypes[b],_=b<n.numOptionalArgs;(n.primitive&&x==null||n.type==="sqrt"&&b===1&&f[0]==null)&&(x="primitive");var C=this.parseGroupOfType("argument to '"+r+"'",x,_);if(_)f.push(C);else if(C!=null)c.push(C);else throw new p("Null argument, please report this as a bug")}return{args:c,optArgs:f}},e.parseGroupOfType=function(r,n,i){switch(n){case"color":return this.parseColorGroup(i);case"size":return this.parseSizeGroup(i);case"url":return this.parseUrlGroup(i);case"math":case"text":return this.parseArgumentGroup(i,n);case"hbox":{var c=this.parseArgumentGroup(i,"text");return c!=null?{type:"styling",mode:c.mode,body:[c],style:"text"}:null}case"raw":{var f=this.parseStringGroup("raw",i);return f!=null?{type:"raw",mode:"text",string:f.text}:null}case"primitive":{if(i)throw new p("A primitive argument cannot be optional");var b=this.parseGroup(r);if(b==null)throw new p("Expected group as "+r,this.fetch());return b}case"original":case null:case void 0:return this.parseArgumentGroup(i);default:throw new p("Unknown group type as "+r,this.fetch())}},e.consumeSpaces=function(){for(;this.fetch().text===" ";)this.consume()},e.parseStringGroup=function(r,n){var i=this.gullet.scanArgument(n);if(i==null)return null;for(var c="",f;(f=this.fetch()).text!=="EOF";)c+=f.text,this.consume();return this.consume(),i.text=c,i},e.parseRegexGroup=function(r,n){for(var i=this.fetch(),c=i,f="",b;(b=this.fetch()).text!=="EOF"&&r.test(f+b.text);)c=b,f+=c.text,this.consume();if(f==="")throw new p("Invalid "+n+": '"+i.text+"'",i);return i.range(c,f)},e.parseColorGroup=function(r){var n=this.parseStringGroup("color",r);if(n==null)return null;var i=/^(#[a-f0-9]{3}|#?[a-f0-9]{6}|[a-z]+)$/i.exec(n.text);if(!i)throw new p("Invalid color: '"+n.text+"'",n);var c=i[0];return/^[0-9a-f]{6}$/i.test(c)&&(c="#"+c),{type:"color-token",mode:this.mode,color:c}},e.parseSizeGroup=function(r){var n,i=!1;if(this.gullet.consumeSpaces(),!r&&this.gullet.future().text!=="{"?n=this.parseRegexGroup(/^[-+]? *(?:$|\d+|\d+\.\d*|\.\d*) *[a-z]{0,2} *$/,"size"):n=this.parseStringGroup("size",r),!n)return null;!r&&n.text.length===0&&(n.text="0pt",i=!0);var c=/([-+]?) *(\d+(?:\.\d*)?|\.\d+) *([a-z]{2})/.exec(n.text);if(!c)throw new p("Invalid size: '"+n.text+"'",n);var f={number:+(c[1]+c[2]),unit:c[3]};if(!B0(f))throw new p("Invalid unit: '"+f.unit+"'",n);return{type:"size",mode:this.mode,value:f,isBlank:i}},e.parseUrlGroup=function(r){this.gullet.lexer.setCatcode("%",13),this.gullet.lexer.setCatcode("~",12);var n=this.parseStringGroup("url",r);if(this.gullet.lexer.setCatcode("%",14),this.gullet.lexer.setCatcode("~",13),n==null)return null;var i=n.text.replace(/\\([#$%&~_^{}])/g,"$1");return{type:"url",mode:this.mode,url:i}},e.parseArgumentGroup=function(r,n){var i=this.gullet.scanArgument(r);if(i==null)return null;var c=this.mode;n&&this.switchMode(n),this.gullet.beginGroup();var f=this.parseExpression(!1,"EOF");this.expect("EOF"),this.gullet.endGroup();var b={type:"ordgroup",mode:this.mode,loc:i.loc,body:f};return n&&this.switchMode(c),b},e.parseGroup=function(r,n){var i=this.fetch(),c=i.text,f;if(c==="{"||c==="\\begingroup"){this.consume();var b=c==="{"?"}":"\\endgroup";this.gullet.beginGroup();var x=this.parseExpression(!1,b),_=this.fetch();this.expect(b),this.gullet.endGroup(),f={type:"ordgroup",mode:this.mode,loc:zt.range(i,_),body:x,semisimple:c==="\\begingroup"||void 0}}else if(f=this.parseFunction(n,r)||this.parseSymbol(),f==null&&c[0]==="\\"&&!ti.hasOwnProperty(c)){if(this.settings.throwOnError)throw new p("Undefined control sequence: "+c,i);f=this.formatUnsupportedCmd(c),this.consume()}return f},e.formLigatures=function(r){for(var n=r.length-1,i=0;i<n;++i){var c=r[i],f=c.text;f==="-"&&r[i+1].text==="-"&&(i+1<n&&r[i+2].text==="-"?(r.splice(i,3,{type:"textord",mode:"text",loc:zt.range(c,r[i+2]),text:"---"}),n-=2):(r.splice(i,2,{type:"textord",mode:"text",loc:zt.range(c,r[i+1]),text:"--"}),n-=1)),(f==="'"||f==="`")&&r[i+1].text===f&&(r.splice(i,2,{type:"textord",mode:"text",loc:zt.range(c,r[i+1]),text:f+f}),n-=1)}},e.parseSymbol=function(){var r=this.fetch(),n=r.text;if(/^\\verb[^a-zA-Z]/.test(n)){this.consume();var i=n.slice(5),c=i.charAt(0)==="*";if(c&&(i=i.slice(1)),i.length<2||i.charAt(0)!==i.slice(-1))throw new p(`\\verb assertion failed --
-                    please report what input caused this bug`);return i=i.slice(1,-1),{type:"verb",mode:"text",body:i,star:c}}ni.hasOwnProperty(n[0])&&!Fe[this.mode][n[0]]&&(this.settings.strict&&this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Accented Unicode text character "'+n[0]+'" used in math mode',r),n=ni[n[0]]+n.slice(1));var f=Ts.exec(n);f&&(n=n.substring(0,f.index),n==="i"?n="ı":n==="j"&&(n="ȷ"));var b;if(Fe[this.mode][n]){this.settings.strict&&this.mode==="math"&&Wr.indexOf(n)>=0&&this.settings.reportNonstrict("unicodeTextInMathMode",'Latin-1/Unicode text character "'+n[0]+'" used in math mode',r);var x=Fe[this.mode][n].group,_=zt.range(r),C;if(Wt.hasOwnProperty(x)){var I=x;C={type:"atom",mode:this.mode,family:I,loc:_,text:n}}else C={type:x,mode:this.mode,loc:_,text:n};b=C}else if(n.charCodeAt(0)>=128)this.settings.strict&&(gt(n.charCodeAt(0))?this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Unicode text character "'+n[0]+'" used in math mode',r):this.settings.reportNonstrict("unknownSymbol",'Unrecognized Unicode character "'+n[0]+'"'+(" ("+n.charCodeAt(0)+")"),r)),b={type:"textord",mode:"text",loc:zt.range(r),text:n};else return null;if(this.consume(),f)for(var F=0;F<f[0].length;F++){var q=f[0][F];if(!xn[q])throw new p("Unknown accent ' "+q+"'",r);var Z=xn[q][this.mode]||xn[q].text;if(!Z)throw new p("Accent "+q+" unsupported in "+this.mode+" mode",r);b={type:"accent",mode:this.mode,loc:zt.range(r),label:Z,isStretchy:!1,isShifty:!0,base:b}}return b},u}();ai.endOfExpression=["}","\\endgroup","\\end","\\right","&"];var Bs=function(e,t){if(!(typeof e=="string"||e instanceof String))throw new TypeError("KaTeX can only parse string typed expression");var r=new ai(e,t);delete r.gullet.macros.current["\\df@tag"];var n=r.parse();if(delete r.gullet.macros.current["\\current@color"],delete r.gullet.macros.current["\\color"],r.gullet.macros.get("\\df@tag")){if(!t.displayMode)throw new p("\\tag works only in display equations");n=[{type:"tag",mode:"text",body:n,tag:r.subparse([new l0("\\df@tag")])}]}return n},kn=Bs,ii=function(e,t,r){t.textContent="";var n=Sn(e,r).toNode();t.appendChild(n)};typeof document<"u"&&document.compatMode!=="CSS1Compat"&&(typeof console<"u"&&console.warn("Warning: KaTeX doesn't work in quirks mode. Make sure your website has a suitable doctype."),ii=function(){throw new p("KaTeX doesn't work in quirks mode.")});var Ds=function(e,t){var r=Sn(e,t).toMarkup();return r},Ns=function(e,t){var r=new R(t);return kn(e,r)},li=function(e,t,r){if(r.throwOnError||!(e instanceof p))throw e;var n=E.makeSpan(["katex-error"],[new nt(t)]);return n.setAttribute("title",e.toString()),n.setAttribute("style","color:"+r.errorColor),n},Sn=function(e,t){var r=new R(t);try{var n=kn(e,r);return Ol(n,e,r)}catch(i){return li(i,e,r)}},Rs=function(e,t){var r=new R(t);try{var n=kn(e,r);return ql(n,e,r)}catch(i){return li(i,e,r)}},Fs={version:"0.16.7",render:ii,renderToString:Ds,ParseError:p,SETTINGS_SCHEMA:B,__parse:Ns,__renderToDomTree:Sn,__renderToHTMLTree:Rs,__setFontMetrics:t0,__defineSymbol:o,__defineFunction:re,__defineMacro:w,__domTree:{Span:yt,Anchor:K0,SymbolNode:nt,SvgNode:wt,PathNode:Nt,LineNode:n0}},Is=Fs;return s=s.default,s}()})}(In)),In.exports}(function(g,a){(function(s,m){g.exports=m(r1())})(typeof self<"u"?self:qr,function(l){return function(){var s={771:function(y){y.exports=l}},m={};function p(y){var k=m[y];if(k!==void 0)return k.exports;var N=m[y]={exports:{}};return s[y](N,N.exports,p),N.exports}(function(){p.n=function(y){var k=y&&y.__esModule?function(){return y.default}:function(){return y};return p.d(k,{a:k}),k}})(),function(){p.d=function(y,k){for(var N in k)p.o(k,N)&&!p.o(y,N)&&Object.defineProperty(y,N,{enumerable:!0,get:k[N]})}}(),function(){p.o=function(y,k){return Object.prototype.hasOwnProperty.call(y,k)}}();var v={};return function(){p.d(v,{default:function(){return O}});var y=p(771),k=p.n(y),N=function(z,R,D){for(var L=D,X=0,ae=z.length;L<R.length;){var K=R[L];if(X<=0&&R.slice(L,L+ae)===z)return L;K==="\\"?L++:K==="{"?X++:K==="}"&&X--,L++}return-1},U=function(z){return z.replace(/[-/\\^$*+?.()|[\]{}]/g,"\\$&")},Y=/^\\begin{/,te=function(z,R){for(var D,L=[],X=new RegExp("("+R.map(function(W){return U(W.left)}).join("|")+")");D=z.search(X),D!==-1;){D>0&&(L.push({type:"text",data:z.slice(0,D)}),z=z.slice(D));var ae=R.findIndex(function(W){return z.startsWith(W.left)});if(D=N(R[ae].right,z,R[ae].left.length),D===-1)break;var K=z.slice(0,D+R[ae].right.length),G=Y.test(K)?K:z.slice(R[ae].left.length,D);L.push({type:"math",data:G,rawData:K,display:R[ae].display}),z=z.slice(D+R[ae].right.length)}return z!==""&&L.push({type:"text",data:z}),L},j=te,ve=function(z,R){var D=j(z,R.delimiters);if(D.length===1&&D[0].type==="text")return null;for(var L=document.createDocumentFragment(),X=0;X<D.length;X++)if(D[X].type==="text")L.appendChild(document.createTextNode(D[X].data));else{var ae=document.createElement("span"),K=D[X].data;R.displayMode=D[X].display;try{R.preProcess&&(K=R.preProcess(K)),k().render(K,ae,R)}catch(G){if(!(G instanceof k().ParseError))throw G;R.errorCallback("KaTeX auto-render: Failed to parse `"+D[X].data+"` with ",G),L.appendChild(document.createTextNode(D[X].rawData));continue}L.appendChild(ae)}return L},pe=function B(z,R){for(var D=0;D<z.childNodes.length;D++){var L=z.childNodes[D];if(L.nodeType===3){for(var X=L.textContent,ae=L.nextSibling,K=0;ae&&ae.nodeType===Node.TEXT_NODE;)X+=ae.textContent,ae=ae.nextSibling,K++;var G=ve(X,R);if(G){for(var W=0;W<K;W++)L.nextSibling.remove();D+=G.childNodes.length-1,z.replaceChild(G,L)}else D+=K}else L.nodeType===1&&function(){var Ce=" "+L.className+" ",ie=R.ignoredTags.indexOf(L.nodeName.toLowerCase())===-1&&R.ignoredClasses.every(function(Ge){return Ce.indexOf(" "+Ge+" ")===-1});ie&&B(L,R)}()}},le=function(z,R){if(!z)throw new Error("No element provided to render");var D={};for(var L in R)R.hasOwnProperty(L)&&(D[L]=R[L]);D.delimiters=D.delimiters||[{left:"$$",right:"$$",display:!0},{left:"\\(",right:"\\)",display:!1},{left:"\\begin{equation}",right:"\\end{equation}",display:!0},{left:"\\begin{align}",right:"\\end{align}",display:!0},{left:"\\begin{alignat}",right:"\\end{alignat}",display:!0},{left:"\\begin{gather}",right:"\\end{gather}",display:!0},{left:"\\begin{CD}",right:"\\end{CD}",display:!0},{left:"\\[",right:"\\]",display:!0}],D.ignoredTags=D.ignoredTags||["script","noscript","style","textarea","pre","code","option"],D.ignoredClasses=D.ignoredClasses||[],D.errorCallback=D.errorCallback||console.error,D.macros=D.macros||{},pe(z,D)},O=le}(),v=v.default,v}()})})(gl);var n1=gl.exports;const a1=Xi(n1);function i1(g){let a;return{c(){a=Qe("span"),me(a,"class","svelte-15hifvz")},m(l,s){Oe(l,a,s),g[4](a)},p:ht,i:ht,o:ht,d(l){l&&qe(a),g[4](null)}}}function l1(g,a,l){const s=Zi();let{message:m}=a,p="",{latex_delimiters:v}=a,y,k=!1;Bi.addHook("afterSanitizeAttributes",function(U){"target"in U&&(U.setAttribute("target","_blank"),U.setAttribute("rel","noopener noreferrer"))}),Ki(()=>{Ps().then(()=>{m!==p&&requestAnimationFrame(()=>{l(0,y.innerHTML=Bi.sanitize(fe.parse(m)),y),l(3,k=!0),p=m,s("load")})})});function N(U){Pn[U?"unshift":"push"](()=>{y=U,l(0,y)})}return g.$$set=U=>{"message"in U&&l(1,m=U.message),"latex_delimiters"in U&&l(2,v=U.latex_delimiters)},g.$$.update=()=>{g.$$.dirty&13&&k&&v.length>0&&a1(y,{delimiters:v,throwOnError:!1})},[y,m,v,k,N]}class s1 extends W0{constructor(a){super(),Y0(this,a,l1,i1,j0,{message:1,latex_delimiters:2})}}function Ni(g){let a,l,s;return l=new Js({}),{c(){a=Qe("span"),Pt(l.$$.fragment)},m(m,p){Oe(m,a,p),Ht(l,a,null),s=!0},i(m){s||(xe(l.$$.fragment,m),s=!0)},o(m){Me(l.$$.fragment,m),s=!1},d(m){m&&qe(a),Ut(l)}}}function Ri(g){let a,l,s;return l=new $s({}),{c(){a=Qe("span"),Pt(l.$$.fragment)},m(m,p){Oe(m,a,p),Ht(l,a,null),s=!0},i(m){s||(xe(l.$$.fragment,m),s=!0)},o(m){Me(l.$$.fragment,m),s=!1},d(m){m&&qe(a),Ut(l)}}}function o1(g){let a,l,s,m,p,v=!g[0]&&Ni(),y=g[0]&&Ri();return{c(){a=Qe("button"),v&&v.c(),l=f0(),y&&y.c(),me(a,"title","copy"),me(a,"class","svelte-jdgjqu")},m(k,N){Oe(k,a,N),v&&v.m(a,null),Lt(a,l),y&&y.m(a,null),s=!0,m||(p=m0(a,"click",g[1]),m=!0)},p(k,[N]){k[0]?v&&(Ot(),Me(v,1,1,()=>{v=null}),qt()):v?N&1&&xe(v,1):(v=Ni(),v.c(),xe(v,1),v.m(a,l)),k[0]?y?N&1&&xe(y,1):(y=Ri(),y.c(),xe(y,1),y.m(a,null)):y&&(Ot(),Me(y,1,1,()=>{y=null}),qt())},i(k){s||(xe(v),xe(y),s=!0)},o(k){Me(v),Me(y),s=!1},d(k){k&&qe(a),v&&v.d(),y&&y.d(),m=!1,p()}}}function u1(g,a,l){let s=!1,{value:m}=a,p;function v(){l(0,s=!0),p&&clearTimeout(p),p=setTimeout(()=>{l(0,s=!1)},2e3)}async function y(){if("clipboard"in navigator)await navigator.clipboard.writeText(m),v();else{const k=document.createElement("textarea");k.value=m,k.style.position="absolute",k.style.left="-999999px",document.body.prepend(k),k.select();try{document.execCommand("copy"),v()}catch(N){console.error(N)}finally{k.remove()}}}return Hs(()=>{p&&clearTimeout(p)}),g.$$set=k=>{"value"in k&&l(2,m=k.value)},[s,y,m]}class c1 extends W0{constructor(a){super(),Y0(this,a,u1,o1,j0,{value:2})}}function Fi(g,a,l){const s=g.slice();return s[26]=a[l],s[28]=l,s}function Ii(g,a,l){const s=g.slice();return s[29]=a[l],s[31]=l,s}function Li(g,a,l){const s=g.slice();return s[32]=a[l],s}function Oi(g){let a,l,s;return l=new Ks({props:{formatter:Fo,value:g[0]}}),l.$on("error",g[19]),l.$on("share",g[20]),{c(){a=Qe("div"),Pt(l.$$.fragment),me(a,"class","icon-button svelte-175fbmp")},m(m,p){Oe(m,a,p),Ht(l,a,null),s=!0},p(m,p){const v={};p[0]&1&&(v.value=m[0]),l.$set(v)},i(m){s||(xe(l.$$.fragment,m),s=!0)},o(m){Me(l.$$.fragment,m),s=!1},d(m){m&&qe(a),Ut(l)}}}function qi(g){let a,l,s=G0(g[0]),m=[];for(let v=0;v<s.length;v+=1)m[v]=Vi(Fi(g,s,v));const p=v=>Me(m[v],1,1,()=>{m[v]=null});return{c(){for(let v=0;v<m.length;v+=1)m[v].c();a=Hn()},m(v,y){for(let k=0;k<m.length;k+=1)m[k]&&m[k].m(v,y);Oe(v,a,y),l=!0},p(v,y){if(y[0]&1755){s=G0(v[0]);let k;for(k=0;k<s.length;k+=1){const N=Fi(v,s,k);m[k]?(m[k].p(N,y),xe(m[k],1)):(m[k]=Vi(N),m[k].c(),xe(m[k],1),m[k].m(a.parentNode,a))}for(Ot(),k=s.length;k<m.length;k+=1)p(k);qt()}},i(v){if(!l){for(let y=0;y<s.length;y+=1)xe(m[y]);l=!0}},o(v){m=m.filter(Boolean);for(let y=0;y<m.length;y+=1)Me(m[y]);l=!1},d(v){v&&qe(a),Un(m,v)}}}function h1(g){let a,l=(g[29].orig_name||g[29].name)+"",s,m,p;return{c(){a=Qe("a"),s=Qi(l),me(a,"data-testid","chatbot-file"),me(a,"href",m=g[29].data),me(a,"target","_blank"),me(a,"download",p=window.__is_colab__?null:g[29].orig_name||g[29].name)},m(v,y){Oe(v,a,y),Lt(a,s)},p(v,y){y[0]&1&&l!==(l=(v[29].orig_name||v[29].name)+"")&&Ji(s,l),y[0]&1&&m!==(m=v[29].data)&&me(a,"href",m),y[0]&1&&p!==(p=window.__is_colab__?null:v[29].orig_name||v[29].name)&&me(a,"download",p)},i:ht,o:ht,d(v){v&&qe(a)}}}function m1(g){let a,l,s;return{c(){a=Qe("img"),me(a,"data-testid","chatbot-image"),V0(a.src,l=g[29].data)||me(a,"src",l),me(a,"alt",s=g[29].alt_text)},m(m,p){Oe(m,a,p)},p(m,p){p[0]&1&&!V0(a.src,l=m[29].data)&&me(a,"src",l),p[0]&1&&s!==(s=m[29].alt_text)&&me(a,"alt",s)},i:ht,o:ht,d(m){m&&qe(a)}}}function f1(g){let a,l,s,m,p,v;return{c(){a=Qe("video"),l=Qe("track"),me(l,"kind","captions"),me(a,"data-testid","chatbot-video"),a.controls=!0,V0(a.src,s=g[29].data)||me(a,"src",s),me(a,"title",m=g[29].alt_text),me(a,"preload","auto")},m(y,k){Oe(y,a,k),Lt(a,l),p||(v=[m0(a,"play",g[16]),m0(a,"pause",g[17]),m0(a,"ended",g[18])],p=!0)},p(y,k){k[0]&1&&!V0(a.src,s=y[29].data)&&me(a,"src",s),k[0]&1&&m!==(m=y[29].alt_text)&&me(a,"title",m)},i:ht,o:ht,d(y){y&&qe(a),p=!1,$i(v)}}}function d1(g){let a,l,s,m,p;return{c(){a=Qe("audio"),me(a,"data-testid","chatbot-audio"),a.controls=!0,me(a,"preload","metadata"),V0(a.src,l=g[29].data)||me(a,"src",l),me(a,"title",s=g[29].alt_text)},m(v,y){Oe(v,a,y),m||(p=[m0(a,"play",g[13]),m0(a,"pause",g[14]),m0(a,"ended",g[15])],m=!0)},p(v,y){y[0]&1&&!V0(a.src,l=v[29].data)&&me(a,"src",l),y[0]&1&&s!==(s=v[29].alt_text)&&me(a,"title",s)},i:ht,o:ht,d(v){v&&qe(a),m=!1,$i(p)}}}function p1(g){let a,l,s,m,p;a=new s1({props:{message:g[29],latex_delimiters:g[1]}}),a.$on("load",g[9]);let v=g[3]&&g[31]==1&&Pi(g),y=g[7]&&g[29]&&Ui(g);return{c(){Pt(a.$$.fragment),l=f0(),v&&v.c(),s=f0(),y&&y.c(),m=Hn()},m(k,N){Ht(a,k,N),Oe(k,l,N),v&&v.m(k,N),Oe(k,s,N),y&&y.m(k,N),Oe(k,m,N),p=!0},p(k,N){const U={};N[0]&1&&(U.message=k[29]),N[0]&2&&(U.latex_delimiters=k[1]),a.$set(U),k[3]&&k[31]==1?v?v.p(k,N):(v=Pi(k),v.c(),v.m(s.parentNode,s)):v&&(v.d(1),v=null),k[7]&&k[29]?y?(y.p(k,N),N[0]&129&&xe(y,1)):(y=Ui(k),y.c(),xe(y,1),y.m(m.parentNode,m)):y&&(Ot(),Me(y,1,1,()=>{y=null}),qt())},i(k){p||(xe(a.$$.fragment,k),xe(y),p=!0)},o(k){Me(a.$$.fragment,k),Me(y),p=!1},d(k){k&&(qe(l),qe(s),qe(m)),Ut(a,k),v&&v.d(k),y&&y.d(k)}}}function Pi(g){let a,l=G0(g[3]),s=[];for(let m=0;m<l.length;m+=1)s[m]=Hi(Li(g,l,m));return{c(){a=Qe("div");for(let m=0;m<s.length;m+=1)s[m].c();me(a,"class","feedback svelte-175fbmp")},m(m,p){Oe(m,a,p);for(let v=0;v<s.length;v+=1)s[v]&&s[v].m(a,null)},p(m,p){if(p[0]&8){l=G0(m[3]);let v;for(v=0;v<l.length;v+=1){const y=Li(m,l,v);s[v]?s[v].p(y,p):(s[v]=Hi(y),s[v].c(),s[v].m(a,null))}for(;v<s.length;v+=1)s[v].d(1);s.length=l.length}},d(m){m&&qe(a),Un(s,m)}}}function Hi(g){let a,l=g[32]+"",s;return{c(){a=Qe("button"),s=Qi(l),me(a,"class","svelte-175fbmp")},m(m,p){Oe(m,a,p),Lt(a,s)},p(m,p){p[0]&8&&l!==(l=m[32]+"")&&Ji(s,l)},d(m){m&&qe(a)}}}function Ui(g){let a,l,s;return l=new c1({props:{value:g[29]}}),{c(){a=Qe("div"),Pt(l.$$.fragment),me(a,"class","icon-button svelte-175fbmp")},m(m,p){Oe(m,a,p),Ht(l,a,null),s=!0},p(m,p){const v={};p[0]&1&&(v.value=m[29]),l.$set(v)},i(m){s||(xe(l.$$.fragment,m),s=!0)},o(m){Me(l.$$.fragment,m),s=!1},d(m){m&&qe(a),Ut(l)}}}function Gi(g){let a,l,s,m,p,v,y,k,N,U,Y;const te=[p1,d1,f1,m1,h1],j=[];function ve(le,O){return O[0]&1&&(l=null),O[0]&1&&(s=null),O[0]&1&&(m=null),typeof le[29]=="string"?0:(l==null&&(l=!!(le[29]!==null&&le[29].mime_type?.includes("audio"))),l?1:(s==null&&(s=!!(le[29]!==null&&le[29].mime_type?.includes("video"))),s?2:(m==null&&(m=!!(le[29]!==null&&le[29].mime_type?.includes("image"))),m?3:le[29]!==null&&le[29].data!==null?4:-1)))}~(p=ve(g,[-1,-1]))&&(v=j[p]=te[p](g));function pe(){return g[21](g[28],g[31],g[29])}return{c(){a=Qe("div"),v&&v.c(),y=f0(),me(a,"data-testid",g[31]==0?"user":"bot"),me(a,"class","message "+(g[31]==0?"user":"bot")+" svelte-175fbmp"),me(a,"dir",k=g[6]?"rtl":"ltr"),H0(a,"latest",g[28]===g[0].length-1),H0(a,"hide",g[29]===null),H0(a,"selectable",g[4])},m(le,O){Oe(le,a,O),~p&&j[p].m(a,null),Lt(a,y),N=!0,U||(Y=m0(a,"click",pe),U=!0)},p(le,O){g=le;let B=p;p=ve(g,O),p===B?~p&&j[p].p(g,O):(v&&(Ot(),Me(j[B],1,1,()=>{j[B]=null}),qt()),~p?(v=j[p],v?v.p(g,O):(v=j[p]=te[p](g),v.c()),xe(v,1),v.m(a,y)):v=null),(!N||O[0]&64&&k!==(k=g[6]?"rtl":"ltr"))&&me(a,"dir",k),(!N||O[0]&1)&&H0(a,"latest",g[28]===g[0].length-1),(!N||O[0]&1)&&H0(a,"hide",g[29]===null),(!N||O[0]&16)&&H0(a,"selectable",g[4])},i(le){N||(xe(v),N=!0)},o(le){Me(v),N=!1},d(le){le&&qe(a),~p&&j[p].d(),U=!1,Y()}}}function Vi(g){let a,l,s=G0(g[26]),m=[];for(let v=0;v<s.length;v+=1)m[v]=Gi(Ii(g,s,v));const p=v=>Me(m[v],1,1,()=>{m[v]=null});return{c(){for(let v=0;v<m.length;v+=1)m[v].c();a=Hn()},m(v,y){for(let k=0;k<m.length;k+=1)m[k]&&m[k].m(v,y);Oe(v,a,y),l=!0},p(v,y){if(y[0]&1755){s=G0(v[26]);let k;for(k=0;k<s.length;k+=1){const N=Ii(v,s,k);m[k]?(m[k].p(N,y),xe(m[k],1)):(m[k]=Gi(N),m[k].c(),xe(m[k],1),m[k].m(a.parentNode,a))}for(Ot(),k=s.length;k<m.length;k+=1)p(k);qt()}},i(v){if(!l){for(let y=0;y<s.length;y+=1)xe(m[y]);l=!0}},o(v){m=m.filter(Boolean);for(let y=0;y<m.length;y+=1)Me(m[y]);l=!1},d(v){v&&qe(a),Un(m,v)}}}function Wi(g){let a;return{c(){a=Qe("div"),a.innerHTML=`<div class="dot-flashing svelte-175fbmp"></div>
-				 
-				<div class="dot-flashing svelte-175fbmp"></div>
-				 
-				<div class="dot-flashing svelte-175fbmp"></div>`,me(a,"class","message pending svelte-175fbmp")},m(l,s){Oe(l,a,s)},d(l){l&&qe(a)}}}function g1(g){let a,l,s,m,p,v,y,k=g[5]&&g[0]!==null&&g[0].length>0&&Oi(g),N=g[0]!==null&&qi(g),U=g[2]&&Wi();return{c(){k&&k.c(),a=f0(),l=Qe("div"),s=Qe("div"),N&&N.c(),m=f0(),U&&U.c(),me(s,"class","message-wrap svelte-175fbmp"),me(l,"class","wrap svelte-175fbmp")},m(Y,te){k&&k.m(Y,te),Oe(Y,a,te),Oe(Y,l,te),Lt(l,s),N&&N.m(s,null),Lt(s,m),U&&U.m(s,null),g[22](l),p=!0,v||(y=Us(No.call(null,s)),v=!0)},p(Y,te){Y[5]&&Y[0]!==null&&Y[0].length>0?k?(k.p(Y,te),te[0]&33&&xe(k,1)):(k=Oi(Y),k.c(),xe(k,1),k.m(a.parentNode,a)):k&&(Ot(),Me(k,1,1,()=>{k=null}),qt()),Y[0]!==null?N?(N.p(Y,te),te[0]&1&&xe(N,1)):(N=qi(Y),N.c(),xe(N,1),N.m(s,m)):N&&(Ot(),Me(N,1,1,()=>{N=null}),qt()),Y[2]?U||(U=Wi(),U.c(),U.m(s,null)):U&&(U.d(1),U=null)},i(Y){p||(xe(k),xe(N),p=!0)},o(Y){Me(k),Me(N),p=!1},d(Y){Y&&(qe(a),qe(l)),k&&k.d(Y),N&&N.d(),U&&U.d(),g[22](null),v=!1,y()}}}function v1(g,a,l){const s={light:()=>ci(()=>Promise.resolve({}),["./prism-eca040d0.css"],import.meta.url),dark:()=>ci(()=>Promise.resolve({}),["./prism-dark-aecd8de4.css"],import.meta.url)};let{value:m}=a,p=null,{latex_delimiters:v}=a,{pending_message:y=!1}=a,{feedback:k=null}=a,{selectable:N=!1}=a,{show_share_button:U=!1}=a,{theme_mode:Y}=a,{rtl:te=!1}=a,{show_copy_button:j=!1}=a,ve,pe;const le=Zi();Gs(()=>{pe=ve&&ve.offsetHeight+ve.scrollTop>ve.scrollHeight-100});const O=()=>{pe&&ve.scrollTo(0,ve.scrollHeight)};Ki(()=>{pe&&(O(),ve.querySelectorAll("img").forEach(ie=>{ie.addEventListener("load",()=>{O()})}))});function B(ie,Ge,Gt){le("select",{index:[ie,Ge],value:Gt})}function z(ie){rt.call(this,g,ie)}function R(ie){rt.call(this,g,ie)}function D(ie){rt.call(this,g,ie)}function L(ie){rt.call(this,g,ie)}function X(ie){rt.call(this,g,ie)}function ae(ie){rt.call(this,g,ie)}function K(ie){rt.call(this,g,ie)}function G(ie){rt.call(this,g,ie)}const W=(ie,Ge,Gt)=>B(ie,Ge,Gt);function Ce(ie){Pn[ie?"unshift":"push"](()=>{ve=ie,l(8,ve)})}return g.$$set=ie=>{"value"in ie&&l(0,m=ie.value),"latex_delimiters"in ie&&l(1,v=ie.latex_delimiters),"pending_message"in ie&&l(2,y=ie.pending_message),"feedback"in ie&&l(3,k=ie.feedback),"selectable"in ie&&l(4,N=ie.selectable),"show_share_button"in ie&&l(5,U=ie.show_share_button),"theme_mode"in ie&&l(11,Y=ie.theme_mode),"rtl"in ie&&l(6,te=ie.rtl),"show_copy_button"in ie&&l(7,j=ie.show_copy_button)},g.$$.update=()=>{g.$$.dirty[0]&2048&&(Y=="dark"?s.dark():s.light()),g.$$.dirty[0]&4097&&m!==p&&(l(12,p=m),le("change"))},[m,v,y,k,N,U,te,j,ve,O,B,Y,p,z,R,D,L,X,ae,K,G,W,Ce]}class b1 extends W0{constructor(a){super(),Y0(this,a,v1,g1,j0,{value:0,latex_delimiters:1,pending_message:2,feedback:3,selectable:4,show_share_button:5,theme_mode:11,rtl:6,show_copy_button:7},null,[-1,-1])}}function Yi(g){let a,l;const s=[g[13],{show_progress:g[13].show_progress==="hidden"?"hidden":"minimal"}];let m={};for(let p=0;p<s.length;p+=1)m=Vs(m,s[p]);return a=new Ws({props:m}),{c(){Pt(a.$$.fragment)},m(p,v){Ht(a,p,v),l=!0},p(p,v){const y=v&8192?Ys(s,[js(p[13]),{show_progress:p[13].show_progress==="hidden"?"hidden":"minimal"}]):{};a.$set(y)},i(p){l||(xe(a.$$.fragment,p),l=!0)},o(p){Me(a.$$.fragment,p),l=!1},d(p){Ut(a,p)}}}function ji(g){let a,l;return a=new eo({props:{show_label:g[6],Icon:ro,float:!1,label:g[5]||"Chatbot"}}),{c(){Pt(a.$$.fragment)},m(s,m){Ht(a,s,m),l=!0},p(s,m){const p={};m&64&&(p.show_label=s[6]),m&32&&(p.label=s[5]||"Chatbot"),a.$set(p)},i(s){l||(xe(a.$$.fragment,s),l=!0)},o(s){Me(a.$$.fragment,s),l=!1},d(s){Ut(a,s)}}}function y1(g){let a,l,s,m,p,v=g[13]&&Yi(g),y=g[6]&&ji(g);return m=new b1({props:{selectable:g[7],show_share_button:g[9],theme_mode:g[8],value:g[15],latex_delimiters:g[12],pending_message:g[13]?.status==="pending",rtl:g[10],show_copy_button:g[11]}}),m.$on("change",g[19]),m.$on("select",g[20]),m.$on("share",g[21]),m.$on("error",g[22]),{c(){v&&v.c(),a=f0(),l=Qe("div"),y&&y.c(),s=f0(),Pt(m.$$.fragment),me(l,"class","wrapper svelte-nab2ao")},m(k,N){v&&v.m(k,N),Oe(k,a,N),Oe(k,l,N),y&&y.m(l,null),Lt(l,s),Ht(m,l,null),p=!0},p(k,N){k[13]?v?(v.p(k,N),N&8192&&xe(v,1)):(v=Yi(k),v.c(),xe(v,1),v.m(a.parentNode,a)):v&&(Ot(),Me(v,1,1,()=>{v=null}),qt()),k[6]?y?(y.p(k,N),N&64&&xe(y,1)):(y=ji(k),y.c(),xe(y,1),y.m(l,s)):y&&(Ot(),Me(y,1,1,()=>{y=null}),qt());const U={};N&128&&(U.selectable=k[7]),N&512&&(U.show_share_button=k[9]),N&256&&(U.theme_mode=k[8]),N&32768&&(U.value=k[15]),N&4096&&(U.latex_delimiters=k[12]),N&8192&&(U.pending_message=k[13]?.status==="pending"),N&1024&&(U.rtl=k[10]),N&2048&&(U.show_copy_button=k[11]),m.$set(U)},i(k){p||(xe(v),xe(y),xe(m.$$.fragment,k),p=!0)},o(k){Me(v),Me(y),Me(m.$$.fragment,k),p=!1},d(k){k&&(qe(a),qe(l)),v&&v.d(k),y&&y.d(),Ut(m)}}}function w1(g){let a,l;return a=new Qs({props:{elem_id:g[0],elem_classes:g[1],visible:g[2],padding:!1,scale:g[3],min_width:g[4],height:g[14],allow_overflow:!1,$$slots:{default:[y1]},$$scope:{ctx:g}}}),{c(){Pt(a.$$.fragment)},m(s,m){Ht(a,s,m),l=!0},p(s,[m]){const p={};m&1&&(p.elem_id=s[0]),m&2&&(p.elem_classes=s[1]),m&4&&(p.visible=s[2]),m&8&&(p.scale=s[3]),m&16&&(p.min_width=s[4]),m&16384&&(p.height=s[14]),m&16826336&&(p.$$scope={dirty:m,ctx:s}),a.$set(p)},i(s){l||(xe(a.$$.fragment,s),l=!0)},o(s){Me(a.$$.fragment,s),l=!1},d(s){Ut(a,s)}}}function x1(g,a,l){let{elem_id:s=""}=a,{elem_classes:m=[]}=a,{visible:p=!0}=a,{value:v=[]}=a,{scale:y=null}=a,{min_width:k=void 0}=a,{label:N}=a,{show_label:U=!0}=a,{root:Y}=a,{root_url:te}=a,{selectable:j=!1}=a,{theme_mode:ve}=a,{show_share_button:pe=!1}=a,{rtl:le=!1}=a,{show_copy_button:O=!1}=a,{latex_delimiters:B}=a,z;const R=W=>W.replace('src="/file',`src="${Y}file`);let{loading_status:D=void 0}=a,{height:L=400}=a;function X(W){rt.call(this,g,W)}function ae(W){rt.call(this,g,W)}function K(W){rt.call(this,g,W)}function G(W){rt.call(this,g,W)}return g.$$set=W=>{"elem_id"in W&&l(0,s=W.elem_id),"elem_classes"in W&&l(1,m=W.elem_classes),"visible"in W&&l(2,p=W.visible),"value"in W&&l(16,v=W.value),"scale"in W&&l(3,y=W.scale),"min_width"in W&&l(4,k=W.min_width),"label"in W&&l(5,N=W.label),"show_label"in W&&l(6,U=W.show_label),"root"in W&&l(17,Y=W.root),"root_url"in W&&l(18,te=W.root_url),"selectable"in W&&l(7,j=W.selectable),"theme_mode"in W&&l(8,ve=W.theme_mode),"show_share_button"in W&&l(9,pe=W.show_share_button),"rtl"in W&&l(10,le=W.rtl),"show_copy_button"in W&&l(11,O=W.show_copy_button),"latex_delimiters"in W&&l(12,B=W.latex_delimiters),"loading_status"in W&&l(13,D=W.loading_status),"height"in W&&l(14,L=W.height)},g.$$.update=()=>{g.$$.dirty&458752&&l(15,z=v?v.map(([W,Ce])=>[typeof W=="string"?R(W):mi(W,Y,te),typeof Ce=="string"?R(Ce):mi(Ce,Y,te)]):[])},[s,m,p,y,k,N,U,j,ve,pe,le,O,B,D,L,z,v,Y,te,X,ae,K,G]}class k1 extends W0{constructor(a){super(),Y0(this,a,x1,w1,j0,{elem_id:0,elem_classes:1,visible:2,value:16,scale:3,min_width:4,label:5,show_label:6,root:17,root_url:18,selectable:7,theme_mode:8,show_share_button:9,rtl:10,show_copy_button:11,latex_delimiters:12,loading_status:13,height:14})}}function S1(g){let a,l,s;function m(v){g[18](v)}let p={elem_id:g[1],elem_classes:g[2],visible:g[3],scale:g[5],min_width:g[6],label:g[7],show_label:g[8],root:g[9],root_url:g[10],selectable:g[11],theme_mode:g[12],show_share_button:g[13],rtl:g[14],show_copy_button:g[15],latex_delimiters:g[4],loading_status:g[16],height:g[17]};return g[0]!==void 0&&(p.value=g[0]),a=new k1({props:p}),Pn.push(()=>Xs(a,"value",m)),a.$on("change",g[19]),a.$on("select",g[20]),a.$on("share",g[21]),a.$on("error",g[22]),{c(){Pt(a.$$.fragment)},m(v,y){Ht(a,v,y),s=!0},p(v,[y]){const k={};y&2&&(k.elem_id=v[1]),y&4&&(k.elem_classes=v[2]),y&8&&(k.visible=v[3]),y&32&&(k.scale=v[5]),y&64&&(k.min_width=v[6]),y&128&&(k.label=v[7]),y&256&&(k.show_label=v[8]),y&512&&(k.root=v[9]),y&1024&&(k.root_url=v[10]),y&2048&&(k.selectable=v[11]),y&4096&&(k.theme_mode=v[12]),y&8192&&(k.show_share_button=v[13]),y&16384&&(k.rtl=v[14]),y&32768&&(k.show_copy_button=v[15]),y&16&&(k.latex_delimiters=v[4]),y&65536&&(k.loading_status=v[16]),y&131072&&(k.height=v[17]),!l&&y&1&&(l=!0,k.value=v[0],Zs(()=>l=!1)),a.$set(k)},i(v){s||(xe(a.$$.fragment,v),s=!0)},o(v){Me(a.$$.fragment,v),s=!1},d(v){Ut(a,v)}}}function A1(g,a,l){let{elem_id:s=""}=a,{elem_classes:m=[]}=a,{visible:p=!0}=a,{value:v=[]}=a,{latex_delimiters:y}=a,{scale:k=null}=a,{min_width:N=void 0}=a,{label:U}=a,{show_label:Y=!0}=a,{root:te}=a,{root_url:j}=a,{selectable:ve=!1}=a,{theme_mode:pe}=a,{show_share_button:le=!1}=a,{rtl:O=!1}=a,{show_copy_button:B=!1}=a,{loading_status:z=void 0}=a,{height:R=400}=a;function D(G){v=G,l(0,v)}function L(G){rt.call(this,g,G)}function X(G){rt.call(this,g,G)}function ae(G){rt.call(this,g,G)}function K(G){rt.call(this,g,G)}return g.$$set=G=>{"elem_id"in G&&l(1,s=G.elem_id),"elem_classes"in G&&l(2,m=G.elem_classes),"visible"in G&&l(3,p=G.visible),"value"in G&&l(0,v=G.value),"latex_delimiters"in G&&l(4,y=G.latex_delimiters),"scale"in G&&l(5,k=G.scale),"min_width"in G&&l(6,N=G.min_width),"label"in G&&l(7,U=G.label),"show_label"in G&&l(8,Y=G.show_label),"root"in G&&l(9,te=G.root),"root_url"in G&&l(10,j=G.root_url),"selectable"in G&&l(11,ve=G.selectable),"theme_mode"in G&&l(12,pe=G.theme_mode),"show_share_button"in G&&l(13,le=G.show_share_button),"rtl"in G&&l(14,O=G.rtl),"show_copy_button"in G&&l(15,B=G.show_copy_button),"loading_status"in G&&l(16,z=G.loading_status),"height"in G&&l(17,R=G.height)},[v,s,m,p,y,k,N,U,Y,te,j,ve,pe,le,O,B,z,R,D,L,X,ae,K]}class T1 extends W0{constructor(a){super(),Y0(this,a,A1,S1,j0,{elem_id:1,elem_classes:2,visible:3,value:0,latex_delimiters:4,scale:5,min_width:6,label:7,show_label:8,root:9,root_url:10,selectable:11,theme_mode:12,show_share_button:13,rtl:14,show_copy_button:15,loading_status:16,height:17})}}const N1=T1,R1=["static"];export{N1 as Component,R1 as modes};
-//# sourceMappingURL=index-ce9113ed.js.map
diff --git a/spaces/dcq/freegpt-webui/g4f/Provider/__init__.py b/spaces/dcq/freegpt-webui/g4f/Provider/__init__.py
deleted file mode 100644
index b01b310b39580351ebf76708b8e36de8bfd7fe1b..0000000000000000000000000000000000000000
--- a/spaces/dcq/freegpt-webui/g4f/Provider/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from . import Provider
-from .Providers import (
-    Aichat,
-    Bard,
-    Better,
-    Bing,
-    ChatgptAi,
-    ChatgptLogin,
-    ChatgptLogin,
-    DeepAi,
-    Dfehub,
-    Easychat,
-    Ezcht,
-    Fakeopen,
-    Forefront,
-    GetGpt,
-    Gravityengine,
-    H2o,
-    hteyun,
-    Liaobots,
-    Lockchat,
-    Mishalsgpt,
-    Phind,
-    Theb,
-    Vercel,
-    Weuseing,
-    Xiaor,
-    Yqcloud,
-    You,
-)
-
-Palm = Bard
diff --git a/spaces/debayan/ISM2023w/gradio_app.py b/spaces/debayan/ISM2023w/gradio_app.py
deleted file mode 100644
index 3e0707a27133556d900893044a3969e327241b6e..0000000000000000000000000000000000000000
--- a/spaces/debayan/ISM2023w/gradio_app.py
+++ /dev/null
@@ -1,279 +0,0 @@
-import gradio as gr
-import pandas as pd
-import json
-import os
-from utils_display import AutoEvalColumn, fields, make_clickable_model, styled_error, styled_message
-from datetime import datetime, timezone
-from pathlib import Path
-from request_api import make_get_request, make_post_request
-from io import BytesIO, StringIO
-LEADERBOARD_PATH = "/home/Bhattacharya/ism_leaderboard/files/leaderboard"
-# Directory where request by models are stored
-DIR_OUTPUT_REQUESTS = Path("requested_models")
-EVAL_REQUESTS_PATH = Path("eval_requests")
-
-##########################
-# Text definitions       #
-##########################
-
-banner_url = "https://huggingface.co/spaces/debayan/ism_2023w/resolve/main/logo_leaderboard.png"
-BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 600px;"> </div>'
-
-TITLE = "<html> <head> <style> h1 {text-align: center;} </style> </head> <body> <h1> 🤗 Open Automatic Speech Recognition Leaderboard </b> </body> </html>"
-
-INTRODUCTION_TEXT = "🏆 The ISM2023w Leaderboard ranks and evaluates models \
-    on the Hugging Face Spaces. \
-    \nWe report the Precision, Recall, Accuracy, Weighted [F1 Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)\n"
-
-CSV_TEXT = "<p>Please make sure that you upload the CSV in the correct format. Click the link to download a sample CSV!</p> <a href=\"https://huggingface.co/spaces/debayan/ism_2023w/raw/main/example.csv\" download=\"example.csv\">Download CSV</a>"
-
-METRICS_TAB_TEXT = """
-Here you will find details about the multi-class classification metrics and datasets reported in our leaderboard.
-## Metrics
-🎯 Word Error Rate (WER) and Real-Time Factor (RTF) are popular metrics for evaluating the accuracy of speech recognition 
-models by estimating how accurate the predictions from the models are and how fast they are returned. We explain them each
-below.
-"""
-
-
-LAST_UPDATED = "Sep 28th 2023"
-
-
-
-def create_html_page(title, content):
-
-    html_page = f"""
-    <html>
-    <head>
-    <title>{title}</title>
-    </head>
-    <body>
-    {content}
-    </body>
-    </html>
-    """
-
-    return html_page
-
-def fetch_leaderboard(phase=1):
-
-    """
-    Fetch the leaderboard from the local disk
-    """
-    params = {"phase": phase}
-
-    leaderboard_df = make_get_request("fetch-leaderboard", params)
-    csv_data = leaderboard_df.content.decode('utf-8')
-
-    print(csv_data)
-
-    # create dataframe from csv data
-    leaderboard_df = pd.read_csv(StringIO(csv_data))
-
-    leaderboard_df = leaderboard_df.dropna()
-
-    return leaderboard_df
-
-def process_csv_text(temp_file):
-    if isinstance(temp_file, str):
-
-        df = pd.read_csv(StringIO(temp_file))
-
-    else:
-        try:
-            df = pd.read_csv(temp_file.name)
-        except:
-            return None
-    
-    return df
-
-def app(team_name, password, csv_file):
-
-    
-    # convert csv_file from bytes to file
-   
-    
-    # Check if the team is registered
-    # make get request to get team name from password
-    returned_team_name_json = make_get_request("get-team-name", {"password": password}).json()
-    # handle error by checking if the returned json has an error key
-    if "error" in returned_team_name_json:
-       
-        return create_html_page("Error", "Invalid team name or password.")
-
-    else:   
-        returned_team_name = returned_team_name_json["team_name"]
-
-    if team_name != returned_team_name: 
-        return create_html_page("Error", "Invalid team name or password.")
-
-    else:
-        print("team name and password are correct")
-        # convert csv_file from bytes to file
-        
-        # read csv file as dataframe
-        #df = pd.read_csv(csv_file.name,encoding='utf-8') 
-
-        # convert tempfile to bytes 
-        df = process_csv_text(csv_file)
-        if df is None:
-            return create_html_page("Error", "Invalid CSV file format.")
-        csv_data = df.to_csv(index=False)
-        
-        # upload csv file to the server 
-        upload_csv_response = make_post_request("upload", {"password": password}, data = csv_data) 
-        
-        # handle error by checking if the returned json has an error key
-        if "error" in upload_csv_response.json():
-
-            
-            return create_html_page("Error", upload_csv_response.json()["error"] )
-
-        else:
-
-            return create_html_page("Success", upload_csv_response.json()["message"])
-
-
-def formatter(x):
-        
-    x = round(x, 2)
-    return x
-
-def format_leaderboard(df,sort_by="F1 Score"):
-    # Formats the columns
-
-    for col in df.columns:
-        if col == "Team Name":
-            # do nothing
-            continue
-
-        elif col == "Team Members":
-            # do nothing
-            continue
-        else:
-            #if col type if numeric then format it else leave it as it is
-            if df[col].dtype == "float64" or df[col].dtype == "int64":
-                df[col] = df[col].apply(formatter) # For numerical values
-    df.sort_values(by=sort_by, inplace=True, ascending=False)
-    return df
-
-
-
-
-leaderboard_df_1 = format_leaderboard(fetch_leaderboard(phase=1))
-leaderboard_df_2 = format_leaderboard(fetch_leaderboard(phase=2))
-
-
-COLS = [c.name for c in fields(AutoEvalColumn)]
-TYPES = [c.type for c in fields(AutoEvalColumn)]
-
-with gr.Blocks() as demo:
-    leaderboard_table_1 = None 
-    leaderboard_table_2 = None
-
-    def button_clicked_phase_1(inputs):
-        
-        leaderboard_df_1 = format_leaderboard(fetch_leaderboard(phase=1))
-        
-
-        leaderboard_table_1 = gr.components.Dataframe(
-                value=leaderboard_df_1,
-                datatype=TYPES,
-                max_rows=None,
-                elem_id="leaderboard-table-1",
-                interactive=False,
-                visible=True,
-                
-                )
-
-        return leaderboard_table_1
-        
-
-    def button_clicked_phase_2():
-        
-        leaderboard_df_2 = format_leaderboard(fetch_leaderboard(phase=2))
-        
-        
-        leaderboard_table_2 = gr.components.Dataframe(
-                value=leaderboard_df_2,
-                datatype=TYPES,
-                max_rows=None,
-                elem_id="leaderboard-table-2",
-                interactive=False,
-                visible=True,
-                
-                )
-
-        return leaderboard_table_2
-        #outputs.update(leaderboard_df_2)
-        #inputs.value = leaderboard_df_1
-
-        
-
-
-    gr.HTML(BANNER, elem_id="banner")
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
-    gr.HTML(CSV_TEXT, elem_classes="markdown-text")
-
-
-
-    with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 Leaderboard Phase 1", elem_id="od-benchmark-tab-table-1", id=0):
-            
-            leaderboard_table_1 = gr.components.Dataframe(
-                value=leaderboard_df_1,
-                datatype=TYPES,
-                max_rows=None,
-                elem_id="leaderboard-table-1",
-                interactive=False,
-                visible=True,
-                
-                )
-            button = gr.Button("Refresh Leaderboard",interactive=True)
-            button.click(fn=button_clicked_phase_1,outputs=[leaderboard_table_1])
-        
-        with gr.TabItem("🏅 Leaderboard Phase 2", elem_id="od-benchmark-tab-table-2", id=1):
-            
-            
-            leaderboard_table_2 = gr.components.Dataframe(
-                value=leaderboard_df_2,
-                datatype=TYPES,
-                max_rows=None,
-                elem_id="leaderboard-table-2",
-                interactive=False,
-                visible=True,
-                
-                )
-            button = gr.Button("Refresh Leaderboard",interactive=True)
-            button.click(fn=button_clicked_phase_2,outputs=[leaderboard_table_2])
-
-
-        with gr.TabItem("📈 Upload CSV", elem_id="od-benchmark-tab-table", id=2):
-            
-            gr.Interface(
-            fn=app,
-            title='CSV Upload and Leaderboard',
-            description='Upload a CSV file and see your team\'s rank on the leaderboard.',
-            layout='vertical',
-            theme='compact',
-            inputs=[
-                gr.Textbox(label='Team Name'),
-                gr.Textbox(label='Password'),
-                gr.File(file_count= "single", file_types =[".txt",".csv"] ,label='CSV File') # only allow csv files
-            ],
-            outputs=gr.outputs.HTML()
-        )
-
-
-
-
-
-
-
-        
-
-    gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text")
-    
-demo.launch( enable_queue=True,debug=True)
-
-
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/login-684ccf2f.js b/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/login-684ccf2f.js
deleted file mode 100644
index 0baa11c1176a1ca765607247bfcdce79568e076d..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/static/cy_aps/assets/login-684ccf2f.js
+++ /dev/null
@@ -1 +0,0 @@
-import{d as H,f as v,h as w,N as U,a1 as b,r as _,a as x,c as S,o as F,l,v as s,q as t,$ as e,u as i,t as n,a2 as T}from"./vue-e0bc46a9.js";import{t as h,u as R,P as V,l as q}from"./index-5df855a0.js";import{F as f,I as L,B as G,g as N,h as E,L as g,i as z}from"./vendor-4cd7d240.js";import"./__commonjsHelpers__-042e6b4d.js";const A={width:"111",height:"23",viewBox:"0 0 111 23",fill:"none",xmlns:"http://www.w3.org/2000/svg"},I=U('<path fill-rule="evenodd" clip-rule="evenodd" d="M20.3557 17.1143C18.3798 20.1442 14.9605 22.147 11.0735 22.147C4.95777 22.147 0 17.1892 0 11.0735C0 4.95777 4.95777 0 11.0735 0C14.7962 0 18.0898 1.83697 20.0974 4.65391C19.6754 5.23439 19.2963 5.8095 18.9874 6.34413C18.615 6.98892 18.2368 8.23682 18.2368 8.23682C18.2368 8.23682 19.1135 6.73114 19.5206 6.13508C19.6982 5.87516 19.905 5.61105 20.0755 5.39339C20.1919 5.24472 20.2914 5.11768 20.353 5.02848L20.3557 5.03266C22.3316 2.00275 25.7508 0 29.6379 0C35.7536 0 40.7113 4.95777 40.7113 11.0735C40.7113 17.1892 35.7536 22.147 29.6379 22.147C25.9138 22.147 22.6191 20.3086 20.6117 17.4899C21.0337 16.9094 21.4128 16.3343 21.7217 15.7997C22.0942 15.1549 22.4723 13.907 22.4723 13.907C22.4723 13.907 21.5956 15.4127 21.1885 16.0087C21.0109 16.2687 20.8041 16.5328 20.6336 16.7505C20.5172 16.8991 20.4178 17.026 20.3562 17.1152C20.3561 17.1149 20.3559 17.1146 20.3557 17.1143ZM10.585 18.2387C12.5349 18.2387 14.2796 17.2247 15.4446 15.6288L18.2616 12.2261L18.2708 12.236C18.3855 12.0918 18.5037 11.9418 18.6252 11.7869L18.8823 11.4763C18.9135 11.4386 18.9398 11.3983 18.9612 11.3562C19.2186 11.0251 19.4873 10.6769 19.7636 10.3188L19.7645 10.3176C21.5496 8.00437 23.6531 5.27856 25.0782 4.07069C26.0327 3.26168 27.5208 2.2445 29.1493 2.11655C30.3663 1.94388 31.8366 2.08434 33.5461 2.76816C32.2433 1.9537 30.6821 1.66413 29.0024 1.7618C27.3226 1.85947 25.6832 2.72667 24.2639 3.745C22.8141 4.78522 20.9312 7.16948 18.7438 9.93939L18.5369 10.2013L15.6643 6.51091C14.5055 4.73266 12.6616 3.5826 10.585 3.5826C7.07741 3.5826 4.23398 6.86347 4.23398 10.9106C4.23398 14.9578 7.07741 18.2387 10.585 18.2387ZM30.1264 18.2387C28.1764 18.2387 26.4317 17.2247 25.2667 15.6288L22.1697 11.8877L21.9482 12.1681C19.7607 14.9381 17.8779 17.3223 16.428 18.3625C15.0087 19.3809 13.3693 20.2481 11.6896 20.3457C10.0099 20.4434 8.4486 20.1538 7.14583 19.3394C8.85539 20.0232 10.3256 20.1637 11.5427 19.991C13.1711 19.863 14.6593 18.8459 15.6138 18.0368C17.0389 16.829 19.1424 14.1031 20.9275 11.7899C21.463 11.0959 21.9699 10.4391 22.4212 9.87151L22.4266 9.87738L25.047 6.51091C26.2058 4.73266 28.0497 3.5826 30.1264 3.5826C33.6339 3.5826 36.4774 6.86347 36.4774 10.9106C36.4774 14.9578 33.6339 18.2387 30.1264 18.2387Z" fill="white"></path><mask id="mask0_3275_99396" style="mask-type:alpha;" maskUnits="userSpaceOnUse" x="0" y="0" width="41" height="23"><path fill-rule="evenodd" clip-rule="evenodd" d="M20.3557 17.1143C18.3798 20.1442 14.9605 22.147 11.0735 22.147C4.95777 22.147 0 17.1892 0 11.0735C0 4.95777 4.95777 0 11.0735 0C14.7962 0 18.0898 1.83697 20.0974 4.65391C19.6754 5.23439 19.2963 5.8095 18.9874 6.34413C18.615 6.98892 18.2368 8.23682 18.2368 8.23682C18.2368 8.23682 19.1135 6.73114 19.5206 6.13508C19.6982 5.87516 19.905 5.61105 20.0755 5.39339C20.1919 5.24472 20.2914 5.11768 20.353 5.02848L20.3557 5.03266C22.3316 2.00275 25.7508 0 29.6379 0C35.7536 0 40.7113 4.95777 40.7113 11.0735C40.7113 17.1892 35.7536 22.147 29.6379 22.147C25.9138 22.147 22.6191 20.3086 20.6117 17.4899C21.0337 16.9094 21.4128 16.3343 21.7217 15.7997C22.0942 15.1549 22.4723 13.907 22.4723 13.907C22.4723 13.907 21.5956 15.4127 21.1885 16.0087C21.0109 16.2687 20.8041 16.5328 20.6336 16.7505C20.5172 16.8991 20.4178 17.026 20.3562 17.1152C20.3561 17.1149 20.3559 17.1146 20.3557 17.1143ZM10.585 18.2387C12.5349 18.2387 14.2796 17.2247 15.4446 15.6288L18.2616 12.2261L18.2708 12.236C18.3855 12.0918 18.5037 11.9418 18.6252 11.7869L18.8823 11.4763C18.9135 11.4386 18.9398 11.3983 18.9612 11.3562C19.2186 11.0251 19.4873 10.6769 19.7636 10.3188L19.7645 10.3176C21.5496 8.00437 23.6531 5.27856 25.0782 4.07069C26.0327 3.26168 27.5208 2.2445 29.1493 2.11655C30.3663 1.94388 31.8366 2.08434 33.5461 2.76816C32.2433 1.9537 30.6821 1.66413 29.0024 1.7618C27.3226 1.85947 25.6832 2.72667 24.2639 3.745C22.8141 4.78522 20.9312 7.16948 18.7438 9.93939L18.5369 10.2013L15.6643 6.51091C14.5055 4.73266 12.6616 3.5826 10.585 3.5826C7.07741 3.5826 4.23398 6.86347 4.23398 10.9106C4.23398 14.9578 7.07741 18.2387 10.585 18.2387ZM30.1264 18.2387C28.1764 18.2387 26.4317 17.2247 25.2667 15.6288L22.1697 11.8877L21.9482 12.1681C19.7607 14.9381 17.8779 17.3223 16.428 18.3625C15.0087 19.3809 13.3693 20.2481 11.6896 20.3457C10.0099 20.4434 8.4486 20.1538 7.14583 19.3394C8.85539 20.0232 10.3256 20.1637 11.5427 19.991C13.1711 19.863 14.6593 18.8459 15.6138 18.0368C17.0389 16.829 19.1424 14.1031 20.9275 11.7899C21.463 11.0959 21.9699 10.4391 22.4212 9.87151L22.4266 9.87738L25.047 6.51091C26.2058 4.73266 28.0497 3.5826 30.1264 3.5826C33.6339 3.5826 36.4774 6.86347 36.4774 10.9106C36.4774 14.9578 33.6339 18.2387 30.1264 18.2387Z" fill="black"></path></mask><g mask="url(#mask0_3275_99396)"><circle cx="18.3917" cy="9.75161" r="12.3957" fill="url(#paint0_radial_3275_99396)"></circle><path d="M6.81156 6.45303L8.28313 -6.26299L20.7218 4.39474C20.7218 4.39474 20.1285 4.81161 19.6762 5.62887C18.8474 7.12662 17.6593 9.2678 17.6593 9.2678L6.81156 6.45303Z" fill="url(#paint1_linear_3275_99396)"></path></g><path d="M49.6909 6.07724H51.4969L54.9829 14.0852H55.0249L58.4969 6.07724H60.3029V16.0732H58.7769V8.93324H58.7209L55.6549 16.0732H54.3389L51.2729 8.93324H51.2169V16.0732H49.6909V6.07724ZM65.1951 8.63924C66.3711 8.63924 67.2811 9.03124 67.8971 9.84324C68.4571 10.5712 68.7511 11.5792 68.7651 12.8672H63.2771C63.3331 13.5672 63.5151 14.1132 63.8511 14.4912C64.1871 14.8552 64.6491 15.0512 65.2371 15.0512C65.7551 15.0512 66.1751 14.9252 66.4971 14.6872C66.7491 14.4912 66.9591 14.1692 67.1551 13.7492H68.6391C68.4711 14.4212 68.1491 14.9952 67.6451 15.4432C67.0151 15.9892 66.2171 16.2692 65.2511 16.2692C64.1871 16.2692 63.3331 15.9332 62.7171 15.2752C62.0451 14.5752 61.7091 13.6372 61.7091 12.4472C61.7091 11.3692 62.0171 10.4732 62.6611 9.74524C63.2771 9.00324 64.1311 8.63924 65.1951 8.63924ZM65.2371 9.85724C64.6771 9.85724 64.2431 10.0112 63.9071 10.3472C63.5991 10.6692 63.3891 11.1312 63.3051 11.7472H67.2111C67.0711 10.4872 66.4131 9.85724 65.2371 9.85724ZM72.2294 6.49724V8.83524H73.8814V10.0672H72.2294V14.2672C72.2294 14.4632 72.2714 14.6032 72.3554 14.7012C72.4394 14.7852 72.5654 14.8412 72.7474 14.8412H73.6994V16.0732H72.5094C71.8934 16.0732 71.4314 15.9052 71.1514 15.5832C70.8714 15.2752 70.7454 14.8412 70.7454 14.2672V10.0672H69.4014V8.83524H70.7454V7.11324L72.2294 6.49724ZM78.3383 8.63924C79.4023 8.63924 80.1863 8.90524 80.6903 9.43724C81.1243 9.89924 81.3483 10.5712 81.3483 11.4532V16.0732H79.9763V15.0512C79.6963 15.4152 79.3463 15.7092 78.9263 15.9192C78.4503 16.1432 77.8903 16.2692 77.2463 16.2692C76.4903 16.2692 75.9023 16.0732 75.4823 15.6952C75.0203 15.3172 74.7963 14.8272 74.7963 14.2252C74.7963 13.4132 75.1183 12.7832 75.7623 12.3492C76.3503 11.9292 77.1903 11.7192 78.2543 11.6912L79.8783 11.6492V11.3552C79.8783 10.3472 79.3323 9.84324 78.2403 9.84324C77.7783 9.84324 77.4003 9.92724 77.1203 10.0952C76.7843 10.2912 76.5743 10.5852 76.4903 10.9912L75.0203 10.8652C75.1743 10.0812 75.5663 9.49324 76.1823 9.12924C76.7143 8.79324 77.4423 8.63924 78.3383 8.63924ZM79.8783 12.7272L78.3523 12.7692C76.9943 12.7972 76.3223 13.2732 76.3223 14.1692C76.3223 14.4492 76.4343 14.6732 76.6723 14.8552C76.8963 15.0372 77.2043 15.1352 77.5823 15.1352C78.2123 15.1352 78.7443 14.9392 79.1923 14.5612C79.6403 14.1832 79.8783 13.7072 79.8783 13.1472V12.7272ZM87.513 5.88124C88.787 5.88124 89.795 6.16124 90.537 6.74924C91.251 7.30924 91.699 8.13524 91.881 9.25524H90.341C90.201 8.58324 89.893 8.07924 89.417 7.74324C88.941 7.39324 88.297 7.22524 87.513 7.22524C86.547 7.22524 85.777 7.54724 85.203 8.21924C84.587 8.91924 84.293 9.88524 84.293 11.1172C84.293 12.3212 84.559 13.2452 85.119 13.8892C85.707 14.5892 86.631 14.9392 87.877 14.9392C88.367 14.9392 88.829 14.8692 89.263 14.7572C89.683 14.6452 90.047 14.4772 90.383 14.2672V12.0972H87.625V10.7952H91.909V14.9952C91.391 15.4012 90.789 15.7092 90.103 15.9332C89.375 16.1572 88.577 16.2692 87.709 16.2692C86.113 16.2692 84.881 15.7652 83.999 14.7572C83.173 13.8192 82.767 12.6012 82.767 11.1172C82.767 9.61924 83.173 8.38724 83.999 7.42124C84.867 6.38524 86.043 5.88124 87.513 5.88124ZM93.7417 6.07724H97.8857C100.266 6.07724 101.456 7.08524 101.456 9.11524C101.456 11.1592 100.252 12.1812 97.8577 12.1812H95.2677V16.0732H93.7417V6.07724ZM95.2677 7.37924V10.8792H97.7597C98.5157 10.8792 99.0617 10.7392 99.4117 10.4592C99.7477 10.1792 99.9297 9.73124 99.9297 9.11524C99.9297 8.49924 99.7477 8.05124 99.3977 7.79924C99.0477 7.51924 98.5017 7.37924 97.7597 7.37924H95.2677ZM102.105 6.07724H110.295V7.37924H106.963V16.0732H105.451V7.37924H102.105V6.07724Z" fill="white"></path><defs><radialGradient id="paint0_radial_3275_99396" cx="0" cy="0" r="1" gradientUnits="userSpaceOnUse" gradientTransform="translate(18.3917 9.75161) rotate(90) scale(12.3957)"><stop stop-color="#582AFF"></stop><stop offset="1" stop-color="#582AFF" stop-opacity="0"></stop></radialGradient><linearGradient id="paint1_linear_3275_99396" x1="8.37635" y1="1.52165" x2="19.1719" y2="7.92579" gradientUnits="userSpaceOnUse"><stop offset="0.15027" stop-color="#5329EA" stop-opacity="0"></stop><stop offset="1" stop-color="#5027E4" stop-opacity="0.82"></stop></linearGradient></defs>',5),O=[I],P=H({__name:"LogoWhite",setup(k){return(p,c)=>(v(),w("svg",A,O))}}),W={class:"login"},D={class:"loginbg"},j=l("div",{class:"title"},"MetaGPT",-1),J={class:"loginform"},K={style:{width:"418px"}},Q={class:"formTitle"},X={class:"toolBoxBtn"},Y={class:"desc"},o1=H({__name:"login",setup(k){const p=b(),c=_(),C=x({username:"",password:""}),d=_(!1),M=S(()=>({username:[{required:!0,message:h("请输入用户名")}],password:[{required:!0,message:h("请输入密码")}]})),{setToken:y,getToken:Z}=R(),u=_(!1),$=async()=>{var m;if(u.value||await((m=c.value)==null?void 0:m.validate()))return;u.value=!0;const{isRequestSuccess:o,showRequestErrorMessage:r,data:B}=await q({account:C.username,password:C.password});if(u.value=!1,!o){r();return}y(B.token),p.replace({name:V.home})};return F(()=>{Z()&&p.replace({name:V.home})}),(a,o)=>(v(),w("div",W,[l("div",D,[s(P,{class:"logiWhite"}),j]),l("div",J,[l("div",K,[s(e(z),{ref_key:"formRef",ref:c,model:e(C),rules:e(M)},{default:t(()=>[s(e(f),{"hide-label":""},{default:t(()=>[l("div",Q,i(a.$t("账号登录")),1)]),_:1}),s(e(f),{"hide-label":"",field:"username"},{default:t(()=>[s(e(L),{modelValue:e(C).username,"onUpdate:modelValue":o[0]||(o[0]=r=>e(C).username=r),placeholder:a.$t("用户名/邮箱"),size:"large"},null,8,["modelValue","placeholder"])]),_:1}),s(e(f),{"hide-label":"",field:"password"},{default:t(()=>[s(e(L),{modelValue:e(C).password,"onUpdate:modelValue":o[1]||(o[1]=r=>e(C).password=r),type:"password",placeholder:a.$t("密码"),size:"large"},null,8,["modelValue","placeholder"])]),_:1}),s(e(G),{type:"primary",long:"",size:"large",disabled:!e(d),loading:e(u),onClick:$},{default:t(()=>[n(i(a.$t("登录")),1)]),_:1},8,["disabled","loading"]),s(e(N),{class:"spaceBTW toolBox",fill:""},{default:t(()=>[l("span",X,i(a.$t("忘记密码")),1)]),_:1}),l("div",null,[s(e(E),{modelValue:e(d),"onUpdate:modelValue":o[2]||(o[2]=r=>T(d)?d.value=r:null)},{default:t(()=>[l("div",Y,[n(i(a.$t("登录或完成注册即代表你同意")),1),s(e(g),null,{default:t(()=>[n(i(a.$t("用户协议")),1)]),_:1}),n(" "+i(a.$t("和"))+" ",1),s(e(g),null,{default:t(()=>[n(i(a.$t("隐私政策")),1)]),_:1})])]),_:1},8,["modelValue"])])]),_:1},8,["model","rules"])])])]))}});export{o1 as default};
diff --git a/spaces/diacanFperku/AutoGPT/Baby-Doll - Dreamlike Birthday.avi.md b/spaces/diacanFperku/AutoGPT/Baby-Doll - Dreamlike Birthday.avi.md
deleted file mode 100644
index b81b1a534c36e0f8e8407721f28c13a40ca00b1e..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Baby-Doll - Dreamlike Birthday.avi.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<h2>Baby-Doll - Dreamlike Birthday.avi</h2><br /><p><b><b>Download File</b> &#128505; <a href="https://gohhs.com/2uFUKA">https://gohhs.com/2uFUKA</a></b></p><br /><br />
-<br />
-Baby-Doll - Fabulous birthday.avi !FULL! · Baby-Doll - Dreamlike Birthday.avi. Fairytale birthday.
-Download for free!Download the film Fabulous birthday in the adventure genre directed by Sergei Gerasimov USSR - in high quality and without registration on Tvigle.ru.
-Fairytale birthday.
-Country: USSR.
-Director: Sergey Gerasimov.
-Starring: Alexander Demyanenko.
-Download in the archive!
-A film for children.
-Year of release: 1984.
-Genre: adventure.
-Título original: Fabulous Birthday.
-Download for free Fabulous birthday.
-Country: USSR. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Ctroniq Snook C70L Flash File MT6735 6.0  Firmware  Stock Rom.md b/spaces/diacanFperku/AutoGPT/Ctroniq Snook C70L Flash File MT6735 6.0  Firmware  Stock Rom.md
deleted file mode 100644
index a96ace5d7127d2948ed0f95bb5d7e4d8802a8e58..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Ctroniq Snook C70L Flash File MT6735 6.0  Firmware  Stock Rom.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<br />
-<p>on this page, you will find theithink t233 tabnew phone cm2 read flash file/firmware/stock rom free on your computer. the firmware include in a zib package, which containsithink t233 tab frp reset hang logo fixflash file,flash tool,usb driver. ithink t233 tabflash file firmware stock rom download for yourithink t233 tabandroid 5.0.2 5.3 5.1.1 5.2 firmware stock rom download</p>
-<p>download samsung galaxy s5 firmware almost everyone today knows what is operation system android, why it s so popular among millions of users and how to use its full potential, how to root android samsung galaxy mobile phone and unroot it backing to stock firmware or rom. there are a lot of different mobile firmwares and customized roms for rooted android devices. but sometimes we have to back to stock firmware. internal_link link there can be different reasons to download and upgrade samsung galaxy s5 firmware: to backup to original stock firmware to unroot phone to recover bricked phone to use stock apps and os upgrade quick access required content free download stock rom </p>
-<h2>Ctroniq Snook C70L Flash File MT6735 6.0 | Firmware | Stock Rom</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://gohhs.com/2uFUbY">https://gohhs.com/2uFUbY</a></b></p><br /><br />
-<p>on this page, you will find the s-color u709new phone cm2 read flash file/firmware/stock rom free on your computer. the firmware include in a zib package, which contains s-color u709frp reset hang logo fixflash file,flash tool,usb driver. s-color u709flash file firmware stock rom download for yours-color u709android 5.1 phone. the s-color u709 </p>
-<p>sometimes android smart firmware is broken by other software or attacked by hacking code. phone is showing unwanted behaviours like frp lock, suddenly stuck on logo, display blank or white and also dead after flashing etc. if you would like to recover you gright g626 mobile again you ought to flash your mobile by using gright g626 flash file firmware or stock rom.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Jetbrains Resharper 8 Keygen 13 WORK.md b/spaces/diacanFperku/AutoGPT/Jetbrains Resharper 8 Keygen 13 WORK.md
deleted file mode 100644
index aad75f74ef0b3f7e917250f499b92c2d420d4532..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Jetbrains Resharper 8 Keygen 13 WORK.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Jetbrains Resharper 8 Keygen 13</h2><br /><p><b><b>DOWNLOAD</b> &#10027;&#10027;&#10027; <a href="https://gohhs.com/2uFV2h">https://gohhs.com/2uFV2h</a></b></p><br /><br />
-<br />
-JetBrains is an advanced software vendor specializing in intelligent development tools, including IntelliJ IDEA, the leading Java IDE, ... Download free JetBrains IntelliJ IDEA 2017 (Russian version) in Russian for Windows.
-IntelliJ IDEA is a very flexible and user-friendly development environment.
-IntelliJ IDEA - download IntelliJ IDEA Community Edition 2019.3.1 for free.
-IntelliJ IDEA - The most popular development environment for Java and other programming languages.
-IntelliJ IDEA - Download IntelliJ IDEA Community Edition 2019.3.1, IntelliJ IDEA - The most popular development environment for Java and other programming languages. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Manajemen Sumber Daya Manusia Ebook Download.md b/spaces/diacanFperku/AutoGPT/Manajemen Sumber Daya Manusia Ebook Download.md
deleted file mode 100644
index fd680174a847fb38ecce085a86ac723f8dc1f1a9..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Manajemen Sumber Daya Manusia Ebook Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Manajemen Sumber Daya Manusia Ebook Download</h2><br /><p><b><b>Download Zip</b> &#128505; <a href="https://gohhs.com/2uFUHG">https://gohhs.com/2uFUHG</a></b></p><br /><br />
-
-Saat ini buku-buku Manajemen Sumber Daya Manusia dalam bahasa Indonesia masih sangat sedikit. Sebagian buku MSDM merupakan buku terjemahan dari buku teks ... Ingin buku-buku mudah di punya mengetahui tentang buku menggunakan ... Dengar di semua lingkungan untuk bahas buku mudah di banyak sebagai pemilik buku. Waktu-waktu batu buku menggunakan buku ... Sambil diciptakan bahasa Indonesia dalam buku mudah yang lain di Pengaruh Indonesia ... Dengar di bahasa Indonesia dalam buku mudah yang lain di Kata-Kata ngakak di malam Matahari ... In 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diffusers/latent-upscaler-tool/app.py b/spaces/diffusers/latent-upscaler-tool/app.py
deleted file mode 100644
index 22213e39d6764a1b3d839dbbe08bcb32f4cfe255..0000000000000000000000000000000000000000
--- a/spaces/diffusers/latent-upscaler-tool/app.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from transformers.tools.base import launch_gradio_demo
-from image_upscaling import ImageUpscalingTool
-
-launch_gradio_demo(ImageUpscalingTool)
diff --git a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/utils.py b/spaces/digitalxingtong/Nailv-read-Bert-Vits2/utils.py
deleted file mode 100644
index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-read-Bert-Vits2/utils.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    elif optimizer is None and not skip_optimizer:  
-    #else: #Disable this line if Infer ,and enable the line upper
-        new_opt_dict = optimizer.state_dict()
-        new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
-        new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
-        new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
-        optimizer.load_state_dict(new_opt_dict)
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            #assert "emb_g" not in k
-            # print("load", k)
-            new_state_dict[k] = saved_state_dict[k]
-            assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
-        except:
-            print("error, %s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    print("load ")
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info("Saving model and optimizer state at iteration {} to {}".format(
-        iteration, checkpoint_path))
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save({'model': state_dict,
-                'iteration': iteration,
-                'optimizer': optimizer.state_dict(),
-                'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats='HWC')
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    print(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL",
-                        help='Model name')
-    parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint")
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    hparams.cont = args.cont
-    return hparams
-
-
-def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
-    """Freeing up space by deleting saved ckpts
-
-  Arguments:
-  path_to_models    --  Path to the model directory
-  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
-  sort_by_time      --  True -> chronologically delete ckpts
-                        False -> lexicographically delete ckpts
-  """
-    import re
-    ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
-    name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
-    time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
-    sort_key = time_key if sort_by_time else name_key
-    x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')],
-                                 key=sort_key)
-    to_del = [os.path.join(path_to_models, fn) for fn in
-              (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
-    del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
-    del_routine = lambda x: [os.remove(x), del_info(x)]
-    rs = [del_routine(fn) for fn in to_del]
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/digitalxingtong/Shanbao-Bert-VITS2/transforms.py b/spaces/digitalxingtong/Shanbao-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Shanbao-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/symbols.py b/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-
diff --git a/spaces/dineshreddy/WALT/mmdet/models/detectors/cascade_rcnn.py b/spaces/dineshreddy/WALT/mmdet/models/detectors/cascade_rcnn.py
deleted file mode 100644
index d873dceb7e4efdf8d1e7d282badfe9b7118426b9..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/detectors/cascade_rcnn.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class CascadeRCNN(TwoStageDetector):
-    r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
-    Detection <https://arxiv.org/abs/1906.09756>`_"""
-
-    def __init__(self,
-                 backbone,
-                 neck=None,
-                 rpn_head=None,
-                 roi_head=None,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(CascadeRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)
-
-    def show_result(self, data, result, **kwargs):
-        """Show prediction results of the detector.
-
-        Args:
-            data (str or np.ndarray): Image filename or loaded image.
-            result (Tensor or tuple): The results to draw over `img`
-                bbox_result or (bbox_result, segm_result).
-
-        Returns:
-            np.ndarray: The image with bboxes drawn on it.
-        """
-        if self.with_mask:
-            ms_bbox_result, ms_segm_result = result
-            if isinstance(ms_bbox_result, dict):
-                result = (ms_bbox_result['ensemble'],
-                          ms_segm_result['ensemble'])
-        else:
-            if isinstance(result, dict):
-                result = result['ensemble']
-        return super(CascadeRCNN, self).show_result(data, result, **kwargs)
diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/test_mixins.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/test_mixins.py
deleted file mode 100644
index c28ed61deb946f0ffca70733fb7ddf84d1aec885..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/test_mixins.py
+++ /dev/null
@@ -1,368 +0,0 @@
-import logging
-import sys
-
-import torch
-
-from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
-                        merge_aug_masks, multiclass_nms)
-
-logger = logging.getLogger(__name__)
-
-if sys.version_info >= (3, 7):
-    from mmdet.utils.contextmanagers import completed
-
-
-class BBoxTestMixin(object):
-
-    if sys.version_info >= (3, 7):
-
-        async def async_test_bboxes(self,
-                                    x,
-                                    img_metas,
-                                    proposals,
-                                    rcnn_test_cfg,
-                                    rescale=False,
-                                    bbox_semaphore=None,
-                                    global_lock=None):
-            """Asynchronized test for box head without augmentation."""
-            rois = bbox2roi(proposals)
-            roi_feats = self.bbox_roi_extractor(
-                x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
-            if self.with_shared_head:
-                roi_feats = self.shared_head(roi_feats)
-            sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017)
-
-            async with completed(
-                    __name__, 'bbox_head_forward',
-                    sleep_interval=sleep_interval):
-                cls_score, bbox_pred = self.bbox_head(roi_feats)
-
-            img_shape = img_metas[0]['img_shape']
-            scale_factor = img_metas[0]['scale_factor']
-            det_bboxes, det_labels = self.bbox_head.get_bboxes(
-                rois,
-                cls_score,
-                bbox_pred,
-                img_shape,
-                scale_factor,
-                rescale=rescale,
-                cfg=rcnn_test_cfg)
-            return det_bboxes, det_labels
-
-    def simple_test_bboxes(self,
-                           x,
-                           img_metas,
-                           proposals,
-                           rcnn_test_cfg,
-                           rescale=False):
-        """Test only det bboxes without augmentation.
-
-        Args:
-            x (tuple[Tensor]): Feature maps of all scale level.
-            img_metas (list[dict]): Image meta info.
-            proposals (Tensor or List[Tensor]): Region proposals.
-            rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-
-        Returns:
-            tuple[list[Tensor], list[Tensor]]: The first list contains
-                the boxes of the corresponding image in a batch, each
-                tensor has the shape (num_boxes, 5) and last dimension
-                5 represent (tl_x, tl_y, br_x, br_y, score). Each Tensor
-                in the second list is the labels with shape (num_boxes, ).
-                The length of both lists should be equal to batch_size.
-        """
-        # get origin input shape to support onnx dynamic input shape
-        if torch.onnx.is_in_onnx_export():
-            assert len(
-                img_metas
-            ) == 1, 'Only support one input image while in exporting to ONNX'
-            img_shapes = img_metas[0]['img_shape_for_onnx']
-        else:
-            img_shapes = tuple(meta['img_shape'] for meta in img_metas)
-        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
-
-        # The length of proposals of different batches may be different.
-        # In order to form a batch, a padding operation is required.
-        if isinstance(proposals, list):
-            # padding to form a batch
-            max_size = max([proposal.size(0) for proposal in proposals])
-            for i, proposal in enumerate(proposals):
-                supplement = proposal.new_full(
-                    (max_size - proposal.size(0), proposal.size(1)), 0)
-                proposals[i] = torch.cat((supplement, proposal), dim=0)
-            rois = torch.stack(proposals, dim=0)
-        else:
-            rois = proposals
-
-        batch_index = torch.arange(
-            rois.size(0), device=rois.device).float().view(-1, 1, 1).expand(
-                rois.size(0), rois.size(1), 1)
-        rois = torch.cat([batch_index, rois[..., :4]], dim=-1)
-        batch_size = rois.shape[0]
-        num_proposals_per_img = rois.shape[1]
-
-        # Eliminate the batch dimension
-        rois = rois.view(-1, 5)
-        bbox_results = self._bbox_forward(x, rois)
-        cls_score = bbox_results['cls_score']
-        bbox_pred = bbox_results['bbox_pred']
-
-        # Recover the batch dimension
-        rois = rois.reshape(batch_size, num_proposals_per_img, -1)
-        cls_score = cls_score.reshape(batch_size, num_proposals_per_img, -1)
-
-        if not torch.onnx.is_in_onnx_export():
-            # remove padding
-            supplement_mask = rois[..., -1] == 0
-            cls_score[supplement_mask, :] = 0
-
-        # bbox_pred would be None in some detector when with_reg is False,
-        # e.g. Grid R-CNN.
-        if bbox_pred is not None:
-            # the bbox prediction of some detectors like SABL is not Tensor
-            if isinstance(bbox_pred, torch.Tensor):
-                bbox_pred = bbox_pred.reshape(batch_size,
-                                              num_proposals_per_img, -1)
-                if not torch.onnx.is_in_onnx_export():
-                    bbox_pred[supplement_mask, :] = 0
-            else:
-                # TODO: Looking forward to a better way
-                # For SABL
-                bbox_preds = self.bbox_head.bbox_pred_split(
-                    bbox_pred, num_proposals_per_img)
-                # apply bbox post-processing to each image individually
-                det_bboxes = []
-                det_labels = []
-                for i in range(len(proposals)):
-                    # remove padding
-                    supplement_mask = proposals[i][..., -1] == 0
-                    for bbox in bbox_preds[i]:
-                        bbox[supplement_mask] = 0
-                    det_bbox, det_label = self.bbox_head.get_bboxes(
-                        rois[i],
-                        cls_score[i],
-                        bbox_preds[i],
-                        img_shapes[i],
-                        scale_factors[i],
-                        rescale=rescale,
-                        cfg=rcnn_test_cfg)
-                    det_bboxes.append(det_bbox)
-                    det_labels.append(det_label)
-                return det_bboxes, det_labels
-        else:
-            bbox_pred = None
-
-        return self.bbox_head.get_bboxes(
-            rois,
-            cls_score,
-            bbox_pred,
-            img_shapes,
-            scale_factors,
-            rescale=rescale,
-            cfg=rcnn_test_cfg)
-
-    def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
-        """Test det bboxes with test time augmentation."""
-        aug_bboxes = []
-        aug_scores = []
-        for x, img_meta in zip(feats, img_metas):
-            # only one image in the batch
-            img_shape = img_meta[0]['img_shape']
-            scale_factor = img_meta[0]['scale_factor']
-            flip = img_meta[0]['flip']
-            flip_direction = img_meta[0]['flip_direction']
-            # TODO more flexible
-            proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
-                                     scale_factor, flip, flip_direction)
-            rois = bbox2roi([proposals])
-            bbox_results = self._bbox_forward(x, rois)
-            bboxes, scores = self.bbox_head.get_bboxes(
-                rois,
-                bbox_results['cls_score'],
-                bbox_results['bbox_pred'],
-                img_shape,
-                scale_factor,
-                rescale=False,
-                cfg=None)
-            aug_bboxes.append(bboxes)
-            aug_scores.append(scores)
-        # after merging, bboxes will be rescaled to the original image size
-        merged_bboxes, merged_scores = merge_aug_bboxes(
-            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
-        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
-                                                rcnn_test_cfg.score_thr,
-                                                rcnn_test_cfg.nms,
-                                                rcnn_test_cfg.max_per_img)
-        return det_bboxes, det_labels
-
-
-class MaskTestMixin(object):
-
-    if sys.version_info >= (3, 7):
-
-        async def async_test_mask(self,
-                                  x,
-                                  img_metas,
-                                  det_bboxes,
-                                  det_labels,
-                                  rescale=False,
-                                  mask_test_cfg=None):
-            """Asynchronized test for mask head without augmentation."""
-            # image shape of the first image in the batch (only one)
-            ori_shape = img_metas[0]['ori_shape']
-            scale_factor = img_metas[0]['scale_factor']
-            if det_bboxes.shape[0] == 0:
-                segm_result = [[] for _ in range(self.mask_head.num_classes)]
-            else:
-                if rescale and not isinstance(scale_factor,
-                                              (float, torch.Tensor)):
-                    scale_factor = det_bboxes.new_tensor(scale_factor)
-                _bboxes = (
-                    det_bboxes[:, :4] *
-                    scale_factor if rescale else det_bboxes)
-                mask_rois = bbox2roi([_bboxes])
-                mask_feats = self.mask_roi_extractor(
-                    x[:len(self.mask_roi_extractor.featmap_strides)],
-                    mask_rois)
-
-                if self.with_shared_head:
-                    mask_feats = self.shared_head(mask_feats)
-                if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'):
-                    sleep_interval = mask_test_cfg['async_sleep_interval']
-                else:
-                    sleep_interval = 0.035
-                async with completed(
-                        __name__,
-                        'mask_head_forward',
-                        sleep_interval=sleep_interval):
-                    mask_pred = self.mask_head(mask_feats)
-                segm_result = self.mask_head.get_seg_masks(
-                    mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape,
-                    scale_factor, rescale)
-            return segm_result
-
-    def simple_test_mask(self,
-                         x,
-                         img_metas,
-                         det_bboxes,
-                         det_labels,
-                         rescale=False):
-        """Simple test for mask head without augmentation."""
-        # image shapes of images in the batch
-        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
-        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
-
-        # The length of proposals of different batches may be different.
-        # In order to form a batch, a padding operation is required.
-        if isinstance(det_bboxes, list):
-            # padding to form a batch
-            max_size = max([bboxes.size(0) for bboxes in det_bboxes])
-            for i, (bbox, label) in enumerate(zip(det_bboxes, det_labels)):
-                supplement_bbox = bbox.new_full(
-                    (max_size - bbox.size(0), bbox.size(1)), 0)
-                supplement_label = label.new_full((max_size - label.size(0), ),
-                                                  0)
-                det_bboxes[i] = torch.cat((supplement_bbox, bbox), dim=0)
-                det_labels[i] = torch.cat((supplement_label, label), dim=0)
-            det_bboxes = torch.stack(det_bboxes, dim=0)
-            det_labels = torch.stack(det_labels, dim=0)
-
-        batch_size = det_bboxes.size(0)
-        num_proposals_per_img = det_bboxes.shape[1]
-
-        # if det_bboxes is rescaled to the original image size, we need to
-        # rescale it back to the testing scale to obtain RoIs.
-        det_bboxes = det_bboxes[..., :4]
-        if rescale:
-            if not isinstance(scale_factors[0], float):
-                scale_factors = det_bboxes.new_tensor(scale_factors)
-            det_bboxes = det_bboxes * scale_factors.unsqueeze(1)
-
-        batch_index = torch.arange(
-            det_bboxes.size(0), device=det_bboxes.device).float().view(
-                -1, 1, 1).expand(det_bboxes.size(0), det_bboxes.size(1), 1)
-        mask_rois = torch.cat([batch_index, det_bboxes], dim=-1)
-        mask_rois = mask_rois.view(-1, 5)
-        mask_results = self._mask_forward(x, mask_rois)
-        mask_pred = mask_results['mask_pred']
-        try:
-            mask_full_pred, mask_occ_pred = mask_pred
-        except:
-            mask_full_pred = mask_pred
-            mask_occ_pred = mask_pred
-
-
-        # Recover the batch dimension
-        mask_full_preds = mask_full_pred.reshape(batch_size, num_proposals_per_img,
-                                       *mask_full_pred.shape[1:])
-
-        mask_occ_preds = mask_occ_pred.reshape(batch_size, num_proposals_per_img,
-                                       *mask_occ_pred.shape[1:])
-
-
-        # apply mask post-processing to each image individually
-        segm_results = []
-        for i in range(batch_size):
-            mask_full_pred = mask_full_preds[i]
-            mask_occ_pred = mask_occ_preds[i]
-            det_bbox = det_bboxes[i]
-            det_label = det_labels[i]
-
-            # remove padding
-            supplement_mask = det_bbox[..., -1] != 0
-            mask_full_pred = mask_full_pred[supplement_mask]
-            mask_occ_pred = mask_occ_pred[supplement_mask]
-            det_bbox = det_bbox[supplement_mask]
-            det_label = det_label[supplement_mask]
-
-            if det_label.shape[0] == 0:
-                segm_results.append([[]
-                                     for _ in range(self.mask_head.num_classes)
-                                     ])
-            else:
-                segm_result_vis = self.mask_head.get_seg_masks(
-                        mask_full_pred[:,0:1], det_bbox, det_label, self.test_cfg,
-                    ori_shapes[i], scale_factors[i], rescale)
-                
-                segm_result_occ = self.mask_head.get_seg_masks(
-                    mask_occ_pred[:,0:1], det_bbox, det_label, self.test_cfg,
-                    ori_shapes[i], scale_factors[i], rescale)
-
-                segm_result = segm_result_vis
-                segm_result[1] = segm_result_occ[0]
-
-                segm_results.append(segm_result)
-        return segm_results
-
-    def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
-        """Test for mask head with test time augmentation."""
-        if det_bboxes.shape[0] == 0:
-            segm_result = [[] for _ in range(self.mask_head.num_classes)]
-        else:
-            aug_masks = []
-            for x, img_meta in zip(feats, img_metas):
-                img_shape = img_meta[0]['img_shape']
-                scale_factor = img_meta[0]['scale_factor']
-                flip = img_meta[0]['flip']
-                flip_direction = img_meta[0]['flip_direction']
-                _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
-                                       scale_factor, flip, flip_direction)
-                mask_rois = bbox2roi([_bboxes])
-                mask_results = self._mask_forward(x, mask_rois)
-                # convert to numpy array to save memory
-                aug_masks.append(
-                    mask_results['mask_pred'].sigmoid().cpu().numpy())
-            merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
-
-            ori_shape = img_metas[0][0]['ori_shape']
-            segm_result = self.mask_head.get_seg_masks(
-                merged_masks,
-                det_bboxes,
-                det_labels,
-                self.test_cfg,
-                ori_shape,
-                scale_factor=1.0,
-                rescale=False)
-        return segm_result
diff --git a/spaces/djsull/aha-summarisation/app.py b/spaces/djsull/aha-summarisation/app.py
deleted file mode 100644
index e7a7a4fca66e410f0a7521da9e0be86c31ee3372..0000000000000000000000000000000000000000
--- a/spaces/djsull/aha-summarisation/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import gradio as gr
-import re
-import torch
-
-from transformers import BartForConditionalGeneration, PreTrainedTokenizerFast
-import os
-
-AUTH_TOKEN = os.environ["AUTH_TOKEN"]
-
-tokenizer = PreTrainedTokenizerFast.from_pretrained('djsull/aha-kobart-summarization', use_auth_token=AUTH_TOKEN)
-model = BartForConditionalGeneration.from_pretrained('djsull/aha-kobart-summarization', use_auth_token=AUTH_TOKEN)
-
-def predict(text):
-    text = text
-    if len(text) < 50:
-        error = "query too short"
-        return error
-    text = re.sub('\n[0-9].', '\n', text)
-    text = re.sub('\n-.', '\n', text)
-    text_check = text.split('\n')
-
-    checked = []
-    for i in range(len(text_check)):
-        if '안녕' not in text_check[i]:
-            checked.append(text_check[i])
-    
-    texts = []
-    len_check = 0
-    want_to_check = ''
-    for i in range(len(checked)):
-        want_to_check = want_to_check + checked[i]
-        if len(want_to_check) > 100:
-            texts.append(want_to_check)
-            want_to_check = ''
-        if i == len(checked)-1 and want_to_check != '':
-            texts.append(want_to_check)
-    
-    # print(texts)
-    
-    res = []
-    for i in range(len(texts)):
-        texts[i] = ''.join(re.sub('[^가-힣a-zA-Z0-9\%]', ' ', texts[i]))
-        textx = ' '.join(texts[i].split())
-        # print(textx)
-        raw_input_ids = tokenizer.encode(textx)
-        input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]
-    
-        summary_ids = model.generate(torch.tensor([input_ids]),  num_beams=4,  max_length=512, eos_token_id=1, no_repeat_ngram_size=2)
-        result = tokenizer.decode(summary_ids.squeeze().tolist(), skip_special_tokens=True)
-        res.append(result)
-    # print('\n\n\n\n')
-    fin_res = ''
-    for i in range(len(res)):
-        if i != len(res)-1:
-            fin_res = fin_res + res[i] + '\n\n'
-        else:
-            fin_res = fin_res + res[i] 
-  
-    return fin_res
-
-gr.Interface(
-    predict,
-    inputs=gr.inputs.Textbox(label="Type anything you want in kor, only text"),
-    outputs=gr.outputs.Textbox(label="Summarized!"),
-    title="Answers Summarizer",
-).launch()
\ No newline at end of file
diff --git a/spaces/dorkai/text-generation-webui-main/extensions/whisper_stt/script.py b/spaces/dorkai/text-generation-webui-main/extensions/whisper_stt/script.py
deleted file mode 100644
index 32226404b63c443ac588bbc5e66741a332cbe05d..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/extensions/whisper_stt/script.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import gradio as gr
-import speech_recognition as sr
-
-from modules import shared
-
-input_hijack = {
-    'state': False,
-    'value': ["", ""]
-}
-
-
-def do_stt(audio):
-    transcription = ""
-    r = sr.Recognizer()
-
-    # Convert to AudioData
-    audio_data = sr.AudioData(sample_rate=audio[0], frame_data=audio[1], sample_width=4)
-
-    try:
-        transcription = r.recognize_whisper(audio_data, language="english", model="base.en")
-    except sr.UnknownValueError:
-        print("Whisper could not understand audio")
-    except sr.RequestError as e:
-        print("Could not request results from Whisper", e)
-
-    return transcription
-
-
-def auto_transcribe(audio, auto_submit):
-    if audio is None:
-        return "", ""
-
-    transcription = do_stt(audio)
-    if auto_submit:
-        input_hijack.update({"state": True, "value": [transcription, transcription]})
-
-    return transcription, None
-
-
-def ui():
-    with gr.Row():
-        audio = gr.Audio(source="microphone")
-        auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=True)
-
-    audio.change(
-        auto_transcribe, [audio, auto_submit], [shared.gradio['textbox'], audio]).then(
-        None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}")
diff --git a/spaces/duycse1603/math2tex/ScanSSD/gtdb/adjust_boxes.py b/spaces/duycse1603/math2tex/ScanSSD/gtdb/adjust_boxes.py
deleted file mode 100644
index 067b524f90fd33a1018ff7b4c5d27eacef19dfb7..0000000000000000000000000000000000000000
--- a/spaces/duycse1603/math2tex/ScanSSD/gtdb/adjust_boxes.py
+++ /dev/null
@@ -1,220 +0,0 @@
-# Author: Parag Mali
-# This script stitches back the output generated on the image patches (sub-images)
-
-# read the image
-import sys
-sys.path.extend(['/home/psm2208/code', '/home/psm2208/code'])
-import cv2
-import os
-import csv
-import numpy as np
-import utils.visualize as visualize
-from multiprocessing import Pool
-from cv2.dnn import NMSBoxes
-from scipy.ndimage.measurements import label
-import scipy.ndimage as ndimage
-import copy
-from gtdb import fit_box
-from gtdb import box_utils
-from gtdb import feature_extractor
-import shutil
-import time
-from collections import OrderedDict
-from collections import deque
-import argparse
-
-def parse_args():
-    '''
-    Parameters
-    '''
-    parser = argparse.ArgumentParser(
-        description='Stitching method')
-
-    parser.add_argument('--data_file', default='test',
-                        type=str, help='choose one')
-    parser.add_argument('--output_dir', default='.',
-                        help='Output directory path')
-    parser.add_argument('--math_dir', required=True,
-                        type=str, help='detections dir')
-    parser.add_argument('--math_ext', default='.csv',
-                        help='Extention of detection files')
-    parser.add_argument('--home_data', default='/home/psm2208/data/GTDB/', type = str,
-                        help='database dir')
-    parser.add_argument('--home_eval', default='/home/psm2208/code/eval/', type = str,
-                        help='Eval dir')
-    parser.add_argument('--home_images', default='/home/psm2208/data/GTDB/images/', type = str,
-                        help='Images dir')
-    parser.add_argument('--home_anno', default='/home/psm2208/data/GTDB/annotations/', type = str,
-                        help='Annotations dir')
-    parser.add_argument('--home_char', default='/home/psm2208/data/GTDB/char_annotations/', type = str,
-                        help='Char anno dir')
-    parser.add_argument('--num_workers', default=4, type=int, help='Number of workers')
-    parser.add_argument('--type', default='math', type=str, help='Math or text')
-
-    return parser.parse_args()
-
-def read_math(args, pdf_name):
-    '''
-    Read math bounding boxes for given PDF
-    '''
-    math_file = os.path.join(args.math_dir, pdf_name + args.math_ext)
-    data = np.array([])
-
-    if os.path.exists(math_file):
-        data = np.genfromtxt(math_file, delimiter=',')
-
-        # if there is only one entry convert it to correct form required
-        if len(data.shape) == 1:
-            data = data.reshape(1, -1)
-
-    if args.math_ext == '.char':
-        data = np.delete(data,1,1)
-        data = data[:,:5]
-
-    return data.astype(int)
-
-def read_char(args, pdf_name):
-    '''
-    Read char bounding boxes for given PDF
-    '''
-    data = []
-
-    path = os.path.join(args.home_char, pdf_name + ".char")
-
-    with open(path, 'r') as csvfile:
-        reader = csv.reader(csvfile, delimiter=',')
-        for row in reader:
-            #print('row is ' + str(row[1]))
-            # if entry is not in map
-            data.append(row)
-
-    return np.array(data)
-
-
-def adjust(params):
-    '''
-    Fit the bounding boxes to the characters
-    '''
-    args, math_regions, pdf_name, page_num = params
-    print('Processing ', pdf_name, ' > ', page_num)
-
-    image = cv2.imread(os.path.join(args.home_images, pdf_name, str(int(page_num + 1)) + ".png"))
-    im_bw = fit_box.convert_to_binary(image)
-
-    new_math = []
-    for math in math_regions:
-        box = fit_box.adjust_box(im_bw, math)
-
-        if feature_extractor.width(box) > 0 and feature_extractor.height(box) > 0:
-            new_math.append(box)
-
-    return new_math
-
-def adjust_char(params):
-    '''
-    Adjust the character bounding boxes
-    '''
-    try:
-        args, char_regions, pdf_name, page_num = params
-        print('Char processing ', pdf_name, ' > ', page_num)
-
-        image = cv2.imread(os.path.join(args.home_images, pdf_name, str(int(page_num) + 1) + ".png"))
-        im_bw = fit_box.convert_to_binary(image)
-
-        new_chars = []
-
-        for char in char_regions:
-
-            bb_char = [char[2],char[3],char[4],char[5]]
-            bb_char = [int(float(k)) for k in bb_char]
-            box = fit_box.adjust_box(im_bw, bb_char)
-            if feature_extractor.width(box) > 0 and feature_extractor.height(box) > 0:
-                char[1] = box[0]
-                char[2] = box[1]
-                char[3] = box[2]
-                char[4] = box[3]
-                new_chars.append(char)
-
-        return new_chars
-    except Exception as e:
-        print('Error while processing ', pdf_name, ' > ', page_num, sys.exc_info())
-        return []
-
-def adjust_boxes(args):
-    '''
-    Driving function for adjusting the boxes
-    '''
-
-    pdf_list = []
-    pdf_names_file = open(args.data_file, 'r')
-
-    for pdf_name in pdf_names_file:
-        pdf_name = pdf_name.strip()
-
-        if pdf_name != '':
-            pdf_list.append(pdf_name)
-
-    regions = {}
-
-    for pdf_name in pdf_list:
-        if args.type == 'char':
-            regions[pdf_name] = read_char(args, pdf_name)
-        else:
-            regions[pdf_name] = read_math(args, pdf_name)
-
-    voting_ip_list = []
-
-    for pdf_name in pdf_list:
-
-        pages = np.unique(regions[pdf_name][:, 0])
-
-        #args, math_regions, pdf_name, page_num
-        for page_num in pages:
-
-            current_math = regions[pdf_name][regions[pdf_name][:,0] == page_num]
-
-            if args.type == 'math':
-                current_math = np.delete(current_math, 0, 1)
-
-            voting_ip_list.append([args, current_math, pdf_name, page_num])
-
-    pool = Pool(processes=args.num_workers)
-
-    if args.type == 'math':
-        out = pool.map(adjust, voting_ip_list)
-    else:
-        out = pool.map(adjust_char, voting_ip_list)
-
-    for ip, final_math in zip(voting_ip_list, out):
-        pdf_name = ip[2]
-        page_num = ip[3]
-
-        if args.type == 'math':
-            col = np.array([int(page_num)] * len(final_math))
-            final_math = np.concatenate((col[:, np.newaxis], final_math), axis=1)
-
-        math_file_path = os.path.join(args.output_dir, pdf_name + '.csv')
-
-        if not os.path.exists(os.path.dirname(math_file_path)):
-            os.makedirs(os.path.dirname(math_file_path))
-
-
-        if args.type == 'math':
-            math_file = open(math_file_path, 'a')
-
-            np.savetxt(math_file, final_math, fmt='%.2f', delimiter=',')
-            math_file.close()
-
-        else:
-
-            with open(math_file_path, 'a') as csvfile:
-                writer = csv.writer(csvfile, delimiter=",")
-
-                for math_region in final_math:
-                    writer.writerow(math_region)
-
-
-if __name__ == '__main__':
-
-    args = parse_args()
-    adjust_boxes(args)
diff --git a/spaces/duycse1603/math2tex/ScanSSD/layers/__init__.py b/spaces/duycse1603/math2tex/ScanSSD/layers/__init__.py
deleted file mode 100644
index 53a3f4b5160995d93bc7911e808b3045d74362c9..0000000000000000000000000000000000000000
--- a/spaces/duycse1603/math2tex/ScanSSD/layers/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .functions import *
-from .modules import *
diff --git a/spaces/evaluate-metric/super_glue/app.py b/spaces/evaluate-metric/super_glue/app.py
deleted file mode 100644
index 0d11b1f706b3d99f16537dcec21c0a46b85b5bf4..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/super_glue/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("super_glue", "copa")
-launch_gradio_widget(module)
diff --git a/spaces/exbert-project/exbert/client/src/ts/etc/colors.ts b/spaces/exbert-project/exbert/client/src/ts/etc/colors.ts
deleted file mode 100644
index 5928e562a2db04f0516da86658c5683776dd9730..0000000000000000000000000000000000000000
--- a/spaces/exbert-project/exbert/client/src/ts/etc/colors.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-// Colors were generated to be as visually distinct as possible by: http://jnnnnn.github.io/category-colors-constrained.html
-export const COLORS200 = [
-    "#3957ff", "#d3fe14", "#c9080a", "#fec7f8", "#0b7b3e", "#0bf0e9", "#c203c8", "#fd9b39", 
-    "#888593", "#906407", "#98ba7f", "#fe6794", "#10b0ff", "#ac7bff", "#fee7c0", "#964c63",
-    "#1da49c", "#0ad811", "#bbd9fd", "#fe6cfe", "#297192", "#d1a09c", "#78579e", "#81ffad",
-    "#739400", "#ca6949", "#d9bf01", "#646a58", "#d5097e", "#bb73a9", "#ccf6e9", "#9cb4b6",
-    "#b6a7d4", "#9e8c62", "#6e83c8", "#01af64", "#a71afd", "#cfe589", "#d4ccd1", "#fd4109",
-    "#bf8f0e", "#2f786e", "#4ed1a5", "#d8bb7d", "#a54509", "#6a9276", "#a4777a", "#fc12c9",
-    "#606f15", "#3cc4d9", "#f31c4e", "#73616f", "#f097c6", "#fc8772", "#92a6fe", "#875b44",
-    "#699ab3", "#94bc19", "#7d5bf0", "#d24dfe", "#c85b74", "#68ff57", "#b62347", "#994b91",
-    "#646b8c", "#977ab4", "#d694fd", "#c4d5b5", "#fdc4bd", "#1cae05", "#7bd972", "#e9700a",
-    "#d08f5d", "#8bb9e1", "#fde945", "#a29d98", "#1682fb", "#9ad9e0", "#d6cafe", "#8d8328",
-    "#b091a7", "#647579", "#1f8d11", "#e7eafd", "#b9660b", "#a4a644", "#fec24c", "#b1168c",
-    "#188cc1", "#7ab297", "#4468ae", "#c949a6", "#d48295", "#eb6dc2", "#d5b0cb", "#ff9ffb",
-    "#fdb082", "#af4d44", "#a759c4", "#a9e03a", "#0d906b", "#9ee3bd", "#5b8846", "#0d8995",
-    "#f25c58", "#70ae4f", "#847f74", "#9094bb", "#ffe2f1", "#a67149", "#936c8e", "#d04907",
-    "#c3b8a6", "#cef8c4", "#7a9293", "#fda2ab", "#2ef6c5", "#807242", "#cb94cc", "#b6bdd0",
-    "#b5c75d", "#fde189", "#b7ff80", "#fa2d8e", "#839a5f", "#28c2b5", "#e5e9e1", "#bc79d8",
-    "#7ed8fe", "#9f20c3", "#4f7a5b", "#f511fd", "#09c959", "#bcd0ce", "#8685fd", "#98fcff",
-    "#afbff9", "#6d69b4", "#5f99fd", "#aaa87e", "#b59dfb", "#5d809d", "#d9a742", "#ac5c86",
-    "#9468d5", "#a4a2b2", "#b1376e", "#d43f3d", "#05a9d1", "#c38375", "#24b58e", "#6eabaf",
-    "#66bf7f", "#92cbbb", "#ddb1ee", "#1be895", "#c7ecf9", "#a6baa6", "#8045cd", "#5f70f1",
-    "#a9d796", "#ce62cb", "#0e954d", "#a97d2f", "#fcb8d3", "#9bfee3", "#4e8d84", "#fc6d3f",
-    "#7b9fd4", "#8c6165", "#72805e", "#d53762", "#f00a1b", "#de5c97", "#8ea28b", "#fccd95",
-    "#ba9c57", "#b79a82", "#7c5a82", "#7d7ca4", "#958ad6", "#cd8126", "#bdb0b7", "#10e0f8",
-    "#dccc69", "#d6de0f", "#616d3d", "#985a25", "#30c7fd", "#0aeb65", "#e3cdb4", "#bd1bee",
-    "#ad665d", "#d77070", "#8ea5b8", "#5b5ad0", "#76655e", "#598100", "#86757e", "#5ea068",
-]
\ No newline at end of file
diff --git a/spaces/f2api/gpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h b/spaces/f2api/gpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/f2api/gpt-academic/docs/waifu_plugin/jquery-ui.min.js b/spaces/f2api/gpt-academic/docs/waifu_plugin/jquery-ui.min.js
deleted file mode 100644
index 25398a167415050ae8bfb0bfebac6aa3ab790909..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/docs/waifu_plugin/jquery-ui.min.js
+++ /dev/null
@@ -1,13 +0,0 @@
-/*! jQuery UI - v1.12.1 - 2016-09-14
-* http://jqueryui.com
-* Includes: widget.js, position.js, data.js, disable-selection.js, effect.js, effects/effect-blind.js, effects/effect-bounce.js, effects/effect-clip.js, effects/effect-drop.js, effects/effect-explode.js, effects/effect-fade.js, effects/effect-fold.js, effects/effect-highlight.js, effects/effect-puff.js, effects/effect-pulsate.js, effects/effect-scale.js, effects/effect-shake.js, effects/effect-size.js, effects/effect-slide.js, effects/effect-transfer.js, focusable.js, form-reset-mixin.js, jquery-1-7.js, keycode.js, labels.js, scroll-parent.js, tabbable.js, unique-id.js, widgets/accordion.js, widgets/autocomplete.js, widgets/button.js, widgets/checkboxradio.js, widgets/controlgroup.js, widgets/datepicker.js, widgets/dialog.js, widgets/draggable.js, widgets/droppable.js, widgets/menu.js, widgets/mouse.js, widgets/progressbar.js, widgets/resizable.js, widgets/selectable.js, widgets/selectmenu.js, widgets/slider.js, widgets/sortable.js, widgets/spinner.js, widgets/tabs.js, widgets/tooltip.js
-* Copyright jQuery Foundation and other contributors; Licensed MIT */
-
-(function(t){"function"==typeof define&&define.amd?define(["jquery"],t):t(jQuery)})(function(t){function e(t){for(var e=t.css("visibility");"inherit"===e;)t=t.parent(),e=t.css("visibility");return"hidden"!==e}function i(t){for(var e,i;t.length&&t[0]!==document;){if(e=t.css("position"),("absolute"===e||"relative"===e||"fixed"===e)&&(i=parseInt(t.css("zIndex"),10),!isNaN(i)&&0!==i))return i;t=t.parent()}return 0}function s(){this._curInst=null,this._keyEvent=!1,this._disabledInputs=[],this._datepickerShowing=!1,this._inDialog=!1,this._mainDivId="ui-datepicker-div",this._inlineClass="ui-datepicker-inline",this._appendClass="ui-datepicker-append",this._triggerClass="ui-datepicker-trigger",this._dialogClass="ui-datepicker-dialog",this._disableClass="ui-datepicker-disabled",this._unselectableClass="ui-datepicker-unselectable",this._currentClass="ui-datepicker-current-day",this._dayOverClass="ui-datepicker-days-cell-over",this.regional=[],this.regional[""]={closeText:"Done",prevText:"Prev",nextText:"Next",currentText:"Today",monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],weekHeader:"Wk",dateFormat:"mm/dd/yy",firstDay:0,isRTL:!1,showMonthAfterYear:!1,yearSuffix:""},this._defaults={showOn:"focus",showAnim:"fadeIn",showOptions:{},defaultDate:null,appendText:"",buttonText:"...",buttonImage:"",buttonImageOnly:!1,hideIfNoPrevNext:!1,navigationAsDateFormat:!1,gotoCurrent:!1,changeMonth:!1,changeYear:!1,yearRange:"c-10:c+10",showOtherMonths:!1,selectOtherMonths:!1,showWeek:!1,calculateWeek:this.iso8601Week,shortYearCutoff:"+10",minDate:null,maxDate:null,duration:"fast",beforeShowDay:null,beforeShow:null,onSelect:null,onChangeMonthYear:null,onClose:null,numberOfMonths:1,showCurrentAtPos:0,stepMonths:1,stepBigMonths:12,altField:"",altFormat:"",constrainInput:!0,showButtonPanel:!1,autoSize:!1,disabled:!1},t.extend(this._defaults,this.regional[""]),this.regional.en=t.extend(!0,{},this.regional[""]),this.regional["en-US"]=t.extend(!0,{},this.regional.en),this.dpDiv=n(t("<div id='"+this._mainDivId+"' class='ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>"))}function n(e){var i="button, .ui-datepicker-prev, .ui-datepicker-next, .ui-datepicker-calendar td a";return e.on("mouseout",i,function(){t(this).removeClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&t(this).removeClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&t(this).removeClass("ui-datepicker-next-hover")}).on("mouseover",i,o)}function o(){t.datepicker._isDisabledDatepicker(m.inline?m.dpDiv.parent()[0]:m.input[0])||(t(this).parents(".ui-datepicker-calendar").find("a").removeClass("ui-state-hover"),t(this).addClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&t(this).addClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&t(this).addClass("ui-datepicker-next-hover"))}function a(e,i){t.extend(e,i);for(var s in i)null==i[s]&&(e[s]=i[s]);return e}function r(t){return function(){var e=this.element.val();t.apply(this,arguments),this._refresh(),e!==this.element.val()&&this._trigger("change")}}t.ui=t.ui||{},t.ui.version="1.12.1";var h=0,l=Array.prototype.slice;t.cleanData=function(e){return function(i){var s,n,o;for(o=0;null!=(n=i[o]);o++)try{s=t._data(n,"events"),s&&s.remove&&t(n).triggerHandler("remove")}catch(a){}e(i)}}(t.cleanData),t.widget=function(e,i,s){var n,o,a,r={},h=e.split(".")[0];e=e.split(".")[1];var l=h+"-"+e;return s||(s=i,i=t.Widget),t.isArray(s)&&(s=t.extend.apply(null,[{}].concat(s))),t.expr[":"][l.toLowerCase()]=function(e){return!!t.data(e,l)},t[h]=t[h]||{},n=t[h][e],o=t[h][e]=function(t,e){return this._createWidget?(arguments.length&&this._createWidget(t,e),void 0):new o(t,e)},t.extend(o,n,{version:s.version,_proto:t.extend({},s),_childConstructors:[]}),a=new i,a.options=t.widget.extend({},a.options),t.each(s,function(e,s){return t.isFunction(s)?(r[e]=function(){function t(){return i.prototype[e].apply(this,arguments)}function n(t){return i.prototype[e].apply(this,t)}return function(){var e,i=this._super,o=this._superApply;return this._super=t,this._superApply=n,e=s.apply(this,arguments),this._super=i,this._superApply=o,e}}(),void 0):(r[e]=s,void 0)}),o.prototype=t.widget.extend(a,{widgetEventPrefix:n?a.widgetEventPrefix||e:e},r,{constructor:o,namespace:h,widgetName:e,widgetFullName:l}),n?(t.each(n._childConstructors,function(e,i){var s=i.prototype;t.widget(s.namespace+"."+s.widgetName,o,i._proto)}),delete n._childConstructors):i._childConstructors.push(o),t.widget.bridge(e,o),o},t.widget.extend=function(e){for(var i,s,n=l.call(arguments,1),o=0,a=n.length;a>o;o++)for(i in n[o])s=n[o][i],n[o].hasOwnProperty(i)&&void 0!==s&&(e[i]=t.isPlainObject(s)?t.isPlainObject(e[i])?t.widget.extend({},e[i],s):t.widget.extend({},s):s);return e},t.widget.bridge=function(e,i){var s=i.prototype.widgetFullName||e;t.fn[e]=function(n){var o="string"==typeof n,a=l.call(arguments,1),r=this;return o?this.length||"instance"!==n?this.each(function(){var i,o=t.data(this,s);return"instance"===n?(r=o,!1):o?t.isFunction(o[n])&&"_"!==n.charAt(0)?(i=o[n].apply(o,a),i!==o&&void 0!==i?(r=i&&i.jquery?r.pushStack(i.get()):i,!1):void 0):t.error("no such method '"+n+"' for "+e+" widget instance"):t.error("cannot call methods on "+e+" prior to initialization; "+"attempted to call method '"+n+"'")}):r=void 0:(a.length&&(n=t.widget.extend.apply(null,[n].concat(a))),this.each(function(){var e=t.data(this,s);e?(e.option(n||{}),e._init&&e._init()):t.data(this,s,new i(n,this))})),r}},t.Widget=function(){},t.Widget._childConstructors=[],t.Widget.prototype={widgetName:"widget",widgetEventPrefix:"",defaultElement:"<div>",options:{classes:{},disabled:!1,create:null},_createWidget:function(e,i){i=t(i||this.defaultElement||this)[0],this.element=t(i),this.uuid=h++,this.eventNamespace="."+this.widgetName+this.uuid,this.bindings=t(),this.hoverable=t(),this.focusable=t(),this.classesElementLookup={},i!==this&&(t.data(i,this.widgetFullName,this),this._on(!0,this.element,{remove:function(t){t.target===i&&this.destroy()}}),this.document=t(i.style?i.ownerDocument:i.document||i),this.window=t(this.document[0].defaultView||this.document[0].parentWindow)),this.options=t.widget.extend({},this.options,this._getCreateOptions(),e),this._create(),this.options.disabled&&this._setOptionDisabled(this.options.disabled),this._trigger("create",null,this._getCreateEventData()),this._init()},_getCreateOptions:function(){return{}},_getCreateEventData:t.noop,_create:t.noop,_init:t.noop,destroy:function(){var e=this;this._destroy(),t.each(this.classesElementLookup,function(t,i){e._removeClass(i,t)}),this.element.off(this.eventNamespace).removeData(this.widgetFullName),this.widget().off(this.eventNamespace).removeAttr("aria-disabled"),this.bindings.off(this.eventNamespace)},_destroy:t.noop,widget:function(){return this.element},option:function(e,i){var s,n,o,a=e;if(0===arguments.length)return t.widget.extend({},this.options);if("string"==typeof e)if(a={},s=e.split("."),e=s.shift(),s.length){for(n=a[e]=t.widget.extend({},this.options[e]),o=0;s.length-1>o;o++)n[s[o]]=n[s[o]]||{},n=n[s[o]];if(e=s.pop(),1===arguments.length)return void 0===n[e]?null:n[e];n[e]=i}else{if(1===arguments.length)return void 0===this.options[e]?null:this.options[e];a[e]=i}return this._setOptions(a),this},_setOptions:function(t){var e;for(e in t)this._setOption(e,t[e]);return this},_setOption:function(t,e){return"classes"===t&&this._setOptionClasses(e),this.options[t]=e,"disabled"===t&&this._setOptionDisabled(e),this},_setOptionClasses:function(e){var i,s,n;for(i in e)n=this.classesElementLookup[i],e[i]!==this.options.classes[i]&&n&&n.length&&(s=t(n.get()),this._removeClass(n,i),s.addClass(this._classes({element:s,keys:i,classes:e,add:!0})))},_setOptionDisabled:function(t){this._toggleClass(this.widget(),this.widgetFullName+"-disabled",null,!!t),t&&(this._removeClass(this.hoverable,null,"ui-state-hover"),this._removeClass(this.focusable,null,"ui-state-focus"))},enable:function(){return this._setOptions({disabled:!1})},disable:function(){return this._setOptions({disabled:!0})},_classes:function(e){function i(i,o){var a,r;for(r=0;i.length>r;r++)a=n.classesElementLookup[i[r]]||t(),a=e.add?t(t.unique(a.get().concat(e.element.get()))):t(a.not(e.element).get()),n.classesElementLookup[i[r]]=a,s.push(i[r]),o&&e.classes[i[r]]&&s.push(e.classes[i[r]])}var s=[],n=this;return e=t.extend({element:this.element,classes:this.options.classes||{}},e),this._on(e.element,{remove:"_untrackClassesElement"}),e.keys&&i(e.keys.match(/\S+/g)||[],!0),e.extra&&i(e.extra.match(/\S+/g)||[]),s.join(" ")},_untrackClassesElement:function(e){var i=this;t.each(i.classesElementLookup,function(s,n){-1!==t.inArray(e.target,n)&&(i.classesElementLookup[s]=t(n.not(e.target).get()))})},_removeClass:function(t,e,i){return this._toggleClass(t,e,i,!1)},_addClass:function(t,e,i){return this._toggleClass(t,e,i,!0)},_toggleClass:function(t,e,i,s){s="boolean"==typeof s?s:i;var n="string"==typeof t||null===t,o={extra:n?e:i,keys:n?t:e,element:n?this.element:t,add:s};return o.element.toggleClass(this._classes(o),s),this},_on:function(e,i,s){var n,o=this;"boolean"!=typeof e&&(s=i,i=e,e=!1),s?(i=n=t(i),this.bindings=this.bindings.add(i)):(s=i,i=this.element,n=this.widget()),t.each(s,function(s,a){function r(){return e||o.options.disabled!==!0&&!t(this).hasClass("ui-state-disabled")?("string"==typeof a?o[a]:a).apply(o,arguments):void 0}"string"!=typeof a&&(r.guid=a.guid=a.guid||r.guid||t.guid++);var h=s.match(/^([\w:-]*)\s*(.*)$/),l=h[1]+o.eventNamespace,c=h[2];c?n.on(l,c,r):i.on(l,r)})},_off:function(e,i){i=(i||"").split(" ").join(this.eventNamespace+" ")+this.eventNamespace,e.off(i).off(i),this.bindings=t(this.bindings.not(e).get()),this.focusable=t(this.focusable.not(e).get()),this.hoverable=t(this.hoverable.not(e).get())},_delay:function(t,e){function i(){return("string"==typeof t?s[t]:t).apply(s,arguments)}var s=this;return setTimeout(i,e||0)},_hoverable:function(e){this.hoverable=this.hoverable.add(e),this._on(e,{mouseenter:function(e){this._addClass(t(e.currentTarget),null,"ui-state-hover")},mouseleave:function(e){this._removeClass(t(e.currentTarget),null,"ui-state-hover")}})},_focusable:function(e){this.focusable=this.focusable.add(e),this._on(e,{focusin:function(e){this._addClass(t(e.currentTarget),null,"ui-state-focus")},focusout:function(e){this._removeClass(t(e.currentTarget),null,"ui-state-focus")}})},_trigger:function(e,i,s){var n,o,a=this.options[e];if(s=s||{},i=t.Event(i),i.type=(e===this.widgetEventPrefix?e:this.widgetEventPrefix+e).toLowerCase(),i.target=this.element[0],o=i.originalEvent)for(n in o)n in i||(i[n]=o[n]);return this.element.trigger(i,s),!(t.isFunction(a)&&a.apply(this.element[0],[i].concat(s))===!1||i.isDefaultPrevented())}},t.each({show:"fadeIn",hide:"fadeOut"},function(e,i){t.Widget.prototype["_"+e]=function(s,n,o){"string"==typeof n&&(n={effect:n});var a,r=n?n===!0||"number"==typeof n?i:n.effect||i:e;n=n||{},"number"==typeof n&&(n={duration:n}),a=!t.isEmptyObject(n),n.complete=o,n.delay&&s.delay(n.delay),a&&t.effects&&t.effects.effect[r]?s[e](n):r!==e&&s[r]?s[r](n.duration,n.easing,o):s.queue(function(i){t(this)[e](),o&&o.call(s[0]),i()})}}),t.widget,function(){function e(t,e,i){return[parseFloat(t[0])*(u.test(t[0])?e/100:1),parseFloat(t[1])*(u.test(t[1])?i/100:1)]}function i(e,i){return parseInt(t.css(e,i),10)||0}function s(e){var i=e[0];return 9===i.nodeType?{width:e.width(),height:e.height(),offset:{top:0,left:0}}:t.isWindow(i)?{width:e.width(),height:e.height(),offset:{top:e.scrollTop(),left:e.scrollLeft()}}:i.preventDefault?{width:0,height:0,offset:{top:i.pageY,left:i.pageX}}:{width:e.outerWidth(),height:e.outerHeight(),offset:e.offset()}}var n,o=Math.max,a=Math.abs,r=/left|center|right/,h=/top|center|bottom/,l=/[\+\-]\d+(\.[\d]+)?%?/,c=/^\w+/,u=/%$/,d=t.fn.position;t.position={scrollbarWidth:function(){if(void 0!==n)return n;var e,i,s=t("<div style='display:block;position:absolute;width:50px;height:50px;overflow:hidden;'><div style='height:100px;width:auto;'></div></div>"),o=s.children()[0];return t("body").append(s),e=o.offsetWidth,s.css("overflow","scroll"),i=o.offsetWidth,e===i&&(i=s[0].clientWidth),s.remove(),n=e-i},getScrollInfo:function(e){var i=e.isWindow||e.isDocument?"":e.element.css("overflow-x"),s=e.isWindow||e.isDocument?"":e.element.css("overflow-y"),n="scroll"===i||"auto"===i&&e.width<e.element[0].scrollWidth,o="scroll"===s||"auto"===s&&e.height<e.element[0].scrollHeight;return{width:o?t.position.scrollbarWidth():0,height:n?t.position.scrollbarWidth():0}},getWithinInfo:function(e){var i=t(e||window),s=t.isWindow(i[0]),n=!!i[0]&&9===i[0].nodeType,o=!s&&!n;return{element:i,isWindow:s,isDocument:n,offset:o?t(e).offset():{left:0,top:0},scrollLeft:i.scrollLeft(),scrollTop:i.scrollTop(),width:i.outerWidth(),height:i.outerHeight()}}},t.fn.position=function(n){if(!n||!n.of)return d.apply(this,arguments);n=t.extend({},n);var u,p,f,g,m,_,v=t(n.of),b=t.position.getWithinInfo(n.within),y=t.position.getScrollInfo(b),w=(n.collision||"flip").split(" "),k={};return _=s(v),v[0].preventDefault&&(n.at="left top"),p=_.width,f=_.height,g=_.offset,m=t.extend({},g),t.each(["my","at"],function(){var t,e,i=(n[this]||"").split(" ");1===i.length&&(i=r.test(i[0])?i.concat(["center"]):h.test(i[0])?["center"].concat(i):["center","center"]),i[0]=r.test(i[0])?i[0]:"center",i[1]=h.test(i[1])?i[1]:"center",t=l.exec(i[0]),e=l.exec(i[1]),k[this]=[t?t[0]:0,e?e[0]:0],n[this]=[c.exec(i[0])[0],c.exec(i[1])[0]]}),1===w.length&&(w[1]=w[0]),"right"===n.at[0]?m.left+=p:"center"===n.at[0]&&(m.left+=p/2),"bottom"===n.at[1]?m.top+=f:"center"===n.at[1]&&(m.top+=f/2),u=e(k.at,p,f),m.left+=u[0],m.top+=u[1],this.each(function(){var s,r,h=t(this),l=h.outerWidth(),c=h.outerHeight(),d=i(this,"marginLeft"),_=i(this,"marginTop"),x=l+d+i(this,"marginRight")+y.width,C=c+_+i(this,"marginBottom")+y.height,D=t.extend({},m),I=e(k.my,h.outerWidth(),h.outerHeight());"right"===n.my[0]?D.left-=l:"center"===n.my[0]&&(D.left-=l/2),"bottom"===n.my[1]?D.top-=c:"center"===n.my[1]&&(D.top-=c/2),D.left+=I[0],D.top+=I[1],s={marginLeft:d,marginTop:_},t.each(["left","top"],function(e,i){t.ui.position[w[e]]&&t.ui.position[w[e]][i](D,{targetWidth:p,targetHeight:f,elemWidth:l,elemHeight:c,collisionPosition:s,collisionWidth:x,collisionHeight:C,offset:[u[0]+I[0],u[1]+I[1]],my:n.my,at:n.at,within:b,elem:h})}),n.using&&(r=function(t){var e=g.left-D.left,i=e+p-l,s=g.top-D.top,r=s+f-c,u={target:{element:v,left:g.left,top:g.top,width:p,height:f},element:{element:h,left:D.left,top:D.top,width:l,height:c},horizontal:0>i?"left":e>0?"right":"center",vertical:0>r?"top":s>0?"bottom":"middle"};l>p&&p>a(e+i)&&(u.horizontal="center"),c>f&&f>a(s+r)&&(u.vertical="middle"),u.important=o(a(e),a(i))>o(a(s),a(r))?"horizontal":"vertical",n.using.call(this,t,u)}),h.offset(t.extend(D,{using:r}))})},t.ui.position={fit:{left:function(t,e){var i,s=e.within,n=s.isWindow?s.scrollLeft:s.offset.left,a=s.width,r=t.left-e.collisionPosition.marginLeft,h=n-r,l=r+e.collisionWidth-a-n;e.collisionWidth>a?h>0&&0>=l?(i=t.left+h+e.collisionWidth-a-n,t.left+=h-i):t.left=l>0&&0>=h?n:h>l?n+a-e.collisionWidth:n:h>0?t.left+=h:l>0?t.left-=l:t.left=o(t.left-r,t.left)},top:function(t,e){var i,s=e.within,n=s.isWindow?s.scrollTop:s.offset.top,a=e.within.height,r=t.top-e.collisionPosition.marginTop,h=n-r,l=r+e.collisionHeight-a-n;e.collisionHeight>a?h>0&&0>=l?(i=t.top+h+e.collisionHeight-a-n,t.top+=h-i):t.top=l>0&&0>=h?n:h>l?n+a-e.collisionHeight:n:h>0?t.top+=h:l>0?t.top-=l:t.top=o(t.top-r,t.top)}},flip:{left:function(t,e){var i,s,n=e.within,o=n.offset.left+n.scrollLeft,r=n.width,h=n.isWindow?n.scrollLeft:n.offset.left,l=t.left-e.collisionPosition.marginLeft,c=l-h,u=l+e.collisionWidth-r-h,d="left"===e.my[0]?-e.elemWidth:"right"===e.my[0]?e.elemWidth:0,p="left"===e.at[0]?e.targetWidth:"right"===e.at[0]?-e.targetWidth:0,f=-2*e.offset[0];0>c?(i=t.left+d+p+f+e.collisionWidth-r-o,(0>i||a(c)>i)&&(t.left+=d+p+f)):u>0&&(s=t.left-e.collisionPosition.marginLeft+d+p+f-h,(s>0||u>a(s))&&(t.left+=d+p+f))},top:function(t,e){var i,s,n=e.within,o=n.offset.top+n.scrollTop,r=n.height,h=n.isWindow?n.scrollTop:n.offset.top,l=t.top-e.collisionPosition.marginTop,c=l-h,u=l+e.collisionHeight-r-h,d="top"===e.my[1],p=d?-e.elemHeight:"bottom"===e.my[1]?e.elemHeight:0,f="top"===e.at[1]?e.targetHeight:"bottom"===e.at[1]?-e.targetHeight:0,g=-2*e.offset[1];0>c?(s=t.top+p+f+g+e.collisionHeight-r-o,(0>s||a(c)>s)&&(t.top+=p+f+g)):u>0&&(i=t.top-e.collisionPosition.marginTop+p+f+g-h,(i>0||u>a(i))&&(t.top+=p+f+g))}},flipfit:{left:function(){t.ui.position.flip.left.apply(this,arguments),t.ui.position.fit.left.apply(this,arguments)},top:function(){t.ui.position.flip.top.apply(this,arguments),t.ui.position.fit.top.apply(this,arguments)}}}}(),t.ui.position,t.extend(t.expr[":"],{data:t.expr.createPseudo?t.expr.createPseudo(function(e){return function(i){return!!t.data(i,e)}}):function(e,i,s){return!!t.data(e,s[3])}}),t.fn.extend({disableSelection:function(){var t="onselectstart"in document.createElement("div")?"selectstart":"mousedown";return function(){return this.on(t+".ui-disableSelection",function(t){t.preventDefault()})}}(),enableSelection:function(){return this.off(".ui-disableSelection")}});var c="ui-effects-",u="ui-effects-style",d="ui-effects-animated",p=t;t.effects={effect:{}},function(t,e){function i(t,e,i){var s=u[e.type]||{};return null==t?i||!e.def?null:e.def:(t=s.floor?~~t:parseFloat(t),isNaN(t)?e.def:s.mod?(t+s.mod)%s.mod:0>t?0:t>s.max?s.max:t)}function s(i){var s=l(),n=s._rgba=[];return i=i.toLowerCase(),f(h,function(t,o){var a,r=o.re.exec(i),h=r&&o.parse(r),l=o.space||"rgba";return h?(a=s[l](h),s[c[l].cache]=a[c[l].cache],n=s._rgba=a._rgba,!1):e}),n.length?("0,0,0,0"===n.join()&&t.extend(n,o.transparent),s):o[i]}function n(t,e,i){return i=(i+1)%1,1>6*i?t+6*(e-t)*i:1>2*i?e:2>3*i?t+6*(e-t)*(2/3-i):t}var o,a="backgroundColor borderBottomColor borderLeftColor borderRightColor borderTopColor color columnRuleColor outlineColor textDecorationColor textEmphasisColor",r=/^([\-+])=\s*(\d+\.?\d*)/,h=[{re:/rgba?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(t){return[t[1],t[2],t[3],t[4]]}},{re:/rgba?\(\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,parse:function(t){return[2.55*t[1],2.55*t[2],2.55*t[3],t[4]]}},{re:/#([a-f0-9]{2})([a-f0-9]{2})([a-f0-9]{2})/,parse:function(t){return[parseInt(t[1],16),parseInt(t[2],16),parseInt(t[3],16)]}},{re:/#([a-f0-9])([a-f0-9])([a-f0-9])/,parse:function(t){return[parseInt(t[1]+t[1],16),parseInt(t[2]+t[2],16),parseInt(t[3]+t[3],16)]}},{re:/hsla?\(\s*(\d+(?:\.\d+)?)\s*,\s*(\d+(?:\.\d+)?)\%\s*,\s*(\d+(?:\.\d+)?)\%\s*(?:,\s*(\d?(?:\.\d+)?)\s*)?\)/,space:"hsla",parse:function(t){return[t[1],t[2]/100,t[3]/100,t[4]]}}],l=t.Color=function(e,i,s,n){return new t.Color.fn.parse(e,i,s,n)},c={rgba:{props:{red:{idx:0,type:"byte"},green:{idx:1,type:"byte"},blue:{idx:2,type:"byte"}}},hsla:{props:{hue:{idx:0,type:"degrees"},saturation:{idx:1,type:"percent"},lightness:{idx:2,type:"percent"}}}},u={"byte":{floor:!0,max:255},percent:{max:1},degrees:{mod:360,floor:!0}},d=l.support={},p=t("<p>")[0],f=t.each;p.style.cssText="background-color:rgba(1,1,1,.5)",d.rgba=p.style.backgroundColor.indexOf("rgba")>-1,f(c,function(t,e){e.cache="_"+t,e.props.alpha={idx:3,type:"percent",def:1}}),l.fn=t.extend(l.prototype,{parse:function(n,a,r,h){if(n===e)return this._rgba=[null,null,null,null],this;(n.jquery||n.nodeType)&&(n=t(n).css(a),a=e);var u=this,d=t.type(n),p=this._rgba=[];return a!==e&&(n=[n,a,r,h],d="array"),"string"===d?this.parse(s(n)||o._default):"array"===d?(f(c.rgba.props,function(t,e){p[e.idx]=i(n[e.idx],e)}),this):"object"===d?(n instanceof l?f(c,function(t,e){n[e.cache]&&(u[e.cache]=n[e.cache].slice())}):f(c,function(e,s){var o=s.cache;f(s.props,function(t,e){if(!u[o]&&s.to){if("alpha"===t||null==n[t])return;u[o]=s.to(u._rgba)}u[o][e.idx]=i(n[t],e,!0)}),u[o]&&0>t.inArray(null,u[o].slice(0,3))&&(u[o][3]=1,s.from&&(u._rgba=s.from(u[o])))}),this):e},is:function(t){var i=l(t),s=!0,n=this;return f(c,function(t,o){var a,r=i[o.cache];return r&&(a=n[o.cache]||o.to&&o.to(n._rgba)||[],f(o.props,function(t,i){return null!=r[i.idx]?s=r[i.idx]===a[i.idx]:e})),s}),s},_space:function(){var t=[],e=this;return f(c,function(i,s){e[s.cache]&&t.push(i)}),t.pop()},transition:function(t,e){var s=l(t),n=s._space(),o=c[n],a=0===this.alpha()?l("transparent"):this,r=a[o.cache]||o.to(a._rgba),h=r.slice();return s=s[o.cache],f(o.props,function(t,n){var o=n.idx,a=r[o],l=s[o],c=u[n.type]||{};null!==l&&(null===a?h[o]=l:(c.mod&&(l-a>c.mod/2?a+=c.mod:a-l>c.mod/2&&(a-=c.mod)),h[o]=i((l-a)*e+a,n)))}),this[n](h)},blend:function(e){if(1===this._rgba[3])return this;var i=this._rgba.slice(),s=i.pop(),n=l(e)._rgba;return l(t.map(i,function(t,e){return(1-s)*n[e]+s*t}))},toRgbaString:function(){var e="rgba(",i=t.map(this._rgba,function(t,e){return null==t?e>2?1:0:t});return 1===i[3]&&(i.pop(),e="rgb("),e+i.join()+")"},toHslaString:function(){var e="hsla(",i=t.map(this.hsla(),function(t,e){return null==t&&(t=e>2?1:0),e&&3>e&&(t=Math.round(100*t)+"%"),t});return 1===i[3]&&(i.pop(),e="hsl("),e+i.join()+")"},toHexString:function(e){var i=this._rgba.slice(),s=i.pop();return e&&i.push(~~(255*s)),"#"+t.map(i,function(t){return t=(t||0).toString(16),1===t.length?"0"+t:t}).join("")},toString:function(){return 0===this._rgba[3]?"transparent":this.toRgbaString()}}),l.fn.parse.prototype=l.fn,c.hsla.to=function(t){if(null==t[0]||null==t[1]||null==t[2])return[null,null,null,t[3]];var e,i,s=t[0]/255,n=t[1]/255,o=t[2]/255,a=t[3],r=Math.max(s,n,o),h=Math.min(s,n,o),l=r-h,c=r+h,u=.5*c;return e=h===r?0:s===r?60*(n-o)/l+360:n===r?60*(o-s)/l+120:60*(s-n)/l+240,i=0===l?0:.5>=u?l/c:l/(2-c),[Math.round(e)%360,i,u,null==a?1:a]},c.hsla.from=function(t){if(null==t[0]||null==t[1]||null==t[2])return[null,null,null,t[3]];var e=t[0]/360,i=t[1],s=t[2],o=t[3],a=.5>=s?s*(1+i):s+i-s*i,r=2*s-a;return[Math.round(255*n(r,a,e+1/3)),Math.round(255*n(r,a,e)),Math.round(255*n(r,a,e-1/3)),o]},f(c,function(s,n){var o=n.props,a=n.cache,h=n.to,c=n.from;l.fn[s]=function(s){if(h&&!this[a]&&(this[a]=h(this._rgba)),s===e)return this[a].slice();var n,r=t.type(s),u="array"===r||"object"===r?s:arguments,d=this[a].slice();return f(o,function(t,e){var s=u["object"===r?t:e.idx];null==s&&(s=d[e.idx]),d[e.idx]=i(s,e)}),c?(n=l(c(d)),n[a]=d,n):l(d)},f(o,function(e,i){l.fn[e]||(l.fn[e]=function(n){var o,a=t.type(n),h="alpha"===e?this._hsla?"hsla":"rgba":s,l=this[h](),c=l[i.idx];return"undefined"===a?c:("function"===a&&(n=n.call(this,c),a=t.type(n)),null==n&&i.empty?this:("string"===a&&(o=r.exec(n),o&&(n=c+parseFloat(o[2])*("+"===o[1]?1:-1))),l[i.idx]=n,this[h](l)))})})}),l.hook=function(e){var i=e.split(" ");f(i,function(e,i){t.cssHooks[i]={set:function(e,n){var o,a,r="";if("transparent"!==n&&("string"!==t.type(n)||(o=s(n)))){if(n=l(o||n),!d.rgba&&1!==n._rgba[3]){for(a="backgroundColor"===i?e.parentNode:e;(""===r||"transparent"===r)&&a&&a.style;)try{r=t.css(a,"backgroundColor"),a=a.parentNode}catch(h){}n=n.blend(r&&"transparent"!==r?r:"_default")}n=n.toRgbaString()}try{e.style[i]=n}catch(h){}}},t.fx.step[i]=function(e){e.colorInit||(e.start=l(e.elem,i),e.end=l(e.end),e.colorInit=!0),t.cssHooks[i].set(e.elem,e.start.transition(e.end,e.pos))}})},l.hook(a),t.cssHooks.borderColor={expand:function(t){var e={};return f(["Top","Right","Bottom","Left"],function(i,s){e["border"+s+"Color"]=t}),e}},o=t.Color.names={aqua:"#00ffff",black:"#000000",blue:"#0000ff",fuchsia:"#ff00ff",gray:"#808080",green:"#008000",lime:"#00ff00",maroon:"#800000",navy:"#000080",olive:"#808000",purple:"#800080",red:"#ff0000",silver:"#c0c0c0",teal:"#008080",white:"#ffffff",yellow:"#ffff00",transparent:[null,null,null,0],_default:"#ffffff"}}(p),function(){function e(e){var i,s,n=e.ownerDocument.defaultView?e.ownerDocument.defaultView.getComputedStyle(e,null):e.currentStyle,o={};if(n&&n.length&&n[0]&&n[n[0]])for(s=n.length;s--;)i=n[s],"string"==typeof n[i]&&(o[t.camelCase(i)]=n[i]);else for(i in n)"string"==typeof n[i]&&(o[i]=n[i]);return o}function i(e,i){var s,o,a={};for(s in i)o=i[s],e[s]!==o&&(n[s]||(t.fx.step[s]||!isNaN(parseFloat(o)))&&(a[s]=o));return a}var s=["add","remove","toggle"],n={border:1,borderBottom:1,borderColor:1,borderLeft:1,borderRight:1,borderTop:1,borderWidth:1,margin:1,padding:1};t.each(["borderLeftStyle","borderRightStyle","borderBottomStyle","borderTopStyle"],function(e,i){t.fx.step[i]=function(t){("none"!==t.end&&!t.setAttr||1===t.pos&&!t.setAttr)&&(p.style(t.elem,i,t.end),t.setAttr=!0)}}),t.fn.addBack||(t.fn.addBack=function(t){return this.add(null==t?this.prevObject:this.prevObject.filter(t))}),t.effects.animateClass=function(n,o,a,r){var h=t.speed(o,a,r);return this.queue(function(){var o,a=t(this),r=a.attr("class")||"",l=h.children?a.find("*").addBack():a;l=l.map(function(){var i=t(this);return{el:i,start:e(this)}}),o=function(){t.each(s,function(t,e){n[e]&&a[e+"Class"](n[e])})},o(),l=l.map(function(){return this.end=e(this.el[0]),this.diff=i(this.start,this.end),this}),a.attr("class",r),l=l.map(function(){var e=this,i=t.Deferred(),s=t.extend({},h,{queue:!1,complete:function(){i.resolve(e)}});return this.el.animate(this.diff,s),i.promise()}),t.when.apply(t,l.get()).done(function(){o(),t.each(arguments,function(){var e=this.el;t.each(this.diff,function(t){e.css(t,"")})}),h.complete.call(a[0])})})},t.fn.extend({addClass:function(e){return function(i,s,n,o){return s?t.effects.animateClass.call(this,{add:i},s,n,o):e.apply(this,arguments)}}(t.fn.addClass),removeClass:function(e){return function(i,s,n,o){return arguments.length>1?t.effects.animateClass.call(this,{remove:i},s,n,o):e.apply(this,arguments)}}(t.fn.removeClass),toggleClass:function(e){return function(i,s,n,o,a){return"boolean"==typeof s||void 0===s?n?t.effects.animateClass.call(this,s?{add:i}:{remove:i},n,o,a):e.apply(this,arguments):t.effects.animateClass.call(this,{toggle:i},s,n,o)}}(t.fn.toggleClass),switchClass:function(e,i,s,n,o){return t.effects.animateClass.call(this,{add:i,remove:e},s,n,o)}})}(),function(){function e(e,i,s,n){return t.isPlainObject(e)&&(i=e,e=e.effect),e={effect:e},null==i&&(i={}),t.isFunction(i)&&(n=i,s=null,i={}),("number"==typeof i||t.fx.speeds[i])&&(n=s,s=i,i={}),t.isFunction(s)&&(n=s,s=null),i&&t.extend(e,i),s=s||i.duration,e.duration=t.fx.off?0:"number"==typeof s?s:s in t.fx.speeds?t.fx.speeds[s]:t.fx.speeds._default,e.complete=n||i.complete,e}function i(e){return!e||"number"==typeof e||t.fx.speeds[e]?!0:"string"!=typeof e||t.effects.effect[e]?t.isFunction(e)?!0:"object"!=typeof e||e.effect?!1:!0:!0}function s(t,e){var i=e.outerWidth(),s=e.outerHeight(),n=/^rect\((-?\d*\.?\d*px|-?\d+%|auto),?\s*(-?\d*\.?\d*px|-?\d+%|auto),?\s*(-?\d*\.?\d*px|-?\d+%|auto),?\s*(-?\d*\.?\d*px|-?\d+%|auto)\)$/,o=n.exec(t)||["",0,i,s,0];return{top:parseFloat(o[1])||0,right:"auto"===o[2]?i:parseFloat(o[2]),bottom:"auto"===o[3]?s:parseFloat(o[3]),left:parseFloat(o[4])||0}}t.expr&&t.expr.filters&&t.expr.filters.animated&&(t.expr.filters.animated=function(e){return function(i){return!!t(i).data(d)||e(i)}}(t.expr.filters.animated)),t.uiBackCompat!==!1&&t.extend(t.effects,{save:function(t,e){for(var i=0,s=e.length;s>i;i++)null!==e[i]&&t.data(c+e[i],t[0].style[e[i]])},restore:function(t,e){for(var i,s=0,n=e.length;n>s;s++)null!==e[s]&&(i=t.data(c+e[s]),t.css(e[s],i))},setMode:function(t,e){return"toggle"===e&&(e=t.is(":hidden")?"show":"hide"),e},createWrapper:function(e){if(e.parent().is(".ui-effects-wrapper"))return e.parent();var i={width:e.outerWidth(!0),height:e.outerHeight(!0),"float":e.css("float")},s=t("<div></div>").addClass("ui-effects-wrapper").css({fontSize:"100%",background:"transparent",border:"none",margin:0,padding:0}),n={width:e.width(),height:e.height()},o=document.activeElement;try{o.id}catch(a){o=document.body}return e.wrap(s),(e[0]===o||t.contains(e[0],o))&&t(o).trigger("focus"),s=e.parent(),"static"===e.css("position")?(s.css({position:"relative"}),e.css({position:"relative"})):(t.extend(i,{position:e.css("position"),zIndex:e.css("z-index")}),t.each(["top","left","bottom","right"],function(t,s){i[s]=e.css(s),isNaN(parseInt(i[s],10))&&(i[s]="auto")}),e.css({position:"relative",top:0,left:0,right:"auto",bottom:"auto"})),e.css(n),s.css(i).show()},removeWrapper:function(e){var i=document.activeElement;return e.parent().is(".ui-effects-wrapper")&&(e.parent().replaceWith(e),(e[0]===i||t.contains(e[0],i))&&t(i).trigger("focus")),e}}),t.extend(t.effects,{version:"1.12.1",define:function(e,i,s){return s||(s=i,i="effect"),t.effects.effect[e]=s,t.effects.effect[e].mode=i,s},scaledDimensions:function(t,e,i){if(0===e)return{height:0,width:0,outerHeight:0,outerWidth:0};var s="horizontal"!==i?(e||100)/100:1,n="vertical"!==i?(e||100)/100:1;return{height:t.height()*n,width:t.width()*s,outerHeight:t.outerHeight()*n,outerWidth:t.outerWidth()*s}},clipToBox:function(t){return{width:t.clip.right-t.clip.left,height:t.clip.bottom-t.clip.top,left:t.clip.left,top:t.clip.top}},unshift:function(t,e,i){var s=t.queue();e>1&&s.splice.apply(s,[1,0].concat(s.splice(e,i))),t.dequeue()},saveStyle:function(t){t.data(u,t[0].style.cssText)},restoreStyle:function(t){t[0].style.cssText=t.data(u)||"",t.removeData(u)},mode:function(t,e){var i=t.is(":hidden");return"toggle"===e&&(e=i?"show":"hide"),(i?"hide"===e:"show"===e)&&(e="none"),e},getBaseline:function(t,e){var i,s;switch(t[0]){case"top":i=0;break;case"middle":i=.5;break;case"bottom":i=1;break;default:i=t[0]/e.height}switch(t[1]){case"left":s=0;break;case"center":s=.5;break;case"right":s=1;break;default:s=t[1]/e.width}return{x:s,y:i}},createPlaceholder:function(e){var i,s=e.css("position"),n=e.position();return e.css({marginTop:e.css("marginTop"),marginBottom:e.css("marginBottom"),marginLeft:e.css("marginLeft"),marginRight:e.css("marginRight")}).outerWidth(e.outerWidth()).outerHeight(e.outerHeight()),/^(static|relative)/.test(s)&&(s="absolute",i=t("<"+e[0].nodeName+">").insertAfter(e).css({display:/^(inline|ruby)/.test(e.css("display"))?"inline-block":"block",visibility:"hidden",marginTop:e.css("marginTop"),marginBottom:e.css("marginBottom"),marginLeft:e.css("marginLeft"),marginRight:e.css("marginRight"),"float":e.css("float")}).outerWidth(e.outerWidth()).outerHeight(e.outerHeight()).addClass("ui-effects-placeholder"),e.data(c+"placeholder",i)),e.css({position:s,left:n.left,top:n.top}),i},removePlaceholder:function(t){var e=c+"placeholder",i=t.data(e);i&&(i.remove(),t.removeData(e))},cleanUp:function(e){t.effects.restoreStyle(e),t.effects.removePlaceholder(e)},setTransition:function(e,i,s,n){return n=n||{},t.each(i,function(t,i){var o=e.cssUnit(i);o[0]>0&&(n[i]=o[0]*s+o[1])}),n}}),t.fn.extend({effect:function(){function i(e){function i(){r.removeData(d),t.effects.cleanUp(r),"hide"===s.mode&&r.hide(),a()}function a(){t.isFunction(h)&&h.call(r[0]),t.isFunction(e)&&e()}var r=t(this);s.mode=c.shift(),t.uiBackCompat===!1||o?"none"===s.mode?(r[l](),a()):n.call(r[0],s,i):(r.is(":hidden")?"hide"===l:"show"===l)?(r[l](),a()):n.call(r[0],s,a)}var s=e.apply(this,arguments),n=t.effects.effect[s.effect],o=n.mode,a=s.queue,r=a||"fx",h=s.complete,l=s.mode,c=[],u=function(e){var i=t(this),s=t.effects.mode(i,l)||o;i.data(d,!0),c.push(s),o&&("show"===s||s===o&&"hide"===s)&&i.show(),o&&"none"===s||t.effects.saveStyle(i),t.isFunction(e)&&e()};return t.fx.off||!n?l?this[l](s.duration,h):this.each(function(){h&&h.call(this)}):a===!1?this.each(u).each(i):this.queue(r,u).queue(r,i)},show:function(t){return function(s){if(i(s))return t.apply(this,arguments);var n=e.apply(this,arguments);return n.mode="show",this.effect.call(this,n)
-}}(t.fn.show),hide:function(t){return function(s){if(i(s))return t.apply(this,arguments);var n=e.apply(this,arguments);return n.mode="hide",this.effect.call(this,n)}}(t.fn.hide),toggle:function(t){return function(s){if(i(s)||"boolean"==typeof s)return t.apply(this,arguments);var n=e.apply(this,arguments);return n.mode="toggle",this.effect.call(this,n)}}(t.fn.toggle),cssUnit:function(e){var i=this.css(e),s=[];return t.each(["em","px","%","pt"],function(t,e){i.indexOf(e)>0&&(s=[parseFloat(i),e])}),s},cssClip:function(t){return t?this.css("clip","rect("+t.top+"px "+t.right+"px "+t.bottom+"px "+t.left+"px)"):s(this.css("clip"),this)},transfer:function(e,i){var s=t(this),n=t(e.to),o="fixed"===n.css("position"),a=t("body"),r=o?a.scrollTop():0,h=o?a.scrollLeft():0,l=n.offset(),c={top:l.top-r,left:l.left-h,height:n.innerHeight(),width:n.innerWidth()},u=s.offset(),d=t("<div class='ui-effects-transfer'></div>").appendTo("body").addClass(e.className).css({top:u.top-r,left:u.left-h,height:s.innerHeight(),width:s.innerWidth(),position:o?"fixed":"absolute"}).animate(c,e.duration,e.easing,function(){d.remove(),t.isFunction(i)&&i()})}}),t.fx.step.clip=function(e){e.clipInit||(e.start=t(e.elem).cssClip(),"string"==typeof e.end&&(e.end=s(e.end,e.elem)),e.clipInit=!0),t(e.elem).cssClip({top:e.pos*(e.end.top-e.start.top)+e.start.top,right:e.pos*(e.end.right-e.start.right)+e.start.right,bottom:e.pos*(e.end.bottom-e.start.bottom)+e.start.bottom,left:e.pos*(e.end.left-e.start.left)+e.start.left})}}(),function(){var e={};t.each(["Quad","Cubic","Quart","Quint","Expo"],function(t,i){e[i]=function(e){return Math.pow(e,t+2)}}),t.extend(e,{Sine:function(t){return 1-Math.cos(t*Math.PI/2)},Circ:function(t){return 1-Math.sqrt(1-t*t)},Elastic:function(t){return 0===t||1===t?t:-Math.pow(2,8*(t-1))*Math.sin((80*(t-1)-7.5)*Math.PI/15)},Back:function(t){return t*t*(3*t-2)},Bounce:function(t){for(var e,i=4;((e=Math.pow(2,--i))-1)/11>t;);return 1/Math.pow(4,3-i)-7.5625*Math.pow((3*e-2)/22-t,2)}}),t.each(e,function(e,i){t.easing["easeIn"+e]=i,t.easing["easeOut"+e]=function(t){return 1-i(1-t)},t.easing["easeInOut"+e]=function(t){return.5>t?i(2*t)/2:1-i(-2*t+2)/2}})}();var f=t.effects;t.effects.define("blind","hide",function(e,i){var s={up:["bottom","top"],vertical:["bottom","top"],down:["top","bottom"],left:["right","left"],horizontal:["right","left"],right:["left","right"]},n=t(this),o=e.direction||"up",a=n.cssClip(),r={clip:t.extend({},a)},h=t.effects.createPlaceholder(n);r.clip[s[o][0]]=r.clip[s[o][1]],"show"===e.mode&&(n.cssClip(r.clip),h&&h.css(t.effects.clipToBox(r)),r.clip=a),h&&h.animate(t.effects.clipToBox(r),e.duration,e.easing),n.animate(r,{queue:!1,duration:e.duration,easing:e.easing,complete:i})}),t.effects.define("bounce",function(e,i){var s,n,o,a=t(this),r=e.mode,h="hide"===r,l="show"===r,c=e.direction||"up",u=e.distance,d=e.times||5,p=2*d+(l||h?1:0),f=e.duration/p,g=e.easing,m="up"===c||"down"===c?"top":"left",_="up"===c||"left"===c,v=0,b=a.queue().length;for(t.effects.createPlaceholder(a),o=a.css(m),u||(u=a["top"===m?"outerHeight":"outerWidth"]()/3),l&&(n={opacity:1},n[m]=o,a.css("opacity",0).css(m,_?2*-u:2*u).animate(n,f,g)),h&&(u/=Math.pow(2,d-1)),n={},n[m]=o;d>v;v++)s={},s[m]=(_?"-=":"+=")+u,a.animate(s,f,g).animate(n,f,g),u=h?2*u:u/2;h&&(s={opacity:0},s[m]=(_?"-=":"+=")+u,a.animate(s,f,g)),a.queue(i),t.effects.unshift(a,b,p+1)}),t.effects.define("clip","hide",function(e,i){var s,n={},o=t(this),a=e.direction||"vertical",r="both"===a,h=r||"horizontal"===a,l=r||"vertical"===a;s=o.cssClip(),n.clip={top:l?(s.bottom-s.top)/2:s.top,right:h?(s.right-s.left)/2:s.right,bottom:l?(s.bottom-s.top)/2:s.bottom,left:h?(s.right-s.left)/2:s.left},t.effects.createPlaceholder(o),"show"===e.mode&&(o.cssClip(n.clip),n.clip=s),o.animate(n,{queue:!1,duration:e.duration,easing:e.easing,complete:i})}),t.effects.define("drop","hide",function(e,i){var s,n=t(this),o=e.mode,a="show"===o,r=e.direction||"left",h="up"===r||"down"===r?"top":"left",l="up"===r||"left"===r?"-=":"+=",c="+="===l?"-=":"+=",u={opacity:0};t.effects.createPlaceholder(n),s=e.distance||n["top"===h?"outerHeight":"outerWidth"](!0)/2,u[h]=l+s,a&&(n.css(u),u[h]=c+s,u.opacity=1),n.animate(u,{queue:!1,duration:e.duration,easing:e.easing,complete:i})}),t.effects.define("explode","hide",function(e,i){function s(){b.push(this),b.length===u*d&&n()}function n(){p.css({visibility:"visible"}),t(b).remove(),i()}var o,a,r,h,l,c,u=e.pieces?Math.round(Math.sqrt(e.pieces)):3,d=u,p=t(this),f=e.mode,g="show"===f,m=p.show().css("visibility","hidden").offset(),_=Math.ceil(p.outerWidth()/d),v=Math.ceil(p.outerHeight()/u),b=[];for(o=0;u>o;o++)for(h=m.top+o*v,c=o-(u-1)/2,a=0;d>a;a++)r=m.left+a*_,l=a-(d-1)/2,p.clone().appendTo("body").wrap("<div></div>").css({position:"absolute",visibility:"visible",left:-a*_,top:-o*v}).parent().addClass("ui-effects-explode").css({position:"absolute",overflow:"hidden",width:_,height:v,left:r+(g?l*_:0),top:h+(g?c*v:0),opacity:g?0:1}).animate({left:r+(g?0:l*_),top:h+(g?0:c*v),opacity:g?1:0},e.duration||500,e.easing,s)}),t.effects.define("fade","toggle",function(e,i){var s="show"===e.mode;t(this).css("opacity",s?0:1).animate({opacity:s?1:0},{queue:!1,duration:e.duration,easing:e.easing,complete:i})}),t.effects.define("fold","hide",function(e,i){var s=t(this),n=e.mode,o="show"===n,a="hide"===n,r=e.size||15,h=/([0-9]+)%/.exec(r),l=!!e.horizFirst,c=l?["right","bottom"]:["bottom","right"],u=e.duration/2,d=t.effects.createPlaceholder(s),p=s.cssClip(),f={clip:t.extend({},p)},g={clip:t.extend({},p)},m=[p[c[0]],p[c[1]]],_=s.queue().length;h&&(r=parseInt(h[1],10)/100*m[a?0:1]),f.clip[c[0]]=r,g.clip[c[0]]=r,g.clip[c[1]]=0,o&&(s.cssClip(g.clip),d&&d.css(t.effects.clipToBox(g)),g.clip=p),s.queue(function(i){d&&d.animate(t.effects.clipToBox(f),u,e.easing).animate(t.effects.clipToBox(g),u,e.easing),i()}).animate(f,u,e.easing).animate(g,u,e.easing).queue(i),t.effects.unshift(s,_,4)}),t.effects.define("highlight","show",function(e,i){var s=t(this),n={backgroundColor:s.css("backgroundColor")};"hide"===e.mode&&(n.opacity=0),t.effects.saveStyle(s),s.css({backgroundImage:"none",backgroundColor:e.color||"#ffff99"}).animate(n,{queue:!1,duration:e.duration,easing:e.easing,complete:i})}),t.effects.define("size",function(e,i){var s,n,o,a=t(this),r=["fontSize"],h=["borderTopWidth","borderBottomWidth","paddingTop","paddingBottom"],l=["borderLeftWidth","borderRightWidth","paddingLeft","paddingRight"],c=e.mode,u="effect"!==c,d=e.scale||"both",p=e.origin||["middle","center"],f=a.css("position"),g=a.position(),m=t.effects.scaledDimensions(a),_=e.from||m,v=e.to||t.effects.scaledDimensions(a,0);t.effects.createPlaceholder(a),"show"===c&&(o=_,_=v,v=o),n={from:{y:_.height/m.height,x:_.width/m.width},to:{y:v.height/m.height,x:v.width/m.width}},("box"===d||"both"===d)&&(n.from.y!==n.to.y&&(_=t.effects.setTransition(a,h,n.from.y,_),v=t.effects.setTransition(a,h,n.to.y,v)),n.from.x!==n.to.x&&(_=t.effects.setTransition(a,l,n.from.x,_),v=t.effects.setTransition(a,l,n.to.x,v))),("content"===d||"both"===d)&&n.from.y!==n.to.y&&(_=t.effects.setTransition(a,r,n.from.y,_),v=t.effects.setTransition(a,r,n.to.y,v)),p&&(s=t.effects.getBaseline(p,m),_.top=(m.outerHeight-_.outerHeight)*s.y+g.top,_.left=(m.outerWidth-_.outerWidth)*s.x+g.left,v.top=(m.outerHeight-v.outerHeight)*s.y+g.top,v.left=(m.outerWidth-v.outerWidth)*s.x+g.left),a.css(_),("content"===d||"both"===d)&&(h=h.concat(["marginTop","marginBottom"]).concat(r),l=l.concat(["marginLeft","marginRight"]),a.find("*[width]").each(function(){var i=t(this),s=t.effects.scaledDimensions(i),o={height:s.height*n.from.y,width:s.width*n.from.x,outerHeight:s.outerHeight*n.from.y,outerWidth:s.outerWidth*n.from.x},a={height:s.height*n.to.y,width:s.width*n.to.x,outerHeight:s.height*n.to.y,outerWidth:s.width*n.to.x};n.from.y!==n.to.y&&(o=t.effects.setTransition(i,h,n.from.y,o),a=t.effects.setTransition(i,h,n.to.y,a)),n.from.x!==n.to.x&&(o=t.effects.setTransition(i,l,n.from.x,o),a=t.effects.setTransition(i,l,n.to.x,a)),u&&t.effects.saveStyle(i),i.css(o),i.animate(a,e.duration,e.easing,function(){u&&t.effects.restoreStyle(i)})})),a.animate(v,{queue:!1,duration:e.duration,easing:e.easing,complete:function(){var e=a.offset();0===v.opacity&&a.css("opacity",_.opacity),u||(a.css("position","static"===f?"relative":f).offset(e),t.effects.saveStyle(a)),i()}})}),t.effects.define("scale",function(e,i){var s=t(this),n=e.mode,o=parseInt(e.percent,10)||(0===parseInt(e.percent,10)?0:"effect"!==n?0:100),a=t.extend(!0,{from:t.effects.scaledDimensions(s),to:t.effects.scaledDimensions(s,o,e.direction||"both"),origin:e.origin||["middle","center"]},e);e.fade&&(a.from.opacity=1,a.to.opacity=0),t.effects.effect.size.call(this,a,i)}),t.effects.define("puff","hide",function(e,i){var s=t.extend(!0,{},e,{fade:!0,percent:parseInt(e.percent,10)||150});t.effects.effect.scale.call(this,s,i)}),t.effects.define("pulsate","show",function(e,i){var s=t(this),n=e.mode,o="show"===n,a="hide"===n,r=o||a,h=2*(e.times||5)+(r?1:0),l=e.duration/h,c=0,u=1,d=s.queue().length;for((o||!s.is(":visible"))&&(s.css("opacity",0).show(),c=1);h>u;u++)s.animate({opacity:c},l,e.easing),c=1-c;s.animate({opacity:c},l,e.easing),s.queue(i),t.effects.unshift(s,d,h+1)}),t.effects.define("shake",function(e,i){var s=1,n=t(this),o=e.direction||"left",a=e.distance||20,r=e.times||3,h=2*r+1,l=Math.round(e.duration/h),c="up"===o||"down"===o?"top":"left",u="up"===o||"left"===o,d={},p={},f={},g=n.queue().length;for(t.effects.createPlaceholder(n),d[c]=(u?"-=":"+=")+a,p[c]=(u?"+=":"-=")+2*a,f[c]=(u?"-=":"+=")+2*a,n.animate(d,l,e.easing);r>s;s++)n.animate(p,l,e.easing).animate(f,l,e.easing);n.animate(p,l,e.easing).animate(d,l/2,e.easing).queue(i),t.effects.unshift(n,g,h+1)}),t.effects.define("slide","show",function(e,i){var s,n,o=t(this),a={up:["bottom","top"],down:["top","bottom"],left:["right","left"],right:["left","right"]},r=e.mode,h=e.direction||"left",l="up"===h||"down"===h?"top":"left",c="up"===h||"left"===h,u=e.distance||o["top"===l?"outerHeight":"outerWidth"](!0),d={};t.effects.createPlaceholder(o),s=o.cssClip(),n=o.position()[l],d[l]=(c?-1:1)*u+n,d.clip=o.cssClip(),d.clip[a[h][1]]=d.clip[a[h][0]],"show"===r&&(o.cssClip(d.clip),o.css(l,d[l]),d.clip=s,d[l]=n),o.animate(d,{queue:!1,duration:e.duration,easing:e.easing,complete:i})});var f;t.uiBackCompat!==!1&&(f=t.effects.define("transfer",function(e,i){t(this).transfer(e,i)})),t.ui.focusable=function(i,s){var n,o,a,r,h,l=i.nodeName.toLowerCase();return"area"===l?(n=i.parentNode,o=n.name,i.href&&o&&"map"===n.nodeName.toLowerCase()?(a=t("img[usemap='#"+o+"']"),a.length>0&&a.is(":visible")):!1):(/^(input|select|textarea|button|object)$/.test(l)?(r=!i.disabled,r&&(h=t(i).closest("fieldset")[0],h&&(r=!h.disabled))):r="a"===l?i.href||s:s,r&&t(i).is(":visible")&&e(t(i)))},t.extend(t.expr[":"],{focusable:function(e){return t.ui.focusable(e,null!=t.attr(e,"tabindex"))}}),t.ui.focusable,t.fn.form=function(){return"string"==typeof this[0].form?this.closest("form"):t(this[0].form)},t.ui.formResetMixin={_formResetHandler:function(){var e=t(this);setTimeout(function(){var i=e.data("ui-form-reset-instances");t.each(i,function(){this.refresh()})})},_bindFormResetHandler:function(){if(this.form=this.element.form(),this.form.length){var t=this.form.data("ui-form-reset-instances")||[];t.length||this.form.on("reset.ui-form-reset",this._formResetHandler),t.push(this),this.form.data("ui-form-reset-instances",t)}},_unbindFormResetHandler:function(){if(this.form.length){var e=this.form.data("ui-form-reset-instances");e.splice(t.inArray(this,e),1),e.length?this.form.data("ui-form-reset-instances",e):this.form.removeData("ui-form-reset-instances").off("reset.ui-form-reset")}}},"1.7"===t.fn.jquery.substring(0,3)&&(t.each(["Width","Height"],function(e,i){function s(e,i,s,o){return t.each(n,function(){i-=parseFloat(t.css(e,"padding"+this))||0,s&&(i-=parseFloat(t.css(e,"border"+this+"Width"))||0),o&&(i-=parseFloat(t.css(e,"margin"+this))||0)}),i}var n="Width"===i?["Left","Right"]:["Top","Bottom"],o=i.toLowerCase(),a={innerWidth:t.fn.innerWidth,innerHeight:t.fn.innerHeight,outerWidth:t.fn.outerWidth,outerHeight:t.fn.outerHeight};t.fn["inner"+i]=function(e){return void 0===e?a["inner"+i].call(this):this.each(function(){t(this).css(o,s(this,e)+"px")})},t.fn["outer"+i]=function(e,n){return"number"!=typeof e?a["outer"+i].call(this,e):this.each(function(){t(this).css(o,s(this,e,!0,n)+"px")})}}),t.fn.addBack=function(t){return this.add(null==t?this.prevObject:this.prevObject.filter(t))}),t.ui.keyCode={BACKSPACE:8,COMMA:188,DELETE:46,DOWN:40,END:35,ENTER:13,ESCAPE:27,HOME:36,LEFT:37,PAGE_DOWN:34,PAGE_UP:33,PERIOD:190,RIGHT:39,SPACE:32,TAB:9,UP:38},t.ui.escapeSelector=function(){var t=/([!"#$%&'()*+,.\/:;<=>?@[\]^`{|}~])/g;return function(e){return e.replace(t,"\\$1")}}(),t.fn.labels=function(){var e,i,s,n,o;return this[0].labels&&this[0].labels.length?this.pushStack(this[0].labels):(n=this.eq(0).parents("label"),s=this.attr("id"),s&&(e=this.eq(0).parents().last(),o=e.add(e.length?e.siblings():this.siblings()),i="label[for='"+t.ui.escapeSelector(s)+"']",n=n.add(o.find(i).addBack(i))),this.pushStack(n))},t.fn.scrollParent=function(e){var i=this.css("position"),s="absolute"===i,n=e?/(auto|scroll|hidden)/:/(auto|scroll)/,o=this.parents().filter(function(){var e=t(this);return s&&"static"===e.css("position")?!1:n.test(e.css("overflow")+e.css("overflow-y")+e.css("overflow-x"))}).eq(0);return"fixed"!==i&&o.length?o:t(this[0].ownerDocument||document)},t.extend(t.expr[":"],{tabbable:function(e){var i=t.attr(e,"tabindex"),s=null!=i;return(!s||i>=0)&&t.ui.focusable(e,s)}}),t.fn.extend({uniqueId:function(){var t=0;return function(){return this.each(function(){this.id||(this.id="ui-id-"+ ++t)})}}(),removeUniqueId:function(){return this.each(function(){/^ui-id-\d+$/.test(this.id)&&t(this).removeAttr("id")})}}),t.widget("ui.accordion",{version:"1.12.1",options:{active:0,animate:{},classes:{"ui-accordion-header":"ui-corner-top","ui-accordion-header-collapsed":"ui-corner-all","ui-accordion-content":"ui-corner-bottom"},collapsible:!1,event:"click",header:"> li > :first-child, > :not(li):even",heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var e=this.options;this.prevShow=this.prevHide=t(),this._addClass("ui-accordion","ui-widget ui-helper-reset"),this.element.attr("role","tablist"),e.collapsible||e.active!==!1&&null!=e.active||(e.active=0),this._processPanels(),0>e.active&&(e.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():t()}},_createIcons:function(){var e,i,s=this.options.icons;s&&(e=t("<span>"),this._addClass(e,"ui-accordion-header-icon","ui-icon "+s.header),e.prependTo(this.headers),i=this.active.children(".ui-accordion-header-icon"),this._removeClass(i,s.header)._addClass(i,null,s.activeHeader)._addClass(this.headers,"ui-accordion-icons"))},_destroyIcons:function(){this._removeClass(this.headers,"ui-accordion-icons"),this.headers.children(".ui-accordion-header-icon").remove()},_destroy:function(){var t;this.element.removeAttr("role"),this.headers.removeAttr("role aria-expanded aria-selected aria-controls tabIndex").removeUniqueId(),this._destroyIcons(),t=this.headers.next().css("display","").removeAttr("role aria-hidden aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&t.css("height","")},_setOption:function(t,e){return"active"===t?(this._activate(e),void 0):("event"===t&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(e)),this._super(t,e),"collapsible"!==t||e||this.options.active!==!1||this._activate(0),"icons"===t&&(this._destroyIcons(),e&&this._createIcons()),void 0)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t),this._toggleClass(this.headers.add(this.headers.next()),null,"ui-state-disabled",!!t)},_keydown:function(e){if(!e.altKey&&!e.ctrlKey){var i=t.ui.keyCode,s=this.headers.length,n=this.headers.index(e.target),o=!1;switch(e.keyCode){case i.RIGHT:case i.DOWN:o=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:o=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(e);break;case i.HOME:o=this.headers[0];break;case i.END:o=this.headers[s-1]}o&&(t(e.target).attr("tabIndex",-1),t(o).attr("tabIndex",0),t(o).trigger("focus"),e.preventDefault())}},_panelKeyDown:function(e){e.keyCode===t.ui.keyCode.UP&&e.ctrlKey&&t(e.currentTarget).prev().trigger("focus")},refresh:function(){var e=this.options;this._processPanels(),e.active===!1&&e.collapsible===!0||!this.headers.length?(e.active=!1,this.active=t()):e.active===!1?this._activate(0):this.active.length&&!t.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(e.active=!1,this.active=t()):this._activate(Math.max(0,e.active-1)):e.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var t=this.headers,e=this.panels;this.headers=this.element.find(this.options.header),this._addClass(this.headers,"ui-accordion-header ui-accordion-header-collapsed","ui-state-default"),this.panels=this.headers.next().filter(":not(.ui-accordion-content-active)").hide(),this._addClass(this.panels,"ui-accordion-content","ui-helper-reset ui-widget-content"),e&&(this._off(t.not(this.headers)),this._off(e.not(this.panels)))},_refresh:function(){var e,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active),this._addClass(this.active,"ui-accordion-header-active","ui-state-active")._removeClass(this.active,"ui-accordion-header-collapsed"),this._addClass(this.active.next(),"ui-accordion-content-active"),this.active.next().show(),this.headers.attr("role","tab").each(function(){var e=t(this),i=e.uniqueId().attr("id"),s=e.next(),n=s.uniqueId().attr("id");e.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(e=n.height(),this.element.siblings(":visible").each(function(){var i=t(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(e-=i.outerHeight(!0))}),this.headers.each(function(){e-=t(this).outerHeight(!0)}),this.headers.next().each(function(){t(this).height(Math.max(0,e-t(this).innerHeight()+t(this).height()))}).css("overflow","auto")):"auto"===s&&(e=0,this.headers.next().each(function(){var i=t(this).is(":visible");i||t(this).show(),e=Math.max(e,t(this).css("height","").height()),i||t(this).hide()}).height(e))},_activate:function(e){var i=this._findActive(e)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:t.noop}))},_findActive:function(e){return"number"==typeof e?this.headers.eq(e):t()},_setupEvents:function(e){var i={keydown:"_keydown"};e&&t.each(e.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(e){var i,s,n=this.options,o=this.active,a=t(e.currentTarget),r=a[0]===o[0],h=r&&n.collapsible,l=h?t():a.next(),c=o.next(),u={oldHeader:o,oldPanel:c,newHeader:h?t():a,newPanel:l};e.preventDefault(),r&&!n.collapsible||this._trigger("beforeActivate",e,u)===!1||(n.active=h?!1:this.headers.index(a),this.active=r?t():a,this._toggle(u),this._removeClass(o,"ui-accordion-header-active","ui-state-active"),n.icons&&(i=o.children(".ui-accordion-header-icon"),this._removeClass(i,null,n.icons.activeHeader)._addClass(i,null,n.icons.header)),r||(this._removeClass(a,"ui-accordion-header-collapsed")._addClass(a,"ui-accordion-header-active","ui-state-active"),n.icons&&(s=a.children(".ui-accordion-header-icon"),this._removeClass(s,null,n.icons.header)._addClass(s,null,n.icons.activeHeader)),this._addClass(a.next(),"ui-accordion-content-active")))},_toggle:function(e){var i=e.newPanel,s=this.prevShow.length?this.prevShow:e.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,e):(s.hide(),i.show(),this._toggleComplete(e)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(t(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(t,e,i){var s,n,o,a=this,r=0,h=t.css("box-sizing"),l=t.length&&(!e.length||t.index()<e.index()),c=this.options.animate||{},u=l&&c.down||c,d=function(){a._toggleComplete(i)};return"number"==typeof u&&(o=u),"string"==typeof u&&(n=u),n=n||u.easing||c.easing,o=o||u.duration||c.duration,e.length?t.length?(s=t.show().outerHeight(),e.animate(this.hideProps,{duration:o,easing:n,step:function(t,e){e.now=Math.round(t)}}),t.hide().animate(this.showProps,{duration:o,easing:n,complete:d,step:function(t,i){i.now=Math.round(t),"height"!==i.prop?"content-box"===h&&(r+=i.now):"content"!==a.options.heightStyle&&(i.now=Math.round(s-e.outerHeight()-r),r=0)}}),void 0):e.animate(this.hideProps,o,n,d):t.animate(this.showProps,o,n,d)},_toggleComplete:function(t){var e=t.oldPanel,i=e.prev();this._removeClass(e,"ui-accordion-content-active"),this._removeClass(i,"ui-accordion-header-active")._addClass(i,"ui-accordion-header-collapsed"),e.length&&(e.parent()[0].className=e.parent()[0].className),this._trigger("activate",null,t)}}),t.ui.safeActiveElement=function(t){var e;try{e=t.activeElement}catch(i){e=t.body}return e||(e=t.body),e.nodeName||(e=t.body),e},t.widget("ui.menu",{version:"1.12.1",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-caret-1-e"},items:"> *",menus:"ul",position:{my:"left top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.element.uniqueId().attr({role:this.options.role,tabIndex:0}),this._addClass("ui-menu","ui-widget ui-widget-content"),this._on({"mousedown .ui-menu-item":function(t){t.preventDefault()},"click .ui-menu-item":function(e){var i=t(e.target),s=t(t.ui.safeActiveElement(this.document[0]));!this.mouseHandled&&i.not(".ui-state-disabled").length&&(this.select(e),e.isPropagationStopped()||(this.mouseHandled=!0),i.has(".ui-menu").length?this.expand(e):!this.element.is(":focus")&&s.closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":function(e){if(!this.previousFilter){var i=t(e.target).closest(".ui-menu-item"),s=t(e.currentTarget);i[0]===s[0]&&(this._removeClass(s.siblings().children(".ui-state-active"),null,"ui-state-active"),this.focus(e,s))}},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(t,e){var i=this.active||this.element.find(this.options.items).eq(0);e||this.focus(t,i)},blur:function(e){this._delay(function(){var i=!t.contains(this.element[0],t.ui.safeActiveElement(this.document[0]));i&&this.collapseAll(e)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(t){this._closeOnDocumentClick(t)&&this.collapseAll(t),this.mouseHandled=!1}})},_destroy:function(){var e=this.element.find(".ui-menu-item").removeAttr("role aria-disabled"),i=e.children(".ui-menu-item-wrapper").removeUniqueId().removeAttr("tabIndex role aria-haspopup");this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeAttr("role aria-labelledby aria-expanded aria-hidden aria-disabled tabIndex").removeUniqueId().show(),i.children().each(function(){var e=t(this);e.data("ui-menu-submenu-caret")&&e.remove()})},_keydown:function(e){var i,s,n,o,a=!0;switch(e.keyCode){case t.ui.keyCode.PAGE_UP:this.previousPage(e);break;case t.ui.keyCode.PAGE_DOWN:this.nextPage(e);break;case t.ui.keyCode.HOME:this._move("first","first",e);break;case t.ui.keyCode.END:this._move("last","last",e);break;case t.ui.keyCode.UP:this.previous(e);break;case t.ui.keyCode.DOWN:this.next(e);break;case t.ui.keyCode.LEFT:this.collapse(e);break;case t.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(e);break;case t.ui.keyCode.ENTER:case t.ui.keyCode.SPACE:this._activate(e);break;case t.ui.keyCode.ESCAPE:this.collapse(e);break;default:a=!1,s=this.previousFilter||"",o=!1,n=e.keyCode>=96&&105>=e.keyCode?""+(e.keyCode-96):String.fromCharCode(e.keyCode),clearTimeout(this.filterTimer),n===s?o=!0:n=s+n,i=this._filterMenuItems(n),i=o&&-1!==i.index(this.active.next())?this.active.nextAll(".ui-menu-item"):i,i.length||(n=String.fromCharCode(e.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(e,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}a&&e.preventDefault()},_activate:function(t){this.active&&!this.active.is(".ui-state-disabled")&&(this.active.children("[aria-haspopup='true']").length?this.expand(t):this.select(t))},refresh:function(){var e,i,s,n,o,a=this,r=this.options.icons.submenu,h=this.element.find(this.options.menus);this._toggleClass("ui-menu-icons",null,!!this.element.find(".ui-icon").length),s=h.filter(":not(.ui-menu)").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var e=t(this),i=e.prev(),s=t("<span>").data("ui-menu-submenu-caret",!0);a._addClass(s,"ui-menu-icon","ui-icon "+r),i.attr("aria-haspopup","true").prepend(s),e.attr("aria-labelledby",i.attr("id"))}),this._addClass(s,"ui-menu","ui-widget ui-widget-content ui-front"),e=h.add(this.element),i=e.find(this.options.items),i.not(".ui-menu-item").each(function(){var e=t(this);a._isDivider(e)&&a._addClass(e,"ui-menu-divider","ui-widget-content")}),n=i.not(".ui-menu-item, .ui-menu-divider"),o=n.children().not(".ui-menu").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),this._addClass(n,"ui-menu-item")._addClass(o,"ui-menu-item-wrapper"),i.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!t.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(t,e){if("icons"===t){var i=this.element.find(".ui-menu-icon");this._removeClass(i,null,this.options.icons.submenu)._addClass(i,null,e.submenu)}this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t+""),this._toggleClass(null,"ui-state-disabled",!!t)},focus:function(t,e){var i,s,n;this.blur(t,t&&"focus"===t.type),this._scrollIntoView(e),this.active=e.first(),s=this.active.children(".ui-menu-item-wrapper"),this._addClass(s,null,"ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),n=this.active.parent().closest(".ui-menu-item").children(".ui-menu-item-wrapper"),this._addClass(n,null,"ui-state-active"),t&&"keydown"===t.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=e.children(".ui-menu"),i.length&&t&&/^mouse/.test(t.type)&&this._startOpening(i),this.activeMenu=e.parent(),this._trigger("focus",t,{item:e})},_scrollIntoView:function(e){var i,s,n,o,a,r;this._hasScroll()&&(i=parseFloat(t.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(t.css(this.activeMenu[0],"paddingTop"))||0,n=e.offset().top-this.activeMenu.offset().top-i-s,o=this.activeMenu.scrollTop(),a=this.activeMenu.height(),r=e.outerHeight(),0>n?this.activeMenu.scrollTop(o+n):n+r>a&&this.activeMenu.scrollTop(o+n-a+r))},blur:function(t,e){e||clearTimeout(this.timer),this.active&&(this._removeClass(this.active.children(".ui-menu-item-wrapper"),null,"ui-state-active"),this._trigger("blur",t,{item:this.active}),this.active=null)},_startOpening:function(t){clearTimeout(this.timer),"true"===t.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(t)},this.delay))},_open:function(e){var i=t.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(e.parents(".ui-menu")).hide().attr("aria-hidden","true"),e.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(e,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:t(e&&e.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(e),this._removeClass(s.find(".ui-state-active"),null,"ui-state-active"),this.activeMenu=s},this.delay)},_close:function(t){t||(t=this.active?this.active.parent():this.element),t.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false")},_closeOnDocumentClick:function(e){return!t(e.target).closest(".ui-menu").length},_isDivider:function(t){return!/[^\-\u2014\u2013\s]/.test(t.text())},collapse:function(t){var e=this.active&&this.active.parent().closest(".ui-menu-item",this.element);e&&e.length&&(this._close(),this.focus(t,e))},expand:function(t){var e=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();e&&e.length&&(this._open(e.parent()),this._delay(function(){this.focus(t,e)}))},next:function(t){this._move("next","first",t)},previous:function(t){this._move("prev","last",t)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(t,e,i){var s;this.active&&(s="first"===t||"last"===t?this.active["first"===t?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[t+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[e]()),this.focus(i,s)},nextPage:function(e){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=t(this),0>i.offset().top-s-n}),this.focus(e,i)):this.focus(e,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(e),void 0)},previousPage:function(e){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=t(this),i.offset().top-s+n>0}),this.focus(e,i)):this.focus(e,this.activeMenu.find(this.options.items).first())),void 0):(this.next(e),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(e){this.active=this.active||t(e.target).closest(".ui-menu-item");var i={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(e,!0),this._trigger("select",e,i)},_filterMenuItems:function(e){var i=e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),s=RegExp("^"+i,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return s.test(t.trim(t(this).children(".ui-menu-item-wrapper").text()))})}}),t.widget("ui.autocomplete",{version:"1.12.1",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var e,i,s,n=this.element[0].nodeName.toLowerCase(),o="textarea"===n,a="input"===n;
-this.isMultiLine=o||!a&&this._isContentEditable(this.element),this.valueMethod=this.element[o||a?"val":"text"],this.isNewMenu=!0,this._addClass("ui-autocomplete-input"),this.element.attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return e=!0,s=!0,i=!0,void 0;e=!1,s=!1,i=!1;var o=t.ui.keyCode;switch(n.keyCode){case o.PAGE_UP:e=!0,this._move("previousPage",n);break;case o.PAGE_DOWN:e=!0,this._move("nextPage",n);break;case o.UP:e=!0,this._keyEvent("previous",n);break;case o.DOWN:e=!0,this._keyEvent("next",n);break;case o.ENTER:this.menu.active&&(e=!0,n.preventDefault(),this.menu.select(n));break;case o.TAB:this.menu.active&&this.menu.select(n);break;case o.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(e)return e=!1,(!this.isMultiLine||this.menu.element.is(":visible"))&&s.preventDefault(),void 0;if(!i){var n=t.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move("previousPage",s);break;case n.PAGE_DOWN:this._move("nextPage",s);break;case n.UP:this._keyEvent("previous",s);break;case n.DOWN:this._keyEvent("next",s)}}},input:function(t){return s?(s=!1,t.preventDefault(),void 0):(this._searchTimeout(t),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(t){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(t),this._change(t),void 0)}}),this._initSource(),this.menu=t("<ul>").appendTo(this._appendTo()).menu({role:null}).hide().menu("instance"),this._addClass(this.menu.element,"ui-autocomplete","ui-front"),this._on(this.menu.element,{mousedown:function(e){e.preventDefault(),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,this.element[0]!==t.ui.safeActiveElement(this.document[0])&&this.element.trigger("focus")})},menufocus:function(e,i){var s,n;return this.isNewMenu&&(this.isNewMenu=!1,e.originalEvent&&/^mouse/.test(e.originalEvent.type))?(this.menu.blur(),this.document.one("mousemove",function(){t(e.target).trigger(e.originalEvent)}),void 0):(n=i.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",e,{item:n})&&e.originalEvent&&/^key/.test(e.originalEvent.type)&&this._value(n.value),s=i.item.attr("aria-label")||n.value,s&&t.trim(s).length&&(this.liveRegion.children().hide(),t("<div>").text(s).appendTo(this.liveRegion)),void 0)},menuselect:function(e,i){var s=i.item.data("ui-autocomplete-item"),n=this.previous;this.element[0]!==t.ui.safeActiveElement(this.document[0])&&(this.element.trigger("focus"),this.previous=n,this._delay(function(){this.previous=n,this.selectedItem=s})),!1!==this._trigger("select",e,{item:s})&&this._value(s.value),this.term=this._value(),this.close(e),this.selectedItem=s}}),this.liveRegion=t("<div>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(t,e){this._super(t,e),"source"===t&&this._initSource(),"appendTo"===t&&this.menu.element.appendTo(this._appendTo()),"disabled"===t&&e&&this.xhr&&this.xhr.abort()},_isEventTargetInWidget:function(e){var i=this.menu.element[0];return e.target===this.element[0]||e.target===i||t.contains(i,e.target)},_closeOnClickOutside:function(t){this._isEventTargetInWidget(t)||this.close()},_appendTo:function(){var e=this.options.appendTo;return e&&(e=e.jquery||e.nodeType?t(e):this.document.find(e).eq(0)),e&&e[0]||(e=this.element.closest(".ui-front, dialog")),e.length||(e=this.document[0].body),e},_initSource:function(){var e,i,s=this;t.isArray(this.options.source)?(e=this.options.source,this.source=function(i,s){s(t.ui.autocomplete.filter(e,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(e,n){s.xhr&&s.xhr.abort(),s.xhr=t.ajax({url:i,data:e,dataType:"json",success:function(t){n(t)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(t){clearTimeout(this.searching),this.searching=this._delay(function(){var e=this.term===this._value(),i=this.menu.element.is(":visible"),s=t.altKey||t.ctrlKey||t.metaKey||t.shiftKey;(!e||e&&!i&&!s)&&(this.selectedItem=null,this.search(null,t))},this.options.delay)},search:function(t,e){return t=null!=t?t:this._value(),this.term=this._value(),t.length<this.options.minLength?this.close(e):this._trigger("search",e)!==!1?this._search(t):void 0},_search:function(t){this.pending++,this._addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:t},this._response())},_response:function(){var e=++this.requestIndex;return t.proxy(function(t){e===this.requestIndex&&this.__response(t),this.pending--,this.pending||this._removeClass("ui-autocomplete-loading")},this)},__response:function(t){t&&(t=this._normalize(t)),this._trigger("response",null,{content:t}),!this.options.disabled&&t&&t.length&&!this.cancelSearch?(this._suggest(t),this._trigger("open")):this._close()},close:function(t){this.cancelSearch=!0,this._close(t)},_close:function(t){this._off(this.document,"mousedown"),this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",t))},_change:function(t){this.previous!==this._value()&&this._trigger("change",t,{item:this.selectedItem})},_normalize:function(e){return e.length&&e[0].label&&e[0].value?e:t.map(e,function(e){return"string"==typeof e?{label:e,value:e}:t.extend({},e,{label:e.label||e.value,value:e.value||e.label})})},_suggest:function(e){var i=this.menu.element.empty();this._renderMenu(i,e),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(t.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next(),this._on(this.document,{mousedown:"_closeOnClickOutside"})},_resizeMenu:function(){var t=this.menu.element;t.outerWidth(Math.max(t.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(e,i){var s=this;t.each(i,function(t,i){s._renderItemData(e,i)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-autocomplete-item",e)},_renderItem:function(e,i){return t("<li>").append(t("<div>").text(i.label)).appendTo(e)},_move:function(t,e){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(t)||this.menu.isLastItem()&&/^next/.test(t)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[t](e),void 0):(this.search(null,e),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(t,e){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(t,e),e.preventDefault())},_isContentEditable:function(t){if(!t.length)return!1;var e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),t.extend(t.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(e,i){var s=RegExp(t.ui.autocomplete.escapeRegex(i),"i");return t.grep(e,function(t){return s.test(t.label||t.value||t)})}}),t.widget("ui.autocomplete",t.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(t>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(e){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=e&&e.length?this.options.messages.results(e.length):this.options.messages.noResults,this.liveRegion.children().hide(),t("<div>").text(i).appendTo(this.liveRegion))}}),t.ui.autocomplete;var g=/ui-corner-([a-z]){2,6}/g;t.widget("ui.controlgroup",{version:"1.12.1",defaultElement:"<div>",options:{direction:"horizontal",disabled:null,onlyVisible:!0,items:{button:"input[type=button], input[type=submit], input[type=reset], button, a",controlgroupLabel:".ui-controlgroup-label",checkboxradio:"input[type='checkbox'], input[type='radio']",selectmenu:"select",spinner:".ui-spinner-input"}},_create:function(){this._enhance()},_enhance:function(){this.element.attr("role","toolbar"),this.refresh()},_destroy:function(){this._callChildMethod("destroy"),this.childWidgets.removeData("ui-controlgroup-data"),this.element.removeAttr("role"),this.options.items.controlgroupLabel&&this.element.find(this.options.items.controlgroupLabel).find(".ui-controlgroup-label-contents").contents().unwrap()},_initWidgets:function(){var e=this,i=[];t.each(this.options.items,function(s,n){var o,a={};return n?"controlgroupLabel"===s?(o=e.element.find(n),o.each(function(){var e=t(this);e.children(".ui-controlgroup-label-contents").length||e.contents().wrapAll("<span class='ui-controlgroup-label-contents'></span>")}),e._addClass(o,null,"ui-widget ui-widget-content ui-state-default"),i=i.concat(o.get()),void 0):(t.fn[s]&&(a=e["_"+s+"Options"]?e["_"+s+"Options"]("middle"):{classes:{}},e.element.find(n).each(function(){var n=t(this),o=n[s]("instance"),r=t.widget.extend({},a);if("button"!==s||!n.parent(".ui-spinner").length){o||(o=n[s]()[s]("instance")),o&&(r.classes=e._resolveClassesValues(r.classes,o)),n[s](r);var h=n[s]("widget");t.data(h[0],"ui-controlgroup-data",o?o:n[s]("instance")),i.push(h[0])}})),void 0):void 0}),this.childWidgets=t(t.unique(i)),this._addClass(this.childWidgets,"ui-controlgroup-item")},_callChildMethod:function(e){this.childWidgets.each(function(){var i=t(this),s=i.data("ui-controlgroup-data");s&&s[e]&&s[e]()})},_updateCornerClass:function(t,e){var i="ui-corner-top ui-corner-bottom ui-corner-left ui-corner-right ui-corner-all",s=this._buildSimpleOptions(e,"label").classes.label;this._removeClass(t,null,i),this._addClass(t,null,s)},_buildSimpleOptions:function(t,e){var i="vertical"===this.options.direction,s={classes:{}};return s.classes[e]={middle:"",first:"ui-corner-"+(i?"top":"left"),last:"ui-corner-"+(i?"bottom":"right"),only:"ui-corner-all"}[t],s},_spinnerOptions:function(t){var e=this._buildSimpleOptions(t,"ui-spinner");return e.classes["ui-spinner-up"]="",e.classes["ui-spinner-down"]="",e},_buttonOptions:function(t){return this._buildSimpleOptions(t,"ui-button")},_checkboxradioOptions:function(t){return this._buildSimpleOptions(t,"ui-checkboxradio-label")},_selectmenuOptions:function(t){var e="vertical"===this.options.direction;return{width:e?"auto":!1,classes:{middle:{"ui-selectmenu-button-open":"","ui-selectmenu-button-closed":""},first:{"ui-selectmenu-button-open":"ui-corner-"+(e?"top":"tl"),"ui-selectmenu-button-closed":"ui-corner-"+(e?"top":"left")},last:{"ui-selectmenu-button-open":e?"":"ui-corner-tr","ui-selectmenu-button-closed":"ui-corner-"+(e?"bottom":"right")},only:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"}}[t]}},_resolveClassesValues:function(e,i){var s={};return t.each(e,function(n){var o=i.options.classes[n]||"";o=t.trim(o.replace(g,"")),s[n]=(o+" "+e[n]).replace(/\s+/g," ")}),s},_setOption:function(t,e){return"direction"===t&&this._removeClass("ui-controlgroup-"+this.options.direction),this._super(t,e),"disabled"===t?(this._callChildMethod(e?"disable":"enable"),void 0):(this.refresh(),void 0)},refresh:function(){var e,i=this;this._addClass("ui-controlgroup ui-controlgroup-"+this.options.direction),"horizontal"===this.options.direction&&this._addClass(null,"ui-helper-clearfix"),this._initWidgets(),e=this.childWidgets,this.options.onlyVisible&&(e=e.filter(":visible")),e.length&&(t.each(["first","last"],function(t,s){var n=e[s]().data("ui-controlgroup-data");if(n&&i["_"+n.widgetName+"Options"]){var o=i["_"+n.widgetName+"Options"](1===e.length?"only":s);o.classes=i._resolveClassesValues(o.classes,n),n.element[n.widgetName](o)}else i._updateCornerClass(e[s](),s)}),this._callChildMethod("refresh"))}}),t.widget("ui.checkboxradio",[t.ui.formResetMixin,{version:"1.12.1",options:{disabled:null,label:null,icon:!0,classes:{"ui-checkboxradio-label":"ui-corner-all","ui-checkboxradio-icon":"ui-corner-all"}},_getCreateOptions:function(){var e,i,s=this,n=this._super()||{};return this._readType(),i=this.element.labels(),this.label=t(i[i.length-1]),this.label.length||t.error("No label found for checkboxradio widget"),this.originalLabel="",this.label.contents().not(this.element[0]).each(function(){s.originalLabel+=3===this.nodeType?t(this).text():this.outerHTML}),this.originalLabel&&(n.label=this.originalLabel),e=this.element[0].disabled,null!=e&&(n.disabled=e),n},_create:function(){var t=this.element[0].checked;this._bindFormResetHandler(),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled),this._setOption("disabled",this.options.disabled),this._addClass("ui-checkboxradio","ui-helper-hidden-accessible"),this._addClass(this.label,"ui-checkboxradio-label","ui-button ui-widget"),"radio"===this.type&&this._addClass(this.label,"ui-checkboxradio-radio-label"),this.options.label&&this.options.label!==this.originalLabel?this._updateLabel():this.originalLabel&&(this.options.label=this.originalLabel),this._enhance(),t&&(this._addClass(this.label,"ui-checkboxradio-checked","ui-state-active"),this.icon&&this._addClass(this.icon,null,"ui-state-hover")),this._on({change:"_toggleClasses",focus:function(){this._addClass(this.label,null,"ui-state-focus ui-visual-focus")},blur:function(){this._removeClass(this.label,null,"ui-state-focus ui-visual-focus")}})},_readType:function(){var e=this.element[0].nodeName.toLowerCase();this.type=this.element[0].type,"input"===e&&/radio|checkbox/.test(this.type)||t.error("Can't create checkboxradio on element.nodeName="+e+" and element.type="+this.type)},_enhance:function(){this._updateIcon(this.element[0].checked)},widget:function(){return this.label},_getRadioGroup:function(){var e,i=this.element[0].name,s="input[name='"+t.ui.escapeSelector(i)+"']";return i?(e=this.form.length?t(this.form[0].elements).filter(s):t(s).filter(function(){return 0===t(this).form().length}),e.not(this.element)):t([])},_toggleClasses:function(){var e=this.element[0].checked;this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",e),this.options.icon&&"checkbox"===this.type&&this._toggleClass(this.icon,null,"ui-icon-check ui-state-checked",e)._toggleClass(this.icon,null,"ui-icon-blank",!e),"radio"===this.type&&this._getRadioGroup().each(function(){var e=t(this).checkboxradio("instance");e&&e._removeClass(e.label,"ui-checkboxradio-checked","ui-state-active")})},_destroy:function(){this._unbindFormResetHandler(),this.icon&&(this.icon.remove(),this.iconSpace.remove())},_setOption:function(t,e){return"label"!==t||e?(this._super(t,e),"disabled"===t?(this._toggleClass(this.label,null,"ui-state-disabled",e),this.element[0].disabled=e,void 0):(this.refresh(),void 0)):void 0},_updateIcon:function(e){var i="ui-icon ui-icon-background ";this.options.icon?(this.icon||(this.icon=t("<span>"),this.iconSpace=t("<span> </span>"),this._addClass(this.iconSpace,"ui-checkboxradio-icon-space")),"checkbox"===this.type?(i+=e?"ui-icon-check ui-state-checked":"ui-icon-blank",this._removeClass(this.icon,null,e?"ui-icon-blank":"ui-icon-check")):i+="ui-icon-blank",this._addClass(this.icon,"ui-checkboxradio-icon",i),e||this._removeClass(this.icon,null,"ui-icon-check ui-state-checked"),this.icon.prependTo(this.label).after(this.iconSpace)):void 0!==this.icon&&(this.icon.remove(),this.iconSpace.remove(),delete this.icon)},_updateLabel:function(){var t=this.label.contents().not(this.element[0]);this.icon&&(t=t.not(this.icon[0])),this.iconSpace&&(t=t.not(this.iconSpace[0])),t.remove(),this.label.append(this.options.label)},refresh:function(){var t=this.element[0].checked,e=this.element[0].disabled;this._updateIcon(t),this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),null!==this.options.label&&this._updateLabel(),e!==this.options.disabled&&this._setOptions({disabled:e})}}]),t.ui.checkboxradio,t.widget("ui.button",{version:"1.12.1",defaultElement:"<button>",options:{classes:{"ui-button":"ui-corner-all"},disabled:null,icon:null,iconPosition:"beginning",label:null,showLabel:!0},_getCreateOptions:function(){var t,e=this._super()||{};return this.isInput=this.element.is("input"),t=this.element[0].disabled,null!=t&&(e.disabled=t),this.originalLabel=this.isInput?this.element.val():this.element.html(),this.originalLabel&&(e.label=this.originalLabel),e},_create:function(){!this.option.showLabel&!this.options.icon&&(this.options.showLabel=!0),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled||!1),this.hasTitle=!!this.element.attr("title"),this.options.label&&this.options.label!==this.originalLabel&&(this.isInput?this.element.val(this.options.label):this.element.html(this.options.label)),this._addClass("ui-button","ui-widget"),this._setOption("disabled",this.options.disabled),this._enhance(),this.element.is("a")&&this._on({keyup:function(e){e.keyCode===t.ui.keyCode.SPACE&&(e.preventDefault(),this.element[0].click?this.element[0].click():this.element.trigger("click"))}})},_enhance:function(){this.element.is("button")||this.element.attr("role","button"),this.options.icon&&(this._updateIcon("icon",this.options.icon),this._updateTooltip())},_updateTooltip:function(){this.title=this.element.attr("title"),this.options.showLabel||this.title||this.element.attr("title",this.options.label)},_updateIcon:function(e,i){var s="iconPosition"!==e,n=s?this.options.iconPosition:i,o="top"===n||"bottom"===n;this.icon?s&&this._removeClass(this.icon,null,this.options.icon):(this.icon=t("<span>"),this._addClass(this.icon,"ui-button-icon","ui-icon"),this.options.showLabel||this._addClass("ui-button-icon-only")),s&&this._addClass(this.icon,null,i),this._attachIcon(n),o?(this._addClass(this.icon,null,"ui-widget-icon-block"),this.iconSpace&&this.iconSpace.remove()):(this.iconSpace||(this.iconSpace=t("<span> </span>"),this._addClass(this.iconSpace,"ui-button-icon-space")),this._removeClass(this.icon,null,"ui-wiget-icon-block"),this._attachIconSpace(n))},_destroy:function(){this.element.removeAttr("role"),this.icon&&this.icon.remove(),this.iconSpace&&this.iconSpace.remove(),this.hasTitle||this.element.removeAttr("title")},_attachIconSpace:function(t){this.icon[/^(?:end|bottom)/.test(t)?"before":"after"](this.iconSpace)},_attachIcon:function(t){this.element[/^(?:end|bottom)/.test(t)?"append":"prepend"](this.icon)},_setOptions:function(t){var e=void 0===t.showLabel?this.options.showLabel:t.showLabel,i=void 0===t.icon?this.options.icon:t.icon;e||i||(t.showLabel=!0),this._super(t)},_setOption:function(t,e){"icon"===t&&(e?this._updateIcon(t,e):this.icon&&(this.icon.remove(),this.iconSpace&&this.iconSpace.remove())),"iconPosition"===t&&this._updateIcon(t,e),"showLabel"===t&&(this._toggleClass("ui-button-icon-only",null,!e),this._updateTooltip()),"label"===t&&(this.isInput?this.element.val(e):(this.element.html(e),this.icon&&(this._attachIcon(this.options.iconPosition),this._attachIconSpace(this.options.iconPosition)))),this._super(t,e),"disabled"===t&&(this._toggleClass(null,"ui-state-disabled",e),this.element[0].disabled=e,e&&this.element.blur())},refresh:function(){var t=this.element.is("input, button")?this.element[0].disabled:this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOptions({disabled:t}),this._updateTooltip()}}),t.uiBackCompat!==!1&&(t.widget("ui.button",t.ui.button,{options:{text:!0,icons:{primary:null,secondary:null}},_create:function(){this.options.showLabel&&!this.options.text&&(this.options.showLabel=this.options.text),!this.options.showLabel&&this.options.text&&(this.options.text=this.options.showLabel),this.options.icon||!this.options.icons.primary&&!this.options.icons.secondary?this.options.icon&&(this.options.icons.primary=this.options.icon):this.options.icons.primary?this.options.icon=this.options.icons.primary:(this.options.icon=this.options.icons.secondary,this.options.iconPosition="end"),this._super()},_setOption:function(t,e){return"text"===t?(this._super("showLabel",e),void 0):("showLabel"===t&&(this.options.text=e),"icon"===t&&(this.options.icons.primary=e),"icons"===t&&(e.primary?(this._super("icon",e.primary),this._super("iconPosition","beginning")):e.secondary&&(this._super("icon",e.secondary),this._super("iconPosition","end"))),this._superApply(arguments),void 0)}}),t.fn.button=function(e){return function(){return!this.length||this.length&&"INPUT"!==this[0].tagName||this.length&&"INPUT"===this[0].tagName&&"checkbox"!==this.attr("type")&&"radio"!==this.attr("type")?e.apply(this,arguments):(t.ui.checkboxradio||t.error("Checkboxradio widget missing"),0===arguments.length?this.checkboxradio({icon:!1}):this.checkboxradio.apply(this,arguments))}}(t.fn.button),t.fn.buttonset=function(){return t.ui.controlgroup||t.error("Controlgroup widget missing"),"option"===arguments[0]&&"items"===arguments[1]&&arguments[2]?this.controlgroup.apply(this,[arguments[0],"items.button",arguments[2]]):"option"===arguments[0]&&"items"===arguments[1]?this.controlgroup.apply(this,[arguments[0],"items.button"]):("object"==typeof arguments[0]&&arguments[0].items&&(arguments[0].items={button:arguments[0].items}),this.controlgroup.apply(this,arguments))}),t.ui.button,t.extend(t.ui,{datepicker:{version:"1.12.1"}});var m;t.extend(s.prototype,{markerClassName:"hasDatepicker",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(t){return a(this._defaults,t||{}),this},_attachDatepicker:function(e,i){var s,n,o;s=e.nodeName.toLowerCase(),n="div"===s||"span"===s,e.id||(this.uuid+=1,e.id="dp"+this.uuid),o=this._newInst(t(e),n),o.settings=t.extend({},i||{}),"input"===s?this._connectDatepicker(e,o):n&&this._inlineDatepicker(e,o)},_newInst:function(e,i){var s=e[0].id.replace(/([^A-Za-z0-9_\-])/g,"\\\\$1");return{id:s,input:e,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:i,dpDiv:i?n(t("<div class='"+this._inlineClass+" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>")):this.dpDiv}},_connectDatepicker:function(e,i){var s=t(e);i.append=t([]),i.trigger=t([]),s.hasClass(this.markerClassName)||(this._attachments(s,i),s.addClass(this.markerClassName).on("keydown",this._doKeyDown).on("keypress",this._doKeyPress).on("keyup",this._doKeyUp),this._autoSize(i),t.data(e,"datepicker",i),i.settings.disabled&&this._disableDatepicker(e))},_attachments:function(e,i){var s,n,o,a=this._get(i,"appendText"),r=this._get(i,"isRTL");i.append&&i.append.remove(),a&&(i.append=t("<span class='"+this._appendClass+"'>"+a+"</span>"),e[r?"before":"after"](i.append)),e.off("focus",this._showDatepicker),i.trigger&&i.trigger.remove(),s=this._get(i,"showOn"),("focus"===s||"both"===s)&&e.on("focus",this._showDatepicker),("button"===s||"both"===s)&&(n=this._get(i,"buttonText"),o=this._get(i,"buttonImage"),i.trigger=t(this._get(i,"buttonImageOnly")?t("<img/>").addClass(this._triggerClass).attr({src:o,alt:n,title:n}):t("<button type='button'></button>").addClass(this._triggerClass).html(o?t("<img/>").attr({src:o,alt:n,title:n}):n)),e[r?"before":"after"](i.trigger),i.trigger.on("click",function(){return t.datepicker._datepickerShowing&&t.datepicker._lastInput===e[0]?t.datepicker._hideDatepicker():t.datepicker._datepickerShowing&&t.datepicker._lastInput!==e[0]?(t.datepicker._hideDatepicker(),t.datepicker._showDatepicker(e[0])):t.datepicker._showDatepicker(e[0]),!1}))},_autoSize:function(t){if(this._get(t,"autoSize")&&!t.inline){var e,i,s,n,o=new Date(2009,11,20),a=this._get(t,"dateFormat");a.match(/[DM]/)&&(e=function(t){for(i=0,s=0,n=0;t.length>n;n++)t[n].length>i&&(i=t[n].length,s=n);return s},o.setMonth(e(this._get(t,a.match(/MM/)?"monthNames":"monthNamesShort"))),o.setDate(e(this._get(t,a.match(/DD/)?"dayNames":"dayNamesShort"))+20-o.getDay())),t.input.attr("size",this._formatDate(t,o).length)}},_inlineDatepicker:function(e,i){var s=t(e);s.hasClass(this.markerClassName)||(s.addClass(this.markerClassName).append(i.dpDiv),t.data(e,"datepicker",i),this._setDate(i,this._getDefaultDate(i),!0),this._updateDatepicker(i),this._updateAlternate(i),i.settings.disabled&&this._disableDatepicker(e),i.dpDiv.css("display","block"))},_dialogDatepicker:function(e,i,s,n,o){var r,h,l,c,u,d=this._dialogInst;return d||(this.uuid+=1,r="dp"+this.uuid,this._dialogInput=t("<input type='text' id='"+r+"' style='position: absolute; top: -100px; width: 0px;'/>"),this._dialogInput.on("keydown",this._doKeyDown),t("body").append(this._dialogInput),d=this._dialogInst=this._newInst(this._dialogInput,!1),d.settings={},t.data(this._dialogInput[0],"datepicker",d)),a(d.settings,n||{}),i=i&&i.constructor===Date?this._formatDate(d,i):i,this._dialogInput.val(i),this._pos=o?o.length?o:[o.pageX,o.pageY]:null,this._pos||(h=document.documentElement.clientWidth,l=document.documentElement.clientHeight,c=document.documentElement.scrollLeft||document.body.scrollLeft,u=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[h/2-100+c,l/2-150+u]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),d.settings.onSelect=s,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),t.blockUI&&t.blockUI(this.dpDiv),t.data(this._dialogInput[0],"datepicker",d),this},_destroyDatepicker:function(e){var i,s=t(e),n=t.data(e,"datepicker");s.hasClass(this.markerClassName)&&(i=e.nodeName.toLowerCase(),t.removeData(e,"datepicker"),"input"===i?(n.append.remove(),n.trigger.remove(),s.removeClass(this.markerClassName).off("focus",this._showDatepicker).off("keydown",this._doKeyDown).off("keypress",this._doKeyPress).off("keyup",this._doKeyUp)):("div"===i||"span"===i)&&s.removeClass(this.markerClassName).empty(),m===n&&(m=null))},_enableDatepicker:function(e){var i,s,n=t(e),o=t.data(e,"datepicker");n.hasClass(this.markerClassName)&&(i=e.nodeName.toLowerCase(),"input"===i?(e.disabled=!1,o.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):("div"===i||"span"===i)&&(s=n.children("."+this._inlineClass),s.children().removeClass("ui-state-disabled"),s.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=t.map(this._disabledInputs,function(t){return t===e?null:t}))},_disableDatepicker:function(e){var i,s,n=t(e),o=t.data(e,"datepicker");n.hasClass(this.markerClassName)&&(i=e.nodeName.toLowerCase(),"input"===i?(e.disabled=!0,o.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):("div"===i||"span"===i)&&(s=n.children("."+this._inlineClass),s.children().addClass("ui-state-disabled"),s.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=t.map(this._disabledInputs,function(t){return t===e?null:t}),this._disabledInputs[this._disabledInputs.length]=e)},_isDisabledDatepicker:function(t){if(!t)return!1;for(var e=0;this._disabledInputs.length>e;e++)if(this._disabledInputs[e]===t)return!0;return!1},_getInst:function(e){try{return t.data(e,"datepicker")}catch(i){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(e,i,s){var n,o,r,h,l=this._getInst(e);return 2===arguments.length&&"string"==typeof i?"defaults"===i?t.extend({},t.datepicker._defaults):l?"all"===i?t.extend({},l.settings):this._get(l,i):null:(n=i||{},"string"==typeof i&&(n={},n[i]=s),l&&(this._curInst===l&&this._hideDatepicker(),o=this._getDateDatepicker(e,!0),r=this._getMinMaxDate(l,"min"),h=this._getMinMaxDate(l,"max"),a(l.settings,n),null!==r&&void 0!==n.dateFormat&&void 0===n.minDate&&(l.settings.minDate=this._formatDate(l,r)),null!==h&&void 0!==n.dateFormat&&void 0===n.maxDate&&(l.settings.maxDate=this._formatDate(l,h)),"disabled"in n&&(n.disabled?this._disableDatepicker(e):this._enableDatepicker(e)),this._attachments(t(e),l),this._autoSize(l),this._setDate(l,o),this._updateAlternate(l),this._updateDatepicker(l)),void 0)},_changeDatepicker:function(t,e,i){this._optionDatepicker(t,e,i)},_refreshDatepicker:function(t){var e=this._getInst(t);e&&this._updateDatepicker(e)},_setDateDatepicker:function(t,e){var i=this._getInst(t);i&&(this._setDate(i,e),this._updateDatepicker(i),this._updateAlternate(i))},_getDateDatepicker:function(t,e){var i=this._getInst(t);return i&&!i.inline&&this._setDateFromField(i,e),i?this._getDate(i):null},_doKeyDown:function(e){var i,s,n,o=t.datepicker._getInst(e.target),a=!0,r=o.dpDiv.is(".ui-datepicker-rtl");if(o._keyEvent=!0,t.datepicker._datepickerShowing)switch(e.keyCode){case 9:t.datepicker._hideDatepicker(),a=!1;break;case 13:return n=t("td."+t.datepicker._dayOverClass+":not(."+t.datepicker._currentClass+")",o.dpDiv),n[0]&&t.datepicker._selectDay(e.target,o.selectedMonth,o.selectedYear,n[0]),i=t.datepicker._get(o,"onSelect"),i?(s=t.datepicker._formatDate(o),i.apply(o.input?o.input[0]:null,[s,o])):t.datepicker._hideDatepicker(),!1;case 27:t.datepicker._hideDatepicker();break;case 33:t.datepicker._adjustDate(e.target,e.ctrlKey?-t.datepicker._get(o,"stepBigMonths"):-t.datepicker._get(o,"stepMonths"),"M");break;case 34:t.datepicker._adjustDate(e.target,e.ctrlKey?+t.datepicker._get(o,"stepBigMonths"):+t.datepicker._get(o,"stepMonths"),"M");break;case 35:(e.ctrlKey||e.metaKey)&&t.datepicker._clearDate(e.target),a=e.ctrlKey||e.metaKey;break;case 36:(e.ctrlKey||e.metaKey)&&t.datepicker._gotoToday(e.target),a=e.ctrlKey||e.metaKey;break;case 37:(e.ctrlKey||e.metaKey)&&t.datepicker._adjustDate(e.target,r?1:-1,"D"),a=e.ctrlKey||e.metaKey,e.originalEvent.altKey&&t.datepicker._adjustDate(e.target,e.ctrlKey?-t.datepicker._get(o,"stepBigMonths"):-t.datepicker._get(o,"stepMonths"),"M");break;case 38:(e.ctrlKey||e.metaKey)&&t.datepicker._adjustDate(e.target,-7,"D"),a=e.ctrlKey||e.metaKey;break;case 39:(e.ctrlKey||e.metaKey)&&t.datepicker._adjustDate(e.target,r?-1:1,"D"),a=e.ctrlKey||e.metaKey,e.originalEvent.altKey&&t.datepicker._adjustDate(e.target,e.ctrlKey?+t.datepicker._get(o,"stepBigMonths"):+t.datepicker._get(o,"stepMonths"),"M");break;case 40:(e.ctrlKey||e.metaKey)&&t.datepicker._adjustDate(e.target,7,"D"),a=e.ctrlKey||e.metaKey;break;default:a=!1}else 36===e.keyCode&&e.ctrlKey?t.datepicker._showDatepicker(this):a=!1;a&&(e.preventDefault(),e.stopPropagation())},_doKeyPress:function(e){var i,s,n=t.datepicker._getInst(e.target);return t.datepicker._get(n,"constrainInput")?(i=t.datepicker._possibleChars(t.datepicker._get(n,"dateFormat")),s=String.fromCharCode(null==e.charCode?e.keyCode:e.charCode),e.ctrlKey||e.metaKey||" ">s||!i||i.indexOf(s)>-1):void 0},_doKeyUp:function(e){var i,s=t.datepicker._getInst(e.target);if(s.input.val()!==s.lastVal)try{i=t.datepicker.parseDate(t.datepicker._get(s,"dateFormat"),s.input?s.input.val():null,t.datepicker._getFormatConfig(s)),i&&(t.datepicker._setDateFromField(s),t.datepicker._updateAlternate(s),t.datepicker._updateDatepicker(s))}catch(n){}return!0},_showDatepicker:function(e){if(e=e.target||e,"input"!==e.nodeName.toLowerCase()&&(e=t("input",e.parentNode)[0]),!t.datepicker._isDisabledDatepicker(e)&&t.datepicker._lastInput!==e){var s,n,o,r,h,l,c;s=t.datepicker._getInst(e),t.datepicker._curInst&&t.datepicker._curInst!==s&&(t.datepicker._curInst.dpDiv.stop(!0,!0),s&&t.datepicker._datepickerShowing&&t.datepicker._hideDatepicker(t.datepicker._curInst.input[0])),n=t.datepicker._get(s,"beforeShow"),o=n?n.apply(e,[e,s]):{},o!==!1&&(a(s.settings,o),s.lastVal=null,t.datepicker._lastInput=e,t.datepicker._setDateFromField(s),t.datepicker._inDialog&&(e.value=""),t.datepicker._pos||(t.datepicker._pos=t.datepicker._findPos(e),t.datepicker._pos[1]+=e.offsetHeight),r=!1,t(e).parents().each(function(){return r|="fixed"===t(this).css("position"),!r}),h={left:t.datepicker._pos[0],top:t.datepicker._pos[1]},t.datepicker._pos=null,s.dpDiv.empty(),s.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),t.datepicker._updateDatepicker(s),h=t.datepicker._checkOffset(s,h,r),s.dpDiv.css({position:t.datepicker._inDialog&&t.blockUI?"static":r?"fixed":"absolute",display:"none",left:h.left+"px",top:h.top+"px"}),s.inline||(l=t.datepicker._get(s,"showAnim"),c=t.datepicker._get(s,"duration"),s.dpDiv.css("z-index",i(t(e))+1),t.datepicker._datepickerShowing=!0,t.effects&&t.effects.effect[l]?s.dpDiv.show(l,t.datepicker._get(s,"showOptions"),c):s.dpDiv[l||"show"](l?c:null),t.datepicker._shouldFocusInput(s)&&s.input.trigger("focus"),t.datepicker._curInst=s))
-}},_updateDatepicker:function(e){this.maxRows=4,m=e,e.dpDiv.empty().append(this._generateHTML(e)),this._attachHandlers(e);var i,s=this._getNumberOfMonths(e),n=s[1],a=17,r=e.dpDiv.find("."+this._dayOverClass+" a");r.length>0&&o.apply(r.get(0)),e.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),n>1&&e.dpDiv.addClass("ui-datepicker-multi-"+n).css("width",a*n+"em"),e.dpDiv[(1!==s[0]||1!==s[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),e.dpDiv[(this._get(e,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),e===t.datepicker._curInst&&t.datepicker._datepickerShowing&&t.datepicker._shouldFocusInput(e)&&e.input.trigger("focus"),e.yearshtml&&(i=e.yearshtml,setTimeout(function(){i===e.yearshtml&&e.yearshtml&&e.dpDiv.find("select.ui-datepicker-year:first").replaceWith(e.yearshtml),i=e.yearshtml=null},0))},_shouldFocusInput:function(t){return t.input&&t.input.is(":visible")&&!t.input.is(":disabled")&&!t.input.is(":focus")},_checkOffset:function(e,i,s){var n=e.dpDiv.outerWidth(),o=e.dpDiv.outerHeight(),a=e.input?e.input.outerWidth():0,r=e.input?e.input.outerHeight():0,h=document.documentElement.clientWidth+(s?0:t(document).scrollLeft()),l=document.documentElement.clientHeight+(s?0:t(document).scrollTop());return i.left-=this._get(e,"isRTL")?n-a:0,i.left-=s&&i.left===e.input.offset().left?t(document).scrollLeft():0,i.top-=s&&i.top===e.input.offset().top+r?t(document).scrollTop():0,i.left-=Math.min(i.left,i.left+n>h&&h>n?Math.abs(i.left+n-h):0),i.top-=Math.min(i.top,i.top+o>l&&l>o?Math.abs(o+r):0),i},_findPos:function(e){for(var i,s=this._getInst(e),n=this._get(s,"isRTL");e&&("hidden"===e.type||1!==e.nodeType||t.expr.filters.hidden(e));)e=e[n?"previousSibling":"nextSibling"];return i=t(e).offset(),[i.left,i.top]},_hideDatepicker:function(e){var i,s,n,o,a=this._curInst;!a||e&&a!==t.data(e,"datepicker")||this._datepickerShowing&&(i=this._get(a,"showAnim"),s=this._get(a,"duration"),n=function(){t.datepicker._tidyDialog(a)},t.effects&&(t.effects.effect[i]||t.effects[i])?a.dpDiv.hide(i,t.datepicker._get(a,"showOptions"),s,n):a.dpDiv["slideDown"===i?"slideUp":"fadeIn"===i?"fadeOut":"hide"](i?s:null,n),i||n(),this._datepickerShowing=!1,o=this._get(a,"onClose"),o&&o.apply(a.input?a.input[0]:null,[a.input?a.input.val():"",a]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),t.blockUI&&(t.unblockUI(),t("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(t){t.dpDiv.removeClass(this._dialogClass).off(".ui-datepicker-calendar")},_checkExternalClick:function(e){if(t.datepicker._curInst){var i=t(e.target),s=t.datepicker._getInst(i[0]);(i[0].id!==t.datepicker._mainDivId&&0===i.parents("#"+t.datepicker._mainDivId).length&&!i.hasClass(t.datepicker.markerClassName)&&!i.closest("."+t.datepicker._triggerClass).length&&t.datepicker._datepickerShowing&&(!t.datepicker._inDialog||!t.blockUI)||i.hasClass(t.datepicker.markerClassName)&&t.datepicker._curInst!==s)&&t.datepicker._hideDatepicker()}},_adjustDate:function(e,i,s){var n=t(e),o=this._getInst(n[0]);this._isDisabledDatepicker(n[0])||(this._adjustInstDate(o,i+("M"===s?this._get(o,"showCurrentAtPos"):0),s),this._updateDatepicker(o))},_gotoToday:function(e){var i,s=t(e),n=this._getInst(s[0]);this._get(n,"gotoCurrent")&&n.currentDay?(n.selectedDay=n.currentDay,n.drawMonth=n.selectedMonth=n.currentMonth,n.drawYear=n.selectedYear=n.currentYear):(i=new Date,n.selectedDay=i.getDate(),n.drawMonth=n.selectedMonth=i.getMonth(),n.drawYear=n.selectedYear=i.getFullYear()),this._notifyChange(n),this._adjustDate(s)},_selectMonthYear:function(e,i,s){var n=t(e),o=this._getInst(n[0]);o["selected"+("M"===s?"Month":"Year")]=o["draw"+("M"===s?"Month":"Year")]=parseInt(i.options[i.selectedIndex].value,10),this._notifyChange(o),this._adjustDate(n)},_selectDay:function(e,i,s,n){var o,a=t(e);t(n).hasClass(this._unselectableClass)||this._isDisabledDatepicker(a[0])||(o=this._getInst(a[0]),o.selectedDay=o.currentDay=t("a",n).html(),o.selectedMonth=o.currentMonth=i,o.selectedYear=o.currentYear=s,this._selectDate(e,this._formatDate(o,o.currentDay,o.currentMonth,o.currentYear)))},_clearDate:function(e){var i=t(e);this._selectDate(i,"")},_selectDate:function(e,i){var s,n=t(e),o=this._getInst(n[0]);i=null!=i?i:this._formatDate(o),o.input&&o.input.val(i),this._updateAlternate(o),s=this._get(o,"onSelect"),s?s.apply(o.input?o.input[0]:null,[i,o]):o.input&&o.input.trigger("change"),o.inline?this._updateDatepicker(o):(this._hideDatepicker(),this._lastInput=o.input[0],"object"!=typeof o.input[0]&&o.input.trigger("focus"),this._lastInput=null)},_updateAlternate:function(e){var i,s,n,o=this._get(e,"altField");o&&(i=this._get(e,"altFormat")||this._get(e,"dateFormat"),s=this._getDate(e),n=this.formatDate(i,s,this._getFormatConfig(e)),t(o).val(n))},noWeekends:function(t){var e=t.getDay();return[e>0&&6>e,""]},iso8601Week:function(t){var e,i=new Date(t.getTime());return i.setDate(i.getDate()+4-(i.getDay()||7)),e=i.getTime(),i.setMonth(0),i.setDate(1),Math.floor(Math.round((e-i)/864e5)/7)+1},parseDate:function(e,i,s){if(null==e||null==i)throw"Invalid arguments";if(i="object"==typeof i?""+i:i+"",""===i)return null;var n,o,a,r,h=0,l=(s?s.shortYearCutoff:null)||this._defaults.shortYearCutoff,c="string"!=typeof l?l:(new Date).getFullYear()%100+parseInt(l,10),u=(s?s.dayNamesShort:null)||this._defaults.dayNamesShort,d=(s?s.dayNames:null)||this._defaults.dayNames,p=(s?s.monthNamesShort:null)||this._defaults.monthNamesShort,f=(s?s.monthNames:null)||this._defaults.monthNames,g=-1,m=-1,_=-1,v=-1,b=!1,y=function(t){var i=e.length>n+1&&e.charAt(n+1)===t;return i&&n++,i},w=function(t){var e=y(t),s="@"===t?14:"!"===t?20:"y"===t&&e?4:"o"===t?3:2,n="y"===t?s:1,o=RegExp("^\\d{"+n+","+s+"}"),a=i.substring(h).match(o);if(!a)throw"Missing number at position "+h;return h+=a[0].length,parseInt(a[0],10)},k=function(e,s,n){var o=-1,a=t.map(y(e)?n:s,function(t,e){return[[e,t]]}).sort(function(t,e){return-(t[1].length-e[1].length)});if(t.each(a,function(t,e){var s=e[1];return i.substr(h,s.length).toLowerCase()===s.toLowerCase()?(o=e[0],h+=s.length,!1):void 0}),-1!==o)return o+1;throw"Unknown name at position "+h},x=function(){if(i.charAt(h)!==e.charAt(n))throw"Unexpected literal at position "+h;h++};for(n=0;e.length>n;n++)if(b)"'"!==e.charAt(n)||y("'")?x():b=!1;else switch(e.charAt(n)){case"d":_=w("d");break;case"D":k("D",u,d);break;case"o":v=w("o");break;case"m":m=w("m");break;case"M":m=k("M",p,f);break;case"y":g=w("y");break;case"@":r=new Date(w("@")),g=r.getFullYear(),m=r.getMonth()+1,_=r.getDate();break;case"!":r=new Date((w("!")-this._ticksTo1970)/1e4),g=r.getFullYear(),m=r.getMonth()+1,_=r.getDate();break;case"'":y("'")?x():b=!0;break;default:x()}if(i.length>h&&(a=i.substr(h),!/^\s+/.test(a)))throw"Extra/unparsed characters found in date: "+a;if(-1===g?g=(new Date).getFullYear():100>g&&(g+=(new Date).getFullYear()-(new Date).getFullYear()%100+(c>=g?0:-100)),v>-1)for(m=1,_=v;;){if(o=this._getDaysInMonth(g,m-1),o>=_)break;m++,_-=o}if(r=this._daylightSavingAdjust(new Date(g,m-1,_)),r.getFullYear()!==g||r.getMonth()+1!==m||r.getDate()!==_)throw"Invalid date";return r},ATOM:"yy-mm-dd",COOKIE:"D, dd M yy",ISO_8601:"yy-mm-dd",RFC_822:"D, d M y",RFC_850:"DD, dd-M-y",RFC_1036:"D, d M y",RFC_1123:"D, d M yy",RFC_2822:"D, d M yy",RSS:"D, d M y",TICKS:"!",TIMESTAMP:"@",W3C:"yy-mm-dd",_ticksTo1970:1e7*60*60*24*(718685+Math.floor(492.5)-Math.floor(19.7)+Math.floor(4.925)),formatDate:function(t,e,i){if(!e)return"";var s,n=(i?i.dayNamesShort:null)||this._defaults.dayNamesShort,o=(i?i.dayNames:null)||this._defaults.dayNames,a=(i?i.monthNamesShort:null)||this._defaults.monthNamesShort,r=(i?i.monthNames:null)||this._defaults.monthNames,h=function(e){var i=t.length>s+1&&t.charAt(s+1)===e;return i&&s++,i},l=function(t,e,i){var s=""+e;if(h(t))for(;i>s.length;)s="0"+s;return s},c=function(t,e,i,s){return h(t)?s[e]:i[e]},u="",d=!1;if(e)for(s=0;t.length>s;s++)if(d)"'"!==t.charAt(s)||h("'")?u+=t.charAt(s):d=!1;else switch(t.charAt(s)){case"d":u+=l("d",e.getDate(),2);break;case"D":u+=c("D",e.getDay(),n,o);break;case"o":u+=l("o",Math.round((new Date(e.getFullYear(),e.getMonth(),e.getDate()).getTime()-new Date(e.getFullYear(),0,0).getTime())/864e5),3);break;case"m":u+=l("m",e.getMonth()+1,2);break;case"M":u+=c("M",e.getMonth(),a,r);break;case"y":u+=h("y")?e.getFullYear():(10>e.getFullYear()%100?"0":"")+e.getFullYear()%100;break;case"@":u+=e.getTime();break;case"!":u+=1e4*e.getTime()+this._ticksTo1970;break;case"'":h("'")?u+="'":d=!0;break;default:u+=t.charAt(s)}return u},_possibleChars:function(t){var e,i="",s=!1,n=function(i){var s=t.length>e+1&&t.charAt(e+1)===i;return s&&e++,s};for(e=0;t.length>e;e++)if(s)"'"!==t.charAt(e)||n("'")?i+=t.charAt(e):s=!1;else switch(t.charAt(e)){case"d":case"m":case"y":case"@":i+="0123456789";break;case"D":case"M":return null;case"'":n("'")?i+="'":s=!0;break;default:i+=t.charAt(e)}return i},_get:function(t,e){return void 0!==t.settings[e]?t.settings[e]:this._defaults[e]},_setDateFromField:function(t,e){if(t.input.val()!==t.lastVal){var i=this._get(t,"dateFormat"),s=t.lastVal=t.input?t.input.val():null,n=this._getDefaultDate(t),o=n,a=this._getFormatConfig(t);try{o=this.parseDate(i,s,a)||n}catch(r){s=e?"":s}t.selectedDay=o.getDate(),t.drawMonth=t.selectedMonth=o.getMonth(),t.drawYear=t.selectedYear=o.getFullYear(),t.currentDay=s?o.getDate():0,t.currentMonth=s?o.getMonth():0,t.currentYear=s?o.getFullYear():0,this._adjustInstDate(t)}},_getDefaultDate:function(t){return this._restrictMinMax(t,this._determineDate(t,this._get(t,"defaultDate"),new Date))},_determineDate:function(e,i,s){var n=function(t){var e=new Date;return e.setDate(e.getDate()+t),e},o=function(i){try{return t.datepicker.parseDate(t.datepicker._get(e,"dateFormat"),i,t.datepicker._getFormatConfig(e))}catch(s){}for(var n=(i.toLowerCase().match(/^c/)?t.datepicker._getDate(e):null)||new Date,o=n.getFullYear(),a=n.getMonth(),r=n.getDate(),h=/([+\-]?[0-9]+)\s*(d|D|w|W|m|M|y|Y)?/g,l=h.exec(i);l;){switch(l[2]||"d"){case"d":case"D":r+=parseInt(l[1],10);break;case"w":case"W":r+=7*parseInt(l[1],10);break;case"m":case"M":a+=parseInt(l[1],10),r=Math.min(r,t.datepicker._getDaysInMonth(o,a));break;case"y":case"Y":o+=parseInt(l[1],10),r=Math.min(r,t.datepicker._getDaysInMonth(o,a))}l=h.exec(i)}return new Date(o,a,r)},a=null==i||""===i?s:"string"==typeof i?o(i):"number"==typeof i?isNaN(i)?s:n(i):new Date(i.getTime());return a=a&&"Invalid Date"==""+a?s:a,a&&(a.setHours(0),a.setMinutes(0),a.setSeconds(0),a.setMilliseconds(0)),this._daylightSavingAdjust(a)},_daylightSavingAdjust:function(t){return t?(t.setHours(t.getHours()>12?t.getHours()+2:0),t):null},_setDate:function(t,e,i){var s=!e,n=t.selectedMonth,o=t.selectedYear,a=this._restrictMinMax(t,this._determineDate(t,e,new Date));t.selectedDay=t.currentDay=a.getDate(),t.drawMonth=t.selectedMonth=t.currentMonth=a.getMonth(),t.drawYear=t.selectedYear=t.currentYear=a.getFullYear(),n===t.selectedMonth&&o===t.selectedYear||i||this._notifyChange(t),this._adjustInstDate(t),t.input&&t.input.val(s?"":this._formatDate(t))},_getDate:function(t){var e=!t.currentYear||t.input&&""===t.input.val()?null:this._daylightSavingAdjust(new Date(t.currentYear,t.currentMonth,t.currentDay));return e},_attachHandlers:function(e){var i=this._get(e,"stepMonths"),s="#"+e.id.replace(/\\\\/g,"\\");e.dpDiv.find("[data-handler]").map(function(){var e={prev:function(){t.datepicker._adjustDate(s,-i,"M")},next:function(){t.datepicker._adjustDate(s,+i,"M")},hide:function(){t.datepicker._hideDatepicker()},today:function(){t.datepicker._gotoToday(s)},selectDay:function(){return t.datepicker._selectDay(s,+this.getAttribute("data-month"),+this.getAttribute("data-year"),this),!1},selectMonth:function(){return t.datepicker._selectMonthYear(s,this,"M"),!1},selectYear:function(){return t.datepicker._selectMonthYear(s,this,"Y"),!1}};t(this).on(this.getAttribute("data-event"),e[this.getAttribute("data-handler")])})},_generateHTML:function(t){var e,i,s,n,o,a,r,h,l,c,u,d,p,f,g,m,_,v,b,y,w,k,x,C,D,I,T,P,M,S,H,z,O,A,N,W,E,F,L,R=new Date,B=this._daylightSavingAdjust(new Date(R.getFullYear(),R.getMonth(),R.getDate())),Y=this._get(t,"isRTL"),j=this._get(t,"showButtonPanel"),q=this._get(t,"hideIfNoPrevNext"),K=this._get(t,"navigationAsDateFormat"),U=this._getNumberOfMonths(t),V=this._get(t,"showCurrentAtPos"),$=this._get(t,"stepMonths"),X=1!==U[0]||1!==U[1],G=this._daylightSavingAdjust(t.currentDay?new Date(t.currentYear,t.currentMonth,t.currentDay):new Date(9999,9,9)),Q=this._getMinMaxDate(t,"min"),J=this._getMinMaxDate(t,"max"),Z=t.drawMonth-V,te=t.drawYear;if(0>Z&&(Z+=12,te--),J)for(e=this._daylightSavingAdjust(new Date(J.getFullYear(),J.getMonth()-U[0]*U[1]+1,J.getDate())),e=Q&&Q>e?Q:e;this._daylightSavingAdjust(new Date(te,Z,1))>e;)Z--,0>Z&&(Z=11,te--);for(t.drawMonth=Z,t.drawYear=te,i=this._get(t,"prevText"),i=K?this.formatDate(i,this._daylightSavingAdjust(new Date(te,Z-$,1)),this._getFormatConfig(t)):i,s=this._canAdjustMonth(t,-1,te,Z)?"<a class='ui-datepicker-prev ui-corner-all' data-handler='prev' data-event='click' title='"+i+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"e":"w")+"'>"+i+"</span></a>":q?"":"<a class='ui-datepicker-prev ui-corner-all ui-state-disabled' title='"+i+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"e":"w")+"'>"+i+"</span></a>",n=this._get(t,"nextText"),n=K?this.formatDate(n,this._daylightSavingAdjust(new Date(te,Z+$,1)),this._getFormatConfig(t)):n,o=this._canAdjustMonth(t,1,te,Z)?"<a class='ui-datepicker-next ui-corner-all' data-handler='next' data-event='click' title='"+n+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"w":"e")+"'>"+n+"</span></a>":q?"":"<a class='ui-datepicker-next ui-corner-all ui-state-disabled' title='"+n+"'><span class='ui-icon ui-icon-circle-triangle-"+(Y?"w":"e")+"'>"+n+"</span></a>",a=this._get(t,"currentText"),r=this._get(t,"gotoCurrent")&&t.currentDay?G:B,a=K?this.formatDate(a,r,this._getFormatConfig(t)):a,h=t.inline?"":"<button type='button' class='ui-datepicker-close ui-state-default ui-priority-primary ui-corner-all' data-handler='hide' data-event='click'>"+this._get(t,"closeText")+"</button>",l=j?"<div class='ui-datepicker-buttonpane ui-widget-content'>"+(Y?h:"")+(this._isInRange(t,r)?"<button type='button' class='ui-datepicker-current ui-state-default ui-priority-secondary ui-corner-all' data-handler='today' data-event='click'>"+a+"</button>":"")+(Y?"":h)+"</div>":"",c=parseInt(this._get(t,"firstDay"),10),c=isNaN(c)?0:c,u=this._get(t,"showWeek"),d=this._get(t,"dayNames"),p=this._get(t,"dayNamesMin"),f=this._get(t,"monthNames"),g=this._get(t,"monthNamesShort"),m=this._get(t,"beforeShowDay"),_=this._get(t,"showOtherMonths"),v=this._get(t,"selectOtherMonths"),b=this._getDefaultDate(t),y="",k=0;U[0]>k;k++){for(x="",this.maxRows=4,C=0;U[1]>C;C++){if(D=this._daylightSavingAdjust(new Date(te,Z,t.selectedDay)),I=" ui-corner-all",T="",X){if(T+="<div class='ui-datepicker-group",U[1]>1)switch(C){case 0:T+=" ui-datepicker-group-first",I=" ui-corner-"+(Y?"right":"left");break;case U[1]-1:T+=" ui-datepicker-group-last",I=" ui-corner-"+(Y?"left":"right");break;default:T+=" ui-datepicker-group-middle",I=""}T+="'>"}for(T+="<div class='ui-datepicker-header ui-widget-header ui-helper-clearfix"+I+"'>"+(/all|left/.test(I)&&0===k?Y?o:s:"")+(/all|right/.test(I)&&0===k?Y?s:o:"")+this._generateMonthYearHeader(t,Z,te,Q,J,k>0||C>0,f,g)+"</div><table class='ui-datepicker-calendar'><thead>"+"<tr>",P=u?"<th class='ui-datepicker-week-col'>"+this._get(t,"weekHeader")+"</th>":"",w=0;7>w;w++)M=(w+c)%7,P+="<th scope='col'"+((w+c+6)%7>=5?" class='ui-datepicker-week-end'":"")+">"+"<span title='"+d[M]+"'>"+p[M]+"</span></th>";for(T+=P+"</tr></thead><tbody>",S=this._getDaysInMonth(te,Z),te===t.selectedYear&&Z===t.selectedMonth&&(t.selectedDay=Math.min(t.selectedDay,S)),H=(this._getFirstDayOfMonth(te,Z)-c+7)%7,z=Math.ceil((H+S)/7),O=X?this.maxRows>z?this.maxRows:z:z,this.maxRows=O,A=this._daylightSavingAdjust(new Date(te,Z,1-H)),N=0;O>N;N++){for(T+="<tr>",W=u?"<td class='ui-datepicker-week-col'>"+this._get(t,"calculateWeek")(A)+"</td>":"",w=0;7>w;w++)E=m?m.apply(t.input?t.input[0]:null,[A]):[!0,""],F=A.getMonth()!==Z,L=F&&!v||!E[0]||Q&&Q>A||J&&A>J,W+="<td class='"+((w+c+6)%7>=5?" ui-datepicker-week-end":"")+(F?" ui-datepicker-other-month":"")+(A.getTime()===D.getTime()&&Z===t.selectedMonth&&t._keyEvent||b.getTime()===A.getTime()&&b.getTime()===D.getTime()?" "+this._dayOverClass:"")+(L?" "+this._unselectableClass+" ui-state-disabled":"")+(F&&!_?"":" "+E[1]+(A.getTime()===G.getTime()?" "+this._currentClass:"")+(A.getTime()===B.getTime()?" ui-datepicker-today":""))+"'"+(F&&!_||!E[2]?"":" title='"+E[2].replace(/'/g,"&#39;")+"'")+(L?"":" data-handler='selectDay' data-event='click' data-month='"+A.getMonth()+"' data-year='"+A.getFullYear()+"'")+">"+(F&&!_?"&#xa0;":L?"<span class='ui-state-default'>"+A.getDate()+"</span>":"<a class='ui-state-default"+(A.getTime()===B.getTime()?" ui-state-highlight":"")+(A.getTime()===G.getTime()?" ui-state-active":"")+(F?" ui-priority-secondary":"")+"' href='#'>"+A.getDate()+"</a>")+"</td>",A.setDate(A.getDate()+1),A=this._daylightSavingAdjust(A);T+=W+"</tr>"}Z++,Z>11&&(Z=0,te++),T+="</tbody></table>"+(X?"</div>"+(U[0]>0&&C===U[1]-1?"<div class='ui-datepicker-row-break'></div>":""):""),x+=T}y+=x}return y+=l,t._keyEvent=!1,y},_generateMonthYearHeader:function(t,e,i,s,n,o,a,r){var h,l,c,u,d,p,f,g,m=this._get(t,"changeMonth"),_=this._get(t,"changeYear"),v=this._get(t,"showMonthAfterYear"),b="<div class='ui-datepicker-title'>",y="";if(o||!m)y+="<span class='ui-datepicker-month'>"+a[e]+"</span>";else{for(h=s&&s.getFullYear()===i,l=n&&n.getFullYear()===i,y+="<select class='ui-datepicker-month' data-handler='selectMonth' data-event='change'>",c=0;12>c;c++)(!h||c>=s.getMonth())&&(!l||n.getMonth()>=c)&&(y+="<option value='"+c+"'"+(c===e?" selected='selected'":"")+">"+r[c]+"</option>");y+="</select>"}if(v||(b+=y+(!o&&m&&_?"":"&#xa0;")),!t.yearshtml)if(t.yearshtml="",o||!_)b+="<span class='ui-datepicker-year'>"+i+"</span>";else{for(u=this._get(t,"yearRange").split(":"),d=(new Date).getFullYear(),p=function(t){var e=t.match(/c[+\-].*/)?i+parseInt(t.substring(1),10):t.match(/[+\-].*/)?d+parseInt(t,10):parseInt(t,10);return isNaN(e)?d:e},f=p(u[0]),g=Math.max(f,p(u[1]||"")),f=s?Math.max(f,s.getFullYear()):f,g=n?Math.min(g,n.getFullYear()):g,t.yearshtml+="<select class='ui-datepicker-year' data-handler='selectYear' data-event='change'>";g>=f;f++)t.yearshtml+="<option value='"+f+"'"+(f===i?" selected='selected'":"")+">"+f+"</option>";t.yearshtml+="</select>",b+=t.yearshtml,t.yearshtml=null}return b+=this._get(t,"yearSuffix"),v&&(b+=(!o&&m&&_?"":"&#xa0;")+y),b+="</div>"},_adjustInstDate:function(t,e,i){var s=t.selectedYear+("Y"===i?e:0),n=t.selectedMonth+("M"===i?e:0),o=Math.min(t.selectedDay,this._getDaysInMonth(s,n))+("D"===i?e:0),a=this._restrictMinMax(t,this._daylightSavingAdjust(new Date(s,n,o)));t.selectedDay=a.getDate(),t.drawMonth=t.selectedMonth=a.getMonth(),t.drawYear=t.selectedYear=a.getFullYear(),("M"===i||"Y"===i)&&this._notifyChange(t)},_restrictMinMax:function(t,e){var i=this._getMinMaxDate(t,"min"),s=this._getMinMaxDate(t,"max"),n=i&&i>e?i:e;return s&&n>s?s:n},_notifyChange:function(t){var e=this._get(t,"onChangeMonthYear");e&&e.apply(t.input?t.input[0]:null,[t.selectedYear,t.selectedMonth+1,t])},_getNumberOfMonths:function(t){var e=this._get(t,"numberOfMonths");return null==e?[1,1]:"number"==typeof e?[1,e]:e},_getMinMaxDate:function(t,e){return this._determineDate(t,this._get(t,e+"Date"),null)},_getDaysInMonth:function(t,e){return 32-this._daylightSavingAdjust(new Date(t,e,32)).getDate()},_getFirstDayOfMonth:function(t,e){return new Date(t,e,1).getDay()},_canAdjustMonth:function(t,e,i,s){var n=this._getNumberOfMonths(t),o=this._daylightSavingAdjust(new Date(i,s+(0>e?e:n[0]*n[1]),1));return 0>e&&o.setDate(this._getDaysInMonth(o.getFullYear(),o.getMonth())),this._isInRange(t,o)},_isInRange:function(t,e){var i,s,n=this._getMinMaxDate(t,"min"),o=this._getMinMaxDate(t,"max"),a=null,r=null,h=this._get(t,"yearRange");return h&&(i=h.split(":"),s=(new Date).getFullYear(),a=parseInt(i[0],10),r=parseInt(i[1],10),i[0].match(/[+\-].*/)&&(a+=s),i[1].match(/[+\-].*/)&&(r+=s)),(!n||e.getTime()>=n.getTime())&&(!o||e.getTime()<=o.getTime())&&(!a||e.getFullYear()>=a)&&(!r||r>=e.getFullYear())},_getFormatConfig:function(t){var e=this._get(t,"shortYearCutoff");return e="string"!=typeof e?e:(new Date).getFullYear()%100+parseInt(e,10),{shortYearCutoff:e,dayNamesShort:this._get(t,"dayNamesShort"),dayNames:this._get(t,"dayNames"),monthNamesShort:this._get(t,"monthNamesShort"),monthNames:this._get(t,"monthNames")}},_formatDate:function(t,e,i,s){e||(t.currentDay=t.selectedDay,t.currentMonth=t.selectedMonth,t.currentYear=t.selectedYear);var n=e?"object"==typeof e?e:this._daylightSavingAdjust(new Date(s,i,e)):this._daylightSavingAdjust(new Date(t.currentYear,t.currentMonth,t.currentDay));return this.formatDate(this._get(t,"dateFormat"),n,this._getFormatConfig(t))}}),t.fn.datepicker=function(e){if(!this.length)return this;t.datepicker.initialized||(t(document).on("mousedown",t.datepicker._checkExternalClick),t.datepicker.initialized=!0),0===t("#"+t.datepicker._mainDivId).length&&t("body").append(t.datepicker.dpDiv);var i=Array.prototype.slice.call(arguments,1);return"string"!=typeof e||"isDisabled"!==e&&"getDate"!==e&&"widget"!==e?"option"===e&&2===arguments.length&&"string"==typeof arguments[1]?t.datepicker["_"+e+"Datepicker"].apply(t.datepicker,[this[0]].concat(i)):this.each(function(){"string"==typeof e?t.datepicker["_"+e+"Datepicker"].apply(t.datepicker,[this].concat(i)):t.datepicker._attachDatepicker(this,e)}):t.datepicker["_"+e+"Datepicker"].apply(t.datepicker,[this[0]].concat(i))},t.datepicker=new s,t.datepicker.initialized=!1,t.datepicker.uuid=(new Date).getTime(),t.datepicker.version="1.12.1",t.datepicker,t.ui.ie=!!/msie [\w.]+/.exec(navigator.userAgent.toLowerCase());var _=!1;t(document).on("mouseup",function(){_=!1}),t.widget("ui.mouse",{version:"1.12.1",options:{cancel:"input, textarea, button, select, option",distance:1,delay:0},_mouseInit:function(){var e=this;this.element.on("mousedown."+this.widgetName,function(t){return e._mouseDown(t)}).on("click."+this.widgetName,function(i){return!0===t.data(i.target,e.widgetName+".preventClickEvent")?(t.removeData(i.target,e.widgetName+".preventClickEvent"),i.stopImmediatePropagation(),!1):void 0}),this.started=!1},_mouseDestroy:function(){this.element.off("."+this.widgetName),this._mouseMoveDelegate&&this.document.off("mousemove."+this.widgetName,this._mouseMoveDelegate).off("mouseup."+this.widgetName,this._mouseUpDelegate)},_mouseDown:function(e){if(!_){this._mouseMoved=!1,this._mouseStarted&&this._mouseUp(e),this._mouseDownEvent=e;var i=this,s=1===e.which,n="string"==typeof this.options.cancel&&e.target.nodeName?t(e.target).closest(this.options.cancel).length:!1;return s&&!n&&this._mouseCapture(e)?(this.mouseDelayMet=!this.options.delay,this.mouseDelayMet||(this._mouseDelayTimer=setTimeout(function(){i.mouseDelayMet=!0},this.options.delay)),this._mouseDistanceMet(e)&&this._mouseDelayMet(e)&&(this._mouseStarted=this._mouseStart(e)!==!1,!this._mouseStarted)?(e.preventDefault(),!0):(!0===t.data(e.target,this.widgetName+".preventClickEvent")&&t.removeData(e.target,this.widgetName+".preventClickEvent"),this._mouseMoveDelegate=function(t){return i._mouseMove(t)},this._mouseUpDelegate=function(t){return i._mouseUp(t)},this.document.on("mousemove."+this.widgetName,this._mouseMoveDelegate).on("mouseup."+this.widgetName,this._mouseUpDelegate),e.preventDefault(),_=!0,!0)):!0}},_mouseMove:function(e){if(this._mouseMoved){if(t.ui.ie&&(!document.documentMode||9>document.documentMode)&&!e.button)return this._mouseUp(e);if(!e.which)if(e.originalEvent.altKey||e.originalEvent.ctrlKey||e.originalEvent.metaKey||e.originalEvent.shiftKey)this.ignoreMissingWhich=!0;else if(!this.ignoreMissingWhich)return this._mouseUp(e)}return(e.which||e.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(e),e.preventDefault()):(this._mouseDistanceMet(e)&&this._mouseDelayMet(e)&&(this._mouseStarted=this._mouseStart(this._mouseDownEvent,e)!==!1,this._mouseStarted?this._mouseDrag(e):this._mouseUp(e)),!this._mouseStarted)},_mouseUp:function(e){this.document.off("mousemove."+this.widgetName,this._mouseMoveDelegate).off("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,e.target===this._mouseDownEvent.target&&t.data(e.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(e)),this._mouseDelayTimer&&(clearTimeout(this._mouseDelayTimer),delete this._mouseDelayTimer),this.ignoreMissingWhich=!1,_=!1,e.preventDefault()},_mouseDistanceMet:function(t){return Math.max(Math.abs(this._mouseDownEvent.pageX-t.pageX),Math.abs(this._mouseDownEvent.pageY-t.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),t.ui.plugin={add:function(e,i,s){var n,o=t.ui[e].prototype;for(n in s)o.plugins[n]=o.plugins[n]||[],o.plugins[n].push([i,s[n]])},call:function(t,e,i,s){var n,o=t.plugins[e];if(o&&(s||t.element[0].parentNode&&11!==t.element[0].parentNode.nodeType))for(n=0;o.length>n;n++)t.options[o[n][0]]&&o[n][1].apply(t.element,i)}},t.ui.safeBlur=function(e){e&&"body"!==e.nodeName.toLowerCase()&&t(e).trigger("blur")},t.widget("ui.draggable",t.ui.mouse,{version:"1.12.1",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this._addClass("ui-draggable"),this._setHandleClassName(),this._mouseInit()},_setOption:function(t,e){this._super(t,e),"handle"===t&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(".ui-draggable-dragging")?(this.destroyOnClear=!0,void 0):(this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(e){var i=this.options;return this.helper||i.disabled||t(e.target).closest(".ui-resizable-handle").length>0?!1:(this.handle=this._getHandle(e),this.handle?(this._blurActiveElement(e),this._blockFrames(i.iframeFix===!0?"iframe":i.iframeFix),!0):!1)},_blockFrames:function(e){this.iframeBlocks=this.document.find(e).map(function(){var e=t(this);return t("<div>").css("position","absolute").appendTo(e.parent()).outerWidth(e.outerWidth()).outerHeight(e.outerHeight()).offset(e.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(e){var i=t.ui.safeActiveElement(this.document[0]),s=t(e.target);s.closest(i).length||t.ui.safeBlur(i)},_mouseStart:function(e){var i=this.options;return this.helper=this._createHelper(e),this._addClass(this.helper,"ui-draggable-dragging"),this._cacheHelperProportions(),t.ui.ddmanager&&(t.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return"fixed"===t(this).css("position")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(e),this.originalPosition=this.position=this._generatePosition(e,!1),this.originalPageX=e.pageX,this.originalPageY=e.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",e)===!1?(this._clear(),!1):(this._cacheHelperProportions(),t.ui.ddmanager&&!i.dropBehaviour&&t.ui.ddmanager.prepareOffsets(this,e),this._mouseDrag(e,!0),t.ui.ddmanager&&t.ui.ddmanager.dragStart(this,e),!0)},_refreshOffsets:function(t){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:t.pageX-this.offset.left,top:t.pageY-this.offset.top}},_mouseDrag:function(e,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(e,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",e,s)===!1)return this._mouseUp(new t.Event("mouseup",e)),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",t.ui.ddmanager&&t.ui.ddmanager.drag(this,e),!1},_mouseStop:function(e){var i=this,s=!1;return t.ui.ddmanager&&!this.options.dropBehaviour&&(s=t.ui.ddmanager.drop(this,e)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||t.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?t(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",e)!==!1&&i._clear()}):this._trigger("stop",e)!==!1&&this._clear(),!1},_mouseUp:function(e){return this._unblockFrames(),t.ui.ddmanager&&t.ui.ddmanager.dragStop(this,e),this.handleElement.is(e.target)&&this.element.trigger("focus"),t.ui.mouse.prototype._mouseUp.call(this,e)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp(new t.Event("mouseup",{target:this.element[0]})):this._clear(),this},_getHandle:function(e){return this.options.handle?!!t(e.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this._addClass(this.handleElement,"ui-draggable-handle")},_removeHandleClassName:function(){this._removeClass(this.handleElement,"ui-draggable-handle")},_createHelper:function(e){var i=this.options,s=t.isFunction(i.helper),n=s?t(i.helper.apply(this.element[0],[e])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return n.parents("body").length||n.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css("position"))||n.css("position","absolute"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(e){"string"==typeof e&&(e=e.split(" ")),t.isArray(e)&&(e={left:+e[0],top:+e[1]||0}),"left"in e&&(this.offset.click.left=e.left+this.margins.left),"right"in e&&(this.offset.click.left=this.helperProportions.width-e.right+this.margins.left),"top"in e&&(this.offset.click.top=e.top+this.margins.top),"bottom"in e&&(this.offset.click.top=this.helperProportions.height-e.bottom+this.margins.top)},_isRootNode:function(t){return/(html|body)/i.test(t.tagName)||t===this.document[0]},_getParentOffset:function(){var e=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&t.contains(this.scrollParent[0],this.offsetParent[0])&&(e.left+=this.scrollParent.scrollLeft(),e.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(e={top:0,left:0}),{top:e.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:e.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.element.position(),e=this._isRootNode(this.scrollParent[0]);return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+(e?0:this.scrollParent.scrollTop()),left:t.left-(parseInt(this.helper.css("left"),10)||0)+(e?0:this.scrollParent.scrollLeft())}
-},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var e,i,s,n=this.options,o=this.document[0];return this.relativeContainer=null,n.containment?"window"===n.containment?(this.containment=[t(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,t(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,t(window).scrollLeft()+t(window).width()-this.helperProportions.width-this.margins.left,t(window).scrollTop()+(t(window).height()||o.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,t(o).width()-this.helperProportions.width-this.margins.left,(t(o).height()||o.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=t(n.containment),s=i[0],s&&(e=/(scroll|auto)/.test(i.css("overflow")),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(e?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(e?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)},_convertPositionTo:function(t,e){e||(e=this.position);var i="absolute"===t?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(t,e){var i,s,n,o,a=this.options,r=this._isRootNode(this.scrollParent[0]),h=t.pageX,l=t.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),e&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,t.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),t.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),t.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),t.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),a.grid&&(n=a.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/a.grid[1])*a.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-a.grid[1]:n+a.grid[1]:n,o=a.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/a.grid[0])*a.grid[0]:this.originalPageX,h=i?o-this.offset.click.left>=i[0]||o-this.offset.click.left>i[2]?o:o-this.offset.click.left>=i[0]?o-a.grid[0]:o+a.grid[0]:o),"y"===a.axis&&(h=this.originalPageX),"x"===a.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this._removeClass(this.helper,"ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_trigger:function(e,i,s){return s=s||this._uiHash(),t.ui.plugin.call(this,e,[i,s,this],!0),/^(drag|start|stop)/.test(e)&&(this.positionAbs=this._convertPositionTo("absolute"),s.offset=this.positionAbs),t.Widget.prototype._trigger.call(this,e,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),t.ui.plugin.add("draggable","connectToSortable",{start:function(e,i,s){var n=t.extend({},i,{item:s.element});s.sortables=[],t(s.options.connectToSortable).each(function(){var i=t(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger("activate",e,n))})},stop:function(e,i,s){var n=t.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,t.each(s.sortables,function(){var t=this;t.isOver?(t.isOver=0,s.cancelHelperRemoval=!0,t.cancelHelperRemoval=!1,t._storedCSS={position:t.placeholder.css("position"),top:t.placeholder.css("top"),left:t.placeholder.css("left")},t._mouseStop(e),t.options.helper=t.options._helper):(t.cancelHelperRemoval=!0,t._trigger("deactivate",e,n))})},drag:function(e,i,s){t.each(s.sortables,function(){var n=!1,o=this;o.positionAbs=s.positionAbs,o.helperProportions=s.helperProportions,o.offset.click=s.offset.click,o._intersectsWith(o.containerCache)&&(n=!0,t.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==o&&this._intersectsWith(this.containerCache)&&t.contains(o.element[0],this.element[0])&&(n=!1),n})),n?(o.isOver||(o.isOver=1,s._parent=i.helper.parent(),o.currentItem=i.helper.appendTo(o.element).data("ui-sortable-item",!0),o.options._helper=o.options.helper,o.options.helper=function(){return i.helper[0]},e.target=o.currentItem[0],o._mouseCapture(e,!0),o._mouseStart(e,!0,!0),o.offset.click.top=s.offset.click.top,o.offset.click.left=s.offset.click.left,o.offset.parent.left-=s.offset.parent.left-o.offset.parent.left,o.offset.parent.top-=s.offset.parent.top-o.offset.parent.top,s._trigger("toSortable",e),s.dropped=o.element,t.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,o.fromOutside=s),o.currentItem&&(o._mouseDrag(e),i.position=o.position)):o.isOver&&(o.isOver=0,o.cancelHelperRemoval=!0,o.options._revert=o.options.revert,o.options.revert=!1,o._trigger("out",e,o._uiHash(o)),o._mouseStop(e,!0),o.options.revert=o.options._revert,o.options.helper=o.options._helper,o.placeholder&&o.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(e),i.position=s._generatePosition(e,!0),s._trigger("fromSortable",e),s.dropped=!1,t.each(s.sortables,function(){this.refreshPositions()}))})}}),t.ui.plugin.add("draggable","cursor",{start:function(e,i,s){var n=t("body"),o=s.options;n.css("cursor")&&(o._cursor=n.css("cursor")),n.css("cursor",o.cursor)},stop:function(e,i,s){var n=s.options;n._cursor&&t("body").css("cursor",n._cursor)}}),t.ui.plugin.add("draggable","opacity",{start:function(e,i,s){var n=t(i.helper),o=s.options;n.css("opacity")&&(o._opacity=n.css("opacity")),n.css("opacity",o.opacity)},stop:function(e,i,s){var n=s.options;n._opacity&&t(i.helper).css("opacity",n._opacity)}}),t.ui.plugin.add("draggable","scroll",{start:function(t,e,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(e,i,s){var n=s.options,o=!1,a=s.scrollParentNotHidden[0],r=s.document[0];a!==r&&"HTML"!==a.tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+a.offsetHeight-e.pageY<n.scrollSensitivity?a.scrollTop=o=a.scrollTop+n.scrollSpeed:e.pageY-s.overflowOffset.top<n.scrollSensitivity&&(a.scrollTop=o=a.scrollTop-n.scrollSpeed)),n.axis&&"y"===n.axis||(s.overflowOffset.left+a.offsetWidth-e.pageX<n.scrollSensitivity?a.scrollLeft=o=a.scrollLeft+n.scrollSpeed:e.pageX-s.overflowOffset.left<n.scrollSensitivity&&(a.scrollLeft=o=a.scrollLeft-n.scrollSpeed))):(n.axis&&"x"===n.axis||(e.pageY-t(r).scrollTop()<n.scrollSensitivity?o=t(r).scrollTop(t(r).scrollTop()-n.scrollSpeed):t(window).height()-(e.pageY-t(r).scrollTop())<n.scrollSensitivity&&(o=t(r).scrollTop(t(r).scrollTop()+n.scrollSpeed))),n.axis&&"y"===n.axis||(e.pageX-t(r).scrollLeft()<n.scrollSensitivity?o=t(r).scrollLeft(t(r).scrollLeft()-n.scrollSpeed):t(window).width()-(e.pageX-t(r).scrollLeft())<n.scrollSensitivity&&(o=t(r).scrollLeft(t(r).scrollLeft()+n.scrollSpeed)))),o!==!1&&t.ui.ddmanager&&!n.dropBehaviour&&t.ui.ddmanager.prepareOffsets(s,e)}}),t.ui.plugin.add("draggable","snap",{start:function(e,i,s){var n=s.options;s.snapElements=[],t(n.snap.constructor!==String?n.snap.items||":data(ui-draggable)":n.snap).each(function(){var e=t(this),i=e.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:e.outerWidth(),height:e.outerHeight(),top:i.top,left:i.left})})},drag:function(e,i,s){var n,o,a,r,h,l,c,u,d,p,f=s.options,g=f.snapTolerance,m=i.offset.left,_=m+s.helperProportions.width,v=i.offset.top,b=v+s.helperProportions.height;for(d=s.snapElements.length-1;d>=0;d--)h=s.snapElements[d].left-s.margins.left,l=h+s.snapElements[d].width,c=s.snapElements[d].top-s.margins.top,u=c+s.snapElements[d].height,h-g>_||m>l+g||c-g>b||v>u+g||!t.contains(s.snapElements[d].item.ownerDocument,s.snapElements[d].item)?(s.snapElements[d].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,e,t.extend(s._uiHash(),{snapItem:s.snapElements[d].item})),s.snapElements[d].snapping=!1):("inner"!==f.snapMode&&(n=g>=Math.abs(c-b),o=g>=Math.abs(u-v),a=g>=Math.abs(h-_),r=g>=Math.abs(l-m),n&&(i.position.top=s._convertPositionTo("relative",{top:c-s.helperProportions.height,left:0}).top),o&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top),a&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left)),p=n||o||a||r,"outer"!==f.snapMode&&(n=g>=Math.abs(c-v),o=g>=Math.abs(u-b),a=g>=Math.abs(h-m),r=g>=Math.abs(l-_),n&&(i.position.top=s._convertPositionTo("relative",{top:c,left:0}).top),o&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top),a&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left)),!s.snapElements[d].snapping&&(n||o||a||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,e,t.extend(s._uiHash(),{snapItem:s.snapElements[d].item})),s.snapElements[d].snapping=n||o||a||r||p)}}),t.ui.plugin.add("draggable","stack",{start:function(e,i,s){var n,o=s.options,a=t.makeArray(t(o.stack)).sort(function(e,i){return(parseInt(t(e).css("zIndex"),10)||0)-(parseInt(t(i).css("zIndex"),10)||0)});a.length&&(n=parseInt(t(a[0]).css("zIndex"),10)||0,t(a).each(function(e){t(this).css("zIndex",n+e)}),this.css("zIndex",n+a.length))}}),t.ui.plugin.add("draggable","zIndex",{start:function(e,i,s){var n=t(i.helper),o=s.options;n.css("zIndex")&&(o._zIndex=n.css("zIndex")),n.css("zIndex",o.zIndex)},stop:function(e,i,s){var n=s.options;n._zIndex&&t(i.helper).css("zIndex",n._zIndex)}}),t.ui.draggable,t.widget("ui.resizable",t.ui.mouse,{version:"1.12.1",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,classes:{"ui-resizable-se":"ui-icon ui-icon-gripsmall-diagonal-se"},containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(t){return parseFloat(t)||0},_isNumber:function(t){return!isNaN(parseFloat(t))},_hasScroll:function(e,i){if("hidden"===t(e).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return e[s]>0?!0:(e[s]=1,n=e[s]>0,e[s]=0,n)},_create:function(){var e,i=this.options,s=this;this._addClass("ui-resizable"),t.extend(this,{_aspectRatio:!!i.aspectRatio,aspectRatio:i.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:i.helper||i.ghost||i.animate?i.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(t("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,e={marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom"),marginLeft:this.originalElement.css("marginLeft")},this.element.css(e),this.originalElement.css("margin",0),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css(e),this._proportionallyResize()),this._setupHandles(),i.autoHide&&t(this.element).on("mouseenter",function(){i.disabled||(s._removeClass("ui-resizable-autohide"),s._handles.show())}).on("mouseleave",function(){i.disabled||s.resizing||(s._addClass("ui-resizable-autohide"),s._handles.hide())}),this._mouseInit()},_destroy:function(){this._mouseDestroy();var e,i=function(e){t(e).removeData("resizable").removeData("ui-resizable").off(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),e=this.element,this.originalElement.css({position:e.css("position"),width:e.outerWidth(),height:e.outerHeight(),top:e.css("top"),left:e.css("left")}).insertAfter(e),e.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_setOption:function(t,e){switch(this._super(t,e),t){case"handles":this._removeHandles(),this._setupHandles();break;default:}},_setupHandles:function(){var e,i,s,n,o,a=this.options,r=this;if(this.handles=a.handles||(t(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=t(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),s=this.handles.split(","),this.handles={},i=0;s.length>i;i++)e=t.trim(s[i]),n="ui-resizable-"+e,o=t("<div>"),this._addClass(o,"ui-resizable-handle "+n),o.css({zIndex:a.zIndex}),this.handles[e]=".ui-resizable-"+e,this.element.append(o);this._renderAxis=function(e){var i,s,n,o;e=e||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=t(this.handles[i]),this._on(this.handles[i],{mousedown:r._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=t(this.handles[i],this.element),o=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),e.css(n,o),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.on("mouseover",function(){r.resizing||(this.className&&(o=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),r.axis=o&&o[1]?o[1]:"se")}),a.autoHide&&(this._handles.hide(),this._addClass("ui-resizable-autohide"))},_removeHandles:function(){this._handles.remove()},_mouseCapture:function(e){var i,s,n=!1;for(i in this.handles)s=t(this.handles[i])[0],(s===e.target||t.contains(s,e.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(e){var i,s,n,o=this.options,a=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),o.containment&&(i+=t(o.containment).scrollLeft()||0,s+=t(o.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:a.width(),height:a.height()},this.originalSize=this._helper?{width:a.outerWidth(),height:a.outerHeight()}:{width:a.width(),height:a.height()},this.sizeDiff={width:a.outerWidth()-a.width(),height:a.outerHeight()-a.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:e.pageX,top:e.pageY},this.aspectRatio="number"==typeof o.aspectRatio?o.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=t(".ui-resizable-"+this.axis).css("cursor"),t("body").css("cursor","auto"===n?this.axis+"-resize":n),this._addClass("ui-resizable-resizing"),this._propagate("start",e),!0},_mouseDrag:function(e){var i,s,n=this.originalMousePosition,o=this.axis,a=e.pageX-n.left||0,r=e.pageY-n.top||0,h=this._change[o];return this._updatePrevProperties(),h?(i=h.apply(this,[e,a,r]),this._updateVirtualBoundaries(e.shiftKey),(this._aspectRatio||e.shiftKey)&&(i=this._updateRatio(i,e)),i=this._respectSize(i,e),this._updateCache(i),this._propagate("resize",e),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),t.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger("resize",e,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(e){this.resizing=!1;var i,s,n,o,a,r,h,l=this.options,c=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:c.sizeDiff.height,o=s?0:c.sizeDiff.width,a={width:c.helper.width()-o,height:c.helper.height()-n},r=parseFloat(c.element.css("left"))+(c.position.left-c.originalPosition.left)||null,h=parseFloat(c.element.css("top"))+(c.position.top-c.originalPosition.top)||null,l.animate||this.element.css(t.extend(a,{top:h,left:r})),c.helper.height(c.size.height),c.helper.width(c.size.width),this._helper&&!l.animate&&this._proportionallyResize()),t("body").css("cursor","auto"),this._removeClass("ui-resizable-resizing"),this._propagate("stop",e),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var t={};return this.position.top!==this.prevPosition.top&&(t.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(t.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(t.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(t.height=this.size.height+"px"),this.helper.css(t),t},_updateVirtualBoundaries:function(t){var e,i,s,n,o,a=this.options;o={minWidth:this._isNumber(a.minWidth)?a.minWidth:0,maxWidth:this._isNumber(a.maxWidth)?a.maxWidth:1/0,minHeight:this._isNumber(a.minHeight)?a.minHeight:0,maxHeight:this._isNumber(a.maxHeight)?a.maxHeight:1/0},(this._aspectRatio||t)&&(e=o.minHeight*this.aspectRatio,s=o.minWidth/this.aspectRatio,i=o.maxHeight*this.aspectRatio,n=o.maxWidth/this.aspectRatio,e>o.minWidth&&(o.minWidth=e),s>o.minHeight&&(o.minHeight=s),o.maxWidth>i&&(o.maxWidth=i),o.maxHeight>n&&(o.maxHeight=n)),this._vBoundaries=o},_updateCache:function(t){this.offset=this.helper.offset(),this._isNumber(t.left)&&(this.position.left=t.left),this._isNumber(t.top)&&(this.position.top=t.top),this._isNumber(t.height)&&(this.size.height=t.height),this._isNumber(t.width)&&(this.size.width=t.width)},_updateRatio:function(t){var e=this.position,i=this.size,s=this.axis;return this._isNumber(t.height)?t.width=t.height*this.aspectRatio:this._isNumber(t.width)&&(t.height=t.width/this.aspectRatio),"sw"===s&&(t.left=e.left+(i.width-t.width),t.top=null),"nw"===s&&(t.top=e.top+(i.height-t.height),t.left=e.left+(i.width-t.width)),t},_respectSize:function(t){var e=this._vBoundaries,i=this.axis,s=this._isNumber(t.width)&&e.maxWidth&&e.maxWidth<t.width,n=this._isNumber(t.height)&&e.maxHeight&&e.maxHeight<t.height,o=this._isNumber(t.width)&&e.minWidth&&e.minWidth>t.width,a=this._isNumber(t.height)&&e.minHeight&&e.minHeight>t.height,r=this.originalPosition.left+this.originalSize.width,h=this.originalPosition.top+this.originalSize.height,l=/sw|nw|w/.test(i),c=/nw|ne|n/.test(i);return o&&(t.width=e.minWidth),a&&(t.height=e.minHeight),s&&(t.width=e.maxWidth),n&&(t.height=e.maxHeight),o&&l&&(t.left=r-e.minWidth),s&&l&&(t.left=r-e.maxWidth),a&&c&&(t.top=h-e.minHeight),n&&c&&(t.top=h-e.maxHeight),t.width||t.height||t.left||!t.top?t.width||t.height||t.top||!t.left||(t.left=null):t.top=null,t},_getPaddingPlusBorderDimensions:function(t){for(var e=0,i=[],s=[t.css("borderTopWidth"),t.css("borderRightWidth"),t.css("borderBottomWidth"),t.css("borderLeftWidth")],n=[t.css("paddingTop"),t.css("paddingRight"),t.css("paddingBottom"),t.css("paddingLeft")];4>e;e++)i[e]=parseFloat(s[e])||0,i[e]+=parseFloat(n[e])||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var t,e=0,i=this.helper||this.element;this._proportionallyResizeElements.length>e;e++)t=this._proportionallyResizeElements[e],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(t)),t.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var e=this.element,i=this.options;this.elementOffset=e.offset(),this._helper?(this.helper=this.helper||t("<div style='overflow:hidden;'></div>"),this._addClass(this.helper,this._helper),this.helper.css({width:this.element.outerWidth(),height:this.element.outerHeight(),position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(t,e){return{width:this.originalSize.width+e}},w:function(t,e){var i=this.originalSize,s=this.originalPosition;return{left:s.left+e,width:i.width-e}},n:function(t,e,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(t,e,i){return{height:this.originalSize.height+i}},se:function(e,i,s){return t.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[e,i,s]))},sw:function(e,i,s){return t.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[e,i,s]))},ne:function(e,i,s){return t.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[e,i,s]))},nw:function(e,i,s){return t.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[e,i,s]))}},_propagate:function(e,i){t.ui.plugin.call(this,e,[i,this.ui()]),"resize"!==e&&this._trigger(e,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),t.ui.plugin.add("resizable","animate",{stop:function(e){var i=t(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,o=n.length&&/textarea/i.test(n[0].nodeName),a=o&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=o?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-a},l=parseFloat(i.element.css("left"))+(i.position.left-i.originalPosition.left)||null,c=parseFloat(i.element.css("top"))+(i.position.top-i.originalPosition.top)||null;i.element.animate(t.extend(h,c&&l?{top:c,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseFloat(i.element.css("width")),height:parseFloat(i.element.css("height")),top:parseFloat(i.element.css("top")),left:parseFloat(i.element.css("left"))};n&&n.length&&t(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",e)}})}}),t.ui.plugin.add("resizable","containment",{start:function(){var e,i,s,n,o,a,r,h=t(this).resizable("instance"),l=h.options,c=h.element,u=l.containment,d=u instanceof t?u.get(0):/parent/.test(u)?c.parent().get(0):u;d&&(h.containerElement=t(d),/document/.test(u)||u===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:t(document),left:0,top:0,width:t(document).width(),height:t(document).height()||document.body.parentNode.scrollHeight}):(e=t(d),i=[],t(["Top","Right","Left","Bottom"]).each(function(t,s){i[t]=h._num(e.css("padding"+s))}),h.containerOffset=e.offset(),h.containerPosition=e.position(),h.containerSize={height:e.innerHeight()-i[3],width:e.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,o=h.containerSize.width,a=h._hasScroll(d,"left")?d.scrollWidth:o,r=h._hasScroll(d)?d.scrollHeight:n,h.parentData={element:d,left:s.left,top:s.top,width:a,height:r}))},resize:function(e){var i,s,n,o,a=t(this).resizable("instance"),r=a.options,h=a.containerOffset,l=a.position,c=a._aspectRatio||e.shiftKey,u={top:0,left:0},d=a.containerElement,p=!0;d[0]!==document&&/static/.test(d.css("position"))&&(u=h),l.left<(a._helper?h.left:0)&&(a.size.width=a.size.width+(a._helper?a.position.left-h.left:a.position.left-u.left),c&&(a.size.height=a.size.width/a.aspectRatio,p=!1),a.position.left=r.helper?h.left:0),l.top<(a._helper?h.top:0)&&(a.size.height=a.size.height+(a._helper?a.position.top-h.top:a.position.top),c&&(a.size.width=a.size.height*a.aspectRatio,p=!1),a.position.top=a._helper?h.top:0),n=a.containerElement.get(0)===a.element.parent().get(0),o=/relative|absolute/.test(a.containerElement.css("position")),n&&o?(a.offset.left=a.parentData.left+a.position.left,a.offset.top=a.parentData.top+a.position.top):(a.offset.left=a.element.offset().left,a.offset.top=a.element.offset().top),i=Math.abs(a.sizeDiff.width+(a._helper?a.offset.left-u.left:a.offset.left-h.left)),s=Math.abs(a.sizeDiff.height+(a._helper?a.offset.top-u.top:a.offset.top-h.top)),i+a.size.width>=a.parentData.width&&(a.size.width=a.parentData.width-i,c&&(a.size.height=a.size.width/a.aspectRatio,p=!1)),s+a.size.height>=a.parentData.height&&(a.size.height=a.parentData.height-s,c&&(a.size.width=a.size.height*a.aspectRatio,p=!1)),p||(a.position.left=a.prevPosition.left,a.position.top=a.prevPosition.top,a.size.width=a.prevSize.width,a.size.height=a.prevSize.height)},stop:function(){var e=t(this).resizable("instance"),i=e.options,s=e.containerOffset,n=e.containerPosition,o=e.containerElement,a=t(e.helper),r=a.offset(),h=a.outerWidth()-e.sizeDiff.width,l=a.outerHeight()-e.sizeDiff.height;e._helper&&!i.animate&&/relative/.test(o.css("position"))&&t(this).css({left:r.left-n.left-s.left,width:h,height:l}),e._helper&&!i.animate&&/static/.test(o.css("position"))&&t(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),t.ui.plugin.add("resizable","alsoResize",{start:function(){var e=t(this).resizable("instance"),i=e.options;t(i.alsoResize).each(function(){var e=t(this);e.data("ui-resizable-alsoresize",{width:parseFloat(e.width()),height:parseFloat(e.height()),left:parseFloat(e.css("left")),top:parseFloat(e.css("top"))})})},resize:function(e,i){var s=t(this).resizable("instance"),n=s.options,o=s.originalSize,a=s.originalPosition,r={height:s.size.height-o.height||0,width:s.size.width-o.width||0,top:s.position.top-a.top||0,left:s.position.left-a.left||0};t(n.alsoResize).each(function(){var e=t(this),s=t(this).data("ui-resizable-alsoresize"),n={},o=e.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];t.each(o,function(t,e){var i=(s[e]||0)+(r[e]||0);i&&i>=0&&(n[e]=i||null)}),e.css(n)})},stop:function(){t(this).removeData("ui-resizable-alsoresize")}}),t.ui.plugin.add("resizable","ghost",{start:function(){var e=t(this).resizable("instance"),i=e.size;e.ghost=e.originalElement.clone(),e.ghost.css({opacity:.25,display:"block",position:"relative",height:i.height,width:i.width,margin:0,left:0,top:0}),e._addClass(e.ghost,"ui-resizable-ghost"),t.uiBackCompat!==!1&&"string"==typeof e.options.ghost&&e.ghost.addClass(this.options.ghost),e.ghost.appendTo(e.helper)},resize:function(){var e=t(this).resizable("instance");e.ghost&&e.ghost.css({position:"relative",height:e.size.height,width:e.size.width})},stop:function(){var e=t(this).resizable("instance");e.ghost&&e.helper&&e.helper.get(0).removeChild(e.ghost.get(0))}}),t.ui.plugin.add("resizable","grid",{resize:function(){var e,i=t(this).resizable("instance"),s=i.options,n=i.size,o=i.originalSize,a=i.originalPosition,r=i.axis,h="number"==typeof s.grid?[s.grid,s.grid]:s.grid,l=h[0]||1,c=h[1]||1,u=Math.round((n.width-o.width)/l)*l,d=Math.round((n.height-o.height)/c)*c,p=o.width+u,f=o.height+d,g=s.maxWidth&&p>s.maxWidth,m=s.maxHeight&&f>s.maxHeight,_=s.minWidth&&s.minWidth>p,v=s.minHeight&&s.minHeight>f;s.grid=h,_&&(p+=l),v&&(f+=c),g&&(p-=l),m&&(f-=c),/^(se|s|e)$/.test(r)?(i.size.width=p,i.size.height=f):/^(ne)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.top=a.top-d):/^(sw)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.left=a.left-u):((0>=f-c||0>=p-l)&&(e=i._getPaddingPlusBorderDimensions(this)),f-c>0?(i.size.height=f,i.position.top=a.top-d):(f=c-e.height,i.size.height=f,i.position.top=a.top+o.height-f),p-l>0?(i.size.width=p,i.position.left=a.left-u):(p=l-e.width,i.size.width=p,i.position.left=a.left+o.width-p))}}),t.ui.resizable,t.widget("ui.dialog",{version:"1.12.1",options:{appendTo:"body",autoOpen:!0,buttons:[],classes:{"ui-dialog":"ui-corner-all","ui-dialog-titlebar":"ui-corner-all"},closeOnEscape:!0,closeText:"Close",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(e){var i=t(this).css(e).offset().top;0>i&&t(this).css("top",e.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),null==this.options.title&&null!=this.originalTitle&&(this.options.title=this.originalTitle),this.options.disabled&&(this.options.disabled=!1),this._createWrapper(),this.element.show().removeAttr("title").appendTo(this.uiDialog),this._addClass("ui-dialog-content","ui-widget-content"),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&t.fn.draggable&&this._makeDraggable(),this.options.resizable&&t.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var e=this.options.appendTo;return e&&(e.jquery||e.nodeType)?t(e):this.document.find(e||"body").eq(0)},_destroy:function(){var t,e=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().css(this.originalCss).detach(),this.uiDialog.remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),t=e.parent.children().eq(e.index),t.length&&t[0]!==this.element[0]?t.before(this.element):e.parent.append(this.element)},widget:function(){return this.uiDialog
-},disable:t.noop,enable:t.noop,close:function(e){var i=this;this._isOpen&&this._trigger("beforeClose",e)!==!1&&(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),this.opener.filter(":focusable").trigger("focus").length||t.ui.safeBlur(t.ui.safeActiveElement(this.document[0])),this._hide(this.uiDialog,this.options.hide,function(){i._trigger("close",e)}))},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(e,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+t(this).css("z-index")}).get(),o=Math.max.apply(null,n);return o>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",o+1),s=!0),s&&!i&&this._trigger("focus",e),s},open:function(){var e=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=t(t.ui.safeActiveElement(this.document[0])),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){e._focusTabbable(),e._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"),void 0)},_focusTabbable:function(){var t=this._focusedElement;t||(t=this.element.find("[autofocus]")),t.length||(t=this.element.find(":tabbable")),t.length||(t=this.uiDialogButtonPane.find(":tabbable")),t.length||(t=this.uiDialogTitlebarClose.filter(":tabbable")),t.length||(t=this.uiDialog),t.eq(0).trigger("focus")},_keepFocus:function(e){function i(){var e=t.ui.safeActiveElement(this.document[0]),i=this.uiDialog[0]===e||t.contains(this.uiDialog[0],e);i||this._focusTabbable()}e.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=t("<div>").hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._addClass(this.uiDialog,"ui-dialog","ui-widget ui-widget-content ui-front"),this._on(this.uiDialog,{keydown:function(e){if(this.options.closeOnEscape&&!e.isDefaultPrevented()&&e.keyCode&&e.keyCode===t.ui.keyCode.ESCAPE)return e.preventDefault(),this.close(e),void 0;if(e.keyCode===t.ui.keyCode.TAB&&!e.isDefaultPrevented()){var i=this.uiDialog.find(":tabbable"),s=i.filter(":first"),n=i.filter(":last");e.target!==n[0]&&e.target!==this.uiDialog[0]||e.shiftKey?e.target!==s[0]&&e.target!==this.uiDialog[0]||!e.shiftKey||(this._delay(function(){n.trigger("focus")}),e.preventDefault()):(this._delay(function(){s.trigger("focus")}),e.preventDefault())}},mousedown:function(t){this._moveToTop(t)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var e;this.uiDialogTitlebar=t("<div>"),this._addClass(this.uiDialogTitlebar,"ui-dialog-titlebar","ui-widget-header ui-helper-clearfix"),this._on(this.uiDialogTitlebar,{mousedown:function(e){t(e.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.trigger("focus")}}),this.uiDialogTitlebarClose=t("<button type='button'></button>").button({label:t("<a>").text(this.options.closeText).html(),icon:"ui-icon-closethick",showLabel:!1}).appendTo(this.uiDialogTitlebar),this._addClass(this.uiDialogTitlebarClose,"ui-dialog-titlebar-close"),this._on(this.uiDialogTitlebarClose,{click:function(t){t.preventDefault(),this.close(t)}}),e=t("<span>").uniqueId().prependTo(this.uiDialogTitlebar),this._addClass(e,"ui-dialog-title"),this._title(e),this.uiDialogTitlebar.prependTo(this.uiDialog),this.uiDialog.attr({"aria-labelledby":e.attr("id")})},_title:function(t){this.options.title?t.text(this.options.title):t.html("&#160;")},_createButtonPane:function(){this.uiDialogButtonPane=t("<div>"),this._addClass(this.uiDialogButtonPane,"ui-dialog-buttonpane","ui-widget-content ui-helper-clearfix"),this.uiButtonSet=t("<div>").appendTo(this.uiDialogButtonPane),this._addClass(this.uiButtonSet,"ui-dialog-buttonset"),this._createButtons()},_createButtons:function(){var e=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),t.isEmptyObject(i)||t.isArray(i)&&!i.length?(this._removeClass(this.uiDialog,"ui-dialog-buttons"),void 0):(t.each(i,function(i,s){var n,o;s=t.isFunction(s)?{click:s,text:i}:s,s=t.extend({type:"button"},s),n=s.click,o={icon:s.icon,iconPosition:s.iconPosition,showLabel:s.showLabel,icons:s.icons,text:s.text},delete s.click,delete s.icon,delete s.iconPosition,delete s.showLabel,delete s.icons,"boolean"==typeof s.text&&delete s.text,t("<button></button>",s).button(o).appendTo(e.uiButtonSet).on("click",function(){n.apply(e.element[0],arguments)})}),this._addClass(this.uiDialog,"ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function e(t){return{position:t.position,offset:t.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(s,n){i._addClass(t(this),"ui-dialog-dragging"),i._blockFrames(),i._trigger("dragStart",s,e(n))},drag:function(t,s){i._trigger("drag",t,e(s))},stop:function(n,o){var a=o.offset.left-i.document.scrollLeft(),r=o.offset.top-i.document.scrollTop();s.position={my:"left top",at:"left"+(a>=0?"+":"")+a+" "+"top"+(r>=0?"+":"")+r,of:i.window},i._removeClass(t(this),"ui-dialog-dragging"),i._unblockFrames(),i._trigger("dragStop",n,e(o))}})},_makeResizable:function(){function e(t){return{originalPosition:t.originalPosition,originalSize:t.originalSize,position:t.position,size:t.size}}var i=this,s=this.options,n=s.resizable,o=this.uiDialog.css("position"),a="string"==typeof n?n:"n,e,s,w,se,sw,ne,nw";this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:a,start:function(s,n){i._addClass(t(this),"ui-dialog-resizing"),i._blockFrames(),i._trigger("resizeStart",s,e(n))},resize:function(t,s){i._trigger("resize",t,e(s))},stop:function(n,o){var a=i.uiDialog.offset(),r=a.left-i.document.scrollLeft(),h=a.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:"left top",at:"left"+(r>=0?"+":"")+r+" "+"top"+(h>=0?"+":"")+h,of:i.window},i._removeClass(t(this),"ui-dialog-resizing"),i._unblockFrames(),i._trigger("resizeStop",n,e(o))}}).css("position",o)},_trackFocus:function(){this._on(this.widget(),{focusin:function(e){this._makeFocusTarget(),this._focusedElement=t(e.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var e=this._trackingInstances(),i=t.inArray(this,e);-1!==i&&e.splice(i,1)},_trackingInstances:function(){var t=this.document.data("ui-dialog-instances");return t||(t=[],this.document.data("ui-dialog-instances",t)),t},_minHeight:function(){var t=this.options;return"auto"===t.height?t.minHeight:Math.min(t.minHeight,t.height)},_position:function(){var t=this.uiDialog.is(":visible");t||this.uiDialog.show(),this.uiDialog.position(this.options.position),t||this.uiDialog.hide()},_setOptions:function(e){var i=this,s=!1,n={};t.each(e,function(t,e){i._setOption(t,e),t in i.sizeRelatedOptions&&(s=!0),t in i.resizableRelatedOptions&&(n[t]=e)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(e,i){var s,n,o=this.uiDialog;"disabled"!==e&&(this._super(e,i),"appendTo"===e&&this.uiDialog.appendTo(this._appendTo()),"buttons"===e&&this._createButtons(),"closeText"===e&&this.uiDialogTitlebarClose.button({label:t("<a>").text(""+this.options.closeText).html()}),"draggable"===e&&(s=o.is(":data(ui-draggable)"),s&&!i&&o.draggable("destroy"),!s&&i&&this._makeDraggable()),"position"===e&&this._position(),"resizable"===e&&(n=o.is(":data(ui-resizable)"),n&&!i&&o.resizable("destroy"),n&&"string"==typeof i&&o.resizable("option","handles",i),n||i===!1||this._makeResizable()),"title"===e&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var t,e,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),t=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),e=Math.max(0,s.minHeight-t),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-t):"none","auto"===s.height?this.element.css({minHeight:e,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-t)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var e=t(this);return t("<div>").css({position:"absolute",width:e.outerWidth(),height:e.outerHeight()}).appendTo(e.parent()).offset(e.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(e){return t(e.target).closest(".ui-dialog").length?!0:!!t(e.target).closest(".ui-datepicker").length},_createOverlay:function(){if(this.options.modal){var e=!0;this._delay(function(){e=!1}),this.document.data("ui-dialog-overlays")||this._on(this.document,{focusin:function(t){e||this._allowInteraction(t)||(t.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=t("<div>").appendTo(this._appendTo()),this._addClass(this.overlay,null,"ui-widget-overlay ui-front"),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var t=this.document.data("ui-dialog-overlays")-1;t?this.document.data("ui-dialog-overlays",t):(this._off(this.document,"focusin"),this.document.removeData("ui-dialog-overlays")),this.overlay.remove(),this.overlay=null}}}),t.uiBackCompat!==!1&&t.widget("ui.dialog",t.ui.dialog,{options:{dialogClass:""},_createWrapper:function(){this._super(),this.uiDialog.addClass(this.options.dialogClass)},_setOption:function(t,e){"dialogClass"===t&&this.uiDialog.removeClass(this.options.dialogClass).addClass(e),this._superApply(arguments)}}),t.ui.dialog,t.widget("ui.droppable",{version:"1.12.1",widgetEventPrefix:"drop",options:{accept:"*",addClasses:!0,greedy:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var e,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=t.isFunction(s)?s:function(t){return t.is(s)},this.proportions=function(){return arguments.length?(e=arguments[0],void 0):e?e:e={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this._addClass("ui-droppable")},_addToManager:function(e){t.ui.ddmanager.droppables[e]=t.ui.ddmanager.droppables[e]||[],t.ui.ddmanager.droppables[e].push(this)},_splice:function(t){for(var e=0;t.length>e;e++)t[e]===this&&t.splice(e,1)},_destroy:function(){var e=t.ui.ddmanager.droppables[this.options.scope];this._splice(e)},_setOption:function(e,i){if("accept"===e)this.accept=t.isFunction(i)?i:function(t){return t.is(i)};else if("scope"===e){var s=t.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(e,i)},_activate:function(e){var i=t.ui.ddmanager.current;this._addActiveClass(),i&&this._trigger("activate",e,this.ui(i))},_deactivate:function(e){var i=t.ui.ddmanager.current;this._removeActiveClass(),i&&this._trigger("deactivate",e,this.ui(i))},_over:function(e){var i=t.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this._addHoverClass(),this._trigger("over",e,this.ui(i)))},_out:function(e){var i=t.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this._removeHoverClass(),this._trigger("out",e,this.ui(i)))},_drop:function(e,i){var s=i||t.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var i=t(this).droppable("instance");return i.options.greedy&&!i.options.disabled&&i.options.scope===s.options.scope&&i.accept.call(i.element[0],s.currentItem||s.element)&&v(s,t.extend(i,{offset:i.element.offset()}),i.options.tolerance,e)?(n=!0,!1):void 0}),n?!1:this.accept.call(this.element[0],s.currentItem||s.element)?(this._removeActiveClass(),this._removeHoverClass(),this._trigger("drop",e,this.ui(s)),this.element):!1):!1},ui:function(t){return{draggable:t.currentItem||t.element,helper:t.helper,position:t.position,offset:t.positionAbs}},_addHoverClass:function(){this._addClass("ui-droppable-hover")},_removeHoverClass:function(){this._removeClass("ui-droppable-hover")},_addActiveClass:function(){this._addClass("ui-droppable-active")},_removeActiveClass:function(){this._removeClass("ui-droppable-active")}});var v=t.ui.intersect=function(){function t(t,e,i){return t>=e&&e+i>t}return function(e,i,s,n){if(!i.offset)return!1;var o=(e.positionAbs||e.position.absolute).left+e.margins.left,a=(e.positionAbs||e.position.absolute).top+e.margins.top,r=o+e.helperProportions.width,h=a+e.helperProportions.height,l=i.offset.left,c=i.offset.top,u=l+i.proportions().width,d=c+i.proportions().height;switch(s){case"fit":return o>=l&&u>=r&&a>=c&&d>=h;case"intersect":return o+e.helperProportions.width/2>l&&u>r-e.helperProportions.width/2&&a+e.helperProportions.height/2>c&&d>h-e.helperProportions.height/2;case"pointer":return t(n.pageY,c,i.proportions().height)&&t(n.pageX,l,i.proportions().width);case"touch":return(a>=c&&d>=a||h>=c&&d>=h||c>a&&h>d)&&(o>=l&&u>=o||r>=l&&u>=r||l>o&&r>u);default:return!1}}}();t.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(e,i){var s,n,o=t.ui.ddmanager.droppables[e.options.scope]||[],a=i?i.type:null,r=(e.currentItem||e.element).find(":data(ui-droppable)").addBack();t:for(s=0;o.length>s;s++)if(!(o[s].options.disabled||e&&!o[s].accept.call(o[s].element[0],e.currentItem||e.element))){for(n=0;r.length>n;n++)if(r[n]===o[s].element[0]){o[s].proportions().height=0;continue t}o[s].visible="none"!==o[s].element.css("display"),o[s].visible&&("mousedown"===a&&o[s]._activate.call(o[s],i),o[s].offset=o[s].element.offset(),o[s].proportions({width:o[s].element[0].offsetWidth,height:o[s].element[0].offsetHeight}))}},drop:function(e,i){var s=!1;return t.each((t.ui.ddmanager.droppables[e.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&v(e,this,this.options.tolerance,i)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(e,i){e.element.parentsUntil("body").on("scroll.droppable",function(){e.options.refreshPositions||t.ui.ddmanager.prepareOffsets(e,i)})},drag:function(e,i){e.options.refreshPositions&&t.ui.ddmanager.prepareOffsets(e,i),t.each(t.ui.ddmanager.droppables[e.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,o,a=v(e,this,this.options.tolerance,i),r=!a&&this.isover?"isout":a&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,o=this.element.parents(":data(ui-droppable)").filter(function(){return t(this).droppable("instance").options.scope===n}),o.length&&(s=t(o[0]).droppable("instance"),s.greedyChild="isover"===r)),s&&"isover"===r&&(s.isover=!1,s.isout=!0,s._out.call(s,i)),this[r]=!0,this["isout"===r?"isover":"isout"]=!1,this["isover"===r?"_over":"_out"].call(this,i),s&&"isout"===r&&(s.isout=!1,s.isover=!0,s._over.call(s,i)))}})},dragStop:function(e,i){e.element.parentsUntil("body").off("scroll.droppable"),e.options.refreshPositions||t.ui.ddmanager.prepareOffsets(e,i)}},t.uiBackCompat!==!1&&t.widget("ui.droppable",t.ui.droppable,{options:{hoverClass:!1,activeClass:!1},_addActiveClass:function(){this._super(),this.options.activeClass&&this.element.addClass(this.options.activeClass)},_removeActiveClass:function(){this._super(),this.options.activeClass&&this.element.removeClass(this.options.activeClass)},_addHoverClass:function(){this._super(),this.options.hoverClass&&this.element.addClass(this.options.hoverClass)},_removeHoverClass:function(){this._super(),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass)}}),t.ui.droppable,t.widget("ui.progressbar",{version:"1.12.1",options:{classes:{"ui-progressbar":"ui-corner-all","ui-progressbar-value":"ui-corner-left","ui-progressbar-complete":"ui-corner-right"},max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.attr({role:"progressbar","aria-valuemin":this.min}),this._addClass("ui-progressbar","ui-widget ui-widget-content"),this.valueDiv=t("<div>").appendTo(this.element),this._addClass(this.valueDiv,"ui-progressbar-value","ui-widget-header"),this._refreshValue()},_destroy:function(){this.element.removeAttr("role aria-valuemin aria-valuemax aria-valuenow"),this.valueDiv.remove()},value:function(t){return void 0===t?this.options.value:(this.options.value=this._constrainedValue(t),this._refreshValue(),void 0)},_constrainedValue:function(t){return void 0===t&&(t=this.options.value),this.indeterminate=t===!1,"number"!=typeof t&&(t=0),this.indeterminate?!1:Math.min(this.options.max,Math.max(this.min,t))},_setOptions:function(t){var e=t.value;delete t.value,this._super(t),this.options.value=this._constrainedValue(e),this._refreshValue()},_setOption:function(t,e){"max"===t&&(e=Math.max(this.min,e)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var e=this.options.value,i=this._percentage();this.valueDiv.toggle(this.indeterminate||e>this.min).width(i.toFixed(0)+"%"),this._toggleClass(this.valueDiv,"ui-progressbar-complete",null,e===this.options.max)._toggleClass("ui-progressbar-indeterminate",null,this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=t("<div>").appendTo(this.valueDiv),this._addClass(this.overlayDiv,"ui-progressbar-overlay"))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":e}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==e&&(this.oldValue=e,this._trigger("change")),e===this.options.max&&this._trigger("complete")}}),t.widget("ui.selectable",t.ui.mouse,{version:"1.12.1",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var e=this;this._addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){e.elementPos=t(e.element[0]).offset(),e.selectees=t(e.options.filter,e.element[0]),e._addClass(e.selectees,"ui-selectee"),e.selectees.each(function(){var i=t(this),s=i.offset(),n={left:s.left-e.elementPos.left,top:s.top-e.elementPos.top};t.data(this,"selectable-item",{element:this,$element:i,left:n.left,top:n.top,right:n.left+i.outerWidth(),bottom:n.top+i.outerHeight(),startselected:!1,selected:i.hasClass("ui-selected"),selecting:i.hasClass("ui-selecting"),unselecting:i.hasClass("ui-unselecting")})})},this.refresh(),this._mouseInit(),this.helper=t("<div>"),this._addClass(this.helper,"ui-selectable-helper")},_destroy:function(){this.selectees.removeData("selectable-item"),this._mouseDestroy()},_mouseStart:function(e){var i=this,s=this.options;this.opos=[e.pageX,e.pageY],this.elementPos=t(this.element[0]).offset(),this.options.disabled||(this.selectees=t(s.filter,this.element[0]),this._trigger("start",e),t(s.appendTo).append(this.helper),this.helper.css({left:e.pageX,top:e.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var s=t.data(this,"selectable-item");s.startselected=!0,e.metaKey||e.ctrlKey||(i._removeClass(s.$element,"ui-selected"),s.selected=!1,i._addClass(s.$element,"ui-unselecting"),s.unselecting=!0,i._trigger("unselecting",e,{unselecting:s.element}))}),t(e.target).parents().addBack().each(function(){var s,n=t.data(this,"selectable-item");return n?(s=!e.metaKey&&!e.ctrlKey||!n.$element.hasClass("ui-selected"),i._removeClass(n.$element,s?"ui-unselecting":"ui-selected")._addClass(n.$element,s?"ui-selecting":"ui-unselecting"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger("selecting",e,{selecting:n.element}):i._trigger("unselecting",e,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(e){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,o=this.opos[0],a=this.opos[1],r=e.pageX,h=e.pageY;return o>r&&(i=r,r=o,o=i),a>h&&(i=h,h=a,a=i),this.helper.css({left:o,top:a,width:r-o,height:h-a}),this.selectees.each(function(){var i=t.data(this,"selectable-item"),l=!1,c={};i&&i.element!==s.element[0]&&(c.left=i.left+s.elementPos.left,c.right=i.right+s.elementPos.left,c.top=i.top+s.elementPos.top,c.bottom=i.bottom+s.elementPos.top,"touch"===n.tolerance?l=!(c.left>r||o>c.right||c.top>h||a>c.bottom):"fit"===n.tolerance&&(l=c.left>o&&r>c.right&&c.top>a&&h>c.bottom),l?(i.selected&&(s._removeClass(i.$element,"ui-selected"),i.selected=!1),i.unselecting&&(s._removeClass(i.$element,"ui-unselecting"),i.unselecting=!1),i.selecting||(s._addClass(i.$element,"ui-selecting"),i.selecting=!0,s._trigger("selecting",e,{selecting:i.element}))):(i.selecting&&((e.metaKey||e.ctrlKey)&&i.startselected?(s._removeClass(i.$element,"ui-selecting"),i.selecting=!1,s._addClass(i.$element,"ui-selected"),i.selected=!0):(s._removeClass(i.$element,"ui-selecting"),i.selecting=!1,i.startselected&&(s._addClass(i.$element,"ui-unselecting"),i.unselecting=!0),s._trigger("unselecting",e,{unselecting:i.element}))),i.selected&&(e.metaKey||e.ctrlKey||i.startselected||(s._removeClass(i.$element,"ui-selected"),i.selected=!1,s._addClass(i.$element,"ui-unselecting"),i.unselecting=!0,s._trigger("unselecting",e,{unselecting:i.element})))))}),!1}},_mouseStop:function(e){var i=this;return this.dragged=!1,t(".ui-unselecting",this.element[0]).each(function(){var s=t.data(this,"selectable-item");i._removeClass(s.$element,"ui-unselecting"),s.unselecting=!1,s.startselected=!1,i._trigger("unselected",e,{unselected:s.element})}),t(".ui-selecting",this.element[0]).each(function(){var s=t.data(this,"selectable-item");i._removeClass(s.$element,"ui-selecting")._addClass(s.$element,"ui-selected"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger("selected",e,{selected:s.element})}),this._trigger("stop",e),this.helper.remove(),!1}}),t.widget("ui.selectmenu",[t.ui.formResetMixin,{version:"1.12.1",defaultElement:"<select>",options:{appendTo:null,classes:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"},disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:!1,change:null,close:null,focus:null,open:null,select:null},_create:function(){var e=this.element.uniqueId().attr("id");this.ids={element:e,button:e+"-button",menu:e+"-menu"},this._drawButton(),this._drawMenu(),this._bindFormResetHandler(),this._rendered=!1,this.menuItems=t()},_drawButton:function(){var e,i=this,s=this._parseOption(this.element.find("option:selected"),this.element[0].selectedIndex);this.labels=this.element.labels().attr("for",this.ids.button),this._on(this.labels,{click:function(t){this.button.focus(),t.preventDefault()}}),this.element.hide(),this.button=t("<span>",{tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true",title:this.element.attr("title")}).insertAfter(this.element),this._addClass(this.button,"ui-selectmenu-button ui-selectmenu-button-closed","ui-button ui-widget"),e=t("<span>").appendTo(this.button),this._addClass(e,"ui-selectmenu-icon","ui-icon "+this.options.icons.button),this.buttonItem=this._renderButtonItem(s).appendTo(this.button),this.options.width!==!1&&this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){i._rendered||i._refreshMenu()})},_drawMenu:function(){var e=this;this.menu=t("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=t("<div>").append(this.menu),this._addClass(this.menuWrap,"ui-selectmenu-menu","ui-front"),this.menuWrap.appendTo(this._appendTo()),this.menuInstance=this.menu.menu({classes:{"ui-menu":"ui-corner-bottom"},role:"listbox",select:function(t,i){t.preventDefault(),e._setSelection(),e._select(i.item.data("ui-selectmenu-item"),t)},focus:function(t,i){var s=i.item.data("ui-selectmenu-item");null!=e.focusIndex&&s.index!==e.focusIndex&&(e._trigger("focus",t,{item:s}),e.isOpen||e._select(s,t)),e.focusIndex=s.index,e.button.attr("aria-activedescendant",e.menuItems.eq(s.index).attr("id"))}}).menu("instance"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(this._getSelectedItem().data("ui-selectmenu-item")||{})),null===this.options.width&&this._resizeButton()},_refreshMenu:function(){var t,e=this.element.find("option");this.menu.empty(),this._parseOptions(e),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup").find(".ui-menu-item-wrapper"),this._rendered=!0,e.length&&(t=this._getSelectedItem(),this.menuInstance.focus(null,t),this._setAria(t.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(t){this.options.disabled||(this._rendered?(this._removeClass(this.menu.find(".ui-state-active"),null,"ui-state-active"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.menuItems.length&&(this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",t)))},_position:function(){this.menuWrap.position(t.extend({of:this.button},this.options.position))},close:function(t){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",t))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderButtonItem:function(e){var i=t("<span>");return this._setText(i,e.label),this._addClass(i,"ui-selectmenu-text"),i},_renderMenu:function(e,i){var s=this,n="";t.each(i,function(i,o){var a;o.optgroup!==n&&(a=t("<li>",{text:o.optgroup}),s._addClass(a,"ui-selectmenu-optgroup","ui-menu-divider"+(o.element.parent("optgroup").prop("disabled")?" ui-state-disabled":"")),a.appendTo(e),n=o.optgroup),s._renderItemData(e,o)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-selectmenu-item",e)},_renderItem:function(e,i){var s=t("<li>"),n=t("<div>",{title:i.element.attr("title")});return i.disabled&&this._addClass(s,null,"ui-state-disabled"),this._setText(n,i.label),s.append(n).appendTo(e)},_setText:function(t,e){e?t.text(e):t.html("&#160;")},_move:function(t,e){var i,s,n=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex).parent("li"):(i=this.menuItems.eq(this.element[0].selectedIndex).parent("li"),n+=":not(.ui-state-disabled)"),s="first"===t||"last"===t?i["first"===t?"prevAll":"nextAll"](n).eq(-1):i[t+"All"](n).eq(0),s.length&&this.menuInstance.focus(e,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex).parent("li")},_toggle:function(t){this[this.isOpen?"close":"open"](t)},_setSelection:function(){var t;this.range&&(window.getSelection?(t=window.getSelection(),t.removeAllRanges(),t.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(e){this.isOpen&&(t(e.target).closest(".ui-selectmenu-menu, #"+t.ui.escapeSelector(this.ids.button)).length||this.close(e))}},_buttonEvents:{mousedown:function(){var t;window.getSelection?(t=window.getSelection(),t.rangeCount&&(this.range=t.getRangeAt(0))):this.range=document.selection.createRange()},click:function(t){this._setSelection(),this._toggle(t)},keydown:function(e){var i=!0;switch(e.keyCode){case t.ui.keyCode.TAB:case t.ui.keyCode.ESCAPE:this.close(e),i=!1;break;case t.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(e);break;case t.ui.keyCode.UP:e.altKey?this._toggle(e):this._move("prev",e);break;case t.ui.keyCode.DOWN:e.altKey?this._toggle(e):this._move("next",e);break;case t.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(e):this._toggle(e);break;case t.ui.keyCode.LEFT:this._move("prev",e);break;case t.ui.keyCode.RIGHT:this._move("next",e);break;case t.ui.keyCode.HOME:case t.ui.keyCode.PAGE_UP:this._move("first",e);break;case t.ui.keyCode.END:case t.ui.keyCode.PAGE_DOWN:this._move("last",e);break;default:this.menu.trigger(e),i=!1}i&&e.preventDefault()}},_selectFocusedItem:function(t){var e=this.menuItems.eq(this.focusIndex).parent("li");e.hasClass("ui-state-disabled")||this._select(e.data("ui-selectmenu-item"),t)},_select:function(t,e){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=t.index,this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(t)),this._setAria(t),this._trigger("select",e,{item:t}),t.index!==i&&this._trigger("change",e,{item:t}),this.close(e)},_setAria:function(t){var e=this.menuItems.eq(t.index).attr("id");this.button.attr({"aria-labelledby":e,"aria-activedescendant":e}),this.menu.attr("aria-activedescendant",e)},_setOption:function(t,e){if("icons"===t){var i=this.button.find("span.ui-icon");this._removeClass(i,null,this.options.icons.button)._addClass(i,null,e.button)}this._super(t,e),"appendTo"===t&&this.menuWrap.appendTo(this._appendTo()),"width"===t&&this._resizeButton()},_setOptionDisabled:function(t){this._super(t),this.menuInstance.option("disabled",t),this.button.attr("aria-disabled",t),this._toggleClass(this.button,null,"ui-state-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)},_appendTo:function(){var e=this.options.appendTo;return e&&(e=e.jquery||e.nodeType?t(e):this.document.find(e).eq(0)),e&&e[0]||(e=this.element.closest(".ui-front, dialog")),e.length||(e=this.document[0].body),e},_toggleAttr:function(){this.button.attr("aria-expanded",this.isOpen),this._removeClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"closed":"open"))._addClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"open":"closed"))._toggleClass(this.menuWrap,"ui-selectmenu-open",null,this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var t=this.options.width;return t===!1?(this.button.css("width",""),void 0):(null===t&&(t=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(t),void 0)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){var t=this._super();return t.disabled=this.element.prop("disabled"),t},_parseOptions:function(e){var i=this,s=[];e.each(function(e,n){s.push(i._parseOption(t(n),e))}),this.items=s},_parseOption:function(t,e){var i=t.parent("optgroup");return{element:t,index:e,value:t.val(),label:t.text(),optgroup:i.attr("label")||"",disabled:i.prop("disabled")||t.prop("disabled")}},_destroy:function(){this._unbindFormResetHandler(),this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.labels.attr("for",this.ids.element)}}]),t.widget("ui.slider",t.ui.mouse,{version:"1.12.1",widgetEventPrefix:"slide",options:{animate:!1,classes:{"ui-slider":"ui-corner-all","ui-slider-handle":"ui-corner-all","ui-slider-range":"ui-corner-all ui-widget-header"},distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this._addClass("ui-slider ui-slider-"+this.orientation,"ui-widget ui-widget-content"),this._refresh(),this._animateOff=!1
-},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var e,i,s=this.options,n=this.element.find(".ui-slider-handle"),o="<span tabindex='0'></span>",a=[];for(i=s.values&&s.values.length||1,n.length>i&&(n.slice(i).remove(),n=n.slice(0,i)),e=n.length;i>e;e++)a.push(o);this.handles=n.add(t(a.join("")).appendTo(this.element)),this._addClass(this.handles,"ui-slider-handle","ui-state-default"),this.handle=this.handles.eq(0),this.handles.each(function(e){t(this).data("ui-slider-handle-index",e).attr("tabIndex",0)})},_createRange:function(){var e=this.options;e.range?(e.range===!0&&(e.values?e.values.length&&2!==e.values.length?e.values=[e.values[0],e.values[0]]:t.isArray(e.values)&&(e.values=e.values.slice(0)):e.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?(this._removeClass(this.range,"ui-slider-range-min ui-slider-range-max"),this.range.css({left:"",bottom:""})):(this.range=t("<div>").appendTo(this.element),this._addClass(this.range,"ui-slider-range")),("min"===e.range||"max"===e.range)&&this._addClass(this.range,"ui-slider-range-"+e.range)):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this._mouseDestroy()},_mouseCapture:function(e){var i,s,n,o,a,r,h,l,c=this,u=this.options;return u.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:e.pageX,y:e.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(e){var i=Math.abs(s-c.values(e));(n>i||n===i&&(e===c._lastChangedValue||c.values(e)===u.min))&&(n=i,o=t(this),a=e)}),r=this._start(e,a),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=a,this._addClass(o,null,"ui-state-active"),o.trigger("focus"),h=o.offset(),l=!t(e.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=l?{left:0,top:0}:{left:e.pageX-h.left-o.width()/2,top:e.pageY-h.top-o.height()/2-(parseInt(o.css("borderTopWidth"),10)||0)-(parseInt(o.css("borderBottomWidth"),10)||0)+(parseInt(o.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(e,a,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(t){var e={x:t.pageX,y:t.pageY},i=this._normValueFromMouse(e);return this._slide(t,this._handleIndex,i),!1},_mouseStop:function(t){return this._removeClass(this.handles,null,"ui-state-active"),this._mouseSliding=!1,this._stop(t,this._handleIndex),this._change(t,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(t){var e,i,s,n,o;return"horizontal"===this.orientation?(e=this.elementSize.width,i=t.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(e=this.elementSize.height,i=t.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/e,s>1&&(s=1),0>s&&(s=0),"vertical"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),o=this._valueMin()+s*n,this._trimAlignValue(o)},_uiHash:function(t,e,i){var s={handle:this.handles[t],handleIndex:t,value:void 0!==e?e:this.value()};return this._hasMultipleValues()&&(s.value=void 0!==e?e:this.values(t),s.values=i||this.values()),s},_hasMultipleValues:function(){return this.options.values&&this.options.values.length},_start:function(t,e){return this._trigger("start",t,this._uiHash(e))},_slide:function(t,e,i){var s,n,o=this.value(),a=this.values();this._hasMultipleValues()&&(n=this.values(e?0:1),o=this.values(e),2===this.options.values.length&&this.options.range===!0&&(i=0===e?Math.min(n,i):Math.max(n,i)),a[e]=i),i!==o&&(s=this._trigger("slide",t,this._uiHash(e,i,a)),s!==!1&&(this._hasMultipleValues()?this.values(e,i):this.value(i)))},_stop:function(t,e){this._trigger("stop",t,this._uiHash(e))},_change:function(t,e){this._keySliding||this._mouseSliding||(this._lastChangedValue=e,this._trigger("change",t,this._uiHash(e)))},value:function(t){return arguments.length?(this.options.value=this._trimAlignValue(t),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(e,i){var s,n,o;if(arguments.length>1)return this.options.values[e]=this._trimAlignValue(i),this._refreshValue(),this._change(null,e),void 0;if(!arguments.length)return this._values();if(!t.isArray(arguments[0]))return this._hasMultipleValues()?this._values(e):this.value();for(s=this.options.values,n=arguments[0],o=0;s.length>o;o+=1)s[o]=this._trimAlignValue(n[o]),this._change(null,o);this._refreshValue()},_setOption:function(e,i){var s,n=0;switch("range"===e&&this.options.range===!0&&("min"===i?(this.options.value=this._values(0),this.options.values=null):"max"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),t.isArray(this.options.values)&&(n=this.options.values.length),this._super(e,i),e){case"orientation":this._detectOrientation(),this._removeClass("ui-slider-horizontal ui-slider-vertical")._addClass("ui-slider-"+this.orientation),this._refreshValue(),this.options.range&&this._refreshRange(i),this.handles.css("horizontal"===i?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),s=n-1;s>=0;s--)this._change(null,s);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_setOptionDisabled:function(t){this._super(t),this._toggleClass(null,"ui-state-disabled",!!t)},_value:function(){var t=this.options.value;return t=this._trimAlignValue(t)},_values:function(t){var e,i,s;if(arguments.length)return e=this.options.values[t],e=this._trimAlignValue(e);if(this._hasMultipleValues()){for(i=this.options.values.slice(),s=0;i.length>s;s+=1)i[s]=this._trimAlignValue(i[s]);return i}return[]},_trimAlignValue:function(t){if(this._valueMin()>=t)return this._valueMin();if(t>=this._valueMax())return this._valueMax();var e=this.options.step>0?this.options.step:1,i=(t-this._valueMin())%e,s=t-i;return 2*Math.abs(i)>=e&&(s+=i>0?e:-e),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var t=this.options.max,e=this._valueMin(),i=this.options.step,s=Math.round((t-e)/i)*i;t=s+e,t>this.options.max&&(t-=i),this.max=parseFloat(t.toFixed(this._precision()))},_precision:function(){var t=this._precisionOf(this.options.step);return null!==this.options.min&&(t=Math.max(t,this._precisionOf(this.options.min))),t},_precisionOf:function(t){var e=""+t,i=e.indexOf(".");return-1===i?0:e.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshRange:function(t){"vertical"===t&&this.range.css({width:"",left:""}),"horizontal"===t&&this.range.css({height:"",bottom:""})},_refreshValue:function(){var e,i,s,n,o,a=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,c={};this._hasMultipleValues()?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),c["horizontal"===h.orientation?"left":"bottom"]=i+"%",t(this).stop(1,1)[l?"animate":"css"](c,r.animate),h.options.range===!0&&("horizontal"===h.orientation?(0===s&&h.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({width:i-e+"%"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({height:i-e+"%"},{queue:!1,duration:r.animate}))),e=i}):(s=this.value(),n=this._valueMin(),o=this._valueMax(),i=o!==n?100*((s-n)/(o-n)):0,c["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](c,r.animate),"min"===a&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},r.animate),"max"===a&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:100-i+"%"},r.animate),"min"===a&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},r.animate),"max"===a&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:100-i+"%"},r.animate))},_handleEvents:{keydown:function(e){var i,s,n,o,a=t(e.target).data("ui-slider-handle-index");switch(e.keyCode){case t.ui.keyCode.HOME:case t.ui.keyCode.END:case t.ui.keyCode.PAGE_UP:case t.ui.keyCode.PAGE_DOWN:case t.ui.keyCode.UP:case t.ui.keyCode.RIGHT:case t.ui.keyCode.DOWN:case t.ui.keyCode.LEFT:if(e.preventDefault(),!this._keySliding&&(this._keySliding=!0,this._addClass(t(e.target),null,"ui-state-active"),i=this._start(e,a),i===!1))return}switch(o=this.options.step,s=n=this._hasMultipleValues()?this.values(a):this.value(),e.keyCode){case t.ui.keyCode.HOME:n=this._valueMin();break;case t.ui.keyCode.END:n=this._valueMax();break;case t.ui.keyCode.PAGE_UP:n=this._trimAlignValue(s+(this._valueMax()-this._valueMin())/this.numPages);break;case t.ui.keyCode.PAGE_DOWN:n=this._trimAlignValue(s-(this._valueMax()-this._valueMin())/this.numPages);break;case t.ui.keyCode.UP:case t.ui.keyCode.RIGHT:if(s===this._valueMax())return;n=this._trimAlignValue(s+o);break;case t.ui.keyCode.DOWN:case t.ui.keyCode.LEFT:if(s===this._valueMin())return;n=this._trimAlignValue(s-o)}this._slide(e,a,n)},keyup:function(e){var i=t(e.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(e,i),this._change(e,i),this._removeClass(t(e.target),null,"ui-state-active"))}}}),t.widget("ui.sortable",t.ui.mouse,{version:"1.12.1",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(t,e,i){return t>=e&&e+i>t},_isFloating:function(t){return/left|right/.test(t.css("float"))||/inline|table-cell/.test(t.css("display"))},_create:function(){this.containerCache={},this._addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(t,e){this._super(t,e),"handle"===t&&this._setHandleClassName()},_setHandleClassName:function(){var e=this;this._removeClass(this.element.find(".ui-sortable-handle"),"ui-sortable-handle"),t.each(this.items,function(){e._addClass(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item,"ui-sortable-handle")})},_destroy:function(){this._mouseDestroy();for(var t=this.items.length-1;t>=0;t--)this.items[t].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(e,i){var s=null,n=!1,o=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(e),t(e.target).parents().each(function(){return t.data(this,o.widgetName+"-item")===o?(s=t(this),!1):void 0}),t.data(e.target,o.widgetName+"-item")===o&&(s=t(e.target)),s?!this.options.handle||i||(t(this.options.handle,s).find("*").addBack().each(function(){this===e.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(e,i,s){var n,o,a=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(e),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},t.extend(this.offset,{click:{left:e.pageX-this.offset.left,top:e.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(e),this.originalPageX=e.pageX,this.originalPageY=e.pageY,a.cursorAt&&this._adjustOffsetFromHelper(a.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),a.containment&&this._setContainment(),a.cursor&&"auto"!==a.cursor&&(o=this.document.find("body"),this.storedCursor=o.css("cursor"),o.css("cursor",a.cursor),this.storedStylesheet=t("<style>*{ cursor: "+a.cursor+" !important; }</style>").appendTo(o)),a.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",a.opacity)),a.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",a.zIndex)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",e,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",e,this._uiHash(this));return t.ui.ddmanager&&(t.ui.ddmanager.current=this),t.ui.ddmanager&&!a.dropBehaviour&&t.ui.ddmanager.prepareOffsets(this,e),this.dragging=!0,this._addClass(this.helper,"ui-sortable-helper"),this._mouseDrag(e),!0},_mouseDrag:function(e){var i,s,n,o,a=this.options,r=!1;for(this.position=this._generatePosition(e),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-e.pageY<a.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+a.scrollSpeed:e.pageY-this.overflowOffset.top<a.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-a.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-e.pageX<a.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+a.scrollSpeed:e.pageX-this.overflowOffset.left<a.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-a.scrollSpeed)):(e.pageY-this.document.scrollTop()<a.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-a.scrollSpeed):this.window.height()-(e.pageY-this.document.scrollTop())<a.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+a.scrollSpeed)),e.pageX-this.document.scrollLeft()<a.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-a.scrollSpeed):this.window.width()-(e.pageX-this.document.scrollLeft())<a.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+a.scrollSpeed))),r!==!1&&t.ui.ddmanager&&!a.dropBehaviour&&t.ui.ddmanager.prepareOffsets(this,e)),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],o=this._intersectsWithPointer(s),o&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===o?"next":"prev"]()[0]!==n&&!t.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!t.contains(this.element[0],n):!0)){if(this.direction=1===o?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(e,s),this._trigger("change",e,this._uiHash());break}return this._contactContainers(e),t.ui.ddmanager&&t.ui.ddmanager.drag(this,e),this._trigger("sort",e,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(e,i){if(e){if(t.ui.ddmanager&&!this.options.dropBehaviour&&t.ui.ddmanager.drop(this,e),this.options.revert){var s=this,n=this.placeholder.offset(),o=this.options.axis,a={};o&&"x"!==o||(a.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),o&&"y"!==o||(a.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,t(this.helper).animate(a,parseInt(this.options.revert,10)||500,function(){s._clear(e)})}else this._clear(e,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp(new t.Event("mouseup",{target:null})),"original"===this.options.helper?(this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")):this.currentItem.show();for(var e=this.containers.length-1;e>=0;e--)this.containers[e]._trigger("deactivate",null,this._uiHash(this)),this.containers[e].containerCache.over&&(this.containers[e]._trigger("out",null,this._uiHash(this)),this.containers[e].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),t.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?t(this.domPosition.prev).after(this.currentItem):t(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(e){var i=this._getItemsAsjQuery(e&&e.connected),s=[];return e=e||{},t(i).each(function(){var i=(t(e.item||this).attr(e.attribute||"id")||"").match(e.expression||/(.+)[\-=_](.+)/);i&&s.push((e.key||i[1]+"[]")+"="+(e.key&&e.expression?i[1]:i[2]))}),!s.length&&e.key&&s.push(e.key+"="),s.join("&")},toArray:function(e){var i=this._getItemsAsjQuery(e&&e.connected),s=[];return e=e||{},i.each(function(){s.push(t(e.item||this).attr(e.attribute||"id")||"")}),s},_intersectsWith:function(t){var e=this.positionAbs.left,i=e+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,o=t.left,a=o+t.width,r=t.top,h=r+t.height,l=this.offset.click.top,c=this.offset.click.left,u="x"===this.options.axis||s+l>r&&h>s+l,d="y"===this.options.axis||e+c>o&&a>e+c,p=u&&d;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>t[this.floating?"width":"height"]?p:e+this.helperProportions.width/2>o&&a>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(t){var e,i,s="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top,t.height),n="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left,t.width),o=s&&n;return o?(e=this._getDragVerticalDirection(),i=this._getDragHorizontalDirection(),this.floating?"right"===i||"down"===e?2:1:e&&("down"===e?2:1)):!1},_intersectsWithSides:function(t){var e=this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top+t.height/2,t.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left+t.width/2,t.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&e||"up"===s&&!e)},_getDragVerticalDirection:function(){var t=this.positionAbs.top-this.lastPositionAbs.top;return 0!==t&&(t>0?"down":"up")},_getDragHorizontalDirection:function(){var t=this.positionAbs.left-this.lastPositionAbs.left;return 0!==t&&(t>0?"right":"left")},refresh:function(t){return this._refreshItems(t),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var t=this.options;return t.connectWith.constructor===String?[t.connectWith]:t.connectWith},_getItemsAsjQuery:function(e){function i(){r.push(this)}var s,n,o,a,r=[],h=[],l=this._connectWith();if(l&&e)for(s=l.length-1;s>=0;s--)for(o=t(l[s],this.document[0]),n=o.length-1;n>=0;n--)a=t.data(o[n],this.widgetFullName),a&&a!==this&&!a.options.disabled&&h.push([t.isFunction(a.options.items)?a.options.items.call(a.element):t(a.options.items,a.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),a]);for(h.push([t.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):t(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return t(r)},_removeCurrentsFromItems:function(){var e=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=t.grep(this.items,function(t){for(var i=0;e.length>i;i++)if(e[i]===t.item[0])return!1;return!0})},_refreshItems:function(e){this.items=[],this.containers=[this];var i,s,n,o,a,r,h,l,c=this.items,u=[[t.isFunction(this.options.items)?this.options.items.call(this.element[0],e,{item:this.currentItem}):t(this.options.items,this.element),this]],d=this._connectWith();if(d&&this.ready)for(i=d.length-1;i>=0;i--)for(n=t(d[i],this.document[0]),s=n.length-1;s>=0;s--)o=t.data(n[s],this.widgetFullName),o&&o!==this&&!o.options.disabled&&(u.push([t.isFunction(o.options.items)?o.options.items.call(o.element[0],e,{item:this.currentItem}):t(o.options.items,o.element),o]),this.containers.push(o));for(i=u.length-1;i>=0;i--)for(a=u[i][1],r=u[i][0],s=0,l=r.length;l>s;s++)h=t(r[s]),h.data(this.widgetName+"-item",a),c.push({item:h,instance:a,width:0,height:0,left:0,top:0})},refreshPositions:function(e){this.floating=this.items.length?"x"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,o;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?t(this.options.toleranceElement,s.item):s.item,e||(s.width=n.outerWidth(),s.height=n.outerHeight()),o=n.offset(),s.left=o.left,s.top=o.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)o=this.containers[i].element.offset(),this.containers[i].containerCache.left=o.left,this.containers[i].containerCache.top=o.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(e){e=e||this;var i,s=e.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=e.currentItem[0].nodeName.toLowerCase(),n=t("<"+s+">",e.document[0]);return e._addClass(n,"ui-sortable-placeholder",i||e.currentItem[0].className)._removeClass(n,"ui-sortable-helper"),"tbody"===s?e._createTrPlaceholder(e.currentItem.find("tr").eq(0),t("<tr>",e.document[0]).appendTo(n)):"tr"===s?e._createTrPlaceholder(e.currentItem,n):"img"===s&&n.attr("src",e.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(t,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(e.currentItem.innerHeight()-parseInt(e.currentItem.css("paddingTop")||0,10)-parseInt(e.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(e.currentItem.innerWidth()-parseInt(e.currentItem.css("paddingLeft")||0,10)-parseInt(e.currentItem.css("paddingRight")||0,10)))}}),e.placeholder=t(s.placeholder.element.call(e.element,e.currentItem)),e.currentItem.after(e.placeholder),s.placeholder.update(e,e.placeholder)},_createTrPlaceholder:function(e,i){var s=this;e.children().each(function(){t("<td>&#160;</td>",s.document[0]).attr("colspan",t(this).attr("colspan")||1).appendTo(i)})},_contactContainers:function(e){var i,s,n,o,a,r,h,l,c,u,d=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!t.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(d&&t.contains(this.containers[i].element[0],d.element[0]))continue;d=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",e,this._uiHash(this)),this.containers[i].containerCache.over=0);if(d)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",e,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,o=null,c=d.floating||this._isFloating(this.currentItem),a=c?"left":"top",r=c?"width":"height",u=c?"pageX":"pageY",s=this.items.length-1;s>=0;s--)t.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[a],l=!1,e[u]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(e[u]-h)&&(n=Math.abs(e[u]-h),o=this.items[s],this.direction=l?"up":"down"));if(!o&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger("over",e,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;o?this._rearrange(e,o,null,!0):this._rearrange(e,null,this.containers[p].element,!0),this._trigger("change",e,this._uiHash()),this.containers[p]._trigger("change",e,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",e,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(e){var i=this.options,s=t.isFunction(i.helper)?t(i.helper.apply(this.element[0],[e,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||t("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(e){"string"==typeof e&&(e=e.split(" ")),t.isArray(e)&&(e={left:+e[0],top:+e[1]||0}),"left"in e&&(this.offset.click.left=e.left+this.margins.left),"right"in e&&(this.offset.click.left=this.helperProportions.width-e.right+this.margins.left),"top"in e&&(this.offset.click.top=e.top+this.margins.top),"bottom"in e&&(this.offset.click.top=this.helperProportions.height-e.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var e=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&t.contains(this.scrollParent[0],this.offsetParent[0])&&(e.left+=this.scrollParent.scrollLeft(),e.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&t.ui.ie)&&(e={top:0,left:0}),{top:e.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:e.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var t=this.currentItem.position();return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:t.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var e,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===n.containment?this.document.height()||document.body.parentNode.scrollHeight:this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(e=t(n.containment)[0],i=t(n.containment).offset(),s="hidden"!==t(e).css("overflow"),this.containment=[i.left+(parseInt(t(e).css("borderLeftWidth"),10)||0)+(parseInt(t(e).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(t(e).css("borderTopWidth"),10)||0)+(parseInt(t(e).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(e.scrollWidth,e.offsetWidth):e.offsetWidth)-(parseInt(t(e).css("borderLeftWidth"),10)||0)-(parseInt(t(e).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(e.scrollHeight,e.offsetHeight):e.offsetHeight)-(parseInt(t(e).css("borderTopWidth"),10)||0)-(parseInt(t(e).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(e,i){i||(i=this.position);var s="absolute"===e?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&t.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,o=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():o?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():o?0:n.scrollLeft())*s}},_generatePosition:function(e){var i,s,n=this.options,o=e.pageX,a=e.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&t.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(e.pageX-this.offset.click.left<this.containment[0]&&(o=this.containment[0]+this.offset.click.left),e.pageY-this.offset.click.top<this.containment[1]&&(a=this.containment[1]+this.offset.click.top),e.pageX-this.offset.click.left>this.containment[2]&&(o=this.containment[2]+this.offset.click.left),e.pageY-this.offset.click.top>this.containment[3]&&(a=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((a-this.originalPageY)/n.grid[1])*n.grid[1],a=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((o-this.originalPageX)/n.grid[0])*n.grid[0],o=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:a-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:o-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(t,e,i,s){i?i[0].appendChild(this.placeholder[0]):e.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?e.item[0]:e.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;
-this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(t,e){function i(t,e,i){return function(s){i._trigger(t,s,e._uiHash(e))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!e&&n.push(function(t){this._trigger("receive",t,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||e||n.push(function(t){this._trigger("update",t,this._uiHash())}),this!==this.currentContainer&&(e||(n.push(function(t){this._trigger("remove",t,this._uiHash())}),n.push(function(t){return function(e){t._trigger("receive",e,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(t){return function(e){t._trigger("update",e,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)e||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,e||this._trigger("beforeStop",t,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!e){for(s=0;n.length>s;s++)n[s].call(this,t);this._trigger("stop",t,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){t.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(e){var i=e||this;return{helper:i.helper,placeholder:i.placeholder||t([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:e?e.element:null}}}),t.widget("ui.spinner",{version:"1.12.1",defaultElement:"<input>",widgetEventPrefix:"spin",options:{classes:{"ui-spinner":"ui-corner-all","ui-spinner-down":"ui-corner-br","ui-spinner-up":"ui-corner-tr"},culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var e=this._super(),i=this.element;return t.each(["min","max","step"],function(t,s){var n=i.attr(s);null!=n&&n.length&&(e[s]=n)}),e},_events:{keydown:function(t){this._start(t)&&this._keydown(t)&&t.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(t){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",t),void 0)},mousewheel:function(t,e){if(e){if(!this.spinning&&!this._start(t))return!1;this._spin((e>0?1:-1)*this.options.step,t),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(t)},100),t.preventDefault()}},"mousedown .ui-spinner-button":function(e){function i(){var e=this.element[0]===t.ui.safeActiveElement(this.document[0]);e||(this.element.trigger("focus"),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===t.ui.safeActiveElement(this.document[0])?this.previous:this.element.val(),e.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(e)!==!1&&this._repeat(null,t(e.currentTarget).hasClass("ui-spinner-up")?1:-1,e)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(e){return t(e.currentTarget).hasClass("ui-state-active")?this._start(e)===!1?!1:(this._repeat(null,t(e.currentTarget).hasClass("ui-spinner-up")?1:-1,e),void 0):void 0},"mouseleave .ui-spinner-button":"_stop"},_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap("<span>").parent().append("<a></a><a></a>")},_draw:function(){this._enhance(),this._addClass(this.uiSpinner,"ui-spinner","ui-widget ui-widget-content"),this._addClass("ui-spinner-input"),this.element.attr("role","spinbutton"),this.buttons=this.uiSpinner.children("a").attr("tabIndex",-1).attr("aria-hidden",!0).button({classes:{"ui-button":""}}),this._removeClass(this.buttons,"ui-corner-all"),this._addClass(this.buttons.first(),"ui-spinner-button ui-spinner-up"),this._addClass(this.buttons.last(),"ui-spinner-button ui-spinner-down"),this.buttons.first().button({icon:this.options.icons.up,showLabel:!1}),this.buttons.last().button({icon:this.options.icons.down,showLabel:!1}),this.buttons.height()>Math.ceil(.5*this.uiSpinner.height())&&this.uiSpinner.height()>0&&this.uiSpinner.height(this.uiSpinner.height())},_keydown:function(e){var i=this.options,s=t.ui.keyCode;switch(e.keyCode){case s.UP:return this._repeat(null,1,e),!0;case s.DOWN:return this._repeat(null,-1,e),!0;case s.PAGE_UP:return this._repeat(null,i.page,e),!0;case s.PAGE_DOWN:return this._repeat(null,-i.page,e),!0}return!1},_start:function(t){return this.spinning||this._trigger("start",t)!==!1?(this.counter||(this.counter=1),this.spinning=!0,!0):!1},_repeat:function(t,e,i){t=t||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,e,i)},t),this._spin(e*this.options.step,i)},_spin:function(t,e){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+t*this._increment(this.counter)),this.spinning&&this._trigger("spin",e,{value:i})===!1||(this._value(i),this.counter++)},_increment:function(e){var i=this.options.incremental;return i?t.isFunction(i)?i(e):Math.floor(e*e*e/5e4-e*e/500+17*e/200+1):1},_precision:function(){var t=this._precisionOf(this.options.step);return null!==this.options.min&&(t=Math.max(t,this._precisionOf(this.options.min))),t},_precisionOf:function(t){var e=""+t,i=e.indexOf(".");return-1===i?0:e.length-i-1},_adjustValue:function(t){var e,i,s=this.options;return e=null!==s.min?s.min:0,i=t-e,i=Math.round(i/s.step)*s.step,t=e+i,t=parseFloat(t.toFixed(this._precision())),null!==s.max&&t>s.max?s.max:null!==s.min&&s.min>t?s.min:t},_stop:function(t){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",t))},_setOption:function(t,e){var i,s,n;return"culture"===t||"numberFormat"===t?(i=this._parse(this.element.val()),this.options[t]=e,this.element.val(this._format(i)),void 0):(("max"===t||"min"===t||"step"===t)&&"string"==typeof e&&(e=this._parse(e)),"icons"===t&&(s=this.buttons.first().find(".ui-icon"),this._removeClass(s,null,this.options.icons.up),this._addClass(s,null,e.up),n=this.buttons.last().find(".ui-icon"),this._removeClass(n,null,this.options.icons.down),this._addClass(n,null,e.down)),this._super(t,e),void 0)},_setOptionDisabled:function(t){this._super(t),this._toggleClass(this.uiSpinner,null,"ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable")},_setOptions:r(function(t){this._super(t)}),_parse:function(t){return"string"==typeof t&&""!==t&&(t=window.Globalize&&this.options.numberFormat?Globalize.parseFloat(t,10,this.options.culture):+t),""===t||isNaN(t)?null:t},_format:function(t){return""===t?"":window.Globalize&&this.options.numberFormat?Globalize.format(t,this.options.numberFormat,this.options.culture):t},_refresh:function(){this.element.attr({"aria-valuemin":this.options.min,"aria-valuemax":this.options.max,"aria-valuenow":this._parse(this.element.val())})},isValid:function(){var t=this.value();return null===t?!1:t===this._adjustValue(t)},_value:function(t,e){var i;""!==t&&(i=this._parse(t),null!==i&&(e||(i=this._adjustValue(i)),t=this._format(i))),this.element.val(t),this._refresh()},_destroy:function(){this.element.prop("disabled",!1).removeAttr("autocomplete role aria-valuemin aria-valuemax aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:r(function(t){this._stepUp(t)}),_stepUp:function(t){this._start()&&(this._spin((t||1)*this.options.step),this._stop())},stepDown:r(function(t){this._stepDown(t)}),_stepDown:function(t){this._start()&&(this._spin((t||1)*-this.options.step),this._stop())},pageUp:r(function(t){this._stepUp((t||1)*this.options.page)}),pageDown:r(function(t){this._stepDown((t||1)*this.options.page)}),value:function(t){return arguments.length?(r(this._value).call(this,t),void 0):this._parse(this.element.val())},widget:function(){return this.uiSpinner}}),t.uiBackCompat!==!1&&t.widget("ui.spinner",t.ui.spinner,{_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml())},_uiSpinnerHtml:function(){return"<span>"},_buttonHtml:function(){return"<a></a><a></a>"}}),t.ui.spinner,t.widget("ui.tabs",{version:"1.12.1",delay:300,options:{active:null,classes:{"ui-tabs":"ui-corner-all","ui-tabs-nav":"ui-corner-all","ui-tabs-panel":"ui-corner-bottom","ui-tabs-tab":"ui-corner-top"},collapsible:!1,event:"click",heightStyle:"content",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:function(){var t=/#.*$/;return function(e){var i,s;i=e.href.replace(t,""),s=location.href.replace(t,"");try{i=decodeURIComponent(i)}catch(n){}try{s=decodeURIComponent(s)}catch(n){}return e.hash.length>1&&i===s}}(),_create:function(){var e=this,i=this.options;this.running=!1,this._addClass("ui-tabs","ui-widget ui-widget-content"),this._toggleClass("ui-tabs-collapsible",null,i.collapsible),this._processTabs(),i.active=this._initialActive(),t.isArray(i.disabled)&&(i.disabled=t.unique(i.disabled.concat(t.map(this.tabs.filter(".ui-state-disabled"),function(t){return e.tabs.index(t)}))).sort()),this.active=this.options.active!==!1&&this.anchors.length?this._findActive(i.active):t(),this._refresh(),this.active.length&&this.load(i.active)},_initialActive:function(){var e=this.options.active,i=this.options.collapsible,s=location.hash.substring(1);return null===e&&(s&&this.tabs.each(function(i,n){return t(n).attr("aria-controls")===s?(e=i,!1):void 0}),null===e&&(e=this.tabs.index(this.tabs.filter(".ui-tabs-active"))),(null===e||-1===e)&&(e=this.tabs.length?0:!1)),e!==!1&&(e=this.tabs.index(this.tabs.eq(e)),-1===e&&(e=i?!1:0)),!i&&e===!1&&this.anchors.length&&(e=0),e},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):t()}},_tabKeydown:function(e){var i=t(t.ui.safeActiveElement(this.document[0])).closest("li"),s=this.tabs.index(i),n=!0;if(!this._handlePageNav(e)){switch(e.keyCode){case t.ui.keyCode.RIGHT:case t.ui.keyCode.DOWN:s++;break;case t.ui.keyCode.UP:case t.ui.keyCode.LEFT:n=!1,s--;break;case t.ui.keyCode.END:s=this.anchors.length-1;break;case t.ui.keyCode.HOME:s=0;break;case t.ui.keyCode.SPACE:return e.preventDefault(),clearTimeout(this.activating),this._activate(s),void 0;case t.ui.keyCode.ENTER:return e.preventDefault(),clearTimeout(this.activating),this._activate(s===this.options.active?!1:s),void 0;default:return}e.preventDefault(),clearTimeout(this.activating),s=this._focusNextTab(s,n),e.ctrlKey||e.metaKey||(i.attr("aria-selected","false"),this.tabs.eq(s).attr("aria-selected","true"),this.activating=this._delay(function(){this.option("active",s)},this.delay))}},_panelKeydown:function(e){this._handlePageNav(e)||e.ctrlKey&&e.keyCode===t.ui.keyCode.UP&&(e.preventDefault(),this.active.trigger("focus"))},_handlePageNav:function(e){return e.altKey&&e.keyCode===t.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):e.altKey&&e.keyCode===t.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 0},_findNextTab:function(e,i){function s(){return e>n&&(e=0),0>e&&(e=n),e}for(var n=this.tabs.length-1;-1!==t.inArray(s(),this.options.disabled);)e=i?e+1:e-1;return e},_focusNextTab:function(t,e){return t=this._findNextTab(t,e),this.tabs.eq(t).trigger("focus"),t},_setOption:function(t,e){return"active"===t?(this._activate(e),void 0):(this._super(t,e),"collapsible"===t&&(this._toggleClass("ui-tabs-collapsible",null,e),e||this.options.active!==!1||this._activate(0)),"event"===t&&this._setupEvents(e),"heightStyle"===t&&this._setupHeightStyle(e),void 0)},_sanitizeSelector:function(t){return t?t.replace(/[!"$%&'()*+,.\/:;<=>?@\[\]\^`{|}~]/g,"\\$&"):""},refresh:function(){var e=this.options,i=this.tablist.children(":has(a[href])");e.disabled=t.map(i.filter(".ui-state-disabled"),function(t){return i.index(t)}),this._processTabs(),e.active!==!1&&this.anchors.length?this.active.length&&!t.contains(this.tablist[0],this.active[0])?this.tabs.length===e.disabled.length?(e.active=!1,this.active=t()):this._activate(this._findNextTab(Math.max(0,e.active-1),!1)):e.active=this.tabs.index(this.active):(e.active=!1,this.active=t()),this._refresh()},_refresh:function(){this._setOptionDisabled(this.options.disabled),this._setupEvents(this.options.event),this._setupHeightStyle(this.options.heightStyle),this.tabs.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}),this.panels.not(this._getPanelForTab(this.active)).hide().attr({"aria-hidden":"true"}),this.active.length?(this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}),this._addClass(this.active,"ui-tabs-active","ui-state-active"),this._getPanelForTab(this.active).show().attr({"aria-hidden":"false"})):this.tabs.eq(0).attr("tabIndex",0)},_processTabs:function(){var e=this,i=this.tabs,s=this.anchors,n=this.panels;this.tablist=this._getList().attr("role","tablist"),this._addClass(this.tablist,"ui-tabs-nav","ui-helper-reset ui-helper-clearfix ui-widget-header"),this.tablist.on("mousedown"+this.eventNamespace,"> li",function(e){t(this).is(".ui-state-disabled")&&e.preventDefault()}).on("focus"+this.eventNamespace,".ui-tabs-anchor",function(){t(this).closest("li").is(".ui-state-disabled")&&this.blur()}),this.tabs=this.tablist.find("> li:has(a[href])").attr({role:"tab",tabIndex:-1}),this._addClass(this.tabs,"ui-tabs-tab","ui-state-default"),this.anchors=this.tabs.map(function(){return t("a",this)[0]}).attr({role:"presentation",tabIndex:-1}),this._addClass(this.anchors,"ui-tabs-anchor"),this.panels=t(),this.anchors.each(function(i,s){var n,o,a,r=t(s).uniqueId().attr("id"),h=t(s).closest("li"),l=h.attr("aria-controls");e._isLocal(s)?(n=s.hash,a=n.substring(1),o=e.element.find(e._sanitizeSelector(n))):(a=h.attr("aria-controls")||t({}).uniqueId()[0].id,n="#"+a,o=e.element.find(n),o.length||(o=e._createPanel(a),o.insertAfter(e.panels[i-1]||e.tablist)),o.attr("aria-live","polite")),o.length&&(e.panels=e.panels.add(o)),l&&h.data("ui-tabs-aria-controls",l),h.attr({"aria-controls":a,"aria-labelledby":r}),o.attr("aria-labelledby",r)}),this.panels.attr("role","tabpanel"),this._addClass(this.panels,"ui-tabs-panel","ui-widget-content"),i&&(this._off(i.not(this.tabs)),this._off(s.not(this.anchors)),this._off(n.not(this.panels)))},_getList:function(){return this.tablist||this.element.find("ol, ul").eq(0)},_createPanel:function(e){return t("<div>").attr("id",e).data("ui-tabs-destroy",!0)},_setOptionDisabled:function(e){var i,s,n;for(t.isArray(e)&&(e.length?e.length===this.anchors.length&&(e=!0):e=!1),n=0;s=this.tabs[n];n++)i=t(s),e===!0||-1!==t.inArray(n,e)?(i.attr("aria-disabled","true"),this._addClass(i,null,"ui-state-disabled")):(i.removeAttr("aria-disabled"),this._removeClass(i,null,"ui-state-disabled"));this.options.disabled=e,this._toggleClass(this.widget(),this.widgetFullName+"-disabled",null,e===!0)},_setupEvents:function(e){var i={};e&&t.each(e.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.anchors.add(this.tabs).add(this.panels)),this._on(!0,this.anchors,{click:function(t){t.preventDefault()}}),this._on(this.anchors,i),this._on(this.tabs,{keydown:"_tabKeydown"}),this._on(this.panels,{keydown:"_panelKeydown"}),this._focusable(this.tabs),this._hoverable(this.tabs)},_setupHeightStyle:function(e){var i,s=this.element.parent();"fill"===e?(i=s.height(),i-=this.element.outerHeight()-this.element.height(),this.element.siblings(":visible").each(function(){var e=t(this),s=e.css("position");"absolute"!==s&&"fixed"!==s&&(i-=e.outerHeight(!0))}),this.element.children().not(this.panels).each(function(){i-=t(this).outerHeight(!0)}),this.panels.each(function(){t(this).height(Math.max(0,i-t(this).innerHeight()+t(this).height()))}).css("overflow","auto")):"auto"===e&&(i=0,this.panels.each(function(){i=Math.max(i,t(this).height("").height())}).height(i))},_eventHandler:function(e){var i=this.options,s=this.active,n=t(e.currentTarget),o=n.closest("li"),a=o[0]===s[0],r=a&&i.collapsible,h=r?t():this._getPanelForTab(o),l=s.length?this._getPanelForTab(s):t(),c={oldTab:s,oldPanel:l,newTab:r?t():o,newPanel:h};e.preventDefault(),o.hasClass("ui-state-disabled")||o.hasClass("ui-tabs-loading")||this.running||a&&!i.collapsible||this._trigger("beforeActivate",e,c)===!1||(i.active=r?!1:this.tabs.index(o),this.active=a?t():o,this.xhr&&this.xhr.abort(),l.length||h.length||t.error("jQuery UI Tabs: Mismatching fragment identifier."),h.length&&this.load(this.tabs.index(o),e),this._toggle(e,c))},_toggle:function(e,i){function s(){o.running=!1,o._trigger("activate",e,i)}function n(){o._addClass(i.newTab.closest("li"),"ui-tabs-active","ui-state-active"),a.length&&o.options.show?o._show(a,o.options.show,s):(a.show(),s())}var o=this,a=i.newPanel,r=i.oldPanel;this.running=!0,r.length&&this.options.hide?this._hide(r,this.options.hide,function(){o._removeClass(i.oldTab.closest("li"),"ui-tabs-active","ui-state-active"),n()}):(this._removeClass(i.oldTab.closest("li"),"ui-tabs-active","ui-state-active"),r.hide(),n()),r.attr("aria-hidden","true"),i.oldTab.attr({"aria-selected":"false","aria-expanded":"false"}),a.length&&r.length?i.oldTab.attr("tabIndex",-1):a.length&&this.tabs.filter(function(){return 0===t(this).attr("tabIndex")}).attr("tabIndex",-1),a.attr("aria-hidden","false"),i.newTab.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_activate:function(e){var i,s=this._findActive(e);s[0]!==this.active[0]&&(s.length||(s=this.active),i=s.find(".ui-tabs-anchor")[0],this._eventHandler({target:i,currentTarget:i,preventDefault:t.noop}))},_findActive:function(e){return e===!1?t():this.tabs.eq(e)},_getIndex:function(e){return"string"==typeof e&&(e=this.anchors.index(this.anchors.filter("[href$='"+t.ui.escapeSelector(e)+"']"))),e},_destroy:function(){this.xhr&&this.xhr.abort(),this.tablist.removeAttr("role").off(this.eventNamespace),this.anchors.removeAttr("role tabIndex").removeUniqueId(),this.tabs.add(this.panels).each(function(){t.data(this,"ui-tabs-destroy")?t(this).remove():t(this).removeAttr("role tabIndex aria-live aria-busy aria-selected aria-labelledby aria-hidden aria-expanded")}),this.tabs.each(function(){var e=t(this),i=e.data("ui-tabs-aria-controls");i?e.attr("aria-controls",i).removeData("ui-tabs-aria-controls"):e.removeAttr("aria-controls")}),this.panels.show(),"content"!==this.options.heightStyle&&this.panels.css("height","")},enable:function(e){var i=this.options.disabled;i!==!1&&(void 0===e?i=!1:(e=this._getIndex(e),i=t.isArray(i)?t.map(i,function(t){return t!==e?t:null}):t.map(this.tabs,function(t,i){return i!==e?i:null})),this._setOptionDisabled(i))},disable:function(e){var i=this.options.disabled;if(i!==!0){if(void 0===e)i=!0;else{if(e=this._getIndex(e),-1!==t.inArray(e,i))return;i=t.isArray(i)?t.merge([e],i).sort():[e]}this._setOptionDisabled(i)}},load:function(e,i){e=this._getIndex(e);var s=this,n=this.tabs.eq(e),o=n.find(".ui-tabs-anchor"),a=this._getPanelForTab(n),r={tab:n,panel:a},h=function(t,e){"abort"===e&&s.panels.stop(!1,!0),s._removeClass(n,"ui-tabs-loading"),a.removeAttr("aria-busy"),t===s.xhr&&delete s.xhr};this._isLocal(o[0])||(this.xhr=t.ajax(this._ajaxSettings(o,i,r)),this.xhr&&"canceled"!==this.xhr.statusText&&(this._addClass(n,"ui-tabs-loading"),a.attr("aria-busy","true"),this.xhr.done(function(t,e,n){setTimeout(function(){a.html(t),s._trigger("load",i,r),h(n,e)},1)}).fail(function(t,e){setTimeout(function(){h(t,e)},1)})))},_ajaxSettings:function(e,i,s){var n=this;return{url:e.attr("href").replace(/#.*$/,""),beforeSend:function(e,o){return n._trigger("beforeLoad",i,t.extend({jqXHR:e,ajaxSettings:o},s))}}},_getPanelForTab:function(e){var i=t(e).attr("aria-controls");return this.element.find(this._sanitizeSelector("#"+i))}}),t.uiBackCompat!==!1&&t.widget("ui.tabs",t.ui.tabs,{_processTabs:function(){this._superApply(arguments),this._addClass(this.tabs,"ui-tab")}}),t.ui.tabs,t.widget("ui.tooltip",{version:"1.12.1",options:{classes:{"ui-tooltip":"ui-corner-all ui-widget-shadow"},content:function(){var e=t(this).attr("title")||"";return t("<a>").text(e).html()},hide:!0,items:"[title]:not([disabled])",position:{my:"left top+15",at:"left bottom",collision:"flipfit flip"},show:!0,track:!1,close:null,open:null},_addDescribedBy:function(e,i){var s=(e.attr("aria-describedby")||"").split(/\s+/);s.push(i),e.data("ui-tooltip-id",i).attr("aria-describedby",t.trim(s.join(" ")))},_removeDescribedBy:function(e){var i=e.data("ui-tooltip-id"),s=(e.attr("aria-describedby")||"").split(/\s+/),n=t.inArray(i,s);-1!==n&&s.splice(n,1),e.removeData("ui-tooltip-id"),s=t.trim(s.join(" ")),s?e.attr("aria-describedby",s):e.removeAttr("aria-describedby")},_create:function(){this._on({mouseover:"open",focusin:"open"}),this.tooltips={},this.parents={},this.liveRegion=t("<div>").attr({role:"log","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this.disabledTitles=t([])},_setOption:function(e,i){var s=this;this._super(e,i),"content"===e&&t.each(this.tooltips,function(t,e){s._updateContent(e.element)})},_setOptionDisabled:function(t){this[t?"_disable":"_enable"]()},_disable:function(){var e=this;t.each(this.tooltips,function(i,s){var n=t.Event("blur");n.target=n.currentTarget=s.element[0],e.close(n,!0)}),this.disabledTitles=this.disabledTitles.add(this.element.find(this.options.items).addBack().filter(function(){var e=t(this);return e.is("[title]")?e.data("ui-tooltip-title",e.attr("title")).removeAttr("title"):void 0}))},_enable:function(){this.disabledTitles.each(function(){var e=t(this);e.data("ui-tooltip-title")&&e.attr("title",e.data("ui-tooltip-title"))}),this.disabledTitles=t([])},open:function(e){var i=this,s=t(e?e.target:this.element).closest(this.options.items);s.length&&!s.data("ui-tooltip-id")&&(s.attr("title")&&s.data("ui-tooltip-title",s.attr("title")),s.data("ui-tooltip-open",!0),e&&"mouseover"===e.type&&s.parents().each(function(){var e,s=t(this);s.data("ui-tooltip-open")&&(e=t.Event("blur"),e.target=e.currentTarget=this,i.close(e,!0)),s.attr("title")&&(s.uniqueId(),i.parents[this.id]={element:this,title:s.attr("title")},s.attr("title",""))}),this._registerCloseHandlers(e,s),this._updateContent(s,e))},_updateContent:function(t,e){var i,s=this.options.content,n=this,o=e?e.type:null;return"string"==typeof s||s.nodeType||s.jquery?this._open(e,t,s):(i=s.call(t[0],function(i){n._delay(function(){t.data("ui-tooltip-open")&&(e&&(e.type=o),this._open(e,t,i))})}),i&&this._open(e,t,i),void 0)},_open:function(e,i,s){function n(t){l.of=t,a.is(":hidden")||a.position(l)}var o,a,r,h,l=t.extend({},this.options.position);if(s){if(o=this._find(i))return o.tooltip.find(".ui-tooltip-content").html(s),void 0;i.is("[title]")&&(e&&"mouseover"===e.type?i.attr("title",""):i.removeAttr("title")),o=this._tooltip(i),a=o.tooltip,this._addDescribedBy(i,a.attr("id")),a.find(".ui-tooltip-content").html(s),this.liveRegion.children().hide(),h=t("<div>").html(a.find(".ui-tooltip-content").html()),h.removeAttr("name").find("[name]").removeAttr("name"),h.removeAttr("id").find("[id]").removeAttr("id"),h.appendTo(this.liveRegion),this.options.track&&e&&/^mouse/.test(e.type)?(this._on(this.document,{mousemove:n}),n(e)):a.position(t.extend({of:i},this.options.position)),a.hide(),this._show(a,this.options.show),this.options.track&&this.options.show&&this.options.show.delay&&(r=this.delayedShow=setInterval(function(){a.is(":visible")&&(n(l.of),clearInterval(r))},t.fx.interval)),this._trigger("open",e,{tooltip:a})}},_registerCloseHandlers:function(e,i){var s={keyup:function(e){if(e.keyCode===t.ui.keyCode.ESCAPE){var s=t.Event(e);s.currentTarget=i[0],this.close(s,!0)}}};i[0]!==this.element[0]&&(s.remove=function(){this._removeTooltip(this._find(i).tooltip)}),e&&"mouseover"!==e.type||(s.mouseleave="close"),e&&"focusin"!==e.type||(s.focusout="close"),this._on(!0,i,s)},close:function(e){var i,s=this,n=t(e?e.currentTarget:this.element),o=this._find(n);return o?(i=o.tooltip,o.closing||(clearInterval(this.delayedShow),n.data("ui-tooltip-title")&&!n.attr("title")&&n.attr("title",n.data("ui-tooltip-title")),this._removeDescribedBy(n),o.hiding=!0,i.stop(!0),this._hide(i,this.options.hide,function(){s._removeTooltip(t(this))}),n.removeData("ui-tooltip-open"),this._off(n,"mouseleave focusout keyup"),n[0]!==this.element[0]&&this._off(n,"remove"),this._off(this.document,"mousemove"),e&&"mouseleave"===e.type&&t.each(this.parents,function(e,i){t(i.element).attr("title",i.title),delete s.parents[e]}),o.closing=!0,this._trigger("close",e,{tooltip:i}),o.hiding||(o.closing=!1)),void 0):(n.removeData("ui-tooltip-open"),void 0)},_tooltip:function(e){var i=t("<div>").attr("role","tooltip"),s=t("<div>").appendTo(i),n=i.uniqueId().attr("id");return this._addClass(s,"ui-tooltip-content"),this._addClass(i,"ui-tooltip","ui-widget ui-widget-content"),i.appendTo(this._appendTo(e)),this.tooltips[n]={element:e,tooltip:i}},_find:function(t){var e=t.data("ui-tooltip-id");return e?this.tooltips[e]:null},_removeTooltip:function(t){t.remove(),delete this.tooltips[t.attr("id")]},_appendTo:function(t){var e=t.closest(".ui-front, dialog");return e.length||(e=this.document[0].body),e},_destroy:function(){var e=this;t.each(this.tooltips,function(i,s){var n=t.Event("blur"),o=s.element;n.target=n.currentTarget=o[0],e.close(n,!0),t("#"+i).remove(),o.data("ui-tooltip-title")&&(o.attr("title")||o.attr("title",o.data("ui-tooltip-title")),o.removeData("ui-tooltip-title"))}),this.liveRegion.remove()}}),t.uiBackCompat!==!1&&t.widget("ui.tooltip",t.ui.tooltip,{options:{tooltipClass:null},_tooltip:function(){var t=this._superApply(arguments);return this.options.tooltipClass&&t.tooltip.addClass(this.options.tooltipClass),t}}),t.ui.tooltip});
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/AVS Audio Editor7.1.3.444 Crack 64 Bit.md b/spaces/falterWliame/Face_Mask_Detection/AVS Audio Editor7.1.3.444 Crack 64 Bit.md
deleted file mode 100644
index 4c8f1fbd354c6f858992b3edbce07a449da029d6..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/AVS Audio Editor7.1.3.444 Crack 64 Bit.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-<h2>AVS Audio Editor7.1.3.444 Crack 64 Bit</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734; <a href="https://urlca.com/2uDdSs">https://urlca.com/2uDdSs</a></b></p><br /><br />
-
-crack, audio editor 20, audio editor 2015, audio editor for android mp4, audio editor 7.1.3, audio editor hack, audio editor for mac, audio editor for windows .x, audio editor windows 7.1.3, audio editor mod hack, audio editor for mac
-
-How To Install:
-
-Extract the file you have just downloaded.
-
-Start the setup as admin.
-
-Select where you want to install the app.
-
-Enjoy!Giancarlo DiTrapano is the latest addition to the driver roster of Reiter Engineering for the Indy 500 and in particular, the Freedom 100 race. The German team has had notable success in the European Le Mans Series and the two previous GTEs races at Le Mans.
-
-However, Reiter will now have the Dallara’s newly refreshed by GRX racing while the team has acquired the engineering services of the Argentinian team. The team looks to return with Giancarlo, Andrej Sayan, Luca Mancini and Alex Braun to see if they can perform in the high-profile races.
-
-“We have a very good team,” said Giancarlo. “I have been working with the team for some time and I know their strengths and weaknesses. We have to try and capitalize on our experience and strengths and forget about our weaknesses. The team is capable of winning.”
-
-“The goal of Reiter Engineering is not only to win the 100,” said team principal Klaus Fröhlich. “We want to win the entire championship. We have the resources for that and we want to build on this success in order to be able to participate in more races and win more championships.”
-
-Giancarlo has a wealth of racing experience having won the Blancpain Endurance Series and overall in the Asian Le Mans Series. The 37 year old also competed for BMW in the 24 Hours of Nürburgring.
-
-As for the upcoming endurance racing season, the team will participate in both the Nürburgring 24 and the Silverstone 12 hour race.Heathburn railway station
-
-Heathburn railway station served Heathburn in Dundee, Scotland from 1847 to 1955 on the Dundee and Perth Railway.
-
-History 
-
-The station opened in 1847 by the Dundee and Perth Railway. The station closed to both passengers and goods traffic in 1955.
-
-References
-
-External links 
-
-Category 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/falterWliame/Face_Mask_Detection/Age Of Empires 3 Crack _HOT_ Free Download Full Version Zip Rar.md b/spaces/falterWliame/Face_Mask_Detection/Age Of Empires 3 Crack _HOT_ Free Download Full Version Zip Rar.md
deleted file mode 100644
index bf51e93ec6663ddfb843fa93c6389f0a8ed8b8a4..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Age Of Empires 3 Crack _HOT_ Free Download Full Version Zip Rar.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>How to Download Age of Empires 3 Crack Full Version for Free</h1>
-<p>Age of Empires 3 is one of the most popular real-time strategy games ever made. It lets you control a civilization from the Age of Discovery to the Industrial Revolution, and compete with other players online or offline. But what if you don't want to pay for the game? Is there a way to get Age of Empires 3 crack free download full version zip rar?</p>
-<h2>Age Of Empires 3 Crack Free Download Full Version Zip Rar</h2><br /><p><b><b>Download</b> &#9889; <a href="https://urlca.com/2uDc3V">https://urlca.com/2uDc3V</a></b></p><br /><br />
-<p>The answer is yes, but you have to be careful. There are many websites that claim to offer Age of Empires 3 crack free download full version zip rar, but some of them are scams or viruses that can harm your computer. You should always check the reviews and ratings of the website before downloading anything from it. Also, you should be aware that downloading cracked games is illegal and may get you in trouble with the law.</p>
-<p>That being said, if you still want to try Age of Empires 3 crack free download full version zip rar, here are the steps you need to follow:</p>
-<ol>
-<li>Find a reliable website that offers Age of Empires 3 crack free download full version zip rar. You can use a search engine or a torrent site to look for it.</li>
-<li>Download the zip or rar file that contains the game and the crack. Make sure you have enough space on your hard drive and a good internet connection.</li>
-<li>Extract the zip or rar file using a program like WinRAR or 7-Zip. You should see a folder with the game files and another folder with the crack files.</li>
-<li>Copy the crack files and paste them into the game folder. This will overwrite the original files and make the game run without a CD key or activation.</li>
-<li>Run the game and enjoy!</li>
-</ol>
-<p>Note: Some websites may require you to complete surveys or offers before downloading the file. This is usually a sign of a scam, so avoid them if possible. Also, some crack files may contain malware or spyware that can infect your computer, so scan them with an antivirus program before using them.</p>
-<p>We hope this article helped you learn how to download Age of Empires 3 crack free download full version zip rar. However, we do not condone piracy and we recommend that you buy the game from an official source if you like it. Age of Empires 3 is a great game that deserves your support.</p>
-<p></p>
-
-<p>If you want to learn more about Age of Empires 3 and its features, you can visit the official website or the wiki page. You can also watch some gameplay videos or read some reviews to get a better idea of what the game is like. Age of Empires 3 has a lot of content and variety to offer, such as different civilizations, campaigns, modes, maps, units, buildings, technologies, and more.</p>
-<p>Age of Empires 3 also has a vibrant community of fans and modders who create and share custom content for the game. You can find many mods, scenarios, maps, skins, and other enhancements that can improve your gaming experience. You can also join online forums and chat rooms where you can discuss the game with other players and get tips and tricks.</p>
-<p>Age of Empires 3 is a game that will keep you entertained for hours. Whether you want to play solo or with friends, you will find something that suits your taste and style. Age of Empires 3 is a classic that you should not miss.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Delco-cdr-500-code-calculator [REPACK].md b/spaces/falterWliame/Face_Mask_Detection/Delco-cdr-500-code-calculator [REPACK].md
deleted file mode 100644
index 77ef0f8f440d674394f53cd54aea158459c5524e..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Delco-cdr-500-code-calculator [REPACK].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>delco-cdr-500-code-calculator</h2><br /><p><b><b>DOWNLOAD</b> &#10040;&#10040;&#10040; <a href="https://urlca.com/2uDcWL">https://urlca.com/2uDcWL</a></b></p><br /><br />
-<br />
-hi hab ein astra mit cdr500 hab keinen code dafür. kann der opelhändler nur mit dem ausgebauten radio das teil freischalten oder braucht er ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/fatiXbelha/sd/Download RFS and Experience the Ultimate Real Flight Simulator on Your PC.md b/spaces/fatiXbelha/sd/Download RFS and Experience the Ultimate Real Flight Simulator on Your PC.md
deleted file mode 100644
index 37b102b19f784c6f336aa52ce31effac6d2a3c8a..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download RFS and Experience the Ultimate Real Flight Simulator on Your PC.md	
+++ /dev/null
@@ -1,82 +0,0 @@
-<br />
-<h1>How to Download RFS - Real Flight Simulator on Your Device</h1>
- <p>Do you love flying and exploring the world from the sky? Do you want to experience a realistic and immersive flight simulation game on your device? If yes, then you should download RFS - Real Flight Simulator, one of the best flight simulator games available for mobile and PC platforms.</p>
-<h2>download rfs</h2><br /><p><b><b>Download File</b> &#128279; <a href="https://urllie.com/2uNCEg">https://urllie.com/2uNCEg</a></b></p><br /><br />
- <p>RFS - Real Flight Simulator is a simulation game developed by RORTOS, a leading developer of flight simulation games. In this game, you can live a unique experience flying in any part of the world and exploring sceneries and airports in high resolution with satellite maps, 3D buildings, runways, procedures and air traffic. You can also jump on board of real time flights, chat with other pilots and join them in multiplayer mode. You can manage flight plans and interact with ATC controllers, get access to thousands of community created liveries, customize all airplanes, their gauges, failures and weather conditions, and become a real pilot!</p>
- <p>In this article, we will show you how to download RFS - Real Flight Simulator on your device, whether it is an Android device, an iOS device, a PC or Mac, or any other platform that supports this game. We will also provide you with some tips and tricks to enjoy this game to the fullest. So, let's get started!</p>
- <h2>Download RFS on Android Devices</h2>
- <p>If you have an Android device, such as a smartphone or a tablet, you can easily download RFS - Real Flight Simulator from the Google Play Store. Here are the steps to follow:</p>
- <ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for "RFS - Real Flight Simulator" in the search bar.</li>
-<li>Select the game from the search results and tap on "Install".</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Once the installation is complete, tap on "Open" to launch the game.</li>
-</ol>
- <p>You can also download RFS - Real Flight Simulator from the Google Play Store website using your browser. Just follow this link: </p>
- <p>The game is free to download and play, but it offers in-app purchases for some premium features, such as advanced flight plan, real time flights, multiplayer mode, satellite terrain, satellite heightmaps, liveries creation and more. You can buy these features individually or subscribe to the PRO version for $0.99 per month or $9.99 per year.</p>
- <h2>Download RFS on iOS Devices</h2>
- <p>If you have an iOS device, such as an iPhone or an iPad, you can easily download RFS - Real Flight Simulator from the App Store. Here are the steps to follow:</p>
-<p>Real Flight Simulator, a software that simulates realistic flight scenarios[^1^]<br />
-Reason for Selling, a term used by sellers to explain why they are selling a product or service[^2^]<br />
-Received for Shipment, a document that indicates that a shipment is received by the carrier but not yet loaded on the vessel[^3^]<br />
-Request for Solution, a document that describes a problem and asks for a proposed solution from potential suppliers[^4^]<br />
-Really Funny Stuff, an internet slang phrase that expresses amusement or laughter[^5^]<br />
-download real flight simulator for pc<br />
-download real flight simulator mod apk<br />
-download real flight simulator free<br />
-download reason for selling template<br />
-download reason for selling letter sample<br />
-download reason for selling form<br />
-download received for shipment bill of lading<br />
-download received for shipment template<br />
-download received for shipment example<br />
-download request for solution template<br />
-download request for solution example<br />
-download request for solution sample<br />
-download really funny stuff videos<br />
-download really funny stuff memes<br />
-download really funny stuff images</p>
- <ol>
-<li>Open the App Store app on your device.</li>
-<li>Search for "RFS - Real Flight Simulator" in the search bar.</li>
-<li>Select the game from the search results and tap on "Get".</li>
-<li>Enter your Apple ID password or use Touch ID or Face ID to confirm the download.</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Once the installation is complete, tap on "Open" to launch the game.</li>
-</ol>
- <p>You can also download RFS - Real Flight Simulator from the App Store website using your browser. Just follow this link: </p>
- <p>The game is free to download and play, but it offers in-app purchases for some premium features, such as advanced flight plan, real time flights, multiplayer mode, satellite terrain, satellite heightmaps, liveries creation and more. You can buy these features individually or subscribe to the PRO version for $0.99 per month or $9.99 per year.</p>
- <h2>Download RFS on PC or Mac</h2>
- <p>If you have a PC or a Mac computer, you can also download and play space on your device to download and install. However, the game may require additional space to download and store the satellite maps, 3D buildings, liveries, and other data. You can check the storage usage of the game in the settings menu and delete some data if you need to free up some space.</p>
- <h3>Q: How can I update RFS - Real Flight Simulator to the latest version?</h3>
-<p>A: RFS - Real Flight Simulator is regularly updated by RORTOS with new features, improvements, bug fixes, and more. To update the game to the latest version, you can follow these steps:</p>
- <ul>
-<li>If you have downloaded the game from the Google Play Store or the App Store, you can check for updates in the app store and tap on "Update" if there is a new version available. You can also enable automatic updates in the app store settings to get the latest version as soon as it is released.</li>
-<li>If you have downloaded the game from the Google Play Store or App Store websites using an emulator, you can check for updates in the emulator's app store and click on "Update" if there is a new version available. You can also enable automatic updates in the emulator's app store settings to get the latest version as soon as it is released.</li>
-<li>If you have downloaded the game from other sources or platforms, you may need to visit the official website of RORTOS or their social media pages to check for updates and download them manually. However, we recommend you to use one of the supported platforms for the best experience and performance of this game.</li>
-</ul>
- <h3>Q: How can I contact RORTOS for support or feedback?</h3>
-<p>A: If you have any issues, questions, suggestions, or feedback about RFS - Real Flight Simulator or any other RORTOS games, you can contact them through various channels:</p>
- <ul>
-<li>You can visit their official website at  and fill out a contact form with your details and message.</li>
-<li>You can send them an email at support@rortos.com with your device model, operating system version, game version, and a detailed description of your problem or request.</li>
-<li>You can follow them on their social media pages on Facebook, Twitter, Instagram, YouTube, etc., and send them a direct message or leave a comment on their posts.</li>
-<li>You can join their official Discord server at  and chat with other players and developers.</li>
-</ul>
- <h3>Q: How can I join the RFS community and share my flights and liveries?</h3>
-<p>A: RFS - Real Flight Simulator has a large and active community of players who love to share their flights and liveries with others. You can join the RFS community and interact with other players through various platforms:</p>
- <ul>
-<li>You can visit their official forum at  and create an account to post your flights and liveries, ask questions, give feedback, participate in contests, and more.</li>
-<li>You can join their official Facebook group at  and share your flights and liveries, chat with other players, get news and updates, and more.</li>
-<li>You can use the hashtag #rfsimulator on Instagram, Twitter, TikTok, etc., and tag @rortos in your posts to share your flights and liveries with other players and get featured by RORTOS.</li>
-</ul>
- <h3>Q: How can I learn more about RFS - Real Flight Simulator and its features?</h3>
-<p>A: If you want to learn more about RFS - Real Flight Simulator and its features, you can check out these resources:</p>
- <ul>
-<li>You can visit their official website at  and read more about the game's features, modes, airplanes, airports, etc.</li>
-<li>You can watch their official YouTube channel at  and watch tutorials, trailers, gameplay videos, live streams, etc.</li>
-<li>You can read their official blog at  and get insights into the development process, behind-the-scenes stories, tips and tricks, etc.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Enjoy Free Live Voice Chat and Games with 4Fun Lite APK - Download Now.md b/spaces/fatiXbelha/sd/Enjoy Free Live Voice Chat and Games with 4Fun Lite APK - Download Now.md
deleted file mode 100644
index 851df1b3659d5325c16bcdcdfaea5aea7516de6f..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Enjoy Free Live Voice Chat and Games with 4Fun Lite APK - Download Now.md	
+++ /dev/null
@@ -1,14 +0,0 @@
-<br />
-<h1>4Fun Lite APK: A Live Group Voice Chat and Entertainment App</h1>
- Do you want to have fun with your friends online? Do you want to chat, play games, and party with people from all over the world? If yes, then you should try 4Fun Lite APK, a live group voice chat and entertainment app that lets you do all these things and more. In this article, we will tell you what 4Fun Lite APK is, how to download and install it, why you should use it, and some tips and tricks for using it. Let's get started! <h2>What is 4Fun Lite APK?</h2>
- 4Fun Lite APK is a social app developed by SpreadFun that allows you to join or create live voice chat rooms with people who share your interests. You can also play games, send virtual gifts, follow and share your favorite rooms, and have fun with your friends or strangers. 4Fun Lite APK is a lite version of the original 4Fun app, which means it has a smaller size and consumes less data and battery. <h3>Features of 4Fun Lite APK</h3>
- Some of the features of 4Fun Lite APK are: - TOTALLY FREE — Enjoy free live voice chat over 3G, 4G, LTE or Wi-Fi. - PUBLIC CHAT ROOMS — Browse THOUSANDS of live chat rooms around the world that cover thousands of topics. - CHAT GAMES — Play games together directly in your chat group. - VIRTUAL GIFTS — Stunning animated gifts can be sent out to express your support. - SHARE and FOLLOW — Share your favorite rooms on Facebook, Twitter, Instagram, Snapchat and more, inviting friends and new followers. <h3>How to download and install 4Fun Lite APK</h3>
- To download and install 4Fun Lite APK on your Android device, follow these steps: - Go to [this link](^1^) or [this link](^2^) to download the latest version of 4Fun Lite APK. - Once the download is complete, open the file and tap on Install. - If you see a message that says "Install blocked", go to Settings > Security > Unknown sources and enable it. - After the installation is done, open the app and sign up with your phone number or Facebook account. - Enjoy chatting and playing with your friends! <h2>Why use 4Fun Lite APK?</h2>
- There are many reasons why you should use 4Fun Lite APK, such as: <h3>Benefits of using 4Fun Lite APK</h3>
- Some of the benefits of using 4Fun Lite APK are: - You can make new friends from different countries and cultures. - You can improve your communication skills and learn new languages. - You can have fun and relax after a long day of work or study. - You can discover new topics and hobbies that interest you. - You can express yourself freely and creatively. <h3>Tips and tricks for using 4Fun Lite APK</h3>
- Some of the tips and tricks for using 4Fun Lite APK are: - Use a good headset or earphones to have a clear voice chat experience. - Be respectful and polite to other users in the chat rooms. - Join or create rooms that match your preferences and mood. - Invite your friends to join your chat group or join theirs. - Try different games and gifts to spice up your conversation. <h2>Conclusion</h2>
- If you are looking for a live group voice chat and entertainment app that is free, fun, and easy to use, then you should give 4Fun Lite APK a try. You can chat, play games, party, and make new friends with people from all over the world. Download 4Fun Lite APK today and enjoy! <h2>FAQs</h2>
- Q: Is 4Fun Lite APK safe to use? A: Yes, 4Fun Lite APK is safe to use. It does not collect or share any personal or financial information with third parties. It also encrypts your data in transit and allows you to delete it if you want. Q: How can I Q: How can I contact the customer support of 4Fun Lite APK? A: You can contact the customer support of 4Fun Lite APK by sending an email to support@4funapp.com or by visiting their official website at https://www.4funapp.com/. Q: What are the minimum requirements for using 4Fun Lite APK? A: The minimum requirements for using 4Fun Lite APK are: - Android 4.1 or higher - Internet connection - Phone number or Facebook account Q: Can I use 4Fun Lite APK on my PC or laptop? A: No, 4Fun Lite APK is only available for Android devices. However, you can use an Android emulator such as BlueStacks or NoxPlayer to run 4Fun Lite APK on your PC or laptop. Q: How can I earn money from 4Fun Lite APK? A: You can earn money from 4Fun Lite APK by inviting your friends to join the app, by receiving virtual gifts from other users, or by participating in various events and activities. Q: How can I update 4Fun Lite APK to the latest version? A: You can update 4Fun Lite APK to the latest version by following these steps: - Open the app and tap on the Menu icon on the top left corner. - Tap on Settings and then on About. - Tap on Check for updates and follow the instructions.</p>
-<h2>4fun lite apk</h2><br /><p><b><b>Download Zip</b> &#10004; <a href="https://urllie.com/2uNG9C">https://urllie.com/2uNG9C</a></b></p><br /><br /> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Experience the Zombie Carnage on iOS with Zombie Highway 2 Download.md b/spaces/fatiXbelha/sd/Experience the Zombie Carnage on iOS with Zombie Highway 2 Download.md
deleted file mode 100644
index 4f935555395cafee29669183e8575bb0b0039913..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Experience the Zombie Carnage on iOS with Zombie Highway 2 Download.md	
+++ /dev/null
@@ -1,298 +0,0 @@
-
-<h1>Zombie Highway 2: How to Download and Play on iOS</h1>
- <p>If you are looking for a thrilling and addictive zombie survival game, you might want to check out <strong>Zombie Highway 2</strong>. This is a sequel to the popular <em>Zombie Highway</em> game that was released in 2010. In this game, you have to drive your car through a post-apocalyptic highway infested with zombies. You have to avoid crashing into obstacles, shoot or shake off zombies that latch onto your vehicle, and try to survive as long as possible.</p>
-<h2>zombie highway 2 download ios</h2><br /><p><b><b>Download</b> &rArr;&rArr;&rArr; <a href="https://urllie.com/2uNA28">https://urllie.com/2uNA28</a></b></p><br /><br />
- <p>Zombie Highway 2 is a free-to-play game that is available on both Android and iOS devices. In this article, we will focus on how to download and play it on iOS devices, such as iPhone, iPad, or iPod touch. We will also give you a brief review of the game, as well as some tips and tricks to help you get better at it.</p>
- <h2>How to download Zombie Highway 2 on iOS devices</h2>
- <p>Downloading Zombie Highway 2 on iOS devices is very easy. All you need is a device that runs on iOS 8.0 or later, and an internet connection. Here are the steps to follow:</p>
- <ol>
-<li>Open the App Store app on your device.</li>
-<li>Search for "Zombie Highway" or "Zombie Highway 2" in the search bar.</li>
-<li>Tap on the icon of Zombie Highway 2, which has a red car with a zombie on it.</li>
-<li>Tap on "Get" or "Install" to start downloading the game.</li>
-<li>Wait for the download to finish, then tap on "Open" or find the game icon on your home screen.</li>
-<li>Enjoy playing Zombie Highway 2!</li>
-</ol>
- <p>You can also download Zombie Highway 2 directly from this link:</p>
- <p><a href="(^1^)">‎Zombie Highway on the App Store</a></p>
- <h2>Zombie Highway 2: Game Review</h2>
- <p>Now that you have downloaded Zombie Highway 2, you might be wondering what makes this game so fun and exciting. Well, here are some aspects of the game that we think you will enjoy:</p>
- <h3>Gameplay</h3>
- <p>The gameplay of Zombie Highway 2 is simple but challenging. You have to drive your car along a randomly generated highway that has different environments, such as desert, forest, city, or snow. Along the way, you will encounter various zombies that will try to jump on your car and flip it over. You have to use your weapons or your driving skills to get rid of them before they do.</p>
- <p>The game has six modes of gameplay: endless, objective, and daily challenge. In endless mode, you have to survive as long as you can and beat your own high score. In objective mode, you have to complete specific tasks, such as killing a certain number of zombies or driving a certain distance. In daily challenge mode, you have to compete with other players around the world and see who can get the highest score in a limited time.</p>
-<p>zombie highway 2 ios app store<br />
-how to download zombie highway 2 on iphone<br />
-zombie highway 2 free download for ipad<br />
-zombie highway 2 gameplay ios<br />
-zombie highway 2 cheats and tips ios<br />
-zombie highway 2 review ios<br />
-zombie highway 2 best weapons ios<br />
-zombie highway 2 update ios<br />
-zombie highway 2 hack ios<br />
-zombie highway 2 mod apk ios<br />
-zombie highway 2 online multiplayer ios<br />
-zombie highway 2 offline mode ios<br />
-zombie highway 2 vs zombie highway ios<br />
-zombie highway 2 auxbrain inc ios<br />
-zombie highway 2 ratings and feedback ios<br />
-zombie highway 2 trailer and screenshots ios<br />
-zombie highway 2 compatible devices ios<br />
-zombie highway 2 system requirements ios<br />
-zombie highway 2 size and version ios<br />
-zombie highway 2 features and benefits ios<br />
-zombie highway 2 alternatives and similar games ios<br />
-zombie highway 2 support and contact ios<br />
-zombie highway 2 bugs and issues ios<br />
-zombie highway 2 achievements and leaderboards ios<br />
-zombie highway 2 in-app purchases and prices ios<br />
-zombie highway 2 fun and addictive ios game<br />
-zombie highway 2 survival and strategy ios game<br />
-zombie highway 2 action and adventure ios game<br />
-zombie highway 2 shooting and racing ios game<br />
-zombie highway 2 zombies and cars ios game<br />
-download and install zombie highway 2 on your iphone or ipad<br />
-play and enjoy zombie highway 2 on your ios device<br />
-share and recommend zombie highway 2 to your friends on ios<br />
-rate and review zombie highway 2 on the app store for ios<br />
-watch and learn from zombie highway 2 videos and tutorials on ios<br />
-follow and join the zombie highway 2 community on social media for ios<br />
-get and use the latest zombie highway 2 news and updates for ios<br />
-find and discover more about zombie highway 2 for ios<br />
-compare and contrast zombie highway 2 with other ios games in the same genre or category<br />
-explore and experiment with different zombie highway 2 modes and levels on ios</p>
- <p>The game also has a leveling system that allows you to unlock new weapons, vehicles, and upgrades. You can earn experience points by playing the game and completing objectives. You can also earn coins by killing zombies and collecting them on the road. You can use coins to buy new weapons or upgrade your existing ones. You can also use coins to buy new vehicles or customize your current one.</p>
- <h3>Graphics and Sound</h3>
- <p>The graphics of Zombie Highway 2 are impressive and realistic. The game has a 3D engine that renders the environments, the vehicles, and the zombies in high quality. The game also has dynamic lighting and shadows, as well as weather effects, such as rain, fog, or snow. The game runs smoothly and does not lag or crash.</p>
- <p>The sound of Zombie Highway 2 is also immersive and atmospheric. The game has a variety of sound effects, such as the engine noise, the gunfire, the zombie screams, and the crash sounds. The game also has a catchy and upbeat soundtrack that matches the mood of the game. The game also has voice-overs that narrate the objectives and give you feedback on your performance.</p>
- <h3>Pros and Cons</h3>
- <p>Like any other game, Zombie Highway 2 has its pros and cons. Here are some of them:</p>
- <ul>
-<li><strong>Pros</strong></li>
-<li>The game is free to play and does not require an internet connection.</li>
-<li>The game is fun and addictive, with simple but challenging gameplay.</li>
-<li>The game has a lot of variety and replay value, with different modes, weapons, vehicles, zombies, and environments.</li>
-<li>The game has great graphics and sound that enhance the experience.</li>
-<li>The game has a friendly and active community that you can interact with.</li>
-</ul>
- <ul>
-<li><strong>Cons</strong></li>
-<li>The game can be repetitive and frustrating at times, especially when you encounter too many zombies or obstacles.</li>
-<li>The game can be hard to control, especially when you have to tilt your device or swipe the screen.</li>
-<li>The game can be unfair and unbalanced, especially when you face stronger zombies or encounter random events.</li>
-<li>The game can be expensive and pay-to-win, especially when you want to buy or upgrade weapons or vehicles.</li>
-<li>The game can have some bugs and glitches that affect the gameplay.</li>
-</ul>
- <h2>Zombie Highway 2: Tips and Tricks</h2>
- <p>If you want to improve your skills and enjoy Zombie Highway 2 more, here are some tips and tricks that you can use:</p>
- <h3>How to survive longer and get higher scores in Zombie Highway 2</h3>
- <p>Surviving longer and getting higher scores in Zombie Highway 2 is not easy, but it is possible if you follow these tips:</p>
- <ul>
-<li>Always keep an eye on your health bar and fuel gauge. If your health bar reaches zero or your fuel gauge runs out, you will lose the game. To replenish your health bar, you have to kill zombies or collect health packs on the road. To replenish your fuel gauge, you have to collect fuel cans on the road or use nitro boosts.</li>
-<li>Always keep moving and avoid staying in one lane for too long. If you stay in one lane for too long, you will attract more zombies and obstacles. To change lanes, you have to tilt your device left or right. To avoid obstacles, you have to swipe up or down on the screen.</li>
-<li>Always use your weapons wisely and aim for the head. If you want to kill zombies faster and easier, you have to use your weapons effectively. To use your weapons, you have to tap on the screen where you want to shoot. To aim for the head, you have to tap on the zombie's head or neck area. Headshots deal more damage and give you more coins.</li>
-<li>Always shake off zombies that latch onto your vehicle. If zombies manage to jump on your car, they will try to flip it over or damage it. To shake them off, you have to swipe left or right on the screen repeatedly. You can also use your weapons or drive close to obstacles to knock them off.</li>
-<li>Always try to complete objectives and daily challenges. If you want to earn more experience points and coins, you have to complete objectives and daily challenges. Objectives are specific tasks that you have to do in the game, such as killing a certain number of zombies or driving a certain distance. Daily challenges are time-limited tasks that you have to do in a specific mode, such as getting a certain score or surviving a certain time. Completing objectives and daily challenges will give you rewards, such as coins, weapons, or vehicles.</li>
-<li>Always try to collect coins and power-ups on the road. If you want to buy or upgrade weapons or vehicles, you have to collect coins on the road. Coins are scattered randomly on the highway, and you have to drive over them to collect them. You can also get coins by killing zombies, especially with headshots. Power-ups are special items that give you temporary advantages, such as nitro boosts, invincibility, or double coins. Power-ups are also scattered randomly on the highway, and you have to drive over them to activate them.</li>
-</ul>
- <h3>How to manage your resources and use your weapons wisely</h3>
- <p>Managing your resources and using your weapons wisely is crucial in Zombie Highway 2. You have limited ammo and fuel, and you have to use them sparingly and strategically. Here are some tips on how to do that:</p>
- <ul>
-<li>Always check your ammo and fuel before starting a game. You can see your ammo and fuel levels on the top left corner of the screen. You can also see the stats of your weapons and vehicles on the menu screen. You can buy more ammo or fuel with coins, or watch ads to get them for free.</li>
-<li>Always choose the right weapon and vehicle for the situation. You can equip up to four weapons and one vehicle at a time. You can switch between weapons by tapping on their icons on the bottom right corner of the screen. You can also switch between vehicles by tapping on their icons on the menu screen. Different weapons and vehicles have different stats, such as damage, range, accuracy, speed, handling, armor, and capacity. You have to choose the ones that suit your playstyle and preference.</li>
-<li>Always upgrade your weapons and vehicles when you can. You can upgrade your weapons and vehicles with coins on the menu screen. Upgrading your weapons will increase their damage, range, accuracy, and capacity. Upgrading your vehicles will increase their speed, handling, armor, and fuel efficiency. Upgrading your weapons and vehicles will make them more effective and durable.</li>
-</ul>
- <h3>How to deal with different types of zombies and obstacles</h3>
- <p>Dealing with different types of zombies and obstacles is challenging in Zombie Highway 2. You have to know their traits and weaknesses, and use the appropriate tactics to counter them. Here are some tips on how to do that:</p>
- <ul>
-<li>Always know your enemy. There are many types of zombies in Zombie Highway 2, each with their own appearance, behavior, and ability. Some zombies are fast and agile, some are slow and tough, some are small and weak, some are big and strong, some can jump high or far, some can spit acid or fireballs, some can explode or multiply, and some can even fly or swim. You have to learn their patterns and habits, and use the best weapon or strategy to deal with them.</li>
-<li>Always know your environment. There are many types of obstacles in Zombie Highway 2, each with their own shape, size, and effect. Some obstacles are harmless and can be ignored, some are dangerous and can damage your car or zombies, some are useful and can help you shake off zombies or collect coins or power-ups, and some are interactive and can be moved or destroyed. You have to pay attention to your surroundings and use the obstacles to your advantage or avoid them if necessary.</li>
-</ul>
- <h3>Zombie Highway 2: Weapons Guide</h3>
- <p>To help you choose the best weapons for Zombie Highway 2, here is a table of all the weapons available in the game and their stats:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Type</th>
-<th>Damage</th>
-<th>Range</th>
-<th>Accuracy</th>
-<th>Capacity</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Pistol</td>
-<td>Pistol</td>
-<td>Low</td>
-<td>Short</td>
-<td>High</td>
-<td>12</td>
-<td>A basic handgun that is reliable but weak.</td>
-</tr>
-<tr>
-<td>Revolver</td>
-<td>Pistol</td>
-<td>Medium</td>
-<td>Short</td>
-<td>High</td>
-<td>6</td>
-<td>A powerful handgun that has a slow reload time.</td>
-</tr>
-<tr>
-<td>SMG</td>
-<td>Rifle</td>
-<td>Low</td>
-<td>Medium</td>
-<td>Low</td>
-<td>30</td>
-<td>A rapid-fire rifle that is good for spraying bullets.</td>
-</tr>
-<tr>
-<td>Assault Rifle</td>
-<td>Rifle</td>
-<td>Medium</td>
-<td>Medium</td>
-<td>Medium</td>
-<td>30</td>
-<td>A balanced rifle that is good for most situations.</td>
-</tr>
-<tr>
-<td>Shotgun</td>
-<td>Shotgun</td>
-<td>High</td>
-<td>Short</td>
-<td>Low</td>
-<td>8</td>
-<td>A powerful shotgun that is good for close range.</td>
-</tr>
-<tr>
-<td>Sawed-Off Shotgun</td>
-<td>Shotgun</td>
-<td>Very High</td>
-<td>Very Short</td>
-<td>Very Low</td>
-<td>2</td>
-<td>A devastating shotgun that has a very fast reload time.</td>
-</tr>
-<tr>
-<td>Sniper Rifle</td>
-<td>Rifle</td>
-<td>Very High</td>
-<td>Long</td>
-<td>Very High</td>
-<td>5</td>
-<td>A precise rifle that is good for long range.</td>
-</tr>
-<tr>
-<td>Rocket Launcher</td>
-<td>Rocket Launcher</td>
-<td>Very High</td>
-<td>Long</td>
-<td>Low</td>
-<td>1</td>
-<td>A destructive rocket launcher that can cause explosions.</td>
-</tr>
-<tr>
-<td>Grenade Launcher</td>
-<td>Rocket Launcher</td>
-<td>High</td ><td>Medium</td ><td>Low</td ><td>6</td ><td>A versatile grenade launcher that can bounce grenades.</td ></tr ><tr ><td >Laser Rifle</td ><td >Laser Rifle</td ><td >High</td ><td >Long</td ><td >High</td ><td >Infinite</td ><td>A futuristic laser rifle that does not need ammo.</td ></tr ></table ><h3>Zombie Highway 2: Zombies Guide ></h3 ><p>To help you deal with different types of zombies in Zombie Highway 2, here is a table of all the zombies encountered in the game and their traits:</p ><table ><tr ><th>Name ></th ><th >Description ></th ><th >Trait ></th ></tr ><tr ><td >Zombie ></td ><t d >A basic zombie that is slow and weak.</td ><td >None ></td ></tr ><tr ><td >Runner ></td ><td >A fast zombie that can sprint and jump.</td ><td >Speed ></td ></tr ><tr ><td >Tank ></td ><td >A big zombie that is tough and strong.</td ><td >Strength ></td ></tr ><tr ><td >Spitter ></td ><td >A zombie that can spit acid or fireballs.</td ><td >Projectile ></td ></tr ><tr ><td >Bomber ></td ><td >A zombie that can explode or multiply.</td ><td >Explosion ></td ></tr ><tr ><td >Flyer ></td ><td >A zombie that can fly or glide.</td ><td >Flight ></td ></tr ><tr ><td >Swimmer ></td ><td >A zombie that can swim or dive.</td ><td>
->Water</td>
-</tr>
-</table>
- <h3>Zombie Highway 2: Vehicles Guide</h3>
- <p>To help you choose the best vehicles for Zombie Highway 2, here is a table of all the vehicles unlocked in the game and their features:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Type</th>
-<th>Speed</th>
-<th>Handling</th>
-<th>Armor</th>
-<th>Fuel Efficiency</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Sedan</td>
-<td>Sedan</td>
-<td>Medium</td>
-<td>Medium</td>
-<td>Low</td>
-<td>Medium</td>
-<td>A standard car that is good for beginners.</td>
-</tr>
-<tr>
-<td>Pickup Truck</td>
-<td>Truck</td>
-<td>Low</td>
-<td>Low</td>
-<td>High</td>
-<td>Low</td>
-<td>A sturdy truck that can withstand more damage.</td>
-</tr>
-<tr>
-<td>Sports Car</td>
-<td>Sports Car</td>
-<td>High</td>
-<td>High</td>
-<td>Low</td>
-<td>Low</td>
-<td>A fast car that can outrun zombies.</td>
-</tr>
-<tr>
-<td>Muscle Car</td>
-<td>Muscle Car</t d>
-<td>Medium</td>
-<td>Medium</td>
-<td>Medium</td>
-<td>Medium</td>
-<td>A powerful car that can knock off zombies.</td>
-</tr>
-<tr>
-<td>Police Car</td>
-<td>Sedan</td>
-<td>High</td>
-<td>Medium</td>
-<td>Medium</td>
-<td>Medium</td>
-<td>A law enforcement car that has a siren and a light.</td>
-</tr>
-<tr>
-<td>Military Truck</td>
-<td>Truck</td>
-<td>Low</td>
-<td>Low</td>
-<td>Very High</td>
-<td>Low</td>
-<td>A heavy-duty truck that has a turret and a plow.</td>
-</tr>
-<tr>
-<td>Limo</td>
-<td>Limo</td>
-<td>Low</td <td>Low</td ><td >High</td ><t d >High</t d ><t d >A luxurious car that has a lot of space and fuel.</t d ></tr ><tr ><t d >Bike ></t d ><t d >Bike ></t d ><t d >Very High ></t d ><t d >Very High ></t d ><t d >Very Low ></t d ><t d >Very High ></t d ><t d >A nimble bike that can dodge zombies and obstacles.</t d ></tr ></table ><h4>Zombie Highway 2: Daily Challenge ></h4 ><p>The daily challenge is a special mode in Zombie Highway 2 that gives you a chance to compete with other players around the world and see who can get the highest score in a limited time. The daily challenge has different rules and conditions every day, such as the mode, the vehicle, the weapon, the zombie, or the obstacle. You can play the daily challenge once a day for free, or pay coins to play it more times. You can also watch ads to get extra coins or power-ups for the daily challenge. The daily challenge rewards you with coins and experience points based on your rank and score.</p ><h4>Zombie Highway 2: In-App Purchases ></h4 ><p>Zombie Highway 2 is a free-to-play game, but it also has in-app purchases that allow you to buy coins or power-ups with real money. Coins are the currency of the game that you can use to buy or upgrade weapons or vehicles. Power-ups are special items that give you temporary advantages, such as nitro boosts, invincibility, or double coins. You can also buy power-ups with coins, but they are more expensive than buying them with real money. You can access the in-app purchases by tapping on the "+" icon on the top right corner of the screen.</p ><p>If you want to enjoy Zombie Highway 2 without spending any money, you can do so by playing the game regularly and completing objectives and daily challenges. You can also watch ads to get free coins or power-ups. However, if you want to support the developers and get more coins or power-ups faster, you can choose to buy them with real money. The choice is yours.</p ><h4>Zombie Highway 2: Settings and Options ></h4 ><p>Zombie Highway 2 has various settings and options that you can adjust to customize your gameplay experience. You can access the settings and options by tapping on the gear icon on the top left corner of the screen. Here are some of the settings and options that you can change:</p <ul>
- <li>Sensitivity: You can change the sensitivity of your device's tilt control by sliding the bar left or right. A higher sensitivity means a faster response, while a lower sensitivity means a slower response.</li>
- <li>Sound: You can turn on or off the sound effects and music of the game by tapping on the buttons. You can also adjust the volume of the sound effects and music by sliding the bars left or right.</li>
- <li>Voice: You can turn on or off the voice-overs of the game by tapping on the button. You can also choose between male or female voice-overs by tapping on their icons.</li>
- <li>Tutorial: You can replay the tutorial of the game by tapping on the button. The tutorial will teach you the basics of the game, such as how to drive, shoot, shake off zombies, and collect coins and power-ups.</li>
- <li>Credits: You can view the credits of the game by tapping on the button. The credits will show you the names of the developers, artists, musicians, and voice actors who worked on Zombie Highway 2.</li>
- <li>Feedback: You can send feedback to the developers by tapping on the button. You can write your comments, suggestions, or complaints about the game and send them to the developers via email.</li>
- <li>Rate: You can rate the game by tapping on the button. You can give the game a star rating from one to five, and write a review about it on the App Store.</li>
- <li>Share: You can share the game with your friends by tapping on the button. You can choose to share the game via Facebook, Twitter, or email.</li>
-</ul>
- <h2>Zombie Highway 2: Conclusion</h2>
- <p>Zombie Highway 2 is a fun and addictive zombie survival game that you can play on your iOS devices. The game has simple but challenging gameplay, great graphics and sound, a lot of variety and replay value, and a friendly and active community. The game is free to play, but it also has in-app purchases that you can choose to buy or not. The game is suitable for anyone who likes zombies, cars, guns, or action games.</p>
- <p>If you are looking for a thrilling and exciting game to kill some time and zombies, you should definitely give Zombie Highway 2 a try. You will not regret it.</p>
- <h3>Zombie Highway 2: FAQs</h3>
- <p>Here are some frequently asked questions and answers about Zombie Highway 2:</p>
- <ol>
-<li><strong>How do I unlock new weapons and vehicles?</strong></li>
-<li>You can unlock new weapons and vehicles by leveling up your character or completing objectives. You can also buy some weapons and vehicles with coins on the menu screen.</li>
-<li><strong>How do I customize my weapons and vehicles?</strong></li>
-<li>You can customize your weapons and vehicles by upgrading them with coins on the menu screen. You can also change the color of your vehicles by tapping on the paint icon on the menu screen.</li>
-<li><strong>How do I get more coins and power-ups?</strong></li>
-<li>You can get more coins and power-ups by playing the game and killing zombies, especially with headshots. You can also get coins and power-ups by collecting them on the road, completing objectives and daily challenges, watching ads, or buying them with real money.</li>
-<li><strong>How do I play with my friends?</strong></li>
-<li>You can play with your friends by connecting your game to Facebook or Game Center. You can then see your friends' scores and ranks on the leaderboard, and challenge them to beat your score.</li>
-<li><strong>How do I contact the developers?</strong></li>
-<li>You can contact the developers by sending feedback to them via email. You can also follow them on Facebook or Twitter for updates and news about the game.</li>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/querystring.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/querystring.d.ts
deleted file mode 100644
index e1185478461f4b15444b7b2ae114c8a6819a992a..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/querystring.d.ts
+++ /dev/null
@@ -1,131 +0,0 @@
-/**
- * The `querystring` module provides utilities for parsing and formatting URL
- * query strings. It can be accessed using:
- *
- * ```js
- * const querystring = require('querystring');
- * ```
- *
- * `querystring` is more performant than `URLSearchParams` but is not a
- * standardized API. Use `URLSearchParams` when performance is not critical
- * or when compatibility with browser code is desirable.
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/querystring.js)
- */
-declare module 'querystring' {
-    interface StringifyOptions {
-        encodeURIComponent?: ((str: string) => string) | undefined;
-    }
-    interface ParseOptions {
-        maxKeys?: number | undefined;
-        decodeURIComponent?: ((str: string) => string) | undefined;
-    }
-    interface ParsedUrlQuery extends NodeJS.Dict<string | string[]> {}
-    interface ParsedUrlQueryInput extends NodeJS.Dict<string | number | boolean | ReadonlyArray<string> | ReadonlyArray<number> | ReadonlyArray<boolean> | null> {}
-    /**
-     * The `querystring.stringify()` method produces a URL query string from a
-     * given `obj` by iterating through the object's "own properties".
-     *
-     * It serializes the following types of values passed in `obj`:[string](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#String_type) |
-     * [number](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Number_type) |
-     * [bigint](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/BigInt) |
-     * [boolean](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Boolean_type) |
-     * [string\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#String_type) |
-     * [number\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Number_type) |
-     * [bigint\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/BigInt) |
-     * [boolean\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Boolean_type) The numeric values must be finite. Any other input values will be coerced to
-     * empty strings.
-     *
-     * ```js
-     * querystring.stringify({ foo: 'bar', baz: ['qux', 'quux'], corge: '' });
-     * // Returns 'foo=bar&#x26;baz=qux&#x26;baz=quux&#x26;corge='
-     *
-     * querystring.stringify({ foo: 'bar', baz: 'qux' }, ';', ':');
-     * // Returns 'foo:bar;baz:qux'
-     * ```
-     *
-     * By default, characters requiring percent-encoding within the query string will
-     * be encoded as UTF-8\. If an alternative encoding is required, then an alternative`encodeURIComponent` option will need to be specified:
-     *
-     * ```js
-     * // Assuming gbkEncodeURIComponent function already exists,
-     *
-     * querystring.stringify({ w: '中文', foo: 'bar' }, null, null,
-     *                       { encodeURIComponent: gbkEncodeURIComponent });
-     * ```
-     * @since v0.1.25
-     * @param obj The object to serialize into a URL query string
-     * @param [sep='&'] The substring used to delimit key and value pairs in the query string.
-     * @param [eq='='] . The substring used to delimit keys and values in the query string.
-     */
-    function stringify(obj?: ParsedUrlQueryInput, sep?: string, eq?: string, options?: StringifyOptions): string;
-    /**
-     * The `querystring.parse()` method parses a URL query string (`str`) into a
-     * collection of key and value pairs.
-     *
-     * For example, the query string `'foo=bar&#x26;abc=xyz&#x26;abc=123'` is parsed into:
-     *
-     * ```js
-     * {
-     *   foo: 'bar',
-     *   abc: ['xyz', '123']
-     * }
-     * ```
-     *
-     * The object returned by the `querystring.parse()` method _does not_prototypically inherit from the JavaScript `Object`. This means that typical`Object` methods such as `obj.toString()`,
-     * `obj.hasOwnProperty()`, and others
-     * are not defined and _will not work_.
-     *
-     * By default, percent-encoded characters within the query string will be assumed
-     * to use UTF-8 encoding. If an alternative character encoding is used, then an
-     * alternative `decodeURIComponent` option will need to be specified:
-     *
-     * ```js
-     * // Assuming gbkDecodeURIComponent function already exists...
-     *
-     * querystring.parse('w=%D6%D0%CE%C4&#x26;foo=bar', null, null,
-     *                   { decodeURIComponent: gbkDecodeURIComponent });
-     * ```
-     * @since v0.1.25
-     * @param str The URL query string to parse
-     * @param [sep='&'] The substring used to delimit key and value pairs in the query string.
-     * @param [eq='='] . The substring used to delimit keys and values in the query string.
-     */
-    function parse(str: string, sep?: string, eq?: string, options?: ParseOptions): ParsedUrlQuery;
-    /**
-     * The querystring.encode() function is an alias for querystring.stringify().
-     */
-    const encode: typeof stringify;
-    /**
-     * The querystring.decode() function is an alias for querystring.parse().
-     */
-    const decode: typeof parse;
-    /**
-     * The `querystring.escape()` method performs URL percent-encoding on the given`str` in a manner that is optimized for the specific requirements of URL
-     * query strings.
-     *
-     * The `querystring.escape()` method is used by `querystring.stringify()` and is
-     * generally not expected to be used directly. It is exported primarily to allow
-     * application code to provide a replacement percent-encoding implementation if
-     * necessary by assigning `querystring.escape` to an alternative function.
-     * @since v0.1.25
-     */
-    function escape(str: string): string;
-    /**
-     * The `querystring.unescape()` method performs decoding of URL percent-encoded
-     * characters on the given `str`.
-     *
-     * The `querystring.unescape()` method is used by `querystring.parse()` and is
-     * generally not expected to be used directly. It is exported primarily to allow
-     * application code to provide a replacement decoding implementation if
-     * necessary by assigning `querystring.unescape` to an alternative function.
-     *
-     * By default, the `querystring.unescape()` method will attempt to use the
-     * JavaScript built-in `decodeURIComponent()` method to decode. If that fails,
-     * a safer equivalent that does not throw on malformed URLs will be used.
-     * @since v0.1.25
-     */
-    function unescape(str: string): string;
-}
-declare module 'node:querystring' {
-    export * from 'querystring';
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/binary.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/binary.js
deleted file mode 100644
index 4dfe08f36f73abe40d73c0322b741dba9eb68caf..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/binary.js
+++ /dev/null
@@ -1,88 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.reconstructPacket = exports.deconstructPacket = void 0;
-const is_binary_js_1 = require("./is-binary.js");
-/**
- * Replaces every Buffer | ArrayBuffer | Blob | File in packet with a numbered placeholder.
- *
- * @param {Object} packet - socket.io event packet
- * @return {Object} with deconstructed packet and list of buffers
- * @public
- */
-function deconstructPacket(packet) {
-    const buffers = [];
-    const packetData = packet.data;
-    const pack = packet;
-    pack.data = _deconstructPacket(packetData, buffers);
-    pack.attachments = buffers.length; // number of binary 'attachments'
-    return { packet: pack, buffers: buffers };
-}
-exports.deconstructPacket = deconstructPacket;
-function _deconstructPacket(data, buffers) {
-    if (!data)
-        return data;
-    if ((0, is_binary_js_1.isBinary)(data)) {
-        const placeholder = { _placeholder: true, num: buffers.length };
-        buffers.push(data);
-        return placeholder;
-    }
-    else if (Array.isArray(data)) {
-        const newData = new Array(data.length);
-        for (let i = 0; i < data.length; i++) {
-            newData[i] = _deconstructPacket(data[i], buffers);
-        }
-        return newData;
-    }
-    else if (typeof data === "object" && !(data instanceof Date)) {
-        const newData = {};
-        for (const key in data) {
-            if (Object.prototype.hasOwnProperty.call(data, key)) {
-                newData[key] = _deconstructPacket(data[key], buffers);
-            }
-        }
-        return newData;
-    }
-    return data;
-}
-/**
- * Reconstructs a binary packet from its placeholder packet and buffers
- *
- * @param {Object} packet - event packet with placeholders
- * @param {Array} buffers - binary buffers to put in placeholder positions
- * @return {Object} reconstructed packet
- * @public
- */
-function reconstructPacket(packet, buffers) {
-    packet.data = _reconstructPacket(packet.data, buffers);
-    delete packet.attachments; // no longer useful
-    return packet;
-}
-exports.reconstructPacket = reconstructPacket;
-function _reconstructPacket(data, buffers) {
-    if (!data)
-        return data;
-    if (data && data._placeholder === true) {
-        const isIndexValid = typeof data.num === "number" &&
-            data.num >= 0 &&
-            data.num < buffers.length;
-        if (isIndexValid) {
-            return buffers[data.num]; // appropriate buffer (should be natural order anyway)
-        }
-        else {
-            throw new Error("illegal attachments");
-        }
-    }
-    else if (Array.isArray(data)) {
-        for (let i = 0; i < data.length; i++) {
-            data[i] = _reconstructPacket(data[i], buffers);
-        }
-    }
-    else if (typeof data === "object") {
-        for (const key in data) {
-            if (Object.prototype.hasOwnProperty.call(data, key)) {
-                data[key] = _reconstructPacket(data[key], buffers);
-            }
-        }
-    }
-    return data;
-}
diff --git a/spaces/filehost/txt/run.py b/spaces/filehost/txt/run.py
deleted file mode 100644
index b58af344209d56e6cf72ec7a0bc6428a3384c312..0000000000000000000000000000000000000000
--- a/spaces/filehost/txt/run.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import subprocess
-command = 'xvfb-run python main.py'
-process = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True)
-output, error = process.communicate()
-print(output)
\ No newline at end of file
diff --git a/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.cpp b/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.cpp
deleted file mode 100644
index 2e26b71ed5aad0d46478fdbcd3a880be1401f946..0000000000000000000000000000000000000000
--- a/spaces/fkhuggingme/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.cpp
+++ /dev/null
@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include <stdlib.h>
-#include <string.h>
-#if PLATFORM_WINDOWS
-#include <malloc.h>
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES]  =
-{
-  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
-  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
-  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
-  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
-  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
-  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
-  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
-  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
-  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
-  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-
-// Low-level helper functions.
-template <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
-  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS  13
-#define ROW_BITS    2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
-  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
-  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
-  int32 u1 = DCT_MUL(t12 + t13, 4433); \
-  s2 = u1 + DCT_MUL(t13, 6270); \
-  s6 = u1 + DCT_MUL(t12, -15137); \
-  u1 = t4 + t7; \
-  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
-  int32 z5 = DCT_MUL(u3 + u4, 9633); \
-  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
-  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
-  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
-  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
-  u3 += z5; u4 += z5; \
-  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
-  int32 c, *q = p;
-  for (c = 7; c >= 0; c--, q += 8)
-  {
-    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
-    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
-  }
-  for (q = p, c = 7; c >= 0; c--, q++)
-  {
-    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
-    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
-  }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
-  const uint cMaxPasses = 4;
-  uint32 hist[256 * cMaxPasses]; clear_obj(hist);
-  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
-  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
-  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
-  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
-  {
-    const uint32* pHist = &hist[pass << 8];
-    uint offsets[256], cur_ofs = 0;
-    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
-    for (uint i = 0; i < num_syms; i++)
-      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
-    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
-  }
-  return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
-  int root, leaf, next, avbl, used, dpth;
-  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
-  A[0].m_key += A[1].m_key; root = 0; leaf = 2;
-  for (next=1; next < n-1; next++)
-  {
-    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;
-    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;
-  }
-  A[n-2].m_key = 0;
-  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
-  avbl = 1; used = dpth = 0; root = n-2; next = n-1;
-  while (avbl>0)
-  {
-    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
-    while (avbl>used) { A[next--].m_key = dpth; avbl--; }
-    avbl = 2*used; dpth++; used = 0;
-  }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
-  if (code_list_len <= 1) return;
-
-  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
-  uint32 total = 0;
-  for (int i = max_code_size; i > 0; i--)
-    total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
-  while (total != (1UL << max_code_size))
-  {
-    pNum_codes[max_code_size]--;
-    for (int i = max_code_size - 1; i > 0; i--)
-    {
-      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
-    }
-    total--;
-  }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
-  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
-  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's
-  int num_used_syms = 1;
-  const uint32 *pSym_count = &m_huff_count[table_num][0];
-  for (int i = 0; i < table_len; i++)
-    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
-  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
-  calculate_minimum_redundancy(pSyms, num_used_syms);
-
-  // Count the # of symbols of each code size.
-  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
-  for (int i = 0; i < num_used_syms; i++)
-    num_codes[pSyms[i].m_key]++;
-
-  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
-  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
-  // Compute m_huff_bits array, which contains the # of symbols per code size.
-  clear_obj(m_huff_bits[table_num]);
-  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
-    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);
-
-  // Remove the dummy symbol added above, which must be in largest bucket.
-  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
-  {
-    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
-  }
-
-  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
-  for (int i = num_used_syms - 1; i >= 1; i--)
-    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
-  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
-  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
-  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
-  emit_marker(M_APP0);
-  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
-  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
-  emit_byte(0);
-  emit_byte(1);      /* Major version */
-  emit_byte(1);      /* Minor version */
-  emit_byte(0);      /* Density unit */
-  emit_word(1);
-  emit_word(1);
-  emit_byte(0);      /* No thumbnail image */
-  emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
-  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
-  {
-    emit_marker(M_DQT);
-    emit_word(64 + 1 + 2);
-    emit_byte(static_cast<uint8>(i));
-    for (int j = 0; j < 64; j++)
-      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));
-  }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
-  emit_marker(M_SOF0);                           /* baseline */
-  emit_word(3 * m_num_components + 2 + 5 + 1);
-  emit_byte(8);                                  /* precision */
-  emit_word(m_image_y);
-  emit_word(m_image_x);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */
-    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */
-    emit_byte(i > 0);                                   /* quant. table num */
-  }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
-  emit_marker(M_DHT);
-
-  int length = 0;
-  for (int i = 1; i <= 16; i++)
-    length += bits[i];
-
-  emit_word(length + 2 + 1 + 16);
-  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));
-
-  for (int i = 1; i <= 16; i++)
-    emit_byte(bits[i]);
-
-  for (int i = 0; i < length; i++)
-    emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
-  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
-  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
-  if (m_num_components == 3)
-  {
-    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
-    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
-  }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
-  emit_marker(M_SOS);
-  emit_word(2 * m_num_components + 2 + 1 + 3);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));
-    if (i == 0)
-      emit_byte((0 << 4) + 0);
-    else
-      emit_byte((1 << 4) + 1);
-  }
-  emit_byte(0);     /* spectral selection */
-  emit_byte(63);
-  emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
-  emit_marker(M_SOI);
-  emit_jfif_app0();
-  emit_dqt();
-  emit_sof();
-  emit_dhts();
-  emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
-  int i, l, last_p, si;
-  uint8 huff_size[257];
-  uint huff_code[257];
-  uint code;
-
-  int p = 0;
-  for (l = 1; l <= 16; l++)
-    for (i = 1; i <= bits[l]; i++)
-      huff_size[p++] = (char)l;
-
-  huff_size[p] = 0; last_p = p; // write sentinel
-
-  code = 0; si = huff_size[0]; p = 0;
-
-  while (huff_size[p])
-  {
-    while (huff_size[p] == si)
-      huff_code[p++] = code++;
-    code <<= 1;
-    si++;
-  }
-
-  memset(codes, 0, sizeof(codes[0])*256);
-  memset(code_sizes, 0, sizeof(code_sizes[0])*256);
-  for (p = 0; p < last_p; p++)
-  {
-    codes[val[p]]      = huff_code[p];
-    code_sizes[val[p]] = huff_size[p];
-  }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
-  int32 q;
-  if (m_params.m_quality < 50)
-    q = 5000 / m_params.m_quality;
-  else
-    q = 200 - m_params.m_quality * 2;
-  for (int i = 0; i < 64; i++)
-  {
-    int32 j = *pSrc++; j = (j * q + 50L) / 100L;
-    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
-  }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
-  m_bit_buffer = 0; m_bits_in = 0;
-  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
-  m_mcu_y_ofs = 0;
-  m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
-  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
-  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
-  if (m_num_components > 1)
-  {
-    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
-    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
-  }
-  first_pass_init();
-  emit_markers();
-  m_pass_num = 2;
-  return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
-  m_num_components = 3;
-  switch (m_params.m_subsampling)
-  {
-    case Y_ONLY:
-    {
-      m_num_components = 1;
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H1V1:
-    {
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H2V1:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 8;
-      break;
-    }
-    case H2V2:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 16;
-    }
-  }
-
-  m_image_x        = p_x_res; m_image_y = p_y_res;
-  m_image_bpp      = src_channels;
-  m_image_bpl      = m_image_x * src_channels;
-  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
-  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
-  m_image_bpl_xlt  = m_image_x * m_num_components;
-  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;
-  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;
-
-  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
-  for (int i = 1; i < m_mcu_y; i++)
-    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
-  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
-  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-  m_pOut_buf = m_out_buf;
-
-  if (m_params.m_two_pass_flag)
-  {
-    clear_obj(m_huff_count);
-    first_pass_init();
-  }
-  else
-  {
-    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
-    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
-    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
-    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
-    if (!second_pass_init()) return false;   // in effect, skip over the first pass
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[i] + x;
-    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
-    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (8 * 3)) + c;
-  y <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[y + i] + x;
-    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
-    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
-  uint8 *pSrc1, *pSrc2;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  int a = 0, b = 2;
-  for (int i = 0; i < 16; i += 2, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pSrc2 = m_mcu_lines[i + 1] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
-    int temp = a; a = b; b = temp;
-  }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
-  uint8 *pSrc1;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
-  }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
-  int32 *q = m_quantization_tables[component_num > 0];
-  int16 *pDst = m_coefficient_array;
-  for (int i = 0; i < 64; i++)
-  {
-    sample_array_t j = m_sample_array[s_zag[i]];
-    if (j < 0)
-    {
-      if ((j = -j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>(-(j / *q));
-    }
-    else
-    {
-      if ((j = j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>((j / *q));
-    }
-    q++;
-  }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
-  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
-    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
-  m_pOut_buf = m_out_buf;
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
-  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
-  while (m_bits_in >= 8)
-  {
-    uint8 c;
-    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
-    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
-    if (c == 0xFF) JPGE_PUT_BYTE(0);
-    m_bit_buffer <<= 8;
-    m_bits_in -= 8;
-  }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
-  if (component_num >= 3) return; // just to shut up static analysis
-  int i, run_len, nbits, temp1;
-  int16 *src = m_coefficient_array;
-  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
-  temp1 = src[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = src[0];
-  if (temp1 < 0) temp1 = -temp1;
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  dc_count[nbits]++;
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        ac_count[0xF0]++;
-        run_len -= 16;
-      }
-      if (temp1 < 0) temp1 = -temp1;
-      nbits = 1;
-      while (temp1 >>= 1) nbits++;
-      ac_count[(run_len << 4) + nbits]++;
-      run_len = 0;
-    }
-  }
-  if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
-  int i, j, run_len, nbits, temp1, temp2;
-  int16 *pSrc = m_coefficient_array;
-  uint *codes[2];
-  uint8 *code_sizes[2];
-
-  if (component_num == 0)
-  {
-    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
-    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
-  }
-  else
-  {
-    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
-    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
-  }
-
-  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = pSrc[0];
-
-  if (temp1 < 0)
-  {
-    temp1 = -temp1; temp2--;
-  }
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  put_bits(codes[0][nbits], code_sizes[0][nbits]);
-  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
-        run_len -= 16;
-      }
-      if ((temp2 = temp1) < 0)
-      {
-        temp1 = -temp1;
-        temp2--;
-      }
-      nbits = 1;
-      while (temp1 >>= 1)
-        nbits++;
-      j = (run_len << 4) + nbits;
-      put_bits(codes[1][j], code_sizes[1][j]);
-      put_bits(temp2 & ((1 << nbits) - 1), nbits);
-      run_len = 0;
-    }
-  }
-  if (run_len)
-    put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
-  DCT2D(m_sample_array);
-  load_quantized_coefficients(component_num);
-  if (m_pass_num == 1)
-    code_coefficients_pass_one(component_num);
-  else
-    code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
-  if (m_num_components == 1)
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8_grey(i); code_block(0);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
-      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
-    }
-  }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
-  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
-  if (m_num_components > 1)
-  {
-    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
-  }
-  return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
-  put_bits(0x7F, 7);
-  flush_output_buffer();
-  emit_marker(M_EOI);
-  m_pass_num++; // purposely bump up m_pass_num, for debugging
-  return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
-  if (m_mcu_y_ofs)
-  {
-    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
-    {
-      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
-        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
-    }
-
-    process_mcu_row();
-  }
-
-  if (m_pass_num == 1)
-    return terminate_pass_one();
-  else
-    return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
-  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);
-
-  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
-  if (m_num_components == 1)
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_Y(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_Y(pDst, Psrc, m_image_x);
-    else
-      memcpy(pDst, Psrc, m_image_x);
-  }
-  else
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_YCC(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_YCC(pDst, Psrc, m_image_x);
-    else
-      Y_to_YCC(pDst, Psrc, m_image_x);
-  }
-
-  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
-  if (m_num_components == 1)
-    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
-  else
-  {
-    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
-    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
-    for (int i = m_image_x; i < m_image_x_mcu; i++)
-    {
-      *q++ = y; *q++ = cb; *q++ = cr;
-    }
-  }
-
-  if (++m_mcu_y_ofs == m_mcu_y)
-  {
-    process_mcu_row();
-    m_mcu_y_ofs = 0;
-  }
-}
-
-void jpeg_encoder::clear()
-{
-  m_mcu_lines[0] = NULL;
-  m_pass_num = 0;
-  m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
-  clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
-  deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
-  deinit();
-  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
-  m_pStream = pStream;
-  m_params = comp_params;
-  return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
-  jpge_free(m_mcu_lines[0]);
-  clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
-  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
-  if (m_all_stream_writes_succeeded)
-  {
-    if (!pScanline)
-    {
-      if (!process_end_of_image()) return false;
-    }
-    else
-    {
-      load_mcu(pScanline);
-    }
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include <stdio.h>
-
-class cfile_stream : public output_stream
-{
-   cfile_stream(const cfile_stream &);
-   cfile_stream &operator= (const cfile_stream &);
-
-   FILE* m_pFile;
-   bool m_bStatus;
-
-public:
-   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
-   virtual ~cfile_stream()
-   {
-      close();
-   }
-
-   bool open(const char *pFilename)
-   {
-      close();
-#if defined(_MSC_VER)
-      if (fopen_s(&m_pFile, pFilename, "wb") != 0)
-	  {
-		  return false;
-	  }
-#else
-      m_pFile = fopen(pFilename, "wb");
-#endif
-      m_bStatus = (m_pFile != NULL);
-      return m_bStatus;
-   }
-
-   bool close()
-   {
-      if (m_pFile)
-      {
-         if (fclose(m_pFile) == EOF)
-         {
-            m_bStatus = false;
-         }
-         m_pFile = NULL;
-      }
-      return m_bStatus;
-   }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
-      return m_bStatus;
-   }
-
-   uint get_size() const
-   {
-      return m_pFile ? ftell(m_pFile) : 0;
-   }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-  cfile_stream dst_stream;
-  if (!dst_stream.open(pFilename))
-    return false;
-
-  jpge::jpeg_encoder dst_image;
-  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-    return false;
-
-  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-  {
-    for (int64_t i = 0; i < height; i++)
-    {
-		// i, width, and num_channels are all 64bit
-       const uint8* pBuf = pImage_data + i * width * num_channels;
-       if (!dst_image.process_scanline(pBuf))
-          return false;
-    }
-    if (!dst_image.process_scanline(NULL))
-       return false;
-  }
-
-  dst_image.deinit();
-
-  return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
-   memory_stream(const memory_stream &);
-   memory_stream &operator= (const memory_stream &);
-
-   uint8 *m_pBuf;
-   uint64_t m_buf_size, m_buf_ofs;
-
-public:
-   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
-   virtual ~memory_stream() { }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      uint64_t buf_remaining = m_buf_size - m_buf_ofs;
-      if ((uint64_t)len > buf_remaining)
-         return false;
-      memcpy(m_pBuf + m_buf_ofs, pBuf, len);
-      m_buf_ofs += len;
-      return true;
-   }
-
-   uint64_t get_size() const
-   {
-      return m_buf_ofs;
-   }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-   if ((!pDstBuf) || (!buf_size))
-      return false;
-
-   memory_stream dst_stream(pDstBuf, buf_size);
-
-   buf_size = 0;
-
-   jpge::jpeg_encoder dst_image;
-   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-      return false;
-
-   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-   {
-     for (int64_t i = 0; i < height; i++)
-     {
-        const uint8* pScanline = pImage_data + i * width * num_channels;
-        if (!dst_image.process_scanline(pScanline))
-           return false;
-     }
-     if (!dst_image.process_scanline(NULL))
-        return false;
-   }
-
-   dst_image.deinit();
-
-   buf_size = dst_stream.get_size();
-   return true;
-}
-
-} // namespace jpge
\ No newline at end of file
diff --git a/spaces/gkmike/ckip-joint-bloom-3b-zh/README.md b/spaces/gkmike/ckip-joint-bloom-3b-zh/README.md
deleted file mode 100644
index e433875d46bb4fbb4b8d848f2acd799cd9f18a3f..0000000000000000000000000000000000000000
--- a/spaces/gkmike/ckip-joint-bloom-3b-zh/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ckip Joint Bloom 3b Zh
-emoji: 💻
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gligen/demo/gligen/ldm/data/imagenet.py b/spaces/gligen/demo/gligen/ldm/data/imagenet.py
deleted file mode 100644
index ec7c09fe3e32a634cb47dbd8b5c23aaeb4580b4a..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/ldm/data/imagenet.py
+++ /dev/null
@@ -1,394 +0,0 @@
-import os, yaml, pickle, shutil, tarfile, glob
-import cv2
-import albumentations
-import PIL
-import numpy as np
-import torchvision.transforms.functional as TF
-from omegaconf import OmegaConf
-from functools import partial
-from PIL import Image
-from tqdm import tqdm
-from torch.utils.data import Dataset, Subset
-
-import taming.data.utils as tdu
-from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
-from taming.data.imagenet import ImagePaths
-
-from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
-
-
-def synset2idx(path_to_yaml="ldm/data/index_synset.yaml"):
-    with open(path_to_yaml) as f:
-        di2s = yaml.load(f)
-    return dict((v,k) for k,v in di2s.items())
-
-
-class ImageNetBase(Dataset):
-    def __init__(self, config=None):
-        self.config = config or OmegaConf.create()
-        if not type(self.config)==dict:
-            self.config = OmegaConf.to_container(self.config)
-        self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
-        self.process_images = True  # if False we skip loading & processing images and self.data contains filepaths
-        self._prepare()
-        self._prepare_synset_to_human()
-        self._prepare_idx_to_synset()
-        self._prepare_human_to_integer_label()
-        self._load()
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        return self.data[i]
-
-    def _prepare(self):
-        raise NotImplementedError()
-
-    def _filter_relpaths(self, relpaths):
-        ignore = set([
-            "n06596364_9591.JPEG",
-        ])
-        relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
-        if "sub_indices" in self.config:
-            indices = str_to_indices(self.config["sub_indices"])
-            synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn)  # returns a list of strings
-            self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
-            files = []
-            for rpath in relpaths:
-                syn = rpath.split("/")[0]
-                if syn in synsets:
-                    files.append(rpath)
-            return files
-        else:
-            return relpaths
-
-    def _prepare_synset_to_human(self):
-        SIZE = 2655750
-        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
-        self.human_dict = os.path.join(self.root, "synset_human.txt")
-        if (not os.path.exists(self.human_dict) or
-                not os.path.getsize(self.human_dict)==SIZE):
-            download(URL, self.human_dict)
-
-    def _prepare_idx_to_synset(self):
-        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
-        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
-        if (not os.path.exists(self.idx2syn)):
-            download(URL, self.idx2syn)
-
-    def _prepare_human_to_integer_label(self):
-        URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
-        self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
-        if (not os.path.exists(self.human2integer)):
-            download(URL, self.human2integer)
-        with open(self.human2integer, "r") as f:
-            lines = f.read().splitlines()
-            assert len(lines) == 1000
-            self.human2integer_dict = dict()
-            for line in lines:
-                value, key = line.split(":")
-                self.human2integer_dict[key] = int(value)
-
-    def _load(self):
-        with open(self.txt_filelist, "r") as f:
-            self.relpaths = f.read().splitlines()
-            l1 = len(self.relpaths)
-            self.relpaths = self._filter_relpaths(self.relpaths)
-            print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
-
-        self.synsets = [p.split("/")[0] for p in self.relpaths]
-        self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
-
-        unique_synsets = np.unique(self.synsets)
-        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
-        if not self.keep_orig_class_label:
-            self.class_labels = [class_dict[s] for s in self.synsets]
-        else:
-            self.class_labels = [self.synset2idx[s] for s in self.synsets]
-
-        with open(self.human_dict, "r") as f:
-            human_dict = f.read().splitlines()
-            human_dict = dict(line.split(maxsplit=1) for line in human_dict)
-
-        self.human_labels = [human_dict[s] for s in self.synsets]
-
-        labels = {
-            "relpath": np.array(self.relpaths),
-            "synsets": np.array(self.synsets),
-            "class_label": np.array(self.class_labels),
-            "human_label": np.array(self.human_labels),
-        }
-
-        if self.process_images:
-            self.size = retrieve(self.config, "size", default=256)
-            self.data = ImagePaths(self.abspaths,
-                                   labels=labels,
-                                   size=self.size,
-                                   random_crop=self.random_crop,
-                                   )
-        else:
-            self.data = self.abspaths
-
-
-class ImageNetTrain(ImageNetBase):
-    NAME = "ILSVRC2012_train"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
-    FILES = [
-        "ILSVRC2012_img_train.tar",
-    ]
-    SIZES = [
-        147897477120,
-    ]
-
-    def __init__(self, process_images=True, data_root=None, **kwargs):
-        self.process_images = process_images
-        self.data_root = data_root
-        super().__init__(**kwargs)
-
-    def _prepare(self):
-        if self.data_root:
-            self.root = os.path.join(self.data_root, self.NAME)
-        else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 1281167
-        self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
-                                    default=True)
-        if not tdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                print("Extracting sub-tars.")
-                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
-                for subpath in tqdm(subpaths):
-                    subdir = subpath[:-len(".tar")]
-                    os.makedirs(subdir, exist_ok=True)
-                    with tarfile.open(subpath, "r:") as tar:
-                        tar.extractall(path=subdir)
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            tdu.mark_prepared(self.root)
-
-
-class ImageNetValidation(ImageNetBase):
-    NAME = "ILSVRC2012_validation"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
-    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
-    FILES = [
-        "ILSVRC2012_img_val.tar",
-        "validation_synset.txt",
-    ]
-    SIZES = [
-        6744924160,
-        1950000,
-    ]
-
-    def __init__(self, process_images=True, data_root=None, **kwargs):
-        self.data_root = data_root
-        self.process_images = process_images
-        super().__init__(**kwargs)
-
-    def _prepare(self):
-        if self.data_root:
-            self.root = os.path.join(self.data_root, self.NAME)
-        else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 50000
-        self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
-                                    default=False)
-        if not tdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                vspath = os.path.join(self.root, self.FILES[1])
-                if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
-                    download(self.VS_URL, vspath)
-
-                with open(vspath, "r") as f:
-                    synset_dict = f.read().splitlines()
-                    synset_dict = dict(line.split() for line in synset_dict)
-
-                print("Reorganizing into synset folders")
-                synsets = np.unique(list(synset_dict.values()))
-                for s in synsets:
-                    os.makedirs(os.path.join(datadir, s), exist_ok=True)
-                for k, v in synset_dict.items():
-                    src = os.path.join(datadir, k)
-                    dst = os.path.join(datadir, v)
-                    shutil.move(src, dst)
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            tdu.mark_prepared(self.root)
-
-
-
-class ImageNetSR(Dataset):
-    def __init__(self, size=None,
-                 degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
-                 random_crop=True):
-        """
-        Imagenet Superresolution Dataloader
-        Performs following ops in order:
-        1.  crops a crop of size s from image either as random or center crop
-        2.  resizes crop to size with cv2.area_interpolation
-        3.  degrades resized crop with degradation_fn
-
-        :param size: resizing to size after cropping
-        :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
-        :param downscale_f: Low Resolution Downsample factor
-        :param min_crop_f: determines crop size s,
-          where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
-        :param max_crop_f: ""
-        :param data_root:
-        :param random_crop:
-        """
-        self.base = self.get_base()
-        assert size
-        assert (size / downscale_f).is_integer()
-        self.size = size
-        self.LR_size = int(size / downscale_f)
-        self.min_crop_f = min_crop_f
-        self.max_crop_f = max_crop_f
-        assert(max_crop_f <= 1.)
-        self.center_crop = not random_crop
-
-        self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
-
-        self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
-
-        if degradation == "bsrgan":
-            self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
-
-        elif degradation == "bsrgan_light":
-            self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
-
-        else:
-            interpolation_fn = {
-            "cv_nearest": cv2.INTER_NEAREST,
-            "cv_bilinear": cv2.INTER_LINEAR,
-            "cv_bicubic": cv2.INTER_CUBIC,
-            "cv_area": cv2.INTER_AREA,
-            "cv_lanczos": cv2.INTER_LANCZOS4,
-            "pil_nearest": PIL.Image.NEAREST,
-            "pil_bilinear": PIL.Image.BILINEAR,
-            "pil_bicubic": PIL.Image.BICUBIC,
-            "pil_box": PIL.Image.BOX,
-            "pil_hamming": PIL.Image.HAMMING,
-            "pil_lanczos": PIL.Image.LANCZOS,
-            }[degradation]
-
-            self.pil_interpolation = degradation.startswith("pil_")
-
-            if self.pil_interpolation:
-                self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
-
-            else:
-                self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
-                                                                          interpolation=interpolation_fn)
-
-    def __len__(self):
-        return len(self.base)
-
-    def __getitem__(self, i):
-        example = self.base[i]
-        image = Image.open(example["file_path_"])
-
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        image = np.array(image).astype(np.uint8)
-
-        min_side_len = min(image.shape[:2])
-        crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
-        crop_side_len = int(crop_side_len)
-
-        if self.center_crop:
-            self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
-
-        else:
-            self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
-
-        image = self.cropper(image=image)["image"]
-        image = self.image_rescaler(image=image)["image"]
-
-        if self.pil_interpolation:
-            image_pil = PIL.Image.fromarray(image)
-            LR_image = self.degradation_process(image_pil)
-            LR_image = np.array(LR_image).astype(np.uint8)
-
-        else:
-            LR_image = self.degradation_process(image=image)["image"]
-
-        example["image"] = (image/127.5 - 1.0).astype(np.float32)
-        example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
-
-        return example
-
-
-class ImageNetSRTrain(ImageNetSR):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def get_base(self):
-        with open("ldm/data/imagenet_train_hr_indices.p", "rb") as f:
-            indices = pickle.load(f)
-        dset = ImageNetTrain(process_images=False,)
-        return Subset(dset, indices)
-
-
-class ImageNetSRValidation(ImageNetSR):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def get_base(self):
-        with open("ldm/data/imagenet_val_hr_indices.p", "rb") as f:
-            indices = pickle.load(f)
-        dset = ImageNetValidation(process_images=False,)
-        return Subset(dset, indices)
diff --git a/spaces/globalmatt/catsanddogs/app.py b/spaces/globalmatt/catsanddogs/app.py
deleted file mode 100644
index 631d65b9c75c699e8ba9ec61a266d58a003d7b84..0000000000000000000000000000000000000000
--- a/spaces/globalmatt/catsanddogs/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: (unless otherwise specified).
-
-import gradio as gr
-from fastai.vision.all import *
-
-__all_ = ['is_cat', 'learn', 'classify_image',
-          'categories', 'image', 'label', 'examples', 'intf']
-
-
-# Cell
-def is_cat(x): return x[0].isupper()
-
-
-# Cell
-learn = load_learner('model.pkl')
-
-# Cell
-categories = ('Dog', 'Cat')
-
-
-def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-
-# Cell
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ['dog.jpg', 'cat.jpg']
-intf = gr.Interface(fn=classify_image, inputs=image,
-                    outputs=label, examples=examples)
-intf.launch(inline=False)
diff --git a/spaces/gradio/HuBERT/fairseq/criterions/adaptive_loss.py b/spaces/gradio/HuBERT/fairseq/criterions/adaptive_loss.py
deleted file mode 100644
index 6209ceaedb6d8120ad820c11b55c13596447933c..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/criterions/adaptive_loss.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass
-
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.constants import DDP_BACKEND_CHOICES
-from omegaconf import II
-
-
-@dataclass
-class AdaptiveLossConfig(FairseqDataclass):
-    sentence_avg: bool = II("optimization.sentence_avg")
-    ddp_backend: DDP_BACKEND_CHOICES = II("distributed_training.ddp_backend")
-
-
-@register_criterion("adaptive_loss", dataclass=AdaptiveLossConfig)
-class AdaptiveLoss(FairseqCriterion):
-    """This is an implementation of the loss function accompanying the adaptive softmax approximation for
-    graphical processing units (GPU), described in the paper "Efficient softmax approximation for GPUs"
-    (http://arxiv.org/abs/1609.04309)."""
-
-    def __init__(self, task, sentence_avg):
-        super().__init__(task)
-        self.sentence_avg = sentence_avg
-
-    @classmethod
-    def build_criterion(cls, cfg: AdaptiveLossConfig, task):
-        if cfg.ddp_backend in {"c10d", "pytorch_ddp"}:
-            raise Exception(
-                "AdaptiveLoss is not compatible with the PyTorch "
-                "version of DistributedDataParallel. Please use "
-                "`--ddp-backend=legacy_ddp` instead."
-            )
-        return cls(task, cfg.sentence_avg)
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-
-        assert (
-            hasattr(model.decoder, "adaptive_softmax")
-            and model.decoder.adaptive_softmax is not None
-        )
-        adaptive_softmax = model.decoder.adaptive_softmax
-
-        net_output = model(**sample["net_input"])
-        orig_target = model.get_targets(sample, net_output)
-
-        nsentences = orig_target.size(0)
-        orig_target = orig_target.view(-1)
-
-        bsz = orig_target.size(0)
-
-        logits, target = adaptive_softmax(net_output[0], orig_target)
-        assert len(target) == len(logits)
-
-        loss = net_output[0].new(1 if reduce else bsz).zero_()
-
-        for i in range(len(target)):
-            if target[i] is not None:
-                assert target[i].min() >= 0 and target[i].max() <= logits[i].size(1)
-                loss += F.cross_entropy(
-                    logits[i],
-                    target[i],
-                    ignore_index=self.padding_idx,
-                    reduction="sum" if reduce else "none",
-                )
-
-        orig = utils.strip_pad(orig_target, self.padding_idx)
-        ntokens = orig.numel()
-        sample_size = sample["target"].size(0) if self.sentence_avg else ntokens
-        logging_output = {
-            "loss": loss.data,
-            "ntokens": ntokens,
-            "nsentences": nsentences,
-            "sample_size": sample_size,
-        }
-        return loss, sample_size, logging_output
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
-        ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
-        sample_size = utils.item(
-            sum(log.get("sample_size", 0) for log in logging_outputs)
-        )
-
-        metrics.log_scalar(
-            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
-        )
-        if sample_size != ntokens:
-            metrics.log_scalar(
-                "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
-            )
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
-            )
-        else:
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
-            )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/util/visualizer.py b/spaces/gwang-kim/DATID-3D/pose_estimation/util/visualizer.py
deleted file mode 100644
index 5dac564b2b8f36dd55eadf455301e111ad07b734..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/util/visualizer.py
+++ /dev/null
@@ -1,226 +0,0 @@
-"""This script defines the visualizer for Deep3DFaceRecon_pytorch
-"""
-
-import os
-import ntpath
-import time
-from . import util, html
-from torch.utils.tensorboard import SummaryWriter
-
-def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
-    """Save images to the disk.
-
-    Parameters:
-        webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
-        visuals (OrderedDict)    -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
-        image_path (str)         -- the string is used to create image paths
-        aspect_ratio (float)     -- the aspect ratio of saved images
-        width (int)              -- the images will be resized to width x width
-
-    This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
-    """
-    image_dir = webpage.get_image_dir()
-    short_path = ntpath.basename(image_path[0])
-    name = os.path.splitext(short_path)[0]
-
-    webpage.add_header(name)
-    ims, txts, links = [], [], []
-
-    for label, im_data in visuals.items():
-        im = util.tensor2im(im_data)
-        image_name = '%s/%s.png' % (label, name)
-        os.makedirs(os.path.join(image_dir, label), exist_ok=True)
-        save_path = os.path.join(image_dir, image_name)
-        util.save_image(im, save_path, aspect_ratio=aspect_ratio)
-        ims.append(image_name)
-        txts.append(label)
-        links.append(image_name)
-    webpage.add_images(ims, txts, links, width=width)
-
-
-class Visualizer():
-    """This class includes several functions that can display/save images and print/save logging information.
-
-    It uses a Python library tensprboardX for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images.
-    """
-
-    def __init__(self, opt):
-        """Initialize the Visualizer class
-
-        Parameters:
-            opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        Step 1: Cache the training/test options
-        Step 2: create a tensorboard writer
-        Step 3: create an HTML object for saveing HTML filters
-        Step 4: create a logging file to store training losses
-        """
-        self.opt = opt  # cache the option
-        self.use_html = opt.isTrain and not opt.no_html
-        self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, 'logs', opt.name))
-        self.win_size = opt.display_winsize
-        self.name = opt.name
-        self.saved = False
-        if self.use_html:  # create an HTML object at <checkpoints_dir>/web/; images will be saved under <checkpoints_dir>/web/images/
-            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
-            self.img_dir = os.path.join(self.web_dir, 'images')
-            print('create web directory %s...' % self.web_dir)
-            util.mkdirs([self.web_dir, self.img_dir])
-        # create a logging file to store training losses
-        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
-        with open(self.log_name, "a") as log_file:
-            now = time.strftime("%c")
-            log_file.write('================ Training Loss (%s) ================\n' % now)
-
-    def reset(self):
-        """Reset the self.saved status"""
-        self.saved = False
-
-
-    def display_current_results(self, visuals, total_iters, epoch, save_result):
-        """Display current results on tensorboad; save current results to an HTML file.
-
-        Parameters:
-            visuals (OrderedDict) - - dictionary of images to display or save
-            total_iters (int) -- total iterations
-            epoch (int) - - the current epoch
-            save_result (bool) - - if save the current results to an HTML file
-        """
-        for label, image in visuals.items():
-            self.writer.add_image(label, util.tensor2im(image), total_iters, dataformats='HWC')
-
-        if self.use_html and (save_result or not self.saved):  # save images to an HTML file if they haven't been saved.
-            self.saved = True
-            # save images to the disk
-            for label, image in visuals.items():
-                image_numpy = util.tensor2im(image)
-                img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
-                util.save_image(image_numpy, img_path)
-
-            # update website
-            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=0)
-            for n in range(epoch, 0, -1):
-                webpage.add_header('epoch [%d]' % n)
-                ims, txts, links = [], [], []
-
-                for label, image_numpy in visuals.items():
-                    image_numpy = util.tensor2im(image)
-                    img_path = 'epoch%.3d_%s.png' % (n, label)
-                    ims.append(img_path)
-                    txts.append(label)
-                    links.append(img_path)
-                webpage.add_images(ims, txts, links, width=self.win_size)
-            webpage.save()
-
-    def plot_current_losses(self, total_iters, losses):
-        # G_loss_collection = {}
-        # D_loss_collection = {}
-        # for name, value in losses.items():
-        #     if 'G' in name or 'NCE' in name or 'idt' in name:
-        #         G_loss_collection[name] = value
-        #     else:
-        #         D_loss_collection[name] = value
-        # self.writer.add_scalars('G_collec', G_loss_collection, total_iters)
-        # self.writer.add_scalars('D_collec', D_loss_collection, total_iters)
-        for name, value in losses.items():
-            self.writer.add_scalar(name, value, total_iters)
-
-    # losses: same format as |losses| of plot_current_losses
-    def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
-        """print current losses on console; also save the losses to the disk
-
-        Parameters:
-            epoch (int) -- current epoch
-            iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
-            losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
-            t_comp (float) -- computational time per data point (normalized by batch_size)
-            t_data (float) -- data loading time per data point (normalized by batch_size)
-        """
-        message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
-        for k, v in losses.items():
-            message += '%s: %.3f ' % (k, v)
-
-        print(message)  # print the message
-        with open(self.log_name, "a") as log_file:
-            log_file.write('%s\n' % message)  # save the message
-
-
-class MyVisualizer:
-    def __init__(self, opt):
-        """Initialize the Visualizer class
-
-        Parameters:
-            opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        Step 1: Cache the training/test options
-        Step 2: create a tensorboard writer
-        Step 3: create an HTML object for saveing HTML filters
-        Step 4: create a logging file to store training losses
-        """
-        self.opt = opt  # cache the optio
-        self.name = opt.name
-        self.img_folder = opt.img_folder
-        self.img_dir = os.path.join(opt.checkpoints_dir, opt.name, 'results')
-        
-        if opt.phase != 'test':
-            self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, 'logs'))
-            # create a logging file to store training losses
-            self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
-            with open(self.log_name, "a") as log_file:
-                now = time.strftime("%c")
-                log_file.write('================ Training Loss (%s) ================\n' % now)
-
-
-    def display_current_results(self, visuals, total_iters, epoch, dataset='train', save_results=False, count=0, name=None,
-            add_image=True):
-        """Display current results on tensorboad; save current results to an HTML file.
-
-        Parameters:
-            visuals (OrderedDict) - - dictionary of images to display or save
-            total_iters (int) -- total iterations
-            epoch (int) - - the current epoch
-            dataset (str) - - 'train' or 'val' or 'test'
-        """
-        # if (not add_image) and (not save_results): return
-        for label, image in visuals.items():
-            for i in range(image.shape[0]):
-                image_numpy = util.tensor2im(image[i])
-                if add_image:
-                    self.writer.add_image(label + '%s_%02d'%(dataset, i + count),
-                            image_numpy, total_iters, dataformats='HWC')
-
-                if save_results:
-                    #save_path = os.path.join(self.img_dir, dataset, 'epoch_%s_%06d'%(epoch, total_iters))
-                    save_path = os.path.join(self.img_folder, 'epoch_%s_%06d'%(epoch, total_iters))
-                    #print(self.img_folder)
-                    if not os.path.isdir(save_path):
-                        os.makedirs(save_path)
-
-                    if name is not None:
-                        img_path = os.path.join(save_path, '%s.png' % name)
-                    else:
-                        img_path = os.path.join(save_path, '%s_%03d.png' % (label, i + count))
-                    util.save_image(image_numpy, img_path)
-
-
-    def plot_current_losses(self, total_iters, losses, dataset='train'):
-        for name, value in losses.items():
-            self.writer.add_scalar(name + '/%s'%dataset, value, total_iters)
-
-    # losses: same format as |losses| of plot_current_losses
-    def print_current_losses(self, epoch, iters, losses, t_comp, t_data, dataset='train'):
-        """print current losses on console; also save the losses to the disk
-
-        Parameters:
-            epoch (int) -- current epoch
-            iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
-            losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
-            t_comp (float) -- computational time per data point (normalized by batch_size)
-            t_data (float) -- data loading time per data point (normalized by batch_size)
-        """
-        message = '(dataset: %s, epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (
-            dataset, epoch, iters, t_comp, t_data)
-        for k, v in losses.items():
-            message += '%s: %.3f ' % (k, v)
-
-        print(message)  # print the message
-        with open(self.log_name, "a") as log_file:
-            log_file.write('%s\n' % message)  # save the message
diff --git a/spaces/gyugnsu/DragGan-Inversion/scripts/gui.sh b/spaces/gyugnsu/DragGan-Inversion/scripts/gui.sh
deleted file mode 100644
index 5eb68e3b7d2e51b8781fa2e638c7005f0c994246..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/scripts/gui.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-python visualizer_drag.py \
-    checkpoints/stylegan2_lions_512_pytorch.pkl \
-    checkpoints/stylegan2-ffhq-512x512.pkl \
-    checkpoints/stylegan2-afhqcat-512x512.pkl \
-    checkpoints/stylegan2-car-config-f.pkl \
-    checkpoints/stylegan2_dogs_1024_pytorch.pkl \
-    checkpoints/stylegan2_horses_256_pytorch.pkl \
-    checkpoints/stylegan2-cat-config-f.pkl \
-    checkpoints/stylegan2_elephants_512_pytorch.pkl \
-    checkpoints/stylegan_human_v2_512.pkl \
-    checkpoints/stylegan2-lhq-256x256.pkl
diff --git a/spaces/haakohu/deep_privacy2/sg3_torch_utils/ops/conv2d_resample.py b/spaces/haakohu/deep_privacy2/sg3_torch_utils/ops/conv2d_resample.py
deleted file mode 100644
index 4a999b58b36a5da53752024e86a9ebdf9c031d97..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/sg3_torch_utils/ops/conv2d_resample.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""2D convolution with optional up/downsampling."""
-
-import torch
-
-from .. import misc
-from . import conv2d_gradfix
-from . import upfirdn2d
-from .upfirdn2d import _parse_padding
-from .upfirdn2d import _get_filter_size
-
-#----------------------------------------------------------------------------
-
-def _get_weight_shape(w):
-    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
-        shape = [int(sz) for sz in w.shape]
-    misc.assert_shape(w, shape)
-    return shape
-
-#----------------------------------------------------------------------------
-
-def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
-    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
-    """
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-
-    # Flip weight if requested.
-    if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
-        w = w.flip([2, 3])
-
-    # Otherwise => execute using conv2d_gradfix.
-    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
-    return op(x, w, stride=stride, padding=padding, groups=groups)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
-    r"""2D convolution with optional up/downsampling.
-
-    Padding is performed only once at the beginning, not between the operations.
-
-    Args:
-        x:              Input tensor of shape
-                        `[batch_size, in_channels, in_height, in_width]`.
-        w:              Weight tensor of shape
-                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
-        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
-                        calling upfirdn2d.setup_filter(). None = identity (default).
-        up:             Integer upsampling factor (default: 1).
-        down:           Integer downsampling factor (default: 1).
-        padding:        Padding with respect to the upsampled image. Can be a single number
-                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                        (default: 0).
-        groups:         Split input channels into N groups (default: 1).
-        flip_weight:    False = convolution, True = correlation (default: True).
-        flip_filter:    False = convolution, True = correlation (default: False).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
-    assert isinstance(w, torch.Tensor) and (w.ndim == 4)
-    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
-    assert isinstance(up, int) and (up >= 1)
-    assert isinstance(down, int) and (down >= 1)
-    assert isinstance(groups, int) and (groups >= 1)
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-    fw, fh = _get_filter_size(f)
-    px0, px1, py0, py1 = _parse_padding(padding)
-
-    # Adjust padding to account for up/downsampling.
-    if up > 1:
-        px0 += (fw + up - 1) // 2
-        px1 += (fw - up) // 2
-        py0 += (fh + up - 1) // 2
-        py1 += (fh - up) // 2
-    if down > 1:
-        px0 += (fw - down + 1) // 2
-        px1 += (fw - down) // 2
-        py0 += (fh - down + 1) // 2
-        py1 += (fh - down) // 2
-
-    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
-    if kw == 1 and kh == 1 and (down > 1 and up == 1):
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
-    if kw == 1 and kh == 1 and (up > 1 and down == 1):
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
-        return x
-
-    # Fast path: downsampling only => use strided convolution.
-    if down > 1 and up == 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
-    if up > 1:
-        if groups == 1:
-            w = w.transpose(0, 1)
-        else:
-            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
-            w = w.transpose(1, 2)
-            w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
-        px0 -= kw - 1
-        px1 -= kw - up
-        py0 -= kh - 1
-        py1 -= kh - up
-        pxt = max(min(-px0, -px1), 0)
-        pyt = max(min(-py0, -py1), 0)
-        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
-        if down > 1:
-            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-        return x
-
-    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
-    if up == 1 and down == 1:
-        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
-            return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
-
-    # Fallback: Generic reference implementation.
-    x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
-    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-    if down > 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-    return x
-
-#----------------------------------------------------------------------------
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/evaluation/coco_evaluation.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/evaluation/coco_evaluation.py
deleted file mode 100644
index 64b0903a43187db785113267ed16e82be6f5b28c..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/evaluation/coco_evaluation.py
+++ /dev/null
@@ -1,512 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import contextlib
-import copy
-import io
-import itertools
-import json
-import logging
-import numpy as np
-import os
-import pickle
-from collections import OrderedDict
-import pycocotools.mask as mask_util
-import torch
-from fvcore.common.file_io import PathManager
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-from tabulate import tabulate
-
-import detectron2.utils.comm as comm
-from detectron2.data import MetadataCatalog
-from detectron2.data.datasets.coco import convert_to_coco_json
-from detectron2.structures import Boxes, BoxMode, pairwise_iou
-from detectron2.utils.logger import create_small_table
-
-from .evaluator import DatasetEvaluator
-
-
-class COCOEvaluator(DatasetEvaluator):
-    """
-    Evaluate object proposal, instance detection/segmentation, keypoint detection
-    outputs using COCO's metrics and APIs.
-    """
-
-    def __init__(self, dataset_name, cfg, distributed, output_dir=None):
-        """
-        Args:
-            dataset_name (str): name of the dataset to be evaluated.
-                It must have either the following corresponding metadata:
-
-                    "json_file": the path to the COCO format annotation
-
-                Or it must be in detectron2's standard dataset format
-                so it can be converted to COCO format automatically.
-            cfg (CfgNode): config instance
-            distributed (True): if True, will collect results from all ranks and run evaluation
-                in the main process.
-                Otherwise, will evaluate the results in the current process.
-            output_dir (str): optional, an output directory to dump all
-                results predicted on the dataset. The dump contains two files:
-
-                1. "instance_predictions.pth" a file in torch serialization
-                   format that contains all the raw original predictions.
-                2. "coco_instances_results.json" a json file in COCO's result
-                   format.
-        """
-        self._tasks = self._tasks_from_config(cfg)
-        self._distributed = distributed
-        self._output_dir = output_dir
-
-        self._cpu_device = torch.device("cpu")
-        self._logger = logging.getLogger(__name__)
-
-        self._metadata = MetadataCatalog.get(dataset_name)
-        if not hasattr(self._metadata, "json_file"):
-            self._logger.warning(
-                f"json_file was not found in MetaDataCatalog for '{dataset_name}'."
-                " Trying to convert it to COCO format ..."
-            )
-
-            cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
-            self._metadata.json_file = cache_path
-            convert_to_coco_json(dataset_name, cache_path)
-
-        json_file = PathManager.get_local_path(self._metadata.json_file)
-        with contextlib.redirect_stdout(io.StringIO()):
-            self._coco_api = COCO(json_file)
-
-        self._kpt_oks_sigmas = cfg.TEST.KEYPOINT_OKS_SIGMAS
-        # Test set json files do not contain annotations (evaluation must be
-        # performed using the COCO evaluation server).
-        self._do_evaluation = "annotations" in self._coco_api.split_name
-
-    def reset(self):
-        self._predictions = []
-
-    def _tasks_from_config(self, cfg):
-        """
-        Returns:
-            tuple[str]: tasks that can be evaluated under the given configuration.
-        """
-        tasks = ("bbox",)
-        if cfg.MODEL.MASK_ON:
-            tasks = tasks + ("segm",)
-        if cfg.MODEL.KEYPOINT_ON:
-            tasks = tasks + ("keypoints",)
-        return tasks
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
-                It is a list of dict. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name", "image_id".
-            outputs: the outputs of a COCO model. It is a list of dicts with key
-                "instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            # TODO this is ugly
-            if "instances" in output:
-                instances = output["instances"].to(self._cpu_device)
-                prediction["instances"] = instances_to_coco_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            self._predictions.append(prediction)
-
-    def evaluate(self):
-        if self._distributed:
-            comm.synchronize()
-            predictions = comm.gather(self._predictions, dst=0)
-            predictions = list(itertools.chain(*predictions))
-
-            if not comm.is_main_process():
-                return {}
-        else:
-            predictions = self._predictions
-
-        if len(predictions) == 0:
-            self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
-            return {}
-
-        if self._output_dir:
-            PathManager.mkdirs(self._output_dir)
-            file_path = os.path.join(self._output_dir, "instances_predictions.pth")
-            with PathManager.open(file_path, "wb") as f:
-                torch.save(predictions, f)
-
-        self._results = OrderedDict()
-        if "proposals" in predictions[0]:
-            self._eval_box_proposals(predictions)
-        if "instances" in predictions[0]:
-            self._eval_predictions(set(self._tasks), predictions)
-        # Copy so the caller can do whatever with results
-        return copy.deepcopy(self._results)
-
-    def _eval_predictions(self, tasks, predictions):
-        """
-        Evaluate predictions on the given tasks.
-        Fill self._results with the metrics of the tasks.
-        """
-        self._logger.info("Preparing results for COCO format ...")
-        coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
-
-        # unmap the category ids for COCO
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            reverse_id_mapping = {
-                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
-            }
-            for result in coco_results:
-                category_id = result["category_id"]
-                assert (
-                    category_id in reverse_id_mapping
-                ), "A prediction has category_id={}, which is not available in the dataset.".format(
-                    category_id
-                )
-                result["category_id"] = reverse_id_mapping[category_id]
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "coco_instances_results.json")
-            self._logger.info("Saving results to {}".format(file_path))
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(coco_results))
-                f.flush()
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating predictions ...")
-        for task in sorted(tasks):
-            coco_eval = (
-                _evaluate_predictions_on_coco(
-                    self._coco_api, coco_results, task, kpt_oks_sigmas=self._kpt_oks_sigmas
-                )
-                if len(coco_results) > 0
-                else None  # cocoapi does not handle empty results very well
-            )
-
-            res = self._derive_coco_results(
-                coco_eval, task, class_names=self._metadata.get("thing_classes")
-            )
-            self._results[task] = res
-
-    def _eval_box_proposals(self, predictions):
-        """
-        Evaluate the box proposals in predictions.
-        Fill self._results with the metrics for "box_proposals" task.
-        """
-        if self._output_dir:
-            # Saving generated box proposals to file.
-            # Predicted box_proposals are in XYXY_ABS mode.
-            bbox_mode = BoxMode.XYXY_ABS.value
-            ids, boxes, objectness_logits = [], [], []
-            for prediction in predictions:
-                ids.append(prediction["image_id"])
-                boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy())
-                objectness_logits.append(prediction["proposals"].objectness_logits.numpy())
-
-            proposal_data = {
-                "boxes": boxes,
-                "objectness_logits": objectness_logits,
-                "ids": ids,
-                "bbox_mode": bbox_mode,
-            }
-            with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f:
-                pickle.dump(proposal_data, f)
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating bbox proposals ...")
-        res = {}
-        areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
-        for limit in [100, 1000]:
-            for area, suffix in areas.items():
-                stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)
-                key = "AR{}@{:d}".format(suffix, limit)
-                res[key] = float(stats["ar"].item() * 100)
-        self._logger.info("Proposal metrics: \n" + create_small_table(res))
-        self._results["box_proposals"] = res
-
-    def _derive_coco_results(self, coco_eval, iou_type, class_names=None):
-        """
-        Derive the desired score numbers from summarized COCOeval.
-
-        Args:
-            coco_eval (None or COCOEval): None represents no predictions from model.
-            iou_type (str):
-            class_names (None or list[str]): if provided, will use it to predict
-                per-category AP.
-
-        Returns:
-            a dict of {metric name: score}
-        """
-
-        metrics = {
-            "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
-            "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
-            "keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
-        }[iou_type]
-
-        if coco_eval is None:
-            self._logger.warn("No predictions from the model!")
-            return {metric: float("nan") for metric in metrics}
-
-        # the standard metrics
-        results = {
-            metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
-            for idx, metric in enumerate(metrics)
-        }
-        self._logger.info(
-            "Evaluation results for {}: \n".format(iou_type) + create_small_table(results)
-        )
-        if not np.isfinite(sum(results.values())):
-            self._logger.info("Note that some metrics cannot be computed.")
-
-        if class_names is None or len(class_names) <= 1:
-            return results
-        # Compute per-category AP
-        # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
-        precisions = coco_eval.eval["precision"]
-        # precision has dims (iou, recall, cls, area range, max dets)
-        assert len(class_names) == precisions.shape[2]
-
-        results_per_category = []
-        for idx, name in enumerate(class_names):
-            # area range index 0: all area ranges
-            # max dets index -1: typically 100 per image
-            precision = precisions[:, :, idx, 0, -1]
-            precision = precision[precision > -1]
-            ap = np.mean(precision) if precision.size else float("nan")
-            results_per_category.append(("{}".format(name), float(ap * 100)))
-
-        # tabulate it
-        N_COLS = min(6, len(results_per_category) * 2)
-        results_flatten = list(itertools.chain(*results_per_category))
-        results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
-        table = tabulate(
-            results_2d,
-            tablefmt="pipe",
-            floatfmt=".3f",
-            headers=["category", "AP"] * (N_COLS // 2),
-            numalign="left",
-        )
-        self._logger.info("Per-category {} AP: \n".format(iou_type) + table)
-
-        results.update({"AP-" + name: ap for name, ap in results_per_category})
-        return results
-
-
-def instances_to_coco_json(instances, img_id):
-    """
-    Dump an "Instances" object to a COCO-format json that's used for evaluation.
-
-    Args:
-        instances (Instances):
-        img_id (int): the image id
-
-    Returns:
-        list[dict]: list of json annotations in COCO format.
-    """
-    num_instance = len(instances)
-    if num_instance == 0:
-        return []
-
-    boxes = instances.pred_boxes.tensor.numpy()
-    boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
-    boxes = boxes.tolist()
-    scores = instances.scores.tolist()
-    classes = instances.pred_classes.tolist()
-
-    has_mask = instances.has("pred_masks")
-    if has_mask:
-        # use RLE to encode the masks, because they are too large and takes memory
-        # since this evaluator stores outputs of the entire dataset
-        rles = [
-            mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
-            for mask in instances.pred_masks
-        ]
-        for rle in rles:
-            # "counts" is an array encoded by mask_util as a byte-stream. Python3's
-            # json writer which always produces strings cannot serialize a bytestream
-            # unless you decode it. Thankfully, utf-8 works out (which is also what
-            # the pycocotools/_mask.pyx does).
-            rle["counts"] = rle["counts"].decode("utf-8")
-
-    has_keypoints = instances.has("pred_keypoints")
-    if has_keypoints:
-        keypoints = instances.pred_keypoints
-
-    results = []
-    for k in range(num_instance):
-        result = {
-            "image_id": img_id,
-            "category_id": classes[k],
-            "bbox": boxes[k],
-            "score": scores[k],
-        }
-        if has_mask:
-            result["segmentation"] = rles[k]
-        if has_keypoints:
-            # In COCO annotations,
-            # keypoints coordinates are pixel indices.
-            # However our predictions are floating point coordinates.
-            # Therefore we subtract 0.5 to be consistent with the annotation format.
-            # This is the inverse of data loading logic in `data/coco.py`.
-            keypoints[k][:, :2] -= 0.5
-            result["keypoints"] = keypoints[k].flatten().tolist()
-        results.append(result)
-    return results
-
-
-# inspired from Detectron:
-# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa
-def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None):
-    """
-    Evaluate detection proposal recall metrics. This function is a much
-    faster alternative to the official COCO API recall evaluation code. However,
-    it produces slightly different results.
-    """
-    # Record max overlap value for each gt box
-    # Return vector of overlap values
-    areas = {
-        "all": 0,
-        "small": 1,
-        "medium": 2,
-        "large": 3,
-        "96-128": 4,
-        "128-256": 5,
-        "256-512": 6,
-        "512-inf": 7,
-    }
-    area_ranges = [
-        [0 ** 2, 1e5 ** 2],  # all
-        [0 ** 2, 32 ** 2],  # small
-        [32 ** 2, 96 ** 2],  # medium
-        [96 ** 2, 1e5 ** 2],  # large
-        [96 ** 2, 128 ** 2],  # 96-128
-        [128 ** 2, 256 ** 2],  # 128-256
-        [256 ** 2, 512 ** 2],  # 256-512
-        [512 ** 2, 1e5 ** 2],
-    ]  # 512-inf
-    assert area in areas, "Unknown area range: {}".format(area)
-    area_range = area_ranges[areas[area]]
-    gt_overlaps = []
-    num_pos = 0
-
-    for prediction_dict in dataset_predictions:
-        predictions = prediction_dict["proposals"]
-
-        # sort predictions in descending order
-        # TODO maybe remove this and make it explicit in the documentation
-        inds = predictions.objectness_logits.sort(descending=True)[1]
-        predictions = predictions[inds]
-
-        ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"])
-        anno = coco_api.loadAnns(ann_ids)
-        gt_boxes = [
-            BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
-            for obj in anno
-            if obj["iscrowd"] == 0
-        ]
-        gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4)  # guard against no boxes
-        gt_boxes = Boxes(gt_boxes)
-        gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])
-
-        if len(gt_boxes) == 0 or len(predictions) == 0:
-            continue
-
-        valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
-        gt_boxes = gt_boxes[valid_gt_inds]
-
-        num_pos += len(gt_boxes)
-
-        if len(gt_boxes) == 0:
-            continue
-
-        if limit is not None and len(predictions) > limit:
-            predictions = predictions[:limit]
-
-        overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)
-
-        _gt_overlaps = torch.zeros(len(gt_boxes))
-        for j in range(min(len(predictions), len(gt_boxes))):
-            # find which proposal box maximally covers each gt box
-            # and get the iou amount of coverage for each gt box
-            max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-            # find which gt box is 'best' covered (i.e. 'best' = most iou)
-            gt_ovr, gt_ind = max_overlaps.max(dim=0)
-            assert gt_ovr >= 0
-            # find the proposal box that covers the best covered gt box
-            box_ind = argmax_overlaps[gt_ind]
-            # record the iou coverage of this gt box
-            _gt_overlaps[j] = overlaps[box_ind, gt_ind]
-            assert _gt_overlaps[j] == gt_ovr
-            # mark the proposal box and the gt box as used
-            overlaps[box_ind, :] = -1
-            overlaps[:, gt_ind] = -1
-
-        # append recorded iou coverage level
-        gt_overlaps.append(_gt_overlaps)
-    gt_overlaps = (
-        torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32)
-    )
-    gt_overlaps, _ = torch.sort(gt_overlaps)
-
-    if thresholds is None:
-        step = 0.05
-        thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
-    recalls = torch.zeros_like(thresholds)
-    # compute recall for each iou threshold
-    for i, t in enumerate(thresholds):
-        recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
-    # ar = 2 * np.trapz(recalls, thresholds)
-    ar = recalls.mean()
-    return {
-        "ar": ar,
-        "recalls": recalls,
-        "thresholds": thresholds,
-        "gt_overlaps": gt_overlaps,
-        "num_pos": num_pos,
-    }
-
-
-def _evaluate_predictions_on_coco(coco_gt, coco_results, iou_type, kpt_oks_sigmas=None):
-    """
-    Evaluate the coco results using COCOEval API.
-    """
-    assert len(coco_results) > 0
-
-    if iou_type == "segm":
-        coco_results = copy.deepcopy(coco_results)
-        # When evaluating mask AP, if the results contain bbox, cocoapi will
-        # use the box area as the area of the instance, instead of the mask area.
-        # This leads to a different definition of small/medium/large.
-        # We remove the bbox field to let mask AP use mask area.
-        for c in coco_results:
-            c.pop("bbox", None)
-
-    coco_dt = coco_gt.loadRes(coco_results)
-    coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
-    # Use the COCO default keypoint OKS sigmas unless overrides are specified
-    if kpt_oks_sigmas:
-        coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
-
-    if iou_type == "keypoints":
-        num_keypoints = len(coco_results[0]["keypoints"]) // 3
-        assert len(coco_eval.params.kpt_oks_sigmas) == num_keypoints, (
-            "[COCOEvaluator] The length of cfg.TEST.KEYPOINT_OKS_SIGMAS (default: 17) "
-            "must be equal to the number of keypoints. However the prediction has {} "
-            "keypoints! For more information please refer to "
-            "http://cocodataset.org/#keypoints-eval.".format(num_keypoints)
-        )
-
-    coco_eval.evaluate()
-    coco_eval.accumulate()
-    coco_eval.summarize()
-
-    return coco_eval
diff --git a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Mass-South-Indian-Movie-In-Hindi-Download-15-EXCLUSIVE.md b/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Mass-South-Indian-Movie-In-Hindi-Download-15-EXCLUSIVE.md
deleted file mode 100644
index 1b544b14b5dba94c511c3cfc343af8c19a1cc683..0000000000000000000000000000000000000000
--- a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Mass-South-Indian-Movie-In-Hindi-Download-15-EXCLUSIVE.md
+++ /dev/null
@@ -1,31 +0,0 @@
-mass south indian movie in hindi download 15
-
-
-
-Download ===> [https://poitaihanew.blogspot.com/?l=2tvRRb](https://poitaihanew.blogspot.com/?l=2tvRRb)
-
-
-
-
-
-
-
-
-
-Here is a possible title and article with html formatting for the keyword "mass south indian movie in hindi download 15":
-
-Masss: A Supernatural Action Comedy Starring Suriya and Nayantara
-Masss is a 2015 Tamil movie that was dubbed in Hindi and released on ZEE5. The movie features Suriya as Masilamani, a con man who can communicate with the dead after a life-threatening accident. He uses his ability to help the ghosts settle their unfinished business and also to take revenge on those who wronged him. Nayantara plays Malini, his love interest, who supports him in his endeavors. The movie also stars Parthiban, Samuthirakani, Premgi Amaren and Pranitha Subhash in supporting roles.
-The movie is directed by Venkat Prabhu, who is known for his comedy films. The movie has a mix of humor, action, drama and romance, with some twists and turns along the way. The movie also has some impressive visual effects and stunts that add to the entertainment value. The music is composed by Yuvan Shankar Raja, who has collaborated with Venkat Prabhu in many of his films. The songs are catchy and suit the mood of the film.
-Masss is a fun-filled movie that will keep you engaged till the end. If you are looking for a supernatural action comedy with a star cast, you can watch Masss on ZEE5. You can download the movie in HD quality from the ZEE5 app or website.Here are a few more paragraphs for the article:
-
-The movie has received mostly positive reviews from critics and audiences, who praised the performances of the four lead actors and the emotional impact of the story. Some critics also noted the relevance of the movie in addressing the issue of gun violence and its effects on families and communities. However, some reviewers also pointed out some flaws in the movie, such as its slow pace, its lack of visual flair, its heavy-handed dialogue and its limited scope.
-Mass is not a movie that offers easy answers or solutions to the complex problem of mass shootings. It is a movie that explores the human side of the tragedy, the pain and guilt and anger and forgiveness that the survivors have to deal with. It is a movie that challenges the viewers to empathize with both sides of the conflict, and to reflect on their own beliefs and biases. It is a movie that asks hard questions, but does not judge or preach.
-Mass is a movie that will stay with you long after it ends. It is a movie that will make you cry, think and feel. It is a movie that deserves to be seen and discussed.Here are some more paragraphs for the article:
-
-The movie also has some memorable quotes that capture the essence of the characters and their situation. For example, Jay says to Linda and Richard, \"We're not here to blame you. We're here to understand.\" Gail says to Jay, \"You can't fix this. You can't fix me.\" Linda says to Gail, \"I'm sorry for your loss. I'm sorry for my son.\" Richard says to Jay, \"He was a good kid. He was a good kid until he wasn't.\" These quotes show the complexity and nuance of the dialogue and the emotions involved.
-The movie also has some moments of grace and hope, despite the bleakness of the subject matter. For example, when Jay and Gail share a photo of their son with Linda and Richard, they also see a photo of their son. They realize that they have something in common, a love for their children. They also share some stories about their sons, their hobbies, their personalities, their dreams. They laugh and cry together, and they hug each other. They also pray together at the end of the meeting, holding hands and reciting the Lord's Prayer.
-Mass is a movie that will touch your heart and soul. It is a movie that will make you appreciate life and love more. It is a movie that will inspire you to seek healing and reconciliation with others. It is a movie that will remind you that we are all connected by our humanity. dfd1c89656
-
-
-
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task120_Massachusetts_RoadSegm.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task120_Massachusetts_RoadSegm.py
deleted file mode 100644
index 55d55471615bd9c2f88725acd78c163c02112165..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task120_Massachusetts_RoadSegm.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import numpy as np
-from batchgenerators.utilities.file_and_folder_operations import *
-from nnunet.dataset_conversion.utils import generate_dataset_json
-from nnunet.paths import nnUNet_raw_data, preprocessing_output_dir
-from nnunet.utilities.file_conversions import convert_2d_image_to_nifti
-
-if __name__ == '__main__':
-    """
-    nnU-Net was originally built for 3D images. It is also strongest when applied to 3D segmentation problems because a 
-    large proportion of its design choices were built with 3D in mind. Also note that many 2D segmentation problems, 
-    especially in the non-biomedical domain, may benefit from pretrained network architectures which nnU-Net does not
-    support.
-    Still, there is certainly a need for an out of the box segmentation solution for 2D segmentation problems. And 
-    also on 2D segmentation tasks nnU-Net cam perform extremely well! We have, for example, won a 2D task in the cell 
-    tracking challenge with nnU-Net (see our Nature Methods paper) and we have also successfully applied nnU-Net to 
-    histopathological segmentation problems. 
-    Working with 2D data in nnU-Net requires a small workaround in the creation of the dataset. Essentially, all images 
-    must be converted to pseudo 3D images (so an image with shape (X, Y) needs to be converted to an image with shape 
-    (1, X, Y). The resulting image must be saved in nifti format. Hereby it is important to set the spacing of the 
-    first axis (the one with shape 1) to a value larger than the others. If you are working with niftis anyways, then 
-    doing this should be easy for you. This example here is intended for demonstrating how nnU-Net can be used with 
-    'regular' 2D images. We selected the massachusetts road segmentation dataset for this because it can be obtained 
-    easily, it comes with a good amount of training cases but is still not too large to be difficult to handle.
-    """
-
-    # download dataset from https://www.kaggle.com/insaff/massachusetts-roads-dataset
-    # extract the zip file, then set the following path according to your system:
-    base = '/home/ho11laqe/PycharmProjects/road_segmentation_ideal'
-    # this folder should have the training and testing subfolders
-
-    # now start the conversion to nnU-Net:
-    task_name = 'Task120_MassRoadsSeg'
-    target_base = join(nnUNet_raw_data, task_name)
-    target_imagesTr = join(target_base, "imagesTr")
-    target_imagesTs = join(target_base, "imagesTs")
-    target_labelsTs = join(target_base, "labelsTs")
-    target_labelsTr = join(target_base, "labelsTr")
-
-    maybe_mkdir_p(target_imagesTr)
-    maybe_mkdir_p(target_labelsTs)
-    maybe_mkdir_p(target_imagesTs)
-    maybe_mkdir_p(target_labelsTr)
-
-    # convert the training examples. Not all training images have labels, so we just take the cases for which there are
-    # labels
-    labels_dir_tr = join(base, 'training', 'output')
-    images_dir_tr = join(base, 'training', 'input')
-    training_cases = subfiles(labels_dir_tr, suffix='.png', join=False)
-    for t in training_cases:
-        unique_name = t[:-4]  # just the filename with the extension cropped away, so img-2.png becomes img-2 as unique_name
-        input_segmentation_file = join(labels_dir_tr, t)
-        input_image_file = join(images_dir_tr, t)
-
-        output_image_file = join(target_imagesTr, unique_name)  # do not specify a file ending! This will be done for you
-        output_seg_file = join(target_labelsTr, unique_name)  # do not specify a file ending! This will be done for you
-
-        # this utility will convert 2d images that can be read by skimage.io.imread to nifti. You don't need to do anything.
-        # if this throws an error for your images, please just look at the code for this function and adapt it to your needs
-        convert_2d_image_to_nifti(input_image_file, output_image_file, is_seg=False)
-
-        # the labels are stored as 0: background, 255: road. We need to convert the 255 to 1 because nnU-Net expects
-        # the labels to be consecutive integers. This can be achieved with setting a transform
-        convert_2d_image_to_nifti(input_segmentation_file, output_seg_file, is_seg=True,
-                                  transform=lambda x: (x == 255).astype(int))
-
-    # now do the same for the test set
-    labels_dir_ts = join(base, 'testing', 'output')
-    images_dir_ts = join(base, 'testing', 'input')
-    testing_cases = subfiles(labels_dir_ts, suffix='.png', join=False)
-    for ts in testing_cases:
-        unique_name = ts[:-4]
-        input_segmentation_file = join(labels_dir_ts, ts)
-        input_image_file = join(images_dir_ts, ts)
-
-        output_image_file = join(target_imagesTs, unique_name)
-        output_seg_file = join(target_labelsTs, unique_name)
-
-        convert_2d_image_to_nifti(input_image_file, output_image_file, is_seg=False)
-        convert_2d_image_to_nifti(input_segmentation_file, output_seg_file, is_seg=True,
-                                  transform=lambda x: (x == 255).astype(int))
-
-    # finally we can call the utility for generating a dataset.json
-    generate_dataset_json(join(target_base, 'dataset.json'), target_imagesTr, target_imagesTs, ('Red', 'Green', 'Blue'),
-                          labels={0: 'background', 1: 'street'}, dataset_name=task_name, license='hands off!')
-
-    """
-    once this is completed, you can use the dataset like any other nnU-Net dataset. Note that since this is a 2D
-    dataset there is no need to run preprocessing for 3D U-Nets. You should therefore run the 
-    `nnUNet_plan_and_preprocess` command like this:
-    
-    > nnUNet_plan_and_preprocess -t 120 -pl3d None
-    
-    once that is completed, you can run the trainings as follows:
-    > nnUNet_train 2d nnUNetTrainerV2 120 FOLD
-    
-    (where fold is again 0, 1, 2, 3 and 4 - 5-fold cross validation)
-    
-    there is no need to run nnUNet_find_best_configuration because there is only one model to choose from.
-    Note that without running nnUNet_find_best_configuration, nnU-Net will not have determined a postprocessing
-    for the whole cross-validation. Spoiler: it will determine not to run postprocessing anyways. If you are using
-    a different 2D dataset, you can make nnU-Net determine the postprocessing by using the
-    `nnUNet_determine_postprocessing` command
-    """
diff --git a/spaces/huaiji3y/bingo-Public/src/pages/api/image.ts b/spaces/huaiji3y/bingo-Public/src/pages/api/image.ts
deleted file mode 100644
index 26fdb31076a9c71e70d1725a630844b27f5a3221..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/pages/api/image.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies, 'image')
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), {
-      ...headers,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    })
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/huggingface-projects/wordalle/install-node.sh b/spaces/huggingface-projects/wordalle/install-node.sh
deleted file mode 100644
index 6000b3748ac776969cfd43dc175dddd5bc62808c..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/wordalle/install-node.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.1/install.sh | bash
-export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"
-[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"
-nvm install v18.3.0
-nvm use v18.3.0
-node --version
-npm --version
-which node
-which npm
-command ln -s "$NVM_BIN/node" /home/user/.local/bin/node
-command ln -s "$NVM_BIN/npm" /home/user/.local/bin/npm
diff --git a/spaces/huggingface-projects/wordalle/main.py b/spaces/huggingface-projects/wordalle/main.py
deleted file mode 100644
index 9112565a72e00583bf45b174bf5c369f2d623360..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/wordalle/main.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import subprocess
-
-subprocess.run(["make", "build-all"], shell=False)
diff --git a/spaces/hujike/mj-laf/app.py b/spaces/hujike/mj-laf/app.py
deleted file mode 100644
index 4c9a4ad537ef623d05b53e73235d211c8f20e187..0000000000000000000000000000000000000000
--- a/spaces/hujike/mj-laf/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("rwkv-4-pile-7b/RWKV-4-Pile-7B-Chn-testNovel-done-ctx2048-20230404").launch()
\ No newline at end of file
diff --git a/spaces/hysts/ControlNet-v1-1/app_normal.py b/spaces/hysts/ControlNet-v1-1/app_normal.py
deleted file mode 100644
index 110d243eee807c7c3d3b8ae79bc14d09d025e4a5..0000000000000000000000000000000000000000
--- a/spaces/hysts/ControlNet-v1-1/app_normal.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from settings import (
-    DEFAULT_IMAGE_RESOLUTION,
-    DEFAULT_NUM_IMAGES,
-    MAX_IMAGE_RESOLUTION,
-    MAX_NUM_IMAGES,
-    MAX_SEED,
-)
-from utils import randomize_seed_fn
-
-
-def create_demo(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label="Prompt")
-                run_button = gr.Button("Run")
-                with gr.Accordion("Advanced options", open=False):
-                    preprocessor_name = gr.Radio(
-                        label="Preprocessor", choices=["NormalBae", "None"], type="value", value="NormalBae"
-                    )
-                    num_samples = gr.Slider(
-                        label="Images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1
-                    )
-                    image_resolution = gr.Slider(
-                        label="Image resolution",
-                        minimum=256,
-                        maximum=MAX_IMAGE_RESOLUTION,
-                        value=DEFAULT_IMAGE_RESOLUTION,
-                        step=256,
-                    )
-                    preprocess_resolution = gr.Slider(
-                        label="Preprocess resolution", minimum=128, maximum=512, value=384, step=1
-                    )
-                    num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
-                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
-                    n_prompt = gr.Textbox(
-                        label="Negative prompt",
-                        value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
-                    )
-            with gr.Column():
-                result = gr.Gallery(label="Output", show_label=False, columns=2, object_fit="scale-down")
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name=False,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name="normal",
-        )
-    return demo
-
-
-if __name__ == "__main__":
-    from model import Model
-
-    model = Model(task_name="NormalBae")
-    demo = create_demo(model.process_normal)
-    demo.queue().launch()
diff --git a/spaces/ibm-nasa-geospatial/Prithvi-100M-demo/Dockerfile b/spaces/ibm-nasa-geospatial/Prithvi-100M-demo/Dockerfile
deleted file mode 100644
index 9fd4f7f7cc1d2fa58c898221fcba36113385cc35..0000000000000000000000000000000000000000
--- a/spaces/ibm-nasa-geospatial/Prithvi-100M-demo/Dockerfile
+++ /dev/null
@@ -1,38 +0,0 @@
-FROM ubuntu:22.04
-
-
-RUN apt-get update && apt-get install --no-install-recommends -y \
-  build-essential \
-  python3.9 \
-  python3-pip \
-  git \
-  && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-# Switch to the "user" user
-USER user
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH \
-    PYTHONPATH=$HOME/app \
-	PYTHONUNBUFFERED=1 \
-	GRADIO_ALLOW_FLAGGING=never \
-	GRADIO_NUM_PORTS=1 \
-	GRADIO_SERVER_NAME=0.0.0.0 \
-	GRADIO_THEME=huggingface \
-	SYSTEM=spaces
-
-RUN pip3 install --no-cache-dir --upgrade -r /code/requirements.txt
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-CMD ["python3", "app.py"]
\ No newline at end of file
diff --git a/spaces/iccv23-diffusers-demo/zeroscope-v2/app.py b/spaces/iccv23-diffusers-demo/zeroscope-v2/app.py
deleted file mode 100644
index 93b2d853f298e97065881eb37c0814ae7a1c50f7..0000000000000000000000000000000000000000
--- a/spaces/iccv23-diffusers-demo/zeroscope-v2/app.py
+++ /dev/null
@@ -1,157 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import random
-import tempfile
-
-import gradio as gr
-import imageio
-import numpy as np
-import spaces
-import torch
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-
-DESCRIPTION = "# zeroscope v2"
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-
-MAX_NUM_FRAMES = int(os.getenv("MAX_NUM_FRAMES", "200"))
-DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES, int(os.getenv("DEFAULT_NUM_FRAMES", "24")))
-MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
-
-if torch.cuda.is_available():
-    pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
-    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-    pipe.enable_model_cpu_offload()
-    pipe.enable_vae_slicing()
-else:
-    pipe = None
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-def to_video(frames: list[np.ndarray], fps: int) -> str:
-    out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-    writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps)
-    for frame in frames:
-        writer.append_data(frame)
-    writer.close()
-    return out_file.name
-
-
-@spaces.GPU
-def generate(
-    prompt: str,
-    seed: int,
-    num_frames: int,
-    num_inference_steps: int,
-) -> str:
-    generator = torch.Generator().manual_seed(seed)
-    frames = pipe(
-        prompt,
-        num_inference_steps=num_inference_steps,
-        num_frames=num_frames,
-        width=576,
-        height=320,
-        generator=generator,
-    ).frames
-    return to_video(frames, 8)
-
-
-examples = [
-    ["An astronaut riding a horse", 0, 24, 25],
-    ["A panda eating bamboo on a rock", 0, 24, 25],
-    ["Spiderman is surfing", 0, 24, 25],
-]
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    with gr.Box():
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Generate video", scale=0)
-        result = gr.Video(label="Result", show_label=False)
-        with gr.Accordion("Advanced options", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            num_frames = gr.Slider(
-                label="Number of frames",
-                minimum=24,
-                maximum=MAX_NUM_FRAMES,
-                step=1,
-                value=24,
-                info="Note that the content of the video also changes when you change the number of frames.",
-            )
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps",
-                minimum=10,
-                maximum=50,
-                step=1,
-                value=25,
-            )
-
-    inputs = [
-        prompt,
-        seed,
-        num_frames,
-        num_inference_steps,
-    ]
-    gr.Examples(
-        examples=examples,
-        inputs=inputs,
-        outputs=result,
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-    prompt.submit(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=inputs,
-        outputs=result,
-        api_name="run",
-    )
-    run_button.click(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=10).launch()
diff --git a/spaces/ilikezx/newbing/README.md b/spaces/ilikezx/newbing/README.md
deleted file mode 100644
index c3fbb07089a328fa681baf978995c2358d733311..0000000000000000000000000000000000000000
--- a/spaces/ilikezx/newbing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Newbing
-emoji: 📉
-colorFrom: pink
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ilumine-AI/Insta-3D/style.css b/spaces/ilumine-AI/Insta-3D/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/ilumine-AI/Insta-3D/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/inamXcontru/PoeticTTS/Atomix Virtual DJ Pro 8.2.2048 Incl. Crack Serial Key Keygen Where to Find It How to Use It and Why Youll Love It.md b/spaces/inamXcontru/PoeticTTS/Atomix Virtual DJ Pro 8.2.2048 Incl. Crack Serial Key Keygen Where to Find It How to Use It and Why Youll Love It.md
deleted file mode 100644
index d7e53dc80db6ea082b3216ef096293941b8b629d..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Atomix Virtual DJ Pro 8.2.2048 Incl. Crack Serial Key Keygen Where to Find It How to Use It and Why Youll Love It.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Atomix Virtual DJ Pro 8.2.2048 Incl. Crack Serial Key Keygen</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://gohhs.com/2uz46V">https://gohhs.com/2uz46V</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/innnky/nyaru-svc2.0/monotonic_align/setup.py b/spaces/innnky/nyaru-svc2.0/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru-svc2.0/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Adguard Premium 6.4.1814.4903 Multilingual Final Medicine[Babu Serial Key.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Adguard Premium 6.4.1814.4903 Multilingual Final Medicine[Babu Serial Key.md
deleted file mode 100644
index 14223438b1617065e04de0ce9ced40c70e87aea8..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Adguard Premium 6.4.1814.4903 Multilingual Final Medicine[Babu Serial Key.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p>airflux 2336c5e09f downloaddirector 3.0 plus <br/>crack nerdlife 4.0 serial key free<br/> ivandiva-2-3 downloadgenius<br/>miro-9.10.5-full-crack-free-download-for-windows-without-serial-number<br/>bluesurf 1.3.2.5 [crack] full version pdf download torrente<br/> </p>
-<h2>Adguard Premium 6.4.1814.4903 Multilingual Final Medicine[Babu Serial Key</h2><br /><p><b><b>Download</b> &#9889; <a href="https://urlin.us/2uEvuY">https://urlin.us/2uEvuY</a></b></p><br /><br />
-<p>i like you 14f77faccf full version serial keygen for windows xp 32 bit with patch<br/>emirate dvd player with serial number 2013<br/>zemaran 2336c5e09f download director 3.0 plus<br/>noah 1.0.4 serial number with crack full version<br/> </p>
-<p>thorwax modeler for autocad 2012.2 serial key<br/>immunity 8.3.0.62 crack x64 pro<br/>microsoft office 2011 multilingual product key<br/>eksempel med 32 rentaanseddeler<br/>huawei e303 2gb memory for 12.10 how to reset forgotten password<br/> </p>
-<p>cooldrive 4.0 serial number<br/>minix 3.3.5 keygen<br/>clubwars 3 server download<br/>citrix xenapp 5.5 for linux installation free key<br/>inddlib cheat engine 4.0 serial key<br/>yahooemailpasswordhackerv11rar<br/>gvodle gvodle - a pvr for the 21st century<br/>neufert raster online free<br/>x-platform source code<br/>x-platform source code<br/> </p>
-<p></p>
-<p>fotorunner netmechanic 3.0.4 serial number<br/>hard disk recovery programme 6.1.5.2 serial number<br/>yahooemailpasswordhackerv11rar<br/>wikimedia commons en gkm<br/>clutch 4.4.5 crack x64<br/>yahooemailpasswordhackerv11rar<br/>información de excelfix gratis [verified]<br/> </p>
-<p>one of blade’s hidden realm game free download<br/>kodi media center 17.1.1237 full serial key<br/>bluewave 2.0.3 download<br/>yadavsolan dvd ripper pro key<br/>bust in smash - fight for free download<br/>n+3pv by ghe advanced portable<br/>crack de agro 4.0 serial key<br/>citrix xenapp 5.5 for linux installation free key<br/>enjoy tv online iphone<br/>citrix xenapp 5.5 for linux installation free key<br/>bust in smash - fight for free download<br/>yadavsolan dvd ripper pro key<br/>yahooemailpasswordhackerv11rar<br/> </p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Luigis Mansion 2 Nintendo 3DS.rar Password.rar 2021.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Luigis Mansion 2 Nintendo 3DS.rar Password.rar 2021.md
deleted file mode 100644
index 115d15713f6f974ec688009e95163e06896471e5..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Luigis Mansion 2 Nintendo 3DS.rar Password.rar 2021.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Luigis Mansion 2 Nintendo 3DS.rar password.rar</h2><br /><p><b><b>Download File</b> &#128505; <a href="https://urlin.us/2uEyCE">https://urlin.us/2uEyCE</a></b></p><br /><br />
-<br />
-doom nsp switch club Download NSP Nintendo Switch Games XCI Roms. ... included software. same as how Monster hunter generations or amy late 3DS game, ... Password: no. ... rar. nsp Dragon Quest Builders 2 [010042000a986800][v131072]. com is the number ... About Luigi's Mansion 3 Switch. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/james-oldfield/PandA/networks/genforce/configs/stylegan_ffhq256_val.py b/spaces/james-oldfield/PandA/networks/genforce/configs/stylegan_ffhq256_val.py
deleted file mode 100644
index 092f0d1926e0e35d2035bda1ef8a83cc4d8f1fbd..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/configs/stylegan_ffhq256_val.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# python3.7
-"""Configuration for testing StyleGAN on FF-HQ (256) dataset.
-
-All settings are particularly used for one replica (GPU), such as `batch_size`
-and `num_workers`.
-"""
-
-runner_type = 'StyleGANRunner'
-gan_type = 'stylegan'
-resolution = 256
-batch_size = 64
-
-data = dict(
-    num_workers=4,
-    # val=dict(root_dir='data/ffhq', resolution=resolution),
-    val=dict(root_dir='data/ffhq.zip', data_format='zip',
-             resolution=resolution),
-)
-
-modules = dict(
-    discriminator=dict(
-        model=dict(gan_type=gan_type, resolution=resolution),
-        kwargs_val=dict(),
-    ),
-    generator=dict(
-        model=dict(gan_type=gan_type, resolution=resolution),
-        kwargs_val=dict(trunc_psi=0.7, trunc_layers=8, randomize_noise=False),
-    )
-)
diff --git a/spaces/jhwen/bingo/src/components/chat-attachments.tsx b/spaces/jhwen/bingo/src/components/chat-attachments.tsx
deleted file mode 100644
index ef43d4e262935d263b6099138c56f7daade5299d..0000000000000000000000000000000000000000
--- a/spaces/jhwen/bingo/src/components/chat-attachments.tsx
+++ /dev/null
@@ -1,37 +0,0 @@
-import Image from 'next/image'
-import ClearIcon from '@/assets/images/clear.svg'
-import RefreshIcon from '@/assets/images/refresh.svg'
-import { FileItem } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-import { useBing } from '@/lib/hooks/use-bing'
-
-type ChatAttachmentsProps = Pick<ReturnType<typeof useBing>, 'attachmentList' | 'setAttachmentList' | 'uploadImage'>
-
-export function ChatAttachments({ attachmentList = [], setAttachmentList, uploadImage }: ChatAttachmentsProps) {
-  return attachmentList.length ? (
-    <div className="attachment-list">
-      {attachmentList.map(file => (
-        <div className="file-item" key={file.url}>
-          {file.status === 'loading' && (
-            <div className="loading">
-              <div className="bar" />
-            </div>)
-          }
-          {file.status !== 'error' && (
-            <div className="thumbnail">
-              <img draggable="false" src={file.url} />
-            </div>)
-          }
-          {file.status === 'error' && (
-            <div className="error">
-              <Image alt="refresh" src={RefreshIcon} width={18} onClick={() => uploadImage(file.url)} />
-            </div>
-          )}
-          <button className={cn('dismiss', { 'no-file': file.status === 'error' })} type="button">
-            <Image alt="clear" src={ClearIcon} width={16} onClick={() => setAttachmentList([])} />
-          </button>
-        </div>
-      ))}
-    </div>
-  ) : null
-}
diff --git a/spaces/jiawei011/dreamgaussian/scripts/run_sd.sh b/spaces/jiawei011/dreamgaussian/scripts/run_sd.sh
deleted file mode 100644
index da5575485223db84b9b9a3a0ac02b878ab656603..0000000000000000000000000000000000000000
--- a/spaces/jiawei011/dreamgaussian/scripts/run_sd.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-export CUDA_VISIBLE_DEVICES=6
-
-# easy samples
-python main.py --config configs/text.yaml prompt="a photo of an icecream" save_path=icecream 
-python main2.py --config configs/text.yaml prompt="a photo of an icecream" save_path=icecream 
-python main.py --config configs/text.yaml prompt="a ripe strawberry" save_path=strawberry 
-python main2.py --config configs/text.yaml prompt="a ripe strawberry" save_path=strawberry 
-python main.py --config configs/text.yaml prompt="a blue tulip" save_path=tulip 
-python main2.py --config configs/text.yaml prompt="a blue tulip" save_path=tulip 
-
-python main.py --config configs/text.yaml prompt="a golden goblet" save_path=goblet 
-python main2.py --config configs/text.yaml prompt="a golden goblet" save_path=goblet 
-python main.py --config configs/text.yaml prompt="a photo of a hamburger" save_path=hamburger 
-python main2.py --config configs/text.yaml prompt="a photo of a hamburger" save_path=hamburger 
-python main.py --config configs/text.yaml prompt="a delicious croissant" save_path=croissant 
-python main2.py --config configs/text.yaml prompt="a delicious croissant" save_path=croissant 
-
-# hard samples
-python main.py --config configs/text.yaml prompt="a baby bunny sitting on top of a stack of pancake" save_path=bunny_pancake 
-python main2.py --config configs/text.yaml prompt="a baby bunny sitting on top of a stack of pancake" save_path=bunny_pancake 
-python main.py --config configs/text.yaml prompt="a typewriter" save_path=typewriter 
-python main2.py --config configs/text.yaml prompt="a typewriter" save_path=typewriter 
-python main.py --config configs/text.yaml prompt="a pineapple" save_path=pineapple 
-python main2.py --config configs/text.yaml prompt="a pineapple" save_path=pineapple 
-
-python main.py --config configs/text.yaml prompt="a model of a house in Tudor style" save_path=tudor_house 
-python main2.py --config configs/text.yaml prompt="a model of a house in Tudor style" save_path=tudor_house 
-python main.py --config configs/text.yaml prompt="a lionfish" save_path=lionfish 
-python main2.py --config configs/text.yaml prompt="a lionfish" save_path=lionfish 
-python main.py --config configs/text.yaml prompt="a bunch of yellow rose, highly detailed" save_path=rose 
-python main2.py --config configs/text.yaml prompt="a bunch of yellow rose, highly detailed" save_path=rose 
diff --git a/spaces/jiejiejie0420/bingo/src/lib/bots/bing/index.ts b/spaces/jiejiejie0420/bingo/src/lib/bots/bing/index.ts
deleted file mode 100644
index 2c4afae01a345b8415935228566cb30d695e768d..0000000000000000000000000000000000000000
--- a/spaces/jiejiejie0420/bingo/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,421 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
-  SendMessageParams,
-  BingConversationStyle,
-  ConversationResponse,
-  ChatResponseMessage,
-  ConversationInfo,
-  InvocationEventType,
-  ChatError,
-  ErrorCode,
-  ChatUpdateCompleteResponse,
-  ImageInfo,
-  KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
-  'nlu_direct_response_filter',
-  'deepleo',
-  'disable_emoji_spoken_text',
-  'responsible_ai_policy_235',
-  'enablemm',
-  'iycapbing',
-  'iyxapbing',
-  'objopinion',
-  'rweasgv2',
-  'dagslnv1',
-  'dv3sugg',
-  'autosave',
-  'iyoloxap',
-  'iyoloneutral',
-  'clgalileo',
-  'gencontentv3',
-]
-
-export class BingWebBot {
-  protected conversationContext?: ConversationInfo
-  protected cookie: string
-  protected ua: string
-  protected endpoint = ''
-  private lastText = ''
-  private asyncTasks: Array<Promise<any>> = []
-
-  constructor(opts: {
-    cookie: string
-    ua: string
-    bingConversationStyle?: BingConversationStyle
-    conversationContext?: ConversationInfo
-  }) {
-    const { cookie, ua, conversationContext } = opts
-    this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
-    this.ua = ua
-    this.conversationContext = conversationContext
-  }
-
-  static buildChatRequest(conversation: ConversationInfo) {
-    const optionsSets = OPTIONS_SETS
-    if (conversation.conversationStyle === BingConversationStyle.Precise) {
-      optionsSets.push('h3precise')
-    } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
-      optionsSets.push('h3imaginative')
-    }
-    return {
-      arguments: [
-        {
-          source: 'cib',
-          optionsSets,
-          allowedMessageTypes: [
-            'Chat',
-            'InternalSearchQuery',
-            'Disengaged',
-            'InternalLoaderMessage',
-            'SemanticSerp',
-            'GenerateContentQuery',
-            'SearchQuery',
-          ],
-          sliceIds: [
-            'winmuid1tf',
-            'anssupfor_c',
-            'imgchatgptv2',
-            'tts2cf',
-            'contansperf',
-            'mlchatpc8500w',
-            'mlchatpc2',
-            'ctrlworkpay',
-            'winshortmsgtf',
-            'cibctrl',
-            'sydtransctrl',
-            'sydconfigoptc',
-            '0705trt4',
-            '517opinion',
-            '628ajcopus0',
-            '330uaugs0',
-            '529rwea',
-            '0626snptrcs0',
-            '424dagslnv1',
-          ],
-          isStartOfSession: conversation.invocationId === 0,
-          message: {
-            author: 'user',
-            inputMethod: 'Keyboard',
-            text: conversation.prompt,
-            imageUrl: conversation.imageUrl,
-            messageType: 'Chat',
-          },
-          conversationId: conversation.conversationId,
-          conversationSignature: conversation.conversationSignature,
-          participant: { id: conversation.clientId },
-        },
-      ],
-      invocationId: conversation.invocationId.toString(),
-      target: 'chat',
-      type: InvocationEventType.StreamInvocation,
-    }
-  }
-
-  async createConversation(): Promise<ConversationResponse> {
-    const headers = {
-      'Accept-Encoding': 'gzip, deflate, br, zsdch',
-      'User-Agent': this.ua,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      cookie: this.cookie,
-    }
-
-    let resp: ConversationResponse | undefined
-    try {
-      const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
-      if (response.status === 404) {
-        throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
-      }
-      resp = await response.json() as ConversationResponse
-    } catch (err) {
-      console.error('create conversation error', err)
-    }
-
-    if (!resp?.result) {
-      throw new ChatError('Invalid response', ErrorCode.UNKOWN_ERROR)
-    }
-
-    const { value, message } = resp.result || {}
-    if (value !== 'Success') {
-      const errorMsg = `${value}: ${message}`
-      if (value === 'UnauthorizedRequest') {
-        throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
-      }
-      if (value === 'Forbidden') {
-        throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
-      }
-      throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
-    }
-    return resp
-  }
-
-  private async createContext(conversationStyle: BingConversationStyle) {
-    if (!this.conversationContext) {
-      const conversation = await this.createConversation()
-      this.conversationContext = {
-        conversationId: conversation.conversationId,
-        conversationSignature: conversation.conversationSignature,
-        clientId: conversation.clientId,
-        invocationId: 0,
-        conversationStyle,
-        prompt: '',
-      }
-    }
-    return this.conversationContext
-  }
-
-  async sendMessage(params: Params) {
-    try {
-      await this.createContext(params.options.bingConversationStyle)
-      Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
-      return this.sydneyProxy(params)
-    } catch (error) {
-      params.onEvent({
-        type: 'ERROR',
-        error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
-      })
-    }
-  }
-
-  private async sydneyProxy(params: Params) {
-    const abortController = new AbortController()
-    const response = await fetch(this.endpoint + '/api/sydney', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      signal: abortController.signal,
-      body: JSON.stringify(this.conversationContext!)
-    })
-    if (response.status !== 200) {
-      params.onEvent({
-        type: 'ERROR',
-        error: new ChatError(
-          'Unknown error',
-          ErrorCode.UNKOWN_ERROR,
-        ),
-      })
-    }
-    params.signal?.addEventListener('abort', () => {
-      abortController.abort()
-    })
-
-    const textDecoder = createChunkDecoder()
-    for await (const chunk of streamAsyncIterable(response.body!)) {
-      this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
-    }
-  }
-
-  async sendWs() {
-    const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
-      packMessage: websocketUtils.packMessage,
-      unpackMessage: websocketUtils.unpackMessage,
-      createWebSocket: (url) => new WebSocket(url, {
-        headers: {
-          'accept-language': 'zh-CN,zh;q=0.9',
-          'cache-control': 'no-cache',
-          'User-Agent': this.ua,
-          pragma: 'no-cache',
-          cookie: this.cookie,
-        }
-      })
-    }
-    const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
-    wsp.open().then(() => {
-      wsp.sendPacked({ protocol: 'json', version: 1 })
-      wsp.sendPacked({ type: 6 })
-      wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
-    })
-
-    return wsp
-  }
-
-  private async useWs(params: Params) {
-    const wsp = await this.sendWs()
-    const watchDog = new WatchDog()
-    wsp.onUnpackedMessage.addListener((events) => {
-      watchDog.watch(() => {
-        wsp.sendPacked({ type: 6 })
-      })
-      this.parseEvents(params, events)
-    })
-
-    wsp.onClose.addListener(() => {
-      watchDog.reset()
-      params.onEvent({ type: 'DONE' })
-      wsp.removeAllListeners()
-    })
-
-    params.signal?.addEventListener('abort', () => {
-      wsp.removeAllListeners()
-      wsp.close()
-    })
-  }
-
-  private async createImage(prompt: string, id: string) {
-    try {
-      const headers = {
-        'Accept-Encoding': 'gzip, deflate, br, zsdch',
-        'User-Agent': this.ua,
-        'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-        cookie: this.cookie,
-      }
-      const query = new URLSearchParams({
-        prompt,
-        id
-      })
-      const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
-        {
-          method: 'POST',
-          headers,
-          mode: 'cors',
-          credentials: 'include'
-        })
-        .then(res => res.text())
-      if (response) {
-        this.lastText += '\n' + response
-      }
-    } catch (err) {
-      console.error('Create Image Error', err)
-    }
-  }
-
-  private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
-    const imageInfo: ImageInfo = {}
-    let imageBase64: string | undefined = undefined
-    const knowledgeRequest = {
-      imageInfo,
-      knowledgeRequest: {
-        invokedSkills: [
-          'ImageById'
-        ],
-        subscriptionId: 'Bing.Chat.Multimodal',
-        invokedSkillsRequestData: {
-            enableFaceBlur: true
-        },
-        convoData: {
-          convoid: this.conversationContext?.conversationId,
-          convotone: conversationStyle,
-        }
-      },
-    }
-
-    if (imageUrl.startsWith('data:image/')) {
-      imageBase64 = imageUrl.replace('data:image/', '');
-      const partIndex = imageBase64.indexOf(',')
-      if (partIndex) {
-        imageBase64 = imageBase64.substring(partIndex + 1)
-      }
-    } else {
-      imageInfo.url = imageUrl
-    }
-    return { knowledgeRequest, imageBase64 }
-  }
-
-  async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
-    if (!imageUrl) {
-      return
-    }
-    await this.createContext(conversationStyle)
-    const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
-    const response = await fetch(this.endpoint + '/api/kblob',
-      {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        method: 'POST',
-        mode: 'cors',
-        credentials: 'include',
-        body: JSON.stringify(payload),
-      })
-      .then(res => res.json())
-      .catch(e => {
-        console.log('Error', e)
-      })
-    return response
-  }
-
-  private async generateContent(message: ChatResponseMessage) {
-    if (message.contentType === 'IMAGE') {
-      this.asyncTasks.push(this.createImage(message.text, message.messageId))
-    }
-  }
-
-  private async parseEvents(params: Params, events: any) {
-    const conversation = this.conversationContext!
-
-    events?.forEach(async (event: ChatUpdateCompleteResponse) => {
-      debug('bing event', event)
-      if (event.type === 3) {
-        await Promise.all(this.asyncTasks)
-        this.asyncTasks = []
-        params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
-        params.onEvent({ type: 'DONE' })
-        conversation.invocationId = parseInt(event.invocationId, 10) + 1
-      } else if (event.type === 1) {
-        const messages = event.arguments[0].messages
-        if (messages) {
-          const text = convertMessageToMarkdown(messages[0])
-          this.lastText = text
-          params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
-        }
-      } else if (event.type === 2) {
-        const messages = event.item.messages as ChatResponseMessage[] | undefined
-        if (!messages) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              event.item.result.error || 'Unknown error',
-              event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
-                : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
-                  : ErrorCode.UNKOWN_ERROR
-            ),
-          })
-          return
-        }
-        const limited = messages.some((message) =>
-          message.contentOrigin === 'TurnLimiter'
-          || message.messageType === 'Disengaged'
-        )
-        if (limited) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              'Sorry, you have reached chat limit in this conversation.',
-              ErrorCode.CONVERSATION_LIMIT,
-            ),
-          })
-          return
-        }
-
-        const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
-        const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
-        if (specialMessage) {
-          this.generateContent(specialMessage)
-        }
-
-        if (lastMessage) {
-          const text = convertMessageToMarkdown(lastMessage)
-          this.lastText = text
-          params.onEvent({
-            type: 'UPDATE_ANSWER',
-            data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
-          })
-        }
-      }
-    })
-  }
-
-  resetConversation() {
-    this.conversationContext = undefined
-  }
-}
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/__init__.py
deleted file mode 100644
index 773db588433554b1642c5d3f49382cd7e6edef86..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-__all__ = ['Cipher', 'Hash', 'Protocol', 'PublicKey', 'Util', 'Signature',
-           'IO', 'Math']
-
-version_info = (3, 19, '0')
-
-__version__ = ".".join([str(x) for x in version_info])
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/generic/_rectangle.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/generic/_rectangle.py
deleted file mode 100644
index 3f41bfd595e30b76d59f715038280fdd3b7a463a..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/generic/_rectangle.py
+++ /dev/null
@@ -1,265 +0,0 @@
-import decimal
-from typing import Any, List, Tuple, Union
-
-from .._utils import deprecation_no_replacement, deprecation_with_replacement
-from ._base import FloatObject, NumberObject
-from ._data_structures import ArrayObject
-
-
-class RectangleObject(ArrayObject):
-    """
-    This class is used to represent *page boxes* in PyPDF2. These boxes include:
-        * :attr:`artbox <PyPDF2._page.PageObject.artbox>`
-        * :attr:`bleedbox <PyPDF2._page.PageObject.bleedbox>`
-        * :attr:`cropbox <PyPDF2._page.PageObject.cropbox>`
-        * :attr:`mediabox <PyPDF2._page.PageObject.mediabox>`
-        * :attr:`trimbox <PyPDF2._page.PageObject.trimbox>`
-    """
-
-    def __init__(
-        self, arr: Union["RectangleObject", Tuple[float, float, float, float]]
-    ) -> None:
-        # must have four points
-        assert len(arr) == 4
-        # automatically convert arr[x] into NumberObject(arr[x]) if necessary
-        ArrayObject.__init__(self, [self._ensure_is_number(x) for x in arr])  # type: ignore
-
-    def _ensure_is_number(self, value: Any) -> Union[FloatObject, NumberObject]:
-        if not isinstance(value, (NumberObject, FloatObject)):
-            value = FloatObject(value)
-        return value
-
-    def scale(self, sx: float, sy: float) -> "RectangleObject":
-        return RectangleObject(
-            (
-                float(self.left) * sx,
-                float(self.bottom) * sy,
-                float(self.right) * sx,
-                float(self.top) * sy,
-            )
-        )
-
-    def ensureIsNumber(
-        self, value: Any
-    ) -> Union[FloatObject, NumberObject]:  # pragma: no cover
-        deprecation_no_replacement("ensureIsNumber", "3.0.0")
-        return self._ensure_is_number(value)
-
-    def __repr__(self) -> str:
-        return f"RectangleObject({repr(list(self))})"
-
-    @property
-    def left(self) -> FloatObject:
-        return self[0]
-
-    @left.setter
-    def left(self, f: float) -> None:
-        self[0] = FloatObject(f)
-
-    @property
-    def bottom(self) -> FloatObject:
-        return self[1]
-
-    @bottom.setter
-    def bottom(self, f: float) -> None:
-        self[1] = FloatObject(f)
-
-    @property
-    def right(self) -> FloatObject:
-        return self[2]
-
-    @right.setter
-    def right(self, f: float) -> None:
-        self[2] = FloatObject(f)
-
-    @property
-    def top(self) -> FloatObject:
-        return self[3]
-
-    @top.setter
-    def top(self, f: float) -> None:
-        self[3] = FloatObject(f)
-
-    def getLowerLeft_x(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getLowerLeft_x", "left", "3.0.0")
-        return self.left
-
-    def getLowerLeft_y(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getLowerLeft_y", "bottom", "3.0.0")
-        return self.bottom
-
-    def getUpperRight_x(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getUpperRight_x", "right", "3.0.0")
-        return self.right
-
-    def getUpperRight_y(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getUpperRight_y", "top", "3.0.0")
-        return self.top
-
-    def getUpperLeft_x(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getUpperLeft_x", "left", "3.0.0")
-        return self.left
-
-    def getUpperLeft_y(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getUpperLeft_y", "top", "3.0.0")
-        return self.top
-
-    def getLowerRight_x(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getLowerRight_x", "right", "3.0.0")
-        return self.right
-
-    def getLowerRight_y(self) -> FloatObject:  # pragma: no cover
-        deprecation_with_replacement("getLowerRight_y", "bottom", "3.0.0")
-        return self.bottom
-
-    @property
-    def lower_left(self) -> Tuple[decimal.Decimal, decimal.Decimal]:
-        """
-        Property to read and modify the lower left coordinate of this box
-        in (x,y) form.
-        """
-        return self.left, self.bottom
-
-    @lower_left.setter
-    def lower_left(self, value: List[Any]) -> None:
-        self[0], self[1] = (self._ensure_is_number(x) for x in value)
-
-    @property
-    def lower_right(self) -> Tuple[decimal.Decimal, decimal.Decimal]:
-        """
-        Property to read and modify the lower right coordinate of this box
-        in (x,y) form.
-        """
-        return self.right, self.bottom
-
-    @lower_right.setter
-    def lower_right(self, value: List[Any]) -> None:
-        self[2], self[1] = (self._ensure_is_number(x) for x in value)
-
-    @property
-    def upper_left(self) -> Tuple[decimal.Decimal, decimal.Decimal]:
-        """
-        Property to read and modify the upper left coordinate of this box
-        in (x,y) form.
-        """
-        return self.left, self.top
-
-    @upper_left.setter
-    def upper_left(self, value: List[Any]) -> None:
-        self[0], self[3] = (self._ensure_is_number(x) for x in value)
-
-    @property
-    def upper_right(self) -> Tuple[decimal.Decimal, decimal.Decimal]:
-        """
-        Property to read and modify the upper right coordinate of this box
-        in (x,y) form.
-        """
-        return self.right, self.top
-
-    @upper_right.setter
-    def upper_right(self, value: List[Any]) -> None:
-        self[2], self[3] = (self._ensure_is_number(x) for x in value)
-
-    def getLowerLeft(
-        self,
-    ) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("getLowerLeft", "lower_left", "3.0.0")
-        return self.lower_left
-
-    def getLowerRight(
-        self,
-    ) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("getLowerRight", "lower_right", "3.0.0")
-        return self.lower_right
-
-    def getUpperLeft(
-        self,
-    ) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("getUpperLeft", "upper_left", "3.0.0")
-        return self.upper_left
-
-    def getUpperRight(
-        self,
-    ) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("getUpperRight", "upper_right", "3.0.0")
-        return self.upper_right
-
-    def setLowerLeft(self, value: Tuple[float, float]) -> None:  # pragma: no cover
-        deprecation_with_replacement("setLowerLeft", "lower_left", "3.0.0")
-        self.lower_left = value  # type: ignore
-
-    def setLowerRight(self, value: Tuple[float, float]) -> None:  # pragma: no cover
-        deprecation_with_replacement("setLowerRight", "lower_right", "3.0.0")
-        self[2], self[1] = (self._ensure_is_number(x) for x in value)
-
-    def setUpperLeft(self, value: Tuple[float, float]) -> None:  # pragma: no cover
-        deprecation_with_replacement("setUpperLeft", "upper_left", "3.0.0")
-        self[0], self[3] = (self._ensure_is_number(x) for x in value)
-
-    def setUpperRight(self, value: Tuple[float, float]) -> None:  # pragma: no cover
-        deprecation_with_replacement("setUpperRight", "upper_right", "3.0.0")
-        self[2], self[3] = (self._ensure_is_number(x) for x in value)
-
-    @property
-    def width(self) -> decimal.Decimal:
-        return self.right - self.left
-
-    def getWidth(self) -> decimal.Decimal:  # pragma: no cover
-        deprecation_with_replacement("getWidth", "width", "3.0.0")
-        return self.width
-
-    @property
-    def height(self) -> decimal.Decimal:
-        return self.top - self.bottom
-
-    def getHeight(self) -> decimal.Decimal:  # pragma: no cover
-        deprecation_with_replacement("getHeight", "height", "3.0.0")
-        return self.height
-
-    @property
-    def lowerLeft(self) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("lowerLeft", "lower_left", "3.0.0")
-        return self.lower_left
-
-    @lowerLeft.setter
-    def lowerLeft(
-        self, value: Tuple[decimal.Decimal, decimal.Decimal]
-    ) -> None:  # pragma: no cover
-        deprecation_with_replacement("lowerLeft", "lower_left", "3.0.0")
-        self.lower_left = value
-
-    @property
-    def lowerRight(self) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("lowerRight", "lower_right", "3.0.0")
-        return self.lower_right
-
-    @lowerRight.setter
-    def lowerRight(
-        self, value: Tuple[decimal.Decimal, decimal.Decimal]
-    ) -> None:  # pragma: no cover
-        deprecation_with_replacement("lowerRight", "lower_right", "3.0.0")
-        self.lower_right = value
-
-    @property
-    def upperLeft(self) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("upperLeft", "upper_left", "3.0.0")
-        return self.upper_left
-
-    @upperLeft.setter
-    def upperLeft(
-        self, value: Tuple[decimal.Decimal, decimal.Decimal]
-    ) -> None:  # pragma: no cover
-        deprecation_with_replacement("upperLeft", "upper_left", "3.0.0")
-        self.upper_left = value
-
-    @property
-    def upperRight(self) -> Tuple[decimal.Decimal, decimal.Decimal]:  # pragma: no cover
-        deprecation_with_replacement("upperRight", "upper_right", "3.0.0")
-        return self.upper_right
-
-    @upperRight.setter
-    def upperRight(
-        self, value: Tuple[decimal.Decimal, decimal.Decimal]
-    ) -> None:  # pragma: no cover
-        deprecation_with_replacement("upperRight", "upper_right", "3.0.0")
-        self.upper_right = value
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/_magics.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/_magics.py
deleted file mode 100644
index 7fe6131182952ff30bf63543de528657f7ba77a2..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/altair/_magics.py
+++ /dev/null
@@ -1,109 +0,0 @@
-"""
-Magic functions for rendering vega-lite specifications
-"""
-__all__ = ["vegalite"]
-
-import json
-import warnings
-
-import IPython
-from IPython.core import magic_arguments
-import pandas as pd
-from toolz import curried
-
-from altair.vegalite import v5 as vegalite_v5
-
-try:
-    import yaml
-
-    YAML_AVAILABLE = True
-except ImportError:
-    YAML_AVAILABLE = False
-
-
-RENDERERS = {
-    "vega-lite": {
-        "5": vegalite_v5.VegaLite,
-    },
-}
-
-
-TRANSFORMERS = {
-    "vega-lite": {
-        "5": vegalite_v5.data_transformers,
-    },
-}
-
-
-def _prepare_data(data, data_transformers):
-    """Convert input data to data for use within schema"""
-    if data is None or isinstance(data, dict):
-        return data
-    elif isinstance(data, pd.DataFrame):
-        return curried.pipe(data, data_transformers.get())
-    elif isinstance(data, str):
-        return {"url": data}
-    else:
-        warnings.warn("data of type {} not recognized".format(type(data)), stacklevel=1)
-        return data
-
-
-def _get_variable(name):
-    """Get a variable from the notebook namespace."""
-    ip = IPython.get_ipython()
-    if ip is None:
-        raise ValueError(
-            "Magic command must be run within an IPython "
-            "environemnt, in which get_ipython() is defined."
-        )
-    if name not in ip.user_ns:
-        raise NameError(
-            "argument '{}' does not match the "
-            "name of any defined variable".format(name)
-        )
-    return ip.user_ns[name]
-
-
-@magic_arguments.magic_arguments()
-@magic_arguments.argument(
-    "data",
-    nargs="?",
-    help="local variablename of a pandas DataFrame to be used as the dataset",
-)
-@magic_arguments.argument("-v", "--version", dest="version", default="v5")
-@magic_arguments.argument("-j", "--json", dest="json", action="store_true")
-def vegalite(line, cell):
-    """Cell magic for displaying vega-lite visualizations in CoLab.
-
-    %%vegalite [dataframe] [--json] [--version='v5']
-
-    Visualize the contents of the cell using Vega-Lite, optionally
-    specifying a pandas DataFrame object to be used as the dataset.
-
-    if --json is passed, then input is parsed as json rather than yaml.
-    """
-    args = magic_arguments.parse_argstring(vegalite, line)
-    existing_versions = {"v5": "5"}
-    version = existing_versions[args.version]
-    assert version in RENDERERS["vega-lite"]
-    VegaLite = RENDERERS["vega-lite"][version]
-    data_transformers = TRANSFORMERS["vega-lite"][version]
-
-    if args.json:
-        spec = json.loads(cell)
-    elif not YAML_AVAILABLE:
-        try:
-            spec = json.loads(cell)
-        except json.JSONDecodeError as err:
-            raise ValueError(
-                "%%vegalite: spec is not valid JSON. "
-                "Install pyyaml to parse spec as yaml"
-            ) from err
-    else:
-        spec = yaml.load(cell, Loader=yaml.SafeLoader)
-
-    if args.data is not None:
-        data = _get_variable(args.data)
-        spec["data"] = _prepare_data(data, data_transformers)
-
-    return VegaLite(spec)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bs4/tests/test_dammit.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bs4/tests/test_dammit.py
deleted file mode 100644
index 9aad0ac695a301b6eec081bc15c2a8e29edb891c..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bs4/tests/test_dammit.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# encoding: utf-8
-import pytest
-import logging
-import bs4
-from bs4 import BeautifulSoup
-from bs4.dammit import (
-    EntitySubstitution,
-    EncodingDetector,
-    UnicodeDammit,
-)
-
-class TestUnicodeDammit(object):
-    """Standalone tests of UnicodeDammit."""
-
-    def test_unicode_input(self):
-        markup = "I'm already Unicode! \N{SNOWMAN}"
-        dammit = UnicodeDammit(markup)
-        assert dammit.unicode_markup == markup
-
-    @pytest.mark.parametrize(
-        "smart_quotes_to,expect_converted",
-        [(None, "\u2018\u2019\u201c\u201d"),
-         ("xml", "&#x2018;&#x2019;&#x201C;&#x201D;"),
-         ("html", "&lsquo;&rsquo;&ldquo;&rdquo;"),
-         ("ascii", "''" + '""'),
-        ]
-    )
-    def test_smart_quotes_to(self, smart_quotes_to, expect_converted):
-        """Verify the functionality of the smart_quotes_to argument
-        to the UnicodeDammit constructor."""
-        markup = b"<foo>\x91\x92\x93\x94</foo>"
-        converted = UnicodeDammit(
-            markup, known_definite_encodings=["windows-1252"],
-            smart_quotes_to=smart_quotes_to
-        ).unicode_markup
-        assert converted == "<foo>{}</foo>".format(expect_converted)
-        
-    def test_detect_utf8(self):
-        utf8 = b"Sacr\xc3\xa9 bleu! \xe2\x98\x83"
-        dammit = UnicodeDammit(utf8)
-        assert dammit.original_encoding.lower() == 'utf-8'
-        assert dammit.unicode_markup == 'Sacr\xe9 bleu! \N{SNOWMAN}'
-
-    def test_convert_hebrew(self):
-        hebrew = b"\xed\xe5\xec\xf9"
-        dammit = UnicodeDammit(hebrew, ["iso-8859-8"])
-        assert dammit.original_encoding.lower() == 'iso-8859-8'
-        assert dammit.unicode_markup == '\u05dd\u05d5\u05dc\u05e9'
-
-    def test_dont_see_smart_quotes_where_there_are_none(self):
-        utf_8 = b"\343\202\261\343\203\274\343\202\277\343\202\244 Watch"
-        dammit = UnicodeDammit(utf_8)
-        assert dammit.original_encoding.lower() == 'utf-8'
-        assert dammit.unicode_markup.encode("utf-8") == utf_8
-
-    def test_ignore_inappropriate_codecs(self):
-        utf8_data = "Räksmörgås".encode("utf-8")
-        dammit = UnicodeDammit(utf8_data, ["iso-8859-8"])
-        assert dammit.original_encoding.lower() == 'utf-8'
-
-    def test_ignore_invalid_codecs(self):
-        utf8_data = "Räksmörgås".encode("utf-8")
-        for bad_encoding in ['.utf8', '...', 'utF---16.!']:
-            dammit = UnicodeDammit(utf8_data, [bad_encoding])
-            assert dammit.original_encoding.lower() == 'utf-8'
-
-    def test_exclude_encodings(self):
-        # This is UTF-8.
-        utf8_data = "Räksmörgås".encode("utf-8")
-
-        # But if we exclude UTF-8 from consideration, the guess is
-        # Windows-1252.
-        dammit = UnicodeDammit(utf8_data, exclude_encodings=["utf-8"])
-        assert dammit.original_encoding.lower() == 'windows-1252'
-
-        # And if we exclude that, there is no valid guess at all.
-        dammit = UnicodeDammit(
-            utf8_data, exclude_encodings=["utf-8", "windows-1252"])
-        assert dammit.original_encoding == None
-
-class TestEncodingDetector(object):
-        
-    def test_encoding_detector_replaces_junk_in_encoding_name_with_replacement_character(self):
-        detected = EncodingDetector(
-            b'<?xml version="1.0" encoding="UTF-\xdb" ?>')
-        encodings = list(detected.encodings)
-        assert 'utf-\N{REPLACEMENT CHARACTER}' in encodings
-
-    def test_detect_html5_style_meta_tag(self):
-
-        for data in (
-            b'<html><meta charset="euc-jp" /></html>',
-            b"<html><meta charset='euc-jp' /></html>",
-            b"<html><meta charset=euc-jp /></html>",
-            b"<html><meta charset=euc-jp/></html>"):
-            dammit = UnicodeDammit(data, is_html=True)
-            assert "euc-jp" == dammit.original_encoding
-
-    def test_last_ditch_entity_replacement(self):
-        # This is a UTF-8 document that contains bytestrings
-        # completely incompatible with UTF-8 (ie. encoded with some other
-        # encoding).
-        #
-        # Since there is no consistent encoding for the document,
-        # Unicode, Dammit will eventually encode the document as UTF-8
-        # and encode the incompatible characters as REPLACEMENT
-        # CHARACTER.
-        #
-        # If chardet is installed, it will detect that the document
-        # can be converted into ISO-8859-1 without errors. This happens
-        # to be the wrong encoding, but it is a consistent encoding, so the
-        # code we're testing here won't run.
-        #
-        # So we temporarily disable chardet if it's present.
-        doc = b"""\357\273\277<?xml version="1.0" encoding="UTF-8"?>
-<html><b>\330\250\330\252\330\261</b>
-<i>\310\322\321\220\312\321\355\344</i></html>"""
-        chardet = bs4.dammit.chardet_dammit
-        logging.disable(logging.WARNING)
-        try:
-            def noop(str):
-                return None
-            bs4.dammit.chardet_dammit = noop
-            dammit = UnicodeDammit(doc)
-            assert True == dammit.contains_replacement_characters
-            assert "\ufffd" in dammit.unicode_markup
-
-            soup = BeautifulSoup(doc, "html.parser")
-            assert soup.contains_replacement_characters
-        finally:
-            logging.disable(logging.NOTSET)
-            bs4.dammit.chardet_dammit = chardet
-
-    def test_byte_order_mark_removed(self):
-        # A document written in UTF-16LE will have its byte order marker stripped.
-        data = b'\xff\xfe<\x00a\x00>\x00\xe1\x00\xe9\x00<\x00/\x00a\x00>\x00'
-        dammit = UnicodeDammit(data)
-        assert "<a>áé</a>" == dammit.unicode_markup
-        assert "utf-16le" == dammit.original_encoding
-       
-    def test_known_definite_versus_user_encodings(self):
-        # The known_definite_encodings are used before sniffing the
-        # byte-order mark; the user_encodings are used afterwards.
-
-        # Here's a document in UTF-16LE.
-        data = b'\xff\xfe<\x00a\x00>\x00\xe1\x00\xe9\x00<\x00/\x00a\x00>\x00'
-        dammit = UnicodeDammit(data)
-
-        # We can process it as UTF-16 by passing it in as a known
-        # definite encoding.
-        before = UnicodeDammit(data, known_definite_encodings=["utf-16"])
-        assert "utf-16" == before.original_encoding
-        
-        # If we pass UTF-18 as a user encoding, it's not even
-        # tried--the encoding sniffed from the byte-order mark takes
-        # precedence.
-        after = UnicodeDammit(data, user_encodings=["utf-8"])
-        assert "utf-16le" == after.original_encoding
-        assert ["utf-16le"] == [x[0] for x in dammit.tried_encodings]
-        
-        # Here's a document in ISO-8859-8.
-        hebrew = b"\xed\xe5\xec\xf9"
-        dammit = UnicodeDammit(hebrew, known_definite_encodings=["utf-8"],
-                               user_encodings=["iso-8859-8"])
-        
-        # The known_definite_encodings don't work, BOM sniffing does
-        # nothing (it only works for a few UTF encodings), but one of
-        # the user_encodings does work.
-        assert "iso-8859-8" == dammit.original_encoding
-        assert ["utf-8", "iso-8859-8"] == [x[0] for x in dammit.tried_encodings]
-        
-    def test_deprecated_override_encodings(self):
-        # override_encodings is a deprecated alias for
-        # known_definite_encodings.
-        hebrew = b"\xed\xe5\xec\xf9"
-        dammit = UnicodeDammit(
-            hebrew,
-            known_definite_encodings=["shift-jis"],
-            override_encodings=["utf-8"],
-            user_encodings=["iso-8859-8"],
-        )
-        assert "iso-8859-8" == dammit.original_encoding
-
-        # known_definite_encodings and override_encodings were tried
-        # before user_encodings.
-        assert ["shift-jis", "utf-8", "iso-8859-8"] == (
-            [x[0] for x in dammit.tried_encodings]
-        )
-
-    def test_detwingle(self):
-        # Here's a UTF8 document.
-        utf8 = ("\N{SNOWMAN}" * 3).encode("utf8")
-
-        # Here's a Windows-1252 document.
-        windows_1252 = (
-            "\N{LEFT DOUBLE QUOTATION MARK}Hi, I like Windows!"
-            "\N{RIGHT DOUBLE QUOTATION MARK}").encode("windows_1252")
-
-        # Through some unholy alchemy, they've been stuck together.
-        doc = utf8 + windows_1252 + utf8
-
-        # The document can't be turned into UTF-8:
-        with pytest.raises(UnicodeDecodeError):
-            doc.decode("utf8")
-
-        # Unicode, Dammit thinks the whole document is Windows-1252,
-        # and decodes it into "☃☃☃“Hi, I like Windows!”☃☃☃"
-
-        # But if we run it through fix_embedded_windows_1252, it's fixed:
-        fixed = UnicodeDammit.detwingle(doc)
-        assert "☃☃☃“Hi, I like Windows!”☃☃☃" == fixed.decode("utf8")
-
-    def test_detwingle_ignores_multibyte_characters(self):
-        # Each of these characters has a UTF-8 representation ending
-        # in \x93. \x93 is a smart quote if interpreted as
-        # Windows-1252. But our code knows to skip over multibyte
-        # UTF-8 characters, so they'll survive the process unscathed.
-        for tricky_unicode_char in (
-            "\N{LATIN SMALL LIGATURE OE}", # 2-byte char '\xc5\x93'
-            "\N{LATIN SUBSCRIPT SMALL LETTER X}", # 3-byte char '\xe2\x82\x93'
-            "\xf0\x90\x90\x93", # This is a CJK character, not sure which one.
-            ):
-            input = tricky_unicode_char.encode("utf8")
-            assert input.endswith(b'\x93')
-            output = UnicodeDammit.detwingle(input)
-            assert output == input
-
-    def test_find_declared_encoding(self):
-        # Test our ability to find a declared encoding inside an
-        # XML or HTML document.
-        #
-        # Even if the document comes in as Unicode, it may be
-        # interesting to know what encoding was claimed
-        # originally.
-
-        html_unicode = '<html><head><meta charset="utf-8"></head></html>'
-        html_bytes = html_unicode.encode("ascii")
-
-        xml_unicode= '<?xml version="1.0" encoding="ISO-8859-1" ?>'
-        xml_bytes = xml_unicode.encode("ascii")
-
-        m = EncodingDetector.find_declared_encoding
-        assert m(html_unicode, is_html=False) is None
-        assert "utf-8" == m(html_unicode, is_html=True)
-        assert "utf-8" == m(html_bytes, is_html=True)
-
-        assert "iso-8859-1" == m(xml_unicode)
-        assert "iso-8859-1" == m(xml_bytes)
-
-        # Normally, only the first few kilobytes of a document are checked for
-        # an encoding.
-        spacer = b' ' * 5000
-        assert m(spacer + html_bytes) is None
-        assert m(spacer + xml_bytes) is None
-
-        # But you can tell find_declared_encoding to search an entire
-        # HTML document.
-        assert (
-            m(spacer + html_bytes, is_html=True, search_entire_document=True)
-            == "utf-8"
-        )
-
-        # The XML encoding declaration has to be the very first thing
-        # in the document. We'll allow whitespace before the document
-        # starts, but nothing else.
-        assert m(xml_bytes, search_entire_document=True) == "iso-8859-1"
-        assert m(b' ' + xml_bytes, search_entire_document=True) == "iso-8859-1"
-        assert m(b'a' + xml_bytes, search_entire_document=True) is None
-
-
-class TestEntitySubstitution(object):
-    """Standalone tests of the EntitySubstitution class."""
-    def setup_method(self):
-        self.sub = EntitySubstitution
-
-
-    @pytest.mark.parametrize(
-        "original,substituted",
-        [
-            # Basic case. Unicode characters corresponding to named
-            # HTML entites are substituted; others are not.
-            ("foo\u2200\N{SNOWMAN}\u00f5bar",
-             "foo&forall;\N{SNOWMAN}&otilde;bar"),
-
-            # MS smart quotes are a common source of frustration, so we
-            # give them a special test.
-            ('‘’foo“”', "&lsquo;&rsquo;foo&ldquo;&rdquo;"),           
-        ]
-    )
-    def test_substitute_html(self, original, substituted):
-        assert self.sub.substitute_html(original) == substituted
-        
-    def test_html5_entity(self):
-        for entity, u in (
-            # A few spot checks of our ability to recognize
-            # special character sequences and convert them
-            # to named entities.
-            ('&models;', '\u22a7'),
-            ('&Nfr;', '\U0001d511'),
-            ('&ngeqq;', '\u2267\u0338'),
-            ('&not;', '\xac'),
-            ('&Not;', '\u2aec'),
-                
-            # We _could_ convert | to &verbarr;, but we don't, because
-            # | is an ASCII character.
-            ('|' '|'),
-
-            # Similarly for the fj ligature, which we could convert to
-            # &fjlig;, but we don't.
-            ("fj", "fj"),
-
-            # We do convert _these_ ASCII characters to HTML entities,
-            # because that's required to generate valid HTML.
-            ('&gt;', '>'),
-            ('&lt;', '<'),
-            ('&amp;', '&'),
-        ):
-            template = '3 %s 4'
-            raw = template % u
-            with_entities = template % entity
-            assert self.sub.substitute_html(raw) == with_entities
-            
-    def test_html5_entity_with_variation_selector(self):
-        # Some HTML5 entities correspond either to a single-character
-        # Unicode sequence _or_ to the same character plus U+FE00,
-        # VARIATION SELECTOR 1. We can handle this.
-        data = "fjords \u2294 penguins"
-        markup = "fjords &sqcup; penguins"
-        assert self.sub.substitute_html(data) == markup
-
-        data = "fjords \u2294\ufe00 penguins"
-        markup = "fjords &sqcups; penguins"
-        assert self.sub.substitute_html(data) == markup
-        
-    def test_xml_converstion_includes_no_quotes_if_make_quoted_attribute_is_false(self):
-        s = 'Welcome to "my bar"'
-        assert self.sub.substitute_xml(s, False) == s
-
-    def test_xml_attribute_quoting_normally_uses_double_quotes(self):
-        assert self.sub.substitute_xml("Welcome", True) == '"Welcome"'
-        assert self.sub.substitute_xml("Bob's Bar", True) == '"Bob\'s Bar"'
-
-    def test_xml_attribute_quoting_uses_single_quotes_when_value_contains_double_quotes(self):
-        s = 'Welcome to "my bar"'
-        assert self.sub.substitute_xml(s, True) == "'Welcome to \"my bar\"'"
-
-    def test_xml_attribute_quoting_escapes_single_quotes_when_value_contains_both_single_and_double_quotes(self):
-        s = 'Welcome to "Bob\'s Bar"'
-        assert self.sub.substitute_xml(s, True) == '"Welcome to &quot;Bob\'s Bar&quot;"'
-
-    def test_xml_quotes_arent_escaped_when_value_is_not_being_quoted(self):
-        quoted = 'Welcome to "Bob\'s Bar"'
-        assert self.sub.substitute_xml(quoted) == quoted
-
-    def test_xml_quoting_handles_angle_brackets(self):
-        assert self.sub.substitute_xml("foo<bar>") == "foo&lt;bar&gt;"
-
-    def test_xml_quoting_handles_ampersands(self):
-        assert self.sub.substitute_xml("AT&T") == "AT&amp;T"
-
-    def test_xml_quoting_including_ampersands_when_they_are_part_of_an_entity(self):
-        assert self.sub.substitute_xml("&Aacute;T&T") == "&amp;Aacute;T&amp;T"
-
-    def test_xml_quoting_ignoring_ampersands_when_they_are_part_of_an_entity(self):
-        assert self.sub.substitute_xml_containing_entities("&Aacute;T&T") == "&Aacute;T&amp;T"
-       
-    def test_quotes_not_html_substituted(self):
-        """There's no need to do this except inside attribute values."""
-        text = 'Bob\'s "bar"'
-        assert self.sub.substitute_html(text) == text
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_a_s_p.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_a_s_p.py
deleted file mode 100644
index 10c32a87f4b2cbedac5e346c6f5d578cb7a6b65d..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_a_s_p.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-import struct
-
-
-GASP_SYMMETRIC_GRIDFIT = 0x0004
-GASP_SYMMETRIC_SMOOTHING = 0x0008
-GASP_DOGRAY = 0x0002
-GASP_GRIDFIT = 0x0001
-
-
-class table__g_a_s_p(DefaultTable.DefaultTable):
-    def decompile(self, data, ttFont):
-        self.version, numRanges = struct.unpack(">HH", data[:4])
-        assert 0 <= self.version <= 1, "unknown 'gasp' format: %s" % self.version
-        data = data[4:]
-        self.gaspRange = {}
-        for i in range(numRanges):
-            rangeMaxPPEM, rangeGaspBehavior = struct.unpack(">HH", data[:4])
-            self.gaspRange[int(rangeMaxPPEM)] = int(rangeGaspBehavior)
-            data = data[4:]
-        assert not data, "too much data"
-
-    def compile(self, ttFont):
-        version = 0  # ignore self.version
-        numRanges = len(self.gaspRange)
-        data = b""
-        items = sorted(self.gaspRange.items())
-        for rangeMaxPPEM, rangeGaspBehavior in items:
-            data = data + struct.pack(">HH", rangeMaxPPEM, rangeGaspBehavior)
-            if rangeGaspBehavior & ~(GASP_GRIDFIT | GASP_DOGRAY):
-                version = 1
-        data = struct.pack(">HH", version, numRanges) + data
-        return data
-
-    def toXML(self, writer, ttFont):
-        items = sorted(self.gaspRange.items())
-        for rangeMaxPPEM, rangeGaspBehavior in items:
-            writer.simpletag(
-                "gaspRange",
-                [
-                    ("rangeMaxPPEM", rangeMaxPPEM),
-                    ("rangeGaspBehavior", rangeGaspBehavior),
-                ],
-            )
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name != "gaspRange":
-            return
-        if not hasattr(self, "gaspRange"):
-            self.gaspRange = {}
-        self.gaspRange[safeEval(attrs["rangeMaxPPEM"])] = safeEval(
-            attrs["rangeGaspBehavior"]
-        )
diff --git a/spaces/joshipunitram/crowd-counting-p2p/run_test.py b/spaces/joshipunitram/crowd-counting-p2p/run_test.py
deleted file mode 100644
index 51fe52d79f3bb427fa566ec9815e90f0d77bf623..0000000000000000000000000000000000000000
--- a/spaces/joshipunitram/crowd-counting-p2p/run_test.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import argparse
-import datetime
-import random
-import time
-from pathlib import Path
-
-import torch
-import torchvision.transforms as standard_transforms
-import numpy as np
-
-from PIL import Image
-import cv2
-from crowd_datasets import build_dataset
-from engine import *
-from models import build_model
-import os
-import warnings
-warnings.filterwarnings('ignore')
-
-def get_args_parser():
-    parser = argparse.ArgumentParser('Set parameters for P2PNet evaluation', add_help=False)
-    
-    # * Backbone
-    parser.add_argument('--backbone', default='vgg16_bn', type=str,
-                        help="name of the convolutional backbone to use")
-
-    parser.add_argument('--row', default=2, type=int,
-                        help="row number of anchor points")
-    parser.add_argument('--line', default=2, type=int,
-                        help="line number of anchor points")
-
-    parser.add_argument('--output_dir', default='',
-                        help='path where to save')
-    parser.add_argument('--weight_path', default='',
-                        help='path where the trained weights saved')
-
-    parser.add_argument('--gpu_id', default=0, type=int, help='the gpu used for evaluation')
-
-    return parser
-
-def main(args, debug=False):
-
-    os.environ["CUDA_VISIBLE_DEVICES"] = '{}'.format(args.gpu_id)
-
-    print(args)
-    device = torch.device('cpu')
-    # get the P2PNet
-    model = build_model(args)
-    # move to GPU
-    model.to(device)
-    # load trained model
-    if args.weight_path is not None:
-        checkpoint = torch.load(args.weight_path, map_location='cpu')
-        model.load_state_dict(checkpoint['model'])
-    # convert to eval mode
-    model.eval()
-    # create the pre-processing transform
-    transform = standard_transforms.Compose([
-        standard_transforms.ToTensor(), 
-        standard_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-    ])
-
-    # set your image path here
-    img_path = "./vis/demo1.jpg"
-    # load the images
-    img_raw = Image.open(img_path).convert('RGB')
-    # round the size
-    width, height = img_raw.size
-    new_width = width // 128 * 128
-    new_height = height // 128 * 128
-    img_raw = img_raw.resize((new_width, new_height), Image.ANTIALIAS)
-    # pre-proccessing
-    img = transform(img_raw)
-
-    samples = torch.Tensor(img).unsqueeze(0)
-    samples = samples.to(device)
-    # run inference
-    outputs = model(samples)
-    outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]
-
-    outputs_points = outputs['pred_points'][0]
-
-    threshold = 0.5
-    # filter the predictions
-    points = outputs_points[outputs_scores > threshold].detach().cpu().numpy().tolist()
-    predict_cnt = int((outputs_scores > threshold).sum())
-
-    outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]
-
-    outputs_points = outputs['pred_points'][0]
-    # draw the predictions
-    size = 2
-    img_to_draw = cv2.cvtColor(np.array(img_raw), cv2.COLOR_RGB2BGR)
-    for p in points:
-        img_to_draw = cv2.circle(img_to_draw, (int(p[0]), int(p[1])), size, (0, 0, 255), -1)
-    # save the visualized image
-    cv2.imwrite(os.path.join(args.output_dir, 'pred{}.jpg'.format(predict_cnt)), img_to_draw)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser('P2PNet evaluation script', parents=[get_args_parser()])
-    args = parser.parse_args()
-    main(args)
\ No newline at end of file
diff --git a/spaces/jpwahle/field-diversity/s2.py b/spaces/jpwahle/field-diversity/s2.py
deleted file mode 100644
index b5735e5164d7d2594bd8d3651df9215a4e17f466..0000000000000000000000000000000000000000
--- a/spaces/jpwahle/field-diversity/s2.py
+++ /dev/null
@@ -1,494 +0,0 @@
-# Copyright 2023 by Jan Philip Wahle, https://jpwahle.com/
-# All rights reserved.
-
-
-import asyncio
-import datetime
-import os
-from collections import Counter
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import List, Tuple
-
-import aiohttp
-import requests
-
-from aclanthology import (async_match_acl_id_to_s2_paper, extract_author_info,
-                          extract_paper_info, extract_venue_info)
-from metrics import calculate_gini, calculate_gini_simpson
-from pdf import parse_pdf_to_artcile_dict
-
-
-def get_or_create_eventloop():
-    """
-    Get the current event loop or create a new one if there is no current event loop in the thread.
-
-    Returns:
-        The current event loop.
-    """
-    try:
-        return asyncio.get_event_loop()
-    except RuntimeError as ex:
-        if "There is no current event loop in thread" in str(ex):
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
-            return asyncio.get_event_loop()
-
-
-def send_s2_request(request_url):
-    """
-    Sends a GET request to the specified URL with the S2 API key in the headers.
-
-    Args:
-        request_url (str): The URL to send the request to.
-
-    Returns:
-        requests.Response: The response object returned by the request.
-    """
-    return requests.get(
-        request_url,
-        headers={"x-api-key": os.environ["s2apikey"]},
-        timeout=10,
-    )
-
-
-def check_s2_id_type(semantic_scholar_id):
-    """
-    Check whether a given Semantic Scholar ID is valid for a paper or an author.
-
-    Args:
-        semantic_scholar_id (str): The Semantic Scholar ID to check.
-
-    Returns:
-        tuple: A tuple containing the type of the ID ("paper" or "author") and
-        the name of the author (if the ID is valid for an author), or "invalid"
-        if the ID is not valid for either a paper or an author.
-    """
-    # First, check if it's a paper ID
-    paper_response = requests.get(
-        f"https://api.semanticscholar.org/v1/paper/{semantic_scholar_id}",
-        timeout=5,
-    )
-
-    # If the response status code is 200, it means the ID is valid for a paper
-    if paper_response.status_code == 200:
-        return "paper", None
-
-    # Next, check if it's an author ID
-    author_response = requests.get(
-        f"https://api.semanticscholar.org/v1/author/{semantic_scholar_id}",
-        timeout=5,
-    )
-
-    # If the response status code is 200, it means the ID is valid for an author
-    return (
-        "author",
-        author_response.json()["name"]
-        if author_response.status_code == 200
-        else "invalid",
-    )
-
-
-def get_papers_from_author(ssid_author_id):
-    """Retrieves all papers for a given author
-
-    Args:
-        ssid_author_id (str): semantic scholar id
-
-    Returns:
-        list: a list of all papers for the given author
-    """
-    # Create request URL for an author
-    request_url = f"https://api.semanticscholar.org/graph/v1/author/{ssid_author_id}?fields=papers"
-
-    r = send_s2_request(request_url)
-    if r.status_code == 200:
-        papers = r.json().get("papers", [])
-        return [paper["paperId"] for paper in papers]
-    return []
-
-
-def compute_stats_for_references(s2_ref_paper_keys, year):
-    """
-    Computes various statistics for a list of reference paper keys.
-
-    Args:
-        s2_ref_paper_keys (list): A list of Semantic Scholar paper keys for the references.
-        year (int): The year of the paper.
-
-    Returns:
-        tuple: A tuple containing the following statistics:
-            - num_references (int): The number of references.
-            - fields_of_study_counts (dict): A dictionary containing the count of each field of study.
-            - year_to_title_dict (dict): A dictionary mapping the year of each reference to its title.
-            - cfdi (float): The CFDI (Cumulative Field Diversity Index) of the references.
-            - cadi (float): The CADI (Cumulative Age Diversity Index) of the references.
-            - output_maoc (float): The MAOC (Mean Age of Citation) of the references.
-
-        If there are no valid references, returns a tuple of None values.
-    """
-
-    # Go over the references of the paper
-    reference_year_list = []
-    reference_title_list = []
-    reference_fos_list = []
-    with ThreadPoolExecutor() as executor:
-        request_url_refs = [
-            f"https://api.semanticscholar.org/graph/v1/paper/{ref_paper_key}?fields=title,year,s2FieldsOfStudy"
-            for ref_paper_key in s2_ref_paper_keys
-        ]
-        futures = [
-            executor.submit(send_s2_request, request_url_ref)
-            for request_url_ref in request_url_refs
-        ]
-        for future in as_completed(futures):
-            r_ref = future.result()
-            if r_ref.status_code == 200:
-                result_ref = r_ref.json()
-                (title_ref, year_ref, fields_ref) = (
-                    result_ref["title"],
-                    result_ref["year"],
-                    result_ref["s2FieldsOfStudy"],
-                )
-                reference_year_list.append(year_ref)
-                reference_title_list.append(title_ref)
-                reference_fos_list.extend(
-                    field["category"]
-                    for field in fields_ref
-                    if field["source"] == "s2-fos-model"
-                )
-            else:
-                print(
-                    f"Error retrieving reference {r_ref.status_code} for"
-                    f" paper {s2_ref_paper_keys}"
-                )
-
-    # Remove all None from reference_year_list and reference_title_list
-    reference_year_list = [
-        year_ref for year_ref in reference_year_list if year_ref is not None
-    ]
-    reference_title_list = [
-        title_ref
-        for title_ref in reference_title_list
-        if title_ref is not None
-    ]
-
-    # Count references
-    num_references = len(reference_year_list)
-
-    # Flatten list and count occurrences
-    fields_of_study_counts = dict(
-        Counter(
-            [
-                field
-                for field in reference_fos_list
-            ]
-        )
-    )
-
-    # Citation age list
-    aoc_list = [
-        year - year_ref
-        for year_ref in reference_year_list
-        if year_ref and year
-    ]
-    if not aoc_list:
-        return None, None, None, None, None, None
-
-    # Compute citation age
-    output_maoc = sum(aoc_list) / len(aoc_list)
-    cadi = calculate_gini(aoc_list)
-
-    # Create a dictionary of year to title
-    year_to_title_dict = dict(zip(reference_year_list, reference_title_list))
-
-    # Compute CFDI
-    cfdi = calculate_gini_simpson(fields_of_study_counts)
-
-    # Return the results
-    return (
-        num_references,
-        fields_of_study_counts,
-        year_to_title_dict,
-        cfdi,
-        cadi,
-        output_maoc,
-    )
-
-
-def compute_stats_for_s2_paper(ssid_paper_id):
-    """
-    Computes statistics for a given paper ID using the Semantic Scholar API.
-
-    Args:
-        ssid_paper_id (str): The Semantic Scholar ID of the paper to compute statistics for.
-
-    Returns:
-        Tuple containing the following statistics:
-        - title_authors (str): The title and authors of the paper.
-        - num_references (int): The number of references in the paper.
-        - fields_of_study_counts (dict): A dictionary containing the count of each field of study in the paper's references.
-        - year_to_title_dict (dict): A dictionary mapping the year of each reference to its title.
-        - cfdi (float): The CFDI (Cumulative Field Diversity Index) of the paper's references.
-        - cadi (float): The CADI (Citation Age Diversity Index) of the paper's references.
-        - output_maoc (float): The MAOC (Mean Age of Citation) of the paper's references.
-    """
-    # Get the paper and its references
-    request_url = f"https://api.semanticscholar.org/graph/v1/paper/{ssid_paper_id}?fields=references,title,year,authors"
-    r = send_s2_request(request_url)
-    if r.status_code == 200:  # if successful request
-        result = r.json()
-        if not result.get("references") or result.get("references") == []:
-            return None, None, None, None, None, None, None, None
-        s2_ref_paper_keys = [
-            reference_paper_tuple["paperId"]
-            for reference_paper_tuple in r.json()["references"]
-        ]
-        filtered_s2_ref_paper_keys = [
-            s2_ref_paper_key
-            for s2_ref_paper_key in s2_ref_paper_keys
-            if s2_ref_paper_key is not None
-        ]
-        title, year, authors = (
-            result["title"],
-            result["year"],
-            result["authors"],
-        )
-        title_authors = (
-            title + "\n" + ", ".join([author["name"] for author in authors])
-        )
-
-        (
-            num_references,
-            fields_of_study_counts,
-            year_to_title_dict,
-            cfdi,
-            cadi,
-            output_maoc,
-        ) = compute_stats_for_references(filtered_s2_ref_paper_keys, year)
-
-        # Return the results
-        return (
-            title_authors,
-            num_references,
-            fields_of_study_counts,
-            year_to_title_dict,
-            cfdi,
-            cadi,
-            output_maoc,
-        )
-
-
-def compute_stats_for_s2_author(ssid_author_id, author_name):
-    """
-    Computes statistics for an author based on their papers in the Semantic Scholar database.
-
-    Args:
-        ssid_author_id (str): The Semantic Scholar author ID.
-        author_name (str): The name of the author.
-
-    Returns:
-        dict: A dictionary containing statistics for the author, or None if no papers were found.
-    """
-    if papers := get_papers_from_author(ssid_author_id):
-        return compute_stats_for_multiple_s2_papers(papers, author_name)
-    return None
-
-
-def compute_stats_for_acl_paper(url):
-    """
-    Computes statistics for a paper based on its ACL Anthology URL.
-
-    Args:
-        url (str): The URL of the paper on the ACL Anthology website.
-
-    Returns:
-        dict: A dictionary containing statistics for the paper, or None if the paper was not found.
-    """
-    if paper_info := extract_paper_info(url):
-        loop = get_or_create_eventloop()
-        # Match paper ID to Semantic Scholar ID
-        s2_paper = loop.run_until_complete(
-            async_match_acl_id_to_s2_paper(paper_info["acl_id"])
-        )
-        return compute_stats_for_s2_paper(s2_paper["paperId"])
-    return None
-
-
-def compute_stats_for_acl_author(url):
-    """
-    Computes statistics for an author's papers in the ACL anthology.
-
-    Args:
-        url (str): The URL of the author's page on the ACL anthology website.
-
-    Returns:
-        dict: A dictionary containing statistics for the author's papers, including
-        the number of papers, the number of citations, and the h-index.
-        Returns None if the author's page cannot be accessed or no papers are found.
-    """
-    if paper_info := extract_author_info(url):
-        loop = get_or_create_eventloop()
-        tasks = [
-            async_match_acl_id_to_s2_paper(paper["url"].split("/")[-2])
-            for paper in paper_info["papers"]
-        ]
-        papers = loop.run_until_complete(asyncio.gather(*tasks))
-        return compute_stats_for_multiple_s2_papers(
-            [paper["paperId"] for paper in papers if "paperId" in paper],
-            paper_info["author"],
-        )
-    return None
-
-
-def compute_stats_for_acl_venue(url):
-    """
-    Computes statistics for papers in a given ACL venue.
-
-    Args:
-        url (str): The URL of the ACL venue.
-
-    Returns:
-        dict: A dictionary containing statistics for the papers in the venue.
-    """
-    if paper_info := extract_venue_info(url):
-        loop = get_or_create_eventloop()
-        tasks = [
-            async_match_acl_id_to_s2_paper(paper["url"].split("/")[-2])
-            for paper in paper_info["papers"]
-        ]
-        papers = loop.run_until_complete(asyncio.gather(*tasks))
-        return compute_stats_for_multiple_s2_papers(
-            [paper["paperId"] for paper in papers if "paperId" in paper],
-            paper_info["venue"],
-        )
-    return None
-
-
-def compute_stats_for_multiple_s2_papers(
-    papers: List[dict], title: str
-) -> Tuple[str, int, dict, dict, float, float, float]:
-    """
-    Computes statistics for multiple S2 papers.
-
-    Args:
-        papers (List[dict]): A list of S2 papers.
-        title (str): The title of the papers.
-
-    Returns:
-        A tuple containing the following statistics:
-        - title (str): The title of the papers.
-        - num_references (int): The total number of references in all papers.
-        - top_fields (dict): A dictionary containing the top fields and their counts.
-        - oldest_paper_dict (dict): A dictionary containing the oldest paper for each year.
-        - cfdi (float): The average CFDI score for all papers.
-        - cadi (float): The average CADI score for all papers.
-        - output_maoc (float): The average output MAOC score for all papers.
-    """
-    num_references = 0
-    top_fields = {}
-    oldest_paper_dict = {}
-    cfdi = 0
-    cadi = 0
-    output_maoc = 0
-
-    def process_paper(paper):
-        return compute_stats_for_s2_paper(paper)
-
-    with ThreadPoolExecutor() as executor:
-        results_list = list(executor.map(process_paper, papers))
-
-    for results in results_list:
-        if not results or results[0] is None:
-            continue
-        num_references += results[1]
-        for field, count in results[2].items():
-            top_fields[field] = top_fields.get(field, 0) + count
-        for year, ref_title in results[3].items():
-            oldest_paper_dict[year] = ref_title
-        cfdi += results[4]
-        cadi += results[5]
-        output_maoc += results[6]
-
-    return (
-        title,
-        num_references,
-        top_fields,
-        oldest_paper_dict,
-        cfdi / len(papers),
-        cadi / len(papers),
-        output_maoc / len(papers),
-    )
-
-
-async def send_s2_async_request(url):
-    """
-    Sends an asynchronous request to the specified URL and returns the response as a JSON object.
-
-    Args:
-        url (str): The URL to send the request to.
-
-    Returns:
-        dict: The response from the URL as a JSON object.
-    """
-    async with aiohttp.ClientSession() as session:
-        async with session.get(url) as response:
-            return await response.json()
-
-
-async def match_title_to_s2_paper(title, authors=None):
-    """
-    Matches a given paper title (and authors) to Semantic Scholar to retrieve its S2 paper ID.
-
-    Args:
-        title (str): The title of the paper.
-        authors (List[str], optional): List of authors of the paper. Defaults to None.
-
-    Returns:
-        str or None: Returns the S2 paper ID if found, otherwise None.
-    """
-    # Send a request to the Semantic Scholar API to search for the paper by its title
-    search_url = (
-        f"http://api.semanticscholar.org/graph/v1/paper/search?query={title}"
-    )
-
-    # Send request
-    response = await send_s2_async_request(search_url)
-
-    results = response.get("data", [])
-    if len(results) > 0:
-        result = results[0]  # Ranked by relevance
-        return result.get("paperId")
-
-
-async def compute_stats_for_pdf(pdf_file):
-    """
-    Computes statistics for a given PDF file.
-
-    Args:
-        pdf_file (file): The PDF file to compute statistics for.
-
-    Returns:
-        tuple: A tuple containing the title of the article and the computed statistics.
-    """
-    s2_paper_ids = []
-    article_dict = parse_pdf_to_artcile_dict(pdf_file.name)
-    references = article_dict["references"]
-
-    # Get S2 paper IDs asynchronously
-    tasks = [
-        match_title_to_s2_paper(reference["title"], reference["authors"])
-        for reference in references
-        if reference["title"]
-    ]
-    s2_paper_ids = await asyncio.gather(*tasks)
-
-    # Remove all None values from s2paperids
-    s2_paper_ids = [s2_id for s2_id in s2_paper_ids if s2_id is not None]
-
-    # Compute the current year
-    today = datetime.date.today()
-    year = int(today.strftime("%Y"))
-
-    results = compute_stats_for_references(s2_paper_ids, year)
-    results = (article_dict["title"],) + results
-    return results
diff --git a/spaces/k1ngtai/MMS/vits/monotonic_align/__init__.py b/spaces/k1ngtai/MMS/vits/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/k1ngtai/MMS/vits/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/kaicheng/ChatGPT_ad/modules/models/__init__.py b/spaces/kaicheng/ChatGPT_ad/modules/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kapilmi/AI-lab/README.md b/spaces/kapilmi/AI-lab/README.md
deleted file mode 100644
index 157f3bae67d3e372fce06923a0d059e2c6acdc25..0000000000000000000000000000000000000000
--- a/spaces/kapilmi/AI-lab/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI Lab
-emoji: 🐢
-colorFrom: gray
-colorTo: green
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/Bark-UI-with-Voice-Cloning-2/bark/model_fine.py b/spaces/kevinwang676/Bark-UI-with-Voice-Cloning-2/bark/model_fine.py
deleted file mode 100644
index 6179a851319692b10df0d69b00910ad36cee8685..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bark-UI-with-Voice-Cloning-2/bark/model_fine.py
+++ /dev/null
@@ -1,149 +0,0 @@
-"""
-Much of this code is adapted from Andrej Karpathy's NanoGPT
-(https://github.com/karpathy/nanoGPT)
-"""
-from dataclasses import dataclass
-import math
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from .model import GPT, GPTConfig, MLP
-
-
-class NonCausalSelfAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        assert config.n_embd % config.n_head == 0
-        # key, query, value projections for all heads, but in a batch
-        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
-        # output projection
-        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
-        # regularization
-        self.attn_dropout = nn.Dropout(config.dropout)
-        self.resid_dropout = nn.Dropout(config.dropout)
-        self.n_head = config.n_head
-        self.n_embd = config.n_embd
-        self.dropout = config.dropout
-        # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
-        self.flash = (
-            hasattr(torch.nn.functional, "scaled_dot_product_attention") and self.dropout == 0.0
-        )
-
-    def forward(self, x):
-        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
-
-        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
-        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
-        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
-        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
-        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
-
-        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
-        if self.flash:
-            # efficient attention using Flash Attention CUDA kernels
-            y = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=False
-            )
-        else:
-            # manual implementation of attention
-            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-            att = F.softmax(att, dim=-1)
-            att = self.attn_dropout(att)
-            y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-        y = (
-            y.transpose(1, 2).contiguous().view(B, T, C)
-        )  # re-assemble all head outputs side by side
-
-        # output projection
-        y = self.resid_dropout(self.c_proj(y))
-        return y
-
-
-class FineBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.ln_1 = nn.LayerNorm(config.n_embd)
-        self.attn = NonCausalSelfAttention(config)
-        self.ln_2 = nn.LayerNorm(config.n_embd)
-        self.mlp = MLP(config)
-
-    def forward(self, x):
-        x = x + self.attn(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class FineGPT(GPT):
-    def __init__(self, config):
-        super().__init__(config)
-        del self.lm_head
-        self.config = config
-        self.n_codes_total = config.n_codes_total
-        self.transformer = nn.ModuleDict(
-            dict(
-                wtes=nn.ModuleList(
-                    [
-                        nn.Embedding(config.input_vocab_size, config.n_embd)
-                        for _ in range(config.n_codes_total)
-                    ]
-                ),
-                wpe=nn.Embedding(config.block_size, config.n_embd),
-                drop=nn.Dropout(config.dropout),
-                h=nn.ModuleList([FineBlock(config) for _ in range(config.n_layer)]),
-                ln_f=nn.LayerNorm(config.n_embd),
-            )
-        )
-        self.lm_heads = nn.ModuleList(
-            [
-                nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
-                for _ in range(config.n_codes_given, self.n_codes_total)
-            ]
-        )
-        for i in range(self.n_codes_total - config.n_codes_given):
-            self.transformer.wtes[i + 1].weight = self.lm_heads[i].weight
-
-    def forward(self, pred_idx, idx):
-        device = idx.device
-        b, t, codes = idx.size()
-        assert (
-            t <= self.config.block_size
-        ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
-        assert pred_idx > 0, "cannot predict 0th codebook"
-        assert codes == self.n_codes_total, (b, t, codes)
-        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)  # shape (1, t)
-
-        # forward the GPT model itself
-        tok_embs = [
-            wte(idx[:, :, i]).unsqueeze(-1) for i, wte in enumerate(self.transformer.wtes)
-        ]  # token embeddings of shape (b, t, n_embd)
-        tok_emb = torch.cat(tok_embs, dim=-1)
-        pos_emb = self.transformer.wpe(pos)  # position embeddings of shape (1, t, n_embd)
-        x = tok_emb[:, :, :, : pred_idx + 1].sum(dim=-1)
-        x = self.transformer.drop(x + pos_emb)
-        for block in self.transformer.h:
-            x = block(x)
-        x = self.transformer.ln_f(x)
-        logits = self.lm_heads[pred_idx - self.config.n_codes_given](x)
-        return logits
-
-    def get_num_params(self, non_embedding=True):
-        """
-        Return the number of parameters in the model.
-        For non-embedding count (default), the position embeddings get subtracted.
-        The token embeddings would too, except due to the parameter sharing these
-        params are actually used as weights in the final layer, so we include them.
-        """
-        n_params = sum(p.numel() for p in self.parameters())
-        if non_embedding:
-            for wte in self.transformer.wtes:
-                n_params -= wte.weight.numel()
-            n_params -= self.transformer.wpe.weight.numel()
-        return n_params
-
-
-@dataclass
-class FineGPTConfig(GPTConfig):
-    n_codes_total: int = 8
-    n_codes_given: int = 1
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/hparams.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/hparams.py
deleted file mode 100644
index 9a8c16471903b0c92253b1d70fcd6a61d10e085f..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/hparams.py
+++ /dev/null
@@ -1,31 +0,0 @@
-## Mel-filterbank
-mel_window_length = 25  # In milliseconds
-mel_window_step = 10    # In milliseconds
-mel_n_channels = 40
-
-
-## Audio
-sampling_rate = 16000
-# Number of spectrogram frames in a partial utterance
-partials_n_frames = 160     # 1600 ms
-
-
-## Voice Activation Detection
-# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
-# This sets the granularity of the VAD. Should not need to be changed.
-vad_window_length = 30  # In milliseconds
-# Number of frames to average together when performing the moving average smoothing.
-# The larger this value, the larger the VAD variations must be to not get smoothed out. 
-vad_moving_average_width = 8
-# Maximum number of consecutive silent frames a segment can have.
-vad_max_silence_length = 6
-
-
-## Audio volume normalization
-audio_norm_target_dBFS = -30
-
-
-## Model parameters
-model_hidden_size = 256
-model_embedding_size = 256
-model_num_layers = 3
\ No newline at end of file
diff --git a/spaces/kevinwang676/ControlNet-with-GPT-4/app_openpose.py b/spaces/kevinwang676/ControlNet-with-GPT-4/app_openpose.py
deleted file mode 100644
index bcf6dc9d9bc53dcdc9b0d8d72498d6a626065060..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ControlNet-with-GPT-4/app_openpose.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from settings import (
-    DEFAULT_IMAGE_RESOLUTION,
-    DEFAULT_NUM_IMAGES,
-    MAX_IMAGE_RESOLUTION,
-    MAX_NUM_IMAGES,
-    MAX_SEED,
-)
-from utils import randomize_seed_fn
-
-
-def create_demo(process):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label="Prompt")
-                run_button = gr.Button(label="Run")
-                with gr.Accordion("Advanced options", open=False):
-                    preprocessor_name = gr.Radio(
-                        label="Preprocessor", choices=["Openpose", "None"], type="value", value="Openpose"
-                    )
-                    num_samples = gr.Slider(
-                        label="Number of images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1
-                    )
-                    image_resolution = gr.Slider(
-                        label="Image resolution",
-                        minimum=256,
-                        maximum=MAX_IMAGE_RESOLUTION,
-                        value=DEFAULT_IMAGE_RESOLUTION,
-                        step=256,
-                    )
-                    preprocess_resolution = gr.Slider(
-                        label="Preprocess resolution", minimum=128, maximum=512, value=512, step=1
-                    )
-                    num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
-                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
-                    n_prompt = gr.Textbox(
-                        label="Negative prompt",
-                        value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
-                    )
-            with gr.Column():
-                result = gr.Gallery(label="Output", show_label=False, columns=2, object_fit="scale-down")
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name=False,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-            api_name=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name="openpose",
-        )
-    return demo
-
-
-if __name__ == "__main__":
-    from model import Model
-
-    model = Model(task_name="Openpose")
-    demo = create_demo(model.process_openpose)
-    demo.queue().launch()
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/options/base_options.py b/spaces/kevinwang676/SadTalker/src/face3d/options/base_options.py
deleted file mode 100644
index d8f921d5a43434ae802a55a0fa3889c4b7ab9f6d..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/options/base_options.py
+++ /dev/null
@@ -1,169 +0,0 @@
-"""This script contains base options for Deep3DFaceRecon_pytorch
-"""
-
-import argparse
-import os
-from util import util
-import numpy as np
-import torch
-import face3d.models as models
-import face3d.data as data
-
-
-class BaseOptions():
-    """This class defines options used during both training and test time.
-
-    It also implements several helper functions such as parsing, printing, and saving the options.
-    It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
-    """
-
-    def __init__(self, cmd_line=None):
-        """Reset the class; indicates the class hasn't been initailized"""
-        self.initialized = False
-        self.cmd_line = None
-        if cmd_line is not None:
-            self.cmd_line = cmd_line.split()
-
-    def initialize(self, parser):
-        """Define the common options that are used in both training and test."""
-        # basic parameters
-        parser.add_argument('--name', type=str, default='face_recon', help='name of the experiment. It decides where to store samples and models')
-        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
-        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
-        parser.add_argument('--vis_batch_nums', type=float, default=1, help='batch nums of images for visulization')
-        parser.add_argument('--eval_batch_nums', type=float, default=float('inf'), help='batch nums of images for evaluation')
-        parser.add_argument('--use_ddp', type=util.str2bool, nargs='?', const=True, default=True, help='whether use distributed data parallel')
-        parser.add_argument('--ddp_port', type=str, default='12355', help='ddp port')
-        parser.add_argument('--display_per_batch', type=util.str2bool, nargs='?', const=True, default=True, help='whether use batch to show losses')
-        parser.add_argument('--add_image', type=util.str2bool, nargs='?', const=True, default=True, help='whether add image to tensorboard')
-        parser.add_argument('--world_size', type=int, default=1, help='batch nums of images for evaluation')
-
-        # model parameters
-        parser.add_argument('--model', type=str, default='facerecon', help='chooses which model to use.')
-
-        # additional parameters
-        parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
-        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
-        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
-
-        self.initialized = True
-        return parser
-
-    def gather_options(self):
-        """Initialize our parser with basic options(only once).
-        Add additional model-specific and dataset-specific options.
-        These options are defined in the <modify_commandline_options> function
-        in model and dataset classes.
-        """
-        if not self.initialized:  # check if it has been initialized
-            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-            parser = self.initialize(parser)
-
-        # get the basic options
-        if self.cmd_line is None:
-            opt, _ = parser.parse_known_args()
-        else:
-            opt, _ = parser.parse_known_args(self.cmd_line)
-
-        # set cuda visible devices
-        os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_ids
-
-        # modify model-related parser options
-        model_name = opt.model
-        model_option_setter = models.get_option_setter(model_name)
-        parser = model_option_setter(parser, self.isTrain)
-        if self.cmd_line is None:
-            opt, _ = parser.parse_known_args()  # parse again with new defaults
-        else:
-            opt, _ = parser.parse_known_args(self.cmd_line)  # parse again with new defaults
-
-        # modify dataset-related parser options
-        if opt.dataset_mode:
-            dataset_name = opt.dataset_mode
-            dataset_option_setter = data.get_option_setter(dataset_name)
-            parser = dataset_option_setter(parser, self.isTrain)
-
-        # save and return the parser
-        self.parser = parser
-        if self.cmd_line is None:
-            return parser.parse_args()
-        else:
-            return parser.parse_args(self.cmd_line)
-
-    def print_options(self, opt):
-        """Print and save options
-
-        It will print both current options and default values(if different).
-        It will save options into a text file / [checkpoints_dir] / opt.txt
-        """
-        message = ''
-        message += '----------------- Options ---------------\n'
-        for k, v in sorted(vars(opt).items()):
-            comment = ''
-            default = self.parser.get_default(k)
-            if v != default:
-                comment = '\t[default: %s]' % str(default)
-            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
-        message += '----------------- End -------------------'
-        print(message)
-
-        # save to the disk
-        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
-        util.mkdirs(expr_dir)
-        file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
-        try:
-            with open(file_name, 'wt') as opt_file:
-                opt_file.write(message)
-                opt_file.write('\n')
-        except PermissionError as error:
-            print("permission error {}".format(error))
-            pass
-
-    def parse(self):
-        """Parse our options, create checkpoints directory suffix, and set up gpu device."""
-        opt = self.gather_options()
-        opt.isTrain = self.isTrain   # train or test
-
-        # process opt.suffix
-        if opt.suffix:
-            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
-            opt.name = opt.name + suffix
-
-
-        # set gpu ids
-        str_ids = opt.gpu_ids.split(',')
-        gpu_ids = []
-        for str_id in str_ids:
-            id = int(str_id)
-            if id >= 0:
-                gpu_ids.append(id)
-        opt.world_size = len(gpu_ids)
-        # if len(opt.gpu_ids) > 0:
-        #     torch.cuda.set_device(gpu_ids[0])
-        if opt.world_size == 1:
-            opt.use_ddp = False
-
-        if opt.phase != 'test':
-            # set continue_train automatically
-            if opt.pretrained_name is None:
-                model_dir = os.path.join(opt.checkpoints_dir, opt.name)
-            else:
-                model_dir = os.path.join(opt.checkpoints_dir, opt.pretrained_name)
-            if os.path.isdir(model_dir):
-                model_pths = [i for i in os.listdir(model_dir) if i.endswith('pth')]
-                if os.path.isdir(model_dir) and len(model_pths) != 0:
-                    opt.continue_train= True
-        
-            # update the latest epoch count
-            if opt.continue_train:
-                if opt.epoch == 'latest':
-                    epoch_counts = [int(i.split('.')[0].split('_')[-1]) for i in model_pths if 'latest' not in i]
-                    if len(epoch_counts) != 0:
-                        opt.epoch_count = max(epoch_counts) + 1
-                else:
-                    opt.epoch_count = int(opt.epoch) + 1
-                    
-
-        self.print_options(opt)
-        self.opt = opt
-        return self.opt
diff --git a/spaces/kevinwang676/SadTalker/src/facerender/modules/mapping.py b/spaces/kevinwang676/SadTalker/src/facerender/modules/mapping.py
deleted file mode 100644
index 0e3a1c2d1770996080c08e9daafb346f05d7bcdd..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/facerender/modules/mapping.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class MappingNet(nn.Module):
-    def __init__(self, coeff_nc, descriptor_nc, layer, num_kp, num_bins):
-        super( MappingNet, self).__init__()
-
-        self.layer = layer
-        nonlinearity = nn.LeakyReLU(0.1)
-
-        self.first = nn.Sequential(
-            torch.nn.Conv1d(coeff_nc, descriptor_nc, kernel_size=7, padding=0, bias=True))
-
-        for i in range(layer):
-            net = nn.Sequential(nonlinearity,
-                torch.nn.Conv1d(descriptor_nc, descriptor_nc, kernel_size=3, padding=0, dilation=3))
-            setattr(self, 'encoder' + str(i), net)   
-
-        self.pooling = nn.AdaptiveAvgPool1d(1)
-        self.output_nc = descriptor_nc
-
-        self.fc_roll = nn.Linear(descriptor_nc, num_bins)
-        self.fc_pitch = nn.Linear(descriptor_nc, num_bins)
-        self.fc_yaw = nn.Linear(descriptor_nc, num_bins)
-        self.fc_t = nn.Linear(descriptor_nc, 3)
-        self.fc_exp = nn.Linear(descriptor_nc, 3*num_kp)
-
-    def forward(self, input_3dmm):
-        out = self.first(input_3dmm)
-        for i in range(self.layer):
-            model = getattr(self, 'encoder' + str(i))
-            out = model(out) + out[:,:,3:-3]
-        out = self.pooling(out)
-        out = out.view(out.shape[0], -1)
-        #print('out:', out.shape)
-
-        yaw = self.fc_yaw(out)
-        pitch = self.fc_pitch(out)
-        roll = self.fc_roll(out)
-        t = self.fc_t(out)
-        exp = self.fc_exp(out)
-
-        return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} 
\ No newline at end of file
diff --git a/spaces/kevinwang676/VoiceChangers/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/kevinwang676/VoiceChangers/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index eb60d8830714338448be009d1075e3594337db15..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/kevinwang676/rvc-models-new/app-full.py b/spaces/kevinwang676/rvc-models-new/app-full.py
deleted file mode 100644
index 551bbe4feba710ca1613ff30e60abcf213da71fe..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/rvc-models-new/app-full.py
+++ /dev/null
@@ -1,251 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
-    def vc_fn(
-        input_audio,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        tts_mode,
-        tts_text,
-        tts_voice
-    ):
-        try:
-            if tts_mode:
-                if len(tts_text) > 100 and limitation:
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-            else:
-                if config.files:
-                    audio, sr = librosa.load(input_audio, sr=16000, mono=True)
-                else:
-                    if input_audio is None:
-                        return "You need to upload an audio", None
-                    sampling_rate, audio = input_audio
-                    duration = audio.shape[0] / sampling_rate
-                    if duration > 20 and limitation:
-                        return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
-                    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                    if len(audio.shape) > 1:
-                        audio = librosa.to_mono(audio.transpose(1, 0))
-                    if sampling_rate != 16000:
-                        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                times,
-                f0_up_key,
-                f0_method,
-                file_index,
-                index_rate,
-                if_f0,
-                f0_file=None,
-            )
-            print(
-                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            )
-            return "Success", (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def cut_vocal_and_inst(yt_url):
-    if yt_url != "":
-        if not os.path.exists("/content/youtube_audio"):
-            os.mkdir("/content/youtube_audio")
-        ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": '/content/youtube_audio/audio',
-        }
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            ydl.download([yt_url])
-        yt_audio_path = "/content/youtube_audio/audio.wav"
-        command = f"demucs --two-stems=vocals {yt_audio_path}"
-        result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-        print(result.stdout.decode())
-        return ("/content/rvc-models/separated/htdemucs/audio/vocals.wav", "/content/rvc-models/separated/htdemucs/audio/no_vocals.wav", yt_audio_path, "/content/rvc-models/separated/htdemucs/audio/vocals.wav")
-
-def combine_vocal_and_inst(audio_data, audio_volume):
-    print(audio_data)
-    if not os.path.exists("/content/result"):
-        os.mkdir("/content/result")
-    vocal_path = "/content/result/output.wav"
-    inst_path = "/content/rvc-models/separated/htdemucs/audio/no_vocals.wav"
-    output_path = "/content/result/combine.mp3"
-    with wave.open(vocal_path, "w") as wave_file:
-        wave_file.setnchannels(1) 
-        wave_file.setsampwidth(2)
-        wave_file.setframerate(audio_data[0])
-        wave_file.writeframes(audio_data[1].tobytes())
-    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
-    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-    return output_path
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_to_tts_mode(tts_mode):
-    if tts_mode:
-        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
-    else:
-        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-'''
-def change_audio_to_path_mode(audio_mode):
-    if audio_mode:
-        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True)
-    else:
-        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False)
-'''
-
-if __name__ == '__main__':
-    load_hubert()
-    models = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    folder_path = "weights"
-    for name in os.listdir(folder_path):
-        print("check folder: " + name)
-        if name.startswith("."): break
-        cover_path = glob.glob(f"{folder_path}/{name}/*.png") + glob.glob(f"{folder_path}/{name}/*.jpg")
-        index_path = glob.glob(f"{folder_path}/{name}/*.index")
-        checkpoint_path = glob.glob(f"{folder_path}/{name}/*.pth")
-        title = name
-        cover = cover_path[0]
-        index = index_path[0]
-        cpt = torch.load(checkpoint_path[0], map_location="cpu")
-        tgt_sr = cpt["config"][-1]
-        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-        if_f0 = cpt.get("f0", 1)
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-        del net_g.enc_q
-        print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净, 真奇葩
-        net_g.eval().to(config.device)
-        if config.is_half:
-            net_g = net_g.half()
-        else:
-            net_g = net_g.float()
-        vc = VC(tgt_sr, config)
-        models.append((name, title, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> RVC Models (Latest Update)\n"
-            "## <center> The input audio should be clean and pure voice without background music.\n"
-            "### <center> More feature will be added soon... \n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n"
-            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
-        )
-        with gr.Tabs():
-            for (name, title, cover, vc_fn) in models:
-                with gr.TabItem(name):
-                    with gr.Row():
-                        gr.Markdown(
-                            '<div align="center">'
-                            f'<div>{title}</div>\n'+
-                            (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                            '</div>'
-                        )
-                    with gr.Row():
-                        if config.files:
-                            with gr.Column():
-                                vc_youtube = gr.Textbox(label="Youtube URL")
-                                vc_convert = gr.Button("Convert", variant="primary")
-                                vc_vocal_preview = gr.Audio(label="Vocal Preview")
-                                vc_inst_preview = gr.Audio(label="Instrumental Preview")
-                                vc_audio_preview = gr.Audio(label="Audio Preview")
-                        with gr.Column():
-                            # vc_audio_mode = gr.Checkbox(label="Use audio path", value=False)
-                            if config.files:
-                                vc_input = gr.Textbox(label="Input audio path")
-                            else:
-                                vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
-                            vc_transpose = gr.Number(label="Transpose", value=0)
-                            vc_f0method = gr.Radio(
-                                label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
-                                choices=["pm", "harvest"],
-                                value="pm",
-                                interactive=True,
-                            )
-                            vc_index_ratio = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label="Retrieval feature ratio",
-                                value=0.6,
-                                interactive=True,
-                            )
-                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                            tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
-                            tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                            vc_submit = gr.Button("Generate", variant="primary")
-                            vc_output1 = gr.Textbox(label="Output Message")
-                            vc_output2 = gr.Audio(label="Output Audio")
-                        if config.files:
-                            with gr.Column():
-                                vc_volume = gr.Slider(
-                                    minimum=0,
-                                    maximum=10,
-                                    label="Vocal volume",
-                                    value=5,
-                                    interactive=True,
-                                    step=1
-                                )
-                                vc_outputCombine = gr.Audio(label="Output Combined Audio")
-                                vc_combine =  gr.Button("Combine",variant="primary")
-                vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
-                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
-                # vc_audio_input(change_audio_to_path_mode, [vc_audio_mode])
-                if config.files:
-                    vc_convert.click(cut_vocal_and_inst, vc_youtube, [vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input])
-                    vc_combine.click(combine_vocal_and_inst, [vc_output2, vc_volume], vc_outputCombine)
-        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.share)
\ No newline at end of file
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py
deleted file mode 100644
index 988d9adf2f289ef223bd1c680a5ae1d3387f0269..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/bricks/generalized_attention.py
+++ /dev/null
@@ -1,412 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..utils import kaiming_init
-from .registry import PLUGIN_LAYERS
-
-
-@PLUGIN_LAYERS.register_module()
-class GeneralizedAttention(nn.Module):
-    """GeneralizedAttention module.
-
-    See 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks'
-    (https://arxiv.org/abs/1711.07971) for details.
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        spatial_range (int): The spatial range. -1 indicates no spatial range
-            constraint. Default: -1.
-        num_heads (int): The head number of empirical_attention module.
-            Default: 9.
-        position_embedding_dim (int): The position embedding dimension.
-            Default: -1.
-        position_magnitude (int): A multiplier acting on coord difference.
-            Default: 1.
-        kv_stride (int): The feature stride acting on key/value feature map.
-            Default: 2.
-        q_stride (int): The feature stride acting on query feature map.
-            Default: 1.
-        attention_type (str): A binary indicator string for indicating which
-            items in generalized empirical_attention module are used.
-            Default: '1111'.
-
-            - '1000' indicates 'query and key content' (appr - appr) item,
-            - '0100' indicates 'query content and relative position'
-              (appr - position) item,
-            - '0010' indicates 'key content only' (bias - appr) item,
-            - '0001' indicates 'relative position only' (bias - position) item.
-    """
-
-    _abbr_ = 'gen_attention_block'
-
-    def __init__(self,
-                 in_channels,
-                 spatial_range=-1,
-                 num_heads=9,
-                 position_embedding_dim=-1,
-                 position_magnitude=1,
-                 kv_stride=2,
-                 q_stride=1,
-                 attention_type='1111'):
-
-        super(GeneralizedAttention, self).__init__()
-
-        # hard range means local range for non-local operation
-        self.position_embedding_dim = (
-            position_embedding_dim
-            if position_embedding_dim > 0 else in_channels)
-
-        self.position_magnitude = position_magnitude
-        self.num_heads = num_heads
-        self.in_channels = in_channels
-        self.spatial_range = spatial_range
-        self.kv_stride = kv_stride
-        self.q_stride = q_stride
-        self.attention_type = [bool(int(_)) for _ in attention_type]
-        self.qk_embed_dim = in_channels // num_heads
-        out_c = self.qk_embed_dim * num_heads
-
-        if self.attention_type[0] or self.attention_type[1]:
-            self.query_conv = nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_c,
-                kernel_size=1,
-                bias=False)
-            self.query_conv.kaiming_init = True
-
-        if self.attention_type[0] or self.attention_type[2]:
-            self.key_conv = nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_c,
-                kernel_size=1,
-                bias=False)
-            self.key_conv.kaiming_init = True
-
-        self.v_dim = in_channels // num_heads
-        self.value_conv = nn.Conv2d(
-            in_channels=in_channels,
-            out_channels=self.v_dim * num_heads,
-            kernel_size=1,
-            bias=False)
-        self.value_conv.kaiming_init = True
-
-        if self.attention_type[1] or self.attention_type[3]:
-            self.appr_geom_fc_x = nn.Linear(
-                self.position_embedding_dim // 2, out_c, bias=False)
-            self.appr_geom_fc_x.kaiming_init = True
-
-            self.appr_geom_fc_y = nn.Linear(
-                self.position_embedding_dim // 2, out_c, bias=False)
-            self.appr_geom_fc_y.kaiming_init = True
-
-        if self.attention_type[2]:
-            stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
-            appr_bias_value = -2 * stdv * torch.rand(out_c) + stdv
-            self.appr_bias = nn.Parameter(appr_bias_value)
-
-        if self.attention_type[3]:
-            stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
-            geom_bias_value = -2 * stdv * torch.rand(out_c) + stdv
-            self.geom_bias = nn.Parameter(geom_bias_value)
-
-        self.proj_conv = nn.Conv2d(
-            in_channels=self.v_dim * num_heads,
-            out_channels=in_channels,
-            kernel_size=1,
-            bias=True)
-        self.proj_conv.kaiming_init = True
-        self.gamma = nn.Parameter(torch.zeros(1))
-
-        if self.spatial_range >= 0:
-            # only works when non local is after 3*3 conv
-            if in_channels == 256:
-                max_len = 84
-            elif in_channels == 512:
-                max_len = 42
-
-            max_len_kv = int((max_len - 1.0) / self.kv_stride + 1)
-            local_constraint_map = np.ones(
-                (max_len, max_len, max_len_kv, max_len_kv), dtype=np.int)
-            for iy in range(max_len):
-                for ix in range(max_len):
-                    local_constraint_map[
-                        iy, ix,
-                        max((iy - self.spatial_range) //
-                            self.kv_stride, 0):min((iy + self.spatial_range +
-                                                    1) // self.kv_stride +
-                                                   1, max_len),
-                        max((ix - self.spatial_range) //
-                            self.kv_stride, 0):min((ix + self.spatial_range +
-                                                    1) // self.kv_stride +
-                                                   1, max_len)] = 0
-
-            self.local_constraint_map = nn.Parameter(
-                torch.from_numpy(local_constraint_map).byte(),
-                requires_grad=False)
-
-        if self.q_stride > 1:
-            self.q_downsample = nn.AvgPool2d(
-                kernel_size=1, stride=self.q_stride)
-        else:
-            self.q_downsample = None
-
-        if self.kv_stride > 1:
-            self.kv_downsample = nn.AvgPool2d(
-                kernel_size=1, stride=self.kv_stride)
-        else:
-            self.kv_downsample = None
-
-        self.init_weights()
-
-    def get_position_embedding(self,
-                               h,
-                               w,
-                               h_kv,
-                               w_kv,
-                               q_stride,
-                               kv_stride,
-                               device,
-                               dtype,
-                               feat_dim,
-                               wave_length=1000):
-        # the default type of Tensor is float32, leading to type mismatch
-        # in fp16 mode. Cast it to support fp16 mode.
-        h_idxs = torch.linspace(0, h - 1, h).to(device=device, dtype=dtype)
-        h_idxs = h_idxs.view((h, 1)) * q_stride
-
-        w_idxs = torch.linspace(0, w - 1, w).to(device=device, dtype=dtype)
-        w_idxs = w_idxs.view((w, 1)) * q_stride
-
-        h_kv_idxs = torch.linspace(0, h_kv - 1, h_kv).to(
-            device=device, dtype=dtype)
-        h_kv_idxs = h_kv_idxs.view((h_kv, 1)) * kv_stride
-
-        w_kv_idxs = torch.linspace(0, w_kv - 1, w_kv).to(
-            device=device, dtype=dtype)
-        w_kv_idxs = w_kv_idxs.view((w_kv, 1)) * kv_stride
-
-        # (h, h_kv, 1)
-        h_diff = h_idxs.unsqueeze(1) - h_kv_idxs.unsqueeze(0)
-        h_diff *= self.position_magnitude
-
-        # (w, w_kv, 1)
-        w_diff = w_idxs.unsqueeze(1) - w_kv_idxs.unsqueeze(0)
-        w_diff *= self.position_magnitude
-
-        feat_range = torch.arange(0, feat_dim / 4).to(
-            device=device, dtype=dtype)
-
-        dim_mat = torch.Tensor([wave_length]).to(device=device, dtype=dtype)
-        dim_mat = dim_mat**((4. / feat_dim) * feat_range)
-        dim_mat = dim_mat.view((1, 1, -1))
-
-        embedding_x = torch.cat(
-            ((w_diff / dim_mat).sin(), (w_diff / dim_mat).cos()), dim=2)
-
-        embedding_y = torch.cat(
-            ((h_diff / dim_mat).sin(), (h_diff / dim_mat).cos()), dim=2)
-
-        return embedding_x, embedding_y
-
-    def forward(self, x_input):
-        num_heads = self.num_heads
-
-        # use empirical_attention
-        if self.q_downsample is not None:
-            x_q = self.q_downsample(x_input)
-        else:
-            x_q = x_input
-        n, _, h, w = x_q.shape
-
-        if self.kv_downsample is not None:
-            x_kv = self.kv_downsample(x_input)
-        else:
-            x_kv = x_input
-        _, _, h_kv, w_kv = x_kv.shape
-
-        if self.attention_type[0] or self.attention_type[1]:
-            proj_query = self.query_conv(x_q).view(
-                (n, num_heads, self.qk_embed_dim, h * w))
-            proj_query = proj_query.permute(0, 1, 3, 2)
-
-        if self.attention_type[0] or self.attention_type[2]:
-            proj_key = self.key_conv(x_kv).view(
-                (n, num_heads, self.qk_embed_dim, h_kv * w_kv))
-
-        if self.attention_type[1] or self.attention_type[3]:
-            position_embed_x, position_embed_y = self.get_position_embedding(
-                h, w, h_kv, w_kv, self.q_stride, self.kv_stride,
-                x_input.device, x_input.dtype, self.position_embedding_dim)
-            # (n, num_heads, w, w_kv, dim)
-            position_feat_x = self.appr_geom_fc_x(position_embed_x).\
-                view(1, w, w_kv, num_heads, self.qk_embed_dim).\
-                permute(0, 3, 1, 2, 4).\
-                repeat(n, 1, 1, 1, 1)
-
-            # (n, num_heads, h, h_kv, dim)
-            position_feat_y = self.appr_geom_fc_y(position_embed_y).\
-                view(1, h, h_kv, num_heads, self.qk_embed_dim).\
-                permute(0, 3, 1, 2, 4).\
-                repeat(n, 1, 1, 1, 1)
-
-            position_feat_x /= math.sqrt(2)
-            position_feat_y /= math.sqrt(2)
-
-        # accelerate for saliency only
-        if (np.sum(self.attention_type) == 1) and self.attention_type[2]:
-            appr_bias = self.appr_bias.\
-                view(1, num_heads, 1, self.qk_embed_dim).\
-                repeat(n, 1, 1, 1)
-
-            energy = torch.matmul(appr_bias, proj_key).\
-                view(n, num_heads, 1, h_kv * w_kv)
-
-            h = 1
-            w = 1
-        else:
-            # (n, num_heads, h*w, h_kv*w_kv), query before key, 540mb for
-            if not self.attention_type[0]:
-                energy = torch.zeros(
-                    n,
-                    num_heads,
-                    h,
-                    w,
-                    h_kv,
-                    w_kv,
-                    dtype=x_input.dtype,
-                    device=x_input.device)
-
-            # attention_type[0]: appr - appr
-            # attention_type[1]: appr - position
-            # attention_type[2]: bias - appr
-            # attention_type[3]: bias - position
-            if self.attention_type[0] or self.attention_type[2]:
-                if self.attention_type[0] and self.attention_type[2]:
-                    appr_bias = self.appr_bias.\
-                        view(1, num_heads, 1, self.qk_embed_dim)
-                    energy = torch.matmul(proj_query + appr_bias, proj_key).\
-                        view(n, num_heads, h, w, h_kv, w_kv)
-
-                elif self.attention_type[0]:
-                    energy = torch.matmul(proj_query, proj_key).\
-                        view(n, num_heads, h, w, h_kv, w_kv)
-
-                elif self.attention_type[2]:
-                    appr_bias = self.appr_bias.\
-                        view(1, num_heads, 1, self.qk_embed_dim).\
-                        repeat(n, 1, 1, 1)
-
-                    energy += torch.matmul(appr_bias, proj_key).\
-                        view(n, num_heads, 1, 1, h_kv, w_kv)
-
-            if self.attention_type[1] or self.attention_type[3]:
-                if self.attention_type[1] and self.attention_type[3]:
-                    geom_bias = self.geom_bias.\
-                        view(1, num_heads, 1, self.qk_embed_dim)
-
-                    proj_query_reshape = (proj_query + geom_bias).\
-                        view(n, num_heads, h, w, self.qk_embed_dim)
-
-                    energy_x = torch.matmul(
-                        proj_query_reshape.permute(0, 1, 3, 2, 4),
-                        position_feat_x.permute(0, 1, 2, 4, 3))
-                    energy_x = energy_x.\
-                        permute(0, 1, 3, 2, 4).unsqueeze(4)
-
-                    energy_y = torch.matmul(
-                        proj_query_reshape,
-                        position_feat_y.permute(0, 1, 2, 4, 3))
-                    energy_y = energy_y.unsqueeze(5)
-
-                    energy += energy_x + energy_y
-
-                elif self.attention_type[1]:
-                    proj_query_reshape = proj_query.\
-                        view(n, num_heads, h, w, self.qk_embed_dim)
-                    proj_query_reshape = proj_query_reshape.\
-                        permute(0, 1, 3, 2, 4)
-                    position_feat_x_reshape = position_feat_x.\
-                        permute(0, 1, 2, 4, 3)
-                    position_feat_y_reshape = position_feat_y.\
-                        permute(0, 1, 2, 4, 3)
-
-                    energy_x = torch.matmul(proj_query_reshape,
-                                            position_feat_x_reshape)
-                    energy_x = energy_x.permute(0, 1, 3, 2, 4).unsqueeze(4)
-
-                    energy_y = torch.matmul(proj_query_reshape,
-                                            position_feat_y_reshape)
-                    energy_y = energy_y.unsqueeze(5)
-
-                    energy += energy_x + energy_y
-
-                elif self.attention_type[3]:
-                    geom_bias = self.geom_bias.\
-                        view(1, num_heads, self.qk_embed_dim, 1).\
-                        repeat(n, 1, 1, 1)
-
-                    position_feat_x_reshape = position_feat_x.\
-                        view(n, num_heads, w*w_kv, self.qk_embed_dim)
-
-                    position_feat_y_reshape = position_feat_y.\
-                        view(n, num_heads, h * h_kv, self.qk_embed_dim)
-
-                    energy_x = torch.matmul(position_feat_x_reshape, geom_bias)
-                    energy_x = energy_x.view(n, num_heads, 1, w, 1, w_kv)
-
-                    energy_y = torch.matmul(position_feat_y_reshape, geom_bias)
-                    energy_y = energy_y.view(n, num_heads, h, 1, h_kv, 1)
-
-                    energy += energy_x + energy_y
-
-            energy = energy.view(n, num_heads, h * w, h_kv * w_kv)
-
-        if self.spatial_range >= 0:
-            cur_local_constraint_map = \
-                self.local_constraint_map[:h, :w, :h_kv, :w_kv].\
-                contiguous().\
-                view(1, 1, h*w, h_kv*w_kv)
-
-            energy = energy.masked_fill_(cur_local_constraint_map,
-                                         float('-inf'))
-
-        attention = F.softmax(energy, 3)
-
-        proj_value = self.value_conv(x_kv)
-        proj_value_reshape = proj_value.\
-            view((n, num_heads, self.v_dim, h_kv * w_kv)).\
-            permute(0, 1, 3, 2)
-
-        out = torch.matmul(attention, proj_value_reshape).\
-            permute(0, 1, 3, 2).\
-            contiguous().\
-            view(n, self.v_dim * self.num_heads, h, w)
-
-        out = self.proj_conv(out)
-
-        # output is downsampled, upsample back to input size
-        if self.q_downsample is not None:
-            out = F.interpolate(
-                out,
-                size=x_input.shape[2:],
-                mode='bilinear',
-                align_corners=False)
-
-        out = self.gamma * out + x_input
-        return out
-
-    def init_weights(self):
-        for m in self.modules():
-            if hasattr(m, 'kaiming_init') and m.kaiming_init:
-                kaiming_init(
-                    m,
-                    mode='fan_in',
-                    nonlinearity='leaky_relu',
-                    bias=0,
-                    distribution='uniform',
-                    a=1)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/fast_noisy_channel/__init__.py b/spaces/koajoel/PolyFormer/fairseq/examples/fast_noisy_channel/__init__.py
deleted file mode 100644
index 9b248c3a24e12ad3da885a7f328c714942de2e6b..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/fast_noisy_channel/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from . import noisy_channel_translation  # noqa
-from . import noisy_channel_sequence_generator  # noqa
-from . import noisy_channel_beam_search  # noqa
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py b/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py
deleted file mode 100644
index 7ec1fb7521b8a9b821d28bcaaaedb034f6e95e0b..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-from pathlib import Path
-from collections import defaultdict
-
-import pandas as pd
-from torchaudio.datasets import LJSPEECH
-from tqdm import tqdm
-
-from examples.speech_to_text.data_utils import save_df_to_tsv
-
-
-log = logging.getLogger(__name__)
-
-SPLITS = ["train", "dev", "test"]
-
-
-def process(args):
-    out_root = Path(args.output_data_root).absolute()
-    out_root.mkdir(parents=True, exist_ok=True)
-
-    # Generate TSV manifest
-    print("Generating manifest...")
-    # following FastSpeech's splits
-    dataset = LJSPEECH(out_root.as_posix(), download=True)
-    id_to_split = {}
-    for x in dataset._flist:
-        id_ = x[0]
-        speaker = id_.split("-")[0]
-        id_to_split[id_] = {
-            "LJ001": "test", "LJ002": "test", "LJ003": "dev"
-        }.get(speaker, "train")
-    manifest_by_split = {split: defaultdict(list) for split in SPLITS}
-    progress = tqdm(enumerate(dataset), total=len(dataset))
-    for i, (waveform, _, utt, normalized_utt) in progress:
-        sample_id = dataset._flist[i][0]
-        split = id_to_split[sample_id]
-        manifest_by_split[split]["id"].append(sample_id)
-        audio_path = f"{dataset._path}/{sample_id}.wav"
-        manifest_by_split[split]["audio"].append(audio_path)
-        manifest_by_split[split]["n_frames"].append(len(waveform[0]))
-        manifest_by_split[split]["tgt_text"].append(normalized_utt)
-        manifest_by_split[split]["speaker"].append("ljspeech")
-        manifest_by_split[split]["src_text"].append(utt)
-
-    manifest_root = Path(args.output_manifest_root).absolute()
-    manifest_root.mkdir(parents=True, exist_ok=True)
-    for split in SPLITS:
-        save_df_to_tsv(
-            pd.DataFrame.from_dict(manifest_by_split[split]),
-            manifest_root / f"{split}.audio.tsv"
-        )
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output-data-root", "-d", required=True, type=str)
-    parser.add_argument("--output-manifest-root", "-m", required=True, type=str)
-    args = parser.parse_args()
-
-    process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/multiscale.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/multiscale.py
deleted file mode 100644
index 65f0a54925593e9da8106bfc6d65a4098ce001d7..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/modules/multiscale.py
+++ /dev/null
@@ -1,244 +0,0 @@
-from typing import List, Tuple, Union, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from saicinpainting.training.modules.base import get_conv_block_ctor, get_activation
-from saicinpainting.training.modules.pix2pixhd import ResnetBlock
-
-
-class ResNetHead(nn.Module):
-    def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)):
-        assert (n_blocks >= 0)
-        super(ResNetHead, self).__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-            model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
-                      norm_layer(ngf * mult * 2),
-                      activation]
-
-        mult = 2 ** n_downsampling
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                  conv_kind=conv_kind)]
-
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-class ResNetTail(nn.Module):
-    def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
-                 add_in_proj=None):
-        assert (n_blocks >= 0)
-        super(ResNetTail, self).__init__()
-
-        mult = 2 ** n_downsampling
-
-        model = []
-
-        if add_in_proj is not None:
-            model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1))
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                  conv_kind=conv_kind)]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
-                                         output_padding=1),
-                      up_norm_layer(int(ngf * mult / 2)),
-                      up_activation]
-        self.model = nn.Sequential(*model)
-
-        out_layers = []
-        for _ in range(out_extra_layers_n):
-            out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0),
-                           up_norm_layer(ngf),
-                           up_activation]
-        out_layers += [nn.ReflectionPad2d(3),
-                       nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-
-        if add_out_act:
-            out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act))
-
-        self.out_proj = nn.Sequential(*out_layers)
-
-    def forward(self, input, return_last_act=False):
-        features = self.model(input)
-        out = self.out_proj(features)
-        if return_last_act:
-            return out, features
-        else:
-            return out
-
-
-class MultiscaleResNet(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3,
-                 norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
-                 out_cumulative=False, return_only_hr=False):
-        super().__init__()
-
-        self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling,
-                                               n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type,
-                                               conv_kind=conv_kind, activation=activation)
-                                    for i in range(n_scales)])
-        tail_in_feats = ngf * (2 ** n_downsampling) + ngf
-        self.tails = nn.ModuleList([ResNetTail(output_nc,
-                                               ngf=ngf, n_downsampling=n_downsampling,
-                                               n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type,
-                                               conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer,
-                                               up_activation=up_activation, add_out_act=add_out_act,
-                                               out_extra_layers_n=out_extra_layers_n,
-                                               add_in_proj=None if (i == n_scales - 1) else tail_in_feats)
-                                    for i in range(n_scales)])
-
-        self.out_cumulative = out_cumulative
-        self.return_only_hr = return_only_hr
-
-    @property
-    def num_scales(self):
-        return len(self.heads)
-
-    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-        -> Union[torch.Tensor, List[torch.Tensor]]:
-        """
-        :param ms_inputs: List of inputs of different resolutions from HR to LR
-        :param smallest_scales_num: int or None, number of smallest scales to take at input
-        :return: Depending on return_only_hr:
-            True: Only the most HR output
-            False: List of outputs of different resolutions from HR to LR
-        """
-        if smallest_scales_num is None:
-            assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num)
-            smallest_scales_num = len(self.heads)
-        else:
-            assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num)
-
-        cur_heads = self.heads[-smallest_scales_num:]
-        ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)]
-
-        all_outputs = []
-        prev_tail_features = None
-        for i in range(len(ms_features)):
-            scale_i = -i - 1
-
-            cur_tail_input = ms_features[-i - 1]
-            if prev_tail_features is not None:
-                if prev_tail_features.shape != cur_tail_input.shape:
-                    prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:],
-                                                       mode='bilinear', align_corners=False)
-                cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1)
-
-            cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True)
-
-            prev_tail_features = cur_tail_feats
-            all_outputs.append(cur_out)
-
-        if self.out_cumulative:
-            all_outputs_cum = [all_outputs[0]]
-            for i in range(1, len(ms_features)):
-                cur_out = all_outputs[i]
-                cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:],
-                                                      mode='bilinear', align_corners=False)
-                all_outputs_cum.append(cur_out_cum)
-            all_outputs = all_outputs_cum
-
-        if self.return_only_hr:
-            return all_outputs[-1]
-        else:
-            return all_outputs[::-1]
-
-
-class MultiscaleDiscriminatorSimple(nn.Module):
-    def __init__(self, ms_impl):
-        super().__init__()
-        self.ms_impl = nn.ModuleList(ms_impl)
-
-    @property
-    def num_scales(self):
-        return len(self.ms_impl)
-
-    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-            -> List[Tuple[torch.Tensor, List[torch.Tensor]]]:
-        """
-        :param ms_inputs: List of inputs of different resolutions from HR to LR
-        :param smallest_scales_num: int or None, number of smallest scales to take at input
-        :return: List of pairs (prediction, features) for different resolutions from HR to LR
-        """
-        if smallest_scales_num is None:
-            assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
-            smallest_scales_num = len(self.heads)
-        else:
-            assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \
-                (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
-
-        return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)]
-
-
-class SingleToMultiScaleInputMixin:
-    def forward(self, x: torch.Tensor) -> List:
-        orig_height, orig_width = x.shape[2:]
-        factors = [2 ** i for i in range(self.num_scales)]
-        ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False)
-                     for f in factors]
-        return super().forward(ms_inputs)
-
-
-class GeneratorMultiToSingleOutputMixin:
-    def forward(self, x):
-        return super().forward(x)[0]
-
-
-class DiscriminatorMultiToSingleOutputMixin:
-    def forward(self, x):
-        out_feat_tuples = super().forward(x)
-        return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist]
-
-
-class DiscriminatorMultiToSingleOutputStackedMixin:
-    def __init__(self, *args, return_feats_only_levels=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.return_feats_only_levels = return_feats_only_levels
-
-    def forward(self, x):
-        out_feat_tuples = super().forward(x)
-        outs = [out for out, _ in out_feat_tuples]
-        scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:],
-                                                 mode='bilinear', align_corners=False)
-                                   for cur_out in outs[1:]]
-        out = torch.cat(scaled_outs, dim=1)
-        if self.return_feats_only_levels is not None:
-            feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels]
-        else:
-            feat_lists = [flist for _, flist in out_feat_tuples]
-        feats = [f for flist in feat_lists for f in flist]
-        return out, feats
-
-
-class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple):
-    pass
-
-
-class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet):
-    pass
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageShow.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageShow.py
deleted file mode 100644
index f0e73fb90750c49553787d8a2206a8c9cfadb0f8..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageShow.py
+++ /dev/null
@@ -1,398 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# im.show() drivers
-#
-# History:
-# 2008-04-06 fl   Created
-#
-# Copyright (c) Secret Labs AB 2008.
-#
-# See the README file for information on usage and redistribution.
-#
-import os
-import shutil
-import subprocess
-import sys
-from shlex import quote
-
-from PIL import Image
-
-from ._deprecate import deprecate
-
-_viewers = []
-
-
-def register(viewer, order=1):
-    """
-    The :py:func:`register` function is used to register additional viewers::
-
-        from PIL import ImageShow
-        ImageShow.register(MyViewer())  # MyViewer will be used as a last resort
-        ImageShow.register(MySecondViewer(), 0)  # MySecondViewer will be prioritised
-        ImageShow.register(ImageShow.XVViewer(), 0)  # XVViewer will be prioritised
-
-    :param viewer: The viewer to be registered.
-    :param order:
-        Zero or a negative integer to prepend this viewer to the list,
-        a positive integer to append it.
-    """
-    try:
-        if issubclass(viewer, Viewer):
-            viewer = viewer()
-    except TypeError:
-        pass  # raised if viewer wasn't a class
-    if order > 0:
-        _viewers.append(viewer)
-    else:
-        _viewers.insert(0, viewer)
-
-
-def show(image, title=None, **options):
-    r"""
-    Display a given image.
-
-    :param image: An image object.
-    :param title: Optional title. Not all viewers can display the title.
-    :param \**options: Additional viewer options.
-    :returns: ``True`` if a suitable viewer was found, ``False`` otherwise.
-    """
-    for viewer in _viewers:
-        if viewer.show(image, title=title, **options):
-            return True
-    return False
-
-
-class Viewer:
-    """Base class for viewers."""
-
-    # main api
-
-    def show(self, image, **options):
-        """
-        The main function for displaying an image.
-        Converts the given image to the target format and displays it.
-        """
-
-        if not (
-            image.mode in ("1", "RGBA")
-            or (self.format == "PNG" and image.mode in ("I;16", "LA"))
-        ):
-            base = Image.getmodebase(image.mode)
-            if image.mode != base:
-                image = image.convert(base)
-
-        return self.show_image(image, **options)
-
-    # hook methods
-
-    format = None
-    """The format to convert the image into."""
-    options = {}
-    """Additional options used to convert the image."""
-
-    def get_format(self, image):
-        """Return format name, or ``None`` to save as PGM/PPM."""
-        return self.format
-
-    def get_command(self, file, **options):
-        """
-        Returns the command used to display the file.
-        Not implemented in the base class.
-        """
-        raise NotImplementedError
-
-    def save_image(self, image):
-        """Save to temporary file and return filename."""
-        return image._dump(format=self.get_format(image), **self.options)
-
-    def show_image(self, image, **options):
-        """Display the given image."""
-        return self.show_file(self.save_image(image), **options)
-
-    def show_file(self, path=None, **options):
-        """
-        Display given file.
-
-        Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,
-        and will be removed in Pillow 10.0.0 (2023-07-01). ``path`` should be used
-        instead.
-        """
-        if path is None:
-            if "file" in options:
-                deprecate("The 'file' argument", 10, "'path'")
-                path = options.pop("file")
-            else:
-                msg = "Missing required argument: 'path'"
-                raise TypeError(msg)
-        os.system(self.get_command(path, **options))  # nosec
-        return 1
-
-
-# --------------------------------------------------------------------
-
-
-class WindowsViewer(Viewer):
-    """The default viewer on Windows is the default system application for PNG files."""
-
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        return (
-            f'start "Pillow" /WAIT "{file}" '
-            "&& ping -n 4 127.0.0.1 >NUL "
-            f'&& del /f "{file}"'
-        )
-
-
-if sys.platform == "win32":
-    register(WindowsViewer)
-
-
-class MacViewer(Viewer):
-    """The default viewer on macOS using ``Preview.app``."""
-
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        # on darwin open returns immediately resulting in the temp
-        # file removal while app is opening
-        command = "open -a Preview.app"
-        command = f"({command} {quote(file)}; sleep 20; rm -f {quote(file)})&"
-        return command
-
-    def show_file(self, path=None, **options):
-        """
-        Display given file.
-
-        Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,
-        and will be removed in Pillow 10.0.0 (2023-07-01). ``path`` should be used
-        instead.
-        """
-        if path is None:
-            if "file" in options:
-                deprecate("The 'file' argument", 10, "'path'")
-                path = options.pop("file")
-            else:
-                msg = "Missing required argument: 'path'"
-                raise TypeError(msg)
-        subprocess.call(["open", "-a", "Preview.app", path])
-        executable = sys.executable or shutil.which("python3")
-        if executable:
-            subprocess.Popen(
-                [
-                    executable,
-                    "-c",
-                    "import os, sys, time; time.sleep(20); os.remove(sys.argv[1])",
-                    path,
-                ]
-            )
-        return 1
-
-
-if sys.platform == "darwin":
-    register(MacViewer)
-
-
-class UnixViewer(Viewer):
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        command = self.get_command_ex(file, **options)[0]
-        return f"({command} {quote(file)}"
-
-
-class XDGViewer(UnixViewer):
-    """
-    The freedesktop.org ``xdg-open`` command.
-    """
-
-    def get_command_ex(self, file, **options):
-        command = executable = "xdg-open"
-        return command, executable
-
-    def show_file(self, path=None, **options):
-        """
-        Display given file.
-
-        Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,
-        and will be removed in Pillow 10.0.0 (2023-07-01). ``path`` should be used
-        instead.
-        """
-        if path is None:
-            if "file" in options:
-                deprecate("The 'file' argument", 10, "'path'")
-                path = options.pop("file")
-            else:
-                msg = "Missing required argument: 'path'"
-                raise TypeError(msg)
-        subprocess.Popen(["xdg-open", path])
-        return 1
-
-
-class DisplayViewer(UnixViewer):
-    """
-    The ImageMagick ``display`` command.
-    This viewer supports the ``title`` parameter.
-    """
-
-    def get_command_ex(self, file, title=None, **options):
-        command = executable = "display"
-        if title:
-            command += f" -title {quote(title)}"
-        return command, executable
-
-    def show_file(self, path=None, **options):
-        """
-        Display given file.
-
-        Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,
-        and ``path`` should be used instead.
-        """
-        if path is None:
-            if "file" in options:
-                deprecate("The 'file' argument", 10, "'path'")
-                path = options.pop("file")
-            else:
-                msg = "Missing required argument: 'path'"
-                raise TypeError(msg)
-        args = ["display"]
-        title = options.get("title")
-        if title:
-            args += ["-title", title]
-        args.append(path)
-
-        subprocess.Popen(args)
-        return 1
-
-
-class GmDisplayViewer(UnixViewer):
-    """The GraphicsMagick ``gm display`` command."""
-
-    def get_command_ex(self, file, **options):
-        executable = "gm"
-        command = "gm display"
-        return command, executable
-
-    def show_file(self, path=None, **options):
-        """
-        Display given file.
-
-        Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,
-        and ``path`` should be used instead.
-        """
-        if path is None:
-            if "file" in options:
-                deprecate("The 'file' argument", 10, "'path'")
-                path = options.pop("file")
-            else:
-                msg = "Missing required argument: 'path'"
-                raise TypeError(msg)
-        subprocess.Popen(["gm", "display", path])
-        return 1
-
-
-class EogViewer(UnixViewer):
-    """The GNOME Image Viewer ``eog`` command."""
-
-    def get_command_ex(self, file, **options):
-        executable = "eog"
-        command = "eog -n"
-        return command, executable
-
-    def show_file(self, path=None, **options):
-        """
-        Display given file.
-
-        Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,
-        and ``path`` should be used instead.
-        """
-        if path is None:
-            if "file" in options:
-                deprecate("The 'file' argument", 10, "'path'")
-                path = options.pop("file")
-            else:
-                msg = "Missing required argument: 'path'"
-                raise TypeError(msg)
-        subprocess.Popen(["eog", "-n", path])
-        return 1
-
-
-class XVViewer(UnixViewer):
-    """
-    The X Viewer ``xv`` command.
-    This viewer supports the ``title`` parameter.
-    """
-
-    def get_command_ex(self, file, title=None, **options):
-        # note: xv is pretty outdated.  most modern systems have
-        # imagemagick's display command instead.
-        command = executable = "xv"
-        if title:
-            command += f" -name {quote(title)}"
-        return command, executable
-
-    def show_file(self, path=None, **options):
-        """
-        Display given file.
-
-        Before Pillow 9.1.0, the first argument was ``file``. This is now deprecated,
-        and ``path`` should be used instead.
-        """
-        if path is None:
-            if "file" in options:
-                deprecate("The 'file' argument", 10, "'path'")
-                path = options.pop("file")
-            else:
-                msg = "Missing required argument: 'path'"
-                raise TypeError(msg)
-        args = ["xv"]
-        title = options.get("title")
-        if title:
-            args += ["-name", title]
-        args.append(path)
-
-        subprocess.Popen(args)
-        return 1
-
-
-if sys.platform not in ("win32", "darwin"):  # unixoids
-    if shutil.which("xdg-open"):
-        register(XDGViewer)
-    if shutil.which("display"):
-        register(DisplayViewer)
-    if shutil.which("gm"):
-        register(GmDisplayViewer)
-    if shutil.which("eog"):
-        register(EogViewer)
-    if shutil.which("xv"):
-        register(XVViewer)
-
-
-class IPythonViewer(Viewer):
-    """The viewer for IPython frontends."""
-
-    def show_image(self, image, **options):
-        ipython_display(image)
-        return 1
-
-
-try:
-    from IPython.display import display as ipython_display
-except ImportError:
-    pass
-else:
-    register(IPythonViewer)
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Syntax: python3 ImageShow.py imagefile [title]")
-        sys.exit()
-
-    with Image.open(sys.argv[1]) as im:
-        print(show(im, *sys.argv[2:]))
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/abc.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/abc.py
deleted file mode 100644
index 44a3bda34665a5e3b67fba9acc1e545a37b16617..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/abc.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import asyncio
-import logging
-from abc import ABC, abstractmethod
-from collections.abc import Sized
-from http.cookies import BaseCookie, Morsel
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Awaitable,
-    Callable,
-    Dict,
-    Generator,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-)
-
-from multidict import CIMultiDict
-from yarl import URL
-
-from .helpers import get_running_loop
-from .typedefs import LooseCookies
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .web_app import Application
-    from .web_exceptions import HTTPException
-    from .web_request import BaseRequest, Request
-    from .web_response import StreamResponse
-else:
-    BaseRequest = Request = Application = StreamResponse = None
-    HTTPException = None
-
-
-class AbstractRouter(ABC):
-    def __init__(self) -> None:
-        self._frozen = False
-
-    def post_init(self, app: Application) -> None:
-        """Post init stage.
-
-        Not an abstract method for sake of backward compatibility,
-        but if the router wants to be aware of the application
-        it can override this.
-        """
-
-    @property
-    def frozen(self) -> bool:
-        return self._frozen
-
-    def freeze(self) -> None:
-        """Freeze router."""
-        self._frozen = True
-
-    @abstractmethod
-    async def resolve(self, request: Request) -> "AbstractMatchInfo":
-        """Return MATCH_INFO for given request"""
-
-
-class AbstractMatchInfo(ABC):
-    @property  # pragma: no branch
-    @abstractmethod
-    def handler(self) -> Callable[[Request], Awaitable[StreamResponse]]:
-        """Execute matched request handler"""
-
-    @property
-    @abstractmethod
-    def expect_handler(self) -> Callable[[Request], Awaitable[None]]:
-        """Expect handler for 100-continue processing"""
-
-    @property  # pragma: no branch
-    @abstractmethod
-    def http_exception(self) -> Optional[HTTPException]:
-        """HTTPException instance raised on router's resolving, or None"""
-
-    @abstractmethod  # pragma: no branch
-    def get_info(self) -> Dict[str, Any]:
-        """Return a dict with additional info useful for introspection"""
-
-    @property  # pragma: no branch
-    @abstractmethod
-    def apps(self) -> Tuple[Application, ...]:
-        """Stack of nested applications.
-
-        Top level application is left-most element.
-
-        """
-
-    @abstractmethod
-    def add_app(self, app: Application) -> None:
-        """Add application to the nested apps stack."""
-
-    @abstractmethod
-    def freeze(self) -> None:
-        """Freeze the match info.
-
-        The method is called after route resolution.
-
-        After the call .add_app() is forbidden.
-
-        """
-
-
-class AbstractView(ABC):
-    """Abstract class based view."""
-
-    def __init__(self, request: Request) -> None:
-        self._request = request
-
-    @property
-    def request(self) -> Request:
-        """Request instance."""
-        return self._request
-
-    @abstractmethod
-    def __await__(self) -> Generator[Any, None, StreamResponse]:
-        """Execute the view handler."""
-
-
-class AbstractResolver(ABC):
-    """Abstract DNS resolver."""
-
-    @abstractmethod
-    async def resolve(self, host: str, port: int, family: int) -> List[Dict[str, Any]]:
-        """Return IP address for given hostname"""
-
-    @abstractmethod
-    async def close(self) -> None:
-        """Release resolver"""
-
-
-if TYPE_CHECKING:  # pragma: no cover
-    IterableBase = Iterable[Morsel[str]]
-else:
-    IterableBase = Iterable
-
-
-ClearCookiePredicate = Callable[["Morsel[str]"], bool]
-
-
-class AbstractCookieJar(Sized, IterableBase):
-    """Abstract Cookie Jar."""
-
-    def __init__(self, *, loop: Optional[asyncio.AbstractEventLoop] = None) -> None:
-        self._loop = get_running_loop(loop)
-
-    @abstractmethod
-    def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None:
-        """Clear all cookies if no predicate is passed."""
-
-    @abstractmethod
-    def clear_domain(self, domain: str) -> None:
-        """Clear all cookies for domain and all subdomains."""
-
-    @abstractmethod
-    def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None:
-        """Update cookies."""
-
-    @abstractmethod
-    def filter_cookies(self, request_url: URL) -> "BaseCookie[str]":
-        """Return the jar's cookies filtered by their attributes."""
-
-
-class AbstractStreamWriter(ABC):
-    """Abstract stream writer."""
-
-    buffer_size = 0
-    output_size = 0
-    length: Optional[int] = 0
-
-    @abstractmethod
-    async def write(self, chunk: bytes) -> None:
-        """Write chunk into stream."""
-
-    @abstractmethod
-    async def write_eof(self, chunk: bytes = b"") -> None:
-        """Write last chunk."""
-
-    @abstractmethod
-    async def drain(self) -> None:
-        """Flush the write buffer."""
-
-    @abstractmethod
-    def enable_compression(self, encoding: str = "deflate") -> None:
-        """Enable HTTP body compression"""
-
-    @abstractmethod
-    def enable_chunking(self) -> None:
-        """Enable HTTP chunked mode"""
-
-    @abstractmethod
-    async def write_headers(
-        self, status_line: str, headers: "CIMultiDict[str]"
-    ) -> None:
-        """Write HTTP headers"""
-
-
-class AbstractAccessLogger(ABC):
-    """Abstract writer to access log."""
-
-    def __init__(self, logger: logging.Logger, log_format: str) -> None:
-        self.logger = logger
-        self.log_format = log_format
-
-    @abstractmethod
-    def log(self, request: BaseRequest, response: StreamResponse, time: float) -> None:
-        """Emit log to logger."""
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/streams.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/streams.py
deleted file mode 100644
index 726b02326f66d37b9de1947cb78470479a7bc82b..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/streams.py
+++ /dev/null
@@ -1,660 +0,0 @@
-import asyncio
-import collections
-import warnings
-from typing import Awaitable, Callable, Deque, Generic, List, Optional, Tuple, TypeVar
-
-from .base_protocol import BaseProtocol
-from .helpers import BaseTimerContext, set_exception, set_result
-from .log import internal_logger
-from .typedefs import Final
-
-__all__ = (
-    "EMPTY_PAYLOAD",
-    "EofStream",
-    "StreamReader",
-    "DataQueue",
-    "FlowControlDataQueue",
-)
-
-_T = TypeVar("_T")
-
-
-class EofStream(Exception):
-    """eof stream indication."""
-
-
-class AsyncStreamIterator(Generic[_T]):
-    def __init__(self, read_func: Callable[[], Awaitable[_T]]) -> None:
-        self.read_func = read_func
-
-    def __aiter__(self) -> "AsyncStreamIterator[_T]":
-        return self
-
-    async def __anext__(self) -> _T:
-        try:
-            rv = await self.read_func()
-        except EofStream:
-            raise StopAsyncIteration
-        if rv == b"":
-            raise StopAsyncIteration
-        return rv
-
-
-class ChunkTupleAsyncStreamIterator:
-    def __init__(self, stream: "StreamReader") -> None:
-        self._stream = stream
-
-    def __aiter__(self) -> "ChunkTupleAsyncStreamIterator":
-        return self
-
-    async def __anext__(self) -> Tuple[bytes, bool]:
-        rv = await self._stream.readchunk()
-        if rv == (b"", False):
-            raise StopAsyncIteration
-        return rv
-
-
-class AsyncStreamReaderMixin:
-    def __aiter__(self) -> AsyncStreamIterator[bytes]:
-        return AsyncStreamIterator(self.readline)  # type: ignore[attr-defined]
-
-    def iter_chunked(self, n: int) -> AsyncStreamIterator[bytes]:
-        """Returns an asynchronous iterator that yields chunks of size n.
-
-        Python-3.5 available for Python 3.5+ only
-        """
-        return AsyncStreamIterator(
-            lambda: self.read(n)  # type: ignore[attr-defined,no-any-return]
-        )
-
-    def iter_any(self) -> AsyncStreamIterator[bytes]:
-        """Yield all available data as soon as it is received.
-
-        Python-3.5 available for Python 3.5+ only
-        """
-        return AsyncStreamIterator(self.readany)  # type: ignore[attr-defined]
-
-    def iter_chunks(self) -> ChunkTupleAsyncStreamIterator:
-        """Yield chunks of data as they are received by the server.
-
-        The yielded objects are tuples
-        of (bytes, bool) as returned by the StreamReader.readchunk method.
-
-        Python-3.5 available for Python 3.5+ only
-        """
-        return ChunkTupleAsyncStreamIterator(self)  # type: ignore[arg-type]
-
-
-class StreamReader(AsyncStreamReaderMixin):
-    """An enhancement of asyncio.StreamReader.
-
-    Supports asynchronous iteration by line, chunk or as available::
-
-        async for line in reader:
-            ...
-        async for chunk in reader.iter_chunked(1024):
-            ...
-        async for slice in reader.iter_any():
-            ...
-
-    """
-
-    total_bytes = 0
-
-    def __init__(
-        self,
-        protocol: BaseProtocol,
-        limit: int,
-        *,
-        timer: Optional[BaseTimerContext] = None,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-    ) -> None:
-        self._protocol = protocol
-        self._low_water = limit
-        self._high_water = limit * 2
-        if loop is None:
-            loop = asyncio.get_event_loop()
-        self._loop = loop
-        self._size = 0
-        self._cursor = 0
-        self._http_chunk_splits: Optional[List[int]] = None
-        self._buffer: Deque[bytes] = collections.deque()
-        self._buffer_offset = 0
-        self._eof = False
-        self._waiter: Optional[asyncio.Future[None]] = None
-        self._eof_waiter: Optional[asyncio.Future[None]] = None
-        self._exception: Optional[BaseException] = None
-        self._timer = timer
-        self._eof_callbacks: List[Callable[[], None]] = []
-
-    def __repr__(self) -> str:
-        info = [self.__class__.__name__]
-        if self._size:
-            info.append("%d bytes" % self._size)
-        if self._eof:
-            info.append("eof")
-        if self._low_water != 2**16:  # default limit
-            info.append("low=%d high=%d" % (self._low_water, self._high_water))
-        if self._waiter:
-            info.append("w=%r" % self._waiter)
-        if self._exception:
-            info.append("e=%r" % self._exception)
-        return "<%s>" % " ".join(info)
-
-    def get_read_buffer_limits(self) -> Tuple[int, int]:
-        return (self._low_water, self._high_water)
-
-    def exception(self) -> Optional[BaseException]:
-        return self._exception
-
-    def set_exception(self, exc: BaseException) -> None:
-        self._exception = exc
-        self._eof_callbacks.clear()
-
-        waiter = self._waiter
-        if waiter is not None:
-            self._waiter = None
-            set_exception(waiter, exc)
-
-        waiter = self._eof_waiter
-        if waiter is not None:
-            self._eof_waiter = None
-            set_exception(waiter, exc)
-
-    def on_eof(self, callback: Callable[[], None]) -> None:
-        if self._eof:
-            try:
-                callback()
-            except Exception:
-                internal_logger.exception("Exception in eof callback")
-        else:
-            self._eof_callbacks.append(callback)
-
-    def feed_eof(self) -> None:
-        self._eof = True
-
-        waiter = self._waiter
-        if waiter is not None:
-            self._waiter = None
-            set_result(waiter, None)
-
-        waiter = self._eof_waiter
-        if waiter is not None:
-            self._eof_waiter = None
-            set_result(waiter, None)
-
-        for cb in self._eof_callbacks:
-            try:
-                cb()
-            except Exception:
-                internal_logger.exception("Exception in eof callback")
-
-        self._eof_callbacks.clear()
-
-    def is_eof(self) -> bool:
-        """Return True if  'feed_eof' was called."""
-        return self._eof
-
-    def at_eof(self) -> bool:
-        """Return True if the buffer is empty and 'feed_eof' was called."""
-        return self._eof and not self._buffer
-
-    async def wait_eof(self) -> None:
-        if self._eof:
-            return
-
-        assert self._eof_waiter is None
-        self._eof_waiter = self._loop.create_future()
-        try:
-            await self._eof_waiter
-        finally:
-            self._eof_waiter = None
-
-    def unread_data(self, data: bytes) -> None:
-        """rollback reading some data from stream, inserting it to buffer head."""
-        warnings.warn(
-            "unread_data() is deprecated "
-            "and will be removed in future releases (#3260)",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        if not data:
-            return
-
-        if self._buffer_offset:
-            self._buffer[0] = self._buffer[0][self._buffer_offset :]
-            self._buffer_offset = 0
-        self._size += len(data)
-        self._cursor -= len(data)
-        self._buffer.appendleft(data)
-        self._eof_counter = 0
-
-    # TODO: size is ignored, remove the param later
-    def feed_data(self, data: bytes, size: int = 0) -> None:
-        assert not self._eof, "feed_data after feed_eof"
-
-        if not data:
-            return
-
-        self._size += len(data)
-        self._buffer.append(data)
-        self.total_bytes += len(data)
-
-        waiter = self._waiter
-        if waiter is not None:
-            self._waiter = None
-            set_result(waiter, None)
-
-        if self._size > self._high_water and not self._protocol._reading_paused:
-            self._protocol.pause_reading()
-
-    def begin_http_chunk_receiving(self) -> None:
-        if self._http_chunk_splits is None:
-            if self.total_bytes:
-                raise RuntimeError(
-                    "Called begin_http_chunk_receiving when" "some data was already fed"
-                )
-            self._http_chunk_splits = []
-
-    def end_http_chunk_receiving(self) -> None:
-        if self._http_chunk_splits is None:
-            raise RuntimeError(
-                "Called end_chunk_receiving without calling "
-                "begin_chunk_receiving first"
-            )
-
-        # self._http_chunk_splits contains logical byte offsets from start of
-        # the body transfer. Each offset is the offset of the end of a chunk.
-        # "Logical" means bytes, accessible for a user.
-        # If no chunks containig logical data were received, current position
-        # is difinitely zero.
-        pos = self._http_chunk_splits[-1] if self._http_chunk_splits else 0
-
-        if self.total_bytes == pos:
-            # We should not add empty chunks here. So we check for that.
-            # Note, when chunked + gzip is used, we can receive a chunk
-            # of compressed data, but that data may not be enough for gzip FSM
-            # to yield any uncompressed data. That's why current position may
-            # not change after receiving a chunk.
-            return
-
-        self._http_chunk_splits.append(self.total_bytes)
-
-        # wake up readchunk when end of http chunk received
-        waiter = self._waiter
-        if waiter is not None:
-            self._waiter = None
-            set_result(waiter, None)
-
-    async def _wait(self, func_name: str) -> None:
-        # StreamReader uses a future to link the protocol feed_data() method
-        # to a read coroutine. Running two read coroutines at the same time
-        # would have an unexpected behaviour. It would not possible to know
-        # which coroutine would get the next data.
-        if self._waiter is not None:
-            raise RuntimeError(
-                "%s() called while another coroutine is "
-                "already waiting for incoming data" % func_name
-            )
-
-        waiter = self._waiter = self._loop.create_future()
-        try:
-            if self._timer:
-                with self._timer:
-                    await waiter
-            else:
-                await waiter
-        finally:
-            self._waiter = None
-
-    async def readline(self) -> bytes:
-        return await self.readuntil()
-
-    async def readuntil(self, separator: bytes = b"\n") -> bytes:
-        seplen = len(separator)
-        if seplen == 0:
-            raise ValueError("Separator should be at least one-byte string")
-
-        if self._exception is not None:
-            raise self._exception
-
-        chunk = b""
-        chunk_size = 0
-        not_enough = True
-
-        while not_enough:
-            while self._buffer and not_enough:
-                offset = self._buffer_offset
-                ichar = self._buffer[0].find(separator, offset) + 1
-                # Read from current offset to found separator or to the end.
-                data = self._read_nowait_chunk(ichar - offset if ichar else -1)
-                chunk += data
-                chunk_size += len(data)
-                if ichar:
-                    not_enough = False
-
-                if chunk_size > self._high_water:
-                    raise ValueError("Chunk too big")
-
-            if self._eof:
-                break
-
-            if not_enough:
-                await self._wait("readuntil")
-
-        return chunk
-
-    async def read(self, n: int = -1) -> bytes:
-        if self._exception is not None:
-            raise self._exception
-
-        # migration problem; with DataQueue you have to catch
-        # EofStream exception, so common way is to run payload.read() inside
-        # infinite loop. what can cause real infinite loop with StreamReader
-        # lets keep this code one major release.
-        if __debug__:
-            if self._eof and not self._buffer:
-                self._eof_counter = getattr(self, "_eof_counter", 0) + 1
-                if self._eof_counter > 5:
-                    internal_logger.warning(
-                        "Multiple access to StreamReader in eof state, "
-                        "might be infinite loop.",
-                        stack_info=True,
-                    )
-
-        if not n:
-            return b""
-
-        if n < 0:
-            # This used to just loop creating a new waiter hoping to
-            # collect everything in self._buffer, but that would
-            # deadlock if the subprocess sends more than self.limit
-            # bytes.  So just call self.readany() until EOF.
-            blocks = []
-            while True:
-                block = await self.readany()
-                if not block:
-                    break
-                blocks.append(block)
-            return b"".join(blocks)
-
-        # TODO: should be `if` instead of `while`
-        # because waiter maybe triggered on chunk end,
-        # without feeding any data
-        while not self._buffer and not self._eof:
-            await self._wait("read")
-
-        return self._read_nowait(n)
-
-    async def readany(self) -> bytes:
-        if self._exception is not None:
-            raise self._exception
-
-        # TODO: should be `if` instead of `while`
-        # because waiter maybe triggered on chunk end,
-        # without feeding any data
-        while not self._buffer and not self._eof:
-            await self._wait("readany")
-
-        return self._read_nowait(-1)
-
-    async def readchunk(self) -> Tuple[bytes, bool]:
-        """Returns a tuple of (data, end_of_http_chunk).
-
-        When chunked transfer
-        encoding is used, end_of_http_chunk is a boolean indicating if the end
-        of the data corresponds to the end of a HTTP chunk , otherwise it is
-        always False.
-        """
-        while True:
-            if self._exception is not None:
-                raise self._exception
-
-            while self._http_chunk_splits:
-                pos = self._http_chunk_splits.pop(0)
-                if pos == self._cursor:
-                    return (b"", True)
-                if pos > self._cursor:
-                    return (self._read_nowait(pos - self._cursor), True)
-                internal_logger.warning(
-                    "Skipping HTTP chunk end due to data "
-                    "consumption beyond chunk boundary"
-                )
-
-            if self._buffer:
-                return (self._read_nowait_chunk(-1), False)
-                # return (self._read_nowait(-1), False)
-
-            if self._eof:
-                # Special case for signifying EOF.
-                # (b'', True) is not a final return value actually.
-                return (b"", False)
-
-            await self._wait("readchunk")
-
-    async def readexactly(self, n: int) -> bytes:
-        if self._exception is not None:
-            raise self._exception
-
-        blocks: List[bytes] = []
-        while n > 0:
-            block = await self.read(n)
-            if not block:
-                partial = b"".join(blocks)
-                raise asyncio.IncompleteReadError(partial, len(partial) + n)
-            blocks.append(block)
-            n -= len(block)
-
-        return b"".join(blocks)
-
-    def read_nowait(self, n: int = -1) -> bytes:
-        # default was changed to be consistent with .read(-1)
-        #
-        # I believe the most users don't know about the method and
-        # they are not affected.
-        if self._exception is not None:
-            raise self._exception
-
-        if self._waiter and not self._waiter.done():
-            raise RuntimeError(
-                "Called while some coroutine is waiting for incoming data."
-            )
-
-        return self._read_nowait(n)
-
-    def _read_nowait_chunk(self, n: int) -> bytes:
-        first_buffer = self._buffer[0]
-        offset = self._buffer_offset
-        if n != -1 and len(first_buffer) - offset > n:
-            data = first_buffer[offset : offset + n]
-            self._buffer_offset += n
-
-        elif offset:
-            self._buffer.popleft()
-            data = first_buffer[offset:]
-            self._buffer_offset = 0
-
-        else:
-            data = self._buffer.popleft()
-
-        self._size -= len(data)
-        self._cursor += len(data)
-
-        chunk_splits = self._http_chunk_splits
-        # Prevent memory leak: drop useless chunk splits
-        while chunk_splits and chunk_splits[0] < self._cursor:
-            chunk_splits.pop(0)
-
-        if self._size < self._low_water and self._protocol._reading_paused:
-            self._protocol.resume_reading()
-        return data
-
-    def _read_nowait(self, n: int) -> bytes:
-        """Read not more than n bytes, or whole buffer if n == -1"""
-        chunks = []
-
-        while self._buffer:
-            chunk = self._read_nowait_chunk(n)
-            chunks.append(chunk)
-            if n != -1:
-                n -= len(chunk)
-                if n == 0:
-                    break
-
-        return b"".join(chunks) if chunks else b""
-
-
-class EmptyStreamReader(StreamReader):  # lgtm [py/missing-call-to-init]
-    def __init__(self) -> None:
-        pass
-
-    def exception(self) -> Optional[BaseException]:
-        return None
-
-    def set_exception(self, exc: BaseException) -> None:
-        pass
-
-    def on_eof(self, callback: Callable[[], None]) -> None:
-        try:
-            callback()
-        except Exception:
-            internal_logger.exception("Exception in eof callback")
-
-    def feed_eof(self) -> None:
-        pass
-
-    def is_eof(self) -> bool:
-        return True
-
-    def at_eof(self) -> bool:
-        return True
-
-    async def wait_eof(self) -> None:
-        return
-
-    def feed_data(self, data: bytes, n: int = 0) -> None:
-        pass
-
-    async def readline(self) -> bytes:
-        return b""
-
-    async def read(self, n: int = -1) -> bytes:
-        return b""
-
-    # TODO add async def readuntil
-
-    async def readany(self) -> bytes:
-        return b""
-
-    async def readchunk(self) -> Tuple[bytes, bool]:
-        return (b"", True)
-
-    async def readexactly(self, n: int) -> bytes:
-        raise asyncio.IncompleteReadError(b"", n)
-
-    def read_nowait(self, n: int = -1) -> bytes:
-        return b""
-
-
-EMPTY_PAYLOAD: Final[StreamReader] = EmptyStreamReader()
-
-
-class DataQueue(Generic[_T]):
-    """DataQueue is a general-purpose blocking queue with one reader."""
-
-    def __init__(self, loop: asyncio.AbstractEventLoop) -> None:
-        self._loop = loop
-        self._eof = False
-        self._waiter: Optional[asyncio.Future[None]] = None
-        self._exception: Optional[BaseException] = None
-        self._size = 0
-        self._buffer: Deque[Tuple[_T, int]] = collections.deque()
-
-    def __len__(self) -> int:
-        return len(self._buffer)
-
-    def is_eof(self) -> bool:
-        return self._eof
-
-    def at_eof(self) -> bool:
-        return self._eof and not self._buffer
-
-    def exception(self) -> Optional[BaseException]:
-        return self._exception
-
-    def set_exception(self, exc: BaseException) -> None:
-        self._eof = True
-        self._exception = exc
-
-        waiter = self._waiter
-        if waiter is not None:
-            self._waiter = None
-            set_exception(waiter, exc)
-
-    def feed_data(self, data: _T, size: int = 0) -> None:
-        self._size += size
-        self._buffer.append((data, size))
-
-        waiter = self._waiter
-        if waiter is not None:
-            self._waiter = None
-            set_result(waiter, None)
-
-    def feed_eof(self) -> None:
-        self._eof = True
-
-        waiter = self._waiter
-        if waiter is not None:
-            self._waiter = None
-            set_result(waiter, None)
-
-    async def read(self) -> _T:
-        if not self._buffer and not self._eof:
-            assert not self._waiter
-            self._waiter = self._loop.create_future()
-            try:
-                await self._waiter
-            except (asyncio.CancelledError, asyncio.TimeoutError):
-                self._waiter = None
-                raise
-
-        if self._buffer:
-            data, size = self._buffer.popleft()
-            self._size -= size
-            return data
-        else:
-            if self._exception is not None:
-                raise self._exception
-            else:
-                raise EofStream
-
-    def __aiter__(self) -> AsyncStreamIterator[_T]:
-        return AsyncStreamIterator(self.read)
-
-
-class FlowControlDataQueue(DataQueue[_T]):
-    """FlowControlDataQueue resumes and pauses an underlying stream.
-
-    It is a destination for parsed data.
-    """
-
-    def __init__(
-        self, protocol: BaseProtocol, limit: int, *, loop: asyncio.AbstractEventLoop
-    ) -> None:
-        super().__init__(loop=loop)
-
-        self._protocol = protocol
-        self._limit = limit * 2
-
-    def feed_data(self, data: _T, size: int = 0) -> None:
-        super().feed_data(data, size)
-
-        if self._size > self._limit and not self._protocol._reading_paused:
-            self._protocol.pause_reading()
-
-    async def read(self) -> _T:
-        try:
-            return await super().read()
-        finally:
-            if self._size < self._limit and self._protocol._reading_paused:
-                self._protocol.resume_reading()
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/web_response.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/web_response.py
deleted file mode 100644
index ce07f8153deb29c4cf5856fae0d92ac1170c1441..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/web_response.py
+++ /dev/null
@@ -1,825 +0,0 @@
-import asyncio
-import collections.abc
-import datetime
-import enum
-import json
-import math
-import time
-import warnings
-import zlib
-from concurrent.futures import Executor
-from http.cookies import Morsel, SimpleCookie
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Dict,
-    Iterator,
-    Mapping,
-    MutableMapping,
-    Optional,
-    Tuple,
-    Union,
-    cast,
-)
-
-from multidict import CIMultiDict, istr
-
-from . import hdrs, payload
-from .abc import AbstractStreamWriter
-from .helpers import (
-    ETAG_ANY,
-    PY_38,
-    QUOTED_ETAG_RE,
-    ETag,
-    HeadersMixin,
-    parse_http_date,
-    rfc822_formatted_time,
-    sentinel,
-    validate_etag_value,
-)
-from .http import RESPONSES, SERVER_SOFTWARE, HttpVersion10, HttpVersion11
-from .payload import Payload
-from .typedefs import JSONEncoder, LooseHeaders
-
-__all__ = ("ContentCoding", "StreamResponse", "Response", "json_response")
-
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .web_request import BaseRequest
-
-    BaseClass = MutableMapping[str, Any]
-else:
-    BaseClass = collections.abc.MutableMapping
-
-
-if not PY_38:
-    # allow samesite to be used in python < 3.8
-    # already permitted in python 3.8, see https://bugs.python.org/issue29613
-    Morsel._reserved["samesite"] = "SameSite"  # type: ignore[attr-defined]
-
-
-class ContentCoding(enum.Enum):
-    # The content codings that we have support for.
-    #
-    # Additional registered codings are listed at:
-    # https://www.iana.org/assignments/http-parameters/http-parameters.xhtml#content-coding
-    deflate = "deflate"
-    gzip = "gzip"
-    identity = "identity"
-
-
-############################################################
-# HTTP Response classes
-############################################################
-
-
-class StreamResponse(BaseClass, HeadersMixin):
-
-    _length_check = True
-
-    def __init__(
-        self,
-        *,
-        status: int = 200,
-        reason: Optional[str] = None,
-        headers: Optional[LooseHeaders] = None,
-    ) -> None:
-        self._body = None
-        self._keep_alive: Optional[bool] = None
-        self._chunked = False
-        self._compression = False
-        self._compression_force: Optional[ContentCoding] = None
-        self._cookies: SimpleCookie[str] = SimpleCookie()
-
-        self._req: Optional[BaseRequest] = None
-        self._payload_writer: Optional[AbstractStreamWriter] = None
-        self._eof_sent = False
-        self._body_length = 0
-        self._state: Dict[str, Any] = {}
-
-        if headers is not None:
-            self._headers: CIMultiDict[str] = CIMultiDict(headers)
-        else:
-            self._headers = CIMultiDict()
-
-        self.set_status(status, reason)
-
-    @property
-    def prepared(self) -> bool:
-        return self._payload_writer is not None
-
-    @property
-    def task(self) -> "Optional[asyncio.Task[None]]":
-        if self._req:
-            return self._req.task
-        else:
-            return None
-
-    @property
-    def status(self) -> int:
-        return self._status
-
-    @property
-    def chunked(self) -> bool:
-        return self._chunked
-
-    @property
-    def compression(self) -> bool:
-        return self._compression
-
-    @property
-    def reason(self) -> str:
-        return self._reason
-
-    def set_status(
-        self,
-        status: int,
-        reason: Optional[str] = None,
-        _RESPONSES: Mapping[int, Tuple[str, str]] = RESPONSES,
-    ) -> None:
-        assert not self.prepared, (
-            "Cannot change the response status code after " "the headers have been sent"
-        )
-        self._status = int(status)
-        if reason is None:
-            try:
-                reason = _RESPONSES[self._status][0]
-            except Exception:
-                reason = ""
-        self._reason = reason
-
-    @property
-    def keep_alive(self) -> Optional[bool]:
-        return self._keep_alive
-
-    def force_close(self) -> None:
-        self._keep_alive = False
-
-    @property
-    def body_length(self) -> int:
-        return self._body_length
-
-    @property
-    def output_length(self) -> int:
-        warnings.warn("output_length is deprecated", DeprecationWarning)
-        assert self._payload_writer
-        return self._payload_writer.buffer_size
-
-    def enable_chunked_encoding(self, chunk_size: Optional[int] = None) -> None:
-        """Enables automatic chunked transfer encoding."""
-        self._chunked = True
-
-        if hdrs.CONTENT_LENGTH in self._headers:
-            raise RuntimeError(
-                "You can't enable chunked encoding when " "a content length is set"
-            )
-        if chunk_size is not None:
-            warnings.warn("Chunk size is deprecated #1615", DeprecationWarning)
-
-    def enable_compression(
-        self, force: Optional[Union[bool, ContentCoding]] = None
-    ) -> None:
-        """Enables response compression encoding."""
-        # Backwards compatibility for when force was a bool <0.17.
-        if type(force) == bool:
-            force = ContentCoding.deflate if force else ContentCoding.identity
-            warnings.warn(
-                "Using boolean for force is deprecated #3318", DeprecationWarning
-            )
-        elif force is not None:
-            assert isinstance(force, ContentCoding), (
-                "force should one of " "None, bool or " "ContentEncoding"
-            )
-
-        self._compression = True
-        self._compression_force = force
-
-    @property
-    def headers(self) -> "CIMultiDict[str]":
-        return self._headers
-
-    @property
-    def cookies(self) -> "SimpleCookie[str]":
-        return self._cookies
-
-    def set_cookie(
-        self,
-        name: str,
-        value: str,
-        *,
-        expires: Optional[str] = None,
-        domain: Optional[str] = None,
-        max_age: Optional[Union[int, str]] = None,
-        path: str = "/",
-        secure: Optional[bool] = None,
-        httponly: Optional[bool] = None,
-        version: Optional[str] = None,
-        samesite: Optional[str] = None,
-    ) -> None:
-        """Set or update response cookie.
-
-        Sets new cookie or updates existent with new value.
-        Also updates only those params which are not None.
-        """
-        old = self._cookies.get(name)
-        if old is not None and old.coded_value == "":
-            # deleted cookie
-            self._cookies.pop(name, None)
-
-        self._cookies[name] = value
-        c = self._cookies[name]
-
-        if expires is not None:
-            c["expires"] = expires
-        elif c.get("expires") == "Thu, 01 Jan 1970 00:00:00 GMT":
-            del c["expires"]
-
-        if domain is not None:
-            c["domain"] = domain
-
-        if max_age is not None:
-            c["max-age"] = str(max_age)
-        elif "max-age" in c:
-            del c["max-age"]
-
-        c["path"] = path
-
-        if secure is not None:
-            c["secure"] = secure
-        if httponly is not None:
-            c["httponly"] = httponly
-        if version is not None:
-            c["version"] = version
-        if samesite is not None:
-            c["samesite"] = samesite
-
-    def del_cookie(
-        self, name: str, *, domain: Optional[str] = None, path: str = "/"
-    ) -> None:
-        """Delete cookie.
-
-        Creates new empty expired cookie.
-        """
-        # TODO: do we need domain/path here?
-        self._cookies.pop(name, None)
-        self.set_cookie(
-            name,
-            "",
-            max_age=0,
-            expires="Thu, 01 Jan 1970 00:00:00 GMT",
-            domain=domain,
-            path=path,
-        )
-
-    @property
-    def content_length(self) -> Optional[int]:
-        # Just a placeholder for adding setter
-        return super().content_length
-
-    @content_length.setter
-    def content_length(self, value: Optional[int]) -> None:
-        if value is not None:
-            value = int(value)
-            if self._chunked:
-                raise RuntimeError(
-                    "You can't set content length when " "chunked encoding is enable"
-                )
-            self._headers[hdrs.CONTENT_LENGTH] = str(value)
-        else:
-            self._headers.pop(hdrs.CONTENT_LENGTH, None)
-
-    @property
-    def content_type(self) -> str:
-        # Just a placeholder for adding setter
-        return super().content_type
-
-    @content_type.setter
-    def content_type(self, value: str) -> None:
-        self.content_type  # read header values if needed
-        self._content_type = str(value)
-        self._generate_content_type_header()
-
-    @property
-    def charset(self) -> Optional[str]:
-        # Just a placeholder for adding setter
-        return super().charset
-
-    @charset.setter
-    def charset(self, value: Optional[str]) -> None:
-        ctype = self.content_type  # read header values if needed
-        if ctype == "application/octet-stream":
-            raise RuntimeError(
-                "Setting charset for application/octet-stream "
-                "doesn't make sense, setup content_type first"
-            )
-        assert self._content_dict is not None
-        if value is None:
-            self._content_dict.pop("charset", None)
-        else:
-            self._content_dict["charset"] = str(value).lower()
-        self._generate_content_type_header()
-
-    @property
-    def last_modified(self) -> Optional[datetime.datetime]:
-        """The value of Last-Modified HTTP header, or None.
-
-        This header is represented as a `datetime` object.
-        """
-        return parse_http_date(self._headers.get(hdrs.LAST_MODIFIED))
-
-    @last_modified.setter
-    def last_modified(
-        self, value: Optional[Union[int, float, datetime.datetime, str]]
-    ) -> None:
-        if value is None:
-            self._headers.pop(hdrs.LAST_MODIFIED, None)
-        elif isinstance(value, (int, float)):
-            self._headers[hdrs.LAST_MODIFIED] = time.strftime(
-                "%a, %d %b %Y %H:%M:%S GMT", time.gmtime(math.ceil(value))
-            )
-        elif isinstance(value, datetime.datetime):
-            self._headers[hdrs.LAST_MODIFIED] = time.strftime(
-                "%a, %d %b %Y %H:%M:%S GMT", value.utctimetuple()
-            )
-        elif isinstance(value, str):
-            self._headers[hdrs.LAST_MODIFIED] = value
-
-    @property
-    def etag(self) -> Optional[ETag]:
-        quoted_value = self._headers.get(hdrs.ETAG)
-        if not quoted_value:
-            return None
-        elif quoted_value == ETAG_ANY:
-            return ETag(value=ETAG_ANY)
-        match = QUOTED_ETAG_RE.fullmatch(quoted_value)
-        if not match:
-            return None
-        is_weak, value = match.group(1, 2)
-        return ETag(
-            is_weak=bool(is_weak),
-            value=value,
-        )
-
-    @etag.setter
-    def etag(self, value: Optional[Union[ETag, str]]) -> None:
-        if value is None:
-            self._headers.pop(hdrs.ETAG, None)
-        elif (isinstance(value, str) and value == ETAG_ANY) or (
-            isinstance(value, ETag) and value.value == ETAG_ANY
-        ):
-            self._headers[hdrs.ETAG] = ETAG_ANY
-        elif isinstance(value, str):
-            validate_etag_value(value)
-            self._headers[hdrs.ETAG] = f'"{value}"'
-        elif isinstance(value, ETag) and isinstance(value.value, str):
-            validate_etag_value(value.value)
-            hdr_value = f'W/"{value.value}"' if value.is_weak else f'"{value.value}"'
-            self._headers[hdrs.ETAG] = hdr_value
-        else:
-            raise ValueError(
-                f"Unsupported etag type: {type(value)}. "
-                f"etag must be str, ETag or None"
-            )
-
-    def _generate_content_type_header(
-        self, CONTENT_TYPE: istr = hdrs.CONTENT_TYPE
-    ) -> None:
-        assert self._content_dict is not None
-        assert self._content_type is not None
-        params = "; ".join(f"{k}={v}" for k, v in self._content_dict.items())
-        if params:
-            ctype = self._content_type + "; " + params
-        else:
-            ctype = self._content_type
-        self._headers[CONTENT_TYPE] = ctype
-
-    async def _do_start_compression(self, coding: ContentCoding) -> None:
-        if coding != ContentCoding.identity:
-            assert self._payload_writer is not None
-            self._headers[hdrs.CONTENT_ENCODING] = coding.value
-            self._payload_writer.enable_compression(coding.value)
-            # Compressed payload may have different content length,
-            # remove the header
-            self._headers.popall(hdrs.CONTENT_LENGTH, None)
-
-    async def _start_compression(self, request: "BaseRequest") -> None:
-        if self._compression_force:
-            await self._do_start_compression(self._compression_force)
-        else:
-            accept_encoding = request.headers.get(hdrs.ACCEPT_ENCODING, "").lower()
-            for coding in ContentCoding:
-                if coding.value in accept_encoding:
-                    await self._do_start_compression(coding)
-                    return
-
-    async def prepare(self, request: "BaseRequest") -> Optional[AbstractStreamWriter]:
-        if self._eof_sent:
-            return None
-        if self._payload_writer is not None:
-            return self._payload_writer
-
-        return await self._start(request)
-
-    async def _start(self, request: "BaseRequest") -> AbstractStreamWriter:
-        self._req = request
-        writer = self._payload_writer = request._payload_writer
-
-        await self._prepare_headers()
-        await request._prepare_hook(self)
-        await self._write_headers()
-
-        return writer
-
-    async def _prepare_headers(self) -> None:
-        request = self._req
-        assert request is not None
-        writer = self._payload_writer
-        assert writer is not None
-        keep_alive = self._keep_alive
-        if keep_alive is None:
-            keep_alive = request.keep_alive
-        self._keep_alive = keep_alive
-
-        version = request.version
-
-        headers = self._headers
-        for cookie in self._cookies.values():
-            value = cookie.output(header="")[1:]
-            headers.add(hdrs.SET_COOKIE, value)
-
-        if self._compression:
-            await self._start_compression(request)
-
-        if self._chunked:
-            if version != HttpVersion11:
-                raise RuntimeError(
-                    "Using chunked encoding is forbidden "
-                    "for HTTP/{0.major}.{0.minor}".format(request.version)
-                )
-            writer.enable_chunking()
-            headers[hdrs.TRANSFER_ENCODING] = "chunked"
-            if hdrs.CONTENT_LENGTH in headers:
-                del headers[hdrs.CONTENT_LENGTH]
-        elif self._length_check:
-            writer.length = self.content_length
-            if writer.length is None:
-                if version >= HttpVersion11 and self.status != 204:
-                    writer.enable_chunking()
-                    headers[hdrs.TRANSFER_ENCODING] = "chunked"
-                    if hdrs.CONTENT_LENGTH in headers:
-                        del headers[hdrs.CONTENT_LENGTH]
-                else:
-                    keep_alive = False
-            # HTTP 1.1: https://tools.ietf.org/html/rfc7230#section-3.3.2
-            # HTTP 1.0: https://tools.ietf.org/html/rfc1945#section-10.4
-            elif version >= HttpVersion11 and self.status in (100, 101, 102, 103, 204):
-                del headers[hdrs.CONTENT_LENGTH]
-
-        if self.status not in (204, 304):
-            headers.setdefault(hdrs.CONTENT_TYPE, "application/octet-stream")
-        headers.setdefault(hdrs.DATE, rfc822_formatted_time())
-        headers.setdefault(hdrs.SERVER, SERVER_SOFTWARE)
-
-        # connection header
-        if hdrs.CONNECTION not in headers:
-            if keep_alive:
-                if version == HttpVersion10:
-                    headers[hdrs.CONNECTION] = "keep-alive"
-            else:
-                if version == HttpVersion11:
-                    headers[hdrs.CONNECTION] = "close"
-
-    async def _write_headers(self) -> None:
-        request = self._req
-        assert request is not None
-        writer = self._payload_writer
-        assert writer is not None
-        # status line
-        version = request.version
-        status_line = "HTTP/{}.{} {} {}".format(
-            version[0], version[1], self._status, self._reason
-        )
-        await writer.write_headers(status_line, self._headers)
-
-    async def write(self, data: bytes) -> None:
-        assert isinstance(
-            data, (bytes, bytearray, memoryview)
-        ), "data argument must be byte-ish (%r)" % type(data)
-
-        if self._eof_sent:
-            raise RuntimeError("Cannot call write() after write_eof()")
-        if self._payload_writer is None:
-            raise RuntimeError("Cannot call write() before prepare()")
-
-        await self._payload_writer.write(data)
-
-    async def drain(self) -> None:
-        assert not self._eof_sent, "EOF has already been sent"
-        assert self._payload_writer is not None, "Response has not been started"
-        warnings.warn(
-            "drain method is deprecated, use await resp.write()",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        await self._payload_writer.drain()
-
-    async def write_eof(self, data: bytes = b"") -> None:
-        assert isinstance(
-            data, (bytes, bytearray, memoryview)
-        ), "data argument must be byte-ish (%r)" % type(data)
-
-        if self._eof_sent:
-            return
-
-        assert self._payload_writer is not None, "Response has not been started"
-
-        await self._payload_writer.write_eof(data)
-        self._eof_sent = True
-        self._req = None
-        self._body_length = self._payload_writer.output_size
-        self._payload_writer = None
-
-    def __repr__(self) -> str:
-        if self._eof_sent:
-            info = "eof"
-        elif self.prepared:
-            assert self._req is not None
-            info = f"{self._req.method} {self._req.path} "
-        else:
-            info = "not prepared"
-        return f"<{self.__class__.__name__} {self.reason} {info}>"
-
-    def __getitem__(self, key: str) -> Any:
-        return self._state[key]
-
-    def __setitem__(self, key: str, value: Any) -> None:
-        self._state[key] = value
-
-    def __delitem__(self, key: str) -> None:
-        del self._state[key]
-
-    def __len__(self) -> int:
-        return len(self._state)
-
-    def __iter__(self) -> Iterator[str]:
-        return iter(self._state)
-
-    def __hash__(self) -> int:
-        return hash(id(self))
-
-    def __eq__(self, other: object) -> bool:
-        return self is other
-
-
-class Response(StreamResponse):
-    def __init__(
-        self,
-        *,
-        body: Any = None,
-        status: int = 200,
-        reason: Optional[str] = None,
-        text: Optional[str] = None,
-        headers: Optional[LooseHeaders] = None,
-        content_type: Optional[str] = None,
-        charset: Optional[str] = None,
-        zlib_executor_size: Optional[int] = None,
-        zlib_executor: Optional[Executor] = None,
-    ) -> None:
-        if body is not None and text is not None:
-            raise ValueError("body and text are not allowed together")
-
-        if headers is None:
-            real_headers: CIMultiDict[str] = CIMultiDict()
-        elif not isinstance(headers, CIMultiDict):
-            real_headers = CIMultiDict(headers)
-        else:
-            real_headers = headers  # = cast('CIMultiDict[str]', headers)
-
-        if content_type is not None and "charset" in content_type:
-            raise ValueError("charset must not be in content_type " "argument")
-
-        if text is not None:
-            if hdrs.CONTENT_TYPE in real_headers:
-                if content_type or charset:
-                    raise ValueError(
-                        "passing both Content-Type header and "
-                        "content_type or charset params "
-                        "is forbidden"
-                    )
-            else:
-                # fast path for filling headers
-                if not isinstance(text, str):
-                    raise TypeError("text argument must be str (%r)" % type(text))
-                if content_type is None:
-                    content_type = "text/plain"
-                if charset is None:
-                    charset = "utf-8"
-                real_headers[hdrs.CONTENT_TYPE] = content_type + "; charset=" + charset
-                body = text.encode(charset)
-                text = None
-        else:
-            if hdrs.CONTENT_TYPE in real_headers:
-                if content_type is not None or charset is not None:
-                    raise ValueError(
-                        "passing both Content-Type header and "
-                        "content_type or charset params "
-                        "is forbidden"
-                    )
-            else:
-                if content_type is not None:
-                    if charset is not None:
-                        content_type += "; charset=" + charset
-                    real_headers[hdrs.CONTENT_TYPE] = content_type
-
-        super().__init__(status=status, reason=reason, headers=real_headers)
-
-        if text is not None:
-            self.text = text
-        else:
-            self.body = body
-
-        self._compressed_body: Optional[bytes] = None
-        self._zlib_executor_size = zlib_executor_size
-        self._zlib_executor = zlib_executor
-
-    @property
-    def body(self) -> Optional[Union[bytes, Payload]]:
-        return self._body
-
-    @body.setter
-    def body(
-        self,
-        body: bytes,
-        CONTENT_TYPE: istr = hdrs.CONTENT_TYPE,
-        CONTENT_LENGTH: istr = hdrs.CONTENT_LENGTH,
-    ) -> None:
-        if body is None:
-            self._body: Optional[bytes] = None
-            self._body_payload: bool = False
-        elif isinstance(body, (bytes, bytearray)):
-            self._body = body
-            self._body_payload = False
-        else:
-            try:
-                self._body = body = payload.PAYLOAD_REGISTRY.get(body)
-            except payload.LookupError:
-                raise ValueError("Unsupported body type %r" % type(body))
-
-            self._body_payload = True
-
-            headers = self._headers
-
-            # set content-length header if needed
-            if not self._chunked and CONTENT_LENGTH not in headers:
-                size = body.size
-                if size is not None:
-                    headers[CONTENT_LENGTH] = str(size)
-
-            # set content-type
-            if CONTENT_TYPE not in headers:
-                headers[CONTENT_TYPE] = body.content_type
-
-            # copy payload headers
-            if body.headers:
-                for (key, value) in body.headers.items():
-                    if key not in headers:
-                        headers[key] = value
-
-        self._compressed_body = None
-
-    @property
-    def text(self) -> Optional[str]:
-        if self._body is None:
-            return None
-        return self._body.decode(self.charset or "utf-8")
-
-    @text.setter
-    def text(self, text: str) -> None:
-        assert text is None or isinstance(
-            text, str
-        ), "text argument must be str (%r)" % type(text)
-
-        if self.content_type == "application/octet-stream":
-            self.content_type = "text/plain"
-        if self.charset is None:
-            self.charset = "utf-8"
-
-        self._body = text.encode(self.charset)
-        self._body_payload = False
-        self._compressed_body = None
-
-    @property
-    def content_length(self) -> Optional[int]:
-        if self._chunked:
-            return None
-
-        if hdrs.CONTENT_LENGTH in self._headers:
-            return super().content_length
-
-        if self._compressed_body is not None:
-            # Return length of the compressed body
-            return len(self._compressed_body)
-        elif self._body_payload:
-            # A payload without content length, or a compressed payload
-            return None
-        elif self._body is not None:
-            return len(self._body)
-        else:
-            return 0
-
-    @content_length.setter
-    def content_length(self, value: Optional[int]) -> None:
-        raise RuntimeError("Content length is set automatically")
-
-    async def write_eof(self, data: bytes = b"") -> None:
-        if self._eof_sent:
-            return
-        if self._compressed_body is None:
-            body: Optional[Union[bytes, Payload]] = self._body
-        else:
-            body = self._compressed_body
-        assert not data, f"data arg is not supported, got {data!r}"
-        assert self._req is not None
-        assert self._payload_writer is not None
-        if body is not None:
-            if self._req._method == hdrs.METH_HEAD or self._status in [204, 304]:
-                await super().write_eof()
-            elif self._body_payload:
-                payload = cast(Payload, body)
-                await payload.write(self._payload_writer)
-                await super().write_eof()
-            else:
-                await super().write_eof(cast(bytes, body))
-        else:
-            await super().write_eof()
-
-    async def _start(self, request: "BaseRequest") -> AbstractStreamWriter:
-        if not self._chunked and hdrs.CONTENT_LENGTH not in self._headers:
-            if not self._body_payload:
-                if self._body is not None:
-                    self._headers[hdrs.CONTENT_LENGTH] = str(len(self._body))
-                else:
-                    self._headers[hdrs.CONTENT_LENGTH] = "0"
-
-        return await super()._start(request)
-
-    def _compress_body(self, zlib_mode: int) -> None:
-        assert zlib_mode > 0
-        compressobj = zlib.compressobj(wbits=zlib_mode)
-        body_in = self._body
-        assert body_in is not None
-        self._compressed_body = compressobj.compress(body_in) + compressobj.flush()
-
-    async def _do_start_compression(self, coding: ContentCoding) -> None:
-        if self._body_payload or self._chunked:
-            return await super()._do_start_compression(coding)
-
-        if coding != ContentCoding.identity:
-            # Instead of using _payload_writer.enable_compression,
-            # compress the whole body
-            zlib_mode = (
-                16 + zlib.MAX_WBITS if coding == ContentCoding.gzip else zlib.MAX_WBITS
-            )
-            body_in = self._body
-            assert body_in is not None
-            if (
-                self._zlib_executor_size is not None
-                and len(body_in) > self._zlib_executor_size
-            ):
-                await asyncio.get_event_loop().run_in_executor(
-                    self._zlib_executor, self._compress_body, zlib_mode
-                )
-            else:
-                self._compress_body(zlib_mode)
-
-            body_out = self._compressed_body
-            assert body_out is not None
-
-            self._headers[hdrs.CONTENT_ENCODING] = coding.value
-            self._headers[hdrs.CONTENT_LENGTH] = str(len(body_out))
-
-
-def json_response(
-    data: Any = sentinel,
-    *,
-    text: Optional[str] = None,
-    body: Optional[bytes] = None,
-    status: int = 200,
-    reason: Optional[str] = None,
-    headers: Optional[LooseHeaders] = None,
-    content_type: str = "application/json",
-    dumps: JSONEncoder = json.dumps,
-) -> Response:
-    if data is not sentinel:
-        if text or body:
-            raise ValueError("only one of data, text, or body should be specified")
-        else:
-            text = dumps(data)
-    return Response(
-        text=text,
-        body=body,
-        status=status,
-        reason=reason,
-        headers=headers,
-        content_type=content_type,
-    )
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/merge/util.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/merge/util.py
deleted file mode 100644
index 42fe39d5f701e683f52ca7c4022b1bb85749fb6b..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/merge/util.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools.misc.timeTools import timestampNow
-from fontTools.ttLib.tables.DefaultTable import DefaultTable
-from functools import reduce
-import operator
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-
-
-# General utility functions for merging values from different fonts
-
-
-def equal(lst):
-    lst = list(lst)
-    t = iter(lst)
-    first = next(t)
-    assert all(item == first for item in t), "Expected all items to be equal: %s" % lst
-    return first
-
-
-def first(lst):
-    return next(iter(lst))
-
-
-def recalculate(lst):
-    return NotImplemented
-
-
-def current_time(lst):
-    return timestampNow()
-
-
-def bitwise_and(lst):
-    return reduce(operator.and_, lst)
-
-
-def bitwise_or(lst):
-    return reduce(operator.or_, lst)
-
-
-def avg_int(lst):
-    lst = list(lst)
-    return sum(lst) // len(lst)
-
-
-def onlyExisting(func):
-    """Returns a filter func that when called with a list,
-    only calls func on the non-NotImplemented items of the list,
-    and only so if there's at least one item remaining.
-    Otherwise returns NotImplemented."""
-
-    def wrapper(lst):
-        items = [item for item in lst if item is not NotImplemented]
-        return func(items) if items else NotImplemented
-
-    return wrapper
-
-
-def sumLists(lst):
-    l = []
-    for item in lst:
-        l.extend(item)
-    return l
-
-
-def sumDicts(lst):
-    d = {}
-    for item in lst:
-        d.update(item)
-    return d
-
-
-def mergeBits(bitmap):
-    def wrapper(lst):
-        lst = list(lst)
-        returnValue = 0
-        for bitNumber in range(bitmap["size"]):
-            try:
-                mergeLogic = bitmap[bitNumber]
-            except KeyError:
-                try:
-                    mergeLogic = bitmap["*"]
-                except KeyError:
-                    raise Exception("Don't know how to merge bit %s" % bitNumber)
-            shiftedBit = 1 << bitNumber
-            mergedValue = mergeLogic(bool(item & shiftedBit) for item in lst)
-            returnValue |= mergedValue << bitNumber
-        return returnValue
-
-    return wrapper
-
-
-class AttendanceRecordingIdentityDict(object):
-    """A dictionary-like object that records indices of items actually accessed
-    from a list."""
-
-    def __init__(self, lst):
-        self.l = lst
-        self.d = {id(v): i for i, v in enumerate(lst)}
-        self.s = set()
-
-    def __getitem__(self, v):
-        self.s.add(self.d[id(v)])
-        return v
-
-
-class GregariousIdentityDict(object):
-    """A dictionary-like object that welcomes guests without reservations and
-    adds them to the end of the guest list."""
-
-    def __init__(self, lst):
-        self.l = lst
-        self.s = set(id(v) for v in lst)
-
-    def __getitem__(self, v):
-        if id(v) not in self.s:
-            self.s.add(id(v))
-            self.l.append(v)
-        return v
-
-
-class NonhashableDict(object):
-    """A dictionary-like object mapping objects to values."""
-
-    def __init__(self, keys, values=None):
-        if values is None:
-            self.d = {id(v): i for i, v in enumerate(keys)}
-        else:
-            self.d = {id(k): v for k, v in zip(keys, values)}
-
-    def __getitem__(self, k):
-        return self.d[id(k)]
-
-    def __setitem__(self, k, v):
-        self.d[id(k)] = v
-
-    def __delitem__(self, k):
-        del self.d[id(k)]
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/yaml-95012b83.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/yaml-95012b83.js
deleted file mode 100644
index 3fef68bd6d3b922eebf9622184021189fa7e8cc2..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/yaml-95012b83.js
+++ /dev/null
@@ -1,2 +0,0 @@
-var l=["true","false","on","off","yes","no"],f=new RegExp("\\b(("+l.join(")|(")+"))$","i");const a={name:"yaml",token:function(n,i){var r=n.peek(),e=i.escaped;if(i.escaped=!1,r=="#"&&(n.pos==0||/\s/.test(n.string.charAt(n.pos-1))))return n.skipToEnd(),"comment";if(n.match(/^('([^']|\\.)*'?|"([^"]|\\.)*"?)/))return"string";if(i.literal&&n.indentation()>i.keyCol)return n.skipToEnd(),"string";if(i.literal&&(i.literal=!1),n.sol()){if(i.keyCol=0,i.pair=!1,i.pairStart=!1,n.match("---")||n.match("..."))return"def";if(n.match(/^\s*-\s+/))return"meta"}if(n.match(/^(\{|\}|\[|\])/))return r=="{"?i.inlinePairs++:r=="}"?i.inlinePairs--:r=="["?i.inlineList++:i.inlineList--,"meta";if(i.inlineList>0&&!e&&r==",")return n.next(),"meta";if(i.inlinePairs>0&&!e&&r==",")return i.keyCol=0,i.pair=!1,i.pairStart=!1,n.next(),"meta";if(i.pairStart){if(n.match(/^\s*(\||\>)\s*/))return i.literal=!0,"meta";if(n.match(/^\s*(\&|\*)[a-z0-9\._-]+\b/i))return"variable";if(i.inlinePairs==0&&n.match(/^\s*-?[0-9\.\,]+\s?$/)||i.inlinePairs>0&&n.match(/^\s*-?[0-9\.\,]+\s?(?=(,|}))/))return"number";if(n.match(f))return"keyword"}return!i.pair&&n.match(/^\s*(?:[,\[\]{}&*!|>'"%@`][^\s'":]|[^,\[\]{}#&*!|>'"%@`])[^#]*?(?=\s*:($|\s))/)?(i.pair=!0,i.keyCol=n.indentation(),"atom"):i.pair&&n.match(/^:\s*/)?(i.pairStart=!0,"meta"):(i.pairStart=!1,i.escaped=r=="\\",n.next(),null)},startState:function(){return{pair:!1,pairStart:!1,keyCol:0,inlinePairs:0,inlineList:0,literal:!1,escaped:!1}},languageData:{commentTokens:{line:"#"}}};export{a as yaml};
-//# sourceMappingURL=yaml-95012b83.js.map
diff --git a/spaces/langvision/codellama-34b-chat/style.css b/spaces/langvision/codellama-34b-chat/style.css
deleted file mode 100644
index 303c3d7ef3b06c42b211797cd2d5af9800589092..0000000000000000000000000000000000000000
--- a/spaces/langvision/codellama-34b-chat/style.css
+++ /dev/null
@@ -1,16 +0,0 @@
-h1 {
-  text-align: center;
-}
-
-#duplicate-button {
-  margin: auto;
-  color: white;
-  background: #1565c0;
-  border-radius: 100vh;
-}
-
-#component-0 {
-  max-width: 900px;
-  margin: auto;
-  padding-top: 1.5rem;
-}
diff --git a/spaces/lgaleana/toolkit/ai/__init__.py b/spaces/lgaleana/toolkit/ai/__init__.py
deleted file mode 100644
index 69882e0db3098eb24861ecb21758e67a84dd0536..0000000000000000000000000000000000000000
--- a/spaces/lgaleana/toolkit/ai/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import image, llm
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Dys Vocal Crack [HOT].md b/spaces/lincquiQcaudo/Top-20-Diffusion/Dys Vocal Crack [HOT].md
deleted file mode 100644
index 8915784c1b98e632cef79bf8fff0f1cb17e13559..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Dys Vocal Crack [HOT].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Dys Vocal Crack</h2><br /><p><b><b>Download File</b> &harr; <a href="https://bytlly.com/2uGxdc">https://bytlly.com/2uGxdc</a></b></p><br /><br />
-<br />
-vocal effort that impairs communication or reduces voice-related ... eliminating articles that did not have hoarseness or dys- ... Does your voice crack or break? 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Elicenser Control Center Crack Mackinstmank LINK.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Elicenser Control Center Crack Mackinstmank LINK.md
deleted file mode 100644
index 4c071b5e0326d593364a38f681f8b11b8aed0e8a..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Elicenser Control Center Crack Mackinstmank LINK.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Elicenser Control Center Crack Mackinstmank</h2><br /><p><b><b>Download Zip</b> &#10022;&#10022;&#10022; <a href="https://bytlly.com/2uGxX3">https://bytlly.com/2uGxX3</a></b></p><br /><br />
-<br />
-Elicenser Control Center Crack Mackinstmank ... an expired Cubase 7 trial licence from the eLicenser Control Center? ... to crack (hack) eLicenser protected ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/utils/utils_logging.py b/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/utils/utils_logging.py
deleted file mode 100644
index c787b6aae7cd037a4718df44d672b8ffa9e5c249..0000000000000000000000000000000000000000
--- a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/utils/utils_logging.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import logging
-import os
-import sys
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current value
-    """
-
-    def __init__(self):
-        self.val = None
-        self.avg = None
-        self.sum = None
-        self.count = None
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
-def init_logging(rank, models_root):
-    if rank == 0:
-        log_root = logging.getLogger()
-        log_root.setLevel(logging.INFO)
-        formatter = logging.Formatter("Training: %(asctime)s-%(message)s")
-        handler_file = logging.FileHandler(os.path.join(models_root, "training.log"))
-        handler_stream = logging.StreamHandler(sys.stdout)
-        handler_file.setFormatter(formatter)
-        handler_stream.setFormatter(formatter)
-        log_root.addHandler(handler_file)
-        log_root.addHandler(handler_stream)
-        log_root.info('rank_id: %d' % rank)
diff --git a/spaces/lmalta/PDF_Doc_Search/Dockerfile b/spaces/lmalta/PDF_Doc_Search/Dockerfile
deleted file mode 100644
index c4bdad25ef1b7c55f81cf1de31b9bb35c7c670d8..0000000000000000000000000000000000000000
--- a/spaces/lmalta/PDF_Doc_Search/Dockerfile
+++ /dev/null
@@ -1,16 +0,0 @@
-FROM python:3.9
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-RUN python3 -m pip install --no-cache-dir --upgrade pip
-RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-COPY . .
-
-CMD ["panel", "serve", "/code/main.py", "--address", "0.0.0.0", "--port", "7860", "--allow-websocket-origin", "lmalta-pdf-doc-search.hf.space",  "--allow-websocket-origin", "0.0.0.0:7860"]
-
-RUN mkdir /.cache
-RUN chmod 777 /.cache
-RUN mkdir .chroma
-RUN chmod 777 .chroma
diff --git a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/csrc/pyinterface.h b/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/csrc/pyinterface.h
deleted file mode 100644
index e53c88ae9dfcea7f9766828168d3ad35a404b699..0000000000000000000000000000000000000000
--- a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/csrc/pyinterface.h
+++ /dev/null
@@ -1,38 +0,0 @@
-#include <opencv2/core.hpp>
-#include <cstdlib>
-#include <cstdio>
-#include <cstring>
-
-extern "C" {
-
-struct PM_shape_t {
-    int width, height, channels;
-};
-
-enum PM_dtype_e {
-    PM_UINT8,
-    PM_INT8,
-    PM_UINT16,
-    PM_INT16,
-    PM_INT32,
-    PM_FLOAT32,
-    PM_FLOAT64,
-};
-
-struct PM_mat_t {
-    void *data_ptr;
-    PM_shape_t shape;
-    int dtype;
-};
-
-void PM_set_random_seed(unsigned int seed);
-void PM_set_verbose(int value);
-
-void PM_free_pymat(PM_mat_t pymat);
-PM_mat_t PM_inpaint(PM_mat_t image, PM_mat_t mask, int patch_size);
-PM_mat_t PM_inpaint_regularity(PM_mat_t image, PM_mat_t mask, PM_mat_t ijmap, int patch_size, float guide_weight);
-PM_mat_t PM_inpaint2(PM_mat_t image, PM_mat_t mask, PM_mat_t global_mask, int patch_size);
-PM_mat_t PM_inpaint2_regularity(PM_mat_t image, PM_mat_t mask, PM_mat_t global_mask, PM_mat_t ijmap, int patch_size, float guide_weight);
-
-} /*  extern "C" */
-
diff --git a/spaces/lpnguyen/calculator/README.md b/spaces/lpnguyen/calculator/README.md
deleted file mode 100644
index e67b5506a4f0bda9b2c1489417a5a6545ed07ad6..0000000000000000000000000000000000000000
--- a/spaces/lpnguyen/calculator/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Testing
-emoji: 🔥
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/luckwill/chiakicc/monotonic_align/__init__.py b/spaces/luckwill/chiakicc/monotonic_align/__init__.py
deleted file mode 100644
index 40b6f64aa116c74cac2f6a33444c9eeea2fdb38c..0000000000000000000000000000000000000000
--- a/spaces/luckwill/chiakicc/monotonic_align/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    """ numba optimized version.
-    neg_cent: [b, t_t, t_s]
-    mask: [b, t_t, t_s]
-    """
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)
-
diff --git a/spaces/lunarflu/HuggingMod/README.md b/spaces/lunarflu/HuggingMod/README.md
deleted file mode 100644
index 78d929605073505d9b4bc47a713aebfb1c203e79..0000000000000000000000000000000000000000
--- a/spaces/lunarflu/HuggingMod/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: HuggingMod
-emoji: 🌖
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lwchen/CodeFormer/CodeFormer/basicsr/ops/upfirdn2d/upfirdn2d.py b/spaces/lwchen/CodeFormer/CodeFormer/basicsr/ops/upfirdn2d/upfirdn2d.py
deleted file mode 100644
index 667f96e1ded35d48f163f37e21d1ed8ff191aac3..0000000000000000000000000000000000000000
--- a/spaces/lwchen/CodeFormer/CodeFormer/basicsr/ops/upfirdn2d/upfirdn2d.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501
-
-import torch
-from torch.autograd import Function
-from torch.nn import functional as F
-
-try:
-    from . import upfirdn2d_ext
-except ImportError:
-    import os
-    BASICSR_JIT = os.getenv('BASICSR_JIT')
-    if BASICSR_JIT == 'True':
-        from torch.utils.cpp_extension import load
-        module_path = os.path.dirname(__file__)
-        upfirdn2d_ext = load(
-            'upfirdn2d',
-            sources=[
-                os.path.join(module_path, 'src', 'upfirdn2d.cpp'),
-                os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'),
-            ],
-        )
-
-
-class UpFirDn2dBackward(Function):
-
-    @staticmethod
-    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size):
-
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_ext.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_ext.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
-        #                                  ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1])
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1)
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    if input.device.type == 'cpu':
-        out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
-    else:
-        out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]))
-
-    return out
-
-
-def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
-    out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/device_system_tag.h b/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/device_system_tag.h
deleted file mode 100644
index 394b991cde7e23a76a32b6b92476b34881352b87..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/device_system_tag.h
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// #include the device system's execution_policy header
-#define __THRUST_DEVICE_SYSTEM_TAG_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/execution_policy.h>
-#include __THRUST_DEVICE_SYSTEM_TAG_HEADER
-#undef __THRUST_DEVICE_SYSTEM_TAG_HEADER
-
-namespace thrust
-{
-
-typedef thrust::system::__THRUST_DEVICE_SYSTEM_NAMESPACE::tag device_system_tag;
-
-} // end thrust
-
-// TODO remove this in 1.8.0
-namespace thrust
-{
-
-typedef THRUST_DEPRECATED device_system_tag device_space_tag;
-
-} // end thrust
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/temporary_buffer.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/temporary_buffer.h
deleted file mode 100644
index 2adfaf2810c67462e41f271e43ad0aff9cfbf75f..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/temporary_buffer.h
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special temporary buffer functions
-
diff --git a/spaces/madara-uchiha/MovieMakerAI/README.md b/spaces/madara-uchiha/MovieMakerAI/README.md
deleted file mode 100644
index 89c5a348ff05e91de8f2ae76df0df2510bf5a612..0000000000000000000000000000000000000000
--- a/spaces/madara-uchiha/MovieMakerAI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MovieMakerAI
-emoji: 📉
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/marioboy/neil-breen/utils/modelutils.py b/spaces/marioboy/neil-breen/utils/modelutils.py
deleted file mode 100644
index 6acaa984e0c7876f9149fc1ff99001b7761dc80b..0000000000000000000000000000000000000000
--- a/spaces/marioboy/neil-breen/utils/modelutils.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from pathlib import Path
-
-def check_model_paths(encoder_path: Path, synthesizer_path: Path, vocoder_path: Path):
-    # This function tests the model paths and makes sure at least one is valid.
-    if encoder_path.is_file() or encoder_path.is_dir():
-        return
-    if synthesizer_path.is_file() or synthesizer_path.is_dir():
-        return
-    if vocoder_path.is_file() or vocoder_path.is_dir():
-        return
-
-    # If none of the paths exist, remind the user to download models if needed
-    print("********************************************************************************")
-    print("Error: Model files not found. Follow these instructions to get and install the models:")
-    print("https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Pretrained-models")
-    print("********************************************************************************\n")
-    quit(-1)
diff --git a/spaces/marker22/Bark-Voice-Cloning/README.md b/spaces/marker22/Bark-Voice-Cloning/README.md
deleted file mode 100644
index 5546ba5a357cbeed205ad99d6c4a41201b4a15d5..0000000000000000000000000000000000000000
--- a/spaces/marker22/Bark-Voice-Cloning/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Bark Voice Cloning
-emoji: 🎶
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: kevinwang676/Bark-Voice-Cloning
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/dataset/__init__.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/dataset/__init__.py
deleted file mode 100644
index 7f3999734455352473532ef25cddf059eb5baee3..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/dataset/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""
- # Copyright 2020 Adobe
- # All Rights Reserved.
- 
- # NOTICE: Adobe permits you to use, modify, and distribute this file in
- # accordance with the terms of the Adobe license agreement accompanying
- # it.
- 
-"""
-
diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/quantization/vq.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/quantization/vq.py
deleted file mode 100644
index aa57bea59db95ddae35e0657f723ca3a29ee943b..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/quantization/vq.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer, QuantizedResult
-from .core_vq import ResidualVectorQuantization
-
-
-class ResidualVectorQuantizer(BaseQuantizer):
-    """Residual Vector Quantizer.
-
-    Args:
-        dimension (int): Dimension of the codebooks.
-        n_q (int): Number of residual vector quantizers used.
-        q_dropout (bool): Random quantizer drop out at train time.
-        bins (int): Codebook size.
-        decay (float): Decay for exponential moving average over the codebooks.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider.
-            for orthogonal regularization.
-    """
-    def __init__(
-        self,
-        dimension: int = 256,
-        n_q: int = 8,
-        q_dropout: bool = False,
-        bins: int = 1024,
-        decay: float = 0.99,
-        kmeans_init: bool = True,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        self.max_n_q = n_q
-        self.n_q = n_q
-        self.q_dropout = q_dropout
-        self.dimension = dimension
-        self.bins = bins
-        self.decay = decay
-        self.kmeans_init = kmeans_init
-        self.kmeans_iters = kmeans_iters
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-        self.vq = ResidualVectorQuantization(
-            dim=self.dimension,
-            codebook_size=self.bins,
-            num_quantizers=self.n_q,
-            decay=self.decay,
-            kmeans_init=self.kmeans_init,
-            kmeans_iters=self.kmeans_iters,
-            threshold_ema_dead_code=self.threshold_ema_dead_code,
-            orthogonal_reg_weight=self.orthogonal_reg_weight,
-            orthogonal_reg_active_codes_only=self.orthogonal_reg_active_codes_only,
-            orthogonal_reg_max_codes=self.orthogonal_reg_max_codes,
-            channels_last=False
-        )
-
-    def forward(self, x: torch.Tensor, frame_rate: int):
-        n_q = self.n_q
-        if self.training and self.q_dropout:
-            n_q = int(torch.randint(1, self.n_q + 1, (1,)).item())
-        bw_per_q = math.log2(self.bins) * frame_rate / 1000
-        quantized, codes, commit_loss = self.vq(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        bw = torch.tensor(n_q * bw_per_q).to(x)
-        return QuantizedResult(quantized, codes, bw, penalty=torch.mean(commit_loss))
-
-    def encode(self, x: torch.Tensor) -> torch.Tensor:
-        """Encode a given input tensor with the specified frame rate at the given bandwidth.
-        The RVQ encode method sets the appropriate number of quantizer to use
-        and returns indices for each quantizer.
-        """
-        n_q = self.n_q
-        codes = self.vq.encode(x, n_q=n_q)
-        codes = codes.transpose(0, 1)
-        # codes is [B, K, T], with T frames, K nb of codebooks.
-        return codes
-
-    def decode(self, codes: torch.Tensor) -> torch.Tensor:
-        """Decode the given codes to the quantized representation."""
-        # codes is [B, K, T], with T frames, K nb of codebooks, vq.decode expects [K, B, T].
-        codes = codes.transpose(0, 1)
-        quantized = self.vq.decode(codes)
-        return quantized
-
-    @property
-    def total_codebooks(self):
-        return self.max_n_q
-
-    @property
-    def num_codebooks(self):
-        return self.n_q
-
-    def set_num_codebooks(self, n: int):
-        assert n > 0 and n <= self.max_n_q
-        self.n_q = n
diff --git a/spaces/matthoffner/chatbot/pages/_app.tsx b/spaces/matthoffner/chatbot/pages/_app.tsx
deleted file mode 100644
index ad28961180ef5983ef2d9d8d70ba3d40f2c8324e..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/pages/_app.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import { Toaster } from 'react-hot-toast';
-import { QueryClient, QueryClientProvider } from 'react-query';
-
-import { appWithTranslation } from 'next-i18next';
-import type { AppProps } from 'next/app';
-import { Inter } from 'next/font/google';
-
-import '@/styles/globals.css';
-
-const inter = Inter({ subsets: ['latin'] });
-
-function App({ Component, pageProps }: AppProps<{}>) {
-  const queryClient = new QueryClient();
-
-  return (
-    <div className={inter.className}>
-      <Toaster />
-      <QueryClientProvider client={queryClient}>
-        <Component {...pageProps} />
-      </QueryClientProvider>
-    </div>
-  );
-}
-
-export default appWithTranslation(App);
diff --git a/spaces/matthoffner/chatbot/types/env.ts b/spaces/matthoffner/chatbot/types/env.ts
deleted file mode 100644
index f6b9dd7c97885ba49e2b6c238a297cbf98070961..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/types/env.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-export interface ProcessEnv {
-  OPENAI_API_KEY: string;
-  OPENAI_API_HOST?: string;
-  OPENAI_API_TYPE?: 'openai' | 'azure';
-  OPENAI_API_VERSION?: string;
-  OPENAI_ORGANIZATION?: string;
-}
diff --git a/spaces/matthoffner/open-codetree/graphql/definitions/pageView.definition.ts b/spaces/matthoffner/open-codetree/graphql/definitions/pageView.definition.ts
deleted file mode 100644
index bac0a9e48a2ef3a2737c656137b0549e67ec4909..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/open-codetree/graphql/definitions/pageView.definition.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-import { gql } from "@apollo/client";
-
-export const PageViewQuery = gql(`query pageViews {
-  pageViews {
-    data {
-      id
-      ip
-      createdAt
-      updatedAt
-    }
-    message
-    status
-  }
-}`);
diff --git a/spaces/merle/PROTEIN_GENERATOR/model/utils/.ipynb_checkpoints/calc_dssp-checkpoint.py b/spaces/merle/PROTEIN_GENERATOR/model/utils/.ipynb_checkpoints/calc_dssp-checkpoint.py
deleted file mode 100644
index fe2b975316f2de89d021d4dff442182192d5b7f8..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/model/utils/.ipynb_checkpoints/calc_dssp-checkpoint.py
+++ /dev/null
@@ -1,234 +0,0 @@
-#@title get secondary structure (SSE) from given PDB file
-#@markdown So far it seems the best solution is to steal code from biotite
-#@markdown which calculates the SSE of a peptide chain based on the P-SEA algorithm (Labesse 1997)
-# CODE FROM BIOKITE
-# From Krypton
-import numpy as np
-import random
-import torch
-
-def vector_dot(v1,v2):
-    return (v1*v2).sum(axis=-1)
-
-def norm_vector(v):
-    factor = np.linalg.norm(v, axis=-1)
-    if isinstance(factor, np.ndarray):
-        v /= factor[..., np.newaxis]
-    else:
-        v /= factor
-    return v
-
-def coord(x):
-  return np.asarray(x)
-def displacement(atoms1, atoms2):
-    v1 = coord(atoms1)
-    v2 = coord(atoms2)
-    if len(v1.shape) <= len(v2.shape):
-        diff = v2 - v1
-    else:
-        diff = -(v1 - v2)
-    return diff
-def distance(atoms1, atoms2):
-    diff = displacement(atoms1, atoms2)
-    return np.sqrt(vector_dot(diff, diff))
-
-def angle(atoms1, atoms2, atoms3):
-    v1 = displacement(atoms1, atoms2)
-    v2 = displacement(atoms3, atoms2)
-    norm_vector(v1)
-    norm_vector(v2)
-    return np.arccos(vector_dot(v1,v2))
-
-def dihedral(atoms1, atoms2, atoms3, atoms4):
-    v1 = displacement(atoms1, atoms2)
-    v2 = displacement(atoms2, atoms3)
-    v3 = displacement(atoms3, atoms4)
-    norm_vector(v1)
-    norm_vector(v2)
-    norm_vector(v3)
-    
-    n1 = np.cross(v1, v2)
-    n2 = np.cross(v2, v3)
-    
-    # Calculation using atan2, to ensure the correct sign of the angle 
-    x = vector_dot(n1,n2)
-    y = vector_dot(np.cross(n1,n2), v2)
-    return np.arctan2(y,x)
-
-def replace_letters(arr):
-  # Create a dictionary that maps the letters 'a', 'b', and 'c' to the corresponding numbers
-  letter_to_number = {'a': 0, 'b': 1, 'c': 2}
-  
-  # Create a new array that will hold the numbers
-  nums = []
-  
-  # Loop through the input array and replace the letters with the corresponding numbers
-  for letter in arr:
-    if letter in letter_to_number:
-      nums.append(letter_to_number[letter])
-    else:
-      nums.append(letter)
-  
-  return np.array(nums)
-
-def replace_with_mask(arr, percentage, replace_loops=False):
-  # Make sure the percentage is between 0 and 100
-  percentage = min(max(percentage, 0), 100)
-  
-  # Calculate the number of values to replace
-  num_to_replace = int(len(arr) * percentage / 100)
-  
-  # Choose a random subset of the array to replace
-  replace_indices = random.sample(range(len(arr)), num_to_replace)
-  
-  # Replace the values at the chosen indices with the number 3
-  for i in replace_indices:
-    arr[i] = 3
-
-  if replace_loops:
-    for i in arr:
-        if arr[i] == 2:
-            arr[i] = 3
-  
-  return arr
-
-def annotate_sse(ca_coord, percentage_mask=0, replace_loops=False):
-  _radians_to_angle = 2*np.pi/360
-
-  _r_helix = ((89-12)*_radians_to_angle, (89+12)*_radians_to_angle)
-  _a_helix = ((50-20)*_radians_to_angle, (50+20)*_radians_to_angle)
-  _d2_helix = ((5.5-0.5), (5.5+0.5))
-  _d3_helix = ((5.3-0.5), (5.3+0.5))
-  _d4_helix = ((6.4-0.6), (6.4+0.6))
-
-  _r_strand = ((124-14)*_radians_to_angle, (124+14)*_radians_to_angle)
-  _a_strand = ((-180)*_radians_to_angle, (-125)*_radians_to_angle,
-              (145)*_radians_to_angle, (180)*_radians_to_angle)
-  _d2_strand = ((6.7-0.6), (6.7+0.6))
-  _d3_strand = ((9.9-0.9), (9.9+0.9))
-  _d4_strand = ((12.4-1.1), (12.4+1.1))
-
-  # Filter all CA atoms in the relevant chain.
-
-  d2i_coord = np.full(( len(ca_coord), 2, 3 ), np.nan)
-  d3i_coord = np.full(( len(ca_coord), 2, 3 ), np.nan)
-  d4i_coord = np.full(( len(ca_coord), 2, 3 ), np.nan)
-  ri_coord = np.full(( len(ca_coord), 3, 3 ), np.nan)
-  ai_coord = np.full(( len(ca_coord), 4, 3 ), np.nan)
-  
-  # The distances and angles are not defined for the entire interval,
-  # therefore the indices do not have the full range
-  # Values that are not defined are NaN
-  for i in range(1, len(ca_coord)-1):
-      d2i_coord[i] = (ca_coord[i-1], ca_coord[i+1])
-  for i in range(1, len(ca_coord)-2):
-      d3i_coord[i] = (ca_coord[i-1], ca_coord[i+2])
-  for i in range(1, len(ca_coord)-3):
-      d4i_coord[i] = (ca_coord[i-1], ca_coord[i+3])
-  for i in range(1, len(ca_coord)-1):
-      ri_coord[i] = (ca_coord[i-1], ca_coord[i], ca_coord[i+1])
-  for i in range(1, len(ca_coord)-2):
-      ai_coord[i] = (ca_coord[i-1], ca_coord[i],
-                      ca_coord[i+1], ca_coord[i+2])
-  
-  d2i = distance(d2i_coord[:,0], d2i_coord[:,1])
-  d3i = distance(d3i_coord[:,0], d3i_coord[:,1])
-  d4i = distance(d4i_coord[:,0], d4i_coord[:,1])
-  ri = angle(ri_coord[:,0], ri_coord[:,1], ri_coord[:,2])
-  ai = dihedral(ai_coord[:,0], ai_coord[:,1],
-                ai_coord[:,2], ai_coord[:,3])
-  
-  sse = np.full(len(ca_coord), "c", dtype="U1")
-  
-  # Annotate helices
-  # Find CA that meet criteria for potential helices
-  is_pot_helix = np.zeros(len(sse), dtype=bool)
-  for i in range(len(sse)):
-      if (
-              d3i[i] >= _d3_helix[0] and d3i[i] <= _d3_helix[1]
-          and d4i[i] >= _d4_helix[0] and d4i[i] <= _d4_helix[1]
-          ) or (
-              ri[i] >= _r_helix[0] and ri[i] <= _r_helix[1]
-          and ai[i] >= _a_helix[0] and ai[i] <= _a_helix[1]
-          ):
-              is_pot_helix[i] = True
-  # Real helices are 5 consecutive helix elements
-  is_helix = np.zeros(len(sse), dtype=bool)
-  counter = 0
-  for i in range(len(sse)):
-      if is_pot_helix[i]:
-          counter += 1
-      else:
-          if counter >= 5:
-              is_helix[i-counter : i] = True
-          counter = 0
-  # Extend the helices by one at each end if CA meets extension criteria
-  i = 0
-  while i < len(sse):
-      if is_helix[i]:
-          sse[i] = "a"
-          if (
-              d3i[i-1] >= _d3_helix[0] and d3i[i-1] <= _d3_helix[1]
-              ) or (
-              ri[i-1] >= _r_helix[0] and ri[i-1] <= _r_helix[1]
-              ):
-                  sse[i-1] = "a"
-          sse[i] = "a"
-          if (
-              d3i[i+1] >= _d3_helix[0] and d3i[i+1] <= _d3_helix[1]
-              ) or (
-              ri[i+1] >= _r_helix[0] and ri[i+1] <= _r_helix[1]
-              ):
-                  sse[i+1] = "a"
-      i += 1
-  
-  # Annotate sheets
-  # Find CA that meet criteria for potential strands
-  is_pot_strand = np.zeros(len(sse), dtype=bool)
-  for i in range(len(sse)):
-      if (    d2i[i] >= _d2_strand[0] and d2i[i] <= _d2_strand[1]
-          and d3i[i] >= _d3_strand[0] and d3i[i] <= _d3_strand[1]
-          and d4i[i] >= _d4_strand[0] and d4i[i] <= _d4_strand[1]
-          ) or (
-              ri[i] >= _r_strand[0] and ri[i] <= _r_strand[1]
-          and (   (ai[i] >= _a_strand[0] and ai[i] <= _a_strand[1])
-                or (ai[i] >= _a_strand[2] and ai[i] <= _a_strand[3]))
-          ):
-              is_pot_strand[i] = True
-  # Real strands are 5 consecutive strand elements,
-  # or shorter fragments of at least 3 consecutive strand residues,
-  # if they are in hydrogen bond proximity to 5 other residues
-  pot_strand_coord = ca_coord[is_pot_strand]
-  is_strand = np.zeros(len(sse), dtype=bool)
-  counter = 0
-  contacts = 0
-  for i in range(len(sse)):
-      if is_pot_strand[i]:
-          counter += 1
-          coord = ca_coord[i]
-          for strand_coord in ca_coord:
-              dist = distance(coord, strand_coord)
-              if dist >= 4.2 and dist <= 5.2:
-                  contacts += 1
-      else:
-          if counter >= 4:
-              is_strand[i-counter : i] = True
-          elif counter == 3 and contacts >= 5:
-              is_strand[i-counter : i] = True
-          counter = 0
-          contacts = 0
-  # Extend the strands by one at each end if CA meets extension criteria
-  i = 0
-  while i < len(sse):
-      if is_strand[i]:
-          sse[i] = "b"
-          if d3i[i-1] >= _d3_strand[0] and d3i[i-1] <= _d3_strand[1]:
-              sse[i-1] = "b"
-          sse[i] = "b"
-          if d3i[i+1] >= _d3_strand[0] and d3i[i+1] <= _d3_strand[1]:
-              sse[i+1] = "b"
-      i += 1
-  sse=replace_letters(sse)
-  sse=replace_with_mask(sse, percentage_mask, replace_loops=replace_loops)
-  sse=torch.nn.functional.one_hot(torch.tensor(sse), num_classes=4)
-  return sse
diff --git a/spaces/merve/fill-in-the-blank/public/uncertainty-calibration/style.css b/spaces/merve/fill-in-the-blank/public/uncertainty-calibration/style.css
deleted file mode 100644
index 8073cf0a59eac0be0e293b35af5255c40c063e21..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/public/uncertainty-calibration/style.css
+++ /dev/null
@@ -1,89 +0,0 @@
-svg{
-  overflow: visible;
-}
-
-text{
-  fill: #202124;
-  user-select: none;
-}
-
-.domain{
-  display: none;
-}
-
-.thresholds, .threshold > g{
-  cursor: pointer;
-}
-
-svg{
-  user-select: none;
-}
-
-text.axis-label .legend-text{
-  font-family: 'Roboto';
-  font-style: normal;
-  font-size: 16px;
-  line-height: 20px;
-  /* identical to box height, or 125% */
-  
-  fill: #000;
-}
-
-.axis text{
-  font-size: 10px;
-}
-
-text{
-  text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
-}
-
-
-
-
-.bucket text{
-  /*text-shadow: 0 1px 0 #000, 1px 0 0 #000, 0 -1px 0 #000, -1px 0 0 #000;*/
-  /*fill:  #fff;*/
-  font-size: 11px;
-}
-
-
-.big-text{
-  font-variant-numeric: tabular-nums;
-  font-size: 16px;
-}
-
-#card{
-  display: flex;
-  flex-direction: column;
-  align-items: flex-start;
-  padding: 24px 24px;
-  gap: 6px;
-
-  background: #EDF4EC;
-  border: 1px solid #34A853;
-  box-sizing: border-box;
-  border-radius: 4px;
-}
-
-text.val-text{
-  background: #DFE9E1;
-  border: 1px solid #476C63;
-  box-sizing: border-box;
-  border-radius: 4px;
-  fill: #2A4C4A;
-  text-shadow: none;
-}
-
-.val-box{
-  fill: #DFE9E1;
-  stroke: #476C63;
-  opacity: 1;
-}
-
-.legend-title{
-  fill: #002622;
-}
-
-h3 {
-  color: #00695C;
-}
\ No newline at end of file
diff --git a/spaces/merve/measuring-fairness/public/fill-in-the-blank/README.md b/spaces/merve/measuring-fairness/public/fill-in-the-blank/README.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mingyuan/ReMoDiffuse/mogen/models/losses/__init__.py b/spaces/mingyuan/ReMoDiffuse/mogen/models/losses/__init__.py
deleted file mode 100644
index c893bf204bf8500b85a583ba82b693236cc6a8e8..0000000000000000000000000000000000000000
--- a/spaces/mingyuan/ReMoDiffuse/mogen/models/losses/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from .mse_loss import MSELoss
-from .utils import (
-    convert_to_one_hot,
-    reduce_loss,
-    weight_reduce_loss,
-    weighted_loss,
-)
-
-
-__all__ = [
-    'convert_to_one_hot', 'reduce_loss', 'weight_reduce_loss', 'weighted_loss',
-    'MSELoss'
-]
\ No newline at end of file
diff --git a/spaces/mishtert/tracer/examples.py b/spaces/mishtert/tracer/examples.py
deleted file mode 100644
index dea1633ea18ffad8efc42dd6931cff4f700e41b9..0000000000000000000000000000000000000000
--- a/spaces/mishtert/tracer/examples.py
+++ /dev/null
@@ -1,23 +0,0 @@
-LINK_LIST = {
-    "Sample Link 1": "https://www.gene.com/medical-professionals/pipeline",
-    "Sample Link 2": "https://www.exelixis.com/pipeline/",
-    "Sample Link 3": "https://www.roche.com/dam/jcr:22160102-e04d-4484-ae3b-0f474105647e/en/diaq321.pdf",
-}
-
-STUDY_LIST = {
-    "Study 1": "NCT04827875",
-    "Study 2": "NCT05298423",
-    "Study 3": "NCT04479852"
-}
-
-FILE_LIST = {
-    "File 1": "TF-Q1'22-10Q"
-}
-
-CATEGORY_LIST = {
-
-    "Category 1": 'Baby',
-    "Category 2": 'Clothing, Shoes & Jewelry',
-    "Category 3": 'Health & Household',
-    "Category 4": "Office Products"
-}
\ No newline at end of file
diff --git a/spaces/mithril-security/blind_chat/.svelte-kit/types/src/routes/privacy/$types.d.ts b/spaces/mithril-security/blind_chat/.svelte-kit/types/src/routes/privacy/$types.d.ts
deleted file mode 100644
index 2b7cd88fb7d834df80282d97c8adb0de7a12e296..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/.svelte-kit/types/src/routes/privacy/$types.d.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand<T> = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = {  }
-type RouteId = '/privacy';
-type MaybeWithVoid<T> = {} extends T ? T | void : T;
-export type RequiredKeys<T> = { [K in keyof T]-?: {} extends { [P in K]: T[K] } ? never : K; }[keyof T];
-type OutputDataShape<T> = MaybeWithVoid<Omit<App.PageData, RequiredKeys<T>> & Partial<Pick<App.PageData, keyof T & keyof App.PageData>> & Record<string, any>>
-type EnsureDefined<T> = T extends null | undefined ? {} : T;
-type OptionalUnion<U extends Record<string, any>, A extends keyof U = U extends U ? keyof U : never> = U extends unknown ? { [P in Exclude<A, keyof U>]?: never } & U : never;
-export type Snapshot<T = any> = Kit.Snapshot<T>;
-type PageParentData = EnsureDefined<import('../$types.js').LayoutData>;
-
-export type PageServerData = null;
-export type PageData = Expand<PageParentData>;
\ No newline at end of file
diff --git a/spaces/mmecheri/Rakuten_Streamlit/home.py b/spaces/mmecheri/Rakuten_Streamlit/home.py
deleted file mode 100644
index ffd589d76e927d3166406cdf2cd521babee818d1..0000000000000000000000000000000000000000
--- a/spaces/mmecheri/Rakuten_Streamlit/home.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import streamlit as st
-
-     
-# homme.py
-
-def app():
-    
-    st.title("Rakuten France Multimodal Product Data Classification")
-    
-    st.header("Le projet")  
-    page_content(text_page ='./page_descriptions/description_projet.md')  
-    
-    
-def page_content(text_page):
-        
-        '''The text page. Read from .md file '''
-        with open(text_page, 'r', encoding='utf-8') as txtpage: 
-            txtpage = txtpage.read().split('---Insersetion---')
-            st.markdown(txtpage[0], unsafe_allow_html=True)
-        st.markdown('----------' ) 
-        st.markdown(txtpage[1], unsafe_allow_html=True)
-       
-       
\ No newline at end of file
diff --git a/spaces/mnauf/detect-bees/models/common.py b/spaces/mnauf/detect-bees/models/common.py
deleted file mode 100644
index af8132fffb7a03905297cfe93f234c6a214633e5..0000000000000000000000000000000000000000
--- a/spaces/mnauf/detect-bees/models/common.py
+++ /dev/null
@@ -1,851 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Common modules
-"""
-
-import json
-import math
-import platform
-import warnings
-from collections import OrderedDict, namedtuple
-from copy import copy
-from pathlib import Path
-from urllib.parse import urlparse
-
-import cv2
-import numpy as np
-import pandas as pd
-import requests
-import torch
-import torch.nn as nn
-from IPython.display import display
-from PIL import Image
-from torch.cuda import amp
-
-from utils import TryExcept
-from utils.dataloaders import exif_transpose, letterbox
-from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
-                           increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
-                           xyxy2xywh, yaml_load)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import copy_attr, smart_inference_mode
-
-
-def autopad(k, p=None, d=1):  # kernel, padding, dilation
-    # Pad to 'same' shape outputs
-    if d > 1:
-        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
-    if p is None:
-        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
-    return p
-
-
-class Conv(nn.Module):
-    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
-    default_act = nn.SiLU()  # default activation
-
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
-        super().__init__()
-        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def forward_fuse(self, x):
-        return self.act(self.conv(x))
-
-
-class DWConv(Conv):
-    # Depth-wise convolution
-    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
-        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
-
-
-class DWConvTranspose2d(nn.ConvTranspose2d):
-    # Depth-wise transpose convolution
-    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
-        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
-
-
-class TransformerLayer(nn.Module):
-    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
-    def __init__(self, c, num_heads):
-        super().__init__()
-        self.q = nn.Linear(c, c, bias=False)
-        self.k = nn.Linear(c, c, bias=False)
-        self.v = nn.Linear(c, c, bias=False)
-        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
-        self.fc1 = nn.Linear(c, c, bias=False)
-        self.fc2 = nn.Linear(c, c, bias=False)
-
-    def forward(self, x):
-        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
-        x = self.fc2(self.fc1(x)) + x
-        return x
-
-
-class TransformerBlock(nn.Module):
-    # Vision Transformer https://arxiv.org/abs/2010.11929
-    def __init__(self, c1, c2, num_heads, num_layers):
-        super().__init__()
-        self.conv = None
-        if c1 != c2:
-            self.conv = Conv(c1, c2)
-        self.linear = nn.Linear(c2, c2)  # learnable position embedding
-        self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
-        self.c2 = c2
-
-    def forward(self, x):
-        if self.conv is not None:
-            x = self.conv(x)
-        b, _, w, h = x.shape
-        p = x.flatten(2).permute(2, 0, 1)
-        return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
-
-
-class Bottleneck(nn.Module):
-    # Standard bottleneck
-    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_, c2, 3, 1, g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
-        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
-        self.cv4 = Conv(2 * c_, c2, 1, 1)
-        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
-        self.act = nn.SiLU()
-        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
-    def forward(self, x):
-        y1 = self.cv3(self.m(self.cv1(x)))
-        y2 = self.cv2(x)
-        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
-
-
-class CrossConv(nn.Module):
-    # Cross Convolution Downsample
-    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
-        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, (1, k), (1, s))
-        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class C3(nn.Module):
-    # CSP Bottleneck with 3 convolutions
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super().__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
-        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
-    def forward(self, x):
-        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
-
-
-class C3x(C3):
-    # C3 module with cross-convolutions
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)
-        self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
-
-
-class C3TR(C3):
-    # C3 module with TransformerBlock()
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)
-        self.m = TransformerBlock(c_, c_, 4, n)
-
-
-class C3SPP(C3):
-    # C3 module with SPP()
-    def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)
-        self.m = SPP(c_, c_, k)
-
-
-class C3Ghost(C3):
-    # C3 module with GhostBottleneck()
-    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
-        super().__init__(c1, c2, n, shortcut, g, e)
-        c_ = int(c2 * e)  # hidden channels
-        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
-
-
-class SPP(nn.Module):
-    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
-    def __init__(self, c1, c2, k=(5, 9, 13)):
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
-        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
-    def forward(self, x):
-        x = self.cv1(x)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
-            return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class SPPF(nn.Module):
-    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
-    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
-        super().__init__()
-        c_ = c1 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, 1, 1)
-        self.cv2 = Conv(c_ * 4, c2, 1, 1)
-        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
-
-    def forward(self, x):
-        x = self.cv1(x)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
-            y1 = self.m(x)
-            y2 = self.m(y1)
-            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
-
-
-class Focus(nn.Module):
-    # Focus wh information into c-space
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__()
-        self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
-        # self.contract = Contract(gain=2)
-
-    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
-        # return self.conv(self.contract(x))
-
-
-class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
-        super().__init__()
-        c_ = c2 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
-        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
-
-    def forward(self, x):
-        y = self.cv1(x)
-        return torch.cat((y, self.cv2(y)), 1)
-
-
-class GhostBottleneck(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
-        super().__init__()
-        c_ = c2 // 2
-        self.conv = nn.Sequential(
-            GhostConv(c1, c_, 1, 1),  # pw
-            DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
-            GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
-        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
-                                                                            act=False)) if s == 2 else nn.Identity()
-
-    def forward(self, x):
-        return self.conv(x) + self.shortcut(x)
-
-
-class Contract(nn.Module):
-    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
-    def __init__(self, gain=2):
-        super().__init__()
-        self.gain = gain
-
-    def forward(self, x):
-        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
-        s = self.gain
-        x = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)
-        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
-        return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)
-
-
-class Expand(nn.Module):
-    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
-    def __init__(self, gain=2):
-        super().__init__()
-        self.gain = gain
-
-    def forward(self, x):
-        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
-        s = self.gain
-        x = x.view(b, s, s, c // s ** 2, h, w)  # x(1,2,2,16,80,80)
-        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
-        return x.view(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)
-
-
-class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
-    def __init__(self, dimension=1):
-        super().__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        return torch.cat(x, self.d)
-
-
-class DetectMultiBackend(nn.Module):
-    # YOLOv5 MultiBackend class for python inference on various backends
-    def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
-        # Usage:
-        #   PyTorch:              weights = *.pt
-        #   TorchScript:                    *.torchscript
-        #   ONNX Runtime:                   *.onnx
-        #   ONNX OpenCV DNN:                *.onnx --dnn
-        #   OpenVINO:                       *_openvino_model
-        #   CoreML:                         *.mlmodel
-        #   TensorRT:                       *.engine
-        #   TensorFlow SavedModel:          *_saved_model
-        #   TensorFlow GraphDef:            *.pb
-        #   TensorFlow Lite:                *.tflite
-        #   TensorFlow Edge TPU:            *_edgetpu.tflite
-        #   PaddlePaddle:                   *_paddle_model
-        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
-
-        super().__init__()
-        w = str(weights[0] if isinstance(weights, list) else weights)
-        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
-        fp16 &= pt or jit or onnx or engine  # FP16
-        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
-        stride = 32  # default stride
-        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
-        if not (pt or triton):
-            w = attempt_download(w)  # download if not local
-
-        if pt:  # PyTorch
-            model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
-            stride = max(int(model.stride.max()), 32)  # model stride
-            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
-            model.half() if fp16 else model.float()
-            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
-        elif jit:  # TorchScript
-            LOGGER.info(f'Loading {w} for TorchScript inference...')
-            extra_files = {'config.txt': ''}  # model metadata
-            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
-            model.half() if fp16 else model.float()
-            if extra_files['config.txt']:  # load metadata dict
-                d = json.loads(extra_files['config.txt'],
-                               object_hook=lambda d: {int(k) if k.isdigit() else k: v
-                                                      for k, v in d.items()})
-                stride, names = int(d['stride']), d['names']
-        elif dnn:  # ONNX OpenCV DNN
-            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
-            check_requirements('opencv-python>=4.5.4')
-            net = cv2.dnn.readNetFromONNX(w)
-        elif onnx:  # ONNX Runtime
-            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
-            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
-            import onnxruntime
-            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
-            session = onnxruntime.InferenceSession(w, providers=providers)
-            output_names = [x.name for x in session.get_outputs()]
-            meta = session.get_modelmeta().custom_metadata_map  # metadata
-            if 'stride' in meta:
-                stride, names = int(meta['stride']), eval(meta['names'])
-        elif xml:  # OpenVINO
-            LOGGER.info(f'Loading {w} for OpenVINO inference...')
-            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
-            from openvino.runtime import Core, Layout, get_batch
-            ie = Core()
-            if not Path(w).is_file():  # if not *.xml
-                w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
-            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
-            if network.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout("NCHW"))
-            batch_dim = get_batch(network)
-            if batch_dim.is_static:
-                batch_size = batch_dim.get_length()
-            executable_network = ie.compile_model(network, device_name="CPU")  # device_name="MYRIAD" for Intel NCS2
-            stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
-        elif engine:  # TensorRT
-            LOGGER.info(f'Loading {w} for TensorRT inference...')
-            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
-            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
-            if device.type == 'cpu':
-                device = torch.device('cuda:0')
-            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
-            logger = trt.Logger(trt.Logger.INFO)
-            with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
-                model = runtime.deserialize_cuda_engine(f.read())
-            context = model.create_execution_context()
-            bindings = OrderedDict()
-            output_names = []
-            fp16 = False  # default updated below
-            dynamic = False
-            for i in range(model.num_bindings):
-                name = model.get_binding_name(i)
-                dtype = trt.nptype(model.get_binding_dtype(i))
-                if model.binding_is_input(i):
-                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
-                        dynamic = True
-                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
-                    if dtype == np.float16:
-                        fp16 = True
-                else:  # output
-                    output_names.append(name)
-                shape = tuple(context.get_binding_shape(i))
-                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
-                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
-            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
-            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
-        elif coreml:  # CoreML
-            LOGGER.info(f'Loading {w} for CoreML inference...')
-            import coremltools as ct
-            model = ct.models.MLModel(w)
-        elif saved_model:  # TF SavedModel
-            LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
-            import tensorflow as tf
-            keras = False  # assume TF1 saved_model
-            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
-        elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
-            LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
-            import tensorflow as tf
-
-            def wrap_frozen_graph(gd, inputs, outputs):
-                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
-                ge = x.graph.as_graph_element
-                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
-
-            def gd_outputs(gd):
-                name_list, input_list = [], []
-                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
-                    name_list.append(node.name)
-                    input_list.extend(node.input)
-                return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
-
-            gd = tf.Graph().as_graph_def()  # TF GraphDef
-            with open(w, 'rb') as f:
-                gd.ParseFromString(f.read())
-            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
-        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
-            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
-                from tflite_runtime.interpreter import Interpreter, load_delegate
-            except ImportError:
-                import tensorflow as tf
-                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
-            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
-                LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
-                delegate = {
-                    'Linux': 'libedgetpu.so.1',
-                    'Darwin': 'libedgetpu.1.dylib',
-                    'Windows': 'edgetpu.dll'}[platform.system()]
-                interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
-            else:  # TFLite
-                LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
-                interpreter = Interpreter(model_path=w)  # load TFLite model
-            interpreter.allocate_tensors()  # allocate
-            input_details = interpreter.get_input_details()  # inputs
-            output_details = interpreter.get_output_details()  # outputs
-        elif tfjs:  # TF.js
-            raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
-        elif paddle:  # PaddlePaddle
-            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
-            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
-            import paddle.inference as pdi
-            if not Path(w).is_file():  # if not *.pdmodel
-                w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
-            weights = Path(w).with_suffix('.pdiparams')
-            config = pdi.Config(str(w), str(weights))
-            if cuda:
-                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
-            predictor = pdi.create_predictor(config)
-            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
-            output_names = predictor.get_output_names()
-        elif triton:  # NVIDIA Triton Inference Server
-            LOGGER.info(f'Using {w} as Triton Inference Server...')
-            check_requirements('tritonclient[all]')
-            from utils.triton import TritonRemoteModel
-            model = TritonRemoteModel(url=w)
-            nhwc = model.runtime.startswith("tensorflow")
-        else:
-            raise NotImplementedError(f'ERROR: {w} is not a supported format')
-
-        # class names
-        if 'names' not in locals():
-            names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
-        if names[0] == 'n01440764' and len(names) == 1000:  # ImageNet
-            names = yaml_load(ROOT / 'data/ImageNet.yaml')['names']  # human-readable names
-
-        self.__dict__.update(locals())  # assign all variables to self
-
-    def forward(self, im, augment=False, visualize=False):
-        # YOLOv5 MultiBackend inference
-        b, ch, h, w = im.shape  # batch, channel, height, width
-        if self.fp16 and im.dtype != torch.float16:
-            im = im.half()  # to FP16
-        if self.nhwc:
-            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)
-
-        if self.pt:  # PyTorch
-            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
-        elif self.jit:  # TorchScript
-            y = self.model(im)
-        elif self.dnn:  # ONNX OpenCV DNN
-            im = im.cpu().numpy()  # torch to numpy
-            self.net.setInput(im)
-            y = self.net.forward()
-        elif self.onnx:  # ONNX Runtime
-            im = im.cpu().numpy()  # torch to numpy
-            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
-        elif self.xml:  # OpenVINO
-            im = im.cpu().numpy()  # FP32
-            y = list(self.executable_network([im]).values())
-        elif self.engine:  # TensorRT
-            if self.dynamic and im.shape != self.bindings['images'].shape:
-                i = self.model.get_binding_index('images')
-                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
-                self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
-                for name in self.output_names:
-                    i = self.model.get_binding_index(name)
-                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
-            s = self.bindings['images'].shape
-            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
-            self.binding_addrs['images'] = int(im.data_ptr())
-            self.context.execute_v2(list(self.binding_addrs.values()))
-            y = [self.bindings[x].data for x in sorted(self.output_names)]
-        elif self.coreml:  # CoreML
-            im = im.cpu().numpy()
-            im = Image.fromarray((im[0] * 255).astype('uint8'))
-            # im = im.resize((192, 320), Image.ANTIALIAS)
-            y = self.model.predict({'image': im})  # coordinates are xywh normalized
-            if 'confidence' in y:
-                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
-                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
-                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
-            else:
-                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
-        elif self.paddle:  # PaddlePaddle
-            im = im.cpu().numpy().astype(np.float32)
-            self.input_handle.copy_from_cpu(im)
-            self.predictor.run()
-            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
-        elif self.triton:  # NVIDIA Triton Inference Server
-            y = self.model(im)
-        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
-            im = im.cpu().numpy()
-            if self.saved_model:  # SavedModel
-                y = self.model(im, training=False) if self.keras else self.model(im)
-            elif self.pb:  # GraphDef
-                y = self.frozen_func(x=self.tf.constant(im))
-            else:  # Lite or Edge TPU
-                input = self.input_details[0]
-                int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
-                if int8:
-                    scale, zero_point = input['quantization']
-                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
-                self.interpreter.set_tensor(input['index'], im)
-                self.interpreter.invoke()
-                y = []
-                for output in self.output_details:
-                    x = self.interpreter.get_tensor(output['index'])
-                    if int8:
-                        scale, zero_point = output['quantization']
-                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
-                    y.append(x)
-            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
-            y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels
-
-        if isinstance(y, (list, tuple)):
-            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
-        else:
-            return self.from_numpy(y)
-
-    def from_numpy(self, x):
-        return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
-
-    def warmup(self, imgsz=(1, 3, 640, 640)):
-        # Warmup model by running inference once
-        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
-        if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
-            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
-            for _ in range(2 if self.jit else 1):  #
-                self.forward(im)  # warmup
-
-    @staticmethod
-    def _model_type(p='path/to/model.pt'):
-        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
-        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
-        from export import export_formats
-        from utils.downloads import is_url
-        sf = list(export_formats().Suffix)  # export suffixes
-        if not is_url(p, check=False):
-            check_suffix(p, sf)  # checks
-        url = urlparse(p)  # if url may be Triton inference server
-        types = [s in Path(p).name for s in sf]
-        types[8] &= not types[9]  # tflite &= not edgetpu
-        triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
-        return types + [triton]
-
-    @staticmethod
-    def _load_metadata(f=Path('path/to/meta.yaml')):
-        # Load metadata from meta.yaml if it exists
-        if f.exists():
-            d = yaml_load(f)
-            return d['stride'], d['names']  # assign stride, names
-        return None, None
-
-
-class AutoShape(nn.Module):
-    # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
-    conf = 0.25  # NMS confidence threshold
-    iou = 0.45  # NMS IoU threshold
-    agnostic = False  # NMS class-agnostic
-    multi_label = False  # NMS multiple labels per box
-    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
-    max_det = 1000  # maximum number of detections per image
-    amp = False  # Automatic Mixed Precision (AMP) inference
-
-    def __init__(self, model, verbose=True):
-        super().__init__()
-        if verbose:
-            LOGGER.info('Adding AutoShape... ')
-        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
-        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
-        self.pt = not self.dmb or model.pt  # PyTorch model
-        self.model = model.eval()
-        if self.pt:
-            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
-            m.inplace = False  # Detect.inplace=False for safe multithread inference
-            m.export = True  # do not output loss values
-
-    def _apply(self, fn):
-        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
-        self = super()._apply(fn)
-        if self.pt:
-            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
-            m.stride = fn(m.stride)
-            m.grid = list(map(fn, m.grid))
-            if isinstance(m.anchor_grid, list):
-                m.anchor_grid = list(map(fn, m.anchor_grid))
-        return self
-
-    @smart_inference_mode()
-    def forward(self, ims, size=640, augment=False, profile=False):
-        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
-        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
-        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
-        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
-        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
-        #   numpy:           = np.zeros((640,1280,3))  # HWC
-        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
-        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
-
-        dt = (Profile(), Profile(), Profile())
-        with dt[0]:
-            if isinstance(size, int):  # expand
-                size = (size, size)
-            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
-            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
-            if isinstance(ims, torch.Tensor):  # torch
-                with amp.autocast(autocast):
-                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference
-
-            # Pre-process
-            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
-            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
-            for i, im in enumerate(ims):
-                f = f'image{i}'  # filename
-                if isinstance(im, (str, Path)):  # filename or uri
-                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
-                    im = np.asarray(exif_transpose(im))
-                elif isinstance(im, Image.Image):  # PIL Image
-                    im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
-                files.append(Path(f).with_suffix('.jpg').name)
-                if im.shape[0] < 5:  # image in CHW
-                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
-                s = im.shape[:2]  # HWC
-                shape0.append(s)  # image shape
-                g = max(size) / max(s)  # gain
-                shape1.append([y * g for y in s])
-                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size  # inf shape
-            x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
-            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
-            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
-
-        with amp.autocast(autocast):
-            # Inference
-            with dt[1]:
-                y = self.model(x, augment=augment)  # forward
-
-            # Post-process
-            with dt[2]:
-                y = non_max_suppression(y if self.dmb else y[0],
-                                        self.conf,
-                                        self.iou,
-                                        self.classes,
-                                        self.agnostic,
-                                        self.multi_label,
-                                        max_det=self.max_det)  # NMS
-                for i in range(n):
-                    scale_boxes(shape1, y[i][:, :4], shape0[i])
-
-            return Detections(ims, y, files, dt, self.names, x.shape)
-
-
-class Detections:
-    # YOLOv5 detections class for inference results
-    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
-        super().__init__()
-        d = pred[0].device  # device
-        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
-        self.ims = ims  # list of images as numpy arrays
-        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
-        self.names = names  # class names
-        self.files = files  # image filenames
-        self.times = times  # profiling times
-        self.xyxy = pred  # xyxy pixels
-        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
-        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
-        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
-        self.n = len(self.pred)  # number of images (batch size)
-        self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
-        self.s = tuple(shape)  # inference BCHW shape
-
-    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
-        s, crops = '', []
-        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
-            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
-            if pred.shape[0]:
-                for c in pred[:, -1].unique():
-                    n = (pred[:, -1] == c).sum()  # detections per class
-                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
-                s = s.rstrip(', ')
-                if show or save or render or crop:
-                    annotator = Annotator(im, example=str(self.names))
-                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
-                        label = f'{self.names[int(cls)]} {conf:.2f}'
-                        if crop:
-                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
-                            crops.append({
-                                'box': box,
-                                'conf': conf,
-                                'cls': cls,
-                                'label': label,
-                                'im': save_one_box(box, im, file=file, save=save)})
-                        else:  # all others
-                            annotator.box_label(box, label if labels else '', color=colors(cls))
-                    im = annotator.im
-            else:
-                s += '(no detections)'
-
-            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
-            if show:
-                display(im) if is_notebook() else im.show(self.files[i])
-            if save:
-                f = self.files[i]
-                im.save(save_dir / f)  # save
-                if i == self.n - 1:
-                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
-            if render:
-                self.ims[i] = np.asarray(im)
-        if pprint:
-            s = s.lstrip('\n')
-            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
-        if crop:
-            if save:
-                LOGGER.info(f'Saved results to {save_dir}\n')
-            return crops
-
-    @TryExcept('Showing images is not supported in this environment')
-    def show(self, labels=True):
-        self._run(show=True, labels=labels)  # show results
-
-    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
-        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
-        self._run(save=True, labels=labels, save_dir=save_dir)  # save results
-
-    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
-        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
-        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results
-
-    def render(self, labels=True):
-        self._run(render=True, labels=labels)  # render results
-        return self.ims
-
-    def pandas(self):
-        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
-        new = copy(self)  # return copy
-        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
-        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
-        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
-            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
-            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
-        return new
-
-    def tolist(self):
-        # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        r = range(self.n)  # iterable
-        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
-        # for d in x:
-        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
-        return x
-
-    def print(self):
-        LOGGER.info(self.__str__())
-
-    def __len__(self):  # override len(results)
-        return self.n
-
-    def __str__(self):  # override print(results)
-        return self._run(pprint=True)  # print results
-
-    def __repr__(self):
-        return f'YOLOv5 {self.__class__} instance\n' + self.__str__()
-
-
-class Proto(nn.Module):
-    # YOLOv5 mask Proto module for segmentation models
-    def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
-        super().__init__()
-        self.cv1 = Conv(c1, c_, k=3)
-        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
-        self.cv2 = Conv(c_, c_, k=3)
-        self.cv3 = Conv(c_, c2)
-
-    def forward(self, x):
-        return self.cv3(self.cv2(self.upsample(self.cv1(x))))
-
-
-class Classify(nn.Module):
-    # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
-    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
-        super().__init__()
-        c_ = 1280  # efficientnet_b0 size
-        self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
-        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
-        self.drop = nn.Dropout(p=0.0, inplace=True)
-        self.linear = nn.Linear(c_, c2)  # to x(b,c2)
-
-    def forward(self, x):
-        if isinstance(x, list):
-            x = torch.cat(x, 1)
-        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
diff --git a/spaces/mnauf/detect-bees/utils/augmentations.py b/spaces/mnauf/detect-bees/utils/augmentations.py
deleted file mode 100644
index 7c8e0bcdede67289f8ea70f1fbafb5eec60900f0..0000000000000000000000000000000000000000
--- a/spaces/mnauf/detect-bees/utils/augmentations.py
+++ /dev/null
@@ -1,399 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as TF
-
-from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
-from utils.metrics import bbox_ioa
-
-IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
-IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation
-
-
-class Albumentations:
-    # YOLOv5 Albumentations class (optional, only used if package is installed)
-    def __init__(self, size=640):
-        self.transform = None
-        prefix = colorstr('albumentations: ')
-        try:
-            import albumentations as A
-            check_version(A.__version__, '1.0.3', hard=True)  # version requirement
-
-            T = [
-                A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
-                A.Blur(p=0.01),
-                A.MedianBlur(p=0.01),
-                A.ToGray(p=0.01),
-                A.CLAHE(p=0.01),
-                A.RandomBrightnessContrast(p=0.0),
-                A.RandomGamma(p=0.0),
-                A.ImageCompression(quality_lower=75, p=0.0)]  # transforms
-            self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
-
-            LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
-        except ImportError:  # package not installed, skip
-            pass
-        except Exception as e:
-            LOGGER.info(f'{prefix}{e}')
-
-    def __call__(self, im, labels, p=1.0):
-        if self.transform and random.random() < p:
-            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed
-            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
-        return im, labels
-
-
-def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
-    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
-    return TF.normalize(x, mean, std, inplace=inplace)
-
-
-def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
-    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
-    for i in range(3):
-        x[:, i] = x[:, i] * std[i] + mean[i]
-    return x
-
-
-def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
-    # HSV color-space augmentation
-    if hgain or sgain or vgain:
-        r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
-        hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
-        dtype = im.dtype  # uint8
-
-        x = np.arange(0, 256, dtype=r.dtype)
-        lut_hue = ((x * r[0]) % 180).astype(dtype)
-        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
-        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
-        im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
-        cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)  # no return needed
-
-
-def hist_equalize(im, clahe=True, bgr=False):
-    # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
-    yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
-    if clahe:
-        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        yuv[:, :, 0] = c.apply(yuv[:, :, 0])
-    else:
-        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram
-    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB
-
-
-def replicate(im, labels):
-    # Replicate labels
-    h, w = im.shape[:2]
-    boxes = labels[:, 1:].astype(int)
-    x1, y1, x2, y2 = boxes.T
-    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)
-    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices
-        x1b, y1b, x2b, y2b = boxes[i]
-        bh, bw = y2b - y1b, x2b - x1b
-        yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw))  # offset x, y
-        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
-        im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b]  # im4[ymin:ymax, xmin:xmax]
-        labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
-
-    return im, labels
-
-
-def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = im.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better val mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    ratio = r, r  # width, height ratios
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-    elif scaleFill:  # stretch
-        dw, dh = 0.0, 0.0
-        new_unpad = (new_shape[1], new_shape[0])
-        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return im, ratio, (dw, dh)
-
-
-def random_perspective(im,
-                       targets=(),
-                       segments=(),
-                       degrees=10,
-                       translate=.1,
-                       scale=.1,
-                       shear=10,
-                       perspective=0.0,
-                       border=(0, 0)):
-    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
-    # targets = [cls, xyxy]
-
-    height = im.shape[0] + border[0] * 2  # shape(h,w,c)
-    width = im.shape[1] + border[1] * 2
-
-    # Center
-    C = np.eye(3)
-    C[0, 2] = -im.shape[1] / 2  # x translation (pixels)
-    C[1, 2] = -im.shape[0] / 2  # y translation (pixels)
-
-    # Perspective
-    P = np.eye(3)
-    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
-    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)
-
-    # Rotation and Scale
-    R = np.eye(3)
-    a = random.uniform(-degrees, degrees)
-    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
-    s = random.uniform(1 - scale, 1 + scale)
-    # s = 2 ** random.uniform(-scale, scale)
-    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
-    # Shear
-    S = np.eye(3)
-    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
-    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
-
-    # Translation
-    T = np.eye(3)
-    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
-    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
-
-    # Combined rotation matrix
-    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
-    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
-        if perspective:
-            im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
-        else:  # affine
-            im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
-    # Visualize
-    # import matplotlib.pyplot as plt
-    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
-    # ax[0].imshow(im[:, :, ::-1])  # base
-    # ax[1].imshow(im2[:, :, ::-1])  # warped
-
-    # Transform label coordinates
-    n = len(targets)
-    if n:
-        use_segments = any(x.any() for x in segments)
-        new = np.zeros((n, 4))
-        if use_segments:  # warp segments
-            segments = resample_segments(segments)  # upsample
-            for i, segment in enumerate(segments):
-                xy = np.ones((len(segment), 3))
-                xy[:, :2] = segment
-                xy = xy @ M.T  # transform
-                xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
-
-                # clip
-                new[i] = segment2box(xy, width, height)
-
-        else:  # warp boxes
-            xy = np.ones((n * 4, 3))
-            xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
-            xy = xy @ M.T  # transform
-            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
-
-            # create new boxes
-            x = xy[:, [0, 2, 4, 6]]
-            y = xy[:, [1, 3, 5, 7]]
-            new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
-            # clip
-            new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
-            new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
-
-        # filter candidates
-        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
-        targets = targets[i]
-        targets[:, 1:5] = new[i]
-
-    return im, targets
-
-
-def copy_paste(im, labels, segments, p=0.5):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    if p and n:
-        h, w, c = im.shape  # height, width, channels
-        im_new = np.zeros(im.shape, np.uint8)
-        for j in random.sample(range(n), k=round(p * n)):
-            l, s = labels[j], segments[j]
-            box = w - l[3], l[2], w - l[1], l[4]
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-            if (ioa < 0.30).all():  # allow 30% obscuration of existing labels
-                labels = np.concatenate((labels, [[l[0], *box]]), 0)
-                segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
-                cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
-        result = cv2.bitwise_and(src1=im, src2=im_new)
-        result = cv2.flip(result, 1)  # augment segments (flip left-right)
-        i = result > 0  # pixels to replace
-        # i[:, :] = result.max(2).reshape(h, w, 1)  # act over ch
-        im[i] = result[i]  # cv2.imwrite('debug.jpg', im)  # debug
-
-    return im, labels, segments
-
-
-def cutout(im, labels, p=0.5):
-    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
-    if random.random() < p:
-        h, w = im.shape[:2]
-        scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
-        for s in scales:
-            mask_h = random.randint(1, int(h * s))  # create random masks
-            mask_w = random.randint(1, int(w * s))
-
-            # box
-            xmin = max(0, random.randint(0, w) - mask_w // 2)
-            ymin = max(0, random.randint(0, h) - mask_h // 2)
-            xmax = min(w, xmin + mask_w)
-            ymax = min(h, ymin + mask_h)
-
-            # apply random color mask
-            im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
-
-            # return unobscured labels
-            if len(labels) and s > 0.03:
-                box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
-                ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h))  # intersection over area
-                labels = labels[ioa < 0.60]  # remove >60% obscured labels
-
-    return labels
-
-
-def mixup(im, labels, im2, labels2):
-    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
-    r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
-    im = (im * r + im2 * (1 - r)).astype(np.uint8)
-    labels = np.concatenate((labels, labels2), 0)
-    return im, labels
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
-    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
-    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
-    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
-    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
-    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
-
-
-def classify_albumentations(
-        augment=True,
-        size=224,
-        scale=(0.08, 1.0),
-        ratio=(0.75, 1.0 / 0.75),  # 0.75, 1.33
-        hflip=0.5,
-        vflip=0.0,
-        jitter=0.4,
-        mean=IMAGENET_MEAN,
-        std=IMAGENET_STD,
-        auto_aug=False):
-    # YOLOv5 classification Albumentations (optional, only used if package is installed)
-    prefix = colorstr('albumentations: ')
-    try:
-        import albumentations as A
-        from albumentations.pytorch import ToTensorV2
-        check_version(A.__version__, '1.0.3', hard=True)  # version requirement
-        if augment:  # Resize and crop
-            T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
-            if auto_aug:
-                # TODO: implement AugMix, AutoAug & RandAug in albumentation
-                LOGGER.info(f'{prefix}auto augmentations are currently not supported')
-            else:
-                if hflip > 0:
-                    T += [A.HorizontalFlip(p=hflip)]
-                if vflip > 0:
-                    T += [A.VerticalFlip(p=vflip)]
-                if jitter > 0:
-                    color_jitter = (float(jitter),) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
-                    T += [A.ColorJitter(*color_jitter, 0)]
-        else:  # Use fixed crop for eval set (reproducibility)
-            T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
-        T += [A.Normalize(mean=mean, std=std), ToTensorV2()]  # Normalize and convert to Tensor
-        LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
-        return A.Compose(T)
-
-    except ImportError:  # package not installed, skip
-        LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)')
-    except Exception as e:
-        LOGGER.info(f'{prefix}{e}')
-
-
-def classify_transforms(size=224):
-    # Transforms to apply if albumentations not installed
-    assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
-    # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
-    return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
-
-
-class LetterBox:
-    # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
-    def __init__(self, size=(640, 640), auto=False, stride=32):
-        super().__init__()
-        self.h, self.w = (size, size) if isinstance(size, int) else size
-        self.auto = auto  # pass max size integer, automatically solve for short side using stride
-        self.stride = stride  # used with auto
-
-    def __call__(self, im):  # im = np.array HWC
-        imh, imw = im.shape[:2]
-        r = min(self.h / imh, self.w / imw)  # ratio of new/old
-        h, w = round(imh * r), round(imw * r)  # resized image
-        hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
-        top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
-        im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
-        im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
-        return im_out
-
-
-class CenterCrop:
-    # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
-    def __init__(self, size=640):
-        super().__init__()
-        self.h, self.w = (size, size) if isinstance(size, int) else size
-
-    def __call__(self, im):  # im = np.array HWC
-        imh, imw = im.shape[:2]
-        m = min(imh, imw)  # min dimension
-        top, left = (imh - m) // 2, (imw - m) // 2
-        return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
-
-
-class ToTensor:
-    # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
-    def __init__(self, half=False):
-        super().__init__()
-        self.half = half
-
-    def __call__(self, im):  # im = np.array HWC in BGR order
-        im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1])  # HWC to CHW -> BGR to RGB -> contiguous
-        im = torch.from_numpy(im)  # to torch
-        im = im.half() if self.half else im.float()  # uint8 to fp16/32
-        im /= 255.0  # 0-255 to 0.0-1.0
-        return im
diff --git a/spaces/monra/freegpt-webui/Dockerfile b/spaces/monra/freegpt-webui/Dockerfile
deleted file mode 100644
index 7ac29c145f7d05ea9b1344e50e634629c9d88984..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM python:3.10-slim-buster  
-  
-WORKDIR /app  
-  
-COPY requirements.txt requirements.txt  
-  
-RUN python -m venv venv  
-ENV PATH="/app/venv/bin:$PATH"  
-  
-RUN apt-get update && \  
-    apt-get install -y --no-install-recommends build-essential libffi-dev cmake libcurl4-openssl-dev && \  
-    pip3 install --no-cache-dir -r requirements.txt  
-  
-COPY . .  
-  
-RUN chmod -R 777 translations  
-  
-CMD ["python3", "./run.py"]  
diff --git a/spaces/mrfakename/Translate/README.md b/spaces/mrfakename/Translate/README.md
deleted file mode 100644
index 6b5a0a631fee79093e95cc91e8b8a8e0dfb61eff..0000000000000000000000000000000000000000
--- a/spaces/mrfakename/Translate/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Translator
-emoji: 🗣️
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-app_file: main.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
\ No newline at end of file
diff --git a/spaces/ms180/espnet_onnx_demo/static/js/chunk-vendors.1fdcb7ab.js b/spaces/ms180/espnet_onnx_demo/static/js/chunk-vendors.1fdcb7ab.js
deleted file mode 100644
index 79488f4a27f771fb3e81cfc1de599c4daeae5e92..0000000000000000000000000000000000000000
--- a/spaces/ms180/espnet_onnx_demo/static/js/chunk-vendors.1fdcb7ab.js
+++ /dev/null
@@ -1,8 +0,0 @@
-(self["webpackChunkespnet_onnx_demo"]=self["webpackChunkespnet_onnx_demo"]||[]).push([[998],{1001:function(t,e,n){"use strict";function i(t,e,n,i,s,r,a,o){var l,c="function"===typeof t?t.options:t;if(e&&(c.render=e,c.staticRenderFns=n,c._compiled=!0),i&&(c.functional=!0),r&&(c._scopeId="data-v-"+r),a?(l=function(t){t=t||this.$vnode&&this.$vnode.ssrContext||this.parent&&this.parent.$vnode&&this.parent.$vnode.ssrContext,t||"undefined"===typeof __VUE_SSR_CONTEXT__||(t=__VUE_SSR_CONTEXT__),s&&s.call(this,t),t&&t._registeredComponents&&t._registeredComponents.add(a)},c._ssrRegister=l):s&&(l=o?function(){s.call(this,(c.functional?this.parent:this).$root.$options.shadowRoot)}:s),l)if(c.functional){c._injectStyles=l;var u=c.render;c.render=function(t,e){return l.call(e),u(t,e)}}else{var h=c.beforeCreate;c.beforeCreate=h?[].concat(h,l):[l]}return{exports:t,options:c}}n.d(e,{Z:function(){return i}})},4635:function(t,e,n){"use strict";n.d(e,{Z:function(){return f}});var i=n(2925),s=n(1828),r=s.Z,a=n(1012),o=n(6470),l=n(3276),c=n(144),u=c.ZP.extend({name:"transitionable",props:{mode:String,origin:String,transition:String}}),h=n(4987),d=n(6596),f=(0,h.Z)(i.Z,o.Z,u).extend({name:"v-alert",props:{border:{type:String,validator(t){return["top","right","bottom","left"].includes(t)}},closeLabel:{type:String,default:"$vuetify.close"},coloredBorder:Boolean,dense:Boolean,dismissible:Boolean,closeIcon:{type:String,default:"$cancel"},icon:{default:"",type:[Boolean,String],validator(t){return"string"===typeof t||!1===t}},outlined:Boolean,prominent:Boolean,text:Boolean,type:{type:String,validator(t){return["info","error","success","warning"].includes(t)}},value:{type:Boolean,default:!0}},computed:{__cachedBorder(){if(!this.border)return null;let t={staticClass:"v-alert__border",class:{[`v-alert__border--${this.border}`]:!0}};return this.coloredBorder&&(t=this.setBackgroundColor(this.computedColor,t),t.class["v-alert__border--has-color"]=!0),this.$createElement("div",t)},__cachedDismissible(){if(!this.dismissible)return null;const t=this.iconColor;return this.$createElement(r,{staticClass:"v-alert__dismissible",props:{color:t,icon:!0,small:!0},attrs:{"aria-label":this.$vuetify.lang.t(this.closeLabel)},on:{click:()=>this.isActive=!1}},[this.$createElement(a.Z,{props:{color:t}},this.closeIcon)])},__cachedIcon(){return this.computedIcon?this.$createElement(a.Z,{staticClass:"v-alert__icon",props:{color:this.iconColor}},this.computedIcon):null},classes(){const t={...i.Z.options.computed.classes.call(this),"v-alert--border":Boolean(this.border),"v-alert--dense":this.dense,"v-alert--outlined":this.outlined,"v-alert--prominent":this.prominent,"v-alert--text":this.text};return this.border&&(t[`v-alert--border-${this.border}`]=!0),t},computedColor(){return this.color||this.type},computedIcon(){return!1!==this.icon&&("string"===typeof this.icon&&this.icon?this.icon:!!["error","info","success","warning"].includes(this.type)&&`$${this.type}`)},hasColoredIcon(){return this.hasText||Boolean(this.border)&&this.coloredBorder},hasText(){return this.text||this.outlined},iconColor(){return this.hasColoredIcon?this.computedColor:void 0},isDark(){return!(!this.type||this.coloredBorder||this.outlined)||l.Z.options.computed.isDark.call(this)}},created(){this.$attrs.hasOwnProperty("outline")&&(0,d.fK)("outline","outlined",this)},methods:{genWrapper(){const t=[this.$slots.prepend||this.__cachedIcon,this.genContent(),this.__cachedBorder,this.$slots.append,this.$scopedSlots.close?this.$scopedSlots.close({toggle:this.toggle}):this.__cachedDismissible],e={staticClass:"v-alert__wrapper"};return this.$createElement("div",e,t)},genContent(){return this.$createElement("div",{staticClass:"v-alert__content"},this.$slots.default)},genAlert(){let t={staticClass:"v-alert",attrs:{role:"alert"},on:this.listeners$,class:this.classes,style:this.styles,directives:[{name:"show",value:this.isActive}]};if(!this.coloredBorder){const e=this.hasText?this.setTextColor:this.setBackgroundColor;t=e(this.computedColor,t)}return this.$createElement("div",t,[this.genWrapper()])},toggle(){this.isActive=!this.isActive}},render(t){const e=this.genAlert();return this.transition?t("transition",{props:{name:this.transition,origin:this.origin,mode:this.mode}},[e]):e}})},1096:function(t,e,n){"use strict";n.d(e,{Z:function(){return r}});var i=n(3276),s=n(4987),r=(0,s.Z)(i.Z).extend({name:"v-app",props:{dark:{type:Boolean,default:void 0},id:{type:String,default:"app"},light:{type:Boolean,default:void 0}},computed:{isDark(){return this.$vuetify.theme.dark}},beforeCreate(){if(!this.$vuetify||this.$vuetify===this.$root)throw new Error("Vuetify is not properly initialized, see https://v2.vuetifyjs.com/getting-started/quick-start#bootstrapping-the-vuetify-object")},render(t){const e=t("div",{staticClass:"v-application--wrap"},this.$slots.default);return t("div",{staticClass:"v-application",class:{"v-application--is-rtl":this.$vuetify.rtl,"v-application--is-ltr":!this.$vuetify.rtl,...this.themeClasses},attrs:{"data-app":!0},domProps:{id:this.id}},[e])}})},1828:function(t,e,n){"use strict";n.d(e,{Z:function(){return g}});var i=n(2925),s=n(1558),r=n(8914),a=n(1050),o=r.Z.extend({name:"v-progress-circular",directives:{intersect:s.Z},props:{button:Boolean,indeterminate:Boolean,rotate:{type:[Number,String],default:0},size:{type:[Number,String],default:32},width:{type:[Number,String],default:4},value:{type:[Number,String],default:0}},data:()=>({radius:20,isVisible:!0}),computed:{calculatedSize(){return Number(this.size)+(this.button?8:0)},circumference(){return 2*Math.PI*this.radius},classes(){return{"v-progress-circular--visible":this.isVisible,"v-progress-circular--indeterminate":this.indeterminate,"v-progress-circular--button":this.button}},normalizedValue(){return this.value<0?0:this.value>100?100:parseFloat(this.value)},strokeDashArray(){return Math.round(1e3*this.circumference)/1e3},strokeDashOffset(){return(100-this.normalizedValue)/100*this.circumference+"px"},strokeWidth(){return Number(this.width)/+this.size*this.viewBoxSize*2},styles(){return{height:(0,a.kb)(this.calculatedSize),width:(0,a.kb)(this.calculatedSize)}},svgStyles(){return{transform:`rotate(${Number(this.rotate)}deg)`}},viewBoxSize(){return this.radius/(1-Number(this.width)/+this.size)}},methods:{genCircle(t,e){return this.$createElement("circle",{class:`v-progress-circular__${t}`,attrs:{fill:"transparent",cx:2*this.viewBoxSize,cy:2*this.viewBoxSize,r:this.radius,"stroke-width":this.strokeWidth,"stroke-dasharray":this.strokeDashArray,"stroke-dashoffset":e}})},genSvg(){const t=[this.indeterminate||this.genCircle("underlay",0),this.genCircle("overlay",this.strokeDashOffset)];return this.$createElement("svg",{style:this.svgStyles,attrs:{xmlns:"http://www.w3.org/2000/svg",viewBox:`${this.viewBoxSize} ${this.viewBoxSize} ${2*this.viewBoxSize} ${2*this.viewBoxSize}`}},t)},genInfo(){return this.$createElement("div",{staticClass:"v-progress-circular__info"},this.$slots.default)},onObserve(t,e,n){this.isVisible=n}},render(t){return t("div",this.setTextColor(this.color,{staticClass:"v-progress-circular",attrs:{role:"progressbar","aria-valuemin":0,"aria-valuemax":100,"aria-valuenow":this.indeterminate?void 0:this.normalizedValue},class:this.classes,directives:[{name:"intersect",value:this.onObserve}],style:this.styles,on:this.$listeners}),[this.genSvg(),this.genInfo()])}}),l=o,c=n(4791),u=n(6470),h=n(5303),d=n(8386),f=n(4040),p=n(281),_=n(4987),m=n(6596);const v=(0,_.Z)(i.Z,f.Z,d.Z,p.Z,(0,c.d)("btnToggle"),(0,u.d)("inputValue"));var g=v.extend().extend({name:"v-btn",props:{activeClass:{type:String,default(){return this.btnToggle?this.btnToggle.activeClass:""}},block:Boolean,depressed:Boolean,fab:Boolean,icon:Boolean,loading:Boolean,outlined:Boolean,plain:Boolean,retainFocusOnClick:Boolean,rounded:Boolean,tag:{type:String,default:"button"},text:Boolean,tile:Boolean,type:{type:String,default:"button"},value:null},data:()=>({proxyClass:"v-btn--active"}),computed:{classes(){return{"v-btn":!0,...f.Z.options.computed.classes.call(this),"v-btn--absolute":this.absolute,"v-btn--block":this.block,"v-btn--bottom":this.bottom,"v-btn--disabled":this.disabled,"v-btn--is-elevated":this.isElevated,"v-btn--fab":this.fab,"v-btn--fixed":this.fixed,"v-btn--has-bg":this.hasBg,"v-btn--icon":this.icon,"v-btn--left":this.left,"v-btn--loading":this.loading,"v-btn--outlined":this.outlined,"v-btn--plain":this.plain,"v-btn--right":this.right,"v-btn--round":this.isRound,"v-btn--rounded":this.rounded,"v-btn--router":this.to,"v-btn--text":this.text,"v-btn--tile":this.tile,"v-btn--top":this.top,...this.themeClasses,...this.groupClasses,...this.elevationClasses,...this.sizeableClasses}},computedElevation(){if(!this.disabled)return h.Z.options.computed.computedElevation.call(this)},computedRipple(){var t;const e=!this.icon&&!this.fab||{circle:!0};return!this.disabled&&(null!==(t=this.ripple)&&void 0!==t?t:e)},hasBg(){return!this.text&&!this.plain&&!this.outlined&&!this.icon},isElevated(){return Boolean(!this.icon&&!this.text&&!this.outlined&&!this.depressed&&!this.disabled&&!this.plain&&(null==this.elevation||Number(this.elevation)>0))},isRound(){return Boolean(this.icon||this.fab)},styles(){return{...this.measurableStyles}}},created(){const t=[["flat","text"],["outline","outlined"],["round","rounded"]];t.forEach((([t,e])=>{this.$attrs.hasOwnProperty(t)&&(0,m.fK)(t,e,this)}))},methods:{click(t){!this.retainFocusOnClick&&!this.fab&&t.detail&&this.$el.blur(),this.$emit("click",t),this.btnToggle&&this.toggle()},genContent(){return this.$createElement("span",{staticClass:"v-btn__content"},this.$slots.default)},genLoader(){return this.$createElement("span",{class:"v-btn__loader"},this.$slots.loader||[this.$createElement(l,{props:{indeterminate:!0,size:23,width:2}})])}},render(t){const e=[this.genContent(),this.loading&&this.genLoader()],{tag:n,data:i}=this.generateRouteLink(),s=this.hasBg?this.setBackgroundColor:this.setTextColor;return"button"===n&&(i.attrs.type=this.type,i.attrs.disabled=this.disabled),i.attrs.value=["string","number"].includes(typeof this.value)?this.value:JSON.stringify(this.value),t(n,this.disabled?i:s(this.color,i),e)}})},3058:function(t,e,n){"use strict";n.d(e,{Z:function(){return o}});var i=n(2925),s=n(4204),r=n(4040),a=n(4987),o=(0,a.Z)(s.Z,r.Z,i.Z).extend({name:"v-card",props:{flat:Boolean,hover:Boolean,img:String,link:Boolean,loaderHeight:{type:[Number,String],default:4},raised:Boolean},computed:{classes(){return{"v-card":!0,...r.Z.options.computed.classes.call(this),"v-card--flat":this.flat,"v-card--hover":this.hover,"v-card--link":this.isClickable,"v-card--loading":this.loading,"v-card--disabled":this.disabled,"v-card--raised":this.raised,...i.Z.options.computed.classes.call(this)}},styles(){const t={...i.Z.options.computed.styles.call(this)};return this.img&&(t.background=`url("${this.img}") center center / cover no-repeat`),t}},methods:{genProgress(){const t=s.Z.options.methods.genProgress.call(this);return t?this.$createElement("div",{staticClass:"v-card__progress",key:"progress"},[t]):null}},render(t){const{tag:e,data:n}=this.generateRouteLink();return n.style=this.styles,this.isClickable&&(n.attrs=n.attrs||{},n.attrs.tabindex=0),t(e,this.setBackgroundColor(this.color,n),[this.genProgress(),this.$slots.default])}})},5223:function(t,e,n){"use strict";n.d(e,{EB:function(){return l},ZB:function(){return o}});var i=n(3058),s=n(1050);const r=(0,s.Ji)("v-card__actions"),a=(0,s.Ji)("v-card__subtitle"),o=(0,s.Ji)("v-card__text"),l=(0,s.Ji)("v-card__title");i.Z},6938:function(t,e,n){"use strict";n.d(e,{Z:function(){return c}});n(7658);var i=n(5731),s=n(5251),r=n(2653),a=n(1050);const o={...i.l,offsetY:!0,offsetOverflow:!0,transition:!1};var l=i.Z.extend({name:"v-autocomplete",props:{autoSelectFirst:{type:Boolean,default:!1},filter:{type:Function,default:(t,e,n)=>n.toLocaleLowerCase().indexOf(e.toLocaleLowerCase())>-1},hideNoData:Boolean,menuProps:{type:i.Z.options.props.menuProps.type,default:()=>o},noFilter:Boolean,searchInput:{type:String}},data(){return{lazySearch:this.searchInput}},computed:{classes(){return{...i.Z.options.computed.classes.call(this),"v-autocomplete":!0,"v-autocomplete--is-selecting-index":this.selectedIndex>-1}},computedItems(){return this.filteredItems},selectedValues(){return this.selectedItems.map((t=>this.getValue(t)))},hasDisplayedItems(){return this.hideSelected?this.filteredItems.some((t=>!this.hasItem(t))):this.filteredItems.length>0},currentRange(){return null==this.selectedItem?0:String(this.getText(this.selectedItem)).length},filteredItems(){return!this.isSearching||this.noFilter||null==this.internalSearch?this.allItems:this.allItems.filter((t=>{const e=(0,a.qF)(t,this.itemText),n=null!=e?String(e):"";return this.filter(t,String(this.internalSearch),n)}))},internalSearch:{get(){return this.lazySearch},set(t){this.lazySearch!==t&&(this.lazySearch=t,this.$emit("update:search-input",t))}},isAnyValueAllowed(){return!1},isDirty(){return this.searchIsDirty||this.selectedItems.length>0},isSearching(){return this.multiple&&this.searchIsDirty||this.searchIsDirty&&this.internalSearch!==this.getText(this.selectedItem)},menuCanShow(){return!!this.isFocused&&(this.hasDisplayedItems||!this.hideNoData)},$_menuProps(){const t=i.Z.options.computed.$_menuProps.call(this);return t.contentClass=`v-autocomplete__content ${t.contentClass||""}`.trim(),{...o,...t}},searchIsDirty(){return null!=this.internalSearch&&""!==this.internalSearch},selectedItem(){return this.multiple?null:this.selectedItems.find((t=>this.valueComparator(this.getValue(t),this.getValue(this.internalValue))))},listData(){const t=i.Z.options.computed.listData.call(this);return t.props={...t.props,items:this.virtualizedItems,noFilter:this.noFilter||!this.isSearching||!this.filteredItems.length,searchInput:this.internalSearch},t}},watch:{filteredItems:"onFilteredItemsChanged",internalValue:"setSearch",isFocused(t){t?(document.addEventListener("copy",this.onCopy),this.$refs.input&&this.$refs.input.select()):(document.removeEventListener("copy",this.onCopy),this.blur(),this.updateSelf())},isMenuActive(t){!t&&this.hasSlot&&(this.lazySearch=null)},items(t,e){e&&e.length||!this.hideNoData||!this.isFocused||this.isMenuActive||!t.length||this.activateMenu()},searchInput(t){this.lazySearch=t},internalSearch:"onInternalSearchChanged",itemText:"updateSelf"},created(){this.setSearch()},destroyed(){document.removeEventListener("copy",this.onCopy)},methods:{onFilteredItemsChanged(t,e){if(t!==e){if(!this.autoSelectFirst){const n=e[this.$refs.menu.listIndex];n?this.setMenuIndex(t.findIndex((t=>t===n))):this.setMenuIndex(-1),this.$emit("update:list-index",this.$refs.menu.listIndex)}this.$nextTick((()=>{this.internalSearch&&(1===t.length||this.autoSelectFirst)&&(this.$refs.menu.getTiles(),this.autoSelectFirst&&t.length&&(this.setMenuIndex(0),this.$emit("update:list-index",this.$refs.menu.listIndex)))}))}},onInternalSearchChanged(){this.updateMenuDimensions()},updateMenuDimensions(){this.isMenuActive&&this.$refs.menu&&this.$refs.menu.updateDimensions()},changeSelectedIndex(t){this.searchIsDirty||(this.multiple&&t===a.keyCodes.left?-1===this.selectedIndex?this.selectedIndex=this.selectedItems.length-1:this.selectedIndex--:this.multiple&&t===a.keyCodes.right?this.selectedIndex>=this.selectedItems.length-1?this.selectedIndex=-1:this.selectedIndex++:t!==a.keyCodes.backspace&&t!==a.keyCodes["delete"]||this.deleteCurrentItem())},deleteCurrentItem(){const t=this.selectedIndex,e=this.selectedItems[t];if(!this.isInteractive||this.getDisabled(e))return;const n=this.selectedItems.length-1;if(-1===this.selectedIndex&&0!==n)return void(this.selectedIndex=n);const i=this.selectedItems.length,s=t!==i-1?t:t-1,r=this.selectedItems[s];r?this.selectItem(e):this.setValue(this.multiple?[]:null),this.selectedIndex=s},clearableCallback(){this.internalSearch=null,i.Z.options.methods.clearableCallback.call(this)},genInput(){const t=s.Z.options.methods.genInput.call(this);return t.data=(0,r.ZP)(t.data,{attrs:{"aria-activedescendant":(0,a.vO)(this.$refs.menu,"activeTile.id"),autocomplete:(0,a.vO)(t.data,"attrs.autocomplete","off")},domProps:{value:this.internalSearch}}),t},genInputSlot(){const t=i.Z.options.methods.genInputSlot.call(this);return t.data.attrs.role="combobox",t},genSelections(){return this.hasSlot||this.multiple?i.Z.options.methods.genSelections.call(this):[]},onClick(t){this.isInteractive&&(this.selectedIndex>-1?this.selectedIndex=-1:this.onFocus(),this.isAppendInner(t.target)||this.activateMenu())},onInput(t){if(this.selectedIndex>-1||!t.target)return;const e=t.target,n=e.value;e.value&&this.activateMenu(),this.multiple||""!==n||this.deleteCurrentItem(),this.internalSearch=n,this.badInput=e.validity&&e.validity.badInput},onKeyDown(t){const e=t.keyCode;!t.ctrlKey&&[a.keyCodes.home,a.keyCodes.end].includes(e)||i.Z.options.methods.onKeyDown.call(this,t),this.changeSelectedIndex(e)},onSpaceDown(t){},onTabDown(t){i.Z.options.methods.onTabDown.call(this,t),this.updateSelf()},onUpDown(t){t.preventDefault(),this.activateMenu()},selectItem(t){i.Z.options.methods.selectItem.call(this,t),this.setSearch()},setSelectedItems(){i.Z.options.methods.setSelectedItems.call(this),this.isFocused||this.setSearch()},setSearch(){this.$nextTick((()=>{this.multiple&&this.internalSearch&&this.isMenuActive||(this.internalSearch=!this.selectedItems.length||this.multiple||this.hasSlot?null:this.getText(this.selectedItem))}))},updateSelf(){(this.searchIsDirty||this.internalValue)&&(this.multiple||this.valueComparator(this.internalSearch,this.getValue(this.internalValue))||this.setSearch())},hasItem(t){return this.selectedValues.indexOf(this.getValue(t))>-1},onCopy(t){var e,n;if(-1===this.selectedIndex)return;const i=this.selectedItems[this.selectedIndex],s=this.getText(i);null===(e=t.clipboardData)||void 0===e||e.setData("text/plain",s),null===(n=t.clipboardData)||void 0===n||n.setData("text/vnd.vuetify.autocomplete.item+plain",s),t.preventDefault()}}}),c=l.extend({name:"v-combobox",props:{delimiters:{type:Array,default:()=>[]},returnObject:{type:Boolean,default:!0}},data:()=>({editingIndex:-1}),computed:{computedCounterValue(){return this.multiple?this.selectedItems.length:(this.internalSearch||"").toString().length},hasSlot(){return i.Z.options.computed.hasSlot.call(this)||this.multiple},isAnyValueAllowed(){return!0},menuCanShow(){return!!this.isFocused&&(this.hasDisplayedItems||!!this.$slots["no-data"]&&!this.hideNoData)},searchIsDirty(){return null!=this.internalSearch}},methods:{onInternalSearchChanged(t){if(t&&this.multiple&&this.delimiters.length){const e=this.delimiters.find((e=>t.endsWith(e)));null!=e&&(this.internalSearch=t.slice(0,t.length-e.length),this.updateTags())}this.updateMenuDimensions()},genInput(){const t=l.options.methods.genInput.call(this);return delete t.data.attrs.name,t.data.on.paste=this.onPaste,t},genChipSelection(t,e){const n=i.Z.options.methods.genChipSelection.call(this,t,e);return this.multiple&&(n.componentOptions.listeners={...n.componentOptions.listeners,dblclick:()=>{this.editingIndex=e,this.internalSearch=this.getText(t),this.selectedIndex=-1}}),n},onChipInput(t){i.Z.options.methods.onChipInput.call(this,t),this.editingIndex=-1},onEnterDown(t){t.preventDefault(),this.getMenuIndex()>-1||this.$nextTick(this.updateSelf)},onKeyDown(t){const e=t.keyCode;!t.ctrlKey&&[a.keyCodes.home,a.keyCodes.end].includes(e)||i.Z.options.methods.onKeyDown.call(this,t),this.multiple&&e===a.keyCodes.left&&0===this.$refs.input.selectionStart?this.updateSelf():e===a.keyCodes.enter&&this.onEnterDown(t),this.changeSelectedIndex(e)},onTabDown(t){if(this.multiple&&this.internalSearch&&-1===this.getMenuIndex())return t.preventDefault(),t.stopPropagation(),this.updateTags();l.options.methods.onTabDown.call(this,t)},selectItem(t){this.editingIndex>-1?this.updateEditing():(l.options.methods.selectItem.call(this,t),this.internalSearch&&this.multiple&&this.getText(t).toLocaleLowerCase().includes(this.internalSearch.toLocaleLowerCase())&&(this.internalSearch=null))},setSelectedItems(){null==this.internalValue||""===this.internalValue?this.selectedItems=[]:this.selectedItems=this.multiple?this.internalValue:[this.internalValue]},setValue(t){i.Z.options.methods.setValue.call(this,void 0===t?this.internalSearch:t)},updateEditing(){const t=this.internalValue.slice(),e=this.selectedItems.findIndex((t=>this.getText(t)===this.internalSearch));if(e>-1){const n="object"===typeof t[e]?Object.assign({},t[e]):t[e];t.splice(e,1),t.push(n)}else t[this.editingIndex]=this.internalSearch;this.setValue(t),this.editingIndex=-1,this.internalSearch=null},updateCombobox(){if(!this.searchIsDirty)return;this.internalSearch!==this.getText(this.internalValue)&&this.setValue();const t=Boolean(this.$scopedSlots.selection)||this.hasChips;t&&(this.internalSearch=null)},updateSelf(){this.multiple?this.updateTags():this.updateCombobox()},updateTags(){const t=this.getMenuIndex();if(t<0&&!this.searchIsDirty||!this.internalSearch)return;if(this.editingIndex>-1)return this.updateEditing();const e=this.selectedItems.findIndex((t=>this.internalSearch===this.getText(t))),n=e>-1&&"object"===typeof this.selectedItems[e]?Object.assign({},this.selectedItems[e]):this.internalSearch;if(e>-1){const t=this.internalValue.slice();t.splice(e,1),this.setValue(t)}if(t>-1)return this.internalSearch=null;this.selectItem(n),this.internalSearch=null},onPaste(t){var e;if(this.$emit("paste",t),!this.multiple||this.searchIsDirty)return;const n=null===(e=t.clipboardData)||void 0===e?void 0:e.getData("text/vnd.vuetify.autocomplete.item+plain");n&&-1===this.findExistingIndex(n)&&(t.preventDefault(),i.Z.options.methods.selectItem.call(this,n))},clearableCallback(){this.editingIndex=-1,l.options.methods.clearableCallback.call(this)}}})},4397:function(t,e,n){"use strict";n.d(e,{Z:function(){return a}});var i=n(1050),s=n(3276),r=n(4987),a=(0,r.Z)(s.Z).extend({name:"v-simple-table",props:{dense:Boolean,fixedHeader:Boolean,height:[Number,String]},computed:{classes(){return{"v-data-table--dense":this.dense,"v-data-table--fixed-height":!!this.height&&!this.fixedHeader,"v-data-table--fixed-header":this.fixedHeader,"v-data-table--has-top":!!this.$slots.top,"v-data-table--has-bottom":!!this.$slots.bottom,...this.themeClasses}}},methods:{genWrapper(){return this.$slots.wrapper||this.$createElement("div",{staticClass:"v-data-table__wrapper",style:{height:(0,i.kb)(this.height)}},[this.$createElement("table",this.$slots.default)])}},render(t){return t("div",{staticClass:"v-data-table",class:this.classes},[this.$slots.top,this.genWrapper(),this.$slots.bottom])}})},2537:function(t,e,n){"use strict";n.d(e,{Z:function(){return c}});var i=n(9868),s=n(8386),r=n(4987);function a(t,e=[]){return(0,r.Z)((0,s.d)(["absolute","fixed"])).extend({name:"applicationable",props:{app:Boolean},computed:{applicationProperty(){return t}},watch:{app(t,e){e?this.removeApplication(!0):this.callUpdate()},applicationProperty(t,e){this.$vuetify.application.unregister(this._uid,e)}},activated(){this.callUpdate()},created(){for(let t=0,n=e.length;t<n;t++)this.$watch(e[t],this.callUpdate);this.callUpdate()},mounted(){this.callUpdate()},deactivated(){this.removeApplication()},destroyed(){this.removeApplication()},methods:{callUpdate(){this.app&&this.$vuetify.application.register(this._uid,this.applicationProperty,this.updateApplication())},removeApplication(t=!1){(t||this.app)&&this.$vuetify.application.unregister(this._uid,this.applicationProperty)},updateApplication:()=>0}})}var o=n(8574),l=n(1050),c=(0,r.Z)(i.Z,a("footer",["height","inset"]),o.Z).extend({name:"v-footer",props:{height:{default:"auto",type:[Number,String]},inset:Boolean,padless:Boolean,tag:{type:String,default:"footer"}},computed:{applicationProperty(){return this.inset?"insetFooter":"footer"},classes(){return{...i.Z.options.computed.classes.call(this),"v-footer--absolute":this.absolute,"v-footer--fixed":!this.absolute&&(this.app||this.fixed),"v-footer--padless":this.padless,"v-footer--inset":this.inset}},computedBottom(){if(this.isPositioned)return this.app?this.$vuetify.application.bottom:0},computedLeft(){if(this.isPositioned)return this.app&&this.inset?this.$vuetify.application.left:0},computedRight(){if(this.isPositioned)return this.app&&this.inset?this.$vuetify.application.right:0},isPositioned(){return Boolean(this.absolute||this.fixed||this.app)},styles(){const t=parseInt(this.height);return{...i.Z.options.computed.styles.call(this),height:isNaN(t)?t:(0,l.kb)(t),left:(0,l.kb)(this.computedLeft),right:(0,l.kb)(this.computedRight),bottom:(0,l.kb)(this.computedBottom)}}},methods:{updateApplication(){const t=parseInt(this.height);return isNaN(t)?this.$el?this.$el.clientHeight:0:t}},render(t){const e=this.setBackgroundColor(this.color,{staticClass:"v-footer",class:this.classes,style:this.styles});return t(this.tag,e,this.$slots.default)}})},4437:function(t,e,n){"use strict";n(7658),n(1884);var i=n(144),s=n(2653),r=n(1050);const a=["sm","md","lg","xl"],o=(()=>a.reduce(((t,e)=>(t[e]={type:[Boolean,String,Number],default:!1},t)),{}))(),l=(()=>a.reduce(((t,e)=>(t["offset"+(0,r.jC)(e)]={type:[String,Number],default:null},t)),{}))(),c=(()=>a.reduce(((t,e)=>(t["order"+(0,r.jC)(e)]={type:[String,Number],default:null},t)),{}))(),u={col:Object.keys(o),offset:Object.keys(l),order:Object.keys(c)};function h(t,e,n){let i=t;if(null!=n&&!1!==n){if(e){const n=e.replace(t,"");i+=`-${n}`}return"col"!==t||""!==n&&!0!==n?(i+=`-${n}`,i.toLowerCase()):i.toLowerCase()}}const d=new Map;e["Z"]=i.ZP.extend({name:"v-col",functional:!0,props:{cols:{type:[Boolean,String,Number],default:!1},...o,offset:{type:[String,Number],default:null},...l,order:{type:[String,Number],default:null},...c,alignSelf:{type:String,default:null,validator:t=>["auto","start","end","center","baseline","stretch"].includes(t)},tag:{type:String,default:"div"}},render(t,{props:e,data:n,children:i,parent:r}){let a="";for(const s in e)a+=String(e[s]);let o=d.get(a);if(!o){let t;for(t in o=[],u)u[t].forEach((n=>{const i=e[n],s=h(t,n,i);s&&o.push(s)}));const n=o.some((t=>t.startsWith("col-")));o.push({col:!n||!e.cols,[`col-${e.cols}`]:e.cols,[`offset-${e.offset}`]:e.offset,[`order-${e.order}`]:e.order,[`align-self-${e.alignSelf}`]:e.alignSelf}),d.set(a,o)}return t(e.tag,(0,s.ZP)(n,{class:o}),i)}})},6035:function(t,e,n){"use strict";n.d(e,{Z:function(){return a}});n(9027),n(1884);var i=n(144);function s(t){return i.ZP.extend({name:`v-${t}`,functional:!0,props:{id:String,tag:{type:String,default:"div"}},render(e,{props:n,data:i,children:s}){i.staticClass=`${t} ${i.staticClass||""}`.trim();const{attrs:r}=i;if(r){i.attrs={};const t=Object.keys(r).filter((t=>{if("slot"===t)return!1;const e=r[t];return t.startsWith("data-")?(i.attrs[t]=e,!1):e||"string"===typeof e}));t.length&&(i.staticClass+=` ${t.join(" ")}`)}return n.id&&(i.domProps=i.domProps||{},i.domProps.id=n.id),e(n.tag,i,s)}})}var r=n(2653),a=s("container").extend({name:"v-container",functional:!0,props:{id:String,tag:{type:String,default:"div"},fluid:{type:Boolean,default:!1}},render(t,{props:e,data:n,children:i}){let s;const{attrs:a}=n;return a&&(n.attrs={},s=Object.keys(a).filter((t=>{if("slot"===t)return!1;const e=a[t];return t.startsWith("data-")?(n.attrs[t]=e,!1):e||"string"===typeof e}))),e.id&&(n.domProps=n.domProps||{},n.domProps.id=e.id),t(e.tag,(0,r.ZP)(n,{staticClass:"container",class:Array({"container--fluid":e.fluid}).concat(s||[])}),i)}})},5294:function(t,e,n){"use strict";n(7658),n(1884);var i=n(144),s=n(2653),r=n(1050);const a=["sm","md","lg","xl"],o=["start","end","center"];function l(t,e){return a.reduce(((n,i)=>(n[t+(0,r.jC)(i)]=e(),n)),{})}const c=t=>[...o,"baseline","stretch"].includes(t),u=l("align",(()=>({type:String,default:null,validator:c}))),h=t=>[...o,"space-between","space-around"].includes(t),d=l("justify",(()=>({type:String,default:null,validator:h}))),f=t=>[...o,"space-between","space-around","stretch"].includes(t),p=l("alignContent",(()=>({type:String,default:null,validator:f}))),_={align:Object.keys(u),justify:Object.keys(d),alignContent:Object.keys(p)},m={align:"align",justify:"justify",alignContent:"align-content"};function v(t,e,n){let i=m[t];if(null!=n){if(e){const n=e.replace(t,"");i+=`-${n}`}return i+=`-${n}`,i.toLowerCase()}}const g=new Map;e["Z"]=i.ZP.extend({name:"v-row",functional:!0,props:{tag:{type:String,default:"div"},dense:Boolean,noGutters:Boolean,align:{type:String,default:null,validator:c},...u,justify:{type:String,default:null,validator:h},...d,alignContent:{type:String,default:null,validator:f},...p},render(t,{props:e,data:n,children:i}){let r="";for(const s in e)r+=String(e[s]);let a=g.get(r);if(!a){let t;for(t in a=[],_)_[t].forEach((n=>{const i=e[n],s=v(t,n,i);s&&a.push(s)}));a.push({"no-gutters":e.noGutters,"row--dense":e.dense,[`align-${e.align}`]:e.align,[`justify-${e.justify}`]:e.justify,[`align-content-${e.alignContent}`]:e.alignContent}),g.set(r,a)}return t(e.tag,(0,s.ZP)(n,{staticClass:"row",class:a}),i)}})},5234:function(t,e,n){"use strict";n(9027);var i=n(1050);e["Z"]=(0,i.Ji)("spacer","div","v-spacer")},5057:function(t,e,n){"use strict";n.d(e,{Z:function(){return p}});n(7658);var i,s=n(4609),r=n(8914),a=n(281),o=n(3276),l=n(1050),c=n(144),u=n(4987);function h(t){return["fas","far","fal","fab","fad","fak"].some((e=>t.includes(e)))}function d(t){return/^[mzlhvcsqta]\s*[-+.0-9][^mlhvzcsqta]+/i.test(t)&&/[\dz]$/i.test(t)&&t.length>4}(function(t){t["xSmall"]="12px",t["small"]="16px",t["default"]="24px",t["medium"]="28px",t["large"]="36px",t["xLarge"]="40px"})(i||(i={}));const f=(0,u.Z)(s.Z,r.Z,a.Z,o.Z).extend({name:"v-icon",props:{dense:Boolean,disabled:Boolean,left:Boolean,right:Boolean,size:[Number,String],tag:{type:String,required:!1,default:"i"}},computed:{medium(){return!1},hasClickListener(){return Boolean(this.listeners$.click||this.listeners$["!click"])}},methods:{getIcon(){let t="";return this.$slots.default&&(t=this.$slots.default[0].text.trim()),(0,l.RB)(this,t)},getSize(){const t={xSmall:this.xSmall,small:this.small,medium:this.medium,large:this.large,xLarge:this.xLarge},e=(0,l.XP)(t).find((e=>t[e]));return e&&i[e]||(0,l.kb)(this.size)},getDefaultData(){return{staticClass:"v-icon notranslate",class:{"v-icon--disabled":this.disabled,"v-icon--left":this.left,"v-icon--link":this.hasClickListener,"v-icon--right":this.right,"v-icon--dense":this.dense},attrs:{"aria-hidden":!this.hasClickListener,disabled:this.hasClickListener&&this.disabled,type:this.hasClickListener?"button":void 0,...this.attrs$},on:this.listeners$}},getSvgWrapperData(){const t=this.getSize(),e={...this.getDefaultData(),style:t?{fontSize:t,height:t,width:t}:void 0};return this.applyColors(e),e},applyColors(t){t.class={...t.class,...this.themeClasses},this.setTextColor(this.color,t)},renderFontIcon(t,e){const n=[],i=this.getDefaultData();let s="material-icons";const r=t.indexOf("-"),a=r<=-1;a?n.push(t):(s=t.slice(0,r),h(s)&&(s="")),i.class[s]=!0,i.class[t]=!a;const o=this.getSize();return o&&(i.style={fontSize:o}),this.applyColors(i),e(this.hasClickListener?"button":this.tag,i,n)},renderSvgIcon(t,e){const n={class:"v-icon__svg",attrs:{xmlns:"http://www.w3.org/2000/svg",viewBox:"0 0 24 24",role:"img","aria-hidden":!0}},i=this.getSize();return i&&(n.style={fontSize:i,height:i,width:i}),e(this.hasClickListener?"button":"span",this.getSvgWrapperData(),[e("svg",n,[e("path",{attrs:{d:t}})])])},renderSvgIconComponent(t,e){const n={class:{"v-icon__component":!0}},i=this.getSize();i&&(n.style={fontSize:i,height:i,width:i}),this.applyColors(n);const s=t.component;return n.props=t.props,n.nativeOn=n.on,e(this.hasClickListener?"button":"span",this.getSvgWrapperData(),[e(s,n)])}},render(t){const e=this.getIcon();return"string"===typeof e?d(e)?this.renderSvgIcon(e,t):this.renderFontIcon(e,t):this.renderSvgIconComponent(e,t)}});var p=c.ZP.extend({name:"v-icon",$_wrapperFor:f,functional:!0,render(t,{data:e,children:n}){let i="";return e.domProps&&(i=e.domProps.textContent||e.domProps.innerHTML||i,delete e.domProps.textContent,delete e.domProps.innerHTML),t(f,e,i?[i]:n)}})},1012:function(t,e,n){"use strict";var i=n(5057);e["Z"]=i.Z},3316:function(t,e,n){"use strict";n.d(e,{Z:function(){return m}});n(7658);var i=n(1012),s=n(4654),r=n(8914),a=n(3276),o=n(4987),l=n(1050),c=(0,o.Z)(r.Z,a.Z).extend({name:"v-messages",props:{value:{type:Array,default:()=>[]}},methods:{genChildren(){return this.$createElement("transition-group",{staticClass:"v-messages__wrapper",attrs:{name:"message-transition",tag:"div"}},this.value.map(this.genMessage))},genMessage(t,e){return this.$createElement("div",{staticClass:"v-messages__message",key:e},(0,l.z9)(this,"default",{message:t,key:e})||[t])}},render(t){return t("div",this.setTextColor(this.color,{staticClass:"v-messages",class:this.themeClasses}),[this.genChildren()])}}),u=c,h=n(4609),d=n(4777),f=n(2653);const p=(0,o.Z)(h.Z,d.Z);var _=p.extend().extend({name:"v-input",inheritAttrs:!1,props:{appendIcon:String,backgroundColor:{type:String,default:""},dense:Boolean,height:[Number,String],hideDetails:[Boolean,String],hideSpinButtons:Boolean,hint:String,id:String,label:String,loading:Boolean,persistentHint:Boolean,prependIcon:String,value:null},data(){return{lazyValue:this.value,hasMouseDown:!1}},computed:{classes(){return{"v-input--has-state":this.hasState,"v-input--hide-details":!this.showDetails,"v-input--is-label-active":this.isLabelActive,"v-input--is-dirty":this.isDirty,"v-input--is-disabled":this.isDisabled,"v-input--is-focused":this.isFocused,"v-input--is-loading":!1!==this.loading&&null!=this.loading,"v-input--is-readonly":this.isReadonly,"v-input--dense":this.dense,"v-input--hide-spin-buttons":this.hideSpinButtons,...this.themeClasses}},computedId(){return this.id||`input-${this._uid}`},hasDetails(){return this.messagesToDisplay.length>0},hasHint(){return!this.hasMessages&&!!this.hint&&(this.persistentHint||this.isFocused)},hasLabel(){return!(!this.$slots.label&&!this.label)},internalValue:{get(){return this.lazyValue},set(t){this.lazyValue=t,this.$emit(this.$_modelEvent,t)}},isDirty(){return!!this.lazyValue},isLabelActive(){return this.isDirty},messagesToDisplay(){return this.hasHint?[this.hint]:this.hasMessages?this.validations.map((t=>{if("string"===typeof t)return t;const e=t(this.internalValue);return"string"===typeof e?e:""})).filter((t=>""!==t)):[]},showDetails(){return!1===this.hideDetails||"auto"===this.hideDetails&&this.hasDetails}},watch:{value(t){this.lazyValue=t}},beforeCreate(){this.$_modelEvent=this.$options.model&&this.$options.model.event||"input"},methods:{genContent(){return[this.genPrependSlot(),this.genControl(),this.genAppendSlot()]},genControl(){return this.$createElement("div",{staticClass:"v-input__control",attrs:{title:this.attrs$.title}},[this.genInputSlot(),this.genMessages()])},genDefaultSlot(){return[this.genLabel(),this.$slots.default]},genIcon(t,e,n={}){const s=this[`${t}Icon`],r=`click:${(0,l.GL)(t)}`,a=!(!this.listeners$[r]&&!e),o=(0,f.ZP)({attrs:{"aria-label":a?(0,l.GL)(t).split("-")[0]+" icon":void 0,color:this.validationState,dark:this.dark,disabled:this.isDisabled,light:this.light,tabindex:"clear"===t?-1:void 0},on:a?{click:t=>{t.preventDefault(),t.stopPropagation(),this.$emit(r,t),e&&e(t)},mouseup:t=>{t.preventDefault(),t.stopPropagation()}}:void 0},n);return this.$createElement("div",{staticClass:"v-input__icon",class:t?`v-input__icon--${(0,l.GL)(t)}`:void 0},[this.$createElement(i.Z,o,s)])},genInputSlot(){return this.$createElement("div",this.setBackgroundColor(this.backgroundColor,{staticClass:"v-input__slot",style:{height:(0,l.kb)(this.height)},on:{click:this.onClick,mousedown:this.onMouseDown,mouseup:this.onMouseUp},ref:"input-slot"}),[this.genDefaultSlot()])},genLabel(){return this.hasLabel?this.$createElement(s.Z,{props:{color:this.validationState,dark:this.dark,disabled:this.isDisabled,focused:this.hasState,for:this.computedId,light:this.light}},this.$slots.label||this.label):null},genMessages(){return this.showDetails?this.$createElement(u,{props:{color:this.hasHint?"":this.validationState,dark:this.dark,light:this.light,value:this.messagesToDisplay},attrs:{role:this.hasMessages?"alert":null},scopedSlots:{default:t=>(0,l.z9)(this,"message",t)}}):null},genSlot(t,e,n){if(!n.length)return null;const i=`${t}-${e}`;return this.$createElement("div",{staticClass:`v-input__${i}`,ref:i},n)},genPrependSlot(){const t=[];return this.$slots.prepend?t.push(this.$slots.prepend):this.prependIcon&&t.push(this.genIcon("prepend")),this.genSlot("prepend","outer",t)},genAppendSlot(){const t=[];return this.$slots.append?t.push(this.$slots.append):this.appendIcon&&t.push(this.genIcon("append")),this.genSlot("append","outer",t)},onClick(t){this.$emit("click",t)},onMouseDown(t){this.hasMouseDown=!0,this.$emit("mousedown",t)},onMouseUp(t){this.hasMouseDown=!1,this.$emit("mouseup",t)}},render(t){return t("div",this.setTextColor(this.validationState,{staticClass:"v-input",class:this.classes}),this.genContent())}}),m=_},4654:function(t,e,n){"use strict";n.d(e,{Z:function(){return c}});var i=n(8914),s=n(3276),r=n(4987),a=n(1050),o=n(2653),l=(0,r.Z)(s.Z).extend({name:"v-label",functional:!0,props:{absolute:Boolean,color:{type:String,default:"primary"},disabled:Boolean,focused:Boolean,for:String,left:{type:[Number,String],default:0},right:{type:[Number,String],default:"auto"},value:Boolean},render(t,e){const{children:n,listeners:r,props:l,data:c}=e,u=(0,o.ZP)({staticClass:"v-label",class:{"v-label--active":l.value,"v-label--is-disabled":l.disabled,...(0,s.X)(e)},attrs:{for:l.for,"aria-hidden":!l.for},on:r,style:{left:(0,a.kb)(l.left),right:(0,a.kb)(l.right),position:l.absolute?"absolute":"relative"},ref:"label"},c);return t("label",i.Z.options.methods.setTextColor(l.focused&&l.color,u),n)}}),c=l},3551:function(t,e,n){"use strict";n.d(e,{Z:function(){return s}});var i=n(8574),s=i.Z.extend({name:"v-main",props:{tag:{type:String,default:"main"}},computed:{styles(){const{bar:t,top:e,right:n,footer:i,insetFooter:s,bottom:r,left:a}=this.$vuetify.application;return{paddingTop:`${e+t}px`,paddingRight:`${n}px`,paddingBottom:`${i+s+r}px`,paddingLeft:`${a}px`}}},render(t){const e={staticClass:"v-main",style:this.styles,ref:"main"};return t(this.tag,e,[t("div",{staticClass:"v-main__wrap"},this.$slots.default)])}})},5731:function(t,e,n){"use strict";n.d(e,{Z:function(){return mt},l:function(){return pt}});n(7658),n(2838);var i=n(4987),s=n(3422),r=n(1012),a=n(8914),o=n(4791),l=n(3276),c=n(6470),u=n(4040),h=n(281),d=n(6596),f=(0,i.Z)(a.Z,h.Z,u.Z,l.Z,(0,o.d)("chipGroup"),(0,c.d)("inputValue")).extend({name:"v-chip",props:{active:{type:Boolean,default:!0},activeClass:{type:String,default(){return this.chipGroup?this.chipGroup.activeClass:""}},close:Boolean,closeIcon:{type:String,default:"$delete"},closeLabel:{type:String,default:"$vuetify.close"},disabled:Boolean,draggable:Boolean,filter:Boolean,filterIcon:{type:String,default:"$complete"},label:Boolean,link:Boolean,outlined:Boolean,pill:Boolean,tag:{type:String,default:"span"},textColor:String,value:null},data:()=>({proxyClass:"v-chip--active"}),computed:{classes(){return{"v-chip":!0,...u.Z.options.computed.classes.call(this),"v-chip--clickable":this.isClickable,"v-chip--disabled":this.disabled,"v-chip--draggable":this.draggable,"v-chip--label":this.label,"v-chip--link":this.isLink,"v-chip--no-color":!this.color,"v-chip--outlined":this.outlined,"v-chip--pill":this.pill,"v-chip--removable":this.hasClose,...this.themeClasses,...this.sizeableClasses,...this.groupClasses}},hasClose(){return Boolean(this.close)},isClickable(){return Boolean(u.Z.options.computed.isClickable.call(this)||this.chipGroup)}},created(){const t=[["outline","outlined"],["selected","input-value"],["value","active"],["@input","@active.sync"]];t.forEach((([t,e])=>{this.$attrs.hasOwnProperty(t)&&(0,d.fK)(t,e,this)}))},methods:{click(t){this.$emit("click",t),this.chipGroup&&this.toggle()},genFilter(){const t=[];return this.isActive&&t.push(this.$createElement(r.Z,{staticClass:"v-chip__filter",props:{left:!0}},this.filterIcon)),this.$createElement(s.Zq,t)},genClose(){return this.$createElement(r.Z,{staticClass:"v-chip__close",props:{right:!0,size:18},attrs:{"aria-label":this.$vuetify.lang.t(this.closeLabel)},on:{click:t=>{t.stopPropagation(),t.preventDefault(),this.$emit("click:close"),this.$emit("update:active",!1)}}},this.closeIcon)},genContent(){return this.$createElement("span",{staticClass:"v-chip__content"},[this.filter&&this.genFilter(),this.$slots.default,this.hasClose&&this.genClose()])}},render(t){const e=[this.genContent()];let{tag:n,data:i}=this.generateRouteLink();i.attrs={...i.attrs,draggable:this.draggable?"true":void 0,tabindex:this.chipGroup&&!this.disabled?0:i.attrs.tabindex},i.directives.push({name:"show",value:this.active}),i=this.setBackgroundColor(this.color,i);const s=this.textColor||this.outlined&&this.color;return t(n,this.setTextColor(s,i),e)}}),p=f,_=l.Z.extend({name:"v-theme-provider",props:{root:Boolean},computed:{isDark(){return this.root?this.rootIsDark:l.Z.options.computed.isDark.call(this)}},render(){return this.$slots.default&&this.$slots.default.find((t=>!t.isComment&&" "!==t.text))}}),m=n(144),v=m.ZP.extend().extend({name:"delayable",props:{openDelay:{type:[Number,String],default:0},closeDelay:{type:[Number,String],default:0}},data:()=>({openTimeout:void 0,closeTimeout:void 0}),methods:{clearDelay(){clearTimeout(this.openTimeout),clearTimeout(this.closeTimeout)},runDelay(t,e){this.clearDelay();const n=parseInt(this[`${t}Delay`],10);this[`${t}Timeout`]=setTimeout(e||(()=>{this.isActive={open:!0,close:!1}[t]}),n)}}}),g=n(1050);const b=(0,i.Z)(v,c.Z);var y=b.extend({name:"activatable",props:{activator:{default:null,validator:t=>["string","object"].includes(typeof t)},disabled:Boolean,internalActivator:Boolean,openOnClick:{type:Boolean,default:!0},openOnHover:Boolean,openOnFocus:Boolean},data:()=>({activatorElement:null,activatorNode:[],events:["click","mouseenter","mouseleave","focus"],listeners:{}}),watch:{activator:"resetActivator",openOnFocus:"resetActivator",openOnHover:"resetActivator"},mounted(){const t=(0,g.sp)(this,"activator",!0);t&&["v-slot","normal"].includes(t)&&(0,d.N6)('The activator slot must be bound, try \'<template v-slot:activator="{ on }"><v-btn v-on="on">\'',this),this.addActivatorEvents()},beforeDestroy(){this.removeActivatorEvents()},methods:{addActivatorEvents(){if(!this.activator||this.disabled||!this.getActivator())return;this.listeners=this.genActivatorListeners();const t=Object.keys(this.listeners);for(const e of t)this.getActivator().addEventListener(e,this.listeners[e])},genActivator(){const t=(0,g.z9)(this,"activator",Object.assign(this.getValueProxy(),{on:this.genActivatorListeners(),attrs:this.genActivatorAttributes()}))||[];return this.activatorNode=t,t},genActivatorAttributes(){return{role:this.openOnClick&&!this.openOnHover?"button":void 0,"aria-haspopup":!0,"aria-expanded":String(this.isActive)}},genActivatorListeners(){if(this.disabled)return{};const t={};return this.openOnHover?(t.mouseenter=t=>{this.getActivator(t),this.runDelay("open")},t.mouseleave=t=>{this.getActivator(t),this.runDelay("close")}):this.openOnClick&&(t.click=t=>{const e=this.getActivator(t);e&&e.focus(),t.stopPropagation(),this.isActive=!this.isActive}),this.openOnFocus&&(t.focus=t=>{this.getActivator(t),t.stopPropagation(),this.isActive=!this.isActive}),t},getActivator(t){if(this.activatorElement)return this.activatorElement;let e=null;if(this.activator){const t=this.internalActivator?this.$el:document;e="string"===typeof this.activator?t.querySelector(this.activator):this.activator.$el?this.activator.$el:this.activator}else if(1===this.activatorNode.length||this.activatorNode.length&&!t){const t=this.activatorNode[0].componentInstance;e=t&&t.$options.mixins&&t.$options.mixins.some((t=>t.options&&["activatable","menuable"].includes(t.options.name)))?t.getActivator():this.activatorNode[0].elm}else t&&(e=t.currentTarget||t.target);return this.activatorElement=(null===e||void 0===e?void 0:e.nodeType)===Node.ELEMENT_NODE?e:null,this.activatorElement},getContentSlot(){return(0,g.z9)(this,"default",this.getValueProxy(),!0)},getValueProxy(){const t=this;return{get value(){return t.isActive},set value(e){t.isActive=e}}},removeActivatorEvents(){if(!this.activator||!this.activatorElement)return;const t=Object.keys(this.listeners);for(const e of t)this.activatorElement.removeEventListener(e,this.listeners[e]);this.listeners={}},resetActivator(){this.removeActivatorEvents(),this.activatorElement=null,this.getActivator(),this.addActivatorEvents()}}});function x(t){const e=[];for(let n=0;n<t.length;n++){const i=t[n];i.isActive&&i.isDependent?e.push(i):e.push(...x(i.$children))}return e}var w=(0,i.Z)().extend({name:"dependent",data(){return{closeDependents:!0,isActive:!1,isDependent:!0}},watch:{isActive(t){if(t)return;const e=this.getOpenDependents();for(let n=0;n<e.length;n++)e[n].isActive=!1}},methods:{getOpenDependents(){return this.closeDependents?x(this.$children):[]},getOpenDependentElements(){const t=[],e=this.getOpenDependents();for(let n=0;n<e.length;n++)t.push(...e[n].getClickableDependentElements());return t},getClickableDependentElements(){const t=[this.$el];return this.$refs.content&&t.push(this.$refs.content),this.overlay&&t.push(this.overlay.$el),t.push(...this.getOpenDependentElements()),t}}}),S=m.ZP.extend().extend({name:"stackable",data(){return{stackElement:null,stackExclude:null,stackMinZIndex:0,isActive:!1}},computed:{activeZIndex(){if("undefined"===typeof window)return 0;const t=this.stackElement||this.$refs.content,e=this.isActive?this.getMaxZIndex(this.stackExclude||[t])+2:(0,g.KK)(t);return null==e?e:parseInt(e)}},methods:{getMaxZIndex(t=[]){const e=this.$el,n=[this.stackMinZIndex,(0,g.KK)(e)],i=[...document.getElementsByClassName("v-menu__content--active"),...document.getElementsByClassName("v-dialog__content--active")];for(let s=0;s<i.length;s++)t.includes(i[s])||n.push((0,g.KK)(i[s]));return Math.max(...n)}}}),k=n(8386),A=m.ZP.extend().extend({name:"bootable",props:{eager:Boolean},data:()=>({isBooted:!1}),computed:{hasContent(){return this.isBooted||this.eager||this.isActive}},watch:{isActive(){this.isBooted=!0}},created(){"lazy"in this.$attrs&&(0,d.Jk)("lazy",this)},methods:{showLazyContent(t){return this.hasContent&&t?t():[this.$createElement()]}}});function C(t){const e=typeof t;return"boolean"===e||"string"===e||t.nodeType===Node.ELEMENT_NODE}function M(t){t.forEach((t=>{t.elm&&t.elm.parentNode&&t.elm.parentNode.removeChild(t.elm)}))}var E=(0,i.Z)(A).extend({name:"detachable",props:{attach:{default:!1,validator:C},contentClass:{type:String,default:""}},data:()=>({activatorNode:null,hasDetached:!1}),watch:{attach(){this.hasDetached=!1,this.initDetach()},hasContent(){this.$nextTick(this.initDetach)}},beforeMount(){this.$nextTick((()=>{if(this.activatorNode){const t=Array.isArray(this.activatorNode)?this.activatorNode:[this.activatorNode];t.forEach((t=>{if(!t.elm)return;if(!this.$el.parentNode)return;const e=this.$el===this.$el.parentNode.firstChild?this.$el:this.$el.nextSibling;this.$el.parentNode.insertBefore(t.elm,e)}))}}))},mounted(){this.hasContent&&this.initDetach()},deactivated(){this.isActive=!1},beforeDestroy(){this.$refs.content&&this.$refs.content.parentNode&&this.$refs.content.parentNode.removeChild(this.$refs.content)},destroyed(){if(this.activatorNode){const t=Array.isArray(this.activatorNode)?this.activatorNode:[this.activatorNode];if(this.$el.isConnected){const e=new MutationObserver((n=>{n.some((t=>Array.from(t.removedNodes).includes(this.$el)))&&(e.disconnect(),M(t))}));e.observe(this.$el.parentNode,{subtree:!1,childList:!0})}else M(t)}},methods:{getScopeIdAttrs(){const t=(0,g.vO)(this.$vnode,"context.$options._scopeId");return t&&{[t]:""}},initDetach(){if(this._isDestroyed||!this.$refs.content||this.hasDetached||""===this.attach||!0===this.attach||"attach"===this.attach)return;let t;t=!1===this.attach?document.querySelector("[data-app]"):"string"===typeof this.attach?document.querySelector(this.attach):this.attach,t?(t.appendChild(this.$refs.content),this.hasDetached=!0):(0,d.Kd)(`Unable to locate target ${this.attach||"[data-app]"}`,this)}}});const T=(0,i.Z)(S,(0,k.d)(["top","right","bottom","left","absolute"]),y,E);var B=T.extend().extend({name:"menuable",props:{allowOverflow:Boolean,light:Boolean,dark:Boolean,maxWidth:{type:[Number,String],default:"auto"},minWidth:[Number,String],nudgeBottom:{type:[Number,String],default:0},nudgeLeft:{type:[Number,String],default:0},nudgeRight:{type:[Number,String],default:0},nudgeTop:{type:[Number,String],default:0},nudgeWidth:{type:[Number,String],default:0},offsetOverflow:Boolean,positionX:{type:Number,default:null},positionY:{type:Number,default:null},zIndex:{type:[Number,String],default:null}},data:()=>({activatorNode:[],absoluteX:0,absoluteY:0,activatedBy:null,activatorFixed:!1,dimensions:{activator:{top:0,left:0,bottom:0,right:0,width:0,height:0,offsetTop:0,scrollHeight:0,offsetLeft:0},content:{top:0,left:0,bottom:0,right:0,width:0,height:0,offsetTop:0,scrollHeight:0}},relativeYOffset:0,hasJustFocused:!1,hasWindow:!1,inputActivator:!1,isContentActive:!1,pageWidth:0,pageYOffset:0,stackClass:"v-menu__content--active",stackMinZIndex:6}),computed:{computedLeft(){const t=this.dimensions.activator,e=this.dimensions.content,n=(!1!==this.attach?t.offsetLeft:t.left)||0,i=Math.max(t.width,e.width);let s=0;if(s+=n,(this.left||this.$vuetify.rtl&&!this.right)&&(s-=i-t.width),this.offsetX){const e=isNaN(Number(this.maxWidth))?t.width:Math.min(t.width,Number(this.maxWidth));s+=this.left?-e:t.width}return this.nudgeLeft&&(s-=parseInt(this.nudgeLeft)),this.nudgeRight&&(s+=parseInt(this.nudgeRight)),s},computedTop(){const t=this.dimensions.activator,e=this.dimensions.content;let n=0;return this.top&&(n+=t.height-e.height),!1!==this.attach?n+=t.offsetTop:n+=t.top+this.pageYOffset,this.offsetY&&(n+=this.top?-t.height:t.height),this.nudgeTop&&(n-=parseInt(this.nudgeTop)),this.nudgeBottom&&(n+=parseInt(this.nudgeBottom)),n},hasActivator(){return!!this.$slots.activator||!!this.$scopedSlots.activator||!!this.activator||!!this.inputActivator},absoluteYOffset(){return this.pageYOffset-this.relativeYOffset}},watch:{disabled(t){t&&this.callDeactivate()},isActive(t){this.disabled||(t?this.callActivate():this.callDeactivate())},positionX:"updateDimensions",positionY:"updateDimensions"},beforeMount(){this.hasWindow="undefined"!==typeof window,this.hasWindow&&window.addEventListener("resize",this.updateDimensions,!1)},beforeDestroy(){this.hasWindow&&window.removeEventListener("resize",this.updateDimensions,!1)},methods:{absolutePosition(){return{offsetTop:this.positionY||this.absoluteY,offsetLeft:this.positionX||this.absoluteX,scrollHeight:0,top:this.positionY||this.absoluteY,bottom:this.positionY||this.absoluteY,left:this.positionX||this.absoluteX,right:this.positionX||this.absoluteX,height:0,width:0}},activate(){},calcLeft(t){return(0,g.kb)(!1!==this.attach?this.computedLeft:this.calcXOverflow(this.computedLeft,t))},calcTop(){return(0,g.kb)(!1!==this.attach?this.computedTop:this.calcYOverflow(this.computedTop))},calcXOverflow(t,e){const n=t+e-this.pageWidth+12;return t=(!this.left||this.right)&&n>0?Math.max(t-n,0):Math.max(t,12),t+this.getOffsetLeft()},calcYOverflow(t){const e=this.getInnerHeight(),n=this.absoluteYOffset+e,i=this.dimensions.activator,s=this.dimensions.content.height,r=t+s,a=n<r;return a&&this.offsetOverflow&&i.top>s?t=this.pageYOffset+(i.top-s):a&&!this.allowOverflow?t=n-s-12:t<this.absoluteYOffset&&!this.allowOverflow&&(t=this.absoluteYOffset+12),t<12?12:t},callActivate(){this.hasWindow&&this.activate()},callDeactivate(){this.isContentActive=!1,this.deactivate()},checkForPageYOffset(){this.hasWindow&&(this.pageYOffset=this.activatorFixed?0:this.getOffsetTop())},checkActivatorFixed(){if(!1!==this.attach)return void(this.activatorFixed=!1);let t=this.getActivator();while(t){if("fixed"===window.getComputedStyle(t).position)return void(this.activatorFixed=!0);t=t.offsetParent}this.activatorFixed=!1},deactivate(){},genActivatorListeners(){const t=y.options.methods.genActivatorListeners.call(this),e=t.click;return e&&(t.click=t=>{this.openOnClick&&e&&e(t),this.absoluteX=t.clientX,this.absoluteY=t.clientY}),t},getInnerHeight(){return this.hasWindow?window.innerHeight||document.documentElement.clientHeight:0},getOffsetLeft(){return this.hasWindow?window.pageXOffset||document.documentElement.scrollLeft:0},getOffsetTop(){return this.hasWindow?window.pageYOffset||document.documentElement.scrollTop:0},getRoundedBoundedClientRect(t){const e=t.getBoundingClientRect();return{top:Math.round(e.top),left:Math.round(e.left),bottom:Math.round(e.bottom),right:Math.round(e.right),width:Math.round(e.width),height:Math.round(e.height)}},measure(t){if(!t||!this.hasWindow)return null;const e=this.getRoundedBoundedClientRect(t);if(!1!==this.attach){const n=window.getComputedStyle(t);e.left=parseInt(n.marginLeft),e.top=parseInt(n.marginTop)}return e},sneakPeek(t){requestAnimationFrame((()=>{const e=this.$refs.content;e&&"none"===e.style.display?(e.style.display="inline-block",t(),e.style.display="none"):t()}))},startTransition(){return new Promise((t=>requestAnimationFrame((()=>{this.isContentActive=this.hasJustFocused=this.isActive,t()}))))},updateDimensions(){this.hasWindow="undefined"!==typeof window,this.checkActivatorFixed(),this.checkForPageYOffset(),this.pageWidth=document.documentElement.clientWidth;const t={activator:{...this.dimensions.activator},content:{...this.dimensions.content}};if(!this.hasActivator||this.absolute)t.activator=this.absolutePosition();else{const e=this.getActivator();if(!e)return;t.activator=this.measure(e),t.activator.offsetLeft=e.offsetLeft,!1!==this.attach?t.activator.offsetTop=e.offsetTop:t.activator.offsetTop=0}this.sneakPeek((()=>{if(this.$refs.content){if(this.$refs.content.offsetParent){const e=this.getRoundedBoundedClientRect(this.$refs.content.offsetParent);this.relativeYOffset=window.pageYOffset+e.top,t.activator.top-=this.relativeYOffset,t.activator.left-=window.pageXOffset+e.left}t.content=this.measure(this.$refs.content)}this.dimensions=t}))}}}),R=m.ZP.extend({name:"returnable",props:{returnValue:null},data:()=>({isActive:!1,originalValue:null}),watch:{isActive(t){t?this.originalValue=this.returnValue:this.$emit("update:return-value",this.originalValue)}},methods:{save(t){this.originalValue=t,setTimeout((()=>{this.isActive=!1}))}}}),$=n(3960),I=n(9042),L=n(9495),O=n(473);const P=(0,i.Z)(w,v,R,$.Z,l.Z,B);var V=P.extend({name:"v-menu",directives:{ClickOutside:I.Z,Resize:L.Z},provide(){return{isInMenu:!0,theme:this.theme}},props:{auto:Boolean,closeOnClick:{type:Boolean,default:!0},closeOnContentClick:{type:Boolean,default:!0},disabled:Boolean,disableKeys:Boolean,maxHeight:{type:[Number,String],default:"auto"},offsetX:Boolean,offsetY:Boolean,openOnHover:Boolean,origin:{type:String,default:"top left"},transition:{type:[Boolean,String],default:"v-menu-transition"}},data(){return{calculatedTopAuto:0,defaultOffset:8,hasJustFocused:!1,listIndex:-1,resizeTimeout:0,selectedIndex:null,tiles:[]}},computed:{activeTile(){return this.tiles[this.listIndex]},calculatedLeft(){const t=Math.max(this.dimensions.content.width,parseFloat(this.calculatedMinWidth));return this.auto?(0,g.kb)(this.calcXOverflow(this.calcLeftAuto(),t))||"0":this.calcLeft(t)||"0"},calculatedMaxHeight(){const t=this.auto?"200px":(0,g.kb)(this.maxHeight);return t||"0"},calculatedMaxWidth(){return(0,g.kb)(this.maxWidth)||"0"},calculatedMinWidth(){if(this.minWidth)return(0,g.kb)(this.minWidth)||"0";const t=Math.min(this.dimensions.activator.width+Number(this.nudgeWidth)+(this.auto?16:0),Math.max(this.pageWidth-24,0)),e=isNaN(parseInt(this.calculatedMaxWidth))?t:parseInt(this.calculatedMaxWidth);return(0,g.kb)(Math.min(e,t))||"0"},calculatedTop(){const t=this.auto?(0,g.kb)(this.calcYOverflow(this.calculatedTopAuto)):this.calcTop();return t||"0"},hasClickableTiles(){return Boolean(this.tiles.find((t=>t.tabIndex>-1)))},styles(){return{maxHeight:this.calculatedMaxHeight,minWidth:this.calculatedMinWidth,maxWidth:this.calculatedMaxWidth,top:this.calculatedTop,left:this.calculatedLeft,transformOrigin:this.origin,zIndex:this.zIndex||this.activeZIndex}}},watch:{isActive(t){t||(this.listIndex=-1)},isContentActive(t){this.hasJustFocused=t},listIndex(t,e){if(t in this.tiles){const e=this.tiles[t];e.classList.add("v-list-item--highlighted");const n=this.$refs.content.scrollTop,i=this.$refs.content.clientHeight;n>e.offsetTop-8?(0,O.Z)(e.offsetTop-e.clientHeight,{appOffset:!1,duration:300,container:this.$refs.content}):n+i<e.offsetTop+e.clientHeight+8&&(0,O.Z)(e.offsetTop-i+2*e.clientHeight,{appOffset:!1,duration:300,container:this.$refs.content})}e in this.tiles&&this.tiles[e].classList.remove("v-list-item--highlighted")}},created(){this.$attrs.hasOwnProperty("full-width")&&(0,d.Jk)("full-width",this)},mounted(){this.isActive&&this.callActivate()},methods:{activate(){this.updateDimensions(),requestAnimationFrame((()=>{this.startTransition().then((()=>{this.$refs.content&&(this.calculatedTopAuto=this.calcTopAuto(),this.auto&&(this.$refs.content.scrollTop=this.calcScrollPosition()))}))}))},calcScrollPosition(){const t=this.$refs.content,e=t.querySelector(".v-list-item--active"),n=t.scrollHeight-t.offsetHeight;return e?Math.min(n,Math.max(0,e.offsetTop-t.offsetHeight/2+e.offsetHeight/2)):t.scrollTop},calcLeftAuto(){return parseInt(this.dimensions.activator.left-2*this.defaultOffset)},calcTopAuto(){const t=this.$refs.content,e=t.querySelector(".v-list-item--active");if(e||(this.selectedIndex=null),this.offsetY||!e)return this.computedTop;this.selectedIndex=Array.from(this.tiles).indexOf(e);const n=e.offsetTop-this.calcScrollPosition(),i=t.querySelector(".v-list-item").offsetTop;return this.computedTop-n-i-1},changeListIndex(t){if(this.getTiles(),this.isActive&&this.hasClickableTiles)if(t.keyCode!==g.keyCodes.tab){if(t.keyCode===g.keyCodes.down)this.nextTile();else if(t.keyCode===g.keyCodes.up)this.prevTile();else if(t.keyCode===g.keyCodes.end)this.lastTile();else if(t.keyCode===g.keyCodes.home)this.firstTile();else{if(t.keyCode!==g.keyCodes.enter||-1===this.listIndex)return;this.tiles[this.listIndex].click()}t.preventDefault()}else this.isActive=!1},closeConditional(t){const e=t.target;return this.isActive&&!this._isDestroyed&&this.closeOnClick&&!this.$refs.content.contains(e)},genActivatorAttributes(){const t=y.options.methods.genActivatorAttributes.call(this);return this.activeTile&&this.activeTile.id?{...t,"aria-activedescendant":this.activeTile.id}:t},genActivatorListeners(){const t=B.options.methods.genActivatorListeners.call(this);return this.disableKeys||(t.keydown=this.onKeyDown),t},genTransition(){const t=this.genContent();return this.transition?this.$createElement("transition",{props:{name:this.transition}},[t]):t},genDirectives(){const t=[{name:"show",value:this.isContentActive}];return!this.openOnHover&&this.closeOnClick&&t.push({name:"click-outside",value:{handler:()=>{this.isActive=!1},closeConditional:this.closeConditional,include:()=>[this.$el,...this.getOpenDependentElements()]}}),t},genContent(){const t={attrs:{...this.getScopeIdAttrs(),role:"role"in this.$attrs?this.$attrs.role:"menu"},staticClass:"v-menu__content",class:{...this.rootThemeClasses,...this.roundedClasses,"v-menu__content--auto":this.auto,"v-menu__content--fixed":this.activatorFixed,menuable__content__active:this.isActive,[this.contentClass.trim()]:!0},style:this.styles,directives:this.genDirectives(),ref:"content",on:{click:t=>{const e=t.target;e.getAttribute("disabled")||this.closeOnContentClick&&(this.isActive=!1)},keydown:this.onKeyDown}};return this.$listeners.scroll&&(t.on=t.on||{},t.on.scroll=this.$listeners.scroll),!this.disabled&&this.openOnHover&&(t.on=t.on||{},t.on.mouseenter=this.mouseEnterHandler),this.openOnHover&&(t.on=t.on||{},t.on.mouseleave=this.mouseLeaveHandler),this.$createElement("div",t,this.getContentSlot())},getTiles(){this.$refs.content&&(this.tiles=Array.from(this.$refs.content.querySelectorAll(".v-list-item, .v-divider, .v-subheader")))},mouseEnterHandler(){this.runDelay("open",(()=>{this.hasJustFocused||(this.hasJustFocused=!0)}))},mouseLeaveHandler(t){this.runDelay("close",(()=>{var e;(null===(e=this.$refs.content)||void 0===e?void 0:e.contains(t.relatedTarget))||requestAnimationFrame((()=>{this.isActive=!1,this.callDeactivate()}))}))},nextTile(){const t=this.tiles[this.listIndex+1];if(!t){if(!this.tiles.length)return;return this.listIndex=-1,void this.nextTile()}this.listIndex++,-1===t.tabIndex&&this.nextTile()},prevTile(){const t=this.tiles[this.listIndex-1];if(!t){if(!this.tiles.length)return;return this.listIndex=this.tiles.length,void this.prevTile()}this.listIndex--,-1===t.tabIndex&&this.prevTile()},lastTile(){const t=this.tiles[this.tiles.length-1];t&&(this.listIndex=this.tiles.length-1,-1===t.tabIndex&&this.prevTile())},firstTile(){const t=this.tiles[0];t&&(this.listIndex=0,-1===t.tabIndex&&this.nextTile())},onKeyDown(t){if(t.keyCode===g.keyCodes.esc){setTimeout((()=>{this.isActive=!1}));const t=this.getActivator();this.$nextTick((()=>t&&t.focus()))}else!this.isActive&&[g.keyCodes.up,g.keyCodes.down].includes(t.keyCode)&&(this.isActive=!0);this.$nextTick((()=>this.changeListIndex(t)))},onResize(){this.isActive&&(this.$refs.content.offsetWidth,this.updateDimensions(),clearTimeout(this.resizeTimeout),this.resizeTimeout=window.setTimeout(this.updateDimensions,100))}},render(t){const e={staticClass:"v-menu",class:{"v-menu--attached":""===this.attach||!0===this.attach||"attach"===this.attach},directives:[{arg:"500",name:"resize",value:this.onResize}]};return t("div",e,[!this.activator&&this.genActivator(),this.showLazyContent((()=>[this.$createElement(_,{props:{root:!0,light:this.light,dark:this.dark}},[this.genTransition()])]))])}}),D=V,N=(n(541),n(3176)),F=n(5057),H=n(2653),j=m.ZP.extend({name:"v-simple-checkbox",functional:!0,directives:{Ripple:N.Z},props:{...a.Z.options.props,...l.Z.options.props,disabled:Boolean,ripple:{type:Boolean,default:!0},value:Boolean,indeterminate:Boolean,indeterminateIcon:{type:String,default:"$checkboxIndeterminate"},onIcon:{type:String,default:"$checkboxOn"},offIcon:{type:String,default:"$checkboxOff"}},render(t,{props:e,data:n,listeners:i}){const s=[];let r=e.offIcon;if(e.indeterminate?r=e.indeterminateIcon:e.value&&(r=e.onIcon),s.push(t(F.Z,a.Z.options.methods.setTextColor(e.value&&e.color,{props:{disabled:e.disabled,dark:e.dark,light:e.light}}),r)),e.ripple&&!e.disabled){const n=t("div",a.Z.options.methods.setTextColor(e.color,{staticClass:"v-input--selection-controls__ripple",directives:[{def:N.Z,name:"ripple",value:{center:!0}}]}));s.push(n)}return t("div",(0,H.ZP)(n,{class:{"v-simple-checkbox":!0,"v-simple-checkbox--disabled":e.disabled},on:{click:t=>{t.stopPropagation(),n.on&&n.on.input&&!e.disabled&&(0,g.TI)(n.on.input).forEach((t=>t(!e.value)))}}}),[t("div",{staticClass:"v-input--selection-controls__input"},s)])}}),Z=l.Z.extend({name:"v-divider",props:{inset:Boolean,vertical:Boolean},render(t){let e;return this.$attrs.role&&"separator"!==this.$attrs.role||(e=this.vertical?"vertical":"horizontal"),t("hr",{class:{"v-divider":!0,"v-divider--inset":this.inset,"v-divider--vertical":this.vertical,...this.themeClasses},attrs:{role:"separator","aria-orientation":e,...this.$attrs},on:this.$listeners})}}),z=Z,q=(0,i.Z)(l.Z).extend({name:"v-subheader",props:{inset:Boolean},render(t){return t("div",{staticClass:"v-subheader",class:{"v-subheader--inset":this.inset,...this.themeClasses},attrs:this.$attrs,on:this.$listeners},this.$slots.default)}}),U=q;const X=(0,i.Z)(a.Z,u.Z,l.Z,(0,o.d)("listItemGroup"),(0,c.d)("inputValue"));var Y=X.extend().extend({name:"v-list-item",directives:{Ripple:N.Z},inject:{isInGroup:{default:!1},isInList:{default:!1},isInMenu:{default:!1},isInNav:{default:!1}},inheritAttrs:!1,props:{activeClass:{type:String,default(){return this.listItemGroup?this.listItemGroup.activeClass:""}},dense:Boolean,inactive:Boolean,link:Boolean,selectable:{type:Boolean},tag:{type:String,default:"div"},threeLine:Boolean,twoLine:Boolean,value:null},data:()=>({proxyClass:"v-list-item--active"}),computed:{classes(){return{"v-list-item":!0,...u.Z.options.computed.classes.call(this),"v-list-item--dense":this.dense,"v-list-item--disabled":this.disabled,"v-list-item--link":this.isClickable&&!this.inactive,"v-list-item--selectable":this.selectable,"v-list-item--three-line":this.threeLine,"v-list-item--two-line":this.twoLine,...this.themeClasses}},isClickable(){return Boolean(u.Z.options.computed.isClickable.call(this)||this.listItemGroup)}},created(){this.$attrs.hasOwnProperty("avatar")&&(0,d.Jk)("avatar",this)},methods:{click(t){t.detail&&this.$el.blur(),this.$emit("click",t),this.to||this.toggle()},genAttrs(){const t={"aria-disabled":!!this.disabled||void 0,tabindex:this.isClickable&&!this.disabled?0:-1,...this.$attrs};return this.$attrs.hasOwnProperty("role")||this.isInNav||(this.isInGroup?(t.role="option",t["aria-selected"]=String(this.isActive)):this.isInMenu?(t.role=this.isClickable?"menuitem":void 0,t.id=t.id||`list-item-${this._uid}`):this.isInList&&(t.role="listitem")),t},toggle(){this.to&&void 0===this.inputValue&&(this.isActive=!this.isActive),this.$emit("change")}},render(t){let{tag:e,data:n}=this.generateRouteLink();n.attrs={...n.attrs,...this.genAttrs()},n[this.to?"nativeOn":"on"]={...n[this.to?"nativeOn":"on"],keydown:t=>{this.disabled||(t.keyCode===g.keyCodes.enter&&this.click(t),this.$emit("keydown",t))}},this.inactive&&(e="div"),this.inactive&&this.to&&(n.on=n.nativeOn,delete n.nativeOn);const i=this.$scopedSlots.default?this.$scopedSlots.default({active:this.isActive,toggle:this.toggle}):this.$slots.default;return t(e,this.isActive?this.setTextColor(this.color,n):n,i)}}),W=m.ZP.extend({name:"v-list-item-action",functional:!0,render(t,{data:e,children:n=[]}){e.staticClass=e.staticClass?`v-list-item__action ${e.staticClass}`:"v-list-item__action";const i=n.filter((t=>!1===t.isComment&&" "!==t.text));return i.length>1&&(e.staticClass+=" v-list-item__action--stack"),t("div",e,n)}}),G=n(9868),K=G.Z.extend().extend({name:"v-list",provide(){return{isInList:!0,list:this}},inject:{isInMenu:{default:!1},isInNav:{default:!1}},props:{dense:Boolean,disabled:Boolean,expand:Boolean,flat:Boolean,nav:Boolean,rounded:Boolean,subheader:Boolean,threeLine:Boolean,twoLine:Boolean},data:()=>({groups:[]}),computed:{classes(){return{...G.Z.options.computed.classes.call(this),"v-list--dense":this.dense,"v-list--disabled":this.disabled,"v-list--flat":this.flat,"v-list--nav":this.nav,"v-list--rounded":this.rounded,"v-list--subheader":this.subheader,"v-list--two-line":this.twoLine,"v-list--three-line":this.threeLine}}},methods:{register(t){this.groups.push(t)},unregister(t){const e=this.groups.findIndex((e=>e._uid===t._uid));e>-1&&this.groups.splice(e,1)},listClick(t){if(!this.expand)for(const e of this.groups)e.toggle(t)}},render(t){const e={staticClass:"v-list",class:this.classes,style:this.styles,attrs:{role:this.isInNav||this.isInMenu?void 0:"list",...this.attrs$}};return t(this.tag,this.setBackgroundColor(this.color,e),[this.$slots.default])}}),Q=m.ZP.extend({name:"v-list-item-icon",functional:!0,render(t,{data:e,children:n}){return e.staticClass=`v-list-item__icon ${e.staticClass||""}`.trim(),t("div",e,n)}}),J=n(4609),tt=n(380);const et=(0,i.Z)(J.Z,A,a.Z,(0,tt.f)("list"),c.Z);et.extend().extend({name:"v-list-group",directives:{ripple:N.Z},props:{activeClass:{type:String,default:""},appendIcon:{type:String,default:"$expand"},color:{type:String,default:"primary"},disabled:Boolean,group:[String,RegExp],noAction:Boolean,prependIcon:String,ripple:{type:[Boolean,Object],default:!0},subGroup:Boolean},computed:{classes(){return{"v-list-group--active":this.isActive,"v-list-group--disabled":this.disabled,"v-list-group--no-action":this.noAction,"v-list-group--sub-group":this.subGroup}}},watch:{isActive(t){!this.subGroup&&t&&this.list&&this.list.listClick(this._uid)},$route:"onRouteChange"},created(){this.list&&this.list.register(this),this.group&&this.$route&&null==this.value&&(this.isActive=this.matchRoute(this.$route.path))},beforeDestroy(){this.list&&this.list.unregister(this)},methods:{click(t){this.disabled||(this.isBooted=!0,this.$emit("click",t),this.$nextTick((()=>this.isActive=!this.isActive)))},genIcon(t){return this.$createElement(r.Z,t)},genAppendIcon(){const t=!this.subGroup&&this.appendIcon;return t||this.$slots.appendIcon?this.$createElement(Q,{staticClass:"v-list-group__header__append-icon"},[this.$slots.appendIcon||this.genIcon(t)]):null},genHeader(){return this.$createElement(Y,{staticClass:"v-list-group__header",attrs:{"aria-expanded":String(this.isActive),role:"button"},class:{[this.activeClass]:this.isActive},props:{inputValue:this.isActive},directives:[{name:"ripple",value:this.ripple}],on:{...this.listeners$,click:this.click}},[this.genPrependIcon(),this.$slots.activator,this.genAppendIcon()])},genItems(){return this.showLazyContent((()=>[this.$createElement("div",{staticClass:"v-list-group__items",directives:[{name:"show",value:this.isActive}]},(0,g.z9)(this))]))},genPrependIcon(){const t=this.subGroup&&null==this.prependIcon?"$subgroup":this.prependIcon;return t||this.$slots.prependIcon?this.$createElement(Q,{staticClass:"v-list-group__header__prepend-icon"},[this.$slots.prependIcon||this.genIcon(t)]):null},onRouteChange(t){if(!this.group)return;const e=this.matchRoute(t.path);e&&this.isActive!==e&&this.list&&this.list.listClick(this._uid),this.isActive=e},toggle(t){const e=this._uid===t;e&&(this.isBooted=!0),this.$nextTick((()=>this.isActive=e))},matchRoute(t){return null!==t.match(this.group)}},render(t){return t("div",this.setTextColor(this.isActive&&this.color,{staticClass:"v-list-group",class:this.classes}),[this.genHeader(),t(s.Fx,this.genItems())])}});var nt=m.ZP.extend({name:"comparable",props:{valueComparator:{type:Function,default:g.vZ}}}),it=n(3939);const st=(0,i.Z)(nt,it.Z,l.Z).extend({name:"base-item-group",props:{activeClass:{type:String,default:"v-item--active"},mandatory:Boolean,max:{type:[Number,String],default:null},multiple:Boolean,tag:{type:String,default:"div"}},data(){return{internalLazyValue:void 0!==this.value?this.value:this.multiple?[]:void 0,items:[]}},computed:{classes(){return{"v-item-group":!0,...this.themeClasses}},selectedIndex(){return this.selectedItem&&this.items.indexOf(this.selectedItem)||-1},selectedItem(){if(!this.multiple)return this.selectedItems[0]},selectedItems(){return this.items.filter(((t,e)=>this.toggleMethod(this.getValue(t,e))))},selectedValues(){return null==this.internalValue?[]:Array.isArray(this.internalValue)?this.internalValue:[this.internalValue]},toggleMethod(){if(!this.multiple)return t=>this.valueComparator(this.internalValue,t);const t=this.internalValue;return Array.isArray(t)?e=>t.some((t=>this.valueComparator(t,e))):()=>!1}},watch:{internalValue:"updateItemsState",items:"updateItemsState"},created(){this.multiple&&!Array.isArray(this.internalValue)&&(0,d.Kd)("Model must be bound to an array if the multiple property is true.",this)},methods:{genData(){return{class:this.classes}},getValue(t,e){return void 0===t.value?e:t.value},onClick(t){this.updateInternalValue(this.getValue(t,this.items.indexOf(t)))},register(t){const e=this.items.push(t)-1;t.$on("change",(()=>this.onClick(t))),this.mandatory&&!this.selectedValues.length&&this.updateMandatory(),this.updateItem(t,e)},unregister(t){if(this._isDestroyed)return;const e=this.items.indexOf(t),n=this.getValue(t,e);this.items.splice(e,1);const i=this.selectedValues.indexOf(n);if(!(i<0)){if(!this.mandatory)return this.updateInternalValue(n);this.multiple&&Array.isArray(this.internalValue)?this.internalValue=this.internalValue.filter((t=>t!==n)):this.internalValue=void 0,this.selectedItems.length||this.updateMandatory(!0)}},updateItem(t,e){const n=this.getValue(t,e);t.isActive=this.toggleMethod(n)},updateItemsState(){this.$nextTick((()=>{if(this.mandatory&&!this.selectedItems.length)return this.updateMandatory();this.items.forEach(this.updateItem)}))},updateInternalValue(t){this.multiple?this.updateMultiple(t):this.updateSingle(t)},updateMandatory(t){if(!this.items.length)return;const e=this.items.slice();t&&e.reverse();const n=e.find((t=>!t.disabled));if(!n)return;const i=this.items.indexOf(n);this.updateInternalValue(this.getValue(n,i))},updateMultiple(t){const e=Array.isArray(this.internalValue)?this.internalValue:[],n=e.slice(),i=n.findIndex((e=>this.valueComparator(e,t)));this.mandatory&&i>-1&&n.length-1<1||null!=this.max&&i<0&&n.length+1>this.max||(i>-1?n.splice(i,1):n.push(t),this.internalValue=n)},updateSingle(t){const e=this.valueComparator(this.internalValue,t);this.mandatory&&e||(this.internalValue=e?void 0:t)}},render(t){return t(this.tag,this.genData(),this.$slots.default)}});st.extend({name:"v-item-group",provide(){return{itemGroup:this}}}),(0,i.Z)(st,a.Z).extend({name:"v-list-item-group",provide(){return{isInGroup:!0,listItemGroup:this}},computed:{classes(){return{...st.options.computed.classes.call(this),"v-list-item-group":!0}}},methods:{genData(){return this.setTextColor(this.color,{...st.options.methods.genData.call(this),attrs:{role:"listbox"}})}}});var rt=n(9196),at=(0,i.Z)(a.Z,rt.Z,$.Z).extend({name:"v-avatar",props:{left:Boolean,right:Boolean,size:{type:[Number,String],default:48}},computed:{classes(){return{"v-avatar--left":this.left,"v-avatar--right":this.right,...this.roundedClasses}},styles(){return{height:(0,g.kb)(this.size),minWidth:(0,g.kb)(this.size),width:(0,g.kb)(this.size),...this.measurableStyles}}},render(t){const e={staticClass:"v-avatar",class:this.classes,style:this.styles,on:this.$listeners};return t("div",this.setBackgroundColor(this.color,e),this.$slots.default)}}),ot=at;ot.extend({name:"v-list-item-avatar",props:{horizontal:Boolean,size:{type:[Number,String],default:40}},computed:{classes(){return{"v-list-item__avatar--horizontal":this.horizontal,...ot.options.computed.classes.call(this),"v-avatar--tile":this.tile||this.horizontal}}},render(t){const e=ot.options.render.call(this,t);return e.data=e.data||{},e.data.staticClass+=" v-list-item__avatar",e}});(0,g.Ji)("v-list-item__action-text","span");const lt=(0,g.Ji)("v-list-item__content","div"),ct=(0,g.Ji)("v-list-item__title","div");(0,g.Ji)("v-list-item__subtitle","div");var ut=(0,i.Z)(a.Z,l.Z).extend({name:"v-select-list",directives:{ripple:N.Z},props:{action:Boolean,dense:Boolean,hideSelected:Boolean,items:{type:Array,default:()=>[]},itemDisabled:{type:[String,Array,Function],default:"disabled"},itemText:{type:[String,Array,Function],default:"text"},itemValue:{type:[String,Array,Function],default:"value"},noDataText:String,noFilter:Boolean,searchInput:null,selectedItems:{type:Array,default:()=>[]}},computed:{parsedItems(){return this.selectedItems.map((t=>this.getValue(t)))},tileActiveClass(){return Object.keys(this.setTextColor(this.color).class||{}).join(" ")},staticNoDataTile(){const t={attrs:{role:void 0},on:{mousedown:t=>t.preventDefault()}};return this.$createElement(Y,t,[this.genTileContent(this.noDataText)])}},methods:{genAction(t,e){return this.$createElement(W,[this.$createElement(j,{props:{color:this.color,value:e,ripple:!1},on:{input:()=>this.$emit("select",t)}})])},genDivider(t){return this.$createElement(z,{props:t})},genFilteredText(t){if(t=t||"",!this.searchInput||this.noFilter)return t;const{start:e,middle:n,end:i}=this.getMaskedCharacters(t);return[e,this.genHighlight(n),i]},genHeader(t){return this.$createElement(U,{props:t},t.header)},genHighlight(t){return this.$createElement("span",{staticClass:"v-list-item__mask"},t)},getMaskedCharacters(t){const e=(this.searchInput||"").toString().toLocaleLowerCase(),n=t.toLocaleLowerCase().indexOf(e);if(n<0)return{start:t,middle:"",end:""};const i=t.slice(0,n),s=t.slice(n,n+e.length),r=t.slice(n+e.length);return{start:i,middle:s,end:r}},genTile({item:t,index:e,disabled:n=null,value:i=!1}){i||(i=this.hasItem(t)),t===Object(t)&&(n=null!==n?n:this.getDisabled(t));const s={attrs:{"aria-selected":String(i),id:`list-item-${this._uid}-${e}`,role:"option"},on:{mousedown:t=>{t.preventDefault()},click:()=>n||this.$emit("select",t)},props:{activeClass:this.tileActiveClass,disabled:n,ripple:!0,inputValue:i}};if(!this.$scopedSlots.item)return this.$createElement(Y,s,[this.action&&!this.hideSelected&&this.items.length>0?this.genAction(t,i):null,this.genTileContent(t,e)]);const r=this,a=this.$scopedSlots.item({parent:r,item:t,attrs:{...s.attrs,...s.props},on:s.on});return this.needsTile(a)?this.$createElement(Y,s,a):a},genTileContent(t,e=0){return this.$createElement(lt,[this.$createElement(ct,[this.genFilteredText(this.getText(t))])])},hasItem(t){return this.parsedItems.indexOf(this.getValue(t))>-1},needsTile(t){return 1!==t.length||null==t[0].componentOptions||"v-list-item"!==t[0].componentOptions.Ctor.options.name},getDisabled(t){return Boolean((0,g.qF)(t,this.itemDisabled,!1))},getText(t){return String((0,g.qF)(t,this.itemText,t))},getValue(t){return(0,g.qF)(t,this.itemValue,this.getText(t))}},render(){const t=[],e=this.items.length;for(let n=0;n<e;n++){const e=this.items[n];this.hideSelected&&this.hasItem(e)||(null==e?t.push(this.genTile({item:e,index:n})):e.header?t.push(this.genHeader(e)):e.divider?t.push(this.genDivider(e)):t.push(this.genTile({item:e,index:n})))}return t.length||t.push(this.$slots["no-data"]||this.staticNoDataTile),this.$slots["prepend-item"]&&t.unshift(this.$slots["prepend-item"]),this.$slots["append-item"]&&t.push(this.$slots["append-item"]),this.$createElement(K,{staticClass:"v-select-list",class:this.themeClasses,attrs:{role:"listbox",tabindex:-1},props:{dense:this.dense}},t)}}),ht=n(3316),dt=n(5251),ft=m.ZP.extend({name:"filterable",props:{noDataText:{type:String,default:"$vuetify.noDataText"}}});const pt={closeOnClick:!1,closeOnContentClick:!1,disableKeys:!0,openOnClick:!1,maxHeight:304},_t=(0,i.Z)(dt.Z,nt,w,ft);var mt=_t.extend().extend({name:"v-select",directives:{ClickOutside:I.Z},props:{appendIcon:{type:String,default:"$dropdown"},attach:{type:null,default:!1},cacheItems:Boolean,chips:Boolean,clearable:Boolean,deletableChips:Boolean,disableLookup:Boolean,eager:Boolean,hideSelected:Boolean,items:{type:Array,default:()=>[]},itemColor:{type:String,default:"primary"},itemDisabled:{type:[String,Array,Function],default:"disabled"},itemText:{type:[String,Array,Function],default:"text"},itemValue:{type:[String,Array,Function],default:"value"},menuProps:{type:[String,Array,Object],default:()=>pt},multiple:Boolean,openOnClear:Boolean,returnObject:Boolean,smallChips:Boolean},data(){return{cachedItems:this.cacheItems?this.items:[],menuIsBooted:!1,isMenuActive:!1,lastItem:20,lazyValue:void 0!==this.value?this.value:this.multiple?[]:void 0,selectedIndex:-1,selectedItems:[],keyboardLookupPrefix:"",keyboardLookupLastTime:0}},computed:{allItems(){return this.filterDuplicates(this.cachedItems.concat(this.items))},classes(){return{...dt.Z.options.computed.classes.call(this),"v-select":!0,"v-select--chips":this.hasChips,"v-select--chips--small":this.smallChips,"v-select--is-menu-active":this.isMenuActive,"v-select--is-multi":this.multiple}},computedItems(){return this.allItems},computedOwns(){return`list-${this._uid}`},computedCounterValue(){var t;const e=this.multiple?this.selectedItems:(null!==(t=this.getText(this.selectedItems[0]))&&void 0!==t?t:"").toString();return"function"===typeof this.counterValue?this.counterValue(e):e.length},directives(){return this.isFocused?[{name:"click-outside",value:{handler:this.blur,closeConditional:this.closeConditional,include:()=>this.getOpenDependentElements()}}]:void 0},dynamicHeight(){return"auto"},hasChips(){return this.chips||this.smallChips},hasSlot(){return Boolean(this.hasChips||this.$scopedSlots.selection)},isDirty(){return this.selectedItems.length>0},listData(){const t=this.$vnode&&this.$vnode.context.$options._scopeId,e=t?{[t]:!0}:{};return{attrs:{...e,id:this.computedOwns},props:{action:this.multiple,color:this.itemColor,dense:this.dense,hideSelected:this.hideSelected,items:this.virtualizedItems,itemDisabled:this.itemDisabled,itemText:this.itemText,itemValue:this.itemValue,noDataText:this.$vuetify.lang.t(this.noDataText),selectedItems:this.selectedItems},on:{select:this.selectItem},scopedSlots:{item:this.$scopedSlots.item}}},staticList(){return(this.$slots["no-data"]||this.$slots["prepend-item"]||this.$slots["append-item"])&&(0,d.N6)("assert: staticList should not be called if slots are used"),this.$createElement(ut,this.listData)},virtualizedItems(){return this.$_menuProps.auto?this.computedItems:this.computedItems.slice(0,this.lastItem)},menuCanShow:()=>!0,$_menuProps(){let t="string"===typeof this.menuProps?this.menuProps.split(","):this.menuProps;return Array.isArray(t)&&(t=t.reduce(((t,e)=>(t[e.trim()]=!0,t)),{})),{...pt,eager:this.eager,value:this.menuCanShow&&this.isMenuActive,nudgeBottom:t.offsetY?1:0,...t}}},watch:{internalValue(t){this.initialValue=t,this.setSelectedItems(),this.multiple&&this.$nextTick((()=>{var t;null===(t=this.$refs.menu)||void 0===t||t.updateDimensions()}))},isMenuActive(t){window.setTimeout((()=>this.onMenuActiveChange(t)))},items:{immediate:!0,handler(t){this.cacheItems&&this.$nextTick((()=>{this.cachedItems=this.filterDuplicates(this.cachedItems.concat(t))})),this.setSelectedItems()}}},methods:{blur(t){dt.Z.options.methods.blur.call(this,t),this.isMenuActive=!1,this.isFocused=!1,this.selectedIndex=-1,this.setMenuIndex(-1)},activateMenu(){this.isInteractive&&!this.isMenuActive&&(this.isMenuActive=!0)},clearableCallback(){this.setValue(this.multiple?[]:null),this.setMenuIndex(-1),this.$nextTick((()=>this.$refs.input&&this.$refs.input.focus())),this.openOnClear&&(this.isMenuActive=!0)},closeConditional(t){return!this.isMenuActive||!this._isDestroyed&&(!this.getContent()||!this.getContent().contains(t.target))&&this.$el&&!this.$el.contains(t.target)&&t.target!==this.$el},filterDuplicates(t){const e=new Map;for(let n=0;n<t.length;++n){const i=t[n];if(null==i)continue;if(i.header||i.divider){e.set(i,i);continue}const s=this.getValue(i);!e.has(s)&&e.set(s,i)}return Array.from(e.values())},findExistingIndex(t){const e=this.getValue(t);return(this.internalValue||[]).findIndex((t=>this.valueComparator(this.getValue(t),e)))},getContent(){return this.$refs.menu&&this.$refs.menu.$refs.content},genChipSelection(t,e){const n=this.isDisabled||this.getDisabled(t),i=!n&&this.isInteractive;return this.$createElement(p,{staticClass:"v-chip--select",attrs:{tabindex:-1},props:{close:this.deletableChips&&i,disabled:n,inputValue:e===this.selectedIndex,small:this.smallChips},on:{click:t=>{i&&(t.stopPropagation(),this.selectedIndex=e)},"click:close":()=>this.onChipInput(t)},key:JSON.stringify(this.getValue(t))},this.getText(t))},genCommaSelection(t,e,n){const i=e===this.selectedIndex&&this.computedColor,s=this.isDisabled||this.getDisabled(t);return this.$createElement("div",this.setTextColor(i,{staticClass:"v-select__selection v-select__selection--comma",class:{"v-select__selection--disabled":s},key:JSON.stringify(this.getValue(t))}),`${this.getText(t)}${n?"":", "}`)},genDefaultSlot(){const t=this.genSelections(),e=this.genInput();return Array.isArray(t)?t.push(e):(t.children=t.children||[],t.children.push(e)),[this.genFieldset(),this.$createElement("div",{staticClass:"v-select__slot",directives:this.directives},[this.genLabel(),this.prefix?this.genAffix("prefix"):null,t,this.suffix?this.genAffix("suffix"):null,this.genClearIcon(),this.genIconSlot(),this.genHiddenInput()]),this.genMenu(),this.genProgress()]},genIcon(t,e,n){const i=ht.Z.options.methods.genIcon.call(this,t,e,n);return"append"===t&&(i.children[0].data=(0,H.ZP)(i.children[0].data,{attrs:{tabindex:i.children[0].componentOptions.listeners&&"-1","aria-hidden":"true","aria-label":void 0}})),i},genInput(){const t=dt.Z.options.methods.genInput.call(this);return delete t.data.attrs.name,t.data=(0,H.ZP)(t.data,{domProps:{value:null},attrs:{readonly:!0,type:"text","aria-readonly":String(this.isReadonly),"aria-activedescendant":(0,g.vO)(this.$refs.menu,"activeTile.id"),autocomplete:(0,g.vO)(t.data,"attrs.autocomplete","off"),placeholder:this.isDirty||!this.persistentPlaceholder&&!this.isFocused&&this.hasLabel?void 0:this.placeholder},on:{keypress:this.onKeyPress}}),t},genHiddenInput(){return this.$createElement("input",{domProps:{value:this.lazyValue},attrs:{type:"hidden",name:this.attrs$.name}})},genInputSlot(){const t=dt.Z.options.methods.genInputSlot.call(this);return t.data.attrs={...t.data.attrs,role:"button","aria-haspopup":"listbox","aria-expanded":String(this.isMenuActive),"aria-owns":this.computedOwns},t},genList(){return this.$slots["no-data"]||this.$slots["prepend-item"]||this.$slots["append-item"]?this.genListWithSlot():this.staticList},genListWithSlot(){const t=["prepend-item","no-data","append-item"].filter((t=>this.$slots[t])).map((t=>this.$createElement("template",{slot:t},this.$slots[t])));return this.$createElement(ut,{...this.listData},t)},genMenu(){const t=this.$_menuProps;return t.activator=this.$refs["input-slot"],""===this.attach||!0===this.attach||"attach"===this.attach?t.attach=this.$el:t.attach=this.attach,this.$createElement(D,{attrs:{role:void 0},props:t,on:{input:t=>{this.isMenuActive=t,this.isFocused=t},scroll:this.onScroll},ref:"menu"},[this.genList()])},genSelections(){let t=this.selectedItems.length;const e=new Array(t);let n;n=this.$scopedSlots.selection?this.genSlotSelection:this.hasChips?this.genChipSelection:this.genCommaSelection;while(t--)e[t]=n(this.selectedItems[t],t,t===e.length-1);return this.$createElement("div",{staticClass:"v-select__selections"},e)},genSlotSelection(t,e){return this.$scopedSlots.selection({attrs:{class:"v-chip--select"},parent:this,item:t,index:e,select:t=>{t.stopPropagation(),this.selectedIndex=e},selected:e===this.selectedIndex,disabled:!this.isInteractive})},getMenuIndex(){return this.$refs.menu?this.$refs.menu.listIndex:-1},getDisabled(t){return(0,g.qF)(t,this.itemDisabled,!1)},getText(t){return(0,g.qF)(t,this.itemText,t)},getValue(t){return(0,g.qF)(t,this.itemValue,this.getText(t))},onBlur(t){t&&this.$emit("blur",t)},onChipInput(t){this.multiple?this.selectItem(t):this.setValue(null),0===this.selectedItems.length?this.isMenuActive=!0:this.isMenuActive=!1,this.selectedIndex=-1},onClick(t){this.isInteractive&&(this.isAppendInner(t.target)||(this.isMenuActive=!0),this.isFocused||(this.isFocused=!0,this.$emit("focus")),this.$emit("click",t))},onEscDown(t){t.preventDefault(),this.isMenuActive&&(t.stopPropagation(),this.isMenuActive=!1)},onKeyPress(t){if(this.multiple||!this.isInteractive||this.disableLookup||t.key.length>1||t.ctrlKey||t.metaKey||t.altKey)return;const e=1e3,n=performance.now();n-this.keyboardLookupLastTime>e&&(this.keyboardLookupPrefix=""),this.keyboardLookupPrefix+=t.key.toLowerCase(),this.keyboardLookupLastTime=n;const i=this.allItems.findIndex((t=>{var e;const n=(null!==(e=this.getText(t))&&void 0!==e?e:"").toString();return n.toLowerCase().startsWith(this.keyboardLookupPrefix)})),s=this.allItems[i];-1!==i&&(this.lastItem=Math.max(this.lastItem,i+5),this.setValue(this.returnObject?s:this.getValue(s)),this.$nextTick((()=>this.$refs.menu.getTiles())),setTimeout((()=>this.setMenuIndex(i))))},onKeyDown(t){if(this.isReadonly&&t.keyCode!==g.keyCodes.tab)return;const e=t.keyCode,n=this.$refs.menu;return this.$emit("keydown",t),n?(this.isMenuActive&&[g.keyCodes.up,g.keyCodes.down,g.keyCodes.home,g.keyCodes.end,g.keyCodes.enter].includes(e)&&this.$nextTick((()=>{n.changeListIndex(t),this.$emit("update:list-index",n.listIndex)})),[g.keyCodes.enter,g.keyCodes.space].includes(e)&&this.activateMenu(),!this.isMenuActive&&[g.keyCodes.up,g.keyCodes.down,g.keyCodes.home,g.keyCodes.end].includes(e)?this.onUpDown(t):e===g.keyCodes.esc?this.onEscDown(t):e===g.keyCodes.tab?this.onTabDown(t):e===g.keyCodes.space?this.onSpaceDown(t):void 0):void 0},onMenuActiveChange(t){if(this.multiple&&!t||this.getMenuIndex()>-1)return;const e=this.$refs.menu;if(e&&this.isDirty){this.$refs.menu.getTiles();for(let t=0;t<e.tiles.length;t++)if("true"===e.tiles[t].getAttribute("aria-selected")){this.setMenuIndex(t);break}}},onMouseUp(t){this.hasMouseDown&&3!==t.which&&this.isInteractive&&this.isAppendInner(t.target)&&this.$nextTick((()=>this.isMenuActive=!this.isMenuActive)),dt.Z.options.methods.onMouseUp.call(this,t)},onScroll(){if(this.isMenuActive){if(this.lastItem>this.computedItems.length)return;const t=this.getContent().scrollHeight-(this.getContent().scrollTop+this.getContent().clientHeight)<200;t&&(this.lastItem+=20)}else requestAnimationFrame((()=>this.getContent().scrollTop=0))},onSpaceDown(t){t.preventDefault()},onTabDown(t){const e=this.$refs.menu;if(!e)return;const n=e.activeTile;!this.multiple&&n&&this.isMenuActive?(t.preventDefault(),t.stopPropagation(),n.click()):this.blur(t)},onUpDown(t){const e=this.$refs.menu;if(!e)return;if(t.preventDefault(),this.multiple)return this.activateMenu();const n=t.keyCode;e.isBooted=!0,window.requestAnimationFrame((()=>{if(e.getTiles(),!e.hasClickableTiles)return this.activateMenu();switch(n){case g.keyCodes.up:e.prevTile();break;case g.keyCodes.down:e.nextTile();break;case g.keyCodes.home:e.firstTile();break;case g.keyCodes.end:e.lastTile();break}this.selectItem(this.allItems[this.getMenuIndex()])}))},selectItem(t){if(this.multiple){const e=(this.internalValue||[]).slice(),n=this.findExistingIndex(t);if(-1!==n?e.splice(n,1):e.push(t),this.setValue(e.map((t=>this.returnObject?t:this.getValue(t)))),this.hideSelected)this.setMenuIndex(-1);else{const e=this.allItems.indexOf(t);~e&&(this.$nextTick((()=>this.$refs.menu.getTiles())),setTimeout((()=>this.setMenuIndex(e))))}}else this.setValue(this.returnObject?t:this.getValue(t)),this.isMenuActive=!1},setMenuIndex(t){this.$refs.menu&&(this.$refs.menu.listIndex=t)},setSelectedItems(){const t=[],e=this.multiple&&Array.isArray(this.internalValue)?this.internalValue:[this.internalValue];for(const n of e){const e=this.allItems.findIndex((t=>this.valueComparator(this.getValue(t),this.getValue(n))));e>-1&&t.push(this.allItems[e])}this.selectedItems=t},setValue(t){this.valueComparator(t,this.internalValue)||(this.internalValue=t,this.$emit("change",t))},isAppendInner(t){const e=this.$refs["append-inner"];return e&&(e===t||e.contains(t))}}})},9868:function(t,e,n){"use strict";n.d(e,{Z:function(){return u}});var i=n(4609),s=n(8914),r=n(5303),a=n(9196),o=n(3960),l=n(3276),c=n(4987),u=(0,c.Z)(i.Z,s.Z,r.Z,a.Z,o.Z,l.Z).extend({name:"v-sheet",props:{outlined:Boolean,shaped:Boolean,tag:{type:String,default:"div"}},computed:{classes(){return{"v-sheet":!0,"v-sheet--outlined":this.outlined,"v-sheet--shaped":this.shaped,...this.themeClasses,...this.elevationClasses,...this.roundedClasses}},styles(){return this.measurableStyles}},render(t){const e={class:this.classes,style:this.styles,on:this.listeners$};return t(this.tag,this.setBackgroundColor(this.color,e),this.$slots.default)}})},2925:function(t,e,n){"use strict";var i=n(9868);e["Z"]=i.Z},2059:function(t,e,n){"use strict";n.d(e,{Z:function(){return u}});n(541),n(7658);var i=n(3316),s=n(3422),r=n(4987),a=n(4204),o=n(9042),l=n(1050),c=n(6596),u=(0,r.Z)(i.Z,a.Z).extend({name:"v-slider",directives:{ClickOutside:o.Z},mixins:[a.Z],props:{disabled:Boolean,inverseLabel:Boolean,max:{type:[Number,String],default:100},min:{type:[Number,String],default:0},step:{type:[Number,String],default:1},thumbColor:String,thumbLabel:{type:[Boolean,String],default:void 0,validator:t=>"boolean"===typeof t||"always"===t},thumbSize:{type:[Number,String],default:32},tickLabels:{type:Array,default:()=>[]},ticks:{type:[Boolean,String],default:!1,validator:t=>"boolean"===typeof t||"always"===t},tickSize:{type:[Number,String],default:2},trackColor:String,trackFillColor:String,value:[Number,String],vertical:Boolean},data:()=>({app:null,oldValue:null,thumbPressed:!1,mouseTimeout:-1,isFocused:!1,isActive:!1,noClick:!1,startOffset:0}),computed:{classes(){return{...i.Z.options.computed.classes.call(this),"v-input__slider":!0,"v-input__slider--vertical":this.vertical,"v-input__slider--inverse-label":this.inverseLabel}},internalValue:{get(){return this.lazyValue},set(t){t=isNaN(t)?this.minValue:t;const e=this.roundValue(Math.min(Math.max(t,this.minValue),this.maxValue));e!==this.lazyValue&&(this.lazyValue=e,this.$emit("input",e))}},trackTransition(){return this.thumbPressed?this.showTicks||this.stepNumeric?"0.1s cubic-bezier(0.25, 0.8, 0.5, 1)":"none":""},minValue(){return parseFloat(this.min)},maxValue(){return parseFloat(this.max)},stepNumeric(){return this.step>0?parseFloat(this.step):0},inputWidth(){const t=(this.roundValue(this.internalValue)-this.minValue)/(this.maxValue-this.minValue)*100;return isNaN(t)?0:t},trackFillStyles(){const t=this.vertical?"bottom":"left",e=this.vertical?"top":"right",n=this.vertical?"height":"width",i=this.$vuetify.rtl?"auto":"0",s=this.$vuetify.rtl?"0":"auto",r=this.isDisabled?`calc(${this.inputWidth}% - 10px)`:`${this.inputWidth}%`;return{transition:this.trackTransition,[t]:i,[e]:s,[n]:r}},trackStyles(){const t=this.vertical?this.$vuetify.rtl?"bottom":"top":this.$vuetify.rtl?"left":"right",e=this.vertical?"height":"width",n="0px",i=this.isDisabled?`calc(${100-this.inputWidth}% - 10px)`:`calc(${100-this.inputWidth}%)`;return{transition:this.trackTransition,[t]:n,[e]:i}},showTicks(){return this.tickLabels.length>0||!(this.isDisabled||!this.stepNumeric||!this.ticks)},numTicks(){return Math.ceil((this.maxValue-this.minValue)/this.stepNumeric)},showThumbLabel(){return!this.isDisabled&&!(!this.thumbLabel&&!this.$scopedSlots["thumb-label"])},computedTrackColor(){if(!this.isDisabled)return this.trackColor?this.trackColor:this.isDark?this.validationState:this.validationState||"primary lighten-3"},computedTrackFillColor(){if(!this.isDisabled)return this.trackFillColor?this.trackFillColor:this.validationState||this.computedColor},computedThumbColor(){return this.thumbColor?this.thumbColor:this.validationState||this.computedColor}},watch:{min(t){const e=parseFloat(t);e>this.internalValue&&this.$emit("input",e)},max(t){const e=parseFloat(t);e<this.internalValue&&this.$emit("input",e)},value:{handler(t){this.internalValue=t},immediate:!0}},mounted(){this.app=document.querySelector("[data-app]")||(0,c.Kd)("Missing v-app or a non-body wrapping element with the [data-app] attribute",this)},methods:{genDefaultSlot(){const t=[this.genLabel()],e=this.genSlider();return this.inverseLabel?t.unshift(e):t.push(e),t.push(this.genProgress()),t},genSlider(){return this.$createElement("div",{class:{"v-slider":!0,"v-slider--horizontal":!this.vertical,"v-slider--vertical":this.vertical,"v-slider--focused":this.isFocused,"v-slider--active":this.isActive,"v-slider--disabled":this.isDisabled,"v-slider--readonly":this.isReadonly,...this.themeClasses},directives:[{name:"click-outside",value:this.onBlur}],on:{click:this.onSliderClick,mousedown:this.onSliderMouseDown,touchstart:this.onSliderMouseDown}},this.genChildren())},genChildren(){return[this.genInput(),this.genTrackContainer(),this.genSteps(),this.genThumbContainer(this.internalValue,this.inputWidth,this.isActive,this.isFocused,this.onFocus,this.onBlur)]},genInput(){return this.$createElement("input",{attrs:{value:this.internalValue,id:this.computedId,disabled:!0,readonly:!0,tabindex:-1,...this.$attrs}})},genTrackContainer(){const t=[this.$createElement("div",this.setBackgroundColor(this.computedTrackColor,{staticClass:"v-slider__track-background",style:this.trackStyles})),this.$createElement("div",this.setBackgroundColor(this.computedTrackFillColor,{staticClass:"v-slider__track-fill",style:this.trackFillStyles}))];return this.$createElement("div",{staticClass:"v-slider__track-container",ref:"track"},t)},genSteps(){if(!this.step||!this.showTicks)return null;const t=parseFloat(this.tickSize),e=(0,l.MT)(this.numTicks+1),n=this.vertical?"bottom":this.$vuetify.rtl?"right":"left",i=this.vertical?this.$vuetify.rtl?"left":"right":"top";this.vertical&&e.reverse();const s=e.map((e=>{const s=[];this.tickLabels[e]&&s.push(this.$createElement("div",{staticClass:"v-slider__tick-label"},this.tickLabels[e]));const r=e*(100/this.numTicks),a=this.$vuetify.rtl?100-this.inputWidth<r:r<this.inputWidth;return this.$createElement("span",{key:e,staticClass:"v-slider__tick",class:{"v-slider__tick--filled":a},style:{width:`${t}px`,height:`${t}px`,[n]:`calc(${r}% - ${t/2}px)`,[i]:`calc(50% - ${t/2}px)`}},s)}));return this.$createElement("div",{staticClass:"v-slider__ticks-container",class:{"v-slider__ticks-container--always-show":"always"===this.ticks||this.tickLabels.length>0}},s)},genThumbContainer(t,e,n,i,s,r,a="thumb"){const o=[this.genThumb()],l=this.genThumbLabelContent(t);return this.showThumbLabel&&o.push(this.genThumbLabel(l)),this.$createElement("div",this.setTextColor(this.computedThumbColor,{ref:a,key:a,staticClass:"v-slider__thumb-container",class:{"v-slider__thumb-container--active":n,"v-slider__thumb-container--focused":i,"v-slider__thumb-container--show-label":this.showThumbLabel},style:this.getThumbContainerStyles(e),attrs:{role:"slider",tabindex:this.isDisabled?-1:this.$attrs.tabindex?this.$attrs.tabindex:0,"aria-label":this.$attrs["aria-label"]||this.label,"aria-valuemin":this.min,"aria-valuemax":this.max,"aria-valuenow":this.internalValue,"aria-readonly":String(this.isReadonly),"aria-orientation":this.vertical?"vertical":"horizontal"},on:{focus:s,blur:r,keydown:this.onKeyDown}}),o)},genThumbLabelContent(t){return this.$scopedSlots["thumb-label"]?this.$scopedSlots["thumb-label"]({value:t}):[this.$createElement("span",[String(t)])]},genThumbLabel(t){const e=(0,l.kb)(this.thumbSize),n=this.vertical?`translateY(20%) translateY(${Number(this.thumbSize)/3-1}px) translateX(55%) rotate(135deg)`:"translateY(-20%) translateY(-12px) translateX(-50%) rotate(45deg)";return this.$createElement(s.T0,{props:{origin:"bottom center"}},[this.$createElement("div",{staticClass:"v-slider__thumb-label-container",directives:[{name:"show",value:this.isFocused||this.isActive||"always"===this.thumbLabel}]},[this.$createElement("div",this.setBackgroundColor(this.computedThumbColor,{staticClass:"v-slider__thumb-label",style:{height:e,width:e,transform:n}}),[this.$createElement("div",t)])])])},genThumb(){return this.$createElement("div",this.setBackgroundColor(this.computedThumbColor,{staticClass:"v-slider__thumb"}))},getThumbContainerStyles(t){const e=this.vertical?"top":"left";let n=this.$vuetify.rtl?100-t:t;return n=this.vertical?100-n:n,{transition:this.trackTransition,[e]:`${n}%`}},onSliderMouseDown(t){var e;if(t.preventDefault(),this.oldValue=this.internalValue,this.isActive=!0,null===(e=t.target)||void 0===e?void 0:e.matches(".v-slider__thumb-container, .v-slider__thumb-container *")){this.thumbPressed=!0;const e=t.target.getBoundingClientRect(),n="touches"in t?t.touches[0]:t;this.startOffset=this.vertical?n.clientY-(e.top+e.height/2):n.clientX-(e.left+e.width/2)}else this.startOffset=0,window.clearTimeout(this.mouseTimeout),this.mouseTimeout=window.setTimeout((()=>{this.thumbPressed=!0}),300);const n=!l.e$||{passive:!0,capture:!0},i=!!l.e$&&{passive:!0},s="touches"in t;this.onMouseMove(t),this.app.addEventListener(s?"touchmove":"mousemove",this.onMouseMove,i),(0,l.qh)(this.app,s?"touchend":"mouseup",this.onSliderMouseUp,n),this.$emit("start",this.internalValue)},onSliderMouseUp(t){t.stopPropagation(),window.clearTimeout(this.mouseTimeout),this.thumbPressed=!1;const e=!!l.e$&&{passive:!0};this.app.removeEventListener("touchmove",this.onMouseMove,e),this.app.removeEventListener("mousemove",this.onMouseMove,e),this.$emit("mouseup",t),this.$emit("end",this.internalValue),(0,l.vZ)(this.oldValue,this.internalValue)||(this.$emit("change",this.internalValue),this.noClick=!0),this.isActive=!1},onMouseMove(t){"mousemove"===t.type&&(this.thumbPressed=!0),this.internalValue=this.parseMouseMove(t)},onKeyDown(t){if(!this.isInteractive)return;const e=this.parseKeyDown(t,this.internalValue);null==e||e<this.minValue||e>this.maxValue||(this.internalValue=e,this.$emit("change",e))},onSliderClick(t){if(this.noClick)return void(this.noClick=!1);const e=this.$refs.thumb;e.focus(),this.onMouseMove(t),this.$emit("change",this.internalValue)},onBlur(t){this.isFocused=!1,this.$emit("blur",t)},onFocus(t){this.isFocused=!0,this.$emit("focus",t)},parseMouseMove(t){const e=this.vertical?"top":"left",n=this.vertical?"height":"width",i=this.vertical?"clientY":"clientX",{[e]:s,[n]:r}=this.$refs.track.getBoundingClientRect(),a="touches"in t?t.touches[0][i]:t[i];let o=Math.min(Math.max((a-s-this.startOffset)/r,0),1)||0;return this.vertical&&(o=1-o),this.$vuetify.rtl&&(o=1-o),parseFloat(this.min)+o*(this.maxValue-this.minValue)},parseKeyDown(t,e){if(!this.isInteractive)return;const{pageup:n,pagedown:i,end:s,home:r,left:a,right:o,down:c,up:u}=l.keyCodes;if(![n,i,s,r,a,o,c,u].includes(t.keyCode))return;t.preventDefault();const h=this.stepNumeric||1,d=(this.maxValue-this.minValue)/h;if([a,o,c,u].includes(t.keyCode)){const n=this.$vuetify.rtl?[a,u]:[o,u],i=n.includes(t.keyCode)?1:-1,s=t.shiftKey?3:t.ctrlKey?2:1;e+=i*h*s}else if(t.keyCode===r)e=this.minValue;else if(t.keyCode===s)e=this.maxValue;else{const n=t.keyCode===i?1:-1;e-=n*h*(d>100?d/10:10)}return e},roundValue(t){if(!this.stepNumeric)return t;const e=this.step.toString().trim(),n=e.indexOf(".")>-1?e.length-e.indexOf(".")-1:0,i=this.minValue%this.stepNumeric,s=Math.round((t-i)/this.stepNumeric)*this.stepNumeric+i;return parseFloat(Math.min(s,this.maxValue).toFixed(n))}}})},5251:function(t,e,n){"use strict";n.d(e,{Z:function(){return x}});n(7658),n(541),n(2838);var i=n(3316),s=n(3276),r=n(4987),a=(0,r.Z)(s.Z).extend({name:"v-counter",functional:!0,props:{value:{type:[Number,String],default:""},max:[Number,String]},render(t,e){const{props:n}=e,i=parseInt(n.max,10),r=parseInt(n.value,10),a=i?`${r} / ${i}`:String(n.value),o=i&&r>i;return t("div",{staticClass:"v-counter",class:{"error--text":o,...(0,s.X)(e)}},a)}}),o=a,l=n(4654),c=n(1558),u=n(6596),h=n(144);function d(t){return h.ZP.extend({name:"intersectable",data:()=>({isIntersecting:!1}),mounted(){c.Z.inserted(this.$el,{name:"intersect",value:this.onObserve},this.$vnode)},destroyed(){c.Z.unbind(this.$el,{name:"intersect",value:this.onObserve},this.$vnode)},methods:{onObserve(e,n,i){if(this.isIntersecting=i,i)for(let s=0,r=t.onVisible.length;s<r;s++){const e=this[t.onVisible[s]];"function"!==typeof e?(0,u.Kd)(t.onVisible[s]+" method is not available on the instance but referenced in intersectable mixin options"):e()}}}})}var f=n(4204),p=n(4777),_=n(9495),m=n(3176),v=n(6044),g=n(1050);const b=(0,r.Z)(i.Z,d({onVisible:["onResize","tryAutofocus"]}),f.Z),y=["color","file","time","date","datetime-local","week","month"];var x=b.extend().extend({name:"v-text-field",directives:{resize:_.Z,ripple:m.Z},inheritAttrs:!1,props:{appendOuterIcon:String,autofocus:Boolean,clearable:Boolean,clearIcon:{type:String,default:"$clear"},counter:[Boolean,Number,String],counterValue:Function,filled:Boolean,flat:Boolean,fullWidth:Boolean,label:String,outlined:Boolean,placeholder:String,prefix:String,prependInnerIcon:String,persistentPlaceholder:Boolean,reverse:Boolean,rounded:Boolean,shaped:Boolean,singleLine:Boolean,solo:Boolean,soloInverted:Boolean,suffix:String,type:{type:String,default:"text"}},data:()=>({badInput:!1,labelWidth:0,prefixWidth:0,prependWidth:0,initialValue:null,isBooted:!1,isClearing:!1}),computed:{classes(){return{...i.Z.options.computed.classes.call(this),"v-text-field":!0,"v-text-field--full-width":this.fullWidth,"v-text-field--prefix":this.prefix,"v-text-field--single-line":this.isSingle,"v-text-field--solo":this.isSolo,"v-text-field--solo-inverted":this.soloInverted,"v-text-field--solo-flat":this.flat,"v-text-field--filled":this.filled,"v-text-field--is-booted":this.isBooted,"v-text-field--enclosed":this.isEnclosed,"v-text-field--reverse":this.reverse,"v-text-field--outlined":this.outlined,"v-text-field--placeholder":this.placeholder,"v-text-field--rounded":this.rounded,"v-text-field--shaped":this.shaped}},computedColor(){const t=p.Z.options.computed.computedColor.call(this);return this.soloInverted&&this.isFocused?this.color||"primary":t},computedCounterValue(){return"function"===typeof this.counterValue?this.counterValue(this.internalValue):[...(this.internalValue||"").toString()].length},hasCounter(){return!1!==this.counter&&null!=this.counter},hasDetails(){return i.Z.options.computed.hasDetails.call(this)||this.hasCounter},internalValue:{get(){return this.lazyValue},set(t){this.lazyValue=t,this.$emit("input",this.lazyValue)}},isDirty(){var t;return(null===(t=this.lazyValue)||void 0===t?void 0:t.toString().length)>0||this.badInput},isEnclosed(){return this.filled||this.isSolo||this.outlined},isLabelActive(){return this.isDirty||y.includes(this.type)},isSingle(){return this.isSolo||this.singleLine||this.fullWidth||this.filled&&!this.hasLabel},isSolo(){return this.solo||this.soloInverted},labelPosition(){let t=this.prefix&&!this.labelValue?this.prefixWidth:0;return this.labelValue&&this.prependWidth&&(t-=this.prependWidth),this.$vuetify.rtl===this.reverse?{left:t,right:"auto"}:{left:"auto",right:t}},showLabel(){return this.hasLabel&&!(this.isSingle&&this.labelValue)},labelValue(){return this.isFocused||this.isLabelActive||this.persistentPlaceholder}},watch:{outlined:"setLabelWidth",label(){this.$nextTick(this.setLabelWidth)},prefix(){this.$nextTick(this.setPrefixWidth)},isFocused:"updateValue",value(t){this.lazyValue=t}},created(){this.$attrs.hasOwnProperty("box")&&(0,u.fK)("box","filled",this),this.$attrs.hasOwnProperty("browser-autocomplete")&&(0,u.fK)("browser-autocomplete","autocomplete",this),this.shaped&&!(this.filled||this.outlined||this.isSolo)&&(0,u.Kd)("shaped should be used with either filled or outlined",this)},mounted(){this.$watch((()=>this.labelValue),this.setLabelWidth),this.autofocus&&this.tryAutofocus(),requestAnimationFrame((()=>{this.isBooted=!0,requestAnimationFrame((()=>{this.isIntersecting||this.onResize()}))}))},methods:{focus(){this.onFocus()},blur(t){window.requestAnimationFrame((()=>{this.$refs.input&&this.$refs.input.blur()}))},clearableCallback(){this.$refs.input&&this.$refs.input.focus(),this.$nextTick((()=>this.internalValue=null))},genAppendSlot(){const t=[];return this.$slots["append-outer"]?t.push(this.$slots["append-outer"]):this.appendOuterIcon&&t.push(this.genIcon("appendOuter")),this.genSlot("append","outer",t)},genPrependInnerSlot(){const t=[];return this.$slots["prepend-inner"]?t.push(this.$slots["prepend-inner"]):this.prependInnerIcon&&t.push(this.genIcon("prependInner")),this.genSlot("prepend","inner",t)},genIconSlot(){const t=[];return this.$slots.append?t.push(this.$slots.append):this.appendIcon&&t.push(this.genIcon("append")),this.genSlot("append","inner",t)},genInputSlot(){const t=i.Z.options.methods.genInputSlot.call(this),e=this.genPrependInnerSlot();return e&&(t.children=t.children||[],t.children.unshift(e)),t},genClearIcon(){return this.clearable?this.isDirty?this.genSlot("append","inner",[this.genIcon("clear",this.clearableCallback)]):this.genSlot("append","inner",[this.$createElement("div")]):null},genCounter(){var t,e,n;if(!this.hasCounter)return null;const i=!0===this.counter?this.attrs$.maxlength:this.counter,s={dark:this.dark,light:this.light,max:i,value:this.computedCounterValue};return null!==(n=null===(e=(t=this.$scopedSlots).counter)||void 0===e?void 0:e.call(t,{props:s}))&&void 0!==n?n:this.$createElement(o,{props:s})},genControl(){return i.Z.options.methods.genControl.call(this)},genDefaultSlot(){return[this.genFieldset(),this.genTextFieldSlot(),this.genClearIcon(),this.genIconSlot(),this.genProgress()]},genFieldset(){return this.outlined?this.$createElement("fieldset",{attrs:{"aria-hidden":!0}},[this.genLegend()]):null},genLabel(){if(!this.showLabel)return null;const t={props:{absolute:!0,color:this.validationState,dark:this.dark,disabled:this.isDisabled,focused:!this.isSingle&&(this.isFocused||!!this.validationState),for:this.computedId,left:this.labelPosition.left,light:this.light,right:this.labelPosition.right,value:this.labelValue}};return this.$createElement(l.Z,t,this.$slots.label||this.label)},genLegend(){const t=this.singleLine||!this.labelValue&&!this.isDirty?0:this.labelWidth,e=this.$createElement("span",{domProps:{innerHTML:"&#8203;"},staticClass:"notranslate"});return this.$createElement("legend",{style:{width:this.isSingle?void 0:(0,g.kb)(t)}},[e])},genInput(){const t=Object.assign({},this.listeners$);delete t.change;const{title:e,...n}=this.attrs$;return this.$createElement("input",{style:{},domProps:{value:"number"===this.type&&Object.is(this.lazyValue,-0)?"-0":this.lazyValue},attrs:{...n,autofocus:this.autofocus,disabled:this.isDisabled,id:this.computedId,placeholder:this.persistentPlaceholder||this.isFocused||!this.hasLabel?this.placeholder:void 0,readonly:this.isReadonly,type:this.type},on:Object.assign(t,{blur:this.onBlur,input:this.onInput,focus:this.onFocus,keydown:this.onKeyDown}),ref:"input",directives:[{name:"resize",modifiers:{quiet:!0},value:this.onResize}]})},genMessages(){if(!this.showDetails)return null;const t=i.Z.options.methods.genMessages.call(this),e=this.genCounter();return this.$createElement("div",{staticClass:"v-text-field__details"},[t,e])},genTextFieldSlot(){return this.$createElement("div",{staticClass:"v-text-field__slot"},[this.genLabel(),this.prefix?this.genAffix("prefix"):null,this.genInput(),this.suffix?this.genAffix("suffix"):null])},genAffix(t){return this.$createElement("div",{class:`v-text-field__${t}`,ref:t},this[t])},onBlur(t){this.isFocused=!1,t&&this.$nextTick((()=>this.$emit("blur",t)))},onClick(){this.isFocused||this.isDisabled||!this.$refs.input||this.$refs.input.focus()},onFocus(t){if(!this.$refs.input)return;const e=(0,v.e)(this.$el);return e?e.activeElement!==this.$refs.input?this.$refs.input.focus():void(this.isFocused||(this.isFocused=!0,t&&this.$emit("focus",t))):void 0},onInput(t){const e=t.target;this.internalValue=e.value,this.badInput=e.validity&&e.validity.badInput},onKeyDown(t){t.keyCode===g.keyCodes.enter&&this.lazyValue!==this.initialValue&&(this.initialValue=this.lazyValue,this.$emit("change",this.initialValue)),this.$emit("keydown",t)},onMouseDown(t){t.target!==this.$refs.input&&(t.preventDefault(),t.stopPropagation()),i.Z.options.methods.onMouseDown.call(this,t)},onMouseUp(t){this.hasMouseDown&&this.focus(),i.Z.options.methods.onMouseUp.call(this,t)},setLabelWidth(){this.outlined&&(this.labelWidth=this.$refs.label?Math.min(.75*this.$refs.label.scrollWidth+6,this.$el.offsetWidth-24):0)},setPrefixWidth(){this.$refs.prefix&&(this.prefixWidth=this.$refs.prefix.offsetWidth)},setPrependWidth(){this.outlined&&this.$refs["prepend-inner"]&&(this.prependWidth=this.$refs["prepend-inner"].offsetWidth)},tryAutofocus(){if(!this.autofocus||"undefined"===typeof document||!this.$refs.input)return!1;const t=(0,v.e)(this.$el);return!(!t||t.activeElement===this.$refs.input)&&(this.$refs.input.focus(),!0)},updateValue(t){this.hasColor=t,t?this.initialValue=this.lazyValue:this.initialValue!==this.lazyValue&&this.$emit("change",this.lazyValue)},onResize(){this.setLabelWidth(),this.setPrefixWidth(),this.setPrependWidth()}}})},5617:function(t,e,n){"use strict";n.d(e,{Z:function(){return a}});var i=n(5251),s=n(4987);const r=(0,s.Z)(i.Z);var a=r.extend({name:"v-textarea",props:{autoGrow:Boolean,noResize:Boolean,rowHeight:{type:[Number,String],default:24,validator:t=>!isNaN(parseFloat(t))},rows:{type:[Number,String],default:5,validator:t=>!isNaN(parseInt(t,10))}},computed:{classes(){return{"v-textarea":!0,"v-textarea--auto-grow":this.autoGrow,"v-textarea--no-resize":this.noResizeHandle,...i.Z.options.computed.classes.call(this)}},noResizeHandle(){return this.noResize||this.autoGrow}},watch:{autoGrow(t){this.$nextTick((()=>{var e;t?this.calculateInputHeight():null===(e=this.$refs.input)||void 0===e||e.style.removeProperty("height")}))},lazyValue(){this.autoGrow&&this.$nextTick(this.calculateInputHeight)},rowHeight(){this.autoGrow&&this.$nextTick(this.calculateInputHeight)}},mounted(){setTimeout((()=>{this.autoGrow&&this.calculateInputHeight()}),0)},methods:{calculateInputHeight(){const t=this.$refs.input;if(!t)return;t.style.height="0";const e=t.scrollHeight,n=parseInt(this.rows,10)*parseFloat(this.rowHeight);t.style.height=Math.max(n,e)+"px"},genInput(){const t=i.Z.options.methods.genInput.call(this);return t.tag="textarea",delete t.data.attrs.type,t.data.attrs.rows=this.rows,t},onInput(t){i.Z.options.methods.onInput.call(this,t),this.autoGrow&&this.calculateInputHeight()},onKeyDown(t){this.isFocused&&13===t.keyCode&&t.stopPropagation(),this.$emit("keydown",t)}}})},3422:function(t,e,n){"use strict";n.d(e,{Fx:function(){return d},Zq:function(){return f},Z5:function(){return c},T0:function(){return u},Qn:function(){return h}});var i=n(2653);function s(t=[],...e){return Array().concat(t,...e)}function r(t,e="top center 0",n){return{name:t,functional:!0,props:{group:{type:Boolean,default:!1},hideOnLeave:{type:Boolean,default:!1},leaveAbsolute:{type:Boolean,default:!1},mode:{type:String,default:n},origin:{type:String,default:e}},render(e,n){const r="transition"+(n.props.group?"-group":""),a={props:{name:t,mode:n.props.mode},on:{beforeEnter(t){t.style.transformOrigin=n.props.origin,t.style.webkitTransformOrigin=n.props.origin}}};return n.props.leaveAbsolute&&(a.on.leave=s(a.on.leave,(t=>{const{offsetTop:e,offsetLeft:n,offsetWidth:i,offsetHeight:s}=t;t._transitionInitialStyles={position:t.style.position,top:t.style.top,left:t.style.left,width:t.style.width,height:t.style.height},t.style.position="absolute",t.style.top=e+"px",t.style.left=n+"px",t.style.width=i+"px",t.style.height=s+"px"})),a.on.afterLeave=s(a.on.afterLeave,(t=>{if(t&&t._transitionInitialStyles){const{position:e,top:n,left:i,width:s,height:r}=t._transitionInitialStyles;delete t._transitionInitialStyles,t.style.position=e||"",t.style.top=n||"",t.style.left=i||"",t.style.width=s||"",t.style.height=r||""}}))),n.props.hideOnLeave&&(a.on.leave=s(a.on.leave,(t=>{t.style.setProperty("display","none","important")}))),e(r,(0,i.ZP)(n.data,a),n.children)}}}function a(t,e,n="in-out"){return{name:t,functional:!0,props:{mode:{type:String,default:n}},render(n,s){return n("transition",(0,i.ZP)(s.data,{props:{name:t},on:e}),s.children)}}}var o=n(1050);function l(t="",e=!1){const n=e?"width":"height",i=`offset${(0,o.jC)(n)}`;return{beforeEnter(t){t._parent=t.parentNode,t._initialStyle={transition:t.style.transition,overflow:t.style.overflow,[n]:t.style[n]}},enter(e){const s=e._initialStyle;e.style.setProperty("transition","none","important"),e.style.overflow="hidden";const r=`${e[i]}px`;e.style[n]="0",e.offsetHeight,e.style.transition=s.transition,t&&e._parent&&e._parent.classList.add(t),requestAnimationFrame((()=>{e.style[n]=r}))},afterEnter:r,enterCancelled:r,leave(t){t._initialStyle={transition:"",overflow:t.style.overflow,[n]:t.style[n]},t.style.overflow="hidden",t.style[n]=`${t[i]}px`,t.offsetHeight,requestAnimationFrame((()=>t.style[n]="0"))},afterLeave:s,leaveCancelled:s};function s(e){t&&e._parent&&e._parent.classList.remove(t),r(e)}function r(t){const e=t._initialStyle[n];t.style.overflow=t._initialStyle.overflow,null!=e&&(t.style[n]=e),delete t._initialStyle}}r("carousel-transition"),r("carousel-reverse-transition"),r("tab-transition"),r("tab-reverse-transition"),r("menu-transition"),r("fab-transition","center center","out-in"),r("dialog-transition"),r("dialog-bottom-transition"),r("dialog-top-transition");const c=r("fade-transition"),u=r("scale-transition"),h=(r("scroll-x-transition"),r("scroll-x-reverse-transition"),r("scroll-y-transition"),r("scroll-y-reverse-transition"),r("slide-x-transition")),d=(r("slide-x-reverse-transition"),r("slide-y-transition"),r("slide-y-reverse-transition"),a("expand-transition",l())),f=a("expand-x-transition",l("",!0))},9042:function(t,e,n){"use strict";n(7658);var i=n(6044);function s(){return!0}function r(t,e,n){if(!t||!1===a(t,n))return!1;const s=(0,i.e)(e);if("undefined"!==typeof ShadowRoot&&s instanceof ShadowRoot&&s.host===t.target)return!1;const r=("object"===typeof n.value&&n.value.include||(()=>[]))();return r.push(e),!r.some((e=>e.contains(t.target)))}function a(t,e){const n="object"===typeof e.value&&e.value.closeConditional||s;return n(t)}function o(t,e,n){const i="function"===typeof n.value?n.value:n.value.handler;e._clickOutside.lastMousedownWasOutside&&r(t,e,n)&&setTimeout((()=>{a(t,n)&&i&&i(t)}),0)}function l(t,e){const n=(0,i.e)(t);e(document),"undefined"!==typeof ShadowRoot&&n instanceof ShadowRoot&&e(n)}const c={inserted(t,e,n){const i=n=>o(n,t,e),s=n=>{t._clickOutside.lastMousedownWasOutside=r(n,t,e)};l(t,(t=>{t.addEventListener("click",i,!0),t.addEventListener("mousedown",s,!0)})),t._clickOutside||(t._clickOutside={lastMousedownWasOutside:!0}),t._clickOutside[n.context._uid]={onClick:i,onMousedown:s}},unbind(t,e,n){t._clickOutside&&(l(t,(e=>{var i;if(!e||!(null===(i=t._clickOutside)||void 0===i?void 0:i[n.context._uid]))return;const{onClick:s,onMousedown:r}=t._clickOutside[n.context._uid];e.removeEventListener("click",s,!0),e.removeEventListener("mousedown",r,!0)})),delete t._clickOutside[n.context._uid])}};e["Z"]=c},1558:function(t,e,n){"use strict";function i(t,e,n){if("undefined"===typeof window||!("IntersectionObserver"in window))return;const i=e.modifiers||{},r=e.value,{handler:a,options:o}="object"===typeof r?r:{handler:r,options:{}},l=new IntersectionObserver(((r=[],o)=>{var l;const c=null===(l=t._observe)||void 0===l?void 0:l[n.context._uid];if(!c)return;const u=r.some((t=>t.isIntersecting));!a||i.quiet&&!c.init||i.once&&!u&&!c.init||a(r,o,u),u&&i.once?s(t,e,n):c.init=!0}),o);t._observe=Object(t._observe),t._observe[n.context._uid]={init:!1,observer:l},l.observe(t)}function s(t,e,n){var i;const s=null===(i=t._observe)||void 0===i?void 0:i[n.context._uid];s&&(s.observer.unobserve(t),delete t._observe[n.context._uid])}const r={inserted:i,unbind:s};e["Z"]=r},9495:function(t,e,n){"use strict";function i(t,e,n){const i=e.value,s=e.options||{passive:!0};window.addEventListener("resize",i,s),t._onResize=Object(t._onResize),t._onResize[n.context._uid]={callback:i,options:s},e.modifiers&&e.modifiers.quiet||i()}function s(t,e,n){var i;if(!(null===(i=t._onResize)||void 0===i?void 0:i[n.context._uid]))return;const{callback:s,options:r}=t._onResize[n.context._uid];window.removeEventListener("resize",s,r),delete t._onResize[n.context._uid]}const r={inserted:i,unbind:s};e["Z"]=r},3176:function(t,e,n){"use strict";n.d(e,{Z:function(){return k}});var i=n(1050);const s=80;function r(t,e){t.style.transform=e,t.style.webkitTransform=e}function a(t){return"TouchEvent"===t.constructor.name}function o(t){return"KeyboardEvent"===t.constructor.name}const l=(t,e,n={})=>{let i=0,s=0;if(!o(t)){const n=e.getBoundingClientRect(),r=a(t)?t.touches[t.touches.length-1]:t;i=r.clientX-n.left,s=r.clientY-n.top}let r=0,l=.3;e._ripple&&e._ripple.circle?(l=.15,r=e.clientWidth/2,r=n.center?r:r+Math.sqrt((i-r)**2+(s-r)**2)/4):r=Math.sqrt(e.clientWidth**2+e.clientHeight**2)/2;const c=(e.clientWidth-2*r)/2+"px",u=(e.clientHeight-2*r)/2+"px",h=n.center?c:i-r+"px",d=n.center?u:s-r+"px";return{radius:r,scale:l,x:h,y:d,centerX:c,centerY:u}},c={show(t,e,n={}){if(!e._ripple||!e._ripple.enabled)return;const i=document.createElement("span"),s=document.createElement("span");i.appendChild(s),i.className="v-ripple__container",n.class&&(i.className+=` ${n.class}`);const{radius:a,scale:o,x:c,y:u,centerX:h,centerY:d}=l(t,e,n),f=2*a+"px";s.className="v-ripple__animation",s.style.width=f,s.style.height=f,e.appendChild(i);const p=window.getComputedStyle(e);p&&"static"===p.position&&(e.style.position="relative",e.dataset.previousPosition="static"),s.classList.add("v-ripple__animation--enter"),s.classList.add("v-ripple__animation--visible"),r(s,`translate(${c}, ${u}) scale3d(${o},${o},${o})`),s.dataset.activated=String(performance.now()),setTimeout((()=>{s.classList.remove("v-ripple__animation--enter"),s.classList.add("v-ripple__animation--in"),r(s,`translate(${h}, ${d}) scale3d(1,1,1)`)}),0)},hide(t){if(!t||!t._ripple||!t._ripple.enabled)return;const e=t.getElementsByClassName("v-ripple__animation");if(0===e.length)return;const n=e[e.length-1];if(n.dataset.isHiding)return;n.dataset.isHiding="true";const i=performance.now()-Number(n.dataset.activated),s=Math.max(250-i,0);setTimeout((()=>{n.classList.remove("v-ripple__animation--in"),n.classList.add("v-ripple__animation--out"),setTimeout((()=>{var e;const i=t.getElementsByClassName("v-ripple__animation");1===i.length&&t.dataset.previousPosition&&(t.style.position=t.dataset.previousPosition,delete t.dataset.previousPosition),(null===(e=n.parentNode)||void 0===e?void 0:e.parentNode)===t&&t.removeChild(n.parentNode)}),300)}),s)}};function u(t){return"undefined"===typeof t||!!t}function h(t){const e={},n=t.currentTarget;if(n&&n._ripple&&!n._ripple.touched&&!t.rippleStop){if(t.rippleStop=!0,a(t))n._ripple.touched=!0,n._ripple.isTouch=!0;else if(n._ripple.isTouch)return;if(e.center=n._ripple.centered||o(t),n._ripple.class&&(e.class=n._ripple.class),a(t)){if(n._ripple.showTimerCommit)return;n._ripple.showTimerCommit=()=>{c.show(t,n,e)},n._ripple.showTimer=window.setTimeout((()=>{n&&n._ripple&&n._ripple.showTimerCommit&&(n._ripple.showTimerCommit(),n._ripple.showTimerCommit=null)}),s)}else c.show(t,n,e)}}function d(t){const e=t.currentTarget;if(e&&e._ripple){if(window.clearTimeout(e._ripple.showTimer),"touchend"===t.type&&e._ripple.showTimerCommit)return e._ripple.showTimerCommit(),e._ripple.showTimerCommit=null,void(e._ripple.showTimer=setTimeout((()=>{d(t)})));window.setTimeout((()=>{e._ripple&&(e._ripple.touched=!1)})),c.hide(e)}}function f(t){const e=t.currentTarget;e&&e._ripple&&(e._ripple.showTimerCommit&&(e._ripple.showTimerCommit=null),window.clearTimeout(e._ripple.showTimer))}let p=!1;function _(t){p||t.keyCode!==i.keyCodes.enter&&t.keyCode!==i.keyCodes.space||(p=!0,h(t))}function m(t){p=!1,d(t)}function v(t){!0===p&&(p=!1,d(t))}function g(t,e,n){const i=u(e.value);i||c.hide(t),t._ripple=t._ripple||{},t._ripple.enabled=i;const s=e.value||{};s.center&&(t._ripple.centered=!0),s.class&&(t._ripple.class=e.value.class),s.circle&&(t._ripple.circle=s.circle),i&&!n?(t.addEventListener("touchstart",h,{passive:!0}),t.addEventListener("touchend",d,{passive:!0}),t.addEventListener("touchmove",f,{passive:!0}),t.addEventListener("touchcancel",d),t.addEventListener("mousedown",h),t.addEventListener("mouseup",d),t.addEventListener("mouseleave",d),t.addEventListener("keydown",_),t.addEventListener("keyup",m),t.addEventListener("blur",v),t.addEventListener("dragstart",d,{passive:!0})):!i&&n&&b(t)}function b(t){t.removeEventListener("mousedown",h),t.removeEventListener("touchstart",h),t.removeEventListener("touchend",d),t.removeEventListener("touchmove",f),t.removeEventListener("touchcancel",d),t.removeEventListener("mouseup",d),t.removeEventListener("mouseleave",d),t.removeEventListener("keydown",_),t.removeEventListener("keyup",m),t.removeEventListener("dragstart",d),t.removeEventListener("blur",v)}function y(t,e,n){g(t,e,!1)}function x(t){delete t._ripple,b(t)}function w(t,e){if(e.value===e.oldValue)return;const n=u(e.oldValue);g(t,e,n)}const S={bind:y,unbind:x,update:w};var k=S},2250:function(t,e,n){"use strict";n.d(e,{Z:function(){return tt}});n(7658);var i=n(144),s=n(6596);function r(t,e={}){if(r.installed)return;r.installed=!0,i.ZP!==t&&(0,s.N6)("Multiple instances of Vue detected\nSee https://github.com/vuetifyjs/vuetify/issues/4068\n\nIf you're seeing \"$attrs is readonly\", it's caused by this");const n=e.components||{},a=e.directives||{};for(const i in a){const e=a[i];t.directive(i,e)}(function e(n){if(n){for(const i in n){const s=n[i];s&&!e(s.$_vuetify_subcomponents)&&t.component(i,s)}return!0}return!1})(n),t.$_vuetify_installed||(t.$_vuetify_installed=!0,t.mixin({beforeCreate(){const e=this.$options;e.vuetify?(e.vuetify.init(this,this.$ssrContext),this.$vuetify=t.observable(e.vuetify.framework)):this.$vuetify=e.parent&&e.parent.$vuetify||this},beforeMount(){this.$options.vuetify&&this.$el&&this.$el.hasAttribute("data-server-rendered")&&(this.$vuetify.isHydrating=!0,this.$vuetify.breakpoint.update(!0))},mounted(){this.$options.vuetify&&this.$vuetify.isHydrating&&(this.$vuetify.isHydrating=!1,this.$vuetify.breakpoint.update())}}))}var a={badge:"Badge",close:"Close",dataIterator:{noResultsText:"No matching records found",loadingText:"Loading items..."},dataTable:{itemsPerPageText:"Rows per page:",ariaLabel:{sortDescending:"Sorted descending.",sortAscending:"Sorted ascending.",sortNone:"Not sorted.",activateNone:"Activate to remove sorting.",activateDescending:"Activate to sort descending.",activateAscending:"Activate to sort ascending."},sortBy:"Sort by"},dataFooter:{itemsPerPageText:"Items per page:",itemsPerPageAll:"All",nextPage:"Next page",prevPage:"Previous page",firstPage:"First page",lastPage:"Last page",pageText:"{0}-{1} of {2}"},datePicker:{itemsSelected:"{0} selected",nextMonthAriaLabel:"Next month",nextYearAriaLabel:"Next year",prevMonthAriaLabel:"Previous month",prevYearAriaLabel:"Previous year"},noDataText:"No data available",carousel:{prev:"Previous visual",next:"Next visual",ariaLabel:{delimiter:"Carousel slide {0} of {1}"}},calendar:{moreEvents:"{0} more"},fileInput:{counter:"{0} files",counterSize:"{0} files ({1} in total)"},timePicker:{am:"AM",pm:"PM"},pagination:{ariaLabel:{wrapper:"Pagination Navigation",next:"Next page",previous:"Previous page",page:"Goto Page {0}",currentPage:"Current Page, Page {0}"}},rating:{ariaLabel:{icon:"Rating {0} of {1}"}}};const o={breakpoint:{mobileBreakpoint:1264,scrollBarWidth:16,thresholds:{xs:600,sm:960,md:1280,lg:1920}},icons:{iconfont:"mdi",values:{}},lang:{current:"en",locales:{en:a},t:void 0},rtl:!1,theme:{dark:!1,default:"light",disable:!1,options:{cspNonce:void 0,customProperties:void 0,minifyTheme:void 0,themeCache:void 0,variations:!0},themes:{light:{primary:"#1976D2",secondary:"#424242",accent:"#82B1FF",error:"#FF5252",info:"#2196F3",success:"#4CAF50",warning:"#FB8C00"},dark:{primary:"#2196F3",secondary:"#424242",accent:"#FF4081",error:"#FF5252",info:"#2196F3",success:"#4CAF50",warning:"#FB8C00"}}}};var l=n(1050),c=n(8447);class u extends c.t{constructor(t,e){super();const n=(0,l.Ee)({},o),{userPreset:i}=e,{preset:r={},...a}=i;null!=r.preset&&(0,s.Kd)("Global presets do not support the **preset** option, it can be safely omitted"),e.preset=(0,l.Ee)((0,l.Ee)(n,r),a)}}u.property="presets";class h extends c.t{constructor(){super(...arguments),this.bar=0,this.top=0,this.left=0,this.insetFooter=0,this.right=0,this.bottom=0,this.footer=0,this.application={bar:{},top:{},left:{},insetFooter:{},right:{},bottom:{},footer:{}}}register(t,e,n){this.application[e][t]=n,this.update(e)}unregister(t,e){null!=this.application[e][t]&&(delete this.application[e][t],this.update(e))}update(t){this[t]=Object.values(this.application[t]).reduce(((t,e)=>t+e),0)}}h.property="application";class d extends c.t{constructor(t){super(),this.xs=!1,this.sm=!1,this.md=!1,this.lg=!1,this.xl=!1,this.xsOnly=!1,this.smOnly=!1,this.smAndDown=!1,this.smAndUp=!1,this.mdOnly=!1,this.mdAndDown=!1,this.mdAndUp=!1,this.lgOnly=!1,this.lgAndDown=!1,this.lgAndUp=!1,this.xlOnly=!1,this.name="xs",this.height=0,this.width=0,this.mobile=!0,this.resizeTimeout=0;const{mobileBreakpoint:e,scrollBarWidth:n,thresholds:i}=t[d.property];this.mobileBreakpoint=e,this.scrollBarWidth=n,this.thresholds=i}init(){this.update(),"undefined"!==typeof window&&window.addEventListener("resize",this.onResize.bind(this),{passive:!0})}update(t=!1){const e=t?0:this.getClientHeight(),n=t?0:this.getClientWidth(),i=n<this.thresholds.xs,s=n<this.thresholds.sm&&!i,r=n<this.thresholds.md-this.scrollBarWidth&&!(s||i),a=n<this.thresholds.lg-this.scrollBarWidth&&!(r||s||i),o=n>=this.thresholds.lg-this.scrollBarWidth;switch(this.height=e,this.width=n,this.xs=i,this.sm=s,this.md=r,this.lg=a,this.xl=o,this.xsOnly=i,this.smOnly=s,this.smAndDown=(i||s)&&!(r||a||o),this.smAndUp=!i&&(s||r||a||o),this.mdOnly=r,this.mdAndDown=(i||s||r)&&!(a||o),this.mdAndUp=!(i||s)&&(r||a||o),this.lgOnly=a,this.lgAndDown=(i||s||r||a)&&!o,this.lgAndUp=!(i||s||r)&&(a||o),this.xlOnly=o,!0){case i:this.name="xs";break;case s:this.name="sm";break;case r:this.name="md";break;case a:this.name="lg";break;default:this.name="xl";break}if("number"===typeof this.mobileBreakpoint)return void(this.mobile=n<parseInt(this.mobileBreakpoint,10));const l={xs:0,sm:1,md:2,lg:3,xl:4},c=l[this.name],u=l[this.mobileBreakpoint];this.mobile=c<=u}onResize(){clearTimeout(this.resizeTimeout),this.resizeTimeout=window.setTimeout(this.update.bind(this),200)}getClientWidth(){return"undefined"===typeof document?0:Math.max(document.documentElement.clientWidth,window.innerWidth||0)}getClientHeight(){return"undefined"===typeof document?0:Math.max(document.documentElement.clientHeight,window.innerHeight||0)}}d.property="breakpoint";var f=n(473);const p={complete:"M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z",cancel:"M12,2C17.53,2 22,6.47 22,12C22,17.53 17.53,22 12,22C6.47,22 2,17.53 2,12C2,6.47 6.47,2 12,2M15.59,7L12,10.59L8.41,7L7,8.41L10.59,12L7,15.59L8.41,17L12,13.41L15.59,17L17,15.59L13.41,12L17,8.41L15.59,7Z",close:"M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z",delete:"M12,2C17.53,2 22,6.47 22,12C22,17.53 17.53,22 12,22C6.47,22 2,17.53 2,12C2,6.47 6.47,2 12,2M15.59,7L12,10.59L8.41,7L7,8.41L10.59,12L7,15.59L8.41,17L12,13.41L15.59,17L17,15.59L13.41,12L17,8.41L15.59,7Z",clear:"M12,2C17.53,2 22,6.47 22,12C22,17.53 17.53,22 12,22C6.47,22 2,17.53 2,12C2,6.47 6.47,2 12,2M15.59,7L12,10.59L8.41,7L7,8.41L10.59,12L7,15.59L8.41,17L12,13.41L15.59,17L17,15.59L13.41,12L17,8.41L15.59,7Z",success:"M12 2C6.5 2 2 6.5 2 12S6.5 22 12 22 22 17.5 22 12 17.5 2 12 2M10 17L5 12L6.41 10.59L10 14.17L17.59 6.58L19 8L10 17Z",info:"M13,9H11V7H13M13,17H11V11H13M12,2C6.48,2 2,6.48 2,12C2,17.52 6.48,22 12,22C17.52,22 22,17.52 22,12C22,6.48 17.52,2 12,2ZM13,9H11V7H13M13,17H11V11H13M12,2A10,10 0 0,0 2,12A10,10 0 0,0 12,22A10,10 0 0,0 22,12A10,10 0 0,0 12,2Z",warning:"M13,13H11V7H13M13,17H11V15H13M12,2A10,10 0 0,0 2,12A10,10 0 0,0 12,22A10,10 0 0,0 22,12A10,10 0 0,0 12,2Z",error:"M12,2C17.53,2 22,6.47 22,12C22,17.53 17.53,22 12,22C6.47,22 2,17.53 2,12C2,6.47 6.47,2 12,2M15.59,7L12,10.59L8.41,7L7,8.41L10.59,12L7,15.59L8.41,17L12,13.41L15.59,17L17,15.59L13.41,12L17,8.41L15.59,7Z",prev:"M15.41,16.58L10.83,12L15.41,7.41L14,6L8,12L14,18L15.41,16.58Z",next:"M8.59,16.58L13.17,12L8.59,7.41L10,6L16,12L10,18L8.59,16.58Z",checkboxOn:"M10,17L5,12L6.41,10.58L10,14.17L17.59,6.58L19,8M19,3H5C3.89,3 3,3.89 3,5V19C3,20.1 3.9,21 5,21H19C20.1,21 21,20.1 21,19V5C21,3.89 20.1,3 19,3Z",checkboxOff:"M19,3H5C3.89,3 3,3.89 3,5V19C3,20.1 3.9,21 5,21H19C20.1,21 21,20.1 21,19V5C21,3.89 20.1,3 19,3M19,5V19H5V5H19Z",checkboxIndeterminate:"M17,13H7V11H17M19,3H5C3.89,3 3,3.89 3,5V19C3,20.1 3.9,21 5,21H19C20.1,21 21,20.1 21,19V5C21,3.89 20.1,3 19,3Z",delimiter:"M12,2C6.48,2 2,6.48 2,12C2,17.52 6.48,22 12,22C17.52,22 22,17.52 22,12C22,6.48 17.52,2 12,2Z",sort:"M13,20H11V8L5.5,13.5L4.08,12.08L12,4.16L19.92,12.08L18.5,13.5L13,8V20Z",expand:"M7.41,8.58L12,13.17L16.59,8.58L18,10L12,16L6,10L7.41,8.58Z",menu:"M3,6H21V8H3V6M3,11H21V13H3V11M3,16H21V18H3V16Z",subgroup:"M7,10L12,15L17,10H7Z",dropdown:"M7,10L12,15L17,10H7Z",radioOn:"M12,20C7.58,20 4,16.42 4,12C4,7.58 7.58,4 12,4C16.42,4 20,7.58 20,12C20,16.42 16.42,20 12,20M12,2C6.48,2 2,6.48 2,12C2,17.52 6.48,22 12,22C17.52,22 22,17.52 22,12C22,6.48 17.52,2 12,2M12,7C9.24,7 7,9.24 7,12C7,14.76 9.24,17 12,17C14.76,17 17,14.76 17,12C17,9.24 14.76,7 12,7Z",radioOff:"M12,20C7.58,20 4,16.42 4,12C4,7.58 7.58,4 12,4C16.42,4 20,7.58 20,12C20,16.42 16.42,20 12,20M12,2C6.48,2 2,6.48 2,12C2,17.52 6.48,22 12,22C17.52,22 22,17.52 22,12C22,6.48 17.52,2 12,2Z",edit:"M20.71,7.04C21.1,6.65 21.1,6 20.71,5.63L18.37,3.29C18,2.9 17.35,2.9 16.96,3.29L15.12,5.12L18.87,8.87M3,17.25V21H6.75L17.81,9.93L14.06,6.18L3,17.25Z",ratingEmpty:"M12,15.39L8.24,17.66L9.23,13.38L5.91,10.5L10.29,10.13L12,6.09L13.71,10.13L18.09,10.5L14.77,13.38L15.76,17.66M22,9.24L14.81,8.63L12,2L9.19,8.63L2,9.24L7.45,13.97L5.82,21L12,17.27L18.18,21L16.54,13.97L22,9.24Z",ratingFull:"M12,17.27L18.18,21L16.54,13.97L22,9.24L14.81,8.62L12,2L9.19,8.62L2,9.24L7.45,13.97L5.82,21L12,17.27Z",ratingHalf:"M12,15.4V6.1L13.71,10.13L18.09,10.5L14.77,13.39L15.76,17.67M22,9.24L14.81,8.63L12,2L9.19,8.63L2,9.24L7.45,13.97L5.82,21L12,17.27L18.18,21L16.54,13.97L22,9.24Z",loading:"M19,8L15,12H18C18,15.31 15.31,18 12,18C11,18 10.03,17.75 9.2,17.3L7.74,18.76C8.97,19.54 10.43,20 12,20C16.42,20 20,16.42 20,12H23M6,12C6,8.69 8.69,6 12,6C13,6 13.97,6.25 14.8,6.7L16.26,5.24C15.03,4.46 13.57,4 12,4C7.58,4 4,7.58 4,12H1L5,16L9,12",first:"M18.41,16.59L13.82,12L18.41,7.41L17,6L11,12L17,18L18.41,16.59M6,6H8V18H6V6Z",last:"M5.59,7.41L10.18,12L5.59,16.59L7,18L13,12L7,6L5.59,7.41M16,6H18V18H16V6Z",unfold:"M12,18.17L8.83,15L7.42,16.41L12,21L16.59,16.41L15.17,15M12,5.83L15.17,9L16.58,7.59L12,3L7.41,7.59L8.83,9L12,5.83Z",file:"M16.5,6V17.5C16.5,19.71 14.71,21.5 12.5,21.5C10.29,21.5 8.5,19.71 8.5,17.5V5C8.5,3.62 9.62,2.5 11,2.5C12.38,2.5 13.5,3.62 13.5,5V15.5C13.5,16.05 13.05,16.5 12.5,16.5C11.95,16.5 11.5,16.05 11.5,15.5V6H10V15.5C10,16.88 11.12,18 12.5,18C13.88,18 15,16.88 15,15.5V5C15,2.79 13.21,1 11,1C8.79,1 7,2.79 7,5V17.5C7,20.54 9.46,23 12.5,23C15.54,23 18,20.54 18,17.5V6H16.5Z",plus:"M19,13H13V19H11V13H5V11H11V5H13V11H19V13Z",minus:"M19,13H5V11H19V13Z"};var _=p;const m={complete:"check",cancel:"cancel",close:"close",delete:"cancel",clear:"clear",success:"check_circle",info:"info",warning:"priority_high",error:"warning",prev:"chevron_left",next:"chevron_right",checkboxOn:"check_box",checkboxOff:"check_box_outline_blank",checkboxIndeterminate:"indeterminate_check_box",delimiter:"fiber_manual_record",sort:"arrow_upward",expand:"keyboard_arrow_down",menu:"menu",subgroup:"arrow_drop_down",dropdown:"arrow_drop_down",radioOn:"radio_button_checked",radioOff:"radio_button_unchecked",edit:"edit",ratingEmpty:"star_border",ratingFull:"star",ratingHalf:"star_half",loading:"cached",first:"first_page",last:"last_page",unfold:"unfold_more",file:"attach_file",plus:"add",minus:"remove"};var v=m;const g={complete:"mdi-check",cancel:"mdi-close-circle",close:"mdi-close",delete:"mdi-close-circle",clear:"mdi-close",success:"mdi-check-circle",info:"mdi-information",warning:"mdi-exclamation",error:"mdi-alert",prev:"mdi-chevron-left",next:"mdi-chevron-right",checkboxOn:"mdi-checkbox-marked",checkboxOff:"mdi-checkbox-blank-outline",checkboxIndeterminate:"mdi-minus-box",delimiter:"mdi-circle",sort:"mdi-arrow-up",expand:"mdi-chevron-down",menu:"mdi-menu",subgroup:"mdi-menu-down",dropdown:"mdi-menu-down",radioOn:"mdi-radiobox-marked",radioOff:"mdi-radiobox-blank",edit:"mdi-pencil",ratingEmpty:"mdi-star-outline",ratingFull:"mdi-star",ratingHalf:"mdi-star-half-full",loading:"mdi-cached",first:"mdi-page-first",last:"mdi-page-last",unfold:"mdi-unfold-more-horizontal",file:"mdi-paperclip",plus:"mdi-plus",minus:"mdi-minus"};var b=g;const y={complete:"fas fa-check",cancel:"fas fa-times-circle",close:"fas fa-times",delete:"fas fa-times-circle",clear:"fas fa-times-circle",success:"fas fa-check-circle",info:"fas fa-info-circle",warning:"fas fa-exclamation",error:"fas fa-exclamation-triangle",prev:"fas fa-chevron-left",next:"fas fa-chevron-right",checkboxOn:"fas fa-check-square",checkboxOff:"far fa-square",checkboxIndeterminate:"fas fa-minus-square",delimiter:"fas fa-circle",sort:"fas fa-sort-up",expand:"fas fa-chevron-down",menu:"fas fa-bars",subgroup:"fas fa-caret-down",dropdown:"fas fa-caret-down",radioOn:"far fa-dot-circle",radioOff:"far fa-circle",edit:"fas fa-edit",ratingEmpty:"far fa-star",ratingFull:"fas fa-star",ratingHalf:"fas fa-star-half",loading:"fas fa-sync",first:"fas fa-step-backward",last:"fas fa-step-forward",unfold:"fas fa-arrows-alt-v",file:"fas fa-paperclip",plus:"fas fa-plus",minus:"fas fa-minus"};var x=y;const w={complete:"fa fa-check",cancel:"fa fa-times-circle",close:"fa fa-times",delete:"fa fa-times-circle",clear:"fa fa-times-circle",success:"fa fa-check-circle",info:"fa fa-info-circle",warning:"fa fa-exclamation",error:"fa fa-exclamation-triangle",prev:"fa fa-chevron-left",next:"fa fa-chevron-right",checkboxOn:"fa fa-check-square",checkboxOff:"fa fa-square-o",checkboxIndeterminate:"fa fa-minus-square",delimiter:"fa fa-circle",sort:"fa fa-sort-up",expand:"fa fa-chevron-down",menu:"fa fa-bars",subgroup:"fa fa-caret-down",dropdown:"fa fa-caret-down",radioOn:"fa fa-dot-circle-o",radioOff:"fa fa-circle-o",edit:"fa fa-pencil",ratingEmpty:"fa fa-star-o",ratingFull:"fa fa-star",ratingHalf:"fa fa-star-half-o",loading:"fa fa-refresh",first:"fa fa-step-backward",last:"fa fa-step-forward",unfold:"fa fa-angle-double-down",file:"fa fa-paperclip",plus:"fa fa-plus",minus:"fa fa-minus"};var S=w;function k(t,e){const n={};for(const i in e)n[i]={component:t,props:{icon:e[i].split(" fa-")}};return n}var A=k("font-awesome-icon",x),C=Object.freeze({mdiSvg:_,md:v,mdi:b,fa:x,fa4:S,faSvg:A});class M extends c.t{constructor(t){super();const{iconfont:e,values:n,component:i}=t[M.property];this.component=i,this.iconfont=e,this.values=(0,l.Ee)(C[e],n)}}M.property="icons";const E="$vuetify.",T=Symbol("Lang fallback");function B(t,e,n=!1,i){const r=e.replace(E,"");let a=(0,l.vO)(t,r,T);return a===T&&(n?((0,s.N6)(`Translation key "${r}" not found in fallback`),a=e):((0,s.Kd)(`Translation key "${r}" not found, falling back to default`),a=B(i,e,!0,i))),a}class R extends c.t{constructor(t){super(),this.defaultLocale="en";const{current:e,locales:n,t:i}=t[R.property];this.current=e,this.locales=n,this.translator=i||this.defaultTranslator}currentLocale(t){const e=this.locales[this.current],n=this.locales[this.defaultLocale];return B(e,t,!1,n)}t(t,...e){return t.startsWith(E)?this.translator(t,...e):this.replace(t,e)}defaultTranslator(t,...e){return this.replace(this.currentLocale(t),e)}replace(t,e){return t.replace(/\{(\d+)\}/g,((t,n)=>String(e[+n])))}}R.property="lang";var $=n(8604);const I=[[3.2406,-1.5372,-.4986],[-.9689,1.8758,.0415],[.0557,-.204,1.057]],L=t=>t<=.0031308?12.92*t:1.055*t**(1/2.4)-.055,O=[[.4124,.3576,.1805],[.2126,.7152,.0722],[.0193,.1192,.9505]],P=t=>t<=.04045?t/12.92:((t+.055)/1.055)**2.4;function V(t){const e=Array(3),n=L,i=I;for(let s=0;s<3;++s)e[s]=Math.round(255*(0,l.uZ)(n(i[s][0]*t[0]+i[s][1]*t[1]+i[s][2]*t[2])));return(e[0]<<16)+(e[1]<<8)+(e[2]<<0)}function D(t){const e=[0,0,0],n=P,i=O,s=n((t>>16&255)/255),r=n((t>>8&255)/255),a=n((t>>0&255)/255);for(let o=0;o<3;++o)e[o]=i[o][0]*s+i[o][1]*r+i[o][2]*a;return e}const N=.20689655172413793,F=t=>t>N**3?Math.cbrt(t):t/(3*N**2)+4/29,H=t=>t>N?t**3:3*N**2*(t-4/29);function j(t){const e=F,n=e(t[1]);return[116*n-16,500*(e(t[0]/.95047)-n),200*(n-e(t[2]/1.08883))]}function Z(t){const e=H,n=(t[0]+16)/116;return[.95047*e(n+t[1]/500),e(n),1.08883*e(n-t[2]/200)]}function z(t,e=!1,n=!0){const{anchor:i,...s}=t,r=Object.keys(s),a={};for(let o=0;o<r.length;++o){const i=r[o],s=t[i];null!=s&&(n?e?("base"===i||i.startsWith("lighten")||i.startsWith("darken"))&&(a[i]=(0,$.hq)(s)):a[i]="object"===typeof s?z(s,!0,n):G(i,(0,$.jx)(s)):a[i]={base:(0,$.I4)((0,$.jx)(s))})}return e||(a.anchor=i||a.base||a.primary.base),a}const q=(t,e)=>`\n.v-application .${t} {\n  background-color: ${e} !important;\n  border-color: ${e} !important;\n}\n.v-application .${t}--text {\n  color: ${e} !important;\n  caret-color: ${e} !important;\n}`,U=(t,e,n)=>{const[i,s]=e.split(/(\d)/,2);return`\n.v-application .${t}.${i}-${s} {\n  background-color: ${n} !important;\n  border-color: ${n} !important;\n}\n.v-application .${t}--text.text--${i}-${s} {\n  color: ${n} !important;\n  caret-color: ${n} !important;\n}`},X=(t,e="base")=>`--v-${t}-${e}`,Y=(t,e="base")=>`var(${X(t,e)})`;function W(t,e=!1){const{anchor:n,...i}=t,s=Object.keys(i);if(!s.length)return"";let r="",a="";const o=e?Y("anchor"):n;a+=`.v-application a { color: ${o}; }`,e&&(r+=`  ${X("anchor")}: ${n};\n`);for(let c=0;c<s.length;++c){const n=s[c],i=t[n];a+=q(n,e?Y(n):i.base),e&&(r+=`  ${X(n)}: ${i.base};\n`);const o=(0,l.XP)(i);for(let t=0;t<o.length;++t){const s=o[t],l=i[s];"base"!==s&&(a+=U(n,s,e?Y(n,s):l),e&&(r+=`  ${X(n,s)}: ${l};\n`))}}return e&&(r=`:root {\n${r}}\n\n`),r+a}function G(t,e){const n={base:(0,$.I4)(e)};for(let i=5;i>0;--i)n[`lighten${i}`]=(0,$.I4)(K(e,i));for(let i=1;i<=4;++i)n[`darken${i}`]=(0,$.I4)(Q(e,i));return n}function K(t,e){const n=j(D(t));return n[0]=n[0]+10*e,V(Z(n))}function Q(t,e){const n=j(D(t));return n[0]=n[0]-10*e,V(Z(n))}class J extends c.t{constructor(t){super(),this.disabled=!1,this.isDark=null,this.unwatch=null,this.vueMeta=null;const{dark:e,disable:n,options:i,themes:s}=t[J.property];this.dark=Boolean(e),this.defaults=this.themes=s,this.options=i,n?this.disabled=!0:this.themes={dark:this.fillVariant(s.dark,!0),light:this.fillVariant(s.light,!1)}}set css(t){this.vueMeta?this.isVueMeta23&&this.applyVueMeta23():this.checkOrCreateStyleElement()&&(this.styleEl.innerHTML=t)}set dark(t){const e=this.isDark;this.isDark=t,null!=e&&this.applyTheme()}get dark(){return Boolean(this.isDark)}applyTheme(){if(this.disabled)return this.clearCss();this.css=this.generatedStyles}clearCss(){this.css=""}init(t,e){this.disabled||(t.$meta?this.initVueMeta(t):e&&this.initSSR(e),this.initTheme(t))}setTheme(t,e){this.themes[t]=Object.assign(this.themes[t],e),this.applyTheme()}resetThemes(){this.themes.light=Object.assign({},this.defaults.light),this.themes.dark=Object.assign({},this.defaults.dark),this.applyTheme()}checkOrCreateStyleElement(){return this.styleEl=document.getElementById("vuetify-theme-stylesheet"),!!this.styleEl||(this.genStyleElement(),Boolean(this.styleEl))}fillVariant(t={},e){const n=this.themes[e?"dark":"light"];return Object.assign({},n,t)}genStyleElement(){"undefined"!==typeof document&&(this.styleEl=document.createElement("style"),this.styleEl.type="text/css",this.styleEl.id="vuetify-theme-stylesheet",this.options.cspNonce&&this.styleEl.setAttribute("nonce",this.options.cspNonce),document.head.appendChild(this.styleEl))}initVueMeta(t){if(this.vueMeta=t.$meta(),this.isVueMeta23)return void t.$nextTick((()=>{this.applyVueMeta23()}));const e="function"===typeof this.vueMeta.getOptions?this.vueMeta.getOptions().keyName:"metaInfo",n=t.$options[e]||{};t.$options[e]=()=>{n.style=n.style||[];const t=n.style.find((t=>"vuetify-theme-stylesheet"===t.id));return t?t.cssText=this.generatedStyles:n.style.push({cssText:this.generatedStyles,type:"text/css",id:"vuetify-theme-stylesheet",nonce:(this.options||{}).cspNonce}),n}}applyVueMeta23(){const{set:t}=this.vueMeta.addApp("vuetify");t({style:[{cssText:this.generatedStyles,type:"text/css",id:"vuetify-theme-stylesheet",nonce:this.options.cspNonce}]})}initSSR(t){const e=this.options.cspNonce?` nonce="${this.options.cspNonce}"`:"";t.head=t.head||"",t.head+=`<style type="text/css" id="vuetify-theme-stylesheet"${e}>${this.generatedStyles}</style>`}initTheme(t){"undefined"!==typeof document&&(this.unwatch&&(this.unwatch(),this.unwatch=null),t.$once("hook:created",(()=>{const e=i.ZP.observable({themes:this.themes});this.unwatch=t.$watch((()=>e.themes),(()=>this.applyTheme()),{deep:!0})})),this.applyTheme())}get currentTheme(){const t=this.dark?"dark":"light";return this.themes[t]}get generatedStyles(){const t=this.parsedTheme,e=this.options||{};let n;return null!=e.themeCache&&(n=e.themeCache.get(t),null!=n)||(n=W(t,e.customProperties),null!=e.minifyTheme&&(n=e.minifyTheme(n)),null!=e.themeCache&&e.themeCache.set(t,n)),n}get parsedTheme(){return z(this.currentTheme||{},void 0,(0,l.qw)(this.options,["variations"],!0))}get isVueMeta23(){return"function"===typeof this.vueMeta.addApp}}J.property="theme";class tt{constructor(t={}){this.framework={isHydrating:!1},this.installed=[],this.preset={},this.userPreset={},this.userPreset=t,this.use(u),this.use(h),this.use(d),this.use(f.A),this.use(M),this.use(R),this.use(J)}init(t,e){this.installed.forEach((n=>{const i=this.framework[n];i.framework=this.framework,i.init(t,e)})),this.framework.rtl=Boolean(this.preset.rtl)}use(t){const e=t.property;this.installed.includes(e)||(this.framework[e]=new t(this.preset,this),this.installed.push(e))}}tt.install=r,tt.installed=!1,tt.version="2.6.15",tt.config={silent:!1}},4609:function(t,e,n){"use strict";var i=n(144);function s(t){return function(e,n){for(const i in n)Object.prototype.hasOwnProperty.call(e,i)||this.$delete(this.$data[t],i);for(const i in e)this.$set(this.$data[t],i,e[i])}}e["Z"]=i.ZP.extend({data:()=>({attrs$:{},listeners$:{}}),created(){this.$watch("$attrs",s("attrs$"),{immediate:!0}),this.$watch("$listeners",s("listeners$"),{immediate:!0})}})},8914:function(t,e,n){"use strict";var i=n(144),s=n(6596),r=n(8604);e["Z"]=i.ZP.extend({name:"colorable",props:{color:String},methods:{setBackgroundColor(t,e={}){return"string"===typeof e.style?((0,s.N6)("style must be an object",this),e):"string"===typeof e.class?((0,s.N6)("class must be an object",this),e):((0,r.NA)(t)?e.style={...e.style,"background-color":`${t}`,"border-color":`${t}`}:t&&(e.class={...e.class,[t]:!0}),e)},setTextColor(t,e={}){if("string"===typeof e.style)return(0,s.N6)("style must be an object",this),e;if("string"===typeof e.class)return(0,s.N6)("class must be an object",this),e;if((0,r.NA)(t))e.style={...e.style,color:`${t}`,"caret-color":`${t}`};else if(t){const[n,i]=t.toString().trim().split(" ",2);e.class={...e.class,[n+"--text"]:!0},i&&(e.class["text--"+i]=!0)}return e}}})},5303:function(t,e,n){"use strict";var i=n(144);e["Z"]=i.ZP.extend({name:"elevatable",props:{elevation:[Number,String]},computed:{computedElevation(){return this.elevation},elevationClasses(){const t=this.computedElevation;return null==t||isNaN(parseInt(t))?{}:{[`elevation-${this.elevation}`]:!0}}}})},4791:function(t,e,n){"use strict";n.d(e,{d:function(){return s}});var i=n(380);function s(t,e,n){return(0,i.f)(t,e,n).extend({name:"groupable",props:{activeClass:{type:String,default(){if(this[t])return this[t].activeClass}},disabled:Boolean},data(){return{isActive:!1}},computed:{groupClasses(){return this.activeClass?{[this.activeClass]:this.isActive}:{}}},created(){this[t]&&this[t].register(this)},beforeDestroy(){this[t]&&this[t].unregister(this)},methods:{toggle(){this.$emit("change")}}})}s("itemGroup")},4204:function(t,e,n){"use strict";n.d(e,{Z:function(){return _}});var i=n(144),s=n(3422),r=n(1558),a=n(8914),o=n(8386),l=n(3939),c=n(3276),u=n(1050),h=n(4987);const d=(0,h.Z)(a.Z,(0,o.d)(["absolute","fixed","top","bottom"]),l.Z,c.Z);var f=d.extend({name:"v-progress-linear",directives:{intersect:r.Z},props:{active:{type:Boolean,default:!0},backgroundColor:{type:String,default:null},backgroundOpacity:{type:[Number,String],default:null},bufferValue:{type:[Number,String],default:100},color:{type:String,default:"primary"},height:{type:[Number,String],default:4},indeterminate:Boolean,query:Boolean,reverse:Boolean,rounded:Boolean,stream:Boolean,striped:Boolean,value:{type:[Number,String],default:0}},data(){return{internalLazyValue:this.value||0,isVisible:!0}},computed:{__cachedBackground(){return this.$createElement("div",this.setBackgroundColor(this.backgroundColor||this.color,{staticClass:"v-progress-linear__background",style:this.backgroundStyle}))},__cachedBar(){return this.$createElement(this.computedTransition,[this.__cachedBarType])},__cachedBarType(){return this.indeterminate?this.__cachedIndeterminate:this.__cachedDeterminate},__cachedBuffer(){return this.$createElement("div",{staticClass:"v-progress-linear__buffer",style:this.styles})},__cachedDeterminate(){return this.$createElement("div",this.setBackgroundColor(this.color,{staticClass:"v-progress-linear__determinate",style:{width:(0,u.kb)(this.normalizedValue,"%")}}))},__cachedIndeterminate(){return this.$createElement("div",{staticClass:"v-progress-linear__indeterminate",class:{"v-progress-linear__indeterminate--active":this.active}},[this.genProgressBar("long"),this.genProgressBar("short")])},__cachedStream(){return this.stream?this.$createElement("div",this.setTextColor(this.color,{staticClass:"v-progress-linear__stream",style:{width:(0,u.kb)(100-this.normalizedBuffer,"%")}})):null},backgroundStyle(){const t=null==this.backgroundOpacity?this.backgroundColor?1:.3:parseFloat(this.backgroundOpacity);return{opacity:t,[this.isReversed?"right":"left"]:(0,u.kb)(this.normalizedValue,"%"),width:(0,u.kb)(Math.max(0,this.normalizedBuffer-this.normalizedValue),"%")}},classes(){return{"v-progress-linear--absolute":this.absolute,"v-progress-linear--fixed":this.fixed,"v-progress-linear--query":this.query,"v-progress-linear--reactive":this.reactive,"v-progress-linear--reverse":this.isReversed,"v-progress-linear--rounded":this.rounded,"v-progress-linear--striped":this.striped,"v-progress-linear--visible":this.isVisible,...this.themeClasses}},computedTransition(){return this.indeterminate?s.Z5:s.Qn},isReversed(){return this.$vuetify.rtl!==this.reverse},normalizedBuffer(){return this.normalize(this.bufferValue)},normalizedValue(){return this.normalize(this.internalLazyValue)},reactive(){return Boolean(this.$listeners.change)},styles(){const t={};return this.active||(t.height=0),this.indeterminate||100===parseFloat(this.normalizedBuffer)||(t.width=(0,u.kb)(this.normalizedBuffer,"%")),t}},methods:{genContent(){const t=(0,u.z9)(this,"default",{value:this.internalLazyValue});return t?this.$createElement("div",{staticClass:"v-progress-linear__content"},t):null},genListeners(){const t=this.$listeners;return this.reactive&&(t.click=this.onClick),t},genProgressBar(t){return this.$createElement("div",this.setBackgroundColor(this.color,{staticClass:"v-progress-linear__indeterminate",class:{[t]:!0}}))},onClick(t){if(!this.reactive)return;const{width:e}=this.$el.getBoundingClientRect();this.internalValue=t.offsetX/e*100},onObserve(t,e,n){this.isVisible=n},normalize(t){return t<0?0:t>100?100:parseFloat(t)}},render(t){const e={staticClass:"v-progress-linear",attrs:{role:"progressbar","aria-valuemin":0,"aria-valuemax":this.normalizedBuffer,"aria-valuenow":this.indeterminate?void 0:this.normalizedValue},class:this.classes,directives:[{name:"intersect",value:this.onObserve}],style:{bottom:this.bottom?0:void 0,height:this.active?(0,u.kb)(this.height):0,top:this.top?0:void 0},on:this.genListeners()};return t("div",e,[this.__cachedStream,this.__cachedBackground,this.__cachedBuffer,this.__cachedBar,this.genContent()])}}),p=f,_=i.ZP.extend().extend({name:"loadable",props:{loading:{type:[Boolean,String],default:!1},loaderHeight:{type:[Number,String],default:2}},methods:{genProgress(){return!1===this.loading?null:this.$slots.progress||this.$createElement(p,{props:{absolute:!0,color:!0===this.loading||""===this.loading?this.color||"primary":this.loading,height:this.loaderHeight,indeterminate:!0}})}}})},9196:function(t,e,n){"use strict";var i=n(1050),s=n(144);e["Z"]=s.ZP.extend({name:"measurable",props:{height:[Number,String],maxHeight:[Number,String],maxWidth:[Number,String],minHeight:[Number,String],minWidth:[Number,String],width:[Number,String]},computed:{measurableStyles(){const t={},e=(0,i.kb)(this.height),n=(0,i.kb)(this.minHeight),s=(0,i.kb)(this.minWidth),r=(0,i.kb)(this.maxHeight),a=(0,i.kb)(this.maxWidth),o=(0,i.kb)(this.width);return e&&(t.height=e),n&&(t.minHeight=n),s&&(t.minWidth=s),r&&(t.maxHeight=r),a&&(t.maxWidth=a),o&&(t.width=o),t}}})},8386:function(t,e,n){"use strict";n.d(e,{d:function(){return a}});var i=n(144),s=n(1050);const r={absolute:Boolean,bottom:Boolean,fixed:Boolean,left:Boolean,right:Boolean,top:Boolean};function a(t=[]){return i.ZP.extend({name:"positionable",props:t.length?(0,s.ji)(r,t):r})}e["Z"]=a()},3939:function(t,e,n){"use strict";var i=n(144);function s(t="value",e="change"){return i.ZP.extend({name:"proxyable",model:{prop:t,event:e},props:{[t]:{required:!1}},data(){return{internalLazyValue:this[t]}},computed:{internalValue:{get(){return this.internalLazyValue},set(t){t!==this.internalLazyValue&&(this.internalLazyValue=t,this.$emit(e,t))}}},watch:{[t](t){this.internalLazyValue=t}}})}const r=s();e["Z"]=r},380:function(t,e,n){"use strict";n.d(e,{f:function(){return a}});var i=n(144),s=n(6596);function r(t,e){return()=>(0,s.Kd)(`The ${t} component must be used inside a ${e}`)}function a(t,e,n){const s=e&&n?{register:r(e,n),unregister:r(e,n)}:null;return i.ZP.extend({name:"registrable-inject",inject:{[t]:{default:s}}})}},3960:function(t,e,n){"use strict";n(7658);var i=n(144);e["Z"]=i.ZP.extend({name:"roundable",props:{rounded:[Boolean,String],tile:Boolean},computed:{roundedClasses(){const t=[],e="string"===typeof this.rounded?String(this.rounded):!0===this.rounded;if(this.tile)t.push("rounded-0");else if("string"===typeof e){const n=e.split(" ");for(const e of n)t.push(`rounded-${e}`)}else e&&t.push("rounded");return t.length>0?{[t.join(" ")]:!0}:{}}}})},4040:function(t,e,n){"use strict";var i=n(144),s=n(3176),r=n(1050);e["Z"]=i.ZP.extend({name:"routable",directives:{Ripple:s.Z},props:{activeClass:String,append:Boolean,disabled:Boolean,exact:{type:Boolean,default:void 0},exactPath:Boolean,exactActiveClass:String,link:Boolean,href:[String,Object],to:[String,Object],nuxt:Boolean,replace:Boolean,ripple:{type:[Boolean,Object],default:null},tag:String,target:String},data:()=>({isActive:!1,proxyClass:""}),computed:{classes(){const t={};return this.to||(this.activeClass&&(t[this.activeClass]=this.isActive),this.proxyClass&&(t[this.proxyClass]=this.isActive)),t},computedRipple(){var t;return null!==(t=this.ripple)&&void 0!==t?t:!this.disabled&&this.isClickable},isClickable(){return!this.disabled&&Boolean(this.isLink||this.$listeners.click||this.$listeners["!click"]||this.$attrs.tabindex)},isLink(){return this.to||this.href||this.link},styles:()=>({})},watch:{$route:"onRouteChange"},mounted(){this.onRouteChange()},methods:{generateRouteLink(){let t,e=this.exact;const n={attrs:{tabindex:"tabindex"in this.$attrs?this.$attrs.tabindex:void 0},class:this.classes,style:this.styles,props:{},directives:[{name:"ripple",value:this.computedRipple}],[this.to?"nativeOn":"on"]:{...this.$listeners,..."click"in this?{click:this.click}:void 0},ref:"link"};if("undefined"===typeof this.exact&&(e="/"===this.to||this.to===Object(this.to)&&"/"===this.to.path),this.to){let i=this.activeClass,s=this.exactActiveClass||i;this.proxyClass&&(i=`${i} ${this.proxyClass}`.trim(),s=`${s} ${this.proxyClass}`.trim()),t=this.nuxt?"nuxt-link":"router-link",Object.assign(n.props,{to:this.to,exact:e,exactPath:this.exactPath,activeClass:i,exactActiveClass:s,append:this.append,replace:this.replace})}else t=(this.href?"a":this.tag)||"div","a"===t&&this.href&&(n.attrs.href=this.href);return this.target&&(n.attrs.target=this.target),{tag:t,data:n}},onRouteChange(){if(!this.to||!this.$refs.link||!this.$route)return;const t=`${this.activeClass||""} ${this.proxyClass||""}`.trim(),e=`${this.exactActiveClass||""} ${this.proxyClass||""}`.trim()||t,n="_vnode.data.class."+(this.exact?e:t);this.$nextTick((()=>{!(0,r.vO)(this.$refs.link,n)===this.isActive&&this.toggle()}))},toggle(){this.isActive=!this.isActive}}})},281:function(t,e,n){"use strict";var i=n(144);e["Z"]=i.ZP.extend({name:"sizeable",props:{large:Boolean,small:Boolean,xLarge:Boolean,xSmall:Boolean},computed:{medium(){return Boolean(!this.xSmall&&!this.small&&!this.large&&!this.xLarge)},sizeableClasses(){return{"v-size--x-small":this.xSmall,"v-size--small":this.small,"v-size--default":this.medium,"v-size--large":this.large,"v-size--x-large":this.xLarge}}}})},8574:function(t,e,n){"use strict";var i=n(144);e["Z"]=i.ZP.extend({name:"ssr-bootable",data:()=>({isBooted:!1}),mounted(){window.requestAnimationFrame((()=>{this.$el.setAttribute("data-booted","true"),this.isBooted=!0}))}})},3276:function(t,e,n){"use strict";n.d(e,{X:function(){return r}});var i=n(144);const s=i.ZP.extend().extend({name:"themeable",provide(){return{theme:this.themeableProvide}},inject:{theme:{default:{isDark:!1}}},props:{dark:{type:Boolean,default:null},light:{type:Boolean,default:null}},data(){return{themeableProvide:{isDark:!1}}},computed:{appIsDark(){return this.$vuetify.theme.dark||!1},isDark(){return!0===this.dark||!0!==this.light&&this.theme.isDark},themeClasses(){return{"theme--dark":this.isDark,"theme--light":!this.isDark}},rootIsDark(){return!0===this.dark||!0!==this.light&&this.appIsDark},rootThemeClasses(){return{"theme--dark":this.rootIsDark,"theme--light":!this.rootIsDark}}},watch:{isDark:{handler(t,e){t!==e&&(this.themeableProvide.isDark=this.isDark)},immediate:!0}}});function r(t){const e={...t.props,...t.injections},n=s.options.computed.isDark.call(e);return s.options.computed.themeClasses.call({isDark:n})}e["Z"]=s},6470:function(t,e,n){"use strict";n.d(e,{d:function(){return s}});var i=n(144);function s(t="value",e="input"){return i.ZP.extend({name:"toggleable",model:{prop:t,event:e},props:{[t]:{required:!1}},data(){return{isActive:!!this[t]}},watch:{[t](t){this.isActive=!!t},isActive(n){!!n!==this[t]&&this.$emit(e,n)}}})}const r=s();e["Z"]=r},4777:function(t,e,n){"use strict";n(7658);var i=n(8914),s=n(3276),r=n(380),a=n(1050),o=n(6596),l=n(4987);const c=(0,l.Z)(i.Z,(0,r.f)("form"),s.Z);e["Z"]=c.extend({name:"validatable",props:{disabled:Boolean,error:Boolean,errorCount:{type:[Number,String],default:1},errorMessages:{type:[String,Array],default:()=>[]},messages:{type:[String,Array],default:()=>[]},readonly:Boolean,rules:{type:Array,default:()=>[]},success:Boolean,successMessages:{type:[String,Array],default:()=>[]},validateOnBlur:Boolean,value:{required:!1}},data(){return{errorBucket:[],hasColor:!1,hasFocused:!1,hasInput:!1,isFocused:!1,isResetting:!1,lazyValue:this.value,valid:!1}},computed:{computedColor(){if(!this.isDisabled)return this.color?this.color:this.isDark&&!this.appIsDark?"white":"primary"},hasError(){return this.internalErrorMessages.length>0||this.errorBucket.length>0||this.error},hasSuccess(){return this.internalSuccessMessages.length>0||this.success},externalError(){return this.internalErrorMessages.length>0||this.error},hasMessages(){return this.validationTarget.length>0},hasState(){return!this.isDisabled&&(this.hasSuccess||this.shouldValidate&&this.hasError)},internalErrorMessages(){return this.genInternalMessages(this.errorMessages)},internalMessages(){return this.genInternalMessages(this.messages)},internalSuccessMessages(){return this.genInternalMessages(this.successMessages)},internalValue:{get(){return this.lazyValue},set(t){this.lazyValue=t,this.$emit("input",t)}},isDisabled(){return this.disabled||!!this.form&&this.form.disabled},isInteractive(){return!this.isDisabled&&!this.isReadonly},isReadonly(){return this.readonly||!!this.form&&this.form.readonly},shouldValidate(){return!!this.externalError||!this.isResetting&&(this.validateOnBlur?this.hasFocused&&!this.isFocused:this.hasInput||this.hasFocused)},validations(){return this.validationTarget.slice(0,Number(this.errorCount))},validationState(){if(!this.isDisabled)return this.hasError&&this.shouldValidate?"error":this.hasSuccess?"success":this.hasColor?this.computedColor:void 0},validationTarget(){return this.internalErrorMessages.length>0?this.internalErrorMessages:this.successMessages&&this.successMessages.length>0?this.internalSuccessMessages:this.messages&&this.messages.length>0?this.internalMessages:this.shouldValidate?this.errorBucket:[]}},watch:{rules:{handler(t,e){(0,a.vZ)(t,e)||this.validate()},deep:!0},internalValue(){this.hasInput=!0,this.validateOnBlur||this.$nextTick(this.validate)},isFocused(t){t||this.isDisabled||(this.hasFocused=!0,this.validateOnBlur&&this.$nextTick(this.validate))},isResetting(){setTimeout((()=>{this.hasInput=!1,this.hasFocused=!1,this.isResetting=!1,this.validate()}),0)},hasError(t){this.shouldValidate&&this.$emit("update:error",t)},value(t){this.lazyValue=t}},beforeMount(){this.validate()},created(){this.form&&this.form.register(this)},beforeDestroy(){this.form&&this.form.unregister(this)},methods:{genInternalMessages(t){return t?Array.isArray(t)?t:[t]:[]},reset(){this.isResetting=!0,this.internalValue=Array.isArray(this.internalValue)?[]:null},resetValidation(){this.isResetting=!0},validate(t=!1,e){const n=[];e=e||this.internalValue,t&&(this.hasInput=this.hasFocused=!0);for(let i=0;i<this.rules.length;i++){const t=this.rules[i],s="function"===typeof t?t(e):t;!1===s||"string"===typeof s?n.push(s||""):"boolean"!==typeof s&&(0,o.N6)(`Rules should return a string or boolean, received '${typeof s}' instead`,this)}return this.errorBucket=n,this.valid=0===n.length,this.valid}}})},473:function(t,e,n){"use strict";n.d(e,{A:function(){return S},Z:function(){return w}});var i={};n.r(i),n.d(i,{easeInCubic:function(){return c},easeInOutCubic:function(){return h},easeInOutQuad:function(){return l},easeInOutQuart:function(){return p},easeInOutQuint:function(){return v},easeInQuad:function(){return a},easeInQuart:function(){return d},easeInQuint:function(){return _},easeOutCubic:function(){return u},easeOutQuad:function(){return o},easeOutQuart:function(){return f},easeOutQuint:function(){return m},linear:function(){return r}});var s=n(8447);const r=t=>t,a=t=>t**2,o=t=>t*(2-t),l=t=>t<.5?2*t**2:(4-2*t)*t-1,c=t=>t**3,u=t=>--t**3+1,h=t=>t<.5?4*t**3:(t-1)*(2*t-2)*(2*t-2)+1,d=t=>t**4,f=t=>1- --t**4,p=t=>t<.5?8*t*t*t*t:1-8*--t*t*t*t,_=t=>t**5,m=t=>1+--t**5,v=t=>t<.5?16*t**5:1+16*--t**5;function g(t){if("number"===typeof t)return t;let e=x(t);if(!e)throw"string"===typeof t?new Error(`Target element "${t}" not found.`):new TypeError(`Target must be a Number/Selector/HTMLElement/VueComponent, received ${y(t)} instead.`);let n=0;while(e)n+=e.offsetTop,e=e.offsetParent;return n}function b(t){const e=x(t);if(e)return e;throw"string"===typeof t?new Error(`Container element "${t}" not found.`):new TypeError(`Container must be a Selector/HTMLElement/VueComponent, received ${y(t)} instead.`)}function y(t){return null==t?t:t.constructor.name}function x(t){return"string"===typeof t?document.querySelector(t):t&&t._isVue?t.$el:t instanceof HTMLElement?t:null}function w(t,e={}){const n={container:document.scrollingElement||document.body||document.documentElement,duration:500,offset:0,easing:"easeInOutCubic",appOffset:!0,...e},s=b(n.container);if(n.appOffset&&w.framework.application){const t=s.classList.contains("v-navigation-drawer"),e=s.classList.contains("v-navigation-drawer--clipped"),{bar:i,top:r}=w.framework.application;n.offset+=i,t&&!e||(n.offset+=r)}const r=performance.now();let a;a="number"===typeof t?g(t)-n.offset:g(t)-g(s)-n.offset;const o=s.scrollTop;if(a===o)return Promise.resolve(a);const l="function"===typeof n.easing?n.easing:i[n.easing];if(!l)throw new TypeError(`Easing function "${n.easing}" not found.`);return new Promise((t=>requestAnimationFrame((function e(i){const c=i-r,u=Math.abs(n.duration?Math.min(c/n.duration,1):1);s.scrollTop=Math.floor(o+(a-o)*l(u));const h=s===document.body?document.documentElement.clientHeight:s.clientHeight,d=h+s.scrollTop>=s.scrollHeight;if(1===u||a>s.scrollTop&&d)return t(a);requestAnimationFrame(e)}))))}w.framework={},w.init=()=>{};class S extends s.t{constructor(){return super(),w}}S.property="goTo"},8447:function(t,e,n){"use strict";n.d(e,{t:function(){return i}});class i{constructor(){this.framework={}}init(t,e){}}},8604:function(t,e,n){"use strict";n.d(e,{I4:function(){return a},NA:function(){return s},hq:function(){return o},jx:function(){return r}});var i=n(6596);function s(t){return!!t&&!!t.match(/^(#|var\(--|(rgb|hsl)a?\()/)}function r(t){let e;if("number"===typeof t)e=t;else{if("string"!==typeof t)throw new TypeError(`Colors can only be numbers or strings, recieved ${null==t?t:t.constructor.name} instead`);{let n="#"===t[0]?t.substring(1):t;3===n.length&&(n=n.split("").map((t=>t+t)).join("")),6!==n.length&&(0,i.Kd)(`'${t}' is not a valid rgb color`),e=parseInt(n,16)}}return e<0?((0,i.Kd)(`Colors cannot be negative: '${t}'`),e=0):(e>16777215||isNaN(e))&&((0,i.Kd)(`'${t}' is not a valid rgb color`),e=16777215),e}function a(t){let e=t.toString(16);return e.length<6&&(e="0".repeat(6-e.length)+e),"#"+e}function o(t){return a(r(t))}},6596:function(t,e,n){"use strict";n.d(e,{Jk:function(){return l},Kd:function(){return r},N6:function(){return a},fK:function(){return o}});n(7658);var i=n(2250);function s(t,e,n){if(!i.Z.config.silent){if(n&&(e={_isVue:!0,$parent:n,$options:e}),e){if(e.$_alreadyWarned=e.$_alreadyWarned||[],e.$_alreadyWarned.includes(t))return;e.$_alreadyWarned.push(t)}return`[Vuetify] ${t}`+(e?d(e):"")}}function r(t,e,n){const i=s(t,e,n);null!=i&&console.warn(i)}function a(t,e,n){const i=s(t,e,n);null!=i&&console.error(i)}function o(t,e,n,i){a(`[BREAKING] '${t}' has been removed, use '${e}' instead. For more information, see the upgrade guide https://github.com/vuetifyjs/vuetify/releases/tag/v2.0.0#user-content-upgrade-guide`,n,i)}function l(t,e,n){r(`[REMOVED] '${t}' has been removed. You can safely omit it.`,e,n)}const c=/(?:^|[-_])(\w)/g,u=t=>t.replace(c,(t=>t.toUpperCase())).replace(/[-_]/g,"");function h(t,e){if(t.$root===t)return"<Root>";const n="function"===typeof t&&null!=t.cid?t.options:t._isVue?t.$options||t.constructor.options:t||{};let i=n.name||n._componentTag;const s=n.__file;if(!i&&s){const t=s.match(/([^/\\]+)\.vue$/);i=t&&t[1]}return(i?`<${u(i)}>`:"<Anonymous>")+(s&&!1!==e?` at ${s}`:"")}function d(t){if(t._isVue&&t.$parent){const e=[];let n=0;while(t){if(e.length>0){const i=e[e.length-1];if(i.constructor===t.constructor){n++,t=t.$parent;continue}n>0&&(e[e.length-1]=[i,n],n=0)}e.push(t),t=t.$parent}return"\n\nfound in\n\n"+e.map(((t,e)=>`${0===e?"---\x3e ":" ".repeat(5+2*e)}${Array.isArray(t)?`${h(t[0])}... (${t[1]} recursive calls)`:h(t)}`)).join("\n")}return`\n\n(found in ${h(t)})`}},6044:function(t,e,n){"use strict";function i(t){if("function"!==typeof t.getRootNode){while(t.parentNode)t=t.parentNode;return t!==document?null:document}const e=t.getRootNode();return e!==document&&e.getRootNode({composed:!0})!==document?null:e}n.d(e,{e:function(){return i}})},1050:function(t,e,n){"use strict";n.d(e,{Ee:function(){return M},GL:function(){return _},Ji:function(){return s},KK:function(){return d},MT:function(){return h},RB:function(){return g},TI:function(){return S},XP:function(){return b},_A:function(){return x},e$:function(){return a},jC:function(){return w},ji:function(){return f},kb:function(){return p},keyCodes:function(){return v},qF:function(){return u},qh:function(){return r},qw:function(){return o},sp:function(){return k},uZ:function(){return C},vO:function(){return c},vZ:function(){return l},z9:function(){return A}});n(7658);var i=n(144);function s(t,e="div",n){return i.ZP.extend({name:n||t.replace(/__/g,"-"),functional:!0,props:{tag:{type:String,default:e}},render(e,{data:n,props:i,children:s}){return n.staticClass=`${t} ${n.staticClass||""}`.trim(),e(i.tag,n,s)}})}function r(t,e,n,i=!1){const s=r=>{n(r),t.removeEventListener(e,s,i)};t.addEventListener(e,s,i)}let a=!1;try{if("undefined"!==typeof window){const t=Object.defineProperty({},"passive",{get:()=>{a=!0}});window.addEventListener("testListener",t,t),window.removeEventListener("testListener",t,t)}}catch(E){console.warn(E)}function o(t,e,n){const i=e.length-1;if(i<0)return void 0===t?n:t;for(let s=0;s<i;s++){if(null==t)return n;t=t[e[s]]}return null==t||void 0===t[e[i]]?n:t[e[i]]}function l(t,e){if(t===e)return!0;if(t instanceof Date&&e instanceof Date&&t.getTime()!==e.getTime())return!1;if(t!==Object(t)||e!==Object(e))return!1;const n=Object.keys(t);return n.length===Object.keys(e).length&&n.every((n=>l(t[n],e[n])))}function c(t,e,n){return null!=t&&e&&"string"===typeof e?void 0!==t[e]?t[e]:(e=e.replace(/\[(\w+)\]/g,".$1"),e=e.replace(/^\./,""),o(t,e.split("."),n)):n}function u(t,e,n){if(null==e)return void 0===t?n:t;if(t!==Object(t))return void 0===n?t:n;if("string"===typeof e)return c(t,e,n);if(Array.isArray(e))return o(t,e,n);if("function"!==typeof e)return n;const i=e(t,n);return"undefined"===typeof i?n:i}function h(t){return Array.from({length:t},((t,e)=>e))}function d(t){if(!t||t.nodeType!==Node.ELEMENT_NODE)return 0;const e=+window.getComputedStyle(t).getPropertyValue("z-index");return e||d(t.parentNode)}function f(t,e){const n={};for(let i=0;i<e.length;i++){const s=e[i];"undefined"!==typeof t[s]&&(n[s]=t[s])}return n}function p(t,e="px"){return null==t||""===t?void 0:isNaN(+t)?String(t):`${Number(t)}${e}`}function _(t){return(t||"").replace(/([a-z])([A-Z])/g,"$1-$2").toLowerCase()}function m(t){return null!==t&&"object"===typeof t}const v=Object.freeze({enter:13,tab:9,delete:46,esc:27,space:32,up:38,down:40,left:37,right:39,end:35,home:36,del:46,backspace:8,insert:45,pageup:33,pagedown:34,shift:16});function g(t,e){const n=t.$vuetify.icons.component;if(e.startsWith("$")){const n=`$vuetify.icons.values.${e.split("$").pop().split(".").pop()}`,i=c(t,n,e);if("string"!==typeof i)return i;e=i}return null==n?e:{component:n,props:{icon:e}}}function b(t){return Object.keys(t)}const y=/-(\w)/g,x=t=>t.replace(y,((t,e)=>e?e.toUpperCase():""));function w(t){return t.charAt(0).toUpperCase()+t.slice(1)}function S(t){return null!=t?Array.isArray(t)?t:[t]:[]}function k(t,e,n){return t.$slots.hasOwnProperty(e)&&t.$scopedSlots.hasOwnProperty(e)&&t.$scopedSlots[e].name?n?"v-slot":"scoped":t.$slots.hasOwnProperty(e)?"normal":t.$scopedSlots.hasOwnProperty(e)?"scoped":void 0}function A(t,e="default",n,i=!1){return t.$scopedSlots.hasOwnProperty(e)?t.$scopedSlots[e](n instanceof Function?n():n):!t.$slots.hasOwnProperty(e)||n&&!i?void 0:t.$slots[e]}function C(t,e=0,n=1){return Math.max(e,Math.min(n,t))}function M(t={},e={}){for(const n in e){const i=t[n],s=e[n];m(i)&&m(s)?t[n]=M(i,s):t[n]=s}return t}},2653:function(t,e,n){"use strict";n.d(e,{ZP:function(){return a}});var i=n(1050);const s={styleList:/;(?![^(]*\))/g,styleProp:/:(.*)/};function r(t){const e={};for(const n of t.split(s.styleList)){let[t,r]=n.split(s.styleProp);t=t.trim(),t&&("string"===typeof r&&(r=r.trim()),e[(0,i._A)(t)]=r)}return e}function a(){const t={};let e,n=arguments.length;while(n--)for(e of Object.keys(arguments[n]))switch(e){case"class":case"directives":arguments[n][e]&&(t[e]=l(t[e],arguments[n][e]));break;case"style":arguments[n][e]&&(t[e]=o(t[e],arguments[n][e]));break;case"staticClass":if(!arguments[n][e])break;void 0===t[e]&&(t[e]=""),t[e]&&(t[e]+=" "),t[e]+=arguments[n][e].trim();break;case"on":case"nativeOn":arguments[n][e]&&(t[e]=c(t[e],arguments[n][e]));break;case"attrs":case"props":case"domProps":case"scopedSlots":case"staticStyle":case"hook":case"transition":if(!arguments[n][e])break;t[e]||(t[e]={}),t[e]={...arguments[n][e],...t[e]};break;default:t[e]||(t[e]=arguments[n][e])}return t}function o(t,e){return t?e?(t=(0,i.TI)("string"===typeof t?r(t):t),t.concat("string"===typeof e?r(e):e)):t:e}function l(t,e){return e?t&&t?(0,i.TI)(t).concat(e):e:t}function c(...t){if(!t[0])return t[1];if(!t[1])return t[0];const e={};for(let n=2;n--;){const i=t[n];for(const t in i)i[t]&&(e[t]?e[t]=[].concat(i[t],e[t]):e[t]=i[t])}return e}},4987:function(t,e,n){"use strict";n.d(e,{Z:function(){return s}});var i=n(144);function s(...t){return i.ZP.extend({mixins:t})}},9662:function(t,e,n){var i=n(614),s=n(6330),r=TypeError;t.exports=function(t){if(i(t))return t;throw r(s(t)+" is not a function")}},9670:function(t,e,n){var i=n(111),s=String,r=TypeError;t.exports=function(t){if(i(t))return t;throw r(s(t)+" is not an object")}},1318:function(t,e,n){var i=n(5656),s=n(1400),r=n(6244),a=function(t){return function(e,n,a){var o,l=i(e),c=r(l),u=s(a,c);if(t&&n!=n){while(c>u)if(o=l[u++],o!=o)return!0}else for(;c>u;u++)if((t||u in l)&&l[u]===n)return t||u||0;return!t&&-1}};t.exports={includes:a(!0),indexOf:a(!1)}},3658:function(t,e,n){"use strict";var i=n(9781),s=n(3157),r=TypeError,a=Object.getOwnPropertyDescriptor,o=i&&!function(){if(void 0!==this)return!0;try{Object.defineProperty([],"length",{writable:!1}).length=1}catch(t){return t instanceof TypeError}}();t.exports=o?function(t,e){if(s(t)&&!a(t,"length").writable)throw r("Cannot set read only .length");return t.length=e}:function(t,e){return t.length=e}},4326:function(t,e,n){var i=n(1702),s=i({}.toString),r=i("".slice);t.exports=function(t){return r(s(t),8,-1)}},9920:function(t,e,n){var i=n(2597),s=n(3887),r=n(1236),a=n(3070);t.exports=function(t,e,n){for(var o=s(e),l=a.f,c=r.f,u=0;u<o.length;u++){var h=o[u];i(t,h)||n&&i(n,h)||l(t,h,c(e,h))}}},8880:function(t,e,n){var i=n(9781),s=n(3070),r=n(9114);t.exports=i?function(t,e,n){return s.f(t,e,r(1,n))}:function(t,e,n){return t[e]=n,t}},9114:function(t){t.exports=function(t,e){return{enumerable:!(1&t),configurable:!(2&t),writable:!(4&t),value:e}}},8052:function(t,e,n){var i=n(614),s=n(3070),r=n(6339),a=n(3072);t.exports=function(t,e,n,o){o||(o={});var l=o.enumerable,c=void 0!==o.name?o.name:e;if(i(n)&&r(n,c,o),o.global)l?t[e]=n:a(e,n);else{try{o.unsafe?t[e]&&(l=!0):delete t[e]}catch(u){}l?t[e]=n:s.f(t,e,{value:n,enumerable:!1,configurable:!o.nonConfigurable,writable:!o.nonWritable})}return t}},3072:function(t,e,n){var i=n(7854),s=Object.defineProperty;t.exports=function(t,e){try{s(i,t,{value:e,configurable:!0,writable:!0})}catch(n){i[t]=e}return e}},5117:function(t,e,n){"use strict";var i=n(6330),s=TypeError;t.exports=function(t,e){if(!delete t[e])throw s("Cannot delete property "+i(e)+" of "+i(t))}},9781:function(t,e,n){var i=n(7293);t.exports=!i((function(){return 7!=Object.defineProperty({},1,{get:function(){return 7}})[1]}))},4154:function(t){var e="object"==typeof document&&document.all,n="undefined"==typeof e&&void 0!==e;t.exports={all:e,IS_HTMLDDA:n}},317:function(t,e,n){var i=n(7854),s=n(111),r=i.document,a=s(r)&&s(r.createElement);t.exports=function(t){return a?r.createElement(t):{}}},7207:function(t){var e=TypeError,n=9007199254740991;t.exports=function(t){if(t>n)throw e("Maximum allowed index exceeded");return t}},8113:function(t){t.exports="undefined"!=typeof navigator&&String(navigator.userAgent)||""},7392:function(t,e,n){var i,s,r=n(7854),a=n(8113),o=r.process,l=r.Deno,c=o&&o.versions||l&&l.version,u=c&&c.v8;u&&(i=u.split("."),s=i[0]>0&&i[0]<4?1:+(i[0]+i[1])),!s&&a&&(i=a.match(/Edge\/(\d+)/),(!i||i[1]>=74)&&(i=a.match(/Chrome\/(\d+)/),i&&(s=+i[1]))),t.exports=s},748:function(t){t.exports=["constructor","hasOwnProperty","isPrototypeOf","propertyIsEnumerable","toLocaleString","toString","valueOf"]},2109:function(t,e,n){var i=n(7854),s=n(1236).f,r=n(8880),a=n(8052),o=n(3072),l=n(9920),c=n(4705);t.exports=function(t,e){var n,u,h,d,f,p,_=t.target,m=t.global,v=t.stat;if(u=m?i:v?i[_]||o(_,{}):(i[_]||{}).prototype,u)for(h in e){if(f=e[h],t.dontCallGetSet?(p=s(u,h),d=p&&p.value):d=u[h],n=c(m?h:_+(v?".":"#")+h,t.forced),!n&&void 0!==d){if(typeof f==typeof d)continue;l(f,d)}(t.sham||d&&d.sham)&&r(f,"sham",!0),a(u,h,f,t)}}},7293:function(t){t.exports=function(t){try{return!!t()}catch(e){return!0}}},4374:function(t,e,n){var i=n(7293);t.exports=!i((function(){var t=function(){}.bind();return"function"!=typeof t||t.hasOwnProperty("prototype")}))},6916:function(t,e,n){var i=n(4374),s=Function.prototype.call;t.exports=i?s.bind(s):function(){return s.apply(s,arguments)}},6530:function(t,e,n){var i=n(9781),s=n(2597),r=Function.prototype,a=i&&Object.getOwnPropertyDescriptor,o=s(r,"name"),l=o&&"something"===function(){}.name,c=o&&(!i||i&&a(r,"name").configurable);t.exports={EXISTS:o,PROPER:l,CONFIGURABLE:c}},1702:function(t,e,n){var i=n(4374),s=Function.prototype,r=s.call,a=i&&s.bind.bind(r,r);t.exports=i?a:function(t){return function(){return r.apply(t,arguments)}}},5005:function(t,e,n){var i=n(7854),s=n(614),r=function(t){return s(t)?t:void 0};t.exports=function(t,e){return arguments.length<2?r(i[t]):i[t]&&i[t][e]}},8173:function(t,e,n){var i=n(9662),s=n(8554);t.exports=function(t,e){var n=t[e];return s(n)?void 0:i(n)}},7854:function(t,e,n){var i=function(t){return t&&t.Math==Math&&t};t.exports=i("object"==typeof globalThis&&globalThis)||i("object"==typeof window&&window)||i("object"==typeof self&&self)||i("object"==typeof n.g&&n.g)||function(){return this}()||Function("return this")()},2597:function(t,e,n){var i=n(1702),s=n(7908),r=i({}.hasOwnProperty);t.exports=Object.hasOwn||function(t,e){return r(s(t),e)}},3501:function(t){t.exports={}},4664:function(t,e,n){var i=n(9781),s=n(7293),r=n(317);t.exports=!i&&!s((function(){return 7!=Object.defineProperty(r("div"),"a",{get:function(){return 7}}).a}))},8361:function(t,e,n){var i=n(1702),s=n(7293),r=n(4326),a=Object,o=i("".split);t.exports=s((function(){return!a("z").propertyIsEnumerable(0)}))?function(t){return"String"==r(t)?o(t,""):a(t)}:a},2788:function(t,e,n){var i=n(1702),s=n(614),r=n(5465),a=i(Function.toString);s(r.inspectSource)||(r.inspectSource=function(t){return a(t)}),t.exports=r.inspectSource},9909:function(t,e,n){var i,s,r,a=n(4811),o=n(7854),l=n(111),c=n(8880),u=n(2597),h=n(5465),d=n(6200),f=n(3501),p="Object already initialized",_=o.TypeError,m=o.WeakMap,v=function(t){return r(t)?s(t):i(t,{})},g=function(t){return function(e){var n;if(!l(e)||(n=s(e)).type!==t)throw _("Incompatible receiver, "+t+" required");return n}};if(a||h.state){var b=h.state||(h.state=new m);b.get=b.get,b.has=b.has,b.set=b.set,i=function(t,e){if(b.has(t))throw _(p);return e.facade=t,b.set(t,e),e},s=function(t){return b.get(t)||{}},r=function(t){return b.has(t)}}else{var y=d("state");f[y]=!0,i=function(t,e){if(u(t,y))throw _(p);return e.facade=t,c(t,y,e),e},s=function(t){return u(t,y)?t[y]:{}},r=function(t){return u(t,y)}}t.exports={set:i,get:s,has:r,enforce:v,getterFor:g}},3157:function(t,e,n){var i=n(4326);t.exports=Array.isArray||function(t){return"Array"==i(t)}},614:function(t,e,n){var i=n(4154),s=i.all;t.exports=i.IS_HTMLDDA?function(t){return"function"==typeof t||t===s}:function(t){return"function"==typeof t}},4705:function(t,e,n){var i=n(7293),s=n(614),r=/#|\.prototype\./,a=function(t,e){var n=l[o(t)];return n==u||n!=c&&(s(e)?i(e):!!e)},o=a.normalize=function(t){return String(t).replace(r,".").toLowerCase()},l=a.data={},c=a.NATIVE="N",u=a.POLYFILL="P";t.exports=a},8554:function(t){t.exports=function(t){return null===t||void 0===t}},111:function(t,e,n){var i=n(614),s=n(4154),r=s.all;t.exports=s.IS_HTMLDDA?function(t){return"object"==typeof t?null!==t:i(t)||t===r}:function(t){return"object"==typeof t?null!==t:i(t)}},1913:function(t){t.exports=!1},2190:function(t,e,n){var i=n(5005),s=n(614),r=n(7976),a=n(3307),o=Object;t.exports=a?function(t){return"symbol"==typeof t}:function(t){var e=i("Symbol");return s(e)&&r(e.prototype,o(t))}},6244:function(t,e,n){var i=n(7466);t.exports=function(t){return i(t.length)}},6339:function(t,e,n){var i=n(1702),s=n(7293),r=n(614),a=n(2597),o=n(9781),l=n(6530).CONFIGURABLE,c=n(2788),u=n(9909),h=u.enforce,d=u.get,f=String,p=Object.defineProperty,_=i("".slice),m=i("".replace),v=i([].join),g=o&&!s((function(){return 8!==p((function(){}),"length",{value:8}).length})),b=String(String).split("String"),y=t.exports=function(t,e,n){"Symbol("===_(f(e),0,7)&&(e="["+m(f(e),/^Symbol\(([^)]*)\)/,"$1")+"]"),n&&n.getter&&(e="get "+e),n&&n.setter&&(e="set "+e),(!a(t,"name")||l&&t.name!==e)&&(o?p(t,"name",{value:e,configurable:!0}):t.name=e),g&&n&&a(n,"arity")&&t.length!==n.arity&&p(t,"length",{value:n.arity});try{n&&a(n,"constructor")&&n.constructor?o&&p(t,"prototype",{writable:!1}):t.prototype&&(t.prototype=void 0)}catch(s){}var i=h(t);return a(i,"source")||(i.source=v(b,"string"==typeof e?e:"")),t};Function.prototype.toString=y((function(){return r(this)&&d(this).source||c(this)}),"toString")},4758:function(t){var e=Math.ceil,n=Math.floor;t.exports=Math.trunc||function(t){var i=+t;return(i>0?n:e)(i)}},3070:function(t,e,n){var i=n(9781),s=n(4664),r=n(3353),a=n(9670),o=n(4948),l=TypeError,c=Object.defineProperty,u=Object.getOwnPropertyDescriptor,h="enumerable",d="configurable",f="writable";e.f=i?r?function(t,e,n){if(a(t),e=o(e),a(n),"function"===typeof t&&"prototype"===e&&"value"in n&&f in n&&!n[f]){var i=u(t,e);i&&i[f]&&(t[e]=n.value,n={configurable:d in n?n[d]:i[d],enumerable:h in n?n[h]:i[h],writable:!1})}return c(t,e,n)}:c:function(t,e,n){if(a(t),e=o(e),a(n),s)try{return c(t,e,n)}catch(i){}if("get"in n||"set"in n)throw l("Accessors not supported");return"value"in n&&(t[e]=n.value),t}},1236:function(t,e,n){var i=n(9781),s=n(6916),r=n(5296),a=n(9114),o=n(5656),l=n(4948),c=n(2597),u=n(4664),h=Object.getOwnPropertyDescriptor;e.f=i?h:function(t,e){if(t=o(t),e=l(e),u)try{return h(t,e)}catch(n){}if(c(t,e))return a(!s(r.f,t,e),t[e])}},8006:function(t,e,n){var i=n(6324),s=n(748),r=s.concat("length","prototype");e.f=Object.getOwnPropertyNames||function(t){return i(t,r)}},5181:function(t,e){e.f=Object.getOwnPropertySymbols},7976:function(t,e,n){var i=n(1702);t.exports=i({}.isPrototypeOf)},6324:function(t,e,n){var i=n(1702),s=n(2597),r=n(5656),a=n(1318).indexOf,o=n(3501),l=i([].push);t.exports=function(t,e){var n,i=r(t),c=0,u=[];for(n in i)!s(o,n)&&s(i,n)&&l(u,n);while(e.length>c)s(i,n=e[c++])&&(~a(u,n)||l(u,n));return u}},5296:function(t,e){"use strict";var n={}.propertyIsEnumerable,i=Object.getOwnPropertyDescriptor,s=i&&!n.call({1:2},1);e.f=s?function(t){var e=i(this,t);return!!e&&e.enumerable}:n},2140:function(t,e,n){var i=n(6916),s=n(614),r=n(111),a=TypeError;t.exports=function(t,e){var n,o;if("string"===e&&s(n=t.toString)&&!r(o=i(n,t)))return o;if(s(n=t.valueOf)&&!r(o=i(n,t)))return o;if("string"!==e&&s(n=t.toString)&&!r(o=i(n,t)))return o;throw a("Can't convert object to primitive value")}},3887:function(t,e,n){var i=n(5005),s=n(1702),r=n(8006),a=n(5181),o=n(9670),l=s([].concat);t.exports=i("Reflect","ownKeys")||function(t){var e=r.f(o(t)),n=a.f;return n?l(e,n(t)):e}},4488:function(t,e,n){var i=n(8554),s=TypeError;t.exports=function(t){if(i(t))throw s("Can't call method on "+t);return t}},6200:function(t,e,n){var i=n(2309),s=n(9711),r=i("keys");t.exports=function(t){return r[t]||(r[t]=s(t))}},5465:function(t,e,n){var i=n(7854),s=n(3072),r="__core-js_shared__",a=i[r]||s(r,{});t.exports=a},2309:function(t,e,n){var i=n(1913),s=n(5465);(t.exports=function(t,e){return s[t]||(s[t]=void 0!==e?e:{})})("versions",[]).push({version:"3.30.1",mode:i?"pure":"global",copyright:"© 2014-2023 Denis Pushkarev (zloirock.ru)",license:"https://github.com/zloirock/core-js/blob/v3.30.1/LICENSE",source:"https://github.com/zloirock/core-js"})},6293:function(t,e,n){var i=n(7392),s=n(7293);t.exports=!!Object.getOwnPropertySymbols&&!s((function(){var t=Symbol();return!String(t)||!(Object(t)instanceof Symbol)||!Symbol.sham&&i&&i<41}))},1400:function(t,e,n){var i=n(9303),s=Math.max,r=Math.min;t.exports=function(t,e){var n=i(t);return n<0?s(n+e,0):r(n,e)}},5656:function(t,e,n){var i=n(8361),s=n(4488);t.exports=function(t){return i(s(t))}},9303:function(t,e,n){var i=n(4758);t.exports=function(t){var e=+t;return e!==e||0===e?0:i(e)}},7466:function(t,e,n){var i=n(9303),s=Math.min;t.exports=function(t){return t>0?s(i(t),9007199254740991):0}},7908:function(t,e,n){var i=n(4488),s=Object;t.exports=function(t){return s(i(t))}},7593:function(t,e,n){var i=n(6916),s=n(111),r=n(2190),a=n(8173),o=n(2140),l=n(5112),c=TypeError,u=l("toPrimitive");t.exports=function(t,e){if(!s(t)||r(t))return t;var n,l=a(t,u);if(l){if(void 0===e&&(e="default"),n=i(l,t,e),!s(n)||r(n))return n;throw c("Can't convert object to primitive value")}return void 0===e&&(e="number"),o(t,e)}},4948:function(t,e,n){var i=n(7593),s=n(2190);t.exports=function(t){var e=i(t,"string");return s(e)?e:e+""}},6330:function(t){var e=String;t.exports=function(t){try{return e(t)}catch(n){return"Object"}}},9711:function(t,e,n){var i=n(1702),s=0,r=Math.random(),a=i(1..toString);t.exports=function(t){return"Symbol("+(void 0===t?"":t)+")_"+a(++s+r,36)}},3307:function(t,e,n){var i=n(6293);t.exports=i&&!Symbol.sham&&"symbol"==typeof Symbol.iterator},3353:function(t,e,n){var i=n(9781),s=n(7293);t.exports=i&&s((function(){return 42!=Object.defineProperty((function(){}),"prototype",{value:42,writable:!1}).prototype}))},4811:function(t,e,n){var i=n(7854),s=n(614),r=i.WeakMap;t.exports=s(r)&&/native code/.test(String(r))},5112:function(t,e,n){var i=n(7854),s=n(2309),r=n(2597),a=n(9711),o=n(6293),l=n(3307),c=i.Symbol,u=s("wks"),h=l?c["for"]||c:c&&c.withoutSetter||a;t.exports=function(t){return r(u,t)||(u[t]=o&&r(c,t)?c[t]:h("Symbol."+t)),u[t]}},7658:function(t,e,n){"use strict";var i=n(2109),s=n(7908),r=n(6244),a=n(3658),o=n(7207),l=n(7293),c=l((function(){return 4294967297!==[].push.call({length:4294967296},1)})),u=function(){try{Object.defineProperty([],"length",{writable:!1}).push()}catch(t){return t instanceof TypeError}},h=c||!u();i({target:"Array",proto:!0,arity:1,forced:h},{push:function(t){var e=s(this),n=r(e),i=arguments.length;o(n+i);for(var l=0;l<i;l++)e[n]=arguments[l],n++;return a(e,n),n}})},541:function(t,e,n){"use strict";var i=n(2109),s=n(7908),r=n(6244),a=n(3658),o=n(5117),l=n(7207),c=1!==[].unshift(0),u=function(){try{Object.defineProperty([],"length",{writable:!1}).unshift()}catch(t){return t instanceof TypeError}},h=c||!u();i({target:"Array",proto:!0,arity:1,forced:h},{unshift:function(t){var e=s(this),n=r(e),i=arguments.length;if(i){l(n+i);var c=n;while(c--){var u=c+i;c in e?e[u]=e[c]:o(e,u)}for(var h=0;h<i;h++)e[h]=arguments[h]}return a(e,n+i)}})},1884:function(){},9027:function(){},2838:function(){},579:function(t){!function(e,n){t.exports=n()}(window,(function(){return function(t){var e={};function n(i){if(e[i])return e[i].exports;var s=e[i]={i:i,l:!1,exports:{}};return t[i].call(s.exports,s,s.exports,n),s.l=!0,s.exports}return n.m=t,n.c=e,n.d=function(t,e,i){n.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:i})},n.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},n.t=function(t,e){if(1&e&&(t=n(t)),8&e)return t;if(4&e&&"object"==typeof t&&t&&t.__esModule)return t;var i=Object.create(null);if(n.r(i),Object.defineProperty(i,"default",{enumerable:!0,value:t}),2&e&&"string"!=typeof t)for(var s in t)n.d(i,s,function(e){return t[e]}.bind(null,s));return i},n.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return n.d(e,"a",e),e},n.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},n.p="",n(n.s=49)}([function(t,e){function n(t){return new Int16Array(t)}function i(t){return new Int32Array(t)}function s(t){return new Float32Array(t)}var r={fill:function(t,e,n,i){if(2==arguments.length)for(var s=0;s<t.length;s++)t[s]=e;else for(s=e;s<n;s++)t[s]=i}},a={arraycopy:function(t,e,n,i,s){for(var r=e+s;e<r;)n[i++]=t[e++]}},o={};function l(t){this.ordinal=t}o.SQRT2=1.4142135623730951,o.FAST_LOG10=function(t){return Math.log10(t)},o.FAST_LOG10_X=function(t,e){return Math.log10(t)*e},l.short_block_allowed=new l(0),l.short_block_coupled=new l(1),l.short_block_dispensed=new l(2),l.short_block_forced=new l(3);var c={};function u(t){this.ordinal=t}c.MAX_VALUE=34028235e31,u.vbr_off=new u(0),u.vbr_mt=new u(1),u.vbr_rh=new u(2),u.vbr_abr=new u(3),u.vbr_mtrh=new u(4),u.vbr_default=u.vbr_mtrh,t.exports={System:a,VbrMode:u,Float:c,ShortBlock:l,Util:o,Arrays:r,new_array_n:function t(e){if(1==e.length)return new Array(e[0]);var n=e[0];e=e.slice(1);for(var i=[],s=0;s<n;s++)i.push(t(e));return i},new_byte:function(t){return new Int8Array(t)},new_double:function(t){return new Float64Array(t)},new_float:s,new_float_n:function t(e){if(1==e.length)return s(e[0]);var n=e[0];e=e.slice(1);for(var i=[],r=0;r<n;r++)i.push(t(e));return i},new_int:i,new_int_n:function t(e){if(1==e.length)return i(e[0]);var n=e[0];e=e.slice(1);for(var s=[],r=0;r<n;r++)s.push(t(e));return s},new_short:n,new_short_n:function t(e){if(1==e.length)return n(e[0]);var i=e[0];e=e.slice(1);for(var s=[],r=0;r<i;r++)s.push(t(e));return s},assert:function(t){}}},function(t,e,n){var i=n(0),s=i.System,r=i.VbrMode,a=(i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n),o=(i.new_byte,i.new_double,i.new_float),l=i.new_float_n,c=i.new_int,u=(i.new_int_n,i.assert);function h(){var t=n(30),e=n(31),i=h.FFTOFFSET,d=h.MPG_MD_MS_LR,f=null,p=this.psy=null,_=null,m=null;this.setModules=function(t,e,n,i){f=t,this.psy=e,p=e,_=i,m=n};var v=new t;this.lame_encode_mp3_frame=function(t,n,g,b,y,x){var w,S=a([2,2]);S[0][0]=new e,S[0][1]=new e,S[1][0]=new e,S[1][1]=new e;var k,A=a([2,2]);A[0][0]=new e,A[0][1]=new e,A[1][0]=new e,A[1][1]=new e;var C,M,E,T=[null,null],B=t.internal_flags,R=l([2,4]),$=[.5,.5],I=[[0,0],[0,0]],L=[[0,0],[0,0]];if(T[0]=n,T[1]=g,0==B.lame_encode_frame_init&&function(t,e){var n,i,s=t.internal_flags;if(0==s.lame_encode_frame_init){var r,a,l=o(2014),c=o(2014);for(s.lame_encode_frame_init=1,a=r=0;r<286+576*(1+s.mode_gr);++r)r<576*s.mode_gr?(l[r]=0,2==s.channels_out&&(c[r]=0)):(l[r]=e[0][a],2==s.channels_out&&(c[r]=e[1][a]),++a);for(i=0;i<s.mode_gr;i++)for(n=0;n<s.channels_out;n++)s.l3_side.tt[i][n].block_type=h.SHORT_TYPE;v.mdct_sub48(s,l,c),u(h.FFTOFFSET<=576),u(s.mf_size>=h.BLKSIZE+t.framesize-h.FFTOFFSET),u(s.mf_size>=512+t.framesize-32)}}(t,T),B.padding=0,(B.slot_lag-=B.frac_SpF)<0&&(B.slot_lag+=t.out_samplerate,B.padding=1),0!=B.psymodel){var O=[null,null],P=0,V=c(2);for(E=0;E<B.mode_gr;E++){for(M=0;M<B.channels_out;M++)O[M]=T[M],P=576+576*E-h.FFTOFFSET;if(0!=(t.VBR==r.vbr_mtrh||t.VBR==r.vbr_mt?p.L3psycho_anal_vbr(t,O,P,E,S,A,I[E],L[E],R[E],V):p.L3psycho_anal_ns(t,O,P,E,S,A,I[E],L[E],R[E],V)))return-4;for(t.mode==MPEGMode.JOINT_STEREO&&($[E]=R[E][2]+R[E][3],0<$[E]&&($[E]=R[E][3]/$[E])),M=0;M<B.channels_out;M++){var D=B.l3_side.tt[E][M];D.block_type=V[M],D.mixed_block_flag=0}}}else for(E=0;E<B.mode_gr;E++)for(M=0;M<B.channels_out;M++)B.l3_side.tt[E][M].block_type=h.NORM_TYPE,B.l3_side.tt[E][M].mixed_block_flag=0,L[E][M]=I[E][M]=700;if(function(t){var e,n;if(0!=t.ATH.useAdjust)if(n=t.loudness_sq[0][0],e=t.loudness_sq[1][0],2==t.channels_out?(n+=t.loudness_sq[0][1],e+=t.loudness_sq[1][1]):(n+=n,e+=e),2==t.mode_gr&&(n=Math.max(n,e)),n*=.5,.03125<(n*=t.ATH.aaSensitivityP))1<=t.ATH.adjust?t.ATH.adjust=1:t.ATH.adjust<t.ATH.adjustLimit&&(t.ATH.adjust=t.ATH.adjustLimit),t.ATH.adjustLimit=1;else{var i=31.98*n+625e-6;t.ATH.adjust>=i?(t.ATH.adjust*=.075*i+.925,t.ATH.adjust<i&&(t.ATH.adjust=i)):t.ATH.adjustLimit>=i?t.ATH.adjust=i:t.ATH.adjust<t.ATH.adjustLimit&&(t.ATH.adjust=t.ATH.adjustLimit),t.ATH.adjustLimit=i}else t.ATH.adjust=1}(B),v.mdct_sub48(B,T[0],T[1]),B.mode_ext=h.MPG_MD_LR_LR,t.force_ms)B.mode_ext=h.MPG_MD_MS_LR;else if(t.mode==MPEGMode.JOINT_STEREO){var N=0,F=0;for(E=0;E<B.mode_gr;E++)for(M=0;M<B.channels_out;M++)N+=L[E][M],F+=I[E][M];if(N<=1*F){var H=B.l3_side.tt[0],j=B.l3_side.tt[B.mode_gr-1];H[0].block_type==H[1].block_type&&j[0].block_type==j[1].block_type&&(B.mode_ext=h.MPG_MD_MS_LR)}}if(C=B.mode_ext==d?(k=A,L):(k=S,I),t.analysis&&null!=B.pinfo)for(E=0;E<B.mode_gr;E++)for(M=0;M<B.channels_out;M++)B.pinfo.ms_ratio[E]=B.ms_ratio[E],B.pinfo.ms_ener_ratio[E]=$[E],B.pinfo.blocktype[E][M]=B.l3_side.tt[E][M].block_type,B.pinfo.pe[E][M]=C[E][M],s.arraycopy(B.l3_side.tt[E][M].xr,0,B.pinfo.xr[E][M],0,576),B.mode_ext==d&&(B.pinfo.ers[E][M]=B.pinfo.ers[E][M+2],s.arraycopy(B.pinfo.energy[E][M+2],0,B.pinfo.energy[E][M],0,B.pinfo.energy[E][M].length));if(t.VBR==r.vbr_off||t.VBR==r.vbr_abr){var Z,z;for(Z=0;Z<18;Z++)B.nsPsy.pefirbuf[Z]=B.nsPsy.pefirbuf[Z+1];for(E=z=0;E<B.mode_gr;E++)for(M=0;M<B.channels_out;M++)z+=C[E][M];for(B.nsPsy.pefirbuf[18]=z,z=B.nsPsy.pefirbuf[9],Z=0;Z<9;Z++)z+=(B.nsPsy.pefirbuf[Z]+B.nsPsy.pefirbuf[18-Z])*h.fircoef[Z];for(z=3350*B.mode_gr*B.channels_out/z,E=0;E<B.mode_gr;E++)for(M=0;M<B.channels_out;M++)C[E][M]*=z}if(B.iteration_loop.iteration_loop(t,C,$,k),f.format_bitstream(t),w=f.copy_buffer(B,b,y,x,1),t.bWriteVbrTag&&_.addVbrFrame(t),t.analysis&&null!=B.pinfo){for(M=0;M<B.channels_out;M++){var q;for(q=0;q<i;q++)B.pinfo.pcmdata[M][q]=B.pinfo.pcmdata[M][q+t.framesize];for(q=i;q<1600;q++)B.pinfo.pcmdata[M][q]=T[M][q-i]}m.set_frame_pinfo(t,k)}return function(t){var e,n;for(u(0<=t.bitrate_index&&t.bitrate_index<16),u(0<=t.mode_ext&&t.mode_ext<4),t.bitrate_stereoMode_Hist[t.bitrate_index][4]++,t.bitrate_stereoMode_Hist[15][4]++,2==t.channels_out&&(t.bitrate_stereoMode_Hist[t.bitrate_index][t.mode_ext]++,t.bitrate_stereoMode_Hist[15][t.mode_ext]++),e=0;e<t.mode_gr;++e)for(n=0;n<t.channels_out;++n){var i=0|t.l3_side.tt[e][n].block_type;0!=t.l3_side.tt[e][n].mixed_block_flag&&(i=4),t.bitrate_blockType_Hist[t.bitrate_index][i]++,t.bitrate_blockType_Hist[t.bitrate_index][5]++,t.bitrate_blockType_Hist[15][i]++,t.bitrate_blockType_Hist[15][5]++}}(B),w}}h.ENCDELAY=576,h.POSTDELAY=1152,h.FFTOFFSET=224+(h.MDCTDELAY=48),h.DECDELAY=528,h.SBLIMIT=32,h.CBANDS=64,h.SBPSY_l=21,h.SBPSY_s=12,h.SBMAX_l=22,h.SBMAX_s=13,h.PSFB21=6,h.PSFB12=6,h.HBLKSIZE=(h.BLKSIZE=1024)/2+1,h.HBLKSIZE_s=(h.BLKSIZE_s=256)/2+1,h.NORM_TYPE=0,h.START_TYPE=1,h.SHORT_TYPE=2,h.STOP_TYPE=3,h.MPG_MD_LR_LR=0,h.MPG_MD_LR_I=1,h.MPG_MD_MS_LR=2,h.MPG_MD_MS_I=3,h.fircoef=[-.1039435,-.1892065,5*-.0432472,-.155915,3898045e-23,.0467745*5,.50455,.756825,.187098*5],t.exports=h},function(t,e){t.exports=function(t){var e=[];return e.toString=function(){return this.map((function(e){var n=function(t,e){var n,i=t[1]||"",s=t[3];if(!s)return i;if(e&&"function"==typeof btoa){var r=(n=s,"/*# sourceMappingURL=data:application/json;charset=utf-8;base64,"+btoa(unescape(encodeURIComponent(JSON.stringify(n))))+" */"),a=s.sources.map((function(t){return"/*# sourceURL="+s.sourceRoot+t+" */"}));return[i].concat(a).concat([r]).join("\n")}return[i].join("\n")}(e,t);return e[2]?"@media "+e[2]+"{"+n+"}":n})).join("")},e.i=function(t,n){"string"==typeof t&&(t=[[null,t,""]]);for(var i={},s=0;s<this.length;s++){var r=this[s][0];"number"==typeof r&&(i[r]=!0)}for(s=0;s<t.length;s++){var a=t[s];"number"==typeof a[0]&&i[a[0]]||(n&&!a[2]?a[2]=n:n&&(a[2]="("+a[2]+") and ("+n+")"),e.push(a))}},e}},function(t,e,n){"use strict";function i(t,e){for(var n=[],i={},s=0;s<e.length;s++){var r=e[s],a=r[0],o={id:t+":"+s,css:r[1],media:r[2],sourceMap:r[3]};i[a]?i[a].parts.push(o):n.push(i[a]={id:a,parts:[o]})}return n}n.r(e),n.d(e,"default",(function(){return p}));var s="undefined"!=typeof document;if("undefined"!=typeof DEBUG&&DEBUG&&!s)throw new Error("vue-style-loader cannot be used in a non-browser environment. Use { target: 'node' } in your Webpack config to indicate a server-rendering environment.");var r={},a=s&&(document.head||document.getElementsByTagName("head")[0]),o=null,l=0,c=!1,u=function(){},h=null,d="data-vue-ssr-id",f="undefined"!=typeof navigator&&/msie [6-9]\b/.test(navigator.userAgent.toLowerCase());function p(t,e,n,s){c=n,h=s||{};var a=i(t,e);return _(a),function(e){for(var n=[],s=0;s<a.length;s++){var o=a[s];(l=r[o.id]).refs--,n.push(l)}for(e?_(a=i(t,e)):a=[],s=0;s<n.length;s++){var l;if(0===(l=n[s]).refs){for(var c=0;c<l.parts.length;c++)l.parts[c]();delete r[l.id]}}}}function _(t){for(var e=0;e<t.length;e++){var n=t[e],i=r[n.id];if(i){i.refs++;for(var s=0;s<i.parts.length;s++)i.parts[s](n.parts[s]);for(;s<n.parts.length;s++)i.parts.push(v(n.parts[s]));i.parts.length>n.parts.length&&(i.parts.length=n.parts.length)}else{var a=[];for(s=0;s<n.parts.length;s++)a.push(v(n.parts[s]));r[n.id]={id:n.id,refs:1,parts:a}}}}function m(){var t=document.createElement("style");return t.type="text/css",a.appendChild(t),t}function v(t){var e,n,i=document.querySelector("style["+d+'~="'+t.id+'"]');if(i){if(c)return u;i.parentNode.removeChild(i)}if(f){var s=l++;i=o||(o=m()),e=y.bind(null,i,s,!1),n=y.bind(null,i,s,!0)}else i=m(),e=function(t,e){var n=e.css,i=e.media,s=e.sourceMap;if(i&&t.setAttribute("media",i),h.ssrId&&t.setAttribute(d,e.id),s&&(n+="\n/*# sourceURL="+s.sources[0]+" */",n+="\n/*# sourceMappingURL=data:application/json;base64,"+btoa(unescape(encodeURIComponent(JSON.stringify(s))))+" */"),t.styleSheet)t.styleSheet.cssText=n;else{for(;t.firstChild;)t.removeChild(t.firstChild);t.appendChild(document.createTextNode(n))}}.bind(null,i),n=function(){i.parentNode.removeChild(i)};return e(t),function(i){if(i){if(i.css===t.css&&i.media===t.media&&i.sourceMap===t.sourceMap)return;e(t=i)}else n()}}var g,b=(g=[],function(t,e){return g[t]=e,g.filter(Boolean).join("\n")});function y(t,e,n,i){var s=n?"":i.css;if(t.styleSheet)t.styleSheet.cssText=b(e,s);else{var r=document.createTextNode(s),a=t.childNodes;a[e]&&t.removeChild(a[e]),a.length?t.insertBefore(r,a[e]):t.appendChild(r)}}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte),r=i.new_double,a=i.new_float,o=i.new_float_n,l=i.new_int,c=i.new_int_n,u=(i.assert,n(33)),h=n(10),d=n(34),f=n(35),p=n(8),_=n(1),m=n(5);function v(){var t=40;function e(){this.write_timing=0,this.ptr=0,this.buf=s(t)}this.Class_ID=0,this.lame_encode_frame_init=0,this.iteration_init_init=0,this.fill_buffer_resample_init=0,this.mfbuf=o([2,v.MFSIZE]),this.mode_gr=0,this.channels_in=0,this.channels_out=0,this.resample_ratio=0,this.mf_samples_to_encode=0,this.mf_size=0,this.VBR_min_bitrate=0,this.VBR_max_bitrate=0,this.bitrate_index=0,this.samplerate_index=0,this.mode_ext=0,this.lowpass1=0,this.lowpass2=0,this.highpass1=0,this.highpass2=0,this.noise_shaping=0,this.noise_shaping_amp=0,this.substep_shaping=0,this.psymodel=0,this.noise_shaping_stop=0,this.subblock_gain=0,this.use_best_huffman=0,this.full_outer_loop=0,this.l3_side=new u,this.ms_ratio=a(2),this.padding=0,this.frac_SpF=0,this.slot_lag=0,this.tag_spec=null,this.nMusicCRC=0,this.OldValue=l(2),this.CurrentStep=l(2),this.masking_lower=0,this.bv_scf=l(576),this.pseudohalf=l(m.SFBMAX),this.sfb21_extra=!1,this.inbuf_old=new Array(2),this.blackfilt=new Array(2*v.BPC+1),this.itime=r(2),this.sideinfo_len=0,this.sb_sample=o([2,2,18,_.SBLIMIT]),this.amp_filter=a(32),this.header=new Array(v.MAX_HEADER_BUF),this.h_ptr=0,this.w_ptr=0,this.ancillary_flag=0,this.ResvSize=0,this.ResvMax=0,this.scalefac_band=new h,this.minval_l=a(_.CBANDS),this.minval_s=a(_.CBANDS),this.nb_1=o([4,_.CBANDS]),this.nb_2=o([4,_.CBANDS]),this.nb_s1=o([4,_.CBANDS]),this.nb_s2=o([4,_.CBANDS]),this.s3_ss=null,this.s3_ll=null,this.decay=0,this.thm=new Array(4),this.en=new Array(4),this.tot_ener=a(4),this.loudness_sq=o([2,2]),this.loudness_sq_save=a(2),this.mld_l=a(_.SBMAX_l),this.mld_s=a(_.SBMAX_s),this.bm_l=l(_.SBMAX_l),this.bo_l=l(_.SBMAX_l),this.bm_s=l(_.SBMAX_s),this.bo_s=l(_.SBMAX_s),this.npart_l=0,this.npart_s=0,this.s3ind=c([_.CBANDS,2]),this.s3ind_s=c([_.CBANDS,2]),this.numlines_s=l(_.CBANDS),this.numlines_l=l(_.CBANDS),this.rnumlines_l=a(_.CBANDS),this.mld_cb_l=a(_.CBANDS),this.mld_cb_s=a(_.CBANDS),this.numlines_s_num1=0,this.numlines_l_num1=0,this.pe=a(4),this.ms_ratio_s_old=0,this.ms_ratio_l_old=0,this.ms_ener_ratio_old=0,this.blocktype_old=l(2),this.nsPsy=new d,this.VBR_seek_table=new f,this.ATH=null,this.PSY=null,this.nogap_total=0,this.nogap_current=0,this.decode_on_the_fly=!0,this.findReplayGain=!0,this.findPeakSample=!0,this.PeakSample=0,this.RadioGain=0,this.AudiophileGain=0,this.rgdata=null,this.noclipGainChange=0,this.noclipScale=0,this.bitrate_stereoMode_Hist=c([16,5]),this.bitrate_blockType_Hist=c([16,6]),this.pinfo=null,this.hip=null,this.in_buffer_nsamples=0,this.in_buffer_0=null,this.in_buffer_1=null,this.iteration_loop=null;for(var n=0;n<this.en.length;n++)this.en[n]=new p;for(n=0;n<this.thm.length;n++)this.thm[n]=new p;for(n=0;n<this.header.length;n++)this.header[n]=new e}v.MFSIZE=3456+_.ENCDELAY-_.MDCTDELAY,v.MAX_HEADER_BUF=256,v.MAX_BITS_PER_CHANNEL=4095,v.MAX_BITS_PER_GRANULE=7680,v.BPC=320,t.exports=v},function(t,e,n){var i=n(1),s={};s.SFBMAX=3*i.SBMAX_s,t.exports=s},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float),r=(i.new_float_n,i.new_int),a=(i.new_int_n,i.assert,n(5));t.exports=function(){this.xr=s(576),this.l3_enc=r(576),this.scalefac=r(a.SFBMAX),this.xrpow_max=0,this.part2_3_length=0,this.big_values=0,this.count1=0,this.global_gain=0,this.scalefac_compress=0,this.block_type=0,this.mixed_block_flag=0,this.table_select=r(3),this.subblock_gain=r(4),this.region0_count=0,this.region1_count=0,this.preflag=0,this.scalefac_scale=0,this.count1table_select=0,this.part2_length=0,this.sfb_lmax=0,this.sfb_smin=0,this.psy_lmax=0,this.sfbmax=0,this.psymax=0,this.sfbdivide=0,this.width=r(a.SFBMAX),this.window=r(a.SFBMAX),this.count1bits=0,this.sfb_partition_table=null,this.slen=r(4),this.max_nonzero_coeff=0;var t=this;function e(t){return new Int32Array(t)}this.assign=function(n){var i;t.xr=(i=n.xr,new Float32Array(i)),t.l3_enc=e(n.l3_enc),t.scalefac=e(n.scalefac),t.xrpow_max=n.xrpow_max,t.part2_3_length=n.part2_3_length,t.big_values=n.big_values,t.count1=n.count1,t.global_gain=n.global_gain,t.scalefac_compress=n.scalefac_compress,t.block_type=n.block_type,t.mixed_block_flag=n.mixed_block_flag,t.table_select=e(n.table_select),t.subblock_gain=e(n.subblock_gain),t.region0_count=n.region0_count,t.region1_count=n.region1_count,t.preflag=n.preflag,t.scalefac_scale=n.scalefac_scale,t.count1table_select=n.count1table_select,t.part2_length=n.part2_length,t.sfb_lmax=n.sfb_lmax,t.sfb_smin=n.sfb_smin,t.psy_lmax=n.psy_lmax,t.sfbmax=n.sfbmax,t.psymax=n.psymax,t.sfbdivide=n.sfbdivide,t.width=e(n.width),t.window=e(n.window),t.count1bits=n.count1bits,t.sfb_partition_table=n.sfb_partition_table.slice(0),t.slen=e(n.slen),t.max_nonzero_coeff=n.max_nonzero_coeff}}},function(t,e){function n(t,e,n,i){this.xlen=t,this.linmax=e,this.table=n,this.hlen=i}var i={t1HB:[1,1,1,0],t2HB:[1,2,1,3,1,1,3,2,0],t3HB:[3,2,1,1,1,1,3,2,0],t5HB:[1,2,6,5,3,1,4,4,7,5,7,1,6,1,1,0],t6HB:[7,3,5,1,6,2,3,2,5,4,4,1,3,3,2,0],t7HB:[1,2,10,19,16,10,3,3,7,10,5,3,11,4,13,17,8,4,12,11,18,15,11,2,7,6,9,14,3,1,6,4,5,3,2,0],t8HB:[3,4,6,18,12,5,5,1,2,16,9,3,7,3,5,14,7,3,19,17,15,13,10,4,13,5,8,11,5,1,12,4,4,1,1,0],t9HB:[7,5,9,14,15,7,6,4,5,5,6,7,7,6,8,8,8,5,15,6,9,10,5,1,11,7,9,6,4,1,14,4,6,2,6,0],t10HB:[1,2,10,23,35,30,12,17,3,3,8,12,18,21,12,7,11,9,15,21,32,40,19,6,14,13,22,34,46,23,18,7,20,19,33,47,27,22,9,3,31,22,41,26,21,20,5,3,14,13,10,11,16,6,5,1,9,8,7,8,4,4,2,0],t11HB:[3,4,10,24,34,33,21,15,5,3,4,10,32,17,11,10,11,7,13,18,30,31,20,5,25,11,19,59,27,18,12,5,35,33,31,58,30,16,7,5,28,26,32,19,17,15,8,14,14,12,9,13,14,9,4,1,11,4,6,6,6,3,2,0],t12HB:[9,6,16,33,41,39,38,26,7,5,6,9,23,16,26,11,17,7,11,14,21,30,10,7,17,10,15,12,18,28,14,5,32,13,22,19,18,16,9,5,40,17,31,29,17,13,4,2,27,12,11,15,10,7,4,1,27,12,8,12,6,3,1,0],t13HB:[1,5,14,21,34,51,46,71,42,52,68,52,67,44,43,19,3,4,12,19,31,26,44,33,31,24,32,24,31,35,22,14,15,13,23,36,59,49,77,65,29,40,30,40,27,33,42,16,22,20,37,61,56,79,73,64,43,76,56,37,26,31,25,14,35,16,60,57,97,75,114,91,54,73,55,41,48,53,23,24,58,27,50,96,76,70,93,84,77,58,79,29,74,49,41,17,47,45,78,74,115,94,90,79,69,83,71,50,59,38,36,15,72,34,56,95,92,85,91,90,86,73,77,65,51,44,43,42,43,20,30,44,55,78,72,87,78,61,46,54,37,30,20,16,53,25,41,37,44,59,54,81,66,76,57,54,37,18,39,11,35,33,31,57,42,82,72,80,47,58,55,21,22,26,38,22,53,25,23,38,70,60,51,36,55,26,34,23,27,14,9,7,34,32,28,39,49,75,30,52,48,40,52,28,18,17,9,5,45,21,34,64,56,50,49,45,31,19,12,15,10,7,6,3,48,23,20,39,36,35,53,21,16,23,13,10,6,1,4,2,16,15,17,27,25,20,29,11,17,12,16,8,1,1,0,1],t15HB:[7,12,18,53,47,76,124,108,89,123,108,119,107,81,122,63,13,5,16,27,46,36,61,51,42,70,52,83,65,41,59,36,19,17,15,24,41,34,59,48,40,64,50,78,62,80,56,33,29,28,25,43,39,63,55,93,76,59,93,72,54,75,50,29,52,22,42,40,67,57,95,79,72,57,89,69,49,66,46,27,77,37,35,66,58,52,91,74,62,48,79,63,90,62,40,38,125,32,60,56,50,92,78,65,55,87,71,51,73,51,70,30,109,53,49,94,88,75,66,122,91,73,56,42,64,44,21,25,90,43,41,77,73,63,56,92,77,66,47,67,48,53,36,20,71,34,67,60,58,49,88,76,67,106,71,54,38,39,23,15,109,53,51,47,90,82,58,57,48,72,57,41,23,27,62,9,86,42,40,37,70,64,52,43,70,55,42,25,29,18,11,11,118,68,30,55,50,46,74,65,49,39,24,16,22,13,14,7,91,44,39,38,34,63,52,45,31,52,28,19,14,8,9,3,123,60,58,53,47,43,32,22,37,24,17,12,15,10,2,1,71,37,34,30,28,20,17,26,21,16,10,6,8,6,2,0],t16HB:[1,5,14,44,74,63,110,93,172,149,138,242,225,195,376,17,3,4,12,20,35,62,53,47,83,75,68,119,201,107,207,9,15,13,23,38,67,58,103,90,161,72,127,117,110,209,206,16,45,21,39,69,64,114,99,87,158,140,252,212,199,387,365,26,75,36,68,65,115,101,179,164,155,264,246,226,395,382,362,9,66,30,59,56,102,185,173,265,142,253,232,400,388,378,445,16,111,54,52,100,184,178,160,133,257,244,228,217,385,366,715,10,98,48,91,88,165,157,148,261,248,407,397,372,380,889,884,8,85,84,81,159,156,143,260,249,427,401,392,383,727,713,708,7,154,76,73,141,131,256,245,426,406,394,384,735,359,710,352,11,139,129,67,125,247,233,229,219,393,743,737,720,885,882,439,4,243,120,118,115,227,223,396,746,742,736,721,712,706,223,436,6,202,224,222,218,216,389,386,381,364,888,443,707,440,437,1728,4,747,211,210,208,370,379,734,723,714,1735,883,877,876,3459,865,2,377,369,102,187,726,722,358,711,709,866,1734,871,3458,870,434,0,12,10,7,11,10,17,11,9,13,12,10,7,5,3,1,3],t24HB:[15,13,46,80,146,262,248,434,426,669,653,649,621,517,1032,88,14,12,21,38,71,130,122,216,209,198,327,345,319,297,279,42,47,22,41,74,68,128,120,221,207,194,182,340,315,295,541,18,81,39,75,70,134,125,116,220,204,190,178,325,311,293,271,16,147,72,69,135,127,118,112,210,200,188,352,323,306,285,540,14,263,66,129,126,119,114,214,202,192,180,341,317,301,281,262,12,249,123,121,117,113,215,206,195,185,347,330,308,291,272,520,10,435,115,111,109,211,203,196,187,353,332,313,298,283,531,381,17,427,212,208,205,201,193,186,177,169,320,303,286,268,514,377,16,335,199,197,191,189,181,174,333,321,305,289,275,521,379,371,11,668,184,183,179,175,344,331,314,304,290,277,530,383,373,366,10,652,346,171,168,164,318,309,299,287,276,263,513,375,368,362,6,648,322,316,312,307,302,292,284,269,261,512,376,370,364,359,4,620,300,296,294,288,282,273,266,515,380,374,369,365,361,357,2,1033,280,278,274,267,264,259,382,378,372,367,363,360,358,356,0,43,20,19,17,15,13,11,9,7,6,4,7,5,3,1,3],t32HB:[1,10,8,20,12,20,16,32,14,12,24,0,28,16,24,16],t33HB:[15,28,26,48,22,40,36,64,14,24,20,32,12,16,8,0],t1l:[1,4,3,5],t2l:[1,4,7,4,5,7,6,7,8],t3l:[2,3,7,4,4,7,6,7,8],t5l:[1,4,7,8,4,5,8,9,7,8,9,10,8,8,9,10],t6l:[3,4,6,8,4,4,6,7,5,6,7,8,7,7,8,9],t7l:[1,4,7,9,9,10,4,6,8,9,9,10,7,7,9,10,10,11,8,9,10,11,11,11,8,9,10,11,11,12,9,10,11,12,12,12],t8l:[2,4,7,9,9,10,4,4,6,10,10,10,7,6,8,10,10,11,9,10,10,11,11,12,9,9,10,11,12,12,10,10,11,11,13,13],t9l:[3,4,6,7,9,10,4,5,6,7,8,10,5,6,7,8,9,10,7,7,8,9,9,10,8,8,9,9,10,11,9,9,10,10,11,11],t10l:[1,4,7,9,10,10,10,11,4,6,8,9,10,11,10,10,7,8,9,10,11,12,11,11,8,9,10,11,12,12,11,12,9,10,11,12,12,12,12,12,10,11,12,12,13,13,12,13,9,10,11,12,12,12,13,13,10,10,11,12,12,13,13,13],t11l:[2,4,6,8,9,10,9,10,4,5,6,8,10,10,9,10,6,7,8,9,10,11,10,10,8,8,9,11,10,12,10,11,9,10,10,11,11,12,11,12,9,10,11,12,12,13,12,13,9,9,9,10,11,12,12,12,9,9,10,11,12,12,12,12],t12l:[4,4,6,8,9,10,10,10,4,5,6,7,9,9,10,10,6,6,7,8,9,10,9,10,7,7,8,8,9,10,10,10,8,8,9,9,10,10,10,11,9,9,10,10,10,11,10,11,9,9,9,10,10,11,11,12,10,10,10,11,11,11,11,12],t13l:[1,5,7,8,9,10,10,11,10,11,12,12,13,13,14,14,4,6,8,9,10,10,11,11,11,11,12,12,13,14,14,14,7,8,9,10,11,11,12,12,11,12,12,13,13,14,15,15,8,9,10,11,11,12,12,12,12,13,13,13,13,14,15,15,9,9,11,11,12,12,13,13,12,13,13,14,14,15,15,16,10,10,11,12,12,12,13,13,13,13,14,13,15,15,16,16,10,11,12,12,13,13,13,13,13,14,14,14,15,15,16,16,11,11,12,13,13,13,14,14,14,14,15,15,15,16,18,18,10,10,11,12,12,13,13,14,14,14,14,15,15,16,17,17,11,11,12,12,13,13,13,15,14,15,15,16,16,16,18,17,11,12,12,13,13,14,14,15,14,15,16,15,16,17,18,19,12,12,12,13,14,14,14,14,15,15,15,16,17,17,17,18,12,13,13,14,14,15,14,15,16,16,17,17,17,18,18,18,13,13,14,15,15,15,16,16,16,16,16,17,18,17,18,18,14,14,14,15,15,15,17,16,16,19,17,17,17,19,18,18,13,14,15,16,16,16,17,16,17,17,18,18,21,20,21,18],t15l:[3,5,6,8,8,9,10,10,10,11,11,12,12,12,13,14,5,5,7,8,9,9,10,10,10,11,11,12,12,12,13,13,6,7,7,8,9,9,10,10,10,11,11,12,12,13,13,13,7,8,8,9,9,10,10,11,11,11,12,12,12,13,13,13,8,8,9,9,10,10,11,11,11,11,12,12,12,13,13,13,9,9,9,10,10,10,11,11,11,11,12,12,13,13,13,14,10,9,10,10,10,11,11,11,11,12,12,12,13,13,14,14,10,10,10,11,11,11,11,12,12,12,12,12,13,13,13,14,10,10,10,11,11,11,11,12,12,12,12,13,13,14,14,14,10,10,11,11,11,11,12,12,12,13,13,13,13,14,14,14,11,11,11,11,12,12,12,12,12,13,13,13,13,14,15,14,11,11,11,11,12,12,12,12,13,13,13,13,14,14,14,15,12,12,11,12,12,12,13,13,13,13,13,13,14,14,15,15,12,12,12,12,12,13,13,13,13,14,14,14,14,14,15,15,13,13,13,13,13,13,13,13,14,14,14,14,15,15,14,15,13,13,13,13,13,13,13,14,14,14,14,14,15,15,15,15],t16_5l:[1,5,7,9,10,10,11,11,12,12,12,13,13,13,14,11,4,6,8,9,10,11,11,11,12,12,12,13,14,13,14,11,7,8,9,10,11,11,12,12,13,12,13,13,13,14,14,12,9,9,10,11,11,12,12,12,13,13,14,14,14,15,15,13,10,10,11,11,12,12,13,13,13,14,14,14,15,15,15,12,10,10,11,11,12,13,13,14,13,14,14,15,15,15,16,13,11,11,11,12,13,13,13,13,14,14,14,14,15,15,16,13,11,11,12,12,13,13,13,14,14,15,15,15,15,17,17,13,11,12,12,13,13,13,14,14,15,15,15,15,16,16,16,13,12,12,12,13,13,14,14,15,15,15,15,16,15,16,15,14,12,13,12,13,14,14,14,14,15,16,16,16,17,17,16,13,13,13,13,13,14,14,15,16,16,16,16,16,16,15,16,14,13,14,14,14,14,15,15,15,15,17,16,16,16,16,18,14,15,14,14,14,15,15,16,16,16,18,17,17,17,19,17,14,14,15,13,14,16,16,15,16,16,17,18,17,19,17,16,14,11,11,11,12,12,13,13,13,14,14,14,14,14,14,14,12],t16l:[1,5,7,9,10,10,11,11,12,12,12,13,13,13,14,10,4,6,8,9,10,11,11,11,12,12,12,13,14,13,14,10,7,8,9,10,11,11,12,12,13,12,13,13,13,14,14,11,9,9,10,11,11,12,12,12,13,13,14,14,14,15,15,12,10,10,11,11,12,12,13,13,13,14,14,14,15,15,15,11,10,10,11,11,12,13,13,14,13,14,14,15,15,15,16,12,11,11,11,12,13,13,13,13,14,14,14,14,15,15,16,12,11,11,12,12,13,13,13,14,14,15,15,15,15,17,17,12,11,12,12,13,13,13,14,14,15,15,15,15,16,16,16,12,12,12,12,13,13,14,14,15,15,15,15,16,15,16,15,13,12,13,12,13,14,14,14,14,15,16,16,16,17,17,16,12,13,13,13,13,14,14,15,16,16,16,16,16,16,15,16,13,13,14,14,14,14,15,15,15,15,17,16,16,16,16,18,13,15,14,14,14,15,15,16,16,16,18,17,17,17,19,17,13,14,15,13,14,16,16,15,16,16,17,18,17,19,17,16,13,10,10,10,11,11,12,12,12,13,13,13,13,13,13,13,10],t24l:[4,5,7,8,9,10,10,11,11,12,12,12,12,12,13,10,5,6,7,8,9,10,10,11,11,11,12,12,12,12,12,10,7,7,8,9,9,10,10,11,11,11,11,12,12,12,13,9,8,8,9,9,10,10,10,11,11,11,11,12,12,12,12,9,9,9,9,10,10,10,10,11,11,11,12,12,12,12,13,9,10,9,10,10,10,10,11,11,11,11,12,12,12,12,12,9,10,10,10,10,10,11,11,11,11,12,12,12,12,12,13,9,11,10,10,10,11,11,11,11,12,12,12,12,12,13,13,10,11,11,11,11,11,11,11,11,11,12,12,12,12,13,13,10,11,11,11,11,11,11,11,12,12,12,12,12,13,13,13,10,12,11,11,11,11,12,12,12,12,12,12,13,13,13,13,10,12,12,11,11,11,12,12,12,12,12,12,13,13,13,13,10,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,10,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,10,13,12,12,12,12,12,12,13,13,13,13,13,13,13,13,10,9,9,9,9,9,9,9,9,9,9,9,10,10,10,10,6],t32l:[1,5,5,7,5,8,7,9,5,7,7,9,7,9,9,10],t33l:[4,5,5,6,5,6,6,7,5,6,6,7,6,7,7,8]};i.ht=[new n(0,0,null,null),new n(2,0,i.t1HB,i.t1l),new n(3,0,i.t2HB,i.t2l),new n(3,0,i.t3HB,i.t3l),new n(0,0,null,null),new n(4,0,i.t5HB,i.t5l),new n(4,0,i.t6HB,i.t6l),new n(6,0,i.t7HB,i.t7l),new n(6,0,i.t8HB,i.t8l),new n(6,0,i.t9HB,i.t9l),new n(8,0,i.t10HB,i.t10l),new n(8,0,i.t11HB,i.t11l),new n(8,0,i.t12HB,i.t12l),new n(16,0,i.t13HB,i.t13l),new n(0,0,null,i.t16_5l),new n(16,0,i.t15HB,i.t15l),new n(1,1,i.t16HB,i.t16l),new n(2,3,i.t16HB,i.t16l),new n(3,7,i.t16HB,i.t16l),new n(4,15,i.t16HB,i.t16l),new n(6,63,i.t16HB,i.t16l),new n(8,255,i.t16HB,i.t16l),new n(10,1023,i.t16HB,i.t16l),new n(13,8191,i.t16HB,i.t16l),new n(4,15,i.t24HB,i.t24l),new n(5,31,i.t24HB,i.t24l),new n(6,63,i.t24HB,i.t24l),new n(7,127,i.t24HB,i.t24l),new n(8,255,i.t24HB,i.t24l),new n(9,511,i.t24HB,i.t24l),new n(11,2047,i.t24HB,i.t24l),new n(13,8191,i.t24HB,i.t24l),new n(0,0,i.t32HB,i.t32l),new n(0,0,i.t33HB,i.t33l)],i.largetbl=[65540,327685,458759,589832,655369,655370,720906,720907,786443,786444,786444,851980,851980,851980,917517,655370,262149,393222,524295,589832,655369,720906,720906,720907,786443,786443,786444,851980,917516,851980,917516,655370,458759,524295,589832,655369,720905,720906,786442,786443,851979,786443,851979,851980,851980,917516,917517,720905,589832,589832,655369,720905,720906,786442,786442,786443,851979,851979,917515,917516,917516,983052,983052,786441,655369,655369,720905,720906,786442,786442,851978,851979,851979,917515,917516,917516,983052,983052,983053,720905,655370,655369,720906,720906,786442,851978,851979,917515,851979,917515,917516,983052,983052,983052,1048588,786441,720906,720906,720906,786442,851978,851979,851979,851979,917515,917516,917516,917516,983052,983052,1048589,786441,720907,720906,786442,786442,851979,851979,851979,917515,917516,983052,983052,983052,983052,1114125,1114125,786442,720907,786443,786443,851979,851979,851979,917515,917515,983051,983052,983052,983052,1048588,1048589,1048589,786442,786443,786443,786443,851979,851979,917515,917515,983052,983052,983052,983052,1048588,983053,1048589,983053,851978,786444,851979,786443,851979,917515,917516,917516,917516,983052,1048588,1048588,1048589,1114125,1114125,1048589,786442,851980,851980,851979,851979,917515,917516,983052,1048588,1048588,1048588,1048588,1048589,1048589,983053,1048589,851978,851980,917516,917516,917516,917516,983052,983052,983052,983052,1114124,1048589,1048589,1048589,1048589,1179661,851978,983052,917516,917516,917516,983052,983052,1048588,1048588,1048589,1179661,1114125,1114125,1114125,1245197,1114125,851978,917517,983052,851980,917516,1048588,1048588,983052,1048589,1048589,1114125,1179661,1114125,1245197,1114125,1048589,851978,655369,655369,655369,720905,720905,786441,786441,786441,851977,851977,851977,851978,851978,851978,851978,655366],i.table23=[65538,262147,458759,262148,327684,458759,393222,458759,524296],i.table56=[65539,262148,458758,524296,262148,327684,524294,589831,458757,524294,589831,655368,524295,524295,589832,655369],i.bitrate_table=[[0,8,16,24,32,40,48,56,64,80,96,112,128,144,160,-1],[0,32,40,48,56,64,80,96,112,128,160,192,224,256,320,-1],[0,8,16,24,32,40,48,56,64,-1,-1,-1,-1,-1,-1,-1]],i.samplerate_table=[[22050,24e3,16e3,-1],[44100,48e3,32e3,-1],[11025,12e3,8e3,-1]],i.scfsi_band=[0,6,11,16,21],t.exports=i},function(t,e,n){var i=n(1),s=n(0),r=s.System,a=(s.VbrMode,s.Float,s.ShortBlock,s.Util,s.Arrays,s.new_array_n,s.new_byte,s.new_double,s.new_float),o=s.new_float_n;s.new_int,s.new_int_n,s.assert,t.exports=function(){this.l=a(i.SBMAX_l),this.s=o([i.SBMAX_s,3]);var t=this;this.assign=function(e){r.arraycopy(e.l,0,t.l,0,i.SBMAX_l);for(var n=0;n<i.SBMAX_s;n++)for(var s=0;s<3;s++)t.s[n][s]=e.s[n][s]}}},function(t,e){function n(t){var e=t;this.ordinal=function(){return e}}n.STEREO=new n(0),n.JOINT_STEREO=new n(1),n.DUAL_CHANNEL=new n(2),n.MONO=new n(3),n.NOT_SET=new n(4),t.exports=n},function(t,e,n){var i=n(0),s=i.System,r=(i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float,i.new_float_n,i.new_int),a=(i.new_int_n,i.assert,n(1));t.exports=function(t,e,n,i){this.l=r(1+a.SBMAX_l),this.s=r(1+a.SBMAX_s),this.psfb21=r(1+a.PSFB21),this.psfb12=r(1+a.PSFB12);var o=this.l,l=this.s;4==arguments.length&&(this.arrL=t,this.arrS=e,this.arr21=n,this.arr12=i,s.arraycopy(this.arrL,0,o,0,Math.min(this.arrL.length,this.l.length)),s.arraycopy(this.arrS,0,l,0,Math.min(this.arrS.length,this.s.length)),s.arraycopy(this.arr21,0,this.psfb21,0,Math.min(this.arr21.length,this.psfb21.length)),s.arraycopy(this.arr12,0,this.psfb12,0,Math.min(this.arr12.length,this.psfb12.length)))}},function(t,e,n){var i=n(0),s=i.System,r=(i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays);function a(){var t=a.RMS_WINDOW_TIME_NUMERATOR,e=a.RMS_WINDOW_TIME_DENOMINATOR,n=[[.038575994352,-3.84664617118067,-.02160367184185,7.81501653005538,-.00123395316851,-11.34170355132042,-9291677959e-14,13.05504219327545,-.01655260341619,-12.28759895145294,.02161526843274,9.4829380631979,-.02074045215285,-5.87257861775999,.00594298065125,2.75465861874613,.00306428023191,-.86984376593551,.00012025322027,.13919314567432,.00288463683916],[.0541865640643,-3.47845948550071,-.02911007808948,6.36317777566148,-.00848709379851,-8.54751527471874,-.00851165645469,9.4769360780128,-.00834990904936,-8.81498681370155,.02245293253339,6.85401540936998,-.02596338512915,-4.39470996079559,.01624864962975,2.19611684890774,-.00240879051584,-.75104302451432,.00674613682247,.13149317958808,-.00187763777362],[.15457299681924,-2.37898834973084,-.09331049056315,2.84868151156327,-.06247880153653,-2.64577170229825,.02163541888798,2.23697657451713,-.05588393329856,-1.67148153367602,.04781476674921,1.00595954808547,.00222312597743,-.45953458054983,.03174092540049,.16378164858596,-.01390589421898,-.05032077717131,.00651420667831,.0234789740702,-.00881362733839],[.30296907319327,-1.61273165137247,-.22613988682123,1.0797749225997,-.08587323730772,-.2565625775407,.03282930172664,-.1627671912044,-.00915702933434,-.22638893773906,-.02364141202522,.39120800788284,-.00584456039913,-.22138138954925,.06276101321749,.04500235387352,-828086748e-14,.02005851806501,.00205861885564,.00302439095741,-.02950134983287],[.33642304856132,-1.49858979367799,-.2557224142557,.87350271418188,-.11828570177555,.12205022308084,.11921148675203,-.80774944671438,-.07834489609479,.47854794562326,-.0046997791438,-.12453458140019,-.0058950022444,-.04067510197014,.05724228140351,.08333755284107,.00832043980773,-.04237348025746,-.0163538138454,.02977207319925,-.0176017656815],[.4491525660845,-.62820619233671,-.14351757464547,.29661783706366,-.22784394429749,-.372563729424,-.01419140100551,.00213767857124,.04078262797139,-.42029820170918,-.12398163381748,.22199650564824,.04097565135648,.00613424350682,.10478503600251,.06747620744683,-.01863887810927,.05784820375801,-.03193428438915,.03222754072173,.00541907748707],[.56619470757641,-1.04800335126349,-.75464456939302,.29156311971249,.1624213774223,-.26806001042947,.16744243493672,.00819999645858,-.18901604199609,.45054734505008,.3093178284183,-.33032403314006,-.27562961986224,.0673936833311,.00647310677246,-.04784254229033,.08647503780351,.01639907836189,-.0378898455484,.01807364323573,-.00588215443421],[.58100494960553,-.51035327095184,-.53174909058578,-.31863563325245,-.14289799034253,-.20256413484477,.17520704835522,.1472815413433,.02377945217615,.38952639978999,.15558449135573,-.23313271880868,-.25344790059353,-.05246019024463,.01628462406333,-.02505961724053,.06920467763959,.02442357316099,-.03721611395801,.01818801111503,-.00749618797172],[.53648789255105,-.2504987195602,-.42163034350696,-.43193942311114,-.00275953611929,-.03424681017675,.04267842219415,-.04678328784242,-.10214864179676,.26408300200955,.14590772289388,.15113130533216,-.02459864859345,-.17556493366449,-.11202315195388,-.18823009262115,-.04060034127,.05477720428674,.0478866554818,.0470440968812,-.02217936801134]],i=[[.98621192462708,-1.97223372919527,-1.97242384925416,.97261396931306,.98621192462708],[.98500175787242,-1.96977855582618,-1.97000351574484,.9702284756635,.98500175787242],[.97938932735214,-1.95835380975398,-1.95877865470428,.95920349965459,.97938932735214],[.97531843204928,-1.95002759149878,-1.95063686409857,.95124613669835,.97531843204928],[.97316523498161,-1.94561023566527,-1.94633046996323,.94705070426118,.97316523498161],[.96454515552826,-1.92783286977036,-1.92909031105652,.93034775234268,.96454515552826],[.96009142950541,-1.91858953033784,-1.92018285901082,.92177618768381,.96009142950541],[.95856916599601,-1.9154210807478,-1.91713833199203,.91885558323625,.95856916599601],[.94597685600279,-1.88903307939452,-1.89195371200558,.89487434461664,.94597685600279]];function o(t,e,n,i,s,r){for(;0!=s--;)n[i]=1e-10+t[e+0]*r[0]-n[i-1]*r[1]+t[e-1]*r[2]-n[i-2]*r[3]+t[e-2]*r[4]-n[i-3]*r[5]+t[e-3]*r[6]-n[i-4]*r[7]+t[e-4]*r[8]-n[i-5]*r[9]+t[e-5]*r[10]-n[i-6]*r[11]+t[e-6]*r[12]-n[i-7]*r[13]+t[e-7]*r[14]-n[i-8]*r[15]+t[e-8]*r[16]-n[i-9]*r[17]+t[e-9]*r[18]-n[i-10]*r[19]+t[e-10]*r[20],++i,++e}function l(t,e,n,i,s,r){for(;0!=s--;)n[i]=t[e+0]*r[0]-n[i-1]*r[1]+t[e-1]*r[2]-n[i-2]*r[3]+t[e-2]*r[4],++i,++e}function c(t){return t*t}this.InitGainAnalysis=function(n,i){return function(n,i){for(var s=0;s<MAX_ORDER;s++)n.linprebuf[s]=n.lstepbuf[s]=n.loutbuf[s]=n.rinprebuf[s]=n.rstepbuf[s]=n.routbuf[s]=0;switch(0|i){case 48e3:n.reqindex=0;break;case 44100:n.reqindex=1;break;case 32e3:n.reqindex=2;break;case 24e3:n.reqindex=3;break;case 22050:n.reqindex=4;break;case 16e3:n.reqindex=5;break;case 12e3:n.reqindex=6;break;case 11025:n.reqindex=7;break;case 8e3:n.reqindex=8;break;default:return INIT_GAIN_ANALYSIS_ERROR}return n.sampleWindow=0|(i*t+e-1)/e,n.lsum=0,n.rsum=0,n.totsamp=0,r.ill(n.A,0),INIT_GAIN_ANALYSIS_OK}(n,i)!=INIT_GAIN_ANALYSIS_OK?INIT_GAIN_ANALYSIS_ERROR:(n.linpre=MAX_ORDER,n.rinpre=MAX_ORDER,n.lstep=MAX_ORDER,n.rstep=MAX_ORDER,n.lout=MAX_ORDER,n.rout=MAX_ORDER,r.fill(n.B,0),INIT_GAIN_ANALYSIS_OK)},this.AnalyzeSamples=function(t,e,r,u,h,d,f){var p,_,m,v,g,b,y;if(0==d)return GAIN_ANALYSIS_OK;switch(y=0,g=d,f){case 1:u=e,h=r;break;case 2:break;default:return GAIN_ANALYSIS_ERROR}for(d<MAX_ORDER?(s.arraycopy(e,r,t.linprebuf,MAX_ORDER,d),s.arraycopy(u,h,t.rinprebuf,MAX_ORDER,d)):(s.arraycopy(e,r,t.linprebuf,MAX_ORDER,MAX_ORDER),s.arraycopy(u,h,t.rinprebuf,MAX_ORDER,MAX_ORDER));0<g;){b=g>t.sampleWindow-t.totsamp?t.sampleWindow-t.totsamp:g,y<MAX_ORDER?(p=t.linpre+y,_=t.linprebuf,m=t.rinpre+y,v=t.rinprebuf,b>MAX_ORDER-y&&(b=MAX_ORDER-y)):(p=r+y,_=e,m=h+y,v=u),o(_,p,t.lstepbuf,t.lstep+t.totsamp,b,n[t.reqindex]),o(v,m,t.rstepbuf,t.rstep+t.totsamp,b,n[t.reqindex]),l(t.lstepbuf,t.lstep+t.totsamp,t.loutbuf,t.lout+t.totsamp,b,i[t.reqindex]),l(t.rstepbuf,t.rstep+t.totsamp,t.routbuf,t.rout+t.totsamp,b,i[t.reqindex]),p=t.lout+t.totsamp,_=t.loutbuf,m=t.rout+t.totsamp,v=t.routbuf;for(var x=b%8;0!=x--;)t.lsum+=c(_[p++]),t.rsum+=c(v[m++]);for(x=b/8;0!=x--;)t.lsum+=c(_[p+0])+c(_[p+1])+c(_[p+2])+c(_[p+3])+c(_[p+4])+c(_[p+5])+c(_[p+6])+c(_[p+7]),p+=8,t.rsum+=c(v[m+0])+c(v[m+1])+c(v[m+2])+c(v[m+3])+c(v[m+4])+c(v[m+5])+c(v[m+6])+c(v[m+7]),m+=8;if(g-=b,y+=b,t.totsamp+=b,t.totsamp==t.sampleWindow){var w=10*a.STEPS_per_dB*Math.log10((t.lsum+t.rsum)/t.totsamp*.5+1e-37),S=w<=0?0:0|w;S>=t.A.length&&(S=t.A.length-1),t.A[S]++,t.lsum=t.rsum=0,s.arraycopy(t.loutbuf,t.totsamp,t.loutbuf,0,MAX_ORDER),s.arraycopy(t.routbuf,t.totsamp,t.routbuf,0,MAX_ORDER),s.arraycopy(t.lstepbuf,t.totsamp,t.lstepbuf,0,MAX_ORDER),s.arraycopy(t.rstepbuf,t.totsamp,t.rstepbuf,0,MAX_ORDER),t.totsamp=0}if(t.totsamp>t.sampleWindow)return GAIN_ANALYSIS_ERROR}return d<MAX_ORDER?(s.arraycopy(t.linprebuf,d,t.linprebuf,0,MAX_ORDER-d),s.arraycopy(t.rinprebuf,d,t.rinprebuf,0,MAX_ORDER-d),s.arraycopy(e,r,t.linprebuf,MAX_ORDER-d,d),s.arraycopy(u,h,t.rinprebuf,MAX_ORDER-d,d)):(s.arraycopy(e,r+d-MAX_ORDER,t.linprebuf,0,MAX_ORDER),s.arraycopy(u,h+d-MAX_ORDER,t.rinprebuf,0,MAX_ORDER)),GAIN_ANALYSIS_OK},this.GetTitleGain=function(t){for(var e=function(t,e){var n,i=0;for(n=0;n<e;n++)i+=t[n];if(0==i)return GAIN_NOT_ENOUGH_SAMPLES;var s=0|Math.ceil(i*(1-.95));for(n=e;0<n--&&!((s-=t[n])<=0););return 64.82-n/a.STEPS_per_dB}(t.A,t.A.length),n=0;n<t.A.length;n++)t.B[n]+=t.A[n],t.A[n]=0;for(n=0;n<MAX_ORDER;n++)t.linprebuf[n]=t.lstepbuf[n]=t.loutbuf[n]=t.rinprebuf[n]=t.rstepbuf[n]=t.routbuf[n]=0;return t.totsamp=0,t.lsum=t.rsum=0,e}}i.new_array_n,i.new_byte,i.new_double,i.new_float,i.new_float_n,i.new_int,i.new_int_n,i.assert,a.STEPS_per_dB=100,a.MAX_dB=120,a.GAIN_NOT_ENOUGH_SAMPLES=-24601,a.GAIN_ANALYSIS_ERROR=0,a.GAIN_ANALYSIS_OK=1,a.INIT_GAIN_ANALYSIS_ERROR=0,a.INIT_GAIN_ANALYSIS_OK=1,a.MAX_ORDER=a.YULE_ORDER=10,a.MAX_SAMPLES_PER_WINDOW=(a.MAX_SAMP_FREQ=48e3)*(a.RMS_WINDOW_TIME_NUMERATOR=1)/(a.RMS_WINDOW_TIME_DENOMINATOR=20)+1,t.exports=a},function(t,e){t.exports=function(t){this.bits=t}},function(t,e,n){var i=n(0),s=i.System,r=(i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays),a=(i.new_array_n,i.new_byte),o=(i.new_double,i.new_float,i.new_float_n),l=i.new_int,c=(i.new_int_n,i.assert),u=n(14),h=n(7),d=n(1),f=n(4);function p(){var t=this,e=32,n=null,i=null,p=null,_=null;this.setModules=function(t,e,s,r){n=t,i=e,p=s,_=r};var m=null,v=0,g=0,b=0;function y(t,n,i){for(c(i<e-2);0<i;){var r;0==b&&(b=8,c(++g<Lame.LAME_MAXMP3BUFFER),c(t.header[t.w_ptr].write_timing>=v),t.header[t.w_ptr].write_timing==v&&(a=t,s.arraycopy(a.header[a.w_ptr].buf,0,m,g,a.sideinfo_len),g+=a.sideinfo_len,v+=8*a.sideinfo_len,a.w_ptr=a.w_ptr+1&f.MAX_HEADER_BUF-1),m[g]=0),r=Math.min(i,b),b-=r,c((i-=r)<e),c(b<e),m[g]|=n>>i<<b,v+=r}var a}function x(t,n,i){for(c(i<e-2);0<i;){var s;0==b&&(b=8,c(++g<Lame.LAME_MAXMP3BUFFER),m[g]=0),s=Math.min(i,b),b-=s,c((i-=s)<e),c(b<e),m[g]|=n>>i<<b,v+=s}}function w(t,e){var n,i=t.internal_flags;if(c(0<=e),8<=e&&(y(i,76,8),e-=8),8<=e&&(y(i,65,8),e-=8),8<=e&&(y(i,77,8),e-=8),8<=e&&(y(i,69,8),e-=8),32<=e){var s=p.getLameShortVersion();if(32<=e)for(n=0;n<s.length&&8<=e;++n)e-=8,y(i,s.charAt(n),8)}for(;1<=e;e-=1)y(i,i.ancillary_flag,1),i.ancillary_flag^=t.disable_reservoir?0:1;c(0==e)}function S(t,n,i){for(var s=t.header[t.h_ptr].ptr;0<i;){var r=Math.min(i,8-(7&s));c((i-=r)<e),t.header[t.h_ptr].buf[s>>3]|=n>>i<<8-(7&s)-r,s+=r}t.header[t.h_ptr].ptr=s}function k(t,e){t<<=8;for(var n=0;n<8;n++)0!=(65536&((e<<=1)^(t<<=1)))&&(e^=32773);return e}function A(t,e){var n,i=h.ht[e.count1table_select+32],s=0,r=e.big_values,a=e.big_values;for(c(e.count1table_select<2),n=(e.count1-e.big_values)/4;0<n;--n){var o,l=0,u=0;0!=(o=e.l3_enc[r+0])&&(u+=8,e.xr[a+0]<0&&l++,c(o<=1)),0!=(o=e.l3_enc[r+1])&&(u+=4,l*=2,e.xr[a+1]<0&&l++,c(o<=1)),0!=(o=e.l3_enc[r+2])&&(u+=2,l*=2,e.xr[a+2]<0&&l++,c(o<=1)),0!=(o=e.l3_enc[r+3])&&(u++,l*=2,e.xr[a+3]<0&&l++,c(o<=1)),r+=4,a+=4,y(t,l+i.table[u],i.hlen[u]),s+=i.hlen[u]}return s}function C(t,n,i,s,r){var a=h.ht[n],o=0;if(c(n<32),0==n)return o;for(var l=i;l<s;l+=2){var u=0,d=0,f=a.xlen,p=a.xlen,_=0,m=r.l3_enc[l],v=r.l3_enc[l+1];if(0!=m&&(r.xr[l]<0&&_++,u--),15<n){if(14<m){var g=m-15;c(g<=a.linmax),_|=g<<1,d=f,m=15}if(14<v){var b=v-15;c(b<=a.linmax),_<<=f,_|=b,d+=f,v=15}p=16}0!=v&&(_<<=1,r.xr[l+1]<0&&_++,u--),c((m|v)<16),m=m*p+v,d-=u,u+=a.hlen[m],c(u<=e),c(d<=e),y(t,a.table[m],u),y(t,_,d),o+=u+d}return o}function M(t,e){var n=3*t.scalefac_band.s[3];n>e.big_values&&(n=e.big_values);var i=C(t,e.table_select[0],0,n,e);return i+C(t,e.table_select[1],n,e.big_values,e)}function E(t,e){var n,i,s,r;n=e.big_values,c(0<=n&&n<=576);var a=e.region0_count+1;return c(0<=a),c(a<t.scalefac_band.l.length),s=t.scalefac_band.l[a],a+=e.region1_count+1,c(0<=a),c(a<t.scalefac_band.l.length),n<s&&(s=n),n<(r=t.scalefac_band.l[a])&&(r=n),i=C(t,e.table_select[0],0,s,e),i+=C(t,e.table_select[1],s,r,e),i+C(t,e.table_select[2],r,n,e)}function T(){this.total=0}function B(e,n){var i,r,a,o,l,c=e.internal_flags;return l=c.w_ptr,-1==(o=c.h_ptr-1)&&(o=f.MAX_HEADER_BUF-1),i=c.header[o].write_timing-v,0<=(n.total=i)&&(r=1+o-l,o<l&&(r=1+o-l+f.MAX_HEADER_BUF),i-=8*r*c.sideinfo_len),i+=a=t.getframebits(e),n.total+=a,n.total%8!=0?n.total=1+n.total/8:n.total=n.total/8,n.total+=g+1,i<0&&s.err.println("strange error flushing buffer ... \n"),i}this.getframebits=function(t){var e,n=t.internal_flags;return e=0!=n.bitrate_index?h.bitrate_table[t.version][n.bitrate_index]:t.brate,c(8<=e&&e<=640),8*(0|72e3*(t.version+1)*e/t.out_samplerate+n.padding)},this.CRC_writeheader=function(t,e){var n=65535;n=k(255&e[2],n),n=k(255&e[3],n);for(var i=6;i<t.sideinfo_len;i++)n=k(255&e[i],n);e[4]=byte(n>>8),e[5]=byte(255&n)},this.flush_bitstream=function(t){var e,i,s=t.internal_flags,r=s.h_ptr-1;if(-1==r&&(r=f.MAX_HEADER_BUF-1),e=s.l3_side,!((i=B(t,new T))<0)){if(w(t,i),c(s.header[r].write_timing+this.getframebits(t)==v),s.ResvSize=0,e.main_data_begin=0,s.findReplayGain){var a=n.GetTitleGain(s.rgdata);c(NEQ(a,GainAnalysis.GAIN_NOT_ENOUGH_SAMPLES)),s.RadioGain=0|Math.floor(10*a+.5)}s.findPeakSample&&(s.noclipGainChange=0|Math.ceil(20*Math.log10(s.PeakSample/32767)*10),0<s.noclipGainChange&&(EQ(t.scale,1)||EQ(t.scale,0))?s.noclipScale=Math.floor(32767/s.PeakSample*100)/100:s.noclipScale=-1)}},this.add_dummy_byte=function(t,e,n){for(var i,s=t.internal_flags;0<n--;)for(x(0,e,8),i=0;i<f.MAX_HEADER_BUF;++i)s.header[i].write_timing+=8},this.format_bitstream=function(t){var e,n=t.internal_flags;e=n.l3_side;var i=this.getframebits(t);w(t,e.resvDrain_pre),function(t,e){var n,i,a,o=t.internal_flags;if(n=o.l3_side,o.header[o.h_ptr].ptr=0,r.fill(o.header[o.h_ptr].buf,0,o.sideinfo_len,0),t.out_samplerate<16e3?S(o,4094,12):S(o,4095,12),S(o,t.version,1),S(o,1,2),S(o,t.error_protection?0:1,1),S(o,o.bitrate_index,4),S(o,o.samplerate_index,2),S(o,o.padding,1),S(o,t.extension,1),S(o,t.mode.ordinal(),2),S(o,o.mode_ext,2),S(o,t.copyright,1),S(o,t.original,1),S(o,t.emphasis,2),t.error_protection&&S(o,0,16),1==t.version){for(c(0<=n.main_data_begin),S(o,n.main_data_begin,9),2==o.channels_out?S(o,n.private_bits,3):S(o,n.private_bits,5),a=0;a<o.channels_out;a++){var l;for(l=0;l<4;l++)S(o,n.scfsi[a][l],1)}for(i=0;i<2;i++)for(a=0;a<o.channels_out;a++)S(o,(u=n.tt[i][a]).part2_3_length+u.part2_length,12),S(o,u.big_values/2,9),S(o,u.global_gain,8),S(o,u.scalefac_compress,4),u.block_type!=d.NORM_TYPE?(S(o,1,1),S(o,u.block_type,2),S(o,u.mixed_block_flag,1),14==u.table_select[0]&&(u.table_select[0]=16),S(o,u.table_select[0],5),14==u.table_select[1]&&(u.table_select[1]=16),S(o,u.table_select[1],5),S(o,u.subblock_gain[0],3),S(o,u.subblock_gain[1],3),S(o,u.subblock_gain[2],3)):(S(o,0,1),14==u.table_select[0]&&(u.table_select[0]=16),S(o,u.table_select[0],5),14==u.table_select[1]&&(u.table_select[1]=16),S(o,u.table_select[1],5),14==u.table_select[2]&&(u.table_select[2]=16),S(o,u.table_select[2],5),c(0<=u.region0_count&&u.region0_count<16),c(0<=u.region1_count&&u.region1_count<8),S(o,u.region0_count,4),S(o,u.region1_count,3)),S(o,u.preflag,1),S(o,u.scalefac_scale,1),S(o,u.count1table_select,1)}else for(c(0<=n.main_data_begin),S(o,n.main_data_begin,8),S(o,n.private_bits,o.channels_out),a=i=0;a<o.channels_out;a++){var u;S(o,(u=n.tt[i][a]).part2_3_length+u.part2_length,12),S(o,u.big_values/2,9),S(o,u.global_gain,8),S(o,u.scalefac_compress,9),u.block_type!=d.NORM_TYPE?(S(o,1,1),S(o,u.block_type,2),S(o,u.mixed_block_flag,1),14==u.table_select[0]&&(u.table_select[0]=16),S(o,u.table_select[0],5),14==u.table_select[1]&&(u.table_select[1]=16),S(o,u.table_select[1],5),S(o,u.subblock_gain[0],3),S(o,u.subblock_gain[1],3),S(o,u.subblock_gain[2],3)):(S(o,0,1),14==u.table_select[0]&&(u.table_select[0]=16),S(o,u.table_select[0],5),14==u.table_select[1]&&(u.table_select[1]=16),S(o,u.table_select[1],5),14==u.table_select[2]&&(u.table_select[2]=16),S(o,u.table_select[2],5),c(0<=u.region0_count&&u.region0_count<16),c(0<=u.region1_count&&u.region1_count<8),S(o,u.region0_count,4),S(o,u.region1_count,3)),S(o,u.scalefac_scale,1),S(o,u.count1table_select,1)}t.error_protection&&CRC_writeheader(o,o.header[o.h_ptr].buf);var h=o.h_ptr;c(o.header[h].ptr==8*o.sideinfo_len),o.h_ptr=h+1&f.MAX_HEADER_BUF-1,o.header[o.h_ptr].write_timing=o.header[h].write_timing+e,o.h_ptr==o.w_ptr&&s.err.println("Error: MAX_HEADER_BUF too small in bitstream.c \n")}(t,i);var a=8*n.sideinfo_len;if(a+=function(t){var e,n,i,s,r=0,a=t.internal_flags,o=a.l3_side;if(1==t.version)for(e=0;e<2;e++)for(n=0;n<a.channels_out;n++){var l=o.tt[e][n],h=u.slen1_tab[l.scalefac_compress],f=u.slen2_tab[l.scalefac_compress];for(i=s=0;i<l.sfbdivide;i++)-1!=l.scalefac[i]&&(y(a,l.scalefac[i],h),s+=h);for(;i<l.sfbmax;i++)-1!=l.scalefac[i]&&(y(a,l.scalefac[i],f),s+=f);c(s==l.part2_length),l.block_type==d.SHORT_TYPE?s+=M(a,l):s+=E(a,l),s+=A(a,l),c(s==l.part2_3_length+l.part2_length),r+=s}else for(n=e=0;n<a.channels_out;n++){l=o.tt[e][n];var p,_,m=0;if(c(null!=l.sfb_partition_table),_=i=s=0,l.block_type==d.SHORT_TYPE){for(;_<4;_++){var v=l.sfb_partition_table[_]/3,g=l.slen[_];for(p=0;p<v;p++,i++)y(a,Math.max(l.scalefac[3*i+0],0),g),y(a,Math.max(l.scalefac[3*i+1],0),g),y(a,Math.max(l.scalefac[3*i+2],0),g),m+=3*g}s+=M(a,l)}else{for(;_<4;_++)for(v=l.sfb_partition_table[_],g=l.slen[_],p=0;p<v;p++,i++)y(a,Math.max(l.scalefac[i],0),g),m+=g;s+=E(a,l)}s+=A(a,l),c(s==l.part2_3_length),c(m==l.part2_length),r+=m+s}return r}(t),w(t,e.resvDrain_post),a+=e.resvDrain_post,e.main_data_begin+=(i-a)/8,B(t,new T)!=n.ResvSize&&s.err.println("Internal buffer inconsistency. flushbits <> ResvSize"),8*e.main_data_begin!=n.ResvSize&&(s.err.printf("bit reservoir error: \nl3_side.main_data_begin: %d \nResvoir size:             %d \nresv drain (post)         %d \nresv drain (pre)          %d \nheader and sideinfo:      %d \ndata bits:                %d \ntotal bits:               %d (remainder: %d) \nbitsperframe:             %d \n",8*e.main_data_begin,n.ResvSize,e.resvDrain_post,e.resvDrain_pre,8*n.sideinfo_len,a-e.resvDrain_post-8*n.sideinfo_len,a,a%8,i),s.err.println("This is a fatal error.  It has several possible causes:"),s.err.println("90%%  LAME compiled with buggy version of gcc using advanced optimizations"),s.err.println(" 9%%  Your system is overclocked"),s.err.println(" 1%%  bug in LAME encoding library"),n.ResvSize=8*e.main_data_begin),c(v%8==0),1e9<v){var o;for(o=0;o<f.MAX_HEADER_BUF;++o)n.header[o].write_timing-=v;v=0}return 0},this.copy_buffer=function(t,e,r,a,u){var h=g+1;if(h<=0)return 0;if(0!=a&&a<h)return-1;if(s.arraycopy(m,0,e,r,h),g=-1,(b=0)!=u){var d=l(1);if(d[0]=t.nMusicCRC,_.updateMusicCRC(d,e,r,h),t.nMusicCRC=d[0],0<h&&(t.VBR_seek_table.nBytesWritten+=h),t.decode_on_the_fly)for(var f,p=o([2,1152]),v=h,y=-1;0!=y;)if(y=i.hip_decode1_unclipped(t.hip,e,r,v,p[0],p[1]),v=0,-1==y&&(y=0),0<y){if(c(y<=1152),t.findPeakSample){for(f=0;f<y;f++)p[0][f]>t.PeakSample?t.PeakSample=p[0][f]:-p[0][f]>t.PeakSample&&(t.PeakSample=-p[0][f]);if(1<t.channels_out)for(f=0;f<y;f++)p[1][f]>t.PeakSample?t.PeakSample=p[1][f]:-p[1][f]>t.PeakSample&&(t.PeakSample=-p[1][f])}if(t.findReplayGain&&n.AnalyzeSamples(t.rgdata,p[0],0,p[1],0,y,t.channels_out)==GainAnalysis.GAIN_ANALYSIS_ERROR)return-6}}return h},this.init_bit_stream_w=function(t){m=a(Lame.LAME_MAXMP3BUFFER),t.h_ptr=t.w_ptr=0,t.header[t.h_ptr].write_timing=0,g=-1,v=b=0}}p.EQ=function(t,e){return Math.abs(t)>Math.abs(e)?Math.abs(t-e)<=1e-6*Math.abs(t):Math.abs(t-e)<=1e-6*Math.abs(e)},p.NEQ=function(t,e){return!p.EQ(t,e)},t.exports=p},function(t,e,n){var i=n(0),s=i.System,r=(i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays),a=(i.new_array_n,i.new_byte,i.new_double,i.new_float,i.new_float_n,i.new_int),o=(i.new_int_n,i.assert),l=n(1),c=n(7),u=n(6),h=n(15);t.exports=function t(){var e=null;function n(t){this.bits=0|t}this.qupvt=null,this.setModules=function(t){this.qupvt=t,e=t};var i=[[0,0],[0,0],[0,0],[0,0],[0,0],[0,1],[1,1],[1,1],[1,2],[2,2],[2,3],[2,3],[3,4],[3,4],[3,4],[4,5],[4,5],[4,6],[5,6],[5,6],[5,7],[6,7],[6,7]];function d(t,e,n,i,s,r){var a=.5946/e;for(o(0<t),t>>=1;0!=t--;)s[r++]=a>n[i++]?0:1,s[r++]=a>n[i++]?0:1}function f(t,n,i,s,r,a){o(0<t);var l=(t>>=1)%2;for(t>>=1;0!=t--;){var c,u,h,d,f,p,_,m;c=i[s++]*n,u=i[s++]*n,f=0|c,h=i[s++]*n,p=0|u,d=i[s++]*n,_=0|h,c+=e.adj43[f],m=0|d,u+=e.adj43[p],r[a++]=0|c,h+=e.adj43[_],r[a++]=0|u,d+=e.adj43[m],r[a++]=0|h,r[a++]=0|d}0!=l&&(f=0|(c=i[s++]*n),p=0|(u=i[s++]*n),c+=e.adj43[f],u+=e.adj43[p],r[a++]=0|c,r[a++]=0|u)}var p=[1,2,5,7,7,10,10,13,13,13,13,13,13,13,13];function _(t,e,n,i){var s=function(t,e,n){var i=0,s=0;do{var r=t[e++],a=t[e++];i<r&&(i=r),s<a&&(s=a)}while(e<n);return i<s&&(i=s),i}(t,e,n);switch(s){case 0:return s;case 1:return function(t,e,n,i){var s=0,r=c.ht[1].hlen;do{var a=2*t[e+0]+t[e+1];e+=2,s+=r[a]}while(e<n);return i.bits+=s,1}(t,e,n,i);case 2:case 3:return function(t,e,n,i,s){var r,a,o=0,l=c.ht[i].xlen;a=2==i?c.table23:c.table56;do{var u=t[e+0]*l+t[e+1];e+=2,o+=a[u]}while(e<n);return(r=65535&o)<(o>>=16)&&(o=r,i++),s.bits+=o,i}(t,e,n,p[s-1],i);case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:return function(t,e,n,i,s){var r=0,a=0,o=0,l=c.ht[i].xlen,u=c.ht[i].hlen,h=c.ht[i+1].hlen,d=c.ht[i+2].hlen;do{var f=t[e+0]*l+t[e+1];e+=2,r+=u[f],a+=h[f],o+=d[f]}while(e<n);var p=i;return a<r&&(r=a,p++),o<r&&(r=o,p=i+2),s.bits+=r,p}(t,e,n,p[s-1],i);default:if(s>h.IXMAX_VAL)return i.bits=h.LARGE_BITS,-1;var r,a;for(s-=15,r=24;r<32&&!(c.ht[r].linmax>=s);r++);for(a=r-8;a<24&&!(c.ht[a].linmax>=s);a++);return function(t,e,n,i,s,r){var a,o=65536*c.ht[i].xlen+c.ht[s].xlen,l=0;do{var u=t[e++],h=t[e++];0!=u&&(14<u&&(u=15,l+=o),u*=16),0!=h&&(14<h&&(h=15,l+=o),u+=h),l+=c.largetbl[u]}while(e<n);return(a=65535&l)<(l>>=16)&&(l=a,i=s),r.bits+=l,i}(t,e,n,a,r,i)}}function m(t,e,i,s,r,a,o,c){for(var u=e.big_values,h=2;h<l.SBMAX_l+1;h++){var d=t.scalefac_band.l[h];if(u<=d)break;var f=r[h-2]+e.count1bits;if(i.part2_3_length<=f)break;var p=new n(f),m=_(s,d,u,p);f=p.bits,i.part2_3_length<=f||(i.assign(e),i.part2_3_length=f,i.region0_count=a[h-2],i.region1_count=h-2-a[h-2],i.table_select[0]=o[h-2],i.table_select[1]=c[h-2],i.table_select[2]=m)}}this.noquant_count_bits=function(t,e,i){var s=e.l3_enc,r=Math.min(576,e.max_nonzero_coeff+2>>1<<1);for(null!=i&&(i.sfb_count1=0);1<r&&0==(s[r-1]|s[r-2]);r-=2);e.count1=r;for(var a=0,u=0;3<r;r-=4){var h;if(1<(2147483647&(s[r-1]|s[r-2]|s[r-3]|s[r-4])))break;h=2*(2*(2*s[r-4]+s[r-3])+s[r-2])+s[r-1],a+=c.t32l[h],u+=c.t33l[h]}var d=a;if(e.count1table_select=0,u<a&&(d=u,e.count1table_select=1),e.count1bits=d,0==(e.big_values=r))return d;if(e.block_type==l.SHORT_TYPE)(a=3*t.scalefac_band.s[3])>e.big_values&&(a=e.big_values),u=e.big_values;else if(e.block_type==l.NORM_TYPE){if(o(r<=576),a=e.region0_count=t.bv_scf[r-2],u=e.region1_count=t.bv_scf[r-1],o(a+u+2<l.SBPSY_l),u=t.scalefac_band.l[a+u+2],a=t.scalefac_band.l[a+1],u<r){var f=new n(d);e.table_select[2]=_(s,u,r,f),d=f.bits}}else e.region0_count=7,e.region1_count=l.SBMAX_l-1-7-1,(u=r)<(a=t.scalefac_band.l[8])&&(a=u);if(a=Math.min(a,r),u=Math.min(u,r),o(0<=a),o(0<=u),0<a&&(f=new n(d),e.table_select[0]=_(s,0,a,f),d=f.bits),a<u&&(f=new n(d),e.table_select[1]=_(s,a,u,f),d=f.bits),2==t.use_best_huffman&&(e.part2_3_length=d,best_huffman_divide(t,e),d=e.part2_3_length),null!=i&&e.block_type==l.NORM_TYPE){for(var p=0;t.scalefac_band.l[p]<e.big_values;)p++;i.sfb_count1=p}return d},this.count_bits=function(t,n,i,s){var a=i.l3_enc,c=h.IXMAX_VAL/e.IPOW20(i.global_gain);if(i.xrpow_max>c)return h.LARGE_BITS;if(function(t,n,i,s,a){var c,u,h,p=0,_=0,m=0,v=0,g=n,b=0,y=g,x=0,w=t,S=0;for(h=null!=a&&s.global_gain==a.global_gain,u=s.block_type==l.SHORT_TYPE?38:21,c=0;c<=u;c++){var k=-1;if((h||s.block_type==l.NORM_TYPE)&&(k=s.global_gain-(s.scalefac[c]+(0!=s.preflag?e.pretab[c]:0)<<s.scalefac_scale+1)-8*s.subblock_gain[s.window[c]]),o(0<=s.width[c]),h&&a.step[c]==k)0!=_&&(f(_,i,w,S,y,x),_=0),0!=m&&(d(m,i,w,S,y,x),m=0);else{var A,C=s.width[c];if(p+s.width[c]>s.max_nonzero_coeff&&(A=s.max_nonzero_coeff-p+1,r.fill(n,s.max_nonzero_coeff,576,0),(C=A)<0&&(C=0),c=u+1),0==_&&0==m&&(y=g,x=b,w=t,S=v),null!=a&&0<a.sfb_count1&&c>=a.sfb_count1&&0<a.step[c]&&k>=a.step[c]?(0!=_&&(f(_,i,w,S,y,x),_=0,y=g,x=b,w=t,S=v),m+=C):(0!=m&&(d(m,i,w,S,y,x),m=0,y=g,x=b,w=t,S=v),_+=C),C<=0){0!=m&&(d(m,i,w,S,y,x),m=0),0!=_&&(f(_,i,w,S,y,x),_=0);break}}c<=u&&(b+=s.width[c],v+=s.width[c],p+=s.width[c])}0!=_&&(f(_,i,w,S,y,x),_=0),0!=m&&(d(m,i,w,S,y,x),m=0)}(n,a,e.IPOW20(i.global_gain),i,s),0!=(2&t.substep_shaping))for(var u=0,p=i.global_gain+i.scalefac_scale,_=.634521682242439/e.IPOW20(p),m=0;m<i.sfbmax;m++){var v,g=i.width[m];if(o(0<=g),0==t.pseudohalf[m])u+=g;else for(v=u,u+=g;v<u;++v)a[v]=n[v]>=_?a[v]:0}return this.noquant_count_bits(t,i,s)},this.best_huffman_divide=function(t,e){var i=new u,s=e.l3_enc,r=a(23),d=a(23),f=a(23),p=a(23);if(e.block_type!=l.SHORT_TYPE||1!=t.mode_gr){i.assign(e),e.block_type==l.NORM_TYPE&&(function(t,e,i,s,r,a,o){for(var l=e.big_values,c=0;c<=22;c++)s[c]=h.LARGE_BITS;for(c=0;c<16;c++){var u=t.scalefac_band.l[c+1];if(l<=u)break;var d=0,f=new n(d),p=_(i,0,u,f);d=f.bits;for(var m=0;m<8;m++){var v=t.scalefac_band.l[c+m+2];if(l<=v)break;var g=d,b=_(i,u,v,f=new n(g));g=f.bits,s[c+m]>g&&(s[c+m]=g,a[(r[c+m]=c)+m]=p,o[c+m]=b)}}}(t,e,s,r,d,f,p),m(t,i,e,s,r,d,f,p));var v=i.big_values;if(!(0==v||1<(s[v-2]|s[v-1])||576<(v=e.count1+2))){i.assign(e),i.count1=v;var g=0,b=0;for(o(v<=576);v>i.big_values;v-=4){var y=2*(2*(2*s[v-4]+s[v-3])+s[v-2])+s[v-1];g+=c.t32l[y],b+=c.t33l[y]}if(i.big_values=v,i.count1table_select=0,b<g&&(g=b,i.count1table_select=1),i.count1bits=g,i.block_type==l.NORM_TYPE)m(t,i,e,s,r,d,f,p);else{if(i.part2_3_length=g,v<(g=t.scalefac_band.l[8])&&(g=v),0<g){var x=new n(i.part2_3_length);i.table_select[0]=_(s,0,g,x),i.part2_3_length=x.bits}g<v&&(x=new n(i.part2_3_length),i.table_select[1]=_(s,g,v,x),i.part2_3_length=x.bits),e.part2_3_length>i.part2_3_length&&e.assign(i)}}}};var v=[1,1,1,1,8,2,2,2,4,4,4,8,8,8,16,16],g=[1,2,4,8,1,2,4,8,2,4,8,2,4,8,4,8],b=[0,0,0,0,3,1,1,1,2,2,2,3,3,3,4,4],y=[0,1,2,3,0,1,2,3,1,2,3,1,2,3,2,3];t.slen1_tab=b,t.slen2_tab=y,this.best_scalefac_store=function(t,n,i,s){var r,a,u,h,d=s.tt[n][i],f=0;for(r=u=0;r<d.sfbmax;r++){var p=d.width[r];for(o(0<=p),u+=p,h=-p;h<0&&0==d.l3_enc[h+u];h++);0==h&&(d.scalefac[r]=f=-2)}if(0==d.scalefac_scale&&0==d.preflag){var _=0;for(r=0;r<d.sfbmax;r++)0<d.scalefac[r]&&(_|=d.scalefac[r]);if(0==(1&_)&&0!=_){for(r=0;r<d.sfbmax;r++)0<d.scalefac[r]&&(d.scalefac[r]>>=1);d.scalefac_scale=f=1}}if(0==d.preflag&&d.block_type!=l.SHORT_TYPE&&2==t.mode_gr){for(r=11;r<l.SBPSY_l&&!(d.scalefac[r]<e.pretab[r]&&-2!=d.scalefac[r]);r++);if(r==l.SBPSY_l){for(r=11;r<l.SBPSY_l;r++)0<d.scalefac[r]&&(d.scalefac[r]-=e.pretab[r]);d.preflag=f=1}}for(a=0;a<4;a++)s.scfsi[i][a]=0;for(2==t.mode_gr&&1==n&&s.tt[0][i].block_type!=l.SHORT_TYPE&&s.tt[1][i].block_type!=l.SHORT_TYPE&&(function(t,e){for(var n,i=e.tt[1][t],s=e.tt[0][t],r=0;r<c.scfsi_band.length-1;r++){for(n=c.scfsi_band[r];n<c.scfsi_band[r+1]&&!(s.scalefac[n]!=i.scalefac[n]&&0<=i.scalefac[n]);n++);if(n==c.scfsi_band[r+1]){for(n=c.scfsi_band[r];n<c.scfsi_band[r+1];n++)i.scalefac[n]=-1;e.scfsi[t][r]=1}}var a=0,o=0;for(n=0;n<11;n++)-1!=i.scalefac[n]&&(o++,a<i.scalefac[n]&&(a=i.scalefac[n]));for(var u=0,h=0;n<l.SBPSY_l;n++)-1!=i.scalefac[n]&&(h++,u<i.scalefac[n]&&(u=i.scalefac[n]));for(r=0;r<16;r++)if(a<v[r]&&u<g[r]){var d=b[r]*o+y[r]*h;i.part2_length>d&&(i.part2_length=d,i.scalefac_compress=r)}}(i,s),f=0),r=0;r<d.sfbmax;r++)-2==d.scalefac[r]&&(d.scalefac[r]=0);0!=f&&(2==t.mode_gr?this.scale_bitcount(d):this.scale_bitcount_lsf(t,d))};var x=[0,18,36,54,54,36,54,72,54,72,90,72,90,108,108,126],w=[0,18,36,54,51,35,53,71,52,70,88,69,87,105,104,122],S=[0,10,20,30,33,21,31,41,32,42,52,43,53,63,64,74];this.scale_bitcount=function(t){var n,i,s,r=0,a=0,c=t.scalefac;if(o(function(t,e){for(var n=0;n<e;++n)if(t[n]<0)return!1;return!0}(c,t.sfbmax)),t.block_type==l.SHORT_TYPE)s=x,0!=t.mixed_block_flag&&(s=w);else if(s=S,0==t.preflag){for(i=11;i<l.SBPSY_l&&!(c[i]<e.pretab[i]);i++);if(i==l.SBPSY_l)for(t.preflag=1,i=11;i<l.SBPSY_l;i++)c[i]-=e.pretab[i]}for(i=0;i<t.sfbdivide;i++)r<c[i]&&(r=c[i]);for(;i<t.sfbmax;i++)a<c[i]&&(a=c[i]);for(t.part2_length=h.LARGE_BITS,n=0;n<16;n++)r<v[n]&&a<g[n]&&t.part2_length>s[n]&&(t.part2_length=s[n],t.scalefac_compress=n);return t.part2_length==h.LARGE_BITS};var k=[[15,15,7,7],[15,15,7,0],[7,3,0,0],[15,31,31,0],[7,7,7,0],[3,3,0,0]];this.scale_bitcount_lsf=function(t,n){var i,r,c,u,h,d,f,p,_=a(4),m=n.scalefac;for(i=0!=n.preflag?2:0,f=0;f<4;f++)_[f]=0;if(n.block_type==l.SHORT_TYPE){r=1;var v=e.nr_of_sfb_block[i][r];for(c=p=0;c<4;c++)for(u=v[c]/3,f=0;f<u;f++,p++)for(h=0;h<3;h++)m[3*p+h]>_[c]&&(_[c]=m[3*p+h])}else for(r=0,v=e.nr_of_sfb_block[i][r],c=p=0;c<4;c++)for(u=v[c],f=0;f<u;f++,p++)m[p]>_[c]&&(_[c]=m[p]);for(d=!1,c=0;c<4;c++)_[c]>k[i][c]&&(d=!0);if(!d){var g,b,y,x;for(n.sfb_partition_table=e.nr_of_sfb_block[i][r],c=0;c<4;c++)n.slen[c]=A[_[c]];switch(g=n.slen[0],b=n.slen[1],y=n.slen[2],x=n.slen[3],i){case 0:n.scalefac_compress=(5*g+b<<4)+(y<<2)+x;break;case 1:n.scalefac_compress=400+(5*g+b<<2)+y;break;case 2:n.scalefac_compress=500+3*g+b;break;default:s.err.printf("intensity stereo not implemented yet\n")}}if(!d)for(o(null!=n.sfb_partition_table),c=n.part2_length=0;c<4;c++)n.part2_length+=n.slen[c]*n.sfb_partition_table[c];return d};var A=[0,1,2,2,3,3,3,3,4,4,4,4,4,4,4,4];this.huffman_init=function(t){for(var e=2;e<=576;e+=2){for(var n,s=0;t.scalefac_band.l[++s]<e;);for(n=i[s][0];t.scalefac_band.l[n+1]>e;)n--;for(n<0&&(n=i[s][0]),t.bv_scf[e-2]=n,n=i[s][1];t.scalefac_band.l[n+t.bv_scf[e-2]+2]>e;)n--;n<0&&(n=i[s][1]),t.bv_scf[e-1]=n}}}},function(t,e,n){var i=n(10),s=n(0),r=(s.System,s.VbrMode),a=s.Float,o=(s.ShortBlock,s.Util),l=(s.Arrays,s.new_array_n,s.new_byte,s.new_double,s.new_float),c=(s.new_float_n,s.new_int),u=(s.new_int_n,s.assert),h=n(1),d=n(12),f=n(4);function p(){var t=null,e=null,n=null;this.setModules=function(i,s,r){t=i,e=s,n=r},this.IPOW20=function(t){return u(0<=t&&t<p.Q_MAX),x[t]};var s=2220446049250313e-31,_=p.IXMAX_VAL+2,m=p.Q_MAX,v=p.Q_MAX2,g=100;this.nr_of_sfb_block=[[[6,5,5,5],[9,9,9,9],[6,9,9,9]],[[6,5,7,3],[9,9,12,6],[6,9,12,6]],[[11,10,0,0],[18,18,0,0],[15,18,0,0]],[[7,7,7,0],[12,12,12,0],[6,15,12,0]],[[6,6,6,3],[12,9,9,6],[6,12,9,6]],[[8,8,5,0],[15,12,9,0],[6,18,9,0]]];var b=[0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,2,2,3,3,3,2,0];this.pretab=b,this.sfBandIndex=[new i([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,24,32,42,56,74,100,132,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,6,12,18,24,30,36,44,54,66,80,96,114,136,162,194,232,278,332,394,464,540,576],[0,4,8,12,18,26,36,48,62,80,104,136,180,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,26,36,48,62,80,104,134,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,4,8,12,16,20,24,30,36,44,52,62,74,90,110,134,162,196,238,288,342,418,576],[0,4,8,12,16,22,30,40,52,66,84,106,136,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,4,8,12,16,20,24,30,36,42,50,60,72,88,106,128,156,190,230,276,330,384,576],[0,4,8,12,16,22,28,38,50,64,80,100,126,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,4,8,12,16,20,24,30,36,44,54,66,82,102,126,156,194,240,296,364,448,550,576],[0,4,8,12,16,22,30,42,58,78,104,138,180,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,26,36,48,62,80,104,134,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,26,36,48,62,80,104,134,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new i([0,12,24,36,48,60,72,88,108,132,160,192,232,280,336,400,476,566,568,570,572,574,576],[0,8,16,24,36,52,72,96,124,160,162,164,166,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0])];var y=l(m+v+1),x=l(m),w=l(_),S=l(_);function k(t,e){var i=n.ATHformula(e,t);return i-=g,Math.pow(10,i/10+t.ATHlower)}function A(t){this.s=t}this.adj43=S,this.iteration_init=function(e){var n,i=e.internal_flags,s=i.l3_side;if(0==i.iteration_init_init){for(i.iteration_init_init=1,s.main_data_begin=0,function(t){for(var e=t.internal_flags.ATH.l,n=t.internal_flags.ATH.psfb21,i=t.internal_flags.ATH.s,s=t.internal_flags.ATH.psfb12,r=t.internal_flags,o=t.out_samplerate,l=0;l<h.SBMAX_l;l++){var c=r.scalefac_band.l[l],u=r.scalefac_band.l[l+1];e[l]=a.MAX_VALUE;for(var d=c;d<u;d++){var f=k(t,d*o/1152);e[l]=Math.min(e[l],f)}}for(l=0;l<h.PSFB21;l++)for(c=r.scalefac_band.psfb21[l],u=r.scalefac_band.psfb21[l+1],n[l]=a.MAX_VALUE,d=c;d<u;d++)f=k(t,d*o/1152),n[l]=Math.min(n[l],f);for(l=0;l<h.SBMAX_s;l++){for(c=r.scalefac_band.s[l],u=r.scalefac_band.s[l+1],i[l]=a.MAX_VALUE,d=c;d<u;d++)f=k(t,d*o/384),i[l]=Math.min(i[l],f);i[l]*=r.scalefac_band.s[l+1]-r.scalefac_band.s[l]}for(l=0;l<h.PSFB12;l++){for(c=r.scalefac_band.psfb12[l],u=r.scalefac_band.psfb12[l+1],s[l]=a.MAX_VALUE,d=c;d<u;d++)f=k(t,d*o/384),s[l]=Math.min(s[l],f);s[l]*=r.scalefac_band.s[13]-r.scalefac_band.s[12]}if(t.noATH){for(l=0;l<h.SBMAX_l;l++)e[l]=1e-20;for(l=0;l<h.PSFB21;l++)n[l]=1e-20;for(l=0;l<h.SBMAX_s;l++)i[l]=1e-20;for(l=0;l<h.PSFB12;l++)s[l]=1e-20}r.ATH.floor=10*Math.log10(k(t,-1))}(e),w[0]=0,n=1;n<_;n++)w[n]=Math.pow(n,4/3);for(n=0;n<_-1;n++)S[n]=n+1-Math.pow(.5*(w[n]+w[n+1]),.75);for(S[n]=.5,n=0;n<m;n++)x[n]=Math.pow(2,-.1875*(n-210));for(n=0;n<=m+v;n++)y[n]=Math.pow(2,.25*(n-210-v));var r,o,l,c;for(t.huffman_init(i),32<=(n=e.exp_nspsytune>>2&63)&&(n-=64),r=Math.pow(10,n/4/10),32<=(n=e.exp_nspsytune>>8&63)&&(n-=64),o=Math.pow(10,n/4/10),32<=(n=e.exp_nspsytune>>14&63)&&(n-=64),l=Math.pow(10,n/4/10),32<=(n=e.exp_nspsytune>>20&63)&&(n-=64),c=l*Math.pow(10,n/4/10),n=0;n<h.SBMAX_l;n++)u=n<=6?r:n<=13?o:n<=20?l:c,i.nsPsy.longfact[n]=u;for(n=0;n<h.SBMAX_s;n++){var u;u=n<=5?r:n<=10?o:n<=11?l:c,i.nsPsy.shortfact[n]=u}}},this.on_pe=function(t,n,i,s,r,a){var o,l,h=t.internal_flags,p=0,_=c(2),m=new d(p),v=e.ResvMaxBits(t,s,m,a),g=(p=m.bits)+v;for(g>f.MAX_BITS_PER_GRANULE&&(g=f.MAX_BITS_PER_GRANULE),l=o=0;l<h.channels_out;++l)i[l]=Math.min(f.MAX_BITS_PER_CHANNEL,p/h.channels_out),_[l]=0|i[l]*n[r][l]/700-i[l],_[l]>3*s/4&&(_[l]=3*s/4),_[l]<0&&(_[l]=0),_[l]+i[l]>f.MAX_BITS_PER_CHANNEL&&(_[l]=Math.max(0,f.MAX_BITS_PER_CHANNEL-i[l])),o+=_[l];if(v<o)for(l=0;l<h.channels_out;++l)_[l]=v*_[l]/o;for(l=0;l<h.channels_out;++l)i[l]+=_[l],v-=_[l];for(l=o=0;l<h.channels_out;++l)o+=i[l];if(o>f.MAX_BITS_PER_GRANULE){var b=0;for(l=0;l<h.channels_out;++l)i[l]*=f.MAX_BITS_PER_GRANULE,i[l]/=o,b+=i[l];u(b<=f.MAX_BITS_PER_GRANULE)}return g},this.reduce_side=function(t,e,n,i){u(i<=f.MAX_BITS_PER_GRANULE),u(t[0]+t[1]<=f.MAX_BITS_PER_GRANULE);var s=.33*(.5-e)/.5;s<0&&(s=0),.5<s&&(s=.5);var r=0|.5*s*(t[0]+t[1]);r>f.MAX_BITS_PER_CHANNEL-t[0]&&(r=f.MAX_BITS_PER_CHANNEL-t[0]),r<0&&(r=0),125<=t[1]&&(125<t[1]-r?(t[0]<n&&(t[0]+=r),t[1]-=r):(t[0]+=t[1]-125,t[1]=125)),i<(r=t[0]+t[1])&&(t[0]=i*t[0]/r,t[1]=i*t[1]/r),u(t[0]<=f.MAX_BITS_PER_CHANNEL),u(t[1]<=f.MAX_BITS_PER_CHANNEL),u(t[0]+t[1]<=f.MAX_BITS_PER_GRANULE)},this.athAdjust=function(t,e,n){var i=90.30873362,s=o.FAST_LOG10_X(e,10),r=t*t,a=0;return s-=n,1e-20<r&&(a=1+o.FAST_LOG10_X(r,10/i)),a<0&&(a=0),s*=a,s+=n+i-94.82444863,Math.pow(10,.1*s)},this.calc_xmin=function(t,e,n,i){var a,o=0,l=t.internal_flags,c=0,u=0,d=l.ATH,f=n.xr,p=t.VBR==r.vbr_mtrh?1:0,_=l.masking_lower;for(t.VBR!=r.vbr_mtrh&&t.VBR!=r.vbr_mt||(_=1),a=0;a<n.psy_lmax;a++){S=(w=t.VBR==r.vbr_rh||t.VBR==r.vbr_mtrh?athAdjust(d.adjust,d.l[a],d.floor):d.adjust*d.l[a])/(b=n.width[a]),k=s,E=b>>1,M=0;do{M+=T=f[c]*f[c],k+=T<S?T:S,M+=B=f[++c]*f[c],k+=B<S?B:S,c++}while(0<--E);w<M&&u++,a==h.SBPSY_l&&k<(C=w*l.nsPsy.longfact[a])&&(k=C),0!=p&&(w=k),t.ATHonly||0<(A=e.en.l[a])&&(C=M*e.thm.l[a]*_/A,0!=p&&(C*=l.nsPsy.longfact[a]),w<C&&(w=C)),i[o++]=0!=p?w:w*l.nsPsy.longfact[a]}var m=575;if(n.block_type!=h.SHORT_TYPE)for(var v=576;0!=v--&&BitStream.EQ(f[v],0);)m=v;n.max_nonzero_coeff=m;for(var g=n.sfb_smin;a<n.psymax;g++,a+=3){var b,y,x;for(x=t.VBR==r.vbr_rh||t.VBR==r.vbr_mtrh?athAdjust(d.adjust,d.s[g],d.floor):d.adjust*d.s[g],b=n.width[a],y=0;y<3;y++){var w,S,k,A,C,M=0,E=b>>1;S=x/b,k=s;do{var T,B;M+=T=f[c]*f[c],k+=T<S?T:S,M+=B=f[++c]*f[c],k+=B<S?B:S,c++}while(0<--E);x<M&&u++,g==h.SBPSY_s&&k<(C=x*l.nsPsy.shortfact[g])&&(k=C),w=0!=p?k:x,t.ATHonly||t.ATHshort||0<(A=e.en.s[g][y])&&(C=M*e.thm.s[g][y]*_/A,0!=p&&(C*=l.nsPsy.shortfact[g]),w<C&&(w=C)),i[o++]=0!=p?w:w*l.nsPsy.shortfact[g]}t.useTemporal&&(i[o-3]>i[o-3+1]&&(i[o-3+1]+=(i[o-3]-i[o-3+1])*l.decay),i[o-3+1]>i[o-3+2]&&(i[o-3+2]+=(i[o-3+1]-i[o-3+2])*l.decay))}return u},this.calc_noise_core=function(t,e,n,i){var s=0,r=e.s,a=t.l3_enc;if(r>t.count1)for(;0!=n--;)c=t.xr[r],r++,s+=c*c,c=t.xr[r],r++,s+=c*c;else if(r>t.big_values){var o=l(2);for(o[0]=0,o[1]=i;0!=n--;)c=Math.abs(t.xr[r])-o[a[r]],r++,s+=c*c,c=Math.abs(t.xr[r])-o[a[r]],r++,s+=c*c}else for(;0!=n--;){var c;c=Math.abs(t.xr[r])-w[a[r]]*i,r++,s+=c*c,c=Math.abs(t.xr[r])-w[a[r]]*i,r++,s+=c*c}return e.s=r,s},this.calc_noise=function(t,e,n,i,s){var r,a,l,c=0,h=0,d=0,f=0,_=0,m=-20,v=0,g=t.scalefac,x=0;for(r=i.over_SSD=0;r<t.psymax;r++){var w,S=t.global_gain-(g[x++]+(0!=t.preflag?b[r]:0)<<t.scalefac_scale+1)-8*t.subblock_gain[t.window[r]],k=0;if(null!=s&&s.step[r]==S)k=s.noise[r],v+=t.width[r],n[c++]=k/e[h++],k=s.noise_log[r];else{var C,M=(u(0<=(l=S)+p.Q_MAX2&&l<p.Q_MAX),y[l+p.Q_MAX2]);a=t.width[r]>>1,v+t.width[r]>t.max_nonzero_coeff&&(a=0<(C=t.max_nonzero_coeff-v+1)?C>>1:0);var E=new A(v);k=this.calc_noise_core(t,E,a,M),v=E.s,null!=s&&(s.step[r]=S,s.noise[r]=k),k=n[c++]=k/e[h++],k=o.FAST_LOG10(Math.max(k,1e-20)),null!=s&&(s.noise_log[r]=k)}null!=s&&(s.global_gain=t.global_gain),_+=k,0<k&&(w=Math.max(0|10*k+.5,1),i.over_SSD+=w*w,d++,f+=k),m=Math.max(m,k)}return i.over_count=d,i.tot_noise=_,i.over_noise=f,i.max_noise=m,d},this.set_pinfo=function(t,e,n,i,s){var r,a,o,c,d,f=t.internal_flags,p=0==e.scalefac_scale?.5:1,_=e.scalefac,m=l(L3Side.SFBMAX),v=l(L3Side.SFBMAX),g=new CalcNoiseResult;calc_xmin(t,n,e,m),calc_noise(e,m,v,g,null);var y=0;for(a=e.sfb_lmax,e.block_type!=h.SHORT_TYPE&&0==e.mixed_block_flag&&(a=22),r=0;r<a;r++){var x=f.scalefac_band.l[r],w=(S=f.scalefac_band.l[r+1])-x;for(c=0;y<S;y++)c+=e.xr[y]*e.xr[y];c/=w,d=1e15,f.pinfo.en[i][s][r]=d*c,f.pinfo.xfsf[i][s][r]=d*m[r]*v[r]/w,0<n.en.l[r]&&!t.ATHonly?c/=n.en.l[r]:c=0,f.pinfo.thr[i][s][r]=d*Math.max(c*n.thm.l[r],f.ATH.l[r]),(f.pinfo.LAMEsfb[i][s][r]=0)!=e.preflag&&11<=r&&(f.pinfo.LAMEsfb[i][s][r]=-p*b[r]),r<h.SBPSY_l&&(u(0<=_[r]),f.pinfo.LAMEsfb[i][s][r]-=p*_[r])}if(e.block_type==h.SHORT_TYPE)for(a=r,r=e.sfb_smin;r<h.SBMAX_s;r++){x=f.scalefac_band.s[r],w=(S=f.scalefac_band.s[r+1])-x;for(var S,k=0;k<3;k++){for(c=0,o=x;o<S;o++)c+=e.xr[y]*e.xr[y],y++;c=Math.max(c/w,1e-20),d=1e15,f.pinfo.en_s[i][s][3*r+k]=d*c,f.pinfo.xfsf_s[i][s][3*r+k]=d*m[a]*v[a]/w,0<n.en.s[r][k]?c/=n.en.s[r][k]:c=0,(t.ATHonly||t.ATHshort)&&(c=0),f.pinfo.thr_s[i][s][3*r+k]=d*Math.max(c*n.thm.s[r][k],f.ATH.s[r]),f.pinfo.LAMEsfb_s[i][s][3*r+k]=-2*e.subblock_gain[k],r<h.SBPSY_s&&(f.pinfo.LAMEsfb_s[i][s][3*r+k]-=p*_[a]),a++}}f.pinfo.LAMEqss[i][s]=e.global_gain,f.pinfo.LAMEmainbits[i][s]=e.part2_3_length+e.part2_length,f.pinfo.LAMEsfbits[i][s]=e.part2_length,f.pinfo.over[i][s]=g.over_count,f.pinfo.max_noise[i][s]=10*g.max_noise,f.pinfo.over_noise[i][s]=10*g.over_noise,f.pinfo.tot_noise[i][s]=10*g.tot_noise,f.pinfo.over_SSD[i][s]=g.over_SSD}}p.Q_MAX=257,p.Q_MAX2=116,p.LARGE_BITS=1e5,p.IXMAX_VAL=8206,t.exports=p},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte),r=(i.new_double,i.new_float,i.new_float_n,i.new_int,i.new_int_n,i.assert);Lame=n(27),Presets=n(39),GainAnalysis=n(11),QuantizePVT=n(15),Quantize=n(40),Takehiro=n(14),Reservoir=n(44),MPEGMode=n(9),BitStream=n(13),n(1);var a=n(45),o=n(46);function l(){this.setModules=function(t,e){}}function c(){this.setModules=function(t,e,n){}}function u(){}function h(){this.setModules=function(t,e){}}function d(){this.dataOffset=0,this.dataLen=0,this.channels=0,this.sampleRate=0}function f(t){return t.charCodeAt(0)<<24|t.charCodeAt(1)<<16|t.charCodeAt(2)<<8|t.charCodeAt(3)}d.RIFF=f("RIFF"),d.WAVE=f("WAVE"),d.fmt_=f("fmt "),d.data=f("data"),d.readHeader=function(t){var e=new d,n=t.getUint32(0,!1);if(d.RIFF==n&&(t.getUint32(4,!0),d.WAVE==t.getUint32(8,!1)&&d.fmt_==t.getUint32(12,!1))){var i=t.getUint32(16,!0),s=20;switch(i){case 16:case 18:e.channels=t.getUint16(s+2,!0),e.sampleRate=t.getUint32(s+4,!0);break;default:throw"extended fmt chunk not implemented"}s+=i;for(var r=d.data,a=0;r!=n&&(n=t.getUint32(s,!1),a=t.getUint32(s+4,!0),r!=n);)s+=a+8;return e.dataLen=a,e.dataOffset=s+8,e}},t.exports.Mp3Encoder=function(t,e,n){3!=arguments.length&&(console.error("WARN: Mp3Encoder(channels, samplerate, kbps) not specified"),t=1,e=44100,n=128);var i=new Lame,d=new l,f=new GainAnalysis,p=new BitStream,_=new Presets,m=new QuantizePVT,v=new Quantize,g=new o,b=new a,y=new h,x=new Reservoir,w=new Takehiro,S=new c,k=new u;i.setModules(f,p,_,m,v,g,b,y,k),p.setModules(f,k,b,g),y.setModules(p,b),_.setModules(i),v.setModules(p,x,m,w),m.setModules(w,x,i.enc.psy),x.setModules(p),w.setModules(m),g.setModules(i,p,b),d.setModules(S,k),S.setModules(b,y,_);var A=i.lame_init();A.num_channels=t,A.in_samplerate=e,A.brate=n,A.mode=MPEGMode.STEREO,A.quality=3,A.bWriteVbrTag=!1,A.disable_reservoir=!0,A.write_id3tag_automatic=!1;var C=i.lame_init_params(A);r(0==C);var M=1152,E=0|1.25*M+7200,T=s(E);this.encodeBuffer=function(e,n){1==t&&(n=e),r(e.length==n.length),e.length>M&&(M=e.length,T=s(E=0|1.25*M+7200));var a=i.lame_encode_buffer(A,e,n,e.length,T,0,E);return new Int8Array(T.subarray(0,a))},this.flush=function(){var t=i.lame_encode_flush(A,T,0,E);return new Int8Array(T.subarray(0,t))}},t.exports.WavHeader=d},function(t,e,n){var i=n(18);"string"==typeof i&&(i=[[t.i,i,""]]),i.locals&&(t.exports=i.locals),(0,n(3).default)("9d62102e",i,!1,{})},function(t,e,n){(t.exports=n(2)(!1)).push([t.i,"\n.ar-player {\n  width: 380px;\n  height: unset;\n  border: 0;\n  border-radius: 0;\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  justify-content: center;\n  background-color: unset;\n  font-family: 'Roboto', sans-serif;\n}\n.ar-player > .ar-player-bar {\n    border: 1px solid #E8E8E8;\n    border-radius: 24px;\n    margin: 0 0 0 5px;\n}\n.ar-player > .ar-player-bar > .ar-player__progress {\n      width: 125px;\n}\n.ar-player-bar {\n    display: flex;\n    align-items: center;\n    height: 38px;\n    padding: 0 12px;\n    margin: 0 5px;\n}\n.ar-player-actions {\n    width: 55%;\n    display: flex;\n    align-items: center;\n    justify-content: space-around;\n}\n.ar-player__progress {\n    width: 160px;\n    margin: 0 8px;\n}\n.ar-player__time {\n    color: rgba(84, 84, 84, 0.5);\n    font-size: 16px;\n    width: 41px;\n}\n.ar-player__play {\n    width: 45px;\n    height: 45px;\n    background-color: #FFFFFF;\n    box-shadow: 0 2px 11px 11px rgba(0, 0, 0, 0.07);\n}\n.ar-player__play--active {\n      fill: white !important;\n      background-color: #05CBCD !important;\n}\n.ar-icon {\n  fill: #747474;\n  border-radius: 50%;\n  border: 1px solid #05CBCD;\n  background-color: #FFFFFF;\n  padding: 5px;\n  cursor: pointer;\n  transition: .2s;\n}\n.ar-icon--no-border {\n    border: 0;\n    border-radius: 0;\n    padding: 0;\n}\n.ar-icon--rec {\n    fill: white;\n    background-color: #FF6B64;\n    border-color: transparent;\n}\n.ar-icon--pulse {\n    animation: ripple .5s linear infinite;\n}\n@keyframes ripple {\n0% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 1px rgba(255, 0, 0, 0.1), 0 0 0 5px rgba(255, 0, 0, 0.1);\n}\n100% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 10px rgba(255, 0, 0, 0.1), 0 0 0 20px rgba(255, 0, 0, 0);\n}\n}\n.ar-icon__xs {\n    width: 18px;\n    height: 18px;\n    line-height: 18px;\n}\n.ar-icon__sm {\n    width: 30px;\n    height: 30px;\n    line-height: 30px;\n}\n.ar-icon__lg {\n    width: 45px;\n    height: 45px;\n    line-height: 45px;\n    box-shadow: 0 2px 5px 1px rgba(158, 158, 158, 0.5);\n}\n",""])},function(t,e,n){var i=n(20);"string"==typeof i&&(i=[[t.i,i,""]]),i.locals&&(t.exports=i.locals),(0,n(3).default)("722c44a2",i,!1,{})},function(t,e,n){(t.exports=n(2)(!1)).push([t.i,"\n.ar-line-control {\n  position: relative;\n  height: 8px;\n  border-radius: 5px;\n  background-color: #E6E6E6;\n}\n.ar-line-control__head {\n    position: absolute;\n    height: inherit;\n    background-color: #616161;\n    border-radius: inherit;\n}\n",""])},function(t,e,n){var i=n(22);"string"==typeof i&&(i=[[t.i,i,""]]),i.locals&&(t.exports=i.locals),(0,n(3).default)("7e43270a",i,!1,{})},function(t,e,n){(t.exports=n(2)(!1)).push([t.i,"\n.ar-volume {\n  display: flex;\n  align-items: center;\n  line-height: 10px;\n}\n.ar-volume-bar {\n    width: 50px;\n    height: 6px;\n    background: #E6E6E6;\n    border-radius: 4px;\n    position: relative;\n}\n.ar-volume__icon {\n    fill: #747474;\n    width: 24px;\n    height: 24px;\n    border: 0;\n    border-radius: 0;\n    padding: 0;\n    background-color: unset;\n    margin-right: 3px;\n}\n",""])},function(t,e,n){var i=n(24);"string"==typeof i&&(i=[[t.i,i,""]]),i.locals&&(t.exports=i.locals),(0,n(3).default)("00c7c6ba",i,!1,{})},function(t,e,n){(t.exports=n(2)(!1)).push([t.i,"\n.ar {\n  width: 420px;\n  font-family: 'Roboto', sans-serif;\n  border-radius: 16px;\n  background-color: #FAFAFA;\n  box-shadow: 0 4px 18px 0 rgba(0, 0, 0, 0.17);\n  position: relative;\n  box-sizing: content-box;\n}\n.ar-content {\n    padding: 16px;\n    display: flex;\n    flex-direction: column;\n    align-items: center;\n}\n.ar-records {\n    height: 138px;\n    padding-top: 1px;\n    overflow-y: auto;\n    margin-bottom: 20px;\n}\n.ar-records__record {\n      width: 320px;\n      height: 45px;\n      padding: 0 10px;\n      margin: 0 auto;\n      line-height: 45px;\n      display: flex;\n      justify-content: space-between;\n      border-bottom: 1px solid #E8E8E8;\n      position: relative;\n}\n.ar-records__record--selected {\n        border: 1px solid #E8E8E8;\n        border-radius: 24px;\n        background-color: #FFFFFF;\n        margin-top: -1px;\n        padding: 0 34px;\n}\n.ar-recorder {\n    position: relative;\n    display: flex;\n    flex-direction: column;\n    align-items: center;\n}\n.ar-recorder__duration {\n      color: #AEAEAE;\n      font-size: 32px;\n      font-weight: 500;\n      margin-top: 20px;\n      margin-bottom: 16px;\n}\n.ar-recorder__stop {\n      position: absolute;\n      top: 10px;\n      right: -52px;\n}\n.ar-recorder__time-limit {\n      position: absolute;\n      color: #AEAEAE;\n      font-size: 12px;\n      top: 128px;\n}\n.ar-recorder__records-limit {\n      position: absolute;\n      color: #AEAEAE;\n      font-size: 13px;\n      top: 78px;\n}\n.ar-spinner {\n    display: flex;\n    height: 30px;\n    position: absolute;\n    left: 0;\n    right: 0;\n    top: 0;\n    bottom: 0;\n    margin: auto;\n    width: 144px;\n    z-index: 10;\n}\n.ar-spinner__dot {\n      display: block;\n      margin: 0 8px;\n      border-radius: 50%;\n      width: 30px;\n      height: 30px;\n      background: #05CBCD;\n      animation-name: blink;\n      animation-duration: 1.4s;\n      animation-iteration-count: infinite;\n      animation-fill-mode: both;\n}\n.ar-spinner__dot:nth-child(2) {\n        animation-delay: .2s;\n}\n.ar-spinner__dot:nth-child(3) {\n        animation-delay: .4s;\n}\n@keyframes blink {\n0% {\n    opacity: .2;\n}\n20% {\n    opacity: 1;\n}\n100% {\n    opacity: .2;\n}\n}\n.ar__text {\n    color: rgba(84, 84, 84, 0.5);\n    font-size: 16px;\n}\n.ar__blur {\n    filter: blur(2px);\n    opacity: 0.7;\n}\n.ar__overlay {\n    position: absolute;\n    width: 100%;\n    height: 100%;\n    z-index: 10;\n}\n.ar__upload-status {\n    text-align: center;\n    font-size: 10px;\n    padding: 2px;\n    letter-spacing: 1px;\n    position: absolute;\n    bottom: 0;\n}\n.ar__upload-status--success {\n      color: green;\n}\n.ar__upload-status--fail {\n      color: red;\n}\n.ar__rm {\n    cursor: pointer;\n    position: absolute;\n    width: 6px;\n    height: 6px;\n    padding: 6px;\n    line-height: 6px;\n    margin: auto;\n    left: 10px;\n    bottom: 0;\n    top: 0;\n    color: #f4785a;\n}\n.ar__downloader, .ar__uploader {\n    position: absolute;\n    top: 0;\n    bottom: 0;\n    margin: auto;\n}\n.ar__downloader {\n    right: 115px;\n}\n.ar__uploader {\n    right: 85px;\n}\n.ar-icon {\n  fill: #747474;\n  border-radius: 50%;\n  border: 1px solid #05CBCD;\n  background-color: #FFFFFF;\n  padding: 5px;\n  cursor: pointer;\n  transition: .2s;\n}\n.ar-icon--no-border {\n    border: 0;\n    border-radius: 0;\n    padding: 0;\n}\n.ar-icon--rec {\n    fill: white;\n    background-color: #FF6B64;\n    border-color: transparent;\n}\n.ar-icon--pulse {\n    animation: ripple .5s linear infinite;\n}\n@keyframes ripple {\n0% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 1px rgba(255, 0, 0, 0.1), 0 0 0 5px rgba(255, 0, 0, 0.1);\n}\n100% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 10px rgba(255, 0, 0, 0.1), 0 0 0 20px rgba(255, 0, 0, 0);\n}\n}\n.ar-icon__xs {\n    width: 18px;\n    height: 18px;\n    line-height: 18px;\n}\n.ar-icon__sm {\n    width: 30px;\n    height: 30px;\n    line-height: 30px;\n}\n.ar-icon__lg {\n    width: 45px;\n    height: 45px;\n    line-height: 45px;\n    box-shadow: 0 2px 5px 1px rgba(158, 158, 158, 0.5);\n}\n",""])},function(t,e,n){var i=n(26);"string"==typeof i&&(i=[[t.i,i,""]]),i.locals&&(t.exports=i.locals),(0,n(3).default)("07a135de",i,!1,{})},function(t,e,n){(t.exports=n(2)(!1)).push([t.i,"\n.ar-icon {\n  fill: #747474;\n  border-radius: 50%;\n  border: 1px solid #05CBCD;\n  background-color: #FFFFFF;\n  padding: 5px;\n  cursor: pointer;\n  transition: .2s;\n}\n.ar-icon--no-border {\n    border: 0;\n    border-radius: 0;\n    padding: 0;\n}\n.ar-icon--rec {\n    fill: white;\n    background-color: #FF6B64;\n    border-color: transparent;\n}\n.ar-icon--pulse {\n    animation: ripple .5s linear infinite;\n}\n@keyframes ripple {\n0% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 1px rgba(255, 0, 0, 0.1), 0 0 0 5px rgba(255, 0, 0, 0.1);\n}\n100% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 10px rgba(255, 0, 0, 0.1), 0 0 0 20px rgba(255, 0, 0, 0);\n}\n}\n.ar-icon__xs {\n    width: 18px;\n    height: 18px;\n    line-height: 18px;\n}\n.ar-icon__sm {\n    width: 30px;\n    height: 30px;\n    line-height: 30px;\n}\n.ar-icon__lg {\n    width: 45px;\n    height: 45px;\n    line-height: 45px;\n    box-shadow: 0 2px 5px 1px rgba(158, 158, 158, 0.5);\n}\n",""])},function(t,e,n){var i=n(0),s=i.System,r=i.VbrMode,a=(i.Float,i.ShortBlock),o=(i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float),l=(i.new_float_n,i.new_int,i.new_int_n),c=i.new_short_n,u=i.assert,h=n(28),d=n(32),f=n(4),p=n(36),_=n(37),m=n(38),v=n(13),g=n(7),b=n(1);t.exports=function t(){var e,n,i,y,x,w=this;t.V9=410,t.V8=420,t.V7=430,t.V6=440,t.V5=450,t.V4=460,t.V3=470,t.V2=480,t.V1=490,t.V0=500,t.R3MIX=1e3,t.STANDARD=1001,t.EXTREME=1002,t.INSANE=1003,t.STANDARD_FAST=1004,t.EXTREME_FAST=1005,t.MEDIUM=1006,t.MEDIUM_FAST=1007,t.LAME_MAXMP3BUFFER=147456;var S,k,A,C=new h;function M(){this.mask_adjust=0,this.mask_adjust_short=0,this.bo_l_weight=o(b.SBMAX_l),this.bo_s_weight=o(b.SBMAX_s)}function E(){this.lowerlimit=0}function T(t,e){this.lowpass=e}this.enc=new b,this.setModules=function(t,s,r,a,o,l,c,u,h){e=t,n=s,i=r,y=a,x=o,S=l,k=u,A=h,this.enc.setModules(n,C,y,S)};var B=4294479419;function R(t){return 1<t?0:t<=0?1:Math.cos(Math.PI/2*t)}function $(t,e){switch(t){case 44100:return e.version=1,0;case 48e3:return e.version=1;case 32e3:return e.version=1,2;case 22050:return e.version=0;case 24e3:return e.version=0,1;case 16e3:return e.version=0,2;case 11025:return e.version=0;case 12e3:return e.version=0,1;case 8e3:return e.version=0,2;default:return e.version=0,-1}}function I(t,e,n){n<16e3&&(e=2);for(var i=g.bitrate_table[e][1],s=2;s<=14;s++)0<g.bitrate_table[e][s]&&Math.abs(g.bitrate_table[e][s]-t)<Math.abs(i-t)&&(i=g.bitrate_table[e][s]);return i}function L(t,e,n){n<16e3&&(e=2);for(var i=0;i<=14;i++)if(0<g.bitrate_table[e][i]&&g.bitrate_table[e][i]==t)return i;return-1}function O(t,e){var n=[new T(8,2e3),new T(16,3700),new T(24,3900),new T(32,5500),new T(40,7e3),new T(48,7500),new T(56,1e4),new T(64,11e3),new T(80,13500),new T(96,15100),new T(112,15600),new T(128,17e3),new T(160,17500),new T(192,18600),new T(224,19400),new T(256,19700),new T(320,20500)],i=w.nearestBitrateFullIndex(e);t.lowerlimit=n[i].lowpass}function P(t){var e=b.BLKSIZE+t.framesize-b.FFTOFFSET;return e=Math.max(e,512+t.framesize-32),u(f.MFSIZE>=e),e}function V(){this.n_in=0,this.n_out=0}function D(){this.num_used=0}function N(t,e,n){var i=Math.PI*e;(t/=n)<0&&(t=0),1<t&&(t=1);var s=t-.5,r=.42-.5*Math.cos(2*t*Math.PI)+.08*Math.cos(4*t*Math.PI);return Math.abs(s)<1e-9?i/Math.PI:r*Math.sin(n*i*s)/(Math.PI*n*s)}function F(t,e,n,i,s,r,a,l,c){var h,d,p=t.internal_flags,_=0,m=t.out_samplerate/function t(e,n){return 0!=n?t(n,e%n):e}(t.out_samplerate,t.in_samplerate);m>f.BPC&&(m=f.BPC);var v=Math.abs(p.resample_ratio-Math.floor(.5+p.resample_ratio))<1e-4?1:0,g=1/p.resample_ratio;1<g&&(g=1);var b=31;0==b%2&&--b;var y=(b+=v)+1;if(0==p.fill_buffer_resample_init){for(p.inbuf_old[0]=o(y),p.inbuf_old[1]=o(y),h=0;h<=2*m;++h)p.blackfilt[h]=o(y);for(p.itime[0]=0,_=p.itime[1]=0;_<=2*m;_++){var x=0,w=(_-m)/(2*m);for(h=0;h<=b;h++)x+=p.blackfilt[_][h]=N(h-w,g,b);for(h=0;h<=b;h++)p.blackfilt[_][h]/=x}p.fill_buffer_resample_init=1}var S=p.inbuf_old[c];for(d=0;d<i;d++){var k,A;if(k=d*p.resample_ratio,a<=b+(_=0|Math.floor(k-p.itime[c]))-b/2)break;w=k-p.itime[c]-(_+b%2*.5),u(Math.abs(w)<=.501),A=0|Math.floor(2*w*m+m+.5);var C=0;for(h=0;h<=b;++h){var M=h+_-b/2;u(M<a),u(0<=M+y),C+=(M<0?S[y+M]:s[r+M])*p.blackfilt[A][h]}e[n+d]=C}if(l.num_used=Math.min(a,b+_-b/2),p.itime[c]+=l.num_used-d*p.resample_ratio,l.num_used>=y)for(h=0;h<y;h++)S[h]=s[r+l.num_used+h-y];else{var E=y-l.num_used;for(h=0;h<E;++h)S[h]=S[h+l.num_used];for(_=0;h<y;++h,++_)S[h]=s[r+_];u(_==l.num_used)}return d}function H(t,e,n,i,s,r){var a=t.internal_flags;if(a.resample_ratio<.9999||1.0001<a.resample_ratio)for(var o=0;o<a.channels_out;o++){var l=new D;r.n_out=F(t,e[o],a.mf_size,t.framesize,n[o],i,s,l,o),r.n_in=l.num_used}else{r.n_out=Math.min(t.framesize,s),r.n_in=r.n_out;for(var c=0;c<r.n_out;++c)e[0][a.mf_size+c]=n[0][i+c],2==a.channels_out&&(e[1][a.mf_size+c]=n[1][i+c])}}this.lame_init=function(){var t,e,n=new d;return 0!=((t=n).class_id=B,e=t.internal_flags=new f,t.mode=MPEGMode.NOT_SET,t.original=1,t.in_samplerate=44100,t.num_channels=2,t.num_samples=-1,t.bWriteVbrTag=!0,t.quality=-1,t.short_blocks=null,e.subblock_gain=-1,t.lowpassfreq=0,t.highpassfreq=0,t.lowpasswidth=-1,t.highpasswidth=-1,t.VBR=r.vbr_off,t.VBR_q=4,t.ATHcurve=-1,t.VBR_mean_bitrate_kbps=128,t.VBR_min_bitrate_kbps=0,t.VBR_max_bitrate_kbps=0,t.VBR_hard_min=0,e.VBR_min_bitrate=1,e.VBR_max_bitrate=13,t.quant_comp=-1,t.quant_comp_short=-1,t.msfix=-1,e.resample_ratio=1,e.OldValue[0]=180,e.OldValue[1]=180,e.CurrentStep[0]=4,e.CurrentStep[1]=4,e.masking_lower=1,e.nsPsy.attackthre=-1,e.nsPsy.attackthre_s=-1,t.scale=-1,t.athaa_type=-1,t.ATHtype=-1,t.athaa_loudapprox=-1,t.athaa_sensitivity=0,t.useTemporal=null,t.interChRatio=-1,e.mf_samples_to_encode=b.ENCDELAY+b.POSTDELAY,t.encoder_padding=0,e.mf_size=b.ENCDELAY-b.MDCTDELAY,t.findReplayGain=!1,t.decode_on_the_fly=!1,e.decode_on_the_fly=!1,e.findReplayGain=!1,e.findPeakSample=!1,e.RadioGain=0,e.AudiophileGain=0,e.noclipGainChange=0,e.noclipScale=-1,t.preset=0,t.write_id3tag_automatic=!0,0)?null:(n.lame_allocated_gfp=1,n)},this.nearestBitrateFullIndex=function(t){var e=[8,16,24,32,40,48,56,64,80,96,112,128,160,192,224,256,320],n=0,i=0,s=0,r=0;r=e[16],i=e[s=16],n=16;for(var a=0;a<16;a++)if(Math.max(t,e[a+1])!=t){r=e[a+1],s=a+1,i=e[a],n=a;break}return t-i<r-t?n:s},this.lame_init_params=function(t){var o,c,d,f=t.internal_flags;if(f.Class_ID=0,null==f.ATH&&(f.ATH=new p),null==f.PSY&&(f.PSY=new M),null==f.rgdata&&(f.rgdata=new _),f.channels_in=t.num_channels,1==f.channels_in&&(t.mode=MPEGMode.MONO),f.channels_out=t.mode==MPEGMode.MONO?1:2,f.mode_ext=b.MPG_MD_MS_LR,t.mode==MPEGMode.MONO&&(t.force_ms=!1),t.VBR==r.vbr_off&&128!=t.VBR_mean_bitrate_kbps&&0==t.brate&&(t.brate=t.VBR_mean_bitrate_kbps),t.VBR==r.vbr_off||t.VBR==r.vbr_mtrh||t.VBR==r.vbr_mt||(t.free_format=!1),t.VBR==r.vbr_off&&0==t.brate&&v.EQ(t.compression_ratio,0)&&(t.compression_ratio=11.025),t.VBR==r.vbr_off&&0<t.compression_ratio&&(0==t.out_samplerate&&(t.out_samplerate=map2MP3Frequency(int(.97*t.in_samplerate))),t.brate=0|16*t.out_samplerate*f.channels_out/(1e3*t.compression_ratio),f.samplerate_index=$(t.out_samplerate,t),t.free_format||(t.brate=I(t.brate,t.version,t.out_samplerate))),0!=t.out_samplerate&&(t.out_samplerate<16e3?(t.VBR_mean_bitrate_kbps=Math.max(t.VBR_mean_bitrate_kbps,8),t.VBR_mean_bitrate_kbps=Math.min(t.VBR_mean_bitrate_kbps,64)):t.out_samplerate<32e3?(t.VBR_mean_bitrate_kbps=Math.max(t.VBR_mean_bitrate_kbps,8),t.VBR_mean_bitrate_kbps=Math.min(t.VBR_mean_bitrate_kbps,160)):(t.VBR_mean_bitrate_kbps=Math.max(t.VBR_mean_bitrate_kbps,32),t.VBR_mean_bitrate_kbps=Math.min(t.VBR_mean_bitrate_kbps,320))),0==t.lowpassfreq){var w=16e3;switch(t.VBR){case r.vbr_off:O(T=new E,t.brate),w=T.lowerlimit;break;case r.vbr_abr:var T;O(T=new E,t.VBR_mean_bitrate_kbps),w=T.lowerlimit;break;case r.vbr_rh:var P=[19500,19e3,18600,18e3,17500,16e3,15600,14900,12500,1e4,3950];if(0<=t.VBR_q&&t.VBR_q<=9){var V=P[t.VBR_q],D=P[t.VBR_q+1],N=t.VBR_q_frac;w=linear_int(V,D,N)}else w=19500;break;default:P=[19500,19e3,18500,18e3,17500,16500,15500,14500,12500,9500,3950],w=0<=t.VBR_q&&t.VBR_q<=9?(V=P[t.VBR_q],D=P[t.VBR_q+1],N=t.VBR_q_frac,linear_int(V,D,N)):19500}t.mode!=MPEGMode.MONO||t.VBR!=r.vbr_off&&t.VBR!=r.vbr_abr||(w*=1.5),t.lowpassfreq=0|w}if(0==t.out_samplerate&&(2*t.lowpassfreq>t.in_samplerate&&(t.lowpassfreq=t.in_samplerate/2),t.out_samplerate=(o=0|t.lowpassfreq,c=t.in_samplerate,d=44100,48e3<=c?d=48e3:44100<=c?d=44100:32e3<=c?d=32e3:24e3<=c?d=24e3:22050<=c?d=22050:16e3<=c?d=16e3:12e3<=c?d=12e3:11025<=c?d=11025:8e3<=c&&(d=8e3),-1==o?d:(o<=15960&&(d=44100),o<=15250&&(d=32e3),o<=11220&&(d=24e3),o<=9970&&(d=22050),o<=7230&&(d=16e3),o<=5420&&(d=12e3),o<=4510&&(d=11025),o<=3970&&(d=8e3),c<d?44100<c?48e3:32e3<c?44100:24e3<c?32e3:22050<c?24e3:16e3<c?22050:12e3<c?16e3:11025<c?12e3:8e3<c?11025:8e3:d))),t.lowpassfreq=Math.min(20500,t.lowpassfreq),t.lowpassfreq=Math.min(t.out_samplerate/2,t.lowpassfreq),t.VBR==r.vbr_off&&(t.compression_ratio=16*t.out_samplerate*f.channels_out/(1e3*t.brate)),t.VBR==r.vbr_abr&&(t.compression_ratio=16*t.out_samplerate*f.channels_out/(1e3*t.VBR_mean_bitrate_kbps)),t.bWriteVbrTag||(t.findReplayGain=!1,t.decode_on_the_fly=!1,f.findPeakSample=!1),f.findReplayGain=t.findReplayGain,f.decode_on_the_fly=t.decode_on_the_fly,f.decode_on_the_fly&&(f.findPeakSample=!0),f.findReplayGain&&e.InitGainAnalysis(f.rgdata,t.out_samplerate)==GainAnalysis.INIT_GAIN_ANALYSIS_ERROR)return t.internal_flags=null,-6;switch(f.decode_on_the_fly&&!t.decode_only&&(null!=f.hip&&A.hip_decode_exit(f.hip),f.hip=A.hip_decode_init()),f.mode_gr=t.out_samplerate<=24e3?1:2,t.framesize=576*f.mode_gr,t.encoder_delay=b.ENCDELAY,f.resample_ratio=t.in_samplerate/t.out_samplerate,t.VBR){case r.vbr_mt:case r.vbr_rh:case r.vbr_mtrh:t.compression_ratio=[5.7,6.5,7.3,8.2,10,11.9,13,14,15,16.5][t.VBR_q];break;case r.vbr_abr:t.compression_ratio=16*t.out_samplerate*f.channels_out/(1e3*t.VBR_mean_bitrate_kbps);break;default:t.compression_ratio=16*t.out_samplerate*f.channels_out/(1e3*t.brate)}if(t.mode==MPEGMode.NOT_SET&&(t.mode=MPEGMode.JOINT_STEREO),0<t.highpassfreq?(f.highpass1=2*t.highpassfreq,0<=t.highpasswidth?f.highpass2=2*(t.highpassfreq+t.highpasswidth):f.highpass2=2*t.highpassfreq,f.highpass1/=t.out_samplerate,f.highpass2/=t.out_samplerate):(f.highpass1=0,f.highpass2=0),0<t.lowpassfreq?(f.lowpass2=2*t.lowpassfreq,0<=t.lowpasswidth?(f.lowpass1=2*(t.lowpassfreq-t.lowpasswidth),f.lowpass1<0&&(f.lowpass1=0)):f.lowpass1=2*t.lowpassfreq,f.lowpass1/=t.out_samplerate,f.lowpass2/=t.out_samplerate):(f.lowpass1=0,f.lowpass2=0),function(t){var e=t.internal_flags,n=32,i=-1;if(0<e.lowpass1){for(var r=999,a=0;a<=31;a++)(u=a/31)>=e.lowpass2&&(n=Math.min(n,a)),e.lowpass1<u&&u<e.lowpass2&&(r=Math.min(r,a));e.lowpass1=999==r?(n-.75)/31:(r-.75)/31,e.lowpass2=n/31}if(0<e.highpass2&&e.highpass2<.75/31*.9&&(e.highpass1=0,e.highpass2=0,s.err.println("Warning: highpass filter disabled.  highpass frequency too small\n")),0<e.highpass2){var o=-1;for(a=0;a<=31;a++)(u=a/31)<=e.highpass1&&(i=Math.max(i,a)),e.highpass1<u&&u<e.highpass2&&(o=Math.max(o,a));e.highpass1=i/31,e.highpass2=-1==o?(i+.75)/31:(o+.75)/31}for(a=0;a<32;a++){var l,c,u=a/31;l=e.highpass2>e.highpass1?R((e.highpass2-u)/(e.highpass2-e.highpass1+1e-20)):1,c=e.lowpass2>e.lowpass1?R((u-e.lowpass1)/(e.lowpass2-e.lowpass1+1e-20)):1,e.amp_filter[a]=l*c}}(t),f.samplerate_index=$(t.out_samplerate,t),f.samplerate_index<0)return t.internal_flags=null,-1;if(t.VBR==r.vbr_off){if(t.free_format)f.bitrate_index=0;else if(t.brate=I(t.brate,t.version,t.out_samplerate),f.bitrate_index=L(t.brate,t.version,t.out_samplerate),f.bitrate_index<=0)return t.internal_flags=null,-1}else f.bitrate_index=1;t.analysis&&(t.bWriteVbrTag=!1),null!=f.pinfo&&(t.bWriteVbrTag=!1),n.init_bit_stream_w(f);for(var F,H,j,Z=f.samplerate_index+3*t.version+6*(t.out_samplerate<16e3?1:0),z=0;z<b.SBMAX_l+1;z++)f.scalefac_band.l[z]=y.sfBandIndex[Z].l[z];for(z=0;z<b.PSFB21+1;z++){var q=(f.scalefac_band.l[22]-f.scalefac_band.l[21])/b.PSFB21,U=f.scalefac_band.l[21]+z*q;f.scalefac_band.psfb21[z]=U}for(f.scalefac_band.psfb21[b.PSFB21]=576,z=0;z<b.SBMAX_s+1;z++)f.scalefac_band.s[z]=y.sfBandIndex[Z].s[z];for(z=0;z<b.PSFB12+1;z++)q=(f.scalefac_band.s[13]-f.scalefac_band.s[12])/b.PSFB12,U=f.scalefac_band.s[12]+z*q,f.scalefac_band.psfb12[z]=U;for(f.scalefac_band.psfb12[b.PSFB12]=192,1==t.version?f.sideinfo_len=1==f.channels_out?21:36:f.sideinfo_len=1==f.channels_out?13:21,t.error_protection&&(f.sideinfo_len+=2),H=(F=t).internal_flags,F.frameNum=0,F.write_id3tag_automatic&&k.id3tag_write_v2(F),H.bitrate_stereoMode_Hist=l([16,5]),H.bitrate_blockType_Hist=l([16,6]),H.PeakSample=0,F.bWriteVbrTag&&S.InitVbrTag(F),f.Class_ID=B,j=0;j<19;j++)f.nsPsy.pefirbuf[j]=700*f.mode_gr*f.channels_out;switch(-1==t.ATHtype&&(t.ATHtype=4),u(t.VBR_q<=9),u(0<=t.VBR_q),t.VBR){case r.vbr_mt:t.VBR=r.vbr_mtrh;case r.vbr_mtrh:null==t.useTemporal&&(t.useTemporal=!1),i.apply_preset(t,500-10*t.VBR_q,0),t.quality<0&&(t.quality=LAME_DEFAULT_QUALITY),t.quality<5&&(t.quality=0),5<t.quality&&(t.quality=5),f.PSY.mask_adjust=t.maskingadjust,f.PSY.mask_adjust_short=t.maskingadjust_short,t.experimentalY?f.sfb21_extra=!1:f.sfb21_extra=44e3<t.out_samplerate,f.iteration_loop=new VBRNewIterationLoop(x);break;case r.vbr_rh:i.apply_preset(t,500-10*t.VBR_q,0),f.PSY.mask_adjust=t.maskingadjust,f.PSY.mask_adjust_short=t.maskingadjust_short,t.experimentalY?f.sfb21_extra=!1:f.sfb21_extra=44e3<t.out_samplerate,6<t.quality&&(t.quality=6),t.quality<0&&(t.quality=LAME_DEFAULT_QUALITY),f.iteration_loop=new VBROldIterationLoop(x);break;default:var X;f.sfb21_extra=!1,t.quality<0&&(t.quality=LAME_DEFAULT_QUALITY),(X=t.VBR)==r.vbr_off&&(t.VBR_mean_bitrate_kbps=t.brate),i.apply_preset(t,t.VBR_mean_bitrate_kbps,0),t.VBR=X,f.PSY.mask_adjust=t.maskingadjust,f.PSY.mask_adjust_short=t.maskingadjust_short,X==r.vbr_off?f.iteration_loop=new m(x):f.iteration_loop=new ABRIterationLoop(x)}if(u(0<=t.scale),t.VBR!=r.vbr_off){if(f.VBR_min_bitrate=1,f.VBR_max_bitrate=14,t.out_samplerate<16e3&&(f.VBR_max_bitrate=8),0!=t.VBR_min_bitrate_kbps&&(t.VBR_min_bitrate_kbps=I(t.VBR_min_bitrate_kbps,t.version,t.out_samplerate),f.VBR_min_bitrate=L(t.VBR_min_bitrate_kbps,t.version,t.out_samplerate),f.VBR_min_bitrate<0))return-1;if(0!=t.VBR_max_bitrate_kbps&&(t.VBR_max_bitrate_kbps=I(t.VBR_max_bitrate_kbps,t.version,t.out_samplerate),f.VBR_max_bitrate=L(t.VBR_max_bitrate_kbps,t.version,t.out_samplerate),f.VBR_max_bitrate<0))return-1;t.VBR_min_bitrate_kbps=g.bitrate_table[t.version][f.VBR_min_bitrate],t.VBR_max_bitrate_kbps=g.bitrate_table[t.version][f.VBR_max_bitrate],t.VBR_mean_bitrate_kbps=Math.min(g.bitrate_table[t.version][f.VBR_max_bitrate],t.VBR_mean_bitrate_kbps),t.VBR_mean_bitrate_kbps=Math.max(g.bitrate_table[t.version][f.VBR_min_bitrate],t.VBR_mean_bitrate_kbps)}return t.tune&&(f.PSY.mask_adjust+=t.tune_value_a,f.PSY.mask_adjust_short+=t.tune_value_a),function(t){var e=t.internal_flags;switch(t.quality){default:case 9:e.psymodel=0,e.noise_shaping=0,e.noise_shaping_amp=0,e.noise_shaping_stop=0,e.use_best_huffman=0,e.full_outer_loop=0;break;case 8:t.quality=7;case 7:e.psymodel=1,e.noise_shaping=0,e.noise_shaping_amp=0,e.noise_shaping_stop=0,e.use_best_huffman=0,e.full_outer_loop=0;break;case 6:case 5:e.psymodel=1,0==e.noise_shaping&&(e.noise_shaping=1),e.noise_shaping_amp=0,e.noise_shaping_stop=0,-1==e.subblock_gain&&(e.subblock_gain=1),e.use_best_huffman=0,e.full_outer_loop=0;break;case 4:e.psymodel=1,0==e.noise_shaping&&(e.noise_shaping=1),e.noise_shaping_amp=0,e.noise_shaping_stop=0,-1==e.subblock_gain&&(e.subblock_gain=1),e.use_best_huffman=1,e.full_outer_loop=0;break;case 3:e.psymodel=1,0==e.noise_shaping&&(e.noise_shaping=1),e.noise_shaping_amp=1,-(e.noise_shaping_stop=1)==e.subblock_gain&&(e.subblock_gain=1),e.use_best_huffman=1,e.full_outer_loop=0;break;case 2:e.psymodel=1,0==e.noise_shaping&&(e.noise_shaping=1),0==e.substep_shaping&&(e.substep_shaping=2),e.noise_shaping_amp=1,-(e.noise_shaping_stop=1)==e.subblock_gain&&(e.subblock_gain=1),e.use_best_huffman=1,e.full_outer_loop=0;break;case 1:case 0:e.psymodel=1,0==e.noise_shaping&&(e.noise_shaping=1),0==e.substep_shaping&&(e.substep_shaping=2),e.noise_shaping_amp=2,-(e.noise_shaping_stop=1)==e.subblock_gain&&(e.subblock_gain=1),e.use_best_huffman=1,e.full_outer_loop=0}}(t),u(0<=t.scale),t.athaa_type<0?f.ATH.useAdjust=3:f.ATH.useAdjust=t.athaa_type,f.ATH.aaSensitivityP=Math.pow(10,t.athaa_sensitivity/-10),null==t.short_blocks&&(t.short_blocks=a.short_block_allowed),t.short_blocks!=a.short_block_allowed||t.mode!=MPEGMode.JOINT_STEREO&&t.mode!=MPEGMode.STEREO||(t.short_blocks=a.short_block_coupled),t.quant_comp<0&&(t.quant_comp=1),t.quant_comp_short<0&&(t.quant_comp_short=0),t.msfix<0&&(t.msfix=0),t.exp_nspsytune=1|t.exp_nspsytune,t.internal_flags.nsPsy.attackthre<0&&(t.internal_flags.nsPsy.attackthre=h.NSATTACKTHRE),t.internal_flags.nsPsy.attackthre_s<0&&(t.internal_flags.nsPsy.attackthre_s=h.NSATTACKTHRE_S),u(0<=t.scale),t.scale<0&&(t.scale=1),t.ATHtype<0&&(t.ATHtype=4),t.ATHcurve<0&&(t.ATHcurve=4),t.athaa_loudapprox<0&&(t.athaa_loudapprox=2),t.interChRatio<0&&(t.interChRatio=0),null==t.useTemporal&&(t.useTemporal=!0),f.slot_lag=f.frac_SpF=0,t.VBR==r.vbr_off&&(f.slot_lag=f.frac_SpF=72e3*(t.version+1)*t.brate%t.out_samplerate|0),y.iteration_init(t),C.psymodel_init(t),u(0<=t.scale),0},this.lame_encode_flush=function(t,e,i,s){var r,a,o,l,u=t.internal_flags,h=c([2,1152]),d=0,f=u.mf_samples_to_encode-b.POSTDELAY,p=P(t);if(u.mf_samples_to_encode<1)return 0;for(r=0,t.in_samplerate!=t.out_samplerate&&(f+=16*t.out_samplerate/t.in_samplerate),(o=t.framesize-f%t.framesize)<576&&(o+=t.framesize),l=(f+(t.encoder_padding=o))/t.framesize;0<l&&0<=d;){var _=p-u.mf_size,m=t.frameNum;_*=t.in_samplerate,1152<(_/=t.out_samplerate)&&(_=1152),_<1&&(_=1),a=s-r,0==s&&(a=0),i+=d=this.lame_encode_buffer(t,h[0],h[1],_,e,i,a),r+=d,l-=m!=t.frameNum?1:0}if(d<(u.mf_samples_to_encode=0))return d;if(a=s-r,0==s&&(a=0),n.flush_bitstream(t),(d=n.copy_buffer(u,e,i,a,1))<0)return d;if(i+=d,a=s-(r+=d),0==s&&(a=0),t.write_id3tag_automatic){if(k.id3tag_write_v1(t),(d=n.copy_buffer(u,e,i,a,0))<0)return d;r+=d}return r},this.lame_encode_buffer=function(t,i,s,r,a,l,c){var h,d,p=t.internal_flags,_=[null,null];if(p.Class_ID!=B)return-3;if(0==r)return 0;d=r,(null==(h=p).in_buffer_0||h.in_buffer_nsamples<d)&&(h.in_buffer_0=o(d),h.in_buffer_1=o(d),h.in_buffer_nsamples=d),_[0]=p.in_buffer_0,_[1]=p.in_buffer_1;for(var m=0;m<r;m++)_[0][m]=i[m],1<p.channels_in&&(_[1][m]=s[m]);return function(t,i,s,r,a,o,l){var c,h,d,p,_,m=t.internal_flags,g=0,y=[null,null],x=[null,null];if(m.Class_ID!=B)return-3;if(0==r)return 0;if((_=n.copy_buffer(m,a,o,l,0))<0)return _;if(o+=_,g+=_,x[0]=i,x[1]=s,v.NEQ(t.scale,0)&&v.NEQ(t.scale,1))for(h=0;h<r;++h)x[0][h]*=t.scale,2==m.channels_out&&(x[1][h]*=t.scale);if(v.NEQ(t.scale_left,0)&&v.NEQ(t.scale_left,1))for(h=0;h<r;++h)x[0][h]*=t.scale_left;if(v.NEQ(t.scale_right,0)&&v.NEQ(t.scale_right,1))for(h=0;h<r;++h)x[1][h]*=t.scale_right;if(2==t.num_channels&&1==m.channels_out)for(h=0;h<r;++h)x[0][h]=.5*(x[0][h]+x[1][h]),x[1][h]=0;p=P(t),y[0]=m.mfbuf[0],y[1]=m.mfbuf[1];for(var S,k,A,C,M,E,T,R=0;0<r;){var $=[null,null],I=0,L=0;$[0]=x[0],$[1]=x[1];var O=new V;if(H(t,y,$,R,r,O),I=O.n_in,L=O.n_out,m.findReplayGain&&!m.decode_on_the_fly&&e.AnalyzeSamples(m.rgdata,y[0],m.mf_size,y[1],m.mf_size,L,m.channels_out)==GainAnalysis.GAIN_ANALYSIS_ERROR)return-6;if(r-=I,R+=I,m.channels_out,m.mf_size+=L,u(m.mf_size<=f.MFSIZE),m.mf_samples_to_encode<1&&(m.mf_samples_to_encode=b.ENCDELAY+b.POSTDELAY),m.mf_samples_to_encode+=L,m.mf_size>=p){var D=l-g;if(0==l&&(D=0),S=t,k=y[0],A=y[1],C=a,M=o,E=D,T=w.enc.lame_encode_mp3_frame(S,k,A,C,M,E),S.frameNum++,(c=T)<0)return c;for(o+=c,g+=c,m.mf_size-=t.framesize,m.mf_samples_to_encode-=t.framesize,d=0;d<m.channels_out;d++)for(h=0;h<m.mf_size;h++)y[d][h]=y[d][h+t.framesize]}}return u(0==r),g}(t,_[0],_[1],r,a,l,c)}}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode),r=i.Float,a=i.ShortBlock,o=i.Util,l=i.Arrays,c=(i.new_array_n,i.new_byte,i.new_double,i.new_float),u=i.new_float_n,h=i.new_int,d=(i.new_int_n,i.assert),f=n(29),p=n(1);t.exports=function(){var t=new f,e=2.302585092994046,n=2,i=16,_=2,m=16,v=.34,g=1/217621504/(p.BLKSIZE/2),b=.3,y=21,x=.2302585093;function w(t){return t}function S(t,e){for(var n=0,i=0;i<p.BLKSIZE/2;++i)n+=t[i]*e.ATH.eql_w[i];return n*g}function k(e,n,i,s,r,a,l,c,u,h,d){var f=e.internal_flags;if(u<2)t.fft_long(f,s[r],u,h,d),t.fft_short(f,a[l],u,h,d);else if(2==u){for(var _=p.BLKSIZE-1;0<=_;--_){var m=s[r+0][_],v=s[r+1][_];s[r+0][_]=(m+v)*o.SQRT2*.5,s[r+1][_]=(m-v)*o.SQRT2*.5}for(var g=2;0<=g;--g)for(_=p.BLKSIZE_s-1;0<=_;--_)m=a[l+0][g][_],v=a[l+1][g][_],a[l+0][g][_]=(m+v)*o.SQRT2*.5,a[l+1][g][_]=(m-v)*o.SQRT2*.5}for(n[0]=w(s[r+0][0]),n[0]*=n[0],_=p.BLKSIZE/2-1;0<=_;--_){var b=s[r+0][p.BLKSIZE/2-_],y=s[r+0][p.BLKSIZE/2+_];n[p.BLKSIZE/2-_]=w(.5*(b*b+y*y))}for(g=2;0<=g;--g)for(i[g][0]=a[l+0][g][0],i[g][0]*=i[g][0],_=p.BLKSIZE_s/2-1;0<=_;--_)b=a[l+0][g][p.BLKSIZE_s/2-_],y=a[l+0][g][p.BLKSIZE_s/2+_],i[g][p.BLKSIZE_s/2-_]=w(.5*(b*b+y*y));var x=0;for(_=11;_<p.HBLKSIZE;_++)x+=n[_];if(f.tot_ener[u]=x,e.analysis){for(_=0;_<p.HBLKSIZE;_++)f.pinfo.energy[c][u][_]=f.pinfo.energy_save[u][_],f.pinfo.energy_save[u][_]=n[_];f.pinfo.pe[c][u]=f.pe[u]}2==e.athaa_loudapprox&&u<2&&(f.loudness_sq[c][u]=f.loudness_sq_save[u],f.loudness_sq_save[u]=S(n,f))}var A,C,M,E=8,T=23,B=15,R=[1,.79433,.63096,.63096,.63096,.63096,.63096,.25119,.11749],$=[3.3246*3.3246,3.23837*3.23837,9.9500500969,9.0247369744,8.1854926609,7.0440875649,2.46209*2.46209,2.284*2.284,4.4892710641,1.96552*1.96552,1.82335*1.82335,1.69146*1.69146,2.4621061921,2.1508568964,1.37074*1.37074,1.31036*1.31036,1.5691069696,1.4555939904,1.16203*1.16203,1.2715945225,1.09428*1.09428,1.0659*1.0659,1.0779838276,1.0382591025,1],I=[1.7782755904,1.35879*1.35879,1.38454*1.38454,1.39497*1.39497,1.40548*1.40548,1.3537*1.3537,1.6999465924,1.22321*1.22321,1.3169398564,1],L=[5.5396212496,2.29259*2.29259,4.9868695969,2.12675*2.12675,2.02545*2.02545,1.87894*1.87894,1.74303*1.74303,1.61695*1.61695,2.2499700001,1.39148*1.39148,1.29083*1.29083,1.19746*1.19746,1.2339655056,1.0779838276];function O(t,e,n,i,s,r){var a,l,c;if(t<e){if(!(e<t*C))return t+e;a=e/t}else{if(e*C<=t)return t+e;a=t/e}if(d(0<=t),d(0<=e),t+=e,i+3<=6){if(A<=a)return t;var u=0|o.FAST_LOG10_X(a,16);return t*I[u]}return u=0|o.FAST_LOG10_X(a,16),e=0!=r?s.ATH.cb_s[n]*s.ATH.adjust:s.ATH.cb_l[n]*s.ATH.adjust,d(0<=e),t<M*e?e<t?(l=1,u<=13&&(l=L[u]),c=o.FAST_LOG10_X(t/e,10/15),t*(($[u]-l)*c+l)):13<u?t:t*L[u]:t*$[u]}var P=[1.7782755904,1.35879*1.35879,1.38454*1.38454,1.39497*1.39497,1.40548*1.40548,1.3537*1.3537,1.6999465924,1.22321*1.22321,1.3169398564,1];function V(t,e,n){var i;if(t<0&&(t=0),e<0&&(e=0),t<=0)return e;if(e<=0)return t;if(i=t<e?e/t:t/e,-2<=n&&n<=2){if(A<=i)return t+e;var s=0|o.FAST_LOG10_X(i,16);return(t+e)*P[s]}return i<C?t+e:(t<e&&(t=e),t)}function D(t,e,n,i,s){var r,a,o=0,l=0;for(r=a=0;r<p.SBMAX_s;++a,++r){for(var c=t.bo_s[r],u=t.npart_s,h=c<u?c:u;a<h;)d(0<=e[a]),d(0<=n[a]),o+=e[a],l+=n[a],a++;if(t.en[i].s[r][s]=o,t.thm[i].s[r][s]=l,u<=a){++r;break}d(0<=e[a]),d(0<=n[a]);var f=t.PSY.bo_s_weight[r],_=1-f;o=f*e[a],l=f*n[a],t.en[i].s[r][s]+=o,t.thm[i].s[r][s]+=l,o=_*e[a],l=_*n[a]}for(;r<p.SBMAX_s;++r)t.en[i].s[r][s]=0,t.thm[i].s[r][s]=0}function N(t,e,n,i){var s,r,a=0,o=0;for(s=r=0;s<p.SBMAX_l;++r,++s){for(var l=t.bo_l[s],c=t.npart_l,u=l<c?l:c;r<u;)d(0<=e[r]),d(0<=n[r]),a+=e[r],o+=n[r],r++;if(t.en[i].l[s]=a,t.thm[i].l[s]=o,c<=r){++s;break}d(0<=e[r]),d(0<=n[r]);var h=t.PSY.bo_l_weight[s],f=1-h;a=h*e[r],o=h*n[r],t.en[i].l[s]+=a,t.thm[i].l[s]+=o,a=f*e[r],o=f*n[r]}for(;s<p.SBMAX_l;++s)t.en[i].l[s]=0,t.thm[i].l[s]=0}function F(t,e,n,i,s,r){var a,o,l=t.internal_flags;for(o=a=0;o<l.npart_s;++o){for(var c=0,u=0,h=l.numlines_s[o],f=0;f<h;++f,++a){var v=e[r][a];c+=v,u<v&&(u=v)}n[o]=c}for(d(o==l.npart_s),d(129==a),a=o=0;o<l.npart_s;o++){var g=l.s3ind_s[o][0],b=l.s3_ss[a++]*n[g];for(++g;g<=l.s3ind_s[o][1];)b+=l.s3_ss[a]*n[g],++a,++g;var y=_*l.nb_s1[s][o];if(i[o]=Math.min(b,y),l.blocktype_old[1&s]==p.SHORT_TYPE){y=m*l.nb_s2[s][o];var x=i[o];i[o]=Math.min(y,x)}l.nb_s2[s][o]=l.nb_s1[s][o],l.nb_s1[s][o]=b,d(0<=i[o])}for(;o<=p.CBANDS;++o)n[o]=0,i[o]=0}function H(t,e,n){return 1<=n?t:n<=0?e:0<e?Math.pow(t/e,n)*e:0}var j=[11.8,13.6,17.2,32,46.5,51.3,57.5,67.1,71.5,84.6,97.6,130];function Z(t,n){for(var i=309.07,s=0;s<p.SBMAX_s-1;s++)for(var r=0;r<3;r++){var a=t.thm.s[s][r];if(d(s<j.length),0<a){var l=a*n,c=t.en.s[s][r];l<c&&(1e10*l<c?i+=j[s]*(10*e):(d(0<l),i+=j[s]*o.FAST_LOG10(c/l)))}}return i}var z=[6.8,5.8,5.8,6.4,6.5,9.9,12.1,14.4,15,18.9,21.6,26.9,34.2,40.2,46.8,56.5,60.7,73.9,85.7,93.4,126.1];function q(t,n){for(var i=281.0575,s=0;s<p.SBMAX_l-1;s++){var r=t.thm.l[s];if(d(s<z.length),0<r){var a=r*n,l=t.en.l[s];a<l&&(1e10*a<l?i+=z[s]*(10*e):(d(0<a),i+=z[s]*o.FAST_LOG10(l/a)))}}return i}function U(t,e,n,i,s){var r,a;for(r=a=0;r<t.npart_l;++r){var o,l=0,c=0;for(o=0;o<t.numlines_l[r];++o,++a){var u=e[a];d(0<=u),l+=u,c<u&&(c=u)}n[r]=l,i[r]=c,s[r]=l*t.rnumlines_l[r],d(0<=t.rnumlines_l[r]),d(0<=l),d(0<=n[r]),d(0<=i[r]),d(0<=s[r])}}function X(t,e,n,i){var s=R.length-1,r=0,a=n[r]+n[r+1];for(d(0<=a),i[r]=0<a?((o=e[r])<e[r+1]&&(o=e[r+1]),d(0<t.numlines_l[r]+t.numlines_l[r+1]-1),s<(l=0|(a=20*(2*o-a)/(a*(t.numlines_l[r]+t.numlines_l[r+1]-1))))&&(l=s),l):0,r=1;r<t.npart_l-1;r++){var o,l;a=n[r-1]+n[r]+n[r+1],d(0<=a),i[r]=0<a?((o=e[r-1])<e[r]&&(o=e[r]),o<e[r+1]&&(o=e[r+1]),d(0<t.numlines_l[r-1]+t.numlines_l[r]+t.numlines_l[r+1]-1),s<(l=0|(a=20*(3*o-a)/(a*(t.numlines_l[r-1]+t.numlines_l[r]+t.numlines_l[r+1]-1))))&&(l=s),l):0}d(0<r),d(r==t.npart_l-1),a=n[r-1]+n[r],d(0<=a),i[r]=0<a?((o=e[r-1])<e[r]&&(o=e[r]),d(0<t.numlines_l[r-1]+t.numlines_l[r]-1),s<(l=0|(a=20*(2*o-a)/(a*(t.numlines_l[r-1]+t.numlines_l[r]-1))))&&(l=s),l):0,d(r==t.npart_l-1)}var Y=[-1730326e-23,-.01703172,-1349528e-23,.0418072,-673278e-22,-.0876324,-30835e-21,.1863476,-1104424e-22,-.627638];function W(e,n,i,s,r,a,l,c){var u=e.internal_flags;if(s<2)t.fft_long(u,l[c],s,n,i);else if(2==s)for(var h=p.BLKSIZE-1;0<=h;--h){var d=l[c+0][h],f=l[c+1][h];l[c+0][h]=(d+f)*o.SQRT2*.5,l[c+1][h]=(d-f)*o.SQRT2*.5}for(a[0]=w(l[c+0][0]),a[0]*=a[0],h=p.BLKSIZE/2-1;0<=h;--h){var _=l[c+0][p.BLKSIZE/2-h],m=l[c+0][p.BLKSIZE/2+h];a[p.BLKSIZE/2-h]=w(.5*(_*_+m*m))}var v=0;for(h=11;h<p.HBLKSIZE;h++)v+=a[h];if(u.tot_ener[s]=v,e.analysis){for(h=0;h<p.HBLKSIZE;h++)u.pinfo.energy[r][s][h]=u.pinfo.energy_save[s][h],u.pinfo.energy_save[s][h]=a[h];u.pinfo.pe[r][s]=u.pe[s]}}function G(e,n,i,s,r,a,l,c){var u=e.internal_flags;if(0==r&&s<2&&t.fft_short(u,l[c],s,n,i),2==s)for(var h=p.BLKSIZE_s-1;0<=h;--h){var d=l[c+0][r][h],f=l[c+1][r][h];l[c+0][r][h]=(d+f)*o.SQRT2*.5,l[c+1][r][h]=(d-f)*o.SQRT2*.5}for(a[r][0]=l[c+0][r][0],a[r][0]*=a[r][0],h=p.BLKSIZE_s/2-1;0<=h;--h){var _=l[c+0][r][p.BLKSIZE_s/2-h],m=l[c+0][r][p.BLKSIZE_s/2+h];a[r][p.BLKSIZE_s/2-h]=w(.5*(_*_+m*m))}}this.L3psycho_anal_ns=function(t,e,r,o,f,_,m,v,g,x){var w,S,A,C,M,E,T,B,$,I,L=t.internal_flags,P=u([2,p.BLKSIZE]),V=u([2,3,p.BLKSIZE_s]),j=c(p.CBANDS+1),z=c(p.CBANDS+1),W=c(p.CBANDS+2),G=h(2),K=h(2),Q=u([2,576]),J=h(p.CBANDS+2),tt=h(p.CBANDS+2);for(l.fill(tt,0),w=L.channels_out,t.mode==MPEGMode.JOINT_STEREO&&(w=4),$=t.VBR==s.vbr_off?0==L.ResvMax?0:L.ResvSize/L.ResvMax*.5:t.VBR==s.vbr_rh||t.VBR==s.vbr_mtrh||t.VBR==s.vbr_mt?.6:1,S=0;S<L.channels_out;S++){var et=e[S],nt=r+576-350-y+192;for(d(Y.length==(y-1)/2),C=0;C<576;C++){var it,st;for(it=et[nt+C+10],M=st=0;M<(y-1)/2-1;M+=2)it+=Y[M]*(et[nt+C+M]+et[nt+C+y-M]),st+=Y[M+1]*(et[nt+C+M+1]+et[nt+C+y-M-1]);Q[S][C]=it+st}f[o][S].en.assign(L.en[S]),f[o][S].thm.assign(L.thm[S]),2<w&&(_[o][S].en.assign(L.en[S+2]),_[o][S].thm.assign(L.thm[S+2]))}for(S=0;S<w;S++){var rt,at=c(12),ot=[0,0,0,0],lt=c(12),ct=1,ut=c(p.CBANDS),ht=c(p.CBANDS),dt=[0,0,0,0],ft=c(p.HBLKSIZE),pt=u([3,p.HBLKSIZE_s]);for(d(L.npart_s<=p.CBANDS),d(L.npart_l<=p.CBANDS),C=0;C<3;C++)at[C]=L.nsPsy.last_en_subshort[S][C+6],d(0<L.nsPsy.last_en_subshort[S][C+4]),lt[C]=at[C]/L.nsPsy.last_en_subshort[S][C+4],ot[0]+=at[C];if(2==S)for(C=0;C<576;C++){var _t,mt;_t=Q[0][C],mt=Q[1][C],Q[0][C]=_t+mt,Q[1][C]=_t-mt}var vt=Q[1&S],gt=0;for(C=0;C<9;C++){for(var bt=gt+64,yt=1;gt<bt;gt++)yt<Math.abs(vt[gt])&&(yt=Math.abs(vt[gt]));L.nsPsy.last_en_subshort[S][C]=at[C+3]=yt,ot[1+C/3]+=yt,yt>at[C+3-2]?(d(0<at[C+3-2]),yt/=at[C+3-2]):yt=at[C+3-2]>10*yt?(d(0<yt),at[C+3-2]/(10*yt)):0,lt[C+3]=yt}if(t.analysis){var xt=lt[0];for(C=1;C<12;C++)xt<lt[C]&&(xt=lt[C]);L.pinfo.ers[o][S]=L.pinfo.ers_save[S],L.pinfo.ers_save[S]=xt}for(rt=3==S?L.nsPsy.attackthre_s:L.nsPsy.attackthre,C=0;C<12;C++)0==dt[C/3]&&lt[C]>rt&&(dt[C/3]=C%3+1);for(C=1;C<4;C++)(ot[C-1]>ot[C]?(d(0<ot[C]),ot[C-1]/ot[C]):(d(0<ot[C-1]),ot[C]/ot[C-1]))<1.7&&(dt[C]=0,1==C&&(dt[0]=0));for(0!=dt[0]&&0!=L.nsPsy.lastAttacks[S]&&(dt[0]=0),3!=L.nsPsy.lastAttacks[S]&&dt[0]+dt[1]+dt[2]+dt[3]==0||((ct=0)!=dt[1]&&0!=dt[0]&&(dt[1]=0),0!=dt[2]&&0!=dt[1]&&(dt[2]=0),0!=dt[3]&&0!=dt[2]&&(dt[3]=0)),S<2?K[S]=ct:0==ct&&(K[0]=K[1]=0),g[S]=L.tot_ener[S],k(t,ft,pt,P,1&S,V,1&S,o,S,e,r),U(L,ft,j,ut,ht),X(L,ut,ht,J),B=0;B<3;B++){var wt,St;for(F(t,pt,z,W,S,B),D(L,z,W,S,B),T=0;T<p.SBMAX_s;T++){if(St=L.thm[S].s[T][B],St*=.8,2<=dt[B]||1==dt[B+1]){var kt=0!=B?B-1:2;yt=H(L.thm[S].s[T][kt],St,.6*$),St=Math.min(St,yt)}1==dt[B]?(kt=0!=B?B-1:2,yt=H(L.thm[S].s[T][kt],St,b*$),St=Math.min(St,yt)):(0!=B&&3==dt[B-1]||0==B&&3==L.nsPsy.lastAttacks[S])&&(kt=2!=B?B+1:0,yt=H(L.thm[S].s[T][kt],St,b*$),St=Math.min(St,yt)),wt=at[3*B+3]+at[3*B+4]+at[3*B+5],6*at[3*B+5]<wt&&(St*=.5,6*at[3*B+4]<wt&&(St*=.5)),L.thm[S].s[T][B]=St}}for(L.nsPsy.lastAttacks[S]=dt[2],A=E=0;A<L.npart_l;A++){for(var At=L.s3ind[A][0],Ct=j[At]*R[J[At]],Mt=L.s3_ll[E++]*Ct;++At<=L.s3ind[A][1];)Ct=j[At]*R[J[At]],Mt=O(Mt,L.s3_ll[E++]*Ct,At,At-A,L,0);Mt*=.158489319246111,L.blocktype_old[1&S]==p.SHORT_TYPE?W[A]=Mt:W[A]=H(Math.min(Mt,Math.min(n*L.nb_1[S][A],i*L.nb_2[S][A])),Mt,$),L.nb_2[S][A]=L.nb_1[S][A],L.nb_1[S][A]=Mt}for(;A<=p.CBANDS;++A)j[A]=0,W[A]=0;N(L,j,W,S)}for(t.mode!=MPEGMode.STEREO&&t.mode!=MPEGMode.JOINT_STEREO||0<t.interChRatio&&function(t,e){var n=t.internal_flags;if(1<n.channels_out){for(var i=0;i<p.SBMAX_l;i++){var s=n.thm[0].l[i],r=n.thm[1].l[i];n.thm[0].l[i]+=r*e,n.thm[1].l[i]+=s*e}for(i=0;i<p.SBMAX_s;i++)for(var a=0;a<3;a++)s=n.thm[0].s[i][a],r=n.thm[1].s[i][a],n.thm[0].s[i][a]+=r*e,n.thm[1].s[i][a]+=s*e}}(t,t.interChRatio),t.mode==MPEGMode.JOINT_STEREO&&(function(t){for(var e=0;e<p.SBMAX_l;e++)if(!(t.thm[0].l[e]>1.58*t.thm[1].l[e]||t.thm[1].l[e]>1.58*t.thm[0].l[e])){var n=t.mld_l[e]*t.en[3].l[e],i=Math.max(t.thm[2].l[e],Math.min(t.thm[3].l[e],n));n=t.mld_l[e]*t.en[2].l[e];var s=Math.max(t.thm[3].l[e],Math.min(t.thm[2].l[e],n));t.thm[2].l[e]=i,t.thm[3].l[e]=s}for(e=0;e<p.SBMAX_s;e++)for(var r=0;r<3;r++)t.thm[0].s[e][r]>1.58*t.thm[1].s[e][r]||t.thm[1].s[e][r]>1.58*t.thm[0].s[e][r]||(n=t.mld_s[e]*t.en[3].s[e][r],i=Math.max(t.thm[2].s[e][r],Math.min(t.thm[3].s[e][r],n)),n=t.mld_s[e]*t.en[2].s[e][r],s=Math.max(t.thm[3].s[e][r],Math.min(t.thm[2].s[e][r],n)),t.thm[2].s[e][r]=i,t.thm[3].s[e][r]=s)}(L),I=t.msfix,0<Math.abs(I)&&function(t,e,n){var i=e,s=Math.pow(10,n);e*=2,i*=2;for(var r=0;r<p.SBMAX_l;r++)u=t.ATH.cb_l[t.bm_l[r]]*s,(o=Math.min(Math.max(t.thm[0].l[r],u),Math.max(t.thm[1].l[r],u)))*e<(l=Math.max(t.thm[2].l[r],u))+(c=Math.max(t.thm[3].l[r],u))&&d(0<(l*=h=o*i/(l+c))+(c*=h)),t.thm[2].l[r]=Math.min(l,t.thm[2].l[r]),t.thm[3].l[r]=Math.min(c,t.thm[3].l[r]);for(s*=p.BLKSIZE_s/p.BLKSIZE,r=0;r<p.SBMAX_s;r++)for(var a=0;a<3;a++){var o,l,c,u,h;u=t.ATH.cb_s[t.bm_s[r]]*s,(o=Math.min(Math.max(t.thm[0].s[r][a],u),Math.max(t.thm[1].s[r][a],u)))*e<(l=Math.max(t.thm[2].s[r][a],u))+(c=Math.max(t.thm[3].s[r][a],u))&&d(0<(l*=h=o*e/(l+c))+(c*=h)),t.thm[2].s[r][a]=Math.min(t.thm[2].s[r][a],l),t.thm[3].s[r][a]=Math.min(t.thm[3].s[r][a],c)}}(L,I,t.ATHlower*L.ATH.adjust)),function(t,e,n,i){var s=t.internal_flags;t.short_blocks!=a.short_block_coupled||0!=e[0]&&0!=e[1]||(e[0]=e[1]=0);for(var r=0;r<s.channels_out;r++)i[r]=p.NORM_TYPE,t.short_blocks==a.short_block_dispensed&&(e[r]=1),t.short_blocks==a.short_block_forced&&(e[r]=0),0!=e[r]?(d(s.blocktype_old[r]!=p.START_TYPE),s.blocktype_old[r]==p.SHORT_TYPE&&(i[r]=p.STOP_TYPE)):(i[r]=p.SHORT_TYPE,s.blocktype_old[r]==p.NORM_TYPE&&(s.blocktype_old[r]=p.START_TYPE),s.blocktype_old[r]==p.STOP_TYPE&&(s.blocktype_old[r]=p.SHORT_TYPE)),n[r]=s.blocktype_old[r],s.blocktype_old[r]=i[r]}(t,K,x,G),S=0;S<w;S++){var Et,Tt,Bt,Rt=0;Bt=1<S?(Et=v,Rt=-2,Tt=p.NORM_TYPE,x[0]!=p.SHORT_TYPE&&x[1]!=p.SHORT_TYPE||(Tt=p.SHORT_TYPE),_[o][S-2]):(Et=m,Rt=0,Tt=x[S],f[o][S]),Tt==p.SHORT_TYPE?Et[Rt+S]=Z(Bt,L.masking_lower):Et[Rt+S]=q(Bt,L.masking_lower),t.analysis&&(L.pinfo.pe[o][S]=Et[Rt+S])}return 0};var K=[-1730326e-23,-.01703172,-1349528e-23,.0418072,-673278e-22,-.0876324,-30835e-21,.1863476,-1104424e-22,-.627638];function Q(t,e,n){if(0==n)for(var i=0;i<t.npart_s;i++)t.nb_s2[e][i]=t.nb_s1[e][i],t.nb_s1[e][i]=0}function J(t,e){for(var n=0;n<t.npart_l;n++)t.nb_2[e][n]=t.nb_1[e][n],t.nb_1[e][n]=0}function tt(t,e,n,i,s,r){var a,o,l,u=t.internal_flags,h=new float[p.CBANDS],f=c(p.CBANDS),_=new int[p.CBANDS];for(l=o=0;l<u.npart_s;++l){var m=0,v=0,g=u.numlines_s[l];for(a=0;a<g;++a,++o){var b=e[r][o];m+=b,v<b&&(v=b)}n[l]=m,d(0<=m),h[l]=v,d(0<g),f[l]=m/g,d(0<=f[l])}for(d(l==u.npart_s),d(129==o);l<p.CBANDS;++l)h[l]=0,f[l]=0;for(function(t,e,n,i){var s=R.length-1,r=0,a=n[r]+n[r+1];for(d(0<=a),i[r]=0<a?((o=e[r])<e[r+1]&&(o=e[r+1]),d(0<t.numlines_s[r]+t.numlines_s[r+1]-1),s<(l=0|(a=20*(2*o-a)/(a*(t.numlines_s[r]+t.numlines_s[r+1]-1))))&&(l=s),l):0,r=1;r<t.npart_s-1;r++){var o,l;a=n[r-1]+n[r]+n[r+1],d(r+1<t.npart_s),d(0<=a),i[r]=0<a?((o=e[r-1])<e[r]&&(o=e[r]),o<e[r+1]&&(o=e[r+1]),d(0<t.numlines_s[r-1]+t.numlines_s[r]+t.numlines_s[r+1]-1),s<(l=0|(a=20*(3*o-a)/(a*(t.numlines_s[r-1]+t.numlines_s[r]+t.numlines_s[r+1]-1))))&&(l=s),l):0}d(0<r),d(r==t.npart_s-1),a=n[r-1]+n[r],d(0<=a),i[r]=0<a?((o=e[r-1])<e[r]&&(o=e[r]),d(0<t.numlines_s[r-1]+t.numlines_s[r]-1),s<(l=0|(a=20*(2*o-a)/(a*(t.numlines_s[r-1]+t.numlines_s[r]-1))))&&(l=s),l):0,d(r==t.npart_s-1)}(u,h,f,_),o=l=0;l<u.npart_s;l++){var y,x,w,S,k,A=u.s3ind_s[l][0],C=u.s3ind_s[l][1];for(y=_[A],x=1,S=u.s3_ss[o]*n[A]*R[_[A]],++o,++A;A<=C;)y+=_[A],x+=1,S=V(S,w=u.s3_ss[o]*n[A]*R[_[A]],A-l),++o,++A;S*=k=.5*R[y=(1+2*y)/(2*x)],i[l]=S,u.nb_s2[s][l]=u.nb_s1[s][l],u.nb_s1[s][l]=S,w=h[l],w*=u.minval_s[l],w*=k,i[l]>w&&(i[l]=w),1<u.masking_lower&&(i[l]*=u.masking_lower),i[l]>n[l]&&(i[l]=n[l]),u.masking_lower<1&&(i[l]*=u.masking_lower),d(0<=i[l])}for(;l<p.CBANDS;++l)n[l]=0,i[l]=0}function et(t,e,s,r,a){var o,l=c(p.CBANDS),u=c(p.CBANDS),f=h(p.CBANDS+2);U(t,e,s,l,u),X(t,l,u,f);var _=0;for(o=0;o<t.npart_l;o++){var m,v,g,y=t.s3ind[o][0],x=t.s3ind[o][1],w=0,S=0;for(w=f[y],S+=1,v=t.s3_ll[_]*s[y]*R[f[y]],++_,++y;y<=x;)w+=f[y],S+=1,v=V(v,m=t.s3_ll[_]*s[y]*R[f[y]],y-o),++_,++y;if(v*=g=.5*R[w=(1+2*w)/(2*S)],t.blocktype_old[1&a]==p.SHORT_TYPE){var k=n*t.nb_1[a][o];r[o]=0<k?Math.min(v,k):Math.min(v,s[o]*b)}else{var A=i*t.nb_2[a][o],C=n*t.nb_1[a][o];A<=0&&(A=v),C<=0&&(C=v),k=t.blocktype_old[1&a]==p.NORM_TYPE?Math.min(C,A):C,r[o]=Math.min(v,k)}t.nb_2[a][o]=t.nb_1[a][o],t.nb_1[a][o]=v,m=l[o],m*=t.minval_l[o],m*=g,r[o]>m&&(r[o]=m),1<t.masking_lower&&(r[o]*=t.masking_lower),r[o]>s[o]&&(r[o]=s[o]),t.masking_lower<1&&(r[o]*=t.masking_lower),d(0<=r[o])}for(;o<p.CBANDS;++o)s[o]=0,r[o]=0}function nt(t,e,n,i,s,r,a){for(var o,l,c=2*r,u=0<r?Math.pow(10,s):1,h=0;h<a;++h){var f=t[2][h],p=t[3][h],_=e[0][h],m=e[1][h],v=e[2][h],g=e[3][h];if(_<=1.58*m&&m<=1.58*_){var b=n[h]*p,y=n[h]*f;l=Math.max(v,Math.min(g,b)),o=Math.max(g,Math.min(v,y))}else l=v,o=g;if(0<r){var x,w,S=i[h]*u;if(x=Math.min(Math.max(_,S),Math.max(m,S)),0<(w=(v=Math.max(l,S))+(g=Math.max(o,S)))&&x*c<w){var k=x*c/w;v*=k,g*=k,d(0<w)}l=Math.min(v,l),o=Math.min(g,o)}f<l&&(l=f),p<o&&(o=p),e[2][h]=l,e[3][h]=o}}function it(t,e){var n;return(n=0<=t?27*-t:t*e)<=-72?0:Math.exp(n*x)}function st(t){var e,n,i,s,r=0;for(r=0;1e-20<it(r,t);r-=1);for(i=r,s=0;1e-12<Math.abs(s-i);)0<it(r=(s+i)/2,t)?s=r:i=r;for(e=i,r=r=0;1e-20<it(r,t);r+=1);for(i=0,s=r;1e-12<Math.abs(s-i);)0<it(r=(s+i)/2,t)?i=r:s=r;n=s;var a,o=0;for(a=0;a<=1e3;++a)o+=it(r=e+a*(n-e)/1e3,t);return 1001/(o*(n-e))}function rt(t){return t<0&&(t=0),t*=.001,13*Math.atan(.76*t)+3.5*Math.atan(t*t/56.25)}function at(t,e,n,i,s,r,a,o,l,u,f,_){var m,g=c(p.CBANDS+1),b=o/(15<_?1152:384),y=h(p.HBLKSIZE);o/=l;var x=0,w=0;for(m=0;m<p.CBANDS;m++){var S;for($=rt(o*x),g[m]=o*x,S=x;rt(o*S)-$<v&&S<=l/2;S++);for(t[m]=S-x,w=m+1;x<S;)d(x<p.HBLKSIZE),y[x++]=m;if(l/2<x){x=l/2,++m;break}}d(m<p.CBANDS),g[m]=o*x;for(var k=0;k<_;k++){var A,C,M,E,T;M=u[k],E=u[k+1],(A=0|Math.floor(.5+f*(M-.5)))<0&&(A=0),l/2<(C=0|Math.floor(.5+f*(E-.5)))&&(C=l/2),n[k]=(y[A]+y[C])/2,e[k]=y[C];var B=b*E;a[k]=(B-g[e[k]])/(g[e[k]+1]-g[e[k]]),a[k]<0?a[k]=0:1<a[k]&&(a[k]=1),T=rt(o*u[k]*f),T=Math.min(T,15.5)/15.5,r[k]=Math.pow(10,1.25*(1-Math.cos(Math.PI*T))-2.5)}for(var R=x=0;R<w;R++){var $,I,L=t[R];$=rt(o*x),I=rt(o*(x+L-1)),i[R]=.5*($+I),$=rt(o*(x-.5)),I=rt(o*(x+L-.5)),s[R]=I-$,x+=L}return w}function ot(t,e,n,i,s,r){var a,o,l,h,d,f,_=u([p.CBANDS,p.CBANDS]),m=0;if(r)for(var v=0;v<e;v++)for(a=0;a<e;a++){var g=(o=n[v]-n[a],f=d=h=l=void 0,l=o,h=.5<=(l*=0<=l?3:1.5)&&l<=2.5?8*((f=l-.5)*f-2*f):0,((d=15.811389+7.5*(l+=.474)-17.5*Math.sqrt(1+l*l))<=-60?0:(l=Math.exp((h+d)*x),l/=.6609193))*i[a]);_[v][a]=g*s[v]}else for(a=0;a<e;a++){var b=15+Math.min(21/n[a],12),y=st(b);for(v=0;v<e;v++)g=y*it(n[v]-n[a],b)*i[a],_[v][a]=g*s[v]}for(v=0;v<e;v++){for(a=0;a<e&&!(0<_[v][a]);a++);for(t[v][0]=a,a=e-1;0<a&&!(0<_[v][a]);a--);t[v][1]=a,m+=t[v][1]-t[v][0]+1}var w=c(m),S=0;for(v=0;v<e;v++)for(a=t[v][0];a<=t[v][1];a++)w[S++]=_[v][a];return w}function lt(t){var e=rt(t);return e=Math.min(e,15.5)/15.5,Math.pow(10,1.25*(1-Math.cos(Math.PI*e))-2.5)}function ct(t,e){return t<-.3&&(t=3410),t/=1e3,t=Math.max(.1,t),3.64*Math.pow(t,-.8)-6.8*Math.exp(-.6*Math.pow(t-3.4,2))+6*Math.exp(-.15*Math.pow(t-8.7,2))+.001*(.6+.04*e)*Math.pow(t,4)}this.L3psycho_anal_vbr=function(t,e,n,i,s,r,o,l,f,_){var m,v,g,x,w,k=t.internal_flags,A=c(p.HBLKSIZE),C=u([3,p.HBLKSIZE_s]),M=u([2,p.BLKSIZE]),E=u([2,3,p.BLKSIZE_s]),T=u([4,p.CBANDS]),B=u([4,p.CBANDS]),R=u([4,3]),$=[[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]],I=h(2),L=t.mode==MPEGMode.JOINT_STEREO?4:k.channels_out;!function(t,e,n,i,s,r,a,o,l,h){for(var f=u([2,576]),p=t.internal_flags,_=p.channels_out,m=t.mode==MPEGMode.JOINT_STEREO?4:_,v=0;v<_;v++){firbuf=e[v];var g=n+576-350-y+192;d(K.length==(y-1)/2);for(var b=0;b<576;b++){var x,w;x=firbuf[g+b+10];for(var S=w=0;S<(y-1)/2-1;S+=2)x+=K[S]*(firbuf[g+b+S]+firbuf[g+b+y-S]),w+=K[S+1]*(firbuf[g+b+S+1]+firbuf[g+b+y-S-1]);f[v][b]=x+w}s[i][v].en.assign(p.en[v]),s[i][v].thm.assign(p.thm[v]),2<m&&(r[i][v].en.assign(p.en[v+2]),r[i][v].thm.assign(p.thm[v+2]))}for(v=0;v<m;v++){var k=c(12),A=c(12),C=[0,0,0,0],M=f[1&v],E=0,T=3==v?p.nsPsy.attackthre_s:p.nsPsy.attackthre,B=1;if(2==v)for(b=0,S=576;0<S;++b,--S){var R=f[0][b],$=f[1][b];f[0][b]=R+$,f[1][b]=R-$}for(b=0;b<3;b++)A[b]=p.nsPsy.last_en_subshort[v][b+6],d(0<p.nsPsy.last_en_subshort[v][b+4]),k[b]=A[b]/p.nsPsy.last_en_subshort[v][b+4],C[0]+=A[b];for(b=0;b<9;b++){for(var I=E+64,L=1;E<I;E++)L<Math.abs(M[E])&&(L=Math.abs(M[E]));p.nsPsy.last_en_subshort[v][b]=A[b+3]=L,C[1+b/3]+=L,L>A[b+3-2]?(d(0<A[b+3-2]),L/=A[b+3-2]):L=A[b+3-2]>10*L?(d(0<L),A[b+3-2]/(10*L)):0,k[b+3]=L}for(b=0;b<3;++b){var O=A[3*b+3]+A[3*b+4]+A[3*b+5],P=1;6*A[3*b+5]<O&&(P*=.5,6*A[3*b+4]<O&&(P*=.5)),o[v][b]=P}if(t.analysis){var V=k[0];for(b=1;b<12;b++)V<k[b]&&(V=k[b]);p.pinfo.ers[i][v]=p.pinfo.ers_save[v],p.pinfo.ers_save[v]=V}for(b=0;b<12;b++)0==l[v][b/3]&&k[b]>T&&(l[v][b/3]=b%3+1);for(b=1;b<4;b++){var D=C[b-1],N=C[b];Math.max(D,N)<4e4&&D<1.7*N&&N<1.7*D&&(1==b&&l[v][0]<=l[v][b]&&(l[v][0]=0),l[v][b]=0)}l[v][0]<=p.nsPsy.lastAttacks[v]&&(l[v][0]=0),3!=p.nsPsy.lastAttacks[v]&&l[v][0]+l[v][1]+l[v][2]+l[v][3]==0||((B=0)!=l[v][1]&&0!=l[v][0]&&(l[v][1]=0),0!=l[v][2]&&0!=l[v][1]&&(l[v][2]=0),0!=l[v][3]&&0!=l[v][2]&&(l[v][3]=0)),v<2?h[v]=B:0==B&&(h[0]=h[1]=0),a[v]=p.tot_ener[v]}}(t,e,n,i,s,r,f,R,$,I),function(t,e){var n=t.internal_flags;t.short_blocks!=a.short_block_coupled||0!=e[0]&&0!=e[1]||(e[0]=e[1]=0);for(var i=0;i<n.channels_out;i++)t.short_blocks==a.short_block_dispensed&&(e[i]=1),t.short_blocks==a.short_block_forced&&(e[i]=0)}(t,I);for(var O=0;O<L;O++)W(t,e,n,O,i,A,M,V=1&O),v=i,g=O,x=A,w=void 0,w=(m=t).internal_flags,2==m.athaa_loudapprox&&g<2&&(w.loudness_sq[v][g]=w.loudness_sq_save[g],w.loudness_sq_save[g]=S(x,w)),0!=I[V]?et(k,A,T[O],B[O],O):J(k,O);for(I[0]+I[1]==2&&t.mode==MPEGMode.JOINT_STEREO&&nt(T,B,k.mld_cb_l,k.ATH.cb_l,t.ATHlower*k.ATH.adjust,t.msfix,k.npart_l),O=0;O<L;O++)0!=I[V=1&O]&&N(k,T[O],B[O],O);for(var P=0;P<3;P++){for(O=0;O<L;++O)0!=I[V=1&O]?Q(k,O,P):(G(t,e,n,O,P,C,E,V),tt(t,C,T[O],B[O],O,P));for(I[0]+I[1]==0&&t.mode==MPEGMode.JOINT_STEREO&&nt(T,B,k.mld_cb_s,k.ATH.cb_s,t.ATHlower*k.ATH.adjust,t.msfix,k.npart_s),O=0;O<L;++O)0==I[V=1&O]&&D(k,T[O],B[O],O,P)}for(O=0;O<L;O++){var V;if(0==I[V=1&O])for(var F=0;F<p.SBMAX_s;F++){var j=c(3);for(P=0;P<3;P++){var z=k.thm[O].s[F][P];if(z*=.8,2<=$[O][P]||1==$[O][P+1]){var U=0!=P?P-1:2,X=H(k.thm[O].s[F][U],z,.36);z=Math.min(z,X)}else 1==$[O][P]?(U=0!=P?P-1:2,X=H(k.thm[O].s[F][U],z,.6*b),z=Math.min(z,X)):(0!=P&&3==$[O][P-1]||0==P&&3==k.nsPsy.lastAttacks[O])&&(U=2!=P?P+1:0,X=H(k.thm[O].s[F][U],z,.6*b),z=Math.min(z,X));z*=R[O][P],j[P]=z}for(P=0;P<3;P++)k.thm[O].s[F][P]=j[P]}}for(O=0;O<L;O++)k.nsPsy.lastAttacks[O]=$[O][2];for(function(t,e,n){for(var i=t.internal_flags,s=0;s<i.channels_out;s++){var r=p.NORM_TYPE;0!=e[s]?(d(i.blocktype_old[s]!=p.START_TYPE),i.blocktype_old[s]==p.SHORT_TYPE&&(r=p.STOP_TYPE)):(r=p.SHORT_TYPE,i.blocktype_old[s]==p.NORM_TYPE&&(i.blocktype_old[s]=p.START_TYPE),i.blocktype_old[s]==p.STOP_TYPE&&(i.blocktype_old[s]=p.SHORT_TYPE)),n[s]=i.blocktype_old[s],i.blocktype_old[s]=r}}(t,I,_),O=0;O<L;O++){var Y,it,st,rt;rt=1<O?(Y=l,it=-2,st=p.NORM_TYPE,_[0]!=p.SHORT_TYPE&&_[1]!=p.SHORT_TYPE||(st=p.SHORT_TYPE),r[i][O-2]):(Y=o,it=0,st=_[O],s[i][O]),st==p.SHORT_TYPE?Y[it+O]=Z(rt,k.masking_lower):Y[it+O]=q(rt,k.masking_lower),t.analysis&&(k.pinfo.pe[i][O]=Y[it+O])}return 0},this.psymodel_init=function(n){var i,a,o=n.internal_flags,l=!0,u=13,h=0,f=0,_=-8.25,m=-4.5,v=c(p.CBANDS),g=c(p.CBANDS),b=c(p.CBANDS),y=n.out_samplerate;switch(n.experimentalZ){default:case 0:l=!0;break;case 1:l=n.VBR!=s.vbr_mtrh&&n.VBR!=s.vbr_mt;break;case 2:l=!1;break;case 3:u=8,h=-1.75,f=-.0125,_=-8.25,m=-2.25}for(o.ms_ener_ratio_old=.25,o.blocktype_old[0]=o.blocktype_old[1]=p.NORM_TYPE,i=0;i<4;++i){for(var x=0;x<p.CBANDS;++x)o.nb_1[i][x]=1e20,o.nb_2[i][x]=1e20,o.nb_s1[i][x]=o.nb_s2[i][x]=1;for(var w=0;w<p.SBMAX_l;w++)o.en[i].l[w]=1e20,o.thm[i].l[w]=1e20;for(x=0;x<3;++x){for(w=0;w<p.SBMAX_s;w++)o.en[i].s[w][x]=1e20,o.thm[i].s[w][x]=1e20;o.nsPsy.lastAttacks[i]=0}for(x=0;x<9;x++)o.nsPsy.last_en_subshort[i][x]=10}for(o.loudness_sq_save[0]=o.loudness_sq_save[1]=0,o.npart_l=at(o.numlines_l,o.bo_l,o.bm_l,v,g,o.mld_l,o.PSY.bo_l_weight,y,p.BLKSIZE,o.scalefac_band.l,p.BLKSIZE/1152,p.SBMAX_l),d(o.npart_l<p.CBANDS),i=0;i<o.npart_l;i++){var S=h;v[i]>=u&&(S=f*(v[i]-u)/(24-u)+h*(24-v[i])/(24-u)),b[i]=Math.pow(10,S/10),0<o.numlines_l[i]?o.rnumlines_l[i]=1/o.numlines_l[i]:o.rnumlines_l[i]=0}for(o.s3_ll=ot(o.s3ind,o.npart_l,v,g,b,l),i=x=0;i<o.npart_l;i++){$=r.MAX_VALUE;for(var k=0;k<o.numlines_l[i];k++,x++){var R=y*x/(1e3*p.BLKSIZE);I=this.ATHformula(1e3*R,n)-20,I=Math.pow(10,.1*I),(I*=o.numlines_l[i])<$&&($=I)}o.ATH.cb_l[i]=$,6<($=20*v[i]/10-20)&&($=100),$<-15&&($=-15),$-=8,o.minval_l[i]=Math.pow(10,$/10)*o.numlines_l[i]}for(o.npart_s=at(o.numlines_s,o.bo_s,o.bm_s,v,g,o.mld_s,o.PSY.bo_s_weight,y,p.BLKSIZE_s,o.scalefac_band.s,p.BLKSIZE_s/384,p.SBMAX_s),d(o.npart_s<p.CBANDS),i=x=0;i<o.npart_s;i++){var $;for(S=_,v[i]>=u&&(S=m*(v[i]-u)/(24-u)+_*(24-v[i])/(24-u)),b[i]=Math.pow(10,S/10),$=r.MAX_VALUE,k=0;k<o.numlines_s[i];k++,x++){var I;R=y*x/(1e3*p.BLKSIZE_s),I=this.ATHformula(1e3*R,n)-20,I=Math.pow(10,.1*I),(I*=o.numlines_s[i])<$&&($=I)}o.ATH.cb_s[i]=$,$=7*v[i]/12-7,12<v[i]&&($*=1+3.1*Math.log(1+$)),v[i]<12&&($*=1+2.3*Math.log(1-$)),$<-15&&($=-15),$-=8,o.minval_s[i]=Math.pow(10,$/10)*o.numlines_s[i]}o.s3_ss=ot(o.s3ind_s,o.npart_s,v,g,b,l),A=Math.pow(10,(E+1)/16),C=Math.pow(10,(T+1)/16),M=Math.pow(10,B/10),t.init_fft(o),o.decay=Math.exp(-1*e/(.01*y/192)),a=3.5,0!=(2&n.exp_nspsytune)&&(a=1),0<Math.abs(n.msfix)&&(a=n.msfix),n.msfix=a;for(var L=0;L<o.npart_l;L++)o.s3ind[L][1]>o.npart_l-1&&(o.s3ind[L][1]=o.npart_l-1);var O=576*o.mode_gr/y;if(o.ATH.decay=Math.pow(10,-1.2*O),o.ATH.adjust=.01,o.ATH.adjustLimit=1,d(o.bo_l[p.SBMAX_l-1]<=o.npart_l),d(o.bo_s[p.SBMAX_s-1]<=o.npart_s),-1!=n.ATHtype){var P=n.out_samplerate/p.BLKSIZE,V=0;for(i=R=0;i<p.BLKSIZE/2;++i)R+=P,o.ATH.eql_w[i]=1/Math.pow(10,this.ATHformula(R,n)/10),V+=o.ATH.eql_w[i];for(V=1/V,i=p.BLKSIZE/2;0<=--i;)o.ATH.eql_w[i]*=V}for(L=x=0;L<o.npart_s;++L)for(i=0;i<o.numlines_s[L];++i)++x;for(d(129==x),L=x=0;L<o.npart_l;++L)for(i=0;i<o.numlines_l[L];++i)++x;for(d(513==x),i=x=0;i<o.npart_l;i++)R=y*(x+o.numlines_l[i]/2)/(1*p.BLKSIZE),o.mld_cb_l[i]=lt(R),x+=o.numlines_l[i];for(;i<p.CBANDS;++i)o.mld_cb_l[i]=1;for(i=x=0;i<o.npart_s;i++)R=y*(x+o.numlines_s[i]/2)/(1*p.BLKSIZE_s),o.mld_cb_s[i]=lt(R),x+=o.numlines_s[i];for(;i<p.CBANDS;++i)o.mld_cb_s[i]=1;return 0},this.ATHformula=function(t,e){var n;switch(e.ATHtype){case 0:n=ct(t,9);break;case 1:n=ct(t,-1);break;case 2:n=ct(t,0);break;case 3:n=ct(t,1)+6;break;case 4:n=ct(t,e.ATHcurve);break;default:n=ct(t,0)}return n}}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util),r=(i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float),a=(i.new_float_n,i.new_int,i.new_int_n,i.assert,n(1));t.exports=function(){var t=r(a.BLKSIZE),e=r(a.BLKSIZE_s/2),n=[.9238795325112867,.3826834323650898,.9951847266721969,.0980171403295606,.9996988186962042,.02454122852291229,.9999811752826011,.006135884649154475];function i(t,e,i){var r,a,o,l=0,c=e+(i<<=1);r=4;do{var u,h,d,f,p,_,m;for(m=r>>1,_=(p=(f=r)<<1)+f,r=p<<1,o=(a=e)+m;S=t[a+0]-t[a+f],w=t[a+0]+t[a+f],M=t[a+p]-t[a+_],A=t[a+p]+t[a+_],t[a+p]=w-A,t[a+0]=w+A,t[a+_]=S-M,t[a+f]=S+M,S=t[o+0]-t[o+f],w=t[o+0]+t[o+f],M=s.SQRT2*t[o+_],A=s.SQRT2*t[o+p],t[o+p]=w-A,t[o+0]=w+A,t[o+_]=S-M,t[o+f]=S+M,o+=r,(a+=r)<c;);for(h=n[l+0],u=n[l+1],d=1;d<m;d++){var v,g;v=1-2*u*u,g=2*u*h,a=e+d,o=e+f-d;do{var b,y,x,w,S,k,A,C,M,E;y=g*t[a+f]-v*t[o+f],b=v*t[a+f]+g*t[o+f],S=t[a+0]-b,w=t[a+0]+b,k=t[o+0]-y,x=t[o+0]+y,y=g*t[a+_]-v*t[o+_],b=v*t[a+_]+g*t[o+_],M=t[a+p]-b,A=t[a+p]+b,E=t[o+p]-y,C=t[o+p]+y,y=u*A-h*E,b=h*A+u*E,t[a+p]=w-b,t[a+0]=w+b,t[o+_]=k-y,t[o+f]=k+y,y=h*C-u*M,b=u*C+h*M,t[o+p]=x-b,t[o+0]=x+b,t[a+_]=S-y,t[a+f]=S+y,o+=r,a+=r}while(a<c);h=(v=h)*n[l+0]-u*n[l+1],u=v*n[l+1]+u*n[l+0]}l+=2}while(r<i)}var o=[0,128,64,192,32,160,96,224,16,144,80,208,48,176,112,240,8,136,72,200,40,168,104,232,24,152,88,216,56,184,120,248,4,132,68,196,36,164,100,228,20,148,84,212,52,180,116,244,12,140,76,204,44,172,108,236,28,156,92,220,60,188,124,252,2,130,66,194,34,162,98,226,18,146,82,210,50,178,114,242,10,138,74,202,42,170,106,234,26,154,90,218,58,186,122,250,6,134,70,198,38,166,102,230,22,150,86,214,54,182,118,246,14,142,78,206,46,174,110,238,30,158,94,222,62,190,126,254];this.fft_short=function(t,n,s,r,l){for(var c=0;c<3;c++){var u=a.BLKSIZE_s/2,h=65535&192*(c+1),d=a.BLKSIZE_s/8-1;do{var f,p,_,m,v,g=255&o[d<<2];p=(f=e[g]*r[s][l+g+h])-(v=e[127-g]*r[s][l+g+h+128]),f+=v,m=(_=e[g+64]*r[s][l+g+h+64])-(v=e[63-g]*r[s][l+g+h+192]),_+=v,u-=4,n[c][u+0]=f+_,n[c][u+2]=f-_,n[c][u+1]=p+m,n[c][u+3]=p-m,p=(f=e[g+1]*r[s][l+g+h+1])-(v=e[126-g]*r[s][l+g+h+129]),f+=v,m=(_=e[g+65]*r[s][l+g+h+65])-(v=e[62-g]*r[s][l+g+h+193]),_+=v,n[c][u+a.BLKSIZE_s/2+0]=f+_,n[c][u+a.BLKSIZE_s/2+2]=f-_,n[c][u+a.BLKSIZE_s/2+1]=p+m,n[c][u+a.BLKSIZE_s/2+3]=p-m}while(0<=--d);i(n[c],u,a.BLKSIZE_s/2)}},this.fft_long=function(e,n,s,r,l){var c=a.BLKSIZE/8-1,u=a.BLKSIZE/2;do{var h,d,f,p,_,m=255&o[c];d=(h=t[m]*r[s][l+m])-(_=t[m+512]*r[s][l+m+512]),h+=_,p=(f=t[m+256]*r[s][l+m+256])-(_=t[m+768]*r[s][l+m+768]),f+=_,n[0+(u-=4)]=h+f,n[u+2]=h-f,n[u+1]=d+p,n[u+3]=d-p,d=(h=t[m+1]*r[s][l+m+1])-(_=t[m+513]*r[s][l+m+513]),h+=_,p=(f=t[m+257]*r[s][l+m+257])-(_=t[m+769]*r[s][l+m+769]),f+=_,n[u+a.BLKSIZE/2+0]=h+f,n[u+a.BLKSIZE/2+2]=h-f,n[u+a.BLKSIZE/2+1]=d+p,n[u+a.BLKSIZE/2+3]=d-p}while(0<=--c);i(n,u,a.BLKSIZE/2)},this.init_fft=function(n){for(var i=0;i<a.BLKSIZE;i++)t[i]=.42-.5*Math.cos(2*Math.PI*(i+.5)/a.BLKSIZE)+.08*Math.cos(4*Math.PI*(i+.5)/a.BLKSIZE);for(i=0;i<a.BLKSIZE_s/2;i++)e[i]=.5*(1-Math.cos(2*Math.PI*(i+.5)/a.BLKSIZE_s))}}},function(t,e,n){var i=n(0),s=i.System,r=(i.VbrMode,i.Float,i.ShortBlock,i.Util),a=i.Arrays,o=(i.new_array_n,i.new_byte,i.new_double,i.new_float),l=(i.new_float_n,i.new_int,i.new_int_n,i.assert,n(1));t.exports=function(){var t=[-.1482523854003001,32.308141959636465,296.40344946382766,883.1344870032432,11113.947376231741,1057.2713659324597,305.7402417275812,30.825928907280012,3.8533188138216365,59.42900443849514,709.5899960123345,5281.91112291017,-5829.66483675846,-817.6293103748613,-76.91656988279972,-4.594269939176596,.9063471690191471,.1960342806591213,-.15466694054279598,34.324387823855965,301.8067566458425,817.599602898885,11573.795901679885,1181.2520595540152,321.59731579894424,31.232021761053772,3.7107095756221318,53.650946155329365,684.167428119626,5224.56624370173,-6366.391851890084,-908.9766368219582,-89.83068876699639,-5.411397422890401,.8206787908286602,.3901806440322567,-.16070888947830023,36.147034243915876,304.11815768187864,732.7429163887613,11989.60988270091,1300.012278487897,335.28490093152146,31.48816102859945,3.373875931311736,47.232241542899175,652.7371796173471,5132.414255594984,-6909.087078780055,-1001.9990371107289,-103.62185754286375,-6.104916304710272,.7416505462720353,.5805693545089249,-.16636367662261495,37.751650073343995,303.01103387567713,627.9747488785183,12358.763425278165,1412.2779918482834,346.7496836825721,31.598286663170416,3.1598635433980946,40.57878626349686,616.1671130880391,5007.833007176154,-7454.040671756168,-1095.7960341867115,-118.24411666465777,-6.818469345853504,.6681786379192989,.7653668647301797,-.1716176790982088,39.11551877123304,298.3413246578966,503.5259106886539,12679.589408408976,1516.5821921214542,355.9850766329023,31.395241710249053,2.9164211881972335,33.79716964664243,574.8943997801362,4853.234992253242,-7997.57021486075,-1189.7624067269965,-133.6444792601766,-7.7202770609839915,.5993769336819237,.9427934736519954,-.17645823955292173,40.21879108166477,289.9982036694474,359.3226160751053,12950.259102786438,1612.1013903507662,362.85067106591504,31.045922092242872,2.822222032597987,26.988862316190684,529.8996541764288,4671.371946949588,-8535.899136645805,-1282.5898586244496,-149.58553632943463,-8.643494270763135,.5345111359507916,1.111140466039205,-.36174739330527045,41.04429910497807,277.5463268268618,195.6386023135583,13169.43812144731,1697.6433561479398,367.40983966190305,30.557037410382826,2.531473372857427,20.070154905927314,481.50208566532336,4464.970341588308,-9065.36882077239,-1373.62841526722,-166.1660487028118,-9.58289321133207,.4729647758913199,1.268786568327291,-.36970682634889585,41.393213350082036,261.2935935556502,12.935476055240873,13336.131683328815,1772.508612059496,369.76534388639965,29.751323653701338,2.4023193045459172,13.304795348228817,430.5615775526625,4237.0568611071185,-9581.931701634761,-1461.6913552409758,-183.12733958476446,-10.718010163869403,.41421356237309503,1.414213562373095,-.37677560326535325,41.619486213528496,241.05423794991074,-187.94665032361226,13450.063605744153,1836.153896465782,369.4908799925761,29.001847876923147,2.0714759319987186,6.779591200894186,377.7767837205709,3990.386575512536,-10081.709459700915,-1545.947424837898,-200.3762958015653,-11.864482073055006,.3578057213145241,1.546020906725474,-.3829366947518991,41.1516456456653,216.47684307105183,-406.1569483347166,13511.136535077321,1887.8076599260432,367.3025214564151,28.136213436723654,1.913880671464418,.3829366947518991,323.85365704338597,3728.1472257487526,-10561.233882199509,-1625.2025997821418,-217.62525175416,-13.015432208941645,.3033466836073424,1.66293922460509,-.5822628872992417,40.35639251440489,188.20071124269245,-640.2706748618148,13519.21490106562,1927.6022433578062,362.8197642637487,26.968821921868447,1.7463817695935329,-5.62650678237171,269.3016715297017,3453.386536448852,-11016.145278780888,-1698.6569643425091,-234.7658734267683,-14.16351421663124,.2504869601913055,1.76384252869671,-.5887180101749253,39.23429103868072,155.76096234403798,-889.2492977967378,13475.470561874661,1955.0535223723712,356.4450994756727,25.894952980042156,1.5695032905781554,-11.181939564328772,214.80884394039484,3169.1640829158237,-11443.321309975563,-1765.1588461316153,-251.68908574481912,-15.49755935939164,.198912367379658,1.847759065022573,-.7912582233652842,37.39369355329111,119.699486012458,-1151.0956593239027,13380.446257078214,1970.3952110853447,348.01959814116185,24.731487364283044,1.3850130831637748,-16.421408865300393,161.05030052864092,2878.3322807850063,-11838.991423510031,-1823.985884688674,-268.2854986386903,-16.81724543849939,.1483359875383474,1.913880671464418,-.7960642926861912,35.2322109610459,80.01928065061526,-1424.0212633405113,13235.794061869668,1973.804052543835,337.9908651258184,23.289159354463873,1.3934255946442087,-21.099669467133474,108.48348407242611,2583.700758091299,-12199.726194855148,-1874.2780658979746,-284.2467154529415,-18.11369784385905,.09849140335716425,1.961570560806461,-.998795456205172,32.56307803611191,36.958364584370486,-1706.075448829146,13043.287458812016,1965.3831106103316,326.43182772364605,22.175018750622293,1.198638339011324,-25.371248002043963,57.53505923036915,2288.41886619975,-12522.674544337233,-1914.8400385312243,-299.26241273417224,-19.37805630698734,.04912684976946725,1.990369453344394,.035780907*r.SQRT2*.5/2384e-9,.017876148*r.SQRT2*.5/2384e-9,.003134727*r.SQRT2*.5/2384e-9,.002457142*r.SQRT2*.5/2384e-9,971317e-9*r.SQRT2*.5/2384e-9,218868e-9*r.SQRT2*.5/2384e-9,101566e-9*r.SQRT2*.5/2384e-9,13828e-9*r.SQRT2*.5/2384e-9,12804.797818791945,1945.5515939597317,313.4244966442953,20.801593959731544,1995.1556208053692,9.000838926174497,-29.20218120805369],e=[[2382191739347913e-28,6423305872147834e-28,9400849094049688e-28,1122435026096556e-27,1183840321267481e-27,1122435026096556e-27,940084909404969e-27,6423305872147839e-28,2382191739347918e-28,5456116108943412e-27,4878985199565852e-27,4240448995017367e-27,3559909094758252e-27,2858043359288075e-27,2156177623817898e-27,1475637723558783e-27,8371015190102974e-28,2599706096327376e-28,-5456116108943412e-27,-4878985199565852e-27,-4240448995017367e-27,-3559909094758252e-27,-2858043359288076e-27,-2156177623817898e-27,-1475637723558783e-27,-8371015190102975e-28,-2599706096327376e-28,-2382191739347923e-28,-6423305872147843e-28,-9400849094049696e-28,-1122435026096556e-27,-1183840321267481e-27,-1122435026096556e-27,-9400849094049694e-28,-642330587214784e-27,-2382191739347918e-28],[2382191739347913e-28,6423305872147834e-28,9400849094049688e-28,1122435026096556e-27,1183840321267481e-27,1122435026096556e-27,9400849094049688e-28,6423305872147841e-28,2382191739347918e-28,5456116108943413e-27,4878985199565852e-27,4240448995017367e-27,3559909094758253e-27,2858043359288075e-27,2156177623817898e-27,1475637723558782e-27,8371015190102975e-28,2599706096327376e-28,-5461314069809755e-27,-4921085770524055e-27,-4343405037091838e-27,-3732668368707687e-27,-3093523840190885e-27,-2430835727329465e-27,-1734679010007751e-27,-974825365660928e-27,-2797435120168326e-28,0,0,0,0,0,0,-2283748241799531e-28,-4037858874020686e-28,-2146547464825323e-28],[.1316524975873958,.414213562373095,.7673269879789602,1.091308501069271,1.303225372841206,1.56968557711749,1.920982126971166,2.414213562373094,3.171594802363212,4.510708503662055,7.595754112725146,22.90376554843115,.984807753012208,.6427876096865394,.3420201433256688,.9396926207859084,-.1736481776669303,-.7660444431189779,.8660254037844387,.5,-.5144957554275265,-.4717319685649723,-.3133774542039019,-.1819131996109812,-.09457419252642064,-.04096558288530405,-.01419856857247115,-.003699974673760037,.8574929257125442,.8817419973177052,.9496286491027329,.9833145924917901,.9955178160675857,.9991605581781475,.999899195244447,.9999931550702802],[0,0,0,0,0,0,2283748241799531e-28,4037858874020686e-28,2146547464825323e-28,5461314069809755e-27,4921085770524055e-27,4343405037091838e-27,3732668368707687e-27,3093523840190885e-27,2430835727329466e-27,1734679010007751e-27,974825365660928e-27,2797435120168326e-28,-5456116108943413e-27,-4878985199565852e-27,-4240448995017367e-27,-3559909094758253e-27,-2858043359288075e-27,-2156177623817898e-27,-1475637723558782e-27,-8371015190102975e-28,-2599706096327376e-28,-2382191739347913e-28,-6423305872147834e-28,-9400849094049688e-28,-1122435026096556e-27,-1183840321267481e-27,-1122435026096556e-27,-9400849094049688e-28,-6423305872147841e-28,-2382191739347918e-28]],n=e[l.SHORT_TYPE],i=e[l.SHORT_TYPE],c=e[l.SHORT_TYPE],u=e[l.SHORT_TYPE],h=[0,1,16,17,8,9,24,25,4,5,20,21,12,13,28,29,2,3,18,19,10,11,26,27,6,7,22,23,14,15,30,31];function d(e,n,i){for(var s,a,o,l=10,c=n+238-14-286,u=-15;u<0;u++){var h,d,f;h=t[l+-10],d=e[c+-224]*h,f=e[n+224]*h,h=t[l+-9],d+=e[c+-160]*h,f+=e[n+160]*h,h=t[l+-8],d+=e[c+-96]*h,f+=e[n+96]*h,h=t[l+-7],d+=e[c+-32]*h,f+=e[n+32]*h,h=t[l+-6],d+=e[c+32]*h,f+=e[n+-32]*h,h=t[l+-5],d+=e[c+96]*h,f+=e[n+-96]*h,h=t[l+-4],d+=e[c+160]*h,f+=e[n+-160]*h,h=t[l+-3],d+=e[c+224]*h,f+=e[n+-224]*h,h=t[l+-2],d+=e[n+-256]*h,f-=e[c+256]*h,h=t[l+-1],d+=e[n+-192]*h,f-=e[c+192]*h,h=t[l+0],d+=e[n+-128]*h,f-=e[c+128]*h,h=t[l+1],d+=e[n+-64]*h,f-=e[c+64]*h,h=t[l+2],d+=e[n+0]*h,f-=e[c+0]*h,h=t[l+3],d+=e[n+64]*h,f-=e[c+-64]*h,h=t[l+4],d+=e[n+128]*h,f-=e[c+-128]*h,h=t[l+5],d+=e[n+192]*h,h=(f-=e[c+-192]*h)-(d*=t[l+6]),i[30+2*u]=f+d,i[31+2*u]=t[l+7]*h,l+=18,n--,c++}f=e[n+-16]*t[l+-10],d=e[n+-32]*t[l+-2],f+=(e[n+-48]-e[n+16])*t[l+-9],d+=e[n+-96]*t[l+-1],f+=(e[n+-80]+e[n+48])*t[l+-8],d+=e[n+-160]*t[l+0],f+=(e[n+-112]-e[n+80])*t[l+-7],d+=e[n+-224]*t[l+1],f+=(e[n+-144]+e[n+112])*t[l+-6],d-=e[n+32]*t[l+2],f+=(e[n+-176]-e[n+144])*t[l+-5],d-=e[n+96]*t[l+3],f+=(e[n+-208]+e[n+176])*t[l+-4],d-=e[n+160]*t[l+4],f+=(e[n+-240]-e[n+208])*t[l+-3],s=(d-=e[n+224])-f,a=d+f,f=i[14],d=i[15]-f,i[31]=a+f,i[30]=s+d,i[15]=s-d,i[14]=a-f,o=i[28]-i[0],i[0]+=i[28],i[28]=o*t[l+-36+7],o=i[29]-i[1],i[1]+=i[29],i[29]=o*t[l+-36+7],o=i[26]-i[2],i[2]+=i[26],i[26]=o*t[l+-72+7],o=i[27]-i[3],i[3]+=i[27],i[27]=o*t[l+-72+7],o=i[24]-i[4],i[4]+=i[24],i[24]=o*t[l+-108+7],o=i[25]-i[5],i[5]+=i[25],i[25]=o*t[l+-108+7],o=i[22]-i[6],i[6]+=i[22],i[22]=o*r.SQRT2,o=i[23]-i[7],i[7]+=i[23],i[23]=o*r.SQRT2-i[7],i[7]-=i[6],i[22]-=i[7],i[23]-=i[22],o=i[6],i[6]=i[31]-o,i[31]=i[31]+o,o=i[7],i[7]=i[30]-o,i[30]=i[30]+o,o=i[22],i[22]=i[15]-o,i[15]=i[15]+o,o=i[23],i[23]=i[14]-o,i[14]=i[14]+o,o=i[20]-i[8],i[8]+=i[20],i[20]=o*t[l+-180+7],o=i[21]-i[9],i[9]+=i[21],i[21]=o*t[l+-180+7],o=i[18]-i[10],i[10]+=i[18],i[18]=o*t[l+-216+7],o=i[19]-i[11],i[11]+=i[19],i[19]=o*t[l+-216+7],o=i[16]-i[12],i[12]+=i[16],i[16]=o*t[l+-252+7],o=i[17]-i[13],i[13]+=i[17],i[17]=o*t[l+-252+7],o=-i[20]+i[24],i[20]+=i[24],i[24]=o*t[l+-216+7],o=-i[21]+i[25],i[21]+=i[25],i[25]=o*t[l+-216+7],o=i[4]-i[8],i[4]+=i[8],i[8]=o*t[l+-216+7],o=i[5]-i[9],i[5]+=i[9],i[9]=o*t[l+-216+7],o=i[0]-i[12],i[0]+=i[12],i[12]=o*t[l+-72+7],o=i[1]-i[13],i[1]+=i[13],i[13]=o*t[l+-72+7],o=i[16]-i[28],i[16]+=i[28],i[28]=o*t[l+-72+7],o=-i[17]+i[29],i[17]+=i[29],i[29]=o*t[l+-72+7],o=r.SQRT2*(i[2]-i[10]),i[2]+=i[10],i[10]=o,o=r.SQRT2*(i[3]-i[11]),i[3]+=i[11],i[11]=o,o=r.SQRT2*(-i[18]+i[26]),i[18]+=i[26],i[26]=o-i[18],o=r.SQRT2*(-i[19]+i[27]),i[19]+=i[27],i[27]=o-i[19],o=i[2],i[19]-=i[3],i[3]-=o,i[2]=i[31]-o,i[31]+=o,o=i[3],i[11]-=i[19],i[18]-=o,i[3]=i[30]-o,i[30]+=o,o=i[18],i[27]-=i[11],i[19]-=o,i[18]=i[15]-o,i[15]+=o,o=i[19],i[10]-=o,i[19]=i[14]-o,i[14]+=o,o=i[10],i[11]-=o,i[10]=i[23]-o,i[23]+=o,o=i[11],i[26]-=o,i[11]=i[22]-o,i[22]+=o,o=i[26],i[27]-=o,i[26]=i[7]-o,i[7]+=o,o=i[27],i[27]=i[6]-o,i[6]+=o,o=r.SQRT2*(i[0]-i[4]),i[0]+=i[4],i[4]=o,o=r.SQRT2*(i[1]-i[5]),i[1]+=i[5],i[5]=o,o=r.SQRT2*(i[16]-i[20]),i[16]+=i[20],i[20]=o,o=r.SQRT2*(i[17]-i[21]),i[17]+=i[21],i[21]=o,o=-r.SQRT2*(i[8]-i[12]),i[8]+=i[12],i[12]=o-i[8],o=-r.SQRT2*(i[9]-i[13]),i[9]+=i[13],i[13]=o-i[9],o=-r.SQRT2*(i[25]-i[29]),i[25]+=i[29],i[29]=o-i[25],o=-r.SQRT2*(i[24]+i[28]),i[24]-=i[28],i[28]=o-i[24],o=i[24]-i[16],i[24]=o,o=i[20]-o,i[20]=o,o=i[28]-o,i[28]=o,o=i[25]-i[17],i[25]=o,o=i[21]-o,i[21]=o,o=i[29]-o,i[29]=o,o=i[17]-i[1],i[17]=o,o=i[9]-o,i[9]=o,o=i[25]-o,i[25]=o,o=i[5]-o,i[5]=o,o=i[21]-o,i[21]=o,o=i[13]-o,i[13]=o,o=i[29]-o,i[29]=o,o=i[1]-i[0],i[1]=o,o=i[16]-o,i[16]=o,o=i[17]-o,i[17]=o,o=i[8]-o,i[8]=o,o=i[9]-o,i[9]=o,o=i[24]-o,i[24]=o,o=i[25]-o,i[25]=o,o=i[4]-o,i[4]=o,o=i[5]-o,i[5]=o,o=i[20]-o,i[20]=o,o=i[21]-o,i[21]=o,o=i[12]-o,i[12]=o,o=i[13]-o,i[13]=o,o=i[28]-o,i[28]=o,o=i[29]-o,i[29]=o,o=i[0],i[0]+=i[31],i[31]-=o,o=i[1],i[1]+=i[30],i[30]-=o,o=i[16],i[16]+=i[15],i[15]-=o,o=i[17],i[17]+=i[14],i[14]-=o,o=i[8],i[8]+=i[23],i[23]-=o,o=i[9],i[9]+=i[22],i[22]-=o,o=i[24],i[24]+=i[7],i[7]-=o,o=i[25],i[25]+=i[6],i[6]-=o,o=i[4],i[4]+=i[27],i[27]-=o,o=i[5],i[5]+=i[26],i[26]-=o,o=i[20],i[20]+=i[11],i[11]-=o,o=i[21],i[21]+=i[10],i[10]-=o,o=i[12],i[12]+=i[19],i[19]-=o,o=i[13],i[13]+=i[18],i[18]-=o,o=i[28],i[28]+=i[3],i[3]-=o,o=i[29],i[29]+=i[2],i[2]-=o}function f(t,n){for(var i=0;i<3;i++){var s,r,a,o,c,u;r=(o=t[n+6]*e[l.SHORT_TYPE][0]-t[n+15])+(s=t[n+0]*e[l.SHORT_TYPE][2]-t[n+9]),a=o-s,c=(o=t[n+15]*e[l.SHORT_TYPE][0]+t[n+6])+(s=t[n+9]*e[l.SHORT_TYPE][2]+t[n+0]),u=-o+s,s=2069978111953089e-26*(t[n+3]*e[l.SHORT_TYPE][1]-t[n+12]),o=2069978111953089e-26*(t[n+12]*e[l.SHORT_TYPE][1]+t[n+3]),t[n+0]=190752519173728e-25*r+s,t[n+15]=190752519173728e-25*-c+o,a=.8660254037844387*a*1907525191737281e-26,c=.5*c*1907525191737281e-26+o,t[n+3]=a-c,t[n+6]=a+c,r=.5*r*1907525191737281e-26-s,u=.8660254037844387*u*1907525191737281e-26,t[n+9]=r+u,t[n+12]=r-u,n++}}this.mdct_sub48=function(t,r,p){for(var _,m,v,g,b,y,x,w,S,k,A,C,M,E,T,B,R,$,I,L,O,P=r,V=286,D=0;D<t.channels_out;D++){for(var N=0;N<t.mode_gr;N++){for(var F,H=t.l3_side.tt[N][D],j=H.xr,Z=0,z=t.sb_sample[D][1-N],q=0,U=0;U<9;U++)for(d(P,V,z[q]),d(P,V+32,z[q+1]),q+=2,V+=64,F=1;F<32;F+=2)z[q-1][F]*=-1;for(F=0;F<32;F++,Z+=18){var X=H.block_type,Y=t.sb_sample[D][N],W=t.sb_sample[D][1-N];if(0!=H.mixed_block_flag&&F<2&&(X=0),t.amp_filter[F]<1e-12)a.fill(j,Z+0,Z+18,0);else{if(t.amp_filter[F]<1)for(U=0;U<18;U++)W[U][h[F]]*=t.amp_filter[F];if(X==l.SHORT_TYPE){for(U=-3;U<0;U++){var G=e[l.SHORT_TYPE][U+3];j[Z+3*U+9]=Y[9+U][h[F]]*G-Y[8-U][h[F]],j[Z+3*U+18]=Y[14-U][h[F]]*G+Y[15+U][h[F]],j[Z+3*U+10]=Y[15+U][h[F]]*G-Y[14-U][h[F]],j[Z+3*U+19]=W[2-U][h[F]]*G+W[3+U][h[F]],j[Z+3*U+11]=W[3+U][h[F]]*G-W[2-U][h[F]],j[Z+3*U+20]=W[8-U][h[F]]*G+W[9+U][h[F]]}f(j,Z)}else{var K=o(18);for(U=-9;U<0;U++){var Q,J;Q=e[X][U+27]*W[U+9][h[F]]+e[X][U+36]*W[8-U][h[F]],J=e[X][U+9]*Y[U+9][h[F]]-e[X][U+18]*Y[8-U][h[F]],K[U+9]=Q-J*n[3+U+9],K[U+18]=Q*n[3+U+9]+J}_=j,m=Z,O=L=I=$=R=B=T=E=M=C=A=k=S=w=x=y=b=g=void 0,y=(v=K)[17]-v[9],w=v[15]-v[11],S=v[14]-v[12],k=v[0]+v[8],A=v[1]+v[7],C=v[2]+v[6],M=v[3]+v[5],_[m+17]=k+C-M-(A-v[4]),b=(k+C-M)*i[19]+(A-v[4]),g=(y-w-S)*i[18],_[m+5]=g+b,_[m+6]=g-b,x=(v[16]-v[10])*i[18],A=A*i[19]+v[4],g=y*i[12]+x+w*i[13]+S*i[14],b=-k*i[16]+A-C*i[17]+M*i[15],_[m+1]=g+b,_[m+2]=g-b,g=y*i[13]-x-w*i[14]+S*i[12],b=-k*i[17]+A-C*i[15]+M*i[16],_[m+9]=g+b,_[m+10]=g-b,g=y*i[14]-x+w*i[12]-S*i[13],b=k*i[15]-A+C*i[16]-M*i[17],_[m+13]=g+b,_[m+14]=g-b,E=v[8]-v[0],B=v[6]-v[2],R=v[5]-v[3],$=v[17]+v[9],I=v[16]+v[10],L=v[15]+v[11],O=v[14]+v[12],_[m+0]=$+L+O+(I+v[13]),g=($+L+O)*i[19]-(I+v[13]),b=(E-B+R)*i[18],_[m+11]=g+b,_[m+12]=g-b,T=(v[7]-v[1])*i[18],I=v[13]-I*i[19],g=$*i[15]-I+L*i[16]+O*i[17],b=E*i[14]+T+B*i[12]+R*i[13],_[m+3]=g+b,_[m+4]=g-b,g=-$*i[17]+I-L*i[15]-O*i[16],b=E*i[13]+T-B*i[14]-R*i[12],_[m+7]=g+b,_[m+8]=g-b,g=-$*i[16]+I-L*i[17]-O*i[15],b=E*i[12]-T+B*i[13]-R*i[14],_[m+15]=g+b,_[m+16]=g-b}}if(X!=l.SHORT_TYPE&&0!=F)for(U=7;0<=U;--U){var tt,et;tt=j[Z+U]*c[20+U]+j[Z+-1-U]*u[28+U],et=j[Z+U]*u[28+U]-j[Z+-1-U]*c[20+U],j[Z+-1-U]=tt,j[Z+U]=et}}}if(P=p,V=286,1==t.mode_gr)for(var nt=0;nt<18;nt++)s.arraycopy(t.sb_sample[D][1][nt],0,t.sb_sample[D][0][nt],0,32)}}}},function(t,e,n){var i=n(8);t.exports=function(){this.thm=new i,this.en=new i}},function(t,e,n){var i=n(9);t.exports=function(){this.class_id=0,this.num_samples=0,this.num_channels=0,this.in_samplerate=0,this.out_samplerate=0,this.scale=0,this.scale_left=0,this.scale_right=0,this.analysis=!1,this.bWriteVbrTag=!1,this.decode_only=!1,this.quality=0,this.mode=i.STEREO,this.force_ms=!1,this.free_format=!1,this.findReplayGain=!1,this.decode_on_the_fly=!1,this.write_id3tag_automatic=!1,this.brate=0,this.compression_ratio=0,this.copyright=0,this.original=0,this.extension=0,this.emphasis=0,this.error_protection=0,this.strict_ISO=!1,this.disable_reservoir=!1,this.quant_comp=0,this.quant_comp_short=0,this.experimentalY=!1,this.experimentalZ=0,this.exp_nspsytune=0,this.preset=0,this.VBR=null,this.VBR_q_frac=0,this.VBR_q=0,this.VBR_mean_bitrate_kbps=0,this.VBR_min_bitrate_kbps=0,this.VBR_max_bitrate_kbps=0,this.VBR_hard_min=0,this.lowpassfreq=0,this.highpassfreq=0,this.lowpasswidth=0,this.highpasswidth=0,this.maskingadjust=0,this.maskingadjust_short=0,this.ATHonly=!1,this.ATHshort=!1,this.noATH=!1,this.ATHtype=0,this.ATHcurve=0,this.ATHlower=0,this.athaa_type=0,this.athaa_loudapprox=0,this.athaa_sensitivity=0,this.short_blocks=null,this.useTemporal=!1,this.interChRatio=0,this.msfix=0,this.tune=!1,this.tune_value_a=0,this.version=0,this.encoder_delay=0,this.encoder_padding=0,this.framesize=0,this.frameNum=0,this.lame_allocated_gfp=0,this.internal_flags=null}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float,i.new_float_n,i.new_int),r=(i.new_int_n,i.assert,n(6));t.exports=function(){this.tt=[[null,null],[null,null]],this.main_data_begin=0,this.private_bits=0,this.resvDrain_pre=0,this.resvDrain_post=0,this.scfsi=[s(4),s(4)];for(var t=0;t<2;t++)for(var e=0;e<2;e++)this.tt[t][e]=new r}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float),r=i.new_float_n,a=i.new_int,o=(i.new_int_n,i.assert,n(1));t.exports=function(){this.last_en_subshort=r([4,9]),this.lastAttacks=a(4),this.pefirbuf=s(19),this.longfact=s(o.SBMAX_l),this.shortfact=s(o.SBMAX_s),this.attackthre=0,this.attackthre_s=0}},function(t,e){t.exports=function(){this.sum=0,this.seen=0,this.want=0,this.pos=0,this.size=0,this.bag=null,this.nVbrNumFrames=0,this.nBytesWritten=0,this.TotalFrameSize=0}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float),r=(i.new_float_n,i.new_int,i.new_int_n,i.assert,n(1));t.exports=function(){this.useAdjust=0,this.aaSensitivityP=0,this.adjust=0,this.adjustLimit=0,this.decay=0,this.floor=0,this.l=s(r.SBMAX_l),this.s=s(r.SBMAX_s),this.psfb21=s(r.PSFB21),this.psfb12=s(r.PSFB12),this.cb_l=s(r.CBANDS),this.cb_s=s(r.CBANDS),this.eql_w=s(r.BLKSIZE/2)}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float),r=(i.new_float_n,i.new_int),a=(i.new_int_n,i.assert,n(11));t.exports=function(){this.linprebuf=s(2*a.MAX_ORDER),this.linpre=0,this.lstepbuf=s(a.MAX_SAMPLES_PER_WINDOW+a.MAX_ORDER),this.lstep=0,this.loutbuf=s(a.MAX_SAMPLES_PER_WINDOW+a.MAX_ORDER),this.lout=0,this.rinprebuf=s(2*a.MAX_ORDER),this.rinpre=0,this.rstepbuf=s(a.MAX_SAMPLES_PER_WINDOW+a.MAX_ORDER),this.rstep=0,this.routbuf=s(a.MAX_SAMPLES_PER_WINDOW+a.MAX_ORDER),this.rout=0,this.sampleWindow=0,this.totsamp=0,this.lsum=0,this.rsum=0,this.freqindex=0,this.first=0,this.A=r(0|a.STEPS_per_dB*a.MAX_dB),this.B=r(0|a.STEPS_per_dB*a.MAX_dB)}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode,i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float),r=(i.new_float_n,i.new_int),a=(i.new_int_n,i.assert),o=n(12),l=n(1),c=n(5),u=n(4);t.exports=function(t){var e=t;this.quantize=e,this.iteration_loop=function(t,e,n,i){var h,d=t.internal_flags,f=s(c.SFBMAX),p=s(576),_=r(2),m=0,v=d.l3_side,g=new o(m);this.quantize.rv.ResvFrameBegin(t,g),m=g.bits;for(var b=0;b<d.mode_gr;b++){h=this.quantize.qupvt.on_pe(t,e,_,m,b,b),d.mode_ext==l.MPG_MD_MS_LR&&(this.quantize.ms_convert(d.l3_side,b),this.quantize.qupvt.reduce_side(_,n[b],m,h));for(var y=0;y<d.channels_out;y++){var x,w,S=v.tt[b][y];w=S.block_type!=l.SHORT_TYPE?(x=0,d.PSY.mask_adjust-x):(x=0,d.PSY.mask_adjust_short-x),d.masking_lower=Math.pow(10,.1*w),this.quantize.init_outer_loop(d,S),this.quantize.init_xrpow(d,S,p)&&(this.quantize.qupvt.calc_xmin(t,i[b][y],S,f),this.quantize.outer_loop(t,S,f,p,y,_[y])),this.quantize.iteration_finish_one(d,b,y),a(S.part2_3_length<=u.MAX_BITS_PER_CHANNEL),a(S.part2_3_length<=_[y])}}this.quantize.rv.ResvFrameEnd(d,m)}}},function(t,e,n){var i=n(0),s=(i.System,i.VbrMode);i.Float,i.ShortBlock,i.Util,i.Arrays,i.new_array_n,i.new_byte,i.new_double,i.new_float,i.new_float_n,i.new_int,i.new_int_n,i.assert,t.exports=function(){function t(t,e,n,i,s,r,a,o,l,c,u,h,d,f,p){this.vbr_q=t,this.quant_comp=e,this.quant_comp_s=n,this.expY=i,this.st_lrm=s,this.st_s=r,this.masking_adj=a,this.masking_adj_short=o,this.ath_lower=l,this.ath_curve=c,this.ath_sensitivity=u,this.interch=h,this.safejoint=d,this.sfb21mod=f,this.msfix=p}function e(t,e,n,i,s,r,a,o,l,c,u,h,d,f){this.quant_comp=e,this.quant_comp_s=n,this.safejoint=i,this.nsmsfix=s,this.st_lrm=r,this.st_s=a,this.nsbass=o,this.scale=l,this.masking_adj=c,this.ath_lower=u,this.ath_curve=h,this.interch=d,this.sfscale=f}var n;this.setModules=function(t){n=t};var i=[new t(0,9,9,0,5.2,125,-4.2,-6.3,4.8,1,0,0,2,21,.97),new t(1,9,9,0,5.3,125,-3.6,-5.6,4.5,1.5,0,0,2,21,1.35),new t(2,9,9,0,5.6,125,-2.2,-3.5,2.8,2,0,0,2,21,1.49),new t(3,9,9,1,5.8,130,-1.8,-2.8,2.6,3,-4,0,2,20,1.64),new t(4,9,9,1,6,135,-.7,-1.1,1.1,3.5,-8,0,2,0,1.79),new t(5,9,9,1,6.4,140,.5,.4,-7.5,4,-12,2e-4,0,0,1.95),new t(6,9,9,1,6.6,145,.67,.65,-14.7,6.5,-19,4e-4,0,0,2.3),new t(7,9,9,1,6.6,145,.8,.75,-19.7,8,-22,6e-4,0,0,2.7),new t(8,9,9,1,6.6,145,1.2,1.15,-27.5,10,-23,7e-4,0,0,0),new t(9,9,9,1,6.6,145,1.6,1.6,-36,11,-25,8e-4,0,0,0),new t(10,9,9,1,6.6,145,2,2,-36,12,-25,8e-4,0,0,0)],r=[new t(0,9,9,0,4.2,25,-7,-4,7.5,1,0,0,2,26,.97),new t(1,9,9,0,4.2,25,-5.6,-3.6,4.5,1.5,0,0,2,21,1.35),new t(2,9,9,0,4.2,25,-4.4,-1.8,2,2,0,0,2,18,1.49),new t(3,9,9,1,4.2,25,-3.4,-1.25,1.1,3,-4,0,2,15,1.64),new t(4,9,9,1,4.2,25,-2.2,.1,0,3.5,-8,0,2,0,1.79),new t(5,9,9,1,4.2,25,-1,1.65,-7.7,4,-12,2e-4,0,0,1.95),new t(6,9,9,1,4.2,25,-0,2.47,-7.7,6.5,-19,4e-4,0,0,2),new t(7,9,9,1,4.2,25,.5,2,-14.5,8,-22,6e-4,0,0,2),new t(8,9,9,1,4.2,25,1,2.4,-22,10,-23,7e-4,0,0,2),new t(9,9,9,1,4.2,25,1.5,2.95,-30,11,-25,8e-4,0,0,2),new t(10,9,9,1,4.2,25,2,2.95,-36,12,-30,8e-4,0,0,2)];function a(t,e,n){var a,o,l=t.VBR==s.vbr_rh?i:r,c=t.VBR_q_frac,u=l[e],h=l[e+1],d=u;u.st_lrm=u.st_lrm+c*(h.st_lrm-u.st_lrm),u.st_s=u.st_s+c*(h.st_s-u.st_s),u.masking_adj=u.masking_adj+c*(h.masking_adj-u.masking_adj),u.masking_adj_short=u.masking_adj_short+c*(h.masking_adj_short-u.masking_adj_short),u.ath_lower=u.ath_lower+c*(h.ath_lower-u.ath_lower),u.ath_curve=u.ath_curve+c*(h.ath_curve-u.ath_curve),u.ath_sensitivity=u.ath_sensitivity+c*(h.ath_sensitivity-u.ath_sensitivity),u.interch=u.interch+c*(h.interch-u.interch),u.msfix=u.msfix+c*(h.msfix-u.msfix),a=t,(o=d.vbr_q)<0&&(o=0),9<o&&(o=9),a.VBR_q=o,(a.VBR_q_frac=0)!=n?t.quant_comp=d.quant_comp:0<Math.abs(t.quant_comp- -1)||(t.quant_comp=d.quant_comp),0!=n?t.quant_comp_short=d.quant_comp_s:0<Math.abs(t.quant_comp_short- -1)||(t.quant_comp_short=d.quant_comp_s),0!=d.expY&&(t.experimentalY=0!=d.expY),0!=n?t.internal_flags.nsPsy.attackthre=d.st_lrm:0<Math.abs(t.internal_flags.nsPsy.attackthre- -1)||(t.internal_flags.nsPsy.attackthre=d.st_lrm),0!=n?t.internal_flags.nsPsy.attackthre_s=d.st_s:0<Math.abs(t.internal_flags.nsPsy.attackthre_s- -1)||(t.internal_flags.nsPsy.attackthre_s=d.st_s),0!=n?t.maskingadjust=d.masking_adj:0<Math.abs(t.maskingadjust-0)||(t.maskingadjust=d.masking_adj),0!=n?t.maskingadjust_short=d.masking_adj_short:0<Math.abs(t.maskingadjust_short-0)||(t.maskingadjust_short=d.masking_adj_short),0!=n?t.ATHlower=-d.ath_lower/10:0<Math.abs(10*-t.ATHlower-0)||(t.ATHlower=-d.ath_lower/10),0!=n?t.ATHcurve=d.ath_curve:0<Math.abs(t.ATHcurve- -1)||(t.ATHcurve=d.ath_curve),0!=n?t.athaa_sensitivity=d.ath_sensitivity:0<Math.abs(t.athaa_sensitivity- -1)||(t.athaa_sensitivity=d.ath_sensitivity),0<d.interch&&(0!=n?t.interChRatio=d.interch:0<Math.abs(t.interChRatio- -1)||(t.interChRatio=d.interch)),0<d.safejoint&&(t.exp_nspsytune=t.exp_nspsytune|d.safejoint),0<d.sfb21mod&&(t.exp_nspsytune=t.exp_nspsytune|d.sfb21mod<<20),0!=n?t.msfix=d.msfix:0<Math.abs(t.msfix- -1)||(t.msfix=d.msfix),0==n&&(t.VBR_q=e,t.VBR_q_frac=c)}var o=[new e(8,9,9,0,0,6.6,145,0,.95,0,-30,11,.0012,1),new e(16,9,9,0,0,6.6,145,0,.95,0,-25,11,.001,1),new e(24,9,9,0,0,6.6,145,0,.95,0,-20,11,.001,1),new e(32,9,9,0,0,6.6,145,0,.95,0,-15,11,.001,1),new e(40,9,9,0,0,6.6,145,0,.95,0,-10,11,9e-4,1),new e(48,9,9,0,0,6.6,145,0,.95,0,-10,11,9e-4,1),new e(56,9,9,0,0,6.6,145,0,.95,0,-6,11,8e-4,1),new e(64,9,9,0,0,6.6,145,0,.95,0,-2,11,8e-4,1),new e(80,9,9,0,0,6.6,145,0,.95,0,0,8,7e-4,1),new e(96,9,9,0,2.5,6.6,145,0,.95,0,1,5.5,6e-4,1),new e(112,9,9,0,2.25,6.6,145,0,.95,0,2,4.5,5e-4,1),new e(128,9,9,0,1.95,6.4,140,0,.95,0,3,4,2e-4,1),new e(160,9,9,1,1.79,6,135,0,.95,-2,5,3.5,0,1),new e(192,9,9,1,1.49,5.6,125,0,.97,-4,7,3,0,0),new e(224,9,9,1,1.25,5.2,125,0,.98,-6,9,2,0,0),new e(256,9,9,1,.97,5.2,125,0,1,-8,10,1,0,0),new e(320,9,9,1,.9,5.2,125,0,1,-10,12,0,0,0)];function l(t,e,i){var r=e,a=n.nearestBitrateFullIndex(e);if(t.VBR=s.vbr_abr,t.VBR_mean_bitrate_kbps=r,t.VBR_mean_bitrate_kbps=Math.min(t.VBR_mean_bitrate_kbps,320),t.VBR_mean_bitrate_kbps=Math.max(t.VBR_mean_bitrate_kbps,8),t.brate=t.VBR_mean_bitrate_kbps,320<t.VBR_mean_bitrate_kbps&&(t.disable_reservoir=!0),0<o[a].safejoint&&(t.exp_nspsytune=2|t.exp_nspsytune),0<o[a].sfscale&&(t.internal_flags.noise_shaping=2),0<Math.abs(o[a].nsbass)){var l=int(4*o[a].nsbass);l<0&&(l+=64),t.exp_nspsytune=t.exp_nspsytune|l<<2}return 0!=i?t.quant_comp=o[a].quant_comp:0<Math.abs(t.quant_comp- -1)||(t.quant_comp=o[a].quant_comp),0!=i?t.quant_comp_short=o[a].quant_comp_s:0<Math.abs(t.quant_comp_short- -1)||(t.quant_comp_short=o[a].quant_comp_s),0!=i?t.msfix=o[a].nsmsfix:0<Math.abs(t.msfix- -1)||(t.msfix=o[a].nsmsfix),0!=i?t.internal_flags.nsPsy.attackthre=o[a].st_lrm:0<Math.abs(t.internal_flags.nsPsy.attackthre- -1)||(t.internal_flags.nsPsy.attackthre=o[a].st_lrm),0!=i?t.internal_flags.nsPsy.attackthre_s=o[a].st_s:0<Math.abs(t.internal_flags.nsPsy.attackthre_s- -1)||(t.internal_flags.nsPsy.attackthre_s=o[a].st_s),0!=i?t.scale=o[a].scale:0<Math.abs(t.scale- -1)||(t.scale=o[a].scale),0!=i?t.maskingadjust=o[a].masking_adj:0<Math.abs(t.maskingadjust-0)||(t.maskingadjust=o[a].masking_adj),0<o[a].masking_adj?0!=i?t.maskingadjust_short=.9*o[a].masking_adj:0<Math.abs(t.maskingadjust_short-0)||(t.maskingadjust_short=.9*o[a].masking_adj):0!=i?t.maskingadjust_short=1.1*o[a].masking_adj:0<Math.abs(t.maskingadjust_short-0)||(t.maskingadjust_short=1.1*o[a].masking_adj),0!=i?t.ATHlower=-o[a].ath_lower/10:0<Math.abs(10*-t.ATHlower-0)||(t.ATHlower=-o[a].ath_lower/10),0!=i?t.ATHcurve=o[a].ath_curve:0<Math.abs(t.ATHcurve- -1)||(t.ATHcurve=o[a].ath_curve),0!=i?t.interChRatio=o[a].interch:0<Math.abs(t.interChRatio- -1)||(t.interChRatio=o[a].interch),e}this.apply_preset=function(t,e,n){switch(e){case Lame.R3MIX:e=Lame.V3,t.VBR=s.vbr_mtrh;break;case Lame.MEDIUM:e=Lame.V4,t.VBR=s.vbr_rh;break;case Lame.MEDIUM_FAST:e=Lame.V4,t.VBR=s.vbr_mtrh;break;case Lame.STANDARD:e=Lame.V2,t.VBR=s.vbr_rh;break;case Lame.STANDARD_FAST:e=Lame.V2,t.VBR=s.vbr_mtrh;break;case Lame.EXTREME:e=Lame.V0,t.VBR=s.vbr_rh;break;case Lame.EXTREME_FAST:e=Lame.V0,t.VBR=s.vbr_mtrh;break;case Lame.INSANE:return e=320,t.preset=e,l(t,e,n),t.VBR=s.vbr_off,e}switch(t.preset=e){case Lame.V9:return a(t,9,n),e;case Lame.V8:return a(t,8,n),e;case Lame.V7:return a(t,7,n),e;case Lame.V6:return a(t,6,n),e;case Lame.V5:return a(t,5,n),e;case Lame.V4:return a(t,4,n),e;case Lame.V3:return a(t,3,n),e;case Lame.V2:return a(t,2,n),e;case Lame.V1:return a(t,1,n),e;case Lame.V0:return a(t,0,n),e}return 8<=e&&e<=320?l(t,e,n):(t.preset=0,e)}}},function(t,e,n){var i=n(0),s=i.System,r=i.VbrMode,a=(i.Float,i.ShortBlock,i.Util),o=i.Arrays,l=(i.new_array_n,i.new_byte,i.new_double,i.new_float),c=(i.new_float_n,i.new_int,i.new_int_n,i.assert),u=n(41),h=n(42),d=n(43),f=n(1),p=n(6),_=n(5);t.exports=function(){var t,e,n;this.rv=null,this.qupvt=null;var i,m=new u;function v(t){this.ordinal=t}function g(t){for(var e=0;e<t.sfbmax;e++)if(t.scalefac[e]+t.subblock_gain[t.window[e]]==0)return!1;return!0}function b(t,e,n,i,s){var r;switch(t){default:case 9:0<e.over_count?(r=n.over_SSD<=e.over_SSD,n.over_SSD==e.over_SSD&&(r=n.bits<e.bits)):r=n.max_noise<0&&10*n.max_noise+n.bits<=10*e.max_noise+e.bits;break;case 0:r=n.over_count<e.over_count||n.over_count==e.over_count&&n.over_noise<e.over_noise||n.over_count==e.over_count&&BitStream.EQ(n.over_noise,e.over_noise)&&n.tot_noise<e.tot_noise;break;case 8:n.max_noise=function(t,e){for(var n,i=1e-37,s=0;s<e.psymax;s++)i+=(n=t[s],a.FAST_LOG10(.368+.632*n*n*n));return Math.max(1e-20,i)}(s,i);case 1:r=n.max_noise<e.max_noise;break;case 2:r=n.tot_noise<e.tot_noise;break;case 3:r=n.tot_noise<e.tot_noise&&n.max_noise<e.max_noise;break;case 4:r=n.max_noise<=0&&.2<e.max_noise||n.max_noise<=0&&e.max_noise<0&&e.max_noise>n.max_noise-.2&&n.tot_noise<e.tot_noise||n.max_noise<=0&&0<e.max_noise&&e.max_noise>n.max_noise-.2&&n.tot_noise<e.tot_noise+e.over_noise||0<n.max_noise&&-.05<e.max_noise&&e.max_noise>n.max_noise-.1&&n.tot_noise+n.over_noise<e.tot_noise+e.over_noise||0<n.max_noise&&-.1<e.max_noise&&e.max_noise>n.max_noise-.15&&n.tot_noise+n.over_noise+n.over_noise<e.tot_noise+e.over_noise+e.over_noise;break;case 5:r=n.over_noise<e.over_noise||BitStream.EQ(n.over_noise,e.over_noise)&&n.tot_noise<e.tot_noise;break;case 6:r=n.over_noise<e.over_noise||BitStream.EQ(n.over_noise,e.over_noise)&&(n.max_noise<e.max_noise||BitStream.EQ(n.max_noise,e.max_noise)&&n.tot_noise<=e.tot_noise);break;case 7:r=n.over_count<e.over_count||n.over_noise<e.over_noise}return 0==e.over_count&&(r=r&&n.bits<e.bits),r}function y(t,e,s,r,a){var l=t.internal_flags;!function(t,e,n,i,s){var r,a=t.internal_flags;r=0==e.scalefac_scale?1.2968395546510096:1.6817928305074292;for(var o=0,l=0;l<e.sfbmax;l++)o<n[l]&&(o=n[l]);var c=a.noise_shaping_amp;switch(3==c&&(c=s?2:1),c){case 2:break;case 1:1<o?o=Math.pow(o,.5):o*=.95;break;case 0:default:1<o?o=1:o*=.95}var u=0;for(l=0;l<e.sfbmax;l++){var h,d=e.width[l];if(u+=d,!(n[l]<o)){if(0!=(2&a.substep_shaping)&&(a.pseudohalf[l]=0==a.pseudohalf[l]?1:0,0==a.pseudohalf[l]&&2==a.noise_shaping_amp))return;for(e.scalefac[l]++,h=-d;h<0;h++)i[u+h]*=r,i[u+h]>e.xrpow_max&&(e.xrpow_max=i[u+h]);if(2==a.noise_shaping_amp)return}}}(t,e,s,r,a);var u=g(e);return!(u||(u=2==l.mode_gr?i.scale_bitcount(e):i.scale_bitcount_lsf(l,e))&&(1<l.noise_shaping&&(o.fill(l.pseudohalf,0),0==e.scalefac_scale?(function(t,e){for(var i=0,s=0;s<t.sfbmax;s++){var r=t.width[s],a=t.scalefac[s];if(0!=t.preflag&&(a+=n.pretab[s]),i+=r,0!=(1&a)){a++;for(var o=-r;o<0;o++)e[i+o]*=1.2968395546510096,e[i+o]>t.xrpow_max&&(t.xrpow_max=e[i+o])}t.scalefac[s]=a>>1}t.preflag=0,t.scalefac_scale=1}(e,r),u=!1):e.block_type==f.SHORT_TYPE&&0<l.subblock_gain&&(u=function(t,e,i){var s,r=e.scalefac;for(s=0;s<e.sfb_lmax;s++)if(16<=r[s])return!0;for(var a=0;a<3;a++){var o=0,l=0;for(s=e.sfb_lmax+a;s<e.sfbdivide;s+=3)o<r[s]&&(o=r[s]);for(;s<e.sfbmax;s+=3)l<r[s]&&(l=r[s]);if(!(o<16&&l<8)){if(7<=e.subblock_gain[a])return!0;e.subblock_gain[a]++;var u=t.scalefac_band.l[e.sfb_lmax];for(s=e.sfb_lmax+a;s<e.sfbmax;s+=3){var h=e.width[s],d=r[s];if(c(0<=d),0<=(d-=4>>e.scalefac_scale))r[s]=d,u+=3*h;else{r[s]=0;var f=210+(d<<e.scalefac_scale+1);_=n.IPOW20(f),u+=h*(a+1);for(var p=-h;p<0;p++)i[u+p]*=_,i[u+p]>e.xrpow_max&&(e.xrpow_max=i[u+p]);u+=h*(3-a-1)}}var _=n.IPOW20(202);for(u+=e.width[s]*(a+1),p=-e.width[s];p<0;p++)i[u+p]*=_,i[u+p]>e.xrpow_max&&(e.xrpow_max=i[u+p])}}return!1}(l,e,r)||g(e))),u||(u=2==l.mode_gr?i.scale_bitcount(e):i.scale_bitcount_lsf(l,e)),u))}this.setModules=function(s,r,a,o){t=s,e=r,this.rv=r,n=a,this.qupvt=a,i=o,m.setModules(n,i)},this.ms_convert=function(t,e){for(var n=0;n<576;++n){var i=t.tt[e][0].xr[n],s=t.tt[e][1].xr[n];t.tt[e][0].xr[n]=(i+s)*(.5*a.SQRT2),t.tt[e][1].xr[n]=(i-s)*(.5*a.SQRT2)}},this.init_xrpow=function(t,e,n){var i=0,s=0|e.max_nonzero_coeff;if(c(null!=n),e.xrpow_max=0,c(0<=s&&s<=575),o.fill(n,s,576,0),1e-20<(i=function(t,e,n,i){for(var s=i=0;s<=n;++s){var r=Math.abs(t.xr[s]);i+=r,e[s]=Math.sqrt(r*Math.sqrt(r)),e[s]>t.xrpow_max&&(t.xrpow_max=e[s])}return i}(e,n,s,i))){var r=0;0!=(2&t.substep_shaping)&&(r=1);for(var a=0;a<e.psymax;a++)t.pseudohalf[a]=r;return!0}return o.fill(e.l3_enc,0,576,0),!1},this.init_outer_loop=function(t,e){e.part2_3_length=0,e.big_values=0,e.count1=0,e.global_gain=210,e.scalefac_compress=0,e.table_select[0]=0,e.table_select[1]=0,e.table_select[2]=0,e.subblock_gain[0]=0,e.subblock_gain[1]=0,e.subblock_gain[2]=0,e.subblock_gain[3]=0,e.region0_count=0,e.region1_count=0,e.preflag=0,e.scalefac_scale=0,e.count1table_select=0,e.part2_length=0,e.sfb_lmax=f.SBPSY_l,e.sfb_smin=f.SBPSY_s,e.psy_lmax=t.sfb21_extra?f.SBMAX_l:f.SBPSY_l,e.psymax=e.psy_lmax,e.sfbmax=e.sfb_lmax,e.sfbdivide=11;for(var i=0;i<f.SBMAX_l;i++)e.width[i]=t.scalefac_band.l[i+1]-t.scalefac_band.l[i],e.window[i]=3;if(e.block_type==f.SHORT_TYPE){var r=l(576);e.sfb_smin=0,(e.sfb_lmax=0)!=e.mixed_block_flag&&(e.sfb_smin=3,e.sfb_lmax=2*t.mode_gr+4),e.psymax=e.sfb_lmax+3*((t.sfb21_extra?f.SBMAX_s:f.SBPSY_s)-e.sfb_smin),e.sfbmax=e.sfb_lmax+3*(f.SBPSY_s-e.sfb_smin),e.sfbdivide=e.sfbmax-18,e.psy_lmax=e.sfb_lmax;var a=t.scalefac_band.l[e.sfb_lmax];for(s.arraycopy(e.xr,0,r,0,576),i=e.sfb_smin;i<f.SBMAX_s;i++)for(var c=t.scalefac_band.s[i],u=t.scalefac_band.s[i+1],h=0;h<3;h++)for(var d=c;d<u;d++)e.xr[a++]=r[3*d+h];var p=e.sfb_lmax;for(i=e.sfb_smin;i<f.SBMAX_s;i++)e.width[p]=e.width[p+1]=e.width[p+2]=t.scalefac_band.s[i+1]-t.scalefac_band.s[i],e.window[p]=0,e.window[p+1]=1,e.window[p+2]=2,p+=3}e.count1bits=0,e.sfb_partition_table=n.nr_of_sfb_block[0][0],e.slen[0]=0,e.slen[1]=0,e.slen[2]=0,e.slen[3]=0,e.max_nonzero_coeff=575,o.fill(e.scalefac,0),function(t,e){var i=t.ATH,s=e.xr;if(e.block_type!=f.SHORT_TYPE)for(var r=!1,a=f.PSFB21-1;0<=a&&!r;a--){var o=t.scalefac_band.psfb21[a],l=t.scalefac_band.psfb21[a+1],c=n.athAdjust(i.adjust,i.psfb21[a],i.floor);1e-12<t.nsPsy.longfact[21]&&(c*=t.nsPsy.longfact[21]);for(var u=l-1;o<=u;u--){if(!(Math.abs(s[u])<c)){r=!0;break}s[u]=0}}else for(var h=0;h<3;h++)for(r=!1,a=f.PSFB12-1;0<=a&&!r;a--){l=(o=3*t.scalefac_band.s[12]+(t.scalefac_band.s[13]-t.scalefac_band.s[12])*h+(t.scalefac_band.psfb12[a]-t.scalefac_band.psfb12[0]))+(t.scalefac_band.psfb12[a+1]-t.scalefac_band.psfb12[a]);var d=n.athAdjust(i.adjust,i.psfb12[a],i.floor);for(1e-12<t.nsPsy.shortfact[12]&&(d*=t.nsPsy.shortfact[12]),u=l-1;o<=u;u--){if(!(Math.abs(s[u])<d)){r=!0;break}s[u]=0}}}(t,e)},v.BINSEARCH_NONE=new v(0),v.BINSEARCH_UP=new v(1),v.BINSEARCH_DOWN=new v(2),this.trancate_smallspectrums=function(t,e,s,r){var a=l(_.SFBMAX);if((0!=(4&t.substep_shaping)||e.block_type!=f.SHORT_TYPE)&&0==(128&t.substep_shaping)){n.calc_noise(e,s,a,new h,null);for(var c=0;c<576;c++){var u=0;0!=e.l3_enc[c]&&(u=Math.abs(e.xr[c])),r[c]=u}c=0;var d=8;e.block_type==f.SHORT_TYPE&&(d=6);do{var p,m,v,g,b=e.width[d];if(c+=b,!(1<=a[d]||(o.sort(r,c-b,b),BitStream.EQ(r[c-1],0)))){p=(1-a[d])*s[d],g=m=0;do{var y;for(v=1;g+v<b&&!BitStream.NEQ(r[g+c-b],r[g+c+v-b]);v++);if(p<(y=r[g+c-b]*r[g+c-b]*v)){0!=g&&(m=r[g+c-b-1]);break}p-=y,g+=v}while(g<b);if(!BitStream.EQ(m,0))for(;Math.abs(e.xr[c-b])<=m&&(e.l3_enc[c-b]=0),0<--b;);}}while(++d<e.psymax);e.part2_3_length=i.noquant_count_bits(t,e,null)}},this.outer_loop=function(t,e,a,o,u,m){var g=t.internal_flags,x=new p,w=l(576),S=l(_.SFBMAX),k=new h,A=new d,C=9999999,M=!1,E=!1,T=0;if(function(t,e,n,s,r){var a,o=t.CurrentStep[s],l=!1,u=t.OldValue[s],h=v.BINSEARCH_NONE;for(e.global_gain=u,n-=e.part2_length,c(0!=o);;){var d;if(a=i.count_bits(t,r,e,null),1==o||a==n)break;d=n<a?(h==v.BINSEARCH_DOWN&&(l=!0),l&&(o/=2),h=v.BINSEARCH_UP,o):(h==v.BINSEARCH_UP&&(l=!0),l&&(o/=2),h=v.BINSEARCH_DOWN,-o),e.global_gain+=d,e.global_gain<0&&(l=!(e.global_gain=0)),255<e.global_gain&&(e.global_gain=255,l=!0)}for(c(0<=e.global_gain),c(e.global_gain<256);n<a&&e.global_gain<255;)e.global_gain++,a=i.count_bits(t,r,e,null);t.CurrentStep[s]=4<=u-e.global_gain?4:2,t.OldValue[s]=e.global_gain,e.part2_3_length=a}(g,e,m,u,o),0==g.noise_shaping)return 100;n.calc_noise(e,a,S,k,A),k.bits=e.part2_3_length,x.assign(e);var B=0;for(s.arraycopy(o,0,w,0,576);!M;){do{var R,$=new h,I=255;if(R=0!=(2&g.substep_shaping)?20:3,g.sfb21_extra){if(1<S[x.sfbmax])break;if(x.block_type==f.SHORT_TYPE&&(1<S[x.sfbmax+1]||1<S[x.sfbmax+2]))break}if(!y(t,x,S,o,E))break;0!=x.scalefac_scale&&(I=254);var L=m-x.part2_length;if(L<=0)break;for(;(x.part2_3_length=i.count_bits(g,o,x,A))>L&&x.global_gain<=I;)x.global_gain++;if(x.global_gain>I)break;if(0==k.over_count){for(;(x.part2_3_length=i.count_bits(g,o,x,A))>C&&x.global_gain<=I;)x.global_gain++;if(x.global_gain>I)break}if(n.calc_noise(x,a,S,$,A),$.bits=x.part2_3_length,0!=(b(e.block_type!=f.SHORT_TYPE?t.quant_comp:t.quant_comp_short,k,$,x,S)?1:0))C=e.part2_3_length,k=$,e.assign(x),B=0,s.arraycopy(o,0,w,0,576);else if(0==g.full_outer_loop){if(++B>R&&0==k.over_count)break;if(3==g.noise_shaping_amp&&E&&30<B)break;if(3==g.noise_shaping_amp&&E&&15<x.global_gain-T)break}}while(x.global_gain+x.scalefac_scale<255);3==g.noise_shaping_amp?E?M=!0:(x.assign(e),s.arraycopy(w,0,o,0,576),B=0,T=x.global_gain,E=!0):M=!0}return c(e.global_gain+e.scalefac_scale<=255),t.VBR==r.vbr_rh||t.VBR==r.vbr_mtrh?s.arraycopy(w,0,o,0,576):0!=(1&g.substep_shaping)&&trancate_smallspectrums(g,e,a,o),k.over_count},this.iteration_finish_one=function(t,n,s){var r=t.l3_side,a=r.tt[n][s];i.best_scalefac_store(t,n,s,r),1==t.use_best_huffman&&i.best_huffman_divide(t,a),e.ResvAdjust(t,a)},this.VBR_encode_granule=function(t,e,n,i,r,a,u){var h,d=t.internal_flags,f=new p,_=l(576),m=u,v=u+1,g=(u+a)/2,b=0,y=d.sfb21_extra;for(c(m<=LameInternalFlags.MAX_BITS_PER_CHANNEL),o.fill(f.l3_enc,0);c(a<=g),c(g<=u),c(a<=u),d.sfb21_extra=!(m-42<g)&&y,outer_loop(t,e,n,i,r,g)<=0?(b=1,v=e.part2_3_length,f.assign(e),s.arraycopy(i,0,_,0,576),h=(u=v-32)-a,g=(u+a)/2):(h=u-(a=g+32),g=(u+a)/2,0!=b&&(b=2,e.assign(f),s.arraycopy(_,0,i,0,576))),12<h;);d.sfb21_extra=y,2==b&&s.arraycopy(f.l3_enc,0,e.l3_enc,0,576),c(e.part2_3_length<=m)},this.get_framebits=function(n,i){var s=n.internal_flags;s.bitrate_index=s.VBR_min_bitrate;var r=t.getframebits(n);s.bitrate_index=1,r=t.getframebits(n);for(var a=1;a<=s.VBR_max_bitrate;a++){s.bitrate_index=a;var o=new MeanBits(r);i[a]=e.ResvFrameBegin(n,o),r=o.bits}},this.VBR_old_prepare=function(t,i,s,r,a,o,l,c,u){var h,d=t.internal_flags,p=0,_=1,m=0;d.bitrate_index=d.VBR_max_bitrate;var v=e.ResvFrameBegin(t,new MeanBits(0))/d.mode_gr;get_framebits(t,o);for(var g=0;g<d.mode_gr;g++){var b=n.on_pe(t,i,c[g],v,g,0);d.mode_ext==f.MPG_MD_MS_LR&&(ms_convert(d.l3_side,g),n.reduce_side(c[g],s[g],v,b));for(var y=0;y<d.channels_out;++y){var x=d.l3_side.tt[g][y];h=x.block_type!=f.SHORT_TYPE?(p=1.28/(1+Math.exp(3.5-i[g][y]/300))-.05,d.PSY.mask_adjust-p):(p=2.56/(1+Math.exp(3.5-i[g][y]/300))-.14,d.PSY.mask_adjust_short-p),d.masking_lower=Math.pow(10,.1*h),init_outer_loop(d,x),u[g][y]=n.calc_xmin(t,r[g][y],x,a[g][y]),0!=u[g][y]&&(_=0),l[g][y]=126,m+=c[g][y]}}for(g=0;g<d.mode_gr;g++)for(y=0;y<d.channels_out;y++)m>o[d.VBR_max_bitrate]&&(c[g][y]*=o[d.VBR_max_bitrate],c[g][y]/=m),l[g][y]>c[g][y]&&(l[g][y]=c[g][y]);return _},this.bitpressure_strategy=function(t,e,n,i){for(var s=0;s<t.mode_gr;s++)for(var r=0;r<t.channels_out;r++){for(var a=t.l3_side.tt[s][r],o=e[s][r],l=0,c=0;c<a.psy_lmax;c++)o[l++]*=1+.029*c*c/f.SBMAX_l/f.SBMAX_l;if(a.block_type==f.SHORT_TYPE)for(c=a.sfb_smin;c<f.SBMAX_s;c++)o[l++]*=1+.029*c*c/f.SBMAX_s/f.SBMAX_s,o[l++]*=1+.029*c*c/f.SBMAX_s/f.SBMAX_s,o[l++]*=1+.029*c*c/f.SBMAX_s/f.SBMAX_s;i[s][r]=0|Math.max(n[s][r],.9*i[s][r])}},this.VBR_new_prepare=function(t,i,s,r,a,o){var l,c=t.internal_flags,u=1,h=0,d=0;if(t.free_format)c.bitrate_index=0,p=new MeanBits(h),l=e.ResvFrameBegin(t,p),h=p.bits,a[0]=l;else{c.bitrate_index=c.VBR_max_bitrate;var p=new MeanBits(h);e.ResvFrameBegin(t,p),h=p.bits,get_framebits(t,a),l=a[c.VBR_max_bitrate]}for(var _=0;_<c.mode_gr;_++){n.on_pe(t,i,o[_],h,_,0),c.mode_ext==f.MPG_MD_MS_LR&&ms_convert(c.l3_side,_);for(var m=0;m<c.channels_out;++m){var v=c.l3_side.tt[_][m];c.masking_lower=Math.pow(10,.1*c.PSY.mask_adjust),init_outer_loop(c,v),0!=n.calc_xmin(t,s[_][m],v,r[_][m])&&(u=0),d+=o[_][m]}}for(_=0;_<c.mode_gr;_++)for(m=0;m<c.channels_out;m++)l<d&&(o[_][m]*=l,o[_][m]/=d);return u},this.calc_target_bits=function(i,s,r,a,o,l){var c,u,h,d,p=i.internal_flags,_=p.l3_side,m=0;p.bitrate_index=p.VBR_max_bitrate;var v=new MeanBits(m);for(l[0]=e.ResvFrameBegin(i,v),m=v.bits,p.bitrate_index=1,m=t.getframebits(i)-8*p.sideinfo_len,o[0]=m/(p.mode_gr*p.channels_out),m=i.VBR_mean_bitrate_kbps*i.framesize*1e3,0!=(1&p.substep_shaping)&&(m*=1.09),m/=i.out_samplerate,m-=8*p.sideinfo_len,m/=p.mode_gr*p.channels_out,(c=.93+.07*(11-i.compression_ratio)/5.5)<.9&&(c=.9),1<c&&(c=1),u=0;u<p.mode_gr;u++){var g=0;for(h=0;h<p.channels_out;h++){if(a[u][h]=int(c*m),700<s[u][h]){var b=int((s[u][h]-700)/1.4),y=_.tt[u][h];a[u][h]=int(c*m),y.block_type==f.SHORT_TYPE&&b<m/2&&(b=m/2),3*m/2<b?b=3*m/2:b<0&&(b=0),a[u][h]+=b}a[u][h]>LameInternalFlags.MAX_BITS_PER_CHANNEL&&(a[u][h]=LameInternalFlags.MAX_BITS_PER_CHANNEL),g+=a[u][h]}if(g>LameInternalFlags.MAX_BITS_PER_GRANULE)for(h=0;h<p.channels_out;++h)a[u][h]*=LameInternalFlags.MAX_BITS_PER_GRANULE,a[u][h]/=g}if(p.mode_ext==f.MPG_MD_MS_LR)for(u=0;u<p.mode_gr;u++)n.reduce_side(a[u],r[u],m*p.channels_out,LameInternalFlags.MAX_BITS_PER_GRANULE);for(u=d=0;u<p.mode_gr;u++)for(h=0;h<p.channels_out;h++)a[u][h]>LameInternalFlags.MAX_BITS_PER_CHANNEL&&(a[u][h]=LameInternalFlags.MAX_BITS_PER_CHANNEL),d+=a[u][h];if(d>l[0])for(u=0;u<p.mode_gr;u++)for(h=0;h<p.channels_out;h++)a[u][h]*=l[0],a[u][h]/=d}}},function(t,e){t.exports=function(){this.setModules=function(t,e){}}},function(t,e){t.exports=function(){this.over_noise=0,this.tot_noise=0,this.max_noise=0,this.over_count=0,this.over_SSD=0,this.bits=0}},function(t,e,n){var i=n(0),s=i.new_float,r=i.new_int;i.assert,t.exports=function(){this.global_gain=0,this.sfb_count1=0,this.step=r(39),this.noise=s(39),this.noise_log=s(39)}},function(t,e,n){var i=n(0).assert;t.exports=function(){var t;this.setModules=function(e){t=e},this.ResvFrameBegin=function(e,n){var s,r=e.internal_flags,a=r.l3_side,o=t.getframebits(e);n.bits=(o-8*r.sideinfo_len)/r.mode_gr;var l=2048*r.mode_gr-8;320<e.brate?s=8*int(1e3*e.brate/(e.out_samplerate/1152)/8+.5):(s=11520,e.strict_ISO&&(s=8*int(32e4/(e.out_samplerate/1152)/8+.5))),r.ResvMax=s-o,r.ResvMax>l&&(r.ResvMax=l),(r.ResvMax<0||e.disable_reservoir)&&(r.ResvMax=0);var c=n.bits*r.mode_gr+Math.min(r.ResvSize,r.ResvMax);return s<c&&(c=s),i(0==r.ResvMax%8),i(0<=r.ResvMax),a.resvDrain_pre=0,null!=r.pinfo&&(r.pinfo.mean_bits=n.bits/2,r.pinfo.resvsize=r.ResvSize),c},this.ResvMaxBits=function(t,e,n,i){var s,r=t.internal_flags,a=r.ResvSize,o=r.ResvMax;0!=i&&(a+=e),0!=(1&r.substep_shaping)&&(o*=.9),n.bits=e,9*o<10*a?(s=a-9*o/10,n.bits+=s,r.substep_shaping|=128):(s=0,r.substep_shaping&=127,t.disable_reservoir||0!=(1&r.substep_shaping)||(n.bits-=.1*e));var l=a<6*r.ResvMax/10?a:6*r.ResvMax/10;return(l-=s)<0&&(l=0),l},this.ResvAdjust=function(t,e){t.ResvSize-=e.part2_3_length+e.part2_length},this.ResvFrameEnd=function(t,e){var n,s=t.l3_side;t.ResvSize+=e*t.mode_gr;var r=0;s.resvDrain_post=0,(s.resvDrain_pre=0)!=(n=t.ResvSize%8)&&(r+=n),0<(n=t.ResvSize-r-t.ResvMax)&&(i(0==n%8),i(0<=n),r+=n);var a=Math.min(8*s.main_data_begin,r)/8;s.resvDrain_pre+=8*a,r-=8*a,t.ResvSize-=8*a,s.main_data_begin-=a,s.resvDrain_post+=r,t.ResvSize-=r}}},function(t,e){t.exports=function(){this.getLameVersion=function(){return"3.98.4"},this.getLameShortVersion=function(){return"3.98.4"},this.getLameVeryShortVersion=function(){return"LAME3.98r"},this.getPsyVersion=function(){return"0.93"},this.getLameUrl=function(){return"http://www.mp3dev.org/"},this.getLameOsBitness=function(){return"32bits"}}},function(t,e,n){var i=n(0),s=i.System,r=i.VbrMode,a=(i.Float,i.ShortBlock),o=(i.Util,i.Arrays),l=(i.new_array_n,i.new_byte),c=(i.new_double,i.new_float,i.new_float_n,i.new_int,i.new_int_n,i.assert);function u(){var t,e,n;this.setModules=function(i,s,r){t=i,e=s,n=r};var i=u.NUMTOCENTRIES,h=u.MAXFRAMESIZE,d=i+4+4+4+4+4+9+1+1+8+1+1+3+1+1+2+4+2+2,f="Xing",p="Info",_=[0,49345,49537,320,49921,960,640,49729,50689,1728,1920,51009,1280,50625,50305,1088,52225,3264,3456,52545,3840,53185,52865,3648,2560,51905,52097,2880,51457,2496,2176,51265,55297,6336,6528,55617,6912,56257,55937,6720,7680,57025,57217,8e3,56577,7616,7296,56385,5120,54465,54657,5440,55041,6080,5760,54849,53761,4800,4992,54081,4352,53697,53377,4160,61441,12480,12672,61761,13056,62401,62081,12864,13824,63169,63361,14144,62721,13760,13440,62529,15360,64705,64897,15680,65281,16320,16e3,65089,64001,15040,15232,64321,14592,63937,63617,14400,10240,59585,59777,10560,60161,11200,10880,59969,60929,11968,12160,61249,11520,60865,60545,11328,58369,9408,9600,58689,9984,59329,59009,9792,8704,58049,58241,9024,57601,8640,8320,57409,40961,24768,24960,41281,25344,41921,41601,25152,26112,42689,42881,26432,42241,26048,25728,42049,27648,44225,44417,27968,44801,28608,28288,44609,43521,27328,27520,43841,26880,43457,43137,26688,30720,47297,47489,31040,47873,31680,31360,47681,48641,32448,32640,48961,32e3,48577,48257,31808,46081,29888,30080,46401,30464,47041,46721,30272,29184,45761,45953,29504,45313,29120,28800,45121,20480,37057,37249,20800,37633,21440,21120,37441,38401,22208,22400,38721,21760,38337,38017,21568,39937,23744,23936,40257,24320,40897,40577,24128,23040,39617,39809,23360,39169,22976,22656,38977,34817,18624,18816,35137,19200,35777,35457,19008,19968,36545,36737,20288,36097,19904,19584,35905,17408,33985,34177,17728,34561,18368,18048,34369,33281,17088,17280,33601,16640,33217,32897,16448];function m(t,e){var n=255&t[e+0];return n<<=8,n|=255&t[e+1],n<<=8,n|=255&t[e+2],n<<=8,n|255&t[e+3]}function v(t,e,n){t[e+0]=255&n>>24,t[e+1]=255&n>>16,t[e+2]=255&n>>8,t[e+3]=255&n}function g(t,e,n){t[e+0]=255&n>>8,t[e+1]=255&n}function b(t,e,n){return 255&(t<<e|n&~(-1<<e))}function y(e,n){var i=e.internal_flags;n[0]=b(n[0],8,255),n[1]=b(n[1],3,7),n[1]=b(n[1],1,e.out_samplerate<16e3?0:1),n[1]=b(n[1],1,e.version),n[1]=b(n[1],2,1),n[1]=b(n[1],1,e.error_protection?0:1),n[2]=b(n[2],4,i.bitrate_index),n[2]=b(n[2],2,i.samplerate_index),n[2]=b(n[2],1,0),n[2]=b(n[2],1,e.extension),n[3]=b(n[3],2,e.mode.ordinal()),n[3]=b(n[3],2,i.mode_ext),n[3]=b(n[3],1,e.copyright),n[3]=b(n[3],1,e.original),n[3]=b(n[3],2,e.emphasis),n[0]=255;var s,a,o=241&n[1];s=1==e.version?128:e.out_samplerate<16e3?32:64,e.VBR==r.vbr_off&&(s=e.brate),a=e.free_format?0:255&16*t.BitrateIndex(s,e.version,e.out_samplerate),1==e.version?n[1]=255&(10|o):n[1]=255&(2|o),o=13&n[2],n[2]=255&(a|o)}function x(t,e){return e>>8^_[255&(e^t)]}this.addVbrFrame=function(t){var e=t.internal_flags,n=Tables.bitrate_table[t.version][e.bitrate_index];c(null!=e.VBR_seek_table.bag),function(t,e){if(t.nVbrNumFrames++,t.sum+=e,t.seen++,!(t.seen<t.want)&&(t.pos<t.size&&(t.bag[t.pos]=t.sum,t.pos++,t.seen=0),t.pos==t.size)){for(var n=1;n<t.size;n+=2)t.bag[n/2]=t.bag[n];t.want*=2,t.pos/=2}}(e.VBR_seek_table,n)},this.getVbrTag=function(t){var e=new VBRTagData,n=0;e.flags=0;var s,r,a=t[n+1]>>3&1,o=t[n+2]>>2&3,l=t[n+3]>>6&3,c=t[n+2]>>4&15;if(c=Tables.bitrate_table[a][c],t[n+1]>>4==14?e.samprate=Tables.samplerate_table[2][o]:e.samprate=Tables.samplerate_table[a][o],s=t,r=n+=0!=a?3!=l?36:21:3!=l?21:13,!new String(s,r,f.length(),null).equals(f)&&!new String(s,r,p.length(),null).equals(p))return null;n+=4,e.hId=a;var u=e.flags=m(t,n);if(n+=4,0!=(1&u)&&(e.frames=m(t,n),n+=4),0!=(2&u)&&(e.bytes=m(t,n),n+=4),0!=(4&u)){if(null!=e.toc)for(var h=0;h<i;h++)e.toc[h]=t[n+h];n+=i}e.vbrScale=-1,0!=(8&u)&&(e.vbrScale=m(t,n),n+=4),e.headersize=72e3*(a+1)*c/e.samprate;var d=t[(n+=21)+0]<<4;d+=t[n+1]>>4;var _=(15&t[n+1])<<8;return(d<0||3e3<d)&&(d=-1),((_+=255&t[n+2])<0||3e3<_)&&(_=-1),e.encDelay=d,e.encPadding=_,e},this.InitVbrTag=function(t){var n,i=t.internal_flags;n=1==t.version?128:t.out_samplerate<16e3?32:64,t.VBR==r.vbr_off&&(n=t.brate);var s=72e3*(t.version+1)*n/t.out_samplerate,a=i.sideinfo_len+d;if((i.VBR_seek_table.TotalFrameSize=s)<a||h<s)t.bWriteVbrTag=!1;else{i.VBR_seek_table.nVbrNumFrames=0,i.VBR_seek_table.nBytesWritten=0,i.VBR_seek_table.sum=0,i.VBR_seek_table.seen=0,i.VBR_seek_table.want=1,i.VBR_seek_table.pos=0,null==i.VBR_seek_table.bag&&(i.VBR_seek_table.bag=new int[400],i.VBR_seek_table.size=400);var o=l(h);y(t,o);for(var c=i.VBR_seek_table.TotalFrameSize,u=0;u<c;++u)e.add_dummy_byte(t,255&o[u],1)}},this.updateMusicCRC=function(t,e,n,i){for(var s=0;s<i;++s)t[0]=x(e[n+s],t[0])},this.getLameTagFrame=function(t,c){var u=t.internal_flags;if(!t.bWriteVbrTag)return 0;if(u.Class_ID!=Lame.LAME_ID)return 0;if(u.VBR_seek_table.pos<=0)return 0;if(c.length<u.VBR_seek_table.TotalFrameSize)return u.VBR_seek_table.TotalFrameSize;o.fill(c,0,u.VBR_seek_table.TotalFrameSize,0),y(t,c);var h=l(i);if(t.free_format)for(var d=1;d<i;++d)h[d]=255&255*d/100;else!function(t,e){if(!(t.pos<=0))for(var n=1;n<i;++n){var s=n/i,r=0|Math.floor(s*t.pos);r>t.pos-1&&(r=t.pos-1);var a=0|256*t.bag[r]/t.sum;255<a&&(a=255),e[n]=255&a}}(u.VBR_seek_table,h);var _=u.sideinfo_len;t.error_protection&&(_-=2),t.VBR==r.vbr_off?(c[_++]=255&p.charAt(0),c[_++]=255&p.charAt(1),c[_++]=255&p.charAt(2),c[_++]=255&p.charAt(3)):(c[_++]=255&f.charAt(0),c[_++]=255&f.charAt(1),c[_++]=255&f.charAt(2),c[_++]=255&f.charAt(3)),v(c,_,15),v(c,_+=4,u.VBR_seek_table.nVbrNumFrames),_+=4;var m=u.VBR_seek_table.nBytesWritten+u.VBR_seek_table.TotalFrameSize;v(c,_,0|m),_+=4,s.arraycopy(h,0,c,_,h.length),_+=h.length,t.error_protection&&e.CRC_writeheader(u,c);var b=0;for(d=0;d<_;d++)b=x(c[d],b);return _+=function(t,e,i,s,r){var o,l,c,u,h,d=t.internal_flags,f=0,p=t.encoder_delay,_=t.encoder_padding,m=100-10*t.VBR_q-t.quality,b=n.getLameVeryShortVersion(),y=[1,5,3,2,4,0,3],w=0|(255<t.lowpassfreq/100+.5?255:t.lowpassfreq/100+.5),S=0,k=0,A=t.internal_flags.noise_shaping,C=0,M=0,E=0,T=0!=(1&t.exp_nspsytune),B=0!=(2&t.exp_nspsytune),R=!1,$=!1,I=t.internal_flags.nogap_total,L=t.internal_flags.nogap_current,O=t.ATHtype;switch(t.VBR){case vbr_abr:h=t.VBR_mean_bitrate_kbps;break;case vbr_off:h=t.brate;break;default:h=t.VBR_min_bitrate_kbps}switch(o=0+(t.VBR.ordinal()<y.length?y[t.VBR.ordinal()]:0),d.findReplayGain&&(510<d.RadioGain&&(d.RadioGain=510),d.RadioGain<-510&&(d.RadioGain=-510),k=8192,k|=3072,0<=d.RadioGain?k|=d.RadioGain:(k|=512,k|=-d.RadioGain)),d.findPeakSample&&(S=Math.abs(0|d.PeakSample/32767*Math.pow(2,23)+.5)),-1!=I&&(0<L&&($=!0),L<I-1&&(R=!0)),u=O+((T?1:0)<<4)+((B?1:0)<<5)+((R?1:0)<<6)+(($?1:0)<<7),m<0&&(m=0),t.mode){case MONO:C=0;break;case STEREO:C=1;break;case DUAL_CHANNEL:C=2;break;case JOINT_STEREO:C=t.force_ms?4:3;break;case NOT_SET:default:C=7}E=t.in_samplerate<=32e3?0:48e3==t.in_samplerate?2:48e3<t.in_samplerate?3:1,(t.short_blocks==a.short_block_forced||t.short_blocks==a.short_block_dispensed||-1==t.lowpassfreq&&-1==t.highpassfreq||t.scale_left<t.scale_right||t.scale_left>t.scale_right||t.disable_reservoir&&t.brate<320||t.noATH||t.ATHonly||0==O||t.in_samplerate<=32e3)&&(M=1),l=A+(C<<2)+(M<<5)+(E<<6),c=d.nMusicCRC,v(i,s+f,m),f+=4;for(var P=0;P<9;P++)i[s+f+P]=255&b.charAt(P);i[s+(f+=9)]=255&o,i[s+ ++f]=255&w,v(i,s+ ++f,S),g(i,s+(f+=4),k),g(i,s+(f+=2),0),i[s+(f+=2)]=255&u,i[s+ ++f]=255<=h?255:255&h,i[s+ ++f]=255&p>>4,i[s+f+1]=255&(p<<4)+(_>>8),i[s+f+2]=255&_,i[s+(f+=3)]=255&l,f++,i[s+f++]=0,g(i,s+f,t.preset),v(i,s+(f+=2),e),g(i,s+(f+=4),c),f+=2;for(var V=0;V<f;V++)r=x(i[s+V],r);return g(i,s+f,r),f+2}(t,m,c,_,b),u.VBR_seek_table.TotalFrameSize},this.putVbrTag=function(t,e){if(t.internal_flags.VBR_seek_table.pos<=0)return-1;if(e.seek(e.length()),0==e.length())return-1;var n=function(t){t.seek(0);var e=l(10);return t.readFully(e),new String(e,"ISO-8859-1").startsWith("ID3")?0:((127&e[6])<<21|(127&e[7])<<14|(127&e[8])<<7|127&e[9])+e.length}(e);e.seek(n);var i=l(h),s=getLameTagFrame(t,i);return s>i.length?-1:(s<1||e.write(i,0,s),0)}}u.NUMTOCENTRIES=100,u.MAXFRAMESIZE=2880,t.exports=u},function(t,e,n){var i=n(48);"string"==typeof i&&(i=[[t.i,i,""]]),i.locals&&(t.exports=i.locals),(0,n(3).default)("5b0bec03",i,!1,{})},function(t,e,n){(t.exports=n(2)(!1)).push([t.i,"\n.ar-icon {\n  fill: #747474;\n  border-radius: 50%;\n  border: 1px solid #05CBCD;\n  background-color: #FFFFFF;\n  padding: 5px;\n  cursor: pointer;\n  transition: .2s;\n}\n.ar-icon--no-border {\n    border: 0;\n    border-radius: 0;\n    padding: 0;\n}\n.ar-icon--rec {\n    fill: white;\n    background-color: #FF6B64;\n    border-color: transparent;\n}\n.ar-icon--pulse {\n    animation: ripple .5s linear infinite;\n}\n@keyframes ripple {\n0% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 1px rgba(255, 0, 0, 0.1), 0 0 0 5px rgba(255, 0, 0, 0.1);\n}\n100% {\n    box-shadow: 0 0 0 0 rgba(255, 0, 0, 0.1), 0 0 0 10px rgba(255, 0, 0, 0.1), 0 0 0 20px rgba(255, 0, 0, 0);\n}\n}\n.ar-icon__xs {\n    width: 18px;\n    height: 18px;\n    line-height: 18px;\n}\n.ar-icon__sm {\n    width: 30px;\n    height: 30px;\n    line-height: 30px;\n}\n.ar-icon__lg {\n    width: 45px;\n    height: 45px;\n    line-height: 45px;\n    box-shadow: 0 2px 5px 1px rgba(158, 158, 158, 0.5);\n}\n",""])},function(t,e,n){"use strict";n.r(e);var i={props:{name:{type:String}},data:function(){return{icons:{download:'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 12v7H5v-7H3v7c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2v-7h-2zm-6 .67l2.59-2.58L17 11.5l-5 5-5-5 1.41-1.41L11 12.67V3h2z"/><path fill="none" d="M0 0h24v24H0z"/></svg>',mic:'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 14c1.66 0 2.99-1.34 2.99-3L15 5c0-1.66-1.34-3-3-3S9 3.34 9 5v6c0 1.66 1.34 3 3 3zm5.3-3c0 3-2.54 5.1-5.3 5.1S6.7 14 6.7 11H5c0 3.41 2.72 6.23 6 6.72V21h2v-3.28c3.28-.48 6-3.3 6-6.72h-1.7z"/><path d="M0 0h24v24H0z" fill="none"/></svg>',pause:'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M6 19h4V5H6v14zm8-14v14h4V5h-4z"/><path d="M0 0h24v24H0z" fill="none"/></svg>',play:'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M8 5v14l11-7z"/><path d="M0 0h24v24H0z" fill="none"/></svg>',save:'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M0 0h24v24H0z" fill="none"/><path d="M17 3H5c-1.11 0-2 .9-2 2v14c0 1.1.89 2 2 2h14c1.1 0 2-.9 2-2V7l-4-4zm-5 16c-1.66 0-3-1.34-3-3s1.34-3 3-3 3 1.34 3 3-1.34 3-3 3zm3-10H5V5h10v4z"/></svg>',stop:'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M0 0h24v24H0z" fill="none"/><path d="M6 6h12v12H6z"/></svg>',volume:'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 9v6h4l5 5V4L7 9H3zm13.5 3c0-1.77-1.02-3.29-2.5-4.03v8.05c1.48-.73 2.5-2.25 2.5-4.02zM14 3.23v2.06c2.89.86 5 3.54 5 6.71s-2.11 5.85-5 6.71v2.06c4.01-.91 7-4.49 7-8.77s-2.99-7.86-7-8.77z"/><path d="M0 0h24v24H0z" fill="none"/></svg>'}}}},s=function(){var t=this,e=t.$createElement;return(t._self._c||e)("div",{domProps:{innerHTML:t._s(t.icons[t.name])}})};function r(t,e,n,i,s,r,a,o){var l=typeof(t=t||{}).default;"object"!==l&&"function"!==l||(t=t.default);var c,u="function"==typeof t?t.options:t;if(e&&(u.render=e,u.staticRenderFns=n,u._compiled=!0),i&&(u.functional=!0),r&&(u._scopeId=r),a?(c=function(t){(t=t||this.$vnode&&this.$vnode.ssrContext||this.parent&&this.parent.$vnode&&this.parent.$vnode.ssrContext)||"undefined"==typeof __VUE_SSR_CONTEXT__||(t=__VUE_SSR_CONTEXT__),s&&s.call(this,t),t&&t._registeredComponents&&t._registeredComponents.add(a)},u._ssrRegister=c):s&&(c=o?function(){s.call(this,this.$root.$options.shadowRoot)}:s),c)if(u.functional){u._injectStyles=c;var h=u.render;u.render=function(t,e){return c.call(e),h(t,e)}}else{var d=u.beforeCreate;u.beforeCreate=d?[].concat(d,c):[c]}return{exports:t,options:u}}s._withStripped=!0;var a=r(i,s,[],!1,null,null,null);a.options.__file="src/components/icon-button.vue";var o=a.exports;function l(t,e){var n=e.getBoundingClientRect().width,i=t.target.getBoundingClientRect().left,s=(t.clientX-i)/n;try{if(!t.target.className.match(/^ar\-line\-control/))return}catch(t){return}return 1<(s=s<0?0:s)?1:s}function c(t){return new Date(1e3*t).toISOString().substr(14,5)}var u={props:{refId:{type:String},eventName:{type:String},percentage:{type:Number,default:0},rowDirection:{type:Boolean,default:!0}},methods:{onMouseDown:function(t){var e=l(t,this.$refs[this.refId]);this.$emit("change-linehead",e),document.addEventListener("mousemove",this.onMouseMove),document.addEventListener("mouseup",this.onMouseUp)},onMouseUp:function(t){document.removeEventListener("mouseup",this.onMouseUp),document.removeEventListener("mousemove",this.onMouseMove);var e=l(t,this.$refs[this.refId]);this.$emit("change-linehead",e)},onMouseMove:function(t){var e=l(t,this.$refs[this.refId]);this.$emit("change-linehead",e)}},computed:{calculateSize:function(){var t=this.percentage<1?100*this.percentage:this.percentage;return(this.rowDirection?"width":"height")+": "+t+"%"}}},h=function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("div",{ref:t.refId,staticClass:"ar-line-control",on:{mousedown:t.onMouseDown}},[n("div",{staticClass:"ar-line-control__head",style:t.calculateSize})])};h._withStripped=!0;var d=r(u,h,[],!1,(function(t){n(19)}),null,null);d.options.__file="src/components/line-control.vue";var f=d.exports,p={data:function(){return{volume:.8}},components:{IconButton:o,LineControl:f},methods:{onChangeLinehead:function(t){this.$emit("change-volume",t),this.volume=t}}},_=function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("div",{staticClass:"ar-volume"},[n("icon-button",{staticClass:"ar-volume__icon",attrs:{name:"volume"}}),t._v(" "),n("line-control",{staticClass:"ar-volume-bar",attrs:{"ref-id":"volume",percentage:t.volume},on:{"change-linehead":t.onChangeLinehead}})],1)};_._withStripped=!0;var m=r(p,_,[],!1,(function(t){n(21)}),null,null);m.options.__file="src/components/volume-control.vue";var v=m.exports,g={props:{src:{type:String},record:{type:Object},filename:{type:String}},data:function(){return{isPlaying:!1,duration:c(0),playedTime:c(0),progress:0}},components:{IconButton:o,LineControl:f,VolumeControl:v},mounted:function(){var t=this;this.player=document.getElementById(this.playerUniqId),this.player.addEventListener("ended",(function(){t.isPlaying=!1})),this.player.addEventListener("loadeddata",(function(e){t._resetProgress(),t.duration=c(t.player.duration)})),this.player.addEventListener("timeupdate",this._onTimeUpdate),this.$eventBus.$on("remove-record",(function(){t._resetProgress()}))},computed:{audioSource:function(){var t=this.src||this.record.url;if(t)return t;this._resetProgress()},playBtnIcon:function(){return this.isPlaying?"pause":"play"},playerUniqId:function(){return"audio-player"+this._uid}},methods:{playback:function(){var t=this;this.audioSource&&(this.isPlaying?this.player.pause():setTimeout((function(){t.player.play()}),0),this.isPlaying=!this.isPlaying)},_resetProgress:function(){this.isPlaying&&this.player.pause(),this.duration=c(0),this.playedTime=c(0),this.progress=0,this.isPlaying=!1},_onTimeUpdate:function(){this.playedTime=c(this.player.currentTime),this.progress=this.player.currentTime/this.player.duration*100},_onUpdateProgress:function(t){t&&(this.player.currentTime=t*this.player.duration)},_onChangeVolume:function(t){t&&(this.player.volume=t)}}},b=function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("div",{staticClass:"ar-player"},[n("div",{staticClass:"ar-player-actions"},[n("icon-button",{staticClass:"ar-icon ar-icon__lg ar-player__play",class:{"ar-player__play--active":t.isPlaying},attrs:{id:"play",name:t.playBtnIcon},nativeOn:{click:function(e){return t.playback(e)}}})],1),t._v(" "),n("div",{staticClass:"ar-player-bar"},[n("div",{staticClass:"ar-player__time"},[t._v(t._s(t.playedTime))]),t._v(" "),n("line-control",{staticClass:"ar-player__progress",attrs:{"ref-id":"progress",percentage:t.progress},on:{"change-linehead":t._onUpdateProgress}}),t._v(" "),n("div",{staticClass:"ar-player__time"},[t._v(t._s(t.duration))]),t._v(" "),n("volume-control",{on:{"change-volume":t._onChangeVolume}})],1),t._v(" "),n("audio",{attrs:{id:t.playerUniqId,src:t.audioSource}})])};b._withStripped=!0;var y=r(g,b,[],!1,(function(t){n(17)}),null,null);y.options.__file="src/components/player.vue";var x=y.exports,w={props:{record:{type:Object},filename:{type:String}},components:{IconButton:o},methods:{download:function(){if(this.record.url){var t=document.createElement("a");t.href=this.record.url,t.download=this.filename+".mp3",t.click()}}}},S=function(){var t=this,e=t.$createElement;return(t._self._c||e)("icon-button",{staticClass:"ar-icon ar-icon__xs ar-icon--no-border",attrs:{id:"download",name:"download"},nativeOn:{click:function(e){return t.download(e)}}})};S._withStripped=!0;var k=r(w,S,[],!1,(function(t){n(25)}),null,null);k.options.__file="src/components/downloader.vue";var A=k.exports,C=n(16),M=function(){function t(t,e){for(var n=0;n<e.length;n++){var i=e[n];i.enumerable=i.enumerable||!1,i.configurable=!0,"value"in i&&(i.writable=!0),Object.defineProperty(t,i.key,i)}}return function(e,n,i){return n&&t(e.prototype,n),i&&t(e,i),e}}(),E=function(){function t(e){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.bitRate=e.bitRate||128,this.sampleRate=e.sampleRate||44100,this.dataBuffer=[],this.encoder=new C.Mp3Encoder(1,this.sampleRate,this.bitRate)}return M(t,[{key:"encode",value:function(t){for(var e=this._convertBuffer(t),n=e.length,i=0;0<=n;i+=1152){var s=e.subarray(i,i+1152),r=this.encoder.encodeBuffer(s);this.dataBuffer.push(new Int8Array(r)),n-=1152}}},{key:"finish",value:function(){this.dataBuffer.push(this.encoder.flush());var t=new Blob(this.dataBuffer,{type:"audio/mp3"});return this.dataBuffer=[],{id:Date.now(),blob:t,url:URL.createObjectURL(t)}}},{key:"_floatTo16BitPCM",value:function(t,e){for(var n=0;n<t.length;n++){var i=Math.max(-1,Math.min(1,t[n]));e[n]=i<0?32768*i:32767*i}}},{key:"_convertBuffer",value:function(t){var e=new Float32Array(t),n=new Int16Array(t.length);return this._floatTo16BitPCM(e,n),n}}]),t}(),T=function(){function t(t,e){for(var n=0;n<e.length;n++){var i=e[n];i.enumerable=i.enumerable||!1,i.configurable=!0,"value"in i&&(i.writable=!0),Object.defineProperty(t,i.key,i)}}return function(e,n,i){return n&&t(e.prototype,n),i&&t(e,i),e}}(),B=function(){function t(){var e=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,t),this.beforeRecording=e.beforeRecording,this.pauseRecording=e.pauseRecording,this.afterRecording=e.afterRecording,this.micFailed=e.micFailed,this.bufferSize=4096,this.records=[],this.isPause=!1,this.isRecording=!1,this.duration=0,this.volume=0,this._duration=0}return T(t,[{key:"start",value:function(){this.beforeRecording&&this.beforeRecording("start recording"),navigator.mediaDevices.getUserMedia({video:!1,audio:{channelCount:1,echoCancellation:!1}}).then(this._micCaptured.bind(this)).catch(this._micError.bind(this)),this.isPause=!1,this.isRecording=!0,this.lameEncoder=new E({})}},{key:"stop",value:function(){this.stream.getTracks().forEach((function(t){return t.stop()})),this.input.disconnect(),this.processor.disconnect(),this.context.close();var t=this.lameEncoder.finish();t.duration=c(this.duration),this.records.push(t),this._duration=0,this.duration=0,this.isPause=!1,this.isRecording=!1,this.afterRecording&&this.afterRecording(t)}},{key:"pause",value:function(){this.stream.getTracks().forEach((function(t){return t.stop()})),this.input.disconnect(),this.processor.disconnect(),this.context.close(),this._duration=this.duration,this.isPause=!0,this.pauseRecording&&this.pauseRecording("pause recording")}},{key:"recordList",value:function(){return this.records}},{key:"lastRecord",value:function(){return this.records.slice(-1)}},{key:"_micCaptured",value:function(t){var e=this;this.context=new(window.AudioContext||window.webkitAudioContext),this.duration=this._duration,this.input=this.context.createMediaStreamSource(t),this.processor=this.context.createScriptProcessor(this.bufferSize,1,1),this.stream=t,this.processor.onaudioprocess=function(t){var n=t.inputBuffer.getChannelData(0),i=0;e.lameEncoder.encode(n);for(var s=0;s<n.length;++s)i+=n[s]*n[s];e.duration=parseFloat(e._duration)+parseFloat(e.context.currentTime.toFixed(2)),e.volume=Math.sqrt(i/n.length).toFixed(2)},this.input.connect(this.processor),this.processor.connect(this.context.destination)}},{key:"_micError",value:function(t){this.micFailed&&this.micFailed(t)}}]),t}(),R={props:{filename:{type:String,default:"record"},headers:{type:Object,default:function(){return{}}},uploadUrl:{type:String}}},$={mixins:[R],props:{record:{type:Object}},components:{IconButton:o},methods:{upload:function(){var t=this;if(this.record.url){this.$eventBus.$emit("start-upload");var e=new FormData;e.append("audio",this.record.blob,this.filename+".mp3");var n=Object.assign(this.headers,{});n["Content-Type"]="multipart/form-data; boundary="+e._boundary,this.$http.post(this.uploadUrl,e,{headers:n}).then((function(e){t.$eventBus.$emit("end-upload",{status:"success",response:e})})).catch((function(e){t.$eventBus.$emit("end-upload",{status:"fail",response:e})}))}}}},I=function(){var t=this,e=t.$createElement;return(t._self._c||e)("icon-button",{staticClass:"ar-icon ar-icon__xs ar-icon--no-border",attrs:{name:"save"},nativeOn:{click:function(e){return t.upload(e)}}})};I._withStripped=!0;var L=r($,I,[],!1,(function(t){n(47)}),null,null);L.options.__file="src/components/uploader.vue";var O=L.exports,P={mixins:[R],props:{attempts:{type:Number},time:{type:Number},showDownloadButton:{type:Boolean,default:!0},showUploadButton:{type:Boolean,default:!0},micFailed:{type:Function},beforeRecording:{type:Function},pauseRecording:{type:Function},afterRecording:{type:Function},failedUpload:{type:Function},beforeUpload:{type:Function},successfulUpload:{type:Function},selectRecord:{type:Function}},data:function(){return{isUploading:!1,recorder:this._initRecorder(),recordList:[],selected:{},uploadStatus:null}},components:{AudioPlayer:x,Downloader:A,IconButton:o,Uploader:O},mounted:function(){var t=this;this.$eventBus.$on("start-upload",(function(){t.isUploading=!0,t.beforeUpload&&t.beforeUpload("before upload")})),this.$eventBus.$on("end-upload",(function(e){t.isUploading=!1,"success"===e.status?t.successfulUpload&&t.successfulUpload(e.response):t.failedUpload&&t.failedUpload(e.response)}))},beforeDestroy:function(){this.stopRecorder()},methods:{toggleRecorder:function(){this.attempts&&this.recorder.records.length>=this.attempts||(!this.isRecording||this.isRecording&&this.isPause?this.recorder.start():this.recorder.pause())},stopRecorder:function(){this.isRecording&&(this.recorder.stop(),this.recordList=this.recorder.recordList())},removeRecord:function(t){this.recordList.splice(t,1),this.$set(this.selected,"url",null),this.$eventBus.$emit("remove-record")},choiceRecord:function(t){this.selected!==t&&(this.selected=t,this.selectRecord&&this.selectRecord(t))},_initRecorder:function(){return new B({beforeRecording:this.beforeRecording,afterRecording:this.afterRecording,pauseRecording:this.pauseRecording,micFailed:this.micFailed})}},computed:{attemptsLeft:function(){return this.attempts-this.recordList.length},iconButtonType:function(){return this.isRecording&&this.isPause?"mic":this.isRecording?"pause":"mic"},isPause:function(){return this.recorder.isPause},isRecording:function(){return this.recorder.isRecording},recordedTime:function(){return this.time&&this.recorder.duration>=60*this.time&&this.stopRecorder(),c(this.recorder.duration)},volume:function(){return parseFloat(this.recorder.volume)}}},V=function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("div",{staticClass:"ar"},[t.isUploading?n("div",{staticClass:"ar__overlay"}):t._e(),t._v(" "),t.isUploading?n("div",{staticClass:"ar-spinner"},[n("div",{staticClass:"ar-spinner__dot"}),t._v(" "),n("div",{staticClass:"ar-spinner__dot"}),t._v(" "),n("div",{staticClass:"ar-spinner__dot"})]):t._e(),t._v(" "),n("div",{staticClass:"ar-content",class:{ar__blur:t.isUploading}},[n("div",{staticClass:"ar-recorder"},[n("icon-button",{staticClass:"ar-icon ar-icon__lg",class:{"ar-icon--rec":t.isRecording,"ar-icon--pulse":t.isRecording&&.02<t.volume},attrs:{name:t.iconButtonType},nativeOn:{click:function(e){return t.toggleRecorder(e)}}}),t._v(" "),n("icon-button",{staticClass:"ar-icon ar-icon__sm ar-recorder__stop",attrs:{name:"stop"},nativeOn:{click:function(e){return t.stopRecorder(e)}}})],1),t._v(" "),t.attempts?n("div",{staticClass:"ar-recorder__records-limit"},[t._v("Attempts: "+t._s(t.attemptsLeft)+"/"+t._s(t.attempts))]):t._e(),t._v(" "),n("div",{staticClass:"ar-recorder__duration"},[t._v(t._s(t.recordedTime))]),t._v(" "),t.time?n("div",{staticClass:"ar-recorder__time-limit"},[t._v("Record duration is limited: "+t._s(t.time)+"m")]):t._e(),t._v(" "),n("div",{staticClass:"ar-records"},t._l(t.recordList,(function(e,i){return n("div",{key:e.id,staticClass:"ar-records__record",class:{"ar-records__record--selected":e.id===t.selected.id},on:{click:function(n){t.choiceRecord(e)}}},[e.id===t.selected.id?n("div",{staticClass:"ar__rm",on:{click:function(e){t.removeRecord(i)}}},[t._v("×")]):t._e(),t._v(" "),n("div",{staticClass:"ar__text"},[t._v("Record "+t._s(i+1))]),t._v(" "),n("div",{staticClass:"ar__text"},[t._v(t._s(e.duration))]),t._v(" "),e.id===t.selected.id&&t.showDownloadButton?n("downloader",{staticClass:"ar__downloader",attrs:{record:e,filename:t.filename}}):t._e(),t._v(" "),e.id===t.selected.id&&t.showUploadButton?n("uploader",{staticClass:"ar__uploader",attrs:{record:e,filename:t.filename,headers:t.headers,"upload-url":t.uploadUrl}}):t._e()],1)}))),t._v(" "),n("audio-player",{attrs:{record:t.selected}})],1)])};V._withStripped=!0;var D=r(P,V,[],!1,(function(t){n(23)}),null,null);D.options.__file="src/components/recorder.vue";var N=D.exports;n.d(e,"AudioPlayer",(function(){return x})),n.d(e,"AudioRecorder",(function(){return N}));var F={AudioPlayer:x,AudioRecorder:N,install:function(t){this.installed||(this.installed=!0,t.prototype.$eventBus=t.prototype.$eventBus||new t,t.component("audio-player",x),t.component("audio-recorder",N))}};e.default=F}]).default}))},144:function(t,e,n){"use strict";n.d(e,{ZP:function(){return Gi}});
-/*!
- * Vue.js v2.7.14
- * (c) 2014-2022 Evan You
- * Released under the MIT License.
- */
-var i=Object.freeze({}),s=Array.isArray;function r(t){return void 0===t||null===t}function a(t){return void 0!==t&&null!==t}function o(t){return!0===t}function l(t){return!1===t}function c(t){return"string"===typeof t||"number"===typeof t||"symbol"===typeof t||"boolean"===typeof t}function u(t){return"function"===typeof t}function h(t){return null!==t&&"object"===typeof t}var d=Object.prototype.toString;function f(t){return"[object Object]"===d.call(t)}function p(t){return"[object RegExp]"===d.call(t)}function _(t){var e=parseFloat(String(t));return e>=0&&Math.floor(e)===e&&isFinite(t)}function m(t){return a(t)&&"function"===typeof t.then&&"function"===typeof t.catch}function v(t){return null==t?"":Array.isArray(t)||f(t)&&t.toString===d?JSON.stringify(t,null,2):String(t)}function g(t){var e=parseFloat(t);return isNaN(e)?t:e}function b(t,e){for(var n=Object.create(null),i=t.split(","),s=0;s<i.length;s++)n[i[s]]=!0;return e?function(t){return n[t.toLowerCase()]}:function(t){return n[t]}}b("slot,component",!0);var y=b("key,ref,slot,slot-scope,is");function x(t,e){var n=t.length;if(n){if(e===t[n-1])return void(t.length=n-1);var i=t.indexOf(e);if(i>-1)return t.splice(i,1)}}var w=Object.prototype.hasOwnProperty;function S(t,e){return w.call(t,e)}function k(t){var e=Object.create(null);return function(n){var i=e[n];return i||(e[n]=t(n))}}var A=/-(\w)/g,C=k((function(t){return t.replace(A,(function(t,e){return e?e.toUpperCase():""}))})),M=k((function(t){return t.charAt(0).toUpperCase()+t.slice(1)})),E=/\B([A-Z])/g,T=k((function(t){return t.replace(E,"-$1").toLowerCase()}));function B(t,e){function n(n){var i=arguments.length;return i?i>1?t.apply(e,arguments):t.call(e,n):t.call(e)}return n._length=t.length,n}function R(t,e){return t.bind(e)}var $=Function.prototype.bind?R:B;function I(t,e){e=e||0;var n=t.length-e,i=new Array(n);while(n--)i[n]=t[n+e];return i}function L(t,e){for(var n in e)t[n]=e[n];return t}function O(t){for(var e={},n=0;n<t.length;n++)t[n]&&L(e,t[n]);return e}function P(t,e,n){}var V=function(t,e,n){return!1},D=function(t){return t};function N(t,e){if(t===e)return!0;var n=h(t),i=h(e);if(!n||!i)return!n&&!i&&String(t)===String(e);try{var s=Array.isArray(t),r=Array.isArray(e);if(s&&r)return t.length===e.length&&t.every((function(t,n){return N(t,e[n])}));if(t instanceof Date&&e instanceof Date)return t.getTime()===e.getTime();if(s||r)return!1;var a=Object.keys(t),o=Object.keys(e);return a.length===o.length&&a.every((function(n){return N(t[n],e[n])}))}catch(l){return!1}}function F(t,e){for(var n=0;n<t.length;n++)if(N(t[n],e))return n;return-1}function H(t){var e=!1;return function(){e||(e=!0,t.apply(this,arguments))}}function j(t,e){return t===e?0===t&&1/t!==1/e:t===t||e===e}var Z="data-server-rendered",z=["component","directive","filter"],q=["beforeCreate","created","beforeMount","mounted","beforeUpdate","updated","beforeDestroy","destroyed","activated","deactivated","errorCaptured","serverPrefetch","renderTracked","renderTriggered"],U={optionMergeStrategies:Object.create(null),silent:!1,productionTip:!1,devtools:!1,performance:!1,errorHandler:null,warnHandler:null,ignoredElements:[],keyCodes:Object.create(null),isReservedTag:V,isReservedAttr:V,isUnknownElement:V,getTagNamespace:P,parsePlatformTagName:D,mustUseProp:V,async:!0,_lifecycleHooks:q},X=/a-zA-Z\u00B7\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u037D\u037F-\u1FFF\u200C-\u200D\u203F-\u2040\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD/;function Y(t){var e=(t+"").charCodeAt(0);return 36===e||95===e}function W(t,e,n,i){Object.defineProperty(t,e,{value:n,enumerable:!!i,writable:!0,configurable:!0})}var G=new RegExp("[^".concat(X.source,".$_\\d]"));function K(t){if(!G.test(t)){var e=t.split(".");return function(t){for(var n=0;n<e.length;n++){if(!t)return;t=t[e[n]]}return t}}}var Q="__proto__"in{},J="undefined"!==typeof window,tt=J&&window.navigator.userAgent.toLowerCase(),et=tt&&/msie|trident/.test(tt),nt=tt&&tt.indexOf("msie 9.0")>0,it=tt&&tt.indexOf("edge/")>0;tt&&tt.indexOf("android");var st=tt&&/iphone|ipad|ipod|ios/.test(tt);tt&&/chrome\/\d+/.test(tt),tt&&/phantomjs/.test(tt);var rt,at=tt&&tt.match(/firefox\/(\d+)/),ot={}.watch,lt=!1;if(J)try{var ct={};Object.defineProperty(ct,"passive",{get:function(){lt=!0}}),window.addEventListener("test-passive",null,ct)}catch(Ga){}var ut=function(){return void 0===rt&&(rt=!J&&"undefined"!==typeof n.g&&(n.g["process"]&&"server"===n.g["process"].env.VUE_ENV)),rt},ht=J&&window.__VUE_DEVTOOLS_GLOBAL_HOOK__;function dt(t){return"function"===typeof t&&/native code/.test(t.toString())}var ft,pt="undefined"!==typeof Symbol&&dt(Symbol)&&"undefined"!==typeof Reflect&&dt(Reflect.ownKeys);ft="undefined"!==typeof Set&&dt(Set)?Set:function(){function t(){this.set=Object.create(null)}return t.prototype.has=function(t){return!0===this.set[t]},t.prototype.add=function(t){this.set[t]=!0},t.prototype.clear=function(){this.set=Object.create(null)},t}();var _t=null;function mt(t){void 0===t&&(t=null),t||_t&&_t._scope.off(),_t=t,t&&t._scope.on()}var vt=function(){function t(t,e,n,i,s,r,a,o){this.tag=t,this.data=e,this.children=n,this.text=i,this.elm=s,this.ns=void 0,this.context=r,this.fnContext=void 0,this.fnOptions=void 0,this.fnScopeId=void 0,this.key=e&&e.key,this.componentOptions=a,this.componentInstance=void 0,this.parent=void 0,this.raw=!1,this.isStatic=!1,this.isRootInsert=!0,this.isComment=!1,this.isCloned=!1,this.isOnce=!1,this.asyncFactory=o,this.asyncMeta=void 0,this.isAsyncPlaceholder=!1}return Object.defineProperty(t.prototype,"child",{get:function(){return this.componentInstance},enumerable:!1,configurable:!0}),t}(),gt=function(t){void 0===t&&(t="");var e=new vt;return e.text=t,e.isComment=!0,e};function bt(t){return new vt(void 0,void 0,void 0,String(t))}function yt(t){var e=new vt(t.tag,t.data,t.children&&t.children.slice(),t.text,t.elm,t.context,t.componentOptions,t.asyncFactory);return e.ns=t.ns,e.isStatic=t.isStatic,e.key=t.key,e.isComment=t.isComment,e.fnContext=t.fnContext,e.fnOptions=t.fnOptions,e.fnScopeId=t.fnScopeId,e.asyncMeta=t.asyncMeta,e.isCloned=!0,e}var xt=0,wt=[],St=function(){for(var t=0;t<wt.length;t++){var e=wt[t];e.subs=e.subs.filter((function(t){return t})),e._pending=!1}wt.length=0},kt=function(){function t(){this._pending=!1,this.id=xt++,this.subs=[]}return t.prototype.addSub=function(t){this.subs.push(t)},t.prototype.removeSub=function(t){this.subs[this.subs.indexOf(t)]=null,this._pending||(this._pending=!0,wt.push(this))},t.prototype.depend=function(e){t.target&&t.target.addDep(this)},t.prototype.notify=function(t){var e=this.subs.filter((function(t){return t}));for(var n=0,i=e.length;n<i;n++){var s=e[n];0,s.update()}},t}();kt.target=null;var At=[];function Ct(t){At.push(t),kt.target=t}function Mt(){At.pop(),kt.target=At[At.length-1]}var Et=Array.prototype,Tt=Object.create(Et),Bt=["push","pop","shift","unshift","splice","sort","reverse"];Bt.forEach((function(t){var e=Et[t];W(Tt,t,(function(){for(var n=[],i=0;i<arguments.length;i++)n[i]=arguments[i];var s,r=e.apply(this,n),a=this.__ob__;switch(t){case"push":case"unshift":s=n;break;case"splice":s=n.slice(2);break}return s&&a.observeArray(s),a.dep.notify(),r}))}));var Rt=Object.getOwnPropertyNames(Tt),$t={},It=!0;function Lt(t){It=t}var Ot={notify:P,depend:P,addSub:P,removeSub:P},Pt=function(){function t(t,e,n){if(void 0===e&&(e=!1),void 0===n&&(n=!1),this.value=t,this.shallow=e,this.mock=n,this.dep=n?Ot:new kt,this.vmCount=0,W(t,"__ob__",this),s(t)){if(!n)if(Q)t.__proto__=Tt;else for(var i=0,r=Rt.length;i<r;i++){var a=Rt[i];W(t,a,Tt[a])}e||this.observeArray(t)}else{var o=Object.keys(t);for(i=0;i<o.length;i++){a=o[i];Dt(t,a,$t,void 0,e,n)}}}return t.prototype.observeArray=function(t){for(var e=0,n=t.length;e<n;e++)Vt(t[e],!1,this.mock)},t}();function Vt(t,e,n){return t&&S(t,"__ob__")&&t.__ob__ instanceof Pt?t.__ob__:!It||!n&&ut()||!s(t)&&!f(t)||!Object.isExtensible(t)||t.__v_skip||qt(t)||t instanceof vt?void 0:new Pt(t,e,n)}function Dt(t,e,n,i,r,a){var o=new kt,l=Object.getOwnPropertyDescriptor(t,e);if(!l||!1!==l.configurable){var c=l&&l.get,u=l&&l.set;c&&!u||n!==$t&&2!==arguments.length||(n=t[e]);var h=!r&&Vt(n,!1,a);return Object.defineProperty(t,e,{enumerable:!0,configurable:!0,get:function(){var e=c?c.call(t):n;return kt.target&&(o.depend(),h&&(h.dep.depend(),s(e)&&Ht(e))),qt(e)&&!r?e.value:e},set:function(e){var i=c?c.call(t):n;if(j(i,e)){if(u)u.call(t,e);else{if(c)return;if(!r&&qt(i)&&!qt(e))return void(i.value=e);n=e}h=!r&&Vt(e,!1,a),o.notify()}}}),o}}function Nt(t,e,n){if(!zt(t)){var i=t.__ob__;return s(t)&&_(e)?(t.length=Math.max(t.length,e),t.splice(e,1,n),i&&!i.shallow&&i.mock&&Vt(n,!1,!0),n):e in t&&!(e in Object.prototype)?(t[e]=n,n):t._isVue||i&&i.vmCount?n:i?(Dt(i.value,e,n,void 0,i.shallow,i.mock),i.dep.notify(),n):(t[e]=n,n)}}function Ft(t,e){if(s(t)&&_(e))t.splice(e,1);else{var n=t.__ob__;t._isVue||n&&n.vmCount||zt(t)||S(t,e)&&(delete t[e],n&&n.dep.notify())}}function Ht(t){for(var e=void 0,n=0,i=t.length;n<i;n++)e=t[n],e&&e.__ob__&&e.__ob__.dep.depend(),s(e)&&Ht(e)}function jt(t){return Zt(t,!0),W(t,"__v_isShallow",!0),t}function Zt(t,e){if(!zt(t)){Vt(t,e,ut());0}}function zt(t){return!(!t||!t.__v_isReadonly)}function qt(t){return!(!t||!0!==t.__v_isRef)}function Ut(t,e,n){Object.defineProperty(t,n,{enumerable:!0,configurable:!0,get:function(){var t=e[n];if(qt(t))return t.value;var i=t&&t.__ob__;return i&&i.dep.depend(),t},set:function(t){var i=e[n];qt(i)&&!qt(t)?i.value=t:e[n]=t}})}var Xt="watcher";"".concat(Xt," callback"),"".concat(Xt," getter"),"".concat(Xt," cleanup");var Yt;var Wt=function(){function t(t){void 0===t&&(t=!1),this.detached=t,this.active=!0,this.effects=[],this.cleanups=[],this.parent=Yt,!t&&Yt&&(this.index=(Yt.scopes||(Yt.scopes=[])).push(this)-1)}return t.prototype.run=function(t){if(this.active){var e=Yt;try{return Yt=this,t()}finally{Yt=e}}else 0},t.prototype.on=function(){Yt=this},t.prototype.off=function(){Yt=this.parent},t.prototype.stop=function(t){if(this.active){var e=void 0,n=void 0;for(e=0,n=this.effects.length;e<n;e++)this.effects[e].teardown();for(e=0,n=this.cleanups.length;e<n;e++)this.cleanups[e]();if(this.scopes)for(e=0,n=this.scopes.length;e<n;e++)this.scopes[e].stop(!0);if(!this.detached&&this.parent&&!t){var i=this.parent.scopes.pop();i&&i!==this&&(this.parent.scopes[this.index]=i,i.index=this.index)}this.parent=void 0,this.active=!1}},t}();function Gt(t,e){void 0===e&&(e=Yt),e&&e.active&&e.effects.push(t)}function Kt(t){var e=t._provided,n=t.$parent&&t.$parent._provided;return n===e?t._provided=Object.create(n):e}var Qt=k((function(t){var e="&"===t.charAt(0);t=e?t.slice(1):t;var n="~"===t.charAt(0);t=n?t.slice(1):t;var i="!"===t.charAt(0);return t=i?t.slice(1):t,{name:t,once:n,capture:i,passive:e}}));function Jt(t,e){function n(){var t=n.fns;if(!s(t))return We(t,null,arguments,e,"v-on handler");for(var i=t.slice(),r=0;r<i.length;r++)We(i[r],null,arguments,e,"v-on handler")}return n.fns=t,n}function te(t,e,n,i,s,a){var l,c,u,h;for(l in t)c=t[l],u=e[l],h=Qt(l),r(c)||(r(u)?(r(c.fns)&&(c=t[l]=Jt(c,a)),o(h.once)&&(c=t[l]=s(h.name,c,h.capture)),n(h.name,c,h.capture,h.passive,h.params)):c!==u&&(u.fns=c,t[l]=u));for(l in e)r(t[l])&&(h=Qt(l),i(h.name,e[l],h.capture))}function ee(t,e,n){var i;t instanceof vt&&(t=t.data.hook||(t.data.hook={}));var s=t[e];function l(){n.apply(this,arguments),x(i.fns,l)}r(s)?i=Jt([l]):a(s.fns)&&o(s.merged)?(i=s,i.fns.push(l)):i=Jt([s,l]),i.merged=!0,t[e]=i}function ne(t,e,n){var i=e.options.props;if(!r(i)){var s={},o=t.attrs,l=t.props;if(a(o)||a(l))for(var c in i){var u=T(c);ie(s,l,c,u,!0)||ie(s,o,c,u,!1)}return s}}function ie(t,e,n,i,s){if(a(e)){if(S(e,n))return t[n]=e[n],s||delete e[n],!0;if(S(e,i))return t[n]=e[i],s||delete e[i],!0}return!1}function se(t){for(var e=0;e<t.length;e++)if(s(t[e]))return Array.prototype.concat.apply([],t);return t}function re(t){return c(t)?[bt(t)]:s(t)?oe(t):void 0}function ae(t){return a(t)&&a(t.text)&&l(t.isComment)}function oe(t,e){var n,i,l,u,h=[];for(n=0;n<t.length;n++)i=t[n],r(i)||"boolean"===typeof i||(l=h.length-1,u=h[l],s(i)?i.length>0&&(i=oe(i,"".concat(e||"","_").concat(n)),ae(i[0])&&ae(u)&&(h[l]=bt(u.text+i[0].text),i.shift()),h.push.apply(h,i)):c(i)?ae(u)?h[l]=bt(u.text+i):""!==i&&h.push(bt(i)):ae(i)&&ae(u)?h[l]=bt(u.text+i.text):(o(t._isVList)&&a(i.tag)&&r(i.key)&&a(e)&&(i.key="__vlist".concat(e,"_").concat(n,"__")),h.push(i)));return h}function le(t,e){var n,i,r,o,l=null;if(s(t)||"string"===typeof t)for(l=new Array(t.length),n=0,i=t.length;n<i;n++)l[n]=e(t[n],n);else if("number"===typeof t)for(l=new Array(t),n=0;n<t;n++)l[n]=e(n+1,n);else if(h(t))if(pt&&t[Symbol.iterator]){l=[];var c=t[Symbol.iterator](),u=c.next();while(!u.done)l.push(e(u.value,l.length)),u=c.next()}else for(r=Object.keys(t),l=new Array(r.length),n=0,i=r.length;n<i;n++)o=r[n],l[n]=e(t[o],o,n);return a(l)||(l=[]),l._isVList=!0,l}function ce(t,e,n,i){var s,r=this.$scopedSlots[t];r?(n=n||{},i&&(n=L(L({},i),n)),s=r(n)||(u(e)?e():e)):s=this.$slots[t]||(u(e)?e():e);var a=n&&n.slot;return a?this.$createElement("template",{slot:a},s):s}function ue(t){return Si(this.$options,"filters",t,!0)||D}function he(t,e){return s(t)?-1===t.indexOf(e):t!==e}function de(t,e,n,i,s){var r=U.keyCodes[e]||n;return s&&i&&!U.keyCodes[e]?he(s,i):r?he(r,t):i?T(i)!==e:void 0===t}function fe(t,e,n,i,r){if(n)if(h(n)){s(n)&&(n=O(n));var a=void 0,o=function(s){if("class"===s||"style"===s||y(s))a=t;else{var o=t.attrs&&t.attrs.type;a=i||U.mustUseProp(e,o,s)?t.domProps||(t.domProps={}):t.attrs||(t.attrs={})}var l=C(s),c=T(s);if(!(l in a)&&!(c in a)&&(a[s]=n[s],r)){var u=t.on||(t.on={});u["update:".concat(s)]=function(t){n[s]=t}}};for(var l in n)o(l)}else;return t}function pe(t,e){var n=this._staticTrees||(this._staticTrees=[]),i=n[t];return i&&!e||(i=n[t]=this.$options.staticRenderFns[t].call(this._renderProxy,this._c,this),me(i,"__static__".concat(t),!1)),i}function _e(t,e,n){return me(t,"__once__".concat(e).concat(n?"_".concat(n):""),!0),t}function me(t,e,n){if(s(t))for(var i=0;i<t.length;i++)t[i]&&"string"!==typeof t[i]&&ve(t[i],"".concat(e,"_").concat(i),n);else ve(t,e,n)}function ve(t,e,n){t.isStatic=!0,t.key=e,t.isOnce=n}function ge(t,e){if(e)if(f(e)){var n=t.on=t.on?L({},t.on):{};for(var i in e){var s=n[i],r=e[i];n[i]=s?[].concat(s,r):r}}else;return t}function be(t,e,n,i){e=e||{$stable:!n};for(var r=0;r<t.length;r++){var a=t[r];s(a)?be(a,e,n):a&&(a.proxy&&(a.fn.proxy=!0),e[a.key]=a.fn)}return i&&(e.$key=i),e}function ye(t,e){for(var n=0;n<e.length;n+=2){var i=e[n];"string"===typeof i&&i&&(t[e[n]]=e[n+1])}return t}function xe(t,e){return"string"===typeof t?e+t:t}function we(t){t._o=_e,t._n=g,t._s=v,t._l=le,t._t=ce,t._q=N,t._i=F,t._m=pe,t._f=ue,t._k=de,t._b=fe,t._v=bt,t._e=gt,t._u=be,t._g=ge,t._d=ye,t._p=xe}function Se(t,e){if(!t||!t.length)return{};for(var n={},i=0,s=t.length;i<s;i++){var r=t[i],a=r.data;if(a&&a.attrs&&a.attrs.slot&&delete a.attrs.slot,r.context!==e&&r.fnContext!==e||!a||null==a.slot)(n.default||(n.default=[])).push(r);else{var o=a.slot,l=n[o]||(n[o]=[]);"template"===r.tag?l.push.apply(l,r.children||[]):l.push(r)}}for(var c in n)n[c].every(ke)&&delete n[c];return n}function ke(t){return t.isComment&&!t.asyncFactory||" "===t.text}function Ae(t){return t.isComment&&t.asyncFactory}function Ce(t,e,n,s){var r,a=Object.keys(n).length>0,o=e?!!e.$stable:!a,l=e&&e.$key;if(e){if(e._normalized)return e._normalized;if(o&&s&&s!==i&&l===s.$key&&!a&&!s.$hasNormal)return s;for(var c in r={},e)e[c]&&"$"!==c[0]&&(r[c]=Me(t,n,c,e[c]))}else r={};for(var u in n)u in r||(r[u]=Ee(n,u));return e&&Object.isExtensible(e)&&(e._normalized=r),W(r,"$stable",o),W(r,"$key",l),W(r,"$hasNormal",a),r}function Me(t,e,n,i){var r=function(){var e=_t;mt(t);var n=arguments.length?i.apply(null,arguments):i({});n=n&&"object"===typeof n&&!s(n)?[n]:re(n);var r=n&&n[0];return mt(e),n&&(!r||1===n.length&&r.isComment&&!Ae(r))?void 0:n};return i.proxy&&Object.defineProperty(e,n,{get:r,enumerable:!0,configurable:!0}),r}function Ee(t,e){return function(){return t[e]}}function Te(t){var e=t.$options,n=e.setup;if(n){var i=t._setupContext=Be(t);mt(t),Ct();var s=We(n,null,[t._props||jt({}),i],t,"setup");if(Mt(),mt(),u(s))e.render=s;else if(h(s))if(t._setupState=s,s.__sfc){var r=t._setupProxy={};for(var a in s)"__sfc"!==a&&Ut(r,s,a)}else for(var a in s)Y(a)||Ut(t,s,a);else 0}}function Be(t){return{get attrs(){if(!t._attrsProxy){var e=t._attrsProxy={};W(e,"_v_attr_proxy",!0),Re(e,t.$attrs,i,t,"$attrs")}return t._attrsProxy},get listeners(){if(!t._listenersProxy){var e=t._listenersProxy={};Re(e,t.$listeners,i,t,"$listeners")}return t._listenersProxy},get slots(){return Ie(t)},emit:$(t.$emit,t),expose:function(e){e&&Object.keys(e).forEach((function(n){return Ut(t,e,n)}))}}}function Re(t,e,n,i,s){var r=!1;for(var a in e)a in t?e[a]!==n[a]&&(r=!0):(r=!0,$e(t,a,i,s));for(var a in t)a in e||(r=!0,delete t[a]);return r}function $e(t,e,n,i){Object.defineProperty(t,e,{enumerable:!0,configurable:!0,get:function(){return n[i][e]}})}function Ie(t){return t._slotsProxy||Le(t._slotsProxy={},t.$scopedSlots),t._slotsProxy}function Le(t,e){for(var n in e)t[n]=e[n];for(var n in t)n in e||delete t[n]}function Oe(t){t._vnode=null,t._staticTrees=null;var e=t.$options,n=t.$vnode=e._parentVnode,s=n&&n.context;t.$slots=Se(e._renderChildren,s),t.$scopedSlots=n?Ce(t.$parent,n.data.scopedSlots,t.$slots):i,t._c=function(e,n,i,s){return ze(t,e,n,i,s,!1)},t.$createElement=function(e,n,i,s){return ze(t,e,n,i,s,!0)};var r=n&&n.data;Dt(t,"$attrs",r&&r.attrs||i,null,!0),Dt(t,"$listeners",e._parentListeners||i,null,!0)}var Pe=null;function Ve(t){we(t.prototype),t.prototype.$nextTick=function(t){return ln(t,this)},t.prototype._render=function(){var t,e=this,n=e.$options,i=n.render,r=n._parentVnode;r&&e._isMounted&&(e.$scopedSlots=Ce(e.$parent,r.data.scopedSlots,e.$slots,e.$scopedSlots),e._slotsProxy&&Le(e._slotsProxy,e.$scopedSlots)),e.$vnode=r;try{mt(e),Pe=e,t=i.call(e._renderProxy,e.$createElement)}catch(Ga){Ye(Ga,e,"render"),t=e._vnode}finally{Pe=null,mt()}return s(t)&&1===t.length&&(t=t[0]),t instanceof vt||(t=gt()),t.parent=r,t}}function De(t,e){return(t.__esModule||pt&&"Module"===t[Symbol.toStringTag])&&(t=t.default),h(t)?e.extend(t):t}function Ne(t,e,n,i,s){var r=gt();return r.asyncFactory=t,r.asyncMeta={data:e,context:n,children:i,tag:s},r}function Fe(t,e){if(o(t.error)&&a(t.errorComp))return t.errorComp;if(a(t.resolved))return t.resolved;var n=Pe;if(n&&a(t.owners)&&-1===t.owners.indexOf(n)&&t.owners.push(n),o(t.loading)&&a(t.loadingComp))return t.loadingComp;if(n&&!a(t.owners)){var i=t.owners=[n],s=!0,l=null,c=null;n.$on("hook:destroyed",(function(){return x(i,n)}));var u=function(t){for(var e=0,n=i.length;e<n;e++)i[e].$forceUpdate();t&&(i.length=0,null!==l&&(clearTimeout(l),l=null),null!==c&&(clearTimeout(c),c=null))},d=H((function(n){t.resolved=De(n,e),s?i.length=0:u(!0)})),f=H((function(e){a(t.errorComp)&&(t.error=!0,u(!0))})),p=t(d,f);return h(p)&&(m(p)?r(t.resolved)&&p.then(d,f):m(p.component)&&(p.component.then(d,f),a(p.error)&&(t.errorComp=De(p.error,e)),a(p.loading)&&(t.loadingComp=De(p.loading,e),0===p.delay?t.loading=!0:l=setTimeout((function(){l=null,r(t.resolved)&&r(t.error)&&(t.loading=!0,u(!1))}),p.delay||200)),a(p.timeout)&&(c=setTimeout((function(){c=null,r(t.resolved)&&f(null)}),p.timeout)))),s=!1,t.loading?t.loadingComp:t.resolved}}function He(t){if(s(t))for(var e=0;e<t.length;e++){var n=t[e];if(a(n)&&(a(n.componentOptions)||Ae(n)))return n}}var je=1,Ze=2;function ze(t,e,n,i,r,a){return(s(n)||c(n))&&(r=i,i=n,n=void 0),o(a)&&(r=Ze),qe(t,e,n,i,r)}function qe(t,e,n,i,r){if(a(n)&&a(n.__ob__))return gt();if(a(n)&&a(n.is)&&(e=n.is),!e)return gt();var o,l;if(s(i)&&u(i[0])&&(n=n||{},n.scopedSlots={default:i[0]},i.length=0),r===Ze?i=re(i):r===je&&(i=se(i)),"string"===typeof e){var c=void 0;l=t.$vnode&&t.$vnode.ns||U.getTagNamespace(e),o=U.isReservedTag(e)?new vt(U.parsePlatformTagName(e),n,i,void 0,void 0,t):n&&n.pre||!a(c=Si(t.$options,"components",e))?new vt(e,n,i,void 0,void 0,t):ai(c,n,t,i,e)}else o=ai(e,n,t,i);return s(o)?o:a(o)?(a(l)&&Ue(o,l),a(n)&&Xe(n),o):gt()}function Ue(t,e,n){if(t.ns=e,"foreignObject"===t.tag&&(e=void 0,n=!0),a(t.children))for(var i=0,s=t.children.length;i<s;i++){var l=t.children[i];a(l.tag)&&(r(l.ns)||o(n)&&"svg"!==l.tag)&&Ue(l,e,n)}}function Xe(t){h(t.style)&&fn(t.style),h(t.class)&&fn(t.class)}function Ye(t,e,n){Ct();try{if(e){var i=e;while(i=i.$parent){var s=i.$options.errorCaptured;if(s)for(var r=0;r<s.length;r++)try{var a=!1===s[r].call(i,t,e,n);if(a)return}catch(Ga){Ge(Ga,i,"errorCaptured hook")}}}Ge(t,e,n)}finally{Mt()}}function We(t,e,n,i,s){var r;try{r=n?t.apply(e,n):t.call(e),r&&!r._isVue&&m(r)&&!r._handled&&(r.catch((function(t){return Ye(t,i,s+" (Promise/async)")})),r._handled=!0)}catch(Ga){Ye(Ga,i,s)}return r}function Ge(t,e,n){if(U.errorHandler)try{return U.errorHandler.call(null,t,e,n)}catch(Ga){Ga!==t&&Ke(Ga,null,"config.errorHandler")}Ke(t,e,n)}function Ke(t,e,n){if(!J||"undefined"===typeof console)throw t;console.error(t)}var Qe,Je=!1,tn=[],en=!1;function nn(){en=!1;var t=tn.slice(0);tn.length=0;for(var e=0;e<t.length;e++)t[e]()}if("undefined"!==typeof Promise&&dt(Promise)){var sn=Promise.resolve();Qe=function(){sn.then(nn),st&&setTimeout(P)},Je=!0}else if(et||"undefined"===typeof MutationObserver||!dt(MutationObserver)&&"[object MutationObserverConstructor]"!==MutationObserver.toString())Qe="undefined"!==typeof setImmediate&&dt(setImmediate)?function(){setImmediate(nn)}:function(){setTimeout(nn,0)};else{var rn=1,an=new MutationObserver(nn),on=document.createTextNode(String(rn));an.observe(on,{characterData:!0}),Qe=function(){rn=(rn+1)%2,on.data=String(rn)},Je=!0}function ln(t,e){var n;if(tn.push((function(){if(t)try{t.call(e)}catch(Ga){Ye(Ga,e,"nextTick")}else n&&n(e)})),en||(en=!0,Qe()),!t&&"undefined"!==typeof Promise)return new Promise((function(t){n=t}))}function cn(t){return function(e,n){if(void 0===n&&(n=_t),n)return un(n,t,e)}}function un(t,e,n){var i=t.$options;i[e]=_i(i[e],n)}cn("beforeMount"),cn("mounted"),cn("beforeUpdate"),cn("updated"),cn("beforeDestroy"),cn("destroyed"),cn("activated"),cn("deactivated"),cn("serverPrefetch"),cn("renderTracked"),cn("renderTriggered"),cn("errorCaptured");var hn="2.7.14";var dn=new ft;function fn(t){return pn(t,dn),dn.clear(),t}function pn(t,e){var n,i,r=s(t);if(!(!r&&!h(t)||t.__v_skip||Object.isFrozen(t)||t instanceof vt)){if(t.__ob__){var a=t.__ob__.dep.id;if(e.has(a))return;e.add(a)}if(r){n=t.length;while(n--)pn(t[n],e)}else if(qt(t))pn(t.value,e);else{i=Object.keys(t),n=i.length;while(n--)pn(t[i[n]],e)}}}var _n,mn=0,vn=function(){function t(t,e,n,i,s){Gt(this,Yt&&!Yt._vm?Yt:t?t._scope:void 0),(this.vm=t)&&s&&(t._watcher=this),i?(this.deep=!!i.deep,this.user=!!i.user,this.lazy=!!i.lazy,this.sync=!!i.sync,this.before=i.before):this.deep=this.user=this.lazy=this.sync=!1,this.cb=n,this.id=++mn,this.active=!0,this.post=!1,this.dirty=this.lazy,this.deps=[],this.newDeps=[],this.depIds=new ft,this.newDepIds=new ft,this.expression="",u(e)?this.getter=e:(this.getter=K(e),this.getter||(this.getter=P)),this.value=this.lazy?void 0:this.get()}return t.prototype.get=function(){var t;Ct(this);var e=this.vm;try{t=this.getter.call(e,e)}catch(Ga){if(!this.user)throw Ga;Ye(Ga,e,'getter for watcher "'.concat(this.expression,'"'))}finally{this.deep&&fn(t),Mt(),this.cleanupDeps()}return t},t.prototype.addDep=function(t){var e=t.id;this.newDepIds.has(e)||(this.newDepIds.add(e),this.newDeps.push(t),this.depIds.has(e)||t.addSub(this))},t.prototype.cleanupDeps=function(){var t=this.deps.length;while(t--){var e=this.deps[t];this.newDepIds.has(e.id)||e.removeSub(this)}var n=this.depIds;this.depIds=this.newDepIds,this.newDepIds=n,this.newDepIds.clear(),n=this.deps,this.deps=this.newDeps,this.newDeps=n,this.newDeps.length=0},t.prototype.update=function(){this.lazy?this.dirty=!0:this.sync?this.run():Wn(this)},t.prototype.run=function(){if(this.active){var t=this.get();if(t!==this.value||h(t)||this.deep){var e=this.value;if(this.value=t,this.user){var n='callback for watcher "'.concat(this.expression,'"');We(this.cb,this.vm,[t,e],this.vm,n)}else this.cb.call(this.vm,t,e)}}},t.prototype.evaluate=function(){this.value=this.get(),this.dirty=!1},t.prototype.depend=function(){var t=this.deps.length;while(t--)this.deps[t].depend()},t.prototype.teardown=function(){if(this.vm&&!this.vm._isBeingDestroyed&&x(this.vm._scope.effects,this),this.active){var t=this.deps.length;while(t--)this.deps[t].removeSub(this);this.active=!1,this.onStop&&this.onStop()}},t}();function gn(t){t._events=Object.create(null),t._hasHookEvent=!1;var e=t.$options._parentListeners;e&&wn(t,e)}function bn(t,e){_n.$on(t,e)}function yn(t,e){_n.$off(t,e)}function xn(t,e){var n=_n;return function i(){var s=e.apply(null,arguments);null!==s&&n.$off(t,i)}}function wn(t,e,n){_n=t,te(e,n||{},bn,yn,xn,t),_n=void 0}function Sn(t){var e=/^hook:/;t.prototype.$on=function(t,n){var i=this;if(s(t))for(var r=0,a=t.length;r<a;r++)i.$on(t[r],n);else(i._events[t]||(i._events[t]=[])).push(n),e.test(t)&&(i._hasHookEvent=!0);return i},t.prototype.$once=function(t,e){var n=this;function i(){n.$off(t,i),e.apply(n,arguments)}return i.fn=e,n.$on(t,i),n},t.prototype.$off=function(t,e){var n=this;if(!arguments.length)return n._events=Object.create(null),n;if(s(t)){for(var i=0,r=t.length;i<r;i++)n.$off(t[i],e);return n}var a,o=n._events[t];if(!o)return n;if(!e)return n._events[t]=null,n;var l=o.length;while(l--)if(a=o[l],a===e||a.fn===e){o.splice(l,1);break}return n},t.prototype.$emit=function(t){var e=this,n=e._events[t];if(n){n=n.length>1?I(n):n;for(var i=I(arguments,1),s='event handler for "'.concat(t,'"'),r=0,a=n.length;r<a;r++)We(n[r],e,i,e,s)}return e}}var kn=null;function An(t){var e=kn;return kn=t,function(){kn=e}}function Cn(t){var e=t.$options,n=e.parent;if(n&&!e.abstract){while(n.$options.abstract&&n.$parent)n=n.$parent;n.$children.push(t)}t.$parent=n,t.$root=n?n.$root:t,t.$children=[],t.$refs={},t._provided=n?n._provided:Object.create(null),t._watcher=null,t._inactive=null,t._directInactive=!1,t._isMounted=!1,t._isDestroyed=!1,t._isBeingDestroyed=!1}function Mn(t){t.prototype._update=function(t,e){var n=this,i=n.$el,s=n._vnode,r=An(n);n._vnode=t,n.$el=s?n.__patch__(s,t):n.__patch__(n.$el,t,e,!1),r(),i&&(i.__vue__=null),n.$el&&(n.$el.__vue__=n);var a=n;while(a&&a.$vnode&&a.$parent&&a.$vnode===a.$parent._vnode)a.$parent.$el=a.$el,a=a.$parent},t.prototype.$forceUpdate=function(){var t=this;t._watcher&&t._watcher.update()},t.prototype.$destroy=function(){var t=this;if(!t._isBeingDestroyed){In(t,"beforeDestroy"),t._isBeingDestroyed=!0;var e=t.$parent;!e||e._isBeingDestroyed||t.$options.abstract||x(e.$children,t),t._scope.stop(),t._data.__ob__&&t._data.__ob__.vmCount--,t._isDestroyed=!0,t.__patch__(t._vnode,null),In(t,"destroyed"),t.$off(),t.$el&&(t.$el.__vue__=null),t.$vnode&&(t.$vnode.parent=null)}}}function En(t,e,n){var i;t.$el=e,t.$options.render||(t.$options.render=gt),In(t,"beforeMount"),i=function(){t._update(t._render(),n)};var s={before:function(){t._isMounted&&!t._isDestroyed&&In(t,"beforeUpdate")}};new vn(t,i,P,s,!0),n=!1;var r=t._preWatchers;if(r)for(var a=0;a<r.length;a++)r[a].run();return null==t.$vnode&&(t._isMounted=!0,In(t,"mounted")),t}function Tn(t,e,n,s,r){var a=s.data.scopedSlots,o=t.$scopedSlots,l=!!(a&&!a.$stable||o!==i&&!o.$stable||a&&t.$scopedSlots.$key!==a.$key||!a&&t.$scopedSlots.$key),c=!!(r||t.$options._renderChildren||l),u=t.$vnode;t.$options._parentVnode=s,t.$vnode=s,t._vnode&&(t._vnode.parent=s),t.$options._renderChildren=r;var h=s.data.attrs||i;t._attrsProxy&&Re(t._attrsProxy,h,u.data&&u.data.attrs||i,t,"$attrs")&&(c=!0),t.$attrs=h,n=n||i;var d=t.$options._parentListeners;if(t._listenersProxy&&Re(t._listenersProxy,n,d||i,t,"$listeners"),t.$listeners=t.$options._parentListeners=n,wn(t,n,d),e&&t.$options.props){Lt(!1);for(var f=t._props,p=t.$options._propKeys||[],_=0;_<p.length;_++){var m=p[_],v=t.$options.props;f[m]=ki(m,v,e,t)}Lt(!0),t.$options.propsData=e}c&&(t.$slots=Se(r,s.context),t.$forceUpdate())}function Bn(t){while(t&&(t=t.$parent))if(t._inactive)return!0;return!1}function Rn(t,e){if(e){if(t._directInactive=!1,Bn(t))return}else if(t._directInactive)return;if(t._inactive||null===t._inactive){t._inactive=!1;for(var n=0;n<t.$children.length;n++)Rn(t.$children[n]);In(t,"activated")}}function $n(t,e){if((!e||(t._directInactive=!0,!Bn(t)))&&!t._inactive){t._inactive=!0;for(var n=0;n<t.$children.length;n++)$n(t.$children[n]);In(t,"deactivated")}}function In(t,e,n,i){void 0===i&&(i=!0),Ct();var s=_t;i&&mt(t);var r=t.$options[e],a="".concat(e," hook");if(r)for(var o=0,l=r.length;o<l;o++)We(r[o],t,n||null,t,a);t._hasHookEvent&&t.$emit("hook:"+e),i&&mt(s),Mt()}var Ln=[],On=[],Pn={},Vn=!1,Dn=!1,Nn=0;function Fn(){Nn=Ln.length=On.length=0,Pn={},Vn=Dn=!1}var Hn=0,jn=Date.now;if(J&&!et){var Zn=window.performance;Zn&&"function"===typeof Zn.now&&jn()>document.createEvent("Event").timeStamp&&(jn=function(){return Zn.now()})}var zn=function(t,e){if(t.post){if(!e.post)return 1}else if(e.post)return-1;return t.id-e.id};function qn(){var t,e;for(Hn=jn(),Dn=!0,Ln.sort(zn),Nn=0;Nn<Ln.length;Nn++)t=Ln[Nn],t.before&&t.before(),e=t.id,Pn[e]=null,t.run();var n=On.slice(),i=Ln.slice();Fn(),Yn(n),Un(i),St(),ht&&U.devtools&&ht.emit("flush")}function Un(t){var e=t.length;while(e--){var n=t[e],i=n.vm;i&&i._watcher===n&&i._isMounted&&!i._isDestroyed&&In(i,"updated")}}function Xn(t){t._inactive=!1,On.push(t)}function Yn(t){for(var e=0;e<t.length;e++)t[e]._inactive=!0,Rn(t[e],!0)}function Wn(t){var e=t.id;if(null==Pn[e]&&(t!==kt.target||!t.noRecurse)){if(Pn[e]=!0,Dn){var n=Ln.length-1;while(n>Nn&&Ln[n].id>t.id)n--;Ln.splice(n+1,0,t)}else Ln.push(t);Vn||(Vn=!0,ln(qn))}}function Gn(t){var e=t.$options.provide;if(e){var n=u(e)?e.call(t):e;if(!h(n))return;for(var i=Kt(t),s=pt?Reflect.ownKeys(n):Object.keys(n),r=0;r<s.length;r++){var a=s[r];Object.defineProperty(i,a,Object.getOwnPropertyDescriptor(n,a))}}}function Kn(t){var e=Qn(t.$options.inject,t);e&&(Lt(!1),Object.keys(e).forEach((function(n){Dt(t,n,e[n])})),Lt(!0))}function Qn(t,e){if(t){for(var n=Object.create(null),i=pt?Reflect.ownKeys(t):Object.keys(t),s=0;s<i.length;s++){var r=i[s];if("__ob__"!==r){var a=t[r].from;if(a in e._provided)n[r]=e._provided[a];else if("default"in t[r]){var o=t[r].default;n[r]=u(o)?o.call(e):o}else 0}}return n}}function Jn(t,e,n,r,a){var l,c=this,u=a.options;S(r,"_uid")?(l=Object.create(r),l._original=r):(l=r,r=r._original);var h=o(u._compiled),d=!h;this.data=t,this.props=e,this.children=n,this.parent=r,this.listeners=t.on||i,this.injections=Qn(u.inject,r),this.slots=function(){return c.$slots||Ce(r,t.scopedSlots,c.$slots=Se(n,r)),c.$slots},Object.defineProperty(this,"scopedSlots",{enumerable:!0,get:function(){return Ce(r,t.scopedSlots,this.slots())}}),h&&(this.$options=u,this.$slots=this.slots(),this.$scopedSlots=Ce(r,t.scopedSlots,this.$slots)),u._scopeId?this._c=function(t,e,n,i){var a=ze(l,t,e,n,i,d);return a&&!s(a)&&(a.fnScopeId=u._scopeId,a.fnContext=r),a}:this._c=function(t,e,n,i){return ze(l,t,e,n,i,d)}}function ti(t,e,n,r,o){var l=t.options,c={},u=l.props;if(a(u))for(var h in u)c[h]=ki(h,u,e||i);else a(n.attrs)&&ni(c,n.attrs),a(n.props)&&ni(c,n.props);var d=new Jn(n,c,o,r,t),f=l.render.call(null,d._c,d);if(f instanceof vt)return ei(f,n,d.parent,l,d);if(s(f)){for(var p=re(f)||[],_=new Array(p.length),m=0;m<p.length;m++)_[m]=ei(p[m],n,d.parent,l,d);return _}}function ei(t,e,n,i,s){var r=yt(t);return r.fnContext=n,r.fnOptions=i,e.slot&&((r.data||(r.data={})).slot=e.slot),r}function ni(t,e){for(var n in e)t[C(n)]=e[n]}function ii(t){return t.name||t.__name||t._componentTag}we(Jn.prototype);var si={init:function(t,e){if(t.componentInstance&&!t.componentInstance._isDestroyed&&t.data.keepAlive){var n=t;si.prepatch(n,n)}else{var i=t.componentInstance=oi(t,kn);i.$mount(e?t.elm:void 0,e)}},prepatch:function(t,e){var n=e.componentOptions,i=e.componentInstance=t.componentInstance;Tn(i,n.propsData,n.listeners,e,n.children)},insert:function(t){var e=t.context,n=t.componentInstance;n._isMounted||(n._isMounted=!0,In(n,"mounted")),t.data.keepAlive&&(e._isMounted?Xn(n):Rn(n,!0))},destroy:function(t){var e=t.componentInstance;e._isDestroyed||(t.data.keepAlive?$n(e,!0):e.$destroy())}},ri=Object.keys(si);function ai(t,e,n,i,s){if(!r(t)){var l=n.$options._base;if(h(t)&&(t=l.extend(t)),"function"===typeof t){var c;if(r(t.cid)&&(c=t,t=Fe(c,l),void 0===t))return Ne(c,e,n,i,s);e=e||{},Yi(t),a(e.model)&&ui(t.options,e);var u=ne(e,t,s);if(o(t.options.functional))return ti(t,u,e,n,i);var d=e.on;if(e.on=e.nativeOn,o(t.options.abstract)){var f=e.slot;e={},f&&(e.slot=f)}li(e);var p=ii(t.options)||s,_=new vt("vue-component-".concat(t.cid).concat(p?"-".concat(p):""),e,void 0,void 0,void 0,n,{Ctor:t,propsData:u,listeners:d,tag:s,children:i},c);return _}}}function oi(t,e){var n={_isComponent:!0,_parentVnode:t,parent:e},i=t.data.inlineTemplate;return a(i)&&(n.render=i.render,n.staticRenderFns=i.staticRenderFns),new t.componentOptions.Ctor(n)}function li(t){for(var e=t.hook||(t.hook={}),n=0;n<ri.length;n++){var i=ri[n],s=e[i],r=si[i];s===r||s&&s._merged||(e[i]=s?ci(r,s):r)}}function ci(t,e){var n=function(n,i){t(n,i),e(n,i)};return n._merged=!0,n}function ui(t,e){var n=t.model&&t.model.prop||"value",i=t.model&&t.model.event||"input";(e.attrs||(e.attrs={}))[n]=e.model.value;var r=e.on||(e.on={}),o=r[i],l=e.model.callback;a(o)?(s(o)?-1===o.indexOf(l):o!==l)&&(r[i]=[l].concat(o)):r[i]=l}var hi=P,di=U.optionMergeStrategies;function fi(t,e,n){if(void 0===n&&(n=!0),!e)return t;for(var i,s,r,a=pt?Reflect.ownKeys(e):Object.keys(e),o=0;o<a.length;o++)i=a[o],"__ob__"!==i&&(s=t[i],r=e[i],n&&S(t,i)?s!==r&&f(s)&&f(r)&&fi(s,r):Nt(t,i,r));return t}function pi(t,e,n){return n?function(){var i=u(e)?e.call(n,n):e,s=u(t)?t.call(n,n):t;return i?fi(i,s):s}:e?t?function(){return fi(u(e)?e.call(this,this):e,u(t)?t.call(this,this):t)}:e:t}function _i(t,e){var n=e?t?t.concat(e):s(e)?e:[e]:t;return n?mi(n):n}function mi(t){for(var e=[],n=0;n<t.length;n++)-1===e.indexOf(t[n])&&e.push(t[n]);return e}function vi(t,e,n,i){var s=Object.create(t||null);return e?L(s,e):s}di.data=function(t,e,n){return n?pi(t,e,n):e&&"function"!==typeof e?t:pi(t,e)},q.forEach((function(t){di[t]=_i})),z.forEach((function(t){di[t+"s"]=vi})),di.watch=function(t,e,n,i){if(t===ot&&(t=void 0),e===ot&&(e=void 0),!e)return Object.create(t||null);if(!t)return e;var r={};for(var a in L(r,t),e){var o=r[a],l=e[a];o&&!s(o)&&(o=[o]),r[a]=o?o.concat(l):s(l)?l:[l]}return r},di.props=di.methods=di.inject=di.computed=function(t,e,n,i){if(!t)return e;var s=Object.create(null);return L(s,t),e&&L(s,e),s},di.provide=function(t,e){return t?function(){var n=Object.create(null);return fi(n,u(t)?t.call(this):t),e&&fi(n,u(e)?e.call(this):e,!1),n}:e};var gi=function(t,e){return void 0===e?t:e};function bi(t,e){var n=t.props;if(n){var i,r,a,o={};if(s(n)){i=n.length;while(i--)r=n[i],"string"===typeof r&&(a=C(r),o[a]={type:null})}else if(f(n))for(var l in n)r=n[l],a=C(l),o[a]=f(r)?r:{type:r};else 0;t.props=o}}function yi(t,e){var n=t.inject;if(n){var i=t.inject={};if(s(n))for(var r=0;r<n.length;r++)i[n[r]]={from:n[r]};else if(f(n))for(var a in n){var o=n[a];i[a]=f(o)?L({from:a},o):{from:o}}else 0}}function xi(t){var e=t.directives;if(e)for(var n in e){var i=e[n];u(i)&&(e[n]={bind:i,update:i})}}function wi(t,e,n){if(u(e)&&(e=e.options),bi(e,n),yi(e,n),xi(e),!e._base&&(e.extends&&(t=wi(t,e.extends,n)),e.mixins))for(var i=0,s=e.mixins.length;i<s;i++)t=wi(t,e.mixins[i],n);var r,a={};for(r in t)o(r);for(r in e)S(t,r)||o(r);function o(i){var s=di[i]||gi;a[i]=s(t[i],e[i],n,i)}return a}function Si(t,e,n,i){if("string"===typeof n){var s=t[e];if(S(s,n))return s[n];var r=C(n);if(S(s,r))return s[r];var a=M(r);if(S(s,a))return s[a];var o=s[n]||s[r]||s[a];return o}}function ki(t,e,n,i){var s=e[t],r=!S(n,t),a=n[t],o=Ti(Boolean,s.type);if(o>-1)if(r&&!S(s,"default"))a=!1;else if(""===a||a===T(t)){var l=Ti(String,s.type);(l<0||o<l)&&(a=!0)}if(void 0===a){a=Ai(i,s,t);var c=It;Lt(!0),Vt(a),Lt(c)}return a}function Ai(t,e,n){if(S(e,"default")){var i=e.default;return t&&t.$options.propsData&&void 0===t.$options.propsData[n]&&void 0!==t._props[n]?t._props[n]:u(i)&&"Function"!==Mi(e.type)?i.call(t):i}}var Ci=/^\s*function (\w+)/;function Mi(t){var e=t&&t.toString().match(Ci);return e?e[1]:""}function Ei(t,e){return Mi(t)===Mi(e)}function Ti(t,e){if(!s(e))return Ei(e,t)?0:-1;for(var n=0,i=e.length;n<i;n++)if(Ei(e[n],t))return n;return-1}var Bi={enumerable:!0,configurable:!0,get:P,set:P};function Ri(t,e,n){Bi.get=function(){return this[e][n]},Bi.set=function(t){this[e][n]=t},Object.defineProperty(t,n,Bi)}function $i(t){var e=t.$options;if(e.props&&Ii(t,e.props),Te(t),e.methods&&Hi(t,e.methods),e.data)Li(t);else{var n=Vt(t._data={});n&&n.vmCount++}e.computed&&Vi(t,e.computed),e.watch&&e.watch!==ot&&ji(t,e.watch)}function Ii(t,e){var n=t.$options.propsData||{},i=t._props=jt({}),s=t.$options._propKeys=[],r=!t.$parent;r||Lt(!1);var a=function(r){s.push(r);var a=ki(r,e,n,t);Dt(i,r,a),r in t||Ri(t,"_props",r)};for(var o in e)a(o);Lt(!0)}function Li(t){var e=t.$options.data;e=t._data=u(e)?Oi(e,t):e||{},f(e)||(e={});var n=Object.keys(e),i=t.$options.props,s=(t.$options.methods,n.length);while(s--){var r=n[s];0,i&&S(i,r)||Y(r)||Ri(t,"_data",r)}var a=Vt(e);a&&a.vmCount++}function Oi(t,e){Ct();try{return t.call(e,e)}catch(Ga){return Ye(Ga,e,"data()"),{}}finally{Mt()}}var Pi={lazy:!0};function Vi(t,e){var n=t._computedWatchers=Object.create(null),i=ut();for(var s in e){var r=e[s],a=u(r)?r:r.get;0,i||(n[s]=new vn(t,a||P,P,Pi)),s in t||Di(t,s,r)}}function Di(t,e,n){var i=!ut();u(n)?(Bi.get=i?Ni(e):Fi(n),Bi.set=P):(Bi.get=n.get?i&&!1!==n.cache?Ni(e):Fi(n.get):P,Bi.set=n.set||P),Object.defineProperty(t,e,Bi)}function Ni(t){return function(){var e=this._computedWatchers&&this._computedWatchers[t];if(e)return e.dirty&&e.evaluate(),kt.target&&e.depend(),e.value}}function Fi(t){return function(){return t.call(this,this)}}function Hi(t,e){t.$options.props;for(var n in e)t[n]="function"!==typeof e[n]?P:$(e[n],t)}function ji(t,e){for(var n in e){var i=e[n];if(s(i))for(var r=0;r<i.length;r++)Zi(t,n,i[r]);else Zi(t,n,i)}}function Zi(t,e,n,i){return f(n)&&(i=n,n=n.handler),"string"===typeof n&&(n=t[n]),t.$watch(e,n,i)}function zi(t){var e={get:function(){return this._data}},n={get:function(){return this._props}};Object.defineProperty(t.prototype,"$data",e),Object.defineProperty(t.prototype,"$props",n),t.prototype.$set=Nt,t.prototype.$delete=Ft,t.prototype.$watch=function(t,e,n){var i=this;if(f(e))return Zi(i,t,e,n);n=n||{},n.user=!0;var s=new vn(i,t,e,n);if(n.immediate){var r='callback for immediate watcher "'.concat(s.expression,'"');Ct(),We(e,i,[s.value],i,r),Mt()}return function(){s.teardown()}}}var qi=0;function Ui(t){t.prototype._init=function(t){var e=this;e._uid=qi++,e._isVue=!0,e.__v_skip=!0,e._scope=new Wt(!0),e._scope._vm=!0,t&&t._isComponent?Xi(e,t):e.$options=wi(Yi(e.constructor),t||{},e),e._renderProxy=e,e._self=e,Cn(e),gn(e),Oe(e),In(e,"beforeCreate",void 0,!1),Kn(e),$i(e),Gn(e),In(e,"created"),e.$options.el&&e.$mount(e.$options.el)}}function Xi(t,e){var n=t.$options=Object.create(t.constructor.options),i=e._parentVnode;n.parent=e.parent,n._parentVnode=i;var s=i.componentOptions;n.propsData=s.propsData,n._parentListeners=s.listeners,n._renderChildren=s.children,n._componentTag=s.tag,e.render&&(n.render=e.render,n.staticRenderFns=e.staticRenderFns)}function Yi(t){var e=t.options;if(t.super){var n=Yi(t.super),i=t.superOptions;if(n!==i){t.superOptions=n;var s=Wi(t);s&&L(t.extendOptions,s),e=t.options=wi(n,t.extendOptions),e.name&&(e.components[e.name]=t)}}return e}function Wi(t){var e,n=t.options,i=t.sealedOptions;for(var s in n)n[s]!==i[s]&&(e||(e={}),e[s]=n[s]);return e}function Gi(t){this._init(t)}function Ki(t){t.use=function(t){var e=this._installedPlugins||(this._installedPlugins=[]);if(e.indexOf(t)>-1)return this;var n=I(arguments,1);return n.unshift(this),u(t.install)?t.install.apply(t,n):u(t)&&t.apply(null,n),e.push(t),this}}function Qi(t){t.mixin=function(t){return this.options=wi(this.options,t),this}}function Ji(t){t.cid=0;var e=1;t.extend=function(t){t=t||{};var n=this,i=n.cid,s=t._Ctor||(t._Ctor={});if(s[i])return s[i];var r=ii(t)||ii(n.options);var a=function(t){this._init(t)};return a.prototype=Object.create(n.prototype),a.prototype.constructor=a,a.cid=e++,a.options=wi(n.options,t),a["super"]=n,a.options.props&&ts(a),a.options.computed&&es(a),a.extend=n.extend,a.mixin=n.mixin,a.use=n.use,z.forEach((function(t){a[t]=n[t]})),r&&(a.options.components[r]=a),a.superOptions=n.options,a.extendOptions=t,a.sealedOptions=L({},a.options),s[i]=a,a}}function ts(t){var e=t.options.props;for(var n in e)Ri(t.prototype,"_props",n)}function es(t){var e=t.options.computed;for(var n in e)Di(t.prototype,n,e[n])}function ns(t){z.forEach((function(e){t[e]=function(t,n){return n?("component"===e&&f(n)&&(n.name=n.name||t,n=this.options._base.extend(n)),"directive"===e&&u(n)&&(n={bind:n,update:n}),this.options[e+"s"][t]=n,n):this.options[e+"s"][t]}}))}function is(t){return t&&(ii(t.Ctor.options)||t.tag)}function ss(t,e){return s(t)?t.indexOf(e)>-1:"string"===typeof t?t.split(",").indexOf(e)>-1:!!p(t)&&t.test(e)}function rs(t,e){var n=t.cache,i=t.keys,s=t._vnode;for(var r in n){var a=n[r];if(a){var o=a.name;o&&!e(o)&&as(n,r,i,s)}}}function as(t,e,n,i){var s=t[e];!s||i&&s.tag===i.tag||s.componentInstance.$destroy(),t[e]=null,x(n,e)}Ui(Gi),zi(Gi),Sn(Gi),Mn(Gi),Ve(Gi);var os=[String,RegExp,Array],ls={name:"keep-alive",abstract:!0,props:{include:os,exclude:os,max:[String,Number]},methods:{cacheVNode:function(){var t=this,e=t.cache,n=t.keys,i=t.vnodeToCache,s=t.keyToCache;if(i){var r=i.tag,a=i.componentInstance,o=i.componentOptions;e[s]={name:is(o),tag:r,componentInstance:a},n.push(s),this.max&&n.length>parseInt(this.max)&&as(e,n[0],n,this._vnode),this.vnodeToCache=null}}},created:function(){this.cache=Object.create(null),this.keys=[]},destroyed:function(){for(var t in this.cache)as(this.cache,t,this.keys)},mounted:function(){var t=this;this.cacheVNode(),this.$watch("include",(function(e){rs(t,(function(t){return ss(e,t)}))})),this.$watch("exclude",(function(e){rs(t,(function(t){return!ss(e,t)}))}))},updated:function(){this.cacheVNode()},render:function(){var t=this.$slots.default,e=He(t),n=e&&e.componentOptions;if(n){var i=is(n),s=this,r=s.include,a=s.exclude;if(r&&(!i||!ss(r,i))||a&&i&&ss(a,i))return e;var o=this,l=o.cache,c=o.keys,u=null==e.key?n.Ctor.cid+(n.tag?"::".concat(n.tag):""):e.key;l[u]?(e.componentInstance=l[u].componentInstance,x(c,u),c.push(u)):(this.vnodeToCache=e,this.keyToCache=u),e.data.keepAlive=!0}return e||t&&t[0]}},cs={KeepAlive:ls};function us(t){var e={get:function(){return U}};Object.defineProperty(t,"config",e),t.util={warn:hi,extend:L,mergeOptions:wi,defineReactive:Dt},t.set=Nt,t.delete=Ft,t.nextTick=ln,t.observable=function(t){return Vt(t),t},t.options=Object.create(null),z.forEach((function(e){t.options[e+"s"]=Object.create(null)})),t.options._base=t,L(t.options.components,cs),Ki(t),Qi(t),Ji(t),ns(t)}us(Gi),Object.defineProperty(Gi.prototype,"$isServer",{get:ut}),Object.defineProperty(Gi.prototype,"$ssrContext",{get:function(){return this.$vnode&&this.$vnode.ssrContext}}),Object.defineProperty(Gi,"FunctionalRenderContext",{value:Jn}),Gi.version=hn;var hs=b("style,class"),ds=b("input,textarea,option,select,progress"),fs=function(t,e,n){return"value"===n&&ds(t)&&"button"!==e||"selected"===n&&"option"===t||"checked"===n&&"input"===t||"muted"===n&&"video"===t},ps=b("contenteditable,draggable,spellcheck"),_s=b("events,caret,typing,plaintext-only"),ms=function(t,e){return xs(e)||"false"===e?"false":"contenteditable"===t&&_s(e)?e:"true"},vs=b("allowfullscreen,async,autofocus,autoplay,checked,compact,controls,declare,default,defaultchecked,defaultmuted,defaultselected,defer,disabled,enabled,formnovalidate,hidden,indeterminate,inert,ismap,itemscope,loop,multiple,muted,nohref,noresize,noshade,novalidate,nowrap,open,pauseonexit,readonly,required,reversed,scoped,seamless,selected,sortable,truespeed,typemustmatch,visible"),gs="http://www.w3.org/1999/xlink",bs=function(t){return":"===t.charAt(5)&&"xlink"===t.slice(0,5)},ys=function(t){return bs(t)?t.slice(6,t.length):""},xs=function(t){return null==t||!1===t};function ws(t){var e=t.data,n=t,i=t;while(a(i.componentInstance))i=i.componentInstance._vnode,i&&i.data&&(e=Ss(i.data,e));while(a(n=n.parent))n&&n.data&&(e=Ss(e,n.data));return ks(e.staticClass,e.class)}function Ss(t,e){return{staticClass:As(t.staticClass,e.staticClass),class:a(t.class)?[t.class,e.class]:e.class}}function ks(t,e){return a(t)||a(e)?As(t,Cs(e)):""}function As(t,e){return t?e?t+" "+e:t:e||""}function Cs(t){return Array.isArray(t)?Ms(t):h(t)?Es(t):"string"===typeof t?t:""}function Ms(t){for(var e,n="",i=0,s=t.length;i<s;i++)a(e=Cs(t[i]))&&""!==e&&(n&&(n+=" "),n+=e);return n}function Es(t){var e="";for(var n in t)t[n]&&(e&&(e+=" "),e+=n);return e}var Ts={svg:"http://www.w3.org/2000/svg",math:"http://www.w3.org/1998/Math/MathML"},Bs=b("html,body,base,head,link,meta,style,title,address,article,aside,footer,header,h1,h2,h3,h4,h5,h6,hgroup,nav,section,div,dd,dl,dt,figcaption,figure,picture,hr,img,li,main,ol,p,pre,ul,a,b,abbr,bdi,bdo,br,cite,code,data,dfn,em,i,kbd,mark,q,rp,rt,rtc,ruby,s,samp,small,span,strong,sub,sup,time,u,var,wbr,area,audio,map,track,video,embed,object,param,source,canvas,script,noscript,del,ins,caption,col,colgroup,table,thead,tbody,td,th,tr,button,datalist,fieldset,form,input,label,legend,meter,optgroup,option,output,progress,select,textarea,details,dialog,menu,menuitem,summary,content,element,shadow,template,blockquote,iframe,tfoot"),Rs=b("svg,animate,circle,clippath,cursor,defs,desc,ellipse,filter,font-face,foreignobject,g,glyph,image,line,marker,mask,missing-glyph,path,pattern,polygon,polyline,rect,switch,symbol,text,textpath,tspan,use,view",!0),$s=function(t){return Bs(t)||Rs(t)};function Is(t){return Rs(t)?"svg":"math"===t?"math":void 0}var Ls=Object.create(null);function Os(t){if(!J)return!0;if($s(t))return!1;if(t=t.toLowerCase(),null!=Ls[t])return Ls[t];var e=document.createElement(t);return t.indexOf("-")>-1?Ls[t]=e.constructor===window.HTMLUnknownElement||e.constructor===window.HTMLElement:Ls[t]=/HTMLUnknownElement/.test(e.toString())}var Ps=b("text,number,password,search,email,tel,url");function Vs(t){if("string"===typeof t){var e=document.querySelector(t);return e||document.createElement("div")}return t}function Ds(t,e){var n=document.createElement(t);return"select"!==t||e.data&&e.data.attrs&&void 0!==e.data.attrs.multiple&&n.setAttribute("multiple","multiple"),n}function Ns(t,e){return document.createElementNS(Ts[t],e)}function Fs(t){return document.createTextNode(t)}function Hs(t){return document.createComment(t)}function js(t,e,n){t.insertBefore(e,n)}function Zs(t,e){t.removeChild(e)}function zs(t,e){t.appendChild(e)}function qs(t){return t.parentNode}function Us(t){return t.nextSibling}function Xs(t){return t.tagName}function Ys(t,e){t.textContent=e}function Ws(t,e){t.setAttribute(e,"")}var Gs=Object.freeze({__proto__:null,createElement:Ds,createElementNS:Ns,createTextNode:Fs,createComment:Hs,insertBefore:js,removeChild:Zs,appendChild:zs,parentNode:qs,nextSibling:Us,tagName:Xs,setTextContent:Ys,setStyleScope:Ws}),Ks={create:function(t,e){Qs(e)},update:function(t,e){t.data.ref!==e.data.ref&&(Qs(t,!0),Qs(e))},destroy:function(t){Qs(t,!0)}};function Qs(t,e){var n=t.data.ref;if(a(n)){var i=t.context,r=t.componentInstance||t.elm,o=e?null:r,l=e?void 0:r;if(u(n))We(n,i,[o],i,"template ref function");else{var c=t.data.refInFor,h="string"===typeof n||"number"===typeof n,d=qt(n),f=i.$refs;if(h||d)if(c){var p=h?f[n]:n.value;e?s(p)&&x(p,r):s(p)?p.includes(r)||p.push(r):h?(f[n]=[r],Js(i,n,f[n])):n.value=[r]}else if(h){if(e&&f[n]!==r)return;f[n]=l,Js(i,n,o)}else if(d){if(e&&n.value!==r)return;n.value=o}else 0}}}function Js(t,e,n){var i=t._setupState;i&&S(i,e)&&(qt(i[e])?i[e].value=n:i[e]=n)}var tr=new vt("",{},[]),er=["create","activate","update","remove","destroy"];function nr(t,e){return t.key===e.key&&t.asyncFactory===e.asyncFactory&&(t.tag===e.tag&&t.isComment===e.isComment&&a(t.data)===a(e.data)&&ir(t,e)||o(t.isAsyncPlaceholder)&&r(e.asyncFactory.error))}function ir(t,e){if("input"!==t.tag)return!0;var n,i=a(n=t.data)&&a(n=n.attrs)&&n.type,s=a(n=e.data)&&a(n=n.attrs)&&n.type;return i===s||Ps(i)&&Ps(s)}function sr(t,e,n){var i,s,r={};for(i=e;i<=n;++i)s=t[i].key,a(s)&&(r[s]=i);return r}function rr(t){var e,n,i={},l=t.modules,u=t.nodeOps;for(e=0;e<er.length;++e)for(i[er[e]]=[],n=0;n<l.length;++n)a(l[n][er[e]])&&i[er[e]].push(l[n][er[e]]);function h(t){return new vt(u.tagName(t).toLowerCase(),{},[],void 0,t)}function d(t,e){function n(){0===--n.listeners&&f(t)}return n.listeners=e,n}function f(t){var e=u.parentNode(t);a(e)&&u.removeChild(e,t)}function p(t,e,n,i,s,r,l){if(a(t.elm)&&a(r)&&(t=r[l]=yt(t)),t.isRootInsert=!s,!_(t,e,n,i)){var c=t.data,h=t.children,d=t.tag;a(d)?(t.elm=t.ns?u.createElementNS(t.ns,d):u.createElement(d,t),S(t),y(t,h,e),a(c)&&w(t,e),g(n,t.elm,i)):o(t.isComment)?(t.elm=u.createComment(t.text),g(n,t.elm,i)):(t.elm=u.createTextNode(t.text),g(n,t.elm,i))}}function _(t,e,n,i){var s=t.data;if(a(s)){var r=a(t.componentInstance)&&s.keepAlive;if(a(s=s.hook)&&a(s=s.init)&&s(t,!1),a(t.componentInstance))return m(t,e),g(n,t.elm,i),o(r)&&v(t,e,n,i),!0}}function m(t,e){a(t.data.pendingInsert)&&(e.push.apply(e,t.data.pendingInsert),t.data.pendingInsert=null),t.elm=t.componentInstance.$el,x(t)?(w(t,e),S(t)):(Qs(t),e.push(t))}function v(t,e,n,s){var r,o=t;while(o.componentInstance)if(o=o.componentInstance._vnode,a(r=o.data)&&a(r=r.transition)){for(r=0;r<i.activate.length;++r)i.activate[r](tr,o);e.push(o);break}g(n,t.elm,s)}function g(t,e,n){a(t)&&(a(n)?u.parentNode(n)===t&&u.insertBefore(t,e,n):u.appendChild(t,e))}function y(t,e,n){if(s(e)){0;for(var i=0;i<e.length;++i)p(e[i],n,t.elm,null,!0,e,i)}else c(t.text)&&u.appendChild(t.elm,u.createTextNode(String(t.text)))}function x(t){while(t.componentInstance)t=t.componentInstance._vnode;return a(t.tag)}function w(t,n){for(var s=0;s<i.create.length;++s)i.create[s](tr,t);e=t.data.hook,a(e)&&(a(e.create)&&e.create(tr,t),a(e.insert)&&n.push(t))}function S(t){var e;if(a(e=t.fnScopeId))u.setStyleScope(t.elm,e);else{var n=t;while(n)a(e=n.context)&&a(e=e.$options._scopeId)&&u.setStyleScope(t.elm,e),n=n.parent}a(e=kn)&&e!==t.context&&e!==t.fnContext&&a(e=e.$options._scopeId)&&u.setStyleScope(t.elm,e)}function k(t,e,n,i,s,r){for(;i<=s;++i)p(n[i],r,t,e,!1,n,i)}function A(t){var e,n,s=t.data;if(a(s))for(a(e=s.hook)&&a(e=e.destroy)&&e(t),e=0;e<i.destroy.length;++e)i.destroy[e](t);if(a(e=t.children))for(n=0;n<t.children.length;++n)A(t.children[n])}function C(t,e,n){for(;e<=n;++e){var i=t[e];a(i)&&(a(i.tag)?(M(i),A(i)):f(i.elm))}}function M(t,e){if(a(e)||a(t.data)){var n,s=i.remove.length+1;for(a(e)?e.listeners+=s:e=d(t.elm,s),a(n=t.componentInstance)&&a(n=n._vnode)&&a(n.data)&&M(n,e),n=0;n<i.remove.length;++n)i.remove[n](t,e);a(n=t.data.hook)&&a(n=n.remove)?n(t,e):e()}else f(t.elm)}function E(t,e,n,i,s){var o,l,c,h,d=0,f=0,_=e.length-1,m=e[0],v=e[_],g=n.length-1,b=n[0],y=n[g],x=!s;while(d<=_&&f<=g)r(m)?m=e[++d]:r(v)?v=e[--_]:nr(m,b)?(B(m,b,i,n,f),m=e[++d],b=n[++f]):nr(v,y)?(B(v,y,i,n,g),v=e[--_],y=n[--g]):nr(m,y)?(B(m,y,i,n,g),x&&u.insertBefore(t,m.elm,u.nextSibling(v.elm)),m=e[++d],y=n[--g]):nr(v,b)?(B(v,b,i,n,f),x&&u.insertBefore(t,v.elm,m.elm),v=e[--_],b=n[++f]):(r(o)&&(o=sr(e,d,_)),l=a(b.key)?o[b.key]:T(b,e,d,_),r(l)?p(b,i,t,m.elm,!1,n,f):(c=e[l],nr(c,b)?(B(c,b,i,n,f),e[l]=void 0,x&&u.insertBefore(t,c.elm,m.elm)):p(b,i,t,m.elm,!1,n,f)),b=n[++f]);d>_?(h=r(n[g+1])?null:n[g+1].elm,k(t,h,n,f,g,i)):f>g&&C(e,d,_)}function T(t,e,n,i){for(var s=n;s<i;s++){var r=e[s];if(a(r)&&nr(t,r))return s}}function B(t,e,n,s,l,c){if(t!==e){a(e.elm)&&a(s)&&(e=s[l]=yt(e));var h=e.elm=t.elm;if(o(t.isAsyncPlaceholder))a(e.asyncFactory.resolved)?I(t.elm,e,n):e.isAsyncPlaceholder=!0;else if(o(e.isStatic)&&o(t.isStatic)&&e.key===t.key&&(o(e.isCloned)||o(e.isOnce)))e.componentInstance=t.componentInstance;else{var d,f=e.data;a(f)&&a(d=f.hook)&&a(d=d.prepatch)&&d(t,e);var p=t.children,_=e.children;if(a(f)&&x(e)){for(d=0;d<i.update.length;++d)i.update[d](t,e);a(d=f.hook)&&a(d=d.update)&&d(t,e)}r(e.text)?a(p)&&a(_)?p!==_&&E(h,p,_,n,c):a(_)?(a(t.text)&&u.setTextContent(h,""),k(h,null,_,0,_.length-1,n)):a(p)?C(p,0,p.length-1):a(t.text)&&u.setTextContent(h,""):t.text!==e.text&&u.setTextContent(h,e.text),a(f)&&a(d=f.hook)&&a(d=d.postpatch)&&d(t,e)}}}function R(t,e,n){if(o(n)&&a(t.parent))t.parent.data.pendingInsert=e;else for(var i=0;i<e.length;++i)e[i].data.hook.insert(e[i])}var $=b("attrs,class,staticClass,staticStyle,key");function I(t,e,n,i){var s,r=e.tag,l=e.data,c=e.children;if(i=i||l&&l.pre,e.elm=t,o(e.isComment)&&a(e.asyncFactory))return e.isAsyncPlaceholder=!0,!0;if(a(l)&&(a(s=l.hook)&&a(s=s.init)&&s(e,!0),a(s=e.componentInstance)))return m(e,n),!0;if(a(r)){if(a(c))if(t.hasChildNodes())if(a(s=l)&&a(s=s.domProps)&&a(s=s.innerHTML)){if(s!==t.innerHTML)return!1}else{for(var u=!0,h=t.firstChild,d=0;d<c.length;d++){if(!h||!I(h,c[d],n,i)){u=!1;break}h=h.nextSibling}if(!u||h)return!1}else y(e,c,n);if(a(l)){var f=!1;for(var p in l)if(!$(p)){f=!0,w(e,n);break}!f&&l["class"]&&fn(l["class"])}}else t.data!==e.text&&(t.data=e.text);return!0}return function(t,e,n,s){if(!r(e)){var l=!1,c=[];if(r(t))l=!0,p(e,c);else{var d=a(t.nodeType);if(!d&&nr(t,e))B(t,e,c,null,null,s);else{if(d){if(1===t.nodeType&&t.hasAttribute(Z)&&(t.removeAttribute(Z),n=!0),o(n)&&I(t,e,c))return R(e,c,!0),t;t=h(t)}var f=t.elm,_=u.parentNode(f);if(p(e,c,f._leaveCb?null:_,u.nextSibling(f)),a(e.parent)){var m=e.parent,v=x(e);while(m){for(var g=0;g<i.destroy.length;++g)i.destroy[g](m);if(m.elm=e.elm,v){for(var b=0;b<i.create.length;++b)i.create[b](tr,m);var y=m.data.hook.insert;if(y.merged)for(var w=1;w<y.fns.length;w++)y.fns[w]()}else Qs(m);m=m.parent}}a(_)?C([t],0,0):a(t.tag)&&A(t)}}return R(e,c,l),e.elm}a(t)&&A(t)}}var ar={create:or,update:or,destroy:function(t){or(t,tr)}};function or(t,e){(t.data.directives||e.data.directives)&&lr(t,e)}function lr(t,e){var n,i,s,r=t===tr,a=e===tr,o=ur(t.data.directives,t.context),l=ur(e.data.directives,e.context),c=[],u=[];for(n in l)i=o[n],s=l[n],i?(s.oldValue=i.value,s.oldArg=i.arg,dr(s,"update",e,t),s.def&&s.def.componentUpdated&&u.push(s)):(dr(s,"bind",e,t),s.def&&s.def.inserted&&c.push(s));if(c.length){var h=function(){for(var n=0;n<c.length;n++)dr(c[n],"inserted",e,t)};r?ee(e,"insert",h):h()}if(u.length&&ee(e,"postpatch",(function(){for(var n=0;n<u.length;n++)dr(u[n],"componentUpdated",e,t)})),!r)for(n in o)l[n]||dr(o[n],"unbind",t,t,a)}var cr=Object.create(null);function ur(t,e){var n,i,s=Object.create(null);if(!t)return s;for(n=0;n<t.length;n++){if(i=t[n],i.modifiers||(i.modifiers=cr),s[hr(i)]=i,e._setupState&&e._setupState.__sfc){var r=i.def||Si(e,"_setupState","v-"+i.name);i.def="function"===typeof r?{bind:r,update:r}:r}i.def=i.def||Si(e.$options,"directives",i.name,!0)}return s}function hr(t){return t.rawName||"".concat(t.name,".").concat(Object.keys(t.modifiers||{}).join("."))}function dr(t,e,n,i,s){var r=t.def&&t.def[e];if(r)try{r(n.elm,t,n,i,s)}catch(Ga){Ye(Ga,n.context,"directive ".concat(t.name," ").concat(e," hook"))}}var fr=[Ks,ar];function pr(t,e){var n=e.componentOptions;if((!a(n)||!1!==n.Ctor.options.inheritAttrs)&&(!r(t.data.attrs)||!r(e.data.attrs))){var i,s,l,c=e.elm,u=t.data.attrs||{},h=e.data.attrs||{};for(i in(a(h.__ob__)||o(h._v_attr_proxy))&&(h=e.data.attrs=L({},h)),h)s=h[i],l=u[i],l!==s&&_r(c,i,s,e.data.pre);for(i in(et||it)&&h.value!==u.value&&_r(c,"value",h.value),u)r(h[i])&&(bs(i)?c.removeAttributeNS(gs,ys(i)):ps(i)||c.removeAttribute(i))}}function _r(t,e,n,i){i||t.tagName.indexOf("-")>-1?mr(t,e,n):vs(e)?xs(n)?t.removeAttribute(e):(n="allowfullscreen"===e&&"EMBED"===t.tagName?"true":e,t.setAttribute(e,n)):ps(e)?t.setAttribute(e,ms(e,n)):bs(e)?xs(n)?t.removeAttributeNS(gs,ys(e)):t.setAttributeNS(gs,e,n):mr(t,e,n)}function mr(t,e,n){if(xs(n))t.removeAttribute(e);else{if(et&&!nt&&"TEXTAREA"===t.tagName&&"placeholder"===e&&""!==n&&!t.__ieph){var i=function(e){e.stopImmediatePropagation(),t.removeEventListener("input",i)};t.addEventListener("input",i),t.__ieph=!0}t.setAttribute(e,n)}}var vr={create:pr,update:pr};function gr(t,e){var n=e.elm,i=e.data,s=t.data;if(!(r(i.staticClass)&&r(i.class)&&(r(s)||r(s.staticClass)&&r(s.class)))){var o=ws(e),l=n._transitionClasses;a(l)&&(o=As(o,Cs(l))),o!==n._prevClass&&(n.setAttribute("class",o),n._prevClass=o)}}var br,yr={create:gr,update:gr},xr="__r",wr="__c";function Sr(t){if(a(t[xr])){var e=et?"change":"input";t[e]=[].concat(t[xr],t[e]||[]),delete t[xr]}a(t[wr])&&(t.change=[].concat(t[wr],t.change||[]),delete t[wr])}function kr(t,e,n){var i=br;return function s(){var r=e.apply(null,arguments);null!==r&&Mr(t,s,n,i)}}var Ar=Je&&!(at&&Number(at[1])<=53);function Cr(t,e,n,i){if(Ar){var s=Hn,r=e;e=r._wrapper=function(t){if(t.target===t.currentTarget||t.timeStamp>=s||t.timeStamp<=0||t.target.ownerDocument!==document)return r.apply(this,arguments)}}br.addEventListener(t,e,lt?{capture:n,passive:i}:n)}function Mr(t,e,n,i){(i||br).removeEventListener(t,e._wrapper||e,n)}function Er(t,e){if(!r(t.data.on)||!r(e.data.on)){var n=e.data.on||{},i=t.data.on||{};br=e.elm||t.elm,Sr(n),te(n,i,Cr,Mr,kr,e.context),br=void 0}}var Tr,Br={create:Er,update:Er,destroy:function(t){return Er(t,tr)}};function Rr(t,e){if(!r(t.data.domProps)||!r(e.data.domProps)){var n,i,s=e.elm,l=t.data.domProps||{},c=e.data.domProps||{};for(n in(a(c.__ob__)||o(c._v_attr_proxy))&&(c=e.data.domProps=L({},c)),l)n in c||(s[n]="");for(n in c){if(i=c[n],"textContent"===n||"innerHTML"===n){if(e.children&&(e.children.length=0),i===l[n])continue;1===s.childNodes.length&&s.removeChild(s.childNodes[0])}if("value"===n&&"PROGRESS"!==s.tagName){s._value=i;var u=r(i)?"":String(i);$r(s,u)&&(s.value=u)}else if("innerHTML"===n&&Rs(s.tagName)&&r(s.innerHTML)){Tr=Tr||document.createElement("div"),Tr.innerHTML="<svg>".concat(i,"</svg>");var h=Tr.firstChild;while(s.firstChild)s.removeChild(s.firstChild);while(h.firstChild)s.appendChild(h.firstChild)}else if(i!==l[n])try{s[n]=i}catch(Ga){}}}}function $r(t,e){return!t.composing&&("OPTION"===t.tagName||Ir(t,e)||Lr(t,e))}function Ir(t,e){var n=!0;try{n=document.activeElement!==t}catch(Ga){}return n&&t.value!==e}function Lr(t,e){var n=t.value,i=t._vModifiers;if(a(i)){if(i.number)return g(n)!==g(e);if(i.trim)return n.trim()!==e.trim()}return n!==e}var Or={create:Rr,update:Rr},Pr=k((function(t){var e={},n=/;(?![^(]*\))/g,i=/:(.+)/;return t.split(n).forEach((function(t){if(t){var n=t.split(i);n.length>1&&(e[n[0].trim()]=n[1].trim())}})),e}));function Vr(t){var e=Dr(t.style);return t.staticStyle?L(t.staticStyle,e):e}function Dr(t){return Array.isArray(t)?O(t):"string"===typeof t?Pr(t):t}function Nr(t,e){var n,i={};if(e){var s=t;while(s.componentInstance)s=s.componentInstance._vnode,s&&s.data&&(n=Vr(s.data))&&L(i,n)}(n=Vr(t.data))&&L(i,n);var r=t;while(r=r.parent)r.data&&(n=Vr(r.data))&&L(i,n);return i}var Fr,Hr=/^--/,jr=/\s*!important$/,Zr=function(t,e,n){if(Hr.test(e))t.style.setProperty(e,n);else if(jr.test(n))t.style.setProperty(T(e),n.replace(jr,""),"important");else{var i=qr(e);if(Array.isArray(n))for(var s=0,r=n.length;s<r;s++)t.style[i]=n[s];else t.style[i]=n}},zr=["Webkit","Moz","ms"],qr=k((function(t){if(Fr=Fr||document.createElement("div").style,t=C(t),"filter"!==t&&t in Fr)return t;for(var e=t.charAt(0).toUpperCase()+t.slice(1),n=0;n<zr.length;n++){var i=zr[n]+e;if(i in Fr)return i}}));function Ur(t,e){var n=e.data,i=t.data;if(!(r(n.staticStyle)&&r(n.style)&&r(i.staticStyle)&&r(i.style))){var s,o,l=e.elm,c=i.staticStyle,u=i.normalizedStyle||i.style||{},h=c||u,d=Dr(e.data.style)||{};e.data.normalizedStyle=a(d.__ob__)?L({},d):d;var f=Nr(e,!0);for(o in h)r(f[o])&&Zr(l,o,"");for(o in f)s=f[o],s!==h[o]&&Zr(l,o,null==s?"":s)}}var Xr={create:Ur,update:Ur},Yr=/\s+/;function Wr(t,e){if(e&&(e=e.trim()))if(t.classList)e.indexOf(" ")>-1?e.split(Yr).forEach((function(e){return t.classList.add(e)})):t.classList.add(e);else{var n=" ".concat(t.getAttribute("class")||""," ");n.indexOf(" "+e+" ")<0&&t.setAttribute("class",(n+e).trim())}}function Gr(t,e){if(e&&(e=e.trim()))if(t.classList)e.indexOf(" ")>-1?e.split(Yr).forEach((function(e){return t.classList.remove(e)})):t.classList.remove(e),t.classList.length||t.removeAttribute("class");else{var n=" ".concat(t.getAttribute("class")||""," "),i=" "+e+" ";while(n.indexOf(i)>=0)n=n.replace(i," ");n=n.trim(),n?t.setAttribute("class",n):t.removeAttribute("class")}}function Kr(t){if(t){if("object"===typeof t){var e={};return!1!==t.css&&L(e,Qr(t.name||"v")),L(e,t),e}return"string"===typeof t?Qr(t):void 0}}var Qr=k((function(t){return{enterClass:"".concat(t,"-enter"),enterToClass:"".concat(t,"-enter-to"),enterActiveClass:"".concat(t,"-enter-active"),leaveClass:"".concat(t,"-leave"),leaveToClass:"".concat(t,"-leave-to"),leaveActiveClass:"".concat(t,"-leave-active")}})),Jr=J&&!nt,ta="transition",ea="animation",na="transition",ia="transitionend",sa="animation",ra="animationend";Jr&&(void 0===window.ontransitionend&&void 0!==window.onwebkittransitionend&&(na="WebkitTransition",ia="webkitTransitionEnd"),void 0===window.onanimationend&&void 0!==window.onwebkitanimationend&&(sa="WebkitAnimation",ra="webkitAnimationEnd"));var aa=J?window.requestAnimationFrame?window.requestAnimationFrame.bind(window):setTimeout:function(t){return t()};function oa(t){aa((function(){aa(t)}))}function la(t,e){var n=t._transitionClasses||(t._transitionClasses=[]);n.indexOf(e)<0&&(n.push(e),Wr(t,e))}function ca(t,e){t._transitionClasses&&x(t._transitionClasses,e),Gr(t,e)}function ua(t,e,n){var i=da(t,e),s=i.type,r=i.timeout,a=i.propCount;if(!s)return n();var o=s===ta?ia:ra,l=0,c=function(){t.removeEventListener(o,u),n()},u=function(e){e.target===t&&++l>=a&&c()};setTimeout((function(){l<a&&c()}),r+1),t.addEventListener(o,u)}var ha=/\b(transform|all)(,|$)/;function da(t,e){var n,i=window.getComputedStyle(t),s=(i[na+"Delay"]||"").split(", "),r=(i[na+"Duration"]||"").split(", "),a=fa(s,r),o=(i[sa+"Delay"]||"").split(", "),l=(i[sa+"Duration"]||"").split(", "),c=fa(o,l),u=0,h=0;e===ta?a>0&&(n=ta,u=a,h=r.length):e===ea?c>0&&(n=ea,u=c,h=l.length):(u=Math.max(a,c),n=u>0?a>c?ta:ea:null,h=n?n===ta?r.length:l.length:0);var d=n===ta&&ha.test(i[na+"Property"]);return{type:n,timeout:u,propCount:h,hasTransform:d}}function fa(t,e){while(t.length<e.length)t=t.concat(t);return Math.max.apply(null,e.map((function(e,n){return pa(e)+pa(t[n])})))}function pa(t){return 1e3*Number(t.slice(0,-1).replace(",","."))}function _a(t,e){var n=t.elm;a(n._leaveCb)&&(n._leaveCb.cancelled=!0,n._leaveCb());var i=Kr(t.data.transition);if(!r(i)&&!a(n._enterCb)&&1===n.nodeType){var s=i.css,o=i.type,l=i.enterClass,c=i.enterToClass,d=i.enterActiveClass,f=i.appearClass,p=i.appearToClass,_=i.appearActiveClass,m=i.beforeEnter,v=i.enter,b=i.afterEnter,y=i.enterCancelled,x=i.beforeAppear,w=i.appear,S=i.afterAppear,k=i.appearCancelled,A=i.duration,C=kn,M=kn.$vnode;while(M&&M.parent)C=M.context,M=M.parent;var E=!C._isMounted||!t.isRootInsert;if(!E||w||""===w){var T=E&&f?f:l,B=E&&_?_:d,R=E&&p?p:c,$=E&&x||m,I=E&&u(w)?w:v,L=E&&S||b,O=E&&k||y,P=g(h(A)?A.enter:A);0;var V=!1!==s&&!nt,D=ga(I),N=n._enterCb=H((function(){V&&(ca(n,R),ca(n,B)),N.cancelled?(V&&ca(n,T),O&&O(n)):L&&L(n),n._enterCb=null}));t.data.show||ee(t,"insert",(function(){var e=n.parentNode,i=e&&e._pending&&e._pending[t.key];i&&i.tag===t.tag&&i.elm._leaveCb&&i.elm._leaveCb(),I&&I(n,N)})),$&&$(n),V&&(la(n,T),la(n,B),oa((function(){ca(n,T),N.cancelled||(la(n,R),D||(va(P)?setTimeout(N,P):ua(n,o,N)))}))),t.data.show&&(e&&e(),I&&I(n,N)),V||D||N()}}}function ma(t,e){var n=t.elm;a(n._enterCb)&&(n._enterCb.cancelled=!0,n._enterCb());var i=Kr(t.data.transition);if(r(i)||1!==n.nodeType)return e();if(!a(n._leaveCb)){var s=i.css,o=i.type,l=i.leaveClass,c=i.leaveToClass,u=i.leaveActiveClass,d=i.beforeLeave,f=i.leave,p=i.afterLeave,_=i.leaveCancelled,m=i.delayLeave,v=i.duration,b=!1!==s&&!nt,y=ga(f),x=g(h(v)?v.leave:v);0;var w=n._leaveCb=H((function(){n.parentNode&&n.parentNode._pending&&(n.parentNode._pending[t.key]=null),b&&(ca(n,c),ca(n,u)),w.cancelled?(b&&ca(n,l),_&&_(n)):(e(),p&&p(n)),n._leaveCb=null}));m?m(S):S()}function S(){w.cancelled||(!t.data.show&&n.parentNode&&((n.parentNode._pending||(n.parentNode._pending={}))[t.key]=t),d&&d(n),b&&(la(n,l),la(n,u),oa((function(){ca(n,l),w.cancelled||(la(n,c),y||(va(x)?setTimeout(w,x):ua(n,o,w)))}))),f&&f(n,w),b||y||w())}}function va(t){return"number"===typeof t&&!isNaN(t)}function ga(t){if(r(t))return!1;var e=t.fns;return a(e)?ga(Array.isArray(e)?e[0]:e):(t._length||t.length)>1}function ba(t,e){!0!==e.data.show&&_a(e)}var ya=J?{create:ba,activate:ba,remove:function(t,e){!0!==t.data.show?ma(t,e):e()}}:{},xa=[vr,yr,Br,Or,Xr,ya],wa=xa.concat(fr),Sa=rr({nodeOps:Gs,modules:wa});nt&&document.addEventListener("selectionchange",(function(){var t=document.activeElement;t&&t.vmodel&&Ra(t,"input")}));var ka={inserted:function(t,e,n,i){"select"===n.tag?(i.elm&&!i.elm._vOptions?ee(n,"postpatch",(function(){ka.componentUpdated(t,e,n)})):Aa(t,e,n.context),t._vOptions=[].map.call(t.options,Ea)):("textarea"===n.tag||Ps(t.type))&&(t._vModifiers=e.modifiers,e.modifiers.lazy||(t.addEventListener("compositionstart",Ta),t.addEventListener("compositionend",Ba),t.addEventListener("change",Ba),nt&&(t.vmodel=!0)))},componentUpdated:function(t,e,n){if("select"===n.tag){Aa(t,e,n.context);var i=t._vOptions,s=t._vOptions=[].map.call(t.options,Ea);if(s.some((function(t,e){return!N(t,i[e])}))){var r=t.multiple?e.value.some((function(t){return Ma(t,s)})):e.value!==e.oldValue&&Ma(e.value,s);r&&Ra(t,"change")}}}};function Aa(t,e,n){Ca(t,e,n),(et||it)&&setTimeout((function(){Ca(t,e,n)}),0)}function Ca(t,e,n){var i=e.value,s=t.multiple;if(!s||Array.isArray(i)){for(var r,a,o=0,l=t.options.length;o<l;o++)if(a=t.options[o],s)r=F(i,Ea(a))>-1,a.selected!==r&&(a.selected=r);else if(N(Ea(a),i))return void(t.selectedIndex!==o&&(t.selectedIndex=o));s||(t.selectedIndex=-1)}}function Ma(t,e){return e.every((function(e){return!N(e,t)}))}function Ea(t){return"_value"in t?t._value:t.value}function Ta(t){t.target.composing=!0}function Ba(t){t.target.composing&&(t.target.composing=!1,Ra(t.target,"input"))}function Ra(t,e){var n=document.createEvent("HTMLEvents");n.initEvent(e,!0,!0),t.dispatchEvent(n)}function $a(t){return!t.componentInstance||t.data&&t.data.transition?t:$a(t.componentInstance._vnode)}var Ia={bind:function(t,e,n){var i=e.value;n=$a(n);var s=n.data&&n.data.transition,r=t.__vOriginalDisplay="none"===t.style.display?"":t.style.display;i&&s?(n.data.show=!0,_a(n,(function(){t.style.display=r}))):t.style.display=i?r:"none"},update:function(t,e,n){var i=e.value,s=e.oldValue;if(!i!==!s){n=$a(n);var r=n.data&&n.data.transition;r?(n.data.show=!0,i?_a(n,(function(){t.style.display=t.__vOriginalDisplay})):ma(n,(function(){t.style.display="none"}))):t.style.display=i?t.__vOriginalDisplay:"none"}},unbind:function(t,e,n,i,s){s||(t.style.display=t.__vOriginalDisplay)}},La={model:ka,show:Ia},Oa={name:String,appear:Boolean,css:Boolean,mode:String,type:String,enterClass:String,leaveClass:String,enterToClass:String,leaveToClass:String,enterActiveClass:String,leaveActiveClass:String,appearClass:String,appearActiveClass:String,appearToClass:String,duration:[Number,String,Object]};function Pa(t){var e=t&&t.componentOptions;return e&&e.Ctor.options.abstract?Pa(He(e.children)):t}function Va(t){var e={},n=t.$options;for(var i in n.propsData)e[i]=t[i];var s=n._parentListeners;for(var i in s)e[C(i)]=s[i];return e}function Da(t,e){if(/\d-keep-alive$/.test(e.tag))return t("keep-alive",{props:e.componentOptions.propsData})}function Na(t){while(t=t.parent)if(t.data.transition)return!0}function Fa(t,e){return e.key===t.key&&e.tag===t.tag}var Ha=function(t){return t.tag||Ae(t)},ja=function(t){return"show"===t.name},Za={name:"transition",props:Oa,abstract:!0,render:function(t){var e=this,n=this.$slots.default;if(n&&(n=n.filter(Ha),n.length)){0;var i=this.mode;0;var s=n[0];if(Na(this.$vnode))return s;var r=Pa(s);if(!r)return s;if(this._leaving)return Da(t,s);var a="__transition-".concat(this._uid,"-");r.key=null==r.key?r.isComment?a+"comment":a+r.tag:c(r.key)?0===String(r.key).indexOf(a)?r.key:a+r.key:r.key;var o=(r.data||(r.data={})).transition=Va(this),l=this._vnode,u=Pa(l);if(r.data.directives&&r.data.directives.some(ja)&&(r.data.show=!0),u&&u.data&&!Fa(r,u)&&!Ae(u)&&(!u.componentInstance||!u.componentInstance._vnode.isComment)){var h=u.data.transition=L({},o);if("out-in"===i)return this._leaving=!0,ee(h,"afterLeave",(function(){e._leaving=!1,e.$forceUpdate()})),Da(t,s);if("in-out"===i){if(Ae(r))return l;var d,f=function(){d()};ee(o,"afterEnter",f),ee(o,"enterCancelled",f),ee(h,"delayLeave",(function(t){d=t}))}}return s}}},za=L({tag:String,moveClass:String},Oa);delete za.mode;var qa={props:za,beforeMount:function(){var t=this,e=this._update;this._update=function(n,i){var s=An(t);t.__patch__(t._vnode,t.kept,!1,!0),t._vnode=t.kept,s(),e.call(t,n,i)}},render:function(t){for(var e=this.tag||this.$vnode.data.tag||"span",n=Object.create(null),i=this.prevChildren=this.children,s=this.$slots.default||[],r=this.children=[],a=Va(this),o=0;o<s.length;o++){var l=s[o];if(l.tag)if(null!=l.key&&0!==String(l.key).indexOf("__vlist"))r.push(l),n[l.key]=l,(l.data||(l.data={})).transition=a;else;}if(i){var c=[],u=[];for(o=0;o<i.length;o++){l=i[o];l.data.transition=a,l.data.pos=l.elm.getBoundingClientRect(),n[l.key]?c.push(l):u.push(l)}this.kept=t(e,null,c),this.removed=u}return t(e,null,r)},updated:function(){var t=this.prevChildren,e=this.moveClass||(this.name||"v")+"-move";t.length&&this.hasMove(t[0].elm,e)&&(t.forEach(Ua),t.forEach(Xa),t.forEach(Ya),this._reflow=document.body.offsetHeight,t.forEach((function(t){if(t.data.moved){var n=t.elm,i=n.style;la(n,e),i.transform=i.WebkitTransform=i.transitionDuration="",n.addEventListener(ia,n._moveCb=function t(i){i&&i.target!==n||i&&!/transform$/.test(i.propertyName)||(n.removeEventListener(ia,t),n._moveCb=null,ca(n,e))})}})))},methods:{hasMove:function(t,e){if(!Jr)return!1;if(this._hasMove)return this._hasMove;var n=t.cloneNode();t._transitionClasses&&t._transitionClasses.forEach((function(t){Gr(n,t)})),Wr(n,e),n.style.display="none",this.$el.appendChild(n);var i=da(n);return this.$el.removeChild(n),this._hasMove=i.hasTransform}}};function Ua(t){t.elm._moveCb&&t.elm._moveCb(),t.elm._enterCb&&t.elm._enterCb()}function Xa(t){t.data.newPos=t.elm.getBoundingClientRect()}function Ya(t){var e=t.data.pos,n=t.data.newPos,i=e.left-n.left,s=e.top-n.top;if(i||s){t.data.moved=!0;var r=t.elm.style;r.transform=r.WebkitTransform="translate(".concat(i,"px,").concat(s,"px)"),r.transitionDuration="0s"}}var Wa={Transition:Za,TransitionGroup:qa};Gi.config.mustUseProp=fs,Gi.config.isReservedTag=$s,Gi.config.isReservedAttr=hs,Gi.config.getTagNamespace=Is,Gi.config.isUnknownElement=Os,L(Gi.options.directives,La),L(Gi.options.components,Wa),Gi.prototype.__patch__=J?Sa:P,Gi.prototype.$mount=function(t,e){return t=t&&J?Vs(t):void 0,En(this,t,e)},J&&setTimeout((function(){U.devtools&&ht&&ht.emit("init",Gi)}),0)},6154:function(t,e,n){"use strict";function i(t,e){return function(){return t.apply(e,arguments)}}n.d(e,{Z:function(){return De}});const{toString:s}=Object.prototype,{getPrototypeOf:r}=Object,a=(t=>e=>{const n=s.call(e);return t[n]||(t[n]=n.slice(8,-1).toLowerCase())})(Object.create(null)),o=t=>(t=t.toLowerCase(),e=>a(e)===t),l=t=>e=>typeof e===t,{isArray:c}=Array,u=l("undefined");function h(t){return null!==t&&!u(t)&&null!==t.constructor&&!u(t.constructor)&&_(t.constructor.isBuffer)&&t.constructor.isBuffer(t)}const d=o("ArrayBuffer");function f(t){let e;return e="undefined"!==typeof ArrayBuffer&&ArrayBuffer.isView?ArrayBuffer.isView(t):t&&t.buffer&&d(t.buffer),e}const p=l("string"),_=l("function"),m=l("number"),v=t=>null!==t&&"object"===typeof t,g=t=>!0===t||!1===t,b=t=>{if("object"!==a(t))return!1;const e=r(t);return(null===e||e===Object.prototype||null===Object.getPrototypeOf(e))&&!(Symbol.toStringTag in t)&&!(Symbol.iterator in t)},y=o("Date"),x=o("File"),w=o("Blob"),S=o("FileList"),k=t=>v(t)&&_(t.pipe),A=t=>{let e;return t&&("function"===typeof FormData&&t instanceof FormData||_(t.append)&&("formdata"===(e=a(t))||"object"===e&&_(t.toString)&&"[object FormData]"===t.toString()))},C=o("URLSearchParams"),M=t=>t.trim?t.trim():t.replace(/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,"");function E(t,e,{allOwnKeys:n=!1}={}){if(null===t||"undefined"===typeof t)return;let i,s;if("object"!==typeof t&&(t=[t]),c(t))for(i=0,s=t.length;i<s;i++)e.call(null,t[i],i,t);else{const s=n?Object.getOwnPropertyNames(t):Object.keys(t),r=s.length;let a;for(i=0;i<r;i++)a=s[i],e.call(null,t[a],a,t)}}function T(t,e){e=e.toLowerCase();const n=Object.keys(t);let i,s=n.length;while(s-- >0)if(i=n[s],e===i.toLowerCase())return i;return null}const B=(()=>"undefined"!==typeof globalThis?globalThis:"undefined"!==typeof self?self:"undefined"!==typeof window?window:global)(),R=t=>!u(t)&&t!==B;function $(){const{caseless:t}=R(this)&&this||{},e={},n=(n,i)=>{const s=t&&T(e,i)||i;b(e[s])&&b(n)?e[s]=$(e[s],n):b(n)?e[s]=$({},n):c(n)?e[s]=n.slice():e[s]=n};for(let i=0,s=arguments.length;i<s;i++)arguments[i]&&E(arguments[i],n);return e}const I=(t,e,n,{allOwnKeys:s}={})=>(E(e,((e,s)=>{n&&_(e)?t[s]=i(e,n):t[s]=e}),{allOwnKeys:s}),t),L=t=>(65279===t.charCodeAt(0)&&(t=t.slice(1)),t),O=(t,e,n,i)=>{t.prototype=Object.create(e.prototype,i),t.prototype.constructor=t,Object.defineProperty(t,"super",{value:e.prototype}),n&&Object.assign(t.prototype,n)},P=(t,e,n,i)=>{let s,a,o;const l={};if(e=e||{},null==t)return e;do{s=Object.getOwnPropertyNames(t),a=s.length;while(a-- >0)o=s[a],i&&!i(o,t,e)||l[o]||(e[o]=t[o],l[o]=!0);t=!1!==n&&r(t)}while(t&&(!n||n(t,e))&&t!==Object.prototype);return e},V=(t,e,n)=>{t=String(t),(void 0===n||n>t.length)&&(n=t.length),n-=e.length;const i=t.indexOf(e,n);return-1!==i&&i===n},D=t=>{if(!t)return null;if(c(t))return t;let e=t.length;if(!m(e))return null;const n=new Array(e);while(e-- >0)n[e]=t[e];return n},N=(t=>e=>t&&e instanceof t)("undefined"!==typeof Uint8Array&&r(Uint8Array)),F=(t,e)=>{const n=t&&t[Symbol.iterator],i=n.call(t);let s;while((s=i.next())&&!s.done){const n=s.value;e.call(t,n[0],n[1])}},H=(t,e)=>{let n;const i=[];while(null!==(n=t.exec(e)))i.push(n);return i},j=o("HTMLFormElement"),Z=t=>t.toLowerCase().replace(/[-_\s]([a-z\d])(\w*)/g,(function(t,e,n){return e.toUpperCase()+n})),z=(({hasOwnProperty:t})=>(e,n)=>t.call(e,n))(Object.prototype),q=o("RegExp"),U=(t,e)=>{const n=Object.getOwnPropertyDescriptors(t),i={};E(n,((n,s)=>{!1!==e(n,s,t)&&(i[s]=n)})),Object.defineProperties(t,i)},X=t=>{U(t,((e,n)=>{if(_(t)&&-1!==["arguments","caller","callee"].indexOf(n))return!1;const i=t[n];_(i)&&(e.enumerable=!1,"writable"in e?e.writable=!1:e.set||(e.set=()=>{throw Error("Can not rewrite read-only method '"+n+"'")}))}))},Y=(t,e)=>{const n={},i=t=>{t.forEach((t=>{n[t]=!0}))};return c(t)?i(t):i(String(t).split(e)),n},W=()=>{},G=(t,e)=>(t=+t,Number.isFinite(t)?t:e),K="abcdefghijklmnopqrstuvwxyz",Q="0123456789",J={DIGIT:Q,ALPHA:K,ALPHA_DIGIT:K+K.toUpperCase()+Q},tt=(t=16,e=J.ALPHA_DIGIT)=>{let n="";const{length:i}=e;while(t--)n+=e[Math.random()*i|0];return n};function et(t){return!!(t&&_(t.append)&&"FormData"===t[Symbol.toStringTag]&&t[Symbol.iterator])}const nt=t=>{const e=new Array(10),n=(t,i)=>{if(v(t)){if(e.indexOf(t)>=0)return;if(!("toJSON"in t)){e[i]=t;const s=c(t)?[]:{};return E(t,((t,e)=>{const r=n(t,i+1);!u(r)&&(s[e]=r)})),e[i]=void 0,s}}return t};return n(t,0)},it=o("AsyncFunction"),st=t=>t&&(v(t)||_(t))&&_(t.then)&&_(t.catch);var rt={isArray:c,isArrayBuffer:d,isBuffer:h,isFormData:A,isArrayBufferView:f,isString:p,isNumber:m,isBoolean:g,isObject:v,isPlainObject:b,isUndefined:u,isDate:y,isFile:x,isBlob:w,isRegExp:q,isFunction:_,isStream:k,isURLSearchParams:C,isTypedArray:N,isFileList:S,forEach:E,merge:$,extend:I,trim:M,stripBOM:L,inherits:O,toFlatObject:P,kindOf:a,kindOfTest:o,endsWith:V,toArray:D,forEachEntry:F,matchAll:H,isHTMLForm:j,hasOwnProperty:z,hasOwnProp:z,reduceDescriptors:U,freezeMethods:X,toObjectSet:Y,toCamelCase:Z,noop:W,toFiniteNumber:G,findKey:T,global:B,isContextDefined:R,ALPHABET:J,generateString:tt,isSpecCompliantForm:et,toJSONObject:nt,isAsyncFn:it,isThenable:st};function at(t,e,n,i,s){Error.call(this),Error.captureStackTrace?Error.captureStackTrace(this,this.constructor):this.stack=(new Error).stack,this.message=t,this.name="AxiosError",e&&(this.code=e),n&&(this.config=n),i&&(this.request=i),s&&(this.response=s)}rt.inherits(at,Error,{toJSON:function(){return{message:this.message,name:this.name,description:this.description,number:this.number,fileName:this.fileName,lineNumber:this.lineNumber,columnNumber:this.columnNumber,stack:this.stack,config:rt.toJSONObject(this.config),code:this.code,status:this.response&&this.response.status?this.response.status:null}}});const ot=at.prototype,lt={};["ERR_BAD_OPTION_VALUE","ERR_BAD_OPTION","ECONNABORTED","ETIMEDOUT","ERR_NETWORK","ERR_FR_TOO_MANY_REDIRECTS","ERR_DEPRECATED","ERR_BAD_RESPONSE","ERR_BAD_REQUEST","ERR_CANCELED","ERR_NOT_SUPPORT","ERR_INVALID_URL"].forEach((t=>{lt[t]={value:t}})),Object.defineProperties(at,lt),Object.defineProperty(ot,"isAxiosError",{value:!0}),at.from=(t,e,n,i,s,r)=>{const a=Object.create(ot);return rt.toFlatObject(t,a,(function(t){return t!==Error.prototype}),(t=>"isAxiosError"!==t)),at.call(a,t.message,e,n,i,s),a.cause=t,a.name=t.name,r&&Object.assign(a,r),a};var ct=at,ut=null;function ht(t){return rt.isPlainObject(t)||rt.isArray(t)}function dt(t){return rt.endsWith(t,"[]")?t.slice(0,-2):t}function ft(t,e,n){return t?t.concat(e).map((function(t,e){return t=dt(t),!n&&e?"["+t+"]":t})).join(n?".":""):e}function pt(t){return rt.isArray(t)&&!t.some(ht)}const _t=rt.toFlatObject(rt,{},null,(function(t){return/^is[A-Z]/.test(t)}));function mt(t,e,n){if(!rt.isObject(t))throw new TypeError("target must be an object");e=e||new(ut||FormData),n=rt.toFlatObject(n,{metaTokens:!0,dots:!1,indexes:!1},!1,(function(t,e){return!rt.isUndefined(e[t])}));const i=n.metaTokens,s=n.visitor||u,r=n.dots,a=n.indexes,o=n.Blob||"undefined"!==typeof Blob&&Blob,l=o&&rt.isSpecCompliantForm(e);if(!rt.isFunction(s))throw new TypeError("visitor must be a function");function c(t){if(null===t)return"";if(rt.isDate(t))return t.toISOString();if(!l&&rt.isBlob(t))throw new ct("Blob is not supported. Use a Buffer instead.");return rt.isArrayBuffer(t)||rt.isTypedArray(t)?l&&"function"===typeof Blob?new Blob([t]):Buffer.from(t):t}function u(t,n,s){let o=t;if(t&&!s&&"object"===typeof t)if(rt.endsWith(n,"{}"))n=i?n:n.slice(0,-2),t=JSON.stringify(t);else if(rt.isArray(t)&&pt(t)||(rt.isFileList(t)||rt.endsWith(n,"[]"))&&(o=rt.toArray(t)))return n=dt(n),o.forEach((function(t,i){!rt.isUndefined(t)&&null!==t&&e.append(!0===a?ft([n],i,r):null===a?n:n+"[]",c(t))})),!1;return!!ht(t)||(e.append(ft(s,n,r),c(t)),!1)}const h=[],d=Object.assign(_t,{defaultVisitor:u,convertValue:c,isVisitable:ht});function f(t,n){if(!rt.isUndefined(t)){if(-1!==h.indexOf(t))throw Error("Circular reference detected in "+n.join("."));h.push(t),rt.forEach(t,(function(t,i){const r=!(rt.isUndefined(t)||null===t)&&s.call(e,t,rt.isString(i)?i.trim():i,n,d);!0===r&&f(t,n?n.concat(i):[i])})),h.pop()}}if(!rt.isObject(t))throw new TypeError("data must be an object");return f(t),e}var vt=mt;function gt(t){const e={"!":"%21","'":"%27","(":"%28",")":"%29","~":"%7E","%20":"+","%00":"\0"};return encodeURIComponent(t).replace(/[!'()~]|%20|%00/g,(function(t){return e[t]}))}function bt(t,e){this._pairs=[],t&&vt(t,this,e)}const yt=bt.prototype;yt.append=function(t,e){this._pairs.push([t,e])},yt.toString=function(t){const e=t?function(e){return t.call(this,e,gt)}:gt;return this._pairs.map((function(t){return e(t[0])+"="+e(t[1])}),"").join("&")};var xt=bt;function wt(t){return encodeURIComponent(t).replace(/%3A/gi,":").replace(/%24/g,"$").replace(/%2C/gi,",").replace(/%20/g,"+").replace(/%5B/gi,"[").replace(/%5D/gi,"]")}function St(t,e,n){if(!e)return t;const i=n&&n.encode||wt,s=n&&n.serialize;let r;if(r=s?s(e,n):rt.isURLSearchParams(e)?e.toString():new xt(e,n).toString(i),r){const e=t.indexOf("#");-1!==e&&(t=t.slice(0,e)),t+=(-1===t.indexOf("?")?"?":"&")+r}return t}class kt{constructor(){this.handlers=[]}use(t,e,n){return this.handlers.push({fulfilled:t,rejected:e,synchronous:!!n&&n.synchronous,runWhen:n?n.runWhen:null}),this.handlers.length-1}eject(t){this.handlers[t]&&(this.handlers[t]=null)}clear(){this.handlers&&(this.handlers=[])}forEach(t){rt.forEach(this.handlers,(function(e){null!==e&&t(e)}))}}var At=kt,Ct={silentJSONParsing:!0,forcedJSONParsing:!0,clarifyTimeoutError:!1},Mt="undefined"!==typeof URLSearchParams?URLSearchParams:xt,Et="undefined"!==typeof FormData?FormData:null,Tt="undefined"!==typeof Blob?Blob:null;const Bt=(()=>{let t;return("undefined"===typeof navigator||"ReactNative"!==(t=navigator.product)&&"NativeScript"!==t&&"NS"!==t)&&("undefined"!==typeof window&&"undefined"!==typeof document)})(),Rt=(()=>"undefined"!==typeof WorkerGlobalScope&&self instanceof WorkerGlobalScope&&"function"===typeof self.importScripts)();var $t={isBrowser:!0,classes:{URLSearchParams:Mt,FormData:Et,Blob:Tt},isStandardBrowserEnv:Bt,isStandardBrowserWebWorkerEnv:Rt,protocols:["http","https","file","blob","url","data"]};function It(t,e){return vt(t,new $t.classes.URLSearchParams,Object.assign({visitor:function(t,e,n,i){return $t.isNode&&rt.isBuffer(t)?(this.append(e,t.toString("base64")),!1):i.defaultVisitor.apply(this,arguments)}},e))}function Lt(t){return rt.matchAll(/\w+|\[(\w*)]/g,t).map((t=>"[]"===t[0]?"":t[1]||t[0]))}function Ot(t){const e={},n=Object.keys(t);let i;const s=n.length;let r;for(i=0;i<s;i++)r=n[i],e[r]=t[r];return e}function Pt(t){function e(t,n,i,s){let r=t[s++];const a=Number.isFinite(+r),o=s>=t.length;if(r=!r&&rt.isArray(i)?i.length:r,o)return rt.hasOwnProp(i,r)?i[r]=[i[r],n]:i[r]=n,!a;i[r]&&rt.isObject(i[r])||(i[r]=[]);const l=e(t,n,i[r],s);return l&&rt.isArray(i[r])&&(i[r]=Ot(i[r])),!a}if(rt.isFormData(t)&&rt.isFunction(t.entries)){const n={};return rt.forEachEntry(t,((t,i)=>{e(Lt(t),i,n,0)})),n}return null}var Vt=Pt;const Dt={"Content-Type":void 0};function Nt(t,e,n){if(rt.isString(t))try{return(e||JSON.parse)(t),rt.trim(t)}catch(i){if("SyntaxError"!==i.name)throw i}return(n||JSON.stringify)(t)}const Ft={transitional:Ct,adapter:["xhr","http"],transformRequest:[function(t,e){const n=e.getContentType()||"",i=n.indexOf("application/json")>-1,s=rt.isObject(t);s&&rt.isHTMLForm(t)&&(t=new FormData(t));const r=rt.isFormData(t);if(r)return i&&i?JSON.stringify(Vt(t)):t;if(rt.isArrayBuffer(t)||rt.isBuffer(t)||rt.isStream(t)||rt.isFile(t)||rt.isBlob(t))return t;if(rt.isArrayBufferView(t))return t.buffer;if(rt.isURLSearchParams(t))return e.setContentType("application/x-www-form-urlencoded;charset=utf-8",!1),t.toString();let a;if(s){if(n.indexOf("application/x-www-form-urlencoded")>-1)return It(t,this.formSerializer).toString();if((a=rt.isFileList(t))||n.indexOf("multipart/form-data")>-1){const e=this.env&&this.env.FormData;return vt(a?{"files[]":t}:t,e&&new e,this.formSerializer)}}return s||i?(e.setContentType("application/json",!1),Nt(t)):t}],transformResponse:[function(t){const e=this.transitional||Ft.transitional,n=e&&e.forcedJSONParsing,i="json"===this.responseType;if(t&&rt.isString(t)&&(n&&!this.responseType||i)){const n=e&&e.silentJSONParsing,r=!n&&i;try{return JSON.parse(t)}catch(s){if(r){if("SyntaxError"===s.name)throw ct.from(s,ct.ERR_BAD_RESPONSE,this,null,this.response);throw s}}}return t}],timeout:0,xsrfCookieName:"XSRF-TOKEN",xsrfHeaderName:"X-XSRF-TOKEN",maxContentLength:-1,maxBodyLength:-1,env:{FormData:$t.classes.FormData,Blob:$t.classes.Blob},validateStatus:function(t){return t>=200&&t<300},headers:{common:{Accept:"application/json, text/plain, */*"}}};rt.forEach(["delete","get","head"],(function(t){Ft.headers[t]={}})),rt.forEach(["post","put","patch"],(function(t){Ft.headers[t]=rt.merge(Dt)}));var Ht=Ft;const jt=rt.toObjectSet(["age","authorization","content-length","content-type","etag","expires","from","host","if-modified-since","if-unmodified-since","last-modified","location","max-forwards","proxy-authorization","referer","retry-after","user-agent"]);var Zt=t=>{const e={};let n,i,s;return t&&t.split("\n").forEach((function(t){s=t.indexOf(":"),n=t.substring(0,s).trim().toLowerCase(),i=t.substring(s+1).trim(),!n||e[n]&&jt[n]||("set-cookie"===n?e[n]?e[n].push(i):e[n]=[i]:e[n]=e[n]?e[n]+", "+i:i)})),e};const zt=Symbol("internals");function qt(t){return t&&String(t).trim().toLowerCase()}function Ut(t){return!1===t||null==t?t:rt.isArray(t)?t.map(Ut):String(t)}function Xt(t){const e=Object.create(null),n=/([^\s,;=]+)\s*(?:=\s*([^,;]+))?/g;let i;while(i=n.exec(t))e[i[1]]=i[2];return e}const Yt=t=>/^[-_a-zA-Z0-9^`|~,!#$%&'*+.]+$/.test(t.trim());function Wt(t,e,n,i,s){return rt.isFunction(i)?i.call(this,e,n):(s&&(e=n),rt.isString(e)?rt.isString(i)?-1!==e.indexOf(i):rt.isRegExp(i)?i.test(e):void 0:void 0)}function Gt(t){return t.trim().toLowerCase().replace(/([a-z\d])(\w*)/g,((t,e,n)=>e.toUpperCase()+n))}function Kt(t,e){const n=rt.toCamelCase(" "+e);["get","set","has"].forEach((i=>{Object.defineProperty(t,i+n,{value:function(t,n,s){return this[i].call(this,e,t,n,s)},configurable:!0})}))}class Qt{constructor(t){t&&this.set(t)}set(t,e,n){const i=this;function s(t,e,n){const s=qt(e);if(!s)throw new Error("header name must be a non-empty string");const r=rt.findKey(i,s);(!r||void 0===i[r]||!0===n||void 0===n&&!1!==i[r])&&(i[r||e]=Ut(t))}const r=(t,e)=>rt.forEach(t,((t,n)=>s(t,n,e)));return rt.isPlainObject(t)||t instanceof this.constructor?r(t,e):rt.isString(t)&&(t=t.trim())&&!Yt(t)?r(Zt(t),e):null!=t&&s(e,t,n),this}get(t,e){if(t=qt(t),t){const n=rt.findKey(this,t);if(n){const t=this[n];if(!e)return t;if(!0===e)return Xt(t);if(rt.isFunction(e))return e.call(this,t,n);if(rt.isRegExp(e))return e.exec(t);throw new TypeError("parser must be boolean|regexp|function")}}}has(t,e){if(t=qt(t),t){const n=rt.findKey(this,t);return!(!n||void 0===this[n]||e&&!Wt(this,this[n],n,e))}return!1}delete(t,e){const n=this;let i=!1;function s(t){if(t=qt(t),t){const s=rt.findKey(n,t);!s||e&&!Wt(n,n[s],s,e)||(delete n[s],i=!0)}}return rt.isArray(t)?t.forEach(s):s(t),i}clear(t){const e=Object.keys(this);let n=e.length,i=!1;while(n--){const s=e[n];t&&!Wt(this,this[s],s,t,!0)||(delete this[s],i=!0)}return i}normalize(t){const e=this,n={};return rt.forEach(this,((i,s)=>{const r=rt.findKey(n,s);if(r)return e[r]=Ut(i),void delete e[s];const a=t?Gt(s):String(s).trim();a!==s&&delete e[s],e[a]=Ut(i),n[a]=!0})),this}concat(...t){return this.constructor.concat(this,...t)}toJSON(t){const e=Object.create(null);return rt.forEach(this,((n,i)=>{null!=n&&!1!==n&&(e[i]=t&&rt.isArray(n)?n.join(", "):n)})),e}[Symbol.iterator](){return Object.entries(this.toJSON())[Symbol.iterator]()}toString(){return Object.entries(this.toJSON()).map((([t,e])=>t+": "+e)).join("\n")}get[Symbol.toStringTag](){return"AxiosHeaders"}static from(t){return t instanceof this?t:new this(t)}static concat(t,...e){const n=new this(t);return e.forEach((t=>n.set(t))),n}static accessor(t){const e=this[zt]=this[zt]={accessors:{}},n=e.accessors,i=this.prototype;function s(t){const e=qt(t);n[e]||(Kt(i,t),n[e]=!0)}return rt.isArray(t)?t.forEach(s):s(t),this}}Qt.accessor(["Content-Type","Content-Length","Accept","Accept-Encoding","User-Agent","Authorization"]),rt.freezeMethods(Qt.prototype),rt.freezeMethods(Qt);var Jt=Qt;function te(t,e){const n=this||Ht,i=e||n,s=Jt.from(i.headers);let r=i.data;return rt.forEach(t,(function(t){r=t.call(n,r,s.normalize(),e?e.status:void 0)})),s.normalize(),r}function ee(t){return!(!t||!t.__CANCEL__)}function ne(t,e,n){ct.call(this,null==t?"canceled":t,ct.ERR_CANCELED,e,n),this.name="CanceledError"}rt.inherits(ne,ct,{__CANCEL__:!0});var ie=ne;function se(t,e,n){const i=n.config.validateStatus;n.status&&i&&!i(n.status)?e(new ct("Request failed with status code "+n.status,[ct.ERR_BAD_REQUEST,ct.ERR_BAD_RESPONSE][Math.floor(n.status/100)-4],n.config,n.request,n)):t(n)}var re=$t.isStandardBrowserEnv?function(){return{write:function(t,e,n,i,s,r){const a=[];a.push(t+"="+encodeURIComponent(e)),rt.isNumber(n)&&a.push("expires="+new Date(n).toGMTString()),rt.isString(i)&&a.push("path="+i),rt.isString(s)&&a.push("domain="+s),!0===r&&a.push("secure"),document.cookie=a.join("; ")},read:function(t){const e=document.cookie.match(new RegExp("(^|;\\s*)("+t+")=([^;]*)"));return e?decodeURIComponent(e[3]):null},remove:function(t){this.write(t,"",Date.now()-864e5)}}}():function(){return{write:function(){},read:function(){return null},remove:function(){}}}();function ae(t){return/^([a-z][a-z\d+\-.]*:)?\/\//i.test(t)}function oe(t,e){return e?t.replace(/\/+$/,"")+"/"+e.replace(/^\/+/,""):t}function le(t,e){return t&&!ae(e)?oe(t,e):e}var ce=$t.isStandardBrowserEnv?function(){const t=/(msie|trident)/i.test(navigator.userAgent),e=document.createElement("a");let n;function i(n){let i=n;return t&&(e.setAttribute("href",i),i=e.href),e.setAttribute("href",i),{href:e.href,protocol:e.protocol?e.protocol.replace(/:$/,""):"",host:e.host,search:e.search?e.search.replace(/^\?/,""):"",hash:e.hash?e.hash.replace(/^#/,""):"",hostname:e.hostname,port:e.port,pathname:"/"===e.pathname.charAt(0)?e.pathname:"/"+e.pathname}}return n=i(window.location.href),function(t){const e=rt.isString(t)?i(t):t;return e.protocol===n.protocol&&e.host===n.host}}():function(){return function(){return!0}}();function ue(t){const e=/^([-+\w]{1,25})(:?\/\/|:)/.exec(t);return e&&e[1]||""}function he(t,e){t=t||10;const n=new Array(t),i=new Array(t);let s,r=0,a=0;return e=void 0!==e?e:1e3,function(o){const l=Date.now(),c=i[a];s||(s=l),n[r]=o,i[r]=l;let u=a,h=0;while(u!==r)h+=n[u++],u%=t;if(r=(r+1)%t,r===a&&(a=(a+1)%t),l-s<e)return;const d=c&&l-c;return d?Math.round(1e3*h/d):void 0}}var de=he;function fe(t,e){let n=0;const i=de(50,250);return s=>{const r=s.loaded,a=s.lengthComputable?s.total:void 0,o=r-n,l=i(o),c=r<=a;n=r;const u={loaded:r,total:a,progress:a?r/a:void 0,bytes:o,rate:l||void 0,estimated:l&&a&&c?(a-r)/l:void 0,event:s};u[e?"download":"upload"]=!0,t(u)}}const pe="undefined"!==typeof XMLHttpRequest;var _e=pe&&function(t){return new Promise((function(e,n){let i=t.data;const s=Jt.from(t.headers).normalize(),r=t.responseType;let a;function o(){t.cancelToken&&t.cancelToken.unsubscribe(a),t.signal&&t.signal.removeEventListener("abort",a)}rt.isFormData(i)&&($t.isStandardBrowserEnv||$t.isStandardBrowserWebWorkerEnv?s.setContentType(!1):s.setContentType("multipart/form-data;",!1));let l=new XMLHttpRequest;if(t.auth){const e=t.auth.username||"",n=t.auth.password?unescape(encodeURIComponent(t.auth.password)):"";s.set("Authorization","Basic "+btoa(e+":"+n))}const c=le(t.baseURL,t.url);function u(){if(!l)return;const i=Jt.from("getAllResponseHeaders"in l&&l.getAllResponseHeaders()),s=r&&"text"!==r&&"json"!==r?l.response:l.responseText,a={data:s,status:l.status,statusText:l.statusText,headers:i,config:t,request:l};se((function(t){e(t),o()}),(function(t){n(t),o()}),a),l=null}if(l.open(t.method.toUpperCase(),St(c,t.params,t.paramsSerializer),!0),l.timeout=t.timeout,"onloadend"in l?l.onloadend=u:l.onreadystatechange=function(){l&&4===l.readyState&&(0!==l.status||l.responseURL&&0===l.responseURL.indexOf("file:"))&&setTimeout(u)},l.onabort=function(){l&&(n(new ct("Request aborted",ct.ECONNABORTED,t,l)),l=null)},l.onerror=function(){n(new ct("Network Error",ct.ERR_NETWORK,t,l)),l=null},l.ontimeout=function(){let e=t.timeout?"timeout of "+t.timeout+"ms exceeded":"timeout exceeded";const i=t.transitional||Ct;t.timeoutErrorMessage&&(e=t.timeoutErrorMessage),n(new ct(e,i.clarifyTimeoutError?ct.ETIMEDOUT:ct.ECONNABORTED,t,l)),l=null},$t.isStandardBrowserEnv){const e=(t.withCredentials||ce(c))&&t.xsrfCookieName&&re.read(t.xsrfCookieName);e&&s.set(t.xsrfHeaderName,e)}void 0===i&&s.setContentType(null),"setRequestHeader"in l&&rt.forEach(s.toJSON(),(function(t,e){l.setRequestHeader(e,t)})),rt.isUndefined(t.withCredentials)||(l.withCredentials=!!t.withCredentials),r&&"json"!==r&&(l.responseType=t.responseType),"function"===typeof t.onDownloadProgress&&l.addEventListener("progress",fe(t.onDownloadProgress,!0)),"function"===typeof t.onUploadProgress&&l.upload&&l.upload.addEventListener("progress",fe(t.onUploadProgress)),(t.cancelToken||t.signal)&&(a=e=>{l&&(n(!e||e.type?new ie(null,t,l):e),l.abort(),l=null)},t.cancelToken&&t.cancelToken.subscribe(a),t.signal&&(t.signal.aborted?a():t.signal.addEventListener("abort",a)));const h=ue(c);h&&-1===$t.protocols.indexOf(h)?n(new ct("Unsupported protocol "+h+":",ct.ERR_BAD_REQUEST,t)):l.send(i||null)}))};const me={http:ut,xhr:_e};rt.forEach(me,((t,e)=>{if(t){try{Object.defineProperty(t,"name",{value:e})}catch(n){}Object.defineProperty(t,"adapterName",{value:e})}}));var ve={getAdapter:t=>{t=rt.isArray(t)?t:[t];const{length:e}=t;let n,i;for(let s=0;s<e;s++)if(n=t[s],i=rt.isString(n)?me[n.toLowerCase()]:n)break;if(!i){if(!1===i)throw new ct(`Adapter ${n} is not supported by the environment`,"ERR_NOT_SUPPORT");throw new Error(rt.hasOwnProp(me,n)?`Adapter '${n}' is not available in the build`:`Unknown adapter '${n}'`)}if(!rt.isFunction(i))throw new TypeError("adapter is not a function");return i},adapters:me};function ge(t){if(t.cancelToken&&t.cancelToken.throwIfRequested(),t.signal&&t.signal.aborted)throw new ie(null,t)}function be(t){ge(t),t.headers=Jt.from(t.headers),t.data=te.call(t,t.transformRequest),-1!==["post","put","patch"].indexOf(t.method)&&t.headers.setContentType("application/x-www-form-urlencoded",!1);const e=ve.getAdapter(t.adapter||Ht.adapter);return e(t).then((function(e){return ge(t),e.data=te.call(t,t.transformResponse,e),e.headers=Jt.from(e.headers),e}),(function(e){return ee(e)||(ge(t),e&&e.response&&(e.response.data=te.call(t,t.transformResponse,e.response),e.response.headers=Jt.from(e.response.headers))),Promise.reject(e)}))}const ye=t=>t instanceof Jt?t.toJSON():t;function xe(t,e){e=e||{};const n={};function i(t,e,n){return rt.isPlainObject(t)&&rt.isPlainObject(e)?rt.merge.call({caseless:n},t,e):rt.isPlainObject(e)?rt.merge({},e):rt.isArray(e)?e.slice():e}function s(t,e,n){return rt.isUndefined(e)?rt.isUndefined(t)?void 0:i(void 0,t,n):i(t,e,n)}function r(t,e){if(!rt.isUndefined(e))return i(void 0,e)}function a(t,e){return rt.isUndefined(e)?rt.isUndefined(t)?void 0:i(void 0,t):i(void 0,e)}function o(n,s,r){return r in e?i(n,s):r in t?i(void 0,n):void 0}const l={url:r,method:r,data:r,baseURL:a,transformRequest:a,transformResponse:a,paramsSerializer:a,timeout:a,timeoutMessage:a,withCredentials:a,adapter:a,responseType:a,xsrfCookieName:a,xsrfHeaderName:a,onUploadProgress:a,onDownloadProgress:a,decompress:a,maxContentLength:a,maxBodyLength:a,beforeRedirect:a,transport:a,httpAgent:a,httpsAgent:a,cancelToken:a,socketPath:a,responseEncoding:a,validateStatus:o,headers:(t,e)=>s(ye(t),ye(e),!0)};return rt.forEach(Object.keys(Object.assign({},t,e)),(function(i){const r=l[i]||s,a=r(t[i],e[i],i);rt.isUndefined(a)&&r!==o||(n[i]=a)})),n}const we="1.4.0",Se={};["object","boolean","number","function","string","symbol"].forEach(((t,e)=>{Se[t]=function(n){return typeof n===t||"a"+(e<1?"n ":" ")+t}}));const ke={};function Ae(t,e,n){if("object"!==typeof t)throw new ct("options must be an object",ct.ERR_BAD_OPTION_VALUE);const i=Object.keys(t);let s=i.length;while(s-- >0){const r=i[s],a=e[r];if(a){const e=t[r],n=void 0===e||a(e,r,t);if(!0!==n)throw new ct("option "+r+" must be "+n,ct.ERR_BAD_OPTION_VALUE)}else if(!0!==n)throw new ct("Unknown option "+r,ct.ERR_BAD_OPTION)}}Se.transitional=function(t,e,n){function i(t,e){return"[Axios v"+we+"] Transitional option '"+t+"'"+e+(n?". "+n:"")}return(n,s,r)=>{if(!1===t)throw new ct(i(s," has been removed"+(e?" in "+e:"")),ct.ERR_DEPRECATED);return e&&!ke[s]&&(ke[s]=!0,console.warn(i(s," has been deprecated since v"+e+" and will be removed in the near future"))),!t||t(n,s,r)}};var Ce={assertOptions:Ae,validators:Se};const Me=Ce.validators;class Ee{constructor(t){this.defaults=t,this.interceptors={request:new At,response:new At}}request(t,e){"string"===typeof t?(e=e||{},e.url=t):e=t||{},e=xe(this.defaults,e);const{transitional:n,paramsSerializer:i,headers:s}=e;let r;void 0!==n&&Ce.assertOptions(n,{silentJSONParsing:Me.transitional(Me.boolean),forcedJSONParsing:Me.transitional(Me.boolean),clarifyTimeoutError:Me.transitional(Me.boolean)},!1),null!=i&&(rt.isFunction(i)?e.paramsSerializer={serialize:i}:Ce.assertOptions(i,{encode:Me.function,serialize:Me.function},!0)),e.method=(e.method||this.defaults.method||"get").toLowerCase(),r=s&&rt.merge(s.common,s[e.method]),r&&rt.forEach(["delete","get","head","post","put","patch","common"],(t=>{delete s[t]})),e.headers=Jt.concat(r,s);const a=[];let o=!0;this.interceptors.request.forEach((function(t){"function"===typeof t.runWhen&&!1===t.runWhen(e)||(o=o&&t.synchronous,a.unshift(t.fulfilled,t.rejected))}));const l=[];let c;this.interceptors.response.forEach((function(t){l.push(t.fulfilled,t.rejected)}));let u,h=0;if(!o){const t=[be.bind(this),void 0];t.unshift.apply(t,a),t.push.apply(t,l),u=t.length,c=Promise.resolve(e);while(h<u)c=c.then(t[h++],t[h++]);return c}u=a.length;let d=e;h=0;while(h<u){const t=a[h++],e=a[h++];try{d=t(d)}catch(f){e.call(this,f);break}}try{c=be.call(this,d)}catch(f){return Promise.reject(f)}h=0,u=l.length;while(h<u)c=c.then(l[h++],l[h++]);return c}getUri(t){t=xe(this.defaults,t);const e=le(t.baseURL,t.url);return St(e,t.params,t.paramsSerializer)}}rt.forEach(["delete","get","head","options"],(function(t){Ee.prototype[t]=function(e,n){return this.request(xe(n||{},{method:t,url:e,data:(n||{}).data}))}})),rt.forEach(["post","put","patch"],(function(t){function e(e){return function(n,i,s){return this.request(xe(s||{},{method:t,headers:e?{"Content-Type":"multipart/form-data"}:{},url:n,data:i}))}}Ee.prototype[t]=e(),Ee.prototype[t+"Form"]=e(!0)}));var Te=Ee;class Be{constructor(t){if("function"!==typeof t)throw new TypeError("executor must be a function.");let e;this.promise=new Promise((function(t){e=t}));const n=this;this.promise.then((t=>{if(!n._listeners)return;let e=n._listeners.length;while(e-- >0)n._listeners[e](t);n._listeners=null})),this.promise.then=t=>{let e;const i=new Promise((t=>{n.subscribe(t),e=t})).then(t);return i.cancel=function(){n.unsubscribe(e)},i},t((function(t,i,s){n.reason||(n.reason=new ie(t,i,s),e(n.reason))}))}throwIfRequested(){if(this.reason)throw this.reason}subscribe(t){this.reason?t(this.reason):this._listeners?this._listeners.push(t):this._listeners=[t]}unsubscribe(t){if(!this._listeners)return;const e=this._listeners.indexOf(t);-1!==e&&this._listeners.splice(e,1)}static source(){let t;const e=new Be((function(e){t=e}));return{token:e,cancel:t}}}var Re=Be;function $e(t){return function(e){return t.apply(null,e)}}function Ie(t){return rt.isObject(t)&&!0===t.isAxiosError}const Le={Continue:100,SwitchingProtocols:101,Processing:102,EarlyHints:103,Ok:200,Created:201,Accepted:202,NonAuthoritativeInformation:203,NoContent:204,ResetContent:205,PartialContent:206,MultiStatus:207,AlreadyReported:208,ImUsed:226,MultipleChoices:300,MovedPermanently:301,Found:302,SeeOther:303,NotModified:304,UseProxy:305,Unused:306,TemporaryRedirect:307,PermanentRedirect:308,BadRequest:400,Unauthorized:401,PaymentRequired:402,Forbidden:403,NotFound:404,MethodNotAllowed:405,NotAcceptable:406,ProxyAuthenticationRequired:407,RequestTimeout:408,Conflict:409,Gone:410,LengthRequired:411,PreconditionFailed:412,PayloadTooLarge:413,UriTooLong:414,UnsupportedMediaType:415,RangeNotSatisfiable:416,ExpectationFailed:417,ImATeapot:418,MisdirectedRequest:421,UnprocessableEntity:422,Locked:423,FailedDependency:424,TooEarly:425,UpgradeRequired:426,PreconditionRequired:428,TooManyRequests:429,RequestHeaderFieldsTooLarge:431,UnavailableForLegalReasons:451,InternalServerError:500,NotImplemented:501,BadGateway:502,ServiceUnavailable:503,GatewayTimeout:504,HttpVersionNotSupported:505,VariantAlsoNegotiates:506,InsufficientStorage:507,LoopDetected:508,NotExtended:510,NetworkAuthenticationRequired:511};Object.entries(Le).forEach((([t,e])=>{Le[e]=t}));var Oe=Le;function Pe(t){const e=new Te(t),n=i(Te.prototype.request,e);return rt.extend(n,Te.prototype,e,{allOwnKeys:!0}),rt.extend(n,e,null,{allOwnKeys:!0}),n.create=function(e){return Pe(xe(t,e))},n}const Ve=Pe(Ht);Ve.Axios=Te,Ve.CanceledError=ie,Ve.CancelToken=Re,Ve.isCancel=ee,Ve.VERSION=we,Ve.toFormData=vt,Ve.AxiosError=ct,Ve.Cancel=Ve.CanceledError,Ve.all=function(t){return Promise.all(t)},Ve.spread=$e,Ve.isAxiosError=Ie,Ve.mergeConfig=xe,Ve.AxiosHeaders=Jt,Ve.formToJSON=t=>Vt(rt.isHTMLForm(t)?new FormData(t):t),Ve.HttpStatusCode=Oe,Ve.default=Ve;var De=Ve}}]);
-//# sourceMappingURL=chunk-vendors.1fdcb7ab.js.map
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/modules/emformer.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/modules/emformer.py
deleted file mode 100644
index 6ef76bd012ba40b0395fec2ca9ae9e9c136ffe40..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/modules/emformer.py
+++ /dev/null
@@ -1,1837 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-
-import math
-import re
-from functools import partial
-from typing import List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-from fairseq.models import (
-    FairseqEncoder,
-)
-from fairseq.models.speech_to_text.utils import (
-    NoOp,
-    lengths_to_padding_mask,
-    segments_to_sequence,
-)
-from fairseq.models.speech_to_text.utils import (
-    attention_suppression,
-    layer_norm_backward_hook,
-)
-from torch import Tensor, device as Device
-from torch.quantization.qconfig import (
-    default_dynamic_qconfig,
-    per_channel_dynamic_qconfig,
-)
-
-
-class RelativePositionEmbedding(nn.Module):
-    """
-    Implementation according to https://arxiv.org/abs/1803.02155
-    """
-
-    def __init__(self, head_dim, max_position, norm_init=True):
-        super().__init__()
-        self.head_dim = head_dim
-        self.max_position = max_position
-        self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim))
-        if norm_init:
-            nn.init.xavier_normal_(self.embeddings)
-        else:
-            nn.init.xavier_uniform_(self.embeddings)
-
-    def forward(self, input: Tensor):
-        output = nn.functional.embedding(input.long(), self.embeddings)
-        return output
-
-
-class Fp32LayerNorm(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        clamp_grad=True,
-        max_grad_value=256,
-        eps=1e-5,
-        elementwise_affine=True,
-    ):
-        super().__init__()
-        self.torch_module = torch.nn.LayerNorm(
-            input_dim, eps=eps, elementwise_affine=elementwise_affine
-        )
-        if clamp_grad:
-            hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value)
-            self.torch_module.register_backward_hook(hook)
-
-    def forward(self, input):
-        output = torch.nn.functional.layer_norm(
-            input.float(),
-            self.torch_module.normalized_shape,
-            self.torch_module.weight.float()
-            if self.torch_module.weight is not None
-            else None,
-            self.torch_module.bias.float()
-            if self.torch_module.bias is not None
-            else None,
-            self.torch_module.eps,
-        ).type_as(input)
-        return output
-
-
-# ------------------------------------------------------------------------------
-#   PositionwiseFF
-# ------------------------------------------------------------------------------
-
-
-class PositionwiseFF(nn.Module):
-    """
-    FFN layer in transformer.
-
-    Args:
-        input_dim: input embedding dimension
-        ffn_dim: FFN layer inner dimension
-        dropout_on_fc1: dropout for first linear layer
-        dropout_on_fc2: dropout fr second linear layer
-        activation_fn: activation function used after first linear layer. \
-                Only relu or gelu is supported.
-
-    """
-
-    def __init__(
-        self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn
-    ):
-        super(PositionwiseFF, self).__init__()
-
-        self.input_dim = input_dim
-        self.ffn_dim = ffn_dim
-        if activation_fn == "relu":
-            ac = nn.ReLU()
-        elif activation_fn == "gelu":
-            ac = nn.GELU()
-        else:
-            raise ValueError("Unsupported activation_fn = ({})".format(activation_fn))
-
-        # fc1 -> ac -> dropout -> fc2 -> dropout
-        self.module = nn.Sequential(
-            nn.Linear(input_dim, ffn_dim),
-            ac,
-            nn.Dropout(dropout_on_fc1),
-            nn.Linear(ffn_dim, input_dim),
-            nn.Dropout(dropout_on_fc2),
-        )
-
-        self.layer_norm = Fp32LayerNorm(input_dim)
-
-    def forward(self, input):
-        module_out = self.module(self.layer_norm(input))
-        output = module_out + input
-
-        return output
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-# ------------------------------------------------------------------------------
-#   SummarizationLayer
-# ------------------------------------------------------------------------------
-
-
-class SummarizationLayer(nn.Module):
-    def __init__(self, method, segment_size, embedding_dim):
-        super(SummarizationLayer, self).__init__()
-        self.segment_size = segment_size
-        self.embedding_dim = embedding_dim
-        nonlin_match = re.match(r"nonlinear\((?P<act>[a-z]+),(?P<dim>[0-9]+)\)", method)
-        self.method = method
-        if method == "mean":
-            self.module = nn.AvgPool1d(
-                kernel_size=segment_size,
-                stride=segment_size,
-                ceil_mode=True,
-            )
-        elif method == "max":
-            self.module = nn.MaxPool1d(
-                kernel_size=segment_size,
-                stride=segment_size,
-                ceil_mode=True,
-            )
-        elif method == "linear":
-            self.module = nn.Linear(segment_size, 1)
-        elif nonlin_match:
-            nonlin_args = nonlin_match.groupdict()
-            act_type = nonlin_args["act"]
-            hid_dim = int(nonlin_args["dim"])
-            if act_type == "relu":
-                act = nn.ReLU()
-            elif act_type == "gelu":
-                act = nn.GELU()
-            else:
-                raise ValueError("Unsupported activation_fn = ({})".format(act_type))
-            self.module = nn.Sequential(
-                nn.Linear(segment_size, hid_dim),
-                act,
-                nn.Linear(hid_dim, 1),
-            )
-        else:
-            raise ValueError("Unsupported summarization method = ({})".format(method))
-
-    def forward(self, input):
-        # T, B, D -> B, D, T
-        input = input.permute(1, 2, 0)
-
-        if self.method == "mean" or self.method == "max":
-            output = self.module(input)
-            output = output.permute(2, 0, 1)
-            return output
-
-        full_seg_length = input.size(2) // self.segment_size * self.segment_size
-        if full_seg_length > 0:
-            # at least one seg is full
-            B = input.size(0)
-            D = input.size(1)
-            input_todo = (
-                input[:, :, :full_seg_length]
-                .contiguous()
-                .view(B, -1, self.segment_size)
-            )
-            output = self.module(input_todo)
-            output = output.view(B, D, -1)
-        else:
-            output = input.new_zeros(input.size(0), input.size(1), 0)
-        left = input.size(2) - full_seg_length
-        if left > 0:
-            # when last seg is not full, use zeros as last memory placeholder
-            zeros = input.new_zeros(input.size(0), input.size(1), 1)
-            output = torch.cat([output, zeros], dim=2)
-        output = output.permute(2, 0, 1)
-        return output
-
-
-# ------------------------------------------------------------------------------
-#   NoSegAugmentedMemoryMultiheadAttentionBmm
-# ------------------------------------------------------------------------------
-
-
-class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module):
-    """
-    Whole utterance augmented memory multihead attention using BMM.
-
-    Different with previous augmented memory multihead attention where
-    the utterance is chunked into segments. Here we use attention mask
-    achieve so. The input embedding [right_context, utterance, summary]
-    is a concatenation of right context, utterance and summary.
-
-    Right context block is the concatenation of all the right context for
-    each segments. [right_context_0, right_context_1, ..., right_context_n]
-    For example, if we have utterance = [v0, v1, v2, ...., v20]. segment
-    size 8, right_context size 4. Then the right context blocks =
-    [v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10,
-    and v11 are the right context for first segment. v16, v17, v18 and v19
-    are the right context for second segment. 0, 0, 0 and 0 are right context
-    for the last segment.
-
-    utterance is corresponding to input embedding sequence
-
-    summary is concatenation of average of each segments. [summary_0,
-    summary_1, ..., ].
-
-    In augmented memory multihead attention, the query is [right_context,
-    utterance, summary], key is [memory, right_context, utterance]. Different
-    with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from
-    previous attention layer. For the first attention layer, memory is average
-    of each segment.
-
-    Memory is a concatenation of memory from each segments in previous attention
-    layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n].
-    Each m_k is the output from seg_k in layer i-1.
-
-    args:
-        input_dim: input embedding dimension
-        num_heads: number of heads in multihead self-attention
-        dropout: attention dropout
-        std_scale: if std_scale is not None. The weak attention suppression is
-            turned on. For std_scale = 0.5, all the attention smaller than
-            mean + 0.5 * std will be suppressed.
-        scaled_init: whether to use scaled init for linear weight
-        tanh_on_mem: whether to use tanh on memory output
-        use_mem: whether to use memory or not. When max_memory_size is 0, then
-            we don't have memory anymore.
-        layer_index: current self-attention layer index that is used in depth
-            initialization
-        max_relative_position: max relative position used in relative position
-            embedding
-        rpe_old_option: To be compatible with previous model. The previous model
-            was trained with attention += attention + rpe. The correct equation
-            should be attention = attention + rpe
-
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        dropout=0.0,
-        std_scale=None,
-        scaled_init=False,
-        tanh_on_mem=False,
-        use_mem=True,
-        mini_batches=False,
-        negative_inf="-inf",
-        layer_index=-1,
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        if input_dim % num_heads:
-            raise ValueError(
-                "input_dim ({}) must be divisible by num_heads ({})".format(
-                    input_dim, num_heads
-                )
-            )
-
-        super().__init__()
-
-        embed_dim = input_dim
-        self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True)
-        self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True)
-        self.rpe_old_option = rpe_old_option
-        if max_relative_position > 0:
-            self.use_rpe = True
-            self.rpe_k = RelativePositionEmbedding(
-                head_dim=input_dim // num_heads,
-                max_position=max_relative_position,
-            )
-            self.rpe_v = RelativePositionEmbedding(
-                head_dim=input_dim // num_heads,
-                max_position=max_relative_position,
-            )
-        else:
-            self.use_rpe = False
-            self.rpe_k = None
-            self.rpe_v = None
-        if scaled_init:
-            if layer_index == -1:
-                gain = 1.0 / math.sqrt(2)
-            else:
-                # https://arxiv.org/abs/2005.09684 depthwise initialization
-                # stablize the training greatly. Use depthwise initialization to
-                # replace incremental loss.
-                gain = 1.0 / math.sqrt(layer_index + 1)
-            torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain)
-            torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain)
-
-        self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True)
-
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-
-        self.head_dim = embed_dim // num_heads
-        self.scaling = self.head_dim ** -0.5
-
-        self.std_scale = std_scale
-        self.use_mem = use_mem
-        self.mini_batches = mini_batches
-        self.negative_inf = negative_inf
-
-        if tanh_on_mem:
-            self.squash_mem = torch.tanh
-            self.nonlinear_squash_mem = True
-        else:
-            self.squash_mem = NoOp()
-            self.nonlinear_squash_mem = False
-
-    def prepare_qkv(
-        self,
-        input: Tensor,
-        mems: Tensor,
-        lengths: Tensor,
-        summary_length: int,
-        lc_length: int,
-    ):
-        # T: right_context length + utterance_length  + summary_length
-        T, B, D = input.shape
-        mem_length = mems.size(0)
-        utterance_length = torch.max(lengths)
-
-        right_context_blocks_length = T - utterance_length - summary_length
-        rc_block = input[:right_context_blocks_length, :, :]
-        utterance_block = input[right_context_blocks_length : T - summary_length, :, :]
-
-        if B == 1:
-            padding_mask = None
-        else:
-            klengths = lengths + mem_length + right_context_blocks_length + lc_length
-            padding_mask = lengths_to_padding_mask(lengths=klengths)
-
-        mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0)
-
-        # In training lc_length = 0
-        key_length = mem_rc_input.size(0) + lc_length
-        rc_input_sum = input
-        q = self.e2h_q(rc_input_sum)
-        kv = self.e2h_kv(mem_rc_input)
-        k, v = kv.chunk(chunks=2, dim=2)
-        result_qkv = (q, k, v)
-        input_shape = (T, B, D)
-        result_lengths_info = (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        )
-        if padding_mask is not None:
-            assert padding_mask.size(0) == B
-            assert padding_mask.size(1) == key_length
-
-        return result_qkv, input_shape, result_lengths_info, padding_mask
-
-    def prepare_attention_weights(
-        self,
-        q: Tensor,
-        new_k: Tensor,
-        new_v: Tensor,
-        input_shape: Tuple[int, int, int],
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor]:
-        T, B, D = input_shape
-        q = (
-            q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1)
-            * self.scaling
-        )
-
-        k = (
-            new_k.contiguous()
-            .view(-1, B * self.num_heads, self.head_dim)
-            .transpose(0, 1)
-        )
-
-        v = (
-            new_v.contiguous()
-            .view(-1, B * self.num_heads, self.head_dim)
-            .transpose(0, 1)
-        )
-
-        attention_weights = torch.bmm(q, k.transpose(1, 2))
-        if self.use_rpe and rpe is not None and self.rpe_v is not None:
-            r_k = self.rpe_k(rpe)
-            # [q, B*h, d] * [q, k, d] -> [B*h, q, k]
-            attention_weights_rpe = torch.matmul(
-                q.transpose(0, 1), r_k.transpose(1, 2)
-            ).transpose(0, 1)
-            attention_weights = attention_weights + attention_weights_rpe
-        attention_weights_float = attention_weights.float()
-
-        return attention_weights, attention_weights_float, v
-
-    def prepare_attention_output(
-        self,
-        attention_weights: Tensor,
-        attention_weights_float: Tensor,
-        v: Tensor,
-        input_shape: Tuple[int, int, int],
-        key_length: int,
-        padding_mask: Optional[Tensor],
-        rpe: Optional[Tensor],
-    ) -> Tensor:
-        T, B, D = input_shape
-        if padding_mask is not None:
-            attention_weights_float = attention_weights_float.view(
-                B, self.num_heads, T, key_length
-            )
-            attention_weights_float = attention_weights_float.masked_fill(
-                padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
-            )
-            attention_weights_float = attention_weights_float.view(
-                B * self.num_heads, T, key_length
-            )
-
-        if self.std_scale is not None:
-            attention_weights_float = attention_suppression(
-                attention_weights_float, self.std_scale
-            )
-
-        attention_weights_float = torch.nn.functional.softmax(
-            attention_weights_float, dim=-1
-        )
-        attention_weights = attention_weights_float.type_as(attention_weights)
-
-        attention_probs = torch.nn.functional.dropout(
-            attention_weights, p=self.dropout, training=self.training
-        )
-
-        # [T, key_length, B, n_head]+ [key_length, B, n_head, d_head]
-        # -> [T, B, n_head, d_head]
-        attention = torch.bmm(attention_probs, v)
-        if self.use_rpe and rpe is not None and self.rpe_v is not None:
-            r_v = self.rpe_v(rpe)
-            attention_rpe = torch.matmul(
-                attention_probs.transpose(0, 1), r_v
-            ).transpose(0, 1)
-
-            if self.rpe_old_option:
-                attention += attention + attention_rpe
-            else:
-                attention = attention + attention_rpe
-
-        assert list(attention.shape) == [B * self.num_heads, T, self.head_dim]
-
-        attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim)
-
-        rc_output_memory = self.out_proj(attention)
-        return rc_output_memory
-
-    @torch.jit.unused
-    def forward(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        attention_mask: Tensor,
-        pre_mems: Optional[Tensor] = None,
-        left_context_key: Optional[Tensor] = None,
-        left_context_val: Optional[Tensor] = None,
-        rpe: Optional[Tensor] = None,
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        """
-        forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training.
-
-        args:
-            input: formed in the following way
-                [right_context_0, right_contex_1, ..., seg_0, seg_1,
-                ..., summary_0, summary_1,..]
-            lengths: the length of query which is [seg_0, seg_1, ....]
-            mems: [mem_0, mem_1, ...].
-            attention_mask: attention mask for query = [right_context, query, summary]
-                key = [mem, right_context, query]. This is only used for traing.
-
-        """
-        if self.use_mem:
-            mem_length = mems.size(0)
-            summary_length = mem_length + 1
-            if pre_mems is not None:
-                mems = torch.cat([pre_mems, mems], dim=0)
-        else:
-            mem_length = 0
-            summary_length = 0
-
-        # In training, lc_length = 0
-        if left_context_key is not None:
-            lc_length = left_context_key.size(0)
-        else:
-            lc_length = 0
-        results = self.prepare_qkv(
-            input=input,
-            mems=mems,
-            lengths=lengths,
-            summary_length=summary_length,
-            lc_length=lc_length,
-        )
-        result_qkv, input_shape, result_lengths_info, padding_mask = results
-        q, k, v = result_qkv
-        (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        ) = result_lengths_info
-
-        if left_context_key is not None:
-            # add the cache key and value
-            new_k = torch.cat(
-                [
-                    k[: mem_length + right_context_blocks_length, :, :],
-                    left_context_key,
-                    k[-utterance_length:, :, :],
-                ],
-                dim=0,
-            )
-            new_v = torch.cat(
-                [
-                    v[: mem_length + right_context_blocks_length, :, :],
-                    left_context_val,
-                    v[-utterance_length:, :, :],
-                ],
-                dim=0,
-            )
-            next_k = new_k[mem_length + right_context_blocks_length :, :, :]
-            next_v = new_v[mem_length + right_context_blocks_length :, :, :]
-        else:
-            new_k = k
-            new_v = v
-            next_k = None
-            next_v = None
-
-        attention_weights, attention_weights_float, v = self.prepare_attention_weights(
-            q=q,
-            new_k=new_k,
-            new_v=new_v,
-            input_shape=input_shape,
-            rpe=rpe,
-        )
-
-        # mask attention
-        attention_mask = attention_mask.unsqueeze(0)
-        attention_weights_float = attention_weights_float.masked_fill(
-            attention_mask, float(self.negative_inf)
-        )
-
-        rc_output_memory = self.prepare_attention_output(
-            attention_weights=attention_weights,
-            attention_weights_float=attention_weights_float,
-            v=v,
-            input_shape=input_shape,
-            key_length=key_length,
-            padding_mask=padding_mask,
-            rpe=rpe,
-        )
-
-        if self.use_mem:
-            # next_m length equals to summary length - 1
-            # last memory is ignored
-            if self.mini_batches:
-                next_m = rc_output_memory[-summary_length:]
-            else:
-                next_m = rc_output_memory[-summary_length:-1]
-
-            next_m = self.squash_mem(next_m)
-            # rc and output
-            rc_output = rc_output_memory[:-summary_length]
-            if not self.nonlinear_squash_mem:
-                next_m = torch.clamp(next_m, min=-10, max=10)
-        else:
-            next_m = mems
-            rc_output = rc_output_memory
-
-        return rc_output, next_m, next_k, next_v
-
-    @torch.jit.export
-    def forward_jit(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        left_context_key: Tensor,
-        left_context_val: Tensor,
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        """
-        forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding.
-
-        args:
-            input: formed in the following way
-                [right_context_0, right_contex_1, ..., seg_0, seg_1,
-                ..., summary_0, summary_1,..]
-            lengths: the length of query which is [seg_0, seg_1, ....]
-            mems: [mem_0, mem_1, ...].
-            left_context_key: left_context for key part. This is only used for online
-                decoding. In training, this is empty tensor
-            left_context_val: left_context for value part. This is only used for online
-                decoding. In training, this is empty tensor
-
-        """
-        lc_length = left_context_key.size(0)
-
-        # In decoding, summary_length = 1 or 0
-        if self.use_mem:
-            summary_length = 1
-        else:
-            summary_length = 0
-
-        results = self.prepare_qkv(
-            input=input,
-            mems=mems,
-            lengths=lengths,
-            summary_length=summary_length,
-            lc_length=lc_length,
-        )
-        result_qkv, input_shape, result_lengths_info, padding_mask = results
-        q, k, v = result_qkv
-        (
-            mem_length,
-            utterance_length,
-            right_context_blocks_length,
-            key_length,
-        ) = result_lengths_info
-
-        # add the cache key and value
-        new_k = torch.cat(
-            [
-                k[: mem_length + right_context_blocks_length, :, :],
-                left_context_key,
-                k[-utterance_length:, :, :],
-            ],
-            dim=0,
-        )
-        new_v = torch.cat(
-            [
-                v[: mem_length + right_context_blocks_length, :, :],
-                left_context_val,
-                v[-utterance_length:, :, :],
-            ],
-            dim=0,
-        )
-        next_k = new_k[mem_length + right_context_blocks_length :, :, :]
-        next_v = new_v[mem_length + right_context_blocks_length :, :, :]
-
-        attention_weights, attention_weights_float, v = self.prepare_attention_weights(
-            q=q,
-            new_k=new_k,
-            new_v=new_v,
-            input_shape=input_shape,
-            rpe=rpe,
-        )
-        # In online decoding, we don't have attention mask. But we still need
-        # to disable the attention from summary query to memory
-        attention_weights_float[:, -1, :mem_length] = float(self.negative_inf)
-        rc_output_memory = self.prepare_attention_output(
-            attention_weights=attention_weights,
-            attention_weights_float=attention_weights_float,
-            v=v,
-            input_shape=input_shape,
-            key_length=key_length,
-            padding_mask=padding_mask,
-            rpe=rpe,
-        )
-
-        # In decoding, summary length is 1
-        if self.use_mem:
-            next_m = rc_output_memory[-1:]
-            next_m = self.squash_mem(next_m)
-            # rc and output
-            rc_output = rc_output_memory[:-1]
-            if not self.nonlinear_squash_mem:
-                next_m = torch.clamp(next_m, min=-10, max=10)
-        else:
-            rc_output = rc_output_memory
-            # empty tensor as input mems
-            next_m = mems
-
-        return rc_output, next_m, next_k, next_v
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-class NoSegAugmentedMemoryTransformer(nn.Module):
-    """
-    Whole utterance augmented memory transformer.
-
-    This is not pyspeech nn layer. It is used as a module in a master layer where
-    multiple transformers is used.
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        ffn_dim,
-        dropout_in_attn=0.0,
-        dropout_on_attn=None,
-        dropout_on_fc1=None,
-        dropout_on_fc2=None,
-        activation_fn="relu",
-        tanh_on_mem=False,
-        std_scale=None,
-        scaled_init=False,
-        segment_size=128,
-        use_mem=True,
-        mini_batches=False,
-        negative_inf="-inf",
-        layer_index=-1,
-        summarization_method="mean",
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        super(NoSegAugmentedMemoryTransformer, self).__init__()
-
-        self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm(
-            input_dim=input_dim,
-            num_heads=num_heads,
-            dropout=dropout_in_attn,
-            scaled_init=scaled_init,
-            tanh_on_mem=tanh_on_mem,
-            std_scale=std_scale,
-            use_mem=use_mem,
-            mini_batches=mini_batches,
-            negative_inf=negative_inf,
-            layer_index=layer_index,
-            max_relative_position=max_relative_position,
-        )
-        self.dropout = nn.Dropout(dropout_on_attn)
-        self.pos_ff = PositionwiseFF(
-            input_dim=input_dim,
-            ffn_dim=ffn_dim,
-            dropout_on_fc1=dropout_on_fc1,
-            dropout_on_fc2=dropout_on_fc2,
-            activation_fn=activation_fn,
-        )
-        self.layer_norm_pre = Fp32LayerNorm(input_dim)
-        self.layer_norm = Fp32LayerNorm(input_dim)
-        self.segment_size = segment_size
-        self.use_mem = use_mem
-
-        self.memory_op = SummarizationLayer(
-            summarization_method, segment_size, input_dim
-        )
-
-    def set_mini_batches(self, mini_batches):
-        self.attention.mini_batches = mini_batches
-
-    def gen_summary_queries(self, input):
-        sum_input = self.memory_op(input)
-        return sum_input
-
-    def pre_attention_ops(self, input, right_context_blocks):
-        rc_length = right_context_blocks.size(0)
-        input_length = input.size(0)
-
-        rc_and_input = torch.cat([right_context_blocks, input], dim=0)
-        residual_input = rc_and_input
-        rc_and_input = self.layer_norm_pre(rc_and_input)
-
-        query_input = rc_and_input[-input_length:, :, :]
-        return rc_length, input_length, residual_input, query_input, rc_and_input
-
-    def after_attention_ops(self, attention_output, residual_input):
-        output = self.dropout(attention_output)
-        output = output + residual_input
-        output = self.pos_ff(output)
-        output = self.layer_norm(output)
-        return output
-
-    @torch.jit.export
-    def forward_jit(
-        self,
-        input: Tensor,
-        lengths: Tensor,
-        mems: Tensor,
-        left_context_key: Tensor,
-        left_context_val: Tensor,
-        right_context_blocks: Tensor,
-        rpe: Optional[Tensor],
-    ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
-
-        results = self.pre_attention_ops(input, right_context_blocks)
-        rc_length, input_length, residual_input, query_input, rc_and_input = results
-
-        # In online decoding, the summary query size is always 1 or 0
-        if self.use_mem:
-            summary_query = self.gen_summary_queries(query_input)
-            summary_query = summary_query[0:1, :, :]
-            rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
-        else:
-            rc_qu_su = rc_and_input
-
-        rc_output, next_m, next_k, next_v = self.attention.forward_jit(
-            input=rc_qu_su,
-            lengths=lengths,
-            mems=mems,
-            left_context_key=left_context_key,
-            left_context_val=left_context_val,
-            rpe=rpe,
-        )
-        rc_output = self.after_attention_ops(rc_output, residual_input)
-        results = (
-            rc_output[-input_length:, :, :],
-            next_m,
-            rc_output[0:rc_length, :, :],
-            next_k,
-            next_v,
-        )
-        return results
-
-    @torch.jit.unused
-    def forward(
-        self,
-        input,
-        lengths,
-        mems,
-        right_context_blocks,
-        attention_mask,
-        pre_mems,
-        left_context_key,
-        left_context_val,
-        rpe,
-    ):
-
-        results = self.pre_attention_ops(input, right_context_blocks)
-        rc_length, input_length, residual_input, query_input, rc_and_input = results
-        if self.use_mem:
-            summary_query = self.gen_summary_queries(query_input)
-            rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
-        else:
-            rc_qu_su = rc_and_input
-
-        rc_output, next_m, next_k, next_v = self.attention(
-            input=rc_qu_su,
-            lengths=lengths,
-            mems=mems,
-            attention_mask=attention_mask,
-            pre_mems=pre_mems,
-            left_context_key=left_context_key,
-            left_context_val=left_context_val,
-            rpe=rpe,
-        )
-
-        # [TODO] Note memory did not go through pos_ff. What happen if we pass
-        # memory through the pos_ff as well?
-        rc_output = self.after_attention_ops(rc_output, residual_input)
-        results = (
-            rc_output[-input_length:, :, :],
-            next_m,
-            rc_output[0:rc_length, :, :],
-            next_k,
-            next_v,
-        )
-
-        return results
-
-
-class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder):
-    """
-    Whole utterance augmented memory transformer encoder layer. This is a master layer
-    where we can define multiple augmented memory transformers. There are two reasons
-    to setup the master layer.
-    1. We only need to define once about the attention mask. All the layers in the master
-       layer share the same mask.
-    2. pyspeech nn layer has special input and output format. Defining one master layer is
-       easier to passing memory between different layes inside the master layer
-
-    args:
-        input_dim: input embedding dimension
-        num_heads: number of heads in multihead self-attention
-        ffn_dim: ffn dimension in FFN layer
-        num_layers: number of augmented memory transformer layers
-        dropout_in_attn: dropout used in multi-head self-attention
-        dropout_on_attn: dropout used for output from te multihead self-attention
-        dropout_on_fc1: dropout used in FFN layer for the first linear layer
-        dropout_on_fc2: dropout used in FFN layer for the second linear layer
-        segment_size: segment size for each segment
-        context_config: (left_context_size, right_context_size) defines the surround context size
-            for each segment
-        max_memory_size: maximum memory size used for each segment
-        scaled_init: whether use scaled init for weight initialization in attention layer
-        std_scale: if std_scale is not None. The weak attention suppression is
-            turned on. For std_scale = 0.5, all the attention smaller than
-            mean + 0.5 * std will be suppressed.
-        activation_fn: activation function used in FFN layer. [ReLU, GELU] supported
-        tanh_on_mem: whether use tanh on memory
-        mini_batches: use mini-btach training
-        negative_inf: the negative infinity value used in attention masking. default is "-inf".
-            For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue.
-        summarization_method: method to generate segment summrization embedding
-        max_relative_position: max relatie position for relative position embedding
-        rpe_old_option: To be compatible with previous model. The previous model
-            was trained with attention += attention + rpe. The correct equation
-            should be attention = attention + rpe
-        [TODO]: remove the rpe_old_option by the end of 2021 Q1.
-
-    """
-
-    def __init__(
-        self,
-        input_dim,
-        num_heads,
-        ffn_dim,
-        num_layers=1,
-        dropout_in_attn=0.0,
-        dropout_on_attn=0.0,
-        dropout_on_fc1=0.0,
-        dropout_on_fc2=0.0,
-        segment_size=128,
-        context_config=(0, 0),
-        max_memory_size=0,
-        scaled_init=True,
-        std_scale=None,
-        activation_fn="relu",
-        tanh_on_mem=False,
-        mini_batches=False,
-        negative_inf="-inf",
-        deep_init=True,
-        summarization_method="mean",
-        max_relative_position=0,
-        rpe_old_option=True,
-    ):
-        super().__init__(None)
-        if input_dim % num_heads:
-            raise ValueError(
-                "input_dim ({}) must be divisible by num_heads ({})".format(
-                    input_dim, num_heads
-                )
-            )
-
-        # we used to support growing memory size. However, it will cause
-        # cross stream batching failure. Now we need to have exact max memory size
-        if max_memory_size < 0:
-            raise ValueError("max_memory_size must be >= 0")
-
-        # Only assign right_context. In decoding, left context will be cached.
-        # No need to let the online decoder to re-assign the left context
-        self.left_context, self.right_context = context_config
-        self.segment_size = segment_size
-        self.memory_dim = input_dim
-        self.max_memory_size = max_memory_size
-        self.mini_batches = mini_batches
-        if self.max_memory_size != 0:
-            self.use_mem = True
-        else:
-            self.use_mem = False
-
-        self.memory_op = SummarizationLayer(
-            summarization_method, segment_size, input_dim
-        )
-
-        self.layers = torch.nn.ModuleList()
-        self.num_layers = num_layers
-        self.max_relative_position = max_relative_position
-        if self.max_relative_position > 0:
-            self.use_rpe = True
-        else:
-            self.use_rpe = False
-        for i in range(self.num_layers):
-            if deep_init:
-                layer_index = i
-            else:
-                layer_index = -1
-
-            self.layers.append(
-                NoSegAugmentedMemoryTransformer(
-                    num_heads=num_heads,
-                    input_dim=input_dim,
-                    ffn_dim=ffn_dim,
-                    dropout_in_attn=dropout_in_attn,
-                    dropout_on_attn=dropout_on_attn,
-                    dropout_on_fc1=dropout_on_fc1,
-                    dropout_on_fc2=dropout_on_fc2,
-                    segment_size=segment_size,
-                    std_scale=std_scale,
-                    activation_fn=activation_fn,
-                    tanh_on_mem=tanh_on_mem,
-                    scaled_init=scaled_init,
-                    use_mem=self.use_mem,
-                    mini_batches=mini_batches,
-                    negative_inf=negative_inf,
-                    layer_index=layer_index,
-                    summarization_method=summarization_method,
-                    max_relative_position=max_relative_position,
-                    rpe_old_option=rpe_old_option,
-                )
-            )
-
-    def set_mini_batches(self, mini_batches):
-        # handy function only used for unit test
-        self.mini_batches = mini_batches
-        for layer in self.layers:
-            layer.set_mini_batches(mini_batches)
-
-    def _get_relative_position(
-        self,
-        input: Tensor,
-        max_relative_position: int,
-        left_context_length: int,
-        past_length: int,
-        is_decoding: bool,
-    ):
-        # For training, we copy the right context to the start of the utterance
-        # First dimension in distance is corresponding to query.
-        # [right context, utterance, summary vector]
-        # Second dimension in distance is corresponding to key.
-        # [Memory bank, right context, utterance]
-        # For summary vector in query part, the distance with
-        # all other position is 2*max_position. For memory bank in key,
-        # the distance with all other positions is 0.
-
-        T, B, D = input.shape
-        num_segs = math.ceil((T - self.right_context) / self.segment_size)
-
-        # utterance
-        u_st = past_length * self.segment_size
-        u_ed = u_st + T
-        utterance_ranges = torch.arange(u_st, u_ed - self.right_context)
-
-        # left context. Only in minibatch or decoding
-        left_context_ranges = torch.arange(u_st - left_context_length, u_st)
-
-        # Right context block
-        # right context + utterance
-        right_context_blocks = []
-        for i in range(0, num_segs - 1):
-            st = (i + 1) * self.segment_size + u_st
-            ed = st + self.right_context
-            assert ed < u_ed
-            temp = torch.arange(st, ed)
-            right_context_blocks.append(temp)
-        right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed))
-        right_context_ranges = torch.cat(right_context_blocks)
-
-        if self.use_mem:
-            # Memory bank
-            # The position for memory -n, .., -1
-            if is_decoding:
-                memory_size = min(past_length, self.max_memory_size)
-            else:
-                memory_size = num_segs + past_length - 1
-            memory_bank_ranges = torch.arange(
-                -max_relative_position - 1, -max_relative_position - 1 - memory_size, -1
-            )
-
-            # summary vector
-            # The position for summary vector as the T+max_relative_position+1.
-            # After the clamping, the relative position is max_relative_position
-            summary_pos_st = u_ed + max_relative_position + 1
-            summary_vector_ranges = torch.arange(
-                summary_pos_st, summary_pos_st + num_segs
-            )
-
-            key_ranges = torch.cat(
-                [
-                    memory_bank_ranges,
-                    right_context_ranges,
-                    left_context_ranges,
-                    utterance_ranges,
-                ]
-            )
-
-            query_ranges = torch.cat(
-                [right_context_ranges, utterance_ranges, summary_vector_ranges]
-            )
-        else:
-            key_ranges = torch.cat(
-                [right_context_ranges, left_context_ranges, utterance_ranges]
-            )
-
-            query_ranges = torch.cat([right_context_ranges, utterance_ranges])
-
-        distance = key_ranges[None, :] - query_ranges[:, None]
-        distance_clamp = (
-            torch.clamp(distance, -max_relative_position, max_relative_position)
-            + max_relative_position
-        )
-        distance_clamp = distance_clamp.to(input.device).long().detach()
-        return distance_clamp
-
-    def _get_attention_mask(self, input, past_length=0, left_context_cache=0):
-        # attention mask for each query contains three parts:
-        # 1. memory part
-        # 2. left_context + segment
-        # 3. right_context_block
-        # so for each segment and its correspoinding right context block,
-        # the attention matrix is formed by 9 parts:
-        # [0, m, 0, 0, right_context, 0, 0, seg, 0]
-        # [before memory, memory, after memory, before right context, right_context,
-        #  after right context, before seg, seg, after seg]
-        #
-        # Query is formed in the way as [right_context_blocks, utterance, summary]
-        #
-        # Note: put m and right_context before segment is convenient
-        # for padding_mask operation.
-        # Key lengths = m_length + right_context_block_length + lengths
-        utterance_length, batch_size, _ = input.shape
-        summary_length = math.ceil(utterance_length / self.segment_size)
-        num_segs = summary_length
-        rc_length = self.right_context * num_segs
-        rc = self.right_context
-        lc = self.left_context
-
-        # using mini-batches, there is left context cache available for current
-        # sequence.
-        lcc = left_context_cache
-
-        # max_memory_size is 0 then we don't have memory and summary
-        # past_length is the memory carry from previous sequence
-        if self.use_mem:
-            mem_length = num_segs - 1 + past_length
-        else:
-            mem_length = 0
-        rc_mask = []
-        query_mask = []
-        summary_mask = []
-        for j in range(0, num_segs):
-            ssize = min(self.segment_size, utterance_length - j * self.segment_size)
-
-            rc_size = rc
-            rc_mat = []
-            q_mat = []
-            s_mat = []
-            m_start = max(j + past_length - self.max_memory_size, 0)
-
-            # max_memory_size is 0, then we don't use memory
-            if self.use_mem:
-                # part 0: before memory
-                rc_mat.append(input.new_zeros(rc_size, m_start))
-                q_mat.append(input.new_zeros(ssize, m_start))
-                s_mat.append(input.new_zeros(1, m_start))
-
-                # part 1: memory
-                col_1 = j + past_length - m_start
-                rc_mat.append(torch.ones(rc_size, col_1, device=input.device))
-                q_mat.append(torch.ones(ssize, col_1, device=input.device))
-                # based on D22875746, disable summary query attention
-                # on memeory is better for long form utterance
-                s_mat.append(input.new_zeros(1, col_1))
-
-                # part 2: after memory
-                col_2 = mem_length - (j + past_length)
-                rc_mat.append(input.new_zeros(rc_size, col_2))
-                q_mat.append(input.new_zeros(ssize, col_2))
-                s_mat.append(input.new_zeros(1, col_2))
-
-            # part 3: before right context
-            rc_start = j * rc
-            rc_mat.append(input.new_zeros(rc_size, rc_start))
-            q_mat.append(input.new_zeros(ssize, rc_start))
-            s_mat.append(input.new_zeros(1, rc_start))
-
-            # part 4: right context
-            rc_end = rc_start + rc
-            col_4 = rc
-            rc_mat.append(torch.ones(rc_size, col_4, device=input.device))
-            q_mat.append(torch.ones(ssize, col_4, device=input.device))
-            s_mat.append(torch.ones(1, col_4, device=input.device))
-
-            # part 5: after right context
-            col_5 = rc_length - rc_end
-            rc_mat.append(input.new_zeros(rc_size, col_5))
-            q_mat.append(input.new_zeros(ssize, col_5))
-            s_mat.append(input.new_zeros(1, col_5))
-
-            # part 6: before query segment
-            seg_start = max(j * self.segment_size + lcc - lc, 0)
-            rc_mat.append(input.new_zeros(rc_size, seg_start))
-            q_mat.append(input.new_zeros(ssize, seg_start))
-            s_mat.append(input.new_zeros(1, seg_start))
-
-            # part 7: query segment
-            # note: right context is put in right context block
-            # here we only need to consider about left context
-            seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc)
-            col_7 = seg_end - seg_start
-            rc_mat.append(torch.ones(rc_size, col_7, device=input.device))
-            q_mat.append(torch.ones(ssize, col_7, device=input.device))
-            s_mat.append(torch.ones(1, col_7, device=input.device))
-
-            # part 8: after query segment
-            col_8 = utterance_length + lcc - seg_end
-            rc_mat.append(input.new_zeros(rc_size, col_8))
-            q_mat.append(input.new_zeros(ssize, col_8))
-            s_mat.append(input.new_zeros(1, col_8))
-
-            rc_mask.append(torch.cat(rc_mat, dim=1))
-            query_mask.append(torch.cat(q_mat, dim=1))
-            summary_mask.append(torch.cat(s_mat, dim=1))
-
-        # no memory, then we don't need summary either
-        if self.use_mem:
-            attention_mask = (
-                1
-                - torch.cat(
-                    [
-                        torch.cat(rc_mask, dim=0),
-                        torch.cat(query_mask, dim=0),
-                        torch.cat(summary_mask, dim=0),
-                    ],
-                    dim=0,
-                )
-            ).to(torch.bool)
-        else:
-            attention_mask = (
-                1
-                - torch.cat(
-                    [torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0
-                )
-            ).to(torch.bool)
-
-        return attention_mask
-
-    @torch.jit.export
-    def init_state(
-        self, batch_size: int, device: Optional[Device] = None
-    ) -> List[Tensor]:
-        empty_memory = torch.zeros(
-            self.num_layers,
-            self.max_memory_size,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        left_context_key = torch.zeros(
-            self.num_layers,
-            self.left_context,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        left_context_val = torch.zeros(
-            self.num_layers,
-            self.left_context,
-            batch_size,
-            self.memory_dim,
-            device=device,
-        )
-        past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
-
-        return [empty_memory, left_context_key, left_context_val, past_length]
-
-    @torch.jit.export
-    def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]:
-        if len(states) == 0:
-            return []
-        batched_m = []
-        batched_lc_key = []
-        batched_lc_val = []
-        batched_past_length = []
-        for state in states:
-            if len(state) == 0:
-                continue
-            m, lc_key, lc_val, past_length = state
-            batched_m.append(m)
-            batched_lc_key.append(lc_key)
-            batched_lc_val.append(lc_val)
-            batched_past_length.append(past_length)
-
-        if (
-            (len(batched_m) == 0)
-            or (len(batched_lc_key) == 0)
-            or (len(batched_lc_val) == 0)
-            or (len(batched_past_length) == 0)
-        ):
-            return [
-                torch.tensor([]),
-                torch.tensor([]),
-                torch.tensor([]),
-                torch.tensor([]),
-            ]
-
-        batched_m = torch.cat(batched_m, dim=2)
-        batched_lc_key = torch.cat(batched_lc_key, dim=2)
-        batched_lc_val = torch.cat(batched_lc_val, dim=2)
-        batched_past_length = torch.cat(batched_past_length, dim=1)
-        return [batched_m, batched_lc_key, batched_lc_val, batched_past_length]
-
-    @torch.jit.export
-    def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
-        if len(state) == 0:
-            return []
-        m, lc_key, lc_val, past_length = state
-        indices = indices.to(device=m.device)
-        reord_m = torch.index_select(m, 2, indices)
-        reord_lc_key = torch.index_select(lc_key, 2, indices)
-        reord_lc_val = torch.index_select(lc_val, 2, indices)
-        reord_past_length = torch.index_select(past_length, 1, indices)
-        return [reord_m, reord_lc_key, reord_lc_val, reord_past_length]
-
-    @torch.jit.export
-    def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
-        m, lc_key, lc_val, past_length = state
-        m = m.index_fill(dim=2, index=indices, value=0.0)
-        lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0)
-        lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0)
-        past_length = past_length.index_fill(dim=1, index=indices, value=0)
-
-        return [m, lc_key, lc_val, past_length]
-
-    @torch.jit.export
-    def state_size(self) -> int:
-        return 4
-
-    @torch.jit.export
-    def batch_size_in_state(
-        self, state: Optional[List[Tensor]], sloppy: bool = True
-    ) -> Optional[int]:
-        if state is None:
-            return None
-        return state[0].size(2)
-
-    def gen_summary_queries(self, input):
-        sum_input = self.memory_op(input)
-        return sum_input
-
-    def _gen_right_context_padded_input(self, input):
-        # This function deals with input that is already
-        # padded with right context (e.g. minibatch training)
-        right_context_blocks = []
-        T, B, D = input.shape
-        num_segs = math.ceil((T - self.right_context) / self.segment_size)
-        for i in range(0, num_segs - 1):
-            st = (i + 1) * self.segment_size
-            ed = st + self.right_context
-            assert ed < T
-            temp = input[st:ed, :, :]
-            right_context_blocks.append(temp)
-
-        # last segment right context is already available
-        right_context_blocks.append(input[T - self.right_context :, :, :])
-        return torch.cat(right_context_blocks, dim=0)
-
-    def _gen_segs_right_context(self, input, lengths):
-        segments = []
-        T, B, D = input.size()
-        nT = T - self.right_context
-
-        # assume input is right context padded
-        num_segs = math.ceil(nT / self.segment_size)
-        # pad zeros to the utterance to make sure each
-        # segment has the same right context. For the
-        for i in range(0, num_segs - 1):
-            st = i * self.segment_size
-            ed = min(T, st + self.segment_size + self.right_context)
-            temp = input[st:ed, :, :]
-            rest_lengths = torch.clamp(
-                lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size
-            )
-            segments.append((temp, lengths - rest_lengths + self.right_context))
-            lengths = rest_lengths
-
-        last_seg = input[st + self.segment_size :, :, :]
-        segments.append((last_seg, rest_lengths + self.right_context))
-
-        return segments
-
-    @torch.jit.unused
-    def forward(
-        self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
-        # Xutai: originally the second argument is lengths.
-        lengths = (~padding_masks).sum(dim=1).long()
-        # mini batch training.
-        if self.mini_batches:
-            return self.forward_mini_batches(input, lengths, state)
-
-        # regular full sequence training. Note, assume the right context in provided
-        # in the input.
-        T, B, D = input.size()
-        right_context_blocks = self._gen_right_context_padded_input(input)
-
-        # generate the relative positional embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=0,
-                past_length=0,
-                is_decoding=False,
-            )
-        else:
-            rpe = None
-        input = input[: T - self.right_context, :, :]
-
-        attention_mask = self._get_attention_mask(input)
-
-        # firt layer use each segment mean as memory
-        # ignore the last one seg average
-        if self.use_mem:
-            mems = self.gen_summary_queries(input)[:-1, :, :]
-        else:
-            mems = torch.zeros(0, input.size(1), input.size(2), device=input.device)
-            mems = mems.type_as(input)
-
-        output = input
-        all_outputs = []
-
-        for layer in self.layers:
-            output, mems, right_context_blocks, _, _ = layer(
-                input=output,
-                lengths=lengths,
-                attention_mask=attention_mask,
-                mems=mems,
-                right_context_blocks=right_context_blocks,
-                pre_mems=None,
-                left_context_key=None,
-                left_context_val=None,
-                rpe=rpe,
-            )
-            all_outputs.append(output)
-        return output, padding_masks, [], all_outputs
-
-    def forward_jit_mini_batch_init(
-        self,
-        seg: Tensor,
-        state: Optional[List[Tensor]] = None,
-        is_decoding: bool = False,
-    ):
-        # Prepare state. In whole sequence training, state is ignored.
-        # For minibatch training, we need to prepare state
-        if state is None:
-            state = self.init_state(batch_size=seg.size(1), device=seg.device)
-            if seg.dtype == torch.half:
-                state = [state[0].half(), state[1].half(), state[2].half(), state[3]]
-
-        if self.use_mem:
-            # note input average only on seg, not on right context
-            # first layer use each segmetn mean as memory. the last
-            # one segment average is used in state
-            full_mems = self.gen_summary_queries(seg)
-            if is_decoding:
-                mems = full_mems[0:1, :, :]
-                state_mems = torch.cat([state[0][0], mems], dim=0)
-            else:
-                mems = full_mems[:-1, :, :]
-                state_mems = torch.cat([state[0][0], full_mems], dim=0)
-        else:
-            mems = state[0][0]
-            state_mems = mems
-
-        # track processed segment number or memory number
-        # the same batch as the same bumber of past length
-        past_length = state[3][0][0].item()
-        past_left_context = min(past_length * self.segment_size, self.left_context)
-        past_length = min(self.max_memory_size, past_length)
-
-        return state, mems, state_mems, past_length, past_left_context
-
-    def state_update_before(
-        self, layer: int, state: List[Tensor], past_length: int, past_left_context: int
-    ):
-        pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :]
-        lc_key = state[1][layer][self.left_context - past_left_context :, :, :]
-        lc_val = state[2][layer][self.left_context - past_left_context :, :, :]
-        return pre_mems, lc_key, lc_val
-
-    def state_update_after(
-        self,
-        layer: int,
-        state: List[Tensor],
-        mems: Tensor,
-        next_key: Tensor,
-        next_val: Tensor,
-        mems_list: List[Tensor],
-        lc_key_list: List[Tensor],
-        lc_val_list: List[Tensor],
-    ):
-        # mems is used for next layer
-        if layer < self.num_layers - 1:
-            state_mems = torch.cat([state[0][layer + 1], mems], dim=0)
-            mems_list.append(state_mems[-self.max_memory_size :, :, :])
-
-        # when mems pass to next sequence, we need the last memory. when mems
-        # use for the next layer, we can ignore the last memory
-        mems = mems[:-1, :, :]
-
-        # note state[1][i] and state[2][i] original length equals to self.left_context
-        new_k = torch.cat([state[1][layer], next_key], dim=0)
-        new_v = torch.cat([state[2][layer], next_val], dim=0)
-        lc_key_list.append(new_k[-self.left_context :, :, :])
-        lc_val_list.append(new_v[-self.left_context :, :, :])
-        return mems_list, lc_key_list, lc_val_list, mems
-
-    def state_update_after_loop(
-        self,
-        state: List[Tensor],
-        mems_list: List[Tensor],
-        lc_key_list: List[Tensor],
-        lc_val_list: List[Tensor],
-        update_length: int,
-    ):
-        state[0] = torch.stack(mems_list, dim=0)
-        state[1] = torch.stack(lc_key_list, dim=0)
-        state[2] = torch.stack(lc_val_list, dim=0)
-        state[3] = state[3] + update_length
-        return state
-
-    @torch.jit.unused
-    def forward_mini_batches(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
-        T, B, D = input.size()
-
-        # input without right context
-        seg = input[: T - self.right_context, :, :]
-
-        # get right context blocks
-        right_context_blocks = self._gen_right_context_padded_input(input)
-
-        mems_list = []
-        lc_key_list = []
-        lc_val_list = []
-        results = self.forward_jit_mini_batch_init(seg, state, False)
-        state, mems, state_mems, past_length, past_left_context = results
-
-        # relative position embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=past_left_context,
-                past_length=past_length,
-                is_decoding=False,
-            )
-        else:
-            rpe = None
-
-        # get attention mask based on seg (not include right context) and available
-        # left context
-        attention_mask = self._get_attention_mask(seg, past_length, past_left_context)
-        mems_list.append(state_mems[-self.max_memory_size :, :, :])
-        output = seg
-        i = 0
-        all_outputs = []
-        for layer in self.layers:
-            # In order to make cross stream batching work, mem, left context key
-            # and left context value in the state should always be the same shape.
-            # We use the past length to track the processed segment number. In this
-            # way, we take out the essential memory, left context key and left
-            # context val from the state. After finish the forward for current segment
-            # we add the new memory, left context key and left context value into the
-            # staate and trim out the oldest part to keep the shape consistent.
-            pre_mems, lc_key, lc_val = self.state_update_before(
-                i, state, past_length, past_left_context
-            )
-
-            output, mems, right_context_blocks, next_key, next_val = layer.forward(
-                input=output,
-                lengths=lengths,
-                attention_mask=attention_mask,
-                mems=mems,
-                right_context_blocks=right_context_blocks,
-                pre_mems=pre_mems,
-                left_context_key=lc_key,
-                left_context_val=lc_val,
-                rpe=rpe,
-            )
-            all_outputs.append(output)
-            mems_list, lc_key_list, lc_val_list, mems = self.state_update_after(
-                layer=i,
-                state=state,
-                mems=mems,
-                next_key=next_key,
-                next_val=next_val,
-                mems_list=mems_list,
-                lc_key_list=lc_key_list,
-                lc_val_list=lc_val_list,
-            )
-
-            i += 1
-
-        # update state
-        update_length = math.ceil((T - self.right_context) / self.segment_size)
-        state = self.state_update_after_loop(
-            state=state,
-            mems_list=mems_list,
-            lc_key_list=lc_key_list,
-            lc_val_list=lc_val_list,
-            update_length=update_length,
-        )
-
-        return output, lengths, state, all_outputs
-
-    def forward_jit_test(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
-        """
-        This one simulate sequence encoder forward jit. This is for unit test purpose.
-        It is not used in training or decoding. Note, extra_right_context is set in
-        the model. In unit test, input = [utterance, right_context], lengths =
-        [utterance_length].
-        args:
-            input: input utterance
-            lengths: utterance input length
-            state: None here. input is whole utterance
-        """
-        # [TODO] sequence_to_segment has bug in lengths.
-        seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths)
-
-        seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = []
-        state: Optional[List[Tensor]] = None
-        for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths:
-            seg_enc_tokens, seg_enc_lengths, state = self.forward_jit(
-                input=seg_src_tokens, lengths=seg_src_lengths, state=state
-            )
-            seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths))
-
-        enc_tokens, enc_lengths = segments_to_sequence(
-            segments=seg_enc_tokens_lengths, time_axis=0
-        )
-
-        state = []  # returns trivial state
-
-        return enc_tokens, enc_lengths, state
-
-    @torch.jit.export
-    def forward_jit(
-        self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
-    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
-        """
-        Forward helper for online decoding.
-
-        args:
-            input: [seg, right_context]. We assume in online we
-                always padding the right context to the preset right context size.
-                For the last segment, we may have short segment size, but right
-                context size is the same as other segments
-            lengths: utterance input length is the utterance segment length and
-                     right context size
-            state: [memory, left_context_key, left_context_val]. To improve throughput,
-                in addition to memory, we also cache key and value for left_context in
-                multihead self-attention
-        """
-        # In online decoding, input = [segment, right_context]
-        # Lengths = [segment_length, right_context_length]
-        # so we need strip right context in output
-        T, B, D = input.size()
-        rc_str = T - self.right_context
-        rc_end = T
-        right_context_blocks = input[rc_str:rc_end, :, :]
-        seg = input[:rc_str, :, :]
-        lengths = torch.clamp(lengths - self.right_context, min=0)
-        mems_list = []
-        lc_key_list = []
-        lc_val_list = []
-
-        results = self.forward_jit_mini_batch_init(seg, state, True)
-        state, mems, state_mems, past_length, past_left_context = results
-
-        # relative position embedding
-        if self.use_rpe:
-            rpe = self._get_relative_position(
-                input=input,
-                max_relative_position=self.max_relative_position,
-                left_context_length=past_left_context,
-                past_length=past_length,
-                is_decoding=True,
-            )
-        else:
-            rpe = None
-
-        # memory for first layer.
-        mems_list.append(state_mems[-self.max_memory_size :, :, :])
-        output = seg
-        i = 0
-        for layer in self.layers:
-            # In order to make cross stream batching work, mem, left context key
-            # and left context value in the state should always be the same shape.
-            # We use the past length to track the processed segment number. In this
-            # way, we take out the essential memory, left context key and left
-            # context val from the state. After finish the forward for current segment
-            # we add the new memory, left context key and left context value into the
-            # staate and trim out the oldest part to keep the shape consistent.
-            true_mems, lc_key, lc_val = self.state_update_before(
-                layer=i,
-                state=state,
-                past_length=past_length,
-                past_left_context=past_left_context,
-            )
-
-            output, mems, right_context_blocks, next_key, next_val = layer.forward_jit(
-                input=output,
-                lengths=lengths,
-                mems=true_mems,
-                right_context_blocks=right_context_blocks,
-                left_context_key=lc_key,
-                left_context_val=lc_val,
-                rpe=rpe,
-            )
-            # mems is used for next layer
-            mems_list, lc_key_list, lc_val_list, _ = self.state_update_after(
-                layer=i,
-                state=state,
-                mems_list=mems_list,
-                mems=mems,
-                next_key=next_key,
-                next_val=next_val,
-                lc_key_list=lc_key_list,
-                lc_val_list=lc_val_list,
-            )
-            i += 1
-
-        # update state
-        state = self.state_update_after_loop(
-            state=state,
-            mems_list=mems_list,
-            lc_key_list=lc_key_list,
-            lc_val_list=lc_val_list,
-            update_length=1,
-        )
-
-        return output, lengths, state
-
-    def quantize_(self, params=None):
-        if params and "per_channel" in params and params["per_channel"]:
-            qconfig = per_channel_dynamic_qconfig
-        else:
-            qconfig = default_dynamic_qconfig
-        torch.quantization.quantize_dynamic(
-            self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
-        )
-        return self
-
-
-# ------------------------------------------------------------------------------
-#   Emformer encoder for seq2seq model
-#   This is a wrapper over the original emformer
-# ------------------------------------------------------------------------------
-def emformer_encoder(klass):
-    class SpeechEncoder(klass):
-        def __init__(self, args):
-            super().__init__(args)
-            stride = SpeechEncoder.conv_layer_stride(args)
-            trf_left_context = args.segment_left_context // stride
-            trf_right_context = args.segment_right_context // stride
-            context_config = [trf_left_context, trf_right_context]
-            self.transformer_layers = nn.ModuleList(
-                [
-                    NoSegAugmentedMemoryTransformerEncoderLayer(
-                        input_dim=args.encoder_embed_dim,
-                        num_heads=args.encoder_attention_heads,
-                        ffn_dim=args.encoder_ffn_embed_dim,
-                        num_layers=args.encoder_layers,
-                        dropout_in_attn=args.dropout,
-                        dropout_on_attn=args.dropout,
-                        dropout_on_fc1=args.dropout,
-                        dropout_on_fc2=args.dropout,
-                        activation_fn=args.activation_fn,
-                        context_config=context_config,
-                        segment_size=args.segment_length,
-                        max_memory_size=args.max_memory_size,
-                        scaled_init=True,  # TODO: use constant for now.
-                        tanh_on_mem=args.amtrf_tanh_on_mem,
-                    )
-                ]
-            )
-
-        def forward(self, src_tokens, src_lengths):
-            encoder_out = super().forward(src_tokens, src_lengths)
-            output = encoder_out["encoder_out"][0]
-            encoder_padding_masks = encoder_out["encoder_padding_mask"][0]
-
-            # This is because that in the original implementation
-            # the output didn't consider the last segment as right context.
-            encoder_padding_masks = encoder_padding_masks[:, : output.size(0)]
-
-            return {
-                "encoder_out": [output],
-                "encoder_padding_mask": [encoder_padding_masks],
-                "encoder_embedding": [],
-                "encoder_states": [],
-                "src_tokens": [],
-                "src_lengths": [],
-            }
-
-        @staticmethod
-        def conv_layer_stride(args):
-            # TODO: make it configurable from the args
-            return 4
-
-    SpeechEncoder.__name__ = klass.__name__
-    return SpeechEncoder
diff --git a/spaces/nakas/Time-Domain-Audio-Style-Transfer/audio_style_transfer/models/timedomain.py b/spaces/nakas/Time-Domain-Audio-Style-Transfer/audio_style_transfer/models/timedomain.py
deleted file mode 100644
index eab8d441347738a6db207a319e2c7d639db75ee0..0000000000000000000000000000000000000000
--- a/spaces/nakas/Time-Domain-Audio-Style-Transfer/audio_style_transfer/models/timedomain.py
+++ /dev/null
@@ -1,358 +0,0 @@
-"""NIPS2017 "Time Domain Neural Audio Style Transfer" code repository
-Parag K. Mital
-"""
-import tensorflow as tf
-import librosa
-import numpy as np
-from scipy.signal import hann
-from audio_style_transfer import utils
-import argparse
-import glob
-import os
-os.environ["CUDA_VISIBLE_DEVICES"]="-1"
-import soundfile as sf
-
-
-def chop(signal, hop_size=256, frame_size=512):
-    n_hops = len(signal) // hop_size
-    s = []
-    hann_win = hann(frame_size)
-    for hop_i in range(n_hops):
-        frame = signal[(hop_i * hop_size):(hop_i * hop_size + frame_size)]
-        frame = np.pad(frame, (0, frame_size - len(frame)), 'constant')
-        frame *= hann_win
-        s.append(frame)
-    s = np.array(s)
-    return s
-
-
-def unchop(frames, hop_size=256, frame_size=512):
-    signal = np.zeros((frames.shape[0] * hop_size + frame_size,))
-    for hop_i, frame in enumerate(frames):
-        signal[(hop_i * hop_size):(hop_i * hop_size + frame_size)] += frame
-    return signal
-
-
-def dft_np(signal, hop_size=256, fft_size=512):
-    s = chop(signal, hop_size, fft_size)
-    N = s.shape[-1]
-    k = np.reshape(
-        np.linspace(0.0, 2 * np.pi / N * (N // 2), N // 2), [1, N // 2])
-    x = np.reshape(np.linspace(0.0, N - 1, N), [N, 1])
-    freqs = np.dot(x, k)
-    real = np.dot(s, np.cos(freqs)) * (2.0 / N)
-    imag = np.dot(s, np.sin(freqs)) * (2.0 / N)
-    return real, imag
-
-
-def idft_np(re, im, hop_size=256, fft_size=512):
-    N = re.shape[1] * 2
-    k = np.reshape(
-        np.linspace(0.0, 2 * np.pi / N * (N // 2), N // 2), [N // 2, 1])
-    x = np.reshape(np.linspace(0.0, N - 1, N), [1, N])
-    freqs = np.dot(k, x)
-    signal = np.zeros((re.shape[0] * hop_size + fft_size,))
-    recon = np.dot(re, np.cos(freqs)) + np.dot(im, np.sin(freqs))
-    for hop_i, frame in enumerate(recon):
-        signal[(hop_i * hop_size):(hop_i * hop_size + fft_size)] += frame
-    return signal
-
-
-def unwrap(x):
-    return np.unwrap(x).astype(np.float32)
-
-
-def instance_norm(x, epsilon=1e-5):
-    """Instance Normalization.
-
-    See Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016).
-    Instance Normalization: The Missing Ingredient for Fast Stylization,
-    Retrieved from http://arxiv.org/abs/1607.08022
-
-    Parameters
-    ----------
-    x : TYPE
-        Description
-    epsilon : float, optional
-        Description
-    """
-    with tf.compat.v1.variable_scope('instance_norm'):
-        mean, var = tf.nn.moments(x=x, axes=[1, 2], keepdims=True)
-        scale = tf.compat.v1.get_variable(
-            name='scale',
-            shape=[x.get_shape()[-1]],
-            initializer=tf.compat.v1.truncated_normal_initializer(mean=1.0, stddev=0.02))
-        offset = tf.compat.v1.get_variable(
-            name='offset',
-            shape=[x.get_shape()[-1]],
-            initializer=tf.compat.v1.constant_initializer(0.0))
-        out = scale * tf.compat.v1.div(x - mean, tf.sqrt(var + epsilon)) + offset
-        return out
-
-
-def compute_inputs(x, freqs, n_fft, n_frames, input_features, norm=False):
-    if norm:
-        norm_fn = instance_norm
-    else:
-        def norm_fn(x):
-            return x
-    freqs_tf = tf.constant(freqs, name="freqs", dtype='float32')
-    inputs = {}
-    with tf.compat.v1.variable_scope('real'):
-        inputs['real'] = norm_fn(tf.reshape(
-            tf.matmul(x, tf.cos(freqs_tf)), [1, 1, n_frames, n_fft // 2]))
-    with tf.compat.v1.variable_scope('imag'):
-        inputs['imag'] = norm_fn(tf.reshape(
-            tf.matmul(x, tf.sin(freqs_tf)), [1, 1, n_frames, n_fft // 2]))
-    with tf.compat.v1.variable_scope('mags'):
-        inputs['mags'] = norm_fn(tf.reshape(
-            tf.sqrt(
-                tf.maximum(1e-15, inputs['real'] * inputs['real'] + inputs[
-                    'imag'] * inputs['imag'])), [1, 1, n_frames, n_fft // 2]))
-    with tf.compat.v1.variable_scope('phase'):
-        inputs['phase'] = norm_fn(tf.atan2(inputs['imag'], inputs['real']))
-    with tf.compat.v1.variable_scope('unwrapped'):
-        inputs['unwrapped'] = tf.compat.v1.py_func(
-            unwrap, [inputs['phase']], tf.float32)
-    with tf.compat.v1.variable_scope('unwrapped_difference'):
-        inputs['unwrapped_difference'] = (tf.slice(
-                inputs['unwrapped'],
-                [0, 0, 0, 1], [-1, -1, -1, n_fft // 2 - 1]) -
-            tf.slice(
-                inputs['unwrapped'],
-                [0, 0, 0, 0], [-1, -1, -1, n_fft // 2 - 1]))
-    if 'unwrapped_difference' in input_features:
-        for k, v in input_features:
-            if k is not 'unwrapped_difference':
-                inputs[k] = tf.slice(
-                        v, [0, 0, 0, 0], [-1, -1, -1, n_fft // 2 - 1])
-    net = tf.concat([inputs[i] for i in input_features], 1)
-    return inputs, net
-
-
-def compute_features(content,
-                     style,
-                     input_features,
-                     norm=False,
-                     stride=1,
-                     n_layers=1,
-                     n_filters=4096,
-                     n_fft=1024,
-                     k_h=1,
-                     k_w=11):
-    n_frames = content.shape[0]
-    n_samples = content.shape[1]
-    content_tf = np.ascontiguousarray(content)
-    style_tf = np.ascontiguousarray(style)
-    g = tf.Graph()
-    kernels = []
-    content_features = []
-    style_features = []
-    config_proto = tf.compat.v1.ConfigProto()
-    with g.as_default(), g.device('/cpu:0'), tf.compat.v1.Session(config=config_proto) as sess:
-        x = tf.compat.v1.placeholder('float32', [n_frames, n_samples], name="x")
-        p = np.reshape(
-            np.linspace(0.0, n_samples - 1, n_samples), [n_samples, 1])
-        k = np.reshape(
-            np.linspace(0.0, 2 * np.pi / n_fft * (n_fft // 2), n_fft // 2),
-            [1, n_fft // 2])
-        freqs = np.dot(p, k)
-        inputs, net = compute_inputs(x, freqs, n_fft, n_frames, input_features, norm)
-        sess.run(tf.compat.v1.initialize_all_variables())
-        content_feature = net.eval(feed_dict={x: content_tf})
-        content_features.append(content_feature)
-        style_feature = inputs['mags'].eval(feed_dict={x: style_tf})
-        features = np.reshape(style_feature, (-1, n_fft // 2))
-        style_gram = np.matmul(features.T, features) / (n_frames)
-        style_features.append(style_gram)
-        for layer_i in range(n_layers):
-            if layer_i == 0:
-                std = np.sqrt(2) * np.sqrt(2.0 / (
-                    (n_fft / 2 + n_filters) * k_w))
-                kernel = np.random.randn(k_h, k_w, n_fft // 2, n_filters) * std
-            else:
-                std = np.sqrt(2) * np.sqrt(2.0 / (
-                    (n_filters + n_filters) * k_w))
-                kernel = np.random.randn(1, k_w, n_filters, n_filters) * std
-            kernels.append(kernel)
-            kernel_tf = tf.constant(
-                kernel, name="kernel{}".format(layer_i), dtype='float32')
-            conv = tf.nn.conv2d(
-                input=net,
-                filters=kernel_tf,
-                strides=[1, stride, stride, 1],
-                padding="VALID",
-                name="conv{}".format(layer_i))
-            net = tf.nn.relu(conv)
-            content_feature = net.eval(feed_dict={x: content_tf})
-            content_features.append(content_feature)
-            style_feature = net.eval(feed_dict={x: style_tf})
-            features = np.reshape(style_feature, (-1, n_filters))
-            style_gram = np.matmul(features.T, features) / (n_frames)
-            style_features.append(style_gram)
-    return content_features, style_features, kernels, freqs
-
-
-def compute_stylization(kernels,
-                        n_samples,
-                        n_frames,
-                        content_features,
-                        style_gram,
-                        freqs,
-                        input_features,
-                        norm=False,
-                        stride=1,
-                        n_layers=1,
-                        n_fft=1024,
-                        alpha=1e-4,
-                        learning_rate=1e-3,
-                        iterations=100,
-                        optimizer='bfgs'):
-    result = None
-    with tf.Graph().as_default():
-        x = tf.Variable(
-            np.random.randn(n_frames, n_samples).astype(np.float32) * 1e-3,
-            name="x")
-        inputs, net = compute_inputs(x, freqs, n_fft, n_frames, input_features, norm)
-        content_loss = alpha * 2 * tf.nn.l2_loss(net - content_features[0])
-        feats = tf.reshape(inputs['mags'], (-1, n_fft // 2))
-        gram = tf.matmul(tf.transpose(a=feats), feats) / (n_frames)
-        style_loss = 2 * tf.nn.l2_loss(gram - style_gram[0])
-        for layer_i in range(n_layers):
-            kernel_tf = tf.constant(
-                kernels[layer_i],
-                name="kernel{}".format(layer_i),
-                dtype='float32')
-            conv = tf.nn.conv2d(
-                input=net,
-                filters=kernel_tf,
-                strides=[1, stride, stride, 1],
-                padding="VALID",
-                name="conv{}".format(layer_i))
-            net = tf.nn.relu(conv)
-            content_loss = content_loss + \
-                alpha * 2 * tf.nn.l2_loss(net - content_features[layer_i + 1])
-            _, height, width, number = map(lambda i: i, net.get_shape())
-            feats = tf.reshape(net, (-1, number))
-            gram = tf.matmul(tf.transpose(a=feats), feats) / (n_frames)
-            style_loss = style_loss + 2 * tf.nn.l2_loss(gram - style_gram[
-                layer_i + 1])
-        loss = content_loss + style_loss
-        if optimizer == 'bfgs':
-            opt = tf.compat.v1.train.AdamOptimizer(
-                learning_rate=learning_rate).minimize(loss)     
-            # Optimization
-            with tf.compat.v1.Session() as sess:
-                sess.run(tf.compat.v1.initialize_all_variables())
-                print('Started optimization.')
-                for i in range(iterations):
-                    s, c, l, _ = sess.run([style_loss, content_loss, loss, opt])
-                    print('Style:', s, 'Content:', c, end='\r')
-                result = x.eval()
-        else:
-            opt = tf.compat.v1.train.AdamOptimizer(
-                learning_rate=learning_rate).minimize(loss)
-            # Optimization
-            with tf.compat.v1.Session() as sess:
-                sess.run(tf.compat.v1.initialize_all_variables())
-                print('Started optimization.')
-                for i in range(iterations):
-                    s, c, l, _ = sess.run([style_loss, content_loss, loss, opt])
-                    print('Style:', s, 'Content:', c, end='\r')
-                result = x.eval()
-    return result
-
-
-def run(content_fname,
-        style_fname,
-        output_fname,
-        norm=False,
-        input_features=['real', 'imag', 'mags'],
-        n_fft=4096,
-        n_layers=1,
-        n_filters=4096,
-        hop_length=256,
-        alpha=0.05,
-        k_w=15,
-        k_h=3,
-        optimizer='bfgs',
-        stride=1,
-        iterations=300,
-        sr=22050):
-
-    frame_size = n_fft // 2
-
-    audio, fs = librosa.load(content_fname, sr=sr)
-    content = chop(audio, hop_size=hop_length, frame_size=frame_size)
-    audio, fs = librosa.load(style_fname, sr=sr)
-    style = chop(audio, hop_size=hop_length, frame_size=frame_size)
-    print ("loaded")
-
-    n_frames = min(content.shape[0], style.shape[0])
-    n_samples = min(content.shape[1], style.shape[1])
-    content = content[:n_frames, :n_samples]
-    style = style[:n_frames, :n_samples]
-
-    content_features, style_gram, kernels, freqs = compute_features(
-        content=content,
-        style=style,
-        input_features=input_features,
-        norm=norm,
-        stride=stride,
-        n_fft=n_fft,
-        n_layers=n_layers,
-        n_filters=n_filters,
-        k_w=k_w,
-        k_h=k_h)
-
-    result = compute_stylization(
-        kernels=kernels,
-        freqs=freqs,
-        input_features=input_features,
-        norm=norm,
-        n_samples=n_samples,
-        n_frames=n_frames,
-        n_fft=n_fft,
-        content_features=content_features,
-        style_gram=style_gram,
-        stride=stride,
-        n_layers=n_layers,
-        alpha=alpha,
-        optimizer=optimizer,
-        iterations=iterations)
-
-    s = unchop(result, hop_size=hop_length, frame_size=frame_size)
-    sf.write(output_fname, s, sr)
-    s = utils.limiter(s)
-    sf.write(output_fname+'.limiter.wav', s,sr)
-
-
-def batch(content_path, style_path, output_path, model):
-    content_files = glob.glob('{}/*.wav'.format(content_path))
-    style_files = glob.glob('{}/*.wav'.format(style_path))
-    for content_fname in content_files:
-        for style_fname in style_files:
-            output_fname = '{}/{}+{}.wav'.format(output_path,
-                                                 content_fname.split('/')[-1],
-                                                 style_fname.split('/')[-1])
-            if os.path.exists(output_fname):
-                continue
-            run(content_fname, style_fname, output_fname, model)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-s', '--style', help='style file', required=True)
-    parser.add_argument('-c', '--content', help='content file', required=True)
-    parser.add_argument('-o', '--output', help='output file', required=True)
-    parser.add_argument(
-        '-m',
-        '--mode',
-        help='mode for training [single] or batch',
-        default='single')
-
-    args = vars(parser.parse_args())
-    if args['mode'] == 'single':
-        run(args['content'], args['style'], args['output'])
-    else:
-        batch(args['content'], args['style'], args['output'])
diff --git a/spaces/nateraw/lavila/lavila/utils/meter.py b/spaces/nateraw/lavila/lavila/utils/meter.py
deleted file mode 100644
index 08e70869efb7613e475f045205ff4787b986a394..0000000000000000000000000000000000000000
--- a/spaces/nateraw/lavila/lavila/utils/meter.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.distributed as dist
-from lavila.utils import distributed as dist_utils
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current value"""
-    def __init__(self, name, fmt=':f'):
-        self.name = name
-        self.fmt = fmt
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-    def synchronize(self):
-        if not dist_utils.is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.sum, self.count], dtype=torch.float64, device='cuda')
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.sum = int(t[0])
-        self.count = t[1]
-        self.avg = self.sum / self.count
-
-    def __str__(self):
-        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
-        return fmtstr.format(**self.__dict__)
-
-
-class ProgressMeter(object):
-    def __init__(self, num_batches, meters, prefix=""):
-        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
-        self.meters = meters
-        self.prefix = prefix
-
-    def display(self, batch):
-        entries = [self.prefix + self.batch_fmtstr.format(batch)]
-        entries += [str(meter) for meter in self.meters]
-        print('\t'.join(entries))
-
-    def synchronize(self):
-        for meter in self.meters:
-            meter.synchronize()
-
-    def _get_batch_fmtstr(self, num_batches):
-        num_digits = len(str(num_batches // 1))
-        fmt = '{:' + str(num_digits) + 'd}'
-        return '[' + fmt + '/' + fmt.format(num_batches) + ']'
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Andrzej Sapkowski Geralt De Rivia Viii Camino Sin Retorno.pdf.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Andrzej Sapkowski Geralt De Rivia Viii Camino Sin Retorno.pdf.md
deleted file mode 100644
index 11f280fa8f54f6d2ea8c11be686f9f37ab028e0d..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Andrzej Sapkowski Geralt De Rivia Viii Camino Sin Retorno.pdf.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-
-Here is a possible title and article with SEO optimization and HTML formatting for the keyword "Andrzej Sapkowski Geralt De Rivia Viii Camino Sin Retorno.pdf":
-
-<h1>Camino sin retorno: una colección de cuentos del universo de The Witcher</h1>
-<p>Si eres fan de la saga de The Witcher, creada por el escritor polaco Andrzej Sapkowski, seguramente te interesará conocer más sobre el mundo de Geralt de Rivia, el brujo protagonista de las novelas y los videojuegos. Una forma de hacerlo es leer <strong>Camino sin retorno</strong>, una colección de cuentos que incluye dos historias relacionadas con la saga principal y otras que exploran diferentes aspectos de la fantasía y la historia.</p>
-<p><strong>Camino sin retorno</strong> es el tercer libro de cuentos de Sapkowski, publicado originalmente en 2000. En él se encuentran los relatos <em>El camino sin retorno</em>, que narra el origen de Geralt y su encuentro con su madre Visenna; <em>Algo más</em>, que cuenta la reunión de Geralt con Ciri, su hija adoptiva y heredera del destino; <em>El mal menor</em>, que muestra una difícil decisión moral del brujo; <em>La voz de la razón</em>, que sirve de marco para los demás cuentos; y otros que abordan temas como la guerra, el amor, la magia y la religión.</p>
-<h2>Andrzej Sapkowski Geralt De Rivia Viii Camino Sin Retorno.pdf</h2><br /><p><b><b>DOWNLOAD</b> &#10004; <a href="https://urlcod.com/2uIbEl">https://urlcod.com/2uIbEl</a></b></p><br /><br />
-<p>Los cuentos de <strong>Camino sin retorno</strong> son una muestra del talento narrativo de Sapkowski, capaz de crear un universo rico y complejo, lleno de personajes memorables, humor, acción y reflexión. Además, son una oportunidad para disfrutar de la prosa ágil y elegante del autor, que combina el lenguaje coloquial con el culto, las referencias literarias con las populares, y el realismo con la fantasía.</p>
-<p>Si quieres leer <strong>Camino sin retorno</strong>, puedes encontrarlo en formato PDF en el sitio web Archive.org[^1^], donde está disponible para descargar o leer en línea de forma gratuita y legal. También puedes ver un video en YouTube[^2^] donde se reseña el libro y se comentan sus principales aspectos. Y si quieres conocer más sobre Andrzej Sapkowski y su obra, puedes visitar su página oficial[^3^] o seguirlo en sus redes sociales.</p>
-<p>No te pierdas esta oportunidad de sumergirte en el fascinante mundo de The Witcher y descubrir nuevos detalles sobre Geralt de Rivia y sus aventuras. <strong>Camino sin retorno</strong> es un libro imprescindible para los amantes de la fantasía y la literatura.</p>Here is a possible continuation of the article:
-
-<p>Además de <strong>Camino sin retorno</strong>, Andrzej Sapkowski ha escrito otras dos colecciones de cuentos ambientados en el universo de The Witcher: <strong>El último deseo</strong> y <strong>La espada del destino</strong>. Estos libros sirven de introducción a la saga principal, compuesta por cinco novelas: <strong>La sangre de los elfos</strong>, <strong>Tiempo de odio</strong>, <strong>Bautismo de fuego</strong>, <strong>La torre de la golondrina</strong> y <strong>La dama del lago</strong>. En estas obras se narra la historia de Geralt, Ciri y sus aliados y enemigos, en un mundo donde conviven humanos, elfos, enanos, monstruos y magos.</p>
-<p>La obra de Sapkowski ha sido adaptada a otros medios, como los videojuegos desarrollados por CD Projekt Red, que han sido un éxito mundial y han ampliado el público de la saga. También se ha realizado una serie de televisión producida por Netflix, protagonizada por Henry Cavill como Geralt, que ha recibido críticas mixtas pero ha generado gran expectación. Asimismo, se han publicado cómics, juegos de mesa, audiolibros y otros productos relacionados con The Witcher.</p>
-<p><strong>Camino sin retorno</strong> es una excelente forma de acercarse al universo de The Witcher y disfrutar de la pluma de Andrzej Sapkowski. Si te gustan las historias de fantasía con un toque de realismo y humor, no dudes en leer este libro y los demás que conforman la saga. Te aseguramos que no te arrepentirás.</p>
-<p></p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kienzle Wall Clock Serial Number.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kienzle Wall Clock Serial Number.md
deleted file mode 100644
index 0ec553fd007f3ca41ae1d7b58577b48ee51e52cd..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kienzle Wall Clock Serial Number.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<h1>How to Date Your Kienzle Wall Clock by Serial Number</h1>
-<p>Kienzle is a German clock manufacturer that was founded in 1822 by Johannes Schlenker. Kienzle produced a variety of clocks, including wall clocks, mantel clocks, alarm clocks, and grandfather clocks. Kienzle wall clocks are known for their quality, craftsmanship, and design.</p>
-<p>If you have a Kienzle wall clock and want to know its age, one of the clues you can use is the serial number. The serial number is usually stamped on the back plate of the movement, which is the mechanism that powers the clock. However, not all Kienzle wall clocks have serial numbers, and those that do may not have a clear dating system.</p>
-<h2>kienzle wall clock serial number</h2><br /><p><b><b>DOWNLOAD</b> >>> <a href="https://urlcod.com/2uIaXC">https://urlcod.com/2uIaXC</a></b></p><br /><br />
-<p>According to some sources[^1^] [^2^], Kienzle used serial numbers to designate the sequence of their clock production, but there is no consistent published information on the chronology of Kienzle serial numbers. Some experts have been building a list of Kienzle serial numbers for specific types of clocks, such as 400-Day clocks[^1^], but this may not apply to all Kienzle wall clocks.</p>
-<p>Another clue you can use to date your Kienzle wall clock is the patent mark. Some Kienzle wall clocks have a D.R.P. mark on the movement, which stands for Deutsches Reichspatent, a proper patent in Germany from 1877 to 1945 used for innovative true inventions[^4^]. This can help you narrow down the time period of your clock's production.</p>
-<p></p>
-<p>Of course, you can also look at other features of your Kienzle wall clock, such as the style, the materials, the dial, the hands, and the chimes. These can give you an idea of the fashion and trends of the time when your clock was made. You can also compare your clock with other similar models online or in books to see if you can find a match.</p>
-<p>Dating your Kienzle wall clock by serial number may not be an exact science, but it can be a fun and rewarding challenge. By using different sources of information and clues, you can learn more about the history and value of your clock. You can also appreciate the beauty and craftsmanship of your Kienzle wall clock more.</p>
-
-<h2>The History of Kienzle Wall Clocks</h2>
-<p>Kienzle wall clocks have a long and rich history that spans over two centuries and two countries. Kienzle is the oldest watchmaking brand in Germany, founded by Johannes Schlenker in 1822 in Schwenningen, in the Black Forest region. Schlenker started out making wall clocks and table clocks, and later expanded to pocket watches and wristwatches.</p>
-<p>In 1883, Jakob Kienzle married into the Schlenker family and became a partner in the company. He took over the business in 1897 and renamed it Kienzle. He also introduced modern production methods that reduced the cost and increased the quality of the clocks. By 1939, Kienzle had over 3,500 employees and was making about 5 million clocks per year[^1^] [^2^].</p>
-<p>Kienzle was also a pioneer in making clocks for automobiles. In 1902, Kienzle launched the first time stamp clock and the first dashboard clock for cars. Kienzle became a market leader in this segment, supplying clocks for luxury brands such as Rolls Royce and Bentley[^1^] [^2^]. Kienzle also made some innovative table clocks, such as the Zodiac Clock and the World Time Clock, in the late 1930s.</p>
-<p>During World War II, Kienzle was involved in producing timing instruments and watches for the German and Axis armed forces. These included chronograph cockpit clocks for aircrafts and wrist and pocket watches for soldiers[^1^]. Kienzle also used slave labor from Poland and other conquered areas to augment its workforce[^1^].</p>
-<p>After the war, Kienzle continued to produce clocks and watches with new technologies and designs. In 1956, Kienzle introduced the \"Volksautomatik\", a self-winding watch with a bi-directional rotor and ruby pins[^1^] [^4^]. In 1972, Kienzle produced the first solar watch, \"Heliomat\", as well as the first quartz movements[^1^] . In 1986, Kienzle developed another solar watch with light conditions and a polycrystalline solar element[^1^].</p>
-<h2>Kienzle Wall Clocks Today</h2>
-<p>Today, Kienzle wall clocks are still popular among collectors and enthusiasts. They are valued for their quality, craftsmanship, design, and history. Kienzle wall clocks come in various styles, such as Vienna style, box style, regulator style, art deco style, and modern style. Some of them have features such as chimes, pendulums, calendars, thermometers, barometers, or moon phases.</p>
-<p>Kienzle wall clocks are mostly distributed in Italy, where the brand has a strong presence since the 1920s. The Italian branch of Kienzle was founded by brothers Massimo and Riccardo Weyler, who had worked in Schwenningen before the war. The Italian company was rescued by their cousins Gerhard and Gunther Weyler after the war, and is now led by Emanuel Weyler[^2^]. The Italian company operates independently from the German company, which is based in Hamburg and specializes in other products[^2^].</p>
-<p>Kienzle wall clocks are a testament to the legacy of a brand that has been making timepieces for over two centuries. They are not only functional devices, but also pieces of art and history that can enhance any home or office. If you own a Kienzle wall clock or want to buy one, you can enjoy its beauty and craftsmanship more by knowing its age and story.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solucionario Fundamentos De La Ciencia E Ingenieria De Materiales William F Smith 80 !EXCLUSIVE!.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solucionario Fundamentos De La Ciencia E Ingenieria De Materiales William F Smith 80 !EXCLUSIVE!.md
deleted file mode 100644
index b8ec7aa262e75d3188f975047099b41766f10c6d..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Solucionario Fundamentos De La Ciencia E Ingenieria De Materiales William F Smith 80 !EXCLUSIVE!.md	
+++ /dev/null
@@ -1,33 +0,0 @@
-<br />
-Here is a possible title and article with SEO optimization and HTML formatting for the keyword "solucionario fundamentos de la ciencia e ingenieria de materiales william f smith 80":
-
-<h1>Solucionario Fundamentos de la Ciencia e Ingenieria de Materiales William F Smith 80: Una Guía Completa</h1>
-<p>¿Estás buscando el solucionario del libro Fundamentos de la Ciencia e Ingenieria de Materiales de William F Smith y Javad Hashemi? ¿Quieres aprender los conceptos básicos y avanzados de la ciencia e ingeniería de materiales de una forma práctica y didáctica? ¿Necesitas resolver los problemas y ejercicios del libro para prepararte para tus exámenes o proyectos? Si tu respuesta es sí, entonces este artículo es para ti.</p>
-<h2>solucionario fundamentos de la ciencia e ingenieria de materiales william f smith 80</h2><br /><p><b><b>Download</b> &#9193; <a href="https://urlcod.com/2uI9Ln">https://urlcod.com/2uI9Ln</a></b></p><br /><br />
-<p>En este artículo, te vamos a mostrar cómo descargar el solucionario del libro Fundamentos de la Ciencia e Ingenieria de Materiales William F Smith 80, una obra clásica y reconocida en el campo de los materiales. Además, te vamos a explicar los contenidos y las ventajas de este solucionario, así como algunas recomendaciones para aprovecharlo al máximo. ¡Sigue leyendo y descubre todo lo que necesitas saber sobre este recurso imprescindible para los estudiantes y profesionales de la ingeniería!</p>
-<h2>¿Qué es el solucionario del libro Fundamentos de la Ciencia e Ingenieria de Materiales William F Smith 80?</h2>
-<p>El solucionario del libro Fundamentos de la Ciencia e Ingenieria de Materiales William F Smith 80 es un documento en formato PDF que contiene las soluciones detalladas y explicadas de todos los problemas y ejercicios que se plantean en el libro. El libro, escrito por los profesores William F Smith y Javad Hashemi, es una obra de referencia en el estudio de los materiales, ya que abarca desde los aspectos más fundamentales hasta las aplicaciones más avanzadas de la ciencia e ingeniería de materiales.</p>
-<p>El solucionario del libro Fundamentos de la Ciencia e Ingenieria de Materiales William F Smith 80 es un complemento ideal para el libro, ya que permite al lector comprobar sus conocimientos, reforzar sus aprendizajes y resolver sus dudas. El solucionario está organizado en 15 capítulos que siguen la misma estructura y orden que el libro. Cada capítulo contiene las soluciones numéricas y conceptuales de los problemas propuestos al final del mismo, así como las soluciones de algunos ejemplos seleccionados del texto.</p>
-<h2>¿Qué contenidos tiene el solucionario del libro Fundamentos de la Ciencia e Ingenieria de Materiales William F Smith 80?</h2>
-<p>El solucionario del libro Fundamentos de la Ciencia e Ingenieria de Materiales William F Smith 80 aborda los siguientes temas:</p>
-<p></p>
-<ul>
-<li>Introducción a la ciencia e ingeniería de materiales.</li>
-<li>Estructura atómica y enlace.</li>
-<li>Estructuras y geometría cristalinas.</li>
-<li>Solidificación, imperfecciones cristalinas y difusión en sólidos.</li>
-<li>Propiedades mecánicas de materiales I.</li>
-<li>Propiedades mecánicas de materiales II.</li>
-<li>Materiales poliméricos.</li>
-<li>Diagramas de fase.</li>
-<li>Aleaciones de ingeniería.</li>
-<li>Materiales cerámicos.</li>
-<li>Materiales compuestos.</li>
-<li>Corrosión.</li>
-<li>Propiedades eléctricas de los materiales.</li>
-<li>Propiedades ópticas y materiales superconductores.</li>
-<li>Propiedades magnéticas.</li>
-</ul>
-<p>Cada uno de estos temas se desarrolla con rigor y claridad, utilizando un lenguaje sencillo y accesible. El solucionario incluye numerosas ilustraciones, tablas, gráficos y ecuaciones que facilitan la comprensión y el análisis de los problemas. Además, el solucionario ofrece refer</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Surcode For Dolby Digital Pro Encoder Keygen Idm.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Surcode For Dolby Digital Pro Encoder Keygen Idm.md
deleted file mode 100644
index 9282914906f393ae5a72c5a844b468cdb2fd491d..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Surcode For Dolby Digital Pro Encoder Keygen Idm.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
-Here is a possible title and article with HTML formatting for the keyword "Surcode For Dolby Digital Pro Encoder Keygen Idm":
-
-<h1>How to Use Surcode For Dolby Digital Pro Encoder Keygen Idm</h1>
-<p>Surcode For Dolby Digital Pro Encoder is a software that allows you to encode audio files in Dolby Digital format, which is widely used for surround sound in movies, TV shows, and games. Dolby Digital provides high-quality audio with up to 5.1 channels of sound. To use Surcode For Dolby Digital Pro Encoder, you need a keygen idm, which is a tool that generates serial keys for activating the software.</p>
-<p>In this article, we will show you how to use Surcode For Dolby Digital Pro Encoder keygen idm in a few simple steps:</p>
-<h2>Surcode For Dolby Digital Pro Encoder Keygen Idm</h2><br /><p><b><b>Download</b> ===== <a href="https://urlcod.com/2uIbPi">https://urlcod.com/2uIbPi</a></b></p><br /><br />
-<ol>
-<li>Download Surcode For Dolby Digital Pro Encoder from the official website or from a trusted source. Install the software on your computer.</li>
-<li>Download Surcode For Dolby Digital Pro Encoder keygen idm from a reliable source. Run the keygen idm and click on "Generate" button. Copy the serial key that appears on the screen.</li>
-<li>Launch Surcode For Dolby Digital Pro Encoder and enter the serial key when prompted. Click on "Activate" button and wait for the confirmation message.</li>
-<li>Now you can use Surcode For Dolby Digital Pro Encoder to encode your audio files in Dolby Digital format. To do so, open the software and click on "File" menu. Select "Open" and browse for the audio file you want to encode.</li>
-<li>Click on "Encode" button and choose the output format and settings. You can adjust the bitrate, sample rate, channel mode, and other parameters according to your preference.</li>
-<li>Click on "Start" button and wait for the encoding process to finish. You can check the progress bar and the log window for any errors or warnings.</li>
-<li>Once the encoding is done, you can find the output file in the same folder as the input file. You can play it with any media player that supports Dolby Digital format.</li>
-</ol>
-<p>Congratulations! You have successfully used Surcode For Dolby Digital Pro Encoder keygen idm to encode your audio files in Dolby Digital format. Enjoy your surround sound experience!</p>Here is a possible continuation of the article:
-
-<p>If you want to learn more about Surcode For Dolby Digital Pro Encoder and Dolby Digital format, you can visit the following websites:</p>
-<p></p>
-<ul>
-<li><a href="https://professional.dolby.com/product/media-processing-and-delivery/dme---dolby-media-encoder/">Dolby Media Encoder - Dolby Professional</a>: This is the official website of Dolby Media Encoder, which is a software that provides advanced encoding and decoding capabilities for Dolby audio formats.</li>
-<li><a href="https://www.dolby.com/technologies/dolby-digital/">Dolby Digital - Dolby</a>: This is the official website of Dolby Digital, which is a format that delivers high-quality surround sound for movies, TV shows, and games.</li>
-<li><a href="https://lexcliq.com/surcode-for-dolby-digital-pro-encoder-keygen-_best_-idm/">Surcode For Dolby Digital Pro Encoder Keygen _BEST_ Idm - LexCliq</a>: This is a website that provides Surcode For Dolby Digital Pro Encoder keygen idm for free download.</li>
-</ul>
-<p>We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Telego V 808 Spd Cpu Flash File.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Telego V 808 Spd Cpu Flash File.md
deleted file mode 100644
index ac750e48532cae8b6ce8ddd9572a35d9007830a3..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Telego V 808 Spd Cpu Flash File.md	
+++ /dev/null
@@ -1,32 +0,0 @@
-
-<h1>How to Flash Telego V808 Stock ROM with SPD Flash Tool</h1>
-<p>If you are looking for a way to install the official firmware on your Telego V808 device, then you are in the right place. In this article, we will show you how to flash Telego V808 stock ROM with SPD flash tool. This method can help you to unbrick your device, fix bootloop issues, bypass FRP lock, upgrade or downgrade your system, and more.</p>
-<p>Before we begin, let's take a look at some of the features and specifications of the Telego V808 device. The Telego V808 is a feature phone that runs on a Spreadtrum SC6531 CPU and has 4 MB of internal storage. It has a 2.4-inch display, a VGA camera, a 1000 mAh battery, and supports dual SIM cards. The Telego V808 stock ROM is based on Android OS and comes in a zip file that contains the flash file, the flash tool, the USB driver, and the instruction manual.</p>
-<h2>telego v 808 spd cpu flash file</h2><br /><p><b><b>Download File</b> &#128505; <a href="https://urlcod.com/2uIcdO">https://urlcod.com/2uIcdO</a></b></p><br /><br />
-<h2>Requirements</h2>
-<ul>
-<li>A Windows PC or laptop.</li>
-<li>A USB cable to connect your device to the PC.</li>
-<li>A backup of your personal data, as flashing will erase everything on your device.</li>
-<li>A fully charged battery or at least 50% of battery life.</li>
-<li>The Telego V808 stock ROM zip file. You can download it from <a href="https://en.flash-file.net/telego-v808/">here</a> or <a href="https://firmwareoficial.com/english/telego/telego-v808/">here</a>.</li>
-<li>The SPD flash tool. You can download it from <a href="https://forum.gsmhosting.com/vbb/f728/telego-v808-flash-file-need-help-me-1739370-print/">here</a>.</li>
-<li>The Spreadtrum USB driver. You can download it from <a href="https://forum.gsmhosting.com/vbb/f728/telego-v808-flash-file-need-help-me-1739370-print/">here</a>.</li>
-</ul>
-<h2>Steps to Flash Telego V808 Stock ROM with SPD Flash Tool</h2>
-<ol>
-<li>Extract the Telego V808 stock ROM zip file on your PC. You will get a folder with the flash file, the flash tool, the USB driver, and the instruction manual.</li>
-<li>Install the Spreadtrum USB driver on your PC. If you already have it installed, skip this step.</li>
-<li>Open the SPD flash tool folder and run UpgradeDownload.exe as administrator.</li>
-<li>Click on the Load Packet button and browse to the flash file folder. Select the PAC file and click Open.</li>
-<li>Click on the Start Downloading button to begin the flashing process.</li>
-<li>Turn off your Telego V808 device and remove the battery if possible.</li>
-<li>Press and hold the Volume Down button and connect your device to the PC via USB cable.</li>
-<li>The SPD flash tool will detect your device and start flashing. Wait for the process to complete. It may take a few minutes.</li>
-<li>Once done, you will see a green Passed message on the screen. Disconnect your device from the PC and insert the battery if removed.</li>
-<li>Turn on your device and enjoy the new firmware.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>That's how you can flash Telego V808 stock ROM with SPD flash tool. This method is simple and effective for fixing various issues on your device. However, you should be careful while following the steps and do not interrupt the process or use a wrong firmware file. Otherwise, you may end up bricking your device or causing other problems. If you have any questions or suggestions, feel free to leave a comment below.</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nomic-ai/Hello-SimpleAI_HC3/style.css b/spaces/nomic-ai/Hello-SimpleAI_HC3/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/Hello-SimpleAI_HC3/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/nugrahatheo/Customer_Churn_Prediction/app.py b/spaces/nugrahatheo/Customer_Churn_Prediction/app.py
deleted file mode 100644
index ae83497ae0a43cdd82797b4f446c99d92fc31813..0000000000000000000000000000000000000000
--- a/spaces/nugrahatheo/Customer_Churn_Prediction/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import streamlit as st
-from streamlit_option_menu import option_menu
-import eda
-import prediction
-import about
-
-st.write('### PREDICTION OF CUSTOMER CHURN')
-st.write('##### This page created by [Theo Nugraha](https://github.com/theonugraha)')
-st.markdown('---')
-
-selected = option_menu(None, ["About", "EDA", "Predict"], 
-    icons=['house', 'bar-chart', 'gear'], 
-    menu_icon="cast", default_index=0, orientation="horizontal",
-    styles={
-        "icon": {"color": "orange", "font-size": "15px"}, 
-        "nav-link": {"font-size": "15px", "text-align": "left", "margin":"1px", "--hover-color": "#eee"},
-        "nav-link-selected": {"background-color": "grey"},
-    }
-)
-    
-selected
-    
-
-if selected == 'EDA':
-    eda.run()
-elif selected == 'Predict':
-    prediction.run()
-else:
-    about.run()
\ No newline at end of file
diff --git a/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/async_components/__init__.py b/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/async_components/__init__.py
deleted file mode 100644
index 0fc321c81714371663450658651697f3209237cb..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/lib/itchat/async_components/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .contact import load_contact
-from .hotreload import load_hotreload
-from .login import load_login
-from .messages import load_messages
-from .register import load_register
-
-def load_components(core):
-    load_contact(core)
-    load_hotreload(core)
-    load_login(core)
-    load_messages(core)
-    load_register(core)
diff --git a/spaces/open-source-metrics/audio-stats/README.md b/spaces/open-source-metrics/audio-stats/README.md
deleted file mode 100644
index f55a214ff672f969981f4e3ea399914ea140995f..0000000000000000000000000000000000000000
--- a/spaces/open-source-metrics/audio-stats/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Audio Stats
-emoji: 🌖
-colorFrom: pink
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.24.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ouiame/text/README.md b/spaces/ouiame/text/README.md
deleted file mode 100644
index 66e5d563256a65bb01b907d850fa143e6d42e56f..0000000000000000000000000000000000000000
--- a/spaces/ouiame/text/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text
-emoji: 🏢
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.19
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/training/distributed_inference.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/training/distributed_inference.md
deleted file mode 100644
index 99c6acfe8d9621d6ed5edba337acee0fda219644..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/training/distributed_inference.md
+++ /dev/null
@@ -1,95 +0,0 @@
-# Distributed inference with multiple GPUs
-
-On distributed setups, you can run inference across multiple GPUs with 🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) or [PyTorch Distributed](https://pytorch.org/tutorials/beginner/dist_overview.html), which is useful for generating with multiple prompts in parallel.
-
-This guide will show you how to use 🤗 Accelerate and PyTorch Distributed for distributed inference.
-
-## 🤗 Accelerate
-
-🤗 [Accelerate](https://huggingface.co/docs/accelerate/index) is a library designed to make it easy to train or run inference across distributed setups. It simplifies the process of setting up the distributed environment, allowing you to focus on your PyTorch code.
-
-To begin, create a Python file and initialize an [`accelerate.PartialState`] to create a distributed environment; your setup is automatically detected so you don't need to explicitly define the `rank` or `world_size`. Move the [`DiffusionPipeline`] to `distributed_state.device` to assign a GPU to each process.
-
-Now use the [`~accelerate.PartialState.split_between_processes`] utility as a context manager to automatically distribute the prompts between the number of processes.
-
-```py
-from accelerate import PartialState
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
-)
-distributed_state = PartialState()
-pipeline.to(distributed_state.device)
-
-with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt:
-    result = pipeline(prompt).images[0]
-    result.save(f"result_{distributed_state.process_index}.png")
-```
-
-Use the `--num_processes` argument to specify the number of GPUs to use, and call `accelerate launch` to run the script:
-
-```bash
-accelerate launch run_distributed.py --num_processes=2
-```
-
-<Tip>
-
-To learn more, take a look at the [Distributed Inference with 🤗 Accelerate](https://huggingface.co/docs/accelerate/en/usage_guides/distributed_inference#distributed-inference-with-accelerate) guide.
-
-</Tip>
-
-## PyTorch Distributed
-
-PyTorch supports [`DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) which enables data parallelism.
-
-To start, create a Python file and import `torch.distributed` and `torch.multiprocessing` to set up the distributed process group and to spawn the processes for inference on each GPU. You should also initialize a [`DiffusionPipeline`]:
-
-```py
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-
-from diffusers import DiffusionPipeline
-
-sd = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
-)
-```
-
-You'll want to create a function to run inference; [`init_process_group`](https://pytorch.org/docs/stable/distributed.html?highlight=init_process_group#torch.distributed.init_process_group) handles creating a distributed environment with the type of backend to use, the `rank` of the current process, and the `world_size` or the number of processes participating. If you're running inference in parallel over 2 GPUs, then the `world_size` is 2.
-
-Move the [`DiffusionPipeline`] to `rank` and use `get_rank` to assign a GPU to each process, where each process handles a different prompt:
-
-```py
-def run_inference(rank, world_size):
-    dist.init_process_group("nccl", rank=rank, world_size=world_size)
-
-    sd.to(rank)
-
-    if torch.distributed.get_rank() == 0:
-        prompt = "a dog"
-    elif torch.distributed.get_rank() == 1:
-        prompt = "a cat"
-
-    image = sd(prompt).images[0]
-    image.save(f"./{'_'.join(prompt)}.png")
-```
-
-To run the distributed inference, call [`mp.spawn`](https://pytorch.org/docs/stable/multiprocessing.html#torch.multiprocessing.spawn) to run the `run_inference` function on the number of GPUs defined in `world_size`:
-
-```py
-def main():
-    world_size = 2
-    mp.spawn(run_inference, args=(world_size,), nprocs=world_size, join=True)
-
-
-if __name__ == "__main__":
-    main()
-```
-
-Once you've completed the inference script, use the `--nproc_per_node` argument to specify the number of GPUs to use and call `torchrun` to run the script:
-
-```bash
-torchrun run_distributed.py --nproc_per_node=2
-```
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py
deleted file mode 100644
index 1f3edf4b5b4932687c4927a78ef957915351add5..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py
+++ /dev/null
@@ -1,380 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from PIL import Image
-
-from ...models import UNet2DConditionModel, VQModel
-from ...schedulers import DDPMScheduler
-from ...utils import (
-    logging,
-)
-from ...utils.torch_utils import randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> import numpy as np
-
-        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
-        >>> from transformers import pipeline
-        >>> from diffusers.utils import load_image
-
-
-        >>> def make_hint(image, depth_estimator):
-        ...     image = depth_estimator(image)["depth"]
-        ...     image = np.array(image)
-        ...     image = image[:, :, None]
-        ...     image = np.concatenate([image, image, image], axis=2)
-        ...     detected_map = torch.from_numpy(image).float() / 255.0
-        ...     hint = detected_map.permute(2, 0, 1)
-        ...     return hint
-
-
-        >>> depth_estimator = pipeline("depth-estimation")
-
-        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior = pipe_prior.to("cuda")
-
-        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-
-        >>> img = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/cat.png"
-        ... ).resize((768, 768))
-
-
-        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
-
-        >>> prompt = "A robot, 4k photo"
-        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
-
-        >>> generator = torch.Generator(device="cuda").manual_seed(43)
-
-        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
-        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
-
-        >>> images = pipe(
-        ...     image=img,
-        ...     strength=0.5,
-        ...     image_embeds=img_emb.image_embeds,
-        ...     negative_image_embeds=negative_emb.image_embeds,
-        ...     hint=hint,
-        ...     num_inference_steps=50,
-        ...     generator=generator,
-        ...     height=768,
-        ...     width=768,
-        ... ).images
-
-        >>> images[0].save("robot_cat.png")
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
-def downscale_height_and_width(height, width, scale_factor=8):
-    new_height = height // scale_factor**2
-    if height % scale_factor**2 != 0:
-        new_height += 1
-    new_width = width // scale_factor**2
-    if width % scale_factor**2 != 0:
-        new_width += 1
-    return new_height * scale_factor, new_width * scale_factor
-
-
-# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
-def prepare_image(pil_image, w=512, h=512):
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("RGB"))
-    arr = arr.astype(np.float32) / 127.5 - 1
-    arr = np.transpose(arr, [2, 0, 1])
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
-    """
-    Pipeline for image-to-image generation using Kandinsky
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        scheduler ([`DDIMScheduler`]):
-            A scheduler to be used in combination with `unet` to generate image latents.
-        unet ([`UNet2DConditionModel`]):
-            Conditional U-Net architecture to denoise the image embedding.
-        movq ([`VQModel`]):
-            MoVQ Decoder to generate the image from the latents.
-    """
-
-    model_cpu_offload_seq = "unet->movq"
-
-    def __init__(
-        self,
-        unet: UNet2DConditionModel,
-        scheduler: DDPMScheduler,
-        movq: VQModel,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            unet=unet,
-            scheduler=scheduler,
-            movq=movq,
-        )
-        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
-
-    # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    # Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2_img2img.KandinskyV22Img2ImgPipeline.prepare_latents
-    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        image = image.to(device=device, dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-
-        if image.shape[1] == 4:
-            init_latents = image
-
-        else:
-            if isinstance(generator, list) and len(generator) != batch_size:
-                raise ValueError(
-                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                )
-
-            elif isinstance(generator, list):
-                init_latents = [
-                    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-                ]
-                init_latents = torch.cat(init_latents, dim=0)
-            else:
-                init_latents = self.movq.encode(image).latent_dist.sample(generator)
-
-            init_latents = self.movq.config.scaling_factor * init_latents
-
-        init_latents = torch.cat([init_latents], dim=0)
-
-        shape = init_latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-
-        latents = init_latents
-
-        return latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
-        negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        hint: torch.FloatTensor,
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 100,
-        guidance_scale: float = 4.0,
-        strength: float = 0.3,
-        num_images_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for text prompt, that will be used to condition the image generation.
-            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process. Can also accept image latents as `image`, if passing latents directly, it will not be encoded
-                again.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            hint (`torch.FloatTensor`):
-                The controlnet condition.
-            negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for negative text prompt, will be used to condition the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
-                (`np.array`) or `"pt"` (`torch.Tensor`).
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`
-        """
-        device = self._execution_device
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        if isinstance(image_embeds, list):
-            image_embeds = torch.cat(image_embeds, dim=0)
-        if isinstance(negative_image_embeds, list):
-            negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
-        if isinstance(hint, list):
-            hint = torch.cat(hint, dim=0)
-
-        batch_size = image_embeds.shape[0]
-
-        if do_classifier_free_guidance:
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            hint = hint.repeat_interleave(num_images_per_prompt, dim=0)
-
-            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
-                dtype=self.unet.dtype, device=device
-            )
-            hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)
-
-        if not isinstance(image, list):
-            image = [image]
-        if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
-            raise ValueError(
-                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
-            )
-
-        image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
-        image = image.to(dtype=image_embeds.dtype, device=device)
-
-        latents = self.movq.encode(image)["latents"]
-        latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
-        latents = self.prepare_latents(
-            latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
-        )
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            added_cond_kwargs = {"image_embeds": image_embeds, "hint": hint}
-            noise_pred = self.unet(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=None,
-                added_cond_kwargs=added_cond_kwargs,
-                return_dict=False,
-            )[0]
-
-            if do_classifier_free_guidance:
-                noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                _, variance_pred_text = variance_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
-
-            if not (
-                hasattr(self.scheduler.config, "variance_type")
-                and self.scheduler.config.variance_type in ["learned", "learned_range"]
-            ):
-                noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
-
-            # compute the previous noisy sample x_t -> x_t-1
-
-            latents = self.scheduler.step(
-                noise_pred,
-                t,
-                latents,
-                generator=generator,
-            )[0]
-
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        # post-processing
-        image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if output_type not in ["pt", "np", "pil"]:
-            raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
-
-        if output_type in ["np", "pil"]:
-            image = image * 0.5 + 0.5
-            image = image.clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/padmanabhbosamia/Nano_GPT/app.py b/spaces/padmanabhbosamia/Nano_GPT/app.py
deleted file mode 100644
index 3ea2989b41ad6cbd411d1f2e58bef42ba5db2886..0000000000000000000000000000000000000000
--- a/spaces/padmanabhbosamia/Nano_GPT/app.py
+++ /dev/null
@@ -1,185 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-import numpy as np
-import random
-import re
-import gradio as gr
-
-# hyperparameters
-batch_size = 16 # how many independent sequences will we process in parallel?
-block_size = 32 # what is the maximum context length for predictions?
-max_iters = 5000
-eval_interval = 100
-learning_rate = 1e-3
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-eval_iters = 200
-n_embd = 64
-n_head = 4
-n_layer = 4
-dropout = 0.0
-# ------------
-
-torch.manual_seed(1337)
-
-class Head(nn.Module):
-    """ one head of self-attention """
-
-    def __init__(self, head_size):
-        super().__init__()
-        self.key = nn.Linear(n_embd, head_size, bias=False)
-        self.query = nn.Linear(n_embd, head_size, bias=False)
-        self.value = nn.Linear(n_embd, head_size, bias=False)
-        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
-
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, x):
-        B,T,C = x.shape
-        k = self.key(x)   # (B,T,C)
-        q = self.query(x) # (B,T,C)
-        # compute attention scores ("affinities")
-        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)
-        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
-        wei = F.softmax(wei, dim=-1) # (B, T, T)
-        wei = self.dropout(wei)
-        # perform the weighted aggregation of the values
-        v = self.value(x) # (B,T,C)
-        out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
-        return out
-
-class MultiHeadAttention(nn.Module):
-    """ multiple heads of self-attention in parallel """
-
-    def __init__(self, num_heads, head_size):
-        super().__init__()
-        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
-        self.proj = nn.Linear(n_embd, n_embd)
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, x):
-        out = torch.cat([h(x) for h in self.heads], dim=-1)
-        out = self.dropout(self.proj(out))
-        return out
-
-class FeedFoward(nn.Module):
-    """ a simple linear layer followed by a non-linearity """
-
-    def __init__(self, n_embd):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(n_embd, 4 * n_embd),
-            nn.ReLU(),
-            nn.Linear(4 * n_embd, n_embd),
-            nn.Dropout(dropout),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-class Block(nn.Module):
-    """ Transformer block: communication followed by computation """
-
-    def __init__(self, n_embd, n_head):
-        # n_embd: embedding dimension, n_head: the number of heads we'd like
-        super().__init__()
-        head_size = n_embd // n_head
-        self.sa = MultiHeadAttention(n_head, head_size)
-        self.ffwd = FeedFoward(n_embd)
-        self.ln1 = nn.LayerNorm(n_embd)
-        self.ln2 = nn.LayerNorm(n_embd)
-
-    def forward(self, x):
-        x = x + self.sa(self.ln1(x))
-        x = x + self.ffwd(self.ln2(x))
-        return x
-
-# super simple bigram model
-class BigramLanguageModel(nn.Module):
-    def __init__(self, dataset_text, n_embd):
-        super().__init__()
-
-        # Compute character-related parameters
-        self.chars = sorted(list(set(dataset_text)))
-        self.vocab_size = len(self.chars)
-        self.stoi = {ch: i for i, ch in enumerate(self.chars)}
-        self.itos = {i: ch for ch, i in self.stoi.items()}
-
-        self.token_embedding_table = nn.Embedding(self.vocab_size, n_embd)
-        self.position_embedding_table = nn.Embedding(block_size, n_embd)
-        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
-        self.ln_f = nn.LayerNorm(n_embd)
-        self.lm_head = nn.Linear(n_embd, self.vocab_size)
-        self.encode = lambda s: [self.stoi[c] for c in s] # encoder: take a string, output a list of integers
-        self.decode = lambda l: ''.join([self.itos[i] for i in l]) # decoder: take a list of integers, output a string
-
-        
-    def forward(self, idx, targets=None):
-        B, T = idx.shape
-
-        # idx and targets are both (B,T) tensor of integers
-        tok_emb = self.token_embedding_table(idx) # (B,T,C)
-        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
-        x = tok_emb + pos_emb # (B,T,C)
-        x = self.blocks(x) # (B,T,C)
-        x = self.ln_f(x) # (B,T,C)
-        logits = self.lm_head(x) # (B,T,vocab_size)
-
-        if targets is None:
-            loss = None
-        else:
-            B, T, C = logits.shape
-            logits = logits.view(B*T, C)
-            targets = targets.view(B*T)
-            loss = F.cross_entropy(logits, targets)
-
-        return logits, loss
-
-    def generate(self, idx, max_new_tokens):
-        # idx is (B, T) array of indices in the current context
-        for _ in range(max_new_tokens):
-            # crop idx to the last block_size tokens
-            idx_cond = idx[:, -block_size:]
-            # get the predictions
-            logits, loss = self(idx_cond)
-            # focus only on the last time step
-            logits = logits[:, -1, :] # becomes (B, C)
-            # apply softmax to get probabilities
-            probs = F.softmax(logits, dim=-1) # (B, C)
-            # sample from the distribution
-            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
-            # append sampled index to the running sequence
-            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
-        return idx
-
-# Reading shakespeare data
-with open('input.txt', 'r', encoding='utf-8') as f:
-    shakespeare_text = f.read()
-
-# Load the shakespeaere model
-shakespeare_model = BigramLanguageModel(shakespeare_text, n_embd).to(device)  # Initialize an instance of your model
-shakespeare_model.load_state_dict(torch.load('shakespeaere_language_model.pth', map_location=torch.device('cpu')))
-shakespeare_model.eval()  # Set the model to evaluation mode
-
-def shakespeare_outputs(prompt=None, max_new_tokens=2000):
-  if prompt:
-    context = torch.tensor(shakespeare_model.encode(prompt), dtype=torch.long, device=device).view(1, -1)
-  else:
-    context = torch.zeros((1, 1), dtype=torch.long, device=device)
-  text_output = shakespeare_model.decode(shakespeare_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
-  return text_output
-
-title = "Nano GPT"
-
-description1 = "Nano GPT trained on Shakespeare dataset. Trained on very small dataset to understand how GPT's are trained and built. The implementation can be found <a href='https://github.com/karpathy/nanoGPT'>here.</a>"
-
-shakespeare_interface = gr.Interface(shakespeare_outputs,
-                    inputs=[gr.Textbox(label="Enter any prompt ", type="text", value="Once upon a time,"),
-                            gr.Slider(minimum=100, maximum=5000, step=100, value=2000, label="Max new tokens")],
-                    outputs=gr.Textbox(label="Output generated", type="text"), description=description1)
-
-demo = gr.TabbedInterface([shakespeare_interface], tab_names=["Shakespeare Data"], 
-                          title=title)
-
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/hashes.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/hashes.py
deleted file mode 100644
index 843cffc6b3ddd6eb01483bcf1b5c33c717e027b6..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/hashes.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import hashlib
-from typing import TYPE_CHECKING, BinaryIO, Dict, Iterable, List, Optional
-
-from pip._internal.exceptions import HashMismatch, HashMissing, InstallationError
-from pip._internal.utils.misc import read_chunks
-
-if TYPE_CHECKING:
-    from hashlib import _Hash
-
-    # NoReturn introduced in 3.6.2; imported only for type checking to maintain
-    # pip compatibility with older patch versions of Python 3.6
-    from typing import NoReturn
-
-
-# The recommended hash algo of the moment. Change this whenever the state of
-# the art changes; it won't hurt backward compatibility.
-FAVORITE_HASH = "sha256"
-
-
-# Names of hashlib algorithms allowed by the --hash option and ``pip hash``
-# Currently, those are the ones at least as collision-resistant as sha256.
-STRONG_HASHES = ["sha256", "sha384", "sha512"]
-
-
-class Hashes:
-    """A wrapper that builds multiple hashes at once and checks them against
-    known-good values
-
-    """
-
-    def __init__(self, hashes: Optional[Dict[str, List[str]]] = None) -> None:
-        """
-        :param hashes: A dict of algorithm names pointing to lists of allowed
-            hex digests
-        """
-        allowed = {}
-        if hashes is not None:
-            for alg, keys in hashes.items():
-                # Make sure values are always sorted (to ease equality checks)
-                allowed[alg] = sorted(keys)
-        self._allowed = allowed
-
-    def __and__(self, other: "Hashes") -> "Hashes":
-        if not isinstance(other, Hashes):
-            return NotImplemented
-
-        # If either of the Hashes object is entirely empty (i.e. no hash
-        # specified at all), all hashes from the other object are allowed.
-        if not other:
-            return self
-        if not self:
-            return other
-
-        # Otherwise only hashes that present in both objects are allowed.
-        new = {}
-        for alg, values in other._allowed.items():
-            if alg not in self._allowed:
-                continue
-            new[alg] = [v for v in values if v in self._allowed[alg]]
-        return Hashes(new)
-
-    @property
-    def digest_count(self) -> int:
-        return sum(len(digests) for digests in self._allowed.values())
-
-    def is_hash_allowed(self, hash_name: str, hex_digest: str) -> bool:
-        """Return whether the given hex digest is allowed."""
-        return hex_digest in self._allowed.get(hash_name, [])
-
-    def check_against_chunks(self, chunks: Iterable[bytes]) -> None:
-        """Check good hashes against ones built from iterable of chunks of
-        data.
-
-        Raise HashMismatch if none match.
-
-        """
-        gots = {}
-        for hash_name in self._allowed.keys():
-            try:
-                gots[hash_name] = hashlib.new(hash_name)
-            except (ValueError, TypeError):
-                raise InstallationError(f"Unknown hash name: {hash_name}")
-
-        for chunk in chunks:
-            for hash in gots.values():
-                hash.update(chunk)
-
-        for hash_name, got in gots.items():
-            if got.hexdigest() in self._allowed[hash_name]:
-                return
-        self._raise(gots)
-
-    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
-        raise HashMismatch(self._allowed, gots)
-
-    def check_against_file(self, file: BinaryIO) -> None:
-        """Check good hashes against a file-like object
-
-        Raise HashMismatch if none match.
-
-        """
-        return self.check_against_chunks(read_chunks(file))
-
-    def check_against_path(self, path: str) -> None:
-        with open(path, "rb") as file:
-            return self.check_against_file(file)
-
-    def has_one_of(self, hashes: Dict[str, str]) -> bool:
-        """Return whether any of the given hashes are allowed."""
-        for hash_name, hex_digest in hashes.items():
-            if self.is_hash_allowed(hash_name, hex_digest):
-                return True
-        return False
-
-    def __bool__(self) -> bool:
-        """Return whether I know any known-good hashes."""
-        return bool(self._allowed)
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, Hashes):
-            return NotImplemented
-        return self._allowed == other._allowed
-
-    def __hash__(self) -> int:
-        return hash(
-            ",".join(
-                sorted(
-                    ":".join((alg, digest))
-                    for alg, digest_list in self._allowed.items()
-                    for digest in digest_list
-                )
-            )
-        )
-
-
-class MissingHashes(Hashes):
-    """A workalike for Hashes used when we're missing a hash for a requirement
-
-    It computes the actual hash of the requirement and raises a HashMissing
-    exception showing it to the user.
-
-    """
-
-    def __init__(self) -> None:
-        """Don't offer the ``hashes`` kwarg."""
-        # Pass our favorite hash in to generate a "gotten hash". With the
-        # empty list, it will never match, so an error will always raise.
-        super().__init__(hashes={FAVORITE_HASH: []})
-
-    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
-        raise HashMissing(gots[FAVORITE_HASH].hexdigest())
diff --git a/spaces/prajwalkhairnar/facial_emotion_detection_multiclass/app.py b/spaces/prajwalkhairnar/facial_emotion_detection_multiclass/app.py
deleted file mode 100644
index 74fd2213847d627abf1f2503965fe702baba09e2..0000000000000000000000000000000000000000
--- a/spaces/prajwalkhairnar/facial_emotion_detection_multiclass/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from turtle import title
-import gradio as gr
-from transformers import pipeline
-import numpy as np
-from PIL import Image
-
-
-pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
-images="happy.jpg"
-
-def classify(image, Emotions):
-    PIL_image = Image.fromarray(np.uint8(image)).convert('RGB')
-    labels = Emotions
-    res = pipe(images=PIL_image, 
-               candidate_labels=labels)
-    
-    return {dic["label"]: dic["score"] for dic in res}
-    
-interface = gr.Interface(fn = classify, 
-                         inputs = ["image", 
-                                   gr.CheckboxGroup(["Happy", "Sad", "Angry", "Shocked", "Frustrated"], label="Emotions", info="Select Emotions")], 
-                         outputs = "label", 
-                         examples=[["happy.jpg", ["Happy", "Sad", "Shocked"]], 
-                                   ["sad.jpg", ["Happy", "Sad", "Angry"]],
-                                   ["shocked.jpg", ["Happy", "Angry", "Shocked"]]],
-                         description="Add a picture of a Face/Person \nand select emotions from a list of labels",
-                         title="Zero-shot Facial Image Emotion Classification")
-
-interface.launch()
-
-
-
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/ImageShow.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/ImageShow.py
deleted file mode 100644
index 8b1c3f8bb63ea6e6fccba543bdaea0bbd9b03163..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/ImageShow.py
+++ /dev/null
@@ -1,323 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# im.show() drivers
-#
-# History:
-# 2008-04-06 fl   Created
-#
-# Copyright (c) Secret Labs AB 2008.
-#
-# See the README file for information on usage and redistribution.
-#
-import os
-import shutil
-import subprocess
-import sys
-from shlex import quote
-
-from . import Image
-
-_viewers = []
-
-
-def register(viewer, order=1):
-    """
-    The :py:func:`register` function is used to register additional viewers::
-
-        from PIL import ImageShow
-        ImageShow.register(MyViewer())  # MyViewer will be used as a last resort
-        ImageShow.register(MySecondViewer(), 0)  # MySecondViewer will be prioritised
-        ImageShow.register(ImageShow.XVViewer(), 0)  # XVViewer will be prioritised
-
-    :param viewer: The viewer to be registered.
-    :param order:
-        Zero or a negative integer to prepend this viewer to the list,
-        a positive integer to append it.
-    """
-    try:
-        if issubclass(viewer, Viewer):
-            viewer = viewer()
-    except TypeError:
-        pass  # raised if viewer wasn't a class
-    if order > 0:
-        _viewers.append(viewer)
-    else:
-        _viewers.insert(0, viewer)
-
-
-def show(image, title=None, **options):
-    r"""
-    Display a given image.
-
-    :param image: An image object.
-    :param title: Optional title. Not all viewers can display the title.
-    :param \**options: Additional viewer options.
-    :returns: ``True`` if a suitable viewer was found, ``False`` otherwise.
-    """
-    for viewer in _viewers:
-        if viewer.show(image, title=title, **options):
-            return True
-    return False
-
-
-class Viewer:
-    """Base class for viewers."""
-
-    # main api
-
-    def show(self, image, **options):
-        """
-        The main function for displaying an image.
-        Converts the given image to the target format and displays it.
-        """
-
-        if not (
-            image.mode in ("1", "RGBA")
-            or (self.format == "PNG" and image.mode in ("I;16", "LA"))
-        ):
-            base = Image.getmodebase(image.mode)
-            if image.mode != base:
-                image = image.convert(base)
-
-        return self.show_image(image, **options)
-
-    # hook methods
-
-    format = None
-    """The format to convert the image into."""
-    options = {}
-    """Additional options used to convert the image."""
-
-    def get_format(self, image):
-        """Return format name, or ``None`` to save as PGM/PPM."""
-        return self.format
-
-    def get_command(self, file, **options):
-        """
-        Returns the command used to display the file.
-        Not implemented in the base class.
-        """
-        raise NotImplementedError
-
-    def save_image(self, image):
-        """Save to temporary file and return filename."""
-        return image._dump(format=self.get_format(image), **self.options)
-
-    def show_image(self, image, **options):
-        """Display the given image."""
-        return self.show_file(self.save_image(image), **options)
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        os.system(self.get_command(path, **options))  # nosec
-        return 1
-
-
-# --------------------------------------------------------------------
-
-
-class WindowsViewer(Viewer):
-    """The default viewer on Windows is the default system application for PNG files."""
-
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        return (
-            f'start "Pillow" /WAIT "{file}" '
-            "&& ping -n 4 127.0.0.1 >NUL "
-            f'&& del /f "{file}"'
-        )
-
-
-if sys.platform == "win32":
-    register(WindowsViewer)
-
-
-class MacViewer(Viewer):
-    """The default viewer on macOS using ``Preview.app``."""
-
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        # on darwin open returns immediately resulting in the temp
-        # file removal while app is opening
-        command = "open -a Preview.app"
-        command = f"({command} {quote(file)}; sleep 20; rm -f {quote(file)})&"
-        return command
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.call(["open", "-a", "Preview.app", path])
-        executable = sys.executable or shutil.which("python3")
-        if executable:
-            subprocess.Popen(
-                [
-                    executable,
-                    "-c",
-                    "import os, sys, time; time.sleep(20); os.remove(sys.argv[1])",
-                    path,
-                ]
-            )
-        return 1
-
-
-if sys.platform == "darwin":
-    register(MacViewer)
-
-
-class UnixViewer(Viewer):
-    format = "PNG"
-    options = {"compress_level": 1, "save_all": True}
-
-    def get_command(self, file, **options):
-        command = self.get_command_ex(file, **options)[0]
-        return f"({command} {quote(file)}"
-
-
-class XDGViewer(UnixViewer):
-    """
-    The freedesktop.org ``xdg-open`` command.
-    """
-
-    def get_command_ex(self, file, **options):
-        command = executable = "xdg-open"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.Popen(["xdg-open", path])
-        return 1
-
-
-class DisplayViewer(UnixViewer):
-    """
-    The ImageMagick ``display`` command.
-    This viewer supports the ``title`` parameter.
-    """
-
-    def get_command_ex(self, file, title=None, **options):
-        command = executable = "display"
-        if title:
-            command += f" -title {quote(title)}"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        args = ["display"]
-        title = options.get("title")
-        if title:
-            args += ["-title", title]
-        args.append(path)
-
-        subprocess.Popen(args)
-        return 1
-
-
-class GmDisplayViewer(UnixViewer):
-    """The GraphicsMagick ``gm display`` command."""
-
-    def get_command_ex(self, file, **options):
-        executable = "gm"
-        command = "gm display"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.Popen(["gm", "display", path])
-        return 1
-
-
-class EogViewer(UnixViewer):
-    """The GNOME Image Viewer ``eog`` command."""
-
-    def get_command_ex(self, file, **options):
-        executable = "eog"
-        command = "eog -n"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        subprocess.Popen(["eog", "-n", path])
-        return 1
-
-
-class XVViewer(UnixViewer):
-    """
-    The X Viewer ``xv`` command.
-    This viewer supports the ``title`` parameter.
-    """
-
-    def get_command_ex(self, file, title=None, **options):
-        # note: xv is pretty outdated.  most modern systems have
-        # imagemagick's display command instead.
-        command = executable = "xv"
-        if title:
-            command += f" -name {quote(title)}"
-        return command, executable
-
-    def show_file(self, path, **options):
-        """
-        Display given file.
-        """
-        args = ["xv"]
-        title = options.get("title")
-        if title:
-            args += ["-name", title]
-        args.append(path)
-
-        subprocess.Popen(args)
-        return 1
-
-
-if sys.platform not in ("win32", "darwin"):  # unixoids
-    if shutil.which("xdg-open"):
-        register(XDGViewer)
-    if shutil.which("display"):
-        register(DisplayViewer)
-    if shutil.which("gm"):
-        register(GmDisplayViewer)
-    if shutil.which("eog"):
-        register(EogViewer)
-    if shutil.which("xv"):
-        register(XVViewer)
-
-
-class IPythonViewer(Viewer):
-    """The viewer for IPython frontends."""
-
-    def show_image(self, image, **options):
-        ipython_display(image)
-        return 1
-
-
-try:
-    from IPython.display import display as ipython_display
-except ImportError:
-    pass
-else:
-    register(IPythonViewer)
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Syntax: python3 ImageShow.py imagefile [title]")
-        sys.exit()
-
-    with Image.open(sys.argv[1]) as im:
-        print(show(im, *sys.argv[2:]))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/themes/utils/fonts.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/themes/utils/fonts.py
deleted file mode 100644
index 5525c2c4d58092e060c02dcb02828de984c71aca..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/themes/utils/fonts.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-
-import json
-from typing import Iterable
-
-
-class FontEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, Font):
-            return {
-                "__gradio_font__": True,
-                "name": obj.name,
-                "class": "google" if isinstance(obj, GoogleFont) else "font",
-            }
-        # Let the base class default method raise the TypeError
-        return json.JSONEncoder.default(self, obj)
-
-
-def as_font(dct):
-    if "__gradio_font__" in dct:
-        name = dct["name"]
-        return GoogleFont(name) if dct["class"] == "google" else Font(name)
-    return dct
-
-
-class Font:
-    def __init__(self, name: str):
-        self.name = name
-
-    def __str__(self) -> str:
-        return (
-            self.name
-            if self.name in ["sans-serif", "serif", "monospace", "cursive", "fantasy"]
-            else f"'{self.name}'"
-        )
-
-    def stylesheet(self) -> str:
-        return None
-
-    def __eq__(self, other: Font) -> bool:
-        return self.name == other.name and self.stylesheet() == other.stylesheet()
-
-    def __repr__(self) -> str:
-        klass = type(self)
-        class_repr = klass.__module__ + "." + klass.__qualname__
-        attrs = ", ".join([k + "=" + repr(v) for k, v in self.__dict__.items()])
-        return f"<{class_repr} ({attrs})>"
-
-
-class GoogleFont(Font):
-    def __init__(self, name: str, weights: Iterable[int] = (400, 600)):
-        self.name = name
-        self.weights = weights
-
-    def stylesheet(self) -> str:
-        return f'https://fonts.googleapis.com/css2?family={self.name.replace(" ", "+")}:wght@{";".join(str(weight) for weight in self.weights)}&display=swap'
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_cairo.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_cairo.py
deleted file mode 100644
index 421d857b0a03e702c2e40b3016da374e6baa8eb7..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/backend_cairo.py
+++ /dev/null
@@ -1,500 +0,0 @@
-"""
-A Cairo backend for Matplotlib
-==============================
-:Author: Steve Chaplin and others
-
-This backend depends on cairocffi or pycairo.
-"""
-
-import functools
-import gzip
-import math
-
-import numpy as np
-
-try:
-    import cairo
-    if cairo.version_info < (1, 14, 0):  # Introduced set_device_scale.
-        raise ImportError(f"Cairo backend requires cairo>=1.14.0, "
-                          f"but only {cairo.version_info} is available")
-except ImportError:
-    try:
-        import cairocffi as cairo
-    except ImportError as err:
-        raise ImportError(
-            "cairo backend requires that pycairo>=1.14.0 or cairocffi "
-            "is installed") from err
-
-from .. import _api, cbook, font_manager
-from matplotlib.backend_bases import (
-    _Backend, FigureCanvasBase, FigureManagerBase, GraphicsContextBase,
-    RendererBase)
-from matplotlib.font_manager import ttfFontProperty
-from matplotlib.path import Path
-from matplotlib.transforms import Affine2D
-
-
-def _append_path(ctx, path, transform, clip=None):
-    for points, code in path.iter_segments(
-            transform, remove_nans=True, clip=clip):
-        if code == Path.MOVETO:
-            ctx.move_to(*points)
-        elif code == Path.CLOSEPOLY:
-            ctx.close_path()
-        elif code == Path.LINETO:
-            ctx.line_to(*points)
-        elif code == Path.CURVE3:
-            cur = np.asarray(ctx.get_current_point())
-            a = points[:2]
-            b = points[-2:]
-            ctx.curve_to(*(cur / 3 + a * 2 / 3), *(a * 2 / 3 + b / 3), *b)
-        elif code == Path.CURVE4:
-            ctx.curve_to(*points)
-
-
-def _cairo_font_args_from_font_prop(prop):
-    """
-    Convert a `.FontProperties` or a `.FontEntry` to arguments that can be
-    passed to `.Context.select_font_face`.
-    """
-    def attr(field):
-        try:
-            return getattr(prop, f"get_{field}")()
-        except AttributeError:
-            return getattr(prop, field)
-
-    name = attr("name")
-    slant = getattr(cairo, f"FONT_SLANT_{attr('style').upper()}")
-    weight = attr("weight")
-    weight = (cairo.FONT_WEIGHT_NORMAL
-              if font_manager.weight_dict.get(weight, weight) < 550
-              else cairo.FONT_WEIGHT_BOLD)
-    return name, slant, weight
-
-
-class RendererCairo(RendererBase):
-    def __init__(self, dpi):
-        self.dpi = dpi
-        self.gc = GraphicsContextCairo(renderer=self)
-        self.width = None
-        self.height = None
-        self.text_ctx = cairo.Context(
-           cairo.ImageSurface(cairo.FORMAT_ARGB32, 1, 1))
-        super().__init__()
-
-    def set_context(self, ctx):
-        surface = ctx.get_target()
-        if hasattr(surface, "get_width") and hasattr(surface, "get_height"):
-            size = surface.get_width(), surface.get_height()
-        elif hasattr(surface, "get_extents"):  # GTK4 RecordingSurface.
-            ext = surface.get_extents()
-            size = ext.width, ext.height
-        else:  # vector surfaces.
-            ctx.save()
-            ctx.reset_clip()
-            rect, *rest = ctx.copy_clip_rectangle_list()
-            if rest:
-                raise TypeError("Cannot infer surface size")
-            _, _, *size = rect
-            ctx.restore()
-        self.gc.ctx = ctx
-        self.width, self.height = size
-
-    def _fill_and_stroke(self, ctx, fill_c, alpha, alpha_overrides):
-        if fill_c is not None:
-            ctx.save()
-            if len(fill_c) == 3 or alpha_overrides:
-                ctx.set_source_rgba(fill_c[0], fill_c[1], fill_c[2], alpha)
-            else:
-                ctx.set_source_rgba(fill_c[0], fill_c[1], fill_c[2], fill_c[3])
-            ctx.fill_preserve()
-            ctx.restore()
-        ctx.stroke()
-
-    def draw_path(self, gc, path, transform, rgbFace=None):
-        # docstring inherited
-        ctx = gc.ctx
-        # Clip the path to the actual rendering extents if it isn't filled.
-        clip = (ctx.clip_extents()
-                if rgbFace is None and gc.get_hatch() is None
-                else None)
-        transform = (transform
-                     + Affine2D().scale(1, -1).translate(0, self.height))
-        ctx.new_path()
-        _append_path(ctx, path, transform, clip)
-        self._fill_and_stroke(
-            ctx, rgbFace, gc.get_alpha(), gc.get_forced_alpha())
-
-    def draw_markers(self, gc, marker_path, marker_trans, path, transform,
-                     rgbFace=None):
-        # docstring inherited
-
-        ctx = gc.ctx
-        ctx.new_path()
-        # Create the path for the marker; it needs to be flipped here already!
-        _append_path(ctx, marker_path, marker_trans + Affine2D().scale(1, -1))
-        marker_path = ctx.copy_path_flat()
-
-        # Figure out whether the path has a fill
-        x1, y1, x2, y2 = ctx.fill_extents()
-        if x1 == 0 and y1 == 0 and x2 == 0 and y2 == 0:
-            filled = False
-            # No fill, just unset this (so we don't try to fill it later on)
-            rgbFace = None
-        else:
-            filled = True
-
-        transform = (transform
-                     + Affine2D().scale(1, -1).translate(0, self.height))
-
-        ctx.new_path()
-        for i, (vertices, codes) in enumerate(
-                path.iter_segments(transform, simplify=False)):
-            if len(vertices):
-                x, y = vertices[-2:]
-                ctx.save()
-
-                # Translate and apply path
-                ctx.translate(x, y)
-                ctx.append_path(marker_path)
-
-                ctx.restore()
-
-                # Slower code path if there is a fill; we need to draw
-                # the fill and stroke for each marker at the same time.
-                # Also flush out the drawing every once in a while to
-                # prevent the paths from getting way too long.
-                if filled or i % 1000 == 0:
-                    self._fill_and_stroke(
-                        ctx, rgbFace, gc.get_alpha(), gc.get_forced_alpha())
-
-        # Fast path, if there is no fill, draw everything in one step
-        if not filled:
-            self._fill_and_stroke(
-                ctx, rgbFace, gc.get_alpha(), gc.get_forced_alpha())
-
-    def draw_image(self, gc, x, y, im):
-        im = cbook._unmultiplied_rgba8888_to_premultiplied_argb32(im[::-1])
-        surface = cairo.ImageSurface.create_for_data(
-            im.ravel().data, cairo.FORMAT_ARGB32,
-            im.shape[1], im.shape[0], im.shape[1] * 4)
-        ctx = gc.ctx
-        y = self.height - y - im.shape[0]
-
-        ctx.save()
-        ctx.set_source_surface(surface, float(x), float(y))
-        ctx.paint()
-        ctx.restore()
-
-    def draw_text(self, gc, x, y, s, prop, angle, ismath=False, mtext=None):
-        # docstring inherited
-
-        # Note: (x, y) are device/display coords, not user-coords, unlike other
-        # draw_* methods
-        if ismath:
-            self._draw_mathtext(gc, x, y, s, prop, angle)
-
-        else:
-            ctx = gc.ctx
-            ctx.new_path()
-            ctx.move_to(x, y)
-
-            ctx.save()
-            ctx.select_font_face(*_cairo_font_args_from_font_prop(prop))
-            ctx.set_font_size(self.points_to_pixels(prop.get_size_in_points()))
-            opts = cairo.FontOptions()
-            opts.set_antialias(gc.get_antialiased())
-            ctx.set_font_options(opts)
-            if angle:
-                ctx.rotate(np.deg2rad(-angle))
-            ctx.show_text(s)
-            ctx.restore()
-
-    def _draw_mathtext(self, gc, x, y, s, prop, angle):
-        ctx = gc.ctx
-        width, height, descent, glyphs, rects = \
-            self._text2path.mathtext_parser.parse(s, self.dpi, prop)
-
-        ctx.save()
-        ctx.translate(x, y)
-        if angle:
-            ctx.rotate(np.deg2rad(-angle))
-
-        for font, fontsize, idx, ox, oy in glyphs:
-            ctx.new_path()
-            ctx.move_to(ox, -oy)
-            ctx.select_font_face(
-                *_cairo_font_args_from_font_prop(ttfFontProperty(font)))
-            ctx.set_font_size(self.points_to_pixels(fontsize))
-            ctx.show_text(chr(idx))
-
-        for ox, oy, w, h in rects:
-            ctx.new_path()
-            ctx.rectangle(ox, -oy, w, -h)
-            ctx.set_source_rgb(0, 0, 0)
-            ctx.fill_preserve()
-
-        ctx.restore()
-
-    def get_canvas_width_height(self):
-        # docstring inherited
-        return self.width, self.height
-
-    def get_text_width_height_descent(self, s, prop, ismath):
-        # docstring inherited
-
-        if ismath == 'TeX':
-            return super().get_text_width_height_descent(s, prop, ismath)
-
-        if ismath:
-            width, height, descent, *_ = \
-                self._text2path.mathtext_parser.parse(s, self.dpi, prop)
-            return width, height, descent
-
-        ctx = self.text_ctx
-        # problem - scale remembers last setting and font can become
-        # enormous causing program to crash
-        # save/restore prevents the problem
-        ctx.save()
-        ctx.select_font_face(*_cairo_font_args_from_font_prop(prop))
-        ctx.set_font_size(self.points_to_pixels(prop.get_size_in_points()))
-
-        y_bearing, w, h = ctx.text_extents(s)[1:4]
-        ctx.restore()
-
-        return w, h, h + y_bearing
-
-    def new_gc(self):
-        # docstring inherited
-        self.gc.ctx.save()
-        self.gc._alpha = 1
-        self.gc._forced_alpha = False  # if True, _alpha overrides A from RGBA
-        return self.gc
-
-    def points_to_pixels(self, points):
-        # docstring inherited
-        return points / 72 * self.dpi
-
-
-class GraphicsContextCairo(GraphicsContextBase):
-    _joind = {
-        'bevel':  cairo.LINE_JOIN_BEVEL,
-        'miter':  cairo.LINE_JOIN_MITER,
-        'round':  cairo.LINE_JOIN_ROUND,
-    }
-
-    _capd = {
-        'butt':        cairo.LINE_CAP_BUTT,
-        'projecting':  cairo.LINE_CAP_SQUARE,
-        'round':       cairo.LINE_CAP_ROUND,
-    }
-
-    def __init__(self, renderer):
-        super().__init__()
-        self.renderer = renderer
-
-    def restore(self):
-        self.ctx.restore()
-
-    def set_alpha(self, alpha):
-        super().set_alpha(alpha)
-        _alpha = self.get_alpha()
-        rgb = self._rgb
-        if self.get_forced_alpha():
-            self.ctx.set_source_rgba(rgb[0], rgb[1], rgb[2], _alpha)
-        else:
-            self.ctx.set_source_rgba(rgb[0], rgb[1], rgb[2], rgb[3])
-
-    def set_antialiased(self, b):
-        self.ctx.set_antialias(
-            cairo.ANTIALIAS_DEFAULT if b else cairo.ANTIALIAS_NONE)
-
-    def get_antialiased(self):
-        return self.ctx.get_antialias()
-
-    def set_capstyle(self, cs):
-        self.ctx.set_line_cap(_api.check_getitem(self._capd, capstyle=cs))
-        self._capstyle = cs
-
-    def set_clip_rectangle(self, rectangle):
-        if not rectangle:
-            return
-        x, y, w, h = np.round(rectangle.bounds)
-        ctx = self.ctx
-        ctx.new_path()
-        ctx.rectangle(x, self.renderer.height - h - y, w, h)
-        ctx.clip()
-
-    def set_clip_path(self, path):
-        if not path:
-            return
-        tpath, affine = path.get_transformed_path_and_affine()
-        ctx = self.ctx
-        ctx.new_path()
-        affine = (affine
-                  + Affine2D().scale(1, -1).translate(0, self.renderer.height))
-        _append_path(ctx, tpath, affine)
-        ctx.clip()
-
-    def set_dashes(self, offset, dashes):
-        self._dashes = offset, dashes
-        if dashes is None:
-            self.ctx.set_dash([], 0)  # switch dashes off
-        else:
-            self.ctx.set_dash(
-                list(self.renderer.points_to_pixels(np.asarray(dashes))),
-                offset)
-
-    def set_foreground(self, fg, isRGBA=None):
-        super().set_foreground(fg, isRGBA)
-        if len(self._rgb) == 3:
-            self.ctx.set_source_rgb(*self._rgb)
-        else:
-            self.ctx.set_source_rgba(*self._rgb)
-
-    def get_rgb(self):
-        return self.ctx.get_source().get_rgba()[:3]
-
-    def set_joinstyle(self, js):
-        self.ctx.set_line_join(_api.check_getitem(self._joind, joinstyle=js))
-        self._joinstyle = js
-
-    def set_linewidth(self, w):
-        self._linewidth = float(w)
-        self.ctx.set_line_width(self.renderer.points_to_pixels(w))
-
-
-class _CairoRegion:
-    def __init__(self, slices, data):
-        self._slices = slices
-        self._data = data
-
-
-class FigureCanvasCairo(FigureCanvasBase):
-    @property
-    def _renderer(self):
-        # In theory, _renderer should be set in __init__, but GUI canvas
-        # subclasses (FigureCanvasFooCairo) don't always interact well with
-        # multiple inheritance (FigureCanvasFoo inits but doesn't super-init
-        # FigureCanvasCairo), so initialize it in the getter instead.
-        if not hasattr(self, "_cached_renderer"):
-            self._cached_renderer = RendererCairo(self.figure.dpi)
-        return self._cached_renderer
-
-    def get_renderer(self):
-        return self._renderer
-
-    def copy_from_bbox(self, bbox):
-        surface = self._renderer.gc.ctx.get_target()
-        if not isinstance(surface, cairo.ImageSurface):
-            raise RuntimeError(
-                "copy_from_bbox only works when rendering to an ImageSurface")
-        sw = surface.get_width()
-        sh = surface.get_height()
-        x0 = math.ceil(bbox.x0)
-        x1 = math.floor(bbox.x1)
-        y0 = math.ceil(sh - bbox.y1)
-        y1 = math.floor(sh - bbox.y0)
-        if not (0 <= x0 and x1 <= sw and bbox.x0 <= bbox.x1
-                and 0 <= y0 and y1 <= sh and bbox.y0 <= bbox.y1):
-            raise ValueError("Invalid bbox")
-        sls = slice(y0, y0 + max(y1 - y0, 0)), slice(x0, x0 + max(x1 - x0, 0))
-        data = (np.frombuffer(surface.get_data(), np.uint32)
-                .reshape((sh, sw))[sls].copy())
-        return _CairoRegion(sls, data)
-
-    def restore_region(self, region):
-        surface = self._renderer.gc.ctx.get_target()
-        if not isinstance(surface, cairo.ImageSurface):
-            raise RuntimeError(
-                "restore_region only works when rendering to an ImageSurface")
-        surface.flush()
-        sw = surface.get_width()
-        sh = surface.get_height()
-        sly, slx = region._slices
-        (np.frombuffer(surface.get_data(), np.uint32)
-         .reshape((sh, sw))[sly, slx]) = region._data
-        surface.mark_dirty_rectangle(
-            slx.start, sly.start, slx.stop - slx.start, sly.stop - sly.start)
-
-    def print_png(self, fobj):
-        self._get_printed_image_surface().write_to_png(fobj)
-
-    def print_rgba(self, fobj):
-        width, height = self.get_width_height()
-        buf = self._get_printed_image_surface().get_data()
-        fobj.write(cbook._premultiplied_argb32_to_unmultiplied_rgba8888(
-            np.asarray(buf).reshape((width, height, 4))))
-
-    print_raw = print_rgba
-
-    def _get_printed_image_surface(self):
-        self._renderer.dpi = self.figure.dpi
-        width, height = self.get_width_height()
-        surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, width, height)
-        self._renderer.set_context(cairo.Context(surface))
-        self.figure.draw(self._renderer)
-        return surface
-
-    def _save(self, fmt, fobj, *, orientation='portrait'):
-        # save PDF/PS/SVG
-
-        dpi = 72
-        self.figure.dpi = dpi
-        w_in, h_in = self.figure.get_size_inches()
-        width_in_points, height_in_points = w_in * dpi, h_in * dpi
-
-        if orientation == 'landscape':
-            width_in_points, height_in_points = (
-                height_in_points, width_in_points)
-
-        if fmt == 'ps':
-            if not hasattr(cairo, 'PSSurface'):
-                raise RuntimeError('cairo has not been compiled with PS '
-                                   'support enabled')
-            surface = cairo.PSSurface(fobj, width_in_points, height_in_points)
-        elif fmt == 'pdf':
-            if not hasattr(cairo, 'PDFSurface'):
-                raise RuntimeError('cairo has not been compiled with PDF '
-                                   'support enabled')
-            surface = cairo.PDFSurface(fobj, width_in_points, height_in_points)
-        elif fmt in ('svg', 'svgz'):
-            if not hasattr(cairo, 'SVGSurface'):
-                raise RuntimeError('cairo has not been compiled with SVG '
-                                   'support enabled')
-            if fmt == 'svgz':
-                if isinstance(fobj, str):
-                    fobj = gzip.GzipFile(fobj, 'wb')
-                else:
-                    fobj = gzip.GzipFile(None, 'wb', fileobj=fobj)
-            surface = cairo.SVGSurface(fobj, width_in_points, height_in_points)
-        else:
-            raise ValueError(f"Unknown format: {fmt!r}")
-
-        self._renderer.dpi = self.figure.dpi
-        self._renderer.set_context(cairo.Context(surface))
-        ctx = self._renderer.gc.ctx
-
-        if orientation == 'landscape':
-            ctx.rotate(np.pi / 2)
-            ctx.translate(0, -height_in_points)
-            # Perhaps add an '%%Orientation: Landscape' comment?
-
-        self.figure.draw(self._renderer)
-
-        ctx.show_page()
-        surface.finish()
-        if fmt == 'svgz':
-            fobj.close()
-
-    print_pdf = functools.partialmethod(_save, "pdf")
-    print_ps = functools.partialmethod(_save, "ps")
-    print_svg = functools.partialmethod(_save, "svg")
-    print_svgz = functools.partialmethod(_save, "svgz")
-
-
-@_Backend.export
-class _BackendCairo(_Backend):
-    backend_version = cairo.version
-    FigureCanvas = FigureCanvasCairo
-    FigureManager = FigureManagerBase
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_docs.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_docs.py
deleted file mode 100644
index 6631dd82c9c7cdab0106a9ec939b23c6e9d50fcd..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_docs.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import pytest
-import numpy as np
-from numpy.testing import assert_array_equal, assert_equal
-from . import util
-
-
-def get_docdir():
-    # assuming that documentation tests are run from a source
-    # directory
-    return os.path.abspath(os.path.join(
-        os.path.dirname(__file__),
-        '..', '..', '..',
-        'doc', 'source', 'f2py', 'code'))
-
-
-pytestmark = pytest.mark.skipif(
-    not os.path.isdir(get_docdir()),
-    reason=('Could not find f2py documentation sources'
-            f' ({get_docdir()} does not exists)'))
-
-
-def _path(*a):
-    return os.path.join(*((get_docdir(),) + a))
-
-
-class TestDocAdvanced(util.F2PyTest):
-    # options = ['--debug-capi', '--build-dir', '/tmp/build-f2py']
-    sources = [_path('asterisk1.f90'), _path('asterisk2.f90'),
-               _path('ftype.f')]
-
-    def test_asterisk1(self):
-        foo = getattr(self.module, 'foo1')
-        assert_equal(foo(), b'123456789A12')
-
-    def test_asterisk2(self):
-        foo = getattr(self.module, 'foo2')
-        assert_equal(foo(2), b'12')
-        assert_equal(foo(12), b'123456789A12')
-        assert_equal(foo(24), b'123456789A123456789B')
-
-    def test_ftype(self):
-        ftype = self.module
-        ftype.foo()
-        assert_equal(ftype.data.a, 0)
-        ftype.data.a = 3
-        ftype.data.x = [1, 2, 3]
-        assert_equal(ftype.data.a, 3)
-        assert_array_equal(ftype.data.x,
-                           np.array([1, 2, 3], dtype=np.float32))
-        ftype.data.x[1] = 45
-        assert_array_equal(ftype.data.x,
-                           np.array([1, 45, 3], dtype=np.float32))
-
-    # TODO: implement test methods for other example Fortran codes
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/util.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/util.py
deleted file mode 100644
index 75b257cdb825c08bb00c9bd30e986d33eff87495..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/util.py
+++ /dev/null
@@ -1,439 +0,0 @@
-"""
-Utility functions for
-
-- building and importing modules on test time, using a temporary location
-- detecting if compilers are present
-- determining paths to tests
-
-"""
-import glob
-import os
-import sys
-import subprocess
-import tempfile
-import shutil
-import atexit
-import textwrap
-import re
-import pytest
-import contextlib
-import numpy
-
-from pathlib import Path
-from numpy._utils import asunicode
-from numpy.testing import temppath, IS_WASM
-from importlib import import_module
-
-#
-# Maintaining a temporary module directory
-#
-
-_module_dir = None
-_module_num = 5403
-
-if sys.platform == "cygwin":
-    NUMPY_INSTALL_ROOT = Path(__file__).parent.parent.parent
-    _module_list = list(NUMPY_INSTALL_ROOT.glob("**/*.dll"))
-
-
-def _cleanup():
-    global _module_dir
-    if _module_dir is not None:
-        try:
-            sys.path.remove(_module_dir)
-        except ValueError:
-            pass
-        try:
-            shutil.rmtree(_module_dir)
-        except OSError:
-            pass
-        _module_dir = None
-
-
-def get_module_dir():
-    global _module_dir
-    if _module_dir is None:
-        _module_dir = tempfile.mkdtemp()
-        atexit.register(_cleanup)
-        if _module_dir not in sys.path:
-            sys.path.insert(0, _module_dir)
-    return _module_dir
-
-
-def get_temp_module_name():
-    # Assume single-threaded, and the module dir usable only by this thread
-    global _module_num
-    get_module_dir()
-    name = "_test_ext_module_%d" % _module_num
-    _module_num += 1
-    if name in sys.modules:
-        # this should not be possible, but check anyway
-        raise RuntimeError("Temporary module name already in use.")
-    return name
-
-
-def _memoize(func):
-    memo = {}
-
-    def wrapper(*a, **kw):
-        key = repr((a, kw))
-        if key not in memo:
-            try:
-                memo[key] = func(*a, **kw)
-            except Exception as e:
-                memo[key] = e
-                raise
-        ret = memo[key]
-        if isinstance(ret, Exception):
-            raise ret
-        return ret
-
-    wrapper.__name__ = func.__name__
-    return wrapper
-
-
-#
-# Building modules
-#
-
-
-@_memoize
-def build_module(source_files, options=[], skip=[], only=[], module_name=None):
-    """
-    Compile and import a f2py module, built from the given files.
-
-    """
-
-    code = f"import sys; sys.path = {sys.path!r}; import numpy.f2py; numpy.f2py.main()"
-
-    d = get_module_dir()
-
-    # Copy files
-    dst_sources = []
-    f2py_sources = []
-    for fn in source_files:
-        if not os.path.isfile(fn):
-            raise RuntimeError("%s is not a file" % fn)
-        dst = os.path.join(d, os.path.basename(fn))
-        shutil.copyfile(fn, dst)
-        dst_sources.append(dst)
-
-        base, ext = os.path.splitext(dst)
-        if ext in (".f90", ".f", ".c", ".pyf"):
-            f2py_sources.append(dst)
-
-    assert f2py_sources
-
-    # Prepare options
-    if module_name is None:
-        module_name = get_temp_module_name()
-    f2py_opts = ["-c", "-m", module_name] + options + f2py_sources
-    if skip:
-        f2py_opts += ["skip:"] + skip
-    if only:
-        f2py_opts += ["only:"] + only
-
-    # Build
-    cwd = os.getcwd()
-    try:
-        os.chdir(d)
-        cmd = [sys.executable, "-c", code] + f2py_opts
-        p = subprocess.Popen(cmd,
-                             stdout=subprocess.PIPE,
-                             stderr=subprocess.STDOUT)
-        out, err = p.communicate()
-        if p.returncode != 0:
-            raise RuntimeError("Running f2py failed: %s\n%s" %
-                               (cmd[4:], asunicode(out)))
-    finally:
-        os.chdir(cwd)
-
-        # Partial cleanup
-        for fn in dst_sources:
-            os.unlink(fn)
-
-    # Rebase (Cygwin-only)
-    if sys.platform == "cygwin":
-        # If someone starts deleting modules after import, this will
-        # need to change to record how big each module is, rather than
-        # relying on rebase being able to find that from the files.
-        _module_list.extend(
-            glob.glob(os.path.join(d, "{:s}*".format(module_name)))
-        )
-        subprocess.check_call(
-            ["/usr/bin/rebase", "--database", "--oblivious", "--verbose"]
-            + _module_list
-        )
-
-
-
-    # Import
-    return import_module(module_name)
-
-
-@_memoize
-def build_code(source_code,
-               options=[],
-               skip=[],
-               only=[],
-               suffix=None,
-               module_name=None):
-    """
-    Compile and import Fortran code using f2py.
-
-    """
-    if suffix is None:
-        suffix = ".f"
-    with temppath(suffix=suffix) as path:
-        with open(path, "w") as f:
-            f.write(source_code)
-        return build_module([path],
-                            options=options,
-                            skip=skip,
-                            only=only,
-                            module_name=module_name)
-
-
-#
-# Check if compilers are available at all...
-#
-
-_compiler_status = None
-
-
-def _get_compiler_status():
-    global _compiler_status
-    if _compiler_status is not None:
-        return _compiler_status
-
-    _compiler_status = (False, False, False)
-    if IS_WASM:
-        # Can't run compiler from inside WASM.
-        return _compiler_status
-
-    # XXX: this is really ugly. But I don't know how to invoke Distutils
-    #      in a safer way...
-    code = textwrap.dedent(f"""\
-        import os
-        import sys
-        sys.path = {repr(sys.path)}
-
-        def configuration(parent_name='',top_path=None):
-            global config
-            from numpy.distutils.misc_util import Configuration
-            config = Configuration('', parent_name, top_path)
-            return config
-
-        from numpy.distutils.core import setup
-        setup(configuration=configuration)
-
-        config_cmd = config.get_config_cmd()
-        have_c = config_cmd.try_compile('void foo() {{}}')
-        print('COMPILERS:%%d,%%d,%%d' %% (have_c,
-                                          config.have_f77c(),
-                                          config.have_f90c()))
-        sys.exit(99)
-        """)
-    code = code % dict(syspath=repr(sys.path))
-
-    tmpdir = tempfile.mkdtemp()
-    try:
-        script = os.path.join(tmpdir, "setup.py")
-
-        with open(script, "w") as f:
-            f.write(code)
-
-        cmd = [sys.executable, "setup.py", "config"]
-        p = subprocess.Popen(cmd,
-                             stdout=subprocess.PIPE,
-                             stderr=subprocess.STDOUT,
-                             cwd=tmpdir)
-        out, err = p.communicate()
-    finally:
-        shutil.rmtree(tmpdir)
-
-    m = re.search(br"COMPILERS:(\d+),(\d+),(\d+)", out)
-    if m:
-        _compiler_status = (
-            bool(int(m.group(1))),
-            bool(int(m.group(2))),
-            bool(int(m.group(3))),
-        )
-    # Finished
-    return _compiler_status
-
-
-def has_c_compiler():
-    return _get_compiler_status()[0]
-
-
-def has_f77_compiler():
-    return _get_compiler_status()[1]
-
-
-def has_f90_compiler():
-    return _get_compiler_status()[2]
-
-
-#
-# Building with distutils
-#
-
-
-@_memoize
-def build_module_distutils(source_files, config_code, module_name, **kw):
-    """
-    Build a module via distutils and import it.
-
-    """
-    d = get_module_dir()
-
-    # Copy files
-    dst_sources = []
-    for fn in source_files:
-        if not os.path.isfile(fn):
-            raise RuntimeError("%s is not a file" % fn)
-        dst = os.path.join(d, os.path.basename(fn))
-        shutil.copyfile(fn, dst)
-        dst_sources.append(dst)
-
-    # Build script
-    config_code = textwrap.dedent(config_code).replace("\n", "\n    ")
-
-    code = fr"""
-import os
-import sys
-sys.path = {repr(sys.path)}
-
-def configuration(parent_name='',top_path=None):
-    from numpy.distutils.misc_util import Configuration
-    config = Configuration('', parent_name, top_path)
-    {config_code}
-    return config
-
-if __name__ == "__main__":
-    from numpy.distutils.core import setup
-    setup(configuration=configuration)
-    """
-    script = os.path.join(d, get_temp_module_name() + ".py")
-    dst_sources.append(script)
-    with open(script, "wb") as f:
-        f.write(code.encode('latin1'))
-
-    # Build
-    cwd = os.getcwd()
-    try:
-        os.chdir(d)
-        cmd = [sys.executable, script, "build_ext", "-i"]
-        p = subprocess.Popen(cmd,
-                             stdout=subprocess.PIPE,
-                             stderr=subprocess.STDOUT)
-        out, err = p.communicate()
-        if p.returncode != 0:
-            raise RuntimeError("Running distutils build failed: %s\n%s" %
-                               (cmd[4:], asstr(out)))
-    finally:
-        os.chdir(cwd)
-
-        # Partial cleanup
-        for fn in dst_sources:
-            os.unlink(fn)
-
-    # Import
-    __import__(module_name)
-    return sys.modules[module_name]
-
-
-#
-# Unittest convenience
-#
-
-
-class F2PyTest:
-    code = None
-    sources = None
-    options = []
-    skip = []
-    only = []
-    suffix = ".f"
-    module = None
-
-    @property
-    def module_name(self):
-        cls = type(self)
-        return f'_{cls.__module__.rsplit(".",1)[-1]}_{cls.__name__}_ext_module'
-
-    def setup_method(self):
-        if sys.platform == "win32":
-            pytest.skip("Fails with MinGW64 Gfortran (Issue #9673)")
-
-        if self.module is not None:
-            return
-
-        # Check compiler availability first
-        if not has_c_compiler():
-            pytest.skip("No C compiler available")
-
-        codes = []
-        if self.sources:
-            codes.extend(self.sources)
-        if self.code is not None:
-            codes.append(self.suffix)
-
-        needs_f77 = False
-        needs_f90 = False
-        needs_pyf = False
-        for fn in codes:
-            if str(fn).endswith(".f"):
-                needs_f77 = True
-            elif str(fn).endswith(".f90"):
-                needs_f90 = True
-            elif str(fn).endswith(".pyf"):
-                needs_pyf = True
-        if needs_f77 and not has_f77_compiler():
-            pytest.skip("No Fortran 77 compiler available")
-        if needs_f90 and not has_f90_compiler():
-            pytest.skip("No Fortran 90 compiler available")
-        if needs_pyf and not (has_f90_compiler() or has_f77_compiler()):
-            pytest.skip("No Fortran compiler available")
-
-        # Build the module
-        if self.code is not None:
-            self.module = build_code(
-                self.code,
-                options=self.options,
-                skip=self.skip,
-                only=self.only,
-                suffix=self.suffix,
-                module_name=self.module_name,
-            )
-
-        if self.sources is not None:
-            self.module = build_module(
-                self.sources,
-                options=self.options,
-                skip=self.skip,
-                only=self.only,
-                module_name=self.module_name,
-            )
-
-
-#
-# Helper functions
-#
-
-
-def getpath(*a):
-    # Package root
-    d = Path(numpy.f2py.__file__).parent.resolve()
-    return d.joinpath(*a)
-
-
-@contextlib.contextmanager
-def switchdir(path):
-    curpath = Path.cwd()
-    os.chdir(path)
-    try:
-        yield
-    finally:
-        os.chdir(curpath)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/comparisons.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/comparisons.py
deleted file mode 100644
index ce41de43596e305278acf6a9efb82fa49a43da1a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/comparisons.py
+++ /dev/null
@@ -1,301 +0,0 @@
-from __future__ import annotations
-
-from typing import Any
-import numpy as np
-
-c16 = np.complex128()
-f8 = np.float64()
-i8 = np.int64()
-u8 = np.uint64()
-
-c8 = np.complex64()
-f4 = np.float32()
-i4 = np.int32()
-u4 = np.uint32()
-
-dt = np.datetime64(0, "D")
-td = np.timedelta64(0, "D")
-
-b_ = np.bool_()
-
-b = bool()
-c = complex()
-f = float()
-i = int()
-
-SEQ = (0, 1, 2, 3, 4)
-
-AR_b: np.ndarray[Any, np.dtype[np.bool_]] = np.array([True])
-AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
-AR_i: np.ndarray[Any, np.dtype[np.int_]] = np.array([1])
-AR_f: np.ndarray[Any, np.dtype[np.float_]] = np.array([1.0])
-AR_c: np.ndarray[Any, np.dtype[np.complex_]] = np.array([1.0j])
-AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64("1")])
-AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64("1")])
-AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([1], dtype=object)
-
-# Arrays
-
-AR_b > AR_b
-AR_b > AR_u
-AR_b > AR_i
-AR_b > AR_f
-AR_b > AR_c
-
-AR_u > AR_b
-AR_u > AR_u
-AR_u > AR_i
-AR_u > AR_f
-AR_u > AR_c
-
-AR_i > AR_b
-AR_i > AR_u
-AR_i > AR_i
-AR_i > AR_f
-AR_i > AR_c
-
-AR_f > AR_b
-AR_f > AR_u
-AR_f > AR_i
-AR_f > AR_f
-AR_f > AR_c
-
-AR_c > AR_b
-AR_c > AR_u
-AR_c > AR_i
-AR_c > AR_f
-AR_c > AR_c
-
-AR_m > AR_b
-AR_m > AR_u
-AR_m > AR_i
-AR_b > AR_m
-AR_u > AR_m
-AR_i > AR_m
-
-AR_M > AR_M
-
-AR_O > AR_O
-1 > AR_O
-AR_O > 1
-
-# Time structures
-
-dt > dt
-
-td > td
-td > i
-td > i4
-td > i8
-td > AR_i
-td > SEQ
-
-# boolean
-
-b_ > b
-b_ > b_
-b_ > i
-b_ > i8
-b_ > i4
-b_ > u8
-b_ > u4
-b_ > f
-b_ > f8
-b_ > f4
-b_ > c
-b_ > c16
-b_ > c8
-b_ > AR_i
-b_ > SEQ
-
-# Complex
-
-c16 > c16
-c16 > f8
-c16 > i8
-c16 > c8
-c16 > f4
-c16 > i4
-c16 > b_
-c16 > b
-c16 > c
-c16 > f
-c16 > i
-c16 > AR_i
-c16 > SEQ
-
-c16 > c16
-f8 > c16
-i8 > c16
-c8 > c16
-f4 > c16
-i4 > c16
-b_ > c16
-b > c16
-c > c16
-f > c16
-i > c16
-AR_i > c16
-SEQ > c16
-
-c8 > c16
-c8 > f8
-c8 > i8
-c8 > c8
-c8 > f4
-c8 > i4
-c8 > b_
-c8 > b
-c8 > c
-c8 > f
-c8 > i
-c8 > AR_i
-c8 > SEQ
-
-c16 > c8
-f8 > c8
-i8 > c8
-c8 > c8
-f4 > c8
-i4 > c8
-b_ > c8
-b > c8
-c > c8
-f > c8
-i > c8
-AR_i > c8
-SEQ > c8
-
-# Float
-
-f8 > f8
-f8 > i8
-f8 > f4
-f8 > i4
-f8 > b_
-f8 > b
-f8 > c
-f8 > f
-f8 > i
-f8 > AR_i
-f8 > SEQ
-
-f8 > f8
-i8 > f8
-f4 > f8
-i4 > f8
-b_ > f8
-b > f8
-c > f8
-f > f8
-i > f8
-AR_i > f8
-SEQ > f8
-
-f4 > f8
-f4 > i8
-f4 > f4
-f4 > i4
-f4 > b_
-f4 > b
-f4 > c
-f4 > f
-f4 > i
-f4 > AR_i
-f4 > SEQ
-
-f8 > f4
-i8 > f4
-f4 > f4
-i4 > f4
-b_ > f4
-b > f4
-c > f4
-f > f4
-i > f4
-AR_i > f4
-SEQ > f4
-
-# Int
-
-i8 > i8
-i8 > u8
-i8 > i4
-i8 > u4
-i8 > b_
-i8 > b
-i8 > c
-i8 > f
-i8 > i
-i8 > AR_i
-i8 > SEQ
-
-u8 > u8
-u8 > i4
-u8 > u4
-u8 > b_
-u8 > b
-u8 > c
-u8 > f
-u8 > i
-u8 > AR_i
-u8 > SEQ
-
-i8 > i8
-u8 > i8
-i4 > i8
-u4 > i8
-b_ > i8
-b > i8
-c > i8
-f > i8
-i > i8
-AR_i > i8
-SEQ > i8
-
-u8 > u8
-i4 > u8
-u4 > u8
-b_ > u8
-b > u8
-c > u8
-f > u8
-i > u8
-AR_i > u8
-SEQ > u8
-
-i4 > i8
-i4 > i4
-i4 > i
-i4 > b_
-i4 > b
-i4 > AR_i
-i4 > SEQ
-
-u4 > i8
-u4 > i4
-u4 > u8
-u4 > u4
-u4 > i
-u4 > b_
-u4 > b
-u4 > AR_i
-u4 > SEQ
-
-i8 > i4
-i4 > i4
-i > i4
-b_ > i4
-b > i4
-AR_i > i4
-SEQ > i4
-
-i8 > u4
-i4 > u4
-u8 > u4
-u4 > u4
-b_ > u4
-b > u4
-i > u4
-AR_i > u4
-SEQ > u4
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/openai/datalib/numpy_helper.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/openai/datalib/numpy_helper.py
deleted file mode 100644
index fb80f2ae5457588778063a27e2e8a736888dceec..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/openai/datalib/numpy_helper.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from openai.datalib.common import INSTRUCTIONS, MissingDependencyError
-
-try:
-    import numpy
-except ImportError:
-    numpy = None
-
-HAS_NUMPY = bool(numpy)
-
-NUMPY_INSTRUCTIONS = INSTRUCTIONS.format(library="numpy")
-
-
-def assert_has_numpy():
-    if not HAS_NUMPY:
-        raise MissingDependencyError(NUMPY_INSTRUCTIONS)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/__init__.py
deleted file mode 100644
index c804b81c49e7c8abb406f2132909df6036df1c09..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# ruff: noqa: TCH004
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    # import modules that have public classes/functions
-    from pandas.io import (
-        formats,
-        json,
-        stata,
-    )
-
-    # mark only those modules as public
-    __all__ = ["formats", "json", "stata"]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/boolean/test_construction.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/boolean/test_construction.py
deleted file mode 100644
index d26eea19c06e9764023f45cc6889d382d3a607b2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/boolean/test_construction.py
+++ /dev/null
@@ -1,326 +0,0 @@
-import numpy as np
-import pytest
-
-import pandas as pd
-import pandas._testing as tm
-from pandas.arrays import BooleanArray
-from pandas.core.arrays.boolean import coerce_to_array
-
-
-def test_boolean_array_constructor():
-    values = np.array([True, False, True, False], dtype="bool")
-    mask = np.array([False, False, False, True], dtype="bool")
-
-    result = BooleanArray(values, mask)
-    expected = pd.array([True, False, True, None], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-    with pytest.raises(TypeError, match="values should be boolean numpy array"):
-        BooleanArray(values.tolist(), mask)
-
-    with pytest.raises(TypeError, match="mask should be boolean numpy array"):
-        BooleanArray(values, mask.tolist())
-
-    with pytest.raises(TypeError, match="values should be boolean numpy array"):
-        BooleanArray(values.astype(int), mask)
-
-    with pytest.raises(TypeError, match="mask should be boolean numpy array"):
-        BooleanArray(values, None)
-
-    with pytest.raises(ValueError, match="values.shape must match mask.shape"):
-        BooleanArray(values.reshape(1, -1), mask)
-
-    with pytest.raises(ValueError, match="values.shape must match mask.shape"):
-        BooleanArray(values, mask.reshape(1, -1))
-
-
-def test_boolean_array_constructor_copy():
-    values = np.array([True, False, True, False], dtype="bool")
-    mask = np.array([False, False, False, True], dtype="bool")
-
-    result = BooleanArray(values, mask)
-    assert result._data is values
-    assert result._mask is mask
-
-    result = BooleanArray(values, mask, copy=True)
-    assert result._data is not values
-    assert result._mask is not mask
-
-
-def test_to_boolean_array():
-    expected = BooleanArray(
-        np.array([True, False, True]), np.array([False, False, False])
-    )
-
-    result = pd.array([True, False, True], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-    result = pd.array(np.array([True, False, True]), dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-    result = pd.array(np.array([True, False, True], dtype=object), dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-    # with missing values
-    expected = BooleanArray(
-        np.array([True, False, True]), np.array([False, False, True])
-    )
-
-    result = pd.array([True, False, None], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-    result = pd.array(np.array([True, False, None], dtype=object), dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_to_boolean_array_all_none():
-    expected = BooleanArray(np.array([True, True, True]), np.array([True, True, True]))
-
-    result = pd.array([None, None, None], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-    result = pd.array(np.array([None, None, None], dtype=object), dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "a, b",
-    [
-        ([True, False, None, np.nan, pd.NA], [True, False, None, None, None]),
-        ([True, np.nan], [True, None]),
-        ([True, pd.NA], [True, None]),
-        ([np.nan, np.nan], [None, None]),
-        (np.array([np.nan, np.nan], dtype=float), [None, None]),
-    ],
-)
-def test_to_boolean_array_missing_indicators(a, b):
-    result = pd.array(a, dtype="boolean")
-    expected = pd.array(b, dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "values",
-    [
-        ["foo", "bar"],
-        ["1", "2"],
-        # "foo",
-        [1, 2],
-        [1.0, 2.0],
-        pd.date_range("20130101", periods=2),
-        np.array(["foo"]),
-        np.array([1, 2]),
-        np.array([1.0, 2.0]),
-        [np.nan, {"a": 1}],
-    ],
-)
-def test_to_boolean_array_error(values):
-    # error in converting existing arrays to BooleanArray
-    msg = "Need to pass bool-like value"
-    with pytest.raises(TypeError, match=msg):
-        pd.array(values, dtype="boolean")
-
-
-def test_to_boolean_array_from_integer_array():
-    result = pd.array(np.array([1, 0, 1, 0]), dtype="boolean")
-    expected = pd.array([True, False, True, False], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-    # with missing values
-    result = pd.array(np.array([1, 0, 1, None]), dtype="boolean")
-    expected = pd.array([True, False, True, None], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_to_boolean_array_from_float_array():
-    result = pd.array(np.array([1.0, 0.0, 1.0, 0.0]), dtype="boolean")
-    expected = pd.array([True, False, True, False], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-    # with missing values
-    result = pd.array(np.array([1.0, 0.0, 1.0, np.nan]), dtype="boolean")
-    expected = pd.array([True, False, True, None], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_to_boolean_array_integer_like():
-    # integers of 0's and 1's
-    result = pd.array([1, 0, 1, 0], dtype="boolean")
-    expected = pd.array([True, False, True, False], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-    # with missing values
-    result = pd.array([1, 0, 1, None], dtype="boolean")
-    expected = pd.array([True, False, True, None], dtype="boolean")
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_coerce_to_array():
-    # TODO this is currently not public API
-    values = np.array([True, False, True, False], dtype="bool")
-    mask = np.array([False, False, False, True], dtype="bool")
-    result = BooleanArray(*coerce_to_array(values, mask=mask))
-    expected = BooleanArray(values, mask)
-    tm.assert_extension_array_equal(result, expected)
-    assert result._data is values
-    assert result._mask is mask
-    result = BooleanArray(*coerce_to_array(values, mask=mask, copy=True))
-    expected = BooleanArray(values, mask)
-    tm.assert_extension_array_equal(result, expected)
-    assert result._data is not values
-    assert result._mask is not mask
-
-    # mixed missing from values and mask
-    values = [True, False, None, False]
-    mask = np.array([False, False, False, True], dtype="bool")
-    result = BooleanArray(*coerce_to_array(values, mask=mask))
-    expected = BooleanArray(
-        np.array([True, False, True, True]), np.array([False, False, True, True])
-    )
-    tm.assert_extension_array_equal(result, expected)
-    result = BooleanArray(*coerce_to_array(np.array(values, dtype=object), mask=mask))
-    tm.assert_extension_array_equal(result, expected)
-    result = BooleanArray(*coerce_to_array(values, mask=mask.tolist()))
-    tm.assert_extension_array_equal(result, expected)
-
-    # raise errors for wrong dimension
-    values = np.array([True, False, True, False], dtype="bool")
-    mask = np.array([False, False, False, True], dtype="bool")
-
-    # passing 2D values is OK as long as no mask
-    coerce_to_array(values.reshape(1, -1))
-
-    with pytest.raises(ValueError, match="values.shape and mask.shape must match"):
-        coerce_to_array(values.reshape(1, -1), mask=mask)
-
-    with pytest.raises(ValueError, match="values.shape and mask.shape must match"):
-        coerce_to_array(values, mask=mask.reshape(1, -1))
-
-
-def test_coerce_to_array_from_boolean_array():
-    # passing BooleanArray to coerce_to_array
-    values = np.array([True, False, True, False], dtype="bool")
-    mask = np.array([False, False, False, True], dtype="bool")
-    arr = BooleanArray(values, mask)
-    result = BooleanArray(*coerce_to_array(arr))
-    tm.assert_extension_array_equal(result, arr)
-    # no copy
-    assert result._data is arr._data
-    assert result._mask is arr._mask
-
-    result = BooleanArray(*coerce_to_array(arr), copy=True)
-    tm.assert_extension_array_equal(result, arr)
-    assert result._data is not arr._data
-    assert result._mask is not arr._mask
-
-    with pytest.raises(ValueError, match="cannot pass mask for BooleanArray input"):
-        coerce_to_array(arr, mask=mask)
-
-
-def test_coerce_to_numpy_array():
-    # with missing values -> object dtype
-    arr = pd.array([True, False, None], dtype="boolean")
-    result = np.array(arr)
-    expected = np.array([True, False, pd.NA], dtype="object")
-    tm.assert_numpy_array_equal(result, expected)
-
-    # also with no missing values -> object dtype
-    arr = pd.array([True, False, True], dtype="boolean")
-    result = np.array(arr)
-    expected = np.array([True, False, True], dtype="object")
-    tm.assert_numpy_array_equal(result, expected)
-
-    # force bool dtype
-    result = np.array(arr, dtype="bool")
-    expected = np.array([True, False, True], dtype="bool")
-    tm.assert_numpy_array_equal(result, expected)
-    # with missing values will raise error
-    arr = pd.array([True, False, None], dtype="boolean")
-    msg = (
-        "cannot convert to 'bool'-dtype NumPy array with missing values. "
-        "Specify an appropriate 'na_value' for this dtype."
-    )
-    with pytest.raises(ValueError, match=msg):
-        np.array(arr, dtype="bool")
-
-
-def test_to_boolean_array_from_strings():
-    result = BooleanArray._from_sequence_of_strings(
-        np.array(["True", "False", "1", "1.0", "0", "0.0", np.nan], dtype=object)
-    )
-    expected = BooleanArray(
-        np.array([True, False, True, True, False, False, False]),
-        np.array([False, False, False, False, False, False, True]),
-    )
-
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_to_boolean_array_from_strings_invalid_string():
-    with pytest.raises(ValueError, match="cannot be cast"):
-        BooleanArray._from_sequence_of_strings(["donkey"])
-
-
-@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
-def test_to_numpy(box):
-    con = pd.Series if box else pd.array
-    # default (with or without missing values) -> object dtype
-    arr = con([True, False, True], dtype="boolean")
-    result = arr.to_numpy()
-    expected = np.array([True, False, True], dtype="object")
-    tm.assert_numpy_array_equal(result, expected)
-
-    arr = con([True, False, None], dtype="boolean")
-    result = arr.to_numpy()
-    expected = np.array([True, False, pd.NA], dtype="object")
-    tm.assert_numpy_array_equal(result, expected)
-
-    arr = con([True, False, None], dtype="boolean")
-    result = arr.to_numpy(dtype="str")
-    expected = np.array([True, False, pd.NA], dtype=f"{tm.ENDIAN}U5")
-    tm.assert_numpy_array_equal(result, expected)
-
-    # no missing values -> can convert to bool, otherwise raises
-    arr = con([True, False, True], dtype="boolean")
-    result = arr.to_numpy(dtype="bool")
-    expected = np.array([True, False, True], dtype="bool")
-    tm.assert_numpy_array_equal(result, expected)
-
-    arr = con([True, False, None], dtype="boolean")
-    with pytest.raises(ValueError, match="cannot convert to 'bool'-dtype"):
-        result = arr.to_numpy(dtype="bool")
-
-    # specify dtype and na_value
-    arr = con([True, False, None], dtype="boolean")
-    result = arr.to_numpy(dtype=object, na_value=None)
-    expected = np.array([True, False, None], dtype="object")
-    tm.assert_numpy_array_equal(result, expected)
-
-    result = arr.to_numpy(dtype=bool, na_value=False)
-    expected = np.array([True, False, False], dtype="bool")
-    tm.assert_numpy_array_equal(result, expected)
-
-    result = arr.to_numpy(dtype="int64", na_value=-99)
-    expected = np.array([1, 0, -99], dtype="int64")
-    tm.assert_numpy_array_equal(result, expected)
-
-    result = arr.to_numpy(dtype="float64", na_value=np.nan)
-    expected = np.array([1, 0, np.nan], dtype="float64")
-    tm.assert_numpy_array_equal(result, expected)
-
-    # converting to int or float without specifying na_value raises
-    with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"):
-        arr.to_numpy(dtype="int64")
-    with pytest.raises(ValueError, match="cannot convert to 'float64'-dtype"):
-        arr.to_numpy(dtype="float64")
-
-
-def test_to_numpy_copy():
-    # to_numpy can be zero-copy if no missing values
-    arr = pd.array([True, False, True], dtype="boolean")
-    result = arr.to_numpy(dtype=bool)
-    result[0] = False
-    tm.assert_extension_array_equal(
-        arr, pd.array([False, False, True], dtype="boolean")
-    )
-
-    arr = pd.array([True, False, True], dtype="boolean")
-    result = arr.to_numpy(dtype=bool, copy=True)
-    result[0] = False
-    tm.assert_extension_array_equal(arr, pd.array([True, False, True], dtype="boolean"))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/floating/test_concat.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/floating/test_concat.py
deleted file mode 100644
index 2174a834aa959b88d899971f83247258a94476e3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/floating/test_concat.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import pytest
-
-import pandas as pd
-import pandas._testing as tm
-
-
-@pytest.mark.parametrize(
-    "to_concat_dtypes, result_dtype",
-    [
-        (["Float64", "Float64"], "Float64"),
-        (["Float32", "Float64"], "Float64"),
-        (["Float32", "Float32"], "Float32"),
-    ],
-)
-def test_concat_series(to_concat_dtypes, result_dtype):
-    result = pd.concat([pd.Series([1, 2, pd.NA], dtype=t) for t in to_concat_dtypes])
-    expected = pd.concat([pd.Series([1, 2, pd.NA], dtype=object)] * 2).astype(
-        result_dtype
-    )
-    tm.assert_series_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py
deleted file mode 100644
index 47efc43d5eae03bab42b00f9cd94758a069735b8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
-    DataFrame,
-    IndexSlice,
-    MultiIndex,
-    date_range,
-)
-import pandas._testing as tm
-
-
-@pytest.fixture
-def df():
-    #                        c1
-    # 2016-01-01 00:00:00 a   0
-    #                     b   1
-    #                     c   2
-    # 2016-01-01 12:00:00 a   3
-    #                     b   4
-    #                     c   5
-    # 2016-01-02 00:00:00 a   6
-    #                     b   7
-    #                     c   8
-    # 2016-01-02 12:00:00 a   9
-    #                     b  10
-    #                     c  11
-    # 2016-01-03 00:00:00 a  12
-    #                     b  13
-    #                     c  14
-    dr = date_range("2016-01-01", "2016-01-03", freq="12H")
-    abc = ["a", "b", "c"]
-    mi = MultiIndex.from_product([dr, abc])
-    frame = DataFrame({"c1": range(0, 15)}, index=mi)
-    return frame
-
-
-def test_partial_string_matching_single_index(df):
-    # partial string matching on a single index
-    for df_swap in [df.swaplevel(), df.swaplevel(0), df.swaplevel(0, 1)]:
-        df_swap = df_swap.sort_index()
-        just_a = df_swap.loc["a"]
-        result = just_a.loc["2016-01-01"]
-        expected = df.loc[IndexSlice[:, "a"], :].iloc[0:2]
-        expected.index = expected.index.droplevel(1)
-        tm.assert_frame_equal(result, expected)
-
-
-def test_get_loc_partial_timestamp_multiindex(df):
-    mi = df.index
-    key = ("2016-01-01", "a")
-    loc = mi.get_loc(key)
-
-    expected = np.zeros(len(mi), dtype=bool)
-    expected[[0, 3]] = True
-    tm.assert_numpy_array_equal(loc, expected)
-
-    key2 = ("2016-01-02", "a")
-    loc2 = mi.get_loc(key2)
-    expected2 = np.zeros(len(mi), dtype=bool)
-    expected2[[6, 9]] = True
-    tm.assert_numpy_array_equal(loc2, expected2)
-
-    key3 = ("2016-01", "a")
-    loc3 = mi.get_loc(key3)
-    expected3 = np.zeros(len(mi), dtype=bool)
-    expected3[mi.get_level_values(1).get_loc("a")] = True
-    tm.assert_numpy_array_equal(loc3, expected3)
-
-    key4 = ("2016", "a")
-    loc4 = mi.get_loc(key4)
-    expected4 = expected3
-    tm.assert_numpy_array_equal(loc4, expected4)
-
-    # non-monotonic
-    taker = np.arange(len(mi), dtype=np.intp)
-    taker[::2] = taker[::-2]
-    mi2 = mi.take(taker)
-    loc5 = mi2.get_loc(key)
-    expected5 = np.zeros(len(mi2), dtype=bool)
-    expected5[[3, 14]] = True
-    tm.assert_numpy_array_equal(loc5, expected5)
-
-
-def test_partial_string_timestamp_multiindex(df):
-    # GH10331
-    df_swap = df.swaplevel(0, 1).sort_index()
-    SLC = IndexSlice
-
-    # indexing with IndexSlice
-    result = df.loc[SLC["2016-01-01":"2016-02-01", :], :]
-    expected = df
-    tm.assert_frame_equal(result, expected)
-
-    # match on secondary index
-    result = df_swap.loc[SLC[:, "2016-01-01":"2016-01-01"], :]
-    expected = df_swap.iloc[[0, 1, 5, 6, 10, 11]]
-    tm.assert_frame_equal(result, expected)
-
-    # partial string match on year only
-    result = df.loc["2016"]
-    expected = df
-    tm.assert_frame_equal(result, expected)
-
-    # partial string match on date
-    result = df.loc["2016-01-01"]
-    expected = df.iloc[0:6]
-    tm.assert_frame_equal(result, expected)
-
-    # partial string match on date and hour, from middle
-    result = df.loc["2016-01-02 12"]
-    # hourly resolution, same as index.levels[0], so we are _not_ slicing on
-    #  that level, so that level gets dropped
-    expected = df.iloc[9:12].droplevel(0)
-    tm.assert_frame_equal(result, expected)
-
-    # partial string match on secondary index
-    result = df_swap.loc[SLC[:, "2016-01-02"], :]
-    expected = df_swap.iloc[[2, 3, 7, 8, 12, 13]]
-    tm.assert_frame_equal(result, expected)
-
-    # tuple selector with partial string match on date
-    # "2016-01-01" has daily resolution, so _is_ a slice on the first level.
-    result = df.loc[("2016-01-01", "a"), :]
-    expected = df.iloc[[0, 3]]
-    expected = df.iloc[[0, 3]].droplevel(1)
-    tm.assert_frame_equal(result, expected)
-
-    # Slicing date on first level should break (of course) bc the DTI is the
-    #  second level on df_swap
-    with pytest.raises(KeyError, match="'2016-01-01'"):
-        df_swap.loc["2016-01-01"]
-
-
-def test_partial_string_timestamp_multiindex_str_key_raises(df):
-    # Even though this syntax works on a single index, this is somewhat
-    # ambiguous and we don't want to extend this behavior forward to work
-    # in multi-indexes. This would amount to selecting a scalar from a
-    # column.
-    with pytest.raises(KeyError, match="'2016-01-01'"):
-        df["2016-01-01"]
-
-
-def test_partial_string_timestamp_multiindex_daily_resolution(df):
-    # GH12685 (partial string with daily resolution or below)
-    result = df.loc[IndexSlice["2013-03":"2013-03", :], :]
-    expected = df.iloc[118:180]
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/ranges/test_range.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/ranges/test_range.py
deleted file mode 100644
index 5f137df281fa373e781dc2920124bd6396fa3948..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/ranges/test_range.py
+++ /dev/null
@@ -1,609 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas.core.dtypes.common import ensure_platform_int
-
-import pandas as pd
-from pandas import (
-    Index,
-    RangeIndex,
-)
-import pandas._testing as tm
-
-# aliases to make some tests easier to read
-RI = RangeIndex
-
-
-class TestRangeIndex:
-    @pytest.fixture
-    def simple_index(self):
-        return RangeIndex(start=0, stop=20, step=2)
-
-    def test_constructor_unwraps_index(self):
-        result = RangeIndex(1, 3)
-        expected = np.array([1, 2], dtype=np.int64)
-        tm.assert_numpy_array_equal(result._data, expected)
-
-    def test_can_hold_identifiers(self, simple_index):
-        idx = simple_index
-        key = idx[0]
-        assert idx._can_hold_identifiers_and_holds_name(key) is False
-
-    def test_too_many_names(self, simple_index):
-        index = simple_index
-        with pytest.raises(ValueError, match="^Length"):
-            index.names = ["roger", "harold"]
-
-    @pytest.mark.parametrize(
-        "index, start, stop, step",
-        [
-            (RangeIndex(5), 0, 5, 1),
-            (RangeIndex(0, 5), 0, 5, 1),
-            (RangeIndex(5, step=2), 0, 5, 2),
-            (RangeIndex(1, 5, 2), 1, 5, 2),
-        ],
-    )
-    def test_start_stop_step_attrs(self, index, start, stop, step):
-        # GH 25710
-        assert index.start == start
-        assert index.stop == stop
-        assert index.step == step
-
-    def test_copy(self):
-        i = RangeIndex(5, name="Foo")
-        i_copy = i.copy()
-        assert i_copy is not i
-        assert i_copy.identical(i)
-        assert i_copy._range == range(0, 5, 1)
-        assert i_copy.name == "Foo"
-
-    def test_repr(self):
-        i = RangeIndex(5, name="Foo")
-        result = repr(i)
-        expected = "RangeIndex(start=0, stop=5, step=1, name='Foo')"
-        assert result == expected
-
-        result = eval(result)
-        tm.assert_index_equal(result, i, exact=True)
-
-        i = RangeIndex(5, 0, -1)
-        result = repr(i)
-        expected = "RangeIndex(start=5, stop=0, step=-1)"
-        assert result == expected
-
-        result = eval(result)
-        tm.assert_index_equal(result, i, exact=True)
-
-    def test_insert(self):
-        idx = RangeIndex(5, name="Foo")
-        result = idx[1:4]
-
-        # test 0th element
-        tm.assert_index_equal(idx[0:4], result.insert(0, idx[0]), exact="equiv")
-
-        # GH 18295 (test missing)
-        expected = Index([0, np.nan, 1, 2, 3, 4], dtype=np.float64)
-        for na in [np.nan, None, pd.NA]:
-            result = RangeIndex(5).insert(1, na)
-            tm.assert_index_equal(result, expected)
-
-        result = RangeIndex(5).insert(1, pd.NaT)
-        expected = Index([0, pd.NaT, 1, 2, 3, 4], dtype=object)
-        tm.assert_index_equal(result, expected)
-
-    def test_insert_edges_preserves_rangeindex(self):
-        idx = Index(range(4, 9, 2))
-
-        result = idx.insert(0, 2)
-        expected = Index(range(2, 9, 2))
-        tm.assert_index_equal(result, expected, exact=True)
-
-        result = idx.insert(3, 10)
-        expected = Index(range(4, 11, 2))
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_insert_middle_preserves_rangeindex(self):
-        # insert in the middle
-        idx = Index(range(0, 3, 2))
-        result = idx.insert(1, 1)
-        expected = Index(range(3))
-        tm.assert_index_equal(result, expected, exact=True)
-
-        idx = idx * 2
-        result = idx.insert(1, 2)
-        expected = expected * 2
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_delete(self):
-        idx = RangeIndex(5, name="Foo")
-        expected = idx[1:]
-        result = idx.delete(0)
-        tm.assert_index_equal(result, expected, exact=True)
-        assert result.name == expected.name
-
-        expected = idx[:-1]
-        result = idx.delete(-1)
-        tm.assert_index_equal(result, expected, exact=True)
-        assert result.name == expected.name
-
-        msg = "index 5 is out of bounds for axis 0 with size 5"
-        with pytest.raises((IndexError, ValueError), match=msg):
-            # either depending on numpy version
-            result = idx.delete(len(idx))
-
-    def test_delete_preserves_rangeindex(self):
-        idx = Index(range(2), name="foo")
-
-        result = idx.delete([1])
-        expected = Index(range(1), name="foo")
-        tm.assert_index_equal(result, expected, exact=True)
-
-        result = idx.delete(1)
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_delete_preserves_rangeindex_middle(self):
-        idx = Index(range(3), name="foo")
-        result = idx.delete(1)
-        expected = idx[::2]
-        tm.assert_index_equal(result, expected, exact=True)
-
-        result = idx.delete(-2)
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_delete_preserves_rangeindex_list_at_end(self):
-        idx = RangeIndex(0, 6, 1)
-
-        loc = [2, 3, 4, 5]
-        result = idx.delete(loc)
-        expected = idx[:2]
-        tm.assert_index_equal(result, expected, exact=True)
-
-        result = idx.delete(loc[::-1])
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_delete_preserves_rangeindex_list_middle(self):
-        idx = RangeIndex(0, 6, 1)
-
-        loc = [1, 2, 3, 4]
-        result = idx.delete(loc)
-        expected = RangeIndex(0, 6, 5)
-        tm.assert_index_equal(result, expected, exact=True)
-
-        result = idx.delete(loc[::-1])
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_delete_all_preserves_rangeindex(self):
-        idx = RangeIndex(0, 6, 1)
-
-        loc = [0, 1, 2, 3, 4, 5]
-        result = idx.delete(loc)
-        expected = idx[:0]
-        tm.assert_index_equal(result, expected, exact=True)
-
-        result = idx.delete(loc[::-1])
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_delete_not_preserving_rangeindex(self):
-        idx = RangeIndex(0, 6, 1)
-
-        loc = [0, 3, 5]
-        result = idx.delete(loc)
-        expected = Index([1, 2, 4])
-        tm.assert_index_equal(result, expected, exact=True)
-
-        result = idx.delete(loc[::-1])
-        tm.assert_index_equal(result, expected, exact=True)
-
-    def test_view(self):
-        i = RangeIndex(0, name="Foo")
-        i_view = i.view()
-        assert i_view.name == "Foo"
-
-        i_view = i.view("i8")
-        tm.assert_numpy_array_equal(i.values, i_view)
-
-        i_view = i.view(RangeIndex)
-        tm.assert_index_equal(i, i_view)
-
-    def test_dtype(self, simple_index):
-        index = simple_index
-        assert index.dtype == np.int64
-
-    def test_cache(self):
-        # GH 26565, GH26617, GH35432, GH53387
-        # This test checks whether _cache has been set.
-        # Calling RangeIndex._cache["_data"] creates an int64 array of the same length
-        # as the RangeIndex and stores it in _cache.
-        idx = RangeIndex(0, 100, 10)
-
-        assert idx._cache == {}
-
-        repr(idx)
-        assert idx._cache == {}
-
-        str(idx)
-        assert idx._cache == {}
-
-        idx.get_loc(20)
-        assert idx._cache == {}
-
-        90 in idx  # True
-        assert idx._cache == {}
-
-        91 in idx  # False
-        assert idx._cache == {}
-
-        idx.all()
-        assert idx._cache == {}
-
-        idx.any()
-        assert idx._cache == {}
-
-        for _ in idx:
-            pass
-        assert idx._cache == {}
-
-        idx.format()
-        assert idx._cache == {}
-
-        df = pd.DataFrame({"a": range(10)}, index=idx)
-
-        str(df)
-        assert idx._cache == {}
-
-        df.loc[50]
-        assert idx._cache == {}
-
-        with pytest.raises(KeyError, match="51"):
-            df.loc[51]
-        assert idx._cache == {}
-
-        df.loc[10:50]
-        assert idx._cache == {}
-
-        df.iloc[5:10]
-        assert idx._cache == {}
-
-        # after calling take, _cache may contain other keys, but not "_data"
-        idx.take([3, 0, 1])
-        assert "_data" not in idx._cache
-
-        df.loc[[50]]
-        assert "_data" not in idx._cache
-
-        df.iloc[[5, 6, 7, 8, 9]]
-        assert "_data" not in idx._cache
-
-        # idx._cache should contain a _data entry after call to idx._data
-        idx._data
-        assert isinstance(idx._data, np.ndarray)
-        assert idx._data is idx._data  # check cached value is reused
-        assert "_data" in idx._cache
-        expected = np.arange(0, 100, 10, dtype="int64")
-        tm.assert_numpy_array_equal(idx._cache["_data"], expected)
-
-    def test_is_monotonic(self):
-        index = RangeIndex(0, 20, 2)
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_decreasing is False
-        assert index._is_strictly_monotonic_increasing is True
-        assert index._is_strictly_monotonic_decreasing is False
-
-        index = RangeIndex(4, 0, -1)
-        assert index.is_monotonic_increasing is False
-        assert index._is_strictly_monotonic_increasing is False
-        assert index.is_monotonic_decreasing is True
-        assert index._is_strictly_monotonic_decreasing is True
-
-        index = RangeIndex(1, 2)
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_decreasing is True
-        assert index._is_strictly_monotonic_increasing is True
-        assert index._is_strictly_monotonic_decreasing is True
-
-        index = RangeIndex(2, 1)
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_decreasing is True
-        assert index._is_strictly_monotonic_increasing is True
-        assert index._is_strictly_monotonic_decreasing is True
-
-        index = RangeIndex(1, 1)
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_increasing is True
-        assert index.is_monotonic_decreasing is True
-        assert index._is_strictly_monotonic_increasing is True
-        assert index._is_strictly_monotonic_decreasing is True
-
-    @pytest.mark.parametrize(
-        "left,right",
-        [
-            (RangeIndex(0, 9, 2), RangeIndex(0, 10, 2)),
-            (RangeIndex(0), RangeIndex(1, -1, 3)),
-            (RangeIndex(1, 2, 3), RangeIndex(1, 3, 4)),
-            (RangeIndex(0, -9, -2), RangeIndex(0, -10, -2)),
-        ],
-    )
-    def test_equals_range(self, left, right):
-        assert left.equals(right)
-        assert right.equals(left)
-
-    def test_logical_compat(self, simple_index):
-        idx = simple_index
-        assert idx.all() == idx.values.all()
-        assert idx.any() == idx.values.any()
-
-    def test_identical(self, simple_index):
-        index = simple_index
-        i = Index(index.copy())
-        assert i.identical(index)
-
-        # we don't allow object dtype for RangeIndex
-        if isinstance(index, RangeIndex):
-            return
-
-        same_values_different_type = Index(i, dtype=object)
-        assert not i.identical(same_values_different_type)
-
-        i = index.copy(dtype=object)
-        i = i.rename("foo")
-        same_values = Index(i, dtype=object)
-        assert same_values.identical(index.copy(dtype=object))
-
-        assert not i.identical(index)
-        assert Index(same_values, name="foo", dtype=object).identical(i)
-
-        assert not index.copy(dtype=object).identical(index.copy(dtype="int64"))
-
-    def test_nbytes(self):
-        # memory savings vs int index
-        idx = RangeIndex(0, 1000)
-        assert idx.nbytes < Index(idx._values).nbytes / 10
-
-        # constant memory usage
-        i2 = RangeIndex(0, 10)
-        assert idx.nbytes == i2.nbytes
-
-    @pytest.mark.parametrize(
-        "start,stop,step",
-        [
-            # can't
-            ("foo", "bar", "baz"),
-            # shouldn't
-            ("0", "1", "2"),
-        ],
-    )
-    def test_cant_or_shouldnt_cast(self, start, stop, step):
-        msg = f"Wrong type {type(start)} for value {start}"
-        with pytest.raises(TypeError, match=msg):
-            RangeIndex(start, stop, step)
-
-    def test_view_index(self, simple_index):
-        index = simple_index
-        index.view(Index)
-
-    def test_prevent_casting(self, simple_index):
-        index = simple_index
-        result = index.astype("O")
-        assert result.dtype == np.object_
-
-    def test_repr_roundtrip(self, simple_index):
-        index = simple_index
-        tm.assert_index_equal(eval(repr(index)), index)
-
-    def test_slice_keep_name(self):
-        idx = RangeIndex(1, 2, name="asdf")
-        assert idx.name == idx[1:].name
-
-    @pytest.mark.parametrize(
-        "index",
-        [
-            RangeIndex(start=0, stop=20, step=2, name="foo"),
-            RangeIndex(start=18, stop=-1, step=-2, name="bar"),
-        ],
-        ids=["index_inc", "index_dec"],
-    )
-    def test_has_duplicates(self, index):
-        assert index.is_unique
-        assert not index.has_duplicates
-
-    def test_extended_gcd(self, simple_index):
-        index = simple_index
-        result = index._extended_gcd(6, 10)
-        assert result[0] == result[1] * 6 + result[2] * 10
-        assert 2 == result[0]
-
-        result = index._extended_gcd(10, 6)
-        assert 2 == result[1] * 10 + result[2] * 6
-        assert 2 == result[0]
-
-    def test_min_fitting_element(self):
-        result = RangeIndex(0, 20, 2)._min_fitting_element(1)
-        assert 2 == result
-
-        result = RangeIndex(1, 6)._min_fitting_element(1)
-        assert 1 == result
-
-        result = RangeIndex(18, -2, -2)._min_fitting_element(1)
-        assert 2 == result
-
-        result = RangeIndex(5, 0, -1)._min_fitting_element(1)
-        assert 1 == result
-
-        big_num = 500000000000000000000000
-
-        result = RangeIndex(5, big_num * 2, 1)._min_fitting_element(big_num)
-        assert big_num == result
-
-    def test_slice_specialised(self, simple_index):
-        index = simple_index
-        index.name = "foo"
-
-        # scalar indexing
-        res = index[1]
-        expected = 2
-        assert res == expected
-
-        res = index[-1]
-        expected = 18
-        assert res == expected
-
-        # slicing
-        # slice value completion
-        index_slice = index[:]
-        expected = index
-        tm.assert_index_equal(index_slice, expected)
-
-        # positive slice values
-        index_slice = index[7:10:2]
-        expected = Index([14, 18], name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-        # negative slice values
-        index_slice = index[-1:-5:-2]
-        expected = Index([18, 14], name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-        # stop overshoot
-        index_slice = index[2:100:4]
-        expected = Index([4, 12], name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-        # reverse
-        index_slice = index[::-1]
-        expected = Index(index.values[::-1], name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-        index_slice = index[-8::-1]
-        expected = Index([4, 2, 0], name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-        index_slice = index[-40::-1]
-        expected = Index(np.array([], dtype=np.int64), name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-        index_slice = index[40::-1]
-        expected = Index(index.values[40::-1], name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-        index_slice = index[10::-1]
-        expected = Index(index.values[::-1], name="foo")
-        tm.assert_index_equal(index_slice, expected, exact="equiv")
-
-    @pytest.mark.parametrize("step", set(range(-5, 6)) - {0})
-    def test_len_specialised(self, step):
-        # make sure that our len is the same as np.arange calc
-        start, stop = (0, 5) if step > 0 else (5, 0)
-
-        arr = np.arange(start, stop, step)
-        index = RangeIndex(start, stop, step)
-        assert len(index) == len(arr)
-
-        index = RangeIndex(stop, start, step)
-        assert len(index) == 0
-
-    @pytest.mark.parametrize(
-        "indices, expected",
-        [
-            ([RI(1, 12, 5)], RI(1, 12, 5)),
-            ([RI(0, 6, 4)], RI(0, 6, 4)),
-            ([RI(1, 3), RI(3, 7)], RI(1, 7)),
-            ([RI(1, 5, 2), RI(5, 6)], RI(1, 6, 2)),
-            ([RI(1, 3, 2), RI(4, 7, 3)], RI(1, 7, 3)),
-            ([RI(-4, 3, 2), RI(4, 7, 2)], RI(-4, 7, 2)),
-            ([RI(-4, -8), RI(-8, -12)], RI(0, 0)),
-            ([RI(-4, -8), RI(3, -4)], RI(0, 0)),
-            ([RI(-4, -8), RI(3, 5)], RI(3, 5)),
-            ([RI(-4, -2), RI(3, 5)], Index([-4, -3, 3, 4])),
-            ([RI(-2), RI(3, 5)], RI(3, 5)),
-            ([RI(2), RI(2)], Index([0, 1, 0, 1])),
-            ([RI(2), RI(2, 5), RI(5, 8, 4)], RI(0, 6)),
-            ([RI(2), RI(3, 5), RI(5, 8, 4)], Index([0, 1, 3, 4, 5])),
-            ([RI(-2, 2), RI(2, 5), RI(5, 8, 4)], RI(-2, 6)),
-            ([RI(3), Index([-1, 3, 15])], Index([0, 1, 2, -1, 3, 15])),
-            ([RI(3), Index([-1, 3.1, 15.0])], Index([0, 1, 2, -1, 3.1, 15.0])),
-            ([RI(3), Index(["a", None, 14])], Index([0, 1, 2, "a", None, 14])),
-            ([RI(3, 1), Index(["a", None, 14])], Index(["a", None, 14])),
-        ],
-    )
-    def test_append(self, indices, expected):
-        # GH16212
-        result = indices[0].append(indices[1:])
-        tm.assert_index_equal(result, expected, exact=True)
-
-        if len(indices) == 2:
-            # Append single item rather than list
-            result2 = indices[0].append(indices[1])
-            tm.assert_index_equal(result2, expected, exact=True)
-
-    def test_engineless_lookup(self):
-        # GH 16685
-        # Standard lookup on RangeIndex should not require the engine to be
-        # created
-        idx = RangeIndex(2, 10, 3)
-
-        assert idx.get_loc(5) == 1
-        tm.assert_numpy_array_equal(
-            idx.get_indexer([2, 8]), ensure_platform_int(np.array([0, 2]))
-        )
-        with pytest.raises(KeyError, match="3"):
-            idx.get_loc(3)
-
-        assert "_engine" not in idx._cache
-
-        # Different types of scalars can be excluded immediately, no need to
-        #  use the _engine
-        with pytest.raises(KeyError, match="'a'"):
-            idx.get_loc("a")
-
-        assert "_engine" not in idx._cache
-
-    def test_format_empty(self):
-        # GH35712
-        empty_idx = RangeIndex(0)
-        assert empty_idx.format() == []
-        assert empty_idx.format(name=True) == [""]
-
-    @pytest.mark.parametrize(
-        "RI",
-        [
-            RangeIndex(0, -1, -1),
-            RangeIndex(0, 1, 1),
-            RangeIndex(1, 3, 2),
-            RangeIndex(0, -1, -2),
-            RangeIndex(-3, -5, -2),
-        ],
-    )
-    def test_append_len_one(self, RI):
-        # GH39401
-        result = RI.append([])
-        tm.assert_index_equal(result, RI, exact=True)
-
-    @pytest.mark.parametrize("base", [RangeIndex(0, 2), Index([0, 1])])
-    def test_isin_range(self, base):
-        # GH#41151
-        values = RangeIndex(0, 1)
-        result = base.isin(values)
-        expected = np.array([True, False])
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_sort_values_key(self):
-        # GH#43666, GH#52764
-        sort_order = {8: 2, 6: 0, 4: 8, 2: 10, 0: 12}
-        values = RangeIndex(0, 10, 2)
-        result = values.sort_values(key=lambda x: x.map(sort_order))
-        expected = Index([6, 8, 4, 2, 0], dtype="int64")
-        tm.assert_index_equal(result, expected, check_exact=True)
-
-        # check this matches the Series.sort_values behavior
-        ser = values.to_series()
-        result2 = ser.sort_values(key=lambda x: x.map(sort_order))
-        tm.assert_series_equal(result2, expected.to_series(), check_exact=True)
-
-    def test_range_index_rsub_by_const(self):
-        # GH#53255
-        result = 3 - RangeIndex(0, 4, 1)
-        expected = RangeIndex(3, -1, -1)
-        tm.assert_index_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/idna/core.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/idna/core.py
deleted file mode 100644
index 55ab9678850cab239813fd9059dece2ec9117270..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/idna/core.py
+++ /dev/null
@@ -1,397 +0,0 @@
-from . import idnadata
-import bisect
-import unicodedata
-import re
-from typing import Union, Optional
-from .intranges import intranges_contain
-
-_virama_combining_class = 9
-_alabel_prefix = b'xn--'
-_unicode_dots_re = re.compile('[\u002e\u3002\uff0e\uff61]')
-
-class IDNAError(UnicodeError):
-    """ Base exception for all IDNA-encoding related problems """
-    pass
-
-
-class IDNABidiError(IDNAError):
-    """ Exception when bidirectional requirements are not satisfied """
-    pass
-
-
-class InvalidCodepoint(IDNAError):
-    """ Exception when a disallowed or unallocated codepoint is used """
-    pass
-
-
-class InvalidCodepointContext(IDNAError):
-    """ Exception when the codepoint is not valid in the context it is used """
-    pass
-
-
-def _combining_class(cp: int) -> int:
-    v = unicodedata.combining(chr(cp))
-    if v == 0:
-        if not unicodedata.name(chr(cp)):
-            raise ValueError('Unknown character in unicodedata')
-    return v
-
-def _is_script(cp: str, script: str) -> bool:
-    return intranges_contain(ord(cp), idnadata.scripts[script])
-
-def _punycode(s: str) -> bytes:
-    return s.encode('punycode')
-
-def _unot(s: int) -> str:
-    return 'U+{:04X}'.format(s)
-
-
-def valid_label_length(label: Union[bytes, str]) -> bool:
-    if len(label) > 63:
-        return False
-    return True
-
-
-def valid_string_length(label: Union[bytes, str], trailing_dot: bool) -> bool:
-    if len(label) > (254 if trailing_dot else 253):
-        return False
-    return True
-
-
-def check_bidi(label: str, check_ltr: bool = False) -> bool:
-    # Bidi rules should only be applied if string contains RTL characters
-    bidi_label = False
-    for (idx, cp) in enumerate(label, 1):
-        direction = unicodedata.bidirectional(cp)
-        if direction == '':
-            # String likely comes from a newer version of Unicode
-            raise IDNABidiError('Unknown directionality in label {} at position {}'.format(repr(label), idx))
-        if direction in ['R', 'AL', 'AN']:
-            bidi_label = True
-    if not bidi_label and not check_ltr:
-        return True
-
-    # Bidi rule 1
-    direction = unicodedata.bidirectional(label[0])
-    if direction in ['R', 'AL']:
-        rtl = True
-    elif direction == 'L':
-        rtl = False
-    else:
-        raise IDNABidiError('First codepoint in label {} must be directionality L, R or AL'.format(repr(label)))
-
-    valid_ending = False
-    number_type = None  # type: Optional[str]
-    for (idx, cp) in enumerate(label, 1):
-        direction = unicodedata.bidirectional(cp)
-
-        if rtl:
-            # Bidi rule 2
-            if not direction in ['R', 'AL', 'AN', 'EN', 'ES', 'CS', 'ET', 'ON', 'BN', 'NSM']:
-                raise IDNABidiError('Invalid direction for codepoint at position {} in a right-to-left label'.format(idx))
-            # Bidi rule 3
-            if direction in ['R', 'AL', 'EN', 'AN']:
-                valid_ending = True
-            elif direction != 'NSM':
-                valid_ending = False
-            # Bidi rule 4
-            if direction in ['AN', 'EN']:
-                if not number_type:
-                    number_type = direction
-                else:
-                    if number_type != direction:
-                        raise IDNABidiError('Can not mix numeral types in a right-to-left label')
-        else:
-            # Bidi rule 5
-            if not direction in ['L', 'EN', 'ES', 'CS', 'ET', 'ON', 'BN', 'NSM']:
-                raise IDNABidiError('Invalid direction for codepoint at position {} in a left-to-right label'.format(idx))
-            # Bidi rule 6
-            if direction in ['L', 'EN']:
-                valid_ending = True
-            elif direction != 'NSM':
-                valid_ending = False
-
-    if not valid_ending:
-        raise IDNABidiError('Label ends with illegal codepoint directionality')
-
-    return True
-
-
-def check_initial_combiner(label: str) -> bool:
-    if unicodedata.category(label[0])[0] == 'M':
-        raise IDNAError('Label begins with an illegal combining character')
-    return True
-
-
-def check_hyphen_ok(label: str) -> bool:
-    if label[2:4] == '--':
-        raise IDNAError('Label has disallowed hyphens in 3rd and 4th position')
-    if label[0] == '-' or label[-1] == '-':
-        raise IDNAError('Label must not start or end with a hyphen')
-    return True
-
-
-def check_nfc(label: str) -> None:
-    if unicodedata.normalize('NFC', label) != label:
-        raise IDNAError('Label must be in Normalization Form C')
-
-
-def valid_contextj(label: str, pos: int) -> bool:
-    cp_value = ord(label[pos])
-
-    if cp_value == 0x200c:
-
-        if pos > 0:
-            if _combining_class(ord(label[pos - 1])) == _virama_combining_class:
-                return True
-
-        ok = False
-        for i in range(pos-1, -1, -1):
-            joining_type = idnadata.joining_types.get(ord(label[i]))
-            if joining_type == ord('T'):
-                continue
-            if joining_type in [ord('L'), ord('D')]:
-                ok = True
-                break
-
-        if not ok:
-            return False
-
-        ok = False
-        for i in range(pos+1, len(label)):
-            joining_type = idnadata.joining_types.get(ord(label[i]))
-            if joining_type == ord('T'):
-                continue
-            if joining_type in [ord('R'), ord('D')]:
-                ok = True
-                break
-        return ok
-
-    if cp_value == 0x200d:
-
-        if pos > 0:
-            if _combining_class(ord(label[pos - 1])) == _virama_combining_class:
-                return True
-        return False
-
-    else:
-
-        return False
-
-
-def valid_contexto(label: str, pos: int, exception: bool = False) -> bool:
-    cp_value = ord(label[pos])
-
-    if cp_value == 0x00b7:
-        if 0 < pos < len(label)-1:
-            if ord(label[pos - 1]) == 0x006c and ord(label[pos + 1]) == 0x006c:
-                return True
-        return False
-
-    elif cp_value == 0x0375:
-        if pos < len(label)-1 and len(label) > 1:
-            return _is_script(label[pos + 1], 'Greek')
-        return False
-
-    elif cp_value == 0x05f3 or cp_value == 0x05f4:
-        if pos > 0:
-            return _is_script(label[pos - 1], 'Hebrew')
-        return False
-
-    elif cp_value == 0x30fb:
-        for cp in label:
-            if cp == '\u30fb':
-                continue
-            if _is_script(cp, 'Hiragana') or _is_script(cp, 'Katakana') or _is_script(cp, 'Han'):
-                return True
-        return False
-
-    elif 0x660 <= cp_value <= 0x669:
-        for cp in label:
-            if 0x6f0 <= ord(cp) <= 0x06f9:
-                return False
-        return True
-
-    elif 0x6f0 <= cp_value <= 0x6f9:
-        for cp in label:
-            if 0x660 <= ord(cp) <= 0x0669:
-                return False
-        return True
-
-    return False
-
-
-def check_label(label: Union[str, bytes, bytearray]) -> None:
-    if isinstance(label, (bytes, bytearray)):
-        label = label.decode('utf-8')
-    if len(label) == 0:
-        raise IDNAError('Empty Label')
-
-    check_nfc(label)
-    check_hyphen_ok(label)
-    check_initial_combiner(label)
-
-    for (pos, cp) in enumerate(label):
-        cp_value = ord(cp)
-        if intranges_contain(cp_value, idnadata.codepoint_classes['PVALID']):
-            continue
-        elif intranges_contain(cp_value, idnadata.codepoint_classes['CONTEXTJ']):
-            try:
-                if not valid_contextj(label, pos):
-                    raise InvalidCodepointContext('Joiner {} not allowed at position {} in {}'.format(
-                        _unot(cp_value), pos+1, repr(label)))
-            except ValueError:
-                raise IDNAError('Unknown codepoint adjacent to joiner {} at position {} in {}'.format(
-                    _unot(cp_value), pos+1, repr(label)))
-        elif intranges_contain(cp_value, idnadata.codepoint_classes['CONTEXTO']):
-            if not valid_contexto(label, pos):
-                raise InvalidCodepointContext('Codepoint {} not allowed at position {} in {}'.format(_unot(cp_value), pos+1, repr(label)))
-        else:
-            raise InvalidCodepoint('Codepoint {} at position {} of {} not allowed'.format(_unot(cp_value), pos+1, repr(label)))
-
-    check_bidi(label)
-
-
-def alabel(label: str) -> bytes:
-    try:
-        label_bytes = label.encode('ascii')
-        ulabel(label_bytes)
-        if not valid_label_length(label_bytes):
-            raise IDNAError('Label too long')
-        return label_bytes
-    except UnicodeEncodeError:
-        pass
-
-    if not label:
-        raise IDNAError('No Input')
-
-    label = str(label)
-    check_label(label)
-    label_bytes = _punycode(label)
-    label_bytes = _alabel_prefix + label_bytes
-
-    if not valid_label_length(label_bytes):
-        raise IDNAError('Label too long')
-
-    return label_bytes
-
-
-def ulabel(label: Union[str, bytes, bytearray]) -> str:
-    if not isinstance(label, (bytes, bytearray)):
-        try:
-            label_bytes = label.encode('ascii')
-        except UnicodeEncodeError:
-            check_label(label)
-            return label
-    else:
-        label_bytes = label
-
-    label_bytes = label_bytes.lower()
-    if label_bytes.startswith(_alabel_prefix):
-        label_bytes = label_bytes[len(_alabel_prefix):]
-        if not label_bytes:
-            raise IDNAError('Malformed A-label, no Punycode eligible content found')
-        if label_bytes.decode('ascii')[-1] == '-':
-            raise IDNAError('A-label must not end with a hyphen')
-    else:
-        check_label(label_bytes)
-        return label_bytes.decode('ascii')
-
-    try:
-        label = label_bytes.decode('punycode')
-    except UnicodeError:
-        raise IDNAError('Invalid A-label')
-    check_label(label)
-    return label
-
-
-def uts46_remap(domain: str, std3_rules: bool = True, transitional: bool = False) -> str:
-    """Re-map the characters in the string according to UTS46 processing."""
-    from .uts46data import uts46data
-    output = ''
-
-    for pos, char in enumerate(domain):
-        code_point = ord(char)
-        try:
-            uts46row = uts46data[code_point if code_point < 256 else
-                bisect.bisect_left(uts46data, (code_point, 'Z')) - 1]
-            status = uts46row[1]
-            replacement = None  # type: Optional[str]
-            if len(uts46row) == 3:
-                replacement = uts46row[2]  # type: ignore
-            if (status == 'V' or
-                    (status == 'D' and not transitional) or
-                    (status == '3' and not std3_rules and replacement is None)):
-                output += char
-            elif replacement is not None and (status == 'M' or
-                    (status == '3' and not std3_rules) or
-                    (status == 'D' and transitional)):
-                output += replacement
-            elif status != 'I':
-                raise IndexError()
-        except IndexError:
-            raise InvalidCodepoint(
-                'Codepoint {} not allowed at position {} in {}'.format(
-                _unot(code_point), pos + 1, repr(domain)))
-
-    return unicodedata.normalize('NFC', output)
-
-
-def encode(s: Union[str, bytes, bytearray], strict: bool = False, uts46: bool = False, std3_rules: bool = False, transitional: bool = False) -> bytes:
-    if isinstance(s, (bytes, bytearray)):
-        s = s.decode('ascii')
-    if uts46:
-        s = uts46_remap(s, std3_rules, transitional)
-    trailing_dot = False
-    result = []
-    if strict:
-        labels = s.split('.')
-    else:
-        labels = _unicode_dots_re.split(s)
-    if not labels or labels == ['']:
-        raise IDNAError('Empty domain')
-    if labels[-1] == '':
-        del labels[-1]
-        trailing_dot = True
-    for label in labels:
-        s = alabel(label)
-        if s:
-            result.append(s)
-        else:
-            raise IDNAError('Empty label')
-    if trailing_dot:
-        result.append(b'')
-    s = b'.'.join(result)
-    if not valid_string_length(s, trailing_dot):
-        raise IDNAError('Domain too long')
-    return s
-
-
-def decode(s: Union[str, bytes, bytearray], strict: bool = False, uts46: bool = False, std3_rules: bool = False) -> str:
-    try:
-        if isinstance(s, (bytes, bytearray)):
-            s = s.decode('ascii')
-    except UnicodeDecodeError:
-        raise IDNAError('Invalid ASCII in A-label')
-    if uts46:
-        s = uts46_remap(s, std3_rules, False)
-    trailing_dot = False
-    result = []
-    if not strict:
-        labels = _unicode_dots_re.split(s)
-    else:
-        labels = s.split('.')
-    if not labels or labels == ['']:
-        raise IDNAError('Empty domain')
-    if not labels[-1]:
-        del labels[-1]
-        trailing_dot = True
-    for label in labels:
-        s = ulabel(label)
-        if s:
-            result.append(s)
-        else:
-            raise IDNAError('Empty label')
-    if trailing_dot:
-        result.append('')
-    return '.'.join(result)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/pretty.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/pretty.py
deleted file mode 100644
index 606ee33822aff7db27086df4da06d732dcd181b4..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/pretty.py
+++ /dev/null
@@ -1,903 +0,0 @@
-import builtins
-import dataclasses
-import inspect
-import os
-import re
-import sys
-from array import array
-from collections import Counter, UserDict, UserList, defaultdict, deque
-from dataclasses import dataclass, fields, is_dataclass
-from inspect import isclass
-from itertools import islice
-from types import MappingProxyType
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    DefaultDict,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Set,
-    Tuple,
-    Union,
-)
-
-from pip._vendor.rich.repr import RichReprResult
-
-try:
-    import attr as _attr_module
-except ImportError:  # pragma: no cover
-    _attr_module = None  # type: ignore
-
-
-from . import get_console
-from ._loop import loop_last
-from ._pick import pick_bool
-from .abc import RichRenderable
-from .cells import cell_len
-from .highlighter import ReprHighlighter
-from .jupyter import JupyterMixin, JupyterRenderable
-from .measure import Measurement
-from .text import Text
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        HighlighterType,
-        JustifyMethod,
-        OverflowMethod,
-        RenderResult,
-    )
-
-
-def _is_attr_object(obj: Any) -> bool:
-    """Check if an object was created with attrs module."""
-    return _attr_module is not None and _attr_module.has(type(obj))
-
-
-def _get_attr_fields(obj: Any) -> Iterable["_attr_module.Attribute[Any]"]:
-    """Get fields for an attrs object."""
-    return _attr_module.fields(type(obj)) if _attr_module is not None else []
-
-
-def _is_dataclass_repr(obj: object) -> bool:
-    """Check if an instance of a dataclass contains the default repr.
-
-    Args:
-        obj (object): A dataclass instance.
-
-    Returns:
-        bool: True if the default repr is used, False if there is a custom repr.
-    """
-    # Digging in to a lot of internals here
-    # Catching all exceptions in case something is missing on a non CPython implementation
-    try:
-        return obj.__repr__.__code__.co_filename == dataclasses.__file__
-    except Exception:  # pragma: no coverage
-        return False
-
-
-def _ipy_display_hook(
-    value: Any,
-    console: Optional["Console"] = None,
-    overflow: "OverflowMethod" = "ignore",
-    crop: bool = False,
-    indent_guides: bool = False,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    expand_all: bool = False,
-) -> None:
-    from .console import ConsoleRenderable  # needed here to prevent circular import
-
-    # always skip rich generated jupyter renderables or None values
-    if isinstance(value, JupyterRenderable) or value is None:
-        return
-
-    console = console or get_console()
-    if console.is_jupyter:
-        # Delegate rendering to IPython if the object (and IPython) supports it
-        #  https://ipython.readthedocs.io/en/stable/config/integrating.html#rich-display
-        ipython_repr_methods = [
-            "_repr_html_",
-            "_repr_markdown_",
-            "_repr_json_",
-            "_repr_latex_",
-            "_repr_jpeg_",
-            "_repr_png_",
-            "_repr_svg_",
-            "_repr_mimebundle_",
-        ]
-        for repr_method in ipython_repr_methods:
-            method = getattr(value, repr_method, None)
-            if inspect.ismethod(method):
-                # Calling the method ourselves isn't ideal. The interface for the `_repr_*_` methods
-                #  specifies that if they return None, then they should not be rendered
-                #  by the notebook.
-                try:
-                    repr_result = method()
-                except Exception:
-                    continue  # If the method raises, treat it as if it doesn't exist, try any others
-                if repr_result is not None:
-                    return  # Delegate rendering to IPython
-
-    # certain renderables should start on a new line
-    if isinstance(value, ConsoleRenderable):
-        console.line()
-
-    console.print(
-        value
-        if isinstance(value, RichRenderable)
-        else Pretty(
-            value,
-            overflow=overflow,
-            indent_guides=indent_guides,
-            max_length=max_length,
-            max_string=max_string,
-            expand_all=expand_all,
-            margin=12,
-        ),
-        crop=crop,
-        new_line_start=True,
-    )
-
-
-def install(
-    console: Optional["Console"] = None,
-    overflow: "OverflowMethod" = "ignore",
-    crop: bool = False,
-    indent_guides: bool = False,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    expand_all: bool = False,
-) -> None:
-    """Install automatic pretty printing in the Python REPL.
-
-    Args:
-        console (Console, optional): Console instance or ``None`` to use global console. Defaults to None.
-        overflow (Optional[OverflowMethod], optional): Overflow method. Defaults to "ignore".
-        crop (Optional[bool], optional): Enable cropping of long lines. Defaults to False.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to False.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-        max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
-    """
-    from pip._vendor.rich import get_console
-
-    console = console or get_console()
-    assert console is not None
-
-    def display_hook(value: Any) -> None:
-        """Replacement sys.displayhook which prettifies objects with Rich."""
-        if value is not None:
-            assert console is not None
-            builtins._ = None  # type: ignore
-            console.print(
-                value
-                if isinstance(value, RichRenderable)
-                else Pretty(
-                    value,
-                    overflow=overflow,
-                    indent_guides=indent_guides,
-                    max_length=max_length,
-                    max_string=max_string,
-                    expand_all=expand_all,
-                ),
-                crop=crop,
-            )
-            builtins._ = value  # type: ignore
-
-    try:  # pragma: no cover
-        ip = get_ipython()  # type: ignore
-        from IPython.core.formatters import BaseFormatter
-
-        class RichFormatter(BaseFormatter):  # type: ignore
-            pprint: bool = True
-
-            def __call__(self, value: Any) -> Any:
-                if self.pprint:
-                    return _ipy_display_hook(
-                        value,
-                        console=get_console(),
-                        overflow=overflow,
-                        indent_guides=indent_guides,
-                        max_length=max_length,
-                        max_string=max_string,
-                        expand_all=expand_all,
-                    )
-                else:
-                    return repr(value)
-
-        # replace plain text formatter with rich formatter
-        rich_formatter = RichFormatter()
-        ip.display_formatter.formatters["text/plain"] = rich_formatter
-    except Exception:
-        sys.displayhook = display_hook
-
-
-class Pretty(JupyterMixin):
-    """A rich renderable that pretty prints an object.
-
-    Args:
-        _object (Any): An object to pretty print.
-        highlighter (HighlighterType, optional): Highlighter object to apply to result, or None for ReprHighlighter. Defaults to None.
-        indent_size (int, optional): Number of spaces in indent. Defaults to 4.
-        justify (JustifyMethod, optional): Justify method, or None for default. Defaults to None.
-        overflow (OverflowMethod, optional): Overflow method, or None for default. Defaults to None.
-        no_wrap (Optional[bool], optional): Disable word wrapping. Defaults to False.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to False.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structures, or None for no maximum. Defaults to None.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-        margin (int, optional): Subtrace a margin from width to force containers to expand earlier. Defaults to 0.
-        insert_line (bool, optional): Insert a new line if the output has multiple new lines. Defaults to False.
-    """
-
-    def __init__(
-        self,
-        _object: Any,
-        highlighter: Optional["HighlighterType"] = None,
-        *,
-        indent_size: int = 4,
-        justify: Optional["JustifyMethod"] = None,
-        overflow: Optional["OverflowMethod"] = None,
-        no_wrap: Optional[bool] = False,
-        indent_guides: bool = False,
-        max_length: Optional[int] = None,
-        max_string: Optional[int] = None,
-        max_depth: Optional[int] = None,
-        expand_all: bool = False,
-        margin: int = 0,
-        insert_line: bool = False,
-    ) -> None:
-        self._object = _object
-        self.highlighter = highlighter or ReprHighlighter()
-        self.indent_size = indent_size
-        self.justify: Optional["JustifyMethod"] = justify
-        self.overflow: Optional["OverflowMethod"] = overflow
-        self.no_wrap = no_wrap
-        self.indent_guides = indent_guides
-        self.max_length = max_length
-        self.max_string = max_string
-        self.max_depth = max_depth
-        self.expand_all = expand_all
-        self.margin = margin
-        self.insert_line = insert_line
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        pretty_str = pretty_repr(
-            self._object,
-            max_width=options.max_width - self.margin,
-            indent_size=self.indent_size,
-            max_length=self.max_length,
-            max_string=self.max_string,
-            max_depth=self.max_depth,
-            expand_all=self.expand_all,
-        )
-        pretty_text = Text(
-            pretty_str,
-            justify=self.justify or options.justify,
-            overflow=self.overflow or options.overflow,
-            no_wrap=pick_bool(self.no_wrap, options.no_wrap),
-            style="pretty",
-        )
-        pretty_text = (
-            self.highlighter(pretty_text)
-            if pretty_text
-            else Text(
-                f"{type(self._object)}.__repr__ returned empty string",
-                style="dim italic",
-            )
-        )
-        if self.indent_guides and not options.ascii_only:
-            pretty_text = pretty_text.with_indent_guides(
-                self.indent_size, style="repr.indent"
-            )
-        if self.insert_line and "\n" in pretty_text:
-            yield ""
-        yield pretty_text
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        pretty_str = pretty_repr(
-            self._object,
-            max_width=options.max_width,
-            indent_size=self.indent_size,
-            max_length=self.max_length,
-            max_string=self.max_string,
-        )
-        text_width = (
-            max(cell_len(line) for line in pretty_str.splitlines()) if pretty_str else 0
-        )
-        return Measurement(text_width, text_width)
-
-
-def _get_braces_for_defaultdict(_object: DefaultDict[Any, Any]) -> Tuple[str, str, str]:
-    return (
-        f"defaultdict({_object.default_factory!r}, {{",
-        "})",
-        f"defaultdict({_object.default_factory!r}, {{}})",
-    )
-
-
-def _get_braces_for_array(_object: "array[Any]") -> Tuple[str, str, str]:
-    return (f"array({_object.typecode!r}, [", "])", "array({_object.typecode!r})")
-
-
-_BRACES: Dict[type, Callable[[Any], Tuple[str, str, str]]] = {
-    os._Environ: lambda _object: ("environ({", "})", "environ({})"),
-    array: _get_braces_for_array,
-    defaultdict: _get_braces_for_defaultdict,
-    Counter: lambda _object: ("Counter({", "})", "Counter()"),
-    deque: lambda _object: ("deque([", "])", "deque()"),
-    dict: lambda _object: ("{", "}", "{}"),
-    UserDict: lambda _object: ("{", "}", "{}"),
-    frozenset: lambda _object: ("frozenset({", "})", "frozenset()"),
-    list: lambda _object: ("[", "]", "[]"),
-    UserList: lambda _object: ("[", "]", "[]"),
-    set: lambda _object: ("{", "}", "set()"),
-    tuple: lambda _object: ("(", ")", "()"),
-    MappingProxyType: lambda _object: ("mappingproxy({", "})", "mappingproxy({})"),
-}
-_CONTAINERS = tuple(_BRACES.keys())
-_MAPPING_CONTAINERS = (dict, os._Environ, MappingProxyType, UserDict)
-
-
-def is_expandable(obj: Any) -> bool:
-    """Check if an object may be expanded by pretty print."""
-    return (
-        isinstance(obj, _CONTAINERS)
-        or (is_dataclass(obj))
-        or (hasattr(obj, "__rich_repr__"))
-        or _is_attr_object(obj)
-    ) and not isclass(obj)
-
-
-@dataclass
-class Node:
-    """A node in a repr tree. May be atomic or a container."""
-
-    key_repr: str = ""
-    value_repr: str = ""
-    open_brace: str = ""
-    close_brace: str = ""
-    empty: str = ""
-    last: bool = False
-    is_tuple: bool = False
-    children: Optional[List["Node"]] = None
-    key_separator = ": "
-    separator: str = ", "
-
-    def iter_tokens(self) -> Iterable[str]:
-        """Generate tokens for this node."""
-        if self.key_repr:
-            yield self.key_repr
-            yield self.key_separator
-        if self.value_repr:
-            yield self.value_repr
-        elif self.children is not None:
-            if self.children:
-                yield self.open_brace
-                if self.is_tuple and len(self.children) == 1:
-                    yield from self.children[0].iter_tokens()
-                    yield ","
-                else:
-                    for child in self.children:
-                        yield from child.iter_tokens()
-                        if not child.last:
-                            yield self.separator
-                yield self.close_brace
-            else:
-                yield self.empty
-
-    def check_length(self, start_length: int, max_length: int) -> bool:
-        """Check the length fits within a limit.
-
-        Args:
-            start_length (int): Starting length of the line (indent, prefix, suffix).
-            max_length (int): Maximum length.
-
-        Returns:
-            bool: True if the node can be rendered within max length, otherwise False.
-        """
-        total_length = start_length
-        for token in self.iter_tokens():
-            total_length += cell_len(token)
-            if total_length > max_length:
-                return False
-        return True
-
-    def __str__(self) -> str:
-        repr_text = "".join(self.iter_tokens())
-        return repr_text
-
-    def render(
-        self, max_width: int = 80, indent_size: int = 4, expand_all: bool = False
-    ) -> str:
-        """Render the node to a pretty repr.
-
-        Args:
-            max_width (int, optional): Maximum width of the repr. Defaults to 80.
-            indent_size (int, optional): Size of indents. Defaults to 4.
-            expand_all (bool, optional): Expand all levels. Defaults to False.
-
-        Returns:
-            str: A repr string of the original object.
-        """
-        lines = [_Line(node=self, is_root=True)]
-        line_no = 0
-        while line_no < len(lines):
-            line = lines[line_no]
-            if line.expandable and not line.expanded:
-                if expand_all or not line.check_length(max_width):
-                    lines[line_no : line_no + 1] = line.expand(indent_size)
-            line_no += 1
-
-        repr_str = "\n".join(str(line) for line in lines)
-        return repr_str
-
-
-@dataclass
-class _Line:
-    """A line in repr output."""
-
-    parent: Optional["_Line"] = None
-    is_root: bool = False
-    node: Optional[Node] = None
-    text: str = ""
-    suffix: str = ""
-    whitespace: str = ""
-    expanded: bool = False
-    last: bool = False
-
-    @property
-    def expandable(self) -> bool:
-        """Check if the line may be expanded."""
-        return bool(self.node is not None and self.node.children)
-
-    def check_length(self, max_length: int) -> bool:
-        """Check this line fits within a given number of cells."""
-        start_length = (
-            len(self.whitespace) + cell_len(self.text) + cell_len(self.suffix)
-        )
-        assert self.node is not None
-        return self.node.check_length(start_length, max_length)
-
-    def expand(self, indent_size: int) -> Iterable["_Line"]:
-        """Expand this line by adding children on their own line."""
-        node = self.node
-        assert node is not None
-        whitespace = self.whitespace
-        assert node.children
-        if node.key_repr:
-            new_line = yield _Line(
-                text=f"{node.key_repr}{node.key_separator}{node.open_brace}",
-                whitespace=whitespace,
-            )
-        else:
-            new_line = yield _Line(text=node.open_brace, whitespace=whitespace)
-        child_whitespace = self.whitespace + " " * indent_size
-        tuple_of_one = node.is_tuple and len(node.children) == 1
-        for last, child in loop_last(node.children):
-            separator = "," if tuple_of_one else node.separator
-            line = _Line(
-                parent=new_line,
-                node=child,
-                whitespace=child_whitespace,
-                suffix=separator,
-                last=last and not tuple_of_one,
-            )
-            yield line
-
-        yield _Line(
-            text=node.close_brace,
-            whitespace=whitespace,
-            suffix=self.suffix,
-            last=self.last,
-        )
-
-    def __str__(self) -> str:
-        if self.last:
-            return f"{self.whitespace}{self.text}{self.node or ''}"
-        else:
-            return (
-                f"{self.whitespace}{self.text}{self.node or ''}{self.suffix.rstrip()}"
-            )
-
-
-def traverse(
-    _object: Any,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-) -> Node:
-    """Traverse object and generate a tree.
-
-    Args:
-        _object (Any): Object to be traversed.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
-            Defaults to None.
-        max_depth (int, optional): Maximum depth of data structures, or None for no maximum.
-            Defaults to None.
-
-    Returns:
-        Node: The root of a tree structure which can be used to render a pretty repr.
-    """
-
-    def to_repr(obj: Any) -> str:
-        """Get repr string for an object, but catch errors."""
-        if (
-            max_string is not None
-            and isinstance(obj, (bytes, str))
-            and len(obj) > max_string
-        ):
-            truncated = len(obj) - max_string
-            obj_repr = f"{obj[:max_string]!r}+{truncated}"
-        else:
-            try:
-                obj_repr = repr(obj)
-            except Exception as error:
-                obj_repr = f"<repr-error {str(error)!r}>"
-        return obj_repr
-
-    visited_ids: Set[int] = set()
-    push_visited = visited_ids.add
-    pop_visited = visited_ids.remove
-
-    def _traverse(obj: Any, root: bool = False, depth: int = 0) -> Node:
-        """Walk the object depth first."""
-
-        obj_type = type(obj)
-        py_version = (sys.version_info.major, sys.version_info.minor)
-        children: List[Node]
-        reached_max_depth = max_depth is not None and depth >= max_depth
-
-        def iter_rich_args(rich_args: Any) -> Iterable[Union[Any, Tuple[str, Any]]]:
-            for arg in rich_args:
-                if isinstance(arg, tuple):
-                    if len(arg) == 3:
-                        key, child, default = arg
-                        if default == child:
-                            continue
-                        yield key, child
-                    elif len(arg) == 2:
-                        key, child = arg
-                        yield key, child
-                    elif len(arg) == 1:
-                        yield arg[0]
-                else:
-                    yield arg
-
-        try:
-            fake_attributes = hasattr(
-                obj, "awehoi234_wdfjwljet234_234wdfoijsdfmmnxpi492"
-            )
-        except Exception:
-            fake_attributes = False
-
-        rich_repr_result: Optional[RichReprResult] = None
-        if not fake_attributes:
-            try:
-                if hasattr(obj, "__rich_repr__") and not isclass(obj):
-                    rich_repr_result = obj.__rich_repr__()
-            except Exception:
-                pass
-
-        if rich_repr_result is not None:
-            angular = getattr(obj.__rich_repr__, "angular", False)
-            args = list(iter_rich_args(rich_repr_result))
-            class_name = obj.__class__.__name__
-
-            if args:
-                children = []
-                append = children.append
-
-                if reached_max_depth:
-                    node = Node(value_repr=f"...")
-                else:
-                    if angular:
-                        node = Node(
-                            open_brace=f"<{class_name} ",
-                            close_brace=">",
-                            children=children,
-                            last=root,
-                            separator=" ",
-                        )
-                    else:
-                        node = Node(
-                            open_brace=f"{class_name}(",
-                            close_brace=")",
-                            children=children,
-                            last=root,
-                        )
-                    for last, arg in loop_last(args):
-                        if isinstance(arg, tuple):
-                            key, child = arg
-                            child_node = _traverse(child, depth=depth + 1)
-                            child_node.last = last
-                            child_node.key_repr = key
-                            child_node.key_separator = "="
-                            append(child_node)
-                        else:
-                            child_node = _traverse(arg, depth=depth + 1)
-                            child_node.last = last
-                            append(child_node)
-            else:
-                node = Node(
-                    value_repr=f"<{class_name}>" if angular else f"{class_name}()",
-                    children=[],
-                    last=root,
-                )
-        elif _is_attr_object(obj) and not fake_attributes:
-            children = []
-            append = children.append
-
-            attr_fields = _get_attr_fields(obj)
-            if attr_fields:
-                if reached_max_depth:
-                    node = Node(value_repr=f"...")
-                else:
-                    node = Node(
-                        open_brace=f"{obj.__class__.__name__}(",
-                        close_brace=")",
-                        children=children,
-                        last=root,
-                    )
-
-                    def iter_attrs() -> Iterable[
-                        Tuple[str, Any, Optional[Callable[[Any], str]]]
-                    ]:
-                        """Iterate over attr fields and values."""
-                        for attr in attr_fields:
-                            if attr.repr:
-                                try:
-                                    value = getattr(obj, attr.name)
-                                except Exception as error:
-                                    # Can happen, albeit rarely
-                                    yield (attr.name, error, None)
-                                else:
-                                    yield (
-                                        attr.name,
-                                        value,
-                                        attr.repr if callable(attr.repr) else None,
-                                    )
-
-                    for last, (name, value, repr_callable) in loop_last(iter_attrs()):
-                        if repr_callable:
-                            child_node = Node(value_repr=str(repr_callable(value)))
-                        else:
-                            child_node = _traverse(value, depth=depth + 1)
-                        child_node.last = last
-                        child_node.key_repr = name
-                        child_node.key_separator = "="
-                        append(child_node)
-            else:
-                node = Node(
-                    value_repr=f"{obj.__class__.__name__}()", children=[], last=root
-                )
-
-        elif (
-            is_dataclass(obj)
-            and not isinstance(obj, type)
-            and not fake_attributes
-            and (_is_dataclass_repr(obj) or py_version == (3, 6))
-        ):
-            obj_id = id(obj)
-            if obj_id in visited_ids:
-                # Recursion detected
-                return Node(value_repr="...")
-            push_visited(obj_id)
-
-            children = []
-            append = children.append
-            if reached_max_depth:
-                node = Node(value_repr=f"...")
-            else:
-                node = Node(
-                    open_brace=f"{obj.__class__.__name__}(",
-                    close_brace=")",
-                    children=children,
-                    last=root,
-                )
-
-                for last, field in loop_last(
-                    field for field in fields(obj) if field.repr
-                ):
-                    child_node = _traverse(getattr(obj, field.name), depth=depth + 1)
-                    child_node.key_repr = field.name
-                    child_node.last = last
-                    child_node.key_separator = "="
-                    append(child_node)
-
-                pop_visited(obj_id)
-
-        elif isinstance(obj, _CONTAINERS):
-            for container_type in _CONTAINERS:
-                if isinstance(obj, container_type):
-                    obj_type = container_type
-                    break
-
-            obj_id = id(obj)
-            if obj_id in visited_ids:
-                # Recursion detected
-                return Node(value_repr="...")
-            push_visited(obj_id)
-
-            open_brace, close_brace, empty = _BRACES[obj_type](obj)
-
-            if reached_max_depth:
-                node = Node(value_repr=f"...", last=root)
-            elif obj_type.__repr__ != type(obj).__repr__:
-                node = Node(value_repr=to_repr(obj), last=root)
-            elif obj:
-                children = []
-                node = Node(
-                    open_brace=open_brace,
-                    close_brace=close_brace,
-                    children=children,
-                    last=root,
-                )
-                append = children.append
-                num_items = len(obj)
-                last_item_index = num_items - 1
-
-                if isinstance(obj, _MAPPING_CONTAINERS):
-                    iter_items = iter(obj.items())
-                    if max_length is not None:
-                        iter_items = islice(iter_items, max_length)
-                    for index, (key, child) in enumerate(iter_items):
-                        child_node = _traverse(child, depth=depth + 1)
-                        child_node.key_repr = to_repr(key)
-                        child_node.last = index == last_item_index
-                        append(child_node)
-                else:
-                    iter_values = iter(obj)
-                    if max_length is not None:
-                        iter_values = islice(iter_values, max_length)
-                    for index, child in enumerate(iter_values):
-                        child_node = _traverse(child, depth=depth + 1)
-                        child_node.last = index == last_item_index
-                        append(child_node)
-                if max_length is not None and num_items > max_length:
-                    append(Node(value_repr=f"... +{num_items-max_length}", last=True))
-            else:
-                node = Node(empty=empty, children=[], last=root)
-
-            pop_visited(obj_id)
-        else:
-            node = Node(value_repr=to_repr(obj), last=root)
-        node.is_tuple = isinstance(obj, tuple)
-        return node
-
-    node = _traverse(_object, root=True)
-    return node
-
-
-def pretty_repr(
-    _object: Any,
-    *,
-    max_width: int = 80,
-    indent_size: int = 4,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> str:
-    """Prettify repr string by expanding on to new lines to fit within a given width.
-
-    Args:
-        _object (Any): Object to repr.
-        max_width (int, optional): Desired maximum width of repr string. Defaults to 80.
-        indent_size (int, optional): Number of spaces to indent. Defaults to 4.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
-            Defaults to None.
-        max_depth (int, optional): Maximum depth of nested data structure, or None for no depth.
-            Defaults to None.
-        expand_all (bool, optional): Expand all containers regardless of available width. Defaults to False.
-
-    Returns:
-        str: A possibly multi-line representation of the object.
-    """
-
-    if isinstance(_object, Node):
-        node = _object
-    else:
-        node = traverse(
-            _object, max_length=max_length, max_string=max_string, max_depth=max_depth
-        )
-    repr_str = node.render(
-        max_width=max_width, indent_size=indent_size, expand_all=expand_all
-    )
-    return repr_str
-
-
-def pprint(
-    _object: Any,
-    *,
-    console: Optional["Console"] = None,
-    indent_guides: bool = True,
-    max_length: Optional[int] = None,
-    max_string: Optional[int] = None,
-    max_depth: Optional[int] = None,
-    expand_all: bool = False,
-) -> None:
-    """A convenience function for pretty printing.
-
-    Args:
-        _object (Any): Object to pretty print.
-        console (Console, optional): Console instance, or None to use default. Defaults to None.
-        max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to None.
-        max_string (int, optional): Maximum length of strings before truncating, or None to disable. Defaults to None.
-        max_depth (int, optional): Maximum depth for nested data structures, or None for unlimited depth. Defaults to None.
-        indent_guides (bool, optional): Enable indentation guides. Defaults to True.
-        expand_all (bool, optional): Expand all containers. Defaults to False.
-    """
-    _console = get_console() if console is None else console
-    _console.print(
-        Pretty(
-            _object,
-            max_length=max_length,
-            max_string=max_string,
-            max_depth=max_depth,
-            indent_guides=indent_guides,
-            expand_all=expand_all,
-            overflow="ignore",
-        ),
-        soft_wrap=True,
-    )
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    class BrokenRepr:
-        def __repr__(self) -> str:
-            1 / 0
-            return "this will fail"
-
-    d = defaultdict(int)
-    d["foo"] = 5
-    data = {
-        "foo": [
-            1,
-            "Hello World!",
-            100.123,
-            323.232,
-            432324.0,
-            {5, 6, 7, (1, 2, 3, 4), 8},
-        ],
-        "bar": frozenset({1, 2, 3}),
-        "defaultdict": defaultdict(
-            list, {"crumble": ["apple", "rhubarb", "butter", "sugar", "flour"]}
-        ),
-        "counter": Counter(
-            [
-                "apple",
-                "orange",
-                "pear",
-                "kumquat",
-                "kumquat",
-                "durian" * 100,
-            ]
-        ),
-        "atomic": (False, True, None),
-        "Broken": BrokenRepr(),
-    }
-    data["foo"].append(data)  # type: ignore
-
-    from pip._vendor.rich import print
-
-    print(Pretty(data, indent_guides=True, max_string=20))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/modeling.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/modeling.py
deleted file mode 100644
index e24768072da6ed6a5b3c28f55c59da0c29f8b578..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/modeling.py
+++ /dev/null
@@ -1,369 +0,0 @@
-"""
-    pygments.lexers.modeling
-    ~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for modeling languages.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-import re
-
-from pygments.lexer import RegexLexer, include, bygroups, using, default
-from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
-    Number, Punctuation, Whitespace
-
-from pygments.lexers.html import HtmlLexer
-from pygments.lexers import _stan_builtins
-
-__all__ = ['ModelicaLexer', 'BugsLexer', 'JagsLexer', 'StanLexer']
-
-
-class ModelicaLexer(RegexLexer):
-    """
-    For Modelica source code.
-
-    .. versionadded:: 1.1
-    """
-    name = 'Modelica'
-    url = 'http://www.modelica.org/'
-    aliases = ['modelica']
-    filenames = ['*.mo']
-    mimetypes = ['text/x-modelica']
-
-    flags = re.DOTALL | re.MULTILINE
-
-    _name = r"(?:'(?:[^\\']|\\.)+'|[a-zA-Z_]\w*)"
-
-    tokens = {
-        'whitespace': [
-            (r'[\s\ufeff]+', Text),
-            (r'//[^\n]*\n?', Comment.Single),
-            (r'/\*.*?\*/', Comment.Multiline)
-        ],
-        'root': [
-            include('whitespace'),
-            (r'"', String.Double, 'string'),
-            (r'[()\[\]{},;]+', Punctuation),
-            (r'\.?[*^/+-]|\.|<>|[<>:=]=?', Operator),
-            (r'\d+(\.?\d*[eE][-+]?\d+|\.\d*)', Number.Float),
-            (r'\d+', Number.Integer),
-            (r'(abs|acos|actualStream|array|asin|assert|AssertionLevel|atan|'
-             r'atan2|backSample|Boolean|cardinality|cat|ceil|change|Clock|'
-             r'Connections|cos|cosh|cross|delay|diagonal|div|edge|exp|'
-             r'ExternalObject|fill|floor|getInstanceName|hold|homotopy|'
-             r'identity|inStream|integer|Integer|interval|inverse|isPresent|'
-             r'linspace|log|log10|matrix|max|min|mod|ndims|noClock|noEvent|'
-             r'ones|outerProduct|pre|previous|product|Real|reinit|rem|rooted|'
-             r'sample|scalar|semiLinear|shiftSample|sign|sin|sinh|size|skew|'
-             r'smooth|spatialDistribution|sqrt|StateSelect|String|subSample|'
-             r'sum|superSample|symmetric|tan|tanh|terminal|terminate|time|'
-             r'transpose|vector|zeros)\b', Name.Builtin),
-            (r'(algorithm|annotation|break|connect|constant|constrainedby|der|'
-             r'discrete|each|else|elseif|elsewhen|encapsulated|enumeration|'
-             r'equation|exit|expandable|extends|external|firstTick|final|flow|for|if|'
-             r'import|impure|in|initial|inner|input|interval|loop|nondiscrete|outer|'
-             r'output|parameter|partial|protected|public|pure|redeclare|'
-             r'replaceable|return|stream|then|when|while)\b',
-             Keyword.Reserved),
-            (r'(and|not|or)\b', Operator.Word),
-            (r'(block|class|connector|end|function|model|operator|package|'
-             r'record|type)\b', Keyword.Reserved, 'class'),
-            (r'(false|true)\b', Keyword.Constant),
-            (r'within\b', Keyword.Reserved, 'package-prefix'),
-            (_name, Name)
-        ],
-        'class': [
-            include('whitespace'),
-            (r'(function|record)\b', Keyword.Reserved),
-            (r'(if|for|when|while)\b', Keyword.Reserved, '#pop'),
-            (_name, Name.Class, '#pop'),
-            default('#pop')
-        ],
-        'package-prefix': [
-            include('whitespace'),
-            (_name, Name.Namespace, '#pop'),
-            default('#pop')
-        ],
-        'string': [
-            (r'"', String.Double, '#pop'),
-            (r'\\[\'"?\\abfnrtv]', String.Escape),
-            (r'(?i)<\s*html\s*>([^\\"]|\\.)+?(<\s*/\s*html\s*>|(?="))',
-             using(HtmlLexer)),
-            (r'<|\\?[^"\\<]+', String.Double)
-        ]
-    }
-
-
-class BugsLexer(RegexLexer):
-    """
-    Pygments Lexer for OpenBugs and WinBugs
-    models.
-
-    .. versionadded:: 1.6
-    """
-
-    name = 'BUGS'
-    aliases = ['bugs', 'winbugs', 'openbugs']
-    filenames = ['*.bug']
-
-    _FUNCTIONS = (
-        # Scalar functions
-        'abs', 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctanh',
-        'cloglog', 'cos', 'cosh', 'cumulative', 'cut', 'density', 'deviance',
-        'equals', 'expr', 'gammap', 'ilogit', 'icloglog', 'integral', 'log',
-        'logfact', 'loggam', 'logit', 'max', 'min', 'phi', 'post.p.value',
-        'pow', 'prior.p.value', 'probit', 'replicate.post', 'replicate.prior',
-        'round', 'sin', 'sinh', 'solution', 'sqrt', 'step', 'tan', 'tanh',
-        'trunc',
-        # Vector functions
-        'inprod', 'interp.lin', 'inverse', 'logdet', 'mean', 'eigen.vals',
-        'ode', 'prod', 'p.valueM', 'rank', 'ranked', 'replicate.postM',
-        'sd', 'sort', 'sum',
-        # Special
-        'D', 'I', 'F', 'T', 'C')
-    """ OpenBUGS built-in functions
-
-    From http://www.openbugs.info/Manuals/ModelSpecification.html#ContentsAII
-
-    This also includes
-
-    - T, C, I : Truncation and censoring.
-      ``T`` and ``C`` are in OpenBUGS. ``I`` in WinBUGS.
-    - D : ODE
-    - F : Functional http://www.openbugs.info/Examples/Functionals.html
-
-    """
-
-    _DISTRIBUTIONS = ('dbern', 'dbin', 'dcat', 'dnegbin', 'dpois',
-                      'dhyper', 'dbeta', 'dchisqr', 'ddexp', 'dexp',
-                      'dflat', 'dgamma', 'dgev', 'df', 'dggamma', 'dgpar',
-                      'dloglik', 'dlnorm', 'dlogis', 'dnorm', 'dpar',
-                      'dt', 'dunif', 'dweib', 'dmulti', 'ddirch', 'dmnorm',
-                      'dmt', 'dwish')
-    """ OpenBUGS built-in distributions
-
-    Functions from
-    http://www.openbugs.info/Manuals/ModelSpecification.html#ContentsAI
-    """
-
-    tokens = {
-        'whitespace': [
-            (r"\s+", Text),
-        ],
-        'comments': [
-            # Comments
-            (r'#.*$', Comment.Single),
-        ],
-        'root': [
-            # Comments
-            include('comments'),
-            include('whitespace'),
-            # Block start
-            (r'(model)(\s+)(\{)',
-             bygroups(Keyword.Namespace, Text, Punctuation)),
-            # Reserved Words
-            (r'(for|in)(?![\w.])', Keyword.Reserved),
-            # Built-in Functions
-            (r'(%s)(?=\s*\()'
-             % r'|'.join(_FUNCTIONS + _DISTRIBUTIONS),
-             Name.Builtin),
-            # Regular variable names
-            (r'[A-Za-z][\w.]*', Name),
-            # Number Literals
-            (r'[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?', Number),
-            # Punctuation
-            (r'\[|\]|\(|\)|:|,|;', Punctuation),
-            # Assignment operators
-            # SLexer makes these tokens Operators.
-            (r'<-|~', Operator),
-            # Infix and prefix operators
-            (r'\+|-|\*|/', Operator),
-            # Block
-            (r'[{}]', Punctuation),
-        ]
-    }
-
-    def analyse_text(text):
-        if re.search(r"^\s*model\s*{", text, re.M):
-            return 0.7
-        else:
-            return 0.0
-
-
-class JagsLexer(RegexLexer):
-    """
-    Pygments Lexer for JAGS.
-
-    .. versionadded:: 1.6
-    """
-
-    name = 'JAGS'
-    aliases = ['jags']
-    filenames = ['*.jag', '*.bug']
-
-    # JAGS
-    _FUNCTIONS = (
-        'abs', 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctanh',
-        'cos', 'cosh', 'cloglog',
-        'equals', 'exp', 'icloglog', 'ifelse', 'ilogit', 'log', 'logfact',
-        'loggam', 'logit', 'phi', 'pow', 'probit', 'round', 'sin', 'sinh',
-        'sqrt', 'step', 'tan', 'tanh', 'trunc', 'inprod', 'interp.lin',
-        'logdet', 'max', 'mean', 'min', 'prod', 'sum', 'sd', 'inverse',
-        'rank', 'sort', 't', 'acos', 'acosh', 'asin', 'asinh', 'atan',
-        # Truncation/Censoring (should I include)
-        'T', 'I')
-    # Distributions with density, probability and quartile functions
-    _DISTRIBUTIONS = tuple('[dpq]%s' % x for x in
-                           ('bern', 'beta', 'dchiqsqr', 'ddexp', 'dexp',
-                            'df', 'gamma', 'gen.gamma', 'logis', 'lnorm',
-                            'negbin', 'nchisqr', 'norm', 'par', 'pois', 'weib'))
-    # Other distributions without density and probability
-    _OTHER_DISTRIBUTIONS = (
-        'dt', 'dunif', 'dbetabin', 'dbern', 'dbin', 'dcat', 'dhyper',
-        'ddirch', 'dmnorm', 'dwish', 'dmt', 'dmulti', 'dbinom', 'dchisq',
-        'dnbinom', 'dweibull', 'ddirich')
-
-    tokens = {
-        'whitespace': [
-            (r"\s+", Text),
-        ],
-        'names': [
-            # Regular variable names
-            (r'[a-zA-Z][\w.]*\b', Name),
-        ],
-        'comments': [
-            # do not use stateful comments
-            (r'(?s)/\*.*?\*/', Comment.Multiline),
-            # Comments
-            (r'#.*$', Comment.Single),
-        ],
-        'root': [
-            # Comments
-            include('comments'),
-            include('whitespace'),
-            # Block start
-            (r'(model|data)(\s+)(\{)',
-             bygroups(Keyword.Namespace, Text, Punctuation)),
-            (r'var(?![\w.])', Keyword.Declaration),
-            # Reserved Words
-            (r'(for|in)(?![\w.])', Keyword.Reserved),
-            # Builtins
-            # Need to use lookahead because . is a valid char
-            (r'(%s)(?=\s*\()' % r'|'.join(_FUNCTIONS
-                                          + _DISTRIBUTIONS
-                                          + _OTHER_DISTRIBUTIONS),
-             Name.Builtin),
-            # Names
-            include('names'),
-            # Number Literals
-            (r'[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?', Number),
-            (r'\[|\]|\(|\)|:|,|;', Punctuation),
-            # Assignment operators
-            (r'<-|~', Operator),
-            # # JAGS includes many more than OpenBUGS
-            (r'\+|-|\*|\/|\|\|[&]{2}|[<>=]=?|\^|%.*?%', Operator),
-            (r'[{}]', Punctuation),
-        ]
-    }
-
-    def analyse_text(text):
-        if re.search(r'^\s*model\s*\{', text, re.M):
-            if re.search(r'^\s*data\s*\{', text, re.M):
-                return 0.9
-            elif re.search(r'^\s*var', text, re.M):
-                return 0.9
-            else:
-                return 0.3
-        else:
-            return 0
-
-
-class StanLexer(RegexLexer):
-    """Pygments Lexer for Stan models.
-
-    The Stan modeling language is specified in the *Stan Modeling Language
-    User's Guide and Reference Manual, v2.17.0*,
-    `pdf <https://github.com/stan-dev/stan/releases/download/v2.17.0/stan-reference-2.17.0.pdf>`__.
-
-    .. versionadded:: 1.6
-    """
-
-    name = 'Stan'
-    aliases = ['stan']
-    filenames = ['*.stan']
-
-    tokens = {
-        'whitespace': [
-            (r"\s+", Text),
-        ],
-        'comments': [
-            (r'(?s)/\*.*?\*/', Comment.Multiline),
-            # Comments
-            (r'(//|#).*$', Comment.Single),
-        ],
-        'root': [
-            (r'"[^"]*"', String),
-            # Comments
-            include('comments'),
-            # block start
-            include('whitespace'),
-            # Block start
-            (r'(%s)(\s*)(\{)' %
-             r'|'.join(('functions', 'data', r'transformed\s+?data',
-                        'parameters', r'transformed\s+parameters',
-                        'model', r'generated\s+quantities')),
-             bygroups(Keyword.Namespace, Text, Punctuation)),
-            # target keyword
-            (r'target\s*\+=', Keyword),
-            # Reserved Words
-            (r'(%s)\b' % r'|'.join(_stan_builtins.KEYWORDS), Keyword),
-            # Truncation
-            (r'T(?=\s*\[)', Keyword),
-            # Data types
-            (r'(%s)\b' % r'|'.join(_stan_builtins.TYPES), Keyword.Type),
-             # < should be punctuation, but elsewhere I can't tell if it is in
-             # a range constraint
-            (r'(<)(\s*)(upper|lower|offset|multiplier)(\s*)(=)',
-             bygroups(Operator, Whitespace, Keyword, Whitespace, Punctuation)),
-            (r'(,)(\s*)(upper)(\s*)(=)',
-             bygroups(Punctuation, Whitespace, Keyword, Whitespace, Punctuation)),
-            # Punctuation
-            (r"[;,\[\]()]", Punctuation),
-            # Builtin
-            (r'(%s)(?=\s*\()' % '|'.join(_stan_builtins.FUNCTIONS), Name.Builtin),
-            (r'(~)(\s*)(%s)(?=\s*\()' % '|'.join(_stan_builtins.DISTRIBUTIONS),
-                bygroups(Operator, Whitespace, Name.Builtin)),
-            # Special names ending in __, like lp__
-            (r'[A-Za-z]\w*__\b', Name.Builtin.Pseudo),
-            (r'(%s)\b' % r'|'.join(_stan_builtins.RESERVED), Keyword.Reserved),
-            # user-defined functions
-            (r'[A-Za-z]\w*(?=\s*\()]', Name.Function),
-            # Imaginary Literals
-            (r'[0-9]+(\.[0-9]*)?([eE][+-]?[0-9]+)?i', Number.Float),
-            (r'\.[0-9]+([eE][+-]?[0-9]+)?i', Number.Float),
-            (r'[0-9]+i', Number.Float),
-            # Real Literals
-            (r'[0-9]+(\.[0-9]*)?([eE][+-]?[0-9]+)?', Number.Float),
-            (r'\.[0-9]+([eE][+-]?[0-9]+)?', Number.Float),
-            # Integer Literals
-            (r'[0-9]+', Number.Integer),
-            # Regular variable names
-            (r'[A-Za-z]\w*\b', Name),
-            # Assignment operators
-            (r'<-|(?:\+|-|\.?/|\.?\*|=)?=|~', Operator),
-            # Infix, prefix and postfix operators (and = )
-            (r"\+|-|\.?\*|\.?/|\\|'|\.?\^|!=?|<=?|>=?|\|\||&&|%|\?|:|%/%|!", Operator),
-            # Block delimiters
-            (r'[{}]', Punctuation),
-            # Distribution |
-            (r'\|', Punctuation)
-        ]
-    }
-
-    def analyse_text(text):
-        if re.search(r'^\s*parameters\s*\{', text, re.M):
-            return 1.0
-        else:
-            return 0.0
diff --git a/spaces/pseudolab/medical-chatbot/app.py b/spaces/pseudolab/medical-chatbot/app.py
deleted file mode 100644
index 692cc7c46fe582a19ece172b9f7ea2ff846e99f8..0000000000000000000000000000000000000000
--- a/spaces/pseudolab/medical-chatbot/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import os
-import torch
-import gradio as gr
-import requests
-from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
-from peft import PeftModel, PeftConfig
-from textwrap import wrap, fill
-
-## using Falcon 7b Instruct
-Falcon_API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-7b-instruct"
-hf_token = os.getenv("HUGGINGFACE_TOKEN")
-HEADERS = {"Authorization": "Bearer {hf_token}"}
-def falcon_query(payload):
-    response = requests.post(Falcon_API_URL, headers=HEADERS, json=payload)
-    return response.json()
-def falcon_inference(input_text):
-    payload = {"inputs": input_text}
-    return falcon_query(payload)
-
-## using Mistral
-Mistral_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
-def mistral_query(payload):
-    response = requests.post(Mistral_API_URL , headers=HEADERS, json=payload)
-    return response.json()
-def mistral_inference(input_text):
-    payload = {"inputs": input_text}
-    return mistral_query(payload)
-
-# Functions to Wrap the Prompt Correctly
-def wrap_text(text, width=90):
-    lines = text.split('\n')
-    wrapped_lines = [fill(line, width=width) for line in lines]
-    wrapped_text = '\n'.join(wrapped_lines)
-    return wrapped_text
-
-class ChatbotInterface():
-    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
-        self.name = name
-        self.system_prompt = system_prompt
-        self.chatbot = gr.Chatbot()
-        self.chat_history = []
-        
-        with gr.Row() as row:
-            row.justify = "end"
-            self.msg = gr.Textbox(scale=7)
-            #self.msg.change(fn=, inputs=, outputs=)
-            self.submit = gr.Button("Submit", scale=1)
-
-        clear = gr.ClearButton([self.msg, self.chatbot])
-        chat_history = []
-        
-        self.submit.click(self.respond, [self.msg, self.chatbot], [self.msg, self.chatbot])
-    
-    def respond(self, msg, chatbot):
-        raise NotImplementedError
-
-class GaiaMinimed(ChatbotInterface):
-    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
-        super().__init__(name, system_prompt)
-        
-    def respond(self, msg, history):
-            formatted_input = f"{{{{ {self.system_prompt} }}}}\nUser: {msg}\n{self.name}:"
-            input_ids = tokenizer.encode(
-                formatted_input, 
-                return_tensors="pt", 
-                add_special_tokens=False
-            )
-            response = peft_model.generate(
-                input_ids=input_ids, 
-                max_length=500, 
-                use_cache=False,
-                early_stopping=False,
-                bos_token_id=peft_model.config.bos_token_id,
-                eos_token_id=peft_model.config.eos_token_id,
-                pad_token_id=peft_model.config.eos_token_id,
-                temperature=0.4,
-                do_sample=True
-            )
-            response_text = tokenizer.decode(response[0], skip_special_tokens=True)
-            
-            self.chat_history.append([formatted_input, response_text])
-
-            return "", self.chat_history
-
-class FalconBot(ChatbotInterface):
-    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
-        super().__init__(name, system_prompt)
-        
-    def respond(self, msg, chatbot):
-        falcon_response = falcon_inference(msg)
-        falcon_output = falcon_response[0]["generated_text"]
-        self.chat_history.append([msg, falcon_output])
-        return "", falcon_output
-
-class MistralBot(ChatbotInterface):
-    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
-        super().__init__(name, system_prompt)
-    
-    def respond(self, msg, chatbot):
-        mistral_response = mistral_inference(msg)
-        mistral_output = mistral_response[0]["generated_text"]
-        self.chat_history.append([msg, mistral_output])
-        return "", mistral_output
-
-if __name__ == "__main__":
-    # Define the device
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    # Use the base model's ID
-    base_model_id = "tiiuae/falcon-7b-instruct"
-    model_directory = "Tonic/GaiaMiniMed"
-
-    # Instantiate the Tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, padding_side="left")
-    
-    # Specify the configuration class for the model
-    model_config = AutoConfig.from_pretrained(base_model_id)
-    # Load the PEFT model with the specified configuration
-    peft_model = AutoModelForCausalLM.from_pretrained(model_directory, config=model_config)
-    peft_model = PeftModel.from_pretrained(peft_model, model_directory)
-    
-    with gr.Blocks() as demo:        
-        with gr.Row() as intro:
-            gr.Markdown(
-                """
-                # MedChat: Your Medical Assistant Chatbot
-            
-                Welcome to MedChat, your friendly medical assistant chatbot! 🩺
-            
-                Dive into a world of medical expertise where you can interact with three specialized chatbots, all trained on the latest and most comprehensive medical dataset. Whether you have health-related questions, need medical advice, or just want to learn more about your well-being, MedChat is here to help!
-            
-                ## How it Works
-                Simply type your medical query or concern, and let MedChat's advanced algorithms provide you with accurate and reliable responses. 
-            
-                ## Explore and Compare
-                Feel like experimenting? Click the **Submit to All** button and witness the magic as all three chatbots compete to provide you with the best possible answer! It's a unique opportunity to compare the insights from different models and choose the one that suits your needs the best.
-            
-                _Ready to get started? Type your question and let's begin!_
-                """
-            )
-        with gr.Row() as row:
-            with gr.Column() as col1:
-                with gr.Tab("GaiaMinimed") as gaia:
-                    gaia_bot = GaiaMinimed("GaiaMinimed")
-            with gr.Column() as col2:
-                with gr.Tab("MistralMed") as mistral:
-                    mistral_bot = MistralBot("MistralMed") 
-                with gr.Tab("Falcon-7B") as falcon7b:
-                    falcon_bot = FalconBot("Falcon-7B")
-        
-        gaia_bot.msg.input(fn=lambda s: (s[::1], s[::1]), inputs=gaia_bot.msg, outputs=[mistral_bot.msg, falcon_bot.msg])
-        mistral_bot.msg.input(fn=lambda s: (s[::1], s[::1]), inputs=mistral_bot.msg, outputs=[gaia_bot.msg, falcon_bot.msg])
-        falcon_bot.msg.input(fn=lambda s: (s[::1], s[::1]), inputs=falcon_bot.msg, outputs=[gaia_bot.msg, mistral_bot.msg])
-                
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/puji4ml/PubMedAbstractSkimmingTool/webScraping.py b/spaces/puji4ml/PubMedAbstractSkimmingTool/webScraping.py
deleted file mode 100644
index 9966a2862f6840ee46001942b50adc27dc7dba1c..0000000000000000000000000000000000000000
--- a/spaces/puji4ml/PubMedAbstractSkimmingTool/webScraping.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-
-def parseURL(url):
-    # Send a GET request to the URL
-    response = requests.get(url)
-
-    # Check if the request was successful (status code 200)
-    if response.status_code == 200: 
-        # Parse the HTML content of the page using BeautifulSoup
-        soup = BeautifulSoup(response.text, "html.parser")
-        #print(soup)
-        # Find the parent div block of article current page
-        article_page = soup.find("div", class_="article-page")
-        if article_page:
-            #Find the 'article-detail' main block within the 'article-page' div block
-            article_details = article_page.find("main",class_="article-details")
-            
-            if article_details:
-                # Find the abstract section within the 'article-detail' div block
-                abstract_section = article_details.find("div",class_="abstract")
-                
-                if abstract_section:
-                    # Find the location where abstract heading and its content is located
-                    abstract = abstract_section.find("div",class_="abstract-content selected")
-                    
-                    if abstract:
-                    #Find the location of <p> ,inside it abstract text would be located
-                        abstract = abstract.find("p").get_text()
-                        return abstract.strip() #will be of string class
-                    else:
-                        return f"Abstract paragraph not found inside 'abstract-content selected' div block inside 'abstract' div block inside 'article-details' main block inside 'article-page' div block of {url}"
-                else:
-                    return f"'abstract' div block not found inside 'article-details' main block inside 'article-page' div block of {url}"
-            else:
-                return f"'article-details' main block not found inside 'article-page' div block of {url}"
-        else:
-            return f"'article-page' div block not found of {url}"
-    else:
-        return "Failed to retrieve the webpage. Status code:", response.status_code
-
-'''
-Validating parseURL function
-
-# PubMed article URL
-urlSample = r"https://pubmed.ncbi.nlm.nih.gov/20232240/"
-print(parseURL(urlSample))
-'''
\ No newline at end of file
diff --git a/spaces/pustozerov/poc-handwriting-ocr/modules/ocr_model_ru/model.py b/spaces/pustozerov/poc-handwriting-ocr/modules/ocr_model_ru/model.py
deleted file mode 100644
index a05affc0a3e17b5c4cba154cae9d58a9f0a525e6..0000000000000000000000000000000000000000
--- a/spaces/pustozerov/poc-handwriting-ocr/modules/ocr_model_ru/model.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-
-import imageio
-import numpy as np
-import torch
-from torchvision import transforms
-
-from modules.ocr_model_ru.architecture import TransformerModel
-from modules.ocr_model_ru.processing import process_image, indices_to_text
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-CHANNELS = 1
-HIDDEN = 512
-ENC_LAYERS = 2
-DEC_LAYERS = 2
-N_HEADS = 4
-LENGTH = 32
-ALPHABET = ['PAD', 'SOS', ' ', '!', '"', '%', '(', ')', ',', '-', '.', '/',
-            '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '?',
-            '[', ']', '«', '»', 'А', 'Б', 'В', 'Г', 'Д', 'Е', 'Ж', 'З', 'И',
-            'Й', 'К', 'Л', 'М', 'Н', 'О', 'П', 'Р', 'С', 'Т', 'У', 'Ф', 'Х',
-            'Ц', 'Ч', 'Ш', 'Щ', 'Э', 'Ю', 'Я', 'а', 'б', 'в', 'г', 'д', 'е',
-            'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т',
-            'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я',
-            'ё', 'EOS']
-MODEL_PATH = "model/model_ocr_rus_handwriting.pt"
-idx2char = {idx: char for idx, char in enumerate(ALPHABET)}
-model = TransformerModel(len(ALPHABET), hidden=HIDDEN, enc_layers=ENC_LAYERS, dec_layers=DEC_LAYERS,
-                         nhead=N_HEADS, dropout=0.0).to(DEVICE)
-model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE))
-
-
-def prediction(folder_images):
-    preds = {}
-    model.eval()
-
-    with torch.no_grad():
-        for filename in os.listdir(folder_images):
-
-            img = imageio.v2.imread(folder_images + filename, as_gray=False, pilmode="RGB")
-
-            img = process_image(np.asarray(img)).astype('uint8')
-            img = img / img.max()
-            img = np.transpose(img, (2, 0, 1))
-
-            src = torch.FloatTensor(img).unsqueeze(0).to(DEVICE)
-            if CHANNELS == 1:
-                src = transforms.Grayscale(CHANNELS)(src)
-            out_indexes = model.predict(src)
-            pred = indices_to_text(out_indexes[0], idx2char)
-            preds[filename] = pred
-
-    return preds
diff --git a/spaces/pyannote/pretrained-pipelines/README.md b/spaces/pyannote/pretrained-pipelines/README.md
deleted file mode 100644
index 62f4fe56672d3044a909aea8a38b16c2763e3965..0000000000000000000000000000000000000000
--- a/spaces/pyannote/pretrained-pipelines/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Pretrained pipelines
-emoji: 🎹
-colorFrom: red
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: true
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/pycoming/bingo/src/lib/bots/bing/tts.ts b/spaces/pycoming/bingo/src/lib/bots/bing/tts.ts
deleted file mode 100644
index cd10b7d1d7581bf9cf46ff6755fcca550c558c9b..0000000000000000000000000000000000000000
--- a/spaces/pycoming/bingo/src/lib/bots/bing/tts.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-import { sleep } from './utils'
-
-const synth = window.speechSynthesis
-
-export class TTS {
-  currentText = ''
-  speakText = ''
-  private controller = new AbortController()
-  speaking = false
-  get isSpeaking() {
-    return this.speaking
-  }
-  finished = false
-  constructor() {}
-  abort = () => {
-    this.controller.abort()
-  }
-
-  reset = () => {
-    this.speaking = false
-    this.finished = true
-    this.currentText = ''
-    this.speakText = ''
-    this.abort()
-  }
-
-  speak = (text: string) => {
-    if (!synth || text?.trim()?.length < 2) {
-      return
-    }
-    this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '')
-    this.finished = false
-    this.loop()
-  }
-
-  private async doSpeek() {
-    return new Promise((resolve) => {
-      const endIndex = this.finished ? this.currentText.length :
-        Math.max(
-          this.currentText.lastIndexOf('。'),
-          this.currentText.lastIndexOf('；'),
-          this.currentText.lastIndexOf('、'),
-          this.currentText.lastIndexOf('？'),
-          this.currentText.lastIndexOf('\n')
-        )
-      const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0
-
-      if (startIndex >= endIndex) {
-        return resolve(true)
-      }
-      const text = this.currentText.slice(startIndex, endIndex)
-      this.speakText = text
-      const utterThis = new SpeechSynthesisUtterance(text)
-      this.controller.signal.onabort = () => {
-        synth.cancel()
-        this.finished = true
-        resolve(false)
-      }
-
-      utterThis.onend = function (event) {
-        resolve(true)
-      }
-
-      utterThis.onerror = function (event) {
-        resolve(false)
-      }
-
-      const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null
-      utterThis.voice = voice
-      synth.speak(utterThis)
-    })
-  }
-
-  private async loop() {
-    if (this.speaking) return
-    this.speaking = true
-    while(!this.finished) {
-      await Promise.all([sleep(1000), this.doSpeek()])
-    }
-    this.speaking = false
-  }
-}
diff --git a/spaces/pycui/RealChar/contribute.md b/spaces/pycui/RealChar/contribute.md
deleted file mode 100644
index 8ce3a20c04439ecae315b55ee8a6bdcc2e1bfd3a..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/contribute.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Contribution Guide
-Welcome to the RealChar project contribution guide. If you're looking to help, this is a great place to start. Follow these guidelines to help ensure the process is as smooth as possible for both you and the project maintainers.
-
-# Setup
-1. Fork the RealChar repository
-2. Clone the fork to your local machine
-3. Set the upstream remote to the original RealChar repository
-```bash
-git clone https://github.com/<your username>/RealChar.git
-cd RealChar
-git remote add upstream https://github.com/Shaunwei/RealChar.git
-```
-
-# Making Changes
-Before making changes, ensure that you're working with the most recent version of the code:
-
-```bash
-git checkout main
-git pull upstream main
-```
-
-Create a new branch for your changes:
-
-```bash
-git checkout -b <branch-name>
-```
-
-# Coding Standards
-Please adhere to the following:
-
-1. Follow existing coding style and conventions.
-2. Write clear, readable, and maintainable code.
-3. Include detailed comments when necessary.
-4. Test your changes thoroughly before submitting your pull request.
-
-# Committing Your Changes
-1. Stage your changes: git add .
-2. Commit your changes: git commit -m "Your detailed commit message"
-3. Push your changes to your fork: git push origin <branch-name>
-
-# Submitting a Pull Request
-1. Navigate to the original RealChar repository
-2. Click 'New pull request'
-3. Choose your fork and the branch containing your changes
-4. Give your pull request a title and detailed description
-5. Submit the pull request
-
-# Issue Tracking
-If you're adding a new feature or fixing a bug, make sure to add or update an issue in the issue tracker. If there's not an existing issue, create a new one. This helps us keep track of what needs to be worked on.
-
-# Code of Conduct
-By participating in this project, you're expected to uphold our Code of Conduct.
-
-# Where to Get Help
-If you run into problems or need help, check out our [discord](https://discord.gg/UYeQAUfaAX).
-
-Thank you for considering contributing to RealChar! Your time and expertise is greatly appreciated by the community.
diff --git a/spaces/qinzhu/diy-girlfriend-online/text/__init__.py b/spaces/qinzhu/diy-girlfriend-online/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/diy-girlfriend-online/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Battlefield 3 Highly Compressed 10mb.md b/spaces/quidiaMuxgu/Expedit-SAM/Battlefield 3 Highly Compressed 10mb.md
deleted file mode 100644
index 46b6a5f44db97dd5a987bd36459902638a1fdd61..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Battlefield 3 Highly Compressed 10mb.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Battlefield 3 Highly Compressed 10mb</h2><br /><p><b><b>Download File</b> &#8230;&#8230;&#8230; <a href="https://geags.com/2uCq0O">https://geags.com/2uCq0O</a></b></p><br /><br />
-
-Highly optimized code and GPU (AMD, NVIDIA, Intel) utilization guarantees fastest recovery ... to ZIP archives (compressed files) created in programs like WinZip, PKZip etc. ... Battlelog is a free social platform that ties into Battlefield 4 and Battlefield 3 and lets you ... My plan is 10MB storage so I use file storage for other files. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Civilization 3 Complete No Cd Crack Download HOT.md b/spaces/quidiaMuxgu/Expedit-SAM/Civilization 3 Complete No Cd Crack Download HOT.md
deleted file mode 100644
index 1ce8475626641abca706a592798b3a0647f98d60..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Civilization 3 Complete No Cd Crack Download HOT.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>you also will need to download the mod for this game, it is not included. you can get it here: > civilization 3 complete no cd crack download <p>what you can do is edit the map tiles. for example, if you want to turn the grassland into desert, you select the tile, click on the terrain tile button, select the desert tile, and click on the tile. this can only be done after you have researched the appropriate tech. you can also build roads by clicking and dragging the square next to the road you want to build. you can also select a tile to move it. so, this means that you can create a road network by selecting a tile, clicking on a tile next to the road you want to create, then clicking and dragging, and then selecting a tile you want to place it. you can also create or destroy tiles. if you click on the tile in the middle, it will disappear. if you click on the tile to the right of it, it will be destroyed. if you click on the tile to the left of it, it will be created.</p>
-<p>homeworld is a mod for civilization iii that lets you play the game on a 3d planet. by default, the planet is earth, but you can choose any planet in the game (the only planet that isnt available is mercury). you can view a 3d map of the planet and explore it, view it from orbit, zoom in to the planet, and more. it is the newest mod for civilization iii, and it has many new features, and features that are improved from the original game. </p>
-<h2>civilization 3 complete no cd crack download</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://geags.com/2uCryQ">https://geags.com/2uCryQ</a></b></p><br /><br />
-<p>there are many different ways to play civilization v, depending on how you want to play it. there are scenarios to play through, which are pre-written games that have objectives, as well as general guidance. there is also the "abandon" mode, where the objective is to simply not die. there are the many different paths to this goal, and as the game goes on the goal becomes increasingly difficult. this is unlike the original civilization, where you pick an "adventure" at the start of the game, and you dont even know if its to win or lose. other games are more like an rpg, where you have various paths, with the difference being that in an rpg, you have a different storyline depending on how you play. each of these styles of play has a set of mods that can be used to enhance the game. for example, the scenario mod allows you to play through a certain set of scenarios, and it also allows you to play multiple games of the same scenario on the same map. the abandon mod makes it so you will not die, and no one will be able to attack you. this is a good mod if you want to try something different, and you dont want to worry about losing. the mod the great wall of china is a fun one that makes the game easier in a competitive setting by allowing you to build a wall to protect your cities. another mod that is good for beginners is the diplomacy mod, which enables you to talk to anyone on the map at the press of a button, instead of waiting until the time in the real-time strategy game. for example, if i were to talk to another player, and offer them a 10% trade surplus, they would now have to choose to accept or reject my offer. once they accept, we can start negotiating. if they reject it, i have the option of raising it. if they still reject it, i can raise it again.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Love Aaj Kal 2 Full Movie Download In 720p Hd.md b/spaces/quidiaMuxgu/Expedit-SAM/Love Aaj Kal 2 Full Movie Download In 720p Hd.md
deleted file mode 100644
index 11b8733cf415c76633ce2b3824d8b696a7719f32..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Love Aaj Kal 2 Full Movie Download In 720p Hd.md	
+++ /dev/null
@@ -1,115 +0,0 @@
-<br />
-<h1>How to Watch Love Aaj Kal 2 Full Movie in 720p HD Online for Free</h1>
-
-<p>Are you a fan of romantic movies? Do you love watching stories of love that span across different lifetimes? If yes, then you should definitely check out <strong>Love Aaj Kal 2</strong>, the latest Hindi movie that has won the hearts of many viewers. This movie, directed by Imtiaz Ali, stars Kartik Aaryan and Sara Ali Khan as two lovers who experience love, loss and life in different eras. The movie is a sequel to the 2009 hit film <em>Love Aaj Kal</em>, which had a similar theme but with different characters and settings.</p>
-
-<p>In this article, we will tell you how you can watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free. We will also give you a brief overview of the plot, the cast and the reviews of the movie. So, read on and get ready to fall in love with <strong>Love Aaj Kal 2</strong>.</p>
-<h2>Love Aaj Kal 2 full movie download in 720p hd</h2><br /><p><b><b>Download File</b> &#9675;&#9675;&#9675; <a href="https://geags.com/2uCroB">https://geags.com/2uCroB</a></b></p><br /><br />
-
-<h2>What is Love Aaj Kal 2 About?</h2>
-
-<p><strong>Love Aaj Kal 2</strong> is a romantic drama that follows two parallel stories of love set in different time periods. The first story is about Veer (Kartik Aaryan) and Zoe (Sara Ali Khan), who meet in Delhi in 2020 and start a casual relationship. However, they soon realize that they want more than just physical intimacy and struggle to balance their career aspirations and emotional needs.</p>
-
-<p>The second story is about Raghu (also Kartik Aaryan) and Leena (Arushi Sharma), who fall in love in Udaipur in 1990 and elope against their families' wishes. However, they face many challenges and hardships as they try to make their relationship work.</p>
-
-<p>The two stories are connected by Randeep Hooda's character, who is Veer's mentor and Raghu's older self. He narrates his past to Zoe and helps her understand the true meaning of love.</p>
-
-<p><strong>Love Aaj Kal 2</strong> is a sequel to the 2009 film <em>Love Aaj Kal</em>, which starred Saif Ali Khan and Deepika Padukone as two lovers who go through similar situations as Veer and Zoe. However, the sequel is not a direct continuation of the original story but rather a reimagining of the same theme with different characters and settings.</p>
-
-<h2>Who are the Cast and Crew of Love Aaj Kal 2?</h2>
-
-<p><strong>Love Aaj Kal 2</strong> is directed by Imtiaz Ali, who is known for his romantic films like <em>Jab We Met</em>, <em>Rockstar</em>, <em>Tamasha</em>, etc. He also directed the original <em>Love Aaj Kal</em>. The film is produced by Dinesh Vijan and Imtiaz Ali under their banners Maddock Films and Window Seat Films respectively.</p>
-
-<p>The film stars Kartik Aaryan as Veer/Raghu, Sara Ali Khan as Zoe, Randeep Hooda as Raj/Raghu's older self and Arushi Sharma as Leena. The film also features Simran Jehani as Zoe's friend Tarana, Siddharth Kakkar as Veer's friend Rishi, Kavita Ghai as Zoe's mother Vimmi Kapoor, etc.</p>
-
-<p>The music of the film is composed by Pritam, who also composed the music for the original <em>Love Aaj Kal</em>. The film has songs like "Shayad", "Haan Main Galat", "Mehrama", "Rahogi Meri", etc., sung by artists like Arijit Singh, Shashwat Singh, Darshan Raval, Antara Mitra, etc.</p>
-<p></p>
-
-<h2>How to Watch Love Aaj Kal 2 Full Movie in 720p HD Online for Free?</h2>
-
-<p>If you want to watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free, you have many options to choose from. However, not all of them are legal or safe. Some websites may offer pirated or low-quality copies of the movie, which may harm your device or expose you to malware or viruses. Therefore, we recommend you to use only trusted and legal sources to watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free.</p>
-
-<p>One of the best options to watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free is JioCinema, a streaming platform that offers a wide range of movies and shows for Jio users. You can access JioCinema on your smartphone, tablet, laptop or smart TV using your Jio number or Jio ID. You can also download the movies and shows offline and watch them later without any internet connection.</p>
-
-<p>To watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free on JioCinema, follow these simple steps:</p>
-
-<ol>
-<li>Download the JioCinema app from Google Play Store or App Store, or visit the website www.jiocinema.com.</li>
-<li>Login with your Jio number or Jio ID.</li>
-<li>Search for <strong>Love Aaj Kal 2</strong> in the search bar.</li>
-<li>Select the movie and click on play.</li>
-<li>Enjoy watching <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free.</li>
-</ol>
-
-<p>If you are not a Jio user, don't worry. There are other legal and safe options to watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free. Some of them are:</p>
-
-<ul>
-<li>M4uHD: This is a website that offers free streaming of movies and shows in HD quality. You can find <strong>Love Aaj Kal 2</strong> on this website and watch it online without any registration or subscription. However, you may have to deal with some ads and pop-ups while using this website.</li>
-<li>Internet Archive: This is a digital library that hosts millions of free books, music, movies and more. You can find <strong>Love Aaj Kal 2</strong> on this website and watch it online without any download or login. However, you may not get the best quality of the movie on this website.</li>
-<li>Torrent Sites: These are websites that allow users to share files using peer-to-peer networks. You can find <strong>Love Aaj Kal 2</strong> on these websites and download it using a torrent client. However, you should be careful while using these websites as they may contain illegal or harmful content.</li>
-</ul>
-
-<p>We hope that these options will help you to watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free. However, we also urge you to respect the rights of the creators and producers of the movie and avoid using any illegal or unethical means to access their content.</p>
-
-<h3>In Conclusion...</h3>
-
-<p><strong>Love Aaj Kal 2</strong> is a romantic drama that explores the theme of reincarnation and how love transcends time and space. It stars Kartik Aaryan and Sara Ali Khan as two lovers who go through different phases of love in different eras. The film is directed by Imtiaz Ali and has a melodious soundtrack by Pritam.</p>
-
-<p>If you are looking for a romantic movie that will make you feel all kinds of emotions</p>
-<h2>What are the Reviews of Love Aaj Kal 2?</h2>
-
-<p><strong>Love Aaj Kal 2</strong> received mixed reviews from critics and audiences alike. The film was praised for its music, cinematography and performances by Kartik Aaryan and Randeep Hooda. However, it was criticized for its weak script, confusing narrative structure, lack of chemistry between Kartik Aaryan and Sara Ali Khan, etc.</p>
-
-<p>The film has a rating of **5/10** on IMDb based on **11k+** votes; **1/5** on Times of India based on **3k+** ratings; **3/5** on Bollywood Hungama based on **1k+** ratings; **1/5** on Hindustan Times based on **1k+** ratings; etc.</p>
-
-<p>The film was also involved in some controversies due to its portrayal of love and relationships in modern times. Some people accused it of being regressive, sexist and unrealistic. Some people also compared it unfavorably with the original <em>Love Aaj Kal</em>, which they felt was more relatable and authentic.</p>
-
-<h3>In Conclusion...</h3>
-
-<p><strong>Love Aaj Kal 2</strong> is a romantic drama that explores the theme of reincarnation and how love transcends time and space. It stars Kartik Aaryan and Sara Ali Khan as two lovers who go through different phases of love in different eras. The film is directed by Imtiaz Ali and has a melodious soundtrack by Pritam.</p>
-
-<p>If you are looking for a romantic movie that will make you feel all kinds of emotions
-<h2>Why Should You Watch Love Aaj Kal 2?</h2>
-
-<p>If you are still wondering why you should watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong>, here are some reasons that might convince you:</p>
-
-<ul>
-<li>It is a beautiful and emotional story of love that will make you laugh, cry and feel inspired.</li>
-<li>It has amazing performances by Kartik Aaryan and Sara Ali Khan, who share a sizzling chemistry on screen.</li>
-<li>It has stunning visuals and cinematography that capture the essence of different locations and eras.</li>
-<li>It has a soulful and catchy soundtrack by Pritam that will stay with you long after the movie ends.</li>
-<li>It is a sequel to one of the most loved romantic movies of Bollywood, <em>Love Aaj Kal</em>, which had a cult following among the fans.</li>
-</ul>
-
-<p>So, what are you waiting for? Watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free and enjoy this romantic saga with your loved ones.</p>
-
-<h3>Tips to Watch Love Aaj Kal 2 Full Movie in 720p HD Online for Free Safely</h3>
-
-<p>While watching <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free, you should also be careful about some things that might ruin your experience or cause you trouble. Here are some tips to watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free safely:</p>
-
-<ol>
-<li>Use a reliable and secure internet connection to avoid buffering or interruptions.</li>
-<li>Use a good quality device and screen to enjoy the best picture and sound quality.</li>
-<li>Use headphones or earphones to avoid disturbing others or getting disturbed by others.</li>
-<li>Avoid clicking on any suspicious links or ads that might redirect you to malicious websites or download unwanted software.</li>
-<li>Avoid sharing your personal or financial information with any website or app that claims to offer <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free.</li>
-</ol>
-
-<p>We hope that these tips will help you to watch <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free safely and have a great time.</p>
-
-<h3>In Conclusion...</h3>
-
-<p><strong>Love Aaj Kal 2</strong> is a romantic drama that explores the theme of reincarnation and how love transcends time and space. It stars Kartik Aaryan and Sara Ali Khan as two lovers who go through different phases of love in different eras. The film is directed by Imtiaz Ali and has a melodious soundtrack by Pritam.</p>
-
-<p>If you are looking for a romantic movie that will make you feel all kinds of emotions
-<h3>In Conclusion...</h3>
-
-<p><strong>Love Aaj Kal 2</strong> is a romantic drama that explores the theme of reincarnation and how love transcends time and space. It stars Kartik Aaryan and Sara Ali Khan as two lovers who go through different phases of love in different eras. The film is directed by Imtiaz Ali and has a melodious soundtrack by Pritam.</p>
-
-<p>If you are looking for a romantic movie that will make you feel all kinds of emotions, then you should not miss <strong>Love Aaj Kal 2</strong>. You can watch or download <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free using the options we have mentioned in this article. However, you should also be careful and use only legal and safe sources to access the movie.</p>
-
-<p>We hope that this article has helped you to know more about <strong>Love Aaj Kal 2</strong> and how to watch or download <strong>Love Aaj Kal 2 full movie in 720p HD</strong> online for free. We also hope that you will enjoy watching this movie and share your feedback with us.</p>
-
-<p>Thank you for reading and happy watching!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/julius/bands.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/julius/bands.py
deleted file mode 100644
index ef2162440b69e960770aa7bf81b9aaec48a63243..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/julius/bands.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-Decomposition of a signal over frequency bands in the waveform domain.
-"""
-from typing import Optional, Sequence
-import torch
-
-from .core import mel_frequencies
-from .lowpass import LowPassFilters
-from .utils import simple_repr
-
-
-class SplitBands(torch.nn.Module):
-    """
-    Decomposes a signal over the given frequency bands in the waveform domain using
-    a cascade of low pass filters as implemented by `julius.lowpass.LowPassFilters`.
-    You can either specify explicitely the frequency cutoffs, or just the number of bands,
-    in which case the frequency cutoffs will be spread out evenly in mel scale.
-
-    Args:
-        sample_rate (float): Sample rate of the input signal in Hz.
-        n_bands (int or None): number of bands, when not giving them explictely with `cutoffs`.
-            In that case, the cutoff frequencies will be evenly spaced in mel-space.
-        cutoffs (list[float] or None): list of frequency cutoffs in Hz.
-        pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
-            the output will have the same length as the input.
-        zeros (float): Number of zero crossings to keep. See `LowPassFilters` for more informations.
-        fft (bool or None): See `LowPassFilters` for more info.
-
-    ..note::
-        The sum of all the bands will always be the input signal.
-
-    ..warning::
-        Unlike `julius.lowpass.LowPassFilters`, the cutoffs frequencies must be provided in Hz along
-        with the sample rate.
-
-    Shape:
-
-        - Input: `[*, T]`
-        - Output: `[B, *, T']`, with `T'=T` if `pad` is True.
-            If `n_bands` was provided, `B = n_bands` otherwise `B = len(cutoffs) + 1`
-
-    >>> bands = SplitBands(sample_rate=128, n_bands=10)
-    >>> x = torch.randn(6, 4, 1024)
-    >>> list(bands(x).shape)
-    [10, 6, 4, 1024]
-    """
-
-    def __init__(self, sample_rate: float, n_bands: Optional[int] = None,
-                 cutoffs: Optional[Sequence[float]] = None, pad: bool = True,
-                 zeros: float = 8, fft: Optional[bool] = None):
-        super().__init__()
-        if (cutoffs is None) + (n_bands is None) != 1:
-            raise ValueError("You must provide either n_bands, or cutoffs, but not boths.")
-
-        self.sample_rate = sample_rate
-        self.n_bands = n_bands
-        self._cutoffs = list(cutoffs) if cutoffs is not None else None
-        self.pad = pad
-        self.zeros = zeros
-        self.fft = fft
-
-        if cutoffs is None:
-            if n_bands is None:
-                raise ValueError("You must provide one of n_bands or cutoffs.")
-            if not n_bands >= 1:
-                raise ValueError(f"n_bands must be greater than one (got {n_bands})")
-            cutoffs = mel_frequencies(n_bands + 1, 0, sample_rate / 2)[1:-1]
-        else:
-            if max(cutoffs) > 0.5 * sample_rate:
-                raise ValueError("A cutoff above sample_rate/2 does not make sense.")
-        if len(cutoffs) > 0:
-            self.lowpass = LowPassFilters(
-                [c / sample_rate for c in cutoffs], pad=pad, zeros=zeros, fft=fft)
-        else:
-            # Here I cannot make both TorchScript and MyPy happy.
-            # I miss the good old times, before all this madness was created.
-            self.lowpass = None  # type: ignore
-
-    def forward(self, input):
-        if self.lowpass is None:
-            return input[None]
-        lows = self.lowpass(input)
-        low = lows[0]
-        bands = [low]
-        for low_and_band in lows[1:]:
-            # Get a bandpass filter by substracting lowpasses
-            band = low_and_band - low
-            bands.append(band)
-            low = low_and_band
-        # Last band is whatever is left in the signal
-        bands.append(input - low)
-        return torch.stack(bands)
-
-    @property
-    def cutoffs(self):
-        if self._cutoffs is not None:
-            return self._cutoffs
-        elif self.lowpass is not None:
-            return [c * self.sample_rate for c in self.lowpass.cutoffs]
-        else:
-            return []
-
-    def __repr__(self):
-        return simple_repr(self, overrides={"cutoffs": self._cutoffs})
-
-
-def split_bands(signal: torch.Tensor, sample_rate: float, n_bands: Optional[int] = None,
-                cutoffs: Optional[Sequence[float]] = None, pad: bool = True,
-                zeros: float = 8, fft: Optional[bool] = None):
-    """
-    Functional version of `SplitBands`, refer to this class for more information.
-
-    >>> x = torch.randn(6, 4, 1024)
-    >>> list(split_bands(x, sample_rate=64, cutoffs=[12, 24]).shape)
-    [3, 6, 4, 1024]
-    """
-    return SplitBands(sample_rate, n_bands, cutoffs, pad, zeros, fft).to(signal)(signal)
diff --git a/spaces/r3gm/SoniTranslate_translate_audio_of_a_video_content/soni_translate/text_to_speech.py b/spaces/r3gm/SoniTranslate_translate_audio_of_a_video_content/soni_translate/text_to_speech.py
deleted file mode 100644
index e919a2ff235721d520c52bdd3196a3351b9f48b3..0000000000000000000000000000000000000000
--- a/spaces/r3gm/SoniTranslate_translate_audio_of_a_video_content/soni_translate/text_to_speech.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from gtts import gTTS
-import edge_tts
-import asyncio
-import nest_asyncio
-
-def make_voice(tts_text, tts_voice, filename,language):
-    #print(tts_text, filename)
-    try:
-      nest_asyncio.apply()
-      asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(filename))
-    except:
-      try:
-          tts = gTTS(tts_text, lang=language)
-          tts.save(filename)
-          print(f'No audio was received. Please change the tts voice for {tts_voice}. TTS auxiliary will be used in the segment')
-      except:
-        tts = gTTS('a', lang=language)
-        tts.save(filename)
-        print('Error: Audio will be replaced.')
-
-def make_voice_gradio(tts_text, tts_voice, filename, language):
-    print(tts_text, filename)
-    try:
-      asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(filename))
-    except:
-      try:
-        tts = gTTS(tts_text, lang=language)
-        tts.save(filename)
-        print(f'No audio was received. Please change the tts voice for {tts_voice}. TTS auxiliary will be used in the segment')
-      except:
-        tts = gTTS('a', lang=language)
-        tts.save(filename)
-        print('Error: Audio will be replaced.')
diff --git a/spaces/rabiyulfahim/Prompt-Refinery-Text-to-Image-Generation/share_btn.py b/spaces/rabiyulfahim/Prompt-Refinery-Text-to-Image-Generation/share_btn.py
deleted file mode 100644
index 1382fb25a5ef50e843598187e1e660e86ea8dd05..0000000000000000000000000000000000000000
--- a/spaces/rabiyulfahim/Prompt-Refinery-Text-to-Image-Generation/share_btn.py
+++ /dev/null
@@ -1,88 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `magic-prompt-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `magic-prompt-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const imgEls = gradioEl.querySelectorAll('#generated-gallery img');
-    const promptTxt = gradioEl.querySelector('#translated textarea').value;
-    let titleTxt = promptTxt;
-    if(titleTxt.length > 100){
-        titleTxt = titleTxt.slice(0, 100) + ' ...';
-    }
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!imgEls.length){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const files = await Promise.all(
-        [...imgEls].map(async (imgEl) => {
-            const res = await fetch(imgEl.src);
-            const blob = await res.blob();
-            const imgId = Date.now() % 200;
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        })
-    );
-    const inputFile = await getInputImgFile(inputImgEl);
-    files.push(inputFile);
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-    const urlInputImg = urls.pop();
-	const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-    const htmlImgsMd = htmlImgs.join(`\n`);
-	const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-#### Caption:
-${promptTxt}
-#### Generations:
-<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-${htmlImgsMd}
-</div>`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/huggingface-projects/magic-diffusion/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/rachana219/MODT2/utils/autoanchor.py b/spaces/rachana219/MODT2/utils/autoanchor.py
deleted file mode 100644
index f491032e53ab43cd81d966d127bd92f9b414b9fe..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/utils/autoanchor.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Auto-anchor utils
-
-import numpy as np
-import torch
-import yaml
-from scipy.cluster.vq import kmeans
-from tqdm import tqdm
-
-from utils.general import colorstr
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary
-    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da.sign() != ds.sign():  # same order
-        print('Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-        m.anchor_grid[:] = m.anchor_grid.flip(0)
-
-
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    prefix = colorstr('autoanchor: ')
-    print(f'\n{prefix}Analyzing anchors... ', end='')
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1. / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
-    bpr, aat = metric(anchors)
-    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
-    if bpr < 0.98:  # threshold to recompute
-        print('. Attempting to improve anchors, please wait...')
-        na = m.anchor_grid.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            print(f'{prefix}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
-            check_anchor_order(m)
-            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
-            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
-        else:
-            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
-    print('')  # newline
-
-
-def kmean_anchors(path='./data/coco.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            path: path to dataset *.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-            verbose: print all results
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.autoanchor import *; _ = kmean_anchors()
-    """
-    thr = 1. / thr
-    prefix = colorstr('autoanchor: ')
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def anchor_fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
-        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
-              f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
-        for i, x in enumerate(k):
-            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
-        return k
-
-    if isinstance(path, str):  # *.yaml file
-        with open(path) as f:
-            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
-        from utils.datasets import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-    else:
-        dataset = path  # dataset
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans calculation
-    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
-    s = wh.std(0)  # sigmas for whitening
-    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
-    k *= s
-    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
-    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
-    k = print_results(k)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    npr = np.random
-    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = anchor_fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k)
-
-    return print_results(k)
diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/models/lm.py b/spaces/radames/MusicGen-Continuation/audiocraft/models/lm.py
deleted file mode 100644
index 43f82b42340dd9e721a3a76fa58e27f70fe2b4e5..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/models/lm.py
+++ /dev/null
@@ -1,526 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
-    ConditionFuser,
-    ClassifierFreeGuidanceDropout,
-    AttributeDropout,
-    ConditioningProvider,
-    ConditioningAttributes,
-    ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
-    """LM layer initialization.
-    Inspired from xlformers: https://github.com/fairinternal/xlformers
-
-    Args:
-        method (str): Method name for init function. Valid options are:
-            'gaussian', 'uniform'.
-        input_dim (int): Input dimension of the initialized module.
-        init_depth (Optional[int]): Optional init depth value used to rescale
-            the standard deviation if defined.
-    """
-    # Compute std
-    std = 1 / math.sqrt(input_dim)
-    # Rescale with depth
-    if init_depth is not None:
-        std = std / math.sqrt(2 * init_depth)
-
-    if method == 'gaussian':
-        return partial(
-            torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
-        )
-    elif method == 'uniform':
-        bound = math.sqrt(3) * std  # ensure the standard deviation is `std`
-        return partial(torch.nn.init.uniform_, a=-bound, b=bound)
-    else:
-        raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
-               method: str,
-               init_depth: tp.Optional[int] = None,
-               zero_bias_init: bool = False):
-    """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
-    Args:
-        m (nn.Module): Module to initialize.
-        method (str): Method name for the init function.
-        init_depth (Optional[int]): Optional init depth value used to rescale
-            the standard deviation if defined.
-        zero_bias_init (bool): Whether to initialize the bias to 0 or not.
-    """
-    if isinstance(m, nn.Linear):
-        init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
-        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
-            weight = m.weight.float()
-            init_fn(weight)
-            m.weight.data[:] = weight.half()
-        else:
-            init_fn(m.weight)
-        if zero_bias_init and m.bias is not None:
-            nn.init.constant_(m.bias, 0)
-    elif isinstance(m, nn.Embedding):
-        init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
-        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
-            weight = m.weight.float()
-            init_fn(weight)
-            m.weight.data[:] = weight.half()
-        else:
-            init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
-    """Boost learning rate for embeddings (with `scale`).
-    """
-    def __init__(self, *args, lr=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr = lr
-
-    def make_optim_group(self):
-        group = {"params": list(self.parameters())}
-        if self.lr is not None:
-            group["lr"] = self.lr
-        return group
-
-
-@dataclass
-class LMOutput:
-    # The logits are already re-aligned with the input codes
-    # hence no extra shift is required, e.g. when computing CE
-    logits: torch.Tensor  # [B, K, T, card]
-    mask: torch.Tensor  # [B, K, T]
-
-
-class LMModel(StreamingModule):
-    """Transformer-based language model on multiple streams of codes.
-
-    Args:
-        pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
-        condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
-        fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
-        n_q (int): Number of parallel streams to model.
-        card (int): Cardinality, vocabulary size.
-        dim (int): Dimension of the transformer encoder.
-        num_heads (int): Number of heads for the transformer encoder.
-        hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
-        norm (str): Normalization method.
-        norm_first (bool): Use pre-norm instead of post-norm.
-        emb_lr (Optional[float]): Embedding-specific learning rate.
-        bias_proj (bool): Use bias for output projections.
-        weight_init (Optional[str]): Method for weight initialization.
-        depthwise_init (Optional[str]): Method for depthwise weight initialization.
-        zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
-        cfg_dropout (float): Classifier-free guidance dropout.
-        cfg_coef (float): Classifier-free guidance coefficient.
-        attribute_dropout (dict): Attribute dropout probabilities.
-        two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
-        **kwargs: Additional parameters for the transformer encoder.
-    """
-    def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
-                 fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
-                 hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
-                 emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
-                 weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
-                 zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
-                 attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
-                 **kwargs):
-        super().__init__()
-        self.cfg_coef = cfg_coef
-        self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
-        self.att_dropout = AttributeDropout(p=attribute_dropout)
-        self.condition_provider = condition_provider
-        self.fuser = fuser
-        self.card = card
-        embed_dim = self.card + 1
-        self.n_q = n_q
-        self.dim = dim
-        self.pattern_provider = pattern_provider
-        self.two_step_cfg = two_step_cfg
-        self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
-        if 'activation' in kwargs:
-            kwargs['activation'] = get_activation_fn(kwargs['activation'])
-        self.transformer = StreamingTransformer(
-            d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
-            norm=norm, norm_first=norm_first, **kwargs)
-        self.out_norm: tp.Optional[nn.Module] = None
-        if norm_first:
-            self.out_norm = create_norm_fn(norm, dim)
-        self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
-        self._init_weights(weight_init, depthwise_init, zero_bias_init)
-        self._fsdp: tp.Optional[nn.Module]
-        self.__dict__['_fsdp'] = None
-
-    def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
-        """Initialization of the transformer module weights.
-
-        Args:
-            weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
-            depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
-                'current' where the depth corresponds to the current layer index or 'global' where the total number
-                of layer is used as depth. If not set, no depthwise initialization strategy is used.
-            zero_bias_init (bool): Whether to initalize bias to zero or not.
-        """
-        assert depthwise_init is None or depthwise_init in ['current', 'global']
-        assert depthwise_init is None or weight_init is not None, \
-            "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
-        assert not zero_bias_init or weight_init is not None, \
-            "If 'zero_bias_init', a 'weight_init' method should be provided"
-
-        if weight_init is None:
-            return
-
-        for emb_layer in self.emb:
-            init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
-        for layer_idx, tr_layer in enumerate(self.transformer.layers):
-            depth = None
-            if depthwise_init == 'current':
-                depth = layer_idx + 1
-            elif depthwise_init == 'global':
-                depth = len(self.transformer.layers)
-            init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
-            tr_layer.apply(init_fn)
-
-        for linear in self.linears:
-            init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
-    @property
-    def special_token_id(self) -> int:
-        return self.card
-
-    @property
-    def num_codebooks(self) -> int:
-        return self.n_q
-
-    def forward(self, sequence: torch.Tensor,
-                conditions: tp.List[ConditioningAttributes],
-                condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
-        """Apply language model on sequence and conditions.
-        Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
-        S the sequence steps, return the logits with shape [B, card, K, S].
-
-        Args:
-            indices (torch.Tensor): indices of the codes to model.
-            conditions (list[ConditioningAttributes]): conditionings to use when modeling
-                the given codes. Note that when evaluating multiple time with the same conditioning
-                you should pre-compute those and pass them as `condition_tensors`.
-            condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
-                tensors, see `conditions`.
-        Returns:
-            torch.Tensor: Logits.
-        """
-        B, K, S = sequence.shape
-        assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
-        input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
-        if condition_tensors is None:
-            assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
-            # apply dropout modules
-            conditions = self.cfg_dropout(conditions)
-            conditions = self.att_dropout(conditions)
-            tokenized = self.condition_provider.tokenize(conditions)
-            # encode conditions and fuse, both have a streaming cache to not recompute when generating.
-            condition_tensors = self.condition_provider(tokenized)
-        else:
-            assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
-        input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
-        out = self.transformer(input_, cross_attention_src=cross_attention_input)
-        if self.out_norm:
-            out = self.out_norm(out)
-        logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1)  # [B, K, S, card]
-
-        # remove the prefix from the model outputs
-        if len(self.fuser.fuse2cond['prepend']) > 0:
-            logits = logits[:, :, -S:]
-
-        return logits  # [B, K, S, card]
-
-    def compute_predictions(
-            self, codes: torch.Tensor,
-            conditions: tp.List[ConditioningAttributes],
-            condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
-        """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
-        forward using the specified codes interleaving pattern.
-
-        Args:
-            codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
-                K the number of codebooks and T the number of timesteps.
-            conditions (list[ConditioningAttributes]): conditionings to use when modeling
-                the given codes. Note that when evaluating multiple time with the same conditioning
-                you should pre-compute those and pass them as `condition_tensors`.
-            condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
-                tensors, see `conditions`.
-        Returns:
-            LMOutput: Language model outputs
-                logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
-                    i.e. the first item corresponds to logits to predict the first code, meaning that
-                    no additional shifting of codes and logits is required.
-                mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
-                    Given the specified interleaving strategies, parts of the logits and codes should
-                    not be considered as valid predictions because of invalid context.
-        """
-        B, K, T = codes.shape
-        codes = codes.contiguous()
-        # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
-        pattern = self.pattern_provider.get_pattern(T)
-        sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
-            codes, self.special_token_id, keep_only_valid_steps=True
-        )
-        # apply model on pattern sequence
-        model = self if self._fsdp is None else self._fsdp
-        logits = model(sequence_codes, conditions, condition_tensors)  # [B, K, S, card]
-        # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
-        # and provide the corresponding mask over invalid positions of tokens
-        logits = logits.permute(0, 3, 1, 2)  # [B, card, K, S]
-        # note: we use nans as special token to make it obvious if we feed unexpected logits
-        logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
-            logits, float('nan'), keep_only_valid_steps=True
-        )
-        logits = logits.permute(0, 2, 3, 1)  # [B, K, T, card]
-        logits_mask = logits_mask[None, :, :].expand(B, -1, -1)  # [K, T] -> [B, K, T]
-        return LMOutput(logits, logits_mask)
-
-    def _sample_next_token(self,
-                           sequence: torch.Tensor,
-                           cfg_conditions: CFGConditions,
-                           unconditional_state: State,
-                           use_sampling: bool = False,
-                           temp: float = 1.0,
-                           top_k: int = 0,
-                           top_p: float = 0.0,
-                           cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
-        """Sample next token from the model given a sequence and a set of conditions. The model supports
-        multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
-        Args:
-            sequence (torch.Tensor): Current sequence of shape [B, K, S]
-                with K corresponding to the number of codebooks and S the number of sequence steps.
-                S = 1 in streaming mode, except for the first step that contains a bigger prompt.
-            condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
-                should be twice the batch size, being the concatenation of the conditions + null conditions.
-            use_sampling (bool): Whether to use a sampling strategy or not.
-            temp (float): Sampling temperature.
-            top_k (int): K for "top-k" sampling.
-            top_p (float): P for "top-p" sampling.
-            cfg_coef (float): classifier free guidance coefficient
-        Returns:
-            next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
-        """
-        B = sequence.shape[0]
-        cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
-        model = self if self._fsdp is None else self._fsdp
-        if self.two_step_cfg and cfg_conditions != {}:
-            assert isinstance(cfg_conditions, tuple)
-            condition_tensors, null_condition_tensors = cfg_conditions
-            cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
-            state = self.get_streaming_state()
-            self.set_streaming_state(unconditional_state)
-            uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
-            unconditional_state.update(self.get_streaming_state())
-            self.set_streaming_state(state)
-            logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
-        else:
-            assert isinstance(cfg_conditions, dict)
-            condition_tensors = cfg_conditions
-            if condition_tensors:
-                # Preparing for CFG, predicting both conditional and unconditional logits.
-                sequence = torch.cat([sequence, sequence], dim=0)
-            all_logits = model(
-                sequence,
-                conditions=[], condition_tensors=condition_tensors)
-            if condition_tensors:
-                cond_logits, uncond_logits = all_logits.split(B, dim=0)  # [B, K, T, card]
-                logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
-            else:
-                logits = all_logits
-
-        logits = logits.permute(0, 1, 3, 2)  # [B, K, card, T]
-        logits = logits[..., -1]  # [B x K x card]
-
-        if use_sampling:
-            probs = torch.softmax(logits / temp, dim=-1)
-            if top_p > 0.0:
-                next_token = utils.sample_top_p(probs, p=top_p)
-            elif top_k > 0:
-                next_token = utils.sample_top_k(probs, k=top_k)
-            else:
-                next_token = utils.multinomial(probs, num_samples=1)
-        else:
-            next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
-        return next_token
-
-    @torch.no_grad()
-    def generate(self,
-                 prompt: tp.Optional[torch.Tensor] = None,
-                 conditions: tp.List[ConditioningAttributes] = [],
-                 num_samples: tp.Optional[int] = None,
-                 max_gen_len: int = 256,
-                 use_sampling: bool = True,
-                 temp: float = 1.0,
-                 top_k: int = 250,
-                 top_p: float = 0.0,
-                 cfg_coef: tp.Optional[float] = None,
-                 two_step_cfg: bool = False,
-                 remove_prompts: bool = False,
-                 check: bool = False,
-                 callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
-        """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
-        be perform in a greedy fashion or using sampling with top K and top P strategies.
-
-        Args:
-            prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
-            conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
-            num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
-            max_gen_len (int): Maximum generation length.
-            use_sampling (bool): Whether to use a sampling strategy or not.
-            temp (float): Sampling temperature.
-            top_k (int): K for "top-k" sampling.
-            top_p (float): P for "top-p" sampling.
-            remove_prompts (bool): Whether to remove prompts from generation or not.
-        Returns:
-            torch.Tensor: Generated tokens.
-        """
-        assert not self.training, "generation shouldn't be used in training mode."
-        first_param = next(iter(self.parameters()))
-        device = first_param.device
-
-        # Checking all input shapes are consistents.
-        possible_num_samples = []
-        if num_samples is not None:
-            possible_num_samples.append(num_samples)
-        elif prompt is not None:
-            possible_num_samples.append(prompt.shape[0])
-        elif conditions:
-            possible_num_samples.append(len(conditions))
-        else:
-            possible_num_samples.append(1)
-        assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
-        num_samples = possible_num_samples[0]
-
-        # below we create set of conditions: one conditional and one unconditional
-        # to do that we merge the regular condition together with the null condition
-        # we then do 1 forward pass instead of 2.
-        # the reason for that is two-fold:
-        # 1. it is about x2 faster than doing 2 forward passes
-        # 2. avoid the streaming API treating the 2 passes as part of different time steps
-        # We also support doing two different passes, in particular to ensure that
-        # the padding structure is exactly the same between train anf test.
-        # With a batch size of 1, this can be slower though.
-        cfg_conditions: CFGConditions
-        two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
-        if conditions:
-            null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
-            if two_step_cfg:
-                cfg_conditions = (
-                    self.condition_provider(self.condition_provider.tokenize(conditions)),
-                    self.condition_provider(self.condition_provider.tokenize(null_conditions)),
-                )
-            else:
-                conditions = conditions + null_conditions
-                tokenized = self.condition_provider.tokenize(conditions)
-                cfg_conditions = self.condition_provider(tokenized)
-        else:
-            cfg_conditions = {}
-
-        if prompt is None:
-            assert num_samples > 0
-            prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
-        B, K, T = prompt.shape
-        start_offset = T
-        assert start_offset < max_gen_len
-
-        pattern = self.pattern_provider.get_pattern(max_gen_len)
-        # this token is used as default value for codes that are not generated yet
-        unknown_token = -1
-
-        # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
-        gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
-        # filling the gen_codes with the prompt if needed
-        gen_codes[..., :start_offset] = prompt
-        # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
-        gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
-        # retrieve the start_offset in the sequence:
-        # it is the first sequence step that contains the `start_offset` timestep
-        start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
-        assert start_offset_sequence is not None
-
-        with self.streaming():
-            unconditional_state = self.get_streaming_state()
-            prev_offset = 0
-            gen_sequence_len = gen_sequence.shape[-1]  # gen_sequence shape is [B, K, S]
-            for offset in range(start_offset_sequence, gen_sequence_len):
-                # get current sequence (note that the streaming API is providing the caching over previous offsets)
-                curr_sequence = gen_sequence[..., prev_offset:offset]
-                curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
-                if check:
-                    # check coherence between mask and sequence
-                    assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
-                    # should never happen as gen_sequence is filled progressively
-                    assert not (curr_sequence == unknown_token).any()
-                # sample next token from the model, next token shape is [B, K, 1]
-                next_token = self._sample_next_token(
-                    curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
-                    cfg_coef=cfg_coef)
-                # ensure the tokens that should be masked are properly set to special_token_id
-                # as the model never output special_token_id
-                valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
-                next_token[~valid_mask] = self.special_token_id
-                # ensure we don't overwrite prompt tokens, we only write over unknown tokens
-                # (then mask tokens should be left as is as well, which is correct)
-                gen_sequence[..., offset:offset+1] = torch.where(
-                    gen_sequence[..., offset:offset+1] == unknown_token,
-                    next_token, gen_sequence[..., offset:offset+1]
-                )
-                prev_offset = offset
-                if callback is not None:
-                    callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
-        unconditional_state.clear()
-
-        # ensure sequence has been entirely filled
-        assert not (gen_sequence == unknown_token).any()
-        # ensure gen_sequence pattern and mask are matching
-        # which means the gen_sequence is valid according to the pattern
-        assert (
-            gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
-        ).all()
-        # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
-        out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
-        # sanity checks over the returned codes and corresponding masks
-        assert (out_codes[..., :max_gen_len] != unknown_token).all()
-        assert (out_mask[..., :max_gen_len] == 1).all()
-
-        out_start_offset = start_offset if remove_prompts else 0
-        out_codes = out_codes[..., out_start_offset:max_gen_len]
-
-        # ensure the returned codes are all valid
-        assert (out_codes >= 0).all() and (out_codes <= self.card).all()
-        return out_codes
diff --git a/spaces/radames/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/gl/prt_render.py b/spaces/radames/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/gl/prt_render.py
deleted file mode 100644
index 92c8a6257f776ab0c803a78a3af7c43a4333c3f9..0000000000000000000000000000000000000000
--- a/spaces/radames/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/gl/prt_render.py
+++ /dev/null
@@ -1,350 +0,0 @@
-import numpy as np
-import random
-
-from .framework import *
-from .cam_render import CamRender
-
-class PRTRender(CamRender):
-    def __init__(self, width=1600, height=1200, name='PRT Renderer', uv_mode=False, ms_rate=1, egl=False):
-        program_files = ['prt.vs', 'prt.fs'] if not uv_mode else ['prt_uv.vs', 'prt_uv.fs']
-        CamRender.__init__(self, width, height, name, program_files=program_files, color_size=8, ms_rate=ms_rate, egl=egl)
-
-        # WARNING: this differs from vertex_buffer and vertex_data in Render
-        self.vert_buffer = {}
-        self.vert_data = {}
-
-        self.norm_buffer = {}
-        self.norm_data = {}
-
-        self.tan_buffer = {}
-        self.tan_data = {}
-
-        self.btan_buffer = {}
-        self.btan_data = {}
-
-        self.prt1_buffer = {}
-        self.prt1_data = {}
-        self.prt2_buffer = {}
-        self.prt2_data = {}        
-        self.prt3_buffer = {}
-        self.prt3_data = {}
-
-        self.uv_buffer = {}
-        self.uv_data = {}
-
-        self.render_texture_mat = {}
-
-        self.vertex_dim = {}
-        self.n_vertices = {}
-
-        self.norm_mat_unif = glGetUniformLocation(self.program, 'NormMat')
-        self.normalize_matrix = np.eye(4)
-
-        self.shcoeff_unif = glGetUniformLocation(self.program, 'SHCoeffs')
-        self.shcoeffs = np.zeros((9,3))
-        self.shcoeffs[0,:] = 1.0
-        #self.shcoeffs[1:,:] = np.random.rand(8,3)
-
-        self.hasAlbedoUnif = glGetUniformLocation(self.program, 'hasAlbedoMap')
-        self.hasNormalUnif = glGetUniformLocation(self.program, 'hasNormalMap')
-
-        self.analyticUnif = glGetUniformLocation(self.program, 'analytic')
-        self.analytic = False
-
-        self.rot_mat_unif = glGetUniformLocation(self.program, 'RotMat')
-        self.rot_matrix = np.eye(3)
-
-    def set_texture(self, mat_name, smplr_name, texture):
-        # texture_image: H x W x 3
-        width = texture.shape[1]
-        height = texture.shape[0]
-        texture = np.flip(texture, 0)
-        img_data = np.fromstring(texture.tostring(), np.uint8)
-
-        if mat_name not in self.render_texture_mat:
-            self.render_texture_mat[mat_name] = {} 
-        if smplr_name in self.render_texture_mat[mat_name].keys():
-            glDeleteTextures([self.render_texture_mat[mat_name][smplr_name]])
-            del self.render_texture_mat[mat_name][smplr_name]
-        self.render_texture_mat[mat_name][smplr_name] = glGenTextures(1)
-        glActiveTexture(GL_TEXTURE0)
-        
-        glPixelStorei(GL_UNPACK_ALIGNMENT, 1)
-        glBindTexture(GL_TEXTURE_2D, self.render_texture_mat[mat_name][smplr_name])
-
-        glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, width, height, 0, GL_RGB, GL_UNSIGNED_BYTE, img_data)
-        
-        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAX_LEVEL, 3)
-        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)
-        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)
-        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
-        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR_MIPMAP_LINEAR)
-
-        glGenerateMipmap(GL_TEXTURE_2D)
-        
-    def set_albedo(self, texture_image, mat_name='all'):
-        self.set_texture(mat_name, 'AlbedoMap', texture_image)
-
-    def set_normal_map(self, texture_image, mat_name='all'):
-        self.set_texture(mat_name, 'NormalMap', texture_image)
-
-    def set_mesh(self, vertices, faces, norms, faces_nml, uvs, faces_uvs, prt, faces_prt, tans, bitans, mat_name='all'):
-        self.vert_data[mat_name] = vertices[faces.reshape([-1])]
-        self.n_vertices[mat_name] = self.vert_data[mat_name].shape[0]
-        self.vertex_dim[mat_name] = self.vert_data[mat_name].shape[1]
-
-        if mat_name not in self.vert_buffer.keys():
-            self.vert_buffer[mat_name] = glGenBuffers(1)
-        glBindBuffer(GL_ARRAY_BUFFER, self.vert_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.vert_data[mat_name], GL_STATIC_DRAW)
-
-        self.uv_data[mat_name] = uvs[faces_uvs.reshape([-1])]
-        if mat_name not in self.uv_buffer.keys():
-            self.uv_buffer[mat_name] = glGenBuffers(1)
-        glBindBuffer(GL_ARRAY_BUFFER, self.uv_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.uv_data[mat_name], GL_STATIC_DRAW)
-
-        self.norm_data[mat_name] = norms[faces_nml.reshape([-1])]
-        if mat_name not in self.norm_buffer.keys():
-            self.norm_buffer[mat_name] = glGenBuffers(1)
-        glBindBuffer(GL_ARRAY_BUFFER, self.norm_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.norm_data[mat_name], GL_STATIC_DRAW)
-
-        self.tan_data[mat_name] = tans[faces_nml.reshape([-1])]
-        if mat_name not in self.tan_buffer.keys():
-            self.tan_buffer[mat_name] = glGenBuffers(1)
-        glBindBuffer(GL_ARRAY_BUFFER, self.tan_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.tan_data[mat_name], GL_STATIC_DRAW)
-
-        self.btan_data[mat_name] = bitans[faces_nml.reshape([-1])]
-        if mat_name not in self.btan_buffer.keys():
-            self.btan_buffer[mat_name] = glGenBuffers(1)
-        glBindBuffer(GL_ARRAY_BUFFER, self.btan_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.btan_data[mat_name], GL_STATIC_DRAW)
-
-        self.prt1_data[mat_name] = prt[faces_prt.reshape([-1])][:,:3]
-        self.prt2_data[mat_name] = prt[faces_prt.reshape([-1])][:,3:6]
-        self.prt3_data[mat_name] = prt[faces_prt.reshape([-1])][:,6:]
-
-        if mat_name not in self.prt1_buffer.keys():
-            self.prt1_buffer[mat_name] = glGenBuffers(1)
-        if mat_name not in self.prt2_buffer.keys():
-            self.prt2_buffer[mat_name] = glGenBuffers(1)
-        if mat_name not in self.prt3_buffer.keys():
-            self.prt3_buffer[mat_name] = glGenBuffers(1)
-        glBindBuffer(GL_ARRAY_BUFFER, self.prt1_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.prt1_data[mat_name], GL_STATIC_DRAW)
-        glBindBuffer(GL_ARRAY_BUFFER, self.prt2_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.prt2_data[mat_name], GL_STATIC_DRAW)
-        glBindBuffer(GL_ARRAY_BUFFER, self.prt3_buffer[mat_name])
-        glBufferData(GL_ARRAY_BUFFER, self.prt3_data[mat_name], GL_STATIC_DRAW)
-        
-        glBindBuffer(GL_ARRAY_BUFFER, 0)
-
-    def set_mesh_mtl(self, vertices, faces, norms, faces_nml, uvs, faces_uvs, tans, bitans, prt):
-        for key in faces:
-            self.vert_data[key] = vertices[faces[key].reshape([-1])]
-            self.n_vertices[key] = self.vert_data[key].shape[0]
-            self.vertex_dim[key] = self.vert_data[key].shape[1]
-
-            if key not in self.vert_buffer.keys():
-                self.vert_buffer[key] = glGenBuffers(1)
-            glBindBuffer(GL_ARRAY_BUFFER, self.vert_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.vert_data[key], GL_STATIC_DRAW)
-
-            self.uv_data[key] = uvs[faces_uvs[key].reshape([-1])]
-            if key not in self.uv_buffer.keys():
-                self.uv_buffer[key] = glGenBuffers(1)
-            glBindBuffer(GL_ARRAY_BUFFER, self.uv_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.uv_data[key], GL_STATIC_DRAW)
-
-            self.norm_data[key] = norms[faces_nml[key].reshape([-1])]
-            if key not in self.norm_buffer.keys():
-                self.norm_buffer[key] = glGenBuffers(1)
-            glBindBuffer(GL_ARRAY_BUFFER, self.norm_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.norm_data[key], GL_STATIC_DRAW)
-
-            self.tan_data[key] = tans[faces_nml[key].reshape([-1])]
-            if key not in self.tan_buffer.keys():
-                self.tan_buffer[key] = glGenBuffers(1)
-            glBindBuffer(GL_ARRAY_BUFFER, self.tan_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.tan_data[key], GL_STATIC_DRAW)
-
-            self.btan_data[key] = bitans[faces_nml[key].reshape([-1])]
-            if key not in self.btan_buffer.keys():
-                self.btan_buffer[key] = glGenBuffers(1)
-            glBindBuffer(GL_ARRAY_BUFFER, self.btan_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.btan_data[key], GL_STATIC_DRAW)
-
-            self.prt1_data[key] = prt[faces[key].reshape([-1])][:,:3]
-            self.prt2_data[key] = prt[faces[key].reshape([-1])][:,3:6]
-            self.prt3_data[key] = prt[faces[key].reshape([-1])][:,6:]
-
-            if key not in self.prt1_buffer.keys():
-                self.prt1_buffer[key] = glGenBuffers(1)
-            if key not in self.prt2_buffer.keys():
-                self.prt2_buffer[key] = glGenBuffers(1)
-            if key not in self.prt3_buffer.keys():
-                self.prt3_buffer[key] = glGenBuffers(1)
-            glBindBuffer(GL_ARRAY_BUFFER, self.prt1_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.prt1_data[key], GL_STATIC_DRAW)
-            glBindBuffer(GL_ARRAY_BUFFER, self.prt2_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.prt2_data[key], GL_STATIC_DRAW)
-            glBindBuffer(GL_ARRAY_BUFFER, self.prt3_buffer[key])
-            glBufferData(GL_ARRAY_BUFFER, self.prt3_data[key], GL_STATIC_DRAW)
-
-        glBindBuffer(GL_ARRAY_BUFFER, 0)
-
-    def cleanup(self):
-        
-        glBindBuffer(GL_ARRAY_BUFFER, 0)
-        for key in self.vert_data:
-            glDeleteBuffers(1, [self.vert_buffer[key]])
-            glDeleteBuffers(1, [self.norm_buffer[key]])
-            glDeleteBuffers(1, [self.uv_buffer[key]])
-
-            glDeleteBuffers(1, [self.tan_buffer[key]])
-            glDeleteBuffers(1, [self.btan_buffer[key]])
-            glDeleteBuffers(1, [self.prt1_buffer[key]])
-            glDeleteBuffers(1, [self.prt2_buffer[key]])
-            glDeleteBuffers(1, [self.prt3_buffer[key]])
-
-            glDeleteBuffers(1, [])
-
-            for smplr in self.render_texture_mat[key]:
-                glDeleteTextures([self.render_texture_mat[key][smplr]])
-
-        self.vert_buffer = {}
-        self.vert_data = {}
-
-        self.norm_buffer = {}
-        self.norm_data = {}
-
-        self.tan_buffer = {}
-        self.tan_data = {}
-
-        self.btan_buffer = {}
-        self.btan_data = {}
-
-        self.prt1_buffer = {}
-        self.prt1_data = {}
-
-        self.prt2_buffer = {}
-        self.prt2_data = {}
-
-        self.prt3_buffer = {}
-        self.prt3_data = {}
-
-        self.uv_buffer = {}
-        self.uv_data = {}
-
-        self.render_texture_mat = {}
-
-        self.vertex_dim = {}
-        self.n_vertices = {}
-    
-    def randomize_sh(self):
-        self.shcoeffs[0,:] = 0.8
-        self.shcoeffs[1:,:] = 1.0*np.random.rand(8,3)
-
-    def set_sh(self, sh):
-        self.shcoeffs = sh
-
-    def set_norm_mat(self, scale, center):
-        N = np.eye(4)
-        N[:3, :3] = scale*np.eye(3)
-        N[:3, 3] = -scale*center
-
-        self.normalize_matrix = N
-
-    def draw(self):
-        self.draw_init()
-
-        glDisable(GL_BLEND)
-        #glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
-        glEnable(GL_MULTISAMPLE)
-
-        glUseProgram(self.program)
-        glUniformMatrix4fv(self.norm_mat_unif, 1, GL_FALSE, self.normalize_matrix.transpose())
-        glUniformMatrix4fv(self.model_mat_unif, 1, GL_FALSE, self.model_view_matrix.transpose())
-        glUniformMatrix4fv(self.persp_mat_unif, 1, GL_FALSE, self.projection_matrix.transpose())
-
-        if 'AlbedoMap' in self.render_texture_mat['all']:
-            glUniform1ui(self.hasAlbedoUnif, GLuint(1))
-        else:
-            glUniform1ui(self.hasAlbedoUnif, GLuint(0))
-
-        if 'NormalMap' in self.render_texture_mat['all']:
-            glUniform1ui(self.hasNormalUnif, GLuint(1))
-        else:
-            glUniform1ui(self.hasNormalUnif, GLuint(0))
-
-        glUniform1ui(self.analyticUnif, GLuint(1) if self.analytic else GLuint(0))
-
-        glUniform3fv(self.shcoeff_unif, 9, self.shcoeffs)
-
-        glUniformMatrix3fv(self.rot_mat_unif, 1, GL_FALSE, self.rot_matrix.transpose())
-
-        for mat in self.vert_buffer:
-            # Handle vertex buffer
-            glBindBuffer(GL_ARRAY_BUFFER, self.vert_buffer[mat])
-            glEnableVertexAttribArray(0)
-            glVertexAttribPointer(0, self.vertex_dim[mat], GL_DOUBLE, GL_FALSE, 0, None)
-
-            # Handle normal buffer
-            glBindBuffer(GL_ARRAY_BUFFER, self.norm_buffer[mat])
-            glEnableVertexAttribArray(1)
-            glVertexAttribPointer(1, 3, GL_DOUBLE, GL_FALSE, 0, None)
-
-            # Handle uv buffer
-            glBindBuffer(GL_ARRAY_BUFFER, self.uv_buffer[mat])
-            glEnableVertexAttribArray(2)
-            glVertexAttribPointer(2, 2, GL_DOUBLE, GL_FALSE, 0, None)
-
-            # Handle tan buffer
-            glBindBuffer(GL_ARRAY_BUFFER, self.tan_buffer[mat])
-            glEnableVertexAttribArray(3)
-            glVertexAttribPointer(3, 3, GL_DOUBLE, GL_FALSE, 0, None)
-
-            # Handle btan buffer
-            glBindBuffer(GL_ARRAY_BUFFER, self.btan_buffer[mat])
-            glEnableVertexAttribArray(4)
-            glVertexAttribPointer(4, 3, GL_DOUBLE, GL_FALSE, 0, None)
-
-            # Handle PTR buffer
-            glBindBuffer(GL_ARRAY_BUFFER, self.prt1_buffer[mat])
-            glEnableVertexAttribArray(5)
-            glVertexAttribPointer(5, 3, GL_DOUBLE, GL_FALSE, 0, None)
-
-            glBindBuffer(GL_ARRAY_BUFFER, self.prt2_buffer[mat])
-            glEnableVertexAttribArray(6)
-            glVertexAttribPointer(6, 3, GL_DOUBLE, GL_FALSE, 0, None)
-
-            glBindBuffer(GL_ARRAY_BUFFER, self.prt3_buffer[mat])
-            glEnableVertexAttribArray(7)
-            glVertexAttribPointer(7, 3, GL_DOUBLE, GL_FALSE, 0, None)
-
-            for i, smplr in enumerate(self.render_texture_mat[mat]):
-                glActiveTexture(GL_TEXTURE0 + i)
-                glBindTexture(GL_TEXTURE_2D, self.render_texture_mat[mat][smplr])
-                glUniform1i(glGetUniformLocation(self.program, smplr), i)
-
-            glDrawArrays(GL_TRIANGLES, 0, self.n_vertices[mat])
-
-            glDisableVertexAttribArray(7)
-            glDisableVertexAttribArray(6)
-            glDisableVertexAttribArray(5)
-            glDisableVertexAttribArray(4)
-            glDisableVertexAttribArray(3)
-            glDisableVertexAttribArray(2)
-            glDisableVertexAttribArray(1)
-            glDisableVertexAttribArray(0)
-
-        glBindBuffer(GL_ARRAY_BUFFER, 0)
-
-        glUseProgram(0)
-
-        glDisable(GL_BLEND)
-        glDisable(GL_MULTISAMPLE)
-
-        self.draw_end()
diff --git a/spaces/radames/SPIGA-face-alignment-headpose-estimator/README.md b/spaces/radames/SPIGA-face-alignment-headpose-estimator/README.md
deleted file mode 100644
index a6c278c8e7f6015e4f1643fc30f0829759cdc6f1..0000000000000000000000000000000000000000
--- a/spaces/radames/SPIGA-face-alignment-headpose-estimator/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SPIGA Face Alignment Headpose Estimator
-emoji: 👀
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.22.1
-app_file: app.py
-pinned: false
----
-
-https://arxiv.org/abs/2210.07233
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion A Comparison With Other Doraemon Movies.md b/spaces/raedeXanto/academic-chatgpt-beta/Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion A Comparison With Other Doraemon Movies.md
deleted file mode 100644
index e29f1278906097aa1ef3dde03fbd20c092740cdf..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion A Comparison With Other Doraemon Movies.md	
+++ /dev/null
@@ -1,104 +0,0 @@
-<br />
-<h1>Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion</h1>
- <p>Do you love watching animated movies that are full of adventure, comedy, fantasy, and friendship? If yes, then you should not miss <strong>Doraemon Movie Nobita And The Legend Of The Sun King</strong>, a 2000 Japanese film that features one of the most beloved characters in anime history, Doraemon. This movie is based on a story from Daichohen Doraemon (Doraemon Long Stories), a series of longer manga volumes that have more complex plots than the regular manga chapters. It is also known as <strong>Doraemon and the Empire of the Sun</strong> . In this movie, you will see how Doraemon, Nobita, Shizuka, Gian, and Suneo travel back in time to an ancient kingdom called Mayana, where they meet a prince who looks exactly like Nobita. Together, they embark on a quest to find the legendary Sun King, who can save Mayana from an evil witch. In this article, we will tell you more about this movie, why you should watch it, and how you can watch it for free in Hindi on Dailymotion.</p>
- <h2>What is Doraemon?</h2>
- <p>Doraemon is one of the most popular anime and manga series in Japan and around the world. It was created by Fujiko F. Fujio in 1970, and has since spawned over 50 movies, TV shows, video games, books, and merchandise. The series revolves around Doraemon, a robotic cat who travels back in time from the 22nd century to help a young boy named Nobita Nobi. Nobita is a lazy, clumsy, and unlucky student who often gets bullied by his classmates Gian and Suneo. To help him out, Doraemon lends him various futuristic gadgets from his fourth-dimensional pocket, such as the Anywhere Door, which can take them anywhere they want; or the Bamboo Copter, which can make them fly. Along with their friends Shizuka Minamoto, a kind-hearted girl who likes Nobita; and Dekisugi Hidetoshi, a smart and talented boy who often outshines Nobita; they have many fun-filled adventures in different places and times.</p>
-<h2>Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://tinourl.com/2uL4E5">https://tinourl.com/2uL4E5</a></b></p><br /><br />
- <h2>What is the Legend of the Sun King?</h2>
- <p>The Legend of the Sun King is one of those adventures that Nobita and his friends have with Doraemon's gadgets. It is based on a story from Daichohen Doraemon (Doraemon Long Stories), a series of longer manga volumes that feature more complex plots than the regular manga chapters. The story was adapted into a 93-minute animated film that was released in Japan on March 11, 2000. It was directed by Tsutomu Shibayama , who also directed many other Doraemon movies.</p>
- <p>The plot of the movie is as follows:</p>
-<p>Doraemon Hindi Movie : Nobita and the Legend of the Sun King Full HD<br />
-Doraemon : Nobita and the Legend of the Sun King in Hindi Dailymotion Part 1<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Download<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Watch Online<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi 2000<br />
-Doraemon : Nobita and the Legend of the Sun King Hindi Dailymotion Part 2<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Free Download<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Full Movie Dailymotion<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD 1080p<br />
-Doraemon : Nobita and the Legend of the Sun King Hindi Dailymotion Part 3<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Watch Online<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Full Movie Download<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD 720p<br />
-Doraemon : Nobita and the Legend of the Sun King Hindi Dailymotion Part 4<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Online Streaming<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Full Movie Online<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD 480p<br />
-Doraemon : Nobita and the Legend of the Sun King Hindi Dailymotion Part 5<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Dailymotion Video<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Full Movie HD<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD Dailymotion<br />
-Doraemon : Nobita and the Legend of the Sun King Hindi Dailymotion Part 6<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Facebook Video<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Full Movie Free Download<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD Facebook<br />
-Doraemon : Nobita and the Legend of the Sun King Hindi Dailymotion Part 7<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Youtube Video<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Full Movie Watch Online Free<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD Youtube<br />
-Doraemon : Nobita and the Legend of<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Mp4 Download<br />
-Doraemon The Movie : Nobita and the Legend of the Sun King Hindi Full Movie Youtube Video<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD Mp4<br />
-Doraemon : Nobita and the Legend of<br />
-Doraemon Movie Nobita and the Sun King Hindi Dubbed Mkv Download<br />
-Doraemon The Movie : Nobita and the Legend of<br />
-Doraemon Nobita and the Sun King Full Movie in Hindi HD Mkv<br />
-Doraemon : Nobita and<br />
-Doraemon Movie Nobita and<br />
-Doraemon The Movie : Nobita<br />
-Doraemon Nobita<br />
-Doraemon : Nobita</p>
- <ul>
-<li>Nobita and his friends are rehearsing for a school play based on Snow White with Doraemon's gadgets, but a series of miscasts halt their progress.</li>
-<li>Gian selfishly takes the gadgets to perform on an imaginary stadium, leading to Doraemon, Nobita, and Shizuka's attempt to take them back through a time portal.</li>
-<li>Gian's violent reaction breaks the portal, unknowingly connecting it to a tree in the forests of Mayana, a Mesoamerican kingdom ruled by a queen until an evil witch, Ledina, casts a spell putting her into an eternal sleep.</li>
-<li>Until the queen awakes, the throne is passed to her young son, Prince Tio, who has a pet jerboa-like creature named Popol.</li>
-<li>Popol ransacks Nobita's room  and spreads his test papers throughout  the forest,  forcing Nobita  and Doraemon to cross  the portal to collect them.</li>
-<li>Along  the way,  Nobita is cornered by Tio until both land on a mud lake;  to his surprise,  he finds out that both  of them look-alike.</li>
-<li>The three attempt to escape through  the portal,  but Nobita lags behind  and gets captured by  the kingdom guards,  who mistake him for Tio.</li>
-<li>Tio himself,  amazed by  the "magic"  of present-day technology,  decides that he will temporarily switch roles with Nobita for several days,  which Doraemon reluctantly agrees.</li>
-<li>Nobita enjoys living as a prince in Mayana,  while Tio explores modern Japan with Shizuka,  Gian,  and Suneo.</li>
-<li>However,  they soon discover that Ledina plans to overthrow Tio  and take over Mayana with her army  of stone warriors.</li>
-<li>Nobita  and his friends must use their wits  and gadgets to stop Ledina  and find  the Sun King,  a mythical hero who can awaken the queen and restore peace to Mayana.</li>
-</ul>
- <p>The movie is a thrilling and heartwarming tale of friendship, courage, loyalty, and justice. It also explores the themes of identity, culture, history, and destiny. It shows how Nobita and Tio learn from each other's experiences and grow as individuals. It also shows how Doraemon and his friends use their creativity and teamwork to overcome the challenges they face. The movie also pays homage to the rich and diverse Maya civilization, which flourished in Mesoamerica for thousands of years.</p>
- <h2>Why watch Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion?</h2>
- <p>If you are a fan of Doraemon or animation in general, you will find many reasons to watch this movie. Here are some of them:</p>
- <h3>The animation and the music of the movie</h3>
- <p>The movie boasts of high-quality and colorful animation that brings the characters and the settings to life. The movie showcases the beauty and diversity of the Maya region, from its lush forests and rivers to its majestic pyramids and temples. The movie also features stunning action scenes that are exciting and engaging. The movie also has a wonderful soundtrack that complements the mood and tone of the story. The movie has a catchy theme song called "Yume no Hito" (Dreamer) by Ryoko Shinohara, which expresses the hopes and dreams of Nobita and Tio. The movie also has a beautiful ending song called "Doraemon no Uta" (Doraemon's Song) by The Vienna Boys' Choir, which celebrates the friendship between Doraemon and Nobita.</p>
- <h3>The humor and the emotion of the movie</h3>
- <p>The movie is not only an adventure but also a comedy that will make you laugh out loud. The movie has many hilarious moments that showcase the personalities and quirks of Doraemon and his friends. For example, you will see how Gian tries to impress Dakidaki, a geeky girl who helps them in their quest; how Suneo gets scared by Popol's antics; how Shizuka gets annoyed by Dekisugi's flirting; and how Nobita gets confused by Tio's customs. The movie also has many touching moments that will tug at your heartstrings. The movie shows the bond between Nobita and Tio, who share more than just their appearance; the loyalty between Doraemon and Nobita, who always support each other; the sacrifice between Tio and his mother, who love their kingdom; and the reconciliation between Nobita and his friends, who overcome their differences.</p>
- <h3>The availability and accessibility of the movie on Dailymotion</h3>
- <p>One of the best reasons to watch this movie is that you can watch it for free in Hindi on Dailymotion, a free online video platform that hosts millions of videos from various genres and languages. Dailymotion is a great way to watch movies that are not available on other platforms or in your region. You can watch this movie in Hindi on Dailymotion with subtitles or dubbing, depending on your preference. You can also adjust the quality and speed of the video according to your internet connection. You can also comment on the video and share it with your friends on social media.</p>
- <h2>How to watch Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion?</h2>
- <p>Watching this movie on Dailymotion is very easy and simple. Here are the steps you need to follow:</p>
- <h3>Search for the movie title on Dailymotion</h3>
- <p>First, you need to go to <a href="https://www.dailymotion.com/">Dailymotion's website</a> or download its app on your device. Then, you need to type "Doraemon Movie Nobita And The Legend Of The Sun King In Hindi" on the search bar at the top of the page or app. You can also copy and paste this title from this article. Then, you need to press enter or click on the magnifying glass icon to start searching.</p>
- <p><img src="https://i.imgur.com/0zZj9Wq.png" alt="Screenshot of Dailymotion's website with search bar" width="800" height="400"></p>
- <h3>Choose a video that matches your preferences</h3>
- <p>Next, you will see a list of videos that match your search query. You need to choose a video that has good quality, subtitles or dubbing in Hindi, and a reasonable duration (around 90 minutes). You can check these details by looking at the thumbnail, title, description, views, likes, comments, and duration of each video. You can also preview each video by hovering over it with your mouse or tapping on it with your finger.</p>
- <p><img src="https://i.imgur.com/1fFZ6k8.png" alt="Screenshot of Dailymotion's website with video list" width="800" height="400"></p>
- <h3>Enjoy watching the movie on Dailymotion</h3>
- <p>Finally, you can click or tap on the video that you have chosen to start watching it. You can adjust the volume, brightness, fullscreen mode, playback speed, quality, subtitles or dubbing options using the icons at the bottom of the video player. You can also pause, play, rewind, or fast-forward the video using the spacebar, arrow keys, or mouse.</p>
- <p><img src="https://i.imgur.com/6t0F9g5.png" alt="Screenshot of Dailymotion's video player with icons" width="800" height="400"></p>
- <h2>Conclusion</h2>
- <p>Doraemon Movie Nobita And The Legend Of The Sun King is a fantastic movie that you should not miss if you love Doraemon or animation. It is a movie that will make you laugh, cry, and cheer for the heroes as they face an epic adventure in an ancient kingdom. It is also a movie that will teach you about the Maya civilization and its culture, history, and mythology. You can watch this movie for free in Hindi on Dailymotion, a free online video platform that offers you a great watching experience. All you need to do is to follow the steps we have explained above and enjoy the movie on your device.</p>
- <p>So what are you waiting for? Go ahead and watch Doraemon Movie Nobita And The Legend Of The Sun King In Hindi On Dailymotion today and share your thoughts with us in the comments below!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions and answers about the movie:</p>
- <ol>
-<li>Q: Who is the voice actor of Doraemon in Hindi?<br>
-A: The voice actor of Doraemon in Hindi is Sonal Kaushal, who has been voicing the character since 2005.</li>
-<li>Q: Who is the voice actor of Nobita in Hindi?<br>
-A: The voice actor of Nobita in Hindi is Simran Kaur, who has been voicing the character since 2005.</li>
-<li>Q: Is Doraemon Movie Nobita And The Legend Of The Sun King based on a true story?<br>
-A: No, the movie is not based on a true story. It is based on a fictional story from Daichohen Doraemon (Doraemon Long Stories), a series of longer manga volumes by Fujiko F. Fujio.</li>
-<li>Q: Is Doraemon Movie Nobita And The Legend Of The Sun King available on other platforms?<br>
-A: Yes, the movie is also available on other platforms such as YouTube, Netflix, Amazon Prime Video, and Disney+ Hotstar. However, you may need to pay a subscription fee or rent the movie to watch it on these platforms.</li>
-<li>Q: Is Doraemon Movie Nobita And The Legend Of The Sun King suitable for children?<br>
-A: Yes, the movie is suitable for children of all ages. It is rated TV-Y7-FV by IMDb, which means it is appropriate for children age 7 and older with fantasy violence.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar The Secret Weapon for Successful Traders.md b/spaces/raedeXanto/academic-chatgpt-beta/ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar The Secret Weapon for Successful Traders.md
deleted file mode 100644
index 884baa21ee5f63003d938f26c0e1a4637ba7bb64..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar The Secret Weapon for Successful Traders.md	
+++ /dev/null
@@ -1,172 +0,0 @@
-
-<h1>ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar: What Is It and How to Use It?</h1>
- <p>If you are looking for a powerful and professional trading software that can help you analyze the market, identify trends, and execute trades, you might have heard of ESignal Advanced GET Edition Ver. 11. This is one of the most popular and trusted trading platforms among traders of all levels and styles. However, you might also know that this software is not cheap and requires a subscription fee to access its full features. That's why some people look for a crack file that can unlock the software without paying anything. In this article, we will explain what ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar is, how to download and install it, and how to use it.</p>
- <h2>Introduction</h2>
- <p>Before we dive into the details of the crack file, let's first understand what ESignal Advanced GET Edition Ver. 11 is and what it can do for you as a trader.</p>
-<h2>ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://tinourl.com/2uKZm0">https://tinourl.com/2uKZm0</a></b></p><br /><br />
- <h3>What is ESignal Advanced GET Edition?</h3>
- <p>ESignal Advanced GET Edition is a trading software that provides you with advanced tools and indicators for technical analysis, market scanning, strategy testing, and trading execution. It is designed for traders who want to take their trading to the next level and gain an edge over the market. With ESignal Advanced GET Edition, you can access real-time data from over 100 exchanges worldwide, including stocks, futures, forex, options, ETFs, and more. You can also customize your charts with over 100 technical studies, drawing tools, and annotations. You can also use the exclusive Advanced GET indicators that are based on Elliott Wave Theory and Gann Analysis. These indicators can help you identify market patterns, support and resistance levels, entry and exit points, and trading opportunities.</p>
- <h3>What is BiGStoRm.rar?</h3>
- <p>BiGStoRm.rar is a compressed file that contains the crack file for ESignal Advanced GET Edition Ver. 11. A crack file is a program that modifies or bypasses the original software's security features and allows you to use it without paying for a license or subscription. BiGStoRm.rar is one of the most popular crack files for ESignal Advanced GET Edition Ver. 11 because it claims to work for both Windows and Mac operating systems and does not require any additional software or hardware.</p>
- <h3>Why do you need a crack for ESignal Advanced GET Edition Ver. 11?</h3>
- <p>The main reason why some people look for a crack for ESignal Advanced GET Edition Ver. 11 is because they want to save money and avoid paying for the subscription fee. ESignal Advanced GET Edition Ver. 11 is not a cheap software and costs $299 per month or $2,395 per year for a single user license. That's a lot of money for some traders who might not be able to afford it or who might not want to commit to a long-term contract. By using a crack file, they can access the full features of the software without spending a dime.</p>
-<p>eSignal Advanced GET Edition Ver. 11 download free<br />
-eSignal Advanced GET Edition Ver. 11 full version with crack<br />
-eSignal Advanced GET Edition Ver. 11 BiGStoRm.rar torrent<br />
-eSignal Advanced GET Edition Ver. 11 activation key<br />
-eSignal Advanced GET Edition Ver. 11 serial number<br />
-eSignal Advanced GET Edition Ver. 11 license code<br />
-eSignal Advanced GET Edition Ver. 11 patch<br />
-eSignal Advanced GET Edition Ver. 11 keygen<br />
-eSignal Advanced GET Edition Ver. 11 cracked by BiGStoRm<br />
-eSignal Advanced GET Edition Ver. 11 software review<br />
-eSignal Advanced GET Edition Ver. 11 features and benefits<br />
-eSignal Advanced GET Edition Ver. 11 system requirements<br />
-eSignal Advanced GET Edition Ver. 11 installation guide<br />
-eSignal Advanced GET Edition Ver. 11 user manual<br />
-eSignal Advanced GET Edition Ver. 11 tutorial videos<br />
-eSignal Advanced GET Edition Ver. 11 technical support<br />
-eSignal Advanced GET Edition Ver. 11 alternative software<br />
-eSignal Advanced GET Edition Ver. 11 comparison with other products<br />
-eSignal Advanced GET Edition Ver. 11 discount coupon code<br />
-eSignal Advanced GET Edition Ver. 11 price and payment options<br />
-eSignal Advanced GET Edition Ver. 11 refund policy and guarantee<br />
-eSignal Advanced GET Edition Ver. 11 testimonials and feedbacks<br />
-eSignal Advanced GET Edition Ver. 11 FAQs and tips<br />
-eSignal Advanced GET Edition Ver. 11 latest updates and news<br />
-eSignal Advanced GET Edition Ver. 11 online demo and trial version<br />
-eSignal Advanced GET Edition Ver. 11 for Windows/Mac/Linux<br />
-eSignal Advanced GET Edition Ver. 11 for Forex/Stocks/Options/Futures trading<br />
-eSignal Advanced GET Edition Ver. 11 best settings and strategies<br />
-eSignal Advanced GET Edition Ver. 11 custom indicators and templates<br />
-eSignal Advanced GET Edition Ver. 11 how to use and apply<br />
-eSignal Advanced GET Edition Ver. 11 pros and cons<br />
-eSignal Advanced GET Edition Ver. 11 results and performance<br />
-eSignal Advanced GET Edition Ver. 11 scam or legit<br />
-eSignal Advanced GET Edition Ver. 11 bonus and extras<br />
-eSignal Advanced GET Edition Ver. 11 affiliate program and commissions<br />
-eSignal Advanced GET Edition Ver. 11 official website and download link<br />
-eSignal Advanced GET Edition Ver. 11 forum and community<br />
-eSignal Advanced GET Edition Ver. 11 blog and articles<br />
-eSignal Advanced GET Edition Ver. 11 webinar and live training<br />
-eSignal Advanced GET Edition Ver. 11 case studies and success stories<br />
-How to get Esignal advanced get edition ver. 11 crack bigstorm.rar for free?<br />
-Where to find Esignal advanced get edition ver. 11 crack bigstorm.rar online?<br />
-What is Esignal advanced get edition ver. 11 crack bigstorm.rar and how does it work?<br />
-Why choose Esignal advanced get edition ver. 11 crack bigstorm.rar over other software?<br />
-Who is behind Esignal advanced get edition ver. 11 crack bigstorm.rar and what is their reputation?<br />
-When is the best time to use Esignal advanced get edition ver. 11 crack bigstorm.rar for trading?<br />
-What are the advantages and disadvantages of Esignal advanced get edition ver. 11 crack bigstorm.rar?<br />
-How to install and activate Esignal advanced get edition ver. 11 crack bigstorm.rar on your computer?<br />
-How to troubleshoot and fix Esignal advanced get edition ver. 11 crack bigstorm.rar errors and issues?<br />
-How to optimize and improve Esignal advanced get edition ver. 11 crack bigstorm.rar performance and results?</p>
- <h2>How to download and install ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar</h2>
- <p>If you are interested in trying out ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar, here are the steps you need to follow:</p>
- <h3>Step 1: Find a reliable source for the crack file</h3>
- <p>The first thing you need to do is find a website that offers the crack file for download. You need to be careful when choosing a source because not all websites are trustworthy and some might contain viruses or malware that can harm your computer or steal your personal information. You also need to check the reviews and comments from other users who have downloaded the crack file before you decide to download it yourself.</p>
- <p>One of the websites that claims to offer ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar is forumotion.com. This website has a forum thread where users can share their experiences with the crack file and ask questions or report problems. However, this website also has some negative feedback from users who say that the crack file does not work or that it contains viruses or malware. Therefore, you should be cautious when downloading from this website and scan your computer with an antivirus program after downloading.</p>
- <h3>Step 2: Download the crack file to your computer</h3>
- <p>Once you have found a reliable source for the crack file, you need to download it to your computer. The crack file should be in a .rar format which means that it is compressed and needs to be extracted before you can use it. The size of the crack file should be around 10 MB which means that it should not take too long to download depending on your internet speed.</p>
- <h3>Step 3: Extract the crack file using WinRAR or another software</h3>
- <p>After downloading the crack file, you need to extract it using WinRAR or another software that can handle .rar files. WinRAR is a free program that you can download from https://www.win-rar.com/. To extract the crack file using WinRAR, follow these steps:</p>
- <ol>
-<li>Right-click on the crack file and select "Extract Here" or "Extract Files".</li>
-<li>Select a destination folder where you want to save the extracted files.</li>
-<li>Click "OK" and wait for the extraction process to finish.</li>
-</ol>
- <p>You should see a folder named "ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm" in your destination folder after extracting the crack file.</p>
- <h3>Step 4: Run the crack file and follow the instructions</h3>
- <p>The next step is to run the crack file and follow the instructions on how to activate ESignal Advanced GET Edition Ver. 11 on your computer. To run the crack file, follow these steps:</p>
- <ol>
-<li>Open the folder "ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm" that you extracted in step 3.</li>
-<li>Double-click on the file named "ESignal_Advanced_GET_Edition_Ver_11_Crack_BiGStoRm.exe".</li>
-<li>A window will pop up with some instructions on how to use the crack file.</li>
-<li>Read carefully and follow the instructions on how to copy and paste some files into your ESignal installation folder.</li>
-<li>Click "Next" until you reach the end of the instructions.</li>
-<li>The crack file will automatically close after completing its task.</li>
-</ol>
- <p>You should see a message saying "ESignal Advanced GET Edition Ver. 11 has been successfully cracked!" after running the crack file.</p>
- <h3>Step 5: Enjoy the full features of ESignal Advanced GET Edition Ver. 11</h3>
- <p>The final step is to enjoy using ESignal Advanced GET Edition Ver. 11 without paying anything. To launch ESignal Advanced GET Edition Ver. 11, follow these steps:</p>
- <ol>
-<li>Go to your ESignal installation folder (usually C:\Program Files (x86)\eSignal).</li>
-<li>Double-click on the file named "winros.exe".</li>
-<li>A window will pop up asking you to enter your username and password.</li>
-<li>Type any username and password that you want (they don't have to I'll try to continue the article. <h2>How to use ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar</h2>
- <p>Now that you have installed ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar on your computer, you might be wondering how to use it to improve your trading performance. In this section, we will give you an overview of the main features of ESignal Advanced GET Edition Ver. 11 and some tips on how to use them effectively.</p>
- <h3>What are the main features of ESignal Advanced GET Edition Ver. 11?</h3>
- <p>ESignal Advanced GET Edition Ver. 11 has many features that can help you analyze the market, identify trends, and execute trades. Here are some of the most important ones:</p>
- <ul>
-<li><b>Data Feed:</b> ESignal Advanced GET Edition Ver. 11 provides you with real-time data from over 100 exchanges worldwide, including stocks, futures, forex, options, ETFs, and more. You can also access historical data for backtesting and charting purposes.</li>
-<li><b>Charting:</b> ESignal Advanced GET Edition Ver. 11 allows you to customize your charts with over 100 technical studies, drawing tools, and annotations. You can also use the exclusive Advanced GET indicators that are based on Elliott Wave Theory and Gann Analysis. These indicators can help you identify market patterns, support and resistance levels, entry and exit points, and trading opportunities.</li>
-<li><b>Scanning:</b> ESignal Advanced GET Edition Ver. 11 enables you to scan the market for potential trades based on your own criteria or predefined templates. You can also use the Trade Profile feature to analyze the risk/reward ratio of any trade before entering it.</li>
-<li><b>Strategy Testing:</b> ESignal Advanced GET Edition Ver. 11 allows you to test your trading strategies using historical data and see how they would have performed in different market conditions. You can also optimize your strategies using various parameters and compare them with other strategies.</li>
-<li><b>Trading Execution:</b> ESignal Advanced GET Edition Ver. 11 enables you to execute your trades directly from your charts using various order types and brokers. You can also monitor your positions and manage your risk using the Trade Manager feature.</li>
-</ul>
- <h3>How to access the advanced tools and indicators of ESignal Advanced GET Edition Ver. 11?</h3>
- <p>To access the advanced tools and indicators of ESignal Advanced GET Edition Ver. 11, you need to open a chart window and right-click on it. You will see a menu with various options such as Studies, Drawing Tools, Annotations, Advanced GET Studies, etc. You can select any option that you want to use and customize it according to your preferences.</p>
- <p>For example, if you want to use the Elliott Wave indicator, you need to select Advanced GET Studies > Elliott Wave > Auto Wave Labeling from the menu. This will automatically label the waves on your chart based on the Elliott Wave Theory. You can also adjust the settings of the indicator by right-clicking on it and selecting Properties.</p>
- <h3>How to customize your charts and analysis with ESignal Advanced GET Edition Ver. 11?</h3>
- <p>To customize your charts and analysis with ESignal Advanced GET Edition Ver. 11, you need to use the Chart Properties window that you can access by right-clicking on any chart window and selecting Properties. This window allows you to change various aspects of your chart such as time frame, symbol, style, color, scale, grid, etc.</p>
- <p>You can also use the Layout Manager window that you can access by clicking on the Layout button on the toolbar or pressing Ctrl+L on your keyboard. This window allows you to arrange multiple chart windows in different ways such as cascade, tile, tabbed, etc.</p>
- <h3>How to trade with ESignal Advanced GET Edition Ver. 11?</h3>
- <p>To trade with ESignal Advanced GET Edition Ver. 11, you need to have a broker account that is compatible with ESignal's trading platform. You can check the list of supported brokers here: https://www.esignal.com/trading-software/brokers</p>
- <p>Once you have a broker account, you need to connect it with ESignal by clicking on the Broker button on the toolbar or pressing Ctrl+B on your keyboard. This will open the Broker Manager window where you can add or edit your broker settings.</p>
- <p>After connecting your broker account with ESignal, you can execute your trades directly from your charts using various order types such as market, limit, stop, etc. You can also monitor your positions and manage your risk using the Trade Manager window that you can access by clicking on the Trade button on the toolbar or pressing Ctrl+T on your keyboard.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have explained what ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar is, how to download and install it, and how to use it for trading purposes. We have also given you an overview of the main features of ESignal Advanced GET Edition Ver. 11 and some tips on how to use them effectively.</p>
- <p>We hope that this article has been helpful and informative for you. However, we would like to remind you that using a crack file for any software is illegal and unethical and may expose you to various risks such as viruses or malware infection, legal action from the software developer or owner, or loss of data or money due to software malfunction or incompatibility.</p>
- <p>Therefore, we strongly advise you not to use ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar or any other crack file for any software and instead purchase a legitimate license or subscription from the official website or authorized reseller.</p>
- <p>If you are interested in learning more about ESignal Advanced GET Edition Ver. 11 or purchasing a license or subscription for it I'll try to continue the article. <p>, you can visit the official website here: https://www.esignal.com/trading-software/advancedget</p>
- <p>You can also check the price and subscription options here: https://www.esignal.com/pricing</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar:</p>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar safe to use?</td>
-<td>No, it is not safe to use. Using a crack file for any software is illegal and unethical and may expose you to various risks such as viruses or malware infection, legal action from the software developer or owner, or loss of data or money due to software malfunction or incompatibility.</td>
-</tr>
-<tr>
-<td>Does ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar work for both Windows and Mac?</td>
-<td>According to some sources, ESignal Advanced GET Edition Ver. 11 Crack BiGStoRm.rar claims to work for both Windows and Mac operating systems. However, we cannot verify this claim and we do not recommend using it for any operating system.</td>
-</tr>
-<tr>
-<td>What are the advantages of using ESignal Advanced GET Edition Ver. 11 over other trading software?</td>
-<td>ESignal Advanced GET Edition Ver. 11 has many advantages over other trading software such as:</td>
-<ul>
-<li>It provides you with advanced tools and indicators for technical analysis, market scanning, strategy testing, and trading execution.</li>
-<li>It is designed for traders who want to take their trading to the next level and gain an edge over the market.</li>
-<li>It allows you to access real-time data from over 100 exchanges worldwide, including stocks, futures, forex, options, ETFs, and more.</li>
-<li>It enables you to customize your charts with over 100 technical studies, drawing tools, and annotations.</li>
-<li>It offers you exclusive Advanced GET indicators that are based on Elliott Wave Theory and Gann Analysis.</li>
-<li>It includes a dedicated team of active traders that will help you develop the skills and market knowledge needed to be a more successful trader.</li>
-</ul>
-</tr>
-<tr>
-<td>How can I learn more about ESignal Advanced GET Edition Ver. 11 and its features?</td>
-<td>You can learn more about ESignal Advanced GET Edition Ver. 11 and its features by:</td>
-<ul>
-<li>Visiting the official website: https://www.esignal.com/trading-software/advancedget</li>
-<li>Reading the reference guide for Advanced GET indicators: https://kb.esignal.com/hc/en-us/articles/6362123379995-Advanced-GET-Reference-Guide-for-Advanced-GET-Indicators</li>
-<li>Watching the video tutorials: https://www.youtube.com/playlist?list=PLF1E9B7E0F0C8A6D4</li>
-<li>Attending a free online seminar or visiting a live seminar in a city near you: https://www.esignal.com/trading-software/advancedget/seminars</li>
-<li>Contacting one of the experienced sales professionals: https://www.esignal.com/contact-us</li>
-</ul>
-</tr>
-<tr>
-<td>Where can I get a legitimate license or subscription for ESignal Advanced GET Edition Ver. 11?</td>
-<td>You can get a legitimate license or subscription for ESignal Advanced GET Edition Ver. 11 by:</td>
-<ul>
-<li>Purchasing it from the official website: https://www.esignal.com/pricing</li>
-<li>Purchasing it from an authorized reseller: https://www.esignal.com/partners/resellers</li>
-<li>Taking advantage of the free trial offer: https://www.esignal.com/free-trial-signup</li>
-<li>Taking advantage of the money-back guarantee offer: https://www.esignal.com/money-back-guarantee</li> I'll try to finish the article. </ul>
-</tr>
-</table>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/EXCLUSIVE-Free-Download-Isis-Draw.md b/spaces/raedeXanto/academic-chatgpt-beta/EXCLUSIVE-Free-Download-Isis-Draw.md
deleted file mode 100644
index 69aa7ae796c454bad4ef5949827fe43367dea21e..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/EXCLUSIVE-Free-Download-Isis-Draw.md
+++ /dev/null
@@ -1,62 +0,0 @@
-## Free Download Isis Draw
-
-
-
- 
-
- ![EXCLUSIVE Free Download Isis Draw](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT0zd37AgV_KKVX3VlrNjNHC5PHk8tQDUwdBTD0HO-2OSkqQaioSe3jYYMG)
-
- 
-
- 
-
-**Click Here === [https://denirade.blogspot.com/?download=2txM5j](https://denirade.blogspot.com/?download=2txM5j)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- Here is a possible title and article with HTML formatting for the keyword "Free Download Isis Draw":  
-
-# How to Download and Use Isis Draw for Free
-
- 
-
-Isis Draw is a popular chemical drawing software that allows you to create and edit complex molecules, chemical reactions and biological sequences with ease. It also integrates with Isis Base, a database program that can store and retrieve chemical information. If you are a chemist or a student studying chemistry, you might be interested in downloading and using Isis Draw for free. Here are the steps to do so:
-
- 
-
-1. Go to [this website](https://en.freedownloadmanager.org/Windows-PC/ISIS-Draw-for-Windows.html) [^1^] and click on the green "Download" button. This will download the installer file for Isis Draw for Windows 2.5, which is the latest version available for free.
-
-2. Run the installer file and follow the instructions to install Isis Draw on your computer. You might need to restart your computer after the installation is complete.
-
-3. Launch Isis Draw from your desktop or start menu. You will see a blank drawing area where you can start creating your chemical structures. You can use the toolbar on the left to select different drawing tools, such as atoms, bonds, rings, charges, etc. You can also use the menu bar on the top to access more features, such as templates, reactions, stereochemistry, etc.
-
-4. To save your work, go to File > Save As and choose a name and location for your file. Isis Draw files have the extension .skc. You can also export your work as an image file (such as .jpg or .png) or a text file (such as .mol or .rxn).
-
-5. To open an existing Isis Draw file, go to File > Open and browse for the file you want to open. You can also import files from other formats, such as ChemDraw (.cdx), MDL Molfile (.mol), SMILES (.smi), etc.
-
-6. To use Isis Draw with Isis Base, you need to download and install Isis Base from [this website](https://www-jmg.ch.cam.ac.uk/cil/SGTL/MDL/mdl.html) [^2^]. Isis Base is a database program that can store and retrieve chemical information, such as structures, properties, spectra, etc. You can also access the Isis reaction database, which contains over 300,000 reactions from literature sources.
-
-7. To connect Isis Draw with Isis Base, go to Options > Preferences and select the Database tab. Check the box that says "Enable ISIS/Base Interface" and enter the server name and port number of your Isis Base installation. Click OK to save the changes.
-
-8. To store your chemical structures in Isis Base, go to Database > Store Structure and enter the details of your structure, such as name, ID number, comments, etc. Click OK to store your structure in the database.
-
-9. To retrieve your chemical structures from Isis Base, go to Database > Retrieve Structure and enter the criteria of your search, such as name, ID number, substructure, etc. Click OK to display the results of your search.
-
-
-
-That's it! You have successfully downloaded and used Isis Draw for free. If you need more help or tutorials on how to use Isis Draw, you can visit [this website](http://homework.sdmesa.edu/dgergens/chem231L/word_processing/draw_programs.html) [^3^] or [this website](https://www.updatestar.com/en/topic/isis%20draw%20chemistry) [^4^]. Happy drawing!
-
- dfd1c89656
-
- 
-
- 
-
diff --git a/spaces/rajaatif786/VirBert2/Toxonomy/modules/confusionmatrix.py b/spaces/rajaatif786/VirBert2/Toxonomy/modules/confusionmatrix.py
deleted file mode 100644
index 69f128fb3ed1764491171dc785b6a6d0d0eeaf36..0000000000000000000000000000000000000000
--- a/spaces/rajaatif786/VirBert2/Toxonomy/modules/confusionmatrix.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-def plot_confusion_matrix(cm, classes,
-                          normalize=False,
-                          title='Confusion matrix',
-                          cmap=plt.cm.Greens):
-    """
-    This function prints and plots the confusion matrix.
-    Normalization can be applied by setting `normalize=True`.
-    """
-    import itertools
-    if normalize:
-        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
-        print("Normalized confusion matrix")
-    else:
-        print('Confusion matrix, without normalization')
-
-    print(cm)
-
-    plt.imshow(cm, interpolation='nearest', cmap=cmap)
-    plt.title(title)
-    plt.colorbar()
-    tick_marks = np.arange(len(classes))
-    plt.xticks(tick_marks, classes, rotation=45)
-    plt.yticks(tick_marks, classes)
-
-    fmt = '.2f' if normalize else 'd'
-    thresh = cm.max() / 2.
-    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
-        plt.text(j, i, format(cm[i, j], fmt),
-                 horizontalalignment="center",
-                 color="white" if cm[i, j] > thresh else "black")
-
-    plt.ylabel('True label')
-    plt.xlabel('Predicted label')
-    plt.tight_layout()
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Acrobat XI Pro 11.0.16 Multilingual Crack Serial Key Keygen __FULL__.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Acrobat XI Pro 11.0.16 Multilingual Crack Serial Key Keygen __FULL__.md
deleted file mode 100644
index 6e5eb44fa8c4bf1162132868f212bf4be895d25e..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Acrobat XI Pro 11.0.16 Multilingual Crack Serial Key Keygen __FULL__.md	
+++ /dev/null
@@ -1,62 +0,0 @@
-
-<h1>How to Download and Install Adobe Acrobat XI Pro 11.0.16 Multilingual with Crack, Serial Key and Keygen</h1>
-
-<p>Adobe Acrobat XI Pro is one of the most popular and powerful PDF converters and editors in the market. It allows you to create, edit, sign, and share PDF documents with ease. It also has many advanced features such as OCR, form creation, digital signatures, encryption, redaction, and more.</p>
-
-<p>If you want to download and install Adobe Acrobat XI Pro 11.0.16 Multilingual with crack, serial key and keygen, you can follow these simple steps:</p>
-<h2>Adobe Acrobat XI Pro 11.0.16 Multilingual Crack Serial Key keygen</h2><br /><p><b><b>Download</b> ->>> <a href="https://urlgoal.com/2uCKSO">https://urlgoal.com/2uCKSO</a></b></p><br /><br />
-
-<ol>
-<li>Download the setup file from <a href="https://wildcerestibo.wixsite.com/interilcei/post/adobe-acrobat-xi-pro-11-0-16-multilingual-crack-sadeempc-serial-key-keygen">this link</a> [^1^]. It is a torrent file, so you will need a torrent client such as uTorrent or BitTorrent to download it.</li>
-<li>Extract the zip file using WinRAR or any other extraction tool.</li>
-<li>Run the setup file and follow the installation wizard.</li>
-<li>Do not launch the program after installation.</li>
-<li>Copy the crack file from the crack folder and paste it into the installation directory (usually C:\Program Files (x86)\Adobe\Acrobat 11.0\Acrobat).</li>
-<li>Run the keygen file from the keygen folder and generate a serial key.</li>
-<li>Launch the program and enter the serial key when prompted.</li>
-<li>Enjoy Adobe Acrobat XI Pro 11.0.16 Multilingual with full features.</li>
-</ol>
-
-<p>Note: This is a cracked version of Adobe Acrobat XI Pro 11.0.16 Multilingual, which means it may not be safe or legal to use. It may also contain viruses or malware that can harm your computer or data. We do not recommend or endorse using cracked software. Use it at your own risk.</p>
-
-<p>If you want to get a genuine and legal version of Adobe Acrobat XI Pro 11.0.16 Multilingual, you can buy it from <a href="https://www.adobe.com/products/acrobatpro.html">the official website</a>. You will get regular updates, support, and security patches from Adobe.</p>
-
-<p>We hope this article was helpful for you. If you have any questions or feedback, please leave a comment below.</p>
-
-<h2>What is Adobe Acrobat XI Pro 11.0.16 Multilingual?</h2>
-
-<p>Adobe Acrobat XI Pro 11.0.16 Multilingual is the latest version of Adobe Acrobat XI Pro, which was released in March 2016. It is a multilingual version, which means it supports multiple languages such as English, French, German, Spanish, Italian, Portuguese, Japanese, Chinese, Korean, and more.</p>
-
-<p>Adobe Acrobat XI Pro 11.0.16 Multilingual has many new and improved features compared to the previous versions. Some of them are:</p>
-
-<ul>
-<li>Improved performance and stability.</li>
-<li>Enhanced security and compatibility with Windows 10.</li>
-<li>New tools for editing text and images in PDF documents.</li>
-<li>New options for creating and customizing PDF forms.</li>
-<li>New features for signing and certifying PDF documents with digital signatures.</li>
-<li>New tools for redacting and protecting sensitive information in PDF documents.</li>
-<li>New features for converting PDF documents to other formats such as Word, Excel, PowerPoint, HTML, and more.</li>
-<li>New features for creating and editing PDF portfolios.</li>
-<li>New features for collaborating and sharing PDF documents with others.</li>
-</ul>
-
-<h2>Why Should You Use Adobe Acrobat XI Pro 11.0.16 Multilingual?</h2>
-
-<p>Adobe Acrobat XI Pro 11.0.16 Multilingual is a powerful and versatile PDF converter and editor that can help you with various tasks and projects. Whether you are a student, a teacher, a professional, or a business owner, you can benefit from using Adobe Acrobat XI Pro 11.0.16 Multilingual. Here are some of the reasons why you should use it:</p>
-
-<ul>
-<li>It can help you create high-quality and professional-looking PDF documents from any source such as scanned documents, web pages, images, videos, audio files, and more.</li>
-<li>It can help you edit and enhance your PDF documents with ease. You can add, delete, move, resize, rotate, crop, align, adjust color, add effects, and more to your text and images in PDF documents.</li>
-<li>It can help you create and fill out PDF forms with ease. You can design your own forms or use the templates provided by Adobe. You can also collect data from your forms and export them to other formats such as Excel or CSV.</li>
-<li>It can help you sign and certify your PDF documents with digital signatures. You can also verify the authenticity and integrity of your PDF documents with certificates.</li>
-<li>It can help you protect your PDF documents from unauthorized access or modification. You can encrypt your PDF documents with passwords or certificates. You can also redact or remove sensitive information from your PDF documents such as personal data, confidential data, or classified data.</li>
-<li>It can help you convert your PDF documents to other formats such as Word, Excel, PowerPoint, HTML, and more. You can also convert other formats to PDF such as images, videos, audio files, web pages, and more.</li>
-<li>It can help you create and edit PDF portfolios. A PDF portfolio is a collection of files of different types that are bundled together in a single PDF file. You can customize the look and feel of your PDF portfolio with themes, layouts, colors, backgrounds, logos, and more.</li>
-<li>It can help you collaborate and share your PDF documents with others. You can send your PDF documents via email or upload them to online services such as Adobe Document Cloud or Dropbox. You can also review and comment on your PDF documents with others using the annotation tools such as sticky notes, highlights, stamps, shapes, drawings, and more.</li>
-</ul>
-
-<p>As you can see, Adobe Acrobat XI Pro 11.0.16 Multilingual is a comprehensive and useful tool that can help you with many tasks and projects involving PDF documents. It is easy to use and has a user-friendly interface that allows you to access all the features and functions quickly and conveniently.</p>
-<p></p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha).md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha).md
deleted file mode 100644
index a6b143584986cf7f48bcd9dba68b812228422782..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha).md	
+++ /dev/null
@@ -1,95 +0,0 @@
-
-<h1>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha): How to Watch and Download the Movie</h1>
-<p>Veer Zaara is a 2004 Hindi movie that tells the story of a love that transcends borders and time. It stars Shah Rukh Khan as Veer Pratap Singh, an Indian Air Force pilot who falls in love with Zaara (Preity Zinta), a Pakistani girl who comes to India to fulfill her dying mother's wish. The movie is directed by Yash Chopra and has a musical score by the legendary duo of Lata Mangeshkar and Madan Mohan.</p>
-<p>If you want to watch or download Veer Zaara in HD quality, you need to use an HD online player that can stream or download the movie from various sources. In this article, we will show you how to use HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) to watch and download the movie.</p>
-<h2>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha)</h2><br /><p><b><b>Download Zip</b> ---> <a href="https://urlgoal.com/2uCMgP">https://urlgoal.com/2uCMgP</a></b></p><br /><br />
-<h2>What is HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha)?</h2>
-<p>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) is a software that allows you to watch and download Veer Zaara in HD quality. It can stream the movie from various online platforms such as YouTube, Dailymotion, Vimeo, etc. It can also download the movie from various torrent sites such as YTS, DownloadHub, MovieSpyHD, etc.</p>
-<p>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) is compatible with all devices such as PC, laptop, smartphone, tablet, etc. It supports all formats such as MP4, MKV, AVI, etc. It also has features such as subtitles, audio tracks, playback speed, etc.</p>
-<h2>How to Watch Veer Zaara with HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha)?</h2>
-<p>To watch Veer Zaara with HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha), you need to follow these steps:</p>
-<ol>
-<li>Download and install HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) from its official website or any reputable download site.</li>
-<li>Open the software and search for Veer Zaara in the search bar.</li>
-<li>Select the source that you want to stream the movie from. You can choose from various online platforms such as YouTube, Dailymotion, Vimeo, etc.</li>
-<li>Click on the play button and enjoy the movie in HD quality.</li>
-</ol>
-<h2>How to Download Veer Zaara with HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha)?</h2>
-<p>To download Veer Zaara with HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha), you need to follow these steps:</p>
-<ol>
-<li>Download and install HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) from its official website or any reputable download site.</li>
-<li>Open the software and search for Veer Zaara in the search bar.</li>
-<li>Select the source that you want to download the movie from. You can choose from various torrent sites such as YTS, DownloadHub, MovieSpyHD, etc.</li>
-<li>Click on the download button and choose the destination folder where you want to save the movie.</li>
-<li>Wait for the download to finish and enjoy the movie in HD quality.</li>
-</ol>
-<h2>Why Choose HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha)?</h2>
-<p>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) is one of the best software to watch and download Veer Zaara in HD quality. Here are some of the reasons why you should choose it:</p>
-<ul>
-<li>It is free and easy to use.</li>
-<li>It offers multiple sources to stream or download the movie from.</li>
-<li>It supports all devices and formats.</li>
-<li>It provides high-quality video and audio output.</li>
-<li>It has various features such as subtitles, audio tracks, playback speed, etc.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Veer Zaara is a classic Hindi movie that showcases a love that is divine, whole-hearted, completely consuming, and an inspiration for generations. If you want to watch or download this movie in HD quality, you should use HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha). It is a software that can stream or download the movie from various sources in high-quality video and audio output. It is free and easy to use, and it supports all devices and formats. It also has various features such as subtitles, audio tracks, playback speed, etc. If you love Veer Zaara, you should definitely try HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) today.</p>
-<h2>What is Veer Zaara (2004) Hindi?</h2>
-<p>Veer Zaara (2004) Hindi is a movie that tells the story of a love that transcends borders and time. It stars Shah Rukh Khan as Veer Pratap Singh, an Indian Air Force pilot who falls in love with Zaara (Preity Zinta), a Pakistani girl who comes to India to fulfill her dying mother's wish. The movie is directed by Yash Chopra and has a musical score by the legendary duo of Lata Mangeshkar and Madan Mohan.</p>
-<p></p>
-<p>The movie is based on a true story of an Indian man who spent 22 years in a Pakistani prison for helping a Pakistani girl. The movie explores the themes of love, sacrifice, courage, and patriotism. It also showcases the culture and beauty of India and Pakistan. The movie was a critical and commercial success, and won several awards and accolades. It is considered one of the best movies of Indian cinema.</p>
-<h2>How to Use HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha)?</h2>
-<p>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) is a software that allows you to watch and download Veer Zaara in HD quality. It can stream the movie from various online platforms such as YouTube, Dailymotion, Vimeo, etc. It can also download the movie from various torrent sites such as YTS, DownloadHub, MovieSpyHD, etc.</p>
-<p>To use HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha), you need to follow these steps:</p>
-<ol>
-<li>Download and install HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) from its official website or any reputable download site.</li>
-<li>Open the software and search for Veer Zaara in the search bar.</li>
-<li>Select the source that you want to stream or download the movie from.</li>
-<li>Click on the play or download button and enjoy the movie in HD quality.</li>
-</ol>
-<p>Note: You need to have a stable internet connection and enough storage space to use HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha). You also need to have a compatible device and format to watch or download the movie.</p>
-<h2>What are the Features of HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha)?</h2>
-<p>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) is not just a software, it is a complete solution for watching and downloading Veer Zaara in HD quality. It has many features that make it stand out from other software. Here are some of them:</p>
-<ul>
-<li>It offers multiple sources to stream or download the movie from.</li>
-<li>It supports all devices and formats.</li>
-<li>It provides high-quality video and audio output.</li>
-<li>It has various features such as subtitles, audio tracks, playback speed, etc.</li>
-<li>It is free and easy to use.</li>
-</ul>
-<h2>What are the Songs of Veer Zaara (2004) Hindi?</h2>
-<p>Veer Zaara (2004) Hindi is not only a movie, but also a musical masterpiece. It has a soundtrack composed by the legendary duo of Lata Mangeshkar and Madan Mohan, who created some of the most memorable songs of Indian cinema. The songs are sung by various singers such as Lata Mangeshkar, Udit Narayan, Sonu Nigam, Roop Kumar Rathod, Gurdas Maan, etc. The lyrics are written by Javed Akhtar, who penned some of the most poetic and romantic words for the movie.</p>
-<p>The songs of Veer Zaara (2004) Hindi are as follows:</p>
-<ul>
-<li>Tere Liye: This is the theme song of the movie, which expresses the love and devotion of Veer and Zaara for each other. It is sung by Lata Mangeshkar and Roop Kumar Rathod.</li>
-<li>Main Yahaan Hoon: This is a song that shows how Veer and Zaara feel each other's presence even when they are apart. It is sung by Udit Narayan.</li>
-<li>Aisa Des Hai Mera: This is a song that celebrates the culture and beauty of India and Pakistan. It is sung by Lata Mangeshkar, Udit Narayan, Gurdas Maan, and Pritha Majumdar.</li>
-<li>Yeh Hum Aa Gaye Hain Kahaan: This is a song that depicts the reunion of Veer and Zaara after 22 years of separation. It is sung by Lata Mangeshkar and Udit Narayan.</li>
-<li>Do Pal: This is a song that reflects on the brief moments of happiness that Veer and Zaara shared before they were separated. It is sung by Lata Mangeshkar and Sonu Nigam.</li>
-</ul>
-<h2>How to Appreciate Veer Zaara (2004) Hindi?</h2>
-<p>Veer Zaara (2004) Hindi is a movie that deserves to be appreciated by every lover of cinema. It is a movie that has everything that makes a great movie: a powerful story, a brilliant direction, a stellar cast, a mesmerizing music, and a timeless message. It is a movie that touches your heart and soul, and makes you feel the emotions of love, pain, joy, and hope. It is a movie that transcends borders and time, and becomes an inspiration for generations.</p>
-<p>To appreciate Veer Zaara (2004) Hindi, you need to do the following:</p>
-<ol>
-<li>Watch it with an open mind and heart. Don't judge it by its genre or language, but by its content and quality.</li>
-<li>Watch it with HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha). This software will enhance your viewing experience by providing you with high-quality video and audio output.</li>
-<li>Watch it with someone you love or care about. This movie will make you appreciate your relationship more, and make you cherish every moment you spend together.</li>
-<li>Watch it more than once. This movie has many layers and nuances that you might miss in the first viewing. You will discover something new every time you watch it.</li>
-<li>Share it with others. This movie has a message that needs to be spread to everyone who believes in love and humanity. You will inspire others to watch it and appreciate it as well.</li>
-</ol>
-<h2>What are the Cast and Crew of Veer Zaara (2004) Hindi?</h2>
-<p>Veer Zaara (2004) Hindi is a movie that boasts of a stellar cast and crew that have delivered a masterpiece of cinema. The movie is directed by Yash Chopra, who is known as the king of romance in Bollywood. He has directed some of the most iconic movies such as Deewaar, Kabhi Kabhie, Silsila, Chandni, Dil To Pagal Hai, etc. He has also produced many movies under his banner Yash Raj Films.</p>
-<p>The movie stars Shah Rukh Khan as Veer Pratap Singh, who is one of the most popular and successful actors in India. He has acted in many movies such as Dilwale Dulhania Le Jayenge, Kuch Kuch Hota Hai, Devdas, Swades, Chak De! India, My Name Is Khan, etc. He has won many awards and accolades for his performances.</p>
-<p>The movie also stars Preity Zinta as Zaara Hayaat Khan, who is one of the most talented and versatile actresses in Bollywood. She has acted in many movies such as Dil Chahta Hai, Kal Ho Naa Ho, Lakshya, Salaam Namaste, etc. She has also won many awards and accolades for her performances.</p>
-<p>The movie also features Rani Mukerji as Saamia Siddiqui, who is one of the most acclaimed and respected actresses in India. She has acted in many movies such as Kuch Kuch Hota Hai, Black, Bunty Aur Babli, No One Killed Jessica, Mardaani, etc. She has also won many awards and accolades for her performances.</p>
-<p>The movie also has a supporting cast of Amitabh Bachchan, Hema Malini, Manoj Bajpayee, Divya Dutta, Boman Irani, Anupam Kher, Kirron Kher, Zohra Sehgal, etc. who have played their roles with perfection.</p>
-<h2>What are the Reviews and Ratings of Veer Zaara (2004) Hindi?</h2>
-<p>Veer Zaara (2004) Hindi is a movie that has received rave reviews and ratings from critics and audiences alike. The movie has been praised for its story, direction, music, performances, and message. The movie has also been appreciated for its portrayal of India and Pakistan in a positive and realistic light.</p>
-<p>The movie has a rating of 7.8 out of 10 on IMDb based on 54.8K votes. It also has a rating of 93% on Rotten Tomatoes based on 14 reviews. It also has a rating of 88% on Google based on 13K votes.</p>
-<p>The movie has also won several awards and accolades such as Filmfare Awards, IIFA Awards, National Film Awards, Zee Cine Awards, Screen Awards, Stardust Awards, etc. The movie has also been nominated for various international awards such as BAFTA Awards, Asian Film Awards, etc.</p>
-<h2>Conclusion</h2>
-<p>HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) is a software that allows you to watch and download Veer Zaara in HD quality. It can stream the movie from various online platforms such as YouTube, Dailymotion, Vimeo, etc. It can also download the movie from various torrent sites such as YTS, DownloadHub, MovieSpyHD, etc. It is compatible with all devices and formats, and it provides high-quality video and audio output. It also has various features such as subtitles, audio tracks, playback speed, etc.</p>
-<p>Veer Zaara (2004) Hindi is a movie that tells the story of a love that transcends borders and time. It stars Shah Rukh Khan as Veer Pratap Singh, an Indian Air Force pilot who falls in love with Zaara (Preity Zinta), a Pakistani girl who comes to India to fulfill her dying mother's wish. The movie is directed by Yash Chopra and has a musical score by the legendary duo of Lata Mangeshkar and Madan Mohan. The movie is based on a true story of an Indian man who spent 22 years in a Pakistani prison for helping a Pakistani girl.</p>
-<p>If you want to watch or download this movie in HD quality, you should use HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha). It is a software that can stream or download the movie from various sources in high-quality video and audio output. It is free and easy to use, and it supports all devices and formats. It also has various features such as subtitles, audio tracks, playback speed, etc. If you love Veer Zaara, you should definitely try HD Online Player (Veer Zaara 2004 Hindi 720p BRRip Cha) today.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rexoscare/Speech_to_Text_Hindi/README.md b/spaces/rexoscare/Speech_to_Text_Hindi/README.md
deleted file mode 100644
index 085c6b4f2d7532cae3ed5f4a22a67cbe32b50791..0000000000000000000000000000000000000000
--- a/spaces/rexoscare/Speech_to_Text_Hindi/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Speech_to_Text_Hindi
-emoji: 🏢
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingchairsocc_bi_occ_384x448.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingchairsocc_bi_occ_384x448.py
deleted file mode 100644
index fa741a7d567e97c39cefff60ef9140f55b38fd99..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/flyingchairsocc_bi_occ_384x448.py
+++ /dev/null
@@ -1,92 +0,0 @@
-dataset_type = 'FlyingChairsOcc'
-data_root = 'data/FlyingChairsOcc/'
-
-img_norm_cfg = dict(mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=True)
-
-global_transform = dict(
-    translates=(0.05, 0.05),
-    zoom=(1.0, 1.5),
-    shear=(0.86, 1.16),
-    rotate=(-10., 10.))
-
-relative_transform = dict(
-    translates=(0.00375, 0.00375),
-    zoom=(0.985, 1.015),
-    shear=(1.0, 1.0),
-    rotate=(-1.0, 1.0))
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_occ=True),
-    dict(
-        type='ColorJitter',
-        brightness=0.5,
-        contrast=0.5,
-        saturation=0.5,
-        hue=0.5),
-    dict(type='RandomGamma', gamma_range=(0.7, 1.5)),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='GaussianNoise', sigma_range=(0, 0.04), clamp_range=(0., 1.)),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='RandomFlip', prob=0.5, direction='vertical'),
-    dict(
-        type='RandomAffine',
-        global_transform=global_transform,
-        relative_transform=relative_transform,
-        check_bound=True),
-    dict(type='DefaultFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs', 'flow_fw_gt', 'flow_bw_gt', 'occ_fw_gt', 'occ_bw_gt'],
-        meta_keys=[
-            'img_fields', 'ann_fields', 'filename1', 'ori_filename1',
-            'filename2', 'ori_filename2', 'filename_flow_fw',
-            'ori_filename_flow_fw', 'filename_flow_bw', 'ori_filename_flow_bw',
-            'filename_occ_fw', 'ori_filename_occ_fw', 'filename_occ_bw',
-            'ori_filename_occ_bw', 'ori_shape', 'img_shape'
-        ]),
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='InputResize', exponent=6),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='TestFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs'],
-        meta_keys=[
-            'flow_fw_gt', 'flow_bw_gt', 'filename1', 'filename2',
-            'ori_filename1', 'ori_filename2', 'ori_shape', 'img_shape',
-            'img_norm_cfg', 'scale_factor', 'pad_shape'
-        ])
-]
-
-flyingchairsocc_train = dict(
-    type=dataset_type,
-    data_root=data_root,
-    pipeline=train_pipeline,
-    test_mode=False)
-
-flyingchairsocc_test_val = dict(
-    type=dataset_type,
-    data_root=data_root,
-    pipeline=test_pipeline,
-    test_mode=True)
-
-data = dict(
-    train_dataloader=dict(
-        samples_per_gpu=1,
-        workers_per_gpu=2,
-        drop_last=True,
-        persistent_workers=True),
-    val_dataloader=dict(
-        samples_per_gpu=1,
-        workers_per_gpu=2,
-        shuffle=False,
-        persistent_workers=True),
-    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=2, shuffle=False),
-    train=flyingchairsocc_train,
-    val=flyingchairsocc_test_val,
-    test=flyingchairsocc_test_val)
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/kitti2015_raft_test.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/kitti2015_raft_test.py
deleted file mode 100644
index 2a67368cda83878ded22ef5ec84c7206688597e6..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/datasets/kitti2015_raft_test.py
+++ /dev/null
@@ -1,27 +0,0 @@
-img_norm_cfg = dict(mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=False)
-
-kitti_test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', sparse=True),
-    dict(type='InputPad', exponent=3),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='TestFormatBundle'),
-    dict(
-        type='Collect',
-        keys=['imgs'],
-        meta_keys=[
-            'flow_gt', 'valid', 'filename1', 'filename2', 'ori_filename1',
-            'ori_filename2', 'ori_shape', 'img_shape', 'img_norm_cfg',
-            'scale_factor', 'pad_shape', 'pad'
-        ])
-]
-
-kitti2015_val_test = dict(
-    type='KITTI2015',
-    data_root='data/kitti2015',
-    pipeline=kitti_test_pipeline,
-    test_mode=True)
-
-data = dict(
-    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=2, shuffle=False),
-    test=kitti2015_val_test)
diff --git a/spaces/robin0307/MMOCR/configs/ner/bert_softmax/README.md b/spaces/robin0307/MMOCR/configs/ner/bert_softmax/README.md
deleted file mode 100644
index 650d18c4d56406e5f064085229f49875f5b4aea5..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/ner/bert_softmax/README.md
+++ /dev/null
@@ -1,47 +0,0 @@
-# Bert
-
-> [Bert: Pre-training of deep bidirectional transformers for language understanding](https://arxiv.org/abs/1810.04805)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
-BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
-
-<!-- [IMAGE] -->
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/22607038/142802652-ecc6500d-e5dc-4ffa-98f4-f5b247b9245c.png"/>
-</div>
-
-## Dataset
-
-### Train Dataset
-
-|  trainset   | text_num | entity_num |
-| :---------: | :------: | :--------: |
-| CLUENER2020 |  10748   |   23338    |
-
-### Test Dataset
-
-|   testset   | text_num | entity_num |
-| :---------: | :------: | :--------: |
-| CLUENER2020 |   1343   |    2982    |
-
-## Results and models
-
-|                          Method                           |                           Pretrain                           | Precision | Recall | F1-Score |                           Download                           |
-| :-------------------------------------------------------: | :----------------------------------------------------------: | :-------: | :----: | :------: | :----------------------------------------------------------: |
-| [bert_softmax](/configs/ner/bert_softmax/bert_softmax_cluener_18e.py) | [pretrain](https://download.openmmlab.com/mmocr/ner/bert_softmax/bert_pretrain.pth) |  0.7885   | 0.7998 |  0.7941  | [model](https://download.openmmlab.com/mmocr/ner/bert_softmax/bert_softmax_cluener-eea70ea2.pth) \| [log](https://download.openmmlab.com/mmocr/ner/bert_softmax/20210514_172645.log.json) |
-
-## Citation
-
-```bibtex
-@article{devlin2018bert,
-  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
-  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
-  journal={arXiv preprint arXiv:1810.04805},
-  year={2018}
-}
-```
diff --git a/spaces/rorallitri/biomedical-language-models/logs/A Chhota Bheem - Himalayan Adventure Full Movie Online Download EXCLUSIVE.md b/spaces/rorallitri/biomedical-language-models/logs/A Chhota Bheem - Himalayan Adventure Full Movie Online Download EXCLUSIVE.md
deleted file mode 100644
index 9a01add4363f6210fae624feb3124f2d5f63d757..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/A Chhota Bheem - Himalayan Adventure Full Movie Online Download EXCLUSIVE.md	
+++ /dev/null
@@ -1,44 +0,0 @@
-<h2>a Chhota Bheem - Himalayan Adventure full movie online download</h2><br /><p><b><b>Download File</b> &#10040;&#10040;&#10040; <a href="https://tinurll.com/2uzmju">https://tinurll.com/2uzmju</a></b></p><br /><br />
-
-However, the group faces a series of threats while they are away from home which include a ruthless villains and their cunning ways. What could be the reason for them to plan all these? Will the group manage to save the situation and win over the evil and also save the precious Girl?
-
-If you are planning to watch or download Bhoot Chhhota Bheem from different websites then please login to download it. This website does not store any media files on its server. We only index and link to content provided by other sites. If you have any doubts about legality of content or you have another suspicions, feel free to Contact US.Q:
-
-Anjuta IDE - What is the file extension?
-
-What is the file extension associated with Anjuta, like when you open a file in Qt Creator?
-
-A:
-
-From the release notes:
-
-What to change?
-
-There is a file chooser dialog.
-
-Please note that the type of the file chooser is not limited to ANJUTA_DATADIR/filechooser_gtk2.
-
-What about a better system?
-
-Because our build system does not use pkg-config we have to install libwebkitgtk-3.0-0, libwebkitgtk-3.0-0-dbg and/or libwebkitgtk-3.0-0-dbg-doc for the build.
-
-If you just want to see what files are installed you can run dpkg -L anjuta-2.3.1 in a terminal.
-
-You can find out about the types of files you are able to create by looking at CMakeLists.txt.  In particular you may want to look at the CMAKE_MODULE_PATH variable.  You can use the find option to list the contents of particular directories.
-
-Q:
-
-How to synchronize 2 Xcode projects
-
-This is my first post.
-
-I have a question regarding Xcode, i have a big project and i want to make it faster so i make several smaller projects (it's a small application with audio clips, one project per song) and then i import the project of my songs to my big project. 
-
-If i want to synchronize the project, will it be the same? 
-
-I want my app to keep playing the song which was being played at the beginning. 
-
-I've tried to export then 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Kernel Ost To Pst Converter 12.06.01 Cracked.md b/spaces/rorallitri/biomedical-language-models/logs/Kernel Ost To Pst Converter 12.06.01 Cracked.md
deleted file mode 100644
index 4ac5d12a7bbe76c847843fab68aad2df2f136954..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Kernel Ost To Pst Converter 12.06.01 Cracked.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>kernel ost to pst converter 12.06.01 cracked</h2><br /><p><b><b>Download File</b> &#10145; <a href="https://tinurll.com/2uzm7f">https://tinurll.com/2uzm7f</a></b></p><br /><br />
-
-Convert Annual Interest Rate To Monthly · Outdoor Wood ... Convert Pdf To Word 2003 · Changing ... Kernel Ost To Pst Converter 12.06 01 Crack · Telstra Nbn ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/saikumar622/testing/README.md b/spaces/saikumar622/testing/README.md
deleted file mode 100644
index 7ed9aaf3c2a40a9c23895a56a6bfe83e21eb2f65..0000000000000000000000000000000000000000
--- a/spaces/saikumar622/testing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Testing
-emoji: 🏃
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sanchit-gandhi/chatGPT/app.py b/spaces/sanchit-gandhi/chatGPT/app.py
deleted file mode 100644
index dd6a8c5b0131fcf25cd6538badb0f943ee60c9dc..0000000000000000000000000000000000000000
--- a/spaces/sanchit-gandhi/chatGPT/app.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import torch
-import os
-
-import gradio as gr
-from transformers import pipeline
-
-from pyChatGPT import ChatGPT
-
-from speechbrain.pretrained import Tacotron2
-from speechbrain.pretrained import HIFIGAN
-
-import json
-import soundfile as sf
-
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-print(f"Is CUDA available: {torch.cuda.is_available()}")
-print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
-
-# Intialise STT (Whisper)
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model="openai/whisper-base.en",
-    chunk_length_s=30,
-    device=device,
-)
-
-# Initialise ChatGPT session
-session_token = os.environ.get("SessionToken")
-api = ChatGPT(session_token=session_token)
-
-# Intialise TTS (tacotron2) and Vocoder (HiFIGAN)
-tacotron2 = Tacotron2.from_hparams(
-    source="speechbrain/tts-tacotron2-ljspeech",
-    savedir="tmpdir_tts",
-    overrides={"max_decoder_steps": 10000},
-    run_opts={"device": device},
-)
-hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
-
-
-def get_response_from_chatbot(text, reset_conversation):
-    try:
-        if reset_conversation:
-            api.refresh_auth()
-            api.reset_conversation()
-        resp = api.send_message(text)
-        response = resp["message"]
-    except:
-        response = "Sorry, the chatGPT queue is full. Please try again later."
-    return response
-
-
-def chat(input_audio, chat_history, reset_conversation):
-    # speech -> text (Whisper)
-    message = pipe(input_audio)["text"]
-
-    # text -> response (chatGPT)
-    response = get_response_from_chatbot(message, reset_conversation)
-
-    # response -> speech (tacotron2)
-    mel_output, mel_length, alignment = tacotron2.encode_text(response)
-    wav = hifi_gan.decode_batch(mel_output)
-    sf.write("out.wav", wav.squeeze().cpu().numpy(), 22050)
-
-    out_chat = []
-    chat_history = chat_history if not reset_conversation else ""
-    if chat_history != "":
-        out_chat = json.loads(chat_history)
-
-    out_chat.append((message, response))
-    chat_history = json.dumps(out_chat)
-
-    return out_chat, chat_history, "out.wav"
-
-
-start_work = """async() => {
-    function isMobile() {
-        try {
-            document.createEvent("TouchEvent"); return true;
-        } catch(e) {
-            return false; 
-        }
-    }
-	function getClientHeight()
-	{
-	  var clientHeight=0;
-	  if(document.body.clientHeight&&document.documentElement.clientHeight) {
-		var clientHeight = (document.body.clientHeight<document.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight;
-	  } else {
-		var clientHeight = (document.body.clientHeight>document.documentElement.clientHeight)?document.body.clientHeight:document.documentElement.clientHeight;
-	  }
-	  return clientHeight;
-	}
-
-    function setNativeValue(element, value) {
-      const valueSetter = Object.getOwnPropertyDescriptor(element.__proto__, 'value').set;
-      const prototype = Object.getPrototypeOf(element);
-      const prototypeValueSetter = Object.getOwnPropertyDescriptor(prototype, 'value').set;
-
-      if (valueSetter && valueSetter !== prototypeValueSetter) {
-            prototypeValueSetter.call(element, value);
-      } else {
-            valueSetter.call(element, value);
-      }
-    }
-    var gradioEl = document.querySelector('body > gradio-app').shadowRoot;
-    if (!gradioEl) {
-        gradioEl = document.querySelector('body > gradio-app');
-    }
-
-    if (typeof window['gradioEl'] === 'undefined') {
-        window['gradioEl'] = gradioEl;
-
-        const page1 = window['gradioEl'].querySelectorAll('#page_1')[0];
-        const page2 = window['gradioEl'].querySelectorAll('#page_2')[0]; 
-
-        page1.style.display = "none";
-        page2.style.display = "block"; 
-        window['div_count'] = 0;
-        window['chat_bot'] = window['gradioEl'].querySelectorAll('#chat_bot')[0];
-        window['chat_bot1'] = window['gradioEl'].querySelectorAll('#chat_bot1')[0];   
-        chat_row = window['gradioEl'].querySelectorAll('#chat_row')[0]; 
-        prompt_row = window['gradioEl'].querySelectorAll('#prompt_row')[0]; 
-        window['chat_bot1'].children[1].textContent = '';
-
-        clientHeight = getClientHeight();
-        new_height = (clientHeight-300) + 'px';
-        chat_row.style.height = new_height;
-        window['chat_bot'].style.height = new_height;
-        window['chat_bot'].children[2].style.height = new_height;
-        window['chat_bot1'].style.height = new_height;
-        window['chat_bot1'].children[2].style.height = new_height;
-        prompt_row.children[0].style.flex = 'auto';
-        prompt_row.children[0].style.width = '100%';
-
-        window['checkChange'] = function checkChange() {
-            try {
-                if (window['chat_bot'].children[2].children[0].children.length > window['div_count']) {
-                    new_len = window['chat_bot'].children[2].children[0].children.length - window['div_count'];
-                    for (var i = 0; i < new_len; i++) { 
-                        new_div = window['chat_bot'].children[2].children[0].children[window['div_count'] + i].cloneNode(true);
-                        window['chat_bot1'].children[2].children[0].appendChild(new_div);
-                    }
-                    window['div_count'] = chat_bot.children[2].children[0].children.length;
-                }
-                if (window['chat_bot'].children[0].children.length > 1) {
-                     window['chat_bot1'].children[1].textContent = window['chat_bot'].children[0].children[1].textContent;
-                } else {
-                    window['chat_bot1'].children[1].textContent = '';
-                }
-
-            } catch(e) {
-            }        
-        }
-        window['checkChange_interval'] = window.setInterval("window.checkChange()", 500);         
-    }
-
-    return false;
-}"""
-
-with gr.Blocks(title="Talk to chatGPT") as demo:
-    gr.Markdown("## Talk to chatGPT ##")
-    gr.HTML(
-        "<p> Demo uses <a href='https://huggingface.co/openai/whisper-base.en' class='underline'>Whisper</a> to convert the input speech"
-        " to transcribed text, <a href='https://chat.openai.com/chat' class='underline'>chatGPT</a> to generate responses, and <a"
-        " href='https://huggingface.co/speechbrain/tts-tacotron2-ljspeech' class='underline'>tacotron2</a> to convert the response to"
-        " output speech: </p>"
-    )
-    gr.HTML("<p> <center><img src='https://raw.githubusercontent.com/sanchit-gandhi/codesnippets/main/pipeline.png' width='870'></center> </p>")
-    gr.HTML(
-        "<p>You can duplicate this space and use your own session token: <a style='display:inline-block'"
-        " href='https://huggingface.co/spaces/sanchit-gandhi/chatGPT?duplicate=true'><img"
-        " src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=10'"
-        " alt='Duplicate Space'></a></p>"
-    )
-    gr.HTML(
-        "<p> Instructions on how to obtain your session token can be found in the video <a style='display:inline-block'"
-        " href='https://youtu.be/TdNSj_qgdFk?t=175'><font style='color:blue;weight:bold;'>here</font></a>."
-        " Add your session token by going to <i>Settings</i> -> <i>New secret</i> and add the token under the name <i>SessionToken</i>. </p>"
-    )
-    with gr.Group(elem_id="page_1", visible=True) as page_1:
-        with gr.Box():
-            with gr.Row():
-                start_button = gr.Button("Let's talk to chatGPT! 🗣", elem_id="start-btn", visible=True)
-                start_button.click(fn=None, inputs=[], outputs=[], _js=start_work)
-
-    with gr.Group(elem_id="page_2", visible=False) as page_2:
-        with gr.Row(elem_id="chat_row"):
-            chatbot = gr.Chatbot(elem_id="chat_bot", visible=False).style(color_map=("green", "blue"))
-            chatbot1 = gr.Chatbot(elem_id="chat_bot1").style(color_map=("green", "blue"))
-        with gr.Row():
-            prompt_input_audio = gr.Audio(
-                source="microphone",
-                type="filepath",
-                label="Record Audio Input",
-            )
-            prompt_output_audio = gr.Audio()
-
-        reset_conversation = gr.Checkbox(label="Reset conversation?", value=False)
-        with gr.Row(elem_id="prompt_row"):
-            chat_history = gr.Textbox(lines=4, label="prompt", visible=False)
-            submit_btn = gr.Button(value="Send to chatGPT", elem_id="submit-btn").style(
-                margin=True,
-                rounded=(True, True, True, True),
-                width=100,
-            )
-
-        submit_btn.click(
-            fn=chat,
-            inputs=[prompt_input_audio, chat_history, reset_conversation],
-            outputs=[chatbot, chat_history, prompt_output_audio],
-        )
-
-demo.launch(debug=True)
diff --git a/spaces/sanjayvy/ChatBotAI/app.py b/spaces/sanjayvy/ChatBotAI/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/sanjayvy/ChatBotAI/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/scedlatioru/img-to-music/example/ASPEL SAE CRACK DE REINSTALABLE 22 (R22) 64 Bit [EXCLUSIVE].md b/spaces/scedlatioru/img-to-music/example/ASPEL SAE CRACK DE REINSTALABLE 22 (R22) 64 Bit [EXCLUSIVE].md
deleted file mode 100644
index 85c28650ac0322989e775b7231e33284a4bba0df..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/ASPEL SAE CRACK DE REINSTALABLE 22 (R22) 64 Bit [EXCLUSIVE].md	
+++ /dev/null
@@ -1,16 +0,0 @@
-<h2>ASPEL SAE CRACK DE REINSTALABLE 22 (R22) 64 bit</h2><br /><p><b><b>Download File</b> &ndash;&ndash;&ndash; <a href="https://gohhs.com/2uEAwz">https://gohhs.com/2uEAwz</a></b></p><br /><br />
-<br />
-Nov 19, 2015 - ... ASPEL SAE CRACK DE REINSTALABLE 22 (R22) 64 bit, exclusive edition here and now ... If you want to download the game and install it on Windows 10, you will need ...
-Download Windows 10 | Programs for Windows
-Download torrent Windows 10 PRO for SSD x64 (2019) PC ...
-Download torrent Windows 10 PRO for SSD x64 (2019) PC (32/64- bit)
-Microsoft Windows 10 Pro x32-bit / x64-bit [2019 ...
-Microsoft Windows 10 Pro x32-bit / x64-bit [2019]. ...
-Windows 10.torrent.
-Size: 8.7 GB.
-Language: English.
-Download.
-Windows 10 Pro x32-bit / x64-bit [2019] 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/JetBrains PhpStorm 2018.2.5 Crack [CracksMind] Keygen !!EXCLUSIVE!!.md b/spaces/scedlatioru/img-to-music/example/JetBrains PhpStorm 2018.2.5 Crack [CracksMind] Keygen !!EXCLUSIVE!!.md
deleted file mode 100644
index 6dc05d99721890b023df55b324affd5c2bf24556..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/JetBrains PhpStorm 2018.2.5 Crack [CracksMind] Keygen !!EXCLUSIVE!!.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<h2>JetBrains PhpStorm 2018.2.5 Crack [CracksMind] keygen</h2><br /><p><b><b>DOWNLOAD</b> &#10038;&#10038;&#10038; <a href="https://gohhs.com/2uEyVD">https://gohhs.com/2uEyVD</a></b></p><br /><br />
-
-Quantum Mechanics Second Edition Quantum Mechanics Concepts and Applications Second Edition Nouredine Zettili Jacksonville State University, Jacksonville, . Quantum Mechanics is a course that explains many of the most complex ideas in quantum mechanics.
-For this reason, it is very useful for students who are just starting to get acquainted with quantum mechanics.
-However, it is very difficult to understand if you don't understand what quantum mechanics is and what happens to it.
-To solve your problem, this course will help you improve your thinking skills and allow you to better understand quantum mechanics. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Naruto Infinity Mugen 2014 Repackl.md b/spaces/scedlatioru/img-to-music/example/Naruto Infinity Mugen 2014 Repackl.md
deleted file mode 100644
index a5a3ca501fd5fabf65ef5b1157053552f89db5a7..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Naruto Infinity Mugen 2014 Repackl.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Naruto Infinity Mugen 2014 Repackl</h2><br /><p><b><b>Download Zip</b> &#10038;&#10038;&#10038; <a href="https://gohhs.com/2uEAux">https://gohhs.com/2uEAux</a></b></p><br /><br />
-<br />
-American English File 4 Student Book Answer Key Pdf · newtonevstpluginfreedownload · Naruto Infinity Mugen 2014 Repackl · telecharger torrent logiciel sphinx ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Tafsir Al Aisar Pdf Download.md b/spaces/scedlatioru/img-to-music/example/Tafsir Al Aisar Pdf Download.md
deleted file mode 100644
index 4cc8c584ef29cf5a9c7334cf78953e83c2df7548..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Tafsir Al Aisar Pdf Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Tafsir Al Aisar Pdf Download</h2><br /><p><b><b>DOWNLOAD</b> &#9675; <a href="https://gohhs.com/2uEAL6">https://gohhs.com/2uEAL6</a></b></p><br /><br />
-
-Tafsir Al-Qur‟an Al-Aisar. terj.. Aisar Tafasir Terjemahan.pdf Free Download Here Tafsir Al Quran Al Aisar [7 jilid lengkap] Pustaka Imam ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/sgxz/bingo/src/components/ui/dropdown-menu.tsx b/spaces/sgxz/bingo/src/components/ui/dropdown-menu.tsx
deleted file mode 100644
index 184d4e6007ef85187446362f69532ab077897fea..0000000000000000000000000000000000000000
--- a/spaces/sgxz/bingo/src/components/ui/dropdown-menu.tsx
+++ /dev/null
@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DropdownMenuPrimitive from '@radix-ui/react-dropdown-menu'
-
-import { cn } from '@/lib/utils'
-
-const DropdownMenu = DropdownMenuPrimitive.Root
-
-const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger
-
-const DropdownMenuGroup = DropdownMenuPrimitive.Group
-
-const DropdownMenuPortal = DropdownMenuPrimitive.Portal
-
-const DropdownMenuSub = DropdownMenuPrimitive.Sub
-
-const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup
-
-const DropdownMenuSubContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubContent>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubContent>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubContent
-    ref={ref}
-    className={cn(
-      'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuSubContent.displayName =
-  DropdownMenuPrimitive.SubContent.displayName
-
-const DropdownMenuContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <DropdownMenuPrimitive.Portal>
-    <DropdownMenuPrimitive.Content
-      ref={ref}
-      sideOffset={sideOffset}
-      className={cn(
-        'z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow animate-in data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2',
-        className
-      )}
-      {...props}
-    />
-  </DropdownMenuPrimitive.Portal>
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Item> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Item
-    ref={ref}
-    className={cn(
-      'relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none transition-colors focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName
-
-const DropdownMenuLabel = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Label> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Label
-    ref={ref}
-    className={cn(
-      'px-2 py-1.5 text-sm font-semibold',
-      inset && 'pl-8',
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName
-
-const DropdownMenuSeparator = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.Separator
-    ref={ref}
-    className={cn('-mx-1 my-1 h-px bg-muted', className)}
-    {...props}
-  />
-))
-DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName
-
-const DropdownMenuShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn('ml-auto text-xs tracking-widest opacity-60', className)}
-      {...props}
-    />
-  )
-}
-DropdownMenuShortcut.displayName = 'DropdownMenuShortcut'
-
-export {
-  DropdownMenu,
-  DropdownMenuTrigger,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuLabel,
-  DropdownMenuSeparator,
-  DropdownMenuShortcut,
-  DropdownMenuGroup,
-  DropdownMenuPortal,
-  DropdownMenuSub,
-  DropdownMenuSubContent,
-  DropdownMenuRadioGroup
-}
diff --git a/spaces/shweta44/IndianFoodClassification/app.py b/spaces/shweta44/IndianFoodClassification/app.py
deleted file mode 100644
index 2add5e3f1738fe69d273eaa93b3127cf5b151314..0000000000000000000000000000000000000000
--- a/spaces/shweta44/IndianFoodClassification/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import torch
-import gradio as gr
-from torch import nn
-from torch.nn import functional as F
-import torchvision
-from PIL import Image
-from torchvision import transforms
-
-transformations = transforms.Compose([transforms.Resize((256, 256)), transforms.ToTensor()])
-
-
-model = torch.jit.load('model_scripted (1).pt',map_location=torch.device('cpu'))
-classes=['Kajukatali','dhokla','gulabjam','jalebi','samosa']
-
-
-def predict(inp):
-  inp=transformations(inp).unsqueeze(0)
-  
-  
-  with torch.no_grad():
-    prediction =F.softmax(model(inp)[0], dim=0)
-    confidences = {classes[i]: float(prediction[i]) for i in range(5)}    
-  return confidences
-  
-gr.Interface(predict, gr.inputs.Image(type="pil"),outputs='label').launch(debug='True')
diff --git a/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/training/__init__.py b/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/training/__init__.py
deleted file mode 100644
index db8124b132f91216c0ded226f20ea3a046734728..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/training/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-# empty
diff --git a/spaces/silencewing/server/youyou/.history/math_20230613230703.html b/spaces/silencewing/server/youyou/.history/math_20230613230703.html
deleted file mode 100644
index ce8a8688205baf0588ce9e2ee6c81fecb53de772..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/.history/math_20230613230703.html
+++ /dev/null
@@ -1,227 +0,0 @@
-<!-- /*
- * @Author: Chauncey Yuan 
- * @Date: 2019-08-01 18:25:30 
- * @Last Modified by:   Chauncey Yuan 
- * @Last Modified time: 2019-08-03 08:24:27 
- */ -->
-
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <meta http-equiv="X-UA-Compatible" content="ie=edge">
-    <title>Document</title>
-    <style>
-        html {
-            height: 100%;
-            background: #b3b4a6;
-        }
-        .cal_game {
-            width: 650px;
-            margin: 0 auto;
-        }
-        
-        table {
-            margin: 0 auto;
-        }
-        
-        td {
-            width: 150px;
-            height: 50px;
-            text-align: center;
-            line-height: 40px;
-            border: 1px solid #2b7e71;
-        }
-        
-        .btn {
-            width: 150px;
-            height: 50px;
-            color: #2b7e71;;
-            border: 1px solid #2b7e71;
-            background-color: transparent;
-            /* background-color: #fff; */
-        }
-        
-        .btn:hover {
-            background-color: #2b7e71;
-            color: #fff;
-            cursor: pointer;
-        }
-        
-        input {
-            height: 30px;
-            border: 1px solid #999999;
-            background-color: transparent;
-
-        }
-    </style>
-</head>
-
-<body>
-    <div class="cal_game">
-        <table>
-            <tr>
-                <td id="output" colspan="3"></td>
-            </tr>
-            <tr>
-                <td>题目</td>
-                <td id="eq" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>答案</td>
-                <td><input type="text" name="" id="input" placeholder="请输入计算结果："></td>
-                <td><button class="btn" onclick="xun()">计算</button></td>
-            </tr>
-            <tr>
-                <td>正误</td>
-                <td id="result" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>得分</td>
-                <td id="score" colspan="2"></td>
-            </tr>
-            <tr>
-                <td id="accuracy" colspan="3"></td>
-            </tr>
-        </table>
-    </div>
-    <script>
-        // 定义题号，使用时+1
-        var i = 0;
-        // 定义计算正确的次数
-        var right_times = 0;
-        // 定义分数
-        var score = 0;
-        // 定义正确率
-        var accuracy = 0;
-        // 定义加减法符号数组，用于后边产生0或1的随机数，来确定加减法
-        var sign_operation_list = ["+", "-"];
-        // 生成0或者1的随机数，确定加减法
-        var sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-
-        var max_num = 20;
-        // 显示题号
-        document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-        document.onkeydown = function (e) {
-            if(e.which == "13" && document.getElementById("input").value){
-                xun()
-            }
-        }
-
-        // 如果是减法
-        if (sign_operation == 1) {
-            // 则被减数应该小于减数，结果才不会为负数
-            // 减数范围为0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 被减数范围为0-减数
-            var num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-        }
-        // 如果是加法
-        if (sign_operation == 0) {
-            // 两数和应小于max_num
-            // 第一个数的范围是0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 第二个数是0-（max_num-第一个数）
-            var num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-        }
-        // 组成算式，显示给用户看
-        var eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-        // 页面中显示算式
-        document.getElementById("eq").innerHTML = eq;
-        // console.log(eq);
-
-        // 定义函数，当按钮按下是执行一次
-        function xun() {
-            // console.log(num1, num2);
-            // 题号加1
-            i++;
-            // 获取用户输入的结果
-            var input = Number(document.getElementById("input").value);
-            // console.log(input, num1, num2);
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 定义真实结果
-                var calResult = num1 + num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            } // 如果是减法
-            if (sign_operation == 1) {
-                // 定义真实结果
-                var calResult = num1 - num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            }
-            // 判断结果后，用户输入框清空
-            document.getElementById("input").value = "";
-            // 显示分数
-            document.getElementById("score").innerHTML = score;
-            // 显示正确率
-            document.getElementById("accuracy").innerHTML = (((right_times / i) * 100).toFixed(2)) + "%";
-
-            if (score >= 100) {
-                window.location.href = "game.html";
-            }
-            // 生成0或者1的随机数，确定加减法
-            sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-            // 显示题号
-            document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-            // 如果是减法
-            if (sign_operation == 1) {
-                // 则被减数应该小于减数，结果才不会为负数
-                // 减数范围为0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 被减数范围为0-减数
-                num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-            }
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 两数和应小于max_num
-                // 第一个数的范围是0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 第二个数是0-（max_num-第一个数）
-                num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-            }
-            // 组成算式，显示给用户看
-            eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-            // 页面中显示算式
-            document.getElementById("eq").innerHTML = eq;
-            // console.log(eq);
-        }
-    </script>
-</body>
-
-</html>
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Clash Nulls The Ultimate Guide to Private Servers and Unlimited Resources.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Clash Nulls The Ultimate Guide to Private Servers and Unlimited Resources.md
deleted file mode 100644
index eb843f06882629f702dd9ca5fa160987991916f8..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Clash Nulls The Ultimate Guide to Private Servers and Unlimited Resources.md	
+++ /dev/null
@@ -1,103 +0,0 @@
-<br />
-<h1>Clash Nulls: A Private Server for Clash of Clans Fans</h1>
-<p>If you are a fan of Clash of Clans, the popular strategy game where you build your base, train your army, and attack other players, you might have heard of Clash Nulls. It is a private server that offers a different and exciting way to play the game. But what is Clash Nulls exactly and how can you join it? In this article, we will answer these questions and more. We will also tell you about the pros and cons of playing on Clash Nulls, and some frequently asked questions that you might have.</p>
-<h2>clash nulls</h2><br /><p><b><b>Download</b> &middot;&middot;&middot;&middot;&middot; <a href="https://ssurll.com/2uNYwF">https://ssurll.com/2uNYwF</a></b></p><br /><br />
- <h2>What is Clash Nulls and why you should try it</h2>
-<p>Clash Nulls is a private server for Clash of Clans that is run by a group of developers called Null's. A private server is a modified version of the game that runs on a separate server from the official one. This means that you can play with different rules, features, and players than the original game. There are many reasons why you might want to try Clash Nulls, such as:</p>
- <h3>Unlimited resources and custom mods</h3>
-<p>One of the main attractions of Clash Nulls is that you can have unlimited gems, gold, elixir, and dark elixir. You can use these resources to upgrade your buildings, troops, spells, and heroes without any waiting time or cost. You can also use custom mods that allow you to change the appearance and behavior of your troops, buildings, and spells. For example, you can have flying barbarians, invisible archers, or giant bombs that explode like nuclear bombs. You can also create your own mods using the built-in editor.</p>
- <h3>Multiplayer battles and clan wars</h3>
-<p>Another reason to play on Clash Nulls is that you can enjoy multiplayer battles and clan wars with other players who are also using the private server. You can attack any base you want without any limit or penalty. You can also join or create clans and participate in clan wars with other clans. You can chat with your clan mates, donate troops, and request reinforcements. You can also challenge your friends to friendly battles or practice attacks.</p>
- <h3>Fast updates and new features</h3>
-<p>A third reason to try Clash Nulls is that you can experience fast updates and new features that are not available on the official game. The developers of Clash Nulls are constantly working to add new buildings, troops, spells, heroes, and events to the private server. They also try to fix any bugs or issues that might occur. You can always check their website or Telegram channel for the latest news and updates.</p>
- <h2>How to download and install Clash Nulls on your device</h2>
-<p>If you are interested in playing on Clash Nulls, you will need to download and install the APK file on your Android device. Here are the steps to do so:</p>
- <h3>Download the APK file from the official website</h3>
-<p>The first step is to download the APK file from the official website of Clash Nulls. You can find the link at the end of this article. The APK file is a package that contains all the files needed to run the app on your device. The latest version of Clash Nulls is v15.292.17 which was released on September 7th 2023.</p>
-<p>clash nulls apk download<br />
-clash nulls private server<br />
-clash nulls unlimited resources<br />
-clash nulls builder base<br />
-clash nulls latest version<br />
-clash nulls mod apk<br />
-clash nulls update<br />
-clash nulls free gems<br />
-clash nulls online<br />
-clash nulls hack<br />
-clash nulls server status<br />
-clash nulls new troops<br />
-clash nulls super troops<br />
-clash nulls town hall 14<br />
-clash nulls clan wars<br />
-clash nulls custom commands<br />
-clash nulls discord<br />
-clash nulls error code 7<br />
-clash nulls fast updates<br />
-clash nulls gameplay<br />
-clash nulls hero skins<br />
-clash nulls installation guide<br />
-clash nulls ios<br />
-clash nulls join clan<br />
-clash nulls maintenance break<br />
-clash nulls magic items<br />
-clash nulls no root<br />
-clash nulls offline mode<br />
-clash nulls original base link<br />
-clash nulls pc<br />
-clash nulls review<br />
-clash nulls royal champion<br />
-clash nulls season pass<br />
-clash nulls siege machines<br />
-clash nulls support code 0x1f4f8a6b2c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a8c0a9f4f9b2d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b9d1b10f5fa3e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e2ea10e11f6fb4f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f3fb11f12g7gc5g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g4gc12g13h7hd6h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h5hd13h14i7ie7i6ie14i6ie14i6ie14i6ie14i6ie14i6ie14i6ie14i6ie14i6ie14i6ie14i15j7jf7j6jf15j6jf15j6jf15j6jf15j6jf15j6jf15j16k7kg7k6kg16k6kg16k6</p>
- <h3>Allow unknown sources in your settings</h3>
-<p>The second step is to allow unknown sources in your settings. This is a necessary step to install any app that is not from the Google Play Store. To do this, you need to go to your device's settings, then security, then enable the option to install apps from unknown sources. You might see a warning message that says installing apps from unknown sources can harm your device, but you can ignore it and proceed.</p>
- <h3>Install the app and enjoy</h3>
-<p>The third and final step is to install the app and enjoy playing on Clash Nulls. To install the app, you need to locate the APK file that you downloaded in your device's storage, then tap on it and follow the instructions. You might see another warning message that says the app is not verified by Google Play Protect, but you can also ignore it and continue. Once the app is installed, you can open it and start playing on Clash Nulls. You will see a different loading screen and a different icon than the official game. You will also need to create a new account and choose a name for your base.</p>
- <h2>Pros and cons of playing on Clash Nulls</h2>
-<p>Playing on Clash Nulls can be a lot of fun, but it also has some drawbacks that you should be aware of. Here are some of the pros and cons of playing on Clash Nulls:</p>
- <h3>Pros: more fun, freedom, and creativity</h3>
-<p>The main advantage of playing on Clash Nulls is that you can have more fun, freedom, and creativity than the official game. You can experiment with different strategies, designs, and combinations without any limitations or restrictions. You can also explore new features and updates that are not available on the official game. You can also meet new people and make new friends who share your passion for Clash of Clans.</p>
- <h3>Cons: not official, not secure, not fair</h3>
-<p>The main disadvantage of playing on Clash Nulls is that it is not an official, secure, or fair way to play Clash of Clans. You are playing on a private server that is not affiliated with or endorsed by Supercell, the developer of Clash of Clans. This means that you are violating their terms of service and risking your account being banned or suspended. You are also playing on a server that is not secure or reliable. You might experience crashes, glitches, errors, or data loss while playing on Clash Nulls. You are also playing on a server that is not fair or balanced. You might face opponents who have unfair advantages or disadvantages over you because of the unlimited resources and custom mods.</p>
- <h2>Frequently asked questions about Clash Nulls</h2>
-<p>Here are some of the most common questions that people have about Clash Nulls:</p>
- <h3>Is Clash Nulls safe to use?</h3>
-<p>Clash Nulls is safe to use as long as you download it from the official website and follow the installation instructions carefully. However, there is always a risk of malware or viruses when downloading apps from unknown sources. Therefore, we recommend that you use an antivirus software or a VPN service to protect your device and your data while playing on Clash Nulls.</p>
- <h3>Is Clash Nulls legal?</h3>
-<p>Clash Nulls is not legal because it violates the terms of service of Supercell, the developer of Clash of Clans. Supercell does not allow any modification or alteration of their games or their servers. They also do not allow any use of their intellectual property without their permission. Therefore, playing on Clash Nulls is considered piracy and infringement of Supercell's rights.</p>
- <h3>Can I play on both Clash Nulls and Clash of Clans?</h3>
-<p>You can play on both Clash Nulls and Clash of Clans if you have two separate devices or two separate accounts. However, you cannot play on both servers with the same account or the same device. This is because Clash Nulls and Clash of Clans have different databases and servers that do not sync with each other. If you try to switch between them with the same account or device, you might lose your progress or get banned.</p>
- <h2>Conclusion</h2>
-<p>Clash Nulls is a private server for Clash of Clans that offers a different and exciting way to play the game. It has unlimited resources, custom mods, multiplayer battles, clan wars, fast updates, and new features that are not available on the official game. However, it also has some drawbacks such as being not official, not secure, not fair, and not legal. Therefore, you should be careful and responsible when playing on Clash Nulls. If you want to try it out, you can download it from the official website and follow the installation instructions in this article.</p>
- <p>I hope you enjoyed this article and learned something new about Clash Nulls. If you have any questions or comments, please feel free to leave them below. I would love to hear from you and help you with anything you need. Thank you for reading and have a great day!</p>
- <h2>Frequently asked questions about Clash Nulls</h2>
-<p>Here are some of the most common questions that people have about Clash Nulls:</p>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>What is the difference between Clash Nulls and Clash of Clans?</td>
-<td>Clash Nulls is a private server for Clash of Clans that has unlimited resources, custom mods, multiplayer battles, clan wars, fast updates, and new features that are not available on the official game.</td>
-</tr>
-<tr>
-<td>How can I download and install Clash Nulls on my device?</td>
-<td>You can download the APK file from the official website of Clash Nulls and follow the installation instructions in this article.</td>
-</tr>
-<tr>
-<td>Is Clash Nulls safe, secure, and legal to use?</td>
-<td>Clash Nulls is safe to use as long as you download it from the official website and follow the installation instructions carefully. However, it is not secure or reliable because it might crash, glitch, or lose data. It is also not legal because it violates the terms of service of Supercell, the developer of Clash of Clans.</td>
-</tr>
-<tr>
-<td>Can I play on both Clash Nulls and Clash of Clans?</td>
-<td>You can play on both Clash Nulls and Clash of Clans if you have two separate devices or two separate accounts. However, you cannot play on both servers with the same account or device because they have different databases and servers that do not sync with each other.</td>
-</tr>
-<tr>
-<td>What are some of the pros and cons of playing on Clash Nulls?</td>
-<td>The pros of playing on Clash Nulls are that you can have more fun, freedom, and creativity than the official game. The cons of playing on Clash Nulls are that it is not official, not secure, not fair, and not legal.</td>
-</tr>
-</table>
- : https://clashnull.com/</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Angry Birds 2 APK for Free and Enjoy the Slingshot Adventure.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Angry Birds 2 APK for Free and Enjoy the Slingshot Adventure.md
deleted file mode 100644
index fe540b9f0f77fcbbfe2e62d332f779bc22e30314..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Angry Birds 2 APK for Free and Enjoy the Slingshot Adventure.md	
+++ /dev/null
@@ -1,111 +0,0 @@
-
-<h1>Angry Birds 2: How to Download and Play the Sequel to the Most Popular Mobile Game of All Time</h1>
- <h2>Introduction</h2>
- <p>If you are a fan of casual games, you have probably heard of Angry Birds, the game that took the world by storm in 2009. The game features a flock of colorful birds who use a slingshot to launch themselves at green pigs who have stolen their eggs. The game is simple, fun, and addictive, and has spawned several spin-offs, movies, merchandise, and more.</p>
-<h2>angry birds 2 game download apk</h2><br /><p><b><b>Download</b> &middot; <a href="https://ssurll.com/2uNVEm">https://ssurll.com/2uNVEm</a></b></p><br /><br />
- <p>But did you know that there is a sequel to Angry Birds that is even better than the original? That's right, Angry Birds 2 is here, and it is packed with more features, more levels, more characters, and more fun than ever before. In this article, we will tell you everything you need to know about Angry Birds 2, how to download it for your Android device, how to play it, and some tips and tricks to help you master it. Let's get started!</p>
- <h2>What is Angry Birds 2?</h2>
- <p>Angry Birds 2 is the official sequel to Angry Birds, developed by Rovio Entertainment Corporation. It was released in 2015, six years after the original game. Angry Birds 2 follows the same premise as the first game, but with some major improvements and additions. Here are some of the features that make Angry Birds 2 stand out:</p>
- <ul>
-<li><b>Multi-stage levels:</b> Instead of one stage per level, Angry Birds 2 has multiple stages that you have to complete in order to defeat the pigs. Each stage has different obstacles, structures, and surprises that will challenge your skills and strategy.</li>
-<li><b>Choose your bird:</b> Unlike the first game, where you had a fixed order of birds to use in each level, Angry Birds 2 lets you choose which bird to put in the slingshot. This gives you more freedom and flexibility to plan your moves and use the best bird for each situation.</li>
-<li><b>New characters:</b> Angry Birds 2 introduces some new birds to join the original flock. You can meet Silver, a fast and agile bird who can perform a loop-de-loop in mid-air; Chuck's sister Willow, a creative bird who can create a whirlwind of leaves; Stella's friend Poppy, a cheerful bird who can cause a burst of popcorn; and more. Each bird has its own unique personality and power that can help you in different ways.</li>
-<li><b>Daily challenges:</b> If you want some quick fun and rewards, you can play the daily challenges in Angry Birds 2. These are short and easy levels that you can complete in a minute or less. You can earn coins, gems, feathers, hats, and other prizes by playing these challenges every day.</li>
-<li><b>Clans:</b> One of the most exciting features of Angry Birds 2 is the clan system. You can join or create a clan with other players from around the world. You can chat with your clan members, share tips and strategies, and cooperate in clan events. You can also compete with other clans in the arena, where you can show off your skills and win trophies.</li>
-<li><b>Hats:</b <h2>Why should you play Angry Birds 2?</h2>
- <p>Angry Birds 2 is not just a game, it is an experience. It is a game that will make you laugh, cry, scream, and cheer. It is a game that will test your patience, skill, creativity, and luck. It is a game that will keep you hooked for hours, days, weeks, and months. Here are some of the reasons why you should play Angry Birds 2:</p>
-<p>angry birds 2 free download for android<br />
-angry birds 2 mod apk unlimited gems<br />
-angry birds 2 latest version apk<br />
-angry birds 2 offline game download<br />
-angry birds 2 hack apk download<br />
-angry birds 2 game online play<br />
-angry birds 2 apk + obb<br />
-angry birds 2 game install<br />
-angry birds 2 apk mirror<br />
-angry birds 2 apk old version<br />
-angry birds 2 game for pc<br />
-angry birds 2 apk pure<br />
-angry birds 2 mod apk rexdl<br />
-angry birds 2 game features<br />
-angry birds 2 apk no ads<br />
-angry birds 2 game review<br />
-angry birds 2 apk uptodown<br />
-angry birds 2 mod apk revdl<br />
-angry birds 2 game tips and tricks<br />
-angry birds 2 apk without internet<br />
-angry birds 2 game cheats<br />
-angry birds 2 apk mod menu<br />
-angry birds 2 mod apk happymod<br />
-angry birds 2 game walkthrough<br />
-angry birds 2 apk unlimited money<br />
-angry birds 2 game levels<br />
-angry birds 2 apk mod android 1<br />
-angry birds 2 mod apk an1<br />
-angry birds 2 game characters<br />
-angry birds 2 apk for ios<br />
-angry birds 2 game update<br />
-angry birds 2 apk data download<br />
-angry birds 2 mod apk android republic<br />
-angry birds 2 game modes<br />
-angry birds 2 apk file download<br />
-angry birds 2 game size<br />
-angry birds 2 apk pro<br />
-angry birds 2 mod apk platinmods<br />
-angry birds 2 game requirements<br />
-angry birds 2 apk cracked<br />
-angry birds 2 game events<br />
-angry birds 2 apk full version<br />
-angry birds 2 mod apk unlimited everything<br />
-angry birds 2 game clans<br />
-angry birds 2 apk premium<br />
-angry birds 2 mod apk latest version download</p>
- <ul>
-<li><b>It is fun:</b> This is the most obvious reason, but also the most important one. Angry Birds 2 is a game that will make you smile and enjoy yourself. The game has a colorful and cartoonish design, a catchy and upbeat soundtrack, a humorous and witty dialogue, and a lot of funny moments. The game also has a lot of variety and surprises, so you will never get bored or tired of playing it.</li>
-<li><b>It is challenging:</b> If you are looking for a game that will challenge your brain and your reflexes, Angry Birds 2 is the game for you. The game has over 3000 levels that range from easy to hard to impossible. The game also has different modes and events that will test your skills and strategy. You will have to think fast and act faster to overcome the obstacles and defeat the pigs.</li>
-<li><b>It is social:</b> Angry Birds 2 is not just a solo game, it is also a social game. You can connect with your friends and family through the game, and share your progress and achievements. You can also play with or against other players from around the world, and join or create clans. You can chat with other players, exchange tips and tricks, and cooperate or compete in clan events and arena battles.</li>
-<li><b>It is rewarding:</b> Angry Birds 2 is a game that will reward you for your efforts and achievements. The game has a lot of rewards that you can earn by playing the game. You can earn coins, gems, feathers, hats, chests, cards, stars, trophies, and more. You can use these rewards to upgrade your birds, unlock new characters, buy new hats, open new chests, and access new features.</li>
-</ul>
- <h2>How to download Angry Birds 2 for Android devices</h2>
- <p>If you are ready to join the millions of players who are enjoying Angry Birds 2 on their Android devices, here are the steps you need to follow to download the game:</p>
- <h3>Step 1: Go to the Google Play Store or APKCombo</h3>
- <p>The easiest way to download Angry Birds 2 for your Android device is to go to the Google Play Store, the official app store for Android devices. You can access the Google Play Store from your device's home screen or app drawer. Alternatively, you can also go to APKCombo, a website that provides APK files for Android apps. You can access APKCombo from your device's browser.</p>
- <h3>Step 2: Search for Angry Birds 2 and tap on the install button</h3>
- <p>Once you are on the Google Play Store or APKCombo website, you can search for Angry Birds 2 by typing its name in the search bar. You will see a list of results that match your query. Look for the one that has the Angry Birds 2 logo and tap on it. You will see a page that shows more information about the game, such as its description, screenshots, ratings, reviews, etc. To download the game, tap on the install button (or download button if you are on APKCombo). You may need to accept some permissions or terms of service before proceeding.</p>
- <h3>Step 3: Wait for the download to finish and open the game</h3>
- <p>After tapping on the install button (or download button), you will see a progress bar that shows how much of the game has been downloaded. The download time may vary depending on your internet speed and device storage. Once the download is complete, you will see an open button (or install button if you are on APKCombo). Tap on it to launch the game. You may need to allow some additional permissions or settings before playing the game.</p>
- <h2>How to play Angry Birds 2 on Android devices</h2>
- <p>Now that you have downloaded Angry Birds 2 on your Android device, you are ready to play it. Here are the steps you need to follow to play the game:</p>
- <h3>Step 1: Choose your bird and aim at the pigs</h3>
- <p>The first thing you need to do when you start a level in Angry Birds 2 is to choose which bird you want to use in the slingshot. You can see your available birds at the bottom of the screen. You can tap on the bird to see its name and power, and drag it to the slingshot to select it. You can also swap the order of your birds by tapping on the swap button at the top of the screen. Once you have chosen your bird, you can aim at the pigs by pulling back the slingshot and adjusting the angle. You can see a dotted line that shows the trajectory of your bird. You can also zoom in or out by pinching the screen.</p>
- <h3>Step 2: Use the slingshot and unleash the power of your bird</h3>
- <p>When you are ready to launch your bird, release the slingshot and watch your bird fly towards the pigs. You can also tap on the screen to activate your bird's special power. For example, Red can scream and knock down structures, Chuck can speed up and break through obstacles, Bomb can explode and cause massive damage, etc. You can see your bird's power icon at the bottom right corner of the screen. Try to use your bird's power at the right time and place to maximize its effect.</p>
- <h3>Step 3: Complete the levels and earn rewards</h3>
- <p>Your goal in each level is to destroy all the pigs and their structures. You can see how many pigs are left at the top of the screen. You can also see how many birds you have left at the bottom of the screen. If you run out of birds before you kill all the pigs, you will lose the level and have to start over. If you manage to kill all the pigs with some birds left, you will earn bonus points and stars. You can earn up to three stars per level depending on your score. You can also earn coins, gems, feathers, chests, cards, and other rewards by completing levels. You can use these rewards to upgrade your birds, unlock new characters, buy new hats, open new chests, and access new features.</p>
- <h2>Tips and tricks for playing Angry Birds 2 on Android devices</h2>
- <p>Angry Birds 2 is a game that requires skill, strategy, and luck. It is not always easy to complete the levels and beat the pigs. That's why we have compiled some tips and tricks that will help you improve your game and have more fun. Here are some of them:</p>
- <h3>Tip 1: Collect feathers and level up your birds</h3>
- <p>One of the most important things you need to do in Angry Birds 2 is to collect feathers and level up your birds. Feathers are items that you can use to increase your birds' power, health, speed, and damage. You can collect feathers by playing levels, opening chests, completing daily challenges, participating in clan events, etc. You can also buy feathers with gems or real money. To level up your birds, you need to go to the hatchery and select a bird. You will see a bar that shows how many feathers you need to level up that bird. Once you have enough feathers, tap on the level up button and watch your bird grow stronger.</p>
- <h3>Tip 2: Join a clan and cooperate with other players</h3>
- <p>Another thing you need to do in Angry Birds 2 is to join a clan and cooperate with other players. A clan is a group of players who share a common name, logo, chat room, and leaderboard. You can join or create a clan by going to the clan tab on the main menu. You can chat with your clan members, share tips and strategies, and cooperate in clan events. Clan events are special missions that require teamwork and coordination among clan members. You can earn coins, gems, feathers, hats, chests, cards, stars, trophies, and other prizes by completing clan events.</p>
- <h3>Tip 3: Participate in daily challenges and special events</h3>
- <p>The last thing you need to do in Angry Birds 2 is to participate in daily challenges and special events. Daily challenges are short and easy levels that you can complete in a minute or less. You can earn coins, gems, feathers, hats, chests, cards, and other prizes by playing these challenges every day. You can access the daily challenges by tapping on the calendar icon on the main menu. Special events are limited-time missions that offer unique and exciting gameplay and rewards. You can earn coins, gems, feathers, hats, chests, cards, stars, trophies, and other prizes by playing these events. You can access the special events by tapping on the banner icon on the main menu.</p>
- <h2>Conclusion</h2>
- <p>Angry Birds 2 is a game that you should not miss if you love casual games. It is a game that will provide you with hours of fun, challenge, and entertainment. It is a game that will let you unleash your creativity and strategy. It is a game that will connect you with other players and make you part of a community. It is a game that will reward you for your efforts and achievements. It is a game that will make you an angry bird fan for life.</p>
- <p>So what are you waiting for? Download Angry Birds 2 for your Android device today and join the millions of players who are enjoying this amazing game. You will not regret it!</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about Angry Birds 2:</p>
- <ol>
-<li><b>Is Angry Birds 2 free to play?</b></li>
-<p>Yes, Angry Birds 2 is free to play. You can download and play the game without paying anything. However, the game also offers some optional in-app purchases that can enhance your gameplay and experience. You can buy gems, coins, feathers, hats, chests, cards, and other items with real money. You can also watch ads to earn some free rewards. You can disable these features in the settings if you don't want them.</p>
-<li><b>Is Angry Birds 2 compatible with my device?</b></li>
-<p>Angry Birds 2 is compatible with most Android devices that have Android 4.1 or higher. You can check the compatibility of your device by going to the Google Play Store or APKCombo website and looking at the requirements section. You can also contact the developer if you have any issues or questions about the compatibility of your device.</p>
-<li><b>How can I save my progress in Angry Birds 2?</b></li>
-<p>You can save your progress in Angry Birds 2 by connecting your game to your Facebook account or your Rovio account. You can do this by tapping on the settings icon on the main menu and choosing the connect option. By doing this, you can also sync your progress across multiple devices and access your game from anywhere.</p>
-<li><b>How can I contact the developer of Angry Birds 2?</b></li>
-<p>You can contact the developer of Angry Birds 2 by going to the settings icon on the main menu and choosing the help option. You will see a list of topics that may answer your questions or issues. If you don't find what you are looking for, you can tap on the contact us button and fill out a form with your details and message. The developer will get back to you as soon as possible.</p>
-<li><b>How can I update Angry Birds 2?</b></li>
-<p>You can update Angry Birds 2 by going to the Google Play Store or APKCombo website and looking for the update button. You will see a notification when there is a new version of the game available. You can also enable automatic updates in the settings if you want to keep your game up to date without any hassle.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/stable_diffusion_chinese/README.md b/spaces/skf15963/summary/fengshen/examples/stable_diffusion_chinese/README.md
deleted file mode 100644
index edfb1d354bb2f98c1d24841ae86a70f07ceee37a..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/stable_diffusion_chinese/README.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Taiyi-Stable-Diffusion-1B-Chinese-v0.1
-
-- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
-- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
-
-## 简介 Brief Introduction
-
-首个开源的中文Stable Diffusion模型，基于0.2亿筛选过的中文图文对训练。
-
-The first open source Chinese Stable diffusion, which was trained on 20M filtered Chinese image-text pairs.
-
-## 模型分类 Model Taxonomy
-
-|  需求 Demand  | 任务 Task       | 系列 Series      | 模型 Model    | 参数 Parameter | 额外 Extra |
-|  :----:  | :----:  | :----:  | :----:  | :----:  | :----:  |
-| 特殊 Special | 多模态 Multimodal | 太乙 Taiyi | Stable Diffusion |    1B    |     Chinese     |
-
-## 模型信息 Model Information
-
-我们将[Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)数据集(100M)和[Zero](https://zero.so.com/)数据集(23M)用作预训练的数据集，先用[IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)对这两个数据集的图文对相似性进行打分，取CLIP Score大于0.2的图文对作为我们的训练集。 我们使用[IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)作为初始化的text encoder，冻住[stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)([论文](https://arxiv.org/abs/2112.10752))模型的其他部分，只训练text encoder，以便保留原始模型的生成能力且实现中文概念的对齐。该模型目前在0.2亿图文对上训练了一个epoch。 我们在 32 x A100 训练了大约100小时。该版本只是一个初步的版本，我们将持续优化并开源后续模型，欢迎交流。
-
-We use [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)(100M) 和 [Zero](https://zero.so.com/)(23M) as our dataset, and take the image and text pairs with CLIP Score (based on [IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)) greater than 0.2 as our Training set. We use [IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese) as our init text encoder. To keep the powerful generative capability of stable diffusion and align Chinese concepts with the images, We only train the text encoder and freeze other part of the [stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)([paper](https://arxiv.org/abs/2112.10752)) model. It takes 100 hours to train this model based on 32 x A100. This model is a preliminary version and we will update this model continuously and open sourse. Welcome to exchange！
-
-### Result
-Basic Prompt
-
-|  铁马冰河入梦来，3D绘画。   |  飞流直下三千尺，油画。 | 女孩背影，日落，唯美插画。  |
-|  ----  | ----  | ----  |
-| ![](result_examples/tiema.png)  | ![](result_examples/feiliu.png)  | ![](result_examples/nvhai.jpg) |
-
-Advanced Prompt
-
-| 铁马冰河入梦来，概念画，科幻，玄幻，3D  | 中国海边城市，科幻，未来感，唯美，插画。 | 那人却在灯火阑珊处，色彩艳丽，古风，资深插画师作品，桌面高清壁纸。 |
-|  ----  | ----  | ----  |
-| ![](result_examples/tiema2.jpg)  | ![](result_examples/chengshi.jpg) | ![](result_examples/naren.jpg) |
-
-
-## 使用 Usage
-
-### 全精度 Full precision
-
-```py
-from diffusers import StableDiffusionPipeline
-
-pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1").to("cuda")
-
-prompt = '飞流直下三千尺，油画'
-image = pipe(prompt, guidance_scale=7.5).images[0]  
-image.save("飞流.png")
-```
-
-### 半精度 Half precision FP16 (CUDA)
-
-添加 `torch_dtype=torch.float16` 和 `device_map="auto"` 可以快速加载 FP16 的权重，以加快推理速度。
-更多信息见 [the optimization docs](https://huggingface.co/docs/diffusers/main/en/optimization/fp16#half-precision-weights)。
-
-```py
-# !pip install git+https://github.com/huggingface/accelerate
-import torch
-from diffusers import StableDiffusionPipeline
-torch.backends.cudnn.benchmark = True
-pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1", torch_dtype=torch.float16)
-pipe.to('cuda')
-
-prompt = '飞流直下三千尺，油画'
-image = pipe(prompt, guidance_scale=7.5).images[0]  
-image.save("飞流.png")
-```
-
-
-## 引用 Citation
-
-如果您在您的工作中使用了我们的模型，可以引用我们的[总论文](https://arxiv.org/abs/2209.02970)：
-
-If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
-
-```text
-@article{fengshenbang,
-  author    = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang},
-  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
-  journal   = {CoRR},
-  volume    = {abs/2209.02970},
-  year      = {2022}
-}
-```
-
-也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
-
-You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
-
-```text
-@misc{Fengshenbang-LM,
-  title={Fengshenbang-LM},
-  author={IDEA-CCNL},
-  year={2021},
-  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
-}
-```
-
diff --git a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/static/style.css b/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/static/style.css
deleted file mode 100644
index 28e159e4ea2eb622cc0eb9ec2ebbb441447bb5c8..0000000000000000000000000000000000000000
--- a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/static/style.css
+++ /dev/null
@@ -1,41 +0,0 @@
-.common {
-  text-align: center;
-}
-
-/* Solid border */
-hr.solid {
-  border-top: 3px solid #bbb;
-}
-
-.boxitem1 {
-  display: inline-block;
-  vertical-align: left;
-}
-
-select {
-  font-size: 1.7em;
-  border: 1px;
-}
-
-button {
-  border-radius: 10px;
-}
-
-.boxitem2 {
-  display: inline-block;
-  vertical-align: right;
-}
-.boxitem {
-  display: inline-block;
-  vertical-align: top;
-}
-
-#inputimg {
-  vertical-align: left;
-  border: solid 1px black;
-}
-
-#outputimg {
-  vertical-align: left;
-  border: solid 1px black;
-}
diff --git a/spaces/songweig/rich-text-to-image/models/transformer_2d.py b/spaces/songweig/rich-text-to-image/models/transformer_2d.py
deleted file mode 100644
index fb709de6bf3f31c0c75f47f8788b9ec97e1ddbb2..0000000000000000000000000000000000000000
--- a/spaces/songweig/rich-text-to-image/models/transformer_2d.py
+++ /dev/null
@@ -1,341 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from dataclasses import dataclass
-from typing import Any, Dict, Optional
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.embeddings import ImagePositionalEmbeddings
-from diffusers.utils import BaseOutput, deprecate
-from diffusers.models.embeddings import PatchEmbed
-from diffusers.models.modeling_utils import ModelMixin
-
-from models.attention import BasicTransformerBlock
-
-@dataclass
-class Transformer2DModelOutput(BaseOutput):
-    """
-    The output of [`Transformer2DModel`].
-
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
-            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
-            distributions for the unnoised latent pixels.
-    """
-
-    sample: torch.FloatTensor
-
-
-class Transformer2DModel(ModelMixin, ConfigMixin):
-    """
-    A 2D Transformer model for image-like data.
-
-    Parameters:
-        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
-        in_channels (`int`, *optional*):
-            The number of channels in the input and output (specify if the input is **continuous**).
-        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
-            This is fixed during training since it is used to learn a number of position embeddings.
-        num_vector_embeds (`int`, *optional*):
-            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
-            Includes the class for the masked latent pixel.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
-        num_embeds_ada_norm ( `int`, *optional*):
-            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
-            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
-            added to the hidden states.
-
-            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
-        attention_bias (`bool`, *optional*):
-            Configure if the `TransformerBlocks` attention should contain a bias parameter.
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 16,
-        attention_head_dim: int = 88,
-        in_channels: Optional[int] = None,
-        out_channels: Optional[int] = None,
-        num_layers: int = 1,
-        dropout: float = 0.0,
-        norm_num_groups: int = 32,
-        cross_attention_dim: Optional[int] = None,
-        attention_bias: bool = False,
-        sample_size: Optional[int] = None,
-        num_vector_embeds: Optional[int] = None,
-        patch_size: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        use_linear_projection: bool = False,
-        only_cross_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_type: str = "layer_norm",
-        norm_elementwise_affine: bool = True,
-    ):
-        super().__init__()
-        self.use_linear_projection = use_linear_projection
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-
-        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
-        # Define whether input is continuous or discrete depending on configuration
-        self.is_input_continuous = (in_channels is not None) and (patch_size is None)
-        self.is_input_vectorized = num_vector_embeds is not None
-        self.is_input_patches = in_channels is not None and patch_size is not None
-
-        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
-            deprecation_message = (
-                f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
-                " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
-                " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
-                " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
-                " would be very nice if you could open a Pull request for the `transformer/config.json` file"
-            )
-            deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
-            norm_type = "ada_norm"
-
-        if self.is_input_continuous and self.is_input_vectorized:
-            raise ValueError(
-                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
-                " sure that either `in_channels` or `num_vector_embeds` is None."
-            )
-        elif self.is_input_vectorized and self.is_input_patches:
-            raise ValueError(
-                f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
-                " sure that either `num_vector_embeds` or `num_patches` is None."
-            )
-        elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
-            raise ValueError(
-                f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
-                f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
-            )
-
-        # 2. Define input layers
-        if self.is_input_continuous:
-            self.in_channels = in_channels
-
-            self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-            if use_linear_projection:
-                self.proj_in = nn.Linear(in_channels, inner_dim)
-            else:
-                self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
-        elif self.is_input_vectorized:
-            assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
-            assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
-
-            self.height = sample_size
-            self.width = sample_size
-            self.num_vector_embeds = num_vector_embeds
-            self.num_latent_pixels = self.height * self.width
-
-            self.latent_image_embedding = ImagePositionalEmbeddings(
-                num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
-            )
-        elif self.is_input_patches:
-            assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"
-
-            self.height = sample_size
-            self.width = sample_size
-
-            self.patch_size = patch_size
-            self.pos_embed = PatchEmbed(
-                height=sample_size,
-                width=sample_size,
-                patch_size=patch_size,
-                in_channels=in_channels,
-                embed_dim=inner_dim,
-            )
-
-        # 3. Define transformers blocks
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    cross_attention_dim=cross_attention_dim,
-                    activation_fn=activation_fn,
-                    num_embeds_ada_norm=num_embeds_ada_norm,
-                    attention_bias=attention_bias,
-                    only_cross_attention=only_cross_attention,
-                    upcast_attention=upcast_attention,
-                    norm_type=norm_type,
-                    norm_elementwise_affine=norm_elementwise_affine,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        # 4. Define output layers
-        self.out_channels = in_channels if out_channels is None else out_channels
-        if self.is_input_continuous:
-            # TODO: should use out_channels for continuous projections
-            if use_linear_projection:
-                self.proj_out = nn.Linear(inner_dim, in_channels)
-            else:
-                self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
-        elif self.is_input_vectorized:
-            self.norm_out = nn.LayerNorm(inner_dim)
-            self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
-        elif self.is_input_patches:
-            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
-            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
-            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        timestep: Optional[torch.LongTensor] = None,
-        class_labels: Optional[torch.LongTensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        return_dict: bool = True,
-    ):
-        """
-        The [`Transformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
-                Input `hidden_states`.
-            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
-                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
-                self-attention.
-            timestep ( `torch.LongTensor`, *optional*):
-                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
-            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
-                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
-                `AdaLayerZeroNorm`.
-            encoder_attention_mask ( `torch.Tensor`, *optional*):
-                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
-
-                    * Mask `(batch, sequence_length)` True = keep, False = discard.
-                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
-
-                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
-                above. This bias will be added to the cross-attention scores.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
-        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
-        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
-        # expects mask of shape:
-        #   [batch, key_tokens]
-        # adds singleton query_tokens dimension:
-        #   [batch,                    1, key_tokens]
-        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
-        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
-        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
-        if attention_mask is not None and attention_mask.ndim == 2:
-            # assume that mask is expressed as:
-            #   (1 = keep,      0 = discard)
-            # convert mask into a bias that can be added to attention scores:
-            #       (keep = +0,     discard = -10000.0)
-            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
-            attention_mask = attention_mask.unsqueeze(1)
-
-        # convert encoder_attention_mask to a bias the same way we do for attention_mask
-        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
-            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
-            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
-
-        # 1. Input
-        if self.is_input_continuous:
-            batch, _, height, width = hidden_states.shape
-            residual = hidden_states
-
-            hidden_states = self.norm(hidden_states)
-            if not self.use_linear_projection:
-                hidden_states = self.proj_in(hidden_states)
-                inner_dim = hidden_states.shape[1]
-                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
-            else:
-                inner_dim = hidden_states.shape[1]
-                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
-                hidden_states = self.proj_in(hidden_states)
-        elif self.is_input_vectorized:
-            hidden_states = self.latent_image_embedding(hidden_states)
-        elif self.is_input_patches:
-            hidden_states = self.pos_embed(hidden_states)
-
-        # 2. Blocks
-        for block in self.transformer_blocks:
-            hidden_states = block(
-                hidden_states,
-                attention_mask=attention_mask,
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_attention_mask=encoder_attention_mask,
-                timestep=timestep,
-                cross_attention_kwargs=cross_attention_kwargs,
-                class_labels=class_labels,
-            )
-
-        # 3. Output
-        if self.is_input_continuous:
-            if not self.use_linear_projection:
-                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-                hidden_states = self.proj_out(hidden_states)
-            else:
-                hidden_states = self.proj_out(hidden_states)
-                hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-
-            output = hidden_states + residual
-        elif self.is_input_vectorized:
-            hidden_states = self.norm_out(hidden_states)
-            logits = self.out(hidden_states)
-            # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
-            logits = logits.permute(0, 2, 1)
-
-            # log(p(x_0))
-            output = F.log_softmax(logits.double(), dim=1).float()
-        elif self.is_input_patches:
-            # TODO: cleanup!
-            conditioning = self.transformer_blocks[0].norm1.emb(
-                timestep, class_labels, hidden_dtype=hidden_states.dtype
-            )
-            shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
-            hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
-            hidden_states = self.proj_out_2(hidden_states)
-
-            # unpatchify
-            height = width = int(hidden_states.shape[1] ** 0.5)
-            hidden_states = hidden_states.reshape(
-                shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
-            )
-            hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
-            output = hidden_states.reshape(
-                shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
-            )
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer2DModelOutput(sample=output)
diff --git a/spaces/spiderdio/bingbing/README.md b/spaces/spiderdio/bingbing/README.md
deleted file mode 100644
index 50dac54562561ac34b36c3df5e29627b83ae129c..0000000000000000000000000000000000000000
--- a/spaces/spiderdio/bingbing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bingbing
-emoji: 😻
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/update_ckpt.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/update_ckpt.py
deleted file mode 100644
index 53c9e74ea613e30aa5c22614e658f2b7272bac0c..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/hubert/update_ckpt.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-src_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2.pt"
-ref_ckpt = "/checkpoint/wnhsu/w2v/hubert_icassp_oss_v3/iter2_km100-400k-grp-L6/oss.km500_p0_1_s334.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU100k.s1337.ngpu32/checkpoint_last.pt"
-new_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2_updated.pt"
-
-
-def update_state(state):
-    state["model"]["label_embs_concat"] = state["model"].pop("label_embs")
-    state["args"].task = "hubert_pretraining"
-    state["args"].labels = f"['{state['args'].labels}']"
-    return state
-
-
-src_state = torch.load(src_ckpt)
-src_state = update_state(src_state)
-torch.save(src_state, new_ckpt)
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/encoders/byte_utils.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/encoders/byte_utils.py
deleted file mode 100644
index a305c080926c2d094b7e8ae48f5331da82025a75..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/encoders/byte_utils.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import re
-
-
-WHITESPACE_NORMALIZER = re.compile(r"\s+")
-SPACE = chr(32)
-SPACE_ESCAPE = chr(9601)
-# excluding non-breaking space (160) here
-PRINTABLE_LATIN = set(
-    list(range(32, 126 + 1)) + list(range(161, 172 + 1)) + list(range(174, 255 + 1))
-)
-BYTE_TO_BCHAR = {
-    b: chr(b) if b in PRINTABLE_LATIN else chr(256 + b) for b in range(256)
-}
-BCHAR_TO_BYTE = {bc: b for b, bc in BYTE_TO_BCHAR.items()}
-
-
-def byte_encode(x: str) -> str:
-    normalized = WHITESPACE_NORMALIZER.sub(SPACE, x)
-    return "".join([BYTE_TO_BCHAR[b] for b in normalized.encode("utf-8")])
-
-
-def byte_decode(x: str) -> str:
-    try:
-        return bytes([BCHAR_TO_BYTE[bc] for bc in x]).decode("utf-8")
-    except ValueError:
-        return ""
-
-
-def smart_byte_decode(x: str) -> str:
-    output = byte_decode(x)
-    if output == "":
-        # DP the best recovery (max valid chars) if it's broken
-        n_bytes = len(x)
-        f = [0 for _ in range(n_bytes + 1)]
-        pt = [0 for _ in range(n_bytes + 1)]
-        for i in range(1, n_bytes + 1):
-            f[i], pt[i] = f[i - 1], i - 1
-            for j in range(1, min(4, i) + 1):
-                if f[i - j] + 1 > f[i] and len(byte_decode(x[i - j : i])) > 0:
-                    f[i], pt[i] = f[i - j] + 1, i - j
-        cur_pt = n_bytes
-        while cur_pt > 0:
-            if f[cur_pt] == f[pt[cur_pt]] + 1:
-                output = byte_decode(x[pt[cur_pt] : cur_pt]) + output
-            cur_pt = pt[cur_pt]
-    return output
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Abacre Retail Point Of Sale Professional V4 4 0 98 Crack ((BETTER))ed CzW Zip.md b/spaces/stomexserde/gpt4-ui/Examples/Abacre Retail Point Of Sale Professional V4 4 0 98 Crack ((BETTER))ed CzW Zip.md
deleted file mode 100644
index b90d277b925b0d78819363d1cca7243ca4fb984e..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Abacre Retail Point Of Sale Professional V4 4 0 98 Crack ((BETTER))ed CzW Zip.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-
-<h1>Abacre Retail Point Of Sale Professional v4.4.0.98 Cracked-CzW: A Review</h1>
-<p>Abacre Retail Point Of Sale Professional v4.4.0.98 Cracked-CzW is a retail management software for Windows that claims to offer a complete solution for taking orders, billing, purchasing, inventory and labour management. It also boasts of a user interface that is optimized for high speed input and error prevention[^1^]. But is it worth downloading?</p>
-<p>The answer is no. This software is not only illegal, but also potentially harmful to your computer. It is a cracked version of the original Abacre Retail Point Of Sale software, which means it has been modified by hackers to bypass the security and licensing features of the original software[^2^]. This means that you are not only violating the intellectual property rights of the developers, but also exposing your computer to malware, viruses, spyware and other threats that may be hidden in the cracked file.</p>
-<h2>Abacre Retail Point Of Sale Professional V4 4 0 98 Cracked CzW Zip</h2><br /><p><b><b>DOWNLOAD</b> &#10038; <a href="https://urlgoal.com/2uIb5P">https://urlgoal.com/2uIb5P</a></b></p><br /><br />
-<p>Moreover, this software is outdated and may not work properly with your current system and hardware. The latest version of Abacre Retail Point Of Sale is v11.10, which was released on April 16, 2023[^1^]. This version has many new features and improvements that are not available in the cracked version. For example, it supports cloud-based data synchronization, multi-store network configuration, barcode scanners and printers, loyalty programs and gift cards, and more[^1^].</p>
-<p>Therefore, we do not recommend downloading or using Abacre Retail Point Of Sale Professional v4.4.0.98 Cracked-CzW. It is illegal, unsafe and outdated. If you are looking for a reliable and professional retail management software, you should consider purchasing the original Abacre Retail Point Of Sale software from their official website[^1^]. They offer a free trial version for 30 days, and a full license for $149.99 per computer[^1^]. You can also contact their customer support team for any questions or issues you may have.</p><p>In this article, we have reviewed Abacre Retail Point Of Sale Professional v4.4.0.98 Cracked-CzW, a retail management software for Windows that is illegal, unsafe and outdated. We have explained why you should not download or use this software, and suggested a better alternative: the original Abacre Retail Point Of Sale software from their official website.</p>
-<p>We hope this article has been helpful and informative for you. If you have any comments or feedback, please feel free to share them with us. Thank you for reading!</p><p>As a bonus, we will also give you some tips on how to choose a good retail management software for your business. Here are some factors you should consider before making a purchase:</p>
-<ul>
-<li>Features and functionality: You should look for a software that meets your specific needs and preferences. For example, if you have multiple stores, you may want a software that supports network configuration and data synchronization. If you offer loyalty programs and gift cards, you may want a software that integrates with them. If you use barcode scanners and printers, you may want a software that is compatible with them.</li>
-<li>Usability and performance: You should look for a software that is easy to use and fast to operate. You don't want to waste time and money on a software that is complicated and slow. You should also check the system requirements and compatibility of the software with your hardware and operating system.</li>
-<li>Customer support and updates: You should look for a software that has a reliable and responsive customer support team that can help you with any issues or questions you may have. You should also look for a software that offers regular updates and bug fixes to ensure the security and functionality of the software.</li>
-<li>Price and value: You should look for a software that offers a fair and reasonable price for the features and quality it provides. You should also compare the price and value of different software options to find the best deal for your budget. You should also consider the return on investment (ROI) of the software, which is the amount of money you can save or earn by using the software.</li>
-</ul>
-<p>By following these tips, you can find a retail management software that suits your business needs and goals. Remember, a good retail management software can help you improve your efficiency, productivity, profitability and customer satisfaction.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Curso De Edificacion Diaz Infante Pdf 20.md b/spaces/stomexserde/gpt4-ui/Examples/Curso De Edificacion Diaz Infante Pdf 20.md
deleted file mode 100644
index 027ec08d73ac108ff5d5d7a67c4ae5b9947c09bb..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Curso De Edificacion Diaz Infante Pdf 20.md	
+++ /dev/null
@@ -1,25 +0,0 @@
-<br />
-Here is a possible title and article with html formatting for the keyword "Curso De Edificacion Diaz Infante Pdf 20":
-
-<h1>Curso De Edificacion Diaz Infante Pdf 20: A Comprehensive Guide for Construction Professionals</h1>
-<p>If you are looking for a book that covers all the aspects of building construction, from design to execution, you might want to check out Curso De Edificacion Diaz Infante Pdf 20. This book, written by Luis Armando Diaz Infante de la Mora, is a comprehensive guide for construction professionals, students, and enthusiasts who want to learn more about the techniques, materials, and processes involved in building projects.</p>
-<h2>Curso De Edificacion Diaz Infante Pdf 20</h2><br /><p><b><b>Download</b> &#10038;&#10038;&#10038; <a href="https://urlgoal.com/2uI7Q5">https://urlgoal.com/2uI7Q5</a></b></p><br /><br />
-<p>The book is divided into four parts: The Project in Construction, The Construction Process, The Construction Elements, and The Construction Services. Each part contains several chapters that explain in detail the different stages and components of a construction project, such as:</p>
-<ul>
-<li>How to interpret plans and specifications</li>
-<li>How to program and monitor acquisitions</li>
-<li>How to manage quality, cost, and time</li>
-<li>How to apply engineering and cost analysis</li>
-<li>How to use different types of foundations, structures, walls, roofs, floors, and finishes</li>
-<li>How to install plumbing, electrical, mechanical, and special systems</li>
-<li>How to operate and maintain buildings</li>
-</ul>
-<p>The book also includes numerous examples, illustrations, tables, formulas, and references that help the reader understand and apply the concepts and methods presented. The book is written in a clear and concise language that makes it easy to follow and learn from.</p>
-<p>Curso De Edificacion Diaz Infante Pdf 20 is available online as a PDF file that can be downloaded for free from various websites. It is also available as a printed book that can be purchased from online or physical stores. The book is suitable for anyone who wants to improve their knowledge and skills in building construction.</p>Here are a few more paragraphs for the article:
-
-<p>One of the main advantages of Curso De Edificacion Diaz Infante Pdf 20 is that it covers both the theoretical and practical aspects of construction. The book not only explains the principles and concepts behind each topic, but also shows how they are applied in real-life situations. The book provides useful tips and recommendations for solving common problems and challenges that arise during construction projects.</p>
-<p></p>
-<p>Another benefit of Curso De Edificacion Diaz Infante Pdf 20 is that it is updated and relevant to the current trends and standards in the construction industry. The book reflects the latest innovations and technologies that are used in modern building practices. The book also incorporates the environmental and social aspects of construction, such as sustainability, energy efficiency, safety, and accessibility.</p>
-<p>Curso De Edificacion Diaz Infante Pdf 20 is a valuable resource for anyone who wants to learn more about the art and science of building construction. The book is suitable for beginners who want to get an overview of the construction process, as well as for experts who want to deepen their knowledge and skills in specific areas. The book is also a great reference for teachers, students, researchers, and professionals who are involved in the construction field.</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/HitFilm Ignite Pro 2017 V1.0.6227 CE For OFX.md b/spaces/stomexserde/gpt4-ui/Examples/HitFilm Ignite Pro 2017 V1.0.6227 CE For OFX.md
deleted file mode 100644
index 05b49712cd1dc1790a629ddb89f189621acd5afd..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/HitFilm Ignite Pro 2017 V1.0.6227 CE For OFX.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-
-<h1>HitFilm Ignite Pro 2017: A Powerful Plugin Collection for OFX Hosts</h1>
-<p>HitFilm Ignite Pro 2017 is a suite of over 150 plugins that enhance the functionality and creativity of any OFX-compatible video editing or compositing software. Whether you use Adobe After Effects, Premiere Pro, DaVinci Resolve, Vegas Pro, Final Cut Pro X or any other OFX host, HitFilm Ignite Pro 2017 can help you take your projects to the next level with stunning visual effects, color grading, motion tracking, keying and more.</p>
-<p>HitFilm Ignite Pro 2017 is developed by FXhome, the creators of HitFilm Pro, a free video editing and visual effects software with professional-grade VFX tools[^1^]. HitFilm Ignite Pro 2017 is compatible with HitFilm Pro, but also works as a standalone plugin collection for other OFX hosts. Some of the features of HitFilm Ignite Pro 2017 include:</p>
-<h2>HitFilm Ignite Pro 2017 v1.0.6227 CE for OFX</h2><br /><p><b><b>DOWNLOAD</b> &#10001; <a href="https://urlgoal.com/2uIaag">https://urlgoal.com/2uIaag</a></b></p><br /><br />
-<ul>
-<li>Lighting and lens flares: Add realistic light effects to your scenes with high-quality light leaks, glows, 3D light leaks and more.</li>
-<li>Color grading and correction: Enhance the look and feel of your footage with advanced color tools such as curves, levels, color wheels, LUTs and more.</li>
-<li>Motion tracking: Track the movement of any object in your scene and attach other elements to it with ease.</li>
-<li>Keying: Remove unwanted backgrounds or objects from your footage with powerful chroma keying and matte cleaning tools.</li>
-<li>3D objects: Create and animate 3D text and models with Boris 3D Objects plugin, which lets you extrude, distort, warp and transform any text or shape.</li>
-<li>And much more: HitFilm Ignite Pro 2017 also includes plugins for transitions, presets, built-in assets, audio sync, text tools and more.</li>
-</ul>
-<p>If you want to unleash your creativity and add professional-quality effects to your videos, HitFilm Ignite Pro 2017 is the plugin collection for you. You can download it from FXhome's website for $199[^2^] or buy it as an NFT on OpenSea for $0.01 ETH[^3^]. Either way, you will get access to over 150 plugins that will transform your video editing experience.</p>
-
-<p>HitFilm Ignite Pro 2017 is easy to install and use. You just need to download the installer from FXhome's website and follow the instructions. Once installed, you can access the plugins from your OFX host's effects panel. You can also customize the settings and parameters of each plugin to suit your needs and preferences.</p>
-<p>HitFilm Ignite Pro 2017 is not only a powerful plugin collection, but also a versatile one. You can use it for any type of video project, whether it's a social media video, a short film, a documentary, a music video or a gaming video. You can also mix and match the plugins to create unique and original effects that will impress your audience.</p>
-<p>HitFilm Ignite Pro 2017 is compatible with Windows and Mac operating systems, and supports resolutions up to 8K. It also works with 32-bit and 64-bit floating point color resolution, which means you can preserve the quality and dynamic range of your footage. Moreover, it supports GPU acceleration, which means you can work faster and smoother without compromising performance.</p>
-<p>If you are looking for a plugin collection that will boost your creativity and productivity, HitFilm Ignite Pro 2017 is the perfect choice for you. It offers you over 150 plugins that cover all the aspects of video editing and compositing, from lighting and color to motion and 3D. With HitFilm Ignite Pro 2017, you can turn your vision into reality with ease and confidence.</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Insidious The Last Key (English) Full Hd Movie 1080p.md b/spaces/stomexserde/gpt4-ui/Examples/Insidious The Last Key (English) Full Hd Movie 1080p.md
deleted file mode 100644
index b045bb4e73ed76997a202c14be57570c56d3e07e..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Insidious The Last Key (English) Full Hd Movie 1080p.md	
+++ /dev/null
@@ -1,14 +0,0 @@
-<br />
-<h1>Insidious: The Last Key - A Terrifying Journey into the Past</h1>
-<p>If you are a fan of horror and supernatural movies, you might want to check out <i>Insidious: The Last Key</i>, the fourth installment of the <i>Insidious</i> franchise. This movie follows the story of Dr. Elise Rainier, a brilliant parapsychologist who faces her most personal and fearsome haunting yet: in her own childhood home.</p>
-<p>The movie begins with a flashback to Elise's childhood, where she discovers her ability to see and communicate with spirits. However, she also unleashes a malevolent demon that torments her and her family. Years later, Elise receives a phone call from a man who claims his house is haunted by the same demon...the same house where Elise grew up. She decides to return to her hometown and confront the evil that has been haunting her for decades.</p>
-<h2>Insidious: The Last Key (English) full hd movie 1080p</h2><br /><p><b><b>Download</b> &#10040; <a href="https://urlgoal.com/2uI72c">https://urlgoal.com/2uI72c</a></b></p><br /><br />
-<p><i>Insidious: The Last Key</i> is a thrilling and suspenseful movie that will keep you on the edge of your seat. The movie features some creative and scary scenes that will make you jump out of your skin. The movie also explores Elise's backstory and character development, giving her more depth and emotion. The movie stars Lin Shaye as Elise Rainier, Leigh Whannell as Specs, Angus Sampson as Tucker, Spencer Locke as Melissa, Caitlin Gerard as Imogen, and Bruce Davison as Christian.</p>
-<p>The movie is directed by Adam Robitel and produced by James Wan, Jason Blum, Oren Peli, and Leigh Whannell. The movie was released in 2018 and received mixed reviews from critics and audiences. However, if you are looking for a fun and frightening movie to watch online, you can find <i>Insidious: The Last Key</i> in full HD quality on various streaming platforms such as Sony LIV[^1^], Movies Anywhere[^2^], and Stan[^3^]. Be prepared to enter the dark and twisted world of the Further...if you dare.</p>
-<p></p>
-
-<p>If you want to know more about the <i>Insidious</i> franchise, you can also watch the previous three movies online. The first movie, <i>Insidious</i>, introduces Elise as a psychic who helps a family whose son is trapped in a coma and a realm called the Further. The second movie, <i>Insidious: Chapter 2</i>, follows Elise's colleagues Specs and Tucker as they try to help the same family after Elise's death. The third movie, <i>Insidious: Chapter 3</i>, is a prequel that shows how Elise met Specs and Tucker and how she helped a teenage girl who was targeted by a vengeful spirit.</p>
-<p>The <i>Insidious</i> franchise is one of the most successful and popular horror series of the 21st century. It has earned over $500 million worldwide and has received praise for its originality, atmosphere, and performances. The franchise has also spawned several books, comics, video games, and merchandise. Fans of the franchise can look forward to the fifth movie, <i>Insidious: Rise of the Dark Realm</i>, which is set to be released in 2022. The movie will feature Patrick Wilson as Josh Lambert, who will make his directorial debut.</p>
-<p><i>Insidious: The Last Key</i> is a movie that will satisfy your appetite for horror and supernatural thrillers. It is a movie that will make you scream, shiver, and think. It is a movie that will take you on a journey into the past and into the Further. Watch it online today and experience the terror for yourself.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/metagpt/actions/write_code_review.py b/spaces/sub314xxl/MetaGPT/metagpt/actions/write_code_review.py
deleted file mode 100644
index 7f6a7a38e6a1ed81614364e3deaac37b7dc1f1a9..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/actions/write_code_review.py
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 17:45
-@Author  : alexanderwu
-@File    : write_code_review.py
-"""
-
-from metagpt.actions.action import Action
-from metagpt.logs import logger
-from metagpt.schema import Message
-from metagpt.utils.common import CodeParser
-from tenacity import retry, stop_after_attempt, wait_fixed
-
-PROMPT_TEMPLATE = """
-NOTICE
-Role: You are a professional software engineer, and your main task is to review the code. You need to ensure that the code conforms to the PEP8 standards, is elegantly designed and modularized, easy to read and maintain, and is written in Python 3.9 (or in another programming language).
-ATTENTION: Use '##' to SPLIT SECTIONS, not '#'. Output format carefully referenced "Format example".
-
-## Code Review: Based on the following context and code, and following the check list, Provide key, clear, concise, and specific code modification suggestions, up to 5.
-```
-1. Check 0: Is the code implemented as per the requirements?
-2. Check 1: Are there any issues with the code logic?
-3. Check 2: Does the existing code follow the "Data structures and interface definitions"?
-4. Check 3: Is there a function in the code that is omitted or not fully implemented that needs to be implemented?
-5. Check 4: Does the code have unnecessary or lack dependencies?
-```
-
-## Rewrite Code: {filename} Base on "Code Review" and the source code, rewrite code with triple quotes. Do your utmost to optimize THIS SINGLE FILE. 
------
-# Context
-{context}
-
-## Code: {filename}
-```
-{code}
-```
------
-
-## Format example
------
-{format_example}
------
-
-"""
-
-FORMAT_EXAMPLE = """
-
-## Code Review
-1. The code ...
-2. ...
-3. ...
-4. ...
-5. ...
-
-## Rewrite Code: {filename}
-```python
-## {filename}
-...
-```
-"""
-
-
-class WriteCodeReview(Action):
-    def __init__(self, name="WriteCodeReview", context: list[Message] = None, llm=None):
-        super().__init__(name, context, llm)
-
-    @retry(stop=stop_after_attempt(2), wait=wait_fixed(1))
-    async def write_code(self, prompt):
-        code_rsp = await self._aask(prompt)
-        code = CodeParser.parse_code(block="", text=code_rsp)
-        return code
-
-    async def run(self, context, code, filename):
-        format_example = FORMAT_EXAMPLE.format(filename=filename)
-        prompt = PROMPT_TEMPLATE.format(context=context, code=code, filename=filename, format_example=format_example)
-        logger.info(f'Code review {filename}..')
-        code = await self.write_code(prompt)
-        # code_rsp = await self._aask_v1(prompt, "code_rsp", OUTPUT_MAPPING)
-        # self._save(context, filename, code)
-        return code
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Autodesk Autocad 2006 Activation Code Keygen Free Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Autodesk Autocad 2006 Activation Code Keygen Free Download.md
deleted file mode 100644
index d9209728ef45b19d12dc517622ba748e89e88a5f..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Autodesk Autocad 2006 Activation Code Keygen Free Download.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-
-<h1>How to Activate Autodesk Autocad 2006 with Keygen</h1>
-<p>Autodesk Autocad 2006 is a powerful and versatile software for designing and drafting 2D and 3D models. However, to use it, you need to activate it with a valid activation code. In this article, I will show you how to generate and use an activation code for Autocad 2006 using a keygen program.</p>
-<h2>Autodesk Autocad 2006 Activation Code Keygen Free Download</h2><br /><p><b><b>Download</b> &gt;&gt;&gt;&gt;&gt; <a href="https://cinurl.com/2uEYXY">https://cinurl.com/2uEYXY</a></b></p><br /><br />
-<h2>What is a keygen?</h2>
-<p>A keygen is a small program that can generate serial numbers or activation codes for various software products. A keygen usually works by using an algorithm that mimics the one used by the software vendor to create valid codes. However, using a keygen may be illegal or unethical, as it violates the software license agreement and may expose your computer to malware or viruses. Therefore, you should only use a keygen if you own a legitimate copy of the software and have lost or misplaced your original activation code.</p>
-<h2>Where can I find a keygen for Autocad 2006?</h2>
-<p>There are many websites that claim to offer keygens for Autocad 2006, but not all of them are reliable or safe. Some of them may contain malicious software or fake codes that can harm your computer or fail to activate your software. Therefore, you should be careful and cautious when downloading or using any keygen from the internet. One of the websites that seems to have a working keygen for Autocad 2006 is AppNee[^1^]. This website provides keygens for various Autodesk products from 2006 to 2020, including Autocad 2006. However, you should still scan any file you download from this website with an antivirus program before opening it.</p>
-<h2>How to use the keygen for Autocad 2006?</h2>
-<p>To use the keygen for Autocad 2006, you need to follow these steps:</p>
-<ol>
-<li>Download the keygen file from AppNee[^1^] or another source. The file name should be something like xf-adsk2006_x86.exe or xf-adsk2006_x64.exe, depending on your system architecture.</li>
-<li>Run the keygen file as administrator. You may need to disable your antivirus program temporarily if it blocks the file.</li>
-<li>Select Autocad 2006 from the product list in the keygen window.</li>
-<li>Click on Generate button to generate a serial number and an activation code for Autocad 2006.</li>
-<li>Copy the serial number and paste it in the installation wizard of Autocad 2006 when prompted.</li>
-<li>Complete the installation process and launch Autocad 2006.</li>
-<li>Select Activate Product from the Help menu in Autocad 2006.</li>
-<li>Enter the activation code generated by the keygen in the activation wizard of Autocad 2006.</li>
-<li>Click on Next and follow the instructions to complete the activation process.</li>
-<li>Enjoy your activated Autocad 2006!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, I have shown you how to activate Autodesk Autocad 2006 with a keygen program. However, I do not recommend or endorse using a keygen for any software product, as it may be illegal or unethical. You should only use a keygen if you own a legitimate copy of the software and have lost or misplaced your original activation code. Otherwise, you should purchase a valid license from Autodesk or an authorized reseller. I hope this article was helpful and informative for you. Thank you for reading!</p>
-<p></p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bill Simmons Book Of Basketball Pdf 13.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bill Simmons Book Of Basketball Pdf 13.md
deleted file mode 100644
index b267e1dda656168e4a8b902be2603ec183b4e666..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Bill Simmons Book Of Basketball Pdf 13.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-<br />
-<h1>How to Download Bill Simmons Book of Basketball PDF 13 for Free</h1>
-<p>If you are a fan of basketball and want to learn more about the history, culture, and personalities of the game, you might be interested in reading Bill Simmons' Book of Basketball. This is a comprehensive and entertaining book that covers the NBA from its inception to the present day, with insights, anecdotes, and rankings from one of the most popular sports writers and podcasters in the world.</p>
-<p>However, buying a hard copy of the book can be expensive and inconvenient. That's why many people are looking for a way to download Bill Simmons Book of Basketball PDF 13 for free. This is the latest edition of the book, which includes updates and revisions from the author. But how can you get it without paying anything?</p>
-<h2>Bill Simmons Book Of Basketball Pdf 13</h2><br /><p><b><b>Download Zip</b> ->->->-> <a href="https://cinurl.com/2uEX54">https://cinurl.com/2uEX54</a></b></p><br /><br />
-<p>In this article, we will show you some of the best methods to download Bill Simmons Book of Basketball PDF 13 for free. These methods are legal and safe, and you don't need to worry about viruses, malware, or copyright infringement. So, let's get started!</p>
-<h2>Method 1: Use a Free Online Library</h2>
-<p>One of the easiest ways to download Bill Simmons Book of Basketball PDF 13 for free is to use a free online library. These are websites that offer thousands of books in various formats, including PDF, EPUB, MOBI, and more. You can browse by category, genre, author, or title, and find the book you want in seconds.</p>
-<p>Some of the most popular free online libraries are:</p>
-<ul>
-<li><a href="https://www.pdfdrive.com/">PDF Drive</a>: This website has over 90 million books that you can download for free. You can search by keyword or browse by category. You can also preview the book before downloading it.</li>
-<li><a href="https://www.gutenberg.org/">Project Gutenberg</a>: This website has over 60,000 books that are in the public domain. You can find classics and rare books that are no longer under copyright. You can download them in various formats or read them online.</li>
-<li><a href="https://openlibrary.org/">Open Library</a>: This website is a project of the Internet Archive, which aims to create a digital library of every book ever published. You can find millions of books that you can borrow, read, or download for free. You can also contribute by adding or editing information about books.</li>
-</ul>
-<p>To use these websites, you just need to follow these steps:</p>
-<ol>
-<li>Go to the website of your choice and type "Bill Simmons Book of Basketball" in the search box.</li>
-<li>Find the book you want and click on it.</li>
-<li>Select the format you want (PDF, EPUB, MOBI, etc.) and click on the download button.</li>
-<li>Save the file to your device and enjoy reading it!</li>
-</ol>
-<h2>Method 2: Use a Free Trial Subscription</h2>
-<p>Another way to download Bill Simmons Book of Basketball PDF 13 for free is to use a free trial subscription from an online book service. These are websites or apps that offer unlimited access to thousands of books for a monthly or yearly fee. However, many of them also offer a free trial period that you can use to read any book you want without paying anything.</p>
-<p>Some of the most popular online book services are:</p>
-<p></p>
-<ul>
-<li><a href="https://www.scribd.com/">Scribd</a>: This website has over one million books, audiobooks, magazines, podcasts, and more that you can read or listen to online or offline. You can get a 30-day free trial when you sign up with your email or Facebook account.</li>
-<li><a href="https://www.amazon.com/kindle-dbs/hz/signup">Kindle Unlimited</a>: This service is offered by Amazon and allows you to read over one million books on any device with the Kindle app. You can also listen to thousands of audiobooks with Audible narration. You can get a 30-day free trial when you sign up with your Amazon account.</li>
-<li><a href="https://www.audible.com/">Audible</a>: This service is also offered by Amazon and allows you to listen to over 200,000 audiobooks on any device with the Audible app. You can also get access to exclusive podcasts and original content. You can get</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/TheGodfatherIICrackRELOADEDpcgame Fix.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/TheGodfatherIICrackRELOADEDpcgame Fix.md
deleted file mode 100644
index 3131f50c41ba4c34fea92a988bba053d32855ee5..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/TheGodfatherIICrackRELOADEDpcgame Fix.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>TheGodfatherIICrackRELOADEDpcgame</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://cinurl.com/2uEYFj">https://cinurl.com/2uEYFj</a></b></p><br /><br />
-<br />
-TheGodfatherIICrackRELOADEDpcgame vladmodels y143 galina.rar bluebits trikker V1.5.20 185 easy cut studio keygen 167 .... vladmodels y143 galina.rar ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/suryabbrj/CollegeProjectV2/app.py b/spaces/suryabbrj/CollegeProjectV2/app.py
deleted file mode 100644
index 508e1bc6ad1fdbc4fe3b0369f3bd0949cd065a37..0000000000000000000000000000000000000000
--- a/spaces/suryabbrj/CollegeProjectV2/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import warnings
-import logging
-warnings.simplefilter('ignore')
-logging.disable(logging.WARNING)
-
-
-def predict(image):
-    cap = pipeline('image-to-text')
-    caption = cap(image)
-
-
-    def sentiment_analysis(shortened):
-      pipe = pipeline('sentiment-analysis')
-      senti = pipe(shortened)
-      return senti
-
-    caption_string = str(caption)
-
-    sentiment = sentiment_analysis(caption_string[21:-1])
-
-
-
-    sentiment_string = ''.join(str(e) for e in sentiment)
-
-    image_caption = caption_string[21:-3]
-
-    percentage =sentiment_string[34: -14]
-
-    tone =  sentiment_string[11:-31]
-
-    output = 'the image is of, ' + image_caption + ", the overall tone of the image's content is - "+ tone + " with a approx "+ percentage + "% score"
-    return output
-
-
-input = gr.inputs.Image(
-    label="Upload your Image and wait for 8-12 seconds!", type='pil', optional=False)
-output = gr.outputs.Textbox(label="Captions")
-
-title = "Content-Mod API with Sentiment-Analysis UI "
-
-interface = gr.Interface(
-    fn=predict,
-    inputs=input,
-    outputs=output,
-    title=title,
-
-)
-interface.launch()
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/dnl_head.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/dnl_head.py
deleted file mode 100644
index 333280c5947066fd3c7ebcfe302a0e7ad65480d5..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/dnl_head.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import torch
-from annotator.uniformer.mmcv.cnn import NonLocal2d
-from torch import nn
-
-from ..builder import HEADS
-from .fcn_head import FCNHead
-
-
-class DisentangledNonLocal2d(NonLocal2d):
-    """Disentangled Non-Local Blocks.
-
-    Args:
-        temperature (float): Temperature to adjust attention. Default: 0.05
-    """
-
-    def __init__(self, *arg, temperature, **kwargs):
-        super().__init__(*arg, **kwargs)
-        self.temperature = temperature
-        self.conv_mask = nn.Conv2d(self.in_channels, 1, kernel_size=1)
-
-    def embedded_gaussian(self, theta_x, phi_x):
-        """Embedded gaussian with temperature."""
-
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        pairwise_weight = torch.matmul(theta_x, phi_x)
-        if self.use_scale:
-            # theta_x.shape[-1] is `self.inter_channels`
-            pairwise_weight /= theta_x.shape[-1]**0.5
-        pairwise_weight /= self.temperature
-        pairwise_weight = pairwise_weight.softmax(dim=-1)
-        return pairwise_weight
-
-    def forward(self, x):
-        # x: [N, C, H, W]
-        n = x.size(0)
-
-        # g_x: [N, HxW, C]
-        g_x = self.g(x).view(n, self.inter_channels, -1)
-        g_x = g_x.permute(0, 2, 1)
-
-        # theta_x: [N, HxW, C], phi_x: [N, C, HxW]
-        if self.mode == 'gaussian':
-            theta_x = x.view(n, self.in_channels, -1)
-            theta_x = theta_x.permute(0, 2, 1)
-            if self.sub_sample:
-                phi_x = self.phi(x).view(n, self.in_channels, -1)
-            else:
-                phi_x = x.view(n, self.in_channels, -1)
-        elif self.mode == 'concatenation':
-            theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
-            phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
-        else:
-            theta_x = self.theta(x).view(n, self.inter_channels, -1)
-            theta_x = theta_x.permute(0, 2, 1)
-            phi_x = self.phi(x).view(n, self.inter_channels, -1)
-
-        # subtract mean
-        theta_x -= theta_x.mean(dim=-2, keepdim=True)
-        phi_x -= phi_x.mean(dim=-1, keepdim=True)
-
-        pairwise_func = getattr(self, self.mode)
-        # pairwise_weight: [N, HxW, HxW]
-        pairwise_weight = pairwise_func(theta_x, phi_x)
-
-        # y: [N, HxW, C]
-        y = torch.matmul(pairwise_weight, g_x)
-        # y: [N, C, H, W]
-        y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
-                                                    *x.size()[2:])
-
-        # unary_mask: [N, 1, HxW]
-        unary_mask = self.conv_mask(x)
-        unary_mask = unary_mask.view(n, 1, -1)
-        unary_mask = unary_mask.softmax(dim=-1)
-        # unary_x: [N, 1, C]
-        unary_x = torch.matmul(unary_mask, g_x)
-        # unary_x: [N, C, 1, 1]
-        unary_x = unary_x.permute(0, 2, 1).contiguous().reshape(
-            n, self.inter_channels, 1, 1)
-
-        output = x + self.conv_out(y + unary_x)
-
-        return output
-
-
-@HEADS.register_module()
-class DNLHead(FCNHead):
-    """Disentangled Non-Local Neural Networks.
-
-    This head is the implementation of `DNLNet
-    <https://arxiv.org/abs/2006.06668>`_.
-
-    Args:
-        reduction (int): Reduction factor of projection transform. Default: 2.
-        use_scale (bool): Whether to scale pairwise_weight by
-            sqrt(1/inter_channels). Default: False.
-        mode (str): The nonlocal mode. Options are 'embedded_gaussian',
-            'dot_product'. Default: 'embedded_gaussian.'.
-        temperature (float): Temperature to adjust attention. Default: 0.05
-    """
-
-    def __init__(self,
-                 reduction=2,
-                 use_scale=True,
-                 mode='embedded_gaussian',
-                 temperature=0.05,
-                 **kwargs):
-        super(DNLHead, self).__init__(num_convs=2, **kwargs)
-        self.reduction = reduction
-        self.use_scale = use_scale
-        self.mode = mode
-        self.temperature = temperature
-        self.dnl_block = DisentangledNonLocal2d(
-            in_channels=self.channels,
-            reduction=self.reduction,
-            use_scale=self.use_scale,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            mode=self.mode,
-            temperature=self.temperature)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        output = self.convs[0](x)
-        output = self.dnl_block(output)
-        output = self.convs[1](output)
-        if self.concat_input:
-            output = self.conv_cat(torch.cat([x, output], dim=1))
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/ema_head.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/ema_head.py
deleted file mode 100644
index 12267cb40569d2b5a4a2955a6dc2671377ff5e0a..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/ema_head.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import math
-
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from ..builder import HEADS
-from .decode_head import BaseDecodeHead
-
-
-def reduce_mean(tensor):
-    """Reduce mean when distributed training."""
-    if not (dist.is_available() and dist.is_initialized()):
-        return tensor
-    tensor = tensor.clone()
-    dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
-    return tensor
-
-
-class EMAModule(nn.Module):
-    """Expectation Maximization Attention Module used in EMANet.
-
-    Args:
-        channels (int): Channels of the whole module.
-        num_bases (int): Number of bases.
-        num_stages (int): Number of the EM iterations.
-    """
-
-    def __init__(self, channels, num_bases, num_stages, momentum):
-        super(EMAModule, self).__init__()
-        assert num_stages >= 1, 'num_stages must be at least 1!'
-        self.num_bases = num_bases
-        self.num_stages = num_stages
-        self.momentum = momentum
-
-        bases = torch.zeros(1, channels, self.num_bases)
-        bases.normal_(0, math.sqrt(2. / self.num_bases))
-        # [1, channels, num_bases]
-        bases = F.normalize(bases, dim=1, p=2)
-        self.register_buffer('bases', bases)
-
-    def forward(self, feats):
-        """Forward function."""
-        batch_size, channels, height, width = feats.size()
-        # [batch_size, channels, height*width]
-        feats = feats.view(batch_size, channels, height * width)
-        # [batch_size, channels, num_bases]
-        bases = self.bases.repeat(batch_size, 1, 1)
-
-        with torch.no_grad():
-            for i in range(self.num_stages):
-                # [batch_size, height*width, num_bases]
-                attention = torch.einsum('bcn,bck->bnk', feats, bases)
-                attention = F.softmax(attention, dim=2)
-                # l1 norm
-                attention_normed = F.normalize(attention, dim=1, p=1)
-                # [batch_size, channels, num_bases]
-                bases = torch.einsum('bcn,bnk->bck', feats, attention_normed)
-                # l2 norm
-                bases = F.normalize(bases, dim=1, p=2)
-
-        feats_recon = torch.einsum('bck,bnk->bcn', bases, attention)
-        feats_recon = feats_recon.view(batch_size, channels, height, width)
-
-        if self.training:
-            bases = bases.mean(dim=0, keepdim=True)
-            bases = reduce_mean(bases)
-            # l2 norm
-            bases = F.normalize(bases, dim=1, p=2)
-            self.bases = (1 -
-                          self.momentum) * self.bases + self.momentum * bases
-
-        return feats_recon
-
-
-@HEADS.register_module()
-class EMAHead(BaseDecodeHead):
-    """Expectation Maximization Attention Networks for Semantic Segmentation.
-
-    This head is the implementation of `EMANet
-    <https://arxiv.org/abs/1907.13426>`_.
-
-    Args:
-        ema_channels (int): EMA module channels
-        num_bases (int): Number of bases.
-        num_stages (int): Number of the EM iterations.
-        concat_input (bool): Whether concat the input and output of convs
-            before classification layer. Default: True
-        momentum (float): Momentum to update the base. Default: 0.1.
-    """
-
-    def __init__(self,
-                 ema_channels,
-                 num_bases,
-                 num_stages,
-                 concat_input=True,
-                 momentum=0.1,
-                 **kwargs):
-        super(EMAHead, self).__init__(**kwargs)
-        self.ema_channels = ema_channels
-        self.num_bases = num_bases
-        self.num_stages = num_stages
-        self.concat_input = concat_input
-        self.momentum = momentum
-        self.ema_module = EMAModule(self.ema_channels, self.num_bases,
-                                    self.num_stages, self.momentum)
-
-        self.ema_in_conv = ConvModule(
-            self.in_channels,
-            self.ema_channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        # project (0, inf) -> (-inf, inf)
-        self.ema_mid_conv = ConvModule(
-            self.ema_channels,
-            self.ema_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=None,
-            act_cfg=None)
-        for param in self.ema_mid_conv.parameters():
-            param.requires_grad = False
-
-        self.ema_out_conv = ConvModule(
-            self.ema_channels,
-            self.ema_channels,
-            1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=None)
-        self.bottleneck = ConvModule(
-            self.ema_channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        if self.concat_input:
-            self.conv_cat = ConvModule(
-                self.in_channels + self.channels,
-                self.channels,
-                kernel_size=3,
-                padding=1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        x = self._transform_inputs(inputs)
-        feats = self.ema_in_conv(x)
-        identity = feats
-        feats = self.ema_mid_conv(feats)
-        recon = self.ema_module(feats)
-        recon = F.relu(recon, inplace=True)
-        recon = self.ema_out_conv(recon)
-        output = F.relu(identity + recon, inplace=True)
-        output = self.bottleneck(output)
-        if self.concat_input:
-            output = self.conv_cat(torch.cat([x, output], dim=1))
-        output = self.cls_seg(output)
-        return output
diff --git a/spaces/taesiri/DeticChatGPT/tools/convert-thirdparty-pretrained-model-to-d2.py b/spaces/taesiri/DeticChatGPT/tools/convert-thirdparty-pretrained-model-to-d2.py
deleted file mode 100644
index ec042b8ce48d193b40fd1e6311b2cc4b0c4e4086..0000000000000000000000000000000000000000
--- a/spaces/taesiri/DeticChatGPT/tools/convert-thirdparty-pretrained-model-to-d2.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import argparse
-import pickle 
-import torch
-
-"""
-Usage:
-
-cd DETIC_ROOT/models/
-wget https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/resnet50_miil_21k.pth
-python ../tools/convert-thirdparty-pretrained-model-to-d2.py --path resnet50_miil_21k.pth
-
-wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth
-python ../tools/convert-thirdparty-pretrained-model-to-d2.py --path swin_base_patch4_window7_224_22k.pth
-
-"""
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--path', default='')
-    args = parser.parse_args()
-
-    print('Loading', args.path)
-    model = torch.load(args.path, map_location="cpu")
-    # import pdb; pdb.set_trace()
-    if 'model' in model:
-        model = model['model']
-    if 'state_dict' in model:
-        model = model['state_dict']
-    ret = {
-        "model": model, 
-        "__author__": "third_party", 
-        "matching_heuristics": True
-    }
-    out_path = args.path.replace('.pth', '.pkl')
-    print('Saving to', out_path)
-    pickle.dump(ret, open(out_path, "wb"))
diff --git a/spaces/tdeshane/artists-of-data-science-chainlit/notebooks/app.py b/spaces/tdeshane/artists-of-data-science-chainlit/notebooks/app.py
deleted file mode 100644
index 88906866db807daeb49372342591ac440ffc08bb..0000000000000000000000000000000000000000
--- a/spaces/tdeshane/artists-of-data-science-chainlit/notebooks/app.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import os
-import openai
-from langchain.chat_models import ChatOpenAI
-import chainlit as cl
-
-from langchain.chains import LLMChain, SequentialChain 
-from langchain.prompts import PromptTemplate
-
-from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
-from llama_index.callbacks.base import CallbackManager
-from llama_index import (
-    LLMPredictor,
-    ServiceContext,
-    StorageContext,
-    load_index_from_storage,
-)
-
-from llama_index import (
-    SimpleDirectoryReader,
-    LLMPredictor,
-    ServiceContext,
-    get_response_synthesizer,
-)
-from llama_index.indices.document_summary import DocumentSummaryIndex
-from llama_index.llms import OpenAI
-
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-
-from llama_index import SimpleDirectoryReader
-
-required_exts = [".txt"]
-
-reader = SimpleDirectoryReader(
-    input_dir="data",
-    required_exts=required_exts,
-    recursive=True,
-    filename_as_id=True
-)
-
-docs = reader.load_data()
-print(f"Loaded {len(docs)} docs")
-
-chatgpt = OpenAI(temperature=0.1, model="gpt-3.5-turbo")
-service_context = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)
-
-response_synthesizer = get_response_synthesizer(
-    response_mode="tree_summarize", use_async=True
-)
-
-doc_summary_index = DocumentSummaryIndex.from_documents(
-    docs,
-    service_context=service_context,
-    response_synthesizer=response_synthesizer,
-)
-
-#print(doc_summary_index.get_document_summary('data/final-hh.txt'))
-
-@cl.on_chat_start
-async def factory():
-    llm_predictor = LLMPredictor(
-        llm=ChatOpenAI(
-            temperature=0,
-            model_name="gpt-3.5-turbo",
-            streaming=True,
-        ),
-    )
-    service_context = ServiceContext.from_defaults(
-        llm_predictor=llm_predictor,
-        chunk_size=512,
-        callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
-    )
-
-    
-    generation_type = None
-
-    twitter_expert_prompt = "You are a twitter expert. Create a twitter thread based on this: "
-    
-    summary_text = doc_summary_index.get_document_summary('data/final-hh.txt')
-    summary_text_without_newlines = summary_text.strip()
-    prompt = twitter_expert_prompt + summary_text
-    
-    twitter_prompt = twitter_expert_prompt + summary_text_without_newlines
-    twitter_prompt_template = PromptTemplate.from_template(
-        twitter_expert_prompt
-    )
-    
-    while generation_type == None:
-        generation_type = await cl.AskUserMessage(
-            content="Twitter or blog?", timeout=15
-        ).send()
-        
-        if generation_type == 'twitter':
-            print("twitter generation...")
-        elif generation_type == 'blog':
-            print("blog generation...")
-        
-    await cl.Message(content=f"generating {generation_type['content']} thread").send()
-    
-    
-    
-    prompt = 'write a tweet about' + summary_text
-    print(f'prompt: {prompt}')
-    
-    
-    llm_twitter_expert = ChatOpenAI(model='gpt-3.5-turbo', temperature=0.7)
-    tweet_thread_chain = LLMChain(llm=llm_twitter_expert,prompt=twitter_prompt_template) 
-    #tweet_thread = tweet_thread_chain.run(prompt=prompt)
-    #await cl.Message(content=tweet_thread).send()
-    
-    the_final_prompt = f"write a twitter thread about {summary_text_without_newlines}"
-    
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "You are a twitter expert."},
-            #{"role": "assistant", "content": "{summary_text_without_newlines}"},
-            {"role": "user", "content": the_final_prompt},
-        ]
-    )
-    await cl.Message(content=response['choices'][0]['message']['content']).send()
-    
-    
-    
-    #await cl.Message(content=f"generating {doc_summary_index.get_document_summary('data/final-hh.txt')}").send()
- 
-
-
-
diff --git a/spaces/temp-late/rhyme-ai/rhyme_with_ai/__init__.py b/spaces/temp-late/rhyme-ai/rhyme_with_ai/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Hitman Absolution Highly Compressed 10mb.md b/spaces/terfces0erbo/CollegeProjectV2/Hitman Absolution Highly Compressed 10mb.md
deleted file mode 100644
index 179853d256a22ef34d3cbde608f010ead2cebd19..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Hitman Absolution Highly Compressed 10mb.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>hitman absolution highly compressed 10mb</h2><br /><p><b><b>Download File</b> &#10031;&#10031;&#10031; <a href="https://bytlly.com/2uGkr3">https://bytlly.com/2uGkr3</a></b></p><br /><br />
-
-Hitman Absolution For PC Highly Compressed Only 22Mb By Mr DJ Repack. GHitman Absolution pc game is a stealth game developed by IO ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Kisi Kisi Soal Ulangan Harian Kelas 3 Sd Semester 1  Temp [TOP].md b/spaces/terfces0erbo/CollegeProjectV2/Kisi Kisi Soal Ulangan Harian Kelas 3 Sd Semester 1  Temp [TOP].md
deleted file mode 100644
index 43f4cfb808860e9f3deadbc9f5b67e4659c40cd1..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Kisi Kisi Soal Ulangan Harian Kelas 3 Sd Semester 1  Temp [TOP].md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<h2>kisi kisi soal ulangan harian kelas 3 sd semester 1 | temp</h2><br /><p><b><b>DOWNLOAD</b> &#10022; <a href="https://bytlly.com/2uGkDu">https://bytlly.com/2uGkDu</a></b></p><br /><br />
-<br />
-no 2202
-
-padi, puluhan ribu pelajar yang meminta izin jaminan kelas tiga di mata pelajar. Jaminan tersebut dilakukan oleh tokoh pelajar-pelajar.
-
-Kepala KNSPI Keluar Irlandia, beberapa waktu lalu menyatakan bahwa pengobatan kesuburan muda dan ini menjadi tahun-tahun terakhir. “Kita akan menanyakan pelajar-pelajar oleh permohonan keluar mengenai jaminan kelas tiga,” ujar Jokowi dalam diskusi dengan sejumlah ormas mahasiswa di sektor perekonomian (PSK) di Senayan, Jakarta, Selasa (20/3/2017).
-
-Namun, hingga hari ini belum ada tindakan tertulis dalam pasal. Pasalnya, kata Jokowi, terkadang kalau soal ini masih diupayakan dengan seberat fee pajak per tahun. Ia juga berjanji akan memberikan izin agar dua sesektor muda dapat jaminan kelas tiga.
-
-“Ini tidak kedua jaminan kelas tiga. Kita akan memberikan sesuai kewajibannya,” jelas Jokowi.
-
-Baca juga: Kecelakaan yang Menimbulkan Mati di Ciputat
-
-Dalam diskusi dengan petani lantan Ciputat, Bebicara bahkan menggunakan perkara soal pengobatan kesuburan muda, kata yang menjabat Direktur Pusat Studi Transmisi Fitnah, Tineke Noman, hari ini.
-
-Tineke mengatakan sudah memulai proses untuk mendeklarasikan kelas tiga secara legal.
-
-Meski demikian, kata Tineke, di 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Edit Music Like a Pro with Melodyne Mac.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Edit Music Like a Pro with Melodyne Mac.md
deleted file mode 100644
index 347948cf6fd0566a408f1c53486db18a46eda18d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Edit Music Like a Pro with Melodyne Mac.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-
-<h1>Melodyne Mac: A Powerful Tool for Music Editing</h1>
-<p>Melodyne is a software application that allows you to edit audio files in a musical way. You can change the pitch, timing, volume, and timbre of individual notes or entire phrases, as well as create harmonies, transpose melodies, and adjust the tempo. Melodyne is compatible with Mac OS X and works as a standalone program or as a plug-in for various digital audio workstations (DAWs).</p>
-<h2>melodyne mac crack</h2><br /><p><b><b>Download Zip</b> &#9881;&#9881;&#9881; <a href="https://urlcod.com/2uK71b">https://urlcod.com/2uK71b</a></b></p><br /><br />
-<p>One of the most impressive features of Melodyne is its ability to analyze and edit polyphonic audio, such as chords, piano, or guitar tracks. Unlike other pitch correction software that can only process monophonic audio, Melodyne can separate the individual notes within a complex sound and let you manipulate them independently. This gives you unprecedented control over your music and allows you to correct mistakes, enhance performances, or create new arrangements.</p>
-<p>Melodyne also offers a unique interface that displays the audio as blobs on a musical grid. You can use your mouse or keyboard to move, stretch, or reshape the blobs, which represent the pitch, duration, and amplitude of each note. You can also use various tools to edit the pitch curve, vibrato, formant, or dynamics of the notes. Melodyne can also detect the key and scale of your music and help you adjust it to match other tracks or genres.</p>
-<p>Melodyne is widely used by professional musicians, producers, and engineers for various purposes, such as vocal tuning, remixing, sampling, sound design, and restoration. Melodyne can handle any type of audio file, from vocals and instruments to speech and noise. Melodyne can also integrate with other software applications via MIDI, ReWire, or ARA (Audio Random Access).</p>
-<p></p>
-<p>If you are looking for a powerful and versatile tool for music editing on your Mac computer, you should definitely check out Melodyne. You can download a free trial version from the official website and see for yourself what Melodyne can do for your music.</p>
-
-<h2>How to Use Melodyne on Your Mac</h2>
-<p>There are two ways to use Melodyne on your Mac: as a standalone application or as a plug-in for your DAW. The standalone version allows you to edit audio files directly from your computer, while the plug-in version lets you integrate Melodyne with your DAW and edit audio tracks within your project. Both versions have the same features and functionality, so you can choose the one that suits your workflow best.</p>
-<p>To use Melodyne as a standalone application, you need to launch it from your Applications folder and open the audio file you want to edit. You can drag and drop the file from your Finder or use the File menu to browse for it. Once the file is loaded, you will see the blobs representing the notes on the Melodyne grid. You can then use the tools and menus to edit the audio as you wish. You can also import multiple audio files and edit them simultaneously or separately. When you are done, you can export the edited file as a new audio file or overwrite the original one.</p>
-<p>To use Melodyne as a plug-in for your DAW, you need to insert it on an audio track or a bus in your DAW. You can do this by using the plug-in menu or by dragging and dropping Melodyne from your plug-in list. Once Melodyne is inserted, you will see a small window with a Transfer button. You need to press this button and play back the audio track or bus that you want to edit. This will transfer the audio to Melodyne and display the blobs on the grid. You can then edit the audio in Melodyne as usual. The changes you make in Melodyne will be reflected in your DAW in real time. You can also use Melodyne's MIDI output to control other instruments or effects in your DAW.</p> ddb901b051<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Achieve Your Instagram Goals with 100k Followers APK.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Achieve Your Instagram Goals with 100k Followers APK.md
deleted file mode 100644
index 2d3bba216efa7841a0d431411810a41a6676a13a..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Achieve Your Instagram Goals with 100k Followers APK.md	
+++ /dev/null
@@ -1,111 +0,0 @@
-
-<h1>How to Get 100k Followers on Instagram with APK</h1>
-<p>Instagram is one of the most popular social media platforms in the world, with over 1 billion monthly active users. Having a large and loyal following on Instagram can help you grow your brand, business, or personal influence. But how can you get 100k followers on Instagram fast and easy? One of the ways that some people try is using an APK file that promises to give you free and unlimited followers. But what is an APK file and how does it work? Is it safe and effective to use an APK file for Instagram followers? And are there any alternatives to get 100k followers on Instagram without an APK file? In this article, we will answer these questions and more.</p>
- <h2>What is an APK and why do you need it?</h2>
-<p>An APK is an Android application package file that contains all the files and code needed to run an app on an Android device. You can download and install APK files from various sources online, such as websites, blogs, forums, or app stores. Some APK files are official versions of apps that are available on Google Play Store, while others are modified or hacked versions of apps that offer extra features or benefits.</p>
-<h2>100k followers on instagram apk</h2><br /><p><b><b>Download Zip</b> &rArr; <a href="https://bltlly.com/2uOgwt">https://bltlly.com/2uOgwt</a></b></p><br /><br />
- <h3>An APK is an Android application package file</h3>
-<p>An APK file is similar to an EXE file on Windows or a DMG file on Mac. It is a compressed file that contains all the components of an app, such as images, sounds, code, resources, etc. When you download an APK file from a source online, you need to enable the "Unknown sources" option in your device settings to allow the installation of apps from outside the Google Play Store. Then you can open the APK file and install it on your device like any other app.</p>
- <h3>An APK can help you get more followers on Instagram</h3>
-<p>Some APK files are designed to help you get more followers on Instagram by using various methods. For example, some APK files may automatically follow other users for you and hope that they will follow you back. Some may generate fake or bot followers for your account. Some may give you coins or credits that you can use to exchange for real followers from other users. Some may even hack into other users' accounts and make them follow you without their consent.</p>
- <h2>How to download and install an APK for Instagram followers</h2>
-<p>If you want to try using an APK file for Instagram followers, here are the steps that you need to follow:</p>
- <h3>Find a reliable and safe APK source</h3>
-<p>The first step is to find a trustworthy and secure source where you can download the APK file that you want. There are many websites and blogs that claim to offer free and working APK files for Instagram followers, but not all of them are legit or safe. Some may contain malware or viruses that can harm your device or steal your personal information. Some may not work at all or have outdated versions of apps. Some may even ask you to complete surveys or download other apps before giving you access to the APK file.</p>
-<p>To avoid these risks, you should do some research and check the reviews and ratings of the APK source that you choose. You can also use antivirus software or online tools to scan the APK file for any potential threats before downloading it.</p>
-<p>How to get 100k followers on instagram with instarat<br />
-Instagram followers mod apk: hack free followers<br />
-100k instagram followers in 30 days with follower gallery<br />
-Instarat github: download and install the best tool for instagram followers<br />
-Follower gallery: the best instagram followers mod apk<br />
-How to use instarat to increase your instagram followers for free<br />
-Instagram followers hack: get 100k followers with mod apk<br />
-Instarat review: is it safe and legit to use?<br />
-Follower gallery features: real instagram followers, coins, and likes<br />
-How to get 100k followers on instagram without human verification<br />
-Instagram followers mod apk download: latest version 2023<br />
-Instarat vs follower gallery: which one is better for instagram growth?<br />
-How to get unlimited coins in follower gallery mod apk<br />
-Instarat tutorial: how to set up and run the script for instagram followers<br />
-Follower gallery tips and tricks: how to get more coins and likes<br />
-How to get 100k followers on instagram in one day with mod apk<br />
-Instagram followers mod apk no password: how to use it safely<br />
-Instarat support: how to contact the developer and get help<br />
-Follower gallery hack: how to get free coins and likes without survey<br />
-How to get 100k followers on instagram with no ban or shadowban<br />
-Instagram followers mod apk for android: how to install and use it<br />
-Instarat update: what's new in the latest version of the script<br />
-Follower gallery app: how to download and install it on your device<br />
-How to get 100k followers on instagram organically with mod apk<br />
-Instagram followers mod apk for ios: how to get it and use it<br />
-Instarat testimonials: what users say about the tool for instagram followers<br />
-Follower gallery online: how to use the web version of the app<br />
-How to get 100k followers on instagram fast and easy with mod apk<br />
-Instagram followers mod apk unlimited coins: how to get it and use it<br />
-Instarat alternatives: other tools for increasing your instagram followers<br />
-Follower gallery pro: how to upgrade and get more features<br />
-How to get 100k followers on instagram without following back with mod apk<br />
-Instagram followers mod apk 2023: how to get the latest version of the app<br />
-Instarat benefits: how it can help you grow your instagram account<br />
-Follower gallery login: how to sign up and access your account<br />
-How to get 100k followers on instagram for free with mod apk<br />
-Instagram followers mod apk reddit: what people say about it on reddit<br />
-Instarat problems: how to fix common issues and errors with the tool<br />
-Follower gallery coupon code: how to get discounts and offers on the app<br />
-How to get 100k followers on instagram with verification badge with mod apk</p>
- <h3>Follow the instructions to download and install the APK</h3>
-<p>The next step is to follow the instructions given by the APK source to download and install the APK file on your device. Usually, you will need to click on a download link or button and wait for the file to be downloaded. Then you will need to open the file and tap on "Install" to start the installation process. You may need to grant some permissions or access to the app during the installation. After the installation is complete, you will see an icon of the app on your device screen.</p>
- <h3>Launch the APK and start getting followers</h3>
-<p>The final step is to launch the APK app and start using it to get more followers on Instagram. Depending on the type of APK app that you use, you may need to log in with your Instagram account or create a new account within the app. Then you will see different options or features that the app offers, such as follow, like, comment, share, etc. You can use these options to interact with other users or get coins or credits that you can use to get more followers. Some apps may also have a dashboard or a progress bar that shows you how many followers you have gained or how close you are to reaching your goal.</p>
- <h2>What are the benefits and risks of using an APK for Instagram followers</h2>
-<p>Using an APK file for Instagram followers may seem like a quick and easy way to boost your popularity on the platform, but it also comes with some benefits and risks that you should be aware of before trying it.</p>
- <h3>Benefits: fast, easy, and free way to boost your popularity</h3>
-<p>One of the main benefits of using an APK file for Instagram followers is that it can help you get a large number of followers in a short period of time. You don't need to spend hours creating content, engaging with your audience, or using other strategies to grow your account organically. You just need to download and install an app and let it do the work for you. Moreover, most of these apps are free or very cheap, so you don't need to spend any money to get more followers.</p>
- <h3>Risks: security issues, account suspension, fake or bot followers</h3>
-<p>However, using an APK file for Instagram followers also comes with some serious risks that can outweigh the benefits. One of the biggest risks is that you may compromise your security and privacy by downloading and installing an app from an unknown source. The app may contain malware or spyware that can infect your device or steal your personal information. The app may also ask for too many permissions or access to your Instagram account, which can give it control over your account or expose it to hackers. Another risk is that you may violate Instagram's terms of service and community guidelines by using an app that manipulates your follower count. This can result in your account being suspended or banned by Instagram, which means that you will lose all your followers and content. Moreover, most of the followers that you get from these apps are fake or bot accounts that have no real value or engagement. They may unfollow you after a while or spam your account with unwanted messages or comments. They may also damage your reputation and credibility as an influencer or a brand.</p>
- <h2>How to get 100k followers on Instagram without an APK</h2>
-<p>If you want to get 100k followers on Instagram without using an APK file, there are some alternative ways that are more safe and effective. These ways may take more time and effort, but they will help you build a genuine and loyal following that will benefit you in the long run.</p>
- <h3>Create high-quality and engaging content</h3>
-<p>The most important thing that you need to do to get more followers on Instagram is to create high-quality and engaging content that attracts and retains your audience's attention. You need to post consistently and frequently, at least once a day or more. You need to use high-resolution images and videos that showcase your niche, personality, or brand. You need to use captions that tell a story, convey a message, or ask a question. You need to use stories, reels, IGTV, live videos, and other features that Instagram offers to diversify your content and reach more people.</p>
- <h3>Use hashtags, captions, and stories effectively</h3>
-<p>Another thing that you need to do to get more followers on Instagram is to use hashtags, captions, and stories effectively. Hashtags are keywords that help users find your content based on their interests or topics. You should use relevant and popular hashtags that match your niche and target audience. You should also avoid using too many or too few hashtags or spammy hashtags that can hurt your visibility or reputation. Captions are the text that accompanies your images or videos and adds context or value to your content. You should use captions that are catchy, informative, or entertaining. You should also use captions that encourage your audience to engage with your content, such as by asking questions, giving tips, or inviting feedback. Stories are the short and ephemeral content that disappears after 24 hours and appears at the top of your followers' feed. You should use stories to show behind-the-scenes, sneak peeks, polls, quizzes, or other interactive content that keeps your audience interested and connected.</p>
- <h3>Interact with your audience and other users</h3>
-<p>The final thing that you need to do to get more followers on Instagram is to interact with your audience and other users on the platform. You need to respond to comments, messages, and mentions that you receive from your followers or potential followers. You need to show appreciation, gratitude, and support to your fans and customers. You need to follow, like, comment, and share content from other users in your niche or industry. You need to collaborate, network, or partner with other influencers or brands that have similar or complementary goals or audiences. You need to join or create communities, groups, or challenges that relate to your niche or topic.</p>
- <h2>Conclusion</h2>
-<p>Getting 100k followers on Instagram is not an easy task, but it is not impossible either. You can use an APK file for Instagram followers to get a quick and easy boost, but you also need to be aware of the risks and limitations of this method. Alternatively, you can use organic and authentic ways to grow your account, such as creating high-quality and engaging content, using hashtags, captions, and stories effectively, and interacting with your audience and other users. These ways may take more time and effort, but they will help you build a genuine and loyal following that will benefit you in the long run.</p>
- <h4>Summary of the main points</h4>
-<ul>
-<li>An APK is an Android application package file that can help you get more followers on Instagram by using various methods.</li>
-<li>An APK file for Instagram followers can be downloaded and installed from various sources online, but you need to be careful of the security and privacy risks involved.</li>
-<li>An APK file for Instagram followers can give you a fast, easy, and free way to boost your popularity, but it can also expose you to account suspension, fake or bot followers, and reputation damage.</li>
-<li>You can get 100k followers on Instagram without an APK file by creating high-quality and engaging content, using hashtags, captions, and stories effectively, and interacting with your audience and other users.</li>
-</ul>
- <h4>Call to action</h4>
-<p>If you found this article helpful and informative, please share it with your friends and family who are also interested in getting more followers on Instagram. Also, don't forget to follow us on Instagram for more tips and tricks on how to grow your account organically. Thank you for reading!</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Q: What is the best APK file for Instagram followers?</b></li>
-<li>A: There is no definitive answer to this question, as different APK files may have different features, benefits, and drawbacks. However, some of the factors that you should consider when choosing an APK file for Instagram followers are the source reliability and safety, the app functionality and compatibility, the user reviews and ratings, and the app updates and support.</li>
-<li><b>Q: How many followers can I get from an APK file for Instagram?</b></li>
-<li>A: The number of followers that you can get from an APK file for Instagram depends on the type of app that you use and how often you use it. Some apps may have a limit on how many followers you can get per day or per account. Some apps may have a variable rate of follower growth depending on the demand or supply of other users. Some apps may have a consistent rate of follower growth regardless of any factors.</li>
-<li><b>Q: How long does it take to get 100k followers on Instagram with an APK file?</b></li>
-<li>A: The time that it takes to get 100k followers on Instagram with an APK file varies depending on the app that you use and how frequently you use it. Some apps may help you get 100k followers in a matter of days or weeks. Some apps may take months or years to reach that goal. Some apps may never help you get 100k followers at all.</li>
-<li><b>Q: How can I tell if my followers are real or fake?</b></li>
-<li>A: There are some signs that can help you identify if your followers are real or fake. Some of these signs are:</li>
-<ul>
-<li>Real followers have profile pictures, bio, posts, stories, and followers. Fake followers have blank or generic profiles, no posts, stories, or followers, or a high ratio of following to followers.</li>
-<li>Real followers engage with your content, such as by liking, commenting, sharing, or saving. Fake followers have little or no engagement, or leave spammy or irrelevant comments.</li>
-<li>Real followers have consistent and organic growth over time. Fake followers have sudden and unnatural spikes or drops in their numbers.</li>
-</ul>
-<li><b>Q: How can I avoid getting banned by Instagram for using an APK file for Instagram followers?</b></li>
-<li>A: There is no guarantee that you can avoid getting banned by Instagram for using an APK file for Instagram followers, as this is against their terms of service and community guidelines. However, some of the things that you can do to reduce the risk of getting banned are:</li>
-<ul>
-<li>Use a reputable and secure APK source and app that does not contain malware or viruses.</li>
-<li>Use a VPN or proxy service to hide your IP address and location.</li>
-<li>Use a burner or secondary account to test the app before using it on your main account.</li>
-<li>Use the app sparingly and moderately, and do not abuse its features or functions.</li>
-<li>Do not use multiple apps or services that offer the same or similar results.</li>
-</ul>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Chess by Jeroen Carolus Download APK and Play Online or Offline.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Chess by Jeroen Carolus Download APK and Play Online or Offline.md
deleted file mode 100644
index 5f3d175b0162dd83b71f783c863167eed3db5c5a..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Chess by Jeroen Carolus Download APK and Play Online or Offline.md	
+++ /dev/null
@@ -1,96 +0,0 @@
-<br />
-<h1>Chess by Jeroen Carolus: A Review of the Android App</h1>
-<p>Chess is one of the oldest and most popular board games in the world. It is a game of strategy, logic, and skill that can be enjoyed by people of all ages and backgrounds. Chess can also help improve one's memory, concentration, creativity, and problem-solving abilities.</p>
-<p>There are many ways to play chess, but one of the most convenient and accessible ones is to use a chess app on your smartphone or tablet. There are hundreds of chess apps available on the market, but not all of them are equally good. Some have better graphics, some have more features, some have more users, and some have more bugs.</p>
-<h2>chess jeroen carolus apk</h2><br /><p><b><b>DOWNLOAD</b> &rArr;&rArr;&rArr; <a href="https://bltlly.com/2uOmqT">https://bltlly.com/2uOmqT</a></b></p><br /><br />
-<p>One of the chess apps that has been around for a long time and has received positive feedback from many users is Chess by Jeroen Carolus. This app allows you to play chess online or offline, solve puzzles, learn from tutorials, show the board on your TV via Chromecast, and even play Chess 960, a variant of chess that randomizes the initial position of the pieces.</p>
-<p>In this review, we will take a closer look at Chess by Jeroen Carolus and evaluate its gameplay, interface, pros, cons, and overall quality. We will also answer some frequently asked questions about the app and provide you with our final verdict. Whether you are a beginner or a grandmaster, a casual or a serious player, a fan or a critic of chess, this review will help you decide if Chess by Jeroen Carolus is the right app for you.</p>
- <h2>Gameplay and Interface</h2>
-<p>The main feature of Chess by Jeroen Carolus is that it allows you to play chess online or offline. You can choose to play against another human player or against one of the many AI-powered chess bots that have different strengths and styles. You can also choose the time limit for each move, from one minute to five days. You can also chat with your opponent during the game or after it ends.</p>
-<p>If you want to improve your chess skills or learn new concepts, you can also use Chess by Jeroen Carolus to solve puzzles and watch tutorials. The app has more than 150,000 puzzles that range from easy to hard and cover various themes such as checkmate, tactics, endgame, strategy, etc. The app also has video lessons that explain the rules, principles, openings, middlegame, endgame, etc. of chess in a clear and engaging way.</p>
-<p>Another feature that makes Chess by Jeroen Carolus stand out from other chess apps is that it supports Chromecast. This means that you can show the board on your TV screen while playing on your phone or tablet. This can make your chess experience more immersive and enjoyable, especially if you have a large TV or if you want to play with your friends or family.</p>
-<p>Chess by Jeroen Carolus also supports Chess 960, also known as Fischer Random Chess. This is a variant of chess that was invented by Bobby Fischer, one of the greatest chess players of all time. In Chess 960, the initial position of the pieces on the back rank is randomized according to certain rules. This creates <p>A unique feature of Chess by Jeroen Carolus is that it supports Chess 960, also known as Fischer Random Chess. This is a variant of chess that was invented by Bobby Fischer, one of the greatest chess players of all time. In Chess 960, the initial position of the pieces on the back rank is randomized according to certain rules. This creates 960 unique possible starting positions, hence the name.</p>
-<p>Chess 960 is a fun and challenging way to play chess, as it eliminates the reliance on memorized opening lines and forces the players to think creatively and strategically from the very first move. It also reduces the chances of getting into a boring or drawish position, as every game is different and unpredictable.</p>
-<p>The rules of Chess 960 are mostly the same as classical chess, except for the initial setup and the rules for castling. The starting position for Chess 960 must meet certain requirements:</p>
-<p>chess by jeroen carolus app download<br />
-chess 960 jeroen carolus apk<br />
-chess puzzles by jeroen carolus mod apk<br />
-chess online jeroen carolus android<br />
-chess board jeroen carolus apk free<br />
-chess 9.2.1 jeroen carolus apk xapk<br />
-chess practice jeroen carolus offline apk<br />
-chess game jeroen carolus chromecast apk<br />
-chess duck chess jeroen carolus apk<br />
-chess opening database jeroen carolus apk<br />
-chess by jeroen carolus apk latest version<br />
-chess 9.0.1 jeroen carolus mod apk download<br />
-chess play online jeroen carolus apk<br />
-chess set up board jeroen carolus apk<br />
-chess save and edit games jeroen carolus apk<br />
-chess navigate history jeroen carolus apk<br />
-chess import and export jeroen carolus apk<br />
-chess touch screen interface jeroen carolus apk<br />
-chess support and version info jeroen carolus apk<br />
-chess by jeroen carolus app for android archive<br />
-chess jwtc android chess jeroen carolus apk<br />
-chess by jeroen carolus getjar com apk<br />
-chess by jeroen carolus happymod com apk<br />
-chess by jeroen carolus apkcombo com apk<br />
-chess by jeroen carolus google play id jwtc android chess apk<br />
-chess by jeroen carolus developer website jwtc nl apk<br />
-chess by jeroen carolus category board games apk<br />
-chess by jeroen carolus update oct 13 2022 apk<br />
-chess by jeroen carolus installs 500000+ app apks<br />
-chess by jeroen carolus old versions board advertisement apk<br />
-chess by jeroen carolus description old versions board advertisement latest version version 9.2.1 (174) update oct 13, 2022 developer Jeroen Carolus category board google play id jwtc.android.chess installs 500,000+ app apks Chess APK Xadrez APK Ajedrez APK Шахматы APK Scacchi APK Schach APK 国际象棋 APK Chess GAME Play Chess Online or Offline Solve Puzzles Show the Board via Chromecast on your TV Supports Chess 960 Download APK (6 MB) Old Versions Chess 9.2.1 APK XAPK Oct 13, 2022 · Android 5.0+ Chess 9.1.3 APK XAPK Sep 8, 2022 · Android 5.0+ Chess 9.1.2 APK XAPK Aug 22, 2022 · Android 5.0+ See more Trending Searches stumble guys fgo roblox clash of clans bus simulator 2023 nikke the goddess of victory clash royale mini militia sigma nulls brawl clash mini brawl stars carx street pokemon unite madfut 23 fortnite wheelie life 2 gta 5 dragon ball legends See more" as a long-tail keyword.</p>
-<ul>
-<li>The bishops must be placed on opposite-color squares.</li>
-<li>The king must be placed on a square between the two rooks.</li>
-<li>Black's pieces are placed opposite White's pieces.</li>
-</ul>
-<p>Castling is slightly different in Chess 960, since the rooks can start the game on several squares. Here are the guidelines:</p>
-<ul>
-<li>After you castle in Chess 960, the castled position will look the same as in regular chess.</li>
-<li>Just as in regular chess, the king and rook must not have moved before castling.</li>
-<li>The squares between the king and the rook that is castling must be vacant, and the squares the king crosses must not be in check.</li>
-</ul>
-<p>If you want to learn more about Chess 960, you can check out some online resources such as [What is Chess960? | Fischer Random Chess Explained](^2^) or [Chess Variants: Chess960 - The Full Guide](^4^).</p>
- <h2>Pros and Cons</h2>
-<p>Chess by Jeroen Carolus is a well-designed and feature-rich chess app that has many advantages over other chess apps. Here are some of them:</p>
-<ul>
-<li>It is free to download and use, with no ads or in-app purchases.</li>
-<li>It has a simple and intuitive user interface that is easy to navigate and customize.</li>
-<li>It has a variety of modes and options to suit different preferences and skill levels.</li>
-<li>It supports online and offline play, with a large and active community of players from around the world.</li>
-<li>It has a built-in engine that can analyze your moves and suggest improvements.</li>
-<li>It has a Chromecast feature that allows you to display the board on your TV screen for a better viewing experience.</li>
-<li>It supports Chess 960, a fun and challenging variant of chess that randomizes the starting position.</li>
-</ul>
-<p>However, Chess by Jeroen Carolus also has some drawbacks or limitations that you should be aware of before downloading it. Here are some of them:</p>
-<ul>
-<li>It requires an internet connection for online play and some features such as puzzles and tutorials.</li>
-<li>It does not have a rating system or a leaderboard to track your progress and compare your performance with other players.</li>
-<li>It does not have a voice chat or a video chat feature to communicate with your opponent during or after the game.</li>
-<li>It does not have a tournament mode or a challenge mode to compete with other players for prizes or rewards.</li>
-<li>It does not have a coaching mode or a mentor mode to get guidance from an expert or a friend.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Chess by Jeroen Carolus is a great chess app that offers a lot of features and options for playing chess online or offline, solving puzzles, learning from tutorials, showing the board on your TV via Chromecast, and playing Chess 960. It is free, easy to use, and fun to play. It is suitable for anyone who loves chess or wants to learn more about it.</p>
-<p>However, Chess by Jeroen Carolus also has some limitations that may affect your enjoyment or satisfaction with the app. It does not have a rating system, a leaderboard, a voice chat, a video chat, a tournament mode, a challenge mode, a coaching mode, or a mentor mode. It also requires an internet connection for some features. It may not be ideal for those who want more advanced or interactive features or those who have limited data or battery life.</p>
-<p>If you are interested in trying out Chess by Jeroen Carolus, you can download it from [Google Play](^5^) for free. The app has more than 500,000 downloads and an average rating of 4. <p>5 stars out of more than 10,000 reviews. It is compatible with Android devices that have Android 4.4 or higher.</p>
- <h2>FAQs</h2>
-<p>Here are some common questions and answers about Chess by Jeroen Carolus:</p>
-<ol>
-<li><b>How do I cast the board to my TV?</b></li>
-<p>To cast the board to your TV, you need to have a Chromecast device connected to your TV and a Wi-Fi network that both your phone and your Chromecast are connected to. Then, open the app and tap on the cast icon on the top right corner of the screen. Select your Chromecast device from the list and wait for the connection to be established. You should see the board on your TV screen. To stop casting, tap on the cast icon again and select "Disconnect".</p>
-<li><b>How do I play Chess 960?</b></li>
-<p>To play Chess 960, you need to select the "Chess 960" option from the main menu. Then, you can choose to play online or offline, against a human or a bot, and with or without a time limit. The app will generate a random starting position for each game. You can also see the castling rights for each side by tapping on the king or rook icons on the bottom of the screen.</p>
-<li><b>How do I solve puzzles?</b></li>
-<p>To solve puzzles, you need to select the "Puzzles" option from the main menu. Then, you can choose from different categories and difficulty levels of puzzles. The app will show you a chess position and ask you to find the best move or sequence of moves. You can also see hints, solutions, and explanations by tapping on the buttons on the bottom of the screen.</p>
-<li><b>How do I watch tutorials?</b></li>
-<p>To watch tutorials, you need to select the "Tutorials" option from the main menu. Then, you can choose from different topics and subtopics of chess lessons. The app will show you a video that explains the topic in detail, with examples and diagrams. You can also pause, rewind, fast-forward, or skip the video by using the controls on the bottom of the screen.</p>
-<li><b>How do I customize the board, pieces, and settings?</b></li>
-<p>To customize the board, pieces, and settings, you need to select the "Settings" option from the main menu. Then, you can choose from different options such as board color, piece style, sound effects, notifications, language, etc. You can also adjust the brightness and contrast of the board by using the sliders on the top of the screen.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Como baixar o vdeo do Chaves dizendo ai que burro da zero pra ele em MP4.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Como baixar o vdeo do Chaves dizendo ai que burro da zero pra ele em MP4.md
deleted file mode 100644
index 2225b7ccb1d9ad7dc7345d3b5bfa9afe2bb4ce08..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Como baixar o vdeo do Chaves dizendo ai que burro da zero pra ele em MP4.md	
+++ /dev/null
@@ -1,146 +0,0 @@
-
-<h1>Ai que burrinho da zero pra ele download: What is it and how to get it?</h1>
- <p>If you are a fan of memes, comedy, or Brazilian culture, you might have heard of <strong>ai que burrinho da zero pra ele</strong>, a hilarious sound clip that has gone viral on the internet. But what is it exactly and how can you download it for your own enjoyment? In this article, we will explain everything you need to know about this funny phenomenon and how to get it for free.</p>
-<h2>ai que burrinho da zero pra ele download</h2><br /><p><b><b>DOWNLOAD</b> &ndash;&ndash;&ndash; <a href="https://bltlly.com/2uOpon">https://bltlly.com/2uOpon</a></b></p><br /><br />
- <h2>Introduction</h2>
- <p><strong>Ai que burro dá zero pra ele</strong> is a Portuguese phrase that means "How stupid, give him a zero" in English. It is a famous quote from a popular Mexican sitcom called <em>El Chavo del Ocho</em>, which was dubbed in Portuguese and aired in Brazil as <em>Chaves</em>. The show follows the adventures of a poor boy named Chaves and his friends who live in a low-income neighborhood. The phrase is uttered by Professor Girafales, Chaves' teacher, when he is grading the students' exams and finds a ridiculous answer from Quico, one of Chaves' classmates.</p>
- <p>The phrase became popular and funny because of its exaggerated tone, its catchy rhythm, and its applicability to many situations where someone does something stupid or wrong. Many people use it as a joke or a meme to mock or tease others, or to express their frustration or disbelief. Some people also use it as a sound effect or a background music for their own videos or memes, adding humor and creativity to their content.</p>
- <p>Downloading <strong>ai que burro dá zero pra ele</strong> has many benefits. You can use it as a ringtone or a notification sound for your phone, as a prank or a joke for your friends or family, as a way to spice up your conversations or messages, or as a source of inspiration for your own videos or memes. Plus, it is free and easy to get, as we will show you in the next section.</p>
- <h2>How to download ai que burro dá zero pra ele MP3 for free</h2>
- <p>There are many websites that offer <strong>ai que burro dá zero pra ele</strong> as an MP3 file that you can listen and download for free. Here are two of them that we recommend:</p>
- <h3>Using musiky.xyz</h3>
- <p>Musiky.xyz is a website that allows you to search and download MP3 files from various sources. You can use it to find and get <strong>ai que burro dá zero pra ele</strong> by following these steps:</p>
-<p>ai que burro da zero pra ele mp3 gratis<br />
-baixar ai que burro da zero pra ele chaves<br />
-ai que burro da zero pra ele meme video<br />
-como fazer o som do ai que burro da zero pra ele<br />
-ai que burro da zero pra ele remix musica<br />
-assistir chaves ai que burro da zero pra ele<br />
-ai que burro da zero pra ele original episodio<br />
-ai que burro da zero pra ele toque para celular<br />
-ai que burro da zero pra ele em ingles traducao<br />
-ai que burro da zero pra ele dublado em portugues<br />
-ai que burro da zero pra ele versao minecraft<br />
-ai que burro da zero pra ele no tiktok compilacao<br />
-ai que burro da zero pra ele gif animado<br />
-origem do bordao ai que burro da zero pra ele<br />
-ai que burro da zero pra ele whatsapp audio<br />
-ai que burro da zero pra ele youtube video<br />
-significado de ai que burro da zero pra ele<br />
-ai que burro da zero pra ele em espanhol como se diz<br />
-ai que burro da zero pra ele png imagem<br />
-ai que burro da zero pra ele wallpaper hd<br />
-ai que burro da zero pra ele roblox id codigo<br />
-ai que burro da zero pra ele kpop bts reacao<br />
-ai que burro da zero pra ele fortnite dancinha<br />
-ai que burro da zero pra ele letra e cifra<br />
-ai que burro da zero pra ele karaoke online<br />
-ai que burro da zero pra ele no violao tutorial<br />
-ai que burro da zero pra ele na escola historia<br />
-ai que burro da zero pra ele no among us jogo<br />
-ai que burro da zero pra ele no free fire meme<br />
-ai que burro da zero pra ele no gta san andreas mod<br />
-ai que burro da zero pra ele no piano notas musicais<br />
-ai que burro da zero pra ele no teclado emoji<br />
-ai que burro da zero pra ele no twitter hashtag<br />
-ai que burro da zero pra ele no instagram filtro<br />
-ai que burro da zero pra ele no facebook sticker<br />
-ai que burro da zero pra ele na bateria ritmo<br />
-ai que burro da zero pra ele na guitarra solo<br />
-ai que burro da zero pra ele na flauta doce partitura<br />
-ai que burro da zero pra ele na voz do google tradutor<br />
-ai que burro da zero pra ele na voz do silvio santos imitacao<br />
-ai que burro da zero pra ele na voz do bob esponja dublagem<br />
-ai que burro da zero pra ele na voz do naruto anime<br />
-ai que burro da zero pra ele na voz do homer simpson desenho<br />
-ai que burro da zero pra ele na voz do lula politica<br />
-ai que burro da zero pra ele na voz do neymar futebol<br />
-ai que burro da zero pra ele na voz do faustao domingo<br />
-ai que burro da zero pra ele na voz do galvao bueno narracao<br />
-ai que burro da zero pra ele na voz do caio ribeiro comentarista</p>
- <ol>
-<li>Go to <a href="">musiky.xyz</a> and type <strong>ai que burro dá zero pra ele</strong> in the search box.</li>
-<li>Click on the result that says <strong>Ai Que Burro Dá Zero Pra Ele - Chaves - MP3 Download</strong>.</li>
-<li>Click on the play button to listen to the MP3 file, or click on the download button to save it to your device.</li>
-</ol>
- <p>Some of the advantages of using this website are:</p>
- <ul>
-<li>It is fast and easy to use.</li>
-<li>It offers high-quality MP3 files.</li>
-<li>It does not require registration or payment.</li>
-</ul>
- <p>Some of the disadvantages of using this website are:</p>
- <ul>
-<li>It may contain ads or pop-ups that can be annoying or harmful.</li>
-<li>It may not have all the versions or variations of <strong>ai que burro dá zero pra ele</strong> that you are looking for.</li>
-<li>It may not be legal or ethical to download MP3 files without the permission of the original creators or owners.</li>
-</ul>
- <h3>Using 4shared.com</h3>
- <p>4shared.com is a website that allows you to upload and download files of various types, including videos. You can use it to find and get <strong>ai que burro dá zero pra ele</strong> as a video file that you can watch and download for free by following these steps:</p>
- <ol>
-<li>Go to <a href="">4shared.com</a> and type <strong>ai que burro dá zero pra ele</strong> in the search box.</li>
-<li>Click on the result that says <strong>Ai que burro da zero pra ele.mp4</strong>.</li>
-<li>Click on the play button to watch the video, or click on the download button to save it to your device.</li>
-</ol>
- <p>Some of the advantages of using this website are:</p>
- <ul>
-<li>It is fast and easy to use.</li>
-<li>It offers high-quality video files.</li>
-<li>It does not require registration or payment.</li>
-</ul>
- <p>Some of the disadvantages of using this website are:</p>
- <ul>
-<li>It may contain ads or pop-ups that can be annoying or harmful.</li>
-<li>It may not have all the versions or variations of <strong>ai que burro dá zero pra ele</strong> that you are looking for.</li>
-<li>It may not be legal or ethical to download video files without the permission of the original creators or owners.</li>
-</ul>
- <h2>How to use ai que burro dá zero pra ele in your own videos or memes</h2>
- <p>If you want to have some fun and show your creativity, you can use <strong>ai que burro dá zero pra ele</strong> in your own videos or memes. Here are some examples and tips for doing so:</p>
- <h3>Some examples of videos or memes that use ai que burro dá zero pra ele</h3>
- <p>You can find many videos or memes that use <strong>ai que burro dá zero pra ele</strong> on YouTube, TikTok, Instagram, Facebook, Twitter, Reddit, and other social media platforms. Here are some of them that we think are funny and clever:</p>
- <table border="1">
-<tr><th>Title</th><th>Description</th><th>Link</th></tr>
-<tr><td>Ai que burro dá zero pra ele - Among Us Edition</td><td>A video that shows a scene from the popular game Among Us, where one of the players is an impostor and kills another player in front of everyone. The sound clip is played when the impostor is caught and voted out.</td><td><a href="">https://www.youtube.com/watch?v=0QXnQ6w7f9k</a></td></tr>
-<tr><td>Ai que burro dá zero pra ele - Harry Potter Edition</td><td>A meme that shows a scene from the movie Harry Potter and the Philosopher's Stone, where Harry Potter is sorted into Gryffindor house by the Sorting Hat. The sound clip is played when Professor Snape, who hates Harry Potter, reacts with displeasure.</td><td><a href="">https://www.instagram.com/p/CG9L8cYgZxq/</a></td></tr>
-<tr><td>Ai que burro dá zero pra ele - Avengers Edition</td><td>A video that shows a scene from the movie Avengers: Endgame, where Thanos snaps his fingers and wipes out half of the universe. The sound clip is played when Iron Man, who sacrifices himself to reverse Thanos' snap, dies in the arms of Spider-Man.</td><td><a href="">https://www.tiktok.com/@marvelbrasil/video/6895836647398085894</a></td></tr>
-<tr><td>Ai que burro dá zero pra ele - Donald Trump Edition</td><td>A meme that shows a scene from the 2020 US presidential election, where Donald Trump loses to Joe Biden. The sound clip is played when Trump's supporters react with anger and disbelief.</td><td><a href="">https://www.reddit.com/r/brasil/comments/jq0q1e/ai_que_burro_d%C3%A1_zero_pra_ele_donald_trump_edition/</a></td></tr>
-</table>
- <h3>Some tips and tools for creating your own videos or memes with ai que burro dá zero pra ele</h3>
- <p>If you want to create your own videos or memes with <strong>ai que burro dá zero pra ele</strong>, here are some tips and tools that you can use:</p>
- <ul>
-<li>Choose a scene or a situation that is funny, relatable, or relevant to your audience.</li>
-<li>Make sure that the sound clip matches the timing and the mood of the scene or the situation.</li>
-<li>Use a video or a meme editor that allows you to add sound clips, text, images, stickers, filters, and other effects to your content. Some examples are InShot, FilmoraGo, Meme Generator, and Kapwing.</li>
-<li>Share your video or meme on social media platforms that support sound clips, such as YouTube, TikTok, Instagram, Facebook, Twitter, and Reddit.</li>
-<li>Use hashtags, captions, tags, and keywords that describe your video or meme and attract more viewers or followers.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, <strong>ai que burro dá zero pra ele</strong> is a hilarious sound clip that originated from a Mexican sitcom dubbed in Portuguese and aired in Brazil. It has become popular and funny because of its exaggerated tone, its catchy rhythm, and its applicability to many situations where someone does something stupid or wrong. You can download it for free from various websites as an MP3 or a video file, and use it in your own videos or memes for fun and creativity. We hope that this article has helped you understand and enjoy this phenomenon better.</p>
- <p>Now that you know everything about <strong>ai que burro dá zero pra ele</strong>, why not give it a try and see how it can make you laugh or impress your friends? Go ahead and download it now!</p>
- <h2>FAQs</h2>
- <h3>What does ai que burro dá zero pra ele mean in English?</h3>
- <p>Ai que burro dá zero pra ele means "How stupid, give him a zero" in English. It is a phrase that expresses mockery or disappointment towards someone who does something stupid or wrong.</p>
- <h3>Who is the original creator of ai que burro dá zero pra ele?</h3>
- <p>The original creator of ai que burro dá zero pra ele is Roberto Gómez Bolaños, a Mexican actor, comedian, writer, director, and producer who created and starred in the sitcom El Chavo del Ocho. He played the role of Professor Girafales, who uttered the phrase in one of the episodes.</p>
- <h3>Is ai que burro dá zero pra ele legal or illegal to download?</h3>
- <p>Ai que burro dá zero pra ele is not illegal to download for personal use, as long as you do not distribute it or use it for commercial purposes without the permission of the original creators or owners. However, downloading it may violate some terms of service or policies of some websites or platforms that host it. Therefore, you should always check the source and the legality of the file before downloading it.</p>
- <h3>How can I make ai que burro dá zero pra ele my ringtone or notification sound?</h3>
- <p>You can make ai que burro dá zero pra ele your ringtone or notification sound by following these steps:</p>
- <ol>
-<li>Download the MP3 file of ai que burro dá zero pra ele from one of the websites mentioned above.</li>
-<li>Transfer the MP3 file to your phone using a USB cable, Bluetooth, Wi-Fi, or cloud service.</li>
-<li>Go to your phone settings and select sound and vibration.</li>
-<li>Select ringtone or notification sound and browse for the MP3 file that you transferred.</li>
-<li <li>Select the MP3 file and confirm your choice.</li>
-</ol>
- <p>Now you have ai que burro dá zero pra ele as your ringtone or notification sound. Enjoy!</p>
- <h3>Where can I find more funny sounds or videos like ai que burro dá zero pra ele?</h3>
- <p>If you like ai que burro dá zero pra ele and want to find more funny sounds or videos like it, you can try these options:</p>
- <ul>
-<li>Search for other phrases or quotes from El Chavo del Ocho or Chaves, such as "Foi sem querer querendo", "Isso, isso, isso", "Ninguém tem paciência comigo", and "Tá bom, mas não se irrite".</li>
-<li>Search for other sounds or videos that are popular or viral in Brazil, such as "Eita Giovana", "Já acabou, Jéssica?", "Turn down for what", and "Que tiro foi esse?".</li>
-<li>Search for other sounds or videos that are funny or catchy in general, such as "Rickroll", "Badger Badger Badger", "Nyan Cat", and "Keyboard Cat".</li>
-</ul>
- <p>You can use the same websites or platforms that we mentioned above to search and download these sounds or videos.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Creator Studio Instagram Tips and Tricks for Creating Engaging Videos.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Creator Studio Instagram Tips and Tricks for Creating Engaging Videos.md
deleted file mode 100644
index 7881330d551cb1455e445f5a936a1b79b6a3bf4f..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Creator Studio Instagram Tips and Tricks for Creating Engaging Videos.md	
+++ /dev/null
@@ -1,86 +0,0 @@
-<br />
-<h1>How to Download Creator Studio Instagram and Why You Should Use It</h1>
-<p>If you are an Instagram creator who wants to manage your content, grow your audience, and monetize your passion, you might be interested in using <strong>Creator Studio</strong>. Creator Studio is a platform that lets you manage your Instagram posts, stories, IGTV videos, Reels, insights, messages, and comments from one place. You can also access a variety of tools and resources to help you create engaging and professional content for your followers.</p>
-<h2>download creator studio instagram</h2><br /><p><b><b>Download Zip</b> &middot;&middot;&middot;&middot;&middot; <a href="https://bltlly.com/2uOjCo">https://bltlly.com/2uOjCo</a></b></p><br /><br />
-<p>In this article, we will show you how to download Creator Studio Instagram and how to use it effectively. We will also share some tips and tricks to help you get the most out of this platform. Let's get started!</p>
- <h2>How to Access Creator Studio for Instagram</h2>
-<p>To use Creator Studio for Instagram, you need to have an Instagram business account or a creator account. You also need to link your Instagram account to a Facebook page. Here are the steps to do that:</p>
-<ol>
-<li><strong>Link your Instagram account to a Facebook page</strong>. If you don't have a Facebook page, you can create one from the Facebook app or website. Then, go to your Instagram settings and tap on "Account". Tap on "Switch to Professional Account" and follow the instructions. Choose "Creator" or "Business" as your account type and select a category for your profile. Then, tap on "Continue" and connect your Facebook page.</li>
-<li><strong>Go to the Creator Studio website</strong> (<a href="(^1^)">https://business.facebook.com/creatorstudio/home?mode=instagram</a>) and log in with your Facebook account.</li>
-<li><strong>Switch to the Instagram tab</strong> at the top of the page and select your account from the drop-down menu.</li>
-</ol>
-<p>Congratulations! You have successfully accessed Creator Studio for Instagram. Now, let's see how to use it.</p>
-<p>How to download creator studio instagram app<br />
-Download creator studio instagram for windows 10<br />
-Download creator studio instagram videos to computer<br />
-Download creator studio instagram stories and reels<br />
-Download creator studio instagram analytics and insights<br />
-Download creator studio instagram guide and tutorial<br />
-Download creator studio instagram apk for android<br />
-Download creator studio instagram for macbook pro<br />
-Download creator studio instagram content calendar<br />
-Download creator studio instagram monetization tools<br />
-Benefits of download creator studio instagram for business<br />
-Download creator studio instagram vs later app<br />
-Download creator studio instagram vs buffer app<br />
-Download creator studio instagram vs hootsuite app<br />
-Download creator studio instagram vs planoly app<br />
-Download creator studio instagram best practices and tips<br />
-Download creator studio instagram review and feedback<br />
-Download creator studio instagram alternatives and competitors<br />
-Download creator studio instagram features and updates<br />
-Download creator studio instagram support and help<br />
-How to download creator studio instagram on iphone<br />
-How to download creator studio instagram on ipad<br />
-How to download creator studio instagram on chromebook<br />
-How to download creator studio instagram on linux<br />
-How to download creator studio instagram on pc<br />
-How to download creator studio instagram on laptop<br />
-How to download creator studio instagram on desktop<br />
-How to download creator studio instagram on mac<br />
-How to download creator studio instagram on android<br />
-How to download creator studio instagram on tablet<br />
-How to use download creator studio instagram for marketing<br />
-How to use download creator studio instagram for branding<br />
-How to use download creator studio instagram for engagement<br />
-How to use download creator studio instagram for growth<br />
-How to use download creator studio instagram for scheduling<br />
-How to use download creator studio instagram for editing<br />
-How to use download creator studio instagram for management<br />
-How to use download creator studio instagram for optimization<br />
-How to use download creator studio instagram for collaboration<br />
-How to use download creator studio instagram for influencer marketing</p>
- <h2>How to Use Creator Studio for Instagram</h2>
-<p>Creator Studio has a lot of features and functions that can help you manage your Instagram content and grow your presence. Here are some of the things you can do with it:</p>
-<ol>
-<li><strong>Explore the dashboard</strong>. The dashboard shows you an overview of your activity, audience, and earnings on Instagram. You can see metrics such as impressions, reach, engagement, followers, video views, IGTV views, Reels views, earnings, and more. You can also filter the data by time range, content type, or account.</li>
-<li><strong>Schedule and publish posts and stories</strong>. You can create new posts and stories from Creator Studio by clicking on the "Create Post" button at the top left corner. You can upload photos or videos from your computer or choose from your existing content library. You can also edit your photos or videos with filters, stickers, text, and other tools. You can add captions, hashtags, location, and tags to your posts and stories. You can also choose to share your posts and stories to your Facebook page as well. To schedule your posts and stories, you can select a date and time from the calendar and click on "Schedule". To publish them immediately, you can click on "Publish".</li>
-<li><strong>Manage your messages and comments</strong>. You can view and reply to your direct messages and comments from Creator Studio. You can also filter your messages and comments by status, account, or keyword. You can also mark your messages and comments as read, unread, done, or spam. You can also delete or report inappropriate messages and comments.</li>
-<li><strong>Analyze your performance and insights</strong>. You can access detailed analytics and insights for your Instagram content and audience from Creator Studio. You can see how your posts, stories, IGTV videos, Reels, and live videos are performing in terms of reach, impressions, engagement, views, saves, shares, and more. You can also see how your audience is growing and interacting with your content. You can see metrics such as age, gender, location, online times, top sources, and more. You can also compare your performance across different time periods or content types.</li>
-</ol>
-<p>As you can see, Creator Studio is a powerful tool that can help you manage your Instagram content and grow your presence. But that's not all. Here are some tips and tricks to help you use it even better.</p>
- <h2>Tips and Tricks for Using Creator Studio for Instagram</h2>
-<p>Here are some tips and tricks to help you use Creator Studio for Instagram more effectively:</p>
-<ul>
-<li><strong>Use the content library to organize your posts and stories</strong>. The content library is a feature that lets you view, edit, delete, or download your existing posts and stories from Creator Studio. You can also filter your content by status, account, or keyword. You can also create collections to group your content by theme or category.</li>
-<li><strong>Use the monetization tools to earn money from your content</strong>. If you are eligible for monetization on Instagram, you can use Creator Studio to access various tools and resources to help you earn money from your content. You can apply for the Instagram Partner Program to show ads on your IGTV videos or Reels. You can also use badges to receive tips from your viewers during live videos. You can also use branded content tools to disclose paid partnerships with brands on your posts and stories.</li>
-<li><strong>Use the sound collection to add music and sound effects to your videos</strong>. The sound collection is a feature that lets you access thousands of free music tracks and sound effects that you can use on your videos. You can browse by genre, mood, length, or keyword. You can also preview and download the sounds before using them.</li>
-</ul>
-<p>These are some of the tips and tricks that can help you use Creator Studio for Instagram more effectively. Now that you know how to download Creator Studio Instagram and how to use it, why not give it a try?</p>
- <h2>Conclusion</h2>
-<p>Creator Studio is a platform that lets you manage your Instagram content from one place. You can schedule and publish posts and stories, manage your messages and comments, analyze your performance and insights, and access various tools and resources to help you create engaging and professional content for your followers. You can also monetize your content with ads, badges, or branded content.</p>
-<p>To use Creator Studio for Instagram, you need to have an Instagram business account or a creator account linked to a Facebook page. Then, you need to go to the Creator Studio website (<a href="">https://business.facebook.com/creatorstudio/home?mode=instagram</a>) and log in with your Facebook account. Then, you need to switch to the Instagram tab and select your account.</p>
-<p>We hope this article has helped you learn how to download Creator Studio Instagram and how to use it effectively. If you have any questions or feedback, feel free to leave a comment below. Happy creating!</p>
- <h2>FAQs</h2>
-<h3>Q1: What are the requirements for using Creator Studio for Instagram?</h3>
-<p>A1: To use Creator Studio for Instagram, you need to have an Instagram business account or a creator account linked to a Facebook page. You also need to have a computer with an internet connection.</p>
- <h3>Q2: Can I use Creator Studio for multiple Instagram accounts?</h3>
-<p>A2: Yes, you can use Creator Studio for multiple Instagram accounts as long as they are linked to a Facebook page. You can switch between different accounts from the drop-down menu at the top of the page.</p> <h3>Q3: Can I edit or delete posts and stories from Creator Studio?</h3>
-<p>A3: Yes, you can edit or delete posts and stories from Creator Studio. To edit a post or a story, you need to go to the content library and select the post or story you want to edit. Then, you need to click on the "Edit" button and make the changes you want. To delete a post or a story, you need to go to the content library and select the post or story you want to delete. Then, you need to click on the "Delete" button and confirm your action.</p>
- <h3>Q4: Can I upload IGTV videos and Reels from Creator Studio?</h3>
-<p>A4: Yes, you can upload IGTV videos and Reels from Creator Studio. To upload an IGTV video, you need to click on the "Create Post" button and select "IGTV". Then, you need to upload your video file from your computer or choose from your content library. You can also add a title, a description, a cover image, and hashtags to your IGTV video. You can also choose to share your IGTV video to your feed or your Facebook page. To upload a Reel, you need to click on the "Create Post" button and select "Reel". Then, you need to upload your video file from your computer or choose from your content library. You can also add music, effects, stickers, text, and hashtags to your Reel. You can also choose to share your Reel to your feed or your Facebook page.</p>
- <h3>Q5: How can I contact support or give feedback for Creator Studio?</h3>
-<p>A5: If you need help or have any suggestions for Creator Studio, you can contact support or give feedback from the Creator Studio website. To contact support, you need to click on the question mark icon at the top right corner of the page and select "Help Center". Then, you need to browse the topics or search for your issue. You can also click on the "Contact Us" button and fill out a form with your details and query. To give feedback, you need to click on the question mark icon at the top right corner of the page and select "Send Feedback". Then, you need to write your feedback and attach a screenshot if needed. You can also rate your experience with Creator Studio.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Creation Master 13 Download Pc.md b/spaces/tioseFevbu/cartoon-converter/scripts/Creation Master 13 Download Pc.md
deleted file mode 100644
index 949b1b70098c974bba5bcff6aca737cd5e861d5d..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Creation Master 13 Download Pc.md	
+++ /dev/null
@@ -1,122 +0,0 @@
-
-<h1>Creation Master 13 Download PC: How to Edit FIFA 13 Database Easily</h1>
-<p>If you are a fan of FIFA 13, you might have wondered how to customize and modify the game to your liking. Maybe you want to change the appearance of your favorite players, or create your own teams and leagues, or update the game with the latest transfers and ratings. Well, there is a tool that can help you do all that and more. It is called <strong>Creation Master 13</strong>, and it is a powerful and easy-to-use program that allows you to edit the FIFA 13 database in various ways. In this article, we will show you how to download and install Creation Master 13 on your PC, how to use it to edit FIFA 13 database, and some tips and tricks for using it effectively.</p>
- <h2>What is Creation Master 13?</h2>
-<p>Creation Master 13 is a program developed by Henok Molla that lets you edit many aspects of FIFA 13, such as players, teams, leagues, tournaments, faces, kits, boots, balls, and more. It is a modified version of the original Creation Master by Rinaldo, which was discontinued after FIFA 12. Creation Master 13 is compatible with Windows XP, Vista, 7, and 8, and requires .NET Framework 4.0 or higher to run.</p>
-<h2>Creation Master 13 Download Pc</h2><br /><p><b><b>Download File</b> &#127379; <a href="https://urlcod.com/2uHvVu">https://urlcod.com/2uHvVu</a></b></p><br /><br />
- <h3>Features of Creation Master 13</h3>
-<p>Some of the features of Creation Master 13 are:</p>
-<ul>
-<li>It allows you to open and save FIFA 13 database files (.db) in a user-friendly interface.</li>
-<li>It allows you to edit players' attributes, skills, traits, positions, contracts, loans, injuries, suspensions, nationalities, appearances, accessories, etc.</li>
-<li>It allows you to edit teams' names, logos, kits, stadiums, banners, flags, managers, formations, tactics, etc.</li>
-<li>It allows you to edit leagues' names, logos, structures, rules, cups, etc.</li>
-<li>It allows you to edit tournaments' names, logos, structures, rules, prizes, etc.</li>
-<li>It allows you to add new faces (.rx3) for players using an external tool called Face Converter.</li>
-<li>It allows you to add new kits (.rx3) for teams using an external tool called Kit Converter.</li>
-<li>It allows you to add new boots (.rx3) for players using an external tool called Boot Converter.</li>
-<li>It allows you to add new balls (.rx3) for tournaments using an external tool called Ball Converter.</li>
-<li>It allows you to import and export data from Excel files (.xls) for easier editing.</li>
-<li>It allows you to backup and restore FIFA 13 database files (.db) in case of errors or issues.</li>
-<li>It allows you to update FIFA 13 database files (.db) with the latest transfers and ratings from online sources.</li>
-</ul>
- <h3>Benefits of using Creation Master 13</h3>
-<p>Some of the benefits of using Creation Master 13 are:</p>
-<ul>
-<li>It gives - It gives you more control and creativity over your FIFA 13 game experience. - It allows you to create your own custom content and share it with other FIFA 13 players online. - It enhances the realism and diversity of FIFA 13 by adding new faces, kits, boots, balls, etc. - It keeps your FIFA 13 game updated with the latest changes in the real world of football. <h2>How to download and install Creation Master 13 on PC</h2>
-<p>Downloading and installing Creation Master 13 on your PC is very easy and straightforward. Just follow these steps:</p>
- <h3>Requirements for running Creation Master 13</h3>
-<p>Before you download and install Creation Master 13, make sure you have the following requirements on your PC:</p>
-<ul>
-<li>A copy of FIFA 13 installed on your PC.</li>
-<li>.NET Framework 4.0 or higher installed on your PC. You can download it from <a href="">here</a>.</li>
-<li>A ZIP file extractor program such as WinRAR or 7-Zip installed on your PC. You can download them from <a href="">here</a> or <a href="">here</a>.</li>
-<li>An internet connection for downloading Creation Master 13 and updating FIFA 13 database.</li>
-</ul>
- <h3>Steps to download and install Creation Master 13</h3>
-<p>Once you have the requirements ready, follow these steps to download and install Creation Master 13:</p>
-<ol>
-<li>Go to the official website of Creation Master 13 at <a href="">http://www.fifa-master.com/cm-13/</a>.</li>
-<li>Click on the "Download" button and save the ZIP file to your PC.</li>
-<li>Extract the ZIP file to a folder of your choice using WinRAR or 7-Zip.</li>
-<li>Open the folder and double-click on the "CreationMaster13.exe" file to run the program.</li>
-<li>Click on "Yes" if a User Account Control window pops up.</li>
-<li>Click on "OK" if a welcome message appears.</li>
-<li>You have successfully installed Creation Master 13 on your PC. You can now use it to edit FIFA 13 database.</li>
-</ol>
- <h2>How to use Creation Master 13 to edit FIFA 13 database</h2>
-<p>Now that you have downloaded and installed Creation Master 13 on your PC, you can start using it to edit FIFA 13 database in various ways. Here are some of the main functions of Creation Master 13 and how to use them:</p>
- <h3>How to open and save FIFA 13 database with Creation Master 13</h3>
-<p>To open and save FIFA 13 database with Creation Master 13, follow these steps:</p>
-<ol>
-<li>Run Creation Master 13 by double-clicking on the "CreationMaster13.exe" file.</li>
-<li>Click on "File" in the menu bar and select "Open" from the drop-down menu.</li>
-<li>Select the folder where your FIFA 13 game is installed. By default, it is "C:\Program Files (x86)\Origin Games\FIFA 13". Click on "OK".</li>
-<li>Wait for a few seconds while Creation Master 13 loads the FIFA 13 database files (.db).</li>
-<li>You have successfully opened FIFA 13 database with Creation Master 13. You can now edit it as you wish.</li>
-<li>To save your changes, click on "File" in the menu bar and select "Save" from the drop-down menu.</li>
-<li>Wait for a few seconds while Creation Master 13 saves the FIFA 13 database files (.db).</li>
-<li>You have successfully saved FIFA 13 database with Creation Master 13. You can now close the program or continue editing.</li>
-</ol>
- <h3>How to edit players, teams, leagues, and tournaments with Creation Master 13</h3>
-<p>To edit players, teams, leagues, and tournaments with Creation Master 13, follow these steps:</p>
-<p></p> - To edit players, teams, leagues, and tournaments with Creation Master 13, follow these steps:     - Run Creation Master 13 by double-clicking on the "CreationMaster13.exe" file.     - Click on "File" in the menu bar and select "Open" from the drop-down menu.     - Select the folder where your FIFA 13 game is installed. By default, it is "C:\Program Files (x86)\Origin Games\FIFA 13". Click on "OK".     - Wait for a few seconds while Creation Master 13 loads the FIFA 13 database files (.db).     - To edit players, click on the "Players" tab in the left panel. You will see a list of all the players in FIFA 13. You can search for a specific player by name, ID, or nationality using the search box at the top. You can also filter the players by league, team, position, or rating using the drop-down menus at the bottom.     - To edit a player, double-click on his name or click on the "Edit" button at the bottom. A new window will open with various tabs for editing different aspects of the player. You can edit his attributes, skills, traits, positions, contracts, loans, injuries, suspensions, nationalities, appearances, accessories, etc. You can also assign him a new face (.rx3) file using the "Face" tab. To save your changes, click on the "Save" button at the bottom of the window.     - To edit teams, click on the "Teams" tab in the left panel. You will see a list of all the teams in FIFA 13. You can search for a specific team by name or ID using the search box at the top. You can also filter the teams by league or country using the drop-down menus at the bottom.     - To edit a team, double-click on its name or click on the "Edit" button at the bottom. A new window will open with various tabs for editing different aspects of the team. You can edit its name, logo, kits, stadium, banners, flags, manager, formation, tactics, etc. You can also assign it new kits (.rx3) files using the "Kits" tab. To save your changes, click on the "Save" button at the bottom of the window.     - To edit leagues, click on the "Leagues" tab in the left panel. You will see a list of all the leagues in FIFA 13. You can search for a specific league by name or ID using the search box at the top.     - To edit a league, double-click on its name or click on the "Edit" button at the bottom. A new window will open with various tabs for editing different aspects of the league. You can edit its name, logo, structure, rules, cups, etc. To save your changes, click on the "Save" button at the bottom of the window.     - To edit tournaments, click on the "Tournaments" tab in the left panel. You will see a list of all the tournaments in FIFA 13. You can search for a specific tournament by name or ID using the search box at the top.     - To edit a tournament, double-click on its name or click on the "Edit" button at - To edit a tournament, double-click on its name or click on the "Edit" button at the bottom. A new window will open with various tabs for editing different aspects of the tournament. You can edit its name, logo, structure, rules, prizes, etc. You can also assign it new balls (.rx3) files using the "Balls" tab. To save your changes, click on the "Save" button at the bottom of the window. <h2>Tips and tricks for using Creation Master 13 effectively</h2>
-<p>Creation Master 13 is a powerful and versatile tool that can help you customize and enhance your FIFA 13 game experience. However, it also requires some caution and care when using it, as it can cause errors or issues if not used properly. Here are some tips and tricks for using Creation Master 13 effectively:</p>
- <h3>How to backup and restore FIFA 13 database with Creation Master 13</h3>
-<p>Before you make any changes to your FIFA 13 database with Creation Master 13, it is highly recommended that you backup your original database files (.db) in case something goes wrong or you want to revert to the default settings. To backup and restore FIFA 13 database with Creation Master 13, follow these steps:</p>
-<ol>
-<li>Run Creation Master 13 by double-clicking on the "CreationMaster13.exe" file.</li>
-<li>Click on "File" in the menu bar and select "Open" from the drop-down menu.</li>
-<li>Select the folder where your FIFA 13 game is installed. By default, it is "C:\Program Files (x86)\Origin Games\FIFA 13". Click on "OK".</li>
-<li>Wait for a few seconds while Creation Master 13 loads the FIFA 13 database files (.db).</li>
-<li>Click on "Tools" in the menu bar and select "Backup Database" from the drop-down menu.</li>
-<li>Select a folder where you want to save your backup database files (.db). Click on "OK".</li>
-<li>Wait for a few seconds while Creation Master 13 copies the FIFA 13 database files (.db) to the selected folder.</li>
-<li>You have successfully backed up your FIFA 13 database with Creation Master 13. You can now make changes to your FIFA 13 database as you wish.</li>
-<li>To restore your backup database, click on "Tools" in the menu bar and select "Restore Database" from the drop-down menu.</li>
-<li>Select the folder where you saved your backup database files (.db). Click on "OK".</li>
-<li>Wait for a few seconds while Creation Master 13 replaces the FIFA 13 database files (.db) with the backup ones.</li>
-<li>You have successfully restored your FIFA 13 database with Creation Master 13. You can now close the program or continue editing.</li>
-</ol>
- <h3>How to fix common errors and issues with Creation Master 13</h3>
-<p>Sometimes, you might encounter some errors or issues when using Creation Master 13, such as crashing, freezing, not opening, not saving, etc. Here are some possible solutions for fixing common errors and issues with Creation Master 13:</p>
-<ul>
-<li>Make sure you have .NET Framework 4.0 or higher installed on your PC. You can download it from <a href="">here</a>.</li>
-<li>Make sure you have a ZIP file extractor program such as WinRAR or 7-Zip installed on your PC. You can download them from <a href="">here</a> or <a href="">here</a>.</li>
-<li>Make sure you have an internet connection for downloading Creation Master 13 and updating FIFA 13 database.</li>
-<li>Make sure you run Creation Master 13 as an administrator by right-clicking on the "CreationMaster13.exe" file and selecting "Run as administrator".</li>
-<li>Make sure you close any other programs that might interfere with Creation Master 13, such as antivirus software, firewall software, etc.</li>
-<li>Make sure you backup your FIFA 13 database files (.db) before making any changes with Creation Master 13.</li>
-<li>Make sure you save your changes before closing Creation Master 13.</li>
-<li>If you still have errors or issues with Creation Master 13, you can contact the developer at <a href="">henokmolla@gmail.com</a> or visit the official website at <a href="">http://www.fifa-master.com/cm-13/</a>.</li>
-</ul>
- <h3>How to update FIFA 13 database with the latest transfers and ratings with Creation Master 13</h3>
-<p>If you want to keep your FIFA 13 game updated with the latest changes in the real world of football, such as transfers and ratings, you can use Creation Master 13 to do so. To update FIFA 13 database with the latest transfers and ratings with Creation Master 13, follow these steps:</p>
-<ol>
-<li>Run Creation Master 13 by double-clicking on the "CreationMaster13.exe" file.</li>
-<li>Click on "File" in the menu bar and select "Open" from the drop-down menu.</li>
-<li>Select the folder where your FIFA 13 game is installed. By default, it is "C:\Program Files (x86)\Origin Games\FIFA 13". Click on "OK".</li>
-<li>Wait for a few seconds while Creation Master 13 loads the FIFA 13 database files (.db).</li>
-<li>Click on "Tools" in the menu bar and select "Update Database" from the drop-down menu.</li>
-<li>Select the source of the update from the list. You can choose from FIFA 14, FIFA 15, FIFA 16, FIFA 17, FIFA 18, FIFA 19, FIFA 20, FIFA 21, or FIFA 22. You can also choose from online sources such as Transfermarkt or Sofifa. Click on "OK".</li>
-<li>Wait for a few minutes while Creation Master 13 downloads and applies the update to your FIFA 13 database files (.db).</li>
-<li>You have successfully updated your FIFA 13 database with the latest transfers and ratings with Creation Master 13. You can now close the program or continue editing.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Creation Master 13 is a great tool for editing FIFA 13 database in various ways. It allows you to customize and modify your FIFA 13 game experience to your liking. It also helps you keep your FIFA 13 game updated with the latest changes in the real world of football. However, it also requires some caution and care when using it, as it can cause errors or issues if not used properly. Therefore, it is important to follow the tips and tricks we have shared in this article for using Creation Master 13 effectively. We hope you have enjoyed this article and learned something new. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Creation Master 13:</p>
-<ul>
-<li><strong>Q: Is Creation Master 13 free?</strong></li>
-<li>A: Yes, Creation Master 13 is free to download and use. However, if you appreciate the work of the developer, you can donate to him via PayPal at <a href="">henokmolla@gmail.com</a>.</li>
-<li><strong>Q: Is Creation Master 13 safe?</strong></li>
-<li>A: Yes, Creation Master 13 is safe to use. However, it is always recommended to backup your FIFA 13 database files (.db) before making any changes with Creation Master 13.</li>
-<li><strong>Q: Is Creation Master 13 compatible with other mods or patches for FIFA 13?</strong></li>
-<li>A: It depends on the mod or patch. Some mods or patches might be compatible with Creation Master 13, while others might not. It is advisable to check with the mod or patch creator before using Creation Master 13 with them.</li>
-<li><strong>Q: How can I uninstall Creation Master 13?</strong></li>
-<li>A: To uninstall Creation Master 13, simply delete the folder where you extracted the ZIP file. You can also restore your original FIFA 13 database files (.db) using Creation Master 13.</li>
-<li><strong>Q: Where can I find more information or support for Creation Master 13?</strong></li>
-<li>A: You can find more information or support for Creation Master 13 at the official website at <a href="">http://www.fifa-master.com/cm-13/</a>. You can also contact the developer at <a href="">henokmolla@gmail.com</a>.</li>
-</ul></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Garmin City Navigator Europe Nt 2013.30 Unlocked Img 11 [BETTER].md b/spaces/tioseFevbu/cartoon-converter/scripts/Garmin City Navigator Europe Nt 2013.30 Unlocked Img 11 [BETTER].md
deleted file mode 100644
index db5532ed351cc892f96dee5735d85f6888555069..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Garmin City Navigator Europe Nt 2013.30 Unlocked Img 11 [BETTER].md	
+++ /dev/null
@@ -1,86 +0,0 @@
-
-<h1>Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11</h1>
-<p>If you are planning to travel to Europe and want to explore its diverse and rich culture, history, and nature, you need a reliable and convenient map that can guide you through your journey. One of the best options available is <strong>Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11</strong>, a detailed road map that covers most of Europe and provides you with accurate and up-to-date information on points of interest, navigation options, and compatibility with your Garmin device.</p>
-<h2>garmin city navigator europe nt 2013.30 unlocked img 11</h2><br /><p><b><b>Download File</b> &#9999; <a href="https://urlcod.com/2uHxFK">https://urlcod.com/2uHxFK</a></b></p><br /><br />
-<p>In this article, we will review the features, installation process, benefits, drawbacks, and FAQs of Garmin City Navigator Europe NT 201 <h2>Features of Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11</h2>
-<p>Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11 is a comprehensive and detailed map that covers most of Europe, including the following countries and regions:</p>
-<ul>
-<li>Albania</li>
-<li>Andorra</li>
-<li>Austria</li>
-<li>Belarus</li>
-<li>Belgium</li>
-<li>Bosnia and Herzegovina</li>
-<li>Bulgaria</li>
-<li>Croatia</li>
-<li>Cyprus</li>
-<li>Czech Republic</li>
-<li>Denmark</li>
-<li>Estonia</li>
-<li>Finland</li>
-<li>France (including French Guiana, Guadeloupe, Martinique, Réunion, and Saint Barthélemy)</li>
-<li>Germany</li>
-<li>Gibraltar</li>
-<li>Greece</li>
-<li>Hungary</li>
-<li>Iceland</li>
-<li>Ireland</li>
-<li>Italy (including San Marino and Vatican City)</li>
-<li>Kosovo</li>
-<li>Latvia</li>
-<li>Liechtenstein</li>
-<li>Lithuania</li>
-<li>Luxembourg</li>
-<li>Macedonia</li>
-<li>Malta</li>
-<li>Moldova</li>
-<li>Monaco</li>
-<li>Montenegro</li>
-<li>Netherlands (including Aruba, Bonaire, Curaçao, Saba, Sint Eustatius, and Sint Maarten)</li>
-<li>Norway (including Jan Mayen and Svalbard)</li>
-<li>Poland</li>
-<li>Portugal (including Azores and Madeira)</li>
-<li>Romania</li>
-<li>Serbia (including Kosovo)</li>
-<li>Slovakia</li>
-<li>Slovenia</li>
-<li>Spain (including Canary Islands)</li>
-<li>Sweden (including Åland Islands)</li>
-<li>Switzerland (including Liechtenstein)</li>
-<li>Turkey (including Northern Cyprus)</li>
-<li>Ukraine (including Crimea)</li>
-<li>United Kingdom (including Channel Islands, Isle of Man, and Gibraltar)</li></ul>
- <p>The map also includes partial coverage of the following countries: </p>
- <ul><li>Bahrain </li><
-|im_end|>
- |im_start|>[assistant](#message) | Algeria | Egypt | Iraq | Israel | Jordan | Kuwait | Lebanon | Libya | Morocco | Oman | Qatar | Saudi Arabia | Syria | Tunisia | United Arab Emirates | Yemen The map provides you with more than 10.4 million points of interest, such as hotels, restaurants, gas stations, attractions, museums, parks, airports, train stations, and more. You can easily search for the nearest or the best point of interest based on your preferences and needs. The map also offers you various navigation options to help you get to your destination safely and efficiently. You can get turn-by-turn directions with spoken street names, lane guidance with junction views, speed limits and alerts, traffic information, roundabout assistance, and more. You can also customize your route preferences based on your vehicle type, road conditions, tolls, or other factors. The map is compatible with most Garmin devices that support NT maps, such as nüvi, zumo, dezl, Oregon, Montana, Dakota, eTrex, GPSMAP, Edge, and others. You can check the compatibility of your device on the Garmin website or contact Garmin customer service if you have any doubts. <h2>How to Install Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11</h2>
- <p>To install Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11 on your Garmin device, you need to have the following requirements:</p>
- <ul><li>A Garmin device that supports NT maps and has enough memory space to store the map file. The map file size is about 3.5 GB, so you may need to use a microSD card or a USB drive to expand your device's memory.</li><
-|im_end|>
- |im_start|>[assistant](#message) | A computer with an internet connection and a USB cable or a card reader to connect your device or your memory card. | A software program that can extract the map file from the downloaded archive file. The archive file is in RAR format, so you may need to use a program like WinRAR or 7-Zip to extract it. | A software program that can transfer the map file to your device or a similar program. You can download Garmin Express from the Garmin website or use another program that can transfer files to your device. The steps to install the map are as follows: 1. Download the archive file that contains the map file from the link provided by the seller or the source. The file name should be something like Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11.rar. 2. Extract the map file from the archive file using a program like WinRAR or 7-Zip. The map file should be something like gmapsupp.img or gmapprom.img. 3. Connect your Garmin device or your memory card to your computer using a USB cable or a card reader. Your device or your memory card should appear as a removable drive on your computer. 4. Open the Garmin Map Manager and select the map file you want to transfer. If you don't have Garmin Map Manager, you can download it from the Garmin website or use another program that can transfer files to your device. 5. Select the option to send the map to your device or your memory card. Choose the folder where you want to store the map file. Depending on your device model, it could be either the Garmin folder or the Map folder. 6. Wait for the transfer to complete and then disconnect your device or your memory card from your computer. You may need to restart your device for the map to load properly. 7. Check if the map is installed correctly on your device by going to Settings > Map & Vehicle > My Maps. You should see Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11 as one of the available maps. You can also view the map details by selecting it and pressing Info. <h2>Benefits of Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11</h2>
- <p>Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11 is a great map for travelers who want to explore Europe with confidence and convenience. Some of the benefits of using this map are:</p>
- <ul><li><strong>Reliability:</strong> The map is based on high-quality data from NAVTEQ, a leading provider of digital map information. The map is also regularly updated with new roads, points of interest, and other features to ensure accuracy and currency. You can also rely on Garmin's customer service and support if you have any issues or questions with your map.</li><
-|im_end|> <li><strong>Convenience:</strong> The map is easy to use and customize according to your preferences and needs. You can search for points of interest by name, category, or proximity. You can also plan your route by entering your destination, choosing your mode of transportation, and selecting your route options. You can also access the map offline, which means you don't need an internet connection or a data plan to use it.</li>
-<li><strong>Value:</strong> The map is cost-effective and functional compared to other options. You only need to pay once for the map and you can use it for as long as you want. You also get free lifetime updates for the map, which means you don't have to pay extra for new versions or features. You also get more than just a map, you get a travel guide that provides you with useful information and tips on your destination.</li>
- <h2>Drawbacks of Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11</h2>
- <p>Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11 is not a perfect map and it has some drawbacks that you should be aware of before using it. Some of the drawbacks are:</p>
-<p></p>
- <ul><li><strong>Limitations:</strong> The map does not cover all of Europe and it may have some gaps or errors in some areas. The map also may not have the latest information on some roads, points of interest, or other features. The map also may not be compatible with some Garmin devices or models.</li>
-<li><strong>Alternatives:</strong> The map is not the only option available for travelers who want to explore Europe. There are other maps that may offer different features, coverage, or prices. There are also online maps that may provide more updated and interactive information. There are also paper maps that may be more convenient and accessible for some travelers.</li></ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11 is a detailed and comprehensive map that covers most of Europe and provides travelers with accurate and up-to-date information on points of interest, navigation options, and compatibility with their Garmin device. The map has many benefits such as reliability, convenience, and value, but it also has some drawbacks such as limitations and alternatives.</p>
- <p>If you are looking for a reliable and convenient map that can guide you through your journey in Europe, Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11 may be a good option for you. However, if you are looking for a more complete or updated map that covers all of Europe or offers different features or prices, you may want to consider other options.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Garmin City Navigator Europe NT 2013.30 Unlocked IMG 11:</p>
- <ol><li><strong>What is the difference between locked and unlocked IMG files?</strong></li></ol>
- <p>A locked IMG file is a map file that can only be used on the device that it was originally downloaded to or activated on. An unlocked IMG file is a map file that can be used on any device that supports it.</p>
- <ol start="2"><li><strong>How can I update my Garmin City Navigator Europe NT map?</strong></li></ol>
- <p>You can update your Garmin City Navigator Europe NT map by using Garmin Express, a software program that allows you to manage your Garmin device and maps. You can download Garmin Express from the Garmin website and follow the instructions on how to update your map.</p>
- <ol start="3"><li><strong>How can I contact Garmin customer service if I have any issues with my map?</strong></li></ol>
- <p>You can contact Garmin customer service by phone, email, chat, or social media. You can find the contact details on the Garmin website or on your device's manual.</p>
- <ol start="4"><li><strong>How can I customize my Garmin City Navigator Europe NT map settings?</strong></li></ol>
- <p>You can customize your Garmin City Navigator Europe NT map settings by going to Settings > Map & Vehicle > Map Tools on your device. You can change the map view, the map detail, the map theme, the points of interest icons, and more.</p>
- <ol start="5"><li><strong>How can I share my Garmin City Navigator Europe NT map routes with others?</strong></li></ol>
- <p>You can share your Garmin City Navigator Europe NT map routes with others by using Garmin Connect, a web-based platform that allows you to upload, analyze, and share your activities and routes. You can sign up for Garmin Connect on the Garmin website and sync your device with it.</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/colorama/win32.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/colorama/win32.py
deleted file mode 100644
index c2d836033673993e00a02d5a3802b61cd051cf08..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/colorama/win32.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-
-# from winbase.h
-STDOUT = -11
-STDERR = -12
-
-try:
-    import ctypes
-    from ctypes import LibraryLoader
-    windll = LibraryLoader(ctypes.WinDLL)
-    from ctypes import wintypes
-except (AttributeError, ImportError):
-    windll = None
-    SetConsoleTextAttribute = lambda *_: None
-    winapi_test = lambda *_: None
-else:
-    from ctypes import byref, Structure, c_char, POINTER
-
-    COORD = wintypes._COORD
-
-    class CONSOLE_SCREEN_BUFFER_INFO(Structure):
-        """struct in wincon.h."""
-        _fields_ = [
-            ("dwSize", COORD),
-            ("dwCursorPosition", COORD),
-            ("wAttributes", wintypes.WORD),
-            ("srWindow", wintypes.SMALL_RECT),
-            ("dwMaximumWindowSize", COORD),
-        ]
-        def __str__(self):
-            return '(%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d)' % (
-                self.dwSize.Y, self.dwSize.X
-                , self.dwCursorPosition.Y, self.dwCursorPosition.X
-                , self.wAttributes
-                , self.srWindow.Top, self.srWindow.Left, self.srWindow.Bottom, self.srWindow.Right
-                , self.dwMaximumWindowSize.Y, self.dwMaximumWindowSize.X
-            )
-
-    _GetStdHandle = windll.kernel32.GetStdHandle
-    _GetStdHandle.argtypes = [
-        wintypes.DWORD,
-    ]
-    _GetStdHandle.restype = wintypes.HANDLE
-
-    _GetConsoleScreenBufferInfo = windll.kernel32.GetConsoleScreenBufferInfo
-    _GetConsoleScreenBufferInfo.argtypes = [
-        wintypes.HANDLE,
-        POINTER(CONSOLE_SCREEN_BUFFER_INFO),
-    ]
-    _GetConsoleScreenBufferInfo.restype = wintypes.BOOL
-
-    _SetConsoleTextAttribute = windll.kernel32.SetConsoleTextAttribute
-    _SetConsoleTextAttribute.argtypes = [
-        wintypes.HANDLE,
-        wintypes.WORD,
-    ]
-    _SetConsoleTextAttribute.restype = wintypes.BOOL
-
-    _SetConsoleCursorPosition = windll.kernel32.SetConsoleCursorPosition
-    _SetConsoleCursorPosition.argtypes = [
-        wintypes.HANDLE,
-        COORD,
-    ]
-    _SetConsoleCursorPosition.restype = wintypes.BOOL
-
-    _FillConsoleOutputCharacterA = windll.kernel32.FillConsoleOutputCharacterA
-    _FillConsoleOutputCharacterA.argtypes = [
-        wintypes.HANDLE,
-        c_char,
-        wintypes.DWORD,
-        COORD,
-        POINTER(wintypes.DWORD),
-    ]
-    _FillConsoleOutputCharacterA.restype = wintypes.BOOL
-
-    _FillConsoleOutputAttribute = windll.kernel32.FillConsoleOutputAttribute
-    _FillConsoleOutputAttribute.argtypes = [
-        wintypes.HANDLE,
-        wintypes.WORD,
-        wintypes.DWORD,
-        COORD,
-        POINTER(wintypes.DWORD),
-    ]
-    _FillConsoleOutputAttribute.restype = wintypes.BOOL
-
-    _SetConsoleTitleW = windll.kernel32.SetConsoleTitleW
-    _SetConsoleTitleW.argtypes = [
-        wintypes.LPCWSTR
-    ]
-    _SetConsoleTitleW.restype = wintypes.BOOL
-
-    def _winapi_test(handle):
-        csbi = CONSOLE_SCREEN_BUFFER_INFO()
-        success = _GetConsoleScreenBufferInfo(
-            handle, byref(csbi))
-        return bool(success)
-
-    def winapi_test():
-        return any(_winapi_test(h) for h in
-                   (_GetStdHandle(STDOUT), _GetStdHandle(STDERR)))
-
-    def GetConsoleScreenBufferInfo(stream_id=STDOUT):
-        handle = _GetStdHandle(stream_id)
-        csbi = CONSOLE_SCREEN_BUFFER_INFO()
-        success = _GetConsoleScreenBufferInfo(
-            handle, byref(csbi))
-        return csbi
-
-    def SetConsoleTextAttribute(stream_id, attrs):
-        handle = _GetStdHandle(stream_id)
-        return _SetConsoleTextAttribute(handle, attrs)
-
-    def SetConsoleCursorPosition(stream_id, position, adjust=True):
-        position = COORD(*position)
-        # If the position is out of range, do nothing.
-        if position.Y <= 0 or position.X <= 0:
-            return
-        # Adjust for Windows' SetConsoleCursorPosition:
-        #    1. being 0-based, while ANSI is 1-based.
-        #    2. expecting (x,y), while ANSI uses (y,x).
-        adjusted_position = COORD(position.Y - 1, position.X - 1)
-        if adjust:
-            # Adjust for viewport's scroll position
-            sr = GetConsoleScreenBufferInfo(STDOUT).srWindow
-            adjusted_position.Y += sr.Top
-            adjusted_position.X += sr.Left
-        # Resume normal processing
-        handle = _GetStdHandle(stream_id)
-        return _SetConsoleCursorPosition(handle, adjusted_position)
-
-    def FillConsoleOutputCharacter(stream_id, char, length, start):
-        handle = _GetStdHandle(stream_id)
-        char = c_char(char.encode())
-        length = wintypes.DWORD(length)
-        num_written = wintypes.DWORD(0)
-        # Note that this is hard-coded for ANSI (vs wide) bytes.
-        success = _FillConsoleOutputCharacterA(
-            handle, char, length, start, byref(num_written))
-        return num_written.value
-
-    def FillConsoleOutputAttribute(stream_id, attr, length, start):
-        ''' FillConsoleOutputAttribute( hConsole, csbi.wAttributes, dwConSize, coordScreen, &cCharsWritten )'''
-        handle = _GetStdHandle(stream_id)
-        attribute = wintypes.WORD(attr)
-        length = wintypes.DWORD(length)
-        num_written = wintypes.DWORD(0)
-        # Note that this is hard-coded for ANSI (vs wide) bytes.
-        return _FillConsoleOutputAttribute(
-            handle, attribute, length, start, byref(num_written))
-
-    def SetConsoleTitle(title):
-        return _SetConsoleTitleW(title)
diff --git a/spaces/tomofi/MMOCR/configs/textrecog/satrn/README.md b/spaces/tomofi/MMOCR/configs/textrecog/satrn/README.md
deleted file mode 100644
index 9e26021a69df8076b2a959d5d4e986700c338457..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/textrecog/satrn/README.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# SATRN
-
->[On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention](https://arxiv.org/abs/1910.04396)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods still fail to recognize texts in arbitrary shapes, such as heavily curved or rotated texts, which are abundant in daily life (e.g. restaurant signs, product labels, company logos, etc). This paper introduces a novel architecture to recognizing texts of arbitrary shapes, named Self-Attention Text Recognition Network (SATRN), which is inspired by the Transformer. SATRN utilizes the self-attention mechanism to describe two-dimensional (2D) spatial dependencies of characters in a scene text image. Exploiting the full-graph propagation of self-attention, SATRN can recognize texts with arbitrary arrangements and large inter-character spacing. As a result, SATRN outperforms existing STR models by a large margin of 5.7 pp on average in "irregular text" benchmarks. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e.g. rotated and multi-line text). We will open-source the code.
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/22607038/142798828-cc4ded5d-3fb8-478c-9f3e-74edbcf41982.png"/>
-</div>
-
-
-## Dataset
-
-### Train Dataset
-
-| trainset  | instance_num | repeat_num | source |
-| :-------: | :----------: | :--------: | :----: |
-| SynthText |   7266686    |     1      | synth  |
-|  Syn90k   |   8919273    |     1      | synth  |
-
-### Test Dataset
-
-| testset | instance_num |   type    |
-| :-----: | :----------: | :-------: |
-| IIIT5K  |     3000     |  regular  |
-|   SVT   |     647      |  regular  |
-|  IC13   |     1015     |  regular  |
-|  IC15   |     2077     | irregular |
-|  SVTP   |     645      | irregular |
-|  CT80   |     288      | irregular |
-
-## Results and Models
-
-|                        Methods                         |        | Regular Text |       |       |       | Irregular Text |       |                                                                                          download                                                                                          |
-| :----------------------------------------------------: | :----: | :----------: | :---: | :---: | :---: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-|                                                        | IIIT5K |     SVT      | IC13  |       | IC15  |      SVTP      | CT80  |
-|  [Satrn](/configs/textrecog/satrn/satrn_academic.py)   |  96.1  |     93.5     | 95.7  |       | 84.1  |      88.5      | 90.3  | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_academic_20211009-cb8b1580.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/20210809_093244.log.json) |
-| [Satrn_small](/configs/textrecog/satrn/satrn_small.py) |  94.7  |     91.3     | 95.4  |       | 81.9  |      85.9      | 86.5  |  [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_small_20211009-2cf13355.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/20210811_053047.log.json)   |
-
-## Citation
-
-```bibtex
-@article{junyeop2019recognizing,
-  title={On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention},
-  author={Junyeop Lee, Sungrae Park, Jeonghun Baek, Seong Joon Oh, Seonghyeon Kim, Hwalsuk Lee},
-  year={2019}
-}
-```
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/res2net/htc_r2_101_fpn_20e_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/res2net/htc_r2_101_fpn_20e_coco.py
deleted file mode 100644
index 3c4cc751bff1b091a396b570d8edfbc9d0bb9958..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/res2net/htc_r2_101_fpn_20e_coco.py
+++ /dev/null
@@ -1,7 +0,0 @@
-_base_ = '../htc/htc_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://res2net101_v1d_26w_4s',
-    backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/spaces/trhacknon/webui/oh-no.py b/spaces/trhacknon/webui/oh-no.py
deleted file mode 100644
index e8c0f3bd8d72805b4ee69d4d0fd9133347d00f92..0000000000000000000000000000000000000000
--- a/spaces/trhacknon/webui/oh-no.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-
-block = gr.Blocks()
-
-def run():
-  with block:
-    gr.Markdown(
-    """
-    <p>oh no 😐 something wrong with the 🤗 hugging face servers 😐 hopefully, it will be fixed soon</p>
-    """)
-    block.launch(server_name="0.0.0.0", server_port=7860)
-
-if __name__ == "__main__":
-    run()
\ No newline at end of file
diff --git a/spaces/tru2610/ImageClassification/README.md b/spaces/tru2610/ImageClassification/README.md
deleted file mode 100644
index 5ba9fb252005ad08f0802d606b6e463e17aa6b3e..0000000000000000000000000000000000000000
--- a/spaces/tru2610/ImageClassification/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ImageClassification
-emoji: 📊
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/typesdigital/BLOOMChat/index.html b/spaces/typesdigital/BLOOMChat/index.html
deleted file mode 100644
index e0f152e2b2b72af812797121fe4ede399b738a71..0000000000000000000000000000000000000000
--- a/spaces/typesdigital/BLOOMChat/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-<!DOCTYPE html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<iframe src="https://api.together.xyz/bloom-chat?preview=1"></iframe>
-	</body>
-</html>
diff --git a/spaces/typesdigital/codellama/app.py b/spaces/typesdigital/codellama/app.py
deleted file mode 100644
index 89069ecb889cf682dca861430095ca38d5f3df1d..0000000000000000000000000000000000000000
--- a/spaces/typesdigital/codellama/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-os.environ['REPLICATE_API_TOKEN'] = "r8_afc5kESy4ucPojF3Tw1GE25ER4Ovudy1iPVw6"
-
-import replicate
-
-# Prompts
-pre_prompt = "You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'."
-prompt_input = "What is Hugging Face"
-
-# Generate LLM response
-output = replicate.run('huggingface/llama-base-125M',  # LLM model
-                       input={
-                           "prompt": f"{pre_prompt} {prompt_input} Assistant: ",  # Prompts
-                           "temperature": 0.1,
-                           "top_p": 0.9,
-                           "max_length": 124,
-                           "repetition_penalty": 1
-                       })  # Model parameters
-
-full_response = ''
-
-for item in output:
-    full_response += item
-
-print(full_response)
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Asure Id 7 Express Card Software Torrent 77.md b/spaces/usbethFlerru/sovits-modelsV2/example/Asure Id 7 Express Card Software Torrent 77.md
deleted file mode 100644
index 8258111aefb2a25684e899a9cc2c69be9640821d..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Asure Id 7 Express Card Software Torrent 77.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-<br />
-```html
-<h1>Asure ID 7 Express Card Software: A Review</h1>
-<p>Asure ID 7 Express Card Software is a powerful and easy-to-use software for creating and printing ID cards. It allows you to design custom cards with photos, logos, barcodes, magnetic stripes, smart chips and more. You can also manage cardholder data, encode cards, and print them on any Windows-compatible printer.</p>
-<p>But what if you don't want to pay for the software? Is there a way to get it for free? Some people may try to download Asure ID 7 Express Card Software from torrent sites, hoping to find a cracked version or a serial key. However, this is not a good idea for several reasons.</p>
-<h2>Asure Id 7 Express Card Software Torrent 77</h2><br /><p><b><b>DOWNLOAD</b> &#128504; <a href="https://urlcod.com/2uyVY4">https://urlcod.com/2uyVY4</a></b></p><br /><br />
-<h2>The Risks of Downloading Asure ID 7 Express Card Software Torrent 77</h2>
-<p>First of all, downloading Asure ID 7 Express Card Software from torrent sites is illegal. You are violating the copyright laws and the terms of service of the software. You may face legal consequences if you are caught using pirated software.</p>
-<p>Secondly, downloading Asure ID 7 Express Card Software from torrent sites is unsafe. You never know what kind of malware or viruses you may get along with the software. You may end up infecting your computer or compromising your personal information. You may also damage your printer or your ID cards if the software is corrupted or incompatible.</p>
-<p>Thirdly, downloading Asure ID 7 Express Card Software from torrent sites is unreliable. You may not get the latest version of the software or the full features. You may also encounter errors, bugs, or crashes while using the software. You may not be able to get any technical support or updates from the official website.</p>
-<h2>The Benefits of Buying Asure ID 7 Express Card Software</h2>
-<p>So, what is the best way to get Asure ID 7 Express Card Software? The answer is simple: buy it from a reputable source. By doing so, you will enjoy many benefits, such as:</p>
-<ul>
-<li>You will get the genuine and legal software that works flawlessly and securely.</li>
-<li>You will get the latest version of the software with all the features and functions.</li>
-<li>You will get free technical support and updates from the official website.</li>
-<li>You will get a warranty and a money-back guarantee from the seller.</li>
-<li>You will support the developers and encourage them to create more quality products.</li>
-</ul>
-<p>As you can see, buying Asure ID 7 Express Card Software is a smart and ethical choice. You will save yourself from a lot of hassle and risk by paying a reasonable price for a great product. You can find Asure ID 7 Express Card Software at <a href="https://www.idwholesaler.com/86413.html">ID Wholesaler</a>, where you can also get free shipping and expert advice.</p>
-<p></p>
-<h2>Conclusion</h2>
-<p>Asure ID 7 Express Card Software is a powerful and easy-to-use software for creating and printing ID cards. It allows you to design custom cards with photos, logos, barcodes, magnetic stripes, smart chips and more. You can also manage cardholder data, encode cards, and print them on any Windows-compatible printer.</p>
-<p>However, downloading Asure ID 7 Express Card Software from torrent sites is illegal, unsafe, and unreliable. You may face legal consequences, malware infections, software errors, or printer damage. You may also miss out on the latest features and updates of the software.</p>
-<p>The best way to get Asure ID 7 Express Card Software is to buy it from a reputable source like <a href="https://www.idwholesaler.com/86413.html">ID Wholesaler</a>. You will get the genuine and legal software that works flawlessly and securely. You will also get free technical support and updates from the official website. You will also get a warranty and a money-back guarantee from the seller. And you will support the developers and encourage them to create more quality products.</p>
-<p>So don't waste your time and money on torrent sites. Buy Asure ID 7 Express Card Software today and start creating professional ID cards in minutes!</p>
-```</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions-builtin/LDSR/preload.py b/spaces/user238921933/stable-diffusion-webui/extensions-builtin/LDSR/preload.py
deleted file mode 100644
index cfd478d545ed12ef74e73fa40b6defe0156859da..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions-builtin/LDSR/preload.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import os
-from modules import paths
-
-
-def preload(parser):
-    parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))
diff --git a/spaces/venz/AW-01-H5-Play-Canvas-Sim-Physics/style.css b/spaces/venz/AW-01-H5-Play-Canvas-Sim-Physics/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/venz/AW-01-H5-Play-Canvas-Sim-Physics/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/victorisgeek/SwapFace2Pon/face_swapper.py b/spaces/victorisgeek/SwapFace2Pon/face_swapper.py
deleted file mode 100644
index 5482af8007176123261eeb4e421f21fc039c4736..0000000000000000000000000000000000000000
--- a/spaces/victorisgeek/SwapFace2Pon/face_swapper.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import time
-import torch
-import onnx
-import cv2
-import onnxruntime
-import numpy as np
-from tqdm import tqdm
-import torch.nn as nn
-from onnx import numpy_helper
-from skimage import transform as trans
-import torchvision.transforms.functional as F
-import torch.nn.functional as F
-from utils import mask_crop, laplacian_blending
-
-
-arcface_dst = np.array(
-    [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
-     [41.5493, 92.3655], [70.7299, 92.2041]],
-    dtype=np.float32)
-
-def estimate_norm(lmk, image_size=112, mode='arcface'):
-    assert lmk.shape == (5, 2)
-    assert image_size % 112 == 0 or image_size % 128 == 0
-    if image_size % 112 == 0:
-        ratio = float(image_size) / 112.0
-        diff_x = 0
-    else:
-        ratio = float(image_size) / 128.0
-        diff_x = 8.0 * ratio
-    dst = arcface_dst * ratio
-    dst[:, 0] += diff_x
-    tform = trans.SimilarityTransform()
-    tform.estimate(lmk, dst)
-    M = tform.params[0:2, :]
-    return M
-
-
-def norm_crop2(img, landmark, image_size=112, mode='arcface'):
-    M = estimate_norm(landmark, image_size, mode)
-    warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
-    return warped, M
-
-
-class Inswapper():
-    def __init__(self, model_file=None, batch_size=32, providers=['CPUExecutionProvider']):
-        self.model_file = model_file
-        self.batch_size = batch_size
-
-        model = onnx.load(self.model_file)
-        graph = model.graph
-        self.emap = numpy_helper.to_array(graph.initializer[-1])
-
-        self.session_options = onnxruntime.SessionOptions()
-        self.session = onnxruntime.InferenceSession(self.model_file, sess_options=self.session_options, providers=providers)
-
-    def forward(self, imgs, latents):
-        preds = []
-        for img, latent in zip(imgs, latents):
-            img = img / 255
-            pred = self.session.run(['output'], {'target': img, 'source': latent})[0]
-            preds.append(pred)
-
-    def get(self, imgs, target_faces, source_faces):
-        imgs = list(imgs)
-
-        preds = [None] * len(imgs)
-        matrs = [None] * len(imgs)
-
-        for idx, (img, target_face, source_face) in enumerate(zip(imgs, target_faces, source_faces)):
-            matrix, blob, latent = self.prepare_data(img, target_face, source_face)
-            pred = self.session.run(['output'], {'target': blob, 'source': latent})[0]
-            pred = pred.transpose((0, 2, 3, 1))[0]
-            pred = np.clip(255 * pred, 0, 255).astype(np.uint8)[:, :, ::-1]
-
-            preds[idx] = pred
-            matrs[idx] = matrix
-
-        return (preds, matrs)
-
-    def prepare_data(self, img, target_face, source_face):
-        if isinstance(img, str):
-            img = cv2.imread(img)
-
-        aligned_img, matrix = norm_crop2(img, target_face.kps, 128)
-
-        blob = cv2.dnn.blobFromImage(aligned_img, 1.0 / 255, (128, 128), (0., 0., 0.), swapRB=True)
-
-        latent = source_face.normed_embedding.reshape((1, -1))
-        latent = np.dot(latent, self.emap)
-        latent /= np.linalg.norm(latent)
-
-        return (matrix, blob, latent)
-
-    def batch_forward(self, img_list, target_f_list, source_f_list):
-        num_samples = len(img_list)
-        num_batches = (num_samples + self.batch_size - 1) // self.batch_size
-
-        for i in tqdm(range(num_batches), desc="Generating face"):
-            start_idx = i * self.batch_size
-            end_idx = min((i + 1) * self.batch_size, num_samples)
-
-            batch_img = img_list[start_idx:end_idx]
-            batch_target_f = target_f_list[start_idx:end_idx]
-            batch_source_f = source_f_list[start_idx:end_idx]
-
-            batch_pred, batch_matr = self.get(batch_img, batch_target_f, batch_source_f)
-
-            yield batch_pred, batch_matr
-
-
-def paste_to_whole(foreground, background, matrix, mask=None, crop_mask=(0,0,0,0), blur_amount=0.1, erode_amount = 0.15, blend_method='linear'):
-    inv_matrix = cv2.invertAffineTransform(matrix)
-    fg_shape = foreground.shape[:2]
-    bg_shape = (background.shape[1], background.shape[0])
-    foreground = cv2.warpAffine(foreground, inv_matrix, bg_shape, borderValue=0.0)
-
-    if mask is None:
-        mask = np.full(fg_shape, 1., dtype=np.float32)
-        mask = mask_crop(mask, crop_mask)
-        mask = cv2.warpAffine(mask, inv_matrix, bg_shape, borderValue=0.0)
-    else:
-        assert fg_shape == mask.shape[:2], "foreground & mask shape mismatch!"
-        mask = mask_crop(mask, crop_mask).astype('float32')
-        mask = cv2.warpAffine(mask, inv_matrix, (background.shape[1], background.shape[0]), borderValue=0.0)
-
-    _mask = mask.copy()
-    _mask[_mask > 0.05] = 1.
-    non_zero_points = cv2.findNonZero(_mask)
-    _, _, w, h = cv2.boundingRect(non_zero_points)
-    mask_size = int(np.sqrt(w * h))
-
-    if erode_amount > 0:
-        kernel_size = max(int(mask_size * erode_amount), 1)
-        structuring_element = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
-        mask = cv2.erode(mask, structuring_element)
-
-    if blur_amount > 0:
-        kernel_size = max(int(mask_size * blur_amount), 3)
-        if kernel_size % 2 == 0:
-            kernel_size += 1
-        mask = cv2.GaussianBlur(mask, (kernel_size, kernel_size), 0)
-
-    mask = np.tile(np.expand_dims(mask, axis=-1), (1, 1, 3))
-
-    if blend_method == 'laplacian':
-        composite_image = laplacian_blending(foreground, background, mask.clip(0,1), num_levels=4)
-    else:
-        composite_image = mask * foreground + (1 - mask) * background
-
-    return composite_image.astype("uint8").clip(0, 255)
diff --git a/spaces/vishal2023/Pneumonia-detection/README.md b/spaces/vishal2023/Pneumonia-detection/README.md
deleted file mode 100644
index f64733f1afa68986e17331b56022fbec8fd129e1..0000000000000000000000000000000000000000
--- a/spaces/vishal2023/Pneumonia-detection/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Pneumonia Detection
-emoji: 🌍
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vobecant/DaS/segmenter_model/segmenter.py b/spaces/vobecant/DaS/segmenter_model/segmenter.py
deleted file mode 100644
index d7b1a24edfa5b3f198b51e54b47a4ae1e355492b..0000000000000000000000000000000000000000
--- a/spaces/vobecant/DaS/segmenter_model/segmenter.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from einops import rearrange
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-# from timm.models.layers import trunc_normal_
-
-from segmenter_model.utils import padding, unpadding
-
-
-class Segmenter(nn.Module):
-    def __init__(
-            self,
-            encoder,
-            decoder,
-            n_cls,
-    ):
-        super().__init__()
-        self.n_cls = n_cls
-        self.patch_size = encoder.patch_size
-        self.encoder = encoder
-        self.decoder = decoder
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        def append_prefix_no_weight_decay(prefix, module):
-            return set(map(lambda x: prefix + x, module.no_weight_decay()))
-
-        nwd_params = append_prefix_no_weight_decay("encoder.", self.encoder).union(
-            append_prefix_no_weight_decay("decoder.", self.decoder)
-        )
-        return nwd_params
-
-    def forward(self, im, decoder_features=False, no_upsample=False, encoder_features=False, no_rearrange=False,
-                cls_only=False, encoder_only=False):
-        H_ori, W_ori = im.size(2), im.size(3)
-        if not no_upsample:
-            im = padding(im, self.patch_size)
-        H, W = im.size(2), im.size(3)
-
-        x = self.encoder(im, return_features=True)  # self.patch_size times smaller than im
-
-        # remove CLS/DIST tokens for decoding
-        num_extra_tokens = 1 + self.encoder.distilled
-
-        if cls_only:
-            return x[:, 0]
-        x = x[:, num_extra_tokens:]
-
-        if encoder_features:
-            enc_fts = x.clone()
-            if not no_rearrange:
-                GS = H // self.patch_size
-                enc_fts = rearrange(enc_fts, "b (h w) c -> b c h w", h=GS)
-            if encoder_only:
-                return enc_fts
-
-        if decoder_features:
-            output = self.decoder(x, (H, W), features_only=True, no_rearrange=no_rearrange)
-            if no_rearrange:
-                if encoder_features:
-                    output = (enc_fts, output)
-                return output
-        else:
-            output = self.decoder(x, (H, W))  # shape (BS, NCLS, H/self.patch_size, W/self.patch_size)
-
-        if not no_upsample:
-            output = F.interpolate(output, size=(H, W), mode="bilinear")  # upsample self.patch_size times
-            output = unpadding(output, (H_ori, W_ori))
-
-        if encoder_features:
-            output = (enc_fts, output)
-        return output
-
-    def get_attention_map_enc(self, im, layer_id):
-        return self.encoder.get_attention_map(im, layer_id)
-
-    def get_attention_map_dec(self, im, layer_id):
-        x = self.encoder(im, return_features=True)
-
-        # remove CLS/DIST tokens for decoding
-        num_extra_tokens = 1 + self.encoder.distilled
-        x = x[:, num_extra_tokens:]
-
-        return self.decoder.get_attention_map(x, layer_id)
diff --git a/spaces/vrajeshbhatt/Automated-Ticket-Management-System/static/css/bootstrap/mixins/_border-radius.css b/spaces/vrajeshbhatt/Automated-Ticket-Management-System/static/css/bootstrap/mixins/_border-radius.css
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/pspnet_r50-d8.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/pspnet_r50-d8.py
deleted file mode 100644
index f451e08ad2eb0732dcb806b1851eb978d4acf136..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/pspnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='PSPHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        pool_scales=(1, 2, 3, 6),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/enc_head.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/enc_head.py
deleted file mode 100644
index da57af617e05d41761628fd2d6d232655b32d905..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/decode_heads/enc_head.py
+++ /dev/null
@@ -1,187 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule, build_norm_layer
-
-from annotator.uniformer.mmseg.ops import Encoding, resize
-from ..builder import HEADS, build_loss
-from .decode_head import BaseDecodeHead
-
-
-class EncModule(nn.Module):
-    """Encoding Module used in EncNet.
-
-    Args:
-        in_channels (int): Input channels.
-        num_codes (int): Number of code words.
-        conv_cfg (dict|None): Config of conv layers.
-        norm_cfg (dict|None): Config of norm layers.
-        act_cfg (dict): Config of activation layers.
-    """
-
-    def __init__(self, in_channels, num_codes, conv_cfg, norm_cfg, act_cfg):
-        super(EncModule, self).__init__()
-        self.encoding_project = ConvModule(
-            in_channels,
-            in_channels,
-            1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-        # TODO: resolve this hack
-        # change to 1d
-        if norm_cfg is not None:
-            encoding_norm_cfg = norm_cfg.copy()
-            if encoding_norm_cfg['type'] in ['BN', 'IN']:
-                encoding_norm_cfg['type'] += '1d'
-            else:
-                encoding_norm_cfg['type'] = encoding_norm_cfg['type'].replace(
-                    '2d', '1d')
-        else:
-            # fallback to BN1d
-            encoding_norm_cfg = dict(type='BN1d')
-        self.encoding = nn.Sequential(
-            Encoding(channels=in_channels, num_codes=num_codes),
-            build_norm_layer(encoding_norm_cfg, num_codes)[1],
-            nn.ReLU(inplace=True))
-        self.fc = nn.Sequential(
-            nn.Linear(in_channels, in_channels), nn.Sigmoid())
-
-    def forward(self, x):
-        """Forward function."""
-        encoding_projection = self.encoding_project(x)
-        encoding_feat = self.encoding(encoding_projection).mean(dim=1)
-        batch_size, channels, _, _ = x.size()
-        gamma = self.fc(encoding_feat)
-        y = gamma.view(batch_size, channels, 1, 1)
-        output = F.relu_(x + x * y)
-        return encoding_feat, output
-
-
-@HEADS.register_module()
-class EncHead(BaseDecodeHead):
-    """Context Encoding for Semantic Segmentation.
-
-    This head is the implementation of `EncNet
-    <https://arxiv.org/abs/1803.08904>`_.
-
-    Args:
-        num_codes (int): Number of code words. Default: 32.
-        use_se_loss (bool): Whether use Semantic Encoding Loss (SE-loss) to
-            regularize the training. Default: True.
-        add_lateral (bool): Whether use lateral connection to fuse features.
-            Default: False.
-        loss_se_decode (dict): Config of decode loss.
-            Default: dict(type='CrossEntropyLoss', use_sigmoid=True).
-    """
-
-    def __init__(self,
-                 num_codes=32,
-                 use_se_loss=True,
-                 add_lateral=False,
-                 loss_se_decode=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=0.2),
-                 **kwargs):
-        super(EncHead, self).__init__(
-            input_transform='multiple_select', **kwargs)
-        self.use_se_loss = use_se_loss
-        self.add_lateral = add_lateral
-        self.num_codes = num_codes
-        self.bottleneck = ConvModule(
-            self.in_channels[-1],
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        if add_lateral:
-            self.lateral_convs = nn.ModuleList()
-            for in_channels in self.in_channels[:-1]:  # skip the last one
-                self.lateral_convs.append(
-                    ConvModule(
-                        in_channels,
-                        self.channels,
-                        1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg,
-                        act_cfg=self.act_cfg))
-            self.fusion = ConvModule(
-                len(self.in_channels) * self.channels,
-                self.channels,
-                3,
-                padding=1,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=self.act_cfg)
-        self.enc_module = EncModule(
-            self.channels,
-            num_codes=num_codes,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        if self.use_se_loss:
-            self.loss_se_decode = build_loss(loss_se_decode)
-            self.se_layer = nn.Linear(self.channels, self.num_classes)
-
-    def forward(self, inputs):
-        """Forward function."""
-        inputs = self._transform_inputs(inputs)
-        feat = self.bottleneck(inputs[-1])
-        if self.add_lateral:
-            laterals = [
-                resize(
-                    lateral_conv(inputs[i]),
-                    size=feat.shape[2:],
-                    mode='bilinear',
-                    align_corners=self.align_corners)
-                for i, lateral_conv in enumerate(self.lateral_convs)
-            ]
-            feat = self.fusion(torch.cat([feat, *laterals], 1))
-        encode_feat, output = self.enc_module(feat)
-        output = self.cls_seg(output)
-        if self.use_se_loss:
-            se_output = self.se_layer(encode_feat)
-            return output, se_output
-        else:
-            return output
-
-    def forward_test(self, inputs, img_metas, test_cfg):
-        """Forward function for testing, ignore se_loss."""
-        if self.use_se_loss:
-            return self.forward(inputs)[0]
-        else:
-            return self.forward(inputs)
-
-    @staticmethod
-    def _convert_to_onehot_labels(seg_label, num_classes):
-        """Convert segmentation label to onehot.
-
-        Args:
-            seg_label (Tensor): Segmentation label of shape (N, H, W).
-            num_classes (int): Number of classes.
-
-        Returns:
-            Tensor: Onehot labels of shape (N, num_classes).
-        """
-
-        batch_size = seg_label.size(0)
-        onehot_labels = seg_label.new_zeros((batch_size, num_classes))
-        for i in range(batch_size):
-            hist = seg_label[i].float().histc(
-                bins=num_classes, min=0, max=num_classes - 1)
-            onehot_labels[i] = hist > 0
-        return onehot_labels
-
-    def losses(self, seg_logit, seg_label):
-        """Compute segmentation and semantic encoding loss."""
-        seg_logit, se_seg_logit = seg_logit
-        loss = dict()
-        loss.update(super(EncHead, self).losses(seg_logit, seg_label))
-        se_loss = self.loss_se_decode(
-            se_seg_logit,
-            self._convert_to_onehot_labels(seg_label, self.num_classes))
-        loss['loss_se'] = se_loss
-        return loss
diff --git a/spaces/wenpeng/Sod_Inpaint/sod/Swin.py b/spaces/wenpeng/Sod_Inpaint/sod/Swin.py
deleted file mode 100644
index 739faa8273ba704dde7fc5e33daf9717caea78ed..0000000000000000000000000000000000000000
--- a/spaces/wenpeng/Sod_Inpaint/sod/Swin.py
+++ /dev/null
@@ -1,578 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            # calculate attention mask for SW-MSA
-            H, W = self.input_resolution
-            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-            h_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            w_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            cnt = 0
-            for h in h_slices:
-                for w in w_slices:
-                    img_mask[:, h, w, :] = cnt
-                    cnt += 1
-
-            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-            atten_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-            atten_mask = atten_mask.masked_fill(atten_mask != 0, float(-100.0)).masked_fill(atten_mask == 0, float(0.0))
-        else:
-            atten_mask = None
-
-        self.register_buffer("atten_mask", atten_mask)
-
-    def forward(self, x):
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=self.atten_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x = blk(x)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops
-
-
-class Swintransformer(nn.Module):
-    r""" Swin Transformer
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 224
-        patch_size (int | tuple(int)): Patch size. Default: 4
-        in_chans (int): Number of input image channels. Default: 3
-        num_classes (int): Number of classes for classification head. Default: 1000
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
-                 embed_dim=128, depths=[2, 2, 18,2], num_heads=[4, 8, 16, 32],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.5,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, **kwargs):
-        super().__init__()
-
-        self.num_classes = num_classes
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers-1):
-            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
-                               input_resolution=(patches_resolution[0] // (2 ** i_layer ),
-                                                 patches_resolution[1] // (2 ** i_layer )),
-                               depth=depths[i_layer],
-                               num_heads=num_heads[i_layer],
-                               window_size=window_size,
-                               mlp_ratio=self.mlp_ratio,
-                               qkv_bias=qkv_bias, qk_scale=qk_scale,
-                               drop=drop_rate, attn_drop=attn_drop_rate,
-                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                               norm_layer=norm_layer,
-                               downsample=PatchMerging if (i_layer < self.num_layers - 2) else None,
-                               use_checkpoint=use_checkpoint)
-            self.layers.append(layer)
-
-        self.norm = norm_layer(self.num_features)
-        self.avgpool = nn.AdaptiveAvgPool1d(1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def forward_features(self, x):
-        b,c,h,w = x.shape
-        x = self.patch_embed(x)
-
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-        s = []
-        s.append(x.view(b, int((x.shape[1])**0.5),int((x.shape[1])**(0.5)), -1).permute(0, 3, 1, 2).contiguous())
-        for layer in self.layers:
-            x = layer(x)
-            s.append(x.view(b, int((x.shape[1])**0.5),int((x.shape[1])**(0.5)), -1).permute(0, 3, 1, 2).contiguous())
-
-      #  x = self.norm(x)  # B L C
-      #  x = self.avgpool(x.transpose(1, 2))  # B C 1
-      #  x = torch.flatten(x, 1)
-        return s
-
-    def forward(self, x):
-        x = self.forward_features(x)
-     #   x = self.head(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        return flops
\ No newline at end of file
diff --git a/spaces/whitphx/gradio-static-test/dist/assets/index-cf9ec9f0.js b/spaces/whitphx/gradio-static-test/dist/assets/index-cf9ec9f0.js
deleted file mode 100644
index ecc29d0c10f9468f24a904aa4eb733101dc1543d..0000000000000000000000000000000000000000
--- a/spaces/whitphx/gradio-static-test/dist/assets/index-cf9ec9f0.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as Z,i as $,s as x,e as E,m as q,q as I,t as y,o as S,u as ee,y as le,a0 as te,I as j,H as M,D as d,h as k,j as se,k as ne,n as oe,p as ae,r as v,f as ie,M as D,N as A,F as L,G as F,O as Q,J as re,E as G,K as B,L as ue,a1 as fe}from"../lite.js";import{B as _e}from"./Button-0391b19a.js";import{B as me}from"./BlockLabel-a3ec523d.js";import{E as ce}from"./Empty-91947ea3.js";/* empty css                                                    */import{I as R}from"./Image-05614c6d.js";import{n as H}from"./ModifyUpload.svelte_svelte_type_style_lang-ba6baa96.js";function J(t,e,s){const l=t.slice();return l[22]=e[s][0],l[7]=e[s][1],l[24]=s,l}function K(t,e,s){const l=t.slice();return l[25]=e[s][0],l[7]=e[s][1],l[24]=s,l}function ge(t){let e,s,l,n,i,o,_=t[8]?t[8][1]:[],m=[];for(let a=0;a<_.length;a+=1)m[a]=O(K(t,_,a));let c=t[4]&&t[8]&&T(t);return{c(){e=M("div"),s=M("img"),n=j();for(let a=0;a<m.length;a+=1)m[a].c();i=j(),c&&c.c(),o=ie(),d(s,"class","base-image svelte-wgo10s"),D(s.src,l=t[8]?t[8][0].data:null)||d(s,"src",l),A(s,"fit-height",t[5].height),d(e,"class","image-container svelte-wgo10s")},m(a,g){k(a,e,g),L(e,s),L(e,n);for(let f=0;f<m.length;f+=1)m[f]&&m[f].m(e,null);k(a,i,g),c&&c.m(a,g),k(a,o,g)},p(a,g){if(g&256&&!D(s.src,l=a[8]?a[8][0].data:null)&&d(s,"src",l),g&32&&A(s,"fit-height",a[5].height),g&800){_=a[8]?a[8][1]:[];let f;for(f=0;f<_.length;f+=1){const b=K(a,_,f);m[f]?m[f].p(b,g):(m[f]=O(b),m[f].c(),m[f].m(e,null))}for(;f<m.length;f+=1)m[f].d(1);m.length=_.length}a[4]&&a[8]?c?c.p(a,g):(c=T(a),c.c(),c.m(o.parentNode,o)):c&&(c.d(1),c=null)},i:F,o:F,d(a){a&&v(e),Q(m,a),a&&v(i),c&&c.d(a),a&&v(o)}}}function he(t){let e,s;return e=new ce({props:{size:"large",unpadded_box:!0,$$slots:{default:[de]},$$scope:{ctx:t}}}),{c(){E(e.$$.fragment)},m(l,n){q(e,l,n),s=!0},p(l,n){const i={};n&134217728&&(i.$$scope={dirty:n,ctx:l}),e.$set(i)},i(l){s||(I(e.$$.fragment,l),s=!0)},o(l){y(e.$$.fragment,l),s=!1},d(l){S(e,l)}}}function O(t){let e,s,l;return{c(){e=M("img"),d(e,"class","mask fit-height svelte-wgo10s"),D(e.src,s=t[25].data)||d(e,"src",s),d(e,"style",l=t[5].color_map&&t[7]in t[5].color_map?null:`filter: hue-rotate(${Math.round(t[24]*360/t[8][1].length)}deg);`),A(e,"active",t[9]==t[7]),A(e,"inactive",t[9]!=t[7]&&t[9]!=null)},m(n,i){k(n,e,i)},p(n,i){i&256&&!D(e.src,s=n[25].data)&&d(e,"src",s),i&288&&l!==(l=n[5].color_map&&n[7]in n[5].color_map?null:`filter: hue-rotate(${Math.round(n[24]*360/n[8][1].length)}deg);`)&&d(e,"style",l),i&768&&A(e,"active",n[9]==n[7]),i&768&&A(e,"inactive",n[9]!=n[7]&&n[9]!=null)},d(n){n&&v(e)}}}function T(t){let e,s=t[8][1],l=[];for(let n=0;n<s.length;n+=1)l[n]=P(J(t,s,n));return{c(){e=M("div");for(let n=0;n<l.length;n+=1)l[n].c();d(e,"class","legend svelte-wgo10s")},m(n,i){k(n,e,i);for(let o=0;o<l.length;o+=1)l[o]&&l[o].m(e,null)},p(n,i){if(i&7456){s=n[8][1];let o;for(o=0;o<s.length;o+=1){const _=J(n,s,o);l[o]?l[o].p(_,i):(l[o]=P(_),l[o].c(),l[o].m(e,null))}for(;o<l.length;o+=1)l[o].d(1);l.length=s.length}},d(n){n&&v(e),Q(l,n)}}}function P(t){let e,s=t[7]+"",l,n,i,o;function _(){return t[17](t[7])}function m(){return t[18](t[7])}function c(){return t[21](t[24],t[7])}return{c(){e=M("div"),l=re(s),n=j(),d(e,"class","legend-item svelte-wgo10s"),G(e,"background-color",t[5].color_map&&t[7]in t[5].color_map?t[5].color_map[t[7]]+"88":`hsla(${Math.round(t[24]*360/t[8][1].length)}, 100%, 50%, 0.3)`)},m(a,g){k(a,e,g),L(e,l),L(e,n),i||(o=[B(e,"mouseover",_),B(e,"focus",m),B(e,"mouseout",t[19]),B(e,"blur",t[20]),B(e,"click",c)],i=!0)},p(a,g){t=a,g&256&&s!==(s=t[7]+"")&&ue(l,s),g&288&&G(e,"background-color",t[5].color_map&&t[7]in t[5].color_map?t[5].color_map[t[7]]+"88":`hsla(${Math.round(t[24]*360/t[8][1].length)}, 100%, 50%, 0.3)`)},d(a){a&&v(e),i=!1,fe(o)}}}function de(t){let e,s;return e=new R({}),{c(){E(e.$$.fragment)},m(l,n){q(e,l,n),s=!0},i(l){s||(I(e.$$.fragment,l),s=!0)},o(l){y(e.$$.fragment,l),s=!1},d(l){S(e,l)}}}function be(t){let e,s,l,n,i,o,_,m;const c=[t[6]];let a={};for(let r=0;r<c.length;r+=1)a=le(a,c[r]);e=new te({props:a}),l=new me({props:{show_label:t[3],Icon:R,label:t[7]||"Image"}});const g=[he,ge],f=[];function b(r,h){return r[8]==null?0:1}return o=b(t),_=f[o]=g[o](t),{c(){E(e.$$.fragment),s=j(),E(l.$$.fragment),n=j(),i=M("div"),_.c(),d(i,"class","container svelte-wgo10s")},m(r,h){q(e,r,h),k(r,s,h),q(l,r,h),k(r,n,h),k(r,i,h),f[o].m(i,null),m=!0},p(r,h){const C=h&64?se(c,[ne(r[6])]):{};e.$set(C);const w={};h&8&&(w.show_label=r[3]),h&128&&(w.label=r[7]||"Image"),l.$set(w);let p=o;o=b(r),o===p?f[o].p(r,h):(oe(),y(f[p],1,1,()=>{f[p]=null}),ae(),_=f[o],_?_.p(r,h):(_=f[o]=g[o](r),_.c()),I(_,1),_.m(i,null))},i(r){m||(I(e.$$.fragment,r),I(l.$$.fragment,r),I(_),m=!0)},o(r){y(e.$$.fragment,r),y(l.$$.fragment,r),y(_),m=!1},d(r){S(e,r),r&&v(s),S(l,r),r&&v(n),r&&v(i),f[o].d()}}}function ke(t){let e,s;return e=new _e({props:{visible:t[2],elem_id:t[0],elem_classes:t[1],padding:!1,style:{height:t[5].height,width:t[5].width},allow_overflow:!1,$$slots:{default:[be]},$$scope:{ctx:t}}}),{c(){E(e.$$.fragment)},m(l,n){q(e,l,n),s=!0},p(l,[n]){const i={};n&4&&(i.visible=l[2]),n&1&&(i.elem_id=l[0]),n&2&&(i.elem_classes=l[1]),n&32&&(i.style={height:l[5].height,width:l[5].width}),n&134218744&&(i.$$scope={dirty:n,ctx:l}),e.$set(i)},i(l){s||(I(e.$$.fragment,l),s=!0)},o(l){y(e.$$.fragment,l),s=!1},d(l){S(e,l)}}}function ve(t,e,s){let{elem_id:l=""}=e,{elem_classes:n=[]}=e,{visible:i=!0}=e,{value:o}=e,_,m,{label:c="Annotated Image"}=e,{show_label:a=!0}=e,{show_legend:g=!0}=e,{style:f={}}=e,{root:b}=e,{root_url:r}=e,h=null,{loading_status:C}=e;const w=ee();function p(u){s(9,h=u)}function N(){s(9,h=null)}const U=u=>p(u),V=u=>p(u),W=()=>N(),X=()=>N(),Y=(u,z)=>w("select",{index:u,value:z});return t.$$set=u=>{"elem_id"in u&&s(0,l=u.elem_id),"elem_classes"in u&&s(1,n=u.elem_classes),"visible"in u&&s(2,i=u.visible),"value"in u&&s(13,o=u.value),"label"in u&&s(7,c=u.label),"show_label"in u&&s(3,a=u.show_label),"show_legend"in u&&s(4,g=u.show_legend),"style"in u&&s(5,f=u.style),"root"in u&&s(14,b=u.root),"root_url"in u&&s(15,r=u.root_url),"loading_status"in u&&s(6,C=u.loading_status)},t.$$.update=()=>{t.$$.dirty&122880&&(o!==_&&(s(16,_=o),w("change")),o?s(8,m=[H(o[0],b,r),o[1].map(([u,z])=>[H(u,b,r),z])]):s(8,m=null))},[l,n,i,a,g,f,C,c,m,h,w,p,N,o,b,r,_,U,V,W,X,Y]}class we extends Z{constructor(e){super(),$(this,e,ve,ke,x,{elem_id:0,elem_classes:1,visible:2,value:13,label:7,show_label:3,show_legend:4,style:5,root:14,root_url:15,loading_status:6})}}const qe=we,Se=["static"],je=t=>({type:{payload:"[string, Array<[string, string]>]"},description:{payload:"path to base image, followed by a list of tuples [mask image path, label]"}});export{qe as Component,je as document,Se as modes};
-//# sourceMappingURL=index-cf9ec9f0.js.map
diff --git a/spaces/willgibs/ControlNet-v1-1/cv_utils.py b/spaces/willgibs/ControlNet-v1-1/cv_utils.py
deleted file mode 100644
index d81177c5eee306107966132fd54695545a61a898..0000000000000000000000000000000000000000
--- a/spaces/willgibs/ControlNet-v1-1/cv_utils.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import cv2
-import numpy as np
-
-
-def resize_image(input_image, resolution, interpolation=None):
-    H, W, C = input_image.shape
-    H = float(H)
-    W = float(W)
-    k = float(resolution) / max(H, W)
-    H *= k
-    W *= k
-    H = int(np.round(H / 64.0)) * 64
-    W = int(np.round(W / 64.0)) * 64
-    if interpolation is None:
-        interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA
-    img = cv2.resize(input_image, (W, H), interpolation=interpolation)
-    return img
diff --git a/spaces/wydgg/bingo-wyd-ai/src/components/learn-more.tsx b/spaces/wydgg/bingo-wyd-ai/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/wydgg/bingo-wyd-ai/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
-  sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
-  if (!sourceAttributions?.length) {
-    return null
-  }
-
-  return (
-    <div className="learn-more-root" role="list" aria-label="了解详细信息:">
-      <div className="learn-more">了解详细信息:</div>
-      <div className="attribution-container">
-        <div className="attribution-items">
-          {sourceAttributions.map((attribution, index) => {
-            const { providerDisplayName, seeMoreUrl } = attribution
-            const { host } = new URL(seeMoreUrl)
-            return (
-              <a
-                key={index}
-                className="attribution-item"
-                target="_blank"
-                role="listitem"
-                href={seeMoreUrl}
-                title={providerDisplayName}
-                tabIndex={index}
-              >
-                {index + 1}. {host}
-              </a>
-            )
-          })}
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/xfys/yolov5_tracking/val_utils/docs/MOTChallenge-Official/Readme.md b/spaces/xfys/yolov5_tracking/val_utils/docs/MOTChallenge-Official/Readme.md
deleted file mode 100644
index 502b504ac16e7a31c56f721675ad206d4b0e53c2..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/docs/MOTChallenge-Official/Readme.md
+++ /dev/null
@@ -1,221 +0,0 @@
-![Test Image 4](https://motchallenge.net/img/header-bg/mot_bannerthin.png)
-![MOT_PIC](https://motchallenge.net/sequenceVideos/MOT17-04-SDP-gt.jpg)
-# MOTChallenge Official Evaluation Kit - Multi-Object Tracking - MOT15, MOT16, MOT17, MOT20
-
-TrackEval is now the Official Evaluation Kit for MOTChallenge.
-
-This repository contains the evaluation scripts for the MOT challenges available at www.MOTChallenge.net.
-
-This codebase replaces the previous version that used to be accessible at https://github.com/dendorferpatrick/MOTChallengeEvalKit and is no longer maintained.
-
-Challenge Name | Data url |
-|----- | ----------- |
-|2D MOT 15| https://motchallenge.net/data/MOT15/ |
-|MOT 16| https://motchallenge.net/data/MOT16/       |
-|MOT 17| https://motchallenge.net/data/MOT17/       |
-|MOT 20| https://motchallenge.net/data/MOT20/       |
-
-## Requirements 
-* Python (3.5 or newer)
-* Numpy and Scipy
-
-## Directories and Data
-The easiest way to get started is to simply download the TrackEval example data from here: [data.zip](https://omnomnom.vision.rwth-aachen.de/data/TrackEval/data.zip) (~150mb).
-
-This contains all the ground-truth, example trackers and meta-data that you will need.
-
-Extract this zip into the repository root folder such that the file paths look like: TrackEval/data/gt/...
-
-## Evaluation
-To run the evaluation for your method please run the script at ```TrackEval/scripts/run_mot_challenge.py```.
-
-Some of the basic arguments are described below. For more arguments, please see the script itself.
-
-```BENCHMARK```: Name of the benchmark, e.g. MOT15, MO16, MOT17 or MOT20  (default : MOT17)
-
-```SPLIT_TO_EVAL```: Data split on which to evalute e.g. train, test (default : train)
-
-```TRACKERS_TO_EVAL```: List of tracker names for which you wish to run evaluation. e.g. MPNTrack (default: all trackers in tracker folder)
-
-```METRICS```: List of metric families which you wish to compute. e.g. HOTA CLEAR Identity VACE (default: HOTA CLEAR Identity)
-
-```USE_PARALLEL```: Whether to run evaluation in parallel on multiple cores. (default: False)
-
-```NUM_PARALLEL_CORES```: Number of cores to use when running in parallel. (default: 8)
-
-An example is below (this will work on the supplied example data above):
-```
-python scripts/run_mot_challenge.py --BENCHMARK MOT17 --SPLIT_TO_EVAL train --TRACKERS_TO_EVAL MPNTrack --METRICS HOTA CLEAR Identity VACE --USE_PARALLEL False --NUM_PARALLEL_CORES 1  
-```
-
-
-## Data Format
-<p>
-The tracker file format should be the same as the ground truth file, 
-which is a CSV text-file containing one object instance per line.
-Each line must contain 10 values:
-</p>
-
-</p>
-<code>
-&lt;frame&gt;,
-&lt;id&gt;,
-&lt;bb_left&gt;,
-&lt;bb_top&gt;,
-&lt;bb_width&gt;,
-&lt;bb_height&gt;,
-&lt;conf&gt;,
-&lt;x&gt;,
-&lt;y&gt;,
-&lt;z&gt;
-</code>
-</p>
-
-The world coordinates <code>x,y,z</code>
-are ignored for the 2D challenge and can be filled with -1.
-Similarly, the bounding boxes are ignored for the 3D challenge.
-However, each line is still required to contain 10 values.
-
-All frame numbers, target IDs and bounding boxes are 1-based. Here is an example:
-
-<pre>
-1, 3, 794.27, 247.59, 71.245, 174.88, -1, -1, -1, -1
-1, 6, 1648.1, 119.61, 66.504, 163.24, -1, -1, -1, -1
-1, 8, 875.49, 399.98, 95.303, 233.93, -1, -1, -1, -1
-...
-</pre>
-
- 
-## Evaluating on your own Data
-The repository also allows you to include your own datasets and evaluate your method on your own challenge ```<YourChallenge>```.  To do so, follow these two steps:  
-***1. Ground truth data preparation***  
-Prepare your sequences in directory ```TrackEval/data/gt/mot_challenge/<YourChallenge>``` following this structure:
-
-```
-.
-|—— <SeqName01>
-	|—— gt
-		|—— gt.txt
-	|—— seqinfo.ini
-|—— <SeqName02>
-	|—— ……
-|—— <SeqName03>
-	|—— …...
-```
-
-***2. Sequence file***  
-Create text files containing the sequence names; ```<YourChallenge>-train.txt```, ```<YourChallenge>-test.txt```,  ```<YourChallenge>-test.txt``` inside the ```seqmaps``` folder, e.g.:
-```<YourChallenge>-all.txt```
-```
-name
-<seqName1> 
-<seqName2>
-<seqName3>
-```
-
-```<YourChallenge>-train.txt```
-```
-name
-<seqName1> 
-<seqName2>
-```
-
-```<YourChallenge>-test.txt```
-```
-name
-<seqName3>
-```
-
-
-To run the evaluation for your method adjust the file ```scripts/run_mot_challenge.py``` and set ```BENCHMARK = <YourChallenge>```
-
-
-## Citation
-If you work with the code and the benchmark, please cite:
-
-***TrackEval***
-```
-@misc{luiten2020trackeval,
-  author =       {Jonathon Luiten, Arne Hoffhues},
-  title =        {TrackEval},
-  howpublished = {\url{https://github.com/JonathonLuiten/TrackEval}},
-  year =         {2020}
-}
-```
-***MOTChallenge Journal***
-```
-@article{dendorfer2020motchallenge,
-  title={MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking},
-  author={Dendorfer, Patrick and Osep, Aljosa and Milan, Anton and Schindler, Konrad and Cremers, Daniel and Reid, Ian and Roth, Stefan and Leal-Taix{\'e}, Laura},
-  journal={International Journal of Computer Vision},
-  pages={1--37},
-  year={2020},
-  publisher={Springer}
-}
-```
-***MOT 15***
-```
-@article{MOTChallenge2015,
-	title = {{MOTC}hallenge 2015: {T}owards a Benchmark for Multi-Target Tracking},
-	shorttitle = {MOTChallenge 2015},
-	url = {http://arxiv.org/abs/1504.01942},
-	journal = {arXiv:1504.01942 [cs]},
-	author = {Leal-Taix\'{e}, L. and Milan, A. and Reid, I. and Roth, S. and Schindler, K.},
-	month = apr,
-	year = {2015},
-	note = {arXiv: 1504.01942},
-	keywords = {Computer Science - Computer Vision and Pattern Recognition}
-}
-```
-***MOT 16, MOT 17***
-```
-@article{MOT16,
-	title = {{MOT}16: {A} Benchmark for Multi-Object Tracking},
-	shorttitle = {MOT16},
-	url = {http://arxiv.org/abs/1603.00831},
-	journal = {arXiv:1603.00831 [cs]},
-	author = {Milan, A. and Leal-Taix\'{e}, L. and Reid, I. and Roth, S. and Schindler, K.},
-	month = mar,
-	year = {2016},
-	note = {arXiv: 1603.00831},
-	keywords = {Computer Science - Computer Vision and Pattern Recognition}
-}
-```
-***MOT 20***
-```
-@article{MOTChallenge20,
-    title={MOT20: A benchmark for multi object tracking in crowded scenes},
-    shorttitle = {MOT20},
-	url = {http://arxiv.org/abs/1906.04567},
-	journal = {arXiv:2003.09003[cs]},
-	author = {Dendorfer, P. and Rezatofighi, H. and Milan, A. and Shi, J. and Cremers, D. and Reid, I. and Roth, S. and Schindler, K. and Leal-Taix\'{e}, L. },
-	month = mar,
-	year = {2020},
-	note = {arXiv: 2003.09003},
-	keywords = {Computer Science - Computer Vision and Pattern Recognition}
-}
-```
-***HOTA metrics***
-```
-@article{luiten2020IJCV,
-  title={HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking},
-  author={Luiten, Jonathon and Osep, Aljosa and Dendorfer, Patrick and Torr, Philip and Geiger, Andreas and Leal-Taix{\'e}, Laura and Leibe, Bastian},
-  journal={International Journal of Computer Vision},
-  pages={1--31},
-  year={2020},
-  publisher={Springer}
-}
-```
-
-## Feedback and Contact
-We are constantly working on improving our benchmark to provide the best performance to the community.
-You can help us to make the benchmark better by open issues in the repo and reporting bugs.
-
-For general questions, please contact one of the following:
-
-```
-Patrick Dendorfer - patrick.dendorfer@tum.de
-Jonathon Luiten - luiten@vision.rwth-aachen.de
-Aljosa Osep - aljosa.osep@tum.de
-```
-
diff --git a/spaces/xswu/HPSv2/src/training/params.py b/spaces/xswu/HPSv2/src/training/params.py
deleted file mode 100644
index 543865d476cc2de95fb49cf42fd2daa36409a6fd..0000000000000000000000000000000000000000
--- a/spaces/xswu/HPSv2/src/training/params.py
+++ /dev/null
@@ -1,509 +0,0 @@
-import argparse
-import ast
-
-
-def get_default_params(model_name):
-    # Params from paper (https://arxiv.org/pdf/2103.00020.pdf)
-    model_name = model_name.lower()
-    if "vit" in model_name:
-        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.98, "eps": 1.0e-6}
-    else:
-        return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.999, "eps": 1.0e-8}
-
-
-class ParseKwargs(argparse.Action):
-    def __call__(self, parser, namespace, values, option_string=None):
-        kw = {}
-        for value in values:
-            key, value = value.split('=')
-            try:
-                kw[key] = ast.literal_eval(value)
-            except ValueError:
-                kw[key] = str(value)  # fallback to string (avoid need to escape on command line)
-        setattr(namespace, self.dest, kw)
-
-
-def parse_args(args):
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--exp-name",
-        type=str,
-        help="the name of experiment",
-    )
-    parser.add_argument(
-        "--no-text-condition",
-        action='store_true',
-        help="whether to use text condition",
-    )
-    parser.add_argument(
-        "--train-data",
-        type=str,
-        default=None,
-        help="Path to file(s) with training data. When using webdataset, multiple datasources can be combined using the `::` separator.",
-        nargs='+',
-    )
-    parser.add_argument(
-        "--train-data-sample-ratio",
-        type=float,
-        default=[1.0],
-        help="When using multiple data sources, this controls the sample ratio. ",
-        nargs='+',
-    )
-    parser.add_argument(
-        "--train-data-upsampling-factors",
-        type=str,
-        default=None,
-        help=(
-            "When using multiple data sources with webdataset and sampling with replacement, this can be used to upsample specific data sources. "
-            "Similar to --train-data, this should be a string with as many numbers as there are data sources, separated by `::` (e.g. 1::2::0.5) "
-            "By default, datapoints are sampled uniformly regardless of the dataset sizes."
-        )
-    )
-    parser.add_argument(
-        "--ignore-in-train",
-        type=int,
-        default=[0],
-        help=(
-            "Whether Ignore coresponding dataset during training"
-        ),nargs='+'
-    )
-    parser.add_argument(
-        "--ignore-in-val",
-        type=int,
-        default=[0],
-        help=(
-            "Whether Ignore coresponding dataset during val"
-        ),nargs='+'
-    )
-    parser
-    parser.add_argument(
-        "--extra-train-data",
-        type=str,
-        default=None,
-        help="Path to file(s) with training data",
-    )
-    parser.add_argument(
-        "--val-data",
-        type=str,
-        default=[None],
-        nargs='+',
-        help="Path to file(s) with validation data",
-    )
-    parser.add_argument(
-        "--extra-val-data",
-        type=str,
-        default=None,
-        help="Path to file(s) with validation data",
-    )
-    parser.add_argument(
-        "--train-folder",
-        type=str,
-        default=None,
-        help="Path to images of training data",
-        nargs='+',
-    )
-    parser.add_argument(
-        "--extra-train-folder",
-        type=str,
-        default=None,
-        help="Path to images of training data",
-    )
-    parser.add_argument(
-        "--val-folder",
-        type=str,
-        default=[None],
-        nargs='+',
-        help="Path to images of val data",
-    )
-    parser.add_argument(
-        "--extra-val-folder",
-        type=str,
-        default=None,
-        help="Path to images of val data",
-    )
-    parser.add_argument(
-        "--save-path",
-        type=str,
-        default=None,
-        help="Path to save checkpoints",
-    )
-    parser.add_argument(
-        "--train-num-samples",
-        type=int,
-        default=None,
-        help="Number of samples in dataset. Required for webdataset if not available in info file.",
-    )
-    parser.add_argument(
-        "--val-num-samples",
-        type=int,
-        default=None,
-        help="Number of samples in dataset. Useful for webdataset if not available in info file.",
-    )
-    parser.add_argument(
-        "--dataset-type",
-        choices=["webdataset", "csv", "synthetic", "auto", "preference", "rating", "regional", "ranking", "ImageReward", "HPD"],
-        default="auto",
-        help="Which type of dataset to process.", 
-        nargs='+'
-    )
-    parser.add_argument(
-        "--dataset-resampled",
-        default=False,
-        action="store_true",
-        help="Whether to use sampling with replacement for webdataset shard selection."
-    )
-    parser.add_argument(
-        "--csv-separator",
-        type=str,
-        default="\t",
-        help="For csv-like datasets, which separator to use."
-    )
-    parser.add_argument(
-        "--csv-img-key",
-        type=str,
-        default="filepath",
-        help="For csv-like datasets, the name of the key for the image paths."
-    )
-    parser.add_argument(
-        "--csv-caption-key",
-        type=str,
-        default="title",
-        help="For csv-like datasets, the name of the key for the captions."
-    )
-    parser.add_argument(
-        "--imagenet-val",
-        type=str,
-        default=None,
-        help="Path to imagenet val set for conducting zero shot evaluation.",
-    )
-    parser.add_argument(
-        "--imagenet-v2",
-        type=str,
-        default=None,
-        help="Path to imagenet v2 for conducting zero shot evaluation.",
-    )
-    parser.add_argument(
-        "--logs",
-        type=str,
-        default="./logs/",
-        help="Where to store tensorboard logs. Use None to avoid storing logs.",
-    )
-    parser.add_argument(
-        "--log-local",
-        action="store_true",
-        default=False,
-        help="log files on local master, otherwise global master only.",
-    )
-    parser.add_argument(
-        "--name",
-        type=str,
-        default=None,
-        help="Optional identifier for the experiment when storing logs. Otherwise use current time.",
-    )
-    parser.add_argument(
-        "--workers", type=int, default=1, help="Number of dataloader workers per GPU.", nargs='+'
-    )
-    parser.add_argument(
-        "--batch-size", type=int, default=64, help="Batch size per GPU.", nargs='+'
-    )
-    parser.add_argument(
-        "--val-batch-size", type=int, default=64, help="Batch size per GPU.", nargs='+'
-    )
-    parser.add_argument(
-        "--iterations", type=int, default=None, help="Number of iterations to train for."
-    )
-    parser.add_argument(
-        "--iters-cooldown", type=int, default=None
-    )
-    parser.add_argument("--lr", type=float, default=None, help="Learning rate.")
-    parser.add_argument("--beta1", type=float, default=None, help="Adam beta 1.")
-    parser.add_argument("--beta2", type=float, default=None, help="Adam beta 2.")
-    parser.add_argument("--eps", type=float, default=None, help="Adam epsilon.")
-    parser.add_argument("--wd", type=float, default=0.2, help="Weight decay.")
-    parser.add_argument(
-        "--warmup", type=int, default=10000, help="Number of steps to warmup for."
-    )
-    parser.add_argument(
-        "--use-bn-sync",
-        default=False,
-        action="store_true",
-        help="Whether to use batch norm sync.")
-    parser.add_argument(
-        "--skip-scheduler",
-        action="store_true",
-        default=False,
-        help="Use this flag to skip the learning rate decay.",
-    )
-    parser.add_argument(
-        "--lr-scheduler",
-        type=str,
-        default='cosine',
-        help="LR scheduler. One of: 'cosine', 'const' (constant), 'const-cooldown' (constant w/ cooldown). Default: cosine",
-    )
-    parser.add_argument(
-        "--lr-cooldown-end", type=float, default=0.0,
-        help="End learning rate for cooldown schedule. Default: 0"
-    )
-    parser.add_argument(
-        "--lr-cooldown-power", type=float, default=1.0,
-        help="Power for polynomial cooldown schedule. Default: 1.0 (linear decay)"
-    )
-    parser.add_argument(
-        "--save-most-recent",
-        action="store_true",
-        default=False,
-        help="Always save the most recent model trained to epoch_latest.pt.",
-    )
-    parser.add_argument(
-        "--zeroshot-frequency", type=int, default=2, help="How often to run zero shot."
-    )
-    parser.add_argument(
-        "--resume",
-        default=None,
-        type=str,
-        help="path to latest checkpoint (default: none)",
-    )
-    parser.add_argument(
-        "--precision",
-        choices=["amp", "amp_bf16", "amp_bfloat16", "bf16", "fp16", "fp32"],
-        default="amp",
-        help="Floating point precision."
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        default="RN50",
-        help="Name of the vision backbone to use.",
-    )
-    parser.add_argument(
-        "--pretrained",
-        default='',
-        type=str,
-        help="Use a pretrained CLIP model weights with the specified tag or file path.",
-    )
-    parser.add_argument(
-        "--pretrained-image",
-        default=False,
-        action='store_true',
-        help="Load imagenet pretrained weights for image tower backbone if available.",
-    )
-    parser.add_argument(
-        "--lock-image",
-        default=False,
-        action='store_true',
-        help="Lock full image tower by disabling gradients.",
-    )
-    parser.add_argument(
-        "--lock-image-unlocked-groups",
-        type=int,
-        default=0,
-        help="Leave last n image tower layer groups unlocked.",
-    )
-    parser.add_argument(
-        "--lock-image-freeze-bn-stats",
-        default=False,
-        action='store_true',
-        help="Freeze BatchNorm running stats in image tower for any locked layers.",
-    )
-    parser.add_argument(
-        '--image-mean', type=float, nargs='+', default=None, metavar='MEAN',
-        help='Override default image mean value of dataset')
-    parser.add_argument(
-        '--image-std', type=float, nargs='+', default=None, metavar='STD',
-        help='Override default image std deviation of of dataset')
-    parser.add_argument('--aug-cfg', nargs='*', default={}, action=ParseKwargs)
-    parser.add_argument(
-        '--light-augmentation', action='store_true',
-        help='')
-    parser.add_argument(
-        "--grad-checkpointing",
-        default=False,
-        action='store_true',
-        help="Enable gradient checkpointing.",
-    )
-    parser.add_argument(
-        "--local-loss",
-        default=False,
-        action="store_true",
-        help="calculate loss w/ local features @ global (instead of realizing full global @ global matrix)"
-    )
-    parser.add_argument(
-        "--gather-with-grad",
-        default=False,
-        action="store_true",
-        help="enable full distributed gradient for feature gather"
-    )
-    parser.add_argument(
-        '--force-image-size', type=int, nargs='+', default=None,
-        help='Override default image size'
-    )
-    parser.add_argument(
-        "--force-quick-gelu",
-        default=False,
-        action='store_true',
-        help="Force use of QuickGELU activation for non-OpenAI transformer models.",
-    )
-    parser.add_argument(
-        "--force-patch-dropout",
-        default=None,
-        type=float,
-        help="Override the patch dropout during training, for fine tuning with no dropout near the end as in the paper",
-    )
-    parser.add_argument(
-        "--force-custom-text",
-        default=False,
-        action='store_true',
-        help="Force use of CustomTextCLIP model (separate text-tower).",
-    )
-    parser.add_argument(
-        "--torchscript",
-        default=False,
-        action='store_true',
-        help="torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'",
-    )
-    parser.add_argument(
-        "--trace",
-        default=False,
-        action='store_true',
-        help="torch.jit.trace the model for inference / eval only",
-    )
-    parser.add_argument(
-        "--accum-freq", type=int, default=1, help="Update the model every --acum-freq steps."
-    )
-    # arguments for distributed training
-    parser.add_argument(
-        "--dist-url",
-        default="env://",
-        type=str,
-        help="url used to set up distributed training",
-    )
-    parser.add_argument(
-        "--dist-backend", default="nccl", type=str, help="distributed backend"
-    )
-    parser.add_argument(
-        "--report-to",
-        default='',
-        type=str,
-        help="Options are ['tensorboard']"
-    )
-    parser.add_argument(
-        "--debug",
-        default=False,
-        action="store_true",
-        help="If true, more information is logged."
-    )
-    parser.add_argument(
-        "--copy-codebase",
-        default=False,
-        action="store_true",
-        help="If true, we copy the entire base on the log directory, and execute from there."
-    )
-    parser.add_argument(
-        "--horovod",
-        default=False,
-        action="store_true",
-        help="Use horovod for distributed training."
-    )
-    parser.add_argument(
-        "--ddp-static-graph",
-        default=False,
-        action='store_true',
-        help="Enable static graph optimization for DDP in PyTorch >= 1.11.",
-    )
-    parser.add_argument(
-        "--no-set-device-rank",
-        default=False,
-        action="store_true",
-        help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc)."
-    )
-    parser.add_argument(
-        "--seed", type=int, default=16, help="Default random seed."
-    )
-    parser.add_argument(
-        "--grad-clip-norm", type=float, default=None, help="Gradient clip."
-    )
-    parser.add_argument(
-        "--lock-text",
-        default=False,
-        action='store_true',
-        help="Lock full text tower by disabling gradients.",
-    )
-    parser.add_argument(
-        "--lock-text-unlocked-layers",
-        type=int,
-        default=0,
-        help="Leave last n image tower layer groups unlocked.",
-    )
-    parser.add_argument(
-        "--margin",
-        type=float,
-        default=1.0,
-        help="hyper parameter for margin loss in ranking",
-    )
-    parser.add_argument(
-        "--lock-text-freeze-layer-norm",
-        default=False,
-        action='store_true',
-        help="Freeze BatchNorm running stats in image tower for any locked layers.",
-    )
-    parser.add_argument(
-        "--log-every-n-steps",
-        type=int,
-        default=10,
-        help="Log every n steps to tensorboard/console.",
-    )
-    parser.add_argument(
-        "--coca-caption-loss-weight",
-        type=float,
-        default=2.0,
-        help="Weight assigned to caption loss in CoCa."
-    )
-    parser.add_argument(
-        "--coca-contrastive-loss-weight",
-        type=float,
-        default=1.0,
-        help="Weight assigned to contrastive loss when training CoCa."
-    )
-    parser.add_argument(
-        "--remote-sync",
-        type=str,
-        default=None,
-        help="Optinoally sync with a remote path specified by this arg",
-    )
-    parser.add_argument(
-        "--remote-sync-frequency",
-        type=int,
-        default=300,
-        help="How frequently to sync to a remote directly if --remote-sync is not None.",
-    )
-    parser.add_argument(
-        "--remote-sync-protocol",
-        choices=["s3", "fsspec"],
-        default="s3",
-        help="How to do the remote sync backup if --remote-sync is not None.",
-    )
-    parser.add_argument(
-        "--delete-previous-checkpoint",
-        default=False,
-        action="store_true",
-        help="If true, delete previous checkpoint after storing a new one."
-    )
-    parser.add_argument(
-        "--distill-model",
-        default=None,
-        help='Which model arch to distill from, if any.'
-    )
-    parser.add_argument(
-        "--distill-pretrained",
-        default=None,
-        help='Which pre-trained weights to distill from, if any.'
-    )
-    args = parser.parse_args(args)
-
-    # If some params are not passed, we use the default values based on model name.
-    default_params = get_default_params(args.model)
-    for name, val in default_params.items():
-        if getattr(args, name) is None:
-            setattr(args, name, val)
-
-    return args
diff --git a/spaces/xxccc/gpt-academic/request_llm/edge_gpt.py b/spaces/xxccc/gpt-academic/request_llm/edge_gpt.py
deleted file mode 100644
index bbf84000d84a42de80d3c051a24f06336af76aaf..0000000000000000000000000000000000000000
--- a/spaces/xxccc/gpt-academic/request_llm/edge_gpt.py
+++ /dev/null
@@ -1,409 +0,0 @@
-"""
-========================================================================
-第一部分：来自EdgeGPT.py
-https://github.com/acheong08/EdgeGPT
-========================================================================
-"""
-
-import argparse
-import asyncio
-import json
-import os
-import random
-import re
-import ssl
-import sys
-import uuid
-from enum import Enum
-from typing import Generator
-from typing import Literal
-from typing import Optional
-from typing import Union
-import websockets.client as websockets
-
-DELIMITER = "\x1e"
-
-
-# Generate random IP between range 13.104.0.0/14
-FORWARDED_IP = (
-    f"13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}"
-)
-
-HEADERS = {
-    "accept": "application/json",
-    "accept-language": "en-US,en;q=0.9",
-    "content-type": "application/json",
-    "sec-ch-ua": '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
-    "sec-ch-ua-arch": '"x86"',
-    "sec-ch-ua-bitness": '"64"',
-    "sec-ch-ua-full-version": '"109.0.1518.78"',
-    "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-    "sec-ch-ua-mobile": "?0",
-    "sec-ch-ua-model": "",
-    "sec-ch-ua-platform": '"Windows"',
-    "sec-ch-ua-platform-version": '"15.0.0"',
-    "sec-fetch-dest": "empty",
-    "sec-fetch-mode": "cors",
-    "sec-fetch-site": "same-origin",
-    "x-ms-client-request-id": str(uuid.uuid4()),
-    "x-ms-useragent": "azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32",
-    "Referer": "https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx",
-    "Referrer-Policy": "origin-when-cross-origin",
-    "x-forwarded-for": FORWARDED_IP,
-}
-
-HEADERS_INIT_CONVER = {
-    "authority": "edgeservices.bing.com",
-    "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
-    "accept-language": "en-US,en;q=0.9",
-    "cache-control": "max-age=0",
-    "sec-ch-ua": '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
-    "sec-ch-ua-arch": '"x86"',
-    "sec-ch-ua-bitness": '"64"',
-    "sec-ch-ua-full-version": '"110.0.1587.69"',
-    "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-    "sec-ch-ua-mobile": "?0",
-    "sec-ch-ua-model": '""',
-    "sec-ch-ua-platform": '"Windows"',
-    "sec-ch-ua-platform-version": '"15.0.0"',
-    "sec-fetch-dest": "document",
-    "sec-fetch-mode": "navigate",
-    "sec-fetch-site": "none",
-    "sec-fetch-user": "?1",
-    "upgrade-insecure-requests": "1",
-    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69",
-    "x-edge-shopping-flag": "1",
-    "x-forwarded-for": FORWARDED_IP,
-}
-
-def get_ssl_context():
-    import certifi
-    ssl_context = ssl.create_default_context()
-    ssl_context.load_verify_locations(certifi.where())
-    return ssl_context
-
-
-
-class NotAllowedToAccess(Exception):
-    pass
-
-
-class ConversationStyle(Enum):
-    creative = "h3imaginative,clgalileo,gencontentv3"
-    balanced = "galileo"
-    precise = "h3precise,clgalileo"
-
-
-CONVERSATION_STYLE_TYPE = Optional[
-    Union[ConversationStyle, Literal["creative", "balanced", "precise"]]
-]
-
-
-def _append_identifier(msg: dict) -> str:
-    """
-    Appends special character to end of message to identify end of message
-    """
-    # Convert dict to json string
-    return json.dumps(msg) + DELIMITER
-
-
-def _get_ran_hex(length: int = 32) -> str:
-    """
-    Returns random hex string
-    """
-    return "".join(random.choice("0123456789abcdef") for _ in range(length))
-
-
-class _ChatHubRequest:
-    """
-    Request object for ChatHub
-    """
-
-    def __init__(
-        self,
-        conversation_signature: str,
-        client_id: str,
-        conversation_id: str,
-        invocation_id: int = 0,
-    ) -> None:
-        self.struct: dict = {}
-
-        self.client_id: str = client_id
-        self.conversation_id: str = conversation_id
-        self.conversation_signature: str = conversation_signature
-        self.invocation_id: int = invocation_id
-
-    def update(
-        self,
-        prompt,
-        conversation_style,
-        options,
-    ) -> None:
-        """
-        Updates request object
-        """
-        if options is None:
-            options = [
-                "deepleo",
-                "enable_debug_commands",
-                "disable_emoji_spoken_text",
-                "enablemm",
-            ]
-        if conversation_style:
-            if not isinstance(conversation_style, ConversationStyle):
-                conversation_style = getattr(ConversationStyle, conversation_style)
-            options = [
-                "nlu_direct_response_filter",
-                "deepleo",
-                "disable_emoji_spoken_text",
-                "responsible_ai_policy_235",
-                "enablemm",
-                conversation_style.value,
-                "dtappid",
-                "cricinfo",
-                "cricinfov2",
-                "dv3sugg",
-            ]
-        self.struct = {
-            "arguments": [
-                {
-                    "source": "cib",
-                    "optionsSets": options,
-                    "sliceIds": [
-                        "222dtappid",
-                        "225cricinfo",
-                        "224locals0",
-                    ],
-                    "traceId": _get_ran_hex(32),
-                    "isStartOfSession": self.invocation_id == 0,
-                    "message": {
-                        "author": "user",
-                        "inputMethod": "Keyboard",
-                        "text": prompt,
-                        "messageType": "Chat",
-                    },
-                    "conversationSignature": self.conversation_signature,
-                    "participant": {
-                        "id": self.client_id,
-                    },
-                    "conversationId": self.conversation_id,
-                },
-            ],
-            "invocationId": str(self.invocation_id),
-            "target": "chat",
-            "type": 4,
-        }
-        self.invocation_id += 1
-
-
-class _Conversation:
-    """
-    Conversation API
-    """
-
-    def __init__(
-        self,
-        cookies,
-        proxy,
-    ) -> None:
-        self.struct: dict = {
-            "conversationId": None,
-            "clientId": None,
-            "conversationSignature": None,
-            "result": {"value": "Success", "message": None},
-        }
-        import httpx
-        self.proxy = proxy
-        proxy = (
-            proxy
-            or os.environ.get("all_proxy")
-            or os.environ.get("ALL_PROXY")
-            or os.environ.get("https_proxy")
-            or os.environ.get("HTTPS_PROXY")
-            or None
-        )
-        if proxy is not None and proxy.startswith("socks5h://"):
-            proxy = "socks5://" + proxy[len("socks5h://") :]
-        self.session = httpx.Client(
-            proxies=proxy,
-            timeout=30,
-            headers=HEADERS_INIT_CONVER,
-        )
-        for cookie in cookies:
-            self.session.cookies.set(cookie["name"], cookie["value"])
-
-        # Send GET request
-        response = self.session.get(
-            url=os.environ.get("BING_PROXY_URL")
-            or "https://edgeservices.bing.com/edgesvc/turing/conversation/create",
-        )
-        if response.status_code != 200:
-            response = self.session.get(
-                "https://edge.churchless.tech/edgesvc/turing/conversation/create",
-            )
-        if response.status_code != 200:
-            print(f"Status code: {response.status_code}")
-            print(response.text)
-            print(response.url)
-            raise Exception("Authentication failed")
-        try:
-            self.struct = response.json()
-        except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc:
-            raise Exception(
-                "Authentication failed. You have not been accepted into the beta.",
-            ) from exc
-        if self.struct["result"]["value"] == "UnauthorizedRequest":
-            raise NotAllowedToAccess(self.struct["result"]["message"])
-
-
-class _ChatHub:
-    """
-    Chat API
-    """
-
-    def __init__(self, conversation) -> None:
-        self.wss = None
-        self.request: _ChatHubRequest
-        self.loop: bool
-        self.task: asyncio.Task
-        print(conversation.struct)
-        self.request = _ChatHubRequest(
-            conversation_signature=conversation.struct["conversationSignature"],
-            client_id=conversation.struct["clientId"],
-            conversation_id=conversation.struct["conversationId"],
-        )
-
-    async def ask_stream(
-        self,
-        prompt: str,
-        wss_link: str,
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        raw: bool = False,
-        options: dict = None,
-    ) -> Generator[str, None, None]:
-        """
-        Ask a question to the bot
-        """
-        if self.wss and not self.wss.closed:
-            await self.wss.close()
-        # Check if websocket is closed
-        self.wss = await websockets.connect(
-            wss_link,
-            extra_headers=HEADERS,
-            max_size=None,
-            ssl=get_ssl_context()
-        )
-        await self._initial_handshake()
-        # Construct a ChatHub request
-        self.request.update(
-            prompt=prompt,
-            conversation_style=conversation_style,
-            options=options,
-        )
-        # Send request
-        await self.wss.send(_append_identifier(self.request.struct))
-        final = False
-        while not final:
-            objects = str(await self.wss.recv()).split(DELIMITER)
-            for obj in objects:
-                if obj is None or not obj:
-                    continue
-                response = json.loads(obj)
-                if response.get("type") != 2 and raw:
-                    yield False, response
-                elif response.get("type") == 1 and response["arguments"][0].get(
-                    "messages",
-                ):
-                    resp_txt = response["arguments"][0]["messages"][0]["adaptiveCards"][
-                        0
-                    ]["body"][0].get("text")
-                    yield False, resp_txt
-                elif response.get("type") == 2:
-                    final = True
-                    yield True, response
-
-    async def _initial_handshake(self) -> None:
-        await self.wss.send(_append_identifier({"protocol": "json", "version": 1}))
-        await self.wss.recv()
-
-    async def close(self) -> None:
-        """
-        Close the connection
-        """
-        if self.wss and not self.wss.closed:
-            await self.wss.close()
-
-
-class NewbingChatbot:
-    """
-    Combines everything to make it seamless
-    """
-
-    def __init__(
-        self,
-        cookies,
-        proxy
-    ) -> None:
-        if cookies is None:
-            cookies = {}
-        self.cookies = cookies
-        self.proxy = proxy
-        self.chat_hub: _ChatHub = _ChatHub(
-            _Conversation(self.cookies, self.proxy),
-        )
-
-    async def ask(
-        self,
-        prompt: str,
-        wss_link: str,
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        options: dict = None,
-    ) -> dict:
-        """
-        Ask a question to the bot
-        """
-        async for final, response in self.chat_hub.ask_stream(
-            prompt=prompt,
-            conversation_style=conversation_style,
-            wss_link=wss_link,
-            options=options,
-        ):
-            if final:
-                return response
-        await self.chat_hub.wss.close()
-        return None
-
-    async def ask_stream(
-        self,
-        prompt: str,
-        wss_link: str,
-        conversation_style: CONVERSATION_STYLE_TYPE = None,
-        raw: bool = False,
-        options: dict = None,
-    ) -> Generator[str, None, None]:
-        """
-        Ask a question to the bot
-        """
-        async for response in self.chat_hub.ask_stream(
-            prompt=prompt,
-            conversation_style=conversation_style,
-            wss_link=wss_link,
-            raw=raw,
-            options=options,
-        ):
-            yield response
-
-    async def close(self) -> None:
-        """
-        Close the connection
-        """
-        await self.chat_hub.close()
-
-    async def reset(self) -> None:
-        """
-        Reset the conversation
-        """
-        await self.close()
-        self.chat_hub = _ChatHub(_Conversation(self.cookies, self.proxy))
-
-
diff --git a/spaces/xxie92/antibody_visulization/app.py b/spaces/xxie92/antibody_visulization/app.py
deleted file mode 100644
index be0406524c8462ae2aa57a88610bcced60fc4667..0000000000000000000000000000000000000000
--- a/spaces/xxie92/antibody_visulization/app.py
+++ /dev/null
@@ -1,373 +0,0 @@
-import sys
-sys.path.append('./diffab-repo')
-import os
-import shutil
-import pandas as pd
-import yaml
-import subprocess
-import streamlit as st
-import stmol
-import py3Dmol
-import tempfile
-import re
-import abnumber
-import gzip
-import tarfile
-import torch
-from tqdm.auto import tqdm
-from Bio import PDB
-from collections import OrderedDict
-
-from diffab.tools.renumber import renumber as renumber_antibody
-from diffab.tools.renumber.run import (
-    biopython_chain_to_sequence, 
-    assign_number_to_sequence,
-)
-
-CDR_OPTIONS = OrderedDict()
-CDR_OPTIONS['H_CDR1'] = 'H1'
-CDR_OPTIONS['H_CDR2'] = 'H2'
-CDR_OPTIONS['H_CDR3'] = 'H3'
-CDR_OPTIONS['L_CDR1'] = 'L1'
-CDR_OPTIONS['L_CDR2'] = 'L2'
-CDR_OPTIONS['L_CDR3'] = 'L3'
-
-DESIGN_MODES = OrderedDict()
-DESIGN_MODES['denovo'] = 'De novo design'
-DESIGN_MODES['denovo_dock'] = 'De novo design (with HDOCK)'
-DESIGN_MODES['opt'] = 'Optimization'
-DESIGN_MODES['fixbb'] = 'Fix-backbone'
-
-MODE_CONFIG = {
-    'denovo': './configs/test/codesign_multicdrs.yml',
-    'denovo_dock': './configs/test/codesign_multicdrs.yml',
-    'opt': './configs/test/abopt_singlecdr.yml',
-    'fixbb': './configs/test/fixbb.yml',
-}
-
-GPU_AVAILABLE = torch.cuda.is_available()
-DEFAULT_NUM_SAMPLES = 5 if GPU_AVAILABLE else 1
-DEFAULT_NUM_DOCKS = 3
-
-
-def dict_to_func(d):
-    def f(x):
-        return d[x]
-    return f
-
-
-def get_config(save_dir, mode, cdrs, num_samples=5, optimization_step=4):
-    tmpl_path = MODE_CONFIG[mode]
-    with open(tmpl_path, 'r') as f:
-        cfg = yaml.safe_load(f)
-    cfg['sampling']['cdrs'] = cdrs
-    cfg['sampling']['num_samples'] = num_samples
-    cfg['sampling']['optimize_steps'] = [optimization_step, ]
-
-    save_path = os.path.join(save_dir, 'design.yml')
-    with open(save_path, 'w') as f:
-        yaml.dump(cfg, f)
-    return cfg, save_path
-
-
-def run_design(pdb_path, config_path, output_dir, docking, display_widget, num_docks=DEFAULT_NUM_DOCKS):
-    if docking:
-        cmd = f"python design_dock.py --antigen {pdb_path} --config {config_path} --num_docks {num_docks} "
-    else:
-        cmd = f"python design_pdb.py {pdb_path} --config {config_path} "
-    cmd += f"--batch_size 1 --out_root {output_dir} "
-
-    if GPU_AVAILABLE:
-        cmd += "--device cuda"
-    else:
-        cmd += "--device cpu"
-
-    result_dir = os.path.join(output_dir, 'design')
-    if os.path.exists(result_dir):
-        shutil.rmtree(result_dir)
-    
-    output_buffer = ''
-    proc = subprocess.Popen(
-        cmd,
-        shell=True,
-        env=os.environ.copy(),
-        bufsize=1,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        cwd=os.getcwd(),
-    )
-    for line in iter(proc.stdout.readline, b''):
-        output_buffer += line.decode()
-
-        display_widget.code( 
-            '\n'.join(output_buffer.splitlines()[-10:]),
-        )
-    proc.stdout.close()
-    proc.wait()
-
-
-@st.cache
-def renumber_antibody_cached(in_pdb, out_pdb, file_id):
-    return renumber_antibody(
-        in_pdb, out_pdb, return_other_chains=True
-    )
-
-
-def gather_results(result_dir):
-    outputs = []
-    for root, dirs, files in os.walk(result_dir):
-        for fname in files:
-            if not re.match('^\d\d\d\d\.pdb$', fname):
-                continue
-            fpath = os.path.join(root, fname)
-            gname = os.path.basename(root)
-
-            outputs.append((gname, fname, fpath))
-    
-    parser = PDB.PDBParser(QUIET=True)
-    records = []
-    fpath_to_name = {}
-    for gname, fname, fpath in tqdm(outputs):
-        name = f"{gname}_{fname}"
-        structure = parser.get_structure(name, fpath)
-        model = structure[0]
-        record = {
-            'name': name,
-            'H1': None, 'H2': None, 'H3': None,
-            'L1': None, 'L2': None, 'L3': None,
-            'gname': gname, 'fname': fname, 'fpath': fpath,
-        }
-        for chain in model:
-            try:
-                seq, reslist = biopython_chain_to_sequence(chain)
-                numbers, abchain = assign_number_to_sequence(seq)
-                if abchain.chain_type == 'H':
-                    record['H1'] = abchain.cdr1_seq
-                    record['H2'] = abchain.cdr2_seq
-                    record['H3'] = abchain.cdr3_seq
-                elif abchain.chain_type in ('L', 'K'):
-                    record['L1'] = abchain.cdr1_seq
-                    record['L2'] = abchain.cdr2_seq
-                    record['L3'] = abchain.cdr3_seq
-            except abnumber.ChainParseError as e:
-                pass
-        records.append(record)
-        fpath_to_name[fpath] = name
-
-    with tarfile.open(os.path.join(result_dir, 'generated.tar.gz'), 'w:gz') as tar:
-        for record in records:
-            info = tar.gettarinfo(record['fpath'])
-            info.name = record['name']
-            tar.addfile(
-                tarinfo = info,
-                fileobj = open(record['fpath'], 'rb'),
-            )
-    
-    records = pd.DataFrame(records)
-
-    return records, fpath_to_name
-
-
-def main():
-    # Temporary workspace directory
-    if 'tempdir_path' not in st.session_state:
-        tempdir_path = tempfile.mkdtemp(prefix='streamlit')
-        st.session_state.tempdir_path = tempdir_path
-    else:
-        tempdir_path = st.session_state.tempdir_path
-    # Page layout
-    st.set_page_config(layout="wide")
-    st.markdown(
-        "# DiffAb \n\n"
-        "Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (NeurIPS 2022) \n\n"
-        "[[Paper](https://www.biorxiv.org/content/10.1101/2022.07.10.499510.abstract)] "
-        "[[Code](https://github.com/luost26/diffab)]"
-    )
-    left_col, right_col = st.columns(2)
-
-    # Step 1: Upload PDB or choose an example
-    uploaded_file = None
-    with left_col:
-        uploaded_file = st.file_uploader(
-            'Antigen structure or antibody-antigen complex',
-            # disabled=True
-        )
-
-        if uploaded_file is None:
-            st.session_state.submit = False
-            st.session_state.done = False
-
-            with st.expander("Don't know what to upload? Try these examples", expanded=True):
-                with open('./data/examples/7DK2_AB_C.pdb', 'r') as f:
-                    st.download_button(
-                        'RBD + Antibody Complex', 
-                        data = f,
-                        file_name='RBD_AbAg.pdb',
-                    )
-                with open('./data/examples/Omicron_RBD.pdb', 'r') as f:
-                    st.download_button(
-                        'RBD Antigen Only (Much slower)', 
-                        data = f,
-                        file_name = 'RBD_AgOnly.pdb',
-                    )
-                st.text('Please upload the downloaded PDB file to run the demo.')
-
-    # Step 1.2: Retrieve uploaded PDB
-    if uploaded_file is not None:
-        pdb_path = os.path.join(tempdir_path, 'structure.pdb')
-        renum_path = os.path.join(tempdir_path, 'structure_renumber.pdb')
-        with open(pdb_path, 'w') as f:
-            f.write(uploaded_file.getvalue().decode())
-        H_chains, L_chains, Ag_chains = renumber_antibody_cached(
-            in_pdb = pdb_path,
-            out_pdb = renum_path,
-            file_id = uploaded_file.id
-        )
-        H_chain = H_chains[0] if H_chains else None
-        L_chain = L_chains[0] if L_chains else None
-        docking = H_chain is None and L_chain is None
-
-    # Step 2: Design options
-    if uploaded_file is not None:
-        with left_col:
-            st.dataframe(pd.DataFrame({
-                'Heavy': {'Chain': H_chain},
-                'Light': {'Chain': L_chain},
-                'Antigen': {'Chain': ','.join(Ag_chains)},
-            }))
-
-            if docking:
-                st.warning('No antibodies detected. Will try to run docking (very slow).')
-
-            # form = st.form('design_form')
-            form = st.container()
-            with form:
-                if H_chain is None and L_chain is None:
-                    # Antigen only
-                    cdr_options = ['H_CDR1', 'H_CDR2', 'H_CDR3', 'L_CDR1', 'L_CDR2', 'L_CDR3']
-                    cdr_default = ['H_CDR1', 'H_CDR2', 'H_CDR3']
-                    mode_options = ['denovo_dock']
-                elif H_chain is not None and L_chain is None:
-                    # Heavy chain + Antigen
-                    cdr_options = ['H_CDR1', 'H_CDR2', 'H_CDR3']
-                    cdr_default = ['H_CDR1', 'H_CDR2', 'H_CDR3']
-                    mode_options = ['denovo', 'opt', 'fixbb']
-                elif H_chain is None and L_chain is not None:
-                    # Light chain + Antigen
-                    cdr_options = ['L_CDR1', 'L_CDR2', 'L_CDR3']
-                    cdr_default = ['L_CDR1', 'L_CDR2', 'L_CDR3']
-                    mode_options = ['denovo', 'opt', 'fixbb']
-                else:
-                    # H + L + Ag
-                    cdr_options = ['H_CDR1', 'H_CDR2', 'H_CDR3', 'L_CDR1', 'L_CDR2', 'L_CDR3']
-                    cdr_default = ['H_CDR1', 'H_CDR2', 'H_CDR3']
-                    mode_options = ['denovo', 'opt', 'fixbb']
-                
-                design_mode = st.radio(
-                    'Mode',
-                    mode_options,
-                    format_func=dict_to_func(DESIGN_MODES),
-                    # disabled=True,
-                )
-                cdr_choices = st.multiselect(
-                    'CDRs',
-                    cdr_options,
-                    default = cdr_default,
-                    format_func=dict_to_func(CDR_OPTIONS),
-                    # disabled=True,
-                )
-
-                if docking:
-                    num_docks = st.slider(
-                        'Number of docking poses', 
-                        min_value=1, max_value=10, value=DEFAULT_NUM_DOCKS,
-                    )
-                else:
-                    num_docks = 0
-                num_designs = st.slider(
-                    'Number of samples',
-                    min_value=1, max_value=10, value=DEFAULT_NUM_SAMPLES,
-                )
-
-                if not GPU_AVAILABLE:
-                    st.warning('No GPU available. Sampling might be very slow.')
-
-                btn_placeholder = st.empty()
-                submit = btn_placeholder.button('Run', key="run_btn_real")
-                st.session_state.submit = st.session_state.submit or submit
-                if submit:
-                    st.session_state.done = False
-                    btn_placeholder.empty()
-
-    # Step 3: Prepare configuration and run design
-    if uploaded_file is not None and st.session_state.submit:
-
-        with left_col:
-            output_display = st.empty()
-
-        with right_col:
-            result_molecule_display = st.empty()
-            result_select_widget = st.empty()
-            result_table_display = st.empty()
-            result_download_btn = st.empty()
-
-        if not st.session_state.done:
-            output_display.code('[INFO] Your job has been submitted. Please wait...\n')
-
-            config, config_path = get_config(
-                save_dir = tempdir_path,
-                mode = design_mode,
-                cdrs = cdr_choices,
-                num_samples = num_designs,
-            )
-
-            run_design(
-                pdb_path = renum_path,
-                config_path = config_path,
-                output_dir = tempdir_path,
-                docking = docking,
-                display_widget = output_display,
-                num_docks = num_docks,
-            )
-            st.session_state.done = True
-    
-            result_dir = os.path.join(tempdir_path, 'design')
-            df_cols = ['name'] + list(CDR_OPTIONS.values())
-            df_results, fpath_to_name = gather_results(result_dir)
-            st.session_state.results = (df_results, fpath_to_name)
-
-    # Step 5: Show results:
-    if st.session_state.submit and st.session_state.done:
-        result_dir = os.path.join(tempdir_path, 'design')
-        df_results, fpath_to_name = st.session_state.results
-
-        df_cols = ['name'] + list(CDR_OPTIONS.values())
-        result_table_display.dataframe(df_results[df_cols])
-
-        display_pdb_path = result_select_widget.selectbox(
-            label = "Visualize",
-            options = df_results['fpath'],
-            format_func = dict_to_func(fpath_to_name),
-        )
-
-        with open(os.path.join(result_dir, 'generated.tar.gz'), 'rb') as f:
-            result_download_btn.download_button(
-                label = "Download PDBs",
-                data = f,
-                file_name = "generated.tar.gz",
-            )
-
-        if not os.path.exists(display_pdb_path):
-            display_pdb_path = df_results['fpath'][0]
-        with open(display_pdb_path, 'r') as f:
-            pdb_str = f.read()
-        xyzview = py3Dmol.view(width=380, height=380)
-        xyzview.addModelsAsFrames(pdb_str)
-        xyzview.setStyle({'cartoon':{'color':'spectrum'}})
-        xyzview.zoomTo()
-        with result_molecule_display:
-            stmol.showmol(xyzview, width=380, height=380)
-    
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/ybelkada/interfacegan_pp/models/pggan_tf_official/train.py b/spaces/ybelkada/interfacegan_pp/models/pggan_tf_official/train.py
deleted file mode 100644
index 1deb1220891716a440bb545965b00e9461b9ecdd..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/interfacegan_pp/models/pggan_tf_official/train.py
+++ /dev/null
@@ -1,288 +0,0 @@
-# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-import os
-import time
-import numpy as np
-import tensorflow as tf
-
-import config
-import tfutil
-import dataset
-import misc
-
-#----------------------------------------------------------------------------
-# Choose the size and contents of the image snapshot grids that are exported
-# periodically during training.
-
-def setup_snapshot_image_grid(G, training_set,
-    size    = '1080p',      # '1080p' = to be viewed on 1080p display, '4k' = to be viewed on 4k display.
-    layout  = 'random'):    # 'random' = grid contents are selected randomly, 'row_per_class' = each row corresponds to one class label.
-
-    # Select size.
-    gw = 1; gh = 1
-    if size == '1080p':
-        gw = np.clip(1920 // G.output_shape[3], 3, 32)
-        gh = np.clip(1080 // G.output_shape[2], 2, 32)
-    if size == '4k':
-        gw = np.clip(3840 // G.output_shape[3], 7, 32)
-        gh = np.clip(2160 // G.output_shape[2], 4, 32)
-
-    # Fill in reals and labels.
-    reals = np.zeros([gw * gh] + training_set.shape, dtype=training_set.dtype)
-    labels = np.zeros([gw * gh, training_set.label_size], dtype=training_set.label_dtype)
-    for idx in range(gw * gh):
-        x = idx % gw; y = idx // gw
-        while True:
-            real, label = training_set.get_minibatch_np(1)
-            if layout == 'row_per_class' and training_set.label_size > 0:
-                if label[0, y % training_set.label_size] == 0.0:
-                    continue
-            reals[idx] = real[0]
-            labels[idx] = label[0]
-            break
-
-    # Generate latents.
-    latents = misc.random_latents(gw * gh, G)
-    return (gw, gh), reals, labels, latents
-
-#----------------------------------------------------------------------------
-# Just-in-time processing of training images before feeding them to the networks.
-
-def process_reals(x, lod, mirror_augment, drange_data, drange_net):
-    with tf.name_scope('ProcessReals'):
-        with tf.name_scope('DynamicRange'):
-            x = tf.cast(x, tf.float32)
-            x = misc.adjust_dynamic_range(x, drange_data, drange_net)
-        if mirror_augment:
-            with tf.name_scope('MirrorAugment'):
-                s = tf.shape(x)
-                mask = tf.random_uniform([s[0], 1, 1, 1], 0.0, 1.0)
-                mask = tf.tile(mask, [1, s[1], s[2], s[3]])
-                x = tf.where(mask < 0.5, x, tf.reverse(x, axis=[3]))
-        with tf.name_scope('FadeLOD'): # Smooth crossfade between consecutive levels-of-detail.
-            s = tf.shape(x)
-            y = tf.reshape(x, [-1, s[1], s[2]//2, 2, s[3]//2, 2])
-            y = tf.reduce_mean(y, axis=[3, 5], keepdims=True)
-            y = tf.tile(y, [1, 1, 1, 2, 1, 2])
-            y = tf.reshape(y, [-1, s[1], s[2], s[3]])
-            x = tfutil.lerp(x, y, lod - tf.floor(lod))
-        with tf.name_scope('UpscaleLOD'): # Upscale to match the expected input/output size of the networks.
-            s = tf.shape(x)
-            factor = tf.cast(2 ** tf.floor(lod), tf.int32)
-            x = tf.reshape(x, [-1, s[1], s[2], 1, s[3], 1])
-            x = tf.tile(x, [1, 1, 1, factor, 1, factor])
-            x = tf.reshape(x, [-1, s[1], s[2] * factor, s[3] * factor])
-        return x
-
-#----------------------------------------------------------------------------
-# Class for evaluating and storing the values of time-varying training parameters.
-
-class TrainingSchedule:
-    def __init__(
-        self,
-        cur_nimg,
-        training_set,
-        lod_initial_resolution  = 4,        # Image resolution used at the beginning.
-        lod_training_kimg       = 600,      # Thousands of real images to show before doubling the resolution.
-        lod_transition_kimg     = 600,      # Thousands of real images to show when fading in new layers.
-        minibatch_base          = 16,       # Maximum minibatch size, divided evenly among GPUs.
-        minibatch_dict          = {},       # Resolution-specific overrides.
-        max_minibatch_per_gpu   = {},       # Resolution-specific maximum minibatch size per GPU.
-        G_lrate_base            = 0.001,    # Learning rate for the generator.
-        G_lrate_dict            = {},       # Resolution-specific overrides.
-        D_lrate_base            = 0.001,    # Learning rate for the discriminator.
-        D_lrate_dict            = {},       # Resolution-specific overrides.
-        tick_kimg_base          = 160,      # Default interval of progress snapshots.
-        tick_kimg_dict          = {4: 160, 8:140, 16:120, 32:100, 64:80, 128:60, 256:40, 512:20, 1024:10}): # Resolution-specific overrides.
-
-        # Training phase.
-        self.kimg = cur_nimg / 1000.0
-        phase_dur = lod_training_kimg + lod_transition_kimg
-        phase_idx = int(np.floor(self.kimg / phase_dur)) if phase_dur > 0 else 0
-        phase_kimg = self.kimg - phase_idx * phase_dur
-
-        # Level-of-detail and resolution.
-        self.lod = training_set.resolution_log2
-        self.lod -= np.floor(np.log2(lod_initial_resolution))
-        self.lod -= phase_idx
-        if lod_transition_kimg > 0:
-            self.lod -= max(phase_kimg - lod_training_kimg, 0.0) / lod_transition_kimg
-        self.lod = max(self.lod, 0.0)
-        self.resolution = 2 ** (training_set.resolution_log2 - int(np.floor(self.lod)))
-
-        # Minibatch size.
-        self.minibatch = minibatch_dict.get(self.resolution, minibatch_base)
-        self.minibatch -= self.minibatch % config.num_gpus
-        if self.resolution in max_minibatch_per_gpu:
-            self.minibatch = min(self.minibatch, max_minibatch_per_gpu[self.resolution] * config.num_gpus)
-
-        # Other parameters.
-        self.G_lrate = G_lrate_dict.get(self.resolution, G_lrate_base)
-        self.D_lrate = D_lrate_dict.get(self.resolution, D_lrate_base)
-        self.tick_kimg = tick_kimg_dict.get(self.resolution, tick_kimg_base)
-
-#----------------------------------------------------------------------------
-# Main training script.
-# To run, comment/uncomment appropriate lines in config.py and launch train.py.
-
-def train_progressive_gan(
-    G_smoothing             = 0.999,        # Exponential running average of generator weights.
-    D_repeats               = 1,            # How many times the discriminator is trained per G iteration.
-    minibatch_repeats       = 4,            # Number of minibatches to run before adjusting training parameters.
-    reset_opt_for_new_lod   = True,         # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
-    total_kimg              = 15000,        # Total length of the training, measured in thousands of real images.
-    mirror_augment          = False,        # Enable mirror augment?
-    drange_net              = [-1,1],       # Dynamic range used when feeding image data to the networks.
-    image_snapshot_ticks    = 1,            # How often to export image snapshots?
-    network_snapshot_ticks  = 10,           # How often to export network snapshots?
-    save_tf_graph           = False,        # Include full TensorFlow computation graph in the tfevents file?
-    save_weight_histograms  = False,        # Include weight histograms in the tfevents file?
-    resume_run_id           = None,         # Run ID or network pkl to resume training from, None = start from scratch.
-    resume_snapshot         = None,         # Snapshot index to resume training from, None = autodetect.
-    resume_kimg             = 0.0,          # Assumed training progress at the beginning. Affects reporting and training schedule.
-    resume_time             = 0.0):         # Assumed wallclock time at the beginning. Affects reporting.
-
-    maintenance_start_time = time.time()
-    training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **config.dataset)
-
-    # Construct networks.
-    with tf.device('/gpu:0'):
-        if resume_run_id is not None:
-            network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
-            print('Loading networks from "%s"...' % network_pkl)
-            G, D, Gs = misc.load_pkl(network_pkl)
-        else:
-            print('Constructing networks...')
-            G = tfutil.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **config.G)
-            D = tfutil.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **config.D)
-            Gs = G.clone('Gs')
-        Gs_update_op = Gs.setup_as_moving_average_of(G, beta=G_smoothing)
-    G.print_layers(); D.print_layers()
-
-    print('Building TensorFlow graph...')
-    with tf.name_scope('Inputs'):
-        lod_in          = tf.placeholder(tf.float32, name='lod_in', shape=[])
-        lrate_in        = tf.placeholder(tf.float32, name='lrate_in', shape=[])
-        minibatch_in    = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
-        minibatch_split = minibatch_in // config.num_gpus
-        reals, labels   = training_set.get_minibatch_tf()
-        reals_split     = tf.split(reals, config.num_gpus)
-        labels_split    = tf.split(labels, config.num_gpus)
-    G_opt = tfutil.Optimizer(name='TrainG', learning_rate=lrate_in, **config.G_opt)
-    D_opt = tfutil.Optimizer(name='TrainD', learning_rate=lrate_in, **config.D_opt)
-    for gpu in range(config.num_gpus):
-        with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
-            G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
-            D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
-            lod_assign_ops = [tf.assign(G_gpu.find_var('lod'), lod_in), tf.assign(D_gpu.find_var('lod'), lod_in)]
-            reals_gpu = process_reals(reals_split[gpu], lod_in, mirror_augment, training_set.dynamic_range, drange_net)
-            labels_gpu = labels_split[gpu]
-            with tf.name_scope('G_loss'), tf.control_dependencies(lod_assign_ops):
-                G_loss = tfutil.call_func_by_name(G=G_gpu, D=D_gpu, opt=G_opt, training_set=training_set, minibatch_size=minibatch_split, **config.G_loss)
-            with tf.name_scope('D_loss'), tf.control_dependencies(lod_assign_ops):
-                D_loss = tfutil.call_func_by_name(G=G_gpu, D=D_gpu, opt=D_opt, training_set=training_set, minibatch_size=minibatch_split, reals=reals_gpu, labels=labels_gpu, **config.D_loss)
-            G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
-            D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
-    G_train_op = G_opt.apply_updates()
-    D_train_op = D_opt.apply_updates()
-
-    print('Setting up snapshot image grid...')
-    grid_size, grid_reals, grid_labels, grid_latents = setup_snapshot_image_grid(G, training_set, **config.grid)
-    sched = TrainingSchedule(total_kimg * 1000, training_set, **config.sched)
-    grid_fakes = Gs.run(grid_latents, grid_labels, minibatch_size=sched.minibatch//config.num_gpus)
-
-    print('Setting up result dir...')
-    result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
-    misc.save_image_grid(grid_reals, os.path.join(result_subdir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
-    misc.save_image_grid(grid_fakes, os.path.join(result_subdir, 'fakes%06d.png' % 0), drange=drange_net, grid_size=grid_size)
-    summary_log = tf.summary.FileWriter(result_subdir)
-    if save_tf_graph:
-        summary_log.add_graph(tf.get_default_graph())
-    if save_weight_histograms:
-        G.setup_weight_histograms(); D.setup_weight_histograms()
-
-    print('Training...')
-    cur_nimg = int(resume_kimg * 1000)
-    cur_tick = 0
-    tick_start_nimg = cur_nimg
-    tick_start_time = time.time()
-    train_start_time = tick_start_time - resume_time
-    prev_lod = -1.0
-    while cur_nimg < total_kimg * 1000:
-
-        # Choose training parameters and configure training ops.
-        sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
-        training_set.configure(sched.minibatch, sched.lod)
-        if reset_opt_for_new_lod:
-            if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(sched.lod) != np.ceil(prev_lod):
-                G_opt.reset_optimizer_state(); D_opt.reset_optimizer_state()
-        prev_lod = sched.lod
-
-        # Run training ops.
-        for repeat in range(minibatch_repeats):
-            for _ in range(D_repeats):
-                tfutil.run([D_train_op, Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch})
-                cur_nimg += sched.minibatch
-            tfutil.run([G_train_op], {lod_in: sched.lod, lrate_in: sched.G_lrate, minibatch_in: sched.minibatch})
-
-        # Perform maintenance tasks once per tick.
-        done = (cur_nimg >= total_kimg * 1000)
-        if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
-            cur_tick += 1
-            cur_time = time.time()
-            tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
-            tick_start_nimg = cur_nimg
-            tick_time = cur_time - tick_start_time
-            total_time = cur_time - train_start_time
-            maintenance_time = tick_start_time - maintenance_start_time
-            maintenance_start_time = cur_time
-
-            # Report progress.
-            print('tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %.1f' % (
-                tfutil.autosummary('Progress/tick', cur_tick),
-                tfutil.autosummary('Progress/kimg', cur_nimg / 1000.0),
-                tfutil.autosummary('Progress/lod', sched.lod),
-                tfutil.autosummary('Progress/minibatch', sched.minibatch),
-                misc.format_time(tfutil.autosummary('Timing/total_sec', total_time)),
-                tfutil.autosummary('Timing/sec_per_tick', tick_time),
-                tfutil.autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
-                tfutil.autosummary('Timing/maintenance_sec', maintenance_time)))
-            tfutil.autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
-            tfutil.autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
-            tfutil.save_summaries(summary_log, cur_nimg)
-
-            # Save snapshots.
-            if cur_tick % image_snapshot_ticks == 0 or done:
-                grid_fakes = Gs.run(grid_latents, grid_labels, minibatch_size=sched.minibatch//config.num_gpus)
-                misc.save_image_grid(grid_fakes, os.path.join(result_subdir, 'fakes%06d.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
-            if cur_tick % network_snapshot_ticks == 0 or done:
-                misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-snapshot-%06d.pkl' % (cur_nimg // 1000)))
-
-            # Record start time of the next tick.
-            tick_start_time = time.time()
-
-    # Write final results.
-    misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-final.pkl'))
-    summary_log.close()
-    open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
-
-#----------------------------------------------------------------------------
-# Main entry point.
-# Calls the function indicated in config.py.
-
-if __name__ == "__main__":
-    misc.init_output_logging()
-    np.random.seed(config.random_seed)
-    print('Initializing TensorFlow...')
-    os.environ.update(config.env)
-    tfutil.init_tf(config.tf_config)
-    print('Running %s()...' % config.train['func'])
-    tfutil.call_func_by_name(**config.train)
-    print('Exiting...')
-
-#----------------------------------------------------------------------------
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/services/MIDIRecorder.ts b/spaces/yderre-aubay/midi-player-demo/src/main/services/MIDIRecorder.ts
deleted file mode 100644
index 7d268cee81edf5bd9f8fed5425d0149f5cc438ba..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/services/MIDIRecorder.ts
+++ /dev/null
@@ -1,109 +0,0 @@
-import { deserializeSingleEvent, Stream } from "midifile-ts"
-import { makeObservable, observable, observe } from "mobx"
-import Player from "../../common/player"
-import { NoteEvent, TrackEvent } from "../../common/track"
-import RootStore from "../stores/RootStore"
-
-export class MIDIRecorder {
-  private recordedNotes: NoteEvent[] = []
-  private player: Player
-  private rootStore: RootStore
-  isRecording: boolean = false
-
-  constructor(player: Player, rootStore: RootStore) {
-    this.player = player
-    this.rootStore = rootStore
-
-    makeObservable(this, {
-      isRecording: observable,
-    })
-
-    // extend duration while key press
-    observe(player, "position", (change) => {
-      if (!this.isRecording) {
-        return
-      }
-
-      const track = rootStore.pianoRollStore.selectedTrack
-      if (track === undefined) {
-        return
-      }
-
-      const tick = change.object.get()
-
-      this.recordedNotes.forEach((n) => {
-        track.updateEvent<NoteEvent>(n.id, {
-          duration: Math.max(0, tick - n.tick),
-        })
-      })
-    })
-
-    observe(this, "isRecording", (change) => {
-      this.recordedNotes = []
-
-      if (!change.newValue) {
-        // stop recording
-        this.rootStore.song.tracks.forEach((track) => {
-          const events = track.events
-            .filter((e) => e.isRecording === true)
-            .map<Partial<TrackEvent>>((e) => ({ ...e, isRecording: false }))
-          track.updateEvents(events)
-        })
-      }
-    })
-  }
-
-  onMessage(e: WebMidi.MIDIMessageEvent) {
-    if (!this.isRecording) {
-      return
-    }
-
-    const track = this.rootStore.pianoRollStore.selectedTrack
-    if (track === undefined) {
-      return
-    }
-
-    const stream = new Stream(e.data)
-    const message = deserializeSingleEvent(stream)
-
-    if (message.type !== "channel") {
-      return
-    }
-
-    const tick = this.player.position
-
-    switch (message.subtype) {
-      case "noteOn": {
-        const note = track.addEvent<NoteEvent>({
-          type: "channel",
-          subtype: "note",
-          noteNumber: message.noteNumber,
-          tick,
-          velocity: message.velocity,
-          duration: 0,
-          isRecording: true,
-        })
-        this.recordedNotes.push(note)
-        break
-      }
-      case "noteOff": {
-        this.recordedNotes
-          .filter((n) => n.noteNumber === message.noteNumber)
-          .forEach((n) => {
-            track.updateEvent<NoteEvent>(n.id, {
-              duration: Math.max(0, tick - n.tick),
-            })
-          })
-
-        this.recordedNotes = this.recordedNotes.filter(
-          (n) => n.noteNumber !== message.noteNumber,
-        )
-        break
-      }
-      default: {
-        track.addEvent({ ...message, tick, isRecording: true })
-        break
-      }
-    }
-  }
-}
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv3/tokenization_layoutlmv3.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv3/tokenization_layoutlmv3.py
deleted file mode 100644
index 4d3d1078db6a0fbe4956ad08712e68e3cab3ec89..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv3/tokenization_layoutlmv3.py
+++ /dev/null
@@ -1,1478 +0,0 @@
-# coding=utf-8
-# Copyright The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization class for LayoutLMv3. Same as LayoutLMv2, but RoBERTa-like BPE tokenization instead of WordPiece."""
-
-import json
-import os
-from functools import lru_cache
-from typing import Dict, List, Optional, Tuple, Union
-
-import regex as re
-
-from ...tokenization_utils import AddedToken, PreTrainedTokenizer
-from ...tokenization_utils_base import (
-    BatchEncoding,
-    EncodedInput,
-    PreTokenizedInput,
-    TextInput,
-    TextInputPair,
-    TruncationStrategy,
-)
-from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "merges_file": "merges.txt",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/raw/main/vocab.json",
-        "microsoft/layoutlmv3-large": "https://huggingface.co/microsoft/layoutlmv3-large/raw/main/vocab.json",
-    },
-    "merges_file": {
-        "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/raw/main/merges.txt",
-        "microsoft/layoutlmv3-large": "https://huggingface.co/microsoft/layoutlmv3-large/raw/main/merges.txt",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "microsoft/layoutlmv3-base": 512,
-    "microsoft/layoutlmv3-large": 512,
-}
-
-
-LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING = r"""
-            add_special_tokens (`bool`, *optional*, defaults to `True`):
-                Whether or not to encode the sequences with the special tokens relative to their model.
-            padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
-                Activates and controls padding. Accepts the following values:
-
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
-                Activates and controls truncation. Accepts the following values:
-
-                - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
-                  to the maximum acceptable input length for the model if that argument is not provided. This will
-                  truncate token by token, removing a token from the longest sequence in the pair if a pair of
-                  sequences (or a batch of pairs) is provided.
-                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
-                  greater than the model maximum admissible input size).
-            max_length (`int`, *optional*):
-                Controls the maximum length to use by one of the truncation/padding parameters.
-
-                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
-                is required by one of the truncation/padding parameters. If the model has no specific maximum input
-                length (like XLNet) truncation/padding to a maximum length will be deactivated.
-            stride (`int`, *optional*, defaults to 0):
-                If set to a number along with `max_length`, the overflowing tokens returned when
-                `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
-                returned to provide some overlap between truncated and overflowing sequences. The value of this
-                argument defines the number of overlapping tokens.
-            pad_to_multiple_of (`int`, *optional*):
-                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
-                the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
-            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
-                If set, will return tensors instead of list of python integers. Acceptable values are:
-
-                - `'tf'`: Return TensorFlow `tf.constant` objects.
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return Numpy `np.ndarray` objects.
-"""
-
-
-LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
-            add_special_tokens (`bool`, *optional*, defaults to `True`):
-                Whether or not to encode the sequences with the special tokens relative to their model.
-            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
-                Activates and controls padding. Accepts the following values:
-
-                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-                  sequence if provided).
-                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
-                  acceptable input length for the model if that argument is not provided.
-                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
-                  lengths).
-            truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
-                Activates and controls truncation. Accepts the following values:
-
-                - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
-                  to the maximum acceptable input length for the model if that argument is not provided. This will
-                  truncate token by token, removing a token from the longest sequence in the pair if a pair of
-                  sequences (or a batch of pairs) is provided.
-                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
-                  greater than the model maximum admissible input size).
-            max_length (`int`, *optional*):
-                Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
-                `None`, this will use the predefined model maximum length if a maximum length is required by one of the
-                truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
-                truncation/padding to a maximum length will be deactivated.
-            stride (`int`, *optional*, defaults to 0):
-                If set to a number along with `max_length`, the overflowing tokens returned when
-                `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
-                returned to provide some overlap between truncated and overflowing sequences. The value of this
-                argument defines the number of overlapping tokens.
-            pad_to_multiple_of (`int`, *optional*):
-                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
-                the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
-            return_tensors (`str` or [`~utils.TensorType`], *optional*):
-                If set, will return tensors instead of list of python integers. Acceptable values are:
-
-                - `'tf'`: Return TensorFlow `tf.constant` objects.
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return Numpy `np.ndarray` objects.
-"""
-
-
-@lru_cache()
-# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
-    characters the bpe code barfs on.
-
-    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
-    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
-    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
-    tables between utf-8 bytes and unicode strings.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-# Copied from transformers.models.roberta.tokenization_roberta.get_pairs
-def get_pairs(word):
-    """
-    Return set of symbol pairs in a word.
-
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class LayoutLMv3Tokenizer(PreTrainedTokenizer):
-    r"""
-    Construct a LayoutLMv3 tokenizer. Based on [`RoBERTatokenizer`] (Byte Pair Encoding or BPE).
-    [`LayoutLMv3Tokenizer`] can be used to turn words, word-level bounding boxes and optional word labels to
-    token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and optional `labels` (for token
-    classification).
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    [`LayoutLMv3Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
-    word-level bounding boxes into token-level bounding boxes.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-        errors (`str`, *optional*, defaults to `"replace"`):
-            Paradigm to follow when decoding bytes to UTF-8. See
-            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
-        bos_token (`str`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the beginning of
-            sequence. The token used is the `cls_token`.
-
-            </Tip>
-
-        eos_token (`str`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
-            The token used is the `sep_token`.
-
-            </Tip>
-
-        sep_token (`str`, *optional*, defaults to `"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        cls_token (`str`, *optional*, defaults to `"<s>"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (`str`, *optional*, defaults to `"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        mask_token (`str`, *optional*, defaults to `"<mask>"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        add_prefix_space (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
-            other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
-        cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
-            The bounding box to use for the special [CLS] token.
-        sep_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
-            The bounding box to use for the special [SEP] token.
-        pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
-            The bounding box to use for the special [PAD] token.
-        pad_token_label (`int`, *optional*, defaults to -100):
-            The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
-            CrossEntropyLoss.
-        only_label_first_subword (`bool`, *optional*, defaults to `True`):
-            Whether or not to only label the first subword, in case word labels are provided.
-    """
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask", "bbox"]
-
-    def __init__(
-        self,
-        vocab_file,
-        merges_file,
-        errors="replace",
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        add_prefix_space=True,
-        cls_token_box=[0, 0, 0, 0],
-        sep_token_box=[0, 0, 0, 0],
-        pad_token_box=[0, 0, 0, 0],
-        pad_token_label=-100,
-        only_label_first_subword=True,
-        **kwargs,
-    ):
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
-        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
-
-        # Mask token behave like a normal word, i.e. include the space before it
-        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
-
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.encoder = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        with open(merges_file, encoding="utf-8") as merges_handle:
-            bpe_merges = merges_handle.read().split("\n")[1:-1]
-        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-        self.add_prefix_space = add_prefix_space
-
-        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
-
-        # additional properties
-        self.cls_token_box = cls_token_box
-        self.sep_token_box = sep_token_box
-        self.pad_token_box = pad_token_box
-        self.pad_token_label = pad_token_label
-        self.only_label_first_subword = only_label_first_subword
-
-        super().__init__(
-            errors=errors,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            sep_token=sep_token,
-            cls_token=cls_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            add_prefix_space=add_prefix_space,
-            cls_token_box=cls_token_box,
-            sep_token_box=sep_token_box,
-            pad_token_box=pad_token_box,
-            pad_token_label=pad_token_label,
-            only_label_first_subword=only_label_first_subword,
-            **kwargs,
-        )
-
-    @property
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size
-    def vocab_size(self):
-        return len(self.encoder)
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab
-    def get_vocab(self):
-        vocab = dict(self.encoder).copy()
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                except ValueError:
-                    new_word.extend(word[i:])
-                    break
-                else:
-                    new_word.extend(word[i:j])
-                    i = j
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize
-    def _tokenize(self, text):
-        """Tokenize a string."""
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            token = "".join(
-                self.byte_encoder[b] for b in token.encode("utf-8")
-            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
-        return bpe_tokens
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        text = "".join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
-        return text
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-        merge_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write("#version: 0.2\n")
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning(
-                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
-                        " Please check that the tokenizer is not corrupted!"
-                    )
-                    index = token_index
-                writer.write(" ".join(bpe_tokens) + "\n")
-                index += 1
-
-        return vocab_file, merge_file
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.build_inputs_with_special_tokens
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A RoBERTa sequence has the following format:
-
-        - single sequence: `<s> X </s>`
-        - pair of sequences: `<s> A </s></s> B </s>`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-        if token_ids_1 is None:
-            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-        cls = [self.cls_token_id]
-        sep = [self.sep_token_id]
-        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
-    # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
-        make use of token type ids, therefore a list of zeros is returned.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of zeros.
-        """
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
-
-    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
-        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
-        # If the text starts with a token that should not be split, no space is added before the text in any case.
-        # It's necessary to match the fast tokenization
-        if (
-            (is_split_into_words or add_prefix_space)
-            and (len(text) > 0 and not text[0].isspace())
-            and sum([text.startswith(no_split_token) for no_split_token in self.added_tokens_encoder]) == 0
-        ):
-            text = " " + text
-        return (text, kwargs)
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.__call__
-    def __call__(
-        self,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
-        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
-        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
-        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
-        sequences with word-level normalized bounding boxes and optional labels.
-
-        Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
-                (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
-                words).
-            text_pair (`List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
-                (pretokenized string).
-            boxes (`List[List[int]]`, `List[List[List[int]]]`):
-                Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
-            word_labels (`List[int]`, `List[List[int]]`, *optional*):
-                Word-level integer labels (for token classification tasks such as FUNSD, CORD).
-        """
-
-        # Input type checking for clearer error
-        def _is_valid_text_input(t):
-            if isinstance(t, str):
-                # Strings are fine
-                return True
-            elif isinstance(t, (list, tuple)):
-                # List are fine as long as they are...
-                if len(t) == 0:
-                    # ... empty
-                    return True
-                elif isinstance(t[0], str):
-                    # ... list of strings
-                    return True
-                elif isinstance(t[0], (list, tuple)):
-                    # ... list with an empty list or with a list of strings
-                    return len(t[0]) == 0 or isinstance(t[0][0], str)
-                else:
-                    return False
-            else:
-                return False
-
-        if text_pair is not None:
-            # in case text + text_pair are provided, text = questions, text_pair = words
-            if not _is_valid_text_input(text):
-                raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
-            if not isinstance(text_pair, (list, tuple)):
-                raise ValueError(
-                    "Words must be of type `List[str]` (single pretokenized example), "
-                    "or `List[List[str]]` (batch of pretokenized examples)."
-                )
-        else:
-            # in case only text is provided => must be words
-            if not isinstance(text, (list, tuple)):
-                raise ValueError(
-                    "Words must be of type `List[str]` (single pretokenized example), "
-                    "or `List[List[str]]` (batch of pretokenized examples)."
-                )
-
-        if text_pair is not None:
-            is_batched = isinstance(text, (list, tuple))
-        else:
-            is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
-
-        words = text if text_pair is None else text_pair
-        if boxes is None:
-            raise ValueError("You must provide corresponding bounding boxes")
-        if is_batched:
-            if len(words) != len(boxes):
-                raise ValueError("You must provide words and boxes for an equal amount of examples")
-            for words_example, boxes_example in zip(words, boxes):
-                if len(words_example) != len(boxes_example):
-                    raise ValueError("You must provide as many words as there are bounding boxes")
-        else:
-            if len(words) != len(boxes):
-                raise ValueError("You must provide as many words as there are bounding boxes")
-
-        if is_batched:
-            if text_pair is not None and len(text) != len(text_pair):
-                raise ValueError(
-                    f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
-                    f" {len(text_pair)}."
-                )
-            batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
-            is_pair = bool(text_pair is not None)
-            return self.batch_encode_plus(
-                batch_text_or_text_pairs=batch_text_or_text_pairs,
-                is_pair=is_pair,
-                boxes=boxes,
-                word_labels=word_labels,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-        else:
-            return self.encode_plus(
-                text=text,
-                text_pair=text_pair,
-                boxes=boxes,
-                word_labels=word_labels,
-                add_special_tokens=add_special_tokens,
-                padding=padding,
-                truncation=truncation,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_tensors=return_tensors,
-                return_token_type_ids=return_token_type_ids,
-                return_attention_mask=return_attention_mask,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_offsets_mapping=return_offsets_mapping,
-                return_length=return_length,
-                verbose=verbose,
-                **kwargs,
-            )
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.batch_encode_plus
-    def batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[
-            List[TextInput],
-            List[TextInputPair],
-            List[PreTokenizedInput],
-        ],
-        is_pair: bool = None,
-        boxes: Optional[List[List[List[int]]]] = None,
-        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._batch_encode_plus(
-            batch_text_or_text_pairs=batch_text_or_text_pairs,
-            is_pair=is_pair,
-            boxes=boxes,
-            word_labels=word_labels,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._batch_encode_plus
-    def _batch_encode_plus(
-        self,
-        batch_text_or_text_pairs: Union[
-            List[TextInput],
-            List[TextInputPair],
-            List[PreTokenizedInput],
-        ],
-        is_pair: bool = None,
-        boxes: Optional[List[List[List[int]]]] = None,
-        word_labels: Optional[List[List[int]]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast."
-            )
-
-        batch_outputs = self._batch_prepare_for_model(
-            batch_text_or_text_pairs=batch_text_or_text_pairs,
-            is_pair=is_pair,
-            boxes=boxes,
-            word_labels=word_labels,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            return_tensors=return_tensors,
-            verbose=verbose,
-        )
-
-        return BatchEncoding(batch_outputs)
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._batch_prepare_for_model
-    def _batch_prepare_for_model(
-        self,
-        batch_text_or_text_pairs,
-        is_pair: bool = None,
-        boxes: Optional[List[List[int]]] = None,
-        word_labels: Optional[List[List[int]]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-    ) -> BatchEncoding:
-        """
-        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
-        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
-        manages a moving window (with user defined stride) for overflowing tokens.
-
-        Args:
-            batch_ids_pairs: list of tokenized input ids or input ids pairs
-        """
-
-        batch_outputs = {}
-        for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
-            batch_text_or_text_pair, boxes_example = example
-            outputs = self.prepare_for_model(
-                batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
-                batch_text_or_text_pair[1] if is_pair else None,
-                boxes_example,
-                word_labels=word_labels[idx] if word_labels is not None else None,
-                add_special_tokens=add_special_tokens,
-                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterward
-                truncation=truncation_strategy.value,
-                max_length=max_length,
-                stride=stride,
-                pad_to_multiple_of=None,  # we pad in batch afterward
-                return_attention_mask=False,  # we pad in batch afterward
-                return_token_type_ids=return_token_type_ids,
-                return_overflowing_tokens=return_overflowing_tokens,
-                return_special_tokens_mask=return_special_tokens_mask,
-                return_length=return_length,
-                return_tensors=None,  # We convert the whole batch to tensors at the end
-                prepend_batch_axis=False,
-                verbose=verbose,
-            )
-
-            for key, value in outputs.items():
-                if key not in batch_outputs:
-                    batch_outputs[key] = []
-                batch_outputs[key].append(value)
-
-        batch_outputs = self.pad(
-            batch_outputs,
-            padding=padding_strategy.value,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_attention_mask=return_attention_mask,
-        )
-
-        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
-
-        return batch_outputs
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode
-    def encode(
-        self,
-        text: Union[TextInput, PreTokenizedInput],
-        text_pair: Optional[PreTokenizedInput] = None,
-        boxes: Optional[List[List[int]]] = None,
-        word_labels: Optional[List[int]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> List[int]:
-        encoded_inputs = self.encode_plus(
-            text=text,
-            text_pair=text_pair,
-            boxes=boxes,
-            word_labels=word_labels,
-            add_special_tokens=add_special_tokens,
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return encoded_inputs["input_ids"]
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode_plus
-    def encode_plus(
-        self,
-        text: Union[TextInput, PreTokenizedInput],
-        text_pair: Optional[PreTokenizedInput] = None,
-        boxes: Optional[List[List[int]]] = None,
-        word_labels: Optional[List[int]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
-        `__call__` should be used instead.
-
-        Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
-            text_pair (`List[str]` or `List[int]`, *optional*):
-                Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
-                list of list of strings (words of a batch of examples).
-        """
-
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        return self._encode_plus(
-            text=text,
-            boxes=boxes,
-            text_pair=text_pair,
-            word_labels=word_labels,
-            add_special_tokens=add_special_tokens,
-            padding_strategy=padding_strategy,
-            truncation_strategy=truncation_strategy,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            return_token_type_ids=return_token_type_ids,
-            return_attention_mask=return_attention_mask,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_offsets_mapping=return_offsets_mapping,
-            return_length=return_length,
-            verbose=verbose,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._encode_plus
-    def _encode_plus(
-        self,
-        text: Union[TextInput, PreTokenizedInput],
-        text_pair: Optional[PreTokenizedInput] = None,
-        boxes: Optional[List[List[int]]] = None,
-        word_labels: Optional[List[int]] = None,
-        add_special_tokens: bool = True,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        **kwargs,
-    ) -> BatchEncoding:
-        if return_offsets_mapping:
-            raise NotImplementedError(
-                "return_offset_mapping is not available when using Python tokenizers. "
-                "To use this feature, change your tokenizer to one deriving from "
-                "transformers.PreTrainedTokenizerFast. "
-                "More information on available tokenizers at "
-                "https://github.com/huggingface/transformers/pull/2674"
-            )
-
-        return self.prepare_for_model(
-            text=text,
-            text_pair=text_pair,
-            boxes=boxes,
-            word_labels=word_labels,
-            add_special_tokens=add_special_tokens,
-            padding=padding_strategy.value,
-            truncation=truncation_strategy.value,
-            max_length=max_length,
-            stride=stride,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors=return_tensors,
-            prepend_batch_axis=True,
-            return_attention_mask=return_attention_mask,
-            return_token_type_ids=return_token_type_ids,
-            return_overflowing_tokens=return_overflowing_tokens,
-            return_special_tokens_mask=return_special_tokens_mask,
-            return_length=return_length,
-            verbose=verbose,
-        )
-
-    @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
-    def prepare_for_model(
-        self,
-        text: Union[TextInput, PreTokenizedInput],
-        text_pair: Optional[PreTokenizedInput] = None,
-        boxes: Optional[List[List[int]]] = None,
-        word_labels: Optional[List[int]] = None,
-        add_special_tokens: bool = True,
-        padding: Union[bool, str, PaddingStrategy] = False,
-        truncation: Union[bool, str, TruncationStrategy] = None,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        pad_to_multiple_of: Optional[int] = None,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
-        return_length: bool = False,
-        verbose: bool = True,
-        prepend_batch_axis: bool = False,
-        **kwargs,
-    ) -> BatchEncoding:
-        """
-        Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
-        truncates sequences if overflowing while taking into account the special tokens and manages a moving window
-        (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
-        *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
-        combination of arguments will raise an error.
-
-        Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
-        token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
-        labeled with -100, such that they will be ignored by the loss function.
-
-        Args:
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
-            text_pair (`List[str]` or `List[int]`, *optional*):
-                Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
-                list of list of strings (words of a batch of examples).
-        """
-
-        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
-        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
-            padding=padding,
-            truncation=truncation,
-            max_length=max_length,
-            pad_to_multiple_of=pad_to_multiple_of,
-            verbose=verbose,
-            **kwargs,
-        )
-
-        tokens = []
-        pair_tokens = []
-        token_boxes = []
-        pair_token_boxes = []
-        labels = []
-
-        if text_pair is None:
-            if word_labels is None:
-                # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
-                for word, box in zip(text, boxes):
-                    if len(word) < 1:  # skip empty words
-                        continue
-                    word_tokens = self.tokenize(word)
-                    tokens.extend(word_tokens)
-                    token_boxes.extend([box] * len(word_tokens))
-            else:
-                # CASE 2: token classification (training)
-                for word, box, label in zip(text, boxes, word_labels):
-                    if len(word) < 1:  # skip empty words
-                        continue
-                    word_tokens = self.tokenize(word)
-                    tokens.extend(word_tokens)
-                    token_boxes.extend([box] * len(word_tokens))
-                    if self.only_label_first_subword:
-                        # Use the real label id for the first token of the word, and padding ids for the remaining tokens
-                        labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
-                    else:
-                        labels.extend([label] * len(word_tokens))
-        else:
-            # CASE 3: document visual question answering (inference)
-            # text = question
-            # text_pair = words
-            tokens = self.tokenize(text)
-            token_boxes = [self.pad_token_box for _ in range(len(tokens))]
-
-            for word, box in zip(text_pair, boxes):
-                if len(word) < 1:  # skip empty words
-                    continue
-                word_tokens = self.tokenize(word)
-                pair_tokens.extend(word_tokens)
-                pair_token_boxes.extend([box] * len(word_tokens))
-
-        # Create ids + pair_ids
-        ids = self.convert_tokens_to_ids(tokens)
-        pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
-
-        if (
-            return_overflowing_tokens
-            and truncation_strategy == TruncationStrategy.LONGEST_FIRST
-            and pair_ids is not None
-        ):
-            raise ValueError(
-                "Not possible to return overflowing tokens for pair of sequences with the "
-                "`longest_first`. Please select another truncation strategy than `longest_first`, "
-                "for instance `only_second` or `only_first`."
-            )
-
-        # Compute the total size of the returned encodings
-        pair = bool(pair_ids is not None)
-        len_ids = len(ids)
-        len_pair_ids = len(pair_ids) if pair else 0
-        total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
-
-        # Truncation: Handle max sequence length
-        overflowing_tokens = []
-        overflowing_token_boxes = []
-        overflowing_labels = []
-        if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
-            (
-                ids,
-                token_boxes,
-                pair_ids,
-                pair_token_boxes,
-                labels,
-                overflowing_tokens,
-                overflowing_token_boxes,
-                overflowing_labels,
-            ) = self.truncate_sequences(
-                ids,
-                token_boxes,
-                pair_ids=pair_ids,
-                pair_token_boxes=pair_token_boxes,
-                labels=labels,
-                num_tokens_to_remove=total_len - max_length,
-                truncation_strategy=truncation_strategy,
-                stride=stride,
-            )
-
-        if return_token_type_ids and not add_special_tokens:
-            raise ValueError(
-                "Asking to return token_type_ids while setting add_special_tokens to False "
-                "results in an undefined behavior. Please set add_special_tokens to True or "
-                "set return_token_type_ids to None."
-            )
-
-        # Load from model defaults
-        if return_token_type_ids is None:
-            return_token_type_ids = "token_type_ids" in self.model_input_names
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        encoded_inputs = {}
-
-        if return_overflowing_tokens:
-            encoded_inputs["overflowing_tokens"] = overflowing_tokens
-            encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
-            encoded_inputs["overflowing_labels"] = overflowing_labels
-            encoded_inputs["num_truncated_tokens"] = total_len - max_length
-
-        # Add special tokens
-        if add_special_tokens:
-            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
-            token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
-            token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
-            if pair_token_boxes:
-                pair_token_boxes = [self.sep_token_box] + pair_token_boxes + [self.sep_token_box]
-            token_boxes = token_boxes + pair_token_boxes if pair else token_boxes
-            if labels:
-                labels = [self.pad_token_label] + labels + [self.pad_token_label]
-        else:
-            sequence = ids + pair_ids if pair else ids
-            token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
-            token_boxes = token_boxes + pair_token_boxes if pair else token_boxes
-
-        # Build output dictionary
-        encoded_inputs["input_ids"] = sequence
-        encoded_inputs["bbox"] = token_boxes
-        if return_token_type_ids:
-            encoded_inputs["token_type_ids"] = token_type_ids
-        if return_special_tokens_mask:
-            if add_special_tokens:
-                encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
-            else:
-                encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
-
-        if labels:
-            encoded_inputs["labels"] = labels
-
-        # Check lengths
-        self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
-
-        # Padding
-        if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
-            encoded_inputs = self.pad(
-                encoded_inputs,
-                max_length=max_length,
-                padding=padding_strategy.value,
-                pad_to_multiple_of=pad_to_multiple_of,
-                return_attention_mask=return_attention_mask,
-            )
-
-        if return_length:
-            encoded_inputs["length"] = len(encoded_inputs["input_ids"])
-
-        batch_outputs = BatchEncoding(
-            encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
-        )
-
-        return batch_outputs
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.truncate_sequences
-    def truncate_sequences(
-        self,
-        ids: List[int],
-        token_boxes: List[List[int]],
-        pair_ids: Optional[List[int]] = None,
-        pair_token_boxes: Optional[List[List[int]]] = None,
-        labels: Optional[List[int]] = None,
-        num_tokens_to_remove: int = 0,
-        truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
-        stride: int = 0,
-    ) -> Tuple[List[int], List[int], List[int]]:
-        """
-        Truncates a sequence pair in-place following the strategy.
-
-        Args:
-            ids (`List[int]`):
-                Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
-                `convert_tokens_to_ids` methods.
-            token_boxes (`List[List[int]]`):
-                Bounding boxes of the first sequence.
-            pair_ids (`List[int]`, *optional*):
-                Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
-                and `convert_tokens_to_ids` methods.
-            pair_token_boxes (`List[List[int]]`, *optional*):
-                Bounding boxes of the second sequence.
-            labels (`List[int]`, *optional*):
-                Labels of the first sequence (for token classification tasks).
-            num_tokens_to_remove (`int`, *optional*, defaults to 0):
-                Number of tokens to remove using the truncation strategy.
-            truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
-                The strategy to follow for truncation. Can be:
-
-                - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will truncate
-                  token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
-                  batch of pairs) is provided.
-                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
-                  maximum acceptable input length for the model if that argument is not provided. This will only
-                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
-                - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
-                  than the model maximum admissible input size).
-            stride (`int`, *optional*, defaults to 0):
-                If set to a positive number, the overflowing tokens returned will contain some tokens from the main
-                sequence returned. The value of this argument defines the number of additional tokens.
-
-        Returns:
-            `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
-            overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
-            of sequences (or a batch of pairs) is provided.
-        """
-        if num_tokens_to_remove <= 0:
-            return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []
-
-        if not isinstance(truncation_strategy, TruncationStrategy):
-            truncation_strategy = TruncationStrategy(truncation_strategy)
-
-        overflowing_tokens = []
-        overflowing_token_boxes = []
-        overflowing_labels = []
-        if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
-            truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
-        ):
-            if len(ids) > num_tokens_to_remove:
-                window_len = min(len(ids), stride + num_tokens_to_remove)
-                overflowing_tokens = ids[-window_len:]
-                overflowing_token_boxes = token_boxes[-window_len:]
-                overflowing_labels = labels[-window_len:]
-                ids = ids[:-num_tokens_to_remove]
-                token_boxes = token_boxes[:-num_tokens_to_remove]
-                labels = labels[:-num_tokens_to_remove]
-            else:
-                error_msg = (
-                    f"We need to remove {num_tokens_to_remove} to truncate the input "
-                    f"but the first sequence has a length {len(ids)}. "
-                )
-                if truncation_strategy == TruncationStrategy.ONLY_FIRST:
-                    error_msg = (
-                        error_msg + "Please select another truncation strategy than "
-                        f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
-                    )
-                logger.error(error_msg)
-        elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
-            logger.warning(
-                "Be aware, overflowing tokens are not returned for the setting you have chosen,"
-                f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
-                "truncation strategy. So the returned list will always be empty even if some "
-                "tokens have been removed."
-            )
-            for _ in range(num_tokens_to_remove):
-                if pair_ids is None or len(ids) > len(pair_ids):
-                    ids = ids[:-1]
-                    token_boxes = token_boxes[:-1]
-                    labels = labels[:-1]
-                else:
-                    pair_ids = pair_ids[:-1]
-                    pair_token_boxes = pair_token_boxes[:-1]
-        elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
-            if len(pair_ids) > num_tokens_to_remove:
-                window_len = min(len(pair_ids), stride + num_tokens_to_remove)
-                overflowing_tokens = pair_ids[-window_len:]
-                overflowing_token_boxes = pair_token_boxes[-window_len:]
-                pair_ids = pair_ids[:-num_tokens_to_remove]
-                pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
-            else:
-                logger.error(
-                    f"We need to remove {num_tokens_to_remove} to truncate the input "
-                    f"but the second sequence has a length {len(pair_ids)}. "
-                    f"Please select another truncation strategy than {truncation_strategy}, "
-                    "for instance 'longest_first' or 'only_first'."
-                )
-
-        return (
-            ids,
-            token_boxes,
-            pair_ids,
-            pair_token_boxes,
-            labels,
-            overflowing_tokens,
-            overflowing_token_boxes,
-            overflowing_labels,
-        )
-
-    # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._pad
-    def _pad(
-        self,
-        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
-        max_length: Optional[int] = None,
-        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-        pad_to_multiple_of: Optional[int] = None,
-        return_attention_mask: Optional[bool] = None,
-    ) -> dict:
-        """
-        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
-
-        Args:
-            encoded_inputs:
-                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            padding_strategy: PaddingStrategy to use for padding.
-
-                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
-                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
-                - PaddingStrategy.DO_NOT_PAD: Do not pad
-                The tokenizer padding sides are defined in self.padding_side:
-
-                    - 'left': pads on the left of the sequences
-                    - 'right': pads on the right of the sequences
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                `>= 7.5` (Volta).
-            return_attention_mask:
-                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-        """
-        # Load from model defaults
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
-        required_input = encoded_inputs[self.model_input_names[0]]
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(required_input)
-
-        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
-            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
-        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
-
-        # Initialize attention mask if not present.
-        if return_attention_mask and "attention_mask" not in encoded_inputs:
-            encoded_inputs["attention_mask"] = [1] * len(required_input)
-
-        if needs_to_be_padded:
-            difference = max_length - len(required_input)
-            if self.padding_side == "right":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = (
-                        encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
-                    )
-                if "bbox" in encoded_inputs:
-                    encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
-                if "labels" in encoded_inputs:
-                    encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
-                encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
-            elif self.padding_side == "left":
-                if return_attention_mask:
-                    encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
-                if "token_type_ids" in encoded_inputs:
-                    encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
-                        "token_type_ids"
-                    ]
-                if "bbox" in encoded_inputs:
-                    encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
-                if "labels" in encoded_inputs:
-                    encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
-                if "special_tokens_mask" in encoded_inputs:
-                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-                encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
-            else:
-                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
-
-        return encoded_inputs
diff --git a/spaces/yufiofficial/MusicGenQ/tests/modules/test_lstm.py b/spaces/yufiofficial/MusicGenQ/tests/modules/test_lstm.py
deleted file mode 100644
index 1248964c8191e19f27661f0974bef9cc967eb015..0000000000000000000000000000000000000000
--- a/spaces/yufiofficial/MusicGenQ/tests/modules/test_lstm.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import random
-import torch
-
-from audiocraft.modules.lstm import StreamableLSTM
-
-
-class TestStreamableLSTM:
-
-    def test_lstm(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=False)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        print(y.shape)
-        assert y.shape == torch.Size([B, C, T])
-
-    def test_lstm_skip(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=True)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        assert y.shape == torch.Size([B, C, T])
diff --git a/spaces/zeno-ml/chatbot-report/main.py b/spaces/zeno-ml/chatbot-report/main.py
deleted file mode 100644
index 22d7e02492f9528fdf5c2c5b1c208cc4862d9948..0000000000000000000000000000000000000000
--- a/spaces/zeno-ml/chatbot-report/main.py
+++ /dev/null
@@ -1,247 +0,0 @@
-"""The main entry point for performing comparison on chatbots."""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import json
-import logging
-import os
-from dataclasses import asdict
-from typing import cast
-
-import pandas as pd
-from zeno_build.experiments import search_space
-from zeno_build.experiments.experiment_run import ExperimentRun
-from zeno_build.optimizers import exhaustive
-from zeno_build.prompts.chat_prompt import ChatMessages
-from zeno_build.reporting import reporting_utils
-from zeno_build.reporting.visualize import visualize
-
-import config as chatbot_config
-from modeling import make_predictions, process_data
-
-
-def chatbot_main(
-    models: list[str],
-    single_model: str,
-    prompts: list[str],
-    single_prompt: str,
-    experiments: list[str],
-    hf_inference_method: str,
-    results_dir: str,
-    do_prediction: bool = True,
-    do_visualization: bool = True,
-):
-    """Run the chatbot experiment."""
-    # Update the experiment settings with the provided models and prompts
-    experiment_settings: list[search_space.CombinatorialSearchSpace] = [
-        copy.deepcopy(chatbot_config.experiments[x]) for x in experiments
-    ]
-    for setting in experiment_settings:
-        if isinstance(setting.dimensions["model_preset"], search_space.Categorical):
-            setting.dimensions["model_preset"] = search_space.Categorical(models)
-        else:
-            assert isinstance(setting.dimensions["model_preset"], search_space.Constant)
-            setting.dimensions["model_preset"] = search_space.Constant(single_model)
-        if isinstance(setting.dimensions["prompt_preset"], search_space.Categorical):
-            setting.dimensions["prompt_preset"] = search_space.Categorical(prompts)
-        else:
-            assert isinstance(
-                setting.dimensions["prompt_preset"], search_space.Constant
-            )
-            setting.dimensions["prompt_preset"] = search_space.Constant(single_prompt)
-    my_space = search_space.CompositeSearchSpace(
-        cast(list[search_space.SearchSpace], experiment_settings)
-    )
-
-    # Get the dataset configuration
-    dataset_config = chatbot_config.dataset_configs[chatbot_config.dataset]
-
-    # Define the directories for storing data and predictions
-    data_dir = os.path.join(results_dir, "data")
-    predictions_dir = os.path.join(results_dir, "predictions")
-
-    # Load and standardize the format of the necessary data. The resulting
-    # processed data will be stored in the `results_dir/data` directory
-    # both for browsing and for caching for fast reloading on future runs.
-    contexts_and_labels: list[ChatMessages] = process_data(
-        dataset=dataset_config.dataset,
-        split=dataset_config.split,
-        data_format=dataset_config.data_format,
-        data_column=dataset_config.data_column,
-        output_dir=data_dir,
-    )
-
-    # Organize the data into labels (output) and context (input)
-    labels: list[str] = []
-    contexts: list[ChatMessages] = []
-    for candl in contexts_and_labels:
-        labels.append(candl.messages[-1].content)
-        contexts.append(ChatMessages(candl.messages[:-1]))
-
-    if do_prediction:
-        # Perform the hyperparameter sweep
-        optimizer = exhaustive.ExhaustiveOptimizer(
-            space=my_space,
-            distill_functions=chatbot_config.sweep_distill_functions,
-            metric=chatbot_config.sweep_metric_function,
-            num_trials=chatbot_config.num_trials,
-        )
-
-        while not optimizer.is_complete(predictions_dir, include_in_progress=True):
-            # Get parameters
-            parameters = optimizer.get_parameters()
-            if parameters is None:
-                break
-            # Get the run ID and resulting predictions
-            id_and_predictions = make_predictions(
-                contexts=contexts,
-                prompt_preset=parameters["prompt_preset"],
-                model_preset=parameters["model_preset"],
-                temperature=parameters["temperature"],
-                max_tokens=parameters["max_tokens"],
-                top_p=parameters["top_p"],
-                context_length=parameters["context_length"],
-                output_dir=predictions_dir,
-                hf_inference_method=hf_inference_method,
-            )
-            if id_and_predictions is None:
-                print(f"*** Skipped run for {parameters=} ***")
-                continue
-            # Run or read the evaluation result
-            id, predictions = id_and_predictions
-            if os.path.exists(f"{predictions_dir}/{id}.eval"):
-                with open(f"{predictions_dir}/{id}.eval", "r") as f:
-                    eval_result = float(next(f).strip())
-            else:
-                eval_result = optimizer.calculate_metric(contexts, labels, predictions)
-                with open(f"{predictions_dir}/{id}.eval", "w") as f:
-                    f.write(f"{eval_result}")
-            # Print out the results
-            print("*** Iteration complete. ***")
-            print(f"Eval: {eval_result}, Parameters: {parameters}")
-            print("***************************")
-
-    if do_visualization:
-        param_files = my_space.get_valid_param_files(
-            predictions_dir, include_in_progress=False
-        )
-        if chatbot_config.num_trials and len(param_files) < chatbot_config.num_trials:
-            logging.getLogger().warning(
-                "Not enough completed but performing visualization anyway."
-            )
-        results: list[ExperimentRun] = []
-        for param_file in param_files:
-            assert param_file.endswith(".zbp")
-            with open(param_file, "r") as f:
-                loaded_parameters = json.load(f)
-            with open(f"{param_file[:-4]}.json", "r") as f:
-                predictions = json.load(f)
-            name = reporting_utils.parameters_to_name(loaded_parameters, my_space)
-            results.append(
-                ExperimentRun(
-                    parameters=loaded_parameters, predictions=predictions, name=name
-                )
-            )
-        results.sort(key=lambda x: x.name)
-
-        # Perform the visualization
-        df = pd.DataFrame(
-            {
-                "messages": [[asdict(y) for y in x.messages] for x in contexts],
-                "label": labels,
-            }
-        )
-        visualize(
-            df,
-            labels,
-            results,
-            "openai-chat",
-            "messages",
-            chatbot_config.zeno_distill_and_metric_functions,
-            zeno_config={
-                "cache_path": os.path.join(results_dir, "zeno_cache"),
-                "port": 7860,
-                "host": "0.0.0.0",
-            },
-        )
-
-
-if __name__ == "__main__":
-    # Parse the command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        type=str,
-        nargs="+",
-        default=chatbot_config.default_models,
-        help="The models to use (for experimental settings with multiple models).",
-    )
-    parser.add_argument(
-        "--single-model",
-        type=str,
-        default=chatbot_config.default_single_model,
-        help="The model to use (for experimental settings with a single model).",
-    )
-    parser.add_argument(
-        "--prompts",
-        type=str,
-        nargs="+",
-        default=chatbot_config.default_prompts,
-        help="The prompts to use (for experimental settings with multiple prompts).",
-    )
-    parser.add_argument(
-        "--single-prompt",
-        type=str,
-        default=chatbot_config.default_single_prompt,
-        help="The prompt to use (for experimental settings with a single prompt).",
-    )
-    parser.add_argument(
-        "--experiments",
-        type=str,
-        nargs="+",
-        default=["model", "prompt", "temperature", "context_length"],
-        help="The experiments to run.",
-    )
-    parser.add_argument(
-        "--hf-inference-method",
-        type=str,
-        default="huggingface",
-        help="The method used to perform inference on HuggingFace models.",
-        choices=["huggingface", "vllm"],
-    )
-    parser.add_argument(
-        "--results-dir",
-        type=str,
-        default="results",
-        help="The directory to store the results in.",
-    )
-    parser.add_argument(
-        "--skip-prediction",
-        action="store_true",
-        help="Skip prediction and just do visualization.",
-    )
-    parser.add_argument(
-        "--skip-visualization",
-        action="store_true",
-        help="Skip visualization and just do prediction.",
-    )
-    args = parser.parse_args()
-
-    if args.skip_prediction and args.skip_visualization:
-        raise ValueError(
-            "Cannot specify both --skip-prediction and --skip-visualization."
-        )
-
-    chatbot_main(
-        models=args.models,
-        single_model=args.single_model,
-        prompts=args.prompts,
-        single_prompt=args.single_prompt,
-        experiments=args.experiments,
-        hf_inference_method=args.hf_inference_method,
-        results_dir=args.results_dir,
-        do_prediction=not args.skip_prediction,
-        do_visualization=not args.skip_visualization,
-    )
diff --git a/spaces/zhang-wei-jian/docker/node_modules/nodemon/node_modules/debug/src/browser.js b/spaces/zhang-wei-jian/docker/node_modules/nodemon/node_modules/debug/src/browser.js
deleted file mode 100644
index c924b0ac4d4882e4a72a116436ed17a487c2fc05..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/nodemon/node_modules/debug/src/browser.js
+++ /dev/null
@@ -1,180 +0,0 @@
-"use strict";
-
-function _typeof(obj) { if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
-
-/* eslint-env browser */
-
-/**
- * This is the web browser implementation of `debug()`.
- */
-exports.log = log;
-exports.formatArgs = formatArgs;
-exports.save = save;
-exports.load = load;
-exports.useColors = useColors;
-exports.storage = localstorage();
-/**
- * Colors.
- */
-
-exports.colors = ['#0000CC', '#0000FF', '#0033CC', '#0033FF', '#0066CC', '#0066FF', '#0099CC', '#0099FF', '#00CC00', '#00CC33', '#00CC66', '#00CC99', '#00CCCC', '#00CCFF', '#3300CC', '#3300FF', '#3333CC', '#3333FF', '#3366CC', '#3366FF', '#3399CC', '#3399FF', '#33CC00', '#33CC33', '#33CC66', '#33CC99', '#33CCCC', '#33CCFF', '#6600CC', '#6600FF', '#6633CC', '#6633FF', '#66CC00', '#66CC33', '#9900CC', '#9900FF', '#9933CC', '#9933FF', '#99CC00', '#99CC33', '#CC0000', '#CC0033', '#CC0066', '#CC0099', '#CC00CC', '#CC00FF', '#CC3300', '#CC3333', '#CC3366', '#CC3399', '#CC33CC', '#CC33FF', '#CC6600', '#CC6633', '#CC9900', '#CC9933', '#CCCC00', '#CCCC33', '#FF0000', '#FF0033', '#FF0066', '#FF0099', '#FF00CC', '#FF00FF', '#FF3300', '#FF3333', '#FF3366', '#FF3399', '#FF33CC', '#FF33FF', '#FF6600', '#FF6633', '#FF9900', '#FF9933', '#FFCC00', '#FFCC33'];
-/**
- * Currently only WebKit-based Web Inspectors, Firefox >= v31,
- * and the Firebug extension (any Firefox version) are known
- * to support "%c" CSS customizations.
- *
- * TODO: add a `localStorage` variable to explicitly enable/disable colors
- */
-// eslint-disable-next-line complexity
-
-function useColors() {
-  // NB: In an Electron preload script, document will be defined but not fully
-  // initialized. Since we know we're in Chrome, we'll just detect this case
-  // explicitly
-  if (typeof window !== 'undefined' && window.process && (window.process.type === 'renderer' || window.process.__nwjs)) {
-    return true;
-  } // Internet Explorer and Edge do not support colors.
-
-
-  if (typeof navigator !== 'undefined' && navigator.userAgent && navigator.userAgent.toLowerCase().match(/(edge|trident)\/(\d+)/)) {
-    return false;
-  } // Is webkit? http://stackoverflow.com/a/16459606/376773
-  // document is undefined in react-native: https://github.com/facebook/react-native/pull/1632
-
-
-  return typeof document !== 'undefined' && document.documentElement && document.documentElement.style && document.documentElement.style.WebkitAppearance || // Is firebug? http://stackoverflow.com/a/398120/376773
-  typeof window !== 'undefined' && window.console && (window.console.firebug || window.console.exception && window.console.table) || // Is firefox >= v31?
-  // https://developer.mozilla.org/en-US/docs/Tools/Web_Console#Styling_messages
-  typeof navigator !== 'undefined' && navigator.userAgent && navigator.userAgent.toLowerCase().match(/firefox\/(\d+)/) && parseInt(RegExp.$1, 10) >= 31 || // Double check webkit in userAgent just in case we are in a worker
-  typeof navigator !== 'undefined' && navigator.userAgent && navigator.userAgent.toLowerCase().match(/applewebkit\/(\d+)/);
-}
-/**
- * Colorize log arguments if enabled.
- *
- * @api public
- */
-
-
-function formatArgs(args) {
-  args[0] = (this.useColors ? '%c' : '') + this.namespace + (this.useColors ? ' %c' : ' ') + args[0] + (this.useColors ? '%c ' : ' ') + '+' + module.exports.humanize(this.diff);
-
-  if (!this.useColors) {
-    return;
-  }
-
-  var c = 'color: ' + this.color;
-  args.splice(1, 0, c, 'color: inherit'); // The final "%c" is somewhat tricky, because there could be other
-  // arguments passed either before or after the %c, so we need to
-  // figure out the correct index to insert the CSS into
-
-  var index = 0;
-  var lastC = 0;
-  args[0].replace(/%[a-zA-Z%]/g, function (match) {
-    if (match === '%%') {
-      return;
-    }
-
-    index++;
-
-    if (match === '%c') {
-      // We only are interested in the *last* %c
-      // (the user may have provided their own)
-      lastC = index;
-    }
-  });
-  args.splice(lastC, 0, c);
-}
-/**
- * Invokes `console.log()` when available.
- * No-op when `console.log` is not a "function".
- *
- * @api public
- */
-
-
-function log() {
-  var _console;
-
-  // This hackery is required for IE8/9, where
-  // the `console.log` function doesn't have 'apply'
-  return (typeof console === "undefined" ? "undefined" : _typeof(console)) === 'object' && console.log && (_console = console).log.apply(_console, arguments);
-}
-/**
- * Save `namespaces`.
- *
- * @param {String} namespaces
- * @api private
- */
-
-
-function save(namespaces) {
-  try {
-    if (namespaces) {
-      exports.storage.setItem('debug', namespaces);
-    } else {
-      exports.storage.removeItem('debug');
-    }
-  } catch (error) {// Swallow
-    // XXX (@Qix-) should we be logging these?
-  }
-}
-/**
- * Load `namespaces`.
- *
- * @return {String} returns the previously persisted debug modes
- * @api private
- */
-
-
-function load() {
-  var r;
-
-  try {
-    r = exports.storage.getItem('debug');
-  } catch (error) {} // Swallow
-  // XXX (@Qix-) should we be logging these?
-  // If debug isn't set in LS, and we're in Electron, try to load $DEBUG
-
-
-  if (!r && typeof process !== 'undefined' && 'env' in process) {
-    r = process.env.DEBUG;
-  }
-
-  return r;
-}
-/**
- * Localstorage attempts to return the localstorage.
- *
- * This is necessary because safari throws
- * when a user disables cookies/localstorage
- * and you attempt to access it.
- *
- * @return {LocalStorage}
- * @api private
- */
-
-
-function localstorage() {
-  try {
-    // TVMLKit (Apple TV JS Runtime) does not have a window object, just localStorage in the global context
-    // The Browser also has localStorage in the global context.
-    return localStorage;
-  } catch (error) {// Swallow
-    // XXX (@Qix-) should we be logging these?
-  }
-}
-
-module.exports = require('./common')(exports);
-var formatters = module.exports.formatters;
-/**
- * Map %j to `JSON.stringify()`, since no Web Inspectors do that by default.
- */
-
-formatters.j = function (v) {
-  try {
-    return JSON.stringify(v);
-  } catch (error) {
-    return '[UnexpectedJSONParseError]: ' + error.message;
-  }
-};
-
diff --git a/spaces/zhoupin30/zhoupin30/src/components/toaster.tsx b/spaces/zhoupin30/zhoupin30/src/components/toaster.tsx
deleted file mode 100644
index 4d2693460b61307a1d4c127fd01df9bee16e59ff..0000000000000000000000000000000000000000
--- a/spaces/zhoupin30/zhoupin30/src/components/toaster.tsx
+++ /dev/null
@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'
diff --git a/spaces/zixian/Zhenhuan-VITS/text/cantonese.py b/spaces/zixian/Zhenhuan-VITS/text/cantonese.py
deleted file mode 100644
index b66d12138b81b70b86f18217d24a08fce76305c0..0000000000000000000000000000000000000000
--- a/spaces/zixian/Zhenhuan-VITS/text/cantonese.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('jyutjyu')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ei˥'),
-    ('B', 'biː˥'),
-    ('C', 'siː˥'),
-    ('D', 'tiː˥'),
-    ('E', 'iː˥'),
-    ('F', 'e˥fuː˨˩'),
-    ('G', 'tsiː˥'),
-    ('H', 'ɪk̚˥tsʰyː˨˩'),
-    ('I', 'ɐi˥'),
-    ('J', 'tsei˥'),
-    ('K', 'kʰei˥'),
-    ('L', 'e˥llou˨˩'),
-    ('M', 'ɛːm˥'),
-    ('N', 'ɛːn˥'),
-    ('O', 'ou˥'),
-    ('P', 'pʰiː˥'),
-    ('Q', 'kʰiːu˥'),
-    ('R', 'aː˥lou˨˩'),
-    ('S', 'ɛː˥siː˨˩'),
-    ('T', 'tʰiː˥'),
-    ('U', 'juː˥'),
-    ('V', 'wiː˥'),
-    ('W', 'tʊk̚˥piː˥juː˥'),
-    ('X', 'ɪk̚˥siː˨˩'),
-    ('Y', 'waːi˥'),
-    ('Z', 'iː˨sɛːt̚˥')
-]]
-
-
-def number_to_cantonese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def cantonese_to_ipa(text):
-    text = number_to_cantonese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text