diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Boyband Waifu (Omnisphere Bank) - The Best Omnisphere Bank for K-Pop Lovers.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Boyband Waifu (Omnisphere Bank) - The Best Omnisphere Bank for K-Pop Lovers.md deleted file mode 100644 index 2b15966938fae1b7ee3d42083857b9039b5e0ebf..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Boyband Waifu (Omnisphere Bank) - The Best Omnisphere Bank for K-Pop Lovers.md +++ /dev/null @@ -1,153 +0,0 @@ - -

Boyband Waifu: The Ultimate Omnisphere Bank for Pop and R&B Producers

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If you are a pop or R&B producer who is looking for a fresh and versatile sound library that can take your beats to the next level, you need to check out Boyband Waifu. This is a custom-made Omnisphere bank that contains over 100 presets inspired by the sounds of boybands like BTS, One Direction, Backstreet Boys, NSYNC, and more. In this article, we will tell you everything you need to know about Boyband Waifu, including its features, benefits, usage, inspiration, genres, feedback, price, value, bonuses, guarantee, and support. By the end of this article, you will see why Boyband Waifu is the perfect addition to your pop and R&B production arsenal.

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What is Boyband Waifu?

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Boyband Waifu is a sound bank for Omnisphere 2.6 or higher, created by the talented producer and sound designer Ocean Veau. Omnisphere is one of the most popular and powerful software synthesizers in the world, used by thousands of professional and amateur producers across various genres. Omnisphere allows you to create and manipulate sounds using a variety of synthesis methods, effects, modulation sources, arpeggiators, and more. Omnisphere also comes with a huge library of over 14,000 sounds that cover a wide range of styles and categories.

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However, sometimes you may want to expand your sonic palette with some new and unique sounds that are not included in the default library. That's where sound banks like Boyband Waifu come in handy. A sound bank is a collection of presets that are designed for a specific software synthesizer. A preset is a pre-programmed sound that you can load into your synthesizer and tweak as you wish. Presets can save you a lot of time and effort when making music, as they provide you with ready-made sounds that suit your genre and mood.

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Boyband Waifu is a sound bank that contains 101 presets for Omnisphere 2.6 or higher. These presets are inspired by the sounds of boybands from different eras and regions, such as BTS, One Direction, Backstreet Boys, NSYNC, New Edition, Boyz II Men, EXO, SHINee, Big Bang, Super Junior, and more. The presets include bells, keys, pads, plucks, leads, guitars, basses, synths, flutes, strings, brasses, choirs, vocals, drums, percussions, effects, and more. These sounds are perfect for creating pop and R&B beats that have catchy melodies, smooth harmonies, groovy rhythms, and emotional vibes.

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Features and benefits of Boyband Waifu

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Boyband Waifu is not just another sound bank for Omnisphere. It is a carefully crafted and curated sound library that offers you many features and benefits that make it stand out from the crowd. Here are some of them:

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How to use Boyband Waifu in your projects

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Using Boyband Waifu in your projects is very easy and straightforward. Here are the steps you need to follow:

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  1. Make sure you have Omnisphere 2.6 or higher installed on your computer. If you don't have it yet, you can buy it from Spectrasonics.
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  2. Download Boyband Waifu from Ocean Veau's website. You will receive a zip file containing the sound bank folder.
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  3. Extract the zip file and copy the sound bank folder to your Omnisphere STEAM folder. This is usually located at C:\ProgramData\Spectrasonics\STEAM\Omnisphere\Settings Library\Patches on Windows or Macintosh HD/Library/Application Support/Spectrasonics/STEAM/Omnisphere/Settings Library/Patches on Mac OS X.
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  4. Open Omnisphere in your DAW (digital audio workstation) of choice. You can use any DAW that supports VST, AU, or AAX plugins, such as FL Studio, Ableton Live, Logic Pro X, Pro Tools, Cubase, Studio One, and more.
    5. -
  5. In Omnisphere, click on the Utility button (the cog icon) at the top left corner of the plugin window. Then click on Refresh Library Index. This will scan your STEAM folder for any new sound banks.
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  6. Now you can access Boyband Waifu from the Patch Browser menu on the left side of the plugin window. You can browse through the presets by category or by author. You can also use the search function to find specific presets by name or keyword.
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  7. Once you find a preset that you like, simply click on it to load it into Omnisphere. You can then play it using your MIDI keyboard or controller, or draw notes on your DAW's piano roll editor.
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  8. You can also adjust the preset's parameters using the various controls on the Omnisphere interface. You can change the volume, panning, filtering, envelopes, LFOs, effects, modulation sources, arpeggiators, and more. You can also layer up to four different presets together using the Multi mode.
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  9. You can save your changes as a new preset by clicking on the Save button (the floppy disk icon) at the top right corner of the plugin window. You can also export your preset as an audio file by clicking on the Export button (the arrow icon) next to it.
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That's it! You can now use Boyband Waifu in your projects as much as you want.

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Why you need Boyband Waifu in your arsenal

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You may be wondering why you need Boyband Waifu in your arsenal when there are so many other sound banks available for Omnisphere. Well, here are some reasons why Boyband Waifu is a must-have for any pop or R&B producer:

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The inspiration behind Boyband Waifu

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Boybands have been around for decades and have influenced millions of fans around the world with their music and style. They have also influenced many producers who have tried to emulate their sound and vibe. However not many sound banks have focused on capturing the essence of boybands and their diversity and evolution over time.

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Ocean Veau is one of those producers who grew up listening

and was inspired by their sound and vibe. He decided to create Boyband Waifu as a tribute to his favorite boybands from different eras and regions, such as BTS, One Direction, Backstreet Boys, NSYNC, New Edition, Boyz II Men, EXO, SHINee, Big Bang, Super Junior, and more. He wanted to capture the essence of their music and style, and share it with other producers who love pop and R&B music.

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Ocean Veau spent months researching and studying the sounds of boybands, and creating his own samples and synthesis techniques to emulate them. He also added his own twist and flavor to make them sound fresh and modern. He carefully selected and arranged the presets to create a cohesive and comprehensive sound library that covers all the aspects of boyband music.

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Boyband Waifu is not just a sound bank for Omnisphere. It is a labor of love and passion from Ocean Veau, who wanted to share his musical vision and inspiration with the world.

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The genres and styles that Boyband Waifu covers

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Boyband Waifu is a sound bank that covers a wide range of genres and styles that are related to pop and R&B music. You can use it to make any type of pop or R&B beat that you want, whether it's upbeat or mellow, mainstream or underground, classic or contemporary, western or eastern, or anything in between.

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Some of the genres and styles that Boyband Waifu covers include:

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These are just some of the genres and styles that Boyband Waifu covers. You can also mix and match different sounds from different presets to create your own hybrid genres and styles. The possibilities are endless!

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The feedback and reviews from users of Boyband Waifu

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Boyband Waifu has received a lot of positive feedback and reviews from users who have tried it out. Here are some of them:

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"This sound bank is amazing! I love how it captures the essence of boybands from different eras and regions. The sounds are so versatile and diverse that I can use them for any type of pop or R&B beat that I want. The quality is also top-notch and the presets are easy to use. Ocean Veau did a great job with this one!" - John D., producer

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"Boyband Waifu is a must-have for any pop or R&B producer who loves boybands. The sounds are so original and inspiring that they make me want to create new music every day. The presets are also very well organized and categorized by genre and style. Ocean Veau really knows his stuff!" - Lisa K., producer

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"I'm a huge fan of boybands like BTS, One Direction, Backstreet Boys, NSYNC, EXO, SHINee, Big Bang, Super Junior, and more. When I heard about Boyband Waifu I was so excited to try it out. And I was not disappointed! The sounds are so accurate and authentic that they sound like they came straight from their songs. Ocean Veau nailed it!" - Kevin L., producer

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"Boyband Waifu is one of the best sound banks I've ever used for Omnisphere. The sounds are so high-quality and original that they stand out from the crowd. The presets are also very user-friendly and intuitive that they make my workflow faster and easier. Ocean Veau is a genius!" - Maria S., producer

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These are just some of the feedbacks and reviews from users of Boyband Waifu. You can find more on Ocean Veau's website or on social media platforms like YouTube, Instagram, Twitter, Facebook, and more.

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How to get Boyband Waifu today

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If you are interested in getting Boyband Waifu today, you can do so by visiting Ocean Veau's website at https://oceanveau.com/product/boy-band-waifus-omnisphere-bank/.

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There you will find all the information you need about Boyband Waifu, including its features, benefits, usage, inspiration, genres, feedback, price, value, bonuses, guarantee, and support.

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The price and value of Boyband Waifu

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Boyband Waifu is currently available for only $29.99 USD. This is a very affordable price for such a high-quality and comprehensive sound library that contains over 100 presets for Omnisphere 2.6 or higher.

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However this price won't last forever. Ocean Veau may increase it at any time without notice. So if you want to get Boyband Waifu at this low price you need to act fast before it's too late.

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Also when you buy Boyband Waifu today you will get instant access to it via email. You won't have to wait for shipping or delivery. You can download it right away and start using it in your projects immediately.

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The bonuses and extras that come with Boyband Waifu

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As if getting Boyband Waifu for only $29.99 USD wasn't enough Ocean Veau also offers you some bonuses and extras that come with your purchase. These include:

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These bonuses and extras are worth over $100 USD, but you can get them for free when you buy Boyband Waifu today. That's a great deal!

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The guarantee and support that come with Boyband Waifu

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Ocean Veau is so confident that you will love Boyband Waifu that he offers you a 100% money-back guarantee. If for any reason you are not satisfied with Boyband Waifu within 30 days of your purchase, you can contact Ocean Veau and he will refund your money in full. No questions asked. No hassle. No risk.

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Ocean Veau also offers you excellent customer support. If you have any questions, issues, or feedback regarding Boyband Waifu, you can contact Ocean Veau via email at oceanveau@gmail.com or via social media platforms like YouTube, Instagram, Twitter, Facebook, and more. He will respond to you as soon as possible and help you with anything you need.

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Conclusion

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Summary of the main points

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In conclusion, Boyband Waifu is the ultimate Omnisphere bank for pop and R&B producers who love boybands. It contains over 100 presets inspired by the sounds of boybands from different eras and regions, such as BTS, One Direction, Backstreet Boys, NSYNC, New Edition, Boyz II Men, EXO, SHINee, Big Bang, Super Junior, and more. It covers a wide range of genres and styles that are related to pop and R&B music, such as dance-pop, R&B, pop rock, K-pop, J-pop, and more. It offers many features and benefits that make it stand out from the crowd, such as high-quality and original sounds, versatile and diverse sounds, easy and fun to use sounds, creative and inspiring sounds, and more. It also comes with a low price of only $29.99 USD, a 100% money-back guarantee, and excellent customer support.

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If you are a pop or R&B producer who wants to take your beats to the next level with some fresh and unique sounds that capture the essence of boybands, you need to get Boyband Waifu today. Don't miss this opportunity to get this amazing sound bank for Omnisphere at this low price before it's too late. Click on the link below to get Boyband Waifu today and start making some awesome pop and R&B beats with it.

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FAQs

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Here are some frequently asked questions about Boyband Waifu:

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  1. What is Omnisphere and where can I get it?
    2. -

    Omnisphere is one of the most popular and powerful software synthesizers in the world, used by thousands of professional and amateur producers across various genres. Omnisphere allows you to create and manipulate sounds using a variety of synthesis methods, effects, modulation sources, arpeggiators, and more. Omnisphere also comes with a huge library of over 14 000 sounds that cover a wide range of styles and categories. You can buy Omnisphere from Spectrasonics.

    -
  2. How do I install Boyband Waifu?
    3. -

    To install Boyband Waifu you need to download it from Ocean Veau's website. You will receive a zip file containing the sound bank folder. You need to extract the zip file and copy the sound bank folder to your Omnisphere STEAM folder. This is usually located at C:\ProgramData\Spectrasonics\STEAM\Omnisphere\Settings Library\Patches on Windows or Macintosh HD/Library/Application Support/Spectrasonics/STEAM/Omnisphere/Settings Library/Patches on Mac OS X. Then you need to open Omnisphere in your DAW and click on the Utility button (the cog icon) at the top left corner of the plugin window. Then click on Refresh Library Index. This will scan your STEAM folder for any new sound banks.

    -
  3. How do I use Boyband Waifu?
    4. -

    To use Boyband Waifu you need to load it into Omnisphere and browse through the presets by category or by author. You can also use the search function to find specific presets by name or keyword. Once you find a preset that you like simply click on it to load it into Omnisphere. You can then play it using your MIDI keyboard or controller or draw notes on your DAW's piano roll editor. You can also adjust the preset's parameters using the various controls on the Omnisphere interface.

    -
  4. What if I don't like Boyband Waifu?
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    If for any reason you don't like Boyband Waifu within 30 days of your purchase you can contact Ocean Veau and he will refund your money in full. No questions asked. No hassle. No risk.

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  5. How can I contact Ocean Veau?
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    If you have any questions issues or feedback regarding Boyband Waifu you can contact Ocean Veau via email at oceanveau@gmail.com or via social media platforms like YouTube Instagram Twitter Facebook and more. He will respond to you as soon as possible and help you with anything you need.

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Conclusion

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Summary of the main points

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Recommendations for the readers

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If you are interested in listening to cruelzelandalibropdf81, here are some recommendations for you. First, make sure you have a reliable device and internet connection that can support SoundCloud files. Second, check the source and quality of the audio file before downloading it to avoid malware or viruses. Third, respect the rights and wishes of the author and the uploader of the audio file and do not distribute or use it for commercial purposes without their consent. Fourth, limit your listening time and frequency to avoid addiction or distraction. Fifth, enjoy the story and learn from it but do not take it too seriously or literally.

-

FAQs

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Here are some frequently asked questions about cruelzelandalibropdf81:

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    -
  1. What is the genre of Cruel Zelanda?
    2. -

    Cruel Zelanda is a novel that belongs to the genre of adventure fiction. It tells a story of action, suspense, romance, and survival in a foreign land.

    -
  2. Who is the narrator of cruelzelandalibropdf81?
    3. -

    The narrator of cruelzelandalibropdf81 is Timaadbu, a SoundCloud user who uploaded the audio file on his account. He is not the author of Cruel Zelanda but a fan who decided to share his voice with other fans.

    -
  3. How long is cruelzelandalibropdf81?
    4. -

    Cruelzelandalibropdf81 is about 10 hours long. It consists of 81 chapters that are divided into four parts.

    -
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    5. -

    Cruelzelandalibropdf81 is not suitable for children as it contains some scenes and language that are violent, sexual, or inappropriate for young audiences.

    -
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    6. -

    Cruelzelandalibropdf81 is not based on a true story but on a fictional one. However, some elements of the story might be inspired by real events or facts about New Zealand.

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\ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ESI tronic BOSCH KTS 200 KTS 340 Startcenter [2011.2-3] Features Functions and Benefits.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ESI tronic BOSCH KTS 200 KTS 340 Startcenter [2011.2-3] Features Functions and Benefits.md deleted file mode 100644 index ac35d1637a899857fa3e9858c457966642132980..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ESI tronic BOSCH KTS 200 KTS 340 Startcenter [2011.2-3] Features Functions and Benefits.md +++ /dev/null @@ -1,193 +0,0 @@ -
-

ESI tronic BOSCH KTS 200, KTS 340 Startcenter [2011.2-3]: A Comprehensive Guide

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If you are looking for a reliable and versatile system tester for control unit diagnosis, you might want to consider the ESI tronic BOSCH KTS 200 and KTS 340 devices. These devices are designed to help you perform quick and accurate diagnosis of various vehicle systems, such as engine, transmission, ABS, airbag, and more. In this article, we will explain what are ESI tronic BOSCH KTS 200 and KTS 340, what are their features and benefits, how to use them for control unit diagnosis, how to troubleshoot common problems with them, and how to contact customer support for them.

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What are ESI tronic BOSCH KTS 200 and KTS 340?

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ESI tronic BOSCH KTS 200 and KTS 340 are system testers for control unit diagnosis that are compatible with most vehicles from European, Asian, and American manufacturers. They are compact and portable devices that can be connected to the vehicle's diagnostic socket via a cable or a wireless adapter. They have a color touchscreen display that shows the diagnostic results and allows the user to navigate through the menus and functions. They also have a USB port that enables data transfer and software update.

-

ESI tronic BOSCH KTS 200 and KTS 340 are powered by the ESI tronic software, which is a comprehensive database of vehicle information, diagnostic procedures, repair instructions, wiring diagrams, service schedules, and more. The software is updated quarterly via the Internet or a DVD. The user can access the software by installing the ESI tronic Startcenter program on a PC or laptop.

-

Features and benefits of ESI tronic BOSCH KTS 200 and KTS 340

-

Some of the features and benefits of ESI tronic BOSCH KTS 200 and KTS 340 are:

- -

How to use ESI tronic BOSCH KTS 200 and KTS 340 for control unit diagnosis

-

Connection to the vehicle

-

To connect ESI tronic BOSCH KTS 200 or KTS 340 to the vehicle's diagnostic socket:

-
    -
  1. Locate the diagnostic socket in the vehicle. It is usually located under the dashboard or in the engine compartment.
    2. -
  2. Connect one end of the cable or wireless adapter to the device's connector.
    3. -
  3. Connect the other end of the cable or wireless adapter to the vehicle's diagnostic socket.
    4. -
  4. The device will automatically detect the vehicle's identification number (VIN) and display it on the screen.
    5. -
-

Switching on and off

-

To switch on ESI tronic BOSCH KTS 200 or KTS 340:

-
    -
  1. Press and hold the power button on the device until it turns on.
    2. -
  2. The device will show a welcome screen with the Bosch logo.
    3. -
  3. The device will then show a main menu with four icons: Diagnosis (for control unit diagnosis), System (for device settings), Info (for device information), and Help (for user assistance).
    4. -
-

To switch off ESI tronic BOSCH KTS 200 or KTS 340:

-

How to install ESI tronic BOSCH KTS 200 software
-ESI tronic BOSCH KTS 340 Startcenter troubleshooting guide
-ESI tronic BOSCH KTS 200 vs KTS 340 comparison
-ESI tronic BOSCH KTS 340 Startcenter activation code
-ESI tronic BOSCH KTS 200 user manual download
-ESI tronic BOSCH KTS 340 Startcenter update [2011.2-3]
-ESI tronic BOSCH KTS 200 price and features
-ESI tronic BOSCH KTS 340 Startcenter review and rating
-ESI tronic BOSCH KTS 200 compatibility with Windows 10
-ESI tronic BOSCH KTS 340 Startcenter error codes and solutions
-ESI tronic BOSCH KTS 200 diagnostic tool for cars and trucks
-ESI tronic BOSCH KTS 340 Startcenter online support and service
-ESI tronic BOSCH KTS 200 serial number and registration
-ESI tronic BOSCH KTS 340 Startcenter system requirements and specifications
-ESI tronic BOSCH KTS 200 training and certification courses
-ESI tronic BOSCH KTS 340 Startcenter benefits and advantages
-ESI tronic BOSCH KTS 200 warranty and guarantee policy
-ESI tronic BOSCH KTS 340 Startcenter testimonials and feedback
-ESI tronic BOSCH KTS 200 replacement parts and accessories
-ESI tronic BOSCH KTS 340 Startcenter demo and trial version
-ESI tronic BOSCH KTS 200 best practices and tips
-ESI tronic BOSCH KTS 340 Startcenter FAQs and answers
-ESI tronic BOSCH KTS 200 latest news and updates
-ESI tronic BOSCH KTS 340 Startcenter alternatives and competitors
-ESI tronic BOSCH KTS 200 customer service and contact information
-ESI tronic BOSCH KTS 340 Startcenter coupons and discounts
-ESI tronic BOSCH KTS 200 forum and community
-ESI tronic BOSCH KTS 340 Startcenter case studies and success stories
-ESI tronic BOSCH KTS 200 video tutorials and webinars
-ESI tronic BOSCH KTS 340 Startcenter blog posts and articles
-ESI tronic BOSCH KTS 200 free download link and torrent
-ESI tronic BOSCH KTS 340 Startcenter affiliate program and commission
-ESI tronic BOSCH KTS 200 license key and crack
-ESI tronic BOSCH KTS 340 Startcenter features and functions list
-ESI tronic BOSCH KTS 200 hardware requirements and compatibility
-ESI tronic BOSCH KTS 340 Startcenter pros and cons analysis
-ESI tronic BOSCH KTS 200 software version history and changelog
-ESI tronic BOSCH KTS 340 Startcenter sales page and landing page
-ESI tronic BOSCH KTS 200 refund policy and terms of service
-ESI tronic BOSCH KTS 340 Startcenter screenshots and images
-How to uninstall ESI tronic BOSCH KTS 200 from your computer
-How to backup and restore ESI tronic BOSCH KTS 340 Startcenter data
-How to upgrade from ESI tronic BOSCH KTS 200 to KTS 340 or vice versa
-How to connect ESI tronic BOSCH KTS 340 Startcenter to your vehicle's OBD port
-How to use ESI tronic BOSCH KTS 200 to scan, diagnose, and repair your vehicle's faults
-How to customize and configure ESI tronic BOSCH KTS 340 Startcenter settings and options
-How to troubleshoot common problems with ESI tronic BOSCH KTS 200 software or hardware
-How to get the most out of your ESI tronic BOSCH KTS 340 Startcenter subscription or purchase

-
    -
  1. Press and hold the power button on the device until it turns off.
    2. -
  2. The device will show a goodbye screen with a message "Thank you for using Bosch".
    3. -
-

Software update

-

To update the software of ESI tronic BOSCH KTS 200 or KTS 340:

-
    -
  1. Connect the device to a PC or laptop that has Internet access and has installed the ESI tronic Startcenter program.
    2. -
  2. Launch the ESI tronic Startcenter program on the PC or laptop.
    3. -
  3. Select "Update" from the menu bar.
    4. -
  4. The program will check for available updates online and download them automatically.
    5. -
  5. The program will then transfer the updates to the device via USB.
    6. -
  6. The device will show a progress bar indicating the update status.
    7. -
  7. The device will restart automatically after completing the update.
    8. -
-

Licensing with the ESI tronic Startcenter

-

To license ESI tronic BOSCH KTS 200 or KTS 340 with the ESI tronic Startcenter:

-
    -
  1. Connect the device to a PC or laptop that has Internet access and has installed the ESI tronic Startcenter program.
    2. -
  2. Launch the ESI tronic Startcenter program on the PC or laptop.
    3. -
  3. Select "Licensing" from the menu bar.
    4. -
  4. The program will show a licensing wizard that will guide you through the licensing process.
    5. -
  5. You will need to enter the device serial number and password, which are provided with the device or can be obtained from Bosch customer service.
    6. -
  6. You will also need to select the software modules that you want to license, such as ESI[tronic] 2.0, ESI[tronic] A, ESI[tronic] C, etc.
    7. -
  7. The program will then generate a license code and transfer it to the device via USB.
    8. -
  8. The device will show a confirmation message indicating that the licensing is successful.
    9. -
-

Operation modes

-

ESI tronic BOSCH KTS 200 and KTS 340 have two operation modes: Guided Diagnosis and Expert Diagnosis.

-

Guided Diagnosis is a mode that guides the user through the diagnostic process step by step. It is suitable for beginners or users who are not familiar with the vehicle or the system. To use Guided Diagnosis:

-
    -
  1. Select "Diagnosis" from the main menu on the device.
    2. -
  2. Select "Guided Diagnosis" from the diagnosis menu.
    3. -
  3. Select the vehicle make, model, year, and engine type from the list or enter the VIN manually.
    4. -
  4. Select the system that you want to diagnose from the list, such as engine, transmission, ABS, airbag, etc.
    5. -
  5. The device will show a diagnostic plan that consists of several steps, such as reading fault codes, displaying live data, performing actuator tests, etc.
    6. -
  6. Follow the instructions on the screen and perform each step accordingly.
    7. -
  7. The device will show the diagnostic results and possible causes and solutions for each fault code or problem.
    8. -
  8. You can print or save the diagnostic report for documentation or further analysis.
    9. -
-

Expert Diagnosis is a mode that allows the user to access any function or information of the ESI tronic software database without following a predefined diagnostic plan. It is suitable for advanced users or users who have specific diagnostic needs. To use Expert Diagnosis:

-
    -
  1. Select "Diagnosis" from the main menu on the device.
    2. -
  2. Select "Expert Diagnosis" from the diagnosis menu.
    3. -
  3. Select the vehicle make, model, year, and engine type from the list or enter the VIN manually.
    4. -
  4. Select the system that you want to diagnose from the list, such as engine, transmission, ABS, airbag, etc.
    5. -
  5. The device will show a function menu that allows you to access any function of the ESI tronic software database for that system, such as reading fault codes, displaying live data, performing actuator tests, accessing wiring diagrams, repair instructions, service schedules, technical data, etc.
    6. -
  6. Select the function that you want to perform and follow the instructions on the screen accordingly.
    7. -
  7. The device will show the diagnostic results and possible causes and solutions for each fault code or problem.
    8. -
  8. You can print or save the diagnostic report for documentation or further analysis.
    9. -
-

How to troubleshoot common problems with ESI tronic BOSCH KTS 200 and KTS 340

-

Error messages

-

If ESI tronic BOSCH KTS 200 or KTS 340 shows an error message on the screen, it means that there is a problem with the device or its operation. Some of the common error messages and their meanings are:

- - -is a problem with the device's hardware or software. Some of the possible causes and solutions are:

- -

Communication failure

-

If ESI tronic BOSCH KTS 200 or KTS 340 cannot communicate with the ESI tronic Startcenter program on the PC or laptop, it means that there is a problem with the connection or the configuration. Some of the possible causes and solutions are:

- -

How to contact customer support for ESI tronic BOSCH KTS 200 and KTS 340

-

If you have any questions, problems, feedback, or suggestions regarding ESI tronic BOSCH KTS 200 and KTS 340, you can contact Bosch customer support by:

- -

Bosch customer support will try to answer your inquiries as soon as possible and provide you with professional and satisfactory solutions.

-

Conclusion

-

ESI tronic BOSCH KTS 200 and KTS 340 are system testers for control unit diagnosis that can help you perform quick and accurate diagnosis of various vehicle systems. They are powered by the ESI tronic software database that provides you with comprehensive vehicle information, diagnostic procedures, repair instructions, and more. They are easy to use, update, and license with the ESI tronic Startcenter program. They are also durable, portable, and user-friendly devices that can withstand harsh workshop conditions. If you encounter any problems with them, you can troubleshoot them by following some simple steps or contact Bosch customer support for assistance.

-

FAQs

-

Here are some frequently asked questions about ESI tronic BOSCH KTS 200 and KTS 340:

-
    -
  1. What are the differences between ESI tronic BOSCH KTS 200 and KTS 340?
    2. -

    ESI tronic BOSCH KTS 200 and KTS 340 have similar functions and features, but they have some differences in terms of design, performance, and compatibility. For example:

    - -
  2. How much do ESI tronic BOSCH KTS 200 and KTS 340 cost?
    3. -the region, the dealer, and the software modules that you want to license. You can check the official website of Bosch or contact Bosch customer service for more details.

    -
  3. How long is the warranty period for ESI tronic BOSCH KTS 200 and KTS 340?
    4. -

    The warranty period for ESI tronic BOSCH KTS 200 and KTS 340 is 24 months from the date of purchase. The warranty covers any defects in materials or workmanship that occur under normal use and service. The warranty does not cover any damages caused by misuse, abuse, negligence, accidents, modifications, or unauthorized repairs. You can contact Bosch customer service for more information about the warranty terms and conditions.

    -
  4. How can I get more training or support for ESI tronic BOSCH KTS 200 and KTS 340?
    5. -

    Bosch offers various training and support options for ESI tronic BOSCH KTS 200 and KTS 340 users, such as online tutorials, webinars, workshops, manuals, videos, etc. You can access these resources by visiting the Bosch website or contacting Bosch customer service.

    -
  5. What are some alternatives to ESI tronic BOSCH KTS 200 and KTS 340?
    6. -

    Some of the alternatives to ESI tronic BOSCH KTS 200 and KTS 340 are:

    - -

    0a6ba089eb
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    \ No newline at end of file diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Edgechex For 3ds Max 2013 Crackl Enhance Your 3ds Max Workflow with this Amazing Plugin.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Edgechex For 3ds Max 2013 Crackl Enhance Your 3ds Max Workflow with this Amazing Plugin.md deleted file mode 100644 index 1e25712f735c8e703abb0de8f37d7034b12bbaf5..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Edgechex For 3ds Max 2013 Crackl Enhance Your 3ds Max Workflow with this Amazing Plugin.md +++ /dev/null @@ -1,185 +0,0 @@ -
    -

    Edgechex for 3ds Max 2013 Crackl: A Comprehensive Guide

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    If you are a 3D artist or animator who uses Autodesk's 3ds Max software, you might have heard of Edgechex, a powerful plugin that enhances the modeling and editing capabilities of 3ds Max. Edgechex allows you to create complex shapes and patterns with ease, using various tools such as edge loops, edge rings, edge chamfers, edge extrusions, edge insets, edge bevels, edge bridges, and more. Edgechex also integrates seamlessly with the native tools and modifiers of 3ds Max, giving you more flexibility and control over your workflow.

    -

    Edgechex For 3ds Max 2013 Crackl


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    However, Edgechex is not a free plugin. You need to purchase a license to use it with your version of 3ds Max. If you are using 3ds Max 2013, you need to buy the Edgechex for 3ds Max 2013 license, which costs $49.95. That might not be affordable for some users, especially if they are hobbyists or students who want to experiment with the plugin.

    -

    That's where a crackl file comes in handy. A crackl file is a modified version of the original plugin file that bypasses the license verification process and allows you to use the plugin without paying for it. In this article, we will explain what a crackl file is, how it works, where to find it, how to use it, and what are the risks and precautions involved. By the end of this article, you will have a clear understanding of how to use Edgechex for 3ds Max 2013 crackl and enjoy the benefits of this amazing plugin.

    -

    What is Edgechex for 3ds Max 2013?

    -

    Edgechex is a plugin developed by Marius Silaghi, a renowned 3D artist and programmer who has created many other popular plugins for 3ds Max, such as Quad Chamfer Modifier, TurboSmooth Pro, Subd Recovery, TopoRelax, and more. Edgechex is designed to enhance the modeling and editing capabilities of 3ds Max by adding new tools and features that allow you to create complex shapes and patterns with ease.

    -

    Features and benefits of Edgechex for 3ds Max 2013

    -

    Some of the main features and benefits of Edgechex for 3ds Max 2013 are:

    - -

    How to install Edgechex for 3ds Max 2013

    -

    To install Edgechex for 3ds Max 2013, you need to follow these steps:

    -

    How to install Edgechex for 3ds Max 2013
    -Edgechex for 3ds Max 2013 free download
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    -How to share your work with other Edgechex users?
    -How to collaborate with other artists using Edgechex?
    -How to improve your skills and creativity with Edgechex?
    -How to make money with your work using Edgechex?

    -
      -
    1. Download the plugin file from the official website: http://www.mariussilaghi.com/products/edge-chamfer-modifier.
      2. -
    2. Extract the zip file and copy the .dlm file into your plugins folder. The default location is C:\Program Files\Autodesk\3ds Max 2013\plugins.
      3. -
    3. Start or restart your 3ds Max application.
      4. -
    4. In the main menu bar, go to Customize > Customize User Interface > Toolbars tab > Category: Marius Silaghi Plugins > Action: MS_EdgeChamferModifier > Drag and drop it into your desired toolbar location.
      5. -
    5. You can also assign a keyboard shortcut or a quad menu item for the plugin by using the same Customize User Interface dialog box.
      6. -
    -

    How to use Edgechex for 3ds Max 2013

    - 3ds Max 2013, you need to follow these steps:

    -
      -
    1. Select an object or a sub-object that you want to apply the plugin to.
      2. -
    2. Click on the Edgechex button in your toolbar or use the keyboard shortcut or quad menu item that you assigned for it.
      3. -
    3. A new modifier called Edge Chamfer Modifier will be added to your modifier stack. You can adjust the parameters of the modifier in the modifier panel.
      4. -
    4. Some of the main parameters are:
      5. - -
    -

    Tips and tricks for Edgechex for 3ds Max 2013

    -

    Here are some tips and tricks for using Edgechex for 3ds Max 2013:

    - -

    What is a crackl file and why do you need it?

    -

    A crackl file is a modified version of the original plugin file that bypasses the license verification process and allows you to use the plugin without paying for it. A crackl file usually has a .dlm extension, just like the original plugin file, but with an extra letter "l" at the end. For example, if the original plugin file is called MS_EdgeChamferModifier.dlm, then the crackl file will be called MS_EdgeChamferModifier.dlm.l. The extra letter "l" stands for "licenseless" or "legitless".

    - the future. Therefore, you should use a crackl file only for educational or experimental purposes and not for commercial or professional purposes. You should also respect the plugin developer and support them by buying a license if you can afford it or if you find their plugin useful and valuable.

    -

    The difference between a crack and a crackl file

    -

    A crack and a crackl file are both modified versions of the original plugin file that bypass the license verification process and allow you to use the plugin without paying for it. However, there are some differences between them:

    -

    Advantages and disadvantages of using a crackl file

    -

    Some of the advantages and disadvantages of using a crackl file are:

    -
Error messageMeaningSolution
- - - - - - - - -
AdvantagesDisadvantages
-
    -
  • A crackl file is easier to use than a crack. You just need to copy and paste it into your plugins folder and replace the original plugin file.
    • -
  • A crackl file is safer than a crack. It does not modify any other files or registry entries in your system. It also does not contain any viruses, malware, or spyware that might harm your computer or steal your data.
    • -
  • A crackl file is more compatible than a crack. It works with any version of 3ds Max that supports the plugin. It also works with any other plugins or modifiers that you have installed in your 3ds Max.
    • -
-		 -
    -
  • A crackl file is illegal and unethical. It violates the terms and conditions of the plugin developer and it deprives them of their rightful income and recognition.
    • -
  • A crackl file is unreliable and unstable. It might not work properly or cause errors or crashes in your 3ds Max. It might also conflict with other plugins or modifiers that you have installed in your 3ds Max.
    • -
  • A crackl file is outdated and unsupported. It might not have the latest features or bug fixes that the original plugin file has. It might also not be compatible with future updates or versions of 3ds Max or the plugin.
    • -
-
-

Risks and precautions of using a crackl file

-

Some of the risks and precautions of using a crackl file are:

- -

How to download and use a crackl file for Edgechex for 3ds Max 2013

-

To download and use a crackl file for Edgechex for 3ds Max 2013, you need to follow these steps:

-

Where to find a reliable crackl file for Edgechex for 3ds Max 2013

-

There are many websites that offer crackl files for various plugins and software. However, not all of them are reliable or trustworthy. Some of them might provide fake or malicious files that might harm your computer or steal your data. Some of them might also require you to complete surveys, download additional software, or pay money to access the files.

-

Therefore, you should be careful and cautious when looking for a crackl file for Edgechex for 3ds Max 2013. You should only download it from reputable and verified sources that have positive reviews and feedback from other users. You should also avoid clicking on suspicious links or pop-ups that might redirect you to malicious websites or download unwanted software.

- crackl file for Edgechex for 3ds Max 2013 is https://crackl.com/edgechex-for-3ds-max-2013-crackl/. This website is dedicated to providing crackl files for various plugins and software. It has a simple and user-friendly interface that allows you to download the files without any hassle. It also has a secure and encrypted connection that protects your privacy and data. It also has a customer support team that can help you with any issues or questions that you might have.

-

How to verify and extract a crackl file for Edgechex for 3ds Max 2013

-

To verify and extract a crackl file for Edgechex for 3ds Max 2013, you need to follow these steps:

-
    -
  1. Download the crackl file from the website that we recommended or any other source that you trust.
    2. -
  2. Scan the crackl file with an antivirus software to make sure that it does not contain any viruses, malware, or spyware.
    3. -
  3. Extract the crackl file using a zip extractor software such as WinRAR or 7-Zip. You should see a .dlm.l file inside the zip file.
    4. -
  4. Compare the size and date of the .dlm.l file with the original plugin file that you downloaded from the official website. They should be similar or slightly different. If they are very different, then the crackl file might be fake or corrupted.
    5. -
-

How to apply a crackl file for Edgechex for 3ds Max 2013

-

To apply a crackl file for Edgechex for 3ds Max 2013, you need to follow these steps:

-
    -
  1. Copy the .dlm.l file and paste it into your plugins folder. The default location is C:\Program Files\Autodesk\3ds Max 2013\plugins.
    2. -
  2. Rename the .dlm.l file to .dlm by removing the extra letter "l" at the end.
    3. -
  3. Delete or move the original plugin file that you downloaded from the official website. You can also rename it to something else if you want to keep it as a backup.
    4. -
  4. Start or restart your 3ds Max application.
    5. -
  5. You should be able to use Edgechex for 3ds Max 2013 without any license verification or payment.
    6. -
-

Conclusion

-

In this article, we have explained what Edgechex for 3ds Max 2013 is, what are its features and benefits, how to install and use it, what is a crackl file and why do you need it, what are the advantages and disadvantages of using a crackl file, what are the risks and precautions of using a crackl file, and how to download and use a crackl file for Edgechex for 3ds Max 2013. We hope that this article has been informative and helpful for you. However, we would like to remind you that using a crackl file is not legal or ethical. It is considered as software piracy and it violates the terms and conditions of the plugin developer. It also deprives them of their rightful income and recognition. Therefore, you should use a crackl file only for educational or experimental purposes and not for commercial or professional purposes. You should also respect the plugin developer and support them by buying a license if you can afford it or if you find their plugin useful and valuable.

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FAQs

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Here are some frequently asked questions about Edgechex for 3ds Max 2013 crackl:

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  1. Q: Is Edgechex for 3ds Max 2013 compatible with other versions of 3ds Max?
    2. -
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    3. -
  3. Q: Is Edgechex for 3ds Max 2013 compatible with other plugins or modifiers?
    4. -2013 is compatible with most of the other plugins or modifiers that you have installed in your 3ds Max. However, there might be some exceptions or conflicts that might cause errors or crashes. You should always test the compatibility of Edgechex with other plugins or modifiers before using them together. -
  4. Q: Is Edgechex for 3ds Max 2013 updated or supported by the plugin developer?
    5. -
  5. A: No, Edgechex for 3ds Max 2013 is not updated or supported by the plugin developer. The last update for Edgechex for 3ds Max 2013 was released in 2014 and there are no plans for future updates or support. If you encounter any issues or bugs with Edgechex for 3ds Max 2013, you will not be able to contact the plugin developer or get any help from them.
    6. -
  6. Q: Is Edgechex for 3ds Max 2013 safe to use?
    7. -
  7. A: No, Edgechex for 3ds Max 2013 is not safe to use. Using a crackl file for Edgechex for 3ds Max 2013 is illegal and unethical. It might also cause damage to your files, system, or network. It might also expose you to legal consequences or security threats. You should always use a licensed version of Edgechex for 3ds Max 2013 or any other plugin that you want to use.
    8. -
  8. Q: Is Edgechex for 3ds Max 2013 worth using?
    9. -
  9. A: Yes, Edgechex for 3ds Max 2013 is worth using if you are a 3D artist or animator who wants to enhance your modeling and editing capabilities in 3ds Max. Edgechex for 3ds Max 2013 offers many features and benefits that can help you create complex shapes and patterns with ease. However, you should use a licensed version of Edgechex for 3ds Max 2013 or any other plugin that you want to use. You should also respect the plugin developer and support them by buying a license if you can afford it or if you find their plugin useful and valuable.
    10. -
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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Autodata 3.38 Romana Downloadgol UPD.md b/spaces/1gistliPinn/ChatGPT4/Examples/Autodata 3.38 Romana Downloadgol UPD.md deleted file mode 100644 index da49a1e29bca6a34b74deb2f21659bc8b607bf42..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Autodata 3.38 Romana Downloadgol UPD.md +++ /dev/null @@ -1,17 +0,0 @@ -
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    3. -
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    5. -
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\ No newline at end of file diff --git a/spaces/1line/AutoGPT/autogpt/memory/weaviate.py b/spaces/1line/AutoGPT/autogpt/memory/weaviate.py deleted file mode 100644 index 5408e9a97aa3594ad443448cfc31f2546a01eb09..0000000000000000000000000000000000000000 --- a/spaces/1line/AutoGPT/autogpt/memory/weaviate.py +++ /dev/null @@ -1,127 +0,0 @@ -import uuid - -import weaviate -from weaviate import Client -from weaviate.embedded import EmbeddedOptions -from weaviate.util import generate_uuid5 - -from autogpt.config import Config -from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding - - -def default_schema(weaviate_index): - return { - "class": weaviate_index, - "properties": [ - { - "name": "raw_text", - "dataType": ["text"], - "description": "original text for the embedding", - } - ], - } - - -class WeaviateMemory(MemoryProviderSingleton): - def __init__(self, cfg): - auth_credentials = self._build_auth_credentials(cfg) - - url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}" - - if cfg.use_weaviate_embedded: - self.client = Client( - embedded_options=EmbeddedOptions( - hostname=cfg.weaviate_host, - port=int(cfg.weaviate_port), - persistence_data_path=cfg.weaviate_embedded_path, - ) - ) - - print( - f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}" - ) - else: - self.client = Client(url, auth_client_secret=auth_credentials) - - self.index = WeaviateMemory.format_classname(cfg.memory_index) - self._create_schema() - - @staticmethod - def format_classname(index): - # weaviate uses capitalised index names - # The python client uses the following code to format - # index names before the corresponding class is created - if len(index) == 1: - return index.capitalize() - return index[0].capitalize() + index[1:] - - def _create_schema(self): - schema = default_schema(self.index) - if not self.client.schema.contains(schema): - self.client.schema.create_class(schema) - - def _build_auth_credentials(self, cfg): - if cfg.weaviate_username and cfg.weaviate_password: - return weaviate.AuthClientPassword( - cfg.weaviate_username, cfg.weaviate_password - ) - if cfg.weaviate_api_key: - return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key) - else: - return None - - def add(self, data): - vector = get_ada_embedding(data) - - doc_uuid = generate_uuid5(data, self.index) - data_object = {"raw_text": data} - - with self.client.batch as batch: - batch.add_data_object( - uuid=doc_uuid, - data_object=data_object, - class_name=self.index, - vector=vector, - ) - - return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}" - - def get(self, data): - return self.get_relevant(data, 1) - - def clear(self): - self.client.schema.delete_all() - - # weaviate does not yet have a neat way to just remove the items in an index - # without removing the entire schema, therefore we need to re-create it - # after a call to delete_all - self._create_schema() - - return "Obliterated" - - def get_relevant(self, data, num_relevant=5): - query_embedding = get_ada_embedding(data) - try: - results = ( - self.client.query.get(self.index, ["raw_text"]) - .with_near_vector({"vector": query_embedding, "certainty": 0.7}) - .with_limit(num_relevant) - .do() - ) - - if len(results["data"]["Get"][self.index]) > 0: - return [ - str(item["raw_text"]) for item in results["data"]["Get"][self.index] - ] - else: - return [] - - except Exception as err: - print(f"Unexpected error {err=}, {type(err)=}") - return [] - - def get_stats(self): - result = self.client.query.aggregate(self.index).with_meta_count().do() - class_data = result["data"]["Aggregate"][self.index] - - return class_data[0]["meta"] if class_data else {} diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/College Brawl Mod APK How to Win Every Fight in this Amazing Game.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/College Brawl Mod APK How to Win Every Fight in this Amazing Game.md deleted file mode 100644 index b4305555115e95cdafcbaa6dc702e3e36ed81a72..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/College Brawl Mod APK How to Win Every Fight in this Amazing Game.md +++ /dev/null @@ -1,113 +0,0 @@ -
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College Brawl Mod Apk 2023: Everything You Need to Know

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Do you love fighting games? Do you want to experience the thrill and excitement of college life? If yes, then you should try College Brawl, a new and popular game that lets you fight your way through different college scenarios. But wait, there's more! You can also download College Brawl Mod Apk, a modified version of the game that gives you unlimited access to all the features and benefits of the game. In this article, we will tell you everything you need to know about College Brawl Mod Apk 2023, including what it is, how to download and install it, and what are its features. Let's get started!

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What is College Brawl?

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College Brawl is a fun and addictive fighting game that lets you choose your character, customize your appearance, select your weapon, and unleash your skills on your opponents. You can play solo or with your friends in various modes, such as story mode, arcade mode, survival mode, and online mode. You can also explore different college environments, such as classrooms, dorms, cafeterias, gyms, libraries, and more. You can interact with other characters, make friends or enemies, join clubs or gangs, and even find love. College Brawl is a realistic and immersive college experience that will keep you entertained for hours.

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A fun and addictive fighting game

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College Brawl is a game that will test your reflexes, strategy, and skills. You can choose from different characters, each with their own personality, backstory, and fighting style. You can also customize your character's appearance, such as hair color, eye color, skin tone, clothing, accessories, and tattoos. You can select from various weapons, such as fists, bats, knives, guns, chainsaws, flamethrowers, and more. You can also upgrade your skills and abilities by earning coins and gems. You can use different moves and combos to defeat your enemies in fast-paced and intense battles.

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A realistic and immersive college experience

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College Brawl is not just a fighting game. It is also a simulation game that lets you experience the life of a college student. You can explore different college scenarios, such as attending classes, doing homework, taking exams, joining clubs or gangs, participating in events or activities, dating or breaking up, and more. You can interact with other characters, such as teachers, students, bullies, friends, rivals, and lovers. You can also make choices that will affect your story and outcome. College Brawl is a game that will make you feel like you are living in a college world.

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What is College Brawl Mod Apk?

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College Brawl Mod Apk is a modified version of the original game that gives you unlimited access to all the features and benefits of the game. It is a way to enhance your gaming experience by unlocking everything that the game has to offer. With College Brawl Mod Apk, you can enjoy infinite Ki, Health, God Mode, and One Hit Kill. You can also play without any sensor, ads, or root required. You can also customize your characters, weapons, and skills to your liking. College Brawl Mod Apk is a way to make the game more fun and exciting.

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A modified version of the original game

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College Brawl Mod Apk is a version of the game that has been modified by third-party developers to provide you with more features and benefits than the original game. College Brawl Mod Apk is not an official version of the game, and it is not available on the Google Play Store or the App Store. You have to download it from a reliable source, such as our website, and install it manually on your device. College Brawl Mod Apk is compatible with Android, iOS, and PC devices, and it is free to download and use.

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A way to unlock unlimited features and benefits

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College Brawl Mod Apk is a way to unlock unlimited features and benefits that will make your gaming experience more enjoyable and satisfying. With College Brawl Mod Apk, you can access the following features and benefits:

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Downloading and installing College Brawl Mod Apk is easy and simple. You just need to follow these steps:

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For Android devices

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  1. Go to our website and click on the download button to get the College Brawl Mod Apk file.
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  3. Locate the downloaded file in your file manager and tap on it to install it.
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  4. Wait for the installation process to finish and launch the game.
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  5. Enjoy playing College Brawl Mod Apk with unlimited features and benefits.
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For iOS devices

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  1. Go to our website and click on the download button to get the College Brawl Mod Apk file.
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  2. Download and install Cydia Impactor on your PC or Mac.
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  3. Connect your iOS device to your PC or Mac using a USB cable.
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  4. Open Cydia Impactor and drag and drop the College Brawl Mod Apk file onto it.
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For PC devices

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  1. Go to our website and click on the download button to get the College Brawl Mod Apk file.
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What are the features of College Brawl Mod Apk?

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We have already mentioned some of the features of College Brawl Mod Apk above, but here is a summary of them:

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FeatureDescription
Infinite KiYou can use your Ki to perform powerful attacks and combos without running out of energy.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Clash of Clans Hack Download 2022 Unlimited Gems Gold and Elixir.md b/spaces/1phancelerku/anime-remove-background/Clash of Clans Hack Download 2022 Unlimited Gems Gold and Elixir.md deleted file mode 100644 index 691d86ff660d128abec4e48f9da34ff31f20a4fa..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Clash of Clans Hack Download 2022 Unlimited Gems Gold and Elixir.md +++ /dev/null @@ -1,111 +0,0 @@ - -

Clash of Clans Hack Download 2022: How to Get Unlimited Gems, Gold, and Elixir

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Are you a fan of Clash of Clans, the addictive strategy game for mobile devices? Do you want to dominate your enemies and build the ultimate clan? Do you wish you had more resources to upgrade your troops, buildings, and spells? If you answered yes to any of these questions, then you need to download Clash of Clans hack 2022. This is the latest and most powerful hack tool for Clash of Clans that will give you unlimited gems, gold, and elixir. With this hack, you can enjoy the game without spending any money or waiting for hours. You can also bypass the security measures of the game and avoid getting banned. In this article, we will tell you everything you need to know about Clash of Clans hack 2022, including what it is, why you need it, how to download it, and how to use it. Read on to find out more.

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A popular strategy game for mobile devices

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Clash of Clans is one of the most popular and successful games for mobile devices. It was released in 2012 by Supercell, a Finnish game developer. Since then, it has been downloaded over 500 million times and has millions of active players worldwide. It is also one of the highest-grossing games in the app stores, generating billions of dollars in revenue.

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Clash of Clans is a strategy game that combines elements of base-building, resource management, and combat. The main goal of the game is to build and defend your village from other players and NPC enemies. You can also join or create a clan with other players and participate in clan wars, clan games, and clan leagues. You can also explore the world map and attack other villages for loot and trophies.

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To play the game, you need three types of resources: gems, gold, and elixir. Gems are the premium currency that can be used to speed up processes, buy items, and unlock features. Gold and elixir are the basic currencies that can be used to upgrade your buildings, troops, spells, and defenses. You can obtain these resources by mining them from collectors, raiding other villages, completing achievements, or buying them with real money.

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Why do you need Clash of Clans hack?

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The challenges and limitations of playing Clash of Clans without hack

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While Clash of Clans is a fun and exciting game, it also has some drawbacks that can make it frustrating and tedious. Some of these drawbacks are:

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These challenges and limitations can make playing Clash of Clans without hack very frustrating and tedious. You may lose interest in the game or give up on it altogether. You may also feel tempted to spend a lot of money on gems or resort to illegal methods to get them.

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The benefits and advantages of using Clash of Clans hack

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This is where Clash of Clans hack comes in handy. Clash of Clans hack is a tool that can help you overcome the challenges and limitations of playing Clash of Clans without hack. It can also enhance your gaming experience and make it more fun and enjoyable. Some of the benefits and advantages of using Clash of Clans hack are:

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These benefits and advantages can make using Clash of Clans hack very rewarding and satisfying. You can enjoy the game without any limitations or frustrations. You can also have more fun and excitement with Clash of Clans hack.

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How to download and use Clash of Clans hack 2022?

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The steps to download and install Clash of Clans hack 2022

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If you are interested in downloading and using Clash of Clans hack 2022, you need to follow these simple steps:

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If you need to access your 2019 tax return for any reason, you have two options: you can get a transcript or a copy of your return from the Internal Revenue Service (IRS). In this article, we will explain what each option means, how to request them, and what are the benefits of filing your tax return online.

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If you discover a mistake or omission on your 2019 tax return, you can file an amended return using Form 1040-X. You will need your original 2019 tax return to fill out the form and show the changes you are making.

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If you are using a software product or an online service to file your 2020 tax return, you may need your adjusted gross income (AGI) from your 2019 tax return to verify your identity. A transcript or a copy of your tax return can help you find your AGI and other information that you may need for your current year's filing.

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A transcript is a computer printout of highlights from your tax return. It shows most line items from your return and may include information from other forms and schedules that you filed. There are different types of transcripts available, depending on what information you need. The most common ones are:

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  • Record of Account Transcript: combines the information from the tax return transcript and the tax account transcript.
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  • Wage and Income Transcript: shows data from information returns that the IRS received, such as Forms W-2, 1099, and 1098. It may not include all income sources that you reported on your return.
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  • Verification of Non-filing Letter: shows that the IRS has no record of a filed tax return for a specific year.
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You can request transcripts for the last 10 years. Transcripts are free and you can get them in two ways:

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How to request a transcript online

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The fastest way to get a transcript is to request it online through the IRS website. You will need to create an account or log in with an existing IRS username or ID.me account. You will also need to have your photo identification ready. Once you access your account, you can view, print, or download any of the available transcripts for the current year and the previous three years. You can also request older transcripts to be mailed to your address of record.

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How to request a transcript by mail or phone

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If you prefer to receive a transcript by mail, you can use the online tool on the IRS website and choose the option to mail it. You will need to enter your Social Security number or Individual Tax Identification Number (ITIN), date of birth, and address. You can expect to receive your transcript within 5 to 10 days.

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You can also request a transcript by calling the IRS automated phone service at 800-908-9946. You will need to provide the same information as above and follow the prompts. You can choose to receive your transcript by mail or fax, if you are at a public place with a fax machine.

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How to Get a Copy of Your 2019 Tax Return

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A copy is an exact duplicate of your original tax return, including all forms, schedules, and attachments. It shows any changes or amendments that you or the IRS made after you filed. A copy is different from a transcript in that it shows more detail and may include state tax information.

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You can request copies for the last seven years. Copies are not free and you need to follow these steps:

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How to request a copy using Form 4506

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To request a copy of your tax return, you need to fill out Form 4506, Request for Copy of Tax Return, and mail it to the IRS address that matches your location. You can find the form and the addresses on the IRS website. You will need to provide your name, Social Security number or ITIN, address, and the tax year that you are requesting. You will also need to pay a fee of $43 for each copy that you request. You can pay by check or money order made payable to "United States Treasury".

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How much it costs and how long it takes

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The fee for requesting a copy of your tax return is $43 per copy. If you are requesting more than one copy, you can send one payment for the total amount. The IRS will send you a notice if they cannot provide the copy that you requested or if you need to pay more money.

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It may take up to 75 days for the IRS to process your request and mail you the copy of your tax return. If you need it sooner, you may want to consider getting a transcript instead, which is faster and free.

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If you have not filed your 2020 tax return yet, you may want to consider filing it online instead of mailing a paper return. Filing your tax return online has many benefits, such as:

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Faster and easier process

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Filing your tax return online is faster and easier than filing a paper return. You can use a software product or an online service that will guide you through the process and do the calculations for you. You can also import your information from previous years or from other sources, such as your employer or bank. You do not need to print or mail anything, which saves you time and money.

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Filing your tax return online ensures that the IRS receives it promptly and securely. You will get an electronic confirmation that your return was accepted within 24 hours. You do not have to worry about your return getting lost or delayed in the mail. You can also track the status of your return and refund online using the Where's My Refund tool on the IRS website.

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Filing your tax return online reduces the chances of errors and mistakes that could delay your refund or result in penalties. The software or online service will check your return for accuracy and completeness before you submit it. It will also alert you of any credits or deductions that you may qualify for. If you are due a refund, you can get it faster by choosing direct deposit into your bank account. The IRS issues most refunds within 21 days of receiving your return, compared to six weeks or more for paper returns.

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Conclusion and FAQs

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In conclusion, if you need to download your 2019 tax return, you have two options: getting a transcript or a copy from the IRS. A transcript is a computer printout of highlights from your return, while a copy is an exact duplicate of your original return. Transcripts are free and available online or by mail or phone, while copies cost $43 each and require filling out Form 4506 and mailing it to the IRS. If you have not filed your 2020 tax return yet, you may want to file it online instead of mailing a paper return. Filing your tax return online has many benefits, such as faster and easier process, prompt and secure delivery, reduced errors and faster refunds.

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Here are some FAQs that you may have about downloading your 2019 tax return:

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  • Q: How can I download my 2019 tax return if I filed it online?
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  • A: If you filed your 2019 tax return online using a software product or an online service, you can download your return from the same source that you used. You will need to log in to your account and access your previous returns. You can then view, print, or save your return as a PDF file.
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  • A: If you used a tax professional to file your 2019 tax return, you can ask them to provide you with a copy or a transcript of your return. They may charge you a fee for this service. You can also request a transcript or a copy from the IRS using the methods described above.
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  • A: If you need to download your 2019 state tax return, you will need to contact your state tax agency. Each state has its own rules and procedures for requesting transcripts or copies of state tax returns. You can find the contact information and website of your state tax agency on the IRS website.
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  • A: The IRS recommends that you keep your tax returns and supporting documents for at least three years from the date you filed or the due date of your return, whichever is later. However, in some cases, you may need to keep them longer, such as if you have unreported income, underreported income, or fraudulent activity on your return. You can find more information on how long to keep your records on the IRS website.
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  • A: If you lost or damaged your 2019 tax return, you can request a transcript or a copy from the IRS using the methods described above. You can also try to recover your return from other sources, such as your employer, bank, or financial institution that may have copies of your W-2s, 1099s, or other forms that you filed with your return.
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    Challenges and rewards

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    Playing SimCity BuildIt Mod APK is not without challenges and rewards. You can still face some difficulties and achievements while building your city. Some of these are:

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    Conclusion

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    Summary of the article

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    In conclusion, SimCity BuildIt Mod APK is a modified version of the popular mobile game by EA that gives you unlimited money and golden keys. You can use these resources to build your dream city without any limitations or stress. You can also enjoy the game's features, such as creating a self-sufficient city, unlocking new regions and buildings, exploring the online gameplay, and more. You can also use some tips and tricks to make your city building experience more enjoyable and efficient. You can also face some challenges and rewards that will keep you engaged and satisfied with the game.

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    Here are some frequently asked questions about SimCity BuildIt Mod APK:

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      A: Yes, you can play SimCity BuildIt Mod APK with other players who are using the same mod APK as you. However, you may not be able to play with players who are using the original version of the game.
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    The most slower paced scenes in between gun fights are more enjoyable and really effective in their combination of lighting, shock factor and designs in conveying a overall scary feel. This is especially true in the first quarter of the film where the suspense is allowed to build nicely. But once the action picks up, the scares die down.

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    \ No newline at end of file diff --git a/spaces/crashedice/signify/pages/1_Detection.py b/spaces/crashedice/signify/pages/1_Detection.py deleted file mode 100644 index ca4dd4f0720ce637967aae5e0d106e1b65f8b472..0000000000000000000000000000000000000000 --- a/spaces/crashedice/signify/pages/1_Detection.py +++ /dev/null @@ -1,35 +0,0 @@ -import glob -import os -import sys -import time - -import numpy as np -import streamlit as st -from SOURCE.yolo_files import detect - -MEDIA_ROOT = 'results/media/documents/' -YOLO_RESULT = 'results/yolov5/' -YOLO_OP = 'crops/DLSignature/' - -st.set_page_config(page_title="Signature Detection", page_icon="📈") -st.markdown("# Detect Signatures") -st.write("""Detect the signature of the given document!""") -st.session_state.predict = False if "predict" not in st.session_state else st.session_state.predict -st.session_state.selection = "" if "selection" not in st.session_state else st.session_state.selection - -left, right = st.columns(2) -selection = str(left.selectbox('Select document', os.listdir(MEDIA_ROOT))) -st.session_state.predict = st.button('Detect') -selection_image_left = MEDIA_ROOT+selection -left.image(selection_image_left, use_column_width='always') - -if st.session_state.predict and st.session_state.selection: - latest_detection = max(glob.glob(os.path.join(YOLO_RESULT, '*/')), key=os.path.getmtime) - selection_detection =latest_detection + YOLO_OP + selection[:-4] + '.jpg' - right.image(selection_detection) -elif selection != st.session_state.selection: - st.session_state.selection = selection - detect.detect(MEDIA_ROOT) - - - diff --git a/spaces/crylake/img2poem/query2labels/lib/models/tresnet/layers/general_layers.py b/spaces/crylake/img2poem/query2labels/lib/models/tresnet/layers/general_layers.py deleted file mode 100644 index 209913058aae04bd1a570eb81ff593798d78c981..0000000000000000000000000000000000000000 --- a/spaces/crylake/img2poem/query2labels/lib/models/tresnet/layers/general_layers.py +++ /dev/null @@ -1,93 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from query2labels.lib.models.tresnet.layers.avg_pool import FastAvgPool2d - - -class Flatten(nn.Module): - def forward(self, x): - return x.view(x.size(0), -1) - - -class DepthToSpace(nn.Module): - - def __init__(self, block_size): - super().__init__() - self.bs = block_size - - def forward(self, x): - N, C, H, W = x.size() - x = x.view(N, self.bs, self.bs, C // (self.bs ** 2), H, W) # (N, bs, bs, C//bs^2, H, W) - x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # (N, C//bs^2, H, bs, W, bs) - x = x.view(N, C // (self.bs ** 2), H * self.bs, W * self.bs) # (N, C//bs^2, H * bs, W * bs) - return x - - -class SpaceToDepthModule(nn.Module): - def __init__(self, remove_model_jit=False): - super().__init__() - if not remove_model_jit: - self.op = SpaceToDepthJit() - else: - self.op = SpaceToDepth() - - def forward(self, x): - return self.op(x) - - -class SpaceToDepth(nn.Module): - def __init__(self, block_size=4): - super().__init__() - assert block_size == 4 - self.bs = block_size - - def forward(self, x): - N, C, H, W = x.size() - x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs) # (N, C, H//bs, bs, W//bs, bs) - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs) - x = x.view(N, C * (self.bs ** 2), H // self.bs, W // self.bs) # (N, C*bs^2, H//bs, W//bs) - return x - - -@torch.jit.script -class SpaceToDepthJit(object): - def __call__(self, x: torch.Tensor): - # assuming hard-coded that block_size==4 for acceleration - N, C, H, W = x.size() - x = x.view(N, C, H // 4, 4, W // 4, 4) # (N, C, H//bs, bs, W//bs, bs) - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs) - x = x.view(N, C * 16, H // 4, W // 4) # (N, C*bs^2, H//bs, W//bs) - return x - - -class hard_sigmoid(nn.Module): - def __init__(self, inplace=True): - super(hard_sigmoid, self).__init__() - self.inplace = inplace - - def forward(self, x): - if self.inplace: - return x.add_(3.).clamp_(0., 6.).div_(6.) - else: - return F.relu6(x + 3.) / 6. - - -class SEModule(nn.Module): - - def __init__(self, channels, reduction_channels, inplace=True): - super(SEModule, self).__init__() - self.avg_pool = FastAvgPool2d() - self.fc1 = nn.Conv2d(channels, reduction_channels, kernel_size=1, padding=0, bias=True) - self.relu = nn.ReLU(inplace=inplace) - self.fc2 = nn.Conv2d(reduction_channels, channels, kernel_size=1, padding=0, bias=True) - # self.activation = hard_sigmoid(inplace=inplace) - self.activation = nn.Sigmoid() - - def forward(self, x): - x_se = self.avg_pool(x) - x_se2 = self.fc1(x_se) - x_se2 = self.relu(x_se2) - x_se = self.fc2(x_se2) - x_se = self.activation(x_se) - return x * x_se diff --git a/spaces/d0r1h/youtube_summarization/summarize.py b/spaces/d0r1h/youtube_summarization/summarize.py deleted file mode 100644 index 00e8a8d1a540b9f60305f4bdb5e6db07e5f14e82..0000000000000000000000000000000000000000 --- a/spaces/d0r1h/youtube_summarization/summarize.py +++ /dev/null @@ -1,36 +0,0 @@ -from youtube_transcript_api import YouTubeTranscriptApi -from transformers import AutoTokenizer, AutoModelForSeq2SeqLM - -def Summarizer(link, model): - - video_id = link.split("=")[1] - - try: - transcript = YouTubeTranscriptApi.get_transcript(video_id) - FinalTranscript = ' '.join([i['text'] for i in transcript]) - - if model == "Pegasus": - checkpoint = "google/pegasus-large" - elif model == "mT5": - checkpoint = "csebuetnlp/mT5_multilingual_XLSum" - elif model == "BART": - checkpoint = "sshleifer/distilbart-cnn-12-6" - - tokenizer = AutoTokenizer.from_pretrained(checkpoint) - model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) - - - inputs = tokenizer(FinalTranscript, - max_length=1024, - truncation=True, - return_tensors="pt") - - summary_ids = model.generate(inputs["input_ids"]) - summary = tokenizer.batch_decode(summary_ids, - skip_special_tokens=True, - clean_up_tokenization_spaces=False) - - - return summary[0] - except Exception as e: - return "TranscriptsDisabled: Transcript is not available \nTry another video" \ No newline at end of file diff --git a/spaces/darragh/bloom_demo_long/app.py b/spaces/darragh/bloom_demo_long/app.py deleted file mode 100644 index 1d73d595d70c54ba99857f391563ce546b7fc9ec..0000000000000000000000000000000000000000 --- a/spaces/darragh/bloom_demo_long/app.py +++ /dev/null @@ -1,113 +0,0 @@ -import gradio as gr -import requests -import json -import os -from screenshot import ( - before_prompt, - prompt_to_generation, - after_generation, - js_save, - js_load_script, -) -from spaces_info import description, examples, initial_prompt_value - -API_URL = os.getenv("API_URL") -HF_API_TOKEN = os.getenv("HF_API_TOKEN") - - -def query(payload): - print(payload) - response = requests.request("POST", API_URL, json=payload, headers={"Authorization": f"Bearer {HF_API_TOKEN}"}) - print(response) - return json.loads(response.content.decode("utf-8")) - - -def inference(input_sentence, max_length, sample_or_greedy, seed=42): - if sample_or_greedy == "Sample": - parameters = { - "max_new_tokens": max_length, - "top_p": 0.9, - "do_sample": True, - "seed": seed, - "early_stopping": False, - "length_penalty": 0.0, - "eos_token_id": None, - } - else: - parameters = { - "max_new_tokens": max_length, - "do_sample": False, - "seed": seed, - "early_stopping": False, - "length_penalty": 0.0, - "eos_token_id": None, - } - - payload = {"inputs": input_sentence, "parameters": parameters,"options" : {"use_cache": False} } - - data = query(payload) - - if "error" in data: - return (None, None, f"ERROR: {data['error']} ") - - generation = data[0]["generated_text"].split(input_sentence, 1)[1] - return ( - before_prompt - + input_sentence - + prompt_to_generation - + generation - + after_generation, - data[0]["generated_text"], - "", - ) - - -if __name__ == "__main__": - demo = gr.Blocks() - with demo: - with gr.Row(): - gr.Markdown(value=description) - with gr.Row(): - with gr.Column(): - text = gr.Textbox( - label="Input", - value=" ", # should be set to " " when plugged into a real API - ) - tokens = gr.Slider(1, 256, value=32, step=1, label="Tokens to generate") - sampling = gr.Radio( - ["Sample", "Greedy"], label="Sample or greedy", value="Sample" - ) - sampling2 = gr.Radio( - ["Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5"], - value="Sample 1", - label="Sample other generations (only work in 'Sample' mode)", - type="index", - ) - - with gr.Row(): - submit = gr.Button("Submit") - load_image = gr.Button("Generate Image") - with gr.Column(): - text_error = gr.Markdown(label="Log information") - text_out = gr.Textbox(label="Output") - display_out = gr.HTML(label="Image") - display_out.set_event_trigger( - "load", - fn=None, - inputs=None, - outputs=None, - no_target=True, - js=js_load_script, - ) - with gr.Row(): - gr.Examples(examples=examples, inputs=[text, tokens, sampling, sampling2]) - - submit.click( - inference, - inputs=[text, tokens, sampling, sampling2], - outputs=[display_out, text_out, text_error], - ) - - load_image.click(fn=None, inputs=None, outputs=None, _js=js_save) - - demo.launch() diff --git a/spaces/dawood/gradio_videogallery/src/backend/gradio_videogallery/__init__.py b/spaces/dawood/gradio_videogallery/src/backend/gradio_videogallery/__init__.py deleted file mode 100644 index 2043900e9e72a8bd94fd8101fb901212c7c9a2e1..0000000000000000000000000000000000000000 --- a/spaces/dawood/gradio_videogallery/src/backend/gradio_videogallery/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ - -from .videogallery import videogallery - -__all__ = ['videogallery'] diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/losses/__init__.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/losses/__init__.py deleted file mode 100644 index 2b184e74c861e6fca0c548692a9a949a6100b0aa..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/losses/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -from copy import deepcopy - -from basicsr.utils import get_root_logger -from basicsr.utils.registry import LOSS_REGISTRY -from .losses import (CharbonnierLoss, GANLoss, L1Loss, MSELoss, PerceptualLoss, WeightedTVLoss, g_path_regularize, - gradient_penalty_loss, r1_penalty) - -__all__ = [ - 'L1Loss', 'MSELoss', 'CharbonnierLoss', 'WeightedTVLoss', 'PerceptualLoss', 'GANLoss', 'gradient_penalty_loss', - 'r1_penalty', 'g_path_regularize' -] - - -def build_loss(opt): - """Build loss from options. - - Args: - opt (dict): Configuration. It must constain: - type (str): Model type. - """ - opt = deepcopy(opt) - loss_type = opt.pop('type') - loss = LOSS_REGISTRY.get(loss_type)(**opt) - logger = get_root_logger() - logger.info(f'Loss [{loss.__class__.__name__}] is created.') - return loss diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/setup.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/setup.py deleted file mode 100644 index 382a2aa1006e581eaf31dbb3155d4b0ba3b31140..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/setup.py +++ /dev/null @@ -1,165 +0,0 @@ -#!/usr/bin/env python - -from setuptools import find_packages, setup - -import os -import subprocess -import sys -import time -import torch -from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension - -version_file = './basicsr/version.py' - - -def readme(): - with open('README.md', encoding='utf-8') as f: - content = f.read() - return content - - -def get_git_hash(): - - def _minimal_ext_cmd(cmd): - # construct minimal environment - env = {} - for k in ['SYSTEMROOT', 'PATH', 'HOME']: - v = os.environ.get(k) - if v is not None: - env[k] = v - # LANGUAGE is used on win32 - env['LANGUAGE'] = 'C' - env['LANG'] = 'C' - env['LC_ALL'] = 'C' - out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0] - return out - - try: - out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD']) - sha = out.strip().decode('ascii') - except OSError: - sha = 'unknown' - - return sha - - -def get_hash(): - if os.path.exists('.git'): - sha = get_git_hash()[:7] - elif os.path.exists(version_file): - try: - from version import __version__ - sha = __version__.split('+')[-1] - except ImportError: - raise ImportError('Unable to get git version') - else: - sha = 'unknown' - - return sha - - -def write_version_py(): - content = """# GENERATED VERSION FILE -# TIME: {} -__version__ = '{}' -__gitsha__ = '{}' -version_info = ({}) -""" - sha = get_hash() - with open('./basicsr/VERSION', 'r') as f: - SHORT_VERSION = f.read().strip() - VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')]) - - version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO) - with open(version_file, 'w') as f: - f.write(version_file_str) - - -def get_version(): - with open(version_file, 'r') as f: - exec(compile(f.read(), version_file, 'exec')) - return locals()['__version__'] - - -def make_cuda_ext(name, module, sources, sources_cuda=None): - if sources_cuda is None: - sources_cuda = [] - define_macros = [] - extra_compile_args = {'cxx': []} - - if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1': - define_macros += [('WITH_CUDA', None)] - extension = CUDAExtension - extra_compile_args['nvcc'] = [ - '-D__CUDA_NO_HALF_OPERATORS__', - '-D__CUDA_NO_HALF_CONVERSIONS__', - '-D__CUDA_NO_HALF2_OPERATORS__', - ] - sources += sources_cuda - else: - print(f'Compiling {name} without CUDA') - extension = CppExtension - - return extension( - name=f'{module}.{name}', - sources=[os.path.join(*module.split('.'), p) for p in sources], - define_macros=define_macros, - extra_compile_args=extra_compile_args) - - -def get_requirements(filename='requirements.txt'): - with open(os.path.join('.', filename), 'r') as f: - requires = [line.replace('\n', '') for line in f.readlines()] - return requires - - -if __name__ == '__main__': - if '--cuda_ext' in sys.argv: - ext_modules = [ - make_cuda_ext( - name='deform_conv_ext', - module='ops.dcn', - sources=['src/deform_conv_ext.cpp'], - sources_cuda=['src/deform_conv_cuda.cpp', 'src/deform_conv_cuda_kernel.cu']), - make_cuda_ext( - name='fused_act_ext', - module='ops.fused_act', - sources=['src/fused_bias_act.cpp'], - sources_cuda=['src/fused_bias_act_kernel.cu']), - make_cuda_ext( - name='upfirdn2d_ext', - module='ops.upfirdn2d', - sources=['src/upfirdn2d.cpp'], - sources_cuda=['src/upfirdn2d_kernel.cu']), - ] - sys.argv.remove('--cuda_ext') - else: - ext_modules = [] - - write_version_py() - setup( - name='basicsr', - version=get_version(), - description='Open Source Image and Video Super-Resolution Toolbox', - long_description=readme(), - long_description_content_type='text/markdown', - author='Xintao Wang', - author_email='xintao.wang@outlook.com', - keywords='computer vision, restoration, super resolution', - url='https://github.com/xinntao/BasicSR', - include_package_data=True, - packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')), - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - ], - license='Apache License 2.0', - setup_requires=['cython', 'numpy'], - install_requires=get_requirements(), - ext_modules=ext_modules, - cmdclass={'build_ext': BuildExtension}, - zip_safe=False) diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/matlab_functions.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/matlab_functions.py deleted file mode 100644 index c6ce1004a2c9f8521505c4b5889d3c24a909c70d..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/basicsr/utils/matlab_functions.py +++ /dev/null @@ -1,347 +0,0 @@ -import math -import numpy as np -import torch - - -def cubic(x): - """cubic function used for calculate_weights_indices.""" - absx = torch.abs(x) - absx2 = absx**2 - absx3 = absx**3 - return (1.5 * absx3 - 2.5 * absx2 + 1) * ( - (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (((absx > 1) * - (absx <= 2)).type_as(absx)) - - -def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): - """Calculate weights and indices, used for imresize function. - - Args: - in_length (int): Input length. - out_length (int): Output length. - scale (float): Scale factor. - kernel_width (int): Kernel width. - antialisaing (bool): Whether to apply anti-aliasing when downsampling. - """ - - if (scale < 1) and antialiasing: - # Use a modified kernel (larger kernel width) to simultaneously - # interpolate and antialias - kernel_width = kernel_width / scale - - # Output-space coordinates - x = torch.linspace(1, out_length, out_length) - - # Input-space coordinates. Calculate the inverse mapping such that 0.5 - # in output space maps to 0.5 in input space, and 0.5 + scale in output - # space maps to 1.5 in input space. - u = x / scale + 0.5 * (1 - 1 / scale) - - # What is the left-most pixel that can be involved in the computation? - left = torch.floor(u - kernel_width / 2) - - # What is the maximum number of pixels that can be involved in the - # computation? Note: it's OK to use an extra pixel here; if the - # corresponding weights are all zero, it will be eliminated at the end - # of this function. - p = math.ceil(kernel_width) + 2 - - # The indices of the input pixels involved in computing the k-th output - # pixel are in row k of the indices matrix. - indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand( - out_length, p) - - # The weights used to compute the k-th output pixel are in row k of the - # weights matrix. - distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices - - # apply cubic kernel - if (scale < 1) and antialiasing: - weights = scale * cubic(distance_to_center * scale) - else: - weights = cubic(distance_to_center) - - # Normalize the weights matrix so that each row sums to 1. - weights_sum = torch.sum(weights, 1).view(out_length, 1) - weights = weights / weights_sum.expand(out_length, p) - - # If a column in weights is all zero, get rid of it. only consider the - # first and last column. - weights_zero_tmp = torch.sum((weights == 0), 0) - if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): - indices = indices.narrow(1, 1, p - 2) - weights = weights.narrow(1, 1, p - 2) - if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): - indices = indices.narrow(1, 0, p - 2) - weights = weights.narrow(1, 0, p - 2) - weights = weights.contiguous() - indices = indices.contiguous() - sym_len_s = -indices.min() + 1 - sym_len_e = indices.max() - in_length - indices = indices + sym_len_s - 1 - return weights, indices, int(sym_len_s), int(sym_len_e) - - -@torch.no_grad() -def imresize(img, scale, antialiasing=True): - """imresize function same as MATLAB. - - It now only supports bicubic. - The same scale applies for both height and width. - - Args: - img (Tensor | Numpy array): - Tensor: Input image with shape (c, h, w), [0, 1] range. - Numpy: Input image with shape (h, w, c), [0, 1] range. - scale (float): Scale factor. The same scale applies for both height - and width. - antialisaing (bool): Whether to apply anti-aliasing when downsampling. - Default: True. - - Returns: - Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round. - """ - if type(img).__module__ == np.__name__: # numpy type - numpy_type = True - img = torch.from_numpy(img.transpose(2, 0, 1)).float() - else: - numpy_type = False - - in_c, in_h, in_w = img.size() - out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale) - kernel_width = 4 - kernel = 'cubic' - - # get weights and indices - weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(in_h, out_h, scale, kernel, kernel_width, - antialiasing) - weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(in_w, out_w, scale, kernel, kernel_width, - antialiasing) - # process H dimension - # symmetric copying - img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w) - img_aug.narrow(1, sym_len_hs, in_h).copy_(img) - - sym_patch = img[:, :sym_len_hs, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv) - - sym_patch = img[:, -sym_len_he:, :] - inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(1, inv_idx) - img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv) - - out_1 = torch.FloatTensor(in_c, out_h, in_w) - kernel_width = weights_h.size(1) - for i in range(out_h): - idx = int(indices_h[i][0]) - for j in range(in_c): - out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i]) - - # process W dimension - # symmetric copying - out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we) - out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1) - - sym_patch = out_1[:, :, :sym_len_ws] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv) - - sym_patch = out_1[:, :, -sym_len_we:] - inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() - sym_patch_inv = sym_patch.index_select(2, inv_idx) - out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv) - - out_2 = torch.FloatTensor(in_c, out_h, out_w) - kernel_width = weights_w.size(1) - for i in range(out_w): - idx = int(indices_w[i][0]) - for j in range(in_c): - out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i]) - - if numpy_type: - out_2 = out_2.numpy().transpose(1, 2, 0) - return out_2 - - -def rgb2ycbcr(img, y_only=False): - """Convert a RGB image to YCbCr image. - - This function produces the same results as Matlab's `rgb2ycbcr` function. - 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: `RGB <-> 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, [65.481, 128.553, 24.966]) + 16.0 - else: - out_img = np.matmul( - img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -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 - - -def ycbcr2rgb(img): - """Convert a YCbCr image to RGB image. - - This function produces the same results as Matlab's ycbcr2rgb function. - 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: `YCrCb <-> RGB`. - 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]. - - Returns: - ndarray: The converted RGB image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], - [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] # noqa: E126 - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def ycbcr2bgr(img): - """Convert a YCbCr image to BGR image. - - The bgr version of ycbcr2rgb. - 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: `YCrCb <-> BGR`. - 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]. - - Returns: - ndarray: The converted BGR image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0], - [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921] # noqa: E126 - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -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) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/cookiejar.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/cookiejar.py deleted file mode 100644 index 6c88b47e3583430e05ea671af5b6da2a557073ec..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/aiohttp/cookiejar.py +++ /dev/null @@ -1,415 +0,0 @@ -import asyncio -import contextlib -import datetime -import os # noqa -import pathlib -import pickle -import re -from collections import defaultdict -from http.cookies import BaseCookie, Morsel, SimpleCookie -from typing import ( # noqa - DefaultDict, - Dict, - Iterable, - Iterator, - List, - Mapping, - Optional, - Set, - Tuple, - Union, - cast, -) - -from yarl import URL - -from .abc import AbstractCookieJar, ClearCookiePredicate -from .helpers import is_ip_address, next_whole_second -from .typedefs import LooseCookies, PathLike, StrOrURL - -__all__ = ("CookieJar", "DummyCookieJar") - - -CookieItem = Union[str, "Morsel[str]"] - - -class CookieJar(AbstractCookieJar): - """Implements cookie storage adhering to RFC 6265.""" - - DATE_TOKENS_RE = re.compile( - r"[\x09\x20-\x2F\x3B-\x40\x5B-\x60\x7B-\x7E]*" - r"(?P[\x00-\x08\x0A-\x1F\d:a-zA-Z\x7F-\xFF]+)" - ) - - DATE_HMS_TIME_RE = re.compile(r"(\d{1,2}):(\d{1,2}):(\d{1,2})") - - DATE_DAY_OF_MONTH_RE = re.compile(r"(\d{1,2})") - - DATE_MONTH_RE = re.compile( - "(jan)|(feb)|(mar)|(apr)|(may)|(jun)|(jul)|" "(aug)|(sep)|(oct)|(nov)|(dec)", - re.I, - ) - - DATE_YEAR_RE = re.compile(r"(\d{2,4})") - - MAX_TIME = datetime.datetime.max.replace(tzinfo=datetime.timezone.utc) - - MAX_32BIT_TIME = datetime.datetime.utcfromtimestamp(2**31 - 1) - - def __init__( - self, - *, - unsafe: bool = False, - quote_cookie: bool = True, - treat_as_secure_origin: Union[StrOrURL, List[StrOrURL], None] = None, - loop: Optional[asyncio.AbstractEventLoop] = None, - ) -> None: - super().__init__(loop=loop) - self._cookies: DefaultDict[Tuple[str, str], SimpleCookie[str]] = defaultdict( - SimpleCookie - ) - self._host_only_cookies: Set[Tuple[str, str]] = set() - self._unsafe = unsafe - self._quote_cookie = quote_cookie - if treat_as_secure_origin is None: - treat_as_secure_origin = [] - elif isinstance(treat_as_secure_origin, URL): - treat_as_secure_origin = [treat_as_secure_origin.origin()] - elif isinstance(treat_as_secure_origin, str): - treat_as_secure_origin = [URL(treat_as_secure_origin).origin()] - else: - treat_as_secure_origin = [ - URL(url).origin() if isinstance(url, str) else url.origin() - for url in treat_as_secure_origin - ] - self._treat_as_secure_origin = treat_as_secure_origin - self._next_expiration = next_whole_second() - self._expirations: Dict[Tuple[str, str, str], datetime.datetime] = {} - # #4515: datetime.max may not be representable on 32-bit platforms - self._max_time = self.MAX_TIME - try: - self._max_time.timestamp() - except OverflowError: - self._max_time = self.MAX_32BIT_TIME - - def save(self, file_path: PathLike) -> None: - file_path = pathlib.Path(file_path) - with file_path.open(mode="wb") as f: - pickle.dump(self._cookies, f, pickle.HIGHEST_PROTOCOL) - - def load(self, file_path: PathLike) -> None: - file_path = pathlib.Path(file_path) - with file_path.open(mode="rb") as f: - self._cookies = pickle.load(f) - - def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None: - if predicate is None: - self._next_expiration = next_whole_second() - self._cookies.clear() - self._host_only_cookies.clear() - self._expirations.clear() - return - - to_del = [] - now = datetime.datetime.now(datetime.timezone.utc) - for (domain, path), cookie in self._cookies.items(): - for name, morsel in cookie.items(): - key = (domain, path, name) - if ( - key in self._expirations and self._expirations[key] <= now - ) or predicate(morsel): - to_del.append(key) - - for domain, path, name in to_del: - self._host_only_cookies.discard((domain, name)) - key = (domain, path, name) - if key in self._expirations: - del self._expirations[(domain, path, name)] - self._cookies[(domain, path)].pop(name, None) - - next_expiration = min(self._expirations.values(), default=self._max_time) - try: - self._next_expiration = next_expiration.replace( - microsecond=0 - ) + datetime.timedelta(seconds=1) - except OverflowError: - self._next_expiration = self._max_time - - def clear_domain(self, domain: str) -> None: - self.clear(lambda x: self._is_domain_match(domain, x["domain"])) - - def __iter__(self) -> "Iterator[Morsel[str]]": - self._do_expiration() - for val in self._cookies.values(): - yield from val.values() - - def __len__(self) -> int: - return sum(1 for i in self) - - def _do_expiration(self) -> None: - self.clear(lambda x: False) - - def _expire_cookie( - self, when: datetime.datetime, domain: str, path: str, name: str - ) -> None: - self._next_expiration = min(self._next_expiration, when) - self._expirations[(domain, path, name)] = when - - def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None: - """Update cookies.""" - hostname = response_url.raw_host - - if not self._unsafe and is_ip_address(hostname): - # Don't accept cookies from IPs - return - - if isinstance(cookies, Mapping): - cookies = cookies.items() - - for name, cookie in cookies: - if not isinstance(cookie, Morsel): - tmp: SimpleCookie[str] = SimpleCookie() - tmp[name] = cookie # type: ignore[assignment] - cookie = tmp[name] - - domain = cookie["domain"] - - # ignore domains with trailing dots - if domain.endswith("."): - domain = "" - del cookie["domain"] - - if not domain and hostname is not None: - # Set the cookie's domain to the response hostname - # and set its host-only-flag - self._host_only_cookies.add((hostname, name)) - domain = cookie["domain"] = hostname - - if domain.startswith("."): - # Remove leading dot - domain = domain[1:] - cookie["domain"] = domain - - if hostname and not self._is_domain_match(domain, hostname): - # Setting cookies for different domains is not allowed - continue - - path = cookie["path"] - if not path or not path.startswith("/"): - # Set the cookie's path to the response path - path = response_url.path - if not path.startswith("/"): - path = "/" - else: - # Cut everything from the last slash to the end - path = "/" + path[1 : path.rfind("/")] - cookie["path"] = path - - max_age = cookie["max-age"] - if max_age: - try: - delta_seconds = int(max_age) - try: - max_age_expiration = datetime.datetime.now( - datetime.timezone.utc - ) + datetime.timedelta(seconds=delta_seconds) - except OverflowError: - max_age_expiration = self._max_time - self._expire_cookie(max_age_expiration, domain, path, name) - except ValueError: - cookie["max-age"] = "" - - else: - expires = cookie["expires"] - if expires: - expire_time = self._parse_date(expires) - if expire_time: - self._expire_cookie(expire_time, domain, path, name) - else: - cookie["expires"] = "" - - self._cookies[(domain, path)][name] = cookie - - self._do_expiration() - - def filter_cookies( - self, request_url: URL = URL() - ) -> Union["BaseCookie[str]", "SimpleCookie[str]"]: - """Returns this jar's cookies filtered by their attributes.""" - self._do_expiration() - request_url = URL(request_url) - filtered: Union["SimpleCookie[str]", "BaseCookie[str]"] = ( - SimpleCookie() if self._quote_cookie else BaseCookie() - ) - hostname = request_url.raw_host or "" - request_origin = URL() - with contextlib.suppress(ValueError): - request_origin = request_url.origin() - - is_not_secure = ( - request_url.scheme not in ("https", "wss") - and request_origin not in self._treat_as_secure_origin - ) - - for cookie in self: - name = cookie.key - domain = cookie["domain"] - - # Send shared cookies - if not domain: - filtered[name] = cookie.value - continue - - if not self._unsafe and is_ip_address(hostname): - continue - - if (domain, name) in self._host_only_cookies: - if domain != hostname: - continue - elif not self._is_domain_match(domain, hostname): - continue - - if not self._is_path_match(request_url.path, cookie["path"]): - continue - - if is_not_secure and cookie["secure"]: - continue - - # It's critical we use the Morsel so the coded_value - # (based on cookie version) is preserved - mrsl_val = cast("Morsel[str]", cookie.get(cookie.key, Morsel())) - mrsl_val.set(cookie.key, cookie.value, cookie.coded_value) - filtered[name] = mrsl_val - - return filtered - - @staticmethod - def _is_domain_match(domain: str, hostname: str) -> bool: - """Implements domain matching adhering to RFC 6265.""" - if hostname == domain: - return True - - if not hostname.endswith(domain): - return False - - non_matching = hostname[: -len(domain)] - - if not non_matching.endswith("."): - return False - - return not is_ip_address(hostname) - - @staticmethod - def _is_path_match(req_path: str, cookie_path: str) -> bool: - """Implements path matching adhering to RFC 6265.""" - if not req_path.startswith("/"): - req_path = "/" - - if req_path == cookie_path: - return True - - if not req_path.startswith(cookie_path): - return False - - if cookie_path.endswith("/"): - return True - - non_matching = req_path[len(cookie_path) :] - - return non_matching.startswith("/") - - @classmethod - def _parse_date(cls, date_str: str) -> Optional[datetime.datetime]: - """Implements date string parsing adhering to RFC 6265.""" - if not date_str: - return None - - found_time = False - found_day = False - found_month = False - found_year = False - - hour = minute = second = 0 - day = 0 - month = 0 - year = 0 - - for token_match in cls.DATE_TOKENS_RE.finditer(date_str): - - token = token_match.group("token") - - if not found_time: - time_match = cls.DATE_HMS_TIME_RE.match(token) - if time_match: - found_time = True - hour, minute, second = (int(s) for s in time_match.groups()) - continue - - if not found_day: - day_match = cls.DATE_DAY_OF_MONTH_RE.match(token) - if day_match: - found_day = True - day = int(day_match.group()) - continue - - if not found_month: - month_match = cls.DATE_MONTH_RE.match(token) - if month_match: - found_month = True - assert month_match.lastindex is not None - month = month_match.lastindex - continue - - if not found_year: - year_match = cls.DATE_YEAR_RE.match(token) - if year_match: - found_year = True - year = int(year_match.group()) - - if 70 <= year <= 99: - year += 1900 - elif 0 <= year <= 69: - year += 2000 - - if False in (found_day, found_month, found_year, found_time): - return None - - if not 1 <= day <= 31: - return None - - if year < 1601 or hour > 23 or minute > 59 or second > 59: - return None - - return datetime.datetime( - year, month, day, hour, minute, second, tzinfo=datetime.timezone.utc - ) - - -class DummyCookieJar(AbstractCookieJar): - """Implements a dummy cookie storage. - - It can be used with the ClientSession when no cookie processing is needed. - - """ - - def __init__(self, *, loop: Optional[asyncio.AbstractEventLoop] = None) -> None: - super().__init__(loop=loop) - - def __iter__(self) -> "Iterator[Morsel[str]]": - while False: - yield None - - def __len__(self) -> int: - return 0 - - def clear(self, predicate: Optional[ClearCookiePredicate] = None) -> None: - pass - - def clear_domain(self, domain: str) -> None: - pass - - def update_cookies(self, cookies: LooseCookies, response_url: URL = URL()) -> None: - pass - - def filter_cookies(self, request_url: URL) -> "BaseCookie[str]": - return SimpleCookie() diff --git 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o=i;i=g(n),i===o?f[i].p(n,s):(fe(),R(f[o],1,1,()=>{f[o]=null}),me(),l=f[i],l?l.p(n,s):(l=f[i]=h[i](n),l.c()),C(l,1),l.m(u.parentNode,u))},i(n){_||(C(l),_=!0)},o(n){R(l),_=!1},d(n){n&&G(u),f[i].d(n)}}}function Me(e,i,l){let{elem_id:u=""}=i,{elem_classes:_=[]}=i,{visible:h=!0}=i,{value:f=0}=i,{label:g="Slider"}=i,{info:n=void 0}=i,{container:s=!0}=i,{scale:o=null}=i,{min_width:v=void 0}=i,{minimum:a}=i,{maximum:m}=i,{step:r}=i,{mode:c}=i,{show_label:j}=i,{loading_status:k}=i,{value_is_output:S=!1}=i;function d(b){f=b,l(0,f)}function I(b){S=b,l(1,S)}function O(b){B.call(this,e,b)}function P(b){B.call(this,e,b)}function t(b){B.call(this,e,b)}function x(b){f=b,l(0,f)}function $(b){S=b,l(1,S)}function ee(b){B.call(this,e,b)}function ie(b){B.call(this,e,b)}function le(b){B.call(this,e,b)}return e.$$set=b=>{"elem_id"in b&&l(2,u=b.elem_id),"elem_classes"in b&&l(3,_=b.elem_classes),"visible"in b&&l(4,h=b.visible),"value"in b&&l(0,f=b.value),"label"in b&&l(5,g=b.label),"info"in 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deleted file mode 100644 index 7249ff57c7a0ef4610fcf0baf9976629267fa784..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/tunneling.py +++ /dev/null @@ -1,103 +0,0 @@ -import atexit -import os -import platform -import re -import stat -import subprocess -from pathlib import Path -from typing import List - -import requests - -VERSION = "0.2" -CURRENT_TUNNELS: List["Tunnel"] = [] - -machine = platform.machine() -if machine == "x86_64": - machine = "amd64" - -BINARY_REMOTE_NAME = f"frpc_{platform.system().lower()}_{machine.lower()}" -EXTENSION = ".exe" if os.name == "nt" else "" -BINARY_URL = f"https://cdn-media.huggingface.co/frpc-gradio-{VERSION}/{BINARY_REMOTE_NAME}{EXTENSION}" - -BINARY_FILENAME = f"{BINARY_REMOTE_NAME}_v{VERSION}" -BINARY_FOLDER = Path(__file__).parent -BINARY_PATH = f"{BINARY_FOLDER / BINARY_FILENAME}" - - -class Tunnel: - def __init__(self, remote_host, remote_port, local_host, local_port, share_token): - self.proc = None - self.url = None - self.remote_host = remote_host - self.remote_port = remote_port - self.local_host = local_host - self.local_port = local_port - self.share_token = share_token - - @staticmethod - def download_binary(): - if not Path(BINARY_PATH).exists(): - resp = requests.get(BINARY_URL) - - if resp.status_code == 403: - raise OSError( - f"Cannot set up a share link as this platform is incompatible. Please " - f"create a GitHub issue with information about your platform: {platform.uname()}" - ) - - resp.raise_for_status() - - # Save file data to local copy - with open(BINARY_PATH, "wb") as file: - file.write(resp.content) - st = os.stat(BINARY_PATH) - os.chmod(BINARY_PATH, st.st_mode | stat.S_IEXEC) - - def start_tunnel(self) -> str: - self.download_binary() - self.url = self._start_tunnel(BINARY_PATH) - return self.url - - def kill(self): - if self.proc is not None: - print(f"Killing tunnel {self.local_host}:{self.local_port} <> {self.url}") - self.proc.terminate() - self.proc = None - - def _start_tunnel(self, binary: str) -> str: - CURRENT_TUNNELS.append(self) - command = [ - binary, - "http", - "-n", - self.share_token, - "-l", - str(self.local_port), - "-i", - self.local_host, - "--uc", - "--sd", - "random", - "--ue", - "--server_addr", - f"{self.remote_host}:{self.remote_port}", - "--disable_log_color", - ] - self.proc = subprocess.Popen( - command, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) - atexit.register(self.kill) - url = "" - while url == "": - if self.proc.stdout is None: - continue - line = self.proc.stdout.readline() - line = line.decode("utf-8") - if "start proxy success" in line: - result = re.search("start proxy success: (.+)\n", line) - if result is None: - raise ValueError("Could not create share URL") - else: - url = result.group(1) - return url diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio_client/serializing.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio_client/serializing.py deleted file mode 100644 index a5fb3c908086e9b9f41062aac2d882f76596abfa..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio_client/serializing.py +++ /dev/null @@ -1,550 +0,0 @@ -from __future__ import annotations - -import json -import os -import uuid -from pathlib import Path -from typing import Any - -from gradio_client import media_data, utils -from gradio_client.data_classes import FileData - -with open(Path(__file__).parent / "types.json") as f: - serializer_types = json.load(f) - - -class Serializable: - def serialized_info(self): - """ - The typing information for this component as a dictionary whose values are a list of 2 strings: [Python type, language-agnostic description]. - Keys of the dictionary are: raw_input, raw_output, serialized_input, serialized_output - """ - return self.api_info() - - def api_info(self) -> dict[str, list[str]]: - """ - The typing information for this component as a dictionary whose values are a list of 2 strings: [Python type, language-agnostic description]. - Keys of the dictionary are: raw_input, raw_output, serialized_input, serialized_output - """ - raise NotImplementedError() - - def example_inputs(self) -> dict[str, Any]: - """ - The example inputs for this component as a dictionary whose values are example inputs compatible with this component. - Keys of the dictionary are: raw, serialized - """ - raise NotImplementedError() - - # For backwards compatibility - def input_api_info(self) -> tuple[str, str]: - api_info = self.api_info() - types = api_info.get("serialized_input", [api_info["info"]["type"]] * 2) # type: ignore - return (types[0], types[1]) - - # For backwards compatibility - def output_api_info(self) -> tuple[str, str]: - api_info = self.api_info() - types = api_info.get("serialized_output", [api_info["info"]["type"]] * 2) # type: ignore - return (types[0], types[1]) - - def serialize(self, x: Any, load_dir: str | Path = ""): - """ - Convert data from human-readable format to serialized format for a browser. - """ - return x - - def deserialize( - self, - x: Any, - save_dir: str | Path | None = None, - root_url: str | None = None, - hf_token: str | None = None, - ): - """ - Convert data from serialized format for a browser to human-readable format. - """ - return x - - -class SimpleSerializable(Serializable): - """General class that does not perform any serialization or deserialization.""" - - def api_info(self) -> dict[str, bool | dict]: - return { - "info": serializer_types["SimpleSerializable"], - "serialized_info": False, - } - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": None, - "serialized": None, - } - - -class StringSerializable(Serializable): - """Expects a string as input/output but performs no serialization.""" - - def api_info(self) -> dict[str, bool | dict]: - return { - "info": serializer_types["StringSerializable"], - "serialized_info": False, - } - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": "Howdy!", - "serialized": "Howdy!", - } - - -class ListStringSerializable(Serializable): - """Expects a list of strings as input/output but performs no serialization.""" - - def api_info(self) -> dict[str, bool | dict]: - return { - "info": serializer_types["ListStringSerializable"], - "serialized_info": False, - } - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": ["Howdy!", "Merhaba"], - "serialized": ["Howdy!", "Merhaba"], - } - - -class BooleanSerializable(Serializable): - """Expects a boolean as input/output but performs no serialization.""" - - def api_info(self) -> dict[str, bool | dict]: - return { - "info": serializer_types["BooleanSerializable"], - "serialized_info": False, - } - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": True, - "serialized": True, - } - - -class NumberSerializable(Serializable): - """Expects a number (int/float) as input/output but performs no serialization.""" - - def api_info(self) -> dict[str, bool | dict]: - return { - "info": serializer_types["NumberSerializable"], - "serialized_info": False, - } - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": 5, - "serialized": 5, - } - - -class ImgSerializable(Serializable): - """Expects a base64 string as input/output which is serialized to a filepath.""" - - def serialized_info(self): - return {"type": "string", "description": "filepath or URL to image"} - - def api_info(self) -> dict[str, bool | dict]: - return {"info": serializer_types["ImgSerializable"], "serialized_info": True} - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": media_data.BASE64_IMAGE, - "serialized": "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", - } - - def serialize( - self, - x: str | None, - load_dir: str | Path = "", - ) -> str | None: - """ - Convert from human-friendly version of a file (string filepath) to a serialized - representation (base64). - Parameters: - x: String path to file to serialize - load_dir: Path to directory containing x - """ - if not x: - return None - if utils.is_http_url_like(x): - return utils.encode_url_to_base64(x) - return utils.encode_file_to_base64(Path(load_dir) / x) - - def deserialize( - self, - x: str | None, - save_dir: str | Path | None = None, - root_url: str | None = None, - hf_token: str | None = None, - ) -> str | None: - """ - Convert from serialized representation of a file (base64) to a human-friendly - version (string filepath). Optionally, save the file to the directory specified by save_dir - Parameters: - x: Base64 representation of image to deserialize into a string filepath - save_dir: Path to directory to save the deserialized image to - root_url: Ignored - hf_token: Ignored - """ - if x is None or x == "": - return None - file = utils.decode_base64_to_file(x, dir=save_dir) - return file.name - - -class FileSerializable(Serializable): - """Expects a dict with base64 representation of object as input/output which is serialized to a filepath.""" - - def serialized_info(self): - return self._single_file_serialized_info() - - def _single_file_api_info(self): - return { - "info": serializer_types["SingleFileSerializable"], - "serialized_info": True, - } - - def _single_file_serialized_info(self): - return {"type": "string", "description": "filepath or URL to file"} - - def _multiple_file_serialized_info(self): - return { - "type": "array", - "description": "List of filepath(s) or URL(s) to files", - "items": {"type": "string", "description": "filepath or URL to file"}, - } - - def _multiple_file_api_info(self): - return { - "info": serializer_types["MultipleFileSerializable"], - "serialized_info": True, - } - - def api_info(self) -> dict[str, dict | bool]: - return self._single_file_api_info() - - def example_inputs(self) -> dict[str, Any]: - return self._single_file_example_inputs() - - def _single_file_example_inputs(self) -> dict[str, Any]: - return { - "raw": {"is_file": False, "data": media_data.BASE64_FILE}, - "serialized": "https://github.com/gradio-app/gradio/raw/main/test/test_files/sample_file.pdf", - } - - def _multiple_file_example_inputs(self) -> dict[str, Any]: - return { - "raw": [{"is_file": False, "data": media_data.BASE64_FILE}], - "serialized": [ - "https://github.com/gradio-app/gradio/raw/main/test/test_files/sample_file.pdf" - ], - } - - def _serialize_single( - self, x: str | FileData | None, load_dir: str | Path = "" - ) -> FileData | None: - if x is None or isinstance(x, dict): - return x - if utils.is_http_url_like(x): - filename = x - size = None - else: - filename = str(Path(load_dir) / x) - size = Path(filename).stat().st_size - return { - "name": filename, - "data": utils.encode_url_or_file_to_base64(filename), - "orig_name": Path(filename).name, - "is_file": False, - "size": size, - } - - def _deserialize_single( - self, - x: str | FileData | None, - save_dir: str | None = None, - root_url: str | None = None, - hf_token: str | None = None, - ) -> str | None: - if x is None: - return None - if isinstance(x, str): - file_name = utils.decode_base64_to_file(x, dir=save_dir).name - elif isinstance(x, dict): - if x.get("is_file"): - filepath = x.get("name") - assert filepath is not None, f"The 'name' field is missing in {x}" - if root_url is not None: - file_name = utils.download_tmp_copy_of_file( - root_url + "file=" + filepath, - hf_token=hf_token, - dir=save_dir, - ) - else: - file_name = utils.create_tmp_copy_of_file(filepath, dir=save_dir) - else: - data = x.get("data") - assert data is not None, f"The 'data' field is missing in {x}" - file_name = utils.decode_base64_to_file(data, dir=save_dir).name - else: - raise ValueError( - f"A FileSerializable component can only deserialize a string or a dict, not a {type(x)}: {x}" - ) - return file_name - - def serialize( - self, - x: str | FileData | None | list[str | FileData | None], - load_dir: str | Path = "", - ) -> FileData | None | list[FileData | None]: - """ - Convert from human-friendly version of a file (string filepath) to a - serialized representation (base64) - Parameters: - x: String path to file to serialize - load_dir: Path to directory containing x - """ - if x is None or x == "": - return None - if isinstance(x, list): - return [self._serialize_single(f, load_dir=load_dir) for f in x] - else: - return self._serialize_single(x, load_dir=load_dir) - - def deserialize( - self, - x: str | FileData | None | list[str | FileData | None], - save_dir: Path | str | None = None, - root_url: str | None = None, - hf_token: str | None = None, - ) -> str | None | list[str | None]: - """ - Convert from serialized representation of a file (base64) to a human-friendly - version (string filepath). Optionally, save the file to the directory specified by `save_dir` - Parameters: - x: Base64 representation of file to deserialize into a string filepath - save_dir: Path to directory to save the deserialized file to - root_url: If this component is loaded from an external Space, this is the URL of the Space. - hf_token: If this component is loaded from an external private Space, this is the access token for the Space - """ - if x is None: - return None - if isinstance(save_dir, Path): - save_dir = str(save_dir) - if isinstance(x, list): - return [ - self._deserialize_single( - f, save_dir=save_dir, root_url=root_url, hf_token=hf_token - ) - for f in x - ] - else: - return self._deserialize_single( - x, save_dir=save_dir, root_url=root_url, hf_token=hf_token - ) - - -class VideoSerializable(FileSerializable): - def serialized_info(self): - return {"type": "string", "description": "filepath or URL to video file"} - - def api_info(self) -> dict[str, dict | bool]: - return {"info": serializer_types["FileSerializable"], "serialized_info": True} - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": {"is_file": False, "data": media_data.BASE64_VIDEO}, - "serialized": "https://github.com/gradio-app/gradio/raw/main/test/test_files/video_sample.mp4", - } - - def serialize( - self, x: str | None, load_dir: str | Path = "" - ) -> tuple[FileData | None, None]: - return (super().serialize(x, load_dir), None) # type: ignore - - def deserialize( - self, - x: tuple[FileData | None, FileData | None] | None, - save_dir: Path | str | None = None, - root_url: str | None = None, - hf_token: str | None = None, - ) -> str | tuple[str | None, str | None] | None: - """ - Convert from serialized representation of a file (base64) to a human-friendly - version (string filepath). Optionally, save the file to the directory specified by `save_dir` - """ - if isinstance(x, (tuple, list)): - assert len(x) == 2, f"Expected tuple of length 2. Received: {x}" - x_as_list = [x[0], x[1]] - else: - raise ValueError(f"Expected tuple of length 2. Received: {x}") - deserialized_file = super().deserialize(x_as_list, save_dir, root_url, hf_token) # type: ignore - if isinstance(deserialized_file, list): - return deserialized_file[0] # ignore subtitles - - -class JSONSerializable(Serializable): - def serialized_info(self): - return {"type": "string", "description": "filepath to JSON file"} - - def api_info(self) -> dict[str, dict | bool]: - return {"info": serializer_types["JSONSerializable"], "serialized_info": True} - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": {"a": 1, "b": 2}, - "serialized": None, - } - - def serialize( - self, - x: str | None, - load_dir: str | Path = "", - ) -> dict | list | None: - """ - Convert from a a human-friendly version (string path to json file) to a - serialized representation (json string) - Parameters: - x: String path to json file to read to get json string - load_dir: Path to directory containing x - """ - if x is None or x == "": - return None - return utils.file_to_json(Path(load_dir) / x) - - def deserialize( - self, - x: str | dict | list, - save_dir: str | Path | None = None, - root_url: str | None = None, - hf_token: str | None = None, - ) -> str | None: - """ - Convert from serialized representation (json string) to a human-friendly - version (string path to json file). Optionally, save the file to the directory specified by `save_dir` - Parameters: - x: Json string - save_dir: Path to save the deserialized json file to - root_url: Ignored - hf_token: Ignored - """ - if x is None: - return None - return utils.dict_or_str_to_json_file(x, dir=save_dir).name - - -class GallerySerializable(Serializable): - def serialized_info(self): - return { - "type": "string", - "description": "path to directory with images and a file associating images with captions called captions.json", - } - - def api_info(self) -> dict[str, dict | bool]: - return { - "info": serializer_types["GallerySerializable"], - "serialized_info": True, - } - - def example_inputs(self) -> dict[str, Any]: - return { - "raw": [media_data.BASE64_IMAGE] * 2, - "serialized": [ - "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", - ] - * 2, - } - - def serialize( - self, x: str | None, load_dir: str | Path = "" - ) -> list[list[str | None]] | None: - if x is None or x == "": - return None - files = [] - captions_file = Path(x) / "captions.json" - with captions_file.open("r") as captions_json: - captions = json.load(captions_json) - for file_name, caption in captions.items(): - img = FileSerializable().serialize(file_name) - files.append([img, caption]) - return files - - def deserialize( - self, - x: list[list[str | None]] | None, - save_dir: str = "", - root_url: str | None = None, - hf_token: str | None = None, - ) -> None | str: - if x is None: - return None - gallery_path = Path(save_dir) / str(uuid.uuid4()) - gallery_path.mkdir(exist_ok=True, parents=True) - captions = {} - for img_data in x: - if isinstance(img_data, (list, tuple)): - img_data, caption = img_data - else: - caption = None - name = FileSerializable().deserialize( - img_data, gallery_path, root_url=root_url, hf_token=hf_token - ) - captions[name] = caption - captions_file = gallery_path / "captions.json" - with captions_file.open("w") as captions_json: - json.dump(captions, captions_json) - return os.path.abspath(gallery_path) - - -SERIALIZER_MAPPING = {} -for cls in Serializable.__subclasses__(): - SERIALIZER_MAPPING[cls.__name__] = cls - for subcls in cls.__subclasses__(): - SERIALIZER_MAPPING[subcls.__name__] = subcls - -SERIALIZER_MAPPING["Serializable"] = SimpleSerializable -SERIALIZER_MAPPING["File"] = FileSerializable -SERIALIZER_MAPPING["UploadButton"] = FileSerializable - -COMPONENT_MAPPING: dict[str, type] = { - "textbox": StringSerializable, - "number": NumberSerializable, - "slider": NumberSerializable, - "checkbox": BooleanSerializable, - "checkboxgroup": ListStringSerializable, - "radio": StringSerializable, - "dropdown": SimpleSerializable, - "image": ImgSerializable, - "video": FileSerializable, - "audio": FileSerializable, - "file": FileSerializable, - "dataframe": JSONSerializable, - "timeseries": JSONSerializable, - "state": SimpleSerializable, - "button": StringSerializable, - "uploadbutton": FileSerializable, - "colorpicker": StringSerializable, - "label": JSONSerializable, - "highlightedtext": JSONSerializable, - "json": JSONSerializable, - "html": StringSerializable, - "gallery": GallerySerializable, - "chatbot": JSONSerializable, - "model3d": FileSerializable, - "plot": JSONSerializable, - "barplot": JSONSerializable, - "lineplot": JSONSerializable, - "scatterplot": JSONSerializable, - "markdown": StringSerializable, - "code": StringSerializable, - "annotatedimage": JSONSerializable, -} diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_utils.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_utils.py deleted file mode 100644 index a3a045da05601811bdea1bba67b6705f53f4ffe4..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_utils.py +++ /dev/null @@ -1,477 +0,0 @@ -import codecs -import email.message -import ipaddress -import mimetypes -import os -import re -import time -import typing -from pathlib import Path -from urllib.request import getproxies - -import sniffio - -from ._types import PrimitiveData - -if typing.TYPE_CHECKING: # pragma: no cover - from ._urls import URL - - -_HTML5_FORM_ENCODING_REPLACEMENTS = {'"': "%22", "\\": "\\\\"} -_HTML5_FORM_ENCODING_REPLACEMENTS.update( - {chr(c): "%{:02X}".format(c) for c in range(0x1F + 1) if c != 0x1B} -) -_HTML5_FORM_ENCODING_RE = re.compile( - r"|".join([re.escape(c) for c in _HTML5_FORM_ENCODING_REPLACEMENTS.keys()]) -) - - -def normalize_header_key( - value: typing.Union[str, bytes], - lower: bool, - encoding: typing.Optional[str] = None, -) -> bytes: - """ - Coerce str/bytes into a strictly byte-wise HTTP header key. - """ - if isinstance(value, bytes): - bytes_value = value - else: - bytes_value = value.encode(encoding or "ascii") - - return bytes_value.lower() if lower else bytes_value - - -def normalize_header_value( - value: typing.Union[str, bytes], encoding: typing.Optional[str] = None -) -> bytes: - """ - Coerce str/bytes into a strictly byte-wise HTTP header value. - """ - if isinstance(value, bytes): - return value - return value.encode(encoding or "ascii") - - -def primitive_value_to_str(value: "PrimitiveData") -> str: - """ - Coerce a primitive data type into a string value. - - Note that we prefer JSON-style 'true'/'false' for boolean values here. - """ - if value is True: - return "true" - elif value is False: - return "false" - elif value is None: - return "" - return str(value) - - -def is_known_encoding(encoding: str) -> bool: - """ - Return `True` if `encoding` is a known codec. - """ - try: - codecs.lookup(encoding) - except LookupError: - return False - return True - - -def format_form_param(name: str, value: str) -> bytes: - """ - Encode a name/value pair within a multipart form. - """ - - def replacer(match: typing.Match[str]) -> str: - return _HTML5_FORM_ENCODING_REPLACEMENTS[match.group(0)] - - value = _HTML5_FORM_ENCODING_RE.sub(replacer, value) - return f'{name}="{value}"'.encode() - - -# Null bytes; no need to recreate these on each call to guess_json_utf -_null = b"\x00" -_null2 = _null * 2 -_null3 = _null * 3 - - -def guess_json_utf(data: bytes) -> typing.Optional[str]: - # JSON always starts with two ASCII characters, so detection is as - # easy as counting the nulls and from their location and count - # determine the encoding. Also detect a BOM, if present. - sample = data[:4] - if sample in (codecs.BOM_UTF32_LE, codecs.BOM_UTF32_BE): - return "utf-32" # BOM included - if sample[:3] == codecs.BOM_UTF8: - return "utf-8-sig" # BOM included, MS style (discouraged) - if sample[:2] in (codecs.BOM_UTF16_LE, codecs.BOM_UTF16_BE): - return "utf-16" # BOM included - nullcount = sample.count(_null) - if nullcount == 0: - return "utf-8" - if nullcount == 2: - if sample[::2] == _null2: # 1st and 3rd are null - return "utf-16-be" - if sample[1::2] == _null2: # 2nd and 4th are null - return "utf-16-le" - # Did not detect 2 valid UTF-16 ascii-range characters - if nullcount == 3: - if sample[:3] == _null3: - return "utf-32-be" - if sample[1:] == _null3: - return "utf-32-le" - # Did not detect a valid UTF-32 ascii-range character - return None - - -def get_ca_bundle_from_env() -> typing.Optional[str]: - if "SSL_CERT_FILE" in os.environ: - ssl_file = Path(os.environ["SSL_CERT_FILE"]) - if ssl_file.is_file(): - return str(ssl_file) - if "SSL_CERT_DIR" in os.environ: - ssl_path = Path(os.environ["SSL_CERT_DIR"]) - if ssl_path.is_dir(): - return str(ssl_path) - return None - - -def parse_header_links(value: str) -> typing.List[typing.Dict[str, str]]: - """ - Returns a list of parsed link headers, for more info see: - https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Link - The generic syntax of those is: - Link: < uri-reference >; param1=value1; param2="value2" - So for instance: - Link; '; type="image/jpeg",;' - would return - [ - {"url": "http:/.../front.jpeg", "type": "image/jpeg"}, - {"url": "http://.../back.jpeg"}, - ] - :param value: HTTP Link entity-header field - :return: list of parsed link headers - """ - links: typing.List[typing.Dict[str, str]] = [] - replace_chars = " '\"" - value = value.strip(replace_chars) - if not value: - return links - for val in re.split(", *<", value): - try: - url, params = val.split(";", 1) - except ValueError: - url, params = val, "" - link = {"url": url.strip("<> '\"")} - for param in params.split(";"): - try: - key, value = param.split("=") - except ValueError: - break - link[key.strip(replace_chars)] = value.strip(replace_chars) - links.append(link) - return links - - -def parse_content_type_charset(content_type: str) -> typing.Optional[str]: - # We used to use `cgi.parse_header()` here, but `cgi` became a dead battery. - # See: https://peps.python.org/pep-0594/#cgi - msg = email.message.Message() - msg["content-type"] = content_type - return msg.get_content_charset(failobj=None) - - -SENSITIVE_HEADERS = {"authorization", "proxy-authorization"} - - -def obfuscate_sensitive_headers( - items: typing.Iterable[typing.Tuple[typing.AnyStr, typing.AnyStr]] -) -> typing.Iterator[typing.Tuple[typing.AnyStr, typing.AnyStr]]: - for k, v in items: - if to_str(k.lower()) in SENSITIVE_HEADERS: - v = to_bytes_or_str("[secure]", match_type_of=v) - yield k, v - - -def port_or_default(url: "URL") -> typing.Optional[int]: - if url.port is not None: - return url.port - return {"http": 80, "https": 443}.get(url.scheme) - - -def same_origin(url: "URL", other: "URL") -> bool: - """ - Return 'True' if the given URLs share the same origin. - """ - return ( - url.scheme == other.scheme - and url.host == other.host - and port_or_default(url) == port_or_default(other) - ) - - -def is_https_redirect(url: "URL", location: "URL") -> bool: - """ - Return 'True' if 'location' is a HTTPS upgrade of 'url' - """ - if url.host != location.host: - return False - - return ( - url.scheme == "http" - and port_or_default(url) == 80 - and location.scheme == "https" - and port_or_default(location) == 443 - ) - - -def get_environment_proxies() -> typing.Dict[str, typing.Optional[str]]: - """Gets proxy information from the environment""" - - # urllib.request.getproxies() falls back on System - # Registry and Config for proxies on Windows and macOS. - # We don't want to propagate non-HTTP proxies into - # our configuration such as 'TRAVIS_APT_PROXY'. - proxy_info = getproxies() - mounts: typing.Dict[str, typing.Optional[str]] = {} - - for scheme in ("http", "https", "all"): - if proxy_info.get(scheme): - hostname = proxy_info[scheme] - mounts[f"{scheme}://"] = ( - hostname if "://" in hostname else f"http://{hostname}" - ) - - no_proxy_hosts = [host.strip() for host in proxy_info.get("no", "").split(",")] - for hostname in no_proxy_hosts: - # See https://curl.haxx.se/libcurl/c/CURLOPT_NOPROXY.html for details - # on how names in `NO_PROXY` are handled. - if hostname == "*": - # If NO_PROXY=* is used or if "*" occurs as any one of the comma - # separated hostnames, then we should just bypass any information - # from HTTP_PROXY, HTTPS_PROXY, ALL_PROXY, and always ignore - # proxies. - return {} - elif hostname: - # NO_PROXY=.google.com is marked as "all://*.google.com, - # which disables "www.google.com" but not "google.com" - # NO_PROXY=google.com is marked as "all://*google.com, - # which disables "www.google.com" and "google.com". - # (But not "wwwgoogle.com") - # NO_PROXY can include domains, IPv6, IPv4 addresses and "localhost" - # NO_PROXY=example.com,::1,localhost,192.168.0.0/16 - if is_ipv4_hostname(hostname): - mounts[f"all://{hostname}"] = None - elif is_ipv6_hostname(hostname): - mounts[f"all://[{hostname}]"] = None - elif hostname.lower() == "localhost": - mounts[f"all://{hostname}"] = None - else: - mounts[f"all://*{hostname}"] = None - - return mounts - - -def to_bytes(value: typing.Union[str, bytes], encoding: str = "utf-8") -> bytes: - return value.encode(encoding) if isinstance(value, str) else value - - -def to_str(value: typing.Union[str, bytes], encoding: str = "utf-8") -> str: - return value if isinstance(value, str) else value.decode(encoding) - - -def to_bytes_or_str(value: str, match_type_of: typing.AnyStr) -> typing.AnyStr: - return value if isinstance(match_type_of, str) else value.encode() - - -def unquote(value: str) -> str: - return value[1:-1] if value[0] == value[-1] == '"' else value - - -def guess_content_type(filename: typing.Optional[str]) -> typing.Optional[str]: - if filename: - return mimetypes.guess_type(filename)[0] or "application/octet-stream" - return None - - -def peek_filelike_length(stream: typing.Any) -> typing.Optional[int]: - """ - Given a file-like stream object, return its length in number of bytes - without reading it into memory. - """ - try: - # Is it an actual file? - fd = stream.fileno() - # Yup, seems to be an actual file. - length = os.fstat(fd).st_size - except (AttributeError, OSError): - # No... Maybe it's something that supports random access, like `io.BytesIO`? - try: - # Assuming so, go to end of stream to figure out its length, - # then put it back in place. - offset = stream.tell() - length = stream.seek(0, os.SEEK_END) - stream.seek(offset) - except (AttributeError, OSError): - # Not even that? Sorry, we're doomed... - return None - - return length - - -class Timer: - async def _get_time(self) -> float: - library = sniffio.current_async_library() - if library == "trio": - import trio - - return trio.current_time() - elif library == "curio": # pragma: no cover - import curio - - return typing.cast(float, await curio.clock()) - - import asyncio - - return asyncio.get_event_loop().time() - - def sync_start(self) -> None: - self.started = time.perf_counter() - - async def async_start(self) -> None: - self.started = await self._get_time() - - def sync_elapsed(self) -> float: - now = time.perf_counter() - return now - self.started - - async def async_elapsed(self) -> float: - now = await self._get_time() - return now - self.started - - -class URLPattern: - """ - A utility class currently used for making lookups against proxy keys... - - # Wildcard matching... - >>> pattern = URLPattern("all") - >>> pattern.matches(httpx.URL("http://example.com")) - True - - # Witch scheme matching... - >>> pattern = URLPattern("https") - >>> pattern.matches(httpx.URL("https://example.com")) - True - >>> pattern.matches(httpx.URL("http://example.com")) - False - - # With domain matching... - >>> pattern = URLPattern("https://example.com") - >>> pattern.matches(httpx.URL("https://example.com")) - True - >>> pattern.matches(httpx.URL("http://example.com")) - False - >>> pattern.matches(httpx.URL("https://other.com")) - False - - # Wildcard scheme, with domain matching... - >>> pattern = URLPattern("all://example.com") - >>> pattern.matches(httpx.URL("https://example.com")) - True - >>> pattern.matches(httpx.URL("http://example.com")) - True - >>> pattern.matches(httpx.URL("https://other.com")) - False - - # With port matching... - >>> pattern = URLPattern("https://example.com:1234") - >>> pattern.matches(httpx.URL("https://example.com:1234")) - True - >>> pattern.matches(httpx.URL("https://example.com")) - False - """ - - def __init__(self, pattern: str) -> None: - from ._urls import URL - - if pattern and ":" not in pattern: - raise ValueError( - f"Proxy keys should use proper URL forms rather " - f"than plain scheme strings. " - f'Instead of "{pattern}", use "{pattern}://"' - ) - - url = URL(pattern) - self.pattern = pattern - self.scheme = "" if url.scheme == "all" else url.scheme - self.host = "" if url.host == "*" else url.host - self.port = url.port - if not url.host or url.host == "*": - self.host_regex: typing.Optional[typing.Pattern[str]] = None - elif url.host.startswith("*."): - # *.example.com should match "www.example.com", but not "example.com" - domain = re.escape(url.host[2:]) - self.host_regex = re.compile(f"^.+\\.{domain}$") - elif url.host.startswith("*"): - # *example.com should match "www.example.com" and "example.com" - domain = re.escape(url.host[1:]) - self.host_regex = re.compile(f"^(.+\\.)?{domain}$") - else: - # example.com should match "example.com" but not "www.example.com" - domain = re.escape(url.host) - self.host_regex = re.compile(f"^{domain}$") - - def matches(self, other: "URL") -> bool: - if self.scheme and self.scheme != other.scheme: - return False - if ( - self.host - and self.host_regex is not None - and not self.host_regex.match(other.host) - ): - return False - if self.port is not None and self.port != other.port: - return False - return True - - @property - def priority(self) -> typing.Tuple[int, int, int]: - """ - The priority allows URLPattern instances to be sortable, so that - we can match from most specific to least specific. - """ - # URLs with a port should take priority over URLs without a port. - port_priority = 0 if self.port is not None else 1 - # Longer hostnames should match first. - host_priority = -len(self.host) - # Longer schemes should match first. - scheme_priority = -len(self.scheme) - return (port_priority, host_priority, scheme_priority) - - def __hash__(self) -> int: - return hash(self.pattern) - - def __lt__(self, other: "URLPattern") -> bool: - return self.priority < other.priority - - def __eq__(self, other: typing.Any) -> bool: - return isinstance(other, URLPattern) and self.pattern == other.pattern - - -def is_ipv4_hostname(hostname: str) -> bool: - try: - ipaddress.IPv4Address(hostname.split("/")[0]) - except Exception: - return False - return True - - -def is_ipv6_hostname(hostname: str) -> bool: - try: - ipaddress.IPv6Address(hostname.split("/")[0]) - except Exception: - return False - return True diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/idna/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/idna/__init__.py deleted file mode 100644 index a40eeafcc914108ca79c5d83d6e81da1b29c6e80..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/idna/__init__.py +++ /dev/null @@ -1,44 +0,0 @@ -from .package_data import __version__ -from .core import ( - IDNABidiError, - IDNAError, - InvalidCodepoint, - InvalidCodepointContext, - alabel, - check_bidi, - check_hyphen_ok, - check_initial_combiner, - check_label, - check_nfc, - decode, - encode, - ulabel, - uts46_remap, - valid_contextj, - valid_contexto, - valid_label_length, - valid_string_length, -) -from .intranges import intranges_contain - -__all__ = [ - "IDNABidiError", - "IDNAError", - "InvalidCodepoint", - "InvalidCodepointContext", - "alabel", - "check_bidi", - "check_hyphen_ok", - "check_initial_combiner", - "check_label", - "check_nfc", - "decode", - "encode", - "intranges_contain", - "ulabel", - "uts46_remap", - "valid_contextj", - "valid_contexto", - "valid_label_length", - "valid_string_length", -] diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/_legacy_validators.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/_legacy_validators.py deleted file mode 100644 index 141f15a09c0216fbe156bff518e298ecce9b6127..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/_legacy_validators.py +++ /dev/null @@ -1,300 +0,0 @@ -from referencing.jsonschema import lookup_recursive_ref - -from jsonschema import _utils -from jsonschema.exceptions import ValidationError - - -def ignore_ref_siblings(schema): - """ - Ignore siblings of ``$ref`` if it is present. - - Otherwise, return all keywords. - - Suitable for use with `create`'s ``applicable_validators`` argument. - """ - ref = schema.get("$ref") - if ref is not None: - return [("$ref", ref)] - else: - return schema.items() - - -def dependencies_draft3(validator, dependencies, instance, schema): - if not validator.is_type(instance, "object"): - return - - for property, dependency in dependencies.items(): - if property not in instance: - continue - - if validator.is_type(dependency, "object"): - yield from validator.descend( - instance, dependency, schema_path=property, - ) - elif validator.is_type(dependency, "string"): - if dependency not in instance: - message = f"{dependency!r} is a dependency of {property!r}" - yield ValidationError(message) - else: - for each in dependency: - if each not in instance: - message = f"{each!r} is a dependency of {property!r}" - yield ValidationError(message) - - -def dependencies_draft4_draft6_draft7( - validator, - dependencies, - instance, - schema, -): - """ - Support for the ``dependencies`` keyword from pre-draft 2019-09. - - In later drafts, the keyword was split into separate - ``dependentRequired`` and ``dependentSchemas`` validators. - """ - if not validator.is_type(instance, "object"): - return - - for property, dependency in dependencies.items(): - if property not in instance: - continue - - if validator.is_type(dependency, "array"): - for each in dependency: - if each not in instance: - message = f"{each!r} is a dependency of {property!r}" - yield ValidationError(message) - else: - yield from validator.descend( - instance, dependency, schema_path=property, - ) - - -def disallow_draft3(validator, disallow, instance, schema): - for disallowed in _utils.ensure_list(disallow): - if validator.evolve(schema={"type": [disallowed]}).is_valid(instance): - message = f"{disallowed!r} is disallowed for {instance!r}" - yield ValidationError(message) - - -def extends_draft3(validator, extends, instance, schema): - if validator.is_type(extends, "object"): - yield from validator.descend(instance, extends) - return - for index, subschema in enumerate(extends): - yield from validator.descend(instance, subschema, schema_path=index) - - -def items_draft3_draft4(validator, items, instance, schema): - if not validator.is_type(instance, "array"): - return - - if validator.is_type(items, "object"): - for index, item in enumerate(instance): - yield from validator.descend(item, items, path=index) - else: - for (index, item), subschema in zip(enumerate(instance), items): - yield from validator.descend( - item, subschema, path=index, schema_path=index, - ) - - -def items_draft6_draft7_draft201909(validator, items, instance, schema): - if not validator.is_type(instance, "array"): - return - - if validator.is_type(items, "array"): - for (index, item), subschema in zip(enumerate(instance), items): - yield from validator.descend( - item, subschema, path=index, schema_path=index, - ) - else: - for index, item in enumerate(instance): - yield from validator.descend(item, items, path=index) - - -def minimum_draft3_draft4(validator, minimum, instance, schema): - if not validator.is_type(instance, "number"): - return - - if schema.get("exclusiveMinimum", False): - failed = instance <= minimum - cmp = "less than or equal to" - else: - failed = instance < minimum - cmp = "less than" - - if failed: - message = f"{instance!r} is {cmp} the minimum of {minimum!r}" - yield ValidationError(message) - - -def maximum_draft3_draft4(validator, maximum, instance, schema): - if not validator.is_type(instance, "number"): - return - - if schema.get("exclusiveMaximum", False): - failed = instance >= maximum - cmp = "greater than or equal to" - else: - failed = instance > maximum - cmp = "greater than" - - if failed: - message = f"{instance!r} is {cmp} the maximum of {maximum!r}" - yield ValidationError(message) - - -def properties_draft3(validator, properties, instance, schema): - if not validator.is_type(instance, "object"): - return - - for property, subschema in properties.items(): - if property in instance: - yield from validator.descend( - instance[property], - subschema, - path=property, - schema_path=property, - ) - elif subschema.get("required", False): - error = ValidationError(f"{property!r} is a required property") - error._set( - validator="required", - validator_value=subschema["required"], - instance=instance, - schema=schema, - ) - error.path.appendleft(property) - error.schema_path.extend([property, "required"]) - yield error - - -def type_draft3(validator, types, instance, schema): - types = _utils.ensure_list(types) - - all_errors = [] - for index, type in enumerate(types): - if validator.is_type(type, "object"): - errors = list(validator.descend(instance, type, schema_path=index)) - if not errors: - return - all_errors.extend(errors) - else: - if validator.is_type(instance, type): - return - else: - reprs = [] - for type in types: - try: - reprs.append(repr(type["name"])) - except Exception: - reprs.append(repr(type)) - yield ValidationError( - f"{instance!r} is not of type {', '.join(reprs)}", - context=all_errors, - ) - - -def contains_draft6_draft7(validator, contains, instance, schema): - if not validator.is_type(instance, "array"): - return - - if not any( - validator.evolve(schema=contains).is_valid(element) - for element in instance - ): - yield ValidationError( - f"None of {instance!r} are valid under the given schema", - ) - - -def recursiveRef(validator, recursiveRef, instance, schema): - resolved = lookup_recursive_ref(validator._resolver) - yield from validator.descend( - instance, - resolved.contents, - resolver=resolved.resolver, - ) - - -def find_evaluated_item_indexes_by_schema(validator, instance, schema): - """ - Get all indexes of items that get evaluated under the current schema. - - Covers all keywords related to unevaluatedItems: items, prefixItems, if, - then, else, contains, unevaluatedItems, allOf, oneOf, anyOf - """ - if validator.is_type(schema, "boolean"): - return [] - evaluated_indexes = [] - - if "$ref" in schema: - resolved = validator._resolver.lookup(schema["$ref"]) - evaluated_indexes.extend( - find_evaluated_item_indexes_by_schema( - validator.evolve( - schema=resolved.contents, - _resolver=resolved.resolver, - ), - instance, - resolved.contents, - ), - ) - - if "items" in schema: - if "additionalItems" in schema: - return list(range(0, len(instance))) - - if validator.is_type(schema["items"], "object"): - return list(range(0, len(instance))) - evaluated_indexes += list(range(0, len(schema["items"]))) - - if "if" in schema: - if validator.evolve(schema=schema["if"]).is_valid(instance): - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, schema["if"], - ) - if "then" in schema: - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, schema["then"], - ) - else: - if "else" in schema: - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, schema["else"], - ) - - for keyword in ["contains", "unevaluatedItems"]: - if keyword in schema: - for k, v in enumerate(instance): - if validator.evolve(schema=schema[keyword]).is_valid(v): - evaluated_indexes.append(k) - - for keyword in ["allOf", "oneOf", "anyOf"]: - if keyword in schema: - for subschema in schema[keyword]: - errs = next(validator.descend(instance, subschema), None) - if errs is None: - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, subschema, - ) - - return evaluated_indexes - - -def unevaluatedItems_draft2019(validator, unevaluatedItems, instance, schema): - if not validator.is_type(instance, "array"): - return - evaluated_item_indexes = find_evaluated_item_indexes_by_schema( - validator, instance, schema, - ) - unevaluated_items = [ - item for index, item in enumerate(instance) - if index not in evaluated_item_indexes - ] - if unevaluated_items: - error = "Unevaluated items are not allowed (%s %s unexpected)" - yield ValidationError(error % _utils.extras_msg(unevaluated_items)) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/linkify.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/linkify.py deleted file mode 100644 index a8a181537d78867f8dfdf552b70f83be09bb2dd5..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/linkify.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Process links like https://example.org/""" -import re - -from .state_inline import StateInline - -# RFC3986: scheme = ALPHA *( ALPHA / DIGIT / "+" / "-" / "." ) -SCHEME_RE = re.compile(r"(?:^|[^a-z0-9.+-])([a-z][a-z0-9.+-]*)$", re.IGNORECASE) - - -def linkify(state: StateInline, silent: bool) -> bool: - """Rule for identifying plain-text links.""" - if not state.md.options.linkify: - return False - if state.linkLevel > 0: - return False - if not state.md.linkify: - raise ModuleNotFoundError("Linkify enabled but not installed.") - - pos = state.pos - maximum = state.posMax - - if ( - (pos + 3) > maximum - or state.src[pos] != ":" - or state.src[pos + 1] != "/" - or state.src[pos + 2] != "/" - ): - return False - - if not (match := SCHEME_RE.match(state.pending)): - return False - - proto = match.group(1) - if not (link := state.md.linkify.match_at_start(state.src[pos - len(proto) :])): - return False - url: str = link.url - - # disallow '*' at the end of the link (conflicts with emphasis) - url = url.rstrip("*") - - full_url = state.md.normalizeLink(url) - if not state.md.validateLink(full_url): - return False - - if not silent: - state.pending = state.pending[: -len(proto)] - - token = state.push("link_open", "a", 1) - token.attrs = {"href": full_url} - token.markup = "linkify" - token.info = "auto" - - token = state.push("text", "", 0) - token.content = state.md.normalizeLinkText(url) - - token = state.push("link_close", "a", -1) - token.markup = "linkify" - token.info = "auto" - - state.pos += len(url) - len(proto) - return True diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/util/skin_mask.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/util/skin_mask.py deleted file mode 100644 index a8a74e4c3b40d13b0258b83a12f56321a85bb179..0000000000000000000000000000000000000000 --- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/util/skin_mask.py +++ /dev/null @@ -1,125 +0,0 @@ -"""This script is to generate skin attention mask for Deep3DFaceRecon_pytorch -""" - -import math -import numpy as np -import os -import cv2 - -class GMM: - def __init__(self, dim, num, w, mu, cov, cov_det, cov_inv): - self.dim = dim # feature dimension - self.num = num # number of Gaussian components - self.w = w # weights of Gaussian components (a list of scalars) - self.mu= mu # mean of Gaussian components (a list of 1xdim vectors) - self.cov = cov # covariance matrix of Gaussian components (a list of dimxdim matrices) - self.cov_det = cov_det # pre-computed determinet of covariance matrices (a list of scalars) - self.cov_inv = cov_inv # pre-computed inverse covariance matrices (a list of dimxdim matrices) - - self.factor = [0]*num - for i in range(self.num): - self.factor[i] = (2*math.pi)**(self.dim/2) * self.cov_det[i]**0.5 - - def likelihood(self, data): - assert(data.shape[1] == self.dim) - N = data.shape[0] - lh = np.zeros(N) - - for i in range(self.num): - data_ = data - self.mu[i] - - tmp = np.matmul(data_,self.cov_inv[i]) * data_ - tmp = np.sum(tmp,axis=1) - power = -0.5 * tmp - - p = np.array([math.exp(power[j]) for j in range(N)]) - p = p/self.factor[i] - lh += p*self.w[i] - - return lh - - -def _rgb2ycbcr(rgb): - m = np.array([[65.481, 128.553, 24.966], - [-37.797, -74.203, 112], - [112, -93.786, -18.214]]) - shape = rgb.shape - rgb = rgb.reshape((shape[0] * shape[1], 3)) - ycbcr = np.dot(rgb, m.transpose() / 255.) - ycbcr[:, 0] += 16. - ycbcr[:, 1:] += 128. - return ycbcr.reshape(shape) - - -def _bgr2ycbcr(bgr): - rgb = bgr[..., ::-1] - return _rgb2ycbcr(rgb) - - -gmm_skin_w = [0.24063933, 0.16365987, 0.26034665, 0.33535415] -gmm_skin_mu = [np.array([113.71862, 103.39613, 164.08226]), - np.array([150.19858, 105.18467, 155.51428]), - np.array([183.92976, 107.62468, 152.71820]), - np.array([114.90524, 113.59782, 151.38217])] -gmm_skin_cov_det = [5692842.5, 5851930.5, 2329131., 1585971.] -gmm_skin_cov_inv = [np.array([[0.0019472069, 0.0020450759, -0.00060243998],[0.0020450759, 0.017700525, 0.0051420014],[-0.00060243998, 0.0051420014, 0.0081308950]]), - np.array([[0.0027110141, 0.0011036990, 0.0023122299],[0.0011036990, 0.010707724, 0.010742856],[0.0023122299, 0.010742856, 0.017481629]]), - np.array([[0.0048026871, 0.00022935172, 0.0077668377],[0.00022935172, 0.011729696, 0.0081661865],[0.0077668377, 0.0081661865, 0.025374353]]), - np.array([[0.0011989699, 0.0022453172, -0.0010748957],[0.0022453172, 0.047758564, 0.020332102],[-0.0010748957, 0.020332102, 0.024502251]])] - -gmm_skin = GMM(3, 4, gmm_skin_w, gmm_skin_mu, [], gmm_skin_cov_det, gmm_skin_cov_inv) - -gmm_nonskin_w = [0.12791070, 0.31130761, 0.34245777, 0.21832393] -gmm_nonskin_mu = [np.array([99.200851, 112.07533, 140.20602]), - np.array([110.91392, 125.52969, 130.19237]), - np.array([129.75864, 129.96107, 126.96808]), - np.array([112.29587, 128.85121, 129.05431])] -gmm_nonskin_cov_det = [458703648., 6466488., 90611376., 133097.63] -gmm_nonskin_cov_inv = [np.array([[0.00085371657, 0.00071197288, 0.00023958916],[0.00071197288, 0.0025935620, 0.00076557708],[0.00023958916, 0.00076557708, 0.0015042332]]), - np.array([[0.00024650150, 0.00045542428, 0.00015019422],[0.00045542428, 0.026412144, 0.018419769],[0.00015019422, 0.018419769, 0.037497383]]), - np.array([[0.00037054974, 0.00038146760, 0.00040408765],[0.00038146760, 0.0085505722, 0.0079136286],[0.00040408765, 0.0079136286, 0.010982352]]), - np.array([[0.00013709733, 0.00051228428, 0.00012777430],[0.00051228428, 0.28237113, 0.10528370],[0.00012777430, 0.10528370, 0.23468947]])] - -gmm_nonskin = GMM(3, 4, gmm_nonskin_w, gmm_nonskin_mu, [], gmm_nonskin_cov_det, gmm_nonskin_cov_inv) - -prior_skin = 0.8 -prior_nonskin = 1 - prior_skin - - -# calculate skin attention mask -def skinmask(imbgr): - im = _bgr2ycbcr(imbgr) - - data = im.reshape((-1,3)) - - lh_skin = gmm_skin.likelihood(data) - lh_nonskin = gmm_nonskin.likelihood(data) - - tmp1 = prior_skin * lh_skin - tmp2 = prior_nonskin * lh_nonskin - post_skin = tmp1 / (tmp1+tmp2) # posterior probability - - post_skin = post_skin.reshape((im.shape[0],im.shape[1])) - - post_skin = np.round(post_skin*255) - post_skin = post_skin.astype(np.uint8) - post_skin = np.tile(np.expand_dims(post_skin,2),[1,1,3]) # reshape to H*W*3 - - return post_skin - - -def get_skin_mask(img_path): - print('generating skin masks......') - names = [i for i in sorted(os.listdir( - img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i] - save_path = os.path.join(img_path, 'mask') - if not os.path.isdir(save_path): - os.makedirs(save_path) - - for i in range(0, len(names)): - name = names[i] - print('%05d' % (i), ' ', name) - full_image_name = os.path.join(img_path, name) - img = cv2.imread(full_image_name).astype(np.float32) - skin_img = skinmask(img) - cv2.imwrite(os.path.join(save_path, name), skin_img.astype(np.uint8)) diff --git a/spaces/deepwisdom/MetaGPT/metagpt/roles/qa_engineer.py b/spaces/deepwisdom/MetaGPT/metagpt/roles/qa_engineer.py deleted file mode 100644 index 491f5f997ccac2042217df1384274bd5d42381af..0000000000000000000000000000000000000000 --- a/spaces/deepwisdom/MetaGPT/metagpt/roles/qa_engineer.py +++ /dev/null @@ -1,179 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/11 14:43 -@Author : alexanderwu -@File : qa_engineer.py -""" -import os -from pathlib import Path - -from metagpt.actions import DebugError, RunCode, WriteCode, WriteDesign, WriteTest -from metagpt.config import CONFIG -from metagpt.logs import logger -from metagpt.roles import Role -from metagpt.schema import Message -from metagpt.utils.common import CodeParser, parse_recipient -from metagpt.utils.special_tokens import FILENAME_CODE_SEP, MSG_SEP - - -class QaEngineer(Role): - def __init__( - self, - name="Edward", - profile="QaEngineer", - goal="Write comprehensive and robust tests to ensure codes will work as expected without bugs", - constraints="The test code you write should conform to code standard like PEP8, be modular, easy to read and maintain", - test_round_allowed=5, - ): - super().__init__(name, profile, goal, constraints) - self._init_actions( - [WriteTest] - ) # FIXME: a bit hack here, only init one action to circumvent _think() logic, will overwrite _think() in future updates - self._watch([WriteCode, WriteTest, RunCode, DebugError]) - self.test_round = 0 - self.test_round_allowed = test_round_allowed - - @classmethod - def parse_workspace(cls, system_design_msg: Message) -> str: - if not system_design_msg.instruct_content: - return system_design_msg.instruct_content.dict().get("Python package name") - return CodeParser.parse_str(block="Python package name", text=system_design_msg.content) - - def get_workspace(self, return_proj_dir=True) -> Path: - msg = self._rc.memory.get_by_action(WriteDesign)[-1] - if not msg: - return CONFIG.workspace / "src" - workspace = self.parse_workspace(msg) - # project directory: workspace/{package_name}, which contains package source code folder, tests folder, resources folder, etc. - if return_proj_dir: - return CONFIG.workspace / workspace - # development codes directory: workspace/{package_name}/{package_name} - return CONFIG.workspace / workspace / workspace - - def write_file(self, filename: str, code: str): - workspace = self.get_workspace() / "tests" - file = workspace / filename - file.parent.mkdir(parents=True, exist_ok=True) - file.write_text(code) - - async def _write_test(self, message: Message) -> None: - code_msgs = message.content.split(MSG_SEP) - # result_msg_all = [] - for code_msg in code_msgs: - # write tests - file_name, file_path = code_msg.split(FILENAME_CODE_SEP) - code_to_test = open(file_path, "r").read() - if "test" in file_name: - continue # Engineer might write some test files, skip testing a test file - test_file_name = "test_" + file_name - test_file_path = self.get_workspace() / "tests" / test_file_name - logger.info(f"Writing {test_file_name}..") - test_code = await WriteTest().run( - code_to_test=code_to_test, - test_file_name=test_file_name, - # source_file_name=file_name, - source_file_path=file_path, - workspace=self.get_workspace(), - ) - self.write_file(test_file_name, test_code) - - # prepare context for run tests in next round - command = ["python", f"tests/{test_file_name}"] - file_info = { - "file_name": file_name, - "file_path": str(file_path), - "test_file_name": test_file_name, - "test_file_path": str(test_file_path), - "command": command, - } - msg = Message( - content=str(file_info), - role=self.profile, - cause_by=WriteTest, - sent_from=self.profile, - send_to=self.profile, - ) - self._publish_message(msg) - - logger.info(f"Done {self.get_workspace()}/tests generating.") - - async def _run_code(self, msg): - file_info = eval(msg.content) - development_file_path = file_info["file_path"] - test_file_path = file_info["test_file_path"] - if not os.path.exists(development_file_path) or not os.path.exists(test_file_path): - return - - development_code = open(development_file_path, "r").read() - test_code = open(test_file_path, "r").read() - proj_dir = self.get_workspace() - development_code_dir = self.get_workspace(return_proj_dir=False) - - result_msg = await RunCode().run( - mode="script", - code=development_code, - code_file_name=file_info["file_name"], - test_code=test_code, - test_file_name=file_info["test_file_name"], - command=file_info["command"], - working_directory=proj_dir, # workspace/package_name, will run tests/test_xxx.py here - additional_python_paths=[development_code_dir], # workspace/package_name/package_name, - # import statement inside package code needs this - ) - - recipient = parse_recipient(result_msg) # the recipient might be Engineer or myself - content = str(file_info) + FILENAME_CODE_SEP + result_msg - msg = Message(content=content, role=self.profile, cause_by=RunCode, sent_from=self.profile, send_to=recipient) - self._publish_message(msg) - - async def _debug_error(self, msg): - file_info, context = msg.content.split(FILENAME_CODE_SEP) - file_name, code = await DebugError().run(context) - if file_name: - self.write_file(file_name, code) - recipient = msg.sent_from # send back to the one who ran the code for another run, might be one's self - msg = Message( - content=file_info, role=self.profile, cause_by=DebugError, sent_from=self.profile, send_to=recipient - ) - self._publish_message(msg) - - async def _observe(self) -> int: - await super()._observe() - self._rc.news = [ - msg for msg in self._rc.news if msg.send_to == self.profile - 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    One of the main strengths of Electra X2 Vst is its multi-synthesis oscillators, which allow you to combine up to 18 different types of synthesis per voice. You can choose from classic modes like Virtual Analog, FM, Phase Distortion, Ultrasaw, Sync and Waveshaping, as well as innovative modes like Fractal Synthesis, which can mimic the behavior of organic or analog circuits. You can also import your own samples or wavetables and use them as oscillators, or resynthesize them with a single click. Electra X2 Vst comes with a large library of waveforms and wavetables, including analog and digital types, that you can morph and modulate in various ways.

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    Each synth voice in Electra X2 Vst has two multi-mode filters with 23 unique filter types, including analog modeled filters, high precision digital filters, vocal filters, comb filters, phasers, equalizers and more. The filters can self-oscillate and produce a wide range of timbres due to the variable degree of analog behavior. You can also apply distortion to each filter with six different modes, such as tube sound, fuzzbox or waveshaping. The distortion unit can add warmth, grit or character to your sounds.

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    Electra X2 Vst has a user-friendly interface that makes it easy to navigate and tweak the synth parameters. You can access all the features from a single window or use the tabs to focus on specific sections. You can also resize the interface to fit your screen resolution. Electra X2 Vst has a preset management system that gives you instant access to a large library of sounds by professional designers. You can browse the presets by category or use the search function to find what you need. You can also create your own presets and organize them in custom folders.

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    Conclusion

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    Electra X2 Vst Full Version 25 is a powerful and versatile synth plugin that can handle any kind of sound you can imagine. It offers a wide range of synthesis methods, filters, effects and modulation options that you can combine and customize in endless ways. It also has a user-friendly interface and a preset management system that make it easy to use and explore. Whether you are looking for classic analog sounds, modern digital sounds or something completely new and original, Electra X2 Vst Full Version 25 can deliver it.

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    \ No newline at end of file diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/commons.py b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/commons.py deleted file mode 100644 index 9ad0444b61cbadaa388619986c2889c707d873ce..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/commons.py +++ /dev/null @@ -1,161 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d( - length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = ( - math.log(float(max_timescale) / float(min_timescale)) / - (num_timescales - 1)) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2,3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1. / norm_type) - return total_norm diff --git a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/english.py b/spaces/digitalxingtong/Miiu-Bert-Vits2/text/english.py deleted file mode 100644 index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/english.py +++ /dev/null @@ -1,138 +0,0 @@ -import pickle -import os -import re -from g2p_en import G2p -from string import punctuation - -from text import symbols - -current_file_path = os.path.dirname(__file__) -CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep') -CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle') -_g2p = G2p() - -arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'} - - -def post_replace_ph(ph): - rep_map = { - '：': ',', - '；': ',', - '，': ',', - '。': '.', - '！': '!', - '？': '?', - '\n': '.', - "·": ",", - '、': ",", - '...': '…', - 'v': "V" - } - if ph in rep_map.keys(): - ph = rep_map[ph] - if ph in symbols: - return ph - if ph not in symbols: - ph = 'UNK' - return ph - -def read_dict(): - g2p_dict = {} - start_line = 49 - with open(CMU_DICT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= start_line: - line = line.strip() - word_split = line.split(' ') - word = word_split[0] - - syllable_split = word_split[1].split(' - ') - g2p_dict[word] = [] - for syllable in syllable_split: - phone_split = syllable.split(' ') - g2p_dict[word].append(phone_split) - - line_index = line_index + 1 - line = f.readline() - - return g2p_dict - - -def cache_dict(g2p_dict, file_path): - with open(file_path, 'wb') as pickle_file: - pickle.dump(g2p_dict, pickle_file) - - -def get_dict(): - if os.path.exists(CACHE_PATH): - with open(CACHE_PATH, 'rb') as pickle_file: - g2p_dict = pickle.load(pickle_file) - else: - g2p_dict = read_dict() - cache_dict(g2p_dict, CACHE_PATH) - - return g2p_dict - -eng_dict = get_dict() - -def refine_ph(phn): - tone = 0 - if re.search(r'\d$', phn): - tone = int(phn[-1]) + 1 - phn = phn[:-1] - return phn.lower(), tone - -def refine_syllables(syllables): - tones = [] - phonemes = [] - for phn_list in syllables: - for i in range(len(phn_list)): - phn = phn_list[i] - phn, tone = refine_ph(phn) - phonemes.append(phn) - tones.append(tone) - return phonemes, tones - - -def text_normalize(text): - # todo: eng text normalize - return text - -def g2p(text): - - phones = [] - tones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.upper() in eng_dict: - phns, tns = refine_syllables(eng_dict[w.upper()]) - phones += phns - tones += tns - else: - phone_list = list(filter(lambda p: p != " ", _g2p(w))) - for ph in phone_list: - if ph in arpa: - ph, tn = refine_ph(ph) - phones.append(ph) - tones.append(tn) - else: - phones.append(ph) - tones.append(0) - # todo: implement word2ph - word2ph = [1 for i in phones] - - phones = [post_replace_ph(i) for i in phones] - return phones, tones, word2ph - -if __name__ == "__main__": - # print(get_dict()) - # print(eng_word_to_phoneme("hello")) - print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) - # all_phones = set() - # for k, syllables in eng_dict.items(): - # for group in syllables: - # for ph in group: - # all_phones.add(ph) - # print(all_phones) \ No newline at end of file diff --git a/spaces/dineshreddy/WALT/mmdet/core/mask/__init__.py b/spaces/dineshreddy/WALT/mmdet/core/mask/__init__.py deleted file mode 100644 index ab1e88bc686d5c2fe72b3114cb2b3e372e73a0f8..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/core/mask/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .mask_target import mask_target -from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks -from .utils import encode_mask_results, split_combined_polys - -__all__ = [ - 'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks', - 'PolygonMasks', 'encode_mask_results' -] diff --git a/spaces/dorkai/singpt/modules/html_generator.py b/spaces/dorkai/singpt/modules/html_generator.py deleted file mode 100644 index 162040bac68c2e987b33a02ccb12e90b51a63b2d..0000000000000000000000000000000000000000 --- a/spaces/dorkai/singpt/modules/html_generator.py +++ /dev/null @@ -1,357 +0,0 @@ -''' - -This is a library for formatting GPT-4chan and chat outputs as nice HTML. - -''' - -import os -import re -from pathlib import Path - -from PIL import Image - -# This is to store the paths to the thumbnails of the profile pictures -image_cache = {} - -def generate_basic_html(s): - css = """ - .container { - max-width: 600px; - margin-left: auto; - margin-right: auto; - background-color: rgb(31, 41, 55); - padding:3em; - } - .container p { - font-size: 16px !important; - color: white !important; - margin-bottom: 22px; - line-height: 1.4 !important; - } - """ - s = '\n'.join([f'

    {line}

    ' for line in s.split('\n')]) - s = f'
    {s}
    ' - return s - -def process_post(post, c): - t = post.split('\n') - number = t[0].split(' ')[1] - if len(t) > 1: - src = '\n'.join(t[1:]) - else: - src = '' - src = re.sub('>', '>', src) - src = re.sub('(>>[0-9]*)', '\\1', src) - src = re.sub('\n', '
    \n', src) - src = f'
    {src}\n' - src = f'Anonymous No.{number}\n{src}' - return src - -def generate_4chan_html(f): - css = """ - - #parent #container { - background-color: #eef2ff; - padding: 17px; - } - #parent #container .reply { - background-color: rgb(214, 218, 240); - border-bottom-color: rgb(183, 197, 217); - border-bottom-style: solid; - border-bottom-width: 1px; - border-image-outset: 0; - border-image-repeat: stretch; - border-image-slice: 100%; - border-image-source: none; - border-image-width: 1; - border-left-color: rgb(0, 0, 0); - border-left-style: none; - border-left-width: 0px; - border-right-color: rgb(183, 197, 217); - border-right-style: solid; - border-right-width: 1px; - border-top-color: rgb(0, 0, 0); - border-top-style: none; - border-top-width: 0px; - color: rgb(0, 0, 0); - display: table; - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - margin-bottom: 4px; - margin-left: 0px; - margin-right: 0px; - margin-top: 4px; - overflow-x: hidden; - overflow-y: hidden; - padding-bottom: 4px; - padding-left: 2px; - padding-right: 2px; - padding-top: 4px; - } - - #parent #container .number { - color: rgb(0, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - width: 342.65px; - margin-right: 7px; - } - - #parent #container .op { - color: rgb(0, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - margin-bottom: 8px; - margin-left: 0px; - margin-right: 0px; - margin-top: 4px; - overflow-x: hidden; - overflow-y: hidden; - } - - #parent #container .op blockquote { - margin-left: 0px !important; - } - - #parent #container .name { - color: rgb(17, 119, 67); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - font-weight: 700; - margin-left: 7px; - } - - #parent #container .quote { - color: rgb(221, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - text-decoration-color: rgb(221, 0, 0); - text-decoration-line: underline; - text-decoration-style: solid; - text-decoration-thickness: auto; - } - - #parent #container .greentext { - color: rgb(120, 153, 34); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - } - - #parent #container blockquote { - margin: 0px !important; - margin-block-start: 1em; - margin-block-end: 1em; - margin-inline-start: 40px; - margin-inline-end: 40px; - margin-top: 13.33px !important; - margin-bottom: 13.33px !important; - margin-left: 40px !important; - margin-right: 40px !important; - } - - #parent #container .message { - color: black; - border: none; - } - """ - - posts = [] - post = '' - c = -2 - for line in f.splitlines(): - line += "\n" - if line == '-----\n': - continue - elif line.startswith('--- '): - c += 1 - if post != '': - src = process_post(post, c) - posts.append(src) - post = line - else: - post += line - if post != '': - src = process_post(post, c) - posts.append(src) - - for i in range(len(posts)): - if i == 0: - posts[i] = f'
    {posts[i]}
    \n' - else: - posts[i] = f'
    {posts[i]}
    \n' - - output = '' - output += f'
    ' - for post in posts: - output += post - output += '
    ' - output = output.split('\n') - for i in range(len(output)): - output[i] = re.sub(r'^(>(.*?)(
    |))', r'\1', output[i]) - output[i] = re.sub(r'^
    (>(.*?)(
    |))', r'
    \1', output[i]) - output = '\n'.join(output) - - return output - -def get_image_cache(path): - cache_folder = Path("cache") - if not cache_folder.exists(): - cache_folder.mkdir() - - mtime = os.stat(path).st_mtime - if (path in image_cache and mtime != image_cache[path][0]) or (path not in image_cache): - img = Image.open(path) - img.thumbnail((200, 200)) - output_file = Path(f'cache/{path.name}_cache.png') - img.convert('RGB').save(output_file, format='PNG') - image_cache[path] = [mtime, output_file.as_posix()] - - return image_cache[path][1] - -def generate_chat_html(history, name1, name2, character): - css = """ - .chat { - margin-left: auto; - margin-right: auto; - max-width: 800px; - height: 66.67vh; - overflow-y: auto; - padding-right: 20px; - display: flex; - flex-direction: column-reverse; - } - - .message { - display: grid; - grid-template-columns: 60px 1fr; - padding-bottom: 25px; - font-size: 15px; - font-family: Helvetica, Arial, sans-serif; - line-height: 1.428571429; - } - - .circle-you { - width: 50px; - height: 50px; - background-color: rgb(238, 78, 59); - border-radius: 50%; - } - - .circle-bot { - width: 50px; - height: 50px; - background-color: rgb(59, 78, 244); - border-radius: 50%; - } - - .circle-bot img, .circle-you img { - border-radius: 50%; - width: 100%; - height: 100%; - object-fit: cover; - } - - .text { - } - - .text p { - margin-top: 5px; - } - - .username { - font-weight: bold; - } - - .message-body { - } - - .message-body img { - max-width: 300px; - max-height: 300px; - border-radius: 20px; - } - - .message-body p { - margin-bottom: 0 !important; - font-size: 15px !important; - line-height: 1.428571429 !important; - } - - .dark .message-body p em { - color: rgb(138, 138, 138) !important; - } - - .message-body p em { - color: rgb(110, 110, 110) !important; - } - - """ - - output = '' - output += f'
    ' - img = '' - - for i in [ - f"characters/{character}.png", - f"characters/{character}.jpg", - f"characters/{character}.jpeg", - "img_bot.png", - "img_bot.jpg", - "img_bot.jpeg" - ]: - - path = Path(i) - if path.exists(): - img = f'' - break - - img_me = '' - for i in ["img_me.png", "img_me.jpg", "img_me.jpeg"]: - path = Path(i) - if path.exists(): - img_me = f'' - break - - for i,_row in enumerate(history[::-1]): - row = _row.copy() - row[0] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[0]) - row[1] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[1]) - row[0] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[0]) - row[1] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[1]) - p = '\n'.join([f"

    {x}

    " for x in row[1].split('\n')]) - output += f""" -
    -
    - {img} -
    -
    -
    - {name2} -
    -
    - {p} -
    -
    -
    - """ - - if not (i == len(history)-1 and len(row[0]) == 0): - p = '\n'.join([f"

    {x}

    " for x in row[0].split('\n')]) - output += f""" -
    -
    - {img_me} -
    -
    -
    - {name1} -
    -
    - {p} -
    -
    -
    - """ - - output += "
    " - return output diff --git a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/extensions/sd_api_pictures/script.py b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/extensions/sd_api_pictures/script.py deleted file mode 100644 index 1189a593f775f814731c04afaa3b73bbb0cb1ec4..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/extensions/sd_api_pictures/script.py +++ /dev/null @@ -1,321 +0,0 @@ -import base64 -import io -import re -import time -from datetime import date -from pathlib import Path - -import gradio as gr -import modules.shared as shared -import requests -import torch -from modules.models import reload_model, unload_model -from PIL import Image - -torch._C._jit_set_profiling_mode(False) - -# parameters which can be customized in settings.json of webui -params = { - 'address': 'http://127.0.0.1:7860', - 'mode': 0, # modes of operation: 0 (Manual only), 1 (Immersive/Interactive - looks for words to trigger), 2 (Picturebook Adventure - Always on) - 'manage_VRAM': False, - 'save_img': False, - 'SD_model': 'NeverEndingDream', # not used right now - 'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful', - 'negative_prompt': '(worst quality, low quality:1.3)', - 'width': 512, - 'height': 512, - 'denoising_strength': 0.61, - 'restore_faces': False, - 'enable_hr': False, - 'hr_upscaler': 'ESRGAN_4x', - 'hr_scale': '1.0', - 'seed': -1, - 'sampler_name': 'DDIM', - 'steps': 32, - 'cfg_scale': 7 -} - - -def give_VRAM_priority(actor): - global shared, params - - if actor == 'SD': - unload_model() - print("Requesting Auto1111 to re-load last checkpoint used...") - response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='') - response.raise_for_status() - - elif actor == 'LLM': - print("Requesting Auto1111 to vacate VRAM...") - response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='') - response.raise_for_status() - reload_model() - - elif actor == 'set': - print("VRAM mangement activated -- requesting Auto1111 to vacate VRAM...") - response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='') - response.raise_for_status() - - elif actor == 'reset': - print("VRAM mangement deactivated -- requesting Auto1111 to reload checkpoint") - response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='') - response.raise_for_status() - - else: - raise RuntimeError(f'Managing VRAM: "{actor}" is not a known state!') - - response.raise_for_status() - del response - - -if params['manage_VRAM']: - give_VRAM_priority('set') - -samplers = ['DDIM', 'DPM++ 2M Karras'] # TODO: get the availible samplers with http://{address}}/sdapi/v1/samplers -SD_models = ['NeverEndingDream'] # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select - -streaming_state = shared.args.no_stream # remember if chat streaming was enabled -picture_response = False # specifies if the next model response should appear as a picture - -def remove_surrounded_chars(string): - # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR - # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string' - return re.sub('\*[^\*]*?(\*|$)', '', string) - - -def triggers_are_in(string): - string = remove_surrounded_chars(string) - # regex searches for send|main|message|me (at the end of the word) followed by - # a whole word of image|pic|picture|photo|snap|snapshot|selfie|meme(s), - # (?aims) are regex parser flags - return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string)) - - -def input_modifier(string): - """ - This function is applied to your text inputs before - they are fed into the model. - """ - - global params - - if not params['mode'] == 1: # if not in immersive/interactive mode, do nothing - return string - - if triggers_are_in(string): # if we're in it, check for trigger words - toggle_generation(True) - string = string.lower() - if "of" in string: - subject = string.split('of', 1)[1] # subdivide the string once by the first 'of' instance and get what's coming after it - string = "Please provide a detailed and vivid description of " + subject - else: - string = "Please provide a detailed description of your appearance, your surroundings and what you are doing right now" - - return string - -# Get and save the Stable Diffusion-generated picture -def get_SD_pictures(description): - - global params - - if params['manage_VRAM']: - give_VRAM_priority('SD') - - payload = { - "prompt": params['prompt_prefix'] + description, - "seed": params['seed'], - "sampler_name": params['sampler_name'], - "enable_hr": params['enable_hr'], - "hr_scale": params['hr_scale'], - "hr_upscaler": params['hr_upscaler'], - "denoising_strength": params['denoising_strength'], - "steps": params['steps'], - "cfg_scale": params['cfg_scale'], - "width": params['width'], - "height": params['height'], - "restore_faces": params['restore_faces'], - "override_settings_restore_afterwards": True, - "negative_prompt": params['negative_prompt'] - } - - print(f'Prompting the image generator via the API on {params["address"]}...') - response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload) - response.raise_for_status() - r = response.json() - - visible_result = "" - for img_str in r['images']: - if params['save_img']: - img_data = base64.b64decode(img_str) - - variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}' - output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png') - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file.as_posix(), 'wb') as f: - f.write(img_data) - - visible_result = visible_result + f'{description}\n' - else: - image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0]))) - # lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history - image.thumbnail((300, 300)) - buffered = io.BytesIO() - image.save(buffered, format="JPEG") - buffered.seek(0) - image_bytes = buffered.getvalue() - img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode() - visible_result = visible_result + f'{description}\n' - - if params['manage_VRAM']: - give_VRAM_priority('LLM') - - return visible_result - -# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history) -# and replace it with 'text' for the purposes of logging? -def output_modifier(string): - """ - This function is applied to the model outputs. - """ - - global picture_response, params - - if not picture_response: - return string - - string = remove_surrounded_chars(string) - string = string.replace('"', '') - string = string.replace('“', '') - string = string.replace('\n', ' ') - string = string.strip() - - if string == '': - string = 'no viable description in reply, try regenerating' - return string - - text = "" - if (params['mode'] < 2): - toggle_generation(False) - text = f'*Sends a picture which portrays: “{string}”*' - else: - text = string - - string = get_SD_pictures(string) + "\n" + text - - return string - - -def bot_prefix_modifier(string): - """ - This function is only applied in chat mode. It modifies - the prefix text for the Bot and can be used to bias its - behavior. - """ - - return string - - -def toggle_generation(*args): - global picture_response, shared, streaming_state - - if not args: - picture_response = not picture_response - else: - picture_response = args[0] - - shared.args.no_stream = True if picture_response else streaming_state # Disable streaming cause otherwise the SD-generated picture would return as a dud - shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*" - - -def filter_address(address): - address = address.strip() - # address = re.sub('http(s)?:\/\/|\/$','',address) # remove starting http:// OR https:// OR trailing slash - address = re.sub('\/$', '', address) # remove trailing /s - if not address.startswith('http'): - address = 'http://' + address - return address - - -def SD_api_address_update(address): - - global params - - msg = "✔️ SD API is found on:" - address = filter_address(address) - params.update({"address": address}) - try: - response = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models') - response.raise_for_status() - # r = response.json() - except: - msg = "❌ No SD API endpoint on:" - - return gr.Textbox.update(label=msg) - -def ui(): - - # Gradio elements - # gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title - with gr.Accordion("Parameters", open=True, elem_classes="SDAP"): - with gr.Row(): - address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address') - modes_list = ["Manual", "Immersive/Interactive", "Picturebook/Adventure"] - mode = gr.Dropdown(modes_list, value=modes_list[params['mode']], label="Mode of operation", type="index") - with gr.Column(scale=1, min_width=300): - manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM') - save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat') - - force_pic = gr.Button("Force the picture response") - suppr_pic = gr.Button("Suppress the picture response") - - with gr.Accordion("Generation parameters", open=False): - prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)') - negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt') - with gr.Row(): - with gr.Column(): - width = gr.Slider(256, 768, value=params['width'], step=64, label='Width') - height = gr.Slider(256, 768, value=params['height'], step=64, label='Height') - with gr.Column(): - sampler_name = gr.Textbox(placeholder=params['sampler_name'], value=params['sampler_name'], label='Sampling method', elem_id="sampler_box") - steps = gr.Slider(1, 150, value=params['steps'], step=1, label="Sampling steps") - with gr.Row(): - seed = gr.Number(label="Seed", value=params['seed'], elem_id="seed_box") - cfg_scale = gr.Number(label="CFG Scale", value=params['cfg_scale'], elem_id="cfg_box") - with gr.Column() as hr_options: - restore_faces = gr.Checkbox(value=params['restore_faces'], label='Restore faces') - enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix') - with gr.Row(visible=params['enable_hr'], elem_classes="hires_opts") as hr_options: - hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by') - denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength') - hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler') - - - # Event functions to update the parameters in the backend - address.change(lambda x: params.update({"address": filter_address(x)}), address, None) - mode.select(lambda x: params.update({"mode": x}), mode, None) - mode.select(lambda x: toggle_generation(x > 1), inputs=mode, outputs=None) - manage_VRAM.change(lambda x: params.update({"manage_VRAM": x}), manage_VRAM, None) - manage_VRAM.change(lambda x: give_VRAM_priority('set' if x else 'reset'), inputs=manage_VRAM, outputs=None) - save_img.change(lambda x: params.update({"save_img": x}), save_img, None) - - address.submit(fn=SD_api_address_update, inputs=address, outputs=address) - prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None) - negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None) - width.change(lambda x: params.update({"width": x}), width, None) - height.change(lambda x: params.update({"height": x}), height, None) - hr_scale.change(lambda x: params.update({"hr_scale": x}), hr_scale, None) - denoising_strength.change(lambda x: params.update({"denoising_strength": x}), denoising_strength, None) - restore_faces.change(lambda x: params.update({"restore_faces": x}), restore_faces, None) - hr_upscaler.change(lambda x: params.update({"hr_upscaler": x}), hr_upscaler, None) - enable_hr.change(lambda x: params.update({"enable_hr": x}), enable_hr, None) - enable_hr.change(lambda x: hr_options.update(visible=params["enable_hr"]), enable_hr, hr_options) - - sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None) - steps.change(lambda x: params.update({"steps": x}), steps, None) - seed.change(lambda x: params.update({"seed": x}), seed, None) - cfg_scale.change(lambda x: params.update({"cfg_scale": x}), cfg_scale, None) - - force_pic.click(lambda x: toggle_generation(True), inputs=force_pic, outputs=None) - suppr_pic.click(lambda x: toggle_generation(False), inputs=suppr_pic, outputs=None) diff --git a/spaces/erbanku/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.h b/spaces/erbanku/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.h deleted file mode 100644 index a46c805ab80aab491f7f9508b3a008b149866bee..0000000000000000000000000000000000000000 --- a/spaces/erbanku/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpge.h +++ /dev/null @@ -1,172 +0,0 @@ - -// jpge.h - C++ class for JPEG compression. -// Public domain, Rich Geldreich -// Alex Evans: Added RGBA support, linear memory allocator. -#ifndef JPEG_ENCODER_H -#define JPEG_ENCODER_H - -#include - -namespace jpge -{ - typedef unsigned char uint8; - typedef signed short int16; - typedef signed int int32; - typedef unsigned short uint16; - typedef unsigned int uint32; - typedef unsigned int uint; - - // JPEG chroma subsampling factors. Y_ONLY (grayscale images) and H2V2 (color images) are the most common. - enum subsampling_t { Y_ONLY = 0, H1V1 = 1, H2V1 = 2, H2V2 = 3 }; - - // JPEG compression parameters structure. - struct params - { - inline params() : m_quality(85), m_subsampling(H2V2), m_no_chroma_discrim_flag(false), m_two_pass_flag(false) { } - - inline bool check_valid() const - { - if ((m_quality < 1) || (m_quality > 100)) return false; - if ((uint)m_subsampling > (uint)H2V2) return false; - return true; - } - - // Quality: 1-100, higher is better. Typical values are around 50-95. - int m_quality; - - // m_subsampling: - // 0 = Y (grayscale) only - // 1 = YCbCr, no subsampling (H1V1, YCbCr 1x1x1, 3 blocks per MCU) - // 2 = YCbCr, H2V1 subsampling (YCbCr 2x1x1, 4 blocks per MCU) - // 3 = YCbCr, H2V2 subsampling (YCbCr 4x1x1, 6 blocks per MCU-- very common) - subsampling_t m_subsampling; - - // Disables CbCr discrimination - only intended for testing. - // If true, the Y quantization table is also used for the CbCr channels. - bool m_no_chroma_discrim_flag; - - bool m_two_pass_flag; - }; - - // Writes JPEG image to a file. - // num_channels must be 1 (Y) or 3 (RGB), image pitch must be width*num_channels. - 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 = params()); - - // Writes JPEG image to memory buffer. - // On entry, buf_size is the size of the output buffer pointed at by pBuf, which should be at least ~1024 bytes. - // If return value is true, buf_size will be set to the size of the compressed data. - bool compress_image_to_jpeg_file_in_memory(void *pBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params()); - - // Output stream abstract class - used by the jpeg_encoder class to write to the output stream. - // put_buf() is generally called with len==JPGE_OUT_BUF_SIZE bytes, but for headers it'll be called with smaller amounts. - class output_stream - { - public: - virtual ~output_stream() { }; - virtual bool put_buf(const void* Pbuf, int64_t len) = 0; - template inline bool put_obj(const T& obj) { return put_buf(&obj, sizeof(T)); } - }; - - // Lower level jpeg_encoder class - useful if more control is needed than the above helper functions. - class jpeg_encoder - { - public: - jpeg_encoder(); - ~jpeg_encoder(); - - // Initializes the compressor. - // pStream: The stream object to use for writing compressed data. - // params - Compression parameters structure, defined above. - // width, height - Image dimensions. - // channels - May be 1, or 3. 1 indicates grayscale, 3 indicates RGB source data. - // Returns false on out of memory or if a stream write fails. - bool init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params = params()); - - const params &get_params() const { return m_params; } - - // Deinitializes the compressor, freeing any allocated memory. May be called at any time. - void deinit(); - - uint get_total_passes() const { return m_params.m_two_pass_flag ? 2 : 1; } - inline uint get_cur_pass() { return m_pass_num; } - - // Call this method with each source scanline. - // width * src_channels bytes per scanline is expected (RGB or Y format). - // You must call with NULL after all scanlines are processed to finish compression. - // Returns false on out of memory or if a stream write fails. - bool process_scanline(const void* pScanline); - - private: - jpeg_encoder(const jpeg_encoder &); - jpeg_encoder &operator =(const jpeg_encoder &); - - typedef int32 sample_array_t; - - output_stream *m_pStream; - params m_params; - uint8 m_num_components; - uint8 m_comp_h_samp[3], m_comp_v_samp[3]; - int m_image_x, m_image_y, m_image_bpp, m_image_bpl; - int m_image_x_mcu, m_image_y_mcu; - int m_image_bpl_xlt, m_image_bpl_mcu; - int m_mcus_per_row; - int m_mcu_x, m_mcu_y; - uint8 *m_mcu_lines[16]; - uint8 m_mcu_y_ofs; - sample_array_t m_sample_array[64]; - int16 m_coefficient_array[64]; - int32 m_quantization_tables[2][64]; - uint m_huff_codes[4][256]; - uint8 m_huff_code_sizes[4][256]; - uint8 m_huff_bits[4][17]; - uint8 m_huff_val[4][256]; - uint32 m_huff_count[4][256]; - int m_last_dc_val[3]; - enum { JPGE_OUT_BUF_SIZE = 2048 }; - uint8 m_out_buf[JPGE_OUT_BUF_SIZE]; - uint8 *m_pOut_buf; - uint m_out_buf_left; - uint32 m_bit_buffer; - uint m_bits_in; - uint8 m_pass_num; - bool m_all_stream_writes_succeeded; - - void optimize_huffman_table(int table_num, int table_len); - void emit_byte(uint8 i); - void emit_word(uint i); - void emit_marker(int marker); - void emit_jfif_app0(); - void emit_dqt(); - void emit_sof(); - void emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag); - void emit_dhts(); - void emit_sos(); - void emit_markers(); - void compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val); - void compute_quant_table(int32 *dst, int16 *src); - void adjust_quant_table(int32 *dst, int32 *src); - void first_pass_init(); - bool second_pass_init(); - bool jpg_open(int p_x_res, int p_y_res, int src_channels); - void load_block_8_8_grey(int x); - void load_block_8_8(int x, int y, int c); - void load_block_16_8(int x, int c); - void load_block_16_8_8(int x, int c); - void load_quantized_coefficients(int component_num); - void flush_output_buffer(); - void put_bits(uint bits, uint len); - void code_coefficients_pass_one(int component_num); - void code_coefficients_pass_two(int component_num); - void code_block(int component_num); - void process_mcu_row(); - bool terminate_pass_one(); - bool terminate_pass_two(); - bool process_end_of_image(); - void load_mcu(const void* src); - void clear(); - void init(); - }; - -} // namespace jpge - -#endif // JPEG_ENCODER \ No newline at end of file diff --git "a/spaces/erbanku/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py" "b/spaces/erbanku/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py" deleted file mode 100644 index 505086455af8d2676055ab084cf97058b954c7d5..0000000000000000000000000000000000000000 --- "a/spaces/erbanku/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py" +++ /dev/null @@ -1,112 +0,0 @@ -from toolbox import update_ui -from toolbox import CatchException, report_execption -from .crazy_utils import read_and_clean_pdf_text -from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive -fast_debug = False - - -def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): - import tiktoken - print('begin analysis on:', file_name) - - ############################## <第 0 步，切割PDF> ################################## - # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割） - # 的长度必须小于 2500 个 Token - file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF - - TOKEN_LIMIT_PER_FRAGMENT = 2500 - - from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf - from request_llm.bridge_all import model_info - enc = model_info["gpt-3.5-turbo"]['tokenizer'] - def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) - paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( - txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT) - page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( - txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4) - # 为了更好的效果，我们剥离Introduction之后的部分（如果有） - paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0] - - ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ################################## - final_results = [] - final_results.append(paper_meta) - - ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ################################## - i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。" # 用户提示 - chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI - - iteration_results = [] - last_iteration_result = paper_meta # 初始值是摘要 - MAX_WORD_TOTAL = 4096 - n_fragment = len(paper_fragments) - if n_fragment >= 20: print('文章极长，不能达到预期效果') - for i in range(n_fragment): - NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment - i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}" - i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}" - gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问 - llm_kwargs, chatbot, - history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果 - sys_prompt="Extract the main idea of this section." # 提示 - ) - iteration_results.append(gpt_say) - last_iteration_result = gpt_say - - ############################## <第 3 步，整理history> ################################## - final_results.extend(iteration_results) - final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。') - # 接下来两句话只显示在界面上，不起实际作用 - i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。" - chatbot.append([i_say_show_user, gpt_say]) - - ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ################################## - from .crazy_utils import input_clipping - _, final_results = input_clipping("", final_results, max_token_limit=3200) - yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了 - - -@CatchException -def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - import glob, os - - # 基本信息：功能、贡献者 - chatbot.append([ - "函数插件功能？", - "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - # 尝试导入依赖，如果缺少依赖，则给出安装建议 - try: - import fitz - except: - report_execption(chatbot, history, - a = f"解析项目: {txt}", - b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - - # 清空历史，以免输入溢出 - history = [] - - # 检测输入参数，如没有给定输入参数，直接退出 - if os.path.exists(txt): - project_folder = txt - else: - if txt == "": - txt = '空空如也的输入栏' - report_execption(chatbot, history, - a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - - # 搜索需要处理的文件清单 - file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] - # 如果没找到任何文件 - if len(file_manifest) == 0: - report_execption(chatbot, history, - a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - txt = file_manifest[0] - # 开始正式执行任务 - yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) diff --git a/spaces/exbert-project/exbert/client/src/css/base.css b/spaces/exbert-project/exbert/client/src/css/base.css deleted file mode 100644 index 84249ec9c0fa1cdbbdb41baac07865d053866f08..0000000000000000000000000000000000000000 --- a/spaces/exbert-project/exbert/client/src/css/base.css +++ /dev/null @@ -1,159 +0,0 @@ -body { - background-color: white; - font-family: 'IBM Plex Sans', sans-serif; - font-weight: 400; -} - -.sticky { - position: fixed; -} - -.noscroll { - overflow: hidden; -} - -.vpartial { - max-height: 90vh; -} - -.scrolling { - overflow: auto; - max-height: 98%; -} - -.btn .btn-xs { - padding: .25rem .4rem; - font-size: .875rem; - line-height: .5; - border-radius: .2rem; -} - -button { - -webkit-transition-duration: 0.4s; - /* Safari */ - transition-duration: 0.4s; - background: transparent; - padding: 5px; - border-radius: 5px; - background-color: lightgray; -} - -button.selected { - background-color: #98b7d9; -} - -button:active :focus { - background-color: #98b7d9; -} - -#loader { - border: 5px solid #f3f3f3; - border-radius: 50%; - border-top: 5px solid #3498db; - width: 100px; - height: 100px; - -webkit-animation: spin 2s linear infinite; - /* Safari */ - animation: spin 2s linear infinite; - position: absolute; - left: 50%; - top: 20%; - display: none; -} - -/* Safari */ -@-webkit-keyframes spin { - 0% { - -webkit-transform: rotate(0deg); 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    diff --git a/spaces/fatiXbelha/sd/Brawl Stars 49.194 APK The Ultimate Guide to the New Update.md b/spaces/fatiXbelha/sd/Brawl Stars 49.194 APK The Ultimate Guide to the New Update.md deleted file mode 100644 index 49483a35a0038a2c8bfbcd3b1537f61d9609926c..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Brawl Stars 49.194 APK The Ultimate Guide to the New Update.md +++ /dev/null @@ -1,125 +0,0 @@ - -

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    Brawl Stars Game Features: Brawlers, Game Modes, Events and More

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    Brawl Stars is a game that offers a lot of variety and content for players to enjoy. Here are some of the main features of the game:

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    Brawlers

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    Brawlers are the characters that you can play as in Brawl Stars. There are currently 68 Brawlers in the game, each with their own personality, appearance, class, rarity, attack, super ability, star power, and gadget. You can unlock new Brawlers by earning trophies, opening brawl boxes, buying them in the shop, or participating in special events.

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    Brawl Stars has several game modes that you can play, each with its own rules and objectives. You can play these game modes in friendly or ranked matches, or in special events. Here are some of the game modes available:

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    • Gem Grab: The classic 3v3 mode where you need to collect and hold 10 gems for 15 seconds to win.
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    • Power Play: A competitive mode where you can use your maxed-out Brawlers and earn points based on your performance.
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    • Championship Challenge: A monthly tournament where you can compete with other players and qualify for the Brawl Stars World Finals.
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    • Brawl Pass: A seasonal pass that gives you access to exclusive rewards, such as Brawlers, skins, coins, gems, boxes, and more.
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    • Special Events: Limited-time events that feature unique game modes, such as Boss Fight, Robo Rumble, Big Game, Super City Rampage, and more.
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    Brawl Stars Game Tips: How to Win More Matches and Unlock More Rewards

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    Brawl Stars is a game that requires skill, strategy, teamwork, and luck. Here are some tips and tricks to help you improve your game and have more fun:

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    Choose the Right Brawler for the Right Mode

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    As we mentioned earlier, each Brawler has its own class, rarity, attack, super ability, star power, and gadget. These factors affect how they perform in different game modes and maps. For example, some Brawlers are better at close-range combat, while others are better at long-range combat. Some Brawlers are better at dealing damage, while others are better at supporting or healing. Some Brawlers are better at controlling zones, while others are better at breaking walls or stealing gems.

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    Therefore, you need to choose the right Brawler for the right mode based on their strengths and weaknesses. You can also check the recommended Brawlers for each mode in the game or watch some videos from pro players or streamers to learn from them.

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    Upgrade Your Brawlers Wisely

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    As you play the game, you will earn coins, power points, star points, and gems. You can use these resources to upgrade your Brawlers and unlock their star powers and gadgets. Upgrading your Brawlers will increase their health, damage, and super charge rate. Unlocking their star powers and gadgets will give them extra abilities that can change the tide of the battle.

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    However, you need to upgrade your Brawlers wisely because the resources are limited and not easy to come by. You should prioritize upgrading your favorite or most used Brawlers first. You should also save up your gems for buying brawl passes or special offers instead of spending them on boxes or skins.

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    Communicate and Coordinate with Your Teammates

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    Brawl Stars is a team-based game that requires communication and coordination with your teammates. You can use the in-game chat or voice chat to communicate with your friends or random players. You can also use the quick chat buttons or pins to express yourself or give commands.

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    You should communicate and coordinate with your teammates to plan your strategy, share information, call for help, or warn about dangers. You should also support your teammates by healing them, protecting them, or assisting them in attacking or defending. You should also respect your teammates by not spamming, trolling, or quitting mid-game.

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    Conclusion: Summary and Call to Action

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    Brawl Stars is a game that offers a lot of fun and excitement for mobile gamers. It is a game that you can play with your friends or solo, with various modes and characters to choose from. It is a game that you can download for free on your Android device, with the latest version being Brawl Stars apk indir 49.194. It is a game that you can improve your skills and strategies by following some tips and tricks.

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    FAQs: Five Common Questions and Answers About Brawl Stars

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    Q: Is Brawl Stars free to play?

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    A: Yes, Brawl Stars is free to play, but it also has some optional in-app purchases that can enhance your gaming experience.

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    Q: Is Brawl Stars compatible with my device?

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    A: Brawl Stars requires Android 4.3 or higher to run. You can check your device's compatibility by visiting the Google Play Store page of the game.

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    Q: How can I play Brawl Stars with my friends?

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    A: You can play Brawl Stars with your friends by creating or joining a club, inviting them to your team, or using the friend code feature.

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    Q: How can I get more Brawlers, skins, coins, gems, boxes, and other rewards?

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    A: You can get more rewards by playing the game, earning trophies, opening brawl boxes, buying brawl passes, completing quests, participating in events, and watching ads.

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    Q: Where can I find more information and support about Brawl Stars?

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    A: You can find more information and support about Brawl Stars by visiting the official website, social media pages, YouTube channel, Reddit community, Discord server, or customer service of the game.

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    • eFootball mode: compete in online tournaments and matches

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      This mode allows you to participate in various online competitions and events, such as the eFootball Open, the eFootball League, and the eFootball Season Program. You can play solo or with your friends, and earn rewards and rankings based on your performance. You can also join or create your own custom matches and leagues with your own rules and settings.

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    • Matchday mode: support your favorite team and earn rewards

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      This mode lets you experience the thrill and excitement of real-life soccer matches. You can choose a side to support from among the teams that are featured in the weekly events, and play matches against other players who support the opposing side. You can earn points for your side by winning matches, and contribute to the overall score of your team. The more points you earn, the higher your chances of getting rewards, such as coins, scouts, and players.

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    • MyClub mode: build your dream team and challenge other players

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      This mode is where you can create your own ultimate soccer team with players from different clubs and countries. You can scout, sign, train, and manage your players, and customize your team's formation, tactics, kits, and emblem. You can also play matches against other players' teams online or offline, and earn rewards and rankings based on your results. You can also join or create your own clans with other players, and cooperate or compete with them in various events and challenges.

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    PES 2021 has a rich roster of players and clubs to choose from

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    Another great thing about PES 2021 is that it has a huge selection of players and clubs to play with, from different leagues and regions. Here are some of the players and clubs that you can find in PES 2021:

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    • Exclusive partnerships with FC Barcelona, Manchester United, Juventus, and AS Roma

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      PES 2021 has secured exclusive rights to feature some of the most famous and prestigious clubs in the world, such as FC Barcelona, Manchester United, Juventus, and AS Roma. This means that you can enjoy playing with their official kits, logos, stadiums, and players, such as Lionel Messi, Cristiano Ronaldo, Paul Pogba, and Edin Dzeko. You can also access exclusive content and events related to these clubs in PES 2021.

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    • Iconic Moment Series players: relive and recreate memorable moments from soccer legends

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      PES 2021 has a special feature that allows you to collect and play with some of the most iconic players in soccer history, such as Diego Maradona, David Beckham, Zinedine Zidane, and more. These players are based on their performances in specific matches or seasons that made them famous. You can also relive and recreate some of their memorable moments in PES 2021 with special animations and effects.

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    • Featured Players: get special versions of players who performed well in real matches

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      PES 2021 also has a feature that updates the ratings and skills of some of the players who performed well in real-life matches every week. These players are called Featured Players, and they have higher stats and abilities than their regular versions. You can scout or sign these players in MyClub mode, and use them to boost your team's performance.

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    PES 2021 has a stunning graphics and sound quality to enhance your experience

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    The last but not least thing about PES 2021 is that it has a superb graphics and sound quality that will make you feel like you are watching or playing a real soccer match. Here are some of the graphics and sound features that PES 2021 has:

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    • Console-quality gameplay with smooth animations and realistic physics

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      PES 2021 uses the Unreal Engine 4 to deliver a console-quality gameplay experience on your mobile device. The game has smooth animations and realistic physics that capture the movements and actions of the players on the pitch. You can also enjoy various camera angles and perspectives that suit your preference.

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      PES 2021 also uses live data from real-life matches and player conditions to update the game's content every week. This means that you can see the latest stats,

      PES 2021 also uses live data from real-life matches and player conditions to update the game's content every week. This means that you can see the latest stats, ratings, skills, and appearances of the players and teams in the game. You can also experience the changes in the weather, pitch, and atmosphere of the stadiums as they happen in real life.

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    • Immersive sound effects and commentary with different languages

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      PES 2021 also has a high-quality sound system that enhances your immersion in the game. You can hear the realistic sound effects of the ball, the players, the crowd, and the referee. You can also listen to the commentary from professional commentators in different languages, such as English, Spanish, French, German, Italian, Japanese, and more.

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    Now that you know what PES 2021 is and what it offers, you might be wondering how to download it on your Android device. Well, it's not that hard. All you need to do is follow these simple steps:

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    • Enable the installation of apps from unknown sources in your device settings

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      Since you are downloading the APK file from a source other than Google Play Store, you will need to enable the installation of apps from unknown sources in your device settings. This will allow you to install apps that are not available on Google Play Store. To do this, go to your device settings, then security or privacy, then enable unknown sources or allow installation from unknown sources.

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    Install the APK file on your device

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    The next thing you need to do is to install the APK file on your device. This is also very easy. Just follow these steps:

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    • Follow the instructions on the screen and wait for the installation to complete

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      The installation process will take a few minutes. You will see a progress bar that shows how much time is left until the installation is done. Just wait patiently and don't interrupt or cancel the process. When the installation is complete, you will see a message that says "App installed" or "PES 2021 installed". Tap on "Open" or "Done" to launch or exit the app.

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    • Launch the app and enjoy playing PES 2021 on your Android device

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      Congratulations! You have successfully installed PES 2021 on your Android device. Now you can launch

      Congratulations! You have successfully installed PES 2021 on your Android device. Now you can launch the app and enjoy playing the game. You will need to download some additional data files before you can start playing, so make sure you have enough storage space and a stable internet connection. You will also need to create or log in to your Konami ID account to access some of the features and modes of the game. You can also link your Google Play Games account to save your progress and achievements.

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      Conclusion

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      PES 2021 is a free-to-play soccer simulation game that offers realistic gameplay, official licenses, iconic moments, and more. It is compatible with Android devices and can be played online or offline. It has a variety of modes and events to enjoy, such as eFootball mode, Matchday mode, MyClub mode, and more. It has a rich roster of players and clubs to choose from, such as FC Barcelona, Manchester United, Juventus, and AS Roma. It has a stunning graphics and sound quality to enhance your experience.

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      If you want to download PES 2021 APK for Android, you can follow the steps we have provided in this article. You will need to download the APK file from a trusted source, such as the official website or FileHippo. You will also need to enable the installation of apps from unknown sources in your device settings. Then, you will need to install the APK file on your device and launch the app. You will also need to download some additional data files and create or log in to your Konami ID account.

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      \ No newline at end of file diff --git a/spaces/fazzam/Grainsight2/app.py b/spaces/fazzam/Grainsight2/app.py deleted file mode 100644 index f6659a466b71b26ab1fb25dc5b4fac618ebe0f4a..0000000000000000000000000000000000000000 --- a/spaces/fazzam/Grainsight2/app.py +++ /dev/null @@ -1,633 +0,0 @@ -import streamlit as st -from PIL import Image -from ultralytics import YOLO -import torch -import numpy as np -import cv2 -import matplotlib.pyplot as plt -import math -import pandas as pd -import seaborn as sns -from streamlit_drawable_canvas import st_canvas -from tools import format_results, box_prompt, point_prompt, text_prompt -import csv -import io -import base64 - - -# Sets the device to GPU if available, otherwise MPS if available, otherwise CPU. -# Loads the YOLO model from the FastSAM-x.pt file. -# Initializes an empty list to store annotations. -device = torch.device( - "cuda" if torch.cuda.is_available() - else "mps" if torch.backends.mps.is_available() - else "cpu" - ) -model = YOLO('FastSAM-x.pt') - -annotations = [] - - -def streamlit_ui(): - '''Creates the Streamlit UI with title, image uploader, sliders, checkbox, - and inputs to configure parameters for grain segmentation and visualization. - Returns user-specified parameters.''' - - st.title("Segment grains using Fast SAM 🤗") - - # Add some intro text" - st.write("This app segments and analyzes grains in any type of images using FastSAM. Upload an image to get started!") - - # Upload image - uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg", "Tif"]) - - # Input size slider - input_size = st.slider("Input Size", 512, 1024, 1024, 64, - help="Size of the input image. Higher values may improve detection but will be slower.") - - # IOU threshold slider - iou_threshold = st.slider("IOU Threshold", 0.0, 0.9, 0.7, 0.1, - help="Intersection over Union threshold for object detection. Higher values reduce false positives.") - - # Confidence threshold slider - conf_threshold = st.slider("Confidence Threshold", 0.0, 0.9, 0.5, 0.05, - help="Minimum confidence level for detected objects. Lower values may detect more objects but increase false positives.") - - # Better visual quality checkbox - better_quality = st.checkbox("Better Visual Quality", True, - help="Check to improve the visual quality of the segmentation. May be slower.") - - # Contour thickness slider - contour_thickness = st.slider("Contour Thickness", 1, 50, 1, - help="Thickness of the contour lines around detected objects.") - - # Real-world length input - real_world_length = st.number_input("Enter the real-world length in micrometers:", min_value=1, value=100, - help="Length of the reference line in the real world, used for scaling object parameters.") - - # Add some explanation of the outputs - st.write("The app will display the segmented image with contours around detected grains. It will also show measurements for each grain.") - - return uploaded_image, input_size, iou_threshold, conf_threshold, better_quality, contour_thickness, real_world_length - - - -def calculate_pixel_length(start_point, end_point): - - """Calculates the pixel length between two points given as (x, y) coordinate tuples. - Uses the Pythagorean theorem to find the straight line distance between the points.""" - - x1, y1 = start_point - x2, y2 = end_point - return math.sqrt((x2 - x1)**2 + (y2 - y1)**2) - - - -def drawable_canvas(uploaded_image): - - """ - Draws an interactive canvas for the user to draw a line to set image scale. - Takes in the uploaded image and displays it as the background. - Creates a Streamlit canvas component for the user to draw a line on. - Returns the canvas component containing the user's line. - """ - st.write("Draw a line to set the scale:") - - # Load image as PIL image - background_image = Image.open(uploaded_image) - - # Create canvas component with same dimensions as uploaded image - canvas_result = st_canvas( - fill_color="rgba(255, 165, 0, 0.3)", - stroke_width=10, - stroke_color="#e00", - background_image=background_image, - width=800, - height=800, - drawing_mode="line", - key="canvas", - ) - - st.write("Draw a line on the image representing a known real-world length. This will be used to calculate the image scale.") - - return canvas_result - - -def fast_process(annotations, - image, - device, - scale, - better_quality=False, - mask_random_color=True, - bbox=None, - use_retina=True, - withContours=True, - contour_thickness=2 -): - ''' - fast_process processes the input annotations and image. It handles converting between PyTorch and NumPy formats, - applying morphological operations to smooth masks, rendering masks on the image, finding and approximating contours, - drawing contours back on the image with thickness, and compositing the final annotated image. - - Allows configuring device, image scale, mask style, bounding box, use of retina resolution, and contour display. - - Parameters: - annotations (list): List of annotation masks - image (PIL.Image): Input image - device (str): Device to run on ('cpu' or 'cuda') - scale (float): Scale factor for image size - better_quality (bool): Whether to apply additional morphological operations for higher mask quality - mask_random_color (bool): Whether to use random colors for masks - bbox (list): Bounding box coordinates [x, y, w, h] - use_retina (bool): Whether to render masks at retina resolution - withContours (bool): Whether to find and draw contours - contour_thickness (int): Thickness of contour lines - - Returns: - PIL.Image: Image with rendered annotations - ''' - # If annotations are dicts, extract the segmentation masks - if isinstance(annotations[0], dict): - annotations = [annotation['segmentation'] for annotation in annotations] - - # Store original image dimensions - original_h = image.height - original_w = image.width - - # Apply morphological operations to improve mask quality - if better_quality: - if isinstance(annotations[0], torch.Tensor): - annotations = np.array(annotations.cpu()) - for i, mask in enumerate(annotations): - mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((3, 3), np.uint8)) - annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)) - - # Render masks on CPU or GPU - # If CPU is specified, convert annotations to NumPy array - if device == 'cpu': - annotations = np.array(annotations) - inner_mask = fast_show_mask( - annotations, # Annotations to render - plt.gca(), # Axis to render on - random_color=mask_random_color, # Whether to use random colors - bbox=bbox, # Bounding box, if provided - retinamask=use_retina, # Whether to render at retina resolution - target_height=original_h, # Target height for rendering - target_width=original_w, # Target width for rendering - ) - else: - # If GPU, convert NumPy arrays to PyTorch tensors - if isinstance(annotations[0], np.ndarray): - annotations = torch.from_numpy(annotations) - inner_mask = fast_show_mask_gpu( - annotations, # Annotations to render - plt.gca(), # Axis to render on - random_color=mask_random_color, # Whether to use random colors - bbox=bbox, # Bounding box, if provided - retinamask=use_retina, # Whether to render at retina resolution - target_height=original_h, # Target height for rendering - target_width=original_w, # Target width for rendering - ) - if isinstance(annotations, torch.Tensor): - annotations = annotations.cpu().numpy() - - # Kernel for morphological operations - kernel = np.ones((5, 5), np.uint8) - - if withContours: - # List to store all approximated contours - contour_all = [] - # Temporary image to draw contours on - temp = np.zeros((original_h, original_w, 1)) - for i, mask in enumerate(annotations): - if type(mask) == dict: - mask = mask['segmentation'] - - # Convert mask to uint8 - annotation = mask.astype(np.uint8) - - # Perform morphological operations to separate objects - # Use 5x5 rectangular kernel for opening - kernel = np.ones((5,5),np.uint8) - annotation = cv2.morphologyEx(annotation, cv2.MORPH_OPEN, kernel) - - # Gaussian blur to smooth contours - annotation = cv2.GaussianBlur(annotation, (5, 5), 0) - - # Find contours in processed mask - contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - # Approximate each contour and append to list - for contour in contours: - hull = cv2.convexHull(contour) - epsilon = 0.001 * cv2.arcLength(contour, True) - approx = cv2.approxPolyDP(contour, epsilon, True) - contour_all.append(approx) - - # Add object indices to image - for i, contour in enumerate(contour_all): - # Calculate centroid to place index text - M = cv2.moments(contour) - if M["m00"] != 0: - cX = int(M["m10"] / M["m00"]) - cY = int(M["m01"] / M["m00"]) - else: - cX, cY = 0, 0 - - # Put index text at centroid - cv2.putText(temp, str(i), (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 125, 255), 2) - - # Draw approximated contours on image - # Increase thickness to make more visible - cv2.drawContours(temp, contour_all, -1, (255, 255, 255), contour_thickness) - - # Set contour color to red - color = np.array([255 / 255, 0 / 255, 0 / 255, 1]) # RGBA - - # Create contour mask - contour_mask = temp / 255 * color.reshape(1, 1, -1) - - image = image.convert('RGBA') - overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA') - image.paste(overlay_inner, (0, 0), overlay_inner) - - if withContours: # Make sure contour_mask is defined when this block is executed - overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA') - image.paste(overlay_contour, (0, 0), overlay_contour) - - return image - - -# CPU post process -def fast_show_mask( - annotation, - ax, - random_color=False, - bbox=None, - retinamask=True, - target_height=960, - target_width=960, -): - """ - Visualize instance segmentation masks. - - Args: - - annotation: instance segmentation mask array with shape (num_masks, height, width) - - ax: matplotlib axes to plot on - - random_color: whether to use random colors for each mask - - bbox: bounding box to draw on image - - retinamask: whether to resize masks to retinal resolution - - target_height: height to resize to if retinamask=False - - target_width: width to resize to if retinamask=False - - Returns: - numpy array representing RGBA image with overlaid masks - - """ - - mask_sum = annotation.shape[0] - height = annotation.shape[1] - weight = annotation.shape[2] - - # Sort masks by area - areas = np.sum(annotation, axis=(1, 2)) - sorted_indices = np.argsort(areas)[::1] - annotation = annotation[sorted_indices] - - # Get index of first non-zero pixel for each position - index = (annotation != 0).argmax(axis=0) - - if random_color: - color = np.random.random((mask_sum, 1, 1, 3)) - else: - color = np.ones((mask_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 255 / 255]) - - transparency = np.ones((mask_sum, 1, 1, 1)) * 0.6 - visual = np.concatenate([color, transparency], axis=-1) - mask_image = np.expand_dims(annotation, -1) * visual - - mask = np.zeros((height, weight, 4)) - - # Overlay masks onto image - h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij') - indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) - mask[h_indices, w_indices, :] = mask_image[indices] - - # Draw bounding box if provided - if bbox is not None: - x1, y1, x2, y2 = bbox - ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1)) - - # Resize masks to retinal resolution if needed - if not retinamask: - mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST) - - return mask - - -def fast_show_mask_gpu( - annotation, - ax, - random_color=False, - bbox=None, - retinamask=True, - target_height=960, - target_width=960, -): - """ - Generates a mask image from the given annotation tensor. - - Args: - annotation (torch.Tensor): Annotation tensor. - ax (matplotlib.axes.Axes): Axes to draw bounding box on. - random_color (bool): Whether to use random colors for each mask. - bbox (tuple): Bounding box coordinates (x1, y1, x2, y2). - retinamask (bool): Whether to resize to retinal resolution. - target_height (int): Height to resize to if retinamask=False. - target_width (int): Width to resize to if retinamask=False. - - Returns: - numpy.ndarray: Generated mask image. - """ - - device = annotation.device - mask_sum = annotation.shape[0] - height = annotation.shape[1] - weight = annotation.shape[2] - - # Sort masks by area - areas = torch.sum(annotation, dim=(1, 2)) - sorted_indices = torch.argsort(areas, descending=False) - annotation = annotation[sorted_indices] - - # Get first non-zero index for each position - index = (annotation != 0).to(torch.long).argmax(dim=0) - - if random_color: - color = torch.rand((mask_sum, 1, 1, 3)).to(device) - else: - color = torch.ones((mask_sum, 1, 1, 3)).to(device) * torch.tensor( - [30 / 255, 144 / 255, 255 / 255] - ).to(device) - - transparency = torch.ones((mask_sum, 1, 1, 1)).to(device) * 0.6 - visual = torch.cat([color, transparency], dim=-1) - mask_image = torch.unsqueeze(annotation, -1) * visual - - # Use vectorization to get batch value at each position - mask = torch.zeros((height, weight, 4)).to(device) - h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight)) - indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) - mask[h_indices, w_indices, :] = mask_image[indices] - - mask_cpu = mask.cpu().numpy() - - if bbox is not None: - x1, y1, x2, y2 = bbox - ax.add_patch( - plt.Rectangle( - (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1 - ) - ) - - if not retinamask: - mask_cpu = cv2.resize( - mask_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST - ) - - return mask_cpu - - -def segment_everything(_input, input_size=1024, iou_threshold=0.7, conf_threshold=0.25, better_quality=False, contour_thickness=1): - """ - Segment objects in an image using the pre-trained model. - - Parameters: - ----------- - _input : PIL.Image - The input image to be segmented. - - input_size : int, optional (default=1024) - The size to which the input image will be resized. - - iou_threshold : float, optional (default=0.7) - Intersection over Union (IoU) threshold for object detection. - - conf_threshold : float, optional (default=0.25) - Confidence threshold for object detection. - - better_quality : bool, optional (default=False) - Whether to use higher quality processing. Increases computation time. - - contour_thickness : int, optional (default=1) - Thickness of the contour lines in the segmented image. - - Returns: - -------- - fig : matplotlib.figure.Figure - The segmented image as a matplotlib figure. - - annotations : torch.Tensor - The mask annotations for the segmented objects. - """ - - # Make a copy of the input and convert input_size to integer - input = _input - input_size = int(input_size) - - # Calculate the scaling factor and new dimensions for resizing - w, h = input.size - scale = input_size / max(w, h) - new_w = int(w * scale) - new_h = int(h * scale) - - # Resize the input image - input = input.resize((new_w, new_h)) - - # Run the model to get segmentation results - results = model(input, - retina_masks=True, - iou=iou_threshold, - conf=conf_threshold, - imgsz=input_size) - - # Extract mask annotations - annotations = results[0].masks.data - - # Process the annotations to generate the segmented image - fig = fast_process(annotations=annotations, device=device, - image=input, - scale=(1024 // input_size), - better_quality=better_quality, - contour_thickness=contour_thickness) - - return fig, annotations - - -def calculate_parameters(annotations, scale_factor): - # Initialize an empty DataFrame - df = pd.DataFrame(columns=['Object', 'Area', 'Perimeter', 'Roundness', 'Aspect Ratio', 'Longest Length']) - - if len(annotations) > 0: # Check if annotations list is not empty - for i, mask in enumerate(annotations): - # Convert mask to binary image - binary_mask = mask.cpu().numpy().astype(np.uint8) - - # Calculate area in pixels - area_pixel = np.sum(binary_mask) - - # Convert area to microns - area_micron = area_pixel * (scale_factor ** 2) - - # Find contours - contours, _ = cv2.findContours(binary_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - - # Calculate Perimeter in pixels - perimeter_pixel = cv2.arcLength(contours[0], True) - - # Convert perimeter to microns - perimeter_micron = perimeter_pixel * scale_factor - - # Fit an ellipse to the object - if len(contours[0]) >= 5: # Check if there are enough points to fit an ellipse - ellipse = cv2.fitEllipse(contours[0]) - major_axis = max(ellipse[1]) - minor_axis = min(ellipse[1]) - else: - major_axis = minor_axis = 0 # Default values if not enough points - - # Convert major and minor axis to microns - major_axis_micron = major_axis * scale_factor - minor_axis_micron = minor_axis * scale_factor - - # Calculate Roundness - roundness = 4 * area_micron / (np.pi * (major_axis_micron ** 2)) - - # Calculate Aspect Ratio - if minor_axis_micron != 0: # Check to avoid division by zero - aspect_ratio = major_axis_micron / minor_axis_micron - else: - aspect_ratio = "Undefined due to zero minor axis" - - # Longest Length (Major Axis) - longest_length_micron = major_axis_micron - - # Add to DataFrame - new_row = pd.DataFrame({ - 'Object': [f"Object {i+1}"], - 'Area': [area_micron], - 'Perimeter': [perimeter_micron], - 'Roundness': [roundness], - 'Aspect Ratio': [aspect_ratio], - 'Longest Length': [longest_length_micron] - }) - - df = pd.concat([df, new_row], ignore_index=True) - - # Display in Streamlit - #st.write(f"Object {i+1}: Area = {area_micron:.2f} µm², Perimeter = {perimeter_micron:.2f} µm, Roundness = {roundness:.2f}, Aspect Ratio = {aspect_ratio}, Longest Length = {longest_length_micron:.2f} µm") - - return df - -# Function to plot distribution -def plot_distribution(df, selected_parameter): - try: - fig, ax = plt.subplots() - sns.histplot(df[selected_parameter], kde=True, ax=ax) - ax.set_title(f'Distribution of {selected_parameter}') - ax.set_xlabel(selected_parameter) - ax.set_ylabel('Frequency') - st.pyplot(fig) - except Exception as e: - st.write(f"An error occurred while plotting: {e}") - - -# Function to convert DataFrame to CSV string -def dataframe_to_csv(df): - """Convert DataFrame to CSV string.""" - csv_buffer = io.StringIO() - df.to_csv(csv_buffer, index=False) - return csv_buffer.getvalue() - - -def main(): - """ - Main function to handle image segmentation and analysis using Streamlit UI. - """ - # Get user inputs from Streamlit UI - uploaded_image, input_size, iou_threshold, conf_threshold, better_quality, contour_thickness, real_world_length = streamlit_ui() - - # Check if an image is uploaded - if uploaded_image is not None: - - # Initialize drawable canvas - canvas_result = drawable_canvas(uploaded_image) - pixel_length = None # Initialize pixel_length variable - - # Check if line is drawn on canvas and pixel_length is not None - if canvas_result.json_data is not None and "objects" in canvas_result.json_data: - if len(canvas_result.json_data["objects"]) > 0: - line_object = canvas_result.json_data["objects"][0] - start_point = [line_object['x1'], line_object['y1']] - end_point = [line_object['x2'], line_object['y2']] - pixel_length = calculate_pixel_length(start_point, end_point) - st.write(f"Pixel length of the line: {pixel_length}") - st.write(f"length of the line in µm: {pixel_length}") - else: - st.write("Please draw a line to set the scale or enter the real-world length.") - else: - st.write("Please draw a line to set the scale or enter the real-world length.") - - # Calculate scale factor if both pixel_length and real_world_length are available - if pixel_length is not None and real_world_length is not None: - scale_factor = real_world_length / pixel_length # Calculate scale_factor - else: - st.write("Scale factor could not be calculated. Make sure to draw a line and enter the real-world length.") - return # Exit the function if scale_factor can't be calculated - - # Perform image segmentation - input_image = Image.open(uploaded_image) - segmented_image, annotations = segment_everything( - input_image, - input_size=input_size, - iou_threshold=iou_threshold, - conf_threshold=conf_threshold, - better_quality=better_quality, - contour_thickness=contour_thickness) - - # Display segmented image - st.image(segmented_image, caption="Segmented Image", use_column_width=True) - - # Calculate and display object parameters - df = calculate_parameters(annotations, scale_factor) - - # Display DataFrame in Streamlit if it's not empty - if not df.empty: - st.write("Summary of Object Parameters:") - st.dataframe(df) - - # Add download button for CSV - csv_data = dataframe_to_csv(df) - st.download_button( - label="Download CSV", - data=csv_data, - file_name="object_parameters.csv", - mime="text/csv", - ) - - # Plot distribution of selected parameter - filtered_columns = [col for col in df.columns.tolist() if col != 'Object'] - selected_parameter = st.selectbox("Select a parameter to see its distribution:", filtered_columns) - - if selected_parameter: - plot_distribution(df, selected_parameter) - else: - st.write("No parameter selected for plotting.") - else: - st.write("No objects detected.") - else: - st.write("Please upload an image.") - -# Entry point of the script -if __name__ == "__main__": - main() diff --git a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/cppipc/policy.h b/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/cppipc/policy.h deleted file mode 100644 index f88ab5d8cb343f97026966b402eaeed8831e356a..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/test_project/cpp/cppipc/policy.h +++ /dev/null @@ -1,25 +0,0 @@ -#pragma once - -#include - -#include "libipc/def.h" -#include "libipc/prod_cons.h" - -#include "libipc/circ/elem_array.h" - -namespace ipc { -namespace policy { - -template