diff --git a/spaces/101-5/gpt4free/.github/ISSUE_TEMPLATE/default_issue.md b/spaces/101-5/gpt4free/.github/ISSUE_TEMPLATE/default_issue.md
deleted file mode 100644
index 9e191df5fc28adb509289321c632433033b42616..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/.github/ISSUE_TEMPLATE/default_issue.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-name: New Issue
-about: 'Please use this template !!'
-title: ''
-labels: bug
-assignees: xtekky
-
----
-
-**Known Issues** // delete this
-- you.com issue / fix: use proxy, or vpn, your country is probably flagged
-- forefront account creation error / use your own session or wait for fix
-
-
-**Bug description**
-What did you do, what happened, which file did you try to run, in which directory
-Describe what you did after downloading repo, such as moving to this repo, running this file.
-
-ex.
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Environement**
-- python version
-- location ( are you in a cloudfare flagged country ) ?
-
-**Additional context**
-Add any other context about the problem here.
diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/utils/__init__.py b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/utils/__init__.py
deleted file mode 100644
index 818c605d2d82680f2014ba8f1a3bb66d0ae741b1..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/usesless/utils/__init__.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import requests
-import random
-import string
-import time
-import sys
-import re
-import os
-
-
-def check_email(mail, logging: bool = False):
- username = mail.split("@")[0]
- domain = mail.split("@")[1]
- reqLink = f"https://www.1secmail.com/api/v1/?action=getMessages&login={username}&domain={domain}"
- req = requests.get(reqLink)
- req.encoding = req.apparent_encoding
- req = req.json()
-
- length = len(req)
-
- if logging:
- os.system("cls" if os.name == "nt" else "clear")
- time.sleep(1)
- print("Your temporary mail:", mail)
-
- if logging and length == 0:
- print(
- "Mailbox is empty. Hold tight. Mailbox is refreshed automatically every 5 seconds.",
- )
- else:
- messages = []
- id_list = []
-
- for i in req:
- for k, v in i.items():
- if k == "id":
- id_list.append(v)
-
- x = "mails" if length > 1 else "mail"
-
- if logging:
- print(
- f"Mailbox has {length} {x}. (Mailbox is refreshed automatically every 5 seconds.)"
- )
-
- for i in id_list:
- msgRead = f"https://www.1secmail.com/api/v1/?action=readMessage&login={username}&domain={domain}&id={i}"
- req = requests.get(msgRead)
- req.encoding = req.apparent_encoding
- req = req.json()
-
- for k, v in req.items():
- if k == "from":
- sender = v
- if k == "subject":
- subject = v
- if k == "date":
- date = v
- if k == "textBody":
- content = v
-
- if logging:
- print(
- "Sender:",
- sender,
- "\nTo:",
- mail,
- "\nSubject:",
- subject,
- "\nDate:",
- date,
- "\nContent:",
- content,
- "\n",
- )
- messages.append(
- {
- "sender": sender,
- "to": mail,
- "subject": subject,
- "date": date,
- "content": content,
- }
- )
-
- if logging:
- os.system("cls" if os.name == "nt" else "clear")
- return messages
-
-
-def create_email(custom_domain: bool = False, logging: bool = False):
- domainList = ["1secmail.com", "1secmail.net", "1secmail.org"]
- domain = random.choice(domainList)
- try:
- if custom_domain:
- custom_domain = input(
- "\nIf you enter 'my-test-email' as your domain name, mail address will look like this: 'my-test-email@1secmail.com'"
- "\nEnter the name that you wish to use as your domain name: "
- )
-
- newMail = f"https://www.1secmail.com/api/v1/?login={custom_domain}&domain={domain}"
- reqMail = requests.get(newMail)
- reqMail.encoding = reqMail.apparent_encoding
-
- username = re.search(r"login=(.*)&", newMail).group(1)
- domain = re.search(r"domain=(.*)", newMail).group(1)
- mail = f"{username}@{domain}"
-
- if logging:
- print("\nYour temporary email was created successfully:", mail)
- return mail
-
- else:
- name = string.ascii_lowercase + string.digits
- random_username = "".join(random.choice(name) for i in range(10))
- newMail = f"https://www.1secmail.com/api/v1/?login={random_username}&domain={domain}"
-
- reqMail = requests.get(newMail)
- reqMail.encoding = reqMail.apparent_encoding
-
- username = re.search(r"login=(.*)&", newMail).group(1)
- domain = re.search(r"domain=(.*)", newMail).group(1)
- mail = f"{username}@{domain}"
-
- if logging:
- print("\nYour temporary email was created successfully:", mail)
- return mail
-
- except KeyboardInterrupt:
- requests.post(
- "https://www.1secmail.com/mailbox",
- data={
- "action": "deleteMailbox",
- "login": f"{username}",
- "domain": f"{domain}",
- },
- )
- if logging:
- print("\nKeyboard Interrupt Detected! \nTemporary mail was disposed!")
- os.system("cls" if os.name == "nt" else "clear")
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cities Skylines Mod Pack [Direct run] - Enhance Your City Building Experience with These Mods.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cities Skylines Mod Pack [Direct run] - Enhance Your City Building Experience with These Mods.md
deleted file mode 100644
index 3391872491ddfc56b1cf32fd7b11e902fcaace60..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cities Skylines Mod Pack [Direct run] - Enhance Your City Building Experience with These Mods.md
+++ /dev/null
@@ -1,79 +0,0 @@
-
-
Cities Skylines Mod Pack [Direct run]: What You Need to Know
-
If you love city-building games, you probably have heard of Cities Skylines, one of the most popular and acclaimed titles in the genre. But did you know that you can make your gaming experience even better with mods? Mods are user-created modifications that add new features, content, or improvements to the game. In this article, we will show you how to install mods for Cities Skylines, and what are some of the best mods you can use in 2023.
-
Cities Skylines Mod Pack [Direct run]Cities Skylines Mod Pack [Direct run]
Cities Skylines is a simulation game developed by Colossal Order and published by Paradox Interactive in 2015. The game lets you create and manage your own city, from laying out roads and zoning areas, to providing services and infrastructure, to dealing with traffic and pollution. You can also customize your city with various policies, districts, landmarks, and scenarios. The game has a realistic physics engine, a dynamic day-night cycle, and a large map size that allows you to build sprawling metropolises.
-
What are Mods and Why Use Them?
-
Mods are modifications that change or add something to the game. They are created by other players who use their creativity and skills to enhance the game in various ways. Some mods add new content, such as buildings, vehicles, maps, or scenarios. Some mods improve existing features, such as graphics, gameplay, or performance. Some mods fix bugs or errors that the developers missed or couldn't fix. And some mods just make the game more fun or challenging.
-
Using mods can make your gaming experience more enjoyable, diverse, and personalized. You can tailor your city to your preferences and needs, or experiment with different styles and possibilities. You can also learn from other players' creations and ideas, or share your own with the community. Mods can also extend the lifespan of the game by adding new challenges and goals.
-
How to Install Mods for Cities Skylines
-
There are two main ways to install mods for Cities Skylines: using the Steam Workshop or using Nexus Mods. Both methods have their advantages and disadvantages, so you can choose whichever one suits you best.
-
Steam Workshop
-
The Steam Workshop is a platform that allows you to browse, subscribe, rate, and comment on mods created by other players. It is integrated with Steam, so you don't need to download or install anything manually. To use the Steam Workshop, you need to have a Steam account and own Cities Skylines on Steam.
-
To access the Steam Workshop, launch Steam and go to your library. Right-click on Cities Skylines and select Properties. Then go to the Workshop tab and click on Browse Workshop. You will see a list of mods sorted by different categories and filters. You can also search for specific mods by name or keyword.
-
To subscribe to a mod, click on its title or thumbnail. You will see a page with more information about the mod, such as description, screenshots, ratings, comments, requirements, etc. If you like the mod and want to use it in your game, click on Subscribe. The mod will be automatically downloaded and enabled in your game.
-
To manage your subscribed mods, go back to Properties > Workshop > Browse Workshop > Subscribed Items. You will see a list of all the mods you have subscribed to. You can unsubscribe from any mod by clicking on Unsubscribe. You can also change the order of loading of your mods by dragging them up or down.
-
Nexus Mods
-
Nexus Mods is a website that hosts thousands of mods for various games. It is not affiliated with Steam or Paradox Interactive, so you need to create a free account on their website to use it. To use Nexus Mods, you also need to download a tool called Vortex, which helps you install and manage your mods.
-
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-Cities Skylines Best Mods [Direct run]
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-How to use Cities Skylines Mod Pack [Direct run]
-How to install Cities Skylines Mod Pack [Direct run]
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-How to uninstall Cities Skylines Mod Pack [Direct run]
-What is Cities Skylines Mod Pack [Direct run]
-What are the best mods in Cities Skylines Mod Pack [Direct run]
-What are the features of Cities Skylines Mod Pack [Direct run]
-What are the requirements for Cities Skylines Mod Pack [Direct run]
-What are the benefits of using Cities Skylines Mod Pack [Direct run]
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-Who reviews Cities Skylines Mod Pack [Direct run]
-
To download Vortex, go to https://www.nexusmods.com/about/vortex/ and click on Download Vortex Now. Follow the instructions on how to install Vortex on your computer. Then launch Vortex and sign in with your Nexus Mods account.
-
To find mods for Cities Skylines on Nexus Mods, go to https://www.nexusmods.com/citiesskylines/mods/ and browse through different categories or use the search function. To download a mod, click on its title or thumbnail. You will see a page with more information about the mod, such as description, screenshots, ratings, comments, requirements, etc. If you like the mod and want to use it in your game, click on Mod Manager Download. The mod will be added to Vortex and ready to install.
-
To install a mod, go back to Vortex and click on Mods on the left sidebar. You will see a list of all the mods you have downloaded. To install a mod, click on the red dot next to its name. The dot will turn green and indicate that the mod is installed. To uninstall a mod, click on the green dot and confirm. You can also enable or disable any mod by clicking on the toggle switch next to its name.
-
Mod Compatibility and Conflicts
-
Not all mods are compatible with each other or with the latest version of the game. Some mods may require other mods or DLCs to work properly. Some mods may conflict with each other or cause errors or crashes in the game. To avoid these problems, you should always read the description and requirements of each mod carefully before installing it. You should also check the comments and ratings of other users to see if they have encountered any issues with the mod. You should also keep your mods updated to the latest version available.
-
If you encounter any conflicts or errors in your game, you can try disabling some of your mods to see if they are causing them. You can also use tools like Harmony or Loading Screen Mod to detect and resolve conflicts between mods. You can find these tools on both Steam Workshop and Nexus Mods.
-
Best Mods for Cities Skylines in 2023
-
There are thousands of mods available for Cities Skylines, but some of them stand out as essential or highly recommended by many players. Here are some of the best mods for Cities Skylines in 2023, sorted by category.
-
Network Extensions 2
-
This mod adds new roads, bridges, and tunnels to the game, giving you more options and flexibility when designing your city's transportation network. You can choose from different types of roads, such as highways, avenues, boulevards, lanes, or alleys, each with different capacities, speed limits, and aesthetics. You can also
0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Download Fusion 360 TOP.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Download Fusion 360 TOP.md
deleted file mode 100644
index ac311b5fb840f782d7122c969fe3bb57991fd5cd..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Download Fusion 360 TOP.md
+++ /dev/null
@@ -1,41 +0,0 @@
-
-
How to Free Download Fusion 360 for Your 3D Design Projects
-
If you are looking for a powerful and versatile 3D design software that can handle CAD, CAM, CAE, and PCB tasks, you might want to try Fusion 360. Fusion 360 is a cloud-based software platform from Autodesk that allows you to turn your ideas into 3D models, prototypes, and products. In this article, we will show you how to free download Fusion 360 for your personal, startup, or educational use.
Fusion 360 is a cloud-based 3D modeling, CAD, CAM, CAE, and PCB software platform for product design and manufacturing. It has the following features and benefits:
-
-
Flexible 3D modeling and design: You can create and edit 3D models with ease using a variety of tools and techniques. You can also ensure aesthetics, form, fit, and function of your products with parametric, freeform, direct, and surface modeling options.
-
CAD + CAM in one: You can design and create prototypes and finished products with professional-grade, integrated design and machining software. You can import files from 50 different file types and access hundreds of posts from a free post processor library.
-
3D printing made easy: You can quickly take your designs from the screen to your hands with easy-to-use 3D printing software. You can connect to a library of 3D printing machines from Ultimaker, EOS, Formlabs, Renishaw, and more.
-
Cloud CAD, to work faster and smarter: You can work from anywhere and on any device with an all-in-one cloud software platform. You can collaborate with your peers and streamline feedback on your designs by communicating in real time and centralizing your project history.
-
-
How to free download Fusion 360 for personal use
-
If you want to use Fusion 360 for personal use, such as for hobby projects or non-commercial purposes, you can download a limited free version that includes basic functionality. Here are the steps to do so:
Select "Personal use" as your user type and fill in your information. Click on "Next".
-
Create an Autodesk account or sign in with your existing one.
-
Verify your email address and click on "Download now".
-
Follow the instructions to install Fusion 360 on your device.
-
Launch Fusion 360 and sign in with your Autodesk account.
-
Enjoy using Fusion 360 for personal use for up to one year. You can renew your license annually as long as you meet the eligibility criteria.
-
-
How to free download Fusion 360 for startup use
-
If you want to use Fusion 360 for startup use, such as for developing new products or services for commercial purposes, you can download a free version that includes all the features and functionality. However, you need to meet the following eligibility criteria:
-
-
-
Your startup is less than three years old.
-
Your startup has 10 or fewer employees.
-
Your startup has annual revenue of less than $100,000 USD.
-
-
If you meet these criteria, here are the steps to download Fusion 360 for startup use:
Select "Startup use" as your user type and fill in your information. Click on "Next".
-
Create an Autodesk account or sign in with your existing one.
-
Verify your email address and click on "Download now".
-
Follow the instructions to install Fusion 360 on your device.
-
Launch Fusion 360 and sign ddb901b051
-
-
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Adobe Audition 2.0 Full Crack 12 A Complete Review and Comparison with Other Versions.md b/spaces/1gistliPinn/ChatGPT4/Examples/Adobe Audition 2.0 Full Crack 12 A Complete Review and Comparison with Other Versions.md
deleted file mode 100644
index 0fe7cd6ea68d4672525d13cbcd8a14e9dfca206a..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Adobe Audition 2.0 Full Crack 12 A Complete Review and Comparison with Other Versions.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
I'm using macOS High Sierra version 10.13.6Audition has been flawless and great up until a few days ago. Audtion no longer opens. No updates to my operating system have been made recently. Any help getting this setteld would be appreciated. Here's what pops up when I attempt to open Audition: \"Check with the developer to make sure Adobe Audition CS6 works with this version of macOS. You may need to reinstall the application. Be sure to install any available updates for the application and macOS. 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const CONVERSATION_PAGE='Community: conversationPage';//evar203 mapped variablesconst CARD_CREATED_DATE='digitalData.community.communityAttributes.cardCreatedDate';const COUNT_CORRECT_ANSWER='digitalData.community.communityAttributes.countCorrectAnswer';const COMMUNITY_FLAG='digitalData.community.communityInfo.communityFlag'; const COUNT_REPLY='digitalData.community.communityAttributes.countReply'; const RELATED_CONVERSATION_ACTION='relatedConversationClick';const COMMUNITY_DD_PROPERTY='digitalData.community';const CONVERSATION_REPORT='Community: conversationReportClick';const REPLY_REPORT='Community: repliesReportClick';const MARKED_CORRECT='Community: Marked as Correct';const UNMARKED_CORRECT='Community: UnMarked as Correct';const REPLY_MARKED_CORRECT='replyMarkedCorrect';const REPLY_UNMARKED_CORRECT='replyUnmarkedCorrect';const CONVERSATION_FOLLOW='Community: conversationFollowClick';const REPLY_FOLLOW='Community: repliesFollowClick';const CONVERSATION_UNFOLLOW='Community: conversationUnfollowClick';const REPLY_UNFOLLOW='Community: repliesUnfollowClick';const SOPHIA_EVENTS = 'digitalData.sophiaResponse.fromPage';const CC_LINK1 = 'Community: CCD_';const CC_LINK2 = 'Click';const CC_LINK_CLICK = 'ccdLinkClick';const CC_MANAGE_ACCOUNT_CLICK = 'manageAccountLinkClick'; const REC_CONVO_FEEDBACK_SHOWN='digitalData.community.communityAttributes.recConvoFeedbackShown';const CONVERSATION_EDIT='Community: conversationEditClick';const CONVERSATION_VIEW_HISTORY='Community: conversationViewHistoryClick';const CONVERSATION_MOVE_MERGE='Community: conversationMoveMergeClick';const CONVERSATION_SPAM='Community: conversationSpamClick';const CONVERSATION_DELETE='Community: conversationDeleteClick';const CONVERSATION_BAN_USER='Community: conversationBanUserClick';const REPLY_BAN_USER='Community: repliesBanUserClick';const REPLY_SPAM='Community: repliesSpamClick';const REPLY_DELETE='Community: repliesDeleteClick';const REPLY_MOVE_MERGE='Community: repliesMoveMergeClick';const REPLY_VIEW_HISTORY='Community: repliesViewHistoryClick';const REPLY_EDIT='Community: repliesEditClick';const REPLIES_IN_RESPONSE_TO ='Community: repliesInResponseToClick';$.when(promise1).done( function () userProfilePromise.then(trackConversationPageLoad);); function trackConversationPageLoad() //Conversation Page Load Tracking const subject = $('.userStrip').attr('data-message-subject');let messageUid = '11125404';const tempDD = digitalData; let boardId = normalizeBoardId('audition'); let community = normalizeCategoryBoardId(); let contentType = getBoardType(boardId); //track new post success trackNewPostSuccess(community, subject, messageUid); //track merge message success trackMergeSuccess(subject,community,'11125404',contentType); //recover digital data property digitalData = tempDD; const valArr = location.pathname.split('/'); let pageName; let layoutView = 'threaded'; if('ForumTopicPage' === 'IdeaPage') layoutView = 'linear'; //Ideas do not support threaded view so it will always be linear let sortOrder = 'by_date_ascending'=="by_date_ascending"?"Earliest":"Latest"; if(PAGE_LANG!=='en') pageName = location.hostname + ':t5:' + boardId + ':' + 'conversationPage'; else if(valArr && valArr.length > 2) pageName = location.hostname + ':' + valArr[1] + ':' + community + ':' + 'conversationPage'; if(pageName) setDigitalDataProperty(PAGE_NAME, pageName); if(messageUid) setDigitalDataProperty(COMMUNITY_ID, messageUid); setDigitalDataProperty(LANGUAGE, getLocale()); setDigitalDataProperty(SITE_SECTION, CONVERSATION_PAGE); setPrimaryEvent(CONVERSATION_PAGE, 'pageload');let replyCount = 0;if($('.reply-count__text').length > 0) replyCount = $('.reply-count__text').attr('data-reply-count'); let status = ''; let voteCount = 0; if($('.message-status-link').length > 0) status = $('.message-status-link')[0].innerText; if($('#messageKudosCount_').length > 0) voteCount = $('#messageKudosCount_')[0].getAttribute('data-upvote-count'); const correctAnswerCount = $('.correct-answer-div').attr('data-correct-answer-count'); const creationDate = $('.roleTimestamp').attr('data-post-time'); setDigitalDataProperty(CARD_CREATED_DATE, creationDate); //setDigitalDataProperty(COUNT_REPLY, replyCount?replyCount:'0'); setDigitalDataProperty(COUNT_CORRECT_ANSWER, correctAnswerCount?correctAnswerCount:'0'); setDigitalDataProperty(COMMUNITY_CONTENT_TYPE, contentType); setDigitalDataProperty(COMMUNITY_CATEGORY, community); setDigitalDataProperty(COMMUNITY_TITLE, subject); let solnType = $('.conversation-page-container').attr('data-solution-type'); if(parseInt(solnType) 0) solnType = '1'; else if($('#special-reply-pinned').length > 0) solnType = '4'; solnType = CONVERSATION_FLAG_TYPE[solnType]; let flag = solnType; if($('.body-outer-container').attr('data-pin-flag') === "true") if(flag != '') flag = flag + ';Pinned'; else flag = 'Pinned'; if(flag != '') setDigitalDataProperty(COMMUNITY_FLAG, flag); if(document.getElementById('feedback_view_1')) setDigitalDataProperty(REC_CONVO_FEEDBACK_SHOWN, 'true'); dnmsTrackConversationFeedback('render', 'feedback-answer', [messageUid, community, null, 'radio button']); setDigitalDataProperty(FILTERS, [createGPSortInfoObj(sortOrder)]); setDigitalDataProperty(SOPHIA_EVENTS,['CampaignId': relatedConvCampaignId, 'ControlGroupId': relatedConvControlGroupId, 'VariationId': relatedConvVariationId, 'ActionBlockId': relatedConvActionBlockId, 'CampaignId': manageAccountCampaignId, 'ControlGroupId': manageAccountControlGroupId, 'VariationId': manageAccountVariationId, 'ActionBlockId': manageAccountActionBlockId]); captureSnapshot('state'); //dunamis api call dnmsConversationPageRender(community, replyCount, subject, getCommunityCurrentPageNum(), getConversationTags().toString(), messageUid, layoutView, flag, status, voteCount); cleanDigitalDataProperties([SOPHIA_EVENTS]); if ($('.promos-wrapper').length > 0) let promotype = $('.promos-wrapper').attr('data-promotype'); let promosubtype = $('.promos-wrapper').attr('data-promosubtype'); dnmsPromoRender(promotype, promosubtype, community, messageUid); //Track related conversation clickdetectRelatedConversationsLoad(); //track status update success if(localStorage.hasOwnProperty('messageStatusUpdate')) trackStatusUpdateSuccess(); //Track reply post success trackReplyPostSuccess(); let lsCleanUpArr = ['gpEditMessageType', 'gpEditMessagePageNum', 'gpReportMessageDetails', 'gpReportMessageType'];clearStorage(lsCleanUpArr);cleanDigitalDataProperties(['digitalData.primaryEvent.eventInfo', FILTERS]); function getPayload(params) var sophiaPayload = []; try params = params.split("&"); var keyMapping = 'aid':'ActionBlockId','campid':'CampaignId', 'cid':'ContainerId','cgid':'ControlGroupId','tid':'TreatmentId','vid':'VariationId','sid':'SurfaceId'; var sophiaMap = ; for(let i=0;i 1 && (keys[0] in keyMapping)) sophiaMap[keyMapping[keys[0]]] = keys[1]; sophiaPayload.push(sophiaMap); catch(err) console.log(err); return sophiaPayload;function trackNewPostSuccess(communityName, subject, messageUid) const npsDD = localStorage.getItem('npsDigitalData'); if(npsDD) const ddVal = JSON.parse(npsDD);if(subject === ddVal.community.communityInfo.communityTitle) digitalData = ddVal; setDigitalDataProperty(COMMUNITY_ID, messageUid); dnmsNewPostSuccess(communityName, subject, messageUid, JSON.parse(npsDD).sophiaResponse); captureSnapshot('event'); cleanDigitalDataProperties([SOPHIA_EVENTS]); localStorage.removeItem('npsDigitalData');function trackMergeSuccess(subject,community,messageId,contentType) try const mergeMsgDD = localStorage.getItem('mergeMsgDigitalData'); if(mergeMsgDD) const ddVal = JSON.parse(mergeMsgDD); if(messageId === ddVal.community.communityInfo.communityId) digitalData = ddVal; setDigitalDataProperty(COMMUNITY_CATEGORY, community); setDigitalDataProperty('digitalData.community.communityInfo.communityContentTab', contentType); setDigitalDataProperty(COMMUNITY_TITLE, subject); captureSnapshot('event'); let cnvrstnIds = []; let slctdCnvrstnArr = ddVal.community.attributes.selectedConversations; for(let i=0;i 4) let triggerBy = moveMergeTriggerDetails[0]; let cName = community; // merged to which community if(cName !== moveMergeTriggerDetails[1])' + moveMergeTriggerDetails[1]; // merged to which community let cId = messageId; let cType = moveMergeTriggerDetails[3]; //merged from which community type let msgType = moveMergeTriggerDetails[4]; let replyType = msgType!=='originalPost'?msgType:null; let xArr = [cName, cId, cType, messageId+' localStorage.removeItem('mergeMsgDigitalData'); catch(err) console.log(err); function clearStorage(items) for(let x=0; x 0) $('.related-conversations-card').on('click', function(e) if(e.target.hasAttribute('data-related-content-type')) //section tab click events let destinationTab = e.target.getAttribute('data-related-content-type'); dnmsCPSectionTabClick(getDigitalDataProperty(COMMUNITY_CATEGORY), 'related conversation', destinationTab); setPrimaryEvent('Community: relatedConversationLabelClick', SECTION_TAB_ACTION); setDigitalDataProperty(COMMUNITY_CONTENT_TYPE, destinationTab); captureSnapshot('event'); else let subject = e.target.getAttribute('data-related-conversation-subject'); let boardId = e.target.getAttribute('data-related-conversation-board'); let relatedCommContentType = getBoardType(boardId); let community = normalizeCategoryBoardId(); let target_href = e.target.href; let convo_id = e.target.getAttribute('data-related-conversation-id'); let org_convo_id = getDigitalDataProperty(COMMUNITY_ID); dnmsRelatedConversationsClick(community, target_href, org_convo_id, convo_id, "", subject, relatedConvCampaignId, relatedConvControlGroupId, relatedConvVariationId, relatedCommContentType); setPrimaryEvent(RELATED_CONVERSATION_CLICK, RELATED_CONVERSATION_ACTION); cleanDigitalDataProperties([COMMUNITY_DD_PROPERTY]); setDigitalDataProperty(COMMUNITY_CATEGORY, community); setDigitalDataProperty(COMMUNITY_CONTENT_TYPE,relatedCommContentType); setDigitalDataProperty(COMMUNITY_ID, convo_id); setDigitalDataProperty(COMMUNITY_TITLE, subject); setDigitalDataProperty(SOPHIA_EVENTS,['CampaignId': relatedConvCampaignId, 'ControlGroupId': relatedConvControlGroupId, 'VariationId': relatedConvVariationId, 'ActionBlockId': relatedConvActionBlockId]); captureSnapshot('event'); cleanDigitalDataProperties([SOPHIA_EVENTS]); ); //Track actions on conversation and repliesif($('.lia-quilt-column-main_content').length > 0) $('.lia-quilt-column-main_content').on('click', function(e) let targetElement = $(e.target); //Track Report if(targetElement.hasClass('report__text')) trackReportClick(targetElement); //Track mark correct answer if(targetElement.hasClass('lia-component-solutions-action-mark-message-as-accepted-solution')) trackMarkUnmarkCorrectAnswer('mark correct answer', targetElement); //Track Unmark correct answer if(targetElement.hasClass('lia-component-solutions-action-unmark-message-as-accepted-solution')) trackMarkUnmarkCorrectAnswer('unmark correct answer', targetElement); //Track view history click if(targetElement.hasClass('view-message-history')) trackViewHistoryClick(targetElement); //Track move merge click if(targetElement.hasClass('move-message')) trackMoveMergeClick(targetElement); if(getDigitalDataProperty(COMMUNITY_CONTENT_TYPE) !== 'Discussion' ) let authorId = $(targetElement).closest('.MessageView').find('.userStrip__link').attr('data-user-id'); if(authorId.length > 0) localStorage.setItem("mergeAuthor", authorId); //Track delete conversation/reply click if(targetElement.hasClass('delete-message-and-replies') );//Track edit message clickif($('.edit-message').length > 0) $('.edit-message').on('click', function(e) trackEditMessageClick($(e.target)); );//Track mark spam clickif($('.lia-component-spam-action-mark-message-as-spam').length > 0) $('.lia-component-spam-action-mark-message-as-spam').on('click', function(e) trackMarkSpamClick($(e.target)); ); //Track conversation page CC clicksvar ccElements = document.querySelectorAll(".cc-links-cta-container__anchor, .cc-links-banner-p2 a button");for (let i = 0; i < ccElements.length; i++) if($(ccElements[i]).length) $(ccElements[i]).on('click', function(e) let ccType = e.currentTarget.getAttribute('data-type'); let ccurl = e.currentTarget.getAttribute('href'); if(ccType && CC_LINKS_TYPE[ccType]) if (ccType == '4') let primaryEvent = "Community: ManageAccountBtn_Click"; setPrimaryEvent(primaryEvent, CC_MANAGE_ACCOUNT_CLICK); setDigitalDataProperty(SOPHIA_EVENTS,['CampaignId': manageAccountCampaignId, 'ControlGroupId': manageAccountControlGroupId, 'VariationId': manageAccountVariationId, 'ActionBlockId': manageAccountActionBlockId]); captureSnapshot('event'); cleanDigitalDataProperties([SOPHIA_EVENTS]); dnmsManageAccountEvent(getDigitalDataProperty(COMMUNITY_CATEGORY), ccurl, 'ManageAccount', 'click', 'Conversation', manageAccountCampaignId, manageAccountVariationId, manageAccountControlGroupId); else let primaryEvent = CC_LINK1+CC_LINKS_TYPE[ccType]+CC_LINK2; setPrimaryEvent(primaryEvent, CC_LINK_CLICK); captureSnapshot('event'); dnmsCCLinkClick(getDigitalDataProperty(COMMUNITY_CATEGORY), ccurl, CC_LINKS_TYPE[ccType], 'Conversation'); ); function trackFollowUnfollowClick(tElement, action) let isFollowAction = action==='follow'; if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(isFollowAction?CONVERSATION_FOLLOW:CONVERSATION_UNFOLLOW, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick(action, getConversationPageDetails()); else setPrimaryEvent(isFollowAction?REPLY_FOLLOW:REPLY_UNFOLLOW, REPLY_ACTION); let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick(action, replyType, getConversationPageDetails()); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackBanUserClick(tElement) if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(CONVERSATION_BAN_USER, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('ban user', getConversationPageDetails()); else let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick('ban user', replyType, getConversationPageDetails()); setPrimaryEvent(REPLY_BAN_USER, REPLY_ACTION); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackMarkSpamClick(tElement) if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(CONVERSATION_SPAM, CONVERSATION_ACTION); //dunamis api call let convArray = getConversationPageDetails(); dnmsConversationActionsClick('mark as spam', convArray); if(convArray.length > 1) syncDataOnS3('Spam', convArray[1]); else let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick('mark as spam', replyType, getConversationPageDetails()); setPrimaryEvent(REPLY_SPAM, REPLY_ACTION); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackDeleteMessageClick(tElement) if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(CONVERSATION_DELETE, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('delete the conversation', getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:originalPost'+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE)); else let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick('delete the reply', replyType, getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:'+replyType+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE)); setPrimaryEvent(REPLY_DELETE, REPLY_ACTION); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackMoveMergeClick(tElement) localStorage.setItem("movingConversationId", getDigitalDataProperty(COMMUNITY_ID)); if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(CONVERSATION_MOVE_MERGE, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('move/merge the conversation', getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:originalPost'+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE)); else let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick('move/merge the conversation', replyType, getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:'+replyType+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE)); setPrimaryEvent(REPLY_MOVE_MERGE, REPLY_ACTION); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackViewHistoryClick(tElement) if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(CONVERSATION_VIEW_HISTORY, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('view history', getConversationPageDetails()); else let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick('view history', replyType, getConversationPageDetails()); setPrimaryEvent(REPLY_VIEW_HISTORY, REPLY_ACTION); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackEditMessageClick(tElement) if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(CONVERSATION_EDIT, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('edit message', getConversationPageDetails()); localStorage.setItem('gpEditMessagePageNum', getCommunityCurrentPageNum()); else let replyType = getReplyType(tElement); if(replyType) localStorage.setItem('gpEditMessagePageNum', getCommunityCurrentPageNum()); dnmsConversationReplyActionsClick('edit message', replyType, getConversationPageDetails()); localStorage.setItem('gpEditMessageType', replyType); setPrimaryEvent(REPLY_EDIT, REPLY_ACTION); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackReportClick(tElement) let tempConversationPageDetails = getConversationPageDetails(); tempConversationPageDetails[2] = encodeURIComponent(tempConversationPageDetails[2]); localStorage.setItem('gpReportMessageDetails', tempConversationPageDetails); if(tElement.closest('.lia-thread-topic').length > 0) setPrimaryEvent(CONVERSATION_REPORT, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('report', getConversationPageDetails()); else let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick('report', replyType, getConversationPageDetails()); localStorage.setItem('gpReportMessageType', replyType); setPrimaryEvent(REPLY_REPORT, REPLY_ACTION); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackMarkUnmarkCorrectAnswer(action, tElement) let correctFlag = action==='mark correct answer'; setPrimaryEvent(correctFlag?MARKED_CORRECT:UNMARKED_CORRECT, correctFlag?REPLY_MARKED_CORRECT:REPLY_UNMARKED_CORRECT); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); convDetails = getConversationPageDetails(); if(correctFlag) convDetails = setSophiaPayload(convDetails); captureSnapshot('event'); let replyType = getReplyType(tElement); if(replyType) dnmsConversationReplyActionsClick(action, replyType, convDetails); cleanDigitalDataProperties([SOPHIA_EVENTS]);function detectRelatedConversationsLoad() { if($('.personalised-related-conversations').length > 0) let targetNode = $('.personalised-related-conversations')[0]; let config = childList: true ; let callback = function(mutationsList, observer) for(let i=0; i 0) status = $('.message-status-link')[0].innerText; dnmsConversationStatusUpdate('success',getConversationPageDetails(), comment, status); setPrimaryEvent('Community: StatusChanged'+status.replace(' ',''),'conversationStatusUpdated'); setDigitalDataProperty(PRIMARY_FILTER, createGPFilterInfoObj(status, 'statusChange')); captureSnapshot('event'); localStorage.removeItem('messageStatusUpdate'); cleanDigitalDataProperties([PRIMARY_FILTER, FILTERS]); catch(e) console.log(e); function isReplyBodyEmpty() { let result = false; let xNode;if($('.mce-edit-area').length > 0 && $('.mce-edit-area').children().length > 0) { let mceEditAreaiFrames = $('.mce-edit-area').children(); for(let i=0; i 0 && (content[0].hasAttribute('data-mce-bogus') || tinymce.innerHTML === '
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-09-feb-2020 - DESCARGAR [PDF][EPUB] Pan Casero Larousse - Libros Ilustrados/ Prá por Ibán Yarza LEER EN LINEA [EBOOK KINDLE] - PDF EPUB Libro. 4d29de3e1b
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/APKGame Explore the World of Android Gaming with Ease.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/APKGame Explore the World of Android Gaming with Ease.md
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What is apkgame and why you should try it
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If you are an Android user, you might have heard of the term "apkgame" or seen some websites that offer free downloads of Android games in APK format. But what exactly is an apkgame and why should you try it? In this article, we will explain everything you need to know about apkgame, including what is an APK file, how to install it, what are the benefits and risks of using it, and what are some of the best apk games to play in 2023.
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What is an APK file and how to install it
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An APK file is a compressed package that contains all the files and data needed to run an Android app or game. APK stands for Android Package Kit, and it is the standard format used by Google Play Store to distribute apps and games. However, not all apps and games are available on Google Play Store, either because they are not approved by Google, they are region-locked, they are removed by the developer, or they are modified by third-party sources. In these cases, you can still download and install them from other websites that offer apk files, such as mob.org or apkpure.com.
To install an apk file on your Android device, you need to follow these steps:
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Download the apk file from a trusted source and save it on your device.
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Go to Settings > Security > Unknown Sources and enable the option to allow installation of apps from unknown sources.
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Locate the apk file on your device using a file manager app and tap on it.
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Follow the instructions on the screen to complete the installation.
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Enjoy your new app or game!
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Benefits of using APK files
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There are many benefits of using apk files to install apps and games on your Android device, such as:
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You can access apps and games that are not available on Google Play Store for various reasons.
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You can get early access to beta versions or updates of apps and games before they are officially released.
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You can download apps and games that are region-locked or restricted in your country.
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You can install modified or hacked versions of apps and games that offer extra features or unlimited resources.
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You can save bandwidth and storage space by downloading only the apk file instead of the whole app or game package.
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Risks of using APK files
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However, there are also some risks of using apk files that you should be aware of, such as:
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You might download malicious or infected apk files that can harm your device or steal your personal data.
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You might violate the terms and conditions of Google Play Store or the app or game developer by installing unauthorized or modified versions of apps and games.
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You might encounter compatibility or performance issues with some apps and games that are not optimized for your device or Android version.
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You might lose access to updates or support from the official app or game developer if you install apk files from third-party sources.
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How to find and download APK games
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If you want to find and download apk games for your Android device, you need to do some research and be careful about the sources you choose. There are many websites that offer free downloads of apk games, but not all of them are safe or reliable. Some of them might contain malware, viruses, adware, spyware, or fake links that can harm your device or trick you into downloading unwanted apps or programs. Therefore, you should always check the reviews, ratings, comments, and feedback from other users before downloading any apk file
Here is the continuation of the article:
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Best APK games to play in 2023
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Now that you know what apk games are and how to install them, you might be wondering what are some of the best apk games to play in 2023. Well, there are plenty of options to choose from, depending on your preferences and tastes. Whether you like action, puzzle, simulation, or any other genre, you can find an apk game that suits you. Here are some of the best apk games to play in 2023, according to various sources .
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Action games
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If you are looking for some adrenaline-pumping action games, you can try these apk games:
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PUBG Mobile
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PUBG Mobile is one of the most popular and addictive battle royale games on Android. You can play solo or with your friends in various modes and maps, and fight against 99 other players to be the last one standing. You can customize your character, weapons, vehicles, and outfits, and enjoy realistic graphics and sound effects. PUBG Mobile is free to play, but it also offers in-app purchases for premium items and features.
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Genshin Impact
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Genshin Impact is a stunning open-world RPG game that lets you explore a vast and beautiful world full of secrets, quests, and enemies. You can play as one of the many characters with different abilities and elements, and switch between them during combat. You can also team up with other players online and take on challenging dungeons and bosses. Genshin Impact is free to play, but it also has a gacha system that lets you unlock more characters and items with real money.
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Puzzle games
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If you are looking for some brain-teasing puzzle games, you can try these apk games:
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Monument Valley 2
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Monument Valley 2 is a sequel to the award-winning puzzle game that features stunning art and music. You can guide a mother and her child through a series of optical illusions and impossible architecture, and discover the secrets of their world. You can also enjoy the story of their bond and their journey. Monument Valley 2 is not free, but it is worth every penny for its quality and creativity.
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The Room: Old Sins
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The Room: Old Sins is the fourth installment in the acclaimed puzzle game series that features intricate and mysterious puzzles. You can explore a creepy dollhouse that hides clues and secrets about a missing couple, and use your touch screen to manipulate objects and solve puzzles. You can also enjoy the atmospheric graphics and sound effects that create a immersive experience. The Room: Old Sins is not free, but it is one of the best puzzle games on Android.
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Simulation games
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If you are looking for some relaxing simulation games, you can try these apk games:
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Stardew Valley
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Stardew Valley is a charming farming simulator that lets you create your own farm and live a peaceful life in a rural community. You can grow crops, raise animals, fish, mine, craft, cook, and more. You can also make friends with the villagers, get married, have children, and explore the secrets of the valley. Stardew Valley is not free, but it offers hours of content and replay value.
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The Sims Mobile
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The Sims Mobile is a mobile version of the popular life simulation game that lets you create your own sims and control their lives. You can customize their appearance, personality, hobbies, careers, relationships, and more. You can also build and decorate their homes, host parties, attend events, and interact with other players online. The Sims Mobile is free to play, but it also has in-app purchases for various items and features.
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Conclusion and FAQs
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In conclusion, apk games are a great way to enjoy Android gaming without relying on Google Play Store. They offer more variety, flexibility, and accessibility than official apps and games. However, they also come with some risks and challenges that you should be aware of before downloading them. Therefore, you should always be careful about the sources you choose and the files you install on your device.
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If you have any questions about apk games or how to install them on your Android device, here are some FAQs that might help you:
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What are the best websites to download apk games? There are many websites that offer free downloads of apk games, but not all of them are safe or reliable. Some of the best websites to download apk games are mob.org, apkpure.com,
Here is the continuation of the article:
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What are the best websites to download apk games? There are many websites that offer free downloads of apk games, but not all of them are safe or reliable. Some of the best websites to download apk games are mob.org, apkpure.com, and apkdone.com. These websites have a large collection of apk games in various genres and categories, and they also provide detailed information, screenshots, reviews, and ratings for each game. They also scan and verify the apk files for malware and viruses, and update them regularly.
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How can I update my apk games? If you download an apk game from a third-party source, you might not receive automatic updates from the official app or game developer. However, you can still update your apk games manually by following these steps:
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Check the version number of your installed apk game and compare it with the latest version available on the website where you downloaded it.
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If there is a newer version available, download the updated apk file and save it on your device.
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Uninstall the old version of the apk game from your device.
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Install the new version of the apk game using the same steps as before.
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Enjoy your updated apk game!
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How can I uninstall my apk games? If you want to uninstall an apk game from your device, you can do so by following these steps:
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Go to Settings > Apps and find the apk game you want to uninstall.
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Tap on the apk game and select Uninstall.
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Confirm your action and wait for the uninstallation process to finish.
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Delete the apk file from your device if you don't need it anymore.
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How can I backup my apk games? If you want to backup your apk games and their data, you can use a third-party app such as Helium or Titanium Backup. These apps allow you to backup and restore your apps and games, along with their settings, preferences, progress, and data. You can also sync your backups to cloud storage services such as Google Drive or Dropbox. However, you might need to root your device to use some of these apps and their features.
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How can I play online multiplayer games with apk files? If you want to play online multiplayer games with apk files, you need to make sure that the apk file is compatible with the official version of the game. Otherwise, you might face issues such as connection errors, mismatched versions, or banned accounts. You also need to have a stable internet connection and a valid account for the game. Some online multiplayer games might require additional files or data to run properly, so you need to download them as well.
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I hope this article has helped you understand what is apkgame and why you should try it. Apk games are a great way to enjoy Android gaming without relying on Google Play Store. They offer more variety, flexibility, and accessibility than official apps and games. However, they also come with some risks and challenges that you should be aware of before downloading them. Therefore, you should always be careful about the sources you choose and the files you install on your device.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ApkJeet How to Earn Money Online from Home with a Simple App in 2023.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ApkJeet How to Earn Money Online from Home with a Simple App in 2023.md
deleted file mode 100644
index 4bf9dac1045fffc1adf38c104ff7dc4cc471ddbc..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/ApkJeet How to Earn Money Online from Home with a Simple App in 2023.md
+++ /dev/null
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-
ApkJeet: A Guide to Online Earning in Pakistan
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Are you looking for a way to make money online in Pakistan without investing anything? Do you want to learn new skills and improve your English while earning from home? If yes, then you might want to check out ApkJeet, a platform that offers a variety of online earning opportunities for Pakistanis. In this article, we will explain what ApkJeet is, how it works, what are the benefits and challenges of using it, and how to get started with it.
ApkJeet is a platform that connects Pakistani users with online earning opportunities from various apps and websites. It was created by Eman Shehzadi, a Pakistani entrepreneur who wanted to help her fellow citizens earn money online in a fast and easy way. ApkJeet consists of two main components: an app and a website.
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ApkJeet app: A platform for online earning opportunities
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The ApkJeet app is the core of the platform, where users can find and access different apps and websites that offer online earning opportunities. These include popular platforms like YouTube, Google Adsense, Amazon, Upwork, Fiverr, Shutterstock, Udemy, Guru, TechRoti, Urdu Inbox, and more. Users can download the app from the official website or Google Play Store, register and create their profile, browse and select the apps and websites that suit their interests and skills, follow the instructions and complete the tasks or projects assigned by the app or website, earn money and withdraw it to their bank account or e-wallet.
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ApkJeet website: A source of information and guidance for online earners
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The ApkJeet website is a complementary resource for users who want to learn more about online earning in Pakistan. It provides information and guidance on various topics related to online earning, such as affiliate marketing, selling gently-used items, on-demand ride service, freelancing work, taking paid surveys, private tutoring, creating YouTube channels, social media influencing, starting profitable blogs, niche freelance content writing, writing paid reviews, part-time photography, and more. Users can visit the website to get tips and tricks on how to succeed in online earning, as well as read testimonials and reviews from other users who have used ApkJeet.
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What are the benefits of using ApkJeet?
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Using ApkJeet has many benefits for users who want to make money online in Pakistan. Some of these benefits are:
-
ApkJeet offers a variety of apps and websites to choose from
-
One of the main advantages of using ApkJeet is that it offers a wide range of apps and websites that cater to different interests and skills. Users can choose from different categories such as education, entertainment, e-commerce, freelancing, photography, blogging, social media, etc. Users can also switch between different apps and websites as they please, depending on their availability, preference, and performance.
ApkJeet provides reliable and trusted sources of income
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Another benefit of using ApkJeet is that it provides reliable and trusted sources of income for users. ApkJeet only features apps and websites that are verified and reputable, and that pay users on time and in full. Users can also check the ratings and reviews of the apps and websites on the ApkJeet app or website, to see how other users have rated their experience and earnings. ApkJeet also has a customer support team that is available 24/7 to assist users with any issues or queries they may have regarding online earning.
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ApkJeet helps you learn new skills and improve your English
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A third benefit of using ApkJeet is that it helps users learn new skills and improve their English while earning money online. Many of the apps and websites featured on ApkJeet require users to have certain skills or knowledge, such as web development, graphic design, content writing, video editing, digital marketing, etc. Users can learn these skills by taking online courses, watching tutorials, reading blogs, or following experts on social media. ApkJeet also helps users improve their English, as many of the apps and websites require users to communicate in English with clients, customers, or audiences. Users can improve their English by reading articles, watching videos, listening to podcasts, or practicing with native speakers online.
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What are the challenges and risks of using ApkJeet?
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While using ApkJeet has many benefits, it also has some challenges and risks that users should be aware of. Some of these challenges and risks are:
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ApkJeet requires internet access and a compatible device
-
One of the main challenges of using ApkJeet is that it requires users to have internet access and a compatible device to use the app and the website. Internet access in Pakistan can be expensive, slow, or unreliable, depending on the location, provider, or plan. Users may also need to have a smartphone, tablet, laptop, or desktop computer that can run the app and the website smoothly and securely. Users may need to invest in these devices or services if they want to use ApkJeet effectively.
-
ApkJeet does not guarantee success or income
-
Another challenge of using ApkJeet is that it does not guarantee success or income for users. Online earning depends on many factors, such as the demand and supply of the market, the quality and quantity of the work, the competition and reputation of the user, the payment terms and methods of the app or website, etc. Users may not always find suitable or profitable opportunities on ApkJeet, or they may face delays or disputes in receiving their payments. Users should be realistic and flexible in their expectations and goals when using ApkJeet.
ApkJeet may expose you to scams or frauds
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A third challenge of using ApkJeet is that it may expose users to scams or frauds that may try to exploit their online earning activities. Some of the apps and websites featured on ApkJeet may not be legitimate or trustworthy, and they may ask users to provide personal or financial information, pay upfront fees, download malware, or perform illegal or unethical tasks. Users should be careful and vigilant when using ApkJeet, and they should research the apps and websites before signing up, avoid sharing sensitive information, report any suspicious or abusive behavior, and seek help from ApkJeet or authorities if they encounter any problems.
-
How to get started with ApkJeet?
-
If you are interested in using ApkJeet to make money online in Pakistan, here are the steps you need to follow:
-
Download the ApkJeet app from the official website or Google Play Store
-
The first step is to download the ApkJeet app from the official website (https://apkjeet.com/) or Google Play Store (https://play.google.com/store/apps/details?id=com.apkjeet). The app is free and easy to install, and it works on most Android devices. You can also scan the QR code on the website to download the app directly.
-
Register and create your profile on the app
-
The second step is to register and create your profile on the app. You will need to provide some basic information, such as your name, email address, phone number, gender, age, location, education level, skills, interests, etc. You will also need to create a password and a username for your account. You can also upload a photo of yourself if you want. Your profile will help you find suitable and relevant online earning opportunities on ApkJeet.
-
Browse and select the apps and websites that suit your interests and skills
-
The third step is to browse and select the apps and websites that suit your interests and skills. You can use the search function or the categories to find the apps and websites that offer online earning opportunities in different fields, such as education, entertainment, e-commerce, freelancing, photography, blogging, social media, etc. You can also see the ratings and reviews of the apps and websites from other users, as well as the estimated earnings and payment methods. You can select as many apps and websites as you want, depending on your availability and preference.
Follow the instructions and complete the tasks or projects assigned by the app or website
-
The fourth step is to follow the instructions and complete the tasks or projects assigned by the app or website. Each app and website has its own rules and requirements for online earning, such as the type and quality of the work, the deadline and duration of the work, the feedback and rating system, the dispute resolution process, etc. You should read and understand these rules and requirements before starting any work, and follow them accordingly. You should also communicate clearly and professionally with the app or website, as well as with any clients, customers, or audiences you may have.
-
Earn money and withdraw it to your bank account or e-wallet
-
The fifth and final step is to earn money and withdraw it to your bank account or e-wallet. After completing the tasks or projects assigned by the app or website, you will receive your payment in your ApkJeet account. You can check your balance and transaction history on the app or website. You can also withdraw your money to your bank account or e-wallet, such as JazzCash, EasyPaisa, Payoneer, PayPal, etc. The minimum withdrawal amount and the withdrawal fee may vary depending on the app or website. You should also keep track of your income and expenses for tax purposes.
-
Conclusion and FAQs
-
ApkJeet is a platform that offers a variety of online earning opportunities for Pakistanis. It consists of an app and a website that connect users with different apps and websites that offer online earning opportunities in different fields. Using ApkJeet has many benefits, such as offering a variety of apps and websites to choose from, providing reliable and trusted sources of income, and helping users learn new skills and improve their English. However, using ApkJeet also has some challenges and risks, such as requiring internet access and a compatible device, not guaranteeing success or income, and exposing users to scams or frauds. To get started with ApkJeet, users need to download the app from the official website or Google Play Store, register and create their profile on the app, browse and select the apps and websites that suit their interests and skills, follow the instructions and complete the tasks or projects assigned by the app or website, earn money and withdraw it to their bank account or e-wallet.
-
If you have any questions about ApkJeet, you may find the answers in these FAQs:
-
-
-
Question
-
Answer
-
-
-
Is ApkJeet free to use?
-
Yes, ApkJeet is free to use for users who want to make money online in Pakistan. However, some of the apps and websites featured on ApkJeet may charge fees for their services or products.
-
-
-
How much money can I make with ApkJeet?
-
The amount of money you can make with ApkJeet depends on many factors, such as the demand and supply of the market, the quality and quantity of your work, the competition and reputation of yourself, the payment terms and methods of the app or website, etc. There is no fixed or guaranteed income with ApkJeet.
-
-
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How can I increase my chances of success with ApkJeet?
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You can increase your chances of success with ApkJeet by following these tips: - Choose the apps and websites that match your interests and skills - Learn new skills and improve your English - Provide high-quality work that meets the expectations of the app or website - Communicate clearly and professionally with the app or website - Be consistent and reliable in your work - Seek feedback and improve your performance - Build your reputation and portfolio - Avoid scams and frauds
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-
-
What if I have a problem with an app or website on ApkJeet?
-
If you have a problem with an app or website on ApkJeet, such as not receiving your payment, having a dispute with a client or customer, encountering a technical issue, etc., you should first try to resolve it directly with the app or website. If that does not work, you can contact ApkJeet's customer support team via email (support@apkjeet.com) or phone (0300-1234567) for assistance.
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Can I use ApkJeet outside Pakistan?
-
No, ApkJeet is only available for Pakistani users who want to make money online in Pakistan. If you are outside Pakistan, you will not be able to access or use ApkJeet.
-
-
-
I hope this article has helped you understand what ApkJeet is, how it works, what are the benefits and challenges of using it, and how to get started with it. If you are interested in making money online in Pakistan, you should give ApkJeet a try and see for yourself how it can help you achieve your online earning goals. Remember to be careful and vigilant when using ApkJeet, and to follow the tips and tricks we shared in this article. Good luck and happy earning!
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diff --git a/spaces/1phancelerku/anime-remove-background/Experience the Thrill of Free Fire MAX on PC with BlueStacks The Only App Player that Supports Android 11.md b/spaces/1phancelerku/anime-remove-background/Experience the Thrill of Free Fire MAX on PC with BlueStacks The Only App Player that Supports Android 11.md
deleted file mode 100644
index 2d1f9d055d460f1588ad4281f92f4a3dda8bf245..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Experience the Thrill of Free Fire MAX on PC with BlueStacks The Only App Player that Supports Android 11.md
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-
Free Fire PC Download Max APK: Everything You Need to Know
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If you are a fan of battle royale games, you might have heard of Free Fire, one of the most popular titles in this genre on mobile devices. But did you know that there is a new version of this game called Free Fire Max, which offers enhanced graphics and performance for high-end devices? And did you know that you can play this game on your PC using an Android emulator?
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In this article, we will tell you everything you need to know about Free Fire PC download max apk, including what it is, how to get it, what are the system requirements, what are the best tips and tricks, and what are the best alternatives. Whether you are new to this game or a seasoned veteran, you will find something useful and interesting in this guide.
Free Fire is a battle royale game that can be played on Android devices or PC. It was developed by Garena International I and released in 2017. It has over 500 million downloads on Google Play Store and has won several awards, such as the Best Popular Vote Game by Google Play Store in 2019.
-
The gameplay of Free Fire is similar to other games in this genre. You have to parachute onto an island with 49 other players and fight for your survival using the weapons and gear that you find on the map. The last player or team standing wins the match.
-
Some of the features of Free Fire are:
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-
Fast-paced action: Each match lasts only 10 minutes, so you have to be quick and decisive.
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Squad mode: You can team up with up to three other players and communicate with them using voice chat.
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Clash Squad: This is a 4v4 mode where you have to manage your economy and buy weapons before each round.
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Characters: You can choose from over 50 characters with unique skills and abilities.
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Pets: You can also have a pet companion that can help you in various ways.
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Events: You can participate in various events and challenges to earn rewards and prizes.
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What is Free Fire Max?
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Free Fire Max
How to Download and Play Free Fire Max on PC?
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Free Fire Max is designed for high-end devices, but you can also play it on your PC using an Android emulator. An emulator is a software that allows you to run Android apps and games on your computer. One of the best emulators for playing Free Fire Max on PC is BlueStacks, which offers high performance, compatibility, and customization.
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To download and play Free Fire Max on PC using BlueStacks, follow these steps:
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Download and install BlueStacks from its official website:
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Launch BlueStacks and log in with your Google account.
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Go to the Google Play Store and search for Free Fire Max.
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Click on the Install button and wait for the download to finish.
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Once the installation is complete, click on the Free Fire Max icon on the home screen.
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Enjoy playing Free Fire Max on PC with enhanced graphics and controls.
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You can also customize the keyboard and mouse settings to suit your preferences. To do this, click on the gear icon on the top right corner of the screen and select Controls. You can choose from predefined layouts or create your own. You can also adjust the sensitivity, resolution, and frame rate of the game.
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What are the System Requirements for Free Fire Max on PC?
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To play Free Fire Max on PC smoothly and without lag, you need to have a decent computer that meets the minimum or recommended system requirements. According to , these are:
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-
Minimum System Requirements
Recommended System Requirements
-
OS: Windows 7 or above
OS: Windows 10
-
CPU: Intel or AMD Processor
CPU: Intel Core i5-680 or higher
-
RAM: 4 GB
RAM: 8 GB or higher
-
HDD: 5 GB free space
HDD: 10 GB free space
-
Graphics: Intel HD 520 or higher
Graphics: NVIDIA GeForce GTX 660 or higher
-
Internet: Broadband connection
Internet: Broadband connection
-
-
If your PC does not meet these requirements, you may experience lag, stuttering, or crashing while playing Free Fire Max. To improve your performance, you can try lowering the graphics settings, closing other applications, updating your drivers, or using a wired connection.
What are the Best Tips and Tricks for Free Fire Max?
-
Free Fire Max is a challenging and competitive game that requires skill, strategy, and luck to win. If you want to improve your win rate and become a better player, you need to follow some tips and tricks that can give you an edge over your enemies. Here are some of the best tips and tricks for Free Fire Max that you can use:
-
-
Play it safe: Don't rush into fights without a plan or backup. Try to avoid hot drops and land in less crowded areas where you can loot safely. Use cover and stealth to move around the map and avoid unnecessary confrontations. Only engage when you have a clear advantage or when the zone forces you to.
-
Master the gloo wall: The gloo wall is one of the most useful items in Free Fire Max, as it can provide instant protection from bullets and grenades. You should always carry some gloo walls with you and use them wisely. You can use them to block doors, windows, bridges, or gaps. You can also use them to create ramps, stairs, or platforms. You can also use them to trap or confuse your enemies.
-
Try to maintain max HP and EP: HP and EP are your health and energy points, respectively. You need to keep them as high as possible to survive longer and heal faster. You can use medkits, mushrooms, bonfires, or character skills to restore your HP and EP. You can also use vests and helmets to reduce the damage you take from enemies.
-
Practice ADS and rotating: ADS stands for aim down sight, which is the mode where you look through your weapon's scope or iron sight. This mode allows you to shoot more accurately and deal more damage, especially at long ranges. You should practice using ADS in different situations and with different weapons. Rotating means moving from one location to another in a strategic way. You should practice rotating according to the zone, the map, and the enemy's position. You should also use vehicles, launch pads, or zip lines to rotate faster and safer.
-
Play with an experienced player when possible: One of the best ways to learn and improve in Free Fire Max is by playing with someone who already knows the game well. You can ask them for advice, tips, feedback, or help. You can also observe how they play and copy their strategies. You can find experienced players in your friends list, clan, guild, or online communities.
-
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What are the Best Alternatives to Free Fire Max?
-
If you are looking for some other games like Free Fire Max that offer similar or better battle royale experiences on mobile or PC, you have plenty of options to choose from. Here are some of the best alternatives to Free Fire Max that you can try out:
-
-
COD Mobile: This is a better battle royale experience with high-quality graphics, realistic weapons, vehicles, maps, modes, and characters. You can also play other modes like multiplayer, zombies, or ranked.
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PUBG New State: This is a great title for gamers who prefer high-quality graphics in their games. It is set in a futuristic world with advanced technology, weapons, vehicles, and features. It also has a dynamic map that changes over time.
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PUBG Mobile: This is one of the biggest rivals to Free Fire in the BR gaming community. It has a more realistic and immersive gameplay with large maps, diverse weapons, vehicles, items, and modes. It also has regular updates, events, collaborations, and tournaments.
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Knives Out: This is a game that offers a unique experience with its gameplay mechanics. It has a more casual and humorous style with cartoonish graphics, quirky weapons, items, vehicles, and characters. It also has a variety of modes like 50v50, sniper mode, treasure hunt mode.
-
Battle Prime: This is a game that offers fast-paced action with stunning graphics and smooth controls. It has a variety of characters with unique abilities and skills that you can customize and upgrade. It also has different modes like team deathmatch, domination.
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Conclusion
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Free Fire Max is an amazing game that offers a thrilling and immersive battle royale experience on mobile devices. It has improved graphics, performance, and compatibility compared to Free Fire. You can also play it on your PC using an Android emulator like BlueStacks. However, you need to have a good computer that meets the system requirements for playing Free Fire Max on PC. You also need to follow some tips and tricks to improve your skills and win more matches. If you are looking for some other games like Free Fire Max, you can try out COD Mobile, PUBG New State, PUBG Mobile, Knives Out, or Battle Prime.
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We hope that this article has helped you learn more about Free Fire PC download max apk and how to enjoy it on your device. If you have any questions or feedback, please let us know in the comments section below. Thank you for reading and happy gaming!
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FAQs
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Here are some of the most frequently asked questions and answers about Free Fire Max and its related topics:
-
-
Is Free Fire Max free to play?
-
Yes, Free Fire Max is free to play on both Android devices and PC. However, it may contain some in-app purchases and ads that can enhance your gameplay or support the developers.
-
Can I play Free Fire Max with Free Fire players?
-
Yes, Free Fire Max and Free Fire are cross-compatible, which means that you can play with or against players from both versions. However, you need to have the same server and update version as the other players.
-
Can I transfer my data from Free Fire to Free Fire Max?
-
Yes, you can transfer your data from Free Fire to Free Fire Max using your Facebook or VK account. You can also use the same account to log in to both versions of the game.
-
What are the best weapons in Free Fire Max?
-
The best weapons in Free Fire Max may vary depending on your personal preference, playstyle, and situation. However, some of the most popular and powerful weapons in the game are:
-
-
Groza: This is an assault rifle that has high damage, accuracy, and fire rate. It is ideal for medium to long-range combat.
-
M82B: This is a sniper rifle that can penetrate through gloo walls and deal massive damage. It is perfect for long-range sniping.
-
M1887: This is a shotgun that can fire two shots in quick succession and deal devastating damage at close range. It is great for rushing and clearing buildings.
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M79: This is a grenade launcher that can fire explosive projectiles that can damage multiple enemies at once. It is useful for breaking gloo walls and creating chaos.
-
Katana: This is a melee weapon that can slash enemies with a single hit and deflect bullets with its blade. It is fun and effective for close combat.
-
-
How can I get diamonds in Free Fire Max?
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Diamonds are the premium currency in Free Fire Max that can be used to buy various items and features in the game. You can get diamonds in several ways, such as:
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Purchasing them with real money: This is the easiest and fastest way to get diamonds, but it may cost you a lot.
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Completing surveys and offers: This is a free way to get diamonds, but it may take you some time and effort.
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Participating in events and giveaways: This is another free way to get diamonds, but it may depend on your luck and availability.
-
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diff --git a/spaces/801artistry/RVC801/utils/i18n.py b/spaces/801artistry/RVC801/utils/i18n.py
deleted file mode 100644
index 8e75d2bc26ff86ab1716b8d7f239ad9f5cc1e32d..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/utils/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = "es_ES"
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "es_ES"
- language = "es_ES"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- # print("Use Language:", self.language)
- print("")
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/utils/autocast.py b/spaces/AIConsultant/MusicGen/audiocraft/utils/autocast.py
deleted file mode 100644
index ed644843bb37cf8a92a20fbd51d6cebaa43b9a08..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/utils/autocast.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-
-class TorchAutocast:
- """TorchAutocast utility class.
- Allows you to enable and disable autocast. This is specially useful
- when dealing with different architectures and clusters with different
- levels of support.
-
- Args:
- enabled (bool): Whether to enable torch.autocast or not.
- args: Additional args for torch.autocast.
- kwargs: Additional kwargs for torch.autocast
- """
- def __init__(self, enabled: bool, *args, **kwargs):
- self.autocast = torch.autocast(*args, **kwargs) if enabled else None
-
- def __enter__(self):
- if self.autocast is None:
- return
- try:
- self.autocast.__enter__()
- except RuntimeError:
- device = self.autocast.device
- dtype = self.autocast.fast_dtype
- raise RuntimeError(
- f"There was an error autocasting with dtype={dtype} device={device}\n"
- "If you are on the FAIR Cluster, you might need to use autocast_dtype=float16"
- )
-
- def __exit__(self, *args, **kwargs):
- if self.autocast is None:
- return
- self.autocast.__exit__(*args, **kwargs)
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco.py
deleted file mode 100644
index f66aa15fc447bce5f510a60bdda1914a8a7b5a76..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco.py
+++ /dev/null
@@ -1,21 +0,0 @@
-_base_ = './yolov6_s_syncbn_fast_8xb32-400e_coco.py'
-
-# ======================= Possible modified parameters =======================
-# -----model related-----
-# The scaling factor that controls the depth of the network structure
-deepen_factor = 0.33
-# The scaling factor that controls the width of the network structure
-widen_factor = 0.25
-
-# -----train val related-----
-lr_factor = 0.02 # Learning rate scaling factor
-
-# ============================== Unmodified in most cases ===================
-model = dict(
- backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
- neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
- bbox_head=dict(
- head_module=dict(widen_factor=widen_factor),
- loss_bbox=dict(iou_mode='siou')))
-
-default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/LayoutChildren.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/LayoutChildren.js
deleted file mode 100644
index e26bc91c852c02a6a2f213c5268544b5e9203c91..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/LayoutChildren.js
+++ /dev/null
@@ -1,102 +0,0 @@
-import PreLayoutChild from '../basesizer/utils/PreLayoutChild.js';
-import LayoutChild from '../basesizer/utils/LayoutChild.js';
-import { GetDisplayWidth, GetDisplayHeight } from '../../../plugins/utils/size/GetDisplaySize.js';
-
-
-var LayoutChildren = function () {
- var innerLineWidth = this.innerWidth;
- var justifyPercentage = this.justifyPercentage;
- var itemSpace = this.space.item,
- lineSpace = this.space.line,
- indentLeftOdd = this.space.indentLeftOdd,
- indentLeftEven = this.space.indentLeftEven,
- indentTopOdd = this.space.indentTopOdd,
- indentTopEven = this.space.indentTopEven;
-
- var child, childConfig, padding, justifySpace = 0, indentLeft, indentTop;
- var startX = this.innerLeft,
- startY = this.innerTop;
- var x, y, width, height; // Align zone
- var lines = this.widthWrapResult.lines;
- var line, lineChlidren, remainderLineWidth;
-
- var itemX,
- itemY = startY;
- for (var i = 0, icnt = lines.length; i < icnt; i++) {
- // Layout this line
- line = lines[i];
- lineChlidren = line.children;
- if (this.rtl) {
- lineChlidren.reverse();
- }
-
- indentLeft = (i % 2) ? indentLeftEven : indentLeftOdd;
- itemX = startX + indentLeft;
-
- remainderLineWidth = (innerLineWidth - line.width);
- switch (this.align) {
- case 0: // left
- break;
- case 1: // right
- itemX += remainderLineWidth;
- break;
- case 2: // center
- itemX += remainderLineWidth / 2;
- break;
- case 3: // justify-left
- justifySpace = GetJustifySpace(innerLineWidth, remainderLineWidth, justifyPercentage, lineChlidren.length);
- break;
- case 4: // justify-right
- justifySpace = GetJustifySpace(innerLineWidth, remainderLineWidth, justifyPercentage, lineChlidren.length);
- if (justifySpace === 0) {
- // Align right
- itemX += remainderLineWidth;
- }
- break;
- case 5: // justify-center
- justifySpace = GetJustifySpace(innerLineWidth, remainderLineWidth, justifyPercentage, lineChlidren.length);
- if (justifySpace === 0) {
- // Align center
- itemX += remainderLineWidth / 2;
- }
- break;
- }
-
- var isFirstChild = true;
- for (var j = 0, jcnt = lineChlidren.length; j < jcnt; j++) {
- child = lineChlidren[j];
- if (child.rexSizer.hidden) {
- continue;
- }
-
- childConfig = child.rexSizer;
- padding = childConfig.padding;
-
- PreLayoutChild.call(this, child);
-
- x = (itemX + padding.left);
-
- if (isFirstChild) {
- isFirstChild = false;
- } else {
- x += itemSpace;
- }
-
- indentTop = (j % 2) ? indentTopEven : indentTopOdd;
- y = (itemY + indentTop + padding.top);
- width = GetDisplayWidth(child);
- height = GetDisplayHeight(child);
- itemX = x + width + padding.right + justifySpace;
-
- LayoutChild.call(this, child, x, y, width, height, childConfig.align);
- }
-
- itemY += line.height + lineSpace;
- }
-}
-
-var GetJustifySpace = function (total, remainder, justifyPercentage, childCount) {
- return ((remainder / total) <= justifyPercentage) ? (remainder / (childCount - 1)) : 0;
-}
-
-export default LayoutChildren;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateTextArea.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateTextArea.js
deleted file mode 100644
index b067a21eadba802a9c5a39368cc6b6253f54c7b6..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateTextArea.js
+++ /dev/null
@@ -1,21 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import TextArea from '../../textarea/TextArea.js';
-import CreateChild from './utils/CreateChild.js';
-import ReplaceSliderConfig from './utils/ReplaceSliderConfig.js';
-
-var CreateTextArea = function (scene, data, view, styles, customBuilders) {
- data = MergeStyle(data, styles);
-
- // Replace data by child game object
- CreateChild(scene, data, 'background', view, styles, customBuilders);
- CreateChild(scene, data, 'text', view, styles, customBuilders);
- ReplaceSliderConfig(scene, data.slider, view, styles, customBuilders);
- CreateChild(scene, data, 'header', view, styles, customBuilders);
- CreateChild(scene, data, 'footer', view, styles, customBuilders);
-
- var gameObject = new TextArea(scene, data);
- scene.add.existing(gameObject);
- return gameObject;
-};
-
-export default CreateTextArea;
\ No newline at end of file
diff --git a/spaces/Alexxggs/ggvpnewen/app.py b/spaces/Alexxggs/ggvpnewen/app.py
deleted file mode 100644
index 1cb36229b97e1ee5e4a1bc17d7a1448d8de8e8ff..0000000000000000000000000000000000000000
--- a/spaces/Alexxggs/ggvpnewen/app.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import time
-
-import gradio as gr
-from sentence_transformers import SentenceTransformer
-
-import httpx
-import json
-
-from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat
-
-minilm = SentenceTransformer('all-MiniLM-L6-v2')
-mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
-
-
-def get_track_by_tags(tags, pat, duration, mode, maxit=20, loop=False):
- if loop:
- mode = "loop"
- else:
- mode = "track"
- r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
- json={
- "method": "RecordTrackTTM",
- "params": {
- "pat": pat,
- "duration": duration,
- "tags": tags,
- "mode": mode
- }
- })
-
- rdata = json.loads(r.text)
- assert rdata['status'] == 1, rdata['error']['text']
- trackurl = rdata['data']['tasks'][0]['download_link']
-
- print('Generating track ', end='')
- for i in range(maxit):
- r = httpx.get(trackurl)
- if r.status_code == 200:
- return trackurl
- time.sleep(1)
-
-
-def generate_track_by_prompt(email, prompt, duration, mode, loop=False):
- try:
- pat = get_pat(email)
- _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0]
- return get_track_by_tags(tags, pat, int(duration), mode, loop=loop), "Success", ",".join(tags)
- except Exception as e:
- return None, str(e), ""
-
-
-block = gr.Blocks(css=".mx-auto{max-width:550px}.svelte-10ogue4 {background: rgba(0,0,0,0.0);width:100%;border: 0px}.gradio-container {background: rgba(0,0,0,0.0);border: 0px}.gr-block {background: rgba(0,0,0,0.0);border: 0px} #component-4 {opacity: 0.8;background: linear-gradient(#233581, #E23F9C);border: 0px;margin-bottom: 17px;border-radius: 10px;}#component-5 {opacity: 0.8;background: linear-gradient(#E23F9C, #233581);border: 0px;margin-bottom: 17px;border-radius: 10px;}.gr-form{background: rgba(0,0,0,0.0);border: 0px}.gr-text-input {background: rgba(255,255,255,1);border: 0px}.text-gray-500 {color: #FFFFFF;font-weight: 600;text-align: center;font-size: 18px;}#component-1 {height: 0px;}#range_id_0 {opacity: 0.5;border-radius: 8px;-webkit-appearance: none; width: 60%; height: 15px; background-color: #E64CAC; background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#233581, #E23F9C), to(#E956B8)); background-image: -webkit-linear-gradient(right, #233581, #E956B8); background-image: -moz-linear-gradient(right, #233581, #E956B8); background-image: -ms-linear-gradient(right, #233581, #E956B8); background-image: -o-linear-gradient(right, #233581, #E956B8)}#component-6{opacity: 0.9;background: linear-gradient(#233581, #515A7F);border-radius: 10px}#component-7{margin-top: 7px;margin-bottom: 7px;text-align: center;display:inline;opacity: 0.9;background: linear-gradient(#515A7F, #515A7F);border-radius: 10px;}.ml-2{color: #FFFFFF;}#component-8 {height: 100px;z-index:99;background: linear-gradient(#515A7F, #515A7F);border-radius: 10px;opacity: 0.9}.absolute{background: linear-gradient(#EC5CC0, #D61B70);border: 0px}.feather{color: #FFFFFF;} .mt-7{z-index:100;background: linear-gradient(#515A7F, #515A7F);border-radius: 10px;} .gr-button{margin-left: 30%;width:40%;justify-content: center; background: linear-gradient(#EC5DC1, #D61A6F); padding: 0 12px; border: none; border-radius: 8px; box-shadow: 0 30px 15px rgba(0, 0, 0, 0.15); outline: none; color: #FFF; font: 400 16px/2.5 Nunito, Sans-serif; text-transform: uppercase; cursor: pointer;}#component-11{justify-content: center;text-align: center;margin-top:10px;border: 0px}.mx-auto{background: rgba(0,0,0,0.0);width:100%;border: 0px;padding:0 0 0 0}#component-9 {margin-top: 5px;opacity: 0.8;padding: 3px;background: linear-gradient(#515A7F, #515A7F);border-radius: 10px;}#component-10{margin-top: 5px;opacity: 0.8;padding: 3px;background: linear-gradient(#515A7F, #515A7F);border-radius: 10px;}#component-12{display:none}.gr-input-label{margin-right: 1px;width:71px;font-weight: 400;background: linear-gradient(#584C84, #2C3D7F);text-align: center;border: 0px}.font-semibold{display:none}")
-
-
-
-with block:
- gr.HTML(
- """
-
-
- """
- )
- with gr.Group():
- with gr.Box():
- email = gr.Textbox(label="email")
- prompt = gr.Textbox(label="Text example (bass drum cyberpunk)")
- duration = gr.Slider(label="Time (seconds)", value=30, maximum=250,)
- mode = gr.Radio(["track", "loop", "jingle", "mix"], label="Track Type")
- out = gr.Audio()
- result_msg = gr.Text(label="System messages")
- tags = gr.Text(label="Generated track tags")
- btn = gr.Button("Create").style(full_width=True)
- is_loop = gr.Checkbox(label="Loop a track")
- btn.click(fn=generate_track_by_prompt, inputs=[email, prompt, duration, mode, is_loop], outputs=[out, result_msg, tags])
- gr.HTML('''
-
- ''')
-
-block.launch()
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/face3d/util/nvdiffrast.py b/spaces/Alpaca233/SadTalker/src/face3d/util/nvdiffrast.py
deleted file mode 100644
index f3245859c650afbfe841a66b74cddefaf28820d9..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/face3d/util/nvdiffrast.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""This script is the differentiable renderer for Deep3DFaceRecon_pytorch
- Attention, antialiasing step is missing in current version.
-"""
-import pytorch3d.ops
-import torch
-import torch.nn.functional as F
-import kornia
-from kornia.geometry.camera import pixel2cam
-import numpy as np
-from typing import List
-from scipy.io import loadmat
-from torch import nn
-
-from pytorch3d.structures import Meshes
-from pytorch3d.renderer import (
- look_at_view_transform,
- FoVPerspectiveCameras,
- DirectionalLights,
- RasterizationSettings,
- MeshRenderer,
- MeshRasterizer,
- SoftPhongShader,
- TexturesUV,
-)
-
-# def ndc_projection(x=0.1, n=1.0, f=50.0):
-# return np.array([[n/x, 0, 0, 0],
-# [ 0, n/-x, 0, 0],
-# [ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)],
-# [ 0, 0, -1, 0]]).astype(np.float32)
-
-class MeshRenderer(nn.Module):
- def __init__(self,
- rasterize_fov,
- znear=0.1,
- zfar=10,
- rasterize_size=224):
- super(MeshRenderer, self).__init__()
-
- # x = np.tan(np.deg2rad(rasterize_fov * 0.5)) * znear
- # self.ndc_proj = torch.tensor(ndc_projection(x=x, n=znear, f=zfar)).matmul(
- # torch.diag(torch.tensor([1., -1, -1, 1])))
- self.rasterize_size = rasterize_size
- self.fov = rasterize_fov
- self.znear = znear
- self.zfar = zfar
-
- self.rasterizer = None
-
- def forward(self, vertex, tri, feat=None):
- """
- Return:
- mask -- torch.tensor, size (B, 1, H, W)
- depth -- torch.tensor, size (B, 1, H, W)
- features(optional) -- torch.tensor, size (B, C, H, W) if feat is not None
-
- Parameters:
- vertex -- torch.tensor, size (B, N, 3)
- tri -- torch.tensor, size (B, M, 3) or (M, 3), triangles
- feat(optional) -- torch.tensor, size (B, N ,C), features
- """
- device = vertex.device
- rsize = int(self.rasterize_size)
- # ndc_proj = self.ndc_proj.to(device)
- # trans to homogeneous coordinates of 3d vertices, the direction of y is the same as v
- if vertex.shape[-1] == 3:
- vertex = torch.cat([vertex, torch.ones([*vertex.shape[:2], 1]).to(device)], dim=-1)
- vertex[..., 0] = -vertex[..., 0]
-
-
- # vertex_ndc = vertex @ ndc_proj.t()
- if self.rasterizer is None:
- self.rasterizer = MeshRasterizer()
- print("create rasterizer on device cuda:%d"%device.index)
-
- # ranges = None
- # if isinstance(tri, List) or len(tri.shape) == 3:
- # vum = vertex_ndc.shape[1]
- # fnum = torch.tensor([f.shape[0] for f in tri]).unsqueeze(1).to(device)
- # fstartidx = torch.cumsum(fnum, dim=0) - fnum
- # ranges = torch.cat([fstartidx, fnum], axis=1).type(torch.int32).cpu()
- # for i in range(tri.shape[0]):
- # tri[i] = tri[i] + i*vum
- # vertex_ndc = torch.cat(vertex_ndc, dim=0)
- # tri = torch.cat(tri, dim=0)
-
- # for range_mode vetex: [B*N, 4], tri: [B*M, 3], for instance_mode vetex: [B, N, 4], tri: [M, 3]
- tri = tri.type(torch.int32).contiguous()
-
- # rasterize
- cameras = FoVPerspectiveCameras(
- device=device,
- fov=self.fov,
- znear=self.znear,
- zfar=self.zfar,
- )
-
- raster_settings = RasterizationSettings(
- image_size=rsize
- )
-
- # print(vertex.shape, tri.shape)
- mesh = Meshes(vertex.contiguous()[...,:3], tri.unsqueeze(0).repeat((vertex.shape[0],1,1)))
-
- fragments = self.rasterizer(mesh, cameras = cameras, raster_settings = raster_settings)
- rast_out = fragments.pix_to_face.squeeze(-1)
- depth = fragments.zbuf
-
- # render depth
- depth = depth.permute(0, 3, 1, 2)
- mask = (rast_out > 0).float().unsqueeze(1)
- depth = mask * depth
-
-
- image = None
- if feat is not None:
- attributes = feat.reshape(-1,3)[mesh.faces_packed()]
- image = pytorch3d.ops.interpolate_face_attributes(fragments.pix_to_face,
- fragments.bary_coords,
- attributes)
- # print(image.shape)
- image = image.squeeze(-2).permute(0, 3, 1, 2)
- image = mask * image
-
- return mask, depth, image
-
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/resnet.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/resnet.py
deleted file mode 100644
index 72aa17ed2c2dccf7b5893832a4b36a6e1e7f7719..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/resnet.py
+++ /dev/null
@@ -1,878 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-# `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from functools import partial
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .activations import get_activation
-from .attention import AdaGroupNorm
-from .attention_processor import SpatialNorm
-from .lora import LoRACompatibleConv, LoRACompatibleLinear
-
-
-class Upsample1D(nn.Module):
- """A 1D upsampling layer with an optional convolution.
-
- Parameters:
- channels (`int`):
- number of channels in the inputs and outputs.
- use_conv (`bool`, default `False`):
- option to use a convolution.
- use_conv_transpose (`bool`, default `False`):
- option to use a convolution transpose.
- out_channels (`int`, optional):
- number of output channels. Defaults to `channels`.
- """
-
- def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_conv_transpose = use_conv_transpose
- self.name = name
-
- self.conv = None
- if use_conv_transpose:
- self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
- elif use_conv:
- self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
-
- def forward(self, inputs):
- assert inputs.shape[1] == self.channels
- if self.use_conv_transpose:
- return self.conv(inputs)
-
- outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")
-
- if self.use_conv:
- outputs = self.conv(outputs)
-
- return outputs
-
-
-class Downsample1D(nn.Module):
- """A 1D downsampling layer with an optional convolution.
-
- Parameters:
- channels (`int`):
- number of channels in the inputs and outputs.
- use_conv (`bool`, default `False`):
- option to use a convolution.
- out_channels (`int`, optional):
- number of output channels. Defaults to `channels`.
- padding (`int`, default `1`):
- padding for the convolution.
- """
-
- def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.padding = padding
- stride = 2
- self.name = name
-
- if use_conv:
- self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
- else:
- assert self.channels == self.out_channels
- self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)
-
- def forward(self, inputs):
- assert inputs.shape[1] == self.channels
- return self.conv(inputs)
-
-
-class Upsample2D(nn.Module):
- """A 2D upsampling layer with an optional convolution.
-
- Parameters:
- channels (`int`):
- number of channels in the inputs and outputs.
- use_conv (`bool`, default `False`):
- option to use a convolution.
- use_conv_transpose (`bool`, default `False`):
- option to use a convolution transpose.
- out_channels (`int`, optional):
- number of output channels. Defaults to `channels`.
- """
-
- def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_conv_transpose = use_conv_transpose
- self.name = name
-
- conv = None
- if use_conv_transpose:
- conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
- elif use_conv:
- conv = LoRACompatibleConv(self.channels, self.out_channels, 3, padding=1)
-
- # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
- if name == "conv":
- self.conv = conv
- else:
- self.Conv2d_0 = conv
-
- def forward(self, hidden_states, output_size=None):
- assert hidden_states.shape[1] == self.channels
-
- if self.use_conv_transpose:
- return self.conv(hidden_states)
-
- # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
- # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
- # https://github.com/pytorch/pytorch/issues/86679
- dtype = hidden_states.dtype
- if dtype == torch.bfloat16:
- hidden_states = hidden_states.to(torch.float32)
-
- # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
- if hidden_states.shape[0] >= 64:
- hidden_states = hidden_states.contiguous()
-
- # if `output_size` is passed we force the interpolation output
- # size and do not make use of `scale_factor=2`
- if output_size is None:
- hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
- else:
- hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
-
- # If the input is bfloat16, we cast back to bfloat16
- if dtype == torch.bfloat16:
- hidden_states = hidden_states.to(dtype)
-
- # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
- if self.use_conv:
- if self.name == "conv":
- hidden_states = self.conv(hidden_states)
- else:
- hidden_states = self.Conv2d_0(hidden_states)
-
- return hidden_states
-
-
-class Downsample2D(nn.Module):
- """A 2D downsampling layer with an optional convolution.
-
- Parameters:
- channels (`int`):
- number of channels in the inputs and outputs.
- use_conv (`bool`, default `False`):
- option to use a convolution.
- out_channels (`int`, optional):
- number of output channels. Defaults to `channels`.
- padding (`int`, default `1`):
- padding for the convolution.
- """
-
- def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.padding = padding
- stride = 2
- self.name = name
-
- if use_conv:
- conv = LoRACompatibleConv(self.channels, self.out_channels, 3, stride=stride, padding=padding)
- else:
- assert self.channels == self.out_channels
- conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
-
- # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
- if name == "conv":
- self.Conv2d_0 = conv
- self.conv = conv
- elif name == "Conv2d_0":
- self.conv = conv
- else:
- self.conv = conv
-
- def forward(self, hidden_states):
- assert hidden_states.shape[1] == self.channels
- if self.use_conv and self.padding == 0:
- pad = (0, 1, 0, 1)
- hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
-
- assert hidden_states.shape[1] == self.channels
- hidden_states = self.conv(hidden_states)
-
- return hidden_states
-
-
-class FirUpsample2D(nn.Module):
- """A 2D FIR upsampling layer with an optional convolution.
-
- Parameters:
- channels (`int`):
- number of channels in the inputs and outputs.
- use_conv (`bool`, default `False`):
- option to use a convolution.
- out_channels (`int`, optional):
- number of output channels. Defaults to `channels`.
- fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
- kernel for the FIR filter.
- """
-
- def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
- super().__init__()
- out_channels = out_channels if out_channels else channels
- if use_conv:
- self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
- self.use_conv = use_conv
- self.fir_kernel = fir_kernel
- self.out_channels = out_channels
-
- def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
- """Fused `upsample_2d()` followed by `Conv2d()`.
-
- Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
- efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
- arbitrary order.
-
- Args:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- weight: Weight tensor of the shape `[filterH, filterW, inChannels,
- outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
- kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
- (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
- factor: Integer upsampling factor (default: 2).
- gain: Scaling factor for signal magnitude (default: 1.0).
-
- Returns:
- output: Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same
- datatype as `hidden_states`.
- """
-
- assert isinstance(factor, int) and factor >= 1
-
- # Setup filter kernel.
- if kernel is None:
- kernel = [1] * factor
-
- # setup kernel
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * (gain * (factor**2))
-
- if self.use_conv:
- convH = weight.shape[2]
- convW = weight.shape[3]
- inC = weight.shape[1]
-
- pad_value = (kernel.shape[0] - factor) - (convW - 1)
-
- stride = (factor, factor)
- # Determine data dimensions.
- output_shape = (
- (hidden_states.shape[2] - 1) * factor + convH,
- (hidden_states.shape[3] - 1) * factor + convW,
- )
- output_padding = (
- output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,
- output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
- )
- assert output_padding[0] >= 0 and output_padding[1] >= 0
- num_groups = hidden_states.shape[1] // inC
-
- # Transpose weights.
- weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
- weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)
- weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
-
- inverse_conv = F.conv_transpose2d(
- hidden_states, weight, stride=stride, output_padding=output_padding, padding=0
- )
-
- output = upfirdn2d_native(
- inverse_conv,
- torch.tensor(kernel, device=inverse_conv.device),
- pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),
- )
- else:
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states,
- torch.tensor(kernel, device=hidden_states.device),
- up=factor,
- pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
- )
-
- return output
-
- def forward(self, hidden_states):
- if self.use_conv:
- height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
- height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
- else:
- height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
-
- return height
-
-
-class FirDownsample2D(nn.Module):
- """A 2D FIR downsampling layer with an optional convolution.
-
- Parameters:
- channels (`int`):
- number of channels in the inputs and outputs.
- use_conv (`bool`, default `False`):
- option to use a convolution.
- out_channels (`int`, optional):
- number of output channels. Defaults to `channels`.
- fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
- kernel for the FIR filter.
- """
-
- def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
- super().__init__()
- out_channels = out_channels if out_channels else channels
- if use_conv:
- self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
- self.fir_kernel = fir_kernel
- self.use_conv = use_conv
- self.out_channels = out_channels
-
- def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
- """Fused `Conv2d()` followed by `downsample_2d()`.
- Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
- efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
- arbitrary order.
-
- Args:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- weight:
- Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
- performed by `inChannels = x.shape[0] // numGroups`.
- kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] *
- factor`, which corresponds to average pooling.
- factor: Integer downsampling factor (default: 2).
- gain: Scaling factor for signal magnitude (default: 1.0).
-
- Returns:
- output: Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and
- same datatype as `x`.
- """
-
- assert isinstance(factor, int) and factor >= 1
- if kernel is None:
- kernel = [1] * factor
-
- # setup kernel
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * gain
-
- if self.use_conv:
- _, _, convH, convW = weight.shape
- pad_value = (kernel.shape[0] - factor) + (convW - 1)
- stride_value = [factor, factor]
- upfirdn_input = upfirdn2d_native(
- hidden_states,
- torch.tensor(kernel, device=hidden_states.device),
- pad=((pad_value + 1) // 2, pad_value // 2),
- )
- output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
- else:
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states,
- torch.tensor(kernel, device=hidden_states.device),
- down=factor,
- pad=((pad_value + 1) // 2, pad_value // 2),
- )
-
- return output
-
- def forward(self, hidden_states):
- if self.use_conv:
- downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
- hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
- else:
- hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
-
- return hidden_states
-
-
-# downsample/upsample layer used in k-upscaler, might be able to use FirDownsample2D/DirUpsample2D instead
-class KDownsample2D(nn.Module):
- def __init__(self, pad_mode="reflect"):
- super().__init__()
- self.pad_mode = pad_mode
- kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]])
- self.pad = kernel_1d.shape[1] // 2 - 1
- self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
-
- def forward(self, inputs):
- inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
- weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
- indices = torch.arange(inputs.shape[1], device=inputs.device)
- kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
- weight[indices, indices] = kernel
- return F.conv2d(inputs, weight, stride=2)
-
-
-class KUpsample2D(nn.Module):
- def __init__(self, pad_mode="reflect"):
- super().__init__()
- self.pad_mode = pad_mode
- kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) * 2
- self.pad = kernel_1d.shape[1] // 2 - 1
- self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
-
- def forward(self, inputs):
- inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
- weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
- indices = torch.arange(inputs.shape[1], device=inputs.device)
- kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
- weight[indices, indices] = kernel
- return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1)
-
-
-class ResnetBlock2D(nn.Module):
- r"""
- A Resnet block.
-
- Parameters:
- in_channels (`int`): The number of channels in the input.
- out_channels (`int`, *optional*, default to be `None`):
- The number of output channels for the first conv2d layer. If None, same as `in_channels`.
- dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
- temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
- groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
- groups_out (`int`, *optional*, default to None):
- The number of groups to use for the second normalization layer. if set to None, same as `groups`.
- eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
- non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
- time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
- By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
- "ada_group" for a stronger conditioning with scale and shift.
- kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
- [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
- output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
- use_in_shortcut (`bool`, *optional*, default to `True`):
- If `True`, add a 1x1 nn.conv2d layer for skip-connection.
- up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
- down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
- conv_shortcut_bias (`bool`, *optional*, default to `True`): If `True`, adds a learnable bias to the
- `conv_shortcut` output.
- conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
- If None, same as `out_channels`.
- """
-
- def __init__(
- self,
- *,
- in_channels,
- out_channels=None,
- conv_shortcut=False,
- dropout=0.0,
- temb_channels=512,
- groups=32,
- groups_out=None,
- pre_norm=True,
- eps=1e-6,
- non_linearity="swish",
- skip_time_act=False,
- time_embedding_norm="default", # default, scale_shift, ada_group, spatial
- kernel=None,
- output_scale_factor=1.0,
- use_in_shortcut=None,
- up=False,
- down=False,
- conv_shortcut_bias: bool = True,
- conv_2d_out_channels: Optional[int] = None,
- ):
- super().__init__()
- self.pre_norm = pre_norm
- self.pre_norm = True
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
- self.up = up
- self.down = down
- self.output_scale_factor = output_scale_factor
- self.time_embedding_norm = time_embedding_norm
- self.skip_time_act = skip_time_act
-
- if groups_out is None:
- groups_out = groups
-
- if self.time_embedding_norm == "ada_group":
- self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
- elif self.time_embedding_norm == "spatial":
- self.norm1 = SpatialNorm(in_channels, temb_channels)
- else:
- self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
-
- self.conv1 = LoRACompatibleConv(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
- if temb_channels is not None:
- if self.time_embedding_norm == "default":
- self.time_emb_proj = LoRACompatibleLinear(temb_channels, out_channels)
- elif self.time_embedding_norm == "scale_shift":
- self.time_emb_proj = LoRACompatibleLinear(temb_channels, 2 * out_channels)
- elif self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
- self.time_emb_proj = None
- else:
- raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
- else:
- self.time_emb_proj = None
-
- if self.time_embedding_norm == "ada_group":
- self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
- elif self.time_embedding_norm == "spatial":
- self.norm2 = SpatialNorm(out_channels, temb_channels)
- else:
- self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
-
- self.dropout = torch.nn.Dropout(dropout)
- conv_2d_out_channels = conv_2d_out_channels or out_channels
- self.conv2 = LoRACompatibleConv(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
-
- self.nonlinearity = get_activation(non_linearity)
-
- self.upsample = self.downsample = None
- if self.up:
- if kernel == "fir":
- fir_kernel = (1, 3, 3, 1)
- self.upsample = lambda x: upsample_2d(x, kernel=fir_kernel)
- elif kernel == "sde_vp":
- self.upsample = partial(F.interpolate, scale_factor=2.0, mode="nearest")
- else:
- self.upsample = Upsample2D(in_channels, use_conv=False)
- elif self.down:
- if kernel == "fir":
- fir_kernel = (1, 3, 3, 1)
- self.downsample = lambda x: downsample_2d(x, kernel=fir_kernel)
- elif kernel == "sde_vp":
- self.downsample = partial(F.avg_pool2d, kernel_size=2, stride=2)
- else:
- self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
-
- self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
-
- self.conv_shortcut = None
- if self.use_in_shortcut:
- self.conv_shortcut = LoRACompatibleConv(
- in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias
- )
-
- def forward(self, input_tensor, temb):
- hidden_states = input_tensor
-
- if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
- hidden_states = self.norm1(hidden_states, temb)
- else:
- hidden_states = self.norm1(hidden_states)
-
- hidden_states = self.nonlinearity(hidden_states)
-
- if self.upsample is not None:
- # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
- if hidden_states.shape[0] >= 64:
- input_tensor = input_tensor.contiguous()
- hidden_states = hidden_states.contiguous()
- input_tensor = self.upsample(input_tensor)
- hidden_states = self.upsample(hidden_states)
- elif self.downsample is not None:
- input_tensor = self.downsample(input_tensor)
- hidden_states = self.downsample(hidden_states)
-
- hidden_states = self.conv1(hidden_states)
-
- if self.time_emb_proj is not None:
- if not self.skip_time_act:
- temb = self.nonlinearity(temb)
- temb = self.time_emb_proj(temb)[:, :, None, None]
-
- if temb is not None and self.time_embedding_norm == "default":
- hidden_states = hidden_states + temb
-
- if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
- hidden_states = self.norm2(hidden_states, temb)
- else:
- hidden_states = self.norm2(hidden_states)
-
- if temb is not None and self.time_embedding_norm == "scale_shift":
- scale, shift = torch.chunk(temb, 2, dim=1)
- hidden_states = hidden_states * (1 + scale) + shift
-
- hidden_states = self.nonlinearity(hidden_states)
-
- hidden_states = self.dropout(hidden_states)
- hidden_states = self.conv2(hidden_states)
-
- if self.conv_shortcut is not None:
- input_tensor = self.conv_shortcut(input_tensor)
-
- output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
-
- return output_tensor
-
-
-# unet_rl.py
-def rearrange_dims(tensor):
- if len(tensor.shape) == 2:
- return tensor[:, :, None]
- if len(tensor.shape) == 3:
- return tensor[:, :, None, :]
- elif len(tensor.shape) == 4:
- return tensor[:, :, 0, :]
- else:
- raise ValueError(f"`len(tensor)`: {len(tensor)} has to be 2, 3 or 4.")
-
-
-class Conv1dBlock(nn.Module):
- """
- Conv1d --> GroupNorm --> Mish
- """
-
- def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
- super().__init__()
-
- self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)
- self.group_norm = nn.GroupNorm(n_groups, out_channels)
- self.mish = nn.Mish()
-
- def forward(self, inputs):
- intermediate_repr = self.conv1d(inputs)
- intermediate_repr = rearrange_dims(intermediate_repr)
- intermediate_repr = self.group_norm(intermediate_repr)
- intermediate_repr = rearrange_dims(intermediate_repr)
- output = self.mish(intermediate_repr)
- return output
-
-
-# unet_rl.py
-class ResidualTemporalBlock1D(nn.Module):
- def __init__(self, inp_channels, out_channels, embed_dim, kernel_size=5):
- super().__init__()
- self.conv_in = Conv1dBlock(inp_channels, out_channels, kernel_size)
- self.conv_out = Conv1dBlock(out_channels, out_channels, kernel_size)
-
- self.time_emb_act = nn.Mish()
- self.time_emb = nn.Linear(embed_dim, out_channels)
-
- self.residual_conv = (
- nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
- )
-
- def forward(self, inputs, t):
- """
- Args:
- inputs : [ batch_size x inp_channels x horizon ]
- t : [ batch_size x embed_dim ]
-
- returns:
- out : [ batch_size x out_channels x horizon ]
- """
- t = self.time_emb_act(t)
- t = self.time_emb(t)
- out = self.conv_in(inputs) + rearrange_dims(t)
- out = self.conv_out(out)
- return out + self.residual_conv(inputs)
-
-
-def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
- r"""Upsample2D a batch of 2D images with the given filter.
- Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
- filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
- `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is
- a: multiple of the upsampling factor.
-
- Args:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
- (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
- factor: Integer upsampling factor (default: 2).
- gain: Scaling factor for signal magnitude (default: 1.0).
-
- Returns:
- output: Tensor of the shape `[N, C, H * factor, W * factor]`
- """
- assert isinstance(factor, int) and factor >= 1
- if kernel is None:
- kernel = [1] * factor
-
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * (gain * (factor**2))
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states,
- kernel.to(device=hidden_states.device),
- up=factor,
- pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
- )
- return output
-
-
-def downsample_2d(hidden_states, kernel=None, factor=2, gain=1):
- r"""Downsample2D a batch of 2D images with the given filter.
- Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
- given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
- specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
- shape is a multiple of the downsampling factor.
-
- Args:
- hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
- kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
- (separable). The default is `[1] * factor`, which corresponds to average pooling.
- factor: Integer downsampling factor (default: 2).
- gain: Scaling factor for signal magnitude (default: 1.0).
-
- Returns:
- output: Tensor of the shape `[N, C, H // factor, W // factor]`
- """
-
- assert isinstance(factor, int) and factor >= 1
- if kernel is None:
- kernel = [1] * factor
-
- kernel = torch.tensor(kernel, dtype=torch.float32)
- if kernel.ndim == 1:
- kernel = torch.outer(kernel, kernel)
- kernel /= torch.sum(kernel)
-
- kernel = kernel * gain
- pad_value = kernel.shape[0] - factor
- output = upfirdn2d_native(
- hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2)
- )
- return output
-
-
-def upfirdn2d_native(tensor, kernel, up=1, down=1, pad=(0, 0)):
- up_x = up_y = up
- down_x = down_y = down
- pad_x0 = pad_y0 = pad[0]
- pad_x1 = pad_y1 = pad[1]
-
- _, channel, in_h, in_w = tensor.shape
- tensor = tensor.reshape(-1, in_h, in_w, 1)
-
- _, in_h, in_w, minor = tensor.shape
- kernel_h, kernel_w = kernel.shape
-
- out = tensor.view(-1, in_h, 1, in_w, 1, minor)
- out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
- out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
- out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
- out = out.to(tensor.device) # Move back to mps if necessary
- out = out[
- :,
- max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
- max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
- :,
- ]
-
- out = out.permute(0, 3, 1, 2)
- out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
- w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
- out = F.conv2d(out, w)
- out = out.reshape(
- -1,
- minor,
- in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
- in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
- )
- out = out.permute(0, 2, 3, 1)
- out = out[:, ::down_y, ::down_x, :]
-
- out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
- out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
- return out.view(-1, channel, out_h, out_w)
-
-
-class TemporalConvLayer(nn.Module):
- """
- Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
- https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
- """
-
- def __init__(self, in_dim, out_dim=None, dropout=0.0):
- super().__init__()
- out_dim = out_dim or in_dim
- self.in_dim = in_dim
- self.out_dim = out_dim
-
- # conv layers
- self.conv1 = nn.Sequential(
- nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0))
- )
- self.conv2 = nn.Sequential(
- nn.GroupNorm(32, out_dim),
- nn.SiLU(),
- nn.Dropout(dropout),
- nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
- )
- self.conv3 = nn.Sequential(
- nn.GroupNorm(32, out_dim),
- nn.SiLU(),
- nn.Dropout(dropout),
- nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
- )
- self.conv4 = nn.Sequential(
- nn.GroupNorm(32, out_dim),
- nn.SiLU(),
- nn.Dropout(dropout),
- nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
- )
-
- # zero out the last layer params,so the conv block is identity
- nn.init.zeros_(self.conv4[-1].weight)
- nn.init.zeros_(self.conv4[-1].bias)
-
- def forward(self, hidden_states, num_frames=1):
- hidden_states = (
- hidden_states[None, :].reshape((-1, num_frames) + hidden_states.shape[1:]).permute(0, 2, 1, 3, 4)
- )
-
- identity = hidden_states
- hidden_states = self.conv1(hidden_states)
- hidden_states = self.conv2(hidden_states)
- hidden_states = self.conv3(hidden_states)
- hidden_states = self.conv4(hidden_states)
-
- hidden_states = identity + hidden_states
-
- hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(
- (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
- )
- return hidden_states
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/text_to_video/test_text_to_video_zero.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/text_to_video/test_text_to_video_zero.py
deleted file mode 100644
index 8fc7254c52d1ac4508910a0671ae9422f616a76f..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/text_to_video/test_text_to_video_zero.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import torch
-
-from diffusers import DDIMScheduler, TextToVideoZeroPipeline
-from diffusers.utils import load_pt, require_torch_gpu, slow
-
-from ..test_pipelines_common import assert_mean_pixel_difference
-
-
-@slow
-@require_torch_gpu
-class TextToVideoZeroPipelineSlowTests(unittest.TestCase):
- def test_full_model(self):
- model_id = "runwayml/stable-diffusion-v1-5"
- pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
- pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
- generator = torch.Generator(device="cuda").manual_seed(0)
-
- prompt = "A bear is playing a guitar on Times Square"
- result = pipe(prompt=prompt, generator=generator).images
-
- expected_result = load_pt(
- "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"
- )
-
- assert_mean_pixel_difference(result, expected_result)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
deleted file mode 100644
index d4c7c98ed3c1116224663376350a9216e2acc015..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
+++ /dev/null
@@ -1,80 +0,0 @@
-_base_ = [
- '../_base_/models/retinanet_r50_fpn.py',
- '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
-]
-cudnn_benchmark = True
-norm_cfg = dict(type='BN', requires_grad=True)
-model = dict(
- pretrained='torchvision://resnet50',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch'),
- neck=dict(
- relu_before_extra_convs=True,
- no_norm_on_lateral=True,
- norm_cfg=norm_cfg),
- bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg),
- # training and testing settings
- train_cfg=dict(assigner=dict(neg_iou_thr=0.5)))
-# dataset settings
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=(640, 640),
- ratio_range=(0.8, 1.2),
- keep_ratio=True),
- dict(type='RandomCrop', crop_size=(640, 640)),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=(640, 640)),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(640, 640),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=64),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=8,
- workers_per_gpu=4,
- train=dict(pipeline=train_pipeline),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(
- type='SGD',
- lr=0.08,
- momentum=0.9,
- weight_decay=0.0001,
- paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=1000,
- warmup_ratio=0.1,
- step=[30, 40])
-# runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=50)
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superbooga/download_urls.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superbooga/download_urls.py
deleted file mode 100644
index 424a98857600dc54caf9cdea92b513d9116d6808..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/superbooga/download_urls.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import concurrent.futures
-
-import requests
-
-
-def download_single(url):
- headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'
- }
- response = requests.get(url, headers=headers, timeout=5)
- if response.status_code == 200:
- return response.content
- else:
- raise Exception("Failed to download URL")
-
-
-def download_urls(urls, threads=1):
- with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
- futures = []
- for url in urls:
- future = executor.submit(download_single, url)
- futures.append(future)
-
- results = []
- i = 0
- for future in concurrent.futures.as_completed(futures):
- try:
- result = future.result()
- results.append(result)
- i += 1
- yield f"{i}/{len(urls)}", results
- except Exception:
- pass
-
- yield "Done", results
diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/__init__.py b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/__init__.py
deleted file mode 100644
index b5c31fa84fe440243e49c26ad5c5d90bd8a83a0e..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/model/__init__.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""This package contains modules related to function, network architectures, and models"""
-
-import importlib
-from .base_model import BaseModel
-
-
-def find_model_using_name(model_name):
- """Import the module "model/[model_name]_model.py"."""
- model_file_name = "model." + model_name + "_model"
- modellib = importlib.import_module(model_file_name)
- model = None
- for name, cls in modellib.__dict__.items():
- if name.lower() == model_name.lower() and issubclass(cls, BaseModel):
- model = cls
-
- if model is None:
- print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_file_name, model_name))
- exit(0)
-
- return model
-
-
-def get_option_setter(model_name):
- """Return the static method of the model class."""
- model = find_model_using_name(model_name)
- return model.modify_options
-
-
-def create_model(opt):
- """Create a model given the option."""
- model = find_model_using_name(opt.model)
- instance = model(opt)
- print("model [%s] was created" % type(instance).__name__)
- return instance
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/models/diffusion/dpm_solver/sampler.py b/spaces/Anonymous-sub/Rerender/ControlNet/ldm/models/diffusion/dpm_solver/sampler.py
deleted file mode 100644
index 7d137b8cf36718c1c58faa09f9dd919e5fb2977b..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/models/diffusion/dpm_solver/sampler.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""SAMPLING ONLY."""
-import torch
-
-from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
-
-
-MODEL_TYPES = {
- "eps": "noise",
- "v": "v"
-}
-
-
-class DPMSolverSampler(object):
- def __init__(self, model, **kwargs):
- super().__init__()
- self.model = model
- to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
- self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
-
- def register_buffer(self, name, attr):
- if type(attr) == torch.Tensor:
- if attr.device != torch.device("cuda"):
- attr = attr.to(torch.device("cuda"))
- setattr(self, name, attr)
-
- @torch.no_grad()
- def sample(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
- if conditioning is not None:
- if isinstance(conditioning, dict):
- cbs = conditioning[list(conditioning.keys())[0]].shape[0]
- if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
- else:
- if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
- # sampling
- C, H, W = shape
- size = (batch_size, C, H, W)
-
- print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
-
- device = self.model.betas.device
- if x_T is None:
- img = torch.randn(size, device=device)
- else:
- img = x_T
-
- ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
-
- model_fn = model_wrapper(
- lambda x, t, c: self.model.apply_model(x, t, c),
- ns,
- model_type=MODEL_TYPES[self.model.parameterization],
- guidance_type="classifier-free",
- condition=conditioning,
- unconditional_condition=unconditional_conditioning,
- guidance_scale=unconditional_guidance_scale,
- )
-
- dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
- x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
-
- return x.to(device), None
\ No newline at end of file
diff --git a/spaces/AriaMei/TTSdemo/emotion_extract.py b/spaces/AriaMei/TTSdemo/emotion_extract.py
deleted file mode 100644
index 78f588ff7ff48aba4624cb704e4a46f18da5ea73..0000000000000000000000000000000000000000
--- a/spaces/AriaMei/TTSdemo/emotion_extract.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import torch
-import torch.nn as nn
-from transformers import Wav2Vec2Processor
-from transformers.models.wav2vec2.modeling_wav2vec2 import (
- Wav2Vec2Model,
- Wav2Vec2PreTrainedModel,
-)
-import os
-import librosa
-import numpy as np
-
-
-class RegressionHead(nn.Module):
- r"""Classification head."""
-
- def __init__(self, config):
- super().__init__()
-
- self.dense = nn.Linear(config.hidden_size, config.hidden_size)
- self.dropout = nn.Dropout(config.final_dropout)
- self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
-
- def forward(self, features, **kwargs):
- x = features
- x = self.dropout(x)
- x = self.dense(x)
- x = torch.tanh(x)
- x = self.dropout(x)
- x = self.out_proj(x)
-
- return x
-
-
-class EmotionModel(Wav2Vec2PreTrainedModel):
- r"""Speech emotion classifier."""
-
- def __init__(self, config):
- super().__init__(config)
-
- self.config = config
- self.wav2vec2 = Wav2Vec2Model(config)
- self.classifier = RegressionHead(config)
- self.init_weights()
-
- def forward(
- self,
- input_values,
- ):
- outputs = self.wav2vec2(input_values)
- hidden_states = outputs[0]
- hidden_states = torch.mean(hidden_states, dim=1)
- logits = self.classifier(hidden_states)
-
- return hidden_states, logits
-
-
-# load model from hub
-device = 'cuda' if torch.cuda.is_available() else "cpu"
-model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
-processor = Wav2Vec2Processor.from_pretrained(model_name)
-model = EmotionModel.from_pretrained(model_name).to(device)
-
-
-def process_func(
- x: np.ndarray,
- sampling_rate: int,
- embeddings: bool = False,
-) -> np.ndarray:
- r"""Predict emotions or extract embeddings from raw audio signal."""
-
- # run through processor to normalize signal
- # always returns a batch, so we just get the first entry
- # then we put it on the device
- y = processor(x, sampling_rate=sampling_rate)
- y = y['input_values'][0]
- y = torch.from_numpy(y).to(device)
-
- # run through model
- with torch.no_grad():
- y = model(y)[0 if embeddings else 1]
-
- # convert to numpy
- y = y.detach().cpu().numpy()
-
- return y
-#
-#
-# def disp(rootpath, wavname):
-# wav, sr = librosa.load(f"{rootpath}/{wavname}", 16000)
-# display(ipd.Audio(wav, rate=sr))
-
-rootpath = "dataset/nene"
-embs = []
-wavnames = []
-def extract_dir(path):
- rootpath = path
- for idx, wavname in enumerate(os.listdir(rootpath)):
- wav, sr =librosa.load(f"{rootpath}/{wavname}", 16000)
- emb = process_func(np.expand_dims(wav, 0), sr, embeddings=True)
- embs.append(emb)
- wavnames.append(wavname)
- np.save(f"{rootpath}/{wavname}.emo.npy", emb.squeeze(0))
- print(idx, wavname)
-
-def extract_wav(path):
- wav, sr = librosa.load(path, 16000)
- emb = process_func(np.expand_dims(wav, 0), sr, embeddings=True)
- return emb
-
-if __name__ == '__main__':
- for spk in ["serena", "koni", "nyaru","shanoa", "mana"]:
- extract_dir(f"dataset/{spk}")
diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/utils.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/utils.py
deleted file mode 100644
index 2472d5b0d40ddb41c31924da4bb7c69245d216cb..0000000000000000000000000000000000000000
--- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/utils.py
+++ /dev/null
@@ -1,135 +0,0 @@
-from pathlib import Path
-from typing import Union
-
-import librosa
-import numpy as np
-import torch
-from PIL import Image
-from torchvision.io import write_video
-from torchvision.transforms.functional import pil_to_tensor
-
-
-def get_timesteps_arr(audio_filepath, offset, duration, fps=30, margin=1.0, smooth=0.0):
- y, sr = librosa.load(audio_filepath, offset=offset, duration=duration)
-
- # librosa.stft hardcoded defaults...
- # n_fft defaults to 2048
- # hop length is win_length // 4
- # win_length defaults to n_fft
- D = librosa.stft(y, n_fft=2048, hop_length=2048 // 4, win_length=2048)
-
- # Extract percussive elements
- D_harmonic, D_percussive = librosa.decompose.hpss(D, margin=margin)
- y_percussive = librosa.istft(D_percussive, length=len(y))
-
- # Get normalized melspectrogram
- spec_raw = librosa.feature.melspectrogram(y=y_percussive, sr=sr)
- spec_max = np.amax(spec_raw, axis=0)
- spec_norm = (spec_max - np.min(spec_max)) / np.ptp(spec_max)
-
- # Resize cumsum of spec norm to our desired number of interpolation frames
- x_norm = np.linspace(0, spec_norm.shape[-1], spec_norm.shape[-1])
- y_norm = np.cumsum(spec_norm)
- y_norm /= y_norm[-1]
- x_resize = np.linspace(0, y_norm.shape[-1], int(duration * fps))
-
- T = np.interp(x_resize, x_norm, y_norm)
-
- # Apply smoothing
- return T * (1 - smooth) + np.linspace(0.0, 1.0, T.shape[0]) * smooth
-
-
-def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
- """helper function to spherically interpolate two arrays v1 v2"""
-
- inputs_are_torch = isinstance(v0, torch.Tensor)
- if inputs_are_torch:
- input_device = v0.device
- v0 = v0.cpu().numpy()
- v1 = v1.cpu().numpy()
-
- dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
- if np.abs(dot) > DOT_THRESHOLD:
- v2 = (1 - t) * v0 + t * v1
- else:
- theta_0 = np.arccos(dot)
- sin_theta_0 = np.sin(theta_0)
- theta_t = theta_0 * t
- sin_theta_t = np.sin(theta_t)
- s0 = np.sin(theta_0 - theta_t) / sin_theta_0
- s1 = sin_theta_t / sin_theta_0
- v2 = s0 * v0 + s1 * v1
-
- if inputs_are_torch:
- v2 = torch.from_numpy(v2).to(input_device)
-
- return v2
-
-
-def make_video_pyav(
- frames_or_frame_dir: Union[str, Path, torch.Tensor],
- audio_filepath: Union[str, Path] = None,
- fps: int = 30,
- audio_offset: int = 0,
- audio_duration: int = 2,
- sr: int = 22050,
- output_filepath: Union[str, Path] = "output.mp4",
- glob_pattern: str = "*.png",
-):
- """
- TODO - docstring here
- frames_or_frame_dir: (Union[str, Path, torch.Tensor]):
- Either a directory of images, or a tensor of shape (T, C, H, W) in range [0, 255].
- """
-
- # Torchvision write_video doesn't support pathlib paths
- output_filepath = str(output_filepath)
-
- if isinstance(frames_or_frame_dir, (str, Path)):
- frames = None
- for img in sorted(Path(frames_or_frame_dir).glob(glob_pattern)):
- frame = pil_to_tensor(Image.open(img)).unsqueeze(0)
- frames = frame if frames is None else torch.cat([frames, frame])
- else:
- frames = frames_or_frame_dir
-
- # TCHW -> THWC
- frames = frames.permute(0, 2, 3, 1)
-
- if audio_filepath:
- # Read audio, convert to tensor
- audio, sr = librosa.load(
- audio_filepath,
- sr=sr,
- mono=True,
- offset=audio_offset,
- duration=audio_duration,
- )
- audio_tensor = torch.tensor(audio).unsqueeze(0)
-
- write_video(
- output_filepath,
- frames,
- fps=fps,
- audio_array=audio_tensor,
- audio_fps=sr,
- audio_codec="aac",
- options={"crf": "10", "pix_fmt": "yuv420p"},
- )
- else:
- write_video(
- output_filepath,
- frames,
- fps=fps,
- options={"crf": "10", "pix_fmt": "yuv420p"},
- )
-
- return output_filepath
-
-
-def pad_along_axis(array: np.ndarray, pad_size: int, axis: int = 0) -> np.ndarray:
- if pad_size <= 0:
- return array
- npad = [(0, 0)] * array.ndim
- npad[axis] = (0, pad_size)
- return np.pad(array, pad_width=npad, mode="constant", constant_values=0)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/metadata.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/metadata.py
deleted file mode 100644
index c329e1977fd1ed403bb65529296d5c803a6b289f..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/metadata.py
+++ /dev/null
@@ -1,1076 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2012 The Python Software Foundation.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-"""Implementation of the Metadata for Python packages PEPs.
-
-Supports all metadata formats (1.0, 1.1, 1.2, 1.3/2.1 and 2.2).
-"""
-from __future__ import unicode_literals
-
-import codecs
-from email import message_from_file
-import json
-import logging
-import re
-
-
-from . import DistlibException, __version__
-from .compat import StringIO, string_types, text_type
-from .markers import interpret
-from .util import extract_by_key, get_extras
-from .version import get_scheme, PEP440_VERSION_RE
-
-logger = logging.getLogger(__name__)
-
-
-class MetadataMissingError(DistlibException):
- """A required metadata is missing"""
-
-
-class MetadataConflictError(DistlibException):
- """Attempt to read or write metadata fields that are conflictual."""
-
-
-class MetadataUnrecognizedVersionError(DistlibException):
- """Unknown metadata version number."""
-
-
-class MetadataInvalidError(DistlibException):
- """A metadata value is invalid"""
-
-# public API of this module
-__all__ = ['Metadata', 'PKG_INFO_ENCODING', 'PKG_INFO_PREFERRED_VERSION']
-
-# Encoding used for the PKG-INFO files
-PKG_INFO_ENCODING = 'utf-8'
-
-# preferred version. Hopefully will be changed
-# to 1.2 once PEP 345 is supported everywhere
-PKG_INFO_PREFERRED_VERSION = '1.1'
-
-_LINE_PREFIX_1_2 = re.compile('\n \\|')
-_LINE_PREFIX_PRE_1_2 = re.compile('\n ')
-_241_FIELDS = ('Metadata-Version', 'Name', 'Version', 'Platform',
- 'Summary', 'Description',
- 'Keywords', 'Home-page', 'Author', 'Author-email',
- 'License')
-
-_314_FIELDS = ('Metadata-Version', 'Name', 'Version', 'Platform',
- 'Supported-Platform', 'Summary', 'Description',
- 'Keywords', 'Home-page', 'Author', 'Author-email',
- 'License', 'Classifier', 'Download-URL', 'Obsoletes',
- 'Provides', 'Requires')
-
-_314_MARKERS = ('Obsoletes', 'Provides', 'Requires', 'Classifier',
- 'Download-URL')
-
-_345_FIELDS = ('Metadata-Version', 'Name', 'Version', 'Platform',
- 'Supported-Platform', 'Summary', 'Description',
- 'Keywords', 'Home-page', 'Author', 'Author-email',
- 'Maintainer', 'Maintainer-email', 'License',
- 'Classifier', 'Download-URL', 'Obsoletes-Dist',
- 'Project-URL', 'Provides-Dist', 'Requires-Dist',
- 'Requires-Python', 'Requires-External')
-
-_345_MARKERS = ('Provides-Dist', 'Requires-Dist', 'Requires-Python',
- 'Obsoletes-Dist', 'Requires-External', 'Maintainer',
- 'Maintainer-email', 'Project-URL')
-
-_426_FIELDS = ('Metadata-Version', 'Name', 'Version', 'Platform',
- 'Supported-Platform', 'Summary', 'Description',
- 'Keywords', 'Home-page', 'Author', 'Author-email',
- 'Maintainer', 'Maintainer-email', 'License',
- 'Classifier', 'Download-URL', 'Obsoletes-Dist',
- 'Project-URL', 'Provides-Dist', 'Requires-Dist',
- 'Requires-Python', 'Requires-External', 'Private-Version',
- 'Obsoleted-By', 'Setup-Requires-Dist', 'Extension',
- 'Provides-Extra')
-
-_426_MARKERS = ('Private-Version', 'Provides-Extra', 'Obsoleted-By',
- 'Setup-Requires-Dist', 'Extension')
-
-# See issue #106: Sometimes 'Requires' and 'Provides' occur wrongly in
-# the metadata. Include them in the tuple literal below to allow them
-# (for now).
-# Ditto for Obsoletes - see issue #140.
-_566_FIELDS = _426_FIELDS + ('Description-Content-Type',
- 'Requires', 'Provides', 'Obsoletes')
-
-_566_MARKERS = ('Description-Content-Type',)
-
-_643_MARKERS = ('Dynamic', 'License-File')
-
-_643_FIELDS = _566_FIELDS + _643_MARKERS
-
-_ALL_FIELDS = set()
-_ALL_FIELDS.update(_241_FIELDS)
-_ALL_FIELDS.update(_314_FIELDS)
-_ALL_FIELDS.update(_345_FIELDS)
-_ALL_FIELDS.update(_426_FIELDS)
-_ALL_FIELDS.update(_566_FIELDS)
-_ALL_FIELDS.update(_643_FIELDS)
-
-EXTRA_RE = re.compile(r'''extra\s*==\s*("([^"]+)"|'([^']+)')''')
-
-
-def _version2fieldlist(version):
- if version == '1.0':
- return _241_FIELDS
- elif version == '1.1':
- return _314_FIELDS
- elif version == '1.2':
- return _345_FIELDS
- elif version in ('1.3', '2.1'):
- # avoid adding field names if already there
- return _345_FIELDS + tuple(f for f in _566_FIELDS if f not in _345_FIELDS)
- elif version == '2.0':
- raise ValueError('Metadata 2.0 is withdrawn and not supported')
- # return _426_FIELDS
- elif version == '2.2':
- return _643_FIELDS
- raise MetadataUnrecognizedVersionError(version)
-
-
-def _best_version(fields):
- """Detect the best version depending on the fields used."""
- def _has_marker(keys, markers):
- for marker in markers:
- if marker in keys:
- return True
- return False
-
- keys = []
- for key, value in fields.items():
- if value in ([], 'UNKNOWN', None):
- continue
- keys.append(key)
-
- possible_versions = ['1.0', '1.1', '1.2', '1.3', '2.1', '2.2'] # 2.0 removed
-
- # first let's try to see if a field is not part of one of the version
- for key in keys:
- if key not in _241_FIELDS and '1.0' in possible_versions:
- possible_versions.remove('1.0')
- logger.debug('Removed 1.0 due to %s', key)
- if key not in _314_FIELDS and '1.1' in possible_versions:
- possible_versions.remove('1.1')
- logger.debug('Removed 1.1 due to %s', key)
- if key not in _345_FIELDS and '1.2' in possible_versions:
- possible_versions.remove('1.2')
- logger.debug('Removed 1.2 due to %s', key)
- if key not in _566_FIELDS and '1.3' in possible_versions:
- possible_versions.remove('1.3')
- logger.debug('Removed 1.3 due to %s', key)
- if key not in _566_FIELDS and '2.1' in possible_versions:
- if key != 'Description': # In 2.1, description allowed after headers
- possible_versions.remove('2.1')
- logger.debug('Removed 2.1 due to %s', key)
- if key not in _643_FIELDS and '2.2' in possible_versions:
- possible_versions.remove('2.2')
- logger.debug('Removed 2.2 due to %s', key)
- # if key not in _426_FIELDS and '2.0' in possible_versions:
- # possible_versions.remove('2.0')
- # logger.debug('Removed 2.0 due to %s', key)
-
- # possible_version contains qualified versions
- if len(possible_versions) == 1:
- return possible_versions[0] # found !
- elif len(possible_versions) == 0:
- logger.debug('Out of options - unknown metadata set: %s', fields)
- raise MetadataConflictError('Unknown metadata set')
-
- # let's see if one unique marker is found
- is_1_1 = '1.1' in possible_versions and _has_marker(keys, _314_MARKERS)
- is_1_2 = '1.2' in possible_versions and _has_marker(keys, _345_MARKERS)
- is_2_1 = '2.1' in possible_versions and _has_marker(keys, _566_MARKERS)
- # is_2_0 = '2.0' in possible_versions and _has_marker(keys, _426_MARKERS)
- is_2_2 = '2.2' in possible_versions and _has_marker(keys, _643_MARKERS)
- if int(is_1_1) + int(is_1_2) + int(is_2_1) + int(is_2_2) > 1:
- raise MetadataConflictError('You used incompatible 1.1/1.2/2.1/2.2 fields')
-
- # we have the choice, 1.0, or 1.2, 2.1 or 2.2
- # - 1.0 has a broken Summary field but works with all tools
- # - 1.1 is to avoid
- # - 1.2 fixes Summary but has little adoption
- # - 2.1 adds more features
- # - 2.2 is the latest
- if not is_1_1 and not is_1_2 and not is_2_1 and not is_2_2:
- # we couldn't find any specific marker
- if PKG_INFO_PREFERRED_VERSION in possible_versions:
- return PKG_INFO_PREFERRED_VERSION
- if is_1_1:
- return '1.1'
- if is_1_2:
- return '1.2'
- if is_2_1:
- return '2.1'
- # if is_2_2:
- # return '2.2'
-
- return '2.2'
-
-# This follows the rules about transforming keys as described in
-# https://www.python.org/dev/peps/pep-0566/#id17
-_ATTR2FIELD = {
- name.lower().replace("-", "_"): name for name in _ALL_FIELDS
-}
-_FIELD2ATTR = {field: attr for attr, field in _ATTR2FIELD.items()}
-
-_PREDICATE_FIELDS = ('Requires-Dist', 'Obsoletes-Dist', 'Provides-Dist')
-_VERSIONS_FIELDS = ('Requires-Python',)
-_VERSION_FIELDS = ('Version',)
-_LISTFIELDS = ('Platform', 'Classifier', 'Obsoletes',
- 'Requires', 'Provides', 'Obsoletes-Dist',
- 'Provides-Dist', 'Requires-Dist', 'Requires-External',
- 'Project-URL', 'Supported-Platform', 'Setup-Requires-Dist',
- 'Provides-Extra', 'Extension', 'License-File')
-_LISTTUPLEFIELDS = ('Project-URL',)
-
-_ELEMENTSFIELD = ('Keywords',)
-
-_UNICODEFIELDS = ('Author', 'Maintainer', 'Summary', 'Description')
-
-_MISSING = object()
-
-_FILESAFE = re.compile('[^A-Za-z0-9.]+')
-
-
-def _get_name_and_version(name, version, for_filename=False):
- """Return the distribution name with version.
-
- If for_filename is true, return a filename-escaped form."""
- if for_filename:
- # For both name and version any runs of non-alphanumeric or '.'
- # characters are replaced with a single '-'. Additionally any
- # spaces in the version string become '.'
- name = _FILESAFE.sub('-', name)
- version = _FILESAFE.sub('-', version.replace(' ', '.'))
- return '%s-%s' % (name, version)
-
-
-class LegacyMetadata(object):
- """The legacy metadata of a release.
-
- Supports versions 1.0, 1.1, 1.2, 2.0 and 1.3/2.1 (auto-detected). You can
- instantiate the class with one of these arguments (or none):
- - *path*, the path to a metadata file
- - *fileobj* give a file-like object with metadata as content
- - *mapping* is a dict-like object
- - *scheme* is a version scheme name
- """
- # TODO document the mapping API and UNKNOWN default key
-
- def __init__(self, path=None, fileobj=None, mapping=None,
- scheme='default'):
- if [path, fileobj, mapping].count(None) < 2:
- raise TypeError('path, fileobj and mapping are exclusive')
- self._fields = {}
- self.requires_files = []
- self._dependencies = None
- self.scheme = scheme
- if path is not None:
- self.read(path)
- elif fileobj is not None:
- self.read_file(fileobj)
- elif mapping is not None:
- self.update(mapping)
- self.set_metadata_version()
-
- def set_metadata_version(self):
- self._fields['Metadata-Version'] = _best_version(self._fields)
-
- def _write_field(self, fileobj, name, value):
- fileobj.write('%s: %s\n' % (name, value))
-
- def __getitem__(self, name):
- return self.get(name)
-
- def __setitem__(self, name, value):
- return self.set(name, value)
-
- def __delitem__(self, name):
- field_name = self._convert_name(name)
- try:
- del self._fields[field_name]
- except KeyError:
- raise KeyError(name)
-
- def __contains__(self, name):
- return (name in self._fields or
- self._convert_name(name) in self._fields)
-
- def _convert_name(self, name):
- if name in _ALL_FIELDS:
- return name
- name = name.replace('-', '_').lower()
- return _ATTR2FIELD.get(name, name)
-
- def _default_value(self, name):
- if name in _LISTFIELDS or name in _ELEMENTSFIELD:
- return []
- return 'UNKNOWN'
-
- def _remove_line_prefix(self, value):
- if self.metadata_version in ('1.0', '1.1'):
- return _LINE_PREFIX_PRE_1_2.sub('\n', value)
- else:
- return _LINE_PREFIX_1_2.sub('\n', value)
-
- def __getattr__(self, name):
- if name in _ATTR2FIELD:
- return self[name]
- raise AttributeError(name)
-
- #
- # Public API
- #
-
-# dependencies = property(_get_dependencies, _set_dependencies)
-
- def get_fullname(self, filesafe=False):
- """Return the distribution name with version.
-
- If filesafe is true, return a filename-escaped form."""
- return _get_name_and_version(self['Name'], self['Version'], filesafe)
-
- def is_field(self, name):
- """return True if name is a valid metadata key"""
- name = self._convert_name(name)
- return name in _ALL_FIELDS
-
- def is_multi_field(self, name):
- name = self._convert_name(name)
- return name in _LISTFIELDS
-
- def read(self, filepath):
- """Read the metadata values from a file path."""
- fp = codecs.open(filepath, 'r', encoding='utf-8')
- try:
- self.read_file(fp)
- finally:
- fp.close()
-
- def read_file(self, fileob):
- """Read the metadata values from a file object."""
- msg = message_from_file(fileob)
- self._fields['Metadata-Version'] = msg['metadata-version']
-
- # When reading, get all the fields we can
- for field in _ALL_FIELDS:
- if field not in msg:
- continue
- if field in _LISTFIELDS:
- # we can have multiple lines
- values = msg.get_all(field)
- if field in _LISTTUPLEFIELDS and values is not None:
- values = [tuple(value.split(',')) for value in values]
- self.set(field, values)
- else:
- # single line
- value = msg[field]
- if value is not None and value != 'UNKNOWN':
- self.set(field, value)
-
- # PEP 566 specifies that the body be used for the description, if
- # available
- body = msg.get_payload()
- self["Description"] = body if body else self["Description"]
- # logger.debug('Attempting to set metadata for %s', self)
- # self.set_metadata_version()
-
- def write(self, filepath, skip_unknown=False):
- """Write the metadata fields to filepath."""
- fp = codecs.open(filepath, 'w', encoding='utf-8')
- try:
- self.write_file(fp, skip_unknown)
- finally:
- fp.close()
-
- def write_file(self, fileobject, skip_unknown=False):
- """Write the PKG-INFO format data to a file object."""
- self.set_metadata_version()
-
- for field in _version2fieldlist(self['Metadata-Version']):
- values = self.get(field)
- if skip_unknown and values in ('UNKNOWN', [], ['UNKNOWN']):
- continue
- if field in _ELEMENTSFIELD:
- self._write_field(fileobject, field, ','.join(values))
- continue
- if field not in _LISTFIELDS:
- if field == 'Description':
- if self.metadata_version in ('1.0', '1.1'):
- values = values.replace('\n', '\n ')
- else:
- values = values.replace('\n', '\n |')
- values = [values]
-
- if field in _LISTTUPLEFIELDS:
- values = [','.join(value) for value in values]
-
- for value in values:
- self._write_field(fileobject, field, value)
-
- def update(self, other=None, **kwargs):
- """Set metadata values from the given iterable `other` and kwargs.
-
- Behavior is like `dict.update`: If `other` has a ``keys`` method,
- they are looped over and ``self[key]`` is assigned ``other[key]``.
- Else, ``other`` is an iterable of ``(key, value)`` iterables.
-
- Keys that don't match a metadata field or that have an empty value are
- dropped.
- """
- def _set(key, value):
- if key in _ATTR2FIELD and value:
- self.set(self._convert_name(key), value)
-
- if not other:
- # other is None or empty container
- pass
- elif hasattr(other, 'keys'):
- for k in other.keys():
- _set(k, other[k])
- else:
- for k, v in other:
- _set(k, v)
-
- if kwargs:
- for k, v in kwargs.items():
- _set(k, v)
-
- def set(self, name, value):
- """Control then set a metadata field."""
- name = self._convert_name(name)
-
- if ((name in _ELEMENTSFIELD or name == 'Platform') and
- not isinstance(value, (list, tuple))):
- if isinstance(value, string_types):
- value = [v.strip() for v in value.split(',')]
- else:
- value = []
- elif (name in _LISTFIELDS and
- not isinstance(value, (list, tuple))):
- if isinstance(value, string_types):
- value = [value]
- else:
- value = []
-
- if logger.isEnabledFor(logging.WARNING):
- project_name = self['Name']
-
- scheme = get_scheme(self.scheme)
- if name in _PREDICATE_FIELDS and value is not None:
- for v in value:
- # check that the values are valid
- if not scheme.is_valid_matcher(v.split(';')[0]):
- logger.warning(
- "'%s': '%s' is not valid (field '%s')",
- project_name, v, name)
- # FIXME this rejects UNKNOWN, is that right?
- elif name in _VERSIONS_FIELDS and value is not None:
- if not scheme.is_valid_constraint_list(value):
- logger.warning("'%s': '%s' is not a valid version (field '%s')",
- project_name, value, name)
- elif name in _VERSION_FIELDS and value is not None:
- if not scheme.is_valid_version(value):
- logger.warning("'%s': '%s' is not a valid version (field '%s')",
- project_name, value, name)
-
- if name in _UNICODEFIELDS:
- if name == 'Description':
- value = self._remove_line_prefix(value)
-
- self._fields[name] = value
-
- def get(self, name, default=_MISSING):
- """Get a metadata field."""
- name = self._convert_name(name)
- if name not in self._fields:
- if default is _MISSING:
- default = self._default_value(name)
- return default
- if name in _UNICODEFIELDS:
- value = self._fields[name]
- return value
- elif name in _LISTFIELDS:
- value = self._fields[name]
- if value is None:
- return []
- res = []
- for val in value:
- if name not in _LISTTUPLEFIELDS:
- res.append(val)
- else:
- # That's for Project-URL
- res.append((val[0], val[1]))
- return res
-
- elif name in _ELEMENTSFIELD:
- value = self._fields[name]
- if isinstance(value, string_types):
- return value.split(',')
- return self._fields[name]
-
- def check(self, strict=False):
- """Check if the metadata is compliant. If strict is True then raise if
- no Name or Version are provided"""
- self.set_metadata_version()
-
- # XXX should check the versions (if the file was loaded)
- missing, warnings = [], []
-
- for attr in ('Name', 'Version'): # required by PEP 345
- if attr not in self:
- missing.append(attr)
-
- if strict and missing != []:
- msg = 'missing required metadata: %s' % ', '.join(missing)
- raise MetadataMissingError(msg)
-
- for attr in ('Home-page', 'Author'):
- if attr not in self:
- missing.append(attr)
-
- # checking metadata 1.2 (XXX needs to check 1.1, 1.0)
- if self['Metadata-Version'] != '1.2':
- return missing, warnings
-
- scheme = get_scheme(self.scheme)
-
- def are_valid_constraints(value):
- for v in value:
- if not scheme.is_valid_matcher(v.split(';')[0]):
- return False
- return True
-
- for fields, controller in ((_PREDICATE_FIELDS, are_valid_constraints),
- (_VERSIONS_FIELDS,
- scheme.is_valid_constraint_list),
- (_VERSION_FIELDS,
- scheme.is_valid_version)):
- for field in fields:
- value = self.get(field, None)
- if value is not None and not controller(value):
- warnings.append("Wrong value for '%s': %s" % (field, value))
-
- return missing, warnings
-
- def todict(self, skip_missing=False):
- """Return fields as a dict.
-
- Field names will be converted to use the underscore-lowercase style
- instead of hyphen-mixed case (i.e. home_page instead of Home-page).
- This is as per https://www.python.org/dev/peps/pep-0566/#id17.
- """
- self.set_metadata_version()
-
- fields = _version2fieldlist(self['Metadata-Version'])
-
- data = {}
-
- for field_name in fields:
- if not skip_missing or field_name in self._fields:
- key = _FIELD2ATTR[field_name]
- if key != 'project_url':
- data[key] = self[field_name]
- else:
- data[key] = [','.join(u) for u in self[field_name]]
-
- return data
-
- def add_requirements(self, requirements):
- if self['Metadata-Version'] == '1.1':
- # we can't have 1.1 metadata *and* Setuptools requires
- for field in ('Obsoletes', 'Requires', 'Provides'):
- if field in self:
- del self[field]
- self['Requires-Dist'] += requirements
-
- # Mapping API
- # TODO could add iter* variants
-
- def keys(self):
- return list(_version2fieldlist(self['Metadata-Version']))
-
- def __iter__(self):
- for key in self.keys():
- yield key
-
- def values(self):
- return [self[key] for key in self.keys()]
-
- def items(self):
- return [(key, self[key]) for key in self.keys()]
-
- def __repr__(self):
- return '<%s %s %s>' % (self.__class__.__name__, self.name,
- self.version)
-
-
-METADATA_FILENAME = 'pydist.json'
-WHEEL_METADATA_FILENAME = 'metadata.json'
-LEGACY_METADATA_FILENAME = 'METADATA'
-
-
-class Metadata(object):
- """
- The metadata of a release. This implementation uses 2.1
- metadata where possible. If not possible, it wraps a LegacyMetadata
- instance which handles the key-value metadata format.
- """
-
- METADATA_VERSION_MATCHER = re.compile(r'^\d+(\.\d+)*$')
-
- NAME_MATCHER = re.compile('^[0-9A-Z]([0-9A-Z_.-]*[0-9A-Z])?$', re.I)
-
- FIELDNAME_MATCHER = re.compile('^[A-Z]([0-9A-Z-]*[0-9A-Z])?$', re.I)
-
- VERSION_MATCHER = PEP440_VERSION_RE
-
- SUMMARY_MATCHER = re.compile('.{1,2047}')
-
- METADATA_VERSION = '2.0'
-
- GENERATOR = 'distlib (%s)' % __version__
-
- MANDATORY_KEYS = {
- 'name': (),
- 'version': (),
- 'summary': ('legacy',),
- }
-
- INDEX_KEYS = ('name version license summary description author '
- 'author_email keywords platform home_page classifiers '
- 'download_url')
-
- DEPENDENCY_KEYS = ('extras run_requires test_requires build_requires '
- 'dev_requires provides meta_requires obsoleted_by '
- 'supports_environments')
-
- SYNTAX_VALIDATORS = {
- 'metadata_version': (METADATA_VERSION_MATCHER, ()),
- 'name': (NAME_MATCHER, ('legacy',)),
- 'version': (VERSION_MATCHER, ('legacy',)),
- 'summary': (SUMMARY_MATCHER, ('legacy',)),
- 'dynamic': (FIELDNAME_MATCHER, ('legacy',)),
- }
-
- __slots__ = ('_legacy', '_data', 'scheme')
-
- def __init__(self, path=None, fileobj=None, mapping=None,
- scheme='default'):
- if [path, fileobj, mapping].count(None) < 2:
- raise TypeError('path, fileobj and mapping are exclusive')
- self._legacy = None
- self._data = None
- self.scheme = scheme
- #import pdb; pdb.set_trace()
- if mapping is not None:
- try:
- self._validate_mapping(mapping, scheme)
- self._data = mapping
- except MetadataUnrecognizedVersionError:
- self._legacy = LegacyMetadata(mapping=mapping, scheme=scheme)
- self.validate()
- else:
- data = None
- if path:
- with open(path, 'rb') as f:
- data = f.read()
- elif fileobj:
- data = fileobj.read()
- if data is None:
- # Initialised with no args - to be added
- self._data = {
- 'metadata_version': self.METADATA_VERSION,
- 'generator': self.GENERATOR,
- }
- else:
- if not isinstance(data, text_type):
- data = data.decode('utf-8')
- try:
- self._data = json.loads(data)
- self._validate_mapping(self._data, scheme)
- except ValueError:
- # Note: MetadataUnrecognizedVersionError does not
- # inherit from ValueError (it's a DistlibException,
- # which should not inherit from ValueError).
- # The ValueError comes from the json.load - if that
- # succeeds and we get a validation error, we want
- # that to propagate
- self._legacy = LegacyMetadata(fileobj=StringIO(data),
- scheme=scheme)
- self.validate()
-
- common_keys = set(('name', 'version', 'license', 'keywords', 'summary'))
-
- none_list = (None, list)
- none_dict = (None, dict)
-
- mapped_keys = {
- 'run_requires': ('Requires-Dist', list),
- 'build_requires': ('Setup-Requires-Dist', list),
- 'dev_requires': none_list,
- 'test_requires': none_list,
- 'meta_requires': none_list,
- 'extras': ('Provides-Extra', list),
- 'modules': none_list,
- 'namespaces': none_list,
- 'exports': none_dict,
- 'commands': none_dict,
- 'classifiers': ('Classifier', list),
- 'source_url': ('Download-URL', None),
- 'metadata_version': ('Metadata-Version', None),
- }
-
- del none_list, none_dict
-
- def __getattribute__(self, key):
- common = object.__getattribute__(self, 'common_keys')
- mapped = object.__getattribute__(self, 'mapped_keys')
- if key in mapped:
- lk, maker = mapped[key]
- if self._legacy:
- if lk is None:
- result = None if maker is None else maker()
- else:
- result = self._legacy.get(lk)
- else:
- value = None if maker is None else maker()
- if key not in ('commands', 'exports', 'modules', 'namespaces',
- 'classifiers'):
- result = self._data.get(key, value)
- else:
- # special cases for PEP 459
- sentinel = object()
- result = sentinel
- d = self._data.get('extensions')
- if d:
- if key == 'commands':
- result = d.get('python.commands', value)
- elif key == 'classifiers':
- d = d.get('python.details')
- if d:
- result = d.get(key, value)
- else:
- d = d.get('python.exports')
- if not d:
- d = self._data.get('python.exports')
- if d:
- result = d.get(key, value)
- if result is sentinel:
- result = value
- elif key not in common:
- result = object.__getattribute__(self, key)
- elif self._legacy:
- result = self._legacy.get(key)
- else:
- result = self._data.get(key)
- return result
-
- def _validate_value(self, key, value, scheme=None):
- if key in self.SYNTAX_VALIDATORS:
- pattern, exclusions = self.SYNTAX_VALIDATORS[key]
- if (scheme or self.scheme) not in exclusions:
- m = pattern.match(value)
- if not m:
- raise MetadataInvalidError("'%s' is an invalid value for "
- "the '%s' property" % (value,
- key))
-
- def __setattr__(self, key, value):
- self._validate_value(key, value)
- common = object.__getattribute__(self, 'common_keys')
- mapped = object.__getattribute__(self, 'mapped_keys')
- if key in mapped:
- lk, _ = mapped[key]
- if self._legacy:
- if lk is None:
- raise NotImplementedError
- self._legacy[lk] = value
- elif key not in ('commands', 'exports', 'modules', 'namespaces',
- 'classifiers'):
- self._data[key] = value
- else:
- # special cases for PEP 459
- d = self._data.setdefault('extensions', {})
- if key == 'commands':
- d['python.commands'] = value
- elif key == 'classifiers':
- d = d.setdefault('python.details', {})
- d[key] = value
- else:
- d = d.setdefault('python.exports', {})
- d[key] = value
- elif key not in common:
- object.__setattr__(self, key, value)
- else:
- if key == 'keywords':
- if isinstance(value, string_types):
- value = value.strip()
- if value:
- value = value.split()
- else:
- value = []
- if self._legacy:
- self._legacy[key] = value
- else:
- self._data[key] = value
-
- @property
- def name_and_version(self):
- return _get_name_and_version(self.name, self.version, True)
-
- @property
- def provides(self):
- if self._legacy:
- result = self._legacy['Provides-Dist']
- else:
- result = self._data.setdefault('provides', [])
- s = '%s (%s)' % (self.name, self.version)
- if s not in result:
- result.append(s)
- return result
-
- @provides.setter
- def provides(self, value):
- if self._legacy:
- self._legacy['Provides-Dist'] = value
- else:
- self._data['provides'] = value
-
- def get_requirements(self, reqts, extras=None, env=None):
- """
- Base method to get dependencies, given a set of extras
- to satisfy and an optional environment context.
- :param reqts: A list of sometimes-wanted dependencies,
- perhaps dependent on extras and environment.
- :param extras: A list of optional components being requested.
- :param env: An optional environment for marker evaluation.
- """
- if self._legacy:
- result = reqts
- else:
- result = []
- extras = get_extras(extras or [], self.extras)
- for d in reqts:
- if 'extra' not in d and 'environment' not in d:
- # unconditional
- include = True
- else:
- if 'extra' not in d:
- # Not extra-dependent - only environment-dependent
- include = True
- else:
- include = d.get('extra') in extras
- if include:
- # Not excluded because of extras, check environment
- marker = d.get('environment')
- if marker:
- include = interpret(marker, env)
- if include:
- result.extend(d['requires'])
- for key in ('build', 'dev', 'test'):
- e = ':%s:' % key
- if e in extras:
- extras.remove(e)
- # A recursive call, but it should terminate since 'test'
- # has been removed from the extras
- reqts = self._data.get('%s_requires' % key, [])
- result.extend(self.get_requirements(reqts, extras=extras,
- env=env))
- return result
-
- @property
- def dictionary(self):
- if self._legacy:
- return self._from_legacy()
- return self._data
-
- @property
- def dependencies(self):
- if self._legacy:
- raise NotImplementedError
- else:
- return extract_by_key(self._data, self.DEPENDENCY_KEYS)
-
- @dependencies.setter
- def dependencies(self, value):
- if self._legacy:
- raise NotImplementedError
- else:
- self._data.update(value)
-
- def _validate_mapping(self, mapping, scheme):
- if mapping.get('metadata_version') != self.METADATA_VERSION:
- raise MetadataUnrecognizedVersionError()
- missing = []
- for key, exclusions in self.MANDATORY_KEYS.items():
- if key not in mapping:
- if scheme not in exclusions:
- missing.append(key)
- if missing:
- msg = 'Missing metadata items: %s' % ', '.join(missing)
- raise MetadataMissingError(msg)
- for k, v in mapping.items():
- self._validate_value(k, v, scheme)
-
- def validate(self):
- if self._legacy:
- missing, warnings = self._legacy.check(True)
- if missing or warnings:
- logger.warning('Metadata: missing: %s, warnings: %s',
- missing, warnings)
- else:
- self._validate_mapping(self._data, self.scheme)
-
- def todict(self):
- if self._legacy:
- return self._legacy.todict(True)
- else:
- result = extract_by_key(self._data, self.INDEX_KEYS)
- return result
-
- def _from_legacy(self):
- assert self._legacy and not self._data
- result = {
- 'metadata_version': self.METADATA_VERSION,
- 'generator': self.GENERATOR,
- }
- lmd = self._legacy.todict(True) # skip missing ones
- for k in ('name', 'version', 'license', 'summary', 'description',
- 'classifier'):
- if k in lmd:
- if k == 'classifier':
- nk = 'classifiers'
- else:
- nk = k
- result[nk] = lmd[k]
- kw = lmd.get('Keywords', [])
- if kw == ['']:
- kw = []
- result['keywords'] = kw
- keys = (('requires_dist', 'run_requires'),
- ('setup_requires_dist', 'build_requires'))
- for ok, nk in keys:
- if ok in lmd and lmd[ok]:
- result[nk] = [{'requires': lmd[ok]}]
- result['provides'] = self.provides
- author = {}
- maintainer = {}
- return result
-
- LEGACY_MAPPING = {
- 'name': 'Name',
- 'version': 'Version',
- ('extensions', 'python.details', 'license'): 'License',
- 'summary': 'Summary',
- 'description': 'Description',
- ('extensions', 'python.project', 'project_urls', 'Home'): 'Home-page',
- ('extensions', 'python.project', 'contacts', 0, 'name'): 'Author',
- ('extensions', 'python.project', 'contacts', 0, 'email'): 'Author-email',
- 'source_url': 'Download-URL',
- ('extensions', 'python.details', 'classifiers'): 'Classifier',
- }
-
- def _to_legacy(self):
- def process_entries(entries):
- reqts = set()
- for e in entries:
- extra = e.get('extra')
- env = e.get('environment')
- rlist = e['requires']
- for r in rlist:
- if not env and not extra:
- reqts.add(r)
- else:
- marker = ''
- if extra:
- marker = 'extra == "%s"' % extra
- if env:
- if marker:
- marker = '(%s) and %s' % (env, marker)
- else:
- marker = env
- reqts.add(';'.join((r, marker)))
- return reqts
-
- assert self._data and not self._legacy
- result = LegacyMetadata()
- nmd = self._data
- # import pdb; pdb.set_trace()
- for nk, ok in self.LEGACY_MAPPING.items():
- if not isinstance(nk, tuple):
- if nk in nmd:
- result[ok] = nmd[nk]
- else:
- d = nmd
- found = True
- for k in nk:
- try:
- d = d[k]
- except (KeyError, IndexError):
- found = False
- break
- if found:
- result[ok] = d
- r1 = process_entries(self.run_requires + self.meta_requires)
- r2 = process_entries(self.build_requires + self.dev_requires)
- if self.extras:
- result['Provides-Extra'] = sorted(self.extras)
- result['Requires-Dist'] = sorted(r1)
- result['Setup-Requires-Dist'] = sorted(r2)
- # TODO: any other fields wanted
- return result
-
- def write(self, path=None, fileobj=None, legacy=False, skip_unknown=True):
- if [path, fileobj].count(None) != 1:
- raise ValueError('Exactly one of path and fileobj is needed')
- self.validate()
- if legacy:
- if self._legacy:
- legacy_md = self._legacy
- else:
- legacy_md = self._to_legacy()
- if path:
- legacy_md.write(path, skip_unknown=skip_unknown)
- else:
- legacy_md.write_file(fileobj, skip_unknown=skip_unknown)
- else:
- if self._legacy:
- d = self._from_legacy()
- else:
- d = self._data
- if fileobj:
- json.dump(d, fileobj, ensure_ascii=True, indent=2,
- sort_keys=True)
- else:
- with codecs.open(path, 'w', 'utf-8') as f:
- json.dump(d, f, ensure_ascii=True, indent=2,
- sort_keys=True)
-
- def add_requirements(self, requirements):
- if self._legacy:
- self._legacy.add_requirements(requirements)
- else:
- run_requires = self._data.setdefault('run_requires', [])
- always = None
- for entry in run_requires:
- if 'environment' not in entry and 'extra' not in entry:
- always = entry
- break
- if always is None:
- always = { 'requires': requirements }
- run_requires.insert(0, always)
- else:
- rset = set(always['requires']) | set(requirements)
- always['requires'] = sorted(rset)
-
- def __repr__(self):
- name = self.name or '(no name)'
- version = self.version or 'no version'
- return '<%s %s %s (%s)>' % (self.__class__.__name__,
- self.metadata_version, name, version)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/package_index.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/package_index.py
deleted file mode 100644
index 14881d2992273f3c76e8c6c8dca156abdeae5375..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/package_index.py
+++ /dev/null
@@ -1,1126 +0,0 @@
-"""PyPI and direct package downloading"""
-import sys
-import os
-import re
-import io
-import shutil
-import socket
-import base64
-import hashlib
-import itertools
-import warnings
-import configparser
-import html
-import http.client
-import urllib.parse
-import urllib.request
-import urllib.error
-from functools import wraps
-
-import setuptools
-from pkg_resources import (
- CHECKOUT_DIST, Distribution, BINARY_DIST, normalize_path, SOURCE_DIST,
- Environment, find_distributions, safe_name, safe_version,
- to_filename, Requirement, DEVELOP_DIST, EGG_DIST, parse_version,
-)
-from distutils import log
-from distutils.errors import DistutilsError
-from fnmatch import translate
-from setuptools.wheel import Wheel
-from setuptools.extern.more_itertools import unique_everseen
-
-
-EGG_FRAGMENT = re.compile(r'^egg=([-A-Za-z0-9_.+!]+)$')
-HREF = re.compile(r"""href\s*=\s*['"]?([^'"> ]+)""", re.I)
-PYPI_MD5 = re.compile(
- r'([^<]+)\n\s+\(md5\)'
-)
-URL_SCHEME = re.compile('([-+.a-z0-9]{2,}):', re.I).match
-EXTENSIONS = ".tar.gz .tar.bz2 .tar .zip .tgz".split()
-
-__all__ = [
- 'PackageIndex', 'distros_for_url', 'parse_bdist_wininst',
- 'interpret_distro_name',
-]
-
-_SOCKET_TIMEOUT = 15
-
-_tmpl = "setuptools/{setuptools.__version__} Python-urllib/{py_major}"
-user_agent = _tmpl.format(
- py_major='{}.{}'.format(*sys.version_info), setuptools=setuptools)
-
-
-def parse_requirement_arg(spec):
- try:
- return Requirement.parse(spec)
- except ValueError as e:
- raise DistutilsError(
- "Not a URL, existing file, or requirement spec: %r" % (spec,)
- ) from e
-
-
-def parse_bdist_wininst(name):
- """Return (base,pyversion) or (None,None) for possible .exe name"""
-
- lower = name.lower()
- base, py_ver, plat = None, None, None
-
- if lower.endswith('.exe'):
- if lower.endswith('.win32.exe'):
- base = name[:-10]
- plat = 'win32'
- elif lower.startswith('.win32-py', -16):
- py_ver = name[-7:-4]
- base = name[:-16]
- plat = 'win32'
- elif lower.endswith('.win-amd64.exe'):
- base = name[:-14]
- plat = 'win-amd64'
- elif lower.startswith('.win-amd64-py', -20):
- py_ver = name[-7:-4]
- base = name[:-20]
- plat = 'win-amd64'
- return base, py_ver, plat
-
-
-def egg_info_for_url(url):
- parts = urllib.parse.urlparse(url)
- scheme, server, path, parameters, query, fragment = parts
- base = urllib.parse.unquote(path.split('/')[-1])
- if server == 'sourceforge.net' and base == 'download': # XXX Yuck
- base = urllib.parse.unquote(path.split('/')[-2])
- if '#' in base:
- base, fragment = base.split('#', 1)
- return base, fragment
-
-
-def distros_for_url(url, metadata=None):
- """Yield egg or source distribution objects that might be found at a URL"""
- base, fragment = egg_info_for_url(url)
- for dist in distros_for_location(url, base, metadata):
- yield dist
- if fragment:
- match = EGG_FRAGMENT.match(fragment)
- if match:
- for dist in interpret_distro_name(
- url, match.group(1), metadata, precedence=CHECKOUT_DIST
- ):
- yield dist
-
-
-def distros_for_location(location, basename, metadata=None):
- """Yield egg or source distribution objects based on basename"""
- if basename.endswith('.egg.zip'):
- basename = basename[:-4] # strip the .zip
- if basename.endswith('.egg') and '-' in basename:
- # only one, unambiguous interpretation
- return [Distribution.from_location(location, basename, metadata)]
- if basename.endswith('.whl') and '-' in basename:
- wheel = Wheel(basename)
- if not wheel.is_compatible():
- return []
- return [Distribution(
- location=location,
- project_name=wheel.project_name,
- version=wheel.version,
- # Increase priority over eggs.
- precedence=EGG_DIST + 1,
- )]
- if basename.endswith('.exe'):
- win_base, py_ver, platform = parse_bdist_wininst(basename)
- if win_base is not None:
- return interpret_distro_name(
- location, win_base, metadata, py_ver, BINARY_DIST, platform
- )
- # Try source distro extensions (.zip, .tgz, etc.)
- #
- for ext in EXTENSIONS:
- if basename.endswith(ext):
- basename = basename[:-len(ext)]
- return interpret_distro_name(location, basename, metadata)
- return [] # no extension matched
-
-
-def distros_for_filename(filename, metadata=None):
- """Yield possible egg or source distribution objects based on a filename"""
- return distros_for_location(
- normalize_path(filename), os.path.basename(filename), metadata
- )
-
-
-def interpret_distro_name(
- location, basename, metadata, py_version=None, precedence=SOURCE_DIST,
- platform=None
-):
- """Generate alternative interpretations of a source distro name
-
- Note: if `location` is a filesystem filename, you should call
- ``pkg_resources.normalize_path()`` on it before passing it to this
- routine!
- """
- # Generate alternative interpretations of a source distro name
- # Because some packages are ambiguous as to name/versions split
- # e.g. "adns-python-1.1.0", "egenix-mx-commercial", etc.
- # So, we generate each possible interpretation (e.g. "adns, python-1.1.0"
- # "adns-python, 1.1.0", and "adns-python-1.1.0, no version"). In practice,
- # the spurious interpretations should be ignored, because in the event
- # there's also an "adns" package, the spurious "python-1.1.0" version will
- # compare lower than any numeric version number, and is therefore unlikely
- # to match a request for it. It's still a potential problem, though, and
- # in the long run PyPI and the distutils should go for "safe" names and
- # versions in distribution archive names (sdist and bdist).
-
- parts = basename.split('-')
- if not py_version and any(re.match(r'py\d\.\d$', p) for p in parts[2:]):
- # it is a bdist_dumb, not an sdist -- bail out
- return
-
- for p in range(1, len(parts) + 1):
- yield Distribution(
- location, metadata, '-'.join(parts[:p]), '-'.join(parts[p:]),
- py_version=py_version, precedence=precedence,
- platform=platform
- )
-
-
-def unique_values(func):
- """
- Wrap a function returning an iterable such that the resulting iterable
- only ever yields unique items.
- """
-
- @wraps(func)
- def wrapper(*args, **kwargs):
- return unique_everseen(func(*args, **kwargs))
-
- return wrapper
-
-
-REL = re.compile(r"""<([^>]*\srel\s*=\s*['"]?([^'">]+)[^>]*)>""", re.I)
-# this line is here to fix emacs' cruddy broken syntax highlighting
-
-
-@unique_values
-def find_external_links(url, page):
- """Find rel="homepage" and rel="download" links in `page`, yielding URLs"""
-
- for match in REL.finditer(page):
- tag, rel = match.groups()
- rels = set(map(str.strip, rel.lower().split(',')))
- if 'homepage' in rels or 'download' in rels:
- for match in HREF.finditer(tag):
- yield urllib.parse.urljoin(url, htmldecode(match.group(1)))
-
- for tag in ("
Home Page", "
Download URL"):
- pos = page.find(tag)
- if pos != -1:
- match = HREF.search(page, pos)
- if match:
- yield urllib.parse.urljoin(url, htmldecode(match.group(1)))
-
-
-class ContentChecker:
- """
- A null content checker that defines the interface for checking content
- """
-
- def feed(self, block):
- """
- Feed a block of data to the hash.
- """
- return
-
- def is_valid(self):
- """
- Check the hash. Return False if validation fails.
- """
- return True
-
- def report(self, reporter, template):
- """
- Call reporter with information about the checker (hash name)
- substituted into the template.
- """
- return
-
-
-class HashChecker(ContentChecker):
- pattern = re.compile(
- r'(?Psha1|sha224|sha384|sha256|sha512|md5)='
- r'(?P[a-f0-9]+)'
- )
-
- def __init__(self, hash_name, expected):
- self.hash_name = hash_name
- self.hash = hashlib.new(hash_name)
- self.expected = expected
-
- @classmethod
- def from_url(cls, url):
- "Construct a (possibly null) ContentChecker from a URL"
- fragment = urllib.parse.urlparse(url)[-1]
- if not fragment:
- return ContentChecker()
- match = cls.pattern.search(fragment)
- if not match:
- return ContentChecker()
- return cls(**match.groupdict())
-
- def feed(self, block):
- self.hash.update(block)
-
- def is_valid(self):
- return self.hash.hexdigest() == self.expected
-
- def report(self, reporter, template):
- msg = template % self.hash_name
- return reporter(msg)
-
-
-class PackageIndex(Environment):
- """A distribution index that scans web pages for download URLs"""
-
- def __init__(
- self, index_url="https://pypi.org/simple/", hosts=('*',),
- ca_bundle=None, verify_ssl=True, *args, **kw
- ):
- super().__init__(*args, **kw)
- self.index_url = index_url + "/" [:not index_url.endswith('/')]
- self.scanned_urls = {}
- self.fetched_urls = {}
- self.package_pages = {}
- self.allows = re.compile('|'.join(map(translate, hosts))).match
- self.to_scan = []
- self.opener = urllib.request.urlopen
-
- def add(self, dist):
- # ignore invalid versions
- try:
- parse_version(dist.version)
- except Exception:
- return
- return super().add(dist)
-
- # FIXME: 'PackageIndex.process_url' is too complex (14)
- def process_url(self, url, retrieve=False): # noqa: C901
- """Evaluate a URL as a possible download, and maybe retrieve it"""
- if url in self.scanned_urls and not retrieve:
- return
- self.scanned_urls[url] = True
- if not URL_SCHEME(url):
- self.process_filename(url)
- return
- else:
- dists = list(distros_for_url(url))
- if dists:
- if not self.url_ok(url):
- return
- self.debug("Found link: %s", url)
-
- if dists or not retrieve or url in self.fetched_urls:
- list(map(self.add, dists))
- return # don't need the actual page
-
- if not self.url_ok(url):
- self.fetched_urls[url] = True
- return
-
- self.info("Reading %s", url)
- self.fetched_urls[url] = True # prevent multiple fetch attempts
- tmpl = "Download error on %s: %%s -- Some packages may not be found!"
- f = self.open_url(url, tmpl % url)
- if f is None:
- return
- if isinstance(f, urllib.error.HTTPError) and f.code == 401:
- self.info("Authentication error: %s" % f.msg)
- self.fetched_urls[f.url] = True
- if 'html' not in f.headers.get('content-type', '').lower():
- f.close() # not html, we can't process it
- return
-
- base = f.url # handle redirects
- page = f.read()
- if not isinstance(page, str):
- # In Python 3 and got bytes but want str.
- if isinstance(f, urllib.error.HTTPError):
- # Errors have no charset, assume latin1:
- charset = 'latin-1'
- else:
- charset = f.headers.get_param('charset') or 'latin-1'
- page = page.decode(charset, "ignore")
- f.close()
- for match in HREF.finditer(page):
- link = urllib.parse.urljoin(base, htmldecode(match.group(1)))
- self.process_url(link)
- if url.startswith(self.index_url) and getattr(f, 'code', None) != 404:
- page = self.process_index(url, page)
-
- def process_filename(self, fn, nested=False):
- # process filenames or directories
- if not os.path.exists(fn):
- self.warn("Not found: %s", fn)
- return
-
- if os.path.isdir(fn) and not nested:
- path = os.path.realpath(fn)
- for item in os.listdir(path):
- self.process_filename(os.path.join(path, item), True)
-
- dists = distros_for_filename(fn)
- if dists:
- self.debug("Found: %s", fn)
- list(map(self.add, dists))
-
- def url_ok(self, url, fatal=False):
- s = URL_SCHEME(url)
- is_file = s and s.group(1).lower() == 'file'
- if is_file or self.allows(urllib.parse.urlparse(url)[1]):
- return True
- msg = (
- "\nNote: Bypassing %s (disallowed host; see "
- "http://bit.ly/2hrImnY for details).\n")
- if fatal:
- raise DistutilsError(msg % url)
- else:
- self.warn(msg, url)
-
- def scan_egg_links(self, search_path):
- dirs = filter(os.path.isdir, search_path)
- egg_links = (
- (path, entry)
- for path in dirs
- for entry in os.listdir(path)
- if entry.endswith('.egg-link')
- )
- list(itertools.starmap(self.scan_egg_link, egg_links))
-
- def scan_egg_link(self, path, entry):
- with open(os.path.join(path, entry)) as raw_lines:
- # filter non-empty lines
- lines = list(filter(None, map(str.strip, raw_lines)))
-
- if len(lines) != 2:
- # format is not recognized; punt
- return
-
- egg_path, setup_path = lines
-
- for dist in find_distributions(os.path.join(path, egg_path)):
- dist.location = os.path.join(path, *lines)
- dist.precedence = SOURCE_DIST
- self.add(dist)
-
- def _scan(self, link):
- # Process a URL to see if it's for a package page
- NO_MATCH_SENTINEL = None, None
- if not link.startswith(self.index_url):
- return NO_MATCH_SENTINEL
-
- parts = list(map(
- urllib.parse.unquote, link[len(self.index_url):].split('/')
- ))
- if len(parts) != 2 or '#' in parts[1]:
- return NO_MATCH_SENTINEL
-
- # it's a package page, sanitize and index it
- pkg = safe_name(parts[0])
- ver = safe_version(parts[1])
- self.package_pages.setdefault(pkg.lower(), {})[link] = True
- return to_filename(pkg), to_filename(ver)
-
- def process_index(self, url, page):
- """Process the contents of a PyPI page"""
-
- # process an index page into the package-page index
- for match in HREF.finditer(page):
- try:
- self._scan(urllib.parse.urljoin(url, htmldecode(match.group(1))))
- except ValueError:
- pass
-
- pkg, ver = self._scan(url) # ensure this page is in the page index
- if not pkg:
- return "" # no sense double-scanning non-package pages
-
- # process individual package page
- for new_url in find_external_links(url, page):
- # Process the found URL
- base, frag = egg_info_for_url(new_url)
- if base.endswith('.py') and not frag:
- if ver:
- new_url += '#egg=%s-%s' % (pkg, ver)
- else:
- self.need_version_info(url)
- self.scan_url(new_url)
-
- return PYPI_MD5.sub(
- lambda m: '%s' % m.group(1, 3, 2), page
- )
-
- def need_version_info(self, url):
- self.scan_all(
- "Page at %s links to .py file(s) without version info; an index "
- "scan is required.", url
- )
-
- def scan_all(self, msg=None, *args):
- if self.index_url not in self.fetched_urls:
- if msg:
- self.warn(msg, *args)
- self.info(
- "Scanning index of all packages (this may take a while)"
- )
- self.scan_url(self.index_url)
-
- def find_packages(self, requirement):
- self.scan_url(self.index_url + requirement.unsafe_name + '/')
-
- if not self.package_pages.get(requirement.key):
- # Fall back to safe version of the name
- self.scan_url(self.index_url + requirement.project_name + '/')
-
- if not self.package_pages.get(requirement.key):
- # We couldn't find the target package, so search the index page too
- self.not_found_in_index(requirement)
-
- for url in list(self.package_pages.get(requirement.key, ())):
- # scan each page that might be related to the desired package
- self.scan_url(url)
-
- def obtain(self, requirement, installer=None):
- self.prescan()
- self.find_packages(requirement)
- for dist in self[requirement.key]:
- if dist in requirement:
- return dist
- self.debug("%s does not match %s", requirement, dist)
- return super(PackageIndex, self).obtain(requirement, installer)
-
- def check_hash(self, checker, filename, tfp):
- """
- checker is a ContentChecker
- """
- checker.report(
- self.debug,
- "Validating %%s checksum for %s" % filename)
- if not checker.is_valid():
- tfp.close()
- os.unlink(filename)
- raise DistutilsError(
- "%s validation failed for %s; "
- "possible download problem?"
- % (checker.hash.name, os.path.basename(filename))
- )
-
- def add_find_links(self, urls):
- """Add `urls` to the list that will be prescanned for searches"""
- for url in urls:
- if (
- self.to_scan is None # if we have already "gone online"
- or not URL_SCHEME(url) # or it's a local file/directory
- or url.startswith('file:')
- or list(distros_for_url(url)) # or a direct package link
- ):
- # then go ahead and process it now
- self.scan_url(url)
- else:
- # otherwise, defer retrieval till later
- self.to_scan.append(url)
-
- def prescan(self):
- """Scan urls scheduled for prescanning (e.g. --find-links)"""
- if self.to_scan:
- list(map(self.scan_url, self.to_scan))
- self.to_scan = None # from now on, go ahead and process immediately
-
- def not_found_in_index(self, requirement):
- if self[requirement.key]: # we've seen at least one distro
- meth, msg = self.info, "Couldn't retrieve index page for %r"
- else: # no distros seen for this name, might be misspelled
- meth, msg = (
- self.warn,
- "Couldn't find index page for %r (maybe misspelled?)")
- meth(msg, requirement.unsafe_name)
- self.scan_all()
-
- def download(self, spec, tmpdir):
- """Locate and/or download `spec` to `tmpdir`, returning a local path
-
- `spec` may be a ``Requirement`` object, or a string containing a URL,
- an existing local filename, or a project/version requirement spec
- (i.e. the string form of a ``Requirement`` object). If it is the URL
- of a .py file with an unambiguous ``#egg=name-version`` tag (i.e., one
- that escapes ``-`` as ``_`` throughout), a trivial ``setup.py`` is
- automatically created alongside the downloaded file.
-
- If `spec` is a ``Requirement`` object or a string containing a
- project/version requirement spec, this method returns the location of
- a matching distribution (possibly after downloading it to `tmpdir`).
- If `spec` is a locally existing file or directory name, it is simply
- returned unchanged. If `spec` is a URL, it is downloaded to a subpath
- of `tmpdir`, and the local filename is returned. Various errors may be
- raised if a problem occurs during downloading.
- """
- if not isinstance(spec, Requirement):
- scheme = URL_SCHEME(spec)
- if scheme:
- # It's a url, download it to tmpdir
- found = self._download_url(scheme.group(1), spec, tmpdir)
- base, fragment = egg_info_for_url(spec)
- if base.endswith('.py'):
- found = self.gen_setup(found, fragment, tmpdir)
- return found
- elif os.path.exists(spec):
- # Existing file or directory, just return it
- return spec
- else:
- spec = parse_requirement_arg(spec)
- return getattr(self.fetch_distribution(spec, tmpdir), 'location', None)
-
- def fetch_distribution( # noqa: C901 # is too complex (14) # FIXME
- self, requirement, tmpdir, force_scan=False, source=False,
- develop_ok=False, local_index=None):
- """Obtain a distribution suitable for fulfilling `requirement`
-
- `requirement` must be a ``pkg_resources.Requirement`` instance.
- If necessary, or if the `force_scan` flag is set, the requirement is
- searched for in the (online) package index as well as the locally
- installed packages. If a distribution matching `requirement` is found,
- the returned distribution's ``location`` is the value you would have
- gotten from calling the ``download()`` method with the matching
- distribution's URL or filename. If no matching distribution is found,
- ``None`` is returned.
-
- If the `source` flag is set, only source distributions and source
- checkout links will be considered. Unless the `develop_ok` flag is
- set, development and system eggs (i.e., those using the ``.egg-info``
- format) will be ignored.
- """
- # process a Requirement
- self.info("Searching for %s", requirement)
- skipped = {}
- dist = None
-
- def find(req, env=None):
- if env is None:
- env = self
- # Find a matching distribution; may be called more than once
-
- for dist in env[req.key]:
-
- if dist.precedence == DEVELOP_DIST and not develop_ok:
- if dist not in skipped:
- self.warn(
- "Skipping development or system egg: %s", dist,
- )
- skipped[dist] = 1
- continue
-
- test = (
- dist in req
- and (dist.precedence <= SOURCE_DIST or not source)
- )
- if test:
- loc = self.download(dist.location, tmpdir)
- dist.download_location = loc
- if os.path.exists(dist.download_location):
- return dist
-
- if force_scan:
- self.prescan()
- self.find_packages(requirement)
- dist = find(requirement)
-
- if not dist and local_index is not None:
- dist = find(requirement, local_index)
-
- if dist is None:
- if self.to_scan is not None:
- self.prescan()
- dist = find(requirement)
-
- if dist is None and not force_scan:
- self.find_packages(requirement)
- dist = find(requirement)
-
- if dist is None:
- self.warn(
- "No local packages or working download links found for %s%s",
- (source and "a source distribution of " or ""),
- requirement,
- )
- else:
- self.info("Best match: %s", dist)
- return dist.clone(location=dist.download_location)
-
- def fetch(self, requirement, tmpdir, force_scan=False, source=False):
- """Obtain a file suitable for fulfilling `requirement`
-
- DEPRECATED; use the ``fetch_distribution()`` method now instead. For
- backward compatibility, this routine is identical but returns the
- ``location`` of the downloaded distribution instead of a distribution
- object.
- """
- dist = self.fetch_distribution(requirement, tmpdir, force_scan, source)
- if dist is not None:
- return dist.location
- return None
-
- def gen_setup(self, filename, fragment, tmpdir):
- match = EGG_FRAGMENT.match(fragment)
- dists = match and [
- d for d in
- interpret_distro_name(filename, match.group(1), None) if d.version
- ] or []
-
- if len(dists) == 1: # unambiguous ``#egg`` fragment
- basename = os.path.basename(filename)
-
- # Make sure the file has been downloaded to the temp dir.
- if os.path.dirname(filename) != tmpdir:
- dst = os.path.join(tmpdir, basename)
- if not (os.path.exists(dst) and os.path.samefile(filename, dst)):
- shutil.copy2(filename, dst)
- filename = dst
-
- with open(os.path.join(tmpdir, 'setup.py'), 'w') as file:
- file.write(
- "from setuptools import setup\n"
- "setup(name=%r, version=%r, py_modules=[%r])\n"
- % (
- dists[0].project_name, dists[0].version,
- os.path.splitext(basename)[0]
- )
- )
- return filename
-
- elif match:
- raise DistutilsError(
- "Can't unambiguously interpret project/version identifier %r; "
- "any dashes in the name or version should be escaped using "
- "underscores. %r" % (fragment, dists)
- )
- else:
- raise DistutilsError(
- "Can't process plain .py files without an '#egg=name-version'"
- " suffix to enable automatic setup script generation."
- )
-
- dl_blocksize = 8192
-
- def _download_to(self, url, filename):
- self.info("Downloading %s", url)
- # Download the file
- fp = None
- try:
- checker = HashChecker.from_url(url)
- fp = self.open_url(url)
- if isinstance(fp, urllib.error.HTTPError):
- raise DistutilsError(
- "Can't download %s: %s %s" % (url, fp.code, fp.msg)
- )
- headers = fp.info()
- blocknum = 0
- bs = self.dl_blocksize
- size = -1
- if "content-length" in headers:
- # Some servers return multiple Content-Length headers :(
- sizes = headers.get_all('Content-Length')
- size = max(map(int, sizes))
- self.reporthook(url, filename, blocknum, bs, size)
- with open(filename, 'wb') as tfp:
- while True:
- block = fp.read(bs)
- if block:
- checker.feed(block)
- tfp.write(block)
- blocknum += 1
- self.reporthook(url, filename, blocknum, bs, size)
- else:
- break
- self.check_hash(checker, filename, tfp)
- return headers
- finally:
- if fp:
- fp.close()
-
- def reporthook(self, url, filename, blocknum, blksize, size):
- pass # no-op
-
- # FIXME:
- def open_url(self, url, warning=None): # noqa: C901 # is too complex (12)
- if url.startswith('file:'):
- return local_open(url)
- try:
- return open_with_auth(url, self.opener)
- except (ValueError, http.client.InvalidURL) as v:
- msg = ' '.join([str(arg) for arg in v.args])
- if warning:
- self.warn(warning, msg)
- else:
- raise DistutilsError('%s %s' % (url, msg)) from v
- except urllib.error.HTTPError as v:
- return v
- except urllib.error.URLError as v:
- if warning:
- self.warn(warning, v.reason)
- else:
- raise DistutilsError("Download error for %s: %s"
- % (url, v.reason)) from v
- except http.client.BadStatusLine as v:
- if warning:
- self.warn(warning, v.line)
- else:
- raise DistutilsError(
- '%s returned a bad status line. The server might be '
- 'down, %s' %
- (url, v.line)
- ) from v
- except (http.client.HTTPException, socket.error) as v:
- if warning:
- self.warn(warning, v)
- else:
- raise DistutilsError("Download error for %s: %s"
- % (url, v)) from v
-
- def _download_url(self, scheme, url, tmpdir):
- # Determine download filename
- #
- name, fragment = egg_info_for_url(url)
- if name:
- while '..' in name:
- name = name.replace('..', '.').replace('\\', '_')
- else:
- name = "__downloaded__" # default if URL has no path contents
-
- if name.endswith('.egg.zip'):
- name = name[:-4] # strip the extra .zip before download
-
- filename = os.path.join(tmpdir, name)
-
- # Download the file
- #
- if scheme == 'svn' or scheme.startswith('svn+'):
- return self._download_svn(url, filename)
- elif scheme == 'git' or scheme.startswith('git+'):
- return self._download_git(url, filename)
- elif scheme.startswith('hg+'):
- return self._download_hg(url, filename)
- elif scheme == 'file':
- return urllib.request.url2pathname(urllib.parse.urlparse(url)[2])
- else:
- self.url_ok(url, True) # raises error if not allowed
- return self._attempt_download(url, filename)
-
- def scan_url(self, url):
- self.process_url(url, True)
-
- def _attempt_download(self, url, filename):
- headers = self._download_to(url, filename)
- if 'html' in headers.get('content-type', '').lower():
- return self._download_html(url, headers, filename)
- else:
- return filename
-
- def _download_html(self, url, headers, filename):
- file = open(filename)
- for line in file:
- if line.strip():
- # Check for a subversion index page
- if re.search(r'([^- ]+ - )?Revision \d+:', line):
- # it's a subversion index page:
- file.close()
- os.unlink(filename)
- return self._download_svn(url, filename)
- break # not an index page
- file.close()
- os.unlink(filename)
- raise DistutilsError("Unexpected HTML page found at " + url)
-
- def _download_svn(self, url, filename):
- warnings.warn("SVN download support is deprecated", UserWarning)
- url = url.split('#', 1)[0] # remove any fragment for svn's sake
- creds = ''
- if url.lower().startswith('svn:') and '@' in url:
- scheme, netloc, path, p, q, f = urllib.parse.urlparse(url)
- if not netloc and path.startswith('//') and '/' in path[2:]:
- netloc, path = path[2:].split('/', 1)
- auth, host = _splituser(netloc)
- if auth:
- if ':' in auth:
- user, pw = auth.split(':', 1)
- creds = " --username=%s --password=%s" % (user, pw)
- else:
- creds = " --username=" + auth
- netloc = host
- parts = scheme, netloc, url, p, q, f
- url = urllib.parse.urlunparse(parts)
- self.info("Doing subversion checkout from %s to %s", url, filename)
- os.system("svn checkout%s -q %s %s" % (creds, url, filename))
- return filename
-
- @staticmethod
- def _vcs_split_rev_from_url(url, pop_prefix=False):
- scheme, netloc, path, query, frag = urllib.parse.urlsplit(url)
-
- scheme = scheme.split('+', 1)[-1]
-
- # Some fragment identification fails
- path = path.split('#', 1)[0]
-
- rev = None
- if '@' in path:
- path, rev = path.rsplit('@', 1)
-
- # Also, discard fragment
- url = urllib.parse.urlunsplit((scheme, netloc, path, query, ''))
-
- return url, rev
-
- def _download_git(self, url, filename):
- filename = filename.split('#', 1)[0]
- url, rev = self._vcs_split_rev_from_url(url, pop_prefix=True)
-
- self.info("Doing git clone from %s to %s", url, filename)
- os.system("git clone --quiet %s %s" % (url, filename))
-
- if rev is not None:
- self.info("Checking out %s", rev)
- os.system("git -C %s checkout --quiet %s" % (
- filename,
- rev,
- ))
-
- return filename
-
- def _download_hg(self, url, filename):
- filename = filename.split('#', 1)[0]
- url, rev = self._vcs_split_rev_from_url(url, pop_prefix=True)
-
- self.info("Doing hg clone from %s to %s", url, filename)
- os.system("hg clone --quiet %s %s" % (url, filename))
-
- if rev is not None:
- self.info("Updating to %s", rev)
- os.system("hg --cwd %s up -C -r %s -q" % (
- filename,
- rev,
- ))
-
- return filename
-
- def debug(self, msg, *args):
- log.debug(msg, *args)
-
- def info(self, msg, *args):
- log.info(msg, *args)
-
- def warn(self, msg, *args):
- log.warn(msg, *args)
-
-
-# This pattern matches a character entity reference (a decimal numeric
-# references, a hexadecimal numeric reference, or a named reference).
-entity_sub = re.compile(r'&(#(\d+|x[\da-fA-F]+)|[\w.:-]+);?').sub
-
-
-def decode_entity(match):
- what = match.group(0)
- return html.unescape(what)
-
-
-def htmldecode(text):
- """
- Decode HTML entities in the given text.
-
- >>> htmldecode(
- ... 'https://../package_name-0.1.2.tar.gz'
- ... '?tokena=A&tokenb=B">package_name-0.1.2.tar.gz')
- 'https://../package_name-0.1.2.tar.gz?tokena=A&tokenb=B">package_name-0.1.2.tar.gz'
- """
- return entity_sub(decode_entity, text)
-
-
-def socket_timeout(timeout=15):
- def _socket_timeout(func):
- def _socket_timeout(*args, **kwargs):
- old_timeout = socket.getdefaulttimeout()
- socket.setdefaulttimeout(timeout)
- try:
- return func(*args, **kwargs)
- finally:
- socket.setdefaulttimeout(old_timeout)
-
- return _socket_timeout
-
- return _socket_timeout
-
-
-def _encode_auth(auth):
- """
- Encode auth from a URL suitable for an HTTP header.
- >>> str(_encode_auth('username%3Apassword'))
- 'dXNlcm5hbWU6cGFzc3dvcmQ='
-
- Long auth strings should not cause a newline to be inserted.
- >>> long_auth = 'username:' + 'password'*10
- >>> chr(10) in str(_encode_auth(long_auth))
- False
- """
- auth_s = urllib.parse.unquote(auth)
- # convert to bytes
- auth_bytes = auth_s.encode()
- encoded_bytes = base64.b64encode(auth_bytes)
- # convert back to a string
- encoded = encoded_bytes.decode()
- # strip the trailing carriage return
- return encoded.replace('\n', '')
-
-
-class Credential:
- """
- A username/password pair. Use like a namedtuple.
- """
-
- def __init__(self, username, password):
- self.username = username
- self.password = password
-
- def __iter__(self):
- yield self.username
- yield self.password
-
- def __str__(self):
- return '%(username)s:%(password)s' % vars(self)
-
-
-class PyPIConfig(configparser.RawConfigParser):
- def __init__(self):
- """
- Load from ~/.pypirc
- """
- defaults = dict.fromkeys(['username', 'password', 'repository'], '')
- super().__init__(defaults)
-
- rc = os.path.join(os.path.expanduser('~'), '.pypirc')
- if os.path.exists(rc):
- self.read(rc)
-
- @property
- def creds_by_repository(self):
- sections_with_repositories = [
- section for section in self.sections()
- if self.get(section, 'repository').strip()
- ]
-
- return dict(map(self._get_repo_cred, sections_with_repositories))
-
- def _get_repo_cred(self, section):
- repo = self.get(section, 'repository').strip()
- return repo, Credential(
- self.get(section, 'username').strip(),
- self.get(section, 'password').strip(),
- )
-
- def find_credential(self, url):
- """
- If the URL indicated appears to be a repository defined in this
- config, return the credential for that repository.
- """
- for repository, cred in self.creds_by_repository.items():
- if url.startswith(repository):
- return cred
-
-
-def open_with_auth(url, opener=urllib.request.urlopen):
- """Open a urllib2 request, handling HTTP authentication"""
-
- parsed = urllib.parse.urlparse(url)
- scheme, netloc, path, params, query, frag = parsed
-
- # Double scheme does not raise on macOS as revealed by a
- # failing test. We would expect "nonnumeric port". Refs #20.
- if netloc.endswith(':'):
- raise http.client.InvalidURL("nonnumeric port: ''")
-
- if scheme in ('http', 'https'):
- auth, address = _splituser(netloc)
- else:
- auth = None
-
- if not auth:
- cred = PyPIConfig().find_credential(url)
- if cred:
- auth = str(cred)
- info = cred.username, url
- log.info('Authenticating as %s for %s (from .pypirc)', *info)
-
- if auth:
- auth = "Basic " + _encode_auth(auth)
- parts = scheme, address, path, params, query, frag
- new_url = urllib.parse.urlunparse(parts)
- request = urllib.request.Request(new_url)
- request.add_header("Authorization", auth)
- else:
- request = urllib.request.Request(url)
-
- request.add_header('User-Agent', user_agent)
- fp = opener(request)
-
- if auth:
- # Put authentication info back into request URL if same host,
- # so that links found on the page will work
- s2, h2, path2, param2, query2, frag2 = urllib.parse.urlparse(fp.url)
- if s2 == scheme and h2 == address:
- parts = s2, netloc, path2, param2, query2, frag2
- fp.url = urllib.parse.urlunparse(parts)
-
- return fp
-
-
-# copy of urllib.parse._splituser from Python 3.8
-def _splituser(host):
- """splituser('user[:passwd]@host[:port]')
- --> 'user[:passwd]', 'host[:port]'."""
- user, delim, host = host.rpartition('@')
- return (user if delim else None), host
-
-
-# adding a timeout to avoid freezing package_index
-open_with_auth = socket_timeout(_SOCKET_TIMEOUT)(open_with_auth)
-
-
-def fix_sf_url(url):
- return url # backward compatibility
-
-
-def local_open(url):
- """Read a local path, with special support for directories"""
- scheme, server, path, param, query, frag = urllib.parse.urlparse(url)
- filename = urllib.request.url2pathname(path)
- if os.path.isfile(filename):
- return urllib.request.urlopen(url)
- elif path.endswith('/') and os.path.isdir(filename):
- files = []
- for f in os.listdir(filename):
- filepath = os.path.join(filename, f)
- if f == 'index.html':
- with open(filepath, 'r') as fp:
- body = fp.read()
- break
- elif os.path.isdir(filepath):
- f += '/'
- files.append('<a href="{name}">{name}</a>'.format(name=f))
- else:
- tmpl = (
- "<html><head><title>{url}"
- "{files}