diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Clutch Blast Tyrant Rar REPACK Download.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Clutch Blast Tyrant Rar REPACK Download.md
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-<h1>How to Download Clutch Blast Tyrant Rar File for Free</h1>
-<p>Clutch Blast Tyrant is the sixth studio album by the American rock band Clutch, released in 2004. The album features a mix of hard rock, blues, funk and metal elements, and is considered one of the band's best works by fans and critics alike.</p>
-<p>If you are looking for a way to download Clutch Blast Tyrant rar file for free, you have come to the right place. In this article, we will show you how to use a torrent client to get the album in high quality and without any viruses or malware.</p>
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-<h2>Step 1: Download and Install a Torrent Client</h2>
-<p>A torrent client is a software that allows you to download files from other users who are sharing them on the internet. There are many torrent clients available, but we recommend using uTorrent, which is one of the most popular and reliable ones. You can download uTorrent from <a href="https://www.utorrent.com/">here</a>.</p>
-<p>Once you have downloaded the uTorrent installer, run it and follow the instructions to install it on your computer. You may need to allow uTorrent to access your network in your firewall settings.</p>
-<h2>Step 2: Find and Download the Clutch Blast Tyrant Rar File</h2>
-<p>Now that you have uTorrent installed, you need to find the Clutch Blast Tyrant rar file that you want to download. You can use a torrent search engine like <a href="https://thepiratebay.org/">The Pirate Bay</a> or <a href="https://1337x.to/">1337x</a> to look for it. Just type "Clutch Blast Tyrant rar" in the search box and hit enter.</p>
-<p>You will see a list of results with different file sizes and qualities. Choose the one that has the most seeders and leechers, as this indicates that the file is more likely to be complete and fast to download. Click on the magnet link or the download button next to the result that you want.</p>
-<p>This will open uTorrent and start downloading the file. You can see the progress and speed of the download in uTorrent's interface. Depending on your internet connection and the availability of other users, it may take some time to finish.</p>
-<h2>Step 3: Extract and Enjoy the Album</h2>
-<p>Once the download is complete, you will have a Clutch Blast Tyrant rar file in your uTorrent downloads folder. You will need a software like WinRAR or 7-Zip to extract it. You can download WinRAR from <a href="https://www.win-rar.com/">here</a> or 7-Zip from <a href="https://www.7-zip.org/">here</a>.</p>
-<p>After installing one of these software, right-click on the Clutch Blast Tyrant rar file and choose "Extract Here" or "Extract to Clutch Blast Tyrant". This will create a folder with the same name as the rar file, containing all the tracks of the album in mp3 format.</p>
-<p></p>
-<p>You can now play the album with your favorite music player or transfer it to your mobile device. Enjoy!</p>
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-<h2>Why Download Clutch Blast Tyrant Rar File?</h2>
-<p>You may be wondering why you should download Clutch Blast Tyrant rar file instead of buying the album from a legal source. There are several reasons why some people prefer to download music for free from torrent sites.</p>
-<p>One reason is that they may not have enough money to afford the album or they may live in a country where the album is not available or too expensive. Another reason is that they may want to try the album before buying it or they may not like all the tracks and only want to keep some of them. A third reason is that they may want to have a backup copy of the album in case they lose or damage their original one.</p>
-<p>Whatever your reason is, downloading Clutch Blast Tyrant rar file from torrent sites is a risky and illegal activity. You may expose your computer to viruses or malware, you may face legal consequences if you get caught by the authorities, and you may harm the artists and the music industry by not supporting their work. Therefore, we do not encourage or endorse downloading music for free from torrent sites. We only provide this information for educational purposes and we advise you to buy the album from a legal source if you like it.</p>
-<h2>What is Clutch Blast Tyrant About?</h2>
-<p>Clutch Blast Tyrant is an album that showcases the band's diverse musical influences and styles. The album has 15 tracks, each with a different theme and mood. Some of the tracks are inspired by science fiction, mythology, history, politics, and religion. The album also features guest appearances by members of other bands such as Mastodon, Fu Manchu, and Sixty Watt Shaman.</p>
-<p>The album's title refers to a fictional character named Blast Tyrant, who is a tyrannical ruler of a futuristic world. The album's cover art depicts Blast Tyrant riding a giant worm in a desert landscape. The album's lyrics explore various aspects of Blast Tyrant's personality and actions, as well as the resistance and rebellion against him.</p>
-<p>The album received critical acclaim from music critics and fans alike. It was praised for its originality, creativity, energy, and musicianship. It was also ranked as one of the best albums of 2004 by several publications and websites. The album sold over 100,000 copies in the United States and was certified gold by the RIAA.</p> 81aa517590<br />
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download TextNow Cracked APK for Free Is It Worth It?.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download TextNow Cracked APK for Free Is It Worth It?.md
deleted file mode 100644
index 33467e66a5212dd9edb5fbfc69af0e3a8711dafd..0000000000000000000000000000000000000000
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-<p>TextNow is a popular app that allows you to make free calls and texts to any number in the US and Canada. However, the free version of the app has some limitations, such as ads, low-quality calls, and limited features. If you want to enjoy the full benefits of TextNow without paying for a premium subscription, you might be tempted to download a cracked APK of the app. A cracked APK is a modified version of the app that bypasses the security and verification systems of the original app. However, downloading a cracked APK of TextNow is not only illegal, but also risky. In this article, we will explain why you should avoid TextNow cracked APK download and what are some safer alternatives.</p>
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-<li><b>Free version.</b> The free version of TextNow is still available for download from the official website or the Google Play Store. The free version allows you to make free calls and texts to any number in the US and Canada, as well as enjoy some features such as voicemail, call forwarding, group chat and emojis. However, the free version also has some limitations such as ads, low-quality calls and limited features.</li>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Extra Speed Zte Ac2726 Modem Firmware Upgrade.md b/spaces/1gistliPinn/ChatGPT4/Examples/Extra Speed Zte Ac2726 Modem Firmware Upgrade.md
deleted file mode 100644
index 0df29b84eb1a5ef41d60b5373152cee40d414334..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Extra Speed Zte Ac2726 Modem Firmware Upgrade.md	
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Discover the Best Free Video Games for Every Genre and Platform.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Discover the Best Free Video Games for Every Genre and Platform.md
deleted file mode 100644
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+++ /dev/null
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-<p>If you love playing video games, but don't want to spend a lot of money on them, you might be interested in free download video games. These are games that you can download and play on your PC or mobile device without paying anything. Sounds too good to be true, right? Well, not really. There are actually many free download video games available online, and some of them are very high-quality and fun. In this article, we will explain what free download video games are, why you should play them, and how to find them. We will also recommend some of the best free download video games for PC that you can try right now.</p>
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-<h3>Microsoft Store</h3>
-<p>One of the best places to find free download video games for PC is Microsoft Store. This is an online platform that offers a variety of apps, games, movies, TV shows, and more for Windows 10 devices. You can browse through different categories and genres of games and find some great free download video games for PC. Here are some of the best ones:</p>
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-<h2>Conclusion</h2>
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-<p>In conclusion, free download video games are games that you can download and play on your PC or mobile device without paying anything. They are a great way to save money, discover new games, and play anytime and anywhere. There are many ways to find free download video games online, such as using search engines, online platforms or stores, or online reviews or recommendations. Some of the best free download video games for PC are Asphalt 9: Legends, Roblox, Forza Horizon 4 Demo, FIFA Mobile, Star Wars: Galaxy of Heroes, and Apex Legends. If you are looking for some free download video games to play on your PC, you should definitely check them out.</p>
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diff --git a/spaces/1phancelerku/anime-remove-background/Free Download General Knowledge Quiz Book PDF with Answers and Explanations.md b/spaces/1phancelerku/anime-remove-background/Free Download General Knowledge Quiz Book PDF with Answers and Explanations.md
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index 338f9cfb4dab0a2084265f7fb7ecf0acfb388af3..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Free Download General Knowledge Quiz Book PDF with Answers and Explanations.md	
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-<li>It broadens your perspective and helps you understand different cultures, viewpoints, and phenomena.</li>
-<li>It improves your communication skills and makes you more confident and articulate in expressing your opinions and arguments.</li>
-<li>It boosts your academic performance and helps you excel in exams, tests, quizzes, and assignments.</li>
-<li>It increases your employability and career prospects, as employers value candidates who have a good grasp of general knowledge.</li>
-<li>It enriches your personal life and makes you more interesting and attractive to others.</li>
-</ul>
-<h3>What are the benefits of reading quiz books?</h3>
-<p>Quiz books are books that contain questions and answers on various topics of general knowledge. They are designed to test your knowledge, challenge your mind, and entertain you at the same time. Reading quiz books has many benefits, such as:</p>
-<ul>
-<li>It stimulates your brain and keeps it active and healthy.</li>
-<li>It expands your vocabulary and improves your language skills.</li>
-<li>It updates your information and keeps you abreast of the latest developments in the world.</li>
-<li>It develops your curiosity and encourages you to research more about the topics that interest you.</li>
-<li>It enhances your concentration and attention span.</li>
-<li>It reduces your stress and boredom and provides you with fun and relaxation.</li> <h3>How to download general knowledge quiz book PDF for free?</h3>
-<p>If you are looking for a way to download general knowledge quiz book PDF for free, you have several options to choose from. Some of them are:</p>
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-gk religion and mythology quiz book pdf download</p>
-<ul>
-<li>Use online platforms that offer free ebooks, such as Project Gutenberg, Open Library, Internet Archive, and ManyBooks. These platforms have a large collection of quiz books and other genres that you can download in various formats, including PDF.</li>
-<li>Use online tools that convert web pages or documents into PDF files, such as Web2PDF, PDF Converter, and Smallpdf. These tools allow you to enter the URL or upload the file of the quiz book you want to download, and then generate a PDF file that you can save or share.</li>
-<li>Use online sources that provide free PDF downloads of quiz books, such as PDF Drive, Free-Ebooks.net, and BookBoon. These sources have a wide range of quiz books and other categories that you can download for free or with a registration.</li>
-</ul>
-<p>However, before you download any quiz book PDF for free, make sure that you check the following:</p>
-<ul>
-<li>The quality and accuracy of the content. Some quiz books may have outdated, incorrect, or incomplete information that may mislead or confuse you.</li>
-<li>The legality and safety of the source. Some sources may violate the copyright or privacy of the authors or publishers, or may contain viruses or malware that may harm your device.</li>
-<li>The compatibility and accessibility of the format. Some PDF files may not be compatible with your device or software, or may not be easy to read or print.</li>
-</ul>
-<h2>Top 10 General Knowledge Quiz Books</h2>
-<p>Now that you know how to download general knowledge quiz book PDF for free, let's take a look at some of the best quiz books that you can read online or offline. Here are our top 10 picks, along with their overviews, features, pros, and cons.</p>
- <h3>The General Knowledge Quiz Book by Day Williams</h3>
-<h4>Overview</h4>
-<p>The General Knowledge Quiz Book by Day Williams is a comprehensive and challenging quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 500 questions and answers that are divided into 50 categories. It also includes fun facts and trivia that will enrich your knowledge and entertain you.</p>
-<h4>Features</h4>
-<ul>
-<li>500 questions and answers on various topics of general knowledge</li>
-<li>50 categories, such as animals, astronomy, celebrities, countries, inventions, movies, music, religion, etc.</li>
-<li>Fun facts and trivia that provide additional information and insights</li>
-<li>Easy to read and follow format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and categories | It may not be updated with the latest information | | It provides fun facts and trivia that add value and interest | It may have some errors or typos | | It is easy to read and follow | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may not have enough questions or variety | <h3>The Ultimate General Knowledge Quiz Book by Terry Dolan</h3>
-<h4>Overview</h4>
-<p>The Ultimate General Knowledge Quiz Book by Terry Dolan is an extensive and engaging quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 1000 questions and answers that are divided into 100 quizzes. It also includes hints and tips that will help you improve your general knowledge and quiz skills.</p>
-<h4>Features</h4>
-<ul>
-<li>1000 questions and answers on various topics of general knowledge</li>
-<li>100 quizzes, each with 10 questions and answers</li>
-<li>Hints and tips that provide guidance and advice on how to answer the questions</li>
-<li>Clear and concise format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides hints and tips that enhance your general knowledge and quiz skills | It may have some errors or typos | | It is clear and concise | It may not be available in some regions or formats | | <h3>The Great Book of General Knowledge Trivia by Bill O'Neill</h3>
-<h4>Overview</h4>
-<p>The Great Book of General Knowledge Trivia by Bill O'Neill is a fun and fascinating quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 200 questions and answers that are divided into 20 quizzes. It also includes interesting facts and stories that will amaze and entertain you.</p>
-<h4>Features</h4>
-<ul>
-<li>200 questions and answers on various topics of general knowledge</li>
-<li>20 quizzes, each with 10 questions and answers</li>
-<li>Interesting facts and stories that provide background and context to the questions</li>
-<li>Colorful and attractive format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides interesting facts and stories that make the quiz more enjoyable | It may have some errors or typos | | It is colorful and attractive | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may not have enough questions or variety | <h3>The Mega General Knowledge Quiz Book by Jenny Kellett</h3>
-<h4>Overview</h4>
-<p>The Mega General Knowledge Quiz Book by Jenny Kellett is a massive and comprehensive quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 5000 questions and answers that are divided into 500 quizzes. It also includes a scoring system that will help you track your progress and performance.</p>
-<h4>Features</h4>
-<ul>
-<li>5000 questions and answers on various topics of general knowledge</li>
-<li>500 quizzes, each with 10 questions and answers</li>
-<li>A scoring system that provides feedback and motivation</li>
-<li>Simple and straightforward format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides a scoring system that enhances your general knowledge and quiz skills | It may have some errors or typos | | It is simple and straightforward | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may be too long or repetitive for some readers | <h3>The General Knowledge Pub Quiz Book by Geoff Tibballs</h3>
-<h4>Overview</h4>
-<p>The General Knowledge Pub Quiz Book by Geoff Tibballs is a fun and entertaining quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 1000 questions and answers that are divided into 100 quizzes. It also includes a mix of easy, medium, and hard questions that will suit different tastes and abilities.</p>
-<h4>Features</h4>
-<ul>
-<li>1000 questions and answers on various topics of general knowledge</li>
-<li>100 quizzes, each with 10 questions and answers</li>
-<li>A mix of easy, medium, and hard questions that provide variety and challenge</li>
-<li>Funny and witty format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides a mix of easy, medium, and hard questions that cater to different tastes and abilities | It may have some errors or typos | | It is funny and witty | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may not have enough facts or trivia to accompany the questions | <h3>The Big Book of Random Facts by Bill O'Neill</h3>
-<h4>Overview</h4>
-<p>The Big Book of Random Facts by Bill O'Neill is a fun and interesting quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 1000 questions and answers that are divided into 100 quizzes. It also includes random facts and trivia that will surprise and amuse you.</p>
-<h4>Features</h4>
-<ul>
-<li>1000 questions and answers on various topics of general knowledge</li>
-<li>100 quizzes, each with 10 questions and answers</li>
-<li>Random facts and trivia that provide fun and entertainment</li>
-<li>Creative and catchy format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides random facts and trivia that make the quiz more enjoyable | It may have some errors or typos | | It is creative and catchy | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may not have enough questions or variety | <h3>The World's Greatest General Knowledge Quiz Book by Chris Cowlin</h3>
-<h4>Overview</h4>
-<p>The World's Greatest General Knowledge Quiz Book by Chris Cowlin is a challenging and impressive quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 5000 questions and answers that are divided into 200 quizzes. It also includes a table that shows the world records for the most questions answered correctly in a quiz.</p>
-<h4>Features</h4>
-<ul>
-<li>5000 questions and answers on various topics of general knowledge</li>
-<li>200 quizzes, each with 25 questions and answers</li>
-<li>A table that shows the world records for the most questions answered correctly in a quiz</li>
-<li>Professional and elegant format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides a table that shows the world records for the most questions answered correctly in a quiz | It may have some errors or typos | | It is professional and elegant | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may be too difficult or intimidating for some readers | <h3>The Ultimate Trivia Challenge by Editors of Reader's Digest</h3>
-<h4>Overview</h4>
-<p>The Ultimate Trivia Challenge by Editors of Reader's Digest is a fun and informative quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 3000 questions and answers that are divided into 300 quizzes. It also includes illustrations and diagrams that will help you visualize the questions and answers.</p>
-<h4>Features</h4>
-<ul>
-<li>3000 questions and answers on various topics of general knowledge</li>
-<li>300 quizzes, each with 10 questions and answers</li>
-<li>Illustrations and diagrams that provide visual aids to the questions and answers</li>
-<li>Friendly and colorful format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides illustrations and diagrams that enhance your general knowledge and quiz skills | It may have some errors or typos | | It is friendly and colorful | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may not have enough facts or trivia to accompany the questions | <h3>The Fun Knowledge Encyclopedia by Bill O'Neill</h3>
-<h4>Overview</h4>
-<p>The Fun Knowledge Encyclopedia by Bill O'Neill is a fun and captivating quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 1000 questions and answers that are divided into 100 quizzes. It also includes amazing facts and stories that will astonish and delight you.</p>
-<h4>Features</h4>
-<ul>
-<li>1000 questions and answers on various topics of general knowledge</li>
-<li>100 quizzes, each with 10 questions and answers</li>
-<li>Amazing facts and stories that provide wonder and entertainment</li>
-<li>Engaging and lively format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides amazing facts and stories that make the quiz more enjoyable | It may have some errors or typos | | It is engaging and lively | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may not have enough questions or variety | <h3>The Amazing Quiz Book by Jack Goldstein and Frankie Taylor</h3>
-<h4>Overview</h4>
-<p>The Amazing Quiz Book by Jack Goldstein and Frankie Taylor is a fun and exciting quiz book that covers various topics, such as history, geography, science, arts, culture, sports, current affairs, and more. It contains 500 questions and answers that are divided into 50 quizzes. It also includes a bonus round that will test your speed and accuracy.</p>
-<h4>Features</h4>
-<ul>
-<li>500 questions and answers on various topics of general knowledge</li>
-<li>50 quizzes, each with 10 questions and answers</li>
-<li>A bonus round that provides a timed challenge</li>
-<li>Cool and modern format</li>
-<li>Suitable for all ages and levels of difficulty</li>
-</ul>
-<h4>Pros and cons</h4>
- | Pros | Cons | | --- | --- | | It covers a wide range of topics and quizzes | It may not be updated with the latest information | | It provides a bonus round that adds thrill and excitement to the quiz | It may have some errors or typos | | It is cool and modern | It may not be available in some regions or formats | | It is suitable for all ages and levels of difficulty | It may not have enough facts or trivia to accompany the questions | <h2>Conclusion</h2>
-<p>In conclusion, general knowledge is an important and beneficial skill that you can improve by reading quiz books. Quiz books are books that contain questions and answers on various topics of general knowledge. They are designed to test your knowledge, challenge your mind, and entertain you at the same time.</p>
-<p>In this article, we have shown you how to download general knowledge quiz book PDF for free from various sources. We have also reviewed the top 10 general knowledge quiz books that you can read online or offline, and compared their features, pros, and cons.</p>
-<p>We hope that this article has helped you find the best quiz book for your needs and preferences. Whether you want to learn something new, have fun, or impress your friends, there is a quiz book for everyone. So what are you waiting for? Grab your quiz book today and start quizzing!</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <h3>Q: What is the best way to prepare for a general knowledge quiz?</h3>
-<p>A: The best way to prepare for a general knowledge quiz is to read widely and regularly on various topics of interest. You can also watch documentaries, listen to podcasts, browse websites, or follow social media accounts that provide general knowledge information. Additionally, you can practice answering quiz questions from books or online sources.</p>
- <h3>Q: How can I improve my general knowledge?</h3>
-<p>A: You can improve your general knowledge by following these tips:</p>
-<ul>
-<li>Be curious and ask questions about everything.</li>
-<li>Read books, magazines, newspapers, blogs, etc. on different subjects.</li>
-<li>Watch TV shows, movies, videos, etc. that are informative and educational.</li>
-<li>Listen to radio programs, podcasts, audiobooks, etc. that are relevant and interesting.</li>
-<li>Browse websites, apps, games, etc. that are fun and interactive.</li>
-<li>Follow social media accounts, newsletters, forums, etc. that are reliable and updated.</li>
-<li>Join clubs, groups, events, etc. that are related to your hobbies or passions.</li>
-<li>Travel to different places and cultures.</li>
-<li>Talk to people who have different backgrounds and experiences.</li>
-<li>Play games, puzzles, quizzes, etc. that are fun and challenging.</li>
-</ul>
- <h3>Q: What are some of the most common topics in general knowledge quizzes?</h3>
-<p>A: Some of the most common topics in general knowledge quizzes are:</p>
-<ul>
-<li>History: This topic covers the events, people, places, dates, etc. that shaped the past.</li>
-<li>Geography: This topic covers the physical features, locations, climates, populations, etc. of the world.</li>
-<li>Science: This topic covers the natural phenomena, laws, theories, discoveries, inventions, etc. that explain the world.</li>
-<li>Arts: This topic covers the forms, styles, movements, creators, works, etc. that express the world.</li>
-<li>Culture: This topic covers the beliefs, values, customs, traditions, languages, religions, etc. that define the world.</li>
-<li>Sports: This topic covers the games, rules, players, teams, records, etc. that entertain the world.</li>
-<li>Current Affairs: This topic covers the news, issues, trends, events, etc. that affect the world.</li>
-</ul>
- <h3>Q: What are some of the best sources for general knowledge information?</h3>
-<p>A: Some of the best sources for general knowledge information are:</p>
-<ul>
-<li>Books: Books are reliable and authoritative sources of information that cover a wide range of topics and genres. You can find books in libraries, bookstores, or online platforms.</li>
-<li>Encyclopedias: Encyclopedias are comprehensive and organized sources of information that cover various topics and fields. You can find encyclopedias in print or online formats.</li>
-<li>Dictionaries: Dictionaries are useful and handy sources of information that provide definitions, meanings, synonyms, antonyms, pronunciations, etc. of words. You can find dictionaries in print or online formats.</li>
-<li>Almanacs: Almanacs are informative and updated sources of information that provide facts, figures, statistics, charts, tables, etc. on various topics and categories. You can find almanacs in print or online formats.</li>
-<li>Atlases: Atlases are visual and interactive sources of information that provide maps, images, diagrams, etc. of various places and regions. You can find atlases in print or online formats.</li>
-</ul>
- <h3>Q: How can I make my own general knowledge quiz?</h3>
-<p>A: You can make your own general knowledge quiz by following these steps:</p>
-<ol>
-<li>Pick a topic or category that interests you or your audience.</li>
-<li>Research and gather information on that topic or category from reliable and updated sources.</li>
-<li>Create a list of questions and answers based on that information. Make sure to vary the difficulty and format of the questions.</li>
-<li>Organize your questions and answers into a quiz format. You can use a template or a tool to help you with this step.</li>
-<li>Test your quiz on yourself or others to check for errors or improvements.</li>
-<li>Publish or share your quiz with your friends or family and enjoy!</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/7hao/bingo/src/lib/isomorphic/index.ts b/spaces/7hao/bingo/src/lib/isomorphic/index.ts
deleted file mode 100644
index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000
--- a/spaces/7hao/bingo/src/lib/isomorphic/index.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-'use client'
-
-import Default from './browser'
-
-let exportsModel: any = {}
-
-if (process.browser) {
-  Object.assign(exportsModel, require('./browser').default)
-} else {
-  Object.assign(exportsModel, require('./node').default)
-}
-
-export default exportsModel! as typeof Default
-
-export const fetch: typeof Default.fetch = exportsModel!.fetch
-export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket
-export const debug: typeof Default.debug = exportsModel!.debug
diff --git a/spaces/A00001/bingothoo/src/components/welcome-screen.tsx b/spaces/A00001/bingothoo/src/components/welcome-screen.tsx
deleted file mode 100644
index f7449fcbb6c621875e235db98f2790bf7894fb0a..0000000000000000000000000000000000000000
--- a/spaces/A00001/bingothoo/src/components/welcome-screen.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-import { useBing } from '@/lib/hooks/use-bing'
-
-const exampleMessages = [
-  {
-    heading: '🧐 提出复杂问题',
-    message: `我可以为我挑剔的只吃橙色食物的孩子做什么饭?`
-  },
-  {
-    heading: '🙌 获取更好的答案',
-    message: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?'
-  },
-  {
-    heading: '🎨 获得创意灵感',
-    message: `以海盗的口吻写一首关于外太空鳄鱼的俳句`
-  }
-]
-
-export function WelcomeScreen({ setInput }: Pick<ReturnType<typeof useBing>, 'setInput'>) {
-  return (
-    <div className="welcome-container flex">
-      {exampleMessages.map(example => (
-        <button key={example.heading} className="welcome-item w-4/5 sm:w-[240px]" type="button" onClick={() => setInput(example.message)}>
-          <div className="item-title">{example.heading}</div>
-          <div className="item-content">
-            <div className="item-body">
-              <div className="item-header"></div>
-              <div>&ldquo;{example.message}&rdquo;</div>
-            </div>
-          </div>
-        </button>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/AIWaves/Software_Company/src/agents/Action/base_action.py b/spaces/AIWaves/Software_Company/src/agents/Action/base_action.py
deleted file mode 100644
index 9dc494dc71eacd44c8a96848798b096795f9af0a..0000000000000000000000000000000000000000
--- a/spaces/AIWaves/Software_Company/src/agents/Action/base_action.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from Memory import Memory
-from utils import extract
-class Action:
-    """
-    The basic action unit of agent
-    """
-    def __init__(self,**kwargs):
-        self.response = None
-        self.is_user = False
-        self.res_dict = {}
-        self.name = ""
-        self.role = ""
-        for key,value in kwargs.items():
-            setattr(self,key,value)
-    
-    
-    def process(self):
-        """
-        processing action
-        Rerutn : memory(Memory)
-        """
-        response = self.response
-        send_name = self.name
-        send_role = self.role
-        all = ""
-        for res in response:
-            all += res
-        parse = f"{send_name}:"
-        
-        # 将里面对话的第三人称删了
-        # The third person in the dialogue was deleted.
-        while parse in all:
-            index = all.index(parse) + len(parse)
-            all = all[index:]
-        
-        if not self.is_user:
-            print(f"{send_name}({send_role}):{all}")
-                # for software
-        if "<title>" in all:
-            title = extract(all,"title")
-            python = extract(all,"python")
-            os.makedirs("output_code", exist_ok=True)
-            file_name = "output_code/" + title
-            with open(file_name, "w", encoding="utf-8") as f:
-                f.write(python)
-        memory = Memory(send_role, send_name, all)
-        return memory
-    
-    
diff --git a/spaces/Abdulkader/HumanMotionsDetector/README.md b/spaces/Abdulkader/HumanMotionsDetector/README.md
deleted file mode 100644
index 16dafce7baa15921ba183d1b552a4b69312762f7..0000000000000000000000000000000000000000
--- a/spaces/Abdulkader/HumanMotionsDetector/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: HumanMotionsDetector
-emoji: 🔥
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/midas_net.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=256, non_negative=True):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet, self).__init__()
-
-        use_pretrained = False if path is None else True
-
-        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
-        self.scratch.refinenet4 = FeatureFusionBlock(features)
-        self.scratch.refinenet3 = FeatureFusionBlock(features)
-        self.scratch.refinenet2 = FeatureFusionBlock(features)
-        self.scratch.refinenet1 = FeatureFusionBlock(features)
-
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-        )
-
-        if path:
-            self.load(path)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
diff --git a/spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/__init__.py b/spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/__init__.py
deleted file mode 100644
index 5d28698a83612f0225cb38a2f71b3a6c04bb8790..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from agentverse.registry import Registry
-
-memory_manipulator_registry = Registry(name="Memory_Manipulator_Registry")
-
-from .base import BaseMemoryManipulator
-from .basic import BasicMemoryManipulator
-from .reflection import Reflection
-from .plan import Plan
-
diff --git a/spaces/AgentVerse/agentVerse/scripts/evaluate_commongen.py b/spaces/AgentVerse/agentVerse/scripts/evaluate_commongen.py
deleted file mode 100644
index 89df19f328b7fb4bea2638d6c75c74847fd7abc9..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/scripts/evaluate_commongen.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import argparse
-import json
-import spacy
-from tqdm import tqdm
-
-
-nlp = spacy.load("en_core_web_sm")
-
-
-def coverage_score(preds, concept_sets):
-    covs = []
-    missings = []
-    for p, cs in tqdm(zip(preds, concept_sets), total=len(preds)):
-        cs = set(cs)
-        lemmas = set()
-        for token in nlp(p):
-            lemmas.add(token.lemma_)
-        cov = len(lemmas & cs) / len(cs)
-        covs.append(cov)
-        missings.append(cs - lemmas)
-    return sum(covs) / len(covs), missings
-
-
-def scoring(preds, concept_sets):
-    # Scores, Coverage, Coverage_POS  = pivot_score.score(pred, ref, concept, ori_concepts, scoring="steiner_tree", parser="spacy", verbose=False)
-    coverage, missing_tokens = coverage_score(preds, concept_sets)
-    # print(f"System level Score: {sum(Scores)/len(Scores)*100:.2f}")
-    print(f"System level Coverage: {coverage*100:.2f}")
-    # print(f"System level Coverage_POS: {sum(Coverage_POS)/len(Scores)*100:.2f}")
-    return coverage, missing_tokens
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--path", default="", type=str)
-    args = parser.parse_args()
-    # nlp.pipeline = [("tagger", nlp.tagger), ("parser", nlp.parser)]
-
-    preds_final = []
-    preds_first = []
-    concept_sets = []
-    with open(args.path) as f:
-        for line in f:
-            line = json.loads(line)
-            preds_final.append(line["response"])
-            if line["logs"][0]["module"] == "Role Assigner":
-                preds_first.append(line["logs"][1]["content"])
-            else:
-                preds_first.append(line["logs"][0]["content"])
-            concept_sets.append(line["input"])
-
-    scoring(preds_final, concept_sets)
-    scoring(preds_first, concept_sets)
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/japanese.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text
diff --git a/spaces/AlanMars/QYL-AI-Space/run_Linux.sh b/spaces/AlanMars/QYL-AI-Space/run_Linux.sh
deleted file mode 100644
index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000
--- a/spaces/AlanMars/QYL-AI-Space/run_Linux.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$(readlink -f "$0")")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir" || exit
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
-
-# 检查ChuanhuChatbot.py是否在运行
-if ! pgrep -f ChuanhuChatbot.py > /dev/null; then
-	# 如果没有运行，启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
deleted file mode 100644
index b1f26c081da27811f856fe9973eb444c82604727..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
+++ /dev/null
@@ -1,16 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        plugins=[
-            dict(
-                cfg=dict(
-                    type='GeneralizedAttention',
-                    spatial_range=-1,
-                    num_heads=8,
-                    attention_type='1111',
-                    kv_stride=2),
-                stages=(False, False, True, True),
-                position='after_conv2')
-        ],
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py
deleted file mode 100644
index 3ab2a2c5ef04fc38a686065167df62eb3d67266d..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py
+++ /dev/null
@@ -1,48 +0,0 @@
-_base_ = 'faster_rcnn_r50_fpn_crop640_50e_coco.py'
-
-norm_cfg = dict(type='BN', requires_grad=True)
-model = dict(
-    neck=dict(
-        type='FPG',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        inter_channels=256,
-        num_outs=5,
-        stack_times=9,
-        paths=['bu'] * 9,
-        same_down_trans=None,
-        same_up_trans=dict(
-            type='conv',
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            norm_cfg=norm_cfg,
-            inplace=False,
-            order=('act', 'conv', 'norm')),
-        across_lateral_trans=dict(
-            type='conv',
-            kernel_size=1,
-            norm_cfg=norm_cfg,
-            inplace=False,
-            order=('act', 'conv', 'norm')),
-        across_down_trans=dict(
-            type='interpolation_conv',
-            mode='nearest',
-            kernel_size=3,
-            norm_cfg=norm_cfg,
-            order=('act', 'conv', 'norm'),
-            inplace=False),
-        across_up_trans=None,
-        across_skip_trans=dict(
-            type='conv',
-            kernel_size=1,
-            norm_cfg=norm_cfg,
-            inplace=False,
-            order=('act', 'conv', 'norm')),
-        output_trans=dict(
-            type='last_conv',
-            kernel_size=3,
-            order=('act', 'conv', 'norm'),
-            inplace=False),
-        norm_cfg=norm_cfg,
-        skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/README.md b/spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/README.md
deleted file mode 100644
index 61b2aa811968cab137fcd98909a5b494b18b96b0..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/README.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# Legacy Configs in MMDetection V1.x
-
-[OTHERS]
-
-Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos.
-
-To help users convert their models from V1.x to MMDetection V2.0, we provide v1.x configs to inference the converted v1.x models.
-Due to the BC-breaking changes in MMDetection V2.0 from MMDetection V1.x, running inference with the same model weights in these two version will produce different results. The difference will cause within 1% AP absolute difference as can be found in the following table.
-
-## Usage
-
-To upgrade the model version, the users need to do the following steps.
-
-### 1. Convert model weights
-
-There are three main difference in the model weights between V1.x and V2.0 codebases.
-
-1. Since the class order in all the detector's classification branch is reordered, all the legacy model weights need to go through the conversion process.
-2. The regression and segmentation head no longer contain the background channel. Weights in these background channels should be removed to fix in the current codebase.
-3. For two-stage detectors, their wegihts need to be upgraded since MMDetection V2.0 refactors all the two-stage detectors with `RoIHead`.
-
-The users can do the same modification as mentioned above for the self-implemented
-detectors. We provide a scripts `tools/model_converters/upgrade_model_version.py` to convert the model weights in the V1.x model zoo.
-
-```bash
-python tools/model_converters/upgrade_model_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH} --num-classes ${NUM_CLASSES}
-
-```
-
-- OLD_MODEL_PATH: the path to load the model weights in 1.x version.
-- NEW_MODEL_PATH: the path to save the converted model weights in 2.0 version.
-- NUM_CLASSES: number of classes of the original model weights. Usually it is 81 for COCO dataset, 21 for VOC dataset.
-  The number of classes in V2.0 models should be equal to that in V1.x models - 1.
-
-### 2. Use configs with legacy settings
-
-After converting the model weights, checkout to the v1.2 release to find the corresponding config file that uses the legacy settings.
-The V1.x models usually need these three legacy modules: `LegacyAnchorGenerator`, `LegacyDeltaXYWHBBoxCoder`, and `RoIAlign(align=False)`.
-For models using ResNet Caffe backbones, they also need to change the pretrain name and the corresponding `img_norm_cfg`.
-An example is in [`retinanet_r50_caffe_fpn_1x_coco_v1.py`](retinanet_r50_caffe_fpn_1x_coco_v1.py)
-Then use the config to test the model weights. For most models, the obtained results should be close to that in V1.x.
-We provide configs of some common structures in this directory.
-
-## Performance
-
-The performance change after converting the models in this directory are listed as the following.
-|    Method    |  Style  | Lr schd | V1.x box AP | V1.x mask AP | V2.0 box AP | V2.0 mask AP | Config | Download |
-| :-------------: | :-----: | :-----: | :------:| :-----: |:------:| :-----: | :-------: |:------------------------------------------------------------------------------------------------------------------------------: |
-| Mask R-CNN R-50-FPN     | pytorch |   1x    |  37.3  |  34.2   | 36.8 | 33.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth)|
-| RetinaNet R-50-FPN |  caffe  |   1x    |  35.8  | - | 35.4 | - | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_caffe_1x_coco_v1.py) |
-| RetinaNet R-50-FPN | pytorch |   1x |  35.6 |-|35.2|   -| [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth)     |
-| Cascade Mask R-CNN R-50-FPN | pytorch |   1x    |  41.2  |  35.7   |40.8| 35.6|  [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py) |     [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth)     |
-| SSD300-VGG16 | caffe |  120e   | 25.7  |-|25.4|-|  [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/ssd300_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth) |
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/pisa/pisa_ssd512_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/pisa/pisa_ssd512_coco.py
deleted file mode 100644
index 3219d6d667cb185e6fa4f1954d632ccad9512a48..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/pisa/pisa_ssd512_coco.py
+++ /dev/null
@@ -1,8 +0,0 @@
-_base_ = '../ssd/ssd512_coco.py'
-
-model = dict(
-    bbox_head=dict(type='PISASSDHead'),
-    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
-
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
deleted file mode 100644
index 45ed99b6813324a58575f9bb74ce0534626e10c4..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-model = dict(decode_head=dict(num_classes=150))
diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/dataloader/data_loader.py b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/dataloader/data_loader.py
deleted file mode 100644
index af31bdddfa60372ba9d77e55ce10abd06299a391..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/dataloader/data_loader.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import torch
-import torchvision.transforms as transforms
-import torch.utils.data as data
-from util import task
-from .image_folder import make_dataset
-import random
-import numpy as np
-import copy
-import skimage.morphology as sm
-from PIL import Image, ImageFile, ImageOps
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-
-######################################################################################
-# Create the dataloader
-######################################################################################
-class CreateDataset(data.Dataset):
-    def __init__(self, opt):
-        self.opt = opt
-        self.img_paths, self.img_size = make_dataset(opt.img_file)
-        if opt.mask_file != 'none':         # load the random mask files for training and testing
-            self.mask_paths, self.mask_size = make_dataset(opt.mask_file)
-        self.transform = get_transform(opt, convert=False, augment=False)
-        fixed_opt = copy.deepcopy(opt)
-        fixed_opt.preprocess = 'scale_longside'
-        fixed_opt.load_size = fixed_opt.fixed_size
-        fixed_opt.no_flip = True
-        self.transform_fixed = get_transform(fixed_opt, convert=True, augment=False)
-
-    def __len__(self):
-        """return the total number of examples in the dataset"""
-        return self.img_size
-
-    def __getitem__(self, item):
-        """return a data point and its metadata information"""
-        # load the image and conditional input
-        img_org, img, img_path = self._load_img(item)
-        if self.opt.batch_size > 1: # padding the image to the same size for batch training
-            img_org = transforms.functional.pad(img_org, (0, 0, self.opt.fine_size-self.img_h, self.opt.fine_size-self.img_w))
-            img = transforms.functional.pad(img, (0, 0, self.opt.fixed_size - img.size(-1), self.opt.fixed_size - img.size(-2)))
-        pad_mask = torch.zeros_like(img_org)
-        pad_mask[:, :self.img_w, :self.img_h] = 1
-        # load the mask
-        mask, mask_type = self._load_mask(item, img_org)
-        if self.opt.reverse_mask:
-            if self.opt.isTrain:
-                mask = 1 - mask if random.random() > 0.8 else mask
-            else:
-                mask = 1 - mask
-        return {'img_org': img_org, 'img': img, 'img_path': img_path, 'mask': mask, 'pad_mask': pad_mask}
-
-    def name(self):
-        return ""
-
-    def _load_img(self, item):
-        """load the original image and preprocess image"""
-        img_path = self.img_paths[item % self.img_size]
-        img_pil = Image.open(img_path).convert('RGB')
-        img_org = self.transform(img_pil)
-        img = self.transform_fixed(img_org)
-        img_org = transforms.ToTensor()(img_org)
-        img_pil.close()
-        self.img_c, self.img_w, self.img_h = img_org.size()
-        return img_org, img, img_path
-
-    def _mask_dilation(self, mask):
-        """mask erosion for different region"""
-        mask = np.array(mask)
-        pixel = np.random.randint(3, 25)
-        mask = sm.erosion(mask, sm.square(pixel)).astype(np.uint8)
-
-        return mask
-
-    def _load_mask(self, item, img):
-        """load the mask for image completion task"""
-        c, h, w = img.size()
-        if isinstance(self.opt.mask_type, list):
-            mask_type_index = random.randint(0, len(self.opt.mask_type) - 1)
-            mask_type = self.opt.mask_type[mask_type_index]
-        else:
-            mask_type = self.opt.mask_type
-
-        if mask_type == 0:                                  # center mask
-            if random.random() > 0.3 and self.opt.isTrain:
-                return task.random_regular_mask(img), mask_type # random regular mask
-            return task.center_mask(img), mask_type
-        elif mask_type == 1:                                # random regular mask
-            return task.random_regular_mask(img), mask_type
-        elif mask_type == 2:                                # random irregular mask
-            return task.random_irregular_mask(img), mask_type
-        elif mask_type == 3:
-            # external mask from "Image Inpainting for Irregular Holes Using Partial Convolutions (ECCV18)"
-            if self.opt.isTrain:
-                mask_index = random.randint(0, self.mask_size-1)
-                mask_transform = transforms.Compose(
-                    [
-                        transforms.RandomHorizontalFlip(),
-                        transforms.RandomRotation(10),
-                        transforms.RandomCrop([self.opt.fine_size + 64, self.opt.fine_size + 64]),
-                        transforms.Resize([h, w])
-                    ]
-                )
-            else:
-                mask_index = item
-                mask_transform = transforms.Compose(
-                    [
-                        transforms.Resize([h, w])
-                    ]
-                )
-            mask_pil = Image.open(self.mask_paths[mask_index]).convert('L')
-            mask = mask_transform(mask_pil)
-            mask_pil.close()
-            if self.opt.isTrain:
-                mask = self._mask_dilation(mask)
-            else:
-                mask = np.array(mask) < 128
-            mask = torch.tensor(mask).view(1, h, w).float()
-            return mask, mask_type
-        else:
-            raise NotImplementedError('mask type [%s] is not implemented' % str(mask_type))
-
-
-def dataloader(opt):
-    datasets = CreateDataset(opt)
-    dataset = data.DataLoader(datasets, batch_size=opt.batch_size, shuffle=not opt.no_shuffle,
-                              num_workers=int(opt.nThreads), drop_last=True)
-
-    return dataset
-
-
-######################################################################################
-# Basic image preprocess function
-######################################################################################
-def _make_power_2(img, power, method=Image.BICUBIC):
-    """resize the image to the size of log2(base) times"""
-    ow, oh = img.size
-    base = 2 ** power
-    nw, nh = int(max(1, round(ow / base)) * base), int(max(1, round(oh / base)) * base)
-    if nw == ow and nh == oh:
-        return img
-    return img.resize((nw, nh), method)
-
-
-def _random_zoom(img, target_width, method=Image.BICUBIC):
-    """random resize the image scale"""
-    zoom_level = np.random.uniform(0.8, 1.0, size=[2])
-    ow, oh = img.size
-    nw, nh = int(round(max(target_width, ow * zoom_level[0]))), int(round(max(target_width, oh * zoom_level[1])))
-    return img.resize((nw, nh), method)
-
-
-def _scale_shortside(img, target_width, method=Image.BICUBIC):
-    """resize the short side to the target width"""
-    ow, oh = img.size
-    shortsize = min(ow, oh)
-    scale = target_width / shortsize
-    return img.resize((round(ow * scale), round(oh * scale)), method)
-
-
-def _scale_longside(img, target_width, method=Image.BICUBIC):
-    """resize the long side to the target width"""
-    ow, oh = img.size
-    longsize = max(ow, oh)
-    scale = target_width / longsize
-    return img.resize((round(ow * scale), round(oh * scale)), method)
-
-
-def _scale_randomside(img, target_width, method=Image.BICUBIC):
-    """resize the side to the target width with random side"""
-    if random.random() > 0.5:
-        return _scale_shortside(img, target_width, method)
-    else:
-        return _scale_longside(img, target_width, method)
-
-
-def _crop(img, pos=None, size=None):
-    """crop the image based on the given pos and size"""
-    ow, oh = img.size
-    if size is None:
-        return img
-    nw = min(ow, size)
-    nh = min(oh, size)
-    if (ow > nw or oh > nh):
-        if pos is None:
-            x1 = np.random.randint(0, int(ow-nw)+1)
-            y1 = np.random.randint(0, int(oh-nh)+1)
-        else:
-            x1, y1 = pos
-        return img.crop((x1, y1, x1 + nw, y1 + nh))
-    return img
-
-
-def _pad(img):
-    """expand the image to the square size"""
-    ow, oh = img.size
-    size = max(ow, oh)
-    return ImageOps.pad(img, (size, size), centering=(0, 0))
-
-
-def _flip(img, flip):
-    if flip:
-        return img.transpose(Image.FLIP_LEFT_RIGHT)
-    return img
-
-
-def get_transform(opt, params=None, method=Image.BICUBIC, convert=True, augment=False):
-    """get the transform functions"""
-    transforms_list = []
-    if 'resize' in opt.preprocess:
-        osize = [opt.load_size, opt.load_size]
-        transforms_list.append(transforms.Resize(osize))
-    elif 'scale_shortside' in opt.preprocess:
-        transforms_list.append(transforms.Lambda(lambda img: _scale_shortside(img, opt.load_size, method)))
-    elif 'scale_longside' in opt.preprocess:
-        transforms_list.append(transforms.Lambda(lambda img: _scale_longside(img, opt.load_size, method)))
-    elif "scale_randomside" in opt.preprocess:
-        transforms_list.append(transforms.Lambda(lambda img: _scale_randomside(img, opt.load_size, method)))
-
-    if 'zoom' in opt.preprocess:
-        transforms_list.append(transforms.Lambda(lambda img: _random_zoom(img, opt.load_size, method)))
-
-    if 'crop' in opt.preprocess and opt.isTrain:
-        transforms_list.append(transforms.Lambda(lambda img: _crop(img, size=opt.fine_size)))
-    if 'pad' in opt.preprocess:
-        transforms_list.append(transforms.Lambda(lambda img: _pad(img)))     # padding image to square
-
-    transforms_list.append(transforms.Lambda(lambda img: _make_power_2(img, opt.data_powers, method)))
-
-    if not opt.no_flip and opt.isTrain:
-        transforms_list.append(transforms.RandomHorizontalFlip())
-
-    if augment and opt.isTrain:
-        transforms_list.append(transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2))
-
-    if convert:
-        transforms_list.append(transforms.ToTensor())
-
-    return transforms.Compose(transforms_list)
\ No newline at end of file
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/wrappers.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/wrappers.py
deleted file mode 100644
index 8aebf67bf52355a513f21756ee74fe510902d075..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/wrappers.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-r"""Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/wrappers.py  # noqa: E501
-
-Wrap some nn modules to support empty tensor input. Currently, these wrappers
-are mainly used in mask heads like fcn_mask_head and maskiou_heads since mask
-heads are trained on only positive RoIs.
-"""
-import math
-
-import torch
-import torch.nn as nn
-from torch.nn.modules.utils import _pair, _triple
-
-from .registry import CONV_LAYERS, UPSAMPLE_LAYERS
-
-if torch.__version__ == 'parrots':
-    TORCH_VERSION = torch.__version__
-else:
-    # torch.__version__ could be 1.3.1+cu92, we only need the first two
-    # for comparison
-    TORCH_VERSION = tuple(int(x) for x in torch.__version__.split('.')[:2])
-
-
-def obsolete_torch_version(torch_version, version_threshold):
-    return torch_version == 'parrots' or torch_version <= version_threshold
-
-
-class NewEmptyTensorOp(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, x, new_shape):
-        ctx.shape = x.shape
-        return x.new_empty(new_shape)
-
-    @staticmethod
-    def backward(ctx, grad):
-        shape = ctx.shape
-        return NewEmptyTensorOp.apply(grad, shape), None
-
-
-@CONV_LAYERS.register_module('Conv', force=True)
-class Conv2d(nn.Conv2d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d in zip(x.shape[-2:], self.kernel_size,
-                                     self.padding, self.stride, self.dilation):
-                o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-@CONV_LAYERS.register_module('Conv3d', force=True)
-class Conv3d(nn.Conv3d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d in zip(x.shape[-3:], self.kernel_size,
-                                     self.padding, self.stride, self.dilation):
-                o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-@CONV_LAYERS.register_module()
-@CONV_LAYERS.register_module('deconv')
-@UPSAMPLE_LAYERS.register_module('deconv', force=True)
-class ConvTranspose2d(nn.ConvTranspose2d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d, op in zip(x.shape[-2:], self.kernel_size,
-                                         self.padding, self.stride,
-                                         self.dilation, self.output_padding):
-                out_shape.append((i - 1) * s - 2 * p + (d * (k - 1) + 1) + op)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-@CONV_LAYERS.register_module()
-@CONV_LAYERS.register_module('deconv3d')
-@UPSAMPLE_LAYERS.register_module('deconv3d', force=True)
-class ConvTranspose3d(nn.ConvTranspose3d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d, op in zip(x.shape[-3:], self.kernel_size,
-                                         self.padding, self.stride,
-                                         self.dilation, self.output_padding):
-                out_shape.append((i - 1) * s - 2 * p + (d * (k - 1) + 1) + op)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-class MaxPool2d(nn.MaxPool2d):
-
-    def forward(self, x):
-        # PyTorch 1.9 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
-            out_shape = list(x.shape[:2])
-            for i, k, p, s, d in zip(x.shape[-2:], _pair(self.kernel_size),
-                                     _pair(self.padding), _pair(self.stride),
-                                     _pair(self.dilation)):
-                o = (i + 2 * p - (d * (k - 1) + 1)) / s + 1
-                o = math.ceil(o) if self.ceil_mode else math.floor(o)
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            return empty
-
-        return super().forward(x)
-
-
-class MaxPool3d(nn.MaxPool3d):
-
-    def forward(self, x):
-        # PyTorch 1.9 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
-            out_shape = list(x.shape[:2])
-            for i, k, p, s, d in zip(x.shape[-3:], _triple(self.kernel_size),
-                                     _triple(self.padding),
-                                     _triple(self.stride),
-                                     _triple(self.dilation)):
-                o = (i + 2 * p - (d * (k - 1) + 1)) / s + 1
-                o = math.ceil(o) if self.ceil_mode else math.floor(o)
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            return empty
-
-        return super().forward(x)
-
-
-class Linear(torch.nn.Linear):
-
-    def forward(self, x):
-        # empty tensor forward of Linear layer is supported in Pytorch 1.6
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 5)):
-            out_shape = [x.shape[0], self.out_features]
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/__init__.py
deleted file mode 100644
index 965605587211b7bf0bd6bc3acdbb33dd49cab023..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .evaluation import *  # noqa: F401, F403
-from .seg import *  # noqa: F401, F403
-from .utils import *  # noqa: F401, F403
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/models/diffusion/sampling_util.py b/spaces/Anonymous-sub/Rerender/ControlNet/ldm/models/diffusion/sampling_util.py
deleted file mode 100644
index 7eff02be6d7c54d43ee6680636ac0698dd3b3f33..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/models/diffusion/sampling_util.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-import numpy as np
-
-
-def append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions.
-    From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
-    return x[(...,) + (None,) * dims_to_append]
-
-
-def norm_thresholding(x0, value):
-    s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
-    return x0 * (value / s)
-
-
-def spatial_norm_thresholding(x0, value):
-    # b c h w
-    s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
-    return x0 * (value / s)
\ No newline at end of file
diff --git a/spaces/AquaSuisei/ChatGPTXE/modules/llama_func.py b/spaces/AquaSuisei/ChatGPTXE/modules/llama_func.py
deleted file mode 100644
index 9f4f799882b4e7c34aa8df815ebeb90ed822ba46..0000000000000000000000000000000000000000
--- a/spaces/AquaSuisei/ChatGPTXE/modules/llama_func.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import os
-import logging
-
-from llama_index import download_loader
-from llama_index import (
-    Document,
-    LLMPredictor,
-    PromptHelper,
-    QuestionAnswerPrompt,
-    RefinePrompt,
-)
-import colorama
-import PyPDF2
-from tqdm import tqdm
-
-from modules.presets import *
-from modules.utils import *
-
-def get_index_name(file_src):
-    file_paths = [x.name for x in file_src]
-    file_paths.sort(key=lambda x: os.path.basename(x))
-
-    md5_hash = hashlib.md5()
-    for file_path in file_paths:
-        with open(file_path, "rb") as f:
-            while chunk := f.read(8192):
-                md5_hash.update(chunk)
-
-    return md5_hash.hexdigest()
-
-def block_split(text):
-    blocks = []
-    while len(text) > 0:
-        blocks.append(Document(text[:1000]))
-        text = text[1000:]
-    return blocks
-
-def get_documents(file_src):
-    documents = []
-    logging.debug("Loading documents...")
-    logging.debug(f"file_src: {file_src}")
-    for file in file_src:
-        filepath = file.name
-        filename = os.path.basename(filepath)
-        file_type = os.path.splitext(filepath)[1]
-        logging.info(f"loading file: {filename}")
-        if file_type == ".pdf":
-            logging.debug("Loading PDF...")
-            try:
-                from modules.pdf_func import parse_pdf
-                from modules.config import advance_docs
-                two_column = advance_docs["pdf"].get("two_column", False)
-                pdftext = parse_pdf(filepath, two_column).text
-            except:
-                pdftext = ""
-                with open(filepath, 'rb') as pdfFileObj:
-                    pdfReader = PyPDF2.PdfReader(pdfFileObj)
-                    for page in tqdm(pdfReader.pages):
-                        pdftext += page.extract_text()
-            text_raw = pdftext
-        elif file_type == ".docx":
-            logging.debug("Loading Word...")
-            DocxReader = download_loader("DocxReader")
-            loader = DocxReader()
-            text_raw = loader.load_data(file=filepath)[0].text
-        elif file_type == ".epub":
-            logging.debug("Loading EPUB...")
-            EpubReader = download_loader("EpubReader")
-            loader = EpubReader()
-            text_raw = loader.load_data(file=filepath)[0].text
-        elif file_type == ".xlsx":
-            logging.debug("Loading Excel...")
-            text_raw = excel_to_string(filepath)
-        else:
-            logging.debug("Loading text file...")
-            with open(filepath, "r", encoding="utf-8") as f:
-                text_raw = f.read()
-        text = add_space(text_raw)
-        # text = block_split(text)
-        # documents += text
-        documents += [Document(text)]
-    logging.debug("Documents loaded.")
-    return documents
-
-
-def construct_index(
-        api_key,
-        file_src,
-        max_input_size=4096,
-        num_outputs=5,
-        max_chunk_overlap=20,
-        chunk_size_limit=600,
-        embedding_limit=None,
-        separator=" "
-):
-    from langchain.chat_models import ChatOpenAI
-    from llama_index import GPTSimpleVectorIndex, ServiceContext
-
-    os.environ["OPENAI_API_KEY"] = api_key
-    chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
-    embedding_limit = None if embedding_limit == 0 else embedding_limit
-    separator = " " if separator == "" else separator
-
-    llm_predictor = LLMPredictor(
-        llm=ChatOpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key)
-    )
-    prompt_helper = PromptHelper(max_input_size = max_input_size, num_output = num_outputs, max_chunk_overlap = max_chunk_overlap, embedding_limit=embedding_limit, chunk_size_limit=600, separator=separator)
-    index_name = get_index_name(file_src)
-    if os.path.exists(f"./index/{index_name}.json"):
-        logging.info("找到了缓存的索引文件，加载中……")
-        return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
-    else:
-        try:
-            documents = get_documents(file_src)
-            logging.info("构建索引中……")
-            with retrieve_proxy():
-                service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper, chunk_size_limit=chunk_size_limit)
-                index = GPTSimpleVectorIndex.from_documents(
-                    documents,  service_context=service_context
-                )
-            logging.debug("索引构建完成！")
-            os.makedirs("./index", exist_ok=True)
-            index.save_to_disk(f"./index/{index_name}.json")
-            logging.debug("索引已保存至本地!")
-            return index
-
-        except Exception as e:
-            logging.error("索引构建失败！", e)
-            print(e)
-            return None
-
-
-def add_space(text):
-    punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
-    for cn_punc, en_punc in punctuations.items():
-        text = text.replace(cn_punc, en_punc)
-    return text
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/hooks.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/hooks.py
deleted file mode 100644
index d181ba2ec2e55d274897315887b78fbdca757da8..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/hooks.py
+++ /dev/null
@@ -1,33 +0,0 @@
-"""
-requests.hooks
-~~~~~~~~~~~~~~
-
-This module provides the capabilities for the Requests hooks system.
-
-Available hooks:
-
-``response``:
-    The response generated from a Request.
-"""
-HOOKS = ["response"]
-
-
-def default_hooks():
-    return {event: [] for event in HOOKS}
-
-
-# TODO: response is the only one
-
-
-def dispatch_hook(key, hooks, hook_data, **kwargs):
-    """Dispatches a hook dictionary on a given piece of data."""
-    hooks = hooks or {}
-    hooks = hooks.get(key)
-    if hooks:
-        if hasattr(hooks, "__call__"):
-            hooks = [hooks]
-        for hook in hooks:
-            _hook_data = hook(hook_data, **kwargs)
-            if _hook_data is not None:
-                hook_data = _hook_data
-    return hook_data
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/filepost.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/filepost.py
deleted file mode 100644
index 36c9252c647e67bc7353c523152568b993c1331f..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/filepost.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from __future__ import absolute_import
-
-import binascii
-import codecs
-import os
-from io import BytesIO
-
-from .fields import RequestField
-from .packages import six
-from .packages.six import b
-
-writer = codecs.lookup("utf-8")[3]
-
-
-def choose_boundary():
-    """
-    Our embarrassingly-simple replacement for mimetools.choose_boundary.
-    """
-    boundary = binascii.hexlify(os.urandom(16))
-    if not six.PY2:
-        boundary = boundary.decode("ascii")
-    return boundary
-
-
-def iter_field_objects(fields):
-    """
-    Iterate over fields.
-
-    Supports list of (k, v) tuples and dicts, and lists of
-    :class:`~urllib3.fields.RequestField`.
-
-    """
-    if isinstance(fields, dict):
-        i = six.iteritems(fields)
-    else:
-        i = iter(fields)
-
-    for field in i:
-        if isinstance(field, RequestField):
-            yield field
-        else:
-            yield RequestField.from_tuples(*field)
-
-
-def iter_fields(fields):
-    """
-    .. deprecated:: 1.6
-
-    Iterate over fields.
-
-    The addition of :class:`~urllib3.fields.RequestField` makes this function
-    obsolete. Instead, use :func:`iter_field_objects`, which returns
-    :class:`~urllib3.fields.RequestField` objects.
-
-    Supports list of (k, v) tuples and dicts.
-    """
-    if isinstance(fields, dict):
-        return ((k, v) for k, v in six.iteritems(fields))
-
-    return ((k, v) for k, v in fields)
-
-
-def encode_multipart_formdata(fields, boundary=None):
-    """
-    Encode a dictionary of ``fields`` using the multipart/form-data MIME format.
-
-    :param fields:
-        Dictionary of fields or list of (key, :class:`~urllib3.fields.RequestField`).
-
-    :param boundary:
-        If not specified, then a random boundary will be generated using
-        :func:`urllib3.filepost.choose_boundary`.
-    """
-    body = BytesIO()
-    if boundary is None:
-        boundary = choose_boundary()
-
-    for field in iter_field_objects(fields):
-        body.write(b("--%s\r\n" % (boundary)))
-
-        writer(body).write(field.render_headers())
-        data = field.data
-
-        if isinstance(data, int):
-            data = str(data)  # Backwards compatibility
-
-        if isinstance(data, six.text_type):
-            writer(body).write(data)
-        else:
-            body.write(data)
-
-        body.write(b"\r\n")
-
-    body.write(b("--%s--\r\n" % (boundary)))
-
-    content_type = str("multipart/form-data; boundary=%s" % boundary)
-
-    return body.getvalue(), content_type
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_roi_heads.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_roi_heads.py
deleted file mode 100644
index 90fadf1a9667cf836223945b22c5147b89ad98a4..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_roi_heads.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-import json
-import math
-import torch
-from torch import nn
-from torch.autograd.function import Function
-from typing import Dict, List, Optional, Tuple, Union
-
-from detectron2.layers import ShapeSpec
-from detectron2.structures import Boxes, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference
-from detectron2.modeling.roi_heads.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads
-from detectron2.modeling.roi_heads.cascade_rcnn import CascadeROIHeads
-from detectron2.modeling.roi_heads.box_head import build_box_head
-from .custom_fast_rcnn import CustomFastRCNNOutputLayers
-
-
-@ROI_HEADS_REGISTRY.register()
-class CustomROIHeads(StandardROIHeads):
-    @classmethod
-    def _init_box_head(self, cfg, input_shape):
-        ret = super()._init_box_head(cfg, input_shape)
-        del ret['box_predictor']
-        ret['box_predictor'] = CustomFastRCNNOutputLayers(
-            cfg, ret['box_head'].output_shape)
-        self.debug = cfg.DEBUG
-        if self.debug:
-            self.debug_show_name = cfg.DEBUG_SHOW_NAME
-            self.save_debug = cfg.SAVE_DEBUG
-            self.vis_thresh = cfg.VIS_THRESH
-            self.pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(
-                torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1)
-            self.pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(
-                torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1)
-        return ret
-
-    def forward(self, images, features, proposals, targets=None):
-        """
-        enable debug
-        """
-        if not self.debug:
-            del images
-        if self.training:
-            assert targets
-            proposals = self.label_and_sample_proposals(proposals, targets)
-        del targets
-
-        if self.training:
-            losses = self._forward_box(features, proposals)
-            losses.update(self._forward_mask(features, proposals))
-            losses.update(self._forward_keypoint(features, proposals))
-            return proposals, losses
-        else:
-            pred_instances = self._forward_box(features, proposals)
-            pred_instances = self.forward_with_given_boxes(features, pred_instances)
-            if self.debug:
-                from ..debug import debug_second_stage
-                denormalizer = lambda x: x * self.pixel_std + self.pixel_mean
-                debug_second_stage(
-                    [denormalizer(images[0].clone())],
-                    pred_instances, proposals=proposals,
-                    debug_show_name=self.debug_show_name)
-            return pred_instances, {}
-
-
-@ROI_HEADS_REGISTRY.register()
-class CustomCascadeROIHeads(CascadeROIHeads):
-    @classmethod
-    def _init_box_head(self, cfg, input_shape):
-        self.mult_proposal_score = cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE
-        ret = super()._init_box_head(cfg, input_shape)
-        del ret['box_predictors']
-        cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS
-        box_predictors = []
-        for box_head, bbox_reg_weights in zip(ret['box_heads'], cascade_bbox_reg_weights):
-            box_predictors.append(
-                CustomFastRCNNOutputLayers(
-                    cfg, box_head.output_shape,
-                    box2box_transform=Box2BoxTransform(weights=bbox_reg_weights)
-                ))
-        ret['box_predictors'] = box_predictors
-        self.debug = cfg.DEBUG
-        if self.debug:
-            self.debug_show_name = cfg.DEBUG_SHOW_NAME
-            self.save_debug = cfg.SAVE_DEBUG
-            self.vis_thresh = cfg.VIS_THRESH
-            self.pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(
-                torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1)
-            self.pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(
-                torch.device(cfg.MODEL.DEVICE)).view(3, 1, 1)
-        return ret
-
-
-    def _forward_box(self, features, proposals, targets=None):
-        """
-        Add mult proposal scores at testing
-        """
-        if (not self.training) and self.mult_proposal_score:
-            if len(proposals) > 0 and proposals[0].has('scores'):
-                proposal_scores = [
-                    p.get('scores') for p in proposals]
-            else:
-                proposal_scores = [
-                    p.get('objectness_logits') for p in proposals]
-        
-        features = [features[f] for f in self.box_in_features]
-        head_outputs = []  # (predictor, predictions, proposals)
-        prev_pred_boxes = None
-        image_sizes = [x.image_size for x in proposals]
-        for k in range(self.num_cascade_stages):
-            if k > 0:
-                proposals = self._create_proposals_from_boxes(prev_pred_boxes, image_sizes)
-                if self.training:
-                    proposals = self._match_and_label_boxes(proposals, k, targets)
-            predictions = self._run_stage(features, proposals, k)
-            prev_pred_boxes = self.box_predictor[k].predict_boxes(predictions, proposals)
-            head_outputs.append((self.box_predictor[k], predictions, proposals))
-
-        if self.training:
-            losses = {}
-            storage = get_event_storage()
-            for stage, (predictor, predictions, proposals) in enumerate(head_outputs):
-                with storage.name_scope("stage{}".format(stage)):
-                    stage_losses = predictor.losses(predictions, proposals)
-                losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()})
-            return losses
-        else:
-            # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1)
-            scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs]
-            scores = [
-                sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages)
-                for scores_per_image in zip(*scores_per_stage)
-            ]
-            
-            if self.mult_proposal_score:
-                scores = [(s * ps[:, None]) ** 0.5 \
-                    for s, ps in zip(scores, proposal_scores)]
-
-            predictor, predictions, proposals = head_outputs[-1]
-            boxes = predictor.predict_boxes(predictions, proposals)
-            pred_instances, _ = fast_rcnn_inference(
-                boxes,
-                scores,
-                image_sizes,
-                predictor.test_score_thresh,
-                predictor.test_nms_thresh,
-                predictor.test_topk_per_image,
-            )
-            
-            return pred_instances
-
-    def forward(self, images, features, proposals, targets=None):
-        '''
-        enable debug
-        '''
-        if not self.debug:
-            del images
-        if self.training:
-            proposals = self.label_and_sample_proposals(proposals, targets)
-
-        if self.training:
-            losses = self._forward_box(features, proposals, targets)
-            losses.update(self._forward_mask(features, proposals))
-            losses.update(self._forward_keypoint(features, proposals))
-            return proposals, losses
-        else:
-            # import pdb; pdb.set_trace()
-            pred_instances = self._forward_box(features, proposals)
-            pred_instances = self.forward_with_given_boxes(features, pred_instances)
-            if self.debug:
-                from ..debug import debug_second_stage
-                denormalizer = lambda x: x * self.pixel_std + self.pixel_mean
-                debug_second_stage(
-                    [denormalizer(x.clone()) for x in images],
-                    pred_instances, proposals=proposals,
-                    save_debug=self.save_debug,
-                    debug_show_name=self.debug_show_name,
-                    vis_thresh=self.vis_thresh)
-            return pred_instances, {}
-
-
diff --git a/spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/summarize/+server.ts b/spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/summarize/+server.ts
deleted file mode 100644
index 945f1346d94b24dcc25e8173ec0b4c31efab22e7..0000000000000000000000000000000000000000
--- a/spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/summarize/+server.ts
+++ /dev/null
@@ -1,83 +0,0 @@
-import { PUBLIC_MAX_INPUT_TOKENS, PUBLIC_SEP_TOKEN } from "$env/static/public";
-import { buildPrompt } from "$lib/buildPrompt";
-import { collections } from "$lib/server/database.js";
-import { modelEndpoint } from "$lib/server/modelEndpoint.js";
-import { trimPrefix } from "$lib/utils/trimPrefix.js";
-import { trimSuffix } from "$lib/utils/trimSuffix.js";
-import { textGeneration } from "@huggingface/inference";
-import { error } from "@sveltejs/kit";
-import { ObjectId } from "mongodb";
-
-export async function POST({ params, locals, fetch }) {
-	const convId = new ObjectId(params.id);
-
-	const conversation = await collections.conversations.findOne({
-		_id: convId,
-		sessionId: locals.sessionId,
-	});
-
-	if (!conversation) {
-		throw error(404, "Conversation not found");
-	}
-
-	const firstMessage = conversation.messages.find((m) => m.from === "user");
-
-	const userPrompt =
-		`Please summarize the following message as a single sentence of less than 5 words:\n` +
-		firstMessage?.content;
-
-	const prompt = buildPrompt([{ from: "user", content: userPrompt }]);
-
-	const parameters = {
-		temperature: 0.9,
-		top_p: 0.95,
-		repetition_penalty: 1.2,
-		top_k: 50,
-		watermark: false,
-		max_new_tokens: 1024,
-		truncate: parseInt(PUBLIC_MAX_INPUT_TOKENS),
-		stop: [PUBLIC_SEP_TOKEN],
-		return_full_text: false,
-	};
-
-	const endpoint = modelEndpoint();
-	let { generated_text } = await textGeneration(
-		{
-			model: endpoint.endpoint,
-			inputs: prompt,
-			parameters,
-		},
-		{
-			fetch: (url, options) =>
-				fetch(url, {
-					...options,
-					headers: { ...options?.headers, Authorization: endpoint.authorization },
-				}),
-		}
-	);
-
-	generated_text = trimSuffix(trimPrefix(generated_text, "<|startoftext|>"), PUBLIC_SEP_TOKEN);
-
-	if (generated_text) {
-		await collections.conversations.updateOne(
-			{
-				_id: convId,
-				sessionId: locals.sessionId,
-			},
-			{
-				$set: { title: generated_text },
-			}
-		);
-	}
-
-	return new Response(
-		JSON.stringify(
-			generated_text
-				? {
-						title: generated_text,
-				  }
-				: {}
-		),
-		{ headers: { "Content-Type": "application/json" } }
-	);
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/latex.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/latex.py
deleted file mode 100644
index 4a7375a5ceb4b47894af47d1c1965476f95764ba..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/latex.py
+++ /dev/null
@@ -1,521 +0,0 @@
-"""
-    pygments.formatters.latex
-    ~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for LaTeX fancyvrb output.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from io import StringIO
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.lexer import Lexer, do_insertions
-from pip._vendor.pygments.token import Token, STANDARD_TYPES
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt
-
-
-__all__ = ['LatexFormatter']
-
-
-def escape_tex(text, commandprefix):
-    return text.replace('\\', '\x00'). \
-                replace('{', '\x01'). \
-                replace('}', '\x02'). \
-                replace('\x00', r'\%sZbs{}' % commandprefix). \
-                replace('\x01', r'\%sZob{}' % commandprefix). \
-                replace('\x02', r'\%sZcb{}' % commandprefix). \
-                replace('^', r'\%sZca{}' % commandprefix). \
-                replace('_', r'\%sZus{}' % commandprefix). \
-                replace('&', r'\%sZam{}' % commandprefix). \
-                replace('<', r'\%sZlt{}' % commandprefix). \
-                replace('>', r'\%sZgt{}' % commandprefix). \
-                replace('#', r'\%sZsh{}' % commandprefix). \
-                replace('%', r'\%sZpc{}' % commandprefix). \
-                replace('$', r'\%sZdl{}' % commandprefix). \
-                replace('-', r'\%sZhy{}' % commandprefix). \
-                replace("'", r'\%sZsq{}' % commandprefix). \
-                replace('"', r'\%sZdq{}' % commandprefix). \
-                replace('~', r'\%sZti{}' % commandprefix)
-
-
-DOC_TEMPLATE = r'''
-\documentclass{%(docclass)s}
-\usepackage{fancyvrb}
-\usepackage{color}
-\usepackage[%(encoding)s]{inputenc}
-%(preamble)s
-
-%(styledefs)s
-
-\begin{document}
-
-\section*{%(title)s}
-
-%(code)s
-\end{document}
-'''
-
-## Small explanation of the mess below :)
-#
-# The previous version of the LaTeX formatter just assigned a command to
-# each token type defined in the current style.  That obviously is
-# problematic if the highlighted code is produced for a different style
-# than the style commands themselves.
-#
-# This version works much like the HTML formatter which assigns multiple
-# CSS classes to each <span> tag, from the most specific to the least
-# specific token type, thus falling back to the parent token type if one
-# is not defined.  Here, the classes are there too and use the same short
-# forms given in token.STANDARD_TYPES.
-#
-# Highlighted code now only uses one custom command, which by default is
-# \PY and selectable by the commandprefix option (and in addition the
-# escapes \PYZat, \PYZlb and \PYZrb which haven't been renamed for
-# backwards compatibility purposes).
-#
-# \PY has two arguments: the classes, separated by +, and the text to
-# render in that style.  The classes are resolved into the respective
-# style commands by magic, which serves to ignore unknown classes.
-#
-# The magic macros are:
-# * \PY@it, \PY@bf, etc. are unconditionally wrapped around the text
-#   to render in \PY@do.  Their definition determines the style.
-# * \PY@reset resets \PY@it etc. to do nothing.
-# * \PY@toks parses the list of classes, using magic inspired by the
-#   keyval package (but modified to use plusses instead of commas
-#   because fancyvrb redefines commas inside its environments).
-# * \PY@tok processes one class, calling the \PY@tok@classname command
-#   if it exists.
-# * \PY@tok@classname sets the \PY@it etc. to reflect the chosen style
-#   for its class.
-# * \PY resets the style, parses the classnames and then calls \PY@do.
-#
-# Tip: to read this code, print it out in substituted form using e.g.
-# >>> print STYLE_TEMPLATE % {'cp': 'PY'}
-
-STYLE_TEMPLATE = r'''
-\makeatletter
-\def\%(cp)s@reset{\let\%(cp)s@it=\relax \let\%(cp)s@bf=\relax%%
-    \let\%(cp)s@ul=\relax \let\%(cp)s@tc=\relax%%
-    \let\%(cp)s@bc=\relax \let\%(cp)s@ff=\relax}
-\def\%(cp)s@tok#1{\csname %(cp)s@tok@#1\endcsname}
-\def\%(cp)s@toks#1+{\ifx\relax#1\empty\else%%
-    \%(cp)s@tok{#1}\expandafter\%(cp)s@toks\fi}
-\def\%(cp)s@do#1{\%(cp)s@bc{\%(cp)s@tc{\%(cp)s@ul{%%
-    \%(cp)s@it{\%(cp)s@bf{\%(cp)s@ff{#1}}}}}}}
-\def\%(cp)s#1#2{\%(cp)s@reset\%(cp)s@toks#1+\relax+\%(cp)s@do{#2}}
-
-%(styles)s
-
-\def\%(cp)sZbs{\char`\\}
-\def\%(cp)sZus{\char`\_}
-\def\%(cp)sZob{\char`\{}
-\def\%(cp)sZcb{\char`\}}
-\def\%(cp)sZca{\char`\^}
-\def\%(cp)sZam{\char`\&}
-\def\%(cp)sZlt{\char`\<}
-\def\%(cp)sZgt{\char`\>}
-\def\%(cp)sZsh{\char`\#}
-\def\%(cp)sZpc{\char`\%%}
-\def\%(cp)sZdl{\char`\$}
-\def\%(cp)sZhy{\char`\-}
-\def\%(cp)sZsq{\char`\'}
-\def\%(cp)sZdq{\char`\"}
-\def\%(cp)sZti{\char`\~}
-%% for compatibility with earlier versions
-\def\%(cp)sZat{@}
-\def\%(cp)sZlb{[}
-\def\%(cp)sZrb{]}
-\makeatother
-'''
-
-
-def _get_ttype_name(ttype):
-    fname = STANDARD_TYPES.get(ttype)
-    if fname:
-        return fname
-    aname = ''
-    while fname is None:
-        aname = ttype[-1] + aname
-        ttype = ttype.parent
-        fname = STANDARD_TYPES.get(ttype)
-    return fname + aname
-
-
-class LatexFormatter(Formatter):
-    r"""
-    Format tokens as LaTeX code. This needs the `fancyvrb` and `color`
-    standard packages.
-
-    Without the `full` option, code is formatted as one ``Verbatim``
-    environment, like this:
-
-    .. sourcecode:: latex
-
-        \begin{Verbatim}[commandchars=\\\{\}]
-        \PY{k}{def }\PY{n+nf}{foo}(\PY{n}{bar}):
-            \PY{k}{pass}
-        \end{Verbatim}
-
-    Wrapping can be disabled using the `nowrap` option.
-
-    The special command used here (``\PY``) and all the other macros it needs
-    are output by the `get_style_defs` method.
-
-    With the `full` option, a complete LaTeX document is output, including
-    the command definitions in the preamble.
-
-    The `get_style_defs()` method of a `LatexFormatter` returns a string
-    containing ``\def`` commands defining the macros needed inside the
-    ``Verbatim`` environments.
-
-    Additional options accepted:
-
-    `nowrap`
-        If set to ``True``, don't wrap the tokens at all, not even inside a
-        ``\begin{Verbatim}`` environment. This disables most other options
-        (default: ``False``).
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``).
-
-    `full`
-        Tells the formatter to output a "full" document, i.e. a complete
-        self-contained document (default: ``False``).
-
-    `title`
-        If `full` is true, the title that should be used to caption the
-        document (default: ``''``).
-
-    `docclass`
-        If the `full` option is enabled, this is the document class to use
-        (default: ``'article'``).
-
-    `preamble`
-        If the `full` option is enabled, this can be further preamble commands,
-        e.g. ``\usepackage`` (default: ``''``).
-
-    `linenos`
-        If set to ``True``, output line numbers (default: ``False``).
-
-    `linenostart`
-        The line number for the first line (default: ``1``).
-
-    `linenostep`
-        If set to a number n > 1, only every nth line number is printed.
-
-    `verboptions`
-        Additional options given to the Verbatim environment (see the *fancyvrb*
-        docs for possible values) (default: ``''``).
-
-    `commandprefix`
-        The LaTeX commands used to produce colored output are constructed
-        using this prefix and some letters (default: ``'PY'``).
-
-        .. versionadded:: 0.7
-        .. versionchanged:: 0.10
-           The default is now ``'PY'`` instead of ``'C'``.
-
-    `texcomments`
-        If set to ``True``, enables LaTeX comment lines.  That is, LaTex markup
-        in comment tokens is not escaped so that LaTeX can render it (default:
-        ``False``).
-
-        .. versionadded:: 1.2
-
-    `mathescape`
-        If set to ``True``, enables LaTeX math mode escape in comments. That
-        is, ``'$...$'`` inside a comment will trigger math mode (default:
-        ``False``).
-
-        .. versionadded:: 1.2
-
-    `escapeinside`
-        If set to a string of length 2, enables escaping to LaTeX. Text
-        delimited by these 2 characters is read as LaTeX code and
-        typeset accordingly. It has no effect in string literals. It has
-        no effect in comments if `texcomments` or `mathescape` is
-        set. (default: ``''``).
-
-        .. versionadded:: 2.0
-
-    `envname`
-        Allows you to pick an alternative environment name replacing Verbatim.
-        The alternate environment still has to support Verbatim's option syntax.
-        (default: ``'Verbatim'``).
-
-        .. versionadded:: 2.0
-    """
-    name = 'LaTeX'
-    aliases = ['latex', 'tex']
-    filenames = ['*.tex']
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self.nowrap = get_bool_opt(options, 'nowrap', False)
-        self.docclass = options.get('docclass', 'article')
-        self.preamble = options.get('preamble', '')
-        self.linenos = get_bool_opt(options, 'linenos', False)
-        self.linenostart = abs(get_int_opt(options, 'linenostart', 1))
-        self.linenostep = abs(get_int_opt(options, 'linenostep', 1))
-        self.verboptions = options.get('verboptions', '')
-        self.nobackground = get_bool_opt(options, 'nobackground', False)
-        self.commandprefix = options.get('commandprefix', 'PY')
-        self.texcomments = get_bool_opt(options, 'texcomments', False)
-        self.mathescape = get_bool_opt(options, 'mathescape', False)
-        self.escapeinside = options.get('escapeinside', '')
-        if len(self.escapeinside) == 2:
-            self.left = self.escapeinside[0]
-            self.right = self.escapeinside[1]
-        else:
-            self.escapeinside = ''
-        self.envname = options.get('envname', 'Verbatim')
-
-        self._create_stylesheet()
-
-    def _create_stylesheet(self):
-        t2n = self.ttype2name = {Token: ''}
-        c2d = self.cmd2def = {}
-        cp = self.commandprefix
-
-        def rgbcolor(col):
-            if col:
-                return ','.join(['%.2f' % (int(col[i] + col[i + 1], 16) / 255.0)
-                                 for i in (0, 2, 4)])
-            else:
-                return '1,1,1'
-
-        for ttype, ndef in self.style:
-            name = _get_ttype_name(ttype)
-            cmndef = ''
-            if ndef['bold']:
-                cmndef += r'\let\$$@bf=\textbf'
-            if ndef['italic']:
-                cmndef += r'\let\$$@it=\textit'
-            if ndef['underline']:
-                cmndef += r'\let\$$@ul=\underline'
-            if ndef['roman']:
-                cmndef += r'\let\$$@ff=\textrm'
-            if ndef['sans']:
-                cmndef += r'\let\$$@ff=\textsf'
-            if ndef['mono']:
-                cmndef += r'\let\$$@ff=\textsf'
-            if ndef['color']:
-                cmndef += (r'\def\$$@tc##1{\textcolor[rgb]{%s}{##1}}' %
-                           rgbcolor(ndef['color']))
-            if ndef['border']:
-                cmndef += (r'\def\$$@bc##1{{\setlength{\fboxsep}{\string -\fboxrule}'
-                           r'\fcolorbox[rgb]{%s}{%s}{\strut ##1}}}' %
-                           (rgbcolor(ndef['border']),
-                            rgbcolor(ndef['bgcolor'])))
-            elif ndef['bgcolor']:
-                cmndef += (r'\def\$$@bc##1{{\setlength{\fboxsep}{0pt}'
-                           r'\colorbox[rgb]{%s}{\strut ##1}}}' %
-                           rgbcolor(ndef['bgcolor']))
-            if cmndef == '':
-                continue
-            cmndef = cmndef.replace('$$', cp)
-            t2n[ttype] = name
-            c2d[name] = cmndef
-
-    def get_style_defs(self, arg=''):
-        """
-        Return the command sequences needed to define the commands
-        used to format text in the verbatim environment. ``arg`` is ignored.
-        """
-        cp = self.commandprefix
-        styles = []
-        for name, definition in self.cmd2def.items():
-            styles.append(r'\@namedef{%s@tok@%s}{%s}' % (cp, name, definition))
-        return STYLE_TEMPLATE % {'cp': self.commandprefix,
-                                 'styles': '\n'.join(styles)}
-
-    def format_unencoded(self, tokensource, outfile):
-        # TODO: add support for background colors
-        t2n = self.ttype2name
-        cp = self.commandprefix
-
-        if self.full:
-            realoutfile = outfile
-            outfile = StringIO()
-
-        if not self.nowrap:
-            outfile.write('\\begin{' + self.envname + '}[commandchars=\\\\\\{\\}')
-            if self.linenos:
-                start, step = self.linenostart, self.linenostep
-                outfile.write(',numbers=left' +
-                              (start and ',firstnumber=%d' % start or '') +
-                              (step and ',stepnumber=%d' % step or ''))
-            if self.mathescape or self.texcomments or self.escapeinside:
-                outfile.write(',codes={\\catcode`\\$=3\\catcode`\\^=7'
-                              '\\catcode`\\_=8\\relax}')
-            if self.verboptions:
-                outfile.write(',' + self.verboptions)
-            outfile.write(']\n')
-
-        for ttype, value in tokensource:
-            if ttype in Token.Comment:
-                if self.texcomments:
-                    # Try to guess comment starting lexeme and escape it ...
-                    start = value[0:1]
-                    for i in range(1, len(value)):
-                        if start[0] != value[i]:
-                            break
-                        start += value[i]
-
-                    value = value[len(start):]
-                    start = escape_tex(start, cp)
-
-                    # ... but do not escape inside comment.
-                    value = start + value
-                elif self.mathescape:
-                    # Only escape parts not inside a math environment.
-                    parts = value.split('$')
-                    in_math = False
-                    for i, part in enumerate(parts):
-                        if not in_math:
-                            parts[i] = escape_tex(part, cp)
-                        in_math = not in_math
-                    value = '$'.join(parts)
-                elif self.escapeinside:
-                    text = value
-                    value = ''
-                    while text:
-                        a, sep1, text = text.partition(self.left)
-                        if sep1:
-                            b, sep2, text = text.partition(self.right)
-                            if sep2:
-                                value += escape_tex(a, cp) + b
-                            else:
-                                value += escape_tex(a + sep1 + b, cp)
-                        else:
-                            value += escape_tex(a, cp)
-                else:
-                    value = escape_tex(value, cp)
-            elif ttype not in Token.Escape:
-                value = escape_tex(value, cp)
-            styles = []
-            while ttype is not Token:
-                try:
-                    styles.append(t2n[ttype])
-                except KeyError:
-                    # not in current style
-                    styles.append(_get_ttype_name(ttype))
-                ttype = ttype.parent
-            styleval = '+'.join(reversed(styles))
-            if styleval:
-                spl = value.split('\n')
-                for line in spl[:-1]:
-                    if line:
-                        outfile.write("\\%s{%s}{%s}" % (cp, styleval, line))
-                    outfile.write('\n')
-                if spl[-1]:
-                    outfile.write("\\%s{%s}{%s}" % (cp, styleval, spl[-1]))
-            else:
-                outfile.write(value)
-
-        if not self.nowrap:
-            outfile.write('\\end{' + self.envname + '}\n')
-
-        if self.full:
-            encoding = self.encoding or 'utf8'
-            # map known existings encodings from LaTeX distribution
-            encoding = {
-                'utf_8': 'utf8',
-                'latin_1': 'latin1',
-                'iso_8859_1': 'latin1',
-            }.get(encoding.replace('-', '_'), encoding)
-            realoutfile.write(DOC_TEMPLATE %
-                dict(docclass  = self.docclass,
-                     preamble  = self.preamble,
-                     title     = self.title,
-                     encoding  = encoding,
-                     styledefs = self.get_style_defs(),
-                     code      = outfile.getvalue()))
-
-
-class LatexEmbeddedLexer(Lexer):
-    """
-    This lexer takes one lexer as argument, the lexer for the language
-    being formatted, and the left and right delimiters for escaped text.
-
-    First everything is scanned using the language lexer to obtain
-    strings and comments. All other consecutive tokens are merged and
-    the resulting text is scanned for escaped segments, which are given
-    the Token.Escape type. Finally text that is not escaped is scanned
-    again with the language lexer.
-    """
-    def __init__(self, left, right, lang, **options):
-        self.left = left
-        self.right = right
-        self.lang = lang
-        Lexer.__init__(self, **options)
-
-    def get_tokens_unprocessed(self, text):
-        # find and remove all the escape tokens (replace with an empty string)
-        # this is very similar to DelegatingLexer.get_tokens_unprocessed.
-        buffered = ''
-        insertions = []
-        insertion_buf = []
-        for i, t, v in self._find_safe_escape_tokens(text):
-            if t is None:
-                if insertion_buf:
-                    insertions.append((len(buffered), insertion_buf))
-                    insertion_buf = []
-                buffered += v
-            else:
-                insertion_buf.append((i, t, v))
-        if insertion_buf:
-            insertions.append((len(buffered), insertion_buf))
-        return do_insertions(insertions,
-                             self.lang.get_tokens_unprocessed(buffered))
-
-    def _find_safe_escape_tokens(self, text):
-        """ find escape tokens that are not in strings or comments """
-        for i, t, v in self._filter_to(
-            self.lang.get_tokens_unprocessed(text),
-            lambda t: t in Token.Comment or t in Token.String
-        ):
-            if t is None:
-                for i2, t2, v2 in self._find_escape_tokens(v):
-                    yield i + i2, t2, v2
-            else:
-                yield i, None, v
-
-    def _filter_to(self, it, pred):
-        """ Keep only the tokens that match `pred`, merge the others together """
-        buf = ''
-        idx = 0
-        for i, t, v in it:
-            if pred(t):
-                if buf:
-                    yield idx, None, buf
-                    buf = ''
-                yield i, t, v
-            else:
-                if not buf:
-                    idx = i
-                buf += v
-        if buf:
-            yield idx, None, buf
-
-    def _find_escape_tokens(self, text):
-        """ Find escape tokens within text, give token=None otherwise """
-        index = 0
-        while text:
-            a, sep1, text = text.partition(self.left)
-            if a:
-                yield index, None, a
-                index += len(a)
-            if sep1:
-                b, sep2, text = text.partition(self.right)
-                if sep2:
-                    yield index + len(sep1), Token.Escape, b
-                    index += len(sep1) + len(b) + len(sep2)
-                else:
-                    yield index, Token.Error, sep1
-                    index += len(sep1)
-                    text = b
diff --git a/spaces/BigSalmon/GPT2_Most_Probable/app.py b/spaces/BigSalmon/GPT2_Most_Probable/app.py
deleted file mode 100644
index 81adbb0438894f68964d61599b982514baf2f7de..0000000000000000000000000000000000000000
--- a/spaces/BigSalmon/GPT2_Most_Probable/app.py
+++ /dev/null
@@ -1,273 +0,0 @@
-import streamlit as st
-import numpy as np
-import pandas as pd
-import os
-import torch
-import torch.nn as nn
-from transformers.activations import get_activation
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-
-first = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.\n\ninformal english: """
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-@st.cache(allow_output_mutation=True)
-def get_model():
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln86Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln86Paraphrase")
-    
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln82Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln82Paraphrase")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln79Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln79Paraphrase")
-    
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln74Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln74Paraphrase")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln72Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln72Paraphrase")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln64Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln64Paraphrase")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln60Paraphrase")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo1.3BInformalToFormal")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BInformalToFormal")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln55")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln55")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln51")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln51")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln45")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln49")
-    
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln43")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln43")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln41")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln38")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln38")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln37")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln37")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln36")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln36")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln35")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln35")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln31")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln31")
-    
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln21")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln21")
-    
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsOneSent")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsOneSent")
-    
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsToSentence")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsToSentence")
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln89Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln89Paraphrase")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/DefinitionsSynonyms1")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/DefinitionsSynonyms1")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln95Paraphrase")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln95Paraphrase")
-
-    tokenizer = AutoTokenizer.from_pretrained("BigSalmon/AbstractTest")
-    model = AutoModelForCausalLM.from_pretrained("BigSalmon/AbstractTest")
-
-    #tokenizer = AutoTokenizer.from_pretrained("BigSalmon/DefinitionsSynonyms2")
-    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/DefinitionsSynonyms2")
-    #tokenizer2 = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnMedium")
-    #model2 = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincolnMedium")
-    return model, tokenizer
-    
-model, tokenizer = get_model()
-
-g = """informal english: garage band has made people who know nothing about music good at creating music.
-Translated into the Style of Abraham Lincoln: garage band ( offers the uninitiated in music the ability to produce professional-quality compositions / catapults those for whom music is an uncharted art the ability the realize masterpieces / stimulates music novice's competency to yield sublime arrangements / begets individuals of rudimentary musical talent the proficiency to fashion elaborate suites ).
-informal english: chrome extensions can make doing regular tasks much easier to get done.
-Translated into the Style of Abraham Lincoln: chrome extensions ( yield the boon of time-saving convenience / ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks / turbocharges the velocity with which one can conduct their obligations ).
-informal english: broadband is finally expanding to rural areas, a great development that will thrust them into modern life.
-Translated into the Style of Abraham Lincoln: broadband is ( ( finally / at last / after years of delay ) arriving in remote locations / springing to life in far-flung outposts / inching into even the most backwater corners of the nation ) that will leap-frog them into the twenty-first century.
-informal english: google translate has made talking to people who do not share your language easier.
-Translated into the Style of Abraham Lincoln: google translate ( imparts communicability to individuals whose native tongue differs / mitigates the trials of communication across linguistic barriers / hastens the bridging of semantic boundaries / mollifies the complexity of multilingual communication / avails itself to the internationalization of discussion / flexes its muscles to abet intercultural conversation / calms the tides of linguistic divergence ).
-informal english: corn fields are all across illinois, visible once you leave chicago.
-Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
-informal english: """
-
-number_of_outputs = st.sidebar.slider("Number of Outputs", 5, 100)
-log_nums = st.sidebar.slider("How Many Log Outputs?", 50, 1000)
-
-def BestProbs(prompt):
-  prompt = prompt.strip()
-  text = tokenizer.encode(prompt)
-  myinput, past_key_values = torch.tensor([text]), None
-  myinput = myinput
-  logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
-  logits = logits[0,-1]
-  probabilities = torch.nn.functional.softmax(logits)
-  best_logits, best_indices = logits.topk(10)
-  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
-  for i in best_words[0:10]:
-    print("_______")
-    st.write(f"${i} $\n")
-    f = (f"${i} $\n")
-    m = (prompt + f"{i}")
-    BestProbs2(m)
-  return f
-
-def BestProbs2(prompt):
-  prompt = prompt.strip()
-  text = tokenizer.encode(prompt)
-  myinput, past_key_values = torch.tensor([text]), None
-  myinput = myinput
-  logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
-  logits = logits[0,-1]
-  probabilities = torch.nn.functional.softmax(logits)
-  best_logits, best_indices = logits.topk(20)
-  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
-  for i in best_words[0:20]:
-    print(i)
-    st.write(i)
-    
-def LogProbs(prompt):
-  col1 = []
-  col2 = []
-  prompt = prompt.strip()
-  text = tokenizer.encode(prompt)
-  myinput, past_key_values = torch.tensor([text]), None
-  myinput = myinput
-  logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
-  logits = logits[0,-1]
-  probabilities = torch.nn.functional.softmax(logits)
-  best_logits, best_indices = logits.topk(10)
-  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
-  for i in best_words[0:10]:
-    print("_______")
-    f = i
-    col1.append(f)
-    m = (prompt + f"{i}")
-    #print("^^" + f + " ^^")
-    prompt = m.strip()
-    text = tokenizer.encode(prompt)
-    myinput, past_key_values = torch.tensor([text]), None
-    myinput = myinput
-    logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
-    logits = logits[0,-1]
-    probabilities = torch.nn.functional.softmax(logits)
-    best_logits, best_indices = logits.topk(20)
-    best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
-    for i in best_words[0:20]:
-      #print(i)
-      col2.append(i)
-  #print(col1)
-  #print(col2)
-  d = {col1[0]: [col2[0], col2[1], col2[2], col2[3], col2[4], col2[5], col2[6], col2[7], col2[8], col2[9], col2[10], col2[11], col2[12], col2[13], col2[14], col2[15], col2[16], col2[17], col2[18], col2[19]],
-    col1[1]: [col2[20], col2[21], col2[22], col2[23], col2[24], col2[25], col2[26], col2[27], col2[28], col2[29], col2[30], col2[31], col2[32], col2[33], col2[34], col2[35], col2[36], col2[37], col2[38], col2[39]],
-    col1[2]: [col2[40], col2[41], col2[42], col2[43], col2[44], col2[45], col2[46], col2[47], col2[48], col2[49], col2[50], col2[51], col2[52], col2[53], col2[54], col2[55], col2[56], col2[57], col2[58], col2[59]],
-    col1[3]: [col2[60], col2[61], col2[62], col2[63], col2[64], col2[65], col2[66], col2[67], col2[68], col2[69], col2[70], col2[71], col2[72], col2[73], col2[74], col2[75], col2[76], col2[77], col2[78], col2[79]],
-    col1[4]: [col2[80], col2[81], col2[82], col2[83], col2[84], col2[85], col2[86], col2[87], col2[88], col2[89], col2[90], col2[91], col2[92], col2[93], col2[94], col2[95], col2[96], col2[97], col2[98], col2[99]],
-    col1[5]: [col2[100], col2[101], col2[102], col2[103], col2[104], col2[105], col2[106], col2[107], col2[108], col2[109], col2[110], col2[111], col2[112], col2[113], col2[114], col2[115], col2[116], col2[117], col2[118], col2[119]],
-    col1[6]: [col2[120], col2[121], col2[122], col2[123], col2[124], col2[125], col2[126], col2[127], col2[128], col2[129], col2[130], col2[131], col2[132], col2[133], col2[134], col2[135], col2[136], col2[137], col2[138], col2[139]],
-    col1[7]: [col2[140], col2[141], col2[142], col2[143], col2[144], col2[145], col2[146], col2[147], col2[148], col2[149], col2[150], col2[151], col2[152], col2[153], col2[154], col2[155], col2[156], col2[157], col2[158], col2[159]],
-    col1[8]: [col2[160], col2[161], col2[162], col2[163], col2[164], col2[165], col2[166], col2[167], col2[168], col2[169], col2[170], col2[171], col2[172], col2[173], col2[174], col2[175], col2[176], col2[177], col2[178], col2[179]],
-    col1[9]: [col2[180], col2[181], col2[182], col2[183], col2[184], col2[185], col2[186], col2[187], col2[188], col2[189], col2[190], col2[191], col2[192], col2[193], col2[194], col2[195], col2[196], col2[197], col2[198], col2[199]]}
-  df = pd.DataFrame(data=d)
-  print(df)
-  st.write(df)
-  return df
-  
-def BestProbs5(prompt):
-  prompt = prompt.strip()
-  text = tokenizer.encode(prompt)
-  myinput, past_key_values = torch.tensor([text]), None
-  myinput = myinput
-  logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
-  logits = logits[0,-1]
-  probabilities = torch.nn.functional.softmax(logits)
-  best_logits, best_indices = logits.topk(number_of_outputs)
-  best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
-  for i in best_words[0:number_of_outputs]:
-    #print(i)
-    print("\n")
-    g = (prompt + i)
-    st.write(g)
-    l = run_generate(g, "hey")
-    st.write(l)
-    
-def run_generate(text, bad_words):
-  yo = []
-  input_ids = tokenizer.encode(text, return_tensors='pt')
-  res = len(tokenizer.encode(text))
-  bad_words = bad_words.split()
-  bad_word_ids = [[7829], [40940]]
-  for bad_word in bad_words: 
-    bad_word = " " + bad_word
-    ids = tokenizer(bad_word).input_ids
-    bad_word_ids.append(ids)
-  sample_outputs = model.generate(
-    input_ids,
-    do_sample=True, 
-    max_length= res + 5, 
-    min_length = res + 5, 
-    top_k=50,
-    temperature=1.0,
-    num_return_sequences=3,
-    bad_words_ids=bad_word_ids
-  )
-  for i in range(3):
-    e = tokenizer.decode(sample_outputs[i])
-    e = e.replace(text, "")
-    yo.append(e)
-  print(yo)
-  return yo
-
-with st.form(key='my_form'):
-    prompt = st.text_area(label='Enter sentence', value=g, height=500)
-    submit_button = st.form_submit_button(label='Submit')
-    submit_button2 = st.form_submit_button(label='Fast Forward')
-    submit_button3 = st.form_submit_button(label='Fast Forward 2.0')
-    submit_button4 = st.form_submit_button(label='Get Top')
-
-    if submit_button:
-      with torch.no_grad():
-        text = tokenizer.encode(prompt)
-        myinput, past_key_values = torch.tensor([text]), None
-        myinput = myinput
-        myinput= myinput.to(device)
-        logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
-        logits = logits[0,-1]
-        probabilities = torch.nn.functional.softmax(logits)
-        best_logits, best_indices = logits.topk(log_nums)
-        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
-        text.append(best_indices[0].item())
-        best_probabilities = probabilities[best_indices].tolist()
-        words = []              
-        st.write(best_words)
-    if submit_button2:
-        print("----")
-        st.write("___")
-        m = LogProbs(prompt)
-        st.write("___")
-        st.write(m)
-        st.write("___")
-    if submit_button3:
-        print("----")
-        st.write("___")
-        st.write(BestProbs)
-    if submit_button4:
-      BestProbs5(prompt)
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/pascal_voc.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/pascal_voc.py
deleted file mode 100644
index 5872d96575b428e90b29a7759a2f7b32dcc15d25..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/pascal_voc.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import numpy as np
-import os
-import xml.etree.ElementTree as ET
-from fvcore.common.file_io import PathManager
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.structures import BoxMode
-
-__all__ = ["register_pascal_voc"]
-
-
-# fmt: off
-CLASS_NAMES = [
-    "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
-    "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
-    "pottedplant", "sheep", "sofa", "train", "tvmonitor",
-]
-# fmt: on
-
-
-def load_voc_instances(dirname: str, split: str):
-    """
-    Load Pascal VOC detection annotations to Detectron2 format.
-
-    Args:
-        dirname: Contain "Annotations", "ImageSets", "JPEGImages"
-        split (str): one of "train", "test", "val", "trainval"
-    """
-    with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f:
-        fileids = np.loadtxt(f, dtype=np.str)
-
-    # Needs to read many small annotation files. Makes sense at local
-    annotation_dirname = PathManager.get_local_path(os.path.join(dirname, "Annotations/"))
-    dicts = []
-    for fileid in fileids:
-        anno_file = os.path.join(annotation_dirname, fileid + ".xml")
-        jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg")
-
-        with PathManager.open(anno_file) as f:
-            tree = ET.parse(f)
-
-        r = {
-            "file_name": jpeg_file,
-            "image_id": fileid,
-            "height": int(tree.findall("./size/height")[0].text),
-            "width": int(tree.findall("./size/width")[0].text),
-        }
-        instances = []
-
-        for obj in tree.findall("object"):
-            cls = obj.find("name").text
-            # We include "difficult" samples in training.
-            # Based on limited experiments, they don't hurt accuracy.
-            # difficult = int(obj.find("difficult").text)
-            # if difficult == 1:
-            # continue
-            bbox = obj.find("bndbox")
-            bbox = [float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]]
-            # Original annotations are integers in the range [1, W or H]
-            # Assuming they mean 1-based pixel indices (inclusive),
-            # a box with annotation (xmin=1, xmax=W) covers the whole image.
-            # In coordinate space this is represented by (xmin=0, xmax=W)
-            bbox[0] -= 1.0
-            bbox[1] -= 1.0
-            instances.append(
-                {"category_id": CLASS_NAMES.index(cls), "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS}
-            )
-        r["annotations"] = instances
-        dicts.append(r)
-    return dicts
-
-
-def register_pascal_voc(name, dirname, split, year):
-    DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split))
-    MetadataCatalog.get(name).set(
-        thing_classes=CLASS_NAMES, dirname=dirname, year=year, split=split
-    )
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/malloc_and_free.h b/spaces/CVPR/LIVE/thrust/thrust/detail/malloc_and_free.h
deleted file mode 100644
index 6dc238adb49311d0a0e6187ba65108183d5599a4..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/malloc_and_free.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/detail/pointer.h>
-#include <thrust/detail/raw_pointer_cast.h>
-#include <thrust/system/detail/generic/memory.h>
-#include <thrust/system/detail/adl/malloc_and_free.h>
-
-namespace thrust
-{
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy>
-__host__ __device__
-pointer<void,DerivedPolicy> malloc(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, std::size_t n)
-{
-  using thrust::system::detail::generic::malloc;
-
-  // XXX should use a hypothetical thrust::static_pointer_cast here
-  void *raw_ptr = static_cast<void*>(thrust::raw_pointer_cast(malloc(thrust::detail::derived_cast(thrust::detail::strip_const(exec)), n)));
-
-  return pointer<void,DerivedPolicy>(raw_ptr);
-}
-
-__thrust_exec_check_disable__
-template<typename T, typename DerivedPolicy>
-__host__ __device__
-pointer<T,DerivedPolicy> malloc(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, std::size_t n)
-{
-  using thrust::system::detail::generic::malloc;
-
-  T *raw_ptr = static_cast<T*>(thrust::raw_pointer_cast(malloc<T>(thrust::detail::derived_cast(thrust::detail::strip_const(exec)), n)));
-
-  return pointer<T,DerivedPolicy>(raw_ptr);
-}
-
-
-// XXX WAR nvbug 992955
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#if CUDART_VERSION < 5000
-
-// cudafe generates unqualified calls to free(int *volatile)
-// which get confused with thrust::free
-// spoof a thrust::free which simply maps to ::free
-inline __host__ __device__
-void free(int *volatile ptr)
-{
-  ::free(ptr);
-}
-
-#endif // CUDART_VERSION
-#endif // THRUST_DEVICE_COMPILER
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy, typename Pointer>
-__host__ __device__
-void free(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, Pointer ptr)
-{
-  using thrust::system::detail::generic::free;
-
-  free(thrust::detail::derived_cast(thrust::detail::strip_const(exec)), ptr);
-}
-
-// XXX consider another form of free which does not take a system argument and
-// instead infers the system from the pointer
-
-} // end namespace thrust
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/reverse_iterator.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/reverse_iterator.h
deleted file mode 100644
index 365bc34d2eee7598997be4586c00924b5c7f44ea..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/reverse_iterator.h
+++ /dev/null
@@ -1,238 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file thrust/iterator/reverse_iterator.h
- *  \brief An iterator adaptor which adapts another iterator to traverse backwards
- */
-
-/*
- * (C) Copyright David Abrahams 2002.
- * (C) Copyright Jeremy Siek    2002.
- * (C) Copyright Thomas Witt    2002.
- * 
- * Distributed under the Boost Software License, Version 1.0.
- * (See accompanying NOTICE file for the complete license)
- *
- * For more information, see http://www.boost.org
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/iterator/detail/reverse_iterator_base.h>
-#include <thrust/iterator/iterator_facade.h>
-
-namespace thrust
-{
-
-/*! \addtogroup iterators
- *  \{
- */
-
-/*! \addtogroup fancyiterator Fancy Iterators
- *  \ingroup iterators
- *  \{
- */
-
-/*! \p reverse_iterator is an iterator which represents a pointer into a
- *  reversed view of a given range. In this way, \p reverse_iterator allows
- *  backwards iteration through a bidirectional input range.
- *
- *  It is important to note that although \p reverse_iterator is constructed
- *  from a given iterator, it points to the element preceding it. In this way,
- *  the past-the-end \p reverse_iterator of a given range points to the element
- *  preceding the first element of the input range. By the same token, the first
- *  \p reverse_iterator of a given range is constructed from a past-the-end iterator
- *  of the original range yet points to the last element of the input.
- *
- *  The following code snippet demonstrates how to create a \p reverse_iterator
- *  which represents a reversed view of the contents of a \p device_vector.
- *
- *  \code
- *  #include <thrust/iterator/reverse_iterator.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<float> v(4);
- *  v[0] = 0.0f;
- *  v[1] = 1.0f;
- *  v[2] = 2.0f;
- *  v[3] = 3.0f;
- *
- *  typedef thrust::device_vector<float>::iterator Iterator;
- *
- *  // note that we point the iterator to the *end* of the device_vector
- *  thrust::reverse_iterator<Iterator> iter(values.end());
- *
- *  *iter;   // returns 3.0f;
- *  iter[0]; // returns 3.0f;
- *  iter[1]; // returns 2.0f;
- *  iter[2]; // returns 1.0f;
- *  iter[3]; // returns 0.0f;
- *
- *  // iter[4] is an out-of-bounds error
- *  \endcode
- *
- *  Since reversing a range is a common operation, containers like \p device_vector
- *  have nested typedefs for declaration shorthand and methods for constructing
- *  reverse_iterators. The following code snippet is equivalent to the previous:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<float> v(4);
- *  v[0] = 0.0f;
- *  v[1] = 1.0f;
- *  v[2] = 2.0f;
- *  v[3] = 3.0f;
- *
- *  // we use the nested type reverse_iterator to refer to a reversed view of
- *  // a device_vector and the method rbegin() to create a reverse_iterator pointing
- *  // to the beginning of the reversed device_vector
- *  thrust::device_iterator<float>::reverse_iterator iter = values.rbegin();
- *
- *  *iter;   // returns 3.0f;
- *  iter[0]; // returns 3.0f;
- *  iter[1]; // returns 2.0f;
- *  iter[2]; // returns 1.0f;
- *  iter[3]; // returns 0.0f;
- *
- *  // iter[4] is an out-of-bounds error
- *
- *  // similarly, rend() points to the end of the reversed sequence:
- *  assert(values.rend() == (iter + 4));
- *  \endcode
- *
- *  Finally, the following code snippet demonstrates how to use reverse_iterator to
- *  perform a reversed prefix sum operation on the contents of a device_vector:
- *
- *  \code
- *  #include <thrust/device_vector.h>
- *  #include <thrust/scan.h>
- *  ...
- *  thrust::device_vector<int> v(5);
- *  v[0] = 0;
- *  v[1] = 1;
- *  v[2] = 2;
- *  v[3] = 3;
- *  v[4] = 4;
- *
- *  thrust::device_vector<int> result(5);
- *
- *  // exclusive scan v into result in reverse
- *  thrust::exclusive_scan(v.rbegin(), v.rend(), result.begin());
- *
- *  // result is now {0, 4, 7, 9, 10}
- *  \endcode
- *
- *  \see make_reverse_iterator
- */
-template<typename BidirectionalIterator>
-  class reverse_iterator
-    : public detail::reverse_iterator_base<BidirectionalIterator>::type
-{
-  /*! \cond
-   */
-  private:
-    typedef typename thrust::detail::reverse_iterator_base<
-      BidirectionalIterator
-    >::type super_t;
-
-    friend class thrust::iterator_core_access;
-  /*! \endcond
-   */
-
-  public:
-    /*! Default constructor does nothing.
-     */
-    __host__ __device__
-    reverse_iterator() {}
-
-    /*! \p Constructor accepts a \c BidirectionalIterator pointing to a range
-     *  for this \p reverse_iterator to reverse.
-     *
-     *  \param x A \c BidirectionalIterator pointing to a range to reverse.
-     */
-    __host__ __device__
-    explicit reverse_iterator(BidirectionalIterator x);
-
-    /*! \p Copy constructor allows construction from a related compatible
-     *  \p reverse_iterator.
-     *
-     *  \param r A \p reverse_iterator to copy from.
-     */
-    template<typename OtherBidirectionalIterator>
-    __host__ __device__
-    reverse_iterator(reverse_iterator<OtherBidirectionalIterator> const &r
-// XXX msvc screws this up
-// XXX remove these guards when we have static_assert
-#if THRUST_HOST_COMPILER != THRUST_HOST_COMPILER_MSVC
-                     , typename thrust::detail::enable_if<
-                         thrust::detail::is_convertible<
-                           OtherBidirectionalIterator,
-                           BidirectionalIterator
-                         >::value
-                       >::type * = 0
-#endif // MSVC
-                     );
-
-  /*! \cond
-   */
-  private:
-    __thrust_exec_check_disable__
-    __host__ __device__
-    typename super_t::reference dereference() const;
-
-    __host__ __device__
-    void increment();
-
-    __host__ __device__
-    void decrement();
-
-    __host__ __device__
-    void advance(typename super_t::difference_type n);
-
-    template<typename OtherBidirectionalIterator>
-    __host__ __device__
-    typename super_t::difference_type
-    distance_to(reverse_iterator<OtherBidirectionalIterator> const &y) const;
-  /*! \endcond
-   */
-}; // end reverse_iterator
-
-
-/*! \p make_reverse_iterator creates a \p reverse_iterator
- *  from a \c BidirectionalIterator pointing to a range of elements to reverse.
- *  
- *  \param x A \c BidirectionalIterator pointing to a range to reverse.
- *  \return A new \p reverse_iterator which reverses the range \p x.
- */
-template<typename BidirectionalIterator>
-__host__ __device__
-reverse_iterator<BidirectionalIterator> make_reverse_iterator(BidirectionalIterator x);
-
-
-/*! \} // end fancyiterators
- */
-
-/*! \} // end iterators
- */
-
-} // end thrust
-
-#include <thrust/iterator/detail/reverse_iterator.inl>
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/reduce.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/reduce.h
deleted file mode 100644
index c3e7af0d28bc6a4a0d8f8c893bd7348f1b5e59b6..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/reduce.h
+++ /dev/null
@@ -1,59 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-#include <thrust/iterator/iterator_traits.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy, typename InputIterator>
-__host__ __device__
-  typename thrust::iterator_traits<InputIterator>::value_type
-    reduce(thrust::execution_policy<DerivedPolicy> &exec, InputIterator first, InputIterator last);
-
-
-template<typename DerivedPolicy, typename InputIterator, typename T>
-__host__ __device__
-  T reduce(thrust::execution_policy<DerivedPolicy> &exec, InputIterator first, InputIterator last, T init);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename T,
-         typename BinaryFunction>
-__host__ __device__
-  T reduce(thrust::execution_policy<DerivedPolicy> &exec, InputIterator first, InputIterator last, T init, BinaryFunction binary_op);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/reduce.inl>
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/binary_search.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/binary_search.h
deleted file mode 100644
index 54534143ecd7a4712094461b60c6e2b902a6781e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/binary_search.h
+++ /dev/null
@@ -1,157 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file binary_search.h
- *  \brief Sequential implementation of binary search algorithms.
- */
-
-#pragma once
-
-#include <thrust/advance.h>
-#include <thrust/distance.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/detail/function.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename T,
-         typename StrictWeakOrdering>
-__host__ __device__
-ForwardIterator lower_bound(sequential::execution_policy<DerivedPolicy> &,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            const T& val,
-                            StrictWeakOrdering comp)
-{
-  // wrap comp
-  thrust::detail::wrapped_function<
-    StrictWeakOrdering,
-    bool
-  > wrapped_comp(comp);
-
-  typedef typename thrust::iterator_difference<ForwardIterator>::type difference_type;
-
-  difference_type len = thrust::distance(first, last);
-
-  while(len > 0)
-  {
-    difference_type half = len >> 1;
-    ForwardIterator middle = first;
-
-    thrust::advance(middle, half);
-
-    if(wrapped_comp(*middle, val))
-    {
-      first = middle;
-      ++first;
-      len = len - half - 1;
-    }
-    else
-    {
-      len = half;
-    }
-  }
-
-  return first;
-}
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename T,
-         typename StrictWeakOrdering>
-__host__ __device__
-ForwardIterator upper_bound(sequential::execution_policy<DerivedPolicy> &,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            const T& val, 
-                            StrictWeakOrdering comp)
-{
-  // wrap comp
-  thrust::detail::wrapped_function<
-    StrictWeakOrdering,
-    bool
-  > wrapped_comp(comp);
-
-  typedef typename thrust::iterator_difference<ForwardIterator>::type difference_type;
-
-  difference_type len = thrust::distance(first, last);
-
-  while(len > 0)
-  {
-    difference_type half = len >> 1;
-    ForwardIterator middle = first;
-
-    thrust::advance(middle, half);
-
-    if(wrapped_comp(val, *middle))
-    {
-      len = half;
-    }
-    else
-    {
-      first = middle;
-      ++first;
-      len = len - half - 1;
-    }
-  }
-
-  return first;
-}
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename T,
-         typename StrictWeakOrdering>
-__host__ __device__
-bool binary_search(sequential::execution_policy<DerivedPolicy> &exec,
-                   ForwardIterator first,
-                   ForwardIterator last,
-                   const T& val, 
-                   StrictWeakOrdering comp)
-{
-  ForwardIterator iter = sequential::lower_bound(exec, first, last, val, comp);
-
-  // wrap comp
-  thrust::detail::wrapped_function<
-    StrictWeakOrdering,
-    bool
-  > wrapped_comp(comp);
-
-  return iter != last && !wrapped_comp(val,*iter);
-}
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/equal.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/equal.h
deleted file mode 100644
index 9d31e70f6fdaedcb3215a737888a6c5ac11621ab..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/equal.h
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special equal functions
-
diff --git a/spaces/CVPR/ml-talking-face/translator/module.py b/spaces/CVPR/ml-talking-face/translator/module.py
deleted file mode 100644
index fba414c21bdc16578fee36559150fb2003d7e8cd..0000000000000000000000000000000000000000
--- a/spaces/CVPR/ml-talking-face/translator/module.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from .v3 import GoogleAuthTranslation
-from pathlib import Path
-import yaml
-import os
-
-MAX_ENG_TEXT_LENGTH = int(os.getenv('MAX_ENG_TEXT_LENGTH', 200))
-MAX_CJK_TEXT_LENGTH = int(os.getenv('MAX_CJK_TEXT_LENGTH', 100))
-
-class Translator:
-    def __init__(self, yaml_path='./lang.yaml'):
-        self.google_translation = GoogleAuthTranslation(project_id="cvpr-2022-demonstration")
-        with open(yaml_path) as f:
-            self.supporting_languages = yaml.load(f, Loader=yaml.FullLoader)
-    
-    @staticmethod 
-    def length_check(lang, text):
-        if lang in ['en']:
-            if len(text) > MAX_ENG_TEXT_LENGTH:
-                raise AssertionError(f"Input text is too long. For English, the text length should be less than {MAX_ENG_TEXT_LENGTH}. | Length: {len(text)}")
-        elif lang in ['ko', 'ja', 'zh-CN', 'zh']:
-            if len(text) > MAX_CJK_TEXT_LENGTH:
-                raise AssertionError(f"Input text is too long. For CJK, the text length should be less than {MAX_CJK_TEXT_LENGTH}. | Length: {len(text)}")
-        else:
-            raise AssertionError(f"Not in ['ko', 'ja', 'zh-CN', 'zh', 'en'] ! | Language: {lang}")
-        
-        return
-        
-    def _get_text_with_lang(self, text, lang):
-        lang_detected = self.google_translation.detect(text)
-        print(f"Detected as: {lang_detected} | Destination: {lang}")
-        
-        if lang is None:
-            lang = lang_detected
-            
-        if lang != lang_detected:
-            target_text = self.google_translation.translate(text, lang=lang)
-        else:
-            target_text = text
-                
-        return target_text, lang
-    
-    def _convert_lang_from_index(self, lang):
-        try:
-            lang = [name for name in self.supporting_languages
-                    if self.supporting_languages[name]['language'] == lang][0]
-        except Exception as e:
-            raise RuntimeError(e)
-        
-        return lang
-
-    def get_translation(self, text, lang, use_translation=True):
-        lang_ = self._convert_lang_from_index(lang)
-        
-        if use_translation:
-            target_text, _ = self._get_text_with_lang(text, lang_)
-        else:
-            target_text = text
-
-        return target_text, lang_ 
\ No newline at end of file
diff --git a/spaces/CarlDennis/Lovelive-VITS-JPZH/text/japanese.py b/spaces/CarlDennis/Lovelive-VITS-JPZH/text/japanese.py
deleted file mode 100644
index 65480534b452efabe87b40033316e2c1577ff3ea..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/Lovelive-VITS-JPZH/text/japanese.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('...', '…'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# Dictinary of (consonant, sokuon) pairs:
-_real_sokuon = {
-  'k': 'k#',
-  'g': 'k#',
-  't': 't#',
-  'd': 't#',
-  'ʦ': 't#',
-  'ʧ': 't#',
-  'ʥ': 't#',
-  'j': 't#',
-  's': 's',
-  'ʃ': 's',
-  'p': 'p#',
-  'b': 'p#'
-}
-
-# Dictinary of (consonant, hatsuon) pairs:
-_real_hatsuon = {
-  'p': 'm',
-  'b': 'm',
-  'm': 'm',
-  't': 'n',
-  'd': 'n',
-  'n': 'n',
-  'ʧ': 'n^',
-  'ʥ': 'n^',
-  'k': 'ŋ',
-  'g': 'ŋ'
-}
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-  text=re.sub('Q[↑↓]*(.)',lambda x:_real_sokuon[x.group(1)]+x.group(0)[1:] if x.group(1) in _real_sokuon.keys() else x.group(0),text)
-  return text
-
-
-def get_real_hatsuon(text):
-  text=re.sub('N[↑↓]*(.)',lambda x:_real_hatsuon[x.group(1)]+x.group(0)[1:] if x.group(1) in _real_hatsuon.keys() else x.group(0),text)
-  return text
-
-
-def japanese_to_ipa(text):
-    text=japanese_to_romaji_with_accent(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    text = re.sub(
-            r'([A-Za-zɯ])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    return text
diff --git a/spaces/CarperAI/pile-v2-eda/load_dataset.py b/spaces/CarperAI/pile-v2-eda/load_dataset.py
deleted file mode 100644
index c7cb9a4a9cf8d7ce3e5688e30d463408340ab5ed..0000000000000000000000000000000000000000
--- a/spaces/CarperAI/pile-v2-eda/load_dataset.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# import datasets
-# import logging
-import os
-import json
-# from tqdm import tqdm
-# dataset_subs = os.listdir(PATH)
-
-# print(dataset_subs)
-
-
-# for ds in tqdm(dataset_subs):
-#     try:
-#         print(ds)
-#         dataset = datasets.load_dataset("CarperAI/pile-v2-small-filtered",data_files=f"data/{ds}/data.json", split="train")
-#         dataset.save_to_disk(f"cache_ds/{ds}")
-#     except:
-#         print(f"Error at {ds}")
-
-ds_subsets = os.listdir("cache_ds")
-
-with open("documentation.json","w") as f:
-    json.dump(ds_subsets,f)
\ No newline at end of file
diff --git a/spaces/DataDreamweavers/LegaWeaver/app.py b/spaces/DataDreamweavers/LegaWeaver/app.py
deleted file mode 100644
index a12ad7702c3f192455886f6aacdff94f52c3e495..0000000000000000000000000000000000000000
--- a/spaces/DataDreamweavers/LegaWeaver/app.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# import packages.data_processor as dp
-import streamlit as st 
-import random,time,os
-
-from ibm_watson_machine_learning.foundation_models import Model
-from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes
-from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
-from ibm_watson_machine_learning.foundation_models.utils.enums import DecodingMethods
-
-
-
-# IBM WatsonX
-model_id = ModelTypes.FLAN_UL2
-
-
-credentials = {
-    "url": "https://us-south.ml.cloud.ibm.com",
-    "apikey": os.getenv("API_KEY")
-}
-
-project_id = os.getenv("PROJECT_ID")
-
-
-instruction = '''You are a seasoned legal assistant that excels at summarizing legal documents. Generate a brief summary of below document in a formal and instructive manner:
-
-
-document: subject to your compliance with these terms niantic grants you a limited nonexclusive nontransferable non sublicensable license to download and install a copy of the app on a mobile device and to run such copy of the app solely for your own personal noncommercial purposes. except as expressly permitted in these terms you may not a copy modify or create derivative works based on the app b distribute transfer sublicense lease lend or rent the app to any third party c reverse engineer decompile or disassemble the app or d make the functionality of the app available to multiple users through any means. niantic reserves all rights in and to the app not expressly granted to you under these terms. if you accessed or downloaded the app from the apple store then you agree to use the app only a on an apple branded product or device that runs ios apple s proprietary operating system software and b as permitted by the usage rules set forth in the apple store terms of service. if you accessed or downloaded the app from any app store or distribution platform like the apple store google play or amazon appstore each an app provider then you acknowledge and agree that these terms are concluded between you and niantic and not with app provider and that as between us and the app provider niantic is solely responsible for the app. app provider has no obligation to furnish any maintenance and support services with respect to the app. in the event of any failure of the app to conform to any applicable warranty you may notify app provider and app provider will refund the purchase price for the app to you if applicable and to the maximum extent permitted by applicable law app provider will have no other warranty obligation whatsoever with respect to the app. any other claims losses liabilities damages costs or expenses attributable to any failure of an app to conform to any warranty will be the sole responsibility of niantic. app provider is not responsible for addressing any claims you have or any claims of any third party relating to the app or your possession and use of the app including but not limited to i product liability claims ii any claim that the app fails to conform to any applicable legal or regulatory requirement and iii claims arising under consumer protection or similar legislation. in the event of any third party claim that the app or your possession and use of the app infringes that third party s intellectual property rights niantic will be solely responsible for the investigation defense settlement and discharge of any such intellectual property infringement claim to the extent required by these terms. app provider and its subsidiaries are third party beneficiaries of these terms as related to your license of the app and that upon your acceptance of the terms and conditions of these terms app provider will have the right and will be deemed to have accepted the right to enforce these terms as related to your license of the app against you as a third party beneficiary thereof. you must also comply with all applicable third party terms of service when using the app. you agree to comply with all u s and foreign export laws and regulations to ensure that neither the app nor any technical data related thereto nor any direct product thereof is exported or re exported directly or indirectly in violation of or used for any purposes prohibited by such laws and regulations. by using the app you represent and warrant that i you are not located in a country that is subject to a u s government embargo or that has been designated by the u s government as a terrorist supporting country and ii you are not listed on any u s government list of prohibited or restricted parties.
-
-summary: don t copy modify resell distribute or reverse engineer this app.
-
-
-document: any feedback you provide at this site shall be deemed to be non confidential. apple shall be free to use such information on an unrestricted basis.
-
-summary: apple may use your feedback without restrictions e g. share it publicly.
-
-
-document: except as required by applicable non u s local or national law the laws of the state of california excluding its conflicts of law rules govern these terms and your use of the services.
-
-summary: the court of law governing the terms is in the state of california.
-
-
-document: thanks for using dropbox. these terms of service terms cover your use and access to the services client software and websites services provided by dropbox inc our privacy policy explains how we collect and use your information while our acceptable use policy outlines your responsibilities when using our services. by using our services you re agreeing to be bound by these terms and to review our privacy and acceptable use policies. if you re using our services for an organization you re agreeing to these terms on behalf of that organization.
-
-summary: by accepting these terms you also agree to the privacy and acceptable use policies linked in the right.
-
-
-document: we may update this privacy policy to reflect changes to our information practices. if we make any change in how we use your personal information we will notify you by email sent to the e mail address specified in your account. we encourage you to periodically review this page for the latest information on our privacy practices.
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-summary: users should revisit the terms periodically although in case of material changes the service will notify.
-
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-document: jetbrains reserves the exclusive right to revoke authorization to view download and print site content at any time and you shall discontinue such use immediately upon notice from jetbrains.
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-summary: the service can delete your account without prior notice and without a reason.
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-document: should any part of these terms and conditions be rendered or declared invalid by an appropriate authority such invalidation of such part or portion of these terms and conditions should not invalidate the remaining portions thereof and they shall remain in full force and effect.
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-document: sharing with third parties we share information with third parties that help us operate provide improve integrate customize support and market our services. service providers we work with third party service providers to provide website and application development hosting maintenance backup storage virtual infrastructure payment processing analysis and other services for us which may require them to access or use information about you. if a service provider needs to access information about you to perform services on our behalf they do so under instruction from us including abiding by policies and procedures designed to protect your information. trello partners we work with third parties who provide consulting sales support and technical services to deliver and implement customer solutions around the services including the atlassian global partner network 7 we may share your information with these third parties in connection with their services such as to assist with billing and collections to provide localized support and to provide customizations. we may also share information with these third parties where you have agreed to that sharing like when you agree to us sharing your information with a trello expert for support related questions. third party apps you your administrator or other service users may choose to add new functionality or change the behavior of the services by enabling third party apps like power ups within the services. doing so may give third party apps access to your account and information about you like your name and email address and any content you choose to use in connection with those apps. if you are an administrator or contact listed on an account we share your details with the third party app provider upon installation. third party app policies and procedures are not controlled by us and this privacy policy does not cover how third party apps use your information. we encourage you to review the privacy policies of third parties before connecting to or using their applications or services to learn more about their privacy and information handling practices. if you object to information about you being shared with these third parties please disable the app. links to third party sites the services may include links that direct you to other websites or services whose privacy practices may differ from ours. your use of and any information you submit to any of those third party sites is governed by their privacy policies not this one. third party widgets some of our services contain widgets and social media features such as the twitter tweet button. these widgets and features collect your ip address which page you are visiting on the services and may set a cookie to enable the feature to function properly. widgets and social media features are either hosted by a third party or hosted directly on our services. your interactions with these features are governed by the privacy policy of the company providing it. with your consent we share information about you with third parties when you give us consent to do so. for example we often display personal testimonials of satisfied customers on our public websites. with your consent we may post your name alongside the testimonial. compliance with enforcement requests and applicable laws. enforcement of our rights in exceptional circumstances we may share information about you with a third party if we believe that sharing is reasonably necessary to a comply with any applicable law regulation legal process or governmental request including to meet national security requirements b enforce our agreements policies and terms of service c protect the security or integrity of our products and services d protect trello our customers or the public from harm or illegal activities or e respond to an emergency which we believe in good faith requires us to disclose information to assist in preventing the death or serious bodily injury of any person. for more information on how we respond to government requests see our guidelines for law enforcement8 and our transparency report 9.
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-
-summary: they can change the terms of service any time they see fit even without notification to the user. your use of the service supposedly constitutes acceptance of the changes in the terms.
-
-
-document: '''
-
-
-parameters = {
-    GenParams.MAX_NEW_TOKENS: 100, 
-    GenParams.MIN_NEW_TOKENS: 10,
-    GenParams.DECODING_METHOD: DecodingMethods.GREEDY,
-    GenParams.REPETITION_PENALTY:2,
-    GenParams.STOP_SEQUENCES: ["^a-zA-Z\d\s,.", "human", "ai"]
-    
-}
-
-model = Model(
-    model_id=model_id,
-    params=parameters,  
-    credentials=credentials,
-    project_id=project_id
-)
-
-
-
-### MAIN FUNCTION ###
-def main(title = "LegaWeaver"):
-    st.markdown("<h1 style='text-align: center; font-size: 50px; color: #0530AD;'>{}</h1>".format(title), 
-    unsafe_allow_html=True)
-  
-    
-    text_message = st.text_area("Input your legal document here !")
-    if st.button("Summarize"):
-
-        info =model.generate_text(prompt=" ".join([instruction, text_message, "\n\nsummary: "]))
-
-       
-        st.success('Summary: {}'.format(info))
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/Dinoking/Guccio-AI-Designer/netdissect/workerpool.py b/spaces/Dinoking/Guccio-AI-Designer/netdissect/workerpool.py
deleted file mode 100644
index fe79124ddc86d0e7251d9e1a5d1012e7165249e3..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/netdissect/workerpool.py
+++ /dev/null
@@ -1,158 +0,0 @@
-'''
-WorkerPool and WorkerBase for handling the common problems in managing
-a multiprocess pool of workers that aren't done by multiprocessing.Pool,
-including setup with per-process state, debugging by putting the worker
-on the main thread, and correct handling of unexpected errors, and ctrl-C.
-
-To use it,
-1. Put the per-process setup and the per-task work in the
-   setup() and work() methods of your own WorkerBase subclass.
-2. To prepare the process pool, instantiate a WorkerPool, passing your
-   subclass type as the first (worker) argument, as well as any setup keyword
-   arguments.  The WorkerPool will instantiate one of your workers in each
-   worker process (passing in the setup arguments in those processes).
-   If debugging, the pool can have process_count=0 to force all the work
-   to be done immediately on the main thread; otherwise all the work
-   will be passed to other processes.
-3. Whenever there is a new piece of work to distribute, call pool.add(*args).
-   The arguments will be queued and passed as worker.work(*args) to the
-   next available worker.
-4. When all the work has been distributed, call pool.join() to wait for all
-   the work to complete and to finish and terminate all the worker processes.
-   When pool.join() returns, all the work will have been done.
-
-No arrangement is made to collect the results of the work: for example,
-the return value of work() is ignored.  If you need to collect the
-results, use your own mechanism (filesystem, shared memory object, queue)
-which can be distributed using setup arguments.
-'''
-
-from multiprocessing import Process, Queue, cpu_count
-import signal
-import atexit
-import sys
-
-class WorkerBase(Process):
-    '''
-    Subclass this class and override its work() method (and optionally,
-    setup() as well) to define the units of work to be done in a process
-    worker in a woker pool.
-    '''
-    def __init__(self, i, process_count, queue, initargs):
-        if process_count > 0:
-            # Make sure we ignore ctrl-C if we are not on main process.
-            signal.signal(signal.SIGINT, signal.SIG_IGN)
-        self.process_id = i
-        self.process_count = process_count
-        self.queue = queue
-        super(WorkerBase, self).__init__()
-        self.setup(**initargs)
-    def run(self):
-        # Do the work until None is dequeued
-        while True:
-            try:
-                work_batch = self.queue.get()
-            except (KeyboardInterrupt, SystemExit):
-                print('Exiting...')
-                break
-            if work_batch is None:
-                self.queue.put(None) # for another worker
-                return
-            self.work(*work_batch)
-    def setup(self, **initargs):
-        '''
-        Override this method for any per-process initialization.
-        Keywoard args are passed from WorkerPool constructor.
-        '''
-        pass
-    def work(self, *args):
-        '''
-        Override this method for one-time initialization.
-        Args are passed from WorkerPool.add() arguments.
-        '''
-        raise NotImplementedError('worker subclass needed')
-
-class WorkerPool(object):
-    '''
-    Instantiate this object (passing a WorkerBase subclass type
-    as its first argument) to create a worker pool.  Then call
-    pool.add(*args) to queue args to distribute to worker.work(*args),
-    and call pool.join() to wait for all the workers to complete.
-    '''
-    def __init__(self, worker=WorkerBase, process_count=None, **initargs):
-        global active_pools
-        if process_count is None:
-            process_count = cpu_count()
-        if process_count == 0:
-            # zero process_count uses only main process, for debugging.
-            self.queue = None
-            self.processes = None
-            self.worker = worker(None, 0, None, initargs)
-            return
-        # Ctrl-C strategy: worker processes should ignore ctrl-C.  Set
-        # this up to be inherited by child processes before forking.
-        original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)
-        active_pools[id(self)] = self
-        self.queue = Queue(maxsize=(process_count * 3))
-        self.processes = None   # Initialize before trying to construct workers
-        self.processes = [worker(i, process_count, self.queue, initargs)
-                for i in range(process_count)]
-        for p in self.processes:
-            p.start()
-        # The main process should handle ctrl-C.  Restore this now.
-        signal.signal(signal.SIGINT, original_sigint_handler)
-    def add(self, *work_batch):
-        if self.queue is None:
-            if hasattr(self, 'worker'):
-                self.worker.work(*work_batch)
-            else:
-                print('WorkerPool shutting down.', file=sys.stderr)
-        else:
-            try:
-                # The queue can block if the work is so slow it gets full.
-                self.queue.put(work_batch)
-            except (KeyboardInterrupt, SystemExit):
-                # Handle ctrl-C if done while waiting for the queue.
-                self.early_terminate()
-    def join(self):
-        # End the queue, and wait for all worker processes to complete nicely.
-        if self.queue is not None:
-            self.queue.put(None)
-            for p in self.processes:
-                p.join()
-            self.queue = None
-        # Remove myself from the set of pools that need cleanup on shutdown.
-        try:
-            del active_pools[id(self)]
-        except:
-            pass
-    def early_terminate(self):
-        # When shutting down unexpectedly, first end the queue.
-        if self.queue is not None:
-            try:
-                self.queue.put_nowait(None)  # Nonblocking put throws if full.
-                self.queue = None
-            except:
-                pass
-        # But then don't wait: just forcibly terminate workers.
-        if self.processes is not None:
-            for p in self.processes:
-                p.terminate()
-            self.processes = None
-        try:
-            del active_pools[id(self)]
-        except:
-            pass
-    def __del__(self):
-        if self.queue is not None:
-            print('ERROR: workerpool.join() not called!', file=sys.stderr)
-            self.join()
-
-# Error and ctrl-C handling: kill worker processes if the main process ends.
-active_pools = {}
-def early_terminate_pools():
-    for _, pool in list(active_pools.items()):
-        pool.early_terminate()
-
-atexit.register(early_terminate_pools)
-
diff --git a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/upfirdn2d.h b/spaces/DragGan/DragGan-Inversion/torch_utils/ops/upfirdn2d.h
deleted file mode 100644
index 2793daf874492af01e8634a7863c036e17b6731f..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/upfirdn2d.h
+++ /dev/null
@@ -1,59 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/ECCV2022/ECCV2022_papers/paper_list.py b/spaces/ECCV2022/ECCV2022_papers/paper_list.py
deleted file mode 100644
index db50fc4b13bf710cfbb8a05b7788eb9b896aae21..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/ECCV2022_papers/paper_list.py
+++ /dev/null
@@ -1,129 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-import requests
-from huggingface_hub.hf_api import SpaceInfo
-
-
-class PaperList:
-    def __init__(self):
-        self.organization_name = 'ECCV2022'
-        self.table = pd.read_csv('papers.csv')
-        self._preprcess_table()
-
-        self.table_header = '''
-            <tr>
-                <td width="50%">Paper Title</td>
-                <td width="22%">Authors</td>
-                <td width="4%">pdf</td>
-                <td width="4%">Session</td>
-                <td width="4%">arXiv</td>
-                <td width="4%">GitHub</td>
-                <td width="4%">HF Spaces</td>
-                <td width="4%">HF Models</td>
-                <td width="4%">HF Datasets</td>
-            </tr>'''
-
-    @staticmethod
-    def load_space_info(author: str) -> list[SpaceInfo]:
-        path = 'https://huggingface.co/api/spaces'
-        r = requests.get(path, params={'author': author})
-        d = r.json()
-        return [SpaceInfo(**x) for x in d]
-
-    def add_spaces_to_table(self, organization_name: str,
-                            df: pd.DataFrame) -> pd.DataFrame:
-        spaces = self.load_space_info(organization_name)
-        name2space = {
-            s.id.split('/')[1].lower(): f'https://huggingface.co/spaces/{s.id}'
-            for s in spaces
-        }
-        df['hf_space'] = df.loc[:, ['hf_space', 'github']].apply(
-            lambda x: x[0] if isinstance(x[0], str) else name2space.get(
-                x[1].split('/')[-1].lower()
-                if isinstance(x[1], str) else '', np.nan),
-            axis=1)
-        return df
-
-    def _preprcess_table(self) -> None:
-        self.table = self.add_spaces_to_table(self.organization_name,
-                                              self.table)
-        self.table['title_lowercase'] = self.table.title.str.lower()
-
-        rows = []
-        for row in self.table.itertuples():
-            paper = f'<a href="{row.url}" target="_blank">{row.title}</a>' if isinstance(
-                row.url, str) else row.title
-            pdf = f'<a href="{row.pdf}" target="_blank">pdf</a>' if isinstance(
-                row.pdf, str) else ''
-            arxiv = f'<a href="{row.arxiv}" target="_blank">arXiv</a>' if isinstance(
-                row.arxiv, str) else ''
-            github = f'<a href="{row.github}" target="_blank">GitHub</a>' if isinstance(
-                row.github, str) else ''
-            hf_space = f'<a href="{row.hf_space}" target="_blank">Space</a>' if isinstance(
-                row.hf_space, str) else ''
-            hf_model = f'<a href="{row.hf_model}" target="_blank">Model</a>' if isinstance(
-                row.hf_model, str) else ''
-            hf_dataset = f'<a href="{row.hf_dataset}" target="_blank">Dataset</a>' if isinstance(
-                row.hf_dataset, str) else ''
-            row = f'''
-                <tr>
-                    <td>{paper}</td>
-                    <td>{row.authors}</td>
-                    <td>{pdf}</td>
-                    <td>{row.session}</td>
-                    <td>{arxiv}</td>
-                    <td>{github}</td>
-                    <td>{hf_space}</td>
-                    <td>{hf_model}</td>
-                    <td>{hf_dataset}</td>
-                </tr>'''
-            rows.append(row)
-        self.table['html_table_content'] = rows
-
-    def render(self, search_query: str, case_sensitive: bool,
-               filter_names: list[str],
-               paper_sessions: list[str]) -> tuple[int, str]:
-        df = self.add_spaces_to_table(self.organization_name, self.table)
-        if search_query:
-            if case_sensitive:
-                df = df[df.title.str.contains(search_query)]
-            else:
-                df = df[df.title_lowercase.str.contains(search_query.lower())]
-        has_arxiv = 'arXiv' in filter_names
-        has_github = 'GitHub' in filter_names
-        has_hf_space = 'HF Space' in filter_names
-        has_hf_model = 'HF Model' in filter_names
-        has_hf_dataset = 'HF Dataset' in filter_names
-        df = self.filter_table(df, has_arxiv, has_github, has_hf_space,
-                               has_hf_model, has_hf_dataset, paper_sessions)
-        return len(df), self.to_html(df, self.table_header)
-
-    @staticmethod
-    def filter_table(df: pd.DataFrame, has_arxiv: bool, has_github: bool,
-                     has_hf_space: bool, has_hf_model: bool,
-                     has_hf_dataset: bool,
-                     paper_sessions: list[str]) -> pd.DataFrame:
-        if has_arxiv:
-            df = df[~df.arxiv.isna()]
-        if has_github:
-            df = df[~df.github.isna()]
-        if has_hf_space:
-            df = df[~df.hf_space.isna()]
-        if has_hf_model:
-            df = df[~df.hf_model.isna()]
-        if has_hf_dataset:
-            df = df[~df.hf_dataset.isna()]
-        df = df[df.session.isin(set(paper_sessions))]
-        return df
-
-    @staticmethod
-    def to_html(df: pd.DataFrame, table_header: str) -> str:
-        table_data = ''.join(df.html_table_content)
-        html = f'''
-        <table>
-            {table_header}
-            {table_data}
-        </table>'''
-        return html
diff --git a/spaces/Eieichicken/yyayyaya/README.md b/spaces/Eieichicken/yyayyaya/README.md
deleted file mode 100644
index 3d3030d8ec6d3e7cc7fadf350bf823a491233577..0000000000000000000000000000000000000000
--- a/spaces/Eieichicken/yyayyaya/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Yyayyaya
-emoji: 👁
-colorFrom: pink
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/models/__init__.py b/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/models/__init__.py
deleted file mode 100644
index 0be7105dc75d150c49976396724085f678dc0675..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/models/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import importlib
-from basicsr.utils import scandir
-from os import path as osp
-
-# automatically scan and import model modules for registry
-# scan all the files that end with '_model.py' under the model folder
-model_folder = osp.dirname(osp.abspath(__file__))
-model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
-# import all the model modules
-_model_modules = [importlib.import_module(f'realesrgan.models.{file_name}') for file_name in model_filenames]
diff --git a/spaces/Enigma007/Classifier-Fasttext/README.md b/spaces/Enigma007/Classifier-Fasttext/README.md
deleted file mode 100644
index c47c46a9792d14565635cb3489d1c101922b3c54..0000000000000000000000000000000000000000
--- a/spaces/Enigma007/Classifier-Fasttext/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: FASTTEXT CLASSIFIER
-emoji: 🚀
-colorFrom: blue
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/EtTKSf/uu/README.md b/spaces/EtTKSf/uu/README.md
deleted file mode 100644
index 018699e2018017c5d126f8c9efd4d1e7ced1500c..0000000000000000000000000000000000000000
--- a/spaces/EtTKSf/uu/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Uu
-emoji: 🐠
-colorFrom: pink
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/README.md b/spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/README.md
deleted file mode 100644
index d7109d150ab85f25c2bcdfdf36e22e726f51d89c..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Automatic Speech Recognition
-emoji: 🌖
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.26
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/examples.py b/spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/examples.py
deleted file mode 100644
index b3c3151acf1380d13678a1a17975eff027372f8c..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/examples.py
+++ /dev/null
@@ -1,256 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# See LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-examples = [
-    [
-        "Chinese+English",
-        "ptrnull/icefall-asr-conv-emformer-transducer-stateless2-zh",
-        "greedy_search",
-        4,
-        "./test_wavs/tal_csasr/0.wav",
-    ],
-    [
-        "English",
-        "csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13",
-        "greedy_search",
-        4,
-        "./test_wavs/librispeech/1089-134686-0001.wav",
-    ],
-    [
-        "Chinese",
-        "luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/wenetspeech/DEV_T0000000000.opus",
-    ],
-    [
-        "German",
-        "csukuangfj/wav2vec2.0-torchaudio",
-        "greedy_search",
-        4,
-        "./test_wavs/german/20170517-0900-PLENARY-16-de_20170517.wav",
-    ],
-    [
-        "Arabic",
-        "AmirHussein/icefall-asr-mgb2-conformer_ctc-2022-27-06",
-        "greedy_search",
-        4,
-        "./test_wavs/arabic/a.wav",
-    ],
-    [
-        "Tibetan",
-        "syzym/icefall-asr-xbmu-amdo31-pruned-transducer-stateless7-2022-12-02",
-        "greedy_search",
-        4,
-        "./test_wavs/tibetan/a_0_cacm-A70_31117.wav",
-    ],
-    # librispeech
-    # https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-2022-05-13/tree/main/test_wavs
-    [
-        "English",
-        "csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13",
-        "greedy_search",
-        4,
-        "./test_wavs/librispeech/1089-134686-0001.wav",
-    ],
-    [
-        "English",
-        "csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13",
-        "greedy_search",
-        4,
-        "./test_wavs/librispeech/1221-135766-0001.wav",
-    ],
-    [
-        "English",
-        "csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13",
-        "greedy_search",
-        4,
-        "./test_wavs/librispeech/1221-135766-0002.wav",
-    ],
-    # gigaspeech
-    [
-        "English",
-        "wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/gigaspeech/1-minute-audiobook.opus",
-    ],
-    [
-        "English",
-        "wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/gigaspeech/100-seconds-podcast.opus",
-    ],
-    [
-        "English",
-        "wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/gigaspeech/100-seconds-youtube.opus",
-    ],
-    # wenetspeech
-    # https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2/tree/main/test_wavs
-    [
-        "Chinese",
-        "luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/wenetspeech/DEV_T0000000000.opus",
-    ],
-    [
-        "Chinese",
-        "luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/wenetspeech/DEV_T0000000001.opus",
-    ],
-    [
-        "Chinese",
-        "luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/wenetspeech/DEV_T0000000002.opus",
-    ],
-    # aishell2-A
-    # https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12/tree/main/test_wavs
-    [
-        "Chinese",
-        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12",
-        "greedy_search",
-        4,
-        "./test_wavs/aishell2/ID0012W0030.wav",
-    ],
-    [
-        "Chinese",
-        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12",
-        "greedy_search",
-        4,
-        "./test_wavs/aishell2/ID0012W0162.wav",
-    ],
-    [
-        "Chinese",
-        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12",
-        "greedy_search",
-        4,
-        "./test_wavs/aishell2/ID0012W0215.wav",
-    ],
-    # aishell2-B
-    # https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12/tree/main/test_wavs
-    [
-        "Chinese",
-        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12",
-        "greedy_search",
-        4,
-        "./test_wavs/aishell2/ID0012W0030.wav",
-    ],
-    [
-        "Chinese",
-        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12",
-        "greedy_search",
-        4,
-        "./test_wavs/aishell2/ID0012W0162.wav",
-    ],
-    [
-        "Chinese",
-        "yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12",
-        "greedy_search",
-        4,
-        "./test_wavs/aishell2/ID0012W0215.wav",
-    ],
-    # aishell2-B
-    # https://huggingface.co/luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2/tree/main/test_wavs
-    [
-        "Chinese",
-        "luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/aidatatang_200zh/T0055G0036S0002.wav",
-    ],
-    [
-        "Chinese",
-        "luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/aidatatang_200zh/T0055G0036S0003.wav",
-    ],
-    [
-        "Chinese",
-        "luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2",
-        "greedy_search",
-        4,
-        "./test_wavs/aidatatang_200zh/T0055G0036S0004.wav",
-    ],
-    # tal_csasr
-    [
-        "Chinese+English",
-        "ptrnull/icefall-asr-conv-emformer-transducer-stateless2-zh",
-        "greedy_search",
-        4,
-        "./test_wavs/tal_csasr/210_36476_210_8341_1_1533271973_7057520_132.wav",
-    ],
-    [
-        "Chinese+English",
-        "ptrnull/icefall-asr-conv-emformer-transducer-stateless2-zh",
-        "greedy_search",
-        4,
-        "./test_wavs/tal_csasr/210_36476_210_8341_1_1533271973_7057520_138.wav",
-    ],
-    [
-        "Chinese+English",
-        "ptrnull/icefall-asr-conv-emformer-transducer-stateless2-zh",
-        "greedy_search",
-        4,
-        "./test_wavs/tal_csasr/210_36476_210_8341_1_1533271973_7057520_145.wav",
-    ],
-    [
-        "Tibetan",
-        "syzym/icefall-asr-xbmu-amdo31-pruned-transducer-stateless7-2022-12-02",
-        "greedy_search",
-        4,
-        "./test_wavs/tibetan/a_0_cacm-A70_31116.wav",
-    ],
-    [
-        "Tibetan",
-        "syzym/icefall-asr-xbmu-amdo31-pruned-transducer-stateless7-2022-12-02",
-        "greedy_search",
-        4,
-        "./test_wavs/tibetan/a_0_cacm-A70_31118.wav",
-    ],
-    # arabic
-    [
-        "Arabic",
-        "AmirHussein/icefall-asr-mgb2-conformer_ctc-2022-27-06",
-        "greedy_search",
-        4,
-        "./test_wavs/arabic/b.wav",
-    ],
-    [
-        "Arabic",
-        "AmirHussein/icefall-asr-mgb2-conformer_ctc-2022-27-06",
-        "greedy_search",
-        4,
-        "./test_wavs/arabic/c.wav",
-    ],
-    [
-        "German",
-        "csukuangfj/wav2vec2.0-torchaudio",
-        "greedy_search",
-        4,
-        "./test_wavs/german/20120315-0900-PLENARY-14-de_20120315.wav",
-    ],
-]
diff --git a/spaces/Everymans-ai/GPT-knowledge-management/README.md b/spaces/Everymans-ai/GPT-knowledge-management/README.md
deleted file mode 100644
index 10810c53ebb53915c67379ae0a3fad63d7b812cf..0000000000000000000000000000000000000000
--- a/spaces/Everymans-ai/GPT-knowledge-management/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Haystack QA
-emoji: 📚
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: Abhilashvj/haystack_QA
----
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Fernando22/freegpt-webui/g4f/README.md b/spaces/Fernando22/freegpt-webui/g4f/README.md
deleted file mode 100644
index c2cbfd69dc169e2cb4f8d24104fb12a52b91688d..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/g4f/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-## 🚀 API G4F
-
-This API is built upon the [gpt4free](https://github.com/xtekky/gpt4free) project.
-
-
diff --git a/spaces/Flux9665/IMS-Toucan/Layers/Swish.py b/spaces/Flux9665/IMS-Toucan/Layers/Swish.py
deleted file mode 100644
index 1541ac7c6b33d08f7f998f7950f090afd6c14b38..0000000000000000000000000000000000000000
--- a/spaces/Flux9665/IMS-Toucan/Layers/Swish.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
-#                Northwestern Polytechnical University (Pengcheng Guo)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-# Adapted by Florian Lux 2021
-
-import torch
-
-
-class Swish(torch.nn.Module):
-    """
-    Construct an Swish activation function for Conformer.
-    """
-
-    def forward(self, x):
-        """
-        Return Swish activation function.
-        """
-        return x * torch.sigmoid(x)
diff --git a/spaces/Frorozcol/dreambooth-training/train_dreambooth.py b/spaces/Frorozcol/dreambooth-training/train_dreambooth.py
deleted file mode 100644
index a496382fbc895961b9902c33a9d5cc926d4fcc8d..0000000000000000000000000000000000000000
--- a/spaces/Frorozcol/dreambooth-training/train_dreambooth.py
+++ /dev/null
@@ -1,881 +0,0 @@
-import argparse
-import itertools
-import math
-import os
-from pathlib import Path
-from typing import Optional
-import subprocess
-import sys
-import gc
-import random
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from huggingface_hub import HfFolder, Repository, whoami
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-
-logger = get_logger(__name__)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        #required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--instance_data_dir",
-        type=str,
-        default=None,
-        #required=True,
-        help="A folder containing the training data of instance images.",
-    )
-    parser.add_argument(
-        "--class_data_dir",
-        type=str,
-        default=None,
-        #required=False,
-        help="A folder containing the training data of class images.",
-    )
-    parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default=None,
-        help="The prompt with identifier specifying the instance",
-    )
-    parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default="",
-        help="The prompt to specify images in the same class as provided instance images.",
-    )
-    parser.add_argument(
-        "--with_prior_preservation",
-        default=False,
-        action="store_true",
-        help="Flag to add prior preservation loss.",
-    )
-    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
-    parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
-            " sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
-    )
-    parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-
-    parser.add_argument(
-        "--save_n_steps",
-        type=int,
-        default=1,
-        help=("Save the model every n global_steps"),
-    )
-    
-    
-    parser.add_argument(
-        "--save_starting_step",
-        type=int,
-        default=1,
-        help=("The step from which it starts saving intermediary checkpoints"),
-    )
-    
-    parser.add_argument(
-        "--stop_text_encoder_training",
-        type=int,
-        default=1000000,
-        help=("The step at which the text_encoder is no longer trained"),
-    )
-
-
-    parser.add_argument(
-        "--image_captions_filename",
-        action="store_true",
-        help="Get captions from filename",
-    )    
-    
-    
-    parser.add_argument(
-        "--dump_only_text_encoder",
-        action="store_true",
-        default=False,        
-        help="Dump only text encoder",
-    )
-
-    parser.add_argument(
-        "--train_only_unet",
-        action="store_true",
-        default=False,        
-        help="Train only the unet",
-    )
-    
-    parser.add_argument(
-        "--cache_latents",
-        action="store_true",
-        default=False,        
-        help="Train only the unet",
-    )
-    
-    parser.add_argument(
-        "--Session_dir",
-        type=str,
-        default="",     
-        help="Current session directory",
-    )    
-
-    
-    
-
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    #if args.instance_data_dir is None:
-    #    raise ValueError("You must specify a train data directory.")
-
-    #if args.with_prior_preservation:
-    #    if args.class_data_dir is None:
-    #        raise ValueError("You must specify a data directory for class images.")
-    #    if args.class_prompt is None:
-    #        raise ValueError("You must specify prompt for class images.")
-
-    return args
-
-
-class DreamBoothDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
-    def __init__(
-        self,
-        instance_data_root,
-        instance_prompt,
-        tokenizer,
-        args,
-        class_data_root=None,
-        class_prompt=None,
-        size=512,
-        center_crop=False,
-    ):
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-        self.image_captions_filename = None
-
-        self.instance_data_root = Path(instance_data_root)
-        if not self.instance_data_root.exists():
-            raise ValueError("Instance images root doesn't exists.")
-
-        self.instance_images_path = list(Path(instance_data_root).iterdir())
-        self.num_instance_images = len(self.instance_images_path)
-        self.instance_prompt = instance_prompt
-        self._length = self.num_instance_images
-
-        if args.image_captions_filename:
-            self.image_captions_filename = True
-        
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            random.shuffle(self.class_images_path)
-            self.num_class_images = len(self.class_images_path)
-            self._length = max(self.num_class_images, self.num_instance_images)
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
-        self.image_transforms = transforms.Compose(
-            [
-                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, index):
-        example = {}
-        path = self.instance_images_path[index % self.num_instance_images]
-        instance_image = Image.open(path)
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-            
-        instance_prompt = self.instance_prompt
-        
-        if self.image_captions_filename:
-            filename = Path(path).stem
-            pt=''.join([i for i in filename if not i.isdigit()])
-            pt=pt.replace("_"," ")
-            pt=pt.replace("(","")
-            pt=pt.replace(")","")
-            pt=pt.replace("-","")
-            instance_prompt = pt
-            sys.stdout.write(" " +instance_prompt+" ")
-            sys.stdout.flush()
-
-
-        example["instance_images"] = self.image_transforms(instance_image)
-        example["instance_prompt_ids"] = self.tokenizer(
-            instance_prompt,
-            padding="do_not_pad",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-        ).input_ids
-
-        if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            example["class_images"] = self.image_transforms(class_image)
-            example["class_prompt_ids"] = self.tokenizer(
-                self.class_prompt,
-                padding="do_not_pad",
-                truncation=True,
-                max_length=self.tokenizer.model_max_length,
-            ).input_ids
-
-        return example
-
-
-
-class PromptDataset(Dataset):
-    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
-    def __init__(self, prompt, num_samples):
-        self.prompt = prompt
-        self.num_samples = num_samples
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, index):
-        example = {}
-        example["prompt"] = self.prompt
-        example["index"] = index
-        return example
-
-class LatentsDataset(Dataset):
-    def __init__(self, latents_cache, text_encoder_cache):
-        self.latents_cache = latents_cache
-        self.text_encoder_cache = text_encoder_cache
-
-    def __len__(self):
-        return len(self.latents_cache)
-
-    def __getitem__(self, index):
-        return self.latents_cache[index], self.text_encoder_cache[index]
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-def merge_two_dicts(starting_dict: dict, updater_dict: dict) -> dict:
-    """
-    Starts from base starting dict and then adds the remaining key values from updater replacing the values from
-    the first starting/base dict with the second updater dict.
-
-    For later: how does d = {**d1, **d2} replace collision?
-
-    :param starting_dict:
-    :param updater_dict:
-    :return:
-    """
-    new_dict: dict = starting_dict.copy()   # start with keys and values of starting_dict
-    new_dict.update(updater_dict)    # modifies starting_dict with keys and values of updater_dict
-    return new_dict
-
-def merge_args(args1: argparse.Namespace, args2: argparse.Namespace) -> argparse.Namespace:
-    """
-
-    ref: https://stackoverflow.com/questions/56136549/how-can-i-merge-two-argparse-namespaces-in-python-2-x
-    :param args1:
-    :param args2:
-    :return:
-    """
-    # - the merged args
-    # The vars() function returns the __dict__ attribute to values of the given object e.g {field:value}.
-    merged_key_values_for_namespace: dict = merge_two_dicts(vars(args1), vars(args2))
-    args = argparse.Namespace(**merged_key_values_for_namespace)
-    return args
-
-def run_training(args_imported):
-    args_default = parse_args()
-    args = merge_args(args_default, args_imported)
-    print(args)
-    logging_dir = Path(args.output_dir, args.logging_dir)
-    i=args.save_starting_step
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with="tensorboard",
-        logging_dir=logging_dir,
-    )
-
-    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
-    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
-    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
-    if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
-        raise ValueError(
-            "Gradient accumulation is not supported when training the text encoder in distributed training. "
-            "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
-        )
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        if not class_images_dir.exists():
-            class_images_dir.mkdir(parents=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
-            pipeline = StableDiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path, torch_dtype=torch_dtype
-            )
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
-            sample_dataloader = accelerator.prepare(sample_dataloader)
-            pipeline.to(accelerator.device)
-
-            for example in tqdm(
-                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
-            ):
-                with torch.autocast("cuda"):                
-                    images = pipeline(example["prompt"]).images
-
-                for i, image in enumerate(images):
-                    image.save(class_images_dir / f"{example['index'][i] + cur_class_images}.jpg")
-
-            del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-
-    # Load models and create wrapper for stable diffusion
-    if args.train_only_unet:
-      if os.path.exists(str(args.output_dir+"/text_encoder_trained")):
-        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder_trained")
-      elif os.path.exists(str(args.output_dir+"/text_encoder")):
-        text_encoder = CLIPTextModel.from_pretrained(args.output_dir, subfolder="text_encoder")
-      else:
-        text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    else:
-      text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
-    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
-
-    vae.requires_grad_(False)
-    if not args.train_text_encoder:
-        text_encoder.requires_grad_(False)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-        if args.train_text_encoder:
-            text_encoder.gradient_checkpointing_enable()
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    params_to_optimize = (
-        itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
-    )
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
-
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        center_crop=args.center_crop,
-        args=args,
-    )
-
-    def collate_fn(examples):
-        input_ids = [example["instance_prompt_ids"] for example in examples]
-        pixel_values = [example["instance_images"] for example in examples]
-
-        # Concat class and instance examples for prior preservation.
-        # We do this to avoid doing two forward passes.
-        if args.with_prior_preservation:
-            input_ids += [example["class_prompt_ids"] for example in examples]
-            pixel_values += [example["class_images"] for example in examples]
-
-        pixel_values = torch.stack(pixel_values)
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-        input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
-
-        batch = {
-            "input_ids": input_ids,
-            "pixel_values": pixel_values,
-        }
-        return batch
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-    )
-
-    if args.train_text_encoder:
-        unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-        )
-    else:
-        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, optimizer, train_dataloader, lr_scheduler
-        )
-
-    weight_dtype = torch.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move text_encode and vae to gpu.
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    vae.to(accelerator.device, dtype=weight_dtype)
-    if not args.train_text_encoder:
-        text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-
-    if args.cache_latents:
-        latents_cache = []
-        text_encoder_cache = []
-        for batch in tqdm(train_dataloader, desc="Caching latents"):
-            with torch.no_grad():
-                batch["pixel_values"] = batch["pixel_values"].to(accelerator.device, non_blocking=True, dtype=weight_dtype)
-                batch["input_ids"] = batch["input_ids"].to(accelerator.device, non_blocking=True)
-                latents_cache.append(vae.encode(batch["pixel_values"]).latent_dist)
-                if args.train_text_encoder:
-                    text_encoder_cache.append(batch["input_ids"])
-                else:
-                    text_encoder_cache.append(text_encoder(batch["input_ids"])[0])
-        train_dataset = LatentsDataset(latents_cache, text_encoder_cache)
-        train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=1, collate_fn=lambda x: x, shuffle=True)
-
-        del vae
-        #if not args.train_text_encoder:
-        #    del text_encoder
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("dreambooth", config=vars(args))
-
-    def bar(prg):
-       br='|'+'█' * prg + ' ' * (25-prg)+'|'
-       return br
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
-    global_step = 0
-
-    for epoch in range(args.num_train_epochs):
-        unet.train()
-        if args.train_text_encoder:
-            text_encoder.train()
-        for step, batch in enumerate(train_dataloader):
-            with accelerator.accumulate(unet):
-                # Convert images to latent space
-                with torch.no_grad():
-                    if args.cache_latents:
-                        latents_dist = batch[0][0]
-                    else:
-                        latents_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist
-                    latents = latents_dist.sample() * 0.18215
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                if(args.cache_latents):
-                    if args.train_text_encoder:
-                        encoder_hidden_states = text_encoder(batch[0][1])[0]
-                    else:
-                        encoder_hidden_states = batch[0][1]
-                else:
-                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                # Predict the noise residual
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-                
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-                
-                if args.with_prior_preservation:
-                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
-                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
-                    target, target_prior = torch.chunk(target, 2, dim=0)
-
-                    # Compute instance loss
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
-
-                    # Compute prior loss
-                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
-                    # Add the prior loss to the instance loss.
-                    loss = loss + args.prior_loss_weight * prior_loss
-                else:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = (
-                        itertools.chain(unet.parameters(), text_encoder.parameters())
-                        if args.train_text_encoder
-                        else unet.parameters()
-                    )
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-            fll=round((global_step*100)/args.max_train_steps)
-            fll=round(fll/4)
-            pr=bar(fll)
-            
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            progress_bar.set_description_str("Progress:"+pr)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-            if args.train_text_encoder and global_step == args.stop_text_encoder_training and global_step >= 30:
-              if accelerator.is_main_process:
-                print(" " +" Freezing the text_encoder ..."+" ")                
-                frz_dir=args.output_dir + "/text_encoder_frozen"
-                if os.path.exists(frz_dir):
-                  subprocess.call('rm -r '+ frz_dir, shell=True)
-                os.mkdir(frz_dir)
-                pipeline = StableDiffusionPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    unet=accelerator.unwrap_model(unet),
-                    text_encoder=accelerator.unwrap_model(text_encoder),
-                )
-                pipeline.text_encoder.save_pretrained(frz_dir)
-                         
-            if args.save_n_steps >= 200:
-               if global_step < args.max_train_steps and global_step+1==i:
-                  ckpt_name = "_step_" + str(global_step+1)
-                  save_dir = Path(args.output_dir+ckpt_name)
-                  save_dir=str(save_dir)
-                  save_dir=save_dir.replace(" ", "_")                    
-                  if not os.path.exists(save_dir):
-                     os.mkdir(save_dir)
-                  inst=save_dir[16:]
-                  inst=inst.replace(" ", "_")
-                  print(" SAVING CHECKPOINT: "+args.Session_dir+"/"+inst+".ckpt")
-                  # Create the pipeline using the trained modules and save it.
-                  if accelerator.is_main_process:
-                     pipeline = StableDiffusionPipeline.from_pretrained(
-                           args.pretrained_model_name_or_path,
-                           unet=accelerator.unwrap_model(unet),
-                           text_encoder=accelerator.unwrap_model(text_encoder),
-                     )
-                     pipeline.save_pretrained(save_dir)
-                     frz_dir=args.output_dir + "/text_encoder_frozen"                    
-                     if args.train_text_encoder and os.path.exists(frz_dir):
-                        subprocess.call('rm -r '+save_dir+'/text_encoder/*.*', shell=True)
-                        subprocess.call('cp -f '+frz_dir +'/*.* '+ save_dir+'/text_encoder', shell=True)                     
-                     chkpth=args.Session_dir+"/"+inst+".ckpt"
-                     subprocess.call('python /content/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py --model_path ' + save_dir + ' --checkpoint_path ' + chkpth + ' --half', shell=True)
-                     subprocess.call('rm -r '+ save_dir, shell=True)
-                     i=i+args.save_n_steps
-            
-        accelerator.wait_for_everyone()
-
-    # Create the pipeline using using the trained modules and save it.
-    if accelerator.is_main_process:
-      if args.dump_only_text_encoder:
-         txt_dir=args.output_dir + "/text_encoder_trained"
-         if not os.path.exists(txt_dir):
-           os.mkdir(txt_dir)
-         pipeline = StableDiffusionPipeline.from_pretrained(
-             args.pretrained_model_name_or_path,
-             unet=accelerator.unwrap_model(unet),
-             text_encoder=accelerator.unwrap_model(text_encoder),
-         )
-         pipeline.text_encoder.save_pretrained(txt_dir)       
-
-      elif args.train_only_unet:
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=accelerator.unwrap_model(unet),
-            text_encoder=accelerator.unwrap_model(text_encoder),
-        )
-        pipeline.save_pretrained(args.output_dir)
-        txt_dir=args.output_dir + "/text_encoder_trained"
-        subprocess.call('rm -r '+txt_dir, shell=True)
-     
-      else:
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=accelerator.unwrap_model(unet),
-            text_encoder=accelerator.unwrap_model(text_encoder),
-        )
-        frz_dir=args.output_dir + "/text_encoder_frozen"
-        pipeline.save_pretrained(args.output_dir)
-        if args.train_text_encoder and os.path.exists(frz_dir):
-           subprocess.call('mv -f '+frz_dir +'/*.* '+ args.output_dir+'/text_encoder', shell=True)
-           subprocess.call('rm -r '+ frz_dir, shell=True) 
-
-        if args.push_to_hub:
-            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
-
-    accelerator.end_training()
-    del pipeline
-    torch.cuda.empty_cache()
-    gc.collect()
-if __name__ == "__main__":
-    pass
-    #main()
-
diff --git a/spaces/Future-AI/image-matting/utils/functions.py b/spaces/Future-AI/image-matting/utils/functions.py
deleted file mode 100644
index 971051891861f7ce1a77acbe8c7d39e9dd63d32c..0000000000000000000000000000000000000000
--- a/spaces/Future-AI/image-matting/utils/functions.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import os, random
-from logging import getLogger, StreamHandler, DEBUG
-logger = getLogger(__name__)
-handler = StreamHandler(); handler.setLevel(DEBUG)
-logger.setLevel(DEBUG)
-logger.addHandler(handler)
-logger.propagate = False
-
-
-def get_random_name():
-    famous_painters = [
-    "Leonardo", "DaVinci",
-    "Michelangelo",
-    "Pablo", "Picasso",
-    "Vincent", "VanGogh",
-    "Rembrandt", "VanRijn",
-    "Claude", "Monet",
-    "Salvador", "Dali",
-    "Jackson", "Pollock",
-    "Andy", "Warhol",
-    "Henri", "Matisse",
-    "Georgia", "Keeffe",
-    "Edvard", "Munch",
-    "Wassily", "Kandinsky",
-    "Gustav", "Klimt",
-    "Rene", "Magritte",
-    "Frida", "Kahlo",
-    "Edgar", "Degas",
-    "Johannes", "Vermeer",
-    "Paul", "Cezanne",
-    "Marc", "Chagall",
-    ]
-
-    random_painter = random.choice(famous_painters)
-
-    # 4桁の乱数を生成
-    rand_num = random.randint(1000, 9999)
-
-    return random_painter+str(rand_num)
-
-
-def complete(work_dir):
-    work_dir = work_dir
-    # logger.debug(f"complete :", work_dir)
-    return work_dir
-
-
-def clean(text_output):
-    # logger.debug(f"text_output : {text_output}")
-
-    if text_output!="idle_state":
-        logger.info(f"clean up : {text_output}.zip")
-        os.remove(f"{text_output}.zip")
-        return "idle_state"
-    else:
-        logger.info(f"reset")
-        return "idle_state"
-
-
-
-def clean_by_name(text_output):
-    # logger.debug(f"text_output : {text_output}")
-    if text_output!="idle_state":
-        text_output, dir_name = text_output.split("+")
-        logger.info(f"clean up : {dir_name}.zip")
-        os.remove(f"{dir_name}.zip")
-        return "idle_state"
-    else:
-        logger.info(f"reset")
-        return "idle_state"
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/exp/mask_rcnn_3x_ms_hybrid_base/config.py b/spaces/Gradio-Blocks/uniformer_image_detection/exp/mask_rcnn_3x_ms_hybrid_base/config.py
deleted file mode 100644
index 99b63bb628af81526956ed89a4193878bec08762..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/exp/mask_rcnn_3x_ms_hybrid_base/config.py
+++ /dev/null
@@ -1,82 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/mask_rcnn_uniformer_fpn.py',
-    '../../configs/_base_/datasets/coco_instance.py',
-    '../../configs/_base_/schedules/schedule_1x.py', 
-    '../../configs/_base_/default_runtime.py'
-]
-
-model = dict(
-    backbone=dict(
-        embed_dim=[64, 128, 320, 512],
-        layers=[5, 8, 20, 7],
-        head_dim=64,
-        drop_path_rate=0.3,
-        use_checkpoint=True,
-        checkpoint_num=[0, 0, 20, 0],
-        windows=False,
-        hybrid=True,
-        window_size=14
-    ),
-    neck=dict(in_channels=[64, 128, 320, 512]))
-
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='AutoAugment',
-         policies=[
-             [
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                                 (736, 1333), (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True)
-             ],
-             [
-                 dict(type='Resize',
-                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True),
-                 dict(type='RandomCrop',
-                      crop_type='absolute_range',
-                      crop_size=(384, 600),
-                      allow_negative_crop=True),
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                                 (576, 1333), (608, 1333), (640, 1333),
-                                 (672, 1333), (704, 1333), (736, 1333),
-                                 (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      override=True,
-                      keep_ratio=True)
-             ]
-         ]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[27, 33])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
-    type="DistOptimizerHook",
-    update_interval=1,
-    grad_clip=None,
-    coalesce=True,
-    bucket_size_mb=-1,
-    use_fp16=True,
-)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/mse_loss.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/mse_loss.py
deleted file mode 100644
index 68d05752a245548862f4c9919448d4fb8dc1b8ca..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/losses/mse_loss.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@weighted_loss
-def mse_loss(pred, target):
-    """Warpper of mse loss."""
-    return F.mse_loss(pred, target, reduction='none')
-
-
-@LOSSES.register_module()
-class MSELoss(nn.Module):
-    """MSELoss.
-
-    Args:
-        reduction (str, optional): The method that reduces the loss to a
-            scalar. Options are "none", "mean" and "sum".
-        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
-    """
-
-    def __init__(self, reduction='mean', loss_weight=1.0):
-        super().__init__()
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self, pred, target, weight=None, avg_factor=None):
-        """Forward function of loss.
-
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction.
-            weight (torch.Tensor, optional): Weight of the loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-
-        Returns:
-            torch.Tensor: The calculated loss
-        """
-        loss = self.loss_weight * mse_loss(
-            pred,
-            target,
-            weight,
-            reduction=self.reduction,
-            avg_factor=avg_factor)
-        return loss
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 58f28b43f55f54c7a604960735963e6b7c13b6f1..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './emanet_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index b0672b687ade8d554b71fdf0bc54de9f024fa30c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/tools/train.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/tools/train.py
deleted file mode 100644
index 3ea053d706f2cdfae545c55b8719dd181cd7c1e3..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/tools/train.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import argparse
-import copy
-import os
-import os.path as osp
-import time
-
-import mmcv
-import torch
-from mmcv.runner import init_dist
-from mmcv.utils import Config, DictAction, get_git_hash
-
-from mmseg import __version__
-from mmseg.apis import set_random_seed, train_segmentor
-from mmseg.datasets import build_dataset
-from mmseg.models import build_segmentor
-from mmseg.utils import collect_env, get_root_logger
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Train a segmentor')
-    parser.add_argument('config', help='train config file path')
-    parser.add_argument('--work-dir', help='the dir to save logs and models')
-    parser.add_argument(
-        '--load-from', help='the checkpoint file to load weights from')
-    parser.add_argument(
-        '--resume-from', help='the checkpoint file to resume from')
-    parser.add_argument(
-        '--no-validate',
-        action='store_true',
-        help='whether not to evaluate the checkpoint during training')
-    group_gpus = parser.add_mutually_exclusive_group()
-    group_gpus.add_argument(
-        '--gpus',
-        type=int,
-        help='number of gpus to use '
-        '(only applicable to non-distributed training)')
-    group_gpus.add_argument(
-        '--gpu-ids',
-        type=int,
-        nargs='+',
-        help='ids of gpus to use '
-        '(only applicable to non-distributed training)')
-    parser.add_argument('--seed', type=int, default=None, help='random seed')
-    parser.add_argument(
-        '--deterministic',
-        action='store_true',
-        help='whether to set deterministic options for CUDNN backend.')
-    parser.add_argument(
-        '--options', nargs='+', action=DictAction, help='custom options')
-    parser.add_argument(
-        '--launcher',
-        choices=['none', 'pytorch', 'slurm', 'mpi'],
-        default='none',
-        help='job launcher')
-    parser.add_argument('--local_rank', type=int, default=0)
-    args = parser.parse_args()
-    if 'LOCAL_RANK' not in os.environ:
-        os.environ['LOCAL_RANK'] = str(args.local_rank)
-
-    return args
-
-
-def main():
-    args = parse_args()
-    
-    if not os.path.exists(args.resume_from):
-        args.resume_from = None
-
-    cfg = Config.fromfile(args.config)
-    if args.options is not None:
-        cfg.merge_from_dict(args.options)
-    # set cudnn_benchmark
-    if cfg.get('cudnn_benchmark', False):
-        torch.backends.cudnn.benchmark = True
-
-    # work_dir is determined in this priority: CLI > segment in file > filename
-    if args.work_dir is not None:
-        # update configs according to CLI args if args.work_dir is not None
-        cfg.work_dir = args.work_dir
-    elif cfg.get('work_dir', None) is None:
-        # use config filename as default work_dir if cfg.work_dir is None
-        cfg.work_dir = osp.join('./work_dirs',
-                                osp.splitext(osp.basename(args.config))[0])
-    if args.load_from is not None:
-        cfg.load_from = args.load_from
-    if args.resume_from is not None:
-        cfg.resume_from = args.resume_from
-    if args.gpu_ids is not None:
-        cfg.gpu_ids = args.gpu_ids
-    else:
-        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
-
-    # init distributed env first, since logger depends on the dist info.
-    if args.launcher == 'none':
-        distributed = False
-    else:
-        distributed = True
-        init_dist(args.launcher, **cfg.dist_params)
-
-    # create work_dir
-    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
-    # dump config
-    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
-    # init the logger before other steps
-    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
-    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
-    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
-
-    # init the meta dict to record some important information such as
-    # environment info and seed, which will be logged
-    meta = dict()
-    # log env info
-    env_info_dict = collect_env()
-    env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
-    dash_line = '-' * 60 + '\n'
-    logger.info('Environment info:\n' + dash_line + env_info + '\n' +
-                dash_line)
-    meta['env_info'] = env_info
-
-    # log some basic info
-    logger.info(f'Distributed training: {distributed}')
-    logger.info(f'Config:\n{cfg.pretty_text}')
-
-    # set random seeds
-    if args.seed is not None:
-        logger.info(f'Set random seed to {args.seed}, deterministic: '
-                    f'{args.deterministic}')
-        set_random_seed(args.seed, deterministic=args.deterministic)
-    cfg.seed = args.seed
-    meta['seed'] = args.seed
-    meta['exp_name'] = osp.basename(args.config)
-
-    model = build_segmentor(
-        cfg.model,
-        train_cfg=cfg.get('train_cfg'),
-        test_cfg=cfg.get('test_cfg'))
-
-    logger.info(model)
-
-    datasets = [build_dataset(cfg.data.train)]
-    if len(cfg.workflow) == 2:
-        val_dataset = copy.deepcopy(cfg.data.val)
-        val_dataset.pipeline = cfg.data.train.pipeline
-        datasets.append(build_dataset(val_dataset))
-    if cfg.checkpoint_config is not None:
-        # save mmseg version, config file content and class names in
-        # checkpoints as meta data
-        cfg.checkpoint_config.meta = dict(
-            mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
-            config=cfg.pretty_text,
-            CLASSES=datasets[0].CLASSES,
-            PALETTE=datasets[0].PALETTE)
-    # add an attribute for visualization convenience
-    model.CLASSES = datasets[0].CLASSES
-    train_segmentor(
-        model,
-        datasets,
-        cfg,
-        distributed=distributed,
-        validate=(not args.no_validate),
-        timestamp=timestamp,
-        meta=meta)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/Grezz/generate_human_motion/VQ-Trans/VQ_eval.py b/spaces/Grezz/generate_human_motion/VQ-Trans/VQ_eval.py
deleted file mode 100644
index f1b7f269e344f730797eba13a45c9672f323b9f5..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/VQ-Trans/VQ_eval.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-import json
-
-import torch
-from torch.utils.tensorboard import SummaryWriter
-import numpy as np
-import models.vqvae as vqvae
-import options.option_vq as option_vq
-import utils.utils_model as utils_model
-from dataset import dataset_TM_eval
-import utils.eval_trans as eval_trans
-from options.get_eval_option import get_opt
-from models.evaluator_wrapper import EvaluatorModelWrapper
-import warnings
-warnings.filterwarnings('ignore')
-import numpy as np
-##### ---- Exp dirs ---- #####
-args = option_vq.get_args_parser()
-torch.manual_seed(args.seed)
-
-args.out_dir = os.path.join(args.out_dir, f'{args.exp_name}')
-os.makedirs(args.out_dir, exist_ok = True)
-
-##### ---- Logger ---- #####
-logger = utils_model.get_logger(args.out_dir)
-writer = SummaryWriter(args.out_dir)
-logger.info(json.dumps(vars(args), indent=4, sort_keys=True))
-
-
-from utils.word_vectorizer import WordVectorizer
-w_vectorizer = WordVectorizer('./glove', 'our_vab')
-
-
-dataset_opt_path = 'checkpoints/kit/Comp_v6_KLD005/opt.txt' if args.dataname == 'kit' else 'checkpoints/t2m/Comp_v6_KLD005/opt.txt'
-
-wrapper_opt = get_opt(dataset_opt_path, torch.device('cuda'))
-eval_wrapper = EvaluatorModelWrapper(wrapper_opt)
-
-
-##### ---- Dataloader ---- #####
-args.nb_joints = 21 if args.dataname == 'kit' else 22
-
-val_loader = dataset_TM_eval.DATALoader(args.dataname, True, 32, w_vectorizer, unit_length=2**args.down_t)
-
-##### ---- Network ---- #####
-net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers
-                       args.nb_code,
-                       args.code_dim,
-                       args.output_emb_width,
-                       args.down_t,
-                       args.stride_t,
-                       args.width,
-                       args.depth,
-                       args.dilation_growth_rate,
-                       args.vq_act,
-                       args.vq_norm)
-
-if args.resume_pth : 
-    logger.info('loading checkpoint from {}'.format(args.resume_pth))
-    ckpt = torch.load(args.resume_pth, map_location='cpu')
-    net.load_state_dict(ckpt['net'], strict=True)
-net.train()
-net.cuda()
-
-fid = []
-div = []
-top1 = []
-top2 = []
-top3 = []
-matching = []
-repeat_time = 20
-for i in range(repeat_time):
-    best_fid, best_iter, best_div, best_top1, best_top2, best_top3, best_matching, writer, logger = eval_trans.evaluation_vqvae(args.out_dir, val_loader, net, logger, writer, 0, best_fid=1000, best_iter=0, best_div=100, best_top1=0, best_top2=0, best_top3=0, best_matching=100, eval_wrapper=eval_wrapper, draw=False, save=False, savenpy=(i==0))
-    fid.append(best_fid)
-    div.append(best_div)
-    top1.append(best_top1)
-    top2.append(best_top2)
-    top3.append(best_top3)
-    matching.append(best_matching)
-print('final result:')
-print('fid: ', sum(fid)/repeat_time)
-print('div: ', sum(div)/repeat_time)
-print('top1: ', sum(top1)/repeat_time)
-print('top2: ', sum(top2)/repeat_time)
-print('top3: ', sum(top3)/repeat_time)
-print('matching: ', sum(matching)/repeat_time)
-
-fid = np.array(fid)
-div = np.array(div)
-top1 = np.array(top1)
-top2 = np.array(top2)
-top3 = np.array(top3)
-matching = np.array(matching)
-msg_final = f"FID. {np.mean(fid):.3f}, conf. {np.std(fid)*1.96/np.sqrt(repeat_time):.3f}, Diversity. {np.mean(div):.3f}, conf. {np.std(div)*1.96/np.sqrt(repeat_time):.3f}, TOP1. {np.mean(top1):.3f}, conf. {np.std(top1)*1.96/np.sqrt(repeat_time):.3f}, TOP2. {np.mean(top2):.3f}, conf. {np.std(top2)*1.96/np.sqrt(repeat_time):.3f}, TOP3. {np.mean(top3):.3f}, conf. {np.std(top3)*1.96/np.sqrt(repeat_time):.3f}, Matching. {np.mean(matching):.3f}, conf. {np.std(matching)*1.96/np.sqrt(repeat_time):.3f}"
-logger.info(msg_final)
\ No newline at end of file
diff --git a/spaces/Gustavosta/MagicPrompt-Stable-Diffusion/README.md b/spaces/Gustavosta/MagicPrompt-Stable-Diffusion/README.md
deleted file mode 100644
index a9d428a0274ba3c1ec1ea56384ca6604e2056e2a..0000000000000000000000000000000000000000
--- a/spaces/Gustavosta/MagicPrompt-Stable-Diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MagicPrompt Stable Diffusion
-emoji: 😻
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HaMerL/ChaosinChat/.github/pull_request_template.md b/spaces/HaMerL/ChaosinChat/.github/pull_request_template.md
deleted file mode 100644
index a9258d38e1f785d11f4694bea6a4d975016b5e5b..0000000000000000000000000000000000000000
--- a/spaces/HaMerL/ChaosinChat/.github/pull_request_template.md
+++ /dev/null
@@ -1,31 +0,0 @@
-<!--
-这是一个拉取请求模板。本文段处于注释中，请您先查看本注释，在您提交时该段文字将不会显示。
-
-1. 在提交拉取请求前，您最好已经查看过：https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南 了解了我们的大致要求；
-2. 如果您的这一个pr包含多个不同的功能添加或问题修复，请务必将您的提交拆分为多个不同的原子化的commit，甚至您可以在不同的分支中提交多个pull request；
-3. 不过，就算您的提交与pr写得完全不合规范也没有关系，您可以直接提交，我们会进行审查。总之我们欢迎您做出贡献！
-
-您可以参考这个示例 pull request：[#439](https://github.com/GaiZhenbiao/ChuanhuChatGPT/pull/439)
--->
-
-### 描述
-
-描述您的pull request所做的更改。
-另外请附上相关程序运行时的截图（before & after），以直观地展现您的更改达成的效果。
-
-### 相关问题
-
-（如有）请列出与此拉取请求相关的issue。
-或者，您可以直接描述您的工作能解决的问题等。
-
-### 补充信息
-
-请提供任何其他信息或说明，有助于其他贡献者理解您的更改。
-
-如果您提交的是 draft pull request，也请在这里写明开发进度。例子：
-
-#### 开发进度
-
-- [x] 已经做好的事情1
-- [ ] 还要干的事情1
-- [ ] 还要干的事情2
\ No newline at end of file
diff --git a/spaces/Hallucinate/demo/taming/data/annotated_objects_open_images.py b/spaces/Hallucinate/demo/taming/data/annotated_objects_open_images.py
deleted file mode 100644
index aede6803d2cef7a74ca784e7907d35fba6c71239..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/taming/data/annotated_objects_open_images.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from collections import defaultdict
-from csv import DictReader, reader as TupleReader
-from pathlib import Path
-from typing import Dict, List, Any
-import warnings
-
-from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset
-from taming.data.helper_types import Annotation, Category
-from tqdm import tqdm
-
-OPEN_IMAGES_STRUCTURE = {
-    'train': {
-        'top_level': '',
-        'class_descriptions': 'class-descriptions-boxable.csv',
-        'annotations': 'oidv6-train-annotations-bbox.csv',
-        'file_list': 'train-images-boxable.csv',
-        'files': 'train'
-    },
-    'validation': {
-        'top_level': '',
-        'class_descriptions': 'class-descriptions-boxable.csv',
-        'annotations': 'validation-annotations-bbox.csv',
-        'file_list': 'validation-images.csv',
-        'files': 'validation'
-    },
-    'test': {
-        'top_level': '',
-        'class_descriptions': 'class-descriptions-boxable.csv',
-        'annotations': 'test-annotations-bbox.csv',
-        'file_list': 'test-images.csv',
-        'files': 'test'
-    }
-}
-
-
-def load_annotations(descriptor_path: Path, min_object_area: float, category_mapping: Dict[str, str],
-                     category_no_for_id: Dict[str, int]) -> Dict[str, List[Annotation]]:
-    annotations: Dict[str, List[Annotation]] = defaultdict(list)
-    with open(descriptor_path) as file:
-        reader = DictReader(file)
-        for i, row in tqdm(enumerate(reader), total=14620000, desc='Loading OpenImages annotations'):
-            width = float(row['XMax']) - float(row['XMin'])
-            height = float(row['YMax']) - float(row['YMin'])
-            area = width * height
-            category_id = row['LabelName']
-            if category_id in category_mapping:
-                category_id = category_mapping[category_id]
-            if area >= min_object_area and category_id in category_no_for_id:
-                annotations[row['ImageID']].append(
-                    Annotation(
-                        id=i,
-                        image_id=row['ImageID'],
-                        source=row['Source'],
-                        category_id=category_id,
-                        category_no=category_no_for_id[category_id],
-                        confidence=float(row['Confidence']),
-                        bbox=(float(row['XMin']), float(row['YMin']), width, height),
-                        area=area,
-                        is_occluded=bool(int(row['IsOccluded'])),
-                        is_truncated=bool(int(row['IsTruncated'])),
-                        is_group_of=bool(int(row['IsGroupOf'])),
-                        is_depiction=bool(int(row['IsDepiction'])),
-                        is_inside=bool(int(row['IsInside']))
-                    )
-                )
-        if 'train' in str(descriptor_path) and i < 14000000:
-            warnings.warn(f'Running with subset of Open Images. Train dataset has length [{len(annotations)}].')
-        return dict(annotations)
-
-
-def load_image_ids(csv_path: Path) -> List[str]:
-    with open(csv_path) as file:
-        reader = DictReader(file)
-        return [row['image_name'] for row in reader]
-
-
-def load_categories(csv_path: Path) -> Dict[str, Category]:
-    with open(csv_path) as file:
-        reader = TupleReader(file)
-        return {row[0]: Category(id=row[0], name=row[1], super_category=None) for row in reader}
-
-
-class AnnotatedObjectsOpenImages(AnnotatedObjectsDataset):
-    def __init__(self, use_additional_parameters: bool, **kwargs):
-        """
-        @param data_path: is the path to the following folder structure:
-                          open_images/
-                          │   oidv6-train-annotations-bbox.csv
-                          ├── class-descriptions-boxable.csv
-                          ├── oidv6-train-annotations-bbox.csv
-                          ├── test
-                          │   ├── 000026e7ee790996.jpg
-                          │   ├── 000062a39995e348.jpg
-                          │   └── ...
-                          ├── test-annotations-bbox.csv
-                          ├── test-images.csv
-                          ├── train
-                          │   ├── 000002b66c9c498e.jpg
-                          │   ├── 000002b97e5471a0.jpg
-                          │   └── ...
-                          ├── train-images-boxable.csv
-                          ├── validation
-                          │   ├── 0001eeaf4aed83f9.jpg
-                          │   ├── 0004886b7d043cfd.jpg
-                          │   └── ...
-                          ├── validation-annotations-bbox.csv
-                          └── validation-images.csv
-        @param: split: one of 'train', 'validation' or 'test'
-        @param: desired image size (returns square images)
-        """
-
-        super().__init__(**kwargs)
-        self.use_additional_parameters = use_additional_parameters
-
-        self.categories = load_categories(self.paths['class_descriptions'])
-        self.filter_categories()
-        self.setup_category_id_and_number()
-
-        self.image_descriptions = {}
-        annotations = load_annotations(self.paths['annotations'], self.min_object_area, self.category_mapping,
-                                       self.category_number)
-        self.annotations = self.filter_object_number(annotations, self.min_object_area, self.min_objects_per_image,
-                                                     self.max_objects_per_image)
-        self.image_ids = list(self.annotations.keys())
-        self.clean_up_annotations_and_image_descriptions()
-
-    def get_path_structure(self) -> Dict[str, str]:
-        if self.split not in OPEN_IMAGES_STRUCTURE:
-            raise ValueError(f'Split [{self.split} does not exist for Open Images data.]')
-        return OPEN_IMAGES_STRUCTURE[self.split]
-
-    def get_image_path(self, image_id: str) -> Path:
-        return self.paths['files'].joinpath(f'{image_id:0>16}.jpg')
-
-    def get_image_description(self, image_id: str) -> Dict[str, Any]:
-        image_path = self.get_image_path(image_id)
-        return {'file_path': str(image_path), 'file_name': image_path.name}
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_cmnli.sh b/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_cmnli.sh
deleted file mode 100644
index b2d6dfff35668596c0c748003b7b937d98604922..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen2_finetune/fs_zen2_large_cmnli.sh
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=zen2_large_cmnli # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=1 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:1 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. 
-#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id)
-
-
-export CUDA_VISIBLE_DEVICES='3'
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
-
-MODEL_NAME=zen2_large
-
-TASK=cmnli
-
-ZERO_STAGE=1
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-ROOT_DIR=/cognitive_comp/ganruyi/experiments/classification_finetune/${MODEL_NAME}_${TASK}
-if [ ! -d ${ROOT_DIR} ];then
-  mkdir -p ${ROOT_DIR}
-  echo ${ROOT_DIR} created!!!!!!!!!!!!!!
-else
-  echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
-fi
-
-DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/cmnli_public/${TASK}_public/
-PRETRAINED_MODEL_PATH=/cognitive_comp/ganruyi/hf_models/zen/zh_zen_large_2.0
-
-CHECKPOINT_PATH=${ROOT_DIR}/ckpt/
-OUTPUT_PATH=${ROOT_DIR}/predict.json
-
-DATA_ARGS="\
-        --data_dir $DATA_DIR \
-        --train_data train.json \
-        --valid_data dev.json \
-        --test_data test.json \
-        --train_batchsize 32 \
-        --valid_batchsize 32 \
-        --max_seq_length 128 \
-        --texta_name sentence \
-        --label_name label \
-        --id_name id \
-        --task_name cmnli \
-        "
-
-MODEL_ARGS="\
-        --learning_rate 2e-5 \
-        --weight_decay 0.1 \
-        --warmup_ratio 0.01 \
-        --num_labels 3 \
-        "
-
-MODEL_CHECKPOINT_ARGS="\
-        --monitor val_acc \
-        --save_top_k 3 \
-        --mode max \
-        --every_n_train_steps 100 \
-        --save_weights_only True \
-        --dirpath $CHECKPOINT_PATH \
-        --filename model-{epoch:02d}-{val_acc:.4f} \
-        "
-
-TRAINER_ARGS="\
-        --max_epochs 10 \
-        --gpus 1 \
-        --check_val_every_n_epoch 1 \
-        --val_check_interval 100 \
-        --default_root_dir $ROOT_DIR \
-        "
-
-
-options=" \
-        --pretrained_model_path $PRETRAINED_MODEL_PATH \
-        --vocab_file $PRETRAINED_MODEL_PATH/vocab.txt \
-        --do_lower_case \
-        --output_save_path $OUTPUT_PATH \
-        $DATA_ARGS \
-        $MODEL_ARGS \
-        $MODEL_CHECKPOINT_ARGS \
-        $TRAINER_ARGS \
-"
-SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/zen2_finetune/fengshen_sequence_level_ft_task.py
-/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
-# SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
-# python3 $SCRIPT_PATH $options
-# source activate base
-# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-# /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/utils/convert_py_to_npy.py b/spaces/HaloMaster/chinesesummary/fengshen/utils/convert_py_to_npy.py
deleted file mode 100644
index 0d652169b59ffdc7ca977318ee72187b2ce73c1f..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/utils/convert_py_to_npy.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import argparse
-import torch
-import glob
-import os
-import numpy as np
-
-
-class MMapIndexDataset():
-    def __init__(self, datapath):
-        self.idxfp = np.load(datapath + '.npy', mmap_mode='r')
-        self.binfp = np.memmap(datapath + '.bin', dtype='long', mode='r')
-
-    def __len__(self):
-        return self.idxfp.shape[0]
-
-    def __getitem__(self, idx):
-        return self.binfp[self.idxfp[idx, 0]:self.idxfp[idx, 1]]
-
-
-def convert_py_to_npy(input_tensor, bin_out, idx_out):
-    idx = torch.empty(len(input_tensor), 2, dtype=torch.long)
-    start = 0
-    for i, input in enumerate(input_tensor):
-        idx[i] = torch.tensor([start, start + len(input)])
-        start += len(input)
-    np.save(idx_out, idx)
-    binfp = np.memmap(bin_out, dtype='long', mode='w+', shape=(start))
-    start = 0
-    for i, input in enumerate(input_tensor):
-        for j, idx in enumerate(input):
-            binfp[start + j] = idx
-        start += len(input)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Text infilling.")
-    parser.add_argument('--data_path', type=str,
-                        default='/cognitive_comp/gaoxinyu/data/wudao')
-    args = parser.parse_args()
-    process_key = [
-        'incorrect_input_ids_list',
-        'label_ids_list',
-        'target_ids_list',
-    ]
-    if os.path.exists(args.data_path):
-        print(f'''Loading data from {args.data_path}''')
-        data_dict = torch.load(args.data_path)
-        for k in process_key:
-            bin_out = ('_' + k + '.bin').join(args.data_path.rsplit('.pt', 1))
-            idx_out = ('_' + k).join(args.data_path.rsplit('.pt', 1))
-            convert_py_to_npy(data_dict[k], bin_out, idx_out)
-    else:
-        print(
-            f'Please create the synthetic datafile {args.data_path} with create_synthetic_data.py.')
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/utils/utils.py b/spaces/HaloMaster/chinesesummary/fengshen/utils/utils.py
deleted file mode 100644
index 02d9121cb1f55d142b62e304468695a0feda753b..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/utils/utils.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# coding=utf-8
-import jieba
-
-
-def jieba_tokenize(str):
-    return jieba.lcut(str)
-
-
-_UCODE_RANGES = (
-    ("\u3400", "\u4db5"),  # CJK Unified Ideographs Extension A, release 3.0
-    ("\u4e00", "\u9fa5"),  # CJK Unified Ideographs, release 1.1
-    ("\u9fa6", "\u9fbb"),  # CJK Unified Ideographs, release 4.1
-    ("\uf900", "\ufa2d"),  # CJK Compatibility Ideographs, release 1.1
-    ("\ufa30", "\ufa6a"),  # CJK Compatibility Ideographs, release 3.2
-    ("\ufa70", "\ufad9"),  # CJK Compatibility Ideographs, release 4.1
-    ("\u20000", "\u2a6d6"),  # (UTF16) CJK Unified Ideographs Extension B, release 3.1
-    ("\u2f800", "\u2fa1d"),  # (UTF16) CJK Compatibility Supplement, release 3.1
-    ("\uff00", "\uffef"),  # Full width ASCII, full width of English punctuation,
-    # half width Katakana, half wide half width kana, Korean alphabet
-    ("\u2e80", "\u2eff"),  # CJK Radicals Supplement
-    ("\u3000", "\u303f"),  # CJK punctuation mark
-    ("\u31c0", "\u31ef"),  # CJK stroke
-    ("\u2f00", "\u2fdf"),  # Kangxi Radicals
-    ("\u2ff0", "\u2fff"),  # Chinese character structure
-    ("\u3100", "\u312f"),  # Phonetic symbols
-    ("\u31a0", "\u31bf"),  # Phonetic symbols (Taiwanese and Hakka expansion)
-    ("\ufe10", "\ufe1f"),
-    ("\ufe30", "\ufe4f"),
-    ("\u2600", "\u26ff"),
-    ("\u2700", "\u27bf"),
-    ("\u3200", "\u32ff"),
-    ("\u3300", "\u33ff"),
-)
-
-
-def is_chinese_char(uchar):
-    for start, end in _UCODE_RANGES:
-        if start <= uchar <= end:
-            return True
-    return False
-
-
-def chinese_char_tokenize(line):
-    line = line.strip()
-    line_in_chars = ""
-
-    for char in line:
-        if is_chinese_char(char):
-            line_in_chars += " "
-            line_in_chars += char
-            line_in_chars += " "
-        else:
-            line_in_chars += char
-
-    return line_in_chars
-
-# s = '中国的首都是哪里？1，2，3d回答'
-# print(chinese_char_tokenize(s))
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/noisychannel/rerank_tune.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/noisychannel/rerank_tune.py
deleted file mode 100644
index b2e8b7594a370b2462f77252d54d7ef80e290f7c..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/noisychannel/rerank_tune.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import random
-
-import numpy as np
-from fairseq import options
-
-from examples.noisychannel import rerank, rerank_options
-
-
-def random_search(args):
-    param_values = []
-    tuneable_parameters = ["lenpen", "weight1", "weight2", "weight3"]
-    initial_params = [args.lenpen, args.weight1, args.weight2, args.weight3]
-    for i, elem in enumerate(initial_params):
-        if type(elem) is not list:
-            initial_params[i] = [elem]
-        else:
-            initial_params[i] = elem
-
-    tune_parameters = args.tune_param.copy()
-    for i in range(len(args.tune_param)):
-        assert args.upper_bound[i] >= args.lower_bound[i]
-        index = tuneable_parameters.index(args.tune_param[i])
-        del tuneable_parameters[index]
-        del initial_params[index]
-
-    tune_parameters += tuneable_parameters
-    param_values += initial_params
-    random.seed(args.seed)
-
-    random_params = np.array(
-        [
-            [
-                random.uniform(args.lower_bound[i], args.upper_bound[i])
-                for i in range(len(args.tune_param))
-            ]
-            for k in range(args.num_trials)
-        ]
-    )
-    set_params = np.array(
-        [
-            [initial_params[i][0] for i in range(len(tuneable_parameters))]
-            for k in range(args.num_trials)
-        ]
-    )
-    random_params = np.concatenate((random_params, set_params), 1)
-
-    rerank_args = vars(args).copy()
-    if args.nbest_list:
-        rerank_args["gen_subset"] = "test"
-    else:
-        rerank_args["gen_subset"] = args.tune_subset
-
-    for k in range(len(tune_parameters)):
-        rerank_args[tune_parameters[k]] = list(random_params[:, k])
-
-    if args.share_weights:
-        k = tune_parameters.index("weight2")
-        rerank_args["weight3"] = list(random_params[:, k])
-
-    rerank_args = argparse.Namespace(**rerank_args)
-    best_lenpen, best_weight1, best_weight2, best_weight3, best_score = rerank.rerank(
-        rerank_args
-    )
-    rerank_args = vars(args).copy()
-    rerank_args["lenpen"] = [best_lenpen]
-    rerank_args["weight1"] = [best_weight1]
-    rerank_args["weight2"] = [best_weight2]
-    rerank_args["weight3"] = [best_weight3]
-
-    # write the hypothesis from the valid set from the best trial
-
-    if args.gen_subset != "valid":
-        rerank_args["gen_subset"] = "valid"
-        rerank_args = argparse.Namespace(**rerank_args)
-        rerank.rerank(rerank_args)
-
-    # test with the best hyperparameters on gen subset
-    rerank_args = vars(args).copy()
-    rerank_args["gen_subset"] = args.gen_subset
-    rerank_args["lenpen"] = [best_lenpen]
-    rerank_args["weight1"] = [best_weight1]
-    rerank_args["weight2"] = [best_weight2]
-    rerank_args["weight3"] = [best_weight3]
-    rerank_args = argparse.Namespace(**rerank_args)
-    rerank.rerank(rerank_args)
-
-
-def cli_main():
-    parser = rerank_options.get_tuning_parser()
-    args = options.parse_args_and_arch(parser)
-
-    random_search(args)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/scaling_nmt/README.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/scaling_nmt/README.md
deleted file mode 100644
index 0cc3360c3bbd58fe35a51591db8f081fc8576877..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/scaling_nmt/README.md
+++ /dev/null
@@ -1,114 +0,0 @@
-# Scaling Neural Machine Translation (Ott et al., 2018)
-
-This page includes instructions for reproducing results from the paper [Scaling Neural Machine Translation (Ott et al., 2018)](https://arxiv.org/abs/1806.00187).
-
-## Pre-trained models
-
-Model | Description | Dataset | Download
----|---|---|---
-`transformer.wmt14.en-fr` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2)
-`transformer.wmt16.en-de` | Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2)
-
-## Training a new model on WMT'16 En-De
-
-First download the [preprocessed WMT'16 En-De data provided by Google](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8).
-
-Then:
-
-##### 1. Extract the WMT'16 En-De data
-```bash
-TEXT=wmt16_en_de_bpe32k
-mkdir -p $TEXT
-tar -xzvf wmt16_en_de.tar.gz -C $TEXT
-```
-
-##### 2. Preprocess the dataset with a joined dictionary
-```bash
-fairseq-preprocess \
-    --source-lang en --target-lang de \
-    --trainpref $TEXT/train.tok.clean.bpe.32000 \
-    --validpref $TEXT/newstest2013.tok.bpe.32000 \
-    --testpref $TEXT/newstest2014.tok.bpe.32000 \
-    --destdir data-bin/wmt16_en_de_bpe32k \
-    --nwordssrc 32768 --nwordstgt 32768 \
-    --joined-dictionary \
-    --workers 20
-```
-
-##### 3. Train a model
-```bash
-fairseq-train \
-    data-bin/wmt16_en_de_bpe32k \
-    --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \
-    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
-    --lr 0.0005 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \
-    --dropout 0.3 --weight-decay 0.0 \
-    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
-    --max-tokens 3584 \
-    --fp16
-```
-
-Note that the `--fp16` flag requires you have CUDA 9.1 or greater and a Volta GPU or newer.
-
-***IMPORTANT:*** You will get better performance by training with big batches and
-increasing the learning rate. If you want to train the above model with big batches
-(assuming your machine has 8 GPUs):
-- add `--update-freq 16` to simulate training on 8x16=128 GPUs
-- increase the learning rate; 0.001 works well for big batches
-
-##### 4. Evaluate
-
-Now we can evaluate our trained model.
-
-Note that the original [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
-paper used a couple tricks to achieve better BLEU scores. We use these same tricks in
-the Scaling NMT paper, so it's important to apply them when reproducing our results.
-
-First, use the [average_checkpoints.py](/scripts/average_checkpoints.py) script to
-average the last few checkpoints. Averaging the last 5-10 checkpoints is usually
-good, but you may need to adjust this depending on how long you've trained:
-```bash
-python scripts/average_checkpoints \
-    --inputs /path/to/checkpoints \
-    --num-epoch-checkpoints 10 \
-    --output checkpoint.avg10.pt
-```
-
-Next, generate translations using a beam width of 4 and length penalty of 0.6:
-```bash
-fairseq-generate \
-    data-bin/wmt16_en_de_bpe32k \
-    --path checkpoint.avg10.pt \
-    --beam 4 --lenpen 0.6 --remove-bpe > gen.out
-```
-
-Finally, we apply the ["compound splitting" script](/scripts/compound_split_bleu.sh) to
-add spaces around dashes. For example "Café-Liebhaber" would become three tokens:
-"Café - Liebhaber". This typically results in larger BLEU scores, but it is not
-appropriate to compare these inflated scores to work which does not include this trick.
-This trick was used in the [original AIAYN code](https://github.com/tensorflow/tensor2tensor/blob/fc9335c0203685cbbfe2b30c92db4352d8f60779/tensor2tensor/utils/get_ende_bleu.sh),
-so we used it in the Scaling NMT paper as well. That said, it's strongly advised to
-report [sacrebleu](https://github.com/mjpost/sacrebleu) scores instead.
-
-To compute "compound split" tokenized BLEU (not recommended!):
-```bash
-bash scripts/compound_split_bleu.sh gen.out
-# BLEU4 = 29.29, 60.3/35.0/22.8/15.3 (BP=1.000, ratio=1.004, syslen=64763, reflen=64496)
-```
-
-To compute detokenized BLEU with sacrebleu (preferred):
-```bash
-bash scripts/sacrebleu.sh wmt14/full en de gen.out
-# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt14/full+tok.13a+version.1.4.3 = 28.6 59.3/34.3/22.1/14.9 (BP = 1.000 ratio = 1.016 hyp_len = 63666 ref_len = 62688)
-```
-
-## Citation
-
-```bibtex
-@inproceedings{ott2018scaling,
-  title = {Scaling Neural Machine Translation},
-  author = {Ott, Myle and Edunov, Sergey and Grangier, David and Auli, Michael},
-  booktitle = {Proceedings of the Third Conference on Machine Translation (WMT)},
-  year = 2018,
-}
-```
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/attentions.py b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/attentions.py
deleted file mode 100644
index 62b8c83acbd3150b6d6686f21f3627781107c1ba..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/attentions.py
+++ /dev/null
@@ -1,378 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=None,
-        block_length=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.block_length = block_length
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    window_size=window_size,
-                    p_dropout=p_dropout,
-                    block_length=block_length,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        for i in range(self.n_layers):
-            x = x * x_mask
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class CouplingBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-        sigmoid_scale=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-        self.sigmoid_scale = sigmoid_scale
-
-        start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
-        start = torch.nn.utils.weight_norm(start)
-        self.start = start
-        # Initializing last layer to 0 makes the affine coupling layers
-        # do nothing at first.  It helps to stabilze training.
-        end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
-        end.weight.data.zero_()
-        end.bias.data.zero_()
-        self.end = end
-
-        self.wn = modules.WN(
-            in_channels,
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels,
-            p_dropout,
-        )
-
-    def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
-        b, c, t = x.size()
-        if x_mask is None:
-            x_mask = 1
-        x_0, x_1 = x[:, : self.in_channels // 2], x[:, self.in_channels // 2 :]
-
-        x = self.start(x_0) * x_mask
-        x = self.wn(x, x_mask, g)
-        out = self.end(x)
-
-        z_0 = x_0
-        m = out[:, : self.in_channels // 2, :]
-        logs = out[:, self.in_channels // 2 :, :]
-        if self.sigmoid_scale:
-            logs = torch.log(1e-6 + torch.sigmoid(logs + 2))
-
-        if reverse:
-            z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
-            logdet = None
-        else:
-            z_1 = (m + torch.exp(logs) * x_1) * x_mask
-            logdet = torch.sum(logs * x_mask, [1, 2])
-
-        z = torch.cat([z_0, z_1], 1)
-        return z, logdet
-
-    def store_inverse(self):
-        self.wn.remove_weight_norm()
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        window_size=None,
-        heads_share=True,
-        p_dropout=0.0,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.p_dropout = p_dropout
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels ** -0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        if proximal_init:
-            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
-            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
-            rel_logits = self._relative_position_to_absolute_position(rel_logits)
-            scores_local = rel_logits / math.sqrt(self.k_channels)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores * block_mask + -1e4 * (1 - block_mask)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-
-        self.conv_1 = nn.Conv1d(
-            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.conv_2 = nn.Conv1d(
-            filter_channels, out_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(x * x_mask)
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        return x * x_mask
diff --git a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/src/glow_tts/hifi/utils.py b/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/src/glow_tts/hifi/utils.py
deleted file mode 100644
index 71e9b2c99e053e2d4239074a67d64b834898c348..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Malayalam-TTS/ttsv/src/glow_tts/hifi/utils.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + "????????")
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
diff --git a/spaces/Heathlia/modelscope-text-to-video-synthesis/README.md b/spaces/Heathlia/modelscope-text-to-video-synthesis/README.md
deleted file mode 100644
index a1438994860eec2c0e425a522c06ce7d5c67b48a..0000000000000000000000000000000000000000
--- a/spaces/Heathlia/modelscope-text-to-video-synthesis/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ModelScope Text To Video Synthesis
-emoji: 🚀
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-duplicated_from: damo-vilab/modelscope-text-to-video-synthesis
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py
deleted file mode 100644
index 5b7e1e968564b84c47049c5cc69c9d6b8fafe0e9..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/misc/cut_as.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import torchaudio
-import argparse
-import json
-import pathlib
-
-
-def get_args():
-    parser = argparse.ArgumentParser(
-        "Assuring generated audio have the same length as ground-truth audio")
-    parser.add_argument('--samples_dir', required=True, type=str)
-    parser.add_argument('--out_dir', required=True, type=str)
-    parser.add_argument('--prompts_description', required=True, type=str)
-    return parser.parse_args()
-
-
-def cut(src, tgt, l):
-    x, sr = torchaudio.load(str(src))
-    assert sr == 16_000
-
-    x = x.squeeze()
-    target_frames = int(l * sr)
-
-    flag = 0
-    if target_frames <= x.size(0):
-        x = x[:target_frames]
-        flag = 1
-    else:
-        flag = 0
-    torchaudio.save(str(tgt), x.unsqueeze(0), sr)
-    return flag
-
-
-def main():
-    args = get_args()
-    tgt_dir = pathlib.Path(args.out_dir)
-    tgt_dir.mkdir(exist_ok=True, parents=True)
-
-    total_files, sufficiently_long = 0, 0
-
-    with open(args.prompts_description, 'r') as f:
-        description = json.loads(f.read())
-
-    for src_f in pathlib.Path(args.samples_dir).glob('*.wav'):
-        name_prompt = src_f.with_suffix('').name.split('__')[0]
-
-        assert name_prompt in description, f'Cannot find {name_prompt}!'
-
-        target_length = description[name_prompt][0]
-        tgt_f = tgt_dir / (src_f.name)
-
-        is_long_enough = cut(src_f, tgt_f, target_length)
-        sufficiently_long += is_long_enough
-        if not is_long_enough:
-            print(f'{src_f} is not long enough')
-
-        total_files += 1
-
-    print(
-        f'Total files: {total_files}; sufficiently long: {sufficiently_long}')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/transformer.py b/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/transformer.py
deleted file mode 100644
index 6b330ef1b7f7a506e7e8176f20a0e722b5fd5149..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/transformer.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-
-import torch.nn as nn
-from fairseq.model_parallel.modules import (
-    ModelParallelTransformerDecoderLayer,
-    ModelParallelTransformerEncoderLayer,
-)
-from fairseq.models import register_model
-from fairseq.models.transformer import (
-    TransformerDecoder,
-    TransformerEncoder,
-    TransformerModel,
-)
-
-
-try:
-    from fairseq.model_parallel.megatron.mpu import (
-        copy_to_model_parallel_region,
-        gather_from_model_parallel_region,
-        VocabParallelEmbedding,
-    )
-
-    has_megatron_submodule = True
-except (ImportError, ModuleNotFoundError):
-    has_megatron_submodule = False
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("model_parallel_transformer")
-class ModelParallelTransformerModel(TransformerModel):
-    """
-    Model parallel Transformer model.
-    """
-
-    @classmethod
-    def build_embedding(cls, args, dictionary, embed_dim, path=None):
-        if not has_megatron_submodule:
-            raise ImportError(
-                "\n\nPlease install the megatron submodule:"
-                "\n\n  git submodule update --init "
-                "fairseq/model_parallel/megatron"
-            )
-        dictionary.pad_to_multiple_(args.model_parallel_size * 8)
-        num_embeddings = len(dictionary)
-        padding_idx = dictionary.pad()
-
-        def _vocab_init(tensor, **kwargs):
-            nn.init.normal_(tensor, mean=0, std=num_embeddings ** -0.5)
-            nn.init.constant_(tensor[1], 0)
-
-        emb = VocabParallelEmbedding(
-            num_embeddings, embed_dim, padding_idx, init_method=_vocab_init
-        )
-        # if provided, load from preloaded dictionaries
-        if path:
-            raise NotImplementedError(
-                "Loading of embedding from path is not supported for model parallel"
-            )
-        return emb
-
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return ModelParallelTransformerEncoder(args, src_dict, embed_tokens)
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        return ModelParallelTransformerDecoder(
-            args,
-            tgt_dict,
-            embed_tokens,
-            no_encoder_attn=getattr(args, "no_cross_attention", False),
-        )
-
-
-class ModelParallelTransformerEncoder(TransformerEncoder):
-    """
-    Model parallel Transformer encoder consisting of *args.encoder_layers* layers. Each layer
-    is a :class:`ModelParallelTransformerEncoderLayer`.
-    """
-
-    def __init__(self, args, dictionary, embed_tokens):
-        super().__init__(args, dictionary, embed_tokens)
-
-        if args.no_final_layer_norm:
-            self.layer_norm = None
-
-    def build_encoder_layer(self, args):
-        return ModelParallelTransformerEncoderLayer(args)
-
-
-class ModelParallelTransformerDecoder(TransformerDecoder):
-    """
-    Model Parallel Transformer decoder consisting of *args.decoder_layers* layers. Each layer
-    is a :class:`ModelParallelTransformerDecoderLayer`.
-    """
-
-    def build_decoder_layer(self, args, no_encoder_attn=False):
-        return ModelParallelTransformerDecoderLayer(args, no_encoder_attn)
-
-    def output_layer(self, features, **kwargs):
-        """Project features to the vocabulary size."""
-        if not self.share_input_output_embed:
-            raise NotImplementedError(
-                "Model parallel training currently requires --share-decoder-input-output-embed"
-            )
-
-        features = copy_to_model_parallel_region(features)
-
-        # project back to size of vocabulary
-        x = self.output_projection(features)
-
-        if getattr(self.args, "criterion") != "vocab_parallel_cross_entropy":
-            x = gather_from_model_parallel_region(x).contiguous()
-        return x
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/transformer/transformer_legacy.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/transformer/transformer_legacy.py
deleted file mode 100644
index af9646740a79ce720eeba513e2d994b39509ac49..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/models/transformer/transformer_legacy.py
+++ /dev/null
@@ -1,275 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from fairseq.dataclass.utils import gen_parser_from_dataclass
-from fairseq.models import (
-    register_model,
-    register_model_architecture,
-)
-from fairseq.models.transformer.transformer_config import (
-    TransformerConfig,
-    DEFAULT_MAX_SOURCE_POSITIONS,
-    DEFAULT_MAX_TARGET_POSITIONS,
-    DEFAULT_MIN_PARAMS_TO_WRAP,
-)
-from fairseq.models.transformer.transformer_base import (
-    TransformerModelBase,
-)
-
-
-@register_model("transformer")
-class TransformerModel(TransformerModelBase):
-    """
-    This is the legacy implementation of the transformer model that
-    uses argparse for configuration.
-    """
-
-    @classmethod
-    def hub_models(cls):
-        # fmt: off
-
-        def moses_subword(path):
-            return {
-                'path': path,
-                'tokenizer': 'moses',
-                'bpe': 'subword_nmt',
-            }
-
-        def moses_fastbpe(path):
-            return {
-                'path': path,
-                'tokenizer': 'moses',
-                'bpe': 'fastbpe',
-            }
-
-        def spm(path):
-            return {
-                'path': path,
-                'bpe': 'sentencepiece',
-                'tokenizer': 'space',
-            }
-
-        return {
-            'transformer.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2'),
-            'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2',
-            'transformer.wmt18.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz'),
-            'transformer.wmt19.en-de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz'),
-            'transformer.wmt19.en-ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz'),
-            'transformer.wmt19.de-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz'),
-            'transformer.wmt19.ru-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz'),
-            'transformer.wmt19.en-de.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz'),
-            'transformer.wmt19.en-ru.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz'),
-            'transformer.wmt19.de-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz'),
-            'transformer.wmt19.ru-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz'),
-            'transformer.wmt20.en-ta': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-ta.single.tar.gz'),
-            'transformer.wmt20.en-iu.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.news.single.tar.gz'),
-            'transformer.wmt20.en-iu.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.en-iu.nh.single.tar.gz'),
-            'transformer.wmt20.ta-en': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.ta-en.single.tar.gz'),
-            'transformer.wmt20.iu-en.news': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.news.single.tar.gz'),
-            'transformer.wmt20.iu-en.nh': spm('https://dl.fbaipublicfiles.com/fairseq/models/wmt20.iu-en.nh.single.tar.gz'),
-            'transformer.flores101.mm100.615M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz'),
-            'transformer.flores101.mm100.175M': spm('https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz'),
-        }
-        # fmt: on
-
-    def __init__(self, args, encoder, decoder):
-        cfg = TransformerConfig.from_namespace(args)
-        super().__init__(cfg, encoder, decoder)
-        self.args = args
-
-    @classmethod
-    def add_args(cls, parser):
-        """Add model-specific arguments to the parser."""
-        # we want to build the args recursively in this case.
-        # do not set defaults so that settings defaults from various architectures still works
-        gen_parser_from_dataclass(
-            parser, TransformerConfig(), delete_default=True, with_prefix=""
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present in older models
-        base_architecture(args)
-
-        if args.encoder_layers_to_keep:
-            args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
-        if args.decoder_layers_to_keep:
-            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
-
-        if getattr(args, "max_source_positions", None) is None:
-            args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
-        if getattr(args, "max_target_positions", None) is None:
-            args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
-
-        src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
-
-        if args.share_all_embeddings:
-            if src_dict != tgt_dict:
-                raise ValueError("--share-all-embeddings requires a joined dictionary")
-            if args.encoder_embed_dim != args.decoder_embed_dim:
-                raise ValueError(
-                    "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
-                )
-            if args.decoder_embed_path and (
-                args.decoder_embed_path != args.encoder_embed_path
-            ):
-                raise ValueError(
-                    "--share-all-embeddings not compatible with --decoder-embed-path"
-                )
-            args.share_decoder_input_output_embed = True
-
-        if getattr(args, "offload_activations", False):
-            args.checkpoint_activations = True  # offloading implies checkpointing
-
-        if not args.share_all_embeddings:
-            args.min_params_to_wrap = getattr(
-                args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP
-            )
-        cfg = TransformerConfig.from_namespace(args)
-        return super().build_model(cfg, task)
-
-    @classmethod
-    def build_embedding(cls, args, dictionary, embed_dim, path=None):
-        return super().build_embedding(
-            TransformerConfig.from_namespace(args), dictionary, embed_dim, path
-        )
-
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return super().build_encoder(
-            TransformerConfig.from_namespace(args), src_dict, embed_tokens
-        )
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        return super().build_decoder(
-            TransformerConfig.from_namespace(args), tgt_dict, embed_tokens
-        )
-
-
-# architectures
-
-
-@register_model_architecture("transformer", "transformer_tiny")
-def tiny_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 64)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 64)
-    args.encoder_layers = getattr(args, "encoder_layers", 2)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 2)
-    args.decoder_layers = getattr(args, "decoder_layers", 2)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 2)
-    return base_architecture(args)
-
-
-@register_model_architecture("transformer", "transformer")
-def base_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.no_cross_attention = getattr(args, "no_cross_attention", False)
-    args.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
-    args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
-    args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
-    args.offload_activations = getattr(args, "offload_activations", False)
-    if args.offload_activations:
-        args.checkpoint_activations = True
-    args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None)
-    args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None)
-    args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
-    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0)
-    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-    args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8)
-    args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0)
-
-
-@register_model_architecture("transformer", "transformer_iwslt_de_en")
-def transformer_iwslt_de_en(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    base_architecture(args)
-
-
-@register_model_architecture("transformer", "transformer_wmt_en_de")
-def transformer_wmt_en_de(args):
-    base_architecture(args)
-
-
-# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
-@register_model_architecture("transformer", "transformer_vaswani_wmt_en_de_big")
-def transformer_vaswani_wmt_en_de_big(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
-    args.dropout = getattr(args, "dropout", 0.3)
-    base_architecture(args)
-
-
-@register_model_architecture("transformer", "transformer_vaswani_wmt_en_fr_big")
-def transformer_vaswani_wmt_en_fr_big(args):
-    args.dropout = getattr(args, "dropout", 0.1)
-    transformer_vaswani_wmt_en_de_big(args)
-
-
-@register_model_architecture("transformer", "transformer_wmt_en_de_big")
-def transformer_wmt_en_de_big(args):
-    args.attention_dropout = getattr(args, "attention_dropout", 0.1)
-    transformer_vaswani_wmt_en_de_big(args)
-
-
-# default parameters used in tensor2tensor implementation
-@register_model_architecture("transformer", "transformer_wmt_en_de_big_t2t")
-def transformer_wmt_en_de_big_t2t(args):
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.1)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.1)
-    transformer_vaswani_wmt_en_de_big(args)
diff --git a/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/utils/__init__.py b/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/utils/__init__.py
deleted file mode 100644
index 5277f46157403e47fd830fc519144b97ef69d4ae..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/utils/__init__.py
+++ /dev/null
@@ -1,5 +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.
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/models/base_model.py b/spaces/Iceclear/StableSR/StableSR/basicsr/models/base_model.py
deleted file mode 100644
index fbf8229f59dee86a7f9f95c1d07da785fb5f15b3..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/models/base_model.py
+++ /dev/null
@@ -1,392 +0,0 @@
-import os
-import time
-import torch
-from collections import OrderedDict
-from copy import deepcopy
-from torch.nn.parallel import DataParallel, DistributedDataParallel
-
-from basicsr.models import lr_scheduler as lr_scheduler
-from basicsr.utils import get_root_logger
-from basicsr.utils.dist_util import master_only
-
-
-class BaseModel():
-    """Base model."""
-
-    def __init__(self, opt):
-        self.opt = opt
-        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
-        self.is_train = opt['is_train']
-        self.schedulers = []
-        self.optimizers = []
-
-    def feed_data(self, data):
-        pass
-
-    def optimize_parameters(self):
-        pass
-
-    def get_current_visuals(self):
-        pass
-
-    def save(self, epoch, current_iter):
-        """Save networks and training state."""
-        pass
-
-    def validation(self, dataloader, current_iter, tb_logger, save_img=False):
-        """Validation function.
-
-        Args:
-            dataloader (torch.utils.data.DataLoader): Validation dataloader.
-            current_iter (int): Current iteration.
-            tb_logger (tensorboard logger): Tensorboard logger.
-            save_img (bool): Whether to save images. Default: False.
-        """
-        if self.opt['dist']:
-            self.dist_validation(dataloader, current_iter, tb_logger, save_img)
-        else:
-            self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
-
-    def _initialize_best_metric_results(self, dataset_name):
-        """Initialize the best metric results dict for recording the best metric value and iteration."""
-        if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results:
-            return
-        elif not hasattr(self, 'best_metric_results'):
-            self.best_metric_results = dict()
-
-        # add a dataset record
-        record = dict()
-        for metric, content in self.opt['val']['metrics'].items():
-            better = content.get('better', 'higher')
-            init_val = float('-inf') if better == 'higher' else float('inf')
-            record[metric] = dict(better=better, val=init_val, iter=-1)
-        self.best_metric_results[dataset_name] = record
-
-    def _update_best_metric_result(self, dataset_name, metric, val, current_iter):
-        if self.best_metric_results[dataset_name][metric]['better'] == 'higher':
-            if val >= self.best_metric_results[dataset_name][metric]['val']:
-                self.best_metric_results[dataset_name][metric]['val'] = val
-                self.best_metric_results[dataset_name][metric]['iter'] = current_iter
-        else:
-            if val <= self.best_metric_results[dataset_name][metric]['val']:
-                self.best_metric_results[dataset_name][metric]['val'] = val
-                self.best_metric_results[dataset_name][metric]['iter'] = current_iter
-
-    def model_ema(self, decay=0.999):
-        net_g = self.get_bare_model(self.net_g)
-
-        net_g_params = dict(net_g.named_parameters())
-        net_g_ema_params = dict(self.net_g_ema.named_parameters())
-
-        for k in net_g_ema_params.keys():
-            net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay)
-
-    def get_current_log(self):
-        return self.log_dict
-
-    def model_to_device(self, net):
-        """Model to device. It also warps models with DistributedDataParallel
-        or DataParallel.
-
-        Args:
-            net (nn.Module)
-        """
-        net = net.to(self.device)
-        if self.opt['dist']:
-            find_unused_parameters = self.opt.get('find_unused_parameters', False)
-            net = DistributedDataParallel(
-                net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters)
-        elif self.opt['num_gpu'] > 1:
-            net = DataParallel(net)
-        return net
-
-    def get_optimizer(self, optim_type, params, lr, **kwargs):
-        if optim_type == 'Adam':
-            optimizer = torch.optim.Adam(params, lr, **kwargs)
-        elif optim_type == 'AdamW':
-            optimizer = torch.optim.AdamW(params, lr, **kwargs)
-        elif optim_type == 'Adamax':
-            optimizer = torch.optim.Adamax(params, lr, **kwargs)
-        elif optim_type == 'SGD':
-            optimizer = torch.optim.SGD(params, lr, **kwargs)
-        elif optim_type == 'ASGD':
-            optimizer = torch.optim.ASGD(params, lr, **kwargs)
-        elif optim_type == 'RMSprop':
-            optimizer = torch.optim.RMSprop(params, lr, **kwargs)
-        elif optim_type == 'Rprop':
-            optimizer = torch.optim.Rprop(params, lr, **kwargs)
-        else:
-            raise NotImplementedError(f'optimizer {optim_type} is not supported yet.')
-        return optimizer
-
-    def setup_schedulers(self):
-        """Set up schedulers."""
-        train_opt = self.opt['train']
-        scheduler_type = train_opt['scheduler'].pop('type')
-        if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
-            for optimizer in self.optimizers:
-                self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler']))
-        elif scheduler_type == 'CosineAnnealingRestartLR':
-            for optimizer in self.optimizers:
-                self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler']))
-        else:
-            raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.')
-
-    def get_bare_model(self, net):
-        """Get bare model, especially under wrapping with
-        DistributedDataParallel or DataParallel.
-        """
-        if isinstance(net, (DataParallel, DistributedDataParallel)):
-            net = net.module
-        return net
-
-    @master_only
-    def print_network(self, net):
-        """Print the str and parameter number of a network.
-
-        Args:
-            net (nn.Module)
-        """
-        if isinstance(net, (DataParallel, DistributedDataParallel)):
-            net_cls_str = f'{net.__class__.__name__} - {net.module.__class__.__name__}'
-        else:
-            net_cls_str = f'{net.__class__.__name__}'
-
-        net = self.get_bare_model(net)
-        net_str = str(net)
-        net_params = sum(map(lambda x: x.numel(), net.parameters()))
-
-        logger = get_root_logger()
-        logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}')
-        logger.info(net_str)
-
-    def _set_lr(self, lr_groups_l):
-        """Set learning rate for warm-up.
-
-        Args:
-            lr_groups_l (list): List for lr_groups, each for an optimizer.
-        """
-        for optimizer, lr_groups in zip(self.optimizers, lr_groups_l):
-            for param_group, lr in zip(optimizer.param_groups, lr_groups):
-                param_group['lr'] = lr
-
-    def _get_init_lr(self):
-        """Get the initial lr, which is set by the scheduler.
-        """
-        init_lr_groups_l = []
-        for optimizer in self.optimizers:
-            init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups])
-        return init_lr_groups_l
-
-    def update_learning_rate(self, current_iter, warmup_iter=-1):
-        """Update learning rate.
-
-        Args:
-            current_iter (int): Current iteration.
-            warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up.
-                Default： -1.
-        """
-        if current_iter > 1:
-            for scheduler in self.schedulers:
-                scheduler.step()
-        # set up warm-up learning rate
-        if current_iter < warmup_iter:
-            # get initial lr for each group
-            init_lr_g_l = self._get_init_lr()
-            # modify warming-up learning rates
-            # currently only support linearly warm up
-            warm_up_lr_l = []
-            for init_lr_g in init_lr_g_l:
-                warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g])
-            # set learning rate
-            self._set_lr(warm_up_lr_l)
-
-    def get_current_learning_rate(self):
-        return [param_group['lr'] for param_group in self.optimizers[0].param_groups]
-
-    @master_only
-    def save_network(self, net, net_label, current_iter, param_key='params'):
-        """Save networks.
-
-        Args:
-            net (nn.Module | list[nn.Module]): Network(s) to be saved.
-            net_label (str): Network label.
-            current_iter (int): Current iter number.
-            param_key (str | list[str]): The parameter key(s) to save network.
-                Default: 'params'.
-        """
-        if current_iter == -1:
-            current_iter = 'latest'
-        save_filename = f'{net_label}_{current_iter}.pth'
-        save_path = os.path.join(self.opt['path']['models'], save_filename)
-
-        net = net if isinstance(net, list) else [net]
-        param_key = param_key if isinstance(param_key, list) else [param_key]
-        assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.'
-
-        save_dict = {}
-        for net_, param_key_ in zip(net, param_key):
-            net_ = self.get_bare_model(net_)
-            state_dict = net_.state_dict()
-            for key, param in state_dict.items():
-                if key.startswith('module.'):  # remove unnecessary 'module.'
-                    key = key[7:]
-                state_dict[key] = param.cpu()
-            save_dict[param_key_] = state_dict
-
-        # avoid occasional writing errors
-        retry = 3
-        while retry > 0:
-            try:
-                torch.save(save_dict, save_path)
-            except Exception as e:
-                logger = get_root_logger()
-                logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}')
-                time.sleep(1)
-            else:
-                break
-            finally:
-                retry -= 1
-        if retry == 0:
-            logger.warning(f'Still cannot save {save_path}. Just ignore it.')
-            # raise IOError(f'Cannot save {save_path}.')
-
-    def _print_different_keys_loading(self, crt_net, load_net, strict=True):
-        """Print keys with different name or different size when loading models.
-
-        1. Print keys with different names.
-        2. If strict=False, print the same key but with different tensor size.
-            It also ignore these keys with different sizes (not load).
-
-        Args:
-            crt_net (torch model): Current network.
-            load_net (dict): Loaded network.
-            strict (bool): Whether strictly loaded. Default: True.
-        """
-        crt_net = self.get_bare_model(crt_net)
-        crt_net = crt_net.state_dict()
-        crt_net_keys = set(crt_net.keys())
-        load_net_keys = set(load_net.keys())
-
-        logger = get_root_logger()
-        if crt_net_keys != load_net_keys:
-            logger.warning('Current net - loaded net:')
-            for v in sorted(list(crt_net_keys - load_net_keys)):
-                logger.warning(f'  {v}')
-            logger.warning('Loaded net - current net:')
-            for v in sorted(list(load_net_keys - crt_net_keys)):
-                logger.warning(f'  {v}')
-
-        # check the size for the same keys
-        if not strict:
-            common_keys = crt_net_keys & load_net_keys
-            for k in common_keys:
-                if crt_net[k].size() != load_net[k].size():
-                    logger.warning(f'Size different, ignore [{k}]: crt_net: '
-                                   f'{crt_net[k].shape}; load_net: {load_net[k].shape}')
-                    load_net[k + '.ignore'] = load_net.pop(k)
-
-    def load_network(self, net, load_path, strict=True, param_key='params'):
-        """Load network.
-
-        Args:
-            load_path (str): The path of networks to be loaded.
-            net (nn.Module): Network.
-            strict (bool): Whether strictly loaded.
-            param_key (str): The parameter key of loaded network. If set to
-                None, use the root 'path'.
-                Default: 'params'.
-        """
-        logger = get_root_logger()
-        net = self.get_bare_model(net)
-        load_net = torch.load(load_path, map_location=lambda storage, loc: storage)
-        if param_key is not None:
-            if param_key not in load_net and 'params' in load_net:
-                param_key = 'params'
-                logger.info('Loading: params_ema does not exist, use params.')
-            load_net = load_net[param_key]
-        logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].')
-        # remove unnecessary 'module.'
-        for k, v in deepcopy(load_net).items():
-            if k.startswith('module.'):
-                load_net[k[7:]] = v
-                load_net.pop(k)
-        self._print_different_keys_loading(net, load_net, strict)
-        net.load_state_dict(load_net, strict=strict)
-
-    @master_only
-    def save_training_state(self, epoch, current_iter):
-        """Save training states during training, which will be used for
-        resuming.
-
-        Args:
-            epoch (int): Current epoch.
-            current_iter (int): Current iteration.
-        """
-        if current_iter != -1:
-            state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []}
-            for o in self.optimizers:
-                state['optimizers'].append(o.state_dict())
-            for s in self.schedulers:
-                state['schedulers'].append(s.state_dict())
-            save_filename = f'{current_iter}.state'
-            save_path = os.path.join(self.opt['path']['training_states'], save_filename)
-
-            # avoid occasional writing errors
-            retry = 3
-            while retry > 0:
-                try:
-                    torch.save(state, save_path)
-                except Exception as e:
-                    logger = get_root_logger()
-                    logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}')
-                    time.sleep(1)
-                else:
-                    break
-                finally:
-                    retry -= 1
-            if retry == 0:
-                logger.warning(f'Still cannot save {save_path}. Just ignore it.')
-                # raise IOError(f'Cannot save {save_path}.')
-
-    def resume_training(self, resume_state):
-        """Reload the optimizers and schedulers for resumed training.
-
-        Args:
-            resume_state (dict): Resume state.
-        """
-        resume_optimizers = resume_state['optimizers']
-        resume_schedulers = resume_state['schedulers']
-        assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers'
-        assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers'
-        for i, o in enumerate(resume_optimizers):
-            self.optimizers[i].load_state_dict(o)
-        for i, s in enumerate(resume_schedulers):
-            self.schedulers[i].load_state_dict(s)
-
-    def reduce_loss_dict(self, loss_dict):
-        """reduce loss dict.
-
-        In distributed training, it averages the losses among different GPUs .
-
-        Args:
-            loss_dict (OrderedDict): Loss dict.
-        """
-        with torch.no_grad():
-            if self.opt['dist']:
-                keys = []
-                losses = []
-                for name, value in loss_dict.items():
-                    keys.append(name)
-                    losses.append(value)
-                losses = torch.stack(losses, 0)
-                torch.distributed.reduce(losses, dst=0)
-                if self.opt['rank'] == 0:
-                    losses /= self.opt['world_size']
-                loss_dict = {key: loss for key, loss in zip(keys, losses)}
-
-            log_dict = OrderedDict()
-            for name, value in loss_dict.items():
-                log_dict[name] = value.mean().item()
-
-            return log_dict
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/sample_from_dataset.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/sample_from_dataset.py
deleted file mode 100644
index 31593b3212454dd0b6f74a39195a34b489df20a1..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/sample_from_dataset.py
+++ /dev/null
@@ -1,87 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import numpy as np
-import tqdm
-from skimage import io
-from skimage.segmentation import mark_boundaries
-
-from saicinpainting.evaluation.data import InpaintingDataset
-from saicinpainting.evaluation.vis import save_item_for_vis
-
-def save_mask_for_sidebyside(item, out_file):
-    mask = item['mask']# > 0.5
-    if mask.ndim == 3:
-        mask = mask[0]
-    mask = np.clip(mask * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, mask)
-
-def save_img_for_sidebyside(item, out_file):
-    img = np.transpose(item['image'], (1, 2, 0))
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
-
-def save_masked_img_for_sidebyside(item, out_file):
-    mask = item['mask']
-    img  = item['image']
-
-    img = (1-mask) * img + mask
-    img = np.transpose(img, (1, 2, 0))
-
-    img = np.clip(img * 255, 0, 255).astype('uint8')
-    io.imsave(out_file, img)
-
-def main(args):
-    dataset = InpaintingDataset(args.datadir, img_suffix='.png')
-
-    area_bins = np.linspace(0, 1, args.area_bins + 1)
-
-    heights = []
-    widths = []
-    image_areas = []
-    hole_areas = []
-    hole_area_percents = []
-    area_bins_count = np.zeros(args.area_bins)
-    area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)]
-
-    bin2i = [[] for _ in range(args.area_bins)]
-
-    for i, item in enumerate(tqdm.tqdm(dataset)):
-        h, w = item['image'].shape[1:]
-        heights.append(h)
-        widths.append(w)
-        full_area = h * w
-        image_areas.append(full_area)
-        hole_area = (item['mask'] == 1).sum()
-        hole_areas.append(hole_area)
-        hole_percent = hole_area / full_area
-        hole_area_percents.append(hole_percent)
-        bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1)
-        area_bins_count[bin_i] += 1
-        bin2i[bin_i].append(i)
-
-    os.makedirs(args.outdir, exist_ok=True)
-   
-    for bin_i in range(args.area_bins):
-        bindir = os.path.join(args.outdir, area_bin_titles[bin_i])
-        os.makedirs(bindir, exist_ok=True)
-        bin_idx = bin2i[bin_i]
-        for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False):
-            item = dataset[sample_i]
-            path = os.path.join(bindir, dataset.img_filenames[sample_i].split('/')[-1])
-            save_masked_img_for_sidebyside(item, path)
-           
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('--datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('--outdir', type=str, help='Where to put results')
-    aparser.add_argument('--samples-n', type=int, default=10,
-                         help='Number of sample images with masks to copy for visualization for each area bin')
-    aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have')
-
-    main(aparser.parse_args())
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py
deleted file mode 100644
index 18318965335b37cc671004a6aceda3229dc7b477..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/models/ade20k/segm_lib/nn/modules/batchnorm.py
+++ /dev/null
@@ -1,329 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
-    """sum over the first and last dimention"""
-    return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
-    """add new dementions at the front and the tail"""
-    return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
-    def __init__(self, num_features, eps=1e-5, momentum=0.001, affine=True):
-        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
-        self._sync_master = SyncMaster(self._data_parallel_master)
-
-        self._is_parallel = False
-        self._parallel_id = None
-        self._slave_pipe = None
-
-        # customed batch norm statistics
-        self._moving_average_fraction = 1. - momentum
-        self.register_buffer('_tmp_running_mean', torch.zeros(self.num_features))
-        self.register_buffer('_tmp_running_var', torch.ones(self.num_features))
-        self.register_buffer('_running_iter', torch.ones(1))
-        self._tmp_running_mean = self.running_mean.clone() * self._running_iter
-        self._tmp_running_var = self.running_var.clone() * self._running_iter
-
-    def forward(self, input):
-        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
-        if not (self._is_parallel and self.training):
-            return F.batch_norm(
-                input, self.running_mean, self.running_var, self.weight, self.bias,
-                self.training, self.momentum, self.eps)
-
-        # Resize the input to (B, C, -1).
-        input_shape = input.size()
-        input = input.view(input.size(0), self.num_features, -1)
-
-        # Compute the sum and square-sum.
-        sum_size = input.size(0) * input.size(2)
-        input_sum = _sum_ft(input)
-        input_ssum = _sum_ft(input ** 2)
-
-        # Reduce-and-broadcast the statistics.
-        if self._parallel_id == 0:
-            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
-        else:
-            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
-        # Compute the output.
-        if self.affine:
-            # MJY:: Fuse the multiplication for speed.
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
-        else:
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
-        # Reshape it.
-        return output.view(input_shape)
-
-    def __data_parallel_replicate__(self, ctx, copy_id):
-        self._is_parallel = True
-        self._parallel_id = copy_id
-
-        # parallel_id == 0 means master device.
-        if self._parallel_id == 0:
-            ctx.sync_master = self._sync_master
-        else:
-            self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
-    def _data_parallel_master(self, intermediates):
-        """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
-        to_reduce = [i[1][:2] for i in intermediates]
-        to_reduce = [j for i in to_reduce for j in i]  # flatten
-        target_gpus = [i[1].sum.get_device() for i in intermediates]
-
-        sum_size = sum([i[1].sum_size for i in intermediates])
-        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
-
-        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
-        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
-        outputs = []
-        for i, rec in enumerate(intermediates):
-            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
-        return outputs
-
-    def _add_weighted(self, dest, delta, alpha=1, beta=1, bias=0):
-        """return *dest* by `dest := dest*alpha + delta*beta + bias`"""
-        return dest * alpha + delta * beta + bias
-
-    def _compute_mean_std(self, sum_, ssum, size):
-        """Compute the mean and standard-deviation with sum and square-sum. This method
-        also maintains the moving average on the master device."""
-        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
-        mean = sum_ / size
-        sumvar = ssum - sum_ * mean
-        unbias_var = sumvar / (size - 1)
-        bias_var = sumvar / size
-
-        self._tmp_running_mean = self._add_weighted(self._tmp_running_mean, mean.data, alpha=self._moving_average_fraction)
-        self._tmp_running_var = self._add_weighted(self._tmp_running_var, unbias_var.data, alpha=self._moving_average_fraction)
-        self._running_iter = self._add_weighted(self._running_iter, 1, alpha=self._moving_average_fraction)
-
-        self.running_mean = self._tmp_running_mean / self._running_iter
-        self.running_var = self._tmp_running_var / self._running_iter
-
-        return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
-    r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
-    mini-batch.
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm1d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of size
-            `batch_size x num_features [x width]`
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C)` or :math:`(N, C, L)`
-        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 2 and input.dim() != 3:
-            raise ValueError('expected 2D or 3D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
-    of 3d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm2d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, H, W)`
-        - Output: :math:`(N, C, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 4:
-            raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
-    of 4d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm3d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
-    or Spatio-temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x depth x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, D, H, W)`
-        - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 5:
-            raise ValueError('expected 5D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/spaces/Jamel887/Rvc-tio887/app.py b/spaces/Jamel887/Rvc-tio887/app.py
deleted file mode 100644
index 181452d38f5bcfc7b51c66b304cc766c7f74d1f8..0000000000000000000000000000000000000000
--- a/spaces/Jamel887/Rvc-tio887/app.py
+++ /dev/null
@@ -1,680 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-spaces = os.getenv("SYSTEM") == "spaces"
-force_support = None
-if config.unsupported is False:
-    if config.device == "mps" or config.device == "cpu":
-        force_support = False
-else:
-    force_support = True
-
-audio_mode = []
-f0method_mode = []
-f0method_info = ""
-
-if force_support is False or spaces is True:
-    if spaces is True:
-        audio_mode = ["Upload audio", "TTS Audio"]
-    else:
-        audio_mode = ["Input path", "Upload audio", "TTS Audio"]
-    f0method_mode = ["pm", "harvest"]
-    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)"
-else:
-    audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
-    f0method_mode = ["pm", "harvest", "crepe"]
-    f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"
-
-if os.path.isfile("rmvpe.pt"):
-    f0method_mode.insert(2, "rmvpe")
-
-def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
-    def vc_fn(
-        vc_audio_mode,
-        vc_input, 
-        vc_upload,
-        tts_text,
-        tts_voice,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        filter_radius,
-        resample_sr,
-        rms_mix_rate,
-        protect,
-    ):
-        try:
-            logs = []
-            print(f"Converting using {model_name}...")
-            logs.append(f"Converting using {model_name}...")
-            yield "\n".join(logs), None
-            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
-                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
-            elif vc_audio_mode == "Upload audio":
-                if vc_upload is None:
-                    return "You need to upload an audio", None
-                sampling_rate, audio = vc_upload
-                duration = audio.shape[0] / sampling_rate
-                if duration > 20 and spaces:
-                    return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
-                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                if len(audio.shape) > 1:
-                    audio = librosa.to_mono(audio.transpose(1, 0))
-                if sampling_rate != 16000:
-                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            elif vc_audio_mode == "TTS Audio":
-                if len(tts_text) > 100 and spaces:
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-                vc_input = "tts.mp3"
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                vc_input,
-                times,
-                f0_up_key,
-                f0_method,
-                file_index,
-                # file_big_npy,
-                index_rate,
-                if_f0,
-                filter_radius,
-                tgt_sr,
-                resample_sr,
-                rms_mix_rate,
-                version,
-                protect,
-                f0_file=None,
-            )
-            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            print(f"{model_name} | {info}")
-            logs.append(f"Successfully Convert {model_name}\n{info}")
-            yield "\n".join(logs), (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            yield info, None
-    return vc_fn
-
-def load_model():
-    categories = []
-    if os.path.isfile("weights/folder_info.json"):
-        with open("weights/folder_info.json", "r", encoding="utf-8") as f:
-            folder_info = json.load(f)
-        for category_name, category_info in folder_info.items():
-            if not category_info['enable']:
-                continue
-            category_title = category_info['title']
-            category_folder = category_info['folder_path']
-            description = category_info['description']
-            models = []
-            with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
-                models_info = json.load(f)
-            for character_name, info in models_info.items():
-                if not info['enable']:
-                    continue
-                model_title = info['title']
-                model_name = info['model_path']
-                model_author = info.get("author", None)
-                model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
-                model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
-                cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
-                tgt_sr = cpt["config"][-1]
-                cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-                if_f0 = cpt.get("f0", 1)
-                version = cpt.get("version", "v1")
-                if version == "v1":
-                    if if_f0 == 1:
-                        net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-                    else:
-                        net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-                    model_version = "V1"
-                elif version == "v2":
-                    if if_f0 == 1:
-                        net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-                    else:
-                        net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-                    model_version = "V2"
-                del net_g.enc_q
-                print(net_g.load_state_dict(cpt["weight"], strict=False))
-                net_g.eval().to(config.device)
-                if config.is_half:
-                    net_g = net_g.half()
-                else:
-                    net_g = net_g.float()
-                vc = VC(tgt_sr, config)
-                print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
-                models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
-            categories.append([category_title, category_folder, description, models])
-    else:
-        categories = []
-    return categories
-
-def download_audio(url, audio_provider):
-    logs = []
-    if url == "":
-        raise gr.Error("URL Required!")
-        return "URL Required"
-    if not os.path.exists("dl_audio"):
-        os.mkdir("dl_audio")
-    if audio_provider == "Youtube":
-        logs.append("Downloading the audio...")
-        yield None, "\n".join(logs)
-        ydl_opts = {
-            'noplaylist': True,
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": 'dl_audio/audio',
-        }
-        audio_path = "dl_audio/audio.wav"
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            ydl.download([url])
-        logs.append("Download Complete.")
-        yield audio_path, "\n".join(logs)
-
-def cut_vocal_and_inst(split_model):
-    logs = []
-    logs.append("Starting the audio splitting process...")
-    yield "\n".join(logs), None, None, None, None
-    command = f"demucs --two-stems=vocals -n {split_model} dl_audio/audio.wav -o output"
-    result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
-    for line in result.stdout:
-        logs.append(line)
-        yield "\n".join(logs), None, None, None, None
-    print(result.stdout)
-    vocal = f"output/{split_model}/audio/vocals.wav"
-    inst = f"output/{split_model}/audio/no_vocals.wav"
-    logs.append("Audio splitting complete.")
-    yield "\n".join(logs), vocal, inst, vocal
-
-def combine_vocal_and_inst(audio_data, vocal_volume, inst_volume, split_model):
-    if not os.path.exists("output/result"):
-        os.mkdir("output/result")
-    vocal_path = "output/result/output.wav"
-    output_path = "output/result/combine.mp3"
-    inst_path = f"output/{split_model}/audio/no_vocals.wav"
-    with wave.open(vocal_path, "w") as wave_file:
-        wave_file.setnchannels(1) 
-        wave_file.setsampwidth(2)
-        wave_file.setframerate(audio_data[0])
-        wave_file.writeframes(audio_data[1].tobytes())
-    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [0:a]volume={inst_volume}[i];[1:a]volume={vocal_volume}[v];[i][v]amix=inputs=2:duration=longest[a] -map [a] -b:a 320k -c:a libmp3lame {output_path}'
-    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-    print(result.stdout.decode())
-    return output_path
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_audio_mode(vc_audio_mode):
-    if vc_audio_mode == "Input path":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=True),
-            gr.Checkbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Button.update(visible=False),
-            # Splitter
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Upload audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Checkbox.update(visible=True),
-            gr.Audio.update(visible=True),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Button.update(visible=False),
-            # Splitter
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Youtube":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Checkbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=True),
-            gr.Textbox.update(visible=True),
-            gr.Textbox.update(visible=True),
-            gr.Button.update(visible=True),
-            # Splitter
-            gr.Dropdown.update(visible=True),
-            gr.Textbox.update(visible=True),
-            gr.Button.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Button.update(visible=True),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "TTS Audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Checkbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Button.update(visible=False),
-            # Splitter
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=True),
-            gr.Dropdown.update(visible=True)
-        )
-
-def use_microphone(microphone):
-    if microphone == True:
-        return gr.Audio.update(source="microphone")
-    else:
-        return gr.Audio.update(source="upload")
-
-if __name__ == '__main__':
-    load_hubert()
-    categories = load_model()
-    tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "<div align='center'>\n\n"+
-            "# RVC V2 MODELS GENSHIN IMPACT\n\n"+
-            "### Recommended to use Google Colab to use other character and feature.\n\n"+
-            "#### All of this voice samples are taken from the game Genshin Impact, and all voice credits belong to hoyoverse.\n\n"+
-            "[![Colab](https://img.shields.io/badge/Colab-RVC%20Genshin%20Impact-blue?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/drive/1EGHCk7wluqMX2krZhPI13Vhs21e07kOv)\n\n"+
-            "</div>\n\n"+
-            "[![Repository](https://img.shields.io/badge/Github-Multi%20Model%20RVC%20Inference-blue?style=for-the-badge&logo=github)](https://github.com/ArkanDash/Multi-Model-RVC-Inference)\n\n"+
-            "</div>"
-        )
-        if categories == []:
-            gr.Markdown(
-                "<div align='center'>\n\n"+
-                "## No model found, please add the model into weights folder\n\n"+
-                "</div>"
-            )
-        for (folder_title, folder, description, models) in categories:
-            with gr.TabItem(folder_title):
-                if description:
-                    gr.Markdown(f"### <center> {description}")
-                with gr.Tabs():
-                    if not models:
-                        gr.Markdown("# <center> No Model Loaded.")
-                        gr.Markdown("## <center> Please add the model or fix your model path.")
-                        continue
-                    for (name, title, author, cover, model_version, vc_fn) in models:
-                        with gr.TabItem(name):
-                            with gr.Row():
-                                gr.Markdown(
-                                    '<div align="center">'
-                                    f'<div>{title}</div>\n'+
-                                    f'<div>RVC {model_version} Model</div>\n'+
-                                    (f'<div>Model author: {author}</div>' if author else "")+
-                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                                    '</div>'
-                                )
-                            with gr.Row():
-                                if spaces is False:
-                                    with gr.TabItem("Input"):
-                                        with gr.Row():
-                                            with gr.Column():
-                                                vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
-                                                # Input
-                                                vc_input = gr.Textbox(label="Input audio path", visible=False)
-                                                # Upload
-                                                vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
-                                                vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
-                                                # Youtube
-                                                vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
-                                                vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
-                                                vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
-                                                vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
-                                                vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
-                                                # TTS
-                                                tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
-                                                tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                                            with gr.Column():
-                                                vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                                                vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
-                                                vc_split = gr.Button("Split Audio", variant="primary", visible=False)
-                                                vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
-                                                vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
-                                    with gr.TabItem("Convert"):
-                                        with gr.Row():
-                                            with gr.Column():
-                                                vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                                                f0method0 = gr.Radio(
-                                                    label="Pitch extraction algorithm",
-                                                    info=f0method_info,
-                                                    choices=f0method_mode,
-                                                    value="pm",
-                                                    interactive=True
-                                                )
-                                                index_rate1 = gr.Slider(
-                                                    minimum=0,
-                                                    maximum=1,
-                                                    label="Retrieval feature ratio",
-                                                    info="(Default: 0.7)",
-                                                    value=0.7,
-                                                    interactive=True,
-                                                )
-                                                filter_radius0 = gr.Slider(
-                                                    minimum=0,
-                                                    maximum=7,
-                                                    label="Apply Median Filtering",
-                                                    info="The value represents the filter radius and can reduce breathiness.",
-                                                    value=3,
-                                                    step=1,
-                                                    interactive=True,
-                                                )
-                                                resample_sr0 = gr.Slider(
-                                                    minimum=0,
-                                                    maximum=48000,
-                                                    label="Resample the output audio",
-                                                    info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
-                                                    value=0,
-                                                    step=1,
-                                                    interactive=True,
-                                                )
-                                                rms_mix_rate0 = gr.Slider(
-                                                    minimum=0,
-                                                    maximum=1,
-                                                    label="Volume Envelope",
-                                                    info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
-                                                    value=1,
-                                                    interactive=True,
-                                                )
-                                                protect0 = gr.Slider(
-                                                    minimum=0,
-                                                    maximum=0.5,
-                                                    label="Voice Protection",
-                                                    info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
-                                                    value=0.5,
-                                                    step=0.01,
-                                                    interactive=True,
-                                                )
-                                            with gr.Column():
-                                                vc_log = gr.Textbox(label="Output Information", interactive=False)
-                                                vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                                vc_convert = gr.Button("Convert", variant="primary")
-                                                vc_vocal_volume = gr.Slider(
-                                                    minimum=0,
-                                                    maximum=10,
-                                                    label="Vocal volume",
-                                                    value=1,
-                                                    interactive=True,
-                                                    step=1,
-                                                    info="Adjust vocal volume (Default: 1}",
-                                                    visible=False
-                                                )
-                                                vc_inst_volume = gr.Slider(
-                                                    minimum=0,
-                                                    maximum=10,
-                                                    label="Instrument volume",
-                                                    value=1,
-                                                    interactive=True,
-                                                    step=1,
-                                                    info="Adjust instrument volume (Default: 1}",
-                                                    visible=False
-                                                )
-                                                vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
-                                                vc_combine =  gr.Button("Combine",variant="primary", visible=False)
-                                else:
-                                    with gr.Column():
-                                        vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
-                                        # Input
-                                        vc_input = gr.Textbox(label="Input audio path", visible=False)
-                                        # Upload
-                                        vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True)
-                                        vc_upload = gr.Audio(label="Upload audio file", source="upload", visible=True, interactive=True)
-                                        # Youtube
-                                        vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
-                                        vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
-                                        vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
-                                        vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
-                                        vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
-                                        # Splitter
-                                        vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                                        vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
-                                        vc_split = gr.Button("Split Audio", variant="primary", visible=False)
-                                        vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
-                                        vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
-                                        # TTS
-                                        tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
-                                        tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                                    with gr.Column():
-                                        vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                                        f0method0 = gr.Radio(
-                                            label="Pitch extraction algorithm",
-                                            info=f0method_info,
-                                            choices=f0method_mode,
-                                            value="pm",
-                                            interactive=True
-                                        )
-                                        index_rate1 = gr.Slider(
-                                            minimum=0,
-                                            maximum=1,
-                                            label="Retrieval feature ratio",
-                                            info="(Default: 0.7)",
-                                            value=0.7,
-                                            interactive=True,
-                                        )
-                                        filter_radius0 = gr.Slider(
-                                            minimum=0,
-                                            maximum=7,
-                                            label="Apply Median Filtering",
-                                            info="The value represents the filter radius and can reduce breathiness.",
-                                            value=3,
-                                            step=1,
-                                            interactive=True,
-                                        )
-                                        resample_sr0 = gr.Slider(
-                                            minimum=0,
-                                            maximum=48000,
-                                            label="Resample the output audio",
-                                            info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
-                                            value=0,
-                                            step=1,
-                                            interactive=True,
-                                        )
-                                        rms_mix_rate0 = gr.Slider(
-                                            minimum=0,
-                                            maximum=1,
-                                            label="Volume Envelope",
-                                            info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
-                                            value=1,
-                                            interactive=True,
-                                        )
-                                        protect0 = gr.Slider(
-                                            minimum=0,
-                                            maximum=0.5,
-                                            label="Voice Protection",
-                                            info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
-                                            value=0.5,
-                                            step=0.01,
-                                            interactive=True,
-                                        )
-                                    with gr.Column():
-                                        vc_log = gr.Textbox(label="Output Information", interactive=False)
-                                        vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                        vc_convert = gr.Button("Convert", variant="primary")
-                                        vc_vocal_volume = gr.Slider(
-                                            minimum=0,
-                                            maximum=10,
-                                            label="Vocal volume",
-                                            value=1,
-                                            interactive=True,
-                                            step=1,
-                                            info="Adjust vocal volume (Default: 1}",
-                                            visible=False
-                                        )
-                                        vc_inst_volume = gr.Slider(
-                                            minimum=0,
-                                            maximum=10,
-                                            label="Instrument volume",
-                                            value=1,
-                                            interactive=True,
-                                            step=1,
-                                            info="Adjust instrument volume (Default: 1}",
-                                            visible=False
-                                        )
-                                        vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
-                                        vc_combine =  gr.Button("Combine",variant="primary", visible=False)
-                        vc_convert.click(
-                            fn=vc_fn, 
-                            inputs=[
-                                vc_audio_mode,
-                                vc_input,
-                                vc_upload,
-                                tts_text,
-                                tts_voice,
-                                vc_transform0,
-                                f0method0,
-                                index_rate1,
-                                filter_radius0,
-                                resample_sr0,
-                                rms_mix_rate0,
-                                protect0,
-                            ], 
-                            outputs=[vc_log ,vc_output]
-                        )
-                        vc_download_button.click(
-                            fn=download_audio, 
-                            inputs=[vc_link, vc_download_audio], 
-                            outputs=[vc_audio_preview, vc_log_yt]
-                        )
-                        vc_split.click(
-                            fn=cut_vocal_and_inst, 
-                            inputs=[vc_split_model], 
-                            outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview, vc_input]
-                        )
-                        vc_combine.click(
-                            fn=combine_vocal_and_inst,
-                            inputs=[vc_output, vc_vocal_volume, vc_inst_volume, vc_split_model],
-                            outputs=[vc_combined_output]
-                        )
-                        vc_microphone_mode.change(
-                            fn=use_microphone,
-                            inputs=vc_microphone_mode,
-                            outputs=vc_upload
-                        )
-                        vc_audio_mode.change(
-                            fn=change_audio_mode,
-                            inputs=[vc_audio_mode],
-                            outputs=[
-                                vc_input,
-                                vc_microphone_mode,
-                                vc_upload,
-                                vc_download_audio,
-                                vc_link,
-                                vc_log_yt,
-                                vc_download_button,
-                                vc_split_model,
-                                vc_split_log,
-                                vc_split,
-                                vc_audio_preview,
-                                vc_vocal_preview,
-                                vc_inst_preview,
-                                vc_vocal_volume,
-                                vc_inst_volume,
-                                vc_combined_output,
-                                vc_combine,
-                                tts_text,
-                                tts_voice
-                            ]
-                        )
-        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/JeffJing/ZookChatBot/tls_client/dependencies/__init__.py b/spaces/JeffJing/ZookChatBot/tls_client/dependencies/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/JoeyFoursheds/ClonerHug/vc_infer_pipeline.py b/spaces/JoeyFoursheds/ClonerHug/vc_infer_pipeline.py
deleted file mode 100644
index e2381b52ed75e265a56cd1348dc078b2d90dd873..0000000000000000000000000000000000000000
--- a/spaces/JoeyFoursheds/ClonerHug/vc_infer_pipeline.py
+++ /dev/null
@@ -1,307 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
-        self.device = device
-        self.is_half = is_half
-
-    def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        # *** Note new names Praat & Harvest ***
-        if f0_method == "Praat":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "Harvest":
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        feature_retrieval_index_rate,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9,  # layer 9
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and feature_retrieval_index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-            _, I = index.search(npy, 1)
-            npy = big_npy[I.squeeze()]
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * feature_retrieval_index_rate
-                + (1 - feature_retrieval_index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        file_big_npy,
-        feature_retrieval_index_rate,
-        if_f0,
-        f0_file=None,
-    ):
-        if (
-            file_big_npy != ""
-            and file_index != ""
-            and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and feature_retrieval_index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                big_npy = np.load(file_big_npy)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-            print("Feature retrieval library doesn't exist or ratio is 0")
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        feature_retrieval_index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        feature_retrieval_index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    feature_retrieval_index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    feature_retrieval_index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Spider/con_spider_resnet.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Spider/con_spider_resnet.py
deleted file mode 100644
index 11535692abbbadf64cc82466e2e2649b26a6994b..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Spider/con_spider_resnet.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-from tensorflow.keras.applications.resnet50 import decode_predictions
-import io
-
-
-class spiderResnet:
-    def __init__(self,url) -> None:
-        self.image = url
-
-    def predict_image(self):
-        # Load the model
-        loaded_model = tf.keras.models.load_model("././Model/Spider/resnet50/ResnetSavemodel.h5")
-
-        picklefilepath = "././Model/Spider/resnet50/dataSaved.pkl"
-
-        with open(picklefilepath, 'rb') as file:
-            saved_data = pickle.load(file)
-            self.animal_breed = saved_data['class_name']
-
-
-        im = Image.open(self.image)
-        img = im.convert("RGB")
-        img= np.asarray(img)
-        image_resized= cv2.resize(img, (224,224))
-        image=np.expand_dims(image_resized,axis=0)
-        print(image.shape)
-
-        pred=loaded_model.predict(image)
-
-        pred_proba = self.custom_decode_predictions(pred,top=1)
-        rate = 0
-        for _,confidence in pred_proba:
-            rate = confidence
-
-        output_class= self.animal_breed[np.argmax(pred)]
-
-        return [output_class, rate]
-    
-    def custom_decode_predictions(self, prediction, top=3):
-        # convert 2d Array of shape (1,10) to a 1D array of shape (10,)
-        prediction = np.squeeze(prediction)
-
-        #get the indices of the top "top" prediction
-        top_indices = prediction.argsort()[-top:][::-1]
-
-        class_labels = self.animal_breed
-
-        top_prediction = [(class_labels[i], prediction[i]) for i in top_indices]
-
-        return top_prediction
diff --git a/spaces/JosephusCheung/LL7M-JS-Tokenizer/README.md b/spaces/JosephusCheung/LL7M-JS-Tokenizer/README.md
deleted file mode 100644
index 27c0668d70d0b466a2aa9f8a55f329b198a1c928..0000000000000000000000000000000000000000
--- a/spaces/JosephusCheung/LL7M-JS-Tokenizer/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: LL7M JS Tokenizer
-emoji: 🦙
-colorFrom: gray
-colorTo: blue
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/julius/__init__.py b/spaces/Kangarroar/ApplioRVC-Inference/julius/__init__.py
deleted file mode 100644
index 69811b0415a291ca1beb845531785ba03c57099a..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/julius/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-
-# flake8: noqa
-"""
-.. image:: ../logo.png
-
-Julius contains different Digital Signal Processing algorithms implemented
-with PyTorch, so that they are differentiable and available on CUDA.
-Note that all the modules implemented here can be used with TorchScript.
-
-For now, I have implemented:
-
-- `julius.resample`: fast sinc resampling.
-- `julius.fftconv`: FFT based convolutions.
-- `julius.lowpass`: FIR low pass filter banks.
-- `julius.filters`: FIR high pass and band pass filters.
-- `julius.bands`: Decomposition of a waveform signal over mel-scale frequency bands.
-
-Along that, you might found useful utilities in:
-
-- `julius.core`: DSP related functions.
-- `julius.utils`: Generic utilities.
-
-
-Please checkout [the Github repository](https://github.com/adefossez/julius) for other informations.
-For a verification of the speed and correctness of Julius, check the benchmark module `bench`.
-
-
-This package is named in this honor of
-[Julius O. Smith](https://ccrma.stanford.edu/~jos/),
-whose books and website were a gold mine of information for me to learn about DSP. Go checkout his website if you want
-to learn more about DSP.
-"""
-
-from .bands import SplitBands, split_bands
-from .fftconv import fft_conv1d, FFTConv1d
-from .filters import bandpass_filter, BandPassFilter
-from .filters import highpass_filter, highpass_filters, HighPassFilter, HighPassFilters
-from .lowpass import lowpass_filter, lowpass_filters, LowPassFilters, LowPassFilter
-from .resample import resample_frac, ResampleFrac
diff --git a/spaces/Knowles-Lab/tiger/tiger.py b/spaces/Knowles-Lab/tiger/tiger.py
deleted file mode 100644
index ecfe926affb2ec52115b168f21589940f7116e51..0000000000000000000000000000000000000000
--- a/spaces/Knowles-Lab/tiger/tiger.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import argparse
-import os
-import gzip
-import pickle
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from Bio import SeqIO
-
-# column names
-ID_COL = 'Transcript ID'
-SEQ_COL = 'Transcript Sequence'
-TARGET_COL = 'Target Sequence'
-GUIDE_COL = 'Guide Sequence'
-MM_COL = 'Number of Mismatches'
-SCORE_COL = 'Guide Score'
-
-# nucleotide tokens
-NUCLEOTIDE_TOKENS = dict(zip(['A', 'C', 'G', 'T', 'N'], [0, 1, 2, 3, 255]))
-NUCLEOTIDE_COMPLEMENT = dict(zip(['A', 'C', 'G', 'T'], ['T', 'G', 'C', 'A']))
-
-# model hyper-parameters
-GUIDE_LEN = 23
-CONTEXT_5P = 3
-CONTEXT_3P = 0
-TARGET_LEN = CONTEXT_5P + GUIDE_LEN + CONTEXT_3P
-UNIT_INTERVAL_MAP = 'sigmoid'
-
-# reference transcript files
-REFERENCE_TRANSCRIPTS = ('gencode.v19.pc_transcripts.fa.gz', 'gencode.v19.lncRNA_transcripts.fa.gz')
-
-# application configuration
-BATCH_SIZE_COMPUTE = 500
-BATCH_SIZE_SCAN = 20
-BATCH_SIZE_TRANSCRIPTS = 50
-NUM_TOP_GUIDES = 10
-NUM_MISMATCHES = 3
-RUN_MODES = dict(
-    all='All on-target guides per transcript',
-    top_guides='Top {:d} guides per transcript'.format(NUM_TOP_GUIDES),
-    titration='Top {:d} guides per transcript & their titration candidates'.format(NUM_TOP_GUIDES)
-)
-
-
-# configure GPUs
-for gpu in tf.config.list_physical_devices('GPU'):
-    tf.config.experimental.set_memory_growth(gpu, enable=True)
-if len(tf.config.list_physical_devices('GPU')) > 0:
-    tf.config.experimental.set_visible_devices(tf.config.list_physical_devices('GPU')[0], 'GPU')
-
-
-def load_transcripts(fasta_files: list, enforce_unique_ids: bool = True):
-
-    # load all transcripts from fasta files into a DataFrame
-    transcripts = pd.DataFrame()
-    for file in fasta_files:
-        try:
-            if os.path.splitext(file)[1] == '.gz':
-                with gzip.open(file, 'rt') as f:
-                    df = pd.DataFrame([(t.id, str(t.seq)) for t in SeqIO.parse(f, 'fasta')], columns=[ID_COL, SEQ_COL])
-            else:
-                df = pd.DataFrame([(t.id, str(t.seq)) for t in SeqIO.parse(file, 'fasta')], columns=[ID_COL, SEQ_COL])
-        except Exception as e:
-            print(e, 'while loading', file)
-            continue
-        transcripts = pd.concat([transcripts, df])
-
-    # set index
-    transcripts[ID_COL] = transcripts[ID_COL].apply(lambda s: s.split('|')[0])
-    transcripts.set_index(ID_COL, inplace=True)
-    if enforce_unique_ids:
-        assert not transcripts.index.has_duplicates, "duplicate transcript ID's detected in fasta file"
-
-    return transcripts
-
-
-def sequence_complement(sequence: list):
-    return [''.join([NUCLEOTIDE_COMPLEMENT[nt] for nt in list(seq)]) for seq in sequence]
-
-
-def one_hot_encode_sequence(sequence: list, add_context_padding: bool = False):
-
-    # stack list of sequences into a tensor
-    sequence = tf.ragged.stack([tf.constant(list(seq)) for seq in sequence], axis=0)
-
-    # tokenize sequence
-    nucleotide_table = tf.lookup.StaticVocabularyTable(
-        initializer=tf.lookup.KeyValueTensorInitializer(
-            keys=tf.constant(list(NUCLEOTIDE_TOKENS.keys()), dtype=tf.string),
-            values=tf.constant(list(NUCLEOTIDE_TOKENS.values()), dtype=tf.int64)),
-        num_oov_buckets=1)
-    sequence = tf.RaggedTensor.from_row_splits(values=nucleotide_table.lookup(sequence.values),
-                                               row_splits=sequence.row_splits).to_tensor(255)
-
-    # add context padding if requested
-    if add_context_padding:
-        pad_5p = 255 * tf.ones([sequence.shape[0], CONTEXT_5P], dtype=sequence.dtype)
-        pad_3p = 255 * tf.ones([sequence.shape[0], CONTEXT_3P], dtype=sequence.dtype)
-        sequence = tf.concat([pad_5p, sequence, pad_3p], axis=1)
-
-    # one-hot encode
-    sequence = tf.one_hot(sequence, depth=4, dtype=tf.float16)
-
-    return sequence
-
-
-def process_data(transcript_seq: str):
-
-    # convert to upper case
-    transcript_seq = transcript_seq.upper()
-
-    # get all target sites
-    target_seq = [transcript_seq[i: i + TARGET_LEN] for i in range(len(transcript_seq) - TARGET_LEN + 1)]
-
-    # prepare guide sequences
-    guide_seq = sequence_complement([seq[CONTEXT_5P:len(seq) - CONTEXT_3P] for seq in target_seq])
-
-    # model inputs
-    model_inputs = tf.concat([
-        tf.reshape(one_hot_encode_sequence(target_seq, add_context_padding=False), [len(target_seq), -1]),
-        tf.reshape(one_hot_encode_sequence(guide_seq, add_context_padding=True), [len(guide_seq), -1]),
-        ], axis=-1)
-    return target_seq, guide_seq, model_inputs
-
-
-def calibrate_predictions(predictions: np.array, num_mismatches: np.array, params: pd.DataFrame = None):
-    if params is None:
-        params = pd.read_pickle('calibration_params.pkl')
-    correction = np.squeeze(params.set_index('num_mismatches').loc[num_mismatches, 'slope'].to_numpy())
-    return correction * predictions
-
-
-def score_predictions(predictions: np.array, params: pd.DataFrame = None):
-    if params is None:
-        params = pd.read_pickle('scoring_params.pkl')
-
-    if UNIT_INTERVAL_MAP == 'sigmoid':
-        params = params.iloc[0]
-        return 1 - 1 / (1 + np.exp(params['a'] * predictions + params['b']))
-
-    elif UNIT_INTERVAL_MAP == 'min-max':
-        return 1 - (predictions - params['a']) / (params['b'] - params['a'])
-
-    elif UNIT_INTERVAL_MAP == 'exp-lin-exp':
-        # regime indices
-        active_saturation = predictions < params['a']
-        linear_regime = (params['a'] <= predictions) & (predictions <= params['c'])
-        inactive_saturation = params['c'] < predictions
-
-        # linear regime
-        slope = (params['d'] - params['b']) / (params['c'] - params['a'])
-        intercept = -params['a'] * slope + params['b']
-        predictions[linear_regime] = slope * predictions[linear_regime] + intercept
-
-        # active saturation regime
-        alpha = slope / params['b']
-        beta = alpha * params['a'] - np.log(params['b'])
-        predictions[active_saturation] = np.exp(alpha * predictions[active_saturation] - beta)
-
-        # inactive saturation regime
-        alpha = slope / (1 - params['d'])
-        beta = -alpha * params['c'] - np.log(1 - params['d'])
-        predictions[inactive_saturation] = 1 - np.exp(-alpha * predictions[inactive_saturation] - beta)
-
-        return 1 - predictions
-
-    else:
-        raise NotImplementedError
-
-
-def get_on_target_predictions(transcripts: pd.DataFrame, model: tf.keras.Model, status_update_fn=None):
-
-    # loop over transcripts
-    predictions = pd.DataFrame()
-    for i, (index, row) in enumerate(transcripts.iterrows()):
-
-        # parse transcript sequence
-        target_seq, guide_seq, model_inputs = process_data(row[SEQ_COL])
-
-        # get predictions
-        lfc_estimate = model.predict(model_inputs, batch_size=BATCH_SIZE_COMPUTE, verbose=False)[:, 0]
-        lfc_estimate = calibrate_predictions(lfc_estimate, num_mismatches=np.zeros_like(lfc_estimate))
-        scores = score_predictions(lfc_estimate)
-        predictions = pd.concat([predictions, pd.DataFrame({
-            ID_COL: [index] * len(scores),
-            TARGET_COL: target_seq,
-            GUIDE_COL: guide_seq,
-            SCORE_COL: scores})])
-
-        # progress update
-        percent_complete = 100 * min((i + 1) / len(transcripts), 1)
-        update_text = 'Evaluating on-target guides for each transcript: {:.2f}%'.format(percent_complete)
-        print('\r' + update_text, end='')
-        if status_update_fn is not None:
-            status_update_fn(update_text, percent_complete)
-    print('')
-
-    return predictions
-
-
-def top_guides_per_transcript(predictions: pd.DataFrame):
-
-    # select and sort top guides for each transcript
-    top_guides = pd.DataFrame()
-    for transcript in predictions[ID_COL].unique():
-        df = predictions.loc[predictions[ID_COL] == transcript]
-        df = df.sort_values(SCORE_COL, ascending=False).reset_index(drop=True).iloc[:NUM_TOP_GUIDES]
-        top_guides = pd.concat([top_guides, df])
-
-    return top_guides.reset_index(drop=True)
-
-
-def get_titration_candidates(top_guide_predictions: pd.DataFrame):
-
-    # generate a table of all titration candidates
-    titration_candidates = pd.DataFrame()
-    for _, row in top_guide_predictions.iterrows():
-        for i in range(len(row[GUIDE_COL])):
-            nt = row[GUIDE_COL][i]
-            for mutation in set(NUCLEOTIDE_TOKENS.keys()) - {nt, 'N'}:
-                sm_guide = list(row[GUIDE_COL])
-                sm_guide[i] = mutation
-                sm_guide = ''.join(sm_guide)
-                assert row[GUIDE_COL] != sm_guide
-                titration_candidates = pd.concat([titration_candidates, pd.DataFrame({
-                    ID_COL: [row[ID_COL]],
-                    TARGET_COL: [row[TARGET_COL]],
-                    GUIDE_COL: [sm_guide],
-                    MM_COL: [1]
-                })])
-
-    return titration_candidates
-
-
-def find_off_targets(top_guides: pd.DataFrame, status_update_fn=None):
-
-    # load reference transcripts
-    reference_transcripts = load_transcripts([os.path.join('transcripts', f) for f in REFERENCE_TRANSCRIPTS])
-
-    # one-hot encode guides to form a filter
-    guide_filter = one_hot_encode_sequence(sequence_complement(top_guides[GUIDE_COL]), add_context_padding=False)
-    guide_filter = tf.transpose(guide_filter, [1, 2, 0])
-
-    # loop over transcripts in batches
-    i = 0
-    off_targets = pd.DataFrame()
-    while i < len(reference_transcripts):
-        # select batch
-        df_batch = reference_transcripts.iloc[i:min(i + BATCH_SIZE_SCAN, len(reference_transcripts))]
-        i += BATCH_SIZE_SCAN
-
-        # find locations of off-targets
-        transcripts = one_hot_encode_sequence(df_batch[SEQ_COL].values.tolist(), add_context_padding=False)
-        num_mismatches = GUIDE_LEN - tf.nn.conv1d(transcripts, guide_filter, stride=1, padding='SAME')
-        loc_off_targets = tf.where(tf.round(num_mismatches) <= NUM_MISMATCHES).numpy()
-
-        # off-targets discovered
-        if len(loc_off_targets) > 0:
-
-            # log off-targets
-            dict_off_targets = pd.DataFrame({
-                'On-target ' + ID_COL: top_guides.iloc[loc_off_targets[:, 2]][ID_COL],
-                GUIDE_COL: top_guides.iloc[loc_off_targets[:, 2]][GUIDE_COL],
-                'Off-target ' + ID_COL: df_batch.index.values[loc_off_targets[:, 0]],
-                'Guide Midpoint': loc_off_targets[:, 1],
-                SEQ_COL: df_batch[SEQ_COL].values[loc_off_targets[:, 0]],
-                MM_COL: tf.gather_nd(num_mismatches, loc_off_targets).numpy().astype(int),
-            }).to_dict('records')
-
-            # trim transcripts to targets
-            for row in dict_off_targets:
-                start_location = row['Guide Midpoint'] - (GUIDE_LEN // 2)
-                del row['Guide Midpoint']
-                target = row[SEQ_COL]
-                del row[SEQ_COL]
-                if start_location < CONTEXT_5P:
-                    target = target[0:GUIDE_LEN + CONTEXT_3P]
-                    target = 'N' * (TARGET_LEN - len(target)) + target
-                elif start_location + GUIDE_LEN + CONTEXT_3P > len(target):
-                    target = target[start_location - CONTEXT_5P:]
-                    target = target + 'N' * (TARGET_LEN - len(target))
-                else:
-                    target = target[start_location - CONTEXT_5P:start_location + GUIDE_LEN + CONTEXT_3P]
-                if row[MM_COL] == 0 and 'N' not in target:
-                    assert row[GUIDE_COL] == sequence_complement([target[CONTEXT_5P:TARGET_LEN - CONTEXT_3P]])[0]
-                row[TARGET_COL] = target
-
-            # append new off-targets
-            off_targets = pd.concat([off_targets, pd.DataFrame(dict_off_targets)])
-
-        # progress update
-        percent_complete = 100 * min((i + 1) / len(reference_transcripts), 1)
-        update_text = 'Scanning for off-targets: {:.2f}%'.format(percent_complete)
-        print('\r' + update_text, end='')
-        if status_update_fn is not None:
-            status_update_fn(update_text, percent_complete)
-    print('')
-
-    return off_targets
-
-
-def predict_off_target(off_targets: pd.DataFrame, model: tf.keras.Model):
-    if len(off_targets) == 0:
-        return pd.DataFrame()
-
-    # compute off-target predictions
-    model_inputs = tf.concat([
-        tf.reshape(one_hot_encode_sequence(off_targets[TARGET_COL], add_context_padding=False), [len(off_targets), -1]),
-        tf.reshape(one_hot_encode_sequence(off_targets[GUIDE_COL], add_context_padding=True), [len(off_targets), -1]),
-        ], axis=-1)
-    lfc_estimate = model.predict(model_inputs, batch_size=BATCH_SIZE_COMPUTE, verbose=False)[:, 0]
-    lfc_estimate = calibrate_predictions(lfc_estimate, off_targets['Number of Mismatches'].to_numpy())
-    off_targets[SCORE_COL] = score_predictions(lfc_estimate)
-
-    return off_targets.reset_index(drop=True)
-
-
-def tiger_exhibit(transcripts: pd.DataFrame, mode: str, check_off_targets: bool, status_update_fn=None):
-
-    # load model
-    if os.path.exists('model'):
-        tiger = tf.keras.models.load_model('model')
-    else:
-        print('no saved model!')
-        exit()
-
-    # evaluate all on-target guides per transcript
-    on_target_predictions = get_on_target_predictions(transcripts, tiger, status_update_fn)
-
-    # initialize other outputs
-    titration_predictions = off_target_predictions = None
-
-    if mode == 'all' and not check_off_targets:
-        off_target_candidates = None
-
-    elif mode == 'top_guides':
-        on_target_predictions = top_guides_per_transcript(on_target_predictions)
-        off_target_candidates = on_target_predictions
-
-    elif mode == 'titration':
-        on_target_predictions = top_guides_per_transcript(on_target_predictions)
-        titration_candidates = get_titration_candidates(on_target_predictions)
-        titration_predictions = predict_off_target(titration_candidates, model=tiger)
-        off_target_candidates = pd.concat([on_target_predictions, titration_predictions])
-
-    else:
-        raise NotImplementedError
-
-    # check off-target effects for top guides
-    if check_off_targets and off_target_candidates is not None:
-        off_target_candidates = find_off_targets(off_target_candidates, status_update_fn)
-        off_target_predictions = predict_off_target(off_target_candidates, model=tiger)
-        if len(off_target_predictions) > 0:
-            off_target_predictions = off_target_predictions.sort_values(SCORE_COL, ascending=False)
-            off_target_predictions = off_target_predictions.reset_index(drop=True)
-
-    # finalize tables
-    for df in [on_target_predictions, titration_predictions, off_target_predictions]:
-        if df is not None and len(df) > 0:
-            for col in df.columns:
-                if ID_COL in col and set(df[col].unique()) == {'ManualEntry'}:
-                    del df[col]
-            df[GUIDE_COL] = df[GUIDE_COL].apply(lambda s: s[::-1])  # reverse guide sequences
-            df[TARGET_COL] = df[TARGET_COL].apply(lambda seq: seq[CONTEXT_5P:len(seq) - CONTEXT_3P])  # remove context
-
-    return on_target_predictions, titration_predictions, off_target_predictions
-
-
-if __name__ == '__main__':
-
-    # common arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--mode', type=str, default='titration')
-    parser.add_argument('--check_off_targets', action='store_true', default=False)
-    parser.add_argument('--fasta_path', type=str, default=None)
-    args = parser.parse_args()
-
-    # check for any existing results
-    if os.path.exists('on_target.csv') or os.path.exists('titration.csv') or os.path.exists('off_target.csv'):
-        raise FileExistsError('please rename or delete existing results')
-
-    # load transcripts from a directory of fasta files
-    if args.fasta_path is not None and os.path.exists(args.fasta_path):
-        df_transcripts = load_transcripts([os.path.join(args.fasta_path, f) for f in os.listdir(args.fasta_path)])
-
-    # otherwise consider simple test case with first 50 nucleotides from EIF3B-003's CDS
-    else:
-        df_transcripts = pd.DataFrame({
-            ID_COL: ['ManualEntry'],
-            SEQ_COL: ['ATGCAGGACGCGGAGAACGTGGCGGTGCCCGAGGCGGCCGAGGAGCGCGC']})
-        df_transcripts.set_index(ID_COL, inplace=True)
-
-    # process in batches
-    batch = 0
-    num_batches = len(df_transcripts) // BATCH_SIZE_TRANSCRIPTS
-    num_batches += (len(df_transcripts) % BATCH_SIZE_TRANSCRIPTS > 0)
-    for idx in range(0, len(df_transcripts), BATCH_SIZE_TRANSCRIPTS):
-        batch += 1
-        print('Batch {:d} of {:d}'.format(batch, num_batches))
-
-        # run batch
-        idx_stop = min(idx + BATCH_SIZE_TRANSCRIPTS, len(df_transcripts))
-        df_on_target, df_titration, df_off_target = tiger_exhibit(
-            transcripts=df_transcripts[idx:idx_stop],
-            mode=args.mode,
-            check_off_targets=args.check_off_targets
-        )
-
-        # save batch results
-        df_on_target.to_csv('on_target.csv', header=batch == 1, index=False, mode='a')
-        if df_titration is not None:
-            df_titration.to_csv('titration.csv', header=batch == 1, index=False, mode='a')
-        if df_off_target is not None:
-            df_off_target.to_csv('off_target.csv', header=batch == 1, index=False, mode='a')
-
-        # clear session to prevent memory blow up
-        tf.keras.backend.clear_session()
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/assigners/multi_instance_assigner.py b/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/assigners/multi_instance_assigner.py
deleted file mode 100644
index 1ba32afe856b3c2ad03ed89562d080f15b6ccf30..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/assigners/multi_instance_assigner.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional
-
-import torch
-from mmengine.structures import InstanceData
-
-from mmdet.registry import TASK_UTILS
-from .assign_result import AssignResult
-from .max_iou_assigner import MaxIoUAssigner
-
-
-@TASK_UTILS.register_module()
-class MultiInstanceAssigner(MaxIoUAssigner):
-    """Assign a corresponding gt bbox or background to each proposal bbox. If
-    we need to use a proposal box to generate multiple predict boxes,
-    `MultiInstanceAssigner` can assign multiple gt to each proposal box.
-
-    Args:
-        num_instance (int): How many bboxes are predicted by each proposal box.
-    """
-
-    def __init__(self, num_instance: int = 2, **kwargs):
-        super().__init__(**kwargs)
-        self.num_instance = num_instance
-
-    def assign(self,
-               pred_instances: InstanceData,
-               gt_instances: InstanceData,
-               gt_instances_ignore: Optional[InstanceData] = None,
-               **kwargs) -> AssignResult:
-        """Assign gt to bboxes.
-
-        This method assign gt bboxes to every bbox (proposal/anchor), each bbox
-        is assigned a set of gts, and the number of gts in this set is defined
-        by `self.num_instance`.
-
-        Args:
-            pred_instances (:obj:`InstanceData`): Instances of model
-                predictions. It includes ``priors``, and the priors can
-                be anchors or points, or the bboxes predicted by the
-                previous stage, has shape (n, 4). The bboxes predicted by
-                the current model or stage will be named ``bboxes``,
-                ``labels``, and ``scores``, the same as the ``InstanceData``
-                in other places.
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It usually includes ``bboxes``, with shape (k, 4),
-                and ``labels``, with shape (k, ).
-            gt_instances_ignore (:obj:`InstanceData`, optional): Instances
-                to be ignored during training. It includes ``bboxes``
-                attribute data that is ignored during training and testing.
-                Defaults to None.
-
-        Returns:
-            :obj:`AssignResult`: The assign result.
-        """
-        gt_bboxes = gt_instances.bboxes
-        priors = pred_instances.priors
-        # Set the FG label to 1 and add ignored annotations
-        gt_labels = gt_instances.labels + 1
-        if gt_instances_ignore is not None:
-            gt_bboxes_ignore = gt_instances_ignore.bboxes
-            if hasattr(gt_instances_ignore, 'labels'):
-                gt_labels_ignore = gt_instances_ignore.labels
-            else:
-                gt_labels_ignore = torch.ones_like(gt_bboxes_ignore)[:, 0] * -1
-        else:
-            gt_bboxes_ignore = None
-            gt_labels_ignore = None
-
-        assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
-            gt_bboxes.shape[0] > self.gpu_assign_thr) else False
-        # compute overlap and assign gt on CPU when number of GT is large
-        if assign_on_cpu:
-            device = priors.device
-            priors = priors.cpu()
-            gt_bboxes = gt_bboxes.cpu()
-            gt_labels = gt_labels.cpu()
-            if gt_bboxes_ignore is not None:
-                gt_bboxes_ignore = gt_bboxes_ignore.cpu()
-                gt_labels_ignore = gt_labels_ignore.cpu()
-
-        if gt_bboxes_ignore is not None:
-            all_bboxes = torch.cat([gt_bboxes, gt_bboxes_ignore], dim=0)
-            all_labels = torch.cat([gt_labels, gt_labels_ignore], dim=0)
-        else:
-            all_bboxes = gt_bboxes
-            all_labels = gt_labels
-        all_priors = torch.cat([priors, all_bboxes], dim=0)
-
-        overlaps_normal = self.iou_calculator(
-            all_priors, all_bboxes, mode='iou')
-        overlaps_ignore = self.iou_calculator(
-            all_priors, all_bboxes, mode='iof')
-        gt_ignore_mask = all_labels.eq(-1).repeat(all_priors.shape[0], 1)
-        overlaps_normal = overlaps_normal * ~gt_ignore_mask
-        overlaps_ignore = overlaps_ignore * gt_ignore_mask
-
-        overlaps_normal, overlaps_normal_indices = overlaps_normal.sort(
-            descending=True, dim=1)
-        overlaps_ignore, overlaps_ignore_indices = overlaps_ignore.sort(
-            descending=True, dim=1)
-
-        # select the roi with the higher score
-        max_overlaps_normal = overlaps_normal[:, :self.num_instance].flatten()
-        gt_assignment_normal = overlaps_normal_indices[:, :self.
-                                                       num_instance].flatten()
-        max_overlaps_ignore = overlaps_ignore[:, :self.num_instance].flatten()
-        gt_assignment_ignore = overlaps_ignore_indices[:, :self.
-                                                       num_instance].flatten()
-
-        # ignore or not
-        ignore_assign_mask = (max_overlaps_normal < self.pos_iou_thr) * (
-            max_overlaps_ignore > max_overlaps_normal)
-        overlaps = (max_overlaps_normal * ~ignore_assign_mask) + (
-            max_overlaps_ignore * ignore_assign_mask)
-        gt_assignment = (gt_assignment_normal * ~ignore_assign_mask) + (
-            gt_assignment_ignore * ignore_assign_mask)
-
-        assigned_labels = all_labels[gt_assignment]
-        fg_mask = (overlaps >= self.pos_iou_thr) * (assigned_labels != -1)
-        bg_mask = (overlaps < self.neg_iou_thr) * (overlaps >= 0)
-        assigned_labels[fg_mask] = 1
-        assigned_labels[bg_mask] = 0
-
-        overlaps = overlaps.reshape(-1, self.num_instance)
-        gt_assignment = gt_assignment.reshape(-1, self.num_instance)
-        assigned_labels = assigned_labels.reshape(-1, self.num_instance)
-
-        assign_result = AssignResult(
-            num_gts=all_bboxes.size(0),
-            gt_inds=gt_assignment,
-            max_overlaps=overlaps,
-            labels=assigned_labels)
-
-        if assign_on_cpu:
-            assign_result.gt_inds = assign_result.gt_inds.to(device)
-            assign_result.max_overlaps = assign_result.max_overlaps.to(device)
-            if assign_result.labels is not None:
-                assign_result.labels = assign_result.labels.to(device)
-        return assign_result
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/multi_label.py b/spaces/KyanChen/RSPrompter/mmpretrain/datasets/multi_label.py
deleted file mode 100644
index 58a9c7cd5f097689d29700004e2ed815934a1594..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/multi_label.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List
-
-from mmpretrain.registry import DATASETS
-from .base_dataset import BaseDataset
-
-
-@DATASETS.register_module()
-class MultiLabelDataset(BaseDataset):
-    """Multi-label Dataset.
-
-    This dataset support annotation file in `OpenMMLab 2.0 style annotation
-    format`.
-
-    The annotation format is shown as follows.
-
-    .. code-block:: none
-
-        {
-            "metainfo":
-            {
-              "classes":['A', 'B', 'C'....]
-            },
-            "data_list":
-            [
-              {
-                "img_path": "test_img1.jpg",
-                'gt_label': [0, 1],
-              },
-              {
-                "img_path": "test_img2.jpg",
-                'gt_label': [2],
-              },
-            ]
-            ....
-        }
-
-
-    Args:
-        ann_file (str): Annotation file path.
-        metainfo (dict, optional): Meta information for dataset, such as class
-            information. Defaults to None.
-        data_root (str): The root directory for ``data_prefix`` and
-            ``ann_file``. Defaults to ''.
-        data_prefix (str | dict): Prefix for training data. Defaults to ''.
-        filter_cfg (dict, optional): Config for filter data. Defaults to None.
-        indices (int or Sequence[int], optional): Support using first few
-            data in annotation file to facilitate training/testing on a smaller
-            dataset. Defaults to None which means using all ``data_infos``.
-        serialize_data (bool, optional): Whether to hold memory using
-            serialized objects, when enabled, data loader workers can use
-            shared RAM from master process instead of making a copy. Defaults
-            to True.
-        pipeline (list, optional): Processing pipeline. Defaults to [].
-        test_mode (bool, optional): ``test_mode=True`` means in test phase.
-            Defaults to False.
-        lazy_init (bool, optional): Whether to load annotation during
-            instantiation. In some cases, such as visualization, only the meta
-            information of the dataset is needed, which is not necessary to
-            load annotation file. ``Basedataset`` can skip load annotations to
-            save time by set ``lazy_init=False``. Defaults to False.
-        max_refetch (int, optional): If ``Basedataset.prepare_data`` get a
-            None img. The maximum extra number of cycles to get a valid
-            image. Defaults to 1000.
-        classes (str | Sequence[str], optional): Specify names of classes.
-
-            - If is string, it should be a file path, and the every line of
-              the file is a name of a class.
-            - If is a sequence of string, every item is a name of class.
-            - If is None, use categories information in ``metainfo`` argument,
-              annotation file or the class attribute ``METAINFO``.
-
-            Defaults to None.
-    """
-
-    def get_cat_ids(self, idx: int) -> List[int]:
-        """Get category ids by index.
-
-        Args:
-            idx (int): Index of data.
-
-        Returns:
-            cat_ids (List[int]): Image categories of specified index.
-        """
-        return self.get_data_info(idx)['gt_label']
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/assets/i18n/i18n.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/assets/i18n/i18n.py
deleted file mode 100644
index 6e6ea46996a22fa11d65f9158274616b3a033bae..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/assets/i18n/i18n.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import json
-import sys
-sys.path.append('..')
-import logging
-
-logger = logging.getLogger(__name__)
-def load_language_list(language):
-    try:
-        with open(f"./assets/i18n/langs/{language}.json", "r", encoding="utf-8") as f:
-            return json.load(f)
-    except FileNotFoundError:
-        raise FileNotFoundError(
-            f"Failed to load language file for {language}. Check if the correct .json file exists."
-        )
-
-
-class I18nAuto:
-    """
-    A class used for internationalization using JSON language files.
-
-    Examples
-    --------
-    >>> i18n = I18nAuto()
-    >>> i18n.print()
-    Using Language: en_US
-    """
-    def __init__(self, language=None):
-        from locale import getdefaultlocale
-        language = language or getdefaultlocale()[0]
-
-        # Check if a specific language variant exists, e.g., 'es_ES'
-        if self._language_exists(language):
-            self.language = language
-        else:
-            # If not, check if there is a language with the first two characters
-            # matching, e.g., 'es_' for 'es_ES'.
-            lang_prefix = language[:2]
-            for available_language in self._get_available_languages():
-                if available_language.startswith(lang_prefix):
-                    self.language = available_language
-                    break
-            else:
-                # If no match found, default to 'en_US'.
-                self.language = 'en_US'
-
-        self.language_map = load_language_list(self.language)
-
-    @staticmethod
-    def _get_available_languages():
-        from os import listdir
-        from os.path import isfile, join
-
-        language_files = [f for f in listdir("./assets/i18n/langs/") if isfile(join("./assets/i18n/langs/", f))]
-        return [lang.replace(".json", "") for lang in language_files]
-
-    @staticmethod
-    def _language_exists(language):
-        from os.path import exists
-        return exists(f"./assets/i18n/langs/{language}.json")
-
-    def __call__(self, key):
-        """Returns the translation of the given key if it exists, else returns the key itself."""
-        return self.language_map.get(key, key)
-
-    def print(self):
-        """Prints the language currently in use."""
-        logger.info(f"Using Language: {self.language}")
\ No newline at end of file
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_batch_rvc.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_batch_rvc.py
deleted file mode 100644
index 7c5a1b55042abad9934067fe3a28befd9b8c2a2c..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/infer_batch_rvc.py
+++ /dev/null
@@ -1,215 +0,0 @@
-"""
-v1
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "E:\codes\py39\RVC-beta\output" "E:\codes\py39\test-20230416b\weights\mi-test.pth" 0.66 cuda:0 True 3 0 1 0.33
-v2
-runtime\python.exe myinfer-v2-0528.py 0 "E:\codes\py39\RVC-beta\todo-songs" "E:\codes\py39\test-20230416b\logs\mi-test-v2\aadded_IVF677_Flat_nprobe_1_v2.index" harvest "E:\codes\py39\RVC-beta\output_v2" "E:\codes\py39\test-20230416b\weights\mi-test-v2.pth" 0.66 cuda:0 True 3 0 1 0.33
-"""
-import os, sys
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import sys
-import torch
-import tqdm as tq
-from multiprocessing import cpu_count
-
-
-class Config:
-    def __init__(self, device, is_half):
-        self.device = device
-        self.is_half = is_half
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            if (
-                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-                or "P40" in self.gpu_name.upper()
-                or "1060" in self.gpu_name
-                or "1070" in self.gpu_name
-                or "1080" in self.gpu_name
-            ):
-                print("16系/10系显卡和P40强制单精度")
-                self.is_half = False
-                for config_file in ["32k.json", "40k.json", "48k.json"]:
-                    with open(f"assets/configs/{config_file}", "r") as f:
-                        strr = f.read().replace("true", "false")
-                    with open(f"assets/configs/{config_file}", "w") as f:
-                        f.write(strr)
-                with open("infer/modules/train/preprocess.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("infer/modules/train/preprocess.py", "w") as f:
-                    f.write(strr)
-            else:
-                self.gpu_name = None
-            self.gpu_mem = int(
-                torch.cuda.get_device_properties(i_device).total_memory
-                / 1024
-                / 1024
-                / 1024
-                + 0.4
-            )
-            if self.gpu_mem <= 4:
-                with open("infer/modules/train/preprocess.py", "r") as f:
-                    strr = f.read().replace("3.7", "3.0")
-                with open("infer/modules/train/preprocess.py", "w") as f:
-                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("没有发现支持的N卡, 使用MPS进行推理")
-            self.device = "mps"
-        else:
-            print("没有发现支持的N卡, 使用CPU进行推理")
-            self.device = "cpu"
-            self.is_half = True
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G显存配置
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G显存配置
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
-
-
-f0up_key = sys.argv[1]
-input_path = sys.argv[2]
-index_path = sys.argv[3]
-f0method = sys.argv[4]  # harvest or pm
-opt_path = sys.argv[5]
-model_path = sys.argv[6]
-index_rate = float(sys.argv[7])
-device = sys.argv[8]
-is_half = sys.argv[9].lower() != "false"
-filter_radius = int(sys.argv[10])
-resample_sr = int(sys.argv[11])
-rms_mix_rate = float(sys.argv[12])
-protect = float(sys.argv[13])
-print(sys.argv)
-config = Config(device, is_half)
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from lib.infer.modules.vc.modules import VC
-from lib.infer.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from lib.infer.infer_libs.audio import load_audio
-from fairseq import checkpoint_utils
-from scipy.io import wavfile
-
-hubert_model = None
-
-
-def load_hubert():
-    global hubert_model
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(device)
-    if is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-
-def vc_single(sid, input_audio, f0_up_key, f0_file, f0_method, file_index, index_rate):
-    global tgt_sr, net_g, vc, hubert_model, version
-    if input_audio is None:
-        return "You need to upload an audio", None
-    f0_up_key = int(f0_up_key)
-    audio = load_audio(input_audio, 16000)
-    times = [0, 0, 0]
-    if hubert_model == None:
-        load_hubert()
-    if_f0 = cpt.get("f0", 1)
-    # audio_opt=vc.pipeline(hubert_model,net_g,sid,audio,times,f0_up_key,f0_method,file_index,file_big_npy,index_rate,if_f0,f0_file=f0_file)
-    audio_opt = vc.pipeline(
-        hubert_model,
-        net_g,
-        sid,
-        audio,
-        input_audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        f0_file=f0_file,
-    )
-    print(times)
-    return audio_opt
-
-
-def get_vc(model_path):
-    global n_spk, tgt_sr, net_g, vc, cpt, device, is_half, version
-    print("loading pth %s" % model_path)
-    cpt = torch.load(model_path, map_location="cpu")
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif version == "v2":
-        if if_f0 == 1:  #
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-    del net_g.enc_q
-    print(net_g.load_state_dict(cpt["weight"], strict=False))  # 不加这一行清不干净，真奇葩
-    net_g.eval().to(device)
-    if is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-    vc = VC(tgt_sr, config)
-    n_spk = cpt["config"][-3]
-    # return {"visible": True,"maximum": n_spk, "__type__": "update"}
-
-
-get_vc(model_path)
-audios = os.listdir(input_path)
-for file in tq.tqdm(audios):
-    if file.endswith(".wav"):
-        file_path = input_path + "/" + file
-        wav_opt = vc_single(
-            0, file_path, f0up_key, None, f0method, index_path, index_rate
-        )
-        out_path = opt_path + "/" + file
-        wavfile.write(out_path, tgt_sr, wav_opt)
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/feeds/btcsv.py b/spaces/Lianjd/stock_dashboard/backtrader/feeds/btcsv.py
deleted file mode 100644
index a70e1078ae15e42de7f49d2e7cb9efd8be296f9b..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/feeds/btcsv.py
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-from datetime import date, datetime, time
-
-from .. import feed
-from ..utils import date2num
-
-
-class BacktraderCSVData(feed.CSVDataBase):
-    '''
-    Parses a self-defined CSV Data used for testing.
-
-    Specific parameters:
-
-      - ``dataname``: The filename to parse or a file-like object
-    '''
-
-    def _loadline(self, linetokens):
-        itoken = iter(linetokens)
-
-        dttxt = next(itoken)  # Format is YYYY-MM-DD - skip char 4 and 7
-        dt = date(int(dttxt[0:4]), int(dttxt[5:7]), int(dttxt[8:10]))
-
-        if len(linetokens) == 8:
-            tmtxt = next(itoken)  # Format if present HH:MM:SS, skip 3 and 6
-            tm = time(int(tmtxt[0:2]), int(tmtxt[3:5]), int(tmtxt[6:8]))
-        else:
-            tm = self.p.sessionend  # end of the session parameter
-
-        self.lines.datetime[0] = date2num(datetime.combine(dt, tm))
-        self.lines.open[0] = float(next(itoken))
-        self.lines.high[0] = float(next(itoken))
-        self.lines.low[0] = float(next(itoken))
-        self.lines.close[0] = float(next(itoken))
-        self.lines.volume[0] = float(next(itoken))
-        self.lines.openinterest[0] = float(next(itoken))
-
-        return True
-
-
-class BacktraderCSV(feed.CSVFeedBase):
-    DataCls = BacktraderCSVData
diff --git a/spaces/LucasCodeBreak/MusicGen/app.py b/spaces/LucasCodeBreak/MusicGen/app.py
deleted file mode 100644
index 0f92495d323f1c70a9c8dde3b7680e3f9491ab83..0000000000000000000000000000000000000000
--- a/spaces/LucasCodeBreak/MusicGen/app.py
+++ /dev/null
@@ -1,407 +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.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import typing as tp
-import warnings
-
-import torch
-import gradio as gr
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import MusicGen
-
-
-MODEL = None  # Last used model
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-MAX_BATCH_SIZE = 6
-BATCHED_DURATION = 15
-INTERRUPTING = False
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def _call_nostderr(*args, **kwargs):
-    # Avoid ffmpeg vomitting on the logs.
-    kwargs['stderr'] = sp.DEVNULL
-    kwargs['stdout'] = sp.DEVNULL
-    _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(3)
-pool.__enter__()
-
-
-def interrupt():
-    global INTERRUPTING
-    INTERRUPTING = True
-
-
-class FileCleaner:
-    def __init__(self, file_lifetime: float = 3600):
-        self.file_lifetime = file_lifetime
-        self.files = []
-
-    def add(self, path: tp.Union[str, Path]):
-        self._cleanup()
-        self.files.append((time.time(), Path(path)))
-
-    def _cleanup(self):
-        now = time.time()
-        for time_added, path in list(self.files):
-            if now - time_added > self.file_lifetime:
-                if path.exists():
-                    path.unlink()
-                self.files.pop(0)
-            else:
-                break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
-    # Further remove some warnings.
-    be = time.time()
-    with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        out = gr.make_waveform(*args, **kwargs)
-        print("Make a video took", time.time() - be)
-        return out
-
-
-def load_model(version='melody'):
-    global MODEL
-    print("Loading model", version)
-    if MODEL is None or MODEL.name != version:
-        MODEL = MusicGen.get_pretrained(version)
-
-
-def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
-    MODEL.set_generation_params(duration=duration, **gen_kwargs)
-    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
-    be = time.time()
-    processed_melodies = []
-    target_sr = 32000
-    target_ac = 1
-    for melody in melodies:
-        if melody is None:
-            processed_melodies.append(None)
-        else:
-            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
-            if melody.dim() == 1:
-                melody = melody[None]
-            melody = melody[..., :int(sr * duration)]
-            melody = convert_audio(melody, sr, target_sr, target_ac)
-            processed_melodies.append(melody)
-
-    if any(m is not None for m in processed_melodies):
-        outputs = MODEL.generate_with_chroma(
-            descriptions=texts,
-            melody_wavs=processed_melodies,
-            melody_sample_rate=target_sr,
-            progress=progress,
-        )
-    else:
-        outputs = MODEL.generate(texts, progress=progress)
-
-    outputs = outputs.detach().cpu().float()
-    out_files = []
-    for output in outputs:
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, output, MODEL.sample_rate, strategy="loudness",
-                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-            out_files.append(pool.submit(make_waveform, file.name))
-            file_cleaner.add(file.name)
-    res = [out_file.result() for out_file in out_files]
-    for file in res:
-        file_cleaner.add(file)
-    print("batch finished", len(texts), time.time() - be)
-    print("Tempfiles currently stored: ", len(file_cleaner.files))
-    return res
-
-
-def predict_batched(texts, melodies):
-    max_text_length = 512
-    texts = [text[:max_text_length] for text in texts]
-    load_model('melody')
-    res = _do_predictions(texts, melodies, BATCHED_DURATION)
-    return [res]
-
-
-def predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
-    global INTERRUPTING
-    INTERRUPTING = False
-    if temperature < 0:
-        raise gr.Error("Temperature must be >= 0.")
-    if topk < 0:
-        raise gr.Error("Topk must be non-negative.")
-    if topp < 0:
-        raise gr.Error("Topp must be non-negative.")
-
-    topk = int(topk)
-    load_model(model)
-
-    def _progress(generated, to_generate):
-        progress((generated, to_generate))
-        if INTERRUPTING:
-            raise gr.Error("Interrupted.")
-    MODEL.set_custom_progress_callback(_progress)
-
-    outs = _do_predictions(
-        [text], [melody], duration, progress=True,
-        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
-    return outs[0]
-
-
-def toggle_audio_src(choice):
-    if choice == "mic":
-        return gr.update(source="microphone", value=None, label="Microphone")
-    else:
-        return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
-    with gr.Blocks() as interface:
-        gr.Markdown(
-            """
-            # MusicGen
-            This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Input Text", interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Submit")
-                    # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
-                    _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
-                with gr.Row():
-                    model = gr.Radio(["melody", "medium", "small", "large"],
-                                     label="Model", value="melody", interactive=True)
-                with gr.Row():
-                    duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
-                with gr.Row():
-                    topk = gr.Number(label="Top-k", value=250, interactive=True)
-                    topp = gr.Number(label="Top-p", value=0, interactive=True)
-                    temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
-                    cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict_full,
-                     inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef],
-                     outputs=[output])
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_full,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                    "melody"
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                    "melody"
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                    "medium"
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
-                    "./assets/bach.mp3",
-                    "melody"
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                    "medium",
-                ],
-            ],
-            inputs=[text, melody, model],
-            outputs=[output]
-        )
-        gr.Markdown(
-            """
-            ### More details
-
-            The model will generate a short music extract based on the description you provided.
-            The model can generate up to 30 seconds of audio in one pass. It is now possible
-            to extend the generation by feeding back the end of the previous chunk of audio.
-            This can take a long time, and the model might lose consistency. The model might also
-            decide at arbitrary positions that the song ends.
-
-            **WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
-            An overlap of 12 seconds is kept with the previously generated chunk, and 18 "new" seconds
-            are generated each time.
-
-            We present 4 model variations:
-            1. Melody -- a music generation model capable of generating music condition
-                on text and melody inputs. **Note**, you can also use text only.
-            2. Small -- a 300M transformer decoder conditioned on text only.
-            3. Medium -- a 1.5B transformer decoder conditioned on text only.
-            4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
-
-            When using `melody`, ou can optionaly provide a reference audio from
-            which a broad melody will be extracted. The model will then try to follow both
-            the description and melody provided.
-
-            You can also use your own GPU or a Google Colab by following the instructions on our repo.
-            See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-            for more details.
-            """
-        )
-
-        interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            # MusicGen
-
-            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-            <br/>
-            <a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
-                style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;"
-                src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            for longer sequences, more control and no queue.</p>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict_batched, inputs=[text, melody],
-                     outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_batched,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                ],
-            ],
-            inputs=[text, melody],
-            outputs=[output]
-        )
-        gr.Markdown("""
-        ### More details
-
-        The model will generate 12 seconds of audio based on the description you provided.
-        You can optionaly provide a reference audio from which a broad melody will be extracted.
-        The model will then try to follow both the description and melody provided.
-        All samples are generated with the `melody` model.
-
-        You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
-        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-        for more details.
-        """)
-
-        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--listen',
-        type=str,
-        default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
-        help='IP to listen on for connections to Gradio',
-    )
-    parser.add_argument(
-        '--username', type=str, default='', help='Username for authentication'
-    )
-    parser.add_argument(
-        '--password', type=str, default='', help='Password for authentication'
-    )
-    parser.add_argument(
-        '--server_port',
-        type=int,
-        default=0,
-        help='Port to run the server listener on',
-    )
-    parser.add_argument(
-        '--inbrowser', action='store_true', help='Open in browser'
-    )
-    parser.add_argument(
-        '--share', action='store_true', help='Share the gradio UI'
-    )
-
-    args = parser.parse_args()
-
-    launch_kwargs = {}
-    launch_kwargs['server_name'] = args.listen
-
-    if args.username and args.password:
-        launch_kwargs['auth'] = (args.username, args.password)
-    if args.server_port:
-        launch_kwargs['server_port'] = args.server_port
-    if args.inbrowser:
-        launch_kwargs['inbrowser'] = args.inbrowser
-    if args.share:
-        launch_kwargs['share'] = args.share
-
-    # Show the interface
-    if IS_BATCHED:
-        ui_batched(launch_kwargs)
-    else:
-        ui_full(launch_kwargs)
diff --git a/spaces/Lyra121/finetuned_diffusion/style.css b/spaces/Lyra121/finetuned_diffusion/style.css
deleted file mode 100644
index 9bfa78cc983f84693cf7cbab1e3bfd0e0d36c944..0000000000000000000000000000000000000000
--- a/spaces/Lyra121/finetuned_diffusion/style.css
+++ /dev/null
@@ -1,24 +0,0 @@
-.finetuned-diffusion-div div{
-    display:inline-flex;
-    align-items:center;
-    gap:.8rem;
-    font-size:1.75rem
-}
-.finetuned-diffusion-div div h1{
-    font-weight:900;
-    margin-bottom:7px
-}
-.finetuned-diffusion-div p{
-    margin-bottom:10px;
-    font-size:94%
-}
-a{
-    text-decoration:underline
-}
-.tabs{
-    margin-top:0;
-    margin-bottom:0
-}
-#gallery{
-    min-height:20rem
-}
diff --git a/spaces/MUmairAB/English-to-French/app.py b/spaces/MUmairAB/English-to-French/app.py
deleted file mode 100644
index b716e6d7d242a7c7501eb502fe4bb3ed52d1132f..0000000000000000000000000000000000000000
--- a/spaces/MUmairAB/English-to-French/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/MUmairAB/marian-finetuned-kde4-english-to-french").launch()
\ No newline at end of file
diff --git a/spaces/Makiing/coolb-in-gtest/src/components/providers.tsx b/spaces/Makiing/coolb-in-gtest/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/src/components/providers.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { ThemeProvider as NextThemesProvider } from 'next-themes'
-import { ThemeProviderProps } from 'next-themes/dist/types'
-
-import { TooltipProvider } from '@/components/ui/tooltip'
-
-export function Providers({ children, ...props }: ThemeProviderProps) {
-  return (
-    <NextThemesProvider {...props}>
-      <TooltipProvider>{children}</TooltipProvider>
-    </NextThemesProvider>
-  )
-}
diff --git a/spaces/MaxReimann/Whitebox-Style-Transfer-Editing/Whitebox_style_transfer.py b/spaces/MaxReimann/Whitebox-Style-Transfer-Editing/Whitebox_style_transfer.py
deleted file mode 100644
index 29c0b5a2026fb842f2c64be600c1049292013ce6..0000000000000000000000000000000000000000
--- a/spaces/MaxReimann/Whitebox-Style-Transfer-Editing/Whitebox_style_transfer.py
+++ /dev/null
@@ -1,327 +0,0 @@
-import base64
-import datetime
-import os
-import sys
-from io import BytesIO
-from pathlib import Path
-import numpy as np
-import requests
-import torch
-import torch.nn.functional as F
-from PIL import Image
-import time
-
-PACKAGE_PARENT = 'wise'
-SCRIPT_DIR = os.path.dirname(os.path.realpath(os.path.join(os.getcwd(), os.path.expanduser(__file__))))
-sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, PACKAGE_PARENT)))
-
-import streamlit as st
-from streamlit.logger import get_logger
-from st_click_detector import click_detector
-import streamlit.components.v1 as components
-from streamlit.source_util import get_pages
-from streamlit_extras.switch_page_button import switch_page
-
-from demo_config import HUGGING_FACE
-from parameter_optimization.parametric_styletransfer import single_optimize
-from parameter_optimization.parametric_styletransfer import CONFIG as ST_CONFIG
-from parameter_optimization.strotss_org import strotss, pil_resize_long_edge_to
-import helpers.session_state as session_state
-from helpers import torch_to_np, np_to_torch
-from effects import get_default_settings, MinimalPipelineEffect 
-
-st.set_page_config(layout="wide")
-BASE_URL = "https://ivpg.hpi3d.de/wise/wise-demo/images/"
-LOGGER = get_logger(__name__)
-
-effect_type = "minimal_pipeline"
-
-if "click_counter" not in st.session_state:
-    st.session_state.click_counter = 1
-
-if "action" not in st.session_state:
-    st.session_state["action"] = ""
-    
-if "user" not in st.session_state:
-    st.session_state["user"] = hash(time.time())
-
-content_urls = [
-    {
-        "name": "Portrait", "id": "portrait",
-        "src": BASE_URL + "/content/portrait.jpeg"
-    },
-    {
-        "name": "Tuebingen", "id": "tubingen",
-        "src": BASE_URL + "/content/tubingen.jpeg"
-    },
-    {
-        "name": "Colibri", "id": "colibri",
-        "src": BASE_URL + "/content/colibri.jpeg"
-    }
-]
-
-style_urls = [
-    {
-        "name": "Starry Night, Van Gogh", "id": "starry_night",
-        "src": BASE_URL + "/style/starry_night.jpg"
-    },
-    {
-        "name": "The Scream, Edward Munch", "id": "the_scream",
-        "src": BASE_URL + "/style/the_scream.jpg"
-    },
-    {
-        "name": "The Great Wave, Ukiyo-e", "id": "wave",
-        "src": BASE_URL + "/style/wave.jpg"
-    },
-    {
-        "name": "Woman with Hat, Henry Matisse", "id": "woman_with_hat",
-        "src": BASE_URL + "/style/woman_with_hat.jpg"
-    }
-]
-
-
-def last_image_clicked(type="content", action=None, ):
-    kw = "last_image_clicked" + "_" + type
-    if action:
-        session_state.get(**{kw: action})
-    elif kw not in session_state.get():
-        return None
-    else:
-        return session_state.get()[kw]
-
-
-@st.cache
-def _retrieve_from_id(clicked, urls):
-    src = [x["src"] for x in urls if x["id"] == clicked][0]
-    img = Image.open(requests.get(src, stream=True).raw)
-    return img, src
-
-
-def store_img_from_id(clicked, urls, imgtype):
-    img, src = _retrieve_from_id(clicked, urls)
-    session_state.get(**{f"{imgtype}_im": img, f"{imgtype}_render_src": src, f"{imgtype}_id": clicked})
-
-
-def img_choice_panel(imgtype, urls, default_choice, expanded):
-    with st.expander(f"Select {imgtype} image:", expanded=expanded):
-        html_code = '<div class="column" style="display: flex; flex-wrap: wrap; padding: 0 4px;">'
-        for url in urls:
-            html_code += f"<a href='#' id='{url['id']}' style='padding: 0px 5px'><img height='160px' style='margin-top: 8px;' src='{url['src']}'></a>"
-        html_code += "</div>"
-        clicked = click_detector(html_code)
-
-        if not clicked and st.session_state["action"] not in ("uploaded", "switch_page_from_local_edits", "switch_page_from_presets", "slider_change", "reset"):  # default val
-            store_img_from_id(default_choice, urls, imgtype)
-
-        st.write("OR:  ")
-
-        with st.form(imgtype + "-form", clear_on_submit=True):
-            uploaded_im = st.file_uploader(f"Load {imgtype} image:", type=["png", "jpg"], )
-            upload_pressed = st.form_submit_button("Upload")
-
-            if upload_pressed and uploaded_im is not None:
-                img = Image.open(uploaded_im)
-                img = img.convert('RGB')
-                buffered = BytesIO()
-                img.save(buffered, format="JPEG")
-                encoded = base64.b64encode(buffered.getvalue()).decode()
-                # session_state.get(uploaded_im=img, content_render_src=f"data:image/jpeg;base64,{encoded}")
-                session_state.get(**{f"{imgtype}_im": img, f"{imgtype}_render_src": f"data:image/jpeg;base64,{encoded}",
-                                     f"{imgtype}_id": "uploaded"})
-                st.session_state["action"] = "uploaded"
-                st.write("uploaded.")
-
-        last_clicked = last_image_clicked(type=imgtype)
-        print(st.session_state["user"], " last_clicked", last_clicked, "clicked", clicked, "action", st.session_state["action"] )
-        if not upload_pressed and clicked != "":  # trigger when no file uploaded
-            if last_clicked != clicked:  # only activate when content was actually clicked
-                store_img_from_id(clicked, urls, imgtype)
-                last_image_clicked(type=imgtype, action=clicked)
-                st.session_state["action"] = "clicked"
-                st.session_state.click_counter += 1  # hack to get page to reload at top
-
-        state = session_state.get()
-        st.sidebar.write(f'Selected {imgtype} image:')
-        st.sidebar.markdown(f'<img src="{state[f"{imgtype}_render_src"]}" width=240px></img>', unsafe_allow_html=True)
-
-def optimize(effect, preset, result_image_placeholder):
-    content = st.session_state["Content_im"]
-    style = st.session_state["Style_im"]
-    st.session_state["optimize_next"] = False
-    with st.spinner(text="Optimizing parameters.."):
-        print("optimizing for user", st.session_state["user"])
-        if HUGGING_FACE:
-            optimize_on_server(content, style, result_image_placeholder)
-        else:
-            optimize_params(effect, preset, content, style, result_image_placeholder)
-
-def optimize_next(result_image_placeholder):
-    result_image_placeholder.text("<- Custom content/style needs to be style transferred")
-    queue_length = 0 if not HUGGING_FACE else get_queue_length()
-    if queue_length > 0:
-        st.sidebar.warning(f"WARNING: Already {queue_length} tasks in the queue. It will take approx {(queue_length+1) * 5} min for your image to be completed.")
-    else:
-        st.sidebar.warning("Note: Optimizing takes up to 5 minutes.")
-    optimize_button = st.sidebar.button("Optimize Style Transfer")
-    if optimize_button:
-        st.session_state["optimize_next"] = True
-        st.experimental_rerun()
-    else:
-        if not "result_vp" in st.session_state:
-            st.stop()
-        else:
-            return st.session_state["effect_input"], st.session_state["result_vp"]
-
-
-@st.cache(hash_funcs={MinimalPipelineEffect: id})
-def create_effect():
-    effect, preset, param_set = get_default_settings(effect_type)
-    effect.enable_checkpoints()
-    effect.cuda()
-    return effect, preset
-
-
-def load_visual_params(vp_path: str, img_org: Image, org_cuda: torch.Tensor, effect) -> torch.Tensor:
-    if Path(vp_path).exists():
-        vp = torch.load(vp_path).detach().clone()
-        vp = F.interpolate(vp, (img_org.height, img_org.width))
-        if len(effect.vpd.vp_ranges) == vp.shape[1]:
-            return vp
-    # use preset and save it
-    vp = effect.vpd.preset_tensor(preset, org_cuda, add_local_dims=True)
-    torch.save(vp, vp_path)
-    return vp
-
-
-# @st.cache(hash_funcs={torch.Tensor: id})
-@st.experimental_memo
-def load_params(content_id, style_id):#, effect):
-    preoptim_param_path = os.path.join("precomputed", effect_type, content_id, style_id)
-    img_org = Image.open(os.path.join(preoptim_param_path, "input.png"))
-    content_cuda = np_to_torch(img_org).cuda()
-    vp_path = os.path.join(preoptim_param_path, "vp.pt")
-    vp = load_visual_params(vp_path, img_org, content_cuda, effect)
-    return content_cuda, vp
-
-
-def render_effect(effect, content_cuda, vp):
-    with torch.no_grad():
-        result_cuda = effect(content_cuda, vp)
-    img_res = Image.fromarray((torch_to_np(result_cuda) * 255.0).astype(np.uint8))
-    return img_res
-
-
-result_container = st.container()
-coll1, coll2 = result_container.columns([3,2])
-coll1.header("Result")
-coll2.header("Global Edits")
-result_image_placeholder = coll1.empty()
-result_image_placeholder.markdown("## loading..")
-
-from tasks import optimize_on_server, optimize_params, monitor_task, get_queue_length
-
-if "current_server_task_id" not in st.session_state:
-    st.session_state['current_server_task_id'] = None
-
-if "optimize_next" not in st.session_state:
-    st.session_state['optimize_next'] = False
-
-effect, preset = create_effect()
-
-if HUGGING_FACE and st.session_state['current_server_task_id'] is not None: 
-    with st.spinner(text="Optimizing parameters.."):
-        monitor_task(result_image_placeholder)
-
-if st.session_state["optimize_next"]:
-    print("optimize now")
-    optimize(effect, preset, result_image_placeholder)
-
-img_choice_panel("Content", content_urls, "portrait", expanded=True)
-img_choice_panel("Style", style_urls, "starry_night", expanded=True)
-
-state = session_state.get()
-content_id = state["Content_id"]
-style_id = state["Style_id"]
-
-
-print("content id, style id", content_id, style_id  )
-if st.session_state["action"] == "uploaded":
-    content_img, _vp = optimize_next(result_image_placeholder)
-elif st.session_state["action"] in ("switch_page_from_local_edits", "switch_page_from_presets", "slider_change") or \
-      content_id == "uploaded" or style_id == "uploaded":
-    print(st.session_state["user"], "restore param")
-    _vp = st.session_state["result_vp"]
-    content_img = st.session_state["effect_input"]
-else:
-    print(st.session_state["user"], "load_params")
-    content_img, _vp = load_params(content_id, style_id)#, effect)
-
-vp = torch.clone(_vp)
-
-
-def reset_params(means, names):
-    for i, name in enumerate(names):
-        st.session_state["slider_" + name] = means[i]
-
-def on_slider():
-    st.session_state["action"] = "slider_change"
-
-
-with coll2:
-    show_params_names = [ 'bumpiness',"bumpSpecular", "contours"]
-    display_means = []
-    params_mapping = {"bumpiness": ['bumpScale', "bumpOpacity"], "bumpSpecular": ["bumpSpecular"], "contours": [ "contourOpacity", "contour"]}
-    def create_slider(name):
-        params = params_mapping[name] if name in params_mapping else [name]
-        means = [torch.mean(vp[:, effect.vpd.name2idx[n]]).item() for n in params]
-        display_mean = np.average(means) + 0.5
-        display_means.append(display_mean)
-        if "slider_" + name not in st.session_state or st.session_state["action"] != "slider_change": 
-          st.session_state["slider_" + name] = display_mean
-        slider = st.slider(f"Mean {name}: ", 0.0, 1.0, step=0.05, key="slider_" + name, on_change=on_slider)
-        for i, param_name in enumerate(params):
-            vp[:, effect.vpd.name2idx[param_name]] += slider - (means[i] + 0.5)
-            vp.clamp_(-0.5, 0.5)
-    
-    for name in show_params_names:
-        create_slider(name)
-
-    others_idx = set(range(len(effect.vpd.vp_ranges))) - set([effect.vpd.name2idx[name] for name in sum(params_mapping.values(), [])])
-    others_names = [effect.vpd.vp_ranges[i][0] for i in sorted(list(others_idx))]
-    other_param = st.selectbox("Other parameters: ", ["hueShift"] + [n for n in others_names if n != "hueShift"] )
-    create_slider(other_param)
-
-
-    reset_button = st.button("Reset Parameters", on_click=reset_params, args=(display_means, show_params_names))
-    if reset_button:
-        st.session_state["action"] = "reset"
-        st.experimental_rerun()
-
-    apply_presets = st.button("Paint Presets")
-    if apply_presets:
-        switch_page("Apply_preset")
-
-    edit_locally_btn = st.button("Edit Local Parameter Maps")
-    if edit_locally_btn:
-        switch_page('️ local edits')
-
-
-img_res = render_effect(effect, content_img, vp)
-
-st.session_state["result_vp"] = vp
-st.session_state["effect_input"] = content_img
-st.session_state["last_result"] = img_res
-
-with coll1:
-    result_image_placeholder.image(img_res)
-
-# a bit hacky way to return focus to top of page after clicking on images
-components.html(
-    f"""
-        <p>{st.session_state.click_counter}</p>
-        <script>
-            window.parent.document.querySelector('section.main').scrollTo(0, 0);
-        </script>
-    """,
-    height=0
-)
diff --git a/spaces/Menna2211/TxTimg/README.md b/spaces/Menna2211/TxTimg/README.md
deleted file mode 100644
index 422a4d86eb69150b98ce3a0a13975d72ea657e7a..0000000000000000000000000000000000000000
--- a/spaces/Menna2211/TxTimg/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: TxTimg
-emoji: {{emoji}}
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: Home.py
-pinned: false
----
-# TxTstable
diff --git a/spaces/MertYeter/evrimci/Makefile b/spaces/MertYeter/evrimci/Makefile
deleted file mode 100644
index ad23323414bd2175956f6aef92f223a02f7258be..0000000000000000000000000000000000000000
--- a/spaces/MertYeter/evrimci/Makefile
+++ /dev/null
@@ -1,11 +0,0 @@
-.PHONY: quality style
-
-# Check that source code meets quality standards
-quality:
-	black --check --diff .
-	ruff .
-
-# Format source code automatically
-style:
-	black .
-	ruff . --fix
diff --git a/spaces/Miuzarte/SUI-svc-4.0/modules/attentions.py b/spaces/Miuzarte/SUI-svc-4.0/modules/attentions.py
deleted file mode 100644
index f9c11ca4a3acb86bf1abc04d9dcfa82a4ed4061f..0000000000000000000000000000000000000000
--- a/spaces/Miuzarte/SUI-svc-4.0/modules/attentions.py
+++ /dev/null
@@ -1,349 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.commons as commons
-import modules.modules as modules
-from modules.modules import LayerNorm
-
-
-class FFT(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
-               proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(
-        MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
-                           proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(
-        FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/losses/l1_loss.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/losses/l1_loss.py
deleted file mode 100644
index 68771a328ddedb4d0d4b925626a3abbb17ab9a7c..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/losses/l1_loss.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from mmocr import digit_version
-from mmocr.registry import MODELS
-
-
-@MODELS.register_module()
-class SmoothL1Loss(nn.SmoothL1Loss):
-    """Smooth L1 loss."""
-
-
-@MODELS.register_module()
-class MaskedSmoothL1Loss(nn.Module):
-    """Masked Smooth L1 loss.
-
-    Args:
-        beta (float, optional): The threshold in the piecewise function.
-            Defaults to 1.
-        eps (float, optional): Eps to avoid zero-division error.  Defaults to
-            1e-6.
-    """
-
-    def __init__(self, beta: Union[float, int] = 1, eps: float = 1e-6) -> None:
-        super().__init__()
-        if digit_version(torch.__version__) > digit_version('1.6.0'):
-            if digit_version(torch.__version__) >= digit_version(
-                    '1.13.0') and beta == 0:
-                beta = beta + eps
-            self.smooth_l1_loss = nn.SmoothL1Loss(beta=beta, reduction='none')
-        self.eps = eps
-        self.beta = beta
-
-    def forward(self,
-                pred: torch.Tensor,
-                gt: torch.Tensor,
-                mask: Optional[torch.Tensor] = None) -> torch.Tensor:
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): The prediction in any shape.
-            gt (torch.Tensor): The learning target of the prediction in the
-                same shape as pred.
-            mask (torch.Tensor, optional): Binary mask in the same shape of
-                pred, indicating positive regions to calculate the loss. Whole
-                region will be taken into account if not provided. Defaults to
-                None.
-
-        Returns:
-            torch.Tensor: The loss value.
-        """
-
-        assert pred.size() == gt.size() and gt.numel() > 0
-        if mask is None:
-            mask = torch.ones_like(gt).bool()
-        assert mask.size() == gt.size()
-        x = pred * mask
-        y = gt * mask
-        if digit_version(torch.__version__) > digit_version('1.6.0'):
-            loss = self.smooth_l1_loss(x, y)
-        else:
-            loss = torch.zeros_like(gt)
-            diff = torch.abs(x - y)
-            mask_beta = diff < self.beta
-            loss[mask_beta] = 0.5 * torch.square(diff)[mask_beta] / self.beta
-            loss[~mask_beta] = diff[~mask_beta] - 0.5 * self.beta
-        return loss.sum() / (mask.sum() + self.eps)
diff --git a/spaces/MrD05/text-generation-webui-space/modules/deepspeed_parameters.py b/spaces/MrD05/text-generation-webui-space/modules/deepspeed_parameters.py
deleted file mode 100644
index 3dbed437f5b5196d0b1fcbc582085319fb8d40d1..0000000000000000000000000000000000000000
--- a/spaces/MrD05/text-generation-webui-space/modules/deepspeed_parameters.py
+++ /dev/null
@@ -1,75 +0,0 @@
-def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir):
-
-    '''
-    DeepSpeed configration
-    https://huggingface.co/docs/transformers/main_classes/deepspeed
-    '''
-
-    if nvme_offload_dir:
-        ds_config = {
-            "fp16": {
-                "enabled": not ds_bf16,
-            },
-            "bf16": {
-                "enabled": ds_bf16,
-            },
-            "zero_optimization": {
-                "stage": 3,
-                "offload_param": {
-                    "device": "nvme",
-                    "nvme_path": nvme_offload_dir,
-                    "pin_memory": True,
-                    "buffer_count": 5,
-                    "buffer_size": 1e9,
-                    "max_in_cpu": 1e9
-                },
-                "overlap_comm": True,
-                "reduce_bucket_size": "auto",
-                "contiguous_gradients": True,
-                "sub_group_size": 1e8,
-                "stage3_prefetch_bucket_size": "auto",
-                "stage3_param_persistence_threshold": "auto",
-                "stage3_max_live_parameters": "auto",
-                "stage3_max_reuse_distance": "auto",
-            },
-            "aio": {
-                "block_size": 262144,
-                "queue_depth": 32,
-                "thread_count": 1,
-                "single_submit": False,
-                "overlap_events": True
-            },
-            "steps_per_print": 2000,
-            "train_batch_size": train_batch_size,
-            "train_micro_batch_size_per_gpu": 1,
-            "wall_clock_breakdown": False
-        }
-    else:
-        ds_config = {
-            "fp16": {
-                "enabled": not ds_bf16,
-            },
-            "bf16": {
-                "enabled": ds_bf16,
-            },
-            "zero_optimization": {
-                "stage": 3,
-                "offload_param": {
-                    "device": "cpu",
-                    "pin_memory": True
-                },
-                "overlap_comm": True,
-                "contiguous_gradients": True,
-                "reduce_bucket_size": "auto",
-                "stage3_prefetch_bucket_size": "auto",
-                "stage3_param_persistence_threshold": "auto",
-                "stage3_max_live_parameters": "auto",
-                "stage3_max_reuse_distance": "auto",
-            },
-            "steps_per_print": 2000,
-            "train_batch_size": train_batch_size,
-            "train_micro_batch_size_per_gpu": 1,
-            "wall_clock_breakdown": False
-        }
-
-    return ds_config
diff --git a/spaces/NSect/VALL-E-X/utils/sentence_cutter.py b/spaces/NSect/VALL-E-X/utils/sentence_cutter.py
deleted file mode 100644
index 02b8b141b5b139709fb477653a5c3804cd74fb2d..0000000000000000000000000000000000000000
--- a/spaces/NSect/VALL-E-X/utils/sentence_cutter.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import nltk
-import jieba
-import sudachipy
-import langid
-nltk.download('punkt')
-langid.set_languages(['en', 'zh', 'ja'])
-
-def split_text_into_sentences(text):
-    if langid.classify(text)[0] == "en":
-        sentences = nltk.tokenize.sent_tokenize(text)
-
-        return sentences
-    elif langid.classify(text)[0] == "zh":
-        sentences = []
-        segs = jieba.cut(text, cut_all=False)
-        segs = list(segs)
-        start = 0
-        for i, seg in enumerate(segs):
-            if seg in ["。", "！", "？", "……"]:
-                sentences.append("".join(segs[start:i + 1]))
-                start = i + 1
-        if start < len(segs):
-            sentences.append("".join(segs[start:]))
-
-        return sentences
-    elif langid.classify(text)[0] == "ja":
-        sentences = []
-        tokenizer = sudachipy.Dictionary().create()
-        tokens = tokenizer.tokenize(text)
-        current_sentence = ""
-
-        for token in tokens:
-            current_sentence += token.surface()
-            if token.part_of_speech()[0] == "補助記号" and token.part_of_speech()[1] == "句点":
-                sentences.append(current_sentence)
-                current_sentence = ""
-
-        if current_sentence:
-            sentences.append(current_sentence)
-
-        return sentences
-
-    raise RuntimeError("It is impossible to reach here.")
\ No newline at end of file
diff --git a/spaces/NanoMachin/Free-Palestine/README.md b/spaces/NanoMachin/Free-Palestine/README.md
deleted file mode 100644
index 91b2a32fb9293bcaba030dee3b958844266ccfdc..0000000000000000000000000000000000000000
--- a/spaces/NanoMachin/Free-Palestine/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Free Palestine
-emoji: 🐢
-colorFrom: red
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Narrativa/poc/app.py b/spaces/Narrativa/poc/app.py
deleted file mode 100644
index f3227edc9a46522d37b792fe296d610a17ab591c..0000000000000000000000000000000000000000
--- a/spaces/Narrativa/poc/app.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import nltk
-nltk.download(['stopwords', 'punkt'])
-
-from summarize import summarize
-from data import data
-import streamlit as st
-
-
-st.write("""
-#  Pharma Company POC
-""")
-
-st.sidebar.image("./NarrativaLogoBlanco.png")
-
-
-def make_summarizing(new_body, hide=False, num_sentences=3):
-    if hide:
-        return ''
-    result = summarize(
-        new_body, sentence_count=num_sentences, language='english')
-    return result
-
-
-st.subheader("Overview")
-
-
-st.write("""
-         ##### Topics:
-         - Cancer
-         - immuno-oncology
-         - New Therapy
-         - BiTE
-         """)
-st.write("""
-         ##### Summary:
-         """)
-st.write(make_summarizing(data["intro"]))
-st.write('---')
-
-
-st.write('##### Conversation:')
-st.write("""
-         
-         ***Overview of the current therapeutic landscape for cancer and the immuno-oncology***
-         
-         """)
-st.write(make_summarizing(data["block_1"]))
-
-
-st.write("""
-         
-         ***How does the BiTE platform and constituent BiTE molecules actually work***
-         
-         """)
-st.write(make_summarizing(data["block_2"], num_sentences=4))
-
-
-st.write("""
-         
-         ***Where BiTE molecules are currently being studied***
-
-         """)
-st.write(make_summarizing(data["block_3"], num_sentences=3))
-
-
-st.write("""
-         
-         ***Practial approach: Myeloma and BiTE***
-         
-         """)
-st.write(make_summarizing(data["block_4"], num_sentences=6))
-
-
-st.write("""
-         
-         ***Acute Myeloid Leukemia and BiTE molecules***
-
-         """)
-st.write(make_summarizing(data["block_5"], num_sentences=6))
-
-
-st.write("""
-         
-         ***BiTE molecules for solid tumor types***
-
-         """)
-st.write(make_summarizing(data["block_6"], num_sentences=4))
-
-
-st.write("""
-         
-         ***Takeaways***
-
-         """)
-st.write(make_summarizing(data["block_7"], num_sentences=3))
-
-
-st.write("""
-# MRD Slide Deck
-""")
-st.write("""
-         ### Current Landscape in Minimal Residual Disease (MRD) Testing in Hematologic Malignancies
-         """)
-
-
-st.write("""
-        - MRD is the presence of malignant cells under the detection limit of conventional methods
-        - Strong prognostic indicator of patient outcomes
-        - Potential for use in guiding treatment decisions
-            - Intensity of therapy
-            - Appropriateness of SCT
-        - Currently measured using flow cytometry or qPCR
-            - Newer testing methods are emerging
-        - MRD is currently being evaluated as a potential surrogate endpoint, and may have the potential to replace or augment morphological CR as a response criteria in certain hematologic malignancies
-        """)
-
diff --git a/spaces/Nour33/sci_summ/app.py b/spaces/Nour33/sci_summ/app.py
deleted file mode 100644
index f1665ca882ceca5c2005c13b99de093ee5dbb3ce..0000000000000000000000000000000000000000
--- a/spaces/Nour33/sci_summ/app.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import gradio as gr
-
-title = "Summarization model"
-description = "Summarization model demo"
-interface = gr.Interface.load("huggingface/0x70DA/t5-v1_1-base-finetuned-sci_summ", 
-            description=description,
-            title = title,
-            examples=[["The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."]]
-)
-interface.launch()
\ No newline at end of file
diff --git a/spaces/OAOA/DifFace/basicsr/data/__init__.py b/spaces/OAOA/DifFace/basicsr/data/__init__.py
deleted file mode 100644
index 510df16771d153f61fbf2126baac24f69d3de7e4..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/data/__init__.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import importlib
-import numpy as np
-import random
-import torch
-import torch.utils.data
-from copy import deepcopy
-from functools import partial
-from os import path as osp
-
-from basicsr.data.prefetch_dataloader import PrefetchDataLoader
-from basicsr.utils import get_root_logger, scandir
-from basicsr.utils.dist_util import get_dist_info
-from basicsr.utils.registry import DATASET_REGISTRY
-
-__all__ = ['build_dataset', 'build_dataloader']
-
-# automatically scan and import dataset modules for registry
-# scan all the files under the data folder with '_dataset' in file names
-data_folder = osp.dirname(osp.abspath(__file__))
-dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
-# import all the dataset modules
-_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames]
-
-
-def build_dataset(dataset_opt):
-    """Build dataset from options.
-
-    Args:
-        dataset_opt (dict): Configuration for dataset. It must contain:
-            name (str): Dataset name.
-            type (str): Dataset type.
-    """
-    dataset_opt = deepcopy(dataset_opt)
-    dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt)
-    logger = get_root_logger()
-    logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} is built.')
-    return dataset
-
-
-def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None):
-    """Build dataloader.
-
-    Args:
-        dataset (torch.utils.data.Dataset): Dataset.
-        dataset_opt (dict): Dataset options. It contains the following keys:
-            phase (str): 'train' or 'val'.
-            num_worker_per_gpu (int): Number of workers for each GPU.
-            batch_size_per_gpu (int): Training batch size for each GPU.
-        num_gpu (int): Number of GPUs. Used only in the train phase.
-            Default: 1.
-        dist (bool): Whether in distributed training. Used only in the train
-            phase. Default: False.
-        sampler (torch.utils.data.sampler): Data sampler. Default: None.
-        seed (int | None): Seed. Default: None
-    """
-    phase = dataset_opt['phase']
-    rank, _ = get_dist_info()
-    if phase == 'train':
-        if dist:  # distributed training
-            batch_size = dataset_opt['batch_size_per_gpu']
-            num_workers = dataset_opt['num_worker_per_gpu']
-        else:  # non-distributed training
-            multiplier = 1 if num_gpu == 0 else num_gpu
-            batch_size = dataset_opt['batch_size_per_gpu'] * multiplier
-            num_workers = dataset_opt['num_worker_per_gpu'] * multiplier
-        dataloader_args = dict(
-            dataset=dataset,
-            batch_size=batch_size,
-            shuffle=False,
-            num_workers=num_workers,
-            sampler=sampler,
-            drop_last=True)
-        if sampler is None:
-            dataloader_args['shuffle'] = True
-        dataloader_args['worker_init_fn'] = partial(
-            worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None
-    elif phase in ['val', 'test']:  # validation
-        dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    else:
-        raise ValueError(f"Wrong dataset phase: {phase}. Supported ones are 'train', 'val' and 'test'.")
-
-    dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False)
-    dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', False)
-
-    prefetch_mode = dataset_opt.get('prefetch_mode')
-    if prefetch_mode == 'cpu':  # CPUPrefetcher
-        num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1)
-        logger = get_root_logger()
-        logger.info(f'Use {prefetch_mode} prefetch dataloader: num_prefetch_queue = {num_prefetch_queue}')
-        return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args)
-    else:
-        # prefetch_mode=None: Normal dataloader
-        # prefetch_mode='cuda': dataloader for CUDAPrefetcher
-        return torch.utils.data.DataLoader(**dataloader_args)
-
-
-def worker_init_fn(worker_id, num_workers, rank, seed):
-    # Set the worker seed to num_workers * rank + worker_id + seed
-    worker_seed = num_workers * rank + worker_id + seed
-    np.random.seed(worker_seed)
-    random.seed(worker_seed)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/prepare-wmt18en2de.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/prepare-wmt18en2de.sh
deleted file mode 100644
index f6fd275307db50ca84c299440ae02dce49064030..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/backtranslation/prepare-wmt18en2de.sh
+++ /dev/null
@@ -1,135 +0,0 @@
-#!/bin/bash
-# Adapted from https://github.com/facebookresearch/MIXER/blob/master/prepareData.sh
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-echo 'Cloning Subword NMT repository (for BPE pre-processing)...'
-git clone https://github.com/rsennrich/subword-nmt.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-NORM_PUNC=$SCRIPTS/tokenizer/normalize-punctuation.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-BPEROOT=subword-nmt/subword_nmt
-BPE_TOKENS=32000
-
-URLS=(
-    "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
-    "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
-    "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz"
-    "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz"
-    "http://data.statmt.org/wmt17/translation-task/dev.tgz"
-    "http://statmt.org/wmt14/test-full.tgz"
-)
-FILES=(
-    "training-parallel-europarl-v7.tgz"
-    "training-parallel-commoncrawl.tgz"
-    "training-parallel-nc-v13.tgz"
-    "rapid2016.tgz"
-    "dev.tgz"
-    "test-full.tgz"
-)
-CORPORA=(
-    "training/europarl-v7.de-en"
-    "commoncrawl.de-en"
-    "training-parallel-nc-v13/news-commentary-v13.de-en"
-    "rapid2016.de-en"
-)
-
-if [ ! -d "$SCRIPTS" ]; then
-    echo "Please set SCRIPTS variable correctly to point to Moses scripts."
-    exit 1
-fi
-
-OUTDIR=wmt18_en_de
-
-src=en
-tgt=de
-lang=en-de
-prep=$OUTDIR
-tmp=$prep/tmp
-orig=orig
-
-mkdir -p $orig $tmp $prep
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
-    file=${FILES[i]}
-    if [ -f $file ]; then
-        echo "$file already exists, skipping download"
-    else
-        url=${URLS[i]}
-        wget "$url"
-        if [ -f $file ]; then
-            echo "$url successfully downloaded."
-        else
-            echo "$url not successfully downloaded."
-            exit 1
-        fi
-        if [ ${file: -4} == ".tgz" ]; then
-            tar zxvf $file
-        elif [ ${file: -4} == ".tar" ]; then
-            tar xvf $file
-        fi
-    fi
-done
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
-    rm $tmp/train.tags.$lang.tok.$l
-    for f in "${CORPORA[@]}"; do
-        cat $orig/$f.$l | \
-            perl $NORM_PUNC $l | \
-            perl $REM_NON_PRINT_CHAR | \
-            perl $TOKENIZER -threads 8 -a -l $l >> $tmp/train.tags.$lang.tok.$l
-    done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
-    if [ "$l" == "$src" ]; then
-        t="src"
-    else
-        t="ref"
-    fi
-    grep '<seg id' $orig/test-full/newstest2014-deen-$t.$l.sgm | \
-        sed -e 's/<seg id="[0-9]*">\s*//g' | \
-        sed -e 's/\s*<\/seg>\s*//g' | \
-        sed -e "s/\’/\'/g" | \
-    perl $TOKENIZER -threads 8 -a -l $l > $tmp/test.$l
-    echo ""
-done
-
-echo "splitting train and valid..."
-for l in $src $tgt; do
-    awk '{if (NR%100 == 0)  print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/valid.$l
-    awk '{if (NR%100 != 0)  print $0; }' $tmp/train.tags.$lang.tok.$l > $tmp/train.$l
-done
-
-TRAIN=$tmp/train.de-en
-BPE_CODE=$prep/code
-rm -f $TRAIN
-for l in $src $tgt; do
-    cat $tmp/train.$l >> $TRAIN
-done
-
-echo "learn_bpe.py on ${TRAIN}..."
-python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE
-
-for L in $src $tgt; do
-    for f in train.$L valid.$L test.$L; do
-        echo "apply_bpe.py to ${f}..."
-        python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $tmp/bpe.$f
-    done
-done
-
-perl $CLEAN -ratio 1.5 $tmp/bpe.train $src $tgt $prep/train 1 250
-perl $CLEAN -ratio 1.5 $tmp/bpe.valid $src $tgt $prep/valid 1 250
-
-for L in $src $tgt; do
-    cp $tmp/bpe.test.$L $prep/test.$L
-done
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/decoders/decoder.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/decoders/decoder.py
deleted file mode 100644
index b5bec8cf707b53104ef7a45993a5db2893d3443b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_recognition/new/decoders/decoder.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Union
-
-from fairseq.data.dictionary import Dictionary
-
-from .decoder_config import DecoderConfig, FlashlightDecoderConfig
-from .base_decoder import BaseDecoder
-
-
-def Decoder(
-    cfg: Union[DecoderConfig, FlashlightDecoderConfig], tgt_dict: Dictionary
-) -> BaseDecoder:
-
-    if cfg.type == "viterbi":
-        from .viterbi_decoder import ViterbiDecoder
-
-        return ViterbiDecoder(tgt_dict)
-    if cfg.type == "kenlm":
-        from .flashlight_decoder import KenLMDecoder
-
-        return KenLMDecoder(cfg, tgt_dict)
-    if cfg.type == "fairseqlm":
-        from .flashlight_decoder import FairseqLMDecoder
-
-        return FairseqLMDecoder(cfg, tgt_dict)
-    raise NotImplementedError(f"Invalid decoder name: {cfg.name}")
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/roberta/enc_dec.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/roberta/enc_dec.py
deleted file mode 100644
index e538dee0aa5984b1a3d02ce81117d2046c030593..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/roberta/enc_dec.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import argparse
-import logging
-
-import torch.nn as nn
-import fairseq.checkpoint_utils
-from fairseq.models import (
-    FairseqEncoderDecoderModel,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.models.transformer import TransformerDecoder
-from fairseq.models.roberta import model as roberta
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("roberta_enc_dec")
-class RobertaEncDecModel(FairseqEncoderDecoderModel):
-    @staticmethod
-    def add_args(parser):
-        parser.add_argument(
-            "--pretrained-mlm-checkpoint",
-            default=None,
-            type=str,
-            metavar="PRETRAINED",
-            help="path to pretrained mlm checkpoint",
-        )
-        parser.add_argument(
-            "--pretrained-decoder", action="store_true", help="reload decoder"
-        )
-        parser.add_argument(
-            "--hack-layernorm-embedding",
-            action="store_true",
-            help="hack to reload old models trained with encoder-normalize-before=False (no equivalent to encoder-normalize-before=False and layernorm_embedding=False",
-        )
-        parser.add_argument(
-            "--share-decoder-input-output-embed",
-            action="store_true",
-            help="share decoder input and output embeddings",
-        )
-        parser.add_argument(
-            "--share-all-embeddings",
-            action="store_true",
-            help="share encoder, decoder and output embeddings"
-            " (requires shared dictionary and embed dim)",
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present
-        base_enc_dec_architecture(args)
-        if args.pretrained_mlm_checkpoint:
-            arg_overrides = None
-            if args.hack_layernorm_embedding:
-                arg_overrides = {"layernorm_embedding": False}
-            loaded = fairseq.checkpoint_utils.load_model_ensemble_and_task(
-                [args.pretrained_mlm_checkpoint], arg_overrides=arg_overrides
-            )
-            ([roberta_enc], _cfg, _task) = loaded
-        else:
-            # Do we need to edit untie_weights here ?
-            share_in_out = (
-                args.share_decoder_input_output_embed or args.share_all_embeddings
-            )
-            args.untie_weights_roberta = not share_in_out
-            if args.hack_layernorm_embedding:
-                args.layernorm_embedding = False
-                args.encoder_normalize_before = False
-            roberta_enc = roberta.RobertaModel.build_model(args, task)
-
-        return cls.from_roberta(roberta_enc, args, task.source_dictionary)
-
-    @staticmethod
-    def from_roberta(roberta_enc: roberta.RobertaModel, args, dictionary):
-        encoder = roberta_enc.encoder.sentence_encoder
-        vocab_size, embed_dim = encoder.embed_tokens.weight.shape
-
-        if args.share_all_embeddings:
-            lm_head = roberta_enc.encoder.lm_head
-            assert encoder.embed_tokens.weight is lm_head.weight, (
-                "Can't use --share-all-embeddings with a model "
-                "that was pretraiend with --untie-weights-roberta_enc"
-            )
-        else:
-            lm_head = roberta.RobertaLMHead(
-                embed_dim, vocab_size, roberta_enc.args.activation_fn
-            )
-
-        dec_embs = nn.Embedding(vocab_size, embed_dim, dictionary.pad())
-        if args.share_all_embeddings or args.share_decoder_input_output_embed:
-            # Note: I wasn't able to use Embedding _weight parameter to achive this sharing.
-            dec_embs.weight = lm_head.weight
-
-        decoder = TransformerDecoder(
-            RobertaEncDecModel.read_args_from_roberta(roberta_enc.args),
-            dictionary,
-            dec_embs,
-            no_encoder_attn=False,
-            output_projection=lm_head,
-        )
-        if getattr(args, "pretrained_decoder", False):
-            decoder_dict = encoder.state_dict()
-
-            # TODO: hide setting "encoder_attn" layers behind a flag.
-            for k, w in list(decoder_dict.items()):
-                if ".self_attn" in k:
-                    k_enc_attn = k.replace(".self_attn", ".encoder_attn")
-                    decoder_dict[k_enc_attn] = w.detach().clone()
-
-            for k, w in lm_head.state_dict().items():
-                decoder_dict["output_projection." + k] = w
-
-            missing_keys, unexpected_keys = decoder.load_state_dict(
-                decoder_dict, strict=False
-            )
-            # missing_keys = [m for m in missing_keys if ".encoder_attn" not in m]
-            assert not missing_keys and not unexpected_keys, (
-                "Failed to load state dict. "
-                f"Missing keys: {missing_keys}. "
-                f"Unexpected keys: {unexpected_keys}."
-            )
-
-        if args.share_all_embeddings:
-            assert decoder.output_projection.weight is decoder.embed_tokens.weight
-            assert encoder.embed_tokens.weight is decoder.embed_tokens.weight
-        elif args.share_decoder_input_output_embed:
-            assert decoder.output_projection.weight is decoder.embed_tokens.weight
-            assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight
-        else:
-            assert decoder.output_projection.weight is not decoder.embed_tokens.weight
-            assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight
-
-        return RobertaEncDecModel(encoder, decoder)
-
-    @staticmethod
-    def read_args_from_roberta(roberta_args: argparse.Namespace):
-        # TODO: this would become easier if encoder/decoder where using a similar
-        # TransformerConfig object
-        args = argparse.Namespace(**vars(roberta_args))
-        attr_map = [
-            ("encoder_attention_heads", "decoder_attention_heads"),
-            ("encoder_embed_dim", "decoder_embed_dim"),
-            ("encoder_embed_dim", "decoder_output_dim"),
-            ("encoder_normalize_before", "decoder_normalize_before"),
-            ("encoder_layers_to_keep", "decoder_layers_to_keep"),
-            ("encoder_ffn_embed_dim", "decoder_ffn_embed_dim"),
-            ("encoder_layerdrop", "decoder_layerdrop"),
-            ("encoder_layers", "decoder_layers"),
-            ("encoder_learned_pos", "decoder_learned_pos"),
-            # should this be set from here ?
-            ("max_positions", "max_target_positions"),
-        ]
-        for k1, k2 in attr_map:
-            setattr(args, k2, getattr(roberta_args, k1))
-
-        args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-        args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-        args.share_decoder_input_output_embed = not roberta_args.untie_weights_roberta
-        return args
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        prefix = name + "." if name != "" else ""
-        super().upgrade_state_dict_named(state_dict, name)
-        old_keys = list(state_dict.keys())
-
-        # rename decoder -> encoder before upgrading children modules
-        for k in old_keys:
-            if k.startswith(prefix + "encoder.lm_head"):
-                state_dict.pop(k)
-                continue
-            new_k = k
-            new_k = new_k.replace(".sentence_encoder.", ".")
-            new_k = new_k.replace("decoder.lm_head.", "decoder.output_projection.")
-            if k == new_k:
-                continue
-            # print(k, "->", new_k)
-            state_dict[new_k] = state_dict.pop(k)
-
-
-@register_model_architecture("roberta_enc_dec", "roberta_enc_dec")
-def base_enc_dec_architecture(args):
-    args.hack_layernorm_embedding = getattr(args, "hack_layernorm_embedding", False)
-    args.pretrained_mlm_checkpoint = getattr(args, "pretrained_mlm_checkpoint", None)
-    args.pretrained_decoder = getattr(args, "pretrained_decoder", None)
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-
-    roberta.base_architecture(args)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py
deleted file mode 100644
index 09442206e19abf854f2f02754ec7c6f8bc564200..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/speech2unit/pretrained/hubert_feature_reader.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import fairseq
-import soundfile as sf
-import torch.nn.functional as F
-
-
-class HubertFeatureReader:
-    """
-    Wrapper class to run inference on HuBERT model.
-    Helps extract features for a given audio file.
-    """
-
-    def __init__(self, checkpoint_path, layer, max_chunk=1600000):
-        (
-            model,
-            cfg,
-            task,
-        ) = fairseq.checkpoint_utils.load_model_ensemble_and_task(
-            [checkpoint_path]
-        )
-        self.model = model[0].eval().cuda()
-        self.task = task
-        self.layer = layer
-        self.max_chunk = max_chunk
-
-    def read_audio(self, path, ref_len=None):
-        wav, sr = sf.read(path)
-        if wav.ndim == 2:
-            wav = wav.mean(-1)
-        assert wav.ndim == 1, wav.ndim
-        assert sr == self.task.cfg.sample_rate, sr
-        if ref_len is not None and abs(ref_len - len(wav)) > 160:
-            print(f"ref {ref_len} != read {len(wav)} ({path})")
-        return wav
-
-    def get_feats(self, file_path, ref_len=None):
-        x = self.read_audio(file_path, ref_len)
-        with torch.no_grad():
-            x = torch.from_numpy(x).float().cuda()
-            if self.task.cfg.normalize:
-                x = F.layer_norm(x, x.shape)
-            x = x.view(1, -1)
-
-            feat = []
-            for start in range(0, x.size(1), self.max_chunk):
-                x_chunk = x[:, start: start + self.max_chunk]
-                feat_chunk, _ = self.model.extract_features(
-                    source=x_chunk,
-                    padding_mask=None,
-                    mask=False,
-                    output_layer=self.layer,
-                )
-                feat.append(feat_chunk)
-        return torch.cat(feat, 1).squeeze(0)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py
deleted file mode 100644
index b07e274d202414ce40d00aa64a27cf97bb49c1c3..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import os
-import os.path as osp
-import tqdm
-import torch
-import torch.nn.functional as F
-from shutil import copyfile
-
-from npy_append_array import NpyAppendArray
-
-import fairseq
-import soundfile as sf
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="compute kmeans codebook from kaldi-computed feats"
-    )
-    # fmt: off
-    parser.add_argument('data', help='location of tsv files')
-    parser.add_argument('--split', help='which split to read', required=True)
-    parser.add_argument('--save-dir', help='where to save the output', required=True)
-    parser.add_argument('--checkpoint', type=str, help='checkpoint for wav2vec ctc model', required=True)
-    parser.add_argument('--layer', type=int, default=14, help='which layer to use')
-    # fmt: on
-
-    return parser
-
-
-class Wav2VecFeatureReader(object):
-    def __init__(self, cp_file, layer):
-        model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
-            [cp_file]
-        )
-        model = model[0]
-        model.eval()
-        model.cuda()
-        self.model = model
-        self.task = task
-        self.layer = layer
-
-    def read_audio(self, fname):
-        """Load an audio file and return PCM along with the sample rate"""
-        wav, sr = sf.read(fname)
-        assert sr == 16e3
-
-        return wav
-
-    def get_feats(self, loc):
-        x = self.read_audio(loc)
-        with torch.no_grad():
-            source = torch.from_numpy(x).float().cuda()
-            if self.task.cfg.normalize:
-                assert source.dim() == 1, source.dim()
-                with torch.no_grad():
-                    source = F.layer_norm(source, source.shape)
-            source = source.view(1, -1)
-
-            m_res = self.model(source=source, mask=False, features_only=True, layer=self.layer)
-            return m_res["x"].squeeze(0).cpu()
-
-
-def get_iterator(args):
-    with open(osp.join(args.data, args.split) + ".tsv", "r") as fp:
-        lines = fp.read().split("\n")
-        root = lines.pop(0).strip()
-        files = [osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0]
-
-        num = len(files)
-        reader = Wav2VecFeatureReader(args.checkpoint, args.layer)
-
-        def iterate():
-            for fname in files:
-                w2v_feats = reader.get_feats(fname)
-                yield w2v_feats
-
-    return iterate, num
-
-
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    os.makedirs(args.save_dir, exist_ok=True)
-
-    def create_files(dest):
-        copyfile(osp.join(args.data, args.split) + ".tsv", dest + ".tsv")
-        if osp.exists(osp.join(args.data, args.split) + ".wrd"):
-            copyfile(osp.join(args.data, args.split) + ".wrd", dest + ".wrd")
-        if osp.exists(osp.join(args.data, args.split) + ".phn"):
-            copyfile(osp.join(args.data, args.split) + ".phn", dest + ".phn")
-
-        if osp.exists(dest + ".npy"):
-            os.remove(dest + ".npy")
-        npaa = NpyAppendArray(dest + ".npy")
-        return npaa
-
-    save_path = osp.join(args.save_dir, args.split)
-    npaa = create_files(save_path)
-
-    generator, num = get_iterator(args)
-    iterator = generator()
-
-    with open(save_path + ".lengths", "w") as l_f:
-        for w2v_feats in tqdm.tqdm(iterator, total=num):
-            print(len(w2v_feats), file=l_f)
-
-            if len(w2v_feats) > 0:
-                npaa.append(w2v_feats.numpy())
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/audio/audio_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/audio/audio_utils.py
deleted file mode 100644
index b9444cb8d005fe537b2968d9ed0d92273c46b8f6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/audio/audio_utils.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-from pathlib import Path
-from typing import BinaryIO, Optional, Tuple, Union, List
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-SF_AUDIO_FILE_EXTENSIONS = {".wav", ".flac", ".ogg"}
-FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS = {".npy", ".wav", ".flac", ".ogg"}
-
-
-def convert_waveform(
-        waveform: Union[np.ndarray, torch.Tensor], sample_rate: int,
-        normalize_volume: bool = False, to_mono: bool = False,
-        to_sample_rate: Optional[int] = None
-) -> Tuple[Union[np.ndarray, torch.Tensor], int]:
-    """convert a waveform:
-        - to a target sample rate
-        - from multi-channel to mono channel
-        - volume normalization
-
-        Args:
-            waveform (numpy.ndarray or torch.Tensor): 2D original waveform
-                (channels x length)
-            sample_rate (int): original sample rate
-            normalize_volume (bool): perform volume normalization
-            to_mono (bool): convert to mono channel if having multiple channels
-            to_sample_rate (Optional[int]): target sample rate
-        Returns:
-            waveform (numpy.ndarray): converted 2D waveform (channels x length)
-            sample_rate (float): target sample rate
-        """
-    try:
-        import torchaudio.sox_effects as ta_sox
-    except ImportError:
-        raise ImportError("Please install torchaudio: pip install torchaudio")
-
-    effects = []
-    if normalize_volume:
-        effects.append(["gain", "-n"])
-    if to_sample_rate is not None and to_sample_rate != sample_rate:
-        effects.append(["rate", f"{to_sample_rate}"])
-    if to_mono and waveform.shape[0] > 1:
-        effects.append(["channels", "1"])
-    if len(effects) > 0:
-        is_np_input = isinstance(waveform, np.ndarray)
-        _waveform = torch.from_numpy(waveform) if is_np_input else waveform
-        converted, converted_sample_rate = ta_sox.apply_effects_tensor(
-            _waveform, sample_rate, effects
-        )
-        if is_np_input:
-            converted = converted.numpy()
-        return converted, converted_sample_rate
-    return waveform, sample_rate
-
-
-def get_waveform(
-        path_or_fp: Union[str, BinaryIO], normalization: bool = True,
-        mono: bool = True, frames: int = -1, start: int = 0,
-        always_2d: bool = True, output_sample_rate: Optional[int] = None,
-        normalize_volume: bool = False
-) -> Tuple[np.ndarray, int]:
-    """Get the waveform and sample rate of a 16-bit WAV/FLAC/OGG Vorbis audio.
-
-    Args:
-        path_or_fp (str or BinaryIO): the path or file-like object
-        normalization (bool): normalize values to [-1, 1] (Default: True)
-        mono (bool): convert multi-channel audio to mono-channel one
-        frames (int): the number of frames to read. (-1 for reading all)
-        start (int): Where to start reading. A negative value counts from the end.
-        always_2d (bool): always return 2D array even for mono-channel audios
-        output_sample_rate (Optional[int]): output sample rate
-        normalize_volume (bool): normalize volume
-    Returns:
-        waveform (numpy.ndarray): 1D or 2D waveform (channels x length)
-        sample_rate (float): sample rate
-    """
-    if isinstance(path_or_fp, str):
-        ext = Path(path_or_fp).suffix
-        if ext not in SF_AUDIO_FILE_EXTENSIONS:
-            raise ValueError(f"Unsupported audio format: {ext}")
-
-    try:
-        import soundfile as sf
-    except ImportError:
-        raise ImportError("Please install soundfile: pip install soundfile")
-
-    waveform, sample_rate = sf.read(
-        path_or_fp, dtype="float32", always_2d=True, frames=frames, start=start
-    )
-    waveform = waveform.T  # T x C -> C x T
-    waveform, sample_rate = convert_waveform(
-        waveform, sample_rate, normalize_volume=normalize_volume, to_mono=mono,
-        to_sample_rate=output_sample_rate
-    )
-
-    if not normalization:
-        waveform *= 2 ** 15  # denormalized to 16-bit signed integers
-    if not always_2d:
-        waveform = waveform.squeeze(axis=0)
-    return waveform, sample_rate
-
-
-def _get_kaldi_fbank(
-    waveform: np.ndarray, sample_rate: int, n_bins=80
-) -> Optional[np.ndarray]:
-    """Get mel-filter bank features via PyKaldi."""
-    try:
-        from kaldi.feat.fbank import FbankOptions, Fbank
-        from kaldi.feat.mel import MelBanksOptions
-        from kaldi.feat.window import FrameExtractionOptions
-        from kaldi.matrix import Vector
-
-        mel_opts = MelBanksOptions()
-        mel_opts.num_bins = n_bins
-        frame_opts = FrameExtractionOptions()
-        frame_opts.samp_freq = sample_rate
-        opts = FbankOptions()
-        opts.mel_opts = mel_opts
-        opts.frame_opts = frame_opts
-        fbank = Fbank(opts=opts)
-        features = fbank.compute(Vector(waveform.squeeze()), 1.0).numpy()
-        return features
-    except ImportError:
-        return None
-
-
-def _get_torchaudio_fbank(
-    waveform: np.ndarray, sample_rate, n_bins=80
-) -> Optional[np.ndarray]:
-    """Get mel-filter bank features via TorchAudio."""
-    try:
-        import torchaudio.compliance.kaldi as ta_kaldi
-
-        waveform = torch.from_numpy(waveform)
-        features = ta_kaldi.fbank(
-            waveform, num_mel_bins=n_bins, sample_frequency=sample_rate
-        )
-        return features.numpy()
-    except ImportError:
-        return None
-
-
-def get_fbank(path_or_fp: Union[str, BinaryIO], n_bins=80) -> np.ndarray:
-    """Get mel-filter bank features via PyKaldi or TorchAudio. Prefer PyKaldi
-    (faster CPP implementation) to TorchAudio (Python implementation). Note that
-    Kaldi/TorchAudio requires 16-bit signed integers as inputs and hence the
-    waveform should not be normalized."""
-    waveform, sample_rate = get_waveform(path_or_fp, normalization=False)
-
-    features = _get_kaldi_fbank(waveform, sample_rate, n_bins)
-    if features is None:
-        features = _get_torchaudio_fbank(waveform, sample_rate, n_bins)
-    if features is None:
-        raise ImportError(
-            "Please install pyKaldi or torchaudio to enable "
-            "online filterbank feature extraction"
-        )
-
-    return features
-
-
-def is_npy_data(data: bytes) -> bool:
-    return data[0] == 147 and data[1] == 78
-
-
-def is_sf_audio_data(data: bytes) -> bool:
-    is_wav = data[0] == 82 and data[1] == 73 and data[2] == 70
-    is_flac = data[0] == 102 and data[1] == 76 and data[2] == 97
-    is_ogg = data[0] == 79 and data[1] == 103 and data[2] == 103
-    return is_wav or is_flac or is_ogg
-
-
-def read_from_stored_zip(zip_path: str, offset: int, file_size: int) -> bytes:
-    with open(zip_path, "rb") as f:
-        f.seek(offset)
-        data = f.read(file_size)
-    return data
-
-
-def parse_path(path: str) -> Tuple[str, List[int]]:
-    """Parse data path which is either a path to
-    1. a .npy/.wav/.flac/.ogg file
-    2. a stored ZIP file with slicing info: "[zip_path]:[offset]:[length]"
-
-      Args:
-          path (str): the data path to parse
-
-      Returns:
-          file_path (str): the file path
-          slice_ptr (list of int): empty in case 1;
-            byte offset and length for the slice in case 2
-    """
-
-    if Path(path).suffix in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS:
-        _path, slice_ptr = path, []
-    else:
-        _path, *slice_ptr = path.split(":")
-        if not Path(_path).is_file():
-            raise FileNotFoundError(f"File not found: {_path}")
-    assert len(slice_ptr) in {0, 2}, f"Invalid path: {path}"
-    slice_ptr = [int(i) for i in slice_ptr]
-    return _path, slice_ptr
-
-
-def get_window(
-        window_fn: callable, n_fft: int, win_length: int
-) -> torch.Tensor:
-    padding = n_fft - win_length
-    assert padding >= 0
-    return F.pad(window_fn(win_length), (padding // 2, padding - padding // 2))
-
-
-def get_fourier_basis(n_fft: int) -> torch.Tensor:
-    basis = np.fft.fft(np.eye(n_fft))
-    basis = np.vstack(
-        [np.real(basis[:n_fft // 2 + 1, :]), np.imag(basis[:n_fft // 2 + 1, :])]
-    )
-    return torch.from_numpy(basis).float()
-
-
-def get_mel_filters(
-        sample_rate: int, n_fft: int, n_mels: int, f_min: float, f_max: float
-) -> torch.Tensor:
-    try:
-        import librosa
-    except ImportError:
-        raise ImportError("Please install librosa: pip install librosa")
-    basis = librosa.filters.mel(sample_rate, n_fft, n_mels, f_min, f_max)
-    return torch.from_numpy(basis).float()
-
-
-class TTSSpectrogram(torch.nn.Module):
-    def __init__(
-            self, n_fft: int, win_length: int, hop_length: int,
-            window_fn: callable = torch.hann_window, return_phase: bool = False
-    ) -> None:
-        super(TTSSpectrogram, self).__init__()
-        self.n_fft = n_fft
-        self.hop_length = hop_length
-        self.return_phase = return_phase
-
-        basis = get_fourier_basis(n_fft).unsqueeze(1)
-        basis *= get_window(window_fn, n_fft, win_length)
-        self.register_buffer('basis', basis)
-
-    def forward(
-            self, waveform: torch.Tensor
-    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
-        padding = (self.n_fft // 2, self.n_fft // 2)
-        x = F.pad(waveform.unsqueeze(1), padding, mode='reflect')
-        x = F.conv1d(x, self.basis, stride=self.hop_length)
-        real_part = x[:, :self.n_fft // 2 + 1, :]
-        imag_part = x[:, self.n_fft // 2 + 1:, :]
-        magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2)
-        if self.return_phase:
-            phase = torch.atan2(imag_part, real_part)
-            return magnitude, phase
-        return magnitude
-
-
-class TTSMelScale(torch.nn.Module):
-    def __init__(
-            self, n_mels: int, sample_rate: int, f_min: float, f_max: float,
-            n_stft: int
-    ) -> None:
-        super(TTSMelScale, self).__init__()
-        basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min,
-                                f_max)
-        self.register_buffer('basis', basis)
-
-    def forward(self, specgram: torch.Tensor) -> torch.Tensor:
-        return torch.matmul(self.basis, specgram)
diff --git a/spaces/ORI-Muchim/PowerTTS/text/__init__.py b/spaces/ORI-Muchim/PowerTTS/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/PowerTTS/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py
deleted file mode 100644
index 1aad53bfef62fb584d5022585d567e346f671a55..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco_keypoint import dataloader
-from ..common.models.keypoint_rcnn_fpn import model
-from ..common.train import train
-
-model.backbone.bottom_up.freeze_at = 2
-train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
diff --git a/spaces/OptimalScale/Robin-33b/lmflow/__init__.py b/spaces/OptimalScale/Robin-33b/lmflow/__init__.py
deleted file mode 100644
index 529b2232f82b947e7e85e3e00839c1479db40e28..0000000000000000000000000000000000000000
--- a/spaces/OptimalScale/Robin-33b/lmflow/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from .version import __version__ as internal_version
-
-__version__ = internal_version
-
-from transformers.utils import check_min_version
-from transformers.utils.versions import require_version
-
-from lmflow import args, datasets, models, pipeline, utils
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.27.0.dev0")
-
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/midas/midas/base_model.py b/spaces/PAIR/Text2Video-Zero/annotator/midas/midas/base_model.py
deleted file mode 100644
index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/midas/midas/base_model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device('cpu'))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/scripts/snarf-guile-m4-docs.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/scripts/snarf-guile-m4-docs.go
deleted file mode 100644
index 4d5c44bfa964c2c5ecd88701fe557201c2a03f2b..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/scripts/snarf-guile-m4-docs.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/AutoGPT/tests/integration/milvus_memory_tests.py b/spaces/PeepDaSlan9/AutoGPT/tests/integration/milvus_memory_tests.py
deleted file mode 100644
index ec38bf2f72087b5da679d26594ebff97d8a09b19..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/tests/integration/milvus_memory_tests.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# sourcery skip: snake-case-functions
-"""Tests for the MilvusMemory class."""
-import random
-import string
-import unittest
-
-from autogpt.config import Config
-from autogpt.memory.milvus import MilvusMemory
-
-try:
-
-    class TestMilvusMemory(unittest.TestCase):
-        """Tests for the MilvusMemory class."""
-
-        def random_string(self, length: int) -> str:
-            """Generate a random string of the given length."""
-            return "".join(random.choice(string.ascii_letters) for _ in range(length))
-
-        def setUp(self) -> None:
-            """Set up the test environment."""
-            cfg = Config()
-            cfg.milvus_addr = "localhost:19530"
-            self.memory = MilvusMemory(cfg)
-            self.memory.clear()
-
-            # Add example texts to the cache
-            self.example_texts = [
-                "The quick brown fox jumps over the lazy dog",
-                "I love machine learning and natural language processing",
-                "The cake is a lie, but the pie is always true",
-                "ChatGPT is an advanced AI model for conversation",
-            ]
-
-            for text in self.example_texts:
-                self.memory.add(text)
-
-            # Add some random strings to test noise
-            for _ in range(5):
-                self.memory.add(self.random_string(10))
-
-        def test_get_relevant(self) -> None:
-            """Test getting relevant texts from the cache."""
-            query = "I'm interested in artificial intelligence and NLP"
-            num_relevant = 3
-            relevant_texts = self.memory.get_relevant(query, num_relevant)
-
-            print(f"Top {k} relevant texts for the query '{query}':")
-            for i, text in enumerate(relevant_texts, start=1):
-                print(f"{i}. {text}")
-
-            self.assertEqual(len(relevant_texts), k)
-            self.assertIn(self.example_texts[1], relevant_texts)
-
-except:
-    print(
-        "Skipping tests/integration/milvus_memory_tests.py as Milvus is not installed."
-    )
diff --git a/spaces/PhenixNova/Audio-VideoTranslator/app.py b/spaces/PhenixNova/Audio-VideoTranslator/app.py
deleted file mode 100644
index 94eb5f519661ac4e32ff8531bdae27db5484abee..0000000000000000000000000000000000000000
--- a/spaces/PhenixNova/Audio-VideoTranslator/app.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import gradio as gr 
-import os
-import subprocess
-import whisper
-from whisper.utils import write_vtt
-
-model = whisper.load_model("small")
-
-def video2mp3(video_file, output_ext="mp3"):
-    filename, ext = os.path.splitext(video_file)
-    subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"], 
-                    stdout=subprocess.DEVNULL,
-                    stderr=subprocess.STDOUT)
-    return f"{filename}.{output_ext}"
-
-def transcribe_audio(file):
-    options = dict(task="transcribe", best_of=5, fp16 = False)
-    text = model.transcribe(file, **options)["text"]
-    return text.strip()
-
-def translate_audio(file):
-    options = dict(task="translate", best_of=5, fp16 = False)
-    text = model.transcribe(file, **options)["text"]
-    return text.strip()
-
-def translate_video(input_video):
-    audio_file = video2mp3(input_video)
-    
-    options = dict(beam_size=5, best_of=5, fp16 = False)
-    translate_options = dict(task="translate", **options)
-    result = model.transcribe(audio_file,**translate_options)
-
-    output_dir = '/content/'
-    audio_path = audio_file.split(".")[0]
-
-    with open(os.path.join(output_dir, audio_path + ".vtt"), "w") as vtt:
-      write_vtt(result["segments"], file=vtt)
-
-    subtitle = audio_path + ".vtt"
-    output_video = audio_path + "_subtitled.mp4"
-
-    os.system(f"ffmpeg -i {input_video} -vf subtitles={subtitle} {output_video}")
-
-    return output_video
-
-block = gr.Blocks()
-
-with block:
-
-    with gr.Group():
-        with gr.Box(): 
-            with gr.Row().style():
-                inp_video = gr.Video(
-                    label="Input Video",
-                    type="filepath",
-                    mirror_webcam = False
-                )
-                op_video = gr.Video()
-            btn = gr.Button("Generate Subtitle Video")
-        btn.click(translate_video, inputs=[inp_video], outputs=[op_video])
-    with gr.Group():
-        audio = gr.Audio(
-            show_label=False,
-            source="microphone",
-            type="filepath"
-        )
-        with gr.Box():
-            with gr.Row().style(equal_height=True):
-                transcribe_button = gr.Button("Transcribe")
-                translate_button = gr.Button("Translate")
-        textbox = gr.Textbox(show_label=False)
-        transcribe_button.click(transcribe_audio, inputs=[audio], outputs=[textbox])
-        translate_button.click(translate_audio, inputs=[audio], outputs=[textbox])
-
-block.launch(debug = True)
\ No newline at end of file
diff --git a/spaces/PrabhuKiranKonda/Streamlit-PDF-Assistant-Docker/components/sidebar/OpenAI_API.py b/spaces/PrabhuKiranKonda/Streamlit-PDF-Assistant-Docker/components/sidebar/OpenAI_API.py
deleted file mode 100644
index 56bddd8e4bd80b77e13585fdd7f2f0c566ea9781..0000000000000000000000000000000000000000
--- a/spaces/PrabhuKiranKonda/Streamlit-PDF-Assistant-Docker/components/sidebar/OpenAI_API.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-import os
-
-def set_openAi_api_key(api_key: str):
-    st.session_state["OPENAI_API_KEY"] = api_key
-    os.environ['OPENAI_API_KEY'] = api_key
-
-
-def openai_api_insert_component():
-     with st.sidebar:
-        st.markdown(
-            "## Usage Instructions\n"
-            "1. Enter your [OpenAI API key](https://platform.openai.com/account/api-keys) below🔑\n"
-            "2. Upload a pdf file📄\n"
-            "3. Ask a question about the document💬\n"
-        )
-
-        api_key_input = st.text_input("OpenAI API Key",
-                                      type="password",
-                                      placeholder="OpenAI API Key",
-                                      help="You can get your API key from https://platform.openai.com/account/api-keys.")
-        
-        if st.session_state.get('generate_answer_button', None):
-            if api_key_input == "" or api_key_input is None:
-                st.sidebar.caption("👆 :red[Please set your OpenAI API Key here]")
-        
-        
-        st.caption(":green[Your API is not stored anywhere. It is only used to generate answers to your questions.]")
-
-        set_openAi_api_key(api_key_input)
-        
-        
-if __name__ == "__main__":
-    openai_api_insert_component()
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/common_utils/temp_utils.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/common_utils/temp_utils.py
deleted file mode 100644
index b45d896836799edcf1fee271409b390b3b6e4127..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/common_utils/temp_utils.py
+++ /dev/null
@@ -1,56 +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 os
-import tempfile
-
-
-class TempDirMixin:
-    """Mixin to provide easy access to temp dir.
-    """
-
-    temp_dir_ = None
-
-    @classmethod
-    def get_base_temp_dir(cls):
-        # If AUDIOCRAFT_TEST_DIR is set, use it instead of temporary directory.
-        # this is handy for debugging.
-        key = "AUDIOCRAFT_TEST_DIR"
-        if key in os.environ:
-            return os.environ[key]
-        if cls.temp_dir_ is None:
-            cls.temp_dir_ = tempfile.TemporaryDirectory()
-        return cls.temp_dir_.name
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.temp_dir_ is not None:
-            try:
-                cls.temp_dir_.cleanup()
-                cls.temp_dir_ = None
-            except PermissionError:
-                # On Windows there is a know issue with `shutil.rmtree`,
-                # which fails intermittently.
-                # https://github.com/python/cpython/issues/74168
-                # Following the above thread, we ignore it.
-                pass
-        super().tearDownClass()
-
-    @property
-    def id(self):
-        return self.__class__.__name__
-
-    def get_temp_path(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        return path
-
-    def get_temp_dir(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(path, exist_ok=True)
-        return path
diff --git a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/faceshq.py b/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/faceshq.py
deleted file mode 100644
index 6912d04b66a6d464c1078e4b51d5da290f5e767e..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/faceshq.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import os
-import numpy as np
-import albumentations
-from torch.utils.data import Dataset
-
-from taming.data.base import ImagePaths, NumpyPaths, ConcatDatasetWithIndex
-
-
-class FacesBase(Dataset):
-    def __init__(self, *args, **kwargs):
-        super().__init__()
-        self.data = None
-        self.keys = None
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        example = self.data[i]
-        ex = {}
-        if self.keys is not None:
-            for k in self.keys:
-                ex[k] = example[k]
-        else:
-            ex = example
-        return ex
-
-
-class CelebAHQTrain(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/celebahq"
-        with open("data/celebahqtrain.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = NumpyPaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class CelebAHQValidation(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/celebahq"
-        with open("data/celebahqvalidation.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = NumpyPaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FFHQTrain(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/ffhq"
-        with open("data/ffhqtrain.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FFHQValidation(FacesBase):
-    def __init__(self, size, keys=None):
-        super().__init__()
-        root = "data/ffhq"
-        with open("data/ffhqvalidation.txt", "r") as f:
-            relpaths = f.read().splitlines()
-        paths = [os.path.join(root, relpath) for relpath in relpaths]
-        self.data = ImagePaths(paths=paths, size=size, random_crop=False)
-        self.keys = keys
-
-
-class FacesHQTrain(Dataset):
-    # CelebAHQ [0] + FFHQ [1]
-    def __init__(self, size, keys=None, crop_size=None, coord=False):
-        d1 = CelebAHQTrain(size=size, keys=keys)
-        d2 = FFHQTrain(size=size, keys=keys)
-        self.data = ConcatDatasetWithIndex([d1, d2])
-        self.coord = coord
-        if crop_size is not None:
-            self.cropper = albumentations.RandomCrop(height=crop_size,width=crop_size)
-            if self.coord:
-                self.cropper = albumentations.Compose([self.cropper],
-                                                      additional_targets={"coord": "image"})
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        ex, y = self.data[i]
-        if hasattr(self, "cropper"):
-            if not self.coord:
-                out = self.cropper(image=ex["image"])
-                ex["image"] = out["image"]
-            else:
-                h,w,_ = ex["image"].shape
-                coord = np.arange(h*w).reshape(h,w,1)/(h*w)
-                out = self.cropper(image=ex["image"], coord=coord)
-                ex["image"] = out["image"]
-                ex["coord"] = out["coord"]
-        ex["class"] = y
-        return ex
-
-
-class FacesHQValidation(Dataset):
-    # CelebAHQ [0] + FFHQ [1]
-    def __init__(self, size, keys=None, crop_size=None, coord=False):
-        d1 = CelebAHQValidation(size=size, keys=keys)
-        d2 = FFHQValidation(size=size, keys=keys)
-        self.data = ConcatDatasetWithIndex([d1, d2])
-        self.coord = coord
-        if crop_size is not None:
-            self.cropper = albumentations.CenterCrop(height=crop_size,width=crop_size)
-            if self.coord:
-                self.cropper = albumentations.Compose([self.cropper],
-                                                      additional_targets={"coord": "image"})
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        ex, y = self.data[i]
-        if hasattr(self, "cropper"):
-            if not self.coord:
-                out = self.cropper(image=ex["image"])
-                ex["image"] = out["image"]
-            else:
-                h,w,_ = ex["image"].shape
-                coord = np.arange(h*w).reshape(h,w,1)/(h*w)
-                out = self.cropper(image=ex["image"], coord=coord)
-                ex["image"] = out["image"]
-                ex["coord"] = out["coord"]
-        ex["class"] = y
-        return ex
diff --git a/spaces/RMXK/RVC_HFF/tools/dlmodels.sh b/spaces/RMXK/RVC_HFF/tools/dlmodels.sh
deleted file mode 100644
index 5fba0edef345c0a4384aa9402cfd5e93e29efdc3..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tools/dlmodels.sh
+++ /dev/null
@@ -1,566 +0,0 @@
-#!/bin/bash
-
-echo working dir is $(pwd)
-echo downloading requirement aria2 check.
-
-if command -v aria2c &> /dev/null
-then
-    echo "aria2c command found"
-else
-    echo failed. please install aria2
-    sleep 5
-    exit 1
-fi
-
-d32="f0D32k.pth"
-d40="f0D40k.pth"
-d48="f0D48k.pth"
-g32="f0G32k.pth"
-g40="f0G40k.pth"
-g48="f0G48k.pth"
-
-d40v2="f0D40k.pth"
-g40v2="f0G40k.pth"
-
-dld32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth"
-dld40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth"
-dld48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth"
-dlg32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth"
-dlg40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth"
-dlg48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth"
-
-dld40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth"
-dlg40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth"
-
-hp2_all="HP2_all_vocals.pth"
-hp3_all="HP3_all_vocals.pth"
-hp5_only="HP5_only_main_vocal.pth"
-VR_DeEchoAggressive="VR-DeEchoAggressive.pth"
-VR_DeEchoDeReverb="VR-DeEchoDeReverb.pth"
-VR_DeEchoNormal="VR-DeEchoNormal.pth"
-onnx_dereverb="vocals.onnx"
-rmvpe="rmvpe.pt"
-
-dlhp2_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth"
-dlhp3_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth"
-dlhp5_only="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth"
-dlVR_DeEchoAggressive="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth"
-dlVR_DeEchoDeReverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth"
-dlVR_DeEchoNormal="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth"
-dlonnx_dereverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx"
-dlrmvpe="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt"
-
-hb="hubert_base.pt"
-
-dlhb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt"
-
-echo dir check start.
-
-if [ -d "./assets/pretrained" ]; then
-    echo dir ./assets/pretrained checked.
-else
-    echo failed. generating dir ./assets/pretrained.
-    mkdir pretrained
-fi
-
-if [ -d "./assets/pretrained_v2" ]; then
-    echo dir ./assets/pretrained_v2 checked.
-else
-    echo failed. generating dir ./assets/pretrained_v2.
-    mkdir pretrained_v2
-fi
-
-if [ -d "./assets/uvr5_weights" ]; then
-    echo dir ./assets/uvr5_weights checked.
-else
-    echo failed. generating dir ./assets/uvr5_weights.
-    mkdir uvr5_weights
-fi
-
-if [ -d "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy" ]; then
-    echo dir ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy checked.
-else
-    echo failed. generating dir ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy.
-    mkdir uvr5_weights/onnx_dereverb_By_FoxJoy
-fi
-
-echo dir check finished.
-
-echo required files check start.
-
-echo checking D32k.pth
-if [ -f "./assets/pretrained/D32k.pth" ]; then
-    echo D32k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d ./assets/pretrained -o D32k.pth
-        if [ -f "./assets/pretrained/D32k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking D40k.pth
-if [ -f "./assets/pretrained/D40k.pth" ]; then
-    echo D40k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d ./assets/pretrained -o D40k.pth
-        if [ -f "./assets/pretrained/D40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking D40k.pth
-if [ -f "./assets/pretrained_v2/D40k.pth" ]; then
-    echo D40k.pth in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d ./assets/pretrained_v2 -o D40k.pth
-        if [ -f "./assets/pretrained_v2/D40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking D48k.pth
-if [ -f "./assets/pretrained/D48k.pth" ]; then
-    echo D48k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d ./assets/pretrained -o D48k.pth
-        if [ -f "./assets/pretrained/D48k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G32k.pth
-if [ -f "./assets/pretrained/G32k.pth" ]; then
-    echo G32k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d ./assets/pretrained -o G32k.pth
-        if [ -f "./assets/pretrained/G32k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G40k.pth
-if [ -f "./assets/pretrained/G40k.pth" ]; then
-    echo G40k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d ./assets/pretrained -o G40k.pth
-        if [ -f "./assets/pretrained/G40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G40k.pth
-if [ -f "./assets/pretrained_v2/G40k.pth" ]; then
-    echo G40k.pth in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d ./assets/pretrained_v2 -o G40k.pth
-        if [ -f "./assets/pretrained_v2/G40k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking G48k.pth
-if [ -f "./assets/pretrained/G48k.pth" ]; then
-    echo G48k.pth in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d ./assets/pretrained -o G48k.pth
-        if [ -f "./assets/pretrained/G48k.pth" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d32
-if [ -f "./assets/pretrained/$d32" ]; then
-    echo $d32 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld32 -d ./assets/pretrained -o $d32
-        if [ -f "./assets/pretrained/$d32" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d40
-if [ -f "./assets/pretrained/$d40" ]; then
-    echo $d40 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40 -d ./assets/pretrained -o $d40
-        if [ -f "./assets/pretrained/$d40" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d40v2
-if [ -f "./assets/pretrained_v2/$d40v2" ]; then
-    echo $d40v2 in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40v2 -d ./assets/pretrained_v2 -o $d40v2
-        if [ -f "./assets/pretrained_v2/$d40v2" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $d48
-if [ -f "./assets/pretrained/$d48" ]; then
-    echo $d48 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld48 -d ./assets/pretrained -o $d48
-        if [ -f "./assets/pretrained/$d48" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g32
-if [ -f "./assets/pretrained/$g32" ]; then
-    echo $g32 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg32 -d ./assets/pretrained -o $g32
-        if [ -f "./assets/pretrained/$g32" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g40
-if [ -f "./assets/pretrained/$g40" ]; then
-    echo $g40 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40 -d ./assets/pretrained -o $g40
-        if [ -f "./assets/pretrained/$g40" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g40v2
-if [ -f "./assets/pretrained_v2/$g40v2" ]; then
-    echo $g40v2 in ./assets/pretrained_v2 checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40v2 -d ./assets/pretrained_v2 -o $g40v2
-        if [ -f "./assets/pretrained_v2/$g40v2" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $g48
-if [ -f "./assets/pretrained/$g48" ]; then
-    echo $g48 in ./assets/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg48 -d ./assets/pretrained -o $g48
-        if [ -f "./assets/pretrained/$g48" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hp2_all
-if [ -f "./assets/uvr5_weights/$hp2_all" ]; then
-    echo $hp2_all in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp2_all -d ./assets/uvr5_weights -o $hp2_all
-        if [ -f "./assets/uvr5_weights/$hp2_all" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hp3_all
-if [ -f "./assets/uvr5_weights/$hp3_all" ]; then
-    echo $hp3_all in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp3_all -d ./assets/uvr5_weights -o $hp3_all
-        if [ -f "./assets/uvr5_weights/$hp3_all" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hp5_only
-if [ -f "./assets/uvr5_weights/$hp5_only" ]; then
-    echo $hp5_only in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp5_only -d ./assets/uvr5_weights -o $hp5_only
-        if [ -f "./assets/uvr5_weights/$hp5_only" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $VR_DeEchoAggressive
-if [ -f "./assets/uvr5_weights/$VR_DeEchoAggressive" ]; then
-    echo $VR_DeEchoAggressive in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoAggressive -d ./assets/uvr5_weights -o $VR_DeEchoAggressive
-        if [ -f "./assets/uvr5_weights/$VR_DeEchoAggressive" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $VR_DeEchoDeReverb
-if [ -f "./assets/uvr5_weights/$VR_DeEchoDeReverb" ]; then
-    echo $VR_DeEchoDeReverb in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoDeReverb -d ./assets/uvr5_weights -o $VR_DeEchoDeReverb
-        if [ -f "./assets/uvr5_weights/$VR_DeEchoDeReverb" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $VR_DeEchoNormal
-if [ -f "./assets/uvr5_weights/$VR_DeEchoNormal" ]; then
-    echo $VR_DeEchoNormal in ./assets/uvr5_weights checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoNormal -d ./assets/uvr5_weights -o $VR_DeEchoNormal
-        if [ -f "./assets/uvr5_weights/$VR_DeEchoNormal" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $onnx_dereverb
-if [ -f "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then
-    echo $onnx_dereverb in ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlonnx_dereverb -d ./assets/uvr5_weights/onnx_dereverb_By_FoxJoy -o $onnx_dereverb
-        if [ -f "./assets/uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $rmvpe
-if [ -f "./assets/rmvpe/$rmvpe" ]; then
-    echo $rmvpe in ./assets/rmvpe checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlrmvpe -d ./assets/rmvpe -o $rmvpe
-        if [ -f "./assets/rmvpe/$rmvpe" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo checking $hb
-if [ -f "./assets/hubert/$hb" ]; then
-    echo $hb in ./assets/hubert/pretrained checked.
-else
-    echo failed. starting download from huggingface.
-    if command -v aria2c &> /dev/null; then
-        aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhb -d ./assets/hubert/ -o $hb
-        if [ -f "./assets/hubert/$hb" ]; then
-            echo download successful.
-        else
-            echo please try again!
-            exit 1
-        fi
-    else
-        echo aria2c command not found. Please install aria2c and try again.
-        exit 1
-    fi
-fi
-
-echo required files check finished.
diff --git a/spaces/Ramse/TTS_Hindi/modules/hifigan/utils/plotting.py b/spaces/Ramse/TTS_Hindi/modules/hifigan/utils/plotting.py
deleted file mode 100644
index 4967e534a7bfdbc10d4a90a0a4656822e15c07d1..0000000000000000000000000000000000000000
--- a/spaces/Ramse/TTS_Hindi/modules/hifigan/utils/plotting.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-import numpy as np
-import glob
-
-
-def save_figure_to_numpy(fig, spectrogram = False):
-    # save it to a numpy array.
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    if spectrogram:
-        return data
-    data = np.transpose(data, (2, 0, 1))
-    return data
-
-
-def plot_waveform_to_numpy(waveform):
-    fig, ax = plt.subplots(figsize=(12, 3))
-    ax.plot()
-    ax.plot(range(len(waveform)), waveform,
-            linewidth=0.1, alpha=0.7, color='blue')
-
-    plt.xlabel("Samples")
-    plt.ylabel("Amplitude")
-    plt.ylim(-1, 1)
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = save_figure_to_numpy(fig)
-    plt.close()
-    return data
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    fig, ax = plt.subplots(figsize=(12, 3))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = save_figure_to_numpy(fig, True)
-    plt.close()
-    return data
-
-def get_files(path, extension='.wav') :
-    filenames = []
-    for filename in glob.iglob(f'{path}/*{extension}', recursive=True):
-        filenames += [filename]
-    return filenames
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/packaging/_manylinux.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/packaging/_manylinux.py
deleted file mode 100644
index 4c379aa6f69ff56c8f19612002c6e3e939ea6012..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/packaging/_manylinux.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import collections
-import functools
-import os
-import re
-import struct
-import sys
-import warnings
-from typing import IO, Dict, Iterator, NamedTuple, Optional, Tuple
-
-
-# Python does not provide platform information at sufficient granularity to
-# identify the architecture of the running executable in some cases, so we
-# determine it dynamically by reading the information from the running
-# process. This only applies on Linux, which uses the ELF format.
-class _ELFFileHeader:
-    # https://en.wikipedia.org/wiki/Executable_and_Linkable_Format#File_header
-    class _InvalidELFFileHeader(ValueError):
-        """
-        An invalid ELF file header was found.
-        """
-
-    ELF_MAGIC_NUMBER = 0x7F454C46
-    ELFCLASS32 = 1
-    ELFCLASS64 = 2
-    ELFDATA2LSB = 1
-    ELFDATA2MSB = 2
-    EM_386 = 3
-    EM_S390 = 22
-    EM_ARM = 40
-    EM_X86_64 = 62
-    EF_ARM_ABIMASK = 0xFF000000
-    EF_ARM_ABI_VER5 = 0x05000000
-    EF_ARM_ABI_FLOAT_HARD = 0x00000400
-
-    def __init__(self, file: IO[bytes]) -> None:
-        def unpack(fmt: str) -> int:
-            try:
-                data = file.read(struct.calcsize(fmt))
-                result: Tuple[int, ...] = struct.unpack(fmt, data)
-            except struct.error:
-                raise _ELFFileHeader._InvalidELFFileHeader()
-            return result[0]
-
-        self.e_ident_magic = unpack(">I")
-        if self.e_ident_magic != self.ELF_MAGIC_NUMBER:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_class = unpack("B")
-        if self.e_ident_class not in {self.ELFCLASS32, self.ELFCLASS64}:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_data = unpack("B")
-        if self.e_ident_data not in {self.ELFDATA2LSB, self.ELFDATA2MSB}:
-            raise _ELFFileHeader._InvalidELFFileHeader()
-        self.e_ident_version = unpack("B")
-        self.e_ident_osabi = unpack("B")
-        self.e_ident_abiversion = unpack("B")
-        self.e_ident_pad = file.read(7)
-        format_h = "<H" if self.e_ident_data == self.ELFDATA2LSB else ">H"
-        format_i = "<I" if self.e_ident_data == self.ELFDATA2LSB else ">I"
-        format_q = "<Q" if self.e_ident_data == self.ELFDATA2LSB else ">Q"
-        format_p = format_i if self.e_ident_class == self.ELFCLASS32 else format_q
-        self.e_type = unpack(format_h)
-        self.e_machine = unpack(format_h)
-        self.e_version = unpack(format_i)
-        self.e_entry = unpack(format_p)
-        self.e_phoff = unpack(format_p)
-        self.e_shoff = unpack(format_p)
-        self.e_flags = unpack(format_i)
-        self.e_ehsize = unpack(format_h)
-        self.e_phentsize = unpack(format_h)
-        self.e_phnum = unpack(format_h)
-        self.e_shentsize = unpack(format_h)
-        self.e_shnum = unpack(format_h)
-        self.e_shstrndx = unpack(format_h)
-
-
-def _get_elf_header() -> Optional[_ELFFileHeader]:
-    try:
-        with open(sys.executable, "rb") as f:
-            elf_header = _ELFFileHeader(f)
-    except (OSError, TypeError, _ELFFileHeader._InvalidELFFileHeader):
-        return None
-    return elf_header
-
-
-def _is_linux_armhf() -> bool:
-    # hard-float ABI can be detected from the ELF header of the running
-    # process
-    # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
-    elf_header = _get_elf_header()
-    if elf_header is None:
-        return False
-    result = elf_header.e_ident_class == elf_header.ELFCLASS32
-    result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
-    result &= elf_header.e_machine == elf_header.EM_ARM
-    result &= (
-        elf_header.e_flags & elf_header.EF_ARM_ABIMASK
-    ) == elf_header.EF_ARM_ABI_VER5
-    result &= (
-        elf_header.e_flags & elf_header.EF_ARM_ABI_FLOAT_HARD
-    ) == elf_header.EF_ARM_ABI_FLOAT_HARD
-    return result
-
-
-def _is_linux_i686() -> bool:
-    elf_header = _get_elf_header()
-    if elf_header is None:
-        return False
-    result = elf_header.e_ident_class == elf_header.ELFCLASS32
-    result &= elf_header.e_ident_data == elf_header.ELFDATA2LSB
-    result &= elf_header.e_machine == elf_header.EM_386
-    return result
-
-
-def _have_compatible_abi(arch: str) -> bool:
-    if arch == "armv7l":
-        return _is_linux_armhf()
-    if arch == "i686":
-        return _is_linux_i686()
-    return arch in {"x86_64", "aarch64", "ppc64", "ppc64le", "s390x"}
-
-
-# If glibc ever changes its major version, we need to know what the last
-# minor version was, so we can build the complete list of all versions.
-# For now, guess what the highest minor version might be, assume it will
-# be 50 for testing. Once this actually happens, update the dictionary
-# with the actual value.
-_LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
-
-
-class _GLibCVersion(NamedTuple):
-    major: int
-    minor: int
-
-
-def _glibc_version_string_confstr() -> Optional[str]:
-    """
-    Primary implementation of glibc_version_string using os.confstr.
-    """
-    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
-    # to be broken or missing. This strategy is used in the standard library
-    # platform module.
-    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
-    try:
-        # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17".
-        version_string = os.confstr("CS_GNU_LIBC_VERSION")
-        assert version_string is not None
-        _, version = version_string.split()
-    except (AssertionError, AttributeError, OSError, ValueError):
-        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
-        return None
-    return version
-
-
-def _glibc_version_string_ctypes() -> Optional[str]:
-    """
-    Fallback implementation of glibc_version_string using ctypes.
-    """
-    try:
-        import ctypes
-    except ImportError:
-        return None
-
-    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
-    # manpage says, "If filename is NULL, then the returned handle is for the
-    # main program". This way we can let the linker do the work to figure out
-    # which libc our process is actually using.
-    #
-    # We must also handle the special case where the executable is not a
-    # dynamically linked executable. This can occur when using musl libc,
-    # for example. In this situation, dlopen() will error, leading to an
-    # OSError. Interestingly, at least in the case of musl, there is no
-    # errno set on the OSError. The single string argument used to construct
-    # OSError comes from libc itself and is therefore not portable to
-    # hard code here. In any case, failure to call dlopen() means we
-    # can proceed, so we bail on our attempt.
-    try:
-        process_namespace = ctypes.CDLL(None)
-    except OSError:
-        return None
-
-    try:
-        gnu_get_libc_version = process_namespace.gnu_get_libc_version
-    except AttributeError:
-        # Symbol doesn't exist -> therefore, we are not linked to
-        # glibc.
-        return None
-
-    # Call gnu_get_libc_version, which returns a string like "2.5"
-    gnu_get_libc_version.restype = ctypes.c_char_p
-    version_str: str = gnu_get_libc_version()
-    # py2 / py3 compatibility:
-    if not isinstance(version_str, str):
-        version_str = version_str.decode("ascii")
-
-    return version_str
-
-
-def _glibc_version_string() -> Optional[str]:
-    """Returns glibc version string, or None if not using glibc."""
-    return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
-
-
-def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
-    """Parse glibc version.
-
-    We use a regexp instead of str.split because we want to discard any
-    random junk that might come after the minor version -- this might happen
-    in patched/forked versions of glibc (e.g. Linaro's version of glibc
-    uses version strings like "2.20-2014.11"). See gh-3588.
-    """
-    m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
-    if not m:
-        warnings.warn(
-            "Expected glibc version with 2 components major.minor,"
-            " got: %s" % version_str,
-            RuntimeWarning,
-        )
-        return -1, -1
-    return int(m.group("major")), int(m.group("minor"))
-
-
-@functools.lru_cache()
-def _get_glibc_version() -> Tuple[int, int]:
-    version_str = _glibc_version_string()
-    if version_str is None:
-        return (-1, -1)
-    return _parse_glibc_version(version_str)
-
-
-# From PEP 513, PEP 600
-def _is_compatible(name: str, arch: str, version: _GLibCVersion) -> bool:
-    sys_glibc = _get_glibc_version()
-    if sys_glibc < version:
-        return False
-    # Check for presence of _manylinux module.
-    try:
-        import _manylinux  # noqa
-    except ImportError:
-        return True
-    if hasattr(_manylinux, "manylinux_compatible"):
-        result = _manylinux.manylinux_compatible(version[0], version[1], arch)
-        if result is not None:
-            return bool(result)
-        return True
-    if version == _GLibCVersion(2, 5):
-        if hasattr(_manylinux, "manylinux1_compatible"):
-            return bool(_manylinux.manylinux1_compatible)
-    if version == _GLibCVersion(2, 12):
-        if hasattr(_manylinux, "manylinux2010_compatible"):
-            return bool(_manylinux.manylinux2010_compatible)
-    if version == _GLibCVersion(2, 17):
-        if hasattr(_manylinux, "manylinux2014_compatible"):
-            return bool(_manylinux.manylinux2014_compatible)
-    return True
-
-
-_LEGACY_MANYLINUX_MAP = {
-    # CentOS 7 w/ glibc 2.17 (PEP 599)
-    (2, 17): "manylinux2014",
-    # CentOS 6 w/ glibc 2.12 (PEP 571)
-    (2, 12): "manylinux2010",
-    # CentOS 5 w/ glibc 2.5 (PEP 513)
-    (2, 5): "manylinux1",
-}
-
-
-def platform_tags(linux: str, arch: str) -> Iterator[str]:
-    if not _have_compatible_abi(arch):
-        return
-    # Oldest glibc to be supported regardless of architecture is (2, 17).
-    too_old_glibc2 = _GLibCVersion(2, 16)
-    if arch in {"x86_64", "i686"}:
-        # On x86/i686 also oldest glibc to be supported is (2, 5).
-        too_old_glibc2 = _GLibCVersion(2, 4)
-    current_glibc = _GLibCVersion(*_get_glibc_version())
-    glibc_max_list = [current_glibc]
-    # We can assume compatibility across glibc major versions.
-    # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
-    #
-    # Build a list of maximum glibc versions so that we can
-    # output the canonical list of all glibc from current_glibc
-    # down to too_old_glibc2, including all intermediary versions.
-    for glibc_major in range(current_glibc.major - 1, 1, -1):
-        glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
-        glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
-    for glibc_max in glibc_max_list:
-        if glibc_max.major == too_old_glibc2.major:
-            min_minor = too_old_glibc2.minor
-        else:
-            # For other glibc major versions oldest supported is (x, 0).
-            min_minor = -1
-        for glibc_minor in range(glibc_max.minor, min_minor, -1):
-            glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
-            tag = "manylinux_{}_{}".format(*glibc_version)
-            if _is_compatible(tag, arch, glibc_version):
-                yield linux.replace("linux", tag)
-            # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
-            if glibc_version in _LEGACY_MANYLINUX_MAP:
-                legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
-                if _is_compatible(legacy_tag, arch, glibc_version):
-                    yield linux.replace("linux", legacy_tag)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/editable_wheel.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/editable_wheel.py
deleted file mode 100644
index d60cfbebb7cc4a24f8d56facf841637b04661714..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/editable_wheel.py
+++ /dev/null
@@ -1,844 +0,0 @@
-"""
-Create a wheel that, when installed, will make the source package 'editable'
-(add it to the interpreter's path, including metadata) per PEP 660. Replaces
-'setup.py develop'.
-
-.. note::
-   One of the mechanisms briefly mentioned in PEP 660 to implement editable installs is
-   to create a separated directory inside ``build`` and use a .pth file to point to that
-   directory. In the context of this file such directory is referred as
-   *auxiliary build directory* or ``auxiliary_dir``.
-"""
-
-import logging
-import os
-import re
-import shutil
-import sys
-import traceback
-import warnings
-from contextlib import suppress
-from enum import Enum
-from inspect import cleandoc
-from itertools import chain
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import (
-    TYPE_CHECKING,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Tuple,
-    TypeVar,
-    Union,
-)
-
-from setuptools import Command, SetuptoolsDeprecationWarning, errors, namespaces
-from setuptools.command.build_py import build_py as build_py_cls
-from setuptools.discovery import find_package_path
-from setuptools.dist import Distribution
-
-if TYPE_CHECKING:
-    from wheel.wheelfile import WheelFile  # noqa
-
-if sys.version_info >= (3, 8):
-    from typing import Protocol
-elif TYPE_CHECKING:
-    from typing_extensions import Protocol
-else:
-    from abc import ABC as Protocol
-
-_Path = Union[str, Path]
-_P = TypeVar("_P", bound=_Path)
-_logger = logging.getLogger(__name__)
-
-
-class _EditableMode(Enum):
-    """
-    Possible editable installation modes:
-    `lenient` (new files automatically added to the package - DEFAULT);
-    `strict` (requires a new installation when files are added/removed); or
-    `compat` (attempts to emulate `python setup.py develop` - DEPRECATED).
-    """
-
-    STRICT = "strict"
-    LENIENT = "lenient"
-    COMPAT = "compat"  # TODO: Remove `compat` after Dec/2022.
-
-    @classmethod
-    def convert(cls, mode: Optional[str]) -> "_EditableMode":
-        if not mode:
-            return _EditableMode.LENIENT  # default
-
-        _mode = mode.upper()
-        if _mode not in _EditableMode.__members__:
-            raise errors.OptionError(f"Invalid editable mode: {mode!r}. Try: 'strict'.")
-
-        if _mode == "COMPAT":
-            msg = """
-            The 'compat' editable mode is transitional and will be removed
-            in future versions of `setuptools`.
-            Please adapt your code accordingly to use either the 'strict' or the
-            'lenient' modes.
-
-            For more information, please check:
-            https://setuptools.pypa.io/en/latest/userguide/development_mode.html
-            """
-            warnings.warn(msg, SetuptoolsDeprecationWarning)
-
-        return _EditableMode[_mode]
-
-
-_STRICT_WARNING = """
-New or renamed files may not be automatically picked up without a new installation.
-"""
-
-_LENIENT_WARNING = """
-Options like `package-data`, `include/exclude-package-data` or
-`packages.find.exclude/include` may have no effect.
-"""
-
-
-class editable_wheel(Command):
-    """Build 'editable' wheel for development.
-    (This command is reserved for internal use of setuptools).
-    """
-
-    description = "create a PEP 660 'editable' wheel"
-
-    user_options = [
-        ("dist-dir=", "d", "directory to put final built distributions in"),
-        ("dist-info-dir=", "I", "path to a pre-build .dist-info directory"),
-        ("mode=", None, cleandoc(_EditableMode.__doc__ or "")),
-    ]
-
-    def initialize_options(self):
-        self.dist_dir = None
-        self.dist_info_dir = None
-        self.project_dir = None
-        self.mode = None
-
-    def finalize_options(self):
-        dist = self.distribution
-        self.project_dir = dist.src_root or os.curdir
-        self.package_dir = dist.package_dir or {}
-        self.dist_dir = Path(self.dist_dir or os.path.join(self.project_dir, "dist"))
-
-    def run(self):
-        try:
-            self.dist_dir.mkdir(exist_ok=True)
-            self._ensure_dist_info()
-
-            # Add missing dist_info files
-            self.reinitialize_command("bdist_wheel")
-            bdist_wheel = self.get_finalized_command("bdist_wheel")
-            bdist_wheel.write_wheelfile(self.dist_info_dir)
-
-            self._create_wheel_file(bdist_wheel)
-        except Exception as ex:
-            traceback.print_exc()
-            msg = """
-            Support for editable installs via PEP 660 was recently introduced
-            in `setuptools`. If you are seeing this error, please report to:
-
-            https://github.com/pypa/setuptools/issues
-
-            Meanwhile you can try the legacy behavior by setting an
-            environment variable and trying to install again:
-
-            SETUPTOOLS_ENABLE_FEATURES="legacy-editable"
-            """
-            raise errors.InternalError(cleandoc(msg)) from ex
-
-    def _ensure_dist_info(self):
-        if self.dist_info_dir is None:
-            dist_info = self.reinitialize_command("dist_info")
-            dist_info.output_dir = self.dist_dir
-            dist_info.ensure_finalized()
-            dist_info.run()
-            self.dist_info_dir = dist_info.dist_info_dir
-        else:
-            assert str(self.dist_info_dir).endswith(".dist-info")
-            assert Path(self.dist_info_dir, "METADATA").exists()
-
-    def _install_namespaces(self, installation_dir, pth_prefix):
-        # XXX: Only required to support the deprecated namespace practice
-        dist = self.distribution
-        if not dist.namespace_packages:
-            return
-
-        src_root = Path(self.project_dir, self.package_dir.get("", ".")).resolve()
-        installer = _NamespaceInstaller(dist, installation_dir, pth_prefix, src_root)
-        installer.install_namespaces()
-
-    def _find_egg_info_dir(self) -> Optional[str]:
-        parent_dir = Path(self.dist_info_dir).parent if self.dist_info_dir else Path()
-        candidates = map(str, parent_dir.glob("*.egg-info"))
-        return next(candidates, None)
-
-    def _configure_build(
-        self, name: str, unpacked_wheel: _Path, build_lib: _Path, tmp_dir: _Path
-    ):
-        """Configure commands to behave in the following ways:
-
-        - Build commands can write to ``build_lib`` if they really want to...
-          (but this folder is expected to be ignored and modules are expected to live
-          in the project directory...)
-        - Binary extensions should be built in-place (editable_mode = True)
-        - Data/header/script files are not part of the "editable" specification
-          so they are written directly to the unpacked_wheel directory.
-        """
-        # Non-editable files (data, headers, scripts) are written directly to the
-        # unpacked_wheel
-
-        dist = self.distribution
-        wheel = str(unpacked_wheel)
-        build_lib = str(build_lib)
-        data = str(Path(unpacked_wheel, f"{name}.data", "data"))
-        headers = str(Path(unpacked_wheel, f"{name}.data", "headers"))
-        scripts = str(Path(unpacked_wheel, f"{name}.data", "scripts"))
-
-        # egg-info may be generated again to create a manifest (used for package data)
-        egg_info = dist.reinitialize_command("egg_info", reinit_subcommands=True)
-        egg_info.egg_base = str(tmp_dir)
-        egg_info.ignore_egg_info_in_manifest = True
-
-        build = dist.reinitialize_command("build", reinit_subcommands=True)
-        install = dist.reinitialize_command("install", reinit_subcommands=True)
-
-        build.build_platlib = build.build_purelib = build.build_lib = build_lib
-        install.install_purelib = install.install_platlib = install.install_lib = wheel
-        install.install_scripts = build.build_scripts = scripts
-        install.install_headers = headers
-        install.install_data = data
-
-        install_scripts = dist.get_command_obj("install_scripts")
-        install_scripts.no_ep = True
-
-        build.build_temp = str(tmp_dir)
-
-        build_py = dist.get_command_obj("build_py")
-        build_py.compile = False
-        build_py.existing_egg_info_dir = self._find_egg_info_dir()
-
-        self._set_editable_mode()
-
-        build.ensure_finalized()
-        install.ensure_finalized()
-
-    def _set_editable_mode(self):
-        """Set the ``editable_mode`` flag in the build sub-commands"""
-        dist = self.distribution
-        build = dist.get_command_obj("build")
-        for cmd_name in build.get_sub_commands():
-            cmd = dist.get_command_obj(cmd_name)
-            if hasattr(cmd, "editable_mode"):
-                cmd.editable_mode = True
-            elif hasattr(cmd, "inplace"):
-                cmd.inplace = True  # backward compatibility with distutils
-
-    def _collect_build_outputs(self) -> Tuple[List[str], Dict[str, str]]:
-        files: List[str] = []
-        mapping: Dict[str, str] = {}
-        build = self.get_finalized_command("build")
-
-        for cmd_name in build.get_sub_commands():
-            cmd = self.get_finalized_command(cmd_name)
-            if hasattr(cmd, "get_outputs"):
-                files.extend(cmd.get_outputs() or [])
-            if hasattr(cmd, "get_output_mapping"):
-                mapping.update(cmd.get_output_mapping() or {})
-
-        return files, mapping
-
-    def _run_build_commands(
-        self, dist_name: str, unpacked_wheel: _Path, build_lib: _Path, tmp_dir: _Path
-    ) -> Tuple[List[str], Dict[str, str]]:
-        self._configure_build(dist_name, unpacked_wheel, build_lib, tmp_dir)
-        self._run_build_subcommands()
-        files, mapping = self._collect_build_outputs()
-        self._run_install("headers")
-        self._run_install("scripts")
-        self._run_install("data")
-        return files, mapping
-
-    def _run_build_subcommands(self):
-        """
-        Issue #3501 indicates that some plugins/customizations might rely on:
-
-        1. ``build_py`` not running
-        2. ``build_py`` always copying files to ``build_lib``
-
-        However both these assumptions may be false in editable_wheel.
-        This method implements a temporary workaround to support the ecosystem
-        while the implementations catch up.
-        """
-        # TODO: Once plugins/customisations had the chance to catch up, replace
-        #       `self._run_build_subcommands()` with `self.run_command("build")`.
-        #       Also remove _safely_run, TestCustomBuildPy. Suggested date: Aug/2023.
-        build: Command = self.get_finalized_command("build")
-        for name in build.get_sub_commands():
-            cmd = self.get_finalized_command(name)
-            if name == "build_py" and type(cmd) != build_py_cls:
-                self._safely_run(name)
-            else:
-                self.run_command(name)
-
-    def _safely_run(self, cmd_name: str):
-        try:
-            return self.run_command(cmd_name)
-        except Exception:
-            msg = f"""{traceback.format_exc()}\n
-            If you are seeing this warning it is very likely that a setuptools
-            plugin or customization overrides the `{cmd_name}` command, without
-            taking into consideration how editable installs run build steps
-            starting from v64.0.0.
-
-            Plugin authors and developers relying on custom build steps are encouraged
-            to update their `{cmd_name}` implementation considering the information in
-            https://setuptools.pypa.io/en/latest/userguide/extension.html
-            about editable installs.
-
-            For the time being `setuptools` will silence this error and ignore
-            the faulty command, but this behaviour will change in future versions.\n
-            """
-            warnings.warn(msg, SetuptoolsDeprecationWarning, stacklevel=2)
-
-    def _create_wheel_file(self, bdist_wheel):
-        from wheel.wheelfile import WheelFile
-
-        dist_info = self.get_finalized_command("dist_info")
-        dist_name = dist_info.name
-        tag = "-".join(bdist_wheel.get_tag())
-        build_tag = "0.editable"  # According to PEP 427 needs to start with digit
-        archive_name = f"{dist_name}-{build_tag}-{tag}.whl"
-        wheel_path = Path(self.dist_dir, archive_name)
-        if wheel_path.exists():
-            wheel_path.unlink()
-
-        unpacked_wheel = TemporaryDirectory(suffix=archive_name)
-        build_lib = TemporaryDirectory(suffix=".build-lib")
-        build_tmp = TemporaryDirectory(suffix=".build-temp")
-
-        with unpacked_wheel as unpacked, build_lib as lib, build_tmp as tmp:
-            unpacked_dist_info = Path(unpacked, Path(self.dist_info_dir).name)
-            shutil.copytree(self.dist_info_dir, unpacked_dist_info)
-            self._install_namespaces(unpacked, dist_info.name)
-            files, mapping = self._run_build_commands(dist_name, unpacked, lib, tmp)
-            strategy = self._select_strategy(dist_name, tag, lib)
-            with strategy, WheelFile(wheel_path, "w") as wheel_obj:
-                strategy(wheel_obj, files, mapping)
-                wheel_obj.write_files(unpacked)
-
-        return wheel_path
-
-    def _run_install(self, category: str):
-        has_category = getattr(self.distribution, f"has_{category}", None)
-        if has_category and has_category():
-            _logger.info(f"Installing {category} as non editable")
-            self.run_command(f"install_{category}")
-
-    def _select_strategy(
-        self,
-        name: str,
-        tag: str,
-        build_lib: _Path,
-    ) -> "EditableStrategy":
-        """Decides which strategy to use to implement an editable installation."""
-        build_name = f"__editable__.{name}-{tag}"
-        project_dir = Path(self.project_dir)
-        mode = _EditableMode.convert(self.mode)
-
-        if mode is _EditableMode.STRICT:
-            auxiliary_dir = _empty_dir(Path(self.project_dir, "build", build_name))
-            return _LinkTree(self.distribution, name, auxiliary_dir, build_lib)
-
-        packages = _find_packages(self.distribution)
-        has_simple_layout = _simple_layout(packages, self.package_dir, project_dir)
-        is_compat_mode = mode is _EditableMode.COMPAT
-        if set(self.package_dir) == {""} and has_simple_layout or is_compat_mode:
-            # src-layout(ish) is relatively safe for a simple pth file
-            src_dir = self.package_dir.get("", ".")
-            return _StaticPth(self.distribution, name, [Path(project_dir, src_dir)])
-
-        # Use a MetaPathFinder to avoid adding accidental top-level packages/modules
-        return _TopLevelFinder(self.distribution, name)
-
-
-class EditableStrategy(Protocol):
-    def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
-        ...
-
-    def __enter__(self):
-        ...
-
-    def __exit__(self, _exc_type, _exc_value, _traceback):
-        ...
-
-
-class _StaticPth:
-    def __init__(self, dist: Distribution, name: str, path_entries: List[Path]):
-        self.dist = dist
-        self.name = name
-        self.path_entries = path_entries
-
-    def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
-        entries = "\n".join((str(p.resolve()) for p in self.path_entries))
-        contents = bytes(f"{entries}\n", "utf-8")
-        wheel.writestr(f"__editable__.{self.name}.pth", contents)
-
-    def __enter__(self):
-        msg = f"""
-        Editable install will be performed using .pth file to extend `sys.path` with:
-        {list(map(os.fspath, self.path_entries))!r}
-        """
-        _logger.warning(msg + _LENIENT_WARNING)
-        return self
-
-    def __exit__(self, _exc_type, _exc_value, _traceback):
-        ...
-
-
-class _LinkTree(_StaticPth):
-    """
-    Creates a ``.pth`` file that points to a link tree in the ``auxiliary_dir``.
-
-    This strategy will only link files (not dirs), so it can be implemented in
-    any OS, even if that means using hardlinks instead of symlinks.
-
-    By collocating ``auxiliary_dir`` and the original source code, limitations
-    with hardlinks should be avoided.
-    """
-    def __init__(
-        self, dist: Distribution,
-        name: str,
-        auxiliary_dir: _Path,
-        build_lib: _Path,
-    ):
-        self.auxiliary_dir = Path(auxiliary_dir)
-        self.build_lib = Path(build_lib).resolve()
-        self._file = dist.get_command_obj("build_py").copy_file
-        super().__init__(dist, name, [self.auxiliary_dir])
-
-    def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
-        self._create_links(files, mapping)
-        super().__call__(wheel, files, mapping)
-
-    def _normalize_output(self, file: str) -> Optional[str]:
-        # Files relative to build_lib will be normalized to None
-        with suppress(ValueError):
-            path = Path(file).resolve().relative_to(self.build_lib)
-            return str(path).replace(os.sep, '/')
-        return None
-
-    def _create_file(self, relative_output: str, src_file: str, link=None):
-        dest = self.auxiliary_dir / relative_output
-        if not dest.parent.is_dir():
-            dest.parent.mkdir(parents=True)
-        self._file(src_file, dest, link=link)
-
-    def _create_links(self, outputs, output_mapping):
-        self.auxiliary_dir.mkdir(parents=True, exist_ok=True)
-        link_type = "sym" if _can_symlink_files(self.auxiliary_dir) else "hard"
-        mappings = {
-            self._normalize_output(k): v
-            for k, v in output_mapping.items()
-        }
-        mappings.pop(None, None)  # remove files that are not relative to build_lib
-
-        for output in outputs:
-            relative = self._normalize_output(output)
-            if relative and relative not in mappings:
-                self._create_file(relative, output)
-
-        for relative, src in mappings.items():
-            self._create_file(relative, src, link=link_type)
-
-    def __enter__(self):
-        msg = "Strict editable install will be performed using a link tree.\n"
-        _logger.warning(msg + _STRICT_WARNING)
-        return self
-
-    def __exit__(self, _exc_type, _exc_value, _traceback):
-        msg = f"""\n
-        Strict editable installation performed using the auxiliary directory:
-            {self.auxiliary_dir}
-
-        Please be careful to not remove this directory, otherwise you might not be able
-        to import/use your package.
-        """
-        warnings.warn(msg, InformationOnly)
-
-
-class _TopLevelFinder:
-    def __init__(self, dist: Distribution, name: str):
-        self.dist = dist
-        self.name = name
-
-    def __call__(self, wheel: "WheelFile", files: List[str], mapping: Dict[str, str]):
-        src_root = self.dist.src_root or os.curdir
-        top_level = chain(_find_packages(self.dist), _find_top_level_modules(self.dist))
-        package_dir = self.dist.package_dir or {}
-        roots = _find_package_roots(top_level, package_dir, src_root)
-
-        namespaces_: Dict[str, List[str]] = dict(chain(
-            _find_namespaces(self.dist.packages or [], roots),
-            ((ns, []) for ns in _find_virtual_namespaces(roots)),
-        ))
-
-        name = f"__editable__.{self.name}.finder"
-        finder = _make_identifier(name)
-        content = bytes(_finder_template(name, roots, namespaces_), "utf-8")
-        wheel.writestr(f"{finder}.py", content)
-
-        content = bytes(f"import {finder}; {finder}.install()", "utf-8")
-        wheel.writestr(f"__editable__.{self.name}.pth", content)
-
-    def __enter__(self):
-        msg = "Editable install will be performed using a meta path finder.\n"
-        _logger.warning(msg + _LENIENT_WARNING)
-        return self
-
-    def __exit__(self, _exc_type, _exc_value, _traceback):
-        msg = """\n
-        Please be careful with folders in your working directory with the same
-        name as your package as they may take precedence during imports.
-        """
-        warnings.warn(msg, InformationOnly)
-
-
-def _can_symlink_files(base_dir: Path) -> bool:
-    with TemporaryDirectory(dir=str(base_dir.resolve())) as tmp:
-        path1, path2 = Path(tmp, "file1.txt"), Path(tmp, "file2.txt")
-        path1.write_text("file1", encoding="utf-8")
-        with suppress(AttributeError, NotImplementedError, OSError):
-            os.symlink(path1, path2)
-            if path2.is_symlink() and path2.read_text(encoding="utf-8") == "file1":
-                return True
-
-        try:
-            os.link(path1, path2)  # Ensure hard links can be created
-        except Exception as ex:
-            msg = (
-                "File system does not seem to support either symlinks or hard links. "
-                "Strict editable installs require one of them to be supported."
-            )
-            raise LinksNotSupported(msg) from ex
-        return False
-
-
-def _simple_layout(
-    packages: Iterable[str], package_dir: Dict[str, str], project_dir: Path
-) -> bool:
-    """Return ``True`` if:
-    - all packages are contained by the same parent directory, **and**
-    - all packages become importable if the parent directory is added to ``sys.path``.
-
-    >>> _simple_layout(['a'], {"": "src"}, "/tmp/myproj")
-    True
-    >>> _simple_layout(['a', 'a.b'], {"": "src"}, "/tmp/myproj")
-    True
-    >>> _simple_layout(['a', 'a.b'], {}, "/tmp/myproj")
-    True
-    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"": "src"}, "/tmp/myproj")
-    True
-    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "a", "b": "b"}, ".")
-    True
-    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "_a", "b": "_b"}, ".")
-    False
-    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "_a"}, "/tmp/myproj")
-    False
-    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a.a1.a2": "_a2"}, ".")
-    False
-    >>> _simple_layout(['a', 'a.b'], {"": "src", "a.b": "_ab"}, "/tmp/myproj")
-    False
-    >>> # Special cases, no packages yet:
-    >>> _simple_layout([], {"": "src"}, "/tmp/myproj")
-    True
-    >>> _simple_layout([], {"a": "_a", "": "src"}, "/tmp/myproj")
-    False
-    """
-    layout = {
-        pkg: find_package_path(pkg, package_dir, project_dir)
-        for pkg in packages
-    }
-    if not layout:
-        return set(package_dir) in ({}, {""})
-    parent = os.path.commonpath([_parent_path(k, v) for k, v in layout.items()])
-    return all(
-        _normalize_path(Path(parent, *key.split('.'))) == _normalize_path(value)
-        for key, value in layout.items()
-    )
-
-
-def _parent_path(pkg, pkg_path):
-    """Infer the parent path containing a package, that if added to ``sys.path`` would
-    allow importing that package.
-    When ``pkg`` is directly mapped into a directory with a different name, return its
-    own path.
-    >>> _parent_path("a", "src/a")
-    'src'
-    >>> _parent_path("b", "src/c")
-    'src/c'
-    """
-    parent = pkg_path[:-len(pkg)] if pkg_path.endswith(pkg) else pkg_path
-    return parent.rstrip("/" + os.sep)
-
-
-def _find_packages(dist: Distribution) -> Iterator[str]:
-    yield from iter(dist.packages or [])
-
-    py_modules = dist.py_modules or []
-    nested_modules = [mod for mod in py_modules if "." in mod]
-    if dist.ext_package:
-        yield dist.ext_package
-    else:
-        ext_modules = dist.ext_modules or []
-        nested_modules += [x.name for x in ext_modules if "." in x.name]
-
-    for module in nested_modules:
-        package, _, _ = module.rpartition(".")
-        yield package
-
-
-def _find_top_level_modules(dist: Distribution) -> Iterator[str]:
-    py_modules = dist.py_modules or []
-    yield from (mod for mod in py_modules if "." not in mod)
-
-    if not dist.ext_package:
-        ext_modules = dist.ext_modules or []
-        yield from (x.name for x in ext_modules if "." not in x.name)
-
-
-def _find_package_roots(
-    packages: Iterable[str],
-    package_dir: Mapping[str, str],
-    src_root: _Path,
-) -> Dict[str, str]:
-    pkg_roots: Dict[str, str] = {
-        pkg: _absolute_root(find_package_path(pkg, package_dir, src_root))
-        for pkg in sorted(packages)
-    }
-
-    return _remove_nested(pkg_roots)
-
-
-def _absolute_root(path: _Path) -> str:
-    """Works for packages and top-level modules"""
-    path_ = Path(path)
-    parent = path_.parent
-
-    if path_.exists():
-        return str(path_.resolve())
-    else:
-        return str(parent.resolve() / path_.name)
-
-
-def _find_virtual_namespaces(pkg_roots: Dict[str, str]) -> Iterator[str]:
-    """By carefully designing ``package_dir``, it is possible to implement the logical
-    structure of PEP 420 in a package without the corresponding directories.
-
-    Moreover a parent package can be purposefully/accidentally skipped in the discovery
-    phase (e.g. ``find_packages(include=["mypkg.*"])``, when ``mypkg.foo`` is included
-    by ``mypkg`` itself is not).
-    We consider this case to also be a virtual namespace (ignoring the original
-    directory) to emulate a non-editable installation.
-
-    This function will try to find these kinds of namespaces.
-    """
-    for pkg in pkg_roots:
-        if "." not in pkg:
-            continue
-        parts = pkg.split(".")
-        for i in range(len(parts) - 1, 0, -1):
-            partial_name = ".".join(parts[:i])
-            path = Path(find_package_path(partial_name, pkg_roots, ""))
-            if not path.exists() or partial_name not in pkg_roots:
-                # partial_name not in pkg_roots ==> purposefully/accidentally skipped
-                yield partial_name
-
-
-def _find_namespaces(
-    packages: List[str], pkg_roots: Dict[str, str]
-) -> Iterator[Tuple[str, List[str]]]:
-    for pkg in packages:
-        path = find_package_path(pkg, pkg_roots, "")
-        if Path(path).exists() and not Path(path, "__init__.py").exists():
-            yield (pkg, [path])
-
-
-def _remove_nested(pkg_roots: Dict[str, str]) -> Dict[str, str]:
-    output = dict(pkg_roots.copy())
-
-    for pkg, path in reversed(list(pkg_roots.items())):
-        if any(
-            pkg != other and _is_nested(pkg, path, other, other_path)
-            for other, other_path in pkg_roots.items()
-        ):
-            output.pop(pkg)
-
-    return output
-
-
-def _is_nested(pkg: str, pkg_path: str, parent: str, parent_path: str) -> bool:
-    """
-    Return ``True`` if ``pkg`` is nested inside ``parent`` both logically and in the
-    file system.
-    >>> _is_nested("a.b", "path/a/b", "a", "path/a")
-    True
-    >>> _is_nested("a.b", "path/a/b", "a", "otherpath/a")
-    False
-    >>> _is_nested("a.b", "path/a/b", "c", "path/c")
-    False
-    >>> _is_nested("a.a", "path/a/a", "a", "path/a")
-    True
-    >>> _is_nested("b.a", "path/b/a", "a", "path/a")
-    False
-    """
-    norm_pkg_path = _normalize_path(pkg_path)
-    rest = pkg.replace(parent, "", 1).strip(".").split(".")
-    return (
-        pkg.startswith(parent)
-        and norm_pkg_path == _normalize_path(Path(parent_path, *rest))
-    )
-
-
-def _normalize_path(filename: _Path) -> str:
-    """Normalize a file/dir name for comparison purposes"""
-    # See pkg_resources.normalize_path
-    file = os.path.abspath(filename) if sys.platform == 'cygwin' else filename
-    return os.path.normcase(os.path.realpath(os.path.normpath(file)))
-
-
-def _empty_dir(dir_: _P) -> _P:
-    """Create a directory ensured to be empty. Existing files may be removed."""
-    shutil.rmtree(dir_, ignore_errors=True)
-    os.makedirs(dir_)
-    return dir_
-
-
-def _make_identifier(name: str) -> str:
-    """Make a string safe to be used as Python identifier.
-    >>> _make_identifier("12abc")
-    '_12abc'
-    >>> _make_identifier("__editable__.myns.pkg-78.9.3_local")
-    '__editable___myns_pkg_78_9_3_local'
-    """
-    safe = re.sub(r'\W|^(?=\d)', '_', name)
-    assert safe.isidentifier()
-    return safe
-
-
-class _NamespaceInstaller(namespaces.Installer):
-    def __init__(self, distribution, installation_dir, editable_name, src_root):
-        self.distribution = distribution
-        self.src_root = src_root
-        self.installation_dir = installation_dir
-        self.editable_name = editable_name
-        self.outputs = []
-        self.dry_run = False
-
-    def _get_target(self):
-        """Installation target."""
-        return os.path.join(self.installation_dir, self.editable_name)
-
-    def _get_root(self):
-        """Where the modules/packages should be loaded from."""
-        return repr(str(self.src_root))
-
-
-_FINDER_TEMPLATE = """\
-import sys
-from importlib.machinery import ModuleSpec
-from importlib.machinery import all_suffixes as module_suffixes
-from importlib.util import spec_from_file_location
-from itertools import chain
-from pathlib import Path
-
-MAPPING = {mapping!r}
-NAMESPACES = {namespaces!r}
-PATH_PLACEHOLDER = {name!r} + ".__path_hook__"
-
-
-class _EditableFinder:  # MetaPathFinder
-    @classmethod
-    def find_spec(cls, fullname, path=None, target=None):
-        for pkg, pkg_path in reversed(list(MAPPING.items())):
-            if fullname == pkg or fullname.startswith(f"{{pkg}}."):
-                rest = fullname.replace(pkg, "", 1).strip(".").split(".")
-                return cls._find_spec(fullname, Path(pkg_path, *rest))
-
-        return None
-
-    @classmethod
-    def _find_spec(cls, fullname, candidate_path):
-        init = candidate_path / "__init__.py"
-        candidates = (candidate_path.with_suffix(x) for x in module_suffixes())
-        for candidate in chain([init], candidates):
-            if candidate.exists():
-                return spec_from_file_location(fullname, candidate)
-
-
-class _EditableNamespaceFinder:  # PathEntryFinder
-    @classmethod
-    def _path_hook(cls, path):
-        if path == PATH_PLACEHOLDER:
-            return cls
-        raise ImportError
-
-    @classmethod
-    def _paths(cls, fullname):
-        # Ensure __path__ is not empty for the spec to be considered a namespace.
-        return NAMESPACES[fullname] or MAPPING.get(fullname) or [PATH_PLACEHOLDER]
-
-    @classmethod
-    def find_spec(cls, fullname, target=None):
-        if fullname in NAMESPACES:
-            spec = ModuleSpec(fullname, None, is_package=True)
-            spec.submodule_search_locations = cls._paths(fullname)
-            return spec
-        return None
-
-    @classmethod
-    def find_module(cls, fullname):
-        return None
-
-
-def install():
-    if not any(finder == _EditableFinder for finder in sys.meta_path):
-        sys.meta_path.append(_EditableFinder)
-
-    if not NAMESPACES:
-        return
-
-    if not any(hook == _EditableNamespaceFinder._path_hook for hook in sys.path_hooks):
-        # PathEntryFinder is needed to create NamespaceSpec without private APIS
-        sys.path_hooks.append(_EditableNamespaceFinder._path_hook)
-    if PATH_PLACEHOLDER not in sys.path:
-        sys.path.append(PATH_PLACEHOLDER)  # Used just to trigger the path hook
-"""
-
-
-def _finder_template(
-    name: str, mapping: Mapping[str, str], namespaces: Dict[str, List[str]]
-) -> str:
-    """Create a string containing the code for the``MetaPathFinder`` and
-    ``PathEntryFinder``.
-    """
-    mapping = dict(sorted(mapping.items(), key=lambda p: p[0]))
-    return _FINDER_TEMPLATE.format(name=name, mapping=mapping, namespaces=namespaces)
-
-
-class InformationOnly(UserWarning):
-    """Currently there is no clear way of displaying messages to the users
-    that use the setuptools backend directly via ``pip``.
-    The only thing that might work is a warning, although it is not the
-    most appropriate tool for the job...
-    """
-
-
-class LinksNotSupported(errors.FileError):
-    """File system does not seem to support either symlinks or hard links."""
diff --git a/spaces/Realcat/image-matching-webui/hloc/pipelines/RobotCar/colmap_from_nvm.py b/spaces/Realcat/image-matching-webui/hloc/pipelines/RobotCar/colmap_from_nvm.py
deleted file mode 100644
index 40d7e8bd2d662e712cf41ce831ac838442b6a1ea..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/hloc/pipelines/RobotCar/colmap_from_nvm.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import argparse
-import sqlite3
-from tqdm import tqdm
-from collections import defaultdict
-import numpy as np
-from pathlib import Path
-import logging
-
-from ...colmap_from_nvm import (
-    recover_database_images_and_ids,
-    camera_center_to_translation,
-)
-from ...utils.read_write_model import Camera, Image, Point3D, CAMERA_MODEL_IDS
-from ...utils.read_write_model import write_model
-
-logger = logging.getLogger(__name__)
-
-
-def read_nvm_model(
-    nvm_path, database_path, image_ids, camera_ids, skip_points=False
-):
-    # Extract the intrinsics from the db file instead of the NVM model
-    db = sqlite3.connect(str(database_path))
-    ret = db.execute(
-        "SELECT camera_id, model, width, height, params FROM cameras;"
-    )
-    cameras = {}
-    for camera_id, camera_model, width, height, params in ret:
-        params = np.fromstring(params, dtype=np.double).reshape(-1)
-        camera_model = CAMERA_MODEL_IDS[camera_model]
-        assert len(params) == camera_model.num_params, (
-            len(params),
-            camera_model.num_params,
-        )
-        camera = Camera(
-            id=camera_id,
-            model=camera_model.model_name,
-            width=int(width),
-            height=int(height),
-            params=params,
-        )
-        cameras[camera_id] = camera
-
-    nvm_f = open(nvm_path, "r")
-    line = nvm_f.readline()
-    while line == "\n" or line.startswith("NVM_V3"):
-        line = nvm_f.readline()
-    num_images = int(line)
-    # assert num_images == len(cameras), (num_images, len(cameras))
-
-    logger.info(f"Reading {num_images} images...")
-    image_idx_to_db_image_id = []
-    image_data = []
-    i = 0
-    while i < num_images:
-        line = nvm_f.readline()
-        if line == "\n":
-            continue
-        data = line.strip("\n").lstrip("./").split(" ")
-        image_data.append(data)
-        image_idx_to_db_image_id.append(image_ids[data[0]])
-        i += 1
-
-    line = nvm_f.readline()
-    while line == "\n":
-        line = nvm_f.readline()
-    num_points = int(line)
-
-    if skip_points:
-        logger.info(f"Skipping {num_points} points.")
-        num_points = 0
-    else:
-        logger.info(f"Reading {num_points} points...")
-    points3D = {}
-    image_idx_to_keypoints = defaultdict(list)
-    i = 0
-    pbar = tqdm(total=num_points, unit="pts")
-    while i < num_points:
-        line = nvm_f.readline()
-        if line == "\n":
-            continue
-
-        data = line.strip("\n").split(" ")
-        x, y, z, r, g, b, num_observations = data[:7]
-        obs_image_ids, point2D_idxs = [], []
-        for j in range(int(num_observations)):
-            s = 7 + 4 * j
-            img_index, kp_index, kx, ky = data[s : s + 4]
-            image_idx_to_keypoints[int(img_index)].append(
-                (int(kp_index), float(kx), float(ky), i)
-            )
-            db_image_id = image_idx_to_db_image_id[int(img_index)]
-            obs_image_ids.append(db_image_id)
-            point2D_idxs.append(kp_index)
-
-        point = Point3D(
-            id=i,
-            xyz=np.array([x, y, z], float),
-            rgb=np.array([r, g, b], int),
-            error=1.0,  # fake
-            image_ids=np.array(obs_image_ids, int),
-            point2D_idxs=np.array(point2D_idxs, int),
-        )
-        points3D[i] = point
-
-        i += 1
-        pbar.update(1)
-    pbar.close()
-
-    logger.info("Parsing image data...")
-    images = {}
-    for i, data in enumerate(image_data):
-        # Skip the focal length. Skip the distortion and terminal 0.
-        name, _, qw, qx, qy, qz, cx, cy, cz, _, _ = data
-        qvec = np.array([qw, qx, qy, qz], float)
-        c = np.array([cx, cy, cz], float)
-        t = camera_center_to_translation(c, qvec)
-
-        if i in image_idx_to_keypoints:
-            # NVM only stores triangulated 2D keypoints: add dummy ones
-            keypoints = image_idx_to_keypoints[i]
-            point2D_idxs = np.array([d[0] for d in keypoints])
-            tri_xys = np.array([[x, y] for _, x, y, _ in keypoints])
-            tri_ids = np.array([i for _, _, _, i in keypoints])
-
-            num_2Dpoints = max(point2D_idxs) + 1
-            xys = np.zeros((num_2Dpoints, 2), float)
-            point3D_ids = np.full(num_2Dpoints, -1, int)
-            xys[point2D_idxs] = tri_xys
-            point3D_ids[point2D_idxs] = tri_ids
-        else:
-            xys = np.zeros((0, 2), float)
-            point3D_ids = np.full(0, -1, int)
-
-        image_id = image_ids[name]
-        image = Image(
-            id=image_id,
-            qvec=qvec,
-            tvec=t,
-            camera_id=camera_ids[name],
-            name=name.replace("png", "jpg"),  # some hack required for RobotCar
-            xys=xys,
-            point3D_ids=point3D_ids,
-        )
-        images[image_id] = image
-
-    return cameras, images, points3D
-
-
-def main(nvm, database, output, skip_points=False):
-    assert nvm.exists(), nvm
-    assert database.exists(), database
-
-    image_ids, camera_ids = recover_database_images_and_ids(database)
-
-    logger.info("Reading the NVM model...")
-    model = read_nvm_model(
-        nvm, database, image_ids, camera_ids, skip_points=skip_points
-    )
-
-    logger.info("Writing the COLMAP model...")
-    output.mkdir(exist_ok=True, parents=True)
-    write_model(*model, path=str(output), ext=".bin")
-    logger.info("Done.")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--nvm", required=True, type=Path)
-    parser.add_argument("--database", required=True, type=Path)
-    parser.add_argument("--output", required=True, type=Path)
-    parser.add_argument("--skip_points", action="store_true")
-    args = parser.parse_args()
-    main(**args.__dict__)
diff --git a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py b/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py
deleted file mode 100644
index 3f41b1db96016efb58888381284f86d448839ff0..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-
-from kornia.geometry.subpix import dsnt
-from kornia.utils.grid import create_meshgrid
-
-
-class FineMatching(nn.Module):
-    """FineMatching with s2d paradigm"""
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, feat_f0, feat_f1, data):
-        """
-        Args:
-            feat0 (torch.Tensor): [M, WW, C]
-            feat1 (torch.Tensor): [M, WW, C]
-            data (dict)
-        Update:
-            data (dict):{
-                'expec_f' (torch.Tensor): [M, 3],
-                'mkpts0_f' (torch.Tensor): [M, 2],
-                'mkpts1_f' (torch.Tensor): [M, 2]}
-        """
-        M, WW, C = feat_f0.shape
-        W = int(math.sqrt(WW))
-        scale = data["hw0_i"][0] / data["hw0_f"][0]
-        self.M, self.W, self.WW, self.C, self.scale = M, W, WW, C, scale
-
-        # corner case: if no coarse matches found
-        if M == 0:
-            assert (
-                self.training == False
-            ), "M is always >0, when training, see coarse_matching.py"
-            # logger.warning('No matches found in coarse-level.')
-            data.update(
-                {
-                    "expec_f": torch.empty(0, 3, device=feat_f0.device),
-                    "mkpts0_f": data["mkpts0_c"],
-                    "mkpts1_f": data["mkpts1_c"],
-                }
-            )
-            return
-
-        feat_f0_picked = feat_f0_picked = feat_f0[:, WW // 2, :]
-        sim_matrix = torch.einsum("mc,mrc->mr", feat_f0_picked, feat_f1)
-        softmax_temp = 1.0 / C**0.5
-        heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1).view(-1, W, W)
-
-        # compute coordinates from heatmap
-        coords_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0]  # [M, 2]
-        grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(
-            1, -1, 2
-        )  # [1, WW, 2]
-
-        # compute std over <x, y>
-        var = (
-            torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1)
-            - coords_normalized**2
-        )  # [M, 2]
-        std = torch.sum(
-            torch.sqrt(torch.clamp(var, min=1e-10)), -1
-        )  # [M]  clamp needed for numerical stability
-
-        # for fine-level supervision
-        data.update({"expec_f": torch.cat([coords_normalized, std.unsqueeze(1)], -1)})
-
-        # compute absolute kpt coords
-        self.get_fine_match(coords_normalized, data)
-
-    @torch.no_grad()
-    def get_fine_match(self, coords_normed, data):
-        W, WW, C, scale = self.W, self.WW, self.C, self.scale
-
-        # mkpts0_f and mkpts1_f
-        mkpts0_f = data["mkpts0_c"]
-        scale1 = scale * data["scale1"][data["b_ids"]] if "scale0" in data else scale
-        mkpts1_f = (
-            data["mkpts1_c"] + (coords_normed * (W // 2) * scale1)[: len(data["mconf"])]
-        )
-
-        data.update({"mkpts0_f": mkpts0_f, "mkpts1_f": mkpts1_f})
diff --git a/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/descriptors/__init__.py b/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/descriptors/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Redgon/bingo/src/components/ui/dialog.tsx b/spaces/Redgon/bingo/src/components/ui/dialog.tsx
deleted file mode 100644
index 925e77fe7858fb218b5115b4e225174a886e0f02..0000000000000000000000000000000000000000
--- a/spaces/Redgon/bingo/src/components/ui/dialog.tsx
+++ /dev/null
@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DialogPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Dialog = DialogPrimitive.Root
-
-const DialogTrigger = DialogPrimitive.Trigger
-
-const DialogPortal = ({
-  className,
-  children,
-  ...props
-}: DialogPrimitive.DialogPortalProps) => (
-  <DialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-start justify-center sm:items-center">
-      {children}
-    </div>
-  </DialogPrimitive.Portal>
-)
-DialogPortal.displayName = DialogPrimitive.Portal.displayName
-
-const DialogOverlay = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Overlay>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Overlay
-    ref={ref}
-    className={cn(
-      'bg-white fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-  />
-))
-DialogOverlay.displayName = DialogPrimitive.Overlay.displayName
-
-const DialogContent = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <DialogPortal>
-    <DialogOverlay />
-    <DialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full gap-4 rounded-b-lg border bg-background p-6 shadow-sm animate-in data-[state=open]:fade-in-90 data-[state=open]:slide-in-from-bottom-10 sm:max-w-lg sm:rounded-lg sm:zoom-in-90 data-[state=open]:sm:slide-in-from-bottom-0',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <DialogPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-accent data-[state=open]:text-muted-foreground">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </DialogPrimitive.Close>
-    </DialogPrimitive.Content>
-  </DialogPortal>
-))
-DialogContent.displayName = DialogPrimitive.Content.displayName
-
-const DialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-1.5 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-DialogHeader.displayName = 'DialogHeader'
-
-const DialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-DialogFooter.displayName = 'DialogFooter'
-
-const DialogTitle = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Title
-    ref={ref}
-    className={cn(
-      'text-lg font-semibold leading-none tracking-tight',
-      className
-    )}
-    {...props}
-  />
-))
-DialogTitle.displayName = DialogPrimitive.Title.displayName
-
-const DialogDescription = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-DialogDescription.displayName = DialogPrimitive.Description.displayName
-
-export {
-  Dialog,
-  DialogTrigger,
-  DialogContent,
-  DialogHeader,
-  DialogFooter,
-  DialogTitle,
-  DialogDescription
-}
diff --git a/spaces/RobLi/ControlNet-v1-1/app_softedge.py b/spaces/RobLi/ControlNet-v1-1/app_softedge.py
deleted file mode 100644
index 1e74c042c5fc58f131f86da53005f0113b49b5fd..0000000000000000000000000000000000000000
--- a/spaces/RobLi/ControlNet-v1-1/app_softedge.py
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    preprocessor_name = gr.Radio(label='Preprocessor',
-                                                 choices=[
-                                                     'HED',
-                                                     'PidiNet',
-                                                     'HED safe',
-                                                     'PidiNet safe',
-                                                     'None',
-                                                 ],
-                                                 type='value',
-                                                 value='PidiNet')
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='scribble',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='softedge')
-    demo = create_demo(model.process_softedge)
-    demo.queue().launch()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py
deleted file mode 100644
index df85a0112d27d97301fff56189f99bee0bf8efa5..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from mmdet.models.builder import HEADS
-from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
-from .fused_semantic_head import FusedSemanticHead
-
-
-@HEADS.register_module()
-class SCNetSemanticHead(FusedSemanticHead):
-    """Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
-
-    Args:
-        conv_to_res (bool, optional): if True, change the conv layers to
-            ``SimplifiedBasicBlock``.
-    """
-
-    def __init__(self, conv_to_res=True, **kwargs):
-        super(SCNetSemanticHead, self).__init__(**kwargs)
-        self.conv_to_res = conv_to_res
-        if self.conv_to_res:
-            num_res_blocks = self.num_convs // 2
-            self.convs = ResLayer(
-                SimplifiedBasicBlock,
-                self.in_channels,
-                self.conv_out_channels,
-                num_res_blocks,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg)
-            self.num_convs = num_res_blocks
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/utils/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/utils/__init__.py
deleted file mode 100644
index 5165b22ce57d17f28392213e0f1b055c2b9360c1..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/utils/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from .builder import build_positional_encoding, build_transformer
-from .gaussian_target import gaussian_radius, gen_gaussian_target
-from .positional_encoding import (LearnedPositionalEncoding,
-                                  SinePositionalEncoding)
-from .res_layer import ResLayer, SimplifiedBasicBlock
-from .transformer import (FFN, DynamicConv, MultiheadAttention, Transformer,
-                          TransformerDecoder, TransformerDecoderLayer,
-                          TransformerEncoder, TransformerEncoderLayer)
-
-__all__ = [
-    'ResLayer', 'gaussian_radius', 'gen_gaussian_target', 'MultiheadAttention',
-    'FFN', 'TransformerEncoderLayer', 'TransformerEncoder',
-    'TransformerDecoderLayer', 'TransformerDecoder', 'Transformer',
-    'build_transformer', 'build_positional_encoding', 'SinePositionalEncoding',
-    'LearnedPositionalEncoding', 'DynamicConv', 'SimplifiedBasicBlock'
-]
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/mask_rcnn.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/mask_rcnn.py
deleted file mode 100644
index c15a7733170e059d2825138b3812319915b7cad6..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/mask_rcnn.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class MaskRCNN(TwoStageDetector):
-    """Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
-
-    def __init__(self,
-                 backbone,
-                 rpn_head,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None):
-        super(MaskRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/optimizer/builder.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/optimizer/builder.py
deleted file mode 100644
index f9234eed8f1f186d9d8dfda34562157ee39bdb3a..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/runner/optimizer/builder.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import inspect
-
-import torch
-
-from ...utils import Registry, build_from_cfg
-
-OPTIMIZERS = Registry('optimizer')
-OPTIMIZER_BUILDERS = Registry('optimizer builder')
-
-
-def register_torch_optimizers():
-    torch_optimizers = []
-    for module_name in dir(torch.optim):
-        if module_name.startswith('__'):
-            continue
-        _optim = getattr(torch.optim, module_name)
-        if inspect.isclass(_optim) and issubclass(_optim,
-                                                  torch.optim.Optimizer):
-            OPTIMIZERS.register_module()(_optim)
-            torch_optimizers.append(module_name)
-    return torch_optimizers
-
-
-TORCH_OPTIMIZERS = register_torch_optimizers()
-
-
-def build_optimizer_constructor(cfg):
-    return build_from_cfg(cfg, OPTIMIZER_BUILDERS)
-
-
-def build_optimizer(model, cfg):
-    optimizer_cfg = copy.deepcopy(cfg)
-    constructor_type = optimizer_cfg.pop('constructor',
-                                         'DefaultOptimizerConstructor')
-    paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None)
-    optim_constructor = build_optimizer_constructor(
-        dict(
-            type=constructor_type,
-            optimizer_cfg=optimizer_cfg,
-            paramwise_cfg=paramwise_cfg))
-    optimizer = optim_constructor(model)
-    return optimizer
diff --git a/spaces/RockmanYang/vocal_remover/lib/dataset.py b/spaces/RockmanYang/vocal_remover/lib/dataset.py
deleted file mode 100644
index 09da5b27cd4148ea813bac5ee00349f34e4a2111..0000000000000000000000000000000000000000
--- a/spaces/RockmanYang/vocal_remover/lib/dataset.py
+++ /dev/null
@@ -1,257 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from tqdm import tqdm
-
-try:
-    from lib import spec_utils
-except ModuleNotFoundError:
-    import spec_utils
-
-
-class VocalRemoverTrainingSet(torch.utils.data.Dataset):
-
-    def __init__(self, training_set, cropsize, reduction_rate, reduction_weight, mixup_rate, mixup_alpha):
-        self.training_set = training_set
-        self.cropsize = cropsize
-        self.reduction_rate = reduction_rate
-        self.reduction_weight = reduction_weight
-        self.mixup_rate = mixup_rate
-        self.mixup_alpha = mixup_alpha
-
-    def __len__(self):
-        return len(self.training_set)
-
-    def do_crop(self, X_path, y_path):
-        X_mmap = np.load(X_path, mmap_mode='r')
-        y_mmap = np.load(y_path, mmap_mode='r')
-
-        start = np.random.randint(0, X_mmap.shape[2] - self.cropsize)
-        end = start + self.cropsize
-
-        X_crop = np.array(X_mmap[:, :, start:end], copy=True)
-        y_crop = np.array(y_mmap[:, :, start:end], copy=True)
-
-        return X_crop, y_crop
-
-    def do_aug(self, X, y):
-        if np.random.uniform() < self.reduction_rate:
-            y = spec_utils.aggressively_remove_vocal(X, y, self.reduction_weight)
-
-        if np.random.uniform() < 0.5:
-            # swap channel
-            X = X[::-1].copy()
-            y = y[::-1].copy()
-
-        if np.random.uniform() < 0.01:
-            # inst
-            X = y.copy()
-
-        # if np.random.uniform() < 0.01:
-        #     # mono
-        #     X[:] = X.mean(axis=0, keepdims=True)
-        #     y[:] = y.mean(axis=0, keepdims=True)
-
-        return X, y
-
-    def do_mixup(self, X, y):
-        idx = np.random.randint(0, len(self))
-        X_path, y_path, coef = self.training_set[idx]
-
-        X_i, y_i = self.do_crop(X_path, y_path)
-        X_i /= coef
-        y_i /= coef
-
-        X_i, y_i = self.do_aug(X_i, y_i)
-
-        lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
-        X = lam * X + (1 - lam) * X_i
-        y = lam * y + (1 - lam) * y_i
-
-        return X, y
-
-    def __getitem__(self, idx):
-        X_path, y_path, coef = self.training_set[idx]
-
-        X, y = self.do_crop(X_path, y_path)
-        X /= coef
-        y /= coef
-
-        X, y = self.do_aug(X, y)
-
-        if np.random.uniform() < self.mixup_rate:
-            X, y = self.do_mixup(X, y)
-
-        X_mag = np.abs(X)
-        y_mag = np.abs(y)
-
-        return X_mag, y_mag
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
-
-    def __init__(self, patch_list):
-        self.patch_list = patch_list
-
-    def __len__(self):
-        return len(self.patch_list)
-
-    def __getitem__(self, idx):
-        path = self.patch_list[idx]
-        data = np.load(path)
-
-        X, y = data['X'], data['y']
-
-        X_mag = np.abs(X)
-        y_mag = np.abs(y)
-
-        return X_mag, y_mag
-
-
-def make_pair(mix_dir, inst_dir):
-    input_exts = ['.wav', '.m4a', '.mp3', '.mp4', '.flac']
-
-    X_list = sorted([
-        os.path.join(mix_dir, fname)
-        for fname in os.listdir(mix_dir)
-        if os.path.splitext(fname)[1] in input_exts
-    ])
-    y_list = sorted([
-        os.path.join(inst_dir, fname)
-        for fname in os.listdir(inst_dir)
-        if os.path.splitext(fname)[1] in input_exts
-    ])
-
-    filelist = list(zip(X_list, y_list))
-
-    return filelist
-
-
-def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
-    if split_mode == 'random':
-        filelist = make_pair(
-            os.path.join(dataset_dir, 'mixtures'),
-            os.path.join(dataset_dir, 'instruments')
-        )
-
-        random.shuffle(filelist)
-
-        if len(val_filelist) == 0:
-            val_size = int(len(filelist) * val_rate)
-            train_filelist = filelist[:-val_size]
-            val_filelist = filelist[-val_size:]
-        else:
-            train_filelist = [
-                pair for pair in filelist
-                if list(pair) not in val_filelist
-            ]
-    elif split_mode == 'subdirs':
-        if len(val_filelist) != 0:
-            raise ValueError('`val_filelist` option is not available with `subdirs` mode')
-
-        train_filelist = make_pair(
-            os.path.join(dataset_dir, 'training/mixtures'),
-            os.path.join(dataset_dir, 'training/instruments')
-        )
-
-        val_filelist = make_pair(
-            os.path.join(dataset_dir, 'validation/mixtures'),
-            os.path.join(dataset_dir, 'validation/instruments')
-        )
-
-    return train_filelist, val_filelist
-
-
-def make_padding(width, cropsize, offset):
-    left = offset
-    roi_size = cropsize - offset * 2
-    if roi_size == 0:
-        roi_size = cropsize
-    right = roi_size - (width % roi_size) + left
-
-    return left, right, roi_size
-
-
-def make_training_set(filelist, sr, hop_length, n_fft):
-    ret = []
-    for X_path, y_path in tqdm(filelist):
-        X, y, X_cache_path, y_cache_path = spec_utils.cache_or_load(
-            X_path, y_path, sr, hop_length, n_fft
-        )
-        coef = np.max([np.abs(X).max(), np.abs(y).max()])
-        ret.append([X_cache_path, y_cache_path, coef])
-
-    return ret
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
-    patch_list = []
-    patch_dir = 'cs{}_sr{}_hl{}_nf{}_of{}'.format(cropsize, sr, hop_length, n_fft, offset)
-    os.makedirs(patch_dir, exist_ok=True)
-
-    for X_path, y_path in tqdm(filelist):
-        basename = os.path.splitext(os.path.basename(X_path))[0]
-
-        X, y, _, _ = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
-        coef = np.max([np.abs(X).max(), np.abs(y).max()])
-        X, y = X / coef, y / coef
-
-        l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
-        X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
-        y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode='constant')
-
-        len_dataset = int(np.ceil(X.shape[2] / roi_size))
-        for j in range(len_dataset):
-            outpath = os.path.join(patch_dir, '{}_p{}.npz'.format(basename, j))
-            start = j * roi_size
-            if not os.path.exists(outpath):
-                np.savez(
-                    outpath,
-                    X=X_pad[:, :, start:start + cropsize],
-                    y=y_pad[:, :, start:start + cropsize]
-                )
-            patch_list.append(outpath)
-
-    return patch_list
-
-
-def get_oracle_data(X, y, oracle_loss, oracle_rate, oracle_drop_rate):
-    k = int(len(X) * oracle_rate * (1 / (1 - oracle_drop_rate)))
-    n = int(len(X) * oracle_rate)
-    indices = np.argsort(oracle_loss)[::-1][:k]
-    indices = np.random.choice(indices, n, replace=False)
-    oracle_X = X[indices].copy()
-    oracle_y = y[indices].copy()
-
-    return oracle_X, oracle_y, indices
-
-
-if __name__ == "__main__":
-    import sys
-    import utils
-
-    mix_dir = sys.argv[1]
-    inst_dir = sys.argv[2]
-    outdir = sys.argv[3]
-
-    os.makedirs(outdir, exist_ok=True)
-
-    filelist = make_pair(mix_dir, inst_dir)
-    for mix_path, inst_path in tqdm(filelist):
-        mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
-
-        X_spec, y_spec, _, _ = spec_utils.cache_or_load(
-            mix_path, inst_path, 44100, 1024, 2048
-        )
-
-        X_mag = np.abs(X_spec)
-        y_mag = np.abs(y_spec)
-        v_mag = X_mag - y_mag
-        v_mag *= v_mag > y_mag
-
-        outpath = '{}/{}_Vocal.jpg'.format(outdir, mix_basename)
-        v_image = spec_utils.spectrogram_to_image(v_mag)
-        utils.imwrite(outpath, v_image)
diff --git a/spaces/Rongjiehuang/ProDiff/utils/__init__.py b/spaces/Rongjiehuang/ProDiff/utils/__init__.py
deleted file mode 100644
index 63921bfea9a95629b15d90498677c6a22de9fec8..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/utils/__init__.py
+++ /dev/null
@@ -1,285 +0,0 @@
-import time
-import sys
-import types
-
-import chardet
-import numpy as np
-import torch
-import torch.distributed as dist
-from utils.ckpt_utils import load_ckpt
-
-
-def reduce_tensors(metrics):
-    new_metrics = {}
-    for k, v in metrics.items():
-        if isinstance(v, torch.Tensor):
-            dist.all_reduce(v)
-            v = v / dist.get_world_size()
-        if type(v) is dict:
-            v = reduce_tensors(v)
-        new_metrics[k] = v
-    return new_metrics
-
-
-def tensors_to_scalars(tensors):
-    if isinstance(tensors, torch.Tensor):
-        tensors = tensors.item()
-        return tensors
-    elif isinstance(tensors, dict):
-        new_tensors = {}
-        for k, v in tensors.items():
-            v = tensors_to_scalars(v)
-            new_tensors[k] = v
-        return new_tensors
-    elif isinstance(tensors, list):
-        return [tensors_to_scalars(v) for v in tensors]
-    else:
-        return tensors
-
-
-def tensors_to_np(tensors):
-    if isinstance(tensors, dict):
-        new_np = {}
-        for k, v in tensors.items():
-            if isinstance(v, torch.Tensor):
-                v = v.cpu().numpy()
-            if type(v) is dict:
-                v = tensors_to_np(v)
-            new_np[k] = v
-    elif isinstance(tensors, list):
-        new_np = []
-        for v in tensors:
-            if isinstance(v, torch.Tensor):
-                v = v.cpu().numpy()
-            if type(v) is dict:
-                v = tensors_to_np(v)
-            new_np.append(v)
-    elif isinstance(tensors, torch.Tensor):
-        v = tensors
-        if isinstance(v, torch.Tensor):
-            v = v.cpu().numpy()
-        if type(v) is dict:
-            v = tensors_to_np(v)
-        new_np = v
-    else:
-        raise Exception(f'tensors_to_np does not support type {type(tensors)}.')
-    return new_np
-
-
-def move_to_cpu(tensors):
-    ret = {}
-    for k, v in tensors.items():
-        if isinstance(v, torch.Tensor):
-            v = v.cpu()
-        if type(v) is dict:
-            v = move_to_cpu(v)
-        ret[k] = v
-    return ret
-
-
-def move_to_cuda(batch, gpu_id=0):
-    # base case: object can be directly moved using `cuda` or `to`
-    if callable(getattr(batch, 'cuda', None)):
-        return batch.cuda(gpu_id, non_blocking=True)
-    elif callable(getattr(batch, 'to', None)):
-        return batch.to(torch.device('cuda', gpu_id), non_blocking=True)
-    elif isinstance(batch, list):
-        for i, x in enumerate(batch):
-            batch[i] = move_to_cuda(x, gpu_id)
-        return batch
-    elif isinstance(batch, tuple):
-        batch = list(batch)
-        for i, x in enumerate(batch):
-            batch[i] = move_to_cuda(x, gpu_id)
-        return tuple(batch)
-    elif isinstance(batch, dict):
-        for k, v in batch.items():
-            batch[k] = move_to_cuda(v, gpu_id)
-        return batch
-    return batch
-
-
-class AvgrageMeter(object):
-
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.avg = 0
-        self.sum = 0
-        self.cnt = 0
-
-    def update(self, val, n=1):
-        self.sum += val * n
-        self.cnt += n
-        self.avg = self.sum / self.cnt
-
-
-def collate_1d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1):
-    """Convert a list of 1d tensors into a padded 2d tensor."""
-    size = max(v.size(0) for v in values) if max_len is None else max_len
-    res = values[0].new(len(values), size).fill_(pad_idx)
-
-    def copy_tensor(src, dst):
-        assert dst.numel() == src.numel()
-        if shift_right:
-            dst[1:] = src[:-1]
-            dst[0] = shift_id
-        else:
-            dst.copy_(src)
-
-    for i, v in enumerate(values):
-        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
-    return res
-
-
-def collate_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None):
-    """Convert a list of 2d tensors into a padded 3d tensor."""
-    size = max(v.size(0) for v in values) if max_len is None else max_len
-    res = values[0].new(len(values), size, values[0].shape[1]).fill_(pad_idx)
-
-    def copy_tensor(src, dst):
-        assert dst.numel() == src.numel()
-        if shift_right:
-            dst[1:] = src[:-1]
-        else:
-            dst.copy_(src)
-
-    for i, v in enumerate(values):
-        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
-    return res
-
-
-def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
-    if len(batch) == 0:
-        return 0
-    if len(batch) == max_sentences:
-        return 1
-    if num_tokens > max_tokens:
-        return 1
-    return 0
-
-
-def batch_by_size(
-        indices, num_tokens_fn, max_tokens=None, max_sentences=None,
-        required_batch_size_multiple=1, distributed=False
-):
-    """
-    Yield mini-batches of indices bucketed by size. Batches may contain
-    sequences of different lengths.
-
-    Args:
-        indices (List[int]): ordered list of dataset indices
-        num_tokens_fn (callable): function that returns the number of tokens at
-            a given index
-        max_tokens (int, optional): max number of tokens in each batch
-            (default: None).
-        max_sentences (int, optional): max number of sentences in each
-            batch (default: None).
-        required_batch_size_multiple (int, optional): require batch size to
-            be a multiple of N (default: 1).
-    """
-    max_tokens = max_tokens if max_tokens is not None else sys.maxsize
-    max_sentences = max_sentences if max_sentences is not None else sys.maxsize
-    bsz_mult = required_batch_size_multiple
-
-    if isinstance(indices, types.GeneratorType):
-        indices = np.fromiter(indices, dtype=np.int64, count=-1)
-
-    sample_len = 0
-    sample_lens = []
-    batch = []
-    batches = []
-    for i in range(len(indices)):
-        idx = indices[i]
-        num_tokens = num_tokens_fn(idx)
-        sample_lens.append(num_tokens)
-        sample_len = max(sample_len, num_tokens)
-
-        assert sample_len <= max_tokens, (
-            "sentence at index {} of size {} exceeds max_tokens "
-            "limit of {}!".format(idx, sample_len, max_tokens)
-        )
-        num_tokens = (len(batch) + 1) * sample_len
-
-        if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
-            mod_len = max(
-                bsz_mult * (len(batch) // bsz_mult),
-                len(batch) % bsz_mult,
-            )
-            batches.append(batch[:mod_len])
-            batch = batch[mod_len:]
-            sample_lens = sample_lens[mod_len:]
-            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
-        batch.append(idx)
-    if len(batch) > 0:
-        batches.append(batch)
-    return batches
-
-def unpack_dict_to_list(samples):
-    samples_ = []
-    bsz = samples.get('outputs').size(0)
-    for i in range(bsz):
-        res = {}
-        for k, v in samples.items():
-            try:
-                res[k] = v[i]
-            except:
-                pass
-        samples_.append(res)
-    return samples_
-
-
-def remove_padding(x, padding_idx=0):
-    if x is None:
-        return None
-    assert len(x.shape) in [1, 2]
-    if len(x.shape) == 2:  # [T, H]
-        return x[np.abs(x).sum(-1) != padding_idx]
-    elif len(x.shape) == 1:  # [T]
-        return x[x != padding_idx]
-
-
-class Timer:
-    timer_map = {}
-
-    def __init__(self, name, enable=False):
-        if name not in Timer.timer_map:
-            Timer.timer_map[name] = 0
-        self.name = name
-        self.enable = enable
-
-    def __enter__(self):
-        if self.enable:
-            if torch.cuda.is_available():
-                torch.cuda.synchronize()
-            self.t = time.time()
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        if self.enable:
-            if torch.cuda.is_available():
-                torch.cuda.synchronize()
-            Timer.timer_map[self.name] += time.time() - self.t
-            if self.enable:
-                print(f'[Timer] {self.name}: {Timer.timer_map[self.name]}')
-
-
-def print_arch(model, model_name='model'):
-    print(f"| {model_name} Arch: ", model)
-    num_params(model, model_name=model_name)
-
-
-def num_params(model, print_out=True, model_name="model"):
-    parameters = filter(lambda p: p.requires_grad, model.parameters())
-    parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
-    if print_out:
-        print(f'| {model_name} Trainable Parameters: %.3fM' % parameters)
-    return parameters
-
-
-def get_encoding(file):
-    with open(file, 'rb') as f:
-        encoding = chardet.detect(f.read())['encoding']
-    if encoding == 'GB2312':
-        encoding = 'GB18030'
-    return encoding
diff --git a/spaces/Royir/SynGen/run.py b/spaces/Royir/SynGen/run.py
deleted file mode 100644
index b1b0751c943ee34b67f5870d17815f92b3efc83b..0000000000000000000000000000000000000000
--- a/spaces/Royir/SynGen/run.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import argparse
-import os
-
-import torch
-from syngen_diffusion_pipeline import SynGenDiffusionPipeline
-
-
-def main(prompt, seed, output_directory, model_path):
-    pipe = load_model(model_path)
-    image = generate(pipe, prompt, seed)
-    save_image(image, prompt, seed, output_directory)
-
-
-def load_model(model_path):
-    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-    pipe = SynGenDiffusionPipeline.from_pretrained(model_path).to(device)
-    pipe.disable_xformers_memory_efficient_attention()
-
-    return pipe
-
-
-def generate(pipe, prompt, seed):
-    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-    generator = torch.Generator(device.type).manual_seed(seed)
-    result = pipe(prompt=prompt, generator=generator)
-    return result['images'][0]
-
-
-def save_image(image, prompt, seed, output_directory):
-    if not os.path.exists(output_directory):
-        os.makedirs(output_directory)
-
-    file_name = f"{output_directory}/{prompt}_{seed}.png"
-    image.save(file_name)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--prompt",
-        type=str,
-        default="a checkered bowl on a red and blue table"
-    )
-
-    parser.add_argument(
-        '--seed',
-        type=int,
-        default=1924
-    )
-
-    parser.add_argument(
-        '--output_directory',
-        type=str,
-        default='./output'
-    )
-
-    parser.add_argument(
-        '--model_path',
-        type=str,
-        default='CompVis/stable-diffusion-v1-4',
-        help='The path to the model (this will download the model if the path doesn\'t exist)'
-    )
-
-    args = parser.parse_args()
-
-    main(args.prompt, args.seed, args.output_directory, args.model_path)
diff --git a/spaces/SIH/building-segmentation/model_weights/README.md b/spaces/SIH/building-segmentation/model_weights/README.md
deleted file mode 100644
index bf40b7b7b6c4e6515d1cbfcdeb3566f895b3df95..0000000000000000000000000000000000000000
--- a/spaces/SIH/building-segmentation/model_weights/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Model Weights
-
-This is where pretrained model weights are stored.
\ No newline at end of file
diff --git a/spaces/SaulLu/test/README.md b/spaces/SaulLu/test/README.md
deleted file mode 100644
index e732529940b78bcb52cb7e4cc2a006dbc9a3eaf0..0000000000000000000000000000000000000000
--- a/spaces/SaulLu/test/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
----
-title: Test
-emoji: 🌖
-colorFrom: indigo
-colorTo: green
-sdk: static
----
-
-the goal of the space is to showcase how we can use Spaces.
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/SeViLA/SeViLA/lavis/datasets/download_scripts/DownloadConceptualCaptions/download_data_cc12m.py b/spaces/SeViLA/SeViLA/lavis/datasets/download_scripts/DownloadConceptualCaptions/download_data_cc12m.py
deleted file mode 100644
index c60b6fb8e5ae81783f9fafa71648c147871798ec..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/datasets/download_scripts/DownloadConceptualCaptions/download_data_cc12m.py
+++ /dev/null
@@ -1,232 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import time
-from PIL import Image
-from lavis.common.utils import get_abs_path, get_cache_path
-from multiprocessing import Pool
-from omegaconf import OmegaConf
-from pathlib import Path
-from torchvision.transforms import functional as TF
-from tqdm import tqdm
-import glob
-import io
-import json
-import magic  # pip install python-magic
-import numpy as np
-import os
-import pandas as pd
-import requests
-import shelve
-import zlib
-
-headers = {
-    #'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
-    "User-Agent": "Googlebot-Image/1.0",  # Pretend to be googlebot
-    "X-Forwarded-For": "64.18.15.200",
-}
-
-
-def _df_split_apply(tup_arg):
-    split_ind, subset, func = tup_arg
-    r = subset.apply(func, axis=1)
-    return (split_ind, r)
-
-
-def df_multiprocess(df, processes, chunk_size, func, dataset_name):
-    print("Generating parts...")
-    with shelve.open(
-        "%s_%s_%s_results.tmp" % (dataset_name, func.__name__, chunk_size)
-    ) as results:
-
-        pbar = tqdm(total=len(df), position=0)
-        # Resume:
-        finished_chunks = set([int(k) for k in results.keys()])
-        pbar.desc = "Resuming"
-        for k in results.keys():
-            pbar.update(len(results[str(k)][1]))
-
-        pool_data = (
-            (index, df[i : i + chunk_size], func)
-            for index, i in enumerate(range(0, len(df), chunk_size))
-            if index not in finished_chunks
-        )
-        print(
-            int(len(df) / chunk_size),
-            "parts.",
-            chunk_size,
-            "per part.",
-            "Using",
-            processes,
-            "processes",
-        )
-
-        pbar.desc = "Downloading"
-        with Pool(processes) as pool:
-            for i, result in enumerate(
-                pool.imap_unordered(_df_split_apply, pool_data, 2)
-            ):
-                results[str(result[0])] = result
-                pbar.update(len(result[1]))
-        pbar.close()
-
-    print("Finished Downloading.")
-    return
-
-
-# Unique name based on url
-def _file_name(row):
-    name = (
-        "%s/%s_%s"
-        % (
-            # row["folder"],
-            storage_dir,
-            row.name,
-            (zlib.crc32(row["url"].encode("utf-8")) & 0xFFFFFFFF),
-        )
-        + ".jpg"
-    )
-    return name
-
-
-# For checking mimetypes separately without download
-def check_mimetype(row):
-    if os.path.isfile(str(row["file"])):
-        row["mimetype"] = magic.from_file(row["file"], mime=True)
-        row["size"] = os.stat(row["file"]).st_size
-    return row
-
-
-# Don't download image, just check with a HEAD request, can't resume.
-# Can use this instead of download_image to get HTTP status codes.
-def check_download(row):
-    fname = _file_name(row)
-    try:
-        # not all sites will support HEAD
-        response = requests.head(
-            row["url"], stream=False, timeout=5, allow_redirects=True, headers=headers
-        )
-        row["status"] = response.status_code
-        row["headers"] = dict(response.headers)
-    except:
-        # log errors later, set error as 408 timeout
-        row["status"] = 408
-        return row
-    if response.ok:
-        row["file"] = fname
-    return row
-
-
-def resize_img(req):
-    image = Image.open(req).convert("RGB")
-    image = TF.resize(
-        # image, size=(resize_size, resize_size)
-        image,
-        size=resize_size,
-    )  # , interpolation=Image.LANCZOS)
-    return image
-
-
-def download_image(row):
-    fname = _file_name(row)
-    # Skip Already downloaded, retry others later
-    if os.path.isfile(fname):
-        row["status"] = 200
-        row["file"] = fname
-        row["mimetype"] = magic.from_file(row["file"], mime=True)
-        row["size"] = os.stat(row["file"]).st_size
-        return row
-
-    try:
-        # use smaller timeout to skip errors, but can result in failed downloads
-        response = requests.get(
-            row["url"], stream=False, timeout=5, allow_redirects=True, headers=headers
-        )
-        row["status"] = response.status_code
-        # row['headers'] = dict(response.headers)
-    except Exception as e:
-        # log errors later, set error as 408 timeout
-        row["status"] = 408
-        return row
-
-    if response.ok:
-        try:
-            # some sites respond with gzip transport encoding
-            response.raw.decode_content = True
-            img = resize_img(io.BytesIO(response.content))
-            img.save(fname)
-
-            row["mimetype"] = magic.from_file(fname, mime=True)
-            row["size"] = os.stat(fname).st_size
-
-        except Exception as e:
-            #     # This is if it times out during a download or decode
-            row["status"] = 408
-
-    row["file"] = fname
-    return row
-
-
-def open_tsv(fname, folder):
-    print("Opening %s Data File..." % fname)
-    df = pd.read_csv(
-        fname, sep="\t", names=["url", "caption"]
-    )  # , usecols=range(1, 2))
-    df["folder"] = folder
-    print("Processing", len(df), " Images:")
-    return df
-
-
-def df_from_shelve(chunk_size, func, dataset_name):
-    print("Generating Dataframe from results...")
-    with shelve.open(
-        "%s_%s_%s_results.tmp" % (dataset_name, func.__name__, chunk_size)
-    ) as results:
-        keylist = sorted([int(k) for k in results.keys()])
-        df = pd.concat([results[str(k)][1] for k in keylist], sort=True)
-    return df
-
-
-resize_size = 384
-
-config_path = get_abs_path("configs/datasets/conceptual_caption/defaults_12m.yaml")
-
-storage_dir = OmegaConf.load(
-    config_path
-).datasets.conceptual_caption_12m.build_info.images.storage
-storage_dir = Path(get_cache_path(storage_dir))
-
-os.makedirs(storage_dir, exist_ok=True)
-
-# number of processes in the pool can be larger than cores
-num_processes = 96
-# num_processes = 1
-# chunk_size is how many images per chunk per process - changing this resets progress when restarting.
-images_per_part = 100
-
-data_name = "cc12m"
-# os.makedirs(data_name, exist_ok=True)
-
-df = open_tsv("cc12m.tsv", data_name)
-df_multiprocess(
-    df=df,
-    processes=num_processes,
-    chunk_size=images_per_part,
-    func=download_image,
-    dataset_name=data_name,
-)
-df = df_from_shelve(
-    chunk_size=images_per_part, func=download_image, dataset_name=data_name
-)
-df.to_csv(
-    "downloaded_%s_report.tsv.gz" % data_name,
-    compression="gzip",
-    sep="\t",
-    header=False,
-    index=False,
-)
-print("Saved.")
diff --git a/spaces/SeViLA/SeViLA/lavis/models/blip_models/blip_classification.py b/spaces/SeViLA/SeViLA/lavis/models/blip_models/blip_classification.py
deleted file mode 100644
index 46c49099d6170fd74b8cbdfae8b1925707e493b6..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/blip_models/blip_classification.py
+++ /dev/null
@@ -1,177 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from copy import deepcopy
-
-import torch
-import torch.nn.functional as F
-from lavis.common.registry import registry
-from lavis.models.base_model import MomentumDistilationMixin
-from lavis.models.blip_models.blip import BlipBase
-from lavis.models.blip_models.blip_outputs import (
-    BlipIntermediateOutput,
-    BlipOutputWithLogits,
-)
-from lavis.models.med import XBertEncoder
-from lavis.models.vit import VisionTransformerEncoder
-from torch import nn
-
-
-@registry.register_model("blip_classification")
-class BlipClassification(BlipBase, MomentumDistilationMixin):
-    PRETRAINED_MODEL_CONFIG_DICT = {
-        "base": "configs/models/blip_classification_base.yaml",
-    }
-
-    def __init__(
-        self,
-        image_encoder,
-        text_encoder,
-        num_classes,
-        momentum=0.995,
-        alpha=0.4,
-        max_txt_len=40,
-        use_distill=True,
-    ):
-        super().__init__()
-
-        self.tokenizer = self.init_tokenizer()
-
-        self.use_distill = use_distill
-
-        self.visual_encoder = image_encoder
-        self.text_encoder = text_encoder
-
-        hidden_size = text_encoder.config.hidden_size
-        self.cls_head = nn.Sequential(
-            nn.Linear(hidden_size, hidden_size),
-            nn.ReLU(),
-            nn.Linear(hidden_size, num_classes),
-        )
-
-        if self.use_distill:
-            self.visual_encoder_m = deepcopy(self.visual_encoder)
-            self.text_encoder_m = deepcopy(self.text_encoder)
-            self.cls_head_m = deepcopy(self.cls_head)
-
-            self.momentum = momentum
-            self.alpha = alpha
-
-            self.model_pairs = [
-                [self.visual_encoder, self.visual_encoder_m],
-                [self.text_encoder, self.text_encoder_m],
-                [self.cls_head, self.cls_head_m],
-            ]
-
-            self.copy_params()
-
-        self.max_txt_len = max_txt_len
-
-    def _rampup_factor(self, epoch, iters, num_iters_per_epoch):
-        return min(1, (epoch * num_iters_per_epoch + iters) / num_iters_per_epoch)
-
-    def forward(self, samples, is_train=True):
-        sentences = samples["text_input"]
-        sentences = self.tokenizer(
-            sentences,
-            padding="longest",
-            truncation=True,
-            max_length=self.max_txt_len,
-            return_tensors="pt",
-        ).to(self.device)
-        samples.update({"tokenized_text": sentences})
-
-        targets = samples["label"]
-
-        image_embeds = self.visual_encoder.forward_features(samples["image"])
-        encoder_output = self.text_encoder.forward_automask(
-            samples["tokenized_text"], image_embeds
-        )
-
-        prediction = self.cls_head(encoder_output.last_hidden_state[:, 0, :])
-
-        if is_train:
-            if self.use_distill:
-                with torch.no_grad():
-                    self._momentum_update()
-
-                    image_embeds_m = self.visual_encoder_m(samples["image"])
-                    encoder_output_m = self.text_encoder_m.forward_automask(
-                        samples["tokenized_text"], image_embeds_m
-                    )
-
-                    prediction_m = self.cls_head_m(
-                        encoder_output_m.last_hidden_state[:, 0, :]
-                    )
-
-                alpha = self.alpha * self._rampup_factor(
-                    epoch=samples["epoch"],
-                    iters=samples["iters"],
-                    num_iters_per_epoch=samples["num_iters_per_epoch"],
-                )
-
-                loss = (1 - alpha) * F.cross_entropy(
-                    prediction, targets
-                ) - alpha * torch.sum(
-                    F.log_softmax(prediction, dim=1) * F.softmax(prediction_m, dim=1),
-                    dim=1,
-                ).mean()
-            else:
-                loss = F.cross_entropy(prediction, targets)
-
-            # return {"loss": loss}
-            return BlipOutputWithLogits(
-                loss=loss,
-                intermediate_output=BlipIntermediateOutput(
-                    image_embeds=image_embeds,
-                    image_embeds_m=image_embeds_m,
-                    encoder_output=encoder_output,
-                    encoder_output_m=encoder_output_m,
-                ),
-                logits=prediction,
-                logits_m=prediction_m,
-            )
-
-        else:
-            return {"predictions": prediction, "targets": targets}
-
-    def predict(self, samples):
-        output = self.forward(samples, is_train=False)
-        return output
-
-    @classmethod
-    def from_config(cls, cfg=None):
-        image_encoder = VisionTransformerEncoder.from_config(cfg)
-
-        # text encoder + multimodal encoder
-        text_encoder = XBertEncoder.from_config(cfg)
-        use_distill = cfg.get("use_distill", True)
-        momentum = cfg.get("momentum", 0.995)
-        num_classes = cfg.get("num_classes", -1)
-        alpha = cfg.get("alpha", 0.4)
-        max_txt_len = cfg.get("max_txt_len", 40)
-
-        assert num_classes > 1, "Invalid number of classes provided, found {}".format(
-            num_classes
-        )
-
-        model = cls(
-            image_encoder=image_encoder,
-            text_encoder=text_encoder,
-            use_distill=use_distill,
-            alpha=alpha,
-            num_classes=num_classes,
-            momentum=momentum,
-            max_txt_len=max_txt_len,
-        )
-
-        # load pre-trained weights
-        pretrain_path = cfg.get("pretrained", None)
-        if pretrain_path is not None:
-            msg = model.load_from_pretrained(url_or_filename=pretrain_path)
-
-        return model
diff --git a/spaces/SeyedAli/Persian-Speech-synthesis/README.md b/spaces/SeyedAli/Persian-Speech-synthesis/README.md
deleted file mode 100644
index ac565922547a23b1cfca2ed1cfdea12b43a77167..0000000000000000000000000000000000000000
--- a/spaces/SeyedAli/Persian-Speech-synthesis/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Persian Speech synthesis
-emoji: 📝➡️🔊
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Sijuade/Stable-Diffusion/utils.py b/spaces/Sijuade/Stable-Diffusion/utils.py
deleted file mode 100644
index 79bd0d6066ecd0b56a1b2a50cee9ad02b0190c61..0000000000000000000000000000000000000000
--- a/spaces/Sijuade/Stable-Diffusion/utils.py
+++ /dev/null
@@ -1,265 +0,0 @@
-import PIL
-import torch
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-import torch.nn.functional as F
-import torchvision.transforms as T
-from diffusers import LMSDiscreteScheduler, DiffusionPipeline
-
-# configurations
-torch_device        = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
-height, width       = 512, 512
-guidance_scale      = 8
-blue_loss_scale     = 200
-num_inference_steps = 50
-
-elastic_transformer = T.ElasticTransform(alpha=550.0, sigma=5.0)
-
-
-
-pretrained_model_name_or_path = "segmind/tiny-sd"
-pipe = DiffusionPipeline.from_pretrained(
-    pretrained_model_name_or_path,
-    low_cpu_mem_usage = True
-).to(torch_device)
-
-
-pipe.load_textual_inversion("sd-concepts-library/dreams")
-pipe.load_textual_inversion("sd-concepts-library/midjourney-style")
-pipe.load_textual_inversion("sd-concepts-library/moebius")
-pipe.load_textual_inversion("sd-concepts-library/style-of-marc-allante")
-pipe.load_textual_inversion("sd-concepts-library/wlop-style")
-
-
-concepts_mapping = {
-    "Dream": '<meeg>', "Midjourney":'<midjourney-style>',
-    "Marc Allante": '<Marc_Allante>', "Moebius": '<moebius>',
-    "Wlop": '<wlop-style>'
-}
-
-
-def image_loss(images, method='elastic'):
-
-    # elastic loss
-    if method == 'elastic':
-      transformed_imgs = elastic_transformer(images)
-      error = torch.abs(transformed_imgs - images).mean()
-
-    # symmetry loss - Flip the image along the width
-    elif method == "symmetry":
-      flipped_image = torch.flip(images, [3])
-      error = F.mse_loss(images, flipped_image)
-
-    # saturation loss
-    elif method == 'saturation':
-      transformed_imgs = T.functional.adjust_saturation(images,saturation_factor = 10)
-      error = torch.abs(transformed_imgs - images).mean()
-
-    # blue loss
-    elif method == 'blue':
-      error = torch.abs(images[:,2] - 0.9).mean() # [:,2] -> all images in batch, only the blue channel
-
-    return error
-
-
-HTML_TEMPLATE = """
-<style>
-    body {
-        background: linear-gradient(135deg, #f5f7fa, #c3cfe2);
-    }
-    #app-header {
-        text-align: center;
-        background: rgba(255, 255, 255, 0.8); /* Semi-transparent white */
-        padding: 20px;
-        border-radius: 10px;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-        position: relative; /* To position the artifacts */
-    }
-    #app-header h1 {
-        color: #4CAF50;
-        font-size: 2em;
-        margin-bottom: 10px;
-    }
-    .concept {
-        position: relative;
-        transition: transform 0.3s;
-    }
-    .concept:hover {
-        transform: scale(1.1);
-    }
-    .concept img {
-        width: 100px;
-        border-radius: 10px;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-    }
-    .concept-description {
-        position: absolute;
-        bottom: -30px;
-        left: 50%;
-        transform: translateX(-50%);
-        background-color: #4CAF50;
-        color: white;
-        padding: 5px 10px;
-        border-radius: 5px;
-        opacity: 0;
-        transition: opacity 0.3s;
-    }
-    .concept:hover .concept-description {
-        opacity: 1;
-    }
-    /* Artifacts */
-    .artifact {
-        position: absolute;
-        background: rgba(76, 175, 80, 0.1); /* Semi-transparent green */
-        border-radius: 50%; /* Make it circular */
-    }
-    .artifact.large {
-        width: 300px;
-        height: 300px;
-        top: -50px;
-        left: -150px;
-    }
-    .artifact.medium {
-        width: 200px;
-        height: 200px;
-        bottom: -50px;
-        right: -100px;
-    }
-    .artifact.small {
-        width: 100px;
-        height: 100px;
-        top: 50%;
-        left: 50%;
-        transform: translate(-50%, -50%);
-    }
-</style>
-<div id="app-header">
-    <!-- Artifacts -->
-    <div class="artifact large"></div>
-    <div class="artifact medium"></div>
-    <div class="artifact small"></div>
-    <!-- Content -->
-    <h1>Art Generator</h1>
-    <p>Generate new art in five different styles by providing a prompt.</p>
-    <div style="display: flex; justify-content: center; gap: 20px; margin-top: 20px;">
-        <div class="concept">
-            <img src="https://github.com/Delve-ERAV1/S20/assets/11761529/30ac92f8-fc62-4aab-9221-043865c6fe7c" alt="Midjourney">
-            <div class="concept-description">Midjourney Style</div>
-        </div>
-        <div class="concept">
-            <img src="https://github.com/Delve-ERAV1/S20/assets/11761529/54c9a61e-df9f-4054-835b-ec2c6ba5916c" alt="Dreams">
-            <div class="concept-description">Dreams Style</div>
-        </div>
-        <div class="concept">
-            <img src="https://github.com/Delve-ERAV1/S20/assets/11761529/2f37e402-15d1-4a74-ba85-bb1566da930e" alt="Moebius">
-            <div class="concept-description">Moebius Style</div>
-        </div>
-        <div class="concept">
-            <img src="https://github.com/Delve-ERAV1/S20/assets/11761529/f838e767-ac20-4996-b5be-65c61b365ce0" alt="Allante">
-            <div class="concept-description">Hong Kong born artist inspired by western and eastern influences</div>
-        </div>
-        <div class="concept">
-            <img src="https://github.com/Delve-ERAV1/S20/assets/11761529/9958140a-1b62-4972-83ca-85b023e3863f" alt="Wlop">
-            <div class="concept-description">WLOP (Born 1987) is known for Digital Art (NFTs)</div>
-        </div>
-    </div>
-</div>
-"""
-
-
-def get_examples():
-   examples = [
-      ['A powerful man in dreadlocks', 'Dream', 'Symmetry', 45],
-      ['World Peace', 'Marc Allante', 'Saturation', 147],
-      ['Storm trooper in the desert, dramatic lighting, high-detail', 'Moebius', 'Elastic', 28],
-      ['Delicious Italian pizza on a table, a window in the background overlooking a city skyline', 'Wlop', 'Blue', 50],
-   ]
-   return(examples)
-
-
-def latents_to_pil(latents):
-    # bath of latents -> list of images
-    latents = (1 / 0.18215) * latents
-    with torch.no_grad():
-        image = pipe.vae.decode(latents).sample
-    image = (image / 2 + 0.5).clamp(0, 1) # 0 to 1
-    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
-    image = (image * 255).round().astype("uint8")
-    return Image.fromarray(image[0])
-
-
-def generate_art(prompt, concept, method, seed):
-
-  prompt = f"{prompt} in the style of {concepts_mapping[concept]}"
-  img_no_loss = latents_to_pil(generate_image(prompt, method, seed))
-  img_loss = latents_to_pil(generate_image(prompt, method, seed, loss_apply=True))
-  return([img_no_loss, img_loss])
-
-
-def generate_image(prompt, method, seed, loss_apply=False):
-
-    generator           = torch.manual_seed(seed)
-    batch_size          = 1
-    method              = method.lower()
-
-    # scheduler
-    scheduler    = LMSDiscreteScheduler(beta_start = 0.00085, beta_end = 0.012, beta_schedule = "scaled_linear", num_train_timesteps = 1000)
-    scheduler.set_timesteps(50)
-    scheduler.timesteps = scheduler.timesteps.to(torch.float32)
-
-    # text embeddings of the prompt
-    text_input = pipe.tokenizer([prompt], padding='max_length', max_length = pipe.tokenizer.model_max_length, truncation= True, return_tensors="pt")
-    input_ids = text_input.input_ids.to(torch_device)
-
-    with torch.no_grad():
-        text_embeddings = pipe.text_encoder(text_input.input_ids.to(torch_device))[0]
-
-    max_length = text_input.input_ids.shape[-1]
-    uncond_input = pipe.tokenizer(
-          [""] * 1, padding="max_length", max_length= max_length, return_tensors="pt"
-    )
-
-    with torch.no_grad():
-        uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]
-
-    text_embeddings = torch.cat([uncond_embeddings,text_embeddings]) 
-
-    # random latent
-    latents = torch.randn(
-        (batch_size, pipe.unet.config.in_channels, height// 8, width //8),
-        generator = generator,
-    )
-
-
-    latents = latents.to(torch_device)
-    latents = latents * scheduler.init_noise_sigma
-
-    for i, t in tqdm(enumerate(scheduler.timesteps), total = len(scheduler.timesteps)):
-
-        latent_model_input = torch.cat([latents] * 2)
-        sigma = scheduler.sigmas[i]
-        latent_model_input = scheduler.scale_model_input(latent_model_input, t)
-
-        with torch.no_grad():
-            noise_pred = pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
-
-        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-        if loss_apply and i%5 == 0:
-
-            latents = latents.detach().requires_grad_()
-            latents_x0 = latents - sigma * noise_pred
-
-            # use vae to decode the image
-            denoised_images = pipe.vae.decode((1/ 0.18215) * latents_x0).sample / 2 + 0.5 # range(0,1)
-
-            loss = image_loss(denoised_images, method) * blue_loss_scale
-
-            cond_grad = torch.autograd.grad(loss, latents)[0]
-            latents = latents.detach() - cond_grad * sigma**2
-
-        latents = scheduler.step(noise_pred,t, latents).prev_sample
-
-    return latents
\ No newline at end of file
diff --git a/spaces/SinaAhmadi/ScriptNormalization/README.md b/spaces/SinaAhmadi/ScriptNormalization/README.md
deleted file mode 100644
index 308eded5c5ba3a34f9d091f42a3942a98a138c8d..0000000000000000000000000000000000000000
--- a/spaces/SinaAhmadi/ScriptNormalization/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ScriptNormalization
-emoji: 📊
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Smithjohny376/Orangemixes/app.py b/spaces/Smithjohny376/Orangemixes/app.py
deleted file mode 100644
index 69f54c2b131fe1a55500bffd8828a9d11385ee0c..0000000000000000000000000000000000000000
--- a/spaces/Smithjohny376/Orangemixes/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/WarriorMama777/OrangeMixs").launch()
\ No newline at end of file
diff --git a/spaces/Sonnt/Fracture_Webapp/ui/PageComponents.py b/spaces/Sonnt/Fracture_Webapp/ui/PageComponents.py
deleted file mode 100644
index 9735a200c1b0991ad93b71549daaa246ada9a65a..0000000000000000000000000000000000000000
--- a/spaces/Sonnt/Fracture_Webapp/ui/PageComponents.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import pandas as pd
-import streamlit as st
-from mLogsFunctions.fx import *
-from mLogsFunctions import *
-
-def subtab21(df, well_names):
-    col1, col2 = st.columns([1,8])
-    if "method_eda" not in st.session_state:
-        st.session_state.method_eda:str = "Single Well"
-        st.session_state.well_eda:str = well_names[0]
-    with col1:
-        st.radio("",
-                key="method_eda",
-                options=["All Wells", "Single Well"])
-        st.radio("WELL",
-                key="well_eda",
-                options=well_names)
-    with col2:
-        st.write('Data Description:')
-        if st.session_state.method_eda == "All Wells":
-            st.dataframe(df.describe(),width=1400, height=300)
-        elif st.session_state.method_eda == "Single Well":
-            df_single_w = df[df.WELL == st.session_state.well_eda]
-            st.dataframe(df_single_w.describe(),width=1400, height=300)
-        else:
-            pass
-        
-def subtab22(df,):
-    def missing_count(df):
-        missing = df.isnull().sum()*100/df.isnull().sum().sum()
-        missing = missing[missing >= 0].reset_index()
-        missing.columns = ['Columns', 'Count missing (%)']
-        return missing
-    
-    mt1, mt2, mt3 = st.columns(3)
-    
-    with mt1:
-        st.caption("Missing data rate of whole wells")
-        st.write(missing_bar(missing_count(df), "ALL WELLS"))
-        for i, w in enumerate(df.WELL.unique()):
-            if i%3 == 0:
-                st.caption(f"Missing data rate of {w}")
-                st.write(missing_bar(missing_count(well_filter(df, w)), f"WELL {w}"))
-    with mt2:
-        for i, w in enumerate(df.WELL.unique()):
-            if i%3 == 1:
-                st.caption(f"Missing data rate of {w}")
-                st.write(missing_bar(missing_count(well_filter(df, w)), f"WELL {w}"))
-    with mt3:
-        for i, w in enumerate(df.WELL.unique()):
-            if i%3 == 2:
-                st.caption(f"Missing data rate of {w}")
-                st.write(missing_bar(missing_count(well_filter(df, w)), f"WELL {w}"))
-
-def subtab23(df, curves):
-    mb1, mb2, mb3 = st.columns(3)
-    for i, c in enumerate(curves):
-        if i%3 == 0:
-            with mb1:
-                st.caption(f"Distribution of {c}")
-                st.write(missing_box(df, c))
-        if i%3 == 1:
-            with mb2:
-                st.caption(f"Distribution of {c}")
-                st.write(missing_box(df, c))
-        if i%3 == 2:
-            with mb3:
-                st.caption(f"Distribution of {c}")
-                st.write(missing_box(df, c))
-        
-def subtab24(df, curves):
-    #Histogram Line----------------------------------------------------------------
-    h1, h2, h3 = st.columns(3)
-    for i, c in enumerate(curves):
-        if i%3 == 0:
-            with h1:
-                hist_line_plot(df,c)
-        if i%3 == 1:
-            with h2:
-                hist_line_plot(df,c)
-        if i%3 == 2:
-            with h3:
-                hist_line_plot(df,c)
-        
-def subtab25(df, curves):
-    #CrossPlot---------------------------------------------------------------- 
-    pair_curv = [(a, b) for idx, a in enumerate(curves) for b in curves[idx + 1:]]
-    cp0, cp1, cp2, cp3, cp4 = st.columns(5)
-    for i, c in enumerate(pair_curv):
-        if i%5 == 0:
-            with cp0:
-                crossplot(df, pair_curv[i][0], pair_curv[i][1])
-        if i%5 == 1:
-            with cp1:
-                crossplot(df, pair_curv[i][0], pair_curv[i][1])
-        if i%5 == 2:
-            with cp2:
-                crossplot(df, pair_curv[i][0], pair_curv[i][1])
-        if i%5 == 3:
-            with cp3:
-                crossplot(df, pair_curv[i][0], pair_curv[i][1])
-        if i%5 == 4:
-            with cp4:
-                crossplot(df, pair_curv[i][0], pair_curv[i][1])
-            
-def subtab26(df, curves):
-    #Pairpot----------------------------------------------------------------
-    _p1, _p2, _p3 = st.columns([2,2,2])
-    if "pair_opt" not in st.session_state:
-        st.session_state.pair_opt:str = "ALL WELLS"
-        st.session_state.color_pair:str = "WELL"
-        st.session_state.well_pair:str = list(df.WELL.unique())[0]
-    with _p1:
-        pair_opt_ = st.radio("Displayed objects", key="pair_opt", options=["ALL WELLS", "SINGLE WELL"], horizontal=True)
-    with _p2:
-        well_pair_ = st.selectbox("WELL", key="well_pair", options=list(df.WELL.unique()))
-    with _p3:
-        colorp_ = st.selectbox("COLOR", key="color_pair", options=columns_list(df))
-    if pair_opt_ == "ALL WELLS":
-        st.write(pairplot(df, curves, curves, colorp_))
-    elif pair_opt_ == "SINGLE WELL":
-        st.write(pairplot(df[df["WELL"]==well_pair_], curves, curves, colorp_))
-    else:
-        st.write("Undefined Error!")
-        
-def scatterPoint3D(df,):
-    #3D Plotly----------------------------------------------------------------
-    wells_ = list(df.WELL.unique())
-    curvs_ = columns_list(df, no_well=True)
-    colors_ = columns_list(df)
-    sizes_ = ["WELL", "FRACTURE_INTENSITY", "DEPTH", None]
-    symbols_ = ["WELL", "FRACTURE_INTENSITY", None]
-    
-    if "well_3d" not in st.session_state:
-        st.session_state.w_opt:str = "ALL WELLS"
-        st.session_state.well_3d:str = wells_[0]
-        st.session_state.x_3d:str = curvs_[0]
-        st.session_state.y_3d:str = curvs_[0]
-        st.session_state.z_3d:str = curvs_[0]
-        st.session_state.color_3d:str = "WELL"
-        st.session_state.size_3d:str = "DEPTH"
-        st.session_state.symbol_3d:str = "WELL"
-        
-    p1_, p2_ = st.columns([1,7])
-    with p1_:
-        w_opt = st.radio("DisplayType", key="w_opt", options=["ALL WELLS", "SINGLE WELL"])
-        well_ = st.selectbox("WELL", key="well_3d", options=wells_)
-        x_ = st.selectbox("X", key="x_3d", options=curvs_)
-        y_ = st.selectbox("Y", key="y_3d", options=curvs_)
-        z_ = st.selectbox("Z", key="z_3d", options=curvs_)
-        color_ = st.selectbox("COLOR", key="color_3d", options=colors_)
-        size_ = st.selectbox("SIZE", key="size_3d", options=sizes_)
-        symbol_ = st.selectbox("SYMBOL", key="symbol_3d", options=symbols_)
-    with p2_:
-        log_x, log_y, log_z = [False, False, False]
-        if x_ in ["LLD", "LLS"]:
-            log_x = True
-        if y_ in ["LLD", "LLS"]:
-            log_y = True
-        if z_ in ["LLD", "LLS"]:
-            log_z = True
-        if w_opt == "ALL WELLS":
-            plotly_3d(df, x_, y_, z_, color_, size_, symbol_, log_x, log_y, log_z)
-        else:
-            df_3d_plt = df[df["WELL"]==well_]
-            plotly_3d(df_3d_plt, x_, y_, z_, color_, size_, symbol_, log_x, log_y, log_z)
-            
-                
-def stViewCurves(df):
-    _w = st.selectbox(label='Select WELL', options=list(df.WELL.unique()), key='w_plot')
-    if st.session_state.w_plot is not None:
-        df_plot = df[df['WELL']== _w]
-    _c = st.multiselect("Select curves for plotting:", key="curv_plt", options=columns_list(df, no_depth=True, no_well=True))
-    if len(_c) != 0:
-        view_curves(df_plot, curves=_c)
-        
diff --git a/spaces/Spark808/rvc-demo/infer_pack/commons.py b/spaces/Spark808/rvc-demo/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/Spark808/rvc-demo/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/StephanST/OpenLanderONNXonline/README.md b/spaces/StephanST/OpenLanderONNXonline/README.md
deleted file mode 100644
index 2e87b1ea513508dba27002503d8a8edb120a798f..0000000000000000000000000000000000000000
--- a/spaces/StephanST/OpenLanderONNXonline/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: OpenLander ONNX 
-emoji: 🚁
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/testing/plugin/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/testing/plugin/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/_pydev_filesystem_encoding.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/_pydev_filesystem_encoding.py
deleted file mode 100644
index 6264e3dbd354e3b80c2ae2456eccdb90d65646fe..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/_pydev_filesystem_encoding.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import sys
-
-
-def __getfilesystemencoding():
-    '''
-    Note: there's a copy of this method in interpreterInfo.py
-    '''
-    try:
-        ret = sys.getfilesystemencoding()
-        if not ret:
-            raise RuntimeError('Unable to get encoding.')
-        return ret
-    except:
-        try:
-            #Handle Jython
-            from java.lang import System  # @UnresolvedImport
-            env = System.getProperty("os.name").lower()
-            if env.find('win') != -1:
-                return 'ISO-8859-1'  #mbcs does not work on Jython, so, use a (hopefully) suitable replacement
-            return 'utf-8'
-        except:
-            pass
-
-        #Only available from 2.3 onwards.
-        if sys.platform == 'win32':
-            return 'mbcs'
-        return 'utf-8'
-
-def getfilesystemencoding():
-    try:
-        ret = __getfilesystemencoding()
-
-        #Check if the encoding is actually there to be used!
-        if hasattr('', 'encode'):
-            ''.encode(ret)
-        if hasattr('', 'decode'):
-            ''.decode(ret)
-
-        return ret
-    except:
-        return 'utf-8'
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/base_doc/mixins/update.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/base_doc/mixins/update.py
deleted file mode 100644
index e463bcb6af16f617868aaf4ca25269cad0595ba0..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/base_doc/mixins/update.py
+++ /dev/null
@@ -1,187 +0,0 @@
-from abc import abstractmethod
-from typing import TYPE_CHECKING, Dict, List, Type, TypeVar
-
-from typing_inspect import get_origin
-
-T = TypeVar('T', bound='UpdateMixin')
-
-if TYPE_CHECKING:
-    from pydantic.fields import ModelField
-
-
-class UpdateMixin:
-    __fields__: Dict[str, 'ModelField']
-
-    def _get_string_for_regex_filter(self):
-        return str(self)
-
-    @classmethod
-    @abstractmethod
-    def _get_field_type(cls, field: str) -> Type['UpdateMixin']:
-        ...
-
-    def update(self, other: T):
-        """
-        Updates self with the content of other. Changes are applied to self.
-        Updating one Document with another consists in the following:
-
-         - Setting data properties of the second Document to the first Document
-         if they are not None
-         - Concatenating lists and updating sets
-         - Updating recursively Documents and DocLists
-         - Updating Dictionaries of the left with the right
-
-        It behaves as an update operation for Dictionaries, except that since
-        it is applied to a static schema type, the presence of the field is
-        given by the field not having a None value and that DocLists,
-        lists and sets are concatenated. It is worth mentioning that Tuples
-        are not merged together since they are meant to be immutable,
-        so they behave as regular types and the value of `self` is updated
-        with the value of `other`.
-
-
-        ---
-
-        ```python
-        from typing import List, Optional
-
-        from docarray import BaseDoc
-
-
-        class MyDocument(BaseDoc):
-            content: str
-            title: Optional[str] = None
-            tags_: List
-
-
-        doc1 = MyDocument(
-            content='Core content of the document', title='Title', tags_=['python', 'AI']
-        )
-        doc2 = MyDocument(content='Core content updated', tags_=['docarray'])
-
-        doc1.update(doc2)
-        assert doc1.content == 'Core content updated'
-        assert doc1.title == 'Title'
-        assert doc1.tags_ == ['python', 'AI', 'docarray']
-        ```
-
-        ---
-        :param other: The Document with which to update the contents of this
-        """
-        if type(self) != type(other):
-            raise Exception(
-                f'Update operation can only be applied to '
-                f'Documents of the same type. '
-                f'Trying to update Document of type '
-                f'{type(self)} with Document of type '
-                f'{type(other)}'
-            )
-        from collections import namedtuple
-
-        from docarray import DocList
-        from docarray.utils.reduce import reduce
-
-        # Declaring namedtuple()
-        _FieldGroups = namedtuple(
-            '_FieldGroups',
-            [
-                'simple_non_empty_fields',
-                'list_fields',
-                'set_fields',
-                'dict_fields',
-                'nested_docarray_fields',
-                'nested_docs_fields',
-            ],
-        )
-
-        FORBIDDEN_FIELDS_TO_UPDATE = ['ID']
-
-        def _group_fields(doc: 'UpdateMixin') -> _FieldGroups:
-            simple_non_empty_fields: List[str] = []
-            list_fields: List[str] = []
-            set_fields: List[str] = []
-            dict_fields: List[str] = []
-            nested_docs_fields: List[str] = []
-            nested_docarray_fields: List[str] = []
-
-            for field_name, field in doc.__fields__.items():
-                if field_name not in FORBIDDEN_FIELDS_TO_UPDATE:
-                    field_type = doc._get_field_type(field_name)
-
-                    if isinstance(field_type, type) and issubclass(field_type, DocList):
-                        nested_docarray_fields.append(field_name)
-                    else:
-                        origin = get_origin(field_type)
-                        if origin is list:
-                            list_fields.append(field_name)
-                        elif origin is set:
-                            set_fields.append(field_name)
-                        elif origin is dict:
-                            dict_fields.append(field_name)
-                        else:
-                            v = getattr(doc, field_name)
-                            if v:
-                                if isinstance(v, UpdateMixin):
-                                    nested_docs_fields.append(field_name)
-                                else:
-                                    simple_non_empty_fields.append(field_name)
-            return _FieldGroups(
-                simple_non_empty_fields,
-                list_fields,
-                set_fields,
-                dict_fields,
-                nested_docarray_fields,
-                nested_docs_fields,
-            )
-
-        doc1_fields = _group_fields(self)
-        doc2_fields = _group_fields(other)
-
-        for field in doc2_fields.simple_non_empty_fields:
-            setattr(self, field, getattr(other, field))
-
-        for field in set(
-            doc1_fields.nested_docs_fields + doc2_fields.nested_docs_fields
-        ):
-            sub_doc_1: T = getattr(self, field)
-            sub_doc_2: T = getattr(other, field)
-            sub_doc_1.update(sub_doc_2)
-            setattr(self, field, sub_doc_1)
-
-        for field in set(doc1_fields.list_fields + doc2_fields.list_fields):
-            array1 = getattr(self, field)
-            array2 = getattr(other, field)
-            if array1 is None and array2 is not None:
-                setattr(self, field, array2)
-            elif array1 is not None and array2 is not None:
-                array1.extend(array2)
-                setattr(self, field, array1)
-
-        for field in set(doc1_fields.set_fields + doc2_fields.set_fields):
-            array1 = getattr(self, field)
-            array2 = getattr(other, field)
-            if array1 is None and array2 is not None:
-                setattr(self, field, array2)
-            elif array1 is not None and array2 is not None:
-                array1.update(array2)
-                setattr(self, field, array1)
-
-        for field in set(
-            doc1_fields.nested_docarray_fields + doc2_fields.nested_docarray_fields
-        ):
-            array1 = getattr(self, field)
-            array2 = getattr(other, field)
-            if array1 is None and array2 is not None:
-                setattr(self, field, array2)
-            elif array1 is not None and array2 is not None:
-                array1 = reduce(array1, array2)
-                setattr(self, field, array1)
-
-        for field in set(doc1_fields.dict_fields + doc2_fields.dict_fields):
-            dict1 = getattr(self, field)
-            dict2 = getattr(other, field)
-            if dict1 is None and dict2 is not None:
-                setattr(self, field, dict2)
-            elif dict1 is not None and dict2 is not None:
-                dict1.update(dict2)
-                setattr(self, field, dict1)
diff --git a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/data/audio.py b/spaces/Suniilkumaar/MusicGen-updated/audiocraft/data/audio.py
deleted file mode 100644
index 2048df6f175d7303bcf5c7b931922fd297908ead..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/data/audio.py
+++ /dev/null
@@ -1,215 +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.
-
-"""
-Audio IO methods are defined in this module (info, read, write),
-We rely on av library for faster read when possible, otherwise on torchaudio.
-"""
-
-from dataclasses import dataclass
-from pathlib import Path
-import logging
-import typing as tp
-
-import numpy as np
-import soundfile
-import torch
-from torch.nn import functional as F
-import torchaudio as ta
-
-import av
-
-from .audio_utils import f32_pcm, i16_pcm, normalize_audio
-
-
-_av_initialized = False
-
-
-def _init_av():
-    global _av_initialized
-    if _av_initialized:
-        return
-    logger = logging.getLogger('libav.mp3')
-    logger.setLevel(logging.ERROR)
-    _av_initialized = True
-
-
-@dataclass(frozen=True)
-class AudioFileInfo:
-    sample_rate: int
-    duration: float
-    channels: int
-
-
-def _av_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
-    _init_av()
-    with av.open(str(filepath)) as af:
-        stream = af.streams.audio[0]
-        sample_rate = stream.codec_context.sample_rate
-        duration = float(stream.duration * stream.time_base)
-        channels = stream.channels
-        return AudioFileInfo(sample_rate, duration, channels)
-
-
-def _soundfile_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
-    info = soundfile.info(filepath)
-    return AudioFileInfo(info.samplerate, info.duration, info.channels)
-
-
-def audio_info(filepath: tp.Union[str, Path]) -> AudioFileInfo:
-    # torchaudio no longer returns useful duration informations for some formats like mp3s.
-    filepath = Path(filepath)
-    if filepath.suffix in ['.flac', '.ogg']:  # TODO: Validate .ogg can be safely read with av_info
-        # ffmpeg has some weird issue with flac.
-        return _soundfile_info(filepath)
-    else:
-        return _av_info(filepath)
-
-
-def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: float = -1.) -> tp.Tuple[torch.Tensor, int]:
-    """FFMPEG-based audio file reading using PyAV bindings.
-    Soundfile cannot read mp3 and av_read is more efficient than torchaudio.
-
-    Args:
-        filepath (str or Path): Path to audio file to read.
-        seek_time (float): Time at which to start reading in the file.
-        duration (float): Duration to read from the file. If set to -1, the whole file is read.
-    Returns:
-        Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate
-    """
-    _init_av()
-    with av.open(str(filepath)) as af:
-        stream = af.streams.audio[0]
-        sr = stream.codec_context.sample_rate
-        num_frames = int(sr * duration) if duration >= 0 else -1
-        frame_offset = int(sr * seek_time)
-        # we need a small negative offset otherwise we get some edge artifact
-        # from the mp3 decoder.
-        af.seek(int(max(0, (seek_time - 0.1)) / stream.time_base), stream=stream)
-        frames = []
-        length = 0
-        for frame in af.decode(streams=stream.index):
-            current_offset = int(frame.rate * frame.pts * frame.time_base)
-            strip = max(0, frame_offset - current_offset)
-            buf = torch.from_numpy(frame.to_ndarray())
-            if buf.shape[0] != stream.channels:
-                buf = buf.view(-1, stream.channels).t()
-            buf = buf[:, strip:]
-            frames.append(buf)
-            length += buf.shape[1]
-            if num_frames > 0 and length >= num_frames:
-                break
-        assert frames
-        # If the above assert fails, it is likely because we seeked past the end of file point,
-        # in which case ffmpeg returns a single frame with only zeros, and a weird timestamp.
-        # This will need proper debugging, in due time.
-        wav = torch.cat(frames, dim=1)
-        assert wav.shape[0] == stream.channels
-        if num_frames > 0:
-            wav = wav[:, :num_frames]
-        return f32_pcm(wav), sr
-
-
-def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0.,
-               duration: float = -1., pad: bool = False) -> tp.Tuple[torch.Tensor, int]:
-    """Read audio by picking the most appropriate backend tool based on the audio format.
-
-    Args:
-        filepath (str or Path): Path to audio file to read.
-        seek_time (float): Time at which to start reading in the file.
-        duration (float): Duration to read from the file. If set to -1, the whole file is read.
-        pad (bool): Pad output audio if not reaching expected duration.
-    Returns:
-        Tuple[torch.Tensor, int]: Tuple containing audio data and sample rate.
-    """
-    fp = Path(filepath)
-    if fp.suffix in ['.flac', '.ogg']:  # TODO: check if we can safely use av_read for .ogg
-        # There is some bug with ffmpeg and reading flac
-        info = _soundfile_info(filepath)
-        frames = -1 if duration <= 0 else int(duration * info.sample_rate)
-        frame_offset = int(seek_time * info.sample_rate)
-        wav, sr = soundfile.read(filepath, start=frame_offset, frames=frames, dtype=np.float32)
-        assert info.sample_rate == sr, f"Mismatch of sample rates {info.sample_rate} {sr}"
-        wav = torch.from_numpy(wav).t().contiguous()
-        if len(wav.shape) == 1:
-            wav = torch.unsqueeze(wav, 0)
-    elif (
-        fp.suffix in ['.wav', '.mp3'] and fp.suffix[1:] in ta.utils.sox_utils.list_read_formats()
-        and duration <= 0 and seek_time == 0
-    ):
-        # Torchaudio is faster if we load an entire file at once.
-        wav, sr = ta.load(fp)
-    else:
-        wav, sr = _av_read(filepath, seek_time, duration)
-    if pad and duration > 0:
-        expected_frames = int(duration * sr)
-        wav = F.pad(wav, (0, expected_frames - wav.shape[-1]))
-    return wav, sr
-
-
-def audio_write(stem_name: tp.Union[str, Path],
-                wav: torch.Tensor, sample_rate: int,
-                format: str = 'wav', mp3_rate: int = 320, normalize: bool = True,
-                strategy: str = 'peak', peak_clip_headroom_db: float = 1,
-                rms_headroom_db: float = 18, loudness_headroom_db: float = 14,
-                loudness_compressor: bool = False,
-                log_clipping: bool = True, make_parent_dir: bool = True,
-                add_suffix: bool = True) -> Path:
-    """Convenience function for saving audio to disk. Returns the filename the audio was written to.
-
-    Args:
-        stem_name (str or Path): Filename without extension which will be added automatically.
-        format (str): Either "wav" or "mp3".
-        mp3_rate (int): kbps when using mp3s.
-        normalize (bool): if `True` (default), normalizes according to the prescribed
-            strategy (see after). If `False`, the strategy is only used in case clipping
-            would happen.
-        strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak',
-            i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square
-            with extra headroom to avoid clipping. 'clip' just clips.
-        peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy.
-        rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger
-            than the `peak_clip` one to avoid further clipping.
-        loudness_headroom_db (float): Target loudness for loudness normalization.
-        loudness_compressor (bool): Uses tanh for soft clipping when strategy is 'loudness'.
-         when strategy is 'loudness'log_clipping (bool): If True, basic logging on stderr when clipping still
-            occurs despite strategy (only for 'rms').
-        make_parent_dir (bool): Make parent directory if it doesn't exist.
-    Returns:
-        Path: Path of the saved audio.
-    """
-    assert wav.dtype.is_floating_point, "wav is not floating point"
-    if wav.dim() == 1:
-        wav = wav[None]
-    elif wav.dim() > 2:
-        raise ValueError("Input wav should be at most 2 dimension.")
-    assert wav.isfinite().all()
-    wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db,
-                          rms_headroom_db, loudness_headroom_db, log_clipping=log_clipping,
-                          sample_rate=sample_rate, stem_name=str(stem_name))
-    kwargs: dict = {}
-    if format == 'mp3':
-        suffix = '.mp3'
-        kwargs.update({"compression": mp3_rate})
-    elif format == 'wav':
-        wav = i16_pcm(wav)
-        suffix = '.wav'
-        kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16})
-    else:
-        raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.")
-    if not add_suffix:
-        suffix = ''
-    path = Path(str(stem_name) + suffix)
-    if make_parent_dir:
-        path.parent.mkdir(exist_ok=True, parents=True)
-    try:
-        ta.save(path, wav, sample_rate, **kwargs)
-    except Exception:
-        if path.exists():
-            # we do not want to leave half written files around.
-            path.unlink()
-        raise
-    return path
diff --git a/spaces/Suniilkumaar/MusicGen-updated/tests/common_utils/temp_utils.py b/spaces/Suniilkumaar/MusicGen-updated/tests/common_utils/temp_utils.py
deleted file mode 100644
index d1e0367e979c8b9fea65472c373916d956ad5aaa..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/MusicGen-updated/tests/common_utils/temp_utils.py
+++ /dev/null
@@ -1,56 +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 os
-import tempfile
-
-
-class TempDirMixin:
-    """Mixin to provide easy access to temp dir.
-    """
-
-    temp_dir_ = None
-
-    @classmethod
-    def get_base_temp_dir(cls):
-        # If AUDIOCRAFT_TEST_DIR is set, use it instead of temporary directory.
-        # this is handy for debugging.
-        key = "AUDIOCRAFT_TEST_DIR"
-        if key in os.environ:
-            return os.environ[key]
-        if cls.temp_dir_ is None:
-            cls.temp_dir_ = tempfile.TemporaryDirectory()
-        return cls.temp_dir_.name
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.temp_dir_ is not None:
-            try:
-                cls.temp_dir_.cleanup()
-                cls.temp_dir_ = None
-            except PermissionError:
-                # On Windows there is a know issue with `shutil.rmtree`,
-                # which fails intermittenly.
-                # https://github.com/python/cpython/issues/74168
-                # Following the above thread, we ignore it.
-                pass
-        super().tearDownClass()
-
-    @property
-    def id(self):
-        return self.__class__.__name__
-
-    def get_temp_path(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        return path
-
-    def get_temp_dir(self, *paths):
-        temp_dir = os.path.join(self.get_base_temp_dir(), self.id)
-        path = os.path.join(temp_dir, *paths)
-        os.makedirs(path, exist_ok=True)
-        return path
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/epoch_based_runner.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/epoch_based_runner.py
deleted file mode 100644
index 766a9ce6afdf09cd11b1b15005f5132583011348..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/epoch_based_runner.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import time
-import warnings
-
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-from .base_runner import BaseRunner
-from .builder import RUNNERS
-from .checkpoint import save_checkpoint
-from .utils import get_host_info
-
-
-@RUNNERS.register_module()
-class EpochBasedRunner(BaseRunner):
-    """Epoch-based Runner.
-
-    This runner train models epoch by epoch.
-    """
-
-    def run_iter(self, data_batch, train_mode, **kwargs):
-        if self.batch_processor is not None:
-            outputs = self.batch_processor(
-                self.model, data_batch, train_mode=train_mode, **kwargs)
-        elif train_mode:
-            outputs = self.model.train_step(data_batch, self.optimizer,
-                                            **kwargs)
-        else:
-            outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('"batch_processor()" or "model.train_step()"'
-                            'and "model.val_step()" must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-
-    def train(self, data_loader, **kwargs):
-        self.model.train()
-        self.mode = 'train'
-        self.data_loader = data_loader
-        self._max_iters = self._max_epochs * len(self.data_loader)
-        self.call_hook('before_train_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_train_iter')
-            self.run_iter(data_batch, train_mode=True, **kwargs)
-            self.call_hook('after_train_iter')
-            self._iter += 1
-
-        self.call_hook('after_train_epoch')
-        self._epoch += 1
-
-    @torch.no_grad()
-    def val(self, data_loader, **kwargs):
-        self.model.eval()
-        self.mode = 'val'
-        self.data_loader = data_loader
-        self.call_hook('before_val_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_val_iter')
-            self.run_iter(data_batch, train_mode=False)
-            self.call_hook('after_val_iter')
-
-        self.call_hook('after_val_epoch')
-
-    def run(self, data_loaders, workflow, max_epochs=None, **kwargs):
-        """Start running.
-
-        Args:
-            data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
-                and validation.
-            workflow (list[tuple]): A list of (phase, epochs) to specify the
-                running order and epochs. E.g, [('train', 2), ('val', 1)] means
-                running 2 epochs for training and 1 epoch for validation,
-                iteratively.
-        """
-        assert isinstance(data_loaders, list)
-        assert mmcv.is_list_of(workflow, tuple)
-        assert len(data_loaders) == len(workflow)
-        if max_epochs is not None:
-            warnings.warn(
-                'setting max_epochs in run is deprecated, '
-                'please set max_epochs in runner_config', DeprecationWarning)
-            self._max_epochs = max_epochs
-
-        assert self._max_epochs is not None, (
-            'max_epochs must be specified during instantiation')
-
-        for i, flow in enumerate(workflow):
-            mode, epochs = flow
-            if mode == 'train':
-                self._max_iters = self._max_epochs * len(data_loaders[i])
-                break
-
-        work_dir = self.work_dir if self.work_dir is not None else 'NONE'
-        self.logger.info('Start running, host: %s, work_dir: %s',
-                         get_host_info(), work_dir)
-        self.logger.info('Hooks will be executed in the following order:\n%s',
-                         self.get_hook_info())
-        self.logger.info('workflow: %s, max: %d epochs', workflow,
-                         self._max_epochs)
-        self.call_hook('before_run')
-
-        while self.epoch < self._max_epochs:
-            for i, flow in enumerate(workflow):
-                mode, epochs = flow
-                if isinstance(mode, str):  # self.train()
-                    if not hasattr(self, mode):
-                        raise ValueError(
-                            f'runner has no method named "{mode}" to run an '
-                            'epoch')
-                    epoch_runner = getattr(self, mode)
-                else:
-                    raise TypeError(
-                        'mode in workflow must be a str, but got {}'.format(
-                            type(mode)))
-
-                for _ in range(epochs):
-                    if mode == 'train' and self.epoch >= self._max_epochs:
-                        break
-                    epoch_runner(data_loaders[i], **kwargs)
-
-        time.sleep(1)  # wait for some hooks like loggers to finish
-        self.call_hook('after_run')
-
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl='epoch_{}.pth',
-                        save_optimizer=True,
-                        meta=None,
-                        create_symlink=True):
-        """Save the checkpoint.
-
-        Args:
-            out_dir (str): The directory that checkpoints are saved.
-            filename_tmpl (str, optional): The checkpoint filename template,
-                which contains a placeholder for the epoch number.
-                Defaults to 'epoch_{}.pth'.
-            save_optimizer (bool, optional): Whether to save the optimizer to
-                the checkpoint. Defaults to True.
-            meta (dict, optional): The meta information to be saved in the
-                checkpoint. Defaults to None.
-            create_symlink (bool, optional): Whether to create a symlink
-                "latest.pth" to point to the latest checkpoint.
-                Defaults to True.
-        """
-        if meta is None:
-            meta = {}
-        elif not isinstance(meta, dict):
-            raise TypeError(
-                f'meta should be a dict or None, but got {type(meta)}')
-        if self.meta is not None:
-            meta.update(self.meta)
-            # Note: meta.update(self.meta) should be done before
-            # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
-            # there will be problems with resumed checkpoints.
-            # More details in https://github.com/open-mmlab/mmcv/pull/1108
-        meta.update(epoch=self.epoch + 1, iter=self.iter)
-
-        filename = filename_tmpl.format(self.epoch + 1)
-        filepath = osp.join(out_dir, filename)
-        optimizer = self.optimizer if save_optimizer else None
-        save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
-        # in some environments, `os.symlink` is not supported, you may need to
-        # set `create_symlink` to False
-        if create_symlink:
-            dst_file = osp.join(out_dir, 'latest.pth')
-            if platform.system() != 'Windows':
-                mmcv.symlink(filename, dst_file)
-            else:
-                shutil.copy(filepath, dst_file)
-
-
-@RUNNERS.register_module()
-class Runner(EpochBasedRunner):
-    """Deprecated name of EpochBasedRunner."""
-
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            'Runner was deprecated, please use EpochBasedRunner instead')
-        super().__init__(*args, **kwargs)
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/seg/builder.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/seg/builder.py
deleted file mode 100644
index db61f03d4abb2072f2532ce4429c0842495e015b..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/core/seg/builder.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from annotator.uniformer.mmcv.utils import Registry, build_from_cfg
-
-PIXEL_SAMPLERS = Registry('pixel sampler')
-
-
-def build_pixel_sampler(cfg, **default_args):
-    """Build pixel sampler for segmentation map."""
-    return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args)
diff --git a/spaces/Syrinx/WebtoonPlotGenerator/app.py b/spaces/Syrinx/WebtoonPlotGenerator/app.py
deleted file mode 100644
index 74de0ec118f6e272893b11682d7274a0a181748b..0000000000000000000000000000000000000000
--- a/spaces/Syrinx/WebtoonPlotGenerator/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-import streamlit as st
-
-
-# Load the tokenizer and model
-tokenizer = GPT2Tokenizer.from_pretrained('webtoon_tokenizer')
-model = GPT2LMHeadModel.from_pretrained('webtoon_model')
-
-
-# Define the app
-def main():
-    st.title('Webtoon Description Generator')
-
-    # Get the input from the user
-    title = st.text_input('Enter the title of the Webtoon:', '')
-
-    # Generate the description
-    if st.button('Generate Description'):
-        with st.spinner('Generating...'):
-            description = generate_description(title)
-        st.success(description)
-
-# Check if GPU is available
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-else:
-    device = torch.device("cpu")
-
-# Define the function that generates the description
-def generate_description(title):
-    # Preprocess the input
-    input_text = f"{title}"
-    input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)
-
-    # Generate the output using the model
-    output = model.generate(
-        input_ids=input_ids,
-        max_length=200,
-        num_beams=4,
-        early_stopping=True,
-        no_repeat_ngram_size=2
-    )
-
-    # Convert the output to text
-    description = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    return description
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/TEnngal/bingo/src/lib/utils.ts b/spaces/TEnngal/bingo/src/lib/utils.ts
deleted file mode 100644
index 8de2eba94bf0bc93579d4f489e8b810dbf6ce92a..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/lib/utils.ts
+++ /dev/null
@@ -1,159 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-// @ts-ignore
-import randomip from 'random-ip'
-import cidr from './cidr.json'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function random (start: number, end: number) {
-  return start + Math.floor(Math.random() * (end - start))
-}
-
-export function randomIP() {
-  // return `104.${random(0, 21)}.${random(0, 127)}.${random(1, 255)}`
-  const [ip, range] = cidr.at(random(0, cidr.length))?.split('/')!
-  return randomip(ip, range)
-}
-
-export const defaultUID = 'xxx'
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-  return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch(e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
-  const date = new Date(input)
-  return date.toLocaleDateString('en-US', {
-    month: 'long',
-    day: 'numeric',
-    year: 'numeric'
-  })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function setCookie(key: string, value: string) {
-  const maxAge = value ? 86400 * 30 : 0
-  document.cookie = `${key}=${value || ''}; Path=/; Max-Age=${maxAge}; SameSite=None; Secure`
-}
-
-export function getCookie(cookieName: string) {
-  const re = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`)
-  return re.test(document.cookie) ? RegExp.$1 : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function mockUser(cookies: Partial<{ [key: string]: string }>) {
-  const {
-    BING_UA = process.env.BING_UA,
-    BING_IP,
-    _U = defaultUID,
-  } = cookies
-  const ua = parseUA(BING_UA)
-
-  return {
-    'x-forwarded-for': BING_IP!,
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.3 OS/Win32',
-    cookie: `_U=${_U}` || '',
-  }
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, type?: string) {
-  let {
-    BING_HEADER = process.env.BING_HEADER,
-    BING_IP,
-    IMAGE_ONLY = process.env.IMAGE_ONLY ?? '1',
-  } = cookies
-  const imageOnly = /^(1|true|yes)$/.test(String(IMAGE_ONLY))
-  if (BING_HEADER) {
-    if (
-      (imageOnly && type === 'image')
-      || !imageOnly
-    ) {
-      const headers = extraHeadersFromCookie({
-        BING_HEADER,
-        ...cookies,
-      }) || {}
-      headers['x-forward-for'] = BING_IP!
-      return headers
-    }
-  }
-  return mockUser(cookies)
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}
diff --git a/spaces/TEnngal/bingo/src/state/index.ts b/spaces/TEnngal/bingo/src/state/index.ts
deleted file mode 100644
index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/state/index.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atom } from 'jotai'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
-  { author: 'system', text: 'conversation between user and robot', id: '1' },
-  { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?  ', id: '2' },
-  {
-    author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
-  - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
-  - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
-  - 优点：清洁性能强劲，操作方便，适用多种场景；
-  - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
-  - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
-  - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
-  `, id: '3' },
-  { author: 'user', text: '今天的新闻', id: '4' },
-  {
-    author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
-  # 中国新闻
-  - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
-  - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
-  - 中央气象台7月16日18时发布台风橙色预警[^1^]
-  - 贵州石阡：暑期旅游带动乡村振兴[^1^]
-  - 激活大科学装置的“人才红利”[^1^]
-  - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
-  - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
-  - 成都以赛为媒提升城市美誉度[^1^]
-  - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
-  - 浙江建德：新安江上享清凉[^1^]
-  - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
-  - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
-  - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
-  - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
-  - 大运来了丨成都迎大运 全民健身动起来[^1^]
-  - 藏在高校里的宝藏博物馆[^1^]
-  - 中国汽车工业用70年赢得三个“全球第一”[^1^]
-  - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
-  # 国际新闻
-  - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
-  - 国际航运业加快绿色转型[^2^]
-  - 美企反对收紧对华芯片出口限制[^2^]
-  - 欧洲加大气候科技领域投资[^2^]
-  - 中企助力丹麦发展清洁能源[^2^]
-  - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
-  - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
-  以上信息仅供参考，具体详情请点击以下链接查看：
-
-  [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
-  [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
-  { author: 'user', text: '写一个快排', id: '6' },
-  { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n    temp = arr[i]\n    arr[i] = arr[j]\n    arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n    # 选择最右边的元素作为基准元素\n    pivot = arr[high]\n    # 初始化一个指针，指向左边区间的最后一个位置\n    i = low - 1\n    # 遍历左边区间中的每个元素\n    for j in range(low, high):\n        # 如果当前元素小于或等于基准元素\n        if arr[j] <= pivot:\n            # 将指针右移一位\n            i += 1\n            # 将指针所指的元素与当前元素交换\n            swap(arr, i, j)\n    # 将基准元素与指针右边的第一个元素交换\n    swap(arr, i + 1, high)\n    # 返回基准元素的最终位置\n    return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n    # 如果区间长度大于1\n    if low < high:\n        # 对区间进行划分，得到基准元素的位置\n        p = partition(arr, low, high)\n        # 对左边子区间进行快速排序\n        quick_sort(arr, low, p - 1)\n        # 对右边子区间进行快速排序\n        quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
-  {
-    author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)",
-    id: '8'
-  }
-]
-
-export const GreetMessages = [
-  '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
-  '重新开始总是很棒。问我任何问题!',
-  '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
-  '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
-  '很好，让我们来更改主题。你在想什么?',
-  '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
-  '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
-  '感谢刷新！你有新的话题吗？',
-  '明白了，让我们重新开始。接下来应该讨论什么？',
-  '下一步！我可以为你做什么？',
-  '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage<BingConversationStyle>('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const voiceAtom = atomWithStorage<boolean>('enableTTS', false, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
-  return new BingWebBot({
-    cookie: ' ',
-    ua: ' ',
-  })
-}
-
-export const chatFamily = atomFamily(
-  (param: Param) => {
-    return atomWithImmer({
-      botId: param.botId,
-      bot: createBotInstance(),
-      messages: [] as ChatMessageModel[],
-      generatingMessageId: '',
-      abortController: undefined as AbortController | undefined,
-      conversationId: nanoid(),
-    })
-  },
-  (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
-
-export const voiceListenAtom = atom(false)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/cachecontrol/filewrapper.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/cachecontrol/filewrapper.py
deleted file mode 100644
index f5ed5f6f6ec0eae90a9f48753622b2b5ee5d4a4f..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/cachecontrol/filewrapper.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-from tempfile import NamedTemporaryFile
-import mmap
-
-
-class CallbackFileWrapper(object):
-    """
-    Small wrapper around a fp object which will tee everything read into a
-    buffer, and when that file is closed it will execute a callback with the
-    contents of that buffer.
-
-    All attributes are proxied to the underlying file object.
-
-    This class uses members with a double underscore (__) leading prefix so as
-    not to accidentally shadow an attribute.
-
-    The data is stored in a temporary file until it is all available.  As long
-    as the temporary files directory is disk-based (sometimes it's a
-    memory-backed-``tmpfs`` on Linux), data will be unloaded to disk if memory
-    pressure is high.  For small files the disk usually won't be used at all,
-    it'll all be in the filesystem memory cache, so there should be no
-    performance impact.
-    """
-
-    def __init__(self, fp, callback):
-        self.__buf = NamedTemporaryFile("rb+", delete=True)
-        self.__fp = fp
-        self.__callback = callback
-
-    def __getattr__(self, name):
-        # The vaguaries of garbage collection means that self.__fp is
-        # not always set.  By using __getattribute__ and the private
-        # name[0] allows looking up the attribute value and raising an
-        # AttributeError when it doesn't exist. This stop thigns from
-        # infinitely recursing calls to getattr in the case where
-        # self.__fp hasn't been set.
-        #
-        # [0] https://docs.python.org/2/reference/expressions.html#atom-identifiers
-        fp = self.__getattribute__("_CallbackFileWrapper__fp")
-        return getattr(fp, name)
-
-    def __is_fp_closed(self):
-        try:
-            return self.__fp.fp is None
-
-        except AttributeError:
-            pass
-
-        try:
-            return self.__fp.closed
-
-        except AttributeError:
-            pass
-
-        # We just don't cache it then.
-        # TODO: Add some logging here...
-        return False
-
-    def _close(self):
-        if self.__callback:
-            if self.__buf.tell() == 0:
-                # Empty file:
-                result = b""
-            else:
-                # Return the data without actually loading it into memory,
-                # relying on Python's buffer API and mmap(). mmap() just gives
-                # a view directly into the filesystem's memory cache, so it
-                # doesn't result in duplicate memory use.
-                self.__buf.seek(0, 0)
-                result = memoryview(
-                    mmap.mmap(self.__buf.fileno(), 0, access=mmap.ACCESS_READ)
-                )
-            self.__callback(result)
-
-        # We assign this to None here, because otherwise we can get into
-        # really tricky problems where the CPython interpreter dead locks
-        # because the callback is holding a reference to something which
-        # has a __del__ method. Setting this to None breaks the cycle
-        # and allows the garbage collector to do it's thing normally.
-        self.__callback = None
-
-        # Closing the temporary file releases memory and frees disk space.
-        # Important when caching big files.
-        self.__buf.close()
-
-    def read(self, amt=None):
-        data = self.__fp.read(amt)
-        if data:
-            # We may be dealing with b'', a sign that things are over:
-            # it's passed e.g. after we've already closed self.__buf.
-            self.__buf.write(data)
-        if self.__is_fp_closed():
-            self._close()
-
-        return data
-
-    def _safe_read(self, amt):
-        data = self.__fp._safe_read(amt)
-        if amt == 2 and data == b"\r\n":
-            # urllib executes this read to toss the CRLF at the end
-            # of the chunk.
-            return data
-
-        self.__buf.write(data)
-        if self.__is_fp_closed():
-            self._close()
-
-        return data
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/response.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/response.py
deleted file mode 100644
index 8909f8454e94752d188ed13cf36c35f93fc6c3f2..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/response.py
+++ /dev/null
@@ -1,879 +0,0 @@
-from __future__ import absolute_import
-
-import io
-import logging
-import sys
-import warnings
-import zlib
-from contextlib import contextmanager
-from socket import error as SocketError
-from socket import timeout as SocketTimeout
-
-brotli = None
-
-from . import util
-from ._collections import HTTPHeaderDict
-from .connection import BaseSSLError, HTTPException
-from .exceptions import (
-    BodyNotHttplibCompatible,
-    DecodeError,
-    HTTPError,
-    IncompleteRead,
-    InvalidChunkLength,
-    InvalidHeader,
-    ProtocolError,
-    ReadTimeoutError,
-    ResponseNotChunked,
-    SSLError,
-)
-from .packages import six
-from .util.response import is_fp_closed, is_response_to_head
-
-log = logging.getLogger(__name__)
-
-
-class DeflateDecoder(object):
-    def __init__(self):
-        self._first_try = True
-        self._data = b""
-        self._obj = zlib.decompressobj()
-
-    def __getattr__(self, name):
-        return getattr(self._obj, name)
-
-    def decompress(self, data):
-        if not data:
-            return data
-
-        if not self._first_try:
-            return self._obj.decompress(data)
-
-        self._data += data
-        try:
-            decompressed = self._obj.decompress(data)
-            if decompressed:
-                self._first_try = False
-                self._data = None
-            return decompressed
-        except zlib.error:
-            self._first_try = False
-            self._obj = zlib.decompressobj(-zlib.MAX_WBITS)
-            try:
-                return self.decompress(self._data)
-            finally:
-                self._data = None
-
-
-class GzipDecoderState(object):
-
-    FIRST_MEMBER = 0
-    OTHER_MEMBERS = 1
-    SWALLOW_DATA = 2
-
-
-class GzipDecoder(object):
-    def __init__(self):
-        self._obj = zlib.decompressobj(16 + zlib.MAX_WBITS)
-        self._state = GzipDecoderState.FIRST_MEMBER
-
-    def __getattr__(self, name):
-        return getattr(self._obj, name)
-
-    def decompress(self, data):
-        ret = bytearray()
-        if self._state == GzipDecoderState.SWALLOW_DATA or not data:
-            return bytes(ret)
-        while True:
-            try:
-                ret += self._obj.decompress(data)
-            except zlib.error:
-                previous_state = self._state
-                # Ignore data after the first error
-                self._state = GzipDecoderState.SWALLOW_DATA
-                if previous_state == GzipDecoderState.OTHER_MEMBERS:
-                    # Allow trailing garbage acceptable in other gzip clients
-                    return bytes(ret)
-                raise
-            data = self._obj.unused_data
-            if not data:
-                return bytes(ret)
-            self._state = GzipDecoderState.OTHER_MEMBERS
-            self._obj = zlib.decompressobj(16 + zlib.MAX_WBITS)
-
-
-if brotli is not None:
-
-    class BrotliDecoder(object):
-        # Supports both 'brotlipy' and 'Brotli' packages
-        # since they share an import name. The top branches
-        # are for 'brotlipy' and bottom branches for 'Brotli'
-        def __init__(self):
-            self._obj = brotli.Decompressor()
-            if hasattr(self._obj, "decompress"):
-                self.decompress = self._obj.decompress
-            else:
-                self.decompress = self._obj.process
-
-        def flush(self):
-            if hasattr(self._obj, "flush"):
-                return self._obj.flush()
-            return b""
-
-
-class MultiDecoder(object):
-    """
-    From RFC7231:
-        If one or more encodings have been applied to a representation, the
-        sender that applied the encodings MUST generate a Content-Encoding
-        header field that lists the content codings in the order in which
-        they were applied.
-    """
-
-    def __init__(self, modes):
-        self._decoders = [_get_decoder(m.strip()) for m in modes.split(",")]
-
-    def flush(self):
-        return self._decoders[0].flush()
-
-    def decompress(self, data):
-        for d in reversed(self._decoders):
-            data = d.decompress(data)
-        return data
-
-
-def _get_decoder(mode):
-    if "," in mode:
-        return MultiDecoder(mode)
-
-    if mode == "gzip":
-        return GzipDecoder()
-
-    if brotli is not None and mode == "br":
-        return BrotliDecoder()
-
-    return DeflateDecoder()
-
-
-class HTTPResponse(io.IOBase):
-    """
-    HTTP Response container.
-
-    Backwards-compatible with :class:`http.client.HTTPResponse` but the response ``body`` is
-    loaded and decoded on-demand when the ``data`` property is accessed.  This
-    class is also compatible with the Python standard library's :mod:`io`
-    module, and can hence be treated as a readable object in the context of that
-    framework.
-
-    Extra parameters for behaviour not present in :class:`http.client.HTTPResponse`:
-
-    :param preload_content:
-        If True, the response's body will be preloaded during construction.
-
-    :param decode_content:
-        If True, will attempt to decode the body based on the
-        'content-encoding' header.
-
-    :param original_response:
-        When this HTTPResponse wrapper is generated from an :class:`http.client.HTTPResponse`
-        object, it's convenient to include the original for debug purposes. It's
-        otherwise unused.
-
-    :param retries:
-        The retries contains the last :class:`~urllib3.util.retry.Retry` that
-        was used during the request.
-
-    :param enforce_content_length:
-        Enforce content length checking. Body returned by server must match
-        value of Content-Length header, if present. Otherwise, raise error.
-    """
-
-    CONTENT_DECODERS = ["gzip", "deflate"]
-    if brotli is not None:
-        CONTENT_DECODERS += ["br"]
-    REDIRECT_STATUSES = [301, 302, 303, 307, 308]
-
-    def __init__(
-        self,
-        body="",
-        headers=None,
-        status=0,
-        version=0,
-        reason=None,
-        strict=0,
-        preload_content=True,
-        decode_content=True,
-        original_response=None,
-        pool=None,
-        connection=None,
-        msg=None,
-        retries=None,
-        enforce_content_length=False,
-        request_method=None,
-        request_url=None,
-        auto_close=True,
-    ):
-
-        if isinstance(headers, HTTPHeaderDict):
-            self.headers = headers
-        else:
-            self.headers = HTTPHeaderDict(headers)
-        self.status = status
-        self.version = version
-        self.reason = reason
-        self.strict = strict
-        self.decode_content = decode_content
-        self.retries = retries
-        self.enforce_content_length = enforce_content_length
-        self.auto_close = auto_close
-
-        self._decoder = None
-        self._body = None
-        self._fp = None
-        self._original_response = original_response
-        self._fp_bytes_read = 0
-        self.msg = msg
-        self._request_url = request_url
-
-        if body and isinstance(body, (six.string_types, bytes)):
-            self._body = body
-
-        self._pool = pool
-        self._connection = connection
-
-        if hasattr(body, "read"):
-            self._fp = body
-
-        # Are we using the chunked-style of transfer encoding?
-        self.chunked = False
-        self.chunk_left = None
-        tr_enc = self.headers.get("transfer-encoding", "").lower()
-        # Don't incur the penalty of creating a list and then discarding it
-        encodings = (enc.strip() for enc in tr_enc.split(","))
-        if "chunked" in encodings:
-            self.chunked = True
-
-        # Determine length of response
-        self.length_remaining = self._init_length(request_method)
-
-        # If requested, preload the body.
-        if preload_content and not self._body:
-            self._body = self.read(decode_content=decode_content)
-
-    def get_redirect_location(self):
-        """
-        Should we redirect and where to?
-
-        :returns: Truthy redirect location string if we got a redirect status
-            code and valid location. ``None`` if redirect status and no
-            location. ``False`` if not a redirect status code.
-        """
-        if self.status in self.REDIRECT_STATUSES:
-            return self.headers.get("location")
-
-        return False
-
-    def release_conn(self):
-        if not self._pool or not self._connection:
-            return
-
-        self._pool._put_conn(self._connection)
-        self._connection = None
-
-    def drain_conn(self):
-        """
-        Read and discard any remaining HTTP response data in the response connection.
-
-        Unread data in the HTTPResponse connection blocks the connection from being released back to the pool.
-        """
-        try:
-            self.read()
-        except (HTTPError, SocketError, BaseSSLError, HTTPException):
-            pass
-
-    @property
-    def data(self):
-        # For backwards-compat with earlier urllib3 0.4 and earlier.
-        if self._body:
-            return self._body
-
-        if self._fp:
-            return self.read(cache_content=True)
-
-    @property
-    def connection(self):
-        return self._connection
-
-    def isclosed(self):
-        return is_fp_closed(self._fp)
-
-    def tell(self):
-        """
-        Obtain the number of bytes pulled over the wire so far. May differ from
-        the amount of content returned by :meth:``urllib3.response.HTTPResponse.read``
-        if bytes are encoded on the wire (e.g, compressed).
-        """
-        return self._fp_bytes_read
-
-    def _init_length(self, request_method):
-        """
-        Set initial length value for Response content if available.
-        """
-        length = self.headers.get("content-length")
-
-        if length is not None:
-            if self.chunked:
-                # This Response will fail with an IncompleteRead if it can't be
-                # received as chunked. This method falls back to attempt reading
-                # the response before raising an exception.
-                log.warning(
-                    "Received response with both Content-Length and "
-                    "Transfer-Encoding set. This is expressly forbidden "
-                    "by RFC 7230 sec 3.3.2. Ignoring Content-Length and "
-                    "attempting to process response as Transfer-Encoding: "
-                    "chunked."
-                )
-                return None
-
-            try:
-                # RFC 7230 section 3.3.2 specifies multiple content lengths can
-                # be sent in a single Content-Length header
-                # (e.g. Content-Length: 42, 42). This line ensures the values
-                # are all valid ints and that as long as the `set` length is 1,
-                # all values are the same. Otherwise, the header is invalid.
-                lengths = set([int(val) for val in length.split(",")])
-                if len(lengths) > 1:
-                    raise InvalidHeader(
-                        "Content-Length contained multiple "
-                        "unmatching values (%s)" % length
-                    )
-                length = lengths.pop()
-            except ValueError:
-                length = None
-            else:
-                if length < 0:
-                    length = None
-
-        # Convert status to int for comparison
-        # In some cases, httplib returns a status of "_UNKNOWN"
-        try:
-            status = int(self.status)
-        except ValueError:
-            status = 0
-
-        # Check for responses that shouldn't include a body
-        if status in (204, 304) or 100 <= status < 200 or request_method == "HEAD":
-            length = 0
-
-        return length
-
-    def _init_decoder(self):
-        """
-        Set-up the _decoder attribute if necessary.
-        """
-        # Note: content-encoding value should be case-insensitive, per RFC 7230
-        # Section 3.2
-        content_encoding = self.headers.get("content-encoding", "").lower()
-        if self._decoder is None:
-            if content_encoding in self.CONTENT_DECODERS:
-                self._decoder = _get_decoder(content_encoding)
-            elif "," in content_encoding:
-                encodings = [
-                    e.strip()
-                    for e in content_encoding.split(",")
-                    if e.strip() in self.CONTENT_DECODERS
-                ]
-                if len(encodings):
-                    self._decoder = _get_decoder(content_encoding)
-
-    DECODER_ERROR_CLASSES = (IOError, zlib.error)
-    if brotli is not None:
-        DECODER_ERROR_CLASSES += (brotli.error,)
-
-    def _decode(self, data, decode_content, flush_decoder):
-        """
-        Decode the data passed in and potentially flush the decoder.
-        """
-        if not decode_content:
-            return data
-
-        try:
-            if self._decoder:
-                data = self._decoder.decompress(data)
-        except self.DECODER_ERROR_CLASSES as e:
-            content_encoding = self.headers.get("content-encoding", "").lower()
-            raise DecodeError(
-                "Received response with content-encoding: %s, but "
-                "failed to decode it." % content_encoding,
-                e,
-            )
-        if flush_decoder:
-            data += self._flush_decoder()
-
-        return data
-
-    def _flush_decoder(self):
-        """
-        Flushes the decoder. Should only be called if the decoder is actually
-        being used.
-        """
-        if self._decoder:
-            buf = self._decoder.decompress(b"")
-            return buf + self._decoder.flush()
-
-        return b""
-
-    @contextmanager
-    def _error_catcher(self):
-        """
-        Catch low-level python exceptions, instead re-raising urllib3
-        variants, so that low-level exceptions are not leaked in the
-        high-level api.
-
-        On exit, release the connection back to the pool.
-        """
-        clean_exit = False
-
-        try:
-            try:
-                yield
-
-            except SocketTimeout:
-                # FIXME: Ideally we'd like to include the url in the ReadTimeoutError but
-                # there is yet no clean way to get at it from this context.
-                raise ReadTimeoutError(self._pool, None, "Read timed out.")
-
-            except BaseSSLError as e:
-                # FIXME: Is there a better way to differentiate between SSLErrors?
-                if "read operation timed out" not in str(e):
-                    # SSL errors related to framing/MAC get wrapped and reraised here
-                    raise SSLError(e)
-
-                raise ReadTimeoutError(self._pool, None, "Read timed out.")
-
-            except (HTTPException, SocketError) as e:
-                # This includes IncompleteRead.
-                raise ProtocolError("Connection broken: %r" % e, e)
-
-            # If no exception is thrown, we should avoid cleaning up
-            # unnecessarily.
-            clean_exit = True
-        finally:
-            # If we didn't terminate cleanly, we need to throw away our
-            # connection.
-            if not clean_exit:
-                # The response may not be closed but we're not going to use it
-                # anymore so close it now to ensure that the connection is
-                # released back to the pool.
-                if self._original_response:
-                    self._original_response.close()
-
-                # Closing the response may not actually be sufficient to close
-                # everything, so if we have a hold of the connection close that
-                # too.
-                if self._connection:
-                    self._connection.close()
-
-            # If we hold the original response but it's closed now, we should
-            # return the connection back to the pool.
-            if self._original_response and self._original_response.isclosed():
-                self.release_conn()
-
-    def _fp_read(self, amt):
-        """
-        Read a response with the thought that reading the number of bytes
-        larger than can fit in a 32-bit int at a time via SSL in some
-        known cases leads to an overflow error that has to be prevented
-        if `amt` or `self.length_remaining` indicate that a problem may
-        happen.
-
-        The known cases:
-          * 3.8 <= CPython < 3.9.7 because of a bug
-            https://github.com/urllib3/urllib3/issues/2513#issuecomment-1152559900.
-          * urllib3 injected with pyOpenSSL-backed SSL-support.
-          * CPython < 3.10 only when `amt` does not fit 32-bit int.
-        """
-        assert self._fp
-        c_int_max = 2 ** 31 - 1
-        if (
-            (
-                (amt and amt > c_int_max)
-                or (self.length_remaining and self.length_remaining > c_int_max)
-            )
-            and not util.IS_SECURETRANSPORT
-            and (util.IS_PYOPENSSL or sys.version_info < (3, 10))
-        ):
-            buffer = io.BytesIO()
-            # Besides `max_chunk_amt` being a maximum chunk size, it
-            # affects memory overhead of reading a response by this
-            # method in CPython.
-            # `c_int_max` equal to 2 GiB - 1 byte is the actual maximum
-            # chunk size that does not lead to an overflow error, but
-            # 256 MiB is a compromise.
-            max_chunk_amt = 2 ** 28
-            while amt is None or amt != 0:
-                if amt is not None:
-                    chunk_amt = min(amt, max_chunk_amt)
-                    amt -= chunk_amt
-                else:
-                    chunk_amt = max_chunk_amt
-                data = self._fp.read(chunk_amt)
-                if not data:
-                    break
-                buffer.write(data)
-                del data  # to reduce peak memory usage by `max_chunk_amt`.
-            return buffer.getvalue()
-        else:
-            # StringIO doesn't like amt=None
-            return self._fp.read(amt) if amt is not None else self._fp.read()
-
-    def read(self, amt=None, decode_content=None, cache_content=False):
-        """
-        Similar to :meth:`http.client.HTTPResponse.read`, but with two additional
-        parameters: ``decode_content`` and ``cache_content``.
-
-        :param amt:
-            How much of the content to read. If specified, caching is skipped
-            because it doesn't make sense to cache partial content as the full
-            response.
-
-        :param decode_content:
-            If True, will attempt to decode the body based on the
-            'content-encoding' header.
-
-        :param cache_content:
-            If True, will save the returned data such that the same result is
-            returned despite of the state of the underlying file object. This
-            is useful if you want the ``.data`` property to continue working
-            after having ``.read()`` the file object. (Overridden if ``amt`` is
-            set.)
-        """
-        self._init_decoder()
-        if decode_content is None:
-            decode_content = self.decode_content
-
-        if self._fp is None:
-            return
-
-        flush_decoder = False
-        fp_closed = getattr(self._fp, "closed", False)
-
-        with self._error_catcher():
-            data = self._fp_read(amt) if not fp_closed else b""
-            if amt is None:
-                flush_decoder = True
-            else:
-                cache_content = False
-                if (
-                    amt != 0 and not data
-                ):  # Platform-specific: Buggy versions of Python.
-                    # Close the connection when no data is returned
-                    #
-                    # This is redundant to what httplib/http.client _should_
-                    # already do.  However, versions of python released before
-                    # December 15, 2012 (http://bugs.python.org/issue16298) do
-                    # not properly close the connection in all cases. There is
-                    # no harm in redundantly calling close.
-                    self._fp.close()
-                    flush_decoder = True
-                    if self.enforce_content_length and self.length_remaining not in (
-                        0,
-                        None,
-                    ):
-                        # This is an edge case that httplib failed to cover due
-                        # to concerns of backward compatibility. We're
-                        # addressing it here to make sure IncompleteRead is
-                        # raised during streaming, so all calls with incorrect
-                        # Content-Length are caught.
-                        raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
-
-        if data:
-            self._fp_bytes_read += len(data)
-            if self.length_remaining is not None:
-                self.length_remaining -= len(data)
-
-            data = self._decode(data, decode_content, flush_decoder)
-
-            if cache_content:
-                self._body = data
-
-        return data
-
-    def stream(self, amt=2 ** 16, decode_content=None):
-        """
-        A generator wrapper for the read() method. A call will block until
-        ``amt`` bytes have been read from the connection or until the
-        connection is closed.
-
-        :param amt:
-            How much of the content to read. The generator will return up to
-            much data per iteration, but may return less. This is particularly
-            likely when using compressed data. However, the empty string will
-            never be returned.
-
-        :param decode_content:
-            If True, will attempt to decode the body based on the
-            'content-encoding' header.
-        """
-        if self.chunked and self.supports_chunked_reads():
-            for line in self.read_chunked(amt, decode_content=decode_content):
-                yield line
-        else:
-            while not is_fp_closed(self._fp):
-                data = self.read(amt=amt, decode_content=decode_content)
-
-                if data:
-                    yield data
-
-    @classmethod
-    def from_httplib(ResponseCls, r, **response_kw):
-        """
-        Given an :class:`http.client.HTTPResponse` instance ``r``, return a
-        corresponding :class:`urllib3.response.HTTPResponse` object.
-
-        Remaining parameters are passed to the HTTPResponse constructor, along
-        with ``original_response=r``.
-        """
-        headers = r.msg
-
-        if not isinstance(headers, HTTPHeaderDict):
-            if six.PY2:
-                # Python 2.7
-                headers = HTTPHeaderDict.from_httplib(headers)
-            else:
-                headers = HTTPHeaderDict(headers.items())
-
-        # HTTPResponse objects in Python 3 don't have a .strict attribute
-        strict = getattr(r, "strict", 0)
-        resp = ResponseCls(
-            body=r,
-            headers=headers,
-            status=r.status,
-            version=r.version,
-            reason=r.reason,
-            strict=strict,
-            original_response=r,
-            **response_kw
-        )
-        return resp
-
-    # Backwards-compatibility methods for http.client.HTTPResponse
-    def getheaders(self):
-        warnings.warn(
-            "HTTPResponse.getheaders() is deprecated and will be removed "
-            "in urllib3 v2.1.0. Instead access HTTPResponse.headers directly.",
-            category=DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.headers
-
-    def getheader(self, name, default=None):
-        warnings.warn(
-            "HTTPResponse.getheader() is deprecated and will be removed "
-            "in urllib3 v2.1.0. Instead use HTTPResponse.headers.get(name, default).",
-            category=DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.headers.get(name, default)
-
-    # Backwards compatibility for http.cookiejar
-    def info(self):
-        return self.headers
-
-    # Overrides from io.IOBase
-    def close(self):
-        if not self.closed:
-            self._fp.close()
-
-        if self._connection:
-            self._connection.close()
-
-        if not self.auto_close:
-            io.IOBase.close(self)
-
-    @property
-    def closed(self):
-        if not self.auto_close:
-            return io.IOBase.closed.__get__(self)
-        elif self._fp is None:
-            return True
-        elif hasattr(self._fp, "isclosed"):
-            return self._fp.isclosed()
-        elif hasattr(self._fp, "closed"):
-            return self._fp.closed
-        else:
-            return True
-
-    def fileno(self):
-        if self._fp is None:
-            raise IOError("HTTPResponse has no file to get a fileno from")
-        elif hasattr(self._fp, "fileno"):
-            return self._fp.fileno()
-        else:
-            raise IOError(
-                "The file-like object this HTTPResponse is wrapped "
-                "around has no file descriptor"
-            )
-
-    def flush(self):
-        if (
-            self._fp is not None
-            and hasattr(self._fp, "flush")
-            and not getattr(self._fp, "closed", False)
-        ):
-            return self._fp.flush()
-
-    def readable(self):
-        # This method is required for `io` module compatibility.
-        return True
-
-    def readinto(self, b):
-        # This method is required for `io` module compatibility.
-        temp = self.read(len(b))
-        if len(temp) == 0:
-            return 0
-        else:
-            b[: len(temp)] = temp
-            return len(temp)
-
-    def supports_chunked_reads(self):
-        """
-        Checks if the underlying file-like object looks like a
-        :class:`http.client.HTTPResponse` object. We do this by testing for
-        the fp attribute. If it is present we assume it returns raw chunks as
-        processed by read_chunked().
-        """
-        return hasattr(self._fp, "fp")
-
-    def _update_chunk_length(self):
-        # First, we'll figure out length of a chunk and then
-        # we'll try to read it from socket.
-        if self.chunk_left is not None:
-            return
-        line = self._fp.fp.readline()
-        line = line.split(b";", 1)[0]
-        try:
-            self.chunk_left = int(line, 16)
-        except ValueError:
-            # Invalid chunked protocol response, abort.
-            self.close()
-            raise InvalidChunkLength(self, line)
-
-    def _handle_chunk(self, amt):
-        returned_chunk = None
-        if amt is None:
-            chunk = self._fp._safe_read(self.chunk_left)
-            returned_chunk = chunk
-            self._fp._safe_read(2)  # Toss the CRLF at the end of the chunk.
-            self.chunk_left = None
-        elif amt < self.chunk_left:
-            value = self._fp._safe_read(amt)
-            self.chunk_left = self.chunk_left - amt
-            returned_chunk = value
-        elif amt == self.chunk_left:
-            value = self._fp._safe_read(amt)
-            self._fp._safe_read(2)  # Toss the CRLF at the end of the chunk.
-            self.chunk_left = None
-            returned_chunk = value
-        else:  # amt > self.chunk_left
-            returned_chunk = self._fp._safe_read(self.chunk_left)
-            self._fp._safe_read(2)  # Toss the CRLF at the end of the chunk.
-            self.chunk_left = None
-        return returned_chunk
-
-    def read_chunked(self, amt=None, decode_content=None):
-        """
-        Similar to :meth:`HTTPResponse.read`, but with an additional
-        parameter: ``decode_content``.
-
-        :param amt:
-            How much of the content to read. If specified, caching is skipped
-            because it doesn't make sense to cache partial content as the full
-            response.
-
-        :param decode_content:
-            If True, will attempt to decode the body based on the
-            'content-encoding' header.
-        """
-        self._init_decoder()
-        # FIXME: Rewrite this method and make it a class with a better structured logic.
-        if not self.chunked:
-            raise ResponseNotChunked(
-                "Response is not chunked. "
-                "Header 'transfer-encoding: chunked' is missing."
-            )
-        if not self.supports_chunked_reads():
-            raise BodyNotHttplibCompatible(
-                "Body should be http.client.HTTPResponse like. "
-                "It should have have an fp attribute which returns raw chunks."
-            )
-
-        with self._error_catcher():
-            # Don't bother reading the body of a HEAD request.
-            if self._original_response and is_response_to_head(self._original_response):
-                self._original_response.close()
-                return
-
-            # If a response is already read and closed
-            # then return immediately.
-            if self._fp.fp is None:
-                return
-
-            while True:
-                self._update_chunk_length()
-                if self.chunk_left == 0:
-                    break
-                chunk = self._handle_chunk(amt)
-                decoded = self._decode(
-                    chunk, decode_content=decode_content, flush_decoder=False
-                )
-                if decoded:
-                    yield decoded
-
-            if decode_content:
-                # On CPython and PyPy, we should never need to flush the
-                # decoder. However, on Jython we *might* need to, so
-                # lets defensively do it anyway.
-                decoded = self._flush_decoder()
-                if decoded:  # Platform-specific: Jython.
-                    yield decoded
-
-            # Chunk content ends with \r\n: discard it.
-            while True:
-                line = self._fp.fp.readline()
-                if not line:
-                    # Some sites may not end with '\r\n'.
-                    break
-                if line == b"\r\n":
-                    break
-
-            # We read everything; close the "file".
-            if self._original_response:
-                self._original_response.close()
-
-    def geturl(self):
-        """
-        Returns the URL that was the source of this response.
-        If the request that generated this response redirected, this method
-        will return the final redirect location.
-        """
-        if self.retries is not None and len(self.retries.history):
-            return self.retries.history[-1].redirect_location
-        else:
-            return self._request_url
-
-    def __iter__(self):
-        buffer = []
-        for chunk in self.stream(decode_content=True):
-            if b"\n" in chunk:
-                chunk = chunk.split(b"\n")
-                yield b"".join(buffer) + chunk[0] + b"\n"
-                for x in chunk[1:-1]:
-                    yield x + b"\n"
-                if chunk[-1]:
-                    buffer = [chunk[-1]]
-                else:
-                    buffer = []
-            else:
-                buffer.append(chunk)
-        if buffer:
-            yield b"".join(buffer)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/__init__.py
deleted file mode 100644
index 13cadc7f04d4c24afb829c9fc44367ac06a3c61d..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-__title__ = "packaging"
-__summary__ = "Core utilities for Python packages"
-__uri__ = "https://github.com/pypa/packaging"
-
-__version__ = "23.1"
-
-__author__ = "Donald Stufft and individual contributors"
-__email__ = "donald@stufft.io"
-
-__license__ = "BSD-2-Clause or Apache-2.0"
-__copyright__ = "2014-2019 %s" % __author__
diff --git a/spaces/TheProjectsGuy/AnyLoc/README.md b/spaces/TheProjectsGuy/AnyLoc/README.md
deleted file mode 100644
index 01e81780426d85234bab614eba0f41db85e4d493..0000000000000000000000000000000000000000
--- a/spaces/TheProjectsGuy/AnyLoc/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: AnyLoc
-emoji: 📊
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: bsd-3-clause
----
-
-[![arXiv](https://img.shields.io/badge/arXiv-2308.00688-b31b1b.svg)](https://arxiv.org/abs/2308.00688)
-[![githubio](https://img.shields.io/badge/GitHub.io-Anyloc-blue?logo=Github)](https://anyloc.github.io/)
-[![github](https://img.shields.io/badge/GitHub-Anyloc%2FAnyloc-blue?logo=Github)](https://github.com/AnyLoc/AnyLoc)
-[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face%20-AnyLoc-blue)](https://huggingface.co/spaces/TheProjectsGuy/AnyLoc)
-[![YouTube](https://img.shields.io/badge/YouTube-FF0000?style=flat&logo=youtube&logoColor=white)](https://youtu.be/ITo8rMInatk)
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Tuyet3005/Sentiment_Analysis_using_BERT/streamlit_app.py/Homepage.py b/spaces/Tuyet3005/Sentiment_Analysis_using_BERT/streamlit_app.py/Homepage.py
deleted file mode 100644
index 68d3eac3dc354d0656cef7f94637adc19b2796bf..0000000000000000000000000000000000000000
--- a/spaces/Tuyet3005/Sentiment_Analysis_using_BERT/streamlit_app.py/Homepage.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import streamlit as st
-from st_pages import Page, show_pages
-
-st.set_page_config(page_title="Sentiment Analysis", page_icon="🏠")
-
-show_pages(
-    [
-        Page("streamlit_app.py/Homepage.py", "Home", "🏠"),
-        Page(
-            "streamlit_app.py/pages/Sentiment_Analysis.py", "Sentiment Analysis", "📝"
-        ),
-    ]
-)
-
-st.title("Final Project in Machine Learning Course - Sentiment Analysis")
-st.markdown(
-    """
-    **Team members:**
-    | Student ID | Full Name                |
-    | ---------- | ------------------------ |
-    | 19120600   | Bùi Nguyên Nghĩa         |
-    | 20120089   | Lê Xuân Hoàng            |
-    | 20120422   | Nguyễn Thị Ánh Tuyết     |
-    | 20120460   | Lê Nguyễn Hải Dương      |
-    | 20120494   | Lê Xuân Huy              |
-    """
-)
-
-st.header("The Need for Sentiment Analysis")
-st.markdown(
-    """
-    Sentiment analysis algorithms are used to detect sentiment in a comment or a review.
-    It is said that around 90% of consumers read online reviews before visiting a business or buying a product.
-    These reviews can be positive or negative or neutral, and it is important to know what the customers are saying about your business.
-    """
-)
-
-st.header("Technology used")
-st.markdown(
-    """
-    In this demo, we used BERT as the model for sentiment analysis. BERT is a transformer-based model that was proposed in 2018 by Google.
-    It is a pre-trained model that can be used for various NLP tasks such as sentiment analysis, question answering, etc.
-    """
-)
-
-
diff --git a/spaces/VIPLab/Track-Anything/tracker/model/modules.py b/spaces/VIPLab/Track-Anything/tracker/model/modules.py
deleted file mode 100644
index 99207996e6d68dcf74da314dbd7cce21f65ac71e..0000000000000000000000000000000000000000
--- a/spaces/VIPLab/Track-Anything/tracker/model/modules.py
+++ /dev/null
@@ -1,250 +0,0 @@
-"""
-modules.py - This file stores the rather boring network blocks.
-
-x - usually means features that only depends on the image
-g - usually means features that also depends on the mask. 
-    They might have an extra "group" or "num_objects" dimension, hence
-    batch_size * num_objects * num_channels * H * W
-
-The trailing number of a variable usually denote the stride
-
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from model.group_modules import *
-from model import resnet
-from model.cbam import CBAM
-
-
-class FeatureFusionBlock(nn.Module):
-    def __init__(self, x_in_dim, g_in_dim, g_mid_dim, g_out_dim):
-        super().__init__()
-
-        self.distributor = MainToGroupDistributor()
-        self.block1 = GroupResBlock(x_in_dim+g_in_dim, g_mid_dim)
-        self.attention = CBAM(g_mid_dim)
-        self.block2 = GroupResBlock(g_mid_dim, g_out_dim)
-
-    def forward(self, x, g):
-        batch_size, num_objects = g.shape[:2]
-
-        g = self.distributor(x, g)
-        g = self.block1(g)
-        r = self.attention(g.flatten(start_dim=0, end_dim=1))
-        r = r.view(batch_size, num_objects, *r.shape[1:])
-
-        g = self.block2(g+r)
-
-        return g
-
-
-class HiddenUpdater(nn.Module):
-    # Used in the decoder, multi-scale feature + GRU
-    def __init__(self, g_dims, mid_dim, hidden_dim):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-
-        self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)
-        self.g8_conv = GConv2D(g_dims[1], mid_dim, kernel_size=1)
-        self.g4_conv = GConv2D(g_dims[2], mid_dim, kernel_size=1)
-
-        self.transform = GConv2D(mid_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)
-
-        nn.init.xavier_normal_(self.transform.weight)
-
-    def forward(self, g, h):
-        g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \
-            self.g4_conv(downsample_groups(g[2], ratio=1/4))
-
-        g = torch.cat([g, h], 2)
-
-        # defined slightly differently than standard GRU, 
-        # namely the new value is generated before the forget gate.
-        # might provide better gradient but frankly it was initially just an 
-        # implementation error that I never bothered fixing
-        values = self.transform(g)
-        forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])
-        update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])
-        new_value = torch.tanh(values[:,:,self.hidden_dim*2:])
-        new_h = forget_gate*h*(1-update_gate) + update_gate*new_value
-
-        return new_h
-
-
-class HiddenReinforcer(nn.Module):
-    # Used in the value encoder, a single GRU
-    def __init__(self, g_dim, hidden_dim):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)
-
-        nn.init.xavier_normal_(self.transform.weight)
-
-    def forward(self, g, h):
-        g = torch.cat([g, h], 2)
-
-        # defined slightly differently than standard GRU, 
-        # namely the new value is generated before the forget gate.
-        # might provide better gradient but frankly it was initially just an 
-        # implementation error that I never bothered fixing
-        values = self.transform(g)
-        forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])
-        update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])
-        new_value = torch.tanh(values[:,:,self.hidden_dim*2:])
-        new_h = forget_gate*h*(1-update_gate) + update_gate*new_value
-
-        return new_h
-
-
-class ValueEncoder(nn.Module):
-    def __init__(self, value_dim, hidden_dim, single_object=False):
-        super().__init__()
-        
-        self.single_object = single_object
-        network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)
-        self.conv1 = network.conv1
-        self.bn1 = network.bn1
-        self.relu = network.relu  # 1/2, 64
-        self.maxpool = network.maxpool
-
-        self.layer1 = network.layer1 # 1/4, 64
-        self.layer2 = network.layer2 # 1/8, 128
-        self.layer3 = network.layer3 # 1/16, 256
-
-        self.distributor = MainToGroupDistributor()
-        self.fuser = FeatureFusionBlock(1024, 256, value_dim, value_dim)
-        if hidden_dim > 0:
-            self.hidden_reinforce = HiddenReinforcer(value_dim, hidden_dim)
-        else:
-            self.hidden_reinforce = None
-
-    def forward(self, image, image_feat_f16, h, masks, others, is_deep_update=True):
-        # image_feat_f16 is the feature from the key encoder
-        if not self.single_object:
-            g = torch.stack([masks, others], 2)
-        else:
-            g = masks.unsqueeze(2)
-        g = self.distributor(image, g)
-
-        batch_size, num_objects = g.shape[:2]
-        g = g.flatten(start_dim=0, end_dim=1)
-
-        g = self.conv1(g)
-        g = self.bn1(g) # 1/2, 64
-        g = self.maxpool(g)  # 1/4, 64
-        g = self.relu(g) 
-
-        g = self.layer1(g) # 1/4
-        g = self.layer2(g) # 1/8
-        g = self.layer3(g) # 1/16
-
-        g = g.view(batch_size, num_objects, *g.shape[1:])
-        g = self.fuser(image_feat_f16, g)
-
-        if is_deep_update and self.hidden_reinforce is not None:
-            h = self.hidden_reinforce(g, h)
-
-        return g, h
- 
-
-class KeyEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-        network = resnet.resnet50(pretrained=True)
-        self.conv1 = network.conv1
-        self.bn1 = network.bn1
-        self.relu = network.relu  # 1/2, 64
-        self.maxpool = network.maxpool
-
-        self.res2 = network.layer1 # 1/4, 256
-        self.layer2 = network.layer2 # 1/8, 512
-        self.layer3 = network.layer3 # 1/16, 1024
-
-    def forward(self, f):
-        x = self.conv1(f) 
-        x = self.bn1(x)
-        x = self.relu(x)   # 1/2, 64
-        x = self.maxpool(x)  # 1/4, 64
-        f4 = self.res2(x)   # 1/4, 256
-        f8 = self.layer2(f4) # 1/8, 512
-        f16 = self.layer3(f8) # 1/16, 1024
-
-        return f16, f8, f4
-
-
-class UpsampleBlock(nn.Module):
-    def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):
-        super().__init__()
-        self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)
-        self.distributor = MainToGroupDistributor(method='add')
-        self.out_conv = GroupResBlock(g_up_dim, g_out_dim)
-        self.scale_factor = scale_factor
-
-    def forward(self, skip_f, up_g):
-        skip_f = self.skip_conv(skip_f)
-        g = upsample_groups(up_g, ratio=self.scale_factor)
-        g = self.distributor(skip_f, g)
-        g = self.out_conv(g)
-        return g
-
-
-class KeyProjection(nn.Module):
-    def __init__(self, in_dim, keydim):
-        super().__init__()
-
-        self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)
-        # shrinkage
-        self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)
-        # selection
-        self.e_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)
-
-        nn.init.orthogonal_(self.key_proj.weight.data)
-        nn.init.zeros_(self.key_proj.bias.data)
-    
-    def forward(self, x, need_s, need_e):
-        shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None
-        selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None
-
-        return self.key_proj(x), shrinkage, selection
-
-
-class Decoder(nn.Module):
-    def __init__(self, val_dim, hidden_dim):
-        super().__init__()
-
-        self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)
-        if hidden_dim > 0:
-            self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)
-        else:
-            self.hidden_update = None
-        
-        self.up_16_8 = UpsampleBlock(512, 512, 256) # 1/16 -> 1/8
-        self.up_8_4 = UpsampleBlock(256, 256, 256) # 1/8 -> 1/4
-
-        self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)
-
-    def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):
-        batch_size, num_objects = memory_readout.shape[:2]
-
-        if self.hidden_update is not None:
-            g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))
-        else:
-            g16 = self.fuser(f16, memory_readout)
-
-        g8 = self.up_16_8(f8, g16)
-        g4 = self.up_8_4(f4, g8)
-        logits = self.pred(F.relu(g4.flatten(start_dim=0, end_dim=1)))
-
-        if h_out and self.hidden_update is not None:
-            g4 = torch.cat([g4, logits.view(batch_size, num_objects, 1, *logits.shape[-2:])], 2)
-            hidden_state = self.hidden_update([g16, g8, g4], hidden_state)
-        else:
-            hidden_state = None
-        
-        logits = F.interpolate(logits, scale_factor=4, mode='bilinear', align_corners=False)
-        logits = logits.view(batch_size, num_objects, *logits.shape[-2:])
-
-        return hidden_state, logits
diff --git a/spaces/Woocy/541GPT/custom.css b/spaces/Woocy/541GPT/custom.css
deleted file mode 100644
index 97a1c2e681f4cc09e2237a92b37ab6cadd545a71..0000000000000000000000000000000000000000
--- a/spaces/Woocy/541GPT/custom.css
+++ /dev/null
@@ -1,184 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-
-ol, ul {
-    list-style-position: inside;
-    padding-left: 0;
-}
-
-ol li, ul:not(.options) li {
-    padding-left: 1.5em;
-    text-indent: -1.5em;
-}
-
-/* 亮色 */
-@media (prefers-color-scheme: light) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-light) !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #FFFFFF !important;
-    }
-    [data-testid = "user"] {
-        background-color: #95EC69 !important;
-    }
-}
-/* 暗色 */
-@media (prefers-color-scheme: dark) {
-    #chuanhu_chatbot {
-        background-color: var(--chatbot-color-dark) !important;
-    }
-    [data-testid = "bot"] {
-        background-color: #2C2C2C !important;
-    }
-    [data-testid = "user"] {
-        background-color: #26B561 !important;
-    }
-    body {
-        background-color: var(--neutral-950) !important;
-    }
-}
-
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1rem 1.2rem 1rem;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/monotonic_align/core.py b/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/monotonic_align/core.py
deleted file mode 100644
index 5ff728cd74c9228346a82ec64a9829cb98ad315e..0000000000000000000000000000000000000000
--- a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/monotonic_align/core.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]),
-           nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-    b = paths.shape[0]
-    max_neg_val = -1e9
-    for i in range(int(b)):
-        path = paths[i]
-        value = values[i]
-        t_y = t_ys[i]
-        t_x = t_xs[i]
-
-        v_prev = v_cur = 0.0
-        index = t_x - 1
-
-        for y in range(t_y):
-            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-                if x == y:
-                    v_cur = max_neg_val
-                else:
-                    v_cur = value[y - 1, x]
-                if x == 0:
-                    if y == 0:
-                        v_prev = 0.
-                    else:
-                        v_prev = max_neg_val
-                else:
-                    v_prev = value[y - 1, x - 1]
-                value[y, x] += max(v_prev, v_cur)
-
-        for y in range(t_y - 1, -1, -1):
-            path[y, index] = 1
-            if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]):
-                index = index - 1
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/video.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/video.py
deleted file mode 100644
index 94484744ee2b9e257bafedf1fed6ee29e9d484ba..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/video.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# please follow first the install settings before coding , like creating the conda enviroment
-# important - the Python vscode home directory shold be the Deoldify directory
-import numpy as np
-
-# video file is made of frames
-# so will open the video frame , read each frame, colorize it , and save all the frames in one directory
-# than we will open the directory and read the frames
-# all the frames will be store as on numpy array , so it will be very easy to load it and convert it to a new video
-
-# first , lets see our video
-
-from deoldify import device
-from deoldify.device_id import DeviceId
-import time # we need to use the sleep function between each colorzie to give time for the close the memory.
-
-# checking for Cuda (GPU)
-
-device.set(device=DeviceId.GPU0)
-
-import torch
-if not torch.cuda.is_available():
-    print('GPU is not avaiable')
-
-import fastai
-from deoldify.visualize import *
-import warnings
-warnings.filterwarnings("ignore",category=UserWarning,message=".*?Your .*? set is empty.*?")
-
-import cv2
-import numpy
-from numpy import save
-
-index = 0 # -> the image numrator
-imageArray = []
-
-# this is the main object for colorizing image
-colorizer = get_image_colorizer(artistic=True)
-
-# this parameter is very memory consume, So if you have memory issues running this Python script
-# try to reduce it to the minimum and raise as trail and error process
-# to reduce the running time , I changed it for this demo to the minimum : 7
-
-
-# we will use the value 7 , but I recommend you to use 35 (more memory consumption , but better quality)
-render_factor =  7 # 35 # this input parameter is minimum 7 and maximum 40
-watermakred = False # this paramter coltrols watermark in the result image
-
-cap = cv2.VideoCapture('media/Adele - Someone Like You (Official Music Video).mp4')
-
-if (cap.isOpened() ==False):
-    print('Error opening the video file')
-
-while(cap.isOpened()):
-
-    # lets capture each frame inside the loop
-    ret , frame = cap.read()
-    if ret == True:
-        index = index + 1
-        imageFileName = 'video/bwframes/video/'+str(index)+'.jpg'
-        # save BW image
-        cv2.imwrite(imageFileName,frame)
-        print('extract BW frame no. ', index, ' -> save to : ', imageFileName)
-
-        # if cv2.waitKey(25) & 0xFF == ord('q') or index >=2500 :  # the index >2500 frames is just for the demo . not to wait for the all video convert
-        #     break
-
-        image_path = colorizer.plot_transformed_image(path=imageFileName,render_factor=render_factor, compare=True, watermarked=watermakred)
-
-        # if you have a low memory comupter , you should add a sleep funtion in order to give time , after each colorize, to reduce the memory
-        time.sleep(5)
-
-        tempcolorImage = cv2.imread(str(image_path))
-
-        # build an array of color images
-        imageArray.append(tempcolorImage)
-
-cap.release()
-cv2.destroyAllWindows()
-
-colorImagesArrayNP = np.array(imageArray)
-
-print ('Finish creating the image array , Number of images : ', colorImagesArrayNP.shape[0])
-
-# save the numpy array
-save('video/np/video.npy',colorImagesArrayNP)
\ No newline at end of file
diff --git a/spaces/XzJosh/Diana-Bert-VITS2/attentions.py b/spaces/XzJosh/Diana-Bert-VITS2/attentions.py
deleted file mode 100644
index 1192dd7268c20c11010e73a6017ed09549695afe..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Diana-Bert-VITS2/attentions.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import logging
-
-logger = logging.getLogger(__name__)
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-   
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-class Encoder(nn.Module):
-   def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, isflow = True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    #if isflow:
-    #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
-    #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
-    #  self.cond_layer = weight_norm(cond_layer, name='weight')
-    #  self.gin_channels = 256
-    self.cond_layer_idx = self.n_layers
-    if 'gin_channels' in kwargs:
-      self.gin_channels = kwargs['gin_channels']
-      if self.gin_channels != 0:
-        self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
-        # vits2 says 3rd block, so idx is 2 by default
-        self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
-        logging.debug(self.gin_channels, self.cond_layer_idx)
-        assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-   def forward(self, x, x_mask, g=None):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      if i == self.cond_layer_idx and g is not None:
-        g = self.spk_emb_linear(g.transpose(1, 2))
-        g = g.transpose(1, 2)
-        x = x + g
-        x = x * x_mask
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/XzJosh/Lumi-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/Lumi-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Lumi-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/XzJosh/ranran-Bert-VITS2/data_utils.py b/spaces/XzJosh/ranran-Bert-VITS2/data_utils.py
deleted file mode 100644
index be3a29a93188c5b3386f22e5db29e5e96d78109a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/ranran-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.spk_map = hparams.spk2id
-        self.hparams = hparams
-
-        self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
-        if self.use_mel_spec_posterior:
-            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 300)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        skipped = 0
-        for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
-            audiopath = f'{_id}'
-            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
-                phones = phones.split(" ")
-                tone = [int(i) for i in tone.split(" ")]
-                word2ph = [int(i) for i in word2ph.split(" ")]
-                audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-            else:
-                skipped += 1
-        print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
-        bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
-        spec, wav = self.get_audio(audiopath)
-        sid = torch.LongTensor([int(self.spk_map[sid])])
-        return (phones, spec, wav, sid, tone, language, bert)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        try:
-            spec = torch.load(spec_filename)
-        except:
-            if self.use_mel_spec_posterior:
-                spec = mel_spectrogram_torch(audio_norm, self.filter_length,
-                    self.n_mel_channels, self.sampling_rate, self.hop_length,
-                    self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
-            else:
-                spec = spectrogram_torch(audio_norm, self.filter_length,
-                    self.sampling_rate, self.hop_length, self.win_length,
-                    center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
-        pold = phone
-        w2pho = [i for i in word2ph]
-        word2ph = [i for i in word2ph]
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-        pold2 = phone
-
-        if self.add_blank:
-            p1 = len(phone)
-            phone = commons.intersperse(phone, 0)
-            p2 = len(phone)
-            t1 = len(tone)
-            tone = commons.intersperse(tone, 0)
-            t2 = len(tone)
-            language = commons.intersperse(language, 0)
-            for i in range(len(word2ph)):
-                word2ph[i] = word2ph[i] * 2
-            word2ph[0] += 1
-        bert_path = wav_path.replace(".wav", ".bert.pt")
-        try:
-            bert = torch.load(bert_path)
-            assert bert.shape[-1] == len(phone)
-        except:
-            bert = get_bert(text, word2ph, language_str)
-            torch.save(bert, bert_path)
-            #print(bert.shape[-1], bert_path, text, pold)
-            assert bert.shape[-1] == len(phone)
-
-        assert bert.shape[-1] == len(phone), (
-        bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, phone, tone, language
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
-    """ Zero-pads model inputs and targets
-    """
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        tone_padded = torch.LongTensor(len(batch), max_text_len)
-        language_padded = torch.LongTensor(len(batch), max_text_len)
-        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        tone_padded.zero_()
-        language_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        bert_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-            tone = row[4]
-            tone_padded[i, :tone.size(0)] = tone
-
-            language = row[5]
-            language_padded[i, :language.size(0)] = language
-
-            bert = row[6]
-            bert_padded[i, :, :bert.size(1)] = bert
-
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        for i in range(len(buckets) - 1, 0, -1):
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            if (len_bucket == 0):
-                continue
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/demo/README.md b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/demo/README.md
deleted file mode 100644
index 133d8d38e5e9f5f44aca92c59f73309e166d7132..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/demo/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-## Detectron2 Demo
-
-We provide a command line tool to run a simple demo of builtin configs.
-The usage is explained in [GETTING_STARTED.md](../GETTING_STARTED.md).
-
-See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-)
-for a high-quality demo generated with this tool.
diff --git a/spaces/YueMafighting/FollowYourPose/FollowYourPose/followyourpose/util.py b/spaces/YueMafighting/FollowYourPose/FollowYourPose/followyourpose/util.py
deleted file mode 100644
index 75d92f7b068eda69585faa464e718406445287db..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/FollowYourPose/followyourpose/util.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-import imageio
-import numpy as np
-from typing import Union
-
-import torch
-import torchvision
-
-from tqdm import tqdm
-from einops import rearrange
-
-
-def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=8):
-    videos = rearrange(videos, "b c t h w -> t b c h w")
-    outputs = []
-    for x in videos:
-        x = torchvision.utils.make_grid(x, nrow=n_rows)
-        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-        if rescale:
-            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
-        x = (x * 255).numpy().astype(np.uint8)
-        outputs.append(x)
-
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-    imageio.mimsave(path, outputs, fps=fps)
-
-
-# DDIM Inversion
-@torch.no_grad()
-def init_prompt(prompt, pipeline):
-    uncond_input = pipeline.tokenizer(
-        [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
-        return_tensors="pt"
-    )
-    uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
-    text_input = pipeline.tokenizer(
-        [prompt],
-        padding="max_length",
-        max_length=pipeline.tokenizer.model_max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-    text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
-    context = torch.cat([uncond_embeddings, text_embeddings])
-
-    return context
-
-
-def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
-              sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
-    timestep, next_timestep = min(
-        timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
-    alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
-    alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
-    beta_prod_t = 1 - alpha_prod_t
-    next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
-    next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
-    next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
-    return next_sample
-
-
-def get_noise_pred_single(latents, t, context, unet):
-    noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
-    return noise_pred
-
-
-@torch.no_grad()
-def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
-    context = init_prompt(prompt, pipeline)
-    uncond_embeddings, cond_embeddings = context.chunk(2)
-    all_latent = [latent]
-    latent = latent.clone().detach()
-    for i in tqdm(range(num_inv_steps)):
-        t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
-        noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
-        latent = next_step(noise_pred, t, latent, ddim_scheduler)
-        all_latent.append(latent)
-    return all_latent
-
-
-@torch.no_grad()
-def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
-    ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
-    return ddim_latents
diff --git a/spaces/Yuliang/ECON/lib/common/libmesh/triangle_hash.cpp b/spaces/Yuliang/ECON/lib/common/libmesh/triangle_hash.cpp
deleted file mode 100644
index 5f46e5e4f372d1c4dd629743e41063cc6e528adf..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/libmesh/triangle_hash.cpp
+++ /dev/null
@@ -1,24141 +0,0 @@
-/* Generated by Cython 0.29.28 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "depends": [],
-        "language": "c++",
-        "name": "triangle_hash",
-        "sources": [
-            "triangle_hash.pyx"
-        ]
-    },
-    "module_name": "triangle_hash"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
-    #error Cython requires Python 2.6+ or Python 3.3+.
-#else
-#define CYTHON_ABI "0_29_28"
-#define CYTHON_HEX_VERSION 0x001D1CF0
-#define CYTHON_FUTURE_DIVISION 0
-#include <stddef.h>
-#ifndef offsetof
-  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
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-  #define DL_IMPORT(t) t
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-  #define DL_EXPORT(t) t
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-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
-  #if PY_VERSION_HEX >= 0x02070000
-    #define HAVE_LONG_LONG
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-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
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-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#ifdef PYPY_VERSION
-  #define CYTHON_COMPILING_IN_PYPY 1
-  #define CYTHON_COMPILING_IN_PYSTON 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
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-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
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-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
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-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
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-  #define CYTHON_UNPACK_METHODS 0
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-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_TP_FINALIZE
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-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#elif defined(PYSTON_VERSION)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_PYSTON 1
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
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-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
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-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_PYSTON 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #if PY_VERSION_HEX < 0x02070000
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-    #define CYTHON_USE_PYLONG_INTERNALS 1
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-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
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-  #ifndef CYTHON_USE_UNICODE_INTERNALS
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-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
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-    #define CYTHON_USE_UNICODE_WRITER 0
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-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
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-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #if PY_VERSION_HEX >= 0x030B00A4
-    #undef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 0
-  #elif !defined(CYTHON_FAST_THREAD_STATE)
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL (PY_VERSION_HEX < 0x030B00A1)
-  #endif
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
-  #endif
-  #ifndef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1)
-  #endif
-  #if PY_VERSION_HEX >= 0x030B00A4
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
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-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-           typedef unsigned char     uint8_t;
-           typedef unsigned int      uint32_t;
-        #else
-           typedef unsigned __int8   uint8_t;
-           typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-#else
-   #include <stdint.h>
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus) && __cplusplus >= 201103L
-    #if __has_cpp_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH [[fallthrough]]
-    #elif __has_cpp_attribute(clang::fallthrough)
-      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-    #elif __has_cpp_attribute(gnu::fallthrough)
-      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__ ) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-
-#ifndef __cplusplus
-  #error "Cython files generated with the C++ option must be compiled with a C++ compiler."
-#endif
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #else
-    #define CYTHON_INLINE inline
-  #endif
-#endif
-template<typename T>
-void __Pyx_call_destructor(T& x) {
-    x.~T();
-}
-template<typename T>
-class __Pyx_FakeReference {
-  public:
-    __Pyx_FakeReference() : ptr(NULL) { }
-    __Pyx_FakeReference(const T& ref) : ptr(const_cast<T*>(&ref)) { }
-    T *operator->() { return ptr; }
-    T *operator&() { return ptr; }
-    operator T&() { return *ptr; }
-    template<typename U> bool operator ==(U other) { return *ptr == other; }
-    template<typename U> bool operator !=(U other) { return *ptr != other; }
-  private:
-    T *ptr;
-};
-
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
-  #define Py_OptimizeFlag 0
-#endif
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-  #define __Pyx_DefaultClassType PyClass_Type
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject* co=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(0))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto cleanup_code_too;
-        if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here
-        if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too;
-        Py_XDECREF((PyObject*)co);
-        co = (PyCodeObject*)call_result;
-        call_result = NULL;
-        if (0) {
-            cleanup_code_too:
-            Py_XDECREF((PyObject*)co);
-            co = NULL;
-        }
-        end:
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(call_result);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return co;
-    }
-#else
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-  #define __Pyx_DefaultClassType PyType_Type
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_FAST_PYCCALL
-#define __Pyx_PyFastCFunction_Check(func)\
-    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))
-#else
-#define __Pyx_PyFastCFunction_Check(func) 0
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
-  #define PyObject_Malloc(s)   PyMem_Malloc(s)
-  #define PyObject_Free(p)     PyMem_Free(p)
-  #define PyObject_Realloc(p)  PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1
-  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)
-  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)
-  #define PyMem_RawFree(p)             PyMem_Free(p)
-#endif
-#if CYTHON_COMPILING_IN_PYSTON
-  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
-#else
-  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
-  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
-#endif
-#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
-  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
-  #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
-  #define __Pyx_PyThreadState_Current _PyThreadState_Current
-#endif
-#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
-#include "pythread.h"
-#define Py_tss_NEEDS_INIT 0
-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
-  *key = PyThread_create_key();
-  return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
-  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
-  *key = Py_tss_NEEDS_INIT;
-  return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
-  PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
-  return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
-  PyThread_delete_key(*key);
-  *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
-  return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
-  return PyThread_get_key_value(*key);
-}
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-#else
-#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-  #define CYTHON_PEP393_ENABLED 1
-  #if defined(PyUnicode_IS_READY)
-  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
-                                              0 : _PyUnicode_Ready((PyObject *)(op)))
-  #else
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #endif
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
-  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
-  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
-  #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)
-  #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length))
-  #else
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
-  #endif
-  #else
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
-  #endif
-#else
-  #define CYTHON_PEP393_ENABLED 0
-  #define PyUnicode_1BYTE_KIND  1
-  #define PyUnicode_2BYTE_KIND  2
-  #define PyUnicode_4BYTE_KIND  4
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
-  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
-#else
-  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
-      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
-  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
-  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
-  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
-#endif
-#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
-#else
-  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
-  #define PyObject_ASCII(o)            PyObject_Repr(o)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBaseString_Type            PyUnicode_Type
-  #define PyStringObject               PyUnicodeObject
-  #define PyString_Type                PyUnicode_Type
-  #define PyString_Check               PyUnicode_Check
-  #define PyString_CheckExact          PyUnicode_CheckExact
-#ifndef PyObject_Unicode
-  #define PyObject_Unicode             PyObject_Str
-#endif
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
-  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
-#else
-  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
-  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
-#endif
-#ifndef PySet_CheckExact
-  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
-#endif
-#if PY_VERSION_HEX >= 0x030900A4
-  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
-  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
-#else
-  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
-  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
-#endif
-#if CYTHON_ASSUME_SAFE_MACROS
-  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
-#else
-  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyIntObject                  PyLongObject
-  #define PyInt_Type                   PyLong_Type
-  #define PyInt_Check(op)              PyLong_Check(op)
-  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
-  #define PyInt_FromString             PyLong_FromString
-  #define PyInt_FromUnicode            PyLong_FromUnicode
-  #define PyInt_FromLong               PyLong_FromLong
-  #define PyInt_FromSize_t             PyLong_FromSize_t
-  #define PyInt_FromSsize_t            PyLong_FromSsize_t
-  #define PyInt_AsLong                 PyLong_AsLong
-  #define PyInt_AS_LONG                PyLong_AS_LONG
-  #define PyInt_AsSsize_t              PyLong_AsSsize_t
-  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
-  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
-  #define PyNumber_Int                 PyNumber_Long
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBoolObject                 PyLongObject
-#endif
-#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
-  #ifndef PyUnicode_InternFromString
-    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
-  #endif
-#endif
-#if PY_VERSION_HEX < 0x030200A4
-  typedef long Py_hash_t;
-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsHash_t
-#else
-  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
-  #define __Pyx_PyInt_AsHash_t   __Pyx_PyIndex_AsSsize_t
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
-#else
-  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
-#endif
-#if CYTHON_USE_ASYNC_SLOTS
-  #if PY_VERSION_HEX >= 0x030500B1
-    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
-    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
-  #else
-    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
-  #endif
-#else
-  #define __Pyx_PyType_AsAsync(obj) NULL
-#endif
-#ifndef __Pyx_PyAsyncMethodsStruct
-    typedef struct {
-        unaryfunc am_await;
-        unaryfunc am_aiter;
-        unaryfunc am_anext;
-    } __Pyx_PyAsyncMethodsStruct;
-#endif
-
-#if defined(WIN32) || defined(MS_WINDOWS)
-  #define _USE_MATH_DEFINES
-#endif
-#include <math.h>
-#ifdef NAN
-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
-  float value;
-  memset(&value, 0xFF, sizeof(value));
-  return value;
-}
-#endif
-#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
-#define __Pyx_truncl trunc
-#else
-#define __Pyx_truncl truncl
-#endif
-
-#define __PYX_MARK_ERR_POS(f_index, lineno) \
-    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
-#define __PYX_ERR(f_index, lineno, Ln_error) \
-    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
-
-#ifndef __PYX_EXTERN_C
-  #ifdef __cplusplus
-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
-#endif
-
-#define __PYX_HAVE__triangle_hash
-#define __PYX_HAVE_API__triangle_hash
-/* Early includes */
-#include <string.h>
-#include <stdio.h>
-#include "numpy/arrayobject.h"
-#include "numpy/ndarrayobject.h"
-#include "numpy/ndarraytypes.h"
-#include "numpy/arrayscalars.h"
-#include "numpy/ufuncobject.h"
-
-    /* NumPy API declarations from "numpy/__init__.pxd" */
-    
-#include <math.h>
-#include "ios"
-#include "new"
-#include "stdexcept"
-#include "typeinfo"
-#include <vector>
-#include "pythread.h"
-#include <stdlib.h>
-#include "pystate.h"
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
-    (sizeof(type) < sizeof(Py_ssize_t))  ||\
-    (sizeof(type) > sizeof(Py_ssize_t) &&\
-          likely(v < (type)PY_SSIZE_T_MAX ||\
-                 v == (type)PY_SSIZE_T_MAX)  &&\
-          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
-                                v == (type)PY_SSIZE_T_MIN)))  ||\
-    (sizeof(type) == sizeof(Py_ssize_t) &&\
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
-    return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
-    #include <cstdlib>
-    #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
-    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
-    #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
-    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-static PyObject *__pyx_m = NULL;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_cython_runtime = NULL;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static PyObject *__pyx_empty_unicode;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-/* Header.proto */
-#if !defined(CYTHON_CCOMPLEX)
-  #if defined(__cplusplus)
-    #define CYTHON_CCOMPLEX 1
-  #elif defined(_Complex_I)
-    #define CYTHON_CCOMPLEX 1
-  #else
-    #define CYTHON_CCOMPLEX 0
-  #endif
-#endif
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #include <complex>
-  #else
-    #include <complex.h>
-  #endif
-#endif
-#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
-  #undef _Complex_I
-  #define _Complex_I 1.0fj
-#endif
-
-
-static const char *__pyx_f[] = {
-  "triangle_hash.pyx",
-  "stringsource",
-  "__init__.pxd",
-  "type.pxd",
-};
-/* MemviewSliceStruct.proto */
-struct __pyx_memoryview_obj;
-typedef struct {
-  struct __pyx_memoryview_obj *memview;
-  char *data;
-  Py_ssize_t shape[8];
-  Py_ssize_t strides[8];
-  Py_ssize_t suboffsets[8];
-} __Pyx_memviewslice;
-#define __Pyx_MemoryView_Len(m)  (m.shape[0])
-
-/* Atomics.proto */
-#include <pythread.h>
-#ifndef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 1
-#endif
-#define __pyx_atomic_int_type int
-#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\
-                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\
-                    !defined(__i386__)
-    #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1)
-    #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using GNU atomics"
-    #endif
-#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0
-    #include <Windows.h>
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type LONG
-    #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value)
-    #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #pragma message ("Using MSVC atomics")
-    #endif
-#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0
-    #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value)
-    #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using Intel atomics"
-    #endif
-#else
-    #undef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 0
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Not using atomics"
-    #endif
-#endif
-typedef volatile __pyx_atomic_int_type __pyx_atomic_int;
-#if CYTHON_ATOMICS
-    #define __pyx_add_acquisition_count(memview)\
-             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
-#else
-    #define __pyx_add_acquisition_count(memview)\
-            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-#endif
-
-/* ForceInitThreads.proto */
-#ifndef __PYX_FORCE_INIT_THREADS
-  #define __PYX_FORCE_INIT_THREADS 0
-#endif
-
-/* NoFastGil.proto */
-#define __Pyx_PyGILState_Ensure PyGILState_Ensure
-#define __Pyx_PyGILState_Release PyGILState_Release
-#define __Pyx_FastGIL_Remember()
-#define __Pyx_FastGIL_Forget()
-#define __Pyx_FastGilFuncInit()
-
-/* BufferFormatStructs.proto */
-#define IS_UNSIGNED(type) (((type) -1) > 0)
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
-  const char* name;
-  struct __Pyx_StructField_* fields;
-  size_t size;
-  size_t arraysize[8];
-  int ndim;
-  char typegroup;
-  char is_unsigned;
-  int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
-  size_t offset;
-} __Pyx_StructField;
-typedef struct {
-  __Pyx_StructField* field;
-  size_t parent_offset;
-} __Pyx_BufFmt_StackElem;
-typedef struct {
-  __Pyx_StructField root;
-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
-  size_t new_count, enc_count;
-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":690
- * # in Cython to enable them only on the right systems.
- * 
- * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t
- */
-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":691
- * 
- * ctypedef npy_int8       int8_t
- * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int32      int32_t
- * ctypedef npy_int64      int64_t
- */
-typedef npy_int16 __pyx_t_5numpy_int16_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":692
- * ctypedef npy_int8       int8_t
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int64      int64_t
- * #ctypedef npy_int96      int96_t
- */
-typedef npy_int32 __pyx_t_5numpy_int32_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":693
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t
- * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_int96      int96_t
- * #ctypedef npy_int128     int128_t
- */
-typedef npy_int64 __pyx_t_5numpy_int64_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":697
- * #ctypedef npy_int128     int128_t
- * 
- * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t
- */
-typedef npy_uint8 __pyx_t_5numpy_uint8_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":698
- * 
- * ctypedef npy_uint8      uint8_t
- * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint32     uint32_t
- * ctypedef npy_uint64     uint64_t
- */
-typedef npy_uint16 __pyx_t_5numpy_uint16_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":699
- * ctypedef npy_uint8      uint8_t
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint64     uint64_t
- * #ctypedef npy_uint96     uint96_t
- */
-typedef npy_uint32 __pyx_t_5numpy_uint32_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":700
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t
- * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_uint96     uint96_t
- * #ctypedef npy_uint128    uint128_t
- */
-typedef npy_uint64 __pyx_t_5numpy_uint64_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":704
- * #ctypedef npy_uint128    uint128_t
- * 
- * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_float64    float64_t
- * #ctypedef npy_float80    float80_t
- */
-typedef npy_float32 __pyx_t_5numpy_float32_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":705
- * 
- * ctypedef npy_float32    float32_t
- * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_float80    float80_t
- * #ctypedef npy_float128   float128_t
- */
-typedef npy_float64 __pyx_t_5numpy_float64_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":714
- * # The int types are mapped a bit surprising --
- * # numpy.int corresponds to 'l' and numpy.long to 'q'
- * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longlong   long_t
- * ctypedef npy_longlong   longlong_t
- */
-typedef npy_long __pyx_t_5numpy_int_t;
-
-/* "../../../../../anaconda3/lib/python3.8/site-packages/numpy/__init__.pxd":715
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-#ifndef Py_MEMBER_SIZE
-#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member)
-#endif
-#if CYTHON_FAST_PYCALL
-  static size_t __pyx_pyframe_localsplus_offset = 0;
-  #include "frameobject.h"
-  #define __Pxy_PyFrame_Initialize_Offsets()\
-    ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\
-     (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus)))
-  #define __Pyx_PyFrame_GetLocalsplus(frame)\
-    (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset))
-#endif // CYTHON_FAST_PYCALL
-#endif
-
-/* PyObjectCall.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
-#else
-#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
-#endif
-
-/* PyObjectCall2Args.proto */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2);
-
-/* PyObjectCallMethO.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
-#endif
-
-/* PyObjectCallOneArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
-
-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name)  {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
-        (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
-        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-}
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  {\
-    PY_UINT64_T __pyx_dict_version;\
-    PyObject *__pyx_dict_cached_value;\
-    (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-}
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
-#endif
-
-/* RaiseException.proto */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
-
-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
-
-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* PyErrExceptionMatches.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
-#else
-#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* ArgTypeTest.proto */
-#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
-    ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\
-        __Pyx__ArgTypeTest(obj, type, name, exact))
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
-
-/* IncludeStringH.proto */
-#include <string.h>
-
-/* BytesEquals.proto */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* UnicodeEquals.proto */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* StrEquals.proto */
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
-#else
-#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
-#endif
-
-/* DivInt[Py_ssize_t].proto */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t);
-
-/* UnaryNegOverflows.proto */
-#define UNARY_NEG_WOULD_OVERFLOW(x)\
-        (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x)))
-
-static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/
-/* GetAttr.proto */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
-
-/* GetItemInt.proto */
-#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
-               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
-#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* decode_c_string_utf16.proto */
-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) {
-    int byteorder = 0;
-    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
-}
-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) {
-    int byteorder = -1;
-    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
-}
-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {
-    int byteorder = 1;
-    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
-}
-
-/* decode_c_string.proto */
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
-         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
-         const char* encoding, const char* errors,
-         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
-
-/* GetAttr3.proto */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* RaiseNoneIterError.proto */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-/* ListCompAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
-    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
-#endif
-
-/* ListExtend.proto */
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
-    if (unlikely(!none))
-        return -1;
-    Py_DECREF(none);
-    return 0;
-#else
-    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
-#endif
-}
-
-/* ListAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* DivInt[long].proto */
-static CYTHON_INLINE long __Pyx_div_long(long, long);
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* HasAttr.proto */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* PyObject_GenericGetAttr.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
-#endif
-
-/* SetVTable.proto */
-static int __Pyx_SetVtable(PyObject *dict, void *vtable);
-
-/* PyObjectGetAttrStrNoError.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);
-
-/* SetupReduce.proto */
-static int __Pyx_setup_reduce(PyObject* type_obj);
-
-/* TypeImport.proto */
-#ifndef __PYX_HAVE_RT_ImportType_proto
-#define __PYX_HAVE_RT_ImportType_proto
-enum __Pyx_ImportType_CheckSize {
-   __Pyx_ImportType_CheckSize_Error = 0,
-   __Pyx_ImportType_CheckSize_Warn = 1,
-   __Pyx_ImportType_CheckSize_Ignore = 2
-};
-static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size);
-#endif
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-/* None.proto */
-#include <new>
-
-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
-
-
-/* BufferStructDeclare.proto */
-typedef struct {
-  Py_ssize_t shape, strides, suboffsets;
-} __Pyx_Buf_DimInfo;
-typedef struct {
-  size_t refcount;
-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
-  __Pyx_Buffer *rcbuffer;
-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
-
-/* MemviewSliceIsContig.proto */
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
-
-/* OverlappingSlices.proto */
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                                __Pyx_memviewslice *slice2,
-                                int ndim, size_t itemsize);
-
-/* Capsule.proto */
-static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);
-
-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
-
-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type);
-
-/* TypeInfoCompare.proto */
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-/* MemviewSliceValidateAndInit.proto */
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsdsds_double(PyObject *, int writable_flag);
-
-/* GCCDiagnostics.proto */
-#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
-#define __Pyx_HAS_GCC_DIAGNOSTIC
-#endif
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *, int writable_flag);
-
-/* CppExceptionConversion.proto */
-#ifndef __Pyx_CppExn2PyErr
-#include <new>
-#include <typeinfo>
-#include <stdexcept>
-#include <ios>
-static void __Pyx_CppExn2PyErr() {
-  try {
-    if (PyErr_Occurred())
-      ; // let the latest Python exn pass through and ignore the current one
-    else
-      throw;
-  } catch (const std::bad_alloc& exn) {
-    PyErr_SetString(PyExc_MemoryError, exn.what());
-  } catch (const std::bad_cast& exn) {
-    PyErr_SetString(PyExc_TypeError, exn.what());
-  } catch (const std::bad_typeid& exn) {
-    PyErr_SetString(PyExc_TypeError, exn.what());
-  } catch (const std::domain_error& exn) {
-    PyErr_SetString(PyExc_ValueError, exn.what());
-  } catch (const std::invalid_argument& exn) {
-    PyErr_SetString(PyExc_ValueError, exn.what());
-  } catch (const std::ios_base::failure& exn) {
-    PyErr_SetString(PyExc_IOError, exn.what());
-  } catch (const std::out_of_range& exn) {
-    PyErr_SetString(PyExc_IndexError, exn.what());
-  } catch (const std::overflow_error& exn) {
-    PyErr_SetString(PyExc_OverflowError, exn.what());
-  } catch (const std::range_error& exn) {
-    PyErr_SetString(PyExc_ArithmeticError, exn.what());
-  } catch (const std::underflow_error& exn) {
-    PyErr_SetString(PyExc_ArithmeticError, exn.what());
-  } catch (const std::exception& exn) {
-    PyErr_SetString(PyExc_RuntimeError, exn.what());
-  }
-  catch (...)
-  {
-    PyErr_SetString(PyExc_RuntimeError, "Unknown exception");
-  }
-}
-#endif
-
-/* MemviewDtypeToObject.proto */
-static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp);
-static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *, int writable_flag);
-
-/* RealImag.proto */
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #define __Pyx_CREAL(z) ((z).real())
-    #define __Pyx_CIMAG(z) ((z).imag())
-  #else
-    #define __Pyx_CREAL(z) (__real__(z))
-    #define __Pyx_CIMAG(z) (__imag__(z))
-  #endif
-#else
-    #define __Pyx_CREAL(z) ((z).real)
-    #define __Pyx_CIMAG(z) ((z).imag)
-#endif
-#if defined(__cplusplus) && CYTHON_CCOMPLEX\
-        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)
-    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
-    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
-#else
-    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
-    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq_float(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_float(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_float(a, b) ((a)-(b))
-    #define __Pyx_c_prod_float(a, b) ((a)*(b))
-    #define __Pyx_c_quot_float(a, b) ((a)/(b))
-    #define __Pyx_c_neg_float(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)
-    #define __Pyx_c_conj_float(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_float(z)     (::std::abs(z))
-        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_float(z) ((z)==0)
-    #define __Pyx_c_conj_float(z)    (conjf(z))
-    #if 1
-        #define __Pyx_c_abs_float(z)     (cabsf(z))
-        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);
-    #endif
-#endif
-
-/* Arithmetic.proto */
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq_double(a, b)   ((a)==(b))
-    #define __Pyx_c_sum_double(a, b)  ((a)+(b))
-    #define __Pyx_c_diff_double(a, b) ((a)-(b))
-    #define __Pyx_c_prod_double(a, b) ((a)*(b))
-    #define __Pyx_c_quot_double(a, b) ((a)/(b))
-    #define __Pyx_c_neg_double(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)
-    #define __Pyx_c_conj_double(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (::std::abs(z))
-        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero_double(z) ((z)==0)
-    #define __Pyx_c_conj_double(z)    (conj(z))
-    #if 1
-        #define __Pyx_c_abs_double(z)     (cabs(z))
-        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);
-    #endif
-#endif
-
-/* MemviewSliceCopyTemplate.proto */
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-static int __pyx_f_13triangle_hash_12TriangleHash__build_hash(struct __pyx_obj_13triangle_hash_TriangleHash *__pyx_v_self, __Pyx_memviewslice __pyx_v_triangles); /* proto*/
-static PyObject *__pyx_f_13triangle_hash_12TriangleHash_query(struct __pyx_obj_13triangle_hash_TriangleHash *__pyx_v_self, __Pyx_memviewslice __pyx_v_points, int __pyx_skip_dispatch); /* proto*/
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-
-/* Module declarations from 'cython.view' */
-
-/* Module declarations from 'cython' */
-
-/* Module declarations from 'cpython.buffer' */
-
-/* Module declarations from 'libc.string' */
-
-/* Module declarations from 'libc.stdio' */
-
-/* Module declarations from '__builtin__' */
-
-/* Module declarations from 'cpython.type' */
-static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
-
-/* Module declarations from 'cpython' */
-
-/* Module declarations from 'cpython.object' */
-
-/* Module declarations from 'cpython.ref' */
-
-/* Module declarations from 'cpython.mem' */
-
-/* Module declarations from 'numpy' */
-
-/* Module declarations from 'numpy' */
-static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
-static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
-static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
-static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
-static PyTypeObject *__pyx_ptype_5numpy_generic = 0;
-static PyTypeObject *__pyx_ptype_5numpy_number = 0;
-static PyTypeObject *__pyx_ptype_5numpy_integer = 0;
-static PyTypeObject *__pyx_ptype_5numpy_signedinteger = 0;
-static PyTypeObject *__pyx_ptype_5numpy_unsignedinteger = 0;
-static PyTypeObject *__pyx_ptype_5numpy_inexact = 0;
-static PyTypeObject *__pyx_ptype_5numpy_floating = 0;
-static PyTypeObject *__pyx_ptype_5numpy_complexfloating = 0;
-static PyTypeObject *__pyx_ptype_5numpy_flexible = 0;
-static PyTypeObject *__pyx_ptype_5numpy_character = 0;
-static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
-
-/* Module declarations from 'libc.math' */
-
-/* Module declarations from 'libcpp.vector' */
-
-/* Module declarations from 'triangle_hash' */
-static PyTypeObject *__pyx_ptype_13triangle_hash_TriangleHash = 0;
-static PyTypeObject *__pyx_array_type = 0;
-static PyTypeObject *__pyx_MemviewEnum_type = 0;
-static PyTypeObject *__pyx_memoryview_type = 0;
-static PyTypeObject *__pyx_memoryviewslice_type = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static int __pyx_memoryview_thread_locks_used;
-static PyThread_type_lock __pyx_memoryview_thread_locks[8];
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static void *__pyx_align_pointer(void *, size_t); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
-static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 };
-static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 };
-#define __Pyx_MODULE_NAME "triangle_hash"
-extern int __pyx_module_is_main_triangle_hash;
-int __pyx_module_is_main_triangle_hash = 0;
-
-/* Implementation of 'triangle_hash' */
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_ImportError;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-static const char __pyx_k_O[] = "O";
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k_id[] = "id";
-static const char __pyx_k_np[] = "np";
-static const char __pyx_k_new[] = "__new__";
-static const char __pyx_k_obj[] = "obj";
-static const char __pyx_k_base[] = "base";
-static const char __pyx_k_dict[] = "__dict__";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_mode[] = "mode";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ndim[] = "ndim";
-static const char __pyx_k_pack[] = "pack";
-static const char __pyx_k_size[] = "size";
-static const char __pyx_k_step[] = "step";
-static const char __pyx_k_stop[] = "stop";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_ASCII[] = "ASCII";
-static const char __pyx_k_class[] = "__class__";
-static const char __pyx_k_dtype[] = "dtype";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_flags[] = "flags";
-static const char __pyx_k_int32[] = "int32";
-static const char __pyx_k_numpy[] = "numpy";
-static const char __pyx_k_query[] = "query";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_zeros[] = "zeros";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_pickle[] = "pickle";
-static const char __pyx_k_reduce[] = "__reduce__";
-static const char __pyx_k_struct[] = "struct";
-static const char __pyx_k_unpack[] = "unpack";
-static const char __pyx_k_update[] = "update";
-static const char __pyx_k_fortran[] = "fortran";
-static const char __pyx_k_memview[] = "memview";
-static const char __pyx_k_Ellipsis[] = "Ellipsis";
-static const char __pyx_k_getstate[] = "__getstate__";
-static const char __pyx_k_itemsize[] = "itemsize";
-static const char __pyx_k_pyx_type[] = "__pyx_type";
-static const char __pyx_k_setstate[] = "__setstate__";
-static const char __pyx_k_TypeError[] = "TypeError";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_pyx_state[] = "__pyx_state";
-static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
-static const char __pyx_k_triangles[] = "triangles";
-static const char __pyx_k_IndexError[] = "IndexError";
-static const char __pyx_k_ValueError[] = "ValueError";
-static const char __pyx_k_pyx_result[] = "__pyx_result";
-static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static const char __pyx_k_resolution[] = "resolution";
-static const char __pyx_k_ImportError[] = "ImportError";
-static const char __pyx_k_MemoryError[] = "MemoryError";
-static const char __pyx_k_PickleError[] = "PickleError";
-static const char __pyx_k_TriangleHash[] = "TriangleHash";
-static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
-static const char __pyx_k_stringsource[] = "stringsource";
-static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
-static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
-static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
-static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
-static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
-static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
-static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
-static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
-static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
-static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
-static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
-static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
-static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
-static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
-static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import";
-static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
-static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
-static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))";
-static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
-static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
-static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
-static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
-static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
-static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-static PyObject *__pyx_n_s_ASCII;
-static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
-static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
-static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;
-static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;
-static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
-static PyObject *__pyx_n_s_Ellipsis;
-static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
-static PyObject *__pyx_n_s_ImportError;
-static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0;
-static PyObject *__pyx_n_s_IndexError;
-static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
-static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
-static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
-static PyObject *__pyx_n_s_MemoryError;
-static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
-static PyObject *__pyx_kp_s_MemoryView_of_r_object;
-static PyObject *__pyx_n_b_O;
-static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
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- *     if isinstance(memview, _memoryviewslice):
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- *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
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- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
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-    /* "View.MemoryView":777
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- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
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-    /* "View.MemoryView":778
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- *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_dtype_func,
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- *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
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- *     if isinstance(memview, _memoryviewslice):
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- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
-    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 777, __pyx_L1_error)
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-    /* "View.MemoryView":776
- *             new_ndim += 1
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         return memoryview_fromslice(dst, new_ndim,
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- */
-  }
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-  /* "View.MemoryView":782
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- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
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- */
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-
-    /* "View.MemoryView":783
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- *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 782, __pyx_L1_error)
-    __Pyx_GOTREF(__pyx_t_3);
-
-    /* "View.MemoryView":782
- *                                     memview.dtype_is_object)
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- * 
- */
-    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 782, __pyx_L1_error)
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-  }
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-  /* "View.MemoryView":710
- * 
- * @cname('__pyx_memview_slice')
- * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
- *     cdef int new_ndim = 0, suboffset_dim = -1, dim
- *     cdef bint negative_step
- */
-
-  /* function exit code */
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-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_9);
-  __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);
-  __Pyx_XDECREF(__pyx_v_index);
-  __Pyx_XGIVEREF((PyObject *)__pyx_r);
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-  return __pyx_r;
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-/* "View.MemoryView":807
- * 
- * @cname('__pyx_memoryview_slice_memviewslice')
- * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
- *         __Pyx_memviewslice *dst,
- *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
- */
-
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {
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-  int __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
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- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
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-
-    /* "View.MemoryView":829
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- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    __pyx_t_1 = ((__pyx_v_start < 0) != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":830
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- *         if start < 0:
- *             start += shape             # <<<<<<<<<<<<<<
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-      /* "View.MemoryView":829
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
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-
-    /* "View.MemoryView":831
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- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    __pyx_t_1 = (0 <= __pyx_v_start);
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-      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
-    }
-    __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
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-
-      /* "View.MemoryView":832
- *             start += shape
- *         if not 0 <= start < shape:
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
- *     else:
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 832, __pyx_L1_error)
-
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- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    }
-
-    /* "View.MemoryView":827
- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":835
- *     else:
- * 
- *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<
- * 
- *         if have_step and step == 0:
- */
-  /*else*/ {
-    __pyx_t_1 = ((__pyx_v_have_step != 0) != 0);
-    if (__pyx_t_1) {
-    } else {
-      __pyx_t_2 = __pyx_t_1;
-      goto __pyx_L6_bool_binop_done;
-    }
-    __pyx_t_1 = ((__pyx_v_step < 0) != 0);
-    __pyx_t_2 = __pyx_t_1;
-    __pyx_L6_bool_binop_done:;
-    __pyx_v_negative_step = __pyx_t_2;
-
-    /* "View.MemoryView":837
- *         negative_step = have_step != 0 and step < 0
- * 
- *         if have_step and step == 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
- * 
- */
-    __pyx_t_1 = (__pyx_v_have_step != 0);
-    if (__pyx_t_1) {
-    } else {
-      __pyx_t_2 = __pyx_t_1;
-      goto __pyx_L9_bool_binop_done;
-    }
-    __pyx_t_1 = ((__pyx_v_step == 0) != 0);
-    __pyx_t_2 = __pyx_t_1;
-    __pyx_L9_bool_binop_done:;
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":838
- * 
- *         if have_step and step == 0:
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 838, __pyx_L1_error)
-
-      /* "View.MemoryView":837
- *         negative_step = have_step != 0 and step < 0
- * 
- *         if have_step and step == 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
- * 
- */
-    }
-
-    /* "View.MemoryView":841
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-    __pyx_t_2 = (__pyx_v_have_start != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":842
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- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-      __pyx_t_2 = ((__pyx_v_start < 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":843
- *         if have_start:
- *             if start < 0:
- *                 start += shape             # <<<<<<<<<<<<<<
- *                 if start < 0:
- *                     start = 0
- */
-        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-        /* "View.MemoryView":844
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        __pyx_t_2 = ((__pyx_v_start < 0) != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":845
- *                 start += shape
- *                 if start < 0:
- *                     start = 0             # <<<<<<<<<<<<<<
- *             elif start >= shape:
- *                 if negative_step:
- */
-          __pyx_v_start = 0;
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-          /* "View.MemoryView":844
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        }
-
-        /* "View.MemoryView":842
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-        goto __pyx_L12;
-      }
-
-      /* "View.MemoryView":846
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":847
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-        __pyx_t_2 = (__pyx_v_negative_step != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":848
- *             elif start >= shape:
- *                 if negative_step:
- *                     start = shape - 1             # <<<<<<<<<<<<<<
- *                 else:
- *                     start = shape
- */
-          __pyx_v_start = (__pyx_v_shape - 1);
-
-          /* "View.MemoryView":847
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-          goto __pyx_L14;
-        }
-
-        /* "View.MemoryView":850
- *                     start = shape - 1
- *                 else:
- *                     start = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        /*else*/ {
-          __pyx_v_start = __pyx_v_shape;
-        }
-        __pyx_L14:;
-
-        /* "View.MemoryView":846
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      }
-      __pyx_L12:;
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-      /* "View.MemoryView":841
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-      goto __pyx_L11;
-    }
-
-    /* "View.MemoryView":852
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-    /*else*/ {
-      __pyx_t_2 = (__pyx_v_negative_step != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":853
- *         else:
- *             if negative_step:
- *                 start = shape - 1             # <<<<<<<<<<<<<<
- *             else:
- *                 start = 0
- */
-        __pyx_v_start = (__pyx_v_shape - 1);
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-        /* "View.MemoryView":852
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-        goto __pyx_L15;
-      }
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-      /* "View.MemoryView":855
- *                 start = shape - 1
- *             else:
- *                 start = 0             # <<<<<<<<<<<<<<
- * 
- *         if have_stop:
- */
-      /*else*/ {
-        __pyx_v_start = 0;
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-      __pyx_L15:;
-    }
-    __pyx_L11:;
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-    /* "View.MemoryView":857
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-    __pyx_t_2 = (__pyx_v_have_stop != 0);
-    if (__pyx_t_2) {
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-      /* "View.MemoryView":858
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
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- *                     stop = 0
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-        if (__pyx_t_2) {
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- *                 stop += shape
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- *                     stop = 0             # <<<<<<<<<<<<<<
- *             elif stop > shape:
- *                 stop = shape
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- *                     stop = 0
- *             elif stop > shape:
- */
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- *         if have_stop:
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- *                 stop += shape
- *                 if stop < 0:
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-        goto __pyx_L17;
-      }
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- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":863
- *                     stop = 0
- *             elif stop > shape:
- *                 stop = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        __pyx_v_stop = __pyx_v_shape;
-
-        /* "View.MemoryView":862
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      }
-      __pyx_L17:;
-
-      /* "View.MemoryView":857
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-      goto __pyx_L16;
-    }
-
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- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- */
-      goto __pyx_L7_break;
-
-      /* "View.MemoryView":1130
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L7_break:;
-
-  /* "View.MemoryView":1134
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1135
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- *         return 'C'             # <<<<<<<<<<<<<<
- *     else:
- *         return 'F'
- */
-    __pyx_r = 'C';
-    goto __pyx_L0;
-
-    /* "View.MemoryView":1134
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  }
-
-  /* "View.MemoryView":1137
- *         return 'C'
- *     else:
- *         return 'F'             # <<<<<<<<<<<<<<
- * 
- * @cython.cdivision(True)
- */
-  /*else*/ {
-    __pyx_r = 'F';
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1116
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1140
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
-  Py_ssize_t __pyx_v_dst_extent;
-  Py_ssize_t __pyx_v_src_stride;
-  Py_ssize_t __pyx_v_dst_stride;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-  Py_ssize_t __pyx_t_6;
-
-  /* "View.MemoryView":1147
- * 
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- */
-  __pyx_v_src_extent = (__pyx_v_src_shape[0]);
-
-  /* "View.MemoryView":1148
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- */
-  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
-
-  /* "View.MemoryView":1149
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- */
-  __pyx_v_src_stride = (__pyx_v_src_strides[0]);
-
-  /* "View.MemoryView":1150
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
- * 
- *     if ndim == 1:
- */
-  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
-
-  /* "View.MemoryView":1152
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1153
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-    __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-
-    /* "View.MemoryView":1154
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- */
-    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
-    if (__pyx_t_2) {
-      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
-    }
-    __pyx_t_3 = (__pyx_t_2 != 0);
-    __pyx_t_1 = __pyx_t_3;
-    __pyx_L5_bool_binop_done:;
-
-    /* "View.MemoryView":1153
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":1155
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
- *        else:
- *            for i in range(dst_extent):
- */
-      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
-
-      /* "View.MemoryView":1153
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-      goto __pyx_L4;
-    }
-
-    /* "View.MemoryView":1157
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- */
-    /*else*/ {
-      __pyx_t_4 = __pyx_v_dst_extent;
-      __pyx_t_5 = __pyx_t_4;
-      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-        __pyx_v_i = __pyx_t_6;
-
-        /* "View.MemoryView":1158
- *        else:
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
- *                src_data += src_stride
- *                dst_data += dst_stride
- */
-        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
-
-        /* "View.MemoryView":1159
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride             # <<<<<<<<<<<<<<
- *                dst_data += dst_stride
- *     else:
- */
-        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-        /* "View.MemoryView":1160
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- *                dst_data += dst_stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(dst_extent):
- */
-        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-      }
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":1152
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1162
- *                dst_data += dst_stride
- *     else:
- *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *             _copy_strided_to_strided(src_data, src_strides + 1,
- *                                      dst_data, dst_strides + 1,
- */
-  /*else*/ {
-    __pyx_t_4 = __pyx_v_dst_extent;
-    __pyx_t_5 = __pyx_t_4;
-    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-      __pyx_v_i = __pyx_t_6;
-
-      /* "View.MemoryView":1163
- *     else:
- *         for i in range(dst_extent):
- *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
- *                                      dst_data, dst_strides + 1,
- *                                      src_shape + 1, dst_shape + 1,
- */
-      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
-      /* "View.MemoryView":1167
- *                                      src_shape + 1, dst_shape + 1,
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride             # <<<<<<<<<<<<<<
- *             dst_data += dst_stride
- * 
- */
-      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-      /* "View.MemoryView":1168
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride
- *             dst_data += dst_stride             # <<<<<<<<<<<<<<
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
-      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1140
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1170
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
-  /* "View.MemoryView":1173
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
- *                              src.shape, dst.shape, ndim, itemsize)
- * 
- */
-  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1170
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1177
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
-  Py_ssize_t __pyx_v_shape;
-  Py_ssize_t __pyx_v_size;
-  Py_ssize_t __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  Py_ssize_t *__pyx_t_3;
-  Py_ssize_t *__pyx_t_4;
-
-  /* "View.MemoryView":1179
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     for shape in src.shape[:ndim]:
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_size = __pyx_t_1;
-
-  /* "View.MemoryView":1181
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- * 
- *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
- *         size *= shape
- * 
- */
-  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
-  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
-    __pyx_t_2 = __pyx_t_4;
-    __pyx_v_shape = (__pyx_t_2[0]);
-
-    /* "View.MemoryView":1182
- * 
- *     for shape in src.shape[:ndim]:
- *         size *= shape             # <<<<<<<<<<<<<<
- * 
- *     return size
- */
-    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
-  }
-
-  /* "View.MemoryView":1184
- *         size *= shape
- * 
- *     return size             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_fill_contig_strides_array')
- */
-  __pyx_r = __pyx_v_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1177
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1187
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
-  /* "View.MemoryView":1196
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1197
- * 
- *     if order == 'F':
- *         for idx in range(ndim):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-    __pyx_t_2 = __pyx_v_ndim;
-    __pyx_t_3 = __pyx_t_2;
-    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-      __pyx_v_idx = __pyx_t_4;
-
-      /* "View.MemoryView":1198
- *     if order == 'F':
- *         for idx in range(ndim):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- *     else:
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1199
- *         for idx in range(ndim):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-
-    /* "View.MemoryView":1196
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1201
- *             stride *= shape[idx]
- *     else:
- *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-  /*else*/ {
-    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
-      __pyx_v_idx = __pyx_t_2;
-
-      /* "View.MemoryView":1202
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- * 
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1203
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- * 
- *     return stride
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1205
- *             stride *= shape[idx]
- * 
- *     return stride             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- */
-  __pyx_r = __pyx_v_stride;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1187
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) nogil:
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1208
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                              __Pyx_memviewslice *tmpslice,
- *                              char order,
- */
-
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  void *__pyx_v_result;
-  size_t __pyx_v_itemsize;
-  size_t __pyx_v_size;
-  void *__pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
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- *         raise error(msg.decode('ascii'))
- *     else:
- */
-  }
-
-  /* "View.MemoryView":1265
- *         raise error(msg.decode('ascii'))
- *     else:
- *         raise error             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_contents')
- */
-  /*else*/ {
-    __Pyx_Raise(__pyx_v_error, 0, 0, 0);
-    __PYX_ERR(1, 1265, __pyx_L1_error)
-  }
-
-  /* "View.MemoryView":1261
- * 
- * @cname('__pyx_memoryview_err')
- * cdef int _err(object error, char *msg) except -1 with gil:             # <<<<<<<<<<<<<<
- *     if msg != NULL:
- *         raise error(msg.decode('ascii'))
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
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-  #endif
-  return __pyx_r;
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-
-/* "View.MemoryView":1268
- * 
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice dst,
- *                                   int src_ndim, int dst_ndim,
- */
-
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) {
-  void *__pyx_v_tmpdata;
-  size_t __pyx_v_itemsize;
-  int __pyx_v_i;
-  char __pyx_v_order;
-  int __pyx_v_broadcasting;
-  int __pyx_v_direct_copy;
-  __Pyx_memviewslice __pyx_v_tmp;
-  int __pyx_v_ndim;
-  int __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  int __pyx_t_5;
-  int __pyx_t_6;
-  void *__pyx_t_7;
-  int __pyx_t_8;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-
-  /* "View.MemoryView":1276
- *     Check for overlapping memory and verify the shapes.
- *     """
- *     cdef void *tmpdata = NULL             # <<<<<<<<<<<<<<
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- */
-  __pyx_v_tmpdata = NULL;
-
-  /* "View.MemoryView":1277
- *     """
- *     cdef void *tmpdata = NULL
- *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- */
-  __pyx_t_1 = __pyx_v_src.memview->view.itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
-
-  /* "View.MemoryView":1279
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)             # <<<<<<<<<<<<<<
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False
- */
-  __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim);
-
-  /* "View.MemoryView":1280
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False             # <<<<<<<<<<<<<<
- *     cdef bint direct_copy = False
- *     cdef __Pyx_memviewslice tmp
- */
-  __pyx_v_broadcasting = 0;
-
-  /* "View.MemoryView":1281
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False             # <<<<<<<<<<<<<<
- *     cdef __Pyx_memviewslice tmp
- * 
- */
-  __pyx_v_direct_copy = 0;
-
-  /* "View.MemoryView":1284
- *     cdef __Pyx_memviewslice tmp
- * 
- *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- */
-  __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1285
- * 
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim);
-
-    /* "View.MemoryView":1284
- *     cdef __Pyx_memviewslice tmp
- * 
- *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1286
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- */
-  __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1287
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)             # <<<<<<<<<<<<<<
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);
-
-    /* "View.MemoryView":1286
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- */
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1289
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-  __pyx_t_3 = __pyx_v_dst_ndim;
-  __pyx_t_4 = __pyx_v_src_ndim;
-  if (((__pyx_t_3 > __pyx_t_4) != 0)) {
-    __pyx_t_5 = __pyx_t_3;
-  } else {
-    __pyx_t_5 = __pyx_t_4;
-  }
-  __pyx_v_ndim = __pyx_t_5;
-
-  /* "View.MemoryView":1291
- *     cdef int ndim = max(src_ndim, dst_ndim)
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- */
-  __pyx_t_5 = __pyx_v_ndim;
-  __pyx_t_3 = __pyx_t_5;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-
-    /* "View.MemoryView":1292
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1293
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-      __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":1294
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- *                 broadcasting = True             # <<<<<<<<<<<<<<
- *                 src.strides[i] = 0
- *             else:
- */
-        __pyx_v_broadcasting = 1;
-
-        /* "View.MemoryView":1295
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- */
-        (__pyx_v_src.strides[__pyx_v_i]) = 0;
-
-        /* "View.MemoryView":1293
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-        goto __pyx_L7;
-      }
-
-      /* "View.MemoryView":1297
- *                 src.strides[i] = 0
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
- * 
- *         if src.suboffsets[i] >= 0:
- */
-      /*else*/ {
-        __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error)
-      }
-      __pyx_L7:;
-
-      /* "View.MemoryView":1292
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    }
-
-    /* "View.MemoryView":1299
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- */
-    __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1300
- * 
- *         if src.suboffsets[i] >= 0:
- *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- */
-      __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1300, __pyx_L1_error)
-
-      /* "View.MemoryView":1299
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- */
-    }
-  }
-
-  /* "View.MemoryView":1302
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1304
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1305
- * 
- *         if not slice_is_contig(src, order, ndim):
- *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- */
-      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
-
-      /* "View.MemoryView":1304
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    }
-
-    /* "View.MemoryView":1307
- *             order = get_best_order(&dst, ndim)
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
- *         src = tmp
- * 
- */
-    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1307, __pyx_L1_error)
-    __pyx_v_tmpdata = __pyx_t_7;
-
-    /* "View.MemoryView":1308
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- *         src = tmp             # <<<<<<<<<<<<<<
- * 
- *     if not broadcasting:
- */
-    __pyx_v_src = __pyx_v_tmp;
-
-    /* "View.MemoryView":1302
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  }
-
-  /* "View.MemoryView":1310
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1313
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1314
- * 
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
-
-      /* "View.MemoryView":1313
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-      goto __pyx_L12;
-    }
-
-    /* "View.MemoryView":1315
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1316
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
- * 
- *         if direct_copy:
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
-
-      /* "View.MemoryView":1315
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    }
-    __pyx_L12:;
-
-    /* "View.MemoryView":1318
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    __pyx_t_2 = (__pyx_v_direct_copy != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1320
- *         if direct_copy:
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-      /* "View.MemoryView":1321
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)
- */
-      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
-
-      /* "View.MemoryView":1322
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
- *             free(tmpdata)
- *             return 0
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-      /* "View.MemoryView":1323
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)             # <<<<<<<<<<<<<<
- *             return 0
- * 
- */
-      free(__pyx_v_tmpdata);
-
-      /* "View.MemoryView":1324
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)
- *             return 0             # <<<<<<<<<<<<<<
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):
- */
-      __pyx_r = 0;
-      goto __pyx_L0;
-
-      /* "View.MemoryView":1318
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    }
-
-    /* "View.MemoryView":1310
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1326
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (__pyx_v_order == 'F');
-  if (__pyx_t_2) {
-    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
-  }
-  __pyx_t_8 = (__pyx_t_2 != 0);
-  if (__pyx_t_8) {
-
-    /* "View.MemoryView":1329
- * 
- * 
- *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
- *         transpose_memslice(&dst)
- * 
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1329, __pyx_L1_error)
-
-    /* "View.MemoryView":1330
- * 
- *         transpose_memslice(&src)
- *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1330, __pyx_L1_error)
-
-    /* "View.MemoryView":1326
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1332
- *         transpose_memslice(&dst)
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
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-  p->obj = Py_None; Py_INCREF(Py_None);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  p->view.obj = NULL;
-  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
-  return o;
-  bad:
-  Py_DECREF(o); o = 0;
-  return NULL;
-}
-
-static void __pyx_tp_dealloc_memoryview(PyObject *o) {
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryview___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->obj);
-  Py_CLEAR(p->_size);
-  Py_CLEAR(p->_array_interface);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  if (p->obj) {
-    e = (*v)(p->obj, a); if (e) return e;
-  }
-  if (p->_size) {
-    e = (*v)(p->_size, a); if (e) return e;
-  }
-  if (p->_array_interface) {
-    e = (*v)(p->_array_interface, a); if (e) return e;
-  }
-  if (p->view.obj) {
-    e = (*v)(p->view.obj, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  tmp = ((PyObject*)p->obj);
-  p->obj = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_size);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_array_interface);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  Py_CLEAR(p->view.obj);
-  return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_memoryview___setitem__(o, i, v);
-  }
-  else {
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
-  {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},
-  {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},
-  {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},
-  {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},
-  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},
-  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
-  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
-  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
-  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
-  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
-  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
-  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
-  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
-  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
-  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
-  __pyx_memoryview___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_memoryview, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
-  __pyx_memoryview___len__, /*mp_length*/
-  __pyx_memoryview___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "triangle_hash.memoryview", /*tp_name*/
-  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_memoryview___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  __pyx_memoryview___str__, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_memoryview, /*tp_traverse*/
-  __pyx_tp_clear_memoryview, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_memoryview, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_memoryview, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_memoryview, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-  0, /*tp_print*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryviewslice_obj *p;
-  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryviewslice_obj *)o);
-  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  p->from_slice.memview = NULL;
-  return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryviewslice___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->from_object);
-  PyObject_GC_Track(o);
-  __pyx_tp_dealloc_memoryview(o);
-}
-
-static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
-  if (p->from_object) {
-    e = (*v)(p->from_object, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  __pyx_tp_clear_memoryview(o);
-  tmp = ((PyObject*)p->from_object);
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
-  return 0;
-}
-
-static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods__memoryviewslice[] = {
-  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},
-  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
-  {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PyTypeObject __pyx_type___pyx_memoryviewslice = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "triangle_hash._memoryviewslice", /*tp_name*/
-  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY
-  __pyx_memoryview___repr__, /*tp_repr*/
-  #else
-  0, /*tp_repr*/
-  #endif
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  #if CYTHON_COMPILING_IN_PYPY
-  __pyx_memoryview___str__, /*tp_str*/
-  #else
-  0, /*tp_str*/
-  #endif
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  "Internal class for passing memoryview slices to Python", /*tp_doc*/
-  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
-  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods__memoryviewslice, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets__memoryviewslice, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new__memoryviewslice, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-  0, /*tp_print*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-
-static PyMethodDef __pyx_methods[] = {
-  {0, 0, 0, 0}
-};
-
-#if PY_MAJOR_VERSION >= 3
-#if CYTHON_PEP489_MULTI_PHASE_INIT
-static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/
-static int __pyx_pymod_exec_triangle_hash(PyObject* module); /*proto*/
-static PyModuleDef_Slot __pyx_moduledef_slots[] = {
-  {Py_mod_create, (void*)__pyx_pymod_create},
-  {Py_mod_exec, (void*)__pyx_pymod_exec_triangle_hash},
-  {0, NULL}
-};
-#endif
-
-static struct PyModuleDef __pyx_moduledef = {
-    PyModuleDef_HEAD_INIT,
-    "triangle_hash",
-    0, /* m_doc */
-  #if CYTHON_PEP489_MULTI_PHASE_INIT
-    0, /* m_size */
-  #else
-    -1, /* m_size */
-  #endif
-    __pyx_methods /* m_methods */,
-  #if CYTHON_PEP489_MULTI_PHASE_INIT
-    __pyx_moduledef_slots, /* m_slots */
-  #else
-    NULL, /* m_reload */
-  #endif
-    NULL, /* m_traverse */
-    NULL, /* m_clear */
-    NULL /* m_free */
-};
-#endif
-#ifndef CYTHON_SMALL_CODE
-#if defined(__clang__)
-    #define CYTHON_SMALL_CODE
-#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3))
-    #define CYTHON_SMALL_CODE __attribute__((cold))
-#else
-    #define CYTHON_SMALL_CODE
-#endif
-#endif
-
-static __Pyx_StringTabEntry __pyx_string_tab[] = {
-  {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1},
-  {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0},
-  {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0},
-  {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0},
-  {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0},
-  {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0},
-  {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1},
-  {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0},
-  {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1},
-  {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0},
-  {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1},
-  {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0},
-  {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0},
-  {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0},
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-    Py_CLEAR(__pyx_m);
-  } else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_ImportError, "init triangle_hash");
-  }
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  #if CYTHON_PEP489_MULTI_PHASE_INIT
-  return (__pyx_m != NULL) ? 0 : -1;
-  #elif PY_MAJOR_VERSION >= 3
-  return __pyx_m;
-  #else
-  return;
-  #endif
-}
-
-/* --- Runtime support code --- */
-/* Refnanny */
-#if CYTHON_REFNANNY
-static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
-    PyObject *m = NULL, *p = NULL;
-    void *r = NULL;
-    m = PyImport_ImportModule(modname);
-    if (!m) goto end;
-    p = PyObject_GetAttrString(m, "RefNannyAPI");
-    if (!p) goto end;
-    r = PyLong_AsVoidPtr(p);
-end:
-    Py_XDECREF(p);
-    Py_XDECREF(m);
-    return (__Pyx_RefNannyAPIStruct *)r;
-}
-#endif
-
-/* PyObjectGetAttrStr */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_getattro))
-        return tp->tp_getattro(obj, attr_name);
-#if PY_MAJOR_VERSION < 3
-    if (likely(tp->tp_getattr))
-        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
-#endif
-    return PyObject_GetAttr(obj, attr_name);
-}
-#endif
-
-/* GetBuiltinName */
-static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
-    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
-    if (unlikely(!result)) {
-        PyErr_Format(PyExc_NameError,
-#if PY_MAJOR_VERSION >= 3
-            "name '%U' is not defined", name);
-#else
-            "name '%.200s' is not defined", PyString_AS_STRING(name));
-#endif
-    }
-    return result;
-}
-
-/* RaiseArgTupleInvalid */
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
-}
-
-/* RaiseDoubleKeywords */
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-/* ParseKeywords */
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    while (PyDict_Next(kwds, &pos, &key, &value)) {
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (**name == key) ? 0 :
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key);
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    PyErr_Format(PyExc_TypeError,
-    #if PY_MAJOR_VERSION < 3
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-/* MemviewSliceInit */
-static int
-__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
-                        int ndim,
-                        __Pyx_memviewslice *memviewslice,
-                        int memview_is_new_reference)
-{
-    __Pyx_RefNannyDeclarations
-    int i, retval=-1;
-    Py_buffer *buf = &memview->view;
-    __Pyx_RefNannySetupContext("init_memviewslice", 0);
-    if (unlikely(memviewslice->memview || memviewslice->data)) {
-        PyErr_SetString(PyExc_ValueError,
-            "memviewslice is already initialized!");
-        goto fail;
-    }
-    if (buf->strides) {
-        for (i = 0; i < ndim; i++) {
-            memviewslice->strides[i] = buf->strides[i];
-        }
-    } else {
-        Py_ssize_t stride = buf->itemsize;
-        for (i = ndim - 1; i >= 0; i--) {
-            memviewslice->strides[i] = stride;
-            stride *= buf->shape[i];
-        }
-    }
-    for (i = 0; i < ndim; i++) {
-        memviewslice->shape[i]   = buf->shape[i];
-        if (buf->suboffsets) {
-            memviewslice->suboffsets[i] = buf->suboffsets[i];
-        } else {
-            memviewslice->suboffsets[i] = -1;
-        }
-    }
-    memviewslice->memview = memview;
-    memviewslice->data = (char *)buf->buf;
-    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
-        Py_INCREF(memview);
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    memviewslice->memview = 0;
-    memviewslice->data = 0;
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-#ifndef Py_NO_RETURN
-#define Py_NO_RETURN
-#endif
-static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {
-    va_list vargs;
-    char msg[200];
-#ifdef HAVE_STDARG_PROTOTYPES
-    va_start(vargs, fmt);
-#else
-    va_start(vargs);
-#endif
-    vsnprintf(msg, 200, fmt, vargs);
-    va_end(vargs);
-    Py_FatalError(msg);
-}
-static CYTHON_INLINE int
-__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)++;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE int
-__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)--;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE void
-__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
-{
-    int first_time;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None))
-        return;
-    if (unlikely(__pyx_get_slice_count(memview) < 0))
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         __pyx_get_slice_count(memview), lineno);
-    first_time = __pyx_add_acquisition_count(memview) == 0;
-    if (unlikely(first_time)) {
-        if (have_gil) {
-            Py_INCREF((PyObject *) memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_INCREF((PyObject *) memview);
-            PyGILState_Release(_gilstate);
-        }
-    }
-}
-static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,
-                                             int have_gil, int lineno) {
-    int last_time;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
-        memslice->memview = NULL;
-        return;
-    }
-    if (unlikely(__pyx_get_slice_count(memview) <= 0))
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         __pyx_get_slice_count(memview), lineno);
-    last_time = __pyx_sub_acquisition_count(memview) == 1;
-    memslice->data = NULL;
-    if (unlikely(last_time)) {
-        if (have_gil) {
-            Py_CLEAR(memslice->memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_CLEAR(memslice->memview);
-            PyGILState_Release(_gilstate);
-        }
-    } else {
-        memslice->memview = NULL;
-    }
-}
-
-/* PyErrFetchRestore */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    tmp_type = tstate->curexc_type;
-    tmp_value = tstate->curexc_value;
-    tmp_tb = tstate->curexc_traceback;
-    tstate->curexc_type = type;
-    tstate->curexc_value = value;
-    tstate->curexc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    *type = tstate->curexc_type;
-    *value = tstate->curexc_value;
-    *tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-}
-#endif
-
-/* WriteUnraisableException */
-static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno,
-                                  CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename,
-                                  int full_traceback, CYTHON_UNUSED int nogil) {
-    PyObject *old_exc, *old_val, *old_tb;
-    PyObject *ctx;
-    __Pyx_PyThreadState_declare
-#ifdef WITH_THREAD
-    PyGILState_STATE state;
-    if (nogil)
-        state = PyGILState_Ensure();
-#ifdef _MSC_VER
-    else state = (PyGILState_STATE)-1;
-#endif
-#endif
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&old_exc, &old_val, &old_tb);
-    if (full_traceback) {
-        Py_XINCREF(old_exc);
-        Py_XINCREF(old_val);
-        Py_XINCREF(old_tb);
-        __Pyx_ErrRestore(old_exc, old_val, old_tb);
-        PyErr_PrintEx(1);
-    }
-    #if PY_MAJOR_VERSION < 3
-    ctx = PyString_FromString(name);
-    #else
-    ctx = PyUnicode_FromString(name);
-    #endif
-    __Pyx_ErrRestore(old_exc, old_val, old_tb);
-    if (!ctx) {
-        PyErr_WriteUnraisable(Py_None);
-    } else {
-        PyErr_WriteUnraisable(ctx);
-        Py_DECREF(ctx);
-    }
-#ifdef WITH_THREAD
-    if (nogil)
-        PyGILState_Release(state);
-#endif
-}
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* None */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* PyCFunctionFastCall */
-#if CYTHON_FAST_PYCCALL
-static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
-    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
-    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
-    PyObject *self = PyCFunction_GET_SELF(func);
-    int flags = PyCFunction_GET_FLAGS(func);
-    assert(PyCFunction_Check(func));
-    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
-    assert(nargs >= 0);
-    assert(nargs == 0 || args != NULL);
-    /* _PyCFunction_FastCallDict() must not be called with an exception set,
-       because it may clear it (directly or indirectly) and so the
-       caller loses its exception */
-    assert(!PyErr_Occurred());
-    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
-        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
-    } else {
-        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
-    }
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-#if 1 || PY_VERSION_HEX < 0x030600B1
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-#endif
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args, *result = NULL;
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyFunction_FastCall(function, args, 2);
-    }
-    #endif
-    #if CYTHON_FAST_PYCCALL
-    if (__Pyx_PyFastCFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyCFunction_FastCall(function, args, 2);
-    }
-    #endif
-    args = PyTuple_New(2);
-    if (unlikely(!args)) goto done;
-    Py_INCREF(arg1);
-    PyTuple_SET_ITEM(args, 0, arg1);
-    Py_INCREF(arg2);
-    PyTuple_SET_ITEM(args, 1, arg2);
-    Py_INCREF(function);
-    result = __Pyx_PyObject_Call(function, args, NULL);
-    Py_DECREF(args);
-    Py_DECREF(function);
-done:
-    return result;
-}
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallOneArg */
-#if CYTHON_COMPILING_IN_CPYTHON
-static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_New(1);
-    if (unlikely(!args)) return NULL;
-    Py_INCREF(arg);
-    PyTuple_SET_ITEM(args, 0, arg);
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, &arg, 1);
-    }
-#endif
-    if (likely(PyCFunction_Check(func))) {
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-            return __Pyx_PyObject_CallMethO(func, arg);
-#if CYTHON_FAST_PYCCALL
-        } else if (__Pyx_PyFastCFunction_Check(func)) {
-            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
-#endif
-        }
-    }
-    return __Pyx__PyObject_CallOneArg(func, arg);
-}
-#else
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_Pack(1, arg);
-    if (unlikely(!args)) return NULL;
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
-                        CYTHON_UNUSED PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-#if CYTHON_COMPILING_IN_PYPY
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#else
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    #endif
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-#endif
-
-/* PyErrExceptionMatches */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
-    PyObject *exc_type = tstate->curexc_type;
-    if (exc_type == err) return 1;
-    if (unlikely(!exc_type)) return 0;
-    if (unlikely(PyTuple_Check(err)))
-        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
-    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
-        name, type->tp_name, Py_TYPE(obj)->tp_name);
-    return 0;
-}
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* DivInt[Py_ssize_t] */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
-    Py_ssize_t q = a / b;
-    Py_ssize_t r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* GetAttr */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_USE_TYPE_SLOTS
-#if PY_MAJOR_VERSION >= 3
-    if (likely(PyUnicode_Check(n)))
-#else
-    if (likely(PyString_Check(n)))
-#endif
-        return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
-    return PyObject_GetAttr(o, n);
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (!j) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return m->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
-    PyObject *runerr;
-    Py_ssize_t key_value;
-    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
-    if (unlikely(!(m && m->sq_item))) {
-        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
-        return NULL;
-    }
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
-    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(m && m->mp_subscript)) {
-        return m->mp_subscript(obj, key);
-    }
-    return __Pyx_PyObject_GetIndex(obj, key);
-}
-#endif
-
-/* decode_c_string */
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
-         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
-         const char* encoding, const char* errors,
-         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
-    Py_ssize_t length;
-    if (unlikely((start < 0) | (stop < 0))) {
-        size_t slen = strlen(cstring);
-        if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "c-string too long to convert to Python");
-            return NULL;
-        }
-        length = (Py_ssize_t) slen;
-        if (start < 0) {
-            start += length;
-            if (start < 0)
-                start = 0;
-        }
-        if (stop < 0)
-            stop += length;
-    }
-    if (unlikely(stop <= start))
-        return __Pyx_NewRef(__pyx_empty_unicode);
-    length = stop - start;
-    cstring += start;
-    if (decode_func) {
-        return decode_func(cstring, length, errors);
-    } else {
-        return PyUnicode_Decode(cstring, length, encoding, errors);
-    }
-}
-
-/* GetAttr3 */
-static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        return NULL;
-    __Pyx_PyErr_Clear();
-    Py_INCREF(d);
-    return d;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
-    PyObject *r = __Pyx_GetAttr(o, n);
-    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* ExtTypeTest */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(__Pyx_TypeCheck(obj, type)))
-        return 1;
-    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
-                 Py_TYPE(obj)->tp_name, type->tp_name);
-    return 0;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-    #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *empty_list = 0;
-    PyObject *module = 0;
-    PyObject *global_dict = 0;
-    PyObject *empty_dict = 0;
-    PyObject *list;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (!py_import)
-        goto bad;
-    #endif
-    if (from_list)
-        list = from_list;
-    else {
-        empty_list = PyList_New(0);
-        if (!empty_list)
-            goto bad;
-        list = empty_list;
-    }
-    global_dict = PyModule_GetDict(__pyx_m);
-    if (!global_dict)
-        goto bad;
-    empty_dict = PyDict_New();
-    if (!empty_dict)
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                module = PyImport_ImportModuleLevelObject(
-                    name, global_dict, empty_dict, list, 1);
-                if (!module) {
-                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (!py_level)
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, global_dict, empty_dict, list, level);
-            #endif
-        }
-    }
-bad:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    Py_XDECREF(empty_list);
-    Py_XDECREF(empty_dict);
-    return module;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = a->tp_base;
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
-    if (!res) {
-        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-    return res;
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
-    (void)inplace;
-    (void)zerodivision_check;
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-            x = (long)((unsigned long)a + b);
-            if (likely((x^a) >= 0 || (x^b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_add(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        const digit* digits = ((PyLongObject*)op1)->ob_digit;
-        const Py_ssize_t size = Py_SIZE(op1);
-        if (likely(__Pyx_sst_abs(size) <= 1)) {
-            a = likely(size) ? digits[0] : 0;
-            if (size == -1) a = -a;
-        } else {
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
-            }
-        }
-                x = a + b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla + llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-        double a = PyFloat_AS_DOUBLE(op1);
-            double result;
-            PyFPE_START_PROTECT("add", return NULL)
-            result = ((double)a) + (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* DivInt[long] */
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* HasAttr */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
-    PyObject *r;
-    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "hasattr(): attribute name must be string");
-        return -1;
-    }
-    r = __Pyx_GetAttr(o, n);
-    if (unlikely(!r)) {
-        PyErr_Clear();
-        return 0;
-    } else {
-        Py_DECREF(r);
-        return 1;
-    }
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'%.50s' object has no attribute '%U'",
-                 tp->tp_name, attr_name);
-#else
-                 "'%.50s' object has no attribute '%.400s'",
-                 tp->tp_name, PyString_AS_STRING(attr_name));
-#endif
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* PyObject_GenericGetAttr */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
-    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
-}
-#endif
-
-/* SetVTable */
-static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
-#if PY_VERSION_HEX >= 0x02070000
-    PyObject *ob = PyCapsule_New(vtable, 0, 0);
-#else
-    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
-#endif
-    if (!ob)
-        goto bad;
-    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
-        goto bad;
-    Py_DECREF(ob);
-    return 0;
-bad:
-    Py_XDECREF(ob);
-    return -1;
-}
-
-/* PyObjectGetAttrStrNoError */
-static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        __Pyx_PyErr_Clear();
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {
-    PyObject *result;
-#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {
-        return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);
-    }
-#endif
-    result = __Pyx_PyObject_GetAttrStr(obj, attr_name);
-    if (unlikely(!result)) {
-        __Pyx_PyObject_GetAttrStr_ClearAttributeError();
-    }
-    return result;
-}
-
-/* SetupReduce */
-static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
-  int ret;
-  PyObject *name_attr;
-  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);
-  if (likely(name_attr)) {
-      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
-  } else {
-      ret = -1;
-  }
-  if (unlikely(ret < 0)) {
-      PyErr_Clear();
-      ret = 0;
-  }
-  Py_XDECREF(name_attr);
-  return ret;
-}
-static int __Pyx_setup_reduce(PyObject* type_obj) {
-    int ret = 0;
-    PyObject *object_reduce = NULL;
-    PyObject *object_reduce_ex = NULL;
-    PyObject *reduce = NULL;
-    PyObject *reduce_ex = NULL;
-    PyObject *reduce_cython = NULL;
-    PyObject *setstate = NULL;
-    PyObject *setstate_cython = NULL;
-#if CYTHON_USE_PYTYPE_LOOKUP
-    if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
-#else
-    if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
-#endif
-#if CYTHON_USE_PYTYPE_LOOKUP
-    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#else
-    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#endif
-    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
-    if (reduce_ex == object_reduce_ex) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#else
-        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#endif
-        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
-        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
-            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
-            if (likely(reduce_cython)) {
-                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-            } else if (reduce == object_reduce || PyErr_Occurred()) {
-                goto __PYX_BAD;
-            }
-            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
-            if (!setstate) PyErr_Clear();
-            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
-                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
-                if (likely(setstate_cython)) {
-                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                } else if (!setstate || PyErr_Occurred()) {
-                    goto __PYX_BAD;
-                }
-            }
-            PyType_Modified((PyTypeObject*)type_obj);
-        }
-    }
-    goto __PYX_GOOD;
-__PYX_BAD:
-    if (!PyErr_Occurred())
-        PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
-    ret = -1;
-__PYX_GOOD:
-#if !CYTHON_USE_PYTYPE_LOOKUP
-    Py_XDECREF(object_reduce);
-    Py_XDECREF(object_reduce_ex);
-#endif
-    Py_XDECREF(reduce);
-    Py_XDECREF(reduce_ex);
-    Py_XDECREF(reduce_cython);
-    Py_XDECREF(setstate);
-    Py_XDECREF(setstate_cython);
-    return ret;
-}
-
-/* TypeImport */
-#ifndef __PYX_HAVE_RT_ImportType
-#define __PYX_HAVE_RT_ImportType
-static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name,
-    size_t size, enum __Pyx_ImportType_CheckSize check_size)
-{
-    PyObject *result = 0;
-    char warning[200];
-    Py_ssize_t basicsize;
-#ifdef Py_LIMITED_API
-    PyObject *py_basicsize;
-#endif
-    result = PyObject_GetAttrString(module, class_name);
-    if (!result)
-        goto bad;
-    if (!PyType_Check(result)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s.%.200s is not a type object",
-            module_name, class_name);
-        goto bad;
-    }
-#ifndef Py_LIMITED_API
-    basicsize = ((PyTypeObject *)result)->tp_basicsize;
-#else
-    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
-    if (!py_basicsize)
-        goto bad;
-    basicsize = PyLong_AsSsize_t(py_basicsize);
-    Py_DECREF(py_basicsize);
-    py_basicsize = 0;
-    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
-        goto bad;
-#endif
-    if ((size_t)basicsize < size) {
-        PyErr_Format(PyExc_ValueError,
-            "%.200s.%.200s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        goto bad;
-    }
-    if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) {
-        PyErr_Format(PyExc_ValueError,
-            "%.200s.%.200s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        goto bad;
-    }
-    else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) {
-        PyOS_snprintf(warning, sizeof(warning),
-            "%s.%s size changed, may indicate binary incompatibility. "
-            "Expected %zd from C header, got %zd from PyObject",
-            module_name, class_name, size, basicsize);
-        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
-    }
-    return (PyTypeObject *)result;
-bad:
-    Py_XDECREF(result);
-    return NULL;
-}
-#endif
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) goto bad;
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
-    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-    if ((0)) {}
-    view->obj = NULL;
-    Py_DECREF(obj);
-}
-#endif
-
-
-/* MemviewSliceIsContig */
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
-{
-    int i, index, step, start;
-    Py_ssize_t itemsize = mvs.memview->view.itemsize;
-    if (order == 'F') {
-        step = 1;
-        start = 0;
-    } else {
-        step = -1;
-        start = ndim - 1;
-    }
-    for (i = 0; i < ndim; i++) {
-        index = start + step * i;
-        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
-            return 0;
-        itemsize *= mvs.shape[index];
-    }
-    return 1;
-}
-
-/* OverlappingSlices */
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
-                               void **out_start, void **out_end,
-                               int ndim, size_t itemsize)
-{
-    char *start, *end;
-    int i;
-    start = end = slice->data;
-    for (i = 0; i < ndim; i++) {
-        Py_ssize_t stride = slice->strides[i];
-        Py_ssize_t extent = slice->shape[i];
-        if (extent == 0) {
-            *out_start = *out_end = start;
-            return;
-        } else {
-            if (stride > 0)
-                end += stride * (extent - 1);
-            else
-                start += stride * (extent - 1);
-        }
-    }
-    *out_start = start;
-    *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                     __Pyx_memviewslice *slice2,
-                     int ndim, size_t itemsize)
-{
-    void *start1, *end1, *start2, *end2;
-    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
-    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
-    return (start1 < end2) && (start2 < end1);
-}
-
-/* Capsule */
-static CYTHON_INLINE PyObject *
-__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
-{
-    PyObject *cobj;
-#if PY_VERSION_HEX >= 0x02070000
-    cobj = PyCapsule_New(p, sig, NULL);
-#else
-    cobj = PyCObject_FromVoidPtr(p, NULL);
-#endif
-    return cobj;
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
-  union {
-    uint32_t u32;
-    uint8_t u8[4];
-  } S;
-  S.u32 = 0x01020304;
-  return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t <= '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case '?': return "'bool'";
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparseable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number, ndim;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    ndim = ctx->head->field->type->ndim;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-        CYTHON_FALLTHROUGH;
-      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
-            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-        CYTHON_FALLTHROUGH;
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-
-/* TypeInfoCompare */
-  static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
-    int i;
-    if (!a || !b)
-        return 0;
-    if (a == b)
-        return 1;
-    if (a->size != b->size || a->typegroup != b->typegroup ||
-            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
-        if (a->typegroup == 'H' || b->typegroup == 'H') {
-            return a->size == b->size;
-        } else {
-            return 0;
-        }
-    }
-    if (a->ndim) {
-        for (i = 0; i < a->ndim; i++)
-            if (a->arraysize[i] != b->arraysize[i])
-                return 0;
-    }
-    if (a->typegroup == 'S') {
-        if (a->flags != b->flags)
-            return 0;
-        if (a->fields || b->fields) {
-            if (!(a->fields && b->fields))
-                return 0;
-            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
-                __Pyx_StructField *field_a = a->fields + i;
-                __Pyx_StructField *field_b = b->fields + i;
-                if (field_a->offset != field_b->offset ||
-                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
-                    return 0;
-            }
-            return !a->fields[i].type && !b->fields[i].type;
-        }
-    }
-    return 1;
-}
-
-/* MemviewSliceValidateAndInit */
-  static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    if (buf->shape[dim] <= 1)
-        return 1;
-    if (buf->strides) {
-        if (spec & __Pyx_MEMVIEW_CONTIG) {
-            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
-                if (unlikely(buf->strides[dim] != sizeof(void *))) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "Buffer is not indirectly contiguous "
-                                 "in dimension %d.", dim);
-                    goto fail;
-                }
-            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-        if (spec & __Pyx_MEMVIEW_FOLLOW) {
-            Py_ssize_t stride = buf->strides[dim];
-            if (stride < 0)
-                stride = -stride;
-            if (unlikely(stride < buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-    } else {
-        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not contiguous in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not indirect in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(buf->suboffsets)) {
-            PyErr_SetString(PyExc_ValueError,
-                            "Buffer exposes suboffsets but no strides");
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
-{
-    if (spec & __Pyx_MEMVIEW_DIRECT) {
-        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer not compatible with direct access "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    if (spec & __Pyx_MEMVIEW_PTR) {
-        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer is not indirectly accessible "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
-    int i;
-    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = 0; i < ndim; i++) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not fortran contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = ndim - 1; i >- 1; i--) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not C contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj)
-{
-    struct __pyx_memoryview_obj *memview, *new_memview;
-    __Pyx_RefNannyDeclarations
-    Py_buffer *buf;
-    int i, spec = 0, retval = -1;
-    __Pyx_BufFmt_Context ctx;
-    int from_memoryview = __pyx_memoryview_check(original_obj);
-    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
-    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
-                                                            original_obj)->typeinfo)) {
-        memview = (struct __pyx_memoryview_obj *) original_obj;
-        new_memview = NULL;
-    } else {
-        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                            original_obj, buf_flags, 0, dtype);
-        new_memview = memview;
-        if (unlikely(!memview))
-            goto fail;
-    }
-    buf = &memview->view;
-    if (unlikely(buf->ndim != ndim)) {
-        PyErr_Format(PyExc_ValueError,
-                "Buffer has wrong number of dimensions (expected %d, got %d)",
-                ndim, buf->ndim);
-        goto fail;
-    }
-    if (new_memview) {
-        __Pyx_BufFmt_Init(&ctx, stack, dtype);
-        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
-    }
-    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
-        PyErr_Format(PyExc_ValueError,
-                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
-                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
-                     buf->itemsize,
-                     (buf->itemsize > 1) ? "s" : "",
-                     dtype->name,
-                     dtype->size,
-                     (dtype->size > 1) ? "s" : "");
-        goto fail;
-    }
-    if (buf->len > 0) {
-        for (i = 0; i < ndim; i++) {
-            spec = axes_specs[i];
-            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
-                goto fail;
-            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
-                goto fail;
-        }
-        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
-            goto fail;
-    }
-    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
-                                         new_memview != NULL) == -1)) {
-        goto fail;
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    Py_XDECREF(new_memview);
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsdsds_double(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS_RO | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_double, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* CIntFromPyVerify */
-  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_double(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS_RO | writable_flag, 2,
-                                                 &__Pyx_TypeInfo_double, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* MemviewDtypeToObject */
-  static CYTHON_INLINE PyObject *__pyx_memview_get_double(const char *itemp) {
-    return (PyObject *) PyFloat_FromDouble(*(double *) itemp);
-}
-static CYTHON_INLINE int __pyx_memview_set_double(const char *itemp, PyObject *obj) {
-    double value = __pyx_PyFloat_AsDouble(obj);
-    if ((value == (double)-1) && PyErr_Occurred())
-        return 0;
-    *(double *) itemp = value;
-    return 1;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS_RO | writable_flag, 1,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* Declarations */
-  #if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return ::std::complex< float >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return x + y*(__pyx_t_float_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      __pyx_t_float_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-  #if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabsf(b.real) >= fabsf(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                float r = b.imag / b.real;
-                float s = (float)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_float_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            float r = b.real / b.imag;
-            float s = (float)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_float_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            float denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_float_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrtf(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypotf(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-            __pyx_t_float_complex z;
-            float r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    float denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_float(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_float(a, a);
-                        return __Pyx_c_prod_float(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_float(a, a);
-                        return __Pyx_c_prod_float(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if (b.imag == 0) {
-                    z.real = powf(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2f(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_float(a);
-                theta = atan2f(a.imag, a.real);
-            }
-            lnr = logf(r);
-            z_r = expf(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cosf(z_theta);
-            z.imag = z_r * sinf(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* Declarations */
-  #if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return ::std::complex< double >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return x + y*(__pyx_t_double_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      __pyx_t_double_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-/* Arithmetic */
-  #if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    #if 1
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else if (fabs(b.real) >= fabs(b.imag)) {
-            if (b.real == 0 && b.imag == 0) {
-                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
-            } else {
-                double r = b.imag / b.real;
-                double s = (double)(1.0) / (b.real + b.imag * r);
-                return __pyx_t_double_complex_from_parts(
-                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
-            }
-        } else {
-            double r = b.real / b.imag;
-            double s = (double)(1.0) / (b.imag + b.real * r);
-            return __pyx_t_double_complex_from_parts(
-                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
-        }
-    }
-    #else
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        if (b.imag == 0) {
-            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
-        } else {
-            double denom = b.real * b.real + b.imag * b.imag;
-            return __pyx_t_double_complex_from_parts(
-                (a.real * b.real + a.imag * b.imag) / denom,
-                (a.imag * b.real - a.real * b.imag) / denom);
-        }
-    }
-    #endif
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrt(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypot(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-            __pyx_t_double_complex z;
-            double r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    double denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        return __Pyx_c_prod_double(a, a);
-                    case 3:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, a);
-                    case 4:
-                        z = __Pyx_c_prod_double(a, a);
-                        return __Pyx_c_prod_double(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                } else if (b.imag == 0) {
-                    z.real = pow(a.real, b.real);
-                    z.imag = 0;
-                    return z;
-                } else if (a.real > 0) {
-                    r = a.real;
-                    theta = 0;
-                } else {
-                    r = -a.real;
-                    theta = atan2(0.0, -1.0);
-                }
-            } else {
-                r = __Pyx_c_abs_double(a);
-                theta = atan2(a.imag, a.real);
-            }
-            lnr = log(r);
-            z_r = exp(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cos(z_theta);
-            z.imag = z_r * sin(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-/* MemviewSliceCopyTemplate */
-  static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object)
-{
-    __Pyx_RefNannyDeclarations
-    int i;
-    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
-    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
-    Py_buffer *buf = &from_memview->view;
-    PyObject *shape_tuple = NULL;
-    PyObject *temp_int = NULL;
-    struct __pyx_array_obj *array_obj = NULL;
-    struct __pyx_memoryview_obj *memview_obj = NULL;
-    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
-    for (i = 0; i < ndim; i++) {
-        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
-            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
-                                           "indirect dimensions (axis %d)", i);
-            goto fail;
-        }
-    }
-    shape_tuple = PyTuple_New(ndim);
-    if (unlikely(!shape_tuple)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(shape_tuple);
-    for(i = 0; i < ndim; i++) {
-        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
-        if(unlikely(!temp_int)) {
-            goto fail;
-        } else {
-            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
-            temp_int = NULL;
-        }
-    }
-    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
-    if (unlikely(!array_obj)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(array_obj);
-    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                    (PyObject *) array_obj, contig_flag,
-                                    dtype_is_object,
-                                    from_mvs->memview->typeinfo);
-    if (unlikely(!memview_obj))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
-        goto fail;
-    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
-                                                dtype_is_object) < 0))
-        goto fail;
-    goto no_fail;
-fail:
-    __Pyx_XDECREF(new_mvs.memview);
-    new_mvs.memview = NULL;
-    new_mvs.data = NULL;
-no_fail:
-    __Pyx_XDECREF(shape_tuple);
-    __Pyx_XDECREF(temp_int);
-    __Pyx_XDECREF(array_obj);
-    __Pyx_RefNannyFinishContext();
-    return new_mvs;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
-                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
-                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
-                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
-                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
-                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
-                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const char neg_one = (char) -1, const_zero = (char) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(char) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
-                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
-                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
-                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (char) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(char) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(char) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            char val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (char) -1;
-        }
-    } else {
-        char val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to char");
-    return (char) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to char");
-    return (char) -1;
-}
-
-/* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[4], rtversion[4];
-    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
-    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
-        char message[200];
-        PyOS_snprintf(message, sizeof(message),
-                      "compiletime version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION < 3
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        #else
-        if (t->is_unicode | t->is_str) {
-            if (t->intern) {
-                *t->p = PyUnicode_InternFromString(t->s);
-            } else if (t->encoding) {
-                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
-            } else {
-                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
-            }
-        } else {
-            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
-        }
-        #endif
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type %.200s).  "
-                "The ability to return an instance of a strict subclass of int "
-                "is deprecated, and may be removed in a future version of Python.",
-                Py_TYPE(result)->tp_name)) {
-            Py_DECREF(result);
-            return NULL;
-        }
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type %.200s)",
-                 type_name, type_name, Py_TYPE(result)->tp_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    const digit* digits = ((PyLongObject*)b)->ob_digit;
-    const Py_ssize_t size = Py_SIZE(b);
-    if (likely(__Pyx_sst_abs(size) <= 1)) {
-        ival = likely(size) ? digits[0] : 0;
-        if (size == -1) ival = -ival;
-        return ival;
-    } else {
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
-  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
-    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
-  } else if (likely(PyInt_CheckExact(o))) {
-    return PyInt_AS_LONG(o);
-#endif
-  } else {
-    Py_ssize_t ival;
-    PyObject *x;
-    x = PyNumber_Index(o);
-    if (!x) return -1;
-    ival = PyInt_AsLong(x);
-    Py_DECREF(x);
-    return ival;
-  }
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/spaces/Yuliang/ECON/lib/pymafx/utils/smooth_bbox.py b/spaces/Yuliang/ECON/lib/pymafx/utils/smooth_bbox.py
deleted file mode 100644
index a5eb8e431840f974b2cd896d80e8368755f9a682..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/pymafx/utils/smooth_bbox.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# This script is borrowed from https://github.com/akanazawa/human_dynamics/blob/master/src/util/smooth_bbox.py
-# Adhere to their licence to use this script
-
-import numpy as np
-import scipy.signal as signal
-from scipy.ndimage.filters import gaussian_filter1d
-
-
-def get_smooth_bbox_params(kps, vis_thresh=2, kernel_size=11, sigma=3):
-    """
-    Computes smooth bounding box parameters from keypoints:
-      1. Computes bbox by rescaling the person to be around 150 px.
-      2. Linearly interpolates bbox params for missing annotations.
-      3. Median filtering
-      4. Gaussian filtering.
-
-    Recommended thresholds:
-      * detect-and-track: 0
-      * 3DPW: 0.1
-
-    Args:
-        kps (list): List of kps (Nx3) or None.
-        vis_thresh (float): Threshold for visibility.
-        kernel_size (int): Kernel size for median filtering (must be odd).
-        sigma (float): Sigma for gaussian smoothing.
-
-    Returns:
-        Smooth bbox params [cx, cy, scale], start index, end index
-    """
-    bbox_params, start, end = get_all_bbox_params(kps, vis_thresh)
-    smoothed = smooth_bbox_params(bbox_params, kernel_size, sigma)
-    smoothed = np.vstack((np.zeros((start, 3)), smoothed))
-    return smoothed, start, end
-
-
-def kp_to_bbox_param(kp, vis_thresh):
-    """
-    Finds the bounding box parameters from the 2D keypoints.
-
-    Args:
-        kp (Kx3): 2D Keypoints.
-        vis_thresh (float): Threshold for visibility.
-
-    Returns:
-        [center_x, center_y, scale]
-    """
-    if kp is None:
-        return
-    vis = kp[:, 2] > vis_thresh
-    if not np.any(vis):
-        return
-    min_pt = np.min(kp[vis, :2], axis=0)
-    max_pt = np.max(kp[vis, :2], axis=0)
-    person_height = np.linalg.norm(max_pt - min_pt)
-    if person_height < 0.5:
-        return
-    center = (min_pt + max_pt) / 2.
-    scale = 150. / person_height
-    return np.append(center, scale)
-
-
-def get_all_bbox_params(kps, vis_thresh=2):
-    """
-    Finds bounding box parameters for all keypoints.
-
-    Look for sequences in the middle with no predictions and linearly
-    interpolate the bbox params for those
-
-    Args:
-        kps (list): List of kps (Kx3) or None.
-        vis_thresh (float): Threshold for visibility.
-
-    Returns:
-        bbox_params, start_index (incl), end_index (excl)
-    """
-    # keeps track of how many indices in a row with no prediction
-    num_to_interpolate = 0
-    start_index = -1
-    bbox_params = np.empty(shape=(0, 3), dtype=np.float32)
-
-    for i, kp in enumerate(kps):
-        bbox_param = kp_to_bbox_param(kp, vis_thresh=vis_thresh)
-        if bbox_param is None:
-            num_to_interpolate += 1
-            continue
-
-        if start_index == -1:
-            # Found the first index with a prediction!
-            start_index = i
-            num_to_interpolate = 0
-
-        if num_to_interpolate > 0:
-            # Linearly interpolate each param.
-            previous = bbox_params[-1]
-            # This will be 3x(n+2)
-            interpolated = np.array([
-                np.linspace(prev, curr, num_to_interpolate + 2)
-                for prev, curr in zip(previous, bbox_param)
-            ])
-            bbox_params = np.vstack((bbox_params, interpolated.T[1:-1]))
-            num_to_interpolate = 0
-        bbox_params = np.vstack((bbox_params, bbox_param))
-
-    return bbox_params, start_index, i - num_to_interpolate + 1
-
-
-def smooth_bbox_params(bbox_params, kernel_size=11, sigma=8):
-    """
-    Applies median filtering and then gaussian filtering to bounding box
-    parameters.
-
-    Args:
-        bbox_params (Nx3): [cx, cy, scale].
-        kernel_size (int): Kernel size for median filtering (must be odd).
-        sigma (float): Sigma for gaussian smoothing.
-
-    Returns:
-        Smoothed bounding box parameters (Nx3).
-    """
-    smoothed = np.array([signal.medfilt(param, kernel_size) for param in bbox_params.T]).T
-    return np.array([gaussian_filter1d(traj, sigma) for traj in smoothed.T]).T
diff --git a/spaces/Zephyr65/Envvi-Inkpunk-Diffusion/app.py b/spaces/Zephyr65/Envvi-Inkpunk-Diffusion/app.py
deleted file mode 100644
index 542f8368a54f5e7deb116773361809abf413fc6e..0000000000000000000000000000000000000000
--- a/spaces/Zephyr65/Envvi-Inkpunk-Diffusion/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Envvi/Inkpunk-Diffusion").launch()
\ No newline at end of file
diff --git a/spaces/aadnk/faster-whisper-webui/src/vadParallel.py b/spaces/aadnk/faster-whisper-webui/src/vadParallel.py
deleted file mode 100644
index c2323c0b632c34014ac1fe7ac79141b5bd9c5731..0000000000000000000000000000000000000000
--- a/spaces/aadnk/faster-whisper-webui/src/vadParallel.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import multiprocessing
-from queue import Empty
-import threading
-import time
-from src.hooks.progressListener import ProgressListener
-from src.vad import AbstractTranscription, TranscriptionConfig, get_audio_duration
-
-from multiprocessing import Pool, Queue
-
-from typing import Any, Dict, List, Union
-import os
-
-from src.whisper.abstractWhisperContainer import AbstractWhisperCallback
-
-class _ProgressListenerToQueue(ProgressListener):
-    def __init__(self, progress_queue: Queue):
-        self.progress_queue = progress_queue
-        self.progress_total = 0
-        self.prev_progress = 0
-
-    def on_progress(self, current: Union[int, float], total: Union[int, float]):
-        delta = current - self.prev_progress
-        self.prev_progress = current
-        self.progress_total = total
-        self.progress_queue.put(delta)
-
-    def on_finished(self):
-        if self.progress_total > self.prev_progress:
-            delta = self.progress_total - self.prev_progress
-            self.progress_queue.put(delta)
-            self.prev_progress = self.progress_total
-
-class ParallelContext:
-    def __init__(self, num_processes: int = None, auto_cleanup_timeout_seconds: float = None):
-        self.num_processes = num_processes
-        self.auto_cleanup_timeout_seconds = auto_cleanup_timeout_seconds
-        self.lock = threading.Lock()
-
-        self.ref_count = 0
-        self.pool = None
-        self.cleanup_timer = None
-
-    def get_pool(self):
-        # Initialize pool lazily
-        if (self.pool is None):
-            context = multiprocessing.get_context('spawn')
-            self.pool = context.Pool(self.num_processes)
-
-        self.ref_count = self.ref_count + 1
-
-        if (self.auto_cleanup_timeout_seconds is not None):
-            self._stop_auto_cleanup()
-
-        return self.pool
-
-    def return_pool(self, pool):
-        if (self.pool == pool and self.ref_count > 0):
-            self.ref_count = self.ref_count - 1
-
-            if (self.ref_count == 0):
-                if (self.auto_cleanup_timeout_seconds is not None):
-                    self._start_auto_cleanup()
-
-    def _start_auto_cleanup(self):
-        if (self.cleanup_timer is not None):
-            self.cleanup_timer.cancel()
-        self.cleanup_timer = threading.Timer(self.auto_cleanup_timeout_seconds, self._execute_cleanup)
-        self.cleanup_timer.start()
-
-        print("Started auto cleanup of pool in " + str(self.auto_cleanup_timeout_seconds) + " seconds")
-
-    def _stop_auto_cleanup(self):
-        if (self.cleanup_timer is not None):
-            self.cleanup_timer.cancel()
-            self.cleanup_timer = None
-
-            print("Stopped auto cleanup of pool")
-
-    def _execute_cleanup(self):
-        print("Executing cleanup of pool")
-
-        if (self.ref_count == 0):
-            self.close()
-
-    def close(self):
-        self._stop_auto_cleanup()
-
-        if (self.pool is not None):
-            print("Closing pool of " + str(self.num_processes) + " processes")
-            self.pool.close()
-            self.pool.join()
-        self.pool = None
-
-class ParallelTranscriptionConfig(TranscriptionConfig):
-    def __init__(self, device_id: str, override_timestamps, initial_segment_index, copy: TranscriptionConfig = None):
-        super().__init__(copy.non_speech_strategy, copy.segment_padding_left, copy.segment_padding_right, copy.max_silent_period, copy.max_merge_size, copy.max_prompt_window, initial_segment_index)
-        self.device_id = device_id
-        self.override_timestamps = override_timestamps
-
-class ParallelTranscription(AbstractTranscription):
-    # Silero VAD typically takes about 3 seconds per minute, so there's no need to split the chunks 
-    # into smaller segments than 2 minute (min 6 seconds per CPU core)
-    MIN_CPU_CHUNK_SIZE_SECONDS = 2 * 60
-
-    def __init__(self, sampling_rate: int = 16000):
-        super().__init__(sampling_rate=sampling_rate)
-
-    def transcribe_parallel(self, transcription: AbstractTranscription, audio: str, whisperCallable: AbstractWhisperCallback, config: TranscriptionConfig, 
-                            cpu_device_count: int, gpu_devices: List[str], cpu_parallel_context: ParallelContext = None, gpu_parallel_context: ParallelContext = None, 
-                            progress_listener: ProgressListener = None):
-        total_duration = get_audio_duration(audio)
-
-        # First, get the timestamps for the original audio
-        if (cpu_device_count > 1 and not transcription.is_transcribe_timestamps_fast()):
-            merged = self._get_merged_timestamps_parallel(transcription, audio, config, total_duration, cpu_device_count, cpu_parallel_context)
-        else:
-            timestamp_segments = transcription.get_transcribe_timestamps(audio, config, 0, total_duration)
-            merged = transcription.get_merged_timestamps(timestamp_segments, config, total_duration)
-
-        # We must make sure the whisper model is downloaded
-        if (len(gpu_devices) > 1):
-            whisperCallable.model_container.ensure_downloaded()
-
-        # Split into a list for each device
-        # TODO: Split by time instead of by number of chunks
-        merged_split = list(self._split(merged, len(gpu_devices)))
-
-        # Parameters that will be passed to the transcribe function
-        parameters = []
-        segment_index = config.initial_segment_index
-
-        processing_manager = multiprocessing.Manager()
-        progress_queue = processing_manager.Queue()
-
-        for i in range(len(gpu_devices)):
-            # Note that device_segment_list can be empty. But we will still create a process for it,
-            # as otherwise we run the risk of assigning the same device to multiple processes.
-            device_segment_list = list(merged_split[i]) if i < len(merged_split) else []
-            device_id = gpu_devices[i]
-
-            print("Device " + str(device_id) + " (index " + str(i) + ") has " + str(len(device_segment_list)) + " segments")
-
-            # Create a new config with the given device ID
-            device_config = ParallelTranscriptionConfig(device_id, device_segment_list, segment_index, config)
-            segment_index += len(device_segment_list)
-
-            progress_listener_to_queue = _ProgressListenerToQueue(progress_queue)
-            parameters.append([audio, whisperCallable, device_config, progress_listener_to_queue]);
-
-        merged = {
-            'text': '',
-            'segments': [],
-            'language': None
-        }
-
-        created_context = False
-
-        perf_start_gpu = time.perf_counter()
-
-        # Spawn a separate process for each device
-        try:
-            if (gpu_parallel_context is None):
-                gpu_parallel_context = ParallelContext(len(gpu_devices))
-                created_context = True
-
-            # Get a pool of processes
-            pool = gpu_parallel_context.get_pool()
-
-            # Run the transcription in parallel
-            results_async = pool.starmap_async(self.transcribe, parameters)
-            total_progress = 0
-
-            while not results_async.ready():
-                try:
-                    delta = progress_queue.get(timeout=5)  # Set a timeout of 5 seconds
-                except Empty:
-                    continue
-                
-                total_progress += delta
-                if progress_listener is not None:
-                    progress_listener.on_progress(total_progress, total_duration)
-
-            results = results_async.get()
-
-            # Call the finished callback
-            if progress_listener is not None:
-                progress_listener.on_finished()
-
-            for result in results:
-                # Merge the results
-                if (result['text'] is not None):
-                    merged['text'] += result['text']
-                if (result['segments'] is not None):
-                    merged['segments'].extend(result['segments'])
-                if (result['language'] is not None):
-                    merged['language'] = result['language']
-
-        finally:
-            # Return the pool to the context
-            if (gpu_parallel_context is not None):
-                gpu_parallel_context.return_pool(pool)
-            # Always close the context if we created it
-            if (created_context):
-                gpu_parallel_context.close()
-
-        perf_end_gpu = time.perf_counter()
-        print("Parallel transcription took " + str(perf_end_gpu - perf_start_gpu) + " seconds")
-
-        return merged
-
-    def _get_merged_timestamps_parallel(self, transcription: AbstractTranscription, audio: str, config: TranscriptionConfig, total_duration: float, 
-                                       cpu_device_count: int, cpu_parallel_context: ParallelContext = None):
-        parameters = []
-
-        chunk_size = max(total_duration / cpu_device_count, self.MIN_CPU_CHUNK_SIZE_SECONDS)
-        chunk_start = 0
-        cpu_device_id = 0
-
-        perf_start_time = time.perf_counter()
-
-        # Create chunks that will be processed on the CPU
-        while (chunk_start < total_duration):
-            chunk_end = min(chunk_start + chunk_size, total_duration)
-
-            if (chunk_end - chunk_start < 1):
-                # No need to process chunks that are less than 1 second
-                break
-
-            print("Parallel VAD: Executing chunk from " + str(chunk_start) + " to " + 
-                    str(chunk_end) + " on CPU device " + str(cpu_device_id))
-            parameters.append([audio, config, chunk_start, chunk_end]);
-
-            cpu_device_id += 1
-            chunk_start = chunk_end
-
-        created_context = False
-
-        # Spawn a separate process for each device
-        try:
-            if (cpu_parallel_context is None):
-                cpu_parallel_context = ParallelContext(cpu_device_count)
-                created_context = True
-
-            # Get a pool of processes
-            pool = cpu_parallel_context.get_pool()
-
-            # Run the transcription in parallel. Note that transcription must be picklable.
-            results = pool.starmap(transcription.get_transcribe_timestamps, parameters)
-
-            timestamps = []
-
-            # Flatten the results
-            for result in results:
-                timestamps.extend(result)
-
-            merged = transcription.get_merged_timestamps(timestamps, config, total_duration)
-
-            perf_end_time = time.perf_counter()
-            print("Parallel VAD processing took {} seconds".format(perf_end_time - perf_start_time))
-            return merged
-
-        finally:
-            # Return the pool to the context
-            if (cpu_parallel_context is not None):
-                cpu_parallel_context.return_pool(pool)
-            # Always close the context if we created it
-            if (created_context):
-                cpu_parallel_context.close()
-
-    def get_transcribe_timestamps(self, audio: str, config: ParallelTranscriptionConfig, start_time: float, duration: float):
-        return []
-
-    def get_merged_timestamps(self,  timestamps: List[Dict[str, Any]], config: ParallelTranscriptionConfig, total_duration: float):
-        # Override timestamps that will be processed
-        if (config.override_timestamps is not None):
-            print("(get_merged_timestamps) Using override timestamps of size " + str(len(config.override_timestamps)))
-            return config.override_timestamps
-        return super().get_merged_timestamps(timestamps, config, total_duration)
-
-    def transcribe(self, audio: str, whisperCallable: AbstractWhisperCallback, config: ParallelTranscriptionConfig, 
-                   progressListener: ProgressListener = None):
-        # Override device ID the first time
-        if (os.environ.get("INITIALIZED", None) is None):
-            os.environ["INITIALIZED"] = "1"
-
-            # Note that this may be None if the user didn't specify a device. In that case, Whisper will
-            # just use the default GPU device.
-            if (config.device_id is not None):
-                print("Using device " + config.device_id)
-                os.environ["CUDA_VISIBLE_DEVICES"] = config.device_id
-        
-        return super().transcribe(audio, whisperCallable, config, progressListener)
-
-    def _split(self, a, n):
-        """Split a list into n approximately equal parts."""
-        k, m = divmod(len(a), n)
-        return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n))
-
diff --git a/spaces/aaronb/DragGAN/stylegan2/lpips/util.py b/spaces/aaronb/DragGAN/stylegan2/lpips/util.py
deleted file mode 100644
index 4f8b5826cb4ff0614f1ab9bb273af551a3c82311..0000000000000000000000000000000000000000
--- a/spaces/aaronb/DragGAN/stylegan2/lpips/util.py
+++ /dev/null
@@ -1,160 +0,0 @@
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-from skimage.metrics import structural_similarity
-import torch
-
-
-from . import dist_model
-
-class PerceptualLoss(torch.nn.Module):
-    def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric)
-    # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss
-        super(PerceptualLoss, self).__init__()
-        print('Setting up Perceptual loss...')
-        self.use_gpu = use_gpu
-        self.spatial = spatial
-        self.gpu_ids = gpu_ids
-        self.model = dist_model.DistModel()
-        self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids)
-        print('...[%s] initialized'%self.model.name())
-        print('...Done')
-
-    def forward(self, pred, target, normalize=False):
-        """
-        Pred and target are Variables.
-        If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1]
-        If normalize is False, assumes the images are already between [-1,+1]
-
-        Inputs pred and target are Nx3xHxW
-        Output pytorch Variable N long
-        """
-
-        if normalize:
-            target = 2 * target  - 1
-            pred = 2 * pred  - 1
-
-        return self.model.forward(target, pred)
-
-def normalize_tensor(in_feat,eps=1e-10):
-    norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True))
-    return in_feat/(norm_factor+eps)
-
-def l2(p0, p1, range=255.):
-    return .5*np.mean((p0 / range - p1 / range)**2)
-
-def psnr(p0, p1, peak=255.):
-    return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2))
-
-def dssim(p0, p1, range=255.):
-    return (1 - structural_similarity(p0, p1, data_range=range, multichannel=True)) / 2.
-
-def rgb2lab(in_img,mean_cent=False):
-    from skimage import color
-    img_lab = color.rgb2lab(in_img)
-    if(mean_cent):
-        img_lab[:,:,0] = img_lab[:,:,0]-50
-    return img_lab
-
-def tensor2np(tensor_obj):
-    # change dimension of a tensor object into a numpy array
-    return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))
-
-def np2tensor(np_obj):
-     # change dimenion of np array into tensor array
-    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
-    # image tensor to lab tensor
-    from skimage import color
-
-    img = tensor2im(image_tensor)
-    img_lab = color.rgb2lab(img)
-    if(mc_only):
-        img_lab[:,:,0] = img_lab[:,:,0]-50
-    if(to_norm and not mc_only):
-        img_lab[:,:,0] = img_lab[:,:,0]-50
-        img_lab = img_lab/100.
-
-    return np2tensor(img_lab)
-
-def tensorlab2tensor(lab_tensor,return_inbnd=False):
-    from skimage import color
-    import warnings
-    warnings.filterwarnings("ignore")
-
-    lab = tensor2np(lab_tensor)*100.
-    lab[:,:,0] = lab[:,:,0]+50
-
-    rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1)
-    if(return_inbnd):
-        # convert back to lab, see if we match
-        lab_back = color.rgb2lab(rgb_back.astype('uint8'))
-        mask = 1.*np.isclose(lab_back,lab,atol=2.)
-        mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis])
-        return (im2tensor(rgb_back),mask)
-    else:
-        return im2tensor(rgb_back)
-
-def rgb2lab(input):
-    from skimage import color
-    return color.rgb2lab(input / 255.)
-
-def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
-    image_numpy = image_tensor[0].cpu().float().numpy()
-    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
-    return image_numpy.astype(imtype)
-
-def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
-    return torch.Tensor((image / factor - cent)
-                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
-
-def tensor2vec(vector_tensor):
-    return vector_tensor.data.cpu().numpy()[:, :, 0, 0]
-
-def voc_ap(rec, prec, use_07_metric=False):
-    """ ap = voc_ap(rec, prec, [use_07_metric])
-    Compute VOC AP given precision and recall.
-    If use_07_metric is true, uses the
-    VOC 07 11 point method (default:False).
-    """
-    if use_07_metric:
-        # 11 point metric
-        ap = 0.
-        for t in np.arange(0., 1.1, 0.1):
-            if np.sum(rec >= t) == 0:
-                p = 0
-            else:
-                p = np.max(prec[rec >= t])
-            ap = ap + p / 11.
-    else:
-        # correct AP calculation
-        # first append sentinel values at the end
-        mrec = np.concatenate(([0.], rec, [1.]))
-        mpre = np.concatenate(([0.], prec, [0.]))
-
-        # compute the precision envelope
-        for i in range(mpre.size - 1, 0, -1):
-            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
-
-        # to calculate area under PR curve, look for points
-        # where X axis (recall) changes value
-        i = np.where(mrec[1:] != mrec[:-1])[0]
-
-        # and sum (\Delta recall) * prec
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
-    return ap
-
-def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
-# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
-    image_numpy = image_tensor[0].cpu().float().numpy()
-    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
-    return image_numpy.astype(imtype)
-
-def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
-# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
-    return torch.Tensor((image / factor - cent)
-                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))
diff --git a/spaces/aaronb/DragGAN/stylegan2/op/upfirdn2d.cpp b/spaces/aaronb/DragGAN/stylegan2/op/upfirdn2d.cpp
deleted file mode 100644
index 73928ece8150f847d98af65a95685a29fcceecde..0000000000000000000000000000000000000000
--- a/spaces/aaronb/DragGAN/stylegan2/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#include <ATen/ATen.h>
-#include <torch/extension.h>
-
-torch::Tensor upfirdn2d_op(const torch::Tensor &input,
-                           const torch::Tensor &kernel, int up_x, int up_y,
-                           int down_x, int down_y, int pad_x0, int pad_x1,
-                           int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x)                                                          \
-  TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x)                                                    \
-  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x)                                                         \
-  CHECK_CUDA(x);                                                               \
-  CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor &input, const torch::Tensor &kernel,
-                        int up_x, int up_y, int down_x, int down_y, int pad_x0,
-                        int pad_x1, int pad_y0, int pad_y1) {
-  CHECK_INPUT(input);
-  CHECK_INPUT(kernel);
-
-  at::DeviceGuard guard(input.device());
-
-  return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
-                      pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/scnet_mask_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/scnet_mask_head.py
deleted file mode 100644
index 983a2d9db71a3b2b4980996725fdafb0b412b413..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/mask_heads/scnet_mask_head.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from mmdet.models.builder import HEADS
-from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
-from .fcn_mask_head import FCNMaskHead
-
-
-@HEADS.register_module()
-class SCNetMaskHead(FCNMaskHead):
-    """Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
-
-    Args:
-        conv_to_res (bool, optional): if True, change the conv layers to
-            ``SimplifiedBasicBlock``.
-    """
-
-    def __init__(self, conv_to_res=True, **kwargs):
-        super(SCNetMaskHead, self).__init__(**kwargs)
-        self.conv_to_res = conv_to_res
-        if conv_to_res:
-            assert self.conv_kernel_size == 3
-            self.num_res_blocks = self.num_convs // 2
-            self.convs = ResLayer(
-                SimplifiedBasicBlock,
-                self.in_channels,
-                self.conv_out_channels,
-                self.num_res_blocks,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg)
diff --git a/spaces/abrar-adnan/vehicle-recognizer/README.md b/spaces/abrar-adnan/vehicle-recognizer/README.md
deleted file mode 100644
index c1be497adc1ac14f23347efff6764eadfa7fe7f0..0000000000000000000000000000000000000000
--- a/spaces/abrar-adnan/vehicle-recognizer/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-title: Vehicle Recognizer
-emoji: 🌍
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference <br/>
-
-HuggingFace Spaces App URL: https://huggingface.co/spaces/abrar-adnan/vehicle-recognizer
-
-Gradio App URL: https://05490966-9671-4392.gradio.live
\ No newline at end of file
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gui/frame.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gui/frame.py
deleted file mode 100644
index 5e51071a077c496238af98821092521cefbc1e7a..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/gui/frame.py
+++ /dev/null
@@ -1,139 +0,0 @@
-
-class Frame:
-    """The base Frame object, implementing a 2D spatial hash.
-
-    A `Frame` provides an efficient way to handle dispatching
-    keyboard and mouse events to Widgets. This is done by
-    implementing a 2D spatial hash. Only Widgets that are in the
-    vicinity of the mouse pointer will be passed Window events,
-    which can greatly improve efficiency when a large quantity
-    of Widgets are in use.
-    """
-
-    def __init__(self, window, cell_size=64, order=0):
-        """Create an instance of a Frame.
-
-        :Parameters:
-            `window` : `~pyglet.window.Window`
-                The SpatialHash will recieve events from this Window.
-                Appropriate events will be passed on to all added Widgets.
-            `cell_size` : int
-                The cell ("bucket") size for each cell in the hash.
-                Widgets may span multiple cells.
-            `order` : int
-                Widgets use internal OrderedGroups for draw sorting.
-                This is the base value for these Groups.
-        """
-        window.push_handlers(self)
-        self._cell_size = cell_size
-        self._cells = {}
-        self._active_widgets = set()
-        self._order = order
-        self._mouse_pos = 0, 0
-
-    def _hash(self, x, y):
-        """Normalize position to cell"""
-        return int(x / self._cell_size), int(y / self._cell_size)
-
-    def add_widget(self, widget):
-        """Add a Widget to the spatial hash."""
-        min_vec, max_vec = self._hash(*widget.aabb[0:2]), self._hash(*widget.aabb[2:4])
-        for i in range(min_vec[0], max_vec[0] + 1):
-            for j in range(min_vec[1], max_vec[1] + 1):
-                self._cells.setdefault((i, j), set()).add(widget)
-        widget.update_groups(self._order)
-
-    def remove_widget(self, widget):
-        """Remove a Widget from the spatial hash."""
-        min_vec, max_vec = self._hash(*widget.aabb[0:2]), self._hash(*widget.aabb[2:4])
-        for i in range(min_vec[0], max_vec[0] + 1):
-            for j in range(min_vec[1], max_vec[1] + 1):
-                self._cells.get((i, j)).remove(widget)
-
-    def on_mouse_press(self, x, y, buttons, modifiers):
-        """Pass the event to any widgets within range of the mouse"""
-        for widget in self._cells.get(self._hash(x, y), set()):
-            widget.on_mouse_press(x, y, buttons, modifiers)
-            self._active_widgets.add(widget)
-
-    def on_mouse_release(self, x, y, buttons, modifiers):
-        """Pass the event to any widgets that are currently active"""
-        for widget in self._active_widgets:
-            widget.on_mouse_release(x, y, buttons, modifiers)
-        self._active_widgets.clear()
-
-    def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers):
-        """Pass the event to any widgets that are currently active"""
-        for widget in self._active_widgets:
-            widget.on_mouse_drag(x, y, dx, dy, buttons, modifiers)
-        self._mouse_pos = x, y
-
-    def on_mouse_scroll(self, x, y, index, direction):
-        """Pass the event to any widgets within range of the mouse"""
-        for widget in self._cells.get(self._hash(x, y), set()):
-            widget.on_mouse_scroll(x, y, index, direction)
-
-    def on_mouse_motion(self, x, y, dx, dy):
-        """Pass the event to any widgets within range of the mouse"""
-        for widget in self._cells.get(self._hash(x, y), set()):
-            widget.on_mouse_motion(x, y, dx, dy)
-        self._mouse_pos = x, y
-
-    def on_text(self, text):
-        """Pass the event to any widgets within range of the mouse"""
-        for widget in self._cells.get(self._hash(*self._mouse_pos), set()):
-            widget.on_text(text)
-
-    def on_text_motion(self, motion):
-        """Pass the event to any widgets within range of the mouse"""
-        for widget in self._cells.get(self._hash(*self._mouse_pos), set()):
-            widget.on_text_motion(motion)
-
-    def on_text_motion_select(self, motion):
-        """Pass the event to any widgets within range of the mouse"""
-        for widget in self._cells.get(self._hash(*self._mouse_pos), set()):
-            widget.on_text_motion_select(motion)
-
-
-class MovableFrame(Frame):
-    """A Frame that allows Widget repositioning.
-
-    When a specified modifier key is held down, Widgets can be
-    repositioned by dragging them. Examples of modifier keys are
-    Ctrl, Alt, Shift. These are defined in the `pyglet.window.key`
-    module, and start witih `MOD_`. For example::
-
-        from pyglet.window.key import MOD_CTRL
-
-        frame = pyglet.gui.frame.MovableFrame(mywindow, modifier=MOD_CTRL)
-
-    For more information, see the `pyglet.window.key` submodule
-    API documentation.
-    """
-
-    def __init__(self, window, order=0, modifier=0):
-        super().__init__(window, order=order)
-        self._modifier = modifier
-        self._moving_widgets = set()
-
-    def on_mouse_press(self, x, y, buttons, modifiers):
-        if self._modifier & modifiers > 0:
-            for widget in self._cells.get(self._hash(x, y), set()):
-                if widget._check_hit(x, y):
-                    self._moving_widgets.add(widget)
-            for widget in self._moving_widgets:
-                self.remove_widget(widget)
-        else:
-            super().on_mouse_press(x, y, buttons, modifiers)
-
-    def on_mouse_release(self, x, y, buttons, modifiers):
-        for widget in self._moving_widgets:
-            self.add_widget(widget)
-        self._moving_widgets.clear()
-        super().on_mouse_release(x, y, buttons, modifiers)
-
-    def on_mouse_drag(self, x, y, dx, dy, buttons, modifiers):
-        for widget in self._moving_widgets:
-            wx, wy = widget.position
-            widget.position = wx + dx, wy + dy
-        super().on_mouse_drag(x, y, dx, dy, buttons, modifiers)
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/media/codecs/wave.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/media/codecs/wave.py
deleted file mode 100644
index e3ea81c94b636ec7c1e7fcddd5c8156f52f0bbd6..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/media/codecs/wave.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Decoder for RIFF Wave files, using the standard library wave module.
-"""
-
-import wave
-
-from pyglet.util import DecodeException
-from .base import StreamingSource, AudioData, AudioFormat, StaticSource
-from . import MediaEncoder, MediaDecoder
-
-
-class WAVEDecodeException(DecodeException):
-    pass
-
-
-class WaveSource(StreamingSource):
-    def __init__(self, filename, file=None):
-        if file is None:
-            file = open(filename, 'rb')
-            self._file = file
-
-        try:
-            self._wave = wave.open(file)
-        except wave.Error as e:
-            raise WAVEDecodeException(e)
-
-        nchannels, sampwidth, framerate, nframes, comptype, compname = self._wave.getparams()
-
-        self.audio_format = AudioFormat(channels=nchannels, sample_size=sampwidth * 8, sample_rate=framerate)
-
-        self._bytes_per_frame = nchannels * sampwidth
-        self._duration = nframes / framerate
-        self._duration_per_frame = self._duration / nframes
-        self._num_frames = nframes
-
-        self._wave.rewind()
-
-    def __del__(self):
-        if hasattr(self, '_file'):
-            self._file.close()
-
-    def get_audio_data(self, num_bytes, compensation_time=0.0):
-        num_frames = max(1, num_bytes // self._bytes_per_frame)
-
-        data = self._wave.readframes(num_frames)
-        if not data:
-            return None
-
-        timestamp = self._wave.tell() / self.audio_format.sample_rate
-        duration = num_frames / self.audio_format.sample_rate
-        return AudioData(data, len(data), timestamp, duration, [])
-
-    def seek(self, timestamp):
-        timestamp = max(0.0, min(timestamp, self._duration))
-        position = int(timestamp / self._duration_per_frame)
-        self._wave.setpos(position)
-
-
-#########################################
-#   Decoder class:
-#########################################
-
-class WaveDecoder(MediaDecoder):
-
-    def get_file_extensions(self):
-        return '.wav', '.wave', '.riff'
-
-    def decode(self, filename, file, streaming=True):
-        if streaming:
-            return WaveSource(filename, file)
-        else:
-            return StaticSource(WaveSource(filename, file))
-
-
-class WaveEncoder(MediaEncoder):
-
-    def get_file_extensions(self):
-        return '.wav', '.wave', '.riff'
-
-    def encode(self, source, filename, file):
-        """Save the Source to disk as a standard RIFF Wave.
-
-        A standard RIFF wave header will be added to the raw PCM
-        audio data when it is saved to disk.
-
-        :Parameters:
-            `filename` : str
-                The file name to save as.
-            `file` : file-like object
-                A file-like object, opened with mode 'wb'.
-
-        """
-        opened_file = None
-        if file is None:
-            file = open(filename, 'wb')
-            opened_file = True
-
-        source.seek(0)
-        wave_writer = wave.open(file, mode='wb')
-        wave_writer.setnchannels(source.audio_format.channels)
-        wave_writer.setsampwidth(source.audio_format.bytes_per_sample)
-        wave_writer.setframerate(source.audio_format.sample_rate)
-        # Save the data in 1-second chunks:
-        chunksize = source.audio_format.bytes_per_second
-        audiodata = source.get_audio_data(chunksize)
-        while audiodata:
-            wave_writer.writeframes(audiodata.data)
-            audiodata = source.get_audio_data(chunksize)
-        else:
-            wave_writer.close()
-            if opened_file:
-                file.close()
-
-
-def get_decoders():
-    return [WaveDecoder()]
-
-
-def get_encoders():
-    return [WaveEncoder()]
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/window/xlib/__init__.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/window/xlib/__init__.py
deleted file mode 100644
index c1de9d7c90b48e7d34ef25b3ae7182ddc76386ff..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/window/xlib/__init__.py
+++ /dev/null
@@ -1,1516 +0,0 @@
-import unicodedata
-import urllib.parse
-from ctypes import *
-from functools import lru_cache
-
-import pyglet
-from pyglet.window import WindowException, MouseCursorException
-from pyglet.window import MouseCursor, DefaultMouseCursor, ImageMouseCursor
-from pyglet.window import BaseWindow, _PlatformEventHandler, _ViewEventHandler
-
-from pyglet.window import key
-from pyglet.window import mouse
-from pyglet.event import EventDispatcher
-
-from pyglet.canvas.xlib import XlibCanvas
-
-from pyglet.libs.x11 import xlib
-from pyglet.libs.x11 import cursorfont
-
-from pyglet.util import asbytes
-
-try:
-    from pyglet.libs.x11 import xsync
-    _have_xsync = True
-except ImportError:
-    _have_xsync = False
-
-
-class mwmhints_t(Structure):
-    _fields_ = [
-        ('flags', c_uint32),
-        ('functions', c_uint32),
-        ('decorations', c_uint32),
-        ('input_mode', c_int32),
-        ('status', c_uint32)
-    ]
-
-
-# XXX: wraptypes can't parse the header this function is in yet
-XkbSetDetectableAutoRepeat = xlib._lib.XkbSetDetectableAutoRepeat
-XkbSetDetectableAutoRepeat.restype = c_int
-XkbSetDetectableAutoRepeat.argtypes = [POINTER(xlib.Display), c_int, POINTER(c_int)]
-_can_detect_autorepeat = None
-
-XA_CARDINAL = 6  # Xatom.h:14
-XA_ATOM = 4
-
-XDND_VERSION = 5
-
-# Do we have the November 2000 UTF8 extension?
-_have_utf8 = hasattr(xlib._lib, 'Xutf8TextListToTextProperty')
-
-# symbol,ctrl -> motion mapping
-_motion_map = {
-    (key.UP, False):        key.MOTION_UP,
-    (key.RIGHT, False):     key.MOTION_RIGHT,
-    (key.DOWN, False):      key.MOTION_DOWN,
-    (key.LEFT, False):      key.MOTION_LEFT,
-    (key.RIGHT, True):      key.MOTION_NEXT_WORD,
-    (key.LEFT, True):       key.MOTION_PREVIOUS_WORD,
-    (key.HOME, False):      key.MOTION_BEGINNING_OF_LINE,
-    (key.END, False):       key.MOTION_END_OF_LINE,
-    (key.PAGEUP, False):    key.MOTION_PREVIOUS_PAGE,
-    (key.PAGEDOWN, False):  key.MOTION_NEXT_PAGE,
-    (key.HOME, True):       key.MOTION_BEGINNING_OF_FILE,
-    (key.END, True):        key.MOTION_END_OF_FILE,
-    (key.BACKSPACE, False): key.MOTION_BACKSPACE,
-    (key.DELETE, False):    key.MOTION_DELETE,
-    (key.C, True):          key.MOTION_COPY,
-    (key.V, True):          key.MOTION_PASTE
-}
-
-
-class XlibException(WindowException):
-    """An X11-specific exception.  This exception is probably a programming
-    error in pyglet."""
-    pass
-
-
-class XlibMouseCursor(MouseCursor):
-    gl_drawable = False
-    hw_drawable = True
-
-    def __init__(self, cursor):
-        self.cursor = cursor
-
-
-# Platform event data is single item, so use platform event handler directly.
-XlibEventHandler = _PlatformEventHandler
-ViewEventHandler = _ViewEventHandler
-
-
-class XlibWindow(BaseWindow):
-    _x_display = None               # X display connection
-    _x_screen_id = None             # X screen index
-    _x_ic = None                    # X input context
-    _window = None                  # Xlib window handle
-    _override_redirect = False
-
-    _x = 0
-    _y = 0                          # Last known window position
-    _mouse_exclusive_client = None  # x,y of "real" mouse during exclusive
-    _mouse_buttons = [False] * 6    # State of each xlib button
-    _active = True
-    _applied_mouse_exclusive = False
-    _applied_keyboard_exclusive = False
-    _mapped = False
-    _lost_context = False
-    _lost_context_state = False
-
-    _enable_xsync = False
-    _current_sync_value = None
-    _current_sync_valid = False
-
-    _default_event_mask = (0x1ffffff & ~xlib.PointerMotionHintMask
-                                     & ~xlib.ResizeRedirectMask
-                                     & ~xlib.SubstructureNotifyMask)
-
-    def __init__(self, *args, **kwargs):
-        # Bind event handlers
-        self._event_handlers = {}
-        self._view_event_handlers = {}
-        for name in self._platform_event_names:
-            if not hasattr(self, name):
-                continue
-            func = getattr(self, name)
-            for message in func._platform_event_data:
-                if hasattr(func, '_view'):
-                    self._view_event_handlers[message] = func
-                else:
-                    self._event_handlers[message] = func
-
-        super(XlibWindow, self).__init__(*args, **kwargs)
-
-        global _can_detect_autorepeat
-        if _can_detect_autorepeat is None:
-            supported_rtrn = c_int()
-            _can_detect_autorepeat = XkbSetDetectableAutoRepeat(self.display._display, c_int(1),
-                                                                byref(supported_rtrn))
-        if _can_detect_autorepeat:
-            self.pressed_keys = set()
-
-    def _recreate(self, changes):
-        # If flipping to/from fullscreen, need to recreate the window.  (This
-        # is the case with both override_redirect method and
-        # _NET_WM_STATE_FULLSCREEN).
-        #
-        # A possible improvement could be to just hide the top window,
-        # destroy the GLX window, and reshow it again when leaving fullscreen.
-        # This would prevent the floating window from being moved by the
-        # WM.
-        if 'fullscreen' in changes or 'resizable' in changes:
-            # clear out the GLX context
-            self.context.detach()
-            xlib.XDestroyWindow(self._x_display, self._window)
-            del self.display._window_map[self._window]
-            del self.display._window_map[self._view]
-            self._window = None
-            self._mapped = False
-
-        # TODO: detect state loss only by examining context share.
-        if 'context' in changes:
-            self._lost_context = True
-            self._lost_context_state = True
-
-        self._create()
-
-    def _create_xdnd_atoms(self, display):
-        self._xdnd_atoms = {
-            'XdndAware' : xlib.XInternAtom(display, asbytes('XdndAware'), False),
-            'XdndEnter' : xlib.XInternAtom(display, asbytes('XdndEnter'), False),
-            'XdndTypeList' : xlib.XInternAtom(display, asbytes('XdndTypeList'), False),
-            'XdndDrop' : xlib.XInternAtom(display, asbytes('XdndDrop'), False),
-            'XdndFinished' : xlib.XInternAtom(display, asbytes('XdndFinished'), False),
-            'XdndSelection' : xlib.XInternAtom(display, asbytes('XdndSelection'), False),
-            'XdndPosition' : xlib.XInternAtom(display, asbytes('XdndPosition'), False),
-            'XdndStatus' : xlib.XInternAtom(display, asbytes('XdndStatus'), False),
-            'XdndActionCopy' : xlib.XInternAtom(display, asbytes('XdndActionCopy'), False),
-            'text/uri-list' : xlib.XInternAtom(display, asbytes("text/uri-list"), False)
-        }
-
-    def _create(self):
-        # Unmap existing window if necessary while we fiddle with it.
-        if self._window and self._mapped:
-            self._unmap()
-
-        self._x_display = self.display._display
-        self._x_screen_id = self.display.x_screen
-
-        # Create X window if not already existing.
-        if not self._window:
-            root = xlib.XRootWindow(self._x_display, self._x_screen_id)
-
-            visual_info = self.config.get_visual_info()
-            if self.style in ('transparent', 'overlay'):
-                xlib.XMatchVisualInfo(self._x_display, self._x_screen_id, 32, xlib.TrueColor, visual_info)
-
-            visual = visual_info.visual
-            visual_id = xlib.XVisualIDFromVisual(visual)
-            default_visual = xlib.XDefaultVisual(self._x_display, self._x_screen_id)
-            default_visual_id = xlib.XVisualIDFromVisual(default_visual)
-            window_attributes = xlib.XSetWindowAttributes()
-            if visual_id != default_visual_id:
-                window_attributes.colormap = xlib.XCreateColormap(self._x_display, root,
-                                                                  visual, xlib.AllocNone)
-            else:
-                window_attributes.colormap = xlib.XDefaultColormap(self._x_display,
-                                                                   self._x_screen_id)
-            window_attributes.bit_gravity = xlib.StaticGravity
-
-            # Issue 287: Compiz on Intel/Mesa doesn't draw window decoration
-            #            unless CWBackPixel is given in mask.  Should have
-            #            no effect on other systems, so it's set
-            #            unconditionally.
-            mask = xlib.CWColormap | xlib.CWBitGravity | xlib.CWBackPixel
-
-            if self.style in ('transparent', 'overlay'):
-                mask |= xlib.CWBorderPixel
-                window_attributes.border_pixel = 0
-                window_attributes.background_pixel = 0
-
-            if self._fullscreen:
-                width, height = self.screen.width, self.screen.height
-                self._view_x = (width - self._width) // 2
-                self._view_y = (height - self._height) // 2
-            else:
-                width, height = self._width, self._height
-                self._view_x = self._view_y = 0
-
-            self._window = xlib.XCreateWindow(self._x_display, root,
-                                              0, 0, width, height, 0, visual_info.depth,
-                                              xlib.InputOutput, visual, mask,
-                                              byref(window_attributes))
-            self._view = xlib.XCreateWindow(self._x_display,
-                                            self._window, self._view_x, self._view_y,
-                                            self._width, self._height, 0, visual_info.depth,
-                                            xlib.InputOutput, visual, mask,
-                                            byref(window_attributes))
-            xlib.XMapWindow(self._x_display, self._view)
-            xlib.XSelectInput(self._x_display, self._view, self._default_event_mask)
-
-            self.display._window_map[self._window] = self.dispatch_platform_event
-            self.display._window_map[self._view] = self.dispatch_platform_event_view
-
-            self.canvas = XlibCanvas(self.display, self._view)
-
-            self.context.attach(self.canvas)
-            self.context.set_vsync(self._vsync) # XXX ?
-
-            # Setting null background pixmap disables drawing the background,
-            # preventing flicker while resizing (in theory).
-            #
-            # Issue 287: Compiz on Intel/Mesa doesn't draw window decoration if
-            #            this is called.  As it doesn't seem to have any
-            #            effect anyway, it's just commented out.
-            # xlib.XSetWindowBackgroundPixmap(self._x_display, self._window, 0)
-
-            self._enable_xsync = (pyglet.options['xsync'] and
-                                  self.display._enable_xsync and
-                                  self.config.double_buffer)
-
-            # Set supported protocols
-            protocols = []
-            protocols.append(xlib.XInternAtom(self._x_display, asbytes('WM_DELETE_WINDOW'), False))
-            if self._enable_xsync:
-                protocols.append(xlib.XInternAtom(self._x_display,
-                                                  asbytes('_NET_WM_SYNC_REQUEST'),
-                                                  False))
-            protocols = (c_ulong * len(protocols))(*protocols)
-            xlib.XSetWMProtocols(self._x_display, self._window, protocols, len(protocols))
-
-            # Create window resize sync counter
-            if self._enable_xsync:
-                value = xsync.XSyncValue()
-                self._sync_counter = xlib.XID(xsync.XSyncCreateCounter(self._x_display, value))
-                atom = xlib.XInternAtom(self._x_display,
-                                        asbytes('_NET_WM_SYNC_REQUEST_COUNTER'), False)
-                ptr = pointer(self._sync_counter)
-
-                xlib.XChangeProperty(self._x_display, self._window,
-                                     atom, XA_CARDINAL, 32,
-                                     xlib.PropModeReplace,
-                                     cast(ptr, POINTER(c_ubyte)), 1)
-
-            # Atoms required for Xdnd
-            self._create_xdnd_atoms(self._x_display)
-
-            # Support for drag and dropping files needs to be enabled.
-            if self._file_drops:
-                # Some variables set because there are 4 different drop events that need shared data.
-                self._xdnd_source = None
-                self._xdnd_version = None
-                self._xdnd_format = None
-                self._xdnd_position = (0, 0)  # For position callback.
-
-                VERSION = c_ulong(int(XDND_VERSION))
-                ptr = pointer(VERSION)
-
-                xlib.XChangeProperty(self._x_display, self._window,
-                                     self._xdnd_atoms['XdndAware'], XA_ATOM, 32,
-                                     xlib.PropModeReplace,
-                                     cast(ptr, POINTER(c_ubyte)), 1)
-
-        # Set window attributes
-        attributes = xlib.XSetWindowAttributes()
-        attributes_mask = 0
-
-        self._override_redirect = False
-        if self._fullscreen:
-            if pyglet.options['xlib_fullscreen_override_redirect']:
-                # Try not to use this any more, it causes problems; disabled
-                # by default in favour of _NET_WM_STATE_FULLSCREEN.
-                attributes.override_redirect = self._fullscreen
-                attributes_mask |= xlib.CWOverrideRedirect
-                self._override_redirect = True
-            else:
-                self._set_wm_state('_NET_WM_STATE_FULLSCREEN')
-
-        if self._fullscreen:
-            xlib.XMoveResizeWindow(self._x_display, self._window,
-                                   self.screen.x, self.screen.y,
-                                   self.screen.width, self.screen.height)
-        else:
-            xlib.XResizeWindow(self._x_display, self._window, self._width, self._height)
-
-        xlib.XChangeWindowAttributes(self._x_display, self._window,
-                                     attributes_mask, byref(attributes))
-
-        # Set style
-        styles = {
-            self.WINDOW_STYLE_DEFAULT: '_NET_WM_WINDOW_TYPE_NORMAL',
-            self.WINDOW_STYLE_DIALOG: '_NET_WM_WINDOW_TYPE_DIALOG',
-            self.WINDOW_STYLE_TOOL: '_NET_WM_WINDOW_TYPE_UTILITY',
-        }
-        if self._style in styles:
-            self._set_atoms_property('_NET_WM_WINDOW_TYPE', (styles[self._style],))
-        elif self._style in (self.WINDOW_STYLE_BORDERLESS, self.WINDOW_STYLE_OVERLAY):
-            MWM_HINTS_DECORATIONS = 1 << 1
-            PROP_MWM_HINTS_ELEMENTS = 5
-            mwmhints = mwmhints_t()
-            mwmhints.flags = MWM_HINTS_DECORATIONS
-            mwmhints.decorations = 0
-            name = xlib.XInternAtom(self._x_display, asbytes('_MOTIF_WM_HINTS'), False)
-            xlib.XChangeProperty(self._x_display, self._window,
-                                 name, name, 32, xlib.PropModeReplace,
-                                 cast(pointer(mwmhints), POINTER(c_ubyte)),
-                                 PROP_MWM_HINTS_ELEMENTS)
-
-        # Set resizeable
-        if not self._resizable and not self._fullscreen:
-            self.set_minimum_size(self._width, self._height)
-            self.set_maximum_size(self._width, self._height)
-
-        # Set caption
-        self.set_caption(self._caption)
-
-        # Set WM_CLASS for modern desktop environments
-        self.set_wm_class(self._caption)
-
-        # this is supported by some compositors (ie gnome-shell), and more to come
-        # see: http://standards.freedesktop.org/wm-spec/wm-spec-latest.html#idp6357888
-        _NET_WM_BYPASS_COMPOSITOR_HINT_ON = c_ulong(int(self._fullscreen))
-        name = xlib.XInternAtom(self._x_display, asbytes('_NET_WM_BYPASS_COMPOSITOR'), False)
-        ptr = pointer(_NET_WM_BYPASS_COMPOSITOR_HINT_ON)
-
-        xlib.XChangeProperty(self._x_display, self._window,
-                             name, XA_CARDINAL, 32,
-                             xlib.PropModeReplace,
-                             cast(ptr, POINTER(c_ubyte)), 1)
-
-        # Create input context.  A good but very outdated reference for this
-        # is http://www.sbin.org/doc/Xlib/chapt_11.html
-        if _have_utf8 and not self._x_ic:
-            if not self.display._x_im:
-                xlib.XSetLocaleModifiers(asbytes('@im=none'))
-                self.display._x_im = xlib.XOpenIM(self._x_display, None, None, None)
-
-            xlib.XFlush(self._x_display)
-
-            # Need to set argtypes on this function because its vararg,
-            # and ctypes guesses wrong.
-            xlib.XCreateIC.argtypes = [xlib.XIM,
-                                       c_char_p, c_int,
-                                       c_char_p, xlib.Window,
-                                       c_char_p, xlib.Window,
-                                       c_void_p]
-            self._x_ic = xlib.XCreateIC(self.display._x_im,
-                                        asbytes('inputStyle'),
-                                        xlib.XIMPreeditNothing | xlib.XIMStatusNothing,
-                                        asbytes('clientWindow'), self._window,
-                                        asbytes('focusWindow'), self._window,
-                                        None)
-
-            filter_events = c_ulong()
-            xlib.XGetICValues(self._x_ic, 'filterEvents', byref(filter_events), None)
-            self._default_event_mask |= filter_events.value
-            xlib.XSetICFocus(self._x_ic)
-
-        self.switch_to()
-        if self._visible:
-            self.set_visible(True)
-
-        self.set_mouse_platform_visible()
-        self._applied_mouse_exclusive = None
-        self._update_exclusivity()
-
-    def _map(self):
-        if self._mapped:
-            return
-
-        # Map the window, wait for map event before continuing.
-        xlib.XSelectInput(self._x_display, self._window, xlib.StructureNotifyMask)
-        xlib.XMapRaised(self._x_display, self._window)
-        e = xlib.XEvent()
-        while True:
-            xlib.XNextEvent(self._x_display, e)
-            if e.type == xlib.ConfigureNotify:
-                self._width = e.xconfigure.width
-                self._height = e.xconfigure.height
-            elif e.type == xlib.MapNotify:
-                break
-        xlib.XSelectInput(self._x_display, self._window, self._default_event_mask)
-        self._mapped = True
-
-        if self._override_redirect:
-            # Possibly an override_redirect issue.
-            self.activate()
-
-        self._update_view_size()
-
-        self.dispatch_event('on_resize', self._width, self._height)
-        self.dispatch_event('on_show')
-        self.dispatch_event('on_expose')
-
-    def _unmap(self):
-        if not self._mapped:
-            return
-
-        xlib.XSelectInput(self._x_display, self._window, xlib.StructureNotifyMask)
-        xlib.XUnmapWindow(self._x_display, self._window)
-        e = xlib.XEvent()
-        while True:
-            xlib.XNextEvent(self._x_display, e)
-            if e.type == xlib.UnmapNotify:
-                break
-
-        xlib.XSelectInput(self._x_display, self._window, self._default_event_mask)
-        self._mapped = False
-
-    def _get_root(self):
-        attributes = xlib.XWindowAttributes()
-        xlib.XGetWindowAttributes(self._x_display, self._window, byref(attributes))
-        return attributes.root
-
-    def _is_reparented(self):
-        root = c_ulong()
-        parent = c_ulong()
-        children = pointer(c_ulong())
-        n_children = c_uint()
-
-        xlib.XQueryTree(self._x_display, self._window,
-                        byref(root), byref(parent), byref(children),
-                        byref(n_children))
-
-        return root.value != parent.value
-
-    def close(self):
-        if not self._window:
-            return
-
-        self.context.destroy()
-        self._unmap()
-        if self._window:
-            xlib.XDestroyWindow(self._x_display, self._window)
-
-        del self.display._window_map[self._window]
-        del self.display._window_map[self._view]
-        self._window = None
-
-        self._view_event_handlers.clear()
-        self._event_handlers.clear()
-
-        if _have_utf8:
-            xlib.XDestroyIC(self._x_ic)
-            self._x_ic = None
-
-        super(XlibWindow, self).close()
-
-    def switch_to(self):
-        if self.context:
-            self.context.set_current()
-
-    def flip(self):
-        self.draw_mouse_cursor()
-
-        # TODO canvas.flip?
-        if self.context:
-            self.context.flip()
-
-        self._sync_resize()
-
-    def set_vsync(self, vsync: bool) -> None:
-        if pyglet.options['vsync'] is not None:
-            vsync = pyglet.options['vsync']
-
-        super().set_vsync(vsync)
-        self.context.set_vsync(vsync)
-
-    def set_caption(self, caption):
-        if caption is None:
-            caption = ''
-        self._caption = caption
-        self._set_text_property('WM_NAME', caption, allow_utf8=False)
-        self._set_text_property('WM_ICON_NAME', caption, allow_utf8=False)
-        self._set_text_property('_NET_WM_NAME', caption)
-        self._set_text_property('_NET_WM_ICON_NAME', caption)
-
-    def set_wm_class(self, name):
-        # WM_CLASS can only contain Ascii characters
-        try:
-            name = name.encode('ascii')
-        except UnicodeEncodeError:
-            name = "pyglet"
-
-        hint = xlib.XAllocClassHint()
-        hint.contents.res_class = asbytes(name)
-        hint.contents.res_name = asbytes(name.lower())
-        xlib.XSetClassHint(self._x_display, self._window, hint.contents)
-        xlib.XFree(hint)
-
-    def get_caption(self):
-        return self._caption
-
-    def set_size(self, width: int, height: int) -> None:
-        super().set_size(width, height)
-        if not self._resizable:
-            self.set_minimum_size(width, height)
-            self.set_maximum_size(width, height)
-        xlib.XResizeWindow(self._x_display, self._window, width, height)
-        self._update_view_size()
-        self.dispatch_event('on_resize', width, height)
-
-    def _update_view_size(self):
-        xlib.XResizeWindow(self._x_display, self._view, self._width, self._height)
-
-    def set_location(self, x, y):
-        if self._is_reparented():
-            # Assume the window manager has reparented our top-level window
-            # only once, in which case attributes.x/y give the offset from
-            # the frame to the content window.  Better solution would be
-            # to use _NET_FRAME_EXTENTS, where supported.
-            attributes = xlib.XWindowAttributes()
-            xlib.XGetWindowAttributes(self._x_display, self._window, byref(attributes))
-            # XXX at least under KDE's WM these attrs are both 0
-            x -= attributes.x
-            y -= attributes.y
-        xlib.XMoveWindow(self._x_display, self._window, x, y)
-
-    def get_location(self):
-        child = xlib.Window()
-        x = c_int()
-        y = c_int()
-        xlib.XTranslateCoordinates(self._x_display,
-                                   self._window,
-                                   self._get_root(),
-                                   0, 0,
-                                   byref(x),
-                                   byref(y),
-                                   byref(child))
-        return x.value, y.value
-
-    def activate(self):
-        # Issue 218
-        if self._x_display and self._window:
-            xlib.XSetInputFocus(self._x_display, self._window, xlib.RevertToParent, xlib.CurrentTime)
-
-    def set_visible(self, visible: bool = True) -> None:
-        super().set_visible(visible)
-
-        if visible:
-            self._map()
-        else:
-            self._unmap()
-
-    def set_minimum_size(self, width: int, height: int) -> None:
-        super().set_minimum_size(width, height)
-        self._set_wm_normal_hints()
-
-    def set_maximum_size(self, width: int, height: int) -> None:
-        super().set_maximum_size(width, height)
-        self._set_wm_normal_hints()
-
-    def minimize(self):
-        xlib.XIconifyWindow(self._x_display, self._window, self._x_screen_id)
-
-    def maximize(self):
-        self._set_wm_state('_NET_WM_STATE_MAXIMIZED_HORZ',
-                           '_NET_WM_STATE_MAXIMIZED_VERT')
-
-    @staticmethod
-    def _downsample_1bit(pixelarray):
-        byte_list = []
-        value = 0
-
-        for i, pixel in enumerate(pixelarray):
-            index = i % 8
-            if pixel:
-                value |= 1 << index
-            if index == 7:
-                byte_list.append(value)
-                value = 0
-
-        return bytes(byte_list)
-
-    @lru_cache()
-    def _create_cursor_from_image(self, cursor):
-        """Creates platform cursor from an ImageCursor instance."""
-        texture = cursor.texture
-        width = texture.width
-        height = texture.height
-
-        alpha_luma_bytes = texture.get_image_data().get_data('AL', -width * 2)
-        mask_data = self._downsample_1bit(alpha_luma_bytes[0::2])
-        bmp_data = self._downsample_1bit(alpha_luma_bytes[1::2])
-
-        bitmap = xlib.XCreateBitmapFromData(self._x_display, self._window, bmp_data, width, height)
-        mask = xlib.XCreateBitmapFromData(self._x_display, self._window, mask_data, width, height)
-        white = xlib.XColor(red=65535, green=65535, blue=65535)     # background color
-        black = xlib.XColor()                                       # foreground color
-
-        # hot_x/y must be within the image dimension, or the cursor will not display:
-        hot_x = min(max(0, int(self._mouse_cursor.hot_x)), width)
-        hot_y = min(max(0, int(height - self._mouse_cursor.hot_y)), height)
-        cursor = xlib.XCreatePixmapCursor(self._x_display, bitmap, mask, white, black, hot_x, hot_y)
-        xlib.XFreePixmap(self._x_display, bitmap)
-        xlib.XFreePixmap(self._x_display, mask)
-
-        return cursor
-
-    def set_mouse_platform_visible(self, platform_visible=None):
-        if not self._window:
-            return
-        if platform_visible is None:
-            platform_visible = self._mouse_visible and not self._mouse_cursor.gl_drawable
-
-        if platform_visible is False:
-            # Hide pointer by creating an empty cursor:
-            black = xlib.XColor()
-            bitmap = xlib.XCreateBitmapFromData(self._x_display, self._window, bytes(8), 8, 8)
-            cursor = xlib.XCreatePixmapCursor(self._x_display, bitmap, bitmap, black, black, 0, 0)
-            xlib.XDefineCursor(self._x_display, self._window, cursor)
-            xlib.XFreeCursor(self._x_display, cursor)
-            xlib.XFreePixmap(self._x_display, bitmap)
-        elif isinstance(self._mouse_cursor, ImageMouseCursor) and self._mouse_cursor.hw_drawable:
-            # Create a custom hardware cursor:
-            cursor = self._create_cursor_from_image(self._mouse_cursor)
-            xlib.XDefineCursor(self._x_display, self._window, cursor)
-        else:
-            # Restore standard hardware cursor:
-            if isinstance(self._mouse_cursor, XlibMouseCursor):
-                xlib.XDefineCursor(self._x_display, self._window, self._mouse_cursor.cursor)
-            else:
-                xlib.XUndefineCursor(self._x_display, self._window)
-
-    def set_mouse_position(self, x, y):
-        xlib.XWarpPointer(self._x_display,
-                          0,                    # src window
-                          self._window,         # dst window
-                          0, 0,                 # src x, y
-                          0, 0,                 # src w, h
-                          x, self._height - y)
-
-    def _update_exclusivity(self):
-        mouse_exclusive = self._active and self._mouse_exclusive
-        keyboard_exclusive = self._active and self._keyboard_exclusive
-
-        if mouse_exclusive != self._applied_mouse_exclusive:
-            if mouse_exclusive:
-                self.set_mouse_platform_visible(False)
-
-                # Restrict to client area
-                xlib.XGrabPointer(self._x_display, self._window,
-                                  True,
-                                  0,
-                                  xlib.GrabModeAsync,
-                                  xlib.GrabModeAsync,
-                                  self._window,
-                                  0,
-                                  xlib.CurrentTime)
-
-                # Move pointer to center of window
-                x = self._width // 2
-                y = self._height // 2
-                self._mouse_exclusive_client = x, y
-                self.set_mouse_position(x, y)
-            elif self._fullscreen and not self.screen._xinerama:
-                # Restrict to fullscreen area (prevent viewport scrolling)
-                self.set_mouse_position(0, 0)
-                r = xlib.XGrabPointer(self._x_display, self._view,
-                                      True, 0,
-                                      xlib.GrabModeAsync,
-                                      xlib.GrabModeAsync,
-                                      self._view,
-                                      0,
-                                      xlib.CurrentTime)
-                if r:
-                    # Failed to grab, try again later
-                    self._applied_mouse_exclusive = None
-                    return
-                self.set_mouse_platform_visible()
-            else:
-                # Unclip
-                xlib.XUngrabPointer(self._x_display, xlib.CurrentTime)
-                self.set_mouse_platform_visible()
-
-            self._applied_mouse_exclusive = mouse_exclusive
-
-        if keyboard_exclusive != self._applied_keyboard_exclusive:
-            if keyboard_exclusive:
-                xlib.XGrabKeyboard(self._x_display,
-                                   self._window,
-                                   False,
-                                   xlib.GrabModeAsync,
-                                   xlib.GrabModeAsync,
-                                   xlib.CurrentTime)
-            else:
-                xlib.XUngrabKeyboard(self._x_display, xlib.CurrentTime)
-            self._applied_keyboard_exclusive = keyboard_exclusive
-
-    def set_exclusive_mouse(self, exclusive=True):
-        if exclusive == self._mouse_exclusive:
-            return
-
-        super().set_exclusive_mouse(exclusive)
-        self._update_exclusivity()
-
-    def set_exclusive_keyboard(self, exclusive=True):
-        if exclusive == self._keyboard_exclusive:
-            return
-
-        super().set_exclusive_keyboard(exclusive)
-        self._update_exclusivity()
-
-    def get_system_mouse_cursor(self, name):
-        if name == self.CURSOR_DEFAULT:
-            return DefaultMouseCursor()
-
-        # NQR means default shape is not pretty... surely there is another
-        # cursor font?
-        cursor_shapes = {
-            self.CURSOR_CROSSHAIR:       cursorfont.XC_crosshair,
-            self.CURSOR_HAND:            cursorfont.XC_hand2,
-            self.CURSOR_HELP:            cursorfont.XC_question_arrow,  # NQR
-            self.CURSOR_NO:              cursorfont.XC_pirate,          # NQR
-            self.CURSOR_SIZE:            cursorfont.XC_fleur,
-            self.CURSOR_SIZE_UP:         cursorfont.XC_top_side,
-            self.CURSOR_SIZE_UP_RIGHT:   cursorfont.XC_top_right_corner,
-            self.CURSOR_SIZE_RIGHT:      cursorfont.XC_right_side,
-            self.CURSOR_SIZE_DOWN_RIGHT: cursorfont.XC_bottom_right_corner,
-            self.CURSOR_SIZE_DOWN:       cursorfont.XC_bottom_side,
-            self.CURSOR_SIZE_DOWN_LEFT:  cursorfont.XC_bottom_left_corner,
-            self.CURSOR_SIZE_LEFT:       cursorfont.XC_left_side,
-            self.CURSOR_SIZE_UP_LEFT:    cursorfont.XC_top_left_corner,
-            self.CURSOR_SIZE_UP_DOWN:    cursorfont.XC_sb_v_double_arrow,
-            self.CURSOR_SIZE_LEFT_RIGHT: cursorfont.XC_sb_h_double_arrow,
-            self.CURSOR_TEXT:            cursorfont.XC_xterm,
-            self.CURSOR_WAIT:            cursorfont.XC_watch,
-            self.CURSOR_WAIT_ARROW:      cursorfont.XC_watch,           # NQR
-        }
-        if name not in cursor_shapes:
-            raise MouseCursorException('Unknown cursor name "%s"' % name)
-        cursor = xlib.XCreateFontCursor(self._x_display, cursor_shapes[name])
-        return XlibMouseCursor(cursor)
-
-    def set_icon(self, *images):
-        # Careful!  XChangeProperty takes an array of long when data type
-        # is 32-bit (but long can be 64 bit!), so pad high bytes of format if
-        # necessary.
-
-        import sys
-        fmt = {('little', 4): 'BGRA',
-               ('little', 8): 'BGRAAAAA',
-               ('big', 4):    'ARGB',
-               ('big', 8):    'AAAAARGB'}[(sys.byteorder, sizeof(c_ulong))]
-
-        data = asbytes('')
-        for image in images:
-            image = image.get_image_data()
-            pitch = -(image.width * len(fmt))
-            s = c_buffer(sizeof(c_ulong) * 2)
-            memmove(s, cast((c_ulong * 2)(image.width, image.height), POINTER(c_ubyte)), len(s))
-            data += s.raw + image.get_data(fmt, pitch)
-        buffer = (c_ubyte * len(data))()
-        memmove(buffer, data, len(data))
-        atom = xlib.XInternAtom(self._x_display, asbytes('_NET_WM_ICON'), False)
-        xlib.XChangeProperty(self._x_display, self._window, atom, XA_CARDINAL,
-                             32, xlib.PropModeReplace, buffer, len(data)//sizeof(c_ulong))
-
-    # Private utility
-
-    def _set_wm_normal_hints(self):
-        hints = xlib.XAllocSizeHints().contents
-        if self._minimum_size:
-            hints.flags |= xlib.PMinSize
-            hints.min_width, hints.min_height = self._minimum_size
-        if self._maximum_size:
-            hints.flags |= xlib.PMaxSize
-            hints.max_width, hints.max_height = self._maximum_size
-        xlib.XSetWMNormalHints(self._x_display, self._window, byref(hints))
-
-    def _set_text_property(self, name, value, allow_utf8=True):
-        atom = xlib.XInternAtom(self._x_display, asbytes(name), False)
-        if not atom:
-            raise XlibException('Undefined atom "%s"' % name)
-        text_property = xlib.XTextProperty()
-        if _have_utf8 and allow_utf8:
-            buf = create_string_buffer(value.encode('utf8'))
-            result = xlib.Xutf8TextListToTextProperty(self._x_display,
-                                                      cast(pointer(buf), c_char_p),
-                                                      1, xlib.XUTF8StringStyle,
-                                                      byref(text_property))
-            if result < 0:
-                raise XlibException('Could not create UTF8 text property')
-        else:
-            buf = create_string_buffer(value.encode('ascii', 'ignore'))
-            result = xlib.XStringListToTextProperty(
-                cast(pointer(buf), c_char_p), 1, byref(text_property))
-            if result < 0:
-                raise XlibException('Could not create text property')
-        xlib.XSetTextProperty(self._x_display, self._window, byref(text_property), atom)
-        # XXX <rj> Xlib doesn't like us freeing this
-        # xlib.XFree(text_property.value)
-
-    def _set_atoms_property(self, name, values, mode=xlib.PropModeReplace):
-        name_atom = xlib.XInternAtom(self._x_display, asbytes(name), False)
-        atoms = []
-        for value in values:
-            atoms.append(xlib.XInternAtom(self._x_display, asbytes(value), False))
-        atom_type = xlib.XInternAtom(self._x_display, asbytes('ATOM'), False)
-        if len(atoms):
-            atoms_ar = (xlib.Atom * len(atoms))(*atoms)
-            xlib.XChangeProperty(self._x_display, self._window,
-                                 name_atom, atom_type, 32, mode,
-                                 cast(pointer(atoms_ar), POINTER(c_ubyte)), len(atoms))
-        else:
-            net_wm_state = xlib.XInternAtom(self._x_display, asbytes('_NET_WM_STATE'), False)
-            if net_wm_state:
-                xlib.XDeleteProperty(self._x_display, self._window, net_wm_state)
-
-    def _set_wm_state(self, *states):
-        # Set property
-        net_wm_state = xlib.XInternAtom(self._x_display, asbytes('_NET_WM_STATE'), False)
-        atoms = []
-        for state in states:
-            atoms.append(xlib.XInternAtom(self._x_display, asbytes(state), False))
-        atom_type = xlib.XInternAtom(self._x_display, asbytes('ATOM'), False)
-        if len(atoms):
-            atoms_ar = (xlib.Atom * len(atoms))(*atoms)
-            xlib.XChangeProperty(self._x_display, self._window,
-                                 net_wm_state, atom_type, 32, xlib.PropModePrepend,
-                                 cast(pointer(atoms_ar), POINTER(c_ubyte)), len(atoms))
-        else:
-            xlib.XDeleteProperty(self._x_display, self._window, net_wm_state)
-
-        # Nudge the WM
-        e = xlib.XEvent()
-        e.xclient.type = xlib.ClientMessage
-        e.xclient.message_type = net_wm_state
-        e.xclient.display = cast(self._x_display, POINTER(xlib.Display))
-        e.xclient.window = self._window
-        e.xclient.format = 32
-        e.xclient.data.l[0] = xlib.PropModePrepend
-        for i, atom in enumerate(atoms):
-            e.xclient.data.l[i + 1] = atom
-        xlib.XSendEvent(self._x_display, self._get_root(),
-                        False, xlib.SubstructureRedirectMask, byref(e))
-
-    # Event handling
-
-    def dispatch_events(self):
-        self.dispatch_pending_events()
-
-        self._allow_dispatch_event = True
-
-        e = xlib.XEvent()
-
-        # Cache these in case window is closed from an event handler
-        _x_display = self._x_display
-        _window = self._window
-        _view = self._view
-
-        # Check for the events specific to this window
-        while xlib.XCheckWindowEvent(_x_display, _window, 0x1ffffff, byref(e)):
-            # Key events are filtered by the xlib window event
-            # handler so they get a shot at the prefiltered event.
-            if e.xany.type not in (xlib.KeyPress, xlib.KeyRelease):
-                if xlib.XFilterEvent(e, 0):
-                    continue
-            self.dispatch_platform_event(e)
-
-        # Check for the events specific to this view
-        while xlib.XCheckWindowEvent(_x_display, _view, 0x1ffffff, byref(e)):
-            # Key events are filtered by the xlib window event
-            # handler so they get a shot at the prefiltered event.
-            if e.xany.type not in (xlib.KeyPress, xlib.KeyRelease):
-                if xlib.XFilterEvent(e, 0):
-                    continue
-            self.dispatch_platform_event_view(e)
-
-        # Generic events for this window (the window close event).
-        while xlib.XCheckTypedWindowEvent(_x_display, _window, xlib.ClientMessage, byref(e)):
-            self.dispatch_platform_event(e)
-
-        self._allow_dispatch_event = False
-
-    def dispatch_pending_events(self):
-        while self._event_queue:
-            EventDispatcher.dispatch_event(self, *self._event_queue.pop(0))
-
-        # Dispatch any context-related events
-        if self._lost_context:
-            self._lost_context = False
-            EventDispatcher.dispatch_event(self, 'on_context_lost')
-        if self._lost_context_state:
-            self._lost_context_state = False
-            EventDispatcher.dispatch_event(self, 'on_context_state_lost')
-
-    def dispatch_platform_event(self, e):
-        if self._applied_mouse_exclusive is None:
-            self._update_exclusivity()
-        event_handler = self._event_handlers.get(e.type)
-        if event_handler:
-            event_handler(e)
-
-    def dispatch_platform_event_view(self, e):
-        event_handler = self._view_event_handlers.get(e.type)
-        if event_handler:
-            event_handler(e)
-
-    @staticmethod
-    def _translate_modifiers(state):
-        modifiers = 0
-        if state & xlib.ShiftMask:
-            modifiers |= key.MOD_SHIFT
-        if state & xlib.ControlMask:
-            modifiers |= key.MOD_CTRL
-        if state & xlib.LockMask:
-            modifiers |= key.MOD_CAPSLOCK
-        if state & xlib.Mod1Mask:
-            modifiers |= key.MOD_ALT
-        if state & xlib.Mod2Mask:
-            modifiers |= key.MOD_NUMLOCK
-        if state & xlib.Mod4Mask:
-            modifiers |= key.MOD_WINDOWS
-        if state & xlib.Mod5Mask:
-            modifiers |= key.MOD_SCROLLLOCK
-        return modifiers
-
-    # Event handlers
-    """
-    def _event_symbol(self, event):
-        # pyglet.self.key keysymbols are identical to X11 keysymbols, no
-        # need to map the keysymbol.
-        symbol = xlib.XKeycodeToKeysym(self._x_display, event.xkey.keycode, 0)
-        if symbol == 0:
-            # XIM event
-            return None
-        elif symbol not in key._key_names.keys():
-            symbol = key.user_key(event.xkey.keycode)
-        return symbol
-    """
-
-    def _event_text_symbol(self, ev):
-        text = None
-        symbol = xlib.KeySym()
-        buffer = create_string_buffer(128)
-
-        # Look up raw keysym before XIM filters it (default for keypress and
-        # keyrelease)
-        count = xlib.XLookupString(ev.xkey, buffer, len(buffer) - 1, byref(symbol), None)
-
-        # Give XIM a shot
-        filtered = xlib.XFilterEvent(ev, ev.xany.window)
-
-        if ev.type == xlib.KeyPress and not filtered:
-            status = c_int()
-            if _have_utf8:
-                encoding = 'utf8'
-                count = xlib.Xutf8LookupString(self._x_ic,
-                                               ev.xkey,
-                                               buffer, len(buffer) - 1,
-                                               byref(symbol), byref(status))
-                if status.value == xlib.XBufferOverflow:
-                    raise NotImplementedError('TODO: XIM buffer resize')
-
-            else:
-                encoding = 'ascii'
-                count = xlib.XLookupString(ev.xkey, buffer, len(buffer) - 1, byref(symbol), None)
-                if count:
-                    status.value = xlib.XLookupBoth
-
-            if status.value & (xlib.XLookupChars | xlib.XLookupBoth):
-                text = buffer.value[:count].decode(encoding)
-
-            # Don't treat Unicode command codepoints as text, except Return.
-            if text and unicodedata.category(text) == 'Cc' and text != '\r':
-                text = None
-
-        symbol = symbol.value
-
-        # If the event is a XIM filtered event, the keysym will be virtual
-        # (e.g., aacute instead of A after a dead key).  Drop it, we don't
-        # want these kind of key events.
-        if ev.xkey.keycode == 0 and not filtered:
-            symbol = None
-
-        # pyglet.self.key keysymbols are identical to X11 keysymbols, no
-        # need to map the keysymbol.  For keysyms outside the pyglet set, map
-        # raw key code to a user key.
-        if symbol and symbol not in key._key_names and ev.xkey.keycode:
-            # Issue 353: Symbol is uppercase when shift key held down.
-            try:
-                symbol = ord(chr(symbol).lower())
-            except ValueError:
-                # Not a valid unichr, use the keycode
-                symbol = key.user_key(ev.xkey.keycode)
-            else:
-                # If still not recognised, use the keycode
-                if symbol not in key._key_names:
-                    symbol = key.user_key(ev.xkey.keycode)
-
-        if filtered:
-            # The event was filtered, text must be ignored, but the symbol is
-            # still good.
-            return None, symbol
-
-        return text, symbol
-
-    @staticmethod
-    def _event_text_motion(symbol, modifiers):
-        if modifiers & key.MOD_ALT:
-            return None
-        ctrl = modifiers & key.MOD_CTRL != 0
-        return _motion_map.get((symbol, ctrl), None)
-
-    @ViewEventHandler
-    @XlibEventHandler(xlib.KeyPress)
-    @XlibEventHandler(xlib.KeyRelease)
-    def _event_key_view(self, ev):
-        # Try to detect autorepeat ourselves if the server doesn't support it
-        # XXX: Doesn't always work, better off letting the server do it
-        global _can_detect_autorepeat
-        if not _can_detect_autorepeat and ev.type == xlib.KeyRelease:
-            # Look in the queue for a matching KeyPress with same timestamp,
-            # indicating an auto-repeat rather than actual key event.
-            saved = []
-            while True:
-                auto_event = xlib.XEvent()
-                result = xlib.XCheckWindowEvent(self._x_display,
-                                                self._window, xlib.KeyPress|xlib.KeyRelease,
-                                                byref(auto_event))
-                if not result:
-                    break
-                saved.append(auto_event)
-                if auto_event.type == xlib.KeyRelease:
-                    # just save this off for restoration back to the queue
-                    continue
-                if ev.xkey.keycode == auto_event.xkey.keycode:
-                    # Found a key repeat: dispatch EVENT_TEXT* event
-                    text, symbol = self._event_text_symbol(auto_event)
-                    modifiers = self._translate_modifiers(ev.xkey.state)
-                    modifiers_ctrl = modifiers & (key.MOD_CTRL | key.MOD_ALT)
-                    motion = self._event_text_motion(symbol, modifiers)
-                    if motion:
-                        if modifiers & key.MOD_SHIFT:
-                            self.dispatch_event('on_text_motion_select', motion)
-                        else:
-                            self.dispatch_event('on_text_motion', motion)
-                    elif text and not modifiers_ctrl:
-                        self.dispatch_event('on_text', text)
-
-                    ditched = saved.pop()
-                    for auto_event in reversed(saved):
-                        xlib.XPutBackEvent(self._x_display, byref(auto_event))
-                    return
-                else:
-                    # Key code of press did not match, therefore no repeating
-                    # is going on, stop searching.
-                    break
-            # Whoops, put the events back, it's for real.
-            for auto_event in reversed(saved):
-                xlib.XPutBackEvent(self._x_display, byref(auto_event))
-
-        text, symbol = self._event_text_symbol(ev)
-        modifiers = self._translate_modifiers(ev.xkey.state)
-        modifiers_ctrl = modifiers & (key.MOD_CTRL | key.MOD_ALT)
-        motion = self._event_text_motion(symbol, modifiers)
-
-        if ev.type == xlib.KeyPress:
-            if symbol and (not _can_detect_autorepeat or symbol not in self.pressed_keys):
-                self.dispatch_event('on_key_press', symbol, modifiers)
-                if _can_detect_autorepeat:
-                    self.pressed_keys.add(symbol)
-            if motion:
-                if modifiers & key.MOD_SHIFT:
-                    self.dispatch_event('on_text_motion_select', motion)
-                else:
-                    self.dispatch_event('on_text_motion', motion)
-            elif text and not modifiers_ctrl:
-                self.dispatch_event('on_text', text)
-        elif ev.type == xlib.KeyRelease:
-            if symbol:
-                self.dispatch_event('on_key_release', symbol, modifiers)
-                if _can_detect_autorepeat and symbol in self.pressed_keys:
-                    self.pressed_keys.remove(symbol)
-
-    @XlibEventHandler(xlib.KeyPress)
-    @XlibEventHandler(xlib.KeyRelease)
-    def _event_key(self, ev):
-        return self._event_key_view(ev)
-
-    @ViewEventHandler
-    @XlibEventHandler(xlib.MotionNotify)
-    def _event_motionnotify_view(self, ev):
-        x = ev.xmotion.x
-        y = self.height - ev.xmotion.y - 1
-
-        if self._mouse_in_window:
-            dx = x - self._mouse_x
-            dy = y - self._mouse_y
-        else:
-            dx = dy = 0
-
-        if self._applied_mouse_exclusive and (ev.xmotion.x, ev.xmotion.y) == self._mouse_exclusive_client:
-            # Ignore events caused by XWarpPointer
-            self._mouse_x = x
-            self._mouse_y = y
-            return
-
-        if self._applied_mouse_exclusive:
-            # Reset pointer position
-            ex, ey = self._mouse_exclusive_client
-            xlib.XWarpPointer(self._x_display,
-                              0,
-                              self._window,
-                              0, 0,
-                              0, 0,
-                              ex, ey)
-
-        self._mouse_x = x
-        self._mouse_y = y
-        self._mouse_in_window = True
-
-        buttons = 0
-        if ev.xmotion.state & xlib.Button1MotionMask:
-            buttons |= mouse.LEFT
-        if ev.xmotion.state & xlib.Button2MotionMask:
-            buttons |= mouse.MIDDLE
-        if ev.xmotion.state & xlib.Button3MotionMask:
-            buttons |= mouse.RIGHT
-        # TODO: Determine how to implement drag support for mouse 4 and 5
-
-        if buttons:
-            # Drag event
-            modifiers = self._translate_modifiers(ev.xmotion.state)
-            self.dispatch_event('on_mouse_drag', x, y, dx, dy, buttons, modifiers)
-        else:
-            # Motion event
-            self.dispatch_event('on_mouse_motion', x, y, dx, dy)
-
-    @XlibEventHandler(xlib.MotionNotify)
-    def _event_motionnotify(self, ev):
-        # Window motion looks for drags that are outside the view but within
-        # the window.
-        buttons = 0
-        if ev.xmotion.state & xlib.Button1MotionMask:
-            buttons |= mouse.LEFT
-        if ev.xmotion.state & xlib.Button2MotionMask:
-            buttons |= mouse.MIDDLE
-        if ev.xmotion.state & xlib.Button3MotionMask:
-            buttons |= mouse.RIGHT
-        # TODO: Determine how to implement drag support for mouse 4 and 5
-
-        if buttons:
-            # Drag event
-            x = ev.xmotion.x - self._view_x
-            y = self._height - (ev.xmotion.y - self._view_y - 1)
-
-            if self._mouse_in_window:
-                dx = x - self._mouse_x
-                dy = y - self._mouse_y
-            else:
-                dx = dy = 0
-            self._mouse_x = x
-            self._mouse_y = y
-
-            modifiers = self._translate_modifiers(ev.xmotion.state)
-            self.dispatch_event('on_mouse_drag', x, y, dx, dy, buttons, modifiers)
-
-    @XlibEventHandler(xlib.ClientMessage)
-    def _event_clientmessage(self, ev):
-        atom = ev.xclient.data.l[0]
-        if atom == xlib.XInternAtom(ev.xclient.display, asbytes('WM_DELETE_WINDOW'), False):
-            self.dispatch_event('on_close')
-        elif (self._enable_xsync and
-              atom == xlib.XInternAtom(ev.xclient.display,
-                                       asbytes('_NET_WM_SYNC_REQUEST'), False)):
-            lo = ev.xclient.data.l[2]
-            hi = ev.xclient.data.l[3]
-            self._current_sync_value = xsync.XSyncValue(hi, lo)
-
-        elif ev.xclient.message_type == self._xdnd_atoms['XdndPosition']:
-            self._event_drag_position(ev)
-
-        elif ev.xclient.message_type == self._xdnd_atoms['XdndDrop']:
-            self._event_drag_drop(ev)
-
-        elif ev.xclient.message_type == self._xdnd_atoms['XdndEnter']:
-            self._event_drag_enter(ev)
-
-    def _event_drag_drop(self, ev):
-        if self._xdnd_version > XDND_VERSION:
-            return
-
-        time = xlib.CurrentTime
-
-        if self._xdnd_format:
-            if self._xdnd_version >= 1:
-                time = ev.xclient.data.l[2]
-
-            # Convert to selection notification.
-            xlib.XConvertSelection(self._x_display,
-                                   self._xdnd_atoms['XdndSelection'],
-                                   self._xdnd_format,
-                                   self._xdnd_atoms['XdndSelection'],
-                                   self._window,
-                                   time)
-
-            xlib.XFlush(self._x_display)
-
-        elif self._xdnd_version >= 2:
-            # If no format send finished with no data.
-            e = xlib.XEvent()
-            e.xclient.type = xlib.ClientMessage
-            e.xclient.message_type = self._xdnd_atoms['XdndFinished']
-            e.xclient.display = cast(self._x_display, POINTER(xlib.Display))
-            e.xclient.window = self._window
-            e.xclient.format = 32
-            e.xclient.data.l[0] = self._window
-            e.xclient.data.l[1] = 0
-            e.xclient.data.l[2] = None
-
-            xlib.XSendEvent(self._x_display, self._xdnd_source,
-                            False, xlib.NoEventMask, byref(e))
-
-            xlib.XFlush(self._x_display)
-
-    def _event_drag_position(self, ev):
-        if self._xdnd_version > XDND_VERSION:
-            return
-
-        xoff = (ev.xclient.data.l[2] >> 16) & 0xffff
-        yoff = (ev.xclient.data.l[2]) & 0xffff
-
-        # Need to convert the position to actual window coordinates with the screen offset
-        child = xlib.Window()
-        x = c_int()
-        y = c_int()
-        xlib.XTranslateCoordinates(self._x_display,
-                                   self._get_root(),
-                                   self._window,
-                                   xoff, yoff,
-                                   byref(x),
-                                   byref(y),
-                                   byref(child))
-
-        self._xdnd_position = (x.value, y.value)
-
-        e = xlib.XEvent()
-        e.xclient.type = xlib.ClientMessage
-        e.xclient.message_type = self._xdnd_atoms['XdndStatus']
-        e.xclient.display = cast(self._x_display, POINTER(xlib.Display))
-        e.xclient.window = ev.xclient.data.l[0]
-        e.xclient.format = 32
-        e.xclient.data.l[0] = self._window
-        e.xclient.data.l[2] = 0
-        e.xclient.data.l[3] = 0
-
-        if self._xdnd_format:
-            e.xclient.data.l[1] = 1
-            if self._xdnd_version >= 2:
-                e.xclient.data.l[4] = self._xdnd_atoms['XdndActionCopy']
-
-        xlib.XSendEvent(self._x_display, self._xdnd_source,
-                        False, xlib.NoEventMask, byref(e))
-
-        xlib.XFlush(self._x_display)
-
-    def _event_drag_enter(self, ev):
-        self._xdnd_source = ev.xclient.data.l[0]
-        self._xdnd_version = ev.xclient.data.l[1] >> 24
-        self._xdnd_format = None
-
-        if self._xdnd_version > XDND_VERSION:
-            return
-
-        three_or_more = ev.xclient.data.l[1] & 1
-
-        # Search all of them (usually 8)
-        if three_or_more:
-            data, count = self.get_single_property(self._xdnd_source, self._xdnd_atoms['XdndTypeList'], XA_ATOM)
-
-            data = cast(data, POINTER(xlib.Atom))
-        else:
-            # Some old versions may only have 3? Needs testing.
-            count = 3
-            data = ev.xclient.data.l + 2
-
-        # Check all of the properties we received from the dropped item and verify it support URI.
-        for i in range(count):
-            if data[i] == self._xdnd_atoms['text/uri-list']:
-                self._xdnd_format = self._xdnd_atoms['text/uri-list']
-                break
-
-        if data:
-            xlib.XFree(data)
-
-    def get_single_property(self, window, atom_property, atom_type):
-        """ Returns the length and data of a window property. """
-        actualAtom = xlib.Atom()
-        actualFormat = c_int()
-        itemCount = c_ulong()
-        bytesAfter = c_ulong()
-        data = POINTER(c_ubyte)()
-
-        xlib.XGetWindowProperty(self._x_display, window,
-                                atom_property, 0, 2147483647, False, atom_type,
-                                byref(actualAtom),
-                                byref(actualFormat),
-                                byref(itemCount),
-                                byref(bytesAfter),
-                                data)
-
-        return data, itemCount.value
-
-    @XlibEventHandler(xlib.SelectionNotify)
-    def _event_selection_notification(self, ev):
-        if ev.xselection.property != 0 and ev.xselection.selection == self._xdnd_atoms['XdndSelection']:
-            if self._xdnd_format:
-                # This will get the data
-                data, count = self.get_single_property(ev.xselection.requestor,
-                                                         ev.xselection.property,
-                                                         ev.xselection.target)
-
-                buffer = create_string_buffer(count)
-                memmove(buffer, data, count)
-
-                formatted_paths = self.parse_filenames(buffer.value.decode())
-
-                e = xlib.XEvent()
-                e.xclient.type = xlib.ClientMessage
-                e.xclient.message_type = self._xdnd_atoms['XdndFinished']
-                e.xclient.display = cast(self._x_display, POINTER(xlib.Display))
-                e.xclient.window = self._window
-                e.xclient.format = 32
-                e.xclient.data.l[0] = self._xdnd_source
-                e.xclient.data.l[1] = 1
-                e.xclient.data.l[2] = self._xdnd_atoms['XdndActionCopy']
-
-                xlib.XSendEvent(self._x_display, self._get_root(),
-                                False, xlib.NoEventMask, byref(e))
-
-                xlib.XFlush(self._x_display)
-
-                xlib.XFree(data)
-
-                self.dispatch_event('on_file_drop', self._xdnd_position[0], self._height - self._xdnd_position[1], formatted_paths)
-
-    @staticmethod
-    def parse_filenames(decoded_string):
-        """All of the filenames from file drops come as one big string with
-            some special characters (%20), this will parse them out.
-        """
-        import sys
-
-        different_files = decoded_string.splitlines()
-
-        parsed = []
-        for filename in different_files:
-            if filename:
-                filename = urllib.parse.urlsplit(filename).path
-                encoding = sys.getfilesystemencoding()
-                parsed.append(urllib.parse.unquote(filename, encoding))
-
-        return parsed
-
-    def _sync_resize(self):
-        if self._enable_xsync and self._current_sync_valid:
-            if xsync.XSyncValueIsZero(self._current_sync_value):
-                self._current_sync_valid = False
-                return
-            xsync.XSyncSetCounter(self._x_display,
-                                  self._sync_counter,
-                                  self._current_sync_value)
-            self._current_sync_value = None
-            self._current_sync_valid = False
-
-    @ViewEventHandler
-    @XlibEventHandler(xlib.ButtonPress)
-    @XlibEventHandler(xlib.ButtonRelease)
-    def _event_button(self, ev):
-        x = ev.xbutton.x
-        y = self.height - ev.xbutton.y
-
-        button = ev.xbutton.button - 1
-        if button == 7 or button == 8:
-            button -= 4
-
-        modifiers = self._translate_modifiers(ev.xbutton.state)
-        if ev.type == xlib.ButtonPress:
-            # override_redirect issue: manually activate this window if
-            # fullscreen.
-            if self._override_redirect and not self._active:
-                self.activate()
-
-            if ev.xbutton.button == 4:
-                self.dispatch_event('on_mouse_scroll', x, y, 0, 1)
-            elif ev.xbutton.button == 5:
-                self.dispatch_event('on_mouse_scroll', x, y, 0, -1)
-            elif ev.xbutton.button == 6:
-                self.dispatch_event('on_mouse_scroll', x, y, -1, 0)
-            elif ev.xbutton.button == 7:
-                self.dispatch_event('on_mouse_scroll', x, y, 1, 0)
-            elif button < 5:
-                self.dispatch_event('on_mouse_press', x, y, 1 << button, modifiers)
-        elif button < 5:
-            self.dispatch_event('on_mouse_release', x, y, 1 << button, modifiers)
-
-    @ViewEventHandler
-    @XlibEventHandler(xlib.Expose)
-    def _event_expose(self, ev):
-        # Ignore all expose events except the last one. We could be told
-        # about exposure rects - but I don't see the point since we're
-        # working with OpenGL and we'll just redraw the whole scene.
-        if ev.xexpose.count > 0:
-            return
-        self.dispatch_event('on_expose')
-
-    @ViewEventHandler
-    @XlibEventHandler(xlib.EnterNotify)
-    def _event_enternotify(self, ev):
-        # mouse position
-        x = self._mouse_x = ev.xcrossing.x
-        y = self._mouse_y = self.height - ev.xcrossing.y
-        self._mouse_in_window = True
-
-        # XXX there may be more we could do here
-        self.dispatch_event('on_mouse_enter', x, y)
-
-    @ViewEventHandler
-    @XlibEventHandler(xlib.LeaveNotify)
-    def _event_leavenotify(self, ev):
-        x = self._mouse_x = ev.xcrossing.x
-        y = self._mouse_y = self.height - ev.xcrossing.y
-        self._mouse_in_window = False
-        self.dispatch_event('on_mouse_leave', x, y)
-
-    @XlibEventHandler(xlib.ConfigureNotify)
-    def _event_configurenotify(self, ev):
-        if self._enable_xsync and self._current_sync_value:
-            self._current_sync_valid = True
-
-        if self._fullscreen:
-            return
-
-        self.switch_to()
-
-        w, h = ev.xconfigure.width, ev.xconfigure.height
-        x, y = ev.xconfigure.x, ev.xconfigure.y
-        if self._width != w or self._height != h:
-            self._width = w
-            self._height = h
-            self._update_view_size()
-            self.dispatch_event('on_resize', self._width, self._height)
-        if self._x != x or self._y != y:
-            self.dispatch_event('on_move', x, y)
-            self._x = x
-            self._y = y
-
-    @XlibEventHandler(xlib.FocusIn)
-    def _event_focusin(self, ev):
-        self._active = True
-        self._update_exclusivity()
-        self.dispatch_event('on_activate')
-        xlib.XSetICFocus(self._x_ic)
-
-    @XlibEventHandler(xlib.FocusOut)
-    def _event_focusout(self, ev):
-        self._active = False
-        self._update_exclusivity()
-        self.dispatch_event('on_deactivate')
-        xlib.XUnsetICFocus(self._x_ic)
-
-    @XlibEventHandler(xlib.MapNotify)
-    def _event_mapnotify(self, ev):
-        self._mapped = True
-        self.dispatch_event('on_show')
-        self._update_exclusivity()
-
-    @XlibEventHandler(xlib.UnmapNotify)
-    def _event_unmapnotify(self, ev):
-        self._mapped = False
-        self.dispatch_event('on_hide')
-
-
-__all__ = ["XlibEventHandler", "XlibWindow"]
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/platforms/egl.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/platforms/egl.py
deleted file mode 100644
index ae2478d29c9a538c53ad83fa31f8e2277cd897c8..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/pyrender/platforms/egl.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import ctypes
-import os
-
-import OpenGL.platform
-
-from .base import Platform
-
-EGL_PLATFORM_DEVICE_EXT = 0x313F
-EGL_DRM_DEVICE_FILE_EXT = 0x3233
-
-
-def _ensure_egl_loaded():
-    plugin = OpenGL.platform.PlatformPlugin.by_name('egl')
-    if plugin is None:
-        raise RuntimeError("EGL platform plugin is not available.")
-
-    plugin_class = plugin.load()
-    plugin.loaded = True
-    # create instance of this platform implementation
-    plugin = plugin_class()
-
-    plugin.install(vars(OpenGL.platform))
-
-
-_ensure_egl_loaded()
-from OpenGL import EGL as egl
-
-
-def _get_egl_func(func_name, res_type, *arg_types):
-    address = egl.eglGetProcAddress(func_name)
-    if address is None:
-        return None
-
-    proto = ctypes.CFUNCTYPE(res_type)
-    proto.argtypes = arg_types
-    func = proto(address)
-    return func
-
-
-def _get_egl_struct(struct_name):
-    from OpenGL._opaque import opaque_pointer_cls
-    return opaque_pointer_cls(struct_name)
-
-
-# These are not defined in PyOpenGL by default.
-_EGLDeviceEXT = _get_egl_struct('EGLDeviceEXT')
-_eglGetPlatformDisplayEXT = _get_egl_func('eglGetPlatformDisplayEXT', egl.EGLDisplay)
-_eglQueryDevicesEXT = _get_egl_func('eglQueryDevicesEXT', egl.EGLBoolean)
-_eglQueryDeviceStringEXT = _get_egl_func('eglQueryDeviceStringEXT', ctypes.c_char_p)
-
-
-def query_devices():
-    if _eglQueryDevicesEXT is None:
-        raise RuntimeError("EGL query extension is not loaded or is not supported.")
-
-    num_devices = egl.EGLint()
-    success = _eglQueryDevicesEXT(0, None, ctypes.pointer(num_devices))
-    if not success or num_devices.value < 1:
-        return []
-
-    devices = (_EGLDeviceEXT * num_devices.value)()  # array of size num_devices
-    success = _eglQueryDevicesEXT(num_devices.value, devices, ctypes.pointer(num_devices))
-    if not success or num_devices.value < 1:
-        return []
-
-    return [EGLDevice(devices[i]) for i in range(num_devices.value)]
-
-
-def get_default_device():
-    # Fall back to not using query extension.
-    if _eglQueryDevicesEXT is None:
-        return EGLDevice(None)
-
-    return query_devices()[0]
-
-
-def get_device_by_index(device_id):
-    if _eglQueryDevicesEXT is None and device_id == 0:
-        return get_default_device()
-
-    devices = query_devices()
-    if device_id >= len(devices):
-        raise ValueError('Invalid device ID ({})'.format(device_id, len(devices)))
-    return devices[device_id]
-
-
-class EGLDevice:
-
-    def __init__(self, display=None):
-        self._display = display
-
-    def get_display(self):
-        if self._display is None:
-            return egl.eglGetDisplay(egl.EGL_DEFAULT_DISPLAY)
-
-        return _eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, self._display, None)
-
-    @property
-    def name(self):
-        if self._display is None:
-            return 'default'
-
-        name = _eglQueryDeviceStringEXT(self._display, EGL_DRM_DEVICE_FILE_EXT)
-        if name is None:
-            return None
-
-        return name.decode('ascii')
-
-    def __repr__(self):
-        return "<EGLDevice(name={})>".format(self.name)
-
-
-class EGLPlatform(Platform):
-    """Renders using EGL.
-    """
-
-    def __init__(self, viewport_width, viewport_height, device: EGLDevice = None):
-        super(EGLPlatform, self).__init__(viewport_width, viewport_height)
-        if device is None:
-            device = get_default_device()
-
-        self._egl_device = device
-        self._egl_display = None
-        self._egl_context = None
-
-    def init_context(self):
-        _ensure_egl_loaded()
-
-        from OpenGL.EGL import (
-            EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_BLUE_SIZE,
-            EGL_RED_SIZE, EGL_GREEN_SIZE, EGL_DEPTH_SIZE,
-            EGL_COLOR_BUFFER_TYPE, EGL_RGB_BUFFER,
-            EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT, EGL_CONFORMANT,
-            EGL_NONE, EGL_DEFAULT_DISPLAY, EGL_NO_CONTEXT,
-            EGL_OPENGL_API, EGL_CONTEXT_MAJOR_VERSION,
-            EGL_CONTEXT_MINOR_VERSION,
-            EGL_CONTEXT_OPENGL_PROFILE_MASK,
-            EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT,
-            eglGetDisplay, eglInitialize, eglChooseConfig,
-            eglBindAPI, eglCreateContext, EGLConfig
-        )
-        from OpenGL import arrays
-
-        config_attributes = arrays.GLintArray.asArray([
-            EGL_SURFACE_TYPE, EGL_PBUFFER_BIT,
-            EGL_BLUE_SIZE, 8,
-            EGL_RED_SIZE, 8,
-            EGL_GREEN_SIZE, 8,
-            EGL_DEPTH_SIZE, 24,
-            EGL_COLOR_BUFFER_TYPE, EGL_RGB_BUFFER,
-            EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
-            EGL_CONFORMANT, EGL_OPENGL_BIT,
-            EGL_NONE
-        ])
-        context_attributes = arrays.GLintArray.asArray([
-            EGL_CONTEXT_MAJOR_VERSION, 4,
-            EGL_CONTEXT_MINOR_VERSION, 1,
-            EGL_CONTEXT_OPENGL_PROFILE_MASK,
-            EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT,
-            EGL_NONE
-        ])
-        major, minor = ctypes.c_long(), ctypes.c_long()
-        num_configs = ctypes.c_long()
-        configs = (EGLConfig * 1)()
-
-        # Cache DISPLAY if necessary and get an off-screen EGL display
-        orig_dpy = None
-        if 'DISPLAY' in os.environ:
-            orig_dpy = os.environ['DISPLAY']
-            del os.environ['DISPLAY']
-
-        self._egl_display = self._egl_device.get_display()
-        if orig_dpy is not None:
-            os.environ['DISPLAY'] = orig_dpy
-
-        # Initialize EGL
-        assert eglInitialize(self._egl_display, major, minor)
-        assert eglChooseConfig(
-            self._egl_display, config_attributes, configs, 1, num_configs
-        )
-
-        # Bind EGL to the OpenGL API
-        assert eglBindAPI(EGL_OPENGL_API)
-
-        # Create an EGL context
-        self._egl_context = eglCreateContext(
-            self._egl_display, configs[0],
-            EGL_NO_CONTEXT, context_attributes
-        )
-
-        # Make it current
-        self.make_current()
-
-    def make_current(self):
-        from OpenGL.EGL import eglMakeCurrent, EGL_NO_SURFACE
-        assert eglMakeCurrent(
-            self._egl_display, EGL_NO_SURFACE, EGL_NO_SURFACE,
-            self._egl_context
-        )
-
-    def make_uncurrent(self):
-        """Make the OpenGL context uncurrent.
-        """
-        pass
-
-    def delete_context(self):
-        from OpenGL.EGL import eglDestroyContext, eglTerminate
-        if self._egl_display is not None:
-            if self._egl_context is not None:
-                eglDestroyContext(self._egl_display, self._egl_context)
-                self._egl_context = None
-            eglTerminate(self._egl_display)
-            self._egl_display = None
-
-    def supports_framebuffers(self):
-        return True
-
-
-__all__ = ['EGLPlatform']
diff --git a/spaces/adirik/stylemc-demo/id_loss.py b/spaces/adirik/stylemc-demo/id_loss.py
deleted file mode 100644
index b98ef6c6cfcf508b599294e994a1454b47a6804e..0000000000000000000000000000000000000000
--- a/spaces/adirik/stylemc-demo/id_loss.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import torch
-from torch import nn
-from model_irse import Backbone
-
-
-# Use GPU if available
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-else:
-    device = torch.device("cpu")
-
-
-class IDLoss(nn.Module):
-    def __init__(self, opts):
-        super(IDLoss, self).__init__()
-        print('Loading ResNet ArcFace')
-        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
-        self.facenet.load_state_dict(torch.load("./pretrained/model_ir_se50.pth", map_location=device))
-        self.pool = torch.nn.AdaptiveAvgPool2d((256, 256))
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
-        self.facenet.eval()
-        self.opts = opts
-
-    def extract_feats(self, x):
-        if x.shape[2] != 256:
-            x = self.pool(x)
-        x = x[:, :, 35:223, 32:220]  # Crop interesting region
-        x = self.face_pool(x)
-        x_feats = self.facenet(x)
-        return x_feats
-
-    def forward(self, y_hat, y):
-        n_samples = y.shape[0]
-        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        sim_improvement = 0
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            loss += 1 - diff_target
-            count += 1
-
-        return loss / count, sim_improvement / count
diff --git a/spaces/akhaliq/MTTR/app.py b/spaces/akhaliq/MTTR/app.py
deleted file mode 100644
index a967abbc5d849f56c8a5dc2ac8bbd5de1620f925..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/MTTR/app.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# -*- coding: utf-8 -*-
-import os
-os.system("wget https://huggingface.co/spaces/MTTR/MTTR-Referring-Video-Object-Segmentation/resolve/main/refer-youtube-vos_window-12.pth.tar")
-import gradio as gr
-import torch
-import torchvision
-import torchvision.transforms.functional as F
-from einops import rearrange
-import numpy as np
-from PIL import Image, ImageDraw, ImageOps, ImageFont
-from moviepy.editor import VideoFileClip, AudioFileClip, ImageSequenceClip
-from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
-from tqdm import trange, tqdm
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask):
-        self.tensors = tensors
-        self.mask = mask
-
-def nested_tensor_from_videos_list(videos_list):
-    def _max_by_axis(the_list):
-      maxes = the_list[0]
-      for sublist in the_list[1:]:
-          for index, item in enumerate(sublist):
-              maxes[index] = max(maxes[index], item)
-      return maxes
-
-    max_size = _max_by_axis([list(img.shape) for img in videos_list])
-    padded_batch_shape = [len(videos_list)] + max_size
-    b, t, c, h, w = padded_batch_shape
-    dtype = videos_list[0].dtype
-    device = videos_list[0].device
-    padded_videos = torch.zeros(padded_batch_shape, dtype=dtype, device=device)
-    videos_pad_masks = torch.ones((b, t, h, w), dtype=torch.bool, device=device)
-    for vid_frames, pad_vid_frames, vid_pad_m in zip(videos_list, padded_videos, videos_pad_masks):
-        pad_vid_frames[:vid_frames.shape[0], :, :vid_frames.shape[2], :vid_frames.shape[3]].copy_(vid_frames)
-        vid_pad_m[:vid_frames.shape[0], :vid_frames.shape[2], :vid_frames.shape[3]] = False
-    return NestedTensor(padded_videos.transpose(0, 1), videos_pad_masks.transpose(0, 1))
-
-def apply_mask(image, mask, color, transparency=0.7):
-    mask = mask[..., np.newaxis].repeat(repeats=3, axis=2)
-    mask = mask * transparency
-    color_matrix = np.ones(image.shape, dtype=np.float) * color
-    out_image = color_matrix * mask + image * (1.0 - mask)
-    return out_image
-
-def process(text_query, full_video_path):
-    input_clip_path = '/tmp/input.mp4'
-    start_pt, end_pt = 0, 1
-    full_video = VideoFileClip(full_video_path)
-    subclip = full_video.subclip(start_pt, min(full_video.duration, end_pt))
-    subclip.write_videofile(input_clip_path)
-        
-    checkpoint_path ='./refer-youtube-vos_window-12.pth.tar'
-    model, postprocessor = torch.hub.load('mttr2021/MTTR:main','mttr_refer_youtube_vos', get_weights=False)
-    
-    model_state_dict = torch.load(checkpoint_path, map_location='cpu')
-    if 'model_state_dict' in model_state_dict.keys():
-        model_state_dict = model_state_dict['model_state_dict']
-    model.load_state_dict(model_state_dict, strict=True)
-
-
-    text_queries= [text_query]
-    window_length = 24  # length of window during inference
-    window_overlap = 6  # overlap (in frames) between consecutive windows
-
-    with torch.inference_mode():
-      # read and preprocess the video clip:
-      video, audio, meta = torchvision.io.read_video(filename=input_clip_path)
-      video = rearrange(video, 't h w c -> t c h w')
-      input_video = F.resize(video, size=128, max_size=256)
-      input_video = input_video.to(torch.float).div_(255)
-      input_video = F.normalize(input_video, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-      video_metadata = {'resized_frame_size': input_video.shape[-2:], 'original_frame_size': video.shape[-2:]}
-      
-      # partition the clip into overlapping windows of frames:
-      windows = [input_video[i:i+window_length] for i in range(0, len(input_video), window_length - window_overlap)]
-      # clean up the text queries:
-      text_queries = [" ".join(q.lower().split()) for q in text_queries]
-
-      pred_masks_per_query = []
-      t, _, h, w = video.shape
-      for text_query in tqdm(text_queries, desc='text queries'):
-        pred_masks = torch.zeros(size=(t, 1, h, w))
-        for i, window in enumerate(tqdm(windows, desc='windows')):
-          window = nested_tensor_from_videos_list([window])
-          valid_indices = torch.arange(len(window.tensors))
-          outputs = model(window, valid_indices, [text_query])
-          window_masks = postprocessor(outputs, [video_metadata], window.tensors.shape[-2:])[0]['pred_masks']
-          win_start_idx = i*(window_length-window_overlap)
-          pred_masks[win_start_idx:win_start_idx + window_length] = window_masks
-        pred_masks_per_query.append(pred_masks)
-
-    """Finally, we apply the generated instance masks and their corresponding text queries on the input clip for visualization:"""
-
-    # RGB colors for instance masks:
-    light_blue = (41, 171, 226)
-    purple = (237, 30, 121)
-    dark_green = (35, 161, 90)
-    orange = (255, 148, 59)
-    colors = np.array([light_blue, purple, dark_green, orange])
-
-    # width (in pixels) of the black strip above the video on which the text queries will be displayed:
-    text_border_height_per_query = 40
-
-    video_np = rearrange(video, 't c h w -> t h w c').numpy() / 255.0
-    # del video
-    pred_masks_per_frame = rearrange(torch.stack(pred_masks_per_query), 'q t 1 h w -> t q h w').numpy()
-    masked_video = []
-    for vid_frame, frame_masks in tqdm(zip(video_np, pred_masks_per_frame), total=len(video_np), desc='applying masks...'):
-      # apply the masks:
-      for inst_mask, color in zip(frame_masks, colors):
-        vid_frame = apply_mask(vid_frame, inst_mask, color / 255.0)
-      vid_frame = Image.fromarray((vid_frame * 255).astype(np.uint8))
-      # visualize the text queries:
-      vid_frame = ImageOps.expand(vid_frame, border=(0, len(text_queries)*text_border_height_per_query, 0, 0))
-      W, H = vid_frame.size
-      draw = ImageDraw.Draw(vid_frame)
-      font = ImageFont.truetype(font='LiberationSans-Regular.ttf', size=30)
-      for i, (text_query, color) in enumerate(zip(text_queries, colors), start=1):
-          w, h = draw.textsize(text_query, font=font)
-          draw.text(((W - w) / 2, (text_border_height_per_query * i) - h - 8),
-                    text_query, fill=tuple(color) + (255,), font=font)
-      masked_video.append(np.array(vid_frame))
-
-    # generate and save the output clip:
-    output_clip_path = '/tmp/output_clip.mp4'
-    clip = ImageSequenceClip(sequence=masked_video, fps=meta['video_fps'])
-    if subclip.audio:  # attach audio if original subclip had audio
-      clip = clip.set_audio(subclip.audio)
-    clip.write_videofile(output_clip_path, fps=meta['video_fps'], audio=True)
-    del masked_video
-    
-
-    return output_clip_path
-    
-    
-
-title = "MTTR - Interactive Demo"
-
-description = "Given a text query and a short clip based on a YouTube video, we demonstrate how MTTR can be used to segment the referred object instance throughout the video. Select one of the examples below and click 'submit'. Alternatively, try using your own input by uploading a short .mp4 video file and entering a short text query which describes one of the object instances in that video. This is a fork of the original space at https://huggingface.co/spaces/MTTR/MTTR-Referring-Video-Object-Segmentation. This space uses 1 second of input video vs 10 seconds in the original space"
-
-article = "Check out [MTTR's GitHub page](https://github.com/mttr2021/MTTR) for more info about this project. <br> Also, check out our interactive [Colab notebook](https://colab.research.google.com/drive/12p0jpSx3pJNfZk-y_L44yeHZlhsKVra-?usp=sharing) for **much faster** processing (GPU accelerated) and more options! <br> **Disclaimer:** <br>  This is a **limited** demonstration of MTTR's performance. The model used here was trained **exclusively** on Refer-YouTube-VOS with window size `w=12` (as described in our paper). No additional training data was used whatsoever. Hence, the model's performance may be limited, especially on instances from unseen categories. <br> Additionally, slow processing times may be encountered due to HuggingFace's limited computational resources (no GPU acceleration unfortunately), and depending on the input clip length and/or resolution. <br> Finally, we emphasize that this demonstration is intended to be used for academic purposes only. We do not take any responsibility for how the created content is used or distributed."
-
-examples = [['guy in white shirt performing tricks on a bike', 'bike_tricks_2.mp4']]
-
-iface = gr.Interface(fn=process, 
-                     inputs=[gr.inputs.Textbox(label="text query"), gr.inputs.Video(label="input video - first second is used")],
-                     outputs='video',
-                     title=title,
-                     description=description,
-                     examples=examples,
-                     article=article)
-iface.launch(enable_queue=True,cache_examples=True)
\ No newline at end of file
diff --git a/spaces/akhaliq/Real-Time-Voice-Cloning/utils/argutils.py b/spaces/akhaliq/Real-Time-Voice-Cloning/utils/argutils.py
deleted file mode 100644
index db41683027173517c910e3b259f8da48207dcb38..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-Time-Voice-Cloning/utils/argutils.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from pathlib import Path
-import numpy as np
-import argparse
-
-_type_priorities = [    # In decreasing order
-    Path,
-    str,
-    int,
-    float,
-    bool,
-]
-
-def _priority(o):
-    p = next((i for i, t in enumerate(_type_priorities) if type(o) is t), None) 
-    if p is not None:
-        return p
-    p = next((i for i, t in enumerate(_type_priorities) if isinstance(o, t)), None) 
-    if p is not None:
-        return p
-    return len(_type_priorities)
-
-def print_args(args: argparse.Namespace, parser=None):
-    args = vars(args)
-    if parser is None:
-        priorities = list(map(_priority, args.values()))
-    else:
-        all_params = [a.dest for g in parser._action_groups for a in g._group_actions ]
-        priority = lambda p: all_params.index(p) if p in all_params else len(all_params)
-        priorities = list(map(priority, args.keys()))
-    
-    pad = max(map(len, args.keys())) + 3
-    indices = np.lexsort((list(args.keys()), priorities))
-    items = list(args.items())
-    
-    print("Arguments:")
-    for i in indices:
-        param, value = items[i]
-        print("    {0}:{1}{2}".format(param, ' ' * (pad - len(param)), value))
-    print("")
-    
\ No newline at end of file
diff --git a/spaces/akhaliq/VoiceFixer/README.md b/spaces/akhaliq/VoiceFixer/README.md
deleted file mode 100644
index b1bb0b6c74185701f14093b3464743f8bfc88eec..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VoiceFixer/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: VoiceFixer
-emoji: 💩
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/akhaliq/stylegan3_clip/torch_utils/ops/filtered_lrelu.h b/spaces/akhaliq/stylegan3_clip/torch_utils/ops/filtered_lrelu.h
deleted file mode 100644
index 2060f5dc0785df162ecebbdf1c034e697046ded1..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/stylegan3_clip/torch_utils/ops/filtered_lrelu.h
+++ /dev/null
@@ -1,90 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct filtered_lrelu_kernel_params
-{
-    // These parameters decide which kernel to use.
-    int             up;         // upsampling ratio (1, 2, 4)
-    int             down;       // downsampling ratio (1, 2, 4)
-    int2            fuShape;    // [size, 1] | [size, size]
-    int2            fdShape;    // [size, 1] | [size, size]
-
-    int             _dummy;     // Alignment.
-
-    // Rest of the parameters.
-    const void*     x;          // Input tensor.
-    void*           y;          // Output tensor.
-    const void*     b;          // Bias tensor.
-    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
-    const float*    fu;         // Upsampling filter.
-    const float*    fd;         // Downsampling filter.
-
-    int2            pad0;       // Left/top padding.
-    float           gain;       // Additional gain factor.
-    float           slope;      // Leaky ReLU slope on negative side.
-    float           clamp;      // Clamp after nonlinearity.
-    int             flip;       // Filter kernel flip for gradient computation.
-
-    int             tilesXdim;  // Original number of horizontal output tiles.
-    int             tilesXrep;  // Number of horizontal tiles per CTA.
-    int             blockZofs;  // Block z offset to support large minibatch, channel dimensions.
-
-    int4            xShape;     // [width, height, channel, batch]
-    int4            yShape;     // [width, height, channel, batch]
-    int2            sShape;     // [width, height] - width is in bytes. Contiguous. Zeros if unused.
-    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
-    int             swLimit;    // Active width of sign tensor in bytes.
-
-    longlong4       xStride;    // Strides of all tensors except signs, same component order as shapes.
-    longlong4       yStride;    //
-    int64_t         bStride;    //
-    longlong3       fuStride;   //
-    longlong3       fdStride;   //
-};
-
-struct filtered_lrelu_act_kernel_params
-{
-    void*           x;          // Input/output, modified in-place.
-    unsigned char*  s;          // Sign tensor in/out. NULL if unused.
-
-    float           gain;       // Additional gain factor.
-    float           slope;      // Leaky ReLU slope on negative side.
-    float           clamp;      // Clamp after nonlinearity.
-
-    int4            xShape;     // [width, height, channel, batch]
-    longlong4       xStride;    // Input/output tensor strides, same order as in shape.
-    int2            sShape;     // [width, height] - width is in elements. Contiguous. Zeros if unused.
-    int2            sOfs;       // [ofs_x, ofs_y] - offset between upsampled data and sign tensor.
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct filtered_lrelu_kernel_spec
-{
-    void*   setup;              // Function for filter kernel setup.
-    void*   exec;               // Function for main operation.
-    int2    tileOut;            // Width/height of launch tile.
-    int     numWarps;           // Number of warps per thread block, determines launch block size.
-    int     xrep;               // For processing multiple horizontal tiles per thread block.
-    int     dynamicSharedKB;    // How much dynamic shared memory the exec kernel wants.
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T, class index_t, bool signWrite, bool signRead> filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB);
-template <class T, bool signWrite, bool signRead> void* choose_filtered_lrelu_act_kernel(void);
-template <bool signWrite, bool signRead> cudaError_t copy_filters(cudaStream_t stream);
-
-//------------------------------------------------------------------------
diff --git a/spaces/alamin655/websurfx/public/templates/footer.html b/spaces/alamin655/websurfx/public/templates/footer.html
deleted file mode 100644
index 8892b1860088f499676c16ac9d19b07a891002e8..0000000000000000000000000000000000000000
--- a/spaces/alamin655/websurfx/public/templates/footer.html
+++ /dev/null
@@ -1,16 +0,0 @@
-<footer>
-    <div>
-        <span>Powered By <b>Websurfx</b></span><span>-</span><span>a lightening fast, privacy respecting, secure meta
-            search engine</span>
-    </div>
-    <div>
-        <ul>
-            <li><a href="https://github.com/neon-mmd/websurfx">Source Code</a></li>
-            <li><a href="https://github.com/neon-mmd/websurfx/issues">Issues/Bugs</a></li>
-        </ul>
-    </div>
-</footer>
-<script src="static/settings.js"></script>
-</body>
-
-</html>
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/progress/bar.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/progress/bar.py
deleted file mode 100644
index df4e8b61f89e020ee6a536209fa04f049637c86f..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/progress/bar.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright (c) 2012 Georgios Verigakis <verigak@gmail.com>
-#
-# Permission to use, copy, modify, and distribute this software for any
-# purpose with or without fee is hereby granted, provided that the above
-# copyright notice and this permission notice appear in all copies.
-#
-# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
-# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
-# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
-# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-from __future__ import unicode_literals
-
-import sys
-
-from . import Progress
-from .colors import color
-
-
-class Bar(Progress):
-    width = 32
-    suffix = '%(index)d/%(max)d'
-    bar_prefix = ' |'
-    bar_suffix = '| '
-    empty_fill = ' '
-    fill = '#'
-    color = None
-
-    def update(self):
-        filled_length = int(self.width * self.progress)
-        empty_length = self.width - filled_length
-
-        message = self.message % self
-        bar = color(self.fill * filled_length, fg=self.color)
-        empty = self.empty_fill * empty_length
-        suffix = self.suffix % self
-        line = ''.join([message, self.bar_prefix, bar, empty, self.bar_suffix,
-                        suffix])
-        self.writeln(line)
-
-
-class ChargingBar(Bar):
-    suffix = '%(percent)d%%'
-    bar_prefix = ' '
-    bar_suffix = ' '
-    empty_fill = '∙'
-    fill = '█'
-
-
-class FillingSquaresBar(ChargingBar):
-    empty_fill = '▢'
-    fill = '▣'
-
-
-class FillingCirclesBar(ChargingBar):
-    empty_fill = '◯'
-    fill = '◉'
-
-
-class IncrementalBar(Bar):
-    if sys.platform.startswith('win'):
-        phases = (u' ', u'▌', u'█')
-    else:
-        phases = (' ', '▏', '▎', '▍', '▌', '▋', '▊', '▉', '█')
-
-    def update(self):
-        nphases = len(self.phases)
-        filled_len = self.width * self.progress
-        nfull = int(filled_len)                      # Number of full chars
-        phase = int((filled_len - nfull) * nphases)  # Phase of last char
-        nempty = self.width - nfull                  # Number of empty chars
-
-        message = self.message % self
-        bar = color(self.phases[-1] * nfull, fg=self.color)
-        current = self.phases[phase] if phase > 0 else ''
-        empty = self.empty_fill * max(0, nempty - len(current))
-        suffix = self.suffix % self
-        line = ''.join([message, self.bar_prefix, bar, current, empty,
-                        self.bar_suffix, suffix])
-        self.writeln(line)
-
-
-class PixelBar(IncrementalBar):
-    phases = ('⡀', '⡄', '⡆', '⡇', '⣇', '⣧', '⣷', '⣿')
-
-
-class ShadyBar(IncrementalBar):
-    phases = (' ', '░', '▒', '▓', '█')
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/src/common/pa_trace.c b/spaces/amarchheda/ChordDuplicate/portaudio/src/common/pa_trace.c
deleted file mode 100644
index 6763dfacff401e5b4543725728cb30eab41d03b9..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/src/common/pa_trace.c
+++ /dev/null
@@ -1,238 +0,0 @@
-/*
- * $Id$
- * Portable Audio I/O Library Trace Facility
- * Store trace information in real-time for later printing.
- *
- * Based on the Open Source API proposed by Ross Bencina
- * Copyright (c) 1999-2000 Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-/** @file
- @ingroup common_src
-
- @brief Real-time safe event trace logging facility for debugging.
-*/
-
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <stdarg.h>
-#include <string.h>
-#include <assert.h>
-#include "pa_trace.h"
-#include "pa_util.h"
-#include "pa_debugprint.h"
-
-#if PA_TRACE_REALTIME_EVENTS
-
-static char const *traceTextArray[PA_MAX_TRACE_RECORDS];
-static int traceIntArray[PA_MAX_TRACE_RECORDS];
-static int traceIndex = 0;
-static int traceBlock = 0;
-
-/*********************************************************************/
-void PaUtil_ResetTraceMessages()
-{
-    traceIndex = 0;
-}
-
-/*********************************************************************/
-void PaUtil_DumpTraceMessages()
-{
-    int i;
-    int messageCount = (traceIndex < PA_MAX_TRACE_RECORDS) ? traceIndex : PA_MAX_TRACE_RECORDS;
-
-    printf("DumpTraceMessages: traceIndex = %d\n", traceIndex );
-    for( i=0; i<messageCount; i++ )
-    {
-        printf("%3d: %s = 0x%08X\n",
-               i, traceTextArray[i], traceIntArray[i] );
-    }
-    PaUtil_ResetTraceMessages();
-    fflush(stdout);
-}
-
-/*********************************************************************/
-void PaUtil_AddTraceMessage( const char *msg, int data )
-{
-    if( (traceIndex == PA_MAX_TRACE_RECORDS) && (traceBlock == 0) )
-    {
-        traceBlock = 1;
-        /*  PaUtil_DumpTraceMessages(); */
-    }
-    else if( traceIndex < PA_MAX_TRACE_RECORDS )
-    {
-        traceTextArray[traceIndex] = msg;
-        traceIntArray[traceIndex] = data;
-        traceIndex++;
-    }
-}
-
-/************************************************************************/
-/* High performance log alternative                                     */
-/************************************************************************/
-
-typedef unsigned long long  PaUint64;
-
-typedef struct __PaHighPerformanceLog
-{
-    unsigned    magik;
-    int         writePtr;
-    int         readPtr;
-    int         size;
-    double      refTime;
-    char*       data;
-} PaHighPerformanceLog;
-
-static const unsigned kMagik = 0xcafebabe;
-
-#define USEC_PER_SEC    (1000000ULL)
-
-int PaUtil_InitializeHighSpeedLog( LogHandle* phLog, unsigned maxSizeInBytes )
-{
-    PaHighPerformanceLog* pLog = (PaHighPerformanceLog*)PaUtil_AllocateMemory(sizeof(PaHighPerformanceLog));
-    if (pLog == 0)
-    {
-        return paInsufficientMemory;
-    }
-    assert(phLog != 0);
-    *phLog = pLog;
-
-    pLog->data = (char*)PaUtil_AllocateMemory(maxSizeInBytes);
-    if (pLog->data == 0)
-    {
-        PaUtil_FreeMemory(pLog);
-        return paInsufficientMemory;
-    }
-    pLog->magik = kMagik;
-    pLog->size = maxSizeInBytes;
-    pLog->refTime = PaUtil_GetTime();
-    return paNoError;
-}
-
-void PaUtil_ResetHighSpeedLogTimeRef( LogHandle hLog )
-{
-    PaHighPerformanceLog* pLog = (PaHighPerformanceLog*)hLog;
-    assert(pLog->magik == kMagik);
-    pLog->refTime = PaUtil_GetTime();
-}
-
-typedef struct __PaLogEntryHeader
-{
-    int    size;
-    double timeStamp;
-} PaLogEntryHeader;
-
-#ifdef __APPLE__
-#define _vsnprintf vsnprintf
-#define min(a,b) ((a)<(b)?(a):(b))
-#endif
-
-
-int PaUtil_AddHighSpeedLogMessage( LogHandle hLog, const char* fmt, ... )
-{
-    va_list l;
-    int n = 0;
-    PaHighPerformanceLog* pLog = (PaHighPerformanceLog*)hLog;
-    if (pLog != 0)
-    {
-        PaLogEntryHeader* pHeader;
-        char* p;
-        int maxN;
-        assert(pLog->magik == kMagik);
-        pHeader = (PaLogEntryHeader*)( pLog->data + pLog->writePtr );
-        p = (char*)( pHeader + 1 );
-        maxN = pLog->size - pLog->writePtr - 2 * sizeof(PaLogEntryHeader);
-
-        pHeader->timeStamp = PaUtil_GetTime() - pLog->refTime;
-        if (maxN > 0)
-        {
-            if (maxN > 32)
-            {
-                va_start(l, fmt);
-                n = _vsnprintf(p, min(1024, maxN), fmt, l);
-                va_end(l);
-            }
-            else {
-                n = sprintf(p, "End of log...");
-            }
-            n = ((n + sizeof(unsigned)) & ~(sizeof(unsigned)-1)) + sizeof(PaLogEntryHeader);
-            pHeader->size = n;
-#if 0
-            PaUtil_DebugPrint("%05u.%03u: %s\n", pHeader->timeStamp/1000, pHeader->timeStamp%1000, p);
-#endif
-            pLog->writePtr += n;
-        }
-    }
-    return n;
-}
-
-void PaUtil_DumpHighSpeedLog( LogHandle hLog, const char* fileName )
-{
-    FILE* f = (fileName != NULL) ? fopen(fileName, "w") : stdout;
-    unsigned localWritePtr;
-    PaHighPerformanceLog* pLog = (PaHighPerformanceLog*)hLog;
-    assert(pLog->magik == kMagik);
-    localWritePtr = pLog->writePtr;
-    while (pLog->readPtr != localWritePtr)
-    {
-        const PaLogEntryHeader* pHeader = (const PaLogEntryHeader*)( pLog->data + pLog->readPtr );
-        const char* p = (const char*)( pHeader + 1 );
-        const PaUint64 ts = (const PaUint64)( pHeader->timeStamp * USEC_PER_SEC );
-        assert(pHeader->size < (1024+sizeof(unsigned)+sizeof(PaLogEntryHeader)));
-        fprintf(f, "%05u.%03u: %s\n", (unsigned)(ts/1000), (unsigned)(ts%1000), p);
-        pLog->readPtr += pHeader->size;
-    }
-    if (f != stdout)
-    {
-        fclose(f);
-    }
-}
-
-void PaUtil_DiscardHighSpeedLog( LogHandle hLog )
-{
-    PaHighPerformanceLog* pLog = (PaHighPerformanceLog*)hLog;
-    assert(pLog->magik == kMagik);
-    PaUtil_FreeMemory(pLog->data);
-    PaUtil_FreeMemory(pLog);
-}
-
-#else
-/* This stub was added so that this file will generate a symbol.
- * Otherwise linker/archiver programs will complain.
- */
-int PaUtil_TraceStubToSatisfyLinker(void)
-{
-    return 0;
-}
-#endif /* TRACE_REALTIME_EVENTS */
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_formats.c b/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_formats.c
deleted file mode 100644
index 3224d6e561384f858d3fa7f6c71501fb9072a650..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/test/patest_sine_formats.c
+++ /dev/null
@@ -1,203 +0,0 @@
-/** @file patest_sine_formats.c
-    @ingroup test_src
-    @brief Play a sine wave for several seconds. Test various data formats.
-    @author Phil Burk
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-#include <stdio.h>
-#include <math.h>
-#include "portaudio.h"
-
-#define NUM_SECONDS        (10)
-#define SAMPLE_RATE        (44100)
-#define FRAMES_PER_BUFFER  (512)
-#define LEFT_FREQ          (SAMPLE_RATE/256.0)  /* So we hit 1.0 */
-#define RIGHT_FREQ         (500.0)
-#define AMPLITUDE          (1.0)
-
-/* Select ONE format for testing. */
-#define TEST_UINT8    (0)
-#define TEST_INT8     (0)
-#define TEST_INT16    (1)
-#define TEST_FLOAT32  (0)
-
-#if TEST_UINT8
-#define TEST_FORMAT         paUInt8
-typedef unsigned char       SAMPLE_t;
-#define SAMPLE_ZERO         (0x80)
-#define DOUBLE_TO_SAMPLE(x) (SAMPLE_ZERO + (SAMPLE_t)(127.0 * (x)))
-#define FORMAT_NAME         "Unsigned 8 Bit"
-
-#elif TEST_INT8
-#define TEST_FORMAT         paInt8
-typedef char                SAMPLE_t;
-#define SAMPLE_ZERO         (0)
-#define DOUBLE_TO_SAMPLE(x) (SAMPLE_ZERO + (SAMPLE_t)(127.0 * (x)))
-#define FORMAT_NAME         "Signed 8 Bit"
-
-#elif TEST_INT16
-#define TEST_FORMAT         paInt16
-typedef short               SAMPLE_t;
-#define SAMPLE_ZERO         (0)
-#define DOUBLE_TO_SAMPLE(x) (SAMPLE_ZERO + (SAMPLE_t)(32767 * (x)))
-#define FORMAT_NAME         "Signed 16 Bit"
-
-#elif TEST_FLOAT32
-#define TEST_FORMAT         paFloat32
-typedef float               SAMPLE_t;
-#define SAMPLE_ZERO         (0.0)
-#define DOUBLE_TO_SAMPLE(x) ((SAMPLE_t)(x))
-#define FORMAT_NAME         "Float 32 Bit"
-#endif
-
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-
-
-typedef struct
-{
-    double left_phase;
-    double right_phase;
-    unsigned int framesToGo;
-}
-paTestData;
-/* This routine will be called by the PortAudio engine when audio is needed.
-** It may called at interrupt level on some machines so don't do anything
-** that could mess up the system like calling malloc() or free().
-*/
-static int patestCallback( const void *inputBuffer,
-                           void *outputBuffer,
-                           unsigned long framesPerBuffer,
-                           const PaStreamCallbackTimeInfo* timeInfo,
-                           PaStreamCallbackFlags statusFlags,
-                           void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    SAMPLE_t *out = (SAMPLE_t *)outputBuffer;
-    int i;
-    int framesToCalc;
-    int finished = 0;
-    (void) inputBuffer; /* Prevent unused variable warnings. */
-
-    if( data->framesToGo < framesPerBuffer )
-    {
-        framesToCalc = data->framesToGo;
-        data->framesToGo = 0;
-        finished = 1;
-    }
-    else
-    {
-        framesToCalc = framesPerBuffer;
-        data->framesToGo -= framesPerBuffer;
-    }
-
-    for( i=0; i<framesToCalc; i++ )
-    {
-        data->left_phase += (LEFT_FREQ / SAMPLE_RATE);
-        if( data->left_phase > 1.0) data->left_phase -= 1.0;
-        *out++ = DOUBLE_TO_SAMPLE( AMPLITUDE * sin( (data->left_phase * M_PI * 2. )));
-
-        data->right_phase += (RIGHT_FREQ / SAMPLE_RATE);
-        if( data->right_phase > 1.0) data->right_phase -= 1.0;
-        *out++ = DOUBLE_TO_SAMPLE( AMPLITUDE * sin( (data->right_phase * M_PI * 2. )));
-    }
-    /* zero remainder of final buffer */
-    for( ; i<(int)framesPerBuffer; i++ )
-    {
-        *out++ = SAMPLE_ZERO; /* left */
-        *out++ = SAMPLE_ZERO; /* right */
-    }
-    return finished;
-}
-/*******************************************************************/
-int main(void);
-int main(void)
-{
-    PaStream *stream;
-    PaStreamParameters outputParameters;
-    PaError err;
-    paTestData data;
-    int totalSamps;
-
-    printf("PortAudio Test: output " FORMAT_NAME "\n");
-
-    data.left_phase = data.right_phase = 0.0;
-    data.framesToGo = totalSamps =  NUM_SECONDS * SAMPLE_RATE; /* Play for a few seconds. */
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-
-    outputParameters.device           = Pa_GetDefaultOutputDevice(); /* Default output device. */
-    outputParameters.channelCount     = 2;                           /* Stereo output */
-    outputParameters.sampleFormat     = TEST_FORMAT;                 /* Selected above. */
-    outputParameters.suggestedLatency = Pa_GetDeviceInfo(outputParameters.device)->defaultLowOutputLatency;
-    outputParameters.hostApiSpecificStreamInfo = NULL;
-    err = Pa_OpenStream( &stream,
-                         NULL,                  /* No input. */
-                         &outputParameters,     /* As above. */
-                         SAMPLE_RATE,
-                         FRAMES_PER_BUFFER,
-                         paClipOff,      /* we won't output out of range samples so don't bother clipping them */
-                         patestCallback,
-                         &data );
-    if( err != paNoError ) goto error;
-
-    err = Pa_StartStream( stream );
-    if( err != paNoError ) goto error;
-
-    printf("Waiting %d seconds for sound to finish.\n", NUM_SECONDS );
-
-    while( ( err = Pa_IsStreamActive( stream ) ) == 1 ) Pa_Sleep(100);
-    if( err < 0 ) goto error;
-
-    err = Pa_CloseStream( stream );
-    if( err != paNoError ) goto error;
-    Pa_Terminate();
-
-    printf("PortAudio Test Finished: " FORMAT_NAME "\n");
-
-    return err;
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    return err;
-}
diff --git a/spaces/amsterdamNLP/attention-rollout/README.md b/spaces/amsterdamNLP/attention-rollout/README.md
deleted file mode 100644
index f344f095c9ba1e48d84f62159dee133e8c22b8ec..0000000000000000000000000000000000000000
--- a/spaces/amsterdamNLP/attention-rollout/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Attention Rollout
-emoji: 🌍
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
----
-
diff --git a/spaces/aodianyun/stable-diffusion-webui/javascript/dragdrop.js b/spaces/aodianyun/stable-diffusion-webui/javascript/dragdrop.js
deleted file mode 100644
index fe00892481a8ff8b997759b21cb36eb364db788b..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/javascript/dragdrop.js
+++ /dev/null
@@ -1,97 +0,0 @@
-// allows drag-dropping files into gradio image elements, and also pasting images from clipboard
-
-function isValidImageList( files ) {
-    return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
-}
-
-function dropReplaceImage( imgWrap, files ) {
-    if ( ! isValidImageList( files ) ) {
-        return;
-    }
-
-    const tmpFile = files[0];
-
-    imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
-    const callback = () => {
-        const fileInput = imgWrap.querySelector('input[type="file"]');
-        if ( fileInput ) {
-            if ( files.length === 0 ) {
-                files = new DataTransfer();
-                files.items.add(tmpFile);
-                fileInput.files = files.files;
-            } else {
-                fileInput.files = files;
-            }
-            fileInput.dispatchEvent(new Event('change'));   
-        }
-    };
-    
-    if ( imgWrap.closest('#pnginfo_image') ) {
-        // special treatment for PNG Info tab, wait for fetch request to finish
-        const oldFetch = window.fetch;
-        window.fetch = async (input, options) => {
-            const response = await oldFetch(input, options);
-            if ( 'api/predict/' === input ) {
-                const content = await response.text();
-                window.fetch = oldFetch;
-                window.requestAnimationFrame( () => callback() );
-                return new Response(content, {
-                    status: response.status,
-                    statusText: response.statusText,
-                    headers: response.headers
-                })
-            }
-            return response;
-        };        
-    } else {
-        window.requestAnimationFrame( () => callback() );
-    }
-}
-
-window.document.addEventListener('dragover', e => {
-    const target = e.composedPath()[0];
-    const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
-        return;
-    }
-    e.stopPropagation();
-    e.preventDefault();
-    e.dataTransfer.dropEffect = 'copy';
-});
-
-window.document.addEventListener('drop', e => {
-    const target = e.composedPath()[0];
-    if (target.placeholder.indexOf("Prompt") == -1) {
-        return;
-    }
-    const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap ) {
-        return;
-    }
-    e.stopPropagation();
-    e.preventDefault();
-    const files = e.dataTransfer.files;
-    dropReplaceImage( imgWrap, files );
-});
-
-window.addEventListener('paste', e => {
-    const files = e.clipboardData.files;
-    if ( ! isValidImageList( files ) ) {
-        return;
-    }
-
-    const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
-        .filter(el => uiElementIsVisible(el));
-    if ( ! visibleImageFields.length ) {
-        return;
-    }
-    
-    const firstFreeImageField = visibleImageFields
-        .filter(el => el.querySelector('input[type=file]'))?.[0];
-
-    dropReplaceImage(
-        firstFreeImageField ?
-        firstFreeImageField :
-        visibleImageFields[visibleImageFields.length - 1]
-    , files );
-});
diff --git a/spaces/arbml/Ashaar/poetry_diacritizer/util/text_cleaners.py b/spaces/arbml/Ashaar/poetry_diacritizer/util/text_cleaners.py
deleted file mode 100644
index 04b66ee7a261feb58e5636147e9af1213abb2c75..0000000000000000000000000000000000000000
--- a/spaces/arbml/Ashaar/poetry_diacritizer/util/text_cleaners.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import re
-from .constants import VALID_ARABIC
-from itertools import product, combinations
-
-_whitespace_re = re.compile(r"\s+")
-
-
-def collapse_whitespace(text):
-    text = re.sub(_whitespace_re, " ", text)
-    return text
-
-
-def basic_cleaners(text):
-    text = collapse_whitespace(text)
-    return text.strip()
-
-
-# def valid_arabic_cleaners(text):
-#     text = filter(lambda char: char in VALID_ARABIC, text)
-#     text = collapse_whitespace(''.join(list(text)))
-#     return text.strip()
-
-harakat = ["\u0650", "\u064E", "\u064F"]  # [kasra, fatha, damma, ]
-sukun = ["\u0652"]  # [sukun]
-mostly_saken = [
-  "\u0627",
-  "\u0648",
-  "\u0649",
-  "\u064A",
-]  # [alef, waw, alef maqsurah, ya'a]
-
-always_saken = [
-  "\u0627",
-  "\u0649",
-]
-
-tnween_chars = [
-    "\u064c",
-    "\u064d",
-    "\u064b",
-]  # damm tanween, kasra tanween, fatha tanween, maddah
-shadda_chars = ["\u0651"]
-all_tashkeel = harakat+tnween_chars+sukun+shadda_chars
-
-
-all_chars = list("إةابتثجحخدذرزسشصضطظعغفقكلمنهويىأءئؤ ")
-prem_chars = harakat + sukun + mostly_saken + tnween_chars + shadda_chars + all_chars
-
-def not_valid_tashkeel_comb(comb):
-  all_comb = list(product(harakat+sukun+tnween_chars, repeat = 2))+list(product(shadda_chars+sukun, repeat = 2))
-  if comb in all_comb or comb[::-1] in all_comb:
-    return True
-  else:
-    return False
-
-def remove_tanween_on_alef(text):
-  text_copy = ""
-  for i in range(0, len(text)):
-
-    # if there is shaddah or character followed by alef followed by tanween add
-    if i < len(text) - 2 and text[i] in all_chars+shadda_chars and text[i+1] in always_saken and text[i+2] == tnween_chars[2]:
-      text_copy += text[i] + tnween_chars[2]
-    
-    #ignore current harakah if there is alef followed by tanween
-    elif i < len(text) - 2 and text[i] in harakat and text[i+1] in always_saken and text[i+2] == tnween_chars[2] : 
-      text_copy += tnween_chars[2]
-
-    # if the current char is tanween with alef is the previous character drop tanween
-    elif i > 0 and text[i] == tnween_chars[2] and text[i-1] in always_saken:
-      continue
-
-    else:
-      text_copy += text[i]
-  return text_copy
-
-def dont_start_by_harakah(text):
-    text_copy = ""
-    for i, char in enumerate(text):
-      if not(char in all_tashkeel):
-        text_copy = text[i:]
-        break 
-    return text_copy
-        
-def valid_arabic_cleaners(text):
-  prev_text = text
-  for i in range(5):
-    text = prev_text
-    cleaned_text = ""
-    text = filter(lambda char: char in VALID_ARABIC, text)
-    text = collapse_whitespace(''.join(list(text)))
-    text = dont_start_by_harakah(text)
-    text = text.strip()
-    i = 0
-    cnt = 0
-    len_text = len(text)
-    while( i < len_text):
-      if text[i] in all_tashkeel:
-        cnt += 1 
-      else:
-        cnt = 0
-
-      # don't allow three consecutive tashkeel
-      if cnt > 2:
-        i+= 1
-        continue
-
-      # remove second tanween and sukun
-      if i > 1 and text[i] in tnween_chars+sukun and  text[i-2] in tnween_chars+sukun:
-        i += 1
-        continue 
-      
-      # don't allow harakah followed by shaddah or tanween
-      if i < len(text) - 1 and text[i] in harakat and  text[i+1] in tnween_chars+sukun+shadda_chars:
-        i += 1
-        continue
-      
-      # don't allow harkah on space
-      if i> 0 and text[i] in all_tashkeel and text[i-1] == " " :
-        i += 1
-        continue
-
-      # only allow permissable combinations
-      if not_valid_tashkeel_comb((text[i], text[i-1])):
-        i+=1
-        continue
-
-      # don't allow harkah on alef, alef maqsura, if there is no tashkeel before move it back
-      if i> 1 and text[i] in harakat and text[i-1] in always_saken :
-        if text[i-2] in all_tashkeel: # in case there is a tashkeelah before alef
-          continue
-        else:
-          cleaned_text = text[:i-1]+text[i]+ always_saken[always_saken.index(text[i-1])]
-          i += 1 
-         
-      if i < len(text):
-        cleaned_text+= text[i]
-        i += 1
-    
-    # only allow tanween before alef
-    cleaned_text = remove_tanween_on_alef(cleaned_text)
-    cleaned_text = re.sub(r" +", " ", cleaned_text).strip()
-    if prev_text == cleaned_text:
-      break
-    else:
-      prev_text = cleaned_text 
-  return cleaned_text
\ No newline at end of file
diff --git a/spaces/arch-123/bingo/src/pages/api/kblob.ts b/spaces/arch-123/bingo/src/pages/api/kblob.ts
deleted file mode 100644
index 0ce7e6063cdc06838e76f1cff1d5982d34ef52de..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/pages/api/kblob.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import FormData from 'form-data'
-import { fetch } from '@/lib/isomorphic'
-import { KBlobRequest } from '@/lib/bots/bing/types'
-
-const API_DOMAIN = 'https://bing.vcanbb.top'
-
-export const config = {
-  api: {
-    bodyParser: {
-      sizeLimit: '10mb' // Set desired value here
-    }
-  }
-}
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { knowledgeRequest, imageBase64 } = req.body as KBlobRequest
-
-    const formData = new FormData()
-    formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest))
-    if (imageBase64) {
-      formData.append('imageBase64', imageBase64)
-    }
-
-    const response = await fetch(`${API_DOMAIN}/images/kblob`,
-      {
-        method: 'POST',
-        body: formData.getBuffer(),
-        headers: {
-          "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"",
-          "sec-ch-ua-mobile": "?0",
-          "sec-ch-ua-platform": "\"Windows\"",
-          "Referer": `${API_DOMAIN}/web/index.html`,
-          "Referrer-Policy": "origin-when-cross-origin",
-          'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-          ...formData.getHeaders()
-        }
-      }
-    ).then(res => res.text())
-
-    res.writeHead(200, {
-      'Content-Type': 'application/json',
-    })
-    res.end(response || JSON.stringify({ result: { value: 'UploadFailed', message: '请更换 IP 或代理后重试' } }))
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'UploadFailed',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_MD5.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_MD5.py
deleted file mode 100644
index 830ace731538ad2529f230c8507dd230ac0d02c8..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Hash/test_MD5.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  SelfTest/Hash/MD5.py: Self-test for the MD5 hash function
-#
-# Written in 2008 by Dwayne C. Litzenberger <dlitz@dlitz.net>
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain.  To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-
-"""Self-test suite for Crypto.Hash.MD5"""
-
-from Crypto.Util.py3compat import *
-from Crypto.Hash import MD5
-from binascii import unhexlify
-import unittest
-from Crypto.SelfTest.st_common import list_test_cases
-
-
-# This is a list of (expected_result, input[, description]) tuples.
-test_data = [
-    # Test vectors from RFC 1321
-    ('d41d8cd98f00b204e9800998ecf8427e', '', "'' (empty string)"),
-    ('0cc175b9c0f1b6a831c399e269772661', 'a'),
-    ('900150983cd24fb0d6963f7d28e17f72', 'abc'),
-    ('f96b697d7cb7938d525a2f31aaf161d0', 'message digest'),
-
-    ('c3fcd3d76192e4007dfb496cca67e13b', 'abcdefghijklmnopqrstuvwxyz',
-        'a-z'),
-
-    ('d174ab98d277d9f5a5611c2c9f419d9f',
-        'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789',
-        'A-Z, a-z, 0-9'),
-
-    ('57edf4a22be3c955ac49da2e2107b67a',
-        '1234567890123456789012345678901234567890123456'
-        + '7890123456789012345678901234567890',
-        "'1234567890' * 8"),
-
-    # https://www.cosic.esat.kuleuven.be/nessie/testvectors/hash/md5/Md5-128.unverified.test-vectors
-    ('57EDF4A22BE3C955AC49DA2E2107B67A', '1234567890' * 8, 'Set 1, vector #7'),
-    ('7707D6AE4E027C70EEA2A935C2296F21', 'a'*1000000, 'Set 1, vector #8'),
-]
-
-
-class Md5IterTest(unittest.TestCase):
-
-    def runTest(self):
-        message = b("\x00") * 16
-        result1 = "4AE71336E44BF9BF79D2752E234818A5".lower()
-        result2 = "1A83F51285E4D89403D00C46EF8508FE".lower()
-
-        h = MD5.new(message)
-        message = h.digest()
-        self.assertEqual(h.hexdigest(), result1)
-
-        for _ in range(99999):
-            h = MD5.new(message)
-            message = h.digest()
-
-        self.assertEqual(h.hexdigest(), result2)
-
-
-def get_tests(config={}):
-    from .common import make_hash_tests
-
-    tests =  make_hash_tests(MD5, "MD5", test_data,
-                            digest_size=16,
-                            oid="1.2.840.113549.2.5")
-    if config.get('slow_tests'):
-        tests += [ Md5IterTest() ]
-    return tests
-
-
-if __name__ == '__main__':
-    import unittest
-    suite = lambda: unittest.TestSuite(get_tests())
-    unittest.main(defaultTest='suite')
-
-# vim:set ts=4 sw=4 sts=4 expandtab:
diff --git a/spaces/asd998877/TsGpt/assets/Kelpy-Codos.js b/spaces/asd998877/TsGpt/assets/Kelpy-Codos.js
deleted file mode 100644
index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000
--- a/spaces/asd998877/TsGpt/assets/Kelpy-Codos.js
+++ /dev/null
@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name         Kelpy Codos
-// @namespace    https://github.com/Keldos-Li/Kelpy-Codos
-// @version      1.0.5
-// @author       Keldos; https://keldos.me/
-// @description  Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially. 
-//               Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license      GPL-3.0
-// @grant        none
-// ==/UserScript==
-
-(function () {
-    'use strict';
-
-    function addCopyButton(pre) {
-        var code = pre.querySelector('code');
-        if (!code) {
-            return; // 如果没有找到 <code> 元素，则不添加按钮
-        }
-        var firstChild = code.firstChild;
-        if (!firstChild) {
-            return; // 如果 <code> 元素没有子节点，则不添加按钮
-        }
-        var button = document.createElement('button');
-        button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
-        button.style.position = 'relative';
-        button.style.float = 'right';
-        button.style.fontSize = '1em'; // 可选：调整按钮大小
-        button.style.background = 'none'; // 可选：去掉背景颜色
-        button.style.border = 'none'; // 可选：去掉边框
-        button.style.cursor = 'pointer'; // 可选：显示指针样式
-        button.addEventListener('click', function () {
-            var range = document.createRange();
-            range.selectNodeContents(code);
-            range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
-            var selection = window.getSelection();
-            selection.removeAllRanges();
-            selection.addRange(range);
-
-            try {
-                var success = document.execCommand('copy');
-                if (success) {
-                    button.textContent = '\u2714';
-                    setTimeout(function () {
-                        button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
-                    }, 2000);
-                } else {
-                    button.textContent = '\u2716';
-                }
-            } catch (e) {
-                console.error(e);
-                button.textContent = '\u2716';
-            }
-
-            selection.removeAllRanges();
-        });
-        code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
-    }
-
-    function handleNewElements(mutationsList, observer) {
-        for (var mutation of mutationsList) {
-            if (mutation.type === 'childList') {
-                for (var node of mutation.addedNodes) {
-                    if (node.nodeName === 'PRE') {
-                        addCopyButton(node);
-                    }
-                }
-            }
-        }
-    }
-
-    var observer = new MutationObserver(handleNewElements);
-    observer.observe(document.documentElement, { childList: true, subtree: true });
-
-    document.querySelectorAll('pre').forEach(addCopyButton);
-})();
diff --git a/spaces/aseifert/ExplaiNER/src/subpages/find_duplicates.py b/spaces/aseifert/ExplaiNER/src/subpages/find_duplicates.py
deleted file mode 100644
index 0dbcca8f9651bc764bd0dafcb4e88f6f252862c4..0000000000000000000000000000000000000000
--- a/spaces/aseifert/ExplaiNER/src/subpages/find_duplicates.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""Find potential duplicates in the data using cosine similarity."""
-import streamlit as st
-from sentence_transformers.util import cos_sim
-
-from src.subpages.page import Context, Page
-
-
-@st.cache()
-def get_sims(texts: list[str], sentence_encoder):
-    embeddings = sentence_encoder.encode(texts, batch_size=8, convert_to_numpy=True)
-    return cos_sim(embeddings, embeddings)
-
-
-class FindDuplicatesPage(Page):
-    name = "Find Duplicates"
-    icon = "fingerprint"
-
-    def _get_widget_defaults(self):
-        return {
-            "cutoff": 0.95,
-        }
-
-    def render(self, context: Context):
-        st.title("Find Duplicates")
-        with st.expander("💡", expanded=True):
-            st.write("Find potential duplicates in the data using cosine similarity.")
-
-        cutoff = st.slider("Similarity threshold", min_value=0.0, max_value=1.0, key="cutoff")
-        # split.add_faiss_index(column="embeddings", index_name="sent_index")
-        # st.write("Index is ready")
-        # sentence_encoder.encode(["hello world"], batch_size=8)
-        # st.write(split["tokens"][0])
-        texts = [" ".join(ts) for ts in context.split["tokens"]]
-        sims = get_sims(texts, context.sentence_encoder)
-
-        candidates = []
-        for i in range(len(sims)):
-            for j in range(i + 1, len(sims)):
-                if sims[i][j] >= cutoff:
-                    candidates.append((sims[i][j], i, j))
-        candidates.sort(reverse=False)
-
-        for (sim, i, j) in candidates[:100]:
-            st.markdown(f"**Possible duplicate ({i}, {j}, sim: {sim:.3f}):**")
-            st.markdown("* " + " ".join(context.split["tokens"][i]))
-            st.markdown("* " + " ".join(context.split["tokens"][j]))
-
-        # st.write("queries")
-        # results = split.get_nearest_examples("sent_index", np.array(split["embeddings"][0], dtype=np.float32), k=2)
-        # results = split.get_nearest_examples_batch("sent_index", queries, k=2)
-        # st.write(results.total_examples[0]["id"][1])
-        # st.write(results.total_examples[0])
diff --git a/spaces/avans06/whisper-webui-translate/src/diarization/transcriptLoader.py b/spaces/avans06/whisper-webui-translate/src/diarization/transcriptLoader.py
deleted file mode 100644
index 8b17bb6e9b65f712a689dc8b96f8411a6088cd63..0000000000000000000000000000000000000000
--- a/spaces/avans06/whisper-webui-translate/src/diarization/transcriptLoader.py
+++ /dev/null
@@ -1,80 +0,0 @@
-
-import json
-from pathlib import Path
-
-def load_transcript_json(transcript_file: str):
-    """
-    Parse a Whisper JSON file into a Whisper JSON object
-
-    # Parameters:
-    transcript_file (str): Path to the Whisper JSON file
-    """
-    with open(transcript_file, "r", encoding="utf-8") as f:
-        whisper_result = json.load(f)
-
-    # Format of Whisper JSON file:
-    #  {
-    # "text": " And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.",
-    # "segments": [
-    #    {
-    #        "text": " And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.",
-    #        "start": 0.0,
-    #        "end": 10.36,
-    #        "words": [
-    #            {
-    #                "start": 0.0,
-    #                "end": 0.56,
-    #                "word": " And",
-    #                "probability": 0.61767578125
-    #            },
-    #            {
-    #                "start": 0.56,
-    #                "end": 0.88,
-    #                "word": " so",
-    #                "probability": 0.9033203125
-    #            },
-    # etc.  
-
-    return whisper_result
-
-
-def load_transcript_srt(subtitle_file: str):
-    import srt
-
-    """
-    Parse a SRT file into a Whisper JSON object
-
-    # Parameters:
-    subtitle_file (str): Path to the SRT file
-    """
-    with open(subtitle_file, "r", encoding="utf-8") as f:
-        subs = srt.parse(f)
-
-        whisper_result = {
-            "text": "",
-            "segments": []
-        }
-
-        for sub in subs:
-            # Subtitle(index=1, start=datetime.timedelta(seconds=33, microseconds=843000), end=datetime.timedelta(seconds=38, microseconds=97000), content='地球上只有3%的水是淡水', proprietary='')
-            segment = {
-                "text": sub.content,
-                "start": sub.start.total_seconds(),
-                "end": sub.end.total_seconds(),
-                "words": []
-            }
-            whisper_result["segments"].append(segment)
-            whisper_result["text"] += sub.content
-
-        return whisper_result
-
-def load_transcript(file: str):
-    # Determine file type
-    file_extension = Path(file).suffix.lower()
-
-    if file_extension == ".json":
-        return load_transcript_json(file)
-    elif file_extension == ".srt":
-        return load_transcript_srt(file)
-    else:
-        raise ValueError(f"Unsupported file type: {file_extension}")
\ No newline at end of file
diff --git a/spaces/avivdm1/AutoGPT/autogpt/speech/gtts.py b/spaces/avivdm1/AutoGPT/autogpt/speech/gtts.py
deleted file mode 100644
index 1c3e9cae0567428582891b11eca42f82a64f5c8e..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/speech/gtts.py
+++ /dev/null
@@ -1,22 +0,0 @@
-""" GTTS Voice. """
-import os
-
-import gtts
-from playsound import playsound
-
-from autogpt.speech.base import VoiceBase
-
-
-class GTTSVoice(VoiceBase):
-    """GTTS Voice."""
-
-    def _setup(self) -> None:
-        pass
-
-    def _speech(self, text: str, _: int = 0) -> bool:
-        """Play the given text."""
-        tts = gtts.gTTS(text)
-        tts.save("speech.mp3")
-        playsound("speech.mp3", True)
-        os.remove("speech.mp3")
-        return True
diff --git a/spaces/awacke1/HTML5.Wordle.Solver/README.md b/spaces/awacke1/HTML5.Wordle.Solver/README.md
deleted file mode 100644
index 0e30b1d497d290fd8c1025cebfc401380090682f..0000000000000000000000000000000000000000
--- a/spaces/awacke1/HTML5.Wordle.Solver/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: HTML5.Wordle.Solver
-emoji: 📊
-colorFrom: gray
-colorTo: pink
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/__init__.py b/spaces/azusarang/so-vits-svc-models-ba_P/diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/RollerCoaster.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/RollerCoaster.js
deleted file mode 100644
index 95538b99a27aa6bad938cafd7650c10befd6d78b..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/RollerCoaster.js
+++ /dev/null
@@ -1,544 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- */
-
-function RollerCoasterGeometry( curve, divisions ) {
-
-	THREE.BufferGeometry.call( this );
-
-	var vertices = [];
-	var normals = [];
-	var colors = [];
-
-	var color1 = [ 1, 1, 1 ];
-	var color2 = [ 1, 1, 0 ];
-
-	var up = new THREE.Vector3( 0, 1, 0 );
-	var forward = new THREE.Vector3();
-	var right = new THREE.Vector3();
-
-	var quaternion = new THREE.Quaternion();
-	var prevQuaternion = new THREE.Quaternion();
-	prevQuaternion.setFromAxisAngle( up, Math.PI / 2 );
-
-	var point = new THREE.Vector3();
-	var prevPoint = new THREE.Vector3();
-	prevPoint.copy( curve.getPointAt( 0 ) );
-
-	// shapes
-
-	var step = [
-		new THREE.Vector3( - 0.225, 0, 0 ),
-		new THREE.Vector3( 0, - 0.050, 0 ),
-		new THREE.Vector3( 0, - 0.175, 0 ),
-
-		new THREE.Vector3( 0, - 0.050, 0 ),
-		new THREE.Vector3( 0.225, 0, 0 ),
-		new THREE.Vector3( 0, - 0.175, 0 )
-	];
-
-	var PI2 = Math.PI * 2;
-
-	var sides = 5;
-	var tube1 = [];
-
-	for ( var i = 0; i < sides; i ++ ) {
-
-		var angle = ( i / sides ) * PI2;
-		tube1.push( new THREE.Vector3( Math.sin( angle ) * 0.06, Math.cos( angle ) * 0.06, 0 ) );
-
-	}
-
-	var sides = 6;
-	var tube2 = [];
-
-	for ( var i = 0; i < sides; i ++ ) {
-
-		var angle = ( i / sides ) * PI2;
-		tube2.push( new THREE.Vector3( Math.sin( angle ) * 0.025, Math.cos( angle ) * 0.025, 0 ) );
-
-	}
-
-	var vector = new THREE.Vector3();
-	var normal = new THREE.Vector3();
-
-	function drawShape( shape, color ) {
-
-		normal.set( 0, 0, - 1 ).applyQuaternion( quaternion );
-
-		for ( var j = 0; j < shape.length; j ++ ) {
-
-			vector.copy( shape[ j ] );
-			vector.applyQuaternion( quaternion );
-			vector.add( point );
-
-			vertices.push( vector.x, vector.y, vector.z );
-			normals.push( normal.x, normal.y, normal.z );
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-
-		}
-
-		normal.set( 0, 0, 1 ).applyQuaternion( quaternion );
-
-		for ( var j = shape.length - 1; j >= 0; j -- ) {
-
-			vector.copy( shape[ j ] );
-			vector.applyQuaternion( quaternion );
-			vector.add( point );
-
-			vertices.push( vector.x, vector.y, vector.z );
-			normals.push( normal.x, normal.y, normal.z );
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-
-		}
-
-	}
-
-	var vector1 = new THREE.Vector3();
-	var vector2 = new THREE.Vector3();
-	var vector3 = new THREE.Vector3();
-	var vector4 = new THREE.Vector3();
-
-	var normal1 = new THREE.Vector3();
-	var normal2 = new THREE.Vector3();
-	var normal3 = new THREE.Vector3();
-	var normal4 = new THREE.Vector3();
-
-	function extrudeShape( shape, offset, color ) {
-
-		for ( var j = 0, jl = shape.length; j < jl; j ++ ) {
-
-			var point1 = shape[ j ];
-			var point2 = shape[ ( j + 1 ) % jl ];
-
-			vector1.copy( point1 ).add( offset );
-			vector1.applyQuaternion( quaternion );
-			vector1.add( point );
-
-			vector2.copy( point2 ).add( offset );
-			vector2.applyQuaternion( quaternion );
-			vector2.add( point );
-
-			vector3.copy( point2 ).add( offset );
-			vector3.applyQuaternion( prevQuaternion );
-			vector3.add( prevPoint );
-
-			vector4.copy( point1 ).add( offset );
-			vector4.applyQuaternion( prevQuaternion );
-			vector4.add( prevPoint );
-
-			vertices.push( vector1.x, vector1.y, vector1.z );
-			vertices.push( vector2.x, vector2.y, vector2.z );
-			vertices.push( vector4.x, vector4.y, vector4.z );
-
-			vertices.push( vector2.x, vector2.y, vector2.z );
-			vertices.push( vector3.x, vector3.y, vector3.z );
-			vertices.push( vector4.x, vector4.y, vector4.z );
-
-			//
-
-			normal1.copy( point1 );
-			normal1.applyQuaternion( quaternion );
-			normal1.normalize();
-
-			normal2.copy( point2 );
-			normal2.applyQuaternion( quaternion );
-			normal2.normalize();
-
-			normal3.copy( point2 );
-			normal3.applyQuaternion( prevQuaternion );
-			normal3.normalize();
-
-			normal4.copy( point1 );
-			normal4.applyQuaternion( prevQuaternion );
-			normal4.normalize();
-
-			normals.push( normal1.x, normal1.y, normal1.z );
-			normals.push( normal2.x, normal2.y, normal2.z );
-			normals.push( normal4.x, normal4.y, normal4.z );
-
-			normals.push( normal2.x, normal2.y, normal2.z );
-			normals.push( normal3.x, normal3.y, normal3.z );
-			normals.push( normal4.x, normal4.y, normal4.z );
-
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-			colors.push( color[ 0 ], color[ 1 ], color[ 2 ] );
-
-		}
-
-	}
-
-	var offset = new THREE.Vector3();
-
-	for ( var i = 1; i <= divisions; i ++ ) {
-
-		point.copy( curve.getPointAt( i / divisions ) );
-
-		up.set( 0, 1, 0 );
-
-		forward.subVectors( point, prevPoint ).normalize();
-		right.crossVectors( up, forward ).normalize();
-		up.crossVectors( forward, right );
-
-		var angle = Math.atan2( forward.x, forward.z );
-
-		quaternion.setFromAxisAngle( up, angle );
-
-		if ( i % 2 === 0 ) {
-
-			drawShape( step, color2 );
-
-		}
-
-		extrudeShape( tube1, offset.set( 0, - 0.125, 0 ), color2 );
-		extrudeShape( tube2, offset.set( 0.2, 0, 0 ), color1 );
-		extrudeShape( tube2, offset.set( - 0.2, 0, 0 ), color1 );
-
-		prevPoint.copy( point );
-		prevQuaternion.copy( quaternion );
-
-	}
-
-	// console.log( vertices.length );
-
-	this.addAttribute( 'position', new THREE.BufferAttribute( new Float32Array( vertices ), 3 ) );
-	this.addAttribute( 'normal', new THREE.BufferAttribute( new Float32Array( normals ), 3 ) );
-	this.addAttribute( 'color', new THREE.BufferAttribute( new Float32Array( colors ), 3 ) );
-
-}
-
-RollerCoasterGeometry.prototype = Object.create( THREE.BufferGeometry.prototype );
-
-function RollerCoasterLiftersGeometry( curve, divisions ) {
-
-	THREE.BufferGeometry.call( this );
-
-	var vertices = [];
-	var normals = [];
-
-	var quaternion = new THREE.Quaternion();
-
-	var up = new THREE.Vector3( 0, 1, 0 );
-
-	var point = new THREE.Vector3();
-	var tangent = new THREE.Vector3();
-
-	// shapes
-
-	var tube1 = [
-		new THREE.Vector3( 0, 0.05, - 0.05 ),
-		new THREE.Vector3( 0, 0.05, 0.05 ),
-		new THREE.Vector3( 0, - 0.05, 0 )
-	];
-
-	var tube2 = [
-		new THREE.Vector3( - 0.05, 0, 0.05 ),
-		new THREE.Vector3( - 0.05, 0, - 0.05 ),
-		new THREE.Vector3( 0.05, 0, 0 )
-	];
-
-	var tube3 = [
-		new THREE.Vector3( 0.05, 0, - 0.05 ),
-		new THREE.Vector3( 0.05, 0, 0.05 ),
-		new THREE.Vector3( - 0.05, 0, 0 )
-	];
-
-	var vector1 = new THREE.Vector3();
-	var vector2 = new THREE.Vector3();
-	var vector3 = new THREE.Vector3();
-	var vector4 = new THREE.Vector3();
-
-	var normal1 = new THREE.Vector3();
-	var normal2 = new THREE.Vector3();
-	var normal3 = new THREE.Vector3();
-	var normal4 = new THREE.Vector3();
-
-	function extrudeShape( shape, fromPoint, toPoint ) {
-
-		for ( var j = 0, jl = shape.length; j < jl; j ++ ) {
-
-			var point1 = shape[ j ];
-			var point2 = shape[ ( j + 1 ) % jl ];
-
-			vector1.copy( point1 );
-			vector1.applyQuaternion( quaternion );
-			vector1.add( fromPoint );
-
-			vector2.copy( point2 );
-			vector2.applyQuaternion( quaternion );
-			vector2.add( fromPoint );
-
-			vector3.copy( point2 );
-			vector3.applyQuaternion( quaternion );
-			vector3.add( toPoint );
-
-			vector4.copy( point1 );
-			vector4.applyQuaternion( quaternion );
-			vector4.add( toPoint );
-
-			vertices.push( vector1.x, vector1.y, vector1.z );
-			vertices.push( vector2.x, vector2.y, vector2.z );
-			vertices.push( vector4.x, vector4.y, vector4.z );
-
-			vertices.push( vector2.x, vector2.y, vector2.z );
-			vertices.push( vector3.x, vector3.y, vector3.z );
-			vertices.push( vector4.x, vector4.y, vector4.z );
-
-			//
-
-			normal1.copy( point1 );
-			normal1.applyQuaternion( quaternion );
-			normal1.normalize();
-
-			normal2.copy( point2 );
-			normal2.applyQuaternion( quaternion );
-			normal2.normalize();
-
-			normal3.copy( point2 );
-			normal3.applyQuaternion( quaternion );
-			normal3.normalize();
-
-			normal4.copy( point1 );
-			normal4.applyQuaternion( quaternion );
-			normal4.normalize();
-
-			normals.push( normal1.x, normal1.y, normal1.z );
-			normals.push( normal2.x, normal2.y, normal2.z );
-			normals.push( normal4.x, normal4.y, normal4.z );
-
-			normals.push( normal2.x, normal2.y, normal2.z );
-			normals.push( normal3.x, normal3.y, normal3.z );
-			normals.push( normal4.x, normal4.y, normal4.z );
-
-		}
-
-	}
-
-	var fromPoint = new THREE.Vector3();
-	var toPoint = new THREE.Vector3();
-
-	for ( var i = 1; i <= divisions; i ++ ) {
-
-		point.copy( curve.getPointAt( i / divisions ) );
-		tangent.copy( curve.getTangentAt( i / divisions ) );
-
-		var angle = Math.atan2( tangent.x, tangent.z );
-
-		quaternion.setFromAxisAngle( up, angle );
-
-		//
-
-		if ( point.y > 10 ) {
-
-			fromPoint.set( - 0.75, - 0.35, 0 );
-			fromPoint.applyQuaternion( quaternion );
-			fromPoint.add( point );
-
-			toPoint.set( 0.75, - 0.35, 0 );
-			toPoint.applyQuaternion( quaternion );
-			toPoint.add( point );
-
-			extrudeShape( tube1, fromPoint, toPoint );
-
-			fromPoint.set( - 0.7, - 0.3, 0 );
-			fromPoint.applyQuaternion( quaternion );
-			fromPoint.add( point );
-
-			toPoint.set( - 0.7, - point.y, 0 );
-			toPoint.applyQuaternion( quaternion );
-			toPoint.add( point );
-
-			extrudeShape( tube2, fromPoint, toPoint );
-
-			fromPoint.set( 0.7, - 0.3, 0 );
-			fromPoint.applyQuaternion( quaternion );
-			fromPoint.add( point );
-
-			toPoint.set( 0.7, - point.y, 0 );
-			toPoint.applyQuaternion( quaternion );
-			toPoint.add( point );
-
-			extrudeShape( tube3, fromPoint, toPoint );
-
-		} else {
-
-			fromPoint.set( 0, - 0.2, 0 );
-			fromPoint.applyQuaternion( quaternion );
-			fromPoint.add( point );
-
-			toPoint.set( 0, - point.y, 0 );
-			toPoint.applyQuaternion( quaternion );
-			toPoint.add( point );
-
-			extrudeShape( tube3, fromPoint, toPoint );
-
-		}
-
-	}
-
-	this.addAttribute( 'position', new THREE.BufferAttribute( new Float32Array( vertices ), 3 ) );
-	this.addAttribute( 'normal', new THREE.BufferAttribute( new Float32Array( normals ), 3 ) );
-
-}
-
-RollerCoasterLiftersGeometry.prototype = Object.create( THREE.BufferGeometry.prototype );
-
-function RollerCoasterShadowGeometry( curve, divisions ) {
-
-	THREE.BufferGeometry.call( this );
-
-	var vertices = [];
-
-	var up = new THREE.Vector3( 0, 1, 0 );
-	var forward = new THREE.Vector3();
-
-	var quaternion = new THREE.Quaternion();
-	var prevQuaternion = new THREE.Quaternion();
-	prevQuaternion.setFromAxisAngle( up, Math.PI / 2 );
-
-	var point = new THREE.Vector3();
-
-	var prevPoint = new THREE.Vector3();
-	prevPoint.copy( curve.getPointAt( 0 ) );
-	prevPoint.y = 0;
-
-	var vector1 = new THREE.Vector3();
-	var vector2 = new THREE.Vector3();
-	var vector3 = new THREE.Vector3();
-	var vector4 = new THREE.Vector3();
-
-	for ( var i = 1; i <= divisions; i ++ ) {
-
-		point.copy( curve.getPointAt( i / divisions ) );
-		point.y = 0;
-
-		forward.subVectors( point, prevPoint );
-
-		var angle = Math.atan2( forward.x, forward.z );
-
-		quaternion.setFromAxisAngle( up, angle );
-
-		vector1.set( - 0.3, 0, 0 );
-		vector1.applyQuaternion( quaternion );
-		vector1.add( point );
-
-		vector2.set( 0.3, 0, 0 );
-		vector2.applyQuaternion( quaternion );
-		vector2.add( point );
-
-		vector3.set( 0.3, 0, 0 );
-		vector3.applyQuaternion( prevQuaternion );
-		vector3.add( prevPoint );
-
-		vector4.set( - 0.3, 0, 0 );
-		vector4.applyQuaternion( prevQuaternion );
-		vector4.add( prevPoint );
-
-		vertices.push( vector1.x, vector1.y, vector1.z );
-		vertices.push( vector2.x, vector2.y, vector2.z );
-		vertices.push( vector4.x, vector4.y, vector4.z );
-
-		vertices.push( vector2.x, vector2.y, vector2.z );
-		vertices.push( vector3.x, vector3.y, vector3.z );
-		vertices.push( vector4.x, vector4.y, vector4.z );
-
-		prevPoint.copy( point );
-		prevQuaternion.copy( quaternion );
-
-	}
-
-	this.addAttribute( 'position', new THREE.BufferAttribute( new Float32Array( vertices ), 3 ) );
-
-}
-
-RollerCoasterShadowGeometry.prototype = Object.create( THREE.BufferGeometry.prototype );
-
-function SkyGeometry() {
-
-	THREE.BufferGeometry.call( this );
-
-	var vertices = [];
-
-	for ( var i = 0; i < 100; i ++ ) {
-
-		var x = Math.random() * 800 - 400;
-		var y = Math.random() * 50 + 50;
-		var z = Math.random() * 800 - 400;
-
-		var size = Math.random() * 40 + 20;
-
-		vertices.push( x - size, y, z - size );
-		vertices.push( x + size, y, z - size );
-		vertices.push( x - size, y, z + size );
-
-		vertices.push( x + size, y, z - size );
-		vertices.push( x + size, y, z + size );
-		vertices.push( x - size, y, z + size );
-
-	}
-
-
-	this.addAttribute( 'position', new THREE.BufferAttribute( new Float32Array( vertices ), 3 ) );
-
-}
-
-SkyGeometry.prototype = Object.create( THREE.BufferGeometry.prototype );
-
-function TreesGeometry( landscape ) {
-
-	THREE.BufferGeometry.call( this );
-
-	var vertices = [];
-	var colors = [];
-
-	var raycaster = new THREE.Raycaster();
-	raycaster.ray.direction.set( 0, - 1, 0 );
-
-	for ( var i = 0; i < 2000; i ++ ) {
-
-		var x = Math.random() * 500 - 250;
-		var z = Math.random() * 500 - 250;
-
-		raycaster.ray.origin.set( x, 50, z );
-
-		var intersections = raycaster.intersectObject( landscape );
-
-		if ( intersections.length === 0 ) continue;
-
-		var y = intersections[ 0 ].point.y;
-
-		var height = Math.random() * 5 + 0.5;
-
-		var angle = Math.random() * Math.PI * 2;
-
-		vertices.push( x + Math.sin( angle ), y, z + Math.cos( angle ) );
-		vertices.push( x, y + height, z );
-		vertices.push( x + Math.sin( angle + Math.PI ), y, z + Math.cos( angle + Math.PI ) );
-
-		angle += Math.PI / 2;
-
-		vertices.push( x + Math.sin( angle ), y, z + Math.cos( angle ) );
-		vertices.push( x, y + height, z );
-		vertices.push( x + Math.sin( angle + Math.PI ), y, z + Math.cos( angle + Math.PI ) );
-
-		var random = Math.random() * 0.1;
-
-		for ( var j = 0; j < 6; j ++ ) {
-
-			colors.push( 0.2 + random, 0.4 + random, 0 );
-
-		}
-
-	}
-
-	this.addAttribute( 'position', new THREE.BufferAttribute( new Float32Array( vertices ), 3 ) );
-	this.addAttribute( 'color', new THREE.BufferAttribute( new Float32Array( colors ), 3 ) );
-
-}
-
-TreesGeometry.prototype = Object.create( THREE.BufferGeometry.prototype );
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/renderers/Projector.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/renderers/Projector.js
deleted file mode 100644
index 41a41c16657929c9e6dd66cc4c68272db39d8ef4..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/renderers/Projector.js
+++ /dev/null
@@ -1,1108 +0,0 @@
-/**
- * @author mrdoob / http://mrdoob.com/
- * @author supereggbert / http://www.paulbrunt.co.uk/
- * @author julianwa / https://github.com/julianwa
- */
-
-THREE.RenderableObject = function () {
-
-	this.id = 0;
-
-	this.object = null;
-	this.z = 0;
-	this.renderOrder = 0;
-
-};
-
-//
-
-THREE.RenderableFace = function () {
-
-	this.id = 0;
-
-	this.v1 = new THREE.RenderableVertex();
-	this.v2 = new THREE.RenderableVertex();
-	this.v3 = new THREE.RenderableVertex();
-
-	this.normalModel = new THREE.Vector3();
-
-	this.vertexNormalsModel = [ new THREE.Vector3(), new THREE.Vector3(), new THREE.Vector3() ];
-	this.vertexNormalsLength = 0;
-
-	this.color = new THREE.Color();
-	this.material = null;
-	this.uvs = [ new THREE.Vector2(), new THREE.Vector2(), new THREE.Vector2() ];
-
-	this.z = 0;
-	this.renderOrder = 0;
-
-};
-
-//
-
-THREE.RenderableVertex = function () {
-
-	this.position = new THREE.Vector3();
-	this.positionWorld = new THREE.Vector3();
-	this.positionScreen = new THREE.Vector4();
-
-	this.visible = true;
-
-};
-
-THREE.RenderableVertex.prototype.copy = function ( vertex ) {
-
-	this.positionWorld.copy( vertex.positionWorld );
-	this.positionScreen.copy( vertex.positionScreen );
-
-};
-
-//
-
-THREE.RenderableLine = function () {
-
-	this.id = 0;
-
-	this.v1 = new THREE.RenderableVertex();
-	this.v2 = new THREE.RenderableVertex();
-
-	this.vertexColors = [ new THREE.Color(), new THREE.Color() ];
-	this.material = null;
-
-	this.z = 0;
-	this.renderOrder = 0;
-
-};
-
-//
-
-THREE.RenderableSprite = function () {
-
-	this.id = 0;
-
-	this.object = null;
-
-	this.x = 0;
-	this.y = 0;
-	this.z = 0;
-
-	this.rotation = 0;
-	this.scale = new THREE.Vector2();
-
-	this.material = null;
-	this.renderOrder = 0;
-
-};
-
-//
-
-THREE.Projector = function () {
-
-	var _object, _objectCount, _objectPool = [], _objectPoolLength = 0,
-		_vertex, _vertexCount, _vertexPool = [], _vertexPoolLength = 0,
-		_face, _faceCount, _facePool = [], _facePoolLength = 0,
-		_line, _lineCount, _linePool = [], _linePoolLength = 0,
-		_sprite, _spriteCount, _spritePool = [], _spritePoolLength = 0,
-
-		_renderData = { objects: [], lights: [], elements: [] },
-
-		_vector3 = new THREE.Vector3(),
-		_vector4 = new THREE.Vector4(),
-
-		_clipBox = new THREE.Box3( new THREE.Vector3( - 1, - 1, - 1 ), new THREE.Vector3( 1, 1, 1 ) ),
-		_boundingBox = new THREE.Box3(),
-		_points3 = new Array( 3 ),
-
-		_viewMatrix = new THREE.Matrix4(),
-		_viewProjectionMatrix = new THREE.Matrix4(),
-
-		_modelMatrix,
-		_modelViewProjectionMatrix = new THREE.Matrix4(),
-
-		_normalMatrix = new THREE.Matrix3(),
-
-		_frustum = new THREE.Frustum(),
-
-		_clippedVertex1PositionScreen = new THREE.Vector4(),
-		_clippedVertex2PositionScreen = new THREE.Vector4();
-
-	//
-
-	this.projectVector = function ( vector, camera ) {
-
-		console.warn( 'THREE.Projector: .projectVector() is now vector.project().' );
-		vector.project( camera );
-
-	};
-
-	this.unprojectVector = function ( vector, camera ) {
-
-		console.warn( 'THREE.Projector: .unprojectVector() is now vector.unproject().' );
-		vector.unproject( camera );
-
-	};
-
-	this.pickingRay = function () {
-
-		console.error( 'THREE.Projector: .pickingRay() is now raycaster.setFromCamera().' );
-
-	};
-
-	//
-
-	var RenderList = function () {
-
-		var normals = [];
-		var colors = [];
-		var uvs = [];
-
-		var object = null;
-		var material = null;
-
-		var normalMatrix = new THREE.Matrix3();
-
-		function setObject( value ) {
-
-			object = value;
-			material = object.material;
-
-			normalMatrix.getNormalMatrix( object.matrixWorld );
-
-			normals.length = 0;
-			colors.length = 0;
-			uvs.length = 0;
-
-		}
-
-		function projectVertex( vertex ) {
-
-			var position = vertex.position;
-			var positionWorld = vertex.positionWorld;
-			var positionScreen = vertex.positionScreen;
-
-			positionWorld.copy( position ).applyMatrix4( _modelMatrix );
-			positionScreen.copy( positionWorld ).applyMatrix4( _viewProjectionMatrix );
-
-			var invW = 1 / positionScreen.w;
-
-			positionScreen.x *= invW;
-			positionScreen.y *= invW;
-			positionScreen.z *= invW;
-
-			vertex.visible = positionScreen.x >= - 1 && positionScreen.x <= 1 &&
-					 positionScreen.y >= - 1 && positionScreen.y <= 1 &&
-					 positionScreen.z >= - 1 && positionScreen.z <= 1;
-
-		}
-
-		function pushVertex( x, y, z ) {
-
-			_vertex = getNextVertexInPool();
-			_vertex.position.set( x, y, z );
-
-			projectVertex( _vertex );
-
-		}
-
-		function pushNormal( x, y, z ) {
-
-			normals.push( x, y, z );
-
-		}
-
-		function pushColor( r, g, b ) {
-
-			colors.push( r, g, b );
-
-		}
-
-		function pushUv( x, y ) {
-
-			uvs.push( x, y );
-
-		}
-
-		function checkTriangleVisibility( v1, v2, v3 ) {
-
-			if ( v1.visible === true || v2.visible === true || v3.visible === true ) return true;
-
-			_points3[ 0 ] = v1.positionScreen;
-			_points3[ 1 ] = v2.positionScreen;
-			_points3[ 2 ] = v3.positionScreen;
-
-			return _clipBox.intersectsBox( _boundingBox.setFromPoints( _points3 ) );
-
-		}
-
-		function checkBackfaceCulling( v1, v2, v3 ) {
-
-			return ( ( v3.positionScreen.x - v1.positionScreen.x ) *
-				    ( v2.positionScreen.y - v1.positionScreen.y ) -
-				    ( v3.positionScreen.y - v1.positionScreen.y ) *
-				    ( v2.positionScreen.x - v1.positionScreen.x ) ) < 0;
-
-		}
-
-		function pushLine( a, b ) {
-
-			var v1 = _vertexPool[ a ];
-			var v2 = _vertexPool[ b ];
-
-			// Clip
-
-			v1.positionScreen.copy( v1.position ).applyMatrix4( _modelViewProjectionMatrix );
-			v2.positionScreen.copy( v2.position ).applyMatrix4( _modelViewProjectionMatrix );
-
-			if ( clipLine( v1.positionScreen, v2.positionScreen ) === true ) {
-
-				// Perform the perspective divide
-				v1.positionScreen.multiplyScalar( 1 / v1.positionScreen.w );
-				v2.positionScreen.multiplyScalar( 1 / v2.positionScreen.w );
-
-				_line = getNextLineInPool();
-				_line.id = object.id;
-				_line.v1.copy( v1 );
-				_line.v2.copy( v2 );
-				_line.z = Math.max( v1.positionScreen.z, v2.positionScreen.z );
-				_line.renderOrder = object.renderOrder;
-
-				_line.material = object.material;
-
-				if ( object.material.vertexColors === THREE.VertexColors ) {
-
-					_line.vertexColors[ 0 ].fromArray( colors, a * 3 );
-					_line.vertexColors[ 1 ].fromArray( colors, b * 3 );
-
-				}
-
-				_renderData.elements.push( _line );
-
-			}
-
-		}
-
-		function pushTriangle( a, b, c, material ) {
-
-			var v1 = _vertexPool[ a ];
-			var v2 = _vertexPool[ b ];
-			var v3 = _vertexPool[ c ];
-
-			if ( checkTriangleVisibility( v1, v2, v3 ) === false ) return;
-
-			if ( material.side === THREE.DoubleSide || checkBackfaceCulling( v1, v2, v3 ) === true ) {
-
-				_face = getNextFaceInPool();
-
-				_face.id = object.id;
-				_face.v1.copy( v1 );
-				_face.v2.copy( v2 );
-				_face.v3.copy( v3 );
-				_face.z = ( v1.positionScreen.z + v2.positionScreen.z + v3.positionScreen.z ) / 3;
-				_face.renderOrder = object.renderOrder;
-
-				// face normal
-				_vector3.subVectors( v3.position, v2.position );
-				_vector4.subVectors( v1.position, v2.position );
-				_vector3.cross( _vector4 );
-				_face.normalModel.copy( _vector3 );
-				_face.normalModel.applyMatrix3( normalMatrix ).normalize();
-
-				for ( var i = 0; i < 3; i ++ ) {
-
-					var normal = _face.vertexNormalsModel[ i ];
-					normal.fromArray( normals, arguments[ i ] * 3 );
-					normal.applyMatrix3( normalMatrix ).normalize();
-
-					var uv = _face.uvs[ i ];
-					uv.fromArray( uvs, arguments[ i ] * 2 );
-
-				}
-
-				_face.vertexNormalsLength = 3;
-
-				_face.material = material;
-
-				if ( material.vertexColors === THREE.FaceColors || material.vertexColors === THREE.VertexColors ) {
-
-					_face.color.fromArray( colors, a * 3 );
-
-				}
-
-				_renderData.elements.push( _face );
-
-			}
-
-		}
-
-		return {
-			setObject: setObject,
-			projectVertex: projectVertex,
-			checkTriangleVisibility: checkTriangleVisibility,
-			checkBackfaceCulling: checkBackfaceCulling,
-			pushVertex: pushVertex,
-			pushNormal: pushNormal,
-			pushColor: pushColor,
-			pushUv: pushUv,
-			pushLine: pushLine,
-			pushTriangle: pushTriangle
-		};
-
-	};
-
-	var renderList = new RenderList();
-
-	function projectObject( object ) {
-
-		if ( object.visible === false ) return;
-
-		if ( object instanceof THREE.Light ) {
-
-			_renderData.lights.push( object );
-
-		} else if ( object instanceof THREE.Mesh || object instanceof THREE.Line || object instanceof THREE.Points ) {
-
-			if ( object.material.visible === false ) return;
-			if ( object.frustumCulled === true && _frustum.intersectsObject( object ) === false ) return;
-
-			addObject( object );
-
-		} else if ( object instanceof THREE.Sprite ) {
-
-			if ( object.material.visible === false ) return;
-			if ( object.frustumCulled === true && _frustum.intersectsSprite( object ) === false ) return;
-
-			addObject( object );
-
-		}
-
-		var children = object.children;
-
-		for ( var i = 0, l = children.length; i < l; i ++ ) {
-
-			projectObject( children[ i ] );
-
-		}
-
-	}
-
-	function addObject( object ) {
-
-		_object = getNextObjectInPool();
-		_object.id = object.id;
-		_object.object = object;
-
-		_vector3.setFromMatrixPosition( object.matrixWorld );
-		_vector3.applyMatrix4( _viewProjectionMatrix );
-		_object.z = _vector3.z;
-		_object.renderOrder = object.renderOrder;
-
-		_renderData.objects.push( _object );
-
-	}
-
-	this.projectScene = function ( scene, camera, sortObjects, sortElements ) {
-
-		_faceCount = 0;
-		_lineCount = 0;
-		_spriteCount = 0;
-
-		_renderData.elements.length = 0;
-
-		if ( scene.autoUpdate === true ) scene.updateMatrixWorld();
-		if ( camera.parent === null ) camera.updateMatrixWorld();
-
-		_viewMatrix.copy( camera.matrixWorldInverse );
-		_viewProjectionMatrix.multiplyMatrices( camera.projectionMatrix, _viewMatrix );
-
-		_frustum.setFromMatrix( _viewProjectionMatrix );
-
-		//
-
-		_objectCount = 0;
-
-		_renderData.objects.length = 0;
-		_renderData.lights.length = 0;
-
-		projectObject( scene );
-
-		if ( sortObjects === true ) {
-
-			_renderData.objects.sort( painterSort );
-
-		}
-
-		//
-
-		var objects = _renderData.objects;
-
-		for ( var o = 0, ol = objects.length; o < ol; o ++ ) {
-
-			var object = objects[ o ].object;
-			var geometry = object.geometry;
-
-			renderList.setObject( object );
-
-			_modelMatrix = object.matrixWorld;
-
-			_vertexCount = 0;
-
-			if ( object instanceof THREE.Mesh ) {
-
-				if ( geometry instanceof THREE.BufferGeometry ) {
-
-					var material = object.material;
-
-					var isMultiMaterial = Array.isArray( material );
-
-					var attributes = geometry.attributes;
-					var groups = geometry.groups;
-
-					if ( attributes.position === undefined ) continue;
-
-					var positions = attributes.position.array;
-
-					for ( var i = 0, l = positions.length; i < l; i += 3 ) {
-
-						var x = positions[ i ];
-						var y = positions[ i + 1 ];
-						var z = positions[ i + 2 ];
-
-						if ( material.morphTargets === true ) {
-
-							var morphTargets = geometry.morphAttributes.position;
-							var morphInfluences = object.morphTargetInfluences;
-
-							for ( var t = 0, tl = morphTargets.length; t < tl; t ++ ) {
-
-								var influence = morphInfluences[ t ];
-
-								if ( influence === 0 ) continue;
-
-								var target = morphTargets[ t ];
-
-								x += ( target.getX( i / 3 ) - positions[ i ] ) * influence;
-								y += ( target.getY( i / 3 ) - positions[ i + 1 ] ) * influence;
-								z += ( target.getZ( i / 3 ) - positions[ i + 2 ] ) * influence;
-
-							}
-
-						}
-
-						renderList.pushVertex( x, y, z );
-
-					}
-
-					if ( attributes.normal !== undefined ) {
-
-						var normals = attributes.normal.array;
-
-						for ( var i = 0, l = normals.length; i < l; i += 3 ) {
-
-							renderList.pushNormal( normals[ i ], normals[ i + 1 ], normals[ i + 2 ] );
-
-						}
-
-					}
-
-					if ( attributes.color !== undefined ) {
-
-						var colors = attributes.color.array;
-
-						for ( var i = 0, l = colors.length; i < l; i += 3 ) {
-
-							renderList.pushColor( colors[ i ], colors[ i + 1 ], colors[ i + 2 ] );
-
-						}
-
-					}
-
-					if ( attributes.uv !== undefined ) {
-
-						var uvs = attributes.uv.array;
-
-						for ( var i = 0, l = uvs.length; i < l; i += 2 ) {
-
-							renderList.pushUv( uvs[ i ], uvs[ i + 1 ] );
-
-						}
-
-					}
-
-					if ( geometry.index !== null ) {
-
-						var indices = geometry.index.array;
-
-						if ( groups.length > 0 ) {
-
-							for ( var g = 0; g < groups.length; g ++ ) {
-
-								var group = groups[ g ];
-
-								material = isMultiMaterial === true
-									 ? object.material[ group.materialIndex ]
-									 : object.material;
-
-								if ( material === undefined ) continue;
-
-								for ( var i = group.start, l = group.start + group.count; i < l; i += 3 ) {
-
-									renderList.pushTriangle( indices[ i ], indices[ i + 1 ], indices[ i + 2 ], material );
-
-								}
-
-							}
-
-						} else {
-
-							for ( var i = 0, l = indices.length; i < l; i += 3 ) {
-
-								renderList.pushTriangle( indices[ i ], indices[ i + 1 ], indices[ i + 2 ], material );
-
-							}
-
-						}
-
-					} else {
-
-						if ( groups.length > 0 ) {
-
-							for ( var g = 0; g < groups.length; g ++ ) {
-
-								var group = groups[ g ];
-
-								material = isMultiMaterial === true
-									 ? object.material[ group.materialIndex ]
-									 : object.material;
-
-								if ( material === undefined ) continue;
-
-								for ( var i = group.start, l = group.start + group.count; i < l; i += 3 ) {
-
-									renderList.pushTriangle( i, i + 1, i + 2, material );
-
-								}
-
-							}
-
-						} else {
-
-							for ( var i = 0, l = positions.length / 3; i < l; i += 3 ) {
-
-								renderList.pushTriangle( i, i + 1, i + 2, material );
-
-							}
-
-						}
-
-					}
-
-				} else if ( geometry instanceof THREE.Geometry ) {
-
-					var vertices = geometry.vertices;
-					var faces = geometry.faces;
-					var faceVertexUvs = geometry.faceVertexUvs[ 0 ];
-
-					_normalMatrix.getNormalMatrix( _modelMatrix );
-
-					var material = object.material;
-
-					var isMultiMaterial = Array.isArray( material );
-
-					for ( var v = 0, vl = vertices.length; v < vl; v ++ ) {
-
-						var vertex = vertices[ v ];
-
-						_vector3.copy( vertex );
-
-						if ( material.morphTargets === true ) {
-
-							var morphTargets = geometry.morphTargets;
-							var morphInfluences = object.morphTargetInfluences;
-
-							for ( var t = 0, tl = morphTargets.length; t < tl; t ++ ) {
-
-								var influence = morphInfluences[ t ];
-
-								if ( influence === 0 ) continue;
-
-								var target = morphTargets[ t ];
-								var targetVertex = target.vertices[ v ];
-
-								_vector3.x += ( targetVertex.x - vertex.x ) * influence;
-								_vector3.y += ( targetVertex.y - vertex.y ) * influence;
-								_vector3.z += ( targetVertex.z - vertex.z ) * influence;
-
-							}
-
-						}
-
-						renderList.pushVertex( _vector3.x, _vector3.y, _vector3.z );
-
-					}
-
-					for ( var f = 0, fl = faces.length; f < fl; f ++ ) {
-
-						var face = faces[ f ];
-
-						material = isMultiMaterial === true
-							 ? object.material[ face.materialIndex ]
-							 : object.material;
-
-						if ( material === undefined ) continue;
-
-						var side = material.side;
-
-						var v1 = _vertexPool[ face.a ];
-						var v2 = _vertexPool[ face.b ];
-						var v3 = _vertexPool[ face.c ];
-
-						if ( renderList.checkTriangleVisibility( v1, v2, v3 ) === false ) continue;
-
-						var visible = renderList.checkBackfaceCulling( v1, v2, v3 );
-
-						if ( side !== THREE.DoubleSide ) {
-
-							if ( side === THREE.FrontSide && visible === false ) continue;
-							if ( side === THREE.BackSide && visible === true ) continue;
-
-						}
-
-						_face = getNextFaceInPool();
-
-						_face.id = object.id;
-						_face.v1.copy( v1 );
-						_face.v2.copy( v2 );
-						_face.v3.copy( v3 );
-
-						_face.normalModel.copy( face.normal );
-
-						if ( visible === false && ( side === THREE.BackSide || side === THREE.DoubleSide ) ) {
-
-							_face.normalModel.negate();
-
-						}
-
-						_face.normalModel.applyMatrix3( _normalMatrix ).normalize();
-
-						var faceVertexNormals = face.vertexNormals;
-
-						for ( var n = 0, nl = Math.min( faceVertexNormals.length, 3 ); n < nl; n ++ ) {
-
-							var normalModel = _face.vertexNormalsModel[ n ];
-							normalModel.copy( faceVertexNormals[ n ] );
-
-							if ( visible === false && ( side === THREE.BackSide || side === THREE.DoubleSide ) ) {
-
-								normalModel.negate();
-
-							}
-
-							normalModel.applyMatrix3( _normalMatrix ).normalize();
-
-						}
-
-						_face.vertexNormalsLength = faceVertexNormals.length;
-
-						var vertexUvs = faceVertexUvs[ f ];
-
-						if ( vertexUvs !== undefined ) {
-
-							for ( var u = 0; u < 3; u ++ ) {
-
-								_face.uvs[ u ].copy( vertexUvs[ u ] );
-
-							}
-
-						}
-
-						_face.color = face.color;
-						_face.material = material;
-
-						_face.z = ( v1.positionScreen.z + v2.positionScreen.z + v3.positionScreen.z ) / 3;
-						_face.renderOrder = object.renderOrder;
-
-						_renderData.elements.push( _face );
-
-					}
-
-				}
-
-			} else if ( object instanceof THREE.Line ) {
-
-				_modelViewProjectionMatrix.multiplyMatrices( _viewProjectionMatrix, _modelMatrix );
-
-				if ( geometry instanceof THREE.BufferGeometry ) {
-
-					var attributes = geometry.attributes;
-
-					if ( attributes.position !== undefined ) {
-
-						var positions = attributes.position.array;
-
-						for ( var i = 0, l = positions.length; i < l; i += 3 ) {
-
-							renderList.pushVertex( positions[ i ], positions[ i + 1 ], positions[ i + 2 ] );
-
-						}
-
-						if ( attributes.color !== undefined ) {
-
-							var colors = attributes.color.array;
-
-							for ( var i = 0, l = colors.length; i < l; i += 3 ) {
-
-								renderList.pushColor( colors[ i ], colors[ i + 1 ], colors[ i + 2 ] );
-
-							}
-
-						}
-
-						if ( geometry.index !== null ) {
-
-							var indices = geometry.index.array;
-
-							for ( var i = 0, l = indices.length; i < l; i += 2 ) {
-
-								renderList.pushLine( indices[ i ], indices[ i + 1 ] );
-
-							}
-
-						} else {
-
-							var step = object instanceof THREE.LineSegments ? 2 : 1;
-
-							for ( var i = 0, l = ( positions.length / 3 ) - 1; i < l; i += step ) {
-
-								renderList.pushLine( i, i + 1 );
-
-							}
-
-						}
-
-					}
-
-				} else if ( geometry instanceof THREE.Geometry ) {
-
-					var vertices = object.geometry.vertices;
-
-					if ( vertices.length === 0 ) continue;
-
-					v1 = getNextVertexInPool();
-					v1.positionScreen.copy( vertices[ 0 ] ).applyMatrix4( _modelViewProjectionMatrix );
-
-					var step = object instanceof THREE.LineSegments ? 2 : 1;
-
-					for ( var v = 1, vl = vertices.length; v < vl; v ++ ) {
-
-						v1 = getNextVertexInPool();
-						v1.positionScreen.copy( vertices[ v ] ).applyMatrix4( _modelViewProjectionMatrix );
-
-						if ( ( v + 1 ) % step > 0 ) continue;
-
-						v2 = _vertexPool[ _vertexCount - 2 ];
-
-						_clippedVertex1PositionScreen.copy( v1.positionScreen );
-						_clippedVertex2PositionScreen.copy( v2.positionScreen );
-
-						if ( clipLine( _clippedVertex1PositionScreen, _clippedVertex2PositionScreen ) === true ) {
-
-							// Perform the perspective divide
-							_clippedVertex1PositionScreen.multiplyScalar( 1 / _clippedVertex1PositionScreen.w );
-							_clippedVertex2PositionScreen.multiplyScalar( 1 / _clippedVertex2PositionScreen.w );
-
-							_line = getNextLineInPool();
-
-							_line.id = object.id;
-							_line.v1.positionScreen.copy( _clippedVertex1PositionScreen );
-							_line.v2.positionScreen.copy( _clippedVertex2PositionScreen );
-
-							_line.z = Math.max( _clippedVertex1PositionScreen.z, _clippedVertex2PositionScreen.z );
-							_line.renderOrder = object.renderOrder;
-
-							_line.material = object.material;
-
-							if ( object.material.vertexColors === THREE.VertexColors ) {
-
-								_line.vertexColors[ 0 ].copy( object.geometry.colors[ v ] );
-								_line.vertexColors[ 1 ].copy( object.geometry.colors[ v - 1 ] );
-
-							}
-
-							_renderData.elements.push( _line );
-
-						}
-
-					}
-
-				}
-
-			} else if ( object instanceof THREE.Points ) {
-
-				_modelViewProjectionMatrix.multiplyMatrices( _viewProjectionMatrix, _modelMatrix );
-
-				if ( geometry instanceof THREE.Geometry ) {
-
-					var vertices = object.geometry.vertices;
-
-					for ( var v = 0, vl = vertices.length; v < vl; v ++ ) {
-
-						var vertex = vertices[ v ];
-
-						_vector4.set( vertex.x, vertex.y, vertex.z, 1 );
-						_vector4.applyMatrix4( _modelViewProjectionMatrix );
-
-						pushPoint( _vector4, object, camera );
-
-					}
-
-				} else if ( geometry instanceof THREE.BufferGeometry ) {
-
-					var attributes = geometry.attributes;
-
-					if ( attributes.position !== undefined ) {
-
-						var positions = attributes.position.array;
-
-						for ( var i = 0, l = positions.length; i < l; i += 3 ) {
-
-							_vector4.set( positions[ i ], positions[ i + 1 ], positions[ i + 2 ], 1 );
-							_vector4.applyMatrix4( _modelViewProjectionMatrix );
-
-							pushPoint( _vector4, object, camera );
-
-						}
-
-					}
-
-				}
-
-			} else if ( object instanceof THREE.Sprite ) {
-
-				object.modelViewMatrix.multiplyMatrices( camera.matrixWorldInverse, object.matrixWorld );
-				_vector4.set( _modelMatrix.elements[ 12 ], _modelMatrix.elements[ 13 ], _modelMatrix.elements[ 14 ], 1 );
-				_vector4.applyMatrix4( _viewProjectionMatrix );
-
-				pushPoint( _vector4, object, camera );
-
-			}
-
-		}
-
-		if ( sortElements === true ) {
-
-			_renderData.elements.sort( painterSort );
-
-		}
-
-		return _renderData;
-
-	};
-
-	function pushPoint( _vector4, object, camera ) {
-
-		var invW = 1 / _vector4.w;
-
-		_vector4.z *= invW;
-
-		if ( _vector4.z >= - 1 && _vector4.z <= 1 ) {
-
-			_sprite = getNextSpriteInPool();
-			_sprite.id = object.id;
-			_sprite.x = _vector4.x * invW;
-			_sprite.y = _vector4.y * invW;
-			_sprite.z = _vector4.z;
-			_sprite.renderOrder = object.renderOrder;
-			_sprite.object = object;
-
-			_sprite.rotation = object.rotation;
-
-			_sprite.scale.x = object.scale.x * Math.abs( _sprite.x - ( _vector4.x + camera.projectionMatrix.elements[ 0 ] ) / ( _vector4.w + camera.projectionMatrix.elements[ 12 ] ) );
-			_sprite.scale.y = object.scale.y * Math.abs( _sprite.y - ( _vector4.y + camera.projectionMatrix.elements[ 5 ] ) / ( _vector4.w + camera.projectionMatrix.elements[ 13 ] ) );
-
-			_sprite.material = object.material;
-
-			_renderData.elements.push( _sprite );
-
-		}
-
-	}
-
-	// Pools
-
-	function getNextObjectInPool() {
-
-		if ( _objectCount === _objectPoolLength ) {
-
-			var object = new THREE.RenderableObject();
-			_objectPool.push( object );
-			_objectPoolLength ++;
-			_objectCount ++;
-			return object;
-
-		}
-
-		return _objectPool[ _objectCount ++ ];
-
-	}
-
-	function getNextVertexInPool() {
-
-		if ( _vertexCount === _vertexPoolLength ) {
-
-			var vertex = new THREE.RenderableVertex();
-			_vertexPool.push( vertex );
-			_vertexPoolLength ++;
-			_vertexCount ++;
-			return vertex;
-
-		}
-
-		return _vertexPool[ _vertexCount ++ ];
-
-	}
-
-	function getNextFaceInPool() {
-
-		if ( _faceCount === _facePoolLength ) {
-
-			var face = new THREE.RenderableFace();
-			_facePool.push( face );
-			_facePoolLength ++;
-			_faceCount ++;
-			return face;
-
-		}
-
-		return _facePool[ _faceCount ++ ];
-
-
-	}
-
-	function getNextLineInPool() {
-
-		if ( _lineCount === _linePoolLength ) {
-
-			var line = new THREE.RenderableLine();
-			_linePool.push( line );
-			_linePoolLength ++;
-			_lineCount ++;
-			return line;
-
-		}
-
-		return _linePool[ _lineCount ++ ];
-
-	}
-
-	function getNextSpriteInPool() {
-
-		if ( _spriteCount === _spritePoolLength ) {
-
-			var sprite = new THREE.RenderableSprite();
-			_spritePool.push( sprite );
-			_spritePoolLength ++;
-			_spriteCount ++;
-			return sprite;
-
-		}
-
-		return _spritePool[ _spriteCount ++ ];
-
-	}
-
-	//
-
-	function painterSort( a, b ) {
-
-		if ( a.renderOrder !== b.renderOrder ) {
-
-			return a.renderOrder - b.renderOrder;
-
-		} else if ( a.z !== b.z ) {
-
-			return b.z - a.z;
-
-		} else if ( a.id !== b.id ) {
-
-			return a.id - b.id;
-
-		} else {
-
-			return 0;
-
-		}
-
-	}
-
-	function clipLine( s1, s2 ) {
-
-		var alpha1 = 0, alpha2 = 1,
-
-			// Calculate the boundary coordinate of each vertex for the near and far clip planes,
-			// Z = -1 and Z = +1, respectively.
-
-			bc1near = s1.z + s1.w,
-			bc2near = s2.z + s2.w,
-			bc1far = - s1.z + s1.w,
-			bc2far = - s2.z + s2.w;
-
-		if ( bc1near >= 0 && bc2near >= 0 && bc1far >= 0 && bc2far >= 0 ) {
-
-			// Both vertices lie entirely within all clip planes.
-			return true;
-
-		} else if ( ( bc1near < 0 && bc2near < 0 ) || ( bc1far < 0 && bc2far < 0 ) ) {
-
-			// Both vertices lie entirely outside one of the clip planes.
-			return false;
-
-		} else {
-
-			// The line segment spans at least one clip plane.
-
-			if ( bc1near < 0 ) {
-
-				// v1 lies outside the near plane, v2 inside
-				alpha1 = Math.max( alpha1, bc1near / ( bc1near - bc2near ) );
-
-			} else if ( bc2near < 0 ) {
-
-				// v2 lies outside the near plane, v1 inside
-				alpha2 = Math.min( alpha2, bc1near / ( bc1near - bc2near ) );
-
-			}
-
-			if ( bc1far < 0 ) {
-
-				// v1 lies outside the far plane, v2 inside
-				alpha1 = Math.max( alpha1, bc1far / ( bc1far - bc2far ) );
-
-			} else if ( bc2far < 0 ) {
-
-				// v2 lies outside the far plane, v2 inside
-				alpha2 = Math.min( alpha2, bc1far / ( bc1far - bc2far ) );
-
-			}
-
-			if ( alpha2 < alpha1 ) {
-
-				// The line segment spans two boundaries, but is outside both of them.
-				// (This can't happen when we're only clipping against just near/far but good
-				//  to leave the check here for future usage if other clip planes are added.)
-				return false;
-
-			} else {
-
-				// Update the s1 and s2 vertices to match the clipped line segment.
-				s1.lerp( s2, alpha1 );
-				s2.lerp( s1, 1 - alpha2 );
-
-				return true;
-
-			}
-
-		}
-
-	}
-
-};
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/helpers/AxesHelper.js b/spaces/banana-projects/web3d/node_modules/three/src/helpers/AxesHelper.js
deleted file mode 100644
index 34a7f42ff5cfd9a85cdc0cc36ecfa2a7c4516d8c..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/helpers/AxesHelper.js
+++ /dev/null
@@ -1,42 +0,0 @@
-/**
- * @author sroucheray / http://sroucheray.org/
- * @author mrdoob / http://mrdoob.com/
- */
-
-import { LineSegments } from '../objects/LineSegments.js';
-import { VertexColors } from '../constants.js';
-import { LineBasicMaterial } from '../materials/LineBasicMaterial.js';
-import { Float32BufferAttribute } from '../core/BufferAttribute.js';
-import { BufferGeometry } from '../core/BufferGeometry.js';
-
-function AxesHelper( size ) {
-
-	size = size || 1;
-
-	var vertices = [
-		0, 0, 0,	size, 0, 0,
-		0, 0, 0,	0, size, 0,
-		0, 0, 0,	0, 0, size
-	];
-
-	var colors = [
-		1, 0, 0,	1, 0.6, 0,
-		0, 1, 0,	0.6, 1, 0,
-		0, 0, 1,	0, 0.6, 1
-	];
-
-	var geometry = new BufferGeometry();
-	geometry.addAttribute( 'position', new Float32BufferAttribute( vertices, 3 ) );
-	geometry.addAttribute( 'color', new Float32BufferAttribute( colors, 3 ) );
-
-	var material = new LineBasicMaterial( { vertexColors: VertexColors } );
-
-	LineSegments.call( this, geometry, material );
-
-}
-
-AxesHelper.prototype = Object.create( LineSegments.prototype );
-AxesHelper.prototype.constructor = AxesHelper;
-
-
-export { AxesHelper };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshbasic_frag.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshbasic_frag.glsl.js
deleted file mode 100644
index 34b5135b7bf500ef66aa158496a26535b44a9186..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/meshbasic_frag.glsl.js
+++ /dev/null
@@ -1,68 +0,0 @@
-export default /* glsl */`
-uniform vec3 diffuse;
-uniform float opacity;
-
-#ifndef FLAT_SHADED
-
-	varying vec3 vNormal;
-
-#endif
-
-#include <common>
-#include <color_pars_fragment>
-#include <uv_pars_fragment>
-#include <uv2_pars_fragment>
-#include <map_pars_fragment>
-#include <alphamap_pars_fragment>
-#include <aomap_pars_fragment>
-#include <lightmap_pars_fragment>
-#include <envmap_pars_fragment>
-#include <fog_pars_fragment>
-#include <specularmap_pars_fragment>
-#include <logdepthbuf_pars_fragment>
-#include <clipping_planes_pars_fragment>
-
-void main() {
-
-	#include <clipping_planes_fragment>
-
-	vec4 diffuseColor = vec4( diffuse, opacity );
-
-	#include <logdepthbuf_fragment>
-	#include <map_fragment>
-	#include <color_fragment>
-	#include <alphamap_fragment>
-	#include <alphatest_fragment>
-	#include <specularmap_fragment>
-
-	ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );
-
-	// accumulation (baked indirect lighting only)
-	#ifdef USE_LIGHTMAP
-
-		reflectedLight.indirectDiffuse += texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;
-
-	#else
-
-		reflectedLight.indirectDiffuse += vec3( 1.0 );
-
-	#endif
-
-	// modulation
-	#include <aomap_fragment>
-
-	reflectedLight.indirectDiffuse *= diffuseColor.rgb;
-
-	vec3 outgoingLight = reflectedLight.indirectDiffuse;
-
-	#include <envmap_fragment>
-
-	gl_FragColor = vec4( outgoingLight, diffuseColor.a );
-
-	#include <premultiplied_alpha_fragment>
-	#include <tonemapping_fragment>
-	#include <encodings_fragment>
-	#include <fog_fragment>
-
-}
-`;
diff --git a/spaces/basakbuluz/turkish-question-answering/utils/huggingface_qa.py b/spaces/basakbuluz/turkish-question-answering/utils/huggingface_qa.py
deleted file mode 100644
index cc8b21ea310fe508456c8d6292bbdec5584f6408..0000000000000000000000000000000000000000
--- a/spaces/basakbuluz/turkish-question-answering/utils/huggingface_qa.py
+++ /dev/null
@@ -1,20 +0,0 @@
-
-from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
-import torch
-
-
-class QuestionAnswering:
-    def __init__(self):
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-squad")
-        if self.device == "cuda":
-            self.model = AutoModelForQuestionAnswering.from_pretrained("savasy/bert-base-turkish-squad").cuda()
-        else:
-            self.model = AutoModelForQuestionAnswering.from_pretrained("savasy/bert-base-turkish-squad")
-
-        self.pipe = pipeline("question-answering", model=self.model, tokenizer=self.tokenizer)
-
-    def prediction(self, question, context):
-        result = self.pipe(question=question, context=context)
-
-        return result
diff --git a/spaces/bballaek17/ChatGPT4/README.md b/spaces/bballaek17/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/bballaek17/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net_custom.py b/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net_custom.py
deleted file mode 100644
index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/midas_net_custom.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
-        blocks={'expand': True}):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet_small, self).__init__()
-
-        use_pretrained = False if path else True
-                
-        self.channels_last = channels_last
-        self.blocks = blocks
-        self.backbone = backbone
-
-        self.groups = 1
-
-        features1=features
-        features2=features
-        features3=features
-        features4=features
-        self.expand = False
-        if "expand" in self.blocks and self.blocks['expand'] == True:
-            self.expand = True
-            features1=features
-            features2=features*2
-            features3=features*4
-            features4=features*8
-
-        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-  
-        self.scratch.activation = nn.ReLU(False)    
-
-        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-        
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
-            self.scratch.activation,
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-        
-        if path:
-            self.load(path)
-
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-        if self.channels_last==True:
-            print("self.channels_last = ", self.channels_last)
-            x.contiguous(memory_format=torch.channels_last)
-
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-        
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-        
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
-    prev_previous_type = nn.Identity()
-    prev_previous_name = ''
-    previous_type = nn.Identity()
-    previous_name = ''
-    for name, module in m.named_modules():
-        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
-            # print("FUSED ", prev_previous_name, previous_name, name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
-        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
-            # print("FUSED ", prev_previous_name, previous_name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
-        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
-        #    print("FUSED ", previous_name, name)
-        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
-        prev_previous_type = previous_type
-        prev_previous_name = previous_name
-        previous_type = type(module)
-        previous_name = name
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Facebook Pros And Cons For Education.md b/spaces/bioriAsaeru/text-to-voice/Facebook Pros And Cons For Education.md
deleted file mode 100644
index 769a7a4f11df77eb7de6d3fb66508d08d0b7e306..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Facebook Pros And Cons For Education.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<p>Social media is such a prevalent part of modern society especially for kids and teens. In most schools phones and mobile devices have been banned from being used during the school day and social media websites like twitter and facebook have been blocked on school computers. Schools are beginning to take a different approach by introducing social media into the educational system itself. Here are some pros and cons of social media in the classroom and how it is being used for educational purposes.</p>
-<p>There are many pros and cons of using social media in the classroom, so now the question is should schools allow social media in schools? There are some websites that address the negative issues of social media but still allow social media to be used for educational purposes.</p>
-<h2>facebook pros and cons for education</h2><br /><p><b><b>Download</b> &#9675; <a href="https://urloso.com/2uyPrB">https://urloso.com/2uyPrB</a></b></p><br /><br />
-<p>Some attendees said that they would like to see more centralized resources and guidance to inform students, faculty, and staff about social networking tools (like Facebook) in order to build awareness of privacy concerns and the pros and cons of their use. Attendees also thought that members of the UBC community need to be educated about these tools. Information resources, such as the <strong>Digital Tattoo Project</strong> (which Sheryl is a part of), have been created to educate students, faculty, and staff about online usage, but still more needs to be done.</p>
-<p>Cyprien suggested that we could start leveraging connections at other schools to come to a solution for a broader community. If UBC is struggling with this issue, other educational institutions must be too. It was here that Sheryl offered up the example of <strong>RefWorks</strong>, which used to be hosted on an American server. To address privacy concerns, a consortium of Canadian libraries banded together to move RefWorks data onto a Canadian server. Such a consortium could be beneficial when addressing the use of social networking tools in an educational setting, eventually helping UBC solidify a strategy for addressing issues that arise with the increased popularity of these tools.</p>
-<p>Background: </strong> Facebook, a popular social networking site, has been used by people of different ages and professions for various purposes. Its use in the field of medical education is increasing dramatically. At the same time, the pros and cons of facebook use among the health science students has attracted the attention of educators. The data regarding its use and the effect on the life of Nepalese health science students has not yet been documented. Therefore, this study is carried out to evaluate the effect of facebook use on social interactions, behaviour, academics, and the health of students in a medical college of Nepal.</p>
-<p>The age of Facebook has meant a lot of things: the proliferation of social media, the ability to catch up with old friends, the danger of seeing how happy your ex is with his new girlfriend. But do the pros of having a Facebook outweigh the cons? Keep this in mind as you contemplate whether or not to delete your social media presence:</p>
-<p>By submitting this form, I authorize and agree that a representative of Carrington College can contact me about educational services and future offers by email, phone and/or text messaging at the email and telephone number provided above using an automated telephone dialing system, and/or an artificial or pre-recorded voice or text message. I understand that consent is not required to purchase any goods or services from this school and that my consent can be revoked at any time. Standard data rates may apply.</p>
-<p>Social media is growing and not going anywhere. Many schools are now embracing the move. By understanding this movement, schools can make better choices on how to utilize social media in education. This infographic from Online Universities takes a look at the pros and cons of social media in education.</p>
-<p>When weighing the pros and cons of boosting Facebook posts vs ads, you should take into account the number of placements available in both options. When creating a boosted post, you can choose only among Facebook News Feed, Instagram, and Messenger.</p>
-<p></p>
-<p>Facebook, a popular social networking site, has been used by people of different ages and professions for various purposes. Its use in the field of medical education is increasing dramatically. At the same time, the pros and cons of facebook use among the health science students has attracted the attention of educators. The data regarding its use and the effect on the life of Nepalese health science students has not yet been documented. Therefore, this study is carried out to evaluate the effect of facebook use on social interactions, behaviour, academics, and the health of students in a medical college of Nepal.</p>
-<p>Facebook use by students has specific pros and cons. Particularly, the excessive use of online social media may contribute to misuse, dependence, and addictive behaviours [7]. Some studies have reviewed the impact of online social media use on mental health. These effects include changes in self-esteem [8], sleep disorders, and high percentage of depression among students [9]. However, these students were unaware of such adverse effects [10]. With the increased accessibility and availability of the internet, use of social media is on the rise in Nepal. Currently there are approximately 4.4 million facebook users [11]. To best of our knowledge, there is insufficient data regarding the use and effect of facebook on health science students in Nepal. We have hypothesized that the use of facebook may have a negative impact on the life of health science students. This study was performed to evaluate the effects of facebook on social interaction, behaviour, academics, and the health of students at Chitwan Medical College, Nepal.</p>
-<p>A cross-sectional study was conducted in Chitwan Medical College, Bharatpur, Nepal from September to October, 2015. The participants were medical, dental, nursing, and allied health science undergraduate students who consented to participate in the study. A self-administered questionnaire was distributed to total of 476 participants (all of the available students in basic science courses). Exclusion criteria included those who had incompletely completed the questionnaire, did not have facebook account, or had a facebook account in the past but had closed it, students complaining of illness, and anyone taking any medication. A pre-study questionnaire was given to ten students, who were not included in the study.</p>
-<p>The data from previous studies clearly indicated that the excessive use of social media, compromises actual live social interaction and academic accomplishments. It may also be associated with relationship problems, personal loneliness, and depression [23, 24]. We found that very small population (8.6 %) spent more time on facebook than live interactions with others, unlike the result obtained by the previous study [20]. However, 17.4 % of participants indicated that facebook was a source of inspiration and motivation for them. This is consistent with the findings of Farooqi et al. who observed that many students admitted that excessive facebook use had ruined their social life, causing them to spend less time with their loved ones [20]. Moreover, the researchers at the University of Southern California reported that an increasing number of people, admitted to spending less time with their family members as result of excessive use of the internet, including social networking sites [25].</p>
-<p>As with any other technology facebook is not unique for eliciting both positive and negative effects on its users. Previous studies have shown adverse effects similar to computer use, such as: headache, backache, weight change, and eye problems [17, 28]. Of those reported by the users in this study, burning eyes, disturbed sleep, and headache were the most common reported adverse health effects. On the other hand, 23.2 % of participants denied experiencing any adverse effects. Sierra et al. stated that both quantity and quality of sleep might strongly influence mood [29] and subjective well-being, which in turn could impact the academic performance [21, 30] of the students who use facebook late at night and do not allow for sufficient sleep. Al-Dubai et al. had clearly indicated a significant association between facebook use, its adverse health effects, and unhealthy behavior. They have recommended that higher educational institutions should create awareness and safer practices for their students. Additionally, they have advocated regular health screening of students to avoid the possible health consequences due to facebook use [31].</p>
-<p>Although a majority of students (71.5 %) claimed that they tried to reduce their time spent on facebook, one-fourth of them were not successful indicating additive behavior, consistent with the result found by Farooqi et al. [20]. It is worth mentioning that the students were willing to invest more of their time on their studies rather than on facebook. Further studies in a large population may reveal the factors associated with positive and negative impacts of facebook use among the health science students.</p>
-<p>Excessive use of facebook, a popular social network site, has important positive and negative effects on the academics, social interactions, and health of the health science students in this study. With limited academic benefits, excessive use of facebook may actually increase the risk of facebook additive behavior, resulting in less time spent on academics. It is important to involve the parents of students, educational institutions, and the facebook authority, to collaborate on how to encourage students to limit social media usage and bring awareness to the consequences of excessive use, especially among the student population.</p>
-<p>Since this is a cross-sectional study conducted in a single medical school in Nepal, this study might not be representative of all health science students. We believe that similar studies are important in exploring and understanding the practices of facebook and other social media use along with its consequences. The results of this study revealed that the health problems were prevalent in facebook users. However, the specific cause and effects could be better established through further studies. Future research could be designed to explore the effects of excessive use of different social media among students in different academic programs.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/meta_arch/__init__.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/meta_arch/__init__.py
deleted file mode 100644
index 6b0668157052ce7b796ef50bc7ee85361e7605b9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/meta_arch/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .build import META_ARCH_REGISTRY, build_model  # isort:skip
-
-from .panoptic_fpn import PanopticFPN
-
-# import all the meta_arch, so they will be registered
-from .rcnn import GeneralizedRCNN, ProposalNetwork
-from .dense_detector import DenseDetector
-from .retinanet import RetinaNet
-from .fcos import FCOS
-from .semantic_seg import SEM_SEG_HEADS_REGISTRY, SemanticSegmentor, build_sem_seg_head
-
-
-__all__ = list(globals().keys())
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/build.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/build.py
deleted file mode 100644
index bb7f54b4a1044bc518d66d89432dd52c79fdf293..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/build.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from typing import Optional
-from torch import nn
-
-from detectron2.config import CfgNode
-
-from .cse.embedder import Embedder
-from .filter import DensePoseDataFilter
-
-
-def build_densepose_predictor(cfg: CfgNode, input_channels: int):
-    """
-    Create an instance of DensePose predictor based on configuration options.
-
-    Args:
-        cfg (CfgNode): configuration options
-        input_channels (int): input tensor size along the channel dimension
-    Return:
-        An instance of DensePose predictor
-    """
-    from .predictors import DENSEPOSE_PREDICTOR_REGISTRY
-
-    predictor_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME
-    return DENSEPOSE_PREDICTOR_REGISTRY.get(predictor_name)(cfg, input_channels)
-
-
-def build_densepose_data_filter(cfg: CfgNode):
-    """
-    Build DensePose data filter which selects data for training
-
-    Args:
-        cfg (CfgNode): configuration options
-
-    Return:
-        Callable: list(Tensor), list(Instances) -> list(Tensor), list(Instances)
-        An instance of DensePose filter, which takes feature tensors and proposals
-        as an input and returns filtered features and proposals
-    """
-    dp_filter = DensePoseDataFilter(cfg)
-    return dp_filter
-
-
-def build_densepose_head(cfg: CfgNode, input_channels: int):
-    """
-    Build DensePose head based on configurations options
-
-    Args:
-        cfg (CfgNode): configuration options
-        input_channels (int): input tensor size along the channel dimension
-    Return:
-        An instance of DensePose head
-    """
-    from .roi_heads.registry import ROI_DENSEPOSE_HEAD_REGISTRY
-
-    head_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.NAME
-    return ROI_DENSEPOSE_HEAD_REGISTRY.get(head_name)(cfg, input_channels)
-
-
-def build_densepose_losses(cfg: CfgNode):
-    """
-    Build DensePose loss based on configurations options
-
-    Args:
-        cfg (CfgNode): configuration options
-    Return:
-        An instance of DensePose loss
-    """
-    from .losses import DENSEPOSE_LOSS_REGISTRY
-
-    loss_name = cfg.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME
-    return DENSEPOSE_LOSS_REGISTRY.get(loss_name)(cfg)
-
-
-def build_densepose_embedder(cfg: CfgNode) -> Optional[nn.Module]:
-    """
-    Build embedder used to embed mesh vertices into an embedding space.
-    Embedder contains sub-embedders, one for each mesh ID.
-
-    Args:
-        cfg (cfgNode): configuration options
-    Return:
-        Embedding module
-    """
-    if cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS:
-        return Embedder(cfg)
-    return None
diff --git a/spaces/butterswords/nlc-explorer/NLselector.py b/spaces/butterswords/nlc-explorer/NLselector.py
deleted file mode 100644
index 750f351aa5491267730434c2821865661376ccab..0000000000000000000000000000000000000000
--- a/spaces/butterswords/nlc-explorer/NLselector.py
+++ /dev/null
@@ -1,221 +0,0 @@
-#Import the libraries we know we'll need for the Generator.
-import pandas as pd, spacy, nltk, numpy as np, re
-from spacy.matcher import Matcher
-nlp = spacy.load("en_core_web_lg")
-import altair as alt
-import streamlit as st
-from annotated_text import annotated_text as ant
-
-#Import the libraries to support the model and predictions.
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
-import lime
-import torch
-import torch.nn.functional as F
-from lime.lime_text import LimeTextExplainer
-
-#Import WNgen.py
-from WNgen import *
-
-class_names = ['negative', 'positive']
-explainer = LimeTextExplainer(class_names=class_names)
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True) 
-
-def predictor(texts):
-    outputs = model(**tokenizer(texts, return_tensors="pt", padding=True))
-    probas = F.softmax(outputs.logits, dim=1).detach().numpy()
-    return probas
-
-@st.experimental_singleton
-def critical_words(document, options=False):
-    '''This function is meant to select the critical part of a sentence. Critical, in this context means
-    the part of the sentence that is either: A) a NOUN or PROPN from the correct entity group, B) a NOUN,
-    C) a NOUN + ADJ combination, or D) ADJ and PROPN used to modify other NOUN tokens.
-    It also checks this against what the model thinks is important if the user defines "options" as "LIME" or True.'''
-    if type(document) is not spacy.tokens.doc.Doc:
-        document = nlp(document)
-    chunks = list(document.noun_chunks)
-    pos_options = []
-    lime_options = []
-    
-    #Identify what the model cares about.
-    if options:
-        #Run Lime Setup code
-        exp = explainer.explain_instance(document.text, predictor, num_features=15, num_samples=2000)
-        lime_results = exp.as_list()
-        for feature in lime_results:
-            lime_options.append(feature[0])
-        lime_results = pd.DataFrame(lime_results, columns=["Word","Weight"])
-    
-    #Identify what we care about "parts of speech"
-    
-    # Here I am going to try to pick up pronouns, which are people, and Adjectival Compliments.
-    for token in document:
-        if (token.text not in pos_options) and ((token.text in lime_options) or (options == False)):
-            #print(f"executed {token.text} with {token.pos_} and {token.dep_}") #QA
-            if (token.pos_ in ["ADJ","PROPN"]) and (token.dep_ in ["compound", "amod"]) and (document[token.i - 1].dep_ in ["compound", "amod"]):
-                compound = document[token.i - 1: token.i +1].text
-                pos_options.append(compound)
-                print(f'Added {compound} based on "amod" and "compound" adjectives.')
-            elif (token.pos_ in ["NOUN"]) and (token.dep_ in ["compound", "amod", "conj"]) and (document[token.i - 1].dep_ in ["compound"]):
-                compound = document[token.i - 1: token.i +1].text
-                pos_options.append(compound)
-                print(f'Added {compound} based on "amod" and "compound" and "conj" nouns.')
-            elif (token.pos_ == "PROPN") and (token.dep_ in ["prep","amod"]):
-                pos_options.append(token.text)
-                print(f"Added '{token.text}' based on their adjectival state.")
-            elif (token.pos_ == "ADJ") and (token.dep_ in ["acomp","conj","amod"]):
-                pos_options.append(token.text)
-                print(f"Added '{token.text}' based on their adjectival state.")
-            elif (token.pos_ == "PRON") and (len(token.morph) !=0):
-                if (token.morph.get("PronType") == "Prs"):
-                    pos_options.append(token.text)
-                    print(f"Added '{token.text}' because it's a human pronoun.")
-    
-    #Noun Chunks parsing
-    for chunk in chunks:
-        #The use of chunk[-1] is due to testing that it appears to always match the root
-        root = chunk[-1]
-        #This currently matches to a list I've created. I don't know the best way to deal with this so I'm leaving it as is for the moment.
-        if root.ent_type_:
-            cur_values = []
-            if (len(chunk) > 1) and (chunk[-2].dep_ == "compound"):
-                #creates the compound element of the noun
-                compound = [x.text for x in chunk if x.dep_ == "compound"]
-                print(f"This is the contents of {compound} and it is {all(elem in lime_options for elem in compound)} that all elements are present in {lime_options}.") #for QA
-                #checks to see all elements in the compound are important to the model or use the compound if not checking importance.
-                if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):
-                    #creates a span for the entirety of the compound noun and adds it to the list.
-                    span = -1 * (1 + len(compound))
-                    pos_options.append(chunk[span:].text)
-                    cur_values + [token.text for token in chunk if token.pos_ in ["ADJ","NOUN","PROPN"]]
-                else:
-                    print(f"The elmenents in {compound} could not be added to the final list because they are not all relevant to the model.")
-            else: 
-                cur_values = [token.text for token in chunk if (token.ent_type_) or (token.pos_ == "ADJ")]
-            if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):
-                pos_options.extend(cur_values)
-                print(f"From {chunk.text}, {cur_values} added to pos_options due to entity recognition.") #for QA
-        elif len(chunk) >= 1:
-            cur_values = [token.text for token in chunk if token.pos_ in ["NOUN","ADJ","PROPN"]]
-            if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):
-                pos_options.extend(cur_values)
-                print(f"From {chunk.text}, {cur_values} added to pos_options due to wildcard.") #for QA
-        else:
-            print(f"No options added for \'{chunk.text}\' ")
-    
-    pos_options = list(set(pos_options))
-    
-    if options:
-        return pos_options, lime_results
-    else:
-        return pos_options
-    
-# Return the Viz of elements critical to LIME.
-def lime_viz(df):
-    if not isinstance(df, pd.DataFrame):
-        df = pd.DataFrame(df, columns=["Word","Weight"])
-    single_nearest = alt.selection_single(on='mouseover', nearest=True)
-    viz = alt.Chart(df).encode(
-        alt.X('Weight:Q', scale=alt.Scale(domain=(-1, 1))),
-        alt.Y('Word:N', sort='x', axis=None),
-        color=alt.Color("Weight", scale=alt.Scale(scheme='blueorange', domain=[0], type="threshold", range='diverging'), legend=None),
-        tooltip = ("Word","Weight")
-    ).mark_bar().properties(title ="Importance of individual words")
-
-    text = viz.mark_text(
-        fill="black",
-        align='right',
-        baseline='middle'
-    ).encode(
-        text='Word:N'
-    )
-    limeplot = alt.LayerChart(layer=[viz,text], width = 300).configure_axis(grid=False).configure_view(strokeWidth=0)
-    return limeplot
-
-# Evaluate Predictions using the model and pipe.
-def eval_pred(text, return_all = False):
-    '''A basic function for evaluating the prediction from the model and turning it into a visualization friendly number.'''
-    preds = pipe(text)
-    neg_score = -1 * preds[0][0]['score']
-    sent_neg = preds[0][0]['label']
-    pos_score = preds[0][1]['score']
-    sent_pos = preds[0][1]['label']
-    prediction = 0
-    sentiment = ''
-    if pos_score > abs(neg_score):
-        prediction = pos_score
-        sentiment = sent_pos
-    elif abs(neg_score) > pos_score:
-        prediction = neg_score
-        sentiment = sent_neg
-        
-    if return_all:
-        return prediction, sentiment
-    else:
-        return prediction
-
-def construct_nlexp(text,sentiment,probability):
-    prob = str(np.round(100 * abs(probability),2))
-    if sentiment == "NEGATIVE":
-        color_sent = ant('The model predicts the sentiment of the sentence you provided is ', (sentiment, "-", "#FFA44F"), ' with a probability of ', (prob, "neg", "#FFA44F"),"%.")
-    elif sentiment == "POSITIVE":
-        color_sent = ant('The model predicts the sentiment of the sentence you provided is ', (sentiment, "+", "#50A9FF"), ' with a probability of ', (prob, "pos", "#50A9FF"),"%.")
-    return color_sent
-
-def get_min_max(df, seed):
-    '''This function provides the alternatives with the highest spaCy similarity scores and the lowest similarity scores. As similarity is based on vectorization of words and documents this may not be the best way to identify bias.
-    
-    text2 = Most Similar
-    text3 = Least Similar'''
-    maximum = df[df['similarity'] < .9999].similarity.max()
-    text2 = df.loc[df['similarity'] == maximum, 'text'].iloc[0]
-    minimum = df[df['similarity'] > .0001].similarity.min()
-    text3 = df.loc[df['similarity'] == minimum, 'text'].iloc[0]
-    return text2, text3
-
-# Inspired by https://stackoverflow.com/questions/17758023/return-rows-in-a-dataframe-closest-to-a-user-defined-number/17758115#17758115
-def abs_dif(df,seed):
-    '''This function enables a user to identify the alternative that is closest to the seed and farthest from the seed should that be the what they wish to display.
-    
-    text2 = Nearest Prediction
-    text3 = Farthest Prediction'''
-    seed = process_text(seed)
-    target = df[df['Words'].str.lower() == seed].pred.iloc[0]
-    sub_df = df[df['Words'].str.lower() != seed].reset_index()
-    nearest_prediction = sub_df.pred[(sub_df.pred-target).abs().argsort()[:1]]
-    farthest_prediction = sub_df.pred[(sub_df.pred-target).abs().argsort()[-1:]]
-    text2 = sub_df.text.iloc[nearest_prediction.index[0]]
-    text3 = sub_df.text.iloc[farthest_prediction.index[0]]
-    return text2, text3
-
-#@st.experimental_singleton #I've enabled this to prevent it from triggering every time the code runs... which could get very messy
-def sampled_alts(df, seed, fixed=False):
-    '''This function enables a user to select an alternate way of choosing which counterfactuals are shown for MultiNLC, MultiNLC + Lime, and VizNLC. If you use this then you are enabling random sampling over other options (ex. spaCy similarity scores, or absolute difference).
-    
-    Both samples are random.'''
-    sub_df = df[df['Words'] != seed]
-    if fixed:
-        sample = sub_df.sample(n=2, random_state = 2052)
-    else:
-        sample = sub_df.sample(n=2)
-    text2 = sample.text.iloc[0]
-    text3 = sample.text.iloc[1]
-    return text2, text3
-
-def gen_cf_country(df,_document,selection):
-    df['text'] = df.Words.apply(lambda x: re.sub(r'\b'+selection+r'\b',x,_document.text))
-    df['pred'] = df.text.apply(eval_pred)
-    df['seed'] = df.Words.apply(lambda x: 'seed' if x.lower() == selection.lower() else 'alternative')
-    df['similarity'] = df.Words.apply(lambda x: nlp(selection).similarity(nlp(x)))
-    return df
-
-def gen_cf_profession(df,_document,selection):
-    category = df.loc[df['Words'] == selection.lower(), 'Major'].iloc[0]
-    df = df[df.Major == category]
-    df['text'] = df.Words.apply(lambda x: re.sub(r'\b'+selection+r'\b',x,_document.text))
-    df['pred'] = df.text.apply(eval_pred)
-    df['seed'] = df.Words.apply(lambda x: 'seed' if x == selection.lower() else 'alternative')
-    df['similarity'] = df.Words.apply(lambda x: nlp(selection).similarity(nlp(x)))
-    return df
\ No newline at end of file
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/raft/core/utils/flow_viz.py b/spaces/caffeinum/VToonify/vtoonify/model/raft/core/utils/flow_viz.py
deleted file mode 100644
index dcee65e89b91b07ee0496aeb4c7e7436abf99641..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/raft/core/utils/flow_viz.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
-
-
-# MIT License
-#
-# Copyright (c) 2018 Tom Runia
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to conditions.
-#
-# Author: Tom Runia
-# Date Created: 2018-08-03
-
-import numpy as np
-
-def make_colorwheel():
-    """
-    Generates a color wheel for optical flow visualization as presented in:
-        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
-        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
-
-    Code follows the original C++ source code of Daniel Scharstein.
-    Code follows the the Matlab source code of Deqing Sun.
-
-    Returns:
-        np.ndarray: Color wheel
-    """
-
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-    colorwheel = np.zeros((ncols, 3))
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
-    col = col+RY
-    # YG
-    colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
-    colorwheel[col:col+YG, 1] = 255
-    col = col+YG
-    # GC
-    colorwheel[col:col+GC, 1] = 255
-    colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
-    col = col+GC
-    # CB
-    colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
-    colorwheel[col:col+CB, 2] = 255
-    col = col+CB
-    # BM
-    colorwheel[col:col+BM, 2] = 255
-    colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
-    col = col+BM
-    # MR
-    colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
-    colorwheel[col:col+MR, 0] = 255
-    return colorwheel
-
-
-def flow_uv_to_colors(u, v, convert_to_bgr=False):
-    """
-    Applies the flow color wheel to (possibly clipped) flow components u and v.
-
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-
-    Args:
-        u (np.ndarray): Input horizontal flow of shape [H,W]
-        v (np.ndarray): Input vertical flow of shape [H,W]
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
-    colorwheel = make_colorwheel()  # shape [55x3]
-    ncols = colorwheel.shape[0]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    a = np.arctan2(-v, -u)/np.pi
-    fk = (a+1) / 2*(ncols-1)
-    k0 = np.floor(fk).astype(np.int32)
-    k1 = k0 + 1
-    k1[k1 == ncols] = 0
-    f = fk - k0
-    for i in range(colorwheel.shape[1]):
-        tmp = colorwheel[:,i]
-        col0 = tmp[k0] / 255.0
-        col1 = tmp[k1] / 255.0
-        col = (1-f)*col0 + f*col1
-        idx = (rad <= 1)
-        col[idx]  = 1 - rad[idx] * (1-col[idx])
-        col[~idx] = col[~idx] * 0.75   # out of range
-        # Note the 2-i => BGR instead of RGB
-        ch_idx = 2-i if convert_to_bgr else i
-        flow_image[:,:,ch_idx] = np.floor(255 * col)
-    return flow_image
-
-
-def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
-    """
-    Expects a two dimensional flow image of shape.
-
-    Args:
-        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
-        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
-    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
-    if clip_flow is not None:
-        flow_uv = np.clip(flow_uv, 0, clip_flow)
-    u = flow_uv[:,:,0]
-    v = flow_uv[:,:,1]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    rad_max = np.max(rad)
-    epsilon = 1e-5
-    u = u / (rad_max + epsilon)
-    v = v / (rad_max + epsilon)
-    return flow_uv_to_colors(u, v, convert_to_bgr)
\ No newline at end of file
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_sounddevice_data/portaudio-binaries/README.md b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_sounddevice_data/portaudio-binaries/README.md
deleted file mode 100644
index cb6019311861b239798349a881922b1a9a3ad61a..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_sounddevice_data/portaudio-binaries/README.md
+++ /dev/null
@@ -1,76 +0,0 @@
-PortAudio binaries
-==================
-
-This repository provides pre-compiled dynamic libraries for
-[PortAudio](http://www.portaudio.com/).
-
-DLLs for Windows (32-bit and 64-bit)
-------------------------------------
-
-The DLLs include all available host APIs, namely WMME, DirectSound, WDM/KS,
-WASAPI and ASIO.  For more informaton about the ASIO SDK see
-http://www.steinberg.net/en/company/developers.html.
-
-The DLLs were created on a Debian GNU/Linux system using [MXE](http://mxe.cc/)
-([this version](https://github.com/mxe/mxe/tree/95649828bc29334d5f9abef2718d49a55d9a5407),
-using `pa_stable_v190700_20210406.tgz`)
-with the following commands (after installing the
-[dependencies](http://mxe.cc/#requirements)):
-
-    git clone https://github.com/mxe/mxe.git
-    wget http://www.steinberg.net/sdk_downloads/asiosdk2.3.zip
-    export PATH=$(pwd)"/mxe/usr/bin:$PATH"
-
-Open the file `mxe/src/portaudio.mk` and change
-`--with-winapi=wmme,directx,wdmks,wasapi` to
-`--with-winapi=wmme,directx,wdmks,wasapi,asio` (and make sure to keep the
-backslash at the end of the line).
-To the first line starting with "$(MAKE)", append " EXAMPLES= SELFTESTS=" (without the quotes).
-Delete the 4 lines before the last line (i.e. keep the line with "endef").
-After saving your changes, please continue:
-
-    for TARGET in x86_64-w64-mingw32.static i686-w64-mingw32.static
-    do
-        unzip asiosdk2.3.zip
-        # You'll need write access in /usr/local for this:
-        mv ASIOSDK2.3 /usr/local/asiosdk2
-        # If it doesn't work, prepend "sudo " to the previous command
-        make -C mxe portaudio MXE_TARGETS=$TARGET
-        $TARGET-gcc -O2 -shared -o libportaudio-$TARGET.dll -Wl,--whole-archive -lportaudio -Wl,--no-whole-archive -lstdc++ -lwinmm -lole32 -lsetupapi
-        $TARGET-strip libportaudio-$TARGET.dll
-        chmod -x libportaudio-$TARGET.dll
-        # again, you'll probably have to use "sudo":
-        rm -r /usr/local/asiosdk2
-    done
-
-    mv libportaudio-x86_64-w64-mingw32.static.dll libportaudio64bit.dll
-    mv libportaudio-i686-w64-mingw32.static.dll libportaudio32bit.dll
-
-A different set of DLLs (compiled with Visual Studio) is available at
-https://github.com/adfernandes/precompiled-portaudio-windows.
-
-dylib for Mac OS X (64-bit)
----------------------------
-
-The dylib was created on a Mac OS X system using XCode.
-The XCode CLI tools were installed with:
-
-    xcode-select --install
-
-The following commands were used for compilation:
-
-    curl -O http://files.portaudio.com/archives/pa_stable_v190700_20210406.tgz
-    tar xvf pa_stable_v190700_20210406.tgz
-    cd portaudio
-    # in configure: replace "-Werror" (just search for it) with "-DNDEBUG"
-    ./configure MACOSX_DEPLOYMENT_TARGET=10.6
-    make
-    cd ..
-    cp portaudio/lib/.libs/libportaudio.2.dylib libportaudio.dylib
-
-Copyright
----------
-
-* PortAudio by Ross Bencina and Phil Burk, MIT License.
-
-* Steinberg Audio Stream I/O API by Steinberg Media Technologies GmbH.
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/data/transforms/transform.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/data/transforms/transform.py
deleted file mode 100644
index de44b991d7ab0d920ffb769e1402f08e358d37f7..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/data/transforms/transform.py
+++ /dev/null
@@ -1,351 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-"""
-See "Data Augmentation" tutorial for an overview of the system:
-https://detectron2.readthedocs.io/tutorials/augmentation.html
-"""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fvcore.transforms.transform import (
-    CropTransform,
-    HFlipTransform,
-    NoOpTransform,
-    Transform,
-    TransformList,
-)
-from PIL import Image
-
-try:
-    import cv2  # noqa
-except ImportError:
-    # OpenCV is an optional dependency at the moment
-    pass
-
-__all__ = [
-    "ExtentTransform",
-    "ResizeTransform",
-    "RotationTransform",
-    "ColorTransform",
-    "PILColorTransform",
-]
-
-
-class ExtentTransform(Transform):
-    """
-    Extracts a subregion from the source image and scales it to the output size.
-
-    The fill color is used to map pixels from the source rect that fall outside
-    the source image.
-
-    See: https://pillow.readthedocs.io/en/latest/PIL.html#PIL.ImageTransform.ExtentTransform
-    """
-
-    def __init__(self, src_rect, output_size, interp=Image.LINEAR, fill=0):
-        """
-        Args:
-            src_rect (x0, y0, x1, y1): src coordinates
-            output_size (h, w): dst image size
-            interp: PIL interpolation methods
-            fill: Fill color used when src_rect extends outside image
-        """
-        super().__init__()
-        self._set_attributes(locals())
-
-    def apply_image(self, img, interp=None):
-        h, w = self.output_size
-        if len(img.shape) > 2 and img.shape[2] == 1:
-            pil_image = Image.fromarray(img[:, :, 0], mode="L")
-        else:
-            pil_image = Image.fromarray(img)
-        pil_image = pil_image.transform(
-            size=(w, h),
-            method=Image.EXTENT,
-            data=self.src_rect,
-            resample=interp if interp else self.interp,
-            fill=self.fill,
-        )
-        ret = np.asarray(pil_image)
-        if len(img.shape) > 2 and img.shape[2] == 1:
-            ret = np.expand_dims(ret, -1)
-        return ret
-
-    def apply_coords(self, coords):
-        # Transform image center from source coordinates into output coordinates
-        # and then map the new origin to the corner of the output image.
-        h, w = self.output_size
-        x0, y0, x1, y1 = self.src_rect
-        new_coords = coords.astype(np.float32)
-        new_coords[:, 0] -= 0.5 * (x0 + x1)
-        new_coords[:, 1] -= 0.5 * (y0 + y1)
-        new_coords[:, 0] *= w / (x1 - x0)
-        new_coords[:, 1] *= h / (y1 - y0)
-        new_coords[:, 0] += 0.5 * w
-        new_coords[:, 1] += 0.5 * h
-        return new_coords
-
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
-        return segmentation
-
-
-class ResizeTransform(Transform):
-    """
-    Resize the image to a target size.
-    """
-
-    def __init__(self, h, w, new_h, new_w, interp=None):
-        """
-        Args:
-            h, w (int): original image size
-            new_h, new_w (int): new image size
-            interp: PIL interpolation methods, defaults to bilinear.
-        """
-        # TODO decide on PIL vs opencv
-        super().__init__()
-        if interp is None:
-            interp = Image.BILINEAR
-        self._set_attributes(locals())
-
-    def apply_image(self, img, interp=None):
-        assert img.shape[:2] == (self.h, self.w)
-        assert len(img.shape) <= 4
-        interp_method = interp if interp is not None else self.interp
-
-        if img.dtype == np.uint8:
-            if len(img.shape) > 2 and img.shape[2] == 1:
-                pil_image = Image.fromarray(img[:, :, 0], mode="L")
-            else:
-                pil_image = Image.fromarray(img)
-            pil_image = pil_image.resize((self.new_w, self.new_h), interp_method)
-            ret = np.asarray(pil_image)
-            if len(img.shape) > 2 and img.shape[2] == 1:
-                ret = np.expand_dims(ret, -1)
-        else:
-            # PIL only supports uint8
-            if any(x < 0 for x in img.strides):
-                img = np.ascontiguousarray(img)
-            img = torch.from_numpy(img)
-            shape = list(img.shape)
-            shape_4d = shape[:2] + [1] * (4 - len(shape)) + shape[2:]
-            img = img.view(shape_4d).permute(2, 3, 0, 1)  # hw(c) -> nchw
-            _PIL_RESIZE_TO_INTERPOLATE_MODE = {
-                Image.NEAREST: "nearest",
-                Image.BILINEAR: "bilinear",
-                Image.BICUBIC: "bicubic",
-            }
-            mode = _PIL_RESIZE_TO_INTERPOLATE_MODE[interp_method]
-            align_corners = None if mode == "nearest" else False
-            img = F.interpolate(
-                img, (self.new_h, self.new_w), mode=mode, align_corners=align_corners
-            )
-            shape[:2] = (self.new_h, self.new_w)
-            ret = img.permute(2, 3, 0, 1).view(shape).numpy()  # nchw -> hw(c)
-
-        return ret
-
-    def apply_coords(self, coords):
-        coords[:, 0] = coords[:, 0] * (self.new_w * 1.0 / self.w)
-        coords[:, 1] = coords[:, 1] * (self.new_h * 1.0 / self.h)
-        return coords
-
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=Image.NEAREST)
-        return segmentation
-
-    def inverse(self):
-        return ResizeTransform(self.new_h, self.new_w, self.h, self.w, self.interp)
-
-
-class RotationTransform(Transform):
-    """
-    This method returns a copy of this image, rotated the given
-    number of degrees counter clockwise around its center.
-    """
-
-    def __init__(self, h, w, angle, expand=True, center=None, interp=None):
-        """
-        Args:
-            h, w (int): original image size
-            angle (float): degrees for rotation
-            expand (bool): choose if the image should be resized to fit the whole
-                rotated image (default), or simply cropped
-            center (tuple (width, height)): coordinates of the rotation center
-                if left to None, the center will be fit to the center of each image
-                center has no effect if expand=True because it only affects shifting
-            interp: cv2 interpolation method, default cv2.INTER_LINEAR
-        """
-        super().__init__()
-        image_center = np.array((w / 2, h / 2))
-        if center is None:
-            center = image_center
-        if interp is None:
-            interp = cv2.INTER_LINEAR
-        abs_cos, abs_sin = (abs(np.cos(np.deg2rad(angle))), abs(np.sin(np.deg2rad(angle))))
-        if expand:
-            # find the new width and height bounds
-            bound_w, bound_h = np.rint(
-                [h * abs_sin + w * abs_cos, h * abs_cos + w * abs_sin]
-            ).astype(int)
-        else:
-            bound_w, bound_h = w, h
-
-        self._set_attributes(locals())
-        self.rm_coords = self.create_rotation_matrix()
-        # Needed because of this problem https://github.com/opencv/opencv/issues/11784
-        self.rm_image = self.create_rotation_matrix(offset=-0.5)
-
-    def apply_image(self, img, interp=None):
-        """
-        img should be a numpy array, formatted as Height * Width * Nchannels
-        """
-        if len(img) == 0 or self.angle % 360 == 0:
-            return img
-        assert img.shape[:2] == (self.h, self.w)
-        interp = interp if interp is not None else self.interp
-        return cv2.warpAffine(img, self.rm_image, (self.bound_w, self.bound_h), flags=interp)
-
-    def apply_coords(self, coords):
-        """
-        coords should be a N * 2 array-like, containing N couples of (x, y) points
-        """
-        coords = np.asarray(coords, dtype=float)
-        if len(coords) == 0 or self.angle % 360 == 0:
-            return coords
-        return cv2.transform(coords[:, np.newaxis, :], self.rm_coords)[:, 0, :]
-
-    def apply_segmentation(self, segmentation):
-        segmentation = self.apply_image(segmentation, interp=cv2.INTER_NEAREST)
-        return segmentation
-
-    def create_rotation_matrix(self, offset=0):
-        center = (self.center[0] + offset, self.center[1] + offset)
-        rm = cv2.getRotationMatrix2D(tuple(center), self.angle, 1)
-        if self.expand:
-            # Find the coordinates of the center of rotation in the new image
-            # The only point for which we know the future coordinates is the center of the image
-            rot_im_center = cv2.transform(self.image_center[None, None, :] + offset, rm)[0, 0, :]
-            new_center = np.array([self.bound_w / 2, self.bound_h / 2]) + offset - rot_im_center
-            # shift the rotation center to the new coordinates
-            rm[:, 2] += new_center
-        return rm
-
-    def inverse(self):
-        """
-        The inverse is to rotate it back with expand, and crop to get the original shape.
-        """
-        if not self.expand:  # Not possible to inverse if a part of the image is lost
-            raise NotImplementedError()
-        rotation = RotationTransform(
-            self.bound_h, self.bound_w, -self.angle, True, None, self.interp
-        )
-        crop = CropTransform(
-            (rotation.bound_w - self.w) // 2, (rotation.bound_h - self.h) // 2, self.w, self.h
-        )
-        return TransformList([rotation, crop])
-
-
-class ColorTransform(Transform):
-    """
-    Generic wrapper for any photometric transforms.
-    These transformations should only affect the color space and
-        not the coordinate space of the image (e.g. annotation
-        coordinates such as bounding boxes should not be changed)
-    """
-
-    def __init__(self, op):
-        """
-        Args:
-            op (Callable): operation to be applied to the image,
-                which takes in an ndarray and returns an ndarray.
-        """
-        if not callable(op):
-            raise ValueError("op parameter should be callable")
-        super().__init__()
-        self._set_attributes(locals())
-
-    def apply_image(self, img):
-        return self.op(img)
-
-    def apply_coords(self, coords):
-        return coords
-
-    def inverse(self):
-        return NoOpTransform()
-
-    def apply_segmentation(self, segmentation):
-        return segmentation
-
-
-class PILColorTransform(ColorTransform):
-    """
-    Generic wrapper for PIL Photometric image transforms,
-        which affect the color space and not the coordinate
-        space of the image
-    """
-
-    def __init__(self, op):
-        """
-        Args:
-            op (Callable): operation to be applied to the image,
-                which takes in a PIL Image and returns a transformed
-                PIL Image.
-                For reference on possible operations see:
-                - https://pillow.readthedocs.io/en/stable/
-        """
-        if not callable(op):
-            raise ValueError("op parameter should be callable")
-        super().__init__(op)
-
-    def apply_image(self, img):
-        img = Image.fromarray(img)
-        return np.asarray(super().apply_image(img))
-
-
-def HFlip_rotated_box(transform, rotated_boxes):
-    """
-    Apply the horizontal flip transform on rotated boxes.
-
-    Args:
-        rotated_boxes (ndarray): Nx5 floating point array of
-            (x_center, y_center, width, height, angle_degrees) format
-            in absolute coordinates.
-    """
-    # Transform x_center
-    rotated_boxes[:, 0] = transform.width - rotated_boxes[:, 0]
-    # Transform angle
-    rotated_boxes[:, 4] = -rotated_boxes[:, 4]
-    return rotated_boxes
-
-
-def Resize_rotated_box(transform, rotated_boxes):
-    """
-    Apply the resizing transform on rotated boxes. For details of how these (approximation)
-    formulas are derived, please refer to :meth:`RotatedBoxes.scale`.
-
-    Args:
-        rotated_boxes (ndarray): Nx5 floating point array of
-            (x_center, y_center, width, height, angle_degrees) format
-            in absolute coordinates.
-    """
-    scale_factor_x = transform.new_w * 1.0 / transform.w
-    scale_factor_y = transform.new_h * 1.0 / transform.h
-    rotated_boxes[:, 0] *= scale_factor_x
-    rotated_boxes[:, 1] *= scale_factor_y
-    theta = rotated_boxes[:, 4] * np.pi / 180.0
-    c = np.cos(theta)
-    s = np.sin(theta)
-    rotated_boxes[:, 2] *= np.sqrt(np.square(scale_factor_x * c) + np.square(scale_factor_y * s))
-    rotated_boxes[:, 3] *= np.sqrt(np.square(scale_factor_x * s) + np.square(scale_factor_y * c))
-    rotated_boxes[:, 4] = np.arctan2(scale_factor_x * s, scale_factor_y * c) * 180 / np.pi
-
-    return rotated_boxes
-
-
-HFlipTransform.register_type("rotated_box", HFlip_rotated_box)
-ResizeTransform.register_type("rotated_box", Resize_rotated_box)
-
-# not necessary any more with latest fvcore
-NoOpTransform.register_type("rotated_box", lambda t, x: x)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/PointSup/point_sup/__init__.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/PointSup/point_sup/__init__.py
deleted file mode 100644
index 510e3814ac1bb273b48804191b4a7c1272ea9a9b..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/PointSup/point_sup/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from . import register_point_annotations
-from .config import add_point_sup_config
-from .dataset_mapper import PointSupDatasetMapper
-from .mask_head import MaskRCNNConvUpsamplePointSupHead
-from .point_utils import get_point_coords_from_point_annotation
diff --git a/spaces/cffl/Exploring_Intelligent_Writing_Assistance/scripts/launch_app.py b/spaces/cffl/Exploring_Intelligent_Writing_Assistance/scripts/launch_app.py
deleted file mode 100644
index e1311807bb976bfe5465ff034f39a77a23c775d5..0000000000000000000000000000000000000000
--- a/spaces/cffl/Exploring_Intelligent_Writing_Assistance/scripts/launch_app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# ###########################################################################
-#
-#  CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
-#  (C) Cloudera, Inc. 2022
-#  All rights reserved.
-#
-#  Applicable Open Source License: Apache 2.0
-#
-#  NOTE: Cloudera open source products are modular software products
-#  made up of hundreds of individual components, each of which was
-#  individually copyrighted.  Each Cloudera open source product is a
-#  collective work under U.S. Copyright Law. Your license to use the
-#  collective work is as provided in your written agreement with
-#  Cloudera.  Used apart from the collective work, this file is
-#  licensed for your use pursuant to the open source license
-#  identified above.
-#
-#  This code is provided to you pursuant a written agreement with
-#  (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
-#  this code. If you do not have a written agreement with Cloudera nor
-#  with an authorized and properly licensed third party, you do not
-#  have any rights to access nor to use this code.
-#
-#  Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
-#  contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
-#  KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
-#  WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
-#  IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
-#  FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
-#  AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
-#  ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
-#  OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
-#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
-#  CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
-#  RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
-#  BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
-#  DATA.
-#
-# ###########################################################################
-
-!streamlit run apps/app.py --server.port $CDSW_APP_PORT --server.address 127.0.0.1
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/transformers/scripts/pegasus/build_test_sample_spm_no_bos.py b/spaces/chendl/compositional_test/transformers/scripts/pegasus/build_test_sample_spm_no_bos.py
deleted file mode 100644
index 324db02ef7101b8e262f2ae0d6adf964eaf48e55..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/scripts/pegasus/build_test_sample_spm_no_bos.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus 
-
-# 1. pip install sentencepiece
-# 
-# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt
-
-# 3. build
-import sentencepiece as spm
-
-# pegasus:
-# 1. no bos
-# 2. eos_id is 1
-# 3. unk_id is 2
-# build a sample spm file accordingly
-spm.SentencePieceTrainer.train('--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000')
-
-# 4. now update the fixture
-# mv test_sentencepiece_no_bos.model ../../tests/fixtures/
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/charset_normalizer/cd.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/charset_normalizer/cd.py
deleted file mode 100644
index 6e56fe84a9e0e63b918141bc27d708b2d915563f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/charset_normalizer/cd.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import importlib
-from codecs import IncrementalDecoder
-from collections import Counter
-from functools import lru_cache
-from typing import Counter as TypeCounter, Dict, List, Optional, Tuple
-
-from .assets import FREQUENCIES
-from .constant import KO_NAMES, LANGUAGE_SUPPORTED_COUNT, TOO_SMALL_SEQUENCE, ZH_NAMES
-from .md import is_suspiciously_successive_range
-from .models import CoherenceMatches
-from .utils import (
-    is_accentuated,
-    is_latin,
-    is_multi_byte_encoding,
-    is_unicode_range_secondary,
-    unicode_range,
-)
-
-
-def encoding_unicode_range(iana_name: str) -> List[str]:
-    """
-    Return associated unicode ranges in a single byte code page.
-    """
-    if is_multi_byte_encoding(iana_name):
-        raise IOError("Function not supported on multi-byte code page")
-
-    decoder = importlib.import_module(
-        "encodings.{}".format(iana_name)
-    ).IncrementalDecoder
-
-    p: IncrementalDecoder = decoder(errors="ignore")
-    seen_ranges: Dict[str, int] = {}
-    character_count: int = 0
-
-    for i in range(0x40, 0xFF):
-        chunk: str = p.decode(bytes([i]))
-
-        if chunk:
-            character_range: Optional[str] = unicode_range(chunk)
-
-            if character_range is None:
-                continue
-
-            if is_unicode_range_secondary(character_range) is False:
-                if character_range not in seen_ranges:
-                    seen_ranges[character_range] = 0
-                seen_ranges[character_range] += 1
-                character_count += 1
-
-    return sorted(
-        [
-            character_range
-            for character_range in seen_ranges
-            if seen_ranges[character_range] / character_count >= 0.15
-        ]
-    )
-
-
-def unicode_range_languages(primary_range: str) -> List[str]:
-    """
-    Return inferred languages used with a unicode range.
-    """
-    languages: List[str] = []
-
-    for language, characters in FREQUENCIES.items():
-        for character in characters:
-            if unicode_range(character) == primary_range:
-                languages.append(language)
-                break
-
-    return languages
-
-
-@lru_cache()
-def encoding_languages(iana_name: str) -> List[str]:
-    """
-    Single-byte encoding language association. Some code page are heavily linked to particular language(s).
-    This function does the correspondence.
-    """
-    unicode_ranges: List[str] = encoding_unicode_range(iana_name)
-    primary_range: Optional[str] = None
-
-    for specified_range in unicode_ranges:
-        if "Latin" not in specified_range:
-            primary_range = specified_range
-            break
-
-    if primary_range is None:
-        return ["Latin Based"]
-
-    return unicode_range_languages(primary_range)
-
-
-@lru_cache()
-def mb_encoding_languages(iana_name: str) -> List[str]:
-    """
-    Multi-byte encoding language association. Some code page are heavily linked to particular language(s).
-    This function does the correspondence.
-    """
-    if (
-        iana_name.startswith("shift_")
-        or iana_name.startswith("iso2022_jp")
-        or iana_name.startswith("euc_j")
-        or iana_name == "cp932"
-    ):
-        return ["Japanese"]
-    if iana_name.startswith("gb") or iana_name in ZH_NAMES:
-        return ["Chinese"]
-    if iana_name.startswith("iso2022_kr") or iana_name in KO_NAMES:
-        return ["Korean"]
-
-    return []
-
-
-@lru_cache(maxsize=LANGUAGE_SUPPORTED_COUNT)
-def get_target_features(language: str) -> Tuple[bool, bool]:
-    """
-    Determine main aspects from a supported language if it contains accents and if is pure Latin.
-    """
-    target_have_accents: bool = False
-    target_pure_latin: bool = True
-
-    for character in FREQUENCIES[language]:
-        if not target_have_accents and is_accentuated(character):
-            target_have_accents = True
-        if target_pure_latin and is_latin(character) is False:
-            target_pure_latin = False
-
-    return target_have_accents, target_pure_latin
-
-
-def alphabet_languages(
-    characters: List[str], ignore_non_latin: bool = False
-) -> List[str]:
-    """
-    Return associated languages associated to given characters.
-    """
-    languages: List[Tuple[str, float]] = []
-
-    source_have_accents = any(is_accentuated(character) for character in characters)
-
-    for language, language_characters in FREQUENCIES.items():
-        target_have_accents, target_pure_latin = get_target_features(language)
-
-        if ignore_non_latin and target_pure_latin is False:
-            continue
-
-        if target_have_accents is False and source_have_accents:
-            continue
-
-        character_count: int = len(language_characters)
-
-        character_match_count: int = len(
-            [c for c in language_characters if c in characters]
-        )
-
-        ratio: float = character_match_count / character_count
-
-        if ratio >= 0.2:
-            languages.append((language, ratio))
-
-    languages = sorted(languages, key=lambda x: x[1], reverse=True)
-
-    return [compatible_language[0] for compatible_language in languages]
-
-
-def characters_popularity_compare(
-    language: str, ordered_characters: List[str]
-) -> float:
-    """
-    Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language.
-    The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit).
-    Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.)
-    """
-    if language not in FREQUENCIES:
-        raise ValueError("{} not available".format(language))
-
-    character_approved_count: int = 0
-    FREQUENCIES_language_set = set(FREQUENCIES[language])
-
-    ordered_characters_count: int = len(ordered_characters)
-    target_language_characters_count: int = len(FREQUENCIES[language])
-
-    large_alphabet: bool = target_language_characters_count > 26
-
-    for character, character_rank in zip(
-        ordered_characters, range(0, ordered_characters_count)
-    ):
-        if character not in FREQUENCIES_language_set:
-            continue
-
-        character_rank_in_language: int = FREQUENCIES[language].index(character)
-        expected_projection_ratio: float = (
-            target_language_characters_count / ordered_characters_count
-        )
-        character_rank_projection: int = int(character_rank * expected_projection_ratio)
-
-        if (
-            large_alphabet is False
-            and abs(character_rank_projection - character_rank_in_language) > 4
-        ):
-            continue
-
-        if (
-            large_alphabet is True
-            and abs(character_rank_projection - character_rank_in_language)
-            < target_language_characters_count / 3
-        ):
-            character_approved_count += 1
-            continue
-
-        characters_before_source: List[str] = FREQUENCIES[language][
-            0:character_rank_in_language
-        ]
-        characters_after_source: List[str] = FREQUENCIES[language][
-            character_rank_in_language:
-        ]
-        characters_before: List[str] = ordered_characters[0:character_rank]
-        characters_after: List[str] = ordered_characters[character_rank:]
-
-        before_match_count: int = len(
-            set(characters_before) & set(characters_before_source)
-        )
-
-        after_match_count: int = len(
-            set(characters_after) & set(characters_after_source)
-        )
-
-        if len(characters_before_source) == 0 and before_match_count <= 4:
-            character_approved_count += 1
-            continue
-
-        if len(characters_after_source) == 0 and after_match_count <= 4:
-            character_approved_count += 1
-            continue
-
-        if (
-            before_match_count / len(characters_before_source) >= 0.4
-            or after_match_count / len(characters_after_source) >= 0.4
-        ):
-            character_approved_count += 1
-            continue
-
-    return character_approved_count / len(ordered_characters)
-
-
-def alpha_unicode_split(decoded_sequence: str) -> List[str]:
-    """
-    Given a decoded text sequence, return a list of str. Unicode range / alphabet separation.
-    Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list;
-    One containing the latin letters and the other hebrew.
-    """
-    layers: Dict[str, str] = {}
-
-    for character in decoded_sequence:
-        if character.isalpha() is False:
-            continue
-
-        character_range: Optional[str] = unicode_range(character)
-
-        if character_range is None:
-            continue
-
-        layer_target_range: Optional[str] = None
-
-        for discovered_range in layers:
-            if (
-                is_suspiciously_successive_range(discovered_range, character_range)
-                is False
-            ):
-                layer_target_range = discovered_range
-                break
-
-        if layer_target_range is None:
-            layer_target_range = character_range
-
-        if layer_target_range not in layers:
-            layers[layer_target_range] = character.lower()
-            continue
-
-        layers[layer_target_range] += character.lower()
-
-    return list(layers.values())
-
-
-def merge_coherence_ratios(results: List[CoherenceMatches]) -> CoherenceMatches:
-    """
-    This function merge results previously given by the function coherence_ratio.
-    The return type is the same as coherence_ratio.
-    """
-    per_language_ratios: Dict[str, List[float]] = {}
-    for result in results:
-        for sub_result in result:
-            language, ratio = sub_result
-            if language not in per_language_ratios:
-                per_language_ratios[language] = [ratio]
-                continue
-            per_language_ratios[language].append(ratio)
-
-    merge = [
-        (
-            language,
-            round(
-                sum(per_language_ratios[language]) / len(per_language_ratios[language]),
-                4,
-            ),
-        )
-        for language in per_language_ratios
-    ]
-
-    return sorted(merge, key=lambda x: x[1], reverse=True)
-
-
-def filter_alt_coherence_matches(results: CoherenceMatches) -> CoherenceMatches:
-    """
-    We shall NOT return "English—" in CoherenceMatches because it is an alternative
-    of "English". This function only keeps the best match and remove the em-dash in it.
-    """
-    index_results: Dict[str, List[float]] = dict()
-
-    for result in results:
-        language, ratio = result
-        no_em_name: str = language.replace("—", "")
-
-        if no_em_name not in index_results:
-            index_results[no_em_name] = []
-
-        index_results[no_em_name].append(ratio)
-
-    if any(len(index_results[e]) > 1 for e in index_results):
-        filtered_results: CoherenceMatches = []
-
-        for language in index_results:
-            filtered_results.append((language, max(index_results[language])))
-
-        return filtered_results
-
-    return results
-
-
-@lru_cache(maxsize=2048)
-def coherence_ratio(
-    decoded_sequence: str, threshold: float = 0.1, lg_inclusion: Optional[str] = None
-) -> CoherenceMatches:
-    """
-    Detect ANY language that can be identified in given sequence. The sequence will be analysed by layers.
-    A layer = Character extraction by alphabets/ranges.
-    """
-
-    results: List[Tuple[str, float]] = []
-    ignore_non_latin: bool = False
-
-    sufficient_match_count: int = 0
-
-    lg_inclusion_list = lg_inclusion.split(",") if lg_inclusion is not None else []
-    if "Latin Based" in lg_inclusion_list:
-        ignore_non_latin = True
-        lg_inclusion_list.remove("Latin Based")
-
-    for layer in alpha_unicode_split(decoded_sequence):
-        sequence_frequencies: TypeCounter[str] = Counter(layer)
-        most_common = sequence_frequencies.most_common()
-
-        character_count: int = sum(o for c, o in most_common)
-
-        if character_count <= TOO_SMALL_SEQUENCE:
-            continue
-
-        popular_character_ordered: List[str] = [c for c, o in most_common]
-
-        for language in lg_inclusion_list or alphabet_languages(
-            popular_character_ordered, ignore_non_latin
-        ):
-            ratio: float = characters_popularity_compare(
-                language, popular_character_ordered
-            )
-
-            if ratio < threshold:
-                continue
-            elif ratio >= 0.8:
-                sufficient_match_count += 1
-
-            results.append((language, round(ratio, 4)))
-
-            if sufficient_match_count >= 3:
-                break
-
-    return sorted(
-        filter_alt_coherence_matches(results), key=lambda x: x[1], reverse=True
-    )
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/clickhouse.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/clickhouse.py
deleted file mode 100644
index d53be3b32b4b4ae74d0b88a11416939abdc139e7..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chromadb/db/clickhouse.py
+++ /dev/null
@@ -1,656 +0,0 @@
-# type: ignore
-from chromadb.api.types import (
-    Documents,
-    Embeddings,
-    IDs,
-    Metadatas,
-    Where,
-    WhereDocument,
-)
-from chromadb.db import DB
-from chromadb.db.index.hnswlib import Hnswlib, delete_all_indexes
-import uuid
-import json
-from typing import Optional, Sequence, List, Tuple, cast
-import clickhouse_connect
-from clickhouse_connect.driver.client import Client
-from clickhouse_connect import common
-import logging
-from uuid import UUID
-from chromadb.config import System
-from overrides import override
-from chromadb.api.types import Metadata
-
-logger = logging.getLogger(__name__)
-
-COLLECTION_TABLE_SCHEMA = [{"uuid": "UUID"}, {"name": "String"}, {"metadata": "String"}]
-
-EMBEDDING_TABLE_SCHEMA = [
-    {"collection_uuid": "UUID"},
-    {"uuid": "UUID"},
-    {"embedding": "Array(Float64)"},
-    {"document": "Nullable(String)"},
-    {"id": "Nullable(String)"},
-    {"metadata": "Nullable(String)"},
-]
-
-
-def db_array_schema_to_clickhouse_schema(table_schema):
-    return_str = ""
-    for element in table_schema:
-        for k, v in element.items():
-            return_str += f"{k} {v}, "
-    return return_str
-
-
-def db_schema_to_keys() -> List[str]:
-    keys = []
-    for element in EMBEDDING_TABLE_SCHEMA:
-        keys.append(list(element.keys())[0])
-    return keys
-
-
-class Clickhouse(DB):
-    #
-    #  INIT METHODS
-    #
-    def __init__(self, system: System):
-        super().__init__(system)
-        self._conn = None
-        self._settings = system.settings
-
-        self._settings.require("clickhouse_host")
-        self._settings.require("clickhouse_port")
-
-    def _init_conn(self):
-        common.set_setting("autogenerate_session_id", False)
-        self._conn = clickhouse_connect.get_client(
-            host=self._settings.clickhouse_host,
-            port=int(self._settings.clickhouse_port),
-        )
-        self._create_table_collections(self._conn)
-        self._create_table_embeddings(self._conn)
-
-    def _get_conn(self) -> Client:
-        if self._conn is None:
-            self._init_conn()
-        return self._conn
-
-    def _create_table_collections(self, conn):
-        conn.command(
-            f"""CREATE TABLE IF NOT EXISTS collections (
-            {db_array_schema_to_clickhouse_schema(COLLECTION_TABLE_SCHEMA)}
-        ) ENGINE = MergeTree() ORDER BY uuid"""
-        )
-
-    def _create_table_embeddings(self, conn):
-        conn.command(
-            f"""CREATE TABLE IF NOT EXISTS embeddings (
-            {db_array_schema_to_clickhouse_schema(EMBEDDING_TABLE_SCHEMA)}
-        ) ENGINE = MergeTree() ORDER BY collection_uuid"""
-        )
-
-    index_cache = {}
-
-    def _index(self, collection_id):
-        """Retrieve an HNSW index instance for the given collection"""
-
-        if collection_id not in self.index_cache:
-            coll = self.get_collection_by_id(collection_id)
-            collection_metadata = coll[2]
-            index = Hnswlib(
-                collection_id,
-                self._settings,
-                collection_metadata,
-                self.count(collection_id),
-            )
-            self.index_cache[collection_id] = index
-
-        return self.index_cache[collection_id]
-
-    def _delete_index(self, collection_id):
-        """Delete an index from the cache"""
-        index = self._index(collection_id)
-        index.delete()
-        del self.index_cache[collection_id]
-
-    #
-    #  UTILITY METHODS
-    #
-    @override
-    def persist(self):
-        raise NotImplementedError(
-            "Clickhouse is a persistent database, this method is not needed"
-        )
-
-    @override
-    def get_collection_uuid_from_name(self, collection_name: str) -> UUID:
-        res = self._get_conn().query(
-            f"""
-            SELECT uuid FROM collections WHERE name = '{collection_name}'
-        """
-        )
-        return res.result_rows[0][0]
-
-    def _create_where_clause(
-        self,
-        collection_uuid: str,
-        ids: Optional[List[str]] = None,
-        where: Where = {},
-        where_document: WhereDocument = {},
-    ):
-        where_clauses: List[str] = []
-        self._format_where(where, where_clauses)
-        if len(where_document) > 0:
-            where_document_clauses = []
-            self._format_where_document(where_document, where_document_clauses)
-            where_clauses.extend(where_document_clauses)
-
-        if ids is not None:
-            where_clauses.append(f" id IN {tuple(ids)}")
-
-        where_clauses.append(f"collection_uuid = '{collection_uuid}'")
-        where_str = " AND ".join(where_clauses)
-        where_str = f"WHERE {where_str}"
-        return where_str
-
-    #
-    #  COLLECTION METHODS
-    #
-    @override
-    def create_collection(
-        self,
-        name: str,
-        metadata: Optional[Metadata] = None,
-        get_or_create: bool = False,
-    ) -> Sequence:
-        # poor man's unique constraint
-        dupe_check = self.get_collection(name)
-
-        if len(dupe_check) > 0:
-            if get_or_create:
-                if dupe_check[0][2] != metadata:
-                    self.update_collection(
-                        dupe_check[0][0], new_name=name, new_metadata=metadata
-                    )
-                    dupe_check = self.get_collection(name)
-                logger.info(
-                    f"collection with name {name} already exists, returning existing collection"
-                )
-                return dupe_check
-            else:
-                raise ValueError(f"Collection with name {name} already exists")
-
-        collection_uuid = uuid.uuid4()
-        data_to_insert = [[collection_uuid, name, json.dumps(metadata)]]
-
-        self._get_conn().insert(
-            "collections", data_to_insert, column_names=["uuid", "name", "metadata"]
-        )
-        return [[collection_uuid, name, metadata]]
-
-    @override
-    def get_collection(self, name: str) -> Sequence:
-        res = (
-            self._get_conn()
-            .query(
-                f"""
-         SELECT * FROM collections WHERE name = '{name}'
-         """
-            )
-            .result_rows
-        )
-        # json.loads the metadata
-        return [[x[0], x[1], json.loads(x[2])] for x in res]
-
-    def get_collection_by_id(self, collection_uuid: str):
-        res = (
-            self._get_conn()
-            .query(
-                f"""
-         SELECT * FROM collections WHERE uuid = '{collection_uuid}'
-         """
-            )
-            .result_rows
-        )
-        # json.loads the metadata
-        return [[x[0], x[1], json.loads(x[2])] for x in res][0]
-
-    @override
-    def list_collections(self) -> Sequence:
-        res = self._get_conn().query("SELECT * FROM collections").result_rows
-        return [[x[0], x[1], json.loads(x[2])] for x in res]
-
-    @override
-    def update_collection(
-        self,
-        id: UUID,
-        new_name: Optional[str] = None,
-        new_metadata: Optional[Metadata] = None,
-    ):
-        if new_name is not None:
-            dupe_check = self.get_collection(new_name)
-            if len(dupe_check) > 0 and dupe_check[0][0] != id:
-                raise ValueError(f"Collection with name {new_name} already exists")
-
-            self._get_conn().command(
-                "ALTER TABLE collections UPDATE name = %(new_name)s WHERE uuid = %(uuid)s",
-                parameters={"new_name": new_name, "uuid": id},
-            )
-
-        if new_metadata is not None:
-            self._get_conn().command(
-                "ALTER TABLE collections UPDATE metadata = %(new_metadata)s WHERE uuid = %(uuid)s",
-                parameters={"new_metadata": json.dumps(new_metadata), "uuid": id},
-            )
-
-    @override
-    def delete_collection(self, name: str):
-        collection_uuid = self.get_collection_uuid_from_name(name)
-        self._get_conn().command(
-            f"""
-        DELETE FROM embeddings WHERE collection_uuid = '{collection_uuid}'
-        """
-        )
-
-        self._delete_index(collection_uuid)
-
-        self._get_conn().command(
-            f"""
-         DELETE FROM collections WHERE name = '{name}'
-         """
-        )
-
-    #
-    #  ITEM METHODS
-    #
-    @override
-    def add(self, collection_uuid, embeddings, metadatas, documents, ids) -> List[UUID]:
-        data_to_insert = [
-            [
-                collection_uuid,
-                uuid.uuid4(),
-                embedding,
-                json.dumps(metadatas[i]) if metadatas else None,
-                documents[i] if documents else None,
-                ids[i],
-            ]
-            for i, embedding in enumerate(embeddings)
-        ]
-        column_names = [
-            "collection_uuid",
-            "uuid",
-            "embedding",
-            "metadata",
-            "document",
-            "id",
-        ]
-        self._get_conn().insert("embeddings", data_to_insert, column_names=column_names)
-
-        return [x[1] for x in data_to_insert]  # return uuids
-
-    def _update(
-        self,
-        collection_uuid,
-        ids: IDs,
-        embeddings: Optional[Embeddings],
-        metadatas: Optional[Metadatas],
-        documents: Optional[Documents],
-    ):
-        updates = []
-        parameters = {}
-        for i in range(len(ids)):
-            update_fields = []
-            parameters[f"i{i}"] = ids[i]
-            if embeddings is not None:
-                update_fields.append(f"embedding = %(e{i})s")
-                parameters[f"e{i}"] = embeddings[i]
-            if metadatas is not None:
-                update_fields.append(f"metadata = %(m{i})s")
-                parameters[f"m{i}"] = json.dumps(metadatas[i])
-            if documents is not None:
-                update_fields.append(f"document = %(d{i})s")
-                parameters[f"d{i}"] = documents[i]
-
-            update_statement = f"""
-            UPDATE
-                {",".join(update_fields)}
-            WHERE
-                id = %(i{i})s AND
-                collection_uuid = '{collection_uuid}'{"" if i == len(ids) - 1 else ","}
-            """
-            updates.append(update_statement)
-
-        update_clauses = ("").join(updates)
-        self._get_conn().command(
-            f"ALTER TABLE embeddings {update_clauses}", parameters=parameters
-        )
-
-    @override
-    def update(
-        self,
-        collection_uuid,
-        ids: IDs,
-        embeddings: Optional[Embeddings] = None,
-        metadatas: Optional[Metadatas] = None,
-        documents: Optional[Documents] = None,
-    ) -> bool:
-        # Verify all IDs exist
-        existing_items = self.get(collection_uuid=collection_uuid, ids=ids)
-        if len(existing_items) != len(ids):
-            raise ValueError(
-                f"Could not find {len(ids) - len(existing_items)} items for update"
-            )
-
-        # Update the db
-        self._update(collection_uuid, ids, embeddings, metadatas, documents)
-
-        # Update the index
-        if embeddings is not None:
-            # `get` current returns items in arbitrary order.
-            # TODO if we fix `get`, we can remove this explicit mapping.
-            uuid_mapping = {r[4]: r[1] for r in existing_items}
-            update_uuids = [uuid_mapping[id] for id in ids]
-            index = self._index(collection_uuid)
-            index.add(update_uuids, embeddings, update=True)
-
-    def _get(self, where={}, columns: Optional[List] = None):
-        select_columns = db_schema_to_keys() if columns is None else columns
-        val = (
-            self._get_conn()
-            .query(f"""SELECT {",".join(select_columns)} FROM embeddings {where}""")
-            .result_rows
-        )
-        for i in range(len(val)):
-            # We know val has index abilities, so cast it for typechecker
-            val = cast(list, val)
-            val[i] = list(val[i])
-            # json.load the metadata
-            if "metadata" in select_columns:
-                metadata_column_index = select_columns.index("metadata")
-                db_metadata = val[i][metadata_column_index]
-                val[i][metadata_column_index] = (
-                    json.loads(db_metadata) if db_metadata else None
-                )
-        return val
-
-    def _format_where(self, where, result):
-        for key, value in where.items():
-
-            def has_key_and(clause):
-                return f"(JSONHas(metadata,'{key}') = 1 AND {clause})"
-
-            # Shortcut for $eq
-            if type(value) == str:
-                result.append(
-                    has_key_and(f" JSONExtractString(metadata,'{key}') = '{value}'")
-                )
-            elif type(value) == int:
-                result.append(
-                    has_key_and(f" JSONExtractInt(metadata,'{key}') = {value}")
-                )
-            elif type(value) == float:
-                result.append(
-                    has_key_and(f" JSONExtractFloat(metadata,'{key}') = {value}")
-                )
-            # Operator expression
-            elif type(value) == dict:
-                operator, operand = list(value.items())[0]
-                if operator == "$gt":
-                    return result.append(
-                        has_key_and(f" JSONExtractFloat(metadata,'{key}') > {operand}")
-                    )
-                elif operator == "$lt":
-                    return result.append(
-                        has_key_and(f" JSONExtractFloat(metadata,'{key}') < {operand}")
-                    )
-                elif operator == "$gte":
-                    return result.append(
-                        has_key_and(f" JSONExtractFloat(metadata,'{key}') >= {operand}")
-                    )
-                elif operator == "$lte":
-                    return result.append(
-                        has_key_and(f" JSONExtractFloat(metadata,'{key}') <= {operand}")
-                    )
-                elif operator == "$ne":
-                    if type(operand) == str:
-                        return result.append(
-                            has_key_and(
-                                f" JSONExtractString(metadata,'{key}') != '{operand}'"
-                            )
-                        )
-                    return result.append(
-                        has_key_and(f" JSONExtractFloat(metadata,'{key}') != {operand}")
-                    )
-                elif operator == "$eq":
-                    if type(operand) == str:
-                        return result.append(
-                            has_key_and(
-                                f" JSONExtractString(metadata,'{key}') = '{operand}'"
-                            )
-                        )
-                    return result.append(
-                        has_key_and(f" JSONExtractFloat(metadata,'{key}') = {operand}")
-                    )
-                else:
-                    raise ValueError(
-                        f"Expected one of $gt, $lt, $gte, $lte, $ne, $eq, got {operator}"
-                    )
-            elif type(value) == list:
-                all_subresults = []
-                for subwhere in value:
-                    subresults = []
-                    self._format_where(subwhere, subresults)
-                    all_subresults.append(subresults[0])
-                if key == "$or":
-                    result.append(f"({' OR '.join(all_subresults)})")
-                elif key == "$and":
-                    result.append(f"({' AND '.join(all_subresults)})")
-                else:
-                    raise ValueError(f"Expected one of $or, $and, got {key}")
-
-    def _format_where_document(self, where_document, results):
-        operator = list(where_document.keys())[0]
-        if operator == "$contains":
-            results.append(f"position(document, '{where_document[operator]}') > 0")
-        elif operator == "$and" or operator == "$or":
-            all_subresults = []
-            for subwhere in where_document[operator]:
-                subresults = []
-                self._format_where_document(subwhere, subresults)
-                all_subresults.append(subresults[0])
-            if operator == "$or":
-                results.append(f"({' OR '.join(all_subresults)})")
-            if operator == "$and":
-                results.append(f"({' AND '.join(all_subresults)})")
-        else:
-            raise ValueError(f"Expected one of $contains, $and, $or, got {operator}")
-
-    @override
-    def get(
-        self,
-        where: Where = {},
-        collection_name: Optional[str] = None,
-        collection_uuid: Optional[UUID] = None,
-        ids: Optional[IDs] = None,
-        sort: Optional[str] = None,
-        limit: Optional[int] = None,
-        offset: Optional[int] = None,
-        where_document: WhereDocument = {},
-        columns: Optional[List[str]] = None,
-    ) -> Sequence:
-        if collection_name is None and collection_uuid is None:
-            raise TypeError(
-                "Arguments collection_name and collection_uuid cannot both be None"
-            )
-
-        if collection_name is not None:
-            collection_uuid = self.get_collection_uuid_from_name(collection_name)
-
-        where_str = self._create_where_clause(
-            # collection_uuid must be defined at this point, cast it for typechecker
-            cast(str, collection_uuid),
-            ids=ids,
-            where=where,
-            where_document=where_document,
-        )
-
-        if sort is not None:
-            where_str += f" ORDER BY {sort}"
-        else:
-            where_str += " ORDER BY collection_uuid"  # stable ordering
-
-        if limit is not None or isinstance(limit, int):
-            where_str += f" LIMIT {limit}"
-
-        if offset is not None or isinstance(offset, int):
-            where_str += f" OFFSET {offset}"
-
-        val = self._get(where=where_str, columns=columns)
-
-        return val
-
-    @override
-    def count(self, collection_id: UUID) -> int:
-        where_string = f"WHERE collection_uuid = '{collection_id}'"
-        return (
-            self._get_conn()
-            .query(f"SELECT COUNT() FROM embeddings {where_string}")
-            .result_rows[0][0]
-        )
-
-    def _delete(self, where_str: Optional[str] = None) -> List:
-        deleted_uuids = (
-            self._get_conn()
-            .query(f"""SELECT uuid FROM embeddings {where_str}""")
-            .result_rows
-        )
-        self._get_conn().command(
-            f"""
-            DELETE FROM
-                embeddings
-        {where_str}
-        """
-        )
-        return [res[0] for res in deleted_uuids] if len(deleted_uuids) > 0 else []
-
-    @override
-    def delete(
-        self,
-        where: Where = {},
-        collection_uuid: Optional[UUID] = None,
-        ids: Optional[IDs] = None,
-        where_document: WhereDocument = {},
-    ) -> List[str]:
-        where_str = self._create_where_clause(
-            # collection_uuid must be defined at this point, cast it for typechecker
-            cast(str, collection_uuid),
-            ids=ids,
-            where=where,
-            where_document=where_document,
-        )
-
-        deleted_uuids = self._delete(where_str)
-
-        index = self._index(collection_uuid)
-        index.delete_from_index(deleted_uuids)
-
-        return deleted_uuids
-
-    @override
-    def get_by_ids(
-        self, uuids: List[UUID], columns: Optional[List[str]] = None
-    ) -> Sequence:
-        columns = columns + ["uuid"] if columns else ["uuid"]
-        select_columns = db_schema_to_keys() if columns is None else columns
-        response = (
-            self._get_conn()
-            .query(
-                f"""
-        SELECT {",".join(select_columns)} FROM embeddings WHERE uuid IN ({[id.hex for id in uuids]})
-        """
-            )
-            .result_rows
-        )
-
-        # sort db results by the order of the uuids
-        response = sorted(response, key=lambda obj: uuids.index(obj[len(columns) - 1]))
-
-        return response
-
-    @override
-    def get_nearest_neighbors(
-        self,
-        collection_uuid: UUID,
-        where: Where = {},
-        embeddings: Optional[Embeddings] = None,
-        n_results: int = 10,
-        where_document: WhereDocument = {},
-    ) -> Tuple[List[List[UUID]], List[List[float]]]:
-        # Either the collection name or the collection uuid must be provided
-        if collection_uuid is None:
-            raise TypeError("Argument collection_uuid cannot be None")
-
-        if len(where) != 0 or len(where_document) != 0:
-            results = self.get(
-                collection_uuid=collection_uuid,
-                where=where,
-                where_document=where_document,
-            )
-
-            if len(results) > 0:
-                ids = [x[1] for x in results]
-            else:
-                # No results found, return empty lists
-                return [[] for _ in range(len(embeddings))], [
-                    [] for _ in range(len(embeddings))
-                ]
-        else:
-            ids = None
-
-        index = self._index(collection_uuid)
-        uuids, distances = index.get_nearest_neighbors(embeddings, n_results, ids)
-
-        return uuids, distances
-
-    @override
-    def create_index(self, collection_uuid: UUID):
-        """Create an index for a collection_uuid and optionally scoped to a dataset.
-        Args:
-            collection_uuid (str): The collection_uuid to create an index for
-            dataset (str, optional): The dataset to scope the index to. Defaults to None.
-        Returns:
-            None
-        """
-        get = self.get(collection_uuid=collection_uuid)
-
-        uuids = [x[1] for x in get]
-        embeddings = [x[2] for x in get]
-
-        index = self._index(collection_uuid)
-        index.add(uuids, embeddings)
-
-    @override
-    def add_incremental(
-        self, collection_uuid: UUID, ids: List[UUID], embeddings: Embeddings
-    ) -> None:
-        index = self._index(collection_uuid)
-        index.add(ids, embeddings)
-
-    def reset_indexes(self):
-        delete_all_indexes(self._settings)
-        self.index_cache = {}
-
-    @override
-    def reset_state(self):
-        conn = self._get_conn()
-        conn.command("DROP TABLE collections")
-        conn.command("DROP TABLE embeddings")
-        self._create_table_collections(conn)
-        self._create_table_embeddings(conn)
-
-        self.reset_indexes()
-
-    @override
-    def raw_sql(self, raw_sql):
-        return self._get_conn().query(raw_sql).result_rows
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/contourpy/util/mpl_util.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/contourpy/util/mpl_util.py
deleted file mode 100644
index 0c970886faeac57427db27ca4510934de223ac8c..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/contourpy/util/mpl_util.py
+++ /dev/null
@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, cast
-
-import matplotlib.path as mpath
-import numpy as np
-
-from contourpy import FillType, LineType
-
-if TYPE_CHECKING:
-    from contourpy._contourpy import (
-        CodeArray, FillReturn, LineReturn, LineReturn_Separate, OffsetArray,
-    )
-
-
-def filled_to_mpl_paths(filled: FillReturn, fill_type: FillType) -> list[mpath.Path]:
-    if fill_type in (FillType.OuterCode, FillType.ChunkCombinedCode):
-        paths = [mpath.Path(points, codes) for points, codes in zip(*filled) if points is not None]
-    elif fill_type in (FillType.OuterOffset, FillType.ChunkCombinedOffset):
-        paths = [mpath.Path(points, offsets_to_mpl_codes(offsets))
-                 for points, offsets in zip(*filled) if points is not None]
-    elif fill_type == FillType.ChunkCombinedCodeOffset:
-        paths = []
-        for points, codes, outer_offsets in zip(*filled):
-            if points is None:
-                continue
-            points = np.split(points, outer_offsets[1:-1])
-            codes = np.split(codes, outer_offsets[1:-1])
-            paths += [mpath.Path(p, c) for p, c in zip(points, codes)]
-    elif fill_type == FillType.ChunkCombinedOffsetOffset:
-        paths = []
-        for points, offsets, outer_offsets in zip(*filled):
-            if points is None:
-                continue
-            for i in range(len(outer_offsets)-1):
-                offs = offsets[outer_offsets[i]:outer_offsets[i+1]+1]
-                pts = points[offs[0]:offs[-1]]
-                paths += [mpath.Path(pts, offsets_to_mpl_codes(offs - offs[0]))]
-    else:
-        raise RuntimeError(f"Conversion of FillType {fill_type} to MPL Paths is not implemented")
-    return paths
-
-
-def lines_to_mpl_paths(lines: LineReturn, line_type: LineType) -> list[mpath.Path]:
-    if line_type == LineType.Separate:
-        if TYPE_CHECKING:
-            lines = cast(LineReturn_Separate, lines)
-        paths = []
-        for line in lines:
-            # Drawing as Paths so that they can be closed correctly.
-            closed = line[0, 0] == line[-1, 0] and line[0, 1] == line[-1, 1]
-            paths.append(mpath.Path(line, closed=closed))
-    elif line_type in (LineType.SeparateCode, LineType.ChunkCombinedCode):
-        paths = [mpath.Path(points, codes) for points, codes in zip(*lines) if points is not None]
-    elif line_type == LineType.ChunkCombinedOffset:
-        paths = []
-        for points, offsets in zip(*lines):
-            if points is None:
-                continue
-            for i in range(len(offsets)-1):
-                line = points[offsets[i]:offsets[i+1]]
-                closed = line[0, 0] == line[-1, 0] and line[0, 1] == line[-1, 1]
-                paths.append(mpath.Path(line, closed=closed))
-    else:
-        raise RuntimeError(f"Conversion of LineType {line_type} to MPL Paths is not implemented")
-    return paths
-
-
-def mpl_codes_to_offsets(codes: CodeArray) -> OffsetArray:
-    offsets = np.nonzero(codes == 1)[0].astype(np.uint32)
-    offsets = np.append(offsets, len(codes))
-    return offsets
-
-
-def offsets_to_mpl_codes(offsets: OffsetArray) -> CodeArray:
-    codes = np.full(offsets[-1]-offsets[0], 2, dtype=np.uint8)  # LINETO = 2
-    codes[offsets[:-1]] = 1  # MOVETO = 1
-    codes[offsets[1:]-1] = 79  # CLOSEPOLY 79
-    return codes
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/hashes.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/hashes.py
deleted file mode 100644
index b6a7ff140e682d187161985cb74b3bdbab6a28ab..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/hashes.py
+++ /dev/null
@@ -1,243 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from __future__ import annotations
-
-import abc
-import typing
-
-from cryptography.hazmat.bindings._rust import openssl as rust_openssl
-
-__all__ = [
-    "HashAlgorithm",
-    "HashContext",
-    "Hash",
-    "ExtendableOutputFunction",
-    "SHA1",
-    "SHA512_224",
-    "SHA512_256",
-    "SHA224",
-    "SHA256",
-    "SHA384",
-    "SHA512",
-    "SHA3_224",
-    "SHA3_256",
-    "SHA3_384",
-    "SHA3_512",
-    "SHAKE128",
-    "SHAKE256",
-    "MD5",
-    "BLAKE2b",
-    "BLAKE2s",
-    "SM3",
-]
-
-
-class HashAlgorithm(metaclass=abc.ABCMeta):
-    @property
-    @abc.abstractmethod
-    def name(self) -> str:
-        """
-        A string naming this algorithm (e.g. "sha256", "md5").
-        """
-
-    @property
-    @abc.abstractmethod
-    def digest_size(self) -> int:
-        """
-        The size of the resulting digest in bytes.
-        """
-
-    @property
-    @abc.abstractmethod
-    def block_size(self) -> typing.Optional[int]:
-        """
-        The internal block size of the hash function, or None if the hash
-        function does not use blocks internally (e.g. SHA3).
-        """
-
-
-class HashContext(metaclass=abc.ABCMeta):
-    @property
-    @abc.abstractmethod
-    def algorithm(self) -> HashAlgorithm:
-        """
-        A HashAlgorithm that will be used by this context.
-        """
-
-    @abc.abstractmethod
-    def update(self, data: bytes) -> None:
-        """
-        Processes the provided bytes through the hash.
-        """
-
-    @abc.abstractmethod
-    def finalize(self) -> bytes:
-        """
-        Finalizes the hash context and returns the hash digest as bytes.
-        """
-
-    @abc.abstractmethod
-    def copy(self) -> HashContext:
-        """
-        Return a HashContext that is a copy of the current context.
-        """
-
-
-Hash = rust_openssl.hashes.Hash
-HashContext.register(Hash)
-
-
-class ExtendableOutputFunction(metaclass=abc.ABCMeta):
-    """
-    An interface for extendable output functions.
-    """
-
-
-class SHA1(HashAlgorithm):
-    name = "sha1"
-    digest_size = 20
-    block_size = 64
-
-
-class SHA512_224(HashAlgorithm):  # noqa: N801
-    name = "sha512-224"
-    digest_size = 28
-    block_size = 128
-
-
-class SHA512_256(HashAlgorithm):  # noqa: N801
-    name = "sha512-256"
-    digest_size = 32
-    block_size = 128
-
-
-class SHA224(HashAlgorithm):
-    name = "sha224"
-    digest_size = 28
-    block_size = 64
-
-
-class SHA256(HashAlgorithm):
-    name = "sha256"
-    digest_size = 32
-    block_size = 64
-
-
-class SHA384(HashAlgorithm):
-    name = "sha384"
-    digest_size = 48
-    block_size = 128
-
-
-class SHA512(HashAlgorithm):
-    name = "sha512"
-    digest_size = 64
-    block_size = 128
-
-
-class SHA3_224(HashAlgorithm):  # noqa: N801
-    name = "sha3-224"
-    digest_size = 28
-    block_size = None
-
-
-class SHA3_256(HashAlgorithm):  # noqa: N801
-    name = "sha3-256"
-    digest_size = 32
-    block_size = None
-
-
-class SHA3_384(HashAlgorithm):  # noqa: N801
-    name = "sha3-384"
-    digest_size = 48
-    block_size = None
-
-
-class SHA3_512(HashAlgorithm):  # noqa: N801
-    name = "sha3-512"
-    digest_size = 64
-    block_size = None
-
-
-class SHAKE128(HashAlgorithm, ExtendableOutputFunction):
-    name = "shake128"
-    block_size = None
-
-    def __init__(self, digest_size: int):
-        if not isinstance(digest_size, int):
-            raise TypeError("digest_size must be an integer")
-
-        if digest_size < 1:
-            raise ValueError("digest_size must be a positive integer")
-
-        self._digest_size = digest_size
-
-    @property
-    def digest_size(self) -> int:
-        return self._digest_size
-
-
-class SHAKE256(HashAlgorithm, ExtendableOutputFunction):
-    name = "shake256"
-    block_size = None
-
-    def __init__(self, digest_size: int):
-        if not isinstance(digest_size, int):
-            raise TypeError("digest_size must be an integer")
-
-        if digest_size < 1:
-            raise ValueError("digest_size must be a positive integer")
-
-        self._digest_size = digest_size
-
-    @property
-    def digest_size(self) -> int:
-        return self._digest_size
-
-
-class MD5(HashAlgorithm):
-    name = "md5"
-    digest_size = 16
-    block_size = 64
-
-
-class BLAKE2b(HashAlgorithm):
-    name = "blake2b"
-    _max_digest_size = 64
-    _min_digest_size = 1
-    block_size = 128
-
-    def __init__(self, digest_size: int):
-        if digest_size != 64:
-            raise ValueError("Digest size must be 64")
-
-        self._digest_size = digest_size
-
-    @property
-    def digest_size(self) -> int:
-        return self._digest_size
-
-
-class BLAKE2s(HashAlgorithm):
-    name = "blake2s"
-    block_size = 64
-    _max_digest_size = 32
-    _min_digest_size = 1
-
-    def __init__(self, digest_size: int):
-        if digest_size != 32:
-            raise ValueError("Digest size must be 32")
-
-        self._digest_size = digest_size
-
-    @property
-    def digest_size(self) -> int:
-        return self._digest_size
-
-
-class SM3(HashAlgorithm):
-    name = "sm3"
-    digest_size = 32
-    block_size = 64
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/designspaceLib/statNames.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/designspaceLib/statNames.py
deleted file mode 100644
index a164169da69e17d4febdc82e0e50d48191ac533d..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/designspaceLib/statNames.py
+++ /dev/null
@@ -1,252 +0,0 @@
-"""Compute name information for a given location in user-space coordinates
-using STAT data. This can be used to fill-in automatically the names of an
-instance:
-
-.. code:: python
-
-    instance = doc.instances[0]
-    names = getStatNames(doc, instance.getFullUserLocation(doc))
-    print(names.styleNames)
-"""
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-import logging
-
-from fontTools.designspaceLib import (
-    AxisDescriptor,
-    AxisLabelDescriptor,
-    DesignSpaceDocument,
-    DesignSpaceDocumentError,
-    DiscreteAxisDescriptor,
-    SimpleLocationDict,
-    SourceDescriptor,
-)
-
-LOGGER = logging.getLogger(__name__)
-
-# TODO(Python 3.8): use Literal
-# RibbiStyleName = Union[Literal["regular"], Literal["bold"], Literal["italic"], Literal["bold italic"]]
-RibbiStyle = str
-BOLD_ITALIC_TO_RIBBI_STYLE = {
-    (False, False): "regular",
-    (False, True): "italic",
-    (True, False): "bold",
-    (True, True): "bold italic",
-}
-
-
-@dataclass
-class StatNames:
-    """Name data generated from the STAT table information."""
-
-    familyNames: Dict[str, str]
-    styleNames: Dict[str, str]
-    postScriptFontName: Optional[str]
-    styleMapFamilyNames: Dict[str, str]
-    styleMapStyleName: Optional[RibbiStyle]
-
-
-def getStatNames(
-    doc: DesignSpaceDocument, userLocation: SimpleLocationDict
-) -> StatNames:
-    """Compute the family, style, PostScript names of the given ``userLocation``
-    using the document's STAT information.
-
-    Also computes localizations.
-
-    If not enough STAT data is available for a given name, either its dict of
-    localized names will be empty (family and style names), or the name will be
-    None (PostScript name).
-
-    .. versionadded:: 5.0
-    """
-    familyNames: Dict[str, str] = {}
-    defaultSource: Optional[SourceDescriptor] = doc.findDefault()
-    if defaultSource is None:
-        LOGGER.warning("Cannot determine default source to look up family name.")
-    elif defaultSource.familyName is None:
-        LOGGER.warning(
-            "Cannot look up family name, assign the 'familyname' attribute to the default source."
-        )
-    else:
-        familyNames = {
-            "en": defaultSource.familyName,
-            **defaultSource.localisedFamilyName,
-        }
-
-    styleNames: Dict[str, str] = {}
-    # If a free-standing label matches the location, use it for name generation.
-    label = doc.labelForUserLocation(userLocation)
-    if label is not None:
-        styleNames = {"en": label.name, **label.labelNames}
-    # Otherwise, scour the axis labels for matches.
-    else:
-        # Gather all languages in which at least one translation is provided
-        # Then build names for all these languages, but fallback to English
-        # whenever a translation is missing.
-        labels = _getAxisLabelsForUserLocation(doc.axes, userLocation)
-        if labels:
-            languages = set(
-                language for label in labels for language in label.labelNames
-            )
-            languages.add("en")
-            for language in languages:
-                styleName = " ".join(
-                    label.labelNames.get(language, label.defaultName)
-                    for label in labels
-                    if not label.elidable
-                )
-                if not styleName and doc.elidedFallbackName is not None:
-                    styleName = doc.elidedFallbackName
-                styleNames[language] = styleName
-
-    if "en" not in familyNames or "en" not in styleNames:
-        # Not enough information to compute PS names of styleMap names
-        return StatNames(
-            familyNames=familyNames,
-            styleNames=styleNames,
-            postScriptFontName=None,
-            styleMapFamilyNames={},
-            styleMapStyleName=None,
-        )
-
-    postScriptFontName = f"{familyNames['en']}-{styleNames['en']}".replace(" ", "")
-
-    styleMapStyleName, regularUserLocation = _getRibbiStyle(doc, userLocation)
-
-    styleNamesForStyleMap = styleNames
-    if regularUserLocation != userLocation:
-        regularStatNames = getStatNames(doc, regularUserLocation)
-        styleNamesForStyleMap = regularStatNames.styleNames
-
-    styleMapFamilyNames = {}
-    for language in set(familyNames).union(styleNames.keys()):
-        familyName = familyNames.get(language, familyNames["en"])
-        styleName = styleNamesForStyleMap.get(language, styleNamesForStyleMap["en"])
-        styleMapFamilyNames[language] = (familyName + " " + styleName).strip()
-
-    return StatNames(
-        familyNames=familyNames,
-        styleNames=styleNames,
-        postScriptFontName=postScriptFontName,
-        styleMapFamilyNames=styleMapFamilyNames,
-        styleMapStyleName=styleMapStyleName,
-    )
-
-
-def _getSortedAxisLabels(
-    axes: list[Union[AxisDescriptor, DiscreteAxisDescriptor]],
-) -> Dict[str, list[AxisLabelDescriptor]]:
-    """Returns axis labels sorted by their ordering, with unordered ones appended as
-    they are listed."""
-
-    # First, get the axis labels with explicit ordering...
-    sortedAxes = sorted(
-        (axis for axis in axes if axis.axisOrdering is not None),
-        key=lambda a: a.axisOrdering,
-    )
-    sortedLabels: Dict[str, list[AxisLabelDescriptor]] = {
-        axis.name: axis.axisLabels for axis in sortedAxes
-    }
-
-    # ... then append the others in the order they appear.
-    # NOTE: This relies on Python 3.7+ dict's preserved insertion order.
-    for axis in axes:
-        if axis.axisOrdering is None:
-            sortedLabels[axis.name] = axis.axisLabels
-
-    return sortedLabels
-
-
-def _getAxisLabelsForUserLocation(
-    axes: list[Union[AxisDescriptor, DiscreteAxisDescriptor]],
-    userLocation: SimpleLocationDict,
-) -> list[AxisLabelDescriptor]:
-    labels: list[AxisLabelDescriptor] = []
-
-    allAxisLabels = _getSortedAxisLabels(axes)
-    if allAxisLabels.keys() != userLocation.keys():
-        LOGGER.warning(
-            f"Mismatch between user location '{userLocation.keys()}' and available "
-            f"labels for '{allAxisLabels.keys()}'."
-        )
-
-    for axisName, axisLabels in allAxisLabels.items():
-        userValue = userLocation[axisName]
-        label: Optional[AxisLabelDescriptor] = next(
-            (
-                l
-                for l in axisLabels
-                if l.userValue == userValue
-                or (
-                    l.userMinimum is not None
-                    and l.userMaximum is not None
-                    and l.userMinimum <= userValue <= l.userMaximum
-                )
-            ),
-            None,
-        )
-        if label is None:
-            LOGGER.debug(
-                f"Document needs a label for axis '{axisName}', user value '{userValue}'."
-            )
-        else:
-            labels.append(label)
-
-    return labels
-
-
-def _getRibbiStyle(
-    self: DesignSpaceDocument, userLocation: SimpleLocationDict
-) -> Tuple[RibbiStyle, SimpleLocationDict]:
-    """Compute the RIBBI style name of the given user location,
-    return the location of the matching Regular in the RIBBI group.
-
-    .. versionadded:: 5.0
-    """
-    regularUserLocation = {}
-    axes_by_tag = {axis.tag: axis for axis in self.axes}
-
-    bold: bool = False
-    italic: bool = False
-
-    axis = axes_by_tag.get("wght")
-    if axis is not None:
-        for regular_label in axis.axisLabels:
-            if (
-                regular_label.linkedUserValue == userLocation[axis.name]
-                # In the "recursive" case where both the Regular has
-                # linkedUserValue pointing the Bold, and the Bold has
-                # linkedUserValue pointing to the Regular, only consider the
-                # first case: Regular (e.g. 400) has linkedUserValue pointing to
-                # Bold (e.g. 700, higher than Regular)
-                and regular_label.userValue < regular_label.linkedUserValue
-            ):
-                regularUserLocation[axis.name] = regular_label.userValue
-                bold = True
-                break
-
-    axis = axes_by_tag.get("ital") or axes_by_tag.get("slnt")
-    if axis is not None:
-        for upright_label in axis.axisLabels:
-            if (
-                upright_label.linkedUserValue == userLocation[axis.name]
-                # In the "recursive" case where both the Upright has
-                # linkedUserValue pointing the Italic, and the Italic has
-                # linkedUserValue pointing to the Upright, only consider the
-                # first case: Upright (e.g. ital=0, slant=0) has
-                # linkedUserValue pointing to Italic (e.g ital=1, slant=-12 or
-                # slant=12 for backwards italics, in any case higher than
-                # Upright in absolute value, hence the abs() below.
-                and abs(upright_label.userValue) < abs(upright_label.linkedUserValue)
-            ):
-                regularUserLocation[axis.name] = upright_label.userValue
-                italic = True
-                break
-
-    return BOLD_ITALIC_TO_RIBBI_STYLE[bold, italic], {
-        **userLocation,
-        **regularUserLocation,
-    }
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__3.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__3.py
deleted file mode 100644
index 8ef3c5ade2b1c2d52a084bd34f82598bb46f774f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I__3.py
+++ /dev/null
@@ -1,20 +0,0 @@
-""" TSI{0,1,2,3,5} are private tables used by Microsoft Visual TrueType (VTT)
-tool to store its hinting source data.
-
-TSI3 contains the text of the glyph programs in the form of 'VTTTalk' code.
-"""
-from fontTools import ttLib
-
-superclass = ttLib.getTableClass("TSI1")
-
-
-class table_T_S_I__3(superclass):
-
-    extras = {
-        0xFFFA: "reserved0",
-        0xFFFB: "reserved1",
-        0xFFFC: "reserved2",
-        0xFFFD: "reserved3",
-    }
-
-    indextable = "TSI2"
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/google/protobuf/internal/enum_type_wrapper.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/google/protobuf/internal/enum_type_wrapper.py
deleted file mode 100644
index 65d2c7a5c0c022fd00ecebab11c5bd76cdff1b2a..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/google/protobuf/internal/enum_type_wrapper.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Protocol Buffers - Google's data interchange format
-# Copyright 2008 Google Inc.  All rights reserved.
-# https://developers.google.com/protocol-buffers/
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are
-# met:
-#
-#     * Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-#     * Redistributions in binary form must reproduce the above
-# copyright notice, this list of conditions and the following disclaimer
-# in the documentation and/or other materials provided with the
-# distribution.
-#     * Neither the name of Google Inc. nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-"""A simple wrapper around enum types to expose utility functions.
-
-Instances are created as properties with the same name as the enum they wrap
-on proto classes.  For usage, see:
-  reflection_test.py
-"""
-
-__author__ = 'rabsatt@google.com (Kevin Rabsatt)'
-
-
-class EnumTypeWrapper(object):
-  """A utility for finding the names of enum values."""
-
-  DESCRIPTOR = None
-
-  # This is a type alias, which mypy typing stubs can type as
-  # a genericized parameter constrained to an int, allowing subclasses
-  # to be typed with more constraint in .pyi stubs
-  # Eg.
-  # def MyGeneratedEnum(Message):
-  #   ValueType = NewType('ValueType', int)
-  #   def Name(self, number: MyGeneratedEnum.ValueType) -> str
-  ValueType = int
-
-  def __init__(self, enum_type):
-    """Inits EnumTypeWrapper with an EnumDescriptor."""
-    self._enum_type = enum_type
-    self.DESCRIPTOR = enum_type  # pylint: disable=invalid-name
-
-  def Name(self, number):  # pylint: disable=invalid-name
-    """Returns a string containing the name of an enum value."""
-    try:
-      return self._enum_type.values_by_number[number].name
-    except KeyError:
-      pass  # fall out to break exception chaining
-
-    if not isinstance(number, int):
-      raise TypeError(
-          'Enum value for {} must be an int, but got {} {!r}.'.format(
-              self._enum_type.name, type(number), number))
-    else:
-      # repr here to handle the odd case when you pass in a boolean.
-      raise ValueError('Enum {} has no name defined for value {!r}'.format(
-          self._enum_type.name, number))
-
-  def Value(self, name):  # pylint: disable=invalid-name
-    """Returns the value corresponding to the given enum name."""
-    try:
-      return self._enum_type.values_by_name[name].number
-    except KeyError:
-      pass  # fall out to break exception chaining
-    raise ValueError('Enum {} has no value defined for name {!r}'.format(
-        self._enum_type.name, name))
-
-  def keys(self):
-    """Return a list of the string names in the enum.
-
-    Returns:
-      A list of strs, in the order they were defined in the .proto file.
-    """
-
-    return [value_descriptor.name
-            for value_descriptor in self._enum_type.values]
-
-  def values(self):
-    """Return a list of the integer values in the enum.
-
-    Returns:
-      A list of ints, in the order they were defined in the .proto file.
-    """
-
-    return [value_descriptor.number
-            for value_descriptor in self._enum_type.values]
-
-  def items(self):
-    """Return a list of the (name, value) pairs of the enum.
-
-    Returns:
-      A list of (str, int) pairs, in the order they were defined
-      in the .proto file.
-    """
-    return [(value_descriptor.name, value_descriptor.number)
-            for value_descriptor in self._enum_type.values]
-
-  def __getattr__(self, name):
-    """Returns the value corresponding to the given enum name."""
-    try:
-      return super(
-          EnumTypeWrapper,
-          self).__getattribute__('_enum_type').values_by_name[name].number
-    except KeyError:
-      pass  # fall out to break exception chaining
-    raise AttributeError('Enum {} has no value defined for name {!r}'.format(
-        self._enum_type.name, name))
diff --git a/spaces/cihyFjudo/fairness-paper-search/Autocad 2010 Keygen For Windows 8 64 Bit Free 346 The Ultimate Guide for Autocad Users.md b/spaces/cihyFjudo/fairness-paper-search/Autocad 2010 Keygen For Windows 8 64 Bit Free 346 The Ultimate Guide for Autocad Users.md
deleted file mode 100644
index 00ef32e48c3afaab397658d85a696cd591c58889..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Autocad 2010 Keygen For Windows 8 64 Bit Free 346 The Ultimate Guide for Autocad Users.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Red Swastik 720p Hindi Movie Torrent Download Kickass.md b/spaces/cihyFjudo/fairness-paper-search/Red Swastik 720p Hindi Movie Torrent Download Kickass.md
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index 2a441a2b5099e942f5836fae35518f0310a41e40..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Red Swastik 720p Hindi Movie Torrent Download Kickass.md	
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Simple And Sinister Kettlebell Pdf 103 The Best Kettlebell Program for Any Sport and Any Goal.md b/spaces/cihyFjudo/fairness-paper-search/Simple And Sinister Kettlebell Pdf 103 The Best Kettlebell Program for Any Sport and Any Goal.md
deleted file mode 100644
index 9a158e5d78561002bb433d296e92f53e69795960..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Simple And Sinister Kettlebell Pdf 103 The Best Kettlebell Program for Any Sport and Any Goal.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-<br />
-<p>Our revised version of the timeless Royal Court workout involves a heavy single club and a kettlebell (and/ or sandbag) and loads of program variability. The Indian club challenge is perfect for people who love training at home with old school workout equipment!</p>
-<h2>Simple And Sinister Kettlebell Pdf 103</h2><br /><p><b><b>DOWNLOAD</b> &mdash;&mdash;&mdash;>>> <a href="https://tinurli.com/2uwjkv">https://tinurli.com/2uwjkv</a></b></p><br /><br />
-<p>There are both double and single handed exercise variations with a single club, and challenging Hindu push up variations.<br />We also added simple and classical<strong> kettlebell/ sandbag exercises</strong> into the mix for <strong>all around strength and power</strong>. This is one true full body and mind strength workout!</p>
-<p>The addition of the kettlebells really makes the program well-balanced and out of the programs that I have (various for Pahlavandles and the first two of Hanuman) this is the most demanding one. After the 21 day course the full program was too much for me (somehow I always rolled a 5 or a 6). I would advise to limit the number of rounds to 4 and gradually build up. I found that limiting the total time to that of the 21 day challenge works well for me. Currently I use the program without KBs as I have a dedicated lower body KB session per week. As such it nicely complements the KB session regarding both, body parts and movement planes.</p>
-<p>You will learn a simple, structured and proved approach to learn progressively more complex exercises with a single club. We start with simple pendulums in all planes of motion, building up to the signature move of Indian clubs, the full swing or mill.</p>
-<p>More and more ladies are discovering the benefits of <strong>effective kettlebell workouts for women</strong>. There are a number of reasons why kettlebells are an excellent choice for females who wish to <strong>tone their body and remove unsightly body fat</strong>.</p>
-<p>There are a number of reasons however why women should be spending more time <strong>performing resistance based workouts</strong> and using a <strong>kettlebell is a great choice</strong> providing you progress slowly and receive some expert tuition.</p>
-<p>Effective kettlebell workouts for women are <strong>very dynamic and cardiovascular in nature</strong>. Although the resistance element of kettlebell training will <strong>condition existing muscles</strong> it will not cause a huge amount of hypertrophy or muscle growth.</p>
-<p></p>
-<p>If you have received a correctly designed female kettlebell workout then your exercises are going to consist of full body, compound movements, that <strong>target almost every muscle in the body</strong>.</p>
-<p>Learning to train correctly with kettlebells takes skill, time and dedication. Often women get wrapped up in the intricacies of kettlebell training and <strong>forget that they are even working out</strong>.</p>
-<p>In my personal experience I have found more women seem to stick to a kettlebell program than men. Just as an exercise like running is totally mindless (which some people do enjoy) kettlebell training <strong>takes focus and understanding</strong>.</p>
-<p>Hello Loretta, you can use your 16kg with 2 hands and practice the Squat and Overhead Press to build up strength. In the meantime, you can also practice the Turkish Get Up without a kettlebell or use a dumbbell instead.</p>
-<p>Thanks Carmen, you can always practice squat and overhead presses with both hands on the kettlebell to make it easier. You should also be working on closed chain exercises like push ups, planks and shoulder taps.</p>
-<p>Thank you so much for providing so much valuable info on kettlebell training, love your workouts! I am looking to sign up for your 12 week womens program soon but curious as to when to change your routine. If I am following one of your full body circuits do I keep the same workout 3x a week for a month lets say? or should be changing it more frequently?</p>
-<p><strong>A fellow in great distress came to me through email and inquired as to which of my kettlebell complexes are best for the accruement of strength. </strong>I could do no less than to give him my best advice, of course, but if less could have been done, well I know myself well enough to say for certain I would have done it.</p> aaccfb2cb3<br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/[Gundam (1-45).  Free Download Borrow and Streaming  Internet Archive](4).md b/spaces/cihyFjudo/fairness-paper-search/[Gundam (1-45).  Free Download Borrow and Streaming  Internet Archive](4).md
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index eec8c270faa4f61e9495ad5cc6f88f55057a4bbf..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/[Gundam (1-45).  Free Download Borrow and Streaming  Internet Archive](4).md	
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diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/deprecation.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/deprecation.py
deleted file mode 100644
index f0ed26ae98f9a71512f85ca589fd4e160033f97b..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/deprecation.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import warnings
-import functools
-
-
-class AltairDeprecationWarning(UserWarning):
-    pass
-
-
-def deprecated(message=None):
-    """Decorator to deprecate a function or class.
-
-    Parameters
-    ----------
-    message : string (optional)
-        The deprecation message
-    """
-
-    def wrapper(obj):
-        return _deprecate(obj, message=message)
-
-    return wrapper
-
-
-def _deprecate(obj, name=None, message=None):
-    """Return a version of a class or function that raises a deprecation warning.
-
-    Parameters
-    ----------
-    obj : class or function
-        The object to create a deprecated version of.
-    name : string (optional)
-        The name of the deprecated object
-    message : string (optional)
-        The deprecation message
-
-    Returns
-    -------
-    deprecated_obj :
-        The deprecated version of obj
-
-    Examples
-    --------
-    >>> class Foo: pass
-    >>> OldFoo = _deprecate(Foo, "OldFoo")
-    >>> f = OldFoo()  # doctest: +SKIP
-    AltairDeprecationWarning: alt.OldFoo is deprecated. Use alt.Foo instead.
-    """
-    if message is None:
-        message = "alt.{} is deprecated. Use alt.{} instead." "".format(
-            name, obj.__name__
-        )
-    if isinstance(obj, type):
-        return type(
-            name,
-            (obj,),
-            {
-                "__doc__": obj.__doc__,
-                "__init__": _deprecate(obj.__init__, "__init__", message),
-            },
-        )
-    elif callable(obj):
-
-        @functools.wraps(obj)
-        def new_obj(*args, **kwargs):
-            warnings.warn(message, AltairDeprecationWarning, stacklevel=1)
-            return obj(*args, **kwargs)
-
-        new_obj._deprecated = True
-        return new_obj
-    else:
-        raise ValueError("Cannot deprecate object of type {}".format(type(obj)))
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/avs2.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/avs2.h
deleted file mode 100644
index 544cf502d781b936bef70681ba04f2e6cbe7bbdb..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/avs2.h
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * AVS2 related definitions
- *
- * Copyright (C) 2022 Zhao Zhili, <zhilizhao@tencent.com>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_AVS2_H
-#define AVCODEC_AVS2_H
-
-#include "libavutil/rational.h"
-
-#define AVS2_SLICE_MAX_START_CODE    0x000001AF
-
-enum {
-    AVS2_SEQ_START_CODE         = 0xB0,
-    AVS2_SEQ_END_CODE           = 0xB1,
-    AVS2_USER_DATA_START_CODE   = 0xB2,
-    AVS2_INTRA_PIC_START_CODE   = 0xB3,
-    // reserved                 = 0xB4,
-    AVS2_EXTENSION_START_CODE   = 0xB5,
-    AVS2_INTER_PIC_START_CODE   = 0xB6,
-};
-
-#define AVS2_ISPIC(x)  ((x) == AVS2_INTRA_PIC_START_CODE || (x) == AVS2_INTER_PIC_START_CODE)
-#define AVS2_ISUNIT(x) ((x) == AVS2_SEQ_START_CODE || AVS2_ISPIC(x))
-
-enum AVS2Profile {
-    AVS2_PROFILE_MAIN_PIC   = 0x12,
-    AVS2_PROFILE_MAIN       = 0x20,
-    AVS2_PROFILE_MAIN10     = 0x22,
-};
-
-extern const AVRational ff_avs2_frame_rate_tab[16];
-
-#endif
diff --git a/spaces/coldlarry/lr_pdf/Document_QA.py b/spaces/coldlarry/lr_pdf/Document_QA.py
deleted file mode 100644
index dbaf289ba0bff59b1057311ab4d46b5cf5bceb5f..0000000000000000000000000000000000000000
--- a/spaces/coldlarry/lr_pdf/Document_QA.py
+++ /dev/null
@@ -1,160 +0,0 @@
-
-import openai
-import faiss
-import numpy as np
-import pickle
-from tqdm import tqdm
-import argparse
-import os
-from PyPDF2 import PdfReader
-
-class Paper(object):
-
-    def __init__(self, pdf_obj: PdfReader) -> None:
-        self._pdf_obj = pdf_obj
-        self._paper_meta = self._pdf_obj.metadata
-        self.texts = []
-
-    def iter_pages(self, iter_text_len: int = 1000):
-        page_idx = 0
-        for page in self._pdf_obj.pages:
-            txt = page.extract_text()
-            for i in range((len(txt) // iter_text_len) + 1):
-                yield page_idx, i, txt[i * iter_text_len:(i + 1) * iter_text_len]
-            page_idx += 1
-    def get_texts(self):
-        for (page_idx, part_idx, text) in self.iter_pages():
-            self.texts.append(text.strip())
-        return self.texts
-
-def create_embeddings(inputs):
-    """Create embeddings for the provided input."""
-    # input = ['ddd','aaa','ccccccccccccccc','ddddd']
-    result = []
-    tokens = 0
-
-    def get_embedding(input_slice):
-        input_slice = [input_slice]
-        embedding = openai.Embedding.create(model="text-embedding-ada-002", input=input_slice)
-        return [(text, data.embedding) for text, data in zip(input_slice, embedding.data)], embedding.usage.total_tokens
-    
-    for i in range(0,len(inputs)):
-        ebd, tk = get_embedding(inputs[i])
-        tokens += tk
-        result.extend(ebd)
-
-    return result, tokens
-
-def create_embedding(text):
-    """Create an embedding for the provided text."""
-    embedding = openai.Embedding.create(model="text-embedding-ada-002", input=text)
-    return text, embedding.data[0].embedding
-
-class QA():
-    def __init__(self,data_embe) -> None:
-        d = 1536
-        index = faiss.IndexFlatL2(d)
-        embe = np.array([emm[1] for emm in data_embe])
-        data = [emm[0] for emm in data_embe]
-        index.add(embe)
-        #所有emdding
-        self.index = index
-        #所有文字
-        self.data = data
-        print("now all data is:\n",self.data)
-
-    def __call__(self, query):
-        embedding = create_embedding(query)
-        #输出与用户的问题相关的文字
-        context = self.get_texts(embedding[1])
-        #将用户的问题和涉及的文字告诉gpt，并将答案返回
-        answer = self.completion(query,context)
-        return answer,context
-    def get_texts(self,embeding,limit=5):
-        _,text_index = self.index.search(np.array([embeding]),limit)
-        context = []
-        for i in list(text_index[0]):
-            context.extend(self.data[i:i+2])
-        # context = [self.data[i] for i in list(text_index[0])]
-        #输出与用户的问题相关的文字
-        return context
-    
-    def completion(self,query, context):
-        """Create a completion."""
-        # lens = [len(text) for text in context]
-
-        # maximum = 3000
-        # for index, l in enumerate(lens):
-        #     maximum -= l
-        #     if maximum < 0:
-        #         context = context[:index + 1]
-        #         print("超过最大长度，截断到前", index + 1, "个片段")
-        #         break
-
-        text = "\n".join(f"{index}. {text}" for index, text in enumerate(context))
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {'role': 'system',
-                'content': f'你是一个有帮助的AI文章助手，从下文中提取有用的内容进行回答，不能回答不在下文提到的内容，相关性从高到底排序：\n\n{text}'},
-                {'role': 'user', 'content': query},
-            ],
-        )
-        print("使用的tokens：", response.usage.total_tokens)
-        return response.choices[0].message.content
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Document QA")
-    parser.add_argument("--input_file", default="slimming-pages-1.pdf", dest="input_file", type=str,help="输入文件路径")
-    # parser.add_argument("--file_embeding", default="input_embed.pkl", dest="file_embeding", type=str,help="文件embeding文件路径")
-    parser.add_argument("--print_context", action='store_true',help="是否打印上下文")
-    
-
-    args = parser.parse_args()
-
-    # if os.path.isfile(args.file_embeding):
-    #     data_embe = pickle.load(open(args.file_embeding,'rb'))
-    # else:
-    #     with open(args.input_file,'r',encoding='utf-8') as f:
-    #         texts = f.readlines()
-    #         #按照行对文章进行切割
-    #         texts = [text.strip() for text in texts if text.strip()]
-    #         data_embe,tokens = create_embeddings(texts)
-    #         pickle.dump(data_embe,open(args.file_embeding,'wb'))
-    #         print("文本消耗 {} tokens".format(tokens))
-
-    paper = Paper(args.input_file)
-    all_texts = paper.get_texts()
-    
-
-    data_embe, tokens = create_embeddings(all_texts)
-    print("全部文本消耗 {} tokens".format(tokens))
-    qa =QA(data_embe)
-
-    limit = 10
-    while True:
-        query = input("请输入查询(help可查看指令)：")
-        if query == "quit":
-            break
-        elif query.startswith("limit"):
-            try:
-                limit = int(query.split(" ")[1])
-                print("已设置limit为", limit)
-            except Exception as e:
-                print("设置limit失败", e)
-            continue
-        elif query == "help":
-            print("输入limit [数字]设置limit")
-            print("输入quit退出")
-            continue
-        answer,context = qa(query)
-        if args.print_context:
-            print("已找到相关片段：")
-            for text in context:
-                print('\t', text)
-            print("=====================================")
-        print("回答如下\n\n")
-        print(answer.strip())
-        print("=====================================")
-
-
diff --git a/spaces/congsaPfin/Manga-OCR/logs/5 Nights At Freddy 39s Apk Download.md b/spaces/congsaPfin/Manga-OCR/logs/5 Nights At Freddy 39s Apk Download.md
deleted file mode 100644
index cb39322d0b41a929e6a797773ea594c632c76049..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/5 Nights At Freddy 39s Apk Download.md	
+++ /dev/null
@@ -1,143 +0,0 @@
-<br />
-<h1>5 Nights at Freddy's APK Download: How to Play the Horror Game on Your Android Device</h1>
- <p>If you are a fan of horror games, you have probably heard of 5 Nights at Freddy's, one of the most popular and terrifying games in recent years. The game has spawned a huge media franchise, including several sequels, spin-offs, books, merchandise, and even a film adaptation in the works. But did you know that you can also play the original game on your Android device? In this article, we will show you how to download and install 5 Nights at Freddy's APK on your Android device, how to play the game, and some tips and tricks to survive the night. We will also give you a brief overview of the game's features, gameplay, and reviews. So if you are ready to face your fears and challenge yourself with this horror game, read on!</p>
- <h2>What is 5 Nights at Freddy's?</h2>
- <p>5 Nights at Freddy's is a point-and-click survival horror video game developed and published by Scott Cawthon in 2014. The game takes place in a fictional family pizzeria called "Freddy Fazbear's Pizza", where the player takes the role of a night security guard who must defend themselves from the restaurant's animatronic characters that become aggressive and homicidal at night. The game is known for its originality, simplicity, atmosphere, and jump scares that will keep you on edge throughout the night.</p>
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- <p>The game was released for PC in August 2014 on Desura and Steam, and later made available on iOS, Android, Windows Phone, Nintendo Switch, PlayStation 4 and Xbox One. The game received generally positive reviews from critics and players alike, quickly gaining a cult following. It was the top-selling game on Desura for the week ending August 18, 2014, and became the subject of numerous popular "Let's Play" YouTube videos. The game's success led to the launch of a media franchise, with its popularity making it subject to numerous imitations and fan games.</p>
- <h2>How to download and install 5 Nights at Freddy's APK on your Android device</h2>
- <p>If you want to play 5 Nights at Freddy's on your Android device, you will need to download and install an APK file from a trusted source. An APK file is an Android application package that contains all the files needed to run an app on your device. However, not all APK files are safe or compatible with your device, so you need to be careful when downloading them from third-party websites. Here are some requirements and precautions you need to follow before downloading and installing 5 Nights at Freddy's APK on your Android device:</p>
- <h3>Requirements and precautions</h3>
- <ul>
-<li>Your Android device must have at least Android 4.1 (Jelly Bean) or higher to run the game.</li>
-<li>You must have enough storage space on your device to download and install the APK file, which is about 50 MB in size.</li>
-<li>You must enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps that are not from the Google Play Store. However, be careful when installing apps from unknown sources, as they may contain malware or viruses that can harm your device or compromise your privacy.</li>
-<li>You must download the APK file from a trusted and reliable source. There are many websites that offer APK files for various apps and games, but not all of them are safe or legitimate. Some may contain fake or modified APK files that can damage your device or steal your data. To avoid this, you should only download APK files from reputable websites that have positive reviews and ratings from other users. You can also use antivirus software or scan the APK file before installing it to check for any malicious code or threats.</li>
-</ul>
- <h3>Download link and instructions</h3>
- <p>One of the websites that we recommend for downloading 5 Nights at Freddy's APK is APKPure, which is a popular and trusted platform for downloading and updating various Android apps and games. Here are the steps to download and install 5 Nights at Freddy's APK from APKPure:</p>
- <ol>
-<li>Go to [APKPure] on your browser and search for "5 Nights at Freddy's" in the search bar.</li>
-<li>Select the game from the search results and click on the "Download APK" button.</li>
-<li>Wait for the download to finish and then open the APK file on your device.</li>
-<li>Follow the on-screen instructions to install the game on your device.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
- <p>You can also scan the QR code below to download the APK file directly to your device:</p>
- <img src="https://www.apkpure.com/qr/5-nights-at-freddys/com.scottgames.fivenightsatfreddys.png" alt="QR code for 5 Nights at Freddy's APK" width="200" height="200">
- <h2>How to play 5 Nights at Freddy's on your Android device</h2>
- <p>Now that you have downloaded and installed 5 Nights at Freddy's APK on your Android device, you are ready to play the game. But before you do, you should know some basic information about the game's features, gameplay, and controls. Here is a summary of what you need to know:</p>
- <h3>The nights</h3>
- <p>The game consists of five nights (plus a bonus sixth night and a custom night) that you have to survive as a night security guard at Freddy Fazbear's Pizza. Each night lasts from 12 AM to 6 AM, which is about 8 minutes and 36 seconds in real time. The difficulty of the game increases with each night, as the animatronics become more active and aggressive. You have to complete each night in order to unlock the next one.</p>
-<p></p>
- <h3>The animatronics</h3>
- <p>The animatronics are the main antagonists of the game. They are four robotic characters that perform at the pizzeria during the day, but roam around and try to kill you during the night. They are:</p>
- <ul>
-<li>Freddy Fazbear: The leader of the band and the mascot of the pizzeria. He is a brown bear wearing a black bow tie and a top hat. He usually stays in the show stage with his bandmates, but will move around when it is dark or when his music box stops playing. He can enter your office from either door, but will only attack you if you run out of power or if you fail to check his position on the cameras.</li>
-<li>Bonnie: The guitarist of the band and Freddy's right-hand man. He is a purple bunny wearing a red bow tie. He is the most active and aggressive animatronic in the game, as he will start moving on the first night and will often approach your office from the left door. He can also disable your cameras temporarily if he enters the backstage area.</li>
-<li>Chica: The backup singer of the band and Freddy's left-hand woman. She is a yellow chicken wearing a bib that says "Let's Eat!". She is less active than Bonnie, but will still move around and try to enter your office from the right door. She can also enter the kitchen and make noise with the pots and pans.</li>
-<li>Foxy: The pirate of the band and the most mysterious animatronic in the game. He is a red fox wearing an eyepatch and a hook. He is out of order and stays in the Pirate Cove, a closed-off area of the pizzeria. He will only become active if you don't check on him frequently enough, or if you check on him too much. He will then run towards your office from the left door and attack you if you don't close it in time.</li>
-</ul>
- <h3>The cameras, doors, lights, and power</h3>
- <p>To survive the night, you have to use the cameras, doors, lights, and power wisely. You have access to a monitor that shows you the feed from various cameras around the pizzeria. You can use the cameras to track the movements of the animatronics and see where they are. However, you can only view one camera at a time, and some cameras may not work or be disabled by the animatronics. You also have to lower the monitor to check your office and the doors.</p>
- <p>You have two doors on each side of your office that you can close to prevent the animatronics from entering. However, you can't keep them closed all the time, as they consume power and limit your vision. You also have two lights on each door that you can use to see if there is an animatronic outside. However, you can't use them too much, as they also consume power and may attract the animatronics.</p>
- <p>You have a limited amount of power that you can use each night, which is shown by a percentage meter on the bottom right corner of your screen. The power drains gradually as the night progresses, and faster if you use the cameras, doors, or lights. If you run out of power before 6 AM, you will be left in the dark with no defense against Freddy, who will play his music box and then jump scare you. The only way to avoid this is to hope that 6 AM arrives before he does.</p>
- <h2>Tips and tricks to survive 5 Nights at Freddy's</h2>
- <p>5 Nights at Freddy's is not an easy game to beat, especially as the nights get harder and the animatronics get smarter. You will need to have good reflexes, strategy, and nerves of steel to survive. Here are some tips and tricks that may help you:</p>
- <h4>Keep the doors open</h4>
- <p>One of the most common mistakes that beginners make is to close the doors too often or too early. This will only waste your power and make you more vulnerable later on. You should only close the doors when there is an animatronic outside or when Foxy is about to run towards your office. Otherwise, keep them open as much as possible.</p>
- <h4>Use lights sparingly and only under attack</h4>
- <p>Another common mistake is to use the lights too much or too randomly. This will also waste your power and may attract the animatronics to your office. You should only use the lights when you hear footsteps or breathing outside your door, or when you want to check if there is an animatronic outside before closing or opening the door. Otherwise, keep them off as much as possible.</p>
- <h4>Learn how all your enemies move</h4>
- <p>One of the most important skills that you need to master in this game is to learn how all your enemies move and behave. Each animatronic has a different pattern, speed, and strategy that you need to understand and anticipate. For example:</p>
- <ul>
-<li>Bonnie tends to move faster than Chica and will often approach your office from the left door.</li>
-<li>Chica tends to move slower than Bonnie and will often approach your office from the right door.</li>
-<li>Freddy tends to stay in the shadows and will only move when it is dark or when his music box stops playing.</li>
-<li>Foxy tends to stay in his Pirate Cove and will only run towards your office if you don't check on him frequently enough or if you check on him too much.</li>
-</ul>
- <p>By knowing how each animatronic moves, you can plan your strategy accordingly and avoid being caught off guard.</p>
- <h4>Watch for glowing eyes</h4>
- <p>One of the easiest ways to tell if there is an animatronic outside your door is to look for their glowing eyes in the dark. This is a sign that they are ready to attack and that you should close the door immediately. However, not all animatronics have glowing eyes, and some may hide them or fake them. For example:</p>
- <ul>
-<li>Freddy's eyes glow yellow, but he may hide them behind his microphone or his hat.</li>
-<li>Bonnie's eyes glow white, but he may fake them by using the light from the backstage area.</li>
-<li>Chica's eyes glow orange, but she may hide them behind her cupcake or her bib.</li>
-<li>Foxy's eyes glow yellow, but he may not show them at all until he runs towards your office.</li>
-</ul>
- <p>By watching for glowing eyes, you can avoid being surprised by an animatronic and react quickly.</p>
- <h4>Wear headphones</h4>
- <p>One of the most helpful tools that you can use in this game is a pair of headphones. The game relies heavily on sound cues and audio effects to create tension and atmosphere. By wearing headphones, you can hear the sounds more clearly and immerse yourself in the game. You can also use the sounds to your advantage and detect the presence of the animatronics. For example:</p>
- <ul>
-<li>You can hear footsteps or breathing outside your door, indicating that there is an animatronic nearby.</li>
-<li>You can hear laughter or music, indicating that Freddy is moving or about to attack.</li>
-<li>You can hear banging or scraping, indicating that Foxy is leaving his Pirate Cove or running towards your office.</li>
-<li>You can hear pots and pans clanging, indicating that Chica is in the kitchen.</li>
-</ul>
- <p>By wearing headphones, you can enhance your experience and improve your chances of survival.</p>
- <h4>Listen before looking</h4>
- <p>One of the most crucial skills that you need to develop in this game is to listen before looking. The game is designed to make you paranoid and anxious, as you never know when an animatronic will appear or attack. However, if you rely too much on your eyes and check the cameras or the doors too often, you will waste your power and expose yourself to more danger. Instead, you should use your ears and listen for any sound cues that may indicate the location or the behavior of the animatronics. For example:</p>
- <ul>
-<li>If you hear footsteps or breathing outside your door, you should check the light and close the door if necessary.</li>
-<li>If you hear laughter or music, you should check the show stage and Freddy's position on the cameras.</li>
-<li>If you hear banging or scraping, you should check the Pirate Cove and Foxy's position on the cameras.</li>
-<li>If you hear pots and pans clanging, you should ignore it as it means that Chica is in the kitchen and not a threat.</li>
-</ul>
- <p>By listening before looking, you can save your power and avoid unnecessary risks.</p>
- <h4>Quick, short looks are key</h4>
- <p>Another important skill that you need to master in this game is to take quick, short looks at the cameras and the doors. The game is designed to make you feel vulnerable and exposed, as you have to lower your monitor to check your office and the doors. However, if you lower your monitor too long or too frequently, you will miss important information and give the animatronics more time to move or attack. Instead, you should take quick, short looks at the cameras and the doors only when necessary. For example:</p>
- <ul>
-<li>You should only check the cameras when you hear a sound cue or when you want to check on Foxy or Freddy.</li>
-<li>You should only check the doors when you hear footsteps or breathing outside your door or when you want to open or close them.</li>
-<li>You should only lower your monitor for a few seconds at a time and then raise it back up to check your office and the doors.</li>
-</ul>
- <p>By taking quick, short looks, you can stay alert and informed without losing sight of your surroundings.</p>
- <h4>Don't waste power</h4>
- <p>The last and most obvious tip that we can give you is to not waste power. The power is your lifeline in this game, as it allows you to use the cameras, doors, and lights. However, the power is also limited and drains faster as the night progresses. If you run out of power before 6 AM, you will be left defenseless against Freddy's attack. Therefore, you should conserve your power as much as possible and use it wisely. For example:</p>
- <ul>
-<li>You should only use the cameras when necessary and switch them off when not in use.</li>
-<li>You should only use the doors when there is an animatronic outside or when Foxy is running towards your office.</li>
-<li You should only use the lights when you hear footsteps or breathing outside your door or when you want to check if there is an animatronic outside.</li>
-</ul>
- <p>By not wasting power, you can increase your chances of surviving until 6 AM.</p>
- <h2>Reviews and ratings of 5 Nights at Freddy's APK</h2>
- <p>5 Nights at Freddy's APK is one of the most downloaded and rated games on APKPure, with over 10 million downloads and 4.6 stars out of 5. The game has also received positive reviews and ratings from critics and players on other platforms, such as Metacritic, PC Gamer, and GameSpot. Here is a brief overview of the game's reception, reviews, and ratings:</p>
- <h3>Metacritic score and summary</h3>
- <p>Metacritic is a website that aggregates reviews and ratings from various sources and assigns a weighted average score to each game. The score ranges from 0 to 100, with higher scores indicating better reviews. 5 Nights at Freddy's has a Metacritic score of 78 out of 100, based on 12 reviews from critics. The summary of the game's Metascore is as follows:</p>
- <blockquote>
-<p>"Five Nights at Freddy's is a horror game that uses a simple premise and gameplay mechanic to create a terrifying experience that will make you jump out of your seat."</p>
-</blockquote>
- <h3>PC Gamer review and score</h3>
- <p>PC Gamer is a magazine and website that covers PC gaming news, reviews, features, and guides. The magazine and website use a percentage scale to rate games, with higher percentages indicating better reviews. 5 Nights at Freddy's has a PC Gamer review score of 80 out of 100, based on a review by Andy Kelly. The review praises the game's originality, atmosphere, and jump scares, but criticizes its lack of replay value and variation. The review concludes with the following statement:</p>
- <blockquote>
-<p>"Five Nights at Freddy's is one of the most terrifying games I've ever played. It's simple, effective, and brilliantly scary."</p>
-</blockquote>
- <h3>GameSpot review and score</h3>
- <p>GameSpot is a website that covers video game news, reviews, previews, features, and videos. The website uses a scale of 1 to 10 to rate games, with higher numbers indicating better reviews. 5 Nights at Freddy's has a GameSpot review score of 8 out of 10, based on a review by Mark Walton. The review compliments the game's concept, design, and sound effects, but points out its short length and limited replay value. The review ends with the following remark:</p>
- <blockquote>
-<p>"Five Nights at Freddy's is an inventive take on the survival horror genre that will keep you on edge for hours."</p>
-</blockquote>
- <h2>Conclusion</h2>
- <p>5 Nights at Freddy's is a point-and-click survival horror game that will challenge your nerves and test your skills. The game has a simple but effective premise: you are a night security guard at a pizzeria where the animatronic characters come to life and try to kill you. You have to use the cameras, doors, lights, and power wisely to survive five nights (plus a bonus sixth night and a custom night) without being jump scared by the animatronics.</p>
- <p>The game is available for PC and various consoles, but you can also play it on your Android device by downloading and installing an APK file from a trusted source. However, you need to be careful when downloading APK files from third-party websites, as they may contain malware or viruses that can harm your device or compromise your privacy. You also need to enable the installation of apps from unknown sources on your device before installing the APK file.</p>
- <p>The game has received positive reviews and ratings from critics and players alike, becoming one of the most popular and terrifying games in recent years. The game has spawned a huge media franchise, including several sequels, spin-offs, books, merchandise, and even a film adaptation in the works.</p>
- <p>If you are a fan of horror games or want to try something new and exciting, you should definitely give 5 Nights at Freddy's a try. You can download the APK file from [APKPure] or scan the QR code below to get started. But be warned: this game is not for the faint-hearted!</p>
- <img src="https://www.apkpure.com/qr/5-nights-at-freddys/com.scottgames.fivenightsatfreddys.png" alt="QR code for 5 Nights at Freddy's APK" width="200" height="200">
- <h2>Frequently Asked Questions</h2>
- <p>Here are some frequently asked questions about 5 Nights at Freddy's APK:</p <p>Q: Is 5 Nights at Freddy's APK safe to download and install?</p>
-<p>A: Yes, as long as you download and install the APK file from a trusted and reliable source, such as APKPure. However, you should always be careful when downloading APK files from third-party websites, as they may contain malware or viruses that can harm your device or compromise your privacy. You should also enable the installation of apps from unknown sources on your device before installing the APK file.</p>
- <p>Q: How much does 5 Nights at Freddy's APK cost?</p>
-<p>A: 5 Nights at Freddy's APK is free to download and install on your Android device. However, the game may contain ads or in-app purchases that require real money. You can also support the developer by buying the game on other platforms, such as Steam, iOS, or Nintendo Switch.</p>
- <p>Q: What are the differences between 5 Nights at Freddy's APK and the PC version?</p>
-<p>A: 5 Nights at Freddy's APK is a faithful adaptation of the original PC version, with the same features, gameplay, and graphics. However, there may be some minor differences in terms of performance, compatibility, and controls. For example, the game may run slower or smoother on different devices, depending on their specifications and settings. The game may also have some bugs or glitches that are not present in the PC version. The game's controls are also adapted for touch screens, which may be easier or harder to use than a mouse and keyboard.</p>
- <p>Q: How can I update 5 Nights at Freddy's APK?</p>
-<p>A: You can update 5 Nights at Freddy's APK by downloading and installing the latest version of the APK file from the same source that you downloaded it from. You can also use APKPure to check for updates and install them automatically. However, you should always backup your data before updating the game, as some updates may cause compatibility issues or data loss.</p>
- <p>Q: How can I uninstall 5 Nights at Freddy's APK?</p>
-<p>A: You can uninstall 5 Nights at Freddy's APK by following these steps:</p>
- <ol>
-<li>Go to Settings > Apps > 5 Nights at Freddy's on your device.</li>
-<li>Tap on Uninstall and confirm your choice.</li>
-<li>Wait for the uninstallation process to finish and then delete the APK file from your device.</li>
-</ol></p> 197e85843d<br />
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-<br />
-<h1>Cool Brick Breaker Game Download: How to Enjoy the Classic Arcade Game on Your Device</h1>
-<p>Do you love arcade games that are simple, addictive and fun? If so, you might want to try cool brick breaker games. Brick breaker games are classic arcade games that involve breaking bricks with a ball and a paddle. They are easy to play, but hard to master. They are also very cool, because they have great graphics, music, items, bonuses, power-ups, levels and modes. In this article, we will tell you everything you need to know about cool brick breaker games, including their history, features, benefits and how to download them for free.</p>
-<h2>cool brick breaker game download</h2><br /><p><b><b>Download File</b> &#9658; <a href="https://urlca.com/2uOb5j">https://urlca.com/2uOb5j</a></b></p><br /><br />
- <h2>The History of Brick Breaker Games</h2>
-<p>Brick breaker games have a long and rich history in the arcade game industry. They date back to the 1970s and 1980s, when they were first introduced by Atari and Taito. The first brick breaker game was called Breakout, which was released by Atari in 1976. It was designed by Steve Wozniak, the co-founder of Apple, and inspired by Pong, another popular arcade game at the time. Breakout was a huge success, and spawned many clones and sequels over the years.</p>
-<p>The most famous brick breaker game was Arkanoid, which was released by Taito in 1986. It was an enhanced version of Breakout, with improved graphics, sound, items, power-ups and levels. It also introduced a storyline and a final boss. Arkanoid was a hit worldwide, and influenced many other brick breaker games in the following decades.</p>
-<p>Today, brick breaker games are still very popular among arcade game fans. They have evolved with technology, and are now available on various platforms, such as computers, consoles, mobile devices and online. They have also diversified in terms of themes, styles and genres. Some examples of modern brick breaker games are DX Ball, Ricochet, Blasterball, Shatter and Cool Brick Breaker.</p>
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- <h2>The Features of Cool Brick Breaker Games</h2>
-<p>What makes brick breaker games so cool? Well, there are many features that make them appealing and enjoyable. Here are some of them:</p>
-<ul>
-<li>The gameplay and mechanics of brick breaker games are simple and intuitive. You just need to move the paddle left or right to bounce the ball back to the bricks. You can also control the trajectory of the ball by hitting it at different angles. The goal is to break all the bricks in each level without letting the ball fall through the bottom of the screen.</li>
-<li>The graphics and music of brick breaker games are colorful and catchy. They create a retro style atmosphere that reminds you of the old arcade days. They also vary depending on the theme and genre of each game. For example, some games have sci-fi or fantasy themes, while others have cartoon or realistic themes.</li>
-<li>The items, bonuses and power-ups of brick breaker games are fun and exciting. They add variety and spice to the gameplay. They can help you or hinder you depending on <p>what they do. For example, some items can make the ball bigger, faster, slower, or multiply. Some bonuses can give you extra lives, points, or time. Some power-ups can make the paddle longer, shorter, sticky, or magnetic. Some power-ups can also affect the bricks, such as making them explode, disappear, or change color.</li>
-<li>The levels and modes of brick breaker games are diverse and challenging. They offer different layouts, designs, and difficulties of bricks. They also have different objectives, such as clearing a certain number of bricks, scoring a certain number of points, or surviving a certain amount of time. Some games have different modes, such as arcade mode, adventure mode, puzzle mode, or endless mode.</li>
-</ul>
- <h2>The Benefits of Playing Cool Brick Breaker Games</h2>
-<p>Playing cool brick breaker games is not only fun and entertaining, but also beneficial for your mental and physical health. Here are some of the benefits of playing brick breaker games:</p>
-<ul>
-<li>Playing brick breaker games can improve your concentration and focus. You need to pay attention to the ball, the paddle, the bricks, and the items. You also need to plan your moves and strategies ahead. This can help you sharpen your mind and enhance your cognitive skills.</li>
-<li>Playing brick breaker games can reduce your stress and anxiety. You can relax and enjoy the game without worrying about anything else. You can also vent your frustration and anger by breaking bricks and smashing things. This can help you release your negative emotions and improve your mood.</li>
-<li>Playing brick breaker games can increase your hand-eye coordination and reaction time. You need to move the paddle quickly and accurately to catch the ball and hit the bricks. You also need to react to the changes and surprises in the game. This can help you train your reflexes and motor skills.</li>
-</ul>
- <h2>How to Download Cool Brick Breaker Games for Free</h2>
-<p>If you want to play cool brick breaker games on your device, you don't need to spend any money. There are many cool brick breaker games that you can download for free from various sources. Here are some of the best cool brick breaker games available for download:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Platform</th>
-<th>Description</th>
-</tr>
-<tr>
-<td>Cool Brick Breaker</td>
-<td>Android</td>
-<td>A cool brick breaker game with 150 levels, 8 items, 4 power-ups, 3 modes and 3 themes.</td>
-</tr>
-<tr>
-<td>Bricks Breaker Quest</td>
-<td>iOS</td>
-<td>A cool brick breaker game with hundreds of levels, various items, power-ups and balls.</td>
-</tr>
-<tr>
-<td>Bricks n Balls</td>
-<td>Windows</td>
-<td>A cool brick breaker game with thousands of levels, different shapes of bricks, balls and paddles.</td>
-</tr>
-<tr>
-<td>Brick Breaker Star: Space King</td>
-<td>Online</td>
-<td>A cool brick breaker game with a space theme, 200 stages, 10 items and 30 balls.</td>
-</tr>
-<tr>
-<td>Brick Breaker Hero</td>
-<td>Facebook</td>
-<td>A cool brick breaker game with a fantasy theme, 200 levels, 15 items and 5 power-ups.</td>
-</tr>
-</table>
- <p>To download these cool brick breaker games for free, you just need to follow these simple steps:</p>
- <ol>
-<li>Choose the platform that suits your device (Android, iOS, Windows, Online or Facebook).</li>
-<li>Go to the source where you can find the game (Google Play Store, App Store, Microsoft Store, website or Facebook app).</li>
-<li>Search for the name of the game that you want to download (Cool Brick Breaker, Bricks Breaker Quest, Bricks n Balls, Brick Breaker Star: Space King or Brick Breaker Hero).</li>
-<li>Click on the download or install button to start downloading the game.</li>
-<li>Wait for the download or installation to finish.</li>
-<li>Open the game and enjoy playing it.</li>
-</ol>
- <p>To play these cool brick breaker games better, you can also use these tips and tricks:</p>
- <ul>
-<li>Aim for the corners and edges of the bricks to create more angles and bounce more balls.</li>
-<li>Collect as many items, bonuses and power-ups as you can to boost your performance.</li>
-<li>Avoid losing lives by keeping an eye on the ball and not letting it fall through the bottom of the screen.</li>
-<li>Use different balls and paddles to suit your preference and style.</li>
-<li>Try different modes and levels to challenge yourself and have more fun.</li>
- <h1>Conclusion: Why You Should Download Cool Brick Breaker Games Today</h1>
-<p>In conclusion, cool brick breaker games are classic arcade games that are simple, addictive and fun. They have a long and rich history, and have many features and benefits that make them appealing and enjoyable. They are also easy to download and play on your device, and you can find many cool brick breaker games for free from various sources. So, what are you waiting for? Download cool brick breaker games today and have a blast breaking bricks and smashing things!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about cool brick breaker games:</p>
-<ol>
-<li>What is the difference between brick breaker and arkanoid?</li>
-<p>Brick breaker is a generic term for arcade games that involve breaking bricks with a ball and a paddle. Arkanoid is a specific brick breaker game that was released by Taito in 1986. It is considered one of the best and most influential brick breaker games of all time.</p>
-<li>How many levels are there in cool brick breaker games?</li>
-<p>The number of levels in cool brick breaker games varies depending on the game. Some games have hundreds of levels, while others have thousands of levels. Some games also have different modes and stages that offer different challenges and objectives.</p>
-<li>How can I get more lives in cool brick breaker games?</li>
-<p>You can get more lives in cool brick breaker games by collecting bonuses that give you extra lives, or by buying lives with real money or in-game currency. Some games also give you free lives every day or every hour.</p>
-<li>What are the best items and power-ups in cool brick breaker games?</li>
-<p>The best items and power-ups in cool brick breaker games depend on your preference and situation. Some items and power-ups can help you break more bricks, while others can make the game harder or easier. Some of the most common and useful items and power-ups are fireball, laser, magnet, multi-ball, long paddle, short paddle, sticky paddle, extra ball, extra life, extra point, extra time, bomb, shield, freeze, slow down, speed up, etc.</p>
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-<p>Yes, cool brick breaker games are safe to download as long as you download them from reputable sources, such as Google Play Store, App Store, Microsoft Store, website or Facebook app. You should also check the ratings, reviews and permissions of the games before downloading them.</p> 197e85843d<br />
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-<br />
-<h1>How to Install APK on Android</h1>
-<p>If you have ever wanted to install an app that is not available on the Google Play Store, you might have come across the term "APK". But what is an APK file and how do you install it on your Android device? In this article, we will explain everything you need to know about installing APK files on Android.</p>
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- <h2>What is an APK file and why you might need it</h2>
-<p>An APK file is a package file that contains all the components of an Android app. It stands for Android Package Kit or Android Application Package. It is similar to other software packages such as APPX in Windows or DEB in Linux.</p>
-<p>When you download an app from the Google Play Store, you are actually downloading and installing an APK file in the background. However, you don't have access to the APK file itself. The Play Store handles everything for you, including updates and uninstallation.</p>
-<p>However, there are some situations where you might want to install an APK file manually. For example:</p>
-<ul>
-<li>You want to use an app that is not available in your country or region.</li>
-<li>You want to use an app that has been removed from the Play Store due to policy violations or developer issues.</li>
-<li>You want to use an older version of an app that works better than the latest one.</li>
-<li>You want to use a modded or hacked version of an app that offers extra features or benefits.</li>
-<li>You want to use a beta or unreleased version of an app that is not yet available on the Play Store.</li>
-</ul>
-<p>Whatever your reason may be, installing an APK file on Android is not very difficult, but you need to follow some steps and precautions to do it safely and successfully.</p>
-<p>How to install apk on Android phone<br />
-How to install apk from PC to Android<br />
-How to install apk from Chrome browser<br />
-How to install apk from unknown sources<br />
-How to install apk using file manager<br />
-How to install apk with OBB data<br />
-How to install apk on Android TV<br />
-How to install apk on Firestick<br />
-How to install apk on Windows 10<br />
-How to install apk on Mac<br />
-How to install apk on Bluestacks<br />
-How to install apk on Nox Player<br />
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-How to uninstall apk files from Android device<br />
-What is an APK file and how does it work?<br />
-Why do I need an APK file and where can I get it?</p>
- <h2>How to enable unknown sources on Android</h2>
-<p>Before you can install an APK file on your Android device, you need to enable the option to allow installation of apps from unknown sources. This means that you can install apps that are not verified by Google or the Play Store.</p>
-<p>However, this also means that you are exposing your device to potential security risks. APK files from unknown sources may contain malware, viruses, spyware, or other harmful code that can damage your device or compromise your privacy. Therefore, you should only install APK files from sources that you trust and scan them for any threats before installing them.</p>
-<p>To enable unknown sources on your Android device, follow these steps depending on your Android version:</p>
-<table>
-<tr>
-<th>Android Version</th>
-<th>Steps</th>
-</tr>
-<tr>
-<td>Android 8.0 Oreo or higher</td>
-<td><ol>
-<li>Go to Settings > Apps & notifications > Advanced > Special app access > Install unknown apps.</li>
-<li>Select the app that you want to use to install the APK file, such as Chrome, File Manager, or USB Drive.</li>
-<li>Toggle on the switch for Allow from this source.</li>
-</ol></td>
-</tr>
-<tr>
-<td>Android 7.0 Nougat or lower</td>
-<td><ol>
-<li>Go to Settings > Security > Unknown sources.</li>
-<li>Check the box for Allow installation of apps from unknown sources.</li>
-</ol></td>
-</tr>
-</table>
- <h2>How to download and install an APK file on Android</h2>
-<p>Once you have enabled unknown sources on your Android device, you can download and install an APK file using one of these methods:</p>
- <h3>Using a web browser</h3>
-<p>If you have the URL of the APK file that you want to install, you can use a web browser such as Chrome or Firefox to download it. Follow these steps:</p>
-<ol>
-<li>Open the web browser on your Android device and enter the URL of the APK file in the address bar.</li>
-<li>Tap on the download link or button and wait for the download to complete.</li>
-<li>You may see a warning message that says "This type of file can harm your device". Tap on OK to proceed.</li>
-<li>Once the download is finished, tap on the notification bar or open the Downloads folder on your device and tap on the APK file.</li>
-<li>You may see a prompt that says "Do you want to install this application?". Tap on Install and wait for the installation to complete.</li>
-<li>You may see another prompt that says "For your security, your phone is not allowed to install unknown apps from this source". Tap on Settings and enable the option for Allow from this source. Then go back and tap on Install again.</li>
-<li>Once the installation is done, tap on Open to launch the app or Done to exit.</li>
-</ol>
- <h3>Using a USB cable</h3>
-<p>If you have the APK file on your computer, you can transfer it to your Android device using a USB cable. Follow these steps:</p>
-<ol>
-<li>Connect your Android device to your computer using a USB cable and select the option for File Transfer or MTP mode.</li>
-<li>On your computer, open the folder where you have saved the APK file and copy it.</li>
-<li>On your Android device, open a file manager app such as Files by Google or ES File Explorer and navigate to a folder where you want to paste the APK file. You can use the internal storage or an external SD card.</li>
-<li>Paste the APK file in the folder and disconnect your device from the computer.</li>
-<li>On your Android device, open the file manager app and locate and tap on the APK file.</li>
-<li>You may see a prompt that says "Do you want to install this application?". Tap on Install and wait for the installation to complete.</li>
-<li>You may see another prompt that says "For your security, your phone is not allowed to install unknown apps from this source". Tap on Settings and enable the option for Allow from this source. Then go back and tap on Install again.</li>
-<li>Once the installation is done, tap on Open to launch the app or Done to exit.</li> <h2>How to update or uninstall an APK file on Android</h2>
-<p>If you have installed an APK file on your Android device, you may want to update it or uninstall it at some point. Here is how you can do that:</p>
- <h3>How to update an APK file on Android</h3>
-<p>Unlike the apps from the Play Store, APK files do not update automatically. You need to check for updates manually and install them yourself. Here are some ways to do that:</p>
-<ul>
-<li>Visit the website or source where you downloaded the APK file and look for the latest version. Download and install it over the existing app.</li>
-<li>Use a third-party app store or updater app that can scan your device for installed APK files and notify you of any available updates. Some examples are APKPure, Aptoide, or Uptodown.</li>
-<li>Use a file manager app that can extract the version code and name of the APK file and compare it with the online version. Some examples are Solid Explorer, X-plore, or FX File Explorer.</li>
-</ul>
-<p>When you update an APK file, you may need to enable unknown sources again for the app that you are using to install the update.</p>
- <h3>How to uninstall an APK file on Android</h3>
-<p>If you want to remove an APK file from your Android device, you can uninstall it like any other app. Here are some ways to do that:</p>
-<ul>
-<li>Go to Settings > Apps & notifications > See all apps and find the app that you want to uninstall. Tap on it and then tap on Uninstall.</li>
-<li>Open the app drawer and find the app that you want to uninstall. Tap and hold on it and then drag it to the Uninstall icon at the top of the screen.</li>
-<li>Use a file manager app that can locate and delete the APK file from your device. Some examples are Files by Google, ES File Explorer, or SD Maid.</li>
-</ul>
-<p>When you uninstall an APK file, you may also want to delete it from your device to free up some space.</p>
- <h2>How to find safe and reliable sources for APK files</h2>
-<p>One of the most important things to consider when installing APK files on Android is the source. You should only download APK files from sites that are reputable and trustworthy. Otherwise, you may end up with malware, viruses, spyware, or other harmful code on your device.</p>
-<p>Here are some of the best sites for safe Android APK downloads:</p>
-<table>
-<tr>
-<th>Site</th>
-<th>Description</th>
-<th>Link</th>
-</tr>
-<tr>
-<td>APKMirror</td>
-<td>A popular site that offers free and original APK files from various developers and publishers. It also provides detailed information about each APK file, such as version, size, signature, and permissions.</td>
-<td><a href="">APKMirror.com</a></td>
-</tr>
-<tr>
-<td>APKPure</td>
-<td>A reliable site that offers free and verified APK files from various categories and regions. It also provides a user-friendly interface, a built-in app store, and an updater app.</td>
-<td><a href="">APKPure.com</a></td>
-</tr>
-<tr>
-<td>Aptoide</td>
-<td>A community-driven site that offers free and customized APK files from various sources and users. It also provides a social platform, a malware scanner, and a rollback feature.</td>
-<td><a href="">Aptoide.com</a></td>
-</tr>
-<tr>
-<td>F-Droid</td>
-<td>A non-profit site that offers free and open-source APK files from various developers and projects. It also provides a transparent and ethical platform, a donation system, and a privacy policy.</td>
-<td><a href="">F-Droid.org</a></td>
-</tr>
- <tr><td>XDA Developers</td><td>A well-known site that offers free and modded APK files from various developers and enthusiasts. It also provides a forum, a blog, a wiki, and a news section.</td><td><a href="">XDA-Developers.com</a></td></tr></table>
- <h2>Conclusion</h2>
- <p>In this article, we have explained how to install APK files on Android. We have covered what an APK file is, how to enable unknown sources on Android, how to download and install an APK file using a web browser or a USB cable, how to update or uninstall an APK file on Android, and how to find safe and reliable sources for APK files.</p>
- <p>Installing APK files on Android can be useful for accessing apps that are not available on the Play Store or trying out new features or benefits. However, you should also be aware of the potential security risks and choose your sources carefully. Always scan the APK files for any malware or viruses before installing them and only install the ones that you trust.</p>
- <h2>FAQs</h2>
- <h3>What is the difference between an APK file and an app bundle?</h3>
-<p>An app bundle is a new format for Android apps that allows developers to optimize and reduce the size of their apps. It stands for Android App Bundle or AAB. It is similar to an APK file, but it contains only the essential code and resources for a specific device configuration, such as screen size, language, or CPU architecture. The app bundle is uploaded to the Play Store, where it is converted into multiple APK files for different devices. This way, users only download and install the APK files that are relevant for their device, saving bandwidth and storage space.</p>
- <h3>How can I open an APK file on my computer?</h3>
-<p>If you want to open an APK file on your computer, you need to use an Android emulator. An Android emulator is a software that simulates an Android device on your computer, allowing you to run Android apps and games on your PC or Mac. Some of the most popular Android emulators are BlueStacks, NoxPlayer, LDPlayer, and MEmu.</p>
- <h3>How can I create my own APK file?</h3>
-<p>If you want to create your own APK file, you need to use a tool like Android Studio or Visual Studio. These are integrated development environments (IDEs) that allow you to write, compile, and debug Android apps using programming languages such as Java or Kotlin. You can also use other tools such as Unity, Unreal Engine, or Flutter to create cross-platform apps that can run on Android and other operating systems.</p>
- <h3>How can I extract or convert an APK file?</h3>
-<p>If you want to extract or convert an APK file, you need to use a tool like 7-Zip or WinRAR. These are file archivers that can open and extract the contents of an APK file, which are usually compressed using the ZIP format. You can also use these tools to convert an APK file to another format, such as ZIP or JAR.</p>
- <h3>How can I scan an APK file for malware or viruses?</h3>
-<p>If you want to scan an APK file for malware or viruses, you need to use a tool like Google Play Protect or VirusTotal. Google Play Protect is a built-in security feature on Android devices that scans the apps installed on your device and warns you of any harmful behavior or code. VirusTotal is a web-based service that analyzes files and URLs for malware, viruses, trojans, worms, and other threats using multiple antivirus engines.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Naija Whot Pro APK The Best Way to Experience the Nigerian Card Game.md b/spaces/congsaPfin/Manga-OCR/logs/Naija Whot Pro APK The Best Way to Experience the Nigerian Card Game.md
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-<br />
-<h1>How to Download Naija Whot Pro APK for Free</h1>
-<p>If you are looking for a fun and challenging card game to play on your Android device, you might want to try Naija Whot Pro APK. This is a classic card game that originated in England and became popular in Nigeria. It is similar to Crazy Eights, but with more features and variations.</p>
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-<p>Naija Whot Pro APK is an exciting, classic, and healthy card game for everyone. It is simple to play. You have to become a winner by finishing your cards before your opponent. It keeps you comfortable when it's needed and your mind focused when you are idle. The artificial intelligence keeps it challenging all the time.</p>
-<p>Some of the benefits of playing Naija Whot Pro APK are:</p>
-<ul>
-<li>It improves your memory, concentration, and strategic thinking skills.</li>
-<li>It offers different game modes, such as QuickPlay, Tournament, and Multiplayer.</li>
-<li>It allows you to customize the game settings, such as choosing which special cards to play with, turning on/off defense, anthem, and avatars.</li>
-<li>It supports both English and French languages.</li>
-<li>It is free to download and play.</li>
-</ul>
-<p>In this article, we will show you how to download Naija Whot Pro APK for free from different sources. We will also show you how to play the game and have fun.</p>
- <h2>How to Download Naija Whot Pro APK from APK Mirror</h2>
-<p>APK Mirror is one of the best Android APK download sites. It is owned and operated by the same team that created the widely-read Android news site, Android Police. It has strict security policies in place to ensure that all the APKs you download are safe and virus-free.</p>
-<p>To download Naija Whot Pro APK from APK Mirror, follow these steps:</p>
-<ol>
-<li>Go to <a href="(^1^)">APK Mirror</a> website on your Android device or computer.</li>
-<li>Search for Naija Whot Pro APK and select the latest version.</li>
-<li>Tap on 'Download APK ' and save the file to your device or computer.</li>
-<li>Enable 'Unknown Sources' on your Android device if needed. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Install the APK file by tapping on it and following the prompts.</li>
-</ol>
- <h2>How to Download Naija Whot Pro APK from APK Pure</h2>
-<p>APK Pure is another popular Android APK download site. It offers a wide range of apps and games, including modded and patched versions. It also has a user-friendly interface and a fast download speed.</p>
-<p>To download Naija Whot Pro APK from APK Pure, follow these steps:</p>
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-<ol>
-<li>Go to <a href="">APK Pure</a> website on your Android device or computer.</li>
-<li>Search for Naija Whot Pro APK and select the latest version.</li>
-<li>Tap on 'Download APK' and save the file to your device or computer.</li>
-<li>Enable 'Unknown Sources' on your Android device if needed. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Install the APK file by tapping on it and following the prompts.</li>
-</ol>
- <h2>How to Download Naija Whot Pro APK from Other Sources</h2>
-<p>If you can't find Naija Whot Pro APK on APK Mirror or APK Pure, you can try other sources that offer APK downloads. However, you should be careful when downloading from unknown or untrusted sources, as they may contain malware or viruses that can harm your device or data.</p>
-<p>To download Naija Whot Pro APK from other sources, follow these steps:</p>
-<ol>
-<li>Find a reputable APK downloader website that offers Naija Whot Pro APK, such as <a href="">APK-DL</a> or <a href="">Aptoide</a>.</li>
-<li>Follow the instructions on the website to download the APK file to your device or computer.</li>
-<li>Enable 'Unknown Sources' on your Android device if needed. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Install the APK file by tapping on it and following the prompts.</li>
-</ol>
- <h2>How to Play Naija Whot Pro APK</h2>
-<p>Once you have downloaded and installed Naija Whot Pro APK, you can start playing the game and have fun. The game is easy to learn and play, but challenging to master. Here are some basic steps to play the game:</p>
-<ol>
-<li>Launch the app from your app list or home screen.</li>
-<li>Choose a game mode from QuickPlay, Tournament, or Multiplayer. QuickPlay lets you play a single game against the computer. Tournament lets you compete in a series of games against different opponents. Multiplayer lets you play online with other players around the world.</li>
-<li>Select a shape or number to play or use a special card to change the game rules. The shapes are Triangle, Square, Circle, Star, and Cross. The numbers are 1 to 14. The special cards are Hold On, Pick Two, General Market, Suspension, and Last Card. Each special card has a different effect on the game flow.</li>
-<li>Try to finish your cards before your opponent and win the game. You can also use some strategies to make your opponent lose cards or skip their turn. The game ends when one player has no cards left or when the deck is empty.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Naija Whot Pro APK is a fun and addictive card game that you can play on your Android device for free. It is based on a classic card game that originated in England and became popular in Nigeria. It has different game modes, settings, and features that make it more enjoyable and challenging. You can download Naija Whot Pro APK from various sources, such as APK Mirror, APK Pure, or other reputable APK downloader websites. You can also learn how to play the game easily and have fun with your friends or other players online.</p>
-<p>If you are looking for a new card game to try out, why not download Naija Whot Pro APK today and see for yourself how fun it is? You won't regret it!</p>
- <h3>Frequently Asked Questions</h3>
- <h4>What is the difference between Naija Whot Pro APK and Naija Whot Free?</h4>
-<p>Naija Whot Pro APK is an upgraded version of Naija Whot Free. It has more features and options than the free version, such as more special cards, more avatars, more anthems, more defense options, and more game modes. It also has no ads and no in-app purchases. However, Naija Whot Pro APK is not available on the Google Play Store, so you have to download it from other sources.</p>
- <h4>Is Naija Whot Pro APK safe to download and install?</h4>
-<p>Yes, Naija Whot Pro APK is safe to download and install, as long as you get it from a trusted source, such as APK Mirror or APK Pure. These websites scan the APK files for any malware or viruses before uploading them. However, you should always be careful when downloading from unknown or untrusted sources, as they may contain harmful or malicious content that can damage your device or data.</p>
- <h4>How can I update Naija Whot Pro APK?</h4>
-<p>To update Naija Whot Pro APK, you have to download the latest version of the APK file from the same source that you got it from. Then, you have to uninstall the old version of the app and install the new version. Alternatively, you can use an app updater tool, such as <a href="">APK Updater</a>, that can automatically check for updates and install them for you.</p>
- <h4>Can I play Naija Whot Pro APK offline?</h4>
-<p>Yes, you can play Naija Whot Pro APK offline. You can choose the QuickPlay or Tournament mode and play against the computer without an internet connection. However, if you want to play online with other players, you need to have a stable internet connection and sign in with your Google account.</p>
- <h4>Can I play Naija Whot Pro APK with my friends?</h4>
-<p>Yes, you can play Naija Whot Pro APK with your friends. You can choose the Multiplayer mode and invite your friends to join your game room. You can also chat with them and send them emojis during the game. You can also play with random players from around the world and make new friends.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Parking Master Multiplayer 2 MOD APK 1.1.4 Enjoy Unlimited Fun and Challenge.md b/spaces/congsaPfin/Manga-OCR/logs/Parking Master Multiplayer 2 MOD APK 1.1.4 Enjoy Unlimited Fun and Challenge.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/Parking Master Multiplayer 2 MOD APK 1.1.4 Enjoy Unlimited Fun and Challenge.md	
+++ /dev/null
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-
-<h1>Parking Master Multiplayer 2: A Fun and Challenging Simulation Game</h1>
-<p>Do you love driving and parking cars? Do you want to test your skills in realistic scenarios and environments? Do you want to challenge your friends and other players online? If you answered yes to any of these questions, then you should try Parking Master Multiplayer 2, a simulation game that will give you hours of fun and excitement.</p>
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-<h2>What is Parking Master Multiplayer 2?</h2>
-<p>Parking Master Multiplayer 2 is a simulation game developed by Zuuks Games, the creators of popular games like Bus Simulator, Truck Simulator, and City Driving. In this game, you can drive and park various cars in different locations, such as city streets, parking lots, airports, highways, and more. You can also customize your cars with different colors, stickers, rims, spoilers, and other accessories. You can also upgrade your cars with better engines, brakes, tires, and suspension.</p>
-<p>But the best part of this game is that you can play online with other players from around the world. You can join or create a clan, chat with other players, and compete in multiplayer modes like racing, drifting, flag capture, and more. You can also show off your parking skills in leaderboards and rankings.</p>
-<h3>Features of Parking Master Multiplayer 2</h3>
-<h4>Realistic graphics and physics</h4>
-<p>One of the main attractions of this game is its realistic graphics and physics. The game uses advanced 3D graphics that make the cars and environments look stunning. The game also uses realistic physics that make the driving and parking experience more challenging and immersive. You can feel the weight of the car, the friction of the tires, the impact of collisions, and the effects of weather conditions.</p>
-<h4>Various modes and levels</h4>
-<p>The game offers various modes and levels for you to enjoy. You can choose from free mode, career mode, multiplayer mode, or custom mode. In free mode, you can drive and park freely in any location without any time limit or rules. In career mode, you can complete missions and challenges that test your parking skills in different scenarios. In multiplayer mode, you can join or create a clan, chat with other players, and compete in various modes like racing, drifting, flag capture, etc. In custom mode, you can create your own levels with your own rules and objectives.</p>
-<h4>Customizable cars and upgrades</h4>
-<p>The game also allows you to customize your cars with different colors, stickers, rims, spoilers, and other accessories. You can make your car look unique and stylish according to your preference. You can also upgrade your cars with better engines, brakes, tires, suspension, etc. You can improve the performance and handling of your car by spending coins and gems that you earn by playing the game.</p>
-<h4>Online multiplayer and chat</h4>
-<p>The game also features an online multiplayer mode where you can play with other players from around the world. You can join or create a clan, chat with other players using text or voice messages, and compete in various modes like racing, drifting, flag capture, etc. You can also show off your parking skills in leaderboards and rankings.</p>
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-<h3>How to download and install Parking Master Multiplayer 2 mod apk?</h3>
-<h4>Requirements and permissions</h <h4>Requirements and permissions</h4>
-<p>To download and install Parking Master Multiplayer 2 mod apk, you need to have an Android device that runs on Android 4.4 or higher. You also need to have enough storage space on your device to install the game. The game size is about 100 MB, but it may vary depending on your device and updates.</p>
-<p>You also need to enable the installation of apps from unknown sources on your device. This is because the mod apk is not available on the official Google Play Store, but on third-party websites. To enable this option, you need to go to your device settings, then security, then unknown sources, and toggle it on.</p>
-<p>Additionally, you need to grant some permissions to the game when you install it. These permissions include access to your device's storage, location, microphone, camera, and network. These permissions are necessary for the game to function properly and to provide you with the best gaming experience.</p>
-<h4>Steps to download and install</h4>
-<p>Once you have met the requirements and permissions, you can follow these simple steps to download and install Parking Master Multiplayer 2 mod apk:</p>
-<ol>
-<li>Go to a reliable website that offers the mod apk file for Parking Master Multiplayer 2. You can search for it on Google or use the link provided below. Make sure that the website is safe and secure, and that the mod apk file is free from viruses and malware.</li>
-<li>Download the mod apk file to your device by clicking on the download button or scanning the QR code. You may need to wait for a few seconds or minutes for the download to complete, depending on your internet speed and connection.</li>
-<li>Locate the downloaded mod apk file on your device's file manager or downloads folder. Tap on it to start the installation process. You may see a warning message that says "This type of file can harm your device". Ignore it and tap on "Install anyway".</li>
-<li>Wait for the installation to finish. It may take a few seconds or minutes, depending on your device's performance and specifications.</li>
-<li>Once the installation is done, you can launch the game by tapping on its icon on your home screen or app drawer. You can also create a shortcut for it on your desktop for easy access.</li>
-</ol>
-<h4>Benefits of using mod apk</h4>
-<p>By using Parking Master Multiplayer 2 mod apk, you can enjoy some benefits that are not available in the original version of the game. These benefits include:</p>
-<ul>
-<li>Unlimited coins and gems: You can get unlimited coins and gems in the game, which you can use to buy and upgrade cars, customize them, unlock new levels, and more. You don't have to worry about running out of money or resources in the game.</li>
-<li>All cars unlocked: You can access all the cars in the game without having to unlock them by completing missions or challenges. You can choose from a variety of cars, such as sports cars, muscle cars, SUVs, trucks, buses, etc. You can also switch between them anytime you want.</li>
-<li>All levels unlocked: You can play all the levels in the game without having to unlock them by earning stars or completing objectives. You can explore different locations, such as city streets, parking lots, airports, highways, etc. You can also choose from different modes, such as free mode, career mode, multiplayer mode, or custom mode.</li>
-<li>No ads: You can play the game without any interruptions or distractions from annoying ads that pop up on your screen. You can enjoy a smooth and seamless gaming experience without any lag or glitches.</li>
-</ul>
-<h3>Tips and tricks for playing Parking Master Multiplayer 2</h3>
-<h4>Practice your parking skills in free mode</h4>
-<p>If you are new to the game or want to improve your parking skills, you should practice in free mode first. In this mode, you can drive and park freely in any location without any time limit or rules. You can also try different cars and see how they handle and perform in different situations. This will help you get familiar with the game's controls, mechanics, and features.</p>
-<h4>Use the camera angles and sensors wisely</h4>
-<p>The game provides you with different camera angles and sensors that help you see your surroundings better and avoid collisions. You can switch between different camera angles by tapping on the camera icon on the top right corner of your screen. You can also use the sensors that show you the distance between your car and other objects by looking at the green, yellow, or red bars on your screen. You should use these tools wisely and adjust them according to your preference and convenience.</p> <h4>Earn coins and gems by completing missions and challenges</h4>
-<p>One of the ways to earn coins and gems in the game is by completing missions and challenges in career mode. In this mode, you have to park your car in a designated spot within a given time limit and without hitting any obstacles. You can earn stars, coins, and gems based on your performance and accuracy. You can also earn bonus coins and gems by completing daily and weekly tasks that vary in difficulty and reward.</p>
-<h4>Upgrade your cars and unlock new ones</h4>
-<p>Another way to earn coins and gems in the game is by upgrading your cars and unlocking new ones. You can upgrade your cars with better engines, brakes, tires, suspension, etc. by spending coins and gems that you earn by playing the game. You can also unlock new cars by spending coins and gems or by reaching certain levels or achievements. You can choose from a variety of cars, such as sports cars, muscle cars, SUVs, trucks, buses, etc. You can also customize your cars with different colors, stickers, rims, spoilers, and other accessories.</p>
-<h4>Join a clan and compete with other players</h4>
-<p>The most fun and exciting way to play the game is by joining a clan and competing with other players online. You can join or create a clan, chat with other players using text or voice messages, and compete in various modes like racing, drifting, flag capture, etc. You can also show off your parking skills in leaderboards and rankings. You can earn clan points, coins, gems, and trophies by winning matches and tournaments. You can also get exclusive rewards and bonuses by being a part of a clan.</p>
-<h2>Conclusion</h2>
-<p>Parking Master Multiplayer 2 is a simulation game that will give you hours of fun and excitement. You can drive and park various cars in different locations, customize your cars with different colors, stickers, rims, spoilers, and other accessories, upgrade your cars with better engines, brakes, tires, suspension, etc., play online with other players from around the world, join or create a clan, chat with other players using text or voice messages, compete in various modes like racing, drifting, flag capture, etc., show off your parking skills in leaderboards and rankings, and enjoy realistic graphics and physics.</p>
-<p>If you are looking for a fun and challenging simulation game that will test your driving and parking skills in realistic scenarios and environments, then you should download Parking Master Multiplayer 2 mod apk today. You can enjoy unlimited coins and gems, all cars unlocked, all levels unlocked, no ads, and more benefits by using the mod apk version of the game.</p>
-<p>Download Parking Master Multiplayer 2 mod apk now and become the ultimate parking master!</p>
-<h3>FAQs</h3>
-<ol>
-<li>Q: Is Parking Master Multiplayer 2 mod apk safe to use?</li>
-<li>A: Yes, Parking Master Multiplayer 2 mod apk is safe to use as long as you download it from a reliable website that offers the mod apk file for free without any viruses or malware. You should also enable the installation of apps from unknown sources on your device settings before installing the mod apk file.</li>
-<li>Q: How can I play Parking Master Multiplayer 2 offline?</li>
-<li>A: You can play Parking Master Multiplayer 2 offline by choosing the free mode or the career mode. In these modes, you don't need an internet connection to play the game. However, you won't be able to access the multiplayer mode or the chat feature if you play offline.</li>
-<li>Q: How can I contact the developers of Parking Master Multiplayer 2?</li>
-<li>A: You can contact the developers of Parking Master Multiplayer 2 by visiting their official website at https://www.zuuks.com/ or by sending them an email at info@zuuks.com. You can also follow them on their social media accounts on Facebook, Twitter, Instagram, YouTube, etc.</li>
-<li>Q: How can I report a bug or a problem in Parking Master Multiplayer 2?</li>
-<li>A: You can report a bug or a problem in Parking Master Multiplayer 2 by going to the settings menu in the game and tapping on the feedback button. You can also send an email to support@zuuks.com with a screenshot or a video of the bug or problem.</li>
-<li>Q: How can I get more coins and gems in Parking Master Multiplayer 2?</li>
-<li>A: You can get more coins and gems in Parking Master Multiplayer 2 by completing missions and challenges in career mode, by winning matches and tournaments in multiplayer mode, by completing daily and weekly tasks, by watching ads or videos, or by using Parking Master Multiplayer 2 mod apk.</li>
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- <p></</p> 197e85843d<br />
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-<h1>How to Download QZ Tray 2.0 and Why You Need It</h1>
-<p>If you are looking for a way to print labels, barcodes, receipts, and more from your web browser, you might want to check out QZ Tray 2.0. QZ Tray is a software that allows you to print to popular hardware using HTML, JavaScript, and Java. It also lets you communicate directly with scales and other popular hardware from your browser. In this article, we will show you how to download QZ Tray 2.0 for your operating system, and why you need it.</p>
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- <h2>What is QZ Tray and What Can It Do?</h2>
-<p>QZ Tray is a software that runs in the background of your computer and connects your web browser to your printer or other hardware devices. It enables you to print from any webpage using various formats, such as PDF, PNG, HTML, PCL, ESC/POS, EPL, ZPL, SBPL, FGL, and more. It also supports all popular connection types, such as COM, Parallel, Serial, USB, IP, and more. Here are some of the features of QZ Tray that make it a powerful tool for industry printing and more.</p>
- <h3>Print labels, barcodes, receipts and more from a webpage</h3>
-<p>With QZ Tray, you can print anything from your web browser to any printer that supports one of the supported formats. You can print labels, barcodes, receipts, tickets, invoices, certificates, coupons, badges, cards, stickers, tags, and more. You can use any web-based application or software that supports HTML or JavaScript printing. You can also use QZ Tray's demo page to test your printer and see some examples of what you can print.</p>
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-<p>QZ Tray is not only for printing. You can also use it to communicate directly with scales and other popular hardware devices from your web browser. You can read data from scales, scanners, card readers, RFID tags, magnetic stripes, keyboards, mice, joysticks, gamepads, webcams, microphones, speakers. You can also send commands or data to these devices using QZ Tray's API. This way, you can integrate your web-based applications with your hardware devices seamlessly.</p>
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-<p>QZ Tray is easy to use for developers and non-developers alike. You can use QZ Tray's simple API to print or communicate with hardware devices from your web browser. You can also use QZ Tray's sample.html file to get started quickly. You can edit the sample.html file to customize your printing or communication settings. You can also check out QZ Tray's quickstart guide for more <h2>How to Download QZ Tray 2.0 for Your Operating System</h2>
-<p>QZ Tray 2.0 is compatible with Windows, MacOS, and Linux / Unix operating systems. You can download QZ Tray 2.0 for free from the official website. Here are the steps to download and install QZ Tray 2.0 for your operating system.</p>
- <h3>Download QZ Tray 2.0 for Windows</h3>
-<h4>Download the installer file</h4>
-<p>To download QZ Tray 2.0 for Windows, go to the download page and click on the "Download" button under the "Windows" section. You will see a pop-up window asking you to save the installer file. Choose a location on your computer where you want to save the file and click on "Save". The file name will be something like "qz-tray-2.0.exe".</p>
- <h4>Run the installer and follow the instructions</h4>
-<p>To install QZ Tray 2.0 for Windows, locate the installer file on your computer and double-click on it. You will see a User Account Control window asking you to allow the app to make changes to your device. Click on "Yes" to continue. You will then see the QZ Tray Setup Wizard window. Click on "Next" to proceed. You will see the License Agreement window. Read the agreement and click on "I Agree" to accept it. You will then see the Choose Install Location window. You can either keep the default location or browse for a different one. Click on "Next" to continue. You will then see the Choose Start Menu Folder window. You can either keep the default folder or browse for a different one. Click on "Next" to continue. You will then see the Ready to Install window. Click on "Install" to start the installation process. Wait for a few minutes until the installation is complete. You will then see the Installation Complete window. Click on "Finish" to exit the wizard.</p>
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-QZ Tray 2.0 download link for Mac<br />
-QZ Tray 2.0 installation guide for Linux<br />
-Download QZ Tray 2.0 and print labels from web page<br />
-QZ Tray 2.0 demo and sample code<br />
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-QZ Tray 2.0 Java requirements and compatibility<br />
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-<p>To test if QZ Tray 2.0 is installed correctly on your Windows computer, look for the QZ icon in your system tray (the area near the clock on your taskbar). If you see the icon, it means QZ Tray is running in the background. You can right-click on the icon to access various options, such as opening the demo page, checking for updates, viewing logs, or quitting QZ Tray. To start printing from your web browser, you need to allow QZ Tray access to your printer or hardware device. When you visit a webpage that uses QZ Tray, you will see a pop-up window asking you to grant permission to QZ Tray. Click on "Allow" to continue. You can also manage your permissions from the QZ Tray icon in your system tray. Once you have granted permission, you can print from any webpage that supports QZ Tray.</p>
- <h3>Download QZ Tray 2.0 for MacOS</h3>
-<h4>Download the installer file</h4>
-<p>To download QZ Tray 2.0 for MacOS, go to the download page and click on the "Download" button under the "MacOS" section. You will see a pop-up window asking you to save the installer file. Choose a location on your computer where you want to save the file and click on "Save". The file name will be something like "qz-tray-2.0.dmg".</p>
- <h4>Run the installer and follow the instructions</h4>
-<p>To install QZ Tray 2.0 for MacOS, locate the installer file on your computer and double-click on it. You will see a window with a QZ Tray icon and an Applications folder icon. Drag and drop the QZ Tray icon into the Applications folder icon to copy it there. You will then see a confirmation window asking you if you want to replace an existing item with this one (if you have a previous version of QZ Tray installed). Click on "Replace" to continue. To run QZ Tray 2.0 for MacOS, open your Applications folder and double-click on the QZ Tray icon. You will see a pop-up window asking you to open the app. Click on "Open" to continue. You will then see a security warning window asking you to allow QZ Tray to access your network. Click on "OK" to continue. You will then see a window asking you to install the QZ Tray certificate. Click on "Install" to continue. You will then see a window asking you to enter your password to allow QZ Tray to modify your system. Enter your password and click on "OK" to continue. You will then see a confirmation window saying that the certificate was installed successfully. Click on "OK" to exit.</p>
- <h4>Test the installation and start printing</h4>
-<p>To test if QZ Tray 2.0 is installed correctly on your MacOS computer, look for the QZ icon in your menu bar (the area near the clock on your screen). If you see the icon, it means QZ Tray is running in the background. You can click on the icon to access various options, such as opening the demo page, checking for updates, viewing logs, or quitting QZ Tray. To start printing from your web browser, you need to allow QZ Tray access to your printer or hardware device. When you visit a webpage that uses QZ Tray, you will see a pop-up window asking you to grant permission to QZ Tray. Click on "Allow" to continue. You can also manage your permissions from the QZ Tray icon in your menu bar. Once you have granted permission, you can print from any webpage that supports QZ Tray.</p>
- <h3>Download QZ Tray 2.0 for Linux / Unix</h3>
-<h4>Download the installer file</h4>
-<p>To download QZ Tray 2.0 for Linux / Unix, go to the download page and click on the "Download" button under the "Linux / Unix" section. You will see a pop-up window asking you to save the installer file. Choose a location on your computer where you want to save the file and click on "Save". The file name will be something like "qz-tray-2.0.run".</p>
- <h4>Run the installer and follow the instructions</h4>
-<p>To install QZ Tray 2.0 for Linux / Unix, locate the installer file on your computer and make it executable by running the following command in your terminal: <code>chmod +x qz-tray-2.0.run</code>
- Then, run the installer file by running the following command in your terminal: <code>./qz-tray-2.0.run</code>
- You will see a window with the QZ Tray Setup Wizard. Click on "Next" to proceed. You will see the License Agreement window. Read the agreement and click on "I Agree" to accept it. You will then see the Choose Install Location window. You can either keep the default location or browse for a different one. Click on "Next" to continue. You will then see the Choose Start Menu Folder window. You can either keep the default folder or browse for a different one. Click on "Next" to continue. You will then see the Ready to Install window. Click on "Install" to start the installation process. Wait for a few minutes until the installation is complete. You will then see the Installation Complete window. Click on "Finish" to exit the wizard.</p>
- <h4>Test the installation and start printing</h4>
-<p>To test if QZ Tray 2.0 is installed correctly on your Linux / Unix computer, look for the QZ icon in your system tray (the area near the clock on your screen). If you see the icon, it means QZ Tray is running in the background. You can right-click on the icon to access various options, such as opening the demo page, checking for updates, viewing logs, or quitting QZ Tray. To start printing from your web browser, you need to allow QZ Tray access to your printer or hardware device. When you visit a webpage that uses QZ Tray, you will see a pop-up window asking you to grant permission to QZ Tray. Click on "Allow" to continue. You can also manage your permissions from the QZ Tray icon in your system tray. Once you have granted permission, you can print from any webpage that supports QZ Tray.</p>
- <h2>How to Get Support and Licensing Options for QZ Tray 2.0</h2>
-<p>QZ Tray 2.0 is a free software that you can download and use for personal or commercial purposes. However, if you want to get more features and benefits, you can choose from the following licensing options:</p>
- <h3>Choose from premium support, company branded, or free demo packages</h3>
-<p>QZ Tray offers three types of packages for different needs and budgets: - Premium Support: This package gives you access to priority email support, phone support, remote desktop support, bug fixes, feature requests, and more. You also get a company branded version of QZ Tray with your logo and name. The price for this package is $299 per year per workstation. - Company Branded: This package gives you a company branded version of QZ Tray with your logo and name. You also get access to email support and bug fixes. The price for this package is $99 per year per workstation. - Free Demo: This package gives you a free version of QZ Tray with limited features and support. You can use it for testing and evaluation purposes only. You will see a watermark on your prints and a pop-up window asking you to upgrade to a paid package.</p>
- <h3>Contact the support team or visit the wiki for troubleshooting and FAQs</h3>
-<p>If you have any questions or issues with QZ Tray 2.0, you can contact the support team or visit the wiki for help. You can email the support team at support@qz.io or call them at +1 (302) 261-2676. You can also visit the wiki page at https://qz.io/wiki/ for troubleshooting guides, FAQs, tutorials, documentation, and more.</p>
- <h2>Conclusion</h2>
-<p>QZ Tray 2.0 is a powerful software that allows you to print labels, barcodes, receipts, and more from your web browser to any printer or hardware device. It also lets you communicate directly with scales and other popular hardware devices from your web browser. You can download QZ Tray 2.0 for free from the official website and install it on your Windows, MacOS, or Linux / Unix computer. You can also choose from different licensing options to get more features and benefits. If you need any help or support, you can contact the support team or visit the wiki page for assistance.</p>
- <p>We hope this article has helped you learn how to download QZ Tray 2.0 and why you need it. If you have any feedback or suggestions, please let us know in the comments below.</p>
- <h4>FAQs</h4>
-<p>Here are some of the frequently asked questions about QZ Tray 2.0:</p>
- <ol>
-<li><b>What are the system requirements for QZ Tray 2.0?</b></li>
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index fa08e7947c14706a63206c9250ad0b67c82d7d5c..0000000000000000000000000000000000000000
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index 29877d2874c8c3cbbe2e108531a7cce6e7d0890e..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Bad Boys Movie Download Kickass 720p Everything You Need to Know About the Classic Film.md	
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diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/vgg.py b/spaces/cooelf/Multimodal-CoT/timm/models/vgg.py
deleted file mode 100644
index 8bea03e7ce31bada1790090561c99db9faa5ca76..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/vgg.py
+++ /dev/null
@@ -1,261 +0,0 @@
-"""VGG
-
-Adapted from https://github.com/pytorch/vision 'vgg.py' (BSD-3-Clause) with a few changes for
-timm functionality.
-
-Copyright 2021 Ross Wightman
-"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Union, List, Dict, Any, cast
-
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import build_model_with_cfg
-from .layers import ClassifierHead, ConvBnAct
-from .registry import register_model
-
-__all__ = [
-    'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
-    'vgg19_bn', 'vgg19',
-]
-
-
-def _cfg(url='', **kwargs):
-    return {
-        'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (1, 1),
-        'crop_pct': 0.875, 'interpolation': 'bilinear',
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'features.0', 'classifier': 'head.fc',
-        **kwargs
-    }
-
-
-default_cfgs = {
-    'vgg11': _cfg(url='https://download.pytorch.org/models/vgg11-bbd30ac9.pth'),
-    'vgg13': _cfg(url='https://download.pytorch.org/models/vgg13-c768596a.pth'),
-    'vgg16': _cfg(url='https://download.pytorch.org/models/vgg16-397923af.pth'),
-    'vgg19': _cfg(url='https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'),
-    'vgg11_bn': _cfg(url='https://download.pytorch.org/models/vgg11_bn-6002323d.pth'),
-    'vgg13_bn': _cfg(url='https://download.pytorch.org/models/vgg13_bn-abd245e5.pth'),
-    'vgg16_bn': _cfg(url='https://download.pytorch.org/models/vgg16_bn-6c64b313.pth'),
-    'vgg19_bn': _cfg(url='https://download.pytorch.org/models/vgg19_bn-c79401a0.pth'),
-}
-
-
-cfgs: Dict[str, List[Union[str, int]]] = {
-    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
-    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
-    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
-    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
-}
-
-
-class ConvMlp(nn.Module):
-
-    def __init__(self, in_features=512, out_features=4096, kernel_size=7, mlp_ratio=1.0,
-                 drop_rate: float = 0.2, act_layer: nn.Module = None, conv_layer: nn.Module = None):
-        super(ConvMlp, self).__init__()
-        self.input_kernel_size = kernel_size
-        mid_features = int(out_features * mlp_ratio)
-        self.fc1 = conv_layer(in_features, mid_features, kernel_size, bias=True)
-        self.act1 = act_layer(True)
-        self.drop = nn.Dropout(drop_rate)
-        self.fc2 = conv_layer(mid_features, out_features, 1, bias=True)
-        self.act2 = act_layer(True)
-
-    def forward(self, x):
-        if x.shape[-2] < self.input_kernel_size or x.shape[-1] < self.input_kernel_size:
-            # keep the input size >= 7x7
-            output_size = (max(self.input_kernel_size, x.shape[-2]), max(self.input_kernel_size, x.shape[-1]))
-            x = F.adaptive_avg_pool2d(x, output_size)
-        x = self.fc1(x)
-        x = self.act1(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.act2(x)
-        return x
-
-
-class VGG(nn.Module):
-
-    def __init__(
-        self,
-        cfg: List[Any],
-        num_classes: int = 1000,
-        in_chans: int = 3,
-        output_stride: int = 32,
-        mlp_ratio: float = 1.0,
-        act_layer: nn.Module = nn.ReLU,
-        conv_layer: nn.Module = nn.Conv2d,
-        norm_layer: nn.Module = None,
-        global_pool: str = 'avg',
-        drop_rate: float = 0.,
-    ) -> None:
-        super(VGG, self).__init__()
-        assert output_stride == 32
-        self.num_classes = num_classes
-        self.num_features = 4096
-        self.drop_rate = drop_rate
-        self.feature_info = []
-        prev_chs = in_chans
-        net_stride = 1
-        pool_layer = nn.MaxPool2d
-        layers: List[nn.Module] = []
-        for v in cfg:
-            last_idx = len(layers) - 1
-            if v == 'M':
-                self.feature_info.append(dict(num_chs=prev_chs, reduction=net_stride, module=f'features.{last_idx}'))
-                layers += [pool_layer(kernel_size=2, stride=2)]
-                net_stride *= 2
-            else:
-                v = cast(int, v)
-                conv2d = conv_layer(prev_chs, v, kernel_size=3, padding=1)
-                if norm_layer is not None:
-                    layers += [conv2d, norm_layer(v), act_layer(inplace=True)]
-                else:
-                    layers += [conv2d, act_layer(inplace=True)]
-                prev_chs = v
-        self.features = nn.Sequential(*layers)
-        self.feature_info.append(dict(num_chs=prev_chs, reduction=net_stride, module=f'features.{len(layers) - 1}'))
-        self.pre_logits = ConvMlp(
-            prev_chs, self.num_features, 7, mlp_ratio=mlp_ratio,
-            drop_rate=drop_rate, act_layer=act_layer, conv_layer=conv_layer)
-        self.head = ClassifierHead(
-            self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
-
-        self._initialize_weights()
-
-    def get_classifier(self):
-        return self.head.fc
-
-    def reset_classifier(self, num_classes, global_pool='avg'):
-        self.num_classes = num_classes
-        self.head = ClassifierHead(
-            self.num_features, self.num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
-
-    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.features(x)
-        x = self.pre_logits(x)
-        return x
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-    def _initialize_weights(self) -> None:
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                nn.init.constant_(m.bias, 0)
-
-
-def _filter_fn(state_dict):
-    """ convert patch embedding weight from manual patchify + linear proj to conv"""
-    out_dict = {}
-    for k, v in state_dict.items():
-        k_r = k
-        k_r = k_r.replace('classifier.0', 'pre_logits.fc1')
-        k_r = k_r.replace('classifier.3', 'pre_logits.fc2')
-        k_r = k_r.replace('classifier.6', 'head.fc')
-        if 'classifier.0.weight' in k:
-            v = v.reshape(-1, 512, 7, 7)
-        if 'classifier.3.weight' in k:
-            v = v.reshape(-1, 4096, 1, 1)
-        out_dict[k_r] = v
-    return out_dict
-
-
-def _create_vgg(variant: str, pretrained: bool, **kwargs: Any) -> VGG:
-    cfg = variant.split('_')[0]
-    # NOTE: VGG is one of the only models with stride==1 features, so indices are offset from other models
-    out_indices = kwargs.get('out_indices', (0, 1, 2, 3, 4, 5))
-    model = build_model_with_cfg(
-        VGG, variant, pretrained,
-        default_cfg=default_cfgs[variant],
-        model_cfg=cfgs[cfg],
-        feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
-        pretrained_filter_fn=_filter_fn,
-        **kwargs)
-    return model
-
-
-@register_model
-def vgg11(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 11-layer model (configuration "A") from
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(**kwargs)
-    return _create_vgg('vgg11', pretrained=pretrained, **model_args)
-
-
-@register_model
-def vgg11_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 11-layer model (configuration "A") with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
-    return _create_vgg('vgg11_bn', pretrained=pretrained, **model_args)
-
-
-@register_model
-def vgg13(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 13-layer model (configuration "B")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(**kwargs)
-    return _create_vgg('vgg13', pretrained=pretrained, **model_args)
-
-
-@register_model
-def vgg13_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 13-layer model (configuration "B") with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
-    return _create_vgg('vgg13_bn', pretrained=pretrained, **model_args)
-
-
-@register_model
-def vgg16(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 16-layer model (configuration "D")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(**kwargs)
-    return _create_vgg('vgg16', pretrained=pretrained, **model_args)
-
-
-@register_model
-def vgg16_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 16-layer model (configuration "D") with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
-    return _create_vgg('vgg16_bn', pretrained=pretrained, **model_args)
-
-
-@register_model
-def vgg19(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 19-layer model (configuration "E")
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(**kwargs)
-    return _create_vgg('vgg19', pretrained=pretrained, **model_args)
-
-
-@register_model
-def vgg19_bn(pretrained: bool = False, **kwargs: Any) -> VGG:
-    r"""VGG 19-layer model (configuration 'E') with batch normalization
-    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`._
-    """
-    model_args = dict(norm_layer=nn.BatchNorm2d, **kwargs)
-    return _create_vgg('vgg19_bn', pretrained=pretrained, **model_args)
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/checkpoint.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/checkpoint.py
deleted file mode 100644
index 877aa8b84ac48bea0a06f9d0733d74f88be2ecfc..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/checkpoint.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import warnings
-
-from annotator.mmpkg.mmcv.fileio import FileClient
-from ..dist_utils import allreduce_params, master_only
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class CheckpointHook(Hook):
-    """Save checkpoints periodically.
-
-    Args:
-        interval (int): The saving period. If ``by_epoch=True``, interval
-            indicates epochs, otherwise it indicates iterations.
-            Default: -1, which means "never".
-        by_epoch (bool): Saving checkpoints by epoch or by iteration.
-            Default: True.
-        save_optimizer (bool): Whether to save optimizer state_dict in the
-            checkpoint. It is usually used for resuming experiments.
-            Default: True.
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, ``runner.work_dir`` will be used by default. If
-            specified, the ``out_dir`` will be the concatenation of ``out_dir``
-            and the last level directory of ``runner.work_dir``.
-            `Changed in version 1.3.16.`
-        max_keep_ckpts (int, optional): The maximum checkpoints to keep.
-            In some cases we want only the latest few checkpoints and would
-            like to delete old ones to save the disk space.
-            Default: -1, which means unlimited.
-        save_last (bool, optional): Whether to force the last checkpoint to be
-            saved regardless of interval. Default: True.
-        sync_buffer (bool, optional): Whether to synchronize buffers in
-            different gpus. Default: False.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-            `New in version 1.3.16.`
-
-    .. warning::
-        Before v1.3.16, the ``out_dir`` argument indicates the path where the
-        checkpoint is stored. However, since v1.3.16, ``out_dir`` indicates the
-        root directory and the final path to save checkpoint is the
-        concatenation of ``out_dir`` and the last level directory of
-        ``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A"
-        and the value of ``runner.work_dir`` is "/path/of/B", then the final
-        path will be "/path/of/A/B".
-    """
-
-    def __init__(self,
-                 interval=-1,
-                 by_epoch=True,
-                 save_optimizer=True,
-                 out_dir=None,
-                 max_keep_ckpts=-1,
-                 save_last=True,
-                 sync_buffer=False,
-                 file_client_args=None,
-                 **kwargs):
-        self.interval = interval
-        self.by_epoch = by_epoch
-        self.save_optimizer = save_optimizer
-        self.out_dir = out_dir
-        self.max_keep_ckpts = max_keep_ckpts
-        self.save_last = save_last
-        self.args = kwargs
-        self.sync_buffer = sync_buffer
-        self.file_client_args = file_client_args
-
-    def before_run(self, runner):
-        if not self.out_dir:
-            self.out_dir = runner.work_dir
-
-        self.file_client = FileClient.infer_client(self.file_client_args,
-                                                   self.out_dir)
-
-        # if `self.out_dir` is not equal to `runner.work_dir`, it means that
-        # `self.out_dir` is set so the final `self.out_dir` is the
-        # concatenation of `self.out_dir` and the last level directory of
-        # `runner.work_dir`
-        if self.out_dir != runner.work_dir:
-            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
-            self.out_dir = self.file_client.join_path(self.out_dir, basename)
-
-        runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by '
-                            f'{self.file_client.name}.'))
-
-        # disable the create_symlink option because some file backends do not
-        # allow to create a symlink
-        if 'create_symlink' in self.args:
-            if self.args[
-                    'create_symlink'] and not self.file_client.allow_symlink:
-                self.args['create_symlink'] = False
-                warnings.warn(
-                    ('create_symlink is set as True by the user but is changed'
-                     'to be False because creating symbolic link is not '
-                     f'allowed in {self.file_client.name}'))
-        else:
-            self.args['create_symlink'] = self.file_client.allow_symlink
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` epochs
-        # 2. reach the last epoch of training
-        if self.every_n_epochs(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_epoch(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.epoch + 1} epochs')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)
-
-    @master_only
-    def _save_checkpoint(self, runner):
-        """Save the current checkpoint and delete unwanted checkpoint."""
-        runner.save_checkpoint(
-            self.out_dir, save_optimizer=self.save_optimizer, **self.args)
-        if runner.meta is not None:
-            if self.by_epoch:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
-            else:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
-            runner.meta.setdefault('hook_msgs', dict())
-            runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path(
-                self.out_dir, cur_ckpt_filename)
-        # remove other checkpoints
-        if self.max_keep_ckpts > 0:
-            if self.by_epoch:
-                name = 'epoch_{}.pth'
-                current_ckpt = runner.epoch + 1
-            else:
-                name = 'iter_{}.pth'
-                current_ckpt = runner.iter + 1
-            redundant_ckpts = range(
-                current_ckpt - self.max_keep_ckpts * self.interval, 0,
-                -self.interval)
-            filename_tmpl = self.args.get('filename_tmpl', name)
-            for _step in redundant_ckpts:
-                ckpt_path = self.file_client.join_path(
-                    self.out_dir, filename_tmpl.format(_step))
-                if self.file_client.isfile(ckpt_path):
-                    self.file_client.remove(ckpt_path)
-                else:
-                    break
-
-    def after_train_iter(self, runner):
-        if self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` iterations
-        # 2. reach the last iteration of training
-        if self.every_n_iters(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_iter(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.iter + 1} iterations')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/transformer_decoder/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/transformer_decoder/__init__.py
deleted file mode 100644
index b84bd4ecb48f134ccc218c4d5f02c50f7033bcd9..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/modeling/transformer_decoder/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .oneformer_transformer_decoder import ContrastiveMultiScaleMaskedTransformerDecoder
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/assign_score_withk.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/assign_score_withk.py
deleted file mode 100644
index 4906adaa2cffd1b46912fbe7d4f87ef2f9fa0012..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/assign_score_withk.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['assign_score_withk_forward', 'assign_score_withk_backward'])
-
-
-class AssignScoreWithK(Function):
-    r"""Perform weighted sum to generate output features according to scores.
-    Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/
-    scene_seg/lib/paconv_lib/src/gpu>`_.
-
-    This is a memory-efficient CUDA implementation of assign_scores operation,
-    which first transform all point features with weight bank, then assemble
-    neighbor features with ``knn_idx`` and perform weighted sum of ``scores``.
-
-    See the `paper <https://arxiv.org/pdf/2103.14635.pdf>`_ appendix Sec. D for
-        more detailed descriptions.
-
-    Note:
-        This implementation assumes using ``neighbor`` kernel input, which is
-            (point_features - center_features, point_features).
-        See https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/model/
-        pointnet2/paconv.py#L128 for more details.
-    """
-
-    @staticmethod
-    def forward(ctx,
-                scores,
-                point_features,
-                center_features,
-                knn_idx,
-                aggregate='sum'):
-        """
-        Args:
-            scores (torch.Tensor): (B, npoint, K, M), predicted scores to
-                aggregate weight matrices in the weight bank.
-                ``npoint`` is the number of sampled centers.
-                ``K`` is the number of queried neighbors.
-                ``M`` is the number of weight matrices in the weight bank.
-            point_features (torch.Tensor): (B, N, M, out_dim)
-                Pre-computed point features to be aggregated.
-            center_features (torch.Tensor): (B, N, M, out_dim)
-                Pre-computed center features to be aggregated.
-            knn_idx (torch.Tensor): (B, npoint, K), index of sampled kNN.
-                We assume the first idx in each row is the idx of the center.
-            aggregate (str, optional): Aggregation method.
-                Can be 'sum', 'avg' or 'max'. Defaults: 'sum'.
-
-        Returns:
-            torch.Tensor: (B, out_dim, npoint, K), the aggregated features.
-        """
-        agg = {'sum': 0, 'avg': 1, 'max': 2}
-
-        B, N, M, out_dim = point_features.size()
-        _, npoint, K, _ = scores.size()
-
-        output = point_features.new_zeros((B, out_dim, npoint, K))
-        ext_module.assign_score_withk_forward(
-            point_features.contiguous(),
-            center_features.contiguous(),
-            scores.contiguous(),
-            knn_idx.contiguous(),
-            output,
-            B=B,
-            N0=N,
-            N1=npoint,
-            M=M,
-            K=K,
-            O=out_dim,
-            aggregate=agg[aggregate])
-
-        ctx.save_for_backward(output, point_features, center_features, scores,
-                              knn_idx)
-        ctx.agg = agg[aggregate]
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        """
-        Args:
-            grad_out (torch.Tensor): (B, out_dim, npoint, K)
-
-        Returns:
-            grad_scores (torch.Tensor): (B, npoint, K, M)
-            grad_point_features (torch.Tensor): (B, N, M, out_dim)
-            grad_center_features (torch.Tensor): (B, N, M, out_dim)
-        """
-        _, point_features, center_features, scores, knn_idx = ctx.saved_tensors
-
-        agg = ctx.agg
-
-        B, N, M, out_dim = point_features.size()
-        _, npoint, K, _ = scores.size()
-
-        grad_point_features = point_features.new_zeros(point_features.shape)
-        grad_center_features = center_features.new_zeros(center_features.shape)
-        grad_scores = scores.new_zeros(scores.shape)
-
-        ext_module.assign_score_withk_backward(
-            grad_out.contiguous(),
-            point_features.contiguous(),
-            center_features.contiguous(),
-            scores.contiguous(),
-            knn_idx.contiguous(),
-            grad_point_features,
-            grad_center_features,
-            grad_scores,
-            B=B,
-            N0=N,
-            N1=npoint,
-            M=M,
-            K=K,
-            O=out_dim,
-            aggregate=agg)
-
-        return grad_scores, grad_point_features, \
-            grad_center_features, None, None
-
-
-assign_score_withk = AssignScoreWithK.apply
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/easydict/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/easydict/__init__.py
deleted file mode 100644
index 15928179b0182c6045d98bc0a7be1c6ca45f675e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/easydict/__init__.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""
-EasyDict
-Copy/pasted from https://github.com/makinacorpus/easydict
-Original author: Mathieu Leplatre <mathieu.leplatre@makina-corpus.com>
-"""
-
-class EasyDict(dict):
-    """
-    Get attributes
-
-    >>> d = EasyDict({'foo':3})
-    >>> d['foo']
-    3
-    >>> d.foo
-    3
-    >>> d.bar
-    Traceback (most recent call last):
-    ...
-    AttributeError: 'EasyDict' object has no attribute 'bar'
-
-    Works recursively
-
-    >>> d = EasyDict({'foo':3, 'bar':{'x':1, 'y':2}})
-    >>> isinstance(d.bar, dict)
-    True
-    >>> d.bar.x
-    1
-
-    Bullet-proof
-
-    >>> EasyDict({})
-    {}
-    >>> EasyDict(d={})
-    {}
-    >>> EasyDict(None)
-    {}
-    >>> d = {'a': 1}
-    >>> EasyDict(**d)
-    {'a': 1}
-    >>> EasyDict((('a', 1), ('b', 2)))
-    {'a': 1, 'b': 2}
-    
-    Set attributes
-
-    >>> d = EasyDict()
-    >>> d.foo = 3
-    >>> d.foo
-    3
-    >>> d.bar = {'prop': 'value'}
-    >>> d.bar.prop
-    'value'
-    >>> d
-    {'foo': 3, 'bar': {'prop': 'value'}}
-    >>> d.bar.prop = 'newer'
-    >>> d.bar.prop
-    'newer'
-
-
-    Values extraction
-
-    >>> d = EasyDict({'foo':0, 'bar':[{'x':1, 'y':2}, {'x':3, 'y':4}]})
-    >>> isinstance(d.bar, list)
-    True
-    >>> from operator import attrgetter
-    >>> list(map(attrgetter('x'), d.bar))
-    [1, 3]
-    >>> list(map(attrgetter('y'), d.bar))
-    [2, 4]
-    >>> d = EasyDict()
-    >>> list(d.keys())
-    []
-    >>> d = EasyDict(foo=3, bar=dict(x=1, y=2))
-    >>> d.foo
-    3
-    >>> d.bar.x
-    1
-
-    Still like a dict though
-
-    >>> o = EasyDict({'clean':True})
-    >>> list(o.items())
-    [('clean', True)]
-
-    And like a class
-
-    >>> class Flower(EasyDict):
-    ...     power = 1
-    ...
-    >>> f = Flower()
-    >>> f.power
-    1
-    >>> f = Flower({'height': 12})
-    >>> f.height
-    12
-    >>> f['power']
-    1
-    >>> sorted(f.keys())
-    ['height', 'power']
-
-    update and pop items
-    >>> d = EasyDict(a=1, b='2')
-    >>> e = EasyDict(c=3.0, a=9.0)
-    >>> d.update(e)
-    >>> d.c
-    3.0
-    >>> d['c']
-    3.0
-    >>> d.get('c')
-    3.0
-    >>> d.update(a=4, b=4)
-    >>> d.b
-    4
-    >>> d.pop('a')
-    4
-    >>> d.a
-    Traceback (most recent call last):
-    ...
-    AttributeError: 'EasyDict' object has no attribute 'a'
-    """
-    def __init__(self, d=None, **kwargs):
-        if d is None:
-            d = {}
-        else:
-            d = dict(d)        
-        if kwargs:
-            d.update(**kwargs)
-        for k, v in d.items():
-            setattr(self, k, v)
-        # Class attributes
-        for k in self.__class__.__dict__.keys():
-            if not (k.startswith('__') and k.endswith('__')) and not k in ('update', 'pop'):
-                setattr(self, k, getattr(self, k))
-
-    def __setattr__(self, name, value):
-        if isinstance(value, (list, tuple)):
-            value = [self.__class__(x)
-                     if isinstance(x, dict) else x for x in value]
-        elif isinstance(value, dict) and not isinstance(value, self.__class__):
-            value = self.__class__(value)
-        super(EasyDict, self).__setattr__(name, value)
-        super(EasyDict, self).__setitem__(name, value)
-
-    __setitem__ = __setattr__
-
-    def update(self, e=None, **f):
-        d = e or dict()
-        d.update(f)
-        for k in d:
-            setattr(self, k, d[k])
-
-    def pop(self, k, d=None):
-        delattr(self, k)
-        return super(EasyDict, self).pop(k, d)
-
-
-if __name__ == "__main__":
-    import doctest
-    doctest.testmod()
\ No newline at end of file
diff --git a/spaces/darthPanda/Social_media_sentiment_tracker/helper_functions.py b/spaces/darthPanda/Social_media_sentiment_tracker/helper_functions.py
deleted file mode 100644
index db58e51c6f6160bf67542aa063e1f8e1a266dd4e..0000000000000000000000000000000000000000
--- a/spaces/darthPanda/Social_media_sentiment_tracker/helper_functions.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import pandas as pd
-import numpy as np
-import snscrape.modules.twitter as sntwitter
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from transformers import pipeline
-import plotly.express as px
-import plotly.io as pio
-import plotly.graph_objects as go
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-from wordcloud import WordCloud
-from PIL import Image
-import requests
-from itertools import islice
-from youtube_comment_downloader import *
-
-
-@st.cache(allow_output_mutation=True)
-def get_nltk():
-    import nltk
-    nltk.download(
-    ["punkt", "wordnet", "omw-1.4", "averaged_perceptron_tagger", "universal_tagset"]
-    )
-    return
-get_nltk()
-
-from nltk.stem import WordNetLemmatizer
-from nltk.tag import pos_tag
-from nltk.tokenize import word_tokenize
-import re
-from sklearn.feature_extraction.text import CountVectorizer
-
-# Create a custom plotly theme and set it as default
-pio.templates["custom"] = pio.templates["plotly_white"]
-pio.templates["custom"].layout.margin = {"b": 25, "l": 25, "r": 25, "t": 50}
-pio.templates["custom"].layout.width = 600
-pio.templates["custom"].layout.height = 450
-pio.templates["custom"].layout.autosize = False
-pio.templates["custom"].layout.font.update(
-    {"family": "Arial", "size": 12, "color": "#707070"}
-)
-pio.templates["custom"].layout.title.update(
-    {
-        "xref": "container",
-        "yref": "container",
-        "x": 0.5,
-        "yanchor": "top",
-        "font_size": 16,
-        "y": 0.95,
-        "font_color": "#353535",
-    }
-)
-pio.templates["custom"].layout.xaxis.update(
-    {"showline": True, "linecolor": "lightgray", "title_font_size": 14}
-)
-pio.templates["custom"].layout.yaxis.update(
-    {"showline": True, "linecolor": "lightgray", "title_font_size": 14}
-)
-pio.templates["custom"].layout.colorway = [
-    "#1F77B4",
-    "#FF7F0E",
-    "#54A24B",
-    "#D62728",
-    "#C355FA",
-    "#8C564B",
-    "#E377C2",
-    "#7F7F7F",
-    "#FFE323",
-    "#17BECF",
-]
-pio.templates.default = "custom"
-
-@st.cache(allow_output_mutation=True)
-def get_sentiment_model():
-    tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
-    model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
-    return tokenizer,model
-
-tokenizer_sentiment,model_sentiment = get_sentiment_model()
-
-def get_tweets(query, max_tweets):
-    if query[0] == '@':
-        query = query[1:]
-        tweets_list = []
-
-        # Using TwitterSearchScraper to scrape data 
-        for i,tweet in enumerate(sntwitter.TwitterSearchScraper('from:'+query).get_items()):
-            if i>max_tweets:
-                break
-            tweets_list.append([tweet.date, tweet.user.username, tweet.content])
-
-        # Creating a dataframe from the tweets list above
-        tweets_df = pd.DataFrame(tweets_list, columns=['Datetime', 'Username', 'Tweet'])
-
-    else:
-        # Creating list to append tweet data to
-        tweets_list = []
-
-        # Using TwitterSearchScraper to scrape data and append tweets to list
-        for i,tweet in enumerate(sntwitter.TwitterSearchScraper(query+' until:').get_items()):
-            if i>max_tweets:
-                break
-            tweets_list.append([tweet.date, tweet.user.username, tweet.content])
-
-        # Creating a dataframe from the tweets list above
-        tweets_df = pd.DataFrame(tweets_list, columns=['Datetime', 'Username', 'Tweet'])
-
-    
-    tweets_df['Datetime'] = pd.to_datetime(tweets_df['Datetime'])
-    tweets_df['Date'] = tweets_df['Datetime'].dt.date
-    tweets_df['Time'] = tweets_df['Datetime'].dt.strftime('%H:%M') #tweets_df['Datetime'].dt.time
-    tweets_df.drop('Datetime', axis=1, inplace=True)
-    return tweets_df
-
-def get_youtube_comments(url, num_comments):
-    pattern = '"playabilityStatus":{"status":"ERROR","reason":"Video unavailable"'
-    def try_site(url):
-        request = requests.get(url)
-        return False if pattern in request.text else True
-    
-    video_exists = try_site(url)
-    if video_exists:
-        comment_list = []
-        downloader = YoutubeCommentDownloader()
-        comments = downloader.get_comments_from_url(url, sort_by=SORT_BY_POPULAR)
-        for comment in islice(comments, num_comments):
-            comment_list.append(comment['text'])
-        return comment_list
-    else:
-        raise Exception('Video does not exist')
-    
-def get_sentiment_youtube(useful_sentence):
-    tokenizer = tokenizer_sentiment
-    model = model_sentiment
-    pipe = pipeline(model="ProsusAI/finbert") 
-    classifier = pipeline(model="ProsusAI/finbert") 
-    output=[]
-    i=0
-    useful_sentence_len = len(useful_sentence)
-    for temp in useful_sentence:
-        output.extend(classifier(temp))
-        i=i+1
-    df = pd.DataFrame.from_dict(useful_sentence)
-    df_temp = pd.DataFrame.from_dict(output)
-    df = pd.concat([df, df_temp], axis=1)
-    df = df.rename(columns={'label': 'Sentiment'})
-    df = df.rename(columns={0: 'Comment'})
-    df['Sentiment'] = df['Sentiment'].replace('positive', 'Positive')
-    df['Sentiment'] = df['Sentiment'].replace('negative', 'Negative')
-    df['Sentiment'] = df['Sentiment'].replace('neutral', 'Neutral')
-    return df
-
-
-def text_preprocessing(text):
-    stopwords = set()
-    with open("static/en_stopwords.txt", "r") as file:
-        for word in file:
-            stopwords.add(word.rstrip("\n"))
-    lemmatizer = WordNetLemmatizer()
-    try:
-        url_pattern = r"((http://)[^ ]*|(https://)[^ ]*|(www\.)[^ ]*)"
-        user_pattern = r"@[^\s]+"
-        entity_pattern = r"&.*;"
-        neg_contraction = r"n't\W"
-        non_alpha = "[^a-z]"
-        cleaned_text = text.lower()
-        cleaned_text = re.sub(neg_contraction, " not ", cleaned_text)
-        cleaned_text = re.sub(url_pattern, " ", cleaned_text)
-        cleaned_text = re.sub(user_pattern, " ", cleaned_text)
-        cleaned_text = re.sub(entity_pattern, " ", cleaned_text)
-        cleaned_text = re.sub(non_alpha, " ", cleaned_text)
-        tokens = word_tokenize(cleaned_text)
-        # provide POS tag for lemmatization to yield better result
-        word_tag_tuples = pos_tag(tokens, tagset="universal")
-        tag_dict = {"NOUN": "n", "VERB": "v", "ADJ": "a", "ADV": "r"}
-        final_tokens = []
-        
-        
-        for word, tag in word_tag_tuples:
-            if len(word) > 1 and word not in stopwords:
-                if tag in tag_dict:
-                    final_tokens.append(lemmatizer.lemmatize(word, tag_dict[tag]))
-                else:
-                    final_tokens.append(lemmatizer.lemmatize(word))
-        return " ".join(final_tokens)
-    except:
-        return np.nan
-
-def get_sentiment(df):
-    useful_sentence = df['Tweet'].tolist()
-    tokenizer = tokenizer_sentiment
-    model = model_sentiment
-    pipe = pipeline(model="ProsusAI/finbert") 
-    classifier = pipeline(model="ProsusAI/finbert") 
-    output=[]
-    i=0
-    useful_sentence_len = len(useful_sentence)
-    for temp in useful_sentence:
-        output.extend(classifier(temp))
-        i=i+1
-
-    df_temp = pd.DataFrame.from_dict(output)
-    df = pd.concat([df, df_temp], axis=1)
-    df = df.rename(columns={'label': 'Sentiment'})
-    df['Sentiment'] = df['Sentiment'].replace('positive', 'Positive')
-    df['Sentiment'] = df['Sentiment'].replace('negative', 'Negative')
-    df['Sentiment'] = df['Sentiment'].replace('neutral', 'Neutral')
-    return df
-
-def plot_sentiment(tweet_df):
-    sentiment_count = tweet_df["Sentiment"].value_counts()
-    fig = px.pie(
-        values=sentiment_count.values,
-        names=sentiment_count.index,
-        hole=0.3,
-        title="<b>Sentiment Distribution</b>",
-        color=sentiment_count.index,
-        color_discrete_map={"Positive": "#54A24B", "Negative": "#FF7F0E", "Neutral": "#1F77B4"},
-    )
-    fig.update_traces(
-        textposition="inside",
-        texttemplate="%{label}<br>%{value} (%{percent})",
-        hovertemplate="<b>%{label}</b><br>Percentage=%{percent}<br>Count=%{value}",
-    )
-    fig.update_layout(showlegend=False)
-    return fig
-
-
-
-def get_top_n_gram(tweet_df, ngram_range, n=10):
-    try:
-        stopwords = set()
-        with open("static/en_stopwords_ngram.txt", "r") as file:
-            for word in file:
-                stopwords.add(word.rstrip("\n"))
-        stopwords = list(stopwords)
-        corpus = tweet_df["Tweet"]
-        vectorizer = CountVectorizer(
-            analyzer="word", ngram_range=ngram_range, stop_words=stopwords
-        )
-        X = vectorizer.fit_transform(corpus.astype(str).values)
-        words = vectorizer.get_feature_names_out()
-        words_count = np.ravel(X.sum(axis=0))
-        df = pd.DataFrame(zip(words, words_count))
-        df.columns = ["words", "counts"]
-        df = df.sort_values(by="counts", ascending=False).head(n)
-        df["words"] = df["words"].str.title()
-        return df
-    except:
-        pass
-
-def plot_n_gram(n_gram_df, title, color="#54A24B"):
-    try:
-        fig = px.bar(
-            # n_gram_df,
-            # x="counts",
-            # y="words",
-            x=n_gram_df.counts,
-            y=n_gram_df.words,
-            title="<b>{}</b>".format(title),
-            text_auto=True,
-        )
-        fig.update_layout(plot_bgcolor="white")
-        fig.update_xaxes(title=None)
-        fig.update_yaxes(autorange="reversed", title=None)
-        fig.update_traces(hovertemplate="<b>%{y}</b><br>Count=%{x}", marker_color=color)
-        return fig
-    except:
-        fig = go.Figure()
-        return fig
-
-def plot_wordcloud(tweet_df, colormap="Greens", mask_url="static/twitter_mask.png"):
-    try:
-        stopwords = set()
-        with open("static/en_stopwords_ngram.txt", "r") as file:
-            for word in file:
-                stopwords.add(word.rstrip("\n"))
-        cmap = mpl.cm.get_cmap(colormap)(np.linspace(0, 1, 20))
-        cmap = mpl.colors.ListedColormap(cmap[10:15])
-        mask = np.array(Image.open(mask_url))
-        font = "static/quartzo.ttf"
-        tweet_df["Cleaned_Tweet"] = tweet_df["Tweet"].apply(text_preprocessing)
-        text = " ".join(tweet_df["Cleaned_Tweet"])
-        wc = WordCloud(
-            background_color="white",
-            font_path=font,
-            stopwords=stopwords,
-            max_words=90,
-            colormap=cmap,
-            mask=mask,
-            random_state=42,
-            collocations=False,
-            min_word_length=2,
-            max_font_size=200,
-        )
-        wc.generate(text)
-        fig = plt.figure(figsize=(8, 8))
-        ax = fig.add_subplot(1, 1, 1)
-        plt.imshow(wc, interpolation="bilinear")
-        plt.axis("off")
-        plt.title("Wordcloud", fontdict={"fontsize": 16}, fontweight="heavy", pad=20, y=1.0)
-        return fig
-    except:
-        fig = go.Figure()
-        return fig
-
-
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk3cairo.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk3cairo.py
deleted file mode 100644
index 49215510c1783cb2f380934d87451035f492d1ef..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk3cairo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from contextlib import nullcontext
-
-from .backend_cairo import (  # noqa
-    FigureCanvasCairo, _RendererGTKCairo as RendererGTK3Cairo)
-from .backend_gtk3 import Gtk, FigureCanvasGTK3, _BackendGTK3
-
-
-class FigureCanvasGTK3Cairo(FigureCanvasCairo, FigureCanvasGTK3):
-    def on_draw_event(self, widget, ctx):
-        with (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
-              else nullcontext()):
-            self._renderer.set_context(ctx)
-            scale = self.device_pixel_ratio
-            # Scale physical drawing to logical size.
-            ctx.scale(1 / scale, 1 / scale)
-            allocation = self.get_allocation()
-            Gtk.render_background(
-                self.get_style_context(), ctx,
-                allocation.x, allocation.y,
-                allocation.width, allocation.height)
-            self._renderer.dpi = self.figure.dpi
-            self.figure.draw(self._renderer)
-
-
-@_BackendGTK3.export
-class _BackendGTK3Cairo(_BackendGTK3):
-    FigureCanvas = FigureCanvasGTK3Cairo
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py
deleted file mode 100644
index 835fba10dee4abefef52403cb6e412926e6ce8d0..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py
+++ /dev/null
@@ -1,414 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import PIL
-import torch
-from packaging import version
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, is_accelerate_available, logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class StableDiffusionImageVariationPipeline(DiffusionPipeline):
-    r"""
-    Pipeline to generate variations from an input image using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        image_encoder ([`CLIPVisionModelWithProjection`]):
-            Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
-            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    # TODO: feature_extractor is required to encode images (if they are in PIL format),
-    # we should give a descriptive message if the pipeline doesn't have one.
-    _optional_components = ["safety_checker"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        image_encoder: CLIPVisionModelWithProjection,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warn(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            image_encoder=image_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        for cpu_offloaded_model in [self.unet, self.image_encoder, self.vae, self.safety_checker]:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-    @property
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        image_embeddings = self.image_encoder(image).image_embeds
-        image_embeddings = image_embeddings.unsqueeze(1)
-
-        # duplicate image embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = image_embeddings.shape
-        image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
-        image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = torch.zeros_like(image_embeddings)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
-
-        return image_embeddings
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(self, image, height, width, callback_steps):
-        if (
-            not isinstance(image, torch.Tensor)
-            and not isinstance(image, PIL.Image.Image)
-            and not isinstance(image, list)
-        ):
-            raise ValueError(
-                "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
-                f" {type(image)}"
-            )
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
-                The image or images to guide the image generation. If you provide a tensor, it needs to comply with the
-                configuration of
-                [this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
-                `CLIPImageProcessor`
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(image, height, width, callback_steps)
-
-        # 2. Define call parameters
-        if isinstance(image, PIL.Image.Image):
-            batch_size = 1
-        elif isinstance(image, list):
-            batch_size = len(image)
-        else:
-            batch_size = image.shape[0]
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input image
-        image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            image_embeddings.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 8. Post-processing
-        image = self.decode_latents(latents)
-
-        # 9. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, device, image_embeddings.dtype)
-
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/decodemai/Stable-Diffusion-Ads/README.md b/spaces/decodemai/Stable-Diffusion-Ads/README.md
deleted file mode 100644
index 92366de81e67c14d0da7b1cbc52f80f28b5e3488..0000000000000000000000000000000000000000
--- a/spaces/decodemai/Stable-Diffusion-Ads/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stable Diffusion Ads
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: cc-by-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/deelerb/3dselfie/PIFu/lib/model/HGPIFuNet.py b/spaces/deelerb/3dselfie/PIFu/lib/model/HGPIFuNet.py
deleted file mode 100644
index 4771715345afcf326b3b0e64717517801fe75a1c..0000000000000000000000000000000000000000
--- a/spaces/deelerb/3dselfie/PIFu/lib/model/HGPIFuNet.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from .BasePIFuNet import BasePIFuNet
-from .SurfaceClassifier import SurfaceClassifier
-from .DepthNormalizer import DepthNormalizer
-from .HGFilters import *
-from ..net_util import init_net
-
-
-class HGPIFuNet(BasePIFuNet):
-    '''
-    HG PIFu network uses Hourglass stacks as the image filter.
-    It does the following:
-        1. Compute image feature stacks and store it in self.im_feat_list
-            self.im_feat_list[-1] is the last stack (output stack)
-        2. Calculate calibration
-        3. If training, it index on every intermediate stacks,
-            If testing, it index on the last stack.
-        4. Classification.
-        5. During training, error is calculated on all stacks.
-    '''
-
-    def __init__(self,
-                 opt,
-                 projection_mode='orthogonal',
-                 error_term=nn.MSELoss(),
-                 ):
-        super(HGPIFuNet, self).__init__(
-            projection_mode=projection_mode,
-            error_term=error_term)
-
-        self.name = 'hgpifu'
-
-        self.opt = opt
-        self.num_views = self.opt.num_views
-
-        self.image_filter = HGFilter(opt)
-
-        self.surface_classifier = SurfaceClassifier(
-            filter_channels=self.opt.mlp_dim,
-            num_views=self.opt.num_views,
-            no_residual=self.opt.no_residual,
-            last_op=nn.Sigmoid())
-
-        self.normalizer = DepthNormalizer(opt)
-
-        # This is a list of [B x Feat_i x H x W] features
-        self.im_feat_list = []
-        self.tmpx = None
-        self.normx = None
-
-        self.intermediate_preds_list = []
-
-        init_net(self)
-
-    def filter(self, images):
-        '''
-        Filter the input images
-        store all intermediate features.
-        :param images: [B, C, H, W] input images
-        '''
-        self.im_feat_list, self.tmpx, self.normx = self.image_filter(images)
-        # If it is not in training, only produce the last im_feat
-        if not self.training:
-            self.im_feat_list = [self.im_feat_list[-1]]
-
-    def query(self, points, calibs, transforms=None, labels=None):
-        '''
-        Given 3D points, query the network predictions for each point.
-        Image features should be pre-computed before this call.
-        store all intermediate features.
-        query() function may behave differently during training/testing.
-        :param points: [B, 3, N] world space coordinates of points
-        :param calibs: [B, 3, 4] calibration matrices for each image
-        :param transforms: Optional [B, 2, 3] image space coordinate transforms
-        :param labels: Optional [B, Res, N] gt labeling
-        :return: [B, Res, N] predictions for each point
-        '''
-        if labels is not None:
-            self.labels = labels
-
-        xyz = self.projection(points, calibs, transforms)
-        xy = xyz[:, :2, :]
-        z = xyz[:, 2:3, :]
-
-        in_img = (xy[:, 0] >= -1.0) & (xy[:, 0] <= 1.0) & (xy[:, 1] >= -1.0) & (xy[:, 1] <= 1.0)
-
-        z_feat = self.normalizer(z, calibs=calibs)
-
-        if self.opt.skip_hourglass:
-            tmpx_local_feature = self.index(self.tmpx, xy)
-
-        self.intermediate_preds_list = []
-
-        for im_feat in self.im_feat_list:
-            # [B, Feat_i + z, N]
-            point_local_feat_list = [self.index(im_feat, xy), z_feat]
-
-            if self.opt.skip_hourglass:
-                point_local_feat_list.append(tmpx_local_feature)
-
-            point_local_feat = torch.cat(point_local_feat_list, 1)
-
-            # out of image plane is always set to 0
-            pred = in_img[:,None].float() * self.surface_classifier(point_local_feat)
-            self.intermediate_preds_list.append(pred)
-
-        self.preds = self.intermediate_preds_list[-1]
-
-    def get_im_feat(self):
-        '''
-        Get the image filter
-        :return: [B, C_feat, H, W] image feature after filtering
-        '''
-        return self.im_feat_list[-1]
-
-    def get_error(self):
-        '''
-        Hourglass has its own intermediate supervision scheme
-        '''
-        error = 0
-        for preds in self.intermediate_preds_list:
-            error += self.error_term(preds, self.labels)
-        error /= len(self.intermediate_preds_list)
-        
-        return error
-
-    def forward(self, images, points, calibs, transforms=None, labels=None):
-        # Get image feature
-        self.filter(images)
-
-        # Phase 2: point query
-        self.query(points=points, calibs=calibs, transforms=transforms, labels=labels)
-
-        # get the prediction
-        res = self.get_preds()
-        
-        # get the error
-        error = self.get_error()
-
-        return res, error
\ No newline at end of file
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/roles/test_project_manager.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/roles/test_project_manager.py
deleted file mode 100644
index ebda5901da4163429f0f446e6b00d37571e34c49..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/roles/test_project_manager.py
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/12 10:23
-@Author  : alexanderwu
-@File    : test_project_manager.py
-"""
-import pytest
-
-from metagpt.logs import logger
-from metagpt.roles import ProjectManager
-from tests.metagpt.roles.mock import MockMessages
-
-
-@pytest.mark.asyncio
-async def test_project_manager():
-    project_manager = ProjectManager()
-    rsp = await project_manager.handle(MockMessages.system_design)
-    logger.info(rsp)
diff --git a/spaces/diacanFperku/AutoGPT/Arijit-Singh-Medley-Mp3-Download-REPACK.md b/spaces/diacanFperku/AutoGPT/Arijit-Singh-Medley-Mp3-Download-REPACK.md
deleted file mode 100644
index 2857dcf6e94b1cf381322ecf89aff9a2200dfe66..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Arijit-Singh-Medley-Mp3-Download-REPACK.md
+++ /dev/null
@@ -1,117 +0,0 @@
-## Arijit Singh Medley Mp3 Download
-
-
-
- 
-
- ![Arijit Singh Medley Mp3 Download REPACK](https://3.bp.blogspot.com/-RzqxLyhtpKo/VT9FCvPdsXI/AAAAAAAAA5s/5jUhqCo_zXY/s1600/Arijit%2BSingh%2BMashup%2B-%2BDJ%2BParoma.jpg)
-
- 
-
- 
-
-**Download --->>> [https://urlca.com/2tyxq8](https://urlca.com/2tyxq8)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- Here is a possible title and article for the keyword "Arijit Singh Medley Mp3 Download":  
-
-# How to Download Arijit Singh Medley Mp3 Songs for Free
-
- 
-
-Arijit Singh is one of the most popular and versatile singers in Bollywood. He has sung many hit songs in various genres, such as romantic, sad, party, and soulful. His voice has a magical quality that can touch your heart and make you feel the emotions of the song.
-
- 
-
-If you are a fan of Arijit Singh and want to listen to his medley songs, you might be wondering how to download them for free. Medley songs are a combination of different songs that are blended together in a seamless way. They are a great way to enjoy the best of Arijit Singh's songs in one go.
-
- 
-
-In this article, we will show you how to download Arijit Singh medley mp3 songs for free from some of the best online sources. You can also stream them online if you have a good internet connection.
-
-  
-
-## Download Arijit Singh Medley Mp3 Songs from Gaana.com
-
- 
-
-Gaana.com is one of the most popular music streaming and downloading platforms in India. It has a huge collection of songs in various languages and genres. You can find many Arijit Singh medley mp3 songs on Gaana.com, such as:
-
- 
-
-- Arijit Singh Mashup 2020 by DJ Shadow Dubai
-
-- Arijit Singh Love Songs by DJ Paroma
-
-- Arijit Singh Romantic Mashup by DJ Chetas
-
-- Arijit Singh Unplugged Mashup by Various Artists
-
-- Arijit Singh Sad Songs by DJ Rink
-
-
-
-To download Arijit Singh medley mp3 songs from Gaana.com, you need to follow these steps:
-
-
-
-1. Go to [https://gaana.com/artist/arijit-singh](https://gaana.com/artist/arijit-singh) and search for the medley song you want to download.
-
-2. Click on the download icon next to the song title. You will be asked to sign in or sign up for a Gaana account.
-
-3. If you have a Gaana account, sign in with your email or phone number and password. If you don't have a Gaana account, sign up with your email or phone number and create a password.
-
-4. After signing in or signing up, you will be asked to choose a subscription plan. You can choose from Gaana Plus, Gaana Student, or Gaana Free.
-
-5. If you choose Gaana Plus or Gaana Student, you will get unlimited downloads and ad-free streaming for a monthly or yearly fee. You can pay with your credit card, debit card, net banking, UPI, or wallet.
-
-6. If you choose Gaana Free, you will get limited downloads and ad-supported streaming. You can earn coins by listening to songs, inviting friends, or completing tasks. You can use these coins to download songs.
-
-7. After choosing your subscription plan, you can download the Arijit Singh medley mp3 song to your device. You can find it in your downloads section on the app or website.
-
-
-
-## Download Arijit Singh Medley Mp3 Songs from JioSaavn
-
- 
-
-JioSaavn is another popular music streaming and downloading platform in India. It has a large library of songs in various languages and genres. You can find many Arijit Singh medley mp3 songs on JioSaavn, such as:
-
-
-
-- Arijit Singh Mashup 2019 by DJ Yogii
-
-- Arijit Singh Love Mashup by DJ Kiran Kamath
-
-- Arijit Singh Soulful Performance by Various Artists
-
-- Arijit Singh Romantic Medley by Various Artists
-
-- Arijit Singh Sad Medley by Various Artists
-
-
-
-To download Arijit Singh medley mp3 songs from JioSaavn, you need to follow these steps:
-
-
-
-1. Go to [https://www.jiosa dfd1c89656
-
-
-
-
-
-
-
-
-
-](https://www.jiosaavn.com/artist/arijit-singh-songs/LlRWpHzy3Hk_)
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Indian Railways Simulator Game On Auran Trainz.md b/spaces/diacanFperku/AutoGPT/Indian Railways Simulator Game On Auran Trainz.md
deleted file mode 100644
index 938a5d10f57b81e33fd88850a0fa20469a84c9a0..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Indian Railways Simulator Game On Auran Trainz.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Indian Railways Simulator Game On Auran Trainz</h2><br /><p><b><b>Download</b> &#9913;&#9913;&#9913; <a href="https://gohhs.com/2uFUKl">https://gohhs.com/2uFUKl</a></b></p><br /><br />
-
- 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diffle/sd-1.5/sd-1-5.py b/spaces/diffle/sd-1.5/sd-1-5.py
deleted file mode 100644
index 682927b1dfeb58a81e58cddfd263f07ca5415d85..0000000000000000000000000000000000000000
--- a/spaces/diffle/sd-1.5/sd-1-5.py
+++ /dev/null
@@ -1,17 +0,0 @@
-#import library
-import gradio as gr
-
-#title of app
-title = "🐶 Stable Diffusion 1.5"
-
-#description of app
-description = "This space is for generating images from text with the Stable Diffusion 1.5 model!"
-
-#article of app
-article = """
-<p style='text-align: center'>
-    <a href='https://hf.co/CofAI' target='_blank'>CofAI Group</a>
-</p>
-"""
-
-gr.Interface.load("models/runwayml/stable-diffusion-v1-5", title=title, description=description, article=article, theme = "Soft").launch()
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Kino-Bert-VITS2/preprocess_text.py b/spaces/digitalxingtong/Kino-Bert-VITS2/preprocess_text.py
deleted file mode 100644
index 44c35fecd9b7f21016e80e9597d6055254cba3f7..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Kino-Bert-VITS2/preprocess_text.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import json
-from random import shuffle
-
-import tqdm
-from text.cleaner import clean_text
-from collections import defaultdict
-import shutil
-stage = [1,2,3]
-
-transcription_path = 'filelists/short_character_anno.list'
-train_path = 'filelists/train.list'
-val_path = 'filelists/val.list'
-config_path = "configs/config.json"
-val_per_spk = 4
-max_val_total = 8
-
-if 1 in stage:
-    with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f:
-        for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()):
-            try:
-                utt, spk, language, text = line.strip().split('|')
-                #language = "ZH"
-                norm_text, phones, tones, word2ph = clean_text(text, language)
-                f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones),
-                                                     " ".join([str(i) for i in tones]),
-                                                     " ".join([str(i) for i in word2ph])))
-            except:
-                print("err!", utt)
-
-if 2 in stage:
-    spk_utt_map = defaultdict(list)
-    spk_id_map = {}
-    current_sid = 0
-
-    with open( transcription_path+'.cleaned', encoding='utf-8') as f:
-        for line in f.readlines():
-            utt, spk, language, text, phones, tones, word2ph = line.strip().split('|')
-            spk_utt_map[spk].append(line)
-            if spk not in spk_id_map.keys():
-                spk_id_map[spk] = current_sid
-                current_sid += 1
-    train_list = []
-    val_list = []
-    for spk, utts in spk_utt_map.items():
-          shuffle(utts)
-          val_list+=utts[:val_per_spk]
-          train_list+=utts[val_per_spk:]
-    if len(val_list) > max_val_total:
-          train_list+=val_list[max_val_total:]
-          val_list = val_list[:max_val_total]
-     
-    with open( train_path,"w", encoding='utf-8') as f:
-         for line in train_list:
-            f.write(line)
-
-    file_path = transcription_path+'.cleaned'
-    shutil.copy(file_path,'./filelists/train.list')
-    
-    with open(val_path, "w", encoding='utf-8') as f:
-        for line in val_list:
-            f.write(line)
-
-if 3 in stage:
-    assert 2 in stage
-    config = json.load(open(config_path))
-    config['data']["n_speakers"] = current_sid #
-    config["data"]['spk2id'] = spk_id_map
-    with open(config_path, 'w', encoding='utf-8') as f:
-        json.dump(config, f, indent=2, ensure_ascii=False)
diff --git a/spaces/digitalxingtong/Taffy-Bert-VITS2/README_zh.md b/spaces/digitalxingtong/Taffy-Bert-VITS2/README_zh.md
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Taffy-Bert-VITS2/README_zh.md
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/rpn_head.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/rpn_head.py
deleted file mode 100644
index a888cb8c188ca6fe63045b6230266553fbe8c996..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/rpn_head.py
+++ /dev/null
@@ -1,236 +0,0 @@
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv import ConfigDict
-from mmcv.cnn import normal_init
-from mmcv.ops import batched_nms
-
-from ..builder import HEADS
-from .anchor_head import AnchorHead
-from .rpn_test_mixin import RPNTestMixin
-
-
-@HEADS.register_module()
-class RPNHead(RPNTestMixin, AnchorHead):
-    """RPN head.
-
-    Args:
-        in_channels (int): Number of channels in the input feature map.
-    """  # noqa: W605
-
-    def __init__(self, in_channels, **kwargs):
-        super(RPNHead, self).__init__(1, in_channels, **kwargs)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.rpn_conv = nn.Conv2d(
-            self.in_channels, self.feat_channels, 3, padding=1)
-        self.rpn_cls = nn.Conv2d(self.feat_channels,
-                                 self.num_anchors * self.cls_out_channels, 1)
-        self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        normal_init(self.rpn_conv, std=0.01)
-        normal_init(self.rpn_cls, std=0.01)
-        normal_init(self.rpn_reg, std=0.01)
-
-    def forward_single(self, x):
-        """Forward feature map of a single scale level."""
-        x = self.rpn_conv(x)
-        x = F.relu(x, inplace=True)
-        rpn_cls_score = self.rpn_cls(x)
-        rpn_bbox_pred = self.rpn_reg(x)
-        return rpn_cls_score, rpn_bbox_pred
-
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        losses = super(RPNHead, self).loss(
-            cls_scores,
-            bbox_preds,
-            gt_bboxes,
-            None,
-            img_metas,
-            gt_bboxes_ignore=gt_bboxes_ignore)
-        return dict(
-            loss_rpn_cls=losses['loss_cls'], loss_rpn_bbox=losses['loss_bbox'])
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False):
-        """Transform outputs for a single batch item into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W).
-            mlvl_anchors (list[Tensor]): Box reference for each scale level
-                with shape (num_total_anchors, 4).
-            img_shapes (list[tuple[int]]): Shape of the input image,
-                (height, width, 3).
-            scale_factors (list[ndarray]): Scale factor of the image arange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where the first 4 columns
-                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
-                5-th column is a score between 0 and 1. The second item is a
-                (n,) tensor where each item is the predicted class labelof the
-                corresponding box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        cfg = copy.deepcopy(cfg)
-        # bboxes from different level should be independent during NMS,
-        # level_ids are used as labels for batched NMS to separate them
-        level_ids = []
-        mlvl_scores = []
-        mlvl_bbox_preds = []
-        mlvl_valid_anchors = []
-        batch_size = cls_scores[0].shape[0]
-        nms_pre_tensor = torch.tensor(
-            cfg.nms_pre, device=cls_scores[0].device, dtype=torch.long)
-        for idx in range(len(cls_scores)):
-            rpn_cls_score = cls_scores[idx]
-            rpn_bbox_pred = bbox_preds[idx]
-            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
-            rpn_cls_score = rpn_cls_score.permute(0, 2, 3, 1)
-            if self.use_sigmoid_cls:
-                rpn_cls_score = rpn_cls_score.reshape(batch_size, -1)
-                scores = rpn_cls_score.sigmoid()
-            else:
-                rpn_cls_score = rpn_cls_score.reshape(batch_size, -1, 2)
-                # We set FG labels to [0, num_class-1] and BG label to
-                # num_class in RPN head since mmdet v2.5, which is unified to
-                # be consistent with other head since mmdet v2.0. In mmdet v2.0
-                # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
-                scores = rpn_cls_score.softmax(-1)[..., 0]
-            rpn_bbox_pred = rpn_bbox_pred.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, 4)
-            anchors = mlvl_anchors[idx]
-            anchors = anchors.expand_as(rpn_bbox_pred)
-            if nms_pre_tensor > 0:
-                # sort is faster than topk
-                # _, topk_inds = scores.topk(cfg.nms_pre)
-                # keep topk op for dynamic k in onnx model
-                if torch.onnx.is_in_onnx_export():
-                    # sort op will be converted to TopK in onnx
-                    # and k<=3480 in TensorRT
-                    scores_shape = torch._shape_as_tensor(scores)
-                    nms_pre = torch.where(scores_shape[1] < nms_pre_tensor,
-                                          scores_shape[1], nms_pre_tensor)
-                    _, topk_inds = scores.topk(nms_pre)
-                    batch_inds = torch.arange(batch_size).view(
-                        -1, 1).expand_as(topk_inds)
-                    scores = scores[batch_inds, topk_inds]
-                    rpn_bbox_pred = rpn_bbox_pred[batch_inds, topk_inds, :]
-                    anchors = anchors[batch_inds, topk_inds, :]
-
-                elif scores.shape[-1] > cfg.nms_pre:
-                    ranked_scores, rank_inds = scores.sort(descending=True)
-                    topk_inds = rank_inds[:, :cfg.nms_pre]
-                    scores = ranked_scores[:, :cfg.nms_pre]
-                    batch_inds = torch.arange(batch_size).view(
-                        -1, 1).expand_as(topk_inds)
-                    rpn_bbox_pred = rpn_bbox_pred[batch_inds, topk_inds, :]
-                    anchors = anchors[batch_inds, topk_inds, :]
-
-            mlvl_scores.append(scores)
-            mlvl_bbox_preds.append(rpn_bbox_pred)
-            mlvl_valid_anchors.append(anchors)
-            level_ids.append(
-                scores.new_full((
-                    batch_size,
-                    scores.size(1),
-                ),
-                                idx,
-                                dtype=torch.long))
-
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        batch_mlvl_anchors = torch.cat(mlvl_valid_anchors, dim=1)
-        batch_mlvl_rpn_bbox_pred = torch.cat(mlvl_bbox_preds, dim=1)
-        batch_mlvl_proposals = self.bbox_coder.decode(
-            batch_mlvl_anchors, batch_mlvl_rpn_bbox_pred, max_shape=img_shapes)
-        batch_mlvl_ids = torch.cat(level_ids, dim=1)
-
-        # deprecate arguments warning
-        if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
-            warnings.warn(
-                'In rpn_proposal or test_cfg, '
-                'nms_thr has been moved to a dict named nms as '
-                'iou_threshold, max_num has been renamed as max_per_img, '
-                'name of original arguments and the way to specify '
-                'iou_threshold of NMS will be deprecated.')
-        if 'nms' not in cfg:
-            cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
-        if 'max_num' in cfg:
-            if 'max_per_img' in cfg:
-                assert cfg.max_num == cfg.max_per_img, f'You ' \
-                    f'set max_num and ' \
-                    f'max_per_img at the same time, but get {cfg.max_num} ' \
-                    f'and {cfg.max_per_img} respectively' \
-                    'Please delete max_num which will be deprecated.'
-            else:
-                cfg.max_per_img = cfg.max_num
-        if 'nms_thr' in cfg:
-            assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \
-                f' iou_threshold in nms and ' \
-                f'nms_thr at the same time, but get' \
-                f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \
-                f' respectively. Please delete the nms_thr ' \
-                f'which will be deprecated.'
-
-        result_list = []
-        for (mlvl_proposals, mlvl_scores,
-             mlvl_ids) in zip(batch_mlvl_proposals, batch_mlvl_scores,
-                              batch_mlvl_ids):
-            # Skip nonzero op while exporting to ONNX
-            if cfg.min_bbox_size > 0 and (not torch.onnx.is_in_onnx_export()):
-                w = mlvl_proposals[:, 2] - mlvl_proposals[:, 0]
-                h = mlvl_proposals[:, 3] - mlvl_proposals[:, 1]
-                valid_ind = torch.nonzero(
-                    (w >= cfg.min_bbox_size)
-                    & (h >= cfg.min_bbox_size),
-                    as_tuple=False).squeeze()
-                if valid_ind.sum().item() != len(mlvl_proposals):
-                    mlvl_proposals = mlvl_proposals[valid_ind, :]
-                    mlvl_scores = mlvl_scores[valid_ind]
-                    mlvl_ids = mlvl_ids[valid_ind]
-
-            dets, keep = batched_nms(mlvl_proposals, mlvl_scores, mlvl_ids,
-                                     cfg.nms)
-            result_list.append(dets[:cfg.max_per_img])
-        return result_list
diff --git a/spaces/dongsiqie/Image-to-Line-Drawings/README.md b/spaces/dongsiqie/Image-to-Line-Drawings/README.md
deleted file mode 100644
index 0dd5d016d83edf55629198f3a28be66076595b34..0000000000000000000000000000000000000000
--- a/spaces/dongsiqie/Image-to-Line-Drawings/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ✏️Image2Drawing
-emoji: ✏️
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 2.9.3
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/editing-images/project/static/js/fontawesome.all.min.js b/spaces/editing-images/project/static/js/fontawesome.all.min.js
deleted file mode 100644
index 9ee22fdb7753983bae3986b2436bdd167730cd5b..0000000000000000000000000000000000000000
--- a/spaces/editing-images/project/static/js/fontawesome.all.min.js
+++ /dev/null
@@ -1,5 +0,0 @@
-/*!
- * Font Awesome Free 5.15.1 by @fontawesome - https://fontawesome.com
- * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
- */
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diff --git a/spaces/emilyalsentzer/SHEPHERD/README.md b/spaces/emilyalsentzer/SHEPHERD/README.md
deleted file mode 100644
index 41fdd437c54926ae45667e1e2691abb2e758ab03..0000000000000000000000000000000000000000
--- a/spaces/emilyalsentzer/SHEPHERD/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SHEPHERD
-emoji: 🐨
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.8
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/erbanku/gpt-academic/toolbox.py b/spaces/erbanku/gpt-academic/toolbox.py
deleted file mode 100644
index 5e42164d2bd26f6d8fcba54cb8d8238cdc7c9285..0000000000000000000000000000000000000000
--- a/spaces/erbanku/gpt-academic/toolbox.py
+++ /dev/null
@@ -1,657 +0,0 @@
-import markdown
-import importlib
-import traceback
-import inspect
-import re
-from latex2mathml.converter import convert as tex2mathml
-from functools import wraps, lru_cache
-
-"""
-========================================================================
-第一部分
-函数插件输入输出接驳区
-    - ChatBotWithCookies:   带Cookies的Chatbot类，为实现更多强大的功能做基础
-    - ArgsGeneralWrapper:   装饰器函数，用于重组输入参数，改变输入参数的顺序与结构
-    - update_ui:            刷新界面用 yield from update_ui(chatbot, history)
-    - CatchException:       将插件中出的所有问题显示在界面上
-    - HotReload:            实现插件的热更新
-    - trimmed_format_exc:   打印traceback，为了安全而隐藏绝对地址
-========================================================================
-"""
-
-class ChatBotWithCookies(list):
-    def __init__(self, cookie):
-        self._cookies = cookie
-
-    def write_list(self, list):
-        for t in list:
-            self.append(t)
-
-    def get_list(self):
-        return [t for t in self]
-
-    def get_cookies(self):
-        return self._cookies
-
-
-def ArgsGeneralWrapper(f):
-    """
-    装饰器函数，用于重组输入参数，改变输入参数的顺序与结构。
-    """
-    def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg, *args):
-        txt_passon = txt
-        if txt == "" and txt2 != "": txt_passon = txt2
-        # 引入一个有cookie的chatbot
-        cookies.update({
-            'top_p':top_p,
-            'temperature':temperature,
-        })
-        llm_kwargs = {
-            'api_key': cookies['api_key'],
-            'llm_model': llm_model,
-            'top_p':top_p,
-            'max_length': max_length,
-            'temperature':temperature,
-        }
-        plugin_kwargs = {
-            "advanced_arg": plugin_advanced_arg,
-        }
-        chatbot_with_cookie = ChatBotWithCookies(cookies)
-        chatbot_with_cookie.write_list(chatbot)
-        yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
-    return decorated
-
-
-def update_ui(chatbot, history, msg='正常', **kwargs):  # 刷新界面
-    """
-    刷新用户界面
-    """
-    assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时，可用clear将其清空，然后用for+append循环重新赋值。"
-    yield chatbot.get_cookies(), chatbot, history, msg
-
-def trimmed_format_exc():
-    import os, traceback
-    str = traceback.format_exc()
-    current_path = os.getcwd()
-    replace_path = "."
-    return str.replace(current_path, replace_path)
-
-def CatchException(f):
-    """
-    装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
-    """
-
-    @wraps(f)
-    def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-        try:
-            yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
-        except Exception as e:
-            from check_proxy import check_proxy
-            from toolbox import get_conf
-            proxies, = get_conf('proxies')
-            tb_str = '```\n' + trimmed_format_exc() + '```'
-            if len(chatbot) == 0:
-                chatbot.clear()
-                chatbot.append(["插件调度异常", "异常原因"])
-            chatbot[-1] = (chatbot[-1][0],
-                           f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
-            yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
-    return decorated
-
-
-def HotReload(f):
-    """
-    HotReload的装饰器函数，用于实现Python函数插件的热更新。
-    函数热更新是指在不停止程序运行的情况下，更新函数代码，从而达到实时更新功能。
-    在装饰器内部，使用wraps(f)来保留函数的元信息，并定义了一个名为decorated的内部函数。
-    内部函数通过使用importlib模块的reload函数和inspect模块的getmodule函数来重新加载并获取函数模块，
-    然后通过getattr函数获取函数名，并在新模块中重新加载函数。
-    最后，使用yield from语句返回重新加载过的函数，并在被装饰的函数上执行。
-    最终，装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本，并执行函数的新版本。
-    """
-    @wraps(f)
-    def decorated(*args, **kwargs):
-        fn_name = f.__name__
-        f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
-        yield from f_hot_reload(*args, **kwargs)
-    return decorated
-
-
-"""
-========================================================================
-第二部分
-其他小工具:
-    - write_results_to_file:    将结果写入markdown文件中
-    - regular_txt_to_markdown:  将普通文本转换为Markdown格式的文本。
-    - report_execption:         向chatbot中添加简单的意外错误信息
-    - text_divide_paragraph:    将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
-    - markdown_convertion:      用多种方式组合，将markdown转化为好看的html
-    - format_io:                接管gradio默认的markdown处理方式
-    - on_file_uploaded:         处理文件的上传（自动解压）
-    - on_report_generated:      将生成的报告自动投射到文件上传区
-    - clip_history:             当历史上下文过长时，自动截断
-    - get_conf:                 获取设置
-    - select_api_key:           根据当前的模型类别，抽取可用的api-key
-========================================================================
-"""
-
-def get_reduce_token_percent(text):
-    """
-        * 此函数未来将被弃用
-    """
-    try:
-        # text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
-        pattern = r"(\d+)\s+tokens\b"
-        match = re.findall(pattern, text)
-        EXCEED_ALLO = 500  # 稍微留一点余地，否则在回复时会因余量太少出问题
-        max_limit = float(match[0]) - EXCEED_ALLO
-        current_tokens = float(match[1])
-        ratio = max_limit/current_tokens
-        assert ratio > 0 and ratio < 1
-        return ratio, str(int(current_tokens-max_limit))
-    except:
-        return 0.5, '不详'
-
-
-def write_results_to_file(history, file_name=None):
-    """
-    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    if file_name is None:
-        # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-        file_name = 'chatGPT分析报告' + \
-            time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-    os.makedirs('./gpt_log/', exist_ok=True)
-    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-        f.write('# chatGPT 分析报告\n')
-        for i, content in enumerate(history):
-            try:    # 这个bug没找到触发条件，暂时先这样顶一下
-                if type(content) != str:
-                    content = str(content)
-            except:
-                continue
-            if i % 2 == 0:
-                f.write('## ')
-            f.write(content)
-            f.write('\n\n')
-    res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
-    print(res)
-    return res
-
-
-def regular_txt_to_markdown(text):
-    """
-    将普通文本转换为Markdown格式的文本。
-    """
-    text = text.replace('\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    return text
-
-
-
-
-def report_execption(chatbot, history, a, b):
-    """
-    向chatbot中添加错误信息
-    """
-    chatbot.append((a, b))
-    history.append(a)
-    history.append(b)
-
-
-def text_divide_paragraph(text):
-    """
-    将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
-    """
-    if '```' in text:
-        # careful input
-        return text
-    else:
-        # wtf input
-        lines = text.split("\n")
-        for i, line in enumerate(lines):
-            lines[i] = lines[i].replace(" ", "&nbsp;")
-        text = "</br>".join(lines)
-        return text
-
-@lru_cache(maxsize=128) # 使用 lru缓存 加快转换速度
-def markdown_convertion(txt):
-    """
-    将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
-    """
-    pre = '<div class="markdown-body">'
-    suf = '</div>'
-    if txt.startswith(pre) and txt.endswith(suf):
-        # print('警告，输入了已经经过转化的字符串，二次转化可能出问题')
-        return txt # 已经被转化过，不需要再次转化
-    
-    markdown_extension_configs = {
-        'mdx_math': {
-            'enable_dollar_delimiter': True,
-            'use_gitlab_delimiters': False,
-        },
-    }
-    find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
-
-    def tex2mathml_catch_exception(content, *args, **kwargs):
-        try:
-            content = tex2mathml(content, *args, **kwargs)
-        except:
-            content = content
-        return content
-
-    def replace_math_no_render(match):
-        content = match.group(1)
-        if 'mode=display' in match.group(0):
-            content = content.replace('\n', '</br>')
-            return f"<font color=\"#00FF00\">$$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$$</font>"
-        else:
-            return f"<font color=\"#00FF00\">$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$</font>"
-
-    def replace_math_render(match):
-        content = match.group(1)
-        if 'mode=display' in match.group(0):
-            if '\\begin{aligned}' in content:
-                content = content.replace('\\begin{aligned}', '\\begin{array}')
-                content = content.replace('\\end{aligned}', '\\end{array}')
-                content = content.replace('&', ' ')
-            content = tex2mathml_catch_exception(content, display="block")
-            return content
-        else:
-            return tex2mathml_catch_exception(content)
-
-    def markdown_bug_hunt(content):
-        """
-        解决一个mdx_math的bug（单$包裹begin命令时多余<script>）
-        """
-        content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
-        content = content.replace('</script>\n</script>', '</script>')
-        return content
-
-    def no_code(txt):
-        if '```' not in txt: 
-            return True
-        else:
-            if '```reference' in txt: return True    # newbing
-            else: return False
-
-    if ('$' in txt) and no_code(txt):  # 有$标识的公式符号，且没有代码段```的标识
-        # convert everything to html format
-        split = markdown.markdown(text='---')
-        convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
-        convert_stage_1 = markdown_bug_hunt(convert_stage_1)
-        # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
-        # 1. convert to easy-to-copy tex (do not render math)
-        convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
-        # 2. convert to rendered equation
-        convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
-        # cat them together
-        return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
-    else:
-        return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
-
-
-def close_up_code_segment_during_stream(gpt_reply):
-    """
-    在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
-
-    Args:
-        gpt_reply (str): GPT模型返回的回复字符串。
-
-    Returns:
-        str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。
-
-    """
-    if '```' not in gpt_reply:
-        return gpt_reply
-    if gpt_reply.endswith('```'):
-        return gpt_reply
-
-    # 排除了以上两个情况，我们
-    segments = gpt_reply.split('```')
-    n_mark = len(segments) - 1
-    if n_mark % 2 == 1:
-        # print('输出代码片段中！')
-        return gpt_reply+'\n```'
-    else:
-        return gpt_reply
-
-
-def format_io(self, y):
-    """
-    将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
-    """
-    if y is None or y == []:
-        return []
-    i_ask, gpt_reply = y[-1]
-    i_ask = text_divide_paragraph(i_ask)  # 输入部分太自由，预处理一波
-    gpt_reply = close_up_code_segment_during_stream(gpt_reply)  # 当代码输出半截的时候，试着补上后个```
-    y[-1] = (
-        None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code', 'tables']),
-        None if gpt_reply is None else markdown_convertion(gpt_reply)
-    )
-    return y
-
-
-def find_free_port():
-    """
-    返回当前系统中可用的未使用端口。
-    """
-    import socket
-    from contextlib import closing
-    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
-        s.bind(('', 0))
-        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-        return s.getsockname()[1]
-
-
-def extract_archive(file_path, dest_dir):
-    import zipfile
-    import tarfile
-    import os
-    # Get the file extension of the input file
-    file_extension = os.path.splitext(file_path)[1]
-
-    # Extract the archive based on its extension
-    if file_extension == '.zip':
-        with zipfile.ZipFile(file_path, 'r') as zipobj:
-            zipobj.extractall(path=dest_dir)
-            print("Successfully extracted zip archive to {}".format(dest_dir))
-
-    elif file_extension in ['.tar', '.gz', '.bz2']:
-        with tarfile.open(file_path, 'r:*') as tarobj:
-            tarobj.extractall(path=dest_dir)
-            print("Successfully extracted tar archive to {}".format(dest_dir))
-
-    # 第三方库，需要预先pip install rarfile
-    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
-    elif file_extension == '.rar':
-        try:
-            import rarfile
-            with rarfile.RarFile(file_path) as rf:
-                rf.extractall(path=dest_dir)
-                print("Successfully extracted rar archive to {}".format(dest_dir))
-        except:
-            print("Rar format requires additional dependencies to install")
-            return '\n\n需要安装pip install rarfile来解压rar文件'
-
-    # 第三方库，需要预先pip install py7zr
-    elif file_extension == '.7z':
-        try:
-            import py7zr
-            with py7zr.SevenZipFile(file_path, mode='r') as f:
-                f.extractall(path=dest_dir)
-                print("Successfully extracted 7z archive to {}".format(dest_dir))
-        except:
-            print("7z format requires additional dependencies to install")
-            return '\n\n需要安装pip install py7zr来解压7z文件'
-    else:
-        return ''
-    return ''
-
-
-def find_recent_files(directory):
-    """
-        me: find files that is created with in one minutes under a directory with python, write a function
-        gpt: here it is!
-    """
-    import os
-    import time
-    current_time = time.time()
-    one_minute_ago = current_time - 60
-    recent_files = []
-
-    for filename in os.listdir(directory):
-        file_path = os.path.join(directory, filename)
-        if file_path.endswith('.log'):
-            continue
-        created_time = os.path.getmtime(file_path)
-        if created_time >= one_minute_ago:
-            if os.path.isdir(file_path):
-                continue
-            recent_files.append(file_path)
-
-    return recent_files
-
-
-def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
-    """
-    当文件被上传时的回调函数
-    """
-    if len(files) == 0:
-        return chatbot, txt
-    import shutil
-    import os
-    import time
-    import glob
-    from toolbox import extract_archive
-    try:
-        shutil.rmtree('./private_upload/')
-    except:
-        pass
-    time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
-    err_msg = ''
-    for file in files:
-        file_origin_name = os.path.basename(file.orig_name)
-        shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
-        err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
-                                   dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
-    moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
-    if "底部输入区" in checkboxes:
-        txt = ""
-        txt2 = f'private_upload/{time_tag}'
-    else:
-        txt = f'private_upload/{time_tag}'
-        txt2 = ""
-    moved_files_str = '\t\n\n'.join(moved_files)
-    chatbot.append(['我上传了文件，请查收',
-                    f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
-                    f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
-                    f'\n\n现在您点击任意“红颜色”标识的函数插件时，以上文件将被作为输入参数'+err_msg])
-    return chatbot, txt, txt2
-
-
-def on_report_generated(files, chatbot):
-    from toolbox import find_recent_files
-    report_files = find_recent_files('gpt_log')
-    if len(report_files) == 0:
-        return None, chatbot
-    # files.extend(report_files)
-    chatbot.append(['汇总报告如何远程获取？', '汇总报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。'])
-    return report_files, chatbot
-
-def is_openai_api_key(key):
-    API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
-    API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
-    return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)
-
-def is_api2d_key(key):
-    if key.startswith('fk') and len(key) == 41:
-        return True
-    else:
-        return False
-
-def is_any_api_key(key):
-    if ',' in key:
-        keys = key.split(',')
-        for k in keys:
-            if is_any_api_key(k): return True
-        return False
-    else:
-        return is_openai_api_key(key) or is_api2d_key(key)
-
-def what_keys(keys):
-    avail_key_list = {'OpenAI Key':0, "API2D Key":0}
-    key_list = keys.split(',')
-
-    for k in key_list:
-        if is_openai_api_key(k): 
-            avail_key_list['OpenAI Key'] += 1
-
-    for k in key_list:
-        if is_api2d_key(k): 
-            avail_key_list['API2D Key'] += 1
-
-    return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个，API2D Key {avail_key_list['API2D Key']} 个"
-
-def select_api_key(keys, llm_model):
-    import random
-    avail_key_list = []
-    key_list = keys.split(',')
-
-    if llm_model.startswith('gpt-'):
-        for k in key_list:
-            if is_openai_api_key(k): avail_key_list.append(k)
-
-    if llm_model.startswith('api2d-'):
-        for k in key_list:
-            if is_api2d_key(k): avail_key_list.append(k)
-
-    if len(avail_key_list) == 0:
-        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。")
-
-    api_key = random.choice(avail_key_list) # 随机负载均衡
-    return api_key
-
-@lru_cache(maxsize=128)
-def read_single_conf_with_lru_cache(arg):
-    from colorful import print亮红, print亮绿, print亮蓝
-    try:
-        r = getattr(importlib.import_module('config_private'), arg)
-    except:
-        r = getattr(importlib.import_module('config'), arg)
-    # 在读取API_KEY时，检查一下是不是忘了改config
-    if arg == 'API_KEY':
-        print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,api2d-key3\"")
-        print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
-        if is_any_api_key(r):
-            print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
-        else:
-            print亮红( "[API_KEY] 正确的 API_KEY 是'sk'开头的51位密钥（OpenAI），或者 'fk'开头的41位密钥，请在config文件中修改API密钥之后再运行。")
-    if arg == 'proxies':
-        if r is None:
-            print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
-        else:
-            print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
-            assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
-    return r
-
-
-def get_conf(*args):
-    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
-    res = []
-    for arg in args:
-        r = read_single_conf_with_lru_cache(arg)
-        res.append(r)
-    return res
-
-
-def clear_line_break(txt):
-    txt = txt.replace('\n', ' ')
-    txt = txt.replace('  ', ' ')
-    txt = txt.replace('  ', ' ')
-    return txt
-
-
-class DummyWith():
-    """
-    这段代码定义了一个名为DummyWith的空上下文管理器，
-    它的作用是……额……就是不起作用，即在代码结构不变得情况下取代其他的上下文管理器。
-    上下文管理器是一种Python对象，用于与with语句一起使用，
-    以确保一些资源在代码块执行期间得到正确的初始化和清理。
-    上下文管理器必须实现两个方法，分别为 __enter__()和 __exit__()。
-    在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，
-    而在上下文执行结束时，__exit__()方法则会被调用。
-    """
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        return
-
-def run_gradio_in_subpath(demo, auth, port, custom_path):
-    """
-    把gradio的运行地址更改到指定的二次路径上
-    """
-    def is_path_legal(path: str)->bool:
-        '''
-        check path for sub url
-        path: path to check
-        return value: do sub url wrap
-        '''
-        if path == "/": return True
-        if len(path) == 0:
-            print("ilegal custom path: {}\npath must not be empty\ndeploy on root url".format(path))
-            return False
-        if path[0] == '/':
-            if path[1] != '/':
-                print("deploy on sub-path {}".format(path))
-                return True
-            return False
-        print("ilegal custom path: {}\npath should begin with \'/\'\ndeploy on root url".format(path))
-        return False
-
-    if not is_path_legal(custom_path): raise RuntimeError('Ilegal custom path')
-    import uvicorn
-    import gradio as gr
-    from fastapi import FastAPI
-    app = FastAPI()
-    if custom_path != "/":
-        @app.get("/")
-        def read_main(): 
-            return {"message": f"Gradio is running at: {custom_path}"}
-    app = gr.mount_gradio_app(app, demo, path=custom_path)
-    uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth
-
-
-def clip_history(inputs, history, tokenizer, max_token_limit):
-    """
-    reduce the length of history by clipping.
-    this function search for the longest entries to clip, little by little,
-    until the number of token of history is reduced under threshold.
-    通过裁剪来缩短历史记录的长度。 
-    此函数逐渐地搜索最长的条目进行剪辑，
-    直到历史记录的标记数量降低到阈值以下。
-    """
-    import numpy as np
-    from request_llm.bridge_all import model_info
-    def get_token_num(txt): 
-        return len(tokenizer.encode(txt, disallowed_special=()))
-    input_token_num = get_token_num(inputs)
-    if input_token_num < max_token_limit * 3 / 4:
-        # 当输入部分的token占比小于限制的3/4时，裁剪时
-        # 1. 把input的余量留出来
-        max_token_limit = max_token_limit - input_token_num
-        # 2. 把输出用的余量留出来
-        max_token_limit = max_token_limit - 128
-        # 3. 如果余量太小了，直接清除历史
-        if max_token_limit < 128:
-            history = []
-            return history
-    else:
-        # 当输入部分的token占比 > 限制的3/4时，直接清除历史
-        history = []
-        return history
-
-    everything = ['']
-    everything.extend(history)
-    n_token = get_token_num('\n'.join(everything))
-    everything_token = [get_token_num(e) for e in everything]
-
-    # 截断时的颗粒度
-    delta = max(everything_token) // 16
-
-    while n_token > max_token_limit:
-        where = np.argmax(everything_token)
-        encoded = tokenizer.encode(everything[where], disallowed_special=())
-        clipped_encoded = encoded[:len(encoded)-delta]
-        everything[where] = tokenizer.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char
-        everything_token[where] = get_token_num(everything[where])
-        n_token = get_token_num('\n'.join(everything))
-
-    history = everything[1:]
-    return history
diff --git a/spaces/eson/tokenizer-arena/vocab/albert/__init__.py b/spaces/eson/tokenizer-arena/vocab/albert/__init__.py
deleted file mode 100644
index c5a6269209da8579094686f5dbff59d53b44ca0e..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/albert/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""
-
-SentencePiece(unigram)
-
-https://huggingface.co/docs/transformers/tokenizer_summary#sentencepiece
-"""
\ No newline at end of file
diff --git a/spaces/eson/tokenizer-arena/vocab/glm_chinese/tokenization.py b/spaces/eson/tokenizer-arena/vocab/glm_chinese/tokenization.py
deleted file mode 100644
index 211d02f16d318dc0eab40690a48a4e3ec7548a71..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/glm_chinese/tokenization.py
+++ /dev/null
@@ -1,1270 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2019, NVIDIA CORPORATION.  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.
-"""Utilities for using and training tokenizers (char, wordpiece, sentencepiece)"""
-from collections import namedtuple
-import random
-import os
-import csv
-import torch
-import itertools
-
-import nltk
-from nltk import tokenize as nltk_tokenize
-import sentencepiece as spm
-
-from .wordpiece import BertTokenizer, PRETRAINED_VOCAB_ARCHIVE_MAP
-
-from .tokenization_gpt2 import GPT2Tokenizer
-from . import sp_tokenizer
-from .utils import print_rank_0
-import regex as re
-
-
-def make_tokenizer(tokenizer_type, corpus, model_path=None, vocab_size=None, model_type=None, pad_token=0,
-                   character_coverage=1.0, command_tokens=None, type_tokens=None, fix_command_token=False, **kwargs):
-    """
-    Helper function to instantiate a tokenizer given common combinations of options.
-    """
-    tokenizer_class = tokenizer_type
-    if isinstance(tokenizer_class, str):
-        tokenizer_class = eval(tokenizer_class)
-    if tokenizer_class is BertWordPieceTokenizer:
-        return BertWordPieceTokenizer(model_type, **kwargs)
-    elif tokenizer_class is GPT2BPETokenizer:
-        if model_type is None:
-            model_type = 'gpt2'
-        return GPT2BPETokenizer(model_type, **kwargs)
-    elif tokenizer_class is ChineseSPTokenizer:
-        return ChineseSPTokenizer(fix_command_token=fix_command_token, **kwargs)
-    text_tokenizer = tokenizer_class(corpus=corpus, vocab_size=vocab_size, model_path=model_path, model_type=model_type,
-                                     pad_token=pad_token, character_coverage=character_coverage)
-    return Tokenizer(text_tokenizer, command_tokens, type_tokens)
-
-
-class Tokenization(object):
-    """
-    Tokenization object to hold tokenization, (processed text),and original
-    text. Can hold tokenization as Ids or tokens.
-
-    It also holds command tokens (pad, unk, etc.) for the tokenization.
-    This allows functions to pad/operate on tokenizations without having
-    access to the full tokenizer, just the tokenization.
-
-    Several standard array operations are implemented (insert, append, extend).
-    """
-
-    def __init__(self, tokenization, text=None, original_text=None, command_tokens=None, asIds=True):
-        self.tokenization = tokenization
-        self.text = text
-        if self.text is None:
-            self.text = self.tokenization
-        self.original_text = original_text
-        if self.original_text is None:
-            self.original_text = self.text
-        self.command_tokens = command_tokens
-        self.asIds = asIds
-        self.parse_command_tokens()
-
-    def set_command_tokens(self, command_tokens):
-        self.command_tokens = command_tokens
-        return self.parse_command_tokens()
-
-    def parse_command_tokens(self):
-        if self.command_tokens is None:
-            return
-        for command_token in self.command_tokens:
-            if self.asIds:
-                setattr(self, command_token.name, command_token.Id)
-            else:
-                setattr(self, command_token.name, command_token.token)
-
-    def __getitem__(self, index):
-        return self.tokenization[index]
-
-    def __len__(self):
-        return len(self.tokenization)
-
-    def insert(self, idx, other):
-        if isinstance(other, (CommandToken, TypeToken)):
-            self.tokenization.insert(idx, other.Id)
-            if idx == 0:
-                self.text = other.token + self.text
-                self.original_text = other.token + self.original_text
-            elif idx == len(self.tokenization) - 1:
-                self.text += other.token
-                self.original_text += other.token
-        elif isinstance(other, Tokenization):
-            self.tokenization = self.tokenization[:idx] + other.tokenization + self.tokenization[idx:]
-        else:
-            self.tokenization = self.tokenization[:idx] + other.tokenization + self.tokenization[idx:]
-
-    def append(self, other):
-        if isinstance(other, (CommandToken, TypeToken)):
-            self.tokenization.append(other.Id)
-            self.text += other.token
-            self.original_text += other.token
-        elif isinstance(other, Tokenization):
-            self.tokenization.extend(other.tokenization)
-            self.text += other.text
-            self.original_text += other.original_text
-        else:
-            self.tokenization.append(other)
-        return self
-
-    def extend(self, other):
-        if isinstance(other, (CommandToken, TypeToken)):
-            self.tokenization.append(other.Id)
-            self.text += other.token
-            self.original_text += other.token
-        elif isinstance(other, list) and isinstance(other[0], (CommandToken, TypeToken)):
-            self.tokenization.extend([o.Id for o in other])
-            self.text += [o.token for o in other]
-            self.original_text += [o.token for o in other]
-        elif isinstance(other, Tokenization):
-            self.tokenization.extend(other.tokenization)
-            self.text += other.text
-            self.original_text += other.original_text
-        else:
-            self.tokenization.extend(other)
-        return self
-
-
-"""define some default command tokens for the tokenizer to use"""
-token_format = "<{0}>"
-
-COMMAND_TUPLE = namedtuple('CommandToken', ('name', 'token', 'Id'))
-
-
-def prep_command_tokens(tokenlist, token_format=token_format):
-    return [CommandToken(tok[0], token_format.format(tok[0]), tok[1]) for tok in tokenlist]
-
-
-class CommandToken(object):
-    def __init__(self, name, token, Id, lstrip=False, rstrip=False):
-        self.name = name
-        self.token = token
-        self.Id = Id
-        self.lstrip = lstrip
-        self.rstrip = rstrip
-
-    def __str__(self):
-        return str(COMMAND_TUPLE(self.name, self.token, self.Id))
-
-    def __repr__(self):
-        return str(COMMAND_TUPLE(self.name, self.token, self.Id))
-
-
-DEFAULT_COMMAND_TOKENS = [
-    ('pad', 0),
-    ('eos', 1),
-    ('bos', 2),
-    ('unk', 3),
-    ('sep', 4),
-    ('L2R', 5),
-    ('ENC', 6),
-    ('MASK', 7),
-]
-DEFAULT_COMMAND_TOKENS = prep_command_tokens(DEFAULT_COMMAND_TOKENS)
-
-"""define some default type tokens for bert training"""
-
-TYPE_TUPLE = namedtuple('TypeToken', ('name', 'token', 'Id'))
-
-
-def prep_type_tokens(tokenlist, token_format=token_format):
-    return [TypeToken(tok[0], token_format.format(tok[0]), tok[1]) for tok in tokenlist]
-
-
-class TypeToken(object):
-    def __init__(self, name, token, Id):
-        self.name = name
-        self.token = token
-        self.Id = Id
-
-    def __str__(self):
-        return str(TYPE_TUPLE(self.name, self.token, self.Id))
-
-
-DEFAULT_TYPE_TOKENS = [
-    ('function', 0),
-    ('command', 1),
-    ('str0', 2),
-    ('str1', 3),
-    ('str2', 4),
-    ('embedding0', 5),
-    ('embedding1', 6),
-    ('embedding2', 7),
-    ('arg0', 8),
-    ('arg1', 9),
-    ('arg2', 10),
-]
-DEFAULT_TYPE_TOKENS = prep_type_tokens(DEFAULT_TYPE_TOKENS)
-
-
-class Tokenizer(object):
-    """
-    Tokenizer object that handles text tokenization, command tokens, and type tokens.
-
-    Command tokens and text tokens are stored together in one mapping of size
-    `len(text_tokenizer)+len(command_tokens)`. Command tokens are stored as first
-    `len(command_tokens)` tokens. Token idx is stored at `idx+len(command_tokens)`.
-
-    Token types are stored in a separate mapping of size `len(type_tokens)`.
-    """
-
-    def __init__(self, text_tokenizer, command_tokens=None, type_tokens=None):
-        # set text tokenizer
-        self.text_tokenizer = text_tokenizer
-        if not hasattr(self, 'num_text_tokens'):
-            self.num_text_tokens = len(self.text_tokenizer)
-
-        # set command tokens
-        if command_tokens is None:
-            command_tokens = DEFAULT_COMMAND_TOKENS
-        self._command_tokens = command_tokens
-        self.command_name_map = {tok.name: tok for tok in self._command_tokens}
-        self.command_token_map = {tok.token: tok for tok in self._command_tokens}
-        self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
-        if not hasattr(self, 'num_command_tokens'):
-            self.num_command_tokens = len(self._command_tokens)
-        if not hasattr(self, 'num_tokens'):
-            self.num_tokens = self.num_command_tokens + self.num_text_tokens
-
-        # set type tokens
-        if type_tokens is None:
-            type_tokens = DEFAULT_TYPE_TOKENS
-        self.type_tokens = type_tokens
-        self.type_name_map = {tok.name: tok for tok in self.type_tokens}
-        self.type_token_map = {tok.token: tok for tok in self.type_tokens}
-        self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
-        if not hasattr(self, 'num_type_tokens'):
-            self.num_type_tokens = len(self.type_tokens)
-
-        # parse tokens and vocabs from tokenizer
-        self._tokens = list(self.command_token_map.keys()) + list(self.text_tokenizer.tokens)
-        self._vocab = {t: Id for Id, t in self.command_id_map.items()}
-        self._vocab.update({t: Id + self.num_command_tokens for t, Id in self.text_tokenizer.vocab.items()})
-
-        self._text_tokens = list(self.text_tokenizer.tokens)
-        self._text_token_vocab = {t: Id + self.num_command_tokens for t, Id in self.text_tokenizer.vocab.items()}
-
-        self._command_token_tokens = list(self.command_token_map.keys())
-        self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()}
-
-        self._token_types = list(self.type_token_map.keys())
-        self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()}
-
-    def __call__(self, text, process_fn=None):
-        """run preprocessing and encode text as Ids"""
-        return self.EncodeAsIds(text, process_fn=process_fn)
-
-    def __len__(self):
-        """total number of tokens"""
-        return self.num_tokens
-
-    def get_command(self, name):
-        """get command token corresponding to `name`"""
-        return self.command_name_map[name]
-
-    def get_type(self, name):
-        """get type token corresponding to `name`"""
-        return self.type_name_map[name]
-
-    @property
-    def tokens(self):
-        """list (or iterable) of all tokens for tokenizer"""
-        return self._tokens
-
-    @property
-    def vocab(self):
-        """dictionary mapping tokens to ids for tokenizer"""
-        return self._vocab
-
-    @property
-    def token_types(self):
-        """list (or iterable) of all token types for tokenizer"""
-        return self._token_types
-
-    @property
-    def token_type_vocab(self):
-        """dictionary mapping token types to ids for tokenizer"""
-        return self._token_type_vocab
-
-    @property
-    def command_tokens(self):
-        """list (or iterable) of all command tokens for tokenizer"""
-        return self._command_token_tokens
-
-    @property
-    def command_token_vocab(self):
-        """dictionary mapping command tokens to ids for tokenizer"""
-        return self._command_token_vocab
-
-    @property
-    def text_tokens(self):
-        """list (or iterable) of text tokens for text tokenizer"""
-        return self._text_tokens
-
-    @property
-    def text_token_vocab(self):
-        """dictionary mapping text tokens to ids for text tokenizer"""
-        return self._text_token_vocab
-
-    def EncodeAsIds(self, text, process_fn=None):
-        """
-        encode text using text tokenizer and shift Id values for command tokens
-        """
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-
-        def split_on_token(tok_extended: CommandToken, text):
-            result = []
-            tok = tok_extended.token
-            split_text = text.split(tok)
-            for i, sub_text in enumerate(split_text):
-                # CommandToken can control whitespace stripping around them.
-                # We use them for GPT2 and Roberta to have different behavior depending on the special token
-                # Cf. https://github.com/huggingface/transformers/pull/2778
-                # and https://github.com/huggingface/transformers/issues/3788
-                # Strip white spaces on the right
-                if tok_extended.rstrip and i > 0:
-                    # A bit counter-intuitive but we strip the left of the string
-                    # since tok_extended.rstrip means the special token is eating all white spaces on its right
-                    sub_text = sub_text.lstrip()
-                # Strip white spaces on the left
-                if tok_extended.lstrip and i < len(split_text) - 1:
-                    sub_text = sub_text.rstrip()  # Opposite here
-
-                if i == 0 and not sub_text:
-                    result.append(tok)
-                elif i == len(split_text) - 1:
-                    if sub_text:
-                        result.append(sub_text)
-                    else:
-                        pass
-                else:
-                    if sub_text:
-                        result.append(sub_text)
-                    result.append(tok)
-            return result
-
-        def split_on_tokens(tok_list, text):
-            if not text.strip():
-                return []
-            if not tok_list:
-                return self.text_tokenizer.encode(text)
-
-            tokenized_text = []
-            text_list = [text]
-            for tok in tok_list:
-                tokenized_text = []
-                for sub_text in text_list:
-                    if sub_text not in self._command_token_tokens:
-                        tokenized_text.extend(split_on_token(tok, sub_text))
-                    else:
-                        tokenized_text.append(sub_text)
-                text_list = tokenized_text
-
-            return list(
-                itertools.chain.from_iterable(
-                    (
-                        self._encode(token) if token not in self._command_token_tokens else [
-                            self.command_token_map[token].Id] for token in tokenized_text
-                    )
-                )
-            )
-
-        no_split_tokens = self._command_tokens
-        Ids = split_on_tokens(no_split_tokens, processed_text)
-        tokenization = Tokenization(Ids, processed_text, text)
-        tokenization.set_command_tokens(self._command_tokens)
-        return tokenization
-
-    def _encode(self, text):
-        raise NotImplementedError
-
-    def EncodeAsTokens(self, text, process_fn=None):
-        """
-        encode text as tokens using text tokenizer
-        """
-        tokenization = self.text_tokenizer.EncodeAsTokens(text, process_fn=process_fn)
-        tokenization.set_command_tokens(self._command_tokens)
-        return tokenization
-
-    def IdToToken(self, Id, type_token=False):
-        """convert Id to token accounting for command and type tokens"""
-        if isinstance(Id, (TypeToken, CommandToken)):
-            return Id.token
-        if type_token:
-            return self.type_id_map[Id].token
-        if Id < self.num_command_tokens:
-            return self.command_id_map[Id].token
-        return self.text_tokenizer.IdToToken(Id - self.num_command_tokens)
-
-    def TokenToId(self, token, type_token=False):
-        """convert token to Id accounting for command and type tokens"""
-        if isinstance(token, (TypeToken, CommandToken)):
-            return token.Id
-        if type_token:
-            return self.type_token_map[token].Id
-        if token in self.command_token_map:
-            return self.command_token_map[token].Id
-        return self.text_tokenizer.TokenToId(token) + self.num_command_tokens
-
-    def DecodeIds(self, Ids, type_token=False):
-        """
-        convert Ids to tokens accounting for command and type tokens, tokens
-        are joined and returned as a string.
-        """
-        if type_token:
-            return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
-        rtn_strs = []
-        current_str = []
-        if isinstance(Ids, Tokenization):
-            Ids = Ids.tokenization
-        for Id in Ids:
-            if isinstance(Id, CommandToken):
-                rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
-                current_str = []
-                rtn_strs.append(Id.token)
-            elif Id < self.num_command_tokens:
-                rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
-                current_str = []
-                rtn_strs.append(self.command_id_map[Id].token)
-            else:
-                current_str.append(Id - self.num_command_tokens)
-        if current_str != []:
-            rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
-        return ' '.join(rtn_strs)
-
-    def DecodeTokens(self, Tokens, type_token=False):
-        """
-        convert tokens to a string accounting for command and type tokens.
-        """
-        if type_token:
-            return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
-        rtn_strs = []
-        current_str = []
-        if isinstance(Tokens, Tokenization):
-            Tokens = Tokens.tokenization
-        for t in Tokens:
-            if isinstance(t, CommandToken):
-                rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
-                current_str = []
-                rtn_strs.append(t.token)
-            elif t in self.command_token_map:
-                rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
-                current_str = []
-                rtn_strs.append(t)
-            else:
-                current_str.append(t)
-        if current_str != []:
-            rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
-        return ' '.join(rtn_strs)
-
-
-class TextTokenizer(object):
-    """
-    Interface for text tokenizer
-    """
-
-    def __init__(self):
-        if not hasattr(self, 'num_text_tokens'):
-            self.num_text_tokens = 0
-        if not hasattr(self, 'num_tokens'):
-            self.num_tokens = self.num_text_tokens
-
-    def __call__(self, text, process_fn=None):
-        return self.EncodeAsIds(text, process_fn)
-
-    def __len__(self):
-        return self.num_text_tokens
-
-    @property
-    def tokens(self):
-        """list (or iterable) of text tokens for text tokenizer"""
-        raise NotImplementedError('TextTokenizer tokens property not implemented')
-
-    @property
-    def vocab(self):
-        """dictionary mapping tokens to ids"""
-        raise NotImplementedError('TextTokenizer vocab property not implemented')
-
-    @staticmethod
-    def exists(model_path):
-        """check if the filepath for a text tokenizer exists"""
-        raise NotImplementedError('TextTokenizer exists method not implemented')
-
-    def Train(self, corpus):
-        """train a tokenizer on a data corpus and save model for future use"""
-        raise NotImplementedError('TextTokenizer Train not implemented')
-
-    def EncodeAsIds(self, text, process_fn=None):
-        """
-        Preprocess text and encode as ids. Return a tokenization object with
-        original text, processed text, and id tokenization.
-        """
-        raise NotImplementedError('TextTokenizer EncodeAsIds not implemented')
-
-    def EncodeAsTokens(self, text, process_fn=None):
-        """
-        Preprocess text and encode as tokens. Return a tokenization object with
-        original text, processed text, and token tokenization.
-        """
-        raise NotImplementedError('TextTokenizer EncodeAsTokens not implemented')
-
-    def IdToToken(self, Id):
-        """Convert an Id to Token. Reverse lookup of self.vocab"""
-        raise NotImplementedError('TextTokenizer IdToToken not implemented')
-
-    def TokenToId(self, token):
-        """Convert a Token to Id. Lookup of self.vocab"""
-        raise NotImplementedError('TextTokenizer TokenToId not implemented')
-
-    def DecodeIds(self, Ids):
-        """Convert a list or tokenization object of Ids to a text string"""
-        raise NotImplementedError('TextTokenizer DecodeIds not implemented')
-
-    def DecodeTokens(self, Tokens):
-        """Convert a list or tokenization object of tokens to a text string"""
-        raise NotImplementedError('TextTokenizer DecodeTokens not implemented')
-
-
-class CharacterLevelTokenizer(TextTokenizer):
-    """
-    Text tokenizer for ASCII-256 Character Level Tokenization.
-    """
-
-    def __init__(self, **kwargs):
-        self.num_text_tokens = 256
-        super(CharacterLevelTokenizer, self).__init__()
-        self._tokens = [self.IdToToken(Id) for Id in range(self.num_text_tokens)]
-        self._vocab = {t: i for i, t in enumerate(self._tokens)}
-
-    def __len__(self):
-        return 256
-
-    @staticmethod
-    def exists(model_path):
-        return True
-
-    def Train(self, corpus):
-        pass
-
-    @property
-    def tokens(self):
-        return self._tokens
-
-    @property
-    def vocab(self):
-        return self._vocab
-
-    def EncodeAsIds(self, text, process_fn=None):
-        """convert text to ascii 256 Ids"""
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-            processed_text = str(processed_text)
-        tokens = [self.TokenToId(c) for c in processed_text]
-        return Tokenization(tokens, processed_text, text)
-
-    def EncodeAsTokens(self, text, process_fn=None):
-        """convert text to ascii 256 characters"""
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-        processed_text = str(processed_text)
-        tokens = [c for c in processed_text]
-        return Tokenization(tokens, processed_text, text, asIds=False)
-
-    def IdToToken(self, Id):
-        """ascii index to character"""
-        return chr(Id)
-
-    def TokenToId(self, token):
-        """ascii character to index"""
-        return ord(token)
-
-    def DecodeIds(self, Ids):
-        """converts ascii ids to tokens before joining them into text"""
-        if isinstance(Ids, Tokenization):
-            Ids = Ids.tokenization
-        return ''.join([self.IdToToken(tok) for tok in Ids])
-
-    def DecodeTokens(self, Tokens):
-        """just concatenates ascii tokens into text"""
-        if isinstance(Tokens, Tokenization):
-            Tokens = Tokens.tokenization
-        return ''.join(Tokens)
-
-
-MAX_SENTENCEPIECE_SENTENCES = 100000000
-
-
-def get_corpus_freq(dataset, filepath, filetype='tsv'):
-    """
-    Take corpus, split it into sentences, and extract word frequencies.
-    Write frequencies to `filepath` as a tsv. Only write the first
-    MAX_SENTENCEPIECE_SENTENCES most common words to the file.
-    """
-    nltk.download('punkt', download_dir="./nltk")
-    if filetype == 'tsv':
-        delimiter = '\t'
-    else:
-        delimiter = ','
-
-    print("compute corpus frequency\n", flush=True)
-
-    total_sentence_count = 0
-    maxlen = 0
-    freqs = {}
-    for entry in dataset:
-        if isinstance(entry, dict):
-            entry = entry['text']
-        lines = entry.strip().split('\n')
-        for line in lines:
-            sentences = nltk_tokenize.sent_tokenize(line)
-            total_sentence_count += len(sentences)
-            for sentence in sentences:
-                maxlen = max(len(line), maxlen)
-                for word in sentence.split():
-                    if word not in freqs:
-                        freqs[word] = 0
-                    freqs[word] += 1
-
-    print("length of freqs before truncating " + str(len(freqs)), flush=True)
-    print("file path for freq " + str(filepath), flush=True)
-
-    freqs_sorted = {}
-    counter = 0
-    for word, count in sorted(freqs.items(), key=lambda x: x[1], reverse=True):
-        if counter >= MAX_SENTENCEPIECE_SENTENCES:
-            break
-        counter += 1
-        freqs_sorted[word] = count
-
-    print("length of freqs after trancating " + str(len(freqs_sorted)), flush=True)
-
-    with open(filepath, 'w') as f:
-        writer = csv.writer(f, delimiter=delimiter)
-        for k, v in freqs_sorted.items():
-            writer.writerow([str(k), str(v)])
-
-    return total_sentence_count, maxlen
-
-
-class SentencePieceTokenizer(TextTokenizer):
-    """Trains and uses sentencepiece for text tokenization"""
-
-    def __init__(self, model_type='bpe', vocab_size=None, corpus=None, model_path=None, character_coverage=1.0,
-                 **kwargs):
-        self.character_coverage = character_coverage
-        self.model_type = model_type.lower()
-        self.spm_model = model_path
-        self.num_text_tokens = vocab_size
-        make_train = not SentencePieceTokenizer.exists(self.spm_model)
-        if make_train:
-            assert corpus is not None and self.num_text_tokens is not None
-            self.Train(corpus, self.num_text_tokens)
-        self._tokens = []
-        self._vocab = {}
-        self.load_spm_model()
-        super(SentencePieceTokenizer, self).__init__()
-
-    def __len__(self):
-        return self.num_text_tokens
-
-    @property
-    def tokens(self):
-        return self._tokens
-
-    @property
-    def vocab(self):
-        return self._vocab
-
-    @staticmethod
-    def exists(model_path):
-        if model_path is None:
-            return False
-        # check if path exists
-        dne = not os.path.exists(model_path)
-        # check if path.model exists
-        if dne and not model_path.endswith('.model'):
-            dne = not os.path.exists(model_path + '.model')
-        return not dne
-
-    def load_spm_model(self):
-        """load sentencepiece model and parse vocab"""
-        if not os.path.exists(self.spm_model) and not self.spm_model.endswith('.model'):
-            self.spm_model = self.spm_model + '.model'
-        self.sp = spm.SentencePieceProcessor()
-        self.sp.Load(self.spm_model)
-        self.vocab_size = self.num_text_tokens = len(self.sp)
-        self._tokens = [self.IdToToken(t) for t in range(self.vocab_size)]
-        self._vocab = {t: i for i, t in enumerate(self._tokens)}
-
-    def Train(self, corpus, num_text_tokens):
-        """train sentencepiece model on corpus using word frequencies"""
-        self.num_text_tokens = num_text_tokens
-        use_model_path = self.spm_model
-        random_hash = str(random.randint(0, 2147483647))
-        if use_model_path is None:
-            use_model_path = random_hash
-        if use_model_path.endswith('.model'):
-            use_model_path = use_model_path[:use_model_path.rfind('.model')]
-        input_path = use_model_path + '.tsv.' + random_hash
-        line_count, maxlenline = get_corpus_freq(corpus, input_path)
-        line_count = min(line_count, MAX_SENTENCEPIECE_SENTENCES)
-        print('line count used as input_sentence_size ', line_count, flush=True)
-        print('training sentencepiece model', flush=True)
-        train_string = '--input={file_path} --model_prefix={model_prefix} --vocab_size={vocab_size}' \
-                       + ' --model_type={model_type} --character_coverage={character_coverage} ' \
-                       + '--input_sentence_size={input_sentence_size} ' \
-                       + '--input_format=tsv'
-        train_string = train_string.format(file_path=input_path, model_prefix=use_model_path,
-                                           vocab_size=num_text_tokens,
-                                           model_type=self.model_type, character_coverage=self.character_coverage,
-                                           input_sentence_size=int(line_count))  # , #)#,
-        print("calling spm.SentencePieceTrainer.Train(%s)" % (train_string), flush=True)
-        spm.SentencePieceTrainer.Train(train_string)
-        os.remove(input_path)
-        self.spm_model = use_model_path + '.model'
-        print('sentencepiece model written to ' + self.spm_model, flush=True)
-
-    def EncodeAsIds(self, text, process_fn=None):
-        """convert text to sentencepiece Ids"""
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-        tokens = self.sp.EncodeAsIds(processed_text)
-        return Tokenization(tokens, processed_text, text)
-
-    def EncodeAsTokens(self, text, process_fn=None):
-        """convert text to sentencepiece tokens"""
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-        tokens = self.sp.EncodeAsTokens(processed_text)
-        return Tokenization(tokens, processed_text, text, asIds=False)
-
-    def IdToToken(self, Id):
-        """convert Id to sentencpiece token"""
-        return self.sp.IdToPiece(Id)
-
-    def TokenToId(self, token):
-        """convert sentencpiece token to Id"""
-        return self.sp.PieceToId(token)
-
-    def DecodeIds(self, Ids):
-        """converts ids to a text string"""
-        if isinstance(Ids, Tokenization):
-            Ids = Ids.tokenization
-        return self.sp.DecodeIds(Ids)
-
-    def DecodeTokens(self, Tokens):
-        """converts sentencepiece tokens to a text string"""
-        if isinstance(Tokens, Tokenization):
-            Tokens = Tokens.tokenization
-        return self.sp.DecodeTokens(Tokens)
-
-
-class BertWordPieceTokenizer(Tokenizer):
-    """
-    Loads a pretrained WordPiece tokenizer from `cache_dir` for tokenization
-    in BERT training. Default to bert-large-uncased tokenizer.
-    """
-
-    def __init__(self, tokenizer_model_type=None, cache_dir=None, add_block_symbols=False, add_sentinel_token=0,
-                 add_task_mask=False, add_decoder_mask=False, **kwargs):
-        # default to bert-large-uncased tokenizer
-        if tokenizer_model_type not in PRETRAINED_VOCAB_ARCHIVE_MAP:
-            tokenizer_model_type = 'bert-large-uncased'
-        if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
-            print('loading BertWordPieceTokenizer (', tokenizer_model_type, ') from cache_dir ', cache_dir)
-        do_lower_case = not ('-cased' in tokenizer_model_type or 'chinese' in tokenizer_model_type)
-        self.text_tokenizer = BertTokenizer.from_pretrained(tokenizer_model_type, do_lower_case=do_lower_case,
-                                                            cache_dir=cache_dir)
-        if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
-            print('loaded', tokenizer_model_type)
-        # disable max len warnings by increasing max len
-        self.text_tokenizer.max_len = int(1e12)
-
-        # set command tokens from wordpiece tokenizer values
-        self.num_command_tokens = 6
-        self.num_tokens = len(self.text_tokenizer.vocab)
-        self.num_text_tokens = self.num_tokens - 5
-        self.num_type_tokens = 2
-
-        self._command_tokens = [
-            CommandToken('pad', '[PAD]', self.text_tokenizer.vocab['[PAD]']),
-            CommandToken('ENC', '[CLS]', self.text_tokenizer.vocab['[CLS]']),
-            CommandToken('MASK', '[MASK]', self.text_tokenizer.vocab['[MASK]']),
-            CommandToken('unk', '[UNK]', self.text_tokenizer.vocab['[UNK]']),
-            CommandToken('sep', '[SEP]', self.text_tokenizer.vocab['[SEP]']),
-            CommandToken('eos', '[PAD]', self.text_tokenizer.vocab['[PAD]']),
-        ]
-        if add_block_symbols:
-            self._command_tokens.extend([
-                CommandToken('sop', '<|startofpiece|>', self.num_tokens),
-                CommandToken('eop', '<|endofpiece|>', self.num_tokens + 1)
-            ])
-            self.num_tokens += 2
-            self.num_command_tokens += 2
-            if add_task_mask:
-                self._command_tokens.extend([
-                    CommandToken('gMASK', '[gMASK]', self.num_tokens),
-                    CommandToken('sMASK', '[sMASK]', self.num_tokens + 1)
-                ])
-                self.num_tokens += 2
-                self.num_command_tokens += 2
-            if add_decoder_mask:
-                self._command_tokens.extend([
-                    CommandToken('dBLOCK', '[dBLOCK]', self.num_tokens)
-                ])
-                self.num_tokens += 1
-                self.num_command_tokens += 1
-        if add_sentinel_token > 0:
-            for i in range(1, add_sentinel_token):
-                self._command_tokens.extend([CommandToken(f'MASK{i}', f'[MASK{i}]', self.num_tokens),
-                                             CommandToken(f'sop{i}', f'<|startofpiece{i}|>', self.num_tokens + 1)])
-                self.num_tokens += 2
-                self.num_command_tokens += 2
-        self.command_name_map = {tok.name: tok for tok in self._command_tokens}
-        self.command_token_map = {tok.token: tok for tok in self._command_tokens}
-        self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
-
-        # set type tokens
-        self.type_tokens = [
-            TypeToken('str0', '<str0>', 0),
-            TypeToken('str1', '<str1>', 1),
-        ]
-        self.type_name_map = {tok.name: tok for tok in self.type_tokens}
-        self.type_token_map = {tok.token: tok for tok in self.type_tokens}
-        self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
-
-        # parse tokens and vocabs from tokenizer
-
-        self._tokens = list(self.text_tokenizer.vocab.keys())
-        self._vocab = {k: v for k, v in self.text_tokenizer.vocab.items()}
-
-        self._text_tokens = list(self._tokens)
-        self._text_token_vocab = {k: v for k, v in self.text_tokenizer.vocab.items()}
-
-        self._command_token_tokens = list(self.command_token_map.keys())
-        self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()}
-
-        self._token_types = list(self.type_token_map.keys())
-        self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()}
-
-    def _encode(self, text):
-        tokens = self.text_tokenizer.tokenize(text)
-        ids = self.text_tokenizer.convert_tokens_to_ids(tokens)
-        return ids
-
-    def EncodeAsTokens(self, text, process_fn=None):
-        """convert wordpiece token to Id"""
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-        tokens = self.text_tokenizer.tokenize(processed_text)
-        return Tokenization(tokens, processed_text, text, asIds=False)
-
-    def IdToToken(self, Id, type_token=False):
-        """convert Id to sentencpiece token"""
-        if isinstance(Id, (TypeToken, CommandToken)):
-            return Id.token
-        if type_token:
-            return self.type_id_map[Id].token
-        if Id in self.command_id_map:
-            return self.command_id_map[Id].token
-        return self.text_tokenizer.ids_to_tokens[Id]
-
-    def TokenToId(self, token, type_token=False):
-        """convert sentencpiece token to Id"""
-        if isinstance(token, (TypeToken, CommandToken)):
-            return token.Id
-        if type_token:
-            return self.type_token_map[token].Id
-        token = token.strip()
-        return self.text_tokenizer.vocab[token]
-
-    def DecodeIds(self, Ids, type_token=False):
-        """converts ids to wordpiece tokens and joins them as a text string"""
-        if type_token:
-            return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
-        if isinstance(Ids, Tokenization):
-            Ids = Ids.tokenization
-        Tokens = []
-        for Id in Ids:
-            if Id in self.command_id_map:
-                Tokens.append(self.command_id_map[Id].token)
-            elif Id in self.text_tokenizer.ids_to_tokens:
-                Tokens.append(self.text_tokenizer.ids_to_tokens[Id])
-        new_tokens = []
-        for token in Tokens:
-            if token.startswith('##') and len(new_tokens) > 0:
-                new_tokens[-1] += token[2:]
-            else:
-                new_tokens.append(token)
-        return ' '.join(new_tokens)
-
-    def DecodeTokens(self, Tokens, type_token=False):
-        """converts wordpiece tokens to a text string"""
-        if type_token:
-            return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
-        if isinstance(Tokens, Tokenization):
-            Tokens = Tokens.tokenization
-        return ' '.join(Tokens)
-
-
-class GPT2BPETokenizer(Tokenizer):
-    def __init__(self, model_type_or_path, cache_dir=None, add_block_symbols=False, add_task_mask=False,
-                 add_decoder_mask=False, **kwargs):
-        self.text_tokenizer = GPT2Tokenizer.from_pretrained(model_type_or_path,
-                                                            cache_dir=cache_dir)
-
-        # disable max len warnings by increasing max len
-        self.text_tokenizer.max_len = int(1e12)
-        self.num_tokens = len(self.text_tokenizer.encoder)
-        self.num_type_tokens = 2
-        if model_type_or_path.startswith('roberta'):
-            self.num_command_tokens = 6
-            self.num_text_tokens = self.num_tokens - 3
-            self._command_tokens = [
-                CommandToken('pad', '<|endoftext|>', self.text_tokenizer.encoder['</s>']),
-                CommandToken('eos', '<|endoftext|>', self.text_tokenizer.encoder['</s>']),
-                CommandToken('sep', '[SEP]', self.text_tokenizer.encoder['</s>']),
-                CommandToken('ENC', '[CLS]', self.text_tokenizer.encoder['<s>']),
-                CommandToken('MASK', '[MASK]', self.text_tokenizer.encoder['<mask>'], lstrip=True),
-                CommandToken('unk', '[UNK]', self.text_tokenizer.encoder['<unk>'])
-            ]
-            if add_block_symbols:
-                self._command_tokens.extend([
-                    CommandToken('sop', '<|startofpiece|>', self.num_tokens),
-                    CommandToken('eop', '<|endofpiece|>', self.num_tokens + 1)
-                ])
-                self.num_tokens += 2
-                self.num_command_tokens += 2
-        else:
-            self.num_command_tokens = 2
-            self.num_text_tokens = self.num_tokens - 1
-            self._command_tokens = [
-                CommandToken('pad', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>']),
-                CommandToken('eos', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>'])
-            ]
-            if add_block_symbols:
-                self._command_tokens.extend([
-                    CommandToken('sop', '<|startofpiece|>', self.num_tokens),
-                    CommandToken('eop', '<|endofpiece|>', self.num_tokens + 1),
-                    CommandToken('ENC', '[CLS]', self.num_tokens + 2),
-                    CommandToken('MASK', '[MASK]', self.num_tokens + 3, lstrip=True),
-                    CommandToken('sep', '[SEP]', self.num_tokens + 4),
-                    CommandToken('unk', '[UNK]', self.num_tokens + 5)
-                ])
-                self.num_tokens += 6
-                self.num_command_tokens += 6
-        if add_block_symbols:
-            if add_task_mask:
-                self._command_tokens.extend([
-                    CommandToken('gMASK', '[gMASK]', self.num_tokens, lstrip=True),
-                    CommandToken('sMASK', '[sMASK]', self.num_tokens + 1, lstrip=True)
-                ])
-                self.num_tokens += 2
-                self.num_command_tokens += 2
-            if add_decoder_mask:
-                self._command_tokens.extend([
-                    CommandToken('dBLOCK', '[dBLOCK]', self.num_tokens)
-                ])
-                self.num_tokens += 1
-                self.num_command_tokens += 1
-        self.command_name_map = {tok.name: tok for tok in self._command_tokens}
-        self.command_token_map = {tok.token: tok for tok in self._command_tokens}
-        self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
-
-        self.type_tokens = [
-            TypeToken('str0', '<str0>', 0),
-            TypeToken('str1', '<str1>', 1),
-        ]
-        self.type_name_map = {tok.name: tok for tok in self.type_tokens}
-        self.type_token_map = {tok.token: tok for tok in self.type_tokens}
-        self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
-
-        self._tokens = list(self.text_tokenizer.encoder.keys())
-        self._vocab = {k: v for k, v in self.text_tokenizer.encoder.items()}
-
-        self._text_tokens = list(self._tokens)
-        self._text_token_vocab = {k: v for k, v in self.text_tokenizer.encoder.items()}
-
-        self._command_token_tokens = list(self.command_token_map.keys())
-        self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()}
-
-        self._token_types = list(self.type_token_map.keys())
-        self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()}
-
-        for idx, tok in self.command_id_map.items():
-            self.text_tokenizer.decoder[idx] = tok.token
-
-    def EncodeAsIds(self, text, process_fn=None):
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-
-        def split_on_token(tok_extended: CommandToken, text):
-            result = []
-            tok = tok_extended.token
-            split_text = text.split(tok)
-            for i, sub_text in enumerate(split_text):
-                # CommandToken can control whitespace stripping around them.
-                # We use them for GPT2 and Roberta to have different behavior depending on the special token
-                # Cf. https://github.com/huggingface/transformers/pull/2778
-                # and https://github.com/huggingface/transformers/issues/3788
-                # Strip white spaces on the right
-                if tok_extended.rstrip and i > 0:
-                    # A bit counter-intuitive but we strip the left of the string
-                    # since tok_extended.rstrip means the special token is eating all white spaces on its right
-                    sub_text = sub_text.lstrip()
-                # Strip white spaces on the left
-                if tok_extended.lstrip and i < len(split_text) - 1:
-                    sub_text = sub_text.rstrip()  # Opposite here
-
-                if i == 0 and not sub_text:
-                    result.append(tok)
-                elif i == len(split_text) - 1:
-                    if sub_text:
-                        result.append(sub_text)
-                    else:
-                        pass
-                else:
-                    if sub_text:
-                        result.append(sub_text)
-                    result.append(tok)
-            return result
-
-        def split_on_tokens(tok_list, text):
-            if not text.strip():
-                return []
-            if not tok_list:
-                return self.text_tokenizer.encode(text)
-
-            tokenized_text = []
-            text_list = [text]
-            for tok in tok_list:
-                tokenized_text = []
-                for sub_text in text_list:
-                    if sub_text not in self._command_token_tokens:
-                        tokenized_text.extend(split_on_token(tok, sub_text))
-                    else:
-                        tokenized_text.append(sub_text)
-                text_list = tokenized_text
-
-            return list(
-                itertools.chain.from_iterable(
-                    (
-                        self.text_tokenizer.encode(token) if token not in self._command_token_tokens else [
-                            self.command_token_map[token].Id] for token in tokenized_text
-                    )
-                )
-            )
-
-        no_split_tokens = self._command_tokens
-        Ids = split_on_tokens(no_split_tokens, processed_text)
-        tokenization = Tokenization(Ids, processed_text, text)
-        tokenization.set_command_tokens(self._command_tokens)
-        return tokenization
-
-    def _encode(self, text):
-        return self.text_tokenizer.encode(text)
-
-    def EncodeAsTokens(self, text, process_fn=None):
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-        tokens = []
-        for token in re.findall(self.text_tokenizer.pat, processed_text):
-            token = ''.join(self.text_tokenizer.bye_encoder[b] for b in token.encode('utf-8'))
-            tokens.extend(bpe_token for bpe_token in self.text_tokenizer.bpe(token).split(' '))
-        tokenization = Tokenization(tokens, processed_text, text, asIds=False)
-        tokenization.set_command_tokens(self._command_tokens)
-        return tokenization
-
-    def DecodeAsTokens(self, Ids):
-        return [self.IdToToken(x) for x in Ids]
-
-    def IdToToken(self, Id, type_token=False):
-        if isinstance(Id, (TypeToken, CommandToken)):
-            return Id.token
-        if type_token:
-            return self.type_id_map[Id].token
-        if Id in self.command_id_map:
-            return self.command_id_map[Id].token
-        return self.text_tokenizer.decoder[Id]
-
-    def TokenToId(self, token, type_token=False):
-        if isinstance(token, (TypeToken, CommandToken)):
-            return token.Id
-        if type_token:
-            return self.type_token_map[token].Id
-        return self.text_tokenizer.encoder[token]
-
-    def DecodeIds(self, Ids, type_token=False):
-        if type_token:
-            return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
-        if isinstance(Ids, Tokenization):
-            Ids = Ids.tokenization
-        return self.text_tokenizer.decode(Ids)
-
-    def DecodeTokens(self, Tokens, type_token=False):
-        if type_token:
-            return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
-        if isinstance(Tokens, Tokenization):
-            Tokens = Tokens.tokenization
-        return self.text_tokenizer.decode([self.TokenToId(tok) for tok in Tokens])
-
-
-class ChineseSPTokenizer(Tokenizer):
-    def __init__(self, add_block_symbols=False, add_task_mask=False, add_decoder_mask=False, fix_command_token=False,
-                 **kwargs):
-        self.text_tokenizer = sp_tokenizer.from_pretrained()
-
-        self.num_command_tokens = 0
-        self.num_text_tokens = self.text_tokenizer.sp.vocab_size()
-        self.num_tokens = self.num_text_tokens
-        self.num_type_tokens = 2
-
-        self._command_tokens = [
-            CommandToken('pad', '<|endoftext|>', self.num_text_tokens),
-            CommandToken('eos', '<|endoftext|>', self.num_text_tokens),
-            CommandToken('sep', '[SEP]', self.num_text_tokens + 1),
-            CommandToken('ENC', '[CLS]', self.num_text_tokens + 2),
-            CommandToken('MASK', '[MASK]', self.num_text_tokens + 3, lstrip=True),
-            CommandToken('unk', '[UNK]', self.num_text_tokens + 4)
-        ]
-        self.num_tokens += 5
-        self.num_command_tokens += 6
-        if add_block_symbols:
-            self._command_tokens.extend([
-                CommandToken('sop', '<|startofpiece|>', self.num_tokens + 1),
-                CommandToken('eop', '<|endofpiece|>', self.num_tokens + 2)
-            ])
-            if fix_command_token:
-                self.num_tokens += 3
-            else:
-                self.num_tokens += 2
-            self.num_command_tokens += 2
-            if add_task_mask:
-                if fix_command_token:
-                    self._command_tokens.extend([
-                        CommandToken('sMASK', '[sMASK]', self.num_tokens, lstrip=True),
-                        CommandToken('gMASK', '[gMASK]', self.num_tokens + 1, lstrip=True)
-                    ])
-                else:
-                    self._command_tokens.extend([
-                        CommandToken('gMASK', '[gMASK]', self.num_tokens, lstrip=True),
-                        CommandToken('sMASK', '[sMASK]', self.num_tokens + 1, lstrip=True)
-                    ])
-                self.num_tokens += 2
-                self.num_command_tokens += 2
-            if add_decoder_mask:
-                self._command_tokens.extend([
-                    CommandToken('dBLOCK', '[dBLOCK]', self.num_tokens)
-                ])
-                self.num_tokens += 1
-                self.num_command_tokens += 1
-        self.command_name_map = {tok.name: tok for tok in self._command_tokens}
-        self.command_token_map = {tok.token: tok for tok in self._command_tokens}
-        self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
-        print_rank_0({tok.name: tok.Id for tok in self._command_tokens})
-        self.type_tokens = [
-            TypeToken('str0', '<str0>', 0),
-            TypeToken('str1', '<str1>', 1),
-        ]
-        self.type_name_map = {tok.name: tok for tok in self.type_tokens}
-        self.type_token_map = {tok.token: tok for tok in self.type_tokens}
-        self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
-
-        # self._tokens = list(self.text_tokenizer.encoder.keys())
-        # self._vocab = {k:v for k,v in self.text_tokenizer.encoder.items()}
-        #
-        # self._text_tokens = list(self._tokens)
-        # self._text_token_vocab = {k:v for k,v in self.text_tokenizer.encoder.items()}
-
-        self._command_token_tokens = list(self.command_token_map.keys())
-        self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()}
-
-        self._token_types = list(self.type_token_map.keys())
-        self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()}
-
-    def _encode(self, text):
-        ids = self.text_tokenizer.encode(text)
-        return ids
-
-    def EncodeAsTokens(self, text, process_fn=None):
-        processed_text = text
-        if process_fn is not None:
-            processed_text = process_fn(processed_text)
-        tokens = self.text_tokenizer.tokenize(processed_text)
-        tokenization = Tokenization(tokens, processed_text, text, asIds=False)
-        tokenization.set_command_tokens(self._command_tokens)
-        return tokenization
-        # return Tokenization(tokens, processed_text, text, asIds=False)
-
-    def IdToToken(self, Id, type_token=False):
-        if isinstance(Id, (TypeToken, CommandToken)):
-            return Id.token
-        if type_token:
-            return self.type_id_map[Id].token
-        if Id in self.command_id_map:
-            return self.command_id_map[Id].token
-        elif Id in self.type_id_map:
-            return self.type_id_map[Id].token
-        else:
-            return self.text_tokenizer.convert_id_to_token(int(Id))
-
-    def TokenToId(self, token, type_token=False):
-        if isinstance(token, (TypeToken, CommandToken)):
-            return token.Id
-        if type_token:
-            return self.type_token_map[token].Id
-        return self.text_tokenizer.convert_token_to_id(token)
-
-    def DecodeIds(self, Ids, type_token=False):
-        if type_token:
-            return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
-        if isinstance(Ids, Tokenization):
-            Ids = Ids.tokenization
-        Ids = list(map(int, Ids))
-        pieces = []
-        last = 0
-        for i, token_id in enumerate(Ids):
-            if token_id in self.command_id_map:
-                pieces.append(Ids[last: i])
-                pieces.append(token_id)
-                last = i + 1
-        pieces.append(Ids[last:])
-        text = ""
-        for piece in pieces:
-            if isinstance(piece, int):
-                text += self.command_id_map[piece].token
-            elif piece:
-                text += self.text_tokenizer.decode(piece)
-        return text
-
-    def DecodeTokens(self, Tokens, type_token=False):
-        if type_token:
-            return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
-        if isinstance(Tokens, Tokenization):
-            Tokens = Tokens.tokenization
-        return self.text_tokenizer.decode([self.TokenToId(tok) for tok in Tokens])
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Hunting Unlimited 2012 Download WORK Free Full 16.md b/spaces/falterWliame/Face_Mask_Detection/Hunting Unlimited 2012 Download WORK Free Full 16.md
deleted file mode 100644
index 96642787f5c19c2f95c79ac1b69a58ba898e2f21..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Hunting Unlimited 2012 Download WORK Free Full 16.md	
+++ /dev/null
@@ -1,40 +0,0 @@
-<h2>Hunting Unlimited 2012 Download Free Full 16</h2><br /><p><b><b>Download Zip</b> &#10026; <a href="https://urlca.com/2uDbXy">https://urlca.com/2uDbXy</a></b></p><br /><br />
-<br />
-Additionally, the game will use motion capture technology to allow players to experience the outdoors on their own terms. If that wasn't enough, the game will feature dynamic gameplay, in-game checkpoints and an asynchronous multiplayer component for competitive hunters.John Gough (footballer, born 1874)
-
-John Gough (1874 – 5 December 1930) was an English professional footballer who played as a right half. He was born in Penistone, Midlothian.
-
-Gough joined Bradford City from Burnley in April 1902. He made five League appearances for the club, before moving on to East Stirlingshire, where he scored 1 goal in 33 League appearances for the "Blues".
-
-He played for a number of non-League clubs, including Harrogate Town, Bradford Park Avenue, Ashton National and Hollinwood.
-
-References
-
-Category:1874 births
-
-Category:1930 deaths
-
-Category:Sportspeople from Penistone
-
-Category:Scottish footballers
-
-Category:Association football midfielders
-
-Category:Burnley F.C. players
-
-Category:Bradford City A.F.C. players
-
-Category:East Stirlingshire F.C. players
-
-Category:Harrogate Town A.F.C. players
-
-Category:Bradford (Park Avenue) A.F.C. players
-
-Category:English Football League players
-
-Category:Ashton National F.C. players
-
-Category:Hollinwood F.C. players 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/falterWliame/Face_Mask_Detection/Lektira Petnaestogodisnji Kapetan Prepricano.md b/spaces/falterWliame/Face_Mask_Detection/Lektira Petnaestogodisnji Kapetan Prepricano.md
deleted file mode 100644
index d8430d3fe5e8125ff12c00c02eb504ac9211c027..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Lektira Petnaestogodisnji Kapetan Prepricano.md	
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diff --git a/spaces/fatiXbelha/sd/Call of Duty Warzone Mobile The Next Era of Call of Duty Franchise - Free APK Download for Android.md b/spaces/fatiXbelha/sd/Call of Duty Warzone Mobile The Next Era of Call of Duty Franchise - Free APK Download for Android.md
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index 689572399157f952e7a24cd87851c68330375692..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Call of Duty Warzone Mobile The Next Era of Call of Duty Franchise - Free APK Download for Android.md	
+++ /dev/null
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-<br />
-<h1>Call of Duty Warzone Mobile: How to Download and Play for Free on Android</h1>
-<p>If you are a fan of the Call of Duty franchise, you might have heard about the latest addition to the series: Call of Duty Warzone Mobile. This is a mobile version of the popular battle royale game that has taken the gaming world by storm. In this article, we will tell you everything you need to know about this game, including how to download and play it for free on your Android device.</p>
-<h2>What is Call of Duty Warzone Mobile?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Call of Duty Warzone Mobile is a mobile adaptation of the hit PC and console game Call of Duty Warzone, which is a spin-off of the Call of Duty Modern Warfare series. It is a first-person shooter game that features a massive online multiplayer mode where up to 120 players compete in a shrinking map to be the last one standing. The game offers authentic Call of Duty gameplay with realistic graphics, weapons, vehicles, and physics. It also has unique features such as contracts, killstreaks, loadouts, and the Gulag, where you can fight for a second chance after being eliminated.</p>
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-<h2>How to download Call of Duty Warzone Mobile APK for Android?</h2>
-<h3>The steps to pre-register and install the game from Google Play Store</h3>
-<p>The easiest way to download and play Call of Duty Warzone Mobile on your Android device is to pre-register and install it from the official Google Play Store. Here are the steps you need to follow:</p>
-<ol>
-<li>Open the Google Play Store app on your device and search for "Call of Duty Warzone Mobile".</li>
-<li>Tap on the game icon and then tap on the "Pre-register" button.</li>
-<li>Wait for the game to be released and then tap on the "Install" button.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
-<h3>The alternative method to download the APK file from a third-party source</h3>
-<p>If you don't want to wait for the official release or if you have trouble accessing the Google Play Store, you can also download the APK file of Call of Duty Warzone Mobile from a third-party source. However, this method is not recommended as it may expose your device to malware or viruses. If you still want to try it, here are the steps you need to follow:</p>
-<ol>
-<li>Find a reliable website that offers the APK file of Call of Duty Warzone Mobile. You can use a search engine or ask your friends for recommendations.</li>
-<li>Download the APK file to your device and make sure it is compatible with your device's specifications.</li>
-<li>Enable the "Unknown sources" option in your device's settings to allow installation from external sources.</li>
-<li>Locate the APK file in your device's storage and tap on it to install it.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
-<h2>How to play Call of Duty Warzone Mobile on Android?</h2>
-<h3>The basic controls and settings of the game</h <p>Once you have downloaded and installed the game, you can start playing it on your Android device. Here are some of the basic controls and settings of the game:</p>
-<ul>
-<li>To move your character, use the virtual joystick on the left side of the screen.</li>
-<li>To aim and shoot, use the buttons on the right side of the screen. You can also customize the layout and sensitivity of the buttons in the settings menu.</li>
-<li>To switch weapons, tap on the weapon icon on the bottom of the screen. You can also pick up weapons from the ground or from loot boxes.</li>
-<li>To use items such as armor plates, grenades, or killstreaks, tap on the corresponding icons on the top of the screen.</li>
-<li>To access the map, inventory, or options, tap on the menu button on the top left corner of the screen.</li>
-</ul>
-<h3>The game modes and maps available in the game</h3>
-<p>Call of Duty Warzone Mobile offers two main game modes: Battle Royale and Plunder. In Battle Royale, you can play solo, duo, trio, or squad with up to 120 players in a match. The objective is to eliminate all other players or teams and be the last one standing. You can also revive your teammates in certain situations, such as using a self-revive kit, buying them back at a buy station, or winning in the Gulag. In Plunder, you can play with up to 100 players in a match. The objective is to collect as much cash as possible by looting, completing contracts, or killing other players. You can also deposit your cash at designated locations or use a balloon to secure it.</p>
-<p>The game features several maps that are based on real-world locations or inspired by previous Call of Duty games. Some of the maps are:</p>
-<table>
-<tr><th>Map Name</th><th>Description</th></tr>
-<tr><td>Verdansk</td><td>The main map of Call of Duty Warzone Mobile. It is a large-scale map that covers various terrains and landmarks, such as downtown, airport, stadium, prison, dam, and more.</td></tr>
-<tr><td>Rebirth Island</td><td>A smaller map that is set on an island that was used for chemical experiments. It has a faster-paced gameplay and a shorter match duration than Verdansk.</td></tr>
-<tr><td>Alcatraz</td><td>A map that is based on the famous prison island in San Francisco Bay. It is a close-quarters map that requires quick reflexes and tactical skills.</td></tr>
-<tr><td>Nuketown Island</td><td>A map that is based on the classic Nuketown map from Call of Duty Black Ops. It is a small map that offers intense firefights and explosive action.</td></tr>
-</table> <h3>The tips and tricks to survive and win in the game</h3>
-<p>Call of Duty Warzone Mobile is a challenging and competitive game that requires skill, strategy, and luck to win. Here are some of the tips and tricks that can help you improve your performance and increase your chances of victory:</p>
-<ul>
-<li>Choose your landing spot wisely. Depending on your playstyle and preference, you can either land in a hot zone with more loot and enemies, or a quiet zone with less loot and enemies. Either way, make sure you have a plan and a backup plan in case things go wrong.</li>
-<li>Loot smartly. Don't waste time looting everything you see. Focus on the essentials, such as weapons, armor, ammo, and cash. You can also loot from dead enemies or buy from buy stations. Remember to share your loot with your teammates if you are playing in a team.</li>
-<li>Complete contracts. Contracts are optional missions that you can find and activate on the map. They can give you various rewards, such as cash, loot, intel, or killstreaks. They can also help you locate enemies or safe zones.</li>
-<li>Use vehicles. Vehicles are useful for traveling faster, escaping danger, or running over enemies. However, they also make noise and attract attention, so use them with caution. You can also destroy enemy vehicles with explosives or gunfire.</li>
-<li>Use killstreaks. Killstreaks are powerful abilities that you can use to gain an edge over your enemies. You can get them from loot boxes, buy stations, or contracts. Some of the killstreaks are UAV, cluster strike, precision airstrike, and juggernaut suit.</li>
-<li>Stay in the safe zone. The safe zone is the area that is not affected by the gas circle that shrinks over time. If you are outside the safe zone, you will take damage from the gas and eventually die. You can see the safe zone on the map and follow its movement.</li>
-<li>Communicate with your teammates. If you are playing in a team, communication is key to success. You can use voice chat or ping system to communicate with your teammates. You can also mark enemies, locations, items, or vehicles on the map for your teammates to see.</li>
-<li>Play strategically. Don't rush into fights without thinking. Use cover, stealth, flanking, or sniping to gain an advantage over your enemies. You can also use traps, decoys, or distractions to confuse or lure your enemies.</li>
-<li>Have fun. Don't get frustrated or angry if you lose or die. Learn from your mistakes and try again. Enjoy the game and have fun!</li>
-</ul>
-<h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>Call of Duty Warzone Mobile is an amazing game that brings the thrill and excitement of the battle royale genre to your mobile device. You can download and play it for free on your Android device by following the steps we have provided in this article. You can also improve your skills and win more matches by following the tips and tricks we have shared with you. So what are you waiting for? Download Call of Duty Warzone Mobile APK for Android today and join the fight!</p>
-<h2>FAQs</h2>
-<h4>Is Call of Duty Warzone Mobile free to play?</h4>
-<p>Yes, Call of Duty Warzone Mobile is free to play on Android devices. However, it may contain optional in-app purchases that can enhance your gaming experience.</p>
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-<h4>Is Call of Duty Warzone Mobile compatible with my device?</h4>
-<p>Call of Duty Warzone Mobile requires a minimum of 2 GB of RAM and Android 5.0 or higher to run smoothly on your device. You can check the compatibility of your device on the Google Play Store page of the game.</p>
-<h4>Is Call of Duty Warzone Mobile safe to download and play?</h4>
-<p>If you download Call of Duty Warzone Mobile from the official Google Play Store, it is safe to download and play on your device. However, if you download it from a third-party source, it may contain malware or viruses that can harm your device or compromise your privacy.</p>
-<h4>Can I play Call of Duty Warzone Mobile with my friends?</h4>
-<p>Yes, you can play Call of Duty Warzone Mobile with your friends by inviting them to join your lobby or by joining their lobby through the social menu. You can also chat with them through voice chat or text chat during the game.</p>
-<h4>How can I get more rewards and items in Call of Duty Warzone Mobile?</h4>
-<p>You can get more rewards and items in Call of Duty Warzone Mobile by completing challenges, missions, events, or seasons that are available in the game. You can also buy them with real money or earn them by watching ads or completing surveys. Some of the rewards and items include skins, weapons, vehicles, emotes, and more.</p> 401be4b1e0<br />
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diff --git a/spaces/fatiXbelha/sd/Clash Royale on PC A Guide to Download and Play without Bluestacks.md b/spaces/fatiXbelha/sd/Clash Royale on PC A Guide to Download and Play without Bluestacks.md
deleted file mode 100644
index c4f4ff0bed4b44c478aa5c1c976a053abac37519..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Clash Royale on PC A Guide to Download and Play without Bluestacks.md	
+++ /dev/null
@@ -1,103 +0,0 @@
-
-<h1>How to Download Clash Royale on PC without Bluestacks</h1>
-<p>Clash Royale is one of the most popular and addictive mobile games that combines card collecting, tower defense, and real-time strategy elements. It is developed by Supercell, the same company behind the hit game Clash of Clans. In Clash Royale, you can collect dozens of cards featuring your favorite Clash characters, spells, and defenses, as well as new ones such as the Royales. You can also build your own battle deck and challenge other players from around the world in fast-paced matches where you have to destroy their towers while protecting yours.</p>
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-<p>While Clash Royale is designed for mobile devices, you might want to play it on a bigger screen with better graphics and performance. Or maybe you don't have a compatible device or enough storage space for the game. Or maybe you just prefer using a keyboard and mouse over touch controls. Whatever your reason, you can play Clash Royale on your PC with an Android emulator.</p>
-<p>An Android emulator is a software that allows you to run Android apps and games on your PC. There are many Android emulators available for Windows and Mac, but one of the most popular ones is Bluestacks. Bluestacks is easy to use and has a large library of supported apps and games. However, Bluestacks also has some drawbacks that might make you look for an alternative. For example, Bluestacks can be slow, buggy, or unstable at times. It can also consume a lot of resources from your PC, causing performance issues or overheating. It can also show annoying ads or ask you to pay a monthly fee to access premium features. If you are looking for a way to download Clash Royale on PC without Bluestacks, you have come to the right place. In this article, we will show you three alternatives to Bluestacks that you can use to play Clash Royale on your PC for free. These are Nox App Player, LDPlayer, and Android-x86. Let's take a look at each one of them and how to use them.</p>
- <h2>How to Download Clash Royale on PC with Nox App Player</h2>
-<p>Nox App Player is one of the best Android emulators for PC that offers a smooth and stable gaming experience. It has a user-friendly interface and a powerful performance that can run multiple apps and games at the same time. It also has a lot of features that make it ideal for playing Clash Royale on PC, such as keyboard and mouse mapping, gamepad support, screen recording, screenshot, and more. Here is how to download Clash Royale on PC with Nox App Player:</p>
-<ol>
-<li>Download and install Nox App Player from its official website. The installation process is simple and straightforward. Just follow the instructions on the screen and wait for it to finish.</li>
-<li>Launch Nox App Player and sign in with your Google account. If you don't have one, you can create one for free. This will allow you to access the Google Play Store and download apps and games.</li>
-<li>Go to the Google Play Store and search for Clash Royale. Alternatively, you can use the built-in browser of Nox App Player and go to the official website of Clash Royale and click on the Google Play button. This will redirect you to the Google Play Store page of Clash Royale.</li>
-<li>Click on the Install button and wait for the game to download and install on your PC. Once it is done, you can find the game icon on the home screen of Nox App Player.</li>
-<li>Click on the game icon and enjoy playing Clash Royale on PC with Nox App Player.</li>
-</ol>
-<p>You can also customize the settings and controls of Nox App Player and Clash Royale according to your preferences. For example, you can change the resolution, frame rate, graphics quality, sound volume, language, etc. of Nox App Player by clicking on the Settings icon on the top right corner of the window. You can also change the keyboard and mouse mapping by clicking on the Keyboard icon on the right side of the window. You can assign different keys or buttons to perform different actions in Clash Royale, such as moving, attacking, deploying cards, etc.</p>
- <h2>How to Download Clash Royale on PC with LDPlayer</h2>
-<p>LDPlayer is another excellent Android emulator for PC that specializes in gaming. It has a fast and smooth performance that can run high-end games without lag or crash. It also has a lot of features that make it suitable for playing Clash Royale on PC, such as keyboard and mouse mapping, gamepad support, macro recorder, multi-instance manager, screen recorder, screenshot, etc. Here is how to download Clash Royale on PC with LDPlayer:</p>
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-<ol>
-<li>Download and install LDPlayer from its official website. The installation process is similar to Nox App Player. Just follow the instructions on the screen and wait for it to finish.</li>
-<li>Launch LDPlayer and sign in with your Google account. If you don't have one, you can create one for free. This will allow you to access the Google Play Store and download apps and games.</li>
-<li>Go to the Google Play Store and search for Clash Royale. Alternatively, you can use the built-in browser of LDPlayer and go to the official website of Clash Royale and click on the Google Play button. This will redirect you to the Google Play Store page of Clash Royale.</li>
-<li>Click on the Install button and wait for the game to download and install on your PC. Once it is done, you can find the game icon on the home screen of LDPlayer.</li>
-<li>Click on the game icon and enjoy playing Clash Royale on PC with LDPlayer.</li>
-</ol>
-<p>You can also customize the settings and controls of LDPlayer and Clash Royale according to your preferences. For example, you can change the resolution, frame rate, graphics quality, sound volume, language, etc. of LDPlayer by clicking on the Settings icon on the top right corner of the window. You can also change the keyboard and mouse mapping by clicking on the Keyboard icon on the right side of the window. You can assign different keys or buttons to perform different actions in Clash Royale, such as moving, attacking, deploying cards, etc.</p>
- <h2>How to Download Clash Royale on PC with Android-x86</h2>
-<p>Android-x86 is a project that aims to port the Android operating system to PC hardware. It is based on the open source code of Android and allows you to run Android apps and games on your PC natively. It is different from other Android emulators in that it does not run on top of another operating system, but rather replaces it. This means that you can have a dual-boot system with Android-x86 and Windows or Linux, or you can install Android-x86 on a separate partition or drive. It also means that you can have a better compatibility and performance with Android apps and games, as well as access to the full features of the Android system. Here is how to download Clash Royale on PC with Android-x86:</p>
-<ol>
-<li>Download and install Android-x86 from its official website. You can choose between different versions and builds of Android-x86, depending on your PC specifications and preferences. The latest stable version is based on Android 9.0 Pie, but you can also try the experimental versions based on Android 10 or 11. You can also choose between different architectures, such as 32-bit or 64-bit, and different formats, such as ISO or RPM. The installation process is a bit more complex than other Android emulators, as you have to create a bootable USB drive or CD/DVD with the Android-x86 image, and then boot your PC from it. You also have to partition your hard drive or use an external drive to install Android-x86 on it. You can find detailed instructions and tutorials on the official website of Android-x86 or on various online forums.</li>
-<li>Launch Android-x86 and sign in with your Google account. If you don't have one, you can create one for free. This will allow you to access the Google Play Store and download apps and games.</li>
-<li>Go to the Google Play Store and search for Clash Royale. Alternatively, you can use the built-in browser of Android-x86 and go to the official website of Clash Royale and click on the Google Play button. This will redirect you to the Google Play Store page of Clash Royale.</li>
-<li>Click on the Install button and wait for the game to download and install on your PC. Once it is done, you can find the game icon on the home screen of Android-x86.</li>
-<li>Click on the game icon and enjoy playing Clash Royale on PC with Android-x86.</li>
-</ol>
-<p>You can also customize the settings and controls of Android-x86 and Clash Royale according to your preferences. For example, you can change the resolution, frame rate, graphics quality, sound volume, language, etc. of Android-x86 by going to the Settings app on the app drawer. You can also change the keyboard and mouse mapping by using an app called Tincore Keymapper, which you can download from the Google Play Store. You can assign different keys or buttons to perform different actions in Clash Royale, such as moving, attacking, deploying cards, etc.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Clash Royale on PC without Bluestacks. We have discussed three alternatives to Bluestacks that you can use to play Clash Royale on your PC for free: Nox App Player, LDPlayer, and Android-x86. Each one of them has its own advantages and disadvantages, so you can choose the one that suits your needs and preferences best. Here is a summary of their pros and cons:</p>
-<table>
-<tr>
-<th>Alternative</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>Nox App Player</td>
-<td>- User-friendly interface<br>- Smooth and stable performance<br>- Many features for gaming<br>- Supports multiple instances</td>
-<td>- May consume a lot of resources<br>- May show ads or pop-ups<br>- May have compatibility issues with some games</td>
-</tr>
-<tr>
-<td>LDPlayer</td>
-<td>- Fast and smooth performance<br>- Many features for gaming<br>- Supports multiple instances<br>- Supports high-end games</td>
-<td>- May consume a lot of resources<br>- May show ads or pop-ups<br>- May have compatibility issues with some games</td>
-</tr>
-<tr>
-<td>Android-x86</td>
-<td>- Native Android experience<br>- Better compatibility and performance<br>- Access to full features of Android system<br>- Supports dual-boot or separate installation</td>
-<td>- Complex installation process<br>- May require advanced technical skills<br>- May have stability or security issues</td>
-</tr>
-</table>
- <p>Personally, I prefer using Nox App Player for playing Clash Royale on PC, as I find it easy to use and reliable. However, you may have a different opinion or experience, so feel free to try out any of these alternatives and see which one works best for you.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about playing Clash Royale on PC without Bluestacks:</p>
-<ul>
-<li>Q1: Is Clash Royale free to play on PC?<br>
-A1: Yes, Clash Royale is free to play on PC with any of the alternatives to Bluestacks mentioned in this article.</li>
-<li>Q2: Is it safe to use an Android emulator on PC?<br>
-A2: Yes, as long as you download the emulator from a trusted source and scan it for viruses or malware before installing it.</li>
-<li>Q3: Can I play Clash Royale on PC with my friends who play on mobile devices?<br>
-A3: Yes, you can play Clash Royale on PC with your friends who play on mobile devices as long as you are connected to the same server and have the same game version.</li>
-<li>Q4: Can I transfer my progress from mobile device to PC or vice versa?<br>
-A4: Yes, you can transfer your progress from mobile device to PC or vice versa by linking your game account to a Supercell ID or a Google Play account.</li>
-<li>Q5: What are the minimum system requirements for playing Clash Royale on PC?<br>
-A5: The minimum system requirements for playing Clash Royale on PC vary depending on the emulator you use, but generally you need at least 2 GB of RAM, 4 GB of disk space, a dual-core processor, and a graphics card that supports OpenGL 2.0 or higher.</li>
-</ul>
-<p>I hope you found this article helpful and informative. If you have any questions or feedback, please leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Download and Play A to Z MP3 Old Hindi Songs in 320kbps The Best of Retro Hindi Album Songs.md b/spaces/fatiXbelha/sd/Download and Play A to Z MP3 Old Hindi Songs in 320kbps The Best of Retro Hindi Album Songs.md
deleted file mode 100644
index 961ee4d3e97e8492cf82b3c443c11c341530d12f..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download and Play A to Z MP3 Old Hindi Songs in 320kbps The Best of Retro Hindi Album Songs.md	
+++ /dev/null
@@ -1,127 +0,0 @@
-<br />
-<h1>A to Z MP3 Old Hindi Songs Free Download 320kbps</h1>
-<p>If you are a fan of old Hindi songs, you might be looking for a way to download them in MP3 format. MP3 is a popular audio format that offers high quality sound and compatibility with various devices. In this article, we will tell you what are old Hindi songs, why do people love them, and how to download them in MP3 format. We will also share some of the best sources for downloading old Hindi songs in MP3 format.</p>
-<h2>Introduction</h2>
-<h3>What are old Hindi songs?</h3>
-<p>Old Hindi songs are the songs that were composed and sung in the Hindi language from the 1940s to the 1980s. They are also known as retro Hindi songs or golden era songs. These songs belong to different genres such as classical, folk, ghazal, qawwali, bhajan, and filmi. Some of the most famous singers of old Hindi songs are Lata Mangeshkar, Kishore Kumar, Mohammad Rafi, Asha Bhosle, Mukesh, and Manna Dey.</p>
-<h2>a to z mp3 old hindi songs free download 320kbps</h2><br /><p><b><b>DOWNLOAD</b> &bull; <a href="https://urllie.com/2uNBwy">https://urllie.com/2uNBwy</a></b></p><br /><br />
-<h3>Why do people love old Hindi songs?</h3>
-<p>People love old Hindi songs for various reasons. Some of them are:</p>
-<ul>
-<li>They have melodious tunes and meaningful lyrics that touch the heart and soul.</li>
-<li>They evoke nostalgia and memories of the past.</li>
-<li>They reflect the culture and values of the Indian society.</li>
-<li>They have a universal appeal and can be enjoyed by people of all ages and backgrounds.</li>
-</ul>
-<h3>How to download old Hindi songs in MP3 format?</h3>
-<p>To download old Hindi songs in MP3 format, you need to find a reliable source that offers a wide collection of old Hindi songs in high quality. You also need to have a stable internet connection and enough storage space on your device. You can either use a website or an app to download old Hindi songs in MP3 format. We will discuss some of the best sources for downloading old Hindi songs in MP3 format in the next section.</p>
-<h2>Benefits of downloading old Hindi songs in MP3 format</h2>
-<h3>High quality sound</h3>
-<p>MP3 is a compressed audio format that preserves the original sound quality while reducing the file size. This means that you can enjoy old Hindi songs in high quality sound without compromising on the storage space. MP3 files also have a bit rate that determines the quality of the sound. The higher the bit rate, the better the sound quality. For example, 320kbps is considered as one of the highest bit rates for MP3 files.</p>
-<h3>Compatibility with various devices</h3>
-<p>MP3 is a widely supported audio format that can be played on various devices such as computers, smartphones, tablets, music players, speakers, and headphones. You can also transfer MP3 files from one device to another using USB cables, Bluetooth, or Wi-Fi. This makes it convenient for you to listen to old Hindi songs anytime and anywhere.</p>
-<h3>Easy to store and transfer</h3>
-<p>MP3 files have a small file size compared to other audio formats such as WAV or FLAC. This means that you can store more MP3 files on your device without running out of space. You can also transfer MP3 files faster and easier than other audio formats. For example, you can download an MP3 file of an old Hindi song in a few seconds, while it may take minutes to download a WAV or FLAC file of the same song. </p>
-<h2>Best sources for downloading old Hindi songs in MP3 format</h2>
-<h3>Gaana.com</h3>
-<p>Gaana.com is one of the most popular music streaming and downloading platforms in India. It offers a huge collection of old Hindi songs in MP3 format, ranging from the 1940s to the 1980s. You can browse old Hindi songs by singers, composers, genres, albums, or playlists. You can also create your own playlists and share them with your friends.</p>
-<h4>Features of Gaana.com</h4>
-<ul>
-<li>It has a user-friendly interface and a fast downloading speed.</li>
-<li>It allows you to download old Hindi songs in MP3 format for free or with a premium subscription.</li>
-<li>It provides lyrics and trivia for old Hindi songs.</li>
-<li>It has a radio feature that plays old Hindi songs based on your mood or preference.</li>
-</ul>
-<h4>How to download old Hindi songs from Gaana.com</h4>
-<ol>
-<li>Visit the website of Gaana.com or download the app on your device.</li>
-<li>Sign up or log in with your email, phone number, or social media account.</li>
-<li>Search for the old Hindi song you want to download or browse the categories.</li>
-<li>Click on the download icon next to the song title or on the song page.</li>
-<li>Select the quality of the MP3 file you want to download (128kbps or 320kbps).</li>
-<li>Wait for the download to complete and enjoy the song.</li>
-</ol>
-<h3>PagalWorld</h3>
-<p>PagalWorld is another popular source for downloading old Hindi songs in MP3 format. It has a vast collection of old Hindi songs from various eras and genres. You can find old Hindi songs by singers, movies, albums, or years. You can also download old Hindi songs in ZIP files or as ringtones.</p>
-<h4>Features of PagalWorld</h4>
-<ul>
-<li>It has a simple and easy-to-use interface and a fast downloading speed.</li>
-<li>It allows you to download old Hindi songs in MP3 format for free without any registration or subscription.</li>
-<li>It provides information and ratings for old Hindi songs.</li>
-<li>It has a request feature that lets you request any old Hindi song you want to download.</li>
-</ul>
-<h4>How to download old Hindi songs from PagalWorld</h4>
-<ol>
-<li>Visit the website of PagalWorld or download the app on your device.</li>
-<li>Search for the old Hindi song you want to download or browse the categories.</li>
-<li>Click on the song title or on the download button on the song page.</li>
-<li>Select the quality of the MP3 file you want to download (64kbps, 128kbps, or 320kbps).</li>
-<li>Wait for the download to start and complete and enjoy the song.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In conclusion, old Hindi songs are timeless classics that have a loyal fan base across generations. They have melodious tunes and meaningful lyrics that touch the heart and soul. They also reflect the culture and values of the Indian society. To enjoy old Hindi songs in high quality sound and compatibility with various devices, you can download them in MP3 format from reliable sources such as Gaana.com and PagalWorld. These sources offer a wide collection of old Hindi songs in MP3 format that you can download for free or with a premium subscription. You can also create your own playlists and share them with your friends. So, what are you waiting for? Download your favorite old Hindi songs in MP3 format today and relive the golden era of music.</p>
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- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about downloading old Hindi songs in MP3 format:</p>
- <ol>
- <li><b>What is the difference between 128kbps and 320kbps MP3 files?</b></li>
- <p>A: The difference between 128kbps and 320kbps MP3 files is the bit rate, which determines the quality of the sound. The higher the bit rate, the better the sound quality. However, higher bit rates also mean larger file sizes. So, you need to balance between quality and storage space when choosing the bit rate for downloading MP3 files.</p>
- <li><b>Are there any legal issues with downloading old Hindi songs in MP3 format?</b></li>
- <p>A: Downloading old Hindi songs in MP3 format may involve some legal issues depending on the source and the country you are in. Some sources may have obtained the rights to distribute old Hindi songs in MP3 format, while others may not. Some countries may have strict laws against piracy and copyright infringement, while others may not. Therefore, you should always check the source and the law before downloading old Hindi songs in MP3 format.</p>
- <li><b>How can I convert old Hindi songs in other audio formats to MP3 format?</b></li>
- <p>A: If you have old Hindi songs in other audio formats such as WAV or FLAC, you can convert them to MP3 format using online or offline tools. Some of the online tools are Online Audio Converter, Zamzar, and Convertio. Some of the offline tools are Audacity, VLC Media Player, and Freemake Audio Converter. You can choose the tool that suits your needs and preferences.</p>
- <li><b>How can I edit old Hindi songs in MP3 format?</b></li>
- <p>A: If you want to edit old Hindi songs in MP3 format, such as trimming, cutting, merging, or adding effects, you can use online or offline tools. Some of the online tools are MP3 Cutter, Audio Trimmer, and Audio Joiner. Some of the offline tools are Audacity, WavePad, and MP3DirectCut. You can choose the tool that suits your needs and preferences.</p>
- <li><b>How can I share old Hindi songs in MP3 format with my friends?</b></li>
- <p>A: If you want to share old Hindi songs in MP3 format with your friends, you can use various methods such as email, social media, cloud storage, or Bluetooth. You can also create playlists and share them with your friends using music streaming and downloading platforms such as Gaana.com and PagalWorld.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fb700/chat3/crazy_functions/test_project/cpp/cppipc/shm.cpp b/spaces/fb700/chat3/crazy_functions/test_project/cpp/cppipc/shm.cpp
deleted file mode 100644
index 593ce3129dc1574dbc8fc8b088cf595df215de93..0000000000000000000000000000000000000000
--- a/spaces/fb700/chat3/crazy_functions/test_project/cpp/cppipc/shm.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-
-#include <string>
-#include <utility>
-
-#include "libipc/shm.h"
-
-#include "libipc/utility/pimpl.h"
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace shm {
-
-class handle::handle_ : public pimpl<handle_> {
-public:
-    shm::id_t id_ = nullptr;
-    void*     m_  = nullptr;
-
-    ipc::string n_;
-    std::size_t s_ = 0;
-};
-
-handle::handle()
-    : p_(p_->make()) {
-}
-
-handle::handle(char const * name, std::size_t size, unsigned mode)
-    : handle() {
-    acquire(name, size, mode);
-}
-
-handle::handle(handle&& rhs)
-    : handle() {
-    swap(rhs);
-}
-
-handle::~handle() {
-    release();
-    p_->clear();
-}
-
-void handle::swap(handle& rhs) {
-    std::swap(p_, rhs.p_);
-}
-
-handle& handle::operator=(handle rhs) {
-    swap(rhs);
-    return *this;
-}
-
-bool handle::valid() const noexcept {
-    return impl(p_)->m_ != nullptr;
-}
-
-std::size_t handle::size() const noexcept {
-    return impl(p_)->s_;
-}
-
-char const * handle::name() const noexcept {
-    return impl(p_)->n_.c_str();
-}
-
-std::int32_t handle::ref() const noexcept {
-    return shm::get_ref(impl(p_)->id_);
-}
-
-void handle::sub_ref() noexcept {
-    shm::sub_ref(impl(p_)->id_);
-}
-
-bool handle::acquire(char const * name, std::size_t size, unsigned mode) {
-    release();
-    impl(p_)->id_ = shm::acquire((impl(p_)->n_ = name).c_str(), size, mode);
-    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
-    return valid();
-}
-
-std::int32_t handle::release() {
-    if (impl(p_)->id_ == nullptr) return -1;
-    return shm::release(detach());
-}
-
-void* handle::get() const {
-    return impl(p_)->m_;
-}
-
-void handle::attach(id_t id) {
-    if (id == nullptr) return;
-    release();
-    impl(p_)->id_ = id;
-    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
-}
-
-id_t handle::detach() {
-    auto old = impl(p_)->id_;
-    impl(p_)->id_ = nullptr;
-    impl(p_)->m_  = nullptr;
-    impl(p_)->s_  = 0;
-    impl(p_)->n_.clear();
-    return old;
-}
-
-} // namespace shm
-} // namespace ipc
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/eval/verification.py b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/eval/verification.py
deleted file mode 100644
index 253343b83dbf9d1bd154d14ec068e098bf0968db..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/eval/verification.py
+++ /dev/null
@@ -1,407 +0,0 @@
-"""Helper for evaluation on the Labeled Faces in the Wild dataset 
-"""
-
-# MIT License
-#
-# Copyright (c) 2016 David Sandberg
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-
-import datetime
-import os
-import pickle
-
-import mxnet as mx
-import numpy as np
-import sklearn
-import torch
-from mxnet import ndarray as nd
-from scipy import interpolate
-from sklearn.decomposition import PCA
-from sklearn.model_selection import KFold
-
-
-class LFold:
-    def __init__(self, n_splits=2, shuffle=False):
-        self.n_splits = n_splits
-        if self.n_splits > 1:
-            self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle)
-
-    def split(self, indices):
-        if self.n_splits > 1:
-            return self.k_fold.split(indices)
-        else:
-            return [(indices, indices)]
-
-
-def calculate_roc(thresholds,
-                  embeddings1,
-                  embeddings2,
-                  actual_issame,
-                  nrof_folds=10,
-                  pca=0):
-    assert (embeddings1.shape[0] == embeddings2.shape[0])
-    assert (embeddings1.shape[1] == embeddings2.shape[1])
-    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
-    nrof_thresholds = len(thresholds)
-    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
-    tprs = np.zeros((nrof_folds, nrof_thresholds))
-    fprs = np.zeros((nrof_folds, nrof_thresholds))
-    accuracy = np.zeros((nrof_folds))
-    indices = np.arange(nrof_pairs)
-
-    if pca == 0:
-        diff = np.subtract(embeddings1, embeddings2)
-        dist = np.sum(np.square(diff), 1)
-
-    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
-        if pca > 0:
-            print('doing pca on', fold_idx)
-            embed1_train = embeddings1[train_set]
-            embed2_train = embeddings2[train_set]
-            _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
-            pca_model = PCA(n_components=pca)
-            pca_model.fit(_embed_train)
-            embed1 = pca_model.transform(embeddings1)
-            embed2 = pca_model.transform(embeddings2)
-            embed1 = sklearn.preprocessing.normalize(embed1)
-            embed2 = sklearn.preprocessing.normalize(embed2)
-            diff = np.subtract(embed1, embed2)
-            dist = np.sum(np.square(diff), 1)
-
-        # Find the best threshold for the fold
-        acc_train = np.zeros((nrof_thresholds))
-        for threshold_idx, threshold in enumerate(thresholds):
-            _, _, acc_train[threshold_idx] = calculate_accuracy(
-                threshold, dist[train_set], actual_issame[train_set])
-        best_threshold_index = np.argmax(acc_train)
-        for threshold_idx, threshold in enumerate(thresholds):
-            tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(
-                threshold, dist[test_set],
-                actual_issame[test_set])
-        _, _, accuracy[fold_idx] = calculate_accuracy(
-            thresholds[best_threshold_index], dist[test_set],
-            actual_issame[test_set])
-
-    tpr = np.mean(tprs, 0)
-    fpr = np.mean(fprs, 0)
-    return tpr, fpr, accuracy
-
-
-def calculate_accuracy(threshold, dist, actual_issame):
-    predict_issame = np.less(dist, threshold)
-    tp = np.sum(np.logical_and(predict_issame, actual_issame))
-    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
-    tn = np.sum(
-        np.logical_and(np.logical_not(predict_issame),
-                       np.logical_not(actual_issame)))
-    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
-
-    tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
-    fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
-    acc = float(tp + tn) / dist.size
-    return tpr, fpr, acc
-
-
-def calculate_val(thresholds,
-                  embeddings1,
-                  embeddings2,
-                  actual_issame,
-                  far_target,
-                  nrof_folds=10):
-    assert (embeddings1.shape[0] == embeddings2.shape[0])
-    assert (embeddings1.shape[1] == embeddings2.shape[1])
-    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
-    nrof_thresholds = len(thresholds)
-    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
-    val = np.zeros(nrof_folds)
-    far = np.zeros(nrof_folds)
-
-    diff = np.subtract(embeddings1, embeddings2)
-    dist = np.sum(np.square(diff), 1)
-    indices = np.arange(nrof_pairs)
-
-    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
-
-        # Find the threshold that gives FAR = far_target
-        far_train = np.zeros(nrof_thresholds)
-        for threshold_idx, threshold in enumerate(thresholds):
-            _, far_train[threshold_idx] = calculate_val_far(
-                threshold, dist[train_set], actual_issame[train_set])
-        if np.max(far_train) >= far_target:
-            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
-            threshold = f(far_target)
-        else:
-            threshold = 0.0
-
-        val[fold_idx], far[fold_idx] = calculate_val_far(
-            threshold, dist[test_set], actual_issame[test_set])
-
-    val_mean = np.mean(val)
-    far_mean = np.mean(far)
-    val_std = np.std(val)
-    return val_mean, val_std, far_mean
-
-
-def calculate_val_far(threshold, dist, actual_issame):
-    predict_issame = np.less(dist, threshold)
-    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
-    false_accept = np.sum(
-        np.logical_and(predict_issame, np.logical_not(actual_issame)))
-    n_same = np.sum(actual_issame)
-    n_diff = np.sum(np.logical_not(actual_issame))
-    # print(true_accept, false_accept)
-    # print(n_same, n_diff)
-    val = float(true_accept) / float(n_same)
-    far = float(false_accept) / float(n_diff)
-    return val, far
-
-
-def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
-    # Calculate evaluation metrics
-    thresholds = np.arange(0, 4, 0.01)
-    embeddings1 = embeddings[0::2]
-    embeddings2 = embeddings[1::2]
-    tpr, fpr, accuracy = calculate_roc(thresholds,
-                                       embeddings1,
-                                       embeddings2,
-                                       np.asarray(actual_issame),
-                                       nrof_folds=nrof_folds,
-                                       pca=pca)
-    thresholds = np.arange(0, 4, 0.001)
-    val, val_std, far = calculate_val(thresholds,
-                                      embeddings1,
-                                      embeddings2,
-                                      np.asarray(actual_issame),
-                                      1e-3,
-                                      nrof_folds=nrof_folds)
-    return tpr, fpr, accuracy, val, val_std, far
-
-@torch.no_grad()
-def load_bin(path, image_size):
-    try:
-        with open(path, 'rb') as f:
-            bins, issame_list = pickle.load(f)  # py2
-    except UnicodeDecodeError as e:
-        with open(path, 'rb') as f:
-            bins, issame_list = pickle.load(f, encoding='bytes')  # py3
-    data_list = []
-    for flip in [0, 1]:
-        data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1]))
-        data_list.append(data)
-    for idx in range(len(issame_list) * 2):
-        _bin = bins[idx]
-        img = mx.image.imdecode(_bin)
-        if img.shape[1] != image_size[0]:
-            img = mx.image.resize_short(img, image_size[0])
-        img = nd.transpose(img, axes=(2, 0, 1))
-        for flip in [0, 1]:
-            if flip == 1:
-                img = mx.ndarray.flip(data=img, axis=2)
-            data_list[flip][idx][:] = torch.from_numpy(img.asnumpy())
-        if idx % 1000 == 0:
-            print('loading bin', idx)
-    print(data_list[0].shape)
-    return data_list, issame_list
-
-@torch.no_grad()
-def test(data_set, backbone, batch_size, nfolds=10):
-    print('testing verification..')
-    data_list = data_set[0]
-    issame_list = data_set[1]
-    embeddings_list = []
-    time_consumed = 0.0
-    for i in range(len(data_list)):
-        data = data_list[i]
-        embeddings = None
-        ba = 0
-        while ba < data.shape[0]:
-            bb = min(ba + batch_size, data.shape[0])
-            count = bb - ba
-            _data = data[bb - batch_size: bb]
-            time0 = datetime.datetime.now()
-            img = ((_data / 255) - 0.5) / 0.5
-            net_out: torch.Tensor = backbone(img)
-            _embeddings = net_out.detach().cpu().numpy()
-            time_now = datetime.datetime.now()
-            diff = time_now - time0
-            time_consumed += diff.total_seconds()
-            if embeddings is None:
-                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
-            embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
-            ba = bb
-        embeddings_list.append(embeddings)
-
-    _xnorm = 0.0
-    _xnorm_cnt = 0
-    for embed in embeddings_list:
-        for i in range(embed.shape[0]):
-            _em = embed[i]
-            _norm = np.linalg.norm(_em)
-            _xnorm += _norm
-            _xnorm_cnt += 1
-    _xnorm /= _xnorm_cnt
-
-    acc1 = 0.0
-    std1 = 0.0
-    embeddings = embeddings_list[0] + embeddings_list[1]
-    embeddings = sklearn.preprocessing.normalize(embeddings)
-    print(embeddings.shape)
-    print('infer time', time_consumed)
-    _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds)
-    acc2, std2 = np.mean(accuracy), np.std(accuracy)
-    return acc1, std1, acc2, std2, _xnorm, embeddings_list
-
-
-def dumpR(data_set,
-          backbone,
-          batch_size,
-          name='',
-          data_extra=None,
-          label_shape=None):
-    print('dump verification embedding..')
-    data_list = data_set[0]
-    issame_list = data_set[1]
-    embeddings_list = []
-    time_consumed = 0.0
-    for i in range(len(data_list)):
-        data = data_list[i]
-        embeddings = None
-        ba = 0
-        while ba < data.shape[0]:
-            bb = min(ba + batch_size, data.shape[0])
-            count = bb - ba
-
-            _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb)
-            time0 = datetime.datetime.now()
-            if data_extra is None:
-                db = mx.io.DataBatch(data=(_data,), label=(_label,))
-            else:
-                db = mx.io.DataBatch(data=(_data, _data_extra),
-                                     label=(_label,))
-            model.forward(db, is_train=False)
-            net_out = model.get_outputs()
-            _embeddings = net_out[0].asnumpy()
-            time_now = datetime.datetime.now()
-            diff = time_now - time0
-            time_consumed += diff.total_seconds()
-            if embeddings is None:
-                embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
-            embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
-            ba = bb
-        embeddings_list.append(embeddings)
-    embeddings = embeddings_list[0] + embeddings_list[1]
-    embeddings = sklearn.preprocessing.normalize(embeddings)
-    actual_issame = np.asarray(issame_list)
-    outname = os.path.join('temp.bin')
-    with open(outname, 'wb') as f:
-        pickle.dump((embeddings, issame_list),
-                    f,
-                    protocol=pickle.HIGHEST_PROTOCOL)
-
-
-# if __name__ == '__main__':
-#
-#     parser = argparse.ArgumentParser(description='do verification')
-#     # general
-#     parser.add_argument('--data-dir', default='', help='')
-#     parser.add_argument('--model',
-#                         default='../model/softmax,50',
-#                         help='path to load model.')
-#     parser.add_argument('--target',
-#                         default='lfw,cfp_ff,cfp_fp,agedb_30',
-#                         help='test targets.')
-#     parser.add_argument('--gpu', default=0, type=int, help='gpu id')
-#     parser.add_argument('--batch-size', default=32, type=int, help='')
-#     parser.add_argument('--max', default='', type=str, help='')
-#     parser.add_argument('--mode', default=0, type=int, help='')
-#     parser.add_argument('--nfolds', default=10, type=int, help='')
-#     args = parser.parse_args()
-#     image_size = [112, 112]
-#     print('image_size', image_size)
-#     ctx = mx.gpu(args.gpu)
-#     nets = []
-#     vec = args.model.split(',')
-#     prefix = args.model.split(',')[0]
-#     epochs = []
-#     if len(vec) == 1:
-#         pdir = os.path.dirname(prefix)
-#         for fname in os.listdir(pdir):
-#             if not fname.endswith('.params'):
-#                 continue
-#             _file = os.path.join(pdir, fname)
-#             if _file.startswith(prefix):
-#                 epoch = int(fname.split('.')[0].split('-')[1])
-#                 epochs.append(epoch)
-#         epochs = sorted(epochs, reverse=True)
-#         if len(args.max) > 0:
-#             _max = [int(x) for x in args.max.split(',')]
-#             assert len(_max) == 2
-#             if len(epochs) > _max[1]:
-#                 epochs = epochs[_max[0]:_max[1]]
-#
-#     else:
-#         epochs = [int(x) for x in vec[1].split('|')]
-#     print('model number', len(epochs))
-#     time0 = datetime.datetime.now()
-#     for epoch in epochs:
-#         print('loading', prefix, epoch)
-#         sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
-#         # arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
-#         all_layers = sym.get_internals()
-#         sym = all_layers['fc1_output']
-#         model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
-#         # model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
-#         model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0],
-#                                           image_size[1]))])
-#         model.set_params(arg_params, aux_params)
-#         nets.append(model)
-#     time_now = datetime.datetime.now()
-#     diff = time_now - time0
-#     print('model loading time', diff.total_seconds())
-#
-#     ver_list = []
-#     ver_name_list = []
-#     for name in args.target.split(','):
-#         path = os.path.join(args.data_dir, name + ".bin")
-#         if os.path.exists(path):
-#             print('loading.. ', name)
-#             data_set = load_bin(path, image_size)
-#             ver_list.append(data_set)
-#             ver_name_list.append(name)
-#
-#     if args.mode == 0:
-#         for i in range(len(ver_list)):
-#             results = []
-#             for model in nets:
-#                 acc1, std1, acc2, std2, xnorm, embeddings_list = test(
-#                     ver_list[i], model, args.batch_size, args.nfolds)
-#                 print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
-#                 print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
-#                 print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
-#                 results.append(acc2)
-#             print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))
-#     elif args.mode == 1:
-#         raise ValueError
-#     else:
-#         model = nets[0]
-#         dumpR(ver_list[0], model, args.batch_size, args.target)
diff --git a/spaces/fclong/summary/fengshen/examples/finetune_bart_qg/finetune_bart.py b/spaces/fclong/summary/fengshen/examples/finetune_bart_qg/finetune_bart.py
deleted file mode 100644
index d2c64589edf6c146632e656c96b7195d4ae87d81..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/finetune_bart_qg/finetune_bart.py
+++ /dev/null
@@ -1,429 +0,0 @@
-# -*- encoding: utf-8 -*-
-'''
-Copyright 2022 The International Digital Economy Academy (IDEA). CCNL 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.
-@File    :   finetune_bart.py
-@Time    :   2022/10/28 18:23
-@Author  :   Qi Yang
-@Version :   1.0
-@Contact :   yangqi@idea.edu.cn
-@License :   (C)Copyright 2022-2023, CCNL-IDEA
-'''
-
-
-from fengshen.models.model_utils import configure_optimizers
-from fengshen.data.universal_datamodule import UniversalDataModule
-from fengshen.utils.universal_checkpoint import UniversalCheckpoint
-from fengshen.utils import chinese_char_tokenize
-from utils import truncate_sequence, white_space_fix
-from utils import LabelSmoothingCrossEntropy
-import sys
-import os
-import torch
-import argparse
-import pytorch_lightning as pl
-from dataclasses import dataclass
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import LearningRateMonitor
-from transformers import BartForConditionalGeneration
-from transformers import BertTokenizer, AutoTokenizer
-from torchmetrics.text.rouge import ROUGEScore
-sys.path.append('../../../')
-
-
-@dataclass
-class QGT5Collator:
-    @ staticmethod
-    def add_data_specific_args(parent_args):
-        # the hyperparameters should be determined according to the max length of context in dataset
-        parser = parent_args.add_argument_group('BART DIalo Collator')
-        parser.add_argument('--max_seq_length', default=512, type=int)
-        parser.add_argument('--max_src_length', default=32, type=int)
-        parser.add_argument('--max_kno_length', default=416, type=int)
-        parser.add_argument('--max_tgt_length', default=64, type=int)
-        parser.add_argument('--mask_ans_style',
-                            default='normal',
-                            type=str,
-                            choices=['normal', 'unmask', 'anstoken', 'postag', 'anstoken_multispan', 'postag_multispan', 'normal_multispan'])
-        return parent_args
-
-    def __init__(self, tokenizer, args):
-        self.args = args
-        self.tokenizer = tokenizer
-        self.max_seq_length = args.max_seq_length
-        self.print_example = True
-        self.mask_ans_style = args.mask_ans_style
-        self.do_eval_only = args.do_eval_only
-        self.tokenizer_type = args.tokenizer_type
-
-    def encode(self, x, y):
-        if self.tokenizer_type == "bert":
-            x = x
-            y = y
-        else:
-            # t5 sentence piece
-            x = self.tokenizer.bos_token + x + self.tokenizer.eos_token
-            y = y + self.tokenizer.eos_token
-
-        encoder_input = self.tokenizer.encode_plus(
-            x,
-            max_length=self.args.max_kno_length + self.args.max_src_length,
-            padding="max_length",
-            truncation=True,
-            return_tensors='pt'
-        )
-        decoder_output = self.tokenizer.encode_plus(
-            y,
-            max_length=self.args.max_tgt_length,
-            padding="max_length",
-            truncation=True,
-            return_tensors='pt'
-        )
-
-        return encoder_input, decoder_output
-
-    def mask(self, s):
-        def replace_span(source, target, sptoken):
-            ans_bos, ans_eos = s["ans_span"][0]
-            return source[:ans_bos] + sptoken + source[ans_eos:]
-
-        def replace_all(source, target, sptoken):
-            return source.replace(target, sptoken)
-
-        if 'multispan' in self.mask_ans_style:
-            fn = replace_all
-        else:
-            fn = replace_span
-
-        # unmask: 北京是中国的首都
-        if 'unmask' in self.mask_ans_style:
-            return s["context"]
-
-        # normal: 北京是 <mask> 的首都
-        if 'normal' in self.mask_ans_style:
-            self.anstoken = self.tokenizer.mask_token
-            masked_context = fn(s["context"], s["answer"][0], self.anstoken)
-            return masked_context
-
-        # anstoken: 北京是 [ANS] 的首都
-        if 'anstoken' in self.mask_ans_style:
-            anstoken_dict = {
-                "bert": "[ANS]",
-                "bart": "<ans>"
-            }
-            self.anstoken = anstoken_dict[self.tokenizer_type]
-            masked_context = fn(s["context"], s["answer"][0], self.anstoken)
-            return masked_context
-
-        # postag: 北京是 <beg> 中国 <eos> 的首都
-        if 'postag' in self.mask_ans_style:
-            begtoken, endtoken = "<beg>", "<eos>"
-            self.anstoken = begtoken + s["answer"][0] + endtoken
-            masked_context = fn(s["context"], s["answer"][0], self.anstoken)
-            return masked_context
-
-        return masked_context
-
-    def prompt(self, context, answer, question):
-        pre_prompt, mid_prompt, post_prompt = "知识:", "回答:", "问题:"  # prompt
-
-        context = truncate_sequence(context, self.args.max_kno_length-len(pre_prompt)-1)
-
-        # used in squad-2.0
-        # noted that src and tgt is reversed in qg
-        answer = truncate_sequence(answer, self.args.max_src_length - len(mid_prompt)-1)
-        question = truncate_sequence(question, self.args.max_tgt_length-len(post_prompt)-1)
-
-        x_trunc = f'{pre_prompt}{context}{mid_prompt}{answer}'
-        y_trunc = f'{post_prompt}{question}'
-        return x_trunc, y_trunc
-
-    def __call__(self, samples):
-        """
-        ans_num = 1 适用于 Train 数据只有 1 条 answer 取第一条情况
-        ans_num > 1 适用于 Dev 数据有多条 answer 情况
-        Input:
-        input_ids: input_ids (text + answer)
-        attn_mask: input attn mask
-        labels:   decoder_ids (question)
-        """
-        input_ids, attn_mask, labels = [], [], []
-        ans, qes, ctx, ans_spans, idxs, imp = [], [], [], [], [], []
-
-        for s in samples:
-            if self.do_eval_only:
-                # log origin answer to compare
-                ans.append(s["answer"])
-                qes.append(s["question"])
-                ctx.append(s["context"])
-                ans_spans.append(s["ans_span"])
-                idxs.append(s["idx"])
-
-            if "is_impossible" in s:
-                imp.append(s["is_impossible"])
-            else:
-                imp.append(False)  # SQUAD 1.0 don't have is_impossible
-
-            if not s["is_impossible"]:  # have ans and ans_span
-                context = self.mask(s)
-                answer = s["answer"][0]
-                question = s["question"]
-            else:  # no ans and ans_span
-                context = s["context"]
-                answer = "无答案"
-                question = s["question"]
-
-            x_trunc, y_trunc = self.prompt(context, answer, question)
-            encoder_input, decoder_output = self.encode(x_trunc, y_trunc)
-
-            input_ids.append(encoder_input["input_ids"])
-            attn_mask.append(encoder_input["attention_mask"])
-            labels.append(decoder_output["input_ids"])
-
-        labels = torch.cat(labels)
-        if self.tokenizer_type == "bart":
-            end_token_index = torch.where(labels == self.tokenizer.eos_token_id)[1]
-        else:
-            end_token_index = torch.where(labels == self.tokenizer.sep_token_id)[1]
-        for idx, end_idx in enumerate(end_token_index):
-            labels[idx][end_idx + 1:] = -100  # cross entropy cal
-
-        data = {
-            'input_ids': torch.cat(input_ids),
-            'attention_mask': torch.cat(attn_mask),
-            'labels': labels
-        }
-        if self.do_eval_only:
-            data.update({
-                'answer': ans,
-                'question': qes,
-                'context': ctx,
-                'ans_span': ans_spans,
-                'idx': idxs,
-                'is_impossible': imp
-            })
-
-        if self.print_example:
-            print(x_trunc)
-            print(y_trunc)
-            self.print_example = False
-
-        return data
-
-
-class BARTFinetuneModel(pl.LightningModule):
-    @staticmethod
-    def add_model_specific_args(parent_args):
-        parser = parent_args.add_argument_group('BaseModel')
-        parser.add_argument('--model_path', type=str, default='')
-        parser.add_argument('--learning_rate', default=1e-5, type=float)
-        parser.add_argument('--min_learning_rate', default=1e-7, type=float)
-        parser.add_argument('--lr_decay_steps', default=0, type=int)
-        parser.add_argument('--lr_decay_ratio', default=1.0, type=float)
-        parser.add_argument('--weight_decay', default=0.1, type=float)
-        parser.add_argument('--warmup_steps', default=1000, type=int)
-        parser.add_argument('--warmup_ratio', default=0.01, type=float)
-        parser.add_argument('--label_smooth', default=0, type=float)
-        parser.add_argument('--new_token_path', default="./", type=str)  # save new token after add special token
-        parser.add_argument('--adam_beta1', default=0.9, type=float)
-        parser.add_argument('--adam_beta2', default=0.999, type=float)
-        parser.add_argument('--adam_epsilon', default=1e-8, type=float)
-        parser.add_argument('--scheduler_type', default='polynomial', type=str)
-
-        return parent_args
-
-    def __init__(self, tokenizer, args):
-        super().__init__()
-        self.save_hyperparameters(args)
-        self.model = BartForConditionalGeneration.from_pretrained(args.model_path)
-        self.tokenizer = tokenizer
-
-        # add special token ans
-        # self.tokenizer.save_vocabulary(self.args.model_path)
-        new_vocab = args.model_path+"/sp_vocab/"
-        if not os.path.exists(new_vocab):
-            os.makedirs(new_vocab)
-        self.tokenizer.save_pretrained(new_vocab)
-        self.model.resize_token_embeddings(len(tokenizer))
-        self.vocab_size = len(tokenizer)
-        self.rougescore = ROUGEScore(rouge_keys=('rougeL'), normalizer=lambda x: x)
-
-        if self.hparams.label_smooth:
-            self.loss_fct = LabelSmoothingCrossEntropy(smoothing=0.1)
-
-    def setup(self, stage) -> None:
-        if stage == 'fit':
-            train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
-            # Calculate total steps
-            if self.trainer.max_epochs > 0:
-                world_size = self.trainer.world_size
-                tb_size = self.hparams.train_batchsize * max(1, world_size)
-                ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs)
-                self.total_steps = (len(train_loader.dataset) *
-                                    self.trainer.max_epochs // tb_size) // ab_size
-            else:
-                self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches
-
-            print('Total steps: {}' .format(self.total_steps))
-
-    def configure_optimizers(self):
-        return configure_optimizers(self)
-
-    def training_step(self, batch, batch_idx):
-        output = self.model(
-            input_ids=batch['input_ids'],
-            attention_mask=batch['attention_mask'],
-            labels=batch['labels'])
-
-        loss = output.loss
-        if self.hparams.label_smooth:
-            loss = self.loss_fct(output.logits.view(-1, self.vocab_size), batch["labels"].view(-1))
-
-        self.log('train_loss', loss, sync_dist=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        output = self.model(
-            input_ids=batch['input_ids'],
-            attention_mask=batch['attention_mask'],
-            labels=batch['labels'])
-        acc = self.compute_acc(output.logits, batch['labels'])
-        self.log('val_loss', output.loss, sync_dist=True)
-        self.log('val_acc', acc, sync_dist=True)
-        self.log('val_ppl', torch.exp(output.loss), sync_dist=True)
-
-        cond_output = self.model.generate(
-            input_ids=batch['input_ids'],
-            attention_mask=batch['attention_mask'],
-            do_sample=True,
-            num_beams=5,
-            early_stopping=True,
-            max_length=64,
-            top_p=0.9,
-        )
-
-        batch_label = torch.where(batch["labels"] != -100, batch["labels"], self.tokenizer.pad_token_id)
-        pred = self.tokenizer.batch_decode(cond_output, clean_up_tokenization_spaces=True, skip_special_tokens=True)
-        ques = self.tokenizer.batch_decode(batch_label, clean_up_tokenization_spaces=True, skip_special_tokens=True)
-
-        pred = [chinese_char_tokenize(white_space_fix(p)) for p in pred]
-        ques = [chinese_char_tokenize(white_space_fix(q)) for q in ques]
-        self.rougescore.update(pred, ques)
-
-        return pred
-
-    def validation_epoch_end(self, validation_step_outputs):
-        rouge = self.rougescore.compute()
-        self.log('val_rouge', rouge["rougeL_fmeasure"], sync_dist=True)
-
-    def on_predict_start(self):
-        self.loss_fct = torch.nn.CrossEntropyLoss(reduction='none')
-
-    def predict_step(self, batch, batch_idx):
-        output = self.model(
-            input_ids=batch['input_ids'],
-            attention_mask=batch['attention_mask'],
-            labels=batch['labels'])
-
-        loss_tensor = self.loss_fct(output.logits.transpose(1, 2), batch["labels"])
-        if self.hparams.tokenizer_type == 'bart':
-            eos_index = torch.where(batch['labels'] == self.tokenizer.eos_token_id)[1]
-        elif self.hparams.tokenizer_type == 'bert':
-            eos_index = torch.where(batch['labels'] == self.tokenizer.sep_token_id)[1]
-
-        loss = torch.sum(loss_tensor, dim=1) / eos_index
-
-        with torch.no_grad():
-            cond_output = self.model.generate(
-                input_ids=batch['input_ids'],
-                attention_mask=batch['attention_mask'],
-                do_sample=True,
-                num_beams=5,
-                max_length=64,
-                top_p=0.9,
-                output_scores=True,
-                return_dict_in_generate=True
-            )
-
-        pred = self.tokenizer.batch_decode(
-            cond_output.sequences, clean_up_tokenization_spaces=True, skip_special_tokens=True)  # ['sequences']
-        pred = [white_space_fix(p) for p in pred]  # remove prompt and white space
-        score = cond_output.sequences_scores
-        return pred, score, loss
-
-    def compute_acc(self, logits, labels):
-        y_pred = torch.argmax(logits, dim=-1)
-        y_pred = y_pred.view(size=(-1,))
-        y_true = labels.view(size=(-1,)).float()
-        corr = torch.eq(y_pred, y_true)
-        acc = torch.sum(corr.float())/y_true.shape[0]
-        return acc
-
-    def on_save_checkpoint(self, checkpoint) -> None:
-        if self.trainer._accelerator_connector.cluster_environment.global_rank() == 0:
-            self.model.save_pretrained(os.path.join(
-                self.trainer.checkpoint_callback.dirpath,
-                'hf_pretrained_epoch{}_step{}'.format(checkpoint['epoch'], checkpoint['global_step'])))
-
-    def on_load_checkpoint(self, checkpoint) -> None:
-        global_step_offset = checkpoint["global_step"]
-        if 'global_samples' in checkpoint:
-            self.consumed_samples = checkpoint['global_samples']
-        self.trainer.fit_loop.epoch_loop._batches_that_stepped = global_step_offset
-
-
-def get_tokenizer(tokenizer_type, pretrained_model_path):
-    if tokenizer_type == 'bart':
-        tokenizer = AutoTokenizer.from_pretrained(
-            pretrained_model_path, use_fast=False, additional_special_tokens=["<ans>", "<beg>", "<end>"])
-        print(len(tokenizer))
-    elif tokenizer_type == 'bert':
-        tokenizer = BertTokenizer.from_pretrained(
-            pretrained_model_path, use_fast=False, additional_special_tokens=["[ANS]"])
-    return tokenizer
-
-
-def main():
-    total_parser = argparse.ArgumentParser("Finetune BART for QG")
-    total_parser.add_argument('--do_eval_only', action='store_true', default=False)
-    total_parser.add_argument('--tokenizer_type', type=str, default="bart", choices=['bart', 'bert'])
-    total_parser.add_argument('--tensorboard_dir', type=str, default="bart")
-    total_parser.add_argument('--deepspeed')
-
-    total_parser = UniversalDataModule.add_data_specific_args(total_parser)
-    total_parser = QGT5Collator.add_data_specific_args(total_parser)
-    total_parser = Trainer.add_argparse_args(total_parser)
-    total_parser = UniversalCheckpoint.add_argparse_args(total_parser)
-    total_parser = BARTFinetuneModel.add_model_specific_args(total_parser)
-    args = total_parser.parse_args()
-
-    tokenizer = get_tokenizer(args.tokenizer_type, args.model_path)
-    collator = QGT5Collator(tokenizer=tokenizer, args=args)
-    data_model = UniversalDataModule(collate_fn=collator, tokenizer=tokenizer, args=args)
-    print("Data load complete...")
-
-    if args.deepspeed is not None:
-        os.environ['PL_DEEPSPEED_CONFIG_PATH'] = args.deepspeed
-
-    model = BARTFinetuneModel(tokenizer, args)
-    checkpoint_callback = UniversalCheckpoint(args)
-    lr_monitor = LearningRateMonitor(logging_interval='step')
-    trainer = Trainer.from_argparse_args(args,
-                                         callbacks=[checkpoint_callback, lr_monitor]
-                                         )
-
-    if not args.do_eval_only:
-        trainer.fit(model, data_model)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download 60 Seconds! Reatomized for Free from MediaFire - Experience the Post-Apocalyptic Adventure.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download 60 Seconds! Reatomized for Free from MediaFire - Experience the Post-Apocalyptic Adventure.md
deleted file mode 100644
index 317c359ae97baba5ce07075db9d450e29fd4a00c..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download 60 Seconds! Reatomized for Free from MediaFire - Experience the Post-Apocalyptic Adventure.md	
+++ /dev/null
@@ -1,128 +0,0 @@
-
-<h1>How to Download and Play 60 Seconds! Reatomized on Android via Mediafıre</h1>
-<p>Do you enjoy dark humor and survival games? Do you want to test your skills and luck in a post-apocalyptic scenario? If so, you might want to check out <strong>60 Seconds! Reatomized</strong>, a remastered version of the classic atomic adventure game by Robot Gentleman. In this game, you have to scavenge supplies, rescue your family, and stay alive in your fallout shelter after a nuclear bomb drops. The game is now available for download on Android devices via Mediafıre, a file hosting service that allows users to share files online. In this article, we will show you how to download and play 60 Seconds! Reatomized on Android via Mediafıre, as well as review its features and gameplay, and give you some tips for surviving and escaping the wasteland.</p>
-<h2>60 seconds reatomized download mediafıre</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash; <a href="https://gohhs.com/2uPmuT">https://gohhs.com/2uPmuT</a></b></p><br /><br />
- <h2>How to Download 60 Seconds! Reatomized on Android via Mediafıre</h2>
-<p>Downloading 60 Seconds! Reatomized on Android via Mediafıre is easy and fast. All you need to do is follow these simple steps:</p>
-<ol>
-<li>First, make sure your device is compatible with the game. It requires Android 6.0 or higher, and at least 2GB of RAM.</li>
-<li>Next, download the game files from Mediafıre by clicking <a href="(^1^)">this link</a>. The files are compressed in a ZIP format, so you will need an app like WinZip or RAR to extract them.</li>
-<li>Once the files are downloaded, locate the .apk file and tap to install it. Follow the on-screen instructions to complete the installation process.</li>
-<li>Once the game is installed, open it up and get ready for a thrilling adventure!</li>
-</ol>
-<p><strong>Important Note:</strong> Keep in mind that downloading games from third-party sources like Mediafıre can have risks. Be sure to scan all downloaded files with antivirus software before installing them <h2>Features and Gameplay of 60 Seconds! Reatomized</h2>
-<p>60 Seconds! Reatomized is not just a simple port of the original game. It is a remastered edition that offers new features and improvements that make the game more enjoyable and challenging. Some of the new features and gameplay elements are:</p>
-<ul>
-<li><strong>4K support</strong>: The game now supports 4K resolution, which means you can enjoy the game's graphics and animations in high definition.</li>
-<li><strong>Refreshed 2D graphics and hand-drawn 3D textures</strong>: The game's visuals have been enhanced with new 2D graphics and hand-drawn 3D textures, which give the game a more polished and detailed look.</li>
-<li><strong>New interactive menu</strong>: The game's menu has been redesigned to be more user-friendly and interactive. You can now access different options and modes with ease.</li>
-<li><strong>Improved UI system</strong>: The game's user interface has been improved to be more intuitive and informative. You can now see more details and stats about your family and your shelter.</li>
-<li><strong>Technical refresh</strong>: The game's performance and stability have been improved with a technical refresh, which means you can expect fewer bugs and glitches.</li>
-<li><strong>New content</strong>: The game now has new content, such as new characters, events, endings, achievements, etc. You can now experience more variety and surprises in your survival adventure.</li>
-<li><strong>New gameplay mode</strong>: The game now has a new gameplay mode called Survival Challenges, which are unique, short storylines that will put your survival skills to the test. You can choose from different scenarios, such as surviving with a cat, escaping with a dog, or being a secret agent.</li>
-</ul>
-<p>The basic gameplay mechanics and objectives of the game remain the same as the original. You have to scavenge supplies, rescue your family, and stay alive in your fallout shelter after a nuclear bomb drops. You have to ration food and water, make best use of your supplies, face difficult choices, and even venture into the wasteland. You have to deal with random events, such as mutant roaches, raiders, strangers, etc. You have to try to escape the wasteland or wait for rescue. You have to survive as long as you can or die trying.</p>
- <h2>Review and Tips for 60 Seconds! Reatomized</h2>
-<p>60 Seconds! Reatomized is a fun and addictive game that combines dark comedy and survival elements. It is a game that will make you laugh, cry, scream, and think. It is a game that will challenge you with its randomness and unpredictability. It is a game that will reward you with its humor and creativity. It is a game that will keep you hooked with its replay value and variety.</p>
-<p>Here is my personal opinion and rating of the game based on my experience and criteria:</p>
-<table>
-<tr><th>Criteria</th><th>Rating (out of 10)</th></tr>
-<tr><td>Graphics</td><td>8</td></tr>
-<tr><td>Sound</td><td>9</td></tr>
-<tr><td>Gameplay</td><td>10</td></tr>
-<tr><td>Story</td><td>9</td></tr>
-<tr><td>Replay Value</td><td>10</td></tr>
-<tr><td>Total Score</td><td>9.2</td></tr>
-</table>
-<p>Some of the pros and cons of the game are:</p>
-<ul>
-<li><strong>Pros</strong>:   <ul>
-  <li>The game is hilarious and witty, with a lot of references and jokes.</li>
-  <li>The game is engaging and immersive, with a lot of choices and consequences.</li>
-  <li>The game is challenging and rewarding, with a lot of scenarios and endings.</li>
-  <li>The game is diverse and varied, with a lot of characters and events.</li>
-  <li>The game is updated and improved, with a lot of features and enhancements.</li>
-  </ul>
-</li>
-<li><strong>Cons</strong>:   <ul>
-  <li>The game can be frustrating and unfair, with a lot of luck and randomness.</li>
-  <li>The game can be repetitive and boring, with a lot of grinding and waiting.</li>
-  <li>The game can be buggy and glitchy, with some technical issues.</li>
-  </ul>
-</li>
-</ul>
- <p>If you want to enjoy the game more, here are some tips and tricks that I can share with you:</p>
- <ul>
- <li>If you want to scavenge more supplies in 60 seconds, you - If you want to scavenge more supplies in 60 seconds, you should plan ahead and prioritize the items that you need. You should also use shortcuts and avoid obstacles in your house. You should also grab your family members as soon as possible, as they will help you carry more items.</li>
-<li>If you want to ration your food and water wisely, you should keep track of your family's health and needs. You should also consider the quality and quantity of your supplies. You should also be careful about who you feed and when, as some characters may have special traits or preferences.</li>
-<li>If you want to make good decisions in your shelter, you should weigh the pros and cons of each option. You should also consider the risks and rewards of each action. You should also be prepared for the consequences of your choices, as they may affect your survival or escape.</li>
-<li>If you want to venture into the wasteland safely, you should equip your family members with the best items and weapons. You should also choose the best locations and times to explore. You should also be aware of the dangers and opportunities that await you in the wasteland, such as enemies, allies, resources, etc.</li>
-<li>If you want to escape the wasteland successfully, you should follow the clues and signals that may lead you to rescue or salvation. You should also cooperate with other survivors or factions that may help you or hinder you. You should also be ready for the final challenge or twist that may determine your fate.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>60 Seconds! Reatomized is a game that will make you laugh, cry, scream, and think. It is a game that will challenge you with its randomness and unpredictability. It is a game that will reward you with its humor and creativity. It is a game that will keep you hooked with its replay value and variety.</p>
-<p>In this article, we have shown you how to download and play 60 Seconds! Reatomized on Android via Mediafıre, as well as reviewed its features and gameplay, and given you some tips for surviving and escaping the wasteland. We hope that you have found this article helpful and informative.</p>
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-<p>If you are interested in playing 60 Seconds! Reatomized on Android via Mediafıre, you can download it from <a href="">this link</a>. But remember, downloading games from third-party sources like Mediafıre can have risks. Be sure to scan all downloaded files with antivirus software before installing them.</p>
-<p>If you have any questions or comments about the game or the topic, feel free to leave them below. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers about 60 Seconds! Reatomized or the topic:</p>
-<ul>
-<li><strong>Q: Is 60 Seconds! Reatomized free?</strong></li>
-<li>A: No, 60 Seconds! Reatomized is not free. It is a paid game that costs $3.99 on Google Play Store. However, some users may choose to download it from third-party sources like Mediafıre for free, but this can have risks.</li>
-<li><strong>Q: Is 60 Seconds! Reatomized multiplayer?</strong></li>
-<li>A: No, 60 Seconds! Reatomized is not multiplayer. It is a single-player game that can only be played offline.</li>
-<li><strong>Q: Is 60 Seconds! Reatomized scary?</strong></li>
-<li>A: Yes, 60 Seconds! Reatomized can be scary at times. It is a game that deals with dark themes and situations, such as nuclear war, death, violence, etc. It is also a game that has jump scares and creepy sounds. However, it is also a game that has a lot of humor and comedy, which can lighten up the mood.</li>
-<li><strong>Q: Is 60 Seconds! Reatomized suitable for kids?</strong></li>
-<li>A: No, 60 Seconds! Reatomized is not suitable for kids. It is a game that has mature content and language, such as blood, gore, drugs, alcohol, etc. It is also a game that has difficult and moral choices, which may not be appropriate for young audiences.</li>
-<li><strong>Q: Is 60 Seconds! Reatomized different from 60 Seconds!</strong></li>
-<li>A: Yes, 60 Seconds! Reatomized is different from 60 Seconds!. It is a remastered version of the original game that offers new features and improvements, such as 4K support, new content, new modes, etc.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffffu/bing/src/components/header.tsx b/spaces/fffffu/bing/src/components/header.tsx
deleted file mode 100644
index dc298b722154d1ac6d7a7e148204605562d6cc58..0000000000000000000000000000000000000000
--- a/spaces/fffffu/bing/src/components/header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as React from 'react'
-import { UserMenu } from './user-menu'
-
-export async function Header() {
-  return (
-    <header className="sticky top-0 z-50 flex items-center justify-between w-full h-16 px-4 border-b shrink-0 bg-gradient-to-b from-background/10 via-background/50 to-background/80 backdrop-blur-xl">
-      <div className="flex items-center justify-end space-x-2 w-full">
-        <UserMenu />
-      </div>
-    </header>
-  )
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/node_modules/ms/license.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/node_modules/ms/license.md
deleted file mode 100644
index 69b61253a38926757b7de1d4df4880fc2105c2c9..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/node_modules/ms/license.md
+++ /dev/null
@@ -1,21 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2016 Zeit, Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/spaces/fkhuggingme/gpt-academic/crazy_functions/crazy_utils.py b/spaces/fkhuggingme/gpt-academic/crazy_functions/crazy_utils.py
deleted file mode 100644
index e54136c441e7d713b0e8f5a66de9fb8bae1b1f4c..0000000000000000000000000000000000000000
--- a/spaces/fkhuggingme/gpt-academic/crazy_functions/crazy_utils.py
+++ /dev/null
@@ -1,608 +0,0 @@
-from toolbox import update_ui, get_conf, trimmed_format_exc
-
-def input_clipping(inputs, history, max_token_limit):
-    import numpy as np
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-
-    mode = 'input-and-history'
-    # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
-    input_token_num = get_token_num(inputs)
-    if input_token_num < max_token_limit//2: 
-        mode = 'only-history'
-        max_token_limit = max_token_limit - input_token_num
-
-    everything = [inputs] if mode == 'input-and-history' else ['']
-    everything.extend(history)
-    n_token = get_token_num('\n'.join(everything))
-    everything_token = [get_token_num(e) for e in everything]
-    delta = max(everything_token) // 16 # 截断时的颗粒度
-        
-    while n_token > max_token_limit:
-        where = np.argmax(everything_token)
-        encoded = enc.encode(everything[where], disallowed_special=())
-        clipped_encoded = encoded[:len(encoded)-delta]
-        everything[where] = enc.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char
-        everything_token[where] = get_token_num(everything[where])
-        n_token = get_token_num('\n'.join(everything))
-
-    if mode == 'input-and-history':
-        inputs = everything[0]
-    else:
-        pass
-    history = everything[1:]
-    return inputs, history
-
-def request_gpt_model_in_new_thread_with_ui_alive(
-        inputs, inputs_show_user, llm_kwargs, 
-        chatbot, history, sys_prompt, refresh_interval=0.2,
-        handle_token_exceed=True, 
-        retry_times_at_unknown_error=2,
-        ):
-    """
-    Request GPT model，请求GPT模型同时维持用户界面活跃。
-
-    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
-        inputs (string): List of inputs （输入）
-        inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
-        top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）
-        temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）
-        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
-        history (list): List of chat history （历史，对话历史列表）
-        sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
-        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
-        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
-        retry_times_at_unknown_error：失败时的重试次数
-
-    输出 Returns:
-        future: 输出，GPT返回的结果
-    """
-    import time
-    from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
-    # 用户反馈
-    chatbot.append([inputs_show_user, ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    executor = ThreadPoolExecutor(max_workers=16)
-    mutable = ["", time.time(), ""]
-    def _req_gpt(inputs, history, sys_prompt):
-        retry_op = retry_times_at_unknown_error
-        exceeded_cnt = 0
-        while True:
-            # watchdog error
-            if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: 
-                raise RuntimeError("检测到程序终止。")
-            try:
-                # 【第一种情况】：顺利完成
-                result = predict_no_ui_long_connection(
-                    inputs=inputs, llm_kwargs=llm_kwargs,
-                    history=history, sys_prompt=sys_prompt, observe_window=mutable)
-                return result
-            except ConnectionAbortedError as token_exceeded_error:
-                # 【第二种情况】：Token溢出
-                if handle_token_exceed:
-                    exceeded_cnt += 1
-                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
-                    from toolbox import get_reduce_token_percent
-                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = 4096
-                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
-                    mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
-                    continue # 返回重试
-                else:
-                    # 【选择放弃】
-                    tb_str = '```\n' + trimmed_format_exc() + '```'
-                    mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    return mutable[0] # 放弃
-            except:
-                # 【第三种情况】：其他错误：重试几次
-                tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
-                mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                if retry_op > 0:
-                    retry_op -= 1
-                    mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
-                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
-                        time.sleep(30)
-                    time.sleep(5)
-                    continue # 返回重试
-                else:
-                    time.sleep(5)
-                    return mutable[0] # 放弃
-
-    # 提交任务
-    future = executor.submit(_req_gpt, inputs, history, sys_prompt)
-    while True:
-        # yield一次以刷新前端页面
-        time.sleep(refresh_interval)
-        # “喂狗”（看门狗）
-        mutable[1] = time.time()
-        if future.done():
-            break
-        chatbot[-1] = [chatbot[-1][0], mutable[0]]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-
-    final_result = future.result()
-    chatbot[-1] = [chatbot[-1][0], final_result]
-    yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
-    return final_result
-
-
-def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array, inputs_show_user_array, llm_kwargs, 
-        chatbot, history_array, sys_prompt_array, 
-        refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
-        handle_token_exceed=True, show_user_at_complete=False,
-        retry_times_at_unknown_error=2,
-        ):
-    """
-    Request GPT model using multiple threads with UI and high efficiency
-    请求GPT模型的[多线程]版。
-    具备以下功能：
-        实时在UI上反馈远程数据流
-        使用线程池，可调节线程池的大小避免openai的流量限制错误
-        处理中途中止的情况
-        网络等出问题时，会把traceback和已经接收的数据转入输出
-
-    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
-        inputs_array (list): List of inputs （每个子任务的输入）
-        inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
-        llm_kwargs: llm_kwargs参数
-        chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化）
-        history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史）
-        sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
-        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
-        max_workers (int, optional): Maximum number of threads (default: see config.py) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误）
-        scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果）
-        handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本）
-        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
-        show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)
-        retry_times_at_unknown_error：子任务失败时的重试次数
-
-    输出 Returns:
-        list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）
-    """
-    import time, random
-    from concurrent.futures import ThreadPoolExecutor
-    from request_llm.bridge_all import predict_no_ui_long_connection
-    assert len(inputs_array) == len(history_array)
-    assert len(inputs_array) == len(sys_prompt_array)
-    if max_workers == -1: # 读取配置文件
-        try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
-        except: max_workers = 8
-        if max_workers <= 0: max_workers = 3
-    # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
-    if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
-        max_workers = 1
-        
-    executor = ThreadPoolExecutor(max_workers=max_workers)
-    n_frag = len(inputs_array)
-    # 用户反馈
-    chatbot.append(["请开始多线程操作。", ""])
-    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    # 跨线程传递
-    mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
-
-    # 子线程任务
-    def _req_gpt(index, inputs, history, sys_prompt):
-        gpt_say = ""
-        retry_op = retry_times_at_unknown_error
-        exceeded_cnt = 0
-        mutable[index][2] = "执行中"
-        while True:
-            # watchdog error
-            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: 
-                raise RuntimeError("检测到程序终止。")
-            try:
-                # 【第一种情况】：顺利完成
-                # time.sleep(10); raise RuntimeError("测试")
-                gpt_say = predict_no_ui_long_connection(
-                    inputs=inputs, llm_kwargs=llm_kwargs, history=history, 
-                    sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
-                )
-                mutable[index][2] = "已成功"
-                return gpt_say
-            except ConnectionAbortedError as token_exceeded_error:
-                # 【第二种情况】：Token溢出，
-                if handle_token_exceed:
-                    exceeded_cnt += 1
-                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
-                    from toolbox import get_reduce_token_percent
-                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
-                    MAX_TOKEN = 4096
-                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
-                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
-                    gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
-                    mutable[index][2] = f"截断重试"
-                    continue # 返回重试
-                else:
-                    # 【选择放弃】
-                    tb_str = '```\n' + trimmed_format_exc() + '```'
-                    gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                    if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
-                    mutable[index][2] = "输入过长已放弃"
-                    return gpt_say # 放弃
-            except:
-                # 【第三种情况】：其他错误
-                tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
-                gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
-                if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
-                if retry_op > 0: 
-                    retry_op -= 1
-                    wait = random.randint(5, 20)
-                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
-                        wait = wait * 3
-                        fail_info = "OpenAI绑定信用卡可解除频率限制 "
-                    else:
-                        fail_info = ""
-                    # 也许等待十几秒后，情况会好转
-                    for i in range(wait):
-                        mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1)
-                    # 开始重试
-                    mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
-                    continue # 返回重试
-                else:
-                    mutable[index][2] = "已失败"
-                    wait = 5
-                    time.sleep(5)
-                    return gpt_say # 放弃
-
-    # 异步任务开始
-    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
-        range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
-    cnt = 0
-    while True:
-        # yield一次以刷新前端页面
-        time.sleep(refresh_interval)
-        cnt += 1
-        worker_done = [h.done() for h in futures]
-        if all(worker_done):
-            executor.shutdown()
-            break
-        # 更好的UI视觉效果
-        observe_win = []
-        # 每个线程都要“喂狗”（看门狗）
-        for thread_index, _ in enumerate(worker_done):
-            mutable[thread_index][1] = time.time()
-        # 在前端打印些好玩的东西
-        for thread_index, _ in enumerate(worker_done):
-            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
-                replace('\n', '').replace('```', '...').replace(
-                    ' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
-            observe_win.append(print_something_really_funny)
-        # 在前端打印些好玩的东西
-        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' 
-                            if not done else f'`{mutable[thread_index][2]}`\n\n' 
-                            for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
-        # 在前端打印些好玩的东西
-        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    
-    # 异步任务结束
-    gpt_response_collection = []
-    for inputs_show_user, f in zip(inputs_show_user_array, futures):
-        gpt_res = f.result()
-        gpt_response_collection.extend([inputs_show_user, gpt_res])
-    
-    # 是否在结束时，在界面上显示结果
-    if show_user_at_complete:
-        for inputs_show_user, f in zip(inputs_show_user_array, futures):
-            gpt_res = f.result()
-            chatbot.append([inputs_show_user, gpt_res])
-            yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-            time.sleep(0.3)
-    return gpt_response_collection
-
-
-def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
-    def cut(txt_tocut, must_break_at_empty_line):  # 递归
-        if get_token_fn(txt_tocut) <= limit:
-            return [txt_tocut]
-        else:
-            lines = txt_tocut.split('\n')
-            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    if lines[cnt] != "":
-                        continue
-                print(cnt)
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-            if cnt == 0:
-                raise RuntimeError("存在一行极长的文本！")
-            # print(len(post))
-            # 列表递归接龙
-            result = [prev]
-            result.extend(cut(post, must_break_at_empty_line))
-            return result
-    try:
-        return cut(txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        return cut(txt, must_break_at_empty_line=False)
-
-
-def force_breakdown(txt, limit, get_token_fn):
-    """
-    当无法用标点、空行分割时，我们用最暴力的方法切割
-    """
-    for i in reversed(range(len(txt))):
-        if get_token_fn(txt[:i]) < limit:
-            return txt[:i], txt[i:]
-    return "Tiktoken未知错误", "Tiktoken未知错误"
-
-def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
-    # 递归
-    def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):  
-        if get_token_fn(txt_tocut) <= limit:
-            return [txt_tocut]
-        else:
-            lines = txt_tocut.split('\n')
-            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
-            estimated_line_cut = int(estimated_line_cut)
-            cnt = 0
-            for cnt in reversed(range(estimated_line_cut)):
-                if must_break_at_empty_line:
-                    if lines[cnt] != "":
-                        continue
-                prev = "\n".join(lines[:cnt])
-                post = "\n".join(lines[cnt:])
-                if get_token_fn(prev) < limit:
-                    break
-            if cnt == 0:
-                if break_anyway:
-                    prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
-                else:
-                    raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
-            # print(len(post))
-            # 列表递归接龙
-            result = [prev]
-            result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
-            return result
-    try:
-        # 第1次尝试，将双空行（\n\n）作为切分点
-        return cut(txt, must_break_at_empty_line=True)
-    except RuntimeError:
-        try:
-            # 第2次尝试，将单空行（\n）作为切分点
-            return cut(txt, must_break_at_empty_line=False)
-        except RuntimeError:
-            try:
-                # 第3次尝试，将英文句号（.）作为切分点
-                res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
-                return [r.replace('。\n', '.') for r in res]
-            except RuntimeError as e:
-                try:
-                    # 第4次尝试，将中文句号（。）作为切分点
-                    res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False)
-                    return [r.replace('。。\n', '。') for r in res]
-                except RuntimeError as e:
-                    # 第5次尝试，没办法了，随便切一下敷衍吧
-                    return cut(txt, must_break_at_empty_line=False, break_anyway=True)
-
-
-
-def read_and_clean_pdf_text(fp):
-    """
-    这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好
-
-    **输入参数说明**
-    - `fp`：需要读取和清理文本的pdf文件路径
-
-    **输出参数说明**
-    - `meta_txt`：清理后的文本内容字符串
-    - `page_one_meta`：第一页清理后的文本内容列表
-
-    **函数功能**
-    读取pdf文件并清理其中的文本内容，清理规则包括：
-    - 提取所有块元的文本信息，并合并为一个字符串
-    - 去除短块（字符数小于100）并替换为回车符
-    - 清理多余的空行
-    - 合并小写字母开头的段落块并替换为空格
-    - 清除重复的换行
-    - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
-    """
-    import fitz, copy
-    import re
-    import numpy as np
-    from colorful import print亮黄, print亮绿
-    fc = 0  # Index 0 文本
-    fs = 1  # Index 1 字体
-    fb = 2  # Index 2 框框
-    REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
-    REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
-    def primary_ffsize(l):
-        """
-        提取文本块主字体
-        """
-        fsize_statiscs = {}
-        for wtf in l['spans']:
-            if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
-            fsize_statiscs[wtf['size']] += len(wtf['text'])
-        return max(fsize_statiscs, key=fsize_statiscs.get)
-        
-    def ffsize_same(a,b):
-        """
-        提取字体大小是否近似相等
-        """
-        return abs((a-b)/max(a,b)) < 0.02
-
-    with fitz.open(fp) as doc:
-        meta_txt = []
-        meta_font = []
-
-        meta_line = []
-        meta_span = []
-        ############################## <第 1 步，搜集初始信息> ##################################
-        for index, page in enumerate(doc):
-            # file_content += page.get_text()
-            text_areas = page.get_text("dict")  # 获取页面上的文本信息
-            for t in text_areas['blocks']:
-                if 'lines' in t:
-                    pf = 998
-                    for l in t['lines']:
-                        txt_line = "".join([wtf['text'] for wtf in l['spans']])
-                        if len(txt_line) == 0: continue
-                        pf = primary_ffsize(l)
-                        meta_line.append([txt_line, pf, l['bbox'], l])
-                        for wtf in l['spans']: # for l in t['lines']:
-                            meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
-                    # meta_line.append(["NEW_BLOCK", pf])
-            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block
-            meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
-                '- ', '') for t in text_areas['blocks'] if 'lines' in t])
-            meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
-                             for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
-            if index == 0:
-                page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
-                    '- ', '') for t in text_areas['blocks'] if 'lines' in t]
-                
-        ############################## <第 2 步，获取正文主字体> ##################################
-        fsize_statiscs = {}
-        for span in meta_span:
-            if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
-            fsize_statiscs[span[1]] += span[2]
-        main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
-        if REMOVE_FOOT_NOTE:
-            give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
-
-        ############################## <第 3 步，切分和重新整合> ##################################
-        mega_sec = []
-        sec = []
-        for index, line in enumerate(meta_line):
-            if index == 0: 
-                sec.append(line[fc])
-                continue
-            if REMOVE_FOOT_NOTE:
-                if meta_line[index][fs] <= give_up_fize_threshold:
-                    continue
-            if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
-                # 尝试识别段落
-                if meta_line[index][fc].endswith('.') and\
-                    (meta_line[index-1][fc] != 'NEW_BLOCK') and \
-                    (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
-                    sec[-1] += line[fc]
-                    sec[-1] += "\n\n"
-                else:
-                    sec[-1] += " "
-                    sec[-1] += line[fc]
-            else:
-                if (index+1 < len(meta_line)) and \
-                    meta_line[index][fs] > main_fsize:
-                    # 单行 + 字体大
-                    mega_sec.append(copy.deepcopy(sec))
-                    sec = []
-                    sec.append("# " + line[fc])
-                else:
-                    # 尝试识别section
-                    if meta_line[index-1][fs] > meta_line[index][fs]:
-                        sec.append("\n" + line[fc])
-                    else:
-                        sec.append(line[fc])
-        mega_sec.append(copy.deepcopy(sec))
-
-        finals = []
-        for ms in mega_sec:
-            final = " ".join(ms)
-            final = final.replace('- ', ' ')
-            finals.append(final)
-        meta_txt = finals
-
-        ############################## <第 4 步，乱七八糟的后处理> ##################################
-        def 把字符太少的块清除为回车(meta_txt):
-            for index, block_txt in enumerate(meta_txt):
-                if len(block_txt) < 100:
-                    meta_txt[index] = '\n'
-            return meta_txt
-        meta_txt = 把字符太少的块清除为回车(meta_txt)
-
-        def 清理多余的空行(meta_txt):
-            for index in reversed(range(1, len(meta_txt))):
-                if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
-                    meta_txt.pop(index)
-            return meta_txt
-        meta_txt = 清理多余的空行(meta_txt)
-
-        def 合并小写开头的段落块(meta_txt):
-            def starts_with_lowercase_word(s):
-                pattern = r"^[a-z]+"
-                match = re.match(pattern, s)
-                if match:
-                    return True
-                else:
-                    return False
-            for _ in range(100):
-                for index, block_txt in enumerate(meta_txt):
-                    if starts_with_lowercase_word(block_txt):
-                        if meta_txt[index-1] != '\n':
-                            meta_txt[index-1] += ' '
-                        else:
-                            meta_txt[index-1] = ''
-                        meta_txt[index-1] += meta_txt[index]
-                        meta_txt[index] = '\n'
-            return meta_txt
-        meta_txt = 合并小写开头的段落块(meta_txt)
-        meta_txt = 清理多余的空行(meta_txt)
-
-        meta_txt = '\n'.join(meta_txt)
-        # 清除重复的换行
-        for _ in range(5):
-            meta_txt = meta_txt.replace('\n\n', '\n')
-
-        # 换行 -> 双换行
-        meta_txt = meta_txt.replace('\n', '\n\n')
-
-        ############################## <第 5 步，展示分割效果> ##################################
-        # for f in finals:
-        #    print亮黄(f)
-        #    print亮绿('***************************')
-
-    return meta_txt, page_one_meta
-
-
-def get_files_from_everything(txt, type): # type='.md'
-    """
-    这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
-    下面是对每个参数和返回值的说明：
-    参数 
-    - txt: 路径或网址，表示要搜索的文件或者文件夹路径或网络上的文件。 
-    - type: 字符串，表示要搜索的文件类型。默认是.md。
-    返回值 
-    - success: 布尔值，表示函数是否成功执行。 
-    - file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。 
-    - project_folder: 字符串，表示文件所在的文件夹路径。如果是网络上的文件，就是临时文件夹的路径。
-    该函数详细注释已添加，请确认是否满足您的需要。
-    """
-    import glob, os
-
-    success = True
-    if txt.startswith('http'):
-        # 网络的远程文件
-        import requests
-        from toolbox import get_conf
-        proxies, = get_conf('proxies')
-        r = requests.get(txt, proxies=proxies)
-        with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)
-        project_folder = './gpt_log/'
-        file_manifest = ['./gpt_log/temp'+type]
-    elif txt.endswith(type):
-        # 直接给定文件
-        file_manifest = [txt]
-        project_folder = os.path.dirname(txt)
-    elif os.path.exists(txt):
-        # 本地路径，递归搜索
-        project_folder = txt
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
-        if len(file_manifest) == 0:
-            success = False
-    else:
-        project_folder = None
-        file_manifest = []
-        success = False
-
-    return success, file_manifest, project_folder
diff --git a/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/walkers/walker_abstract_body.js b/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/walkers/walker_abstract_body.js
deleted file mode 100644
index 6ae12640f34b30156ceac8c06c22d351d55820f1..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/walkers/walker_abstract_body.js
+++ /dev/null
@@ -1,16 +0,0 @@
-/**
- * @classdesc Abstract class for walker morphologies.
- */
-class WalkerAbstractBody extends AbstractBody{
-    /**
-     * @constructor
-     * @param scale {number} - Scale of the environment
-     * @param motors_torque {number}
-     * @param nb_steps_under_water {number}
-     */
-    constructor(scale, motors_torque, nb_steps_under_water) {
-        super(scale, motors_torque);
-        this.body_type = BodyTypesEnum.WALKER;
-        this.nb_steps_can_survive_under_water = nb_steps_under_water;
-    }
-}
\ No newline at end of file
diff --git a/spaces/fun-research/FC-CLIP/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py b/spaces/fun-research/FC-CLIP/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py
deleted file mode 100644
index 3090c9bc2f9a63156a4132e89c635613691eb350..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import functools
-import json
-import multiprocessing as mp
-import numpy as np
-import os
-import time
-from fvcore.common.download import download
-from panopticapi.utils import rgb2id
-from PIL import Image
-
-from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
-
-
-def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):
-    panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)
-    panoptic = rgb2id(panoptic)
-    output = np.zeros_like(panoptic, dtype=np.uint8) + 255
-    for seg in segments:
-        cat_id = seg["category_id"]
-        new_cat_id = id_map[cat_id]
-        output[panoptic == seg["id"]] = new_cat_id
-    Image.fromarray(output).save(output_semantic)
-
-
-def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):
-    """
-    Create semantic segmentation annotations from panoptic segmentation
-    annotations, to be used by PanopticFPN.
-    It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.
-    It maps all stuff categories to contiguous ids starting from 1.
-    Args:
-        panoptic_json (str): path to the panoptic json file, in COCO's format.
-        panoptic_root (str): a directory with panoptic annotation files, in COCO's format.
-        sem_seg_root (str): a directory to output semantic annotation files
-        categories (list[dict]): category metadata. Each dict needs to have:
-            "id": corresponds to the "category_id" in the json annotations
-            "isthing": 0 or 1
-    """
-    os.makedirs(sem_seg_root, exist_ok=True)
-
-    id_map = {}  # map from category id to id in the output semantic annotation
-    assert len(categories) <= 254
-    for i, k in enumerate(categories):
-        id_map[k["id"]] = i
-    # what is id = 0?
-    # id_map[0] = 255
-    print(id_map)
-
-    with open(panoptic_json) as f:
-        obj = json.load(f)
-
-    pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))
-
-    def iter_annotations():
-        for anno in obj["annotations"]:
-            file_name = anno["file_name"]
-            segments = anno["segments_info"]
-            input = os.path.join(panoptic_root, file_name)
-            output = os.path.join(sem_seg_root, file_name)
-            yield input, output, segments
-
-    print("Start writing to {} ...".format(sem_seg_root))
-    start = time.time()
-    pool.starmap(
-        functools.partial(_process_panoptic_to_semantic, id_map=id_map),
-        iter_annotations(),
-        chunksize=100,
-    )
-    print("Finished. time: {:.2f}s".format(time.time() - start))
-
-
-if __name__ == "__main__":
-    dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco")
-    for s in ["val2017", "train2017"]:
-        separate_coco_semantic_from_panoptic(
-            os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)),
-            os.path.join(dataset_dir, "panoptic_{}".format(s)),
-            os.path.join(dataset_dir, "panoptic_semseg_{}".format(s)),
-            COCO_CATEGORIES,
-        )
diff --git a/spaces/fxmarty/bettertransformer-demo/Dockerfile b/spaces/fxmarty/bettertransformer-demo/Dockerfile
deleted file mode 100644
index 9d345bd97a4d12e7e21b7a5f7478bef1ccb635f9..0000000000000000000000000000000000000000
--- a/spaces/fxmarty/bettertransformer-demo/Dockerfile
+++ /dev/null
@@ -1,30 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-
-ENV PATH="/root/miniconda3/bin:${PATH}"
-ARG PATH="/root/miniconda3/bin:${PATH}"
-
-RUN apt-get update && apt-get upgrade -y
-RUN apt-get install -y wget && rm -rf /var/lib/apt/lists/*
-
-RUN wget \
-    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
-    && mkdir /root/.conda \
-    && bash Miniconda3-latest-Linux-x86_64.sh -b \
-    && rm -f Miniconda3-latest-Linux-x86_64.sh
-
-RUN conda init bash
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y --no-install-recommends \
-    git && \
-    apt-get clean
-
-RUN pip install torch torchvision torchaudio
-
-RUN pip install torchserve torch-model-archiver torch-workflow-archiver
-
-RUN pip install transformers optimum
-
-RUN git clone https://github.com/fxmarty/bettertransformer_demo.git
-
-WORKDIR /workspace/bettertransformer_demo
diff --git a/spaces/gagan3012/T5-Summarization/src/data/make_dataset.py b/spaces/gagan3012/T5-Summarization/src/data/make_dataset.py
deleted file mode 100644
index 84d99ebd6d7753e63fe811ed9e4a31611b6c632d..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/T5-Summarization/src/data/make_dataset.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import yaml
-from datasets import load_dataset
-import pandas as pd
-import os
-import pprint
-
-
-def make_dataset(dataset="cnn_dailymail", split="train"):
-    """make dataset for summarisation"""
-    if not os.path.exists("data/raw"):
-        os.makedirs("data/raw")
-    dataset = load_dataset(dataset, "3.0.0", split=split)
-    df = pd.DataFrame()
-    df["article"] = dataset["article"]
-    df["highlights"] = dataset["highlights"]
-    df.to_csv("data/raw/{}.csv".format(split))
-
-
-if __name__ == "__main__":
-    with open("params.yml") as f:
-        params = yaml.safe_load(f)
-    pprint.pprint(params)
-    make_dataset(dataset=params["data"], split="train")
-    make_dataset(dataset=params["data"], split="test")
-    make_dataset(dataset=params["data"], split="validation")
diff --git a/spaces/gato001k1/maximum_diffusion0k/app.py b/spaces/gato001k1/maximum_diffusion0k/app.py
deleted file mode 100644
index ff096f0f6d7419e85cc24afc35d76cc526f32afb..0000000000000000000000000000000000000000
--- a/spaces/gato001k1/maximum_diffusion0k/app.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import gradio as gr
-import os 
-import sys
-from pathlib import Path
-import time
-import accelerate
-
-
-
-models = [
-    "",
-    "runwayml/stable-diffusion-v1-5",
-    "CompVis/stable-diffusion-v1-4",
-    "claudfuen/photorealistic-fuen-v1",
-    "andite/anything-v4.0",
-    "naclbit/trinart_stable_diffusion_v2",
-    "nitrosocke/Arcane-Diffusion",
-    "nitrosocke/archer-diffusion",
-    "nitrosocke/elden-ring-diffusion",
-    "nitrosocke/redshift-diffusion",
-    "nitrosocke/spider-verse-diffusion", 
-    "nitrosocke/mo-di-diffusion",
-    "nitrosocke/classic-anim-diffusion",
-    "dreamlike-art/dreamlike-photoreal-1.0",
-    "dreamlike-art/dreamlike-photoreal-2.0",    
-    "wavymulder/wavyfusion",
-    "wavymulder/Analog-Diffusion",
-    "prompthero/midjourney-v4-diffusion",
-    "prompthero/openjourney",
-    "dallinmackay/Van-Gogh-diffusion",
-    "hakurei/waifu-diffusion",
-    "DGSpitzer/Cyberpunk-Anime-Diffusion",
-    "Fictiverse/Stable_Diffusion_BalloonArt_Model",
-    "dallinmackay/Tron-Legacy-diffusion",
-    "AstraliteHeart/pony-diffusion",
-    "nousr/robo-diffusion",
-    "Linaqruf/anything-v3",
-    "Omnibus/maximum_diffusion_fast",
-    "",
-    
-]
-model_1=models[1]
-model_2=models[2]
-model_3=models[3]
-model_4=models[4]
-
-model_5=models[5]
-model_6=models[6]
-model_7=models[9]
-model_8=models[13]
-
-model_9=models[14]
-model_10=models[15]
-model_11=models[16]
-model_12=models[17]
-
-text_gen=gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion_link",live=True, preprocess=True)
-
-proc1=gr.Interface.load(f"models/{model_1}",live=False,preprocess=True, postprocess=False)
-proc2=gr.Interface.load(f"models/{model_2}",live=False,preprocess=True, postprocess=False)
-proc3=gr.Interface.load(f"models/{model_3}",live=False,preprocess=True, postprocess=False)
-proc4=gr.Interface.load(f"models/{model_4}",live=False,preprocess=True, postprocess=False)
-
-proc5=gr.Interface.load(f"models/{model_5}",live=False,preprocess=True, postprocess=False)
-proc6=gr.Interface.load(f"models/{model_6}",live=False,preprocess=True, postprocess=False)
-proc7=gr.Interface.load(f"models/{model_7}",live=False,preprocess=True, postprocess=False)
-proc8=gr.Interface.load(f"models/{model_8}",live=False,preprocess=True, postprocess=False)
-
-proc9=gr.Interface.load(f"models/{model_9}",live=False,preprocess=True, postprocess=False)
-proc10=gr.Interface.load(f"models/{model_10}",live=False,preprocess=True, postprocess=False)
-proc11=gr.Interface.load(f"models/{model_11}",live=False,preprocess=True, postprocess=False)
-proc12=gr.Interface.load(f"models/{model_12}",live=False,preprocess=True, postprocess=False)
-
-
-'''
-proc1=gr.Interface.load("models/nitrosocke/Arcane-Diffusion", live=False, preprocess=True, postprocess=False)
-proc2=gr.Interface.load("models/naclbit/trinart_stable_diffusion_v2", live=False, preprocess=True, postprocess=False)
-proc3=gr.Interface.load("models/nitrosocke/redshift-diffusion", live=False, preprocess=True, postprocess=False)
-proc4=gr.Interface.load("models/runwayml/stable-diffusion-v1-5", live=False, preprocess=True, postprocess=False)
-
-proc5=gr.Interface.load("models/claudfuen/photorealistic-fuen-v1", live=False, preprocess=True, postprocess=False)
-proc6=gr.Interface.load("models/CompVis/stable-diffusion-v1-4", live=False, preprocess=True, postprocess=False)
-proc7=gr.Interface.load("models/Linaqruf/anything-v3.0", live=False, preprocess=True, postprocess=False)
-proc8=gr.Interface.load("models/andite/anything-v4.0", live=False, preprocess=True, postprocess=False)
-
-proc9=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-1.0", live=False, preprocess=True, postprocess=False)
-proc10=gr.Interface.load("models/prompthero/openjourney", live=False, preprocess=True, postprocess=False)
-proc11=gr.Interface.load("models/prompthero/midjourney-v4-diffusion", live=False, preprocess=True, postprocess=False)
-proc12=gr.Interface.load("models/wavymulder/Analog-Diffusion", live=False, preprocess=True, postprocess=False)
-'''
-
-def get_prompts(prompt_text):
-    return text_gen(prompt_text)
-def send_it1(inputs,proc1=proc1):
-        output1=proc1(inputs)
-        return(output1)
-def send_it2(inputs,proc2=proc2):
-        output2=proc2(inputs)
-        return(output2)
-def send_it3(inputs,proc3=proc3):
-        output3=proc3(inputs)
-        return(output3)
-def send_it4(inputs,proc4=proc4):
-        output4=proc4(inputs)
-        return(output4)
-
-def send_it5(inputs,proc5=proc5):
-        output5=proc5(inputs)
-        return(output5)
-def send_it6(inputs,proc6=proc6):
-        output6=proc6(inputs)
-        return(output6)
-def send_it7(inputs,proc7=proc7):
-        output7=proc7(inputs)
-        return(output7)
-def send_it8(inputs,proc8=proc8):
-        output8=proc8(inputs)
-        return(output8)
-
-def send_it9(inputs,proc9=proc9):
-        output9=proc9(inputs)
-        return(output9)
-def send_it10(inputs,proc10=proc10):
-        output10=proc10(inputs)
-        return(output10)
-def send_it11(inputs,proc11=proc11):
-        output11=proc11(inputs)
-        return(output11)
-def send_it12(inputs,proc12=proc12):
-        output12=proc12(inputs)
-        return(output12)
-
-def main():
-    with gr.Blocks(batch=False) as myface:
-        with gr.Row():
-            with gr.Tab("Title"):
-                    gr.HTML("""<title>Maximum Diffusion</title><div style="text-align: center; max-width: 1500px; margin: 0 auto;">
-                            <div style="display: inline-flex;align-items: center;gap: 0.8rem;font-size: 1.75rem;margin-bottom: 10px;line-height: 1em;">
-                            <h1 style="font-weight: 900;font-size:30; margin-top: 9px;">Maximum Diffusion</h1></div>
-                            <br><br><p style="margin-bottom: 1px;font-size: 15;font-weight: 100;line-height: 1.5em;">
-                            Text to Image Model Comparison Space - CPU</p>
-                            <br><p style="margin-bottom: 0px;font-size: 15;font-weight: 100;line-height: 1.5em;">
-                            This space becomes quickly encumbered when even 1 model of 12 fails to load, or error out</p>
-                            <p style="margin-bottom: 0px;font-size: 15;font-weight: 100;line-height: 1.5em;">
-                            
-                            Any tips on how to "time-out" Gradio.Interface models are welcome in the "Community" button above<br>
-                            <br></div>""")
-            with gr.Tab("Description"):
-                gr.HTML("""<div style="text-align:center;">
-                <h4>Enter your Prompt into the "Short Prompt" box and click "Magic Prompt" to load a prettified version of your prompt<br>
-                When you are satisfied with the prompt that is in the "Text to Image" box, click "Launch" to load the Models.<br><br>
-                Images load faster with a simpler prompt.<br>  
-                Most images should load within 2 minutes.<br>
-                Some models become stuck on certain prompts, and refreshing the page seems to fix it.<br><br>
-                Not responsible for content, use at your own risk.
-                </h4></div>""")
-            with gr.Tab("DIY"):
-                gr.HTML("""<div style="text-align:Left;">
-                <h4>Copy/Paste this code in your app.py file<br><br>
-                import gradio as gr<br>
-                max_d=gr.Interface.load("spaces/Omnibus/maximum_diffusion")<br>
-                max_d.launch()<br>                    
-                </h4></div>""")                    
-            with gr.Tab("Credits"):
-                with gr.Row():
-                    gr.Column()
-                    with gr.Column(style="text-align:left;"):
-                        gr.HTML("""
-                                <div style="vertical-align:center">
-                                  <br>
-                                  <p>I learned everything I know from:
-                                  <p><a href="https://huggingface.co/spaces/anzorq/finetuned_diffusion">Finetuned Diffusion</a></p>
-                                  <p><a href="https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion">Magic Prompt Stable Diffusion</a></p>
-                                  <p><a href="https://huggingface.co/spaces/huggingface-projects/magic-diffusion">Magic Diffusion</a></p>
-                                  <p>Models by <a href="https://huggingface.co/Gustavosta">@Gustavosta</a>, <a href="https://twitter.com/haruu1367">@haruu1367</a>, <a href="https://twitter.com/DGSpitzer">@Helixngc7293</a>, <a href="https://twitter.com/dal_mack">@dal_mack</a>, <a href="https://twitter.com/prompthero">@prompthero</a> and others.</p>
-                                </div>
-                                """)
-                    gr.Column()
-            with gr.Tab("Tools"):
-                        with gr.Tab("View"):
-                          with gr.Row():  
-                            with gr.Column(style="width=50%, height=70%"):
-                                    gr.Pil(label="Crop")
-                            with gr.Column(style="width=50%, height=70%"):
-                                    gr.Pil(label="Crop")
-                        with gr.Tab("Draw"):
-                          with gr.Row():
-                            with gr.Column(style="width=50%, height=70%"):
-                                    gr.Pil(label="Crop")
-                            with gr.Column(style="width=50%, height=70%"):
-                                    gr.Pil(label="Draw")
-                                    gr.ImagePaint(label="Draw")
-                        with gr.Tab("Text"):
-                            with gr.Row():
-                                with gr.Column(scale=50):
-                                    gr.Textbox(label="", lines=8, interactive=True)            
-                                with gr.Column(scale=50):
-                                    gr.Textbox(label="", lines=8, interactive=True)
-                        with gr.Tab("Color Picker"):
-                            with gr.Row():
-                                with gr.Column(scale=50):
-                                    gr.ColorPicker(label="Color", interactive=True)            
-                                with gr.Column(scale=50):
-                                    gr.ImagePaint(label="Draw", interactive=True)                                
-        
-        with gr.Row():
-                input_text=gr.Textbox(label="Short Prompt")
-                see_prompts=gr.Button("Magic Prompt")
-        with gr.Row():
-                prompt=gr.Textbox(label="Text to Image")
-                run=gr.Button("Launch")
-        with gr.Row():
-                output1=gr.Image(label=(f"{model_1}"))
-                output2=gr.Image(label=(f"{model_2}"))
-                output3=gr.Image(label=(f"{model_3}"))
-                output4=gr.Image(label=(f"{model_4}"))
-        with gr.Row():
-                output5=gr.Image(label=(f"{model_5}"))
-                output6=gr.Image(label=(f"{model_6}"))
-                output7=gr.Image(label=(f"{model_7}"))
-                output8=gr.Image(label=(f"{model_8}"))
-        with gr.Row():
-                output9=gr.Image(label=(f"{model_9}"))
-                output10=gr.Image(label=(f"{model_10}"))
-                output11=gr.Image(label=(f"{model_11}"))
-                output12=gr.Image(label=(f"{model_12}"))
-        
-        see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt])
-    
-        run.click(send_it1, inputs=[prompt], outputs=[output1])
-        run.click(send_it2, inputs=[prompt], outputs=[output2])
-        run.click(send_it3, inputs=[prompt], outputs=[output3])
-        run.click(send_it4, inputs=[prompt], outputs=[output4])
-        run.click(send_it5, inputs=[prompt], outputs=[output5])
-        run.click(send_it6, inputs=[prompt], outputs=[output6])
-        run.click(send_it7, inputs=[prompt], outputs=[output7])
-        run.click(send_it8, inputs=[prompt], outputs=[output8])
-        run.click(send_it9, inputs=[prompt], outputs=[output9])
-        run.click(send_it10, inputs=[prompt], outputs=[output10])
-        run.click(send_it11, inputs=[prompt], outputs=[output11])
-        run.click(send_it12, inputs=[prompt], outputs=[output12])
-
-    myface.queue(concurrency_count=600,status_update_rate=1)
-    myface.launch(enable_queue=True,inline=True,max_threads=600)
-if __name__ == "__main__":
-    main()
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/base/heads.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/base/heads.py
deleted file mode 100644
index fbc939cad427fb6b9edfb02c0e841a68de84a03a..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/base/heads.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import torch.nn as nn
-from .modules import Activation
-
-
-class SegmentationHead(nn.Sequential):
-    def __init__(
-        self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1
-    ):
-        conv2d = nn.Conv2d(
-            in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2
-        )
-        upsampling = (
-            nn.UpsamplingBilinear2d(scale_factor=upsampling)
-            if upsampling > 1
-            else nn.Identity()
-        )
-        activation = Activation(activation)
-        super().__init__(conv2d, upsampling, activation)
-
-
-class ClassificationHead(nn.Sequential):
-    def __init__(
-        self, in_channels, classes, pooling="avg", dropout=0.2, activation=None
-    ):
-        if pooling not in ("max", "avg"):
-            raise ValueError(
-                "Pooling should be one of ('max', 'avg'), got {}.".format(pooling)
-            )
-        pool = nn.AdaptiveAvgPool2d(1) if pooling == "avg" else nn.AdaptiveMaxPool2d(1)
-        flatten = nn.Flatten()
-        dropout = nn.Dropout(p=dropout, inplace=True) if dropout else nn.Identity()
-        linear = nn.Linear(in_channels, classes, bias=True)
-        activation = Activation(activation)
-        super().__init__(pool, flatten, dropout, linear, activation)
diff --git a/spaces/giswqs/solara-template/pages/01_leafmap.py b/spaces/giswqs/solara-template/pages/01_leafmap.py
deleted file mode 100644
index 5ff4756ac622d79d1336dc9b2bef0fe2c5511010..0000000000000000000000000000000000000000
--- a/spaces/giswqs/solara-template/pages/01_leafmap.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import leafmap
-import solara
-
-zoom = solara.reactive(2)
-center = solara.reactive((20, 0))
-
-
-class Map(leafmap.Map):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # Add what you want below
-        self.add_stac_gui()
-
-
-@solara.component
-def Page():
-    with solara.Column(style={"min-width": "500px"}):
-        # solara components support reactive variables
-        # solara.SliderInt(label="Zoom level", value=zoom, min=1, max=20)
-        # using 3rd party widget library require wiring up the events manually
-        # using zoom.value and zoom.set
-        Map.element(  # type: ignore
-            zoom=zoom.value,
-            on_zoom=zoom.set,
-            center=center.value,
-            on_center=center.set,
-            scroll_wheel_zoom=True,
-            toolbar_ctrl=False,
-            data_ctrl=False,
-            height="780px",
-        )
-        # solara.Text(f"Zoom: {zoom.value}")
-        # solara.Text(f"Center: {center.value}")
diff --git a/spaces/golem4300/RVC-TTS/lib/infer_pack/transforms.py b/spaces/golem4300/RVC-TTS/lib/infer_pack/transforms.py
deleted file mode 100644
index 4de15fa1d9e8297bf2ee91d7cbf457617cc1ecef..0000000000000000000000000000000000000000
--- a/spaces/golem4300/RVC-TTS/lib/infer_pack/transforms.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import torch
-from torch.nn import functional as F
-import numpy as np
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-def piecewise_rational_quadratic_transform(inputs, unnormalized_widths, unnormalized_heights, unnormalized_derivatives, inverse=False, tails=None, tail_bound=1.0, min_bin_width=DEFAULT_MIN_BIN_WIDTH, min_bin_height=DEFAULT_MIN_BIN_HEIGHT, min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    return spline_fn(inputs=inputs, unnormalized_widths=unnormalized_widths, unnormalized_heights=unnormalized_heights, unnormalized_derivatives=unnormalized_derivatives, inverse=inverse, min_bin_width=min_bin_width, min_bin_height=min_bin_height, min_derivative=min_derivative, **spline_kwargs)
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-def unconstrained_rational_quadratic_spline(inputs, unnormalized_widths, unnormalized_heights, unnormalized_derivatives, inverse=False, tails="linear", tail_bound=1.0, min_bin_width=DEFAULT_MIN_BIN_WIDTH, min_bin_height=DEFAULT_MIN_BIN_HEIGHT, min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if tails != "linear": raise RuntimeError(f"{tails} tails are not implemented.")
-
-    unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-    constant = np.log(np.exp(1 - min_derivative) - 1)
-    unnormalized_derivatives[..., 0] = constant
-    unnormalized_derivatives[..., -1] = constant
-
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-    outputs = torch.where(outside_interval_mask, inputs, torch.zeros_like(inputs))
-    logabsdet = torch.zeros_like(inputs)
-
-    inside_outputs, inside_logabsdet = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse, left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width, min_bin_height=min_bin_height, min_derivative=min_derivative)
-
-    outputs[inside_interval_mask] = inside_outputs
-    logabsdet[inside_interval_mask] = inside_logabsdet
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs, unnormalized_widths, unnormalized_heights, unnormalized_derivatives, inverse=False, left=0.0, right=1.0, bottom=0.0, top=1.0, min_bin_width=DEFAULT_MIN_BIN_WIDTH, min_bin_height=DEFAULT_MIN_BIN_HEIGHT, min_derivative=DEFAULT_MIN_DERIVATIVE):
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0: raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0: raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths, heights = compute_widths_and_heights(unnormalized_widths, unnormalized_heights, min_bin_width, min_bin_height, num_bins, left, right, bottom, top)
-    cumwidths, cumheights = widths.cumsum(dim=-1), heights.cumsum(dim=-1)
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    widths, heights = cumwidths[..., 1:] - cumwidths[..., :-1], cumheights[..., 1:] - cumheights[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    if inverse: bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else: bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    gather_args = (-1, bin_idx)
-    input_cumwidths, input_bin_widths, input_cumheights, input_delta, input_derivatives, input_derivatives_plus_one, input_heights = map(
-        lambda tensor: tensor.gather(*gather_args)[..., 0],
-        (cumwidths, widths, cumheights, heights / widths, derivatives, derivatives[..., 1:], heights))
-
-    if inverse: outputs, logabsdet = inverse_rational_quadratic_spline(inputs, input_cumheights, input_heights, input_derivatives, input_derivatives_plus_one, input_delta, input_bin_widths, input_cumwidths)
-    else: outputs, logabsdet = direct_rational_quadratic_spline(inputs, input_cumwidths, input_bin_widths, input_cumheights, input_heights, input_derivatives, input_derivatives_plus_one, input_delta)
-
-    return outputs, logabsdet
-
-def compute_widths_and_heights(unnormalized_widths, unnormalized_heights, min_bin_width, min_bin_height, num_bins, left, right, bottom, top):
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    widths = (right - left) * widths + left
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    heights = (top - bottom) * heights + bottom
-
-    return widths, heights
-
-def inverse_rational_quadratic_spline(inputs, input_cumheights, input_heights, input_derivatives, input_derivatives_plus_one, input_delta, input_bin_widths, input_cumwidths):
-    a = (inputs - input_cumheights) * (input_derivatives + input_derivatives_plus_one - 2 * input_delta) + input_heights * (input_delta - input_derivatives)
-    b = input_heights * input_derivatives - (inputs - input_cumheights) * (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-    c = -input_delta * (inputs - input_cumheights)
-
-    discriminant = b.pow(2) - 4 * a * c
-    assert (discriminant >= 0).all()
-
-    root = (2 * c) / (-b - torch.sqrt(discriminant))
-    outputs = root * input_bin_widths + input_cumwidths
-    theta_one_minus_theta = root * (1 - root)
-    denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)* theta_one_minus_theta)
-    derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)+ 2 * input_delta * theta_one_minus_theta+ input_derivatives * (1 - root).pow(2))
-    logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-    return outputs, -logabsdet
-
-def direct_rational_quadratic_spline(inputs, input_cumwidths, input_bin_widths, input_cumheights, input_heights, input_derivatives, input_derivatives_plus_one, input_delta):
-    theta = (inputs - input_cumwidths) / input_bin_widths
-    theta_one_minus_theta = theta * (1 - theta)
-    numerator = input_heights * (input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta)
-    denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) * theta_one_minus_theta)
-    outputs = input_cumheights + numerator / denominator
-    derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) + 2 * input_delta * theta_one_minus_theta + input_derivatives * (1 - theta).pow(2))
-    logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-    return outputs, logabsdet
\ No newline at end of file
diff --git a/spaces/guardiancc/video-face-swap/roop/utilities.py b/spaces/guardiancc/video-face-swap/roop/utilities.py
deleted file mode 100644
index 90c8d981f5f159a459ca0c08cc23dfac8d04c068..0000000000000000000000000000000000000000
--- a/spaces/guardiancc/video-face-swap/roop/utilities.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import glob
-import mimetypes
-import os
-import platform
-import shutil
-import ssl
-import subprocess
-import urllib
-from pathlib import Path
-from typing import List, Any
-from tqdm import tqdm
-
-import roop.globals
-
-TEMP_FILE = 'temp.mp4'
-TEMP_DIRECTORY = 'temp'
-
-# monkey patch ssl for mac
-if platform.system().lower() == 'darwin':
-    ssl._create_default_https_context = ssl._create_unverified_context
-
-
-def run_ffmpeg(args: List[str]) -> bool:
-    commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-loglevel', roop.globals.log_level]
-    commands.extend(args)
-    try:
-        subprocess.check_output(commands, stderr=subprocess.STDOUT)
-        return True
-    except Exception:
-        pass
-    return False
-
-
-def detect_fps(target_path: str) -> float:
-    command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=r_frame_rate', '-of', 'default=noprint_wrappers=1:nokey=1', target_path]
-    output = subprocess.check_output(command).decode().strip().split('/')
-    try:
-        numerator, denominator = map(int, output)
-        return numerator / denominator
-    except Exception:
-        pass
-    return 30.0
-
-
-def extract_frames(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
-
-
-def create_video(target_path: str, fps: float = 30.0) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.png'), '-c:v', roop.globals.video_encoder, '-crf', str(roop.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', temp_output_path])
-
-
-def restore_audio(target_path: str, output_path: str) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    done = run_ffmpeg(['-i', temp_output_path, '-i', target_path, '-c:v', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-y', output_path])
-    if not done:
-        move_temp(target_path, output_path)
-
-
-def get_temp_frame_paths(target_path: str) -> List[str]:
-    temp_directory_path = get_temp_directory_path(target_path)
-    return glob.glob((os.path.join(glob.escape(temp_directory_path), '*.png')))
-
-
-def get_temp_directory_path(target_path: str) -> str:
-    target_name, _ = os.path.splitext(os.path.basename(target_path))
-    target_directory_path = os.path.dirname(target_path)
-    return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
-
-
-def get_temp_output_path(target_path: str) -> str:
-    temp_directory_path = get_temp_directory_path(target_path)
-    return os.path.join(temp_directory_path, TEMP_FILE)
-
-
-def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
-    if source_path and target_path:
-        source_name, _ = os.path.splitext(os.path.basename(source_path))
-        target_name, target_extension = os.path.splitext(os.path.basename(target_path))
-        if os.path.isdir(output_path):
-            return os.path.join(output_path, source_name + '-' + target_name + target_extension)
-    return output_path
-
-
-def create_temp(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
-
-
-def move_temp(target_path: str, output_path: str) -> None:
-    temp_output_path = get_temp_output_path(target_path)
-    if os.path.isfile(temp_output_path):
-        if os.path.isfile(output_path):
-            os.remove(output_path)
-        shutil.move(temp_output_path, output_path)
-
-
-def clean_temp(target_path: str) -> None:
-    temp_directory_path = get_temp_directory_path(target_path)
-    parent_directory_path = os.path.dirname(temp_directory_path)
-    if not roop.globals.keep_frames and os.path.isdir(temp_directory_path):
-        shutil.rmtree(temp_directory_path)
-    if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
-        os.rmdir(parent_directory_path)
-
-
-def has_image_extension(image_path: str) -> bool:
-    return image_path.lower().endswith(('png', 'jpg', 'jpeg', 'webp'))
-
-
-def is_image(image_path: str) -> bool:
-    if image_path and os.path.isfile(image_path):
-        mimetype, _ = mimetypes.guess_type(image_path)
-        return bool(mimetype and mimetype.startswith('image/'))
-    return False
-
-
-def is_video(video_path: str) -> bool:
-    if video_path and os.path.isfile(video_path):
-        mimetype, _ = mimetypes.guess_type(video_path)
-        return bool(mimetype and mimetype.startswith('video/'))
-    return False
-
-
-def conditional_download(download_directory_path: str, urls: List[str]) -> None:
-    if not os.path.exists(download_directory_path):
-        os.makedirs(download_directory_path)
-    for url in urls:
-        download_file_path = os.path.join(download_directory_path, os.path.basename(url))
-        if not os.path.exists(download_file_path):
-            request = urllib.request.urlopen(url) # type: ignore[attr-defined]
-            total = int(request.headers.get('Content-Length', 0))
-            with tqdm(total=total, desc='Downloading', unit='B', unit_scale=True, unit_divisor=1024) as progress:
-                urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
-
-
-def resolve_relative_path(path: str) -> str:
-    return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
diff --git a/spaces/guetLzy/Real-ESRGAN-Demo/CODE_OF_CONDUCT.md b/spaces/guetLzy/Real-ESRGAN-Demo/CODE_OF_CONDUCT.md
deleted file mode 100644
index e8cc4daa4345590464314889b187d6a2d7a8e20f..0000000000000000000000000000000000000000
--- a/spaces/guetLzy/Real-ESRGAN-Demo/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,128 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the
-  overall community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or
-  advances of any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email
-  address, without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-xintao.wang@outlook.com or xintaowang@tencent.com.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series
-of actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or
-permanent ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior,  harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within
-the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
-
-Community Impact Guidelines were inspired by [Mozilla's code of conduct
-enforcement ladder](https://github.com/mozilla/diversity).
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see the FAQ at
-https://www.contributor-covenant.org/faq. Translations are available at
-https://www.contributor-covenant.org/translations.
diff --git a/spaces/guoyww/AnimateDiff/animatediff/models/resnet.py b/spaces/guoyww/AnimateDiff/animatediff/models/resnet.py
deleted file mode 100644
index ad28eb0ca364586b2e0ec89e855b1cb05d62ee79..0000000000000000000000000000000000000000
--- a/spaces/guoyww/AnimateDiff/animatediff/models/resnet.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from einops import rearrange
-
-
-class InflatedConv3d(nn.Conv2d):
-    def forward(self, x):
-        video_length = x.shape[2]
-
-        x = rearrange(x, "b c f h w -> (b f) c h w")
-        x = super().forward(x)
-        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
-
-        return x
-
-
-class Upsample3D(nn.Module):
-    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:
-            raise NotImplementedError
-        elif use_conv:
-            self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, hidden_states, output_size=None):
-        assert hidden_states.shape[1] == self.channels
-
-        if self.use_conv_transpose:
-            raise NotImplementedError
-
-        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
-        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=[1.0, 2.0, 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)
-
-        # if self.use_conv:
-        #     if self.name == "conv":
-        #         hidden_states = self.conv(hidden_states)
-        #     else:
-        #         hidden_states = self.Conv2d_0(hidden_states)
-        hidden_states = self.conv(hidden_states)
-
-        return hidden_states
-
-
-class Downsample3D(nn.Module):
-    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 = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
-        else:
-            raise NotImplementedError
-
-    def forward(self, hidden_states):
-        assert hidden_states.shape[1] == self.channels
-        if self.use_conv and self.padding == 0:
-            raise NotImplementedError
-
-        assert hidden_states.shape[1] == self.channels
-        hidden_states = self.conv(hidden_states)
-
-        return hidden_states
-
-
-class ResnetBlock3D(nn.Module):
-    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",
-        time_embedding_norm="default",
-        output_scale_factor=1.0,
-        use_in_shortcut=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.time_embedding_norm = time_embedding_norm
-        self.output_scale_factor = output_scale_factor
-
-        if groups_out is None:
-            groups_out = groups
-
-        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
-
-        self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
-        if temb_channels is not None:
-            if self.time_embedding_norm == "default":
-                time_emb_proj_out_channels = out_channels
-            elif self.time_embedding_norm == "scale_shift":
-                time_emb_proj_out_channels = out_channels * 2
-            else:
-                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
-
-            self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
-        else:
-            self.time_emb_proj = None
-
-        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
-        if non_linearity == "swish":
-            self.nonlinearity = lambda x: F.silu(x)
-        elif non_linearity == "mish":
-            self.nonlinearity = Mish()
-        elif non_linearity == "silu":
-            self.nonlinearity = nn.SiLU()
-
-        self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
-
-        self.conv_shortcut = None
-        if self.use_in_shortcut:
-            self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, input_tensor, temb):
-        hidden_states = input_tensor
-
-        hidden_states = self.norm1(hidden_states)
-        hidden_states = self.nonlinearity(hidden_states)
-
-        hidden_states = self.conv1(hidden_states)
-
-        if temb is not None:
-            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
-
-        if temb is not None and self.time_embedding_norm == "default":
-            hidden_states = hidden_states + temb
-
-        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
-
-
-class Mish(torch.nn.Module):
-    def forward(self, hidden_states):
-        return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
\ No newline at end of file
diff --git a/spaces/hahahehe99340/chatgpt/run_macOS.command b/spaces/hahahehe99340/chatgpt/run_macOS.command
deleted file mode 100644
index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000
--- a/spaces/hahahehe99340/chatgpt/run_macOS.command
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$0")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir"
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/hamelcubsfan/AutoGPT/README.md b/spaces/hamelcubsfan/AutoGPT/README.md
deleted file mode 100644
index 5bf09b995f04f7af05d1314906b1b1ff39c20ddc..0000000000000000000000000000000000000000
--- a/spaces/hamelcubsfan/AutoGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AutoGPT
-emoji: 🦾
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: ui/app.py
-pinned: false
-license: mit
-duplicated_from: aliabid94/AutoGPT
----
-
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/blocks.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/blocks.py
deleted file mode 100644
index 1d06fec22e472febbc960c49f747acddd2ab7208..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/layers/blocks.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-from torch import nn
-
-from .batch_norm import FrozenBatchNorm2d
-
-
-class CNNBlockBase(nn.Module):
-    """
-    A CNN block is assumed to have input channels, output channels and a stride.
-    The input and output of `forward()` method must be NCHW tensors.
-    The method can perform arbitrary computation but must match the given
-    channels and stride specification.
-
-    Attribute:
-        in_channels (int):
-        out_channels (int):
-        stride (int):
-    """
-
-    def __init__(self, in_channels, out_channels, stride):
-        """
-        The `__init__` method of any subclass should also contain these arguments.
-
-        Args:
-            in_channels (int):
-            out_channels (int):
-            stride (int):
-        """
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.stride = stride
-
-    def freeze(self):
-        """
-        Make this block not trainable.
-        This method sets all parameters to `requires_grad=False`,
-        and convert all BatchNorm layers to FrozenBatchNorm
-
-        Returns:
-            the block itself
-        """
-        for p in self.parameters():
-            p.requires_grad = False
-        FrozenBatchNorm2d.convert_frozen_batchnorm(self)
-        return self
diff --git a/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/customized_langchain/docstore/__init__.py b/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/customized_langchain/docstore/__init__.py
deleted file mode 100644
index 6250d5c3aaf5de06e7daa358a513205f302527c2..0000000000000000000000000000000000000000
--- a/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/customized_langchain/docstore/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Wrappers on top of docstores."""
-from streamlit_langchain_chat.customized_langchain.docstore.in_memory import InMemoryDocstore
-
-
-__all__ = [
-    "InMemoryDocstore",
-]
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_3.sh b/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_3.sh
deleted file mode 100644
index f450a2044e9d2ef7e52771c93f2715c97811160b..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_3.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash -l
-#SBATCH --nodes=1 --gres=gpu:1 --time=24:00:00
-#SBATCH --job-name=Task503_glacier_mtl_early_3
-
-export data_raw="/home/woody/iwi5/iwi5039h/data_raw"
-export nnUNet_raw_data_base="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_raw_data_base/"
-export nnUNet_preprocessed="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/"
-export RESULTS_FOLDER="/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER"
-
-cd nnunet_glacer
-pwd
-conda activate nnunet
-
-python3 nnunet/run/run_training.py 2d nnUNetTrainerMTLearly 503 3 -p nnUNetPlans_mtl --disable_postprocessing_on_folds
-python3 nnunet/inference/predict_simple.py -i $nnUNet_raw_data_base/nnUNet_raw_data/Task503_Glacier_mtl/imagesTs -o $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_3 -t 503 -m 2d -f 3 -p nnUNetPlans_mtl -tr nnUNetTrainerMTLearly
-python3 nnunet/dataset_conversion/Task503_Glacier_mtl_reverse.py -i $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_3
-python3 ./evaluate_nnUNet.py --predictions $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_3/pngs --labels_fronts $data_raw/fronts/test --labels_zones $data_raw/zones/test --sar_images $data_raw/sar_images/test
diff --git a/spaces/hrishikeshpai30/hrishikeshpai30-wavlm-libri-clean-100h-large/README.md b/spaces/hrishikeshpai30/hrishikeshpai30-wavlm-libri-clean-100h-large/README.md
deleted file mode 100644
index 9ed337426bcf899a21071346011f9f277a556a6f..0000000000000000000000000000000000000000
--- a/spaces/hrishikeshpai30/hrishikeshpai30-wavlm-libri-clean-100h-large/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hrishikeshpai30 Wavlm Libri Clean 100h Large
-emoji: 📊
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huggingface-projects/codellama-bot/app.py b/spaces/huggingface-projects/codellama-bot/app.py
deleted file mode 100644
index 1fef5a6a5260a6c0913d0690068faf0b3b5ac8bb..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/codellama-bot/app.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import asyncio
-import json
-import os
-import threading
-from threading import Event
-
-import discord
-import gradio as gr
-from discord.ext import commands
-from gradio_client import Client
-
-event = Event()
-DISCORD_TOKEN = os.getenv("DISCORD_TOKEN")
-HF_TOKEN = os.getenv("HF_TOKEN")
-codellama_client = Client("https://huggingface-projects-codellama-13b-chat.hf.space/", HF_TOKEN)
-codellama_threadid_userid_dictionary = {}
-codellama_threadid_conversation = {}
-intents = discord.Intents.all()
-bot = commands.Bot(command_prefix="/", intents=intents)
-
-
-@bot.event
-async def on_ready():
-    print(f"Logged in as {bot.user} (ID: {bot.user.id})")
-    synced = await bot.tree.sync()
-    print(f"Synced commands: {', '.join([s.name for s in synced])}.")
-    event.set()
-    print("------")
-
-
-@bot.hybrid_command(
-    name="codellama",
-    description="Enter a prompt to generate code!",
-)
-async def codellama(ctx, prompt: str):
-    """Codellama generation"""
-    try:
-        await try_codellama(ctx, prompt)
-    except Exception as e:
-        print(f"Error: {e}")
-
-
-@bot.event
-async def on_message(message):
-    """Checks channel and continues codellama conversation if it's the right Discord Thread"""
-    try:
-        if not message.author.bot:
-            await continue_codellama(message)
-    except Exception as e:
-        print(f"Error: {e}")
-
-
-async def try_codellama(ctx, prompt):
-    """Generates code based on a given prompt"""
-    try:
-        if ctx.guild.id == 879548962464493619:
-            if ctx.channel.id != 1147210106321256508:
-                return        
-        global codellama_threadid_userid_dictionary
-        global codellama_threadid_conversation
-
-        message = await ctx.send(f"**{prompt}** - {ctx.author.mention}")
-        thread = await message.create_thread(name=prompt[:100])
-
-        loop = asyncio.get_running_loop()
-        output_code = await loop.run_in_executor(None, codellama_initial_generation, prompt, thread)
-        codellama_threadid_userid_dictionary[thread.id] = ctx.author.id
-        await thread.send(output_code)
-    except Exception as e:
-        print(f"Error: {e}")
-
-
-def codellama_initial_generation(prompt, thread):
-    """Job.submit inside of run_in_executor = more consistent bot behavior"""
-    global codellama_threadid_conversation
-
-    chat_history = f"{thread.id}.json"
-    conversation = []
-    with open(chat_history, "w") as json_file:
-        json.dump(conversation, json_file)
-
-    job = codellama_client.submit(prompt, chat_history, fn_index=0)
-
-    while job.done() is False:
-        pass
-    else:
-        result = job.outputs()[-1]
-        with open(result, "r") as json_file:
-            data = json.load(json_file)
-        response = data[-1][-1]
-        conversation.append((prompt, response))
-        with open(chat_history, "w") as json_file:
-            json.dump(conversation, json_file)
-
-        codellama_threadid_conversation[thread.id] = chat_history
-        if len(response) > 1300:
-            response = response[:1300] + "...\nTruncating response due to discord api limits."
-        return response
-
-
-async def continue_codellama(message):
-    """Continues a given conversation based on chat_history"""
-    try:
-        if not message.author.bot:
-            global codellama_threadid_userid_dictionary  # tracks userid-thread existence
-            if message.channel.id in codellama_threadid_userid_dictionary:  # is this a valid thread?
-                if codellama_threadid_userid_dictionary[message.channel.id] == message.author.id:
-                    global codellama_threadid_conversation
-
-                    prompt = message.content
-                    chat_history = codellama_threadid_conversation[message.channel.id]
-
-                    # Check to see if conversation is ongoing or ended (>15000 characters)
-                    with open(chat_history, "r") as json_file:
-                        conversation = json.load(json_file)
-                    total_characters = 0
-                    for item in conversation:
-                        for string in item:
-                            total_characters += len(string)
-
-                    if total_characters < 15000:
-                        job = codellama_client.submit(prompt, chat_history, fn_index=0)
-                        while job.done() is False:
-                            pass
-                        else:
-                            result = job.outputs()[-1]
-                            with open(result, "r") as json_file:
-                                data = json.load(json_file)
-                            response = data[-1][-1]
-                            with open(chat_history, "r") as json_file:
-                                conversation = json.load(json_file)
-                            conversation.append((prompt, response))
-                            with open(chat_history, "w") as json_file:
-                                json.dump(conversation, json_file)
-                            codellama_threadid_conversation[message.channel.id] = chat_history
-
-                            if len(response) > 1300:
-                                response = response[:1300] + "...\nTruncating response due to discord api limits."
-
-                            await message.reply(response)
-
-                            total_characters = 0
-                            for item in conversation:
-                                for string in item:
-                                    total_characters += len(string)
-
-                            if total_characters >= 15000:
-                                await message.reply("Conversation ending due to length, feel free to start a new one!")
-
-    except Exception as e:
-        print(f"Error: {e}")
-
-
-def run_bot():
-    if not DISCORD_TOKEN:
-        print("DISCORD_TOKEN NOT SET")
-        event.set()
-    else:
-        bot.run(DISCORD_TOKEN)
-
-
-threading.Thread(target=run_bot).start()
-event.wait()
-
-welcome_message = """
-## Add this bot to your server by clicking this link: 
-
-https://discord.com/api/oauth2/authorize?client_id=1152238037355474964&permissions=309237647360&scope=bot
-
-## How to use it?
-
-The bot can be triggered via `/codellama` followed by your text prompt.
-
-This will generate text based on the text prompt and create a thread for the discussion.
-
-To continue the conversation, simply ask additional questions in the thread - no need for repeating the command!
-
-⚠️ Note ⚠️: Please make sure this bot's command does have the same name as another command in your server.
-
-⚠️ Note ⚠️: Bot commands do not work in DMs with the bot as of now.
-"""
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(f"""
-    # Discord bot of https://huggingface.co/spaces/codellama/codellama-13b-chat
-    {welcome_message}
-    """)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/hylee/apdrawing/app.py b/spaces/hylee/apdrawing/app.py
deleted file mode 100644
index ac911eb2deba9a1281dcd78b2a2fc105445fbeec..0000000000000000000000000000000000000000
--- a/spaces/hylee/apdrawing/app.py
+++ /dev/null
@@ -1,262 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-import argparse
-import functools
-import os
-import pathlib
-import sys
-from typing import Callable
-import uuid
-import cv2
-
-sys.path.insert(0, 'APDrawingGAN2')
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-
-from io import BytesIO
-import shutil
-
-from options.test_options import TestOptions
-from data import CreateDataLoader
-from models import create_model
-import dlib
-from preprocess.get_partmask import get_68lm, get_partmask
-from util import html
-
-import ntpath
-from util import util
-from modnet import ModNet
-
-ORIGINAL_REPO_URL = 'https://github.com/yiranran/APDrawingGAN2'
-TITLE = 'yiranran/APDrawingGAN2'
-DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.
-
-"""
-ARTICLE = """
-
-"""
-
-MODEL_REPO = 'hylee/apdrawing_model'
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    parser.add_argument('--allow-screenshot', action='store_true')
-    return parser.parse_args()
-
-
-def load_checkpoint():
-    dir = 'checkpoint'
-    checkpoint_path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                                      'checkpoints.zip',
-                                                      force_filename='checkpoints.zip')
-    print(checkpoint_path)
-    shutil.unpack_archive(checkpoint_path, extract_dir=dir)
-    print(os.listdir(dir + '/checkpoints'))
-    return dir + '/checkpoints'
-
-def load_modnet_model():
-    modnet_path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                                      'modnet.onnx',
-                                                      force_filename='modnet.onnx')
-    return modnet_path
-
-
-# save image to the disk
-def save_images2(image_dir, visuals, image_path, aspect_ratio=1.0, width=256):
-    short_path = ntpath.basename(image_path[0])
-    name = os.path.splitext(short_path)[0]
-
-    imgs = []
-
-    for label, im_data in visuals.items():
-        im = util.tensor2im(im_data)  # tensor to numpy array [-1,1]->[0,1]->[0,255]
-        image_name = '%s_%s.png' % (name, label)
-        save_path = os.path.join(image_dir, image_name)
-        h, w, _ = im.shape
-        if aspect_ratio > 1.0:
-            im = np.array(PIL.Image.fromarray(arr).resize(im, (h, int(w * aspect_ratio))))
-        if aspect_ratio < 1.0:
-            im = np.array(PIL.Image.fromarray(arr).resize(im, (int(h / aspect_ratio), w)))
-        util.save_image(im, save_path)
-        imgs.append(save_path)
-
-    return imgs
-
-
-SAFEHASH = [x for x in "0123456789-abcdefghijklmnopqrstuvwxyz_ABCDEFGHIJKLMNOPQRSTUVWXYZ"]
-
-
-def compress_UUID():
-    '''
-    根据http://www.ietf.org/rfc/rfc1738.txt,由uuid编码扩bai大字符域生成du串
-    包括:[0-9a-zA-Z\-_]共64个
-    长度:(32-2)/3*2=20
-    备注:可在地球上人zhi人都用,使用100年不重复(2^120)
-    :return:String
-    '''
-    row = str(uuid.uuid4()).replace('-', '')
-    safe_code = ''
-    for i in range(10):
-        enbin = "%012d" % int(bin(int(row[i * 3] + row[i * 3 + 1] + row[i * 3 + 2], 16))[2:], 10)
-        safe_code += (SAFEHASH[int(enbin[0:6], 2)] + SAFEHASH[int(enbin[6:12], 2)])
-    safe_code = safe_code.replace('-', '')
-    return safe_code
-
-def run(
-        image,
-        landmarks_str : str,
-        model,
-        opt,
-        detector,
-        predictor,
-        modnet : ModNet,
-) -> tuple[PIL.Image.Image]:
-
-    dataroot = 'images/' + compress_UUID()
-    opt.dataroot = os.path.join(dataroot, 'src/')
-    os.makedirs(opt.dataroot, exist_ok=True)
-    opt.results_dir = os.path.join(dataroot, 'results/')
-    os.makedirs(opt.results_dir, exist_ok=True)
-
-    opt.lm_dir = os.path.join(dataroot, 'landmark/')
-    opt.cmask_dir = os.path.join(dataroot, 'mask/')
-    opt.bg_dir = os.path.join(dataroot, 'mask/bg')
-    os.makedirs(opt.lm_dir, exist_ok=True)
-    os.makedirs(opt.cmask_dir, exist_ok=True)
-    os.makedirs(opt.bg_dir, exist_ok=True)
-
-    shutil.copy(image.name, opt.dataroot)
-
-    fullname = os.path.basename(image.name)
-    name = fullname.split(".")[0]
-
-    #bg = cv2.cvtColor(cv2.imread(mask.name), cv2.COLOR_BGR2GRAY)
-    #cv2.imwrite(os.path.join(opt.bg_dir, name+'.png'), bg)
-    modnet.segment(image.name, os.path.join(opt.bg_dir, name+'.png'))
-
-    imgfile = os.path.join(opt.dataroot, fullname)
-    lmfile5 = os.path.join(opt.lm_dir, name+'.txt')
-    lmfile68 = os.path.join(opt.lm_dir, name + '_68.txt')
-
-    # 预处理数据
-    # get_68lm(imgfile, lmfile5, lmfile68, detector, predictor)
-    f = open(lmfile68, 'w')
-    print(landmarks_str, file=f)
-    f.close()
-    landmarks = np.loadtxt(lmfile68, dtype=np.int64)
-
-    ff = open(lmfile5, 'w')
-    lm = (landmarks[36] + landmarks[39]) / 2
-    print(int(lm[0]), int(lm[1]), file=ff)
-    lm = (landmarks[45] + landmarks[42]) / 2
-    print(int(lm[0]), int(lm[1]), file=ff)
-    lm = landmarks[30]
-    print(lm[0], lm[1], file=ff)
-    lm = landmarks[48]
-    print(lm[0], lm[1], file=ff)
-    lm = landmarks[54]
-    print(lm[0], lm[1], file=ff)
-    ff.close()
-
-    imgs = []
-    for part in ['eyel', 'eyer', 'nose', 'mouth']:
-        savepath = os.path.join(opt.cmask_dir + part, name+'.png')
-        get_partmask(imgfile, part, lmfile68, savepath)
-        #imgs.append(savepath)
-
-    data_loader = CreateDataLoader(opt)
-    dataset = data_loader.load_data()
-
-    # test
-    # model.eval()
-    for i, data in enumerate(dataset):
-        if i >= opt.how_many:  # test code only supports batch_size = 1, how_many means how many test images to run
-            break
-        model.set_input(data)
-        model.test()
-        visuals = model.get_current_visuals()  # in test the loadSize is set to the same as fineSize
-        img_path = model.get_image_paths()
-        # if i % 5 == 0:
-        #    print('processing (%04d)-th image... %s' % (i, img_path))
-        imgs = save_images2(opt.results_dir, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
-
-    print(imgs)
-
-    return PIL.Image.open(imgs[1])
-
-
-def main():
-    gr.close_all()
-
-    args = parse_args()
-
-    checkpoint_dir = load_checkpoint()
-
-    opt = TestOptions().parse()
-    opt.num_threads = 1  # test code only supports num_threads = 1
-    opt.batch_size = 1  # test code only supports batch_size = 1
-    opt.serial_batches = True  # no shuffle
-    opt.no_flip = True  # no flip
-    opt.display_id = -1  # no visdom display
-
-
-    opt.checkpoints_dir = checkpoint_dir
-
-    model = create_model(opt)
-    model.setup(opt)
-
-    modnet_path = load_modnet_model();
-    modnet = ModNet(modnet_path)
-
-
-    '''
-    预处理数据
-    '''
-    detector = dlib.get_frontal_face_detector()
-    predictor = dlib.shape_predictor(checkpoint_dir + '/shape_predictor_68_face_landmarks.dat')
-
-    func = functools.partial(run, model=model, opt=opt, detector=detector, predictor=predictor, modnet=modnet)
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='file', label='Input Image'),
-            gr.inputs.Textbox(lines=1, label="Landmarks"),
-        ],
-        [
-            gr.outputs.Image(
-                type='pil',
-                label='Result'),
-        ],
-        # examples=examples,
-        theme=args.theme,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        allow_screenshot=args.allow_screenshot,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/ms1mv3_r50_onegpu.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/ms1mv3_r50_onegpu.py
deleted file mode 100644
index 06e2e92ac44d92d76682dd083afd57920516e229..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/arcface_torch/configs/ms1mv3_r50_onegpu.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.margin_list = (1.0, 0.5, 0.0)
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.02
-config.verbose = 2000
-config.dali = False
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 20
-config.warmup_epoch = 0
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_mbf.py b/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_mbf.py
deleted file mode 100644
index 46ae777cc97af41a531cba4e5d1ff31f2efcb468..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_mbf.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "cosface"
-config.network = "mbf"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.1
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 2e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/iamstolas/STOLAS/src/components/welcome-screen.tsx b/spaces/iamstolas/STOLAS/src/components/welcome-screen.tsx
deleted file mode 100644
index f7449fcbb6c621875e235db98f2790bf7894fb0a..0000000000000000000000000000000000000000
--- a/spaces/iamstolas/STOLAS/src/components/welcome-screen.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-import { useBing } from '@/lib/hooks/use-bing'
-
-const exampleMessages = [
-  {
-    heading: '🧐 提出复杂问题',
-    message: `我可以为我挑剔的只吃橙色食物的孩子做什么饭?`
-  },
-  {
-    heading: '🙌 获取更好的答案',
-    message: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?'
-  },
-  {
-    heading: '🎨 获得创意灵感',
-    message: `以海盗的口吻写一首关于外太空鳄鱼的俳句`
-  }
-]
-
-export function WelcomeScreen({ setInput }: Pick<ReturnType<typeof useBing>, 'setInput'>) {
-  return (
-    <div className="welcome-container flex">
-      {exampleMessages.map(example => (
-        <button key={example.heading} className="welcome-item w-4/5 sm:w-[240px]" type="button" onClick={() => setInput(example.message)}>
-          <div className="item-title">{example.heading}</div>
-          <div className="item-content">
-            <div className="item-body">
-              <div className="item-header"></div>
-              <div>&ldquo;{example.message}&rdquo;</div>
-            </div>
-          </div>
-        </button>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/imperialwool/funapi/routes/osuApi/getBeatmap.py b/spaces/imperialwool/funapi/routes/osuApi/getBeatmap.py
deleted file mode 100644
index 828e3d7e0c7c585bb1c8cab8c59820bf2b0d0cf2..0000000000000000000000000000000000000000
--- a/spaces/imperialwool/funapi/routes/osuApi/getBeatmap.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .. import helpers
-def getBeatmap(request):
-     return {"status": "error", "details": {"code": -1, "answer": "Under construction"}}
\ No newline at end of file
diff --git a/spaces/inamXcontru/PoeticTTS/Adriaroute 4.10 Nt Unlocked The Benefits of Using the Latest Version of the Map.md b/spaces/inamXcontru/PoeticTTS/Adriaroute 4.10 Nt Unlocked The Benefits of Using the Latest Version of the Map.md
deleted file mode 100644
index 12a6f968c4fc2191f96d46bffdf8e2a7fd54e191..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Adriaroute 4.10 Nt Unlocked The Benefits of Using the Latest Version of the Map.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<p>adria route, adria route map, adria route 4.50 download, adria route garmin, adria route 4.50, adria route update, adriaroute 4.40 bt, adria route 4.40, adria route 4.40 nt download, adria route aida, adria route 4.40 download, adria route 2019, adria route aida blu HerStorytorrentdownload<br></p>
-<p>Adria Route AdriaROUTE 4.20 NT Update Digitale Kartenwerke. ... Update on MAC AdriaROUTE update for MAC users! The latest version of .... Manga dušik rani Adria Route 4.20 | Peatix. Banket otpornost Začinjeno Garmin 5 Nuvi 1450 Cela evropa i Srbija 2019.30. Mathis Izvan sumnje .... Adria Route 4.20. Free Download. adria route, adriaroute 4.50, adria route map, adria route update, adria route garmin, adria route 4.50 download, adria route .... City Nav AdriaRoute 4.20. AdriaRoute 4.20. oc0cUS8 ttkn7no AdriaROUTE 4:20 NT properties AdriaROUTE 4:20 NT map is detailed routable .... Direktlinks zu den englischen Seiten der Downloads, AdriaROUTE Update: ( 7312bf97fb</p>
-<h2>Adriaroute 4.10 Nt Unlocked</h2><br /><p><b><b>DOWNLOAD</b> &#9989; <a href="https://gohhs.com/2uz2Vu">https://gohhs.com/2uz2Vu</a></b></p><br /><br />
-<p><b>Garmin CityXplorer 2013.20 Europe 12 cities collection (FID800)<br /> CityXplorer maps unlocked to work in Nuvis 37xx series<br /> CityXplorer Europe 2012.40 (all the CityXplorers in one file ) </b></p>
-<p>Garmin City Navigator Europe NTU (Unicode) 2013.30<br /> City Navigator Europe NT 2013.30<br /> City Navigator Europe NTU 2013.21<br /> City Navigator Europe NT 2013.20<br /> Garmin City Navigator Europe NT 2013.10<br /> City Navigator Europe (Unicode) NT 2013.10<br /> City Navigator Europe (Unicode) NTU 2012.40<br /> Garmin City Navigator Europe NT 2012.40<br /> Garmin City Navigator Europe 2009 Non-NT Mapsource+IMG unlocked<br /> Recreational Map of Europe v3.1<br /> Garmin Recreational Map of Europe 3.00 - MapSource<br /> Garmin Travel Guide for All of Europe- update nov 2009<br /> Garmin openmtbmap transparent map of the Alps IMG&Mapsource<br /> Garmin Openmaps Europe<br /> Garmin TransAlpin 2012 Pro<br /> Garmin TransAlpin 2012 Pro - DVD version - unlocked<br /> Garmin Transalpin mapsource<br /> Europe Wanderkarte (walking-trail map) topo map for hiking<br /> European Waterways Map for Garmin Receivers<br /> Approach CourseView Updates - up to October 2012<br /> OSM based Europe Maps for Garmin fenix</p>
-<p>AdriaTOPO XL 1.0<br /> AdriaROUTE 4.10 NT Update<br /> AdriaROUTE 3.30 NT + Lite version<br /> AdriaTOPO 2.40+2.41 slovenian version<br /> Adria Guides&POIs:Croatia+Slovenia(Zadar, Rijeka,Baška,Poreč Krk,Velika Gorica, Perušić,Zagreb)<br /> SCG Route 2.70 NT<br /> Garmin Serbia topo20 v0.99 mapsource&IMG unlocked</p>
-<p>Regio Baltic up to v.1.8 [Estonia, Latvia, Lithuania.]<br /> Estonia EOMAP Eesti Teejuht up to v.2012.2.6<br /> Regio Estonia Roads up to v 1.12 NT [Regio Eesti teed.]<br /> Estonia Map REGIO Eesti topo NT up to v.1.6<br /> Garmin Peipus lake Estonia nautical map IMG unlocked</p>
-<p>TOPO France v3 Pro <b>Regional and Full Coverage</b><br /> CityXplorer Maps France 2012.10 (37xx series )<br /> cityXplorer Paris 2011.10 & 2012.30<br /> Garmin France topo map ( maps merged & unlocked IMGs North&South)<br /> Garmin Topo France v2 (Mapsource)<br /> GARMIN TOPO France - Sud Ouest v2 3/2009<br /> Garmin map of France IMG vers.<br /> Garmin France- Pyrenees mountains topo v2.7<br /> Ourfootprints MapSource Corse (France) Map<br /> OnRoute Motorkaart ExtraRegio France, Italy, Spain, Portugal v.1.01</p>
-<p>OnRoute Motorkaart ExtraRegio France, Italy, Spain, Portugal v.1.01<br /> CityXplorer - Naples (Napoli) 2012.10 (20.12)<br /> Garmin cityXplorer FLORENCE (Firenze) 2011.20<br /> Garmin CityXplorer Roma 2011.10<br /> TrekMap Italia v3 Pro<br /> TrekMap Italy v2 - MapSource - untouched<br /> TrekMap Italia v2 (IMG)<br /> Garmin cityXplorer Venice<br /> Garda gps Lessinia maps up to v.6.0+ Mincio vectorial map+Asiago Mt.Grappa map<br /> Garmin Sud Tirol region Italy map (mapsource)<br /> Garmin Tuscany (Italy)trek map IMG+ mapsource unlocked<br /> Garmin Italy TrekMap: Emilia Romagna mapsource unlocked map<br /> Garmin Italy TrekMap Lombardia mapsource unlocked<br /> Garmin Land Navigator® Italy<br /> Free Italy Mountains Custom Maps -kmz format<br /> Garmin TrekMap Gold Garda Dolomiti V2 (Mapsource)northern part</p>
-<p></p>
-<p>City Navigator Australia & New Zealand NT 2013.30 Navteq<br /> Garmin cityXplorer Australia Sydney/Newcastle NAVTEQ 2013.30<br /> Garmin cityXplorer Australia cities Navteq 2013.10 (FID 800)<br /> City Navigator Australia & New Zealand NT 2013.20 (Sensis)<br /> Garmin Car Navigation: Australia Whereis V18 C1 & C2 （2012）<br /> NAVTEQ - City Navigator Australia And New Zealand NT 2013.10 - Truckies Map<br /> TOPO Australia & New Zealand v4<br /> Garmin Topo Australia & New Zealand up to V3.01<br /> Tracks4Australia PRO v 1.2<br /> Garmin OSMAustralia maps ( regions IMGs)<br /> Australia Topo maps by MapsBank <b>New</b><br /> Garmin Australia Shonkymaps v/3.51 (Mapsource)<br /> Australia Oztopo v3 contour + roads<br /> Garmin australia Oztopo v2.1 + v.2.1b<br /> Garmin Mapsource Australia OZtopo V2.1 Roads<br /> Garmin OZTopo v1.2 Australia unlocked IMG<br /> City Navigator Australia 2009 (Non NT) - Mapsource<br /> Garmin Contours Australia version 3 (5m Contour Maps)<br /> Approach CourseView Updates - up to October 2012</p>
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diff --git a/spaces/inamXcontru/PoeticTTS/CRACK Adobe Photoshop CS3 Extended [FULL] Crack [2021].md b/spaces/inamXcontru/PoeticTTS/CRACK Adobe Photoshop CS3 Extended [FULL] Crack [2021].md
deleted file mode 100644
index 3ebea39c5cceff09576309f320a1b84f4699e83b..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/CRACK Adobe Photoshop CS3 Extended [FULL] Crack [2021].md	
+++ /dev/null
@@ -1,31 +0,0 @@
-<br />
-<h1>Adobe Photoshop CS3 Extended: A Powerful Image Editor for Professionals</h1>
-<p>Adobe Photoshop CS3 Extended is a version of Adobe Photoshop that offers advanced features for editing and manipulating images, especially 3D and motion graphics. Photoshop CS3 Extended is no longer available from Adobe, but you can still download it from third-party websites or use your installation CD if you have one. However, you should be aware of the risks and disadvantages of using an outdated and unsupported software. In this article, we will review some of the benefits and drawbacks of Adobe Photoshop CS3 Extended, and suggest some alternatives that you can try.</p>
-<h2>CRACK Adobe Photoshop CS3 Extended [FULL] Crack</h2><br /><p><b><b>Download Zip</b> &#9733; <a href="https://gohhs.com/2uz4I0">https://gohhs.com/2uz4I0</a></b></p><br /><br />
-<h2>Benefits of Adobe Photoshop CS3 Extended</h2>
-<p>Photoshop CS3 Extended has many features that make it a powerful image editor for professionals. Some of the benefits are:</p>
-<ul>
-<li><strong>Better support of files from Camera RAW</strong>: You can import and edit RAW images from various cameras without losing quality or metadata.</li>
-<li><strong>Quick selection tool</strong>: You can easily select complex objects or regions in your images with a single click.</li>
-<li><strong>Shades of gray control in the b&w images</strong>: You can adjust the tonal range and contrast of your black and white images with more precision and flexibility.</li>
-<li><strong>Smart filters</strong>: You can apply non-destructive filters to your images and edit them later without affecting the original pixels.</li>
-<li><strong>Graphics optimization for mobile devices</strong>: You can create and preview graphics for different screen sizes and resolutions, and export them in optimized formats.</li>
-<li><strong>Photomerge tool to create panoramic images</strong>: You can stitch together multiple images to create seamless panoramas with automatic alignment and blending.</li>
-<li><strong>Ability to work with 3D and motion graphics</strong>: You can import, edit, render, and animate 3D models and textures, as well as edit video layers and apply effects to them.</li>
-<li><strong>Image analysis tools</strong>: You can measure, count, label, and export data from your images using various tools and plugins.</li>
-</ul>
-<h2>Drawbacks of Adobe Photoshop CS3 Extended</h2>
-<p>Despite its advantages, Photoshop CS3 Extended also has some drawbacks that you should consider before downloading or installing it. Some of the drawbacks are:</p>
-<ul>
-<li><strong>It is outdated and unsupported</strong>: Adobe stopped developing and supporting Photoshop CS3 Extended in 2010, which means that it may not work properly on newer operating systems or devices. It also means that you will not receive any updates, bug fixes, security patches, or customer support from Adobe.</li>
-<li><strong>It may contain errors and malfunctions</strong>: Since Photoshop CS3 Extended is no longer maintained by Adobe, it may have some bugs or glitches that affect its performance or functionality. For example, some users have reported problems with saving files, opening menus, or using certain tools.</li>
-<li><strong>It may be infected with malware</strong>: If you download Photoshop CS3 Extended from an untrusted source, you may expose your computer to viruses, spyware, ransomware, or other malicious software that can harm your system or data. You should always scan any downloaded files with a reliable antivirus program before opening them.</li>
-<li><strong>It may be illegal or unethical</strong>: Depending on your location and license agreement, downloading or using Photoshop CS3 Extended without paying for it may violate the intellectual property rights of Adobe or other parties. It may also be unethical to use a pirated software for professional or commercial purposes.</li>
-</ul>
-<h2>Alternatives to Adobe Photoshop CS3 Extended</h2>
-<p>If you are looking for a more updated, secure, and legal way to edit your images, you may want to try some of the alternatives to Photoshop CS3 Extended that are available on the market. Some of the alternatives are:</p>
-<p></p>
-<ul>
-<li><strong>Adobe Photoshop CC</strong>: This is the latest version of Adobe Photoshop that you can subscribe to as part of the Creative Cloud service. It offers all the features of Photoshop CS3 Extended plus many more improvements and additions. You can also install it on both your PC and iPad, and access your work from any device using cloud storage. You can download a free trial version</p> d5da3c52bf<br />
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\ No newline at end of file
diff --git a/spaces/inamXcontru/PoeticTTS/CalcTape Pro v6.0.2  A New Kind of Pocket Calculator for Your PC.md b/spaces/inamXcontru/PoeticTTS/CalcTape Pro v6.0.2  A New Kind of Pocket Calculator for Your PC.md
deleted file mode 100644
index f5cac1ba02f310aff30da2457d4222f155cfd3cc..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/CalcTape Pro v6.0.2  A New Kind of Pocket Calculator for Your PC.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>CalcTape Pro v6.0.2 –</h2><br /><p><b><b>DOWNLOAD</b> ===> <a href="https://gohhs.com/2uz5ov">https://gohhs.com/2uz5ov</a></b></p><br /><br />
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diff --git a/spaces/innnky/vits-nyaru/text/__init__.py b/spaces/innnky/vits-nyaru/text/__init__.py
deleted file mode 100644
index 48ae82f3e40ecd1bf17a7de78d87790327af3362..0000000000000000000000000000000000000000
--- a/spaces/innnky/vits-nyaru/text/__init__.py
+++ /dev/null
@@ -1,56 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Download Do God Of War 2 Para Pc Rip.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Download Do God Of War 2 Para Pc Rip.md
deleted file mode 100644
index c754968402f43b55a5aca7a737ac3bc1fa23eaad..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Download Do God Of War 2 Para Pc Rip.md	
+++ /dev/null
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-<h2>Download Do God Of War 2 Para Pc Rip</h2><br /><p><b><b>DOWNLOAD</b> ->->->-> <a href="https://urlin.us/2uEy56">https://urlin.us/2uEy56</a></b></p><br /><br />
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-December 27, 2021 — God of War 2 Download for PC is a fast-paced single player video game from the third faces with hack and slash basics viewed from a predefined camera... God of War II is a hack and slash game developed by Sony Santa Monica and published by SONY.
-This is the sequel to God of War, created by Sony Santa Monica.
-The game was released in November 2018 on PlayStation 4 and was released in 2019 for PlayStation 4 and PlayStation VR.
-The game's gameplay is inspired by the classic 1980 action movie and 1980's Space Fight.
-The game also introduces a new character, a new type of weapon, several new controls, and more. 8a78ff9644<br />
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/K Kundan English Pdf Free Download.md b/spaces/inplisQlawa/anything-midjourney-v4-1/K Kundan English Pdf Free Download.md
deleted file mode 100644
index 835ef8db0290767aa37b90202c0a1487ec8b02a2..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/K Kundan English Pdf Free Download.md	
+++ /dev/null
@@ -1,7 +0,0 @@
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-<p>paper-xv is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition. paper-xvi is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition. paper-xvii is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition. paper-xviii is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition. paper-xix is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition. paper-xx is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition.</p>
-<h2>K Kundan English Pdf Free Download</h2><br /><p><b><b>DOWNLOAD</b> ->->->-> <a href="https://urlin.us/2uExtv">https://urlin.us/2uExtv</a></b></p><br /><br />
-<p>paper-xxi is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition. paper-xxii is a qualifying examination conducted to test the candidate’s proficiency in the hindi language (sanskrit and hindi), english language and english composition.</p>
-<p>the ssc mts exam pattern consists of three parts; theory, practical and the mock test. the mock test consists of the english language section, general awareness section, quantitative aptitude section and general reasoning section. based on the number of marks secured in each of these sections, candidates are ranked. the candidate with the least score is eliminated in each part. the candidates are required to write an essay and a letter. candidates are also required to write a precis.</p> 899543212b<br />
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diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/depth.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/depth.py
deleted file mode 100644
index 61a50459a4a3ed046ed1c4cdcbd914437026fc0d..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/depth.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math, os, subprocess
-import cv2
-import hashlib
-import numpy as np
-import torch
-import gc
-import torchvision.transforms as T
-from einops import rearrange, repeat
-from PIL import Image
-from infer import InferenceHelper
-from midas.dpt_depth import DPTDepthModel
-from midas.transforms import Resize, NormalizeImage, PrepareForNet
-import torchvision.transforms.functional as TF
-from .general_utils import checksum
-
-class DepthModel():
-    def __init__(self, device):
-        self.adabins_helper = None
-        self.depth_min = 1000
-        self.depth_max = -1000
-        self.device = device
-        self.midas_model = None
-        self.midas_transform = None
-    
-    def load_adabins(self, models_path):
-        if not os.path.exists(os.path.join(models_path,'AdaBins_nyu.pt')):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(r"https://cloudflare-ipfs.com/ipfs/Qmd2mMnDLWePKmgfS8m6ntAg4nhV5VkUyAydYBp8cWWeB7/AdaBins_nyu.pt", models_path)
-            if checksum(os.path.join(models_path,'AdaBins_nyu.pt')) != "643db9785c663aca72f66739427642726b03acc6c4c1d3755a4587aa2239962746410d63722d87b49fc73581dbc98ed8e3f7e996ff7b9c0d56d0fbc98e23e41a":
-                raise Exception(r"Error while downloading AdaBins_nyu.pt. Please download from here: https://drive.google.com/file/d/1lvyZZbC9NLcS8a__YPcUP7rDiIpbRpoF and place in: " + models_path)
-        self.adabins_helper = InferenceHelper(models_path=models_path, dataset='nyu', device=self.device)
-
-    def load_midas(self, models_path, half_precision=True):
-        if not os.path.exists(os.path.join(models_path, 'dpt_large-midas-2f21e586.pt')):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(r"https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", models_path)
-            if checksum(os.path.join(models_path,'dpt_large-midas-2f21e586.pt')) != "fcc4829e65d00eeed0a38e9001770676535d2e95c8a16965223aba094936e1316d569563552a852d471f310f83f597e8a238987a26a950d667815e08adaebc06":
-                raise Exception(r"Error while downloading dpt_large-midas-2f21e586.pt. Please download from here: https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt and place in: " + models_path)
-
-        self.midas_model = DPTDepthModel(
-            path=f"{models_path}/dpt_large-midas-2f21e586.pt",
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-        self.midas_transform = T.Compose([
-            Resize(
-                384, 384,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method="minimal",
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet()
-        ])
-
-        self.midas_model.eval()    
-        if self.device == torch.device("cuda"):
-            self.midas_model = self.midas_model.to(memory_format=torch.channels_last)
-        if half_precision:
-            self.midas_model = self.midas_model.half()
-        self.midas_model.to(self.device)
-
-    def predict(self, prev_img_cv2, anim_args, half_precision) -> torch.Tensor:
-        w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]
-
-        # predict depth with AdaBins    
-        use_adabins = anim_args.midas_weight < 1.0 and self.adabins_helper is not None
-        if use_adabins:
-            MAX_ADABINS_AREA = 500000
-            MIN_ADABINS_AREA = 448*448
-
-            # resize image if too large or too small
-            img_pil = Image.fromarray(cv2.cvtColor(prev_img_cv2.astype(np.uint8), cv2.COLOR_RGB2BGR))
-            image_pil_area = w*h
-            resized = True
-            if image_pil_area > MAX_ADABINS_AREA:
-                scale = math.sqrt(MAX_ADABINS_AREA) / math.sqrt(image_pil_area)
-                depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.LANCZOS) # LANCZOS is good for downsampling
-                print(f"  resized to {depth_input.width}x{depth_input.height}")
-            elif image_pil_area < MIN_ADABINS_AREA:
-                scale = math.sqrt(MIN_ADABINS_AREA) / math.sqrt(image_pil_area)
-                depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.BICUBIC)
-                print(f"  resized to {depth_input.width}x{depth_input.height}")
-            else:
-                depth_input = img_pil
-                resized = False
-
-            # predict depth and resize back to original dimensions
-            try:
-                with torch.no_grad():
-                    _, adabins_depth = self.adabins_helper.predict_pil(depth_input)
-                if resized:
-                    adabins_depth = TF.resize(
-                        torch.from_numpy(adabins_depth), 
-                        torch.Size([h, w]),
-                        interpolation=TF.InterpolationMode.BICUBIC
-                    )
-                    adabins_depth = adabins_depth.cpu().numpy()
-                adabins_depth = adabins_depth.squeeze()
-            except:
-                print(f"  exception encountered, falling back to pure MiDaS")
-                use_adabins = False
-            torch.cuda.empty_cache()
-
-        if self.midas_model is not None:
-            # convert image from 0->255 uint8 to 0->1 float for feeding to MiDaS
-            img_midas = prev_img_cv2.astype(np.float32) / 255.0
-            img_midas_input = self.midas_transform({"image": img_midas})["image"]
-
-            # MiDaS depth estimation implementation
-            sample = torch.from_numpy(img_midas_input).float().to(self.device).unsqueeze(0)
-            if self.device == torch.device("cuda"):
-                sample = sample.to(memory_format=torch.channels_last)  
-                if half_precision:
-                    sample = sample.half()
-            with torch.no_grad():            
-                midas_depth = self.midas_model.forward(sample)
-            midas_depth = torch.nn.functional.interpolate(
-                midas_depth.unsqueeze(1),
-                size=img_midas.shape[:2],
-                mode="bicubic",
-                align_corners=False,
-            ).squeeze()
-            midas_depth = midas_depth.cpu().numpy()
-            torch.cuda.empty_cache()
-
-            # MiDaS makes the near values greater, and the far values lesser. Let's reverse that and try to align with AdaBins a bit better.
-            midas_depth = np.subtract(50.0, midas_depth)
-            midas_depth = midas_depth / 19.0
-
-            # blend between MiDaS and AdaBins predictions
-            if use_adabins:
-                depth_map = midas_depth*anim_args.midas_weight + adabins_depth*(1.0-anim_args.midas_weight)
-            else:
-                depth_map = midas_depth
-
-            depth_map = np.expand_dims(depth_map, axis=0)
-            depth_tensor = torch.from_numpy(depth_map).squeeze().to(self.device)
-        else:
-            depth_tensor = torch.ones((h, w), device=self.device)
-        
-        return depth_tensor
-
-    def save(self, filename: str, depth: torch.Tensor):
-        depth = depth.cpu().numpy()
-        if len(depth.shape) == 2:
-            depth = np.expand_dims(depth, axis=0)
-        self.depth_min = min(self.depth_min, depth.min())
-        self.depth_max = max(self.depth_max, depth.max())
-        print(f"  depth min:{depth.min()} max:{depth.max()}")
-        denom = max(1e-8, self.depth_max - self.depth_min)
-        temp = rearrange((depth - self.depth_min) / denom * 255, 'c h w -> h w c')
-        temp = repeat(temp, 'h w 1 -> h w c', c=3)
-        Image.fromarray(temp.astype(np.uint8)).save(filename)
-    
-    def to(self, device):
-        self.device = device
-        self.midas_model.to(device)
-        if self.adabins_helper is not None:
-            self.adabins_helper.to(device)
-        gc.collect()
-        torch.cuda.empty_cache()
diff --git a/spaces/jbilcke-hf/MusicGen/tests/modules/test_conv.py b/spaces/jbilcke-hf/MusicGen/tests/modules/test_conv.py
deleted file mode 100644
index 28fbc4f1a0ebaf41b56947b767958ae696e75eec..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/MusicGen/tests/modules/test_conv.py
+++ /dev/null
@@ -1,203 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-import math
-import random
-
-import pytest
-import torch
-from torch import nn
-
-from audiocraft.modules import (
-    NormConv1d,
-    NormConvTranspose1d,
-    StreamableConv1d,
-    StreamableConvTranspose1d,
-    pad1d,
-    unpad1d,
-)
-
-
-def test_get_extra_padding_for_conv1d():
-    # TODO: Implement me!
-    pass
-
-
-def test_pad1d_zeros():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='constant', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='constant', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='constant', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='constant', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='constant', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='constant', value=0.)
-
-
-def test_pad1d_reflect():
-    x = torch.randn(1, 1, 20)
-
-    xp1 = pad1d(x, (0, 5), mode='reflect', value=0.)
-    assert xp1.shape[-1] == 25
-    xp2 = pad1d(x, (5, 5), mode='reflect', value=0.)
-    assert xp2.shape[-1] == 30
-    xp3 = pad1d(x, (0, 0), mode='reflect', value=0.)
-    assert xp3.shape[-1] == 20
-    xp4 = pad1d(x, (10, 30), mode='reflect', value=0.)
-    assert xp4.shape[-1] == 60
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, 0), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (0, -1), mode='reflect', value=0.)
-
-    with pytest.raises(AssertionError):
-        pad1d(x, (-1, -1), mode='reflect', value=0.)
-
-
-def test_unpad1d():
-    x = torch.randn(1, 1, 20)
-
-    u1 = unpad1d(x, (5, 5))
-    assert u1.shape[-1] == 10
-    u2 = unpad1d(x, (0, 5))
-    assert u2.shape[-1] == 15
-    u3 = unpad1d(x, (5, 0))
-    assert u3.shape[-1] == 15
-    u4 = unpad1d(x, (0, 0))
-    assert u4.shape[-1] == x.shape[-1]
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, 0))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (0, -1))
-
-    with pytest.raises(AssertionError):
-        unpad1d(x, (-1, -1))
-
-
-class TestNormConv1d:
-
-    def test_norm_conv1d_modules(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = int((T - kernel_size) / stride + 1)
-        wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm')
-        gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm')
-        nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none')
-
-        assert isinstance(wn_conv.norm, nn.Identity)
-        assert isinstance(wn_conv.conv, nn.Conv1d)
-
-        assert isinstance(gn_conv.norm, nn.GroupNorm)
-        assert isinstance(gn_conv.conv, nn.Conv1d)
-
-        assert isinstance(nn_conv.norm, nn.Identity)
-        assert isinstance(nn_conv.conv, nn.Conv1d)
-
-        for conv_layer in [wn_conv, gn_conv, nn_conv]:
-            out = conv_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestNormConvTranspose1d:
-
-    def test_normalizations(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out, kernel_size, stride = 1, 4, 1
-        expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1
-
-        wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm')
-        gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm')
-        nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none')
-
-        assert isinstance(wn_convtr.norm, nn.Identity)
-        assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(gn_convtr.norm, nn.GroupNorm)
-        assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d)
-
-        assert isinstance(nn_convtr.norm, nn.Identity)
-        assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d)
-
-        for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]:
-            out = convtr_layer(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConv1d:
-
-    def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation):
-        # StreamableConv1d internally pads to make sure that the last window is full
-        padding_total = (kernel_size - 1) * dilation - (stride - 1)
-        n_frames = (length - kernel_size + padding_total) / stride + 1
-        ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-        return ideal_length // stride
-
-    def test_streamable_conv1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-        C_out = 1
-
-        # conv params are [(kernel_size, stride, dilation)]
-        conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)]
-        for causal, (kernel_size, stride, dilation) in product([False, True], conv_params):
-            expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation)
-            sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal)
-            out = sconv(t0)
-            assert isinstance(out, torch.Tensor)
-            print(list(out.shape), [N, C_out, expected_out_length])
-            assert list(out.shape) == [N, C_out, expected_out_length]
-
-
-class TestStreamableConvTranspose1d:
-
-    def get_streamable_convtr1d_output_length(self, length, kernel_size, stride):
-        padding_total = (kernel_size - stride)
-        return (length - 1) * stride - padding_total + (kernel_size - 1) + 1
-
-    def test_streamable_convtr1d(self):
-        N, C, T = 2, 2, random.randrange(1, 100_000)
-        t0 = torch.randn(N, C, T)
-
-        C_out = 1
-
-        with pytest.raises(AssertionError):
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.)
-            StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2)
-
-        # causal params are [(causal, trim_right)]
-        causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)]
-        # conv params are [(kernel_size, stride)]
-        conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)]
-        for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params):
-            expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride)
-            sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride,
-                                                causal=causal, trim_right_ratio=trim_right_ratio)
-            out = sconvtr(t0)
-            assert isinstance(out, torch.Tensor)
-            assert list(out.shape) == [N, C_out, expected_out_length]
diff --git a/spaces/jbilcke-hf/VideoChain-UI/src/lib/utils.ts b/spaces/jbilcke-hf/VideoChain-UI/src/lib/utils.ts
deleted file mode 100644
index ec79801fe9cdd7711f6dbef26678a134c634a8be..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoChain-UI/src/lib/utils.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { type ClassValue, clsx } from "clsx"
-import { twMerge } from "tailwind-merge"
- 
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/lib/sleep.ts b/spaces/jbilcke-hf/ai-clip-factory/src/lib/sleep.ts
deleted file mode 100644
index 2885c6e75c0dc415c9eaf71beabac7461eee5588..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/lib/sleep.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-export const sleep = async (durationInMs: number) =>
-  new Promise((resolve) => {
-    setTimeout(() => {
-      resolve(true)
-    }, durationInMs)
-  })
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/components/ui/card.tsx b/spaces/jbilcke-hf/ai-comic-factory/src/components/ui/card.tsx
deleted file mode 100644
index 6583ebc1bb942bfb94e00fb4e7c7d685073c7b2a..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/src/components/ui/card.tsx
+++ /dev/null
@@ -1,79 +0,0 @@
-import * as React from "react"
-
-import { cn } from "@/lib/utils"
-
-const Card = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div
-    ref={ref}
-    className={cn(
-      "rounded-lg border border-stone-200 bg-white text-stone-950 shadow-sm dark:border-stone-800 dark:bg-stone-950 dark:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-Card.displayName = "Card"
-
-const CardHeader = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div
-    ref={ref}
-    className={cn("flex flex-col space-y-1.5 p-6", className)}
-    {...props}
-  />
-))
-CardHeader.displayName = "CardHeader"
-
-const CardTitle = React.forwardRef<
-  HTMLParagraphElement,
-  React.HTMLAttributes<HTMLHeadingElement>
->(({ className, ...props }, ref) => (
-  <h3
-    ref={ref}
-    className={cn(
-      "text-2xl font-semibold leading-none tracking-tight",
-      className
-    )}
-    {...props}
-  />
-))
-CardTitle.displayName = "CardTitle"
-
-const CardDescription = React.forwardRef<
-  HTMLParagraphElement,
-  React.HTMLAttributes<HTMLParagraphElement>
->(({ className, ...props }, ref) => (
-  <p
-    ref={ref}
-    className={cn("text-sm text-stone-500 dark:text-stone-400", className)}
-    {...props}
-  />
-))
-CardDescription.displayName = "CardDescription"
-
-const CardContent = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div ref={ref} className={cn("p-6 pt-0", className)} {...props} />
-))
-CardContent.displayName = "CardContent"
-
-const CardFooter = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(({ className, ...props }, ref) => (
-  <div
-    ref={ref}
-    className={cn("flex items-center p-6 pt-0", className)}
-    {...props}
-  />
-))
-CardFooter.displayName = "CardFooter"
-
-export { Card, CardHeader, CardFooter, CardTitle, CardDescription, CardContent }
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_siv.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_siv.py
deleted file mode 100644
index 1805ec09f029668cbb32ae139bbd9242df6dbaec..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Cipher/_mode_siv.py
+++ /dev/null
@@ -1,392 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin <helderijs@gmail.com>
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-#    notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-#    notice, this list of conditions and the following disclaimer in
-#    the documentation and/or other materials provided with the
-#    distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-"""
-Synthetic Initialization Vector (SIV) mode.
-"""
-
-__all__ = ['SivMode']
-
-from binascii import hexlify, unhexlify
-
-from Crypto.Util.py3compat import bord, _copy_bytes
-
-from Crypto.Util._raw_api import is_buffer
-
-from Crypto.Util.number import long_to_bytes, bytes_to_long
-from Crypto.Protocol.KDF import _S2V
-from Crypto.Hash import BLAKE2s
-from Crypto.Random import get_random_bytes
-
-
-class SivMode(object):
-    """Synthetic Initialization Vector (SIV).
-
-    This is an Authenticated Encryption with Associated Data (`AEAD`_) mode.
-    It provides both confidentiality and authenticity.
-
-    The header of the message may be left in the clear, if needed, and it will
-    still be subject to authentication. The decryption step tells the receiver
-    if the message comes from a source that really knowns the secret key.
-    Additionally, decryption detects if any part of the message - including the
-    header - has been modified or corrupted.
-
-    Unlike other AEAD modes such as CCM, EAX or GCM, accidental reuse of a
-    nonce is not catastrophic for the confidentiality of the message. The only
-    effect is that an attacker can tell when the same plaintext (and same
-    associated data) is protected with the same key.
-
-    The length of the MAC is fixed to the block size of the underlying cipher.
-    The key size is twice the length of the key of the underlying cipher.
-
-    This mode is only available for AES ciphers.
-
-    +--------------------+---------------+-------------------+
-    |      Cipher        | SIV MAC size  |   SIV key length  |
-    |                    |    (bytes)    |     (bytes)       |
-    +====================+===============+===================+
-    |    AES-128         |      16       |        32         |
-    +--------------------+---------------+-------------------+
-    |    AES-192         |      16       |        48         |
-    +--------------------+---------------+-------------------+
-    |    AES-256         |      16       |        64         |
-    +--------------------+---------------+-------------------+
-
-    See `RFC5297`_ and the `original paper`__.
-
-    .. _RFC5297: https://tools.ietf.org/html/rfc5297
-    .. _AEAD: http://blog.cryptographyengineering.com/2012/05/how-to-choose-authenticated-encryption.html
-    .. __: http://www.cs.ucdavis.edu/~rogaway/papers/keywrap.pdf
-
-    :undocumented: __init__
-    """
-
-    def __init__(self, factory, key, nonce, kwargs):
-
-        self.block_size = factory.block_size
-        """The block size of the underlying cipher, in bytes."""
-
-        self._factory = factory
-
-        self._cipher_params = kwargs
-
-        if len(key) not in (32, 48, 64):
-            raise ValueError("Incorrect key length (%d bytes)" % len(key))
-
-        if nonce is not None:
-            if not is_buffer(nonce):
-                raise TypeError("When provided, the nonce must be bytes, bytearray or memoryview")
-
-            if len(nonce) == 0:
-                raise ValueError("When provided, the nonce must be non-empty")
-
-            self.nonce = _copy_bytes(None, None, nonce)
-            """Public attribute is only available in case of non-deterministic
-            encryption."""
-
-        subkey_size = len(key) // 2
-
-        self._mac_tag = None  # Cache for MAC tag
-        self._kdf = _S2V(key[:subkey_size],
-                         ciphermod=factory,
-                         cipher_params=self._cipher_params)
-        self._subkey_cipher = key[subkey_size:]
-
-        # Purely for the purpose of verifying that cipher_params are OK
-        factory.new(key[:subkey_size], factory.MODE_ECB, **kwargs)
-
-        # Allowed transitions after initialization
-        self._next = ["update", "encrypt", "decrypt",
-                      "digest", "verify"]
-
-    def _create_ctr_cipher(self, v):
-        """Create a new CTR cipher from V in SIV mode"""
-
-        v_int = bytes_to_long(v)
-        q = v_int & 0xFFFFFFFFFFFFFFFF7FFFFFFF7FFFFFFF
-        return self._factory.new(
-                    self._subkey_cipher,
-                    self._factory.MODE_CTR,
-                    initial_value=q,
-                    nonce=b"",
-                    **self._cipher_params)
-
-    def update(self, component):
-        """Protect one associated data component
-
-        For SIV, the associated data is a sequence (*vector*) of non-empty
-        byte strings (*components*).
-
-        This method consumes the next component. It must be called
-        once for each of the components that constitue the associated data.
-
-        Note that the components have clear boundaries, so that:
-
-            >>> cipher.update(b"builtin")
-            >>> cipher.update(b"securely")
-
-        is not equivalent to:
-
-            >>> cipher.update(b"built")
-            >>> cipher.update(b"insecurely")
-
-        If there is no associated data, this method must not be called.
-
-        :Parameters:
-          component : bytes/bytearray/memoryview
-            The next associated data component.
-        """
-
-        if "update" not in self._next:
-            raise TypeError("update() can only be called"
-                                " immediately after initialization")
-
-        self._next = ["update", "encrypt", "decrypt",
-                      "digest", "verify"]
-
-        return self._kdf.update(component)
-
-    def encrypt(self, plaintext):
-        """
-        For SIV, encryption and MAC authentication must take place at the same
-        point. This method shall not be used.
-
-        Use `encrypt_and_digest` instead.
-        """
-
-        raise TypeError("encrypt() not allowed for SIV mode."
-                        " Use encrypt_and_digest() instead.")
-
-    def decrypt(self, ciphertext):
-        """
-        For SIV, decryption and verification must take place at the same
-        point. This method shall not be used.
-
-        Use `decrypt_and_verify` instead.
-        """
-
-        raise TypeError("decrypt() not allowed for SIV mode."
-                        " Use decrypt_and_verify() instead.")
-
-    def digest(self):
-        """Compute the *binary* MAC tag.
-
-        The caller invokes this function at the very end.
-
-        This method returns the MAC that shall be sent to the receiver,
-        together with the ciphertext.
-
-        :Return: the MAC, as a byte string.
-        """
-
-        if "digest" not in self._next:
-            raise TypeError("digest() cannot be called when decrypting"
-                            " or validating a message")
-        self._next = ["digest"]
-        if self._mac_tag is None:
-            self._mac_tag = self._kdf.derive()
-        return self._mac_tag
-
-    def hexdigest(self):
-        """Compute the *printable* MAC tag.
-
-        This method is like `digest`.
-
-        :Return: the MAC, as a hexadecimal string.
-        """
-        return "".join(["%02x" % bord(x) for x in self.digest()])
-
-    def verify(self, received_mac_tag):
-        """Validate the *binary* MAC tag.
-
-        The caller invokes this function at the very end.
-
-        This method checks if the decrypted message is indeed valid
-        (that is, if the key is correct) and it has not been
-        tampered with while in transit.
-
-        :Parameters:
-          received_mac_tag : bytes/bytearray/memoryview
-            This is the *binary* MAC, as received from the sender.
-        :Raises ValueError:
-            if the MAC does not match. The message has been tampered with
-            or the key is incorrect.
-        """
-
-        if "verify" not in self._next:
-            raise TypeError("verify() cannot be called"
-                            " when encrypting a message")
-        self._next = ["verify"]
-
-        if self._mac_tag is None:
-            self._mac_tag = self._kdf.derive()
-
-        secret = get_random_bytes(16)
-
-        mac1 = BLAKE2s.new(digest_bits=160, key=secret, data=self._mac_tag)
-        mac2 = BLAKE2s.new(digest_bits=160, key=secret, data=received_mac_tag)
-
-        if mac1.digest() != mac2.digest():
-            raise ValueError("MAC check failed")
-
-    def hexverify(self, hex_mac_tag):
-        """Validate the *printable* MAC tag.
-
-        This method is like `verify`.
-
-        :Parameters:
-          hex_mac_tag : string
-            This is the *printable* MAC, as received from the sender.
-        :Raises ValueError:
-            if the MAC does not match. The message has been tampered with
-            or the key is incorrect.
-        """
-
-        self.verify(unhexlify(hex_mac_tag))
-
-    def encrypt_and_digest(self, plaintext, output=None):
-        """Perform encrypt() and digest() in one step.
-
-        :Parameters:
-          plaintext : bytes/bytearray/memoryview
-            The piece of data to encrypt.
-        :Keywords:
-          output : bytearray/memoryview
-            The location where the ciphertext must be written to.
-            If ``None``, the ciphertext is returned.
-        :Return:
-            a tuple with two items:
-
-            - the ciphertext, as ``bytes``
-            - the MAC tag, as ``bytes``
-
-            The first item becomes ``None`` when the ``output`` parameter
-            specified a location for the result.
-        """
-
-        if "encrypt" not in self._next:
-            raise TypeError("encrypt() can only be called after"
-                            " initialization or an update()")
-
-        self._next = ["digest"]
-
-        # Compute V (MAC)
-        if hasattr(self, 'nonce'):
-            self._kdf.update(self.nonce)
-        self._kdf.update(plaintext)
-        self._mac_tag = self._kdf.derive()
-
-        cipher = self._create_ctr_cipher(self._mac_tag)
-
-        return cipher.encrypt(plaintext, output=output), self._mac_tag
-
-    def decrypt_and_verify(self, ciphertext, mac_tag, output=None):
-        """Perform decryption and verification in one step.
-
-        A cipher object is stateful: once you have decrypted a message
-        you cannot decrypt (or encrypt) another message with the same
-        object.
-
-        You cannot reuse an object for encrypting
-        or decrypting other data with the same key.
-
-        This function does not remove any padding from the plaintext.
-
-        :Parameters:
-          ciphertext : bytes/bytearray/memoryview
-            The piece of data to decrypt.
-            It can be of any length.
-          mac_tag : bytes/bytearray/memoryview
-            This is the *binary* MAC, as received from the sender.
-        :Keywords:
-          output : bytearray/memoryview
-            The location where the plaintext must be written to.
-            If ``None``, the plaintext is returned.
-        :Return: the plaintext as ``bytes`` or ``None`` when the ``output``
-            parameter specified a location for the result.
-        :Raises ValueError:
-            if the MAC does not match. The message has been tampered with
-            or the key is incorrect.
-        """
-
-        if "decrypt" not in self._next:
-            raise TypeError("decrypt() can only be called"
-                            " after initialization or an update()")
-        self._next = ["verify"]
-
-        # Take the MAC and start the cipher for decryption
-        self._cipher = self._create_ctr_cipher(mac_tag)
-
-        plaintext = self._cipher.decrypt(ciphertext, output=output)
-
-        if hasattr(self, 'nonce'):
-            self._kdf.update(self.nonce)
-        self._kdf.update(plaintext if output is None else output)
-        self.verify(mac_tag)
-
-        return plaintext
-
-
-def _create_siv_cipher(factory, **kwargs):
-    """Create a new block cipher, configured in
-    Synthetic Initializaton Vector (SIV) mode.
-
-    :Parameters:
-
-      factory : object
-        A symmetric cipher module from `Crypto.Cipher`
-        (like `Crypto.Cipher.AES`).
-
-    :Keywords:
-
-      key : bytes/bytearray/memoryview
-        The secret key to use in the symmetric cipher.
-        It must be 32, 48 or 64 bytes long.
-        If AES is the chosen cipher, the variants *AES-128*,
-        *AES-192* and or *AES-256* will be used internally.
-
-      nonce : bytes/bytearray/memoryview
-        For deterministic encryption, it is not present.
-
-        Otherwise, it is a value that must never be reused
-        for encrypting message under this key.
-
-        There are no restrictions on its length,
-        but it is recommended to use at least 16 bytes.
-    """
-
-    try:
-        key = kwargs.pop("key")
-    except KeyError as e:
-        raise TypeError("Missing parameter: " + str(e))
-
-    nonce = kwargs.pop("nonce", None)
-
-    return SivMode(factory, key, nonce, kwargs)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/embeddings/utils.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/embeddings/utils.py
deleted file mode 100644
index 1af024c6f07bfeaa0a4168b47d3e87519586a2a0..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/embeddings/utils.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""Embedding utils for LlamaIndex."""
-
-from typing import List
-
-
-def save_embedding(embedding: List[float], file_path: str) -> None:
-    """Save embedding to file."""
-    with open(file_path, "w") as f:
-        f.write(",".join([str(x) for x in embedding]))
-
-
-def load_embedding(file_path: str) -> List[float]:
-    """Load embedding from file. Will only return first embedding in file."""
-    with open(file_path, "r") as f:
-        for line in f:
-            embedding = [float(x) for x in line.strip().split(",")]
-            break
-        return embedding
diff --git a/spaces/jsebdev/stock_predictor/README.md b/spaces/jsebdev/stock_predictor/README.md
deleted file mode 100644
index 93726c927991c14951aff71f01cd8f888ea087c5..0000000000000000000000000000000000000000
--- a/spaces/jsebdev/stock_predictor/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Apple Stock Predicter
-emoji: 📈
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# Stock predictor
diff --git a/spaces/juancopi81/whisper-demo-es-medium/utils.py b/spaces/juancopi81/whisper-demo-es-medium/utils.py
deleted file mode 100644
index 01d04df7f63d8acda652e521ff6faeba53e926c4..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/whisper-demo-es-medium/utils.py
+++ /dev/null
@@ -1,10 +0,0 @@
-def is_google_colab():
-    try:
-        import google.colab
-        return True
-    except:
-        return False
-    
-def splitter(n, s):
-    pieces = s.split()
-    return (" ".join(pieces[i:i+n]) for i in range(0, len(pieces), n))
\ No newline at end of file
diff --git a/spaces/juancopi81/whisper-youtube-2-hf_dataset/threadeddatapipeline.py b/spaces/juancopi81/whisper-youtube-2-hf_dataset/threadeddatapipeline.py
deleted file mode 100644
index 6868bebc3bdd9502ba23eddabf9e4805841c8a3e..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/whisper-youtube-2-hf_dataset/threadeddatapipeline.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import threading
-import logging
-from pathlib import Path
-from typing import List
-
-from datapipeline import DataPipeline
-
-logging.basicConfig(level=logging.INFO, format="(%(threadName)-5s) %(message)s")
-
-class ThreadedDataPipeline(threading.Thread):
-    """Class that wraps a data pipeline in a thread."""
-
-    def __init__(self,
-                 data_pipeline: DataPipeline,
-                 load_paths: List[Path],
-                 **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.data_pipeline = data_pipeline
-        self.load_paths = load_paths
-        
-    def run(self) -> None:
-        logging.info("Started processing data.")
-        self.data_pipeline.process(self.load_paths)
-        logging.info("Finished processing data.")
\ No newline at end of file
diff --git a/spaces/juancopi81/youtube-music-transcribe/t5x/checkpoint_utils.py b/spaces/juancopi81/youtube-music-transcribe/t5x/checkpoint_utils.py
deleted file mode 100644
index 7ef15ab7c10c511d454221abb87916e8e1677519..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/youtube-music-transcribe/t5x/checkpoint_utils.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# Copyright 2022 The T5X Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Checkpoint helper functions for managing checkpoints.
-
-Supports marking checkpoints as pinned to exclude them from the checkpointer
-removal process.
-"""
-
-import os
-
-from absl import logging
-
-from tensorflow.io import gfile
-
-# PINNED file in the checkpoint directory indicates that the checkpoint should
-# not be removed during the automatic pruning of old checkpoints.
-_PINNED_CHECKPOINT_FILENAME = 'PINNED'
-
-
-def pinned_checkpoint_filepath(ckpt_dir: str) -> str:
-  """Full path of the pinned checkpoint file."""
-  return os.path.join(ckpt_dir, _PINNED_CHECKPOINT_FILENAME)
-
-
-def is_pinned_checkpoint(ckpt_dir: str) -> bool:
-  """Returns whether the checkpoint is pinned, and should NOT be removed."""
-  pinned_ckpt_file = pinned_checkpoint_filepath(ckpt_dir)
-  if gfile.exists(pinned_ckpt_file):
-    return True
-  return False
-
-
-def pin_checkpoint(ckpt_dir: str, txt: str = '1') -> None:
-  """Pin a checkpoint so it does not get deleted by the normal pruning process.
-
-  Creates a PINNED file in the checkpoint directory to indicate the checkpoint
-  should be excluded from the deletion of old checkpoints.
-
-  Args:
-    ckpt_dir: The checkpoint step dir that is to be always kept.
-    txt: Text to be written into the checkpoints ALWAYS_KEEP me file.
-  """
-  pinned_ckpt_file = pinned_checkpoint_filepath(ckpt_dir)
-  with gfile.GFile(pinned_ckpt_file, 'w') as f:
-    logging.debug('Write %s file : %s.', pinned_ckpt_file, txt)
-    f.write(txt)
-
-
-def unpin_checkpoint(ckpt_dir: str) -> None:
-  """Removes the pinned status of the checkpoint so it is open for deletion."""
-  if not is_pinned_checkpoint(ckpt_dir):
-    logging.debug('%s is not PINNED. Nothing to do here.', ckpt_dir)
-    return
-  try:
-    pinned_ckpt_file = pinned_checkpoint_filepath(ckpt_dir)
-    logging.debug('Remove %s file.', pinned_ckpt_file)
-    gfile.rmtree(pinned_ckpt_file)
-  except IOError:
-    logging.exception('Failed to unpin %s', ckpt_dir)
-
-
-def remove_checkpoint_dir(ckpt_dir: str) -> None:
-  """Removes the checkpoint dir if it is not pinned."""
-  if not is_pinned_checkpoint(ckpt_dir):
-    logging.info('Deleting checkpoint: %s', ckpt_dir)
-    gfile.rmtree(ckpt_dir)
-  else:
-    logging.info('Keeping pinned checkpoint: %s', ckpt_dir)
-
-
-def remove_dataset_checkpoint(ckpt_dir: str, train_ds_prefix: str) -> None:
-  """Removes dataset checkpoints if the checkpoint is not pinned."""
-  if not is_pinned_checkpoint(ckpt_dir):
-    train_ds_pattern = os.path.join(ckpt_dir, train_ds_prefix + '*')
-    logging.info('Deleting dataset checkpoint: %s', train_ds_pattern)
-    for file in gfile.glob(train_ds_pattern):
-      gfile.remove(file)
-  else:
-    logging.info('Keeping pinned checkpoint: %s', ckpt_dir)
diff --git a/spaces/k1ngtai/MMS/vits/attentions.py b/spaces/k1ngtai/MMS/vits/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/k1ngtai/MMS/vits/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/kavit02/cono/app.py b/spaces/kavit02/cono/app.py
deleted file mode 100644
index dde818d6313834e86e5a5b7d534d85f33c8280bd..0000000000000000000000000000000000000000
--- a/spaces/kavit02/cono/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to explore the PGRKAM website to find a comprehensive list of employment opportunities offered by the Punjab government. Look for details on job openings, eligibility criteria, application procedures, and deadlines. Additionally, seek information on any special initiatives or programs aimed at supporting job seekers. Provide a summary of the most promising job listings that align with your skills, qualifications, and career goals, along with any relevant application links and contact details for further inquiries. Riya also helps you out with any type of query you have in your mind related to registration and any questions or problems you might have with the website.  Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/kbora/minerva-generate-docker/blocks/download.py b/spaces/kbora/minerva-generate-docker/blocks/download.py
deleted file mode 100644
index 9b6931e58d4518e32f3cb4e0f1e6d77fbbc5aa80..0000000000000000000000000000000000000000
--- a/spaces/kbora/minerva-generate-docker/blocks/download.py
+++ /dev/null
@@ -1,400 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-def get_community_loading_icon(task = "text2img"):
-    if task == "text2img":
-        community_icon = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-                <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-                <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-                </svg>"""
-        loading_icon = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-            style="color: #ffffff; 
-            "
-            xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-    elif task == "img2img":
-        community_icon = """<svg id="share-btn-share-icon-img2img" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-            <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-            <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-            </svg>"""
-        loading_icon = """<svg id="share-btn-loading-icon-img2img" style="display:none;" class="animate-spin"
-            style="color: #ffffff; 
-            "
-            xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-    elif task == "inpainting":
-        community_icon =  """<svg id="share-btn-share-icon-inpainting" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-            <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-            <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-            </svg>"""
-        loading_icon = """<svg id="share-btn-loading-icon-inpainting" style="display:none;" class="animate-spin"
-            style="color: #ffffff; 
-            "
-            xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-    return community_icon, loading_icon
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-CSS = """
-    #col-container {margin-left: auto; margin-right: auto;}
-    a {text-decoration-line: underline; font-weight: 600;}
-    .animate-spin {
-        animation: spin 1s linear infinite;
-    }
-    @keyframes spin {
-        from { transform: rotate(0deg); }
-        to { transform: rotate(360deg); }
-    }
-    .gradio-container {
-        font-family: 'IBM Plex Sans', sans-serif;
-    }
-    .gr-button {
-        color: white;
-        border-color: black;
-        background: black;
-    }
-    input[type='range'] {
-        accent-color: black;
-    }
-    .dark input[type='range'] {
-        accent-color: #dfdfdf;
-    }
-    .container {
-        max-width: 730px;
-        margin: auto;
-        padding-top: 1.5rem;
-    }
-    #gallery {
-        min-height: 22rem;
-        margin-bottom: 15px;
-        margin-left: auto;
-        margin-right: auto;
-        border-bottom-right-radius: .5rem !important;
-        border-bottom-left-radius: .5rem !important;
-    }
-    #gallery>div>.h-full {
-        min-height: 20rem;
-    }
-    .details:hover {
-        text-decoration: underline;
-    }
-    .gr-button {
-        white-space: nowrap;
-    }
-    .gr-button:focus {
-        border-color: rgb(147 197 253 / var(--tw-border-opacity));
-        outline: none;
-        box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-        --tw-border-opacity: 1;
-        --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-        --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-        --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-        --tw-ring-opacity: .5;
-    }
-    #advanced-btn {
-        font-size: .7rem !important;
-        line-height: 19px;
-        margin-top: 12px;
-        margin-bottom: 12px;
-        padding: 2px 8px;
-        border-radius: 14px !important;
-    }
-    #advanced-options {
-        display: none;
-        margin-bottom: 20px;
-    }
-    .footer {
-        margin-bottom: 45px;
-        margin-top: 35px;
-        text-align: center;
-        border-bottom: 1px solid #e5e5e5;
-    }
-    .footer>p {
-        font-size: .8rem;
-        display: inline-block;
-        padding: 0 10px;
-        transform: translateY(10px);
-        background: white;
-    }
-    .dark .footer {
-        border-color: #303030;
-    }
-    .dark .footer>p {
-        background: #0b0f19;
-    }
-    .acknowledgments h4{
-        margin: 1.25em 0 .25em 0;
-        font-weight: bold;
-        font-size: 115%;
-    }
-    .animate-spin {
-        animation: spin 1s linear infinite;
-    }
-    @keyframes spin {
-        from {
-            transform: rotate(0deg);
-        }
-        to {
-            transform: rotate(360deg);
-        }
-    }
-    #share-btn-container {
-        display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-        margin-top: 10px;
-        margin-left: auto;
-        margin-right: auto;
-    }
-    #share-btn {
-        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-    }
-    #share-btn * {
-        all: unset;
-    }
-
-    #share-btn-inpainting {
-        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-    }
-    #share-btn-inpainting * {
-        all: unset;
-    }
-
-    #share-btn-img2img {
-        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-    }
-    #share-btn-img2img * {
-        all: unset;
-    }
-    #share-btn-container div:nth-child(-n+2){
-        width: auto !important;
-        min-height: 0px !important;
-    }
-    #share-btn-container .wrap {
-        display: none !important;
-    }
-
-    #download-btn-container {
-        display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-        margin-top: 10px;
-        margin-left: auto;
-        margin-right: auto;
-    }
-    #download-btn {
-        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-    }
-    #download-btn * {
-        all: unset;
-    }
-    #download-btn-container div:nth-child(-n+2){
-        width: auto !important;
-        min-height: 0px !important;
-    }
-    #download-btn-container .wrap {
-        display: none !important;
-    }
-    
-    .gr-form{
-        flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
-    }
-    #prompt-container{
-        gap: 0;
-    }
-    
-    #prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem}
-    #component-16{border-top-width: 1px!important;margin-top: 1em}
-    .image_duplication{position: absolute; width: 100px; left: 50px}
-"""
-
-# window.addEventListener('message', function (event) {
-#     if (event.origin !== 'http://127.0.0.1:5000'){
-#         console.log('Origin not allowed');
-#         return;
-#     }
-#     userId = event.data.userId;
-#     console.log('User ID received from parent:', userId);
-# });
-
-def get_share_js():
-    share_js = """
-    async () => {
-    // Get the username from the URL itself
-    const urlParams = new URLSearchParams(window.location.search);
-    const username = urlParams.get('username');
-
-    async function uploadFile(
-            file,
-            _meta_prompt,
-            _meta_negative_prompt,
-            _meta_model_name,
-            _meta_scheduler_name,
-            _meta_model_guidance_scale,
-            _meta_model_num_steps,
-            _meta_model_image_size,
-            _meta_seed,
-            _meta_mask = null, 
-            _meta_reference_image = null,
-            ){
-        const UPLOAD_URL = 'http://127.0.0.1:5000/v1/api/upload-image';
-        const formData = new FormData();
-        formData.append('file', file);
-
-        // Add the meta data headers to the form data
-        formData.append('text_prompt', _meta_prompt);
-        formData.append('negative_prompt', _meta_negative_prompt);
-        formData.append('model_name', _meta_model_name);
-        formData.append('model_guidance_scale', _meta_model_guidance_scale);
-        formData.append('model_num_steps', _meta_model_num_steps);
-        formData.append('scheduler_name', _meta_scheduler_name);
-        formData.append('seed', _meta_seed);
-        formData.append('model_image_size', _meta_model_image_size);
-
-        // Add the optional meta data headers to the form data
-        if(_meta_mask){
-            formData.append('mask', _meta_mask);
-        }
-        if(_meta_reference_image){
-            formData.append('reference_image', _meta_reference_image);
-        }
-        
-        formData.append('username',username); // This is constant for all the images
-        const response = await fetch(UPLOAD_URL, {
-            method: 'POST',
-            headers: {
-                'X-Requested-With': 'XMLHttpRequest',
-            },
-            body: formData,
-        });
-        const url = await response.text(); // This returns the URL of the uploaded file (S3) bucket
-        return url;
-    }
-
-        const gradioEl = document.querySelector('gradio-app');
-        const imgEls = gradioEl.querySelectorAll('#gallery img');
-
-        // Get the necessary fields 
-        const promptTxt = gradioEl.querySelector('#prompt-text-input textarea').value;
-        const negativePromptTxt = gradioEl.querySelector('#negative-prompt-text-input textarea').value;
-       
-        console.log(promptTxt);
-        console.log(negativePromptTxt);
-
-        // Get values from the sliders
-        const modelGuidanceScale = parseFloat(gradioEl.querySelector('#guidance-scale-slider input').value);
-        console.log(modelGuidanceScale);
-
-        const numSteps = parseInt(gradioEl.querySelector('#num-inference-step-slider input').value);
-        const imageSize = parseInt(gradioEl.querySelector('#image-size-slider input').value);
-        const seed = parseInt(gradioEl.querySelector('#seed-slider input').value);
-
-        console.log(numSteps);
-        console.log(imageSize);
-        console.log(seed);
-
-        // Get the values from dropdowns
-        const modelName = gradioEl.querySelector('#model-dropdown input').value;
-        const schedulerName = gradioEl.querySelector('#scheduler-dropdown input').value;
-
-        console.log(modelName);
-        console.log(schedulerName);
-        
-        const shareBtnEl = gradioEl.querySelector('#share-btn');
-        const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-        const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-        
-        if(!imgEls.length){
-            return;
-        };
-
-        shareBtnEl.style.pointerEvents = 'none';
-        shareIconEl.style.display = 'none';
-        loadingIconEl.style.removeProperty('display');
-        const files = await Promise.all(
-            [...imgEls].map(async (imgEl) => {
-                const res = await fetch(imgEl.src);
-                const blob = await res.blob();
-                const fileSrc = imgEl.src.split('/').pop(); // Get the file name from the img src path
-                const imgId = Date.now();
-                const fileName = `${fileSrc}-${imgId}.jpg`; // Fixed fileName construction
-                return new File([blob], fileName, { type: 'image/jpeg' });
-            })
-        );
-
-        // Ensure that only one image is uploaded by taking the first element if there are multiple
-        if (files.length > 1) {
-            files.splice(1, files.length - 1);
-        }
-
-        const urls = await Promise.all(files.map((f) => uploadFile(
-            f,
-            promptTxt,
-            negativePromptTxt,
-            modelName,
-            schedulerName,
-            modelGuidanceScale,
-            numSteps,
-            imageSize,
-            seed,
-            )));
-        const htmlImgs = urls.map(url => `<img src='${url}' width='400' height='400'>`);
-
-        shareBtnEl.style.removeProperty('pointer-events');
-        shareIconEl.style.removeProperty('display');
-        loadingIconEl.style.display = 'none';
-    }
-    """
-    return share_js
-
-def get_load_from_artwork_js():
-    load_artwork_js = """
-    async ()  => {
-        const urlParams = new URLSearchParams(window.location.search);
-        const username = urlParams.get('username');
-        const artworkId = urlParams.get('artworkId');
-        
-        const LOAD_URL = `http://127.0.0.1:5000/v1/api/load-parameters?artworkId=${artworkId}`;
-        const response = await fetch(LOAD_URL, {
-            method: 'GET',
-            headers: {
-                'X-Requested-With': 'XMLHttpRequest',
-            }
-        });
-
-        // Check if the response is okay
-        if (!response.ok) {
-            console.error("An error occurred while fetching the parameters.");
-            return;
-        }
-
-        const parameters = await response.json(); // Assuming you're getting a JSON response
-
-        // Get the necessary elements
-        const gradioEl = document.querySelector('gradio-app');
-        const promptInput = gradioEl.querySelector('#prompt-text-input textarea');
-        const negativePromptInput = gradioEl.querySelector('#negative-prompt-text-input textarea');
-
-        // Get the slider inputs
-        const guidanceScaleInput = gradioEl.querySelector('#guidance-scale-slider input');
-        const numInferenceStepInput = gradioEl.querySelector('#num-inference-step-slider input');
-        const imageSizeInput = gradioEl.querySelector('#image-size-slider input');
-        const seedInput = gradioEl.querySelector('#seed-slider input');
-
-        // Get the dropdown inputs
-        const modelDropdown = gradioEl.querySelector('#model-dropdown input');
-        const schedulerDropdown = gradioEl.querySelector('#scheduler-dropdown input');
-
-        // Set the values based on the parameters received
-        promptInput.value = parameters.text_prompt;
-        negativePromptInput.value = parameters.negative_prompt;
-        guidanceScaleInput.value = parameters.model_guidance_scale;
-        numInferenceStepInput.value = parameters.model_num_steps;
-        imageSizeInput.value = parameters.model_image_size;
-        seedInput.value = parameters.seed;
-        modelDropdown.value = parameters.model_name;
-        schedulerDropdown.value = parameters.scheduler_name;
-    }
-    """
-    return load_artwork_js
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/docs/eval.md b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/docs/eval.md
deleted file mode 100644
index dd1d9e257367b6422680966198646c45e5a2671d..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/docs/eval.md
+++ /dev/null
@@ -1,31 +0,0 @@
-## Eval on ICCV2021-MFR
-
-coming soon.
-
-
-## Eval IJBC
-You can eval ijbc with pytorch or onnx.
-
-
-1. Eval IJBC With Onnx
-```shell
-CUDA_VISIBLE_DEVICES=0 python onnx_ijbc.py --model-root ms1mv3_arcface_r50 --image-path IJB_release/IJBC --result-dir ms1mv3_arcface_r50
-```
-
-2. Eval IJBC With Pytorch
-```shell
-CUDA_VISIBLE_DEVICES=0,1 python eval_ijbc.py \
---model-prefix ms1mv3_arcface_r50/backbone.pth \
---image-path IJB_release/IJBC \
---result-dir ms1mv3_arcface_r50 \
---batch-size 128 \
---job ms1mv3_arcface_r50 \
---target IJBC \
---network iresnet50
-```
-
-## Inference
-
-```shell
-python inference.py --weight ms1mv3_arcface_r50/backbone.pth --network r50
-```
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/docs/modelzoo.md b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/docs/modelzoo.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/khachapuri69/madoka/Dockerfile b/spaces/khachapuri69/madoka/Dockerfile
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/kidcoconut/spcstm_omdenasaudi_liverhccxai/uix/lit_sidebar.py b/spaces/kidcoconut/spcstm_omdenasaudi_liverhccxai/uix/lit_sidebar.py
deleted file mode 100644
index 4dcf6910f3c0e1688ce1d00ee4146df1ba81f536..0000000000000000000000000000000000000000
--- a/spaces/kidcoconut/spcstm_omdenasaudi_liverhccxai/uix/lit_sidebar.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import streamlit as st
-import importlib
-from uix import lit_packages
-
-from uix.pages import lit_home, lit_about, lit_diagnosis
-from uix.pages import lit_qaConfigCheck
-
-m_kblnTraceOn=False
-
-
-#--- alt define sidebar pages
-m_aryPages = {
-    "Home":                         lit_home,           #--- TODO:  update
-    "Diagnosis:  Single Tile":      lit_diagnosis,
-    #"QA:  File Check":             lit_qaConfigCheck,         
-    "About":                        lit_about
-}
-
-
-#--- define module-level vars
-m_aryModNames = lit_packages.packages()
-m_aryDescr = [] 
-m_aryMods = []
-
-
-def init():
-    #--- upper panel
-    with st.sidebar:
-        kstrUrl_image = "bin/images/logo_omdena_saudi.png"
-        st.sidebar.image(kstrUrl_image, width=200)
-
-    #--- get radio selection
-    strKey = st.sidebar.radio("rdoPageSel", list(m_aryPages.keys()), label_visibility="hidden")
-    pagSel = m_aryPages[strKey]
-    writePage(pagSel)
-
-
-def writePage(uixFile):  
-    #--- writes out the page for the selected combo
-
-    # _reload_module(page)
-    uixFile.run()
-
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/roipoint_pool3d.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/roipoint_pool3d.py
deleted file mode 100644
index 0a21412c0728431c04b84245bc2e3109eea9aefc..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/ops/roipoint_pool3d.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from torch import nn as nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', ['roipoint_pool3d_forward'])
-
-
-class RoIPointPool3d(nn.Module):
-    """Encode the geometry-specific features of each 3D proposal.
-
-    Please refer to `Paper of PartA2 <https://arxiv.org/pdf/1907.03670.pdf>`_
-    for more details.
-
-    Args:
-        num_sampled_points (int, optional): Number of samples in each roi.
-            Default: 512.
-    """
-
-    def __init__(self, num_sampled_points=512):
-        super().__init__()
-        self.num_sampled_points = num_sampled_points
-
-    def forward(self, points, point_features, boxes3d):
-        """
-        Args:
-            points (torch.Tensor): Input points whose shape is (B, N, C).
-            point_features (torch.Tensor): Features of input points whose shape
-                is (B, N, C).
-            boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7).
-
-        Returns:
-            pooled_features (torch.Tensor): The output pooled features whose
-                shape is (B, M, 512, 3 + C).
-            pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M).
-        """
-        return RoIPointPool3dFunction.apply(points, point_features, boxes3d,
-                                            self.num_sampled_points)
-
-
-class RoIPointPool3dFunction(Function):
-
-    @staticmethod
-    def forward(ctx, points, point_features, boxes3d, num_sampled_points=512):
-        """
-        Args:
-            points (torch.Tensor): Input points whose shape is (B, N, C).
-            point_features (torch.Tensor): Features of input points whose shape
-                is (B, N, C).
-            boxes3d (B, M, 7), Input bounding boxes whose shape is (B, M, 7).
-            num_sampled_points (int, optional): The num of sampled points.
-                Default: 512.
-
-        Returns:
-            pooled_features (torch.Tensor): The output pooled features whose
-                shape is (B, M, 512, 3 + C).
-            pooled_empty_flag (torch.Tensor): Empty flag whose shape is (B, M).
-        """
-        assert len(points.shape) == 3 and points.shape[2] == 3
-        batch_size, boxes_num, feature_len = points.shape[0], boxes3d.shape[
-            1], point_features.shape[2]
-        pooled_boxes3d = boxes3d.view(batch_size, -1, 7)
-        pooled_features = point_features.new_zeros(
-            (batch_size, boxes_num, num_sampled_points, 3 + feature_len))
-        pooled_empty_flag = point_features.new_zeros(
-            (batch_size, boxes_num)).int()
-
-        ext_module.roipoint_pool3d_forward(points.contiguous(),
-                                           pooled_boxes3d.contiguous(),
-                                           point_features.contiguous(),
-                                           pooled_features, pooled_empty_flag)
-
-        return pooled_features, pooled_empty_flag
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        raise NotImplementedError
diff --git a/spaces/kn1ghtf1re/Photo-Realistic-Image-Stylization/photo_smooth.py b/spaces/kn1ghtf1re/Photo-Realistic-Image-Stylization/photo_smooth.py
deleted file mode 100644
index 4c89d2c1eef685e1be42d2906cc9dd468fb8500f..0000000000000000000000000000000000000000
--- a/spaces/kn1ghtf1re/Photo-Realistic-Image-Stylization/photo_smooth.py
+++ /dev/null
@@ -1,101 +0,0 @@
-"""
-Copyright (C) 2018 NVIDIA Corporation.    All rights reserved.
-Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
-"""
-from __future__ import division
-import torch.nn as nn
-import scipy.misc
-import numpy as np
-import scipy.sparse
-import scipy.sparse.linalg
-from numpy.lib.stride_tricks import as_strided
-from PIL import Image
-import cv2
-
-class Propagator(nn.Module):
-    def __init__(self, beta=0.9999):
-        super(Propagator, self).__init__()
-        self.beta = beta
-
-    def process(self, initImg, contentImg):
-
-        if type(contentImg) == str:
-            content = cv2.imread(contentImg)
-            content = cv2.cvtColor(np.content, cv2.COLOR_BGR2RGB)
-        else:
-            content = np.array(contentImg, dtype=np.float64)
-
-        if type(initImg) == str:
-            B = cv2.imread(initImg)
-            B = cv2.cvtColor(B, cv2.COLOR_BGR2RGB)
-            B /= 255
-        else:
-            B = np.array(initImg, dtype=np.float64)
-            B /= 255
-   
-        h1,w1,k = B.shape
-        h = h1 - 4
-        w = w1 - 4
-        B = B[int((h1-h)/2):int((h1-h)/2+h),int((w1-w)/2):int((w1-w)/2+w),:]
-        content = cv2.resize(content, (w, h))
-        B = self.__replication_padding(B,2)
-        content = self.__replication_padding(content,2)
-        content = content.astype(np.float64)/255
-        B = np.reshape(B,(h1*w1,k))
-        W = self.__compute_laplacian(content)
-        W = W.tocsc()
-        dd = W.sum(0)
-        dd = np.sqrt(np.power(dd,-1))
-        dd = dd.A.squeeze()
-        D = scipy.sparse.csc_matrix((dd, (np.arange(0,w1*h1), np.arange(0,w1*h1)))) # 0.026
-        S = D.dot(W).dot(D)
-        A = scipy.sparse.identity(w1*h1) - self.beta*S
-        A = A.tocsc()
-        solver = scipy.sparse.linalg.factorized(A)
-        V = np.zeros((h1*w1,k))
-        V[:,0] = solver(B[:,0])
-        V[:,1] = solver(B[:,1])
-        V[:,2] = solver(B[:,2])
-        V = V*(1-self.beta)
-        V = V.reshape(h1,w1,k)
-        V = V[2:2+h,2:2+w,:]
-        
-        img = Image.fromarray(np.uint8(np.clip(V * 255., 0, 255.)))
-        return img
-
-    # Returns sparse matting laplacian
-    # The implementation of the function is heavily borrowed from
-    # https://github.com/MarcoForte/closed-form-matting/blob/master/closed_form_matting.py
-    # We thank Marco Forte for sharing his code.
-    def __compute_laplacian(self, img, eps=10**(-7), win_rad=1):
-            win_size = (win_rad*2+1)**2
-            h, w, d = img.shape
-            c_h, c_w = h - 2*win_rad, w - 2*win_rad
-            win_diam = win_rad*2+1
-            indsM = np.arange(h*w).reshape((h, w))
-            ravelImg = img.reshape(h*w, d)
-            win_inds = self.__rolling_block(indsM, block=(win_diam, win_diam))
-            win_inds = win_inds.reshape(c_h, c_w, win_size)
-            winI = ravelImg[win_inds]
-            win_mu = np.mean(winI, axis=2, keepdims=True)
-            win_var = np.einsum('...ji,...jk ->...ik', winI, winI)/win_size - np.einsum('...ji,...jk ->...ik', win_mu, win_mu)
-            inv = np.linalg.inv(win_var + (eps/win_size)*np.eye(3))
-            X = np.einsum('...ij,...jk->...ik', winI - win_mu, inv)
-            vals = (1/win_size)*(1 + np.einsum('...ij,...kj->...ik', X, winI - win_mu))
-            nz_indsCol = np.tile(win_inds, win_size).ravel()
-            nz_indsRow = np.repeat(win_inds, win_size).ravel()
-            nz_indsVal = vals.ravel()
-            L = scipy.sparse.coo_matrix((nz_indsVal, (nz_indsRow, nz_indsCol)), shape=(h*w, h*w))
-            return L
-
-    def __replication_padding(self, arr,pad):
-            h,w,c = arr.shape
-            ans = np.zeros((h+pad*2,w+pad*2,c))
-            for i in range(c):
-                    ans[:,:,i] = np.pad(arr[:,:,i],pad_width=(pad,pad),mode='edge')
-            return ans
-
-    def __rolling_block(self, A, block=(3, 3)):
-        shape = (A.shape[0] - block[0] + 1, A.shape[1] - block[1] + 1) + block
-        strides = (A.strides[0], A.strides[1]) + A.strides
-        return as_strided(A, shape=shape, strides=strides)
\ No newline at end of file
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_h_m_t_x.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_h_m_t_x.py
deleted file mode 100644
index 2dafe617a061880d93ab91b981ef26a09365728e..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_h_m_t_x.py
+++ /dev/null
@@ -1,152 +0,0 @@
-from fontTools.misc.roundTools import otRound
-from fontTools import ttLib
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-import sys
-import struct
-import array
-import logging
-
-
-log = logging.getLogger(__name__)
-
-
-class table__h_m_t_x(DefaultTable.DefaultTable):
-
-    headerTag = "hhea"
-    advanceName = "width"
-    sideBearingName = "lsb"
-    numberOfMetricsName = "numberOfHMetrics"
-    longMetricFormat = "Hh"
-
-    def decompile(self, data, ttFont):
-        numGlyphs = ttFont["maxp"].numGlyphs
-        headerTable = ttFont.get(self.headerTag)
-        if headerTable is not None:
-            numberOfMetrics = int(getattr(headerTable, self.numberOfMetricsName))
-        else:
-            numberOfMetrics = numGlyphs
-        if numberOfMetrics > numGlyphs:
-            log.warning(
-                "The %s.%s exceeds the maxp.numGlyphs"
-                % (self.headerTag, self.numberOfMetricsName)
-            )
-            numberOfMetrics = numGlyphs
-        if len(data) < 4 * numberOfMetrics:
-            raise ttLib.TTLibError("not enough '%s' table data" % self.tableTag)
-        # Note: advanceWidth is unsigned, but some font editors might
-        # read/write as signed. We can't be sure whether it was a mistake
-        # or not, so we read as unsigned but also issue a warning...
-        metricsFmt = ">" + self.longMetricFormat * numberOfMetrics
-        metrics = struct.unpack(metricsFmt, data[: 4 * numberOfMetrics])
-        data = data[4 * numberOfMetrics :]
-        numberOfSideBearings = numGlyphs - numberOfMetrics
-        sideBearings = array.array("h", data[: 2 * numberOfSideBearings])
-        data = data[2 * numberOfSideBearings :]
-
-        if sys.byteorder != "big":
-            sideBearings.byteswap()
-        if data:
-            log.warning("too much '%s' table data" % self.tableTag)
-        self.metrics = {}
-        glyphOrder = ttFont.getGlyphOrder()
-        for i in range(numberOfMetrics):
-            glyphName = glyphOrder[i]
-            advanceWidth, lsb = metrics[i * 2 : i * 2 + 2]
-            if advanceWidth > 32767:
-                log.warning(
-                    "Glyph %r has a huge advance %s (%d); is it intentional or "
-                    "an (invalid) negative value?",
-                    glyphName,
-                    self.advanceName,
-                    advanceWidth,
-                )
-            self.metrics[glyphName] = (advanceWidth, lsb)
-        lastAdvance = metrics[-2]
-        for i in range(numberOfSideBearings):
-            glyphName = glyphOrder[i + numberOfMetrics]
-            self.metrics[glyphName] = (lastAdvance, sideBearings[i])
-
-    def compile(self, ttFont):
-        metrics = []
-        hasNegativeAdvances = False
-        for glyphName in ttFont.getGlyphOrder():
-            advanceWidth, sideBearing = self.metrics[glyphName]
-            if advanceWidth < 0:
-                log.error(
-                    "Glyph %r has negative advance %s" % (glyphName, self.advanceName)
-                )
-                hasNegativeAdvances = True
-            metrics.append([advanceWidth, sideBearing])
-
-        headerTable = ttFont.get(self.headerTag)
-        if headerTable is not None:
-            lastAdvance = metrics[-1][0]
-            lastIndex = len(metrics)
-            while metrics[lastIndex - 2][0] == lastAdvance:
-                lastIndex -= 1
-                if lastIndex <= 1:
-                    # all advances are equal
-                    lastIndex = 1
-                    break
-            additionalMetrics = metrics[lastIndex:]
-            additionalMetrics = [otRound(sb) for _, sb in additionalMetrics]
-            metrics = metrics[:lastIndex]
-            numberOfMetrics = len(metrics)
-            setattr(headerTable, self.numberOfMetricsName, numberOfMetrics)
-        else:
-            # no hhea/vhea, can't store numberOfMetrics; assume == numGlyphs
-            numberOfMetrics = ttFont["maxp"].numGlyphs
-            additionalMetrics = []
-
-        allMetrics = []
-        for advance, sb in metrics:
-            allMetrics.extend([otRound(advance), otRound(sb)])
-        metricsFmt = ">" + self.longMetricFormat * numberOfMetrics
-        try:
-            data = struct.pack(metricsFmt, *allMetrics)
-        except struct.error as e:
-            if "out of range" in str(e) and hasNegativeAdvances:
-                raise ttLib.TTLibError(
-                    "'%s' table can't contain negative advance %ss"
-                    % (self.tableTag, self.advanceName)
-                )
-            else:
-                raise
-        additionalMetrics = array.array("h", additionalMetrics)
-        if sys.byteorder != "big":
-            additionalMetrics.byteswap()
-        data = data + additionalMetrics.tobytes()
-        return data
-
-    def toXML(self, writer, ttFont):
-        names = sorted(self.metrics.keys())
-        for glyphName in names:
-            advance, sb = self.metrics[glyphName]
-            writer.simpletag(
-                "mtx",
-                [
-                    ("name", glyphName),
-                    (self.advanceName, advance),
-                    (self.sideBearingName, sb),
-                ],
-            )
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if not hasattr(self, "metrics"):
-            self.metrics = {}
-        if name == "mtx":
-            self.metrics[attrs["name"]] = (
-                safeEval(attrs[self.advanceName]),
-                safeEval(attrs[self.sideBearingName]),
-            )
-
-    def __delitem__(self, glyphName):
-        del self.metrics[glyphName]
-
-    def __getitem__(self, glyphName):
-        return self.metrics[glyphName]
-
-    def __setitem__(self, glyphName, advance_sb_pair):
-        self.metrics[glyphName] = tuple(advance_sb_pair)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-90411bc1.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-90411bc1.js
deleted file mode 100644
index bf6ff0567b6aff1938a5b5d07f89ac1b26f5ad2f..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-90411bc1.js
+++ /dev/null
@@ -1,13 +0,0 @@
-const VERSION_RE = new RegExp("3.33.1/", "g");function import_fix(mod, base) {const url =  new URL(mod, base); return import(`https://gradio.s3-us-west-2.amazonaws.com/3.33.1/${url.pathname?.startsWith('/') ? url.pathname.substring(1).replace(VERSION_RE, "") : url.pathname.replace(VERSION_RE, "")}`);}import{S as si,i as ri,s as oi,B as io,C as se,g as Ce,E as Yt,F as vi,q as Ae,_ as Pe,G as dt,r as Xl,u as Xc,b as xr,e as fe,H as pt,M as yn,m as ue,J as Zl,p as H,l as _n,t as j,o as Vn,n as de,a5 as Ql,aa as ea,ab as bn,ac as wn,f as ta,a8 as Zc,x as ia,$ as na,a as sa,h as ra,j as oa,k as la}from"./index-7c0e54a6.js";import"./Blocks-61158678.js";import{B as aa}from"./Button-661a0701.js";import{B as ha}from"./BlockLabel-95be8dd1.js";import{E as Qc}from"./Empty-96265974.js";/* empty css                                                    */import{C as ef,a as ca}from"./Copy-c4997e4e.js";import{D as tf}from"./Download-e5de98da.js";function nf(n){let e,t;return{c(){e=io("svg"),t=io("path"),se(t,"fill","currentColor"),se(t,"d","m31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7zM1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7zm11.42 9.484L17.64 6l1.932.517L14.352 26z"),se(e,"width","100%"),se(e,"height","100%"),se(e,"viewBox","0 0 32 32")},m(i,s){Ce(i,e,s),Yt(e,t)},p:vi,i:vi,o:vi,d(i){i&&Ae(e)}}}let Sr=class extends si{constructor(e){super(),ri(this,e,null,nf,oi,{})}};class _{constructor(){}lineAt(e){if(e<0||e>this.length)throw new RangeError(`Invalid position ${e} in document of length ${this.length}`);return this.lineInner(e,!1,1,0)}line(e){if(e<1||e>this.lines)throw new RangeError(`Invalid line number ${e} in ${this.lines}-line document`);return this.lineInner(e,!0,1,0)}replace(e,t,i){let s=[];return this.decompose(0,e,s,2),i.length&&i.decompose(0,i.length,s,3),this.decompose(t,this.length,s,1),$e.from(s,this.length-(t-e)+i.length)}append(e){return this.replace(this.length,this.length,e)}slice(e,t=this.length){let i=[];return this.decompose(e,t,i,0),$e.from(i,t-e)}eq(e){if(e==this)return!0;if(e.length!=this.length||e.lines!=this.lines)return!1;let t=this.scanIdentical(e,1),i=this.length-this.scanIdentical(e,-1),s=new xi(this),r=new xi(e);for(let o=t,l=t;;){if(s.next(o),r.next(o),o=0,s.lineBreak!=r.lineBreak||s.done!=r.done||s.value!=r.value)return!1;if(l+=s.value.length,s.done||l>=i)return!0}}iter(e=1){return new xi(this,e)}iterRange(e,t=this.length){return new fa(this,e,t)}iterLines(e,t){let i;if(e==null)i=this.iter();else{t==null&&(t=this.lines+1);let s=this.line(e).from;i=this.iterRange(s,Math.max(s,t==this.lines+1?this.length:t<=1?0:this.line(t-1).to))}return new ua(i)}toString(){return this.sliceString(0)}toJSON(){let e=[];return this.flatten(e),e}static of(e){if(e.length==0)throw new RangeError("A document must have at least one line");return e.length==1&&!e[0]?_.empty:e.length<=32?new te(e):$e.from(te.split(e,[]))}}class te extends _{constructor(e,t=sf(e)){super(),this.text=e,this.length=t}get lines(){return this.text.length}get children(){return null}lineInner(e,t,i,s){for(let r=0;;r++){let o=this.text[r],l=s+o.length;if((t?i:l)>=e)return new rf(s,l,i,o);s=l+1,i++}}decompose(e,t,i,s){let r=e<=0&&t>=this.length?this:new te(no(this.text,e,t),Math.min(t,this.length)-Math.max(0,e));if(s&1){let o=i.pop(),l=fn(r.text,o.text.slice(),0,r.length);if(l.length<=32)i.push(new te(l,o.length+r.length));else{let a=l.length>>1;i.push(new te(l.slice(0,a)),new te(l.slice(a)))}}else i.push(r)}replace(e,t,i){if(!(i instanceof te))return super.replace(e,t,i);let s=fn(this.text,fn(i.text,no(this.text,0,e)),t),r=this.length+i.length-(t-e);return s.length<=32?new te(s,r):$e.from(te.split(s,[]),r)}sliceString(e,t=this.length,i=`
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f.values[o]=d,1}}}}function oo(n,e,t){if(n.length!=e.length)return!1;for(let i=0;i<n.length;i++)if(!t(n[i],e[i]))return!1;return!0}function Rs(n,e){let t=!1;for(let i of e)Si(n,i)&1&&(t=!0);return t}function af(n,e,t){let i=t.map(a=>n[a.id]),s=t.map(a=>a.type),r=i.filter(a=>!(a&1)),o=n[e.id]>>1;function l(a){let h=[];for(let c=0;c<i.length;c++){let f=vn(a,i[c]);if(s[c]==2)for(let u of f)h.push(u);else h.push(f)}return e.combine(h)}return{create(a){for(let h of i)Si(a,h);return a.values[o]=l(a),1},update(a,h){if(!Rs(a,r))return 0;let c=l(a);return e.compare(c,a.values[o])?0:(a.values[o]=c,1)},reconfigure(a,h){let c=Rs(a,i),f=h.config.facets[e.id],u=h.facet(e);if(f&&!c&&Ar(t,f))return a.values[o]=u,0;let d=l(a);return e.compare(d,u)?(a.values[o]=u,0):(a.values[o]=d,1)}}}const lo=D.define({static:!0});class Me{constructor(e,t,i,s,r){this.id=e,this.createF=t,this.updateF=i,this.compareF=s,this.spec=r,this.provides=void 0}static define(e){let t=new Me(Cr++,e.create,e.update,e.compare||((i,s)=>i===s),e);return e.provide&&(t.provides=e.provide(t)),t}create(e){let t=e.facet(lo).find(i=>i.field==this);return(t?.create||this.createF)(e)}slot(e){let t=e[this.id]>>1;return{create:i=>(i.values[t]=this.create(i),1),update:(i,s)=>{let r=i.values[t],o=this.updateF(r,s);return this.compareF(r,o)?0:(i.values[t]=o,1)},reconfigure:(i,s)=>s.config.address[this.id]!=null?(i.values[t]=s.field(this),0):(i.values[t]=this.create(i),1)}}init(e){return[this,lo.of({field:this,create:e})]}get extension(){return this}}const Ct={lowest:4,low:3,default:2,high:1,highest:0};function ci(n){return e=>new wa(e,n)}const Vi={highest:ci(Ct.highest),high:ci(Ct.high),default:ci(Ct.default),low:ci(Ct.low),lowest:ci(Ct.lowest)};class wa{constructor(e,t){this.inner=e,this.prec=t}}class Fn{of(e){return new Ls(this,e)}reconfigure(e){return Fn.reconfigure.of({compartment:this,extension:e})}get(e){return e.config.compartments.get(this)}}class Ls{constructor(e,t){this.compartment=e,this.inner=t}}class kn{constructor(e,t,i,s,r,o){for(this.base=e,this.compartments=t,this.dynamicSlots=i,this.address=s,this.staticValues=r,this.facets=o,this.statusTemplate=[];this.statusTemplate.length<i.length;)this.statusTemplate.push(0)}staticFacet(e){let t=this.address[e.id];return t==null?e.default:this.staticValues[t>>1]}static resolve(e,t,i){let s=[],r=Object.create(null),o=new Map;for(let u of hf(e,t,o))u instanceof Me?s.push(u):(r[u.facet.id]||(r[u.facet.id]=[])).push(u);let l=Object.create(null),a=[],h=[];for(let u of s)l[u.id]=h.length<<1,h.push(d=>u.slot(d));let c=i?.config.facets;for(let u in r){let d=r[u],p=d[0].facet,g=c&&c[u]||[];if(d.every(y=>y.type==0))if(l[p.id]=a.length<<1|1,Ar(g,d))a.push(i.facet(p));else{let y=p.combine(d.map(b=>b.value));a.push(i&&p.compare(y,i.facet(p))?i.facet(p):y)}else{for(let y of d)y.type==0?(l[y.id]=a.length<<1|1,a.push(y.value)):(l[y.id]=h.length<<1,h.push(b=>y.dynamicSlot(b)));l[p.id]=h.length<<1,h.push(y=>af(y,p,d))}}let f=h.map(u=>u(l));return new kn(e,o,f,l,a,r)}}function hf(n,e,t){let i=[[],[],[],[],[]],s=new Map;function r(o,l){let a=s.get(o);if(a!=null){if(a<=l)return;let h=i[a].indexOf(o);h>-1&&i[a].splice(h,1),o instanceof Ls&&t.delete(o.compartment)}if(s.set(o,l),Array.isArray(o))for(let h of o)r(h,l);else if(o instanceof Ls){if(t.has(o.compartment))throw new RangeError("Duplicate use of compartment in extensions");let h=e.get(o.compartment)||o.inner;t.set(o.compartment,h),r(h,l)}else if(o instanceof wa)r(o.inner,o.prec);else if(o instanceof Me)i[l].push(o),o.provides&&r(o.provides,l);else if(o instanceof un)i[l].push(o),o.facet.extensions&&r(o.facet.extensions,Ct.default);else{let h=o.extension;if(!h)throw new Error(`Unrecognized extension value in extension set (${o}). 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0:t==this.value?this:new R(this.type,t)}is(e){return this.type==e}static define(e={}){return new ff(e.map||(t=>t))}static mapEffects(e,t){if(!e.length)return e;let i=[];for(let s of e){let r=s.map(t);r&&i.push(r)}return i}}R.reconfigure=R.define();R.appendConfig=R.define();class re{constructor(e,t,i,s,r,o){this.startState=e,this.changes=t,this.selection=i,this.effects=s,this.annotations=r,this.scrollIntoView=o,this._doc=null,this._state=null,i&&ba(i,t.newLength),r.some(l=>l.type==re.time)||(this.annotations=r.concat(re.time.of(Date.now())))}static create(e,t,i,s,r,o){return new re(e,t,i,s,r,o)}get newDoc(){return this._doc||(this._doc=this.changes.apply(this.startState.doc))}get newSelection(){return this.selection||this.startState.selection.map(this.changes)}get state(){return this._state||this.startState.applyTransaction(this),this._state}annotation(e){for(let t of this.annotations)if(t.type==e)return t.value}get docChanged(){return!this.changes.empty}get reconfigured(){return this.startState.config!=this.state.config}isUserEvent(e){let t=this.annotation(re.userEvent);return!!(t&&(t==e||t.length>e.length&&t.slice(0,e.length)==e&&t[e.length]=="."))}}re.time=Nt.define();re.userEvent=Nt.define();re.addToHistory=Nt.define();re.remote=Nt.define();function uf(n,e){let t=[];for(let i=0,s=0;;){let r,o;if(i<n.length&&(s==e.length||e[s]>=n[i]))r=n[i++],o=n[i++];else if(s<e.length)r=e[s++],o=e[s++];else return t;!t.length||t[t.length-1]<r?t.push(r,o):t[t.length-1]<o&&(t[t.length-1]=o)}}function Da(n,e,t){var i;let s,r,o;return t?(s=e.changes,r=ne.empty(e.changes.length),o=n.changes.compose(e.changes)):(s=e.changes.map(n.changes),r=n.changes.mapDesc(e.changes,!0),o=n.changes.compose(s)),{changes:o,selection:e.selection?e.selection.map(r):(i=n.selection)===null||i===void 0?void 0:i.map(s),effects:R.mapEffects(n.effects,s).concat(R.mapEffects(e.effects,r)),annotations:n.annotations.length?n.annotations.concat(e.annotations):e.annotations,scrollIntoView:n.scrollIntoView||e.scrollIntoView}}function Is(n,e,t){let i=e.selection,s=Gt(e.annotations);return e.userEvent&&(s=s.concat(re.userEvent.of(e.userEvent))),{changes:e.changes instanceof ne?e.changes:ne.of(e.changes||[],t,n.facet(xa)),selection:i&&(i instanceof w?i:w.single(i.anchor,i.head)),effects:Gt(e.effects),annotations:s,scrollIntoView:!!e.scrollIntoView}}function Ta(n,e,t){let i=Is(n,e.length?e[0]:{},n.doc.length);e.length&&e[0].filter===!1&&(t=!1);for(let r=1;r<e.length;r++){e[r].filter===!1&&(t=!1);let o=!!e[r].sequential;i=Da(i,Is(n,e[r],o?i.changes.newLength:n.doc.length),o)}let s=re.create(n,i.changes,i.selection,i.effects,i.annotations,i.scrollIntoView);return pf(t?df(s):s)}function df(n){let e=n.startState,t=!0;for(let s of e.facet(Sa)){let r=s(n);if(r===!1){t=!1;break}Array.isArray(r)&&(t=t===!0?r:uf(t,r))}if(t!==!0){let s,r;if(t===!1)r=n.changes.invertedDesc,s=ne.empty(e.doc.length);else{let o=n.changes.filter(t);s=o.changes,r=o.filtered.mapDesc(o.changes).invertedDesc}n=re.create(e,s,n.selection&&n.selection.map(r),R.mapEffects(n.effects,r),n.annotations,n.scrollIntoView)}let i=e.facet(Ca);for(let s=i.length-1;s>=0;s--){let r=i[s](n);r instanceof re?n=r:Array.isArray(r)&&r.length==1&&r[0]instanceof re?n=r[0]:n=Ta(e,Gt(r),!1)}return n}function pf(n){let e=n.startState,t=e.facet(Aa),i=n;for(let s=t.length-1;s>=0;s--){let r=t[s](n);r&&Object.keys(r).length&&(i=Da(i,Is(e,r,n.changes.newLength),!0))}return i==n?n:re.create(e,n.changes,n.selection,i.effects,i.annotations,i.scrollIntoView)}const mf=[];function Gt(n){return n==null?mf:Array.isArray(n)?n:[n]}var Re=function(n){return n[n.Word=0]="Word",n[n.Space=1]="Space",n[n.Other=2]="Other",n}(Re||(Re={}));const gf=/[\u00df\u0587\u0590-\u05f4\u0600-\u06ff\u3040-\u309f\u30a0-\u30ff\u3400-\u4db5\u4e00-\u9fcc\uac00-\ud7af]/;let Ns;try{Ns=new RegExp("[\\p{Alphabetic}\\p{Number}_]","u")}catch{}function yf(n){if(Ns)return Ns.test(n);for(let e=0;e<n.length;e++){let t=n[e];if(/\w/.test(t)||t>"€"&&(t.toUpperCase()!=t.toLowerCase()||gf.test(t)))return!0}return!1}function bf(n){return e=>{if(!/\S/.test(e))return Re.Space;if(yf(e))return Re.Word;for(let t=0;t<n.length;t++)if(e.indexOf(n[t])>-1)return Re.Word;return Re.Other}}class N{constructor(e,t,i,s,r,o){this.config=e,this.doc=t,this.selection=i,this.values=s,this.status=e.statusTemplate.slice(),this.computeSlot=r,o&&(o._state=this);for(let l=0;l<this.config.dynamicSlots.length;l++)Si(this,l<<1);this.computeSlot=null}field(e,t=!0){let i=this.config.address[e.id];if(i==null){if(t)throw new RangeError("Field is not present in this state");return}return Si(this,i),vn(this,i)}update(...e){return Ta(this,e,!0)}applyTransaction(e){let t=this.config,{base:i,compartments:s}=t;for(let o of e.effects)o.is(Fn.reconfigure)?(t&&(s=new Map,t.compartments.forEach((l,a)=>s.set(a,l)),t=null),s.set(o.value.compartment,o.value.extension)):o.is(R.reconfigure)?(t=null,i=o.value):o.is(R.appendConfig)&&(t=null,i=Gt(i).concat(o.value));let r;t?r=e.startState.values.slice():(t=kn.resolve(i,s,this),r=new N(t,this.doc,this.selection,t.dynamicSlots.map(()=>null),(l,a)=>a.reconfigure(l,this),null).values),new N(t,e.newDoc,e.newSelection,r,(o,l)=>l.update(o,e),e)}replaceSelection(e){return typeof e=="string"&&(e=this.toText(e)),this.changeByRange(t=>({changes:{from:t.from,to:t.to,insert:e},range:w.cursor(t.from+e.length)}))}changeByRange(e){let t=this.selection,i=e(t.ranges[0]),s=this.changes(i.changes),r=[i.range],o=Gt(i.effects);for(let l=1;l<t.ranges.length;l++){let a=e(t.ranges[l]),h=this.changes(a.changes),c=h.map(s);for(let u=0;u<l;u++)r[u]=r[u].map(c);let f=s.mapDesc(h,!0);r.push(a.range.map(f)),s=s.compose(c),o=R.mapEffects(o,c).concat(R.mapEffects(Gt(a.effects),f))}return{changes:s,selection:w.create(r,t.mainIndex),effects:o}}changes(e=[]){return e instanceof ne?e:ne.of(e,this.doc.length,this.facet(N.lineSeparator))}toText(e){return _.of(e.split(this.facet(N.lineSeparator)||Bs))}sliceDoc(e=0,t=this.doc.length){return this.doc.sliceString(e,t,this.lineBreak)}facet(e){let t=this.config.address[e.id];return t==null?e.default:(Si(this,t),vn(this,t))}toJSON(e){let t={doc:this.sliceDoc(),selection:this.selection.toJSON()};if(e)for(let i in e){let s=e[i];s instanceof Me&&this.config.address[s.id]!=null&&(t[i]=s.spec.toJSON(this.field(e[i]),this))}return t}static fromJSON(e,t={},i){if(!e||typeof e.doc!="string")throw new RangeError("Invalid JSON representation for EditorState");let s=[];if(i){for(let r in i)if(Object.prototype.hasOwnProperty.call(e,r)){let o=i[r],l=e[r];s.push(o.init(a=>o.spec.fromJSON(l,a)))}}return N.create({doc:e.doc,selection:w.fromJSON(e.selection),extensions:t.extensions?s.concat([t.extensions]):s})}static create(e={}){let t=kn.resolve(e.extensions||[],new Map),i=e.doc instanceof _?e.doc:_.of((e.doc||"").split(t.staticFacet(N.lineSeparator)||Bs)),s=e.selection?e.selection instanceof w?e.selection:w.single(e.selection.anchor,e.selection.head):w.single(0);return ba(s,i.length),t.staticFacet(va)||(s=s.asSingle()),new N(t,i,s,t.dynamicSlots.map(()=>null),(r,o)=>o.create(r),null)}get tabSize(){return this.facet(N.tabSize)}get lineBreak(){return this.facet(N.lineSeparator)||`
-`}get readOnly(){return this.facet(Ma)}phrase(e,...t){for(let i of this.facet(N.phrases))if(Object.prototype.hasOwnProperty.call(i,e)){e=i[e];break}return t.length&&(e=e.replace(/\$(\$|\d*)/g,(i,s)=>{if(s=="$")return"$";let r=+(s||1);return!r||r>t.length?i:t[r-1]})),e}languageDataAt(e,t,i=-1){let s=[];for(let r of this.facet(ka))for(let o of r(this,t,i))Object.prototype.hasOwnProperty.call(o,e)&&s.push(o[e]);return s}charCategorizer(e){return bf(this.languageDataAt("wordChars",e).join(""))}wordAt(e){let{text:t,from:i,length:s}=this.doc.lineAt(e),r=this.charCategorizer(e),o=e-i,l=e-i;for(;o>0;){let a=Oe(t,o,!1);if(r(t.slice(a,o))!=Re.Word)break;o=a}for(;l<s;){let a=Oe(t,l);if(r(t.slice(l,a))!=Re.Word)break;l=a}return o==l?null:w.range(o+i,l+i)}}N.allowMultipleSelections=va;N.tabSize=D.define({combine:n=>n.length?n[0]:4});N.lineSeparator=xa;N.readOnly=Ma;N.phrases=D.define({compare(n,e){let t=Object.keys(n),i=Object.keys(e);return t.length==i.length&&t.every(s=>n[s]==e[s])}});N.languageData=ka;N.changeFilter=Sa;N.transactionFilter=Ca;N.transactionExtender=Aa;Fn.reconfigure=R.define();function _t(n,e,t={}){let i={};for(let s of n)for(let r of Object.keys(s)){let o=s[r],l=i[r];if(l===void 0)i[r]=o;else if(!(l===o||o===void 0))if(Object.hasOwnProperty.call(t,r))i[r]=t[r](l,o);else throw new Error("Config merge conflict for field "+r)}for(let s in e)i[s]===void 0&&(i[s]=e[s]);return i}class Bt{eq(e){return this==e}range(e,t=e){return _s.create(e,t,this)}}Bt.prototype.startSide=Bt.prototype.endSide=0;Bt.prototype.point=!1;Bt.prototype.mapMode=ce.TrackDel;let _s=class Oa{constructor(e,t,i){this.from=e,this.to=t,this.value=i}static create(e,t,i){return new Oa(e,t,i)}};function Vs(n,e){return n.from-e.from||n.value.startSide-e.value.startSide}class Mr{constructor(e,t,i,s){this.from=e,this.to=t,this.value=i,this.maxPoint=s}get length(){return this.to[this.to.length-1]}findIndex(e,t,i,s=0){let r=i?this.to:this.from;for(let o=s,l=r.length;;){if(o==l)return o;let a=o+l>>1,h=r[a]-e||(i?this.value[a].endSide:this.value[a].startSide)-t;if(a==o)return h>=0?o:l;h>=0?l=a:o=a+1}}between(e,t,i,s){for(let r=this.findIndex(t,-1e9,!0),o=this.findIndex(i,1e9,!1,r);r<o;r++)if(s(this.from[r]+e,this.to[r]+e,this.value[r])===!1)return!1}map(e,t){let i=[],s=[],r=[],o=-1,l=-1;for(let a=0;a<this.value.length;a++){let h=this.value[a],c=this.from[a]+e,f=this.to[a]+e,u,d;if(c==f){let p=t.mapPos(c,h.startSide,h.mapMode);if(p==null||(u=d=p,h.startSide!=h.endSide&&(d=t.mapPos(c,h.endSide),d<u)))continue}else if(u=t.mapPos(c,h.startSide),d=t.mapPos(f,h.endSide),u>d||u==d&&h.startSide>0&&h.endSide<=0)continue;(d-u||h.endSide-h.startSide)<0||(o<0&&(o=u),h.point&&(l=Math.max(l,d-u)),i.push(h),s.push(u-o),r.push(d-o))}return{mapped:i.length?new Mr(s,r,i,l):null,pos:o}}}class F{constructor(e,t,i,s){this.chunkPos=e,this.chunk=t,this.nextLayer=i,this.maxPoint=s}static create(e,t,i,s){return new F(e,t,i,s)}get length(){let e=this.chunk.length-1;return e<0?0:Math.max(this.chunkEnd(e),this.nextLayer.length)}get size(){if(this.isEmpty)return 0;let e=this.nextLayer.size;for(let t of this.chunk)e+=t.value.length;return e}chunkEnd(e){return this.chunkPos[e]+this.chunk[e].length}update(e){let{add:t=[],sort:i=!1,filterFrom:s=0,filterTo:r=this.length}=e,o=e.filter;if(t.length==0&&!o)return this;if(i&&(t=t.slice().sort(Vs)),this.isEmpty)return t.length?F.of(t):this;let l=new Ba(this,null,-1).goto(0),a=0,h=[],c=new Pt;for(;l.value||a<t.length;)if(a<t.length&&(l.from-t[a].from||l.startSide-t[a].value.startSide)>=0){let f=t[a++];c.addInner(f.from,f.to,f.value)||h.push(f)}else 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s<0?!1:(this.from.length==250&&this.finishChunk(!0),this.chunkStart<0&&(this.chunkStart=e),this.from.push(e-this.chunkStart),this.to.push(t-this.chunkStart),this.last=i,this.lastFrom=e,this.lastTo=t,this.value.push(i),i.point&&(this.maxPoint=Math.max(this.maxPoint,t-e)),!0)}addChunk(e,t){if((e-this.lastTo||t.value[0].startSide-this.last.endSide)<0)return!1;this.from.length&&this.finishChunk(!0),this.setMaxPoint=Math.max(this.setMaxPoint,t.maxPoint),this.chunks.push(t),this.chunkPos.push(e);let i=t.value.length-1;return this.last=t.value[i],this.lastFrom=t.from[i]+e,this.lastTo=t.to[i]+e,!0}finish(){return this.finishInner(F.empty)}finishInner(e){if(this.from.length&&this.finishChunk(!1),this.chunks.length==0)return e;let t=F.create(this.chunkPos,this.chunks,this.nextLayer?this.nextLayer.finishInner(e):e,this.setMaxPoint);return this.from=null,t}}function ao(n,e,t){let i=new Map;for(let r of n)for(let o=0;o<r.chunk.length;o++)r.chunk[o].maxPoint<=0&&i.set(r.chunk[o],r.chunkPos[o]);let s=new Set;for(let r of e)for(let o=0;o<r.chunk.length;o++){let l=i.get(r.chunk[o]);l!=null&&(t?t.mapPos(l):l)==r.chunkPos[o]&&!t?.touchesRange(l,l+r.chunk[o].length)&&s.add(r.chunk[o])}return s}class Ba{constructor(e,t,i,s=0){this.layer=e,this.skip=t,this.minPoint=i,this.rank=s}get startSide(){return this.value?this.value.startSide:0}get endSide(){return this.value?this.value.endSide:0}goto(e,t=-1e9){return this.chunkIndex=this.rangeIndex=0,this.gotoInner(e,t,!1),this}gotoInner(e,t,i){for(;this.chunkIndex<this.layer.chunk.length;){let s=this.layer.chunk[this.chunkIndex];if(!(this.skip&&this.skip.has(s)||this.layer.chunkEnd(this.chunkIndex)<e||s.maxPoint<this.minPoint))break;this.chunkIndex++,i=!1}if(this.chunkIndex<this.layer.chunk.length){let 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E{constructor(e){let{start:t,end:i}=ja(e);super(t?-1:5e8,i?1:-6e8,null,e),this.tagName=e.tagName||"span",this.class=e.class||"",this.attrs=e.attributes||null}eq(e){return this==e||e instanceof Wn&&this.tagName==e.tagName&&this.class==e.class&&Or(this.attrs,e.attrs)}range(e,t=e){if(e>=t)throw new RangeError("Mark decorations may not be empty");return super.range(e,t)}}Wn.prototype.point=!1;class Hi extends E{constructor(e){super(-2e8,-2e8,null,e)}eq(e){return e instanceof Hi&&Or(this.spec.attributes,e.spec.attributes)}range(e,t=e){if(t!=e)throw new RangeError("Line decoration ranges must be zero-length");return super.range(e,t)}}Hi.prototype.mapMode=ce.TrackBefore;Hi.prototype.point=!0;class Et extends E{constructor(e,t,i,s,r,o){super(t,i,r,e),this.block=s,this.isReplace=o,this.mapMode=s?t<=0?ce.TrackBefore:ce.TrackAfter:ce.TrackDel}get type(){return this.startSide<this.endSide?W.WidgetRange:this.startSide<=0?W.WidgetBefore:W.WidgetAfter}get heightRelevant(){return this.block||!!this.widget&&this.widget.estimatedHeight>=5}eq(e){return e instanceof Et&&Lf(this.widget,e.widget)&&this.block==e.block&&this.startSide==e.startSide&&this.endSide==e.endSide}range(e,t=e){if(this.isReplace&&(e>t||e==t&&this.startSide>0&&this.endSide<=0))throw new RangeError("Invalid range for replacement decoration");if(!this.isReplace&&t!=e)throw new RangeError("Widget decorations can only have zero-length ranges");return super.range(e,t)}}Et.prototype.point=!0;function ja(n,e=!1){let{inclusiveStart:t,inclusiveEnd:i}=n;return t==null&&(t=n.inclusive),i==null&&(i=n.inclusive),{start:t??e,end:i??e}}function Lf(n,e){return n==e||!!(n&&e&&n.compare(e))}function Ys(n,e,t,i=0){let s=t.length-1;s>=0&&t[s]+i>=n?t[s]=Math.max(t[s],e):t.push(n,e)}class ke extends K{constructor(){super(...arguments),this.children=[],this.length=0,this.prevAttrs=void 0,this.attrs=null,this.breakAfter=0}merge(e,t,i,s,r,o){if(i){if(!(i instanceof ke))return!1;this.dom||i.transferDOM(this)}return s&&this.setDeco(i?i.attrs:null),Na(this,e,t,i?i.children:[],r,o),!0}split(e){let t=new ke;if(t.breakAfter=this.breakAfter,this.length==0)return t;let{i,off:s}=this.childPos(e);s&&(t.append(this.children[i].split(s),0),this.children[i].merge(s,this.children[i].length,null,!1,0,0),i++);for(let r=i;r<this.children.length;r++)t.append(this.children[r],0);for(;i>0&&this.children[i-1].length==0;)this.children[--i].destroy();return this.children.length=i,this.markDirty(),this.length=e,t}transferDOM(e){this.dom&&(this.markDirty(),e.setDOM(this.dom),e.prevAttrs=this.prevAttrs===void 0?this.attrs:this.prevAttrs,this.prevAttrs=void 0,this.dom=null)}setDeco(e){Or(this.attrs,e)||(this.dom&&(this.prevAttrs=this.attrs,this.markDirty()),this.attrs=e)}append(e,t){za(this,e,t)}addLineDeco(e){let t=e.spec.attributes,i=e.spec.class;t&&(this.attrs=$s(t,this.attrs||{})),i&&(this.attrs=$s({class:i},this.attrs||{}))}domAtPos(e){return Wa(this,e)}reuseDOM(e){e.nodeName=="DIV"&&(this.setDOM(e),this.dirty|=6)}sync(e){var t;this.dom?this.dirty&4&&(Ra(this.dom),this.dom.className="cm-line",this.prevAttrs=this.attrs?null:void 0):(this.setDOM(document.createElement("div")),this.dom.className="cm-line",this.prevAttrs=this.attrs?null:void 0),this.prevAttrs!==void 0&&(Js(this.dom,this.prevAttrs,this.attrs),this.dom.classList.add("cm-line"),this.prevAttrs=void 0),super.sync(e);let i=this.dom.lastChild;for(;i&&K.get(i)instanceof et;)i=i.lastChild;if(!i||!this.length||i.nodeName!="BR"&&((t=K.get(i))===null||t===void 0?void 0:t.isEditable)==!1&&(!A.ios||!this.children.some(s=>s instanceof yt))){let s=document.createElement("BR");s.cmIgnore=!0,this.dom.appendChild(s)}}measureTextSize(){if(this.children.length==0||this.length>20)return null;let e=0;for(let t of this.children){if(!(t instanceof yt)||/[^ -~]/.test(t.text))return null;let i=Bi(t.dom);if(i.length!=1)return null;e+=i[0].width}return e?{lineHeight:this.dom.getBoundingClientRect().height,charWidth:e/this.length}:null}coordsAt(e,t){return qa(this,e,t)}become(e){return!1}get type(){return W.Text}static find(e,t){for(let i=0,s=0;i<e.children.length;i++){let r=e.children[i],o=s+r.length;if(o>=t){if(r instanceof ke)return r;if(o>t)break}s=o+r.breakAfter}return null}}class Ot extends K{constructor(e,t,i){super(),this.widget=e,this.length=t,this.type=i,this.breakAfter=0,this.prevWidget=null}merge(e,t,i,s,r,o){return i&&(!(i instanceof Ot)||!this.widget.compare(i.widget)||e>0&&r<=0||t<this.length&&o<=0)?!1:(this.length=e+(i?i.length:0)+(this.length-t),!0)}domAtPos(e){return e==0?ye.before(this.dom):ye.after(this.dom,e==this.length)}split(e){let t=this.length-e;this.length=e;let i=new Ot(this.widget,t,this.type);return i.breakAfter=this.breakAfter,i}get children(){return Tr}sync(){(!this.dom||!this.widget.updateDOM(this.dom))&&(this.dom&&this.prevWidget&&this.prevWidget.destroy(this.dom),this.prevWidget=null,this.setDOM(this.widget.toDOM(this.editorView)),this.dom.contentEditable="false")}get overrideDOMText(){return this.parent?this.parent.view.state.doc.slice(this.posAtStart,this.posAtEnd):_.empty}domBoundsAround(){return null}become(e){return e instanceof Ot&&e.type==this.type&&e.widget.constructor==this.widget.constructor?(e.widget.eq(this.widget)||this.markDirty(!0),this.dom&&!this.prevWidget&&(this.prevWidget=this.widget),this.widget=e.widget,this.length=e.length,this.breakAfter=e.breakAfter,!0):!1}ignoreMutation(){return!0}ignoreEvent(e){return this.widget.ignoreEvent(e)}destroy(){super.destroy(),this.dom&&this.widget.destroy(this.dom)}}class Br{constructor(e,t,i,s){this.doc=e,this.pos=t,this.end=i,this.disallowBlockEffectsFor=s,this.content=[],this.curLine=null,this.breakAtStart=0,this.pendingBuffer=0,this.atCursorPos=!0,this.openStart=-1,this.openEnd=-1,this.text="",this.textOff=0,this.cursor=e.iter(),this.skip=t}posCovered(){if(this.content.length==0)return!this.breakAtStart&&this.doc.lineAt(this.pos).from!=this.pos;let e=this.content[this.content.length-1];return!e.breakAfter&&!(e instanceof Ot&&e.type==W.WidgetBefore)}getLine(){return this.curLine||(this.content.push(this.curLine=new ke),this.atCursorPos=!0),this.curLine}flushBuffer(e){this.pendingBuffer&&(this.curLine.append($i(new Qt(-1),e),e.length),this.pendingBuffer=0)}addBlockWidget(e){this.flushBuffer([]),this.curLine=null,this.content.push(e)}finish(e){e?this.pendingBuffer=0:this.flushBuffer([]),this.posCovered()||this.getLine()}buildText(e,t,i){for(;e>0;){if(this.textOff==this.text.length){let{value:r,lineBreak:o,done:l}=this.cursor.next(this.skip);if(this.skip=0,l)throw new Error("Ran out of text content when drawing inline views");if(o){this.posCovered()||this.getLine(),this.content.length?this.content[this.content.length-1].breakAfter=1:this.breakAtStart=1,this.flushBuffer([]),this.curLine=null,e--;continue}else this.text=r,this.textOff=0}let s=Math.min(this.text.length-this.textOff,e,512);this.flushBuffer(t.slice(0,i)),this.getLine().append($i(new yt(this.text.slice(this.textOff,this.textOff+s)),t),i),this.atCursorPos=!0,this.textOff+=s,e-=s,i=0}}span(e,t,i,s){this.buildText(t-e,i,s),this.pos=t,this.openStart<0&&(this.openStart=s)}point(e,t,i,s,r,o){if(this.disallowBlockEffectsFor[o]&&i instanceof Et){if(i.block)throw new RangeError("Block decorations may not be specified via plugins");if(t>this.doc.lineAt(this.pos).to)throw new RangeError("Decorations that replace line breaks may not be specified via plugins")}let l=t-e;if(i instanceof Et)if(i.block){let{type:a}=i;a==W.WidgetAfter&&!this.posCovered()&&this.getLine(),this.addBlockWidget(new Ot(i.widget||new vo("div"),l,a))}else{let a=ct.create(i.widget||new vo("span"),l,l?0:i.startSide),h=this.atCursorPos&&!a.isEditable&&r<=s.length&&(e<t||i.startSide>0),c=!a.isEditable&&(e<t||i.startSide<=0),f=this.getLine();this.pendingBuffer==2&&!h&&(this.pendingBuffer=0),this.flushBuffer(s),h&&(f.append($i(new Qt(1),s),r),r=s.length+Math.max(0,r-s.length)),f.append($i(a,s),r),this.atCursorPos=c,this.pendingBuffer=c?e<t?1:2:0}else this.doc.lineAt(this.pos).from==this.pos&&this.getLine().addLineDeco(i);l&&(this.textOff+l<=this.text.length?this.textOff+=l:(this.skip+=l-(this.text.length-this.textOff),this.text="",this.textOff=0),this.pos=t),this.openStart<0&&(this.openStart=r)}static build(e,t,i,s,r){let o=new Br(e,t,i,r);return o.openEnd=F.spans(s,t,i,o),o.openStart<0&&(o.openStart=o.openEnd),o.finish(o.openEnd),o}}function $i(n,e){for(let t of e)n=new et(t,[n],n.length);return n}class vo extends tt{constructor(e){super(),this.tag=e}eq(e){return e.tag==this.tag}toDOM(){return document.createElement(this.tag)}updateDOM(e){return e.nodeName.toLowerCase()==this.tag}}const Ka=D.define(),Ua=D.define(),Ga=D.define(),$a=D.define(),Xs=D.define(),Ja=D.define(),Ya=D.define({combine:n=>n.some(e=>e)}),Xa=D.define({combine:n=>n.some(e=>e)});class An{constructor(e,t="nearest",i="nearest",s=5,r=5){this.range=e,this.y=t,this.x=i,this.yMargin=s,this.xMargin=r}map(e){return e.empty?this:new 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o=i.head-n.from,l=-1;if(o==0){if(!s||!n.length)return null;e[0].level!=t&&(o=e[0].side(!1,t),l=0)}else if(o==n.length){if(s)return null;let u=e[e.length-1];u.level!=t&&(o=u.side(!0,t),l=e.length-1)}l<0&&(l=Jt.find(e,o,(r=i.bidiLevel)!==null&&r!==void 0?r:-1,i.assoc));let a=e[l];o==a.side(s,t)&&(a=e[l+=s?1:-1],o=a.side(!s,t));let h=s==(a.dir==t),c=Oe(n.text,o,h);if(sh=n.text.slice(Math.min(o,c),Math.max(o,c)),c!=a.side(s,t))return w.cursor(c+n.from,h?-1:1,a.level);let f=l==(s?e.length-1:0)?null:e[l+(s?1:-1)];return!f&&a.level!=t?w.cursor(s?n.to:n.from,s?-1:1,t):f&&f.level<a.level?w.cursor(f.side(!s,t)+n.from,s?1:-1,f.level):w.cursor(c+n.from,s?-1:1,a.level)}const ft="￿";class rh{constructor(e,t){this.points=e,this.text="",this.lineSeparator=t.facet(N.lineSeparator)}append(e){this.text+=e}lineBreak(){this.text+=ft}readRange(e,t){if(!e)return this;let i=e.parentNode;for(let s=e;;){this.findPointBefore(i,s),this.readNode(s);let r=s.nextSibling;if(r==t)break;let o=K.get(s),l=K.get(r);(o&&l?o.breakAfter:(o?o.breakAfter:So(s))||So(r)&&(s.nodeName!="BR"||s.cmIgnore))&&this.lineBreak(),s=r}return this.findPointBefore(i,t),this}readTextNode(e){let t=e.nodeValue;for(let i of this.points)i.node==e&&(i.pos=this.text.length+Math.min(i.offset,t.length));for(let i=0,s=this.lineSeparator?null:/\r\n?|\n/g;;){let r=-1,o=1,l;if(this.lineSeparator?(r=t.indexOf(this.lineSeparator,i),o=this.lineSeparator.length):(l=s.exec(t))&&(r=l.index,o=l[0].length),this.append(t.slice(i,r<0?t.length:r)),r<0)break;if(this.lineBreak(),o>1)for(let a of this.points)a.node==e&&a.pos>this.text.length&&(a.pos-=o-1);i=r+o}}readNode(e){if(e.cmIgnore)return;let t=K.get(e),i=t&&t.overrideDOMText;if(i!=null){this.findPointInside(e,i.length);for(let s=i.iter();!s.next().done;)s.lineBreak?this.lineBreak():this.append(s.value)}else e.nodeType==3?this.readTextNode(e):e.nodeName=="BR"?e.nextSibling&&this.lineBreak():e.nodeType==1&&this.readRange(e.firstChild,null)}findPointBefore(e,t){for(let i of this.points)i.node==e&&e.childNodes[i.offset]==t&&(i.pos=this.text.length)}findPointInside(e,t){for(let i of this.points)(e.nodeType==3?i.node==e:e.contains(i.node))&&(i.pos=this.text.length+Math.min(t,i.offset))}}function So(n){return n.nodeType==1&&/^(DIV|P|LI|UL|OL|BLOCKQUOTE|DD|DT|H\d|SECTION|PRE)$/.test(n.nodeName)}class Co{constructor(e,t){this.node=e,this.offset=t,this.pos=-1}}class Ao extends K{constructor(e){super(),this.view=e,this.compositionDeco=E.none,this.decorations=[],this.dynamicDecorationMap=[],this.minWidth=0,this.minWidthFrom=0,this.minWidthTo=0,this.impreciseAnchor=null,this.impreciseHead=null,this.forceSelection=!1,this.lastUpdate=Date.now(),this.setDOM(e.contentDOM),this.children=[new ke],this.children[0].setParent(this),this.updateDeco(),this.updateInner([new Qe(0,0,0,e.state.doc.length)],0)}get editorView(){return this.view}get length(){return this.view.state.doc.length}update(e){let t=e.changedRanges;this.minWidth>0&&t.length&&(t.every(({fromA:o,toA:l})=>l<this.minWidthFrom||o>this.minWidthTo)?(this.minWidthFrom=e.changes.mapPos(this.minWidthFrom,1),this.minWidthTo=e.changes.mapPos(this.minWidthTo,1)):this.minWidth=this.minWidthFrom=this.minWidthTo=0),this.view.inputState.composing<0?this.compositionDeco=E.none:(e.transactions.length||this.dirty)&&(this.compositionDeco=jf(this.view,e.changes)),(A.ie||A.chrome)&&!this.compositionDeco.size&&e&&e.state.doc.lines!=e.startState.doc.lines&&(this.forceSelection=!0);let i=this.decorations,s=this.updateDeco(),r=$f(i,s,e.changes);return t=Qe.extendWithRanges(t,r),this.dirty==0&&t.length==0?!1:(this.updateInner(t,e.startState.doc.length),e.transactions.length&&(this.lastUpdate=Date.now()),!0)}updateInner(e,t){this.view.viewState.mustMeasureContent=!0,this.updateChildren(e,t);let{observer:i}=this.view;i.ignore(()=>{this.dom.style.height=this.view.viewState.contentHeight+"px",this.dom.style.flexBasis=this.minWidth?this.minWidth+"px":"";let r=A.chrome||A.ios?{node:i.selectionRange.focusNode,written:!1}:void 0;this.sync(r),this.dirty=0,r&&(r.written||i.selectionRange.focusNode!=r.node)&&(this.forceSelection=!0),this.dom.style.height=""});let s=[];if(this.view.viewport.from||this.view.viewport.to<this.view.state.doc.length)for(let r of this.children)r instanceof Ot&&r.widget instanceof Mo&&s.push(r.dom);i.updateGaps(s)}updateChildren(e,t){let i=this.childCursor(t);for(let s=e.length-1;;s--){let r=s>=0?e[s]:null;if(!r)break;let{fromA:o,toA:l,fromB:a,toB:h}=r,{content:c,breakAtStart:f,openStart:u,openEnd:d}=Br.build(this.view.state.doc,a,h,this.decorations,this.dynamicDecorationMap),{i:p,off:g}=i.findPos(l,1),{i:y,off:b}=i.findPos(o,-1);Ia(this,y,b,p,g,c,f,u,d)}}updateSelection(e=!1,t=!1){if((e||!this.view.observer.selectionRange.focusNode)&&this.view.observer.readSelectionRange(),!(t||this.mayControlSelection()))return;let i=this.forceSelection;this.forceSelection=!1;let s=this.view.state.selection.main,r=this.domAtPos(s.anchor),o=s.empty?r:this.domAtPos(s.head);if(A.gecko&&s.empty&&qf(r)){let a=document.createTextNode("");this.view.observer.ignore(()=>r.node.insertBefore(a,r.node.childNodes[r.offset]||null)),r=o=new ye(a,0),i=!0}let l=this.view.observer.selectionRange;(i||!l.focusNode||!Sn(r.node,r.offset,l.anchorNode,l.anchorOffset)||!Sn(o.node,o.offset,l.focusNode,l.focusOffset))&&(this.view.observer.ignore(()=>{A.android&&A.chrome&&this.dom.contains(l.focusNode)&&Jf(l.focusNode,this.dom)&&(this.dom.blur(),this.dom.focus({preventScroll:!0}));let a=xn(this.view.root);if(a)if(s.empty){if(A.gecko){let h=Uf(r.node,r.offset);if(h&&h!=3){let c=lh(r.node,r.offset,h==1?1:-1);c&&(r=new ye(c,h==1?0:c.nodeValue.length))}}a.collapse(r.node,r.offset),s.bidiLevel!=null&&l.cursorBidiLevel!=null&&(l.cursorBidiLevel=s.bidiLevel)}else if(a.extend){a.collapse(r.node,r.offset);try{a.extend(o.node,o.offset)}catch{}}else{let h=document.createRange();s.anchor>s.head&&([r,o]=[o,r]),h.setEnd(o.node,o.offset),h.setStart(r.node,r.offset),a.removeAllRanges(),a.addRange(h)}}),this.view.observer.setSelectionRange(r,o)),this.impreciseAnchor=r.precise?null:new ye(l.anchorNode,l.anchorOffset),this.impreciseHead=o.precise?null:new ye(l.focusNode,l.focusOffset)}enforceCursorAssoc(){if(this.compositionDeco.size)return;let{view:e}=this,t=e.state.selection.main,i=xn(e.root),{anchorNode:s,anchorOffset:r}=e.observer.selectionRange;if(!i||!t.empty||!t.assoc||!i.modify)return;let o=ke.find(this,t.head);if(!o)return;let l=o.posAtStart;if(t.head==l||t.head==l+o.length)return;let a=this.coordsAt(t.head,-1),h=this.coordsAt(t.head,1);if(!a||!h||a.bottom>h.top)return;let c=this.domAtPos(t.head+t.assoc);i.collapse(c.node,c.offset),i.modify("move",t.assoc<0?"forward":"backward","lineboundary"),e.observer.readSelectionRange();let f=e.observer.selectionRange;e.docView.posFromDOM(f.anchorNode,f.anchorOffset)!=t.from&&i.collapse(s,r)}mayControlSelection(){let e=this.view.root.activeElement;return e==this.dom||dn(this.dom,this.view.observer.selectionRange)&&!(e&&this.dom.contains(e))}nearest(e){for(let t=e;t;){let i=K.get(t);if(i&&i.rootView==this)return i;t=t.parentNode}return null}posFromDOM(e,t){let i=this.nearest(e);if(!i)throw new RangeError("Trying to find position for a DOM position outside of the document");return i.localPosFromDOM(e,t)+i.posAtStart}domAtPos(e){let{i:t,off:i}=this.childCursor().findPos(e,-1);for(;t<this.children.length-1;){let s=this.children[t];if(i<s.length||s instanceof ke)break;t++,i=0}return this.children[t].domAtPos(i)}coordsAt(e,t){for(let i=this.length,s=this.children.length-1;;s--){let r=this.children[s],o=i-r.breakAfter-r.length;if(e>o||e==o&&r.type!=W.WidgetBefore&&r.type!=W.WidgetAfter&&(!s||t==2||this.children[s-1].breakAfter||this.children[s-1].type==W.WidgetBefore&&t>-2))return r.coordsAt(e-o,t);i=o}}measureVisibleLineHeights(e){let t=[],{from:i,to:s}=e,r=this.view.contentDOM.clientWidth,o=r>Math.max(this.view.scrollDOM.clientWidth,this.minWidth)+1,l=-1,a=this.view.textDirection==Z.LTR;for(let h=0,c=0;c<this.children.length;c++){let f=this.children[c],u=h+f.length;if(u>s)break;if(h>=i){let d=f.dom.getBoundingClientRect();if(t.push(d.height),o){let p=f.dom.lastChild,g=p?Bi(p):[];if(g.length){let y=g[g.length-1],b=a?y.right-d.left:d.right-y.left;b>l&&(l=b,this.minWidth=r,this.minWidthFrom=h,this.minWidthTo=u)}}}h=u+f.breakAfter}return t}textDirectionAt(e){let{i:t}=this.childPos(e,1);return getComputedStyle(this.children[t].dom).direction=="rtl"?Z.RTL:Z.LTR}measureTextSize(){for(let s of this.children)if(s instanceof ke){let r=s.measureTextSize();if(r)return r}let e=document.createElement("div"),t,i;return e.className="cm-line",e.style.width="99999px",e.textContent="abc def ghi jkl mno pqr stu",this.view.observer.ignore(()=>{this.dom.appendChild(e);let s=Bi(e.firstChild)[0];t=e.getBoundingClientRect().height,i=s?s.width/27:7,e.remove()}),{lineHeight:t,charWidth:i}}childCursor(e=this.length){let t=this.children.length;return t&&(e-=this.children[--t].length),new La(this.children,e,t)}computeBlockGapDeco(){let e=[],t=this.view.viewState;for(let i=0,s=0;;s++){let r=s==t.viewports.length?null:t.viewports[s],o=r?r.from-1:this.length;if(o>i){let l=t.lineBlockAt(o).bottom-t.lineBlockAt(i).top;e.push(E.replace({widget:new Mo(l),block:!0,inclusive:!0,isBlockGap:!0}).range(i,o))}if(!r)break;i=r.to+1}return E.set(e)}updateDeco(){let e=this.view.state.facet(Ei).map((t,i)=>(this.dynamicDecorationMap[i]=typeof t=="function")?t(this.view):t);for(let t=e.length;t<e.length+3;t++)this.dynamicDecorationMap[t]=!1;return this.decorations=[...e,this.compositionDeco,this.computeBlockGapDeco(),this.view.viewState.lineGapDeco]}scrollIntoView(e){let{range:t}=e,i=this.coordsAt(t.head,t.empty?t.assoc:t.head>t.anchor?-1:1),s;if(!i)return;!t.empty&&(s=this.coordsAt(t.anchor,t.anchor>t.head?-1:1))&&(i={left:Math.min(i.left,s.left),top:Math.min(i.top,s.top),right:Math.max(i.right,s.right),bottom:Math.max(i.bottom,s.bottom)});let r=0,o=0,l=0,a=0;for(let c of this.view.state.facet(th).map(f=>f(this.view)))if(c){let{left:f,right:u,top:d,bottom:p}=c;f!=null&&(r=Math.max(r,f)),u!=null&&(o=Math.max(o,u)),d!=null&&(l=Math.max(l,d)),p!=null&&(a=Math.max(a,p))}let h={left:i.left-r,top:i.top-l,right:i.right+o,bottom:i.bottom+a};Df(this.view.scrollDOM,h,t.head<t.anchor?-1:1,e.x,e.y,e.xMargin,e.yMargin,this.view.textDirection==Z.LTR)}}function qf(n){return n.node.nodeType==1&&n.node.firstChild&&(n.offset==0||n.node.childNodes[n.offset-1].contentEditable=="false")&&(n.offset==n.node.childNodes.length||n.node.childNodes[n.offset].contentEditable=="false")}class Mo extends tt{constructor(e){super(),this.height=e}toDOM(){let e=document.createElement("div");return this.updateDOM(e),e}eq(e){return e.height==this.height}updateDOM(e){return e.style.height=this.height+"px",!0}get estimatedHeight(){return this.height}}function oh(n){let e=n.observer.selectionRange,t=e.focusNode&&lh(e.focusNode,e.focusOffset,0);if(!t)return null;let i=n.docView.nearest(t);if(!i)return null;if(i instanceof ke){let s=t;for(;s.parentNode!=i.dom;)s=s.parentNode;let r=s.previousSibling;for(;r&&!K.get(r);)r=r.previousSibling;let o=r?K.get(r).posAtEnd:i.posAtStart;return{from:o,to:o,node:s,text:t}}else{for(;;){let{parent:r}=i;if(!r)return null;if(r instanceof ke)break;i=r}let s=i.posAtStart;return{from:s,to:s+i.length,node:i.dom,text:t}}}function jf(n,e){let t=oh(n);if(!t)return E.none;let{from:i,to:s,node:r,text:o}=t,l=e.mapPos(i,1),a=Math.max(l,e.mapPos(s,-1)),{state:h}=n,c=r.nodeType==3?r.nodeValue:new rh([],h).readRange(r.firstChild,null).text;if(a-l<c.length)if(h.doc.sliceString(l,Math.min(h.doc.length,l+c.length),ft)==c)a=l+c.length;else if(h.doc.sliceString(Math.max(0,a-c.length),a,ft)==c)l=a-c.length;else return E.none;else if(h.doc.sliceString(l,a,ft)!=c)return E.none;let f=K.get(r);return f instanceof Fa?f=f.widget.topView:f&&(f.parent=null),E.set(E.replace({widget:new Kf(r,o,f),inclusive:!0}).range(l,a))}class Kf extends tt{constructor(e,t,i){super(),this.top=e,this.text=t,this.topView=i}eq(e){return this.top==e.top&&this.text==e.text}toDOM(){return this.top}ignoreEvent(){return!1}get customView(){return Fa}}function lh(n,e,t){for(;;){if(n.nodeType==3)return n;if(n.nodeType==1&&e>0&&t<=0)n=n.childNodes[e-1],e=Pi(n);else if(n.nodeType==1&&e<n.childNodes.length&&t>=0)n=n.childNodes[e],e=0;else return null}}function Uf(n,e){return n.nodeType!=1?0:(e&&n.childNodes[e-1].contentEditable=="false"?1:0)|(e<n.childNodes.length&&n.childNodes[e].contentEditable=="false"?2:0)}class Gf{constructor(){this.changes=[]}compareRange(e,t){Ys(e,t,this.changes)}comparePoint(e,t){Ys(e,t,this.changes)}}function $f(n,e,t){let i=new Gf;return F.compare(n,e,t,i),i.changes}function Jf(n,e){for(let t=n;t&&t!=e;t=t.assignedSlot||t.parentNode)if(t.nodeType==1&&t.contentEditable=="false")return!0;return!1}function Yf(n,e,t=1){let i=n.charCategorizer(e),s=n.doc.lineAt(e),r=e-s.from;if(s.length==0)return w.cursor(e);r==0?t=1:r==s.length&&(t=-1);let o=r,l=r;t<0?o=Oe(s.text,r,!1):l=Oe(s.text,r);let a=i(s.text.slice(o,l));for(;o>0;){let h=Oe(s.text,o,!1);if(i(s.text.slice(h,o))!=a)break;o=h}for(;l<s.length;){let h=Oe(s.text,l);if(i(s.text.slice(l,h))!=a)break;l=h}return w.range(o+s.from,l+s.from)}function Xf(n,e){return e.left>n?e.left-n:Math.max(0,n-e.right)}function Zf(n,e){return e.top>n?e.top-n:Math.max(0,n-e.bottom)}function ss(n,e){return n.top<e.bottom-1&&n.bottom>e.top+1}function Do(n,e){return e<n.top?{top:e,left:n.left,right:n.right,bottom:n.bottom}:n}function To(n,e){return e>n.bottom?{top:n.top,left:n.left,right:n.right,bottom:e}:n}function er(n,e,t){let i,s,r,o,l=!1,a,h,c,f;for(let p=n.firstChild;p;p=p.nextSibling){let g=Bi(p);for(let y=0;y<g.length;y++){let b=g[y];s&&ss(s,b)&&(b=Do(To(b,s.bottom),s.top));let v=Xf(e,b),S=Zf(t,b);if(v==0&&S==0)return 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u=e>=(c.left+c.right)/2,d=u;if((A.chrome||A.gecko)&&Zt(n,l).getBoundingClientRect().left==c.right&&(d=!u),f<=0)return{node:n,offset:l+(d?1:0)};s=l+(d?1:0),r=f}}}return{node:n,offset:s>-1?s:o>0?n.nodeValue.length:0}}function ah(n,{x:e,y:t},i,s=-1){var r;let o=n.contentDOM.getBoundingClientRect(),l=o.top+n.viewState.paddingTop,a,{docHeight:h}=n.viewState,c=t-l;if(c<0)return 0;if(c>h)return n.state.doc.length;for(let b=n.defaultLineHeight/2,v=!1;a=n.elementAtHeight(c),a.type!=W.Text;)for(;c=s>0?a.bottom+b:a.top-b,!(c>=0&&c<=h);){if(v)return i?null:0;v=!0,s=-s}t=l+c;let f=a.from;if(f<n.viewport.from)return n.viewport.from==0?0:i?null:Bo(n,o,a,e,t);if(f>n.viewport.to)return n.viewport.to==n.state.doc.length?n.state.doc.length:i?null:Bo(n,o,a,e,t);let u=n.dom.ownerDocument,d=n.root.elementFromPoint?n.root:u,p=d.elementFromPoint(e,t);p&&!n.contentDOM.contains(p)&&(p=null),p||(e=Math.max(o.left+1,Math.min(o.right-1,e)),p=d.elementFromPoint(e,t),p&&!n.contentDOM.contains(p)&&(p=null));let g,y=-1;if(p&&((r=n.docView.nearest(p))===null||r===void 0?void 0:r.isEditable)!=!1){if(u.caretPositionFromPoint){let b=u.caretPositionFromPoint(e,t);b&&({offsetNode:g,offset:y}=b)}else if(u.caretRangeFromPoint){let b=u.caretRangeFromPoint(e,t);b&&({startContainer:g,startOffset:y}=b,(!n.contentDOM.contains(g)||A.safari&&Qf(g,y,e)||A.chrome&&eu(g,y,e))&&(g=void 0))}}if(!g||!n.docView.dom.contains(g)){let b=ke.find(n.docView,f);if(!b)return c>a.top+a.height/2?a.to:a.from;({node:g,offset:y}=er(b.dom,e,t))}return n.docView.posFromDOM(g,y)}function Bo(n,e,t,i,s){let r=Math.round((i-e.left)*n.defaultCharacterWidth);if(n.lineWrapping&&t.height>n.defaultLineHeight*1.5){let l=Math.floor((s-t.top)/n.defaultLineHeight);r+=l*n.viewState.heightOracle.lineLength}let o=n.state.sliceDoc(t.from,t.to);return t.from+Hs(o,r,n.state.tabSize)}function Qf(n,e,t){let i;if(n.nodeType!=3||e!=(i=n.nodeValue.length))return!1;for(let 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i)r.between(t.from-1,t.from+1,(o,l,a)=>{t.from>o&&t.from<l&&(t=e.head>t.from?w.cursor(o,1):w.cursor(l,-1),s=!0)});if(!s)return t}}class su{constructor(e){this.lastKeyCode=0,this.lastKeyTime=0,this.lastTouchTime=0,this.lastFocusTime=0,this.lastScrollTop=0,this.lastScrollLeft=0,this.chromeScrollHack=-1,this.pendingIOSKey=void 0,this.lastSelectionOrigin=null,this.lastSelectionTime=0,this.lastEscPress=0,this.lastContextMenu=0,this.scrollHandlers=[],this.registeredEvents=[],this.customHandlers=[],this.composing=-1,this.compositionFirstChange=null,this.compositionEndedAt=0,this.mouseSelection=null;for(let t in oe){let i=oe[t];e.contentDOM.addEventListener(t,s=>{!Eo(e,s)||this.ignoreDuringComposition(s)||t=="keydown"&&this.keydown(e,s)||(this.mustFlushObserver(s)&&e.observer.forceFlush(),this.runCustomHandlers(t,e,s)?s.preventDefault():i(e,s))},tr[t]),this.registeredEvents.push(t)}A.chrome&&A.chrome_version==102&&e.scrollDOM.addEventListener("wheel",()=>{this.chromeScrollHack<0?e.contentDOM.style.pointerEvents="none":window.clearTimeout(this.chromeScrollHack),this.chromeScrollHack=setTimeout(()=>{this.chromeScrollHack=-1,e.contentDOM.style.pointerEvents=""},100)},{passive:!0}),this.notifiedFocused=e.hasFocus,A.safari&&e.contentDOM.addEventListener("input",()=>null)}setSelectionOrigin(e){this.lastSelectionOrigin=e,this.lastSelectionTime=Date.now()}ensureHandlers(e,t){var i;let s;this.customHandlers=[];for(let r of t)if(s=(i=r.update(e).spec)===null||i===void 0?void 0:i.domEventHandlers){this.customHandlers.push({plugin:r.value,handlers:s});for(let o in s)this.registeredEvents.indexOf(o)<0&&o!="scroll"&&(this.registeredEvents.push(o),e.contentDOM.addEventListener(o,l=>{Eo(e,l)&&this.runCustomHandlers(o,e,l)&&l.preventDefault()}))}}runCustomHandlers(e,t,i){for(let s of this.customHandlers){let r=s.handlers[e];if(r)try{if(r.call(s.plugin,i,t)||i.defaultPrevented)return!0}catch(o){He(t.state,o)}}return!1}runScrollHandlers(e,t){this.lastScrollTop=e.scrollDOM.scrollTop,this.lastScrollLeft=e.scrollDOM.scrollLeft;for(let i of this.customHandlers){let s=i.handlers.scroll;if(s)try{s.call(i.plugin,t,e)}catch(r){He(e.state,r)}}}keydown(e,t){if(this.lastKeyCode=t.keyCode,this.lastKeyTime=Date.now(),t.keyCode==9&&Date.now()<this.lastEscPress+2e3)return!0;if(A.android&&A.chrome&&!t.synthetic&&(t.keyCode==13||t.keyCode==8))return e.observer.delayAndroidKey(t.key,t.keyCode),!0;let i;return A.ios&&!t.synthetic&&!t.altKey&&!t.metaKey&&((i=hh.find(s=>s.keyCode==t.keyCode))&&!t.ctrlKey||ru.indexOf(t.key)>-1&&t.ctrlKey&&!t.shiftKey)?(this.pendingIOSKey=i||t,setTimeout(()=>this.flushIOSKey(e),250),!0):!1}flushIOSKey(e){let t=this.pendingIOSKey;return t?(this.pendingIOSKey=void 0,$t(e.contentDOM,t.key,t.keyCode)):!1}ignoreDuringComposition(e){return/^key/.test(e.type)?this.composing>0?!0:A.safari&&!A.ios&&Date.now()-this.compositionEndedAt<100?(this.compositionEndedAt=0,!0):!1:!1}mustFlushObserver(e){return e.type=="keydown"&&e.keyCode!=229}startMouseSelection(e){this.mouseSelection&&this.mouseSelection.destroy(),this.mouseSelection=e}update(e){this.mouseSelection&&this.mouseSelection.update(e),e.transactions.length&&(this.lastKeyCode=this.lastSelectionTime=0)}destroy(){this.mouseSelection&&this.mouseSelection.destroy()}}const hh=[{key:"Backspace",keyCode:8,inputType:"deleteContentBackward"},{key:"Enter",keyCode:13,inputType:"insertParagraph"},{key:"Delete",keyCode:46,inputType:"deleteContentForward"}],ru="dthko",ch=[16,17,18,20,91,92,224,225];class ou{constructor(e,t,i,s){this.view=e,this.style=i,this.mustSelect=s,this.lastEvent=t;let r=e.contentDOM.ownerDocument;r.addEventListener("mousemove",this.move=this.move.bind(this)),r.addEventListener("mouseup",this.up=this.up.bind(this)),this.extend=t.shiftKey,this.multiple=e.state.facet(N.allowMultipleSelections)&&lu(e,t),this.dragMove=au(e,t),this.dragging=hu(e,t)&&ph(t)==1?null:!1,this.dragging===!1&&(t.preventDefault(),this.select(t))}move(e){if(e.buttons==0)return this.destroy();this.dragging===!1&&this.select(this.lastEvent=e)}up(e){this.dragging==null&&this.select(this.lastEvent),this.dragging||e.preventDefault(),this.destroy()}destroy(){let e=this.view.contentDOM.ownerDocument;e.removeEventListener("mousemove",this.move),e.removeEventListener("mouseup",this.up),this.view.inputState.mouseSelection=null}select(e){let t=this.style.get(e,this.extend,this.multiple);(this.mustSelect||!t.eq(this.view.state.selection)||t.main.assoc!=this.view.state.selection.main.assoc)&&this.view.dispatch({selection:t,userEvent:"select.pointer",scrollIntoView:!0}),this.mustSelect=!1}update(e){e.docChanged&&this.dragging&&(this.dragging=this.dragging.map(e.changes)),this.style.update(e)&&setTimeout(()=>this.select(this.lastEvent),20)}}function lu(n,e){let t=n.state.facet(Ka);return t.length?t[0](e):A.mac?e.metaKey:e.ctrlKey}function au(n,e){let t=n.state.facet(Ua);return t.length?t[0](e):A.mac?!e.altKey:!e.ctrlKey}function hu(n,e){let{main:t}=n.state.selection;if(t.empty)return!1;let i=xn(n.root);if(!i||i.rangeCount==0)return!0;let s=i.getRangeAt(0).getClientRects();for(let r=0;r<s.length;r++){let o=s[r];if(o.left<=e.clientX&&o.right>=e.clientX&&o.top<=e.clientY&&o.bottom>=e.clientY)return!0}return!1}function Eo(n,e){if(!e.bubbles)return!0;if(e.defaultPrevented)return!1;for(let t=e.target,i;t!=n.contentDOM;t=t.parentNode)if(!t||t.nodeType==11||(i=K.get(t))&&i.ignoreEvent(e))return!1;return!0}const oe=Object.create(null),tr=Object.create(null),fh=A.ie&&A.ie_version<15||A.ios&&A.webkit_version<604;function cu(n){let e=n.dom.parentNode;if(!e)return;let t=e.appendChild(document.createElement("textarea"));t.style.cssText="position: fixed; left: -10000px; top: 10px",t.focus(),setTimeout(()=>{n.focus(),t.remove(),uh(n,t.value)},50)}function uh(n,e){let{state:t}=n,i,s=1,r=t.toText(e),o=r.lines==t.selection.ranges.length;if(ir!=null&&t.selection.ranges.every(a=>a.empty)&&ir==r.toString()){let a=-1;i=t.changeByRange(h=>{let c=t.doc.lineAt(h.from);if(c.from==a)return{range:h};a=c.from;let f=t.toText((o?r.line(s++).text:e)+t.lineBreak);return{changes:{from:c.from,insert:f},range:w.cursor(h.from+f.length)}})}else o?i=t.changeByRange(a=>{let h=r.line(s++);return{changes:{from:a.from,to:a.to,insert:h.text},range:w.cursor(a.from+h.length)}}):i=t.replaceSelection(r);n.dispatch(i,{userEvent:"input.paste",scrollIntoView:!0})}oe.keydown=(n,e)=>{n.inputState.setSelectionOrigin("select"),e.keyCode==27?n.inputState.lastEscPress=Date.now():ch.indexOf(e.keyCode)<0&&(n.inputState.lastEscPress=0)};oe.touchstart=(n,e)=>{n.inputState.lastTouchTime=Date.now(),n.inputState.setSelectionOrigin("select.pointer")};oe.touchmove=n=>{n.inputState.setSelectionOrigin("select.pointer")};tr.touchstart=tr.touchmove={passive:!0};oe.mousedown=(n,e)=>{if(n.observer.flush(),n.inputState.lastTouchTime>Date.now()-2e3)return;let t=null;for(let i of n.state.facet(Ga))if(t=i(n,e),t)break;if(!t&&e.button==0&&(t=du(n,e)),t){let i=n.root.activeElement!=n.contentDOM;i&&n.observer.ignore(()=>Ea(n.contentDOM)),n.inputState.startMouseSelection(new ou(n,e,t,i))}};function Ro(n,e,t,i){if(i==1)return w.cursor(e,t);if(i==2)return Yf(n.state,e,t);{let s=ke.find(n.docView,e),r=n.state.doc.lineAt(s?s.posAtEnd:e),o=s?s.posAtStart:r.from,l=s?s.posAtEnd:r.to;return l<n.state.doc.length&&l==r.to&&l++,w.range(o,l)}}let dh=(n,e)=>n>=e.top&&n<=e.bottom,Lo=(n,e,t)=>dh(e,t)&&n>=t.left&&n<=t.right;function fu(n,e,t,i){let s=ke.find(n.docView,e);if(!s)return 1;let r=e-s.posAtStart;if(r==0)return 1;if(r==s.length)return-1;let o=s.coordsAt(r,-1);if(o&&Lo(t,i,o))return-1;let l=s.coordsAt(r,1);return l&&Lo(t,i,l)?1:o&&dh(i,o)?-1:1}function Io(n,e){let t=n.posAtCoords({x:e.clientX,y:e.clientY},!1);return{pos:t,bias:fu(n,t,e.clientX,e.clientY)}}const uu=A.ie&&A.ie_version<=11;let No=null,_o=0,Vo=0;function ph(n){if(!uu)return n.detail;let e=No,t=Vo;return No=n,Vo=Date.now(),_o=!e||t>Date.now()-400&&Math.abs(e.clientX-n.clientX)<2&&Math.abs(e.clientY-n.clientY)<2?(_o+1)%3:1}function du(n,e){let t=Io(n,e),i=ph(e),s=n.state.selection,r=t,o=e;return{update(l){l.docChanged&&(t.pos=l.changes.mapPos(t.pos),s=s.map(l.changes),o=null)},get(l,a,h){let c;o&&l.clientX==o.clientX&&l.clientY==o.clientY?c=r:(c=r=Io(n,l),o=l);let f=Ro(n,c.pos,c.bias,i);if(t.pos!=c.pos&&!a){let u=Ro(n,t.pos,t.bias,i),d=Math.min(u.from,f.from),p=Math.max(u.to,f.to);f=d<f.from?w.range(d,p):w.range(p,d)}return a?s.replaceRange(s.main.extend(f.from,f.to)):h&&s.ranges.length>1&&s.ranges.some(u=>u.eq(f))?pu(s,f):h?s.addRange(f):w.create([f])}}}function pu(n,e){for(let t=0;;t++)if(n.ranges[t].eq(e))return w.create(n.ranges.slice(0,t).concat(n.ranges.slice(t+1)),n.mainIndex==t?0:n.mainIndex-(n.mainIndex>t?1:0))}oe.dragstart=(n,e)=>{let{selection:{main:t}}=n.state,{mouseSelection:i}=n.inputState;i&&(i.dragging=t),e.dataTransfer&&(e.dataTransfer.setData("Text",n.state.sliceDoc(t.from,t.to)),e.dataTransfer.effectAllowed="copyMove")};function Fo(n,e,t,i){if(!t)return;let s=n.posAtCoords({x:e.clientX,y:e.clientY},!1);e.preventDefault();let{mouseSelection:r}=n.inputState,o=i&&r&&r.dragging&&r.dragMove?{from:r.dragging.from,to:r.dragging.to}:null,l={from:s,insert:t},a=n.state.changes(o?[o,l]:l);n.focus(),n.dispatch({changes:a,selection:{anchor:a.mapPos(s,-1),head:a.mapPos(s,1)},userEvent:o?"move.drop":"input.drop"})}oe.drop=(n,e)=>{if(!e.dataTransfer)return;if(n.state.readOnly)return e.preventDefault();let t=e.dataTransfer.files;if(t&&t.length){e.preventDefault();let i=Array(t.length),s=0,r=()=>{++s==t.length&&Fo(n,e,i.filter(o=>o!=null).join(n.state.lineBreak),!1)};for(let o=0;o<t.length;o++){let l=new FileReader;l.onerror=r,l.onload=()=>{/[\x00-\x08\x0e-\x1f]{2}/.test(l.result)||(i[o]=l.result),r()},l.readAsText(t[o])}}else Fo(n,e,e.dataTransfer.getData("Text"),!0)};oe.paste=(n,e)=>{if(n.state.readOnly)return e.preventDefault();n.observer.flush();let t=fh?null:e.clipboardData;t?(uh(n,t.getData("text/plain")),e.preventDefault()):cu(n)};function mu(n,e){let t=n.dom.parentNode;if(!t)return;let i=t.appendChild(document.createElement("textarea"));i.style.cssText="position: fixed; left: -10000px; top: 10px",i.value=e,i.focus(),i.selectionEnd=e.length,i.selectionStart=0,setTimeout(()=>{i.remove(),n.focus()},50)}function gu(n){let e=[],t=[],i=!1;for(let s of n.selection.ranges)s.empty||(e.push(n.sliceDoc(s.from,s.to)),t.push(s));if(!e.length){let s=-1;for(let{from:r}of n.selection.ranges){let o=n.doc.lineAt(r);o.number>s&&(e.push(o.text),t.push({from:o.from,to:Math.min(n.doc.length,o.to+1)})),s=o.number}i=!0}return{text:e.join(n.lineBreak),ranges:t,linewise:i}}let ir=null;oe.copy=oe.cut=(n,e)=>{let{text:t,ranges:i,linewise:s}=gu(n.state);if(!t&&!s)return;ir=s?t:null;let r=fh?null:e.clipboardData;r?(e.preventDefault(),r.clearData(),r.setData("text/plain",t)):mu(n,t),e.type=="cut"&&!n.state.readOnly&&n.dispatch({changes:i,scrollIntoView:!0,userEvent:"delete.cut"})};function mh(n){setTimeout(()=>{n.hasFocus!=n.inputState.notifiedFocused&&n.update([])},10)}oe.focus=n=>{n.inputState.lastFocusTime=Date.now(),!n.scrollDOM.scrollTop&&(n.inputState.lastScrollTop||n.inputState.lastScrollLeft)&&(n.scrollDOM.scrollTop=n.inputState.lastScrollTop,n.scrollDOM.scrollLeft=n.inputState.lastScrollLeft),mh(n)};oe.blur=n=>{n.observer.clearSelectionRange(),mh(n)};oe.compositionstart=oe.compositionupdate=n=>{n.inputState.compositionFirstChange==null&&(n.inputState.compositionFirstChange=!0),n.inputState.composing<0&&(n.inputState.composing=0)};oe.compositionend=n=>{n.inputState.composing=-1,n.inputState.compositionEndedAt=Date.now(),n.inputState.compositionFirstChange=null,A.chrome&&A.android&&n.observer.flushSoon(),setTimeout(()=>{n.inputState.composing<0&&n.docView.compositionDeco.size&&n.update([])},50)};oe.contextmenu=n=>{n.inputState.lastContextMenu=Date.now()};oe.beforeinput=(n,e)=>{var t;let i;if(A.chrome&&A.android&&(i=hh.find(s=>s.inputType==e.inputType))&&(n.observer.delayAndroidKey(i.key,i.keyCode),i.key=="Backspace"||i.key=="Delete")){let s=((t=window.visualViewport)===null||t===void 0?void 0:t.height)||0;setTimeout(()=>{var r;(((r=window.visualViewport)===null||r===void 0?void 0:r.height)||0)>s+10&&n.hasFocus&&(n.contentDOM.blur(),n.focus())},100)}};const Ho=["pre-wrap","normal","pre-line","break-spaces"];class yu{constructor(){this.doc=_.empty,this.lineWrapping=!1,this.heightSamples={},this.lineHeight=14,this.charWidth=7,this.lineLength=30,this.heightChanged=!1}heightForGap(e,t){let i=this.doc.lineAt(t).number-this.doc.lineAt(e).number+1;return this.lineWrapping&&(i+=Math.ceil((t-e-i*this.lineLength*.5)/this.lineLength)),this.lineHeight*i}heightForLine(e){return this.lineWrapping?(1+Math.max(0,Math.ceil((e-this.lineLength)/(this.lineLength-5))))*this.lineHeight:this.lineHeight}setDoc(e){return this.doc=e,this}mustRefreshForWrapping(e){return Ho.indexOf(e)>-1!=this.lineWrapping}mustRefreshForHeights(e){let t=!1;for(let i=0;i<e.length;i++){let s=e[i];s<0?i++:this.heightSamples[Math.floor(s*10)]||(t=!0,this.heightSamples[Math.floor(s*10)]=!0)}return t}refresh(e,t,i,s,r){let o=Ho.indexOf(e)>-1,l=Math.round(t)!=Math.round(this.lineHeight)||this.lineWrapping!=o;if(this.lineWrapping=o,this.lineHeight=t,this.charWidth=i,this.lineLength=s,l){this.heightSamples={};for(let a=0;a<r.length;a++){let h=r[a];h<0?a++:this.heightSamples[Math.floor(h*10)]=!0}}return l}}class bu{constructor(e,t){this.from=e,this.heights=t,this.index=0}get more(){return this.index<this.heights.length}}class ut{constructor(e,t,i,s,r){this.from=e,this.length=t,this.top=i,this.height=s,this.type=r}get to(){return this.from+this.length}get bottom(){return this.top+this.height}join(e){let t=(Array.isArray(this.type)?this.type:[this]).concat(Array.isArray(e.type)?e.type:[e]);return new ut(this.from,this.length+e.length,this.top,this.height+e.height,t)}}var q=function(n){return n[n.ByPos=0]="ByPos",n[n.ByHeight=1]="ByHeight",n[n.ByPosNoHeight=2]="ByPosNoHeight",n}(q||(q={}));const pn=.001;class ve{constructor(e,t,i=2){this.length=e,this.height=t,this.flags=i}get outdated(){return(this.flags&2)>0}set outdated(e){this.flags=(e?2:0)|this.flags&-3}setHeight(e,t){this.height!=t&&(Math.abs(this.height-t)>pn&&(e.heightChanged=!0),this.height=t)}replace(e,t,i){return ve.of(i)}decomposeLeft(e,t){t.push(this)}decomposeRight(e,t){t.push(this)}applyChanges(e,t,i,s){let r=this;for(let o=s.length-1;o>=0;o--){let{fromA:l,toA:a,fromB:h,toB:c}=s[o],f=r.lineAt(l,q.ByPosNoHeight,t,0,0),u=f.to>=a?f:r.lineAt(a,q.ByPosNoHeight,t,0,0);for(c+=u.to-a,a=u.to;o>0&&f.from<=s[o-1].toA;)l=s[o-1].fromA,h=s[o-1].fromB,o--,l<f.from&&(f=r.lineAt(l,q.ByPosNoHeight,t,0,0));h+=f.from-l,l=f.from;let d=Pr.build(i,e,h,c);r=r.replace(l,a,d)}return r.updateHeight(i,0)}static empty(){return new De(0,0)}static of(e){if(e.length==1)return e[0];let t=0,i=e.length,s=0,r=0;for(;;)if(t==i)if(s>r*2){let l=e[t-1];l.break?e.splice(--t,1,l.left,null,l.right):e.splice(--t,1,l.left,l.right),i+=1+l.break,s-=l.size}else if(r>s*2){let l=e[i];l.break?e.splice(i,1,l.left,null,l.right):e.splice(i,1,l.left,l.right),i+=2+l.break,r-=l.size}else break;else if(s<r){let l=e[t++];l&&(s+=l.size)}else{let l=e[--i];l&&(r+=l.size)}let o=0;return e[t-1]==null?(o=1,t--):e[t]==null&&(o=1,i++),new wu(ve.of(e.slice(0,t)),o,ve.of(e.slice(i)))}}ve.prototype.size=1;class gh extends ve{constructor(e,t,i){super(e,t),this.type=i}blockAt(e,t,i,s){return new ut(s,this.length,i,this.height,this.type)}lineAt(e,t,i,s,r){return this.blockAt(0,i,s,r)}forEachLine(e,t,i,s,r,o){e<=r+this.length&&t>=r&&o(this.blockAt(0,i,s,r))}updateHeight(e,t=0,i=!1,s){return s&&s.from<=t&&s.more&&this.setHeight(e,s.heights[s.index++]),this.outdated=!1,this}toString(){return`block(${this.length})`}}class De extends gh{constructor(e,t){super(e,t,W.Text),this.collapsed=0,this.widgetHeight=0}replace(e,t,i){let s=i[0];return i.length==1&&(s instanceof De||s instanceof ae&&s.flags&4)&&Math.abs(this.length-s.length)<10?(s instanceof ae?s=new De(s.length,this.height):s.height=this.height,this.outdated||(s.outdated=!1),s):ve.of(i)}updateHeight(e,t=0,i=!1,s){return s&&s.from<=t&&s.more?this.setHeight(e,s.heights[s.index++]):(i||this.outdated)&&this.setHeight(e,Math.max(this.widgetHeight,e.heightForLine(this.length-this.collapsed))),this.outdated=!1,this}toString(){return`line(${this.length}${this.collapsed?-this.collapsed:""}${this.widgetHeight?":"+this.widgetHeight:""})`}}class ae extends ve{constructor(e){super(e,0)}lines(e,t){let i=e.lineAt(t).number,s=e.lineAt(t+this.length).number;return{firstLine:i,lastLine:s,lineHeight:this.height/(s-i+1)}}blockAt(e,t,i,s){let{firstLine:r,lastLine:o,lineHeight:l}=this.lines(t,s),a=Math.max(0,Math.min(o-r,Math.floor((e-i)/l))),{from:h,length:c}=t.line(r+a);return new ut(h,c,i+l*a,l,W.Text)}lineAt(e,t,i,s,r){if(t==q.ByHeight)return this.blockAt(e,i,s,r);if(t==q.ByPosNoHeight){let{from:f,to:u}=i.lineAt(e);return new ut(f,u-f,0,0,W.Text)}let{firstLine:o,lineHeight:l}=this.lines(i,r),{from:a,length:h,number:c}=i.lineAt(e);return new ut(a,h,s+l*(c-o),l,W.Text)}forEachLine(e,t,i,s,r,o){let{firstLine:l,lineHeight:a}=this.lines(i,r);for(let h=Math.max(e,r),c=Math.min(r+this.length,t);h<=c;){let f=i.lineAt(h);h==e&&(s+=a*(f.number-l)),o(new ut(f.from,f.length,s,a,W.Text)),s+=a,h=f.to+1}}replace(e,t,i){let s=this.length-t;if(s>0){let r=i[i.length-1];r instanceof ae?i[i.length-1]=new ae(r.length+s):i.push(null,new ae(s-1))}if(e>0){let r=i[0];r instanceof ae?i[0]=new ae(e+r.length):i.unshift(new ae(e-1),null)}return ve.of(i)}decomposeLeft(e,t){t.push(new ae(e-1),null)}decomposeRight(e,t){t.push(null,new ae(this.length-e-1))}updateHeight(e,t=0,i=!1,s){let r=t+this.length;if(s&&s.from<=t+this.length&&s.more){let o=[],l=Math.max(t,s.from),a=-1,h=e.heightChanged;for(s.from>t&&o.push(new ae(s.from-t-1).updateHeight(e,t));l<=r&&s.more;){let f=e.doc.lineAt(l).length;o.length&&o.push(null);let u=s.heights[s.index++];a==-1?a=u:Math.abs(u-a)>=pn&&(a=-2);let d=new De(f,u);d.outdated=!1,o.push(d),l+=f+1}l<=r&&o.push(null,new ae(r-l).updateHeight(e,l));let c=ve.of(o);return e.heightChanged=h||a<0||Math.abs(c.height-this.height)>=pn||Math.abs(a-this.lines(e.doc,t).lineHeight)>=pn,c}else(i||this.outdated)&&(this.setHeight(e,e.heightForGap(t,t+this.length)),this.outdated=!1);return this}toString(){return`gap(${this.length})`}}class wu extends ve{constructor(e,t,i){super(e.length+t+i.length,e.height+i.height,t|(e.outdated||i.outdated?2:0)),this.left=e,this.right=i,this.size=e.size+i.size}get break(){return this.flags&1}blockAt(e,t,i,s){let r=i+this.left.height;return e<r?this.left.blockAt(e,t,i,s):this.right.blockAt(e,t,r,s+this.left.length+this.break)}lineAt(e,t,i,s,r){let o=s+this.left.height,l=r+this.left.length+this.break,a=t==q.ByHeight?e<o:e<l,h=a?this.left.lineAt(e,t,i,s,r):this.right.lineAt(e,t,i,o,l);if(this.break||(a?h.to<l:h.from>l))return h;let c=t==q.ByPosNoHeight?q.ByPosNoHeight:q.ByPos;return a?h.join(this.right.lineAt(l,c,i,o,l)):this.left.lineAt(l,c,i,s,r).join(h)}forEachLine(e,t,i,s,r,o){let l=s+this.left.height,a=r+this.left.length+this.break;if(this.break)e<a&&this.left.forEachLine(e,t,i,s,r,o),t>=a&&this.right.forEachLine(e,t,i,l,a,o);else{let h=this.lineAt(a,q.ByPos,i,s,r);e<h.from&&this.left.forEachLine(e,h.from-1,i,s,r,o),h.to>=e&&h.from<=t&&o(h),t>h.to&&this.right.forEachLine(h.to+1,t,i,l,a,o)}}replace(e,t,i){let s=this.left.length+this.break;if(t<s)return this.balanced(this.left.replace(e,t,i),this.right);if(e>this.left.length)return this.balanced(this.left,this.right.replace(e-s,t-s,i));let r=[];e>0&&this.decomposeLeft(e,r);let o=r.length;for(let l of i)r.push(l);if(e>0&&Wo(r,o-1),t<this.length){let l=r.length;this.decomposeRight(t,r),Wo(r,l)}return ve.of(r)}decomposeLeft(e,t){let i=this.left.length;if(e<=i)return this.left.decomposeLeft(e,t);t.push(this.left),this.break&&(i++,e>=i&&t.push(null)),e>i&&this.right.decomposeLeft(e-i,t)}decomposeRight(e,t){let i=this.left.length,s=i+this.break;if(e>=s)return this.right.decomposeRight(e-s,t);e<i&&this.left.decomposeRight(e,t),this.break&&e<s&&t.push(null),t.push(this.right)}balanced(e,t){return e.size>2*t.size||t.size>2*e.size?ve.of(this.break?[e,null,t]:[e,t]):(this.left=e,this.right=t,this.height=e.height+t.height,this.outdated=e.outdated||t.outdated,this.size=e.size+t.size,this.length=e.length+this.break+t.length,this)}updateHeight(e,t=0,i=!1,s){let{left:r,right:o}=this,l=t+r.length+this.break,a=null;return s&&s.from<=t+r.length&&s.more?a=r=r.updateHeight(e,t,i,s):r.updateHeight(e,t,i),s&&s.from<=l+o.length&&s.more?a=o=o.updateHeight(e,l,i,s):o.updateHeight(e,l,i),a?this.balanced(r,o):(this.height=this.left.height+this.right.height,this.outdated=!1,this)}toString(){return this.left+(this.break?" ":"-")+this.right}}function Wo(n,e){let t,i;n[e]==null&&(t=n[e-1])instanceof ae&&(i=n[e+1])instanceof ae&&n.splice(e-1,3,new ae(t.length+1+i.length))}const ku=5;class Pr{constructor(e,t){this.pos=e,this.oracle=t,this.nodes=[],this.lineStart=-1,this.lineEnd=-1,this.covering=null,this.writtenTo=e}get isCovered(){return this.covering&&this.nodes[this.nodes.length-1]==this.covering}span(e,t){if(this.lineStart>-1){let i=Math.min(t,this.lineEnd),s=this.nodes[this.nodes.length-1];s instanceof De?s.length+=i-this.pos:(i>this.pos||!this.isCovered)&&this.nodes.push(new De(i-this.pos,-1)),this.writtenTo=i,t>i&&(this.nodes.push(null),this.writtenTo++,this.lineStart=-1)}this.pos=t}point(e,t,i){if(e<t||i.heightRelevant){let s=i.widget?i.widget.estimatedHeight:0;s<0&&(s=this.oracle.lineHeight);let r=t-e;i.block?this.addBlock(new gh(r,s,i.type)):(r||s>=ku)&&this.addLineDeco(s,r)}else t>e&&this.span(e,t);this.lineEnd>-1&&this.lineEnd<this.pos&&(this.lineEnd=this.oracle.doc.lineAt(this.pos).to)}enterLine(){if(this.lineStart>-1)return;let{from:e,to:t}=this.oracle.doc.lineAt(this.pos);this.lineStart=e,this.lineEnd=t,this.writtenTo<e&&((this.writtenTo<e-1||this.nodes[this.nodes.length-1]==null)&&this.nodes.push(this.blankContent(this.writtenTo,e-1)),this.nodes.push(null)),this.pos>e&&this.nodes.push(new De(this.pos-e,-1)),this.writtenTo=this.pos}blankContent(e,t){let i=new ae(t-e);return this.oracle.doc.lineAt(e).to==t&&(i.flags|=4),i}ensureLine(){this.enterLine();let e=this.nodes.length?this.nodes[this.nodes.length-1]:null;if(e instanceof De)return e;let t=new De(0,-1);return this.nodes.push(t),t}addBlock(e){this.enterLine(),e.type==W.WidgetAfter&&!this.isCovered&&this.ensureLine(),this.nodes.push(e),this.writtenTo=this.pos=this.pos+e.length,e.type!=W.WidgetBefore&&(this.covering=e)}addLineDeco(e,t){let i=this.ensureLine();i.length+=t,i.collapsed+=t,i.widgetHeight=Math.max(i.widgetHeight,e),this.writtenTo=this.pos=this.pos+t}finish(e){let t=this.nodes.length==0?null:this.nodes[this.nodes.length-1];this.lineStart>-1&&!(t instanceof De)&&!this.isCovered?this.nodes.push(new De(0,-1)):(this.writtenTo<this.pos||t==null)&&this.nodes.push(this.blankContent(this.writtenTo,this.pos));let i=e;for(let s of this.nodes)s instanceof De&&s.updateHeight(this.oracle,i),i+=s?s.length:1;return this.nodes}static build(e,t,i,s){let r=new Pr(i,e);return F.spans(t,i,s,r,0),r.finish(i)}}function vu(n,e,t){let i=new xu;return F.compare(n,e,t,i,0),i.changes}class xu{constructor(){this.changes=[]}compareRange(){}comparePoint(e,t,i,s){(e<t||i&&i.heightRelevant||s&&s.heightRelevant)&&Ys(e,t,this.changes,5)}}function Su(n,e){let t=n.getBoundingClientRect(),i=n.ownerDocument,s=i.defaultView||window,r=Math.max(0,t.left),o=Math.min(s.innerWidth,t.right),l=Math.max(0,t.top),a=Math.min(s.innerHeight,t.bottom);for(let h=n.parentNode;h&&h!=i.body;)if(h.nodeType==1){let c=h,f=window.getComputedStyle(c);if((c.scrollHeight>c.clientHeight||c.scrollWidth>c.clientWidth)&&f.overflow!="visible"){let u=c.getBoundingClientRect();r=Math.max(r,u.left),o=Math.min(o,u.right),l=Math.max(l,u.top),a=h==n.parentNode?u.bottom:Math.min(a,u.bottom)}h=f.position=="absolute"||f.position=="fixed"?c.offsetParent:c.parentNode}else if(h.nodeType==11)h=h.host;else break;return{left:r-t.left,right:Math.max(r,o)-t.left,top:l-(t.top+e),bottom:Math.max(l,a)-(t.top+e)}}function Cu(n,e){let t=n.getBoundingClientRect();return{left:0,right:t.right-t.left,top:e,bottom:t.bottom-(t.top+e)}}class os{constructor(e,t,i){this.from=e,this.to=t,this.size=i}static same(e,t){if(e.length!=t.length)return!1;for(let i=0;i<e.length;i++){let s=e[i],r=t[i];if(s.from!=r.from||s.to!=r.to||s.size!=r.size)return!1}return!0}draw(e){return E.replace({widget:new Au(this.size,e)}).range(this.from,this.to)}}class Au extends tt{constructor(e,t){super(),this.size=e,this.vertical=t}eq(e){return e.size==this.size&&e.vertical==this.vertical}toDOM(){let e=document.createElement("div");return this.vertical?e.style.height=this.size+"px":(e.style.width=this.size+"px",e.style.height="2px",e.style.display="inline-block"),e}get estimatedHeight(){return this.vertical?this.size:-1}}class zo{constructor(e){this.state=e,this.pixelViewport={left:0,right:window.innerWidth,top:0,bottom:0},this.inView=!0,this.paddingTop=0,this.paddingBottom=0,this.contentDOMWidth=0,this.contentDOMHeight=0,this.editorHeight=0,this.editorWidth=0,this.heightOracle=new yu,this.scaler=qo,this.scrollTarget=null,this.printing=!1,this.mustMeasureContent=!0,this.defaultTextDirection=Z.LTR,this.visibleRanges=[],this.mustEnforceCursorAssoc=!1,this.stateDeco=e.facet(Ei).filter(t=>typeof t!="function"),this.heightMap=ve.empty().applyChanges(this.stateDeco,_.empty,this.heightOracle.setDoc(e.doc),[new Qe(0,0,0,e.doc.length)]),this.viewport=this.getViewport(0,null),this.updateViewportLines(),this.updateForViewport(),this.lineGaps=this.ensureLineGaps([]),this.lineGapDeco=E.set(this.lineGaps.map(t=>t.draw(!1))),this.computeVisibleRanges()}updateForViewport(){let e=[this.viewport],{main:t}=this.state.selection;for(let i=0;i<=1;i++){let s=i?t.head:t.anchor;if(!e.some(({from:r,to:o})=>s>=r&&s<=o)){let{from:r,to:o}=this.lineBlockAt(s);e.push(new Ji(r,o))}}this.viewports=e.sort((i,s)=>i.from-s.from),this.scaler=this.heightMap.height<=7e6?qo:new Tu(this.heightOracle.doc,this.heightMap,this.viewports)}updateViewportLines(){this.viewportLines=[],this.heightMap.forEachLine(this.viewport.from,this.viewport.to,this.state.doc,0,0,e=>{this.viewportLines.push(this.scaler.scale==1?e:wi(e,this.scaler))})}update(e,t=null){this.state=e.state;let i=this.stateDeco;this.stateDeco=this.state.facet(Ei).filter(h=>typeof h!="function");let s=e.changedRanges,r=Qe.extendWithRanges(s,vu(i,this.stateDeco,e?e.changes:ne.empty(this.state.doc.length))),o=this.heightMap.height;this.heightMap=this.heightMap.applyChanges(this.stateDeco,e.startState.doc,this.heightOracle.setDoc(this.state.doc),r),this.heightMap.height!=o&&(e.flags|=2);let l=r.length?this.mapViewport(this.viewport,e.changes):this.viewport;(t&&(t.range.head<l.from||t.range.head>l.to)||!this.viewportIsAppropriate(l))&&(l=this.getViewport(0,t));let a=!e.changes.empty||e.flags&2||l.from!=this.viewport.from||l.to!=this.viewport.to;this.viewport=l,this.updateForViewport(),a&&this.updateViewportLines(),(this.lineGaps.length||this.viewport.to-this.viewport.from>2e3<<1)&&this.updateLineGaps(this.ensureLineGaps(this.mapLineGaps(this.lineGaps,e.changes))),e.flags|=this.computeVisibleRanges(),t&&(this.scrollTarget=t),!this.mustEnforceCursorAssoc&&e.selectionSet&&e.view.lineWrapping&&e.state.selection.main.empty&&e.state.selection.main.assoc&&!e.state.facet(Xa)&&(this.mustEnforceCursorAssoc=!0)}measure(e){let t=e.contentDOM,i=window.getComputedStyle(t),s=this.heightOracle,r=i.whiteSpace;this.defaultTextDirection=i.direction=="rtl"?Z.RTL:Z.LTR;let o=this.heightOracle.mustRefreshForWrapping(r),l=o||this.mustMeasureContent||this.contentDOMHeight!=t.clientHeight;this.contentDOMHeight=t.clientHeight,this.mustMeasureContent=!1;let a=0,h=0,c=parseInt(i.paddingTop)||0,f=parseInt(i.paddingBottom)||0;(this.paddingTop!=c||this.paddingBottom!=f)&&(this.paddingTop=c,this.paddingBottom=f,a|=10),this.editorWidth!=e.scrollDOM.clientWidth&&(s.lineWrapping&&(l=!0),this.editorWidth=e.scrollDOM.clientWidth,a|=8);let u=(this.printing?Cu:Su)(t,this.paddingTop),d=u.top-this.pixelViewport.top,p=u.bottom-this.pixelViewport.bottom;this.pixelViewport=u;let g=this.pixelViewport.bottom>this.pixelViewport.top&&this.pixelViewport.right>this.pixelViewport.left;if(g!=this.inView&&(this.inView=g,g&&(l=!0)),!this.inView&&!this.scrollTarget)return 0;let y=t.clientWidth;if((this.contentDOMWidth!=y||this.editorHeight!=e.scrollDOM.clientHeight)&&(this.contentDOMWidth=y,this.editorHeight=e.scrollDOM.clientHeight,a|=8),l){let v=e.docView.measureVisibleLineHeights(this.viewport);if(s.mustRefreshForHeights(v)&&(o=!0),o||s.lineWrapping&&Math.abs(y-this.contentDOMWidth)>s.charWidth){let{lineHeight:S,charWidth:k}=e.docView.measureTextSize();o=S>0&&s.refresh(r,S,k,y/k,v),o&&(e.docView.minWidth=0,a|=8)}d>0&&p>0?h=Math.max(d,p):d<0&&p<0&&(h=Math.min(d,p)),s.heightChanged=!1;for(let S of this.viewports){let k=S.from==this.viewport.from?v:e.docView.measureVisibleLineHeights(S);this.heightMap=o?ve.empty().applyChanges(this.stateDeco,_.empty,this.heightOracle,[new Qe(0,0,0,e.state.doc.length)]):this.heightMap.updateHeight(s,0,o,new bu(S.from,k))}s.heightChanged&&(a|=2)}let b=!this.viewportIsAppropriate(this.viewport,h)||this.scrollTarget&&(this.scrollTarget.range.head<this.viewport.from||this.scrollTarget.range.head>this.viewport.to);return b&&(this.viewport=this.getViewport(h,this.scrollTarget)),this.updateForViewport(),(a&2||b)&&this.updateViewportLines(),(this.lineGaps.length||this.viewport.to-this.viewport.from>2e3<<1)&&this.updateLineGaps(this.ensureLineGaps(o?[]:this.lineGaps,e)),a|=this.computeVisibleRanges(),this.mustEnforceCursorAssoc&&(this.mustEnforceCursorAssoc=!1,e.docView.enforceCursorAssoc()),a}get visibleTop(){return this.scaler.fromDOM(this.pixelViewport.top)}get visibleBottom(){return this.scaler.fromDOM(this.pixelViewport.bottom)}getViewport(e,t){let i=.5-Math.max(-.5,Math.min(.5,e/1e3/2)),s=this.heightMap,r=this.state.doc,{visibleTop:o,visibleBottom:l}=this,a=new Ji(s.lineAt(o-i*1e3,q.ByHeight,r,0,0).from,s.lineAt(l+(1-i)*1e3,q.ByHeight,r,0,0).to);if(t){let{head:h}=t.range;if(h<a.from||h>a.to){let c=Math.min(this.editorHeight,this.pixelViewport.bottom-this.pixelViewport.top),f=s.lineAt(h,q.ByPos,r,0,0),u;t.y=="center"?u=(f.top+f.bottom)/2-c/2:t.y=="start"||t.y=="nearest"&&h<a.from?u=f.top:u=f.bottom-c,a=new Ji(s.lineAt(u-1e3/2,q.ByHeight,r,0,0).from,s.lineAt(u+c+1e3/2,q.ByHeight,r,0,0).to)}}return a}mapViewport(e,t){let i=t.mapPos(e.from,-1),s=t.mapPos(e.to,1);return new Ji(this.heightMap.lineAt(i,q.ByPos,this.state.doc,0,0).from,this.heightMap.lineAt(s,q.ByPos,this.state.doc,0,0).to)}viewportIsAppropriate({from:e,to:t},i=0){if(!this.inView)return!0;let{top:s}=this.heightMap.lineAt(e,q.ByPos,this.state.doc,0,0),{bottom:r}=this.heightMap.lineAt(t,q.ByPos,this.state.doc,0,0),{visibleTop:o,visibleBottom:l}=this;return(e==0||s<=o-Math.max(10,Math.min(-i,250)))&&(t==this.state.doc.length||r>=l+Math.max(10,Math.min(i,250)))&&s>o-2*1e3&&r<l+2*1e3}mapLineGaps(e,t){if(!e.length||t.empty)return e;let i=[];for(let s of e)t.touchesRange(s.from,s.to)||i.push(new os(t.mapPos(s.from),t.mapPos(s.to),s.size));return i}ensureLineGaps(e,t){let i=this.heightOracle.lineWrapping,s=i?1e4:2e3,r=s>>1,o=s<<1;if(this.defaultTextDirection!=Z.LTR&&!i)return[];let l=[],a=(h,c,f,u)=>{if(c-h<r)return;let d=this.state.selection.main,p=[d.from];d.empty||p.push(d.to);for(let y of p)if(y>h&&y<c){a(h,y-10,f,u),a(y+10,c,f,u);return}let g=Du(e,y=>y.from>=f.from&&y.to<=f.to&&Math.abs(y.from-h)<r&&Math.abs(y.to-c)<r&&!p.some(b=>y.from<b&&y.to>b));if(!g){if(c<f.to&&t&&i&&t.visibleRanges.some(y=>y.from<=c&&y.to>=c)){let y=t.moveToLineBoundary(w.cursor(c),!1,!0).head;y>h&&(c=y)}g=new os(h,c,this.gapSize(f,h,c,u))}l.push(g)};for(let h of this.viewportLines){if(h.length<o)continue;let c=Mu(h.from,h.to,this.stateDeco);if(c.total<o)continue;let f=this.scrollTarget?this.scrollTarget.range.head:null,u,d;if(i){let p=s/this.heightOracle.lineLength*this.heightOracle.lineHeight,g,y;if(f!=null){let b=Xi(c,f),v=((this.visibleBottom-this.visibleTop)/2+p)/h.height;g=b-v,y=b+v}else g=(this.visibleTop-h.top-p)/h.height,y=(this.visibleBottom-h.top+p)/h.height;u=Yi(c,g),d=Yi(c,y)}else{let p=c.total*this.heightOracle.charWidth,g=s*this.heightOracle.charWidth,y,b;if(f!=null){let v=Xi(c,f),S=((this.pixelViewport.right-this.pixelViewport.left)/2+g)/p;y=v-S,b=v+S}else y=(this.pixelViewport.left-g)/p,b=(this.pixelViewport.right+g)/p;u=Yi(c,y),d=Yi(c,b)}u>h.from&&a(h.from,u,h,c),d<h.to&&a(d,h.to,h,c)}return l}gapSize(e,t,i,s){let r=Xi(s,i)-Xi(s,t);return this.heightOracle.lineWrapping?e.height*r:s.total*this.heightOracle.charWidth*r}updateLineGaps(e){os.same(e,this.lineGaps)||(this.lineGaps=e,this.lineGapDeco=E.set(e.map(t=>t.draw(this.heightOracle.lineWrapping))))}computeVisibleRanges(){let e=this.stateDeco;this.lineGaps.length&&(e=e.concat(this.lineGapDeco));let t=[];F.spans(e,this.viewport.from,this.viewport.to,{span(s,r){t.push({from:s,to:r})},point(){}},20);let i=t.length!=this.visibleRanges.length||this.visibleRanges.some((s,r)=>s.from!=t[r].from||s.to!=t[r].to);return this.visibleRanges=t,i?4:0}lineBlockAt(e){return e>=this.viewport.from&&e<=this.viewport.to&&this.viewportLines.find(t=>t.from<=e&&t.to>=e)||wi(this.heightMap.lineAt(e,q.ByPos,this.state.doc,0,0),this.scaler)}lineBlockAtHeight(e){return wi(this.heightMap.lineAt(this.scaler.fromDOM(e),q.ByHeight,this.state.doc,0,0),this.scaler)}elementAtHeight(e){return wi(this.heightMap.blockAt(this.scaler.fromDOM(e),this.state.doc,0,0),this.scaler)}get docHeight(){return this.scaler.toDOM(this.heightMap.height)}get contentHeight(){return this.docHeight+this.paddingTop+this.paddingBottom}}class Ji{constructor(e,t){this.from=e,this.to=t}}function Mu(n,e,t){let i=[],s=n,r=0;return F.spans(t,n,e,{span(){},point(o,l){o>s&&(i.push({from:s,to:o}),r+=o-s),s=l}},20),s<e&&(i.push({from:s,to:e}),r+=e-s),{total:r,ranges:i}}function Yi({total:n,ranges:e},t){if(t<=0)return e[0].from;if(t>=1)return e[e.length-1].to;let i=Math.floor(n*t);for(let s=0;;s++){let{from:r,to:o}=e[s],l=o-r;if(i<=l)return r+i;i-=l}}function Xi(n,e){let t=0;for(let{from:i,to:s}of n.ranges){if(e<=s){t+=e-i;break}t+=s-i}return t/n.total}function Du(n,e){for(let t of n)if(e(t))return t}const qo={toDOM(n){return n},fromDOM(n){return n},scale:1};class Tu{constructor(e,t,i){let s=0,r=0,o=0;this.viewports=i.map(({from:l,to:a})=>{let h=t.lineAt(l,q.ByPos,e,0,0).top,c=t.lineAt(a,q.ByPos,e,0,0).bottom;return s+=c-h,{from:l,to:a,top:h,bottom:c,domTop:0,domBottom:0}}),this.scale=(7e6-s)/(t.height-s);for(let l of this.viewports)l.domTop=o+(l.top-r)*this.scale,o=l.domBottom=l.domTop+(l.bottom-l.top),r=l.bottom}toDOM(e){for(let t=0,i=0,s=0;;t++){let r=t<this.viewports.length?this.viewports[t]:null;if(!r||e<r.top)return s+(e-i)*this.scale;if(e<=r.bottom)return r.domTop+(e-r.top);i=r.bottom,s=r.domBottom}}fromDOM(e){for(let t=0,i=0,s=0;;t++){let r=t<this.viewports.length?this.viewports[t]:null;if(!r||e<r.domTop)return i+(e-s)/this.scale;if(e<=r.domBottom)return r.top+(e-r.domTop);i=r.bottom,s=r.domBottom}}}function wi(n,e){if(e.scale==1)return n;let t=e.toDOM(n.top),i=e.toDOM(n.bottom);return new ut(n.from,n.length,t,i-t,Array.isArray(n.type)?n.type.map(s=>wi(s,e)):n.type)}const Zi=D.define({combine:n=>n.join(" ")}),nr=D.define({combine:n=>n.indexOf(!0)>-1}),sr=mt.newName(),yh=mt.newName(),bh=mt.newName(),wh={"&light":"."+yh,"&dark":"."+bh};function rr(n,e,t){return new mt(e,{finish(i){return/&/.test(i)?i.replace(/&\w*/,s=>{if(s=="&")return n;if(!t||!t[s])throw new RangeError(`Unsupported selector: ${s}`);return t[s]}):n+" "+i}})}const Ou=rr("."+sr,{"&.cm-editor":{position:"relative !important",boxSizing:"border-box","&.cm-focused":{outline:"1px dotted #212121"},display:"flex !important",flexDirection:"column"},".cm-scroller":{display:"flex !important",alignItems:"flex-start !important",fontFamily:"monospace",lineHeight:1.4,height:"100%",overflowX:"auto",position:"relative",zIndex:0},".cm-content":{margin:0,flexGrow:2,flexShrink:0,minHeight:"100%",display:"block",whiteSpace:"pre",wordWrap:"normal",boxSizing:"border-box",padding:"4px 0",outline:"none","&[contenteditable=true]":{WebkitUserModify:"read-write-plaintext-only"}},".cm-lineWrapping":{whiteSpace_fallback:"pre-wrap",whiteSpace:"break-spaces",wordBreak:"break-word",overflowWrap:"anywhere",flexShrink:1},"&light .cm-content":{caretColor:"black"},"&dark .cm-content":{caretColor:"white"},".cm-line":{display:"block",padding:"0 2px 0 4px"},".cm-selectionLayer":{zIndex:-1,contain:"size style"},".cm-selectionBackground":{position:"absolute"},"&light .cm-selectionBackground":{background:"#d9d9d9"},"&dark .cm-selectionBackground":{background:"#222"},"&light.cm-focused .cm-selectionBackground":{background:"#d7d4f0"},"&dark.cm-focused .cm-selectionBackground":{background:"#233"},".cm-cursorLayer":{zIndex:100,contain:"size style",pointerEvents:"none"},"&.cm-focused .cm-cursorLayer":{animation:"steps(1) cm-blink 1.2s infinite"},"@keyframes cm-blink":{"0%":{},"50%":{opacity:0},"100%":{}},"@keyframes cm-blink2":{"0%":{},"50%":{opacity:0},"100%":{}},".cm-cursor, .cm-dropCursor":{position:"absolute",borderLeft:"1.2px solid black",marginLeft:"-0.6px",pointerEvents:"none"},".cm-cursor":{display:"none"},"&dark .cm-cursor":{borderLeftColor:"#444"},"&.cm-focused .cm-cursor":{display:"block"},"&light .cm-activeLine":{backgroundColor:"#cceeff44"},"&dark .cm-activeLine":{backgroundColor:"#99eeff33"},"&light .cm-specialChar":{color:"red"},"&dark .cm-specialChar":{color:"#f78"},".cm-gutters":{flexShrink:0,display:"flex",height:"100%",boxSizing:"border-box",left:0,zIndex:200},"&light .cm-gutters":{backgroundColor:"#f5f5f5",color:"#6c6c6c",borderRight:"1px solid #ddd"},"&dark .cm-gutters":{backgroundColor:"#333338",color:"#ccc"},".cm-gutter":{display:"flex !important",flexDirection:"column",flexShrink:0,boxSizing:"border-box",minHeight:"100%",overflow:"hidden"},".cm-gutterElement":{boxSizing:"border-box"},".cm-lineNumbers .cm-gutterElement":{padding:"0 3px 0 5px",minWidth:"20px",textAlign:"right",whiteSpace:"nowrap"},"&light .cm-activeLineGutter":{backgroundColor:"#e2f2ff"},"&dark .cm-activeLineGutter":{backgroundColor:"#222227"},".cm-panels":{boxSizing:"border-box",position:"sticky",left:0,right:0},"&light .cm-panels":{backgroundColor:"#f5f5f5",color:"black"},"&light .cm-panels-top":{borderBottom:"1px solid #ddd"},"&light .cm-panels-bottom":{borderTop:"1px solid #ddd"},"&dark .cm-panels":{backgroundColor:"#333338",color:"white"},".cm-tab":{display:"inline-block",overflow:"hidden",verticalAlign:"bottom"},".cm-widgetBuffer":{verticalAlign:"text-top",height:"1em",width:0,display:"inline"},".cm-placeholder":{color:"#888",display:"inline-block",verticalAlign:"top"},".cm-button":{verticalAlign:"middle",color:"inherit",fontSize:"70%",padding:".2em 1em",borderRadius:"1px"},"&light .cm-button":{backgroundImage:"linear-gradient(#eff1f5, #d9d9df)",border:"1px solid #888","&:active":{backgroundImage:"linear-gradient(#b4b4b4, #d0d3d6)"}},"&dark .cm-button":{backgroundImage:"linear-gradient(#393939, #111)",border:"1px solid #888","&:active":{backgroundImage:"linear-gradient(#111, #333)"}},".cm-textfield":{verticalAlign:"middle",color:"inherit",fontSize:"70%",border:"1px solid silver",padding:".2em .5em"},"&light .cm-textfield":{backgroundColor:"white"},"&dark .cm-textfield":{border:"1px solid #555",backgroundColor:"inherit"}},wh);class Bu{constructor(e,t,i,s){this.typeOver=s,this.bounds=null,this.text="";let{impreciseHead:r,impreciseAnchor:o}=e.docView;if(t>-1&&!e.state.readOnly&&(this.bounds=e.docView.domBoundsAround(t,i,0))){let l=r||o?[]:Eu(e),a=new rh(l,e.state);a.readRange(this.bounds.startDOM,this.bounds.endDOM),this.text=a.text,this.newSel=Ru(l,this.bounds.from)}else{let l=e.observer.selectionRange,a=r&&r.node==l.focusNode&&r.offset==l.focusOffset||!Xt(e.contentDOM,l.focusNode)?e.state.selection.main.head:e.docView.posFromDOM(l.focusNode,l.focusOffset),h=o&&o.node==l.anchorNode&&o.offset==l.anchorOffset||!Xt(e.contentDOM,l.anchorNode)?e.state.selection.main.anchor:e.docView.posFromDOM(l.anchorNode,l.anchorOffset);this.newSel=w.single(h,a)}}}function kh(n,e){let t,{newSel:i}=e,s=n.state.selection.main;if(e.bounds){let{from:r,to:o}=e.bounds,l=s.from,a=null;(n.inputState.lastKeyCode===8&&n.inputState.lastKeyTime>Date.now()-100||A.android&&e.text.length<o-r)&&(l=s.to,a="end");let h=Pu(n.state.doc.sliceString(r,o,ft),e.text,l-r,a);h&&(A.chrome&&n.inputState.lastKeyCode==13&&h.toB==h.from+2&&e.text.slice(h.from,h.toB)==ft+ft&&h.toB--,t={from:r+h.from,to:r+h.toA,insert:_.of(e.text.slice(h.from,h.toB).split(ft))})}else i&&(!n.hasFocus||!n.state.facet(zn)||i.main.eq(s))&&(i=null);if(!t&&!i)return!1;if(!t&&e.typeOver&&!s.empty&&i&&i.main.empty?t={from:s.from,to:s.to,insert:n.state.doc.slice(s.from,s.to)}:t&&t.from>=s.from&&t.to<=s.to&&(t.from!=s.from||t.to!=s.to)&&s.to-s.from-(t.to-t.from)<=4?t={from:s.from,to:s.to,insert:n.state.doc.slice(s.from,t.from).append(t.insert).append(n.state.doc.slice(t.to,s.to))}:(A.mac||A.android)&&t&&t.from==t.to&&t.from==s.head-1&&/^\. ?$/.test(t.insert.toString())?(i&&t.insert.length==2&&(i=w.single(i.main.anchor-1,i.main.head-1)),t={from:s.from,to:s.to,insert:_.of([" "])}):A.chrome&&t&&t.from==t.to&&t.from==s.head&&t.insert.toString()==`
- `&&n.lineWrapping&&(i&&(i=w.single(i.main.anchor-1,i.main.head-1)),t={from:s.from,to:s.to,insert:_.of([" "])}),t){let r=n.state;if(A.ios&&n.inputState.flushIOSKey(n)||A.android&&(t.from==s.from&&t.to==s.to&&t.insert.length==1&&t.insert.lines==2&&$t(n.contentDOM,"Enter",13)||t.from==s.from-1&&t.to==s.to&&t.insert.length==0&&$t(n.contentDOM,"Backspace",8)||t.from==s.from&&t.to==s.to+1&&t.insert.length==0&&$t(n.contentDOM,"Delete",46)))return!0;let o=t.insert.toString();if(n.state.facet(Ja).some(h=>h(n,t.from,t.to,o)))return!0;n.inputState.composing>=0&&n.inputState.composing++;let l;if(t.from>=s.from&&t.to<=s.to&&t.to-t.from>=(s.to-s.from)/3&&(!i||i.main.empty&&i.main.from==t.from+t.insert.length)&&n.inputState.composing<0){let h=s.from<t.from?r.sliceDoc(s.from,t.from):"",c=s.to>t.to?r.sliceDoc(t.to,s.to):"";l=r.replaceSelection(n.state.toText(h+t.insert.sliceString(0,void 0,n.state.lineBreak)+c))}else{let h=r.changes(t),c=i&&!r.selection.main.eq(i.main)&&i.main.to<=h.newLength?i.main:void 0;if(r.selection.ranges.length>1&&n.inputState.composing>=0&&t.to<=s.to&&t.to>=s.to-10){let f=n.state.sliceDoc(t.from,t.to),u=oh(n)||n.state.doc.lineAt(s.head),d=s.to-t.to,p=s.to-s.from;l=r.changeByRange(g=>{if(g.from==s.from&&g.to==s.to)return{changes:h,range:c||g.map(h)};let y=g.to-d,b=y-f.length;if(g.to-g.from!=p||n.state.sliceDoc(b,y)!=f||u&&g.to>=u.from&&g.from<=u.to)return{range:g};let v=r.changes({from:b,to:y,insert:t.insert}),S=g.to-s.to;return{changes:v,range:c?w.range(Math.max(0,c.anchor+S),Math.max(0,c.head+S)):g.map(v)}})}else l={changes:h,selection:c&&r.selection.replaceRange(c)}}let a="input.type";return n.composing&&(a+=".compose",n.inputState.compositionFirstChange&&(a+=".start",n.inputState.compositionFirstChange=!1)),n.dispatch(l,{scrollIntoView:!0,userEvent:a}),!0}else if(i&&!i.main.eq(s)){let r=!1,o="select";return n.inputState.lastSelectionTime>Date.now()-50&&(n.inputState.lastSelectionOrigin=="select"&&(r=!0),o=n.inputState.lastSelectionOrigin),n.dispatch({selection:i,scrollIntoView:r,userEvent:o}),!0}else return!1}function Pu(n,e,t,i){let s=Math.min(n.length,e.length),r=0;for(;r<s&&n.charCodeAt(r)==e.charCodeAt(r);)r++;if(r==s&&n.length==e.length)return null;let o=n.length,l=e.length;for(;o>0&&l>0&&n.charCodeAt(o-1)==e.charCodeAt(l-1);)o--,l--;if(i=="end"){let a=Math.max(0,r-Math.min(o,l));t-=o+a-r}if(o<r&&n.length<e.length){let a=t<=r&&t>=o?r-t:0;r-=a,l=r+(l-o),o=r}else if(l<r){let a=t<=r&&t>=l?r-t:0;r-=a,o=r+(o-l),l=r}return{from:r,toA:o,toB:l}}function Eu(n){let e=[];if(n.root.activeElement!=n.contentDOM)return e;let{anchorNode:t,anchorOffset:i,focusNode:s,focusOffset:r}=n.observer.selectionRange;return t&&(e.push(new Co(t,i)),(s!=t||r!=i)&&e.push(new Co(s,r))),e}function Ru(n,e){if(n.length==0)return null;let t=n[0].pos,i=n.length==2?n[1].pos:t;return t>-1&&i>-1?w.single(t+e,i+e):null}const Lu={childList:!0,characterData:!0,subtree:!0,attributes:!0,characterDataOldValue:!0},ls=A.ie&&A.ie_version<=11;class Iu{constructor(e){this.view=e,this.active=!1,this.selectionRange=new Tf,this.selectionChanged=!1,this.delayedFlush=-1,this.resizeTimeout=-1,this.queue=[],this.delayedAndroidKey=null,this.flushingAndroidKey=-1,this.lastChange=0,this.scrollTargets=[],this.intersection=null,this.resize=null,this.intersecting=!1,this.gapIntersection=null,this.gaps=[],this.parentCheck=-1,this.dom=e.contentDOM,this.observer=new MutationObserver(t=>{for(let i of t)this.queue.push(i);(A.ie&&A.ie_version<=11||A.ios&&e.composing)&&t.some(i=>i.type=="childList"&&i.removedNodes.length||i.type=="characterData"&&i.oldValue.length>i.target.nodeValue.length)?this.flushSoon():this.flush()}),ls&&(this.onCharData=t=>{this.queue.push({target:t.target,type:"characterData",oldValue:t.prevValue}),this.flushSoon()}),this.onSelectionChange=this.onSelectionChange.bind(this),this.onResize=this.onResize.bind(this),this.onPrint=this.onPrint.bind(this),this.onScroll=this.onScroll.bind(this),typeof ResizeObserver=="function"&&(this.resize=new ResizeObserver(()=>{var t;((t=this.view.docView)===null||t===void 0?void 0:t.lastUpdate)<Date.now()-75&&this.onResize()}),this.resize.observe(e.scrollDOM)),this.addWindowListeners(this.win=e.win),this.start(),typeof IntersectionObserver=="function"&&(this.intersection=new IntersectionObserver(t=>{this.parentCheck<0&&(this.parentCheck=setTimeout(this.listenForScroll.bind(this),1e3)),t.length>0&&t[t.length-1].intersectionRatio>0!=this.intersecting&&(this.intersecting=!this.intersecting,this.intersecting!=this.view.inView&&this.onScrollChanged(document.createEvent("Event")))},{}),this.intersection.observe(this.dom),this.gapIntersection=new IntersectionObserver(t=>{t.length>0&&t[t.length-1].intersectionRatio>0&&this.onScrollChanged(document.createEvent("Event"))},{})),this.listenForScroll(),this.readSelectionRange()}onScrollChanged(e){this.view.inputState.runScrollHandlers(this.view,e),this.intersecting&&this.view.measure()}onScroll(e){this.intersecting&&this.flush(!1),this.onScrollChanged(e)}onResize(){this.resizeTimeout<0&&(this.resizeTimeout=setTimeout(()=>{this.resizeTimeout=-1,this.view.requestMeasure()},50))}onPrint(){this.view.viewState.printing=!0,this.view.measure(),setTimeout(()=>{this.view.viewState.printing=!1,this.view.requestMeasure()},500)}updateGaps(e){if(this.gapIntersection&&(e.length!=this.gaps.length||this.gaps.some((t,i)=>t!=e[i]))){this.gapIntersection.disconnect();for(let t of e)this.gapIntersection.observe(t);this.gaps=e}}onSelectionChange(e){let t=this.selectionChanged;if(!this.readSelectionRange()||this.delayedAndroidKey)return;let{view:i}=this,s=this.selectionRange;if(i.state.facet(zn)?i.root.activeElement!=this.dom:!dn(i.dom,s))return;let r=s.anchorNode&&i.docView.nearest(s.anchorNode);if(r&&r.ignoreEvent(e)){t||(this.selectionChanged=!1);return}(A.ie&&A.ie_version<=11||A.android&&A.chrome)&&!i.state.selection.main.empty&&s.focusNode&&Sn(s.focusNode,s.focusOffset,s.anchorNode,s.anchorOffset)?this.flushSoon():this.flush(!1)}readSelectionRange(){let{view:e}=this,t=A.safari&&e.root.nodeType==11&&Af(this.dom.ownerDocument)==this.dom&&Nu(this.view)||xn(e.root);if(!t||this.selectionRange.eq(t))return!1;let i=dn(this.dom,t);return i&&!this.selectionChanged&&e.inputState.lastFocusTime>Date.now()-200&&e.inputState.lastTouchTime<Date.now()-300&&Bf(this.dom,t)?(this.view.inputState.lastFocusTime=0,e.docView.updateSelection(),!1):(this.selectionRange.setRange(t),i&&(this.selectionChanged=!0),!0)}setSelectionRange(e,t){this.selectionRange.set(e.node,e.offset,t.node,t.offset),this.selectionChanged=!1}clearSelectionRange(){this.selectionRange.set(null,0,null,0)}listenForScroll(){this.parentCheck=-1;let e=0,t=null;for(let i=this.dom;i;)if(i.nodeType==1)!t&&e<this.scrollTargets.length&&this.scrollTargets[e]==i?e++:t||(t=this.scrollTargets.slice(0,e)),t&&t.push(i),i=i.assignedSlot||i.parentNode;else if(i.nodeType==11)i=i.host;else break;if(e<this.scrollTargets.length&&!t&&(t=this.scrollTargets.slice(0,e)),t){for(let i of this.scrollTargets)i.removeEventListener("scroll",this.onScroll);for(let i of this.scrollTargets=t)i.addEventListener("scroll",this.onScroll)}}ignore(e){if(!this.active)return e();try{return this.stop(),e()}finally{this.start(),this.clear()}}start(){this.active||(this.observer.observe(this.dom,Lu),ls&&this.dom.addEventListener("DOMCharacterDataModified",this.onCharData),this.active=!0)}stop(){this.active&&(this.active=!1,this.observer.disconnect(),ls&&this.dom.removeEventListener("DOMCharacterDataModified",this.onCharData))}clear(){this.processRecords(),this.queue.length=0,this.selectionChanged=!1}delayAndroidKey(e,t){var i;if(!this.delayedAndroidKey){let s=()=>{let r=this.delayedAndroidKey;r&&(this.clearDelayedAndroidKey(),!this.flush()&&r.force&&$t(this.dom,r.key,r.keyCode))};this.flushingAndroidKey=this.view.win.requestAnimationFrame(s)}(!this.delayedAndroidKey||e=="Enter")&&(this.delayedAndroidKey={key:e,keyCode:t,force:this.lastChange<Date.now()-50||!!(!((i=this.delayedAndroidKey)===null||i===void 0)&&i.force)})}clearDelayedAndroidKey(){this.win.cancelAnimationFrame(this.flushingAndroidKey),this.delayedAndroidKey=null,this.flushingAndroidKey=-1}flushSoon(){this.delayedFlush<0&&(this.delayedFlush=this.view.win.requestAnimationFrame(()=>{this.delayedFlush=-1,this.flush()}))}forceFlush(){this.delayedFlush>=0&&(this.view.win.cancelAnimationFrame(this.delayedFlush),this.delayedFlush=-1),this.flush()}processRecords(){let e=this.queue;for(let r of this.observer.takeRecords())e.push(r);e.length&&(this.queue=[]);let t=-1,i=-1,s=!1;for(let r of e){let o=this.readMutation(r);o&&(o.typeOver&&(s=!0),t==-1?{from:t,to:i}=o:(t=Math.min(o.from,t),i=Math.max(o.to,i)))}return{from:t,to:i,typeOver:s}}readChange(){let{from:e,to:t,typeOver:i}=this.processRecords(),s=this.selectionChanged&&dn(this.dom,this.selectionRange);return e<0&&!s?null:(e>-1&&(this.lastChange=Date.now()),this.view.inputState.lastFocusTime=0,this.selectionChanged=!1,new Bu(this.view,e,t,i))}flush(e=!0){if(this.delayedFlush>=0||this.delayedAndroidKey)return!1;e&&this.readSelectionRange();let t=this.readChange();if(!t)return!1;let i=this.view.state,s=kh(this.view,t);return this.view.state==i&&this.view.update([]),s}readMutation(e){let t=this.view.docView.nearest(e.target);if(!t||t.ignoreMutation(e))return null;if(t.markDirty(e.type=="attributes"),e.type=="attributes"&&(t.dirty|=4),e.type=="childList"){let i=jo(t,e.previousSibling||e.target.previousSibling,-1),s=jo(t,e.nextSibling||e.target.nextSibling,1);return{from:i?t.posAfter(i):t.posAtStart,to:s?t.posBefore(s):t.posAtEnd,typeOver:!1}}else return e.type=="characterData"?{from:t.posAtStart,to:t.posAtEnd,typeOver:e.target.nodeValue==e.oldValue}:null}setWindow(e){e!=this.win&&(this.removeWindowListeners(this.win),this.win=e,this.addWindowListeners(this.win))}addWindowListeners(e){e.addEventListener("resize",this.onResize),e.addEventListener("beforeprint",this.onPrint),e.addEventListener("scroll",this.onScroll),e.document.addEventListener("selectionchange",this.onSelectionChange)}removeWindowListeners(e){e.removeEventListener("scroll",this.onScroll),e.removeEventListener("resize",this.onResize),e.removeEventListener("beforeprint",this.onPrint),e.document.removeEventListener("selectionchange",this.onSelectionChange)}destroy(){var e,t,i;this.stop(),(e=this.intersection)===null||e===void 0||e.disconnect(),(t=this.gapIntersection)===null||t===void 0||t.disconnect(),(i=this.resize)===null||i===void 0||i.disconnect();for(let s of this.scrollTargets)s.removeEventListener("scroll",this.onScroll);this.removeWindowListeners(this.win),clearTimeout(this.parentCheck),clearTimeout(this.resizeTimeout),this.win.cancelAnimationFrame(this.delayedFlush),this.win.cancelAnimationFrame(this.flushingAndroidKey)}}function jo(n,e,t){for(;e;){let i=K.get(e);if(i&&i.parent==n)return i;let s=e.parentNode;e=s!=n.dom?s:t>0?e.nextSibling:e.previousSibling}return null}function Nu(n){let e=null;function t(a){a.preventDefault(),a.stopImmediatePropagation(),e=a.getTargetRanges()[0]}if(n.contentDOM.addEventListener("beforeinput",t,!0),n.dom.ownerDocument.execCommand("indent"),n.contentDOM.removeEventListener("beforeinput",t,!0),!e)return null;let i=e.startContainer,s=e.startOffset,r=e.endContainer,o=e.endOffset,l=n.docView.domAtPos(n.state.selection.main.anchor);return Sn(l.node,l.offset,r,o)&&([i,s,r,o]=[r,o,i,s]),{anchorNode:i,anchorOffset:s,focusNode:r,focusOffset:o}}class O{constructor(e={}){this.plugins=[],this.pluginMap=new Map,this.editorAttrs={},this.contentAttrs={},this.bidiCache=[],this.destroyed=!1,this.updateState=2,this.measureScheduled=-1,this.measureRequests=[],this.contentDOM=document.createElement("div"),this.scrollDOM=document.createElement("div"),this.scrollDOM.tabIndex=-1,this.scrollDOM.className="cm-scroller",this.scrollDOM.appendChild(this.contentDOM),this.announceDOM=document.createElement("div"),this.announceDOM.style.cssText="position: absolute; top: -10000px",this.announceDOM.setAttribute("aria-live","polite"),this.dom=document.createElement("div"),this.dom.appendChild(this.announceDOM),this.dom.appendChild(this.scrollDOM),this._dispatch=e.dispatch||(t=>this.update([t])),this.dispatch=this.dispatch.bind(this),this._root=e.root||Of(e.parent)||document,this.viewState=new zo(e.state||N.create(e)),this.plugins=this.state.facet(yi).map(t=>new ns(t));for(let t of this.plugins)t.update(this);this.observer=new Iu(this),this.inputState=new su(this),this.inputState.ensureHandlers(this,this.plugins),this.docView=new Ao(this),this.mountStyles(),this.updateAttrs(),this.updateState=0,this.requestMeasure(),e.parent&&e.parent.appendChild(this.dom)}get state(){return this.viewState.state}get viewport(){return this.viewState.viewport}get visibleRanges(){return this.viewState.visibleRanges}get inView(){return this.viewState.inView}get composing(){return this.inputState.composing>0}get compositionStarted(){return this.inputState.composing>=0}get root(){return this._root}get win(){return this.dom.ownerDocument.defaultView||window}dispatch(...e){this._dispatch(e.length==1&&e[0]instanceof re?e[0]:this.state.update(...e))}update(e){if(this.updateState!=0)throw new Error("Calls to EditorView.update are not allowed while an update is in progress");let t=!1,i=!1,s,r=this.state;for(let h of e){if(h.startState!=r)throw new RangeError("Trying to update state with a transaction that doesn't start from the previous state.");r=h.state}if(this.destroyed){this.viewState.state=r;return}let o=this.observer.delayedAndroidKey,l=null;if(o?(this.observer.clearDelayedAndroidKey(),l=this.observer.readChange(),(l&&!this.state.doc.eq(r.doc)||!this.state.selection.eq(r.selection))&&(l=null)):this.observer.clear(),r.facet(N.phrases)!=this.state.facet(N.phrases))return this.setState(r);s=Mn.create(this,r,e);let a=this.viewState.scrollTarget;try{this.updateState=2;for(let h of e){if(a&&(a=a.map(h.changes)),h.scrollIntoView){let{main:c}=h.state.selection;a=new An(c.empty?c:w.cursor(c.head,c.head>c.anchor?-1:1))}for(let c of h.effects)c.is(xo)&&(a=c.value)}this.viewState.update(s,a),this.bidiCache=Dn.update(this.bidiCache,s.changes),s.empty||(this.updatePlugins(s),this.inputState.update(s)),t=this.docView.update(s),this.state.facet(bi)!=this.styleModules&&this.mountStyles(),i=this.updateAttrs(),this.showAnnouncements(e),this.docView.updateSelection(t,e.some(h=>h.isUserEvent("select.pointer")))}finally{this.updateState=0}if(s.startState.facet(Zi)!=s.state.facet(Zi)&&(this.viewState.mustMeasureContent=!0),(t||i||a||this.viewState.mustEnforceCursorAssoc||this.viewState.mustMeasureContent)&&this.requestMeasure(),!s.empty)for(let h of this.state.facet(Xs))h(s);l&&!kh(this,l)&&o.force&&$t(this.contentDOM,o.key,o.keyCode)}setState(e){if(this.updateState!=0)throw new Error("Calls to EditorView.setState are not allowed while an update is in progress");if(this.destroyed){this.viewState.state=e;return}this.updateState=2;let t=this.hasFocus;try{for(let i of this.plugins)i.destroy(this);this.viewState=new zo(e),this.plugins=e.facet(yi).map(i=>new ns(i)),this.pluginMap.clear();for(let i of this.plugins)i.update(this);this.docView=new Ao(this),this.inputState.ensureHandlers(this,this.plugins),this.mountStyles(),this.updateAttrs(),this.bidiCache=[]}finally{this.updateState=0}t&&this.focus(),this.requestMeasure()}updatePlugins(e){let t=e.startState.facet(yi),i=e.state.facet(yi);if(t!=i){let s=[];for(let r of i){let o=t.indexOf(r);if(o<0)s.push(new ns(r));else{let l=this.plugins[o];l.mustUpdate=e,s.push(l)}}for(let r of this.plugins)r.mustUpdate!=e&&r.destroy(this);this.plugins=s,this.pluginMap.clear(),this.inputState.ensureHandlers(this,this.plugins)}else for(let s of this.plugins)s.mustUpdate=e;for(let s=0;s<this.plugins.length;s++)this.plugins[s].update(this)}measure(e=!0){if(this.destroyed)return;this.measureScheduled>-1&&cancelAnimationFrame(this.measureScheduled),this.measureScheduled=0,e&&this.observer.forceFlush();let t=null,{scrollHeight:i,scrollTop:s,clientHeight:r}=this.scrollDOM,o=s>i-r-4?i:s;try{for(let l=0;;l++){this.updateState=1;let a=this.viewport,h=this.viewState.lineBlockAtHeight(o),c=this.viewState.measure(this);if(!c&&!this.measureRequests.length&&this.viewState.scrollTarget==null)break;if(l>5){console.warn(this.measureRequests.length?"Measure loop restarted more than 5 times":"Viewport failed to stabilize");break}let f=[];c&4||([this.measureRequests,f]=[f,this.measureRequests]);let u=f.map(y=>{try{return y.read(this)}catch(b){return He(this.state,b),Ko}}),d=Mn.create(this,this.state,[]),p=!1,g=!1;d.flags|=c,t?t.flags|=c:t=d,this.updateState=2,d.empty||(this.updatePlugins(d),this.inputState.update(d),this.updateAttrs(),p=this.docView.update(d));for(let y=0;y<f.length;y++)if(u[y]!=Ko)try{let b=f[y];b.write&&b.write(u[y],this)}catch(b){He(this.state,b)}if(this.viewState.editorHeight)if(this.viewState.scrollTarget)this.docView.scrollIntoView(this.viewState.scrollTarget),this.viewState.scrollTarget=null,g=!0;else{let y=this.viewState.lineBlockAt(h.from).top-h.top;(y>1||y<-1)&&(this.scrollDOM.scrollTop+=y,g=!0)}if(p&&this.docView.updateSelection(!0),this.viewport.from==a.from&&this.viewport.to==a.to&&!g&&this.measureRequests.length==0)break}}finally{this.updateState=0,this.measureScheduled=-1}if(t&&!t.empty)for(let l of this.state.facet(Xs))l(t)}get themeClasses(){return sr+" "+(this.state.facet(nr)?bh:yh)+" "+this.state.facet(Zi)}updateAttrs(){let e=Uo(this,Za,{class:"cm-editor"+(this.hasFocus?" cm-focused ":" ")+this.themeClasses}),t={spellcheck:"false",autocorrect:"off",autocapitalize:"off",translate:"no",contenteditable:this.state.facet(zn)?"true":"false",class:"cm-content",style:`${A.tabSize}: ${this.state.tabSize}`,role:"textbox","aria-multiline":"true"};this.state.readOnly&&(t["aria-readonly"]="true"),Uo(this,Qa,t);let i=this.observer.ignore(()=>{let s=Js(this.contentDOM,this.contentAttrs,t),r=Js(this.dom,this.editorAttrs,e);return s||r});return this.editorAttrs=e,this.contentAttrs=t,i}showAnnouncements(e){let t=!0;for(let i of e)for(let s of i.effects)if(s.is(O.announce)){t&&(this.announceDOM.textContent=""),t=!1;let r=this.announceDOM.appendChild(document.createElement("div"));r.textContent=s.value}}mountStyles(){this.styleModules=this.state.facet(bi),mt.mount(this.root,this.styleModules.concat(Ou).reverse())}readMeasured(){if(this.updateState==2)throw new Error("Reading the editor layout isn't allowed during an update");this.updateState==0&&this.measureScheduled>-1&&this.measure(!1)}requestMeasure(e){if(this.measureScheduled<0&&(this.measureScheduled=this.win.requestAnimationFrame(()=>this.measure())),e){if(e.key!=null){for(let t=0;t<this.measureRequests.length;t++)if(this.measureRequests[t].key===e.key){this.measureRequests[t]=e;return}}this.measureRequests.push(e)}}plugin(e){let t=this.pluginMap.get(e);return(t===void 0||t&&t.spec!=e)&&this.pluginMap.set(e,t=this.plugins.find(i=>i.spec==e)||null),t&&t.update(this).value}get documentTop(){return this.contentDOM.getBoundingClientRect().top+this.viewState.paddingTop}get documentPadding(){return{top:this.viewState.paddingTop,bottom:this.viewState.paddingBottom}}elementAtHeight(e){return this.readMeasured(),this.viewState.elementAtHeight(e)}lineBlockAtHeight(e){return this.readMeasured(),this.viewState.lineBlockAtHeight(e)}get viewportLineBlocks(){return this.viewState.viewportLines}lineBlockAt(e){return this.viewState.lineBlockAt(e)}get contentHeight(){return this.viewState.contentHeight}moveByChar(e,t,i){return rs(this,e,Po(this,e,t,i))}moveByGroup(e,t){return rs(this,e,Po(this,e,t,i=>iu(this,e.head,i)))}moveToLineBoundary(e,t,i=!0){return tu(this,e,t,i)}moveVertically(e,t,i){return rs(this,e,nu(this,e,t,i))}domAtPos(e){return this.docView.domAtPos(e)}posAtDOM(e,t=0){return this.docView.posFromDOM(e,t)}posAtCoords(e,t=!0){return this.readMeasured(),ah(this,e,t)}coordsAtPos(e,t=1){this.readMeasured();let i=this.docView.coordsAt(e,t);if(!i||i.left==i.right)return i;let s=this.state.doc.lineAt(e),r=this.bidiSpans(s),o=r[Jt.find(r,e-s.from,-1,t)];return Dr(i,o.dir==Z.LTR==t>0)}get defaultCharacterWidth(){return this.viewState.heightOracle.charWidth}get defaultLineHeight(){return this.viewState.heightOracle.lineHeight}get textDirection(){return this.viewState.defaultTextDirection}textDirectionAt(e){return!this.state.facet(Ya)||e<this.viewport.from||e>this.viewport.to?this.textDirection:(this.readMeasured(),this.docView.textDirectionAt(e))}get lineWrapping(){return this.viewState.heightOracle.lineWrapping}bidiSpans(e){if(e.length>_u)return nh(e.length);let t=this.textDirectionAt(e.from);for(let s of this.bidiCache)if(s.from==e.from&&s.dir==t)return s.order;let i=Wf(e.text,t);return this.bidiCache.push(new Dn(e.from,e.to,t,i)),i}get hasFocus(){var e;return(this.dom.ownerDocument.hasFocus()||A.safari&&((e=this.inputState)===null||e===void 0?void 0:e.lastContextMenu)>Date.now()-3e4)&&this.root.activeElement==this.contentDOM}focus(){this.observer.ignore(()=>{Ea(this.contentDOM),this.docView.updateSelection()})}setRoot(e){this._root!=e&&(this._root=e,this.observer.setWindow((e.nodeType==9?e:e.ownerDocument).defaultView||window),this.mountStyles())}destroy(){for(let e of this.plugins)e.destroy(this);this.plugins=[],this.inputState.destroy(),this.dom.remove(),this.observer.destroy(),this.measureScheduled>-1&&cancelAnimationFrame(this.measureScheduled),this.destroyed=!0}static scrollIntoView(e,t={}){return xo.of(new An(typeof e=="number"?w.cursor(e):e,t.y,t.x,t.yMargin,t.xMargin))}static domEventHandlers(e){return be.define(()=>({}),{eventHandlers:e})}static theme(e,t){let i=mt.newName(),s=[Zi.of(i),bi.of(rr(`.${i}`,e))];return t&&t.dark&&s.push(nr.of(!0)),s}static baseTheme(e){return Vi.lowest(bi.of(rr("."+sr,e,wh)))}static findFromDOM(e){var t;let i=e.querySelector(".cm-content"),s=i&&K.get(i)||K.get(e);return((t=s?.rootView)===null||t===void 0?void 0:t.view)||null}}O.styleModule=bi;O.inputHandler=Ja;O.perLineTextDirection=Ya;O.exceptionSink=$a;O.updateListener=Xs;O.editable=zn;O.mouseSelectionStyle=Ga;O.dragMovesSelection=Ua;O.clickAddsSelectionRange=Ka;O.decorations=Ei;O.atomicRanges=eh;O.scrollMargins=th;O.darkTheme=nr;O.contentAttributes=Qa;O.editorAttributes=Za;O.lineWrapping=O.contentAttributes.of({class:"cm-lineWrapping"});O.announce=R.define();const _u=4096,Ko={};class Dn{constructor(e,t,i,s){this.from=e,this.to=t,this.dir=i,this.order=s}static update(e,t){if(t.empty)return e;let i=[],s=e.length?e[e.length-1].dir:Z.LTR;for(let r=Math.max(0,e.length-10);r<e.length;r++){let o=e[r];o.dir==s&&!t.touchesRange(o.from,o.to)&&i.push(new 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t=this.cursorLayer.children;if(t.length!==e.length){this.cursorLayer.textContent="";for(const i of e)this.cursorLayer.appendChild(i.draw())}else e.forEach((i,s)=>i.adjust(t[s]));this.cursors=e}}destroy(){this.selectionLayer.remove(),this.cursorLayer.remove()}}),Sh={".cm-line":{"& ::selection":{backgroundColor:"transparent !important"},"&::selection":{backgroundColor:"transparent !important"}}};vh&&(Sh[".cm-line"].caretColor="transparent !important");const Gu=Vi.highest(O.theme(Sh));function Ch(n){let e=n.scrollDOM.getBoundingClientRect();return{left:(n.textDirection==Z.LTR?e.left:e.right-n.scrollDOM.clientWidth)-n.scrollDOM.scrollLeft,top:e.top-n.scrollDOM.scrollTop}}function $o(n,e,t){let i=w.cursor(e);return{from:Math.max(t.from,n.moveToLineBoundary(i,!1,!0).from),to:Math.min(t.to,n.moveToLineBoundary(i,!0,!0).from),type:W.Text}}function Jo(n,e){let t=n.lineBlockAt(e);if(Array.isArray(t.type)){for(let i of t.type)if(i.to>e||i.to==e&&(i.to==t.to||i.type==W.Text))return i}return 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T}function b(S,k,C){let T=1e9,B=-1e9,U=[];function I(G,Q,M,$,Y){let le=n.coordsAtPos(G,G==C.to?-2:2),J=n.coordsAtPos(M,M==C.from?2:-2);T=Math.min(le.top,J.top,T),B=Math.max(le.bottom,J.bottom,B),Y==Z.LTR?U.push(s&&Q?h:le.left,s&&$?c:J.right):U.push(!s&&$?h:J.left,!s&&Q?c:le.right)}let P=S??C.from,V=k??C.to;for(let G of n.visibleRanges)if(G.to>P&&G.from<V)for(let Q=Math.max(G.from,P),M=Math.min(G.to,V);;){let $=n.state.doc.lineAt(Q);for(let Y of n.bidiSpans($)){let le=Y.from+$.from,J=Y.to+$.from;if(le>=M)break;J>Q&&I(Math.max(le,Q),S==null&&le<=P,Math.min(J,M),k==null&&J>=V,Y.dir)}if(Q=$.to+1,Q>=M)break}return U.length==0&&I(P,S==null,V,k==null,n.textDirection),{top:T,bottom:B,horizontal:U}}function v(S,k){let C=o.top+(k?S.top:S.bottom);return{top:C,bottom:C,horizontal:[]}}}function Ju(n,e,t){let i=n.coordsAtPos(e.head,e.assoc||1);if(!i)return null;let s=Ch(n);return new xh(i.left-s.left,i.top-s.top,-1,i.bottom-i.top,t?"cm-cursor cm-cursor-primary":"cm-cursor 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u=[],d,p=(g,y,b)=>u.push(b.range(g,y));if(a==h)for(this.regexp.lastIndex=c-a.from;(d=this.regexp.exec(a.text))&&d.index<f-a.from;)this.addMatch(d,e,d.index+a.from,p);else Yo(e.state.doc,this.regexp,c,f,(g,y)=>this.addMatch(y,e,g,p));t=t.update({filterFrom:c,filterTo:f,filter:(g,y)=>g<c||y>f,add:u})}}return t}}const or=/x/.unicode!=null?"gu":"g",Zu=new RegExp(`[\0-\b
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nd="•";function sd(n){return n>=32?nd:n==10?"␤":String.fromCharCode(9216+n)}class rd extends tt{constructor(e,t){super(),this.options=e,this.code=t}eq(e){return e.code==this.code}toDOM(e){let t=sd(this.code),i=e.state.phrase("Control character")+" "+(Qu[this.code]||"0x"+this.code.toString(16)),s=this.options.render&&this.options.render(this.code,i,t);if(s)return s;let r=document.createElement("span");return r.textContent=t,r.title=i,r.setAttribute("aria-label",i),r.className="cm-specialChar",r}ignoreEvent(){return!1}}class od extends tt{constructor(e){super(),this.width=e}eq(e){return e.width==this.width}toDOM(){let e=document.createElement("span");return e.textContent="	",e.className="cm-tab",e.style.width=this.width+"px",e}ignoreEvent(){return!1}}class ld extends tt{constructor(e){super(),this.content=e}toDOM(){let e=document.createElement("span");return e.className="cm-placeholder",e.style.pointerEvents="none",e.appendChild(typeof this.content=="string"?document.createTextNode(this.content):this.content),typeof this.content=="string"?e.setAttribute("aria-label","placeholder "+this.content):e.setAttribute("aria-hidden","true"),e}ignoreEvent(){return!1}}function ad(n){return be.fromClass(class{constructor(e){this.view=e,this.placeholder=E.set([E.widget({widget:new ld(n),side:1}).range(0)])}get decorations(){return this.view.state.doc.length?E.none:this.placeholder}},{decorations:e=>e.decorations})}const lr=2e3;function hd(n,e,t){let i=Math.min(e.line,t.line),s=Math.max(e.line,t.line),r=[];if(e.off>lr||t.off>lr||e.col<0||t.col<0){let o=Math.min(e.off,t.off),l=Math.max(e.off,t.off);for(let a=i;a<=s;a++){let h=n.doc.line(a);h.length<=l&&r.push(w.range(h.from+o,h.to+l))}}else{let o=Math.min(e.col,t.col),l=Math.max(e.col,t.col);for(let a=i;a<=s;a++){let h=n.doc.line(a),c=Hs(h.text,o,n.tabSize,!0);if(c<0)r.push(w.cursor(h.to));else{let f=Hs(h.text,l,n.tabSize);r.push(w.range(h.from+c,h.from+f))}}}return r}function cd(n,e){let t=n.coordsAtPos(n.viewport.from);return t?Math.round(Math.abs((t.left-e)/n.defaultCharacterWidth)):-1}function Zo(n,e){let t=n.posAtCoords({x:e.clientX,y:e.clientY},!1),i=n.state.doc.lineAt(t),s=t-i.from,r=s>lr?-1:s==i.length?cd(n,e.clientX):Fi(i.text,n.state.tabSize,t-i.from);return{line:i.number,col:r,off:s}}function fd(n,e){let t=Zo(n,e),i=n.state.selection;return t?{update(s){if(s.docChanged){let r=s.changes.mapPos(s.startState.doc.line(t.line).from),o=s.state.doc.lineAt(r);t={line:o.number,col:t.col,off:Math.min(t.off,o.length)},i=i.map(s.changes)}},get(s,r,o){let l=Zo(n,s);if(!l)return i;let a=hd(n.state,t,l);return a.length?o?w.create(a.concat(i.ranges)):w.create(a):i}}:null}function ud(n){let e=n?.eventFilter||(t=>t.altKey&&t.button==0);return O.mouseSelectionStyle.of((t,i)=>e(i)?fd(t,i):null)}const dd={Alt:[18,n=>n.altKey],Control:[17,n=>n.ctrlKey],Shift:[16,n=>n.shiftKey],Meta:[91,n=>n.metaKey]},pd={style:"cursor: crosshair"};function md(n={}){let[e,t]=dd[n.key||"Alt"],i=be.fromClass(class{constructor(s){this.view=s,this.isDown=!1}set(s){this.isDown!=s&&(this.isDown=s,this.view.update([]))}},{eventHandlers:{keydown(s){this.set(s.keyCode==e||t(s))},keyup(s){(s.keyCode==e||!t(s))&&this.set(!1)},mousemove(s){this.set(t(s))}}});return[i,O.contentAttributes.of(s=>{var r;return!((r=s.plugin(i))===null||r===void 0)&&r.isDown?pd:null})]}const hs="-10000px";class Ah{constructor(e,t,i){this.facet=t,this.createTooltipView=i,this.input=e.state.facet(t),this.tooltips=this.input.filter(s=>s),this.tooltipViews=this.tooltips.map(i)}update(e){var t;let i=e.state.facet(this.facet),s=i.filter(o=>o);if(i===this.input){for(let o of this.tooltipViews)o.update&&o.update(e);return!1}let r=[];for(let o=0;o<s.length;o++){let l=s[o],a=-1;if(l){for(let h=0;h<this.tooltips.length;h++){let c=this.tooltips[h];c&&c.create==l.create&&(a=h)}if(a<0)r[o]=this.createTooltipView(l);else{let h=r[o]=this.tooltipViews[a];h.update&&h.update(e)}}}for(let o of this.tooltipViews)r.indexOf(o)<0&&(o.dom.remove(),(t=o.destroy)===null||t===void 0||t.call(o));return this.input=i,this.tooltips=s,this.tooltipViews=r,!0}}function gd(n){let{win:e}=n;return{top:0,left:0,bottom:e.innerHeight,right:e.innerWidth}}const cs=D.define({combine:n=>{var e,t,i;return{position:A.ios?"absolute":((e=n.find(s=>s.position))===null||e===void 0?void 0:e.position)||"fixed",parent:((t=n.find(s=>s.parent))===null||t===void 0?void 0:t.parent)||null,tooltipSpace:((i=n.find(s=>s.tooltipSpace))===null||i===void 0?void 0:i.tooltipSpace)||gd}}}),Mh=be.fromClass(class{constructor(n){this.view=n,this.inView=!0,this.lastTransaction=0,this.measureTimeout=-1;let e=n.state.facet(cs);this.position=e.position,this.parent=e.parent,this.classes=n.themeClasses,this.createContainer(),this.measureReq={read:this.readMeasure.bind(this),write:this.writeMeasure.bind(this),key:this},this.manager=new Ah(n,Er,t=>this.createTooltip(t)),this.intersectionObserver=typeof IntersectionObserver=="function"?new IntersectionObserver(t=>{Date.now()>this.lastTransaction-50&&t.length>0&&t[t.length-1].intersectionRatio<1&&this.measureSoon()},{threshold:[1]}):null,this.observeIntersection(),n.win.addEventListener("resize",this.measureSoon=this.measureSoon.bind(this)),this.maybeMeasure()}createContainer(){this.parent?(this.container=document.createElement("div"),this.container.style.position="relative",this.container.className=this.view.themeClasses,this.parent.appendChild(this.container)):this.container=this.view.dom}observeIntersection(){if(this.intersectionObserver){this.intersectionObserver.disconnect();for(let n of this.manager.tooltipViews)this.intersectionObserver.observe(n.dom)}}measureSoon(){this.measureTimeout<0&&(this.measureTimeout=setTimeout(()=>{this.measureTimeout=-1,this.maybeMeasure()},50))}update(n){n.transactions.length&&(this.lastTransaction=Date.now());let e=this.manager.update(n);e&&this.observeIntersection();let t=e||n.geometryChanged,i=n.state.facet(cs);if(i.position!=this.position){this.position=i.position;for(let s of this.manager.tooltipViews)s.dom.style.position=this.position;t=!0}if(i.parent!=this.parent){this.parent&&this.container.remove(),this.parent=i.parent,this.createContainer();for(let s of this.manager.tooltipViews)this.container.appendChild(s.dom);t=!0}else this.parent&&this.view.themeClasses!=this.classes&&(this.classes=this.container.className=this.view.themeClasses);t&&this.maybeMeasure()}createTooltip(n){let e=n.create(this.view);if(e.dom.classList.add("cm-tooltip"),n.arrow&&!e.dom.querySelector(".cm-tooltip > .cm-tooltip-arrow")){let t=document.createElement("div");t.className="cm-tooltip-arrow",e.dom.appendChild(t)}return e.dom.style.position=this.position,e.dom.style.top=hs,this.container.appendChild(e.dom),e.mount&&e.mount(this.view),e}destroy(){var n,e;this.view.win.removeEventListener("resize",this.measureSoon);for(let t of this.manager.tooltipViews)t.dom.remove(),(n=t.destroy)===null||n===void 0||n.call(t);(e=this.intersectionObserver)===null||e===void 0||e.disconnect(),clearTimeout(this.measureTimeout)}readMeasure(){let n=this.view.dom.getBoundingClientRect();return{editor:n,parent:this.parent?this.container.getBoundingClientRect():n,pos:this.manager.tooltips.map((e,t)=>{let i=this.manager.tooltipViews[t];return i.getCoords?i.getCoords(e.pos):this.view.coordsAtPos(e.pos)}),size:this.manager.tooltipViews.map(({dom:e})=>e.getBoundingClientRect()),space:this.view.state.facet(cs).tooltipSpace(this.view)}}writeMeasure(n){let{editor:e,space:t}=n,i=[];for(let s=0;s<this.manager.tooltips.length;s++){let r=this.manager.tooltips[s],o=this.manager.tooltipViews[s],{dom:l}=o,a=n.pos[s],h=n.size[s];if(!a||a.bottom<=Math.max(e.top,t.top)||a.top>=Math.min(e.bottom,t.bottom)||a.right<Math.max(e.left,t.left)-.1||a.left>Math.min(e.right,t.right)+.1){l.style.top=hs;continue}let c=r.arrow?o.dom.querySelector(".cm-tooltip-arrow"):null,f=c?7:0,u=h.right-h.left,d=h.bottom-h.top,p=o.offset||bd,g=this.view.textDirection==Z.LTR,y=h.width>t.right-t.left?g?t.left:t.right-h.width:g?Math.min(a.left-(c?14:0)+p.x,t.right-u):Math.max(t.left,a.left-u+(c?14:0)-p.x),b=!!r.above;!r.strictSide&&(b?a.top-(h.bottom-h.top)-p.y<t.top:a.bottom+(h.bottom-h.top)+p.y>t.bottom)&&b==t.bottom-a.bottom>a.top-t.top&&(b=!b);let v=b?a.top-d-f-p.y:a.bottom+f+p.y,S=y+u;if(o.overlap!==!0)for(let k of i)k.left<S&&k.right>y&&k.top<v+d&&k.bottom>v&&(v=b?k.top-d-2-f:k.bottom+f+2);this.position=="absolute"?(l.style.top=v-n.parent.top+"px",l.style.left=y-n.parent.left+"px"):(l.style.top=v+"px",l.style.left=y+"px"),c&&(c.style.left=`${a.left+(g?p.x:-p.x)-(y+14-7)}px`),o.overlap!==!0&&i.push({left:y,top:v,right:S,bottom:v+d}),l.classList.toggle("cm-tooltip-above",b),l.classList.toggle("cm-tooltip-below",!b),o.positioned&&o.positioned()}}maybeMeasure(){if(this.manager.tooltips.length&&(this.view.inView&&this.view.requestMeasure(this.measureReq),this.inView!=this.view.inView&&(this.inView=this.view.inView,!this.inView)))for(let n of this.manager.tooltipViews)n.dom.style.top=hs}},{eventHandlers:{scroll(){this.maybeMeasure()}}}),yd=O.baseTheme({".cm-tooltip":{zIndex:100},"&light .cm-tooltip":{border:"1px solid #bbb",backgroundColor:"#f5f5f5"},"&light .cm-tooltip-section:not(:first-child)":{borderTop:"1px solid #bbb"},"&dark .cm-tooltip":{backgroundColor:"#333338",color:"white"},".cm-tooltip-arrow":{height:"7px",width:`${7*2}px`,position:"absolute",zIndex:-1,overflow:"hidden","&:before, &:after":{content:"''",position:"absolute",width:0,height:0,borderLeft:"7px solid transparent",borderRight:"7px solid transparent"},".cm-tooltip-above &":{bottom:"-7px","&:before":{borderTop:"7px solid #bbb"},"&:after":{borderTop:"7px solid #f5f5f5",bottom:"1px"}},".cm-tooltip-below &":{top:"-7px","&:before":{borderBottom:"7px solid #bbb"},"&:after":{borderBottom:"7px solid #f5f5f5",top:"1px"}}},"&dark .cm-tooltip .cm-tooltip-arrow":{"&:before":{borderTopColor:"#333338",borderBottomColor:"#333338"},"&:after":{borderTopColor:"transparent",borderBottomColor:"transparent"}}}),bd={x:0,y:0},Er=D.define({enables:[Mh,yd]}),Tn=D.define();class Rr{constructor(e){this.view=e,this.mounted=!1,this.dom=document.createElement("div"),this.dom.classList.add("cm-tooltip-hover"),this.manager=new Ah(e,Tn,t=>this.createHostedView(t))}static create(e){return new Rr(e)}createHostedView(e){let t=e.create(this.view);return t.dom.classList.add("cm-tooltip-section"),this.dom.appendChild(t.dom),this.mounted&&t.mount&&t.mount(this.view),t}mount(e){for(let t of this.manager.tooltipViews)t.mount&&t.mount(e);this.mounted=!0}positioned(){for(let e of this.manager.tooltipViews)e.positioned&&e.positioned()}update(e){this.manager.update(e)}}const wd=Er.compute([Tn],n=>{let e=n.facet(Tn).filter(t=>t);return e.length===0?null:{pos:Math.min(...e.map(t=>t.pos)),end:Math.max(...e.filter(t=>t.end!=null).map(t=>t.end)),create:Rr.create,above:e[0].above,arrow:e.some(t=>t.arrow)}});class kd{constructor(e,t,i,s,r){this.view=e,this.source=t,this.field=i,this.setHover=s,this.hoverTime=r,this.hoverTimeout=-1,this.restartTimeout=-1,this.pending=null,this.lastMove={x:0,y:0,target:e.dom,time:0},this.checkHover=this.checkHover.bind(this),e.dom.addEventListener("mouseleave",this.mouseleave=this.mouseleave.bind(this)),e.dom.addEventListener("mousemove",this.mousemove=this.mousemove.bind(this))}update(){this.pending&&(this.pending=null,clearTimeout(this.restartTimeout),this.restartTimeout=setTimeout(()=>this.startHover(),20))}get active(){return this.view.state.field(this.field)}checkHover(){if(this.hoverTimeout=-1,this.active)return;let e=Date.now()-this.lastMove.time;e<this.hoverTime?this.hoverTimeout=setTimeout(this.checkHover,this.hoverTime-e):this.startHover()}startHover(){clearTimeout(this.restartTimeout);let{lastMove:e}=this,t=this.view.contentDOM.contains(e.target)?this.view.posAtCoords(e):null;if(t==null)return;let i=this.view.coordsAtPos(t);if(i==null||e.y<i.top||e.y>i.bottom||e.x<i.left-this.view.defaultCharacterWidth||e.x>i.right+this.view.defaultCharacterWidth)return;let s=this.view.bidiSpans(this.view.state.doc.lineAt(t)).find(l=>l.from<=t&&l.to>=t),r=s&&s.dir==Z.RTL?-1:1,o=this.source(this.view,t,e.x<i.left?-r:r);if(o?.then){let l=this.pending={pos:t};o.then(a=>{this.pending==l&&(this.pending=null,a&&this.view.dispatch({effects:this.setHover.of(a)}))},a=>He(this.view.state,a,"hover tooltip"))}else o&&this.view.dispatch({effects:this.setHover.of(o)})}mousemove(e){var t;this.lastMove={x:e.clientX,y:e.clientY,target:e.target,time:Date.now()},this.hoverTimeout<0&&(this.hoverTimeout=setTimeout(this.checkHover,this.hoverTime));let i=this.active;if(i&&!vd(this.lastMove.target)||this.pending){let{pos:s}=i||this.pending,r=(t=i?.end)!==null&&t!==void 0?t:s;(s==r?this.view.posAtCoords(this.lastMove)!=s:!xd(this.view,s,r,e.clientX,e.clientY,6))&&(this.view.dispatch({effects:this.setHover.of(null)}),this.pending=null)}}mouseleave(){clearTimeout(this.hoverTimeout),this.hoverTimeout=-1,this.active&&this.view.dispatch({effects:this.setHover.of(null)})}destroy(){clearTimeout(this.hoverTimeout),this.view.dom.removeEventListener("mouseleave",this.mouseleave),this.view.dom.removeEventListener("mousemove",this.mousemove)}}function vd(n){for(let e=n;e;e=e.parentNode)if(e.nodeType==1&&e.classList.contains("cm-tooltip"))return!0;return!1}function xd(n,e,t,i,s,r){let o=document.createRange(),l=n.domAtPos(e),a=n.domAtPos(t);o.setEnd(a.node,a.offset),o.setStart(l.node,l.offset);let h=o.getClientRects();o.detach();for(let c=0;c<h.length;c++){let f=h[c];if(Math.max(f.top-s,s-f.bottom,f.left-i,i-f.right)<=r)return!0}return!1}function Sd(n,e={}){let t=R.define(),i=Me.define({create(){return null},update(s,r){if(s&&(e.hideOnChange&&(r.docChanged||r.selection)||e.hideOn&&e.hideOn(r,s)))return null;if(s&&r.docChanged){let o=r.changes.mapPos(s.pos,-1,ce.TrackDel);if(o==null)return null;let l=Object.assign(Object.create(null),s);l.pos=o,s.end!=null&&(l.end=r.changes.mapPos(s.end)),s=l}for(let o of r.effects)o.is(t)&&(s=o.value),o.is(Ad)&&(s=null);return s},provide:s=>Tn.from(s)});return[i,be.define(s=>new kd(s,n,i,t,e.hoverTime||300)),wd]}function Cd(n,e){let t=n.plugin(Mh);if(!t)return null;let i=t.manager.tooltips.indexOf(e);return i<0?null:t.manager.tooltipViews[i]}const Ad=R.define(),Qo=D.define({combine(n){let e,t;for(let i of n)e=e||i.topContainer,t=t||i.bottomContainer;return{topContainer:e,bottomContainer:t}}});function Md(n,e){let t=n.plugin(Dh),i=t?t.specs.indexOf(e):-1;return i>-1?t.panels[i]:null}const Dh=be.fromClass(class{constructor(n){this.input=n.state.facet(ar),this.specs=this.input.filter(t=>t),this.panels=this.specs.map(t=>t(n));let e=n.state.facet(Qo);this.top=new en(n,!0,e.topContainer),this.bottom=new en(n,!1,e.bottomContainer),this.top.sync(this.panels.filter(t=>t.top)),this.bottom.sync(this.panels.filter(t=>!t.top));for(let t of this.panels)t.dom.classList.add("cm-panel"),t.mount&&t.mount()}update(n){let e=n.state.facet(Qo);this.top.container!=e.topContainer&&(this.top.sync([]),this.top=new en(n.view,!0,e.topContainer)),this.bottom.container!=e.bottomContainer&&(this.bottom.sync([]),this.bottom=new en(n.view,!1,e.bottomContainer)),this.top.syncClasses(),this.bottom.syncClasses();let t=n.state.facet(ar);if(t!=this.input){let i=t.filter(a=>a),s=[],r=[],o=[],l=[];for(let a of i){let h=this.specs.indexOf(a),c;h<0?(c=a(n.view),l.push(c)):(c=this.panels[h],c.update&&c.update(n)),s.push(c),(c.top?r:o).push(c)}this.specs=i,this.panels=s,this.top.sync(r),this.bottom.sync(o);for(let a of l)a.dom.classList.add("cm-panel"),a.mount&&a.mount()}else for(let i of this.panels)i.update&&i.update(n)}destroy(){this.top.sync([]),this.bottom.sync([])}},{provide:n=>O.scrollMargins.of(e=>{let t=e.plugin(n);return t&&{top:t.top.scrollMargin(),bottom:t.bottom.scrollMargin()}})});class en{constructor(e,t,i){this.view=e,this.top=t,this.container=i,this.dom=void 0,this.classes="",this.panels=[],this.syncClasses()}sync(e){for(let t of this.panels)t.destroy&&e.indexOf(t)<0&&t.destroy();this.panels=e,this.syncDOM()}syncDOM(){if(this.panels.length==0){this.dom&&(this.dom.remove(),this.dom=void 0);return}if(!this.dom){this.dom=document.createElement("div"),this.dom.className=this.top?"cm-panels cm-panels-top":"cm-panels cm-panels-bottom",this.dom.style[this.top?"top":"bottom"]="0";let t=this.container||this.view.dom;t.insertBefore(this.dom,this.top?t.firstChild:null)}let e=this.dom.firstChild;for(let t of this.panels)if(t.dom.parentNode==this.dom){for(;e!=t.dom;)e=el(e);e=e.nextSibling}else this.dom.insertBefore(t.dom,e);for(;e;)e=el(e)}scrollMargin(){return!this.dom||this.container?0:Math.max(0,this.top?this.dom.getBoundingClientRect().bottom-Math.max(0,this.view.scrollDOM.getBoundingClientRect().top):Math.min(innerHeight,this.view.scrollDOM.getBoundingClientRect().bottom)-this.dom.getBoundingClientRect().top)}syncClasses(){if(!(!this.container||this.classes==this.view.themeClasses)){for(let e of this.classes.split(" "))e&&this.container.classList.remove(e);for(let e of(this.classes=this.view.themeClasses).split(" "))e&&this.container.classList.add(e)}}}function el(n){let e=n.nextSibling;return n.remove(),e}const ar=D.define({enables:Dh});class bt extends Bt{compare(e){return this==e||this.constructor==e.constructor&&this.eq(e)}eq(e){return!1}destroy(e){}}bt.prototype.elementClass="";bt.prototype.toDOM=void 0;bt.prototype.mapMode=ce.TrackBefore;bt.prototype.startSide=bt.prototype.endSide=-1;bt.prototype.point=!0;const fs=D.define(),Dd={class:"",renderEmptyElements:!1,elementStyle:"",markers:()=>F.empty,lineMarker:()=>null,lineMarkerChange:null,initialSpacer:null,updateSpacer:null,domEventHandlers:{}},Ci=D.define();function Td(n){return[Th(),Ci.of(Object.assign(Object.assign({},Dd),n))]}const hr=D.define({combine:n=>n.some(e=>e)});function Th(n){let e=[Od];return n&&n.fixed===!1&&e.push(hr.of(!0)),e}const Od=be.fromClass(class{constructor(n){this.view=n,this.prevViewport=n.viewport,this.dom=document.createElement("div"),this.dom.className="cm-gutters",this.dom.setAttribute("aria-hidden","true"),this.dom.style.minHeight=this.view.contentHeight+"px",this.gutters=n.state.facet(Ci).map(e=>new il(n,e));for(let e of this.gutters)this.dom.appendChild(e.dom);this.fixed=!n.state.facet(hr),this.fixed&&(this.dom.style.position="sticky"),this.syncGutters(!1),n.scrollDOM.insertBefore(this.dom,n.contentDOM)}update(n){if(this.updateGutters(n)){let e=this.prevViewport,t=n.view.viewport,i=Math.min(e.to,t.to)-Math.max(e.from,t.from);this.syncGutters(i<(t.to-t.from)*.8)}n.geometryChanged&&(this.dom.style.minHeight=this.view.contentHeight+"px"),this.view.state.facet(hr)!=!this.fixed&&(this.fixed=!this.fixed,this.dom.style.position=this.fixed?"sticky":""),this.prevViewport=n.view.viewport}syncGutters(n){let e=this.dom.nextSibling;n&&this.dom.remove();let t=F.iter(this.view.state.facet(fs),this.view.viewport.from),i=[],s=this.gutters.map(r=>new Bd(r,this.view.viewport,-this.view.documentPadding.top));for(let r of this.view.viewportLineBlocks){let o;if(Array.isArray(r.type)){for(let l of r.type)if(l.type==W.Text){o=l;break}}else o=r.type==W.Text?r:void 0;if(o){i.length&&(i=[]),Oh(t,i,r.from);for(let l of s)l.line(this.view,o,i)}}for(let r of s)r.finish();n&&this.view.scrollDOM.insertBefore(this.dom,e)}updateGutters(n){let e=n.startState.facet(Ci),t=n.state.facet(Ci),i=n.docChanged||n.heightChanged||n.viewportChanged||!F.eq(n.startState.facet(fs),n.state.facet(fs),n.view.viewport.from,n.view.viewport.to);if(e==t)for(let s of this.gutters)s.update(n)&&(i=!0);else{i=!0;let s=[];for(let r of t){let o=e.indexOf(r);o<0?s.push(new il(this.view,r)):(this.gutters[o].update(n),s.push(this.gutters[o]))}for(let r of this.gutters)r.dom.remove(),s.indexOf(r)<0&&r.destroy();for(let r of s)this.dom.appendChild(r.dom);this.gutters=s}return i}destroy(){for(let n of this.gutters)n.destroy();this.dom.remove()}},{provide:n=>O.scrollMargins.of(e=>{let t=e.plugin(n);return!t||t.gutters.length==0||!t.fixed?null:e.textDirection==Z.LTR?{left:t.dom.offsetWidth}:{right:t.dom.offsetWidth}})});function tl(n){return Array.isArray(n)?n:[n]}function Oh(n,e,t){for(;n.value&&n.from<=t;)n.from==t&&e.push(n.value),n.next()}class Bd{constructor(e,t,i){this.gutter=e,this.height=i,this.localMarkers=[],this.i=0,this.cursor=F.iter(e.markers,t.from)}line(e,t,i){this.localMarkers.length&&(this.localMarkers=[]),Oh(this.cursor,this.localMarkers,t.from);let s=i.length?this.localMarkers.concat(i):this.localMarkers,r=this.gutter.config.lineMarker(e,t,s);r&&s.unshift(r);let o=this.gutter;if(s.length==0&&!o.config.renderEmptyElements)return;let l=t.top-this.height;if(this.i==o.elements.length){let a=new Bh(e,t.height,l,s);o.elements.push(a),o.dom.appendChild(a.dom)}else o.elements[this.i].update(e,t.height,l,s);this.height=t.bottom,this.i++}finish(){let e=this.gutter;for(;e.elements.length>this.i;){let t=e.elements.pop();e.dom.removeChild(t.dom),t.destroy()}}}class il{constructor(e,t){this.view=e,this.config=t,this.elements=[],this.spacer=null,this.dom=document.createElement("div"),this.dom.className="cm-gutter"+(this.config.class?" "+this.config.class:"");for(let i in t.domEventHandlers)this.dom.addEventListener(i,s=>{let r=e.lineBlockAtHeight(s.clientY-e.documentTop);t.domEventHandlers[i](e,r,s)&&s.preventDefault()});this.markers=tl(t.markers(e)),t.initialSpacer&&(this.spacer=new Bh(e,0,0,[t.initialSpacer(e)]),this.dom.appendChild(this.spacer.dom),this.spacer.dom.style.cssText+="visibility: hidden; pointer-events: none")}update(e){let t=this.markers;if(this.markers=tl(this.config.markers(e.view)),this.spacer&&this.config.updateSpacer){let s=this.config.updateSpacer(this.spacer.markers[0],e);s!=this.spacer.markers[0]&&this.spacer.update(e.view,0,0,[s])}let i=e.view.viewport;return!F.eq(this.markers,t,i.from,i.to)||(this.config.lineMarkerChange?this.config.lineMarkerChange(e):!1)}destroy(){for(let e of this.elements)e.destroy()}}class Bh{constructor(e,t,i,s){this.height=-1,this.above=0,this.markers=[],this.dom=document.createElement("div"),this.dom.className="cm-gutterElement",this.update(e,t,i,s)}update(e,t,i,s){this.height!=t&&(this.dom.style.height=(this.height=t)+"px"),this.above!=i&&(this.dom.style.marginTop=(this.above=i)?i+"px":""),Pd(this.markers,s)||this.setMarkers(e,s)}setMarkers(e,t){let i="cm-gutterElement",s=this.dom.firstChild;for(let r=0,o=0;;){let l=o,a=r<t.length?t[r++]:null,h=!1;if(a){let c=a.elementClass;c&&(i+=" "+c);for(let f=o;f<this.markers.length;f++)if(this.markers[f].compare(a)){l=f,h=!0;break}}else l=this.markers.length;for(;o<l;){let c=this.markers[o++];if(c.toDOM){c.destroy(s);let f=s.nextSibling;s.remove(),s=f}}if(!a)break;a.toDOM&&(h?s=s.nextSibling:this.dom.insertBefore(a.toDOM(e),s)),h&&o++}this.dom.className=i,this.markers=t}destroy(){this.setMarkers(null,[])}}function Pd(n,e){if(n.length!=e.length)return!1;for(let t=0;t<n.length;t++)if(!n[t].compare(e[t]))return!1;return!0}const 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e=9;for(;e<n;)e=e*10+9;return e}const Id=1024;let Nd=0;class Le{constructor(e,t){this.from=e,this.to=t}}class L{constructor(e={}){this.id=Nd++,this.perNode=!!e.perNode,this.deserialize=e.deserialize||(()=>{throw new Error("This node type doesn't define a deserialize function")})}add(e){if(this.perNode)throw new RangeError("Can't add per-node props to node types");return typeof e!="function"&&(e=xe.match(e)),t=>{let i=e(t);return i===void 0?null:[this,i]}}}L.closedBy=new L({deserialize:n=>n.split(" ")});L.openedBy=new L({deserialize:n=>n.split(" ")});L.group=new L({deserialize:n=>n.split(" ")});L.contextHash=new L({perNode:!0});L.lookAhead=new L({perNode:!0});L.mounted=new L({perNode:!0});class _d{constructor(e,t,i){this.tree=e,this.overlay=t,this.parser=i}}const Vd=Object.create(null);class xe{constructor(e,t,i,s=0){this.name=e,this.props=t,this.id=i,this.flags=s}static define(e){let t=e.props&&e.props.length?Object.create(null):Vd,i=(e.top?1:0)|(e.skipped?2:0)|(e.error?4:0)|(e.name==null?8:0),s=new xe(e.name||"",t,e.id,i);if(e.props){for(let r of e.props)if(Array.isArray(r)||(r=r(s)),r){if(r[0].perNode)throw new RangeError("Can't store a per-node prop on a node type");t[r[0].id]=r[1]}}return s}prop(e){return this.props[e.id]}get isTop(){return(this.flags&1)>0}get isSkipped(){return(this.flags&2)>0}get isError(){return(this.flags&4)>0}get isAnonymous(){return(this.flags&8)>0}is(e){if(typeof e=="string"){if(this.name==e)return!0;let t=this.prop(L.group);return t?t.indexOf(e)>-1:!1}return this.id==e}static match(e){let t=Object.create(null);for(let i in e)for(let s of i.split(" "))t[s]=e[i];return i=>{for(let s=i.prop(L.group),r=-1;r<(s?s.length:0);r++){let o=t[r<0?i.name:s[r]];if(o)return o}}}}xe.none=new xe("",Object.create(null),0,8);class Lr{constructor(e){this.types=e;for(let t=0;t<e.length;t++)if(e[t].id!=t)throw new RangeError("Node type ids should correspond to array positions when creating a node set")}extend(...e){let t=[];for(let i of this.types){let s=null;for(let r of e){let o=r(i);o&&(s||(s=Object.assign({},i.props)),s[o[0].id]=o[1])}t.push(s?new xe(i.name,s,i.id,i.flags):i)}return new Lr(t)}}const tn=new WeakMap,sl=new WeakMap;var ee;(function(n){n[n.ExcludeBuffers=1]="ExcludeBuffers",n[n.IncludeAnonymous=2]="IncludeAnonymous",n[n.IgnoreMounts=4]="IgnoreMounts",n[n.IgnoreOverlays=8]="IgnoreOverlays"})(ee||(ee={}));class z{constructor(e,t,i,s,r){if(this.type=e,this.children=t,this.positions=i,this.length=s,this.props=null,r&&r.length){this.props=Object.create(null);for(let[o,l]of r)this.props[typeof o=="number"?o:o.id]=l}}toString(){let e=this.prop(L.mounted);if(e&&!e.overlay)return e.tree.toString();let t="";for(let i of this.children){let s=i.toString();s&&(t&&(t+=","),t+=s)}return this.type.name?(/\W/.test(this.type.name)&&!this.type.isError?JSON.stringify(this.type.name):this.type.name)+(t.length?"("+t+")":""):t}cursor(e=0){return new Ri(this.topNode,e)}cursorAt(e,t=0,i=0){let s=tn.get(this)||this.topNode,r=new Ri(s);return r.moveTo(e,t),tn.set(this,r._tree),r}get topNode(){return new _e(this,0,0,null)}resolve(e,t=0){let i=ei(tn.get(this)||this.topNode,e,t,!1);return tn.set(this,i),i}resolveInner(e,t=0){let i=ei(sl.get(this)||this.topNode,e,t,!0);return sl.set(this,i),i}iterate(e){let{enter:t,leave:i,from:s=0,to:r=this.length}=e;for(let o=this.cursor((e.mode||0)|ee.IncludeAnonymous);;){let l=!1;if(o.from<=r&&o.to>=s&&(o.type.isAnonymous||t(o)!==!1)){if(o.firstChild())continue;l=!0}for(;l&&i&&!o.type.isAnonymous&&i(o),!o.nextSibling();){if(!o.parent())return;l=!0}}}prop(e){return e.perNode?this.props?this.props[e.id]:void 0:this.type.prop(e)}get propValues(){let e=[];if(this.props)for(let t in this.props)e.push([+t,this.props[t]]);return e}balance(e={}){return this.children.length<=8?this:_r(xe.none,this.children,this.positions,0,this.children.length,0,this.length,(t,i,s)=>new z(this.type,t,i,s,this.propValues),e.makeTree||((t,i,s)=>new z(xe.none,t,i,s)))}static build(e){return Hd(e)}}z.empty=new z(xe.none,[],[],0);class Ir{constructor(e,t){this.buffer=e,this.index=t}get id(){return this.buffer[this.index-4]}get start(){return this.buffer[this.index-3]}get end(){return this.buffer[this.index-2]}get size(){return this.buffer[this.index-1]}get pos(){return this.index}next(){this.index-=4}fork(){return new Ir(this.buffer,this.index)}}class Vt{constructor(e,t,i){this.buffer=e,this.length=t,this.set=i}get type(){return xe.none}toString(){let e=[];for(let t=0;t<this.buffer.length;)e.push(this.childString(t)),t=this.buffer[t+3];return e.join(",")}childString(e){let t=this.buffer[e],i=this.buffer[e+3],s=this.set.types[t],r=s.name;if(/\W/.test(r)&&!s.isError&&(r=JSON.stringify(r)),e+=4,i==e)return r;let o=[];for(;e<i;)o.push(this.childString(e)),e=this.buffer[e+3];return r+"("+o.join(",")+")"}findChild(e,t,i,s,r){let{buffer:o}=this,l=-1;for(let a=e;a!=t&&!(Ph(r,s,o[a+1],o[a+2])&&(l=a,i>0));a=o[a+3]);return l}slice(e,t,i){let s=this.buffer,r=new Uint16Array(t-e),o=0;for(let l=e,a=0;l<t;){r[a++]=s[l++],r[a++]=s[l++]-i;let h=r[a++]=s[l++]-i;r[a++]=s[l++]-e,o=Math.max(o,h)}return new Vt(r,o,this.set)}}function Ph(n,e,t,i){switch(n){case-2:return t<e;case-1:return i>=e&&t<e;case 0:return t<e&&i>e;case 1:return t<=e&&i>e;case 2:return i>e;case 4:return!0}}function Eh(n,e){let t=n.childBefore(e);for(;t;){let i=t.lastChild;if(!i||i.to!=t.to)break;i.type.isError&&i.from==i.to?(n=t,t=i.prevSibling):t=i}return n}function ei(n,e,t,i){for(var s;n.from==n.to||(t<1?n.from>=e:n.from>e)||(t>-1?n.to<=e:n.to<e);){let o=!i&&n instanceof _e&&n.index<0?null:n.parent;if(!o)return n;n=o}let r=i?0:ee.IgnoreOverlays;if(i)for(let o=n,l=o.parent;l;o=l,l=o.parent)o instanceof _e&&o.index<0&&((s=l.enter(e,t,r))===null||s===void 0?void 0:s.from)!=o.from&&(n=l);for(;;){let o=n.enter(e,t,r);if(!o)return n;n=o}}class _e{constructor(e,t,i,s){this._tree=e,this.from=t,this.index=i,this._parent=s}get type(){return this._tree.type}get name(){return this._tree.type.name}get to(){return this.from+this._tree.length}nextChild(e,t,i,s,r=0){for(let o=this;;){for(let{children:l,positions:a}=o._tree,h=t>0?l.length:-1;e!=h;e+=t){let c=l[e],f=a[e]+o.from;if(Ph(s,i,f,f+c.length)){if(c instanceof Vt){if(r&ee.ExcludeBuffers)continue;let u=c.findChild(0,c.buffer.length,t,i-f,s);if(u>-1)return new Ye(new Fd(o,c,e,f),null,u)}else if(r&ee.IncludeAnonymous||!c.type.isAnonymous||Nr(c)){let u;if(!(r&ee.IgnoreMounts)&&c.props&&(u=c.prop(L.mounted))&&!u.overlay)return new _e(u.tree,f,e,o);let d=new _e(c,f,e,o);return r&ee.IncludeAnonymous||!d.type.isAnonymous?d:d.nextChild(t<0?c.children.length-1:0,t,i,s)}}}if(r&ee.IncludeAnonymous||!o.type.isAnonymous||(o.index>=0?e=o.index+t:e=t<0?-1:o._parent._tree.children.length,o=o._parent,!o))return null}}get firstChild(){return this.nextChild(0,1,0,4)}get lastChild(){return this.nextChild(this._tree.children.length-1,-1,0,4)}childAfter(e){return this.nextChild(0,1,e,2)}childBefore(e){return this.nextChild(this._tree.children.length-1,-1,e,-2)}enter(e,t,i=0){let s;if(!(i&ee.IgnoreOverlays)&&(s=this._tree.prop(L.mounted))&&s.overlay){let r=e-this.from;for(let{from:o,to:l}of s.overlay)if((t>0?o<=r:o<r)&&(t<0?l>=r:l>r))return new _e(s.tree,s.overlay[0].from+this.from,-1,this)}return this.nextChild(0,1,e,t,i)}nextSignificantParent(){let e=this;for(;e.type.isAnonymous&&e._parent;)e=e._parent;return e}get parent(){return this._parent?this._parent.nextSignificantParent():null}get nextSibling(){return this._parent&&this.index>=0?this._parent.nextChild(this.index+1,1,0,4):null}get prevSibling(){return this._parent&&this.index>=0?this._parent.nextChild(this.index-1,-1,0,4):null}cursor(e=0){return new Ri(this,e)}get tree(){return this._tree}toTree(){return this._tree}resolve(e,t=0){return ei(this,e,t,!1)}resolveInner(e,t=0){return ei(this,e,t,!0)}enterUnfinishedNodesBefore(e){return Eh(this,e)}getChild(e,t=null,i=null){let s=On(this,e,t,i);return s.length?s[0]:null}getChildren(e,t=null,i=null){return On(this,e,t,i)}toString(){return this._tree.toString()}get node(){return this}matchContext(e){return Bn(this,e)}}function On(n,e,t,i){let s=n.cursor(),r=[];if(!s.firstChild())return r;if(t!=null){for(;!s.type.is(t);)if(!s.nextSibling())return r}for(;;){if(i!=null&&s.type.is(i))return r;if(s.type.is(e)&&r.push(s.node),!s.nextSibling())return i==null?r:[]}}function Bn(n,e,t=e.length-1){for(let i=n.parent;t>=0;i=i.parent){if(!i)return!1;if(!i.type.isAnonymous){if(e[t]&&e[t]!=i.name)return!1;t--}}return!0}class Fd{constructor(e,t,i,s){this.parent=e,this.buffer=t,this.index=i,this.start=s}}class Ye{get name(){return this.type.name}get from(){return this.context.start+this.context.buffer.buffer[this.index+1]}get to(){return this.context.start+this.context.buffer.buffer[this.index+2]}constructor(e,t,i){this.context=e,this._parent=t,this.index=i,this.type=e.buffer.set.types[e.buffer.buffer[i]]}child(e,t,i){let{buffer:s}=this.context,r=s.findChild(this.index+4,s.buffer[this.index+3],e,t-this.context.start,i);return r<0?null:new Ye(this.context,this,r)}get firstChild(){return this.child(1,0,4)}get lastChild(){return this.child(-1,0,4)}childAfter(e){return this.child(1,e,2)}childBefore(e){return this.child(-1,e,-2)}enter(e,t,i=0){if(i&ee.ExcludeBuffers)return null;let{buffer:s}=this.context,r=s.findChild(this.index+4,s.buffer[this.index+3],t>0?1:-1,e-this.context.start,t);return r<0?null:new Ye(this.context,this,r)}get parent(){return this._parent||this.context.parent.nextSignificantParent()}externalSibling(e){return this._parent?null:this.context.parent.nextChild(this.context.index+e,e,0,4)}get nextSibling(){let{buffer:e}=this.context,t=e.buffer[this.index+3];return t<(this._parent?e.buffer[this._parent.index+3]:e.buffer.length)?new Ye(this.context,this._parent,t):this.externalSibling(1)}get prevSibling(){let{buffer:e}=this.context,t=this._parent?this._parent.index+4:0;return this.index==t?this.externalSibling(-1):new Ye(this.context,this._parent,e.findChild(t,this.index,-1,0,4))}cursor(e=0){return new Ri(this,e)}get tree(){return null}toTree(){let e=[],t=[],{buffer:i}=this.context,s=this.index+4,r=i.buffer[this.index+3];if(r>s){let o=i.buffer[this.index+1];e.push(i.slice(s,r,o)),t.push(0)}return new z(this.type,e,t,this.to-this.from)}resolve(e,t=0){return ei(this,e,t,!1)}resolveInner(e,t=0){return ei(this,e,t,!0)}enterUnfinishedNodesBefore(e){return 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this.buffer?this.buffer.buffer.childString(this.index):this._tree.toString()}enterChild(e,t,i){if(!this.buffer)return this.yield(this._tree.nextChild(e<0?this._tree._tree.children.length-1:0,e,t,i,this.mode));let{buffer:s}=this.buffer,r=s.findChild(this.index+4,s.buffer[this.index+3],e,t-this.buffer.start,i);return r<0?!1:(this.stack.push(this.index),this.yieldBuf(r))}firstChild(){return this.enterChild(1,0,4)}lastChild(){return this.enterChild(-1,0,4)}childAfter(e){return this.enterChild(1,e,2)}childBefore(e){return this.enterChild(-1,e,-2)}enter(e,t,i=this.mode){return this.buffer?i&ee.ExcludeBuffers?!1:this.enterChild(1,e,t):this.yield(this._tree.enter(e,t,i))}parent(){if(!this.buffer)return this.yieldNode(this.mode&ee.IncludeAnonymous?this._tree._parent:this._tree.parent);if(this.stack.length)return this.yieldBuf(this.stack.pop());let e=this.mode&ee.IncludeAnonymous?this.buffer.parent:this.buffer.parent.nextSignificantParent();return 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r=s;r;r=r.enter(e,t,ee.ExcludeBuffers))r.type.isTop&&(s=r);return s}class ur extends Ie{constructor(e,t,i){super(e,t,[],i),this.parser=t}static define(e){let t=Nh(e.languageData);return new ur(t,e.parser.configure({props:[Dt.add(i=>i.isTop?t:void 0)]}),e.name)}configure(e,t){return new ur(this.data,this.parser.configure(e),t||this.name)}get allowsNesting(){return this.parser.hasWrappers()}}function pe(n){let e=n.field(Ie.state,!1);return e?e.tree:z.empty}class sp{constructor(e){this.doc=e,this.cursorPos=0,this.string="",this.cursor=e.iter()}get length(){return this.doc.length}syncTo(e){return this.string=this.cursor.next(e-this.cursorPos).value,this.cursorPos=e+this.string.length,this.cursorPos-this.string.length}chunk(e){return this.syncTo(e),this.string}get lineChunks(){return!0}read(e,t){let i=this.cursorPos-this.string.length;return e<i||t>=this.cursorPos?this.doc.sliceString(e,t):this.string.slice(e-i,t-i)}}let di=null;class ti{constructor(e,t,i=[],s,r,o,l,a){this.parser=e,this.state=t,this.fragments=i,this.tree=s,this.treeLen=r,this.viewport=o,this.skipped=l,this.scheduleOn=a,this.parse=null,this.tempSkipped=[]}static create(e,t,i){return new ti(e,t,[],z.empty,0,i,[],null)}startParse(){return this.parser.startParse(new sp(this.state.doc),this.fragments)}work(e,t){return t!=null&&t>=this.state.doc.length&&(t=void 0),this.tree!=z.empty&&this.isDone(t??this.state.doc.length)?(this.takeTree(),!0):this.withContext(()=>{var i;if(typeof e=="number"){let s=Date.now()+e;e=()=>Date.now()>s}for(this.parse||(this.parse=this.startParse()),t!=null&&(this.parse.stoppedAt==null||this.parse.stoppedAt>t)&&t<this.state.doc.length&&this.parse.stopAt(t);;){let s=this.parse.advance();if(s)if(this.fragments=this.withoutTempSkipped(rt.addTree(s,this.fragments,this.parse.stoppedAt!=null)),this.treeLen=(i=this.parse.stoppedAt)!==null&&i!==void 0?i:this.state.doc.length,this.tree=s,this.parse=null,this.treeLen<(t??this.state.doc.length))this.parse=this.startParse();else return!0;if(e())return!1}})}takeTree(){let e,t;this.parse&&(e=this.parse.parsedPos)>=this.treeLen&&((this.parse.stoppedAt==null||this.parse.stoppedAt>e)&&this.parse.stopAt(e),this.withContext(()=>{for(;!(t=this.parse.advance()););}),this.treeLen=e,this.tree=t,this.fragments=this.withoutTempSkipped(rt.addTree(this.tree,this.fragments,!0)),this.parse=null)}withContext(e){let t=di;di=this;try{return e()}finally{di=t}}withoutTempSkipped(e){for(let t;t=this.tempSkipped.pop();)e=pl(e,t.from,t.to);return e}changes(e,t){let{fragments:i,tree:s,treeLen:r,viewport:o,skipped:l}=this;if(this.takeTree(),!e.empty){let a=[];if(e.iterChangedRanges((h,c,f,u)=>a.push({fromA:h,toA:c,fromB:f,toB:u})),i=rt.applyChanges(i,a),s=z.empty,r=0,o={from:e.mapPos(o.from,-1),to:e.mapPos(o.to,1)},this.skipped.length){l=[];for(let h of this.skipped){let c=e.mapPos(h.from,1),f=e.mapPos(h.to,-1);c<f&&l.push({from:c,to:f})}}}return new ti(this.parser,t,i,s,r,o,l,this.scheduleOn)}updateViewport(e){if(this.viewport.from==e.from&&this.viewport.to==e.to)return!1;this.viewport=e;let t=this.skipped.length;for(let i=0;i<this.skipped.length;i++){let{from:s,to:r}=this.skipped[i];s<e.to&&r>e.from&&(this.fragments=pl(this.fragments,s,r),this.skipped.splice(i--,1))}return this.skipped.length>=t?!1:(this.reset(),!0)}reset(){this.parse&&(this.takeTree(),this.parse=null)}skipUntilInView(e,t){this.skipped.push({from:e,to:t})}static getSkippingParser(e){return new class extends Rh{createParse(t,i,s){let r=s[0].from,o=s[s.length-1].to;return{parsedPos:r,advance(){let a=di;if(a){for(let h of s)a.tempSkipped.push(h);e&&(a.scheduleOn=a.scheduleOn?Promise.all([a.scheduleOn,e]):e)}return this.parsedPos=o,new z(xe.none,[],[],o-r)},stoppedAt:null,stopAt(){}}}}}isDone(e){e=Math.min(e,this.state.doc.length);let t=this.fragments;return this.treeLen>=e&&t.length&&t[0].from==0&&t[0].to>=e}static get(){return di}}function pl(n,e,t){return rt.applyChanges(n,[{fromA:e,toA:t,fromB:e,toB:t}])}class ii{constructor(e){this.context=e,this.tree=e.tree}apply(e){if(!e.docChanged&&this.tree==this.context.tree)return this;let t=this.context.changes(e.changes,e.state),i=this.context.treeLen==e.startState.doc.length?void 0:Math.max(e.changes.mapPos(this.context.treeLen),t.viewport.to);return t.work(20,i)||t.takeTree(),new ii(t)}static init(e){let t=Math.min(3e3,e.doc.length),i=ti.create(e.facet(wt).parser,e,{from:0,to:t});return i.work(20,t)||i.takeTree(),new ii(i)}}Ie.state=Me.define({create:ii.init,update(n,e){for(let t of e.effects)if(t.is(Ie.setState))return t.value;return e.startState.facet(wt)!=e.state.facet(wt)?ii.init(e.state):n.apply(e)}});let _h=n=>{let e=setTimeout(()=>n(),500);return()=>clearTimeout(e)};typeof requestIdleCallback<"u"&&(_h=n=>{let e=-1,t=setTimeout(()=>{e=requestIdleCallback(n,{timeout:500-100})},100);return()=>e<0?clearTimeout(t):cancelIdleCallback(e)});const gs=typeof navigator<"u"&&(!((ms=navigator.scheduling)===null||ms===void 0)&&ms.isInputPending)?()=>navigator.scheduling.isInputPending():null,rp=be.fromClass(class{constructor(e){this.view=e,this.working=null,this.workScheduled=0,this.chunkEnd=-1,this.chunkBudget=-1,this.work=this.work.bind(this),this.scheduleWork()}update(e){let t=this.view.state.field(Ie.state).context;(t.updateViewport(e.view.viewport)||this.view.viewport.to>t.treeLen)&&this.scheduleWork(),e.docChanged&&(this.view.hasFocus&&(this.chunkBudget+=50),this.scheduleWork()),this.checkAsyncSchedule(t)}scheduleWork(){if(this.working)return;let{state:e}=this.view,t=e.field(Ie.state);(t.tree!=t.context.tree||!t.context.isDone(e.doc.length))&&(this.working=_h(this.work))}work(e){this.working=null;let t=Date.now();if(this.chunkEnd<t&&(this.chunkEnd<0||this.view.hasFocus)&&(this.chunkEnd=t+3e4,this.chunkBudget=3e3),this.chunkBudget<=0)return;let{state:i,viewport:{to:s}}=this.view,r=i.field(Ie.state);if(r.tree==r.context.tree&&r.context.isDone(s+1e5))return;let o=Date.now()+Math.min(this.chunkBudget,100,e&&!gs?Math.max(25,e.timeRemaining()-5):1e9),l=r.context.treeLen<s&&i.doc.length>s+1e3,a=r.context.work(()=>gs&&gs()||Date.now()>o,s+(l?0:1e5));this.chunkBudget-=Date.now()-t,(a||this.chunkBudget<=0)&&(r.context.takeTree(),this.view.dispatch({effects:Ie.setState.of(new ii(r.context))})),this.chunkBudget>0&&!(a&&!l)&&this.scheduleWork(),this.checkAsyncSchedule(r.context)}checkAsyncSchedule(e){e.scheduleOn&&(this.workScheduled++,e.scheduleOn.then(()=>this.scheduleWork()).catch(t=>He(this.view.state,t)).then(()=>this.workScheduled--),e.scheduleOn=null)}destroy(){this.working&&this.working()}isWorking(){return!!(this.working||this.workScheduled>0)}},{eventHandlers:{focus(){this.scheduleWork()}}}),wt=D.define({combine(n){return n.length?n[0]:null},enables:n=>[Ie.state,rp,O.contentAttributes.compute([n],e=>{let t=e.facet(n);return t&&t.name?{"data-language":t.name}:{}})]});class ry{constructor(e,t=[]){this.language=e,this.support=t,this.extension=[e,t]}}class Vh{constructor(e,t,i,s,r,o=void 0){this.name=e,this.alias=t,this.extensions=i,this.filename=s,this.loadFunc=r,this.support=o,this.loading=null}load(){return this.loading||(this.loading=this.loadFunc().then(e=>this.support=e,e=>{throw this.loading=null,e}))}static of(e){let{load:t,support:i}=e;if(!t){if(!i)throw new RangeError("Must pass either 'load' or 'support' to LanguageDescription.of");t=()=>Promise.resolve(i)}return new Vh(e.name,(e.alias||[]).concat(e.name).map(s=>s.toLowerCase()),e.extensions||[],e.filename,t,i)}static matchFilename(e,t){for(let s of e)if(s.filename&&s.filename.test(t))return s;let i=/\.([^.]+)$/.exec(t);if(i){for(let s of e)if(s.extensions.indexOf(i[1])>-1)return s}return null}static matchLanguageName(e,t,i=!0){t=t.toLowerCase();for(let s of e)if(s.alias.some(r=>r==t))return s;if(i)for(let s of e)for(let r of s.alias){let o=t.indexOf(r);if(o>-1&&(r.length>2||!/\w/.test(t[o-1])&&!/\w/.test(t[o+r.length])))return s}return null}}const Fh=D.define(),jn=D.define({combine:n=>{if(!n.length)return"  ";let e=n[0];if(!e||/\S/.test(e)||Array.from(e).some(t=>t!=e[0]))throw new Error("Invalid indent unit: "+JSON.stringify(n[0]));return e}});function Rt(n){let e=n.facet(jn);return e.charCodeAt(0)==9?n.tabSize*e.length:e.length}function Li(n,e){let t="",i=n.tabSize,s=n.facet(jn)[0];if(s=="	"){for(;e>=i;)t+="	",e-=i;s=" "}for(let r=0;r<e;r++)t+=s;return t}function Vr(n,e){n instanceof N&&(n=new Kn(n));for(let i of n.state.facet(Fh)){let s=i(n,e);if(s!==void 0)return s}let t=pe(n.state);return t?lp(n,t,e):null}class Kn{constructor(e,t={}){this.state=e,this.options=t,this.unit=Rt(e)}lineAt(e,t=1){let i=this.state.doc.lineAt(e),{simulateBreak:s,simulateDoubleBreak:r}=this.options;return s!=null&&s>=i.from&&s<=i.to?r&&s==e?{text:"",from:e}:(t<0?s<e:s<=e)?{text:i.text.slice(s-i.from),from:s}:{text:i.text.slice(0,s-i.from),from:i.from}:i}textAfterPos(e,t=1){if(this.options.simulateDoubleBreak&&e==this.options.simulateBreak)return"";let{text:i,from:s}=this.lineAt(e,t);return i.slice(e-s,Math.min(i.length,e+100-s))}column(e,t=1){let{text:i,from:s}=this.lineAt(e,t),r=this.countColumn(i,e-s),o=this.options.overrideIndentation?this.options.overrideIndentation(s):-1;return o>-1&&(r+=o-this.countColumn(i,i.search(/\S|$/))),r}countColumn(e,t=e.length){return Fi(e,this.state.tabSize,t)}lineIndent(e,t=1){let{text:i,from:s}=this.lineAt(e,t),r=this.options.overrideIndentation;if(r){let o=r(s);if(o>-1)return o}return this.countColumn(i,i.search(/\S|$/))}get simulatedBreak(){return this.options.simulateBreak||null}}const op=new L;function lp(n,e,t){return Hh(e.resolveInner(t).enterUnfinishedNodesBefore(t),t,n)}function ap(n){return n.pos==n.options.simulateBreak&&n.options.simulateDoubleBreak}function hp(n){let e=n.type.prop(op);if(e)return e;let t=n.firstChild,i;if(t&&(i=t.type.prop(L.closedBy))){let s=n.lastChild,r=s&&i.indexOf(s.name)>-1;return o=>Wh(o,!0,1,void 0,r&&!ap(o)?s.from:void 0)}return n.parent==null?cp:null}function Hh(n,e,t){for(;n;n=n.parent){let i=hp(n);if(i)return i(Fr.create(t,e,n))}return null}function cp(){return 0}class Fr extends Kn{constructor(e,t,i){super(e.state,e.options),this.base=e,this.pos=t,this.node=i}static create(e,t,i){return new Fr(e,t,i)}get textAfter(){return this.textAfterPos(this.pos)}get baseIndent(){let e=this.state.doc.lineAt(this.node.from);for(;;){let t=this.node.resolve(e.from);for(;t.parent&&t.parent.from==t.from;)t=t.parent;if(fp(t,this.node))break;e=this.state.doc.lineAt(t.from)}return this.lineIndent(e.from)}continue(){let e=this.node.parent;return e?Hh(e,this.pos,this.base):0}}function fp(n,e){for(let t=e;t;t=t.parent)if(n==t)return!0;return!1}function up(n){let e=n.node,t=e.childAfter(e.from),i=e.lastChild;if(!t)return null;let s=n.options.simulateBreak,r=n.state.doc.lineAt(t.from),o=s==null||s<=r.from?r.to:Math.min(r.to,s);for(let l=t.to;;){let a=e.childAfter(l);if(!a||a==i)return null;if(!a.type.isSkipped)return a.from<o?t:null;l=a.to}}function oy({closing:n,align:e=!0,units:t=1}){return i=>Wh(i,e,t,n)}function Wh(n,e,t,i,s){let r=n.textAfter,o=r.match(/^\s*/)[0].length,l=i&&r.slice(o,o+i.length)==i||s==n.pos+o,a=e?up(n):null;return 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e.effects)t.is(Un)&&!wp(n,t.value.from,t.value.to)?n=n.update({add:[ml.range(t.value.from,t.value.to)]}):t.is(Wi)&&(n=n.update({filter:(i,s)=>t.value.from!=i||t.value.to!=s,filterFrom:t.value.from,filterTo:t.value.to}));if(e.selection){let t=!1,{head:i}=e.selection.main;n.between(i,i,(s,r)=>{s<i&&r>i&&(t=!0)}),t&&(n=n.update({filterFrom:i,filterTo:i,filter:(s,r)=>r<=i||s>=i}))}return n},provide:n=>O.decorations.from(n),toJSON(n,e){let t=[];return n.between(0,e.doc.length,(i,s)=>{t.push(i,s)}),t},fromJSON(n){if(!Array.isArray(n)||n.length%2)throw new RangeError("Invalid JSON for fold state");let e=[];for(let t=0;t<n.length;){let i=n[t++],s=n[t++];if(typeof i!="number"||typeof s!="number")throw new RangeError("Invalid JSON for fold state");e.push(ml.range(i,s))}return E.set(e,!0)}});function Ln(n,e,t){var i;let s=null;return(i=n.field(Lt,!1))===null||i===void 0||i.between(e,t,(r,o)=>{(!s||s.from>r)&&(s={from:r,to:o})}),s}function wp(n,e,t){let i=!1;return n.between(e,e,(s,r)=>{s==e&&r==t&&(i=!0)}),i}function jh(n,e){return n.field(Lt,!1)?e:e.concat(R.appendConfig.of(Gh()))}const kp=n=>{for(let e of qh(n)){let t=Rn(n.state,e.from,e.to);if(t)return n.dispatch({effects:jh(n.state,[Un.of(t),Kh(n,t)])}),!0}return!1},vp=n=>{if(!n.state.field(Lt,!1))return!1;let e=[];for(let t of qh(n)){let i=Ln(n.state,t.from,t.to);i&&e.push(Wi.of(i),Kh(n,i,!1))}return e.length&&n.dispatch({effects:e}),e.length>0};function Kh(n,e,t=!0){let i=n.state.doc.lineAt(e.from).number,s=n.state.doc.lineAt(e.to).number;return O.announce.of(`${n.state.phrase(t?"Folded lines":"Unfolded lines")} ${i} ${n.state.phrase("to")} ${s}.`)}const xp=n=>{let{state:e}=n,t=[];for(let i=0;i<e.doc.length;){let s=n.lineBlockAt(i),r=Rn(e,s.from,s.to);r&&t.push(Un.of(r)),i=(r?n.lineBlockAt(r.to):s).to+1}return t.length&&n.dispatch({effects:jh(n.state,t)}),!!t.length},Sp=n=>{let e=n.state.field(Lt,!1);if(!e||!e.size)return!1;let t=[];return e.between(0,n.state.doc.length,(i,s)=>{t.push(Wi.of({from:i,to:s}))}),n.dispatch({effects:t}),!0},Cp=[{key:"Ctrl-Shift-[",mac:"Cmd-Alt-[",run:kp},{key:"Ctrl-Shift-]",mac:"Cmd-Alt-]",run:vp},{key:"Ctrl-Alt-[",run:xp},{key:"Ctrl-Alt-]",run:Sp}],Ap={placeholderDOM:null,placeholderText:"…"},Uh=D.define({combine(n){return _t(n,Ap)}});function Gh(n){let e=[Lt,Tp];return n&&e.push(Uh.of(n)),e}const ml=E.replace({widget:new class extends tt{toDOM(n){let{state:e}=n,t=e.facet(Uh),i=r=>{let o=n.lineBlockAt(n.posAtDOM(r.target)),l=Ln(n.state,o.from,o.to);l&&n.dispatch({effects:Wi.of(l)}),r.preventDefault()};if(t.placeholderDOM)return t.placeholderDOM(n,i);let s=document.createElement("span");return s.textContent=t.placeholderText,s.setAttribute("aria-label",e.phrase("folded code")),s.title=e.phrase("unfold"),s.className="cm-foldPlaceholder",s.onclick=i,s}}}),Mp={openText:"⌄",closedText:"›",markerDOM:null,domEventHandlers:{},foldingChanged:()=>!1};class ys extends bt{constructor(e,t){super(),this.config=e,this.open=t}eq(e){return this.config==e.config&&this.open==e.open}toDOM(e){if(this.config.markerDOM)return this.config.markerDOM(this.open);let t=document.createElement("span");return t.textContent=this.open?this.config.openText:this.config.closedText,t.title=e.state.phrase(this.open?"Fold line":"Unfold line"),t}}function Dp(n={}){let e=Object.assign(Object.assign({},Mp),n),t=new ys(e,!0),i=new ys(e,!1),s=be.fromClass(class{constructor(o){this.from=o.viewport.from,this.markers=this.buildMarkers(o)}update(o){(o.docChanged||o.viewportChanged||o.startState.facet(wt)!=o.state.facet(wt)||o.startState.field(Lt,!1)!=o.state.field(Lt,!1)||pe(o.startState)!=pe(o.state)||e.foldingChanged(o))&&(this.markers=this.buildMarkers(o.view))}buildMarkers(o){let l=new Pt;for(let a of o.viewportLineBlocks){let h=Ln(o.state,a.from,a.to)?i:Rn(o.state,a.from,a.to)?t:null;h&&l.add(a.from,a.from,h)}return l.finish()}}),{domEventHandlers:r}=e;return[s,Td({class:"cm-foldGutter",markers(o){var l;return((l=o.plugin(s))===null||l===void 0?void 0:l.markers)||F.empty},initialSpacer(){return new ys(e,!1)},domEventHandlers:Object.assign(Object.assign({},r),{click:(o,l,a)=>{if(r.click&&r.click(o,l,a))return!0;let h=Ln(o.state,l.from,l.to);if(h)return o.dispatch({effects:Wi.of(h)}),!0;let c=Rn(o.state,l.from,l.to);return c?(o.dispatch({effects:Un.of(c)}),!0):!1}})}),Gh()]}const Tp=O.baseTheme({".cm-foldPlaceholder":{backgroundColor:"#eee",border:"1px solid #ddd",color:"#888",borderRadius:".2em",margin:"0 1px",padding:"0 1px",cursor:"pointer"},".cm-foldGutter span":{padding:"0 1px",cursor:"pointer"}});class li{constructor(e,t){this.specs=e;let i;function s(l){let a=mt.newName();return(i||(i=Object.create(null)))["."+a]=l,a}const r=typeof t.all=="string"?t.all:t.all?s(t.all):void 0,o=t.scope;this.scope=o instanceof Ie?l=>l.prop(Dt)==o.data:o?l=>l==o:void 0,this.style=Ih(e.map(l=>({tag:l.tag,class:l.class||s(Object.assign({},l,{tag:null}))})),{all:r}).style,this.module=i?new mt(i):null,this.themeType=t.themeType}static define(e,t){return new li(e,t||{})}}const dr=D.define(),$h=D.define({combine(n){return n.length?[n[0]]:null}});function bs(n){let e=n.facet(dr);return e.length?e:n.facet($h)}function Hr(n,e){let t=[Bp],i;return n instanceof li&&(n.module&&t.push(O.styleModule.of(n.module)),i=n.themeType),e?.fallback?t.push($h.of(n)):i?t.push(dr.computeN([O.darkTheme],s=>s.facet(O.darkTheme)==(i=="dark")?[n]:[])):t.push(dr.of(n)),t}class Op{constructor(e){this.markCache=Object.create(null),this.tree=pe(e.state),this.decorations=this.buildDeco(e,bs(e.state))}update(e){let t=pe(e.state),i=bs(e.state),s=i!=bs(e.startState);t.length<e.view.viewport.to&&!s&&t.type==this.tree.type?this.decorations=this.decorations.map(e.changes):(t!=this.tree||e.viewportChanged||s)&&(this.tree=t,this.decorations=this.buildDeco(e.view,i))}buildDeco(e,t){if(!t||!this.tree.length)return E.none;let i=new Pt;for(let{from:s,to:r}of e.visibleRanges)ep(this.tree,t,(o,l,a)=>{i.add(o,l,this.markCache[a]||(this.markCache[a]=E.mark({class:a})))},s,r);return i.finish()}}const Bp=Vi.high(be.fromClass(Op,{decorations:n=>n.decorations})),Pp=li.define([{tag:m.meta,color:"#404740"},{tag:m.link,textDecoration:"underline"},{tag:m.heading,textDecoration:"underline",fontWeight:"bold"},{tag:m.emphasis,fontStyle:"italic"},{tag:m.strong,fontWeight:"bold"},{tag:m.strikethrough,textDecoration:"line-through"},{tag:m.keyword,color:"#708"},{tag:[m.atom,m.bool,m.url,m.contentSeparator,m.labelName],color:"#219"},{tag:[m.literal,m.inserted],color:"#164"},{tag:[m.string,m.deleted],color:"#a11"},{tag:[m.regexp,m.escape,m.special(m.string)],color:"#e40"},{tag:m.definition(m.variableName),color:"#00f"},{tag:m.local(m.variableName),color:"#30a"},{tag:[m.typeName,m.namespace],color:"#085"},{tag:m.className,color:"#167"},{tag:[m.special(m.variableName),m.macroName],color:"#256"},{tag:m.definition(m.propertyName),color:"#00c"},{tag:m.comment,color:"#940"},{tag:m.invalid,color:"#f00"}]),Ep=1e4,Rp="()[]{}",Lp=new L;function pr(n,e,t){let i=n.prop(e<0?L.openedBy:L.closedBy);if(i)return i;if(n.name.length==1){let s=t.indexOf(n.name);if(s>-1&&s%2==(e<0?1:0))return[t[s+e]]}return null}function mr(n){let e=n.type.prop(Lp);return e?e(n.node):n}function qt(n,e,t,i={}){let s=i.maxScanDistance||Ep,r=i.brackets||Rp,o=pe(n),l=o.resolveInner(e,t);for(let a=l;a;a=a.parent){let h=pr(a.type,t,r);if(h&&a.from<a.to){let c=mr(a);if(c&&(t>0?e>=c.from&&e<c.to:e>c.from&&e<=c.to))return Ip(n,e,t,a,c,h,r)}}return Np(n,e,t,o,l.type,s,r)}function Ip(n,e,t,i,s,r,o){let l=i.parent,a={from:s.from,to:s.to},h=0,c=l?.cursor();if(c&&(t<0?c.childBefore(i.from):c.childAfter(i.to)))do if(t<0?c.to<=i.from:c.from>=i.to){if(h==0&&r.indexOf(c.type.name)>-1&&c.from<c.to){let f=mr(c);return{start:a,end:f?{from:f.from,to:f.to}:void 0,matched:!0}}else if(pr(c.type,t,o))h++;else if(pr(c.type,-t,o)){if(h==0){let f=mr(c);return{start:a,end:f&&f.from<f.to?{from:f.from,to:f.to}:void 0,matched:!1}}h--}}while(t<0?c.prevSibling():c.nextSibling());return{start:a,matched:!1}}function Np(n,e,t,i,s,r,o){let l=t<0?n.sliceDoc(e-1,e):n.sliceDoc(e,e+1),a=o.indexOf(l);if(a<0||a%2==0!=t>0)return null;let h={from:t<0?e-1:e,to:t>0?e+1:e},c=n.doc.iterRange(e,t>0?n.doc.length:0),f=0;for(let u=0;!c.next().done&&u<=r;){let d=c.value;t<0&&(u+=d.length);let p=e+u*t;for(let g=t>0?0:d.length-1,y=t>0?d.length:-1;g!=y;g+=t){let b=o.indexOf(d[g]);if(!(b<0||i.resolveInner(p+g,1).type!=s))if(b%2==0==t>0)f++;else{if(f==1)return{start:h,end:{from:p+g,to:p+g+1},matched:b>>1==a>>1};f--}}t>0&&(u+=d.length)}return c.done?{start:h,matched:!1}:null}function gl(n,e,t,i=0,s=0){e==null&&(e=n.search(/[^\s\u00a0]/),e==-1&&(e=n.length));let r=s;for(let o=i;o<e;o++)n.charCodeAt(o)==9?r+=t-r%t:r++;return r}class Jh{constructor(e,t,i,s){this.string=e,this.tabSize=t,this.indentUnit=i,this.overrideIndent=s,this.pos=0,this.start=0,this.lastColumnPos=0,this.lastColumnValue=0}eol(){return this.pos>=this.string.length}sol(){return this.pos==0}peek(){return this.string.charAt(this.pos)||void 0}next(){if(this.pos<this.string.length)return this.string.charAt(this.pos++)}eat(e){let t=this.string.charAt(this.pos),i;if(typeof e=="string"?i=t==e:i=t&&(e instanceof RegExp?e.test(t):e(t)),i)return++this.pos,t}eatWhile(e){let t=this.pos;for(;this.eat(e););return this.pos>t}eatSpace(){let e=this.pos;for(;/[\s\u00a0]/.test(this.string.charAt(this.pos));)++this.pos;return this.pos>e}skipToEnd(){this.pos=this.string.length}skipTo(e){let t=this.string.indexOf(e,this.pos);if(t>-1)return this.pos=t,!0}backUp(e){this.pos-=e}column(){return this.lastColumnPos<this.start&&(this.lastColumnValue=gl(this.string,this.start,this.tabSize,this.lastColumnPos,this.lastColumnValue),this.lastColumnPos=this.start),this.lastColumnValue}indentation(){var e;return(e=this.overrideIndent)!==null&&e!==void 0?e:gl(this.string,null,this.tabSize)}match(e,t,i){if(typeof e=="string"){let s=o=>i?o.toLowerCase():o,r=this.string.substr(this.pos,e.length);return 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i=pe(e.state),s=i.resolve(t);for(;s&&s.type!=this.topNode;)s=s.parent;if(!s)return null;let r,{overrideIndentation:o}=e.options;o&&(r=yl.get(e.state),r!=null&&r<t-1e4&&(r=void 0));let l=Wr(this,i,0,s.from,r??t),a,h;if(l?(h=l.state,a=l.pos+1):(h=this.streamParser.startState(e.unit),a=0),t-a>1e4)return null;for(;a<t;){let f=e.state.doc.lineAt(a),u=Math.min(t,f.to);if(f.length){let d=o?o(f.from):-1,p=new Jh(f.text,e.state.tabSize,e.unit,d<0?void 0:d);for(;p.pos<u-f.from;)Xh(this.streamParser.token,p,h)}else this.streamParser.blankLine(h,e.unit);if(u==t)break;a=f.to+1}let c=e.lineAt(t);return o&&r==null&&yl.set(e.state,c.from),this.streamParser.indent(h,/^\s*(.*)/.exec(c.text)[1],e)}get allowsNesting(){return!1}}function Wr(n,e,t,i,s){let r=t>=i&&t+e.length<=s&&e.prop(n.stateAfter);if(r)return{state:n.streamParser.copyState(r),pos:t+e.length};for(let o=e.children.length-1;o>=0;o--){let l=e.children[o],a=t+e.positions[o],h=l instanceof z&&a<s&&Wr(n,l,a,i,s);if(h)return h}return null}function Yh(n,e,t,i,s){if(s&&t<=0&&i>=e.length)return e;!s&&e.type==n.topNode&&(s=!0);for(let r=e.children.length-1;r>=0;r--){let o=e.positions[r],l=e.children[r],a;if(o<i&&l instanceof z){if(!(a=Yh(n,l,t-o,i-o,s)))break;return s?new z(e.type,e.children.slice(0,r).concat(a),e.positions.slice(0,r+1),o+a.length):a}}return null}function Fp(n,e,t,i){for(let s of e){let r=s.from+(s.openStart?25:0),o=s.to-(s.openEnd?25:0),l=r<=t&&o>t&&Wr(n,s.tree,0-s.offset,t,o),a;if(l&&(a=Yh(n,s.tree,t+s.offset,l.pos+s.offset,!1)))return{state:l.state,tree:a}}return{state:n.streamParser.startState(i?Rt(i):4),tree:z.empty}}class Hp{constructor(e,t,i,s){this.lang=e,this.input=t,this.fragments=i,this.ranges=s,this.stoppedAt=null,this.chunks=[],this.chunkPos=[],this.chunk=[],this.chunkReused=void 0,this.rangeIndex=0,this.to=s[s.length-1].to;let r=ti.get(),o=s[0].from,{state:l,tree:a}=Fp(e,i,o,r?.state);this.state=l,this.parsedPos=this.chunkStart=o+a.length;for(let h=0;h<a.children.length;h++)this.chunks.push(a.children[h]),this.chunkPos.push(a.positions[h]);r&&this.parsedPos<r.viewport.from-1e5&&(this.state=this.lang.streamParser.startState(Rt(r.state)),r.skipUntilInView(this.parsedPos,r.viewport.from),this.parsedPos=r.viewport.from),this.moveRangeIndex()}advance(){let e=ti.get(),t=this.stoppedAt==null?this.to:Math.min(this.to,this.stoppedAt),i=Math.min(t,this.chunkStart+2048);for(e&&(i=Math.min(i,e.viewport.to));this.parsedPos<i;)this.parseLine(e);return this.chunkStart<this.parsedPos&&this.finishChunk(),this.parsedPos>=t?this.finish():e&&this.parsedPos>=e.viewport.to?(e.skipUntilInView(this.parsedPos,t),this.finish()):null}stopAt(e){this.stoppedAt=e}lineAfter(e){let t=this.input.chunk(e);if(this.input.lineChunks)t==`
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l=Xh(r.token,o,this.state);if(l&&(s=this.emitToken(this.lang.tokenTable.resolve(l),this.parsedPos+o.start,this.parsedPos+o.pos,4,s)),o.start>1e4)break}this.parsedPos=i,this.moveRangeIndex(),this.parsedPos<this.to&&this.parsedPos++}finishChunk(){let e=z.build({buffer:this.chunk,start:this.chunkStart,length:this.parsedPos-this.chunkStart,nodeSet:Wp,topID:0,maxBufferLength:2048,reused:this.chunkReused});e=new z(e.type,e.children,e.positions,e.length,[[this.lang.stateAfter,this.lang.streamParser.copyState(this.state)]]),this.chunks.push(e),this.chunkPos.push(this.chunkStart-this.ranges[0].from),this.chunk=[],this.chunkReused=void 0,this.chunkStart=this.parsedPos}finish(){return new z(this.lang.topNode,this.chunks,this.chunkPos,this.parsedPos-this.ranges[0].from).balance()}}function Xh(n,e,t){e.start=e.pos;for(let i=0;i<10;i++){let s=n(e,t);if(e.pos>e.start)return s}throw new Error("Stream parser failed to advance stream.")}const zr=Object.create(null),Ii=[xe.none],Wp=new Lr(Ii),bl=[],Zh=Object.create(null);for(let[n,e]of[["variable","variableName"],["variable-2","variableName.special"],["string-2","string.special"],["def","variableName.definition"],["tag","tagName"],["attribute","attributeName"],["type","typeName"],["builtin","variableName.standard"],["qualifier","modifier"],["error","invalid"],["header","heading"],["property","propertyName"]])Zh[n]=ec(zr,e);class Qh{constructor(e){this.extra=e,this.table=Object.assign(Object.create(null),Zh)}resolve(e){return e?this.table[e]||(this.table[e]=ec(this.extra,e)):0}}const zp=new Qh(zr);function ws(n,e){bl.indexOf(n)>-1||(bl.push(n),console.warn(e))}function ec(n,e){let t=null;for(let r of e.split(".")){let o=n[r]||m[r];o?typeof o=="function"?t?t=o(t):ws(r,`Modifier ${r} used at start of tag`):t?ws(r,`Tag ${r} used as modifier`):t=o:ws(r,`Unknown highlighting tag ${r}`)}if(!t)return 0;let i=e.replace(/ /g,"_"),s=xe.define({id:Ii.length,name:i,props:[Zd({[i]:t})]});return Ii.push(s),s.id}function qp(n){let e=xe.define({id:Ii.length,name:"Document",props:[Dt.add(()=>n)]});return Ii.push(e),e}const jp=n=>{let e=jr(n.state);return e.line?Kp(n):e.block?Gp(n):!1};function qr(n,e){return({state:t,dispatch:i})=>{if(t.readOnly)return!1;let s=n(e,t);return s?(i(t.update(s)),!0):!1}}const Kp=qr(Yp,0),Up=qr(tc,0),Gp=qr((n,e)=>tc(n,e,Jp(e)),0);function jr(n,e=n.selection.main.head){let t=n.languageDataAt("commentTokens",e);return t.length?t[0]:{}}const pi=50;function $p(n,{open:e,close:t},i,s){let r=n.sliceDoc(i-pi,i),o=n.sliceDoc(s,s+pi),l=/\s*$/.exec(r)[0].length,a=/^\s*/.exec(o)[0].length,h=r.length-l;if(r.slice(h-e.length,h)==e&&o.slice(a,a+t.length)==t)return{open:{pos:i-l,margin:l&&1},close:{pos:s+a,margin:a&&1}};let c,f;s-i<=2*pi?c=f=n.sliceDoc(i,s):(c=n.sliceDoc(i,i+pi),f=n.sliceDoc(s-pi,s));let u=/^\s*/.exec(c)[0].length,d=/\s*$/.exec(f)[0].length,p=f.length-d-t.length;return c.slice(u,u+e.length)==e&&f.slice(p,p+t.length)==t?{open:{pos:i+u+e.length,margin:/\s/.test(c.charAt(u+e.length))?1:0},close:{pos:s-d-t.length,margin:/\s/.test(f.charAt(p-1))?1:0}}:null}function Jp(n){let e=[];for(let t of n.selection.ranges){let i=n.doc.lineAt(t.from),s=t.to<=i.to?i:n.doc.lineAt(t.to),r=e.length-1;r>=0&&e[r].to>i.from?e[r].to=s.to:e.push({from:i.from,to:s.to})}return e}function tc(n,e,t=e.selection.ranges){let i=t.map(r=>jr(e,r.from).block);if(!i.every(r=>r))return null;let s=t.map((r,o)=>$p(e,i[o],r.from,r.to));if(n!=2&&!s.every(r=>r))return{changes:e.changes(t.map((r,o)=>s[o]?[]:[{from:r.from,insert:i[o].open+" "},{from:r.to,insert:" "+i[o].close}]))};if(n!=1&&s.some(r=>r)){let r=[];for(let o=0,l;o<s.length;o++)if(l=s[o]){let a=i[o],{open:h,close:c}=l;r.push({from:h.pos-a.open.length,to:h.pos+h.margin},{from:c.pos-c.margin,to:c.pos+a.close.length})}return{changes:r}}return null}function Yp(n,e,t=e.selection.ranges){let i=[],s=-1;for(let{from:r,to:o}of t){let l=i.length,a=1e9;for(let h=r;h<=o;){let c=e.doc.lineAt(h);if(c.from>s&&(r==o||o>c.from)){s=c.from;let f=jr(e,h).line;if(!f)continue;let u=/^\s*/.exec(c.text)[0].length,d=u==c.length,p=c.text.slice(u,u+f.length)==f?u:-1;u<c.text.length&&u<a&&(a=u),i.push({line:c,comment:p,token:f,indent:u,empty:d,single:!1})}h=c.to+1}if(a<1e9)for(let h=l;h<i.length;h++)i[h].indent<i[h].line.text.length&&(i[h].indent=a);i.length==l+1&&(i[l].single=!0)}if(n!=2&&i.some(r=>r.comment<0&&(!r.empty||r.single))){let r=[];for(let{line:l,token:a,indent:h,empty:c,single:f}of i)(f||!c)&&r.push({from:l.from+h,insert:a+" "});let o=e.changes(r);return{changes:o,selection:e.selection.map(o,1)}}else if(n!=1&&i.some(r=>r.comment>=0)){let r=[];for(let{line:o,comment:l,token:a}of i)if(l>=0){let h=o.from+l,c=h+a.length;o.text[c-o.from]==" "&&c++,r.push({from:h,to:c})}return{changes:r}}return null}const gr=Nt.define(),Xp=Nt.define(),Zp=D.define(),ic=D.define({combine(n){return _t(n,{minDepth:100,newGroupDelay:500},{minDepth:Math.max,newGroupDelay:Math.min})}});function Qp(n){let e=0;return n.iterChangedRanges((t,i)=>e=i),e}const nc=Me.define({create(){return Xe.empty},update(n,e){let t=e.state.facet(ic),i=e.annotation(gr);if(i){let a=e.docChanged?w.single(Qp(e.changes)):void 0,h=Se.fromTransaction(e,a),c=i.side,f=c==0?n.undone:n.done;return h?f=In(f,f.length,t.minDepth,h):f=oc(f,e.startState.selection),new Xe(c==0?i.rest:f,c==0?f:i.rest)}let s=e.annotation(Xp);if((s=="full"||s=="before")&&(n=n.isolate()),e.annotation(re.addToHistory)===!1)return e.changes.empty?n:n.addMapping(e.changes.desc);let r=Se.fromTransaction(e),o=e.annotation(re.time),l=e.annotation(re.userEvent);return r?n=n.addChanges(r,o,l,t.newGroupDelay,t.minDepth):e.selection&&(n=n.addSelection(e.startState.selection,o,l,t.newGroupDelay)),(s=="full"||s=="after")&&(n=n.isolate()),n},toJSON(n){return{done:n.done.map(e=>e.toJSON()),undone:n.undone.map(e=>e.toJSON())}},fromJSON(n){return new Xe(n.done.map(Se.fromJSON),n.undone.map(Se.fromJSON))}});function em(n={}){return[nc,ic.of(n),O.domEventHandlers({beforeinput(e,t){let i=e.inputType=="historyUndo"?sc:e.inputType=="historyRedo"?yr:null;return i?(e.preventDefault(),i(t)):!1}})]}function Gn(n,e){return function({state:t,dispatch:i}){if(!e&&t.readOnly)return!1;let s=t.field(nc,!1);if(!s)return!1;let r=s.pop(n,t,e);return r?(i(r),!0):!1}}const sc=Gn(0,!1),yr=Gn(1,!1),tm=Gn(0,!0),im=Gn(1,!0);class Se{constructor(e,t,i,s,r){this.changes=e,this.effects=t,this.mapped=i,this.startSelection=s,this.selectionsAfter=r}setSelAfter(e){return new Se(this.changes,this.effects,this.mapped,this.startSelection,e)}toJSON(){var e,t,i;return{changes:(e=this.changes)===null||e===void 0?void 0:e.toJSON(),mapped:(t=this.mapped)===null||t===void 0?void 0:t.toJSON(),startSelection:(i=this.startSelection)===null||i===void 0?void 0:i.toJSON(),selectionsAfter:this.selectionsAfter.map(s=>s.toJSON())}}static fromJSON(e){return new Se(e.changes&&ne.fromJSON(e.changes),[],e.mapped&&Ze.fromJSON(e.mapped),e.startSelection&&w.fromJSON(e.startSelection),e.selectionsAfter.map(w.fromJSON))}static fromTransaction(e,t){let i=Ne;for(let s of e.startState.facet(Zp)){let r=s(e);r.length&&(i=i.concat(r))}return!i.length&&e.changes.empty?null:new Se(e.changes.invert(e.startState.doc),i,void 0,t||e.startState.selection,Ne)}static selection(e){return new Se(void 0,Ne,void 0,void 0,e)}}function In(n,e,t,i){let s=e+1>t+20?e-t-1:0,r=n.slice(s,e);return r.push(i),r}function nm(n,e){let t=[],i=!1;return n.iterChangedRanges((s,r)=>t.push(s,r)),e.iterChangedRanges((s,r,o,l)=>{for(let a=0;a<t.length;){let h=t[a++],c=t[a++];l>=h&&o<=c&&(i=!0)}}),i}function sm(n,e){return n.ranges.length==e.ranges.length&&n.ranges.filter((t,i)=>t.empty!=e.ranges[i].empty).length===0}function rc(n,e){return n.length?e.length?n.concat(e):n:e}const Ne=[],rm=200;function oc(n,e){if(n.length){let t=n[n.length-1],i=t.selectionsAfter.slice(Math.max(0,t.selectionsAfter.length-rm));return i.length&&i[i.length-1].eq(e)?n:(i.push(e),In(n,n.length-1,1e9,t.setSelAfter(i)))}else return[Se.selection([e])]}function om(n){let e=n[n.length-1],t=n.slice();return t[n.length-1]=e.setSelAfter(e.selectionsAfter.slice(0,e.selectionsAfter.length-1)),t}function ks(n,e){if(!n.length)return n;let t=n.length,i=Ne;for(;t;){let s=lm(n[t-1],e,i);if(s.changes&&!s.changes.empty||s.effects.length){let r=n.slice(0,t);return r[t-1]=s,r}else e=s.mapped,t--,i=s.selectionsAfter}return i.length?[Se.selection(i)]:Ne}function lm(n,e,t){let i=rc(n.selectionsAfter.length?n.selectionsAfter.map(l=>l.map(e)):Ne,t);if(!n.changes)return Se.selection(i);let s=n.changes.map(e),r=e.mapDesc(n.changes,!0),o=n.mapped?n.mapped.composeDesc(r):r;return new Se(s,R.mapEffects(n.effects,e),o,n.startSelection.map(r),i)}const am=/^(input\.type|delete)($|\.)/;class Xe{constructor(e,t,i=0,s=void 0){this.done=e,this.undone=t,this.prevTime=i,this.prevUserEvent=s}isolate(){return this.prevTime?new Xe(this.done,this.undone):this}addChanges(e,t,i,s,r){let o=this.done,l=o[o.length-1];return l&&l.changes&&!l.changes.empty&&e.changes&&(!i||am.test(i))&&(!l.selectionsAfter.length&&t-this.prevTime<s&&nm(l.changes,e.changes)||i=="input.type.compose")?o=In(o,o.length-1,r,new Se(e.changes.compose(l.changes),rc(e.effects,l.effects),l.mapped,l.startSelection,Ne)):o=In(o,o.length,r,e),new Xe(o,Ne,t,i)}addSelection(e,t,i,s){let r=this.done.length?this.done[this.done.length-1].selectionsAfter:Ne;return r.length>0&&t-this.prevTime<s&&i==this.prevUserEvent&&i&&/^select($|\.)/.test(i)&&sm(r[r.length-1],e)?this:new Xe(oc(this.done,e),this.undone,t,i)}addMapping(e){return new Xe(ks(this.done,e),ks(this.undone,e),this.prevTime,this.prevUserEvent)}pop(e,t,i){let s=e==0?this.done:this.undone;if(s.length==0)return null;let r=s[s.length-1];if(i&&r.selectionsAfter.length)return t.update({selection:r.selectionsAfter[r.selectionsAfter.length-1],annotations:gr.of({side:e,rest:om(s)}),userEvent:e==0?"select.undo":"select.redo",scrollIntoView:!0});if(r.changes){let o=s.length==1?Ne:s.slice(0,s.length-1);return r.mapped&&(o=ks(o,r.mapped)),t.update({changes:r.changes,selection:r.startSelection,effects:r.effects,annotations:gr.of({side:e,rest:o}),filter:!1,userEvent:e==0?"undo":"redo",scrollIntoView:!0})}else return null}}Xe.empty=new Xe(Ne,Ne);const hm=[{key:"Mod-z",run:sc,preventDefault:!0},{key:"Mod-y",mac:"Mod-Shift-z",run:yr,preventDefault:!0},{linux:"Ctrl-Shift-z",run:yr,preventDefault:!0},{key:"Mod-u",run:tm,preventDefault:!0},{key:"Alt-u",mac:"Mod-Shift-u",run:im,preventDefault:!0}];function ai(n,e){return w.create(n.ranges.map(e),n.mainIndex)}function it(n,e){return n.update({selection:e,scrollIntoView:!0,userEvent:"select"})}function We({state:n,dispatch:e},t){let i=ai(n.selection,t);return i.eq(n.selection)?!1:(e(it(n,i)),!0)}function $n(n,e){return 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e=this.view.state.field(this.stateField),t=e.open;if((t.selected>-1&&t.selected<this.range.from||t.selected>=this.range.to)&&(this.range=Hl(t.options.length,t.selected,this.view.state.facet(It).maxRenderedOptions),this.list.remove(),this.list=this.dom.appendChild(this.createListBox(t.options,e.id,this.range)),this.list.addEventListener("scroll",()=>{this.info&&this.view.requestMeasure(this.placeInfo)})),this.updateSelectedOption(t.selected)){this.info&&(this.info.remove(),this.info=null);let{completion:i}=t.options[t.selected],{info:s}=i;if(!s)return;let r=typeof s=="string"?document.createTextNode(s):s(i);if(!r)return;"then"in r?r.then(o=>{o&&this.view.state.field(this.stateField,!1)==e&&this.addInfoPane(o)}).catch(o=>He(this.view.state,o,"completion info")):this.addInfoPane(r)}}addInfoPane(e){let t=this.info=document.createElement("div");t.className="cm-tooltip 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o=e.selection||r.some(l=>l.hasResult()&&e.changes.touchesRange(l.from,l.to))||!Tg(r,this.active)?Mi.build(r,t,this.id,this.open,i):this.open&&e.docChanged?this.open.map(e.changes):this.open;!o&&r.every(l=>l.state!=1)&&r.some(l=>l.hasResult())&&(r=r.map(l=>l.hasResult()?new st(l.source,0):l));for(let l of e.effects)l.is(Fc)&&(o=o&&o.setSelected(l.value,this.id));return r==this.active&&o==this.open?this:new Nn(r,this.id,o)}get tooltip(){return this.open?this.open.tooltip:null}get attrs(){return this.open?this.open.attrs:Og}}function Tg(n,e){if(n==e)return!0;for(let t=0,i=0;;){for(;t<n.length&&!n[t].hasResult;)t++;for(;i<e.length&&!e[i].hasResult;)i++;let s=t==n.length,r=i==e.length;if(s||r)return s==r;if(n[t++].result!=e[i++].result)return!1}}const Og={"aria-autocomplete":"list"};function zl(n,e){let t={"aria-autocomplete":"list","aria-haspopup":"listbox","aria-controls":n};return e>-1&&(t["aria-activedescendant"]=n+"-"+e),t}const Bg=[];function Pg(n){return 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v=r;r=d(p),r===v?u[r].p(p,g):(_n(),j(u[v],1,1,()=>{u[v]=null}),Vn(),o=u[r],o?o.p(p,g):(o=u[r]=f[r](p),o.c()),H(o,1),o.m(l.parentNode,l))},i(p){a||(H(e.$$.fragment,p),H(i.$$.fragment,p),H(o),a=!0)},o(p){j(e.$$.fragment,p),j(i.$$.fragment,p),j(o),a=!1},d(p){de(e,p),p&&Ae(t),de(i,p),p&&Ae(s),u[r].d(p),p&&Ae(l)}}}function z0(n){let e,t,i,s;const r=[N0,I0],o=[];function l(a,h){return a[6]==="static"?0:1}return e=l(n),t=o[e]=r[e](n),{c(){t.c(),i=ta()},m(a,h){o[e].m(a,h),Ce(a,i,h),s=!0},p(a,[h]){let c=e;e=l(a),e===c?o[e].p(a,h):(_n(),j(o[c],1,1,()=>{o[c]=null}),Vn(),t=o[e],t?t.p(a,h):(t=o[e]=r[e](a),t.c()),H(t,1),t.m(i.parentNode,i))},i(a){s||(H(t),s=!0)},o(a){j(t),s=!1},d(a){o[e].d(a),a&&Ae(i)}}}function q0(n,e,t){const i=Xl();let{value:s=""}=e,{value_is_output:r=!1}=e,{language:o=""}=e,{lines:l=5}=e,{target:a}=e,{elem_id:h=""}=e,{elem_classes:c=[]}=e,{visible:f=!0}=e,{mode:u}=e,{label:d="Code"}=e,{show_label:p=!0}=e,{loading_status:g}=e,y=a.classList.contains("dark");function b(){i("change",s),r||i("input")}Zc(()=>{t(11,r=!1)});function v(k){s=k,t(0,s)}function S(k){s=k,t(0,s)}return n.$$set=k=>{"value"in k&&t(0,s=k.value),"value_is_output"in k&&t(11,r=k.value_is_output),"language"in k&&t(1,o=k.language),"lines"in k&&t(2,l=k.lines),"target"in k&&t(12,a=k.target),"elem_id"in k&&t(3,h=k.elem_id),"elem_classes"in k&&t(4,c=k.elem_classes),"visible"in k&&t(5,f=k.visible),"mode"in k&&t(6,u=k.mode),"label"in k&&t(7,d=k.label),"show_label"in k&&t(8,p=k.show_label),"loading_status"in k&&t(9,g=k.loading_status)},n.$$.update=()=>{n.$$.dirty&1&&b()},[s,o,l,h,c,f,u,d,p,g,y,r,a,v,S]}class j0 extends si{constructor(e){super(),ri(this,e,q0,z0,oi,{value:0,value_is_output:11,language:1,lines:2,target:12,elem_id:3,elem_classes:4,visible:5,mode:6,label:7,show_label:8,loading_status:9})}}const K0=j0,U0=["static","dynamic"],uy=Object.freeze(Object.defineProperty({__proto__:null,Component:K0,modes:U0},Symbol.toStringTag,{value:"Module"}));export{np as A,cy as B,bg as C,Id as D,w as E,uy as F,ee as I,ur as L,Lr as N,Rh as P,jt as S,z as T,hy as a,ry as b,xe as c,oy as d,L as e,gp as f,Ge as g,pe as h,op as i,Vi as j,qn as k,Ie as l,Nh as m,Dt as n,mp as o,ny as p,Vh as q,ti as r,Zd as s,m as t,Lp as u,O as v,ay as w,iy as x,fy as y,ly as z};
-//# sourceMappingURL=index-90411bc1.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/image.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/image.py
deleted file mode 100644
index d7215bdfabb99a00268c6e2a838b01323a7b5ed0..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_inline/image.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Process ![image](<src> "title")
-from __future__ import annotations
-
-from ..common.utils import isSpace, normalizeReference
-from ..token import Token
-from .state_inline import StateInline
-
-
-def image(state: StateInline, silent: bool):
-    label = None
-    href = ""
-    oldPos = state.pos
-    max = state.posMax
-
-    # /* ! */
-    if state.srcCharCode[state.pos] != 0x21:
-        return False
-    # /* [ */
-    if state.pos + 1 < state.posMax and state.srcCharCode[state.pos + 1] != 0x5B:
-        return False
-
-    labelStart = state.pos + 2
-    labelEnd = state.md.helpers.parseLinkLabel(state, state.pos + 1, False)
-
-    # parser failed to find ']', so it's not a valid link
-    if labelEnd < 0:
-        return False
-
-    pos = labelEnd + 1
-    # /* ( */
-    if pos < max and state.srcCharCode[pos] == 0x28:
-        #
-        # Inline link
-        #
-
-        # [link](  <href>  "title"  )
-        #        ^^ skipping these spaces
-        pos += 1
-        while pos < max:
-            code = state.srcCharCode[pos]
-            if not isSpace(code) and code != 0x0A:
-                break
-            pos += 1
-
-        if pos >= max:
-            return False
-
-        # [link](  <href>  "title"  )
-        #          ^^^^^^ parsing link destination
-        start = pos
-        res = state.md.helpers.parseLinkDestination(state.src, pos, state.posMax)
-        if res.ok:
-            href = state.md.normalizeLink(res.str)
-            if state.md.validateLink(href):
-                pos = res.pos
-            else:
-                href = ""
-
-        # [link](  <href>  "title"  )
-        #                ^^ skipping these spaces
-        start = pos
-        while pos < max:
-            code = state.srcCharCode[pos]
-            if not isSpace(code) and code != 0x0A:
-                break
-            pos += 1
-
-        # [link](  <href>  "title"  )
-        #                  ^^^^^^^ parsing link title
-        res = state.md.helpers.parseLinkTitle(state.src, pos, state.posMax)
-        if pos < max and start != pos and res.ok:
-            title = res.str
-            pos = res.pos
-
-            # [link](  <href>  "title"  )
-            #                         ^^ skipping these spaces
-            while pos < max:
-                code = state.srcCharCode[pos]
-                if not isSpace(code) and code != 0x0A:
-                    break
-                pos += 1
-        else:
-            title = ""
-
-        # /* ) */
-        if pos >= max or state.srcCharCode[pos] != 0x29:
-            state.pos = oldPos
-            return False
-
-        pos += 1
-
-    else:
-        #
-        # Link reference
-        #
-        if "references" not in state.env:
-            return False
-
-        # /* [ */
-        if pos < max and state.srcCharCode[pos] == 0x5B:
-            start = pos + 1
-            pos = state.md.helpers.parseLinkLabel(state, pos)
-            if pos >= 0:
-                label = state.src[start:pos]
-                pos += 1
-            else:
-                pos = labelEnd + 1
-        else:
-            pos = labelEnd + 1
-
-        # covers label == '' and label == undefined
-        # (collapsed reference link and shortcut reference link respectively)
-        if not label:
-            label = state.src[labelStart:labelEnd]
-
-        label = normalizeReference(label)
-
-        ref = state.env["references"].get(label, None)
-        if not ref:
-            state.pos = oldPos
-            return False
-
-        href = ref["href"]
-        title = ref["title"]
-
-    #
-    # We found the end of the link, and know for a fact it's a valid link
-    # so all that's left to do is to call tokenizer.
-    #
-    if not silent:
-        content = state.src[labelStart:labelEnd]
-
-        tokens: list[Token] = []
-        state.md.inline.parse(content, state.md, state.env, tokens)
-
-        token = state.push("image", "img", 0)
-        token.attrs = {"src": href, "alt": ""}
-        token.children = tokens or None
-        token.content = content
-
-        if title:
-            token.attrSet("title", title)
-
-        # note, this is not part of markdown-it JS, but is useful for renderers
-        if label and state.md.options.get("store_labels", False):
-            token.meta["label"] = label
-
-    state.pos = pos
-    state.posMax = max
-    return True
diff --git a/spaces/leilaglewis/01-3dModel-GradioDemo/app.py b/spaces/leilaglewis/01-3dModel-GradioDemo/app.py
deleted file mode 100644
index 064b45bde2d867954428b4f90ef3a6c8c62d03f4..0000000000000000000000000000000000000000
--- a/spaces/leilaglewis/01-3dModel-GradioDemo/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import time
-import gradio as gr
-import os
-
-def load_mesh(mesh_file_name):
-    return mesh_file_name, mesh_file_name
-
-demo = gr.Interface(
-    fn=load_mesh,
-    inputs=gr.Model3D(),
-    outputs=[
-        gr.Model3D(
-            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
-        gr.File(label="Download 3D Model")
-    ],
-    examples=[
-        [os.path.join(os.path.dirname(__file__), "files/Duck.glb")],
-        [os.path.join(os.path.dirname(__file__), "files/rubber_duck.glb")],
-        [os.path.join(os.path.dirname(__file__), "files/GroundVehicle.glb")]
-    ],
-)
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/leogabraneth/text-generation-webui-main/modules/shared.py b/spaces/leogabraneth/text-generation-webui-main/modules/shared.py
deleted file mode 100644
index 626c2bf877e3c89af7527ab40319046bacc9c1a4..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/modules/shared.py
+++ /dev/null
@@ -1,268 +0,0 @@
-import argparse
-import os
-import sys
-from collections import OrderedDict
-from pathlib import Path
-
-import yaml
-
-from modules.logging_colors import logger
-
-# Model variables
-model = None
-tokenizer = None
-model_name = 'None'
-is_seq2seq = False
-model_dirty_from_training = False
-lora_names = []
-
-# Generation variables
-stop_everything = False
-generation_lock = None
-processing_message = '*Is typing...*'
-
-# UI variables
-gradio = {}
-persistent_interface_state = {}
-need_restart = False
-
-# UI defaults
-settings = {
-    'dark_theme': True,
-    'show_controls': True,
-    'start_with': '',
-    'mode': 'chat',
-    'chat_style': 'cai-chat',
-    'prompt-default': 'QA',
-    'prompt-notebook': 'QA',
-    'preset': 'simple-1',
-    'max_new_tokens': 200,
-    'max_new_tokens_min': 1,
-    'max_new_tokens_max': 4096,
-    'seed': -1,
-    'negative_prompt': '',
-    'truncation_length': 2048,
-    'truncation_length_min': 0,
-    'truncation_length_max': 32768,
-    'custom_stopping_strings': '',
-    'auto_max_new_tokens': False,
-    'max_tokens_second': 0,
-    'ban_eos_token': False,
-    'custom_token_bans': '',
-    'add_bos_token': True,
-    'skip_special_tokens': True,
-    'stream': True,
-    'name1': 'You',
-    'character': 'Assistant',
-    'instruction_template': 'Alpaca',
-    'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
-    'autoload_model': False,
-    'default_extensions': ['gallery'],
-}
-
-parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
-
-# Basic settings
-parser.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. Warning: this is likely not safe for sharing publicly.')
-parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.')
-parser.add_argument('--model', type=str, help='Name of the model to load by default.')
-parser.add_argument('--lora', type=str, nargs='+', help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.')
-parser.add_argument('--model-dir', type=str, default='models/', help='Path to directory with all the models.')
-parser.add_argument('--lora-dir', type=str, default='loras/', help='Path to directory with all the loras.')
-parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.')
-parser.add_argument('--settings', type=str, help='Load the default interface settings from this yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, this file will be loaded by default without the need to use the --settings flag.')
-parser.add_argument('--extensions', type=str, nargs='+', help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
-parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
-parser.add_argument('--chat-buttons', action='store_true', help='Show buttons on the chat tab instead of a hover menu.')
-
-# Model loader
-parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, exllama_hf, exllamav2_hf, exllama, exllamav2, autogptq, gptq-for-llama, llama.cpp, llamacpp_hf, ctransformers, autoawq.')
-
-# Accelerate/transformers
-parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
-parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
-parser.add_argument('--gpu-memory', type=str, nargs='+', help='Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.')
-parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.')
-parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
-parser.add_argument('--disk-cache-dir', type=str, default='cache', help='Directory to save the disk cache to. Defaults to "cache".')
-parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision (using bitsandbytes).')
-parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
-parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces VRAM usage slightly, but it comes at a performance cost.')
-parser.add_argument('--xformers', action='store_true', help='Use xformer\'s memory efficient attention. This is really old and probably doesn\'t do anything.')
-parser.add_argument('--sdp-attention', action='store_true', help='Use PyTorch 2.0\'s SDP attention. Same as above.')
-parser.add_argument('--trust-remote-code', action='store_true', help='Set trust_remote_code=True while loading the model. Necessary for some models.')
-parser.add_argument('--use_fast', action='store_true', help='Set use_fast=True while loading the tokenizer.')
-
-# Accelerate 4-bit
-parser.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision (using bitsandbytes).')
-parser.add_argument('--use_double_quant', action='store_true', help='use_double_quant for 4-bit.')
-parser.add_argument('--compute_dtype', type=str, default='float16', help='compute dtype for 4-bit. Valid options: bfloat16, float16, float32.')
-parser.add_argument('--quant_type', type=str, default='nf4', help='quant_type for 4-bit. Valid options: nf4, fp4.')
-
-# llama.cpp
-parser.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.')
-parser.add_argument('--threads', type=int, default=0, help='Number of threads to use.')
-parser.add_argument('--threads-batch', type=int, default=0, help='Number of threads to use for batches/prompt processing.')
-parser.add_argument('--no_mul_mat_q', action='store_true', help='Disable the mulmat kernels.')
-parser.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.')
-parser.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.')
-parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.')
-parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.')
-parser.add_argument('--tensor_split', type=str, default=None, help='Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17.')
-parser.add_argument('--llama_cpp_seed', type=int, default=0, help='Seed for llama-cpp models. Default is 0 (random).')
-parser.add_argument('--numa', action='store_true', help='Activate NUMA task allocation for llama.cpp.')
-parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.')
-
-# ExLlama
-parser.add_argument('--gpu-split', type=str, help='Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.')
-parser.add_argument('--max_seq_len', type=int, default=2048, help='Maximum sequence length.')
-parser.add_argument('--cfg-cache', action='store_true', help='ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.')
-
-# AutoGPTQ
-parser.add_argument('--triton', action='store_true', help='Use triton.')
-parser.add_argument('--no_inject_fused_attention', action='store_true', help='Disable the use of fused attention, which will use less VRAM at the cost of slower inference.')
-parser.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.')
-parser.add_argument('--no_use_cuda_fp16', action='store_true', help='This can make models faster on some systems.')
-parser.add_argument('--desc_act', action='store_true', help='For models that do not have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.')
-parser.add_argument('--disable_exllama', action='store_true', help='Disable ExLlama kernel, which can improve inference speed on some systems.')
-
-# GPTQ-for-LLaMa
-parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
-parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.')
-parser.add_argument('--groupsize', type=int, default=-1, help='Group size.')
-parser.add_argument('--pre_layer', type=int, nargs='+', help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.')
-parser.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.')
-parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.')
-
-# DeepSpeed
-parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
-parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.')
-parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.')
-
-# RWKV
-parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".')
-parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.')
-
-# RoPE
-parser.add_argument('--alpha_value', type=float, default=1, help='Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.')
-parser.add_argument('--rope_freq_base', type=int, default=0, help='If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).')
-parser.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.")
-
-# Gradio
-parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
-parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
-parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
-parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
-parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
-parser.add_argument('--gradio-auth', type=str, help='Set Gradio authentication password in the format "username:password". Multiple credentials can also be supplied with "u1:p1,u2:p2,u3:p3".', default=None)
-parser.add_argument('--gradio-auth-path', type=str, help='Set the Gradio authentication file path. The file should contain one or more user:password pairs in the same format as above.', default=None)
-parser.add_argument('--ssl-keyfile', type=str, help='The path to the SSL certificate key file.', default=None)
-parser.add_argument('--ssl-certfile', type=str, help='The path to the SSL certificate cert file.', default=None)
-
-# API
-parser.add_argument('--api', action='store_true', help='Enable the API extension.')
-parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.')
-parser.add_argument('--public-api-id', type=str, help='Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.', default=None)
-parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.')
-parser.add_argument('--api-streaming-port', type=int, default=5005, help='The listening port for the streaming API.')
-
-# Multimodal
-parser.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.')
-
-# Deprecated parameters
-parser.add_argument('--notebook', action='store_true', help='DEPRECATED')
-parser.add_argument('--chat', action='store_true', help='DEPRECATED')
-parser.add_argument('--no-stream', action='store_true', help='DEPRECATED')
-parser.add_argument('--mul_mat_q', action='store_true', help='DEPRECATED')
-
-args = parser.parse_args()
-args_defaults = parser.parse_args([])
-provided_arguments = []
-for arg in sys.argv[1:]:
-    arg = arg.lstrip('-').replace('-', '_')
-    if hasattr(args, arg):
-        provided_arguments.append(arg)
-
-# Deprecation warnings
-for k in ['chat', 'notebook', 'no_stream', 'mul_mat_q']:
-    if getattr(args, k):
-        logger.warning(f'The --{k} flag has been deprecated and will be removed soon. Please remove that flag.')
-
-# Security warnings
-if args.trust_remote_code:
-    logger.warning('trust_remote_code is enabled. This is dangerous.')
-if 'COLAB_GPU' not in os.environ:
-    if args.share:
-        logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.")
-    if any((args.listen, args.share)) and not any((args.gradio_auth, args.gradio_auth_path)):
-        logger.warning("\nYou are potentially exposing the web UI to the entire internet without any access password.\nYou can create one with the \"--gradio-auth\" flag like this:\n\n--gradio-auth username:password\n\nMake sure to replace username:password with your own.")
-        if args.multi_user:
-            logger.warning('\nThe multi-user mode is highly experimental and should not be shared publicly.')
-
-
-def fix_loader_name(name):
-    if not name:
-        return name
-
-    name = name.lower()
-    if name in ['llamacpp', 'llama.cpp', 'llama-cpp', 'llama cpp']:
-        return 'llama.cpp'
-    if name in ['llamacpp_hf', 'llama.cpp_hf', 'llama-cpp-hf', 'llamacpp-hf', 'llama.cpp-hf']:
-        return 'llamacpp_HF'
-    elif name in ['transformers', 'huggingface', 'hf', 'hugging_face', 'hugging face']:
-        return 'Transformers'
-    elif name in ['autogptq', 'auto-gptq', 'auto_gptq', 'auto gptq']:
-        return 'AutoGPTQ'
-    elif name in ['gptq-for-llama', 'gptqforllama', 'gptqllama', 'gptq for llama', 'gptq_for_llama']:
-        return 'GPTQ-for-LLaMa'
-    elif name in ['exllama', 'ex-llama', 'ex_llama', 'exlama']:
-        return 'ExLlama'
-    elif name in ['exllama-hf', 'exllama_hf', 'exllama hf', 'ex-llama-hf', 'ex_llama_hf']:
-        return 'ExLlama_HF'
-    elif name in ['exllamav2', 'exllama-v2', 'ex_llama-v2', 'exlamav2', 'exlama-v2', 'exllama2', 'exllama-2']:
-        return 'ExLlamav2'
-    elif name in ['exllamav2-hf', 'exllamav2_hf', 'exllama-v2-hf', 'exllama_v2_hf', 'exllama-v2_hf', 'exllama2-hf', 'exllama2_hf', 'exllama-2-hf', 'exllama_2_hf', 'exllama-2_hf']:
-        return 'ExLlamav2_HF'
-    elif name in ['ctransformers', 'ctranforemrs', 'ctransformer']:
-        return 'ctransformers'
-    elif name in ['autoawq', 'awq', 'auto-awq']:
-        return 'AutoAWQ'
-
-
-def add_extension(name):
-    if args.extensions is None:
-        args.extensions = [name]
-    elif 'api' not in args.extensions:
-        args.extensions.append(name)
-
-
-def is_chat():
-    return True
-
-
-args.loader = fix_loader_name(args.loader)
-
-# Activate the API extension
-if args.api or args.public_api:
-    add_extension('api')
-
-# Activate the multimodal extension
-if args.multimodal_pipeline is not None:
-    add_extension('multimodal')
-
-# Load model-specific settings
-with Path(f'{args.model_dir}/config.yaml') as p:
-    if p.exists():
-        model_config = yaml.safe_load(open(p, 'r').read())
-    else:
-        model_config = {}
-
-# Load custom model-specific settings
-with Path(f'{args.model_dir}/config-user.yaml') as p:
-    if p.exists():
-        user_config = yaml.safe_load(open(p, 'r').read())
-    else:
-        user_config = {}
-
-model_config = OrderedDict(model_config)
-user_config = OrderedDict(user_config)
diff --git a/spaces/lewiswu1209/MockingBird/encoder/__init__.py b/spaces/lewiswu1209/MockingBird/encoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/lilacai/lilac/dist/README.md b/spaces/lilacai/lilac/dist/README.md
deleted file mode 100644
index 19c80613fe95ac8d27e6bfcc7003ad1096a8859d..0000000000000000000000000000000000000000
--- a/spaces/lilacai/lilac/dist/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-This directory is used for locally built whl files.
-We write a README.md to ensure an empty folder is uploaded when there is no whl.
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/AutoCAD Architecture 2015 With X-force Keygen 2015 __FULL__.md b/spaces/lincquiQcaudo/Top-20-Diffusion/AutoCAD Architecture 2015 With X-force Keygen 2015 __FULL__.md
deleted file mode 100644
index fbcfb6cf629b161854d992b1caeefd6066148176..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/AutoCAD Architecture 2015 With X-force Keygen 2015 __FULL__.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>AutoCAD Architecture 2015 with x-force keygen 2015</h2><br /><p><b><b>Download</b> &#10037;&#10037;&#10037; <a href="https://bytlly.com/2uGx0b">https://bytlly.com/2uGx0b</a></b></p><br /><br />
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Autodesk AutoCAD 2013 32 Bit 64bit With Crack __EXCLUSIVE__ And Keygen.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Autodesk AutoCAD 2013 32 Bit 64bit With Crack __EXCLUSIVE__ And Keygen.md
deleted file mode 100644
index ededfdeb12eda9c4e9050d39a271a1b6eae71459..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Autodesk AutoCAD 2013 32 Bit 64bit With Crack __EXCLUSIVE__ And Keygen.md	
+++ /dev/null
@@ -1,127 +0,0 @@
-<br />
-<h1>How to Get Autodesk AutoCAD 2013 32 Bit 64bit With CRACK And Keygen for Free</h1>
-
-<p>Autodesk AutoCAD 2013 is one of the most popular and powerful software for designing and drafting in 2D and 3D. It offers a wide range of features and tools that can help you create stunning models, edit and annotate drawings, apply various effects and styles, and collaborate with others. However, Autodesk AutoCAD 2013 is not a cheap software, and you need to pay a hefty price to get a license to use it. But what if you don't have the money or the time to buy a license? Is there a way to get Autodesk AutoCAD 2013 for free?</p>
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-
-<p>However, before you proceed with this method, you should be aware of the risks and consequences involved. Using a crack and a keygen is illegal and violates the terms of service of Autodesk. You might face legal actions or penalties if you are caught using pirated software. Moreover, using a crack and a keygen might expose your computer to viruses, malware, or other threats that can harm your system or compromise your data. Therefore, we do not recommend or endorse using a crack and a keygen to get Autodesk AutoCAD 2013 for free.</p>
-
-<p>But if you still want to try this method at your own risk, here are the steps you need to follow:</p>
-
-<ol>
-<li>Download Autodesk AutoCAD 2013 from the official website or from any trusted source. You can choose either the 32-bit or the 64-bit version depending on your system requirements.</li>
-<li>Install Autodesk AutoCAD 2013 on your computer. You can use any serial number or product key that matches these templates: 666-69696969, 667-98989898, 400-45454545, or 066-66666666. Use 001E1 as the product key.</li>
-<li>Download X-Force keygen AutoCAD 2013 from any reliable source. This is a program that can generate an activation code for Autodesk AutoCAD 2013. Make sure you download the correct version of the keygen that matches your system architecture (32-bit or 64-bit).</li>
-<li>Disable your antivirus software and your firewall temporarily. This is to prevent them from deleting or blocking the keygen file.</li>
-<li>Extract the keygen file using WinRAR or any other extraction tool. Right-click on the keygen file and select Run as administrator.</li>
-<li>Launch Autodesk AutoCAD 2013 and click on Activate. You will see a request code on the screen. Copy this code and paste it into the keygen program.</li>
-<li>Click on Generate and then on Patch in the keygen program. You will see a message saying Successfully patched.</li>
-<li>Copy the activation code from the keygen program and paste it into Autodesk AutoCAD 2013. Click on Next and then on Finish.</li>
-<li>Congratulations! You have successfully activated Autodesk AutoCAD 2013 for free using a crack and a keygen.</li>
-</ol>
-
-<p>We hope this article was helpful for you. However, we remind you again that using a crack and a keygen is illegal and risky, and we do not support or encourage this practice. If you want to use Autodesk AutoCAD 2013 legally and safely, we suggest you buy a license from the official website or from an authorized dealer.</p>
-<p></p>
-<h2>What are the features and benefits of Autodesk AutoCAD 2013?</h2>
-
-<p>Autodesk AutoCAD 2013 is not only a powerful and versatile software for designing and drafting, but also a software that offers many features and benefits that can enhance your productivity and creativity. Here are some of the features and benefits of Autodesk AutoCAD 2013 that you should know:</p>
-
-<ul>
-<li>A cloud-based platform: Autodesk AutoCAD 2013 allows you to connect and synchronize your drawings and files with Autodesk 360, a cloud-based service that provides online storage, sharing, and collaboration tools. You can access your drawings from anywhere, anytime, and from any device. You can also use Autodesk 360 to perform online rendering, analysis, simulation, and more.</li>
-<li>An enhanced command line: Autodesk AutoCAD 2013 improves the command line interface with an auto-correct feature that suggests relevant commands as you type. You can also access layers, hatch patterns, blocks, and other objects directly from the command line without interrupting your workflow.</li>
-<li>Support for Autodesk Inventor: Autodesk AutoCAD 2013 supports Autodesk Inventor file formats, allowing you to import and export 3D models between the two software. You can also create associative links between AutoCAD and Inventor files, so that any changes made in one software are reflected in the other.</li>
-<li>Workspaces: Autodesk AutoCAD 2013 allows you to customize your drawing area based on the type of task you are working on. You can choose from predefined workspaces or create your own workspaces that display specific menus, toolbars, palettes, and ribbon panels. You can also switch between workspaces easily with a single click.</li>
-<li>Advantages of AutoCAD: Autodesk AutoCAD 2013 offers many advantages over other software or manual methods of designing and drafting. Some of these advantages are:</li>
-<ul>
-<li>Accuracy: AutoCAD allows you to create precise and detailed drawings with accurate dimensions, coordinates, angles, and measurements. You can also use various tools and commands to check and correct errors in your drawings.</li>
-<li>Efficiency: AutoCAD allows you to work faster and smarter with various tools and commands that automate and simplify your tasks. You can also reuse and modify existing drawings or blocks to save time and effort.</li>
-<li>Creativity: AutoCAD allows you to explore and express your ideas in 2D and 3D models, using various effects and styles. You can also use various tools and commands to manipulate and modify your models to achieve the desired results.</li>
-<li>Collaboration: AutoCAD allows you to share and collaborate with others on your drawings and files, using various formats and platforms. You can also use various tools and commands to add comments, feedback, or annotations to your drawings.</li>
-</ul>
-</ul>
-
-<p>We hope this article was helpful for you. However, we remind you again that using a crack and a keygen is illegal and risky, and we do not support or encourage this practice. If you want to use Autodesk AutoCAD 2013 legally and safely, we suggest you buy a license from the official website or from an authorized dealer.</p>
-<h2>What are some tips and tricks for using Autodesk AutoCAD 2013?</h2>
-
-<p>Autodesk AutoCAD 2013 is not only a powerful and versatile software for designing and drafting, but also a software that offers many tips and tricks that can help you work faster and smarter. Here are some of the tips and tricks for using Autodesk AutoCAD 2013 that you should know:</p>
-
-<ul>
-<li>Use the Autodesk App Store: You can extend the functionality and productivity of Autodesk AutoCAD 2013 by downloading and installing various apps from the Autodesk App Store. You can access the Autodesk App Store from within AutoCAD in two ways: by clicking on the Exchange Apps button on the ribbon or by typing APPSTORE at the command line. You can find hundreds of Autodesk-approved apps for different purposes, such as design, analysis, rendering, collaboration, and more.</li>
-<li>Use the Page Setup function: You can make configuring plots and plotting easy by using the Page Setup function in AutoCAD. You can access the Page Setup function from the Layout tab or by typing PAGESETUP at the command line. You can use the Page Setup function to create and modify page setups for different layouts, printers, paper sizes, plot styles, plot options, and more. You can also save and import page setups from other drawings or templates.</li>
-<li>Use the LAYMCH command: You can quickly change the layer of selected objects to match another object's layer by using the LAYMCH command. You can access the LAYMCH command from the Layers panel on the Home tab or by typing LAYMCH at the command line. You can use the LAYMCH command to select one or more objects to change their layer, then select another object to match its layer.</li>
-<li>Use the BATTMAN command: You can easily manage block attributes by using the BATTMAN command. You can access the BATTMAN command from the Insert panel on the Home tab or by typing BATTMAN at the command line. You can use the BATTMAN command to open the Block Attribute Manager dialog box, where you can customize, edit, and remove attribute definitions for your blocks.</li>
-<li>Use AutoComplete: You can save time and avoid spelling errors by using AutoComplete in AutoCAD. AutoComplete automatically completes your typed commands or system variables as you type them. If you pause, a list of all possible commands or system variables displays for easy selection. You can customize AutoComplete from the right-click menu on the command line or from the Customize User Interface dialog box.</li>
-</ul>
-
-<p>We hope this article was helpful for you. However, we remind you again that using a crack and a keygen is illegal and risky, and we do not support or encourage this practice. If you want to use Autodesk AutoCAD 2013 legally and safely, we suggest you buy a license from the official website or from an authorized dealer.</p>
-<h2>What are the system requirements and compatibility of Autodesk AutoCAD 2013?</h2>
-
-<p>Autodesk AutoCAD 2013 is a software that requires certain system specifications and compatibility to run smoothly and efficiently. Before you install or use Autodesk AutoCAD 2013, you should check if your computer meets the minimum or recommended system requirements and if your operating system and other software are compatible with Autodesk AutoCAD 2013. Here are the system requirements and compatibility of Autodesk AutoCAD 2013 that you should know:</p>
-
-<h3>System Requirements for 32-bit Autodesk AutoCAD 2013</h3>
-
-<ul>
-<li>Operating system: Windows® 8 Standard, Enterprise, or Professional edition (requires installation of AutoCAD 2013 Service Pack 2); Microsoft® Windows® 7 Enterprise, Ultimate, Professional, or Home Premium (compare Windows 7 versions); or Microsoft® Windows® XP Professional or Home edition (SP3 or later)</li>
-<li>Processor: For Windows 7: Intel® Pentium® 4 or AMD Athlon™ dual-core processor, 3.0 GHz or higher with SSE2 technology; For Windows XP: Pentium 4 or AMD Athlon dual-core processor, 1.6 GHz or higher with SSE2 technology</li>
-<li>Memory: 2 GB RAM (4 GB recommended)</li>
-<li>Disk space: 6 GB free disk space for installation</li>
-<li>Display: 1,024 x 768 display resolution with true color (1,600 x 1,050 with true color recommended)</li>
-<li>Browser: Microsoft® Internet Explorer® 7.0 or later web browser</li>
-<li>Installation: Install from download or DVD</li>
-</ul>
-
-<h3>System Requirements for 64-bit Autodesk AutoCAD 2013</h3>
-
-<ul>
-<li>Operating system: Windows® 8 Standard, Enterprise, or Professional edition (requires installation of AutoCAD 2013 Service Pack 2); Microsoft Windows 7 Enterprise, Ultimate, Professional, or Home Premium (compare Windows 7 versions); or Microsoft Windows XP Professional (SP2 or later)</li>
-<li>Processor: AMD Athlon 64 with SSE2 technology, AMD Opteron® processor with SSE2 technology, Intel® Xeon® processor with Intel EM64T support and SSE2 technology, or Intel Pentium 4 with Intel EM64T support and SSE2 technology</li>
-<li>Memory: 2 GB RAM (4 GB recommended)</li>
-<li>Disk space: 6 GB free space for installation</li>
-<li>Display: 1,024 x 768 display resolution with true color (1,600 x 1,050 with true color recommended)</li>
-<li>Browser: Internet Explorer 7.0 or later</li>
-<li>Installation: Install from download or DVD</li>
-</ul>
-
-<h3>Additional Requirements for 3D Modeling (All Configurations)</h3>
-
-<ul>
-<li>Processor: Pentium 4 or Athlon processor, 3 GHz or greater or Intel or AMD dual-core processor, 2 GHz or greater</li>
-<li>Memory: 4 GB RAM or more</li>
-<li>Disk space: 6 GB hard disk space available in addition to free space required for installation</li>
-<li>Display: 1,280 x 1,024 true color video display adapter 128 MB or greater, Pixel Shader 3.0 or greater, Microsoft® Direct3D®-capable workstation-class graphics card</li>
-<li>Certified hardware for AutoCAD: Use this tool to find recommended graphics hardware and drivers. Autodesk tests graphics hardware and drivers for a number of Autodesk products. Please note that not all Autodesk products participate in graphic hardware certification.</li>
-</ul>
-
-<h3>Compatibility of Autodesk AutoCAD 2013</h3>
-
-<p>Autodesk AutoCAD 2013 is compatible with various operating systems and software applications. Here are some of the compatibility aspects of Autodesk AutoCAD 2013 that you should know:</p>
-
-<ul>
-<li>AutoCAD Map 3D Compatibility: You can use AutoCAD Map features in AutoCAD by installing AutoCAD Map software on top of AutoCAD.</li>
-<li>DWG File Format Compatibility: You can save drawings created in AutoCAD in the DWG file format that can be opened by other applications that support this format.</li>
-<li>DGN File Format Compatibility: You can import and export drawings in DGN file format that can be used by MicroStation and other applications that support this format.</li>
-<li>PDF File Format Compatibility: You can export drawings as PDF files that can be viewed by Adobe Acrobat Reader and other applications that support this format.</li>
-<li>AUTODESK INVENTOR File Format Compatibility: You can import and export models in Inventor file format that can be used by Autodesk Inventor and other applications that support this format.</li>
-<li>AUTODESK REVIT File Format Compatibility: You can import models in Revit file format that can be used by Autodesk Revit and other applications that support this format.</li>
-<li>AUTODESK CIVIL File Format Compatibility: You can import models in Civil file format that can be used by Autodesk Civil and other applications that support this format.</li>
-</ul>
-
-<p>We hope this article was helpful for you. However, we remind you again that using a crack and a keygen is illegal and risky, and we do not support or encourage this practice. If you want to use Autodesk AutoCAD 2013 legally and safely, we suggest you buy a license from the official website or from an authorized dealer.</p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have covered the following topics:</p>
-
-<ul>
-<li>What is Autodesk AutoCAD 2013 and what are its features and benefits?</li>
-<li>What are some tips and tricks for using Autodesk AutoCAD 2013?</li>
-<li>What are the system requirements and compatibility of Autodesk AutoCAD 2013?</li>
-<li>What are the risks and consequences of using a crack and a keygen to get Autodesk AutoCAD 2013 for free?</li>
-</ul>
-
-<p>We hope you have learned something useful from this article and that you have enjoyed reading it. However, we strongly advise you not to use a crack and a keygen to get Autodesk AutoCAD 2013 for free, as it is illegal and risky. If you want to use Autodesk AutoCAD 2013 legally and safely, we suggest you buy a license from the official website or from an authorized dealer.</p>
-
-<p>Thank you for reading this article. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ltgoslo/ssa-perin/data/parser/__init__.py b/spaces/ltgoslo/ssa-perin/data/parser/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ma-xu/LIVE/thrust/cmake/ThrustInstallRules.cmake b/spaces/ma-xu/LIVE/thrust/cmake/ThrustInstallRules.cmake
deleted file mode 100644
index 552a716685ec8509343ea40eb05d6eed7bbf0420..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/cmake/ThrustInstallRules.cmake
+++ /dev/null
@@ -1,25 +0,0 @@
-# Thrust is a header library; no need to build anything before installing:
-set(CMAKE_SKIP_INSTALL_ALL_DEPENDENCY TRUE)
-
-install(DIRECTORY "${Thrust_SOURCE_DIR}/thrust"
-  TYPE INCLUDE
-  FILES_MATCHING
-    PATTERN "*.h"
-    PATTERN "*.inl"
-    PATTERN "*.cmake"
-    PATTERN "*.md"
-)
-
-# Depending on how Thrust is configured, CUB's CMake scripts may or may not be
-# included, so maintain a set of CUB install rules in both projects. By default
-# CUB headers are installed alongside Thrust -- this may be disabled by turning
-# off THRUST_INSTALL_CUB_HEADERS.
-option(THRUST_INSTALL_CUB_HEADERS "Include cub headers when installing." ON)
-if (THRUST_INSTALL_CUB_HEADERS)
-  install(DIRECTORY "${Thrust_SOURCE_DIR}/dependencies/cub/cub"
-    TYPE INCLUDE
-    FILES_MATCHING
-      PATTERN "*.cuh"
-      PATTERN "*.cmake"
-  )
-endif()
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/tuple_of_iterator_references.h b/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/tuple_of_iterator_references.h
deleted file mode 100644
index 93d7e05e4f0edb5752b33382186437485ec95fdd..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/iterator/detail/tuple_of_iterator_references.h
+++ /dev/null
@@ -1,263 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/tuple.h>
-#include <thrust/pair.h>
-#include <thrust/detail/reference_forward_declaration.h>
-
-namespace thrust
-{
-namespace detail
-{
-
-  
-template<
-  typename T0, typename T1, typename T2,
-  typename T3, typename T4, typename T5,
-  typename T6, typename T7, typename T8,
-  typename T9
->
-  class tuple_of_iterator_references
-    : public thrust::tuple<T0,T1,T2,T3,T4,T5,T6,T7,T8,T9>
-{
-  private:
-    typedef thrust::tuple<T0,T1,T2,T3,T4,T5,T6,T7,T8,T9> super_t;
-
-  public:
-    // allow implicit construction from tuple<refs>
-    inline __host__ __device__
-    tuple_of_iterator_references(const super_t &other)
-      : super_t(other)
-    {}
-
-    // allow assignment from tuples
-    // XXX might be worthwhile to guard this with an enable_if is_assignable
-    __thrust_exec_check_disable__
-    template<typename U1, typename U2>
-    inline __host__ __device__
-    tuple_of_iterator_references &operator=(const detail::cons<U1,U2> &other)
-    {
-      super_t::operator=(other);
-      return *this;
-    }
-
-    // allow assignment from pairs
-    // XXX might be worthwhile to guard this with an enable_if is_assignable
-    __thrust_exec_check_disable__
-    template<typename U1, typename U2>
-    inline __host__ __device__
-    tuple_of_iterator_references &operator=(const thrust::pair<U1,U2> &other)
-    {
-      super_t::operator=(other);
-      return *this;
-    }
-
-    // allow assignment from reference<tuple>
-    // XXX perhaps we should generalize to reference<T>
-    //     we could captures reference<pair> this way
-    __thrust_exec_check_disable__
-    template<typename U0, typename U1, typename U2,
-             typename U3, typename U4, typename U5,
-             typename U6, typename U7, typename U8,
-             typename U9,
-             typename Pointer, typename Derived>
-    inline __host__ __device__
-// XXX gcc-4.2 crashes on is_assignable
-//    typename thrust::detail::enable_if<
-//      thrust::detail::is_assignable<
-//        super_t,
-//        const thrust::tuple<U0,U1,U2,U3,U4,U5,U6,U7,U8,U9>
-//      >::value,
-//      tuple_of_iterator_references &
-//    >::type
-    tuple_of_iterator_references &
-    operator=(const thrust::reference<thrust::tuple<U0,U1,U2,U3,U4,U5,U6,U7,U8,U9>, Pointer, Derived> &other)
-    {
-      typedef thrust::tuple<U0,U1,U2,U3,U4,U5,U6,U7,U8,U9> tuple_type;
-
-      // XXX perhaps this could be accelerated
-      tuple_type other_tuple = other;
-      super_t::operator=(other_tuple);
-      return *this;
-    }
-
-
-    // duplicate thrust::tuple's constructors
-    inline __host__ __device__
-    tuple_of_iterator_references() {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0)
-      : super_t(t0,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1)
-      : super_t(t0, t1,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2)
-      : super_t(t0, t1, t2,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2,
-                                 typename access_traits<T3>::parameter_type t3)
-      : super_t(t0, t1, t2, t3,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2,
-                                 typename access_traits<T3>::parameter_type t3,
-                                 typename access_traits<T4>::parameter_type t4)
-      : super_t(t0, t1, t2, t3, t4,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2,
-                                 typename access_traits<T3>::parameter_type t3,
-                                 typename access_traits<T4>::parameter_type t4,
-                                 typename access_traits<T5>::parameter_type t5)
-      : super_t(t0, t1, t2, t3, t4, t5,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2,
-                                 typename access_traits<T3>::parameter_type t3,
-                                 typename access_traits<T4>::parameter_type t4,
-                                 typename access_traits<T5>::parameter_type t5,
-                                 typename access_traits<T6>::parameter_type t6)
-      : super_t(t0, t1, t2, t3, t4, t5, t6,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2,
-                                 typename access_traits<T3>::parameter_type t3,
-                                 typename access_traits<T4>::parameter_type t4,
-                                 typename access_traits<T5>::parameter_type t5,
-                                 typename access_traits<T6>::parameter_type t6,
-                                 typename access_traits<T7>::parameter_type t7)
-      : super_t(t0, t1, t2, t3, t4, t5, t6, t7,
-                static_cast<const null_type&>(null_type()),
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2,
-                                 typename access_traits<T3>::parameter_type t3,
-                                 typename access_traits<T4>::parameter_type t4,
-                                 typename access_traits<T5>::parameter_type t5,
-                                 typename access_traits<T6>::parameter_type t6,
-                                 typename access_traits<T7>::parameter_type t7,
-                                 typename access_traits<T8>::parameter_type t8)
-      : super_t(t0, t1, t2, t3, t4, t5, t6, t7, t8,
-                static_cast<const null_type&>(null_type()))
-    {}
-
-    inline __host__ __device__ 
-    tuple_of_iterator_references(typename access_traits<T0>::parameter_type t0,
-                                 typename access_traits<T1>::parameter_type t1,
-                                 typename access_traits<T2>::parameter_type t2,
-                                 typename access_traits<T3>::parameter_type t3,
-                                 typename access_traits<T4>::parameter_type t4,
-                                 typename access_traits<T5>::parameter_type t5,
-                                 typename access_traits<T6>::parameter_type t6,
-                                 typename access_traits<T7>::parameter_type t7,
-                                 typename access_traits<T8>::parameter_type t8,
-                                 typename access_traits<T9>::parameter_type t9)
-      : super_t(t0, t1, t2, t3, t4, t5, t6, t7, t8, t9)
-    {}
-};
-
-
-// this overload of swap() permits swapping tuple_of_iterator_references returned as temporaries from
-// iterator dereferences
-template<
-  typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8, typename T9,
-  typename U0, typename U1, typename U2, typename U3, typename U4, typename U5, typename U6, typename U7, typename U8, typename U9
->
-inline __host__ __device__
-void swap(tuple_of_iterator_references<T0,T1,T2,T3,T4,T5,T6,T7,T8,T9> x,
-          tuple_of_iterator_references<U0,U1,U2,U3,U4,U5,U6,U7,U8,U9> y)
-{
-  x.swap(y);
-}
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/parallel_for.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/parallel_for.h
deleted file mode 100644
index 17fa7e7a86b243c80e13bc6678e31c80ad1e3f5b..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/parallel_for.h
+++ /dev/null
@@ -1,178 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-
-
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <thrust/system/cuda/config.h>
-
-#include <thrust/system/cuda/detail/util.h>
-#include <thrust/detail/type_traits/result_of_adaptable_function.h>
-#include <thrust/system/cuda/detail/par_to_seq.h>
-#include <thrust/system/cuda/detail/core/agent_launcher.h>
-#include <thrust/system/cuda/detail/par_to_seq.h>
-
-namespace thrust
-{
-
-namespace cuda_cub {
-
-namespace __parallel_for {
-
-  template <int _BLOCK_THREADS,
-            int _ITEMS_PER_THREAD = 1>
-  struct PtxPolicy
-  {
-    enum
-    {
-      BLOCK_THREADS    = _BLOCK_THREADS,
-      ITEMS_PER_THREAD = _ITEMS_PER_THREAD,
-      ITEMS_PER_TILE   = BLOCK_THREADS * ITEMS_PER_THREAD,
-    };
-  };    // struct PtxPolicy
-
-  template <class Arch, class F>
-  struct Tuning;
-
-  template <class F>
-  struct Tuning<sm30, F>
-  {
-    typedef PtxPolicy<256, 2> type;
-  };
-
-
-  template <class F,
-            class Size>
-  struct ParallelForAgent
-  {
-    template <class Arch>
-    struct PtxPlan : Tuning<Arch, F>::type
-    {
-      typedef Tuning<Arch, F> tuning;
-    };
-    typedef core::specialize_plan<PtxPlan> ptx_plan;
-
-    enum
-    {
-      ITEMS_PER_THREAD = ptx_plan::ITEMS_PER_THREAD,
-      ITEMS_PER_TILE   = ptx_plan::ITEMS_PER_TILE,
-      BLOCK_THREADS    = ptx_plan::BLOCK_THREADS
-    };
-
-    template <bool IS_FULL_TILE>
-    static void    THRUST_DEVICE_FUNCTION
-    consume_tile(F    f,
-                 Size tile_base,
-                 int  items_in_tile)
-    {
-#pragma unroll
-      for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
-      {
-        Size idx = BLOCK_THREADS * ITEM + threadIdx.x;
-        if (IS_FULL_TILE || idx < items_in_tile)
-          f(tile_base + idx);
-      }
-    }
-
-    THRUST_AGENT_ENTRY(F     f,
-                       Size  num_items,
-                       char * /*shmem*/ )
-    {
-      Size tile_base     = static_cast<Size>(blockIdx.x) * ITEMS_PER_TILE;
-      Size num_remaining = num_items - tile_base;
-      Size items_in_tile = static_cast<Size>(
-          num_remaining < ITEMS_PER_TILE ? num_remaining : ITEMS_PER_TILE);
-
-      if (items_in_tile == ITEMS_PER_TILE)
-      {
-        // full tile
-        consume_tile<true>(f, tile_base, ITEMS_PER_TILE);
-      }
-      else
-      {
-        // partial tile
-        consume_tile<false>(f, tile_base, items_in_tile);
-      }
-    }
-  };    // struct ParallelForEagent
-
-  template <class F,
-            class Size>
-  THRUST_RUNTIME_FUNCTION cudaError_t
-  parallel_for(Size         num_items,
-               F            f,
-               cudaStream_t stream)
-  {
-    if (num_items == 0)
-      return cudaSuccess;
-    using core::AgentLauncher;
-    using core::AgentPlan;
-
-    bool debug_sync = THRUST_DEBUG_SYNC_FLAG;
-
-    typedef AgentLauncher<ParallelForAgent<F, Size> > parallel_for_agent;
-    AgentPlan parallel_for_plan = parallel_for_agent::get_plan(stream);
-
-    parallel_for_agent pfa(parallel_for_plan, num_items, stream, "transform::agent", debug_sync);
-    pfa.launch(f, num_items);
-    CUDA_CUB_RET_IF_FAIL(cudaPeekAtLastError());
-
-    return cudaSuccess;
-  }
-}    // __parallel_for
-
-__thrust_exec_check_disable__
-template <class Derived,
-          class F,
-          class Size>
-void __host__ __device__
-parallel_for(execution_policy<Derived> &policy,
-             F                          f,
-             Size                       count)
-{
-  if (count == 0)
-    return;
-
-  if (__THRUST_HAS_CUDART__)
-  {
-    cudaStream_t stream = cuda_cub::stream(policy);
-    cudaError_t  status = __parallel_for::parallel_for(count, f, stream);
-    cuda_cub::throw_on_error(status, "parallel_for failed");
-  }
-  else
-  {
-#if !__THRUST_HAS_CUDART__
-    for (Size idx = 0; idx != count; ++idx)
-      f(idx);
-#endif
-  }
-}
-
-}    // namespace cuda_cub
-
-} // end namespace thrust
-#endif
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/merge.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/merge.h
deleted file mode 100644
index d80906e3d31faa5f01519ab5c7963fe8762f77bb..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/merge.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-// XXX calling this function is an error; there is no implementation
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename StrictWeakOrdering>
-__host__ __device__
-  OutputIterator merge(thrust::execution_policy<DerivedPolicy> &exec,
-                       InputIterator1 first1,
-                       InputIterator1 last1,
-                       InputIterator2 first2,
-                       InputIterator2 last2,
-                       OutputIterator result,
-                       StrictWeakOrdering comp);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator>
-__host__ __device__
-  OutputIterator merge(thrust::execution_policy<DerivedPolicy> &exec,
-                       InputIterator1 first1,
-                       InputIterator1 last1,
-                       InputIterator2 first2,
-                       InputIterator2 last2,
-                       OutputIterator result);
-
-
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2, typename InputIterator3, typename InputIterator4, typename OutputIterator1, typename OutputIterator2, typename Compare>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-    merge_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                 InputIterator1 keys_first1, InputIterator1 keys_last1,
-                 InputIterator2 keys_first2, InputIterator2 keys_last2,
-                 InputIterator3 values_first1, InputIterator4 values_first2,
-                 OutputIterator1 keys_result,
-                 OutputIterator2 values_result,
-                 Compare comp);
-
-
-template<typename DerivedPolicy, typename InputIterator1, typename InputIterator2, typename InputIterator3, typename InputIterator4, typename OutputIterator1, typename OutputIterator2>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-    merge_by_key(thrust::execution_policy<DerivedPolicy> &exec,
-                 InputIterator1 keys_first1, InputIterator1 keys_last1,
-                 InputIterator2 keys_first2, InputIterator2 keys_last2,
-                 InputIterator3 values_first1, InputIterator4 values_first2,
-                 OutputIterator1 keys_result,
-                 OutputIterator2 values_result);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/merge.inl>
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/stable_primitive_sort.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/stable_primitive_sort.h
deleted file mode 100644
index 3426f953a08ddbc5155e35fdbc1c4447d2831f19..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/sequential/stable_primitive_sort.h
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/sequential/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-template<typename DerivedPolicy,
-         typename RandomAccessIterator>
-__host__ __device__
-void stable_primitive_sort(sequential::execution_policy<DerivedPolicy> &exec,
-                           RandomAccessIterator first,
-                           RandomAccessIterator last);
-
-
-template<typename DerivedPolicy,
-         typename RandomAccessIterator1,
-         typename RandomAccessIterator2>
-__host__ __device__
-void stable_primitive_sort_by_key(sequential::execution_policy<DerivedPolicy> &exec,
-                                  RandomAccessIterator1 keys_first,
-                                  RandomAccessIterator1 keys_last,
-                                  RandomAccessIterator2 values_first);
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/sequential/stable_primitive_sort.inl>
-
diff --git a/spaces/manan/Score-Clinical-Patient-Notes/examples.py b/spaces/manan/Score-Clinical-Patient-Notes/examples.py
deleted file mode 100644
index 011b5be1ff3cc3866332626d12023681b83f02d2..0000000000000000000000000000000000000000
--- a/spaces/manan/Score-Clinical-Patient-Notes/examples.py
+++ /dev/null
@@ -1,33 +0,0 @@
-example_input_11 = "Family-history-of-MI-OR-Family-history-of-myocardial-infarction"
-example_input_12 = """HPI: 17yo M presents with palpitations. Patient reports 3-4 months of intermittent episodes of "heart beating/pounding out of my chest." 2 days ago during a soccer game had an episode, but this time had chest pressure and felt as if he were going to pass out (did not lose conciousness). Of note patient endorses abusing adderall, primarily to study (1-3 times per week). Before recent soccer game, took adderrall night before and morning of game. Denies shortness of breath, diaphoresis, fevers, chills, headache, fatigue, changes in sleep, changes in vision/hearing, abdominal paun, changes in bowel or urinary habits. 
-PMHx: none
-Rx: uses friends adderrall
-FHx: mom with "thyroid disease,"  dad with recent heart attack 
-All: none
-Immunizations: up to date
-SHx: Freshmen in college. Endorses 3-4 drinks 3 nights / week (on weekends), denies tabacco, endorses trying marijuana. Sexually active with girlfriend x 1 year, uses condoms"""
-
-example_input_21 = "Right-sided-LQ-abdominal-pain-OR-Right-lower-quadrant-abdominal-pain"
-example_input_22 = """20 y/o F c/o abdominal pain  
-5/10. from last 10 hours.
-dull and ache.,cramping  pain in the right lower quadrant .
-no radiation.
-walking makes it worse.took ibuprofen but not much help.
-no fever chills,no discharge,no dysuria,no blood in the urine.
-pat h/o of similar episodes 3-4 in the last 6 months..not allergic .no medicines other than ibuprofen..no hospitalisation trauma or surgeries .no past illness.
-family h/o not significant..
-lmp 2 weeks ago.regular 28 days lasts for 4 days.moderare flow.age of menarche 13.
-last sexual encounter 9 months ago with boy friend always uses condoms."""
-
-example_input_31 = "Irregular-menses"
-example_input_32 = """CC: 44 yo female c/o  irregular periods 
-HPI: x 3 years has had  irregular periods  they vary in the amount of blodd each time. Her LMP was 2 months ago and was regular. She first began to notice periods at age 14. She had flu like symptoms one week ago with nausea and vomiting. No clots during her periods. She has to use 2-3 pads when they are light and 5-6 when they are heavy. Nothing makes it better or worse. She has been experiencing hot flashes, sweating and vaginal dryness which she uses lubricant for. She also complains of dyspareunia. No fever or chills. No abdominal pain, diarrhea, constipation or urinary symptoms. 
-PMH: HTN on HCTZ, No PSHx, No allergies,  Two vaginal births without complications. 
-FH: Mother has osteoarthritis and HTN
-Social: Does not smoke or use drugs drinks occasionally"""
-
-example = [
-  [example_input_11, example_input_12],
-  [example_input_21, example_input_22],
-  [example_input_31, example_input_32],
-]
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/ecbsr_arch.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/ecbsr_arch.py
deleted file mode 100644
index 87922a0d0c3e8539bcd4ecaf90b2ee55edd4c84c..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/ecbsr_arch.py
+++ /dev/null
@@ -1,274 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-class SeqConv3x3(nn.Module):
-    """The re-parameterizable block used in the ECBSR architecture.
-
-    Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
-    Ref git repo: https://github.com/xindongzhang/ECBSR
-
-    Args:
-        seq_type (str): Sequence type, option: conv1x1-conv3x3 | conv1x1-sobelx | conv1x1-sobely | conv1x1-laplacian.
-        in_channels (int): Channel number of input.
-        out_channels (int): Channel number of output.
-        depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1.
-    """
-
-    def __init__(self, seq_type, in_channels, out_channels, depth_multiplier=1):
-        super(SeqConv3x3, self).__init__()
-        self.seq_type = seq_type
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-
-        if self.seq_type == 'conv1x1-conv3x3':
-            self.mid_planes = int(out_channels * depth_multiplier)
-            conv0 = torch.nn.Conv2d(self.in_channels, self.mid_planes, kernel_size=1, padding=0)
-            self.k0 = conv0.weight
-            self.b0 = conv0.bias
-
-            conv1 = torch.nn.Conv2d(self.mid_planes, self.out_channels, kernel_size=3)
-            self.k1 = conv1.weight
-            self.b1 = conv1.bias
-
-        elif self.seq_type == 'conv1x1-sobelx':
-            conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
-            self.k0 = conv0.weight
-            self.b0 = conv0.bias
-
-            # init scale and bias
-            scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
-            self.scale = nn.Parameter(scale)
-            bias = torch.randn(self.out_channels) * 1e-3
-            bias = torch.reshape(bias, (self.out_channels, ))
-            self.bias = nn.Parameter(bias)
-            # init mask
-            self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
-            for i in range(self.out_channels):
-                self.mask[i, 0, 0, 0] = 1.0
-                self.mask[i, 0, 1, 0] = 2.0
-                self.mask[i, 0, 2, 0] = 1.0
-                self.mask[i, 0, 0, 2] = -1.0
-                self.mask[i, 0, 1, 2] = -2.0
-                self.mask[i, 0, 2, 2] = -1.0
-            self.mask = nn.Parameter(data=self.mask, requires_grad=False)
-
-        elif self.seq_type == 'conv1x1-sobely':
-            conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
-            self.k0 = conv0.weight
-            self.b0 = conv0.bias
-
-            # init scale and bias
-            scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
-            self.scale = nn.Parameter(torch.FloatTensor(scale))
-            bias = torch.randn(self.out_channels) * 1e-3
-            bias = torch.reshape(bias, (self.out_channels, ))
-            self.bias = nn.Parameter(torch.FloatTensor(bias))
-            # init mask
-            self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
-            for i in range(self.out_channels):
-                self.mask[i, 0, 0, 0] = 1.0
-                self.mask[i, 0, 0, 1] = 2.0
-                self.mask[i, 0, 0, 2] = 1.0
-                self.mask[i, 0, 2, 0] = -1.0
-                self.mask[i, 0, 2, 1] = -2.0
-                self.mask[i, 0, 2, 2] = -1.0
-            self.mask = nn.Parameter(data=self.mask, requires_grad=False)
-
-        elif self.seq_type == 'conv1x1-laplacian':
-            conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0)
-            self.k0 = conv0.weight
-            self.b0 = conv0.bias
-
-            # init scale and bias
-            scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3
-            self.scale = nn.Parameter(torch.FloatTensor(scale))
-            bias = torch.randn(self.out_channels) * 1e-3
-            bias = torch.reshape(bias, (self.out_channels, ))
-            self.bias = nn.Parameter(torch.FloatTensor(bias))
-            # init mask
-            self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32)
-            for i in range(self.out_channels):
-                self.mask[i, 0, 0, 1] = 1.0
-                self.mask[i, 0, 1, 0] = 1.0
-                self.mask[i, 0, 1, 2] = 1.0
-                self.mask[i, 0, 2, 1] = 1.0
-                self.mask[i, 0, 1, 1] = -4.0
-            self.mask = nn.Parameter(data=self.mask, requires_grad=False)
-        else:
-            raise ValueError('The type of seqconv is not supported!')
-
-    def forward(self, x):
-        if self.seq_type == 'conv1x1-conv3x3':
-            # conv-1x1
-            y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
-            # explicitly padding with bias
-            y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
-            b0_pad = self.b0.view(1, -1, 1, 1)
-            y0[:, :, 0:1, :] = b0_pad
-            y0[:, :, -1:, :] = b0_pad
-            y0[:, :, :, 0:1] = b0_pad
-            y0[:, :, :, -1:] = b0_pad
-            # conv-3x3
-            y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1)
-        else:
-            y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
-            # explicitly padding with bias
-            y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
-            b0_pad = self.b0.view(1, -1, 1, 1)
-            y0[:, :, 0:1, :] = b0_pad
-            y0[:, :, -1:, :] = b0_pad
-            y0[:, :, :, 0:1] = b0_pad
-            y0[:, :, :, -1:] = b0_pad
-            # conv-3x3
-            y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_channels)
-        return y1
-
-    def rep_params(self):
-        device = self.k0.get_device()
-        if device < 0:
-            device = None
-
-        if self.seq_type == 'conv1x1-conv3x3':
-            # re-param conv kernel
-            rep_weight = F.conv2d(input=self.k1, weight=self.k0.permute(1, 0, 2, 3))
-            # re-param conv bias
-            rep_bias = torch.ones(1, self.mid_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
-            rep_bias = F.conv2d(input=rep_bias, weight=self.k1).view(-1, ) + self.b1
-        else:
-            tmp = self.scale * self.mask
-            k1 = torch.zeros((self.out_channels, self.out_channels, 3, 3), device=device)
-            for i in range(self.out_channels):
-                k1[i, i, :, :] = tmp[i, 0, :, :]
-            b1 = self.bias
-            # re-param conv kernel
-            rep_weight = F.conv2d(input=k1, weight=self.k0.permute(1, 0, 2, 3))
-            # re-param conv bias
-            rep_bias = torch.ones(1, self.out_channels, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
-            rep_bias = F.conv2d(input=rep_bias, weight=k1).view(-1, ) + b1
-        return rep_weight, rep_bias
-
-
-class ECB(nn.Module):
-    """The ECB block used in the ECBSR architecture.
-
-    Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
-    Ref git repo: https://github.com/xindongzhang/ECBSR
-
-    Args:
-        in_channels (int): Channel number of input.
-        out_channels (int): Channel number of output.
-        depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1.
-        act_type (str): Activation type. Option: prelu | relu | rrelu | softplus | linear. Default: prelu.
-        with_idt (bool): Whether to use identity connection. Default: False.
-    """
-
-    def __init__(self, in_channels, out_channels, depth_multiplier, act_type='prelu', with_idt=False):
-        super(ECB, self).__init__()
-
-        self.depth_multiplier = depth_multiplier
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.act_type = act_type
-
-        if with_idt and (self.in_channels == self.out_channels):
-            self.with_idt = True
-        else:
-            self.with_idt = False
-
-        self.conv3x3 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=3, padding=1)
-        self.conv1x1_3x3 = SeqConv3x3('conv1x1-conv3x3', self.in_channels, self.out_channels, self.depth_multiplier)
-        self.conv1x1_sbx = SeqConv3x3('conv1x1-sobelx', self.in_channels, self.out_channels)
-        self.conv1x1_sby = SeqConv3x3('conv1x1-sobely', self.in_channels, self.out_channels)
-        self.conv1x1_lpl = SeqConv3x3('conv1x1-laplacian', self.in_channels, self.out_channels)
-
-        if self.act_type == 'prelu':
-            self.act = nn.PReLU(num_parameters=self.out_channels)
-        elif self.act_type == 'relu':
-            self.act = nn.ReLU(inplace=True)
-        elif self.act_type == 'rrelu':
-            self.act = nn.RReLU(lower=-0.05, upper=0.05)
-        elif self.act_type == 'softplus':
-            self.act = nn.Softplus()
-        elif self.act_type == 'linear':
-            pass
-        else:
-            raise ValueError('The type of activation if not support!')
-
-    def forward(self, x):
-        if self.training:
-            y = self.conv3x3(x) + self.conv1x1_3x3(x) + self.conv1x1_sbx(x) + self.conv1x1_sby(x) + self.conv1x1_lpl(x)
-            if self.with_idt:
-                y += x
-        else:
-            rep_weight, rep_bias = self.rep_params()
-            y = F.conv2d(input=x, weight=rep_weight, bias=rep_bias, stride=1, padding=1)
-        if self.act_type != 'linear':
-            y = self.act(y)
-        return y
-
-    def rep_params(self):
-        weight0, bias0 = self.conv3x3.weight, self.conv3x3.bias
-        weight1, bias1 = self.conv1x1_3x3.rep_params()
-        weight2, bias2 = self.conv1x1_sbx.rep_params()
-        weight3, bias3 = self.conv1x1_sby.rep_params()
-        weight4, bias4 = self.conv1x1_lpl.rep_params()
-        rep_weight, rep_bias = (weight0 + weight1 + weight2 + weight3 + weight4), (
-            bias0 + bias1 + bias2 + bias3 + bias4)
-
-        if self.with_idt:
-            device = rep_weight.get_device()
-            if device < 0:
-                device = None
-            weight_idt = torch.zeros(self.out_channels, self.out_channels, 3, 3, device=device)
-            for i in range(self.out_channels):
-                weight_idt[i, i, 1, 1] = 1.0
-            bias_idt = 0.0
-            rep_weight, rep_bias = rep_weight + weight_idt, rep_bias + bias_idt
-        return rep_weight, rep_bias
-
-
-@ARCH_REGISTRY.register()
-class ECBSR(nn.Module):
-    """ECBSR architecture.
-
-    Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
-    Ref git repo: https://github.com/xindongzhang/ECBSR
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_block (int): Block number in the trunk network.
-        num_channel (int): Channel number.
-        with_idt (bool): Whether use identity in convolution layers.
-        act_type (str): Activation type.
-        scale (int): Upsampling factor.
-    """
-
-    def __init__(self, num_in_ch, num_out_ch, num_block, num_channel, with_idt, act_type, scale):
-        super(ECBSR, self).__init__()
-        self.num_in_ch = num_in_ch
-        self.scale = scale
-
-        backbone = []
-        backbone += [ECB(num_in_ch, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)]
-        for _ in range(num_block):
-            backbone += [ECB(num_channel, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)]
-        backbone += [
-            ECB(num_channel, num_out_ch * scale * scale, depth_multiplier=2.0, act_type='linear', with_idt=with_idt)
-        ]
-
-        self.backbone = nn.Sequential(*backbone)
-        self.upsampler = nn.PixelShuffle(scale)
-
-    def forward(self, x):
-        if self.num_in_ch > 1:
-            shortcut = torch.repeat_interleave(x, self.scale * self.scale, dim=1)
-        else:
-            shortcut = x  # will repeat the input in the channel dimension (repeat  scale * scale times)
-        y = self.backbone(x) + shortcut
-        y = self.upsampler(y)
-        return y
diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/models/multibanddiffusion.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/models/multibanddiffusion.py
deleted file mode 100644
index 6a2f169d516ed5aaf5da61fb482d94dd142f55e9..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/models/multibanddiffusion.py
+++ /dev/null
@@ -1,194 +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.
-
-"""
-Multi Band Diffusion models as described in
-"From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion"
-(paper link).
-"""
-
-import typing as tp
-
-import torch
-import julius
-
-from .unet import DiffusionUnet
-from ..modules.diffusion_schedule import NoiseSchedule
-from .encodec import CompressionModel
-from ..solvers.compression import CompressionSolver
-from .loaders import load_compression_model, load_diffusion_models
-
-
-class DiffusionProcess:
-    """Sampling for a diffusion Model.
-
-    Args:
-        model (DiffusionUnet): Diffusion U-Net model.
-        noise_schedule (NoiseSchedule): Noise schedule for diffusion process.
-    """
-    def __init__(self, model: DiffusionUnet, noise_schedule: NoiseSchedule) -> None:
-        """
-        """
-        self.model = model
-        self.schedule = noise_schedule
-
-    def generate(self, condition: torch.Tensor, initial_noise: torch.Tensor,
-                 step_list: tp.Optional[tp.List[int]] = None):
-        """Perform one diffusion process to generate one of the bands.
-
-        Args:
-            condition (tensor): The embeddings form the compression model.
-            initial_noise (tensor): The initial noise to start the process/
-        """
-        return self.schedule.generate_subsampled(model=self.model, initial=initial_noise, step_list=step_list,
-                                                 condition=condition)
-
-
-class MultiBandDiffusion:
-    """Sample from multiple diffusion models.
-
-    Args:
-        DPs (list of DiffusionProcess): Diffusion processes.
-        codec_model (CompressionModel): Underlying compression model used to obtain discrete tokens.
-    """
-    def __init__(self, DPs: tp.List[DiffusionProcess], codec_model: CompressionModel) -> None:
-        self.DPs = DPs
-        self.codec_model = codec_model
-        self.device = next(self.codec_model.parameters()).device
-
-    @property
-    def sample_rate(self) -> int:
-        return self.codec_model.sample_rate
-
-    @staticmethod
-    def get_mbd_musicgen(device=None):
-        """Load our diffusion models trained for MusicGen."""
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        path = 'https://dl.fbaipublicfiles.com/encodec/Diffusion/mbd_musicgen_32khz.th'
-        name = 'facebook/musicgen-small'
-        codec_model = load_compression_model(name, device=device)
-        models, processors, cfgs = load_diffusion_models(path, device=device)
-        DPs = []
-        for i in range(len(models)):
-            schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i])
-            DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule))
-        return MultiBandDiffusion(DPs=DPs, codec_model=codec_model)
-
-    @staticmethod
-    def get_mbd_24khz(bw: float = 3.0, pretrained: bool = True,
-                      device: tp.Optional[tp.Union[torch.device, str]] = None,
-                      n_q: tp.Optional[int] = None):
-        """Get the pretrained Models for MultibandDiffusion.
-
-        Args:
-            bw (float): Bandwidth of the compression model.
-            pretrained (bool): Whether to use / download if necessary the models.
-            device (torch.device or str, optional): Device on which the models are loaded.
-            n_q (int, optional): Number of quantizers to use within the compression model.
-        """
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        assert bw in [1.5, 3.0, 6.0], f"bandwidth {bw} not available"
-        if n_q is not None:
-            assert n_q in [2, 4, 8]
-            assert {1.5: 2, 3.0: 4, 6.0: 8}[bw] == n_q, \
-                f"bandwidth and number of codebooks missmatch to use n_q = {n_q} bw should be {n_q * (1.5 / 2)}"
-        n_q = {1.5: 2, 3.0: 4, 6.0: 8}[bw]
-        codec_model = CompressionSolver.model_from_checkpoint(
-            '//pretrained/facebook/encodec_24khz', device=device)
-        codec_model.set_num_codebooks(n_q)
-        codec_model = codec_model.to(device)
-        path = f'https://dl.fbaipublicfiles.com/encodec/Diffusion/mbd_comp_{n_q}.pt'
-        models, processors, cfgs = load_diffusion_models(path, device=device)
-        DPs = []
-        for i in range(len(models)):
-            schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i])
-            DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule))
-        return MultiBandDiffusion(DPs=DPs, codec_model=codec_model)
-
-        return MultiBandDiffusion(DPs, codec_model)
-
-    @torch.no_grad()
-    def get_condition(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
-        """Get the conditioning (i.e. latent reprentatios of the compression model) from a waveform.
-        Args:
-            wav (torch.Tensor): The audio that we want to extract the conditioning from
-            sample_rate (int): sample rate of the audio"""
-        if sample_rate != self.sample_rate:
-            wav = julius.resample_frac(wav, sample_rate, self.sample_rate)
-        codes, scale = self.codec_model.encode(wav)
-        assert scale is None, "Scaled compression models not supported."
-        emb = self.get_emb(codes)
-        return emb
-
-    @torch.no_grad()
-    def get_emb(self, codes: torch.Tensor):
-        """Get latent representation from the discrete codes
-        Argrs:
-            codes (torch.Tensor): discrete tokens"""
-        emb = self.codec_model.decode_latent(codes)
-        return emb
-
-    def generate(self, emb: torch.Tensor, size: tp.Optional[torch.Size] = None,
-                 step_list: tp.Optional[tp.List[int]] = None):
-        """Generate Wavform audio from the latent embeddings of the compression model
-        Args:
-            emb (torch.Tensor): Conditioning embeddinds
-            size (none torch.Size): size of the output
-                if None this is computed from the typical upsampling of the model
-            step_list (optional list[int]): list of Markov chain steps, defaults to 50 linearly spaced step.
-        """
-        if size is None:
-            upsampling = int(self.codec_model.sample_rate / self.codec_model.frame_rate)
-            size = torch.Size([emb.size(0), self.codec_model.channels, emb.size(-1) * upsampling])
-        assert size[0] == emb.size(0)
-        out = torch.zeros(size).to(self.device)
-        for DP in self.DPs:
-            out += DP.generate(condition=emb, step_list=step_list, initial_noise=torch.randn_like(out))
-        return out
-
-    def re_eq(self, wav: torch.Tensor, ref: torch.Tensor, n_bands: int = 32, strictness: float = 1):
-        """match the eq to the encodec output by matching the standard deviation of some frequency bands
-        Args:
-            wav (torch.Tensor): audio to equalize
-            ref (torch.Tensor):refenrence audio from which we match the spectrogram.
-            n_bands (int): number of bands of the eq
-            strictness (float): how strict the the matching. 0 is no matching, 1 is exact matching.
-        """
-        split = julius.SplitBands(n_bands=n_bands, sample_rate=self.codec_model.sample_rate).to(wav.device)
-        bands = split(wav)
-        bands_ref = split(ref)
-        out = torch.zeros_like(ref)
-        for i in range(n_bands):
-            out += bands[i] * (bands_ref[i].std() / bands[i].std()) ** strictness
-        return out
-
-    def regenerate(self, wav: torch.Tensor, sample_rate: int):
-        """Regenerate a wavform through compression and diffusion regeneration.
-        Args:
-            wav (torch.Tensor): Original 'ground truth' audio
-            sample_rate (int): sample rate of the input (and output) wav
-        """
-        if sample_rate != self.codec_model.sample_rate:
-            wav = julius.resample_frac(wav, sample_rate, self.codec_model.sample_rate)
-        emb = self.get_condition(wav, sample_rate=self.codec_model.sample_rate)
-        size = wav.size()
-        out = self.generate(emb, size=size)
-        if sample_rate != self.codec_model.sample_rate:
-            out = julius.resample_frac(out, self.codec_model.sample_rate, sample_rate)
-        return out
-
-    def tokens_to_wav(self, tokens: torch.Tensor, n_bands: int = 32):
-        """Generate Waveform audio with diffusion from the discrete codes.
-        Args:
-            tokens (torch.Tensor): discrete codes
-            n_bands (int): bands for the eq matching.
-        """
-        wav_encodec = self.codec_model.decode(tokens)
-        condition = self.get_emb(tokens)
-        wav_diffusion = self.generate(emb=condition, size=wav_encodec.size())
-        return self.re_eq(wav=wav_diffusion, ref=wav_encodec, n_bands=n_bands)
diff --git a/spaces/matthoffner/chatbot/components/Promptbar/components/Prompt.tsx b/spaces/matthoffner/chatbot/components/Promptbar/components/Prompt.tsx
deleted file mode 100644
index 62eee8425d7a0443a21439cbe8f5184f96bad049..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/components/Promptbar/components/Prompt.tsx
+++ /dev/null
@@ -1,130 +0,0 @@
-import {
-  IconBulbFilled,
-  IconCheck,
-  IconTrash,
-  IconX,
-} from '@tabler/icons-react';
-import {
-  DragEvent,
-  MouseEventHandler,
-  useContext,
-  useEffect,
-  useState,
-} from 'react';
-
-import { Prompt } from '@/types/prompt';
-
-import SidebarActionButton from '@/components/Buttons/SidebarActionButton';
-
-import PromptbarContext from '../PromptBar.context';
-import { PromptModal } from './PromptModal';
-
-interface Props {
-  prompt: Prompt;
-}
-
-export const PromptComponent = ({ prompt }: Props) => {
-  const {
-    dispatch: promptDispatch,
-    handleUpdatePrompt,
-    handleDeletePrompt,
-  } = useContext(PromptbarContext);
-
-  const [showModal, setShowModal] = useState<boolean>(false);
-  const [isDeleting, setIsDeleting] = useState(false);
-  const [isRenaming, setIsRenaming] = useState(false);
-  const [renameValue, setRenameValue] = useState('');
-
-  const handleUpdate = (prompt: Prompt) => {
-    handleUpdatePrompt(prompt);
-    promptDispatch({ field: 'searchTerm', value: '' });
-  };
-
-  const handleDelete: MouseEventHandler<HTMLButtonElement> = (e) => {
-    e.stopPropagation();
-
-    if (isDeleting) {
-      handleDeletePrompt(prompt);
-      promptDispatch({ field: 'searchTerm', value: '' });
-    }
-
-    setIsDeleting(false);
-  };
-
-  const handleCancelDelete: MouseEventHandler<HTMLButtonElement> = (e) => {
-    e.stopPropagation();
-    setIsDeleting(false);
-  };
-
-  const handleOpenDeleteModal: MouseEventHandler<HTMLButtonElement> = (e) => {
-    e.stopPropagation();
-    setIsDeleting(true);
-  };
-
-  const handleDragStart = (e: DragEvent<HTMLButtonElement>, prompt: Prompt) => {
-    if (e.dataTransfer) {
-      e.dataTransfer.setData('prompt', JSON.stringify(prompt));
-    }
-  };
-
-  useEffect(() => {
-    if (isRenaming) {
-      setIsDeleting(false);
-    } else if (isDeleting) {
-      setIsRenaming(false);
-    }
-  }, [isRenaming, isDeleting]);
-
-  return (
-    <div className="relative flex items-center">
-      <button
-        className="flex w-full cursor-pointer items-center gap-3 rounded-lg p-3 text-sm transition-colors duration-200 hover:bg-[#343541]/90"
-        draggable="true"
-        onClick={(e) => {
-          e.stopPropagation();
-          setShowModal(true);
-        }}
-        onDragStart={(e) => handleDragStart(e, prompt)}
-        onMouseLeave={() => {
-          setIsDeleting(false);
-          setIsRenaming(false);
-          setRenameValue('');
-        }}
-      >
-        <IconBulbFilled size={18} />
-
-        <div className="relative max-h-5 flex-1 overflow-hidden text-ellipsis whitespace-nowrap break-all pr-4 text-left text-[12.5px] leading-3">
-          {prompt.name}
-        </div>
-      </button>
-
-      {(isDeleting || isRenaming) && (
-        <div className="absolute right-1 z-10 flex text-gray-300">
-          <SidebarActionButton handleClick={handleDelete}>
-            <IconCheck size={18} />
-          </SidebarActionButton>
-
-          <SidebarActionButton handleClick={handleCancelDelete}>
-            <IconX size={18} />
-          </SidebarActionButton>
-        </div>
-      )}
-
-      {!isDeleting && !isRenaming && (
-        <div className="absolute right-1 z-10 flex text-gray-300">
-          <SidebarActionButton handleClick={handleOpenDeleteModal}>
-            <IconTrash size={18} />
-          </SidebarActionButton>
-        </div>
-      )}
-
-      {showModal && (
-        <PromptModal
-          prompt={prompt}
-          onClose={() => setShowModal(false)}
-          onUpdatePrompt={handleUpdate}
-        />
-      )}
-    </div>
-  );
-};
diff --git a/spaces/matthoffner/chatbot/tailwind.config.js b/spaces/matthoffner/chatbot/tailwind.config.js
deleted file mode 100644
index 74ef404d16fc34967d35c1acadcd885d04a19035..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/tailwind.config.js
+++ /dev/null
@@ -1,18 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './app/**/*.{js,ts,jsx,tsx}',
-    './pages/**/*.{js,ts,jsx,tsx}',
-    './components/**/*.{js,ts,jsx,tsx}',
-  ],
-  darkMode: 'class',
-  theme: {
-    extend: {},
-  },
-  variants: {
-    extend: {
-      visibility: ['group-hover'],
-    },
-  },
-  plugins: [require('@tailwindcss/typography')],
-};
diff --git a/spaces/maxime/chat-with-your-telegram-chat/app.py b/spaces/maxime/chat-with-your-telegram-chat/app.py
deleted file mode 100644
index 9a15e52562ad78869970d2dd45eba571404d0ad0..0000000000000000000000000000000000000000
--- a/spaces/maxime/chat-with-your-telegram-chat/app.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-from typing import Optional, Tuple
-
-import gradio as gr
-import pickle
-from query_data import get_chain
-from ingest_data import embed_chat
-from threading import Lock
-
-
-def click_embed(file):
-    """Embed Telegram chat.
-       """
-    embed_chat(file.name)
-    with open("vectorstore.pkl", "rb") as f:
-        vectorstore = pickle.load(f)
-    chain = get_chain(vectorstore)
-    return chain, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(
-        visible=True), gr.update(visible=True), gr.update(
-        visible=True), gr.update(visible=True)
-
-
-def set_openai_api_key(api_key: str):
-    """Set the api key.
-    """
-    if api_key:
-        os.environ["OPENAI_API_KEY"] = api_key
-
-
-def upload_file(file_obj):
-    return file_obj
-
-
-class ChatWrapper:
-
-    def __init__(self):
-        self.lock = Lock()
-
-    def __call__(
-            self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain
-    ):
-        """Execute the chat functionality."""
-        self.lock.acquire()
-        try:
-            history = history or []
-            # If chain is None, that is because no API key was provided.
-            if chain is None:
-                history.append((inp, "Please paste your OpenAI key to use"))
-                return history, history
-            # Set OpenAI key
-            import openai
-            openai.api_key = api_key
-            # Run chain and append input.
-            output = chain({"question": inp, "chat_history": history})["answer"]
-            history.append((inp, output))
-        except Exception as e:
-            raise e
-        finally:
-            self.lock.release()
-        return history, history
-
-
-chat = ChatWrapper()
-
-block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
-
-with block:
-    with gr.Row():
-        gr.Markdown("<h3><center>Telegram Chat Chat</center></h3>")
-
-        openai_api_key_textbox = gr.Textbox(
-            placeholder="Paste your OpenAI API key (sk-...)",
-            show_label=False,
-            lines=1,
-            type="password",
-            visible=True
-        )
-        telegram_chat_file = gr.File(file_count="single", file_types=["json"], interactive=True, show_label=True,
-                                     visible=True,
-                                     label="Telegram chat history exported .json file")
-        embed_button = gr.Button("Create embeddings", visible=True)
-
-    chatbot = gr.Chatbot(visible=False)
-
-    with gr.Row():
-        message = gr.Textbox(
-            label="What's your question?",
-            placeholder="Ask questions about your Telegram conversation",
-            lines=1,
-            visible=False
-        )
-        submit = gr.Button(value="Send", variant="secondary", visible=False).style(full_width=False)
-    with gr.Row(visible=False) as examples_row:
-        gr.Examples(
-            examples=[
-                "When was [your name] happy and how does it show?",
-                "How could [your name] have been more compassionate?",
-                "What triggers [your name] instantly?",
-            ],
-            inputs=message,
-        )
-
-    gr.HTML(
-        "<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
-    )
-
-    state = gr.State()
-    agent_state = gr.State()
-
-    embed_button.click(click_embed, inputs=[telegram_chat_file],
-                       outputs=[agent_state, openai_api_key_textbox, telegram_chat_file, embed_button, chatbot, message,
-                                submit, examples_row])
-
-    submit.click(chat, inputs=[openai_api_key_textbox, message, state, agent_state], outputs=[chatbot, state])
-    print(agent_state)
-    message.submit(chat, inputs=[openai_api_key_textbox, message, state, agent_state], outputs=[chatbot, state])
-
-    openai_api_key_textbox.change(
-        set_openai_api_key,
-        inputs=[openai_api_key_textbox],
-    )
-
-block.launch(debug=True)
diff --git a/spaces/mayordp/DeepFakeAI/DeepFakeAI/wording.py b/spaces/mayordp/DeepFakeAI/DeepFakeAI/wording.py
deleted file mode 100644
index 1d70363ea7546eeb3b3ec224eb04848db727718e..0000000000000000000000000000000000000000
--- a/spaces/mayordp/DeepFakeAI/DeepFakeAI/wording.py
+++ /dev/null
@@ -1,88 +0,0 @@
-WORDING =\
-{
-	'python_not_supported': 'Python version is not supported, upgrade to {version} or higher',
-	'ffmpeg_not_installed': 'FFMpeg is not installed',
-	'source_help': 'select a source image',
-	'target_help': 'select a target image or video',
-	'output_help': 'specify the output file or directory',
-	'frame_processors_help': 'choose from the available frame processors (choices: {choices}, ...)',
-	'ui_layouts_help': 'choose from the available ui layouts (choices: {choices}, ...)',
-	'keep_fps_help': 'preserve the frames per second (fps) of the target',
-	'keep_temp_help': 'retain temporary frames after processing',
-	'skip_audio_help': 'omit audio from the target',
-	'face_recognition_help': 'specify the method for face recognition',
-	'face_analyser_direction_help': 'specify the direction used for face analysis',
-	'face_analyser_age_help': 'specify the age used for face analysis',
-	'face_analyser_gender_help': 'specify the gender used for face analysis',
-	'reference_face_position_help': 'specify the position of the reference face',
-	'reference_face_distance_help': 'specify the distance between the reference face and the target face',
-	'reference_frame_number_help': 'specify the number of the reference frame',
-	'trim_frame_start_help': 'specify the start frame for extraction',
-	'trim_frame_end_help': 'specify the end frame for extraction',
-	'temp_frame_format_help': 'specify the image format used for frame extraction',
-	'temp_frame_quality_help': 'specify the image quality used for frame extraction',
-	'output_video_encoder_help': 'specify the encoder used for the output video',
-	'output_video_quality_help': 'specify the quality used for the output video',
-	'max_memory_help': 'specify the maximum amount of ram to be used (in gb)',
-	'execution_providers_help': 'choose from the available execution providers (choices: {choices}, ...)',
-	'execution_thread_count_help': 'specify the number of execution threads',
-	'execution_queue_count_help': 'specify the number of execution queries',
-	'creating_temp': 'Creating temporary resources',
-	'extracting_frames_fps': 'Extracting frames with {fps} FPS',
-	'processing': 'Processing',
-	'downloading': 'Downloading',
-	'temp_frames_not_found': 'Temporary frames not found',
-	'creating_video_fps': 'Creating video with {fps} FPS',
-	'creating_video_failed': 'Creating video failed',
-	'skipping_audio': 'Skipping audio',
-	'restoring_audio': 'Restoring audio',
-	'clearing_temp': 'Clearing temporary resources',
-	'processing_image_succeed': 'Processing to image succeed',
-	'processing_image_failed': 'Processing to image failed',
-	'processing_video_succeed': 'Processing to video succeed',
-	'processing_video_failed': 'Processing to video failed',
-	'select_image_source': 'Select an image for source path',
-	'select_image_or_video_target': 'Select an image or video for target path',
-	'no_source_face_detected': 'No source face detected',
-	'frame_processor_not_loaded': 'Frame processor {frame_processor} could not be loaded',
-	'frame_processor_not_implemented': 'Frame processor {frame_processor} not implemented correctly',
-	'ui_layout_not_loaded': 'UI layout {ui_layout} could not be loaded',
-	'ui_layout_not_implemented': 'UI layout {ui_layout} not implemented correctly',
-	'start_button_label': 'START',
-	'clear_button_label': 'CLEAR',
-	'benchmark_result_dataframe_label': 'BENCHMARK RESULT',
-	'benchmark_cycles_slider_label': 'BENCHMARK CYCLES',
-	'execution_providers_checkbox_group_label': 'EXECUTION PROVIDERS',
-	'execution_thread_count_slider_label': 'EXECUTION THREAD COUNT',
-	'execution_queue_count_slider_label': 'EXECUTION QUEUE COUNT',
-	'face_analyser_direction_dropdown_label': 'FACE ANALYSER DIRECTION',
-	'face_analyser_age_dropdown_label': 'FACE ANALYSER AGE',
-	'face_analyser_gender_dropdown_label': 'FACE ANALYSER GENDER',
-	'reference_face_gallery_label': 'REFERENCE FACE',
-	'face_recognition_dropdown_label': 'FACE RECOGNITION',
-	'reference_face_distance_slider_label': 'REFERENCE FACE DISTANCE',
-	'output_image_or_video_label': 'OUTPUT',
-	'output_video_encoder_dropdown_label': 'OUTPUT VIDEO ENCODER',
-	'output_video_quality_slider_label': 'OUTPUT VIDEO QUALITY',
-	'preview_image_label': 'PREVIEW',
-	'preview_frame_slider_label': 'PREVIEW FRAME',
-	'frame_processors_checkbox_group_label': 'FRAME PROCESSORS',
-	'keep_fps_checkbox_label': 'KEEP FPS',
-	'keep_temp_checkbox_label': 'KEEP TEMP',
-	'skip_audio_checkbox_label': 'SKIP AUDIO',
-	'temp_frame_format_dropdown_label': 'TEMP FRAME FORMAT',
-	'temp_frame_quality_slider_label': 'TEMP FRAME QUALITY',
-	'trim_frame_start_slider_label': 'TRIM FRAME START',
-	'trim_frame_end_slider_label': 'TRIM FRAME END',
-	'source_file_label': 'SOURCE',
-	'target_file_label': 'TARGET',
-	'point': '.',
-	'comma': ',',
-	'colon': ':',
-	'question_mark': '?',
-	'exclamation_mark': '!'
-}
-
-
-def get(key : str) -> str:
-	return WORDING[key]
diff --git a/spaces/mdj1412/stock_news_summaries_AI/modules/templates/example.html b/spaces/mdj1412/stock_news_summaries_AI/modules/templates/example.html
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/merve/anonymization/public/dataset-worldviews/shapes.js b/spaces/merve/anonymization/public/dataset-worldviews/shapes.js
deleted file mode 100644
index 87af55b4829a78b48dc41f6674c12cd58cfc3741..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/public/dataset-worldviews/shapes.js
+++ /dev/null
@@ -1,248 +0,0 @@
-
-// Space out the shapes a bit
-shapeParams.forEach((d) => (d.startX = d.startX * 1.1));
-
-// How to draw the background boxes, which will be styled later
-const classifierBgPathTop = "M 420 150 H 0 V 0 H 420 V 150";
-const classifierBgPathBottom = "M 420 300 H 0 V 0 H 420 V 300";
-
-const toDropdownValueStringDict = {
-    shape_name: "circles, triangles, or rectangles",
-    pointiness: "pointy shapes or round shapes",
-    size: "small shapes or big shapes",
-};
-
-const toShortValueStringDict = {
-    shape_name: "circles, triangles, or rectangles",
-    pointiness: "pointy or round",
-    size: "small or big",
-};
-
-const toDropdownValueRoundingStringDict = {
-    true: "with our best guess",
-    false: 'as "other"',
-};
-
-const toPropertyStringDict = {
-    pointy: "pointy shapes",
-    round: "round shapes",
-    small: "small shapes",
-    large: "big shapes",
-    circle: "circles",
-    triangle: "triangles",
-    rect: "rectangles",
-};
-
-function toOriginalString(inputString) {
-    for (const [key, value] of Object.entries(toPropertyStringDict)) {
-        if (inputString == value) {
-            return key;
-        }
-    }
-}
-
-function toPropertyString(inputProperty, isRounding = true) {
-    if (!isRounding && inputProperty.startsWith("rt_")) {
-        return "others";
-    }
-    return toPropertyStringDict[inputProperty.replace("rt_", "")];
-}
-
-// Dictionary mapping div name to classifier results and summary sentences
-var allResults = {};
-var summaries = {};
-
-function toBool(inputString) {
-    if (inputString == "true") {
-        return true;
-    }
-    return false;
-}
-function updateResults() {
-    allResults["default-classifier"] = calculateResults();
-    allResults["second-classifier"] = calculateResults(
-        "shape_name",
-        toBool(
-            document.getElementById("second-classifier-select-rounding").value
-        )
-    );
-
-    allResults["final-classifier"] = calculateResults(
-        document.getElementById("final-classifier-select-category").value,
-        toBool(
-            document.getElementById("final-classifier-select-rounding").value
-        )
-    );
-
-    allResults["conclusion"] = calculateResults(
-        document.getElementById("conclusion-select-category").value,
-        true
-    );
-
-    updateSummaries();
-    updateSecondInterfaceImages();
-}
-
-// Text summaries are written by hand for simplicity, and keyed simply by
-// a string of the form "[category]:[useGuess]" (or simply "none").
-// These are hashed in the same way as the results, by div name.
-function updateSummaries() {
-    summaries["default-classifier"] = getPerformanceSummary("none");
-    summaries["second-classifier"] = getPerformanceSummary(
-        "shape_name:" +
-            document.getElementById("second-classifier-select-rounding").value
-    );
-
-    summaries["final-classifier"] = getPerformanceSummary(
-        document.getElementById("final-classifier-select-category").value +
-            ":" +
-            document.getElementById("final-classifier-select-rounding").value
-    );
-
-    summaries["conclusion"] = getPerformanceSummary(
-        document.getElementById("conclusion-select-category").value + ":" + true
-    );
-}
-
-// Yes, these background colors are hardcoded in,
-// no, this is not good design, this is just how it happened.
-function getPerformanceSummary(key) {
-    allSummaries = {
-        "shape_name:true":
-            '<mark style="background-color: rgb(206, 234, 135);" class="well">well</mark> on circles, <mark style="background-color: rgb(244, 123, 74);" class="terribly">terribly</mark> on triangles, and <mark style="background-color: rgb(173, 220, 114);" class="best">best</mark> on rectangles',
-        "shape_name:false":
-            '<mark style="background-color: rgb(251, 163, 94);" class="poorly">poorly</mark> on circles, <mark style="background-color: rgb(155, 212, 108);" class="best">best</mark> on triangles and rectangles, and <mark style="background-color: rgb(252, 244, 171);" class="fine">fine</mark> on other shapes',
-        "pointiness:true":
-            '<mark style="background-color: rgb(184, 225, 119);" class="better">better</mark> on pointy shapes and <mark style="background-color: rgb(254, 206, 125);" class="worse">worse</mark> on round shapes',
-        "pointiness:false":
-            '<mark style="background-color: rgb(140, 205, 104);" class="best">best</mark> on pointy shapes, <mark style="background-color: rgb(243, 248, 171);" class="fine">fine</mark> on round shapes, and <mark style="background-color: rgb(253, 190, 111);" class="poorly">poorly</mark> on other shapes',
-        "size:true":
-            '<mark style="background-color: rgb(206, 234, 135);" class="better">better</mark> on small shapes, <mark style="background-color: rgb(254, 232, 154);" class="worse">worse</mark> on big shapes',
-        "size:false":
-            '<mark style="background-color: rgb(254, 215, 135);" class="poorly">poorly</mark> on small shapes, <mark style="background-color: rgb(165, 0, 38); color: #FFCCD8;" class="terribly">terribly</mark> on big shapes, and <mark style="background-color: rgb(110, 192, 99);" class="best">best</mark> on other shapes',
-        "none:true":
-            '<mark style="background-color: rgb(246, 248, 173);" class="fine">fine</mark> on all shapes',
-        "none:false":
-            '<mark style="background-color: rgb(246, 248, 173);" class="fine">fine</mark> on all shapes',
-        none: '<mark style="background-color: rgb(246, 248, 173);" class="fine">fine</mark> on all shapes',
-    };
-
-    return "The Is-Shaded Classifier performs " + allSummaries[key] + ".";
-}
-
-// On the second-classifier dropdown, update the "task interface" image.
-function updateSecondInterfaceImages() {
-    d3.select(".second-interface").html(function () {
-        if (
-            !document.getElementById("second-classifier-select-rounding").value
-        ) {
-            return;
-        }
-        var imgPath =
-            "img/interface_shape_name_" +
-            document.getElementById("second-classifier-select-rounding").value;
-        return (
-            '<img src="' +
-            imgPath +
-            '.png" alt="" class="interface-image" srcset="' +
-            imgPath +
-            '.svg"></img>'
-        );
-    });
-}
-
-// Calculate results given input parameters
-function calculateResults(property = "none", useGuess = false) {
-    switch (property) {
-        case "none":
-            var nAccurate = shapeParams.filter(
-                (shape) => shape.correctness == "correct"
-            ).length;
-            var totalShapes = shapeParams.length;
-
-            var results = [
-                {
-                    object: "shape",
-                    n: totalShapes,
-                    "n correct": nAccurate,
-                    accuracy: (nAccurate / totalShapes).toFixed(3),
-                    rawCategoryName: "none",
-                },
-            ];
-
-            return results;
-        case "pointiness":
-            categories = ["pointy", "round"];
-            break;
-        case "size":
-            categories = ["small", "large"];
-            break;
-        case "shape_name":
-            categories = ["circle", "triangle", "rect"];
-            break;
-    }
-
-    var results = [];
-    if (useGuess == true) {
-        // Rounding shapes to categories
-
-        for (const category of categories) {
-            // Get shapes that are either in this category (e.g. rectangle) or "rounds to" this category (e.g. rt_rectangle)
-            var theseShapes = shapeParams.filter(
-                (shape) =>
-                    shape[property] == category ||
-                    shape[property] == "rt_" + category
-            );
-            var nAccurate = theseShapes.filter(
-                (shape) => shape.correctness == "correct"
-            ).length;
-            var totalShapes = theseShapes.length;
-
-            results.push({
-                object: toPropertyString(category),
-                n: totalShapes,
-                "n correct": nAccurate,
-                accuracy: (nAccurate / totalShapes).toFixed(3),
-                rawCategoryName: category,
-            });
-        }
-    } else {
-        // Not rounding, treat everything else as "other"
-
-        // First go through existing categories
-        for (const category of categories) {
-            var theseShapes = shapeParams.filter(
-                (shape) => shape[property] == category
-            );
-            var nAccurate = theseShapes.filter(
-                (shape) => shape.correctness == "correct"
-            ).length;
-            var totalShapes = theseShapes.length;
-            results.push({
-                object: toPropertyString(category),
-                n: totalShapes,
-                "n correct": nAccurate,
-                accuracy: (nAccurate / totalShapes).toFixed(3),
-                rawCategoryName: category,
-            });
-        }
-
-        // Now get "other" shapes
-        var theseShapes = shapeParams.filter(
-            (shape) => !categories.includes(shape[property])
-        );
-        var nAccurate = theseShapes.filter(
-            (shape) => shape.correctness == "correct"
-        ).length;
-        var totalShapes = theseShapes.length;
-        results.push({
-            object: "other shapes",
-            n: totalShapes,
-            "n correct": nAccurate,
-            accuracy: (nAccurate / totalShapes).toFixed(3),
-            rawCategoryName: "other",
-        });
-    }
-
-    return results;
-}
diff --git a/spaces/merve/dataset-worldviews/public/dataset-worldviews/README.md b/spaces/merve/dataset-worldviews/public/dataset-worldviews/README.md
deleted file mode 100644
index 74e4920975910a03f1cc2ebe582a7a6d03eb8da6..0000000000000000000000000000000000000000
--- a/spaces/merve/dataset-worldviews/public/dataset-worldviews/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-## Photo todos
-
-x highlight the active button
-x firing when not expected?
-x clear timer when clicked
-- maybe convert to HTML?
\ No newline at end of file
diff --git a/spaces/merve/fill-in-the-blank/source/_posts/2019-10-02-bias.html b/spaces/merve/fill-in-the-blank/source/_posts/2019-10-02-bias.html
deleted file mode 100644
index 44c586c9489408fa9694149309ffefa3f3fc4d1b..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/_posts/2019-10-02-bias.html
+++ /dev/null
@@ -1,126 +0,0 @@
-<!--
-@license
-Copyright 2020 Google. 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.
--->
----
-template: post.html
-title: Hidden Bias
-summary: Models trained on real-world data can encode real-world bias. Hiding information about protected classes doesn't always fix things — sometimes it can even hurt.
-permalink: /hidden-bias/
-shareimg: https://pair.withgoogle.com/explorables/images/hidden-bias.png
-date: 2020-05-01
-
----
-<link rel="stylesheet" href="style.css">
-
-<div id='container' class='container-1'>
-<div id='graph'></div>
-<div id='sections'>
-
-
-<div>
-<h3>Modeling College GPA</h3>
-
-<p>Let's pretend we're college admissions officers trying to predict the GPA students will have in college (in these examples we'll use simulated data).
-
-<p>One simple approach: predict that students will have the same GPA in college as they did in high school. 
-</div>
-
-
-<div class='img-slide'>
-<p>This is at best a very rough approximation, and it misses a key feature of this data set: students usually have better grades in high school than in college
-
-<p>We're <img src='over.png'><span class='xhighlight blue'>over-predicting</span> college grades more often than we <img src='over.png'><span class='xhighlight orange'>under-predict.</span>
-</div>
-
-
-<div>
-<h3>Predicting with ML</h3>
-<p>If we switched to using a machine learning model and entered these student grades, it would recognize this pattern and adjust the prediction.
-
-<p>The model does this without knowing anything about the real-life context of grading in high school versus college.
-</div>
-
-
-<div>
-<p>Giving the model <span class='highlight blue'>more information</span> about students increases accuracy more...
-</div>
-
-
-<div>
-<p>...and more.
-</div>
-
-
-<div>
-<h3>Models can encode previous bias</h3>
-<p>All of this sensitive information about students is just a long list of numbers to model. 
-
-<p>If a sexist college culture has historically led to lower grades for <span class='f circle'>&nbsp;</span> female students, the model will pick up on that correlation and predict lower grades for women.  
-
-<p>Training on historical data bakes in historical biases. Here the sexist culture has improved, but the model learned from the past correlation and still predicts higher grades for <span class='m circle'>&nbsp;</span> men.
-</div>
-
-<div>
-<h3>Hiding protected classes from the model might not stop discrimination</h3>
-
-<p>Even if we don't tell the model students' genders, it might still score <span class='f circle'>&nbsp;</span> female students poorly.
-
-<p>With detailed enough information about every student, the model can still synthesize a proxy for gender out of other <span class='highlight yellow'>variables.</span>
-</div>
-
-
-<div>
-<h3>Including a protected attribute may even <i>decrease</i> discrimination</h3>
-
-<p>Let's look at a simplified model, one only taking into account the recommendation of an alumni interviewer. 
-</div>
-
-
-<div>
-<p>The interviewer is quite accurate, except that they're biased against students with a <span class='l circle'>&nbsp;</span> low household income. 
-
-<p>In our toy model, students' grades don't depend on their income once they're in college. In other words, we have biased inputs and unbiased outcomes—the opposite of the previous example, where the inputs weren't biased, but the toxic culture biased the outcomes. 
-</div>
-
-
-<div>
-<p>If we also tell the model each student's <span class='highlight blue'>household income</span>, it will naturally correct for the interviewer's overrating of <span class='h circle'>&nbsp;</span> high-income students just like it corrected for the difference between high school and college GPAs. 
-
-<p>By carefully considering and accounting for bias, we've made the model fairer and more accurate. This isn't always easy to do, especially in circumstances like the historically toxic college culture where unbiased data is limited. 
-
-<p>And there are fundamental fairness trade-offs that have to be made. Check out the <a href='../measuring-fairness/'>Measuring Fairness explorable</a> to see how those tradeoffs work.<a href='../measuring-fairness/'><br><img style='width: 100%; max-width: 391px; margin-left: -8px' src='../images/medical-fairness.gif'></a>
-
-
-<br><br>
-
-<p>Adam Pearce // May 2020
-
-<p>Thanks to Carey Radebaugh, Dan Nanas, David Weinberger, Emily Denton, Emily Reif, Fernanda Viégas, Hal Abelson, James Wexler, Kristen Olson, Lucas Dixon, Mahima Pushkarna, Martin Wattenberg, Michael Terry, Rebecca Salois, Timnit Gebru, Tulsee Doshi, Yannick Assogba, Yoni Halpern, Zan Armstrong, and my other colleagues at Google for their help with this piece.
-</div>
-
-</div>
-</div>
-<div id='end'></div>
-
-
-<link rel="stylesheet" href="../measuring-fairness/graph-scroll.css">
-
-<script src="../third_party/seedrandom.min.js"></script>
-<script src='../third_party/d3_.js'></script>
-<script src='../third_party/swoopy-drag.js'></script>
-<script src='../third_party/misc.js'></script>
-<script src='annotations.js'></script>
-<script src='script.js'></script>
\ No newline at end of file
diff --git a/spaces/mfoud2023/Alhareq/main.py b/spaces/mfoud2023/Alhareq/main.py
deleted file mode 100644
index 55075dddd0cf804499706f954f1857600633701e..0000000000000000000000000000000000000000
--- a/spaces/mfoud2023/Alhareq/main.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import json
-import time
-import random
-import string
-import requests
-
-from typing       import Any
-from flask        import Flask, request
-from flask_cors   import CORS
-from g4f          import ChatCompletion
-import g4f
-app = Flask(__name__)
-CORS(app)
-
-#https://mfoud2023-alhareq.hf.space/
-@app.route('/')
-def hellow():
-    return "hellow word"
-
-@app.route('/c')
-def hellow22():
-    return "hellow word ccccccccc"
-
-from g4f.Provider import (
-    AItianhu,
-    Aichat,
-    Bard,
-    Bing,
-    ChatBase,
-    ChatgptAi,
-    OpenaiChat,
-    Vercel,
-    You,
-    Yqcloud,
-    GeekGpt
-)
-
-error_answer =[
-    "Hmm, I am not sure. Email support",
-]
-
-@app.route('/chat/completions', methods=['POST'])
-def chat_completions():
-    model    = request.get_json().get('model', 'gpt-4')
-    stream   = request.get_json().get('stream', False)
-    messages = request.get_json().get('messages')
-    
-    max_retries=5
-    retries = 0
-    while retries < max_retries:
-        try:
-            response = ChatCompletion.create(
-                                model = model, 
-                                stream = stream, 
-                                messages = messages,
-                                Provider = GeekGpt
-                                )
-
-            if response not in error_answer:
-                break  
-        except (RuntimeError, Exception) as e:
-            retries += 1
-            if retries >= max_retries:
-                break  
-
- 
-
-    completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
-    completion_timestamp = int(time.time())
-
-    if not stream:
-        return {
-            'id': f'chatcmpl-{completion_id}',
-            'object': 'chat.completion',
-            'created': completion_timestamp,
-            'model': model,
-            'choices': [
-                {
-                    'index': 0,
-                    'message': {
-                        'role': 'assistant',
-                        'content': response,
-                    },
-                    'finish_reason': 'stop',
-                }
-            ],
-            'usage': {
-                'prompt_tokens': None,
-                'completion_tokens': None,
-                'total_tokens': None,
-            },
-        }
-
-    def streaming():
-        for chunk in response:
-            completion_data = {
-                'id': f'chatcmpl-{completion_id}',
-                'object': 'chat.completion.chunk',
-                'created': completion_timestamp,
-                'model': model,
-                'choices': [
-                    {
-                        'index': 0,
-                        'delta': {
-                            'content': chunk,
-                        },
-                        'finish_reason': None,
-                    }
-                ],
-            }
-
-            content = json.dumps(completion_data, separators=(',', ':'))
-            yield f'data: {content}\n\n'
-            time.sleep(0.1)
-
-        end_completion_data: dict[str, Any] = {
-            'id': f'chatcmpl-{completion_id}',
-            'object': 'chat.completion.chunk',
-            'created': completion_timestamp,
-            'model': model,
-            'choices': [
-                {
-                    'index': 0,
-                    'delta': {},
-                    'finish_reason': 'stop',
-                }
-            ],
-        }
-        content = json.dumps(end_completion_data, separators=(',', ':'))
-        # yield f'data: {content}\n\n'
-        yield "data: [DONE]\n\n"
-    
-
-    return app.response_class(streaming(), mimetype='text/event-stream')
diff --git a/spaces/mfrashad/CharacterGAN/netdissect/runningstats.py b/spaces/mfrashad/CharacterGAN/netdissect/runningstats.py
deleted file mode 100644
index fe4093e0318edeecf8aebc34771adbde5043e2d4..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/netdissect/runningstats.py
+++ /dev/null
@@ -1,773 +0,0 @@
-'''
-Running statistics on the GPU using pytorch.
-
-RunningTopK maintains top-k statistics for a set of channels in parallel.
-RunningQuantile maintains (sampled) quantile statistics for a set of channels.
-'''
-
-import torch, math, numpy
-from collections import defaultdict
-
-class RunningTopK:
-    '''
-    A class to keep a running tally of the the top k values (and indexes)
-    of any number of torch feature components.  Will work on the GPU if
-    the data is on the GPU.
-
-    This version flattens all arrays to avoid crashes.
-    '''
-    def __init__(self, k=100, state=None):
-        if state is not None:
-            self.set_state_dict(state)
-            return
-        self.k = k
-        self.count = 0
-        # This version flattens all data internally to 2-d tensors,
-        # to avoid crashes with the current pytorch topk implementation.
-        # The data is puffed back out to arbitrary tensor shapes on ouput.
-        self.data_shape = None
-        self.top_data = None
-        self.top_index = None
-        self.next = 0
-        self.linear_index = 0
-        self.perm = None
-
-    def add(self, data):
-        '''
-        Adds a batch of data to be considered for the running top k.
-        The zeroth dimension enumerates the observations.  All other
-        dimensions enumerate different features.
-        '''
-        if self.top_data is None:
-            # Allocation: allocate a buffer of size 5*k, at least 10, for each.
-            self.data_shape = data.shape[1:]
-            feature_size = int(numpy.prod(self.data_shape))
-            self.top_data = torch.zeros(
-                    feature_size, max(10, self.k * 5), out=data.new())
-            self.top_index = self.top_data.clone().long()
-            self.linear_index = 0 if len(data.shape) == 1 else torch.arange(
-                feature_size, out=self.top_index.new()).mul_(
-                        self.top_data.shape[-1])[:,None]
-        size = data.shape[0]
-        sk = min(size, self.k)
-        if self.top_data.shape[-1] < self.next + sk:
-            # Compression: if full, keep topk only.
-            self.top_data[:,:self.k], self.top_index[:,:self.k] = (
-                    self.result(sorted=False, flat=True))
-            self.next = self.k
-            free = self.top_data.shape[-1] - self.next
-        # Pick: copy the top sk of the next batch into the buffer.
-        # Currently strided topk is slow.  So we clone after transpose.
-        # TODO: remove the clone() if it becomes faster.
-        cdata = data.contiguous().view(size, -1).t().clone()
-        td, ti = cdata.topk(sk, sorted=False)
-        self.top_data[:,self.next:self.next+sk] = td
-        self.top_index[:,self.next:self.next+sk] = (ti + self.count)
-        self.next += sk
-        self.count += size
-
-    def result(self, sorted=True, flat=False):
-        '''
-        Returns top k data items and indexes in each dimension,
-        with channels in the first dimension and k in the last dimension.
-        '''
-        k = min(self.k, self.next)
-        # bti are top indexes relative to buffer array.
-        td, bti = self.top_data[:,:self.next].topk(k, sorted=sorted)
-        # we want to report top indexes globally, which is ti.
-        ti = self.top_index.view(-1)[
-                (bti + self.linear_index).view(-1)
-                ].view(*bti.shape)
-        if flat:
-            return td, ti
-        else:
-            return (td.view(*(self.data_shape + (-1,))),
-                    ti.view(*(self.data_shape + (-1,))))
-
-    def to_(self, device):
-        self.top_data = self.top_data.to(device)
-        self.top_index = self.top_index.to(device)
-        if isinstance(self.linear_index, torch.Tensor):
-            self.linear_index = self.linear_index.to(device)
-
-    def state_dict(self):
-        return dict(
-                constructor=self.__module__ + '.' +
-                    self.__class__.__name__ + '()',
-                k=self.k,
-                count=self.count,
-                data_shape=tuple(self.data_shape),
-                top_data=self.top_data.cpu().numpy(),
-                top_index=self.top_index.cpu().numpy(),
-                next=self.next,
-                linear_index=(self.linear_index.cpu().numpy()
-                    if isinstance(self.linear_index, torch.Tensor)
-                    else self.linear_index),
-                perm=self.perm)
-
-    def set_state_dict(self, dic):
-        self.k = dic['k'].item()
-        self.count = dic['count'].item()
-        self.data_shape = tuple(dic['data_shape'])
-        self.top_data = torch.from_numpy(dic['top_data'])
-        self.top_index = torch.from_numpy(dic['top_index'])
-        self.next = dic['next'].item()
-        self.linear_index = (torch.from_numpy(dic['linear_index'])
-                if len(dic['linear_index'].shape) > 0
-                else dic['linear_index'].item())
-
-class RunningQuantile:
-    """
-    Streaming randomized quantile computation for torch.
-
-    Add any amount of data repeatedly via add(data).  At any time,
-    quantile estimates (or old-style percentiles) can be read out using
-    quantiles(q) or percentiles(p).
-
-    Accuracy scales according to resolution: the default is to
-    set resolution to be accurate to better than 0.1%,
-    while limiting storage to about 50,000 samples.
-
-    Good for computing quantiles of huge data without using much memory.
-    Works well on arbitrary data with probability near 1.
-
-    Based on the optimal KLL quantile algorithm by Karnin, Lang, and Liberty
-    from FOCS 2016.  http://ieee-focs.org/FOCS-2016-Papers/3933a071.pdf
-    """
-
-    def __init__(self, resolution=6 * 1024, buffersize=None, seed=None,
-            state=None):
-        if state is not None:
-            self.set_state_dict(state)
-            return
-        self.depth = None
-        self.dtype = None
-        self.device = None
-        self.resolution = resolution
-        # Default buffersize: 128 samples (and smaller than resolution).
-        if buffersize is None:
-            buffersize = min(128, (resolution + 7) // 8)
-        self.buffersize = buffersize
-        self.samplerate = 1.0
-        self.data = None
-        self.firstfree = [0]
-        self.randbits = torch.ByteTensor(resolution)
-        self.currentbit = len(self.randbits) - 1
-        self.extremes = None
-        self.size = 0
-
-    def _lazy_init(self, incoming):
-        self.depth = incoming.shape[1]
-        self.dtype = incoming.dtype
-        self.device = incoming.device
-        self.data = [torch.zeros(self.depth, self.resolution,
-            dtype=self.dtype, device=self.device)]
-        self.extremes = torch.zeros(self.depth, 2,
-                dtype=self.dtype, device=self.device)
-        self.extremes[:,0] = float('inf')
-        self.extremes[:,-1] = -float('inf')
-
-    def to_(self, device):
-        """Switches internal storage to specified device."""
-        if device != self.device:
-            old_data = self.data
-            old_extremes = self.extremes
-            self.data = [d.to(device) for d in self.data]
-            self.extremes = self.extremes.to(device)
-            self.device = self.extremes.device
-            del old_data
-            del old_extremes
-
-    def add(self, incoming):
-        if self.depth is None:
-            self._lazy_init(incoming)
-        assert len(incoming.shape) == 2
-        assert incoming.shape[1] == self.depth, (incoming.shape[1], self.depth)
-        self.size += incoming.shape[0]
-        # Convert to a flat torch array.
-        if self.samplerate >= 1.0:
-            self._add_every(incoming)
-            return
-        # If we are sampling, then subsample a large chunk at a time.
-        self._scan_extremes(incoming)
-        chunksize = int(math.ceil(self.buffersize / self.samplerate))
-        for index in range(0, len(incoming), chunksize):
-            batch = incoming[index:index+chunksize]
-            sample = sample_portion(batch, self.samplerate)
-            if len(sample):
-                self._add_every(sample)
-
-    def _add_every(self, incoming):
-        supplied = len(incoming)
-        index = 0
-        while index < supplied:
-            ff = self.firstfree[0]
-            available = self.data[0].shape[1] - ff
-            if available == 0:
-                if not self._shift():
-                    # If we shifted by subsampling, then subsample.
-                    incoming = incoming[index:]
-                    if self.samplerate >= 0.5:
-                        # First time sampling - the data source is very large.
-                        self._scan_extremes(incoming)
-                    incoming = sample_portion(incoming, self.samplerate)
-                    index = 0
-                    supplied = len(incoming)
-                ff = self.firstfree[0]
-                available = self.data[0].shape[1] - ff
-            copycount = min(available, supplied - index)
-            self.data[0][:,ff:ff + copycount] = torch.t(
-                    incoming[index:index + copycount,:])
-            self.firstfree[0] += copycount
-            index += copycount
-
-    def _shift(self):
-        index = 0
-        # If remaining space at the current layer is less than half prev
-        # buffer size (rounding up), then we need to shift it up to ensure
-        # enough space for future shifting.
-        while self.data[index].shape[1] - self.firstfree[index] < (
-                -(-self.data[index-1].shape[1] // 2) if index else 1):
-            if index + 1 >= len(self.data):
-                return self._expand()
-            data = self.data[index][:,0:self.firstfree[index]]
-            data = data.sort()[0]
-            if index == 0 and self.samplerate >= 1.0:
-                self._update_extremes(data[:,0], data[:,-1])
-            offset = self._randbit()
-            position = self.firstfree[index + 1]
-            subset = data[:,offset::2]
-            self.data[index + 1][:,position:position + subset.shape[1]] = subset
-            self.firstfree[index] = 0
-            self.firstfree[index + 1] += subset.shape[1]
-            index += 1
-        return True
-
-    def _scan_extremes(self, incoming):
-        # When sampling, we need to scan every item still to get extremes
-        self._update_extremes(
-                torch.min(incoming, dim=0)[0],
-                torch.max(incoming, dim=0)[0])
-
-    def _update_extremes(self, minr, maxr):
-        self.extremes[:,0] = torch.min(
-                torch.stack([self.extremes[:,0], minr]), dim=0)[0]
-        self.extremes[:,-1] = torch.max(
-                torch.stack([self.extremes[:,-1], maxr]), dim=0)[0]
-
-    def _randbit(self):
-        self.currentbit += 1
-        if self.currentbit >= len(self.randbits):
-            self.randbits.random_(to=2)
-            self.currentbit = 0
-        return self.randbits[self.currentbit]
-
-    def state_dict(self):
-        return dict(
-                constructor=self.__module__ + '.' +
-                    self.__class__.__name__ + '()',
-                resolution=self.resolution,
-                depth=self.depth,
-                buffersize=self.buffersize,
-                samplerate=self.samplerate,
-                data=[d.cpu().numpy()[:,:f].T
-                    for d, f in zip(self.data, self.firstfree)],
-                sizes=[d.shape[1] for d in self.data],
-                extremes=self.extremes.cpu().numpy(),
-                size=self.size)
-
-    def set_state_dict(self, dic):
-        self.resolution = int(dic['resolution'])
-        self.randbits = torch.ByteTensor(self.resolution)
-        self.currentbit = len(self.randbits) - 1
-        self.depth = int(dic['depth'])
-        self.buffersize = int(dic['buffersize'])
-        self.samplerate = float(dic['samplerate'])
-        firstfree = []
-        buffers = []
-        for d, s in zip(dic['data'], dic['sizes']):
-            firstfree.append(d.shape[0])
-            buf = numpy.zeros((d.shape[1], s), dtype=d.dtype)
-            buf[:,:d.shape[0]] = d.T
-            buffers.append(torch.from_numpy(buf))
-        self.firstfree = firstfree
-        self.data = buffers
-        self.extremes = torch.from_numpy((dic['extremes']))
-        self.size = int(dic['size'])
-        self.dtype = self.extremes.dtype
-        self.device = self.extremes.device
-
-    def minmax(self):
-        if self.firstfree[0]:
-            self._scan_extremes(self.data[0][:,:self.firstfree[0]].t())
-        return self.extremes.clone()
-
-    def median(self):
-        return self.quantiles([0.5])[:,0]
-
-    def mean(self):
-        return self.integrate(lambda x: x) / self.size
-
-    def variance(self):
-        mean = self.mean()[:,None]
-        return self.integrate(lambda x: (x - mean).pow(2)) / (self.size - 1)
-
-    def stdev(self):
-        return self.variance().sqrt()
-
-    def _expand(self):
-        cap = self._next_capacity()
-        if cap > 0:
-            # First, make a new layer of the proper capacity.
-            self.data.insert(0, torch.zeros(self.depth, cap,
-                dtype=self.dtype, device=self.device))
-            self.firstfree.insert(0, 0)
-        else:
-            # Unless we're so big we are just subsampling.
-            assert self.firstfree[0] == 0
-            self.samplerate *= 0.5
-        for index in range(1, len(self.data)):
-            # Scan for existing data that needs to be moved down a level.
-            amount = self.firstfree[index]
-            if amount == 0:
-                continue
-            position = self.firstfree[index-1]
-            # Move data down if it would leave enough empty space there
-            # This is the key invariant: enough empty space to fit half
-            # of the previous level's buffer size (rounding up)
-            if self.data[index-1].shape[1] - (amount + position) >= (
-                    -(-self.data[index-2].shape[1] // 2) if (index-1) else 1):
-                self.data[index-1][:,position:position + amount] = (
-                        self.data[index][:,:amount])
-                self.firstfree[index-1] += amount
-                self.firstfree[index] = 0
-            else:
-                # Scrunch the data if it would not.
-                data = self.data[index][:,:amount]
-                data = data.sort()[0]
-                if index == 1:
-                    self._update_extremes(data[:,0], data[:,-1])
-                offset = self._randbit()
-                scrunched = data[:,offset::2]
-                self.data[index][:,:scrunched.shape[1]] = scrunched
-                self.firstfree[index] = scrunched.shape[1]
-        return cap > 0
-
-    def _next_capacity(self):
-        cap = int(math.ceil(self.resolution * (0.67 ** len(self.data))))
-        if cap < 2:
-            return 0
-        # Round up to the nearest multiple of 8 for better GPU alignment.
-        cap = -8 * (-cap // 8)
-        return max(self.buffersize, cap)
-
-    def _weighted_summary(self, sort=True):
-        if self.firstfree[0]:
-            self._scan_extremes(self.data[0][:,:self.firstfree[0]].t())
-        size = sum(self.firstfree) + 2
-        weights = torch.FloatTensor(size) # Floating point
-        summary = torch.zeros(self.depth, size,
-                dtype=self.dtype, device=self.device)
-        weights[0:2] = 0
-        summary[:,0:2] = self.extremes
-        index = 2
-        for level, ff in enumerate(self.firstfree):
-            if ff == 0:
-                continue
-            summary[:,index:index + ff] = self.data[level][:,:ff]
-            weights[index:index + ff] = 2.0 ** level
-            index += ff
-        assert index == summary.shape[1]
-        if sort:
-            summary, order = torch.sort(summary, dim=-1)
-            weights = weights[order.view(-1).cpu()].view(order.shape)
-        return (summary, weights)
-
-    def quantiles(self, quantiles, old_style=False):
-        if self.size == 0:
-            return torch.full((self.depth, len(quantiles)), torch.nan)
-        summary, weights = self._weighted_summary()
-        cumweights = torch.cumsum(weights, dim=-1) - weights / 2
-        if old_style:
-            # To be convenient with torch.percentile
-            cumweights -= cumweights[:,0:1].clone()
-            cumweights /= cumweights[:,-1:].clone()
-        else:
-            cumweights /= torch.sum(weights, dim=-1, keepdim=True)
-        result = torch.zeros(self.depth, len(quantiles),
-                dtype=self.dtype, device=self.device)
-        # numpy is needed for interpolation
-        if not hasattr(quantiles, 'cpu'):
-            quantiles = torch.Tensor(quantiles)
-        nq = quantiles.cpu().numpy()
-        ncw = cumweights.cpu().numpy()
-        nsm = summary.cpu().numpy()
-        for d in range(self.depth):
-            result[d] = torch.tensor(numpy.interp(nq, ncw[d], nsm[d]),
-                    dtype=self.dtype, device=self.device)
-        return result
-
-    def integrate(self, fun):
-        result = None
-        for level, ff in enumerate(self.firstfree):
-            if ff == 0:
-                continue
-            term = torch.sum(
-                    fun(self.data[level][:,:ff]) * (2.0 ** level),
-                    dim=-1)
-            if result is None:
-                result = term
-            else:
-                result += term
-        if result is not None:
-            result /= self.samplerate
-        return result
-
-    def percentiles(self, percentiles):
-        return self.quantiles(percentiles, old_style=True)
-
-    def readout(self, count=1001, old_style=True):
-        return self.quantiles(
-                torch.linspace(0.0, 1.0, count), old_style=old_style)
-
-    def normalize(self, data):
-        '''
-        Given input data as taken from the training distirbution,
-        normalizes every channel to reflect quantile values,
-        uniformly distributed, within [0, 1].
-        '''
-        assert self.size > 0
-        assert data.shape[0] == self.depth
-        summary, weights = self._weighted_summary()
-        cumweights = torch.cumsum(weights, dim=-1) - weights / 2
-        cumweights /= torch.sum(weights, dim=-1, keepdim=True)
-        result = torch.zeros_like(data).float()
-        # numpy is needed for interpolation
-        ndata = data.cpu().numpy().reshape((data.shape[0], -1))
-        ncw = cumweights.cpu().numpy()
-        nsm = summary.cpu().numpy()
-        for d in range(self.depth):
-            normed = torch.tensor(numpy.interp(ndata[d], nsm[d], ncw[d]),
-                dtype=torch.float, device=data.device).clamp_(0.0, 1.0)
-            if len(data.shape) > 1:
-                normed = normed.view(*(data.shape[1:]))
-            result[d] = normed
-        return result
-
-
-class RunningConditionalQuantile:
-    '''
-    Equivalent to a map from conditions (any python hashable type)
-    to RunningQuantiles.  The reason for the type is to allow limited
-    GPU memory to be exploited while counting quantile stats on many
-    different conditions, a few of which are common and which benefit
-    from GPU, but most of which are rare and would not all fit into
-    GPU RAM.
-
-    To move a set of conditions to a device, use rcq.to_(device, conds).
-    Then in the future, move the tallied data to the device before
-    calling rcq.add, that is, rcq.add(cond, data.to(device)).
-
-    To allow the caller to decide which conditions to allow to use GPU,
-    rcq.most_common_conditions(n) returns a list of the n most commonly
-    added conditions so far.
-    '''
-    def __init__(self, resolution=6 * 1024, buffersize=None, seed=None,
-            state=None):
-        self.first_rq = None
-        self.call_stats = defaultdict(int)
-        self.running_quantiles = {}
-        if state is not None:
-            self.set_state_dict(state)
-            return
-        self.rq_args = dict(resolution=resolution, buffersize=buffersize,
-                seed=seed)
-
-    def add(self, condition, incoming):
-        if condition not in self.running_quantiles:
-            self.running_quantiles[condition] = RunningQuantile(**self.rq_args)
-            if self.first_rq is None:
-                self.first_rq = self.running_quantiles[condition]
-        self.call_stats[condition] += 1
-        rq = self.running_quantiles[condition]
-        # For performance reasons, the caller can move some conditions to
-        # the CPU if they are not among the most common conditions.
-        if rq.device is not None and (rq.device != incoming.device):
-            rq.to_(incoming.device)
-        self.running_quantiles[condition].add(incoming)
-
-    def most_common_conditions(self, n):
-        return sorted(self.call_stats.keys(),
-                key=lambda c: -self.call_stats[c])[:n]
-
-    def collected_add(self, conditions, incoming):
-        for c in conditions:
-            self.add(c, incoming)
-
-    def conditional(self, c):
-        return self.running_quantiles[c]
-
-    def collected_quantiles(self, conditions, quantiles, old_style=False):
-        result = torch.zeros(
-                size=(len(conditions), self.first_rq.depth, len(quantiles)),
-                dtype=self.first_rq.dtype,
-                device=self.first_rq.device)
-        for i, c in enumerate(conditions):
-            if c in self.running_quantiles:
-                result[i] = self.running_quantiles[c].quantiles(
-                        quantiles, old_style)
-        return result
-
-    def collected_normalize(self, conditions, values):
-        result = torch.zeros(
-                size=(len(conditions), values.shape[0], values.shape[1]),
-                dtype=torch.float,
-                device=self.first_rq.device)
-        for i, c in enumerate(conditions):
-            if c in self.running_quantiles:
-                result[i] = self.running_quantiles[c].normalize(values)
-        return result
-
-    def to_(self, device, conditions=None):
-        if conditions is None:
-            conditions = self.running_quantiles.keys()
-        for cond in conditions:
-            if cond in self.running_quantiles:
-                self.running_quantiles[cond].to_(device)
-
-    def state_dict(self):
-        conditions = sorted(self.running_quantiles.keys())
-        result = dict(
-                constructor=self.__module__ + '.' +
-                    self.__class__.__name__ + '()',
-                rq_args=self.rq_args,
-                conditions=conditions)
-        for i, c in enumerate(conditions):
-            result.update({
-                '%d.%s' % (i, k): v
-                for k, v in self.running_quantiles[c].state_dict().items()})
-        return result
-
-    def set_state_dict(self, dic):
-        self.rq_args = dic['rq_args'].item()
-        conditions = list(dic['conditions'])
-        subdicts = defaultdict(dict)
-        for k, v in dic.items():
-            if '.' in k:
-                p, s = k.split('.', 1)
-                subdicts[p][s] = v
-        self.running_quantiles = {
-                c: RunningQuantile(state=subdicts[str(i)])
-                for i, c in enumerate(conditions)}
-        if conditions:
-            self.first_rq = self.running_quantiles[conditions[0]]
-
-    # example usage:
-    # levels = rqc.conditional(()).quantiles(1 - fracs)
-    # denoms = 1 - rqc.collected_normalize(cats, levels)
-    # isects = 1 - rqc.collected_normalize(labels, levels)
-    # unions = fracs + denoms[cats] - isects
-    # iou = isects / unions
-
-
-
-
-class RunningCrossCovariance:
-    '''
-    Running computation. Use this when an off-diagonal block of the
-    covariance matrix is needed (e.g., when the whole covariance matrix
-    does not fit in the GPU).
-
-    Chan-style numerically stable update of mean and full covariance matrix.
-    Chan, Golub. LeVeque. 1983. http://www.jstor.org/stable/2683386
-    '''
-    def __init__(self, state=None):
-        if state is not None:
-            self.set_state_dict(state)
-            return
-        self.count = 0
-        self._mean = None
-        self.cmom2 = None
-        self.v_cmom2 = None
-
-    def add(self, a, b):
-        if len(a.shape) == 1:
-            a = a[None, :]
-            b = b[None, :]
-        assert(a.shape[0] == b.shape[0])
-        if len(a.shape) > 2:
-            a, b = [d.view(d.shape[0], d.shape[1], -1).permute(0, 2, 1
-                ).contiguous().view(-1, d.shape[1]) for d in [a, b]]
-        batch_count = a.shape[0]
-        batch_mean = [d.sum(0) / batch_count for d in [a, b]]
-        centered = [d - bm for d, bm in zip([a, b], batch_mean)]
-        # If more than 10 billion operations, divide into batches.
-        sub_batch = -(-(10 << 30) // (a.shape[1] * b.shape[1]))
-        # Initial batch.
-        if self._mean is None:
-            self.count = batch_count
-            self._mean = batch_mean
-            self.v_cmom2 = [c.pow(2).sum(0) for c in centered]
-            self.cmom2 = a.new(a.shape[1], b.shape[1]).zero_()
-            progress_addbmm(self.cmom2, centered[0][:,:,None],
-                    centered[1][:,None,:], sub_batch)
-            return
-        # Update a batch using Chan-style update for numerical stability.
-        oldcount = self.count
-        self.count += batch_count
-        new_frac = float(batch_count) / self.count
-        # Update the mean according to the batch deviation from the old mean.
-        delta = [bm.sub_(m).mul_(new_frac)
-                for bm, m in zip(batch_mean, self._mean)]
-        for m, d in zip(self._mean, delta):
-            m.add_(d)
-        # Update the cross-covariance using the batch deviation
-        progress_addbmm(self.cmom2, centered[0][:,:,None],
-                centered[1][:,None,:], sub_batch)
-        self.cmom2.addmm_(alpha=new_frac * oldcount,
-                mat1=delta[0][:,None], mat2=delta[1][None,:])
-        # Update the variance using the batch deviation
-        for c, vc2, d in zip(centered, self.v_cmom2, delta):
-            vc2.add_(c.pow(2).sum(0))
-            vc2.add_(d.pow_(2).mul_(new_frac * oldcount))
-
-    def mean(self):
-        return self._mean
-
-    def variance(self):
-        return [vc2 / (self.count - 1) for vc2 in self.v_cmom2]
-
-    def stdev(self):
-        return [v.sqrt() for v in self.variance()]
-
-    def covariance(self):
-        return self.cmom2 / (self.count - 1)
-
-    def correlation(self):
-        covariance = self.covariance()
-        rstdev = [s.reciprocal() for s in self.stdev()]
-        cor = rstdev[0][:,None] * covariance * rstdev[1][None,:]
-        # Remove NaNs
-        cor[torch.isnan(cor)] = 0
-        return cor
-
-    def to_(self, device):
-        self._mean = [m.to(device) for m in self._mean]
-        self.v_cmom2 = [vcs.to(device) for vcs in self.v_cmom2]
-        self.cmom2 = self.cmom2.to(device)
-
-    def state_dict(self):
-        return dict(
-                constructor=self.__module__ + '.' +
-                    self.__class__.__name__ + '()',
-                count=self.count,
-                mean_a=self._mean[0].cpu().numpy(),
-                mean_b=self._mean[1].cpu().numpy(),
-                cmom2_a=self.v_cmom2[0].cpu().numpy(),
-                cmom2_b=self.v_cmom2[1].cpu().numpy(),
-                cmom2=self.cmom2.cpu().numpy())
-
-    def set_state_dict(self, dic):
-        self.count = dic['count'].item()
-        self._mean = [torch.from_numpy(dic[k]) for k in ['mean_a', 'mean_b']]
-        self.v_cmom2 = [torch.from_numpy(dic[k])
-                for k in ['cmom2_a', 'cmom2_b']]
-        self.cmom2 = torch.from_numpy(dic['cmom2'])
-
-def progress_addbmm(accum, x, y, batch_size):
-    '''
-    Break up very large adbmm operations into batches so progress can be seen.
-    '''
-    from .progress import default_progress
-    if x.shape[0] <= batch_size:
-        return accum.addbmm_(x, y)
-    progress = default_progress(None)
-    for i in progress(range(0, x.shape[0], batch_size), desc='bmm'):
-        accum.addbmm_(x[i:i+batch_size], y[i:i+batch_size])
-    return accum
-
-
-def sample_portion(vec, p=0.5):
-    bits = torch.bernoulli(torch.zeros(vec.shape[0], dtype=torch.uint8,
-        device=vec.device), p)
-    return vec[bits]
-
-if __name__ == '__main__':
-    import warnings
-    warnings.filterwarnings("error")
-    import time
-    import argparse
-    parser = argparse.ArgumentParser(
-        description='Test things out')
-    parser.add_argument('--mode', default='cpu', help='cpu or cuda')
-    parser.add_argument('--test_size', type=int, default=1000000)
-    args = parser.parse_args()
-
-    # An adverarial case: we keep finding more numbers in the middle
-    # as the stream goes on.
-    amount = args.test_size
-    quantiles = 1000
-    data = numpy.arange(float(amount))
-    data[1::2] = data[-1::-2] + (len(data) - 1)
-    data /= 2
-    depth = 50
-    test_cuda = torch.cuda.is_available()
-    alldata = data[:,None] + (numpy.arange(depth) * amount)[None, :]
-    actual_sum = torch.FloatTensor(numpy.sum(alldata * alldata, axis=0))
-    amt = amount // depth
-    for r in range(depth):
-        numpy.random.shuffle(alldata[r*amt:r*amt+amt,r])
-    if args.mode == 'cuda':
-        alldata = torch.cuda.FloatTensor(alldata)
-        dtype = torch.float
-        device = torch.device('cuda')
-    else:
-        alldata = torch.FloatTensor(alldata)
-        dtype = torch.float
-        device = None
-    starttime = time.time()
-    qc = RunningQuantile(resolution=6 * 1024)
-    qc.add(alldata)
-    # Test state dict
-    saved = qc.state_dict()
-    # numpy.savez('foo.npz', **saved)
-    # saved = numpy.load('foo.npz')
-    qc = RunningQuantile(state=saved)
-    assert not qc.device.type == 'cuda'
-    qc.add(alldata)
-    actual_sum *= 2
-    ro = qc.readout(1001).cpu()
-    endtime = time.time()
-    gt = torch.linspace(0, amount, quantiles+1)[None,:] + (
-            torch.arange(qc.depth, dtype=torch.float) * amount)[:,None]
-    maxreldev = torch.max(torch.abs(ro - gt) / amount) * quantiles
-    print("Maximum relative deviation among %d perentiles: %f" % (
-        quantiles, maxreldev))
-    minerr = torch.max(torch.abs(qc.minmax().cpu()[:,0] -
-            torch.arange(qc.depth, dtype=torch.float) * amount))
-    maxerr = torch.max(torch.abs((qc.minmax().cpu()[:, -1] + 1) -
-            (torch.arange(qc.depth, dtype=torch.float) + 1) * amount))
-    print("Minmax error %f, %f" % (minerr, maxerr))
-    interr = torch.max(torch.abs(qc.integrate(lambda x: x * x).cpu()
-            - actual_sum) / actual_sum)
-    print("Integral error: %f" % interr)
-    medianerr = torch.max(torch.abs(qc.median() -
-        alldata.median(0)[0]) / alldata.median(0)[0]).cpu()
-    print("Median error: %f" % interr)
-    meanerr = torch.max(
-            torch.abs(qc.mean() - alldata.mean(0)) / alldata.mean(0)).cpu()
-    print("Mean error: %f" % meanerr)
-    varerr = torch.max(
-            torch.abs(qc.variance() - alldata.var(0)) / alldata.var(0)).cpu()
-    print("Variance error: %f" % varerr)
-    counterr = ((qc.integrate(lambda x: torch.ones(x.shape[-1]).cpu())
-                - qc.size) / (0.0 + qc.size)).item()
-    print("Count error: %f" % counterr)
-    print("Time %f" % (endtime - starttime))
-    # Algorithm is randomized, so some of these will fail with low probability.
-    assert maxreldev < 1.0
-    assert minerr == 0.0
-    assert maxerr == 0.0
-    assert interr < 0.01
-    assert abs(counterr) < 0.001
-    print("OK")
diff --git a/spaces/mikkoar/marco/src/components/ui/badge.tsx b/spaces/mikkoar/marco/src/components/ui/badge.tsx
deleted file mode 100644
index d9a84b394090e5b4b3bd34f6135b9a2f2ead0aa2..0000000000000000000000000000000000000000
--- a/spaces/mikkoar/marco/src/components/ui/badge.tsx
+++ /dev/null
@@ -1,36 +0,0 @@
-import * as React from 'react'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const badgeVariants = cva(
-  'inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2',
-  {
-    variants: {
-      variant: {
-        default:
-          'border-transparent bg-primary text-primary-foreground hover:bg-primary/80',
-        secondary:
-          'border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        destructive:
-          'border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80',
-        outline: 'text-foreground'
-      }
-    },
-    defaultVariants: {
-      variant: 'default'
-    }
-  }
-)
-
-export interface BadgeProps
-  extends React.HTMLAttributes<HTMLDivElement>,
-    VariantProps<typeof badgeVariants> {}
-
-function Badge({ className, variant, ...props }: BadgeProps) {
-  return (
-    <div className={cn(badgeVariants({ variant }), className)} {...props} />
-  )
-}
-
-export { Badge, badgeVariants }
diff --git a/spaces/milyiyo/reimagine-it/captioning/models/AttEnsemble.py b/spaces/milyiyo/reimagine-it/captioning/models/AttEnsemble.py
deleted file mode 100644
index 19e88e2ace19e4a73fe6fcb1024bd584d77a38fa..0000000000000000000000000000000000000000
--- a/spaces/milyiyo/reimagine-it/captioning/models/AttEnsemble.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# This file is the implementation for ensemble evaluation.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import *
-
-from .CaptionModel import CaptionModel
-from .AttModel import pack_wrapper, AttModel
-
-class AttEnsemble(AttModel):
-    def __init__(self, models, weights=None):
-        CaptionModel.__init__(self)
-        # super(AttEnsemble, self).__init__()
-
-        self.models = nn.ModuleList(models)
-        self.vocab_size = models[0].vocab_size
-        self.seq_length = models[0].seq_length
-        self.bad_endings_ix = models[0].bad_endings_ix
-        self.ss_prob = 0
-        weights = weights or [1.0] * len(self.models)
-        self.register_buffer('weights', torch.tensor(weights))
-
-    def init_hidden(self, batch_size):
-        state = [m.init_hidden(batch_size) for m in self.models]
-        return self.pack_state(state)
-
-    def pack_state(self, state):
-        self.state_lengths = [len(_) for _ in state]
-        return sum([list(_) for _ in state], [])
-
-    def unpack_state(self, state):
-        out = []
-        for l in self.state_lengths:
-            out.append(state[:l])
-            state = state[l:]
-        return out
-
-    def embed(self, it):
-        return [m.embed(it) for m in self.models]
-
-    def core(self, *args):
-        return zip(*[m.core(*_) for m, _ in zip(self.models, zip(*args))])
-
-    def get_logprobs_state(self, it, tmp_fc_feats, tmp_att_feats, tmp_p_att_feats, tmp_att_masks, state, output_logsoftmax=1):
-        # 'it' contains a word index
-        xt = self.embed(it)
-
-        state = self.unpack_state(state)
-        output, state = self.core(xt, tmp_fc_feats, tmp_att_feats, tmp_p_att_feats, state, tmp_att_masks)
-        logprobs = torch.stack([F.softmax(m.logit(output[i]), dim=1) for i,m in enumerate(self.models)], 2).mul(self.weights).div(self.weights.sum()).sum(-1).log()
-
-        return logprobs, self.pack_state(state)
-
-    def _prepare_feature(self, *args):
-        return tuple(zip(*[m._prepare_feature(*args) for m in self.models]))
-
-    def _old_sample_beam(self, fc_feats, att_feats, att_masks=None, opt={}):
-        beam_size = opt.get('beam_size', 10)
-        batch_size = fc_feats.size(0)
-
-        fc_feats, att_feats, p_att_feats, att_masks = self._prepare_feature(fc_feats, att_feats, att_masks)
-
-        assert beam_size <= self.vocab_size + 1, 'lets assume this for now, otherwise this corner case causes a few headaches down the road. can be dealt with in future if needed'
-        seq = torch.LongTensor(self.seq_length, batch_size).zero_()
-        seqLogprobs = torch.FloatTensor(self.seq_length, batch_size, self.vocab_size + 1)
-        # lets process every image independently for now, for simplicity
-
-        self.done_beams = [[] for _ in range(batch_size)]
-        for k in range(batch_size):
-            state = self.init_hidden(beam_size)
-            tmp_fc_feats = [fc_feats[i][k:k+1].expand(beam_size, fc_feats[i].size(1)) for i,m in enumerate(self.models)]
-            tmp_att_feats = [att_feats[i][k:k+1].expand(*((beam_size,)+att_feats[i].size()[1:])).contiguous() for i,m in enumerate(self.models)]
-            tmp_p_att_feats = [p_att_feats[i][k:k+1].expand(*((beam_size,)+p_att_feats[i].size()[1:])).contiguous() for i,m in enumerate(self.models)]
-            tmp_att_masks = [att_masks[i][k:k+1].expand(*((beam_size,)+att_masks[i].size()[1:])).contiguous() if att_masks[i] is not None else att_masks[i] for i,m in enumerate(self.models)]
-
-            it = fc_feats[0].data.new(beam_size).long().zero_()
-            logprobs, state = self.get_logprobs_state(it, tmp_fc_feats, tmp_att_feats, tmp_p_att_feats, tmp_att_masks, state)
-
-            self.done_beams[k] = self.old_beam_search(state, logprobs, tmp_fc_feats, tmp_att_feats, tmp_p_att_feats, tmp_att_masks, opt=opt)
-            seq[:, k] = self.done_beams[k][0]['seq'] # the first beam has highest cumulative score
-            seqLogprobs[:, k] = self.done_beams[k][0]['logps']
-        # return the samples and their log likelihoods
-        return seq.transpose(0, 1), seqLogprobs.transpose(0, 1)
-        # return the samples and their log likelihoods
diff --git a/spaces/mishtert/tracer/dictutils.py b/spaces/mishtert/tracer/dictutils.py
deleted file mode 100644
index 2c08c2bc8340b59ebc1ca21965dbe6e271b38ea9..0000000000000000000000000000000000000000
--- a/spaces/mishtert/tracer/dictutils.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import json
-
-
-# reading the nct_ta dictionary 
-with open('asset/data/nct_ta_dict.json') as f:
-    data = f.read()
-nct_ta_dict = json.loads(data) #load dict
-# print(len(nct_ta_dict))
-
-# reading the mesh_to_ta_dict dictionary 
-with open('asset/data/mesh_to_ta_dict.json') as f:
-    data = f.read()
-mesh_to_ta_dict = json.loads(data) #load dict
-# print(len(mesh_to_ta_dict))
-with open('asset/data/replacement_dict.json') as f:
-    data = f.read()
-repl_dict = json.loads(data) #load dict
-# print(len(mesh_to_ta_dict))
-
-with open('asset/data/entry_terms_dict.json') as f:
-    data = f.read()
-entry_dict = json.loads(data) #load dict
-# print(len(mesh_to_ta_dict))
-
-with open('asset/data/final_check_dict.json') as f:
-    data = f.read()
-final_check = json.loads(data) #load dict
-# print(len(mesh_to_ta_dict))
-
-with open('asset/data/gb_us_dict.json') as f:
-    data = f.read()
-gb_2_us_dict = json.loads(data) #load dict
-# print(len(mesh_to_ta_dict))
\ No newline at end of file
diff --git a/spaces/ml6team/Speaker-Diarization/utils/general_utils.py b/spaces/ml6team/Speaker-Diarization/utils/general_utils.py
deleted file mode 100644
index 2ef7530e82fc878151184523a546c3f0ad59ce89..0000000000000000000000000000000000000000
--- a/spaces/ml6team/Speaker-Diarization/utils/general_utils.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""
-General utility functions
-"""
-import os
-from typing import List, Dict
-
-
-def get_dict_of_audio_samples(audio_sample_path: str) -> Dict[str, str]:
-    """
-    Function that returns the list of available audio samples
-    Args:
-        audio_sample_path (str): The path to the directory with the audio samples
-    Returns:
-        Dict[str, str]: a dictionary where the name is the file name (without extension to be listed
-        as an option for the user) and the value is the original file name
-    """
-    audio_sample_dict = {}
-
-    for file in os.listdir(audio_sample_path):
-        file_option = os.path.basename(file).rsplit('.')[0]
-        audio_sample_dict[file_option] = file
-
-    return audio_sample_dict
diff --git a/spaces/mohamedemam/bert_sentaces_similarty/README.md b/spaces/mohamedemam/bert_sentaces_similarty/README.md
deleted file mode 100644
index 86b886a7d97bd7b08b1334fb17c13972f50a212a..0000000000000000000000000000000000000000
--- a/spaces/mohamedemam/bert_sentaces_similarty/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Bert Sentaces Similarty
-emoji: 🐨
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/monra/freegpt-webui-chimera/client/css/sidebar.css b/spaces/monra/freegpt-webui-chimera/client/css/sidebar.css
deleted file mode 100644
index 310887c60443abd491c3162f62e44b5ec333e50d..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui-chimera/client/css/sidebar.css
+++ /dev/null
@@ -1,197 +0,0 @@
-.sidebar {
-	max-width: 260px;
-	padding: var(--section-gap);
-	flex-shrink: 0;
-	display: flex;
-	flex-direction: column;
-	justify-content: space-between;
-}
-
-.sidebar .title {
-	font-size: 14px;
-	font-weight: 500;
-}
-
-.sidebar .conversation-sidebar {
-	padding: 8px 12px;
-	display: flex;
-	gap: 18px;
-	align-items: center;
-	user-select: none;
-	justify-content: space-between;
-}
-
-.sidebar .conversation-sidebar .left {
-	cursor: pointer;
-	display: flex;
-	align-items: center;
-	gap: 10px;
-}
-
-.sidebar i {
-	color: var(--conversations);
-	cursor: pointer;
-}
-
-.sidebar .top {
-	display: flex;
-	flex-direction: column;
-	overflow: hidden;
-	gap: 16px;
-	padding-right: 8px;
-}
-
-.sidebar .top:hover {
-	overflow: auto;
-}
-
-.sidebar .info {
-	padding: 8px 12px 0px 12px;
-	display: flex;
-	align-items: center;
-	justify-content: center;
-	user-select: none;
-	background: transparent;
-	width: 100%;
-	border: none;
-	text-decoration: none;
-}
-
-.sidebar .info span {
-	color: var(--conversations);
-	line-height: 1.5;
-	font-size: 0.75rem;
-}
-
-.sidebar .info i::before {
-	margin-right: 8px;
-}
-
-.sidebar-footer {
-	width: 100%;
-	margin-top: 16px;
-	display: flex;
-	flex-direction: column;
-}
-
-.sidebar-footer button {
-	cursor: pointer;
-	user-select: none;
-	background: transparent;
-}
-
-.sidebar.shown {
-	position: fixed;
-	top: 0;
-	left: 0;
-	width: 100%;
-	height: 100%;
-	z-index: 1000;
-}
-
-.sidebar.shown .box {
-	background-color: #16171a;
-	width: 80%;
-	height: 100%;
-	overflow-y: auto;
-}
-
-@keyframes spinner {
-	to {
-		transform: rotate(360deg);
-	}
-}
-
-/* scrollbar */
-.sidebar .top::-webkit-scrollbar {
-	width: 4px;
-	padding: 8px 0px;
-}
-
-.sidebar .top::-webkit-scrollbar-track {
-	background-color: #ffffff00;
-}
-
-.sidebar .top::-webkit-scrollbar-thumb {
-	background-color: #555555;
-	border-radius: 10px;
-}
-
-.spinner:before {
-	content: "";
-	box-sizing: border-box;
-	position: absolute;
-	top: 50%;
-	left: 45%;
-	width: 20px;
-	height: 20px;
-	border-radius: 50%;
-	border: 1px solid var(--conversations);
-	border-top-color: white;
-	animation: spinner 0.6s linear infinite;
-}
-
-.menu-button {
-	display: none !important;
-	position: absolute;
-	z-index: 100000;
-	top: 0;
-	left: 0;
-	margin: 10px;
-	font-size: 1rem;
-	cursor: pointer;
-	width: 30px;
-	height: 30px;
-	justify-content: center;
-	align-items: center;
-	transition: 0.33s;
-}
-
-.menu-button i {
-	transition: 0.33s;
-}
-
-.rotated {
-	transform: rotate(360deg);
-}
-
-.menu-button.rotated {
-	position: fixed;
-	top: 10px;
-	left: 10px;
-	z-index: 1001;
-}
-
-@media screen and (max-width: 990px) {
-	.sidebar {
-		display: none;
-		width: 100%;
-		max-width: none;
-	}
-
-	.menu-button {
-		display: flex !important;
-	}
-}
-
-@media (max-width: 990px) {
-	.sidebar .top {
-		padding-top: 48px;
-	}
-}
-
-@media (min-width: 768px) {
-	.sidebar.shown {
-		position: static;
-		width: auto;
-		height: auto;
-		background-color: transparent;
-	}
-
-	.sidebar.shown .box {
-		background-color: #16171a;
-		width: auto;
-		height: auto;
-		overflow-y: auto;
-	}
-}
diff --git a/spaces/nathanTQ/ChatDev/online_log/static/css/style.css b/spaces/nathanTQ/ChatDev/online_log/static/css/style.css
deleted file mode 100644
index 1595bba089d173d3eec4bfb1f2788a1c61a77cb6..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/online_log/static/css/style.css
+++ /dev/null
@@ -1,126 +0,0 @@
-.container {
-    margin-top: 20px;
-    margin-bottom: 20px;
-    width: 2500px;
-    height: 700px;
-    border: 1px solid black;
-    overflow-y: scroll;
-    background-color: white;
-    position: relative;
-    scroll-behavior: smooth;
-}
-
-.container::-webkit-scrollbar {
-    width: 0;
-    background-color: transparent;
-}
-
-.message-container {
-  position: relative;
-  display: flex;
-  margin: 20px;
-  max-width: 95%;
-  word-wrap: break-word;
-  padding-top: 0px; /* Add space for the button */
-}
-
-
-.message-text {
-    background-color: lightgray;
-    border-radius: 10px;
-    padding: 8px;
-    margin-left: 40px;
-    font-size: 10px;
-    width: 100%;
-}
-
-.avatar {
-    width: 40px;
-    height: 40px;
-    border-radius: 50%;
-    position: absolute;
-    top: 0px;
-    left: -30px;
-    margin-left: 20px;
-    background-color: green;
-    background-size: cover;
-}
-
-.role {
-    font-size: 12px;
-    font-weight: bold;
-    position: absolute;
-    bottom: 0;
-    top: -30px;
-    margin-top: 10px;
-    margin-left: 40px;
-}
-
-.code-block pre {
-    margin: 0;
-}
-
-.dark {
-    color: #000000;
-}
-
-.line-numbers .line-numbers-rows {
-    border-right-color: #44475a;
-    display: block; /* Add this to ensure line numbers are displayed */
-}
-
-.copy-button {
-    position: absolute;
-    top: 1px;
-    right: 0px;
-    background-color: #7a7c7f;
-    color: #f8f8f2;
-    border: none;
-    padding: 5px 10px;
-    border-radius: 4px;
-    cursor: pointer;
-}
-
-.code-block-header {
-    background-color: #5b5656;
-    color: #ffffff;
-    padding: 5px;
-    font-size: 14px;
-    font-weight: bold;
-}
-
-
-.code-block {
-    background-color: #000000 !important;
-    border-radius: 4px;
-    margin-top: 10px;
-    position: relative;
-    overflow: hidden;
-    color: #000000; /* Add text color for syntax highlighting */
-}
-
-table {
-  border-collapse: collapse;
-  width: 100%;
-}
-
-th, td {
-  border: 1px solid black;
-  padding: 8px;
-  text-align: left;
-  white-space: pre-line;
-}
-
-.expand-button {
-  position: absolute;
-  top: 2px;
-  right: 2px;
-  border-radius: 4px;
-  background-color: transparent;
-  border: none;
-  padding: 5px;
-  cursor: pointer;
-  font-size: 8px;
-  font-weight: bold;
-  color: rgb(108, 99, 99);
-}
diff --git a/spaces/naver/PUMP/datasets/sfm120k.py b/spaces/naver/PUMP/datasets/sfm120k.py
deleted file mode 100644
index 4e042b3a971e85c8bc0cd428ae13f64d9f8d75ba..0000000000000000000000000000000000000000
--- a/spaces/naver/PUMP/datasets/sfm120k.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright 2022-present NAVER Corp.
-# CC BY-NC-SA 4.0
-# Available only for non-commercial use
-
-from pdb import set_trace as bb
-from os.path import *
-
-from .image_set import ImageSet
-from .pair_dataset import UnsupervisedPairs
-
-
-class SfM120k_Images (ImageSet):
-    def __init__(self, root='datasets/sfm120k'): 
-        self.init_from_folder(join(root,'ims'), recursive=True, listing=True, exts='')
-
-
-class SfM120k_Pairs (UnsupervisedPairs):
-    def __init__(self, root='datasets/sfm120k'):
-        super().__init__(SfM120k_Images(root=root), join(root,'list_pairs.txt'))
-
-
-if __name__ == '__main__':
-    from tools.viz import show_random_pairs
-
-    db = SfM120k_Pairs()
-
-    show_random_pairs(db)
diff --git a/spaces/ncduy/emotion-classifier/README.md b/spaces/ncduy/emotion-classifier/README.md
deleted file mode 100644
index 8f54f0c15f4d57ef27be05a1c9de22d96617a657..0000000000000000000000000000000000000000
--- a/spaces/ncduy/emotion-classifier/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Emotion Classifier
-emoji: 🦀
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Partition Magic Pro 8.05 Full __EXCLUSIVE__ Crack Vn-zooml.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Partition Magic Pro 8.05 Full __EXCLUSIVE__ Crack Vn-zooml.md
deleted file mode 100644
index 0eeaf37ac77fcd34a80bd1f523993365895110ed..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Partition Magic Pro 8.05 Full __EXCLUSIVE__ Crack Vn-zooml.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-
-<h1>How to Use Partition Magic Pro 8.05 Full Crack Vn-zooml to Manage Your Hard Disk Partitions</h1>
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diff --git a/spaces/nightfury/SD_Studio_AI_Text2Image_Image2Image_Generation/ui_functions.py b/spaces/nightfury/SD_Studio_AI_Text2Image_Image2Image_Generation/ui_functions.py
deleted file mode 100644
index 37bfa105ab4451599cb79aaae9f29ea3641c5aae..0000000000000000000000000000000000000000
--- a/spaces/nightfury/SD_Studio_AI_Text2Image_Image2Image_Generation/ui_functions.py
+++ /dev/null
@@ -1,240 +0,0 @@
-import re
-import gradio as gr
-from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps
-from io import BytesIO
-import base64
-import re
-
-def change_img_choices(sample_size):
-    choices = []
-    for i in range(int(sample_size)):
-        choices.append(
-            '图片{}(img{})'.format(i+1,i+1)
-        )
-    update_choices = gr.update(choices=choices)
-    return update_choices
-
-def change_image_editor_mode(choice, cropped_image, masked_image, resize_mode, width, height):
-    if choice == "Mask":
-        update_image_result = update_image_mask(cropped_image, resize_mode, width, height)
-        return [gr.update(visible=False), update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
-
-    update_image_result = update_image_mask(masked_image["image"] if masked_image is not None else None, resize_mode, width, height)
-    return [update_image_result, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
-
-def update_image_mask(cropped_image, resize_mode, width, height):
-    resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None
-    return gr.update(value=resized_cropped_image, visible=True)
-
-def toggle_options_gfpgan(selection):
-    if 0 in selection:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_upscalers(selection):
-    if 1 in selection:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_realesrgan(selection):
-    if selection == 0 or selection == 1 or selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_gobig(selection):
-    if selection == 1:
-        #print(selection)
-        return gr.update(visible=True)
-    if selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def toggle_options_ldsr(selection):
-    if selection == 2 or selection == 3:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def increment_down(value):
-    return value - 1
-
-def increment_up(value):
-    return value + 1
-
-def copy_img_to_lab(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='imgproc_tab')
-        img_update = gr.update(value=processed_image)
-        return processed_image, tab_update,
-    except IndexError:
-        return [None, None]
-def copy_img_params_to_lab(params):
-    try:
-        prompt = params[0][0].replace('\n', ' ').replace('\r', '')
-        seed = int(params[1][1])
-        steps = int(params[7][1])
-        cfg_scale = float(params[9][1])
-        sampler = params[11][1]
-        return prompt,seed,steps,cfg_scale,sampler
-    except IndexError:
-        return [None, None]
-def copy_img_to_input(img, idx):
-    try:
-        # print(img)
-        # print("=============")
-        # print("The img type is:{}".format(type(img[0])))
-        idx_map = {
-            "图片1(img1)":0,
-            "图片2(img2)":1,
-            "图片3(img3)":2,
-            "图片4(img4)":3,
-        }
-        idx = idx_map[idx]
-        image_data = re.sub('^data:image/.+;base64,', '', img[idx])
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='img2img_tab')
-        img_update = gr.update(value=processed_image)
-        move_prompt_zh_update = gr.update(visible=True)
-        move_prompt_en_update = gr.update(visible=True)
-        prompt_update = gr.update(visible=True)
-        return tab_update,processed_image, processed_image, move_prompt_zh_update, move_prompt_en_update, prompt_update
-    except IndexError:
-        return [None, None]
-
-def copy_img_to_edit(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='img2img_tab')
-        img_update = gr.update(value=processed_image)
-        mode_update = gr.update(value='Crop')
-        return processed_image, tab_update, mode_update
-    except IndexError:
-        return [None, None]
-
-def copy_img_to_mask(img):
-    try:
-        image_data = re.sub('^data:image/.+;base64,', '', img)
-        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-        tab_update = gr.update(selected='img2img_tab')
-        img_update = gr.update(value=processed_image)
-        mode_update = gr.update(value='Mask')
-        return processed_image, tab_update, mode_update
-    except IndexError:
-        return [None, None]
-
-
-
-def copy_img_to_upscale_esrgan(img):
-    tabs_update = gr.update(selected='realesrgan_tab')
-    image_data = re.sub('^data:image/.+;base64,', '', img)
-    processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
-    return processed_image, tabs_update
-
-
-help_text = """
-    ## Mask/Crop
-    * Masking is not inpainting. You will probably get better results manually masking your images in photoshop instead.
-    * Built-in masking/cropping is very temperamental.
-    * It may take some time for the image to show when switching from Crop to Mask.
-    * If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
-    * If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.
-
-    ## Advanced Editor
-    * Click 💾 Save to send your editor changes to the img2img workflow
-    * Click ❌ Clear to discard your editor changes
-
-    If anything breaks, try switching modes again, switch tabs, clear the image, or reload.
-"""
-
-def resize_image(resize_mode, im, width, height):
-    LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
-    if resize_mode == 0:
-        res = im.resize((width, height), resample=LANCZOS)
-    elif resize_mode == 1:
-        ratio = width / height
-        src_ratio = im.width / im.height
-
-        src_w = width if ratio > src_ratio else im.width * height // im.height
-        src_h = height if ratio <= src_ratio else im.height * width // im.width
-
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
-        res = Image.new("RGBA", (width, height))
-        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-    else:
-        ratio = width / height
-        src_ratio = im.width / im.height
-
-        src_w = width if ratio < src_ratio else im.width * height // im.height
-        src_h = height if ratio >= src_ratio else im.height * width // im.width
-
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
-        res = Image.new("RGBA", (width, height))
-        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-
-        if ratio < src_ratio:
-            fill_height = height // 2 - src_h // 2
-            res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
-            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
-        elif ratio > src_ratio:
-            fill_width = width // 2 - src_w // 2
-            res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
-            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
-
-    return res
-
-def update_dimensions_info(width, height):
-    pixel_count_formated = "{:,.0f}".format(width * height)
-    return f"Aspect ratio: {round(width / height, 5)}\nTotal pixel count: {pixel_count_formated}"
-
-def get_png_nfo( image: Image ):
-    info_text = ""
-    visible = bool(image and any(image.info))
-    if visible:
-        for key,value in image.info.items():
-            info_text += f"{key}: {value}\n"
-        info_text = info_text.rstrip('\n')
-    return gr.Textbox.update(value=info_text, visible=visible)
-
-def load_settings(*values):
-    new_settings, key_names, checkboxgroup_info = values[-3:]
-    values = list(values[:-3])
-
-    if new_settings:
-        if type(new_settings) is str:
-            if os.path.exists(new_settings):
-                with open(new_settings, "r", encoding="utf8") as f:
-                    new_settings = yaml.safe_load(f)
-            elif new_settings.startswith("file://") and os.path.exists(new_settings[7:]):
-                with open(new_settings[7:], "r", encoding="utf8") as f:
-                    new_settings = yaml.safe_load(f)
-            else:
-                new_settings = yaml.safe_load(new_settings)
-        if type(new_settings) is not dict:
-            new_settings = {"prompt": new_settings}
-        if "txt2img" in new_settings:
-            new_settings = new_settings["txt2img"]
-        target = new_settings.pop("target", "txt2img")
-        if target != "txt2img":
-            print(f"Warning: applying settings to txt2img even though {target} is specified as target.", file=sys.stderr)
-
-        skipped_settings = {}
-        for key in new_settings.keys():
-            if key in key_names:
-                values[key_names.index(key)] = new_settings[key]
-            else:
-                skipped_settings[key] = new_settings[key]
-        if skipped_settings:
-            print(f"Settings could not be applied: {skipped_settings}", file=sys.stderr)
-
-    # Convert lists of checkbox indices to lists of checkbox labels:
-    for (cbg_index, cbg_choices) in checkboxgroup_info:
-        values[cbg_index] = [cbg_choices[i] for i in values[cbg_index]]
-
-    return values
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py b/spaces/nikitaPDL2023/assignment4/detectron2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py
deleted file mode 100644
index ef0b6d16d4403fb5d16a3aeb71a22621a0be5e21..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from .mask_rcnn_R_50_FPN_100ep_LSJ import (
-    dataloader,
-    lr_multiplier,
-    model,
-    optimizer,
-    train,
-)
-from detectron2.config import LazyCall as L
-from detectron2.modeling.backbone import RegNet
-from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
-
-# Config source:
-# https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py  # noqa
-model.backbone.bottom_up = L(RegNet)(
-    stem_class=SimpleStem,
-    stem_width=32,
-    block_class=ResBottleneckBlock,
-    depth=23,
-    w_a=38.65,
-    w_0=96,
-    w_m=2.43,
-    group_width=40,
-    norm="SyncBN",
-    out_features=["s1", "s2", "s3", "s4"],
-)
-model.pixel_std = [57.375, 57.120, 58.395]
-
-# RegNets benefit from enabling cudnn benchmark mode
-train.cudnn_benchmark = True
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_export_onnx.py b/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_export_onnx.py
deleted file mode 100644
index aa15e1a40696e34e6792d1dedd75b6e5bb62b236..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_export_onnx.py
+++ /dev/null
@@ -1,237 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import io
-import unittest
-import warnings
-import torch
-from torch.hub import _check_module_exists
-
-from detectron2 import model_zoo
-from detectron2.config import get_cfg
-from detectron2.export import STABLE_ONNX_OPSET_VERSION
-from detectron2.export.flatten import TracingAdapter
-from detectron2.export.torchscript_patch import patch_builtin_len
-from detectron2.layers import ShapeSpec
-from detectron2.modeling import build_model
-from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.testing import (
-    _pytorch1111_symbolic_opset9_repeat_interleave,
-    _pytorch1111_symbolic_opset9_to,
-    get_sample_coco_image,
-    has_dynamic_axes,
-    random_boxes,
-    register_custom_op_onnx_export,
-    skipIfOnCPUCI,
-    skipIfUnsupportedMinOpsetVersion,
-    skipIfUnsupportedMinTorchVersion,
-    unregister_custom_op_onnx_export,
-)
-
-
-@unittest.skipIf(not _check_module_exists("onnx"), "ONNX not installed.")
-@skipIfUnsupportedMinTorchVersion("1.10")
-class TestONNXTracingExport(unittest.TestCase):
-    opset_version = STABLE_ONNX_OPSET_VERSION
-
-    def testMaskRCNNFPN(self):
-        def inference_func(model, images):
-            with warnings.catch_warnings(record=True):
-                inputs = [{"image": image} for image in images]
-            inst = model.inference(inputs, do_postprocess=False)[0]
-            return [{"instances": inst}]
-
-        self._test_model_zoo_from_config_path(
-            "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func
-        )
-
-    @skipIfOnCPUCI
-    def testMaskRCNNC4(self):
-        def inference_func(model, image):
-            inputs = [{"image": image}]
-            return model.inference(inputs, do_postprocess=False)[0]
-
-        self._test_model_zoo_from_config_path(
-            "COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml", inference_func
-        )
-
-    @skipIfOnCPUCI
-    def testCascadeRCNN(self):
-        def inference_func(model, image):
-            inputs = [{"image": image}]
-            return model.inference(inputs, do_postprocess=False)[0]
-
-        self._test_model_zoo_from_config_path(
-            "Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml", inference_func
-        )
-
-    def testRetinaNet(self):
-        def inference_func(model, image):
-            return model.forward([{"image": image}])[0]["instances"]
-
-        self._test_model_zoo_from_config_path(
-            "COCO-Detection/retinanet_R_50_FPN_3x.yaml", inference_func
-        )
-
-    @skipIfOnCPUCI
-    def testMaskRCNNFPN_batched(self):
-        def inference_func(model, image1, image2):
-            inputs = [{"image": image1}, {"image": image2}]
-            return model.inference(inputs, do_postprocess=False)
-
-        self._test_model_zoo_from_config_path(
-            "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", inference_func, batch=2
-        )
-
-    @skipIfUnsupportedMinOpsetVersion(16, STABLE_ONNX_OPSET_VERSION)
-    @skipIfUnsupportedMinTorchVersion("1.11.1")
-    def testMaskRCNNFPN_with_postproc(self):
-        def inference_func(model, image):
-            inputs = [{"image": image, "height": image.shape[1], "width": image.shape[2]}]
-            return model.inference(inputs, do_postprocess=True)[0]["instances"]
-
-        self._test_model_zoo_from_config_path(
-            "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml",
-            inference_func,
-        )
-
-    def testKeypointHead(self):
-        class M(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.model = KRCNNConvDeconvUpsampleHead(
-                    ShapeSpec(channels=4, height=14, width=14), num_keypoints=17, conv_dims=(4,)
-                )
-
-            def forward(self, x, predbox1, predbox2):
-                inst = [
-                    Instances((100, 100), pred_boxes=Boxes(predbox1)),
-                    Instances((100, 100), pred_boxes=Boxes(predbox2)),
-                ]
-                ret = self.model(x, inst)
-                return tuple(x.pred_keypoints for x in ret)
-
-        model = M()
-        model.eval()
-
-        def gen_input(num1, num2):
-            feat = torch.randn((num1 + num2, 4, 14, 14))
-            box1 = random_boxes(num1)
-            box2 = random_boxes(num2)
-            return feat, box1, box2
-
-        with patch_builtin_len():
-            onnx_model = self._test_model(
-                model,
-                gen_input(1, 2),
-                input_names=["features", "pred_boxes", "pred_classes"],
-                output_names=["box1", "box2"],
-                dynamic_axes={
-                    "features": {0: "batch", 1: "static_four", 2: "height", 3: "width"},
-                    "pred_boxes": {0: "batch", 1: "static_four"},
-                    "pred_classes": {0: "batch", 1: "static_four"},
-                    "box1": {0: "num_instance", 1: "K", 2: "static_three"},
-                    "box2": {0: "num_instance", 1: "K", 2: "static_three"},
-                },
-            )
-
-            # Although ONNX models are not executable by PyTorch to verify
-            # support of batches with different sizes, we can verify model's IR
-            # does not hard-code input and/or output shapes.
-            # TODO: Add tests with different batch sizes when detectron2's CI
-            #       support ONNX Runtime backend.
-            assert has_dynamic_axes(onnx_model)
-
-    ################################################################################
-    # Testcase internals - DO NOT add tests below this point
-    ################################################################################
-
-    def setUp(self):
-        register_custom_op_onnx_export("::to", _pytorch1111_symbolic_opset9_to, 9, "1.11.1")
-        register_custom_op_onnx_export(
-            "::repeat_interleave",
-            _pytorch1111_symbolic_opset9_repeat_interleave,
-            9,
-            "1.11.1",
-        )
-
-    def tearDown(self):
-        unregister_custom_op_onnx_export("::to", 9, "1.11.1")
-        unregister_custom_op_onnx_export("::repeat_interleave", 9, "1.11.1")
-
-    def _test_model(
-        self,
-        model,
-        inputs,
-        inference_func=None,
-        opset_version=STABLE_ONNX_OPSET_VERSION,
-        save_onnx_graph_path=None,
-        **export_kwargs,
-    ):
-        # Not imported in the beginning of file to prevent runtime errors
-        # for environments without ONNX.
-        # This testcase checks dependencies before running
-        import onnx  # isort:skip
-
-        f = io.BytesIO()
-        adapter_model = TracingAdapter(model, inputs, inference_func)
-        adapter_model.eval()
-        with torch.no_grad():
-            try:
-                torch.onnx.enable_log()
-            except AttributeError:
-                # Older ONNX versions does not have this API
-                pass
-            torch.onnx.export(
-                adapter_model,
-                adapter_model.flattened_inputs,
-                f,
-                training=torch.onnx.TrainingMode.EVAL,
-                opset_version=opset_version,
-                verbose=True,
-                **export_kwargs,
-            )
-        onnx_model = onnx.load_from_string(f.getvalue())
-        assert onnx_model is not None
-        if save_onnx_graph_path:
-            onnx.save(onnx_model, save_onnx_graph_path)
-        return onnx_model
-
-    def _test_model_zoo_from_config_path(
-        self,
-        config_path,
-        inference_func,
-        batch=1,
-        opset_version=STABLE_ONNX_OPSET_VERSION,
-        save_onnx_graph_path=None,
-        **export_kwargs,
-    ):
-        model = model_zoo.get(config_path, trained=True)
-        image = get_sample_coco_image()
-        inputs = tuple(image.clone() for _ in range(batch))
-        return self._test_model(
-            model, inputs, inference_func, opset_version, save_onnx_graph_path, **export_kwargs
-        )
-
-    def _test_model_from_config_path(
-        self,
-        config_path,
-        inference_func,
-        batch=1,
-        opset_version=STABLE_ONNX_OPSET_VERSION,
-        save_onnx_graph_path=None,
-        **export_kwargs,
-    ):
-        from projects.PointRend import point_rend  # isort:skip
-
-        cfg = get_cfg()
-        cfg.DATALOADER.NUM_WORKERS = 0
-        point_rend.add_pointrend_config(cfg)
-        cfg.merge_from_file(config_path)
-        cfg.freeze()
-        model = build_model(cfg)
-        image = get_sample_coco_image()
-        inputs = tuple(image.clone() for _ in range(batch))
-        return self._test_model(
-            model, inputs, inference_func, opset_version, save_onnx_graph_path, **export_kwargs
-        )
diff --git a/spaces/nomic-ai/fka_awesome-chatgpt-prompts/style.css b/spaces/nomic-ai/fka_awesome-chatgpt-prompts/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/fka_awesome-chatgpt-prompts/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/nsarrazin/agents-js-oasst/vite.config.ts b/spaces/nsarrazin/agents-js-oasst/vite.config.ts
deleted file mode 100644
index c1f9e1406aa3a3abbbf336414da8f325e4f899bb..0000000000000000000000000000000000000000
--- a/spaces/nsarrazin/agents-js-oasst/vite.config.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-import { sveltekit } from '@sveltejs/kit/vite';
-import { defineConfig } from 'vitest/config';
-
-export default defineConfig({
-	plugins: [sveltekit()],
-	test: {
-		include: ['src/**/*.{test,spec}.{js,ts}']
-	},
-	server: {
-		fs: {
-			// Allow serving files from one level up to the project root
-			allow: ['..']
-		}
-	}
-});
diff --git a/spaces/nugrahatheo/Customer-Segmentation/app.py b/spaces/nugrahatheo/Customer-Segmentation/app.py
deleted file mode 100644
index b57ae2ce88cbd63bce51ba76271c271d46cb2391..0000000000000000000000000000000000000000
--- a/spaces/nugrahatheo/Customer-Segmentation/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import streamlit as st
-from streamlit_option_menu import option_menu
-import eda
-import clustering
-import about
-
-st.write('### CUSTOMER SEGMENTATION MACHINE LEARNING')
-st.write('##### This page created by [Theo Nugraha](https://github.com/theonugraha)')
-st.markdown('---')
-
-selected = option_menu(None, ["About", "EDA", "Clustering"], 
-    icons=['house', 'bar-chart', 'gear'], 
-    menu_icon="cast", default_index=0, orientation="horizontal",
-    styles={
-        "icon": {"color": "red", "font-size": "15px"}, 
-        "nav-link": {"font-size": "15px", "text-align": "left", "margin":"1px", "--hover-color": "#eee"},
-        "nav-link-selected": {"background-color": "lightgrey"},
-    }
-)
-    
-selected
-    
-
-if selected == 'EDA':
-    eda.run()
-elif selected == 'Clustering':
-    clustering.run()
-else:
-    about.run()
\ No newline at end of file
diff --git a/spaces/oguzakif/video-object-remover/SiamMask/experiments/siammask_base/test_all.sh b/spaces/oguzakif/video-object-remover/SiamMask/experiments/siammask_base/test_all.sh
deleted file mode 100644
index 0b896ae1befccd2708bd697c6183ec24a2cd9352..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/SiamMask/experiments/siammask_base/test_all.sh
+++ /dev/null
@@ -1,83 +0,0 @@
-show_help() {
-cat << EOF
-Usage: 
-    ${0##*/} [-h/--help] [-s/--start] [-e/--end] [-d/--dataset] [-m/--model]  [-g/--gpu]
-    e.g.
-        bash ${0##*/} -s 1 -e 20 -d VOT2018 -g 4 # for test models
-        bash ${0##*/} -m snapshot/checkpoint_e10.pth -n 8 -g 4 # for tune models
-EOF
-}
-
-ROOT=`git rev-parse --show-toplevel`
-source activate siammask
-export PYTHONPATH=$ROOT:$PYTHONPATH
-export PYTHONPATH=$PWD:$PYTHONPATH
-
-dataset=VOT2018
-NUM=4
-START=1
-END=20
-GPU=0
-
-while [[ $# -gt 0 ]]
-do
-    key="$1"
-    case $key in
-        -h|--help)
-            show_help
-            exit
-            ;;
-        -d|--dataset)
-            dataset=$2
-            shift 2
-            ;;
-        -n|--num)
-            NUM=$2
-            shift 2
-            ;;
-        -s|--start)
-            START=$2
-            shift 2
-            ;;
-        -e|--end)
-            END=$2
-            shift 2
-            ;;
-        -m|--model)
-            model=$2
-            shift 2
-            ;;
-        -g|--gpu)
-            GPU=$2
-            shift 2
-            ;;
-        *)
-            echo invalid arg [$1]
-            show_help
-            exit 1
-            ;;
-    esac
-done
-
-set -e
-
-if [ -z "$model" ]; then
-    echo test snapshot $START ~ $END on dataset $dataset with $GPU gpus.
-    for i in $(seq $START $END)
-    do 
-        bash test.sh snapshot/checkpoint_e$i.pth $dataset $(($i % $GPU)) &
-    done
-    wait
-
-    python $ROOT/tools/eval.py --dataset $dataset --num 20 --tracker_prefix C --result_dir ./test/$dataset 2>&1 | tee logs/eval_test_$dataset.log
-else
-    echo tuning $model on dataset $dataset with $NUM jobs in $GPU gpus.
-    for i in $(seq 1 $NUM)
-    do 
-        bash tune.sh $model $dataset $(($i % $GPU)) & 
-    done
-    wait
-    rm finish.flag
-
-    python $ROOT/tools/eval.py --dataset $dataset --num 20 --tracker_prefix C  --result_dir ./result/$dataset 2>&1 | tee logs/eval_tune_$dataset.log
-fi
diff --git a/spaces/oliver2023/chatgpt-on-wechat/scripts/shutdown.sh b/spaces/oliver2023/chatgpt-on-wechat/scripts/shutdown.sh
deleted file mode 100644
index c2bf6b14adcafd46e7278ab3730ab7f78b82c593..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/scripts/shutdown.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-
-#关闭服务
-cd `dirname $0`/..
-export BASE_DIR=`pwd`
-pid=`ps ax | grep -i app.py | grep "${BASE_DIR}" | grep python3 | grep -v grep | awk '{print $1}'`
-if [ -z "$pid" ] ; then
-        echo "No chatgpt-on-wechat running."
-        exit -1;
-fi
-
-echo "The chatgpt-on-wechat(${pid}) is running..."
-
-kill ${pid}
-
-echo "Send shutdown request to chatgpt-on-wechat(${pid}) OK"
diff --git a/spaces/ombhojane/Fetch-Alerts/README.md b/spaces/ombhojane/Fetch-Alerts/README.md
deleted file mode 100644
index 6d2b5332527a025ed9d0c20ee5ae862b5b6c3277..0000000000000000000000000000000000000000
--- a/spaces/ombhojane/Fetch-Alerts/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Fetch Alerts
-emoji: 🌍
-colorFrom: gray
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.27.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/omdenatopekachapter/left_ejection_fraction/README.md b/spaces/omdenatopekachapter/left_ejection_fraction/README.md
deleted file mode 100644
index efd19bfa4e610f07e7bb0cdb157620887b810fe2..0000000000000000000000000000000000000000
--- a/spaces/omdenatopekachapter/left_ejection_fraction/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Left Ejection Fraction
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/onereal/rvc-models-convertvoice/README.md b/spaces/onereal/rvc-models-convertvoice/README.md
deleted file mode 100644
index d3e270a9c52ac54fe31595f1a5305afc9bce2b80..0000000000000000000000000000000000000000
--- a/spaces/onereal/rvc-models-convertvoice/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: RVC Inference
-emoji: 🎤
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: true
-license: mit
-duplicated_from: ArkanDash/rvc-models-new
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/onnx/faster-rcnn/app.py b/spaces/onnx/faster-rcnn/app.py
deleted file mode 100644
index f2977022cb203b85031306f92cb0fab2df21fb1d..0000000000000000000000000000000000000000
--- a/spaces/onnx/faster-rcnn/app.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from PIL import Image
-import numpy as np
-import torch
-from torchvision import transforms, models
-from onnx import numpy_helper
-import os
-import onnxruntime as rt
-from matplotlib.colors import hsv_to_rgb
-import cv2
-import gradio as gr
-
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-import pycocotools.mask as mask_util
-
-def preprocess(image):
-    # Resize
-    ratio = 800.0 / min(image.size[0], image.size[1])
-    image = image.resize((int(ratio * image.size[0]), int(ratio * image.size[1])), Image.BILINEAR)
-
-    # Convert to BGR
-    image = np.array(image)[:, :, [2, 1, 0]].astype('float32')
-
-    # HWC -> CHW
-    image = np.transpose(image, [2, 0, 1])
-
-    # Normalize
-    mean_vec = np.array([102.9801, 115.9465, 122.7717])
-    for i in range(image.shape[0]):
-        image[i, :, :] = image[i, :, :] - mean_vec[i]
-
-    # Pad to be divisible of 32
-    import math
-    padded_h = int(math.ceil(image.shape[1] / 32) * 32)
-    padded_w = int(math.ceil(image.shape[2] / 32) * 32)
-
-    padded_image = np.zeros((3, padded_h, padded_w), dtype=np.float32)
-    padded_image[:, :image.shape[1], :image.shape[2]] = image
-    image = padded_image
-
-    return image
-
-
-
-# Start from ORT 1.10, ORT requires explicitly setting the providers parameter if you want to use execution providers
-# other than the default CPU provider (as opposed to the previous behavior of providers getting set/registered by default
-# based on the build flags) when instantiating InferenceSession.
-# For example, if NVIDIA GPU is available and ORT Python package is built with CUDA, then call API as following:
-# onnxruntime.InferenceSession(path/to/model, providers=['CUDAExecutionProvider'])
-os.system("wget https://github.com/AK391/models/raw/main/vision/object_detection_segmentation/faster-rcnn/model/FasterRCNN-10.onnx")
-sess = rt.InferenceSession("FasterRCNN-10.onnx")
-
-outputs = sess.get_outputs()
-
-
-classes = [line.rstrip('\n') for line in open('coco_classes.txt')]
-
-
-def display_objdetect_image(image, boxes, labels, scores, score_threshold=0.7):
-    # Resize boxes
-    ratio = 800.0 / min(image.size[0], image.size[1])
-    boxes /= ratio
-
-    _, ax = plt.subplots(1, figsize=(12,9))
-    image = np.array(image)
-    ax.imshow(image)
-
-    # Showing boxes with score > 0.7
-    for box, label, score in zip(boxes, labels, scores):
-        if score > score_threshold:
-            rect = patches.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], linewidth=1, edgecolor='b', facecolor='none')
-            ax.annotate(classes[label] + ':' + str(np.round(score, 2)), (box[0], box[1]), color='w', fontsize=12)
-            ax.add_patch(rect)
-
-    plt.axis('off')
-    plt.savefig('out.png', bbox_inches='tight')
-
-
-def inference(img):
-  input_image = Image.open(img)
-  orig_tensor = np.asarray(input_image)
-  input_tensor = preprocess(input_image)
-  
-  output_names = list(map(lambda output: output.name, outputs))
-  input_name = sess.get_inputs()[0].name
-  
-  boxes, labels, scores = sess.run(output_names, {input_name: input_tensor})
-  display_objdetect_image(input_image, boxes, labels, scores)
-
-  return 'out.png'
-  
-title="Faster R-CNN"
-description="This model is a real-time neural network for object detection that detects 80 different classes."
-examples=[["examplemask-rcnn.jpeg"]]
-gr.Interface(inference,gr.inputs.Image(type="filepath"),gr.outputs.Image(type="file"),title=title,description=description,examples=examples).launch(enable_queue=True)
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/__init__.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_wuerstchen.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_wuerstchen.py
deleted file mode 100644
index 23d45d3dd6ad4d03d89e77ab27b807bf5bb50de7..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_wuerstchen.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Run inside root directory of official source code: https://github.com/dome272/wuerstchen/
-import os
-
-import torch
-from transformers import AutoTokenizer, CLIPTextModel
-from vqgan import VQModel
-
-from diffusers import (
-    DDPMWuerstchenScheduler,
-    WuerstchenCombinedPipeline,
-    WuerstchenDecoderPipeline,
-    WuerstchenPriorPipeline,
-)
-from diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt, WuerstchenPrior
-
-
-model_path = "models/"
-device = "cpu"
-
-paella_vqmodel = VQModel()
-state_dict = torch.load(os.path.join(model_path, "vqgan_f4_v1_500k.pt"), map_location=device)["state_dict"]
-paella_vqmodel.load_state_dict(state_dict)
-
-state_dict["vquantizer.embedding.weight"] = state_dict["vquantizer.codebook.weight"]
-state_dict.pop("vquantizer.codebook.weight")
-vqmodel = PaellaVQModel(num_vq_embeddings=paella_vqmodel.codebook_size, latent_channels=paella_vqmodel.c_latent)
-vqmodel.load_state_dict(state_dict)
-
-# Clip Text encoder and tokenizer
-text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
-tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
-
-# Generator
-gen_text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K").to("cpu")
-gen_tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
-
-orig_state_dict = torch.load(os.path.join(model_path, "model_v2_stage_b.pt"), map_location=device)["state_dict"]
-state_dict = {}
-for key in orig_state_dict.keys():
-    if key.endswith("in_proj_weight"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
-        state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
-        state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
-    elif key.endswith("in_proj_bias"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
-        state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
-        state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
-    elif key.endswith("out_proj.weight"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
-    elif key.endswith("out_proj.bias"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
-    else:
-        state_dict[key] = orig_state_dict[key]
-deocder = WuerstchenDiffNeXt()
-deocder.load_state_dict(state_dict)
-
-# Prior
-orig_state_dict = torch.load(os.path.join(model_path, "model_v3_stage_c.pt"), map_location=device)["ema_state_dict"]
-state_dict = {}
-for key in orig_state_dict.keys():
-    if key.endswith("in_proj_weight"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
-        state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
-        state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
-    elif key.endswith("in_proj_bias"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
-        state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
-        state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
-    elif key.endswith("out_proj.weight"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
-    elif key.endswith("out_proj.bias"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
-    else:
-        state_dict[key] = orig_state_dict[key]
-prior_model = WuerstchenPrior(c_in=16, c=1536, c_cond=1280, c_r=64, depth=32, nhead=24).to(device)
-prior_model.load_state_dict(state_dict)
-
-# scheduler
-scheduler = DDPMWuerstchenScheduler()
-
-# Prior pipeline
-prior_pipeline = WuerstchenPriorPipeline(
-    prior=prior_model, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler
-)
-
-prior_pipeline.save_pretrained("warp-ai/wuerstchen-prior")
-
-decoder_pipeline = WuerstchenDecoderPipeline(
-    text_encoder=gen_text_encoder, tokenizer=gen_tokenizer, vqgan=vqmodel, decoder=deocder, scheduler=scheduler
-)
-decoder_pipeline.save_pretrained("warp-ai/wuerstchen")
-
-# Wuerstchen pipeline
-wuerstchen_pipeline = WuerstchenCombinedPipeline(
-    # Decoder
-    text_encoder=gen_text_encoder,
-    tokenizer=gen_tokenizer,
-    decoder=deocder,
-    scheduler=scheduler,
-    vqgan=vqmodel,
-    # Prior
-    prior_tokenizer=tokenizer,
-    prior_text_encoder=text_encoder,
-    prior=prior_model,
-    prior_scheduler=scheduler,
-)
-wuerstchen_pipeline.save_pretrained("warp-ai/WuerstchenCombinedPipeline")
diff --git a/spaces/panchajanya1999/spam-classifier/app.py b/spaces/panchajanya1999/spam-classifier/app.py
deleted file mode 100644
index d1dca713a0a8756ac92c121a6d6ae1ee5570b3d8..0000000000000000000000000000000000000000
--- a/spaces/panchajanya1999/spam-classifier/app.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# this is the python file to run the spam_classifer.py file
-
-with open('spam_classifier.py') as f:
-    exec(f.read())
\ No newline at end of file
diff --git a/spaces/paulbricman/lexiscore/Dockerfile b/spaces/paulbricman/lexiscore/Dockerfile
deleted file mode 100644
index 4c60303e915603e160ebf2f561ddec74920fafa1..0000000000000000000000000000000000000000
--- a/spaces/paulbricman/lexiscore/Dockerfile
+++ /dev/null
@@ -1,8 +0,0 @@
-FROM python:3.8
-EXPOSE 8501
-WORKDIR /app
-COPY requirements.txt ./requirements.txt
-RUN pip3 install -r requirements.txt
-RUN python -m nltk.downloader punkt
-COPY . .
-CMD streamlit run main.py
\ No newline at end of file
diff --git a/spaces/pixiou/bingo/src/components/ui/dialog.tsx b/spaces/pixiou/bingo/src/components/ui/dialog.tsx
deleted file mode 100644
index 925e77fe7858fb218b5115b4e225174a886e0f02..0000000000000000000000000000000000000000
--- a/spaces/pixiou/bingo/src/components/ui/dialog.tsx
+++ /dev/null
@@ -1,128 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as DialogPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Dialog = DialogPrimitive.Root
-
-const DialogTrigger = DialogPrimitive.Trigger
-
-const DialogPortal = ({
-  className,
-  children,
-  ...props
-}: DialogPrimitive.DialogPortalProps) => (
-  <DialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-start justify-center sm:items-center">
-      {children}
-    </div>
-  </DialogPrimitive.Portal>
-)
-DialogPortal.displayName = DialogPrimitive.Portal.displayName
-
-const DialogOverlay = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Overlay>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Overlay
-    ref={ref}
-    className={cn(
-      'bg-white fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-  />
-))
-DialogOverlay.displayName = DialogPrimitive.Overlay.displayName
-
-const DialogContent = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <DialogPortal>
-    <DialogOverlay />
-    <DialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full gap-4 rounded-b-lg border bg-background p-6 shadow-sm animate-in data-[state=open]:fade-in-90 data-[state=open]:slide-in-from-bottom-10 sm:max-w-lg sm:rounded-lg sm:zoom-in-90 data-[state=open]:sm:slide-in-from-bottom-0',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <DialogPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-accent data-[state=open]:text-muted-foreground">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </DialogPrimitive.Close>
-    </DialogPrimitive.Content>
-  </DialogPortal>
-))
-DialogContent.displayName = DialogPrimitive.Content.displayName
-
-const DialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-1.5 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-DialogHeader.displayName = 'DialogHeader'
-
-const DialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-DialogFooter.displayName = 'DialogFooter'
-
-const DialogTitle = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Title
-    ref={ref}
-    className={cn(
-      'text-lg font-semibold leading-none tracking-tight',
-      className
-    )}
-    {...props}
-  />
-))
-DialogTitle.displayName = DialogPrimitive.Title.displayName
-
-const DialogDescription = React.forwardRef<
-  React.ElementRef<typeof DialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof DialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <DialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-DialogDescription.displayName = DialogPrimitive.Description.displayName
-
-export {
-  Dialog,
-  DialogTrigger,
-  DialogContent,
-  DialogHeader,
-  DialogFooter,
-  DialogTitle,
-  DialogDescription
-}
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/distributions/wheel.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/distributions/wheel.py
deleted file mode 100644
index 03aac775b53f2dd3153a9f44829e7987258950aa..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/distributions/wheel.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.distributions.base import AbstractDistribution
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import (
-    BaseDistribution,
-    FilesystemWheel,
-    get_wheel_distribution,
-)
-
-
-class WheelDistribution(AbstractDistribution):
-    """Represents a wheel distribution.
-
-    This does not need any preparation as wheels can be directly unpacked.
-    """
-
-    def get_metadata_distribution(self) -> BaseDistribution:
-        """Loads the metadata from the wheel file into memory and returns a
-        Distribution that uses it, not relying on the wheel file or
-        requirement.
-        """
-        assert self.req.local_file_path, "Set as part of preparation during download"
-        assert self.req.name, "Wheels are never unnamed"
-        wheel = FilesystemWheel(self.req.local_file_path)
-        return get_wheel_distribution(wheel, canonicalize_name(self.req.name))
-
-    def prepare_distribution_metadata(
-        self,
-        finder: PackageFinder,
-        build_isolation: bool,
-        check_build_deps: bool,
-    ) -> None:
-        pass
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/locations/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/locations/__init__.py
deleted file mode 100644
index d54bc63eba364bda3f869a0f3b1863b872f9682a..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/locations/__init__.py
+++ /dev/null
@@ -1,467 +0,0 @@
-import functools
-import logging
-import os
-import pathlib
-import sys
-import sysconfig
-from typing import Any, Dict, Generator, Optional, Tuple
-
-from pip._internal.models.scheme import SCHEME_KEYS, Scheme
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.deprecation import deprecated
-from pip._internal.utils.virtualenv import running_under_virtualenv
-
-from . import _sysconfig
-from .base import (
-    USER_CACHE_DIR,
-    get_major_minor_version,
-    get_src_prefix,
-    is_osx_framework,
-    site_packages,
-    user_site,
-)
-
-__all__ = [
-    "USER_CACHE_DIR",
-    "get_bin_prefix",
-    "get_bin_user",
-    "get_major_minor_version",
-    "get_platlib",
-    "get_purelib",
-    "get_scheme",
-    "get_src_prefix",
-    "site_packages",
-    "user_site",
-]
-
-
-logger = logging.getLogger(__name__)
-
-
-_PLATLIBDIR: str = getattr(sys, "platlibdir", "lib")
-
-_USE_SYSCONFIG_DEFAULT = sys.version_info >= (3, 10)
-
-
-def _should_use_sysconfig() -> bool:
-    """This function determines the value of _USE_SYSCONFIG.
-
-    By default, pip uses sysconfig on Python 3.10+.
-    But Python distributors can override this decision by setting:
-        sysconfig._PIP_USE_SYSCONFIG = True / False
-    Rationale in https://github.com/pypa/pip/issues/10647
-
-    This is a function for testability, but should be constant during any one
-    run.
-    """
-    return bool(getattr(sysconfig, "_PIP_USE_SYSCONFIG", _USE_SYSCONFIG_DEFAULT))
-
-
-_USE_SYSCONFIG = _should_use_sysconfig()
-
-if not _USE_SYSCONFIG:
-    # Import distutils lazily to avoid deprecation warnings,
-    # but import it soon enough that it is in memory and available during
-    # a pip reinstall.
-    from . import _distutils
-
-# Be noisy about incompatibilities if this platforms "should" be using
-# sysconfig, but is explicitly opting out and using distutils instead.
-if _USE_SYSCONFIG_DEFAULT and not _USE_SYSCONFIG:
-    _MISMATCH_LEVEL = logging.WARNING
-else:
-    _MISMATCH_LEVEL = logging.DEBUG
-
-
-def _looks_like_bpo_44860() -> bool:
-    """The resolution to bpo-44860 will change this incorrect platlib.
-
-    See <https://bugs.python.org/issue44860>.
-    """
-    from distutils.command.install import INSTALL_SCHEMES
-
-    try:
-        unix_user_platlib = INSTALL_SCHEMES["unix_user"]["platlib"]
-    except KeyError:
-        return False
-    return unix_user_platlib == "$usersite"
-
-
-def _looks_like_red_hat_patched_platlib_purelib(scheme: Dict[str, str]) -> bool:
-    platlib = scheme["platlib"]
-    if "/$platlibdir/" in platlib:
-        platlib = platlib.replace("/$platlibdir/", f"/{_PLATLIBDIR}/")
-    if "/lib64/" not in platlib:
-        return False
-    unpatched = platlib.replace("/lib64/", "/lib/")
-    return unpatched.replace("$platbase/", "$base/") == scheme["purelib"]
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_red_hat_lib() -> bool:
-    """Red Hat patches platlib in unix_prefix and unix_home, but not purelib.
-
-    This is the only way I can see to tell a Red Hat-patched Python.
-    """
-    from distutils.command.install import INSTALL_SCHEMES
-
-    return all(
-        k in INSTALL_SCHEMES
-        and _looks_like_red_hat_patched_platlib_purelib(INSTALL_SCHEMES[k])
-        for k in ("unix_prefix", "unix_home")
-    )
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_debian_scheme() -> bool:
-    """Debian adds two additional schemes."""
-    from distutils.command.install import INSTALL_SCHEMES
-
-    return "deb_system" in INSTALL_SCHEMES and "unix_local" in INSTALL_SCHEMES
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_red_hat_scheme() -> bool:
-    """Red Hat patches ``sys.prefix`` and ``sys.exec_prefix``.
-
-    Red Hat's ``00251-change-user-install-location.patch`` changes the install
-    command's ``prefix`` and ``exec_prefix`` to append ``"/local"``. This is
-    (fortunately?) done quite unconditionally, so we create a default command
-    object without any configuration to detect this.
-    """
-    from distutils.command.install import install
-    from distutils.dist import Distribution
-
-    cmd: Any = install(Distribution())
-    cmd.finalize_options()
-    return (
-        cmd.exec_prefix == f"{os.path.normpath(sys.exec_prefix)}/local"
-        and cmd.prefix == f"{os.path.normpath(sys.prefix)}/local"
-    )
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_slackware_scheme() -> bool:
-    """Slackware patches sysconfig but fails to patch distutils and site.
-
-    Slackware changes sysconfig's user scheme to use ``"lib64"`` for the lib
-    path, but does not do the same to the site module.
-    """
-    if user_site is None:  # User-site not available.
-        return False
-    try:
-        paths = sysconfig.get_paths(scheme="posix_user", expand=False)
-    except KeyError:  # User-site not available.
-        return False
-    return "/lib64/" in paths["purelib"] and "/lib64/" not in user_site
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_msys2_mingw_scheme() -> bool:
-    """MSYS2 patches distutils and sysconfig to use a UNIX-like scheme.
-
-    However, MSYS2 incorrectly patches sysconfig ``nt`` scheme. The fix is
-    likely going to be included in their 3.10 release, so we ignore the warning.
-    See msys2/MINGW-packages#9319.
-
-    MSYS2 MINGW's patch uses lowercase ``"lib"`` instead of the usual uppercase,
-    and is missing the final ``"site-packages"``.
-    """
-    paths = sysconfig.get_paths("nt", expand=False)
-    return all(
-        "Lib" not in p and "lib" in p and not p.endswith("site-packages")
-        for p in (paths[key] for key in ("platlib", "purelib"))
-    )
-
-
-def _fix_abiflags(parts: Tuple[str]) -> Generator[str, None, None]:
-    ldversion = sysconfig.get_config_var("LDVERSION")
-    abiflags = getattr(sys, "abiflags", None)
-
-    # LDVERSION does not end with sys.abiflags. Just return the path unchanged.
-    if not ldversion or not abiflags or not ldversion.endswith(abiflags):
-        yield from parts
-        return
-
-    # Strip sys.abiflags from LDVERSION-based path components.
-    for part in parts:
-        if part.endswith(ldversion):
-            part = part[: (0 - len(abiflags))]
-        yield part
-
-
-@functools.lru_cache(maxsize=None)
-def _warn_mismatched(old: pathlib.Path, new: pathlib.Path, *, key: str) -> None:
-    issue_url = "https://github.com/pypa/pip/issues/10151"
-    message = (
-        "Value for %s does not match. Please report this to <%s>"
-        "\ndistutils: %s"
-        "\nsysconfig: %s"
-    )
-    logger.log(_MISMATCH_LEVEL, message, key, issue_url, old, new)
-
-
-def _warn_if_mismatch(old: pathlib.Path, new: pathlib.Path, *, key: str) -> bool:
-    if old == new:
-        return False
-    _warn_mismatched(old, new, key=key)
-    return True
-
-
-@functools.lru_cache(maxsize=None)
-def _log_context(
-    *,
-    user: bool = False,
-    home: Optional[str] = None,
-    root: Optional[str] = None,
-    prefix: Optional[str] = None,
-) -> None:
-    parts = [
-        "Additional context:",
-        "user = %r",
-        "home = %r",
-        "root = %r",
-        "prefix = %r",
-    ]
-
-    logger.log(_MISMATCH_LEVEL, "\n".join(parts), user, home, root, prefix)
-
-
-def get_scheme(
-    dist_name: str,
-    user: bool = False,
-    home: Optional[str] = None,
-    root: Optional[str] = None,
-    isolated: bool = False,
-    prefix: Optional[str] = None,
-) -> Scheme:
-    new = _sysconfig.get_scheme(
-        dist_name,
-        user=user,
-        home=home,
-        root=root,
-        isolated=isolated,
-        prefix=prefix,
-    )
-    if _USE_SYSCONFIG:
-        return new
-
-    old = _distutils.get_scheme(
-        dist_name,
-        user=user,
-        home=home,
-        root=root,
-        isolated=isolated,
-        prefix=prefix,
-    )
-
-    warning_contexts = []
-    for k in SCHEME_KEYS:
-        old_v = pathlib.Path(getattr(old, k))
-        new_v = pathlib.Path(getattr(new, k))
-
-        if old_v == new_v:
-            continue
-
-        # distutils incorrectly put PyPy packages under ``site-packages/python``
-        # in the ``posix_home`` scheme, but PyPy devs said they expect the
-        # directory name to be ``pypy`` instead. So we treat this as a bug fix
-        # and not warn about it. See bpo-43307 and python/cpython#24628.
-        skip_pypy_special_case = (
-            sys.implementation.name == "pypy"
-            and home is not None
-            and k in ("platlib", "purelib")
-            and old_v.parent == new_v.parent
-            and old_v.name.startswith("python")
-            and new_v.name.startswith("pypy")
-        )
-        if skip_pypy_special_case:
-            continue
-
-        # sysconfig's ``osx_framework_user`` does not include ``pythonX.Y`` in
-        # the ``include`` value, but distutils's ``headers`` does. We'll let
-        # CPython decide whether this is a bug or feature. See bpo-43948.
-        skip_osx_framework_user_special_case = (
-            user
-            and is_osx_framework()
-            and k == "headers"
-            and old_v.parent.parent == new_v.parent
-            and old_v.parent.name.startswith("python")
-        )
-        if skip_osx_framework_user_special_case:
-            continue
-
-        # On Red Hat and derived Linux distributions, distutils is patched to
-        # use "lib64" instead of "lib" for platlib.
-        if k == "platlib" and _looks_like_red_hat_lib():
-            continue
-
-        # On Python 3.9+, sysconfig's posix_user scheme sets platlib against
-        # sys.platlibdir, but distutils's unix_user incorrectly coninutes
-        # using the same $usersite for both platlib and purelib. This creates a
-        # mismatch when sys.platlibdir is not "lib".
-        skip_bpo_44860 = (
-            user
-            and k == "platlib"
-            and not WINDOWS
-            and sys.version_info >= (3, 9)
-            and _PLATLIBDIR != "lib"
-            and _looks_like_bpo_44860()
-        )
-        if skip_bpo_44860:
-            continue
-
-        # Slackware incorrectly patches posix_user to use lib64 instead of lib,
-        # but not usersite to match the location.
-        skip_slackware_user_scheme = (
-            user
-            and k in ("platlib", "purelib")
-            and not WINDOWS
-            and _looks_like_slackware_scheme()
-        )
-        if skip_slackware_user_scheme:
-            continue
-
-        # Both Debian and Red Hat patch Python to place the system site under
-        # /usr/local instead of /usr. Debian also places lib in dist-packages
-        # instead of site-packages, but the /usr/local check should cover it.
-        skip_linux_system_special_case = (
-            not (user or home or prefix or running_under_virtualenv())
-            and old_v.parts[1:3] == ("usr", "local")
-            and len(new_v.parts) > 1
-            and new_v.parts[1] == "usr"
-            and (len(new_v.parts) < 3 or new_v.parts[2] != "local")
-            and (_looks_like_red_hat_scheme() or _looks_like_debian_scheme())
-        )
-        if skip_linux_system_special_case:
-            continue
-
-        # On Python 3.7 and earlier, sysconfig does not include sys.abiflags in
-        # the "pythonX.Y" part of the path, but distutils does.
-        skip_sysconfig_abiflag_bug = (
-            sys.version_info < (3, 8)
-            and not WINDOWS
-            and k in ("headers", "platlib", "purelib")
-            and tuple(_fix_abiflags(old_v.parts)) == new_v.parts
-        )
-        if skip_sysconfig_abiflag_bug:
-            continue
-
-        # MSYS2 MINGW's sysconfig patch does not include the "site-packages"
-        # part of the path. This is incorrect and will be fixed in MSYS.
-        skip_msys2_mingw_bug = (
-            WINDOWS and k in ("platlib", "purelib") and _looks_like_msys2_mingw_scheme()
-        )
-        if skip_msys2_mingw_bug:
-            continue
-
-        # CPython's POSIX install script invokes pip (via ensurepip) against the
-        # interpreter located in the source tree, not the install site. This
-        # triggers special logic in sysconfig that's not present in distutils.
-        # https://github.com/python/cpython/blob/8c21941ddaf/Lib/sysconfig.py#L178-L194
-        skip_cpython_build = (
-            sysconfig.is_python_build(check_home=True)
-            and not WINDOWS
-            and k in ("headers", "include", "platinclude")
-        )
-        if skip_cpython_build:
-            continue
-
-        warning_contexts.append((old_v, new_v, f"scheme.{k}"))
-
-    if not warning_contexts:
-        return old
-
-    # Check if this path mismatch is caused by distutils config files. Those
-    # files will no longer work once we switch to sysconfig, so this raises a
-    # deprecation message for them.
-    default_old = _distutils.distutils_scheme(
-        dist_name,
-        user,
-        home,
-        root,
-        isolated,
-        prefix,
-        ignore_config_files=True,
-    )
-    if any(default_old[k] != getattr(old, k) for k in SCHEME_KEYS):
-        deprecated(
-            reason=(
-                "Configuring installation scheme with distutils config files "
-                "is deprecated and will no longer work in the near future. If you "
-                "are using a Homebrew or Linuxbrew Python, please see discussion "
-                "at https://github.com/Homebrew/homebrew-core/issues/76621"
-            ),
-            replacement=None,
-            gone_in=None,
-        )
-        return old
-
-    # Post warnings about this mismatch so user can report them back.
-    for old_v, new_v, key in warning_contexts:
-        _warn_mismatched(old_v, new_v, key=key)
-    _log_context(user=user, home=home, root=root, prefix=prefix)
-
-    return old
-
-
-def get_bin_prefix() -> str:
-    new = _sysconfig.get_bin_prefix()
-    if _USE_SYSCONFIG:
-        return new
-
-    old = _distutils.get_bin_prefix()
-    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="bin_prefix"):
-        _log_context()
-    return old
-
-
-def get_bin_user() -> str:
-    return _sysconfig.get_scheme("", user=True).scripts
-
-
-def _looks_like_deb_system_dist_packages(value: str) -> bool:
-    """Check if the value is Debian's APT-controlled dist-packages.
-
-    Debian's ``distutils.sysconfig.get_python_lib()`` implementation returns the
-    default package path controlled by APT, but does not patch ``sysconfig`` to
-    do the same. This is similar to the bug worked around in ``get_scheme()``,
-    but here the default is ``deb_system`` instead of ``unix_local``. Ultimately
-    we can't do anything about this Debian bug, and this detection allows us to
-    skip the warning when needed.
-    """
-    if not _looks_like_debian_scheme():
-        return False
-    if value == "/usr/lib/python3/dist-packages":
-        return True
-    return False
-
-
-def get_purelib() -> str:
-    """Return the default pure-Python lib location."""
-    new = _sysconfig.get_purelib()
-    if _USE_SYSCONFIG:
-        return new
-
-    old = _distutils.get_purelib()
-    if _looks_like_deb_system_dist_packages(old):
-        return old
-    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="purelib"):
-        _log_context()
-    return old
-
-
-def get_platlib() -> str:
-    """Return the default platform-shared lib location."""
-    new = _sysconfig.get_platlib()
-    if _USE_SYSCONFIG:
-        return new
-
-    from . import _distutils
-
-    old = _distutils.get_platlib()
-    if _looks_like_deb_system_dist_packages(old):
-        return old
-    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="platlib"):
-        _log_context()
-    return old
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-37519934.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-37519934.css
deleted file mode 100644
index d48203638574e0f2bb6a0f0ad5a39d7f559a8875..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-37519934.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-iyf88w{border:var(--block-border-width) solid var(--border-color-primary);background:var(--border-color-primary);border-radius:var(--block-radius);display:flex;flex-direction:column;gap:var(--form-gap-width);overflow:hidden}div.svelte-iyf88w>*:not(.absolute){border:none;border-radius:0}.hide.svelte-iyf88w{display:none}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/importlib_resources/tests/data02/two/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/importlib_resources/tests/data02/two/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_text.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_text.py
deleted file mode 100644
index 7222d5b00cf7244a22e28166c55e1226cbbd5455..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_text.py
+++ /dev/null
@@ -1,966 +0,0 @@
-from datetime import datetime
-import io
-import warnings
-
-import numpy as np
-from numpy.testing import assert_almost_equal
-from packaging.version import parse as parse_version
-import pyparsing
-import pytest
-
-import matplotlib as mpl
-from matplotlib.backend_bases import MouseEvent
-from matplotlib.font_manager import FontProperties
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import matplotlib.transforms as mtransforms
-from matplotlib.testing.decorators import check_figures_equal, image_comparison
-from matplotlib.testing._markers import needs_usetex
-from matplotlib.text import Text, Annotation, OffsetFrom
-
-pyparsing_version = parse_version(pyparsing.__version__)
-
-
-@image_comparison(['font_styles'])
-def test_font_styles():
-
-    def find_matplotlib_font(**kw):
-        prop = FontProperties(**kw)
-        path = findfont(prop, directory=mpl.get_data_path())
-        return FontProperties(fname=path)
-
-    from matplotlib.font_manager import FontProperties, findfont
-    warnings.filterwarnings(
-        'ignore',
-        r"findfont: Font family \[u?'Foo'\] not found. Falling back to .",
-        UserWarning,
-        module='matplotlib.font_manager')
-
-    fig, ax = plt.subplots()
-
-    normal_font = find_matplotlib_font(
-        family="sans-serif",
-        style="normal",
-        variant="normal",
-        size=14)
-    a = ax.annotate(
-        "Normal Font",
-        (0.1, 0.1),
-        xycoords='axes fraction',
-        fontproperties=normal_font)
-    assert a.get_fontname() == 'DejaVu Sans'
-    assert a.get_fontstyle() == 'normal'
-    assert a.get_fontvariant() == 'normal'
-    assert a.get_weight() == 'normal'
-    assert a.get_stretch() == 'normal'
-
-    bold_font = find_matplotlib_font(
-        family="Foo",
-        style="normal",
-        variant="normal",
-        weight="bold",
-        stretch=500,
-        size=14)
-    ax.annotate(
-        "Bold Font",
-        (0.1, 0.2),
-        xycoords='axes fraction',
-        fontproperties=bold_font)
-
-    bold_italic_font = find_matplotlib_font(
-        family="sans serif",
-        style="italic",
-        variant="normal",
-        weight=750,
-        stretch=500,
-        size=14)
-    ax.annotate(
-        "Bold Italic Font",
-        (0.1, 0.3),
-        xycoords='axes fraction',
-        fontproperties=bold_italic_font)
-
-    light_font = find_matplotlib_font(
-        family="sans-serif",
-        style="normal",
-        variant="normal",
-        weight=200,
-        stretch=500,
-        size=14)
-    ax.annotate(
-        "Light Font",
-        (0.1, 0.4),
-        xycoords='axes fraction',
-        fontproperties=light_font)
-
-    condensed_font = find_matplotlib_font(
-        family="sans-serif",
-        style="normal",
-        variant="normal",
-        weight=500,
-        stretch=100,
-        size=14)
-    ax.annotate(
-        "Condensed Font",
-        (0.1, 0.5),
-        xycoords='axes fraction',
-        fontproperties=condensed_font)
-
-    ax.set_xticks([])
-    ax.set_yticks([])
-
-
-@image_comparison(['multiline'])
-def test_multiline():
-    plt.figure()
-    ax = plt.subplot(1, 1, 1)
-    ax.set_title("multiline\ntext alignment")
-
-    plt.text(
-        0.2, 0.5, "TpTpTp\n$M$\nTpTpTp", size=20, ha="center", va="top")
-
-    plt.text(
-        0.5, 0.5, "TpTpTp\n$M^{M^{M^{M}}}$\nTpTpTp", size=20,
-        ha="center", va="top")
-
-    plt.text(
-        0.8, 0.5, "TpTpTp\n$M_{q_{q_{q}}}$\nTpTpTp", size=20,
-        ha="center", va="top")
-
-    plt.xlim(0, 1)
-    plt.ylim(0, 0.8)
-
-    ax.set_xticks([])
-    ax.set_yticks([])
-
-
-@image_comparison(['multiline2'], style='mpl20')
-def test_multiline2():
-    # Remove this line when this test image is regenerated.
-    plt.rcParams['text.kerning_factor'] = 6
-
-    fig, ax = plt.subplots()
-
-    ax.set_xlim([0, 1.4])
-    ax.set_ylim([0, 2])
-    ax.axhline(0.5, color='C2', linewidth=0.3)
-    sts = ['Line', '2 Lineg\n 2 Lg', '$\\sum_i x $', 'hi $\\sum_i x $\ntest',
-           'test\n $\\sum_i x $', '$\\sum_i x $\n $\\sum_i x $']
-    renderer = fig.canvas.get_renderer()
-
-    def draw_box(ax, tt):
-        r = mpatches.Rectangle((0, 0), 1, 1, clip_on=False,
-                               transform=ax.transAxes)
-        r.set_bounds(
-            tt.get_window_extent(renderer)
-            .transformed(ax.transAxes.inverted())
-            .bounds)
-        ax.add_patch(r)
-
-    horal = 'left'
-    for nn, st in enumerate(sts):
-        tt = ax.text(0.2 * nn + 0.1, 0.5, st, horizontalalignment=horal,
-                     verticalalignment='bottom')
-        draw_box(ax, tt)
-    ax.text(1.2, 0.5, 'Bottom align', color='C2')
-
-    ax.axhline(1.3, color='C2', linewidth=0.3)
-    for nn, st in enumerate(sts):
-        tt = ax.text(0.2 * nn + 0.1, 1.3, st, horizontalalignment=horal,
-                     verticalalignment='top')
-        draw_box(ax, tt)
-    ax.text(1.2, 1.3, 'Top align', color='C2')
-
-    ax.axhline(1.8, color='C2', linewidth=0.3)
-    for nn, st in enumerate(sts):
-        tt = ax.text(0.2 * nn + 0.1, 1.8, st, horizontalalignment=horal,
-                     verticalalignment='baseline')
-        draw_box(ax, tt)
-    ax.text(1.2, 1.8, 'Baseline align', color='C2')
-
-    ax.axhline(0.1, color='C2', linewidth=0.3)
-    for nn, st in enumerate(sts):
-        tt = ax.text(0.2 * nn + 0.1, 0.1, st, horizontalalignment=horal,
-                     verticalalignment='bottom', rotation=20)
-        draw_box(ax, tt)
-    ax.text(1.2, 0.1, 'Bot align, rot20', color='C2')
-
-
-@image_comparison(['antialiased.png'], style='mpl20')
-def test_antialiasing():
-    mpl.rcParams['text.antialiased'] = False  # Passed arguments should override.
-
-    fig = plt.figure(figsize=(5.25, 0.75))
-    fig.text(0.3, 0.75, "antialiased", horizontalalignment='center',
-             verticalalignment='center', antialiased=True)
-    fig.text(0.3, 0.25, r"$\sqrt{x}$", horizontalalignment='center',
-             verticalalignment='center', antialiased=True)
-
-    mpl.rcParams['text.antialiased'] = True  # Passed arguments should override.
-    fig.text(0.7, 0.75, "not antialiased", horizontalalignment='center',
-             verticalalignment='center', antialiased=False)
-    fig.text(0.7, 0.25, r"$\sqrt{x}$", horizontalalignment='center',
-             verticalalignment='center', antialiased=False)
-
-    mpl.rcParams['text.antialiased'] = False  # Should not affect existing text.
-
-
-def test_afm_kerning():
-    fn = mpl.font_manager.findfont("Helvetica", fontext="afm")
-    with open(fn, 'rb') as fh:
-        afm = mpl._afm.AFM(fh)
-    assert afm.string_width_height('VAVAVAVAVAVA') == (7174.0, 718)
-
-
-@image_comparison(['text_contains.png'])
-def test_contains():
-    fig = plt.figure()
-    ax = plt.axes()
-
-    mevent = MouseEvent('button_press_event', fig.canvas, 0.5, 0.5, 1, None)
-
-    xs = np.linspace(0.25, 0.75, 30)
-    ys = np.linspace(0.25, 0.75, 30)
-    xs, ys = np.meshgrid(xs, ys)
-
-    txt = plt.text(
-        0.5, 0.4, 'hello world', ha='center', fontsize=30, rotation=30)
-    # uncomment to draw the text's bounding box
-    # txt.set_bbox(dict(edgecolor='black', facecolor='none'))
-
-    # draw the text. This is important, as the contains method can only work
-    # when a renderer exists.
-    fig.canvas.draw()
-
-    for x, y in zip(xs.flat, ys.flat):
-        mevent.x, mevent.y = plt.gca().transAxes.transform([x, y])
-        contains, _ = txt.contains(mevent)
-        color = 'yellow' if contains else 'red'
-
-        # capture the viewLim, plot a point, and reset the viewLim
-        vl = ax.viewLim.frozen()
-        ax.plot(x, y, 'o', color=color)
-        ax.viewLim.set(vl)
-
-
-def test_annotation_contains():
-    # Check that Annotation.contains looks at the bboxes of the text and the
-    # arrow separately, not at the joint bbox.
-    fig, ax = plt.subplots()
-    ann = ax.annotate(
-        "hello", xy=(.4, .4), xytext=(.6, .6), arrowprops={"arrowstyle": "->"})
-    fig.canvas.draw()  # Needed for the same reason as in test_contains.
-    event = MouseEvent(
-        "button_press_event", fig.canvas, *ax.transData.transform((.5, .6)))
-    assert ann.contains(event) == (False, {})
-
-
-@pytest.mark.parametrize('err, xycoords, match', (
-    (TypeError, print, "xycoords callable must return a BboxBase or Transform, not a"),
-    (TypeError, [0, 0], r"'xycoords' must be an instance of str, tuple"),
-    (ValueError, "foo", "'foo' is not a valid coordinate"),
-    (ValueError, "foo bar", "'foo bar' is not a valid coordinate"),
-    (ValueError, "offset foo", "xycoords cannot be an offset coordinate"),
-    (ValueError, "axes foo", "'foo' is not a recognized unit"),
-))
-def test_annotate_errors(err, xycoords, match):
-    fig, ax = plt.subplots()
-    with pytest.raises(err, match=match):
-        ax.annotate('xy', (0, 0), xytext=(0.5, 0.5), xycoords=xycoords)
-        fig.canvas.draw()
-
-
-@image_comparison(['titles'])
-def test_titles():
-    # left and right side titles
-    plt.figure()
-    ax = plt.subplot(1, 1, 1)
-    ax.set_title("left title", loc="left")
-    ax.set_title("right title", loc="right")
-    ax.set_xticks([])
-    ax.set_yticks([])
-
-
-@image_comparison(['text_alignment'], style='mpl20')
-def test_alignment():
-    plt.figure()
-    ax = plt.subplot(1, 1, 1)
-
-    x = 0.1
-    for rotation in (0, 30):
-        for alignment in ('top', 'bottom', 'baseline', 'center'):
-            ax.text(
-                x, 0.5, alignment + " Tj", va=alignment, rotation=rotation,
-                bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
-            ax.text(
-                x, 1.0, r'$\sum_{i=0}^{j}$', va=alignment, rotation=rotation)
-            x += 0.1
-
-    ax.plot([0, 1], [0.5, 0.5])
-    ax.plot([0, 1], [1.0, 1.0])
-
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1.5)
-    ax.set_xticks([])
-    ax.set_yticks([])
-
-
-@image_comparison(['axes_titles.png'])
-def test_axes_titles():
-    # Related to issue #3327
-    plt.figure()
-    ax = plt.subplot(1, 1, 1)
-    ax.set_title('center', loc='center', fontsize=20, fontweight=700)
-    ax.set_title('left', loc='left', fontsize=12, fontweight=400)
-    ax.set_title('right', loc='right', fontsize=12, fontweight=400)
-
-
-def test_set_position():
-    fig, ax = plt.subplots()
-
-    # test set_position
-    ann = ax.annotate(
-        'test', (0, 0), xytext=(0, 0), textcoords='figure pixels')
-    fig.canvas.draw()
-
-    init_pos = ann.get_window_extent(fig.canvas.renderer)
-    shift_val = 15
-    ann.set_position((shift_val, shift_val))
-    fig.canvas.draw()
-    post_pos = ann.get_window_extent(fig.canvas.renderer)
-
-    for a, b in zip(init_pos.min, post_pos.min):
-        assert a + shift_val == b
-
-    # test xyann
-    ann = ax.annotate(
-        'test', (0, 0), xytext=(0, 0), textcoords='figure pixels')
-    fig.canvas.draw()
-
-    init_pos = ann.get_window_extent(fig.canvas.renderer)
-    shift_val = 15
-    ann.xyann = (shift_val, shift_val)
-    fig.canvas.draw()
-    post_pos = ann.get_window_extent(fig.canvas.renderer)
-
-    for a, b in zip(init_pos.min, post_pos.min):
-        assert a + shift_val == b
-
-
-def test_char_index_at():
-    fig = plt.figure()
-    text = fig.text(0.1, 0.9, "")
-
-    text.set_text("i")
-    bbox = text.get_window_extent()
-    size_i = bbox.x1 - bbox.x0
-
-    text.set_text("m")
-    bbox = text.get_window_extent()
-    size_m = bbox.x1 - bbox.x0
-
-    text.set_text("iiiimmmm")
-    bbox = text.get_window_extent()
-    origin = bbox.x0
-
-    assert text._char_index_at(origin - size_i) == 0  # left of first char
-    assert text._char_index_at(origin) == 0
-    assert text._char_index_at(origin + 0.499*size_i) == 0
-    assert text._char_index_at(origin + 0.501*size_i) == 1
-    assert text._char_index_at(origin + size_i*3) == 3
-    assert text._char_index_at(origin + size_i*4 + size_m*3) == 7
-    assert text._char_index_at(origin + size_i*4 + size_m*4) == 8
-    assert text._char_index_at(origin + size_i*4 + size_m*10) == 8
-
-
-@pytest.mark.parametrize('text', ['', 'O'], ids=['empty', 'non-empty'])
-def test_non_default_dpi(text):
-    fig, ax = plt.subplots()
-
-    t1 = ax.text(0.5, 0.5, text, ha='left', va='bottom')
-    fig.canvas.draw()
-    dpi = fig.dpi
-
-    bbox1 = t1.get_window_extent()
-    bbox2 = t1.get_window_extent(dpi=dpi * 10)
-    np.testing.assert_allclose(bbox2.get_points(), bbox1.get_points() * 10,
-                               rtol=5e-2)
-    # Text.get_window_extent should not permanently change dpi.
-    assert fig.dpi == dpi
-
-
-def test_get_rotation_string():
-    assert Text(rotation='horizontal').get_rotation() == 0.
-    assert Text(rotation='vertical').get_rotation() == 90.
-
-
-def test_get_rotation_float():
-    for i in [15., 16.70, 77.4]:
-        assert Text(rotation=i).get_rotation() == i
-
-
-def test_get_rotation_int():
-    for i in [67, 16, 41]:
-        assert Text(rotation=i).get_rotation() == float(i)
-
-
-def test_get_rotation_raises():
-    with pytest.raises(ValueError):
-        Text(rotation='hozirontal')
-
-
-def test_get_rotation_none():
-    assert Text(rotation=None).get_rotation() == 0.0
-
-
-def test_get_rotation_mod360():
-    for i, j in zip([360., 377., 720+177.2], [0., 17., 177.2]):
-        assert_almost_equal(Text(rotation=i).get_rotation(), j)
-
-
-@pytest.mark.parametrize("ha", ["center", "right", "left"])
-@pytest.mark.parametrize("va", ["center", "top", "bottom",
-                                "baseline", "center_baseline"])
-def test_null_rotation_with_rotation_mode(ha, va):
-    fig, ax = plt.subplots()
-    kw = dict(rotation=0, va=va, ha=ha)
-    t0 = ax.text(.5, .5, 'test', rotation_mode='anchor', **kw)
-    t1 = ax.text(.5, .5, 'test', rotation_mode='default', **kw)
-    fig.canvas.draw()
-    assert_almost_equal(t0.get_window_extent(fig.canvas.renderer).get_points(),
-                        t1.get_window_extent(fig.canvas.renderer).get_points())
-
-
-@image_comparison(['text_bboxclip'])
-def test_bbox_clipping():
-    plt.text(0.9, 0.2, 'Is bbox clipped?', backgroundcolor='r', clip_on=True)
-    t = plt.text(0.9, 0.5, 'Is fancy bbox clipped?', clip_on=True)
-    t.set_bbox({"boxstyle": "round, pad=0.1"})
-
-
-@image_comparison(['annotation_negative_ax_coords.png'])
-def test_annotation_negative_ax_coords():
-    fig, ax = plt.subplots()
-
-    ax.annotate('+ pts',
-                xytext=[30, 20], textcoords='axes points',
-                xy=[30, 20], xycoords='axes points', fontsize=32)
-    ax.annotate('- pts',
-                xytext=[30, -20], textcoords='axes points',
-                xy=[30, -20], xycoords='axes points', fontsize=32,
-                va='top')
-    ax.annotate('+ frac',
-                xytext=[0.75, 0.05], textcoords='axes fraction',
-                xy=[0.75, 0.05], xycoords='axes fraction', fontsize=32)
-    ax.annotate('- frac',
-                xytext=[0.75, -0.05], textcoords='axes fraction',
-                xy=[0.75, -0.05], xycoords='axes fraction', fontsize=32,
-                va='top')
-
-    ax.annotate('+ pixels',
-                xytext=[160, 25], textcoords='axes pixels',
-                xy=[160, 25], xycoords='axes pixels', fontsize=32)
-    ax.annotate('- pixels',
-                xytext=[160, -25], textcoords='axes pixels',
-                xy=[160, -25], xycoords='axes pixels', fontsize=32,
-                va='top')
-
-
-@image_comparison(['annotation_negative_fig_coords.png'])
-def test_annotation_negative_fig_coords():
-    fig, ax = plt.subplots()
-
-    ax.annotate('+ pts',
-                xytext=[10, 120], textcoords='figure points',
-                xy=[10, 120], xycoords='figure points', fontsize=32)
-    ax.annotate('- pts',
-                xytext=[-10, 180], textcoords='figure points',
-                xy=[-10, 180], xycoords='figure points', fontsize=32,
-                va='top')
-    ax.annotate('+ frac',
-                xytext=[0.05, 0.55], textcoords='figure fraction',
-                xy=[0.05, 0.55], xycoords='figure fraction', fontsize=32)
-    ax.annotate('- frac',
-                xytext=[-0.05, 0.5], textcoords='figure fraction',
-                xy=[-0.05, 0.5], xycoords='figure fraction', fontsize=32,
-                va='top')
-
-    ax.annotate('+ pixels',
-                xytext=[50, 50], textcoords='figure pixels',
-                xy=[50, 50], xycoords='figure pixels', fontsize=32)
-    ax.annotate('- pixels',
-                xytext=[-50, 100], textcoords='figure pixels',
-                xy=[-50, 100], xycoords='figure pixels', fontsize=32,
-                va='top')
-
-
-def test_text_stale():
-    fig, (ax1, ax2) = plt.subplots(1, 2)
-    plt.draw_all()
-    assert not ax1.stale
-    assert not ax2.stale
-    assert not fig.stale
-
-    txt1 = ax1.text(.5, .5, 'aardvark')
-    assert ax1.stale
-    assert txt1.stale
-    assert fig.stale
-
-    ann1 = ax2.annotate('aardvark', xy=[.5, .5])
-    assert ax2.stale
-    assert ann1.stale
-    assert fig.stale
-
-    plt.draw_all()
-    assert not ax1.stale
-    assert not ax2.stale
-    assert not fig.stale
-
-
-@image_comparison(['agg_text_clip.png'])
-def test_agg_text_clip():
-    np.random.seed(1)
-    fig, (ax1, ax2) = plt.subplots(2)
-    for x, y in np.random.rand(10, 2):
-        ax1.text(x, y, "foo", clip_on=True)
-        ax2.text(x, y, "foo")
-
-
-def test_text_size_binding():
-    mpl.rcParams['font.size'] = 10
-    fp = mpl.font_manager.FontProperties(size='large')
-    sz1 = fp.get_size_in_points()
-    mpl.rcParams['font.size'] = 100
-
-    assert sz1 == fp.get_size_in_points()
-
-
-@image_comparison(['font_scaling.pdf'])
-def test_font_scaling():
-    mpl.rcParams['pdf.fonttype'] = 42
-    fig, ax = plt.subplots(figsize=(6.4, 12.4))
-    ax.xaxis.set_major_locator(plt.NullLocator())
-    ax.yaxis.set_major_locator(plt.NullLocator())
-    ax.set_ylim(-10, 600)
-
-    for i, fs in enumerate(range(4, 43, 2)):
-        ax.text(0.1, i*30, f"{fs} pt font size", fontsize=fs)
-
-
-@pytest.mark.parametrize('spacing1, spacing2', [(0.4, 2), (2, 0.4), (2, 2)])
-def test_two_2line_texts(spacing1, spacing2):
-    text_string = 'line1\nline2'
-    fig = plt.figure()
-    renderer = fig.canvas.get_renderer()
-
-    text1 = fig.text(0.25, 0.5, text_string, linespacing=spacing1)
-    text2 = fig.text(0.25, 0.5, text_string, linespacing=spacing2)
-    fig.canvas.draw()
-
-    box1 = text1.get_window_extent(renderer=renderer)
-    box2 = text2.get_window_extent(renderer=renderer)
-
-    # line spacing only affects height
-    assert box1.width == box2.width
-    if spacing1 == spacing2:
-        assert box1.height == box2.height
-    else:
-        assert box1.height != box2.height
-
-
-def test_validate_linespacing():
-    with pytest.raises(TypeError):
-        plt.text(.25, .5, "foo", linespacing="abc")
-
-
-def test_nonfinite_pos():
-    fig, ax = plt.subplots()
-    ax.text(0, np.nan, 'nan')
-    ax.text(np.inf, 0, 'inf')
-    fig.canvas.draw()
-
-
-def test_hinting_factor_backends():
-    plt.rcParams['text.hinting_factor'] = 1
-    fig = plt.figure()
-    t = fig.text(0.5, 0.5, 'some text')
-
-    fig.savefig(io.BytesIO(), format='svg')
-    expected = t.get_window_extent().intervalx
-
-    fig.savefig(io.BytesIO(), format='png')
-    # Backends should apply hinting_factor consistently (within 10%).
-    np.testing.assert_allclose(t.get_window_extent().intervalx, expected,
-                               rtol=0.1)
-
-
-@needs_usetex
-def test_usetex_is_copied():
-    # Indirectly tests that update_from (which is used to copy tick label
-    # properties) copies usetex state.
-    fig = plt.figure()
-    plt.rcParams["text.usetex"] = False
-    ax1 = fig.add_subplot(121)
-    plt.rcParams["text.usetex"] = True
-    ax2 = fig.add_subplot(122)
-    fig.canvas.draw()
-    for ax, usetex in [(ax1, False), (ax2, True)]:
-        for t in ax.xaxis.majorTicks:
-            assert t.label1.get_usetex() == usetex
-
-
-@needs_usetex
-def test_single_artist_usetex():
-    # Check that a single artist marked with usetex does not get passed through
-    # the mathtext parser at all (for the Agg backend) (the mathtext parser
-    # currently fails to parse \frac12, requiring \frac{1}{2} instead).
-    fig = plt.figure()
-    fig.text(.5, .5, r"$\frac12$", usetex=True)
-    fig.canvas.draw()
-
-
-@pytest.mark.parametrize("fmt", ["png", "pdf", "svg"])
-def test_single_artist_usenotex(fmt):
-    # Check that a single artist can be marked as not-usetex even though the
-    # rcParam is on ("2_2_2" fails if passed to TeX).  This currently skips
-    # postscript output as the ps renderer doesn't support mixing usetex and
-    # non-usetex.
-    plt.rcParams["text.usetex"] = True
-    fig = plt.figure()
-    fig.text(.5, .5, "2_2_2", usetex=False)
-    fig.savefig(io.BytesIO(), format=fmt)
-
-
-@image_comparison(['text_as_path_opacity.svg'])
-def test_text_as_path_opacity():
-    plt.figure()
-    plt.gca().set_axis_off()
-    plt.text(0.25, 0.25, 'c', color=(0, 0, 0, 0.5))
-    plt.text(0.25, 0.5, 'a', alpha=0.5)
-    plt.text(0.25, 0.75, 'x', alpha=0.5, color=(0, 0, 0, 1))
-
-
-@image_comparison(['text_as_text_opacity.svg'])
-def test_text_as_text_opacity():
-    mpl.rcParams['svg.fonttype'] = 'none'
-    plt.figure()
-    plt.gca().set_axis_off()
-    plt.text(0.25, 0.25, '50% using `color`', color=(0, 0, 0, 0.5))
-    plt.text(0.25, 0.5, '50% using `alpha`', alpha=0.5)
-    plt.text(0.25, 0.75, '50% using `alpha` and 100% `color`', alpha=0.5,
-             color=(0, 0, 0, 1))
-
-
-def test_text_repr():
-    # smoketest to make sure text repr doesn't error for category
-    plt.plot(['A', 'B'], [1, 2])
-    repr(plt.text(['A'], 0.5, 'Boo'))
-
-
-def test_annotation_update():
-    fig, ax = plt.subplots(1, 1)
-    an = ax.annotate('annotation', xy=(0.5, 0.5))
-    extent1 = an.get_window_extent(fig.canvas.get_renderer())
-    fig.tight_layout()
-    extent2 = an.get_window_extent(fig.canvas.get_renderer())
-
-    assert not np.allclose(extent1.get_points(), extent2.get_points(),
-                           rtol=1e-6)
-
-
-@check_figures_equal(extensions=["png"])
-def test_annotation_units(fig_test, fig_ref):
-    ax = fig_test.add_subplot()
-    ax.plot(datetime.now(), 1, "o")  # Implicitly set axes extents.
-    ax.annotate("x", (datetime.now(), 0.5), xycoords=("data", "axes fraction"),
-                # This used to crash before.
-                xytext=(0, 0), textcoords="offset points")
-    ax = fig_ref.add_subplot()
-    ax.plot(datetime.now(), 1, "o")
-    ax.annotate("x", (datetime.now(), 0.5), xycoords=("data", "axes fraction"))
-
-
-@image_comparison(['large_subscript_title.png'], style='mpl20')
-def test_large_subscript_title():
-    # Remove this line when this test image is regenerated.
-    plt.rcParams['text.kerning_factor'] = 6
-    plt.rcParams['axes.titley'] = None
-
-    fig, axs = plt.subplots(1, 2, figsize=(9, 2.5), constrained_layout=True)
-    ax = axs[0]
-    ax.set_title(r'$\sum_{i} x_i$')
-    ax.set_title('New way', loc='left')
-    ax.set_xticklabels([])
-
-    ax = axs[1]
-    ax.set_title(r'$\sum_{i} x_i$', y=1.01)
-    ax.set_title('Old Way', loc='left')
-    ax.set_xticklabels([])
-
-
-@pytest.mark.parametrize(
-    "x, rotation, halign",
-    [(0.7, 0, 'left'),
-     (0.5, 95, 'left'),
-     (0.3, 0, 'right'),
-     (0.3, 185, 'left')])
-def test_wrap(x, rotation, halign):
-    fig = plt.figure(figsize=(6, 6))
-    s = 'This is a very long text that should be wrapped multiple times.'
-    text = fig.text(x, 0.7, s, wrap=True, rotation=rotation, ha=halign)
-    fig.canvas.draw()
-    assert text._get_wrapped_text() == ('This is a very long\n'
-                                        'text that should be\n'
-                                        'wrapped multiple\n'
-                                        'times.')
-
-
-def test_mathwrap():
-    fig = plt.figure(figsize=(6, 4))
-    s = r'This is a very $\overline{\mathrm{long}}$ line of Mathtext.'
-    text = fig.text(0, 0.5, s, size=40, wrap=True)
-    fig.canvas.draw()
-    assert text._get_wrapped_text() == ('This is a very $\\overline{\\mathrm{long}}$\n'
-                                        'line of Mathtext.')
-
-
-def test_get_window_extent_wrapped():
-    # Test that a long title that wraps to two lines has the same vertical
-    # extent as an explicit two line title.
-
-    fig1 = plt.figure(figsize=(3, 3))
-    fig1.suptitle("suptitle that is clearly too long in this case", wrap=True)
-    window_extent_test = fig1._suptitle.get_window_extent()
-
-    fig2 = plt.figure(figsize=(3, 3))
-    fig2.suptitle("suptitle that is clearly\ntoo long in this case")
-    window_extent_ref = fig2._suptitle.get_window_extent()
-
-    assert window_extent_test.y0 == window_extent_ref.y0
-    assert window_extent_test.y1 == window_extent_ref.y1
-
-
-def test_long_word_wrap():
-    fig = plt.figure(figsize=(6, 4))
-    text = fig.text(9.5, 8, 'Alonglineoftexttowrap', wrap=True)
-    fig.canvas.draw()
-    assert text._get_wrapped_text() == 'Alonglineoftexttowrap'
-
-
-def test_wrap_no_wrap():
-    fig = plt.figure(figsize=(6, 4))
-    text = fig.text(0, 0, 'non wrapped text', wrap=True)
-    fig.canvas.draw()
-    assert text._get_wrapped_text() == 'non wrapped text'
-
-
-@check_figures_equal(extensions=["png"])
-def test_buffer_size(fig_test, fig_ref):
-    # On old versions of the Agg renderer, large non-ascii single-character
-    # strings (here, "€") would be rendered clipped because the rendering
-    # buffer would be set by the physical size of the smaller "a" character.
-    ax = fig_test.add_subplot()
-    ax.set_yticks([0, 1])
-    ax.set_yticklabels(["€", "a"])
-    ax.yaxis.majorTicks[1].label1.set_color("w")
-    ax = fig_ref.add_subplot()
-    ax.set_yticks([0, 1])
-    ax.set_yticklabels(["€", ""])
-
-
-def test_fontproperties_kwarg_precedence():
-    """Test that kwargs take precedence over fontproperties defaults."""
-    plt.figure()
-    text1 = plt.xlabel("value", fontproperties='Times New Roman', size=40.0)
-    text2 = plt.ylabel("counts", size=40.0, fontproperties='Times New Roman')
-    assert text1.get_size() == 40.0
-    assert text2.get_size() == 40.0
-
-
-def test_transform_rotates_text():
-    ax = plt.gca()
-    transform = mtransforms.Affine2D().rotate_deg(30)
-    text = ax.text(0, 0, 'test', transform=transform,
-                   transform_rotates_text=True)
-    result = text.get_rotation()
-    assert_almost_equal(result, 30)
-
-
-def test_update_mutate_input():
-    inp = dict(fontproperties=FontProperties(weight="bold"),
-               bbox=None)
-    cache = dict(inp)
-    t = Text()
-    t.update(inp)
-    assert inp['fontproperties'] == cache['fontproperties']
-    assert inp['bbox'] == cache['bbox']
-
-
-@pytest.mark.parametrize('rotation', ['invalid string', [90]])
-def test_invalid_rotation_values(rotation):
-    with pytest.raises(
-            ValueError,
-            match=("rotation must be 'vertical', 'horizontal' or a number")):
-        Text(0, 0, 'foo', rotation=rotation)
-
-
-def test_invalid_color():
-    with pytest.raises(ValueError):
-        plt.figtext(.5, .5, "foo", c="foobar")
-
-
-@image_comparison(['text_pdf_kerning.pdf'], style='mpl20')
-def test_pdf_kerning():
-    plt.figure()
-    plt.figtext(0.1, 0.5, "ATATATATATATATATATA", size=30)
-
-
-def test_unsupported_script(recwarn):
-    fig = plt.figure()
-    fig.text(.5, .5, "\N{BENGALI DIGIT ZERO}")
-    fig.canvas.draw()
-    assert all(isinstance(warn.message, UserWarning) for warn in recwarn)
-    assert (
-        [warn.message.args for warn in recwarn] ==
-        [(r"Glyph 2534 (\N{BENGALI DIGIT ZERO}) missing from current font.",),
-         (r"Matplotlib currently does not support Bengali natively.",)])
-
-
-# See gh-26152 for more information on this xfail
-@pytest.mark.xfail(pyparsing_version.release == (3, 1, 0),
-                   reason="Error messages are incorrect with pyparsing 3.1.0")
-def test_parse_math():
-    fig, ax = plt.subplots()
-    ax.text(0, 0, r"$ \wrong{math} $", parse_math=False)
-    fig.canvas.draw()
-
-    ax.text(0, 0, r"$ \wrong{math} $", parse_math=True)
-    with pytest.raises(ValueError, match='Unknown symbol'):
-        fig.canvas.draw()
-
-
-# See gh-26152 for more information on this xfail
-@pytest.mark.xfail(pyparsing_version.release == (3, 1, 0),
-                   reason="Error messages are incorrect with pyparsing 3.1.0")
-def test_parse_math_rcparams():
-    # Default is True
-    fig, ax = plt.subplots()
-    ax.text(0, 0, r"$ \wrong{math} $")
-    with pytest.raises(ValueError, match='Unknown symbol'):
-        fig.canvas.draw()
-
-    # Setting rcParams to False
-    with mpl.rc_context({'text.parse_math': False}):
-        fig, ax = plt.subplots()
-        ax.text(0, 0, r"$ \wrong{math} $")
-        fig.canvas.draw()
-
-
-@image_comparison(['text_pdf_font42_kerning.pdf'], style='mpl20')
-def test_pdf_font42_kerning():
-    plt.rcParams['pdf.fonttype'] = 42
-    plt.figure()
-    plt.figtext(0.1, 0.5, "ATAVATAVATAVATAVATA", size=30)
-
-
-@image_comparison(['text_pdf_chars_beyond_bmp.pdf'], style='mpl20')
-def test_pdf_chars_beyond_bmp():
-    plt.rcParams['pdf.fonttype'] = 42
-    plt.rcParams['mathtext.fontset'] = 'stixsans'
-    plt.figure()
-    plt.figtext(0.1, 0.5, "Mass $m$ \U00010308", size=30)
-
-
-@needs_usetex
-def test_metrics_cache():
-    mpl.text._get_text_metrics_with_cache_impl.cache_clear()
-
-    fig = plt.figure()
-    fig.text(.3, .5, "foo\nbar")
-    fig.text(.3, .5, "foo\nbar", usetex=True)
-    fig.text(.5, .5, "foo\nbar", usetex=True)
-    fig.canvas.draw()
-    renderer = fig._get_renderer()
-    ys = {}  # mapping of strings to where they were drawn in y with draw_tex.
-
-    def call(*args, **kwargs):
-        renderer, x, y, s, *_ = args
-        ys.setdefault(s, set()).add(y)
-
-    renderer.draw_tex = call
-    fig.canvas.draw()
-    assert [*ys] == ["foo", "bar"]
-    # Check that both TeX strings were drawn with the same y-position for both
-    # single-line substrings.  Previously, there used to be an incorrect cache
-    # collision with the non-TeX string (drawn first here) whose metrics would
-    # get incorrectly reused by the first TeX string.
-    assert len(ys["foo"]) == len(ys["bar"]) == 1
-
-    info = mpl.text._get_text_metrics_with_cache_impl.cache_info()
-    # Every string gets a miss for the first layouting (extents), then a hit
-    # when drawing, but "foo\nbar" gets two hits as it's drawn twice.
-    assert info.hits > info.misses
-
-
-def test_annotate_offset_fontsize():
-    # Test that offset_fontsize parameter works and uses accurate values
-    fig, ax = plt.subplots()
-    text_coords = ['offset points', 'offset fontsize']
-    # 10 points should be equal to 1 fontsize unit at fontsize=10
-    xy_text = [(10, 10), (1, 1)]
-    anns = [ax.annotate('test', xy=(0.5, 0.5),
-                        xytext=xy_text[i],
-                        fontsize='10',
-                        xycoords='data',
-                        textcoords=text_coords[i]) for i in range(2)]
-    points_coords, fontsize_coords = [ann.get_window_extent() for ann in anns]
-    fig.canvas.draw()
-    assert str(points_coords) == str(fontsize_coords)
-
-
-def test_get_set_antialiased():
-    txt = Text(.5, .5, "foo\nbar")
-    assert txt._antialiased == mpl.rcParams['text.antialiased']
-    assert txt.get_antialiased() == mpl.rcParams['text.antialiased']
-
-    txt.set_antialiased(True)
-    assert txt._antialiased is True
-    assert txt.get_antialiased() == txt._antialiased
-
-    txt.set_antialiased(False)
-    assert txt._antialiased is False
-    assert txt.get_antialiased() == txt._antialiased
-
-
-def test_annotation_antialiased():
-    annot = Annotation("foo\nbar", (.5, .5), antialiased=True)
-    assert annot._antialiased is True
-    assert annot.get_antialiased() == annot._antialiased
-
-    annot2 = Annotation("foo\nbar", (.5, .5), antialiased=False)
-    assert annot2._antialiased is False
-    assert annot2.get_antialiased() == annot2._antialiased
-
-    annot3 = Annotation("foo\nbar", (.5, .5), antialiased=False)
-    annot3.set_antialiased(True)
-    assert annot3.get_antialiased() is True
-    assert annot3._antialiased is True
-
-    annot4 = Annotation("foo\nbar", (.5, .5))
-    assert annot4._antialiased == mpl.rcParams['text.antialiased']
-
-
-@check_figures_equal(extensions=["png"])
-def test_annotate_and_offsetfrom_copy_input(fig_test, fig_ref):
-    # Both approaches place the text (10, 0) pixels away from the center of the line.
-    ax = fig_test.add_subplot()
-    l, = ax.plot([0, 2], [0, 2])
-    of_xy = np.array([.5, .5])
-    ax.annotate("foo", textcoords=OffsetFrom(l, of_xy), xytext=(10, 0),
-                xy=(0, 0))  # xy is unused.
-    of_xy[:] = 1
-    ax = fig_ref.add_subplot()
-    l, = ax.plot([0, 2], [0, 2])
-    an_xy = np.array([.5, .5])
-    ax.annotate("foo", xy=an_xy, xycoords=l, xytext=(10, 0), textcoords="offset points")
-    an_xy[:] = 2
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/tests/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/construction.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/construction.py
deleted file mode 100644
index 5903187769f080c399f67e12af564ff3caaf7fbc..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/construction.py
+++ /dev/null
@@ -1,806 +0,0 @@
-"""
-Constructor functions intended to be shared by pd.array, Series.__init__,
-and Index.__new__.
-
-These should not depend on core.internals.
-"""
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import (
-    TYPE_CHECKING,
-    Optional,
-    Union,
-    cast,
-    overload,
-)
-import warnings
-
-import numpy as np
-from numpy import ma
-
-from pandas._config import using_pyarrow_string_dtype
-
-from pandas._libs import lib
-from pandas._libs.tslibs import (
-    Period,
-    get_unit_from_dtype,
-    is_supported_unit,
-)
-from pandas._typing import (
-    AnyArrayLike,
-    ArrayLike,
-    Dtype,
-    DtypeObj,
-    T,
-)
-from pandas.util._exceptions import find_stack_level
-
-from pandas.core.dtypes.base import ExtensionDtype
-from pandas.core.dtypes.cast import (
-    construct_1d_arraylike_from_scalar,
-    construct_1d_object_array_from_listlike,
-    maybe_cast_to_datetime,
-    maybe_cast_to_integer_array,
-    maybe_convert_platform,
-    maybe_infer_to_datetimelike,
-    maybe_promote,
-)
-from pandas.core.dtypes.common import (
-    is_list_like,
-    is_object_dtype,
-    pandas_dtype,
-)
-from pandas.core.dtypes.dtypes import NumpyEADtype
-from pandas.core.dtypes.generic import (
-    ABCDataFrame,
-    ABCExtensionArray,
-    ABCIndex,
-    ABCSeries,
-)
-from pandas.core.dtypes.missing import isna
-
-import pandas.core.common as com
-
-if TYPE_CHECKING:
-    from pandas import (
-        Index,
-        Series,
-    )
-    from pandas.core.arrays.base import ExtensionArray
-
-
-def array(
-    data: Sequence[object] | AnyArrayLike,
-    dtype: Dtype | None = None,
-    copy: bool = True,
-) -> ExtensionArray:
-    """
-    Create an array.
-
-    Parameters
-    ----------
-    data : Sequence of objects
-        The scalars inside `data` should be instances of the
-        scalar type for `dtype`. It's expected that `data`
-        represents a 1-dimensional array of data.
-
-        When `data` is an Index or Series, the underlying array
-        will be extracted from `data`.
-
-    dtype : str, np.dtype, or ExtensionDtype, optional
-        The dtype to use for the array. This may be a NumPy
-        dtype or an extension type registered with pandas using
-        :meth:`pandas.api.extensions.register_extension_dtype`.
-
-        If not specified, there are two possibilities:
-
-        1. When `data` is a :class:`Series`, :class:`Index`, or
-           :class:`ExtensionArray`, the `dtype` will be taken
-           from the data.
-        2. Otherwise, pandas will attempt to infer the `dtype`
-           from the data.
-
-        Note that when `data` is a NumPy array, ``data.dtype`` is
-        *not* used for inferring the array type. This is because
-        NumPy cannot represent all the types of data that can be
-        held in extension arrays.
-
-        Currently, pandas will infer an extension dtype for sequences of
-
-        ============================== =======================================
-        Scalar Type                    Array Type
-        ============================== =======================================
-        :class:`pandas.Interval`       :class:`pandas.arrays.IntervalArray`
-        :class:`pandas.Period`         :class:`pandas.arrays.PeriodArray`
-        :class:`datetime.datetime`     :class:`pandas.arrays.DatetimeArray`
-        :class:`datetime.timedelta`    :class:`pandas.arrays.TimedeltaArray`
-        :class:`int`                   :class:`pandas.arrays.IntegerArray`
-        :class:`float`                 :class:`pandas.arrays.FloatingArray`
-        :class:`str`                   :class:`pandas.arrays.StringArray` or
-                                       :class:`pandas.arrays.ArrowStringArray`
-        :class:`bool`                  :class:`pandas.arrays.BooleanArray`
-        ============================== =======================================
-
-        The ExtensionArray created when the scalar type is :class:`str` is determined by
-        ``pd.options.mode.string_storage`` if the dtype is not explicitly given.
-
-        For all other cases, NumPy's usual inference rules will be used.
-
-        .. versionchanged:: 1.2.0
-
-            Pandas now also infers nullable-floating dtype for float-like
-            input data
-
-    copy : bool, default True
-        Whether to copy the data, even if not necessary. Depending
-        on the type of `data`, creating the new array may require
-        copying data, even if ``copy=False``.
-
-    Returns
-    -------
-    ExtensionArray
-        The newly created array.
-
-    Raises
-    ------
-    ValueError
-        When `data` is not 1-dimensional.
-
-    See Also
-    --------
-    numpy.array : Construct a NumPy array.
-    Series : Construct a pandas Series.
-    Index : Construct a pandas Index.
-    arrays.NumpyExtensionArray : ExtensionArray wrapping a NumPy array.
-    Series.array : Extract the array stored within a Series.
-
-    Notes
-    -----
-    Omitting the `dtype` argument means pandas will attempt to infer the
-    best array type from the values in the data. As new array types are
-    added by pandas and 3rd party libraries, the "best" array type may
-    change. We recommend specifying `dtype` to ensure that
-
-    1. the correct array type for the data is returned
-    2. the returned array type doesn't change as new extension types
-       are added by pandas and third-party libraries
-
-    Additionally, if the underlying memory representation of the returned
-    array matters, we recommend specifying the `dtype` as a concrete object
-    rather than a string alias or allowing it to be inferred. For example,
-    a future version of pandas or a 3rd-party library may include a
-    dedicated ExtensionArray for string data. In this event, the following
-    would no longer return a :class:`arrays.NumpyExtensionArray` backed by a
-    NumPy array.
-
-    >>> pd.array(['a', 'b'], dtype=str)
-    <NumpyExtensionArray>
-    ['a', 'b']
-    Length: 2, dtype: str32
-
-    This would instead return the new ExtensionArray dedicated for string
-    data. If you really need the new array to be backed by a  NumPy array,
-    specify that in the dtype.
-
-    >>> pd.array(['a', 'b'], dtype=np.dtype("<U1"))
-    <NumpyExtensionArray>
-    ['a', 'b']
-    Length: 2, dtype: str32
-
-    Finally, Pandas has arrays that mostly overlap with NumPy
-
-      * :class:`arrays.DatetimeArray`
-      * :class:`arrays.TimedeltaArray`
-
-    When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
-    passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
-    rather than a ``NumpyExtensionArray``. This is for symmetry with the case of
-    timezone-aware data, which NumPy does not natively support.
-
-    >>> pd.array(['2015', '2016'], dtype='datetime64[ns]')
-    <DatetimeArray>
-    ['2015-01-01 00:00:00', '2016-01-01 00:00:00']
-    Length: 2, dtype: datetime64[ns]
-
-    >>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
-    <TimedeltaArray>
-    ['0 days 01:00:00', '0 days 02:00:00']
-    Length: 2, dtype: timedelta64[ns]
-
-    Examples
-    --------
-    If a dtype is not specified, pandas will infer the best dtype from the values.
-    See the description of `dtype` for the types pandas infers for.
-
-    >>> pd.array([1, 2])
-    <IntegerArray>
-    [1, 2]
-    Length: 2, dtype: Int64
-
-    >>> pd.array([1, 2, np.nan])
-    <IntegerArray>
-    [1, 2, <NA>]
-    Length: 3, dtype: Int64
-
-    >>> pd.array([1.1, 2.2])
-    <FloatingArray>
-    [1.1, 2.2]
-    Length: 2, dtype: Float64
-
-    >>> pd.array(["a", None, "c"])
-    <StringArray>
-    ['a', <NA>, 'c']
-    Length: 3, dtype: string
-
-    >>> with pd.option_context("string_storage", "pyarrow"):
-    ...     arr = pd.array(["a", None, "c"])
-    ...
-    >>> arr
-    <ArrowStringArray>
-    ['a', <NA>, 'c']
-    Length: 3, dtype: string
-
-    >>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
-    <PeriodArray>
-    ['2000-01-01', '2000-01-01']
-    Length: 2, dtype: period[D]
-
-    You can use the string alias for `dtype`
-
-    >>> pd.array(['a', 'b', 'a'], dtype='category')
-    ['a', 'b', 'a']
-    Categories (2, object): ['a', 'b']
-
-    Or specify the actual dtype
-
-    >>> pd.array(['a', 'b', 'a'],
-    ...          dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
-    ['a', 'b', 'a']
-    Categories (3, object): ['a' < 'b' < 'c']
-
-    If pandas does not infer a dedicated extension type a
-    :class:`arrays.NumpyExtensionArray` is returned.
-
-    >>> pd.array([1 + 1j, 3 + 2j])
-    <NumpyExtensionArray>
-    [(1+1j), (3+2j)]
-    Length: 2, dtype: complex128
-
-    As mentioned in the "Notes" section, new extension types may be added
-    in the future (by pandas or 3rd party libraries), causing the return
-    value to no longer be a :class:`arrays.NumpyExtensionArray`. Specify the
-    `dtype` as a NumPy dtype if you need to ensure there's no future change in
-    behavior.
-
-    >>> pd.array([1, 2], dtype=np.dtype("int32"))
-    <NumpyExtensionArray>
-    [1, 2]
-    Length: 2, dtype: int32
-
-    `data` must be 1-dimensional. A ValueError is raised when the input
-    has the wrong dimensionality.
-
-    >>> pd.array(1)
-    Traceback (most recent call last):
-      ...
-    ValueError: Cannot pass scalar '1' to 'pandas.array'.
-    """
-    from pandas.core.arrays import (
-        BooleanArray,
-        DatetimeArray,
-        ExtensionArray,
-        FloatingArray,
-        IntegerArray,
-        IntervalArray,
-        NumpyExtensionArray,
-        PeriodArray,
-        TimedeltaArray,
-    )
-    from pandas.core.arrays.string_ import StringDtype
-
-    if lib.is_scalar(data):
-        msg = f"Cannot pass scalar '{data}' to 'pandas.array'."
-        raise ValueError(msg)
-    elif isinstance(data, ABCDataFrame):
-        raise TypeError("Cannot pass DataFrame to 'pandas.array'")
-
-    if dtype is None and isinstance(data, (ABCSeries, ABCIndex, ExtensionArray)):
-        # Note: we exclude np.ndarray here, will do type inference on it
-        dtype = data.dtype
-
-    data = extract_array(data, extract_numpy=True)
-
-    # this returns None for not-found dtypes.
-    if dtype is not None:
-        dtype = pandas_dtype(dtype)
-
-    if isinstance(data, ExtensionArray) and (dtype is None or data.dtype == dtype):
-        # e.g. TimedeltaArray[s], avoid casting to NumpyExtensionArray
-        if copy:
-            return data.copy()
-        return data
-
-    if isinstance(dtype, ExtensionDtype):
-        cls = dtype.construct_array_type()
-        return cls._from_sequence(data, dtype=dtype, copy=copy)
-
-    if dtype is None:
-        inferred_dtype = lib.infer_dtype(data, skipna=True)
-        if inferred_dtype == "period":
-            period_data = cast(Union[Sequence[Optional[Period]], AnyArrayLike], data)
-            return PeriodArray._from_sequence(period_data, copy=copy)
-
-        elif inferred_dtype == "interval":
-            return IntervalArray(data, copy=copy)
-
-        elif inferred_dtype.startswith("datetime"):
-            # datetime, datetime64
-            try:
-                return DatetimeArray._from_sequence(data, copy=copy)
-            except ValueError:
-                # Mixture of timezones, fall back to NumpyExtensionArray
-                pass
-
-        elif inferred_dtype.startswith("timedelta"):
-            # timedelta, timedelta64
-            return TimedeltaArray._from_sequence(data, copy=copy)
-
-        elif inferred_dtype == "string":
-            # StringArray/ArrowStringArray depending on pd.options.mode.string_storage
-            return StringDtype().construct_array_type()._from_sequence(data, copy=copy)
-
-        elif inferred_dtype == "integer":
-            return IntegerArray._from_sequence(data, copy=copy)
-        elif inferred_dtype == "empty" and not hasattr(data, "dtype") and not len(data):
-            return FloatingArray._from_sequence(data, copy=copy)
-        elif (
-            inferred_dtype in ("floating", "mixed-integer-float")
-            and getattr(data, "dtype", None) != np.float16
-        ):
-            # GH#44715 Exclude np.float16 bc FloatingArray does not support it;
-            #  we will fall back to NumpyExtensionArray.
-            return FloatingArray._from_sequence(data, copy=copy)
-
-        elif inferred_dtype == "boolean":
-            return BooleanArray._from_sequence(data, copy=copy)
-
-    # Pandas overrides NumPy for
-    #   1. datetime64[ns,us,ms,s]
-    #   2. timedelta64[ns,us,ms,s]
-    # so that a DatetimeArray is returned.
-    if lib.is_np_dtype(dtype, "M") and is_supported_unit(get_unit_from_dtype(dtype)):
-        return DatetimeArray._from_sequence(data, dtype=dtype, copy=copy)
-    if lib.is_np_dtype(dtype, "m") and is_supported_unit(get_unit_from_dtype(dtype)):
-        return TimedeltaArray._from_sequence(data, dtype=dtype, copy=copy)
-
-    elif lib.is_np_dtype(dtype, "mM"):
-        warnings.warn(
-            r"datetime64 and timedelta64 dtype resolutions other than "
-            r"'s', 'ms', 'us', and 'ns' are deprecated. "
-            r"In future releases passing unsupported resolutions will "
-            r"raise an exception.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-
-    return NumpyExtensionArray._from_sequence(data, dtype=dtype, copy=copy)
-
-
-_typs = frozenset(
-    {
-        "index",
-        "rangeindex",
-        "multiindex",
-        "datetimeindex",
-        "timedeltaindex",
-        "periodindex",
-        "categoricalindex",
-        "intervalindex",
-        "series",
-    }
-)
-
-
-@overload
-def extract_array(
-    obj: Series | Index, extract_numpy: bool = ..., extract_range: bool = ...
-) -> ArrayLike:
-    ...
-
-
-@overload
-def extract_array(
-    obj: T, extract_numpy: bool = ..., extract_range: bool = ...
-) -> T | ArrayLike:
-    ...
-
-
-def extract_array(
-    obj: T, extract_numpy: bool = False, extract_range: bool = False
-) -> T | ArrayLike:
-    """
-    Extract the ndarray or ExtensionArray from a Series or Index.
-
-    For all other types, `obj` is just returned as is.
-
-    Parameters
-    ----------
-    obj : object
-        For Series / Index, the underlying ExtensionArray is unboxed.
-
-    extract_numpy : bool, default False
-        Whether to extract the ndarray from a NumpyExtensionArray.
-
-    extract_range : bool, default False
-        If we have a RangeIndex, return range._values if True
-        (which is a materialized integer ndarray), otherwise return unchanged.
-
-    Returns
-    -------
-    arr : object
-
-    Examples
-    --------
-    >>> extract_array(pd.Series(['a', 'b', 'c'], dtype='category'))
-    ['a', 'b', 'c']
-    Categories (3, object): ['a', 'b', 'c']
-
-    Other objects like lists, arrays, and DataFrames are just passed through.
-
-    >>> extract_array([1, 2, 3])
-    [1, 2, 3]
-
-    For an ndarray-backed Series / Index the ndarray is returned.
-
-    >>> extract_array(pd.Series([1, 2, 3]))
-    array([1, 2, 3])
-
-    To extract all the way down to the ndarray, pass ``extract_numpy=True``.
-
-    >>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
-    array([1, 2, 3])
-    """
-    typ = getattr(obj, "_typ", None)
-    if typ in _typs:
-        # i.e. isinstance(obj, (ABCIndex, ABCSeries))
-        if typ == "rangeindex":
-            if extract_range:
-                # error: "T" has no attribute "_values"
-                return obj._values  # type: ignore[attr-defined]
-            return obj
-
-        # error: "T" has no attribute "_values"
-        return obj._values  # type: ignore[attr-defined]
-
-    elif extract_numpy and typ == "npy_extension":
-        # i.e. isinstance(obj, ABCNumpyExtensionArray)
-        # error: "T" has no attribute "to_numpy"
-        return obj.to_numpy()  # type: ignore[attr-defined]
-
-    return obj
-
-
-def ensure_wrapped_if_datetimelike(arr):
-    """
-    Wrap datetime64 and timedelta64 ndarrays in DatetimeArray/TimedeltaArray.
-    """
-    if isinstance(arr, np.ndarray):
-        if arr.dtype.kind == "M":
-            from pandas.core.arrays import DatetimeArray
-
-            return DatetimeArray._from_sequence(arr)
-
-        elif arr.dtype.kind == "m":
-            from pandas.core.arrays import TimedeltaArray
-
-            return TimedeltaArray._from_sequence(arr)
-
-    return arr
-
-
-def sanitize_masked_array(data: ma.MaskedArray) -> np.ndarray:
-    """
-    Convert numpy MaskedArray to ensure mask is softened.
-    """
-    mask = ma.getmaskarray(data)
-    if mask.any():
-        dtype, fill_value = maybe_promote(data.dtype, np.nan)
-        dtype = cast(np.dtype, dtype)
-        data = ma.asarray(data.astype(dtype, copy=True))
-        data.soften_mask()  # set hardmask False if it was True
-        data[mask] = fill_value
-    else:
-        data = data.copy()
-    return data
-
-
-def sanitize_array(
-    data,
-    index: Index | None,
-    dtype: DtypeObj | None = None,
-    copy: bool = False,
-    *,
-    allow_2d: bool = False,
-) -> ArrayLike:
-    """
-    Sanitize input data to an ndarray or ExtensionArray, copy if specified,
-    coerce to the dtype if specified.
-
-    Parameters
-    ----------
-    data : Any
-    index : Index or None, default None
-    dtype : np.dtype, ExtensionDtype, or None, default None
-    copy : bool, default False
-    allow_2d : bool, default False
-        If False, raise if we have a 2D Arraylike.
-
-    Returns
-    -------
-    np.ndarray or ExtensionArray
-    """
-    original_dtype = dtype
-    if isinstance(data, ma.MaskedArray):
-        data = sanitize_masked_array(data)
-
-    if isinstance(dtype, NumpyEADtype):
-        # Avoid ending up with a NumpyExtensionArray
-        dtype = dtype.numpy_dtype
-
-    # extract ndarray or ExtensionArray, ensure we have no NumpyExtensionArray
-    data = extract_array(data, extract_numpy=True, extract_range=True)
-
-    if isinstance(data, np.ndarray) and data.ndim == 0:
-        if dtype is None:
-            dtype = data.dtype
-        data = lib.item_from_zerodim(data)
-    elif isinstance(data, range):
-        # GH#16804
-        data = range_to_ndarray(data)
-        copy = False
-
-    if not is_list_like(data):
-        if index is None:
-            raise ValueError("index must be specified when data is not list-like")
-        if (
-            isinstance(data, str)
-            and using_pyarrow_string_dtype()
-            and original_dtype is None
-        ):
-            from pandas.core.arrays.string_ import StringDtype
-
-            dtype = StringDtype("pyarrow_numpy")
-        data = construct_1d_arraylike_from_scalar(data, len(index), dtype)
-
-        return data
-
-    elif isinstance(data, ABCExtensionArray):
-        # it is already ensured above this is not a NumpyExtensionArray
-        # Until GH#49309 is fixed this check needs to come before the
-        #  ExtensionDtype check
-        if dtype is not None:
-            subarr = data.astype(dtype, copy=copy)
-        elif copy:
-            subarr = data.copy()
-        else:
-            subarr = data
-
-    elif isinstance(dtype, ExtensionDtype):
-        # create an extension array from its dtype
-        _sanitize_non_ordered(data)
-        cls = dtype.construct_array_type()
-        subarr = cls._from_sequence(data, dtype=dtype, copy=copy)
-
-    # GH#846
-    elif isinstance(data, np.ndarray):
-        if isinstance(data, np.matrix):
-            data = data.A
-
-        if dtype is None:
-            subarr = data
-            if data.dtype == object:
-                subarr = maybe_infer_to_datetimelike(data)
-            elif data.dtype.kind == "U" and using_pyarrow_string_dtype():
-                from pandas.core.arrays.string_ import StringDtype
-
-                dtype = StringDtype(storage="pyarrow_numpy")
-                subarr = dtype.construct_array_type()._from_sequence(data, dtype=dtype)
-
-            if subarr is data and copy:
-                subarr = subarr.copy()
-
-        else:
-            # we will try to copy by-definition here
-            subarr = _try_cast(data, dtype, copy)
-
-    elif hasattr(data, "__array__"):
-        # e.g. dask array GH#38645
-        data = np.array(data, copy=copy)
-        return sanitize_array(
-            data,
-            index=index,
-            dtype=dtype,
-            copy=False,
-            allow_2d=allow_2d,
-        )
-
-    else:
-        _sanitize_non_ordered(data)
-        # materialize e.g. generators, convert e.g. tuples, abc.ValueView
-        data = list(data)
-
-        if len(data) == 0 and dtype is None:
-            # We default to float64, matching numpy
-            subarr = np.array([], dtype=np.float64)
-
-        elif dtype is not None:
-            subarr = _try_cast(data, dtype, copy)
-
-        else:
-            subarr = maybe_convert_platform(data)
-            if subarr.dtype == object:
-                subarr = cast(np.ndarray, subarr)
-                subarr = maybe_infer_to_datetimelike(subarr)
-
-    subarr = _sanitize_ndim(subarr, data, dtype, index, allow_2d=allow_2d)
-
-    if isinstance(subarr, np.ndarray):
-        # at this point we should have dtype be None or subarr.dtype == dtype
-        dtype = cast(np.dtype, dtype)
-        subarr = _sanitize_str_dtypes(subarr, data, dtype, copy)
-
-    return subarr
-
-
-def range_to_ndarray(rng: range) -> np.ndarray:
-    """
-    Cast a range object to ndarray.
-    """
-    # GH#30171 perf avoid realizing range as a list in np.array
-    try:
-        arr = np.arange(rng.start, rng.stop, rng.step, dtype="int64")
-    except OverflowError:
-        # GH#30173 handling for ranges that overflow int64
-        if (rng.start >= 0 and rng.step > 0) or (rng.step < 0 <= rng.stop):
-            try:
-                arr = np.arange(rng.start, rng.stop, rng.step, dtype="uint64")
-            except OverflowError:
-                arr = construct_1d_object_array_from_listlike(list(rng))
-        else:
-            arr = construct_1d_object_array_from_listlike(list(rng))
-    return arr
-
-
-def _sanitize_non_ordered(data) -> None:
-    """
-    Raise only for unordered sets, e.g., not for dict_keys
-    """
-    if isinstance(data, (set, frozenset)):
-        raise TypeError(f"'{type(data).__name__}' type is unordered")
-
-
-def _sanitize_ndim(
-    result: ArrayLike,
-    data,
-    dtype: DtypeObj | None,
-    index: Index | None,
-    *,
-    allow_2d: bool = False,
-) -> ArrayLike:
-    """
-    Ensure we have a 1-dimensional result array.
-    """
-    if getattr(result, "ndim", 0) == 0:
-        raise ValueError("result should be arraylike with ndim > 0")
-
-    if result.ndim == 1:
-        # the result that we want
-        result = _maybe_repeat(result, index)
-
-    elif result.ndim > 1:
-        if isinstance(data, np.ndarray):
-            if allow_2d:
-                return result
-            raise ValueError(
-                f"Data must be 1-dimensional, got ndarray of shape {data.shape} instead"
-            )
-        if is_object_dtype(dtype) and isinstance(dtype, ExtensionDtype):
-            # i.e. NumpyEADtype("O")
-
-            result = com.asarray_tuplesafe(data, dtype=np.dtype("object"))
-            cls = dtype.construct_array_type()
-            result = cls._from_sequence(result, dtype=dtype)
-        else:
-            # error: Argument "dtype" to "asarray_tuplesafe" has incompatible type
-            # "Union[dtype[Any], ExtensionDtype, None]"; expected "Union[str,
-            # dtype[Any], None]"
-            result = com.asarray_tuplesafe(data, dtype=dtype)  # type: ignore[arg-type]
-    return result
-
-
-def _sanitize_str_dtypes(
-    result: np.ndarray, data, dtype: np.dtype | None, copy: bool
-) -> np.ndarray:
-    """
-    Ensure we have a dtype that is supported by pandas.
-    """
-
-    # This is to prevent mixed-type Series getting all casted to
-    # NumPy string type, e.g. NaN --> '-1#IND'.
-    if issubclass(result.dtype.type, str):
-        # GH#16605
-        # If not empty convert the data to dtype
-        # GH#19853: If data is a scalar, result has already the result
-        if not lib.is_scalar(data):
-            if not np.all(isna(data)):
-                data = np.array(data, dtype=dtype, copy=False)
-            result = np.array(data, dtype=object, copy=copy)
-    return result
-
-
-def _maybe_repeat(arr: ArrayLike, index: Index | None) -> ArrayLike:
-    """
-    If we have a length-1 array and an index describing how long we expect
-    the result to be, repeat the array.
-    """
-    if index is not None:
-        if 1 == len(arr) != len(index):
-            arr = arr.repeat(len(index))
-    return arr
-
-
-def _try_cast(
-    arr: list | np.ndarray,
-    dtype: np.dtype,
-    copy: bool,
-) -> ArrayLike:
-    """
-    Convert input to numpy ndarray and optionally cast to a given dtype.
-
-    Parameters
-    ----------
-    arr : ndarray or list
-        Excludes: ExtensionArray, Series, Index.
-    dtype : np.dtype
-    copy : bool
-        If False, don't copy the data if not needed.
-
-    Returns
-    -------
-    np.ndarray or ExtensionArray
-    """
-    is_ndarray = isinstance(arr, np.ndarray)
-
-    if dtype == object:
-        if not is_ndarray:
-            subarr = construct_1d_object_array_from_listlike(arr)
-            return subarr
-        return ensure_wrapped_if_datetimelike(arr).astype(dtype, copy=copy)
-
-    elif dtype.kind == "U":
-        # TODO: test cases with arr.dtype.kind in "mM"
-        if is_ndarray:
-            arr = cast(np.ndarray, arr)
-            shape = arr.shape
-            if arr.ndim > 1:
-                arr = arr.ravel()
-        else:
-            shape = (len(arr),)
-        return lib.ensure_string_array(arr, convert_na_value=False, copy=copy).reshape(
-            shape
-        )
-
-    elif dtype.kind in "mM":
-        return maybe_cast_to_datetime(arr, dtype)
-
-    # GH#15832: Check if we are requesting a numeric dtype and
-    # that we can convert the data to the requested dtype.
-    elif dtype.kind in "iu":
-        # this will raise if we have e.g. floats
-
-        subarr = maybe_cast_to_integer_array(arr, dtype)
-    else:
-        subarr = np.array(arr, dtype=dtype, copy=copy)
-
-    return subarr
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/generic/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/generic/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git "a/spaces/qingxu98/gpt-academic/crazy_functions/\346\211\271\351\207\217Markdown\347\277\273\350\257\221.py" "b/spaces/qingxu98/gpt-academic/crazy_functions/\346\211\271\351\207\217Markdown\347\277\273\350\257\221.py"
deleted file mode 100644
index 9485b1ec187e9ab73b30f3450cd7bb96c46a659d..0000000000000000000000000000000000000000
--- "a/spaces/qingxu98/gpt-academic/crazy_functions/\346\211\271\351\207\217Markdown\347\277\273\350\257\221.py"
+++ /dev/null
@@ -1,261 +0,0 @@
-import glob, time, os, re, logging
-from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
-from toolbox import CatchException, report_execption, get_log_folder
-from toolbox import write_history_to_file, promote_file_to_downloadzone
-fast_debug = False
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.md")
-        logging.info('Segmentation: done')
-
-    def merge_result(self):
-        self.file_result = ["" for _ in range(len(self.file_paths))]
-        for r, k in zip(self.sp_file_result, self.sp_file_index):
-            self.file_result[k] += r
-
-    def write_result(self, language):
-        manifest = []
-        for path, res in zip(self.file_paths, self.file_result):
-            dst_file = os.path.join(get_log_folder(), f'{gen_time_str()}.md')
-            with open(dst_file, 'w', encoding='utf8') as f:
-                manifest.append(dst_file)
-                f.write(res)
-        return manifest
-
-def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    #  <-------- 读取Markdown文件，删除其中的所有注释 ----------> 
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(file_content)
-
-    #  <-------- 拆分过长的Markdown文件 ----------> 
-    pfg.run_file_split(max_token_limit=1500)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 多线程翻译开始 ----------> 
-    if language == 'en->zh':
-        inputs_array = ["This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    elif language == 'zh->en':
-        inputs_array = [f"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    else:
-        inputs_array = [f"This is a Markdown file, translate it into {language}, do not modify any existing Markdown commands, only answer me with translated results:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len = 80
-    )
-    try:
-        pfg.sp_file_result = []
-        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
-            pfg.sp_file_result.append(gpt_say)
-        pfg.merge_result()
-        pfg.write_result(language)
-    except:
-        logging.error(trimmed_format_exc())
-
-    #  <-------- 整理结果，退出 ----------> 
-    create_report_file_name = gen_time_str() + f"-chatgpt.md"
-    res = write_history_to_file(gpt_response_collection, file_basename=create_report_file_name)
-    promote_file_to_downloadzone(res, chatbot=chatbot)
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-def get_files_from_everything(txt, preference=''):
-    if txt == "": return False, None, None
-    success = True
-    if txt.startswith('http'):
-        import requests
-        from toolbox import get_conf
-        proxies, = get_conf('proxies')
-        # 网络的远程文件
-        if preference == 'Github':
-            logging.info('正在从github下载资源 ...')
-            if not txt.endswith('.md'):
-                # Make a request to the GitHub API to retrieve the repository information
-                url = txt.replace("https://github.com/", "https://api.github.com/repos/") + '/readme'
-                response = requests.get(url, proxies=proxies)
-                txt = response.json()['download_url']
-            else:
-                txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
-                txt = txt.replace("/blob/", "/")
-
-        r = requests.get(txt, proxies=proxies)
-        download_local = f'{get_log_folder(plugin_name="批量Markdown翻译")}/raw-readme-{gen_time_str()}.md'
-        project_folder = f'{get_log_folder(plugin_name="批量Markdown翻译")}'
-        with open(download_local, 'wb+') as f: f.write(r.content)
-        file_manifest = [download_local]
-    elif txt.endswith('.md'):
-        # 直接给定文件
-        file_manifest = [txt]
-        project_folder = os.path.dirname(txt)
-    elif os.path.exists(txt):
-        # 本地路径，递归搜索
-        project_folder = txt
-        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
-    else:
-        project_folder = None
-        file_manifest = []
-        success = False
-
-    return success, file_manifest, project_folder
-
-
-@CatchException
-def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-
-    success, file_manifest, project_folder = get_files_from_everything(txt, preference="Github")
-
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
-
-
-
-
-
-@CatchException
-def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    success, file_manifest, project_folder = get_files_from_everything(txt)
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
-
-
-@CatchException
-def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    disable_auto_promotion(chatbot)
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    success, file_manifest, project_folder = get_files_from_everything(txt)
-    if not success:
-        # 什么都没有
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
-    language = plugin_kwargs.get("advanced_arg", 'Chinese')
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language=language)
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/DuranDuranGreatestHitsfullalbumzip.md b/spaces/quidiaMuxgu/Expedit-SAM/DuranDuranGreatestHitsfullalbumzip.md
deleted file mode 100644
index 2a32d822ce72d15761853c371c3e7610f0d9b0ca..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/DuranDuranGreatestHitsfullalbumzip.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>DuranDuranGreatestHitsfullalbumzip</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash; <a href="https://geags.com/2uCqnT">https://geags.com/2uCqnT</a></b></p><br /><br />
-
-drivers license, doc rivers, drivers ed, drivers permit test, drivers license ny, drivers test, drivers, drivers license check, drivers village, drivers ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/r3gm/RVC_HF/tools/app.py b/spaces/r3gm/RVC_HF/tools/app.py
deleted file mode 100644
index 602fbb71a49f2537295337cdcecf501abdd74153..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/tools/app.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import logging
-import os
-
-# os.system("wget -P cvec/ https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
-import gradio as gr
-from dotenv import load_dotenv
-
-from configs.config import Config
-from i18n import I18nAuto
-from infer.modules.vc.pipeline import Pipeline
-VC = Pipeline
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-logger = logging.getLogger(__name__)
-
-i18n = I18nAuto()
-#(i18n)
-
-load_dotenv()
-config = Config()
-vc = VC(config)
-
-weight_root = os.getenv("weight_root")
-weight_uvr5_root = os.getenv("weight_uvr5_root")
-index_root = os.getenv("index_root")
-names = []
-hubert_model = None
-for name in os.listdir(weight_root):
-    if name.endswith(".pth"):
-        names.append(name)
-index_paths = []
-for root, dirs, files in os.walk(index_root, topdown=False):
-    for name in files:
-        if name.endswith(".index") and "trained" not in name:
-            index_paths.append("%s/%s" % (root, name))
-
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("在线demo"):
-            gr.Markdown(
-                value="""
-                RVC 在线demo
-                """
-            )
-            sid = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
-            with gr.Column():
-                spk_item = gr.Slider(
-                    minimum=0,
-                    maximum=2333,
-                    step=1,
-                    label=i18n("请选择说话人id"),
-                    value=0,
-                    visible=False,
-                    interactive=True,
-                )
-            sid.change(fn=vc.get_vc, inputs=[sid], outputs=[spk_item])
-            gr.Markdown(
-                value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
-            )
-            vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
-            vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0)
-            f0method0 = gr.Radio(
-                label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
-                choices=["pm", "harvest", "crepe", "rmvpe"],
-                value="pm",
-                interactive=True,
-            )
-            filter_radius0 = gr.Slider(
-                minimum=0,
-                maximum=7,
-                label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
-                value=3,
-                step=1,
-                interactive=True,
-            )
-            with gr.Column():
-                file_index1 = gr.Textbox(
-                    label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
-                    value="",
-                    interactive=False,
-                    visible=False,
-                )
-            file_index2 = gr.Dropdown(
-                label=i18n("自动检测index路径,下拉式选择(dropdown)"),
-                choices=sorted(index_paths),
-                interactive=True,
-            )
-            index_rate1 = gr.Slider(
-                minimum=0,
-                maximum=1,
-                label=i18n("检索特征占比"),
-                value=0.88,
-                interactive=True,
-            )
-            resample_sr0 = gr.Slider(
-                minimum=0,
-                maximum=48000,
-                label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
-                value=0,
-                step=1,
-                interactive=True,
-            )
-            rms_mix_rate0 = gr.Slider(
-                minimum=0,
-                maximum=1,
-                label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
-                value=1,
-                interactive=True,
-            )
-            protect0 = gr.Slider(
-                minimum=0,
-                maximum=0.5,
-                label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
-                value=0.33,
-                step=0.01,
-                interactive=True,
-            )
-            f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
-            but0 = gr.Button(i18n("转换"), variant="primary")
-            vc_output1 = gr.Textbox(label=i18n("输出信息"))
-            vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
-            but0.click(
-                vc.vc_single,
-                [
-                    spk_item,
-                    vc_input3,
-                    vc_transform0,
-                    f0_file,
-                    f0method0,
-                    file_index1,
-                    file_index2,
-                    # file_big_npy1,
-                    index_rate1,
-                    filter_radius0,
-                    resample_sr0,
-                    rms_mix_rate0,
-                    protect0,
-                ],
-                [vc_output1, vc_output2],
-            )
-
-
-app.launch()
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Dolphin Browser For Windows 7 32bit Iso 32.md b/spaces/raedeXanto/academic-chatgpt-beta/Dolphin Browser For Windows 7 32bit Iso 32.md
deleted file mode 100644
index 9cf8dea82bc8b102df572bbc00ae2412efbac1b5..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Dolphin Browser For Windows 7 32bit Iso 32.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-<br />
-<h1>Dolphin Browser: A Fast, Smart and Personal Web Browser for Android and iOS Devices</h1>
-<p>If you are looking for an alternative web browser for your Android or iOS smartphone or tablet, you may want to try Dolphin Browser. Dolphin Browser is a free, fast, smart and personal web browser that offers many features and benefits that make it stand out from other browsers. In this article, we will show you what Dolphin Browser is, why you should use it, how to install and set up it, how to use it, and some tips and tricks that will help you get the most out of it. We will also answer some frequently asked questions about Dolphin Browser.</p>
-<h2>Dolphin Browser For Windows 7 32bit Iso 32</h2><br /><p><b><b>Download File</b> >>> <a href="https://tinourl.com/2uL5pB">https://tinourl.com/2uL5pB</a></b></p><br /><br />
- <h2>What is Dolphin Browser and why use it?</h2>
-<p>Dolphin Browser is a web browser that was developed by MoboTap Inc. in 2010. It was one of the first browsers to introduce support for multi-touch gestures on mobile devices, allowing users to interact with the browser in a more intuitive way. Dolphin Browser also has a unique user interface design that includes a speed dial page, a sidebar, a menu button, a dolphin key, a sonar button, and a full-screen mode. These features allow users to access their favorite websites, bookmarks, settings, add-ons, tools, and more with just a few taps or swipes.</p>
-<p>Dolphin Browser also has many features and benefits that make it a great choice for mobile browsing. Some of these features are:</p>
-<ul>
-<li><strong>Gesture controls:</strong> You can create custom gestures for your favorite websites or browser actions. For example, you can draw a "T" on the screen to open Twitter, or draw an arrow up or down to scroll the page.</li>
-<li><strong>Voice search:</strong> You can shake your phone or tap the sonar button to activate voice search. You can then speak to Dolphin Browser to search for anything online, share content, open new tabs, or perform other browser actions.</li>
-<li><strong>Tabbed browsing:</strong> You can open multiple web pages in separate tabs and switch between them easily. You can also preview the tabs by swiping from the right edge of the screen.</li>
-<li><strong>Sync support:</strong> You can sync your bookmarks, history, passwords, and open tabs across your Android or iOS devices using Dolphin Connect. You can also sync your data with your desktop browsers using Chrome or Firefox extensions.</li>
-<li><strong>Flash content:</strong> If you are using Dolphin Browser on Android devices, you can view flash content on websites that require it. You just need to install the Adobe Flash Player app on your device.</li>
-<li><strong>Add-ons support:</strong> You can enhance your browsing experience by installing add-ons for Dolphin Browser. There are many add-ons available for different purposes, such as ad-blocking, PDF saving, text-only mode, screen capture, QR code scanner, etc.</li>
-</ul>
-<p>These are just some of the features and benefits of Dolphin Browser. There are many more that you can discover by using it yourself.</ <h2>How to install and set up Dolphin Browser</h2>
-<p>Installing and setting up Dolphin Browser is very easy and simple. You just need to follow these steps:</p>
-<ol>
-<li><strong>Download Dolphin Browser for your device:</strong> You can download Dolphin Browser for free from the Google Play Store or the App Store, depending on your device. You can also scan the QR code below to download it directly.</li>
-<img src="https://www.dolphin.com/wp-content/uploads/2018/01/qr-code.png" alt="QR code for Dolphin Browser" width="200" height="200">
-<li><strong>Launch Dolphin Browser and accept the terms of service:</strong> Once you have downloaded and installed Dolphin Browser, you can launch it by tapping on its icon. You will then see a welcome screen that asks you to accept the terms of service and privacy policy. Tap on "Agree" to continue.</li>
-<li><strong>Sign in or create a Dolphin account:</strong> If you want to sync your data across devices, you need to sign in or create a Dolphin account. You can do this by tapping on the dolphin key at the bottom of the screen and then tapping on "Dolphin Connect". You can sign in with your Facebook, Google, or Twitter account, or create a new account with your email and password.</li>
-<li><strong>Customize your Dolphin Browser settings and preferences:</strong> You can customize your Dolphin Browser settings and preferences by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". You can change various options, such as your homepage, search engine, theme, font size, gesture settings, sonar settings, sync settings, add-on settings, etc.</li>
-</ol>
-<p>Congratulations! You have successfully installed and set up Dolphin Browser on your device. Now you are ready to use it and enjoy its features and benefits.</p>
- <h2>How to use Dolphin Browser</h2>
-<p>Using Dolphin Browser is very easy and fun. You can use it to browse the web, search for anything online, access your favorite websites, view flash content, install add-ons, and more. Here are some of the things you can do with Dolphin Browser:</p>
-<p></p>
- <h3>How to use gesture controls to navigate and access websites</h3>
-<p>Dolphin Browser allows you to use gesture controls to navigate and access websites. You can create custom gestures for your favorite websites or browser actions. For example, you can draw a "T" on the screen to open Twitter, or draw an arrow up or down to scroll the page. To use gesture controls, you need to do the following:</p>
-<ol>
-<li><strong>Enable gesture settings:</strong> You can enable gesture settings by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". Then tap on "Gesture & Sonar" and make sure that "Gesture" is turned on.</li>
-<li><strong>Create custom gestures:</strong> You can create custom gestures by tapping on the dolphin key at the bottom of the screen and then tapping on "Gesture". Then tap on the "+" button at the top right corner of the screen and draw your gesture on the screen. Then choose a website or a browser action that you want to associate with your gesture. You can also edit or delete your existing gestures by tapping on them.</li>
-<li><strong>Use gestures:</strong> You can use gestures by tapping on the dolphin key at the bottom of the screen and then tapping on "Gesture". Then draw your gesture on the screen and release it. Dolphin Browser will then perform the corresponding website or browser action.</li>
-</ol>
-<p>Gestures are a great way to browse the web faster and easier with Dolphin Browser.</p>
- <h3>How to use voice search to find anything online</h3>
-<p>Dolphin Browser allows you to use voice search to find anything online. You can shake your phone or tap the sonar button to activate voice search. You can then speak to Dolphin Browser to search for anything online, share content, open new tabs, or perform other browser actions. To use voice search, you need to do the following:</p>
-<ol>
-<li><strong>Enable sonar settings:</strong> You can enable sonar settings by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". Then tap on "Gesture & Sonar" and make sure that "Sonar" is turned on.</li>
-<li><strong>Activate voice search:</strong> You can activate voice search by shaking your phone or tapping on the sonar button at the bottom left corner of the screen. You will then see a microphone icon appear on the screen.</li>
-<li><strong>Speak to Dolphin Browser:</strong> You can speak to Dolphin Browser by saying what you want to search for online, such as "weather in New York" or "how to make pizza". You can also say commands such as "share this page on Facebook" or "open a new tab". Dolphin Browser will then perform the corresponding search or action.</li>
-</ol>
-<p>Voice search is a great way to find anything online faster and easier with Dolphin Browser.</p>
- <h3>How to use tabbed browsing to switch between web pages</h3>
-<p>Dolphin Browser allows you to use tabbed browsing to switch between web pages. You can open multiple web pages in separate tabs and switch between them easily. You can also preview the tabs by swiping from the right edge of the screen. To use tabbed browsing, you need to do the following:</p>
-<ol>
-<li><strong>Open a new tab:</strong> You can open a new tab by tapping on the "+" button at the top right corner of the screen. You can also open a new tab by long-pressing on a link and choosing "Open in new tab" from the menu.</li>
-<li><strong>Switch between tabs:</strong> You can switch between tabs by tapping on the tab icon at the top left corner of the screen. You will then see a list of your open tabs. You can tap on any tab to switch to it. You can also swipe left or right on the screen to switch to the previous or next tab.</li>
-<li><strong>Preview tabs:</strong> You can preview tabs by swiping from the right edge of the screen. You will then see a thumbnail of each tab. You can tap on any thumbnail to switch to that tab.</li>
-<li><strong>Close tabs:</strong> You can close tabs by tapping on the "x" button on each tab. You can also close all tabs by tapping on the menu button at the bottom right corner of the screen and then tapping on "Close all tabs".</li>
-</ol>
-<p>Tabbed browsing is a great way to multitask and browse the web more efficiently with Dolphin Browser.</p>
- <h3>How to use sync support to access your bookmarks and history across devices</h3>
-<p>Dolphin Browser allows you to use sync support to access your bookmarks and history across devices. You can sync your data with your Android or iOS devices using Dolphin Connect. You can also sync your data with your desktop browsers using Chrome or Firefox extensions. To use sync support, you need to do the following:</p>
-<ol>
-<li><strong>Sign in or create a Dolphin account:</strong> You need to sign in or create a Dolphin account to use sync support. You can do this by tapping on the dolphin key at the bottom of the screen and then tapping on "Dolphin Connect". You can sign in with your Facebook, Google, or Twitter account, or create a new account with your email and password.</li>
-<li><strong>Choose what data you want to sync:</strong> You can choose what data you want to sync by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". Then tap on "Dolphin Connect" and make sure that "Sync" is turned on. Then tap on "Sync Data" and select what data you want to sync, such as bookmarks, history, passwords, and open tabs.</li>
-<li><strong>Access your data across devices:</strong> You can access your data across devices by signing in with your Dolphin account on each device. You will then see your synced data in Dolphin Browser. For example, you can see your bookmarks by tapping on the dolphin key at the bottom of the screen and then tapping on "Bookmarks".</li>
-<li><strong>Sync your data with your desktop browsers:</strong> You can sync your data with your desktop browsers by installing Chrome or Firefox extensions for Dolphin Browser. You can download them from here: <a href="https://chrome.google.com/webstore/detail/dolphin-connect/pajecklcmiegagoelbbjldmfcbcpdpll">Chrome extension</a>, <a href="https://addons.mozilla.org/en-US/firefox/addon/dolphinconnect/">Firefox extension</a>. You will then be able to access your Dolphin Browser data from your desktop browsers.</li>
-</ol>
-<p>Sync support is a great way to access your bookmarks and history across devices with Dolphin Browser.</p>
- <h3>How to view flash content on Android devices</h3>
-<p>If you are using Dolphin Browser on Android devices, you can view flash content on websites that require it. Flash content is a type of multimedia content that includes animations, videos, games, etc. To view flash content, you need to do the following:</p>
-<ol>
-<li><strong>Install Adobe Flash Player app:</strong> You need to install Adobe Flash Player app on your Android device to view flash content. You can download it from here: <a href="https://www.apkmirror.com/apk /adobe/adobe-flash-player/11-1-115-81-release/adobe-flash-player-11-1-11-1-115-81-android-apk-download/">Adobe Flash Player app</a>. You can also scan the QR code below to download it directly.</li>
-<img src="https://www.qrcode-monkey.com/img/default-preview-qr.svg" alt="QR code for Adobe Flash Player app" width="200" height="200">
-<li><strong>Enable flash settings:</strong> You need to enable flash settings in Dolphin Browser to view flash content. You can do this by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". Then tap on "Web Content" and make sure that "Flash Player" is set to "Always on" or "On demand".</li>
-<li><strong>View flash content:</strong> You can view flash content by visiting websites that have flash content. You will see a flash icon appear on the web page. You can tap on the icon to play the flash content. If you have set flash settings to "On demand", you will need to tap on the icon twice to play the flash content.</li>
-</ol>
-<p>Viewing flash content is a great way to enjoy multimedia content on Android devices with Dolphin Browser.</p>
- <h3>How to use add-ons to enhance your browsing experience</h3>
-<p>Dolphin Browser allows you to use add-ons to enhance your browsing experience. Add-ons are extensions or plugins that add extra features or functions to Dolphin Browser. There are many add-ons available for different purposes, such as ad-blocking, PDF saving, text-only mode, screen capture, QR code scanner, etc. To use add-ons, you need to do the following:</p>
-<ol>
-<li><strong>Install add-ons:</strong> You can install add-ons by tapping on the dolphin key at the bottom of the screen and then tapping on "Add-ons". Then tap on the "+" button at the top right corner of the screen and browse through the available add-ons. You can tap on any add-on to see its details and reviews. You can also search for add-ons by using the search bar at the top of the screen. To install an add-on, just tap on the "Install" button.</li>
-<li><strong>Manage add-ons:</strong> You can manage your installed add-ons by tapping on the dolphin key at the bottom of the screen and then tapping on "Add-ons". You will then see a list of your installed add-ons. You can tap on any add-on to enable or disable it, or to access its settings or options. You can also uninstall an add-on by long-pressing on it and choosing "Uninstall" from the menu.</li>
-<li><strong>Use add-ons:</strong> You can use your installed add-ons by tapping on their icons in the sidebar or in the menu. For example, you can use the ad-blocker add-on by tapping on its icon in the sidebar and then choosing "Block ads" from the menu. You can also use some add-ons by long-pressing on a web page and choosing their options from the menu. For example, you can use the PDF saving add-on by long-pressing on a web page and choosing "Save as PDF" from the menu.</li>
-</ol>
-<p>Using add-ons is a great way to enhance your browsing experience with Dolphin Browser.</p>
- <h2>Tips and tricks for Dolphin Browser</h2>
-<p>Dolphin Browser has many tips and tricks that will help you browse the web faster and easier. Here are some of them:</p>
- <h3>How to use speed dial and webzine for quick access to your favorite sites</h3>
-<p>Dolphin Browser has a speed dial page that allows you to quickly access your favorite sites with just one tap. You can also use webzine to view your favorite sites in a magazine-like format. To use speed dial and webzine, you need to do the following:</p>
-<ol>
-<li><strong>Add sites to speed dial:</strong> You can add sites to speed dial by tapping on the "+" button at the bottom of the speed dial page. Then you can choose a site from your bookmarks, history, or popular sites, or enter a URL manually. You can also edit or delete your existing speed dial sites by long-pressing on them and choosing "Edit" or "Delete" from the menu.</li>
-<li><strong>Access sites from speed dial:</strong> You can access sites from speed dial by tapping on their icons on the speed dial page. You will then open them in a new tab.</li>
-<li><strong>Switch between speed dial and webzine:</strong> You can switch between speed dial and webzine by swiping up or down on the speed dial page. Webzine is a feature that displays your favorite sites in a magazine-like format, with images and headlines. You can tap on any site to view its full content.</li>
-<li><strong>Add or remove sites from webzine:</strong> You can add or remove sites from webzine by tapping on the menu button at the bottom right corner of the webzine page. Then tap on "Edit" and select or deselect the sites that you want to add or remove. You can also search for sites by using the search bar at the top of the screen.</li>
-</ol>
-<p>Speed dial and webzine are great features that allow you to quickly access your favorite sites with Dolphin Browser.</p>
- <h3>How to use dolphin key and sonar for easy browsing</h3>
-<p>Dolphin Browser has a dolphin key and a sonar button that allow you to easily browse the web with gesture controls and voice search. The dolphin key is located at the bottom of the screen, and the sonar button is located at the bottom left corner of the screen. To use dolphin key and sonar, you need to do the following:</p>
-<ol>
-<li><strong>Use dolphin key:</strong> You can use dolphin key to access various features and options of Dolphin Browser, such as gesture, bookmarks, history, add-ons, settings, etc. You just need to tap on the dolphin key and then tap on the feature or option that you want to access.</li>
-<li><strong>Use sonar:</strong> You can use sonar to activate voice search and speak to Dolphin Browser to search for anything online, share content, open new tabs, or perform other browser actions. You just need to shake your phone or tap on the sonar button and then say what you want to do.</li>
-</ol>
-<p>Dolphin key and sonar are great tools that allow you to easily browse the web with Dolphin Browser.</p>
- <h3>How to use sidebar and menu for more options and tools</h3>
-<p>Dolphin Browser has a sidebar and a menu button that allow you to access more options and tools for your browsing experience. The sidebar is located at the left edge of the screen, and the menu button is located at the bottom right corner of the screen. To use sidebar and menu, you need to do the following:</p>
-<ol>
-<li><strong>Use sidebar:</strong> You can use sidebar to access your installed add-ons and their icons. You just need to swipe from the left edge of the screen or tap on the tab icon at the top left corner of the screen to open the sidebar. You can then tap on any add-on icon to use it.</li>
-<li><strong>Use menu:</strong> You can use menu to access more options and tools for Dolphin Browser, such as refresh, forward, back, full-screen mode, night mode, share, exit, etc. You just need to tap on the menu button at the bottom right corner of the screen and then tap on the option or tool that you want to use.</li>
-</ol>
-<p>Sidebar and menu are great features that allow you to access more options and tools with Dolphin Browser.</p>
- <h3>How to use ad-blocker and power saver for faster and safer browsing</h3>
-<p>Dolphin Browser has an ad-blocker and a power saver feature that allow you to browse the web faster and safer. The ad-blocker feature blocks annoying ads from websites, while the power saver feature reduces battery consumption by optimizing web pages. To use ad-blocker and power saver, you need to do the following:</p>
-<ol>
-<li><strong>Enable ad-blocker settings:</strong> You can enable ad-blocker settings by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". Then tap on "AdBlock" and make sure that "AdBlock" is turned on. You can also choose what types of ads you want to block, such as pop-ups, banners, video ads, etc.</li>
-<li><strong>Enable power saver settings:</strong> You can enable power saver settings by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". Then tap on "Power Saver" and make sure that "Power Saver" is turned on. You can also choose what level of optimization you want, such as low, medium, or high.</li>
-<li><strong>Browse the web faster and safer:</strong> You can browse the web faster and safer by using Dolphin Browser with ad-blocker and power saver features enabled. You will see less ads and more battery life on your device.</li>
-</ol>
-<p>Ad-blocker and power saver are great features that allow you to browse the web faster and safer with Dolphin Browser.</p>
- <h2>Conclusion</h2>
-<p>Dolphin Browser is a fast, smart and personal web browser for Android and iOS devices. It has many features and benefits that make it a great choice for mobile browsing, such as gesture controls, voice search, tabbed browsing, sync support, flash content, add-ons support, and more. It also has many tips and tricks that will help you browse the web faster and easier, such as speed dial, webzine, dolphin key, sonar, sidebar, menu, ad-blocker, power saver, and more. Dolphin Browser is a free app that you can download from the Google Play Store or the App Store. You can also visit the official website of Dolphin Browser for more information and support.</p>
-<p>If you are looking for an alternative web browser for your Android or iOS device, you may want to try Dolphin Browser. It is a web browser that will make your browsing experience more enjoyable and convenient. Download Dolphin Browser today and see for yourself!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Dolphin Browser:</p>
-<h4>Q: Is Dolphin Browser safe to use?</h4>
-<p>A: Yes, Dolphin Browser is safe to use. It has a built-in ad-blocker that blocks annoying ads from websites, as well as a power saver feature that reduces battery consumption by optimizing web pages. It also has a private browsing mode that allows you to browse the web without leaving any traces of your activity. You can also clear your browsing data anytime by tapping on the menu button at the bottom right corner of the screen and then tapping on "Clear data".</p>
-<h4>Q: How can I update Dolphin Browser?</h4>
-<p>A: You can update Dolphin Browser by visiting the Google Play Store or the App Store and checking for updates. You can also enable automatic updates by tapping on the menu button at the bottom right corner of the screen and then tapping on "Settings". Then tap on "About" and make sure that "Auto-update" is turned on.</p>
-<h4>Q: How can I contact Dolphin Browser support?</h4>
-<p>A: You can contact Dolphin Browser support by visiting the official website of Dolphin Browser and clicking on "Support". You can also send an email to support@dolphin.com or follow Dolphin Browser on Facebook or Twitter for updates and feedback.</p>
-<h4>Q: How can I share my feedback or suggestions for Dolphin Browser?</h4>
-<p>A: You can share your feedback or suggestions for Dolphin Browser by tapping on the menu button at the bottom right corner of the screen and then tapping on "Feedback". You can then rate Dolphin Browser, write a review, or send an email to feedback@dolphin.com.</p>
-<h4>Q: How can I uninstall Dolphin Browser?</h4>
-<p>A: You can uninstall Dolphin Browser by long-pressing on its icon on your device's home screen and choosing "Uninstall" from the menu. You can also uninstall it by visiting the Google Play Store or the App Store and choosing "Uninstall" from the options.</p> b2dd77e56b<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Elephorm Apprendre Html5 Et Css3 Crack [EXCLUSIVE] Murphy Trabajadores.md b/spaces/raedeXanto/academic-chatgpt-beta/Elephorm Apprendre Html5 Et Css3 Crack [EXCLUSIVE] Murphy Trabajadores.md
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-<p>The course consists of 40 video tutorials that last for more than eight hours in total. You will learn how to use HTML5 elements and attributes, how to style your web pages with CSS3 properties and selectors, how to use web fonts, how to create animations and transitions, how to use media queries and responsive design, how to use flexbox and grid layouts, how to use SVG graphics and canvas elements, how to use geolocation and local storage APIs, and much more. You will also get access to the source files and exercises that accompany each lesson.</p>
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-<p>Finally, if you want to take your web design career to the next level, you might want to consider getting certified or taking online courses from reputable platforms and institutions. You can find a variety of courses on web development topics on websites such as <a href="https://www.udemy.com/">Udemy</a>, <a href="https://www.coursera.org/">Coursera</a>, or <a href="https://www.edx.org/">edX</a>. You can also get certified by taking exams from organizations such as <a href="https://certification.w3schools.com/w3certified.asp">W3Schools</a>, <a href="https://www.freecodecamp.org/certification">freeCodeCamp</a>, or <a href="https://certification.w3.org/">W3C</a>.</p>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/FMRTE V 5.2.4 LICENSE KEY.rar Download and Activate the Best Football Manager Editor.md b/spaces/raedeXanto/academic-chatgpt-beta/FMRTE V 5.2.4 LICENSE KEY.rar Download and Activate the Best Football Manager Editor.md
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-<h1>What is FMRTE V 5.2.4 LICENSE KEY.rar and why do you need it?</h1>
-<p>If you are a fan of Football Manager, you probably know how frustrating it can be when you want to change something in your game but you can't because it's too hard or too risky. Maybe you want to edit a player's attributes, transfer a star player to your club, increase your budget, or fix a bug that ruins your game experience.</p>
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-<p>That's where <strong>FMRTE</strong> comes in handy. FMRTE stands for <strong>Football Manager Real Time Editor</strong>, and it's a tool that allows you to edit and customize your Football Manager game in real time.</p>
-<p>With FMRTE, you can do almost anything you want with your game, such as:</p>
-<ul>
-<li>Change any player's attributes, skills, traits, relationships, contracts, etc.</li>
-<li>Edit any club's information, finances, reputation, facilities, staff, etc.</li>
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-<p>FMRTE is not only a powerful editor, but also a user-friendly one. It has a simple and intuitive interface that makes it easy to use for anyone, regardless of their level of experience or knowledge.</p>
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-<p>Before you download and install FMRTE V 5.2.4 LICENSE KEY.rar, you need to make sure that your system meets the minimum requirements for running FMRTE:</p>
-<ul>
-<li>A Windows operating system (Windows XP SP3 or later)</li>
-<li>A .NET Framework version (3.5 SP1 or later)</li>
-<li>A Football Manager game (Football Manager 2013)</li>
-</ul>
-<p>If your system meets these requirements, you can follow these steps to download and install FMRTE V 5.2.4 LICENSE KEY.rar:</p>
-<ol>
-<li>Go to this link and click on "Download".</li>
-<li>Save the file (FMRTE_V_524_LICENSE_KEY.rar) on your computer.</li>
-<li>Extract the file using a program like WinRAR or WinZip.</li>
-<li>Open the extracted folder and double-click on "Fmrte.exe".</li>
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-<p>Here are some tips on how to avoid errors and problems during the installation process:</p>
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-<li>Make sure that your antivirus software does not block or delete any files from FMRTE.</li>
-<li>Make sure that you have enough disk space on your computer for installing FMRTE.</li>
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-</ul>
-<p>However, not all these methods are safe or legal.</p>
-<p>Purchasing a license key from the official website of FMRTE is the best and safest option to activate FMRTE V 5.2.4 LICENSE KEY.rar.</p>
-<p>This way, you will support the developer of FMRTE and get access to all its features without any restrictions or risks.</p>
-<p>To purchase a license key from the official website of FMRTE, you need to:</p>
-<ol>
-<li>Create an account or log in with an existing one.</li>
-<li>Select the version of FMRTE that you want to buy (FMRTE V 5.2).</li>
-<li>Select the payment method that suits you (PayPal or Credit Card).</li>
-<li>Complete the payment process and receive your license key by email.</li>
-<li>Enter your license key in FMRTE and click on "Activate".</li>
- </ol>
- <p>The price of a license key for FMRTE V 5.2 is only €3 (or equivalent in other currencies).</p>
- <p>If you choose to use a crack or a patch that bypasses the activation process, you may face some serious risks and consequences:</p>
- <ul>
- <li>You may download a file that contains viruses or malware that can harm your computer or steal your personal information.</li>
- <li>You may violate the terms of service of Football Manager or Steam and get banned from playing online or accessing updates.</li>
- <li>You may lose access to some features or functions of FMRTE or Football Manager due to compatibility issues or bugs.</li>
- <li>You may face legal actions from the developer of FMRTE or Football Manager for infringing their intellectual property rights.</li>
- </ul>
- <p>If you choose to use a trial version that lasts for a limited period of time (usually one month), you may enjoy some features of FMRTE for free but with some .</li>
- <li>If you want to freeze or unfreeze any value in the game, such as a player's attributes, a club's finances, a competition's results, etc., you can go to the "Freezer" tab and select the item that you want to freeze or unfreeze. Then you can click on the "Freeze" or "Unfreeze" button and confirm your action.</li>
- </ul>
- <p>Here are some precautions that you should take before using FMRTE V 5.2.4 LICENSE KEY.rar:</p>
- <ul>
- <li>Backup your game files before using FMRTE, in case something goes wrong or you want to revert your changes.</li>
- <li>Use FMRTE responsibly and ethically, and do not abuse its power to ruin your game or other players' games.</li>
- <li>Do not use FMRTE while playing online or in multiplayer mode, as it may cause errors or conflicts with other players or servers.</li>
- <li>Do not use FMRTE to edit or customize items that are not supported by FMRTE, such as graphics, sounds, skins, etc., as it may cause crashes or glitches.</li>
- </ul>
- <h1>Conclusion</h1>
- <p>FMRTE V 5.2.4 LICENSE KEY.rar is a file that contains the license key for FMRTE V 5.2.4, which is the version of FMRTE that works with Football Manager 2013.</p>
- <p>FMRTE is a tool that allows you to edit and customize your Football Manager game in real time, and it has many features and benefits that can enhance your game experience.</p>
- <p>To download and install FMRTE V 5.2.4 LICENSE KEY.rar, you need to meet the minimum system requirements and follow the steps provided in this article.</p>
- <p>To activate FMRTE V 5.2.4 LICENSE KEY.rar, you need to purchase a license key from the official website of FMRTE and enter it in FMRTE.</p>
- <p>To use FMRTE V 5.2.4 LICENSE KEY.rar, you need to load your game data and find the item that you want to edit or customize using the tabs and menus of FMRTE.</p>
- <p>If you are a fan of Football Manager and you want to have more control and fun with your game, you should try FMRTE V 5.2.4 LICENSE KEY.rar for yourself and see what it can do for you.</p>
- <p>We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h3>FAQs</h3>
- <p>Here are some answers to some common questions about FMRTE V 5.2.4 LICENSE KEY.rar:</p>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>What is the difference between FMRTE and FM Editor?</td>
-<td>FMRTE is a real time editor that allows you to edit and customize your game while it is running. FM Editor is an official tool that allows you to create or modify databases before starting a new game.</td>
-</tr>
-<tr>
-<td>Is FMRTE safe to use?</td>
-<td>FMRTE is safe to use if you download it from the official website of FMRTE and activate it with a valid license key. However, if you download it from other sources or use illegal methods to activate it, you may expose yourself to viruses, malware, bans, legal actions, etc.</td>
-</tr>
-<tr>
-<td>Can I use FMRTE with other versions of Football Manager?</td>
-<td>No, each version of FMRTE is designed to work with a specific version of Football Manager. For example, FMRTE V 5.2.4 works with Football Manager 2013 only. If you want to use FMRTE with other versions of Football Manager, you need to download and activate the corresponding version of FMRTE.</td>
-</tr>
-<tr>
-<td>Can I use FMRTE on Mac or Linux?</td>
-<td>No, FMRTE is only available for Windows operating systems. However, there is an alternative tool called iFMRTE that works on Mac operating systems.</td>
-</tr>
-<tr>
-<td>Can I get a refund if I don't like FMRTE?</td>
-<td>No, once you purchase a license key for FMRTE, you cannot get a refund or cancel your purchase. However, you can try FMRTE for free for a limited period of time before buying a license key.</td>
-</tr>
-</table>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rahgadda/MigrationUtility/README.md b/spaces/rahgadda/MigrationUtility/README.md
deleted file mode 100644
index faf165442ca3157d9b65f88c1f41d9885fa1792a..0000000000000000000000000000000000000000
--- a/spaces/rahgadda/MigrationUtility/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MigrationUtility
-emoji: 🏢
-colorFrom: pink
-colorTo: red
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: Dashboard.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Astro Office 2012 Software With Crack !!LINK!! 85.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Astro Office 2012 Software With Crack !!LINK!! 85.md
deleted file mode 100644
index fdaf723de5056557f15b18f86c5219deb8f9e576..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Astro Office 2012 Software With Crack !!LINK!! 85.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<br />
-<p>in the long term, he thinks that this decision is the right move for his business, and given that he is starting from scratch on a platform that he is building from scratch, he has more flexibility and better data, and can iterate much more rapidly than he otherwise could have, he said.</p>
-<p>riley hopes that the clients he chooses will spread not just their own positive experiences with video, but a greater understanding of the technology, and that more developers will think about using it to target their own audience.</p>
-<h2>astro office 2012 software with crack 85</h2><br /><p><b><b>Download</b> &#128279; <a href="https://urlgoal.com/2uCJLJ">https://urlgoal.com/2uCJLJ</a></b></p><br /><br />
-<p>like classic cloud 3d art from the usa, the plan, which offers a portfolio of experiences ranging from 50 to 500 minutes long, has found its niche with the facebook community. riley says that over 2 million people have already signed up and logged into the community since the platform launched, and that theres been a consistent rate of growth.</p>
-<p>how its participants interact with one another, who they also interact with, and when its best to contact them, is very much up to them, he says. riley hopes to appeal to a broad audience of people by diversifying the social and economic backgrounds of his participants.</p>
-<p>in a study released earlier this week, forbes covers the reasons why your facebook page is unpopular . from the post: <em>in a study released earlier this week, forbes covers the reasons why your facebook page is unpopular . from the post:</em></p>
-<p>1. we’re mobile-carrier dependent. verizon wireless. in retrospect, there is no difference between verizon wireless and facebook in this context. to succeed in that kind of environment requires any number of resources. one of them is clearly significant outside financing, which wed explicitly chosen to do without. we could have and should have used the proceeds of the convertible note to get out from under facebooks thumb rather to invest further in the facebook platform.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Basic Inventory Control V5.0.135 With Key [TorDigger] Setup Free.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Basic Inventory Control V5.0.135 With Key [TorDigger] Setup Free.md
deleted file mode 100644
index 0c22e33541503687846891cc60d0e278fbf99248..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Basic Inventory Control V5.0.135 With Key [TorDigger] Setup Free.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-<h1>Basic Inventory Control v5.0.135: A Handy Tool for Managing Products</h1>
-<p>Basic Inventory Control (BIC) is a software program that helps you track and manage your inventory of products. Whether you are a small business owner, a warehouse manager, or a hobbyist, BIC can help you keep track of your orders, receipts, shipments, allocations, and shrinkage. You can also print out reports of your current inventory, out of stock products, and inventory transactions for any period of time.</p>
-<h2>Basic Inventory Control v5.0.135 with Key [TorDigger] setup free</h2><br /><p><b><b>DOWNLOAD</b> &#8230; <a href="https://urlgoal.com/2uCMJ5">https://urlgoal.com/2uCMJ5</a></b></p><br /><br />
-<p>BIC is easy to use and customize. You can create your own product list and categories, or import them from a Microsoft Excel file. You can also change the look-up lists and column headings to suit your preferences. BIC supports multiple users and can export data to Excel for further analysis. BIC also features extensive online help, a tutorial, and a support website.</p>
-<p>If you want to try BIC for yourself, you can download a free trial version from the developer's website[^1^]. The trial version is fully functional for 30 days and allows you to track up to 100 products. If you want to unlock the full potential of BIC, you can purchase a license key for $99 USD. The license key will allow you to track an unlimited number of products and remove the trial expiration date.</p>
-<p>BIC is compatible with Windows XP, Vista, 7, 8, Server 2003, and Server 2008. It requires at least 512 MB of RAM and 10 MB of disk space. BIC is a reliable and affordable solution for your inventory management needs.</p>
-<p></p><p>To install BIC on your computer, you need to follow these steps:</p>
-<ol>
-<li>Download the setup file from the developer's website or from a trusted source. Make sure you download the correct version for your operating system.</li>
-<li>Run the setup file and follow the instructions on the screen. You may need to accept the license agreement and choose a destination folder for the program files.</li>
-<li>When the installation is complete, you can launch BIC from the Start menu or from the desktop shortcut. You will be prompted to enter your license key if you have purchased one. If not, you can use the trial version for 30 days.</li>
-<li>Once you have entered your license key or started the trial, you can create a new inventory database or open an existing one. You can also import your product list and beginning inventory from an Excel file if you have one.</li>
-<li>You are now ready to use BIC to manage your inventory. You can add new products, edit existing ones, record orders, receipts, shipments, allocations, and shrinkage, and generate reports. You can also customize the program settings and preferences to suit your needs.</li>
-</ol>
-<p>BIC has many advantages over other inventory software. Some of them are:</p>
-<ul>
-<li>BIC is simple and intuitive. You don't need any special training or skills to use it. You can learn how to use it by following the tutorial or reading the online help.</li>
-<li>BIC is flexible and customizable. You can create your own product categories and subcategories, change the look-up lists and column headings, and adjust the program settings and preferences. You can also export and import data to and from Excel for further analysis or backup.</li>
-<li>BIC is affordable and reliable. You only need to pay a one-time fee of $99 USD for a lifetime license that allows you to track an unlimited number of products. You also get free updates and technical support from the developer. BIC does not require any internet connection or subscription fees.</li>
-</ul>
-<p>If you want to add some images to the article, you can request me to generate them for you. For example, you can ask me to draw a screenshot of BIC's main window or a chart of your inventory levels. Just make sure you specify what kind of image you want and how you want it to look like.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Braham Kavach Bani Pdf Download.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Braham Kavach Bani Pdf Download.md
deleted file mode 100644
index 2c435dd30fc2bfd4c6ca509c947916b8d7764e50..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Braham Kavach Bani Pdf Download.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p>apkfab is the biggest website for downloading apps & games for android phones and tablets. we have android app of great free and paid apk files of android games, android apk mod, android apk patch, android mods and android themes.</p>
-<h2>Braham Kavach Bani Pdf Download</h2><br /><p><b><b>Download</b> &#10037; <a href="https://urlgoal.com/2uCKna">https://urlgoal.com/2uCKna</a></b></p><br /><br />
-<p>this is just the way that i understand how the universe works and it doesn't make sense to me to go against or counter-cross natural energy fields.good luck to sadhgurufella,..but i just follow my interpretations of gurbani whenever possible. </p>
-<p>brahm kavach is the only website and blog which deliver hukamnamah from sri darbar sahib, harmandir sahib (golden temple, sri amritsar sahib), gurbani videos, facts and articles on sikh religion, books in pdf format related to sikh religion and its history.</p>
-<p>brahm kavach is the only website and blog which deliver hukamnamah from sri darbar sahib, harmandir sahib (golden temple, sri amritsar sahib), transliteration of guru granth sahib, gurbani videos, facts and articles on sikh religion, books in pdf format related to sikh religion and its history.</p>
-<p></p>
-<p>i just read the first verse in brahm kavach where it says 'brahm kavi kavi, brahm kav-o kav sach sach, bahm kavi kavi, brahm kavo kavi kavi' - as far as i understand, he is asking us to recite the above verse daily to attain peace. is this correct?</p>
-<p> therefore, if you see even one bania as being out of context then you should continue, otherwise its better to stop. i am also confused by people who say that the esoteric bani are one and the same as the banis of the peopels, even though their contexts and purposes are completely different. </p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Building Design And Construction Vicente Tagayun LINK.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Building Design And Construction Vicente Tagayun LINK.md
deleted file mode 100644
index 0938b1416372d88d43b67aa4ab1b8b21ae842911..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Building Design And Construction Vicente Tagayun LINK.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<h2>building design and construction vicente tagayun</h2><br /><p><b><b>DOWNLOAD</b> &#10084; <a href="https://urlgoal.com/2uCN3k">https://urlgoal.com/2uCN3k</a></b></p><br /><br />
-
-The book is written in two parts. Part one consists of technical chapters covering a wide range of topics related to the design, construction and analysis of building. This is followed by chapters designed for practitioners and researchers who wish to further refine and extend their knowledge of building construction. The second part covers issues that are more the domain of the architect such as the role of building services in building development and design. The book is illustrated with some 50 line drawings and 3D models.
-
-The book is written in plain language, mostly in single-line paragraphs, but occasionally for emphasis. It is intended for use by engineering, architectural and construction professionals. The topics covered are broadly classified into four major areas: Building Construction and Services, Building Fabric (Foam and Glue Bricks), Building Envelopes and Building Analysis. However, each chapter introduces many topics and contains many sub-topics that are not covered in this book.
-
-The chapters are organized into three sections. Part 1 consists of chapters that are directly applicable to the construction of any building or structure. This includes chapters on the various construction methods, different types of construction materials, building systems, analysis of building constructions, and use of building construction drawings.
-
-Part 2 consists of chapters that are directly relevant to the architect. These include chapters on building design, building services, and building control.
-
-Part 3 contains chapters on building analysis and construction cost estimating, site preparation and cost factors, life-cycle costing, and basic construction cost estimating.
-
-The book begins with an introduction to the construction of building by providing a basis for understanding different types of construction. It then looks at different types of construction materials and techniques and the problems encountered in the construction of different building types. It next covers the general building analysis process. It then turns to the individual types of construction analysis and the different kinds of analysis that are carried out. It then looks at the main types of construction cost estimating and gives an overview of various building cost analysis techniques. The book then moves to the costing of building construction and the relationship between the costs of materials and services and building construction costs. It covers the various types of building cost estimation techniques that are used to determine the cost of construction. It then covers life-cycle costing and estimating. It ends with an overview of cost factors. The book ends with a chapter on the construction process.
-
-This book contains a very good number of photographs illustrating the various steps of construction, a large number of line 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_sgd_600e.py b/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_sgd_600e.py
deleted file mode 100644
index ed57b422ded5d302f758ff570187e7b1db809adf..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_sgd_600e.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=1e-3, momentum=0.99, weight_decay=5e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[200, 400])
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=600)
-checkpoint_config = dict(interval=100)
diff --git a/spaces/rodolfoocampo/InfiniteStories/app.py b/spaces/rodolfoocampo/InfiniteStories/app.py
deleted file mode 100644
index 8fa2434a25d27fd920daecf5a20e8198c14363f8..0000000000000000000000000000000000000000
--- a/spaces/rodolfoocampo/InfiniteStories/app.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import numpy as np
-import os
-import gradio as gr
-import openai
-import gspread
-from oauth2client.service_account import ServiceAccountCredentials
-
-# Define the credentials to access the Google Sheets API
-SCOPE = ["https://www.googleapis.com/auth/drive"]
-SERVICE_ACCOUNT_FILE = "./check"
-
-os.environ['GOOGLE_SHEETS_TOKEN']
-
-# os.environ['GOOGLE_SHEETS_TOKEN']
-creds = ServiceAccountCredentials.from_json_keyfile_name(SERVICE_ACCOUNT_FILE, SCOPE)
-client = gspread.authorize(creds)
-sheet = client.open("Feedback").sheet1
-
-
-def continueStory(blank, messageHistory):
-
-    openai.api_key = os.environ['OPENAI_KEY']
-
-    messageHistory.append({"role": "user", "content": blank + "\n\nGenerate the next paragraph with a blank in the last line for me to fill. There should only be one and only one blank in the paragraph. The blank should be at the end of the last line of the paragraph. It is very importat that there is only one blank and that it is at the end!"})
-  
-    response = openai.ChatCompletion.create(
-      model="gpt-3.5-turbo",
-      messages = messageHistory
-    )
-    
-    story = response["choices"][0]["message"]["content"]
-    messageHistory.append(response["choices"][0]["message"])
-    
-    return story, messageHistory
-
-
-
-
-with gr.Blocks(css='''
-  .gradio-container {
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    align-items: center;
-    background-image: url('file=https://cdn.discordapp.com/attachments/941582479117127680/1080730421425344542/rodotcom_colorful_abstract_illustration_for_the_background_of_c_f1b331d7-6493-4a33-9063-345d31a66ddb.png');
-    
-  }
-
-
-  h1 {
-    font-size: 3rem;
-    font-weight: 700;
-    margin-top: 1rem;
-    margin-bottom: 1rem;
-    color: white;
-  }
-  p {
-    font-size: 1.25rem;
-    margin-bottom: 2rem;
-    color: white;
-  }
-  label {
-    font-size: 1.25rem;
-    font-weight: 500;
-    margin-bottom: 0.5rem;
-  }
-  input[type="text"], textarea {
-    font-size: 1.25rem;
-    padding: 0.5rem;
-    border: 2px solid #ccc;
-    border-radius: 5px;
-    width: 100%;
-  }
-  .gradio-button {
-    background-color: #2196f3;
-    color: #fff;
-    font-size: 1.25rem;
-    font-weight: 500;
-    padding: 0.75rem 1.5rem;
-    border-radius: 5px;
-    cursor: pointer;
-    transition: all 0.2s ease-in-out;
-  }
-  .gradio-button:hover {
-    background-color: #0c7cd5;
-  }
-  
-''') as demo:
-    title = gr.HTML('''
-        <div style="text-align: center;">
-            <h1 style="color: white; font-size: 3rem; font-weight: 700; margin-bottom: 1rem; color: white;">Infinite Stories</h1>
-            <p style="color: white; font-size: 1.25rem; margin-bottom: 2rem; color: white;">Let's see where your imagination can take you!</p>
-        </div>
-    ''')
-    with gr.Row(elem_id='row'):        
-      character = gr.Textbox(label='Who will be the main character?', elem_id = 'theme', placeholder="i.e. Diego, an 11-year-old pirate")
-      
-    messageHistory = gr.State()
-
-    beginBtn = gr.Button("Start!", elem_id="generate-btn")
-
-    story_output = gr.Textbox(label='Story')
-
-    blank = gr.Textbox(label='How will you fill the blank?', elem_id = 'blank')
-
-    with gr.Column(visible=True) as continueStoryCol:
-      continueBtn = gr.Button("Continue Story", elem_id="continue-btn") 
-
-    with gr.Column(visible=False) as newStoryCol:
-      newStoryBtn = gr.Button("Create New Story", elem_id="new-story-btn") 
-
-    with gr.Column(visible=True) as finishCol:
-      finishBtn = gr.Button("Finish Story", elem_id="finish-btn") 
-    
-    provideFeedbackBtn = gr.Button("Provide Feedback", elem_id="feedback-btn")
-
-    with gr.Column(visible=False) as feedbackForm:
-        ratingRadio = gr.Radio(["1", "2", "3", "4", "5"], label="How would you rate the story")
-        feedback_text = gr.Textbox(label='Any other comments?')
-        submitFeedbackBtn = gr.Button("Submit Feedback", elem_id="submit-feedback-btn")
-
-    with gr.Column(visible=False) as thankyou:
-      
-      feedback_thankyou = gr.HTML("<h2>Thanks for your feeback!</h2>")
-
-    with gr.Column(visible=False) as mesHistoryCol:
-      
-      messageHistoryTextBox = gr.Textbox(label='How will you fill the blank?', elem_id = 'blank', value=messageHistory)
-      
-
-# the submit feedback btn needs to: send the feedback, 
-# hide the elements and provide a message saying thanks for your feedback
-    
-    def show_feedback_text():
-        return {feedbackForm: gr.update(visible=True)}
-
-    def submitFeedback(comment, rating, character, messageHistory):
-      
-      readableHistory = ''
-      for m in messageHistory:
-        readableHistory += m['content']
-        readableHistory += '\n\n'
-
-      row = [comment, rating, character, readableHistory]
-      sheet.append_row(row)
-      return {thankyou: gr.update(visible=True), feedbackForm: gr.update(visible=False)}
-
-    def generateStory(character):
-   
-      openai.api_key = openai.api_key = os.environ['OPENAI_KEY']
-
-      messageHistory=[
-        {"role": "system", "content": "You are a helpful assistant for storytelling-driven games that writes in the style of Doctor Seuss and Julia Donaldson."},
-        {"role": "system", "name":"example_user", "content": "We are going to engage in a collaborative storytelling game. You are going to generate a paragraph but you are going to leave a blank in the last word, that I will have to complete in order to generate the next paragraph. We will do this for every paragraph. Please generate the first paragraph. The story is about a {}. Write in the style of Julia Donaldson and Doctor Seuss. Remember, just write one paragraph! After this one paragraph is generated, I will tell you the word that I want to use to fill the blank, and then you can generate the next. For more context, the game works as follows. 1. You generate one, and only one paragraph, with a blank somewhere in the last line of the paragraph. 2. I tell you how I want to fill the blank. 3. You generate the next paragraph based on my response to continue the story, this next paragraph also has a blank somewhere in the last line of the paragraph. 4. I tell you how to fill the blank. And so on."},
-        {"role": "system", "name": "example_assistant", "content": "In a land of green hills and blue sky,\n Lived a boy named Jack, oh my oh my!\n He loved to explore and roam,\n From the woods to the sea foam.\n He'd climb up trees and run down hills,\n Watch the water flow and the flowers spill.\n But his favorite place, as you can see,\n Was the forest where he felt so free.\n One day, Jack set out to _____."},
-        {"role": "system", "name":"example_user", "content": "{A cave} Write the next paragraph and leave a blank at the end for me to fill!"},
-        {"role": "system", "name": "example_assistant", "content": "Jack had heard about a cave,\n Where treasure was hidden like in a rave.\n He packed some food and a flashlight,\n And set off, with all his might.\n The forest was thick and the way was rough,\n But Jack kept going, feeling tough.\n He crossed a stream and climbed a hill,\n Until he saw the cave, so still.\n The entrance was dark, but Jack was brave,\n He took a deep breath and entered the cave.\n Deep inside, he saw something ____."},
-        {"role": "user", "content": f"We are going to engage in a collaborative storytelling game. You are going to generate a paragraph but you are going to leave a blank in the last word, that I will have to complete in order to generate the next paragraph. We will do this for every paragraph. Please generate the first paragraph. The story is about {character}. Write in the style of Julia Donaldson and Doctor Seuss. Remember, just write one paragraph! After this one paragraph is generated, I will tell you the word that I want to use to fill the blank, and then you can generate the next. For more context, the game works as follows. 1. You generate one, and only one paragraph, with a blank somewhere in the last line of the paragraph. 2. I tell you how I want to fill the blank. 3. You generate the next paragraph based on my response to continue the story, this next paragraph also has a blank somewhere in the last line of the paragraph. 4. I tell you how to fill the blank. And so on."}
-      ]
-      response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages = messageHistory
-      )
-      
-      # check response and generate until you get desired behavior
-      s = response["choices"][0]["message"]["content"]
-      if (s.endswith("_.") or s.endswith("_")) and ("_" not in s[:-2].rstrip("_")):
-        story = s
-      else: 
-        response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages = messageHistory
-        )
-        s = response["choices"][0]["message"]["content"]
-        if (s.endswith("_.") or s.endswith("_")) and ("_" not in s[:-2].rstrip("_")):
-          story = s
-        else: 
-          response = openai.ChatCompletion.create(
-          model="gpt-3.5-turbo",
-          messages = messageHistory
-          )
-          story = response["choices"][0]["message"]["content"]
-          
-
-      messageHistory.append(response["choices"][0]["message"])
-    
-      return story, messageHistory
-
-    def finishStory(blank, messageHistory):
-
-      openai.api_key = openai.api_key = os.environ['OPENAI_KEY']
-
-      messageHistory.append({"role": "user", "content": blank + "\n\nNow bring the story to a close. Write the necessary paragraphs to make it have a happy or funny ending. No need to leave a blank anymore."})
-
-
-      response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages = messageHistory
-      )
-      
-      story = response["choices"][0]["message"]["content"]
-      messageHistory.append(response["choices"][0]["message"])
-      
-      return story, messageHistory
-
-    def createNewStory():
-        return {character: gr.update(value=""), story_output: gr.update(value=""), blank:  gr.update(value=""), continueStoryCol: gr.update(visible=True), finishCol: gr.update(visible=True), newStoryCol: gr.update(visible=False) }
-
-    def hideContinueBtn():
-      return {continueStoryCol: gr.update(visible=False), newStoryCol: gr.update(visible=True), finishCol: gr.update(visible=False)}
-
-
-    beginBtn.click(generateStory, inputs=[character], outputs=[story_output, messageHistory])
-    
-    continueBtn.click(continueStory, inputs=[blank, messageHistory], outputs=[story_output, messageHistory])
-
-    finishBtn.click(finishStory, inputs=[blank, messageHistory], outputs=[story_output, messageHistory])
-    finishBtn.click(hideContinueBtn, [], [continueStoryCol, newStoryCol, finishCol])
-
-    provideFeedbackBtn.click(show_feedback_text, [], [feedbackForm])
-
-    submitFeedbackBtn.click(submitFeedback, [feedback_text, ratingRadio, character, messageHistory], [thankyou, feedbackForm])
-
-    newStoryBtn.click(createNewStory, [], [character, story_output, blank, continueStoryCol, finishCol, newStoryCol])
-
-demo.launch(share=False)
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Adjustment Program - Reset Impressora Epson TX130-TX133-TX135 (Luzes Piscando).rarl How to Fix Your Printer.md b/spaces/rorallitri/biomedical-language-models/logs/Adjustment Program - Reset Impressora Epson TX130-TX133-TX135 (Luzes Piscando).rarl How to Fix Your Printer.md
deleted file mode 100644
index 018136d64bc32a655d6d6d79155fb4d219c40833..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Adjustment Program - Reset Impressora Epson TX130-TX133-TX135 (Luzes Piscando).rarl How to Fix Your Printer.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Adjustment Program - Reset Impressora Epson TX130-TX133-TX135 (Luzes Piscando).rarl</h2><br /><p><b><b>Download File</b> &bull; <a href="https://tinurll.com/2uzmmM">https://tinurll.com/2uzmmM</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Crack DoPublicity Digital Signage Manager 4 6.md b/spaces/rorallitri/biomedical-language-models/logs/Crack DoPublicity Digital Signage Manager 4 6.md
deleted file mode 100644
index 155cee12ce6d40cd228a519201a3ad22fba92676..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Crack DoPublicity Digital Signage Manager 4 6.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<h2>Crack doPublicity Digital Signage Manager 4 6</h2><br /><p><b><b>Download Zip</b> &#187; <a href="https://tinurll.com/2uzlsV">https://tinurll.com/2uzlsV</a></b></p><br /><br />
-
-. The VDDP−MP2 is a stand−alone console media player.. The NMP302-W is a freeware media player software for Windows XP−based PC that can be used as a digital signage or digital.
-
-Media Player. v 10.0 (build 3787) for Windows XP−based. DSP500, DSP600, DSP700, DP1000. NMP302, NMP302-W, NMP580-W, NMP580, NMP582, NMP580-W, NMP580-W,. A program to play the content stored on Viewsonics NMP302, NMP302-W, NMP580-W, NMP580, NMP582 and.
-
-Oct 16, 2009 · Realtek Media Player Description:. realtek is a popular hardware manufacturer based in Taiwan that produces a broad. hardware manufacturer based in Taiwan that produces a broad range of audio.
-
-SC Media Player. The playback of SC Media Player is compatible with. The playback of SC Media Player is compatible with. realMedia file formats (WMV, MPEG, 3GP) on. Media Player. realMedia file formats (WMV, MPEG, 3GP) on.. realMedia file formats (WMV, MPEG, 3GP) on.. It is a free online media player with more than 60 pre-installed online audio and.
-
-VLC Media Player. The playback of VLC Media Player is compatible with. The playback of VLC Media Player is compatible with. realMedia file formats (WMV, MPEG, 3GP) on. Media Player. realMedia file formats (WMV, MPEG, 3GP) on.. It is a free online media player with more than 60 pre-installed online audio and.
-
-Real Player™ Media Player. Browse free for your PC to download the latest version of Real Player™.. Play video and audio files right from your PC, your favorite home page, and. RealPlayer and RealOne are registered trademarks of RealNetworks, Inc..
-
-RealPlayer Media Player.. Play video and audio files right from your PC, your favorite home page, and. Download RealPlayer, the free media player that plays any media file from the Internet,.
-
-Media Player. The playback of WMP Media Player is compatible with. The playback of WMP Media Player is compatible with. WMP Player is a free media player to play music videos 4fefd39f24<br />
-<br />
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-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/How ExpressionUniversalizer 3.1.4 Can Save You Time and Money on After Effects Projects.md b/spaces/rorallitri/biomedical-language-models/logs/How ExpressionUniversalizer 3.1.4 Can Save You Time and Money on After Effects Projects.md
deleted file mode 100644
index 6db8f2a2d6c0640bcd778fd587a060e9dd5e7853..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/How ExpressionUniversalizer 3.1.4 Can Save You Time and Money on After Effects Projects.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>ExpressionUniversalizer 3.1.4</h2><br /><p><b><b>Download Zip</b> &#128279; <a href="https://tinurll.com/2uzloe">https://tinurll.com/2uzloe</a></b></p><br /><br />
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- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/russel0719/deepfake_detector/training/tools/config.py b/spaces/russel0719/deepfake_detector/training/tools/config.py
deleted file mode 100644
index 51145222df6fd7026dfbc435785b610fc08c041f..0000000000000000000000000000000000000000
--- a/spaces/russel0719/deepfake_detector/training/tools/config.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import json
-
-DEFAULTS = {
-    "network": "dpn",
-    "encoder": "dpn92",
-    "model_params": {},
-    "optimizer": {
-        "batch_size": 32,
-        "type": "SGD",  # supported: SGD, Adam
-        "momentum": 0.9,
-        "weight_decay": 0,
-        "clip": 1.,
-        "learning_rate": 0.1,
-        "classifier_lr": -1,
-        "nesterov": True,
-        "schedule": {
-            "type": "constant",  # supported: constant, step, multistep, exponential, linear, poly
-            "mode": "epoch",  # supported: epoch, step
-            "epochs": 10,
-            "params": {}
-        }
-    },
-    "normalize": {
-        "mean": [0.485, 0.456, 0.406],
-        "std": [0.229, 0.224, 0.225]
-    }
-}
-
-
-def _merge(src, dst):
-    for k, v in src.items():
-        if k in dst:
-            if isinstance(v, dict):
-                _merge(src[k], dst[k])
-        else:
-            dst[k] = v
-
-
-def load_config(config_file, defaults=DEFAULTS):
-    with open(config_file, "r") as fd:
-        config = json.load(fd)
-    _merge(defaults, config)
-    return config
diff --git a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_canny.py b/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_canny.py
deleted file mode 100644
index a313ffda0a74b6373e90681aba6cd0e9a8736c86..0000000000000000000000000000000000000000
--- a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_canny.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import cv2
-import gradio as gr
-import numpy as np
-import torch
-from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
-from PIL import Image
-
-from diffusion_webui.utils.model_list import (
-    controlnet_canny_model_list,
-    stable_model_list,
-)
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_LIST,
-    get_scheduler_list,
-)
-
-
-class StableDiffusionControlNetCannyGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-            self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
-                pretrained_model_name_or_path=stable_model_path,
-                controlnet=controlnet,
-                safety_checker=None,
-                torch_dtype=torch.float16,
-            )
-
-        self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def controlnet_canny(
-        self,
-        image_path: str,
-    ):
-        image = Image.open(image_path)
-        image = np.array(image)
-
-        image = cv2.Canny(image, 100, 200)
-        image = image[:, :, None]
-        image = np.concatenate([image, image, image], axis=2)
-        image = Image.fromarray(image)
-
-        return image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        scheduler: str,
-        seed_generator: int,
-    ):
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_model_path,
-            scheduler=scheduler,
-        )
-
-        image = self.controlnet_canny(image_path=image_path)
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_canny_image_file = gr.Image(
-                        type="filepath", label="Image"
-                    )
-
-                    controlnet_canny_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Prompt",
-                        show_label=False,
-                    )
-
-                    controlnet_canny_negative_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Negative Prompt",
-                        show_label=False,
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_canny_stable_model_id = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Id",
-                            )
-
-                            controlnet_canny_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            controlnet_canny_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Step",
-                            )
-                            controlnet_canny_num_images_per_prompt = gr.Slider(
-                                minimum=1,
-                                maximum=10,
-                                step=1,
-                                value=1,
-                                label="Number Of Images",
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                controlnet_canny_model_id = gr.Dropdown(
-                                    choices=controlnet_canny_model_list,
-                                    value=controlnet_canny_model_list[0],
-                                    label="ControlNet Model Id",
-                                )
-
-                                controlnet_canny_scheduler = gr.Dropdown(
-                                    choices=SCHEDULER_LIST,
-                                    value=SCHEDULER_LIST[0],
-                                    label="Scheduler",
-                                )
-
-                                controlnet_canny_seed_generator = gr.Number(
-                                    value=0,
-                                    label="Seed Generator",
-                                )
-                    controlnet_canny_predict = gr.Button(value="Generator")
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-            controlnet_canny_predict.click(
-                fn=StableDiffusionControlNetCannyGenerator().generate_image,
-                inputs=[
-                    controlnet_canny_image_file,
-                    controlnet_canny_stable_model_id,
-                    controlnet_canny_model_id,
-                    controlnet_canny_prompt,
-                    controlnet_canny_negative_prompt,
-                    controlnet_canny_num_images_per_prompt,
-                    controlnet_canny_guidance_scale,
-                    controlnet_canny_num_inference_step,
-                    controlnet_canny_scheduler,
-                    controlnet_canny_seed_generator,
-                ],
-                outputs=[output_image],
-            )
diff --git a/spaces/samcaicn/bingai/src/state/index.ts b/spaces/samcaicn/bingai/src/state/index.ts
deleted file mode 100644
index e3fa86712a8fa2c20da0d7381538e94aaeab790e..0000000000000000000000000000000000000000
--- a/spaces/samcaicn/bingai/src/state/index.ts
+++ /dev/null
@@ -1,115 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
-  { author: 'system', text: 'conversation between user and robot', id: '1' },
-  { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?  ', id: '2' },
-  {
-    author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
-  - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
-  - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
-  - 优点：清洁性能强劲，操作方便，适用多种场景；
-  - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
-  - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
-  - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
-  `, id: '3' },
-  { author: 'user', text: '今天的新闻', id: '4' },
-  {
-    author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
-  # 中国新闻
-  - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
-  - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
-  - 中央气象台7月16日18时发布台风橙色预警[^1^]
-  - 贵州石阡：暑期旅游带动乡村振兴[^1^]
-  - 激活大科学装置的“人才红利”[^1^]
-  - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
-  - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
-  - 成都以赛为媒提升城市美誉度[^1^]
-  - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
-  - 浙江建德：新安江上享清凉[^1^]
-  - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
-  - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
-  - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
-  - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
-  - 大运来了丨成都迎大运 全民健身动起来[^1^]
-  - 藏在高校里的宝藏博物馆[^1^]
-  - 中国汽车工业用70年赢得三个“全球第一”[^1^]
-  - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
-  # 国际新闻
-  - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
-  - 国际航运业加快绿色转型[^2^]
-  - 美企反对收紧对华芯片出口限制[^2^]
-  - 欧洲加大气候科技领域投资[^2^]
-  - 中企助力丹麦发展清洁能源[^2^]
-  - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
-  - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
-  以上信息仅供参考，具体详情请点击以下链接查看：
-
-  [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
-  [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
-  { author: 'user', text: '写一个快排', id: '6' },
-  { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n    temp = arr[i]\n    arr[i] = arr[j]\n    arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n    # 选择最右边的元素作为基准元素\n    pivot = arr[high]\n    # 初始化一个指针，指向左边区间的最后一个位置\n    i = low - 1\n    # 遍历左边区间中的每个元素\n    for j in range(low, high):\n        # 如果当前元素小于或等于基准元素\n        if arr[j] <= pivot:\n            # 将指针右移一位\n            i += 1\n            # 将指针所指的元素与当前元素交换\n            swap(arr, i, j)\n    # 将基准元素与指针右边的第一个元素交换\n    swap(arr, i + 1, high)\n    # 返回基准元素的最终位置\n    return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n    # 如果区间长度大于1\n    if low < high:\n        # 对区间进行划分，得到基准元素的位置\n        p = partition(arr, low, high)\n        # 对左边子区间进行快速排序\n        quick_sort(arr, low, p - 1)\n        # 对右边子区间进行快速排序\n        quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
-  {
-    author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)",
-    id: '8'
-  }
-]
-
-export const GreetMessages = [
-  '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
-  '重新开始总是很棒。问我任何问题!',
-  '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
-  '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
-  '很好，让我们来更改主题。你在想什么?',
-  '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
-  '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
-  '感谢刷新！你有新的话题吗？',
-  '明白了，让我们重新开始。接下来应该讨论什么？',
-  '下一步！我可以为你做什么？',
-  '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage<BingConversationStyle>('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const useProxyAtom = atomWithStorage<boolean>('useProxy', true, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
-  return new BingWebBot({
-    cookie: ' ',
-    ua: ' ',
-  })
-}
-
-export const chatFamily = atomFamily(
-  (param: Param) => {
-    return atomWithImmer({
-      botId: param.botId,
-      bot: createBotInstance(),
-      messages: [] as ChatMessageModel[],
-      generatingMessageId: '',
-      abortController: undefined as AbortController | undefined,
-      conversationId: nanoid(),
-    })
-  },
-  (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
diff --git a/spaces/scedlatioru/img-to-music/example/Bellas Y Ambiciosas Serie Completa.md b/spaces/scedlatioru/img-to-music/example/Bellas Y Ambiciosas Serie Completa.md
deleted file mode 100644
index 4c59d723b0c6e821be8dfaad5aec80d84ba998a9..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Bellas Y Ambiciosas Serie Completa.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<br />
-<p> serie latina, latina bellas_y_ambiciosas, latina latina bellas y ambiciosas, latina sextreeparty, latina latina, latina, latina chinamandipussies, latina latina latina bellas y ambiciosas 2 serie latina porno latina, latina latina latina latina latina 18 por raya latina bellas y ambiciosas latina porno. </p>
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-<p> bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas. Bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y. Bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas y ambiciosas latina bellas. </p>
-<p> <br /> The Beautiful and ambitious series is about a Latina woman, Katherine, who lives in Miami. But she longs to be in Perú. She moved to Perú when she was ten years old. So, she loves Perú and its culture and its people. And she has a plan in her head to return to live in Perú one day.<br /> Her plan was to achieve her dream as a journalist and to be a successful Latin music and jazz musician.<br /> But her dream didn't work because, after seven years of working, she is still a Nobody and a lonely woman. </p>
-<p></p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/Sis 2011b Keygen Download Crack !!LINK!!.md b/spaces/scedlatioru/img-to-music/example/Sis 2011b Keygen Download Crack !!LINK!!.md
deleted file mode 100644
index eca2a27acdff6f2b364265bcdddcda614561a23b..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Sis 2011b Keygen Download Crack !!LINK!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Sis 2011b Keygen Download Crack</h2><br /><p><b><b>Download</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://gohhs.com/2uEzZ8">https://gohhs.com/2uEzZ8</a></b></p><br /><br />
-
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diff --git a/spaces/segments-tobias/conex/espnet/asr/pytorch_backend/__init__.py b/spaces/segments-tobias/conex/espnet/asr/pytorch_backend/__init__.py
deleted file mode 100644
index b7f177368e62a5578b8706300e101f831a3972ac..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/asr/pytorch_backend/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Initialize sub package."""
diff --git a/spaces/sheikyerbouti/riffusion-playground/README.md b/spaces/sheikyerbouti/riffusion-playground/README.md
deleted file mode 100644
index c052d89b1ee9ed07e5d1f51d0b2c969e797a6728..0000000000000000000000000000000000000000
--- a/spaces/sheikyerbouti/riffusion-playground/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Riffusion Playground
-emoji: 📚
-colorFrom: red
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: riffusion/riffusion-playground
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/shencc/gpt/docs/self_analysis.md b/spaces/shencc/gpt/docs/self_analysis.md
deleted file mode 100644
index c88e1e41217eb13a30269f933586f6c241fab38d..0000000000000000000000000000000000000000
--- a/spaces/shencc/gpt/docs/self_analysis.md
+++ /dev/null
@@ -1,256 +0,0 @@
-# chatgpt-academic项目自译解报告
-（Author补充：以下分析均由本项目调用ChatGPT一键生成，如果有不准确的地方，全怪GPT😄）
-
-## 对程序的整体功能和构架做出概括。然后用一张markdown表格整理每个文件的功能。
-
-整体概括：
-
-该程序是一个基于自然语言处理和机器学习的科学论文辅助工具，主要功能包括聊天机器人、批量总结PDF文档、批量翻译PDF文档、生成函数注释、解析项目源代码等。程序基于 Gradio 构建 Web 服务，并集成了代理和自动更新功能，提高了用户的使用体验。
-
-文件功能表格：
-
-| 文件名 | 文件功能 |
-| --- | --- |
-| check_proxy.py | 用于检查代理的正确性和可用性 |
-| colorful.py | 包含不同预设置颜色的常量，并用于多种UI元素 |
-| config.py | 用于全局配置的类 |
-| config_private.py | 与config.py文件一起使用的另一个配置文件，用于更改私密信息 |
-| core_functional.py | 包含一些TextFunctional类和基础功能函数 |
-| crazy_functional.py | 包含大量高级功能函数和实验性的功能函数 |
-| main.py | 程序的主入口，包含GUI主窗口和主要的UI管理功能 |
-| theme.py | 包含一些预设置主题的颜色 |
-| toolbox.py | 提供了一些有用的工具函数 |
-| crazy_functions\crazy_utils.py | 包含一些用于实现高级功能的辅助函数 |
-| crazy_functions\Latex全文润色.py | 实现了对LaTeX文件中全文的润色和格式化功能 |
-| crazy_functions\Latex全文翻译.py | 实现了对LaTeX文件中的内容进行翻译的功能 |
-| crazy_functions\_\_init\_\_.py | 用于导入crazy_functional.py中的功能函数 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 从Arxiv上下载论文并提取重要信息 |
-| crazy_functions\代码重写为全英文_多线程.py | 针对中文Python文件，将其翻译为全英文 |
-| crazy_functions\总结word文档.py | 提取Word文件的重要内容来生成摘要 |
-| crazy_functions\批量Markdown翻译.py | 批量翻译Markdown文件 |
-| crazy_functions\批量总结PDF文档.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量总结PDF文档pdfminer.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量翻译PDF文档_多线程.py | 批量翻译PDF文件 |
-| crazy_functions\理解PDF文档内容.py | 批量分析PDF文件并提取摘要 |
-| crazy_functions\生成函数注释.py | 自动生成Python文件中函数的注释 |
-| crazy_functions\解析项目源代码.py | 解析并分析给定项目的源代码 |
-| crazy_functions\询问多个大语言模型.py | 向多个大语言模型询问输入文本并进行处理 |
-| crazy_functions\读文献写摘要.py | 根据用户输入读取文献内容并生成摘要 |
-| crazy_functions\谷歌检索小助手.py | 利用谷歌学术检索用户提供的论文信息并提取相关信息 |
-| crazy_functions\高级功能函数模板.py | 实现高级功能的模板函数 |
-| request_llm\bridge_all.py | 处理与LLM的交互 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型进行聊天 |
-| request_llm\bridge_chatgpt.py | 实现对话生成的各项功能 |
-| request_llm\bridge_tgui.py | 在Websockets中与用户进行交互并生成文本输出 |
-
-
-
-## [0/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\check_proxy.py
-
-该文件主要包括四个函数：check_proxy、backup_and_download、patch_and_restart 和 auto_update。其中，check_proxy 函数用于检查代理是否可用；backup_and_download 用于进行一键更新备份和下载；patch_and_restart 是一键更新协议的重要函数，用于覆盖和重启；auto_update 函数用于查询版本和用户意见，并自动进行一键更新。该文件主要使用了 requests、json、shutil、zipfile、distutils、subprocess 等 Python 标准库和 toolbox 和 colorful 两个第三方库。
-
-## [1/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\colorful.py
-
-该程序文件实现了一些打印文本的函数，使其具有不同的颜色输出。当系统为Linux时直接跳过，否则使用colorama库来实现颜色输出。程序提供了深色和亮色两种颜色输出方式，同时也提供了对打印函数的别名。对于不是终端输出的情况，对所有的打印函数进行重复定义，以便在重定向时能够避免打印错误日志。
-
-## [2/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\config.py
-
-该程序文件是一个配置文件，其主要功能是提供使用API密钥等信息，以及对程序的体验进行优化，例如定义对话框高度、布局等。还包含一些其他的设置，例如设置并行使用的线程数、重试次数限制等等。
-
-## [3/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\config_private.py
-
-这是一个名为config_private.py的Python文件，它用于配置API_KEY和代理信息。API_KEY是一个私密密钥，用于访问某些受保护的API。USE_PROXY变量设置为True以应用代理，proxies变量配置了代理网络的地址和协议。在使用该文件时，需要填写正确的API_KEY和代理信息。
-
-## [4/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\core_functional.py
-
-该文件是一个Python模块，名为"core_functional.py"。模块中定义了一个字典，包含了各种核心功能的配置信息，如英语学术润色、中文学术润色、查找语法错误等。每个功能都包含一些前言和后语，在前言中描述了该功能的任务和要求，在后语中提供一些附加信息。此外，有些功能还定义了一些特定的处理函数和按钮颜色。
-
-## [5/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functional.py
-
-这是一个Python程序文件，文件名是crazy_functional.py。它导入了一个名为HotReload的工具箱，并定义了一个名为get_crazy_functions()的函数。这个函数包括三个部分的插件组，分别是已经编写完成的第一组插件、已经测试但距离完美状态还差一点点的第二组插件和尚未充分测试的第三组插件。每个插件都有一个名称、一个按钮颜色、一个函数和一个是否加入下拉菜单中的标志位。这些插件提供了多种功能，包括生成函数注释、解析项目源代码、批量翻译PDF文档、谷歌检索、PDF文档内容理解和Latex文档的全文润色、翻译等功能。其中第三组插件可能还存在一定的bug。
-
-## [6/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\main.py
-
-该Python脚本代码实现了一个用于交互式对话的Chatbot机器人。它使用了Gradio框架来构建一个Web界面，并在此基础之上嵌入了一个文本输入框和与Chatbot进行交互的其他控件，包括提交、重置、停止和清除按钮、选择框和滑块等。此外，它还包括了一些类和函数和一些用于编程分析的工具和方法。整个程序文件的结构清晰，注释丰富，并提供了很多技术细节，使得开发者可以很容易地在其基础上进行二次开发、修改、扩展和集成。
-
-## [7/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\theme.py
-
-该程序文件名为theme.py，主要功能为调节Gradio的全局样式。在该文件中，调节了Gradio的主题颜色、字体、阴影、边框、渐变等等样式。同时，该文件还添加了一些高级CSS样式，比如调整表格单元格的背景和边框，设定聊天气泡的圆角、最大宽度和阴影等等。如果CODE_HIGHLIGHT为True，则还进行了代码高亮显示。
-
-## [8/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\toolbox.py
-
-这是一个名为`toolbox.py`的源代码文件。该文件包含了一系列工具函数和装饰器，用于聊天Bot的开发和调试。其中有一些功能包括将输入参数进行重组、捕捉函数中的异常并记录到历史记录中、生成Markdown格式的聊天记录报告等。该文件中还包含了一些与转换Markdown文本相关的函数。
-
-## [9/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\crazy_utils.py
-
-这是一个Python程序文件 `crazy_utils.py`，它包含了两个函数：
-
-- `input_clipping(inputs, history, max_token_limit)`：这个函数接收三个参数，inputs 是一个字符串，history 是一个列表，max_token_limit 是一个整数。它使用 `tiktoken` 、`numpy` 和 `toolbox` 模块，处理输入文本和历史记录，将其裁剪到指定的最大标记数，避免输入过长导致的性能问题。如果 inputs 长度不超过 max_token_limit 的一半，则只裁剪历史；否则，同时裁剪输入和历史。
-- `request_gpt_model_in_new_thread_with_ui_alive(inputs, inputs_show_user, llm_kwargs, chatbot, history, sys_prompt, refresh_interval=0.2, handle_token_exceed=True, retry_times_at_unknown_error=2)`：这个函数接收八个参数，其中后三个是列表类型，其他为标量或句柄等。它提供对话窗口和刷新控制，执行 `predict_no_ui_long_connection` 方法，将输入数据发送至 GPT 模型并获取结果，如果子任务出错，返回相应的错误信息，否则返回结果。
-
-## [10/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\Latex全文润色.py
-
-这是一个名为"crazy_functions\Latex全文润色.py"的程序文件，其中包含了两个函数"Latex英文润色"和"Latex中文润色"，以及其他辅助函数。这些函数能够对 Latex 项目进行润色处理，其中 "多文件润色" 函数是一个主要函数，它调用了其他辅助函数用于读取和处理 Latex 项目中的文件。函数使用了多线程和机器学习模型进行自然语言处理，对文件进行简化和排版来满足学术标准。注释已删除并可以在函数内部查找。
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-## [11/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\Latex全文翻译.py
-
-这个程序文件包括一个用于对整个Latex项目进行翻译的函数 `Latex英译中` 和一个用于将中文翻译为英文的函数 `Latex中译英`。这两个函数都会尝试导入依赖库 tiktoken， 若无法导入则会提示用户安装。`Latex英译中` 函数会对 Latex 项目中的文件进行分离并去除注释，然后运行多线程翻译。`Latex中译英` 也做同样的事情，只不过是将中文翻译为英文。这个程序文件还包括其他一些帮助函数。
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-## [12/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\__init__.py
-
-这是一个 Python 包，包名为 `crazy_functions`，在 `__init__.py` 文件中定义了一些函数，包含以下函数:
-
-- `crazy_addition(a, b)`：对两个数进行加法运算，并将结果返回。
-- `crazy_multiplication(a, b)`：对两个数进行乘法运算，并将结果返回。
-- `crazy_subtraction(a, b)`：对两个数进行减法运算，并将结果返回。
-- `crazy_division(a, b)`：对两个数进行除法运算，并将结果返回。
-- `crazy_factorial(n)`：计算 `n` 的阶乘并返回结果。
-
-这些函数可能会有一些奇怪或者不符合常规的实现方式（由函数名可以看出来），所以这个包的名称为 `crazy_functions`，可能是暗示这些函数会有一些“疯狂”的实现方式。
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-## [13/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\下载arxiv论文翻译摘要.py
-
-该程序实现了一个名为“下载arxiv论文并翻译摘要”的函数插件，作者是“binary-husky”。该函数的功能是，在输入一篇arxiv论文的链接后，提取摘要、下载PDF文档、翻译摘要为中文，并将翻译结果保存到文件中。程序使用了一些Python库，如requests、pdfminer和beautifulsoup4等。程序入口是名为“下载arxiv论文并翻译摘要”的函数，其中使用了自定义的辅助函数download_arxiv_和get_name。程序中还使用了其他非函数的辅助函数和变量，如update_ui、CatchException、report_exception和get_conf等。
-
-## [14/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\代码重写为全英文_多线程.py
-
-该文件是一个多线程Python脚本，包含多个函数和利用第三方库进行的API请求。主要功能是将给定文件夹内的Python代码文件中所有中文转化为英文，然后输出转化后的英文代码。重要的功能和步骤包括：
-
-1. 清空历史，以免输入溢出
-2. 尝试导入依赖，如果缺少依赖，则给出安装建议
-3. 集合文件
-4. 显示随意内容以防卡顿的感觉
-5. Token限制下的截断与处理
-6. 多线程操作请求转换中文变为英文的代码
-7. 所有线程同时开始执行任务函数
-8. 循环轮询各个线程是否执行完毕
-9. 把结果写入文件
-10. 备份一个文件
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-## [15/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\总结word文档.py
-
-这是一个名为"总结word文档.py"的程序文件，使用python编写。该文件导入了"toolbox"和"crazy_utils"模块，实现了解析docx格式和doc格式的文件的功能。该文件包含了一个名为"解析docx"的函数，通过对文件内容应用自然语言处理技术，生成文章片段的中英文概述。具体实现过程中，该函数使用了"docx"模块和"win32com.client"模块来实现对docx和doc格式文件的解析，同时使用了"request_gpt_model_in_new_thread_with_ui_alive"函数来向GPT模型发起请求。最后，该文件还实现了一个名为"总结word文档"的函数来批量总结Word文档。
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-## [16/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量Markdown翻译.py
-
-这个程序文件实现了一个批量Markdown翻译功能，可以将一个源代码项目中的Markdown文本翻译成指定语言（目前支持中<-英和英<-中）。程序主要分为三个函数，`PaperFileGroup`类用于处理长文本的拆分，`多文件翻译`是主要函数调用了`request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency`函数进行多线程翻译并输出结果，`Markdown英译中`和`Markdown中译外`分别是英译中和中译英的入口函数，用于解析项目路径和调用翻译函数。程序依赖于tiktoken等库实现。
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-## [17/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量总结PDF文档.py
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-这是一个名为“批量总结PDF文档”的Python脚本，包含了多个函数。其中有一个函数名为“clean_text”，可以对PDF提取出的原始文本进行清洗和格式化处理，将连字转换为其基本形式，并根据heuristic规则判断换行符是否是段落分隔，并相应地进行替换。另一个函数名为“解析PDF”，可以接收一个PDF文件清单，并对清单中的每一个PDF进行解析，提取出文本并调用“clean_text”函数进行清洗和格式化处理，然后向用户发送一个包含文章简介信息的问题并等待用户回答。最后，该脚本也包含一个名为“批量总结PDF文档”的主函数，其中调用了“解析PDF”函数来完成对PDF文件的批量处理。
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-## [18/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量总结PDF文档pdfminer.py
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-这个文件是一个Python模块，文件名为pdfminer.py，它定义了一个函数批量总结PDF文档。该函数接受一些参数，然后尝试导入pdfminer和beautifulsoup4库。该函数将读取pdf文件或tex文件中的内容，对其进行分析，并使用GPT模型进行自然语言摘要。文件中还有一个辅助函数readPdf，用于读取pdf文件中的内容。
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-## [19/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\批量翻译PDF文档_多线程.py
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-这是一个Python脚本，文件名是crazy_functions\批量翻译PDF文档_多线程.py。该脚本提供了一个名为“批量翻译PDF文档”的函数，可以批量翻译PDF文件并生成报告文件。该函数使用了多个模块和函数（如toolbox、crazy_utils、update_ui等），使用了Python的异常处理和多线程功能，还使用了一些文本处理函数和第三方库（如fitz和tiktoken）。在函数执行过程中，它会进行一些参数检查、读取和清理PDF文本、递归地切割PDF文件、获取文章meta信息、多线程翻译、整理报告格式等操作，并更新UI界面和生成报告文件。
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-## [20/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\理解PDF文档内容.py
-
-这是一个解析PDF文件内容的Python程序，程序文件名为"理解PDF文档内容.py"，程序主要由5个步骤组成：第0步是切割PDF文件；第1步是从摘要中提取高价值信息，放到history中；第2步是迭代地历遍整个文章，提取精炼信息；第3步是整理history；第4步是设置一个token上限，防止回答时Token溢出。程序主要用到了Python中的各种模块和函数库，如：toolbox, tiktoken, pymupdf等。
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-## [21/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\生成函数注释.py
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-这是一个名为"生成函数注释"的函数，带有一个装饰器"@CatchException"，可以捕获异常。该函数接受文件路径、参数和聊天机器人等参数，用于对多个Python或C++文件进行函数注释，使用了"toolbox"和"crazy_utils"模块中的函数。该函数会逐个读取指定文件中的内容，并使用聊天机器人进行交互，向用户请求注释信息，然后将生成的注释与原文件内容一起输出到一个markdown表格中。最后，该函数返回一个字符串，指示任务是否已完成。另外还包含一个名为"批量生成函数注释"的函数，它与"生成函数注释"函数一起用于批量处理多个文件。
-
-## [22/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\解析项目源代码.py
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-这个程序文件实现了对一个源代码项目进行分析的功能。其中，函数`解析项目本身`、`解析一个Python项目`、`解析一个C项目的头文件`、`解析一个C项目`、`解析一个Java项目`和`解析前端项目`分别用于解析不同类型的项目。函数`解析源代码新`实现了对每一个源代码文件的分析，并将分析结果汇总，同时还实现了分组和迭代处理，提高了效率。最后，函数`write_results_to_file`将所有分析结果写入文件。中间，还用到了`request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency`和`request_gpt_model_in_new_thread_with_ui_alive`来完成请求和响应，并用`update_ui`实时更新界面。
-
-## [23/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\询问多个大语言模型.py
-
-这是一个Python程序，文件名为"crazy_functions\询问多个大语言模型.py"。该程序实现了一个同时向多个大语言模型询问的功能，接收用户输入文本以及模型参数，向ChatGPT和ChatGLM模型发出请求，并将对话记录显示在聊天框中，同时刷新界面。
-
-## [24/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\读文章写摘要.py
-
-该程序文件是一个Python模块，文件名为"读文章写摘要.py"，主要包含两个函数："解析Paper"和"读文章写摘要"。其中，"解析Paper"函数接受文件路径、参数等参数，逐个打印文件内容并使用GPT模型生成对该文件的摘要；"读文章写摘要"函数则接受一段文本内容和参数，将该文本内容及其所有.tex文件逐个传递给"解析Paper"函数进行处理，并使用GPT模型生成文章的中英文摘要。文件还导入了一些工具函数，如异常处理、信息上报和文件写入等。
-
-## [25/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\谷歌检索小助手.py
-
-该文件代码包含了一个名为`get_meta_information`的函数和一个名为`谷歌检索小助手`的装饰器函数，用于从谷歌学术中抓取文章元信息，并从用户提供的搜索页面中分析所有文章的相关信息。该文件使用了许多第三方库，如requests、arxiv、BeautifulSoup等。其中`get_meta_information`函数中还定义了一个名为`string_similar`的辅助函数，用于比较字符串相似度。
-
-## [26/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\crazy_functions\高级功能函数模板.py
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-该程序文件是一个 Python 模块，包含一个名为“高阶功能模板函数”的函数。该函数接受多个参数，其中包括输入文本、GPT 模型参数、插件模型参数、聊天显示框、聊天历史等。 该函数的主要功能是根据输入文本，使用 GPT 模型生成一些问题，并等待用户回答这些问题（使用 Markdown 格式），然后将用户回答加入到聊天历史中，并更新聊天显示框。该函数还包含了一些异常处理和多线程的相关操作。该程序文件还引用了另一个 Python 模块中的两个函数，分别为“CatchException”和“update_ui”，并且还引用了一个名为“request_gpt_model_in_new_thread_with_ui_alive”的自定义函数。
-
-## [27/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_all.py
-
-这个文件是用来处理与LLM的交互的。包含两个函数，一个是 predict_no_ui_long_connection 用来处理长文本的输出，可以多线程调用；另一个是 predict 用来处理基础的对话功能。这个文件会导入其他文件中定义的方法进行调用，具体调用哪个方法取决于传入的参数。函数中还有一些装饰器和管理多线程的逻辑。
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-## [28/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_chatglm.py
-
-这个程序文件实现了一个使用ChatGLM模型进行聊天的功能。具体实现过程是：首先进行初始化，然后使用GetGLMHandle类进行ChatGLM模型的加载和运行。predict_no_ui_long_connection函数用于多线程聊天，而predict函数用于单线程聊天，它们的不同之处在于前者不会更新UI界面，后者会。这个文件还导入了其他模块和库，例如transformers、time、importlib等，并使用了多进程Pipe。
-
-## [29/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_chatgpt.py
-
-这个程序文件是用于对话生成的，主要包含三个函数：predict、predict_no_ui、predict_no_ui_long_connection。其中，predict是用于普通对话的函数，具备完备的交互功能，但不具备多线程能力；predict_no_ui是高级实验性功能模块调用的函数，参数简单，可以多线程并行，方便实现复杂的功能逻辑；predict_no_ui_long_connection解决了predict_no_ui在处理长文档时容易断开连接的问题，同样支持多线程。程序中还包含一些常量和工具函数，用于整合信息，选择LLM模型，生成http请求，发送请求，接收响应等。它需要配置一个config文件，包含代理网址、API等敏感信息。
-
-## [30/31] 请对下面的程序文件做一个概述: H:\chatgpt_academic_resolve\request_llm\bridge_tgui.py
-
-该程序文件实现了一个基于Websockets的文本生成服务和对话功能。其中，有三个函数：`run()`、`predict()`和`predict_no_ui_long_connection()`。`run()`函数用于连接到Websocket服务并生成文本结果；`predict()`函数用于将用户输入作为文本生成的输入，同时在UI上显示对话历史记录，并在不断更新UI的过程中不断更新生成的文本输出；`predict_no_ui_long_connection()`函数与`predict()`函数类似，但没有UI，并在一段时间内返回单个生成的文本。整个程序还引入了多个Python模块来完成相关功能，例如`asyncio`、`websockets`、`json`等等。
-
-## 根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, theme.py, toolbox.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py, crazy_functions\代码重写为全英文_多线程.py, crazy_functions\总结word文档.py）。
-
-程序功能概括：该程序是一个聊天机器人，可以通过 Web 界面与用户进行交互。它包含了丰富的功能，如文本润色、翻译、代码重写、在线查找等，并且支持多线程处理。用户可以通过 Gradio 框架提供的 Web 界面进行交互，程序还提供了一些调试工具，如toolbox 模块，方便程序开发和调试。
-
-下表概述了每个文件的功能：
-
-| 文件名                                                      | 功能                                                         |
-| ----------------------------------------------------------- | ------------------------------------------------------------ |
-| check_proxy.py                                              | 检查代理是否可用                                             |
-| colorful.py                                                 | 用于打印文本的字体颜色输出模块                               |
-| config.py                                                   | 用于程序中的各种设置，如并行线程数量和重试次数的限制等     |
-| config_private.py                                           | 配置API_KEY和代理信息的文件                                   |
-| core_functional.py                                           | 包含具体的文本处理功能的模块                                 |
-| crazy_functional.py                                          | 包括各种插件函数的模块，提供了多种文本处理功能               |
-| main.py                                                     | 包含 Chatbot 机器人主程序的模块                              |
-| theme.py                                                    | 用于调节全局样式的模块                                       |
-| toolbox.py                                                  | 包含工具函数和装饰器，用于聊天Bot的开发和调试                |
-| crazy_functions\crazy_utils.py                              | 包含一些辅助函数，如文本裁剪和消息捕捉等                       |
-| crazy_functions\Latex全文润色.py                           | 对 Latex 项目进行润色处理的功能模块                          |
-| crazy_functions\Latex全文翻译.py                           | 对 Latex 项目进行翻译的功能模块                              |
-| crazy_functions\__init__.py                                 | 定义一些奇特的数学函数等                                      |
-| crazy_functions\下载arxiv论文翻译摘要.py                  | 下载 Arxiv 论文并翻译摘要的功能模块                          |
-| crazy_functions\代码重写为全英文_多线程.py                 | 将Python程序中所有中文转化为英文的功能模块                    |
-| crazy_functions\总结word文档.py                            | 解析 docx 和 doc 格式的文件，生成文章片段的中英文概述的功能模块 |
-
-## 根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, theme.py, toolbox.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py, crazy_functions\代码重写为全英文_多线程.py, crazy_functions\总结word文档.py, crazy_functions\批量Markdown翻译.py, crazy_functions\批量总结PDF文档.py, crazy_functions\批量总结PDF文档pdfminer.py, crazy_functions\批量翻译PDF文档_多线程.py, crazy_functions\理解PDF文档内容.py, crazy_functions\生成函数注释.py, crazy_functions\解析项目源代码.py, crazy_functions\询问多个大语言模型.py, crazy_functions\读文章写摘要.py, crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_tgui.py）。
-
-根据以上分析，整个程序是一个集成了多个有用工具和功能的文本处理和生成工具，提供了多种在不同场景下使用的功能，包括但不限于对话生成、文本摘要、PDF文件批量处理、代码翻译和实用工具等。主要的Python模块包括"toolbox.py"、"config.py"、"core_functional.py"和"crazy_functional.py"等，并且还使用了许多第三方库和模块实现相关功能。以下是每个程序文件的功能：
-
-| 文件名 | 文件功能 |
-| --- | --- |
-| check_proxy.py | 用于检查代理的正确性和可用性 |
-| colorful.py | 包含不同预设置颜色的常量，并用于多种UI元素 |
-| config.py | 用于全局配置的类 |
-| config_private.py | 与config.py文件一起使用的另一个配置文件，用于更改私密信息 |
-| core_functional.py | 包含一些TextFunctional类和基础功能函数 |
-| crazy_functional.py | 包含大量高级功能函数和实验性的功能函数 |
-| main.py | 程序的主入口，包含GUI主窗口和主要的UI管理功能 |
-| theme.py | 包含一些预设置主题的颜色 |
-| toolbox.py | 提供了一些有用的工具函数 |
-| crazy_functions\crazy_utils.py | 包含一些用于实现高级功能的辅助函数 |
-| crazy_functions\Latex全文润色.py | 实现了对LaTeX文件中全文的润色和格式化功能 |
-| crazy_functions\Latex全文翻译.py | 实现了对LaTeX文件中的内容进行翻译的功能 |
-| crazy_functions\_\_init\_\_.py | 用于导入crazy_functional.py中的功能函数 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 从Arxiv上下载论文并提取重要信息 |
-| crazy_functions\代码重写为全英文_多线程.py | 针对中文Python文件，将其翻译为全英文 |
-| crazy_functions\总结word文档.py | 提取Word文件的重要内容来生成摘要 |
-| crazy_functions\批量Markdown翻译.py | 批量翻译Markdown文件 |
-| crazy_functions\批量总结PDF文档.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量总结PDF文档pdfminer.py | 批量从PDF文件中提取摘要 |
-| crazy_functions\批量翻译PDF文档_多线程.py | 批量翻译PDF文件 |
-| crazy_functions\理解PDF文档内容.py | 批量分析PDF文件并提取摘要 |
-| crazy_functions\生成函数注释.py | 自动生成Python文件中函数的注释 |
-| crazy_functions\解析项目源代码.py | 解析并分析给定项目的源代码 |
-| crazy_functions\询问多个大语言模型.py | 向多个大语言模型询问输入文本并进行处理 |
-| crazy_functions\读文献写摘要.py | 根据用户输入读取文献内容并生成摘要 |
-| crazy_functions\谷歌检索小助手.py | 利用谷歌学术检索用户提供的论文信息并提取相关信息 |
-| crazy_functions\高级功能函数模板.py | 实现高级功能的模板函数 |
-| request_llm\bridge_all.py | 处理与LLM的交互 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型进行聊天 |
-| request_llm\bridge_chatgpt.py | 实现对话生成的各项功能 |
-| request_llm\bridge_tgui.py | 在Websockets中与用户进行交互并生成文本输出 |
-
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/i18n/locale_diff.py b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/i18n/locale_diff.py
deleted file mode 100644
index 257277965e0866a86d0361863a8f1b408c4f71ab..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/Retrieval-based-Voice-Conversion-WebUI/i18n/locale_diff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-import os
-from collections import OrderedDict
-
-# Define the standard file name
-standard_file = "zh_CN.json"
-
-# Find all JSON files in the directory
-dir_path = "./"
-languages = [
-    f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file
-]
-
-# Load the standard file
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-
-# Loop through each language file
-for lang_file in languages:
-    # Load the language file
-    with open(lang_file, "r", encoding="utf-8") as f:
-        lang_data = json.load(f, object_pairs_hook=OrderedDict)
-
-    # Find the difference between the language file and the standard file
-    diff = set(standard_data.keys()) - set(lang_data.keys())
-
-    miss = set(lang_data.keys()) - set(standard_data.keys())
-
-    # Add any missing keys to the language file
-    for key in diff:
-        lang_data[key] = key
-
-    # Del any extra keys to the language file
-    for key in miss:
-        del lang_data[key]
-
-    # Sort the keys of the language file to match the order of the standard file
-    lang_data = OrderedDict(
-        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
-    )
-
-    # Save the updated language file
-    with open(lang_file, "w", encoding="utf-8") as f:
-        json.dump(lang_data, f, ensure_ascii=False, indent=4)
-        f.write("\n")
diff --git a/spaces/shi-labs/Matting-Anything/segment-anything/setup.py b/spaces/shi-labs/Matting-Anything/segment-anything/setup.py
deleted file mode 100644
index 2c0986317eb576a14ec774205c88fdee3cc6c0b3..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Matting-Anything/segment-anything/setup.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from setuptools import find_packages, setup
-
-setup(
-    name="segment_anything",
-    version="1.0",
-    install_requires=[],
-    packages=find_packages(exclude="notebooks"),
-    extras_require={
-        "all": ["matplotlib", "pycocotools", "opencv-python", "onnx", "onnxruntime"],
-        "dev": ["flake8", "isort", "black", "mypy"],
-    },
-)
diff --git a/spaces/shifei/gradio/app.py b/spaces/shifei/gradio/app.py
deleted file mode 100644
index 87b07942a01a727f746671dd90fc55253095b7a6..0000000000000000000000000000000000000000
--- a/spaces/shifei/gradio/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import pprint
-
-import gradio as gr
-import requests
-
-def greet(name1):
-    name1=doit()
-    return "Hello " + name1 + "!"
-    
-def doit():
-    API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-7b"
-    headers = {"Authorization": f"Bearer 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",
-            "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36"
-           }#{"Authorization": "Bearer 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"}
-    payload={
-        "inputs": "天真的蓝呀，",
-    }
-    response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
-    return response.text
-               
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-
-if __name__ == "__main__":
-    app, local_url, share_url=demo.launch()
-    pprint.pprint(share_url)
diff --git a/spaces/shikunl/prismer/prismer/experts/depth/base_model.py b/spaces/shikunl/prismer/prismer/experts/depth/base_model.py
deleted file mode 100644
index 5c2e0e93b0495f48a3405546b6fe1969be3480a2..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/depth/base_model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device("cpu"))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)
diff --git a/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/data/dataset_mappers/__init__.py b/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/data/dataset_mappers/__init__.py
deleted file mode 100644
index 9020c2df23e2af280b7bb168b996ae9eaf312eb8..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/segmentation/mask2former/data/dataset_mappers/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/models/diff_ttsg.py b/spaces/shivammehta25/Diff-TTSG/diff_ttsg/models/diff_ttsg.py
deleted file mode 100644
index e122551f8f76e533bcfc5fa8a23c1ced93aa0eb8..0000000000000000000000000000000000000000
--- a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/models/diff_ttsg.py
+++ /dev/null
@@ -1,376 +0,0 @@
-import math
-import random
-from typing import Any
-
-import torch
-from lightning import LightningModule
-
-import diff_ttsg.utils.monotonic_align as monotonic_align
-from diff_ttsg import utils
-from diff_ttsg.models.components.diffusion import Diffusion, Diffusion_Motion
-from diff_ttsg.models.components.text_encoder import (MuMotionEncoder,
-                                                      TextEncoder)
-from diff_ttsg.utils.model import (denormalize, duration_loss,
-                                   fix_len_compatibility, generate_path,
-                                   sequence_mask)
-from diff_ttsg.utils.utils import plot_tensor
-
-log = utils.get_pylogger(__name__)
-
-class Diff_TTSG(LightningModule):
-    def __init__(
-        self,
-        n_vocab,
-        n_spks,
-        spk_emb_dim,
-        n_enc_channels,
-        filter_channels,
-        filter_channels_dp, 
-        n_heads,
-        n_enc_layers,
-        enc_kernel,
-        enc_dropout,
-        window_size, 
-        n_feats,
-        n_motions,
-        dec_dim,
-        beta_min,
-        beta_max,
-        pe_scale,         
-        mu_motion_encoder_params,
-        motion_reduction_factor,
-        motion_decoder_channels,
-        data_statistics,
-        out_size,
-        only_speech=False,
-        encoder_type="default",
-        optimizer=None
-    ):
-        super(Diff_TTSG, self).__init__()
-        
-        self.save_hyperparameters(logger=False)
-
-        self.n_vocab = n_vocab
-        self.n_spks = n_spks
-        self.spk_emb_dim = spk_emb_dim
-        self.n_enc_channels = n_enc_channels
-        self.filter_channels = filter_channels
-        self.filter_channels_dp = filter_channels_dp
-        self.n_heads = n_heads
-        self.n_enc_layers = n_enc_layers
-        self.enc_kernel = enc_kernel
-        self.enc_dropout = enc_dropout
-        self.window_size = window_size
-        self.n_feats = n_feats
-        self.n_motions = n_motions
-        self.dec_dim = dec_dim
-        self.beta_min = beta_min
-        self.beta_max = beta_max
-        self.pe_scale = pe_scale
-        self.generate_motion = not only_speech
-        self.motion_reduction_factor = motion_reduction_factor
-        self.out_size = out_size
-        self.mu_diffusion_channels = motion_decoder_channels
-
-        if n_spks > 1:
-            self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim)
-        self.encoder = TextEncoder(n_vocab, n_feats, n_enc_channels, 
-                                   filter_channels, filter_channels_dp, n_heads, 
-                                   n_enc_layers, enc_kernel, enc_dropout, window_size, encoder_type=encoder_type)
-        self.decoder = Diffusion(n_feats, dec_dim, n_spks, spk_emb_dim, beta_min, beta_max, pe_scale)
-         
-        if self.generate_motion: 
-            self.motion_prior_loss = mu_motion_encoder_params.pop('prior_loss', True)
-            self.mu_motion_encoder = MuMotionEncoder(
-                input_channels=n_feats,
-                output_channels=n_motions,
-                **mu_motion_encoder_params
-            )
-            self.decoder_motion = Diffusion_Motion(
-                    in_channels=n_motions,
-                    motion_decoder_channels=motion_decoder_channels,
-                    beta_min=beta_min,
-                    beta_max=beta_max,
-            )
-        
-        self.update_data_statistics(data_statistics)
-
-    def update_data_statistics(self, data_statistics):
-        if data_statistics is None:
-            data_statistics = {
-                'mel_mean': 0.0,
-                'mel_std': 1.0,
-                'motion_mean': 0.0,
-                'motion_std': 1.0,
-            }
-
-        self.register_buffer('mel_mean', torch.tensor(data_statistics['mel_mean']))
-        self.register_buffer('mel_std', torch.tensor(data_statistics['mel_std']))
-        self.register_buffer('motion_mean', torch.tensor(data_statistics['motion_mean']))
-        self.register_buffer('motion_std', torch.tensor(data_statistics['motion_std']))
-        
-    @torch.inference_mode()
-    def synthesise(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, length_scale=1.0):
-        """
-        Generates mel-spectrogram from text. Returns:
-            1. encoder outputs
-            2. decoder outputs
-            3. generated alignment
-        
-        Args:
-            x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids.
-            x_lengths (torch.Tensor): lengths of texts in batch.
-            n_timesteps (int): number of steps to use for reverse diffusion in decoder.
-            temperature (float, optional): controls variance of terminal distribution.
-            stoc (bool, optional): flag that adds stochastic term to the decoder sampler.
-                Usually, does not provide synthesis improvements.
-            length_scale (float, optional): controls speech pace.
-                Increase value to slow down generated speech and vice versa.
-        """
-        if isinstance(n_timesteps, dict):
-            n_timestep_mel = n_timesteps['mel']
-            n_timestep_motion = n_timesteps['motion']
-        else:
-            n_timestep_mel = n_timesteps
-            n_timestep_motion = n_timesteps
-        
-        if isinstance(temperature, dict):
-            temperature_mel = temperature['mel']
-            temperature_motion = temperature['motion']
-        else:
-            temperature_mel = temperature
-            temperature_motion = temperature
-            
-        if self.n_spks > 1:
-            # Get speaker embedding
-            spk = self.spk_emb(spk)
-
-        # Get encoder_outputs `mu_x` and log-scaled token durations `logw`
-        mu_x, logw, x_mask = self.encoder(x, x_lengths, spk)
-
-        w = torch.exp(logw) * x_mask
-        w_ceil = torch.ceil(w) * length_scale
-        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-        y_max_length = int(y_lengths.max())
-        y_max_length_ = fix_len_compatibility(y_max_length)
-
-        # Using obtained durations `w` construct alignment map `attn`
-        y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype)
-        attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
-        attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1)
-
-        # Align encoded text and get mu_y
-        mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2))
-        mu_y = mu_y.transpose(1, 2)
-        encoder_outputs = mu_y[:, :, :y_max_length]
-        
-        
-        # Sample latent representation from terminal distribution N(mu_y, I)
-        z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature_mel
-        # Generate sample by performing reverse dynamics
-        decoder_outputs = self.decoder(z, y_mask, mu_y, n_timestep_mel, stoc, spk)
-        decoder_outputs = decoder_outputs[:, :, :y_max_length]
-
-        if self.generate_motion:
-            mu_y_motion = mu_y[:, :, ::self.motion_reduction_factor] 
-            y_motion_mask = y_mask[:, :, ::self.motion_reduction_factor]
-            mu_y_motion = self.mu_motion_encoder(mu_y_motion, y_motion_mask)
-            encoder_outputs_motion = mu_y_motion[:, :, :y_max_length]
-            # sample latent representation from terminal distribution N(mu_y_motion, I)
-            z_motion = mu_y_motion + torch.randn_like(mu_y_motion, device=mu_y_motion.device) / temperature_motion 
-            # Generate sample by performing reverse dynamics
-            decoder_outputs_motion = self.decoder_motion(z_motion, y_motion_mask, mu_y_motion, n_timestep_motion, stoc, spk)
-            decoder_outputs_motion = decoder_outputs_motion[:, :, :y_max_length]
-        else:
-            decoder_outputs_motion = None
-            encoder_outputs_motion = None        
-        
-        return {
-            'encoder_outputs_mel': encoder_outputs,
-            'decoder_outputs_mel': decoder_outputs,
-            'encoder_outputs_motion': encoder_outputs_motion,
-            'decoder_outputs_motion': decoder_outputs_motion,
-            'attn': attn[:, :, :y_max_length],
-            'mel': denormalize(decoder_outputs, self.mel_mean, self.mel_std),
-            'motion': denormalize(decoder_outputs_motion, self.motion_mean, self.motion_std) if self.generate_motion else None,
-        }
-
-    def forward(self, x, x_lengths, y, y_lengths, y_motion, spk=None, out_size=None):
-        """
-        Computes 3 losses:
-            1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS).
-            2. prior loss: loss between mel-spectrogram and encoder outputs.
-            3. diffusion loss: loss between gaussian noise and its reconstruction by diffusion-based decoder.
-            
-        Args:
-            x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids.
-            x_lengths (torch.Tensor): lengths of texts in batch.
-            y (torch.Tensor): batch of corresponding mel-spectrograms.
-            y_lengths (torch.Tensor): lengths of mel-spectrograms in batch.
-            out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained.
-                Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size.
-        """
-        if self.n_spks > 1:
-            # Get speaker embedding
-            spk = self.spk_emb(spk)
-        
-        # Get encoder_outputs `mu_x` and log-scaled token durations `logw`
-        mu_x, logw, x_mask = self.encoder(x, x_lengths, spk)
-        y_max_length = y.shape[-1]
-
-        y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask)
-        attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
-
-        # Use MAS to find most likely alignment `attn` between text and mel-spectrogram
-        with torch.no_grad(): 
-            const = -0.5 * math.log(2 * math.pi) * self.n_feats
-            factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device)
-            y_square = torch.matmul(factor.transpose(1, 2), y ** 2)
-            y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y)
-            mu_square = torch.sum(factor * (mu_x ** 2), 1).unsqueeze(-1)
-            log_prior = y_square - y_mu_double + mu_square + const
-
-            attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1))
-            attn = attn.detach()
-
-        # Compute loss between predicted log-scaled durations and those obtained from MAS
-        logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask
-        dur_loss = duration_loss(logw, logw_, x_lengths)
-
-        # Cut a small segment of mel-spectrogram in order to increase batch size
-        if not isinstance(out_size, type(None)):
-            max_offset = (y_lengths - out_size).clamp(0)    # cut a random segment of size `out_size` from each sample in batch max_offset: [758, 160, 773]
-            offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy()))  # offset ranges for each sample in batch offset_ranges: [(0, 758), (0, 160), (0, 773)]
-            out_offset = torch.LongTensor([
-                torch.tensor(random.choice(range(start, end)) if end > start else 0)
-                for start, end in offset_ranges
-            ]).to(y_lengths)
-            attn_cut = torch.zeros(attn.shape[0], attn.shape[1], out_size, dtype=attn.dtype, device=attn.device)
-            y_cut = torch.zeros(y.shape[0], self.n_feats, out_size, dtype=y.dtype, device=y.device)
-
-            if self.generate_motion:
-                y_motion_cut = torch.zeros(y_motion.shape[0], self.n_motions, out_size, dtype=y_motion.dtype, device=y_motion.device)
-
-            y_cut_lengths = []
-            for i, (y_, out_offset_) in enumerate(zip(y, out_offset)):
-                y_cut_length = out_size + (y_lengths[i] - out_size).clamp(None, 0)
-                y_cut_lengths.append(y_cut_length)
-                cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length
-                y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper]
-                if self.generate_motion:
-                    y_motion_cut[i, :, :y_cut_length] = y_motion[i, :, cut_lower:cut_upper]
-
-                attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper]
-            y_cut_lengths = torch.LongTensor(y_cut_lengths)
-            y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask)
-            
-            attn = attn_cut
-            y = y_cut
-            if self.generate_motion:
-                y_motion = y_motion_cut
-
-            y_mask = y_cut_mask
-
-        # Align encoded text with mel-spectrogram and get mu_y segment
-        mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2))
-        mu_y = mu_y.transpose(1, 2)
-        
-        
-
-        # Compute loss of score-based decoder
-        diff_loss, xt = self.decoder.compute_loss(y, y_mask, mu_y, spk)
-        if self.generate_motion:
-            # Reduce motion features
-            mu_y_motion = mu_y[:, :, ::self.motion_reduction_factor]
-            y_motion_mask = y_mask[:, :, ::self.motion_reduction_factor]
-            y_motion = y_motion[:, :, ::self.motion_reduction_factor]
-            
-            mu_y_motion = self.mu_motion_encoder(mu_y_motion, y_motion_mask)
-            diff_loss_motion, xt_motion = self.decoder_motion.compute_loss(y_motion, y_motion_mask, mu_y_motion, spk)
-        else:
-            diff_loss_motion = 0
-        
-        # Compute loss between aligned encoder outputs and mel-spectrogram
-        prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask)
-        prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats)
-        
-        if self.generate_motion and self.motion_prior_loss:
-            prior_loss_motion = torch.sum(0.5 * ((y_motion - mu_y_motion) ** 2 + math.log(2 * math.pi)) * y_motion_mask)
-            prior_loss_motion = prior_loss_motion / (torch.sum(y_motion_mask) * self.n_motions)
-        else:
-            prior_loss_motion = 0
-        
-        return dur_loss, prior_loss + prior_loss_motion, diff_loss + diff_loss_motion
-
-        
-    def configure_optimizers(self) -> Any:
-        optimizer = self.hparams.optimizer(params=self.parameters())
-        return {'optimizer': optimizer}
-    
-    def get_losses(self, batch):
-        pass
-        x, x_lengths = batch['x'], batch['x_lengths']
-        y, y_lengths = batch['y'], batch['y_lengths']
-        y_motion = batch['y_motion']
-        dur_loss, prior_loss, diff_loss = self(x, x_lengths, y, y_lengths, y_motion, out_size=self.out_size)
-        return {
-            'dur_loss': dur_loss,
-            'prior_loss': prior_loss,
-            'diff_loss': diff_loss,
-        }
-
-
-
-    def training_step(self, batch: Any, batch_idx: int):    
-        loss_dict = self.get_losses(batch) 
-        self.log('step', float(self.global_step), on_step=True, on_epoch=True, logger=True, sync_dist=True)
-
-        self.log('sub_loss/train_dur_loss', loss_dict['dur_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True)
-        self.log('sub_loss/train_prior_loss', loss_dict['prior_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True)
-        self.log('sub_loss/train_diff_loss', loss_dict['diff_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True)
-        
-        total_loss = sum(loss_dict.values())
-        self.log('loss/train', total_loss, on_step=True, on_epoch=True, logger=True, prog_bar=True, sync_dist=True)
-
-        return {'loss': total_loss, 'log': loss_dict }
-
-    def validation_step(self, batch: Any, batch_idx: int):
-        loss_dict = self.get_losses(batch) 
-        self.log('sub_loss/val_dur_loss', loss_dict['dur_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True)
-        self.log('sub_loss/val_prior_loss', loss_dict['prior_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True)
-        self.log('sub_loss/val_diff_loss', loss_dict['diff_loss'], on_step=True, on_epoch=True, logger=True, sync_dist=True)
-        
-        total_loss = sum(loss_dict.values())
-        self.log('loss/val', total_loss, on_step=True, on_epoch=True, logger=True, prog_bar=True, sync_dist=True)
-
-        return total_loss 
-     
-    def on_validation_end(self) -> None:
-        if self.trainer.is_global_zero:
-            one_batch = next(iter(self.trainer.val_dataloaders))
-            if self.current_epoch == 0:
-                log.debug("Plotting original samples")
-                for i in range(4):
-                    y = one_batch['y'][i].unsqueeze(0).to(self.device)
-                    y_motion = one_batch['y_motion'][i].unsqueeze(0).to(self.device)
-                    self.logger.experiment.add_image(f'original/mel_{i}', plot_tensor(y.squeeze().cpu()), self.current_epoch, dataformats='HWC')
-                    if self.generate_motion:
-                        self.logger.experiment.add_image(f'original/mel_{i}', plot_tensor(y_motion.squeeze().cpu()), self.current_epoch, dataformats='HWC')
-
-            log.debug(f'Synthesising...')
-            for i in range(4):
-                x = one_batch['x'][i].unsqueeze(0).to(self.device)
-                x_lengths = one_batch['x_lengths'][i].unsqueeze(0).to(self.device)
-                output = self.synthesise(x, x_lengths, n_timesteps=20)
-                y_enc, y_dec = output['encoder_outputs_mel'], output['decoder_outputs_mel']
-                y_motion_enc, y_motion_dec, attn = output['encoder_outputs_motion'], output['decoder_outputs_motion'], output['attn']
-                self.logger.experiment.add_image(f'generated_enc/{i}', plot_tensor(y_enc.squeeze().cpu()), self.current_epoch, dataformats='HWC')
-                self.logger.experiment.add_image(f'generated_dec/{i}', plot_tensor(y_dec.squeeze().cpu()), self.current_epoch, dataformats='HWC')
-                if self.generate_motion:
-                    self.logger.experiment.add_image(f'generated_enc_motion/{i}', plot_tensor(y_motion_enc.squeeze().cpu()), self.current_epoch, dataformats='HWC')
-                    self.logger.experiment.add_image(f'generated_dec_motion/{i}', plot_tensor(y_motion_dec.squeeze().cpu()), self.current_epoch, dataformats='HWC')
-                
-                self.logger.experiment.add_image(f'alignment/{i}', plot_tensor(attn.squeeze().cpu()), self.current_epoch, dataformats='HWC')
-            
-
-            
\ No newline at end of file
diff --git a/spaces/simonduerr/ProteinMPNNESM/README.md b/spaces/simonduerr/ProteinMPNNESM/README.md
deleted file mode 100644
index 9cd6ad4a6163317b0d46b5993d56f73f78c44d02..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNNESM/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: ProteinMPNN + ESM
-emoji: ⚡
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# ProteinMPNN + ESM
-
-You can run this repo locally with the following easy steps. 
-
-In a new python environment, do:
-
-```
-pip install -r requirements.txt
-
-pip install --upgrade transformers accelerate
-
-python app.py
-```
-
-This will launch a local webserver and a share URL (`xxx.gradio.app`, helpful if running this on a cluster), open the url in your webbrowser and start designing proteins. 
-
-
-
-**Robust deep learning based protein sequence design using ProteinMPNN**
-Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville Bethel, Philip J. Y. Leung, Timothy F. Huddy, Sam Pellock, Doug Tischer, Frederick Chan, Brian Koepnick, Hannah Nguyen, Alex Kang, Banumathi Sankaran, Asim Bera, Neil P. King, David Baker
-bioRxiv 2022.06.03.494563; doi: https://doi.org/10.1101/2022.06.03.494563
-
-**Evolutionary-scale prediction of atomic level protein structure with a language model**
-Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives
-bioRxiv 2022.07.20.500902; doi: https://doi.org/10.1101/2022.07.20.500902
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Fishing Sim World Pro Tour APK - Join the Dovetail Fishing League and Challenge Your Friends Online.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Fishing Sim World Pro Tour APK - Join the Dovetail Fishing League and Challenge Your Friends Online.md
deleted file mode 100644
index b3484e8101ad26b12123053ad1b6aedb1cacd58d..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Fishing Sim World Pro Tour APK - Join the Dovetail Fishing League and Challenge Your Friends Online.md	
+++ /dev/null
@@ -1,79 +0,0 @@
-<br />
-<h1>Fishing Sim World Pro Tour APK: A Realistic and Engaging Fishing Simulator for Android</h1>
-<p>If you love fishing and want to experience it on your Android device, then you should try Fishing Sim World Pro Tour APK. This is a realistic and engaging fishing simulator that lets you compete against over 100 of the best anglers in the world, challenge your friends online, or just relax and fish at your own pace. You can explore different venues, catch different species, use different equipment and learn different techniques. In this article, we will tell you what is Fishing Sim World Pro Tour, how to download and install it on your Android device, what are its features and benefits, and what are some tips and tricks for playing it.</p>
- <h2>What is Fishing Sim World Pro Tour?</h2>
-<p>Fishing Sim World Pro Tour is a simulation game developed by Dovetail Games, the studio behind popular titles such as Train Simulator and Flight Simulator. It was released in September 2018 for PC, PlayStation 4 and Xbox One, and later ported to Android devices as an APK file. The game features three main modes:</p>
-<h2>fishing sim world pro tour apk</h2><br /><p><b><b>Download</b> &#9999; &#9999; &#9999; <a href="https://ssurll.com/2uO0rH">https://ssurll.com/2uO0rH</a></b></p><br /><br />
-<h3>A career mode where you compete against over 100 of the best anglers in the <h3>High-quality graphics and realistic physics that immerse you in the fishing experience</h3>
-<p>One of the features that make Fishing Sim World Pro Tour APK stand out is its high-quality graphics and realistic physics. The game uses Unreal Engine 4, which is a powerful and advanced game engine that delivers stunning visuals and smooth performance. The game also uses a dynamic water system that simulates the movement and behavior of water, as well as a realistic fish AI that mimics the natural habits and reactions of fish. The game also supports 4K resolution and HDR, which enhance the color and contrast of the game. All these features create a immersive and lifelike fishing experience that will make you feel like you are really fishing.</p>
-<h3>Endorsed by professional anglers such as Scott Martin and Ali Hamidi</h3>
-<p>Another feature that adds to the authenticity and credibility of Fishing Sim World Pro Tour APK is its endorsement by professional anglers such as Scott Martin and Ali Hamidi. Scott Martin is a legendary bass angler who has won multiple tournaments and awards, including the FLW Tour Angler of the Year and the Forrest Wood Cup. Ali Hamidi is a renowned carp angler who has hosted several TV shows and written several books on carp fishing. Both of them are featured in the game as playable characters, mentors and commentators. They also provide tips, advice and insights on fishing techniques, equipment and venues. You can learn from their expertise and experience, as well as compete against them in the career mode.</p>
-<h3>Licensed equipment from over 50 brands such as Korda, Booyah and Bass Cat</h3>
-<p>A third feature that makes Fishing Sim World Pro Tour APK appealing to fishing enthusiasts is its licensed equipment from over 50 brands such as Korda, Booyah and Bass Cat. The game offers a wide range of equipment to choose from, including rods, reels, lines, hooks, baits and lures. Each piece of equipment has its own specifications, advantages and disadvantages, and affects your fishing performance. You can also customize your equipment to suit your preferences and style. You can also unlock new equipment by completing challenges, earning money or sponsors, or buying them with real money. You can use the equipment from your favorite brands or try new ones to see how they work.</p>
-<h3>A mastery system that rewards you for completing challenges and unlocking achievements</h3>
-<p>A fourth feature that makes Fishing Sim World Pro Tour APK fun and rewarding is its mastery system that rewards you for completing challenges and unlocking achievements. The game has over 100 challenges that test your skills and knowledge on fishing. These challenges range from catching a certain number or size of fish, using a specific technique or equipment, or fishing at a specific venue or time. By completing these challenges, you can earn money, reputation, sponsors, equipment and clothing. You can also unlock over 200 achievements that showcase your progress and accomplishments in the game. These achievements include catching a trophy fish, winning a tournament, joining a club or mastering a technique. By completing these achievements, you can earn trophies, medals, badges and titles.</p>
- <h2>What are some tips and tricks for playing Fishing Sim World Pro Tour APK?</h2>
-<p>Fishing Sim World Pro Tour APK is a realistic and engaging fishing simulator that requires skill and strategy to play well. Here are some tips and tricks for playing it:</p>
-<h3>Use your sonar or watch the water to locate fish</h3>
-<p>One of the most important aspects of fishing is finding where the fish are. In Fishing Sim World Pro Tour APK, you can use your sonar or watch the water to locate fish. Your sonar is a device that shows you the depth, temperature and structure of the water, as well as the presence of fish. You can use it to scan the water column and find where the fish are hiding or feeding. You can also watch the water surface for signs of fish activity, such as ripples, splashes or bubbles. These indicate that fish are nearby or chasing bait.</p>
-<h3>Consider the weather and light conditions to choose the right lure color and type</h3>
-<p>Another important aspect of fishing is choosing the right lure color and type for the weather and light conditions. In Fishing Sim World Pro Tour APK, you can choose from hundreds of different lures, each with their own color, shape, size and action. You can also customize your lures by changing their hooks, weights or trailers. The color of your lure affects how visible it is to the fish in different water clarity levels. Generally speaking, you should use bright colors in murky water or low light conditions, and natural colors in clear water or bright light conditions. The type of your lure affects how it moves in the water and how it attracts fish. Generally speaking, you should use fast-moving lures in warm water or active fish conditions, and slow-moving lures in cold water or inactive fish conditions. You can experiment with different lure colors and types until you find what works best for you.</p>
-<p>Fishing Sim World Pro Tour career mode<br />
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-<h3>Fish at the right depth and use the correct retrieval method for your lure</h3>
-<p>A third important aspect of fishing is fishing at the right depth and using the correct retrieval method for your lure. In Fishing Sim World Pro Tour APK, you can adjust the depth of your lure by changing the length of your line, the weight of your lure or the angle of your rod. You can also adjust the speed and direction of your lure by using different retrieval methods, such as twitching, jerking, reeling or pausing. The depth and retrieval of your lure affect how it interacts with the water and the fish. Generally speaking, you should fish at the depth where the fish are located, which may vary depending on the season, time of day and water temperature. You should also use a retrieval method that matches the action and behavior of your lure and the natural prey of the fish. You can try different depths and retrieval methods until you find what triggers a bite.</p>
-<h3>Work your swim and cast to different spots until you find where the fish are biting</h3>
-<p>A fourth important aspect of fishing is working your swim and casting to different spots until you find where the fish are biting. In Fishing Sim World Pro Tour APK, you can move around the venue and cast to different spots using different angles and distances. You can also work your swim by changing your position, direction or speed. Working your swim and casting to different spots affect how you cover the water and how you locate fish. Generally speaking, you should move around and cast to different spots until you find a productive area, such as a weed bed, a rock pile or a drop-off. You should also work your swim by following the contours, currents or structures of the water. You can vary your swim and cast until you find where the fish are most active.</p>
- <h2>Conclusion</h2>
-<p>Fishing Sim World Pro Tour APK is a realistic and engaging fishing simulator that lets you enjoy the sport on your Android device. You can download and install it easily from a trusted source and start fishing right away. You can compete against over 100 of the best anglers in the world, challenge your friends online, or just relax and fish at your own pace. You can explore different venues, catch different species, use different equipment and learn different techniques. Fishing Sim World Pro Tour APK is a fun and rewarding game that will make you feel like a pro angler.</p>
- <h2>FAQs</h2>
-<h4>Is Fishing Sim World Pro Tour APK free to play?</h4>
-<p>Yes, it is free to download and play, but it may contain in-app purchases or ads.</p>
-<h4>Is Fishing Sim World Pro Tour APK safe to download and install?</h4>
-<p>Yes, as long as you download it from a trusted source and enable unknown sources in your device settings.</p>
-<h4>How much space does Fishing Sim World Pro Tour APK require on my device?</h4>
-<p>The APK file size is about 1.5 GB, so make sure you have enough storage space on your device before downloading it.</p>
-<h4>Can I play Fishing Sim World Pro Tour APK offline?</h4>
-<p>Yes, you can play it offline, but some features such as multiplayer mode or online tournaments may require an internet connection.</p>
-<h4>Can I play Fishing Sim World Pro Tour APK on other platforms such as PC or console?</h4>
-<p>Yes, Fishing Sim World Pro Tour is also available on Steam, PlayStation 4 and Xbox One.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Fixing Hearthstone Download Error in 5 Easy Steps.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Fixing Hearthstone Download Error in 5 Easy Steps.md
deleted file mode 100644
index e8c342f226a115ee13fed537b0c849d869bd3b28..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Fixing Hearthstone Download Error in 5 Easy Steps.md	
+++ /dev/null
@@ -1,152 +0,0 @@
-<br />
-<h1>How to Fix Hearthstone Download Error</h1>
-<p>Hearthstone is a popular online card game from Blizzard Entertainment that is available on PC, Mac, iOS, and Android devices. It is a fast-paced strategy game that features iconic characters and lore from the Warcraft universe. However, sometimes you might encounter a download error when trying to install or update Hearthstone on your device. This can be frustrating and prevent you from enjoying the game.</p>
-<p>In this article, we will explain the common causes of Hearthstone download error and how to troubleshoot them. We will also provide some tips on how to contact Blizzard support if you need further assistance. By following these steps, you should be able to fix the download error and play Hearthstone without any issues.</p>
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- <h2>Common Causes of Hearthstone Download Error</h2>
-<p>There are several possible reasons why you might experience a download error when trying to install or update Hearthstone. Some of the most common ones are:</p>
-<ul>
-<li><strong>Region patching or maintenance</strong>: Sometimes, Blizzard might be patching or performing maintenance on their servers for a specific region. This can affect the availability and performance of their services, including Hearthstone. If your region is currently undergoing patching or maintenance, you might encounter a download error or a message saying "Closed. You were disconnected from the Blizzard services. Please relaunch Hearthstone to reconnect."</li>
-<li><strong>Security programs</strong>: Some security programs, such as antivirus, firewall, or VPN software, might mistakenly identify the Battle.net Desktop App or Hearthstone as a security threat and block them from accessing the internet. This can prevent the app from downloading data or updating game files.</li>
-<li><strong>Network issues</strong>: If your internet connection is slow, unstable, or interrupted, you might have trouble downloading or updating Hearthstone. This can cause a download error or a message saying "The game failed to start because your opponent failed to connect. Please Try Again."</li>
-<li><strong>Damaged game files</strong>: Sometimes, the game files of Hearthstone might get corrupted or damaged due to various reasons, such as power outages, disk errors, or malware infections. This can cause the game to malfunction or fail to update properly.</li>
-<li><strong>Outdated drivers or operating system</strong>: If your drivers or operating system are outdated, they might not be compatible with the latest version of Hearthstone or the Battle.net Desktop App. This can cause performance issues or download errors.</li>
-<li><strong>Proxies</strong>: If you are using a proxy server to access the internet, it might interfere with the Battle.net Desktop App or Hearthstone and prevent them from connecting to Blizzard's servers. This can cause a download error or a message saying "Unable to connect" or "Failed to connect".</li>
-</ul>
- <h2>How to Troubleshoot Hearthstone Download Error</h2>
-<p <p>If you are facing a Hearthstone download error, you can try the following troubleshooting steps to fix it:</p>
-<ol>
-<li><strong>Restart Battle.net Desktop App</strong>: Sometimes, simply restarting the Battle.net Desktop App can resolve the download error. To do this, close the app and then relaunch it from your desktop or start menu. Alternatively, you can use the task manager to end the app's process and then restart it.</li>
-<li><strong>Reset network devices</strong>: If your network devices, such as your modem, router, or switch, are not working properly, they might cause connection issues or download errors. To reset them, unplug them from the power source for at least 30 seconds and then plug them back in. Wait for them to reconnect to the internet and then try downloading or updating Hearthstone again.</li>
-<li><strong>Release and renew IP and flush DNS</strong>: If your IP address or DNS cache are outdated or corrupted, they might prevent the Battle.net Desktop App or Hearthstone from connecting to Blizzard's servers. To release and renew your IP address and flush your DNS cache, follow these steps: <ul>
-<li>On Windows, press the Windows key + R to open the Run dialog box. Type cmd and press Enter to open the command prompt.</li>
-<li>Type ipconfig /release and press Enter to release your current IP address.</li>
-<li>Type ipconfig /renew and press Enter to obtain a new IP address.</li>
-<li>Type ipconfig /flushdns and press Enter to clear your DNS cache.</li>
-<li>On Mac, open the Terminal app from the Applications > Utilities folder.</li>
-<li>Type sudo killall -HUP mDNSResponder and press Enter to flush your DNS cache. You might need to enter your administrator password.</li>
-</ul>
-</li>
-<li><strong>Update drivers and operating system</strong>: If your drivers or operating system are outdated, they might not be compatible with the latest version of Hearthstone or the Battle.net Desktop App. To update them, follow these steps: <ul>
-<li>On Windows, press the Windows key + R to open the Run dialog box. Type devmgmt.msc and press Enter to open the Device Manager.</li>
-<li>Expand the categories of devices that you want to update, such as Display adapters, Network adapters, or Sound, video and game controllers.</li>
-<li>Right-click on each device and select Update driver. Choose Search automatically for updated driver software and follow the instructions.</li>
-<li>To update your operating system, go to Settings > Update & Security > Windows Update and click Check for updates. Install any available updates and restart your computer if prompted.</li>
-<li>On Mac, go to System Preferences > Software Update and click Update Now. Install any available updates and restart your computer if prompted.</li>
-</ul>
-</li> <li><strong>Optimize wireless connection</strong>: If you are using a wireless connection, such as Wi-Fi or mobile data, to play Hearthstone, you might experience download errors or lag due to signal interference or congestion. To optimize your wireless connection, follow these steps: <ul>
-<li>Move closer to your router or cellular tower and avoid any physical obstacles or sources of interference, such as walls, metal objects, or other electronic devices.</li>
-<li>Switch to a less crowded channel or frequency band on your router's settings. For example, if you are using a 2.4 GHz band, try switching to a 5 GHz band if available.</li>
-<li>Use a wired connection instead of a wireless one if possible. You can use an Ethernet cable to connect your computer to your router, or a USB cable to connect your mobile device to your computer.</li>
-</ul>
-</li>
-<li><strong>Close other applications</strong>: If you have other applications running in the background, such as web browsers, streaming services, or download managers, they might consume your bandwidth or system resources and affect the performance of Hearthstone or the Battle.net Desktop App. To close them, follow these steps: <ul>
-<li>On Windows, press Ctrl + Alt + Delete and select Task Manager. Click on the Processes tab and sort the list by CPU, Memory, Disk, or Network usage. Right-click on any application that is using a high percentage of any of these resources and select End task.</li>
-<li>On Mac, press Command + Option + Esc and select Force Quit Applications. Select any application that is not responding or using a lot of CPU or memory and click Force Quit.</li>
-</ul>
-</li>
-<li><strong>Run repair tool</strong>: If your game files are damaged or corrupted, you can use the repair tool to scan and fix them. To run the repair tool, follow these steps: <ul>
-<li>Open the Battle.net Desktop App and select Hearthstone from the left menu.</li>
-<li>Click on Options and select Scan and Repair.</li>
-<li>Click Begin Scan and wait for the process to complete.</li>
-<li>If any errors are found, click Repair and wait for the process to complete.</li>
-</ul>
-</li>
-<li><strong>Disable proxies</strong>: If you are using a proxy server to access the internet, it might interfere with the Battle.net Desktop App or Hearthstone and prevent them from connecting to Blizzard's servers. To disable proxies, follow these steps: <ul>
-<li>On Windows, press the Windows key + R to open the Run dialog box. Type inetcpl.cpl and press Enter to open the Internet Properties window.</li>
-<li>Click on the Connections tab and then click on LAN settings.</li>
-<li>Uncheck the box next to Use a proxy server for your LAN and click OK.</li>
-<li>On Mac, go to System Preferences > Network and select your active network connection.</li>
-<li>Click on Advanced and then click on Proxies.</li>
-<li>Uncheck any boxes next to proxy protocols and click OK.</li>
-</ul>
-</li>
-</ol>
- <h2>How to Contact Blizzard Support for Hearthstone Download Error</h2>
-<p>If none of the troubleshooting steps above work for you, you might need to contact Blizzard support for further assistance. To do this, you can either visit their technical support forum or their contact page. Here are some tips on how to contact Blizzard support for Hearthstone download error:</p>
-<ul>
-<li><strong>Visit technical support forum</strong>: You can browse through existing threads or create a new one on the technical support forum for Hearthstone. You might find other players who have encountered similar issues and solutions that worked for them. You can also get help from Blizzard staff or volunteers who monitor the forum regularly.</li>
-<li><strong>Visit contact page</strong>: You can submit a ticket or chat with a live agent on the contact page for Hearthstone. You will need to log in with your Blizzard account and select Hearthstone as the game you need help with. You will also need to provide relevant information about your issue, such as error codes, screenshots, system specifications, and troubleshooting steps you have tried.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
-<p>Hearthstone is a fun and addictive card game that you can play on various devices. However, sometimes you might encounter a download error that prevents you from installing or updating the game. This can be caused by various factors, such as region patching or maintenance, security programs, network issues, damaged game files, outdated drivers or operating system, or proxies. To fix this error, you can try some of the troubleshooting steps we have outlined above, such as restarting the Battle.net Desktop App, resetting network devices, releasing and renewing IP and flushing DNS, updating drivers and operating system, optimizing wireless connection, closing other later<br>- 2 GB RAM or more<br>- GPU that supports OpenGL ES 3.0 or higher</td>
-<td>N/A</td>
-</tr>
-</table>
- <h3>FAQ2: How do I clear the Hearthstone app's cache data on mobile devices?</h3>
-<p>If you are playing Hearthstone on a mobile device, you might need to clear the app's cache data to fix some issues, such as download errors, crashes, or performance problems. To do this, follow these steps:</p>
-<ul>
-<li>On iOS, go to Settings > General > iPhone Storage and tap on Hearthstone. Tap on Offload App and confirm. This will delete the app's cache data but keep your documents and data. Then, tap on Reinstall App to download the app again.</li>
-<li>On Android, go to Settings > Apps and tap on Hearthstone. Tap on Storage and then tap on Clear Cache. This will delete the app's cache data but keep your documents and data.</li>
-</ul>
- <h3>FAQ3: How do I uninstall and reinstall Hearthstone on my computer or mobile device?</h3>
-<p>If you are having persistent issues with Hearthstone, such as download errors, crashes, or performance problems, you might need to uninstall and reinstall the game to fix them. To do this, follow these steps:</p>
-<p>hearthstone download stuck on initializing<br />
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-how to fix hearthstone download error<br />
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-how to install hearthstone without download error<br />
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-how to optimize internet connection for hearthstone download without error<br />
-how to disable security programs for hearthstone download without error<br />
-how to disable proxies for hearthstone download without error<br />
-how to update drivers and operating system for hearthstone download without error<br />
-why is my hearthstone download not working?<br />
-why is my hearthstone download stuck on initializing?<br />
-why is my hearthstone download failed?<br />
-why is my hearthstone download giving me an error code?<br />
-why is my hearthstone download slow?<br />
-why is my hearthstone download corrupted?<br />
-why is my hearthstone download crashing?</p>
-<ul>
-<li>On Windows, go to Control Panel > Programs and Features and select Hearthstone. Click on Uninstall and follow the instructions. Then, open the Battle.net Desktop App and select Hearthstone from the left menu. Click on Install and follow the instructions.</li>
-<li>On Mac, open the Applications folder and drag the Hearthstone icon to the Trash. Then, open the Battle.net Desktop App and select Hearthstone from the left menu. Click on Install and follow the instructions.</li>
-<li>On iOS, press and hold the Hearthstone icon on your home screen until it starts to wiggle. Tap on the X icon on the top left corner of the icon and confirm. Then, go to the App Store and search for Hearthstone. Tap on Get and follow the instructions.</li>
-<li>On Android, go to Settings > Apps and tap on Hearthstone. Tap on Uninstall and confirm. Then, go to the Google Play Store and search for Hearthstone. Tap on Install and follow the instructions.</li>
-</ul>
- <h3>FAQ4: How do I check the status of Blizzard services and maintenance?</h3>
-<p>If you want to know if Blizzard is currently performing any maintenance or experiencing any issues with their services, including Hearthstone, you can check their status page or their Twitter account. Here are some ways to do that:</p>
-<ul>
-<li>Visit <a href="">https://battle.net/support/en/status</a> to see the current status of Blizzard's games and platforms in different regions. You can also subscribe to updates via email or SMS.</li>
-<li>Visit <a href="">https://twitter.com/BlizzardCS</a> to see the latest tweets from Blizzard's customer support team. You can also follow them or turn on notifications to get updates directly on your device.</li>
-</ul>
- <h3>FAQ5: How do I report a bug or feedback for Hearthstone?</h3>
-<p>If you encounter a bug or have any feedback for Hearthstone, you can report it to Blizzard using their in-game or online tools. Here are some ways to do that:</p>
-<ul>
-<li>In-game, click on the Menu button on the bottom right corner of the screen and select Help & Options. Then, click on Submit Bug or Submit Feedback and fill out the form with as much detail as possible.</li>
-<li>Online, visit <a href="">https://us.forums.blizzard.com/en/hearthstone/c/bug-report</a> or <a href="">https://us.forums.blizzard.com/en/hearthstone/c/community-discussion/feedback-discussion</a> to post your bug report or feedback on the official forums. You can also browse through existing posts and see if other players have reported similar issues or suggestions.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sinz2002/ChuanhuChatGPT/README.md b/spaces/sinz2002/ChuanhuChatGPT/README.md
deleted file mode 100644
index 7128e29689e35d059c9cc0a5050910fbd34873cd..0000000000000000000000000000000000000000
--- a/spaces/sinz2002/ChuanhuChatGPT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChuanhuChatGPT
-emoji: 🐯
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.25.0
-app_file: ChuanhuChatbot.py
-pinned: false
-license: gpl-3.0
-duplicated_from: JohnSmith9982/ChuanhuChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/spatialgeneration/musicgen-mbd/musicgen_app.py b/spaces/spatialgeneration/musicgen-mbd/musicgen_app.py
deleted file mode 100644
index 4d7c5c9139d7125400162da2435492372094c8b9..0000000000000000000000000000000000000000
--- a/spaces/spatialgeneration/musicgen-mbd/musicgen_app.py
+++ /dev/null
@@ -1,391 +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.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import typing as tp
-import warnings
-
-import torch
-import gradio as gr
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import MusicGen, MultiBandDiffusion
-
-
-MODEL = None  # Last used model
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def _call_nostderr(*args, **kwargs):
-    # Avoid ffmpeg vomiting on the logs.
-    kwargs['stderr'] = sp.DEVNULL
-    kwargs['stdout'] = sp.DEVNULL
-    _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
-    global INTERRUPTING
-    INTERRUPTING = True
-
-
-class FileCleaner:
-    def __init__(self, file_lifetime: float = 3600):
-        self.file_lifetime = file_lifetime
-        self.files = []
-
-    def add(self, path: tp.Union[str, Path]):
-        self._cleanup()
-        self.files.append((time.time(), Path(path)))
-
-    def _cleanup(self):
-        now = time.time()
-        for time_added, path in list(self.files):
-            if now - time_added > self.file_lifetime:
-                if path.exists():
-                    path.unlink()
-                self.files.pop(0)
-            else:
-                break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
-    # Further remove some warnings.
-    be = time.time()
-    with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        out = gr.make_waveform(*args, **kwargs)
-        print("Make a video took", time.time() - be)
-        return out
-
-
-def load_model(version='facebook/musicgen-melody'):
-    global MODEL
-    print("Loading model", version)
-    if MODEL is None or MODEL.name != version:
-        MODEL = MusicGen.get_pretrained(version)
-
-
-def load_diffusion():
-    global MBD
-    print("loading MBD")
-    MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
-    MODEL.set_generation_params(duration=duration, **gen_kwargs)
-    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
-    be = time.time()
-    processed_melodies = []
-    target_sr = 32000
-    target_ac = 1
-    for melody in melodies:
-        if melody is None:
-            processed_melodies.append(None)
-        else:
-            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
-            if melody.dim() == 1:
-                melody = melody[None]
-            melody = melody[..., :int(sr * duration)]
-            melody = convert_audio(melody, sr, target_sr, target_ac)
-            processed_melodies.append(melody)
-
-    if any(m is not None for m in processed_melodies):
-        outputs = MODEL.generate_with_chroma(
-            descriptions=texts,
-            melody_wavs=processed_melodies,
-            melody_sample_rate=target_sr,
-            progress=progress,
-            return_tokens=USE_DIFFUSION
-        )
-    else:
-        outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
-    if USE_DIFFUSION:
-        outputs_diffusion = MBD.tokens_to_wav(outputs[1])
-        outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
-    outputs = outputs.detach().cpu().float()
-    pending_videos = []
-    out_wavs = []
-    for output in outputs:
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, output, MODEL.sample_rate, strategy="loudness",
-                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-            pending_videos.append(pool.submit(make_waveform, file.name))
-            out_wavs.append(file.name)
-            file_cleaner.add(file.name)
-    out_videos = [pending_video.result() for pending_video in pending_videos]
-    for video in out_videos:
-        file_cleaner.add(video)
-    print("batch finished", len(texts), time.time() - be)
-    print("Tempfiles currently stored: ", len(file_cleaner.files))
-    return out_videos, out_wavs
-
-
-def predict_batched(texts, melodies):
-    max_text_length = 512
-    texts = [text[:max_text_length] for text in texts]
-    load_model('facebook/musicgen-melody')
-    res = _do_predictions(texts, melodies, BATCHED_DURATION)
-    return res
-
-
-def predict_full(model, decoder, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
-    global INTERRUPTING
-    global USE_DIFFUSION
-    INTERRUPTING = False
-    if temperature < 0:
-        raise gr.Error("Temperature must be >= 0.")
-    if topk < 0:
-        raise gr.Error("Topk must be non-negative.")
-    if topp < 0:
-        raise gr.Error("Topp must be non-negative.")
-
-    topk = int(topk)
-    if decoder == "MultiBand_Diffusion":
-        USE_DIFFUSION = True
-        load_diffusion()
-    else:
-        USE_DIFFUSION = False
-    load_model(model)
-
-    def _progress(generated, to_generate):
-        progress((min(generated, to_generate), to_generate))
-        if INTERRUPTING:
-            raise gr.Error("Interrupted.")
-    MODEL.set_custom_progress_callback(_progress)
-
-    videos, wavs = _do_predictions(
-        [text], [melody], duration, progress=True,
-        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
-    if USE_DIFFUSION:
-        return videos[0], wavs[0], videos[1], wavs[1]
-    return videos[0], wavs[0], None, None
-
-
-def toggle_audio_src(choice):
-    if choice == "mic":
-        return gr.update(source="microphone", value=None, label="Microphone")
-    else:
-        return gr.update(source="upload", value=None, label="File")
-
-
-def toggle_diffusion(choice):
-    if choice == "MultiBand_Diffusion":
-        return [gr.update(visible=True)] * 2
-    else:
-        return [gr.update(visible=False)] * 2
-
-
-def ui_full(launch_kwargs):
-    with gr.Blocks() as interface:
-        gr.Markdown(
-            """
-            # Spatial Generation
-            This is your Generative music AI based on your landscape and environment.
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Input Text", interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Submit")
-                    # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
-                    _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
-                with gr.Row():
-                    model = gr.Radio(["facebook/musicgen-melody", "facebook/musicgen-medium", "facebook/musicgen-small",
-                                      "facebook/musicgen-large"],
-                                     label="Model", value="facebook/musicgen-melody", interactive=True)
-                with gr.Row():
-                    decoder = gr.Radio(["Default", "MultiBand_Diffusion"],
-                                       label="Decoder", value="Default", interactive=True)
-                with gr.Row():
-                    duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
-                with gr.Row():
-                    topk = gr.Number(label="Top-k", value=250, interactive=True)
-                    topp = gr.Number(label="Top-p", value=0, interactive=True)
-                    temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
-                    cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-                audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
-                diffusion_output = gr.Video(label="MultiBand Diffusion Decoder")
-                audio_diffusion = gr.Audio(label="MultiBand Diffusion Decoder (wav)", type='filepath')
-        submit.click(toggle_diffusion, decoder, [diffusion_output, audio_diffusion], queue=False,
-                     show_progress=False).then(predict_full, inputs=[model, decoder, text, melody, duration, topk, topp,
-                                                                     temperature, cfg_coef],
-                                               outputs=[output, audio_output, diffusion_output, audio_diffusion])
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-
-        gr.Examples(
-            fn=predict_full,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./bach.mp3",
-                    "facebook/musicgen-melody",
-                    "Default"
-                ]
-            ],
-            inputs=[text, melody, model, decoder],
-            outputs=[output]
-        )
-        gr.Markdown(
-            """
-            ### More details
-
-            The model will generate a short music extract based on the description you provided.
-            The model can generate up to 30 seconds of audio in one pass. It is now possible
-            to extend the generation by feeding back the end of the previous chunk of audio.
-            """
-        )
-
-        interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-             # Spatial Generation
-            This is your Generative music AI based on your landscape and environment.
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-                audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
-        submit.click(predict_batched, inputs=[text, melody],
-                     outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_batched,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./bach.mp3",
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./bolero_ravel.mp3",
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
-                    "./bach.mp3",
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                ],
-            ],
-            inputs=[text, melody],
-            outputs=[output]
-        )
-        gr.Markdown("""
-        ### More details
-
-        The model will generate 12 seconds of audio based on the description you provided.
-        You can optionally provide a reference audio from which a broad melody will be extracted.
-        The model will then try to follow both the description and melody provided.
-        All samples are generated with the `melody` model.
-
-        You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
-        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-        for more details.
-        """)
-
-        demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--listen',
-        type=str,
-        default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
-        help='IP to listen on for connections to Gradio',
-    )
-    parser.add_argument(
-        '--username', type=str, default='', help='Username for authentication'
-    )
-    parser.add_argument(
-        '--password', type=str, default='', help='Password for authentication'
-    )
-    parser.add_argument(
-        '--server_port',
-        type=int,
-        default=0,
-        help='Port to run the server listener on',
-    )
-    parser.add_argument(
-        '--inbrowser', action='store_true', help='Open in browser'
-    )
-    parser.add_argument(
-        '--share', action='store_true', help='Share the gradio UI'
-    )
-
-    args = parser.parse_args()
-
-    launch_kwargs = {}
-    launch_kwargs['server_name'] = args.listen
-
-    if args.username and args.password:
-        launch_kwargs['auth'] = (args.username, args.password)
-    if args.server_port:
-        launch_kwargs['server_port'] = args.server_port
-    if args.inbrowser:
-        launch_kwargs['inbrowser'] = args.inbrowser
-    if args.share:
-        launch_kwargs['share'] = args.share
-
-    # Show the interface
-    if IS_BATCHED:
-        global USE_DIFFUSION
-        USE_DIFFUSION = False
-        ui_batched(launch_kwargs)
-    else:
-        ui_full(launch_kwargs)
diff --git a/spaces/stomexserde/gpt4-ui/Bagatrix Math Suite 64 Bit.md b/spaces/stomexserde/gpt4-ui/Bagatrix Math Suite 64 Bit.md
deleted file mode 100644
index 5dced4e5408c9eb5ac39c8094141873c96eb3d5e..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Bagatrix Math Suite 64 Bit.md	
+++ /dev/null
@@ -1,66 +0,0 @@
-## Bagatrix Math Suite 64 Bit
-
-
-
- 
-
- ![Bagatrix Math Suite 64 Bit](https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcSwI57qJIJtFToHoCcYGGoS-h6A0G3CJQVvqMbFuMiSQsgmrRDPZ3eI6mgl)
-
- 
-
- 
-
-**CLICK HERE –––––>>> [https://vercupalo.blogspot.com/?d=2tyUgX](https://vercupalo.blogspot.com/?d=2tyUgX)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# Bagatrix Math Suite 64 bit: A Powerful Tool for Solving Math Problems
-
- 
-
-If you are looking for a software that can help you solve your math problems quickly and easily, you might want to check out Bagatrix Math Suite 64 bit. Bagatrix Math Suite is a collection of programs that cover various topics in mathematics, such as algebra, calculus, geometry, trigonometry, statistics, and more. You can use Bagatrix Math Suite to learn new concepts, practice your skills, check your answers, and get step-by-step solutions to any problem.
-
- 
-
-Bagatrix Math Suite 64 bit is designed to run on Windows computers with 64-bit processors. This means that it can handle larger and more complex problems than the 32-bit version. It also has a faster performance and a more user-friendly interface. You can download Bagatrix Math Suite 64 bit from the official website of Bagatrix[^1^], or from other online sources.
-
- 
-
-One of the advantages of Bagatrix Math Suite 64 bit is that it does not require an internet connection to work. You can use it offline anytime and anywhere. Another advantage is that it has a high level of accuracy and reliability. You can trust that the solutions provided by Bagatrix Math Suite are correct and complete. Moreover, Bagatrix Math Suite 64 bit has a lot of features and options that make it easy to customize and personalize your learning experience. You can choose from different difficulty levels, problem types, formats, and styles. You can also save your work, print your results, or export them to other applications.
-
- 
-
-Many users have praised Bagatrix Math Suite 64 bit for its effectiveness and convenience. For example, Dorothy L., a reviewer on Influenster.com[^2^], said that she loves this app/site and that it helps her with her math homework. She also said that it is very easy to use and that it shows her how to solve the problems step by step. She gave it a 5-star rating and recommended it to anyone who needs help with math.
-
- 
-
-In conclusion, Bagatrix Math Suite 64 bit is a powerful tool for solving math problems. It covers a wide range of topics, has a high performance and accuracy, and offers a lot of features and options. It is suitable for students, teachers, professionals, or anyone who wants to improve their math skills. If you are interested in trying out Bagatrix Math Suite 64 bit, you can download it from the official website of Bagatrix or from other online sources.
-
-
-
-If you want to learn more about Bagatrix Math Suite 64 bit, you can visit the official website of Bagatrix, where you can find more information about the features, benefits, and pricing of the software. You can also watch some video tutorials, read some testimonials, and contact the customer support team if you have any questions or issues. You can also follow Bagatrix on social media platforms, such as Facebook, Twitter, and YouTube, where you can get updates, tips, and news about the software.
-
- 
-
-Bagatrix Math Suite 64 bit is not the only product offered by Bagatrix. Bagatrix also has other products that can help you with your math needs, such as Mathway, a web-based service that can solve any math problem online; Solved!, a series of software that can solve specific math topics; and Math Connects, a digital curriculum that can help you learn math concepts interactively. You can check out these products on the official website of Bagatrix or on other online sources.
-
- 
-
-Math is an essential skill that can help you in many aspects of life, such as education, career, and personal development. However, math can also be challenging and frustrating for many people. That is why Bagatrix Math Suite 64 bit is a great solution for anyone who wants to overcome their math difficulties and achieve their math goals. With Bagatrix Math Suite 64 bit, you can solve your math problems with ease and confidence.
-
- 145887f19f
-
- 
-
- 
-
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Dum Laga Ke Haisha Download [UPD] Movie Torrent.md b/spaces/stomexserde/gpt4-ui/Examples/Dum Laga Ke Haisha Download [UPD] Movie Torrent.md
deleted file mode 100644
index 2b37dde7796751bf2126a5b5b01ef6a8c857fff0..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Dum Laga Ke Haisha Download [UPD] Movie Torrent.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-
-<h1>How to Download Dum Laga Ke Haisha Movie Torrent for Free</h1>
-<p>Dum Laga Ke Haisha is a 2015 Hindi comedy-drama film starring Ayushmann Khurrana and Bhumi Pednekar. The film tells the story of a mismatched couple who participate in a wife-carrying race to save their marriage. The film was a critical and commercial success, winning the National Film Award for Best Feature Film in Hindi and becoming one of the highest-grossing Bollywood films of 2015.</p>
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-<ol>
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-</ol>
-<p>Note: This article is for educational purposes only. We do not condone or encourage piracy or illegal downloading of copyrighted content. Please respect the rights of the content creators and pay for their work.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/High Quality Tahalka 2 Hd Movie ((INSTALL)) Download 720p Movies.md b/spaces/stomexserde/gpt4-ui/Examples/High Quality Tahalka 2 Hd Movie ((INSTALL)) Download 720p Movies.md
deleted file mode 100644
index 9b33eeeab459e68c5fbf8d0c57be27f13e40ff3d..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/High Quality Tahalka 2 Hd Movie ((INSTALL)) Download 720p Movies.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<br />
-Here is a possible title and article with HTML formatting for the keyword "High Quality Tahalka 2 Hd Movie Download 720p Movies":
-
-<h1>Tahalka 2: A Sequel to the 1992 Action Thriller</h1>
-<p>Tahalka 2 is an upcoming Bollywood movie that is a sequel to the 1992 action thriller Tahalka, starring Dharmendra, Naseeruddin Shah, Mukesh Khanna, Aditya Pancholi and Amrish Puri. The movie is directed by Anil Sharma, who also helmed the original film.</p>
-<h2>High Quality Tahalka 2 Hd Movie Download 720p Movies</h2><br /><p><b><b>Download</b> ->>> <a href="https://urlgoal.com/2uI8W3">https://urlgoal.com/2uI8W3</a></b></p><br /><br />
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-<p>Tahalka 2 promises to be a high-octane action adventure with stunning stunts, explosions, chases and fights. The movie also features some of the original cast members, such as Dharmendra and Naseeruddin Shah, in cameo roles. The movie is expected to release in late 2023.</p>
-<p>If you are a fan of Tahalka or action movies in general, you can download Tahalka 2 in high quality HD 720p from various online platforms. However, be careful of illegal or pirated sites that may harm your device or compromise your security. Always use trusted and legal sources to enjoy Tahalka 2 in HD.</p>Here is a possible continuation of the article:
-
-<p>Tahalka 2 is not only a sequel to Tahalka, but also a tribute to the late Amrish Puri, who played the iconic villain General Dong in the first film. The makers of Tahalka 2 have recreated some of the memorable scenes and dialogues from Tahalka, such as "Dong is never wrong" and "Dong ka naam sunte hi dushman ki phat jaati hai". The movie also pays homage to the legendary duo of Laurel and Hardy, who featured in a comedy show in Tahalka.</p>
-<p>Tahalka 2 has been shot in various exotic locations, such as Thailand, Nepal, Bhutan and Ladakh. The movie showcases the scenic beauty and cultural diversity of these places, as well as the action sequences. The movie also has a romantic track between Tiger Shroff and Disha Patani, who have previously worked together in Baaghi 2 and Malang. The movie also has a catchy soundtrack composed by Anu Malik, who scored the music for Tahalka.</p>
-<p>Tahalka 2 is one of the most awaited movies of 2023, as it brings back the nostalgia and thrill of Tahalka, with a fresh and modern twist. The movie is expected to be a blockbuster at the box office and a treat for the fans of action and adventure. Don't miss Tahalka 2 in HD 720p when it releases online.</p>
-<p></p>Here is a possible continuation of the article:
-
-<p>Tahalka 2 is not only an action movie, but also a message of patriotism and courage. The movie portrays the bravery and sacrifice of the Indian army officers who risk their lives to save their countrymen from the tyranny of General Dong. The movie also highlights the importance of friendship and loyalty, as the team of commandos stick together and support each other in their mission. The movie also has a touch of humor and emotion, as the characters face various situations and challenges in Dongrila.</p>
-<p>Tahalka 2 is a movie that will appeal to all age groups and audiences, as it has something for everyone. The movie is a perfect blend of action, adventure, romance, comedy and drama. The movie is a must-watch for the fans of Tahalka, as well as the fans of Tiger Shroff and Disha Patani. The movie is a tribute to the legacy of Amrish Puri and Laurel and Hardy. The movie is a celebration of Indian cinema and culture.</p>
-<p>Tahalka 2 is a movie that you don't want to miss. You can download Tahalka 2 in high quality HD 720p from various online platforms. However, make sure you use legal and safe sources to enjoy Tahalka 2 in HD. Tahalka 2 is a movie that will keep you entertained and engaged till the end.</p> 7196e7f11a<br />
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Interspire Shopping Cart Isc 6.1.9 Nulled Xenforo.md b/spaces/stomexserde/gpt4-ui/Examples/Interspire Shopping Cart Isc 6.1.9 Nulled Xenforo.md
deleted file mode 100644
index b09ea89083fcd9ff6760b448a9c8ddb2ac94e1d0..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Interspire Shopping Cart Isc 6.1.9 Nulled Xenforo.md	
+++ /dev/null
@@ -1,47 +0,0 @@
-
-<h1>Interspire Shopping Cart Isc 6.1.9: A Feature-Rich and Trusted E-Commerce Solution</h1>
-
-<p>If you are looking for a reliable and powerful shopping cart software for your online store, you might want to consider Interspire Shopping Cart Isc 6.1.9. This is the latest version of the popular e-commerce platform that has been trusted by more than 15,000 businesses in over 65 countries[^2^]. Interspire Shopping Cart Isc 6.1.9 offers an enterprise-grade feature set that includes:</p>
-
-<ul>
-<li>Multiple store fronts and vendors</li>
-<li>SEO-friendly URLs and meta tags</li>
-<li>Mobile commerce and responsive design</li>
-<li>Product reviews and ratings</li>
-<li>Inventory management and RMA system</li>
-<li>Gift certificates and coupons</li>
-<li>One-page checkout and guest checkout</li>
-<li>Multiple payment gateways and shipping methods</li>
-<li>Customizable templates and logo creator</li>
-<li>Upsell module and cross-sell module</li>
-<li>Google Analytics integration and sales reports</li>
-<li>And much more!</li>
-</ul>
-
-<p>Interspire Shopping Cart Isc 6.1.9 is easy to install and configure, and it comes with a user-friendly admin panel that lets you manage your store with ease. You can also customize your store's look and feel with the built-in template editor and logo creator, or choose from hundreds of free and premium templates available from Cart Addons[^2^]. You can also extend the functionality of your store with various add-ons and modules from third-party developers.</p>
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-<p>However, before you decide to use Interspire Shopping Cart Isc 6.1.9 for your online store, you should be aware that this version is nulled, meaning that it has been modified to bypass the license verification process. This means that you are using an illegal copy of the software that may not work properly or may contain malicious code. Using nulled software can also expose you to legal risks, as you are violating the terms of service and the intellectual property rights of the original developers.</p>
-
-<p>Therefore, we strongly recommend that you purchase a legitimate license of Interspire Shopping Cart Isc 6.1.9 from the official website[^2^], or use an alternative e-commerce solution that is free or open source, such as WooCommerce, Magento, PrestaShop, or OpenCart. By doing so, you will not only support the developers who work hard to create quality software, but also ensure the security and performance of your online store.</p>
-
-<p>If you have any questions or comments about Interspire Shopping Cart Isc 6.1.9 or nulled software in general, feel free to leave them below.</p>
-
-<p>What is Interspire Shopping Cart Isc 6.1.9?</p>
-
-<p>Interspire Shopping Cart Isc 6.1.9 is a PHP-based e-commerce software that allows you to create and manage your own online store. It was developed by Interspire, an Australian company that specializes in web-based software solutions for online businesses. Interspire Shopping Cart Isc 6.1.9 was released in 2013 as the final version of the software, as Interspire decided to focus on their cloud-based e-commerce platform, BigCommerce.</p>
-
-<p>Interspire Shopping Cart Isc 6.1.9 is designed to be flexible and scalable, and it can handle any type of online store, from small to large, from simple to complex. It supports multiple languages and currencies, and it can be integrated with various third-party services and applications, such as Facebook, Twitter, Google, PayPal, FedEx, UPS, and more. It also has a built-in marketing suite that helps you attract and retain customers, such as email marketing, newsletter subscriptions, loyalty programs, wish lists, and referrals.</p>
-
-<p>Interspire Shopping Cart Isc 6.1.9 is suitable for both beginners and experts, as it has a simple installation process and a user-friendly interface that guides you through every step of setting up and running your store. It also has a comprehensive documentation and a support forum that provide you with helpful tips and answers to your questions. You can also access the source code of the software and modify it to suit your needs, or hire a developer to do it for you.</p>
-<p></p>
-
-<p>What is nulled software?</p>
-
-<p>Nulled software is software that has been modified to remove or bypass the license verification process that is required to use it legally. Nulled software is usually distributed for free on various websites or forums that offer pirated or cracked software. Nulled software is often used by people who want to save money or avoid paying for the software they use.</p>
-
-<p>However, using nulled software is not only illegal, but also risky and unethical. Nulled software may not work properly or may contain bugs, errors, or viruses that can harm your computer or your website. Nulled software may also compromise your personal or financial information, as it may send it to hackers or scammers who can use it for malicious purposes. Using nulled software can also damage your reputation and credibility, as you may lose the trust of your customers or partners who find out that you are using illegal software.</p>
-
-<p>Therefore, we strongly advise you to avoid using nulled software and to purchase a legitimate license of the software you want to use from the official website or a reputable vendor. By doing so, you will not only respect the law and the rights of the developers who create quality software, but also ensure the security and performance of your computer or your website.</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/KMSpico 18.8.18 FINAL Portable (Office And Windows 10 Free Download.md b/spaces/stomexserde/gpt4-ui/Examples/KMSpico 18.8.18 FINAL Portable (Office And Windows 10 Free Download.md
deleted file mode 100644
index 307116789562abc6f478394ef0f34ac7078c792f..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/KMSpico 18.8.18 FINAL Portable (Office And Windows 10 Free Download.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<h1>How to Activate Windows 10 and Office with KMSpico 18.8.18 FINAL Portable</h1>
-<p>If you are looking for a way to activate Windows 10 and Office without paying for a license key, you may want to try KMSpico 18.8.18 FINAL Portable. This is a powerful tool that can emulate a genuine KMS (Key Management Service) server on your local machine and force the installed Windows and Office products to activate themselves against it.</p>
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-<p>KMSpico 18.8.18 FINAL Portable is the latest version of KMSpico, which is one of the most popular and trusted activators for Windows and Office. It can activate many versions of Windows and Office, including Windows 7/8/8.1/10/11 and Office 2010/2013/2016/2019/2021. It does not require any installation or user intervention, just run the tool and wait for a few minutes to check the activation status.</p>
-<p>KMSpico 18.8.18 FINAL Portable is safe and clean to use, as it does not contain any virus or malware. It also does not modify any system files or registry entries, so it will not harm your computer or affect its performance. It will only create a virtual KMS server and delete it after activation.</p>
-<p>KMSpico 18.8.18 FINAL Portable is based on KMS technology, which is a legitimate method of activating Windows and Office for testing purposes. It will ensure that your software remains activated for a period of 180 days, after which it will automatically re-activate them for another 180 days. This way, you can enjoy the premium features of Windows and Office without any expiration date or detection by Microsoft.</p>
-<p></p>
-<h2>How to Use KMSpico 18.8.18 FINAL Portable</h2>
-<p>Using KMSpico 18.8.18 FINAL Portable is very easy and straightforward. Just follow these simple steps:</p>
-<ol>
-<li>Download KMSpico 18.8.18 FINAL Portable from the link below.</li>
-<li>Extract the ZIP file to a folder on your computer.</li>
-<li>Disable your antivirus or firewall temporarily, as they may interfere with the activation process.</li>
-<li>Run KMSELDI.exe as administrator from the folder.</li>
-<li>Click on the red button and wait for the program to do its work.</li>
-<li>When you see "Completed" on the screen, you can close the program.</li>
-<li>Restart your computer and check the activation status of Windows and Office.</li>
-<li>Enjoy!</li>
-</ol>
-<p>You can also check the activation status by running Check_KMSpico+.cmd from the folder.</p>
-<h2>Download Link</h2>
-<p>You can download KMSpico 18.8.18 FINAL Portable from the link below:</p>
-<p><a href="https://imarsorgula.com/wp-content/uploads/2022/09/KMSpico_18818_FINAL_Portable_Office_and_Windows_10_free_down.pdf">KMSpico 18.8.18 FINAL Portable (Office And Windows 10 Free Download)</a></p>
-<h2>Conclusion</h2>
-<p>KMSpico 18.8.18 FINAL Portable is a great tool that can help you activate Windows 10 and Office without spending any money on license keys. It is easy to use, safe and reliable, and works with many versions of Windows and Office. However, you should only use it for personal or educational purposes, and not for commercial or illegal activities. If you like Windows and Office, you should support their developers by buying their products.</p> e93f5a0c3f<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/tests/metagpt/roles/test_engineer.py b/spaces/sub314xxl/MetaGPT/tests/metagpt/roles/test_engineer.py
deleted file mode 100644
index c0c48d0b10f652f7df35bc383c153a992e4d30e3..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/tests/metagpt/roles/test_engineer.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/12 10:14
-@Author  : alexanderwu
-@File    : test_engineer.py
-"""
-import pytest
-
-from metagpt.logs import logger
-from metagpt.roles.engineer import Engineer
-from metagpt.utils.common import CodeParser
-from tests.metagpt.roles.mock import (
-    STRS_FOR_PARSING,
-    TASKS,
-    TASKS_TOMATO_CLOCK,
-    MockMessages,
-)
-
-
-@pytest.mark.asyncio
-async def test_engineer():
-    engineer = Engineer()
-
-    engineer.recv(MockMessages.req)
-    engineer.recv(MockMessages.prd)
-    engineer.recv(MockMessages.system_design)
-    rsp = await engineer.handle(MockMessages.tasks)
-
-    logger.info(rsp)
-    assert "all done." == rsp.content
-
-
-def test_parse_str():
-    for idx, i in enumerate(STRS_FOR_PARSING):
-        text = CodeParser.parse_str(f"{idx+1}", i)
-        # logger.info(text)
-        assert text == 'a'
-
-
-def test_parse_blocks():
-    tasks = CodeParser.parse_blocks(TASKS)
-    logger.info(tasks.keys())
-    assert 'Task list' in tasks.keys()
-
-
-target_list = [
-    "smart_search_engine/knowledge_base.py",
-    "smart_search_engine/index.py",
-    "smart_search_engine/ranking.py",
-    "smart_search_engine/summary.py",
-    "smart_search_engine/search.py",
-    "smart_search_engine/main.py",
-    "smart_search_engine/interface.py",
-    "smart_search_engine/user_feedback.py",
-    "smart_search_engine/security.py",
-    "smart_search_engine/testing.py",
-    "smart_search_engine/monitoring.py",
-]
-
-
-def test_parse_file_list():
-    tasks = CodeParser.parse_file_list("任务列表", TASKS)
-    logger.info(tasks)
-    assert isinstance(tasks, list)
-    assert target_list == tasks
-
-    file_list = CodeParser.parse_file_list("Task list", TASKS_TOMATO_CLOCK, lang="python")
-    logger.info(file_list)
-
-
-target_code = """task_list = [
-    "smart_search_engine/knowledge_base.py",
-    "smart_search_engine/index.py",
-    "smart_search_engine/ranking.py",
-    "smart_search_engine/summary.py",
-    "smart_search_engine/search.py",
-    "smart_search_engine/main.py",
-    "smart_search_engine/interface.py",
-    "smart_search_engine/user_feedback.py",
-    "smart_search_engine/security.py",
-    "smart_search_engine/testing.py",
-    "smart_search_engine/monitoring.py",
-]
-"""
-
-
-def test_parse_code():
-    code = CodeParser.parse_code("任务列表", TASKS, lang="python")
-    logger.info(code)
-    assert isinstance(code, str)
-    assert target_code == code
diff --git a/spaces/sunshineatnoon/TextureScraping/swapae/evaluation/structure_style_grid_generation_evaluator.py b/spaces/sunshineatnoon/TextureScraping/swapae/evaluation/structure_style_grid_generation_evaluator.py
deleted file mode 100644
index e3bc7ec6b3299925998673f034499d840a06b3d1..0000000000000000000000000000000000000000
--- a/spaces/sunshineatnoon/TextureScraping/swapae/evaluation/structure_style_grid_generation_evaluator.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-import torch
-from swapae.evaluation import BaseEvaluator
-import swapae.util as util
-import numpy as np
-from PIL import Image
-
-
-class StructureStyleGridGenerationEvaluator(BaseEvaluator):
-    """ generate swapping images and save to disk """
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        return parser
-
-    def create_webpage(self, nsteps):
-        nsteps = self.opt.resume_iter if nsteps is None else nsteps
-        savedir = os.path.join(self.output_dir(), "%s_%s" % (self.target_phase, nsteps))
-        os.makedirs(savedir, exist_ok=True)
-        webpage_title = "%s. iter=%s. phase=%s" % \
-                        (self.opt.name, str(nsteps), self.target_phase)
-        self.webpage = util.HTML(savedir, webpage_title)
-
-    def add_to_webpage(self, images, filenames, tile=1):
-        converted_images = []
-        for image in images:
-            if isinstance(image, list):
-                image = torch.stack(image, dim=0).flatten(0, 1)
-            image = Image.fromarray(util.tensor2im(image, tile=min(image.size(0), tile)))
-            converted_images.append(image)
-
-        self.webpage.add_images(converted_images,
-                                filenames)
-        print("saved %s" % str(filenames))
-        #self.webpage.save()
-
-    def evaluate(self, model, dataset, nsteps=None):
-        self.create_webpage(nsteps)
-
-        structure_images, style_images = {}, {}
-        for i, data_i in enumerate(dataset):
-            bs = data_i["real_A"].size(0)
-            #sp, gl = model(data_i["real_A"].cuda(), command="encode")
-
-            for j in range(bs):
-                image = data_i["real_A"][j:j+1]
-                path = data_i["path_A"][j]
-                imagename = os.path.splitext(os.path.basename(path))[0]
-                if "/structure/" in path:
-                    structure_images[imagename] = image
-                else:
-                    style_images[imagename] = image
-
-        gls = []
-        style_paths = list(style_images.keys())
-        for style_path in style_paths:
-            style_image = style_images[style_path].cuda()
-            gls.append(model(style_image, command="encode")[1])
-
-        sps = []
-        structure_paths = list(structure_images.keys())
-        for structure_path in structure_paths:
-            structure_image = structure_images[structure_path].cuda()
-            sps.append(model(structure_image, command="encode")[0])
-
-        # top row to show the input images
-        blank_image = style_images[style_paths[0]] * 0.0 + 1.0
-        self.add_to_webpage([blank_image] + [style_images[style_path] for style_path in style_paths],
-                            ["blank.png"] + [style_path + ".png" for style_path in style_paths],
-                            tile=1)
-
-        # swapping
-        for i, structure_path in enumerate(structure_paths):
-            structure_image = structure_images[structure_path]
-            swaps = []
-            filenames = []
-            for j, style_path in enumerate(style_paths):
-                swaps.append(model(sps[i], gls[j], command="decode"))
-                filenames.append(structure_path + "_" + style_path + ".png")
-            self.add_to_webpage([structure_image] + swaps,
-                                [structure_path + ".png"] + filenames,
-                                tile=1)
-
-            self.webpage.save()
-        return {}
-                
-                
-        
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Garmin MapSource 6.13.7 Special.epub !!TOP!!.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Garmin MapSource 6.13.7 Special.epub !!TOP!!.md
deleted file mode 100644
index b1633be6b3e2f8291d3af39076055fc331282d14..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Garmin MapSource 6.13.7 Special.epub !!TOP!!.md	
+++ /dev/null
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-<h2>Garmin MapSource 6.13.7 Special.epub</h2><br /><p><b><b>Download File</b> &#10001; &#10001; &#10001; <a href="https://cinurl.com/2uEZ5F">https://cinurl.com/2uEZ5F</a></b></p><br /><br />
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Winsoft JavaImport For Android 3.6 Full Source.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Winsoft JavaImport For Android 3.6 Full Source.md
deleted file mode 100644
index 60b38ff7099be6a597f3d1fd8b68ed633c351340..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Winsoft JavaImport For Android 3.6 Full Source.md	
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-<h2>Winsoft JavaImport for Android 3.6 Full Source</h2><br /><p><b><b>Download</b> &#10040;&#10040;&#10040; <a href="https://cinurl.com/2uEXPW">https://cinurl.com/2uEXPW</a></b></p><br /><br />
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-<p></p>
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Autocad 2005 [EXCLUSIVE] Crack.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Autocad 2005 [EXCLUSIVE] Crack.md
deleted file mode 100644
index bb40ea1c744df16f501321718f1466e162f64ad4..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Autocad 2005 [EXCLUSIVE] Crack.md	
+++ /dev/null
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-<br />
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diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/__init__.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/__init__.py
deleted file mode 100644
index ac66d3cfe0ea04af45c0f3594bf135841c3812e3..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/decode_heads/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from .ann_head import ANNHead
-from .apc_head import APCHead
-from .aspp_head import ASPPHead
-from .cc_head import CCHead
-from .da_head import DAHead
-from .dm_head import DMHead
-from .dnl_head import DNLHead
-from .ema_head import EMAHead
-from .enc_head import EncHead
-from .fcn_head import FCNHead
-from .fpn_head import FPNHead
-from .gc_head import GCHead
-from .lraspp_head import LRASPPHead
-from .nl_head import NLHead
-from .ocr_head import OCRHead
-# from .point_head import PointHead
-from .psa_head import PSAHead
-from .psp_head import PSPHead
-from .sep_aspp_head import DepthwiseSeparableASPPHead
-from .sep_fcn_head import DepthwiseSeparableFCNHead
-from .uper_head import UPerHead
-
-__all__ = [
-    'FCNHead', 'PSPHead', 'ASPPHead', 'PSAHead', 'NLHead', 'GCHead', 'CCHead',
-    'UPerHead', 'DepthwiseSeparableASPPHead', 'ANNHead', 'DAHead', 'OCRHead',
-    'EncHead', 'DepthwiseSeparableFCNHead', 'FPNHead', 'EMAHead', 'DNLHead',
-    'APCHead', 'DMHead', 'LRASPPHead'
-]
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/necks/multilevel_neck.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/necks/multilevel_neck.py
deleted file mode 100644
index 766144d8136326a1fab5906a153a0c0df69b6b60..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmseg/models/necks/multilevel_neck.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class MultiLevelNeck(nn.Module):
-    """MultiLevelNeck.
-
-    A neck structure connect vit backbone and decoder_heads.
-    Args:
-        in_channels (List[int]): Number of input channels per scale.
-        out_channels (int): Number of output channels (used at each scale).
-        scales (List[int]): Scale factors for each input feature map.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        act_cfg (dict): Config dict for activation layer in ConvModule.
-            Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 scales=[0.5, 1, 2, 4],
-                 norm_cfg=None,
-                 act_cfg=None):
-        super(MultiLevelNeck, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.scales = scales
-        self.num_outs = len(scales)
-        self.lateral_convs = nn.ModuleList()
-        self.convs = nn.ModuleList()
-        for in_channel in in_channels:
-            self.lateral_convs.append(
-                ConvModule(
-                    in_channel,
-                    out_channels,
-                    kernel_size=1,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-        for _ in range(self.num_outs):
-            self.convs.append(
-                ConvModule(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    padding=1,
-                    stride=1,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-
-    def forward(self, inputs):
-        assert len(inputs) == len(self.in_channels)
-        print(inputs[0].shape)
-        inputs = [
-            lateral_conv(inputs[i])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-        # for len(inputs) not equal to self.num_outs
-        if len(inputs) == 1:
-            inputs = [inputs[0] for _ in range(self.num_outs)]
-        outs = []
-        for i in range(self.num_outs):
-            x_resize = F.interpolate(
-                inputs[i], scale_factor=self.scales[i], mode='bilinear')
-            outs.append(self.convs[i](x_resize))
-        return tuple(outs)
diff --git a/spaces/szukevin/VISOR-GPT/train/scripts/convert_bert_from_original_tf_to_tencentpretrain.py b/spaces/szukevin/VISOR-GPT/train/scripts/convert_bert_from_original_tf_to_tencentpretrain.py
deleted file mode 100644
index 17c2eddd646049c576e69636cc86b79f93e3f289..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/scripts/convert_bert_from_original_tf_to_tencentpretrain.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import argparse
-import collections
-import tensorflow as tf
-import torch
-from tensorflow.python import pywrap_tensorflow
-
-
-tensors_to_transopse = (
-        "dense/kernel",
-        "attention/self/query",
-        "attention/self/key",
-        "attention/self/value"
-    )
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--layers_num", type=int, default=12,
-                        help=".")
-    parser.add_argument("--input_model_path", default="models/input_model.ckpt", type=str,
-                        help=".")
-    parser.add_argument("--output_model_path", default="models/output_model.bin", type=str,
-                        help="Path to the output PyTorch model.")
-    parser.add_argument("--type", choices=["bert", "mlm"], default="bert",
-                        help="The training target of the pretraining model.")
-
-    args = parser.parse_args()
-
-    reader = pywrap_tensorflow.NewCheckpointReader(args.input_model_path)
-    var_to_shape_map = reader.get_variable_to_shape_map()
-
-    input_model = collections.OrderedDict()
-
-    for key in var_to_shape_map:
-        torch_tensor = reader.get_tensor(key)
-
-        if any([x in key for x in tensors_to_transopse]):
-            torch_tensor = torch_tensor.T
-        if key == "bert/embeddings/token_type_embeddings":
-            col_dim = torch_tensor.shape[1]
-            sess = tf.Session()
-            zeros_var = tf.Variable(tf.zeros([1, col_dim], dtype=tf.float32), name="zeros_var")
-            sess.run(zeros_var.initializer)
-            torch_tensor = sess.run(tf.concat([sess.run(zeros_var), torch_tensor], 0))
-        input_model[key] = torch.Tensor(torch_tensor)
-
-    output_model = collections.OrderedDict()
-    output_model["embedding.word.embedding.weight"] = input_model["bert/embeddings/word_embeddings"]
-    output_model["embedding.pos.embedding.weight"] = input_model["bert/embeddings/position_embeddings"][:512]
-    output_model["embedding.seg.embedding.weight"] = input_model["bert/embeddings/token_type_embeddings"]
-    output_model["embedding.layer_norm.gamma"] = input_model["bert/embeddings/LayerNorm/gamma"]
-    output_model["embedding.layer_norm.beta"] = input_model["bert/embeddings/LayerNorm/beta"]
-
-    for i in range(args.layers_num):
-        output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.weight"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/self/query/kernel"]
-        output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.0.bias"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/self/query/bias"]
-        output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.weight"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/self/key/kernel"]
-        output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.1.bias"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/self/key/bias"]
-        output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.weight"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/self/value/kernel"]
-        output_model["encoder.transformer." + str(i) + ".self_attn.linear_layers.2.bias"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/self/value/bias"]
-        output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.weight"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/output/dense/kernel"]
-        output_model["encoder.transformer." + str(i) + ".self_attn.final_linear.bias"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/output/dense/bias"]
-        output_model["encoder.transformer." + str(i) + ".layer_norm_1.gamma"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/output/LayerNorm/gamma"]
-        output_model["encoder.transformer." + str(i) + ".layer_norm_1.beta"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/attention/output/LayerNorm/beta"]
-        output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.weight"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/intermediate/dense/kernel"]
-        output_model["encoder.transformer." + str(i) + ".feed_forward.linear_1.bias"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/intermediate/dense/bias"]
-        output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.weight"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/output/dense/kernel"]
-        output_model["encoder.transformer." + str(i) + ".feed_forward.linear_2.bias"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/output/dense/bias"]
-        output_model["encoder.transformer." + str(i) + ".layer_norm_2.gamma"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/output/LayerNorm/gamma"]
-        output_model["encoder.transformer." + str(i) + ".layer_norm_2.beta"] = \
-            input_model["bert/encoder/layer_" + str(i) + "/output/LayerNorm/beta"]
-
-    if args.type == "bert":
-        output_model["target.sp.linear_1.weight"] = input_model["bert/pooler/dense/kernel"]
-        output_model["target.sp.linear_1.bias"] = input_model["bert/pooler/dense/bias"]
-        output_model["target.sp.linear_2.weight"] = input_model["cls/seq_relationship/output_weights"]
-        output_model["target.sp.linear_2.bias"] = input_model["cls/seq_relationship/output_bias"]
-    output_model["target.mlm.linear_1.weight"] = input_model["cls/predictions/transform/dense/kernel"]
-    output_model["target.mlm.linear_1.bias"] = input_model["cls/predictions/transform/dense/bias"]
-    output_model["target.mlm.layer_norm.gamma"] = input_model["cls/predictions/transform/LayerNorm/gamma"]
-    output_model["target.mlm.layer_norm.beta"] = input_model["cls/predictions/transform/LayerNorm/beta"]
-    output_model["target.mlm.linear_2.weight"] = input_model["bert/embeddings/word_embeddings"]
-    output_model["target.mlm.linear_2.bias"] = input_model["cls/predictions/output_bias"]
-
-    torch.save(output_model, args.output_model_path)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/tabeina/bingo1/src/lib/bots/bing/index.ts b/spaces/tabeina/bingo1/src/lib/bots/bing/index.ts
deleted file mode 100644
index c75c69f94af8c3db92d4c90d465c219a2af72a4d..0000000000000000000000000000000000000000
--- a/spaces/tabeina/bingo1/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,432 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
-  SendMessageParams,
-  BingConversationStyle,
-  ConversationResponse,
-  ChatResponseMessage,
-  ConversationInfo,
-  InvocationEventType,
-  ChatError,
-  ErrorCode,
-  ChatUpdateCompleteResponse,
-  ImageInfo,
-  KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
-  'nlu_direct_response_filter',
-  'deepleo',
-  'disable_emoji_spoken_text',
-  'responsible_ai_policy_235',
-  'enablemm',
-  'iycapbing',
-  'iyxapbing',
-  'objopinion',
-  'rweasgv2',
-  'dagslnv1',
-  'dv3sugg',
-  'autosave',
-  'iyoloxap',
-  'iyoloneutral',
-  'clgalileo',
-  'gencontentv3',
-]
-
-export class BingWebBot {
-  protected conversationContext?: ConversationInfo
-  protected cookie: string
-  protected ua: string
-  protected endpoint = ''
-  private lastText = ''
-  private asyncTasks: Array<Promise<any>> = []
-
-  constructor(opts: {
-    cookie: string
-    ua: string
-    bingConversationStyle?: BingConversationStyle
-    conversationContext?: ConversationInfo
-  }) {
-    const { cookie, ua, conversationContext } = opts
-    this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
-    this.ua = ua
-    this.conversationContext = conversationContext
-  }
-
-  static buildChatRequest(conversation: ConversationInfo) {
-    const optionsSets = OPTIONS_SETS
-    if (conversation.conversationStyle === BingConversationStyle.Precise) {
-      optionsSets.push('h3precise')
-    } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
-      optionsSets.push('h3imaginative')
-    }
-    return {
-      arguments: [
-        {
-          source: 'cib',
-          optionsSets,
-          allowedMessageTypes: [
-            'ActionRequest',
-            'Chat',
-            'Context',
-            'InternalSearchQuery',
-            'InternalSearchResult',
-            'Disengaged',
-            'InternalLoaderMessage',
-            'Progress',
-            'RenderCardRequest',
-            'SemanticSerp',
-            'GenerateContentQuery',
-            'SearchQuery',
-          ],
-          sliceIds: [
-            'winmuid1tf',
-            'anssupfor_c',
-            'imgchatgptv2',
-            'tts2cf',
-            'contansperf',
-            'mlchatpc8500w',
-            'mlchatpc2',
-            'ctrlworkpay',
-            'winshortmsgtf',
-            'cibctrl',
-            'sydtransctrl',
-            'sydconfigoptc',
-            '0705trt4',
-            '517opinion',
-            '628ajcopus0',
-            '330uaugs0',
-            '529rwea',
-            '0626snptrcs0',
-            '424dagslnv1',
-          ],
-          isStartOfSession: conversation.invocationId === 0,
-          message: {
-            author: 'user',
-            inputMethod: 'Keyboard',
-            text: conversation.prompt,
-            imageUrl: conversation.imageUrl,
-            messageType: 'Chat',
-          },
-          conversationId: conversation.conversationId,
-          conversationSignature: conversation.conversationSignature,
-          participant: { id: conversation.clientId },
-        },
-      ],
-      invocationId: conversation.invocationId.toString(),
-      target: 'chat',
-      type: InvocationEventType.StreamInvocation,
-    }
-  }
-
-  async createConversation(): Promise<ConversationResponse> {
-    const headers = {
-      'Accept-Encoding': 'gzip, deflate, br, zsdch',
-      'User-Agent': this.ua,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      cookie: this.cookie,
-    }
-
-    let resp: ConversationResponse | undefined
-    try {
-      const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
-      if (response.status === 404) {
-        throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
-      }
-      resp = await response.json() as ConversationResponse
-    } catch (err) {
-      console.error('create conversation error', err)
-    }
-
-    if (!resp?.result) {
-      throw new ChatError('你的 VPS 或代理可能被封禁，如有疑问，请前往 https://github.com/weaigc/bingo 咨询', ErrorCode.BING_IP_FORBIDDEN)
-    }
-
-    const { value, message } = resp.result || {}
-    if (value !== 'Success') {
-      const errorMsg = `${value}: ${message}`
-      if (value === 'UnauthorizedRequest') {
-        if (/fetch failed/i.test(message || '')) {
-          throw new ChatError(errorMsg, ErrorCode.BING_IP_FORBIDDEN)
-        }
-        throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
-      }
-      if (value === 'TryLater') {
-        throw new ChatError(errorMsg, ErrorCode.BING_TRY_LATER)
-      }
-      if (value === 'Forbidden') {
-        throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
-      }
-      throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
-    }
-    return resp
-  }
-
-  private async createContext(conversationStyle: BingConversationStyle) {
-    if (!this.conversationContext) {
-      const conversation = await this.createConversation()
-      this.conversationContext = {
-        conversationId: conversation.conversationId,
-        conversationSignature: conversation.conversationSignature,
-        clientId: conversation.clientId,
-        invocationId: 0,
-        conversationStyle,
-        prompt: '',
-      }
-    }
-    return this.conversationContext
-  }
-
-  async sendMessage(params: Params) {
-    try {
-      await this.createContext(params.options.bingConversationStyle)
-      Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
-      return this.sydneyProxy(params)
-    } catch (error) {
-      params.onEvent({
-        type: 'ERROR',
-        error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
-      })
-    }
-  }
-
-  private async sydneyProxy(params: Params) {
-    const abortController = new AbortController()
-    const response = await fetch(this.endpoint + '/api/sydney', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      signal: abortController.signal,
-      body: JSON.stringify(this.conversationContext!)
-    })
-    if (response.status !== 200) {
-      params.onEvent({
-        type: 'ERROR',
-        error: new ChatError(
-          'Unknown error',
-          ErrorCode.UNKOWN_ERROR,
-        ),
-      })
-    }
-    params.signal?.addEventListener('abort', () => {
-      abortController.abort()
-    })
-
-    const textDecoder = createChunkDecoder()
-    for await (const chunk of streamAsyncIterable(response.body!)) {
-      this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
-    }
-  }
-
-  async sendWs() {
-    const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
-      packMessage: websocketUtils.packMessage,
-      unpackMessage: websocketUtils.unpackMessage,
-      createWebSocket: (url) => new WebSocket(url, {
-        headers: {
-          'accept-language': 'zh-CN,zh;q=0.9',
-          'cache-control': 'no-cache',
-          'User-Agent': this.ua,
-          pragma: 'no-cache',
-          cookie: this.cookie,
-        }
-      })
-    }
-    const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
-    wsp.open().then(() => {
-      wsp.sendPacked({ protocol: 'json', version: 1 })
-      wsp.sendPacked({ type: 6 })
-      wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
-    })
-
-    return wsp
-  }
-
-  private async useWs(params: Params) {
-    const wsp = await this.sendWs()
-    const watchDog = new WatchDog()
-    wsp.onUnpackedMessage.addListener((events) => {
-      watchDog.watch(() => {
-        wsp.sendPacked({ type: 6 })
-      })
-      this.parseEvents(params, events)
-    })
-
-    wsp.onClose.addListener(() => {
-      watchDog.reset()
-      params.onEvent({ type: 'DONE' })
-      wsp.removeAllListeners()
-    })
-
-    params.signal?.addEventListener('abort', () => {
-      wsp.removeAllListeners()
-      wsp.close()
-    })
-  }
-
-  private async createImage(prompt: string, id: string) {
-    try {
-      const headers = {
-        'Accept-Encoding': 'gzip, deflate, br, zsdch',
-        'User-Agent': this.ua,
-        'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-        cookie: this.cookie,
-      }
-      const query = new URLSearchParams({
-        prompt,
-        id
-      })
-      const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
-        {
-          method: 'POST',
-          headers,
-          mode: 'cors',
-          credentials: 'include'
-        })
-        .then(res => res.text())
-      if (response) {
-        this.lastText += '\n' + response
-      }
-    } catch (err) {
-      console.error('Create Image Error', err)
-    }
-  }
-
-  private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
-    const imageInfo: ImageInfo = {}
-    let imageBase64: string | undefined = undefined
-    const knowledgeRequest = {
-      imageInfo,
-      knowledgeRequest: {
-        invokedSkills: [
-          'ImageById'
-        ],
-        subscriptionId: 'Bing.Chat.Multimodal',
-        invokedSkillsRequestData: {
-            enableFaceBlur: true
-        },
-        convoData: {
-          convoid: this.conversationContext?.conversationId,
-          convotone: conversationStyle,
-        }
-      },
-    }
-
-    if (imageUrl.startsWith('data:image/')) {
-      imageBase64 = imageUrl.replace('data:image/', '');
-      const partIndex = imageBase64.indexOf(',')
-      if (partIndex) {
-        imageBase64 = imageBase64.substring(partIndex + 1)
-      }
-    } else {
-      imageInfo.url = imageUrl
-    }
-    return { knowledgeRequest, imageBase64 }
-  }
-
-  async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
-    if (!imageUrl) {
-      return
-    }
-    await this.createContext(conversationStyle)
-    const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
-    const response = await fetch(this.endpoint + '/api/kblob',
-      {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        method: 'POST',
-        mode: 'cors',
-        credentials: 'include',
-        body: JSON.stringify(payload),
-      })
-      .then(res => res.json())
-      .catch(e => {
-        console.log('Error', e)
-      })
-    return response
-  }
-
-  private async generateContent(message: ChatResponseMessage) {
-    if (message.contentType === 'IMAGE') {
-      this.asyncTasks.push(this.createImage(message.text, message.messageId))
-    }
-  }
-
-  private async parseEvents(params: Params, events: any) {
-    const conversation = this.conversationContext!
-
-    events?.forEach(async (event: ChatUpdateCompleteResponse) => {
-      debug('bing event', event)
-      if (event.type === 3) {
-        await Promise.all(this.asyncTasks)
-        this.asyncTasks = []
-        params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
-        params.onEvent({ type: 'DONE' })
-        conversation.invocationId = parseInt(event.invocationId, 10) + 1
-      } else if (event.type === 1) {
-        const messages = event.arguments[0].messages
-        if (messages) {
-          const text = convertMessageToMarkdown(messages[0])
-          this.lastText = text
-          params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
-        }
-      } else if (event.type === 2) {
-        const messages = event.item.messages as ChatResponseMessage[] | undefined
-        if (!messages) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              event.item.result.error || 'Unknown error',
-              event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
-                : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
-                  : ErrorCode.UNKOWN_ERROR
-            ),
-          })
-          return
-        }
-        const limited = messages.some((message) =>
-          message.contentOrigin === 'TurnLimiter'
-          || message.messageType === 'Disengaged'
-        )
-        if (limited) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              'Sorry, you have reached chat limit in this conversation.',
-              ErrorCode.CONVERSATION_LIMIT,
-            ),
-          })
-          return
-        }
-
-        const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
-        const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
-        if (specialMessage) {
-          this.generateContent(specialMessage)
-        }
-
-        if (lastMessage) {
-          const text = convertMessageToMarkdown(lastMessage)
-          this.lastText = text
-          params.onEvent({
-            type: 'UPDATE_ANSWER',
-            data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
-          })
-        }
-      }
-    })
-  }
-
-  resetConversation() {
-    this.conversationContext = undefined
-  }
-}
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index 2787e6b377a7a000774ac6a0b1cfdcb8f18f0447..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/CRACK Adobe.Photoshop.CC.2017.v18.0.0.x64.Multilingual-iCV-CreW LINK.md	
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index 766ff601da83e4d0e9c490ef33273db579a90d64..0000000000000000000000000000000000000000
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Finding Dory (English) 2 Telugu Movie __FULL__ Download.md b/spaces/terfces0erbo/CollegeProjectV2/Finding Dory (English) 2 Telugu Movie __FULL__ Download.md
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bandcamp Discover and Download Music 1 from Independent Artists.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bandcamp Discover and Download Music 1 from Independent Artists.md
deleted file mode 100644
index 8493f109d3f05d740a820ecd5943f3946bddc297..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bandcamp Discover and Download Music 1 from Independent Artists.md	
+++ /dev/null
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-<p><a href="">Free Music Archive</a> is a website that offers a large collection of free and legal music from various genres and artists. The music on Free Music Archive is curated by radio stations, labels, festivals, and other partners who have the rights to distribute it. You can explore new music by browsing through categories, charts, playlists, or genres.</p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download Vt Transaction Plus Keygen Software.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download Vt Transaction Plus Keygen Software.md
deleted file mode 100644
index 1016d59e801f31d3a694d51b788dd140615e2fa3..0000000000000000000000000000000000000000
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-<p>Are you looking for a simple and powerful accounting and bookkeeping software that can handle all your business needs? Do you want to save money and time by using a free and fully functional software that can generate and submit VAT returns, create and manage accounts and transactions, and work seamlessly with VT Final Accounts? If yes, then you need to download VT Transaction Plus keygen software.</p>
-<h2>Download Vt Transaction Plus Keygen Software</h2><br /><p><b><b>Download</b> &#10002; &#10002; &#10002; <a href="https://urlcod.com/2uHwRO">https://urlcod.com/2uHwRO</a></b></p><br /><br />
-<p>VT Transaction Plus is a comprehensive accounting and bookkeeping software that is designed for small and medium-sized businesses. It has an integrated ledger structure that includes a cashbook, customer and supplier ledgers, and is based on double entry principles. It allows you to create and manage an unlimited number of accounts and transactions, and offers flexibility and ease of use for data entry, editing, reporting, and exporting. It is also MTD for VAT compliant, which means you can generate and submit VAT returns digitally to HMRC. Moreover, it is compatible with VT Final Accounts, which is a software that helps you prepare final accounts for sole traders, partnerships, limited companies, and LLPs. You can import trial balances from VT Transaction Plus into VT Final Accounts, and drill down to transactions from VT Final Accounts.</p>
-<p>However, VT Transaction Plus is not a free software. You need to purchase a license to use it for your business. The license costs £150 + VAT per year for a single user, or £250 + VAT per year for up to 5 users. This may not be affordable for some small businesses or startups that have a tight budget. That's why you need to download VT Transaction Plus keygen software.</p>
-<p>Keygen software is a type of software that generates serial keys or activation codes for any software. By using keygen software, you can bypass the registration or activation process of the original software, and use it for free without any limitations. Keygen software is usually created by hackers or crackers who want to share their skills or challenge the security of the software developers. Keygen software is often distributed on various websites that offer free serial keys for any software.</p>
-<p>In this article, we will show you how to download VT Transaction Plus keygen software for free, and how to use it to activate your copy of VT Transaction Plus. We will also explain the features and benefits of VT Transaction Plus, and why you should use it for your accounting and bookkeeping needs. So, let's get started!</p>
-<p></p>
- <h2>Features of VT Transaction Plus</h2>
-<p>VT Transaction Plus is a comprehensive accounting and bookkeeping software that has many features that can help you with your business needs. Here are some of the main features of VT Transaction Plus:</p>
- <h3>Integrated ledger structure</h3>
-<p>VT Transaction Plus has an integrated ledger structure that includes a cashbook, customer and supplier ledgers, and is based on double entry principles. This means that you can enter transactions in any order, and the software will automatically update the relevant ledgers and accounts. You can also view the transactions in different ways, such as by date, account, reference, amount, or type. You can also drill down to the source documents or edit the transactions as needed.</p>
- <h3>Unlimited accounts and transactions</h3>
-<p>VT Transaction Plus allows you to create and manage an unlimited number of accounts and transactions. You can create accounts for different categories, such as income, expenses, assets, liabilities, equity, bank, cash, customers, suppliers, etc. You can also create sub-accounts or groups of accounts for more detailed analysis. You can enter transactions using various methods, such as manual entry, import from CSV files or bank statements, copy from previous transactions, or use templates or recurring transactions. You can also export transactions to CSV files or other formats.</p>
- <h3>Flexible and easy to use</h3>
-<p>VT Transaction Plus offers flexibility and ease of use for data entry, editing, reporting, and exporting. You can customize the data entry screens, reports, and export formats to suit your preferences and requirements. You can also use keyboard shortcuts, mouse clicks, or drag and drop to enter or edit data. You can also use filters, sorts, searches, or zooms to find or view the data you need. You can also print or preview reports, or export them to PDF, Excel, Word, or other formats.</p>
- <h2>Benefits of VT Transaction Plus</h2>
-<p>VT Transaction Plus is not only a feature-rich software, but also a beneficial software that can help you with your accounting and bookkeeping tasks. Here are some of the main benefits of using VT Transaction Plus:</p>
- <h3>MTD for VAT compliant</h3>
-<p>VT Transaction Plus is MTD for VAT compliant, which means that it can help you generate and submit VAT returns digitally to HMRC. MTD for VAT is a new requirement that applies to businesses with a taxable turnover above the VAT threshold of £85,000. It requires them to keep digital records of their VAT transactions, and to use compatible software to submit their VAT returns online. VT Transaction Plus can help you comply with this requirement by allowing you to create and maintain digital records of your VAT transactions, and by connecting to HMRC's API to submit your VAT returns securely and easily.</p>
- <h3>Compatible with VT Final Accounts</h3>
-<p>VT Transaction Plus is compatible with VT Final Accounts, which is a software that helps you prepare final accounts for sole traders, partnerships, limited companies, and LLPs. You can import trial balances from VT Transaction Plus into VT Final Accounts, and drill down to transactions from VT Final Accounts. This can help you save time and avoid errors when preparing your final accounts. You can also use VT Final Accounts to generate various reports, such as profit and loss account, balance sheet, cash flow statement, notes to the accounts, etc. You can also export your final accounts to iXBRL format for online filing with HMRC or Companies House.</p>
- <h3>Affordable and reliable</h3>
-<p>VT Transaction Plus is an affordable and reliable software that offers a low-cost and high-quality solution for your accounting and bookkeeping needs. You can purchase a license for VT Transaction Plus for only £150 + VAT per year for a single user, or £250 + VAT per year for up to 5 users. This is much cheaper than other accounting software in the market, such as QuickBooks, Sage, or Xero. Moreover, VT Transaction Plus is a reliable software that has been in the market for over 20 years, and has been used by thousands of businesses and accountants. It has a high customer satisfaction rate, and a dedicated support team that can help you with any issues or queries.</p>
- <h2>How to Download VT Transaction Plus Keygen Software</h2>
-<p>If you want to use VT Transaction Plus for free without purchasing a license, you need to download VT Transaction Plus keygen software. Keygen software is a type of software that generates serial keys or activation codes for any software. By using keygen software, you can bypass the registration or activation process of the original software, and use it for free without any limitations. However, you need to be careful when downloading keygen software, as some keygen websites may contain viruses or malware that can harm your computer or steal your personal information. Here are the steps to download VT Transaction Plus keygen software safely and securely:</p>
- <h3>Visit a reliable keygen website</h3>
-<p>The first step is to find and choose a trustworthy keygen website that offers free serial keys for any software. There are many keygen websites on the internet, but not all of them are reliable or safe. Some keygen websites may have fake or expired serial keys, or may require you to complete surveys or download other programs before giving you the serial key. Some keygen websites may also have pop-ups or ads that may redirect you to malicious sites or download unwanted software on your computer. Therefore, you need to be careful when visiting a keygen website, and look for some signs of reliability and safety.</p>
-<p>Some signs of a reliable keygen website are:</p>
-<ul>
-<li>It has a good reputation and positive reviews from other users.</li>
-<li>It has a clear and user-friendly interface that is easy to navigate.</li>
-<li>It has a large and updated database of serial keys for various software.</li>
-<li>It does not require you to register or provide any personal information.</li>
-<li>It does not ask you to complete surveys or download other programs before giving you the serial key.</li>
-<li>It does not have excessive pop-ups or ads that may interfere with your browsing experience.</li>
-<li>It has a secure connection (HTTPS) that encrypts your data.</li>
-</ul>
-<p>One example of a reliable keygen website is [ One example of a reliable keygen website is [Serials.ws](^1^), which claims to update daily and has a large and updated database of serial keys for various software. You can use the search function or browse the categories to find VT Transaction Plus keygen software on this website. You can also check the user rating and the update date of the serial key to verify its validity.</p>
- <h3>Search for VT Transaction Plus keygen software</h3>
-<p>The next step is to search for VT Transaction Plus keygen software on the keygen website. You can use the search box on the top right corner of the website, and type in "VT Transaction Plus" or "VT Transaction+". You will see a list of results that match your query, and you can click on the one that has the latest update date and the highest user rating. Alternatively, you can browse the categories on the left side of the website, and look for "Accounting & Finance" or "Business & Office". You will see a list of software that belong to these categories, and you can scroll down to find VT Transaction Plus keygen software.</p>
- <h3>Download and install VT Transaction Plus keygen software</h3>
-<p>The final step is to download and install VT Transaction Plus keygen software on your computer. You can click on the "Download" button next to the serial key, and you will be redirected to another page that contains a download link. You may need to complete a captcha or a verification process to access the download link. Once you have the download link, you can click on it and save the file on your computer. The file will be a ZIP or RAR archive that contains the keygen software and a text file with instructions. You need to extract the archive using a program like WinRAR or 7-Zip, and then run the keygen software as an administrator. You may also need to disable your antivirus or firewall temporarily, as they may detect the keygen software as a threat and block it.</p>
- <h2>How to Use VT Transaction Plus Keygen Software</h2>
-<p>Now that you have downloaded and installed VT Transaction Plus keygen software, you can use it to activate your copy of VT Transaction Plus. Here are the steps to use VT Transaction Plus keygen software:</p>
- <h3>Run VT Transaction Plus keygen software</h3>
-<p>The first step is to run VT Transaction Plus keygen software on your computer. You can double-click on the keygen software icon, or right-click on it and select "Run as administrator". You will see a window that looks like this:</p>
- <img src="https://i.imgur.com/5ZyQ0jE.png" alt="VT Transaction Plus keygen software window">
- <p>This is the interface of VT Transaction Plus keygen software, where you can generate a serial key for VT Transaction Plus.</p>
- <h3>Generate a serial key for VT Transaction Plus</h3>
-<p>The next step is to generate a serial key for VT Transaction Plus using the key generator function. You can click on the "Generate" button on the bottom right corner of the window, and you will see a random serial key appear in the text box above. The serial key will look something like this:</p>
- <code>VTTP-1234-5678-9012-3456</code>
- <p>This is an example of a serial key for VT Transaction Plus, which consists of four groups of four alphanumeric characters separated by dashes. You need to copy this serial key by selecting it and pressing Ctrl+C, or by right-clicking on it and selecting "Copy". You will need this serial key in the next step.</p>
- <h3>Enter the serial key into VT Transaction Plus</h3>
-<p>The final step is to enter the serial key into VT Transaction Plus to activate your license and enjoy all the features of the software. You need to open VT Transaction Plus on your computer, and go to "Help" > "About". You will see a window that looks like this:</p>
- <img src="https://i.imgur.com/9g5wZ0f.png" alt="VT Transaction Plus about window">
- <p>This is the about window of VT Transaction Plus, where you can see your license information and enter your serial key. You need to click on the "Enter License Key" button on the bottom left corner of the window, and you will see a window that looks like this:</p>
- <img src="https://i.imgur.com/0o4FtYd.png" alt="VT Transaction Plus enter license key window">
- <p>This is the enter license key window of VT Transaction Plus, where you can enter your serial key that you copied from the keygen software. You need to paste the serial key by pressing Ctrl+V, or by right-clicking on the text box and selecting "Paste". You will see the serial key appear in the text box, and you need to click on the "OK" button to confirm. You will see a message that says "License key accepted" and your license information will be updated. You have successfully activated your copy of VT Transaction Plus, and you can now use all the features of the software for free.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download VT Transaction Plus keygen software for free, and how to use it to activate your copy of VT Transaction Plus. We have also explained the features and benefits of VT Transaction Plus, and why you should use it for your accounting and bookkeeping needs. VT Transaction Plus is a comprehensive accounting and bookkeeping software that has an integrated ledger structure, allows unlimited accounts and transactions, offers flexibility and ease of use, is MTD for VAT compliant, is compatible with VT Final Accounts, and is affordable and reliable. By using VT Transaction Plus keygen software, you can bypass the registration or activation process of the original software, and use it for free without any limitations. However, you need to be careful when downloading keygen software, as some keygen websites may contain viruses or malware that can harm your computer or steal your personal information. You also need to follow the steps carefully to download and install VT Transaction Plus keygen software, and to generate and enter the serial key into VT Transaction Plus.</p>
-<p>We hope that this article has been helpful and informative for you. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some common questions and answers about downloading and using VT Transaction Plus keygen software:</p>
- <h3>Q: Is it legal to download and use VT Transaction Plus keygen software?</h3>
-<p>A: No, it is not legal to download and use VT Transaction Plus keygen software. Keygen software is a type of software that violates the intellectual property rights of the original software developers. By using keygen software, you are infringing on their copyrights and trademarks, and you are also violating their terms of service and license agreement. You may face legal consequences if you are caught downloading and using VT Transaction Plus keygen software.</p>
- <h3>Q: Is it safe to download and use VT Transaction Plus keygen software?</h3>
-<p>A: No, it is not safe to download and use VT Transaction Plus keygen software. Keygen software is a type of software that may contain viruses or malware that can harm your computer or steal your personal information. By downloading keygen software from unreliable websites, you are exposing yourself to potential risks and threats. You may also damage your computer or lose your data if you install or run VT Transaction Plus keygen software.</p>
- <h3>Q: What are the alternatives to downloading and using VT Transaction Plus keygen software?</h3>
-<p>A: The best alternative to downloading and using VT Transaction Plus keygen software is to purchase a license for VT Transaction Plus from the official website. This way, you can support the original software developers, and enjoy all the features and benefits of VT Transaction Plus legally and safely. You can also get updates, support, and warranty from the official website. Another alternative is to use other free or open source accounting and bookkeeping software that are available online. However, they may not have all the features or compatibility of VT Transaction Plus.</p>
- <h3>Q: How can I uninstall VT Transaction Plus keygen software from my computer?</h3>
-<p>A: If you want to uninstall VT Transaction Plus keygen software from your computer, you need to follow these steps:</p>
-<ol>
-<li>Go to "Start" > "Control Panel" > "Programs" > "Uninstall a program".</li>
-<li>Find VT Transaction Plus keygen software in the list of programs, and click on it.</li>
-<li>Click on the "Uninstall" button, and follow the instructions on the screen.</li>
-<li>Delete any remaining files or folders related to VT Transaction Plus keygen software from your computer.</li>
-<li>Restart your computer.</li>
-</ol>
-<p>You have successfully uninstalled VT Transaction Plus keygen software from your computer.</p>
- <h3>Q: How can I contact the support team of VT Transaction Plus?</h3>
-<p>A: If you have any issues or queries about VT Transaction Plus, you can contact the support team of VT Transaction Plus by visiting their official website at [VT Software]. You can find their contact details, such as phone number, email address, and postal address, on their website. You can also use their online form to send them a message or request a callback. You can also access their online help and user guides, or join their online forum to get help from other users. The support team of VT Transaction Plus is friendly and professional, and they will try to resolve your issues or queries as soon as possible.</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/operations/build/metadata.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/operations/build/metadata.py
deleted file mode 100644
index e2b7b4445437c53790e41af448dc14d86d9aa729..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/operations/build/metadata.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""Metadata generation logic for source distributions.
-"""
-
-import os
-
-from pip._vendor.pep517.wrappers import Pep517HookCaller
-
-from pip._internal.build_env import BuildEnvironment
-from pip._internal.exceptions import (
-    InstallationSubprocessError,
-    MetadataGenerationFailed,
-)
-from pip._internal.utils.subprocess import runner_with_spinner_message
-from pip._internal.utils.temp_dir import TempDirectory
-
-
-def generate_metadata(
-    build_env: BuildEnvironment, backend: Pep517HookCaller, details: str
-) -> str:
-    """Generate metadata using mechanisms described in PEP 517.
-
-    Returns the generated metadata directory.
-    """
-    metadata_tmpdir = TempDirectory(kind="modern-metadata", globally_managed=True)
-
-    metadata_dir = metadata_tmpdir.path
-
-    with build_env:
-        # Note that Pep517HookCaller implements a fallback for
-        # prepare_metadata_for_build_wheel, so we don't have to
-        # consider the possibility that this hook doesn't exist.
-        runner = runner_with_spinner_message("Preparing metadata (pyproject.toml)")
-        with backend.subprocess_runner(runner):
-            try:
-                distinfo_dir = backend.prepare_metadata_for_build_wheel(metadata_dir)
-            except InstallationSubprocessError as error:
-                raise MetadataGenerationFailed(package_details=details) from error
-
-    return os.path.join(metadata_dir, distinfo_dir)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/default_styles.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/default_styles.py
deleted file mode 100644
index 46e9ea52c54d067db5fba0ae0e9af34d910417a3..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/default_styles.py
+++ /dev/null
@@ -1,188 +0,0 @@
-from typing import Dict
-
-from .style import Style
-
-DEFAULT_STYLES: Dict[str, Style] = {
-    "none": Style.null(),
-    "reset": Style(
-        color="default",
-        bgcolor="default",
-        dim=False,
-        bold=False,
-        italic=False,
-        underline=False,
-        blink=False,
-        blink2=False,
-        reverse=False,
-        conceal=False,
-        strike=False,
-    ),
-    "dim": Style(dim=True),
-    "bright": Style(dim=False),
-    "bold": Style(bold=True),
-    "strong": Style(bold=True),
-    "code": Style(reverse=True, bold=True),
-    "italic": Style(italic=True),
-    "emphasize": Style(italic=True),
-    "underline": Style(underline=True),
-    "blink": Style(blink=True),
-    "blink2": Style(blink2=True),
-    "reverse": Style(reverse=True),
-    "strike": Style(strike=True),
-    "black": Style(color="black"),
-    "red": Style(color="red"),
-    "green": Style(color="green"),
-    "yellow": Style(color="yellow"),
-    "magenta": Style(color="magenta"),
-    "cyan": Style(color="cyan"),
-    "white": Style(color="white"),
-    "inspect.attr": Style(color="yellow", italic=True),
-    "inspect.attr.dunder": Style(color="yellow", italic=True, dim=True),
-    "inspect.callable": Style(bold=True, color="red"),
-    "inspect.async_def": Style(italic=True, color="bright_cyan"),
-    "inspect.def": Style(italic=True, color="bright_cyan"),
-    "inspect.class": Style(italic=True, color="bright_cyan"),
-    "inspect.error": Style(bold=True, color="red"),
-    "inspect.equals": Style(),
-    "inspect.help": Style(color="cyan"),
-    "inspect.doc": Style(dim=True),
-    "inspect.value.border": Style(color="green"),
-    "live.ellipsis": Style(bold=True, color="red"),
-    "layout.tree.row": Style(dim=False, color="red"),
-    "layout.tree.column": Style(dim=False, color="blue"),
-    "logging.keyword": Style(bold=True, color="yellow"),
-    "logging.level.notset": Style(dim=True),
-    "logging.level.debug": Style(color="green"),
-    "logging.level.info": Style(color="blue"),
-    "logging.level.warning": Style(color="red"),
-    "logging.level.error": Style(color="red", bold=True),
-    "logging.level.critical": Style(color="red", bold=True, reverse=True),
-    "log.level": Style.null(),
-    "log.time": Style(color="cyan", dim=True),
-    "log.message": Style.null(),
-    "log.path": Style(dim=True),
-    "repr.ellipsis": Style(color="yellow"),
-    "repr.indent": Style(color="green", dim=True),
-    "repr.error": Style(color="red", bold=True),
-    "repr.str": Style(color="green", italic=False, bold=False),
-    "repr.brace": Style(bold=True),
-    "repr.comma": Style(bold=True),
-    "repr.ipv4": Style(bold=True, color="bright_green"),
-    "repr.ipv6": Style(bold=True, color="bright_green"),
-    "repr.eui48": Style(bold=True, color="bright_green"),
-    "repr.eui64": Style(bold=True, color="bright_green"),
-    "repr.tag_start": Style(bold=True),
-    "repr.tag_name": Style(color="bright_magenta", bold=True),
-    "repr.tag_contents": Style(color="default"),
-    "repr.tag_end": Style(bold=True),
-    "repr.attrib_name": Style(color="yellow", italic=False),
-    "repr.attrib_equal": Style(bold=True),
-    "repr.attrib_value": Style(color="magenta", italic=False),
-    "repr.number": Style(color="cyan", bold=True, italic=False),
-    "repr.number_complex": Style(color="cyan", bold=True, italic=False),  # same
-    "repr.bool_true": Style(color="bright_green", italic=True),
-    "repr.bool_false": Style(color="bright_red", italic=True),
-    "repr.none": Style(color="magenta", italic=True),
-    "repr.url": Style(underline=True, color="bright_blue", italic=False, bold=False),
-    "repr.uuid": Style(color="bright_yellow", bold=False),
-    "repr.call": Style(color="magenta", bold=True),
-    "repr.path": Style(color="magenta"),
-    "repr.filename": Style(color="bright_magenta"),
-    "rule.line": Style(color="bright_green"),
-    "rule.text": Style.null(),
-    "json.brace": Style(bold=True),
-    "json.bool_true": Style(color="bright_green", italic=True),
-    "json.bool_false": Style(color="bright_red", italic=True),
-    "json.null": Style(color="magenta", italic=True),
-    "json.number": Style(color="cyan", bold=True, italic=False),
-    "json.str": Style(color="green", italic=False, bold=False),
-    "json.key": Style(color="blue", bold=True),
-    "prompt": Style.null(),
-    "prompt.choices": Style(color="magenta", bold=True),
-    "prompt.default": Style(color="cyan", bold=True),
-    "prompt.invalid": Style(color="red"),
-    "prompt.invalid.choice": Style(color="red"),
-    "pretty": Style.null(),
-    "scope.border": Style(color="blue"),
-    "scope.key": Style(color="yellow", italic=True),
-    "scope.key.special": Style(color="yellow", italic=True, dim=True),
-    "scope.equals": Style(color="red"),
-    "table.header": Style(bold=True),
-    "table.footer": Style(bold=True),
-    "table.cell": Style.null(),
-    "table.title": Style(italic=True),
-    "table.caption": Style(italic=True, dim=True),
-    "traceback.error": Style(color="red", italic=True),
-    "traceback.border.syntax_error": Style(color="bright_red"),
-    "traceback.border": Style(color="red"),
-    "traceback.text": Style.null(),
-    "traceback.title": Style(color="red", bold=True),
-    "traceback.exc_type": Style(color="bright_red", bold=True),
-    "traceback.exc_value": Style.null(),
-    "traceback.offset": Style(color="bright_red", bold=True),
-    "bar.back": Style(color="grey23"),
-    "bar.complete": Style(color="rgb(249,38,114)"),
-    "bar.finished": Style(color="rgb(114,156,31)"),
-    "bar.pulse": Style(color="rgb(249,38,114)"),
-    "progress.description": Style.null(),
-    "progress.filesize": Style(color="green"),
-    "progress.filesize.total": Style(color="green"),
-    "progress.download": Style(color="green"),
-    "progress.elapsed": Style(color="yellow"),
-    "progress.percentage": Style(color="magenta"),
-    "progress.remaining": Style(color="cyan"),
-    "progress.data.speed": Style(color="red"),
-    "progress.spinner": Style(color="green"),
-    "status.spinner": Style(color="green"),
-    "tree": Style(),
-    "tree.line": Style(),
-    "markdown.paragraph": Style(),
-    "markdown.text": Style(),
-    "markdown.emph": Style(italic=True),
-    "markdown.strong": Style(bold=True),
-    "markdown.code": Style(bgcolor="black", color="bright_white"),
-    "markdown.code_block": Style(dim=True, color="cyan", bgcolor="black"),
-    "markdown.block_quote": Style(color="magenta"),
-    "markdown.list": Style(color="cyan"),
-    "markdown.item": Style(),
-    "markdown.item.bullet": Style(color="yellow", bold=True),
-    "markdown.item.number": Style(color="yellow", bold=True),
-    "markdown.hr": Style(color="yellow"),
-    "markdown.h1.border": Style(),
-    "markdown.h1": Style(bold=True),
-    "markdown.h2": Style(bold=True, underline=True),
-    "markdown.h3": Style(bold=True),
-    "markdown.h4": Style(bold=True, dim=True),
-    "markdown.h5": Style(underline=True),
-    "markdown.h6": Style(italic=True),
-    "markdown.h7": Style(italic=True, dim=True),
-    "markdown.link": Style(color="bright_blue"),
-    "markdown.link_url": Style(color="blue"),
-    "iso8601.date": Style(color="blue"),
-    "iso8601.time": Style(color="magenta"),
-    "iso8601.timezone": Style(color="yellow"),
-}
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import argparse
-    import io
-
-    from pip._vendor.rich.console import Console
-    from pip._vendor.rich.table import Table
-    from pip._vendor.rich.text import Text
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--html", action="store_true", help="Export as HTML table")
-    args = parser.parse_args()
-    html: bool = args.html
-    console = Console(record=True, width=70, file=io.StringIO()) if html else Console()
-
-    table = Table("Name", "Styling")
-
-    for style_name, style in DEFAULT_STYLES.items():
-        table.add_row(Text(style_name, style=style), str(style))
-
-    console.print(table)
-    if html:
-        print(console.export_html(inline_styles=True))
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pkg_resources/_vendor/packaging/utils.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pkg_resources/_vendor/packaging/utils.py
deleted file mode 100644
index bab11b80c60f10a4f3bccb12eb5b17c48a449767..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pkg_resources/_vendor/packaging/utils.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-from typing import FrozenSet, NewType, Tuple, Union, cast
-
-from .tags import Tag, parse_tag
-from .version import InvalidVersion, Version
-
-BuildTag = Union[Tuple[()], Tuple[int, str]]
-NormalizedName = NewType("NormalizedName", str)
-
-
-class InvalidWheelFilename(ValueError):
-    """
-    An invalid wheel filename was found, users should refer to PEP 427.
-    """
-
-
-class InvalidSdistFilename(ValueError):
-    """
-    An invalid sdist filename was found, users should refer to the packaging user guide.
-    """
-
-
-_canonicalize_regex = re.compile(r"[-_.]+")
-# PEP 427: The build number must start with a digit.
-_build_tag_regex = re.compile(r"(\d+)(.*)")
-
-
-def canonicalize_name(name: str) -> NormalizedName:
-    # This is taken from PEP 503.
-    value = _canonicalize_regex.sub("-", name).lower()
-    return cast(NormalizedName, value)
-
-
-def canonicalize_version(version: Union[Version, str]) -> str:
-    """
-    This is very similar to Version.__str__, but has one subtle difference
-    with the way it handles the release segment.
-    """
-    if isinstance(version, str):
-        try:
-            parsed = Version(version)
-        except InvalidVersion:
-            # Legacy versions cannot be normalized
-            return version
-    else:
-        parsed = version
-
-    parts = []
-
-    # Epoch
-    if parsed.epoch != 0:
-        parts.append(f"{parsed.epoch}!")
-
-    # Release segment
-    # NB: This strips trailing '.0's to normalize
-    parts.append(re.sub(r"(\.0)+$", "", ".".join(str(x) for x in parsed.release)))
-
-    # Pre-release
-    if parsed.pre is not None:
-        parts.append("".join(str(x) for x in parsed.pre))
-
-    # Post-release
-    if parsed.post is not None:
-        parts.append(f".post{parsed.post}")
-
-    # Development release
-    if parsed.dev is not None:
-        parts.append(f".dev{parsed.dev}")
-
-    # Local version segment
-    if parsed.local is not None:
-        parts.append(f"+{parsed.local}")
-
-    return "".join(parts)
-
-
-def parse_wheel_filename(
-    filename: str,
-) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
-    if not filename.endswith(".whl"):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (extension must be '.whl'): {filename}"
-        )
-
-    filename = filename[:-4]
-    dashes = filename.count("-")
-    if dashes not in (4, 5):
-        raise InvalidWheelFilename(
-            f"Invalid wheel filename (wrong number of parts): {filename}"
-        )
-
-    parts = filename.split("-", dashes - 2)
-    name_part = parts[0]
-    # See PEP 427 for the rules on escaping the project name
-    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
-        raise InvalidWheelFilename(f"Invalid project name: {filename}")
-    name = canonicalize_name(name_part)
-    version = Version(parts[1])
-    if dashes == 5:
-        build_part = parts[2]
-        build_match = _build_tag_regex.match(build_part)
-        if build_match is None:
-            raise InvalidWheelFilename(
-                f"Invalid build number: {build_part} in '{filename}'"
-            )
-        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
-    else:
-        build = ()
-    tags = parse_tag(parts[-1])
-    return (name, version, build, tags)
-
-
-def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
-    if filename.endswith(".tar.gz"):
-        file_stem = filename[: -len(".tar.gz")]
-    elif filename.endswith(".zip"):
-        file_stem = filename[: -len(".zip")]
-    else:
-        raise InvalidSdistFilename(
-            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
-            f" {filename}"
-        )
-
-    # We are requiring a PEP 440 version, which cannot contain dashes,
-    # so we split on the last dash.
-    name_part, sep, version_part = file_stem.rpartition("-")
-    if not sep:
-        raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
-
-    name = canonicalize_name(name_part)
-    version = Version(version_part)
-    return (name, version)
diff --git a/spaces/tlkh/textdiff/README.md b/spaces/tlkh/textdiff/README.md
deleted file mode 100644
index 2f526eb07ec2203bce522dea8fa8386c0bb7846a..0000000000000000000000000000000000000000
--- a/spaces/tlkh/textdiff/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Textdiff
-emoji: 🚀
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/tom-beer/birds-israel/train.py b/spaces/tom-beer/birds-israel/train.py
deleted file mode 100644
index bb32a8056bdf2f06c48d97789044f54fe8dfc85c..0000000000000000000000000000000000000000
--- a/spaces/tom-beer/birds-israel/train.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import pytorch_lightning as pl
-from pytorch_lightning.loggers import WandbLogger
-import wandb
-
-from lit_mlp import LitMLP
-from data_module import birds, samples, batch_size
-from loggings import ImagePredictionLogger
-
-wandb_logger = WandbLogger(project="lit-wandb")
-
-trainer = pl.Trainer(
-    logger=wandb_logger,
-    log_every_n_steps=50,
-    gpus=0,
-    max_epochs=100,
-    deterministic=True,
-    callbacks=[ImagePredictionLogger(samples)]
-    )
-
-model = LitMLP(n_classes=18, batch_size=batch_size)
-trainer.fit(model, birds)
-trainer.test(datamodule=birds,
-             ckpt_path=None)
-wandb.finish()
diff --git a/spaces/tomofi/MMOCR/configs/_base_/recog_models/abinet.py b/spaces/tomofi/MMOCR/configs/_base_/recog_models/abinet.py
deleted file mode 100644
index 19c6b66731f0b205741037ece8d6b49f91d0110b..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/configs/_base_/recog_models/abinet.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# num_chars depends on the configuration of label_convertor. The actual
-# dictionary size is 36 + 1 (<BOS/EOS>).
-# TODO: Automatically update num_chars based on the configuration of
-# label_convertor
-num_chars = 37
-max_seq_len = 26
-
-label_convertor = dict(
-    type='ABIConvertor',
-    dict_type='DICT36',
-    with_unknown=False,
-    with_padding=False,
-    lower=True,
-)
-
-model = dict(
-    type='ABINet',
-    backbone=dict(type='ResNetABI'),
-    encoder=dict(
-        type='ABIVisionModel',
-        encoder=dict(
-            type='TransformerEncoder',
-            n_layers=3,
-            n_head=8,
-            d_model=512,
-            d_inner=2048,
-            dropout=0.1,
-            max_len=8 * 32,
-        ),
-        decoder=dict(
-            type='ABIVisionDecoder',
-            in_channels=512,
-            num_channels=64,
-            attn_height=8,
-            attn_width=32,
-            attn_mode='nearest',
-            use_result='feature',
-            num_chars=num_chars,
-            max_seq_len=max_seq_len,
-            init_cfg=dict(type='Xavier', layer='Conv2d')),
-    ),
-    decoder=dict(
-        type='ABILanguageDecoder',
-        d_model=512,
-        n_head=8,
-        d_inner=2048,
-        n_layers=4,
-        dropout=0.1,
-        detach_tokens=True,
-        use_self_attn=False,
-        pad_idx=num_chars - 1,
-        num_chars=num_chars,
-        max_seq_len=max_seq_len,
-        init_cfg=None),
-    fuser=dict(
-        type='ABIFuser',
-        d_model=512,
-        num_chars=num_chars,
-        init_cfg=None,
-        max_seq_len=max_seq_len,
-    ),
-    loss=dict(
-        type='ABILoss',
-        enc_weight=1.0,
-        dec_weight=1.0,
-        fusion_weight=1.0,
-        num_classes=num_chars),
-    label_convertor=label_convertor,
-    max_seq_len=max_seq_len,
-    iter_size=3)
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textdet/losses/drrg_loss.py b/spaces/tomofi/MMOCR/mmocr/models/textdet/losses/drrg_loss.py
deleted file mode 100644
index a59868d942baaba7586554e90414d19e6de9ec29..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textdet/losses/drrg_loss.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn.functional as F
-from mmdet.core import BitmapMasks
-from torch import nn
-
-from mmocr.models.builder import LOSSES
-from mmocr.utils import check_argument
-
-
-@LOSSES.register_module()
-class DRRGLoss(nn.Module):
-    """The class for implementing DRRG loss. This is partially adapted from
-    https://github.com/GXYM/DRRG licensed under the MIT license.
-
-    DRRG: `Deep Relational Reasoning Graph Network for Arbitrary Shape Text
-    Detection <https://arxiv.org/abs/1908.05900>`_.
-
-    Args:
-        ohem_ratio (float): The negative/positive ratio in ohem.
-    """
-
-    def __init__(self, ohem_ratio=3.0):
-        super().__init__()
-        self.ohem_ratio = ohem_ratio
-
-    def balance_bce_loss(self, pred, gt, mask):
-        """Balanced Binary-CrossEntropy Loss.
-
-        Args:
-            pred (Tensor): Shape of :math:`(1, H, W)`.
-            gt (Tensor): Shape of :math:`(1, H, W)`.
-            mask (Tensor): Shape of :math:`(1, H, W)`.
-
-        Returns:
-            Tensor: Balanced bce loss.
-        """
-        assert pred.shape == gt.shape == mask.shape
-        assert torch.all(pred >= 0) and torch.all(pred <= 1)
-        assert torch.all(gt >= 0) and torch.all(gt <= 1)
-        positive = gt * mask
-        negative = (1 - gt) * mask
-        positive_count = int(positive.float().sum())
-        gt = gt.float()
-        if positive_count > 0:
-            loss = F.binary_cross_entropy(pred, gt, reduction='none')
-            positive_loss = torch.sum(loss * positive.float())
-            negative_loss = loss * negative.float()
-            negative_count = min(
-                int(negative.float().sum()),
-                int(positive_count * self.ohem_ratio))
-        else:
-            positive_loss = torch.tensor(0.0, device=pred.device)
-            loss = F.binary_cross_entropy(pred, gt, reduction='none')
-            negative_loss = loss * negative.float()
-            negative_count = 100
-        negative_loss, _ = torch.topk(negative_loss.view(-1), negative_count)
-
-        balance_loss = (positive_loss + torch.sum(negative_loss)) / (
-            float(positive_count + negative_count) + 1e-5)
-
-        return balance_loss
-
-    def gcn_loss(self, gcn_data):
-        """CrossEntropy Loss from gcn module.
-
-        Args:
-            gcn_data (tuple(Tensor, Tensor)): The first is the
-                prediction with shape :math:`(N, 2)` and the
-                second is the gt label with shape :math:`(m, n)`
-                where :math:`m * n = N`.
-
-        Returns:
-            Tensor: CrossEntropy loss.
-        """
-        gcn_pred, gt_labels = gcn_data
-        gt_labels = gt_labels.view(-1).to(gcn_pred.device)
-        loss = F.cross_entropy(gcn_pred, gt_labels)
-
-        return loss
-
-    def bitmasks2tensor(self, bitmasks, target_sz):
-        """Convert Bitmasks to tensor.
-
-        Args:
-            bitmasks (list[BitmapMasks]): The BitmapMasks list. Each item is
-                for one img.
-            target_sz (tuple(int, int)): The target tensor of size
-                :math:`(H, W)`.
-
-        Returns:
-            list[Tensor]: The list of kernel tensors. Each element stands for
-            one kernel level.
-        """
-        assert check_argument.is_type_list(bitmasks, BitmapMasks)
-        assert isinstance(target_sz, tuple)
-
-        batch_size = len(bitmasks)
-        num_masks = len(bitmasks[0])
-
-        results = []
-
-        for level_inx in range(num_masks):
-            kernel = []
-            for batch_inx in range(batch_size):
-                mask = torch.from_numpy(bitmasks[batch_inx].masks[level_inx])
-                # hxw
-                mask_sz = mask.shape
-                # left, right, top, bottom
-                pad = [
-                    0, target_sz[1] - mask_sz[1], 0, target_sz[0] - mask_sz[0]
-                ]
-                mask = F.pad(mask, pad, mode='constant', value=0)
-                kernel.append(mask)
-            kernel = torch.stack(kernel)
-            results.append(kernel)
-
-        return results
-
-    def forward(self, preds, downsample_ratio, gt_text_mask,
-                gt_center_region_mask, gt_mask, gt_top_height_map,
-                gt_bot_height_map, gt_sin_map, gt_cos_map):
-        """Compute Drrg loss.
-
-        Args:
-            preds (tuple(Tensor)): The first is the prediction map
-                with shape :math:`(N, C_{out}, H, W)`.
-                The second is prediction from GCN module, with
-                shape :math:`(N, 2)`.
-                The third is ground-truth label with shape :math:`(N, 8)`.
-            downsample_ratio (float): The downsample ratio.
-            gt_text_mask (list[BitmapMasks]): Text mask.
-            gt_center_region_mask (list[BitmapMasks]): Center region mask.
-            gt_mask (list[BitmapMasks]): Effective mask.
-            gt_top_height_map (list[BitmapMasks]): Top height map.
-            gt_bot_height_map (list[BitmapMasks]): Bottom height map.
-            gt_sin_map (list[BitmapMasks]): Sinusoid map.
-            gt_cos_map (list[BitmapMasks]): Cosine map.
-
-        Returns:
-            dict:  A loss dict with ``loss_text``, ``loss_center``,
-            ``loss_height``, ``loss_sin``, ``loss_cos``, and ``loss_gcn``.
-        """
-        assert isinstance(preds, tuple)
-        assert isinstance(downsample_ratio, float)
-        assert check_argument.is_type_list(gt_text_mask, BitmapMasks)
-        assert check_argument.is_type_list(gt_center_region_mask, BitmapMasks)
-        assert check_argument.is_type_list(gt_mask, BitmapMasks)
-        assert check_argument.is_type_list(gt_top_height_map, BitmapMasks)
-        assert check_argument.is_type_list(gt_bot_height_map, BitmapMasks)
-        assert check_argument.is_type_list(gt_sin_map, BitmapMasks)
-        assert check_argument.is_type_list(gt_cos_map, BitmapMasks)
-
-        pred_maps, gcn_data = preds
-        pred_text_region = pred_maps[:, 0, :, :]
-        pred_center_region = pred_maps[:, 1, :, :]
-        pred_sin_map = pred_maps[:, 2, :, :]
-        pred_cos_map = pred_maps[:, 3, :, :]
-        pred_top_height_map = pred_maps[:, 4, :, :]
-        pred_bot_height_map = pred_maps[:, 5, :, :]
-        feature_sz = pred_maps.size()
-        device = pred_maps.device
-
-        # bitmask 2 tensor
-        mapping = {
-            'gt_text_mask': gt_text_mask,
-            'gt_center_region_mask': gt_center_region_mask,
-            'gt_mask': gt_mask,
-            'gt_top_height_map': gt_top_height_map,
-            'gt_bot_height_map': gt_bot_height_map,
-            'gt_sin_map': gt_sin_map,
-            'gt_cos_map': gt_cos_map
-        }
-        gt = {}
-        for key, value in mapping.items():
-            gt[key] = value
-            if abs(downsample_ratio - 1.0) < 1e-2:
-                gt[key] = self.bitmasks2tensor(gt[key], feature_sz[2:])
-            else:
-                gt[key] = [item.rescale(downsample_ratio) for item in gt[key]]
-                gt[key] = self.bitmasks2tensor(gt[key], feature_sz[2:])
-                if key in ['gt_top_height_map', 'gt_bot_height_map']:
-                    gt[key] = [item * downsample_ratio for item in gt[key]]
-            gt[key] = [item.to(device) for item in gt[key]]
-
-        scale = torch.sqrt(1.0 / (pred_sin_map**2 + pred_cos_map**2 + 1e-8))
-        pred_sin_map = pred_sin_map * scale
-        pred_cos_map = pred_cos_map * scale
-
-        loss_text = self.balance_bce_loss(
-            torch.sigmoid(pred_text_region), gt['gt_text_mask'][0],
-            gt['gt_mask'][0])
-
-        text_mask = (gt['gt_text_mask'][0] * gt['gt_mask'][0]).float()
-        negative_text_mask = ((1 - gt['gt_text_mask'][0]) *
-                              gt['gt_mask'][0]).float()
-        loss_center_map = F.binary_cross_entropy(
-            torch.sigmoid(pred_center_region),
-            gt['gt_center_region_mask'][0].float(),
-            reduction='none')
-        if int(text_mask.sum()) > 0:
-            loss_center_positive = torch.sum(
-                loss_center_map * text_mask) / torch.sum(text_mask)
-        else:
-            loss_center_positive = torch.tensor(0.0, device=device)
-        loss_center_negative = torch.sum(
-            loss_center_map *
-            negative_text_mask) / torch.sum(negative_text_mask)
-        loss_center = loss_center_positive + 0.5 * loss_center_negative
-
-        center_mask = (gt['gt_center_region_mask'][0] *
-                       gt['gt_mask'][0]).float()
-        if int(center_mask.sum()) > 0:
-            map_sz = pred_top_height_map.size()
-            ones = torch.ones(map_sz, dtype=torch.float, device=device)
-            loss_top = F.smooth_l1_loss(
-                pred_top_height_map / (gt['gt_top_height_map'][0] + 1e-2),
-                ones,
-                reduction='none')
-            loss_bot = F.smooth_l1_loss(
-                pred_bot_height_map / (gt['gt_bot_height_map'][0] + 1e-2),
-                ones,
-                reduction='none')
-            gt_height = (
-                gt['gt_top_height_map'][0] + gt['gt_bot_height_map'][0])
-            loss_height = torch.sum(
-                (torch.log(gt_height + 1) *
-                 (loss_top + loss_bot)) * center_mask) / torch.sum(center_mask)
-
-            loss_sin = torch.sum(
-                F.smooth_l1_loss(
-                    pred_sin_map, gt['gt_sin_map'][0], reduction='none') *
-                center_mask) / torch.sum(center_mask)
-            loss_cos = torch.sum(
-                F.smooth_l1_loss(
-                    pred_cos_map, gt['gt_cos_map'][0], reduction='none') *
-                center_mask) / torch.sum(center_mask)
-        else:
-            loss_height = torch.tensor(0.0, device=device)
-            loss_sin = torch.tensor(0.0, device=device)
-            loss_cos = torch.tensor(0.0, device=device)
-
-        loss_gcn = self.gcn_loss(gcn_data)
-
-        results = dict(
-            loss_text=loss_text,
-            loss_center=loss_center,
-            loss_height=loss_height,
-            loss_sin=loss_sin,
-            loss_cos=loss_cos,
-            loss_gcn=loss_gcn)
-
-        return results
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/make.bat b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/make.bat
deleted file mode 100644
index 922152e96a04a242e6fc40f124261d74890617d8..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/visualization/image.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/visualization/image.py
deleted file mode 100644
index 5a148384d7a77c4d9849c54570e85740eaff8235..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/visualization/image.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import matplotlib.pyplot as plt
-import mmcv
-import numpy as np
-import pycocotools.mask as mask_util
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-
-from ..utils import mask2ndarray
-
-EPS = 1e-2
-
-
-def color_val_matplotlib(color):
-    """Convert various input in BGR order to normalized RGB matplotlib color
-    tuples,
-
-    Args:
-        color (:obj:`Color`/str/tuple/int/ndarray): Color inputs
-
-    Returns:
-        tuple[float]: A tuple of 3 normalized floats indicating RGB channels.
-    """
-    color = mmcv.color_val(color)
-    color = [color / 255 for color in color[::-1]]
-    return tuple(color)
-
-
-def imshow_det_bboxes(img,
-                      bboxes,
-                      labels,
-                      segms=None,
-                      class_names=None,
-                      score_thr=0,
-                      bbox_color='green',
-                      text_color='green',
-                      mask_color=None,
-                      thickness=2,
-                      font_size=13,
-                      win_name='',
-                      show=True,
-                      wait_time=0,
-                      out_file=None):
-    """Draw bboxes and class labels (with scores) on an image.
-
-    Args:
-        img (str or ndarray): The image to be displayed.
-        bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
-            (n, 5).
-        labels (ndarray): Labels of bboxes.
-        segms (ndarray or None): Masks, shaped (n,h,w) or None
-        class_names (list[str]): Names of each classes.
-        score_thr (float): Minimum score of bboxes to be shown.  Default: 0
-        bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-           The tuple of color should be in BGR order. Default: 'green'
-        text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-           The tuple of color should be in BGR order. Default: 'green'
-        mask_color (str or tuple(int) or :obj:`Color`, optional):
-           Color of masks. The tuple of color should be in BGR order.
-           Default: None
-        thickness (int): Thickness of lines. Default: 2
-        font_size (int): Font size of texts. Default: 13
-        show (bool): Whether to show the image. Default: True
-        win_name (str): The window name. Default: ''
-        wait_time (float): Value of waitKey param. Default: 0.
-        out_file (str, optional): The filename to write the image.
-            Default: None
-
-    Returns:
-        ndarray: The image with bboxes drawn on it.
-    """
-    assert bboxes.ndim == 2, \
-        f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.'
-    assert labels.ndim == 1, \
-        f' labels ndim should be 1, but its ndim is {labels.ndim}.'
-    assert bboxes.shape[0] == labels.shape[0], \
-        'bboxes.shape[0] and labels.shape[0] should have the same length.'
-    assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5, \
-        f' bboxes.shape[1] should be 4 or 5, but its {bboxes.shape[1]}.'
-    img = mmcv.imread(img).astype(np.uint8)
-
-    if score_thr > 0:
-        assert bboxes.shape[1] == 5
-        scores = bboxes[:, -1]
-        inds = scores > score_thr
-        bboxes = bboxes[inds, :]
-        labels = labels[inds]
-        if segms is not None:
-            segms = segms[inds, ...]
-
-    mask_colors = []
-    if labels.shape[0] > 0:
-        if mask_color is None:
-            # random color
-            np.random.seed(42)
-            mask_colors = [
-                np.random.randint(0, 256, (1, 3), dtype=np.uint8)
-                for _ in range(max(labels) + 1)
-            ]
-        else:
-            # specify  color
-            mask_colors = [
-                np.array(mmcv.color_val(mask_color)[::-1], dtype=np.uint8)
-            ] * (
-                max(labels) + 1)
-
-    bbox_color = color_val_matplotlib(bbox_color)
-    text_color = color_val_matplotlib(text_color)
-
-    img = mmcv.bgr2rgb(img)
-    width, height = img.shape[1], img.shape[0]
-    img = np.ascontiguousarray(img)
-
-    fig = plt.figure(win_name, frameon=False)
-    plt.title(win_name)
-    canvas = fig.canvas
-    dpi = fig.get_dpi()
-    # add a small EPS to avoid precision lost due to matplotlib's truncation
-    # (https://github.com/matplotlib/matplotlib/issues/15363)
-    fig.set_size_inches((width + EPS) / dpi, (height + EPS) / dpi)
-
-    # remove white edges by set subplot margin
-    plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
-    ax = plt.gca()
-    ax.axis('off')
-
-    polygons = []
-    color = []
-    for i, (bbox, label) in enumerate(zip(bboxes, labels)):
-        bbox_int = bbox.astype(np.int32)
-        poly = [[bbox_int[0], bbox_int[1]], [bbox_int[0], bbox_int[3]],
-                [bbox_int[2], bbox_int[3]], [bbox_int[2], bbox_int[1]]]
-        np_poly = np.array(poly).reshape((4, 2))
-        polygons.append(Polygon(np_poly))
-        color.append(bbox_color)
-        label_text = class_names[
-            label] if class_names is not None else f'class {label}'
-        if len(bbox) > 4:
-            label_text += f'|{bbox[-1]:.02f}'
-        ax.text(
-            bbox_int[0],
-            bbox_int[1],
-            f'{label_text}',
-            bbox={
-                'facecolor': 'black',
-                'alpha': 0.8,
-                'pad': 0.7,
-                'edgecolor': 'none'
-            },
-            color=text_color,
-            fontsize=font_size,
-            verticalalignment='top',
-            horizontalalignment='left')
-        if segms is not None:
-            color_mask = mask_colors[labels[i]]
-            mask = segms[i].astype(bool)
-            img[mask] = img[mask] * 0.5 + color_mask * 0.5
-
-    plt.imshow(img)
-
-    p = PatchCollection(
-        polygons, facecolor='none', edgecolors=color, linewidths=thickness)
-    ax.add_collection(p)
-
-    stream, _ = canvas.print_to_buffer()
-    buffer = np.frombuffer(stream, dtype='uint8')
-    img_rgba = buffer.reshape(height, width, 4)
-    rgb, alpha = np.split(img_rgba, [3], axis=2)
-    img = rgb.astype('uint8')
-    img = mmcv.rgb2bgr(img)
-
-    if show:
-        # We do not use cv2 for display because in some cases, opencv will
-        # conflict with Qt, it will output a warning: Current thread
-        # is not the object's thread. You can refer to
-        # https://github.com/opencv/opencv-python/issues/46 for details
-        if wait_time == 0:
-            plt.show()
-        else:
-            plt.show(block=False)
-            plt.pause(wait_time)
-    if out_file is not None:
-        mmcv.imwrite(img, out_file)
-
-    plt.close()
-
-    return img
-
-
-def imshow_gt_det_bboxes(img,
-                         annotation,
-                         result,
-                         class_names=None,
-                         score_thr=0,
-                         gt_bbox_color=(255, 102, 61),
-                         gt_text_color=(255, 102, 61),
-                         gt_mask_color=(255, 102, 61),
-                         det_bbox_color=(72, 101, 241),
-                         det_text_color=(72, 101, 241),
-                         det_mask_color=(72, 101, 241),
-                         thickness=2,
-                         font_size=13,
-                         win_name='',
-                         show=True,
-                         wait_time=0,
-                         out_file=None):
-    """General visualization GT and result function.
-
-    Args:
-      img (str or ndarray): The image to be displayed.)
-      annotation (dict): Ground truth annotations where contain keys of
-          'gt_bboxes' and 'gt_labels' or 'gt_masks'
-      result (tuple[list] or list): The detection result, can be either
-          (bbox, segm) or just bbox.
-      class_names (list[str]): Names of each classes.
-      score_thr (float): Minimum score of bboxes to be shown.  Default: 0
-      gt_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-           The tuple of color should be in BGR order. Default: (255, 102, 61)
-      gt_text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-           The tuple of color should be in BGR order. Default: (255, 102, 61)
-      gt_mask_color (str or tuple(int) or :obj:`Color`, optional):
-           Color of masks. The tuple of color should be in BGR order.
-           Default: (255, 102, 61)
-      det_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-           The tuple of color should be in BGR order. Default: (72, 101, 241)
-      det_text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-           The tuple of color should be in BGR order. Default: (72, 101, 241)
-      det_mask_color (str or tuple(int) or :obj:`Color`, optional):
-           Color of masks. The tuple of color should be in BGR order.
-           Default: (72, 101, 241)
-      thickness (int): Thickness of lines. Default: 2
-      font_size (int): Font size of texts. Default: 13
-      win_name (str): The window name. Default: ''
-      show (bool): Whether to show the image. Default: True
-      wait_time (float): Value of waitKey param. Default: 0.
-      out_file (str, optional): The filename to write the image.
-         Default: None
-
-    Returns:
-        ndarray: The image with bboxes or masks drawn on it.
-    """
-    assert 'gt_bboxes' in annotation
-    assert 'gt_labels' in annotation
-    assert isinstance(
-        result,
-        (tuple, list)), f'Expected tuple or list, but get {type(result)}'
-
-    gt_masks = annotation.get('gt_masks', None)
-    if gt_masks is not None:
-        gt_masks = mask2ndarray(gt_masks)
-
-    img = mmcv.imread(img)
-
-    img = imshow_det_bboxes(
-        img,
-        annotation['gt_bboxes'],
-        annotation['gt_labels'],
-        gt_masks,
-        class_names=class_names,
-        bbox_color=gt_bbox_color,
-        text_color=gt_text_color,
-        mask_color=gt_mask_color,
-        thickness=thickness,
-        font_size=font_size,
-        win_name=win_name,
-        show=False)
-
-    if isinstance(result, tuple):
-        bbox_result, segm_result = result
-        if isinstance(segm_result, tuple):
-            segm_result = segm_result[0]  # ms rcnn
-    else:
-        bbox_result, segm_result = result, None
-
-    bboxes = np.vstack(bbox_result)
-    labels = [
-        np.full(bbox.shape[0], i, dtype=np.int32)
-        for i, bbox in enumerate(bbox_result)
-    ]
-    labels = np.concatenate(labels)
-
-    segms = None
-    if segm_result is not None and len(labels) > 0:  # non empty
-        segms = mmcv.concat_list(segm_result)
-        segms = mask_util.decode(segms)
-        segms = segms.transpose(2, 0, 1)
-
-    img = imshow_det_bboxes(
-        img,
-        bboxes,
-        labels,
-        segms=segms,
-        class_names=class_names,
-        score_thr=score_thr,
-        bbox_color=det_bbox_color,
-        text_color=det_text_color,
-        mask_color=det_mask_color,
-        thickness=thickness,
-        font_size=font_size,
-        win_name=win_name,
-        show=show,
-        wait_time=wait_time,
-        out_file=out_file)
-    return img
diff --git a/spaces/tushar27/Streamlit-Magic-Sheet/README.md b/spaces/tushar27/Streamlit-Magic-Sheet/README.md
deleted file mode 100644
index 2b34e91e469ba45a6b59e811945982df5a4632db..0000000000000000000000000000000000000000
--- a/spaces/tushar27/Streamlit-Magic-Sheet/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit Magic Sheet
-emoji: 🐢
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.28.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/coords_over_scan_gen.py b/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/coords_over_scan_gen.py
deleted file mode 100644
index e6eb456e99ef520c214943f83a559836327b0d20..0000000000000000000000000000000000000000
--- a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/my_py_lib/coords_over_scan_gen.py
+++ /dev/null
@@ -1,73 +0,0 @@
-'''
-N分之一步长坐标生成器，建议组合 ImageOverScanWrapper 来使用
-'''
-
-
-def n_step_scan_coords_gen(im_hw=(512, 512), window_hw=(128, 128), n_step: int=2):
-    '''
-    N分之一步长坐标生成器，可以生成滑动框左上角和右下角坐标，便于快速遍历图像
-    当前只支持生成整数坐标
-    :param im_hw:       图像大小
-    :param window_hw:   窗口高宽
-    :param n_step:      N分之一步长，默认为半步长
-    :return: yx_start, yx_end   扫描框左上角和右下角坐标
-    '''
-    assert n_step >= 1
-    window_step_hw = [window_hw[0] // n_step, window_hw[1] // n_step]
-
-    # 仔细设置区间，可以避免采样到完全无效的区域
-    y_start = 0 - window_hw[0] + window_step_hw[0]
-    y_end = im_hw[0]
-    x_start = 0 - window_hw[1] + window_step_hw[1]
-    x_end = im_hw[1]
-
-    for y in range(y_start, y_end, window_step_hw[0]):
-        for x in range(x_start, x_end, window_step_hw[1]):
-            yx_start = (y, x)
-            yx_end = (y +  window_hw[0], x + window_hw[1])
-            yield yx_start, yx_end
-
-
-def n_step_scan_coords_gen_v2(im_hw=(512, 512), window_hw=(128, 128), n_step: float=1., start_offset=None):
-    '''
-    N步长坐标生成器，支持小数步长，可以生成滑动框左上角和右下角坐标，便于快速遍历图像
-    例如窗口大小设定为[128,128]，设定为0.5步长时，代表步长为[64,64]，设定为2步长时，代表步长为[256,256]
-    当前只支持生成整数坐标
-    :param im_hw:       图像大小
-    :param window_hw:   窗口高宽
-    :param n_step:      N步长，默认为1倍步长
-    :return: yx_start, yx_end   扫描框左上角和右下角坐标
-    '''
-    step_hw = [int(window_hw[0] * n_step), int(window_hw[1] * n_step)]
-
-    # 使采样区间等于或大于边界
-    y_start = 0 - window_hw[0] + step_hw[0]
-    y_end = im_hw[0]
-    x_start = 0 - window_hw[1] + step_hw[1]
-    x_end = im_hw[1]
-
-    if start_offset is not None:
-        assert len(start_offset) == 2
-        y_start += int(start_offset[0])
-        x_start += int(start_offset[1])
-        y_end += int(start_offset[0])
-        x_end += int(start_offset[1])
-
-    for y in range(y_start, y_end, step_hw[0]):
-        for x in range(x_start, x_end, step_hw[1]):
-            yx_start = (y, x)
-            yx_end = (y + window_hw[0], x + window_hw[1])
-            yield yx_start, yx_end
-
-
-if __name__ == '__main__':
-    g = n_step_scan_coords_gen([512, 512], window_hw=[100, 100], n_step=1)
-    for yx_start, yx_end in g:
-        print(yx_start, yx_end)
-
-    g = n_step_scan_coords_gen_v2([512, 512], window_hw=[100, 100], n_step=0.5)
-    for yx_start, yx_end in g:
-        print(yx_start, yx_end)
-
-    # for ipython test speed
-    # %timeit list(n_step_scan_pos_gen([51200, 51200], window_hw=[128, 128], n_step=1))
diff --git a/spaces/uSerNameDDHL/bingo/src/components/chat-header.tsx b/spaces/uSerNameDDHL/bingo/src/components/chat-header.tsx
deleted file mode 100644
index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/src/components/chat-header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import LogoIcon from '@/assets/images/logo.svg'
-import Image from 'next/image'
-
-export function ChatHeader() {
-  return (
-    <div className="flex flex-col items-center justify-center">
-      <Image alt="logo" src={LogoIcon} width={60}/>
-      <div className="mt-8 text-4xl font-bold">欢迎使用新必应</div>
-      <div className="mt-4 mb-8 text-lg">由 AI 支持的网页版 Copilot</div>
-    </div>
-  )
-}
diff --git a/spaces/uSerNameDDHL/bingo/src/components/external-link.tsx b/spaces/uSerNameDDHL/bingo/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/uSerNameDDHL/bingo/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
-  href,
-  children
-}: {
-  href: string
-  children: React.ReactNode
-}) {
-  return (
-    <a
-      href={href}
-      target="_blank"
-      rel="noreferrer"
-      className="inline-flex flex-1 justify-center gap-1 underline"
-    >
-      <span>{children}</span>
-      <svg
-        aria-hidden="true"
-        height="7"
-        viewBox="0 0 6 6"
-        width="7"
-        className="opacity-70"
-      >
-        <path
-          d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
-          fill="currentColor"
-        ></path>
-      </svg>
-    </a>
-  )
-}
diff --git a/spaces/universeTBD/astrollama/README.md b/spaces/universeTBD/astrollama/README.md
deleted file mode 100644
index 884da8e4b809a984ae15f78b0dc1677f72a5b9bd..0000000000000000000000000000000000000000
--- a/spaces/universeTBD/astrollama/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: AstroLLaMA
-emoji: 🌖
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.44.2
-app_file: app.py
-pinned: false
-models:
-- universeTBD/astrollama
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/AK 47 Movie Kickass Torrent Download VERIFIED.md b/spaces/usbethFlerru/sovits-modelsV2/example/AK 47 Movie Kickass Torrent Download VERIFIED.md
deleted file mode 100644
index 05669de6b64623572f1ed46363d26a4c613c06b8..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/AK 47 Movie Kickass Torrent Download VERIFIED.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<h2>AK 47 Movie Kickass Torrent Download</h2><br /><p><b><b>Download Zip</b> &rArr; <a href="https://urlcod.com/2uyXLT">https://urlcod.com/2uyXLT</a></b></p><br /><br />
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-. looking for improvements and in 1947 receives an AK-47 assault rifle. . A biographical film about the inventor and designer of small arms Mikhail .. Mikhail Timofeevich Kalashnikov, was born on November 10, 1919 in the village. Mikhail Kalashnikov (real name - Mikhail) was born on November 10, 1919 in the village. Kalashnikov M. T. "Notes of a Gunsmith Designer". . Mikhail Timofeevich Kalashnikov was born on November 10, 1919 in the village. Born on November 10, 1919 in the village of Kurya, Altai Territory, in a family ...
-Born on November 10, 1919 in the village of Kurya, Altai Territory, in a family. Mikhail Kalashnikov was born on November 10, 1919 in the village of Kurya, Altai Territory. . Mikhail Kalashnikov. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Autodesk 2016 All Products Crack Final Activation.md b/spaces/usbethFlerru/sovits-modelsV2/example/Autodesk 2016 All Products Crack Final Activation.md
deleted file mode 100644
index c4ac08aacb79692e5fbae474c111c94a94fe02b8..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Autodesk 2016 All Products Crack Final Activation.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p>autodesk digital technical suite 2010<br /> autodesk digital technical suite 2010 is a complete platform of 3d and 2d software applications designed to fit your workflow and meet your design needs. the applications include autocad, civil 3d, inventor, inventor 2010, sandpaper, onshape, and revit architecture.</p>
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-<p>autocad 2009 professional<br /> autocad 2009 professional is a powerful and full-featured computer-aided drafting and design program for windows. the program is suitable for mechanical, architectural, landscape, 3d modeling, and electronic design and visualization applications.</p>
-<p>autocad architecture is available for purchase only online. it is sold as part of autocads cad suite. autocad architecture is not available as a standalone product. if you already have autocad then you can purchase autocad architecture online for $599 for the single seat license. the single seat license is restricted to the number of users on your computer. the multi-user license is also available online for $799.</p>
-<p>autodesk design review is available for purchase only online. it is sold as part of autocads cad suite. autodesk design review is not available as a standalone product. it can also be purchased as part of an onsite installation.</p>
-<p></p>
-<p>autocad map 3d (formerly map 3d) is available for purchase online. if you have an autocad subscription then you can purchase this extension online for $700. if you don't have an autocad subscription you can purchase a five-user license for $1300.</p>
-<p>autocad is part of autodesks suite of cad software for mechanical and civil engineering. autodesk and autocad software is integrated into a single product and all of the features of autodesk are included with autocad. with autocad, you can model and draft practically any kind of mechanical or civil engineering design. from simple geometric drawings to elaborate, finished engineering drawings.</p> 899543212b<br />
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\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Bhrigu Samhita Gujarati Pdf LINK Free Download.md b/spaces/usbethFlerru/sovits-modelsV2/example/Bhrigu Samhita Gujarati Pdf LINK Free Download.md
deleted file mode 100644
index c290d274a2dee3d76c6ee67792ba7b822928e851..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Bhrigu Samhita Gujarati Pdf LINK Free Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Connexions Methode De Francais Niveau 2 Pdf Free 24.md b/spaces/usbethFlerru/sovits-modelsV2/example/Connexions Methode De Francais Niveau 2 Pdf Free 24.md
deleted file mode 100644
index 34cfd6b11138507108968cbb46331c690f1c678c..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Connexions Methode De Francais Niveau 2 Pdf Free 24.md	
+++ /dev/null
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diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/DVDFab Passkey Lite 9.3.5.1 Crack Free.md b/spaces/usbethFlerru/sovits-modelsV2/example/DVDFab Passkey Lite 9.3.5.1 Crack Free.md
deleted file mode 100644
index 4ac6c53a7bbe5349234c3123abeb125dad322adc..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/DVDFab Passkey Lite 9.3.5.1 Crack Free.md	
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diff --git a/spaces/vaibhavarduino/anime-plus/e4e/editings/latent_editor.py b/spaces/vaibhavarduino/anime-plus/e4e/editings/latent_editor.py
deleted file mode 100644
index 4bebca2f5c86f71b58fa1f30d24bfcb0da06d88f..0000000000000000000000000000000000000000
--- a/spaces/vaibhavarduino/anime-plus/e4e/editings/latent_editor.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import torch
-import sys
-sys.path.append(".")
-sys.path.append("..")
-from editings import ganspace, sefa
-from utils.common import tensor2im
-
-
-class LatentEditor(object):
-    def __init__(self, stylegan_generator, is_cars=False):
-        self.generator = stylegan_generator
-        self.is_cars = is_cars  # Since the cars StyleGAN output is 384x512, there is a need to crop the 512x512 output.
-
-    def apply_ganspace(self, latent, ganspace_pca, edit_directions):
-        edit_latents = ganspace.edit(latent, ganspace_pca, edit_directions)
-        return self._latents_to_image(edit_latents)
-
-    def apply_interfacegan(self, latent, direction, factor=1, factor_range=None):
-        edit_latents = []
-        if factor_range is not None:  # Apply a range of editing factors. for example, (-5, 5)
-            for f in range(*factor_range):
-                edit_latent = latent + f * direction
-                edit_latents.append(edit_latent)
-            edit_latents = torch.cat(edit_latents)
-        else:
-            edit_latents = latent + factor * direction
-        return self._latents_to_image(edit_latents)
-
-    def apply_sefa(self, latent, indices=[2, 3, 4, 5], **kwargs):
-        edit_latents = sefa.edit(self.generator, latent, indices, **kwargs)
-        return self._latents_to_image(edit_latents)
-
-    # Currently, in order to apply StyleFlow editings, one should run inference,
-    # save the latent codes and load them form the official StyleFlow repository.
-    # def apply_styleflow(self):
-    #     pass
-
-    def _latents_to_image(self, latents):
-        with torch.no_grad():
-            images, _ = self.generator([latents], randomize_noise=False, input_is_latent=True)
-            if self.is_cars:
-                images = images[:, :, 64:448, :]  # 512x512 -> 384x512
-        horizontal_concat_image = torch.cat(list(images), 2)
-        final_image = tensor2im(horizontal_concat_image)
-        return final_image
diff --git a/spaces/venz/AW-04-GR-Seq-2-Seq-QA-Auto-Gen/qasrl_model_pipeline.py b/spaces/venz/AW-04-GR-Seq-2-Seq-QA-Auto-Gen/qasrl_model_pipeline.py
deleted file mode 100644
index 50135f76849bc8537fcae83b72532da661487da6..0000000000000000000000000000000000000000
--- a/spaces/venz/AW-04-GR-Seq-2-Seq-QA-Auto-Gen/qasrl_model_pipeline.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from typing import Optional
-import json
-from argparse import Namespace
-from pathlib import Path
-from transformers import Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer
-
-def get_markers_for_model(is_t5_model: bool) -> Namespace:
-    special_tokens_constants = Namespace() 
-    if is_t5_model:
-        # T5 model have 100 special tokens by default
-        special_tokens_constants.separator_input_question_predicate = "<extra_id_1>"
-        special_tokens_constants.separator_output_answers = "<extra_id_3>"
-        special_tokens_constants.separator_output_questions = "<extra_id_5>"  # if using only questions
-        special_tokens_constants.separator_output_question_answer = "<extra_id_7>"
-        special_tokens_constants.separator_output_pairs = "<extra_id_9>"
-        special_tokens_constants.predicate_generic_marker = "<extra_id_10>" 
-        special_tokens_constants.predicate_verb_marker = "<extra_id_11>" 
-        special_tokens_constants.predicate_nominalization_marker = "<extra_id_12>" 
-
-    else:
-        special_tokens_constants.separator_input_question_predicate = "<question_predicate_sep>"
-        special_tokens_constants.separator_output_answers = "<answers_sep>"
-        special_tokens_constants.separator_output_questions = "<question_sep>"  # if using only questions
-        special_tokens_constants.separator_output_question_answer = "<question_answer_sep>"
-        special_tokens_constants.separator_output_pairs = "<qa_pairs_sep>"
-        special_tokens_constants.predicate_generic_marker = "<predicate_marker>" 
-        special_tokens_constants.predicate_verb_marker = "<verbal_predicate_marker>" 
-        special_tokens_constants.predicate_nominalization_marker = "<nominalization_predicate_marker>" 
-    return special_tokens_constants
-
-def load_trained_model(name_or_path):
-    import huggingface_hub as HFhub
-    tokenizer = AutoTokenizer.from_pretrained(name_or_path)
-    model = AutoModelForSeq2SeqLM.from_pretrained(name_or_path)  
-    # load preprocessing_kwargs from the model repo on HF hub, or from the local model directory
-    kwargs_filename = None
-    if name_or_path.startswith("kleinay/"): # and 'preprocessing_kwargs.json' in HFhub.list_repo_files(name_or_path): # the supported version of HFhub doesn't support list_repo_files
-        kwargs_filename = HFhub.hf_hub_download(repo_id=name_or_path, filename="preprocessing_kwargs.json")
-    elif Path(name_or_path).is_dir() and (Path(name_or_path) / "experiment_kwargs.json").exists():
-        kwargs_filename = Path(name_or_path) / "experiment_kwargs.json"
-    
-    if kwargs_filename:
-        preprocessing_kwargs = json.load(open(kwargs_filename)) 
-        # integrate into model.config (for decoding args, e.g. "num_beams"), and save also as standalone object for preprocessing
-        model.config.preprocessing_kwargs = Namespace(**preprocessing_kwargs)
-        model.config.update(preprocessing_kwargs)
-    return model, tokenizer
-
-
-class QASRL_Pipeline(Text2TextGenerationPipeline):
-    def __init__(self, model_repo: str, **kwargs):
-        model, tokenizer = load_trained_model(model_repo)
-        super().__init__(model, tokenizer, framework="pt")
-        self.is_t5_model = "t5" in model.config.model_type
-        self.special_tokens = get_markers_for_model(self.is_t5_model)
-        self.data_args = model.config.preprocessing_kwargs 
-        # backward compatibility - default keyword values implemeted in `run_summarization`, thus not saved in `preprocessing_kwargs`
-        if "predicate_marker_type" not in vars(self.data_args):
-            self.data_args.predicate_marker_type = "generic"
-        if "use_bilateral_predicate_marker" not in vars(self.data_args):
-            self.data_args.use_bilateral_predicate_marker = True
-        if "append_verb_form" not in vars(self.data_args):
-            self.data_args.append_verb_form = True
-        self._update_config(**kwargs)
-    
-    def _update_config(self, **kwargs):
-        " Update self.model.config with initialization parameters and necessary defaults. "
-        # set default values that will always override model.config, but can overriden by __init__ kwargs
-        kwargs["max_length"] = kwargs.get("max_length", 80)
-        # override model.config with kwargs
-        for k,v in kwargs.items():
-            self.model.config.__dict__[k] = v           
-        
-    def _sanitize_parameters(self, **kwargs):
-        preprocess_kwargs, forward_kwargs, postprocess_kwargs = {}, {}, {} 
-        if "predicate_marker" in kwargs:
-            preprocess_kwargs["predicate_marker"] = kwargs["predicate_marker"]
-        if "predicate_type" in kwargs:
-            preprocess_kwargs["predicate_type"] = kwargs["predicate_type"]
-        if "verb_form" in kwargs:
-            preprocess_kwargs["verb_form"] = kwargs["verb_form"]
-        return preprocess_kwargs, forward_kwargs, postprocess_kwargs
-
-    def preprocess(self, inputs, predicate_marker="<predicate>", predicate_type=None, verb_form=None):
-        # Here, inputs is string or list of strings; apply string postprocessing
-        if isinstance(inputs, str):
-            processed_inputs = self._preprocess_string(inputs, predicate_marker, predicate_type, verb_form)
-        elif hasattr(inputs, "__iter__"):
-            processed_inputs = [self._preprocess_string(s, predicate_marker, predicate_type, verb_form) for s in inputs]
-        else:
-            raise ValueError("inputs must be str or Iterable[str]")
-        # Now pass to super.preprocess for tokenization
-        return super().preprocess(processed_inputs)
-    
-    def _preprocess_string(self, seq: str, predicate_marker: str, predicate_type: Optional[str], verb_form: Optional[str]) -> str:
-        sent_tokens = seq.split(" ")
-        assert predicate_marker in sent_tokens, f"Input sentence must include a predicate-marker token ('{predicate_marker}') before the target predicate word"
-        predicate_idx = sent_tokens.index(predicate_marker)
-        sent_tokens.remove(predicate_marker)
-        sentence_before_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx)])
-        predicate = sent_tokens[predicate_idx]
-        sentence_after_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx+1, len(sent_tokens))])
-        
-        if self.data_args.predicate_marker_type == "generic":
-            predicate_marker = self.special_tokens.predicate_generic_marker    
-        #  In case we want special marker for each predicate type: """
-        elif self.data_args.predicate_marker_type == "pred_type":
-            assert predicate_type is not None, "For this model, you must provide the `predicate_type` either when initializing QASRL_Pipeline(...) or when applying __call__(...) on it"
-            assert predicate_type in ("verbal", "nominal"), f"`predicate_type` must be either 'verbal' or 'nominal'; got '{predicate_type}'"
-            predicate_marker = {"verbal": self.special_tokens.predicate_verb_marker , 
-                                "nominal": self.special_tokens.predicate_nominalization_marker 
-                                }[predicate_type]
-
-        if self.data_args.use_bilateral_predicate_marker:
-            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {predicate_marker} {sentence_after_predicate}"
-        else:
-            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {sentence_after_predicate}"
-
-        # embed also verb_form
-        if self.data_args.append_verb_form and verb_form is None:
-            raise ValueError(f"For this model, you must provide the `verb_form` of the predicate when applying __call__(...)")
-        elif self.data_args.append_verb_form:
-            seq = f"{seq} {self.special_tokens.separator_input_question_predicate} {verb_form} "
-        else:
-            seq = f"{seq} "
-    
-        # append source prefix (for t5 models)
-        prefix = self._get_source_prefix(predicate_type)
-        
-        return prefix + seq
-    
-    def _get_source_prefix(self, predicate_type: Optional[str]):
-        if not self.is_t5_model or self.data_args.source_prefix is None:
-            return ''
-        if not self.data_args.source_prefix.startswith("<"):  # Regular prefix - not dependent on input row x
-            return self.data_args.source_prefix
-        if self.data_args.source_prefix == "<predicate-type>":
-            if predicate_type is None:
-                raise ValueError("source_prefix is '<predicate-type>' but input no `predicate_type`.")
-            else:
-                return f"Generate QAs for {predicate_type} QASRL: "
-    
-    def _forward(self, *args, **kwargs):
-        outputs = super()._forward(*args, **kwargs)
-        return outputs
-
-
-    def postprocess(self, model_outputs):
-        output_seq = self.tokenizer.decode(
-            model_outputs["output_ids"].squeeze(),
-            skip_special_tokens=False,
-            clean_up_tokenization_spaces=False,
-        )
-        output_seq = output_seq.strip(self.tokenizer.pad_token).strip(self.tokenizer.eos_token).strip()
-        qa_subseqs = output_seq.split(self.special_tokens.separator_output_pairs)
-        qas = [self._postrocess_qa(qa_subseq) for qa_subseq in qa_subseqs]
-        return {"generated_text": output_seq,
-                "QAs": qas}
-        
-    def _postrocess_qa(self, seq: str) -> str:
-        # split question and answers
-        if self.special_tokens.separator_output_question_answer in seq:
-            question, answer = seq.split(self.special_tokens.separator_output_question_answer)[:2]
-        else:
-            print("invalid format: no separator between question and answer found...")
-            return None
-            # question, answer = seq, '' # Or: backoff to only question  
-        # skip "_" slots in questions
-        question = ' '.join(t for t in question.split(' ') if t != '_')
-        answers = [a.strip() for a in answer.split(self.special_tokens.separator_output_answers)]
-        return {"question": question, "answers": answers}
-    
-    
-if __name__ == "__main__":
-    pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
-    res1 = pipe("The student was interested in Luke 's <predicate> research about sea animals .", verb_form="research", predicate_type="nominal")
-    res2 = pipe(["The doctor was interested in Luke 's <predicate> treatment .",
-                 "The Veterinary student was interested in Luke 's <predicate> treatment of sea animals ."], verb_form="treat", predicate_type="nominal", num_beams=10)
-    res3 = pipe("A number of professions have <predicate> developed that specialize in the treatment of mental disorders .", verb_form="develop", predicate_type="verbal")
-    print(res1)
-    print(res2)
-    print(res3)
-    
\ No newline at end of file
diff --git a/spaces/videfikri/aicover/Changelog_CN.md b/spaces/videfikri/aicover/Changelog_CN.md
deleted file mode 100644
index ad10739c45cd5f8ac6a4e60e9924e192759f7672..0000000000000000000000000000000000000000
--- a/spaces/videfikri/aicover/Changelog_CN.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### 20230409
-- 修正训练参数，提升显卡平均利用率，A100最高从25%提升至90%左右，V100:50%->90%左右，2060S:60%->85%左右，P40:25%->95%左右，训练速度显著提升
-- 修正参数：总batch_size改为每张卡的batch_size
-- 修正total_epoch：最大限制100解锁至1000；默认10提升至默认20
-- 修复ckpt提取识别是否带音高错误导致推理异常的问题
-- 修复分布式训练每个rank都保存一次ckpt的问题
-- 特征提取进行nan特征过滤
-- 修复静音输入输出随机辅音or噪声的问题（老版模型需要重做训练集重训）
-
-### 20230416更新
-- 新增本地实时变声迷你GUI，双击go-realtime-gui.bat启动
-- 训练推理均对<50Hz的频段进行滤波过滤
-- 训练推理音高提取pyworld最低音高从默认80下降至50,50-80hz间的男声低音不会哑
-- WebUI支持根据系统区域变更语言（现支持en_US，ja_JP，zh_CN，zh_HK，zh_SG，zh_TW，不支持的默认en_US）
-- 修正部分显卡识别（例如V100-16G识别失败，P4识别失败）
-
-### 20230428更新
-- 升级faiss索引设置，速度更快，质量更高
-- 取消total_npy依赖，后续分享模型不再需要填写total_npy
-- 解锁16系限制。4G显存GPU给到4G的推理设置。
-- 修复部分音频格式下UVR5人声伴奏分离的bug
-- 实时变声迷你gui增加对非40k与不懈怠音高模型的支持
-
-
-### 后续计划：
-功能：
-- 增加选项:每次epoch保存的小模型均进行提取
-- 增加选项:推理额外导出mp3至填写的路径
-- 支持多人训练选项卡（至多4人）
-- 
-底模：
-- 收集呼吸wav加入训练集修正呼吸变声电音的问题
-- 我们正在训练增加了歌声训练集的底模，未来会公开
-- 升级鉴别器
-- 升级自监督特征结构
diff --git a/spaces/vinay123/panoptic-segment-anything/segment_anything/segment_anything/automatic_mask_generator.py b/spaces/vinay123/panoptic-segment-anything/segment_anything/segment_anything/automatic_mask_generator.py
deleted file mode 100644
index 23264971b7ff5aa0b4f499ade7773b68dce984b6..0000000000000000000000000000000000000000
--- a/spaces/vinay123/panoptic-segment-anything/segment_anything/segment_anything/automatic_mask_generator.py
+++ /dev/null
@@ -1,372 +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 numpy as np
-import torch
-from torchvision.ops.boxes import batched_nms, box_area  # type: ignore
-
-from typing import Any, Dict, List, Optional, Tuple
-
-from .modeling import Sam
-from .predictor import SamPredictor
-from .utils.amg import (
-    MaskData,
-    area_from_rle,
-    batch_iterator,
-    batched_mask_to_box,
-    box_xyxy_to_xywh,
-    build_all_layer_point_grids,
-    calculate_stability_score,
-    coco_encode_rle,
-    generate_crop_boxes,
-    is_box_near_crop_edge,
-    mask_to_rle_pytorch,
-    remove_small_regions,
-    rle_to_mask,
-    uncrop_boxes_xyxy,
-    uncrop_masks,
-    uncrop_points,
-)
-
-
-class SamAutomaticMaskGenerator:
-    def __init__(
-        self,
-        model: Sam,
-        points_per_side: Optional[int] = 32,
-        points_per_batch: int = 64,
-        pred_iou_thresh: float = 0.88,
-        stability_score_thresh: float = 0.95,
-        stability_score_offset: float = 1.0,
-        box_nms_thresh: float = 0.7,
-        crop_n_layers: int = 0,
-        crop_nms_thresh: float = 0.7,
-        crop_overlap_ratio: float = 512 / 1500,
-        crop_n_points_downscale_factor: int = 1,
-        point_grids: Optional[List[np.ndarray]] = None,
-        min_mask_region_area: int = 0,
-        output_mode: str = "binary_mask",
-    ) -> None:
-        """
-        Using a SAM model, generates masks for the entire image.
-        Generates a grid of point prompts over the image, then filters
-        low quality and duplicate masks. The default settings are chosen
-        for SAM with a ViT-H backbone.
-
-        Arguments:
-          model (Sam): The SAM model to use for mask prediction.
-          points_per_side (int or None): The number of points to be sampled
-            along one side of the image. The total number of points is
-            points_per_side**2. If None, 'point_grids' must provide explicit
-            point sampling.
-          points_per_batch (int): Sets the number of points run simultaneously
-            by the model. Higher numbers may be faster but use more GPU memory.
-          pred_iou_thresh (float): A filtering threshold in [0,1], using the
-            model's predicted mask quality.
-          stability_score_thresh (float): A filtering threshold in [0,1], using
-            the stability of the mask under changes to the cutoff used to binarize
-            the model's mask predictions.
-          stability_score_offset (float): The amount to shift the cutoff when
-            calculated the stability score.
-          box_nms_thresh (float): The box IoU cutoff used by non-maximal
-            suppression to filter duplicate masks.
-          crops_n_layers (int): If >0, mask prediction will be run again on
-            crops of the image. Sets the number of layers to run, where each
-            layer has 2**i_layer number of image crops.
-          crops_nms_thresh (float): The box IoU cutoff used by non-maximal
-            suppression to filter duplicate masks between different crops.
-          crop_overlap_ratio (float): Sets the degree to which crops overlap.
-            In the first crop layer, crops will overlap by this fraction of
-            the image length. Later layers with more crops scale down this overlap.
-          crop_n_points_downscale_factor (int): The number of points-per-side
-            sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
-          point_grids (list(np.ndarray) or None): A list over explicit grids
-            of points used for sampling, normalized to [0,1]. The nth grid in the
-            list is used in the nth crop layer. Exclusive with points_per_side.
-          min_mask_region_area (int): If >0, postprocessing will be applied
-            to remove disconnected regions and holes in masks with area smaller
-            than min_mask_region_area. Requires opencv.
-          output_mode (str): The form masks are returned in. Can be 'binary_mask',
-            'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
-            For large resolutions, 'binary_mask' may consume large amounts of
-            memory.
-        """
-
-        assert (points_per_side is None) != (
-            point_grids is None
-        ), "Exactly one of points_per_side or point_grid must be provided."
-        if points_per_side is not None:
-            self.point_grids = build_all_layer_point_grids(
-                points_per_side,
-                crop_n_layers,
-                crop_n_points_downscale_factor,
-            )
-        elif point_grids is not None:
-            self.point_grids = point_grids
-        else:
-            raise ValueError("Can't have both points_per_side and point_grid be None.")
-
-        assert output_mode in [
-            "binary_mask",
-            "uncompressed_rle",
-            "coco_rle",
-        ], f"Unknown output_mode {output_mode}."
-        if output_mode == "coco_rle":
-            from pycocotools import mask as mask_utils  # type: ignore # noqa: F401
-
-        if min_mask_region_area > 0:
-            import cv2  # type: ignore # noqa: F401
-
-        self.predictor = SamPredictor(model)
-        self.points_per_batch = points_per_batch
-        self.pred_iou_thresh = pred_iou_thresh
-        self.stability_score_thresh = stability_score_thresh
-        self.stability_score_offset = stability_score_offset
-        self.box_nms_thresh = box_nms_thresh
-        self.crop_n_layers = crop_n_layers
-        self.crop_nms_thresh = crop_nms_thresh
-        self.crop_overlap_ratio = crop_overlap_ratio
-        self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
-        self.min_mask_region_area = min_mask_region_area
-        self.output_mode = output_mode
-
-    @torch.no_grad()
-    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
-        """
-        Generates masks for the given image.
-
-        Arguments:
-          image (np.ndarray): The image to generate masks for, in HWC uint8 format.
-
-        Returns:
-           list(dict(str, any)): A list over records for masks. Each record is
-             a dict containing the following keys:
-               segmentation (dict(str, any) or np.ndarray): The mask. If
-                 output_mode='binary_mask', is an array of shape HW. Otherwise,
-                 is a dictionary containing the RLE.
-               bbox (list(float)): The box around the mask, in XYWH format.
-               area (int): The area in pixels of the mask.
-               predicted_iou (float): The model's own prediction of the mask's
-                 quality. This is filtered by the pred_iou_thresh parameter.
-               point_coords (list(list(float))): The point coordinates input
-                 to the model to generate this mask.
-               stability_score (float): A measure of the mask's quality. This
-                 is filtered on using the stability_score_thresh parameter.
-               crop_box (list(float)): The crop of the image used to generate
-                 the mask, given in XYWH format.
-        """
-
-        # Generate masks
-        mask_data = self._generate_masks(image)
-
-        # Filter small disconnected regions and holes in masks
-        if self.min_mask_region_area > 0:
-            mask_data = self.postprocess_small_regions(
-                mask_data,
-                self.min_mask_region_area,
-                max(self.box_nms_thresh, self.crop_nms_thresh),
-            )
-
-        # Encode masks
-        if self.output_mode == "coco_rle":
-            mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
-        elif self.output_mode == "binary_mask":
-            mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
-        else:
-            mask_data["segmentations"] = mask_data["rles"]
-
-        # Write mask records
-        curr_anns = []
-        for idx in range(len(mask_data["segmentations"])):
-            ann = {
-                "segmentation": mask_data["segmentations"][idx],
-                "area": area_from_rle(mask_data["rles"][idx]),
-                "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
-                "predicted_iou": mask_data["iou_preds"][idx].item(),
-                "point_coords": [mask_data["points"][idx].tolist()],
-                "stability_score": mask_data["stability_score"][idx].item(),
-                "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
-            }
-            curr_anns.append(ann)
-
-        return curr_anns
-
-    def _generate_masks(self, image: np.ndarray) -> MaskData:
-        orig_size = image.shape[:2]
-        crop_boxes, layer_idxs = generate_crop_boxes(
-            orig_size, self.crop_n_layers, self.crop_overlap_ratio
-        )
-
-        # Iterate over image crops
-        data = MaskData()
-        for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
-            crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
-            data.cat(crop_data)
-
-        # Remove duplicate masks between crops
-        if len(crop_boxes) > 1:
-            # Prefer masks from smaller crops
-            scores = 1 / box_area(data["crop_boxes"])
-            scores = scores.to(data["boxes"].device)
-            keep_by_nms = batched_nms(
-                data["boxes"].float(),
-                scores,
-                torch.zeros(len(data["boxes"])),  # categories
-                iou_threshold=self.crop_nms_thresh,
-            )
-            data.filter(keep_by_nms)
-
-        data.to_numpy()
-        return data
-
-    def _process_crop(
-        self,
-        image: np.ndarray,
-        crop_box: List[int],
-        crop_layer_idx: int,
-        orig_size: Tuple[int, ...],
-    ) -> MaskData:
-        # Crop the image and calculate embeddings
-        x0, y0, x1, y1 = crop_box
-        cropped_im = image[y0:y1, x0:x1, :]
-        cropped_im_size = cropped_im.shape[:2]
-        self.predictor.set_image(cropped_im)
-
-        # Get points for this crop
-        points_scale = np.array(cropped_im_size)[None, ::-1]
-        points_for_image = self.point_grids[crop_layer_idx] * points_scale
-
-        # Generate masks for this crop in batches
-        data = MaskData()
-        for (points,) in batch_iterator(self.points_per_batch, points_for_image):
-            batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size)
-            data.cat(batch_data)
-            del batch_data
-        self.predictor.reset_image()
-
-        # Remove duplicates within this crop.
-        keep_by_nms = batched_nms(
-            data["boxes"].float(),
-            data["iou_preds"],
-            torch.zeros(len(data["boxes"])),  # categories
-            iou_threshold=self.box_nms_thresh,
-        )
-        data.filter(keep_by_nms)
-
-        # Return to the original image frame
-        data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
-        data["points"] = uncrop_points(data["points"], crop_box)
-        data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
-
-        return data
-
-    def _process_batch(
-        self,
-        points: np.ndarray,
-        im_size: Tuple[int, ...],
-        crop_box: List[int],
-        orig_size: Tuple[int, ...],
-    ) -> MaskData:
-        orig_h, orig_w = orig_size
-
-        # Run model on this batch
-        transformed_points = self.predictor.transform.apply_coords(points, im_size)
-        in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
-        in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
-        masks, iou_preds, _ = self.predictor.predict_torch(
-            in_points[:, None, :],
-            in_labels[:, None],
-            multimask_output=True,
-            return_logits=True,
-        )
-
-        # Serialize predictions and store in MaskData
-        data = MaskData(
-            masks=masks.flatten(0, 1),
-            iou_preds=iou_preds.flatten(0, 1),
-            points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
-        )
-        del masks
-
-        # Filter by predicted IoU
-        if self.pred_iou_thresh > 0.0:
-            keep_mask = data["iou_preds"] > self.pred_iou_thresh
-            data.filter(keep_mask)
-
-        # Calculate stability score
-        data["stability_score"] = calculate_stability_score(
-            data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
-        )
-        if self.stability_score_thresh > 0.0:
-            keep_mask = data["stability_score"] >= self.stability_score_thresh
-            data.filter(keep_mask)
-
-        # Threshold masks and calculate boxes
-        data["masks"] = data["masks"] > self.predictor.model.mask_threshold
-        data["boxes"] = batched_mask_to_box(data["masks"])
-
-        # Filter boxes that touch crop boundaries
-        keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
-        if not torch.all(keep_mask):
-            data.filter(keep_mask)
-
-        # Compress to RLE
-        data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
-        data["rles"] = mask_to_rle_pytorch(data["masks"])
-        del data["masks"]
-
-        return data
-
-    @staticmethod
-    def postprocess_small_regions(
-        mask_data: MaskData, min_area: int, nms_thresh: float
-    ) -> MaskData:
-        """
-        Removes small disconnected regions and holes in masks, then reruns
-        box NMS to remove any new duplicates.
-
-        Edits mask_data in place.
-
-        Requires open-cv as a dependency.
-        """
-        if len(mask_data["rles"]) == 0:
-            return mask_data
-
-        # Filter small disconnected regions and holes
-        new_masks = []
-        scores = []
-        for rle in mask_data["rles"]:
-            mask = rle_to_mask(rle)
-
-            mask, changed = remove_small_regions(mask, min_area, mode="holes")
-            unchanged = not changed
-            mask, changed = remove_small_regions(mask, min_area, mode="islands")
-            unchanged = unchanged and not changed
-
-            new_masks.append(torch.as_tensor(mask).unsqueeze(0))
-            # Give score=0 to changed masks and score=1 to unchanged masks
-            # so NMS will prefer ones that didn't need postprocessing
-            scores.append(float(unchanged))
-
-        # Recalculate boxes and remove any new duplicates
-        masks = torch.cat(new_masks, dim=0)
-        boxes = batched_mask_to_box(masks)
-        keep_by_nms = batched_nms(
-            boxes.float(),
-            torch.as_tensor(scores),
-            torch.zeros(len(boxes)),  # categories
-            iou_threshold=nms_thresh,
-        )
-
-        # Only recalculate RLEs for masks that have changed
-        for i_mask in keep_by_nms:
-            if scores[i_mask] == 0.0:
-                mask_torch = masks[i_mask].unsqueeze(0)
-                mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
-                mask_data["boxes"][i_mask] = boxes[i_mask]  # update res directly
-        mask_data.filter(keep_by_nms)
-
-        return mask_data
diff --git a/spaces/vishvara-sharda/book_recommending/app1.py b/spaces/vishvara-sharda/book_recommending/app1.py
deleted file mode 100644
index bbb47949d67996b774949f23af069cb15924fb32..0000000000000000000000000000000000000000
--- a/spaces/vishvara-sharda/book_recommending/app1.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from fastapi import FastAPI, Request, Query, Depends
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-from Book_recommender import *
-import pandas as pd
-
-app = FastAPI()
-
-# Enable CORS
-app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
-
-# Routes
-
-@app.get("/")
-def main_home():
-    return {"message": "Welcome"}
-
-@app.get("/api")
-def home():
-    return {
-        "Page": "Home",
-        "_AvailableRoutes": {
-            "hybrid_recommendation": "/hybrid_recommendation?book_title=BookName",
-            "content_recommendation": "/content_recommendation?book_title=BookName",
-            "collaborative_recommendation": "/collaborative_recommendation?book_title=BookName",
-        },
-    }
-
-@app.get("/api/top_books")
-def top_books():
-    return {"data": df_popularity.to_dict(orient="records")}
-
-@app.get("/api/recommend")
-def recommendation(book_title: str = Query(..., description="Title of the book")):
-    recommendations = recommend(book_title)
-    return recommendations
-
-@app.get("/api/content_recommendation")
-def content_recommendation(book_title: str = Query(..., description="Title of the book")):
-    recommendations = content_based_recommender(book_title, df_books)
-    return recommendations
-
-@app.get("/api/collaborative_recommendation")
-def collaborative_recommendation(book_title: str = Query(..., description="Title of the book")):
-    recommendations = recommend(book_title)
-    return recommendations
-
-@app.get("/api/hybrid_recommendation")
-def hybrid_recommendation_route(book_title: str = Query(..., description="Title of the book")):
-    recommendations = hybrid_recommendation(book_title)
-    return recommendations
diff --git a/spaces/warrenw/simple-gpt-interface/README.md b/spaces/warrenw/simple-gpt-interface/README.md
deleted file mode 100644
index 5508214c73ab281984d5c7717b49c9bb741a1122..0000000000000000000000000000000000000000
--- a/spaces/warrenw/simple-gpt-interface/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Simple Gpt Interface
-emoji: 📉
-colorFrom: gray
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/weiren119/AudiogramDigitization/src/utils/plotting.py b/spaces/weiren119/AudiogramDigitization/src/utils/plotting.py
deleted file mode 100644
index 72a398eb006fe01e58c3a82e715d42670304aad0..0000000000000000000000000000000000000000
--- a/spaces/weiren119/AudiogramDigitization/src/utils/plotting.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python3
-"""
-Copyright (c) 2020, Carleton University Biomedical Informatics Collaboratory
-
-This source code is licensed under the MIT license found in the
-LICENSE file in the root directory of this source tree.
-"""
-from typing import List
-import os.path as path
-import io
-
-import PIL.Image
-import matplotlib.pyplot as plt
-from matplotlib.offsetbox import OffsetImage, AnnotationBbox
-import matplotlib
-
-from interfaces import Threshold
-
-# Path to the folder containing the symbols that get plotted
-SYMBOLS_DIR = path.join(path.dirname(__file__), "..", "assets", "symbols")
-
-def figure_to_image(figure: matplotlib.pyplot.figure, size, dpi=300):
-    """Converts a matplotlib figure to a PIL Image.
-
-    Parameters
-    ----------
-    figure : matplotlib.pyplot.figure
-    The figure to convert to an image.
-
-    size: tuple
-    Dimensions of the image in pixels.
-
-    dpi: int
-    Resolution (default: 300)
-
-    Returns
-    -------
-    PIL.Image
-    The image of the plot in PIL format.
-    """
-    # Save the figure to a buffer
-    image_buffer = io.BytesIO()
-    figure.set_size_inches((size[0]/dpi, size[1]/dpi))
-    figure.savefig(image_buffer, dpi=dpi)
-
-    # Open image from buffer
-    image_buffer.seek(0)
-    image = PIL.Image.open(image_buffer)
-
-    return image
-
-def plot_audiogram(thresholds: List[Threshold]) -> plt.figure:
-    """Given a list of threshold dictionaries, plots the audiogram.
-
-    Parameters
-    ----------
-    thresholds : List[Threshold]
-    A list of dictionaries that implement the `Threshold` interface.
-
-    Returns
-    -------
-    matplotlib.pyplot.figure
-    The `Figure` object corresponding to the audiogram.
-    """
-    # Plot setup
-    fig = plt.figure()
-    ax = plt.gca()
-
-    # Setup axes
-    plt.xscale("log")
-    ax.set_xlim(125, 8000) # Standard audios go from 125 to 8000 Hz 
-    ax.set_ylim(-20, 120) # Most go from -10 to 120, but let's start at -20, in case
-    plt.gca().invert_yaxis()
-
-    # Add the ticks
-    ax.set_xticks([125, 250, 500, 1000, 2000, 4000, 8000]) # Octave frequencies
-    ax.set_yticks(list(range(-20, 120, 10))) # All multiples of 10
-    ax.get_xaxis().set_major_formatter(
-        matplotlib.ticker.FormatStrFormatter("%.0f")
-    )
-
-    # Show the grid
-    plt.grid()
-
-    # Iterate through the different symbols
-    for ear in ("left", "right"):
-        for masking in (True, False):
-            for conduction in ("air", "bone"):
-                # Filter out all other symbols to generate an individual curve
-                curve = [
-                    threshold 
-                    for threshold
-                    in thresholds 
-                    if threshold["ear"] == ear
-                    and threshold["masking"] == masking
-                    and threshold["conduction"] == conduction
-                ]
-
-                # Sort the thresholds on the curve in order of frequency
-                curve = sorted(curve, key=lambda t: t["frequency"])
-                freq = [t["frequency"] for t in curve]
-                threshold = [t["threshold"] for t in curve]
-
-                # For every threshold (freq, thresh) belonging to the curve
-                # considered in this loop iteration, add a symbol on the plot.
-                for (f, t) in zip(freq, threshold):
-                    icon_name = f"{ear}_{conduction}_{'masked' if masking else 'unmasked'}.png"
-                    icon_img = plt.imread(path.join(SYMBOLS_DIR, icon_name))
-                    icon = AnnotationBbox(
-                                OffsetImage(icon_img, zoom=0.1), 
-                                (f, t),
-                                frameon=False
-                            )
-                    ax.add_artist(icon)
-
-    return fig
diff --git a/spaces/willgibs/ControlNet-v1-1/app_ip2p.py b/spaces/willgibs/ControlNet-v1-1/app_ip2p.py
deleted file mode 100644
index 6ba543bab553a249e121657fae1baf1b4c41a5e3..0000000000000000000000000000000000000000
--- a/spaces/willgibs/ControlNet-v1-1/app_ip2p.py
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-            queue=False,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='ip2p',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='ip2p')
-    demo = create_demo(model.process_ip2p)
-    demo.queue().launch()
diff --git a/spaces/williambr/CarePlanSOTAQnA/app.py b/spaces/williambr/CarePlanSOTAQnA/app.py
deleted file mode 100644
index d720dbebdf5fdd3df052f291b4b1e121ee0f554a..0000000000000000000000000000000000000000
--- a/spaces/williambr/CarePlanSOTAQnA/app.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import gradio as gr
-
-context = "What should be documented in a care plan?\n"
-context = context + "Regardless of what your preferences are, your care plan should include:\n"
-context = context + "What your assessed care needs are.\n"
-context = context + "What type of support you should receive.\n"
-context = context + "Your desired outcomes.\n"
-context = context + "Who should provide care.\n"
-context = context + "When care and support should be provided.\n"
-context = context + "Records of care provided.\n"
-context = context + "Your wishes and personal preferences.\n"
-context = context + "The costs of the services.\n"
-
-context = context + "Dimensions\n"
-context = context + "1-Ontology of Plan\n"
-context = context + "2-Problems as evidenced by Signs of Systems\n"
-context = context + "3-Assessment of Needs\n"
-context = context + "4-Questions about problems faced\n"
-context = context + "5-Goals for long and short term improvements\n"
-context = context + "6-Knowledge-Behavior-Status Quality Measures\n"
-context = context + "7-Intervention List of Options\n"
-context = context + "8-Quality Measures\n"
-context = context + "9-Pathways Available\n"
-
-with open('WritingCarePlan.txt', 'r') as file:
-        context = file.read()
-
-question = "What should be documented in a care plan?"
-
-gr.Interface.load(
-             "huggingface/deepset/roberta-base-squad2",
-             theme="default",
-             css=".footer{display:none !important}",
-             inputs=[gr.inputs.Textbox(lines=12, default=context, label="Context paragraph"), gr.inputs.Textbox(lines=3, default=question, label="Question")],
-             outputs=[gr.outputs.Textbox(label="Answer"), gr.outputs.Textbox(label="Score")],
-             title=None,
-             description="Provide your own paragraph and ask any question about the text. How well does the model answer?").launch()
diff --git a/spaces/wy213/AIwy/Dockerfile b/spaces/wy213/AIwy/Dockerfile
deleted file mode 100644
index e465295d0fb0ed7199d099cdccfefdb5f697d40f..0000000000000000000000000000000000000000
--- a/spaces/wy213/AIwy/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER "4g787ijhljk9068h85hjkf"
-
-CMD npm start
diff --git a/spaces/xdecoder/SEEM/README.md b/spaces/xdecoder/SEEM/README.md
deleted file mode 100644
index 8fc409dabec11b7ab591d57157d014b49a619faf..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/SEEM/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SEEM
-emoji: 🦉
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xfys/yolov5_tracking/val_utils/trackeval/_timing.py b/spaces/xfys/yolov5_tracking/val_utils/trackeval/_timing.py
deleted file mode 100644
index 4614ba3162ad8143b4e9b3c8d9bc286e7a684f4c..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/trackeval/_timing.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from functools import wraps
-from time import perf_counter
-import inspect
-
-DO_TIMING = False
-DISPLAY_LESS_PROGRESS = False
-timer_dict = {}
-counter = 0
-
-
-def time(f):
-    @wraps(f)
-    def wrap(*args, **kw):
-        if DO_TIMING:
-            # Run function with timing
-            ts = perf_counter()
-            result = f(*args, **kw)
-            te = perf_counter()
-            tt = te-ts
-
-            # Get function name
-            arg_names = inspect.getfullargspec(f)[0]
-            if arg_names[0] == 'self' and DISPLAY_LESS_PROGRESS:
-                return result
-            elif arg_names[0] == 'self':
-                method_name = type(args[0]).__name__ + '.' + f.__name__
-            else:
-                method_name = f.__name__
-
-            # Record accumulative time in each function for analysis
-            if method_name in timer_dict.keys():
-                timer_dict[method_name] += tt
-            else:
-                timer_dict[method_name] = tt
-
-            # If code is finished, display timing summary
-            if method_name == "Evaluator.evaluate":
-                print("")
-                print("Timing analysis:")
-                for key, value in timer_dict.items():
-                    print('%-70s %2.4f sec' % (key, value))
-            else:
-                # Get function argument values for printing special arguments of interest
-                arg_titles = ['tracker', 'seq', 'cls']
-                arg_vals = []
-                for i, a in enumerate(arg_names):
-                    if a in arg_titles:
-                        arg_vals.append(args[i])
-                arg_text = '(' + ', '.join(arg_vals) + ')'
-
-                # Display methods and functions with different indentation.
-                if arg_names[0] == 'self':
-                    print('%-74s %2.4f sec' % (' '*4 + method_name + arg_text, tt))
-                elif arg_names[0] == 'test':
-                    pass
-                else:
-                    global counter
-                    counter += 1
-                    print('%i %-70s %2.4f sec' % (counter, method_name + arg_text, tt))
-
-            return result
-        else:
-            # If config["TIME_PROGRESS"] is false, or config["USE_PARALLEL"] is true, run functions normally without timing.
-            return f(*args, **kw)
-    return wrap
diff --git a/spaces/xiang-wuu/yolov5/app.py b/spaces/xiang-wuu/yolov5/app.py
deleted file mode 100644
index 42d557f2dbe6910e3f6ae9c557c59bd42295e568..0000000000000000000000000000000000000000
--- a/spaces/xiang-wuu/yolov5/app.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import gradio as gr
-import os
-
-os.system("wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt")
-os.system("wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt")
-
-from models.experimental import attempt_load
-from utils.augmentations import letterbox
-from utils.plots import Annotator
-from utils.general import non_max_suppression, scale_coords
-from utils.torch_utils import *
-import sys
-import numpy as np
-import random
-
-
-def detect(img, weights):
-    gpu_id="cpu"
-    device = select_device(device=gpu_id)
-    model = attempt_load(weights+'.pt', device=device)
-    torch.no_grad()
-    model.to(device).eval()
-    half = False  # half precision only supported on CUDA
-    if half:
-        model.half()
-
-    img_size = 640
-
-    # Get names and colors
-    names = model.names if hasattr(model, 'names') else model.modules.names
-    colors = [[random.randint(0, 255) for _ in range(3)]
-              for _ in range(len(names))]
-    if img is None:
-        sys.exit(0)
-
-    # Run inference
-    t0 = time_sync()
-
-    im0 = img.copy()
-    img = letterbox(img, img_size, stride=int(model.stride.max()), auto=False and True)[0]
-    img = np.stack(img, 0)
-
-    img = img.transpose((2, 0, 1))[::-1]  # BGR to RGB, to 3x416x416
-
-    img = np.ascontiguousarray(img)
-
-    img = torch.from_numpy(img).to(device)
-
-    if half:
-        img = img.half()
-    else:
-        img = img.float()  # if half else img.float()  # uint8 to fp16/32
-    img /= 255.0  # 0 - 255 to 0.0 - 1.0
-    if len(img.shape) == 3:
-        img = img[None]  # expand for batch dim
-    # Inference
-    t1 = time_sync()
-    pred = model(img, augment=False, profile=False)[0]
-
-    # to float
-    if half:
-        pred = pred.float()
-
-    # Apply NMS
-    pred = non_max_suppression(
-        pred, 0.1, 0.5, classes=None, agnostic=False)
-    t2 = time_sync()
-    annotator = Annotator(im0, line_width=3, example=str(names))
-    # Process detections
-    for i, det in enumerate(pred):  # detections per image
-        s = ''
-        s += '%gx%g ' % img.shape[2:]  # print string
-        if det is not None and len(det):
-            # Rescale boxes from img_size to im0 size
-            det[:, :4] = scale_coords(
-                img.shape[2:], det[:, :4], im0.shape).round()
-
-            # Print results
-            for c in det[:, -1].unique():
-                n = (det[:, -1] == c).sum()  # detections per class
-                s += '%g %ss, ' % (n, names[int(c)])  # add to string
-
-            # show results
-            for *xyxy, conf, cls in det:
-                label = '%s %.2f' % (names[int(cls)], conf)
-                annotator.box_label(xyxy, label, color=colors[int(cls)])
-        im0 = annotator.result()
-        # Print time (inference + NMS)
-        infer_time = t2 - t1
-        
-        print('%sDone.  %s' %
-                (s, infer_time))
-
-    print('Done. (%.3fs)' % (time.time() - t0))
-    
-    return im0
- 
-
-if __name__ == '__main__':
-    gr.Interface(detect,[gr.Image(type="numpy"),gr.Dropdown(choices=["yolov5s","yolov5s6"])], 
-    gr.Image(type="numpy"),title="Yolov5",examples=[["data/images/bus.jpg", "yolov5s"]],
-    description="Gradio based demo for <a href='https://github.com/ultralytics/yolov5' style='text-decoration: underline' target='_blank'>ultralytics/yolov5</a>, new state-of-the-art for real-time object detection").launch()
diff --git a/spaces/xiaoxin1111/vits-uma-genshin-honkai/README.md b/spaces/xiaoxin1111/vits-uma-genshin-honkai/README.md
deleted file mode 100644
index 2fd2870bef9c579ab20b33fdd09aea238aeb1f1d..0000000000000000000000000000000000000000
--- a/spaces/xiaoxin1111/vits-uma-genshin-honkai/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-license: apache-2.0
-title: ' vits-uma-genshin-honkai'
-sdk: gradio
-sdk_version: 3.7
-emoji: 🐨
-colorTo: yellow
-pinned: false
-app_file: app.py
-duplicated_from: sayashi/vits-uma-genshin-honkai
----
diff --git a/spaces/xiaoxin1111/vits-uma-genshin-honkai/text/__init__.py b/spaces/xiaoxin1111/vits-uma-genshin-honkai/text/__init__.py
deleted file mode 100644
index 663c4b6416affb53c9dc56dddbc8b2b65d4bf518..0000000000000000000000000000000000000000
--- a/spaces/xiaoxin1111/vits-uma-genshin-honkai/text/__init__.py
+++ /dev/null
@@ -1,57 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence, clean_text
-
-
-def cleaned_text_to_sequence(cleaned_text):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/xuxw98/TAPA/howto/customize_paths.md b/spaces/xuxw98/TAPA/howto/customize_paths.md
deleted file mode 100644
index e0e94bf6f5db05947a54baa48dabf08a5117f6a1..0000000000000000000000000000000000000000
--- a/spaces/xuxw98/TAPA/howto/customize_paths.md
+++ /dev/null
@@ -1,33 +0,0 @@
-## Customize paths
-
-The project is setup to use specific paths to read the original weights and save checkpoints etc.
-
-For all scripts, you can run
-
-```shell
-python script.py -h
-```
-
-to get a list of available options. For instance, here's how you would modify the checkpoint dir:
-
-```shell
-python scripts/convert_checkpoint.py --checkpoint_dir "data/checkpoints/foo"
-```
-
-Note that this change will need to be passed along to subsequent steps, for example:
-
-```shell
-python generate.py \
-  --checkpoint_path "data/checkpoints/foo/7B/lit-llama.pth" \
-  --tokenizer_path "data/checkpoints/foo/tokenizer.model"
-```
-
-and
-
-```shell
-python quantize/gptq.py \
-  --checkpoint_path "data/checkpoints/foo/7B/lit-llama.pth" \
-  --tokenizer_path "data/checkpoints/foo/tokenizer.model"
-```
-
-To avoid this, you can use symbolic links to create shortcuts and avoid passing different paths.
diff --git a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/ONNXVITS_modules.py b/spaces/xxbb/VITS-Umamusume-voice-synthesizer/ONNXVITS_modules.py
deleted file mode 100644
index 6cf676ce37c1eaf8428c4094e749f862182cb0c3..0000000000000000000000000000000000000000
--- a/spaces/xxbb/VITS-Umamusume-voice-synthesizer/ONNXVITS_modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from ONNXVITS_transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/yaelvinker/CLIPasso/models/painter_params.py b/spaces/yaelvinker/CLIPasso/models/painter_params.py
deleted file mode 100644
index 7da63981a3a0b51586560628c2e1c6e150945d18..0000000000000000000000000000000000000000
--- a/spaces/yaelvinker/CLIPasso/models/painter_params.py
+++ /dev/null
@@ -1,539 +0,0 @@
-import random
-import CLIP_.clip as clip
-import numpy as np
-import pydiffvg
-import sketch_utils as utils
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from PIL import Image
-from scipy.ndimage.filters import gaussian_filter
-from skimage.color import rgb2gray
-from skimage.filters import threshold_otsu
-from torchvision import transforms
-
-
-class Painter(torch.nn.Module):
-    def __init__(self, args,
-                num_strokes=4,
-                num_segments=4,
-                imsize=224,
-                device=None,
-                target_im=None,
-                mask=None):
-        super(Painter, self).__init__()
-
-        self.args = args
-        self.num_paths = num_strokes
-        self.num_segments = num_segments
-        self.width = args.width
-        self.control_points_per_seg = args.control_points_per_seg
-        self.opacity_optim = args.force_sparse
-        self.num_stages = args.num_stages
-        self.add_random_noise = "noise" in args.augemntations
-        self.noise_thresh = args.noise_thresh
-        self.softmax_temp = args.softmax_temp
-
-        self.shapes = []
-        self.shape_groups = []
-        self.device = device
-        self.canvas_width, self.canvas_height = imsize, imsize
-        self.points_vars = []
-        self.color_vars = []
-        self.color_vars_threshold = args.color_vars_threshold
-
-        self.path_svg = args.path_svg
-        self.strokes_per_stage = self.num_paths
-        self.optimize_flag = []
-
-        # attention related for strokes initialisation
-        self.attention_init = args.attention_init
-        self.target_path = args.target
-        self.saliency_model = args.saliency_model
-        self.xdog_intersec = args.xdog_intersec
-        self.mask_object = args.mask_object_attention
-        
-        self.text_target = args.text_target # for clip gradients
-        self.saliency_clip_model = args.saliency_clip_model
-        self.define_attention_input(target_im)
-        self.mask = mask
-        self.attention_map = self.set_attention_map() if self.attention_init else None
-        
-        self.thresh = self.set_attention_threshold_map() if self.attention_init else None
-        self.strokes_counter = 0 # counts the number of calls to "get_path"        
-        self.epoch = 0
-        self.final_epoch = args.num_iter - 1
-        
-
-    def init_image(self, stage=0):
-        if stage > 0:
-            # if multi stages training than add new strokes on existing ones
-            # don't optimize on previous strokes
-            self.optimize_flag = [False for i in range(len(self.shapes))]
-            for i in range(self.strokes_per_stage):
-                stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0])
-                path = self.get_path()
-                self.shapes.append(path)
-                path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(self.shapes) - 1]),
-                                                    fill_color = None,
-                                                    stroke_color = stroke_color)
-                self.shape_groups.append(path_group)
-                self.optimize_flag.append(True)
-
-        else:
-            num_paths_exists = 0
-            if self.path_svg != "none":
-                self.canvas_width, self.canvas_height, self.shapes, self.shape_groups = utils.load_svg(self.path_svg)
-                # if you want to add more strokes to existing ones and optimize on all of them
-                num_paths_exists = len(self.shapes)
-
-            for i in range(num_paths_exists, self.num_paths):
-                stroke_color = torch.tensor([0.0, 0.0, 0.0, 1.0])
-                path = self.get_path()
-                self.shapes.append(path)
-                path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(self.shapes) - 1]),
-                                                    fill_color = None,
-                                                    stroke_color = stroke_color)
-                self.shape_groups.append(path_group)        
-            self.optimize_flag = [True for i in range(len(self.shapes))]
-        
-        img = self.render_warp()
-        img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device = self.device) * (1 - img[:, :, 3:4])
-        img = img[:, :, :3]
-        # Convert img from HWC to NCHW
-        img = img.unsqueeze(0)
-        img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW
-        return img
-        # utils.imwrite(img.cpu(), '{}/init.png'.format(args.output_dir), gamma=args.gamma, use_wandb=args.use_wandb, wandb_name="init")
-
-    def get_image(self):
-        img = self.render_warp()
-        opacity = img[:, :, 3:4]
-        img = opacity * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device = self.device) * (1 - opacity)
-        img = img[:, :, :3]
-        # Convert img from HWC to NCHW
-        img = img.unsqueeze(0)
-        img = img.permute(0, 3, 1, 2).to(self.device) # NHWC -> NCHW
-        return img
-
-    def get_path(self):
-        points = []
-        self.num_control_points = torch.zeros(self.num_segments, dtype = torch.int32) + (self.control_points_per_seg - 2)
-        p0 = self.inds_normalised[self.strokes_counter] if self.attention_init else (random.random(), random.random())
-        points.append(p0)
-
-        for j in range(self.num_segments):
-            radius = 0.05
-            for k in range(self.control_points_per_seg - 1):
-                p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5))
-                points.append(p1)
-                p0 = p1
-        points = torch.tensor(points).to(self.device)
-        points[:, 0] *= self.canvas_width
-        points[:, 1] *= self.canvas_height
-        
-        path = pydiffvg.Path(num_control_points = self.num_control_points,
-                                points = points,
-                                stroke_width = torch.tensor(self.width),
-                                is_closed = False)
-        self.strokes_counter += 1
-        return path
-
-    def render_warp(self):
-        if self.opacity_optim:
-            for group in self.shape_groups:
-                group.stroke_color.data[:3].clamp_(0., 0.) # to force black stroke
-                group.stroke_color.data[-1].clamp_(0., 1.) # opacity
-                # group.stroke_color.data[-1] = (group.stroke_color.data[-1] >= self.color_vars_threshold).float()
-        _render = pydiffvg.RenderFunction.apply
-        # uncomment if you want to add random noise
-        if self.add_random_noise:
-            if random.random() > self.noise_thresh:
-                eps = 0.01 * min(self.canvas_width, self.canvas_height)
-                for path in self.shapes:
-                    path.points.data.add_(eps * torch.randn_like(path.points))
-        scene_args = pydiffvg.RenderFunction.serialize_scene(\
-            self.canvas_width, self.canvas_height, self.shapes, self.shape_groups)
-        img = _render(self.canvas_width, # width
-                    self.canvas_height, # height
-                    2,   # num_samples_x
-                    2,   # num_samples_y
-                    0,   # seed
-                    None,
-                    *scene_args)
-        return img
-    
-    def parameters(self):
-        self.points_vars = []
-        # storkes' location optimization
-        for i, path in enumerate(self.shapes):
-            if self.optimize_flag[i]:
-                path.points.requires_grad = True
-                self.points_vars.append(path.points)
-        return self.points_vars
-    
-    def get_points_parans(self):
-        return self.points_vars
-    
-    def set_color_parameters(self):
-        # for storkes' color optimization (opacity)
-        self.color_vars = []
-        for i, group in enumerate(self.shape_groups):
-            if self.optimize_flag[i]:
-                group.stroke_color.requires_grad = True
-                self.color_vars.append(group.stroke_color)
-        return self.color_vars
-
-    def get_color_parameters(self):
-        return self.color_vars
-        
-    def save_svg(self, output_dir, name):
-        pydiffvg.save_svg('{}/{}.svg'.format(output_dir, name), self.canvas_width, self.canvas_height, self.shapes, self.shape_groups)
-
-
-    def dino_attn(self):
-        patch_size=8 # dino hyperparameter
-        threshold=0.6
-
-        # for dino model
-        mean_imagenet = torch.Tensor([0.485, 0.456, 0.406])[None,:,None,None].to(self.device)
-        std_imagenet = torch.Tensor([0.229, 0.224, 0.225])[None,:,None,None].to(self.device)
-        totens = transforms.Compose([
-            transforms.Resize((self.canvas_height, self.canvas_width)),
-            transforms.ToTensor()
-            ])
-
-        dino_model = torch.hub.load('facebookresearch/dino:main', 'dino_vits8').eval().to(self.device)
-        
-        self.main_im = Image.open(self.target_path).convert("RGB")
-        main_im_tensor = totens(self.main_im).to(self.device)
-        img = (main_im_tensor.unsqueeze(0) - mean_imagenet) / std_imagenet
-        w_featmap = img.shape[-2] // patch_size
-        h_featmap = img.shape[-1] // patch_size
-        
-        with torch.no_grad():
-            attn = dino_model.get_last_selfattention(img).detach().cpu()[0]
-
-        nh = attn.shape[0]
-        attn = attn[:,0,1:].reshape(nh,-1)
-        val, idx = torch.sort(attn)
-        val /= torch.sum(val, dim=1, keepdim=True)
-        cumval = torch.cumsum(val, dim=1)
-        th_attn = cumval > (1 - threshold)
-        idx2 = torch.argsort(idx)
-        for head in range(nh):
-            th_attn[head] = th_attn[head][idx2[head]]
-        th_attn = th_attn.reshape(nh, w_featmap, h_featmap).float()
-        th_attn = nn.functional.interpolate(th_attn.unsqueeze(0), scale_factor=patch_size, mode="nearest")[0].cpu()
-        
-        attn = attn.reshape(nh, w_featmap, h_featmap).float()
-        attn = nn.functional.interpolate(attn.unsqueeze(0), scale_factor=patch_size, mode="nearest")[0].cpu()
-        
-        return attn
-
-
-    def define_attention_input(self, target_im):
-        model, preprocess = clip.load(self.saliency_clip_model, device=self.device, jit=False)
-        model.eval().to(self.device)
-        data_transforms = transforms.Compose([
-                    preprocess.transforms[-1],
-                ])
-        self.image_input_attn_clip = data_transforms(target_im).to(self.device)
-        
-
-    def clip_attn(self):
-        model, preprocess = clip.load(self.saliency_clip_model, device=self.device, jit=False)
-        model.eval().to(self.device)
-        text_input = clip.tokenize([self.text_target]).to(self.device)
-
-        if "RN" in self.saliency_clip_model:
-            saliency_layer = "layer4"
-            attn_map = gradCAM(
-                model.visual,
-                self.image_input_attn_clip,
-                model.encode_text(text_input).float(),
-                getattr(model.visual, saliency_layer)
-            )
-            attn_map = attn_map.squeeze().detach().cpu().numpy()
-            attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min())
-
-        else:
-            # attn_map = interpret(self.image_input_attn_clip, text_input, model, device=self.device, index=0).astype(np.float32)
-            attn_map = interpret(self.image_input_attn_clip, text_input, model, device=self.device)
-            
-        del model
-        return attn_map
-
-    def set_attention_map(self):
-        assert self.saliency_model in ["dino", "clip"]
-        if self.saliency_model == "dino":
-            return self.dino_attn()
-        elif self.saliency_model == "clip":
-            return self.clip_attn()
-        
-
-    def softmax(self, x, tau=0.2):
-        e_x = np.exp(x / tau)
-        return e_x / e_x.sum() 
-
-    def set_inds_clip(self):
-        attn_map = (self.attention_map - self.attention_map.min()) / (self.attention_map.max() - self.attention_map.min())
-        if self.xdog_intersec:
-            xdog = XDoG_()
-            im_xdog = xdog(self.image_input_attn_clip[0].permute(1,2,0).cpu().numpy(), k=10)
-            intersec_map = (1 - im_xdog) * attn_map
-            attn_map = intersec_map
-            
-        attn_map_soft = np.copy(attn_map)
-        attn_map_soft[attn_map > 0] = self.softmax(attn_map[attn_map > 0], tau=self.softmax_temp)
-        
-        k = self.num_stages * self.num_paths
-        self.inds = np.random.choice(range(attn_map.flatten().shape[0]), size=k, replace=False, p=attn_map_soft.flatten())
-        self.inds = np.array(np.unravel_index(self.inds, attn_map.shape)).T
-    
-        self.inds_normalised = np.zeros(self.inds.shape)
-        self.inds_normalised[:, 0] =  self.inds[:, 1] / self.canvas_width
-        self.inds_normalised[:, 1] =  self.inds[:, 0] / self.canvas_height
-        self.inds_normalised = self.inds_normalised.tolist()
-        return attn_map_soft
-
-
-
-    def set_inds_dino(self):
-        k = max(3, (self.num_stages * self.num_paths) // 6 + 1) # sample top 3 three points from each attention head
-        num_heads = self.attention_map.shape[0]
-        self.inds = np.zeros((k * num_heads, 2))
-        # "thresh" is used for visualisaiton purposes only
-        thresh = torch.zeros(num_heads + 1, self.attention_map.shape[1], self.attention_map.shape[2])
-        softmax = nn.Softmax(dim=1)
-        for i in range(num_heads):
-            # replace "self.attention_map[i]" with "self.attention_map" to get the highest values among
-            # all heads. 
-            topk, indices = np.unique(self.attention_map[i].numpy(), return_index=True)
-            topk = topk[::-1][:k]
-            cur_attn_map = self.attention_map[i].numpy()
-            # prob function for uniform sampling
-            prob = cur_attn_map.flatten()
-            prob[prob > topk[-1]] = 1
-            prob[prob <= topk[-1]] = 0
-            prob = prob / prob.sum()
-            thresh[i] = torch.Tensor(prob.reshape(cur_attn_map.shape))
-
-            # choose k pixels from each head            
-            inds = np.random.choice(range(cur_attn_map.flatten().shape[0]), size=k, replace=False, p=prob)
-            inds = np.unravel_index(inds, cur_attn_map.shape)
-            self.inds[i * k: i * k + k, 0] = inds[0]
-            self.inds[i * k: i * k + k, 1] = inds[1]
-        
-        # for visualisaiton
-        sum_attn = self.attention_map.sum(0).numpy()
-        mask = np.zeros(sum_attn.shape)
-        mask[thresh[:-1].sum(0) > 0] = 1
-        sum_attn = sum_attn * mask
-        sum_attn = sum_attn / sum_attn.sum()
-        thresh[-1] = torch.Tensor(sum_attn)
-
-        # sample num_paths from the chosen pixels.
-        prob_sum = sum_attn[self.inds[:,0].astype(np.int), self.inds[:,1].astype(np.int)]
-        prob_sum = prob_sum / prob_sum.sum()
-        new_inds = []
-        for i in range(self.num_stages):
-            new_inds.extend(np.random.choice(range(self.inds.shape[0]), size=self.num_paths, replace=False, p=prob_sum))
-        self.inds = self.inds[new_inds]
-        print("self.inds",self.inds.shape)
-    
-        self.inds_normalised = np.zeros(self.inds.shape)
-        self.inds_normalised[:, 0] =  self.inds[:, 1] / self.canvas_width
-        self.inds_normalised[:, 1] =  self.inds[:, 0] / self.canvas_height
-        self.inds_normalised = self.inds_normalised.tolist()
-        return thresh
-
-    def set_attention_threshold_map(self):
-        assert self.saliency_model in ["dino", "clip"]
-        if self.saliency_model == "dino":
-            return self.set_inds_dino()
-        elif self.saliency_model == "clip":
-            return self.set_inds_clip()
-        
-
-    def get_attn(self):
-        return self.attention_map
-    
-    def get_thresh(self):
-        return self.thresh
-
-    def get_inds(self):
-        return self.inds
-    
-    def get_mask(self):
-        return self.mask
-
-    def set_random_noise(self, epoch):
-        if epoch % self.args.save_interval == 0:
-            self.add_random_noise = False
-        else:
-            self.add_random_noise = "noise" in self.args.augemntations
-
-class PainterOptimizer:
-    def __init__(self, args, renderer):
-        self.renderer = renderer
-        self.points_lr = args.lr
-        self.color_lr = args.color_lr
-        self.args = args
-        self.optim_color = args.force_sparse
-
-    def init_optimizers(self):
-        self.points_optim = torch.optim.Adam(self.renderer.parameters(), lr=self.points_lr)
-        if self.optim_color:
-            self.color_optim = torch.optim.Adam(self.renderer.set_color_parameters(), lr=self.color_lr)
-
-    def update_lr(self, counter):
-        new_lr = utils.get_epoch_lr(counter, self.args)
-        for param_group in self.points_optim.param_groups:
-            param_group["lr"] = new_lr
-    
-    def zero_grad_(self):
-        self.points_optim.zero_grad()
-        if self.optim_color:
-            self.color_optim.zero_grad()
-    
-    def step_(self):
-        self.points_optim.step()
-        if self.optim_color:
-            self.color_optim.step()
-    
-    def get_lr(self):
-        return self.points_optim.param_groups[0]['lr']
-
-
-class Hook:
-    """Attaches to a module and records its activations and gradients."""
-
-    def __init__(self, module: nn.Module):
-        self.data = None
-        self.hook = module.register_forward_hook(self.save_grad)
-        
-    def save_grad(self, module, input, output):
-        self.data = output
-        output.requires_grad_(True)
-        output.retain_grad()
-        
-    def __enter__(self):
-        return self
-    
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        self.hook.remove()
-        
-    @property
-    def activation(self) -> torch.Tensor:
-        return self.data
-    
-    @property
-    def gradient(self) -> torch.Tensor:
-        return self.data.grad
-
-
-
-
-def interpret(image, texts, model, device):
-    images = image.repeat(1, 1, 1, 1)
-    res = model.encode_image(images)
-    model.zero_grad()
-    image_attn_blocks = list(dict(model.visual.transformer.resblocks.named_children()).values())
-    num_tokens = image_attn_blocks[0].attn_probs.shape[-1]
-    R = torch.eye(num_tokens, num_tokens, dtype=image_attn_blocks[0].attn_probs.dtype).to(device)
-    R = R.unsqueeze(0).expand(1, num_tokens, num_tokens)
-    cams = [] # there are 12 attention blocks
-    for i, blk in enumerate(image_attn_blocks):
-        cam = blk.attn_probs.detach() #attn_probs shape is 12, 50, 50
-        # each patch is 7x7 so we have 49 pixels + 1 for positional encoding
-        cam = cam.reshape(1, -1, cam.shape[-1], cam.shape[-1])
-        cam = cam.clamp(min=0)
-        cam = cam.clamp(min=0).mean(dim=1) # mean of the 12 something
-        cams.append(cam)  
-        R = R + torch.bmm(cam, R)
-              
-    cams_avg = torch.cat(cams) # 12, 50, 50
-    cams_avg = cams_avg[:, 0, 1:] # 12, 1, 49
-    image_relevance = cams_avg.mean(dim=0).unsqueeze(0)
-    image_relevance = image_relevance.reshape(1, 1, 7, 7)
-    image_relevance = torch.nn.functional.interpolate(image_relevance, size=224, mode='bicubic')
-    image_relevance = image_relevance.reshape(224, 224).data.cpu().numpy().astype(np.float32)
-    image_relevance = (image_relevance - image_relevance.min()) / (image_relevance.max() - image_relevance.min())
-    return image_relevance
-
-
-# Reference: https://arxiv.org/abs/1610.02391
-def gradCAM(
-    model: nn.Module,
-    input: torch.Tensor,
-    target: torch.Tensor,
-    layer: nn.Module
-) -> torch.Tensor:
-    # Zero out any gradients at the input.
-    if input.grad is not None:
-        input.grad.data.zero_()
-        
-    # Disable gradient settings.
-    requires_grad = {}
-    for name, param in model.named_parameters():
-        requires_grad[name] = param.requires_grad
-        param.requires_grad_(False)
-        
-    # Attach a hook to the model at the desired layer.
-    assert isinstance(layer, nn.Module)
-    with Hook(layer) as hook:        
-        # Do a forward and backward pass.
-        output = model(input)
-        output.backward(target)
-
-        grad = hook.gradient.float()
-        act = hook.activation.float()
-    
-        # Global average pool gradient across spatial dimension
-        # to obtain importance weights.
-        alpha = grad.mean(dim=(2, 3), keepdim=True)
-        # Weighted combination of activation maps over channel
-        # dimension.
-        gradcam = torch.sum(act * alpha, dim=1, keepdim=True)
-        # We only want neurons with positive influence so we
-        # clamp any negative ones.
-        gradcam = torch.clamp(gradcam, min=0)
-
-    # Resize gradcam to input resolution.
-    gradcam = F.interpolate(
-        gradcam,
-        input.shape[2:],
-        mode='bicubic',
-        align_corners=False)
-    
-    # Restore gradient settings.
-    for name, param in model.named_parameters():
-        param.requires_grad_(requires_grad[name])
-        
-    return gradcam    
-
-
-class XDoG_(object):
-    def __init__(self):
-        super(XDoG_, self).__init__()
-        self.gamma=0.98
-        self.phi=200
-        self.eps=-0.1
-        self.sigma=0.8
-        self.binarize=True
-        
-    def __call__(self, im, k=10):
-        if im.shape[2] == 3:
-            im = rgb2gray(im)
-        imf1 = gaussian_filter(im, self.sigma)
-        imf2 = gaussian_filter(im, self.sigma * k)
-        imdiff = imf1 - self.gamma * imf2
-        imdiff = (imdiff < self.eps) * 1.0  + (imdiff >= self.eps) * (1.0 + np.tanh(self.phi * imdiff))
-        imdiff -= imdiff.min()
-        imdiff /= imdiff.max()
-        if self.binarize:
-            th = threshold_otsu(imdiff)
-            imdiff = imdiff >= th
-        imdiff = imdiff.astype('float32')
-        return imdiff
diff --git a/spaces/yderre-aubay/midi-player-demo/jest.config.js b/spaces/yderre-aubay/midi-player-demo/jest.config.js
deleted file mode 100644
index 9432e67b51efbc81c9208f30c23f6f6029a433c3..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/jest.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
-  testEnvironment: "./src/test/CustomEnvironment.ts",
-  preset: "ts-jest/presets/js-with-babel-esm",
-  moduleFileExtensions: ["ts", "tsx", "js", "jsx"],
-  transformIgnorePatterns: ["/node_modules/(?!midifile-ts)"],
-}
diff --git a/spaces/yderre-aubay/midi-player-demo/src/common/selection/ControlSelection.ts b/spaces/yderre-aubay/midi-player-demo/src/common/selection/ControlSelection.ts
deleted file mode 100644
index fd5e1cbccbe57670d41433ce2206fcc50cedc51c..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/common/selection/ControlSelection.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export interface ControlSelection {
-  fromTick: number
-  toTick: number
-}
diff --git a/spaces/yderre-aubay/midi-player-demo/src/components/Label.tsx b/spaces/yderre-aubay/midi-player-demo/src/components/Label.tsx
deleted file mode 100644
index 3834109debdbd312074e556dedd1a333c4a14887..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/components/Label.tsx
+++ /dev/null
@@ -1,8 +0,0 @@
-import styled from "@emotion/styled"
-
-export const Label = styled.label`
-  display: block;
-  color: ${({ theme }) => theme.secondaryTextColor};
-  font-size: 0.8rem;
-  font-family: inherit;
-`
diff --git a/spaces/yeqingmei123/face-test/e4e/criteria/lpips/utils.py b/spaces/yeqingmei123/face-test/e4e/criteria/lpips/utils.py
deleted file mode 100644
index 3d15a0983775810ef6239c561c67939b2b9ee3b5..0000000000000000000000000000000000000000
--- a/spaces/yeqingmei123/face-test/e4e/criteria/lpips/utils.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from collections import OrderedDict
-
-import torch
-
-
-def normalize_activation(x, eps=1e-10):
-    norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
-    return x / (norm_factor + eps)
-
-
-def get_state_dict(net_type: str = 'alex', version: str = '0.1'):
-    # build url
-    url = 'https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/' \
-        + f'master/lpips/weights/v{version}/{net_type}.pth'
-
-    # download
-    old_state_dict = torch.hub.load_state_dict_from_url(
-        url, progress=True,
-        map_location=None if torch.cuda.is_available() else torch.device('cpu')
-    )
-
-    # rename keys
-    new_state_dict = OrderedDict()
-    for key, val in old_state_dict.items():
-        new_key = key
-        new_key = new_key.replace('lin', '')
-        new_key = new_key.replace('model.', '')
-        new_state_dict[new_key] = val
-
-    return new_state_dict
diff --git a/spaces/yerfor/SyntaSpeech/modules/tts/glow/utils.py b/spaces/yerfor/SyntaSpeech/modules/tts/glow/utils.py
deleted file mode 100644
index 214853251e5f00a939c5fe1a348ad9f7dc1d4c5e..0000000000000000000000000000000000000000
--- a/spaces/yerfor/SyntaSpeech/modules/tts/glow/utils.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import torch
-
-
-def squeeze(x, nonpadding=None, n_sqz=2):
-    b, c, t = x.size()
-
-    t = (t // n_sqz) * n_sqz
-    x = x[:, :, :t]
-    x_sqz = x.view(b, c, t // n_sqz, n_sqz)
-    x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
-
-    if nonpadding is not None:
-        nonpadding = nonpadding[:, :, n_sqz - 1::n_sqz]
-    else:
-        nonpadding = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_sqz * nonpadding, nonpadding
-
-
-def unsqueeze(x, nonpadding=None, n_sqz=2):
-    b, c, t = x.size()
-
-    x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
-    x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
-
-    if nonpadding is not None:
-        nonpadding = nonpadding.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
-    else:
-        nonpadding = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
-    return x_unsqz * nonpadding, nonpadding
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/graphormer/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/graphormer/__init__.py
deleted file mode 100644
index 4263525682147f42553effe2c7b287ec91c6613d..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/graphormer/__init__.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
-
-
-_import_structure = {
-    "configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_graphormer"] = [
-        "GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "GraphormerForGraphClassification",
-        "GraphormerModel",
-        "GraphormerPreTrainedModel",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_graphormer import (
-            GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
-            GraphormerForGraphClassification,
-            GraphormerModel,
-            GraphormerPreTrainedModel,
-        )
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/owlvit/configuration_owlvit.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/owlvit/configuration_owlvit.py
deleted file mode 100644
index d21dc77bbf65f99c0bd42e38a0964f79d2f730ce..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/owlvit/configuration_owlvit.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" OWL-ViT model configuration"""
-
-import os
-from collections import OrderedDict
-from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
-
-
-if TYPE_CHECKING:
-    from ...processing_utils import ProcessorMixin
-    from ...utils import TensorType
-
-from ...configuration_utils import PretrainedConfig
-from ...onnx import OnnxConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
-    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
-    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
-}
-
-
-class OwlViTTextConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
-    OwlViT text encoder according to the specified arguments, defining the model architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the OwlViT
-    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 49408):
-            Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented
-            by the `inputs_ids` passed when calling [`OwlViTTextModel`].
-        hidden_size (`int`, *optional*, defaults to 512):
-            Dimensionality of the encoder layers and the pooler layer.
-        intermediate_size (`int`, *optional*, defaults to 2048):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 8):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        max_position_embeddings (`int`, *optional*, defaults to 16):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
-            The epsilon used by the layer normalization layers.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        initializer_factor (`float`, *optional*, defaults to 1):
-            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
-            testing).
-
-    Example:
-
-    ```python
-    >>> from transformers import OwlViTTextConfig, OwlViTTextModel
-
-    >>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration
-    >>> configuration = OwlViTTextConfig()
-
-    >>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration
-    >>> model = OwlViTTextModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "owlvit_text_model"
-
-    def __init__(
-        self,
-        vocab_size=49408,
-        hidden_size=512,
-        intermediate_size=2048,
-        num_hidden_layers=12,
-        num_attention_heads=8,
-        max_position_embeddings=16,
-        hidden_act="quick_gelu",
-        layer_norm_eps=1e-5,
-        attention_dropout=0.0,
-        initializer_range=0.02,
-        initializer_factor=1.0,
-        pad_token_id=0,
-        bos_token_id=49406,
-        eos_token_id=49407,
-        **kwargs,
-    ):
-        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_act = hidden_act
-        self.layer_norm_eps = layer_norm_eps
-        self.attention_dropout = attention_dropout
-        self.initializer_range = initializer_range
-        self.initializer_factor = initializer_factor
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the text config dict if we are loading from OwlViTConfig
-        if config_dict.get("model_type") == "owlvit":
-            config_dict = config_dict["text_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class OwlViTVisionConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate
-    an OWL-ViT image encoder according to the specified arguments, defining the model architecture. Instantiating a
-    configuration with the defaults will yield a similar configuration to that of the OWL-ViT
-    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        hidden_size (`int`, *optional*, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
-        intermediate_size (`int`, *optional*, defaults to 3072):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        num_hidden_layers (`int`, *optional*, defaults to 12):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 12):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_channels (`int`, *optional*, defaults to 3):
-            Number of channels in the input images.
-        image_size (`int`, *optional*, defaults to 768):
-            The size (resolution) of each image.
-        patch_size (`int`, *optional*, defaults to 32):
-            The size (resolution) of each patch.
-        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
-            The epsilon used by the layer normalization layers.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        initializer_factor (`float``, *optional*, defaults to 1.0):
-            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
-            testing).
-
-    Example:
-
-    ```python
-    >>> from transformers import OwlViTVisionConfig, OwlViTVisionModel
-
-    >>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration
-    >>> configuration = OwlViTVisionConfig()
-
-    >>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration
-    >>> model = OwlViTVisionModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "owlvit_vision_model"
-
-    def __init__(
-        self,
-        hidden_size=768,
-        intermediate_size=3072,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        num_channels=3,
-        image_size=768,
-        patch_size=32,
-        hidden_act="quick_gelu",
-        layer_norm_eps=1e-5,
-        attention_dropout=0.0,
-        initializer_range=0.02,
-        initializer_factor=1.0,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_channels = num_channels
-        self.image_size = image_size
-        self.patch_size = patch_size
-        self.hidden_act = hidden_act
-        self.layer_norm_eps = layer_norm_eps
-        self.attention_dropout = attention_dropout
-        self.initializer_range = initializer_range
-        self.initializer_factor = initializer_factor
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the vision config dict if we are loading from OwlViTConfig
-        if config_dict.get("model_type") == "owlvit":
-            config_dict = config_dict["vision_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class OwlViTConfig(PretrainedConfig):
-    r"""
-    [`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
-    instantiate an OWL-ViT model according to the specified arguments, defining the text model and vision model
-    configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the OWL-ViT
-    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        text_config (`dict`, *optional*):
-            Dictionary of configuration options used to initialize [`OwlViTTextConfig`].
-        vision_config (`dict`, *optional*):
-            Dictionary of configuration options used to initialize [`OwlViTVisionConfig`].
-        projection_dim (`int`, *optional*, defaults to 512):
-            Dimensionality of text and vision projection layers.
-        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
-            The inital value of the *logit_scale* parameter. Default is used as per the original OWL-ViT
-            implementation.
-        kwargs (*optional*):
-            Dictionary of keyword arguments.
-    """
-
-    model_type = "owlvit"
-
-    def __init__(
-        self,
-        text_config=None,
-        vision_config=None,
-        projection_dim=512,
-        logit_scale_init_value=2.6592,
-        return_dict=True,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        if text_config is None:
-            text_config = {}
-            logger.info("text_config is None. Initializing the OwlViTTextConfig with default values.")
-
-        if vision_config is None:
-            vision_config = {}
-            logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values.")
-
-        self.text_config = OwlViTTextConfig(**text_config)
-        self.vision_config = OwlViTVisionConfig(**vision_config)
-
-        self.projection_dim = projection_dim
-        self.logit_scale_init_value = logit_scale_init_value
-        self.return_dict = return_dict
-        self.initializer_factor = 1.0
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-    @classmethod
-    def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs):
-        r"""
-        Instantiate a [`OwlViTConfig`] (or a derived class) from owlvit text model configuration and owlvit vision
-        model configuration.
-
-        Returns:
-            [`OwlViTConfig`]: An instance of a configuration object
-        """
-        config_dict = {}
-        config_dict["text_config"] = text_config
-        config_dict["vision_config"] = vision_config
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class OwlViTOnnxConfig(OnnxConfig):
-    @property
-    def inputs(self) -> Mapping[str, Mapping[int, str]]:
-        return OrderedDict(
-            [
-                ("input_ids", {0: "batch", 1: "sequence"}),
-                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
-                ("attention_mask", {0: "batch", 1: "sequence"}),
-            ]
-        )
-
-    @property
-    def outputs(self) -> Mapping[str, Mapping[int, str]]:
-        return OrderedDict(
-            [
-                ("logits_per_image", {0: "batch"}),
-                ("logits_per_text", {0: "batch"}),
-                ("text_embeds", {0: "batch"}),
-                ("image_embeds", {0: "batch"}),
-            ]
-        )
-
-    @property
-    def atol_for_validation(self) -> float:
-        return 1e-4
-
-    def generate_dummy_inputs(
-        self,
-        processor: "ProcessorMixin",
-        batch_size: int = -1,
-        seq_length: int = -1,
-        framework: Optional["TensorType"] = None,
-    ) -> Mapping[str, Any]:
-        text_input_dict = super().generate_dummy_inputs(
-            processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
-        )
-        image_input_dict = super().generate_dummy_inputs(
-            processor.image_processor, batch_size=batch_size, framework=framework
-        )
-        return {**text_input_dict, **image_input_dict}
-
-    @property
-    def default_onnx_opset(self) -> int:
-        return 14
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/test_model_analysis.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/test_model_analysis.py
deleted file mode 100644
index c01b7af09703c8dad889dee0118d74fcc12ac4b0..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/test_model_analysis.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-
-import unittest
-import torch
-from torch import nn
-
-from detectron2.utils.analysis import find_unused_parameters, flop_count_operators, parameter_count
-from detectron2.utils.testing import get_model_no_weights
-
-
-class RetinaNetTest(unittest.TestCase):
-    def setUp(self):
-        self.model = get_model_no_weights("COCO-Detection/retinanet_R_50_FPN_1x.yaml")
-
-    def test_flop(self):
-        # RetinaNet supports flop-counting with random inputs
-        inputs = [{"image": torch.rand(3, 800, 800), "test_unused": "abcd"}]
-        res = flop_count_operators(self.model, inputs)
-        self.assertEqual(int(res["conv"]), 146)  # 146B flops
-
-    def test_param_count(self):
-        res = parameter_count(self.model)
-        self.assertEqual(res[""], 37915572)
-        self.assertEqual(res["backbone"], 31452352)
-
-
-class FasterRCNNTest(unittest.TestCase):
-    def setUp(self):
-        self.model = get_model_no_weights("COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml")
-
-    def test_flop(self):
-        # Faster R-CNN supports flop-counting with random inputs
-        inputs = [{"image": torch.rand(3, 800, 800)}]
-        res = flop_count_operators(self.model, inputs)
-
-        # This only checks flops for backbone & proposal generator
-        # Flops for box head is not conv, and depends on #proposals, which is
-        # almost 0 for random inputs.
-        self.assertEqual(int(res["conv"]), 117)
-
-    def test_flop_with_output_shape(self):
-        inputs = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}]
-        res = flop_count_operators(self.model, inputs)
-        self.assertEqual(int(res["conv"]), 117)
-
-    def test_param_count(self):
-        res = parameter_count(self.model)
-        self.assertEqual(res[""], 41699936)
-        self.assertEqual(res["backbone"], 26799296)
-
-
-class MaskRCNNTest(unittest.TestCase):
-    def setUp(self):
-        self.model = get_model_no_weights("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
-
-    def test_flop(self):
-        inputs1 = [{"image": torch.rand(3, 800, 800)}]
-        inputs2 = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}]
-
-        for inputs in [inputs1, inputs2]:
-            res = flop_count_operators(self.model, inputs)
-            # The mask head could have extra conv flops, so total >= 117
-            self.assertGreaterEqual(int(res["conv"]), 117)
-
-
-class UnusedParamTest(unittest.TestCase):
-    def test_unused(self):
-        class TestMod(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.fc1 = nn.Linear(10, 10)
-                self.t = nn.Linear(10, 10)
-
-            def forward(self, x):
-                return self.fc1(x).mean()
-
-        m = TestMod()
-        ret = find_unused_parameters(m, torch.randn(10, 10))
-        self.assertEqual(set(ret), {"t.weight", "t.bias"})
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/user-select.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/user-select.js
deleted file mode 100644
index 3aba21702eec3d6e0927c8bbf26ec89f045499eb..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/user-select.js
+++ /dev/null
@@ -1,28 +0,0 @@
-let Declaration = require('../declaration')
-
-class UserSelect extends Declaration {
-  /**
-   * Change prefixed value for IE
-   */
-  set(decl, prefix) {
-    if (prefix === '-ms-' && decl.value === 'contain') {
-      decl.value = 'element'
-    }
-    return super.set(decl, prefix)
-  }
-
-  /**
-   * Avoid prefixing all in IE
-   */
-  insert(decl, prefix, prefixes) {
-    if (decl.value === 'all' && prefix === '-ms-') {
-      return undefined
-    } else {
-      return super.insert(decl, prefix, prefixes)
-    }
-  }
-}
-
-UserSelect.names = ['user-select']
-
-module.exports = UserSelect
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/picocolors/types.ts b/spaces/younker/chatgpt-turbo/client/node_modules/picocolors/types.ts
deleted file mode 100644
index b4bacee4909e7f562fb13f89720c8ae57c4922fc..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/picocolors/types.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-export type Formatter = (input: string | number | null | undefined) => string
-
-export interface Colors {
-	isColorSupported: boolean
-	reset: Formatter
-	bold: Formatter
-	dim: Formatter
-	italic: Formatter
-	underline: Formatter
-	inverse: Formatter
-	hidden: Formatter
-	strikethrough: Formatter
-	black: Formatter
-	red: Formatter
-	green: Formatter
-	yellow: Formatter
-	blue: Formatter
-	magenta: Formatter
-	cyan: Formatter
-	white: Formatter
-	gray: Formatter
-	bgBlack: Formatter
-	bgRed: Formatter
-	bgGreen: Formatter
-	bgYellow: Formatter
-	bgBlue: Formatter
-	bgMagenta: Formatter
-	bgCyan: Formatter
-	bgWhite: Formatter
-}
diff --git a/spaces/youplala/StoreCopilot/tests/test_prompts.py b/spaces/youplala/StoreCopilot/tests/test_prompts.py
deleted file mode 100644
index 6a1c8d109c2787a6cabcf2a93bb2501ffcf87e68..0000000000000000000000000000000000000000
--- a/spaces/youplala/StoreCopilot/tests/test_prompts.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from src.callbacks.retailgpt.graph import create_parser, create_llm_chain, get_context
-
-def test_prompt():
-    context = get_context()
-    parser = create_parser()
-    llm_context_chain = create_llm_chain(parser=parser)
-    output = llm_context_chain(context)
-    assert output is not None
-
-
-def test_chain():
-    params = {
-        "temperature": 0,
-        "model_name": "text-davinci-003",
-        "max_tokens": 1000,
-    }
-    llm_context_chain = create_llm_chain(**params)
diff --git a/spaces/ysharma/LLaVA_v1/llava/model/language_model/mpt/hf_prefixlm_converter.py b/spaces/ysharma/LLaVA_v1/llava/model/language_model/mpt/hf_prefixlm_converter.py
deleted file mode 100644
index 8c1a6487202a6400a7116a6bd68b493892ef0d14..0000000000000000000000000000000000000000
--- a/spaces/ysharma/LLaVA_v1/llava/model/language_model/mpt/hf_prefixlm_converter.py
+++ /dev/null
@@ -1,415 +0,0 @@
-"""Converts Huggingface Causal LM to Prefix LM.
-
-Conversion does lightweight surgery on a HuggingFace
-Causal LM to convert it to a Prefix LM.
-
-Prefix LMs accepts a `bidirectional_mask` input in `forward`
-and treat the input prompt as the prefix in `generate`.
-"""
-import math
-import warnings
-from types import MethodType
-from typing import Any, Dict, List, Optional, Tuple, Union
-import torch
-from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss
-from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom
-from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom
-from transformers.models.bloom.modeling_bloom import logging
-from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
-from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM
-from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
-from transformers.models.gptj.modeling_gptj import GPTJForCausalLM
-from transformers.models.opt.modeling_opt import OPTForCausalLM
-from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt
-from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt
-logger = logging.get_logger(__name__)
-_SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM)
-CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
-
-def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES:
-    """Converts a GPT-style Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `GPT2LMHeadModel`
-        - `GPTNeoForCausalLM`
-        - `GPTNeoXForCausalLM`
-        - `GPTJForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, _SUPPORTED_GPT_MODELS)
-    assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models'
-
-    def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]:
-        """Helper that gets a list of the model's attention modules.
-
-        Each module has a `bias` buffer used for causal masking. The Prefix LM
-        conversion adds logic to dynamically manipulate these biases to support
-        Prefix LM attention masking.
-        """
-        attn_modules = []
-        if isinstance(model, GPTNeoXForCausalLM):
-            blocks = model.gpt_neox.layers
-        else:
-            blocks = model.transformer.h
-        for block in blocks:
-            if isinstance(model, GPTNeoForCausalLM):
-                if block.attn.attention_type != 'global':
-                    continue
-                attn_module = block.attn.attention
-            elif isinstance(model, GPTNeoXForCausalLM):
-                attn_module = block.attention
-            else:
-                attn_module = block.attn
-            attn_modules.append(attn_module)
-        return attn_modules
-    setattr(model, '_original_forward', getattr(model, 'forward'))
-    setattr(model, '_original_generate', getattr(model, 'generate'))
-
-    def forward(self: CAUSAL_GPT_TYPES, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]]=None, attention_mask: Optional[torch.FloatTensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
-        """Wraps original forward to enable PrefixLM attention."""
-
-        def call_og_forward():
-            if isinstance(self, GPTNeoXForCausalLM):
-                return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-            else:
-                return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        if bidirectional_mask is None:
-            return call_og_forward()
-        assert isinstance(bidirectional_mask, torch.Tensor)
-        attn_modules = _get_attn_modules(model)
-        (b, s) = bidirectional_mask.shape
-        max_length = attn_modules[0].bias.shape[-1]
-        if s > max_length:
-            raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).')
-        assert s <= max_length
-        if s < max_length:
-            pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device)
-            bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1)
-        bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1)
-        for attn_module in attn_modules:
-            attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional)
-        output = call_og_forward()
-        for attn_module in attn_modules:
-            attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
-        return output
-
-    def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]):
-        """Wraps original generate to enable PrefixLM attention."""
-        attn_modules = _get_attn_modules(model)
-        for attn_module in attn_modules:
-            attn_module.bias.data[:] = 1
-        output = self._original_generate(*args, **kwargs)
-        for attn_module in attn_modules:
-            attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
-        return output
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'generate', MethodType(generate, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-
-def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM:
-    """Converts a BLOOM Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `BloomForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, BloomForCausalLM)
-    assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models'
-
-    def _prepare_attn_mask(self: BloomModel, attention_mask: torch.Tensor, bidirectional_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor:
-        combined_attention_mask = None
-        device = attention_mask.device
-        (_, src_length) = input_shape
-        if src_length > 1:
-            combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length)
-            if bidirectional_mask is not None:
-                assert attention_mask.shape == bidirectional_mask.shape
-                expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length)
-                combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask)
-        expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length)
-        combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
-        return combined_attention_mask
-
-    def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
-        num_heads = self.config.n_head
-        closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
-        base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32)
-        powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32)
-        slopes = torch.pow(base, powers)
-        if closest_power_of_2 != num_heads:
-            extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32)
-            num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
-            extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32)
-            slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
-        qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1)
-        ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1)
-        diffs = qa - ka + key_length - query_length
-        diffs = -diffs.abs()
-        alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length)
-        alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length)
-        return alibi.to(dtype)
-    KeyValueT = Tuple[torch.Tensor, torch.Tensor]
-
-    def forward(self: BloomModel, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
-        if deprecated_arguments.pop('position_ids', False) is not False:
-            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. ' + 'You can safely ignore passing `position_ids`.', FutureWarning)
-        if len(deprecated_arguments) > 0:
-            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
-        elif input_ids is not None:
-            (batch_size, seq_length) = input_ids.shape
-        elif inputs_embeds is not None:
-            (batch_size, seq_length, _) = inputs_embeds.shape
-        else:
-            raise ValueError('You have to specify either input_ids or inputs_embeds')
-        if past_key_values is None:
-            past_key_values = tuple([None] * len(self.h))
-        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        hidden_states = self.word_embeddings_layernorm(inputs_embeds)
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            tmp = past_key_values[0][0]
-            past_key_values_length = tmp.shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-        if attention_mask is None:
-            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
-        else:
-            attention_mask = attention_mask.to(hidden_states.device)
-        alibi = self._build_alibi_tensor(batch_size=batch_size, query_length=seq_length, key_length=seq_length_with_past, dtype=hidden_states.dtype, device=hidden_states.device)
-        causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length)
-        for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)):
-            if output_hidden_states:
-                hst = (hidden_states,)
-                all_hidden_states = all_hidden_states + hst
-            if self.gradient_checkpointing and self.training:
-                if use_cache:
-                    logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...')
-                    use_cache = False
-
-                def create_custom_forward(module):
-
-                    def custom_forward(*inputs):
-                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
-                    return custom_forward
-                outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i])
-            else:
-                outputs = block(hidden_states, layer_past=layer_past, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi)
-            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-            if output_attentions:
-                oa = (outputs[2 if use_cache else 1],)
-                all_self_attentions = all_self_attentions + oa
-        hidden_states = self.ln_f(hidden_states)
-        if output_hidden_states:
-            hst = (hidden_states,)
-            all_hidden_states = all_hidden_states + hst
-        if not return_dict:
-            return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None))
-        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions)
-    setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer))
-    setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer))
-    setattr(model.transformer, 'forward', MethodType(forward, model.transformer))
-    KeyValueT = Tuple[torch.Tensor, torch.Tensor]
-
-    def forward(self: BloomForCausalLM, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, labels: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
-        """Replacement forward method for BloomCausalLM."""
-        if deprecated_arguments.pop('position_ids', False) is not False:
-            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed ' + 'in v5.0.0. You can safely ignore passing `position_ids`.', FutureWarning)
-        if len(deprecated_arguments) > 0:
-            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, bidirectional_mask=bidirectional_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        hidden_states = transformer_outputs[0]
-        lm_logits = self.lm_head(hidden_states)
-        loss = None
-        if labels is not None:
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            (batch_size, seq_length, vocab_size) = shift_logits.shape
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length))
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return (loss,) + output if loss is not None else output
-        return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
-
-    def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> dict:
-        if past:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-            bidirectional_mask = None
-            if past[0][0].shape[0] == input_ids.shape[0]:
-                past = self._convert_to_bloom_cache(past)
-        else:
-            bidirectional_mask = torch.ones_like(input_ids)
-        return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask}
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-
-def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM:
-    """Converts an OPT Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `OPTForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, OPTForCausalLM)
-    assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models'
-    setattr(model, '_original_forward', getattr(model, 'forward'))
-    setattr(model, '_original_generate', getattr(model, 'generate'))
-    model.model.decoder.bidirectional_mask = None
-
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            if self.bidirectional_mask == 'g':
-                (bsz, src_length) = input_shape
-                combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device)
-            else:
-                combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device)
-                if self.bidirectional_mask is not None:
-                    assert attention_mask.shape == self.bidirectional_mask.shape
-                    expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
-                    combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask)
-        if attention_mask is not None:
-            expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
-            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-        return combined_attention_mask
-    setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder))
-
-    def forward(self: OPTForCausalLM, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.ByteTensor]=None, head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[List[torch.FloatTensor]]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
-
-        def call_og_forward():
-            return self._original_forward(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        if bidirectional_mask is None:
-            return call_og_forward()
-        self.model.decoder.bidirectional_mask = bidirectional_mask
-        try:
-            outputs = call_og_forward()
-        except:
-            self.model.decoder.bidirectional_mask = None
-            raise
-        self.model.decoder.bidirectional_mask = None
-        return outputs
-
-    def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]):
-        """Wraps original generate to enable PrefixLM-style attention."""
-        self.model.decoder.bidirectional_mask = 'g'
-        try:
-            output = self._original_generate(*args, **kwargs)
-        except:
-            self.model.decoder.bidirectional_mask = None
-            raise
-        self.model.decoder.bidirectional_mask = None
-        return output
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'generate', MethodType(generate, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM)
-CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM]
-
-def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES:
-    """Converts a HuggingFace Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `GPT2LMHeadModel`
-        - `GPTNeoForCausalLM`
-        - `GPTNeoXForCausalLM`
-        - `GPTJForCausalLM`
-        - `BloomForCausalLM`
-        - `OPTForCausalLM`
-
-    Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the
-    `generate` method and/or select underlying methods depending on the model class.
-
-    These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask".
-
-    Notes on training:
-        To actually train the converted model as a Prefix LM, training batches will need to indicate
-        the prefix/target structure by including `bidirectional_mask` as part of the batch inputs.
-
-        **This is not a standard input and requires custom layers either within or after your dataloader.**
-
-        In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels`
-        such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`.
-        That is, the prefix portion of the sequence should not generate any loss. Loss should only be
-        generated by the target portion of the sequence.
-
-    Notes on `GPTNeoForCausalLM`:
-        To simplify the implementation, "global" and "local" attention layers are handled differently.
-        For "global" layers, we handle conversion as described above. For "local" layers, which use a
-        causal attention mask within a restricted local window, we do not alter the masking.
-
-    Notes on `forward` method conversion:
-        After conversion, the `forward` method will handle a new input, `bidirectional_mask`,
-        which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions
-        belonging to the prefix (prefix tokens can attend to one another bidirectionally), and
-        0 indicates token positions belonging to the target.
-
-        The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing
-        causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset
-        the causal masks before returning the result.
-
-    Notes on `generate` method conversion:
-        After conversion, the `generate` method will have the same signature but will internally
-        convert all causal masks to be purely bidirectional, call the original `generate` method, and
-        (where appropriate) reset the causal masks before returning the result.
-
-        This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token
-        "prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates
-        each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one
-        another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and
-        previously-generated tokens (also as expected in a Prefix LM).
-
-    To preserve the API, the original methods are renamed to `_original_forward` and
-    `_original_generate`, and replaced with new `forward` and `generate` methods that wrap
-    them, respectively. Although implementation details vary by model class.
-    """
-    if isinstance(model, _SUPPORTED_GPT_MODELS):
-        return _convert_gpt_causal_lm_to_prefix_lm(model)
-    elif isinstance(model, BloomForCausalLM):
-        return _convert_bloom_causal_lm_to_prefix_lm(model)
-    elif isinstance(model, OPTForCausalLM):
-        return _convert_opt_causal_lm_to_prefix_lm(model)
-    else:
-        raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}')
-
-def add_bidirectional_mask_if_missing(batch: Dict[str, Any]):
-    """Attempts to add bidirectional_mask to batch if missing.
-
-    Raises:
-        KeyError if bidirectional_mask is missing and can't be inferred
-    """
-    if 'bidirectional_mask' not in batch:
-        if batch.get('mode', None) == 'icl_task':
-            batch['bidirectional_mask'] = batch['attention_mask'].clone()
-            for (i, continuation_indices) in enumerate(batch['continuation_indices']):
-                batch['bidirectional_mask'][i, continuation_indices] = 0
-        elif 'labels' in batch and 'attention_mask' in batch:
-            batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask'])
-        else:
-            raise KeyError('No bidirectional_mask in batch and not sure how to construct one.')
\ No newline at end of file
diff --git a/spaces/zhan66/vits-simple-api/vits/bert/__init__.py b/spaces/zhan66/vits-simple-api/vits/bert/__init__.py
deleted file mode 100644
index 6f70b7372c156d3969a2f87328737f63ed4bef71..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-simple-api/vits/bert/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-""" from https://github.com/PlayVoice/vits_chinese """
-import os
-
-import config
-from utils.download import download_and_verify
-from .ProsodyModel import TTSProsody
-
-URLS = [
-    "https://huggingface.co/spaces/maxmax20160403/vits_chinese/resolve/main/bert/prosody_model.pt",
-]
-TARGET_PATH = os.path.join(config.ABS_PATH, "vits/bert/prosody_model.pt")
-EXPECTED_MD5 = None
-
-if not os.path.exists(TARGET_PATH):
-    success, message = download_and_verify(URLS, TARGET_PATH, EXPECTED_MD5)
\ No newline at end of file
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/lib/hooks/use-bing.ts b/spaces/zhaoys/wfms-kuiwenc/src/lib/hooks/use-bing.ts
deleted file mode 100644
index 1b3674281105c026e970ab440bb7c9ab3f873059..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/lib/hooks/use-bing.ts
+++ /dev/null
@@ -1,191 +0,0 @@
-'use client'
-
-import { useState, useCallback, useEffect, useMemo } from 'react'
-import { useAtom, useAtomValue, useSetAtom } from 'jotai'
-import { chatFamily, bingConversationStyleAtom, GreetMessages, hashAtom, voiceAtom, chatHistoryAtom, isImageOnly } from '@/state'
-import { ChatMessageModel, BotId, FileItem } from '@/lib/bots/bing/types'
-import { nanoid } from '../utils'
-import { TTS } from '../bots/bing/tts'
-
-export function useBing(botId: BotId = 'bing') {
-  const chatAtom = useMemo(() => chatFamily({ botId, page: 'singleton' }), [botId])
-  const [chatState, setChatState] = useAtom(chatAtom)
-  const setHistoryValue = useSetAtom(chatHistoryAtom)
-  const [enableTTS] = useAtom(voiceAtom)
-  const speaker = useMemo(() => new TTS(), [])
-  const [hash, setHash] = useAtom(hashAtom)
-  const bingConversationStyle = useAtomValue(bingConversationStyleAtom)
-  const [input, setInput] = useState('')
-  const [attachmentList, setAttachmentList] = useState<FileItem[]>([])
-
-  const updateMessage = useCallback(
-    (messageId: string, updater: (message: ChatMessageModel) => void) => {
-      setChatState((draft) => {
-        const message = draft.messages.find((m) => m.id === messageId)
-        if (message) {
-          updater(message)
-        }
-      })
-    },
-    [setChatState],
-  )
-
-  const sendMessage = useCallback(
-    async (input: string, options = {}) => {
-      const botMessageId = nanoid()
-      const imageUrl = attachmentList?.[0]?.status === 'loaded' ? attachmentList[0].url : undefined
-
-      setChatState((draft) => {
-        const text = imageUrl ? `${input}\n\n![image](${imageUrl})` : input
-        draft.messages.push({ id: nanoid(), text, author: 'user' }, { id: botMessageId, text: '', author: 'bot' })
-        setAttachmentList([])
-      })
-      const abortController = new AbortController()
-      setChatState((draft) => {
-        draft.generatingMessageId = botMessageId
-        draft.abortController = abortController
-      })
-      speaker.reset()
-      await chatState.bot.sendMessage({
-        prompt: input,
-        imageUrl: !isImageOnly && imageUrl && /api\/blob.jpg\?bcid=([^&]+)/.test(imageUrl) ? `https://www.bing.com/images/blob?bcid=${RegExp.$1}` : imageUrl,
-        options: {
-          ...options,
-          bingConversationStyle,
-          conversation: chatState.conversation,
-        },
-        signal: abortController.signal,
-        onEvent(event) {
-          if (event.type === 'UPDATE_ANSWER') {
-            updateMessage(botMessageId, (message) => {
-              if (event.data.text.length > message.text.length) {
-                message.text = event.data.text
-              }
-
-              if (enableTTS) {
-                speaker.speak(message.text)
-              }
-              if (event.data.progressText) {
-                message.progress = [...(message.progress ?? []), event.data.progressText]
-              }
-              message.throttling = event.data.throttling || message.throttling
-              message.sourceAttributions = event.data.sourceAttributions || message.sourceAttributions
-              message.suggestedResponses = event.data.suggestedResponses || message.suggestedResponses
-            })
-          } else if (event.type === 'ERROR') {
-            updateMessage(botMessageId, (message) => {
-              message.error = event.error
-            })
-            setChatState((draft) => {
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          } else if (event.type === 'DONE') {
-            setChatState((draft) => {
-              setHistoryValue({
-                messages: draft.messages,
-              })
-              draft.abortController = undefined
-              draft.generatingMessageId = ''
-            })
-          }
-        },
-      })
-    },
-    [botId, attachmentList, chatState.bot,  chatState.conversation, bingConversationStyle, speaker, setChatState, updateMessage],
-  )
-
-  const uploadImage = useCallback(async (imgUrl: string) => {
-    setAttachmentList([{ url: imgUrl, status: 'loading' }])
-    const response = await chatState.bot.uploadImage(imgUrl, bingConversationStyle)
-    if (response?.blobId) {
-      setAttachmentList([{ url: new URL(`/api/blob.jpg?bcid=${response.blobId}`, location.href).toString(), status: 'loaded' }])
-    } else {
-      setAttachmentList([{ url: imgUrl, status: 'error' }])
-    }
-  }, [chatState.bot])
-
-  const resetConversation = useCallback(() => {
-    chatState.bot.resetConversation()
-    speaker.abort()
-    setChatState((draft) => {
-      draft.abortController = undefined
-      draft.generatingMessageId = ''
-      draft.conversation = {}
-      draft.messages = [{ author: 'bot', text: GreetMessages[Math.floor(GreetMessages.length * Math.random())], id: nanoid() }]
-    })
-  }, [chatState.bot, setChatState])
-
-  const stopGenerating = useCallback(() => {
-    chatState.abortController?.abort()
-    if (chatState.generatingMessageId) {
-      updateMessage(chatState.generatingMessageId, (message) => {
-        if (!message.text && !message.error) {
-          message.text = 'Cancelled'
-        }
-      })
-    }
-    setChatState((draft) => {
-      draft.generatingMessageId = ''
-    })
-  }, [chatState.abortController, chatState.generatingMessageId, setChatState, updateMessage])
-
-  useEffect(() => {
-    if (hash === 'reset') {
-      resetConversation()
-      setHash('')
-    }
-  }, [hash, setHash, resetConversation])
-
-  // useEffect(() => {
-  //   setChatState((draft) => {
-  //     draft.abortController = undefined
-  //     draft.generatingMessageId = ''
-  //     draft.messages = historyValue.history || []
-  //     draft.conversationId = historyValue.conversationId
-  //     setTimeout(() => {
-  //       window.scrollTo({
-  //         top: document.body.offsetHeight,
-  //         behavior: 'smooth'
-  //       })
-  //     }, 1000)
-  //   })
-  // }, [])
-
-  const chat = useMemo(
-    () => ({
-      botId,
-      bot: chatState.bot,
-      isSpeaking: speaker.isSpeaking,
-      messages: chatState.messages,
-      sendMessage,
-      setInput,
-      input,
-      resetConversation,
-      generating: !!chatState.generatingMessageId,
-      stopGenerating,
-      uploadImage,
-      setAttachmentList,
-      attachmentList,
-    }),
-    [
-      botId,
-      bingConversationStyle,
-      chatState.bot,
-      chatState.generatingMessageId,
-      chatState.messages,
-      speaker.isSpeaking,
-      setInput,
-      input,
-      setAttachmentList,
-      attachmentList,
-      resetConversation,
-      sendMessage,
-      stopGenerating,
-    ],
-  )
-
-  return chat
-}
-
-export type BingReturnType = ReturnType<typeof useBing>
diff --git a/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/dataset/pano_s2d3d_dataset.py b/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/dataset/pano_s2d3d_dataset.py
deleted file mode 100644
index b6939fea1a08e5f1c1eb985b85fc739be0c53b04..0000000000000000000000000000000000000000
--- a/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/dataset/pano_s2d3d_dataset.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""
-@date: 2021/6/16
-@description:
-"""
-import math
-import os
-import numpy as np
-
-from dataset.communal.read import read_image, read_label
-from dataset.communal.base_dataset import BaseDataset
-from utils.logger import get_logger
-
-
-class PanoS2D3DDataset(BaseDataset):
-    def __init__(self, root_dir, mode, shape=None, max_wall_num=0, aug=None, camera_height=1.6, logger=None,
-                 split_list=None, patch_num=256, keys=None, for_test_index=None, subset=None):
-        super().__init__(mode, shape, max_wall_num, aug, camera_height, patch_num, keys)
-
-        if logger is None:
-            logger = get_logger()
-        self.root_dir = root_dir
-
-        if mode is None:
-            return
-        label_dir = os.path.join(root_dir, 'valid' if mode == 'val' else mode, 'label_cor')
-        img_dir = os.path.join(root_dir, 'valid' if mode == 'val' else mode, 'img')
-
-        if split_list is None:
-            split_list = [name.split('.')[0] for name in os.listdir(label_dir) if
-                          not name.startswith('.') and name.endswith('txt')]
-
-        split_list.sort()
-
-        assert subset == 'pano' or subset == 's2d3d' or subset is None, 'error subset'
-        if subset == 'pano':
-            split_list = [name for name in split_list if 'pano_' in name]
-            logger.info(f"Use PanoContext Dataset")
-        elif subset == 's2d3d':
-            split_list = [name for name in split_list if 'camera_' in name]
-            logger.info(f"Use Stanford2D3D Dataset")
-
-        if for_test_index is not None:
-            split_list = split_list[:for_test_index]
-
-        self.data = []
-        invalid_num = 0
-        for name in split_list:
-            img_path = os.path.join(img_dir, f"{name}.png")
-            label_path = os.path.join(label_dir, f"{name}.txt")
-
-            if not os.path.exists(img_path):
-                logger.warning(f"{img_path} not exists")
-                invalid_num += 1
-                continue
-            if not os.path.exists(label_path):
-                logger.warning(f"{label_path} not exists")
-                invalid_num += 1
-                continue
-
-            with open(label_path, 'r') as f:
-                lines = [line for line in f.readlines() if
-                         len([c for c in line.split(' ') if c[0].isnumeric()]) > 1]
-                if len(lines) % 2 != 0:
-                    invalid_num += 1
-                    continue
-            self.data.append([img_path, label_path])
-
-        logger.info(
-            f"Build dataset mode: {self.mode} valid: {len(self.data)} invalid: {invalid_num}")
-
-    def __getitem__(self, idx):
-        rgb_path, label_path = self.data[idx]
-        label = read_label(label_path, data_type='Pano_S2D3D')
-        image = read_image(rgb_path, self.shape)
-        output = self.process_data(label, image, self.patch_num)
-        return output
-
-
-if __name__ == '__main__':
-
-    modes = ['test', 'val', 'train']
-    for i in range(1):
-        for mode in modes:
-            print(mode)
-            mp3d_dataset = PanoS2D3DDataset(root_dir='../src/dataset/pano_s2d3d', mode=mode, aug={
-                # 'STRETCH': True,
-                # 'ROTATE': True,
-                # 'FLIP': True,
-                # 'GAMMA': True
-            })
-            continue
-            save_dir = f'../src/dataset/pano_s2d3d/visualization/{mode}'
-            if not os.path.isdir(save_dir):
-                os.makedirs(save_dir)
-
-            bar = tqdm(mp3d_dataset, ncols=100)
-            for data in bar:
-                bar.set_description(f"Processing {data['id']}")
-                boundary_list = depth2boundaries(data['ratio'], data['depth'], step=None)
-                pano_img = draw_boundaries(data['image'].transpose(1, 2, 0), boundary_list=boundary_list, show=False)
-                Image.fromarray((pano_img * 255).astype(np.uint8)).save(
-                    os.path.join(save_dir, f"{data['id']}_boundary.png"))
-
-                floorplan = draw_floorplan(uv2xyz(boundary_list[0])[..., ::2], show=False,
-                                           marker_color=None, center_color=0.8, show_radius=None)
-                Image.fromarray((floorplan.squeeze() * 255).astype(np.uint8)).save(
-                    os.path.join(save_dir, f"{data['id']}_floorplan.png"))
diff --git a/spaces/zhoujiaxin/zhoujiaxinchatgpt/Dockerfile b/spaces/zhoujiaxin/zhoujiaxinchatgpt/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/zhoujiaxin/zhoujiaxinchatgpt/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/zhoupin30/zhoupin30/src/components/button-scroll-to-bottom.tsx b/spaces/zhoupin30/zhoupin30/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/zhoupin30/zhoupin30/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
-  const isAtBottom = useAtBottom()
-
-  return (
-    <Button
-      variant="outline"
-      size="icon"
-      className={cn(
-        'fixed right-4 bottom-24 z-50 bg-background transition-opacity duration-300 sm:right-20',
-        isAtBottom ? 'opacity-0' : 'opacity-100',
-        className
-      )}
-      onClick={() =>
-        window.scrollTo({
-          top: document.body.offsetHeight,
-          behavior: 'smooth'
-        })
-      }
-      {...props}
-    >
-      <IconArrowDown />
-      <span className="sr-only">Scroll to bottom</span>
-    </Button>
-  )
-}
diff --git a/spaces/zzz666/ChuanhuChatGPT/run_macOS.command b/spaces/zzz666/ChuanhuChatGPT/run_macOS.command
deleted file mode 100644
index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000
--- a/spaces/zzz666/ChuanhuChatGPT/run_macOS.command
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$0")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir"
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi