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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>
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-<p>You can now play the album with your favorite music player or transfer it to your mobile device. Enjoy!</p>
-<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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-<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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-<p>Downloading a cracked APK of TextNow may seem like a good way to save money and enjoy unlimited free calls and texts, but there are many drawbacks and dangers associated with it. Here are some of them:</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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-<h1>Free Download Video Game: How to Find and Play the Best Games for Free</h1>
-<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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-<p>Asphalt 9: Legends is a free download video game for PC that lets you experience the thrill of racing in some of the most amazing cars in the world. You can choose from over 50 models of cars from famous brands like Ferrari, Lamborghini, Porsche, and more. You can customize your car with different colors, decals, and parts. You can also compete with other players online or offline in various modes and events. You can perform stunning stunts and drifts on realistic tracks and environments. Asphalt 9: Legends is a fast-paced and adrenaline-pumping game that will keep you on the edge of your seat.</p>
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-<p>Roblox is a free download video game for PC that lets you create and play millions of games with other players around the world. Roblox is a platform where you can unleash your imagination and creativity. You can use the Roblox Studio to design your own games, or play games made by other users. You can explore different genres and themes of games, such as adventure, role-playing, simulation, horror, and more. You can also chat and socialize with other players, join groups, and earn virtual currency. Roblox is a game that will never get boring, as there is always something new and exciting to discover.</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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-<h1>General Knowledge Quiz Book PDF Free Download</h1>
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-<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 />

spaces/7hao/bingo/src/lib/isomorphic/index.ts DELETED Viewed

@@ -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

spaces/A00001/bingothoo/src/components/welcome-screen.tsx DELETED Viewed

@@ -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>
-  )
-}

spaces/AIWaves/Software_Company/src/agents/Action/base_action.py DELETED Viewed

@@ -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

spaces/Abdulkader/HumanMotionsDetector/README.md DELETED Viewed

@@ -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

spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/midas/midas_net.py DELETED Viewed

@@ -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)

spaces/AgentVerse/agentVerse/agentverse/memory_manipulator/__init__.py DELETED Viewed

@@ -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

spaces/AgentVerse/agentVerse/scripts/evaluate_commongen.py DELETED Viewed

@@ -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)

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/japanese.py DELETED Viewed

@@ -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

spaces/AlanMars/QYL-AI-Space/run_Linux.sh DELETED Viewed

@@ -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

spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py DELETED Viewed

@@ -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)))

spaces/Andy1621/uniformer_image_detection/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py DELETED Viewed

@@ -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, ), ()]))

spaces/Andy1621/uniformer_image_detection/configs/legacy_1.x/README.md DELETED Viewed

@@ -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) |

spaces/Andy1621/uniformer_image_detection/configs/pisa/pisa_ssd512_coco.py DELETED Viewed

@@ -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))

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py DELETED Viewed

@@ -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))

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/dataloader/data_loader.py DELETED Viewed

@@ -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)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/wrappers.py DELETED Viewed

@@ -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)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/__init__.py DELETED Viewed

@@ -1,3 +0,0 @@
-from .evaluation import *  # noqa: F401, F403
-from .seg import *  # noqa: F401, F403
-from .utils import *  # noqa: F401, F403

spaces/Anonymous-sub/Rerender/ControlNet/ldm/models/diffusion/sampling_util.py DELETED Viewed

@@ -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)

spaces/AquaSuisei/ChatGPTXE/modules/llama_func.py DELETED Viewed

@@ -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

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/hooks.py DELETED Viewed

@@ -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

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/filepost.py DELETED Viewed

@@ -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

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/roi_heads/custom_roi_heads.py DELETED Viewed

@@ -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, {}

spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/summarize/+server.ts DELETED Viewed

@@ -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" } }
-	);
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/latex.py DELETED Viewed

@@ -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

spaces/BigSalmon/GPT2_Most_Probable/app.py DELETED Viewed

@@ -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)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/pascal_voc.py DELETED Viewed

@@ -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
-    )

spaces/CVPR/LIVE/thrust/thrust/detail/malloc_and_free.h DELETED Viewed

@@ -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

spaces/CVPR/LIVE/thrust/thrust/iterator/reverse_iterator.h DELETED Viewed

@@ -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>

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/reduce.h DELETED Viewed

@@ -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>

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/binary_search.h DELETED Viewed

@@ -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

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/equal.h DELETED Viewed

@@ -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

spaces/CVPR/ml-talking-face/translator/module.py DELETED Viewed

@@ -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_

spaces/CarlDennis/Lovelive-VITS-JPZH/text/japanese.py DELETED Viewed

@@ -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

spaces/CarperAI/pile-v2-eda/load_dataset.py DELETED Viewed

@@ -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)

spaces/DataDreamweavers/LegaWeaver/app.py DELETED Viewed

@@ -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.
-summary: users should revisit the terms periodically although in case of material changes the service will notify.
-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.
-summary: the service can delete your account without prior notice and without a reason.
-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.
-summary: invalidity of any portion of the terms of service does not entail invalidity of its remainder.
-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.
-summary: third parties may be involved in operating the service.
-document: you also agree to register one account only for your use of the site. the site administrators reserve the right to ban users because of accessing the site with more than one account. the information we obtain through your use of this site including your registration data is subject to our privacy policy which is specifically incorporated by reference into these terms of use.
-summary: service does not allow alternative accounts.
-document: personal data that you transmit are never disclosed or sold by qwant to third parties except for job applications that may be shared with recruiting partners unless you ask us not to.
-summary: this service does not sell your personal data.
-document: please note that this privacy policy may change from time to time. we will not reduce your rights under this policy without your explicit consent and we expect most such changes will be minor. regardless we will post any policy changes on this page. we may amend this privacy policy at any time by posting the amend terms on the site. all amended terms shall automatically be effective thirty 30 days after they are initially posted on the site. each version of this policy will be identified at the top of the page by its effective date.
-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()

spaces/Dinoking/Guccio-AI-Designer/netdissect/workerpool.py DELETED Viewed

@@ -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)

spaces/DragGan/DragGan-Inversion/torch_utils/ops/upfirdn2d.h DELETED Viewed

@@ -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);
-//------------------------------------------------------------------------

spaces/ECCV2022/ECCV2022_papers/paper_list.py DELETED Viewed

@@ -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

spaces/Eieichicken/yyayyaya/README.md DELETED Viewed

@@ -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

spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/models/__init__.py DELETED Viewed

@@ -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]

spaces/Enigma007/Classifier-Fasttext/README.md DELETED Viewed

@@ -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

spaces/EtTKSf/uu/README.md DELETED Viewed

@@ -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

spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/README.md DELETED Viewed

@@ -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

spaces/EuroPython2022/automatic-speech-recognition-with-next-gen-kaldi/examples.py DELETED Viewed

@@ -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",
-    ],
-]

spaces/Everymans-ai/GPT-knowledge-management/README.md DELETED Viewed

@@ -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