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spaces/1acneusushi/gradio-2dmoleculeeditor/Bioquimica De Harper 15 Edicion Pdf 123.md

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-## Bioquimica De Harper 15 Edicion Pdf 123
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-**Download File ===== [https://www.google.com/url?q=https%3A%2F%2Ftinurll.com%2F2txKMT&sa=D&sntz=1&usg=AOvVaw3n4oLhS1Ahd7NcDo9vj3Qn](https://www.google.com/url?q=https%3A%2F%2Ftinurll.com%2F2txKMT&sa=D&sntz=1&usg=AOvVaw3n4oLhS1Ahd7NcDo9vj3Qn)**
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-# Bioquimica De Harper 15 Edicion Pdf 123
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-Bioquimica De Harper 15 Edicion Pdf 123 is a keyword that refers to a book of biochemistry written by Robert K. Murray, Victor W. Rodwell, David A. Bender, Kathleen M. Botham, Peter J. Kennelly, P. Anthony Weil and published by McGraw-Hill Education. The book is also known as Harper's Illustrated Biochemistry and is one of the most widely used textbooks in the field of biochemistry.
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-The book covers the basic principles of biochemistry and its applications to human health and disease. It includes topics such as molecular biology, metabolism, nutrition, genetics, cell signaling, hormones, immunology, and more. The book also features clinical cases, illustrations, tables, diagrams, and summaries to help students understand and apply the concepts.
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-The 15th edition of the book was published in 1996 and has 123 chapters. It is available in PDF format for free download from various online sources[^1^] [^2^]. However, the latest edition of the book is the 31st edition, which was published in 2018 and has 832 pages. The 31st edition has been updated with the latest advances in biochemistry research and clinical practice.
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-Biochemistry is a branch of science that explores the chemical processes within and related to living organisms. It is a laboratory-based science that combines chemistry and biology. By using chemical knowledge and techniques, biochemists can understand and solve biological problems. [^1^]
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-Biochemistry has a wide range of applications in fields such as medicine, genetics, biotechnology, agriculture, and environmental science. Biochemists can study the molecular structure and function of biomolecules, such as proteins, nucleic acids, carbohydrates, and lipids. They can also investigate how these molecules interact with each other and with other cellular components, such as membranes, organelles, and enzymes. They can also examine how these interactions regulate cellular processes, such as metabolism, signaling, transcription, translation, and replication. [^2^]
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-Biochemistry is a young science that emerged in the 20th century as a distinct discipline from physiology and chemistry. However, its roots can be traced back to ancient times, when people observed and experimented with natural phenomena related to life. Some of the pioneers of biochemistry include Robert Boyle, Antoine Lavoisier, Justus von Liebig, Louis Pasteur, Emil Fischer, Eduard Buchner, Frederick Sanger, James Watson, Francis Crick, and many others. [^3^]
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-Biochemistry has many applications in different fields of science and society. Some examples of biochemistry research are:
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-- The discovery of the structure and function of DNA, RNA, and proteins, which are the key molecules of life and heredity.
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-- The development of new drugs and vaccines based on the understanding of biochemical pathways and targets.
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-- The engineering of enzymes and microorganisms for biotechnology and bioremediation purposes.
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-- The investigation of the molecular mechanisms of diseases such as cancer, diabetes, Alzheimer's, and COVID-19.
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-- The analysis of the biochemical composition and diversity of living organisms and ecosystems.
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download 3ds Max 2012 11 Everything You Need to Know About the Latest Version of the Industry-Leading 3D Software.md

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-<h1>Download 3ds Max 2012 11: A Step-by-Step Guide</h1>
- <p>If you are looking for a way to create stunning 3D graphics, animations, and renderings, you might want to consider using <strong>3ds Max</strong>, one of the most popular and powerful software in the industry. Whether you are a professional or a hobbyist, you can use 3ds Max to bring your ideas to life.</p>
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- <p>By the end of this article, you will be able to use 3ds Max 2012 11 to create amazing 3D projects. So let's get started!</p>
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- <p><strong>3ds Max</strong> has been around since <strong>1996</strong>, when it was first released by Autodesk. Since then, it has been updated regularly with new features and improvements. The latest version of this software is <strong>2024</strong>, which was released in <strong>2021</strong>. However, some users might prefer to use older versions of the software for various reasons.</p>
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- <ul>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download VMware 7 ISO and Experience the Power of VMware Workstation 16 Pro.md

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-<h1>How to Download VMware 7 ISO for Free</h1>
-<p>If you are looking for a way to run multiple operating systems on your PC, you might want to download VMware 7 ISO for free. VMware 7 is a virtualization software that allows you to create and manage virtual machines on your computer. You can use it to test new software, run legacy applications, or experiment with different operating systems without affecting your main system.</p>
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-<h2>What is VMware 7 ISO?</h2>
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-<p>Congratulations! You have successfully downloaded VMware 7 ISO for free. You can now use it to install VMware 7 on your PC or create a bootable USB drive or DVD.</p>
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-<p>To install VMware 7 from ISO, you need to have a PC that meets the minimum system requirements for VMware 7. These are:</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Downloadbotautohuntperfectworld.md

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-<h1>What is downloadbotautohuntperfectworld and why you need it</h1>
-<p>If you are a fan of Perfect World, a popular MMORPG game, you might have heard of downloadbotautohuntperfectworld. This is a tool that allows you to automate your hunting and questing activities in the game, without having to manually control your character. In this article, we will explain what downloadbotautohuntperfectworld is, how it works, what are its benefits, how to download and use it, where to find it, and what are the risks and precautions of using it.</p>
- <h2>How downloadbotautohuntperfectworld works</h2>
-<p>Downloadbotautohuntperfectworld is a program that uses Selenium Webdriver, a framework for automating web browser actions, to interact with the Perfect World game client. It can perform various tasks such as moving, attacking, looting, healing, buffing, using skills, completing quests, and more. It can also detect enemies, NPCs, items, and other objects in the game environment. It can run in the background while you do other things on your computer, or you can watch it play on your screen.</p>
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- <h3>The benefits of using downloadbotautohuntperfectworld</h3>
-<p>Using downloadbotautohuntperfectworld can bring you many advantages in Perfect World. Here are some of them:</p>
- <h4>Save time and energy</h4>
-<p>Perfect World is a game that requires a lot of time and effort to progress and level up your character. You have to grind for hours to complete quests, kill monsters, collect items, craft equipment, and more. This can be tedious and boring, especially if you have other things to do in real life. With downloadbotautohuntperfectworld, you can let the bot do all the work for you while you relax or focus on other tasks. You can also set the bot to run overnight or when you are away from your computer, so you can wake up or come back to a higher level character with more resources and rewards.</p>
- <h4>Enhance your gaming experience</h4>
-which skills to use or avoid, which items to keep or discard, and more. You can also switch between manual and automatic mode anytime you want.</p>
- <h4>Avoid bans and detection</h4>
-<p>One of the main concerns of using bots in online games is getting banned or detected by the game developers or administrators. This can result in losing your account, your progress, your items, and your reputation. However, with downloadbotautohuntperfectworld, you don't have to worry about this. Downloadbotautohuntperfectworld is designed to be undetectable by Perfect World's anti-cheat system. It does not inject any code into the game client or modify any game files. It also mimics human-like behavior and movements to avoid suspicion. It also has features such as auto-restart, auto-login, auto-repair, auto-sell, and more to prevent any errors or glitches that might expose the bot.</p>
- <h3>How to download and use downloadbotautohuntperfectworld</h3>
-<p>If you are interested in trying downloadbotautohuntperfectworld, here are the steps you need to follow:</p>
- <h4>Requirements and compatibility</h4>
-<p>Before you download downloadbotautohuntperfectworld, you need to make sure that your computer meets the minimum requirements for running the bot. You need to have:</p>
-<ul>
-<li>A Windows operating system (Windows 7 or higher)</li>
-<li>A Perfect World game client (any version)</li>
-<li>A Selenium Webdriver (Chrome or Firefox)</li>
-<li>A Python interpreter (version 3.6 or higher)</li>
-<li>A stable internet connection</li>
-</ul>
-<p>You also need to make sure that your Perfect World account is not banned or suspended, and that you have enough space on your hard drive for the bot files.</p>
- <h4>Installation and configuration</h4>
-<p>Once you have the requirements ready, you can proceed to download downloadbotautohuntperfectworld from one of the sources listed below. You will receive a zip file containing the bot files and a readme file with instructions. You need to extract the zip file to a folder of your choice and open the readme file for further guidance. You will need to edit some configuration files to set up your bot's preferences and settings. For example, you will need to enter your Perfect World account information, your character name, your hunting location, your quest list, your skill list, your item list, and more. You can also adjust other options such as the bot's speed, delay, mode, and more.</p>
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-<p>After you have installed and configured downloadbotautohuntperfectworld, you can start using it by running the main script file. The bot will launch the Perfect World game client and log in to your account automatically. It will then start performing the tasks you have assigned it according to your settings. You can monitor the bot's progress on your screen or on a separate window that shows the bot's logs and messages. You can also pause, resume, or stop the bot anytime you want by pressing certain keys on your keyboard. Here are some tips and tricks for using downloadbotautohuntperfectworld effectively:</p>
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-<li>Make sure that your computer is not overloaded with other programs or processes that might interfere with the bot's performance.</li>
-<li>Make sure that your internet connection is stable and fast enough to avoid lagging or disconnecting issues.</li>
-<li>Make sure that your game client is updated to the latest version and compatible with the bot's version.</li>
-<li>Make sure that you have enough inventory space and currency for looting and selling items.</li>
-<li>Make sure that you have enough potions and consumables for healing and buffing yourself.</li>
-<li>Make sure that you have enough skill points and cultivation points for upgrading your skills and meridians.</li>
-<li>Make sure that you are not in a crowded or contested area where other players might attack or report you.</li>
-<li>Make sure that you are not violating any game rules or terms of service by using the bot.</li>
-</ul>
- <h2>Where to find downloadbotautohuntperfectworld</h2>
-, you might be wondering where to look. There are many websites and forums that claim to offer downloadbotautohuntperfectworld, but not all of them are trustworthy or safe. Some of them might contain malware, viruses, or outdated versions of the bot that might harm your computer or your account. To avoid these risks, you should only download downloadbotautohuntperfectworld from reputable and verified sources that have positive reviews and feedback from other users. Here are some of the best sources for downloadbotautohuntperfectworld:</p>
- <h3>The best sources for downloadbotautohuntperfectworld</h3>
-<p>These are some of the websites and forums that offer downloadbotautohuntperfectworld with high quality and security:</p>
- <h4>elitepvpers.com</h4>
-<p>This is one of the most popular and trusted forums for all kinds of MMORPG hacks, cheats, and bots. You can find downloadbotautohuntperfectworld in the Perfect World section of the forum, along with other tools and guides for the game. You can also interact with other users and get support and feedback from them. You need to register an account to access the forum and download the bot.</p>
- <h4>vegetarentusiast.no</h4>
-<p>This is a website that offers downloadbotautohuntperfectworld as a PDF file that you can download for free. The PDF file contains a link to a torrent file that contains the bot files and instructions. You need to have a torrent client to download the bot files. The website also provides some information and tips about downloadbotautohuntperfectworld and Perfect World.</p>
- <h4>trello.com</h4>
-<p>This is a website that offers downloadbotautohuntperfectworld as a Trello board that you can access for free. The Trello board contains a link to a Google Drive folder that contains the bot files and instructions. You need to have a Google account to access the folder and download the bot files. The Trello board also provides some updates and news about downloadbotautohuntperfectworld and Perfect World.</p>
- <h3>The risks and precautions of using downloadbotautohuntperfectworld</h3>
-<p>While using downloadbotautohuntperfectworld can be beneficial and enjoyable, it also comes with some risks and challenges that you should be aware of and prepared for. Here are some of them:</p>
- <h4>Potential malware and viruses</h4>
-<p>As mentioned earlier, not all sources for downloadbotautohuntperfectworld are safe and reliable. Some of them might contain malicious software that can infect your computer or steal your personal information. To avoid this, you should always scan the bot files with an antivirus program before installing or running them. You should also avoid clicking on any suspicious links or pop-ups that might appear while using the bot.</p>
- <h4>Legal and ethical issues</h4>
-<p>Using bots in online games is generally considered cheating and unfair by the game developers and administrators, as well as by other players who play by the rules. This can result in legal actions or penalties against you or your account, such as bans, suspensions, fines, or lawsuits. To avoid this, you should always follow the game's terms of service and code of conduct while using the bot. You should also respect other players' rights and feelings while playing the game.</p>
- <h4>How to protect yourself and your account</h4>
-, you should take some precautions and measures to protect yourself and your account. Here are some of them:</p>
-<ul>
-<li>Use a VPN or proxy service to hide your IP address and location while using the bot.</li>
-<li>Use a separate or disposable email address and password for your Perfect World account and your bot account.</li>
-<li>Use a different character name and appearance for your bot character than your main character.</li>
-<li>Use the bot only for a limited amount of time per day or per week, and vary your schedule and activities.</li>
-<li>Do not use the bot in public or crowded areas where other players might notice or report you.</li>
-<li>Do not brag or boast about using the bot or your achievements in the game.</li>
-<li>Do not abuse or harass other players or interfere with their gameplay while using the bot.</li>
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- <p>If you are looking for a library of authentic and diverse ethnic winds for your music production, you might want to check out ETHNAUDIO Breath of Anatolia (KONTAKT). This product is a collection of 15+ ethnic winds from Turkey and neighboring regions, such as doudouk, kaval, kawala, mey, ney, tulum, Turkish clarinet and zurna. You can use these instruments to create rich and realistic melodies, harmonies and soundscapes for your musical projects. In this article, we will review ETHNAUDIO Breath of Anatolia (KONTAKT) and tell you everything you need to know about it.</p>
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- <p>ETHNAUDIO Breath of Anatolia (KONTAKT) is a library of different ethnic winds that you can use with Kontakt or Kontakt Player, which are Native Instruments products. Kontakt is a powerful sampler that allows you to load and play various sounds and instruments with high quality and flexibility. Kontakt Player is a free version of Kontakt that you can download from Native Instruments website.</p>
- <p>ETHNAUDIO Breath of Anatolia (KONTAKT) includes over 1 GB of data, featuring 15+ ethnic winds from Anatolia and surrounding regions. Each instrument has its own unique sound, character and expression. You can control various parameters such as volume, pan, reverb, delay, microtonal tuning, effects and more. You can also switch between different articulations such as legato, staccato, vibrato and zone.</p>
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-<li>You can add a touch of exoticism and diversity to your music with these ethnic winds. They can create a sense of atmosphere, emotion and culture for your listeners.</li>
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- <ol>
-<li>Download ETHNAUDIO Breath of Anatolia (KONTAKT) from the official website or an authorized dealer. You will receive a zip file containing the library files.</li>
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-<li>Open Kontakt or Kontakt Player on your computer. You can download Kontakt Player for free from Native Instruments website if you don't have it already.</li>
-<li>Add ETHNAUDIO Breath of Anatolia (KONTAKT) library to your Kontakt libraries tab by clicking on the "Add Library" button. You will need to locate the folder where you extracted the library files.</li>
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-<li>Load any instrument from ETHNAUDIO Breath of Anatolia (KONTAKT) library by double-clicking on its name in the libraries tab. You will see its interface on the main window.</li>
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-</ol>
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-<tr><th>Parameter</th><th>Value</th></tr>
-<tr><td>Operating System</td><td>Windows XP/Vista/7/8/8.1/10 or Mac OS X 10.9 or higher</td></tr>
-<tr><td>Memory (RAM)</td><td>1 GB or more</td></tr>
-<tr><td>Hard Disk Space</td><td>1.5 GB or more</td></tr>
-<tr><td>Processor</td><td>Intel Dual Core processor or higher</td></tr>
-<tr><td>Kontakt Version</td><td>Kontakt 5.6.5 or higher (full or player)</td></tr>
-<tr><td>Library Size</td><td>1.1 GB uncompressed</td></tr>
-<tr><td>Instruments</td><td>15+ ethnic winds from Turkey and neighboring regions</td></tr>
-<tr><td>Samples</td><td>24 bit / 44.1 kHz stereo WAV format</td></tr>
-<tr><td>User Interface</td><td>Modern intuitive design with easy access to parameters</td></tr>
-<tr><td>NKS Compatibility</td><td>Yes (version 2.0)</td></tr>
-<tr><td>Price</td><td>$159 regular price / $139 discounted price</td></tr>
-<tr><td>Contact Details</td><td>Email: [email protected] / Website: https://ethnaudio.com / Facebook: https://www.facebook.com/ethnaudio / Twitter: https://twitter.com/ethnaudio / YouTube: https://www.youtube.com/channel/UCYwXZqQnJmQx9t0mQZVwLjg </td></tr>
-</table>
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- of Anatolia (KONTAKT) sounds like, you can check out some examples of music created with this library. Here are some links to YouTube videos that showcase the product's ethnic winds library:</p>
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-<li><a href="https://www.youtube.com/watch?v=6Ygq7Z0Jl9g">ETHNAUDIO Breath Of Anatolia Demo</a> by Ethnaudio</li>
-<li><a href="https://www.youtube.com/watch?v=0w1y8fQ2OZw">ETHNAUDIO Breath Of Anatolia - Doudouk A Demo</a> by Ethnaudio</li>
-<li><a href="https://www.youtube.com/watch?v=4W1J3ZnQyqo">ETHNAUDIO Breath Of Anatolia - Kaval Demo</a> by Ethnaudio</li>
-<li><a href="https://www.youtube.com/watch?v=9zXVx8mz5uI">ETHNAUDIO Breath Of Anatolia - Mey A Demo</a> by Ethnaudio</li>
-<li><a href="https://www.youtube.com/watch?v=7f6L4u0oFtM">ETHNAUDIO Breath Of Anatolia - Ney A Demo</a> by Ethnaudio</li>
-<li><a href="https://www.youtube.com/watch?v=HcKXr2jx4dE">ETHNAUDIO Breath Of Anatolia - Turkish Clarinet Demo</a> by Ethnaudio</li>
-<li><a href="https://www.youtube.com/watch?v=3sGvLsWwU6k">ETHNAUDIO Breath Of Anatolia - Zurna A Demo</a> by Ethnaudio</li>
-</ul>
- <p>You can also visit the product's website and listen to more demos and testimonials from other users and customers.</p>
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- <p>In conclusion, ETHNAUDIO Breath of Anatolia (KONTAKT) is a library of 15+ ethnic winds from Turkey and neighboring regions that you can use with Kontakt or Kontakt Player. It is a great product for music producers and composers who want to add a touch of exoticism and diversity to their music with these authentic and realistic instruments. It has many features and benefits such as microtonal tuning, effects, NKS compatibility and more. It is easy to install and use, and it has a reasonable price and customer support.</p>
- <p>If you want to buy ETHNAUDIO Breath of Anatolia (KONTAKT), you can get it from the official website or an authorized dealer or reseller at a discounted price of $139 before the end of the month. You can also try your luck in a giveaway or contest that may be organized by the product's developers or partners. However, you should avoid downloading illegal copies of the product from untrusted sources, as they may harm your computer and personal information.</p>
- <p>If you have any questions, issues or feedback about ETHNAUDIO Breath of Anatolia (KONTAKT), you can contact ETHNAUDIO by email, website or social media. They will be happy to help you and improve their products.</p>
- <p>We hope this article has been helpful and informative for you. If you are interested in ETHNAUDIO Breath of Anatolia (KONTAKT), don't hesitate to get it now and enjoy creating amazing music with these ethnic winds.</p>
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-<li><b>What is Kontakt?</b><br>Kontakt is a powerful sampler that allows you to load and play various sounds and instruments with high quality and flexibility. It is developed by Native Instruments, a leading company in music technology.</li>
-<li><b>Do I need Kontakt to use ETHNAUDIO Breath of Anatolia?</b><br>You need Kontakt 5.6.5 or higher (full or player) to use ETHNAUDIO Breath of Anatolia (KONTAKT). You can download Kontakt Player for free from Native Instruments website if you don't have it already.</li>
-<li><b>What are ethnic winds?</b><br>Ethnic winds are musical instruments that produce sound by blowing air into them. They are usually made of wood, metal or clay, and they have different shapes, sizes and sounds depending on their origin and culture.</li>
-<li><b>What are microtonal tuning and NKS compatibility?</b><br>Microtonal tuning is a feature that allows you to tune each note or key of an instrument according to different scales or modes that are different from the Western music system. NKS compatibility is a feature that allows you to integrate your instrument with Native Instruments hardware such as keyboards or controllers.</li>
-<li><b>How can I learn more about ETHNAUDIO Breath of Anatolia?</b><br>You can learn more about ETHNAUDIO Breath of Anatolia (KONTAKT) by visiting their website, watching their videos, reading their testimonials, contacting them for support and feedback, or trying their demos.</li>
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-<p>If you are a fan of match-three puzzle games, you have probably heard of Candy Crush Saga, one of the most popular and addictive games of its genre. But did you know that there is a way to enjoy the game even more, with unlimited lives, boosters, and other perks? That's right, we are talking about Candy Crush Saga Mod Apk, a modified version of the original game that gives you access to all the features and levels without spending a dime. In this article, we will tell you everything you need to know about Candy Crush Saga Mod Apk, including what it is, how to download and install it, and how to play it like a pro.</p>
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-<p>Candy Crush Saga is a free-to-play tile-matching video game released by King in 2012. It is available for various platforms, such as Facebook, iOS, Android, Windows Phone, and Windows 10. It is a variation of their browser game Candy Crush.</p>
- <h3>The basics of the game</h3>
-<p>The game's premise is simple. You have a level full of candies. Match three or more candies of the same color to clear them from the board and score points. You can also create special candies by matching four or more candies in different ways. These special candies have different effects when matched, such as clearing a row, column, or other section of the board. You have a limited number of moves or time to complete each level's objective, which can vary from reaching a certain score to clearing all the jelly blocks.</p>
- <h3>The different level types and objectives</h3>
-<p>There are five types of levels in Candy Crush Saga, each with its own color and objective:</p>
-<ul>
-<li>Moves Levels are colored orange. You just have to reach the target score in a limited number of moves.</li>
-<li>Jelly Levels are colored blue. You have to clear all the jelly blocks in a limited number of moves.</li>
-<li>Ingredient Levels are colored green. You have to get the cherries or hazelnuts to certain spaces on the board within a limited number of moves.</li>
-<li>Time Levels are colored purple. You have to reach the target score in a limited amount of time.</li>
-<li>Color Order Levels are colored pink. You have to collect a specific number of candies in a certain order within a limited number of moves.</li>
-</ul>
- <h2>What is Candy Crush Saga Mod Apk?</h2>
-<p>Candy Crush Saga Mod Apk is a modified version of the original game that has been altered to include additional features, such as unlimited lives and boosters that can help you progress through levels faster. The mod apk also removes all advertisements from the game so you won't be interrupted while playing.</p>
- <h3>The benefits of using the mod apk</h3>
-<p>There are many benefits of using the mod apk over the original game, such as:</p>
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-<li>You can use any booster you want without spending real money or gold bars.</li>
-<li>You can skip hard levels or retry failed levels without losing lives or boosters.</li>
-<li>You can enjoy the game without annoying ads or pop-ups.</li>
-</ul>
- <h3>The features of the mod apk</h3>
-<p>The mod apk has many features that make it different from the original game, such as:</p>
-<ul>
-<li>All levels and episodes are unlocked.</li>
-<li>All boosters are unlocked and unlimited.</ <h2>How to Download and Install Candy Crush Saga Mod Apk?</h2>
-<p>If you are interested in trying out the mod apk version of Candy Crush Saga, you will need to follow some simple steps to download and install it on your device. Here is how you can do it:</p>
- <h3>The steps to download and install the mod apk</h3>
-<ol>
-<li>First, you will need to uninstall the original Candy Crush Saga game from your device, if you have it installed. This is because the mod apk will replace the original game and you cannot have both versions at the same time.</li>
-<li>Next, you will need to find a reliable source to download the mod apk file. You can use the link provided by  or search for other websites that offer the mod apk. Make sure you download the latest version of the mod apk, which is 1.254.2.1 as of this writing.</li>
-<li>Once you have downloaded the mod apk file, you will need to enable the installation of apps from unknown sources on your device. To do this, go to your device's settings, then security, and then toggle on the option that allows installing apps from unknown sources.</li>
-<li>Now, you can go to your device's file manager and locate the mod apk file that you downloaded. Tap on it and follow the instructions to install it on your device.</li>
-<li>After the installation is complete, you can launch the game and enjoy all the features of the mod apk.</li>
-</ol>
- <h3>The precautions to take before installing the mod apk</h3>
-<p>While installing and using the mod apk can be fun and easy, there are some risks and drawbacks that you should be aware of before doing so. Here are some precautions that you should take before installing the mod apk:</p>
-<ul>
-<li>Make sure you back up your progress and data from the original game before uninstalling it. You can do this by connecting your game to Facebook or using other cloud services. This way, you can restore your progress if you decide to switch back to the original game or if something goes wrong with the mod apk.</li>
-<li>Be careful about where you download the mod apk file from. Some websites may offer fake or malicious files that can harm your device or steal your personal information. Only download from trusted sources and scan the file with an antivirus before installing it.</li>
-<li>Be aware that using the mod apk may violate the terms of service of Candy Crush Saga and King. This means that you may face consequences such as losing your account, getting banned, or facing legal action. Use the mod apk at your own risk and discretion.</li>
-</ul>
- <h2>Tips and Tricks to Play Candy Crush Saga Like a Pro</h2>
-<p>Now that you have installed the mod apk version of Candy Crush Saga, you may be wondering how to make the most of it and play like a pro. Here are some tips and tricks that can help you improve your skills and score higher in the game:</p>
- <h3>The best and worst combos to use</h3>
-<p>As we mentioned earlier, combining two special candies can create a powerful effect that can clear a lot of candies from the board. However, not all combos are equally useful or effective. Here are some of the best and worst combos to use in Candy Crush Saga:</p>
- <table>
-<tr><th>Best Combos</th><th>Worst Combos</th></tr>
-<tr><td>Striped + Wrapped: This combo clears three rows and three columns of candies, creating a huge explosion that can help you complete any level objective.</td><td>Wrapped + Wrapped: This combo only destroys eight candies around each wrapped candy twice, which is not very impressive compared to other combos.</td></tr>
-<tr><td>Striped + Color Bomb: This combo turns all candies of the same color as the striped candy into striped candies, and then activates them all at once. This can clear almost half of the board in one move.</td><td>Color Bomb + Wrapped: This combo turns all candies of the same color as the wrapped candy into wrapped candies, and then activates them all at once. This can create a lot of explosions, but they are not very effective at clearing jellies or ingredients.</td></tr>
-<tr><td>Color Bomb + Color Bomb: This combo clears all candies from the board, giving you a huge score boost and making any level objective easier to achieve.</td><td>Striped + Striped: This combo clears one row and one column of candies, which is not very impressive compared to other combos. It can be useful in some situations, but it is better to save your striped candies for other combos.</td></tr>
-</table>
- <h3>The strategies to clear levels faster and score higher</h3>
-<p>Apart from using special candies and combos, there are some <p>strategies that can help you clear levels faster and score higher in Candy Crush Saga. Here are some of them:</p>
-<ul>
-<li>Focus on the level objective. Don't waste your moves or time on clearing candies that are not related to the level objective. For example, if you need to clear jelly blocks, focus on matching candies on or near them, rather than on the other side of the board.</li>
-<li>Plan your moves ahead. Try to think of the consequences of each move before you make it. Look for opportunities to create special candies or combos, or to clear obstacles or blockers. Also, try to avoid moves that can ruin your chances of creating special candies or combos in the future.</li>
-<li>Use boosters wisely. Boosters can be very helpful in some levels, but they are not unlimited. You can either buy them with real money or gold bars, or earn them by completing quests or events. Therefore, you should use them sparingly and only when you really need them. For example, you can use a lollipop hammer to clear a stubborn candy that is preventing you from completing the level, or a free switch to swap two candies that can create a powerful combo.</li>
-<li>Learn from your mistakes. If you fail a level, don't give up or get frustrated. Instead, try to analyze what went wrong and what you can do better next time. You can also watch videos of other players who have completed the level and learn from their strategies and moves.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Candy Crush Saga is a fun and addictive game that can keep you entertained for hours. However, if you want to enjoy the game even more, you can try the mod apk version that gives you unlimited lives, boosters, and other perks. In this article, we have explained what Candy Crush Saga Mod Apk is, how to download and install it, and how to play it like a pro. We hope you found this article helpful and informative. Now go ahead and crush some candies!</p>
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-<p>Here are some frequently asked questions about Candy Crush Saga Mod Apk:</p>
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-<p>Yes, Candy Crush Saga Mod Apk is safe to use as long as you download it from a trusted source and scan it with an antivirus before installing it. However, you should be aware that using the mod apk may violate the terms of service of Candy Crush Saga and King, and you may face consequences such as losing your account, getting banned, or facing legal action.</p>
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-<p>No, Candy Crush Saga Mod Apk is not compatible with the online features of the original game. You cannot connect your game to Facebook or play with your friends or other players online. You can only play offline with the mod apk.</p>
-<li>Can I update Candy Crush Saga Mod Apk to the latest version?</li>
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-<p>Championship Manager 01/02 is a legendary football (or soccer) management game that retains a considerable fan base. It might not include a fancy 3D match engine, but for many fans this is the greatest game of its series that most of the more recent Football Manager titles have not eclipsed. This is a footie management game that gets the balance between realistic simulation and accessibility just right.</p>
-<p>If you're one of those fans who still enjoy playing CM 01/02, or if you're curious about what makes this game so special, you might be wondering how to play it on your Android device. After all, playing on a mobile device has many advantages, such as portability, convenience, and battery life. Luckily, there are ways to play CM 01/02 on Android, and in this article, we'll show you how.</p>
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-<p>The first thing you need to do is to download and install an Android emulator on your PC or Mac. An emulator is a software that mimics the Android operating system on your computer, allowing you to run Android apps and games. There are many emulators available, but we recommend <a href="(^8^)">BlueStacks</a>, which is one of the most popular and easy-to-use options.</p>
-<p>Once you have BlueStacks installed, you need to download the CM 01/02 ISO file from the <a href="(^11^)">Champman 01/02 website</a>. This is a free download that contains the original game files. You'll also need a software called Daemon Tools Lite, which can mount the ISO file as a virtual CD drive on your computer.</p>
-<p>Here are the steps to follow:</p>
-<ol>
-<li>Open BlueStacks and sign in with your Google account.</li>
-<li>Go to <a href="(^11^)">Champman 01/02 website</a> and click on Downloads.</li>
-<li>Click on Championship Manager 01/02 - Official Download - Attempt #2.</li>
-<li>Click on Download Now and save the ZIP file on your computer.</li>
-<li>Extract the ZIP file using WinRAR or any other extraction tool.</li>
-<li>You should see a file called CM0102.iso. This is the ISO file that contains the game.</li>
-<li>Go to <a href="(^16^)">Daemon Tools Lite website</a> and download and install the software.</li>
-<li>Open Daemon Tools Lite and click on Add Image.</li>
-<li>Browse to the location where you saved the CM0102.iso file and select it.</li>
-<li>The ISO file should appear as a virtual CD drive in Daemon Tools Lite.</li>
-<li>Right-click on it and select Mount.</li>
-<li>A window should pop up with the CM 01/02 installation wizard. Follow the instructions to install the game on your computer.</li>
-<li>You should see a shortcut for CM 01/02 on your desktop. Right-click on it and select Properties.</li>
-<li>Go to Compatibility tab and check Run this program in compatibility mode for Windows XP (Service Pack 3).</li>
-<li>Click OK to save the changes.</li>
-<li>Double-click on the shortcut to launch the game on your computer.</li>
-</ol>
-<p>Congratulations, you have successfully installed CM 01/02 on your PC or Mac. Now, you need to transfer the game files to your Android device. Here's how:</p>
-<ol>
-<li>Connect your Android device to your computer using a USB cable.</li>
-<li>Open File Explorer on your computer and navigate to the folder where you installed CM 01/02. It should be something like C:\Program Files (x86)\Eidos Interactive\Championship Manager 01-02.</li>
-<li>Select all the files and folders in that folder and copy them.</li>
-<li>Open File Explorer on your Android device and create a new folder called CM0102 in the internal storage or SD card.</li>
-<li>Paste the files and folders you copied into the CM0102 folder.</li>
-<li>Disconnect your Android device from your computer.</li>
-</ol>
-<p>Now, you need to download and install an app called <a href="">ExaGear Strategies</a> on your Android device. This is an emulator that can run PC games on Android, including CM 01/02. You can get it from the Google Play Store for free.</p>
-<ol>
-<li>Open Google Play Store on your Android device and search for ExaGear Strategies.</li>
-<li>Tap on Install and wait for the app to download and install.</li>
-<li>Open ExaGear Strategies and tap on the + button at the bottom right corner.</li>
-<li>Select Championship Manager 01/02 from the list of games. If you don't see it, tap on Browse and locate the CM0102 folder you created earlier.</li>
-<li>Select the CM0102.exe file and tap on OK.</li>
-<li>The game should appear on the main screen of ExaGear Strategies. Tap on it to launch it.</li>
-</ol>
-<p>You're done! You can now play CM 01/02 on your Android device. Enjoy managing your favorite team and leading them to glory!</p>
-<p></p>
-<h2>How to update the game with the latest data and patches</h2>
-<p>One of the amazing things about CM 01/02 is that it still receives updates from its dedicated community of fans. You can find the latest data updates, patches, mods, and more on the <a href="">Champman 01/02 website</a> and forum. These updates can fix bugs, improve performance, add new features, and most importantly, update the player database with the latest transfers, ratings, and attributes.</p>
-<p>To update the game with the latest data and patches, you need to download them from the website or forum and replace the existing files in your CM0102 folder. Here are some of the most popular updates you can get:</p>
-<ul>
-<li><a href="">The October 2020 Data Update</a>: This is the most recent data update that includes all the transfers and changes from the summer 2020 window. It also includes some tweaks and fixes to make the game more realistic and balanced.</li>
-<li><a href="">The Tapani Patch</a>: This is a patch that adds many new features and options to the game, such as new leagues, new staff roles, new player attributes, new tactics, new skins, new sounds, and more. It also fixes some bugs and improves performance. There are different versions of this patch, but we recommend version 2.22 which is compatible with ExaGear Strategies.</li>
-<li><a href="">The Saturn Patch</a>: This is another patch that adds some features and fixes to the game, such as improved AI, improved match engine, improved regens, improved finances, improved editor, improved compatibility, and more. It also works well with ExaGear Strategies.</li>
-</ul>
-<p>To install these updates, you need to follow these steps:</p>
-<ol>
-<li>Download the update files from the links above or from any other source you trust.</li>
-<li>Extract the files using WinRAR or any other extraction tool.</li>
-<li>Connect your Android device to your computer using a USB cable.</li>
-<li>Open File Explorer on your computer and navigate to the CM0102 folder on your Android device.</li>
-<li>Select all the files in that folder and copy them to a backup folder on your computer in case something goes wrong.</li>
-<li>Delete all the files in the CM0102 folder except for cm0102.exe and cm0102.gdi.</li>
-<li>Copy all the files from the update folder you extracted earlier into the CM0102 folder on your Android device.</li>
-<li>Disconnect your Android device from your computer.</li>
-<li>Open ExaGear Strategies and launch CM 01/02.</li>
-<li>You should see a message saying that the game has been updated. Click OK to continue.</li>
-</ol>
-<p>That's it! You have successfully updated the game with the latest data and patches. You can now enjoy the game with more realism and variety.</p>
-<h2>How to choose the best formation and tactics</h2>
-<p>One of the most important aspects of CM 01/02 is choosing the right formation and tactics for your team. There are many factors to consider, such as your players' attributes, roles, preferences, morale, fitness, and form, as well as your opponents' strengths and weaknesses, the weather, the pitch condition, and the match situation.</p>
-<p>There is no definitive answer to what is the best formation and tactics for every team and every match, but there are some general guidelines and tips that can help you make better decisions. Here are some of them:</p>
-<ul>
-<li>Know your players: Study your players' attributes, roles, and preferences, and try to find the best position and duty for each of them. For example, if you have a fast and skillful winger, you might want to play him as an attacking midfielder on the flank and give him a forward run instruction. If you have a strong and tall striker, you might want to play him as a target man and give him a hold up ball instruction.</li>
-<li>Know your opponents: Scout your opponents before each match and try to identify their strengths and weaknesses. For example, if they have a weak defense, you might want to play more aggressively and exploit their flanks. If they have a strong attack, you might want to play more defensively and mark their key players.</li>
-<li>Know your style: Decide what kind of football you want to play and choose a formation and tactics that suit your style. For example, if you want to play a possession-based game, you might want to choose a formation with more midfielders and less strikers, such as 4-5-1 or 4-3-3. If you want to play a counter-attacking game, you might want to choose a formation with more strikers and less midfielders, such as 4-4-2 or 3-5-2.</li>
-<li>Know your options: Experiment with different formations and tactics and see how they affect your team's performance. You can use the pre-match screen or the in-game screen to change your formation and tactics at any time. You can also use the tactic wizard or the preset tactics to get some suggestions based on your team's attributes.</li>
-</ul>
-<p>To give you some examples of some of the most effective formations and tactical options in CM 01/02, here are some tables that compare them:</p>
- <table>
-<tr><th>Formation</th><th>Advantages</th><th>Disadvantages</th></tr>
-<tr><td>4-4-2</td><td>A balanced formation that provides width, depth, and support in both attack and defense.</td><td>A vulnerable formation that can be outnumbered in midfield or exposed on the flanks.</td></tr>
-<tr><td>4-3-3</td><td>A flexible formation that can create numerical superiority in attack or midfield depending on the movement of the wingers.</td><td>A demanding formation that requires high stamina, work rate, and teamwork from the wingers.</td></tr>
-<tr><td>4-5-1</td><td>A defensive formation that can dominate midfield and frustrate opponents with its compactness.</td><td>A boring formation that can lack creativity and firepower in attack.</td></tr>
-<tr><td>3-5-2</td><td>An attacking formation that can overload opponents with its wing-backs and strikers.</td><td>A risky formation that can leave gaps in defense or midfield if the wing-backs or central midfielders are caught out of position.</td></tr>
-<tr><td>5-3-2</td><td>A solid formation that can provide security in defense and support in attack with its sweeper and wing-backs.</td><td>A conservative formation that can be predictable and passive in attack.</td></tr>
-</table>
- <table>
-<tr><th>Tactic</th><th>Advantages</th><th>Disadvantages</th></tr>
-<tr><td>Attacking</td><td>A positive tactic that can create more chances and score more goals.</td><td>A risky tactic that can leave your defense exposed and concede more goals.</td></tr>
-<tr><td>Defensive</td><td>A cautious tactic that can protect your lead and prevent your opponents from scoring.</td><td>A negative tactic that can invite pressure and reduce your chances of scoring.</td></tr>
-<tr><td>Long Ball</td><td>A direct tactic that can bypass midfield and exploit the pace and strength of your strikers.</td><td>A crude tactic that can waste possession and rely on luck and individual quality.</td></tr>
-<tr><td>Short Passing</td><td>A patient tactic that can control the game and create openings with clever movement and passing.</td><td>A slow tactic that can be easily disrupted by aggressive pressing and tackling.</td></tr>
-<tr><td>Counter Attack</td><td>A smart tactic that can exploit the space behind your opponents' defense and catch them off guard.</td><td>A reactive tactic that can depend on your opponents' mistakes and require fast transitions.</td></tr>
-</table>
- <p>Of course, these are just some examples of formations and tactics, and you can always customize them to suit your preferences and needs. The key is to find the right balance between attack and defense, creativity and discipline, and risk and reward. Remember, you are the manager, and you have the final say on how your team plays.</p>
-<h2>How to find and sign the best players</h2>
-<p>No matter how good your formation and tactics are, you still need quality players to execute them. Finding and signing the best players is one of the most challenging and rewarding aspects of CM 01/02. There are thousands of players in the game, but not all of them are worth your time and money. You need to scout them, negotiate with them, and convince them to join your team.</p>
-<p>There are many factors to consider when looking for players, such as their attributes, potential, age, nationality, personality, wage, value, contract, availability, etc. You also need to consider your team's needs, budget, reputation, vision, etc. It's not easy to find the perfect player for every position, but there are some tips that can help you:</p>
-<ul>
-<li>Use filters: The game has a powerful search engine that allows you to filter players by various criteria. You can use this to narrow down your search and find players that match your requirements. For example, if you need a young striker with high finishing and pace attributes, you can set the filters accordingly and see who comes up.</li>
-<li>Use scouts: The game has a scouting system that allows you to assign scouts to different regions or countries. You can use this to discover new players that might not be in your database. For example, if you want to find some hidden gems from South America or Africa, you can send your scouts there and see what they report back.</li>
-<li>Use forums: The game has a vibrant community of fans who share their knowledge and experience on various forums. You can use this to get some recommendations or feedback on players that you might be interested in. For example, if you want to know if a certain player is worth signing or not, you can ask other players who have used him or faced him.</li>
-</ul>
-<p>To give you some examples of some of the best players in CM 01/02, here are some tables that list them by position:</p>
- <table>
-<tr><th>Goalkeepers</th><th>Nationality</th><th>Club</th></tr>
-<tr><td>Gianluigi Buffon</td><td>Italy</td><td>Juventus</td></tr>
-<tr><td>Iker Casillas</td><td>Spain</td><td>Real Madrid</td></tr>
-<tr><td>Fabien Barthez</td><td>France</td><td>Manchester United</td></tr>
-<tr><td>Oliver Kahn</td><td>Germany</td><td>Bayern Munich</td></tr>
-<tr><td>Petr Cech</td><td>Czech Republic</td><td>Rennes</td></tr>
-</table>
- <table>
-<tr><th>Defenders</th><th>Nationality</th><th>Club</th></tr>
-<tr><td>Lilian Thuram</td><td>France</<td><td>Juventus</td></tr>
-<tr><td>Alessandro Nesta</ <td>Italy</td><td>Lazio</td></tr>
-<tr><td>Roberto Carlos</td><td>Brazil</td><td>Real Madrid</td></tr>
-<tr><td>Paolo Maldini</td><td>Italy</td><td>AC Milan</td></tr>
-<tr><td>Rio Ferdinand</td><td>England</td><td>Leeds United</td></tr>
-</table>
- <table>
-<tr><th>Midfielders</th><th>Nationality</th><th>Club</th></tr>
-<tr><td>Zinedine Zidane</td><td>France</td><td>Real Madrid</td></tr>
-<tr><td>Luis Figo</td><td>Portugal</td><td>Real Madrid</td></tr>
-<tr><td>David Beckham</td><td>England</td><td>Manchester United</td></tr>
-<tr><td>Pavel Nedved</td><td>Czech Republic</td><td>Juventus</td></tr>
-<tr><td>Ronaldinho</td><td>Brazil</<td><<a href="">Paris Saint-Germain</a></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href=""></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</a></<a href="">Paris Saint-Germain</ [assistant](#message) <td></tr>
-<tr><td>Claude Makelele</td><td>France</<td><<td>Real Madrid</td></tr>
-<tr><td>Pirlo Andrea Pirlo </ td >< td > Italy </ td >< td > AC Milan </ td ></ tr >
-<tr>< td > Steven Gerrard </ td >< td > England </ td >< td > Liverpool </ td ></ tr >
-<tr >< td > Michael Ballack </ td >< td > Germany </ td >< td > Bayer Leverkusen </ td ></ tr >
-<table>
- <table>
-<tr >< th > Strikers </ th >< th > Nationality </ th >< th > Club </ th ></ tr >
-<tr >< td > Ronaldo </ td >< td > Brazil </ td >< td > Real Madrid </ td ></ tr >
-<tr >< td > Thierry Henry </ td >< td > France </ td >< td > Arsenal </ td ></ tr >
-<tr >< td > Ruud van Nistelrooy </ td >< td > Netherlands </ td >< td > Manchester United </ td ></ tr >
-<tr >< td > Raul Gonzalez </ td >< td > Spain </ td >< td > Real Madrid </ td ></ tr >
-<tr >< td > Andriy Shevchenko </ td >< td > Ukraine </ td >< < a   h r e f = " ( ^ 2 8 ^ ) "   t i t l e = " A C   M i l a n "   t a r g e t = " _ b l a n k "   r e l = " n o f o l l o w "   c l a s s = " e x t e r n a l   t e x t "   h r e f = " ( ^ 2 8 ^ ) "   t i t l e = " AC Milan</a></td></tr>
-</table>
- <p>These are just some of the best players in CM 01/02, but there are many more that you can discover and sign. You can also use the editor to create your own players or edit the existing ones. However, be careful not to ruin the balance and fun of the game by making unrealistic changes.</p>
-<h2>How to rotate your squad and manage fitness</h2>
-<p>Another important aspect of CM 01/02 is managing your squad and keeping your players fit and happy. You can have the best players in the world, but if they are tired, injured, or unhappy, they won't perform well on the pitch. You need to rotate your squad and give your players enough rest and recovery time, as well as motivate them and keep them satisfied.</p>
-<p>There are many factors that affect your players' fitness and morale, such as their age, injury history, personality, form, playing time, contract, etc. You need to monitor these factors and make adjustments accordingly. Here are some tips that can help you:</p>
-<ul>
-<li>Use the fitness report: The game has a fitness report that shows you the condition and stamina of each player in your squad. You can access it by clicking on Squad > Fitness Report. You can use this to see which players need a rest or a boost.</li>
-<li>Use the squad status: The game has a squad status that shows you the happiness and morale of each player in your squad. You can access it by clicking on Squad > Squad Status. You can use this to see which players are unhappy or unsettled.</li>
-<li>Use the rotation policy: The game has a rotation policy that allows you to set how often you want to rotate your players. You can access it by clicking on Tactics > Rotation Policy. You can use this to automate your rotation and save time.</li>
-<li>Use the team talks: The game has a team talk feature that allows you to talk to your players before, during, and after each match. You can access it by clicking on Team Talk. You can use this to motivate your players and influence their performance.</li>
-<li>Use the player interaction: The game has a player interaction feature that allows you to talk to your players individually. You can access it by clicking on Player > Interaction. You can use this to praise, criticize, warn, or advise your players and affect their morale and attitude.</li>
-</ul>
-<p>By using these features, you can rotate your squad and manage fitness effectively. Remember, a happy and fit squad is a winning squad.</p>
-<h2>How to use cheats and editors</h2>
-<p>Finally, we come to the controversial topic of cheats and editors. Some players might argue that using cheats and editors is unethical and spoils the fun of the game. Others might argue that using cheats and editors is harmless and enhances the fun of the game. Ultimately, it's up to you to decide whether you want to use them or not.</p>
-<p>Cheats and editors are tools that allow you to modify the game in various ways, such as changing player attributes, adding money, editing competitions, etc. There are many cheats and editors available for CM 01/02, but we recommend using them with caution and moderation. Here are some of the most popular cheats and editors:</p>
-<ul>
-<li><a href="">CM Scout</a>: This is a tool that allows you to scout any player in the game without sending scouts or paying fees. You can see their attributes, potential, value, contract, etc.</li>
-<li><a href="">CM Explorer</a>: This is a tool that allows you to edit any player or club in the game. You can change their attributes, finances, reputation, staff, etc.</li>
-<li><a href="">CM Cheat</a>: This is a tool that allows you to cheat in various ways in the game. You can add money, heal injuries, improve morale, etc.</li>
-</ul>
-<p>To use these cheats and editors, you need to follow these steps:</p>
-<ol>
-<li>Download the cheat or editor files from the links above or from any other source you trust.</li>
-<li>Extract the files using WinRAR or any other extraction tool.</li>
-<li>Run the cheat or editor program on your computer.</li>
-<li>Select CM 01/02 as the target game.</li>
-<li>Make the changes you want using the cheat or editor interface.</li>
-<li>Save the changes and exit the program.</li>
-<li>Launch CM 01/02 on your Android device using ExaGear Strategies.</li>
-<li>You should see the changes reflected in the game.</li>
-</ol>
-<p>Please note that using cheats and editors might cause errors or crashes in the game, so use them at your own risk. Also note that using cheats and editors might ruin [user the challenge and fun of the game, so use them sparingly and wisely.</p>
-<h2>Conclusion</h2>
-<p>CM 01/02 is a timeless game that still offers hours of enjoyment and satisfaction to football fans. If you want to play it on your Android device, you can follow the steps in this article to download and install it, as well as update it with the latest data and patches. You can also learn some tips on how to choose the best formation and tactics, how to find and sign the best players, how to rotate your squad and manage fitness, and how to use cheats and editors. We hope this article has been helpful and informative, and we wish you good luck and have fun with CM 01/02!</p>
-<h2>FAQs</h2>
-<p>Here are some of the most frequently asked questions and answers about CM 01/02:</p>
-<ol>
-<li><b>Is CM 01/02 free?</b><br>Yes, CM 01/02 is free to download and play. You can get it from the <a href="">Champman 01/02 website</a> or from any other source you trust.</li>
-<li><b>Is CM 01/02 compatible with Windows 10?</b><br>Yes, CM 01/02 is compatible with Windows 10. You just need to run it in compatibility mode for Windows XP (Service Pack 3) and as an administrator.</li>
-<li><b>Is CM 01/02 multiplayer?</b><br>Yes, CM 01/02 is multiplayer. You can play it online or offline with up to 16 human players. You just need to create or join a network game and follow the instructions.</li>
-<li><b>What are some of the best mods for CM 01/02?</b><br>There are many mods for CM 01/02 that add new features, graphics, sounds, leagues, etc. Some of the best mods are <a href="">CM Legends</a>, <a href="">CM Club Update</a>, <a href="">CM Retro</a>, <a href="">CM World</a>, and <a href="">CM Fantasy</a>.</li>
-<li><b>Where can I get more help or support for CM 01/02?</b><br>You can get more help or support for CM 01/02 from the <a href="">Champman 01/02 website</a> and forum, where you can find guides, tutorials, tips, tricks, FAQs, downloads, etc. You can also join the <a href="">CM 01/02 Facebook group</a> or the <a href="">CM 01/02 Discord server</a>, where you can chat with other players and fans.</li>
-</ol></p> 197e85843d<br />
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-<h1>Download Java Real Football Manager 2009: A Guide for Football Fans</h1>
-<p>If you are a football fan and you love to play games on your mobile phone, then you might be interested in downloading Java Real Football Manager 2009. This is a game that lets you experience the thrill of managing your own football club, from choosing your players to competing in matches. In this article, we will show you how to download and play this game, as well as some tips and tricks to help you succeed.</p>
-<h2>Introduction</h2>
-<h3>What is Java Real Football Manager 2009?</h3>
-<p>Java Real Football Manager 2009 is a mobile game developed by Gameloft, one of the leading publishers of mobile games. It is part of the Real Football series, which also includes games like Real Football 2010 and Real Football 2011. Java Real Football Manager 2009 is a game that focuses on the managerial aspect of football, rather than the gameplay. You can choose from one of eight different leagues and more than 200 teams, and all transfers are updated for the 2008/2009 season. You can also create your own custom team and league, if you prefer.</p>
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-<h3>Step 2: Manage your team and transfers</h3>
-<p>Once you have chosen your club and league, you will enter the main menu of the game, where you can access various options to manage your team and transfers. You can view your squad, tactics, fixtures, standings, statistics, etc. You can also buy and sell players in the transfer market, where you can bid for players or accept offers from other clubs. You can also scout for new talents or loan players from other teams. You have to balance your budget and keep an eye on your players' contracts and salaries.</p>
-<h3>Step 3: Compete in matches and tournaments</h3>
-<p>The most exciting part of the game is competing in matches and tournaments against other teams. You can play in various competitions, such as league matches, cup matches, friendly matches, etc. You can also participate in international tournaments, such as the World Cup or the European Championship. Before each match, you can set your lineup, formation, strategy, etc. During the match, you can watch the action unfold on the screen, or you can skip to the result. You can also make substitutions or change tactics during the match. After each match, you can view the highlights, statistics, ratings, etc.</p>
-<h2>Tips and tricks for Java Real Football Manager 2009</h2>
-<h3>Tip 1: Use the simplified interface and improved AI</h3>
-<p>One of the features of Java Real Football Manager 2009 is that it has a simplified interface and improved AI compared to previous versions of the game. This means that you can navigate through the menus faster and easier, and that the game will run smoother and more realistic on your device. The AI of the game will also adapt to your style of play and offer you more challenge and variety.</p>
-<h3>Tip 2: Keep an eye on your finances and morale</h3>
-<p>Another important aspect of managing a football club is keeping an eye on your finances and morale. You have to make sure that you have enough money to pay for your players' salaries, transfers, scouts, etc., as well as for maintaining your stadium and facilities. You also have to make sure that your players are happy and motivated, as this will affect their performance on the pitch. You can improve your finances and morale by winning matches and trophies, selling tickets and merchandise, signing sponsors, etc.</p>
-<h3>Tip 3: Experiment with different tactics and formations</h3>
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-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Java Real Football Manager 2009:</p>
-<ul>
-<li><b>Q: How much does the game cost?</b></li>
-<li>A: The game is free to download from some websites, such as dedomil.net, but it might have some ads or limitations. You can also buy the game from other websites or app stores, such as Gameloft.com, for a reasonable price.</li>
-<li><b>Q: What are the minimum requirements for the game?</b></li>
-<li>A: The game requires a device that supports Java games, such as Nokia, Samsung, Motorola, Sony Ericsson, etc. The game also requires a resolution of at least 128x128 pixels, but it might vary depending on the device and version of the game.</li>
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-<li>A: The game does not have an official update feature, but you can download the latest version of the game from the website or app store where you got it. You can also check for updates on Gameloft's website or social media pages.</li>
-<li><b>Q: How can I contact Gameloft for support or feedback?</b></li>
-<li>A: You can contact Gameloft for support or feedback by visiting their website or social media pages, or by sending an email to [email protected]. You can also check their FAQ section or forum for more information.</li>
-<li><b>Q: How can I share my progress or achievements with other players?</b></li>
-<li>A: You can share your progress or achievements with other players by using the online mode of the game, which allows you to connect with other players via Bluetooth or Wi-Fi. You can also share your screenshots or videos of the game on social media platforms, such as Facebook, Twitter, Instagram, etc.</li>
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- <p>There are many reasons why you should download Color Rings Puzzle on your Android device. Here are some of them:</p>
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-<li>The game is fun and relaxing. You can play it anytime and anywhere, without any time limit or pressure. You can also pause and resume the game whenever you want.</li>
-<li>The game is suitable for all ages and skill levels. You can choose from different modes and difficulty levels, depending on your preference and mood. You can also switch between different themes and backgrounds, such as classic, neon, wood, or marble.</li>
-<li>The game is good for your brain and your eyes. It can help you improve your concentration, memory, logic, and color perception. It can also help you reduce stress and boredom.</li>
-<li>The game is free and safe to download. You don't need to pay anything or register anything to play the game. You also don't need to worry about any viruses or malware infecting your device.</li>
-</ul>
- <h2>How to download Color Rings Puzzle APK</h2>
- <p>If you want to download Color Rings Puzzle on your Android device, you have two options: you can either download it from the Google Play Store or from an APK file. In this section, we will explain what an APK file is and how to download and install it.</p>
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- <h3>What is an APK file and why you need it</h3>
- <p>An APK file is a file format that contains all the data and code needed to run an Android app. It stands for Android Package Kit. You can think of it as a zip file that contains everything that an app needs to work properly.</p>
- <p>You might need an APK file if you want to download an app that is not available in your region or in the Google Play Store. You might also need it if you want to update an app manually or install an older version of an app.</p>
- <h3>How to install Color Rings Puzzle APK on your Android device</h3>
- <p>To install Color Rings Puzzle APK on your Android device, you need to follow these steps:</p>
- <ol>
-<li>Download the APK file from a trusted source, such as [APKCombo](^1 ^)^. You can use the link below to access the download page. Make sure you download the latest version of the game, which is 3.0.9 as of June 2023.</li>
-<li>Before you install the APK file, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. You might also need to grant permission to your browser or file manager to install apps.</li>
-<li>Locate the downloaded APK file on your device, either in your Downloads folder or in the folder where you saved it. Tap on the file and follow the instructions on the screen to install it. You might need to allow some permissions for the app to work properly.</li>
-<li>Once the installation is complete, you can launch the game from your app drawer or home screen and enjoy playing it.</li>
-</ol>
- <h3>How to update Color Rings Puzzle APK</h3>
- <p>To update Color Rings Puzzle APK, you need to repeat the same steps as above, but with a newer version of the APK file. You can check for updates on the [APKCombo] website or on the app itself. You don't need to uninstall the previous version of the app before installing the new one.</p>
- <h2>Tips and tricks for playing Color Rings Puzzle</h2>
- <p>Now that you have downloaded and installed Color Rings Puzzle APK on your Android device, you might want to know some tips and tricks for playing it better. Here are some of them:</p>
- <h3>Use the undo button wisely</h3>
- <p>The game has an undo button that allows you to undo your last move if you make a mistake or change your mind. However, you can only use it once per game, so use it wisely. Don't waste it on a minor error or a move that doesn't affect your score much. Save it for a situation where you really need it, such as when you are about to lose the game or when you can clear a lot of rings with one move.</p>
- <h3>Plan ahead and avoid filling up the board</h3>
- <p>The game gets harder as you progress, as more rings of different colors appear on the bottom row. You need to plan ahead and think carefully before placing each ring on the board. Try to create as many lines as possible with each move, and avoid placing rings that don't match any existing lines. Also, avoid filling up the board with rings that have no space to move or clear. Leave some empty slots for future moves and opportunities.</p>
- <h3>Try different modes and themes</h3>
- <p>The game has different modes and themes that you can try for more variety and fun. You can choose from Classic, Time Attack, Move Limit, Bomb, Hexa, and Star modes, each with its own rules and challenges. You can also switch between different themes and backgrounds, such as Classic, Neon, Wood, or Marble, each with its own colors and sounds. Experiment with different combinations and find your favorite one.</p>
- <h2>Conclusion</h2>
- <p>Color Rings Puzzle is a fun and relaxing game that you can download and play on your Android device. It is easy to play but hard to master, and it can keep you entertained and relaxed for hours. You can download it from the Google Play Store or from an APK file, depending on your preference and availability. You can also follow some tips and tricks for playing it better, such as using the undo button wisely, planning ahead, and trying different modes and themes. If you are looking for a simple yet addictive puzzle game that challenges your brain and your eyes, you should give Color Rings Puzzle a try.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Color Rings Puzzle:</p>
- <ol>
-<li><b>Is Color Rings Puzzle free?</b><br>
-Yes, Color Rings Puzzle is free to download and play. However, it contains ads that you can remove by purchasing the ad-free version of the game.</li>
-<li><b>Is Color Rings Puzzle offline?</b><br>
-Yes, Color Rings Puzzle is an offline game that does not require an internet connection to play. However, you might need an internet connection to download updates or access some features of the game.</li>
-<li><b>Is Color Rings Puzzle safe?</b><br>
-Yes, Color Rings Puzzle is safe to download and play. It does not contain any viruses or malware that can harm your device. However, make sure you download it from a trusted source, such as [APKCombo] or the Google Play Store.</li>
-<li><b>How do I get more points in Color Rings Puzzle?</b><br>
-You can get more points in Color Rings Puzzle by clearing more rings at once, clearing multiple lines at once, clearing special rings such as bombs or stars, and playing on higher difficulty levels or modes.</li>
-<li><b>How do I reset Color Rings Puzzle?</b><br>
-You can reset Color Rings Puzzle by clearing the app data and cache on your device. To do this, go to Settings > Apps > Color Rings Puzzle > Storage > Clear Data and Clear Cache. This will erase your progress and settings, and restore the game to its default state.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />

spaces/2023Liu2023/bingo/src/lib/bots/bing/sr.ts

@@ -1,106 +0,0 @@
-// @ts-ignore
-const SpeechRecognitionPolyfill: typeof webkitSpeechRecognition = typeof window !== 'undefined' ? (
-  // @ts-ignore
-  window.SpeechRecognition ||
-  window.webkitSpeechRecognition ||
-  // @ts-ignore
-  window.mozSpeechRecognition ||
-  // @ts-ignore
-  window.msSpeechRecognition ||
-  // @ts-ignore
-  window.oSpeechRecognition
-) as typeof webkitSpeechRecognition : undefined
-
-type subscriber = (msg: string, command?: string) => void
-
-export class SR {
-  recognition?: SpeechRecognition
-  onchange?: subscriber
-  transcript: boolean = false
-  listening: boolean = false
-  private commandsRe?: RegExp
-  constructor(commands: string[]) {
-    this.recognition = SpeechRecognitionPolyfill ? new SpeechRecognitionPolyfill() : undefined
-    if (!this.recognition) {
-      return
-    }
-    this.configuration('zh-CN')
-    if (commands.length) {
-      this.commandsRe = new RegExp(`^(${commands.join('|')})。?$`)
-    }
-    this.recognition.onresult = this.speechRecognition
-    this.recognition.onerror = (err) => {
-      console.log('err', err.error)
-      this.stop()
-    }
-    this.recognition.onend = () => {
-      if (this.recognition && this.listening) {
-        this.recognition.start()
-      }
-    }
-  }
-
-  speechRecognition = (event: SpeechRecognitionEvent) => {
-    if (!this.listening) return
-    for (var i = event.resultIndex; i < event.results.length; i++) {
-      let result = event.results[i]
-      if (result.isFinal) {
-        var alt = result[0]
-        const text = alt.transcript.trim()
-        if (this.commandsRe && this.commandsRe.test(text)) {
-          return this.onchange?.('', RegExp.$1)
-        }
-        if (!this.transcript) return
-        this.onchange?.(text)
-      }
-    }
-  }
-
-  private configuration = async (lang: string = 'zh-CN') => {
-    return new Promise((resolve) => {
-      if (this.recognition) {
-        this.recognition.continuous = true
-        this.recognition.lang = lang
-        this.recognition.onstart = resolve
-      }
-    })
-  }
-
-  start = async () => {
-    if (this.recognition && !this.listening) {
-      await this.recognition.start()
-      this.transcript = true
-      this.listening = true
-    }
-  }
-
-  stop = () => {
-    if (this.recognition) {
-      this.recognition.stop()
-      this.transcript = false
-      this.listening = false
-    }
-  }
-
-
-  pause = () => {
-    if (this.recognition) {
-      this.transcript = false
-    }
-  }
-
-  resume = () => {
-    if (this.recognition) {
-      this.transcript = true
-    }
-  }
-
-  abort = () => {
-    if (this.recognition && this.transcript) {
-      this.recognition.abort()
-      this.transcript = false
-      this.listening = false
-    }
-  }
-}
-

spaces/AIFILMS/generate_human_motion/VQ-Trans/visualize/joints2smpl/src/prior.py

@@ -1,230 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: [email protected]
-
-from __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import sys
-import os
-
-import time
-import pickle
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-
-DEFAULT_DTYPE = torch.float32
-
-
-def create_prior(prior_type, **kwargs):
-    if prior_type == 'gmm':
-        prior = MaxMixturePrior(**kwargs)
-    elif prior_type == 'l2':
-        return L2Prior(**kwargs)
-    elif prior_type == 'angle':
-        return SMPLifyAnglePrior(**kwargs)
-    elif prior_type == 'none' or prior_type is None:
-        # Don't use any pose prior
-        def no_prior(*args, **kwargs):
-            return 0.0
-        prior = no_prior
-    else:
-        raise ValueError('Prior {}'.format(prior_type) + ' is not implemented')
-    return prior
-
-
-class SMPLifyAnglePrior(nn.Module):
-    def __init__(self, dtype=torch.float32, **kwargs):
-        super(SMPLifyAnglePrior, self).__init__()
-
-        # Indices for the roration angle of
-        # 55: left elbow,  90deg bend at -np.pi/2
-        # 58: right elbow, 90deg bend at np.pi/2
-        # 12: left knee,   90deg bend at np.pi/2
-        # 15: right knee,  90deg bend at np.pi/2
-        angle_prior_idxs = np.array([55, 58, 12, 15], dtype=np.int64)
-        angle_prior_idxs = torch.tensor(angle_prior_idxs, dtype=torch.long)
-        self.register_buffer('angle_prior_idxs', angle_prior_idxs)
-
-        angle_prior_signs = np.array([1, -1, -1, -1],
-                                     dtype=np.float32 if dtype == torch.float32
-                                     else np.float64)
-        angle_prior_signs = torch.tensor(angle_prior_signs,
-                                         dtype=dtype)
-        self.register_buffer('angle_prior_signs', angle_prior_signs)
-
-    def forward(self, pose, with_global_pose=False):
-        ''' Returns the angle prior loss for the given pose
-
-        Args:
-            pose: (Bx[23 + 1] * 3) torch tensor with the axis-angle
-            representation of the rotations of the joints of the SMPL model.
-        Kwargs:
-            with_global_pose: Whether the pose vector also contains the global
-            orientation of the SMPL model. If not then the indices must be
-            corrected.
-        Returns:
-            A sze (B) tensor containing the angle prior loss for each element
-            in the batch.
-        '''
-        angle_prior_idxs = self.angle_prior_idxs - (not with_global_pose) * 3
-        return torch.exp(pose[:, angle_prior_idxs] *
-                         self.angle_prior_signs).pow(2)
-
-
-class L2Prior(nn.Module):
-    def __init__(self, dtype=DEFAULT_DTYPE, reduction='sum', **kwargs):
-        super(L2Prior, self).__init__()
-
-    def forward(self, module_input, *args):
-        return torch.sum(module_input.pow(2))
-
-
-class MaxMixturePrior(nn.Module):
-
-    def __init__(self, prior_folder='prior',
-                 num_gaussians=6, dtype=DEFAULT_DTYPE, epsilon=1e-16,
-                 use_merged=True,
-                 **kwargs):
-        super(MaxMixturePrior, self).__init__()
-
-        if dtype == DEFAULT_DTYPE:
-            np_dtype = np.float32
-        elif dtype == torch.float64:
-            np_dtype = np.float64
-        else:
-            print('Unknown float type {}, exiting!'.format(dtype))
-            sys.exit(-1)
-
-        self.num_gaussians = num_gaussians
-        self.epsilon = epsilon
-        self.use_merged = use_merged
-        gmm_fn = 'gmm_{:02d}.pkl'.format(num_gaussians)
-
-        full_gmm_fn = os.path.join(prior_folder, gmm_fn)
-        if not os.path.exists(full_gmm_fn):
-            print('The path to the mixture prior "{}"'.format(full_gmm_fn) +
-                  ' does not exist, exiting!')
-            sys.exit(-1)
-
-        with open(full_gmm_fn, 'rb') as f:
-            gmm = pickle.load(f, encoding='latin1')
-
-        if type(gmm) == dict:
-            means = gmm['means'].astype(np_dtype)
-            covs = gmm['covars'].astype(np_dtype)
-            weights = gmm['weights'].astype(np_dtype)
-        elif 'sklearn.mixture.gmm.GMM' in str(type(gmm)):
-            means = gmm.means_.astype(np_dtype)
-            covs = gmm.covars_.astype(np_dtype)
-            weights = gmm.weights_.astype(np_dtype)
-        else:
-            print('Unknown type for the prior: {}, exiting!'.format(type(gmm)))
-            sys.exit(-1)
-
-        self.register_buffer('means', torch.tensor(means, dtype=dtype))
-
-        self.register_buffer('covs', torch.tensor(covs, dtype=dtype))
-
-        precisions = [np.linalg.inv(cov) for cov in covs]
-        precisions = np.stack(precisions).astype(np_dtype)
-
-        self.register_buffer('precisions',
-                             torch.tensor(precisions, dtype=dtype))
-
-        # The constant term:
-        sqrdets = np.array([(np.sqrt(np.linalg.det(c)))
-                            for c in gmm['covars']])
-        const = (2 * np.pi)**(69 / 2.)
-
-        nll_weights = np.asarray(gmm['weights'] / (const *
-                                                   (sqrdets / sqrdets.min())))
-        nll_weights = torch.tensor(nll_weights, dtype=dtype).unsqueeze(dim=0)
-        self.register_buffer('nll_weights', nll_weights)
-
-        weights = torch.tensor(gmm['weights'], dtype=dtype).unsqueeze(dim=0)
-        self.register_buffer('weights', weights)
-
-        self.register_buffer('pi_term',
-                             torch.log(torch.tensor(2 * np.pi, dtype=dtype)))
-
-        cov_dets = [np.log(np.linalg.det(cov.astype(np_dtype)) + epsilon)
-                    for cov in covs]
-        self.register_buffer('cov_dets',
-                             torch.tensor(cov_dets, dtype=dtype))
-
-        # The dimensionality of the random variable
-        self.random_var_dim = self.means.shape[1]
-
-    def get_mean(self):
-        ''' Returns the mean of the mixture '''
-        mean_pose = torch.matmul(self.weights, self.means)
-        return mean_pose
-
-    def merged_log_likelihood(self, pose, betas):
-        diff_from_mean = pose.unsqueeze(dim=1) - self.means
-
-        prec_diff_prod = torch.einsum('mij,bmj->bmi',
-                                      [self.precisions, diff_from_mean])
-        diff_prec_quadratic = (prec_diff_prod * diff_from_mean).sum(dim=-1)
-
-        curr_loglikelihood = 0.5 * diff_prec_quadratic - \
-            torch.log(self.nll_weights)
-        #  curr_loglikelihood = 0.5 * (self.cov_dets.unsqueeze(dim=0) +
-        #  self.random_var_dim * self.pi_term +
-        #  diff_prec_quadratic
-        #  ) - torch.log(self.weights)
-
-        min_likelihood, _ = torch.min(curr_loglikelihood, dim=1)
-        return min_likelihood
-
-    def log_likelihood(self, pose, betas, *args, **kwargs):
-        ''' Create graph operation for negative log-likelihood calculation
-        '''
-        likelihoods = []
-
-        for idx in range(self.num_gaussians):
-            mean = self.means[idx]
-            prec = self.precisions[idx]
-            cov = self.covs[idx]
-            diff_from_mean = pose - mean
-
-            curr_loglikelihood = torch.einsum('bj,ji->bi',
-                                              [diff_from_mean, prec])
-            curr_loglikelihood = torch.einsum('bi,bi->b',
-                                              [curr_loglikelihood,
-                                               diff_from_mean])
-            cov_term = torch.log(torch.det(cov) + self.epsilon)
-            curr_loglikelihood += 0.5 * (cov_term +
-                                         self.random_var_dim *
-                                         self.pi_term)
-            likelihoods.append(curr_loglikelihood)
-
-        log_likelihoods = torch.stack(likelihoods, dim=1)
-        min_idx = torch.argmin(log_likelihoods, dim=1)
-        weight_component = self.nll_weights[:, min_idx]
-        weight_component = -torch.log(weight_component)
-
-        return weight_component + log_likelihoods[:, min_idx]
-
-    def forward(self, pose, betas):
-        if self.use_merged:
-            return self.merged_log_likelihood(pose, betas)
-        else:
-            return self.log_likelihood(pose, betas)

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/normalizing_flow/res_flow.py

@@ -1,61 +0,0 @@
-import torch
-from torch import nn
-from modules.commons.conv import ConditionalConvBlocks
-from modules.commons.wavenet import WN
-
-
-class FlipLayer(nn.Module):
-    def forward(self, x, nonpadding, cond=None, reverse=False):
-        x = torch.flip(x, [1])
-        return x
-
-
-class CouplingLayer(nn.Module):
-    def __init__(self, c_in, hidden_size, kernel_size, n_layers, p_dropout=0, c_in_g=0, nn_type='wn'):
-        super().__init__()
-        self.channels = c_in
-        self.hidden_size = hidden_size
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.c_half = c_in // 2
-
-        self.pre = nn.Conv1d(self.c_half, hidden_size, 1)
-        if nn_type == 'wn':
-            self.enc = WN(hidden_size, kernel_size, 1, n_layers, p_dropout=p_dropout,
-                          c_cond=c_in_g)
-        elif nn_type == 'conv':
-            self.enc = ConditionalConvBlocks(
-                hidden_size, c_in_g, hidden_size, None, kernel_size,
-                layers_in_block=1, is_BTC=False, num_layers=n_layers)
-        self.post = nn.Conv1d(hidden_size, self.c_half, 1)
-
-    def forward(self, x, nonpadding, cond=None, reverse=False):
-        x0, x1 = x[:, :self.c_half], x[:, self.c_half:]
-        x_ = self.pre(x0) * nonpadding
-        x_ = self.enc(x_, nonpadding=nonpadding, cond=cond)
-        m = self.post(x_)
-        x1 = m + x1 if not reverse else x1 - m
-        x = torch.cat([x0, x1], 1)
-        return x * nonpadding
-
-
-class ResFlow(nn.Module):
-    def __init__(self,
-                 c_in,
-                 hidden_size,
-                 kernel_size,
-                 n_flow_layers,
-                 n_flow_steps=4,
-                 c_cond=0,
-                 nn_type='wn'):
-        super().__init__()
-        self.flows = nn.ModuleList()
-        for i in range(n_flow_steps):
-            self.flows.append(
-                CouplingLayer(c_in, hidden_size, kernel_size, n_flow_layers, c_in_g=c_cond, nn_type=nn_type))
-            self.flows.append(FlipLayer())
-
-    def forward(self, x, nonpadding, cond=None, reverse=False):
-        for flow in (self.flows if not reverse else reversed(self.flows)):
-            x = flow(x, nonpadding, cond=cond, reverse=reverse)
-        return x

spaces/AbandonedMuse/UnlimitedMusicGen/setup.py

@@ -1,65 +0,0 @@
-"""
- Copyright (c) Meta Platforms, Inc. and affiliates.
- All rights reserved.
- 
- This source code is licensed under the license found in the
- LICENSE file in the root directory of this source tree.
- 
-"""
-
-from pathlib import Path
-
-from setuptools import setup, find_packages
-
-
-NAME = 'audiocraft'
-DESCRIPTION = 'Audio research library for PyTorch'
-
-URL = 'https://github.com/fairinternal/audiocraft'
-AUTHOR = 'FAIR Speech & Audio'
-EMAIL = '[email protected]'
-REQUIRES_PYTHON = '>=3.8.0'
-
-for line in open('audiocraft/__init__.py'):
-    line = line.strip()
-    if '__version__' in line:
-        context = {}
-        exec(line, context)
-        VERSION = context['__version__']
-
-HERE = Path(__file__).parent
-
-try:
-    with open(HERE / "README.md", encoding='utf-8') as f:
-        long_description = '\n' + f.read()
-except FileNotFoundError:
-    long_description = DESCRIPTION
-
-REQUIRED = [i.strip() for i in open(HERE / 'requirements.txt') if not i.startswith('#')]
-
-setup(
-    name=NAME,
-    version=VERSION,
-    description=DESCRIPTION,
-    author_email=EMAIL,
-    long_description=long_description,
-    long_description_content_type='text/markdown',
-    author=AUTHOR,
-    url=URL,
-    python_requires=REQUIRES_PYTHON,
-    install_requires=REQUIRED,
-    extras_require={
-        'dev': ['coverage', 'flake8', 'mypy', 'pdoc3', 'pytest'],
-    },
-    packages=find_packages(),
-    package_data={'audiocraft': ['py.typed']},
-    include_package_data=True,
-    license='MIT License',
-    classifiers=[
-        # Trove classifiers
-        # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
-        'License :: OSI Approved :: MIT License',
-        'Topic :: Multimedia :: Sound/Audio',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-    ],
-)

spaces/AchyuthGamer/OpenGPT-v1/Dockerfile

@@ -1,15 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
-FROM python:3.10
-
-COPY app.py .
-COPY requirements.txt .
-
-RUN python -m venv venv
-RUN ./venv/bin/pip install -r requirements.txt
-
-ENV H2O_WAVE_LISTEN=":7860"
-ENV H2O_WAVE_ADDRESS="http://127.0.0.1:7860"
-
-CMD ["./venv/bin/wave", "run", "app.py", "--no-reload"]

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Forefront.py

@@ -1,30 +0,0 @@
-import os
-import json
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://forefront.com'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    json_data = {
-        'text': messages[-1]['content'],
-        'action': 'noauth',
-        'id': '',
-        'parentId': '',
-        'workspaceId': '',
-        'messagePersona': '607e41fe-95be-497e-8e97-010a59b2e2c0',
-        'model': 'gpt-4',
-        'messages': messages[:-1] if len(messages) > 1 else [],
-        'internetMode': 'auto'
-    }
-    response = requests.post( 'https://streaming.tenant-forefront-default.knative.chi.coreweave.com/free-chat',
-        json=json_data, stream=True)
-    for token in response.iter_lines(): 
-        if b'delta' in token:
-            token = json.loads(token.decode().split('data: ')[1])['delta']
-            yield (token)
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/ninepatch/Factory.d.ts

@@ -1,39 +0,0 @@
-import NinePatch from "./NinePatch";
-
-export default function (
-    config?: NinePatch.IConfig
-): NinePatch;
-
-export default function (
-    x: number, y: number,
-    config?: NinePatch.IConfig
-): NinePatch;
-
-export default function (
-    x: number, y: number,
-    width: number, height: number,
-    config?: NinePatch.IConfig
-): NinePatch;
-
-export default function (
-    x: number, y: number,
-    width: number, height: number,
-    key: string,
-    config?: NinePatch.IConfig
-): NinePatch;
-
-export default function (
-    x: number, y: number,
-    width: number, height: number,
-    key: string,
-    columns: (number | undefined)[], rows: (number | undefined)[],
-    config?: NinePatch.IConfig
-): NinePatch;
-
-export default function (
-    x: number, y: number,
-    width: number, height: number,
-    key: string, baseFrame: string,
-    columns: (number | undefined)[], rows: (number | undefined)[],
-    config?: NinePatch.IConfig
-): NinePatch;

spaces/Alealejandrooo/deathCertReader/app.py

@@ -1,299 +0,0 @@
-import re
-import cv2
-import numpy as np
-from paddleocr import PaddleOCR
-from PIL import Image
-import matplotlib.pyplot as plt
-import pandas as pd
-import matplotlib.pyplot as plt
-import onnxruntime
-import gradio as gr
-
-# initialize the OCR
-ocr = PaddleOCR(lang='sl',
-                enable_mkldnn=True,
-                cls=False,
-                show_log= False)
-
-# initialize the models 
-model_deskew = onnxruntime.InferenceSession("./models/CNN_deskew_v0.0.2.onnx")
-model_denoise = onnxruntime.InferenceSession("./models/autoencoder_denoise_v0.0.2.onnx")
-
-##### All Functions #####
-
-def preprocess_image(image):
-    '''
-    Function: preprocess image to make it lighter to work on 
-    Input: resized image
-    Output: image
-    '''
-    image = np.array(image)   
-    scale = 1.494
-    width = int(image.shape[1] / scale)
-    height = int(image.shape[0] / scale)
-    dim = (width, height)
-    image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)  
-    return image
-
-
-def deskew(image, model):
-    '''
-    Function: deskew an image
-    Input: takes an image as an array
-    Output: deskewed image
-    '''
-    # map the model classes to the actual degree of skew
-    map = { 0: '-1', 1: '-10', 2: '-11', 3: '-12', 4: '-13',
-            5: '-14',6: '-15', 7: '-2',  8: '-3',  9: '-4',
-            10: '-5',11: '-6',12: '-7', 13: '-8', 14: '-9',
-            15: '0', 16: '1', 17: '10', 18: '11', 19: '12',
-            20: '13',21: '14',22: '15', 23: '180',24: '2',
-            25: '270',26: '3',27: '4',  28: '5',  29: '6',
-            30: '7',  31: '8',32: '9',  33: '90'}
-
-    image_d = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    width = int(image_d.shape[1] * 0.2)
-    height = int(image_d.shape[0] * 0.2)
-    dim = (width, height)
-    # resize image
-    res = cv2.resize(image_d, dim, interpolation = cv2.INTER_AREA)  
-    resized = cv2.resize(res, (200, 200))
-    # add two dimensions to feed to the model 
-    resized = resized.astype('float32').reshape(1, 200, 200 ,1)
-    # normalize
-    resized = resized/255  
-    # predictions
-    predictions = model.run(None, {'conv2d_input': resized})
-    # best prediction
-    pred = predictions[0].argmax()
-    # angle of skew
-    angle = int(map[pred])
-    skew_confidence = predictions[0][0][pred] * 100
-    # deskew original image
-    if angle == 90:
-        deskewed_image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)
-        return deskewed_image, angle, skew_confidence
-    if angle == 270:
-        deskewed_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
-        return deskewed_image, angle, skew_confidence
-    
-    (h, w) = image.shape[:2]
-    center = (w // 2, h // 2)
-    M = cv2.getRotationMatrix2D(center, -angle, 1.0)
-    deskewed_image = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC,
-                            borderMode=cv2.BORDER_REPLICATE)
-    return deskewed_image, angle, skew_confidence
-
-
-def prepare_image_to_autoencoder(image):
-    '''
-    Function: prepare the image to be passed to the autoencoder.
-    Input: image (_type_): deskewed image
-    Output: resized image to be passed to the autoencoder
-    '''
-    height, width = image.shape[:2]
-    target_height = 600 
-    target_width = 600
-    image = image[int(height/3.6): int(height/1.87), int(width/3.67): int(width/1.575)]
-    # reshape image to fixed size 
-    image = cv2.resize(image, (target_width, target_height))
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-     # normalize images
-    image = image / 255.0 
-    # reshape to pass image to autoencoder
-    image = image.reshape(target_height, target_width, 1) 
-    return image
-
-
-def autoencode_ONNX(image, model):
-    '''
-    Function: remove noise from image
-    Input: image and autoencoder model
-    Output: image
-    '''
-    image = image.astype(np.float32).reshape(1, 600, 600, 1)
-    image = model.run(None, {'input_2': image})
-    image = image[0]
-    image = image.squeeze()
-    image = image * 255
-    image = image.astype('uint8')
-    return image
-
-def extract_detected_entries_pdl(image):
-    """
-    Extracts text, scores, and boundary boxes from an image using OCR and returns a DataFrame.
-
-    This function takes an input image, applies OCR to detect text in the image, and then extracts
-    the detected text, confidence scores, and boundary boxes for each text entry. The extracted
-    information is returned in a DataFrame with columns "Text", "Score", and "Boundary Box".
-
-    Parameters
-    ----------
-    image : numpy.ndarray
-        The input image to be processed.
-
-    Returns
-    -------
-    pandas.DataFrame
-        A DataFrame containing the extracted text, confidence scores, and boundary boxes
-        for each detected text entry. The DataFrame has the following columns:
-        - "Text": The detected text.
-        - "Score": The confidence score for the detected text.
-        - "Boundary Box": The coordinates of the boundary box for the detected text.
-    """
-    # run the OCR
-    result = ocr.ocr(image)
-    # creates Pandas Dataframe
-    txt = []
-    scores = []
-    boxes = []
-    for r in result[0]:
-        txt.append(cleanString_basic(r[-1][0]))
-        scores.append(r[-1][1])
-        boxes.append(tuple(map(tuple, r[0])))
-
-    return pd.DataFrame({"Text": txt, "Score": scores, "Boundary Box": boxes})
-
-def cleanString_basic(word):
-  word = word.replace("$", "s")
-  return word
-
-def clean_string_start(string: 'str'):
-
-    names_flags = "√"
-    chars_to_remove = ['!', "'", '[', ']', '*', '|', '.', ':', '\\', '/']
-    if string.startswith(tuple(chars_to_remove)):
-        names_flags = string[0]
-        string = string[1:]
-    return string, names_flags
-
-def clean_string_end(string: 'str'):
-
-    names_flags = "√"
-    chars_to_remove = ['!', "'", '[', ']', '*', '|', '.', ':', '\\', '/']
-    if string.endswith(tuple(chars_to_remove)):
-        names_flags = string[-1]
-        string = string[:-1]
-    return string, names_flags
-
-def clean_dates(date: 'str'):
-    '''
-    Function: cleans the fields "datum smrti" and returns the char removed.
-    Input: date (string format)
-    Output: cleaned frame
-    '''
-
-    date_flags = "Y"
-    # finds special characters in the string
-    special_char = re.findall(r'[a-zA-Z!\[\|]', date)
-    if len(special_char) > 0:
-        date_flags = special_char
-    # remove special characters in the string
-    string = re.sub(r'[a-zA-Z!\[\|]', '', date)
-    return string, date_flags
-
-
-##### Main Function #####
-
-def pdf_extract_gr(image):
-  extractimg = preprocess_image(image)
-  #extractimg = np.array(image)
-  # deskew the image 
-  deskewed_image, angle, skew_confidence = deskew(extractimg, model_deskew)
-  # prepare the image for the autoencoder
-  cleanimg = prepare_image_to_autoencoder(deskewed_image)
-  # clean the image
-  img = autoencode_ONNX(cleanimg, model_denoise)
-  # extract the entries from the image
-  df = extract_detected_entries_pdl(img)
-  # first name 
-  firstnamerow = df.iloc[0]
-  firstname = firstnamerow[0]
-  firstnameconfidence = round(float(firstnamerow[1]) * 100,3)
-  firstnameconfidence = f"{firstnameconfidence}%"
-  # surname 
-  surnamerow = df.iloc[1]
-  surname = surnamerow[0]
-  surnameconfidence = round(float(surnamerow[1]) * 100,3)
-  surnameconfidence = f"{surnameconfidence}%"
-  # death date condifence
-  dodrow = df.iloc[2]
-  dodname = dodrow[0]
-  dodconfidence = round(float(dodrow[1]) * 100,3)
-  dodconfidence = f"{dodconfidence}%"
-  # return all the results 
-  return df, deskewed_image, angle, skew_confidence, img, firstname, firstnameconfidence, surname, surnameconfidence, dodname, dodconfidence
-
-
-##### Gradio Style #####
-
-css = """
-.run_container {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-  gap: 10px;
-}
-.run_btn {
-  margin: auto;
-  width: 50%;
-  display: flex;
-}
-.upload_cell {
-  margin: auto;
-  display: flex;
-}
-.results_container {
-  display: flex;
-  justify-content: space-evenly;
-}
-.results_cell {
-}
-"""
-
-##### Gradio Blocks #####
-
-with gr.Blocks(css = css) as demo:
-  gr.Markdown("""
-      # Death Certificate Extraction
-      """, elem_classes = "h1")
-  gr.Markdown("Upload a PDF, extract data")
-  with gr.Box(elem_classes = "run_container"):
-    # ExtractInput = gr.File(label = "Death Certificate", elem_classes="upload_cell")
-    ExtractButton = gr.Button(label = "Extract", elem_classes="run_btn")
-  with gr.Row(elem_id = "hide"):
-    with gr.Column():
-      ExtractInput = gr.Image()
-    with gr.Column():
-      # ExtractResult = gr.Image(label = "result")
-      with gr.Row(elem_classes = "results_container"):
-        FirstNameBox = gr.Textbox(label = "First Name", elem_classes = "results_cell")
-        FirstNameConfidenceBox = gr.Textbox(label = "First Name Confidence", elem_classes = "results_cell")
-      with gr.Row(elem_classes = "results_container"):
-        SurnameNameBox = gr.Textbox(label = "Surname", elem_classes = "results_cell")
-        SurnameNameConfidenceBox = gr.Textbox(label = "Surname Confidence", elem_classes = "results_cell")
-      with gr.Row(elem_classes = "results_container"):
-        DODBox = gr.Textbox(label = "Date of Death", elem_classes = "results_cell")
-        DODConfidenceBox = gr.Textbox(label = "Date of Death Confidence", elem_classes = "results_cell")
-
-      with gr.Accordion("Full Results", open = False):
-        ExtractDF = gr.Dataframe(label = "Results")
-
-      with gr.Accordion("Clean Image", open = False):
-        CleanOutput = gr.Image()
-
-      with gr.Accordion("Deskew", open = False):
-        DeskewOutput = gr.Image()
-        with gr.Column():
-          DeskewAngle = gr.Number(label = "Angle")
-        with gr.Column():
-          DeskewConfidence = gr.Number(label = "Confidence")
-
-  ExtractButton.click(fn=pdf_extract_gr, 
-                      inputs = ExtractInput, 
-                      outputs = [ExtractDF, DeskewOutput, DeskewAngle, 
-                                 DeskewConfidence, CleanOutput, FirstNameBox, 
-                                 FirstNameConfidenceBox, SurnameNameBox, 
-                                 SurnameNameConfidenceBox, DODBox, DODConfidenceBox])
-
-demo.launch(show_api=True, share=False, debug=True)

spaces/Aleistair/anything5/README.md

@@ -1,13 +0,0 @@
----
-title: Anything V3.0
-emoji: 🏃
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-duplicated_from: vntonie/anything-v3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlexWang/lama/saicinpainting/evaluation/utils.py

@@ -1,28 +0,0 @@
-from enum import Enum
-
-import yaml
-from easydict import EasyDict as edict
-import torch.nn as nn
-import torch
-
-
-def load_yaml(path):
-    with open(path, 'r') as f:
-        return edict(yaml.safe_load(f))
-
-
-def move_to_device(obj, device):
-    if isinstance(obj, nn.Module):
-        return obj.to(device)
-    if torch.is_tensor(obj):
-        return obj.to(device)
-    if isinstance(obj, (tuple, list)):
-        return [move_to_device(el, device) for el in obj]
-    if isinstance(obj, dict):
-        return {name: move_to_device(val, device) for name, val in obj.items()}
-    raise ValueError(f'Unexpected type {type(obj)}')
-
-
-class SmallMode(Enum):
-    DROP = "drop"
-    UPSCALE = "upscale"

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/optimization/mps.md

@@ -1,71 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Apple Silicon (M1/M2)에서 Stable Diffusion을 사용하는 방법
-
-Diffusers는 Stable Diffusion 추론을 위해 PyTorch `mps`를 사용해 Apple 실리콘과 호환됩니다. 다음은 Stable Diffusion이 있는 M1 또는 M2 컴퓨터를 사용하기 위해 따라야 하는 단계입니다.
-
-## 요구 사항
-
-- Apple silicon (M1/M2) 하드웨어의 Mac 컴퓨터.
-- macOS 12.6 또는 이후 (13.0 또는 이후 추천).
-- Python arm64 버전
-- PyTorch 2.0(추천) 또는 1.13(`mps`를 지원하는 최소 버전). Yhttps://pytorch.org/get-started/locally/의 지침에 따라 `pip` 또는 `conda`로 설치할 수 있습니다.
-
-
-## 추론 파이프라인
-
-아래 코도는 익숙한 `to()` 인터페이스를 사용하여 `mps` 백엔드로 Stable Diffusion 파이프라인을 M1 또는 M2 장치로 이동하는 방법을 보여줍니다.
-
-
-<Tip warning={true}>
-
-**PyTorch 1.13을 사용 중일 때 ** 추가 일회성 전달을 사용하여 파이프라인을 "프라이밍"하는 것을 추천합니다. 이것은 발견한 이상한 문제에 대한 임시 해결 방법입니다. 첫 번째 추론 전달은 후속 전달와 약간 다른 결과를 생성합니다. 이 전달은 한 번만 수행하면 되며 추론 단계를 한 번만 사용하고 결과를 폐기해도 됩니다.
-
-</Tip>
-
-이전 팁에서 설명한 것들을 포함한 여러 문제를 해결하므로 PyTorch 2 이상을 사용하는 것이 좋습니다.
-
-
-```python
-# `huggingface-cli login`에 로그인되어 있음을 확인
-from diffusers import DiffusionPipeline
-
-pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-pipe = pipe.to("mps")
-
-# 컴퓨터가 64GB 이하의 RAM 램일 때 추천
-pipe.enable_attention_slicing()
-
-prompt = "a photo of an astronaut riding a horse on mars"
-
-# 처음 "워밍업" 전달 (위 설명을 보세요)
-_ = pipe(prompt, num_inference_steps=1)
-
-# 결과는 워밍업 전달 후의 CPU 장치의 결과와 일치합니다.
-image = pipe(prompt).images[0]
-```
-
-## 성능 추천
-
-M1/M2 성능은 메모리 압력에 매우 민감합니다. 시스템은 필요한 경우 자동으로 스왑되지만 스왑할 때 성능이 크게 저하됩니다.
-
-
-특히 컴퓨터의 시스템 RAM이 64GB 미만이거나 512 × 512픽셀보다 큰 비표준 해상도에서 이미지를 생성하는 경우, 추론 중에 메모리 압력을 줄이고 스와핑을 방지하기 위해 *어텐션 슬라이싱*을 사용하는 것이 좋습니다. 어텐션 슬라이싱은 비용이 많이 드는 어텐션 작업을 한 번에 모두 수행하는 대신 여러 단계로 수행합니다. 일반적으로 범용 메모리가 없는 컴퓨터에서 ~20%의 성능 영향을 미치지만 64GB 이상이 아닌 경우 대부분의 Apple Silicon 컴퓨터에서 *더 나은 성능*이 관찰되었습니다.
-
-```python
-pipeline.enable_attention_slicing()
-```
-
-## Known Issues
-
-- 여러 프롬프트를 배치로 생성하는 것은 [충돌이 발생하거나 안정적으로 작동하지 않습니다](https://github.com/huggingface/diffusers/issues/363). 우리는 이것이 [PyTorch의 `mps` 백엔드](https://github.com/pytorch/pytorch/issues/84039)와 관련이 있다고 생각합니다. 이 문제는 해결되고 있지만 지금은 배치 대신 반복 방법을 사용하는 것이 좋습니다.

spaces/Andy1621/uniformer_image_detection/configs/swin/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py

@@ -1,140 +0,0 @@
-_base_ = [
-    '../_base_/models/cascade_mask_rcnn_swin_fpn.py',
-    '../_base_/datasets/coco_instance.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-model = dict(
-    backbone=dict(
-        embed_dim=128,
-        depths=[2, 2, 18, 2],
-        num_heads=[4, 8, 16, 32],
-        window_size=7,
-        ape=False,
-        drop_path_rate=0.3,
-        patch_norm=True,
-        use_checkpoint=False
-    ),
-    neck=dict(in_channels=[128, 256, 512, 1024]),
-    roi_head=dict(
-        bbox_head=[
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.1, 0.1, 0.2, 0.2]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.05, 0.05, 0.1, 0.1]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
-            dict(
-                type='ConvFCBBoxHead',
-                num_shared_convs=4,
-                num_shared_fcs=1,
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.033, 0.033, 0.067, 0.067]),
-                reg_class_agnostic=False,
-                reg_decoded_bbox=True,
-                norm_cfg=dict(type='SyncBN', requires_grad=True),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-                loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
-        ]))
-
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='AutoAugment',
-         policies=[
-             [
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                                 (736, 1333), (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True)
-             ],
-             [
-                 dict(type='Resize',
-                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True),
-                 dict(type='RandomCrop',
-                      crop_type='absolute_range',
-                      crop_size=(384, 600),
-                      allow_negative_crop=True),
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                                 (576, 1333), (608, 1333), (640, 1333),
-                                 (672, 1333), (704, 1333), (736, 1333),
-                                 (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      override=True,
-                      keep_ratio=True)
-             ]
-         ]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[27, 33])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
-    type="DistOptimizerHook",
-    update_interval=1,
-    grad_clip=None,
-    coalesce=True,
-    bucket_size_mb=-1,
-    use_fp16=True,
-)

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/__init__.py

@@ -1,27 +0,0 @@
-from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner,
-                        MaxIoUAssigner, RegionAssigner)
-from .builder import build_assigner, build_bbox_coder, build_sampler
-from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, PseudoBBoxCoder,
-                    TBLRBBoxCoder)
-from .iou_calculators import BboxOverlaps2D, bbox_overlaps
-from .samplers import (BaseSampler, CombinedSampler,
-                       InstanceBalancedPosSampler, IoUBalancedNegSampler,
-                       OHEMSampler, PseudoSampler, RandomSampler,
-                       SamplingResult, ScoreHLRSampler)
-from .transforms import (bbox2distance, bbox2result, bbox2roi,
-                         bbox_cxcywh_to_xyxy, bbox_flip, bbox_mapping,
-                         bbox_mapping_back, bbox_rescale, bbox_xyxy_to_cxcywh,
-                         distance2bbox, roi2bbox)
-
-__all__ = [
-    'bbox_overlaps', 'BboxOverlaps2D', 'BaseAssigner', 'MaxIoUAssigner',
-    'AssignResult', 'BaseSampler', 'PseudoSampler', 'RandomSampler',
-    'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler',
-    'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler', 'build_assigner',
-    'build_sampler', 'bbox_flip', 'bbox_mapping', 'bbox_mapping_back',
-    'bbox2roi', 'roi2bbox', 'bbox2result', 'distance2bbox', 'bbox2distance',
-    'build_bbox_coder', 'BaseBBoxCoder', 'PseudoBBoxCoder',
-    'DeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'CenterRegionAssigner',
-    'bbox_rescale', 'bbox_cxcywh_to_xyxy', 'bbox_xyxy_to_cxcywh',
-    'RegionAssigner'
-]

spaces/Andy1621/uniformer_image_segmentation/configs/_base_/models/gcnet_r50-d8.py

@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='GCHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        ratio=1 / 4.,
-        pooling_type='att',
-        fusion_types=('channel_add', ),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/tin_shift.py

@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# Code reference from "Temporal Interlacing Network"
-# https://github.com/deepcs233/TIN/blob/master/cuda_shift/rtc_wrap.py
-# Hao Shao, Shengju Qian, Yu Liu
-# [email protected], [email protected], [email protected]
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext',
-                                 ['tin_shift_forward', 'tin_shift_backward'])
-
-
-class TINShiftFunction(Function):
-
-    @staticmethod
-    def forward(ctx, input, shift):
-        C = input.size(2)
-        num_segments = shift.size(1)
-        if C // num_segments <= 0 or C % num_segments != 0:
-            raise ValueError('C should be a multiple of num_segments, '
-                             f'but got C={C} and num_segments={num_segments}.')
-
-        ctx.save_for_backward(shift)
-
-        out = torch.zeros_like(input)
-        ext_module.tin_shift_forward(input, shift, out)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-
-        shift = ctx.saved_tensors[0]
-        data_grad_input = grad_output.new(*grad_output.size()).zero_()
-        shift_grad_input = shift.new(*shift.size()).zero_()
-        ext_module.tin_shift_backward(grad_output, shift, data_grad_input)
-
-        return data_grad_input, shift_grad_input
-
-
-tin_shift = TINShiftFunction.apply
-
-
-class TINShift(nn.Module):
-    """Temporal Interlace Shift.
-
-    Temporal Interlace shift is a differentiable temporal-wise frame shifting
-    which is proposed in "Temporal Interlacing Network"
-
-    Please refer to https://arxiv.org/abs/2001.06499 for more details.
-    Code is modified from https://github.com/mit-han-lab/temporal-shift-module
-    """
-
-    def forward(self, input, shift):
-        """Perform temporal interlace shift.
-
-        Args:
-            input (Tensor): Feature map with shape [N, num_segments, C, H * W].
-            shift (Tensor): Shift tensor with shape [N, num_segments].
-
-        Returns:
-            Feature map after temporal interlace shift.
-        """
-        return tin_shift(input, shift)

spaces/Arsenii2023/Demo1/logistic.py

@@ -1,40 +0,0 @@
-#Author: Arsenii Kostenko
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-import gradio as gr
-
-# Данные для обучения модели
-x_train = np.array([[0, 0], [1, 1], [2, 2]])
-y_train = np.array([0, 1, 2])
-
-# Обучение модели
-model = LogisticRegression()
-model.fit(x_train, y_train)
-
-# Функция для предсказания значений
-def predict(x, y):
-    # Преобразование строк в списки списков
-    x_nested_list = [list(map(int, sublist.split(","))) for sublist in x.split(";")]
-    y_nested_list = [list(map(int, sublist.split(","))) for sublist in y.split(";")]
-
-    # Преобразование списков списков в numpy arrays
-    x_array = np.array(x_nested_list)
-    y_array = np.array(y_nested_list)
-
-    # Проверка исходных данных на соответствие
-    if x_array.shape != y_array.shape:
-        return "Ошибка: x и y должны иметь одинаковую размерность"
-    
-    # Предсказание значений
-    predictions = model.predict(x_array)
-
-    return predictions
-
-# Создание интерфейса gradio
-iface = gr.Interface(
-    fn=predict,
-    inputs=["text", "text"],
-    outputs="text"
-)
-
-iface.launch(debug=True)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/bcppcompiler.py

@@ -1,408 +0,0 @@
-"""distutils.bcppcompiler
-
-Contains BorlandCCompiler, an implementation of the abstract CCompiler class
-for the Borland C++ compiler.
-"""
-
-# This implementation by Lyle Johnson, based on the original msvccompiler.py
-# module and using the directions originally published by Gordon Williams.
-
-# XXX looks like there's a LOT of overlap between these two classes:
-# someone should sit down and factor out the common code as
-# WindowsCCompiler!  --GPW
-
-
-import os
-import warnings
-
-from distutils.errors import (
-    DistutilsExecError,
-    CompileError,
-    LibError,
-    LinkError,
-    UnknownFileError,
-)
-from distutils.ccompiler import CCompiler, gen_preprocess_options
-from distutils.file_util import write_file
-from distutils.dep_util import newer
-from distutils import log
-
-
-warnings.warn(
-    "bcppcompiler is deprecated and slated to be removed "
-    "in the future. Please discontinue use or file an issue "
-    "with pypa/distutils describing your use case.",
-    DeprecationWarning,
-)
-
-
-class BCPPCompiler(CCompiler):
-    """Concrete class that implements an interface to the Borland C/C++
-    compiler, as defined by the CCompiler abstract class.
-    """
-
-    compiler_type = 'bcpp'
-
-    # Just set this so CCompiler's constructor doesn't barf.  We currently
-    # don't use the 'set_executables()' bureaucracy provided by CCompiler,
-    # as it really isn't necessary for this sort of single-compiler class.
-    # Would be nice to have a consistent interface with UnixCCompiler,
-    # though, so it's worth thinking about.
-    executables = {}
-
-    # Private class data (need to distinguish C from C++ source for compiler)
-    _c_extensions = ['.c']
-    _cpp_extensions = ['.cc', '.cpp', '.cxx']
-
-    # Needed for the filename generation methods provided by the
-    # base class, CCompiler.
-    src_extensions = _c_extensions + _cpp_extensions
-    obj_extension = '.obj'
-    static_lib_extension = '.lib'
-    shared_lib_extension = '.dll'
-    static_lib_format = shared_lib_format = '%s%s'
-    exe_extension = '.exe'
-
-    def __init__(self, verbose=0, dry_run=0, force=0):
-
-        super().__init__(verbose, dry_run, force)
-
-        # These executables are assumed to all be in the path.
-        # Borland doesn't seem to use any special registry settings to
-        # indicate their installation locations.
-
-        self.cc = "bcc32.exe"
-        self.linker = "ilink32.exe"
-        self.lib = "tlib.exe"
-
-        self.preprocess_options = None
-        self.compile_options = ['/tWM', '/O2', '/q', '/g0']
-        self.compile_options_debug = ['/tWM', '/Od', '/q', '/g0']
-
-        self.ldflags_shared = ['/Tpd', '/Gn', '/q', '/x']
-        self.ldflags_shared_debug = ['/Tpd', '/Gn', '/q', '/x']
-        self.ldflags_static = []
-        self.ldflags_exe = ['/Gn', '/q', '/x']
-        self.ldflags_exe_debug = ['/Gn', '/q', '/x', '/r']
-
-    # -- Worker methods ------------------------------------------------
-
-    def compile(  # noqa: C901
-        self,
-        sources,
-        output_dir=None,
-        macros=None,
-        include_dirs=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        depends=None,
-    ):
-
-        macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
-            output_dir, macros, include_dirs, sources, depends, extra_postargs
-        )
-        compile_opts = extra_preargs or []
-        compile_opts.append('-c')
-        if debug:
-            compile_opts.extend(self.compile_options_debug)
-        else:
-            compile_opts.extend(self.compile_options)
-
-        for obj in objects:
-            try:
-                src, ext = build[obj]
-            except KeyError:
-                continue
-            # XXX why do the normpath here?
-            src = os.path.normpath(src)
-            obj = os.path.normpath(obj)
-            # XXX _setup_compile() did a mkpath() too but before the normpath.
-            # Is it possible to skip the normpath?
-            self.mkpath(os.path.dirname(obj))
-
-            if ext == '.res':
-                # This is already a binary file -- skip it.
-                continue  # the 'for' loop
-            if ext == '.rc':
-                # This needs to be compiled to a .res file -- do it now.
-                try:
-                    self.spawn(["brcc32", "-fo", obj, src])
-                except DistutilsExecError as msg:
-                    raise CompileError(msg)
-                continue  # the 'for' loop
-
-            # The next two are both for the real compiler.
-            if ext in self._c_extensions:
-                input_opt = ""
-            elif ext in self._cpp_extensions:
-                input_opt = "-P"
-            else:
-                # Unknown file type -- no extra options.  The compiler
-                # will probably fail, but let it just in case this is a
-                # file the compiler recognizes even if we don't.
-                input_opt = ""
-
-            output_opt = "-o" + obj
-
-            # Compiler command line syntax is: "bcc32 [options] file(s)".
-            # Note that the source file names must appear at the end of
-            # the command line.
-            try:
-                self.spawn(
-                    [self.cc]
-                    + compile_opts
-                    + pp_opts
-                    + [input_opt, output_opt]
-                    + extra_postargs
-                    + [src]
-                )
-            except DistutilsExecError as msg:
-                raise CompileError(msg)
-
-        return objects
-
-    # compile ()
-
-    def create_static_lib(
-        self, objects, output_libname, output_dir=None, debug=0, target_lang=None
-    ):
-
-        (objects, output_dir) = self._fix_object_args(objects, output_dir)
-        output_filename = self.library_filename(output_libname, output_dir=output_dir)
-
-        if self._need_link(objects, output_filename):
-            lib_args = [output_filename, '/u'] + objects
-            if debug:
-                pass  # XXX what goes here?
-            try:
-                self.spawn([self.lib] + lib_args)
-            except DistutilsExecError as msg:
-                raise LibError(msg)
-        else:
-            log.debug("skipping %s (up-to-date)", output_filename)
-
-    # create_static_lib ()
-
-    def link(  # noqa: C901
-        self,
-        target_desc,
-        objects,
-        output_filename,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        export_symbols=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        build_temp=None,
-        target_lang=None,
-    ):
-
-        # XXX this ignores 'build_temp'!  should follow the lead of
-        # msvccompiler.py
-
-        (objects, output_dir) = self._fix_object_args(objects, output_dir)
-        (libraries, library_dirs, runtime_library_dirs) = self._fix_lib_args(
-            libraries, library_dirs, runtime_library_dirs
-        )
-
-        if runtime_library_dirs:
-            log.warn(
-                "I don't know what to do with 'runtime_library_dirs': %s",
-                str(runtime_library_dirs),
-            )
-
-        if output_dir is not None:
-            output_filename = os.path.join(output_dir, output_filename)
-
-        if self._need_link(objects, output_filename):
-
-            # Figure out linker args based on type of target.
-            if target_desc == CCompiler.EXECUTABLE:
-                startup_obj = 'c0w32'
-                if debug:
-                    ld_args = self.ldflags_exe_debug[:]
-                else:
-                    ld_args = self.ldflags_exe[:]
-            else:
-                startup_obj = 'c0d32'
-                if debug:
-                    ld_args = self.ldflags_shared_debug[:]
-                else:
-                    ld_args = self.ldflags_shared[:]
-
-            # Create a temporary exports file for use by the linker
-            if export_symbols is None:
-                def_file = ''
-            else:
-                head, tail = os.path.split(output_filename)
-                modname, ext = os.path.splitext(tail)
-                temp_dir = os.path.dirname(objects[0])  # preserve tree structure
-                def_file = os.path.join(temp_dir, '%s.def' % modname)
-                contents = ['EXPORTS']
-                for sym in export_symbols or []:
-                    contents.append('  {}=_{}'.format(sym, sym))
-                self.execute(write_file, (def_file, contents), "writing %s" % def_file)
-
-            # Borland C++ has problems with '/' in paths
-            objects2 = map(os.path.normpath, objects)
-            # split objects in .obj and .res files
-            # Borland C++ needs them at different positions in the command line
-            objects = [startup_obj]
-            resources = []
-            for file in objects2:
-                (base, ext) = os.path.splitext(os.path.normcase(file))
-                if ext == '.res':
-                    resources.append(file)
-                else:
-                    objects.append(file)
-
-            for ell in library_dirs:
-                ld_args.append("/L%s" % os.path.normpath(ell))
-            ld_args.append("/L.")  # we sometimes use relative paths
-
-            # list of object files
-            ld_args.extend(objects)
-
-            # XXX the command-line syntax for Borland C++ is a bit wonky;
-            # certain filenames are jammed together in one big string, but
-            # comma-delimited.  This doesn't mesh too well with the
-            # Unix-centric attitude (with a DOS/Windows quoting hack) of
-            # 'spawn()', so constructing the argument list is a bit
-            # awkward.  Note that doing the obvious thing and jamming all
-            # the filenames and commas into one argument would be wrong,
-            # because 'spawn()' would quote any filenames with spaces in
-            # them.  Arghghh!.  Apparently it works fine as coded...
-
-            # name of dll/exe file
-            ld_args.extend([',', output_filename])
-            # no map file and start libraries
-            ld_args.append(',,')
-
-            for lib in libraries:
-                # see if we find it and if there is a bcpp specific lib
-                # (xxx_bcpp.lib)
-                libfile = self.find_library_file(library_dirs, lib, debug)
-                if libfile is None:
-                    ld_args.append(lib)
-                    # probably a BCPP internal library -- don't warn
-                else:
-                    # full name which prefers bcpp_xxx.lib over xxx.lib
-                    ld_args.append(libfile)
-
-            # some default libraries
-            ld_args.append('import32')
-            ld_args.append('cw32mt')
-
-            # def file for export symbols
-            ld_args.extend([',', def_file])
-            # add resource files
-            ld_args.append(',')
-            ld_args.extend(resources)
-
-            if extra_preargs:
-                ld_args[:0] = extra_preargs
-            if extra_postargs:
-                ld_args.extend(extra_postargs)
-
-            self.mkpath(os.path.dirname(output_filename))
-            try:
-                self.spawn([self.linker] + ld_args)
-            except DistutilsExecError as msg:
-                raise LinkError(msg)
-
-        else:
-            log.debug("skipping %s (up-to-date)", output_filename)
-
-    # link ()
-
-    # -- Miscellaneous methods -----------------------------------------
-
-    def find_library_file(self, dirs, lib, debug=0):
-        # List of effective library names to try, in order of preference:
-        # xxx_bcpp.lib is better than xxx.lib
-        # and xxx_d.lib is better than xxx.lib if debug is set
-        #
-        # The "_bcpp" suffix is to handle a Python installation for people
-        # with multiple compilers (primarily Distutils hackers, I suspect
-        # ;-).  The idea is they'd have one static library for each
-        # compiler they care about, since (almost?) every Windows compiler
-        # seems to have a different format for static libraries.
-        if debug:
-            dlib = lib + "_d"
-            try_names = (dlib + "_bcpp", lib + "_bcpp", dlib, lib)
-        else:
-            try_names = (lib + "_bcpp", lib)
-
-        for dir in dirs:
-            for name in try_names:
-                libfile = os.path.join(dir, self.library_filename(name))
-                if os.path.exists(libfile):
-                    return libfile
-        else:
-            # Oops, didn't find it in *any* of 'dirs'
-            return None
-
-    # overwrite the one from CCompiler to support rc and res-files
-    def object_filenames(self, source_filenames, strip_dir=0, output_dir=''):
-        if output_dir is None:
-            output_dir = ''
-        obj_names = []
-        for src_name in source_filenames:
-            # use normcase to make sure '.rc' is really '.rc' and not '.RC'
-            (base, ext) = os.path.splitext(os.path.normcase(src_name))
-            if ext not in (self.src_extensions + ['.rc', '.res']):
-                raise UnknownFileError(
-                    "unknown file type '{}' (from '{}')".format(ext, src_name)
-                )
-            if strip_dir:
-                base = os.path.basename(base)
-            if ext == '.res':
-                # these can go unchanged
-                obj_names.append(os.path.join(output_dir, base + ext))
-            elif ext == '.rc':
-                # these need to be compiled to .res-files
-                obj_names.append(os.path.join(output_dir, base + '.res'))
-            else:
-                obj_names.append(os.path.join(output_dir, base + self.obj_extension))
-        return obj_names
-
-    # object_filenames ()
-
-    def preprocess(
-        self,
-        source,
-        output_file=None,
-        macros=None,
-        include_dirs=None,
-        extra_preargs=None,
-        extra_postargs=None,
-    ):
-
-        (_, macros, include_dirs) = self._fix_compile_args(None, macros, include_dirs)
-        pp_opts = gen_preprocess_options(macros, include_dirs)
-        pp_args = ['cpp32.exe'] + pp_opts
-        if output_file is not None:
-            pp_args.append('-o' + output_file)
-        if extra_preargs:
-            pp_args[:0] = extra_preargs
-        if extra_postargs:
-            pp_args.extend(extra_postargs)
-        pp_args.append(source)
-
-        # We need to preprocess: either we're being forced to, or the
-        # source file is newer than the target (or the target doesn't
-        # exist).
-        if self.force or output_file is None or newer(source, output_file):
-            if output_file:
-                self.mkpath(os.path.dirname(output_file))
-            try:
-                self.spawn(pp_args)
-            except DistutilsExecError as msg:
-                print(msg)
-                raise CompileError(msg)
-
-    # preprocess()

spaces/Audio-AGI/WavJourney/examples/examples.py

@@ -1,87 +0,0 @@
-
-example1 = {
-    'text': "An introduction to AI-assisted audio content creation.",
-    'table_script': """
-| Audio Type   | Layout     | ID | Character | Action | Volume | Description                                                      | Length |
-|--------------|------------|----|-----------|--------|--------|------------------------------------------------------------------|--------|
-| music        | background | 1  | N/A       | begin  | -35    | Inspirational technology-themed music                            | Auto   |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Welcome to the future of audio content creation.                 | Auto   |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Digital startup sound                                            | 2      |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | With evolving technology, we are introducing AI-assisted tools for pristine audio production. | Auto |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Keyboard typing noise                                            | 3      |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Imagine crafting audio content with the power of AI at your fingertips. | Auto |
-| sound_effect | background | 2  | N/A       | begin  | -35    | Ambiance of a busy control room                                   | Auto   |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Enhanced quality, efficient production and limitless creativity, all under one roof. | Auto |
-| sound_effect | background | 2  | N/A       | end    | N/A    | N/A                                                              | Auto   |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Unleash your potential with AI-assisted audio content creation.  | Auto   |
-| music        | background | 1  | N/A       | end    | N/A    | N/A                                                              | Auto   |
-
-""",
-    'table_voice': """
-| Character   | Voice     |
-|-------------|-----------|
-| Narrator    | News_Male_En |
-
-""",
-    'wav_file': 'examples/1.mp4',
-}
-
-example2 = {
-    'text': "A couple dating in a cafe.",
-    'table_script': """
-| Audio Type   | Layout     | ID | Character | Action | Volume | Description                                   | Length |
-|--------------|------------|----|-----------|--------|--------|-----------------------------------------------|--------|
-| sound_effect | background | 1  | N/A       | begin  | -35    | Soft chattering in a cafe                     | Auto   |
-| sound_effect | background | 2  | N/A       | begin  | -38    | Coffee brewing noises                         | Auto   |
-| music        | background | 3  | N/A       | begin  | -35    | Soft jazz playing in the background           | Auto   |
-| speech       | foreground | N/A| Man       | N/A    | -15    | It’s really nice to finally get out and relax a little, isn’t it? | Auto |
-| speech       | foreground | N/A| Woman     | N/A    | -15    | I know, right? We should do this more often.  | Auto   |
-| sound_effect | background | 2  | N/A       | end    | N/A    | N/A                                           | Auto   |
-| speech       | foreground | N/A| Man       | N/A    | -15    | Here’s your coffee, just as you like it.      | Auto   |
-| speech       | foreground | N/A| Woman     | N/A    | -15    | Thank you, it smells wonderful.               | Auto   |
-| music        | background | 3  | N/A       | end    | N/A    | N/A                                           | Auto   |
-| sound_effect | background | 1  | N/A       | end    | N/A    | N/A                                           | Auto   |
-
-""",
-    'table_voice': """
-| Character   | Voice     |
-|-------------|-----------|
-| Man         | Male1_En    |
-| Woman       | Female1_En   |
-
-""",
-    'wav_file': 'examples/2.mp4',
-}
-
-
-example3 = {
-    'text': "A child is participating in a farting contest.",
-    'table_script': """
-| Audio Type   | Layout     | ID | Character | Action | Volume | Description                                          | Length |
-|--------------|------------|----|-----------|--------|--------|------------------------------------------------------|--------|
-| sound_effect | background | 1  | N/A       | begin  | -35    | Outdoor park ambiance, people chattering             | Auto   |
-| music        | background | 2  | N/A       | begin  | -35    | Light comedy theme music, quirky                     | Auto   |
-| speech       | foreground | N/A| Host      | N/A    | -15    | Welcome to the annual Fart Competition.              | Auto   |
-| speech       | foreground | N/A| Host      | N/A    | -15    | Now, let’s welcome our youngest participant.         | Auto   |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Clapping sound                                       | 2      |
-| speech       | foreground | N/A| Child     | N/A    | -15    | Hi, I’m excited to be here.                          | Auto   |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Short, cartoonish duration of a fart sound           | 4      |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Audience laughing and applauding                     | 2      |
-| speech       | foreground | N/A| Host      | N/A    | -15    | Wow, that was impressive! Let’s give another round of applause! | Auto |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Audience clapping and cheering                       | 3      |
-| music        | background | 2  | N/A       | end    | N/A    | N/A                                                  | Auto   |
-| sound_effect | background | 1  | N/A       | end    | N/A    | N/A                                                  | Auto   |
-""",
-    'table_voice': """
-| Character   | Voice     |
-|-------------|-----------|
-| Host        | Male1_En     |
-| Child       | Child_En     |
-
-""",
-    'wav_file': 'examples/3.mp4',
-}
-
-
-
-examples = [example1, example2, example3]

spaces/Awesimo/jojogan/e4e/criteria/id_loss.py

@@ -1,47 +0,0 @@
-import torch
-from torch import nn
-from configs.paths_config import model_paths
-from models.encoders.model_irse import Backbone
-
-
-class IDLoss(nn.Module):
-    def __init__(self):
-        super(IDLoss, self).__init__()
-        print('Loading ResNet ArcFace')
-        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
-        self.facenet.load_state_dict(torch.load(model_paths['ir_se50']))
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
-        self.facenet.eval()
-        for module in [self.facenet, self.face_pool]:
-            for param in module.parameters():
-                param.requires_grad = False
-
-    def extract_feats(self, x):
-        x = x[:, :, 35:223, 32:220]  # Crop interesting region
-        x = self.face_pool(x)
-        x_feats = self.facenet(x)
-        return x_feats
-
-    def forward(self, y_hat, y, x):
-        n_samples = x.shape[0]
-        x_feats = self.extract_feats(x)
-        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        sim_improvement = 0
-        id_logs = []
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            diff_input = y_hat_feats[i].dot(x_feats[i])
-            diff_views = y_feats[i].dot(x_feats[i])
-            id_logs.append({'diff_target': float(diff_target),
-                            'diff_input': float(diff_input),
-                            'diff_views': float(diff_views)})
-            loss += 1 - diff_target
-            id_diff = float(diff_target) - float(diff_views)
-            sim_improvement += id_diff
-            count += 1
-
-        return loss / count, sim_improvement / count, id_logs

spaces/Bart92/RVC_HF/tools/torchgate/torchgate.py

@@ -1,264 +0,0 @@
-import torch
-from torch.nn.functional import conv1d, conv2d
-from typing import Union, Optional
-from .utils import linspace, temperature_sigmoid, amp_to_db
-
-
-class TorchGate(torch.nn.Module):
-    """
-    A PyTorch module that applies a spectral gate to an input signal.
-
-    Arguments:
-        sr {int} -- Sample rate of the input signal.
-        nonstationary {bool} -- Whether to use non-stationary or stationary masking (default: {False}).
-        n_std_thresh_stationary {float} -- Number of standard deviations above mean to threshold noise for
-                                           stationary masking (default: {1.5}).
-        n_thresh_nonstationary {float} -- Number of multiplies above smoothed magnitude spectrogram. for
-                                        non-stationary masking (default: {1.3}).
-        temp_coeff_nonstationary {float} -- Temperature coefficient for non-stationary masking (default: {0.1}).
-        n_movemean_nonstationary {int} -- Number of samples for moving average smoothing in non-stationary masking
-                                          (default: {20}).
-        prop_decrease {float} -- Proportion to decrease signal by where the mask is zero (default: {1.0}).
-        n_fft {int} -- Size of FFT for STFT (default: {1024}).
-        win_length {[int]} -- Window length for STFT. If None, defaults to `n_fft` (default: {None}).
-        hop_length {[int]} -- Hop length for STFT. If None, defaults to `win_length` // 4 (default: {None}).
-        freq_mask_smooth_hz {float} -- Frequency smoothing width for mask (in Hz). If None, no smoothing is applied
-                                     (default: {500}).
-        time_mask_smooth_ms {float} -- Time smoothing width for mask (in ms). If None, no smoothing is applied
-                                     (default: {50}).
-    """
-
-    @torch.no_grad()
-    def __init__(
-        self,
-        sr: int,
-        nonstationary: bool = False,
-        n_std_thresh_stationary: float = 1.5,
-        n_thresh_nonstationary: float = 1.3,
-        temp_coeff_nonstationary: float = 0.1,
-        n_movemean_nonstationary: int = 20,
-        prop_decrease: float = 1.0,
-        n_fft: int = 1024,
-        win_length: bool = None,
-        hop_length: int = None,
-        freq_mask_smooth_hz: float = 500,
-        time_mask_smooth_ms: float = 50,
-    ):
-        super().__init__()
-
-        # General Params
-        self.sr = sr
-        self.nonstationary = nonstationary
-        assert 0.0 <= prop_decrease <= 1.0
-        self.prop_decrease = prop_decrease
-
-        # STFT Params
-        self.n_fft = n_fft
-        self.win_length = self.n_fft if win_length is None else win_length
-        self.hop_length = self.win_length // 4 if hop_length is None else hop_length
-
-        # Stationary Params
-        self.n_std_thresh_stationary = n_std_thresh_stationary
-
-        # Non-Stationary Params
-        self.temp_coeff_nonstationary = temp_coeff_nonstationary
-        self.n_movemean_nonstationary = n_movemean_nonstationary
-        self.n_thresh_nonstationary = n_thresh_nonstationary
-
-        # Smooth Mask Params
-        self.freq_mask_smooth_hz = freq_mask_smooth_hz
-        self.time_mask_smooth_ms = time_mask_smooth_ms
-        self.register_buffer("smoothing_filter", self._generate_mask_smoothing_filter())
-
-    @torch.no_grad()
-    def _generate_mask_smoothing_filter(self) -> Union[torch.Tensor, None]:
-        """
-        A PyTorch module that applies a spectral gate to an input signal using the STFT.
-
-        Returns:
-            smoothing_filter (torch.Tensor): a 2D tensor representing the smoothing filter,
-            with shape (n_grad_freq, n_grad_time), where n_grad_freq is the number of frequency
-            bins to smooth and n_grad_time is the number of time frames to smooth.
-            If both self.freq_mask_smooth_hz and self.time_mask_smooth_ms are None, returns None.
-        """
-        if self.freq_mask_smooth_hz is None and self.time_mask_smooth_ms is None:
-            return None
-
-        n_grad_freq = (
-            1
-            if self.freq_mask_smooth_hz is None
-            else int(self.freq_mask_smooth_hz / (self.sr / (self.n_fft / 2)))
-        )
-        if n_grad_freq < 1:
-            raise ValueError(
-                f"freq_mask_smooth_hz needs to be at least {int((self.sr / (self._n_fft / 2)))} Hz"
-            )
-
-        n_grad_time = (
-            1
-            if self.time_mask_smooth_ms is None
-            else int(self.time_mask_smooth_ms / ((self.hop_length / self.sr) * 1000))
-        )
-        if n_grad_time < 1:
-            raise ValueError(
-                f"time_mask_smooth_ms needs to be at least {int((self.hop_length / self.sr) * 1000)} ms"
-            )
-
-        if n_grad_time == 1 and n_grad_freq == 1:
-            return None
-
-        v_f = torch.cat(
-            [
-                linspace(0, 1, n_grad_freq + 1, endpoint=False),
-                linspace(1, 0, n_grad_freq + 2),
-            ]
-        )[1:-1]
-        v_t = torch.cat(
-            [
-                linspace(0, 1, n_grad_time + 1, endpoint=False),
-                linspace(1, 0, n_grad_time + 2),
-            ]
-        )[1:-1]
-        smoothing_filter = torch.outer(v_f, v_t).unsqueeze(0).unsqueeze(0)
-
-        return smoothing_filter / smoothing_filter.sum()
-
-    @torch.no_grad()
-    def _stationary_mask(
-        self, X_db: torch.Tensor, xn: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        """
-        Computes a stationary binary mask to filter out noise in a log-magnitude spectrogram.
-
-        Arguments:
-            X_db (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the log-magnitude spectrogram.
-            xn (torch.Tensor): 1D tensor containing the audio signal corresponding to X_db.
-
-        Returns:
-            sig_mask (torch.Tensor): Binary mask of the same shape as X_db, where values greater than the threshold
-            are set to 1, and the rest are set to 0.
-        """
-        if xn is not None:
-            XN = torch.stft(
-                xn,
-                n_fft=self.n_fft,
-                hop_length=self.hop_length,
-                win_length=self.win_length,
-                return_complex=True,
-                pad_mode="constant",
-                center=True,
-                window=torch.hann_window(self.win_length).to(xn.device),
-            )
-
-            XN_db = amp_to_db(XN).to(dtype=X_db.dtype)
-        else:
-            XN_db = X_db
-
-        # calculate mean and standard deviation along the frequency axis
-        std_freq_noise, mean_freq_noise = torch.std_mean(XN_db, dim=-1)
-
-        # compute noise threshold
-        noise_thresh = mean_freq_noise + std_freq_noise * self.n_std_thresh_stationary
-
-        # create binary mask by thresholding the spectrogram
-        sig_mask = X_db > noise_thresh.unsqueeze(2)
-        return sig_mask
-
-    @torch.no_grad()
-    def _nonstationary_mask(self, X_abs: torch.Tensor) -> torch.Tensor:
-        """
-        Computes a non-stationary binary mask to filter out noise in a log-magnitude spectrogram.
-
-        Arguments:
-            X_abs (torch.Tensor): 2D tensor of shape (frames, freq_bins) containing the magnitude spectrogram.
-
-        Returns:
-            sig_mask (torch.Tensor): Binary mask of the same shape as X_abs, where values greater than the threshold
-            are set to 1, and the rest are set to 0.
-        """
-        X_smoothed = (
-            conv1d(
-                X_abs.reshape(-1, 1, X_abs.shape[-1]),
-                torch.ones(
-                    self.n_movemean_nonstationary,
-                    dtype=X_abs.dtype,
-                    device=X_abs.device,
-                ).view(1, 1, -1),
-                padding="same",
-            ).view(X_abs.shape)
-            / self.n_movemean_nonstationary
-        )
-
-        # Compute slowness ratio and apply temperature sigmoid
-        slowness_ratio = (X_abs - X_smoothed) / (X_smoothed + 1e-6)
-        sig_mask = temperature_sigmoid(
-            slowness_ratio, self.n_thresh_nonstationary, self.temp_coeff_nonstationary
-        )
-
-        return sig_mask
-
-    def forward(
-        self, x: torch.Tensor, xn: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        """
-        Apply the proposed algorithm to the input signal.
-
-        Arguments:
-            x (torch.Tensor): The input audio signal, with shape (batch_size, signal_length).
-            xn (Optional[torch.Tensor]): The noise signal used for stationary noise reduction. If `None`, the input
-                                         signal is used as the noise signal. Default: `None`.
-
-        Returns:
-            torch.Tensor: The denoised audio signal, with the same shape as the input signal.
-        """
-        assert x.ndim == 2
-        if x.shape[-1] < self.win_length * 2:
-            raise Exception(f"x must be bigger than {self.win_length * 2}")
-
-        assert xn is None or xn.ndim == 1 or xn.ndim == 2
-        if xn is not None and xn.shape[-1] < self.win_length * 2:
-            raise Exception(f"xn must be bigger than {self.win_length * 2}")
-
-        # Compute short-time Fourier transform (STFT)
-        X = torch.stft(
-            x,
-            n_fft=self.n_fft,
-            hop_length=self.hop_length,
-            win_length=self.win_length,
-            return_complex=True,
-            pad_mode="constant",
-            center=True,
-            window=torch.hann_window(self.win_length).to(x.device),
-        )
-
-        # Compute signal mask based on stationary or nonstationary assumptions
-        if self.nonstationary:
-            sig_mask = self._nonstationary_mask(X.abs())
-        else:
-            sig_mask = self._stationary_mask(amp_to_db(X), xn)
-
-        # Propagate decrease in signal power
-        sig_mask = self.prop_decrease * (sig_mask * 1.0 - 1.0) + 1.0
-
-        # Smooth signal mask with 2D convolution
-        if self.smoothing_filter is not None:
-            sig_mask = conv2d(
-                sig_mask.unsqueeze(1),
-                self.smoothing_filter.to(sig_mask.dtype),
-                padding="same",
-            )
-
-        # Apply signal mask to STFT magnitude and phase components
-        Y = X * sig_mask.squeeze(1)
-
-        # Inverse STFT to obtain time-domain signal
-        y = torch.istft(
-            Y,
-            n_fft=self.n_fft,
-            hop_length=self.hop_length,
-            win_length=self.win_length,
-            center=True,
-            window=torch.hann_window(self.win_length).to(Y.device),
-        )
-
-        return y.to(dtype=x.dtype)

spaces/Benson/text-generation/Examples/9 Yukle Apps.md

@@ -1,167 +0,0 @@
-<br />
-<h1>9apps yukle: Cómo descargar y usar la mejor tienda de aplicaciones para Android</h1>
-<p>Si usted está buscando una manera de descubrir y descargar las mejores aplicaciones y juegos para su dispositivo Android, es posible que desee probar 9apps yukle. Esta es una tienda de aplicaciones potente y versátil que le ofrece una amplia gama de aplicaciones útiles, desde el entretenimiento a la productividad, desde las redes sociales a la educación, y más. En este artículo, te mostraremos qué es 9apps yukle, por qué lo necesitas, cómo descargarlo y usarlo en tu dispositivo Android y cómo ejecutarlo en tu PC o Mac con BlueStacks. También compartiremos contigo algunas de las mejores aplicaciones y juegos que puedes encontrar en 9apps yukle, para que puedas disfrutarlas en tu dispositivo u ordenador. </p>
- <h2>¿Qué es 9apps yukle y por qué lo necesita</h2>
-<p>9apps yukle es una aplicación de herramientas desarrollada por 9Apps que sirve como una tienda de aplicaciones alternativa para usuarios de Android. A diferencia de la Google Play Store predeterminada, 9apps yukle le ofrece más opciones, más características y más beneficios cuando se trata de encontrar y descargar aplicaciones y juegos. Estas son algunas de las razones por las que necesita 9apps yukle:</p>
-<h2>9 yukle apps</h2><br /><p><b><b>Download Zip</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://bltlly.com/2v6ITi">https://bltlly.com/2v6ITi</a></b></p><br /><br />
-<ul>
-<li>Tiene una gran colección de aplicaciones y juegos en varias categorías y géneros, por lo que siempre puede encontrar algo que se adapte a sus necesidades y preferencias. </li>
-<li> Tiene un sistema de recomendación inteligente que le sugiere las mejores aplicaciones y juegos basados en sus intereses, hábitos y calificaciones. </li>
-<li> Tiene un proceso de descarga rápido y fácil que le ahorra tiempo y datos. También puede pausar y reanudar sus descargas en cualquier momento. </li>
-<li> Tiene una interfaz fácil de usar que hace que sea fácil navegar, buscar y administrar sus aplicaciones y juegos. </li>
-<li> Tiene actualizaciones regulares que mantienen sus aplicaciones y juegos actualizados con las últimas características y correcciones de errores. </li>
-<li> Tiene una plataforma segura que protege su dispositivo de malware, virus y otras amenazas. </li>
-</ul>
- <h2>Cómo descargar e instalar 9apps yukle en tu dispositivo Android</h2>
-
-<ol>
-<li>Ir al sitio web oficial de 9apps yukle (https://www.9appsyukle.com/) o escanear el código QR a continuación con la cámara de su dispositivo. </li>
-<img src="https://www.9appsyukle.com/images/qr-code.png" alt="QR code for 9apps yukle website" width="200" height="200">
-<li>Toque en el botón "Descargar" para comenzar a descargar el archivo APK de 9apps yukle. </li>
-<li>Una vez que la descarga se haya completado, abra el archivo APK desde el administrador de archivos o la barra de notificaciones de su dispositivo. </li>
-<li>Si se le solicita, active la opción "Fuentes desconocidas" en la configuración de su dispositivo para permitir la instalación de aplicaciones desde fuentes distintas de Google Play Store.</ <li>Siga las instrucciones en pantalla para completar la instalación de 9apps yukle. </li>
-<li>Inicie 9apps yukle desde el cajón de aplicaciones de su dispositivo o la pantalla de inicio. </li>
-</ol>
-<p>Felicidades! Usted ha instalado con éxito 9apps yukle en su dispositivo Android. Ahora puedes empezar a explorar y descargar las mejores aplicaciones y juegos para tu dispositivo. </p>
- <h2>Cómo usar 9apps yukle para encontrar y descargar las mejores aplicaciones y juegos</h2>
-<p>Usar 9apps yukle para encontrar y descargar las mejores aplicaciones y juegos es muy fácil y divertido. Aquí hay algunos consejos sobre cómo usar 9apps yukle:</p>
-<ul>
-<li>En la pantalla de inicio de 9apps yukle, puede ver las aplicaciones y juegos destacados, las últimas actualizaciones, las listas de éxitos y las categorías. Puede deslizar hacia la izquierda o hacia la derecha para navegar a través de ellos. </li>
-<li> También puede utilizar la barra de búsqueda en la parte superior para escribir el nombre o la palabra clave de la aplicación o juego que está buscando. </li>
-<li>Cuando encuentre una aplicación o juego que le interese, puede pulsar en ella para ver más detalles, como la descripción, capturas de pantalla, calificaciones, reseñas y aplicaciones y juegos relacionados. </li>
-<li> Si desea descargar una aplicación o juego, puede tocar en el "Descargar" botón en la parte inferior. También puedes pulsar en el botón "Compartir" para compartir la aplicación o el juego con tus amigos a través de redes sociales, correo electrónico u otras aplicaciones. </li>
-
-<li>Puede administrar sus aplicaciones y juegos descargados tocando el icono "Aplicaciones" en la esquina inferior izquierda de la pantalla. También puede desinstalar, actualizar o mover sus aplicaciones y juegos desde allí. </li>
-</ul>
-<p>¡Eso es todo! Has aprendido a usar 9apps yukle para encontrar y descargar las mejores aplicaciones y juegos para tu dispositivo Android. ¡Disfruta! </p>
- <h2>Las ventajas de usar 9apps yukle en PC y Mac</h2>
-<p>Si desea disfrutar de las mejores aplicaciones y juegos en una pantalla más grande, con mejores gráficos, sonido y rendimiento, es posible que desee probar el uso de 9apps yukle en su PC o Mac. Esto es posible con la ayuda de un emulador de Android llamado BlueStacks. BlueStacks es un software que te permite ejecutar aplicaciones y juegos para Android en tu PC o Mac como si fueran aplicaciones nativas. Estas son algunas de las ventajas de usar 9apps yukle en PC y Mac con BlueStacks:</p>
-<ul>
-<li> Puede acceder a una colección más grande de aplicaciones y juegos que no están disponibles o compatibles con su dispositivo Android. </li>
-<li> Usted puede jugar juegos de Android con mejores gráficos, sonido y rendimiento, sin retraso o estrellarse. </li>
-<li>Puedes usar tu teclado, ratón o gamepad para controlar tus juegos para Android, lo que puede darte una ventaja sobre otros jugadores. </li>
-<li> Puede realizar múltiples tareas y ejecutar múltiples aplicaciones y juegos al mismo tiempo en diferentes ventanas o pestañas. </li>
-<li>Puede hacer copias de seguridad y sincronizar sus datos y configuraciones en su dispositivo Android y PC o Mac con Google Play Services.</li>
-</ul> <h2>Cómo ejecutar 9apps yukle en tu PC o Mac con BlueStacks</h2>
-<p>Ejecutar 9apps yukle en tu PC o Mac con BlueStacks es muy fácil y conveniente. Solo tienes que seguir estos pasos:</p>
-<p></p>
-<ol>
-<li>Descargue e instale BlueStacks en su PC o Mac desde el sitio web oficial (https://www.bluestacks.com/) o desde los enlaces de abajo  . </li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google o cree una nueva. </li>
-
-<li>Seleccione el archivo APK de 9apps yukle que ha descargado antes y espere a que se instale. </li>
-<li>Una vez que la instalación se ha completado, puede ver 9apps yukle en la pantalla de inicio de BlueStacks. Haga clic en él para lanzarlo. </li>
-</ol>
-<p>Felicidades! Ha ejecutado con éxito 9apps yukle en su PC o Mac con BlueStacks. Ahora puedes disfrutar de las mejores aplicaciones y juegos en tu ordenador. </p>
- <h2>Los beneficios de usar BlueStacks para jugar juegos Android en tu PC o Mac</h2>
-<p>Usar BlueStacks para jugar juegos Android en tu PC o Mac tiene muchos beneficios que pueden mejorar tu experiencia de juego. Estos son algunos de ellos:</p>
-<ul>
-<li>Puedes usar la función BlueStacks Game Controls para personalizar la configuración de tu teclado, ratón o gamepad para cada juego. También puedes usar los controles de juego predefinidos para juegos populares o crear los tuyos propios. </li>
-<li>Puedes usar la función BlueStacks Eco Mode para optimizar el uso de tu CPU y RAM y reducir el consumo de energía mientras juegas varios juegos al mismo tiempo. </li>
-<li>Puede utilizar la función BlueStacks Multi-Instance para crear y ejecutar varias instancias de BlueStacks con diferentes cuentas, configuraciones y aplicaciones. También puede sincronizar sus acciones en todas las instancias con la función de sincronización de múltiples posiciones. </li>
-<li>Puedes usar la función BlueStacks Macros para grabar y reproducir tus acciones en cualquier juego con una sola pulsación. También puede editar, compartir e importar macros de otros usuarios. </li>
-<li>Puede utilizar la función BlueStacks Screen Recorder para capturar y guardar sus vídeos de juego en alta calidad. También puede transmitir su juego en vivo a Twitch, YouTube, Facebook u otras plataformas con la función BlueStacks Streaming Mode. </li>
-</ul>
- <h2>Cómo personalizar la configuración de BlueStacks para un rendimiento y experiencia óptimos</h2>
-<p>Para personalizar la configuración de BlueStacks para un rendimiento y una experiencia óptimos, puede seguir estos consejos:</p>
-<ul>
-
-<li>Para la configuración de pantalla, puede elegir la resolución, la orientación y el DPI de la ventana BlueStacks. También puede activar o desactivar el modo de pantalla completa, las altas tasas de fotogramas y las notificaciones. </li>
-<li> Para la configuración de sonido, puede ajustar el volumen de los altavoces y el micrófono. También puede activar o desactivar los efectos de sonido y el chat de voz. </li>
-<li>Para la configuración del motor, puede elegir el modo de rendimiento, el modo de gráficos, el motor de gráficos y la asignación de memoria de sus BlueStacks. También puede activar o desactivar la tecnología de virtualización, la textura ASTC y la configuración ABI. </li>
-<li>Para la configuración de preferencias, puede elegir el idioma, la ubicación, el diseño del teclado y la zona horaria de sus BlueStacks. También puede habilitar o deshabilitar las actualizaciones automáticas, notificaciones de aplicaciones, recomendaciones de centros de aplicaciones, copias de seguridad de datos y limpieza de discos. </li>
-<li>Para la configuración de los controles del juego, puede personalizar la configuración del teclado, ratón o gamepad para cada juego. También puede habilitar o desactivar la guía del juego, los controles inteligentes, el modo MOBA, el modo de disparo y el modo de panorámica de objetivos. </li>
-</ul> <h2>Las mejores aplicaciones y juegos que puedes encontrar en 9apps yukle</h2>
-<p>Una de las mejores cosas de 9apps yukle es que tiene una gran colección de aplicaciones y juegos en varias categorías y géneros. Puedes encontrar aplicaciones y juegos para entretenimiento, productividad, redes sociales, educación, salud, estilo de vida y más. También puedes encontrar aplicaciones y juegos para diferentes grupos de edad, intereses y niveles de habilidad. Estas son algunas de las mejores aplicaciones y juegos que puedes encontrar en 9apps yukle:</p>
- <h3>Las principales categorías y géneros de aplicaciones y juegos en 9apps yukle</h3>
-<p>Según las estadísticas de 9apps yukle, las principales categorías y géneros de aplicaciones y juegos en 9apps yukle son los siguientes:</p>
-<tabla>
-<tr>
-<th>Categoría</th>
-<th>Género</th>
-<th>Ejemplo</th>
-</tr>
-<tr>
-<td>Entretenimiento</td>
-<td>Reproductores de vídeo y editores</td>
-<td>VidMate, Reproductor MX, KineMaster</td>
-</tr>
-<tr>
-<td>Productividad</td>
-<td>Herramientas</td>
-<td>Shareit, Xender, CamScanner</td>
-
-<tr>
-<td>Redes sociales</td>
-<td>Comunicación</td>
-<td>WhatsApp, Facebook, Instagram</td>
-</tr>
-<tr>
-<td>Educación</td>
-<td>Educación</td>
-<td>Duolingo, Academia Khan, Udemy</td>
-</tr>
-<tr>
-<td>Salud</td>
-<td>Fitness</td>
-<td>Noom, Fitbit, Calma</td>
-</tr>
-<tr>
-<td>Estilo de vida</td>
-<td>Compras</td>
-<td>Amazon, Flipkart, AliExpress</td>
-</tr>
-<tr>
-<td>Juegos</td>
-<td>Acción</td>
-<td>PUBG móvil, fuego libre, llamada de servicio móvil</td>
-</tr>
-<tr>
-<td>Juegos</td>
-<td>Casual</td>
-<td>Candy Crush Saga, Surfistas del metro, Temple Run 2</td>
-</tr>
-<tr>
-<td>Juegos</td>
-<td>Puzzle</td>
-<td>Explosión de dibujos animados, Cerebro fuera, Cortar la cuerda 2</td>
-</tr>
- <tr> </tr>
-</tabla>
-<p>Estas son algunas de las categorías y géneros más populares y ampliamente utilizados de aplicaciones y juegos en 9apps yukle. Puede explorar más categorías y géneros tocando el botón "Más" en la pantalla de inicio de 9apps yukle. </p>
- <h3>Las aplicaciones y juegos más populares y populares en 9apps yukle</h3>
-<p>Otra manera de encontrar las mejores aplicaciones y juegos en 9apps yukle es echar un vistazo a las aplicaciones y juegos más populares y trending en 9apps yukle. Estas son las aplicaciones y juegos que tienen la mayoría de descargas, calificaciones, comentarios y recomendaciones de otros usuarios. Puede ver las aplicaciones y juegos más populares y trending en 9apps yukle tocando el botón "Top" en la pantalla de inicio de 9apps yukle. Estas son algunas de las aplicaciones y juegos más populares y populares en 9apps yukle:</p>
-<ul>
-<li>VidMate: Un potente descargador de vídeos que te permite descargar vídeos de YouTube, Facebook, Instagram y otras plataformas en varios formatos y resoluciones. </li>
-<li>PUBG Mobile: Un emocionante juego de batalla real que enfrenta a otros 99 jugadores en una lucha por la supervivencia. Puedes jugar solo, dúo o modo escuadrón, y personalizar tus armas, vehículos, trajes y más. </li>
-
-<li>Candy Crush Saga: Un juego de puzzle dulce y adictivo que te desafía a combinar tres o más dulces del mismo color y limpiar el tablero. También puedes jugar con tus amigos y competir por la puntuación más alta. </li>
-<li>Duolingo: Una aplicación de aprendizaje de idiomas divertida y eficaz que te enseña un nuevo idioma a través de lecciones, juegos, concursos e historias. Puede elegir entre más de 30 idiomas y realizar un seguimiento de su progreso. </li>
-</ul>
- <h3>Las gemas ocultas y aplicaciones y juegos subestimados en 9apps yukle</h3>
-<p>Además de las aplicaciones y juegos más populares y populares en 9apps yukle, también hay algunas gemas ocultas y aplicaciones y juegos subestimados en 9apps yukle que merecen su atención. Estas son las aplicaciones y juegos que tienen gran calidad, características y potencial, pero no son tan conocidos o apreciados como deberían ser. Puede descubrir estas gemas ocultas y aplicaciones y juegos subestimados en 9apps yukle tocando el botón "Descubrir" en la pantalla de inicio de 9apps yukle. Aquí están algunas de las gemas ocultas y aplicaciones y juegos subestimados en 9apps yukle:</p>
-<ul>
-<li>CamScanner: Una práctica aplicación de escáner que convierte la cámara de tu dispositivo en un escáner. Puede escanear documentos, recibos, notas, fotos y más, y guardarlos como archivos PDF o JPG. También puede editar, compartir, imprimir o sincronizar sus escaneos. </li>
-<li>Free Fire: Un trepidante juego de battle royale que te ofrece una experiencia de supervivencia de 10 minutos. Puedes elegir tu punto de aterrizaje, saquear armas y objetos, disparar a los enemigos, y ser el último en pie. </li>
-<li>Instagram: Una popular aplicación de redes sociales que te permite compartir tus fotos y videos con tus seguidores. También puedes aplicar filtros, pegatinas, efectos y más a tus publicaciones. También puedes seguir a tus celebridades, marcas, influencers y mucho más. </li>
-
-<li>Udemy: Una plataforma de aprendizaje que ofrece miles de cursos sobre diversos temas, como negocios, diseño, fotografía, programación, desarrollo personal y más. Puedes aprender de instructores expertos a tu propio ritmo. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, 9apps yukle es una gran tienda de aplicaciones para usuarios de Android que quieren descubrir y descargar las mejores aplicaciones y juegos para su dispositivo. Tiene una gran colección de aplicaciones y juegos en varias categorías y géneros, un sistema de recomendación inteligente, un proceso de descarga rápido y fácil, una interfaz fácil de usar, actualizaciones regulares y una plataforma segura. También te permite ejecutar aplicaciones y juegos de Android en tu PC o Mac con BlueStacks, que te ofrece muchos beneficios como mejores gráficos, sonido y rendimiento, controles de teclado, ratón y gamepad, multitarea, copia de seguridad y sincronización, y más. También puede encontrar las mejores aplicaciones y juegos en 9apps yukle revisando las principales categorías y géneros, las aplicaciones y juegos más populares y populares, y las gemas ocultas y las aplicaciones y juegos subestimados. Esperamos que haya disfrutado de este artículo y aprendido algo nuevo sobre 9apps yukle. Si estás interesado en probar 9apps yukle y BlueStacks, puedes descargarlos desde los enlaces de abajo. ¡Feliz descarga! </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí están algunas de las preguntas más frecuentes sobre 9apps yukle y BlueStacks:</p>
-<ol>
-<li> ¿9apps yukle es libre de usar? </li>
-<p>Sí, 9apps yukle es de uso gratuito. Puede descargarlo e instalarlo en su dispositivo Android sin ningún costo. También puedes descargar y usar cualquier aplicación o juego en 9apps yukle gratis. </p>
-<li> ¿Es seguro usar 9apps yukle? </li>
-<p>Sí, 9apps yukle es seguro de usar. Tiene un estricto sistema de seguridad que escanea y verifica cada aplicación y juego antes de subirlo a la plataforma. También protege tu dispositivo de malware, virus y otras amenazas. </p>
-<li> ¿BlueStacks es de uso gratuito? </li>
-
-¿Es seguro usar BlueStacks? </li>
-<p>Sí, BlueStacks es seguro de usar. Tiene un sistema de seguridad confiable que garantiza su privacidad y protección de datos. También cumple con las políticas y términos de servicio de Google Play. </p>
-<li> ¿Cómo puedo contactar al equipo de soporte de 9apps yukle o BlueStacks? </li>
-<p>Si tiene alguna pregunta, problema o comentario sobre 9apps yukle o BlueStacks, puede ponerse en contacto con su equipo de soporte visitando sus sitios web oficiales (https://www.9appsyukle.com/ o https:/www.bluestacks.com/) y haciendo clic en el botón "Contáctenos" o "Soporte". También puede enviarlos por correo electrónico a [email protected] o [email protected]. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/taming-transformers/taming/data/coco.py

@@ -1,176 +0,0 @@
-import os
-import json
-import albumentations
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-from taming.data.sflckr import SegmentationBase # for examples included in repo
-
-
-class Examples(SegmentationBase):
-    def __init__(self, size=256, random_crop=False, interpolation="bicubic"):
-        super().__init__(data_csv="data/coco_examples.txt",
-                         data_root="data/coco_images",
-                         segmentation_root="data/coco_segmentations",
-                         size=size, random_crop=random_crop,
-                         interpolation=interpolation,
-                         n_labels=183, shift_segmentation=True)
-
-
-class CocoBase(Dataset):
-    """needed for (image, caption, segmentation) pairs"""
-    def __init__(self, size=None, dataroot="", datajson="", onehot_segmentation=False, use_stuffthing=False,
-                 crop_size=None, force_no_crop=False, given_files=None):
-        self.split = self.get_split()
-        self.size = size
-        if crop_size is None:
-            self.crop_size = size
-        else:
-            self.crop_size = crop_size
-
-        self.onehot = onehot_segmentation       # return segmentation as rgb or one hot
-        self.stuffthing = use_stuffthing        # include thing in segmentation
-        if self.onehot and not self.stuffthing:
-            raise NotImplemented("One hot mode is only supported for the "
-                                 "stuffthings version because labels are stored "
-                                 "a bit different.")
-
-        data_json = datajson
-        with open(data_json) as json_file:
-            self.json_data = json.load(json_file)
-            self.img_id_to_captions = dict()
-            self.img_id_to_filepath = dict()
-            self.img_id_to_segmentation_filepath = dict()
-
-        assert data_json.split("/")[-1] in ["captions_train2017.json",
-                                            "captions_val2017.json"]
-        if self.stuffthing:
-            self.segmentation_prefix = (
-                "data/cocostuffthings/val2017" if
-                data_json.endswith("captions_val2017.json") else
-                "data/cocostuffthings/train2017")
-        else:
-            self.segmentation_prefix = (
-                "data/coco/annotations/stuff_val2017_pixelmaps" if
-                data_json.endswith("captions_val2017.json") else
-                "data/coco/annotations/stuff_train2017_pixelmaps")
-
-        imagedirs = self.json_data["images"]
-        self.labels = {"image_ids": list()}
-        for imgdir in tqdm(imagedirs, desc="ImgToPath"):
-            self.img_id_to_filepath[imgdir["id"]] = os.path.join(dataroot, imgdir["file_name"])
-            self.img_id_to_captions[imgdir["id"]] = list()
-            pngfilename = imgdir["file_name"].replace("jpg", "png")
-            self.img_id_to_segmentation_filepath[imgdir["id"]] = os.path.join(
-                self.segmentation_prefix, pngfilename)
-            if given_files is not None:
-                if pngfilename in given_files:
-                    self.labels["image_ids"].append(imgdir["id"])
-            else:
-                self.labels["image_ids"].append(imgdir["id"])
-
-        capdirs = self.json_data["annotations"]
-        for capdir in tqdm(capdirs, desc="ImgToCaptions"):
-            # there are in average 5 captions per image
-            self.img_id_to_captions[capdir["image_id"]].append(np.array([capdir["caption"]]))
-
-        self.rescaler = albumentations.SmallestMaxSize(max_size=self.size)
-        if self.split=="validation":
-            self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size)
-        else:
-            self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size)
-        self.preprocessor = albumentations.Compose(
-            [self.rescaler, self.cropper],
-            additional_targets={"segmentation": "image"})
-        if force_no_crop:
-            self.rescaler = albumentations.Resize(height=self.size, width=self.size)
-            self.preprocessor = albumentations.Compose(
-                [self.rescaler],
-                additional_targets={"segmentation": "image"})
-
-    def __len__(self):
-        return len(self.labels["image_ids"])
-
-    def preprocess_image(self, image_path, segmentation_path):
-        image = Image.open(image_path)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-
-        segmentation = Image.open(segmentation_path)
-        if not self.onehot and not segmentation.mode == "RGB":
-            segmentation = segmentation.convert("RGB")
-        segmentation = np.array(segmentation).astype(np.uint8)
-        if self.onehot:
-            assert self.stuffthing
-            # stored in caffe format: unlabeled==255. stuff and thing from
-            # 0-181. to be compatible with the labels in
-            # https://github.com/nightrome/cocostuff/blob/master/labels.txt
-            # we shift stuffthing one to the right and put unlabeled in zero
-            # as long as segmentation is uint8 shifting to right handles the
-            # latter too
-            assert segmentation.dtype == np.uint8
-            segmentation = segmentation + 1
-
-        processed = self.preprocessor(image=image, segmentation=segmentation)
-        image, segmentation = processed["image"], processed["segmentation"]
-        image = (image / 127.5 - 1.0).astype(np.float32)
-
-        if self.onehot:
-            assert segmentation.dtype == np.uint8
-            # make it one hot
-            n_labels = 183
-            flatseg = np.ravel(segmentation)
-            onehot = np.zeros((flatseg.size, n_labels), dtype=np.bool)
-            onehot[np.arange(flatseg.size), flatseg] = True
-            onehot = onehot.reshape(segmentation.shape + (n_labels,)).astype(int)
-            segmentation = onehot
-        else:
-            segmentation = (segmentation / 127.5 - 1.0).astype(np.float32)
-        return image, segmentation
-
-    def __getitem__(self, i):
-        img_path = self.img_id_to_filepath[self.labels["image_ids"][i]]
-        seg_path = self.img_id_to_segmentation_filepath[self.labels["image_ids"][i]]
-        image, segmentation = self.preprocess_image(img_path, seg_path)
-        captions = self.img_id_to_captions[self.labels["image_ids"][i]]
-        # randomly draw one of all available captions per image
-        caption = captions[np.random.randint(0, len(captions))]
-        example = {"image": image,
-                   "caption": [str(caption[0])],
-                   "segmentation": segmentation,
-                   "img_path": img_path,
-                   "seg_path": seg_path,
-                   "filename_": img_path.split(os.sep)[-1]
-                    }
-        return example
-
-
-class CocoImagesAndCaptionsTrain(CocoBase):
-    """returns a pair of (image, caption)"""
-    def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False):
-        super().__init__(size=size,
-                         dataroot="data/coco/train2017",
-                         datajson="data/coco/annotations/captions_train2017.json",
-                         onehot_segmentation=onehot_segmentation,
-                         use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop)
-
-    def get_split(self):
-        return "train"
-
-
-class CocoImagesAndCaptionsValidation(CocoBase):
-    """returns a pair of (image, caption)"""
-    def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,
-                 given_files=None):
-        super().__init__(size=size,
-                         dataroot="data/coco/val2017",
-                         datajson="data/coco/annotations/captions_val2017.json",
-                         onehot_segmentation=onehot_segmentation,
-                         use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
-                         given_files=given_files)
-
-    def get_split(self):
-        return "validation"

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/install/wheel.py

@@ -1,740 +0,0 @@
-"""Support for installing and building the "wheel" binary package format.
-"""
-
-import collections
-import compileall
-import contextlib
-import csv
-import importlib
-import logging
-import os.path
-import re
-import shutil
-import sys
-import warnings
-from base64 import urlsafe_b64encode
-from email.message import Message
-from itertools import chain, filterfalse, starmap
-from typing import (
-    IO,
-    TYPE_CHECKING,
-    Any,
-    BinaryIO,
-    Callable,
-    Dict,
-    Generator,
-    Iterable,
-    Iterator,
-    List,
-    NewType,
-    Optional,
-    Sequence,
-    Set,
-    Tuple,
-    Union,
-    cast,
-)
-from zipfile import ZipFile, ZipInfo
-
-from pip._vendor.distlib.scripts import ScriptMaker
-from pip._vendor.distlib.util import get_export_entry
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.exceptions import InstallationError
-from pip._internal.locations import get_major_minor_version
-from pip._internal.metadata import (
-    BaseDistribution,
-    FilesystemWheel,
-    get_wheel_distribution,
-)
-from pip._internal.models.direct_url import DIRECT_URL_METADATA_NAME, DirectUrl
-from pip._internal.models.scheme import SCHEME_KEYS, Scheme
-from pip._internal.utils.filesystem import adjacent_tmp_file, replace
-from pip._internal.utils.misc import captured_stdout, ensure_dir, hash_file, partition
-from pip._internal.utils.unpacking import (
-    current_umask,
-    is_within_directory,
-    set_extracted_file_to_default_mode_plus_executable,
-    zip_item_is_executable,
-)
-from pip._internal.utils.wheel import parse_wheel
-
-if TYPE_CHECKING:
-    from typing import Protocol
-
-    class File(Protocol):
-        src_record_path: "RecordPath"
-        dest_path: str
-        changed: bool
-
-        def save(self) -> None:
-            pass
-
-
-logger = logging.getLogger(__name__)
-
-RecordPath = NewType("RecordPath", str)
-InstalledCSVRow = Tuple[RecordPath, str, Union[int, str]]
-
-
-def rehash(path: str, blocksize: int = 1 << 20) -> Tuple[str, str]:
-    """Return (encoded_digest, length) for path using hashlib.sha256()"""
-    h, length = hash_file(path, blocksize)
-    digest = "sha256=" + urlsafe_b64encode(h.digest()).decode("latin1").rstrip("=")
-    return (digest, str(length))
-
-
-def csv_io_kwargs(mode: str) -> Dict[str, Any]:
-    """Return keyword arguments to properly open a CSV file
-    in the given mode.
-    """
-    return {"mode": mode, "newline": "", "encoding": "utf-8"}
-
-
-def fix_script(path: str) -> bool:
-    """Replace #!python with #!/path/to/python
-    Return True if file was changed.
-    """
-    # XXX RECORD hashes will need to be updated
-    assert os.path.isfile(path)
-
-    with open(path, "rb") as script:
-        firstline = script.readline()
-        if not firstline.startswith(b"#!python"):
-            return False
-        exename = sys.executable.encode(sys.getfilesystemencoding())
-        firstline = b"#!" + exename + os.linesep.encode("ascii")
-        rest = script.read()
-    with open(path, "wb") as script:
-        script.write(firstline)
-        script.write(rest)
-    return True
-
-
-def wheel_root_is_purelib(metadata: Message) -> bool:
-    return metadata.get("Root-Is-Purelib", "").lower() == "true"
-
-
-def get_entrypoints(dist: BaseDistribution) -> Tuple[Dict[str, str], Dict[str, str]]:
-    console_scripts = {}
-    gui_scripts = {}
-    for entry_point in dist.iter_entry_points():
-        if entry_point.group == "console_scripts":
-            console_scripts[entry_point.name] = entry_point.value
-        elif entry_point.group == "gui_scripts":
-            gui_scripts[entry_point.name] = entry_point.value
-    return console_scripts, gui_scripts
-
-
-def message_about_scripts_not_on_PATH(scripts: Sequence[str]) -> Optional[str]:
-    """Determine if any scripts are not on PATH and format a warning.
-    Returns a warning message if one or more scripts are not on PATH,
-    otherwise None.
-    """
-    if not scripts:
-        return None
-
-    # Group scripts by the path they were installed in
-    grouped_by_dir: Dict[str, Set[str]] = collections.defaultdict(set)
-    for destfile in scripts:
-        parent_dir = os.path.dirname(destfile)
-        script_name = os.path.basename(destfile)
-        grouped_by_dir[parent_dir].add(script_name)
-
-    # We don't want to warn for directories that are on PATH.
-    not_warn_dirs = [
-        os.path.normcase(os.path.normpath(i)).rstrip(os.sep)
-        for i in os.environ.get("PATH", "").split(os.pathsep)
-    ]
-    # If an executable sits with sys.executable, we don't warn for it.
-    #     This covers the case of venv invocations without activating the venv.
-    not_warn_dirs.append(
-        os.path.normcase(os.path.normpath(os.path.dirname(sys.executable)))
-    )
-    warn_for: Dict[str, Set[str]] = {
-        parent_dir: scripts
-        for parent_dir, scripts in grouped_by_dir.items()
-        if os.path.normcase(os.path.normpath(parent_dir)) not in not_warn_dirs
-    }
-    if not warn_for:
-        return None
-
-    # Format a message
-    msg_lines = []
-    for parent_dir, dir_scripts in warn_for.items():
-        sorted_scripts: List[str] = sorted(dir_scripts)
-        if len(sorted_scripts) == 1:
-            start_text = "script {} is".format(sorted_scripts[0])
-        else:
-            start_text = "scripts {} are".format(
-                ", ".join(sorted_scripts[:-1]) + " and " + sorted_scripts[-1]
-            )
-
-        msg_lines.append(
-            "The {} installed in '{}' which is not on PATH.".format(
-                start_text, parent_dir
-            )
-        )
-
-    last_line_fmt = (
-        "Consider adding {} to PATH or, if you prefer "
-        "to suppress this warning, use --no-warn-script-location."
-    )
-    if len(msg_lines) == 1:
-        msg_lines.append(last_line_fmt.format("this directory"))
-    else:
-        msg_lines.append(last_line_fmt.format("these directories"))
-
-    # Add a note if any directory starts with ~
-    warn_for_tilde = any(
-        i[0] == "~" for i in os.environ.get("PATH", "").split(os.pathsep) if i
-    )
-    if warn_for_tilde:
-        tilde_warning_msg = (
-            "NOTE: The current PATH contains path(s) starting with `~`, "
-            "which may not be expanded by all applications."
-        )
-        msg_lines.append(tilde_warning_msg)
-
-    # Returns the formatted multiline message
-    return "\n".join(msg_lines)
-
-
-def _normalized_outrows(
-    outrows: Iterable[InstalledCSVRow],
-) -> List[Tuple[str, str, str]]:
-    """Normalize the given rows of a RECORD file.
-
-    Items in each row are converted into str. Rows are then sorted to make
-    the value more predictable for tests.
-
-    Each row is a 3-tuple (path, hash, size) and corresponds to a record of
-    a RECORD file (see PEP 376 and PEP 427 for details).  For the rows
-    passed to this function, the size can be an integer as an int or string,
-    or the empty string.
-    """
-    # Normally, there should only be one row per path, in which case the
-    # second and third elements don't come into play when sorting.
-    # However, in cases in the wild where a path might happen to occur twice,
-    # we don't want the sort operation to trigger an error (but still want
-    # determinism).  Since the third element can be an int or string, we
-    # coerce each element to a string to avoid a TypeError in this case.
-    # For additional background, see--
-    # https://github.com/pypa/pip/issues/5868
-    return sorted(
-        (record_path, hash_, str(size)) for record_path, hash_, size in outrows
-    )
-
-
-def _record_to_fs_path(record_path: RecordPath, lib_dir: str) -> str:
-    return os.path.join(lib_dir, record_path)
-
-
-def _fs_to_record_path(path: str, lib_dir: str) -> RecordPath:
-    # On Windows, do not handle relative paths if they belong to different
-    # logical disks
-    if os.path.splitdrive(path)[0].lower() == os.path.splitdrive(lib_dir)[0].lower():
-        path = os.path.relpath(path, lib_dir)
-
-    path = path.replace(os.path.sep, "/")
-    return cast("RecordPath", path)
-
-
-def get_csv_rows_for_installed(
-    old_csv_rows: List[List[str]],
-    installed: Dict[RecordPath, RecordPath],
-    changed: Set[RecordPath],
-    generated: List[str],
-    lib_dir: str,
-) -> List[InstalledCSVRow]:
-    """
-    :param installed: A map from archive RECORD path to installation RECORD
-        path.
-    """
-    installed_rows: List[InstalledCSVRow] = []
-    for row in old_csv_rows:
-        if len(row) > 3:
-            logger.warning("RECORD line has more than three elements: %s", row)
-        old_record_path = cast("RecordPath", row[0])
-        new_record_path = installed.pop(old_record_path, old_record_path)
-        if new_record_path in changed:
-            digest, length = rehash(_record_to_fs_path(new_record_path, lib_dir))
-        else:
-            digest = row[1] if len(row) > 1 else ""
-            length = row[2] if len(row) > 2 else ""
-        installed_rows.append((new_record_path, digest, length))
-    for f in generated:
-        path = _fs_to_record_path(f, lib_dir)
-        digest, length = rehash(f)
-        installed_rows.append((path, digest, length))
-    for installed_record_path in installed.values():
-        installed_rows.append((installed_record_path, "", ""))
-    return installed_rows
-
-
-def get_console_script_specs(console: Dict[str, str]) -> List[str]:
-    """
-    Given the mapping from entrypoint name to callable, return the relevant
-    console script specs.
-    """
-    # Don't mutate caller's version
-    console = console.copy()
-
-    scripts_to_generate = []
-
-    # Special case pip and setuptools to generate versioned wrappers
-    #
-    # The issue is that some projects (specifically, pip and setuptools) use
-    # code in setup.py to create "versioned" entry points - pip2.7 on Python
-    # 2.7, pip3.3 on Python 3.3, etc. But these entry points are baked into
-    # the wheel metadata at build time, and so if the wheel is installed with
-    # a *different* version of Python the entry points will be wrong. The
-    # correct fix for this is to enhance the metadata to be able to describe
-    # such versioned entry points, but that won't happen till Metadata 2.0 is
-    # available.
-    # In the meantime, projects using versioned entry points will either have
-    # incorrect versioned entry points, or they will not be able to distribute
-    # "universal" wheels (i.e., they will need a wheel per Python version).
-    #
-    # Because setuptools and pip are bundled with _ensurepip and virtualenv,
-    # we need to use universal wheels. So, as a stopgap until Metadata 2.0, we
-    # override the versioned entry points in the wheel and generate the
-    # correct ones. This code is purely a short-term measure until Metadata 2.0
-    # is available.
-    #
-    # To add the level of hack in this section of code, in order to support
-    # ensurepip this code will look for an ``ENSUREPIP_OPTIONS`` environment
-    # variable which will control which version scripts get installed.
-    #
-    # ENSUREPIP_OPTIONS=altinstall
-    #   - Only pipX.Y and easy_install-X.Y will be generated and installed
-    # ENSUREPIP_OPTIONS=install
-    #   - pipX.Y, pipX, easy_install-X.Y will be generated and installed. Note
-    #     that this option is technically if ENSUREPIP_OPTIONS is set and is
-    #     not altinstall
-    # DEFAULT
-    #   - The default behavior is to install pip, pipX, pipX.Y, easy_install
-    #     and easy_install-X.Y.
-    pip_script = console.pop("pip", None)
-    if pip_script:
-        if "ENSUREPIP_OPTIONS" not in os.environ:
-            scripts_to_generate.append("pip = " + pip_script)
-
-        if os.environ.get("ENSUREPIP_OPTIONS", "") != "altinstall":
-            scripts_to_generate.append(
-                "pip{} = {}".format(sys.version_info[0], pip_script)
-            )
-
-        scripts_to_generate.append(f"pip{get_major_minor_version()} = {pip_script}")
-        # Delete any other versioned pip entry points
-        pip_ep = [k for k in console if re.match(r"pip(\d+(\.\d+)?)?$", k)]
-        for k in pip_ep:
-            del console[k]
-    easy_install_script = console.pop("easy_install", None)
-    if easy_install_script:
-        if "ENSUREPIP_OPTIONS" not in os.environ:
-            scripts_to_generate.append("easy_install = " + easy_install_script)
-
-        scripts_to_generate.append(
-            "easy_install-{} = {}".format(
-                get_major_minor_version(), easy_install_script
-            )
-        )
-        # Delete any other versioned easy_install entry points
-        easy_install_ep = [
-            k for k in console if re.match(r"easy_install(-\d+\.\d+)?$", k)
-        ]
-        for k in easy_install_ep:
-            del console[k]
-
-    # Generate the console entry points specified in the wheel
-    scripts_to_generate.extend(starmap("{} = {}".format, console.items()))
-
-    return scripts_to_generate
-
-
-class ZipBackedFile:
-    def __init__(
-        self, src_record_path: RecordPath, dest_path: str, zip_file: ZipFile
-    ) -> None:
-        self.src_record_path = src_record_path
-        self.dest_path = dest_path
-        self._zip_file = zip_file
-        self.changed = False
-
-    def _getinfo(self) -> ZipInfo:
-        return self._zip_file.getinfo(self.src_record_path)
-
-    def save(self) -> None:
-        # directory creation is lazy and after file filtering
-        # to ensure we don't install empty dirs; empty dirs can't be
-        # uninstalled.
-        parent_dir = os.path.dirname(self.dest_path)
-        ensure_dir(parent_dir)
-
-        # When we open the output file below, any existing file is truncated
-        # before we start writing the new contents. This is fine in most
-        # cases, but can cause a segfault if pip has loaded a shared
-        # object (e.g. from pyopenssl through its vendored urllib3)
-        # Since the shared object is mmap'd an attempt to call a
-        # symbol in it will then cause a segfault. Unlinking the file
-        # allows writing of new contents while allowing the process to
-        # continue to use the old copy.
-        if os.path.exists(self.dest_path):
-            os.unlink(self.dest_path)
-
-        zipinfo = self._getinfo()
-
-        with self._zip_file.open(zipinfo) as f:
-            with open(self.dest_path, "wb") as dest:
-                shutil.copyfileobj(f, dest)
-
-        if zip_item_is_executable(zipinfo):
-            set_extracted_file_to_default_mode_plus_executable(self.dest_path)
-
-
-class ScriptFile:
-    def __init__(self, file: "File") -> None:
-        self._file = file
-        self.src_record_path = self._file.src_record_path
-        self.dest_path = self._file.dest_path
-        self.changed = False
-
-    def save(self) -> None:
-        self._file.save()
-        self.changed = fix_script(self.dest_path)
-
-
-class MissingCallableSuffix(InstallationError):
-    def __init__(self, entry_point: str) -> None:
-        super().__init__(
-            "Invalid script entry point: {} - A callable "
-            "suffix is required. Cf https://packaging.python.org/"
-            "specifications/entry-points/#use-for-scripts for more "
-            "information.".format(entry_point)
-        )
-
-
-def _raise_for_invalid_entrypoint(specification: str) -> None:
-    entry = get_export_entry(specification)
-    if entry is not None and entry.suffix is None:
-        raise MissingCallableSuffix(str(entry))
-
-
-class PipScriptMaker(ScriptMaker):
-    def make(
-        self, specification: str, options: Optional[Dict[str, Any]] = None
-    ) -> List[str]:
-        _raise_for_invalid_entrypoint(specification)
-        return super().make(specification, options)
-
-
-def _install_wheel(
-    name: str,
-    wheel_zip: ZipFile,
-    wheel_path: str,
-    scheme: Scheme,
-    pycompile: bool = True,
-    warn_script_location: bool = True,
-    direct_url: Optional[DirectUrl] = None,
-    requested: bool = False,
-) -> None:
-    """Install a wheel.
-
-    :param name: Name of the project to install
-    :param wheel_zip: open ZipFile for wheel being installed
-    :param scheme: Distutils scheme dictating the install directories
-    :param req_description: String used in place of the requirement, for
-        logging
-    :param pycompile: Whether to byte-compile installed Python files
-    :param warn_script_location: Whether to check that scripts are installed
-        into a directory on PATH
-    :raises UnsupportedWheel:
-        * when the directory holds an unpacked wheel with incompatible
-          Wheel-Version
-        * when the .dist-info dir does not match the wheel
-    """
-    info_dir, metadata = parse_wheel(wheel_zip, name)
-
-    if wheel_root_is_purelib(metadata):
-        lib_dir = scheme.purelib
-    else:
-        lib_dir = scheme.platlib
-
-    # Record details of the files moved
-    #   installed = files copied from the wheel to the destination
-    #   changed = files changed while installing (scripts #! line typically)
-    #   generated = files newly generated during the install (script wrappers)
-    installed: Dict[RecordPath, RecordPath] = {}
-    changed: Set[RecordPath] = set()
-    generated: List[str] = []
-
-    def record_installed(
-        srcfile: RecordPath, destfile: str, modified: bool = False
-    ) -> None:
-        """Map archive RECORD paths to installation RECORD paths."""
-        newpath = _fs_to_record_path(destfile, lib_dir)
-        installed[srcfile] = newpath
-        if modified:
-            changed.add(newpath)
-
-    def is_dir_path(path: RecordPath) -> bool:
-        return path.endswith("/")
-
-    def assert_no_path_traversal(dest_dir_path: str, target_path: str) -> None:
-        if not is_within_directory(dest_dir_path, target_path):
-            message = (
-                "The wheel {!r} has a file {!r} trying to install"
-                " outside the target directory {!r}"
-            )
-            raise InstallationError(
-                message.format(wheel_path, target_path, dest_dir_path)
-            )
-
-    def root_scheme_file_maker(
-        zip_file: ZipFile, dest: str
-    ) -> Callable[[RecordPath], "File"]:
-        def make_root_scheme_file(record_path: RecordPath) -> "File":
-            normed_path = os.path.normpath(record_path)
-            dest_path = os.path.join(dest, normed_path)
-            assert_no_path_traversal(dest, dest_path)
-            return ZipBackedFile(record_path, dest_path, zip_file)
-
-        return make_root_scheme_file
-
-    def data_scheme_file_maker(
-        zip_file: ZipFile, scheme: Scheme
-    ) -> Callable[[RecordPath], "File"]:
-        scheme_paths = {key: getattr(scheme, key) for key in SCHEME_KEYS}
-
-        def make_data_scheme_file(record_path: RecordPath) -> "File":
-            normed_path = os.path.normpath(record_path)
-            try:
-                _, scheme_key, dest_subpath = normed_path.split(os.path.sep, 2)
-            except ValueError:
-                message = (
-                    "Unexpected file in {}: {!r}. .data directory contents"
-                    " should be named like: '<scheme key>/<path>'."
-                ).format(wheel_path, record_path)
-                raise InstallationError(message)
-
-            try:
-                scheme_path = scheme_paths[scheme_key]
-            except KeyError:
-                valid_scheme_keys = ", ".join(sorted(scheme_paths))
-                message = (
-                    "Unknown scheme key used in {}: {} (for file {!r}). .data"
-                    " directory contents should be in subdirectories named"
-                    " with a valid scheme key ({})"
-                ).format(wheel_path, scheme_key, record_path, valid_scheme_keys)
-                raise InstallationError(message)
-
-            dest_path = os.path.join(scheme_path, dest_subpath)
-            assert_no_path_traversal(scheme_path, dest_path)
-            return ZipBackedFile(record_path, dest_path, zip_file)
-
-        return make_data_scheme_file
-
-    def is_data_scheme_path(path: RecordPath) -> bool:
-        return path.split("/", 1)[0].endswith(".data")
-
-    paths = cast(List[RecordPath], wheel_zip.namelist())
-    file_paths = filterfalse(is_dir_path, paths)
-    root_scheme_paths, data_scheme_paths = partition(is_data_scheme_path, file_paths)
-
-    make_root_scheme_file = root_scheme_file_maker(wheel_zip, lib_dir)
-    files: Iterator[File] = map(make_root_scheme_file, root_scheme_paths)
-
-    def is_script_scheme_path(path: RecordPath) -> bool:
-        parts = path.split("/", 2)
-        return len(parts) > 2 and parts[0].endswith(".data") and parts[1] == "scripts"
-
-    other_scheme_paths, script_scheme_paths = partition(
-        is_script_scheme_path, data_scheme_paths
-    )
-
-    make_data_scheme_file = data_scheme_file_maker(wheel_zip, scheme)
-    other_scheme_files = map(make_data_scheme_file, other_scheme_paths)
-    files = chain(files, other_scheme_files)
-
-    # Get the defined entry points
-    distribution = get_wheel_distribution(
-        FilesystemWheel(wheel_path),
-        canonicalize_name(name),
-    )
-    console, gui = get_entrypoints(distribution)
-
-    def is_entrypoint_wrapper(file: "File") -> bool:
-        # EP, EP.exe and EP-script.py are scripts generated for
-        # entry point EP by setuptools
-        path = file.dest_path
-        name = os.path.basename(path)
-        if name.lower().endswith(".exe"):
-            matchname = name[:-4]
-        elif name.lower().endswith("-script.py"):
-            matchname = name[:-10]
-        elif name.lower().endswith(".pya"):
-            matchname = name[:-4]
-        else:
-            matchname = name
-        # Ignore setuptools-generated scripts
-        return matchname in console or matchname in gui
-
-    script_scheme_files: Iterator[File] = map(
-        make_data_scheme_file, script_scheme_paths
-    )
-    script_scheme_files = filterfalse(is_entrypoint_wrapper, script_scheme_files)
-    script_scheme_files = map(ScriptFile, script_scheme_files)
-    files = chain(files, script_scheme_files)
-
-    for file in files:
-        file.save()
-        record_installed(file.src_record_path, file.dest_path, file.changed)
-
-    def pyc_source_file_paths() -> Generator[str, None, None]:
-        # We de-duplicate installation paths, since there can be overlap (e.g.
-        # file in .data maps to same location as file in wheel root).
-        # Sorting installation paths makes it easier to reproduce and debug
-        # issues related to permissions on existing files.
-        for installed_path in sorted(set(installed.values())):
-            full_installed_path = os.path.join(lib_dir, installed_path)
-            if not os.path.isfile(full_installed_path):
-                continue
-            if not full_installed_path.endswith(".py"):
-                continue
-            yield full_installed_path
-
-    def pyc_output_path(path: str) -> str:
-        """Return the path the pyc file would have been written to."""
-        return importlib.util.cache_from_source(path)
-
-    # Compile all of the pyc files for the installed files
-    if pycompile:
-        with captured_stdout() as stdout:
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore")
-                for path in pyc_source_file_paths():
-                    success = compileall.compile_file(path, force=True, quiet=True)
-                    if success:
-                        pyc_path = pyc_output_path(path)
-                        assert os.path.exists(pyc_path)
-                        pyc_record_path = cast(
-                            "RecordPath", pyc_path.replace(os.path.sep, "/")
-                        )
-                        record_installed(pyc_record_path, pyc_path)
-        logger.debug(stdout.getvalue())
-
-    maker = PipScriptMaker(None, scheme.scripts)
-
-    # Ensure old scripts are overwritten.
-    # See https://github.com/pypa/pip/issues/1800
-    maker.clobber = True
-
-    # Ensure we don't generate any variants for scripts because this is almost
-    # never what somebody wants.
-    # See https://bitbucket.org/pypa/distlib/issue/35/
-    maker.variants = {""}
-
-    # This is required because otherwise distlib creates scripts that are not
-    # executable.
-    # See https://bitbucket.org/pypa/distlib/issue/32/
-    maker.set_mode = True
-
-    # Generate the console and GUI entry points specified in the wheel
-    scripts_to_generate = get_console_script_specs(console)
-
-    gui_scripts_to_generate = list(starmap("{} = {}".format, gui.items()))
-
-    generated_console_scripts = maker.make_multiple(scripts_to_generate)
-    generated.extend(generated_console_scripts)
-
-    generated.extend(maker.make_multiple(gui_scripts_to_generate, {"gui": True}))
-
-    if warn_script_location:
-        msg = message_about_scripts_not_on_PATH(generated_console_scripts)
-        if msg is not None:
-            logger.warning(msg)
-
-    generated_file_mode = 0o666 & ~current_umask()
-
-    @contextlib.contextmanager
-    def _generate_file(path: str, **kwargs: Any) -> Generator[BinaryIO, None, None]:
-        with adjacent_tmp_file(path, **kwargs) as f:
-            yield f
-        os.chmod(f.name, generated_file_mode)
-        replace(f.name, path)
-
-    dest_info_dir = os.path.join(lib_dir, info_dir)
-
-    # Record pip as the installer
-    installer_path = os.path.join(dest_info_dir, "INSTALLER")
-    with _generate_file(installer_path) as installer_file:
-        installer_file.write(b"pip\n")
-    generated.append(installer_path)
-
-    # Record the PEP 610 direct URL reference
-    if direct_url is not None:
-        direct_url_path = os.path.join(dest_info_dir, DIRECT_URL_METADATA_NAME)
-        with _generate_file(direct_url_path) as direct_url_file:
-            direct_url_file.write(direct_url.to_json().encode("utf-8"))
-        generated.append(direct_url_path)
-
-    # Record the REQUESTED file
-    if requested:
-        requested_path = os.path.join(dest_info_dir, "REQUESTED")
-        with open(requested_path, "wb"):
-            pass
-        generated.append(requested_path)
-
-    record_text = distribution.read_text("RECORD")
-    record_rows = list(csv.reader(record_text.splitlines()))
-
-    rows = get_csv_rows_for_installed(
-        record_rows,
-        installed=installed,
-        changed=changed,
-        generated=generated,
-        lib_dir=lib_dir,
-    )
-
-    # Record details of all files installed
-    record_path = os.path.join(dest_info_dir, "RECORD")
-
-    with _generate_file(record_path, **csv_io_kwargs("w")) as record_file:
-        # Explicitly cast to typing.IO[str] as a workaround for the mypy error:
-        # "writer" has incompatible type "BinaryIO"; expected "_Writer"
-        writer = csv.writer(cast("IO[str]", record_file))
-        writer.writerows(_normalized_outrows(rows))
-
-
-@contextlib.contextmanager
-def req_error_context(req_description: str) -> Generator[None, None, None]:
-    try:
-        yield
-    except InstallationError as e:
-        message = "For req: {}. {}".format(req_description, e.args[0])
-        raise InstallationError(message) from e
-
-
-def install_wheel(
-    name: str,
-    wheel_path: str,
-    scheme: Scheme,
-    req_description: str,
-    pycompile: bool = True,
-    warn_script_location: bool = True,
-    direct_url: Optional[DirectUrl] = None,
-    requested: bool = False,
-) -> None:
-    with ZipFile(wheel_path, allowZip64=True) as z:
-        with req_error_context(req_description):
-            _install_wheel(
-                name=name,
-                wheel_zip=z,
-                wheel_path=wheel_path,
-                scheme=scheme,
-                pycompile=pycompile,
-                warn_script_location=warn_script_location,
-                direct_url=direct_url,
-                requested=requested,
-            )

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/install_egg_info.py

@@ -1,91 +0,0 @@
-"""
-distutils.command.install_egg_info
-
-Implements the Distutils 'install_egg_info' command, for installing
-a package's PKG-INFO metadata.
-"""
-
-import os
-import sys
-import re
-
-from distutils.cmd import Command
-from distutils import log, dir_util
-
-
-class install_egg_info(Command):
-    """Install an .egg-info file for the package"""
-
-    description = "Install package's PKG-INFO metadata as an .egg-info file"
-    user_options = [
-        ('install-dir=', 'd', "directory to install to"),
-    ]
-
-    def initialize_options(self):
-        self.install_dir = None
-
-    @property
-    def basename(self):
-        """
-        Allow basename to be overridden by child class.
-        Ref pypa/distutils#2.
-        """
-        return "%s-%s-py%d.%d.egg-info" % (
-            to_filename(safe_name(self.distribution.get_name())),
-            to_filename(safe_version(self.distribution.get_version())),
-            *sys.version_info[:2],
-        )
-
-    def finalize_options(self):
-        self.set_undefined_options('install_lib', ('install_dir', 'install_dir'))
-        self.target = os.path.join(self.install_dir, self.basename)
-        self.outputs = [self.target]
-
-    def run(self):
-        target = self.target
-        if os.path.isdir(target) and not os.path.islink(target):
-            dir_util.remove_tree(target, dry_run=self.dry_run)
-        elif os.path.exists(target):
-            self.execute(os.unlink, (self.target,), "Removing " + target)
-        elif not os.path.isdir(self.install_dir):
-            self.execute(
-                os.makedirs, (self.install_dir,), "Creating " + self.install_dir
-            )
-        log.info("Writing %s", target)
-        if not self.dry_run:
-            with open(target, 'w', encoding='UTF-8') as f:
-                self.distribution.metadata.write_pkg_file(f)
-
-    def get_outputs(self):
-        return self.outputs
-
-
-# The following routines are taken from setuptools' pkg_resources module and
-# can be replaced by importing them from pkg_resources once it is included
-# in the stdlib.
-
-
-def safe_name(name):
-    """Convert an arbitrary string to a standard distribution name
-
-    Any runs of non-alphanumeric/. characters are replaced with a single '-'.
-    """
-    return re.sub('[^A-Za-z0-9.]+', '-', name)
-
-
-def safe_version(version):
-    """Convert an arbitrary string to a standard version string
-
-    Spaces become dots, and all other non-alphanumeric characters become
-    dashes, with runs of multiple dashes condensed to a single dash.
-    """
-    version = version.replace(' ', '.')
-    return re.sub('[^A-Za-z0-9.]+', '-', version)
-
-
-def to_filename(name):
-    """Convert a project or version name to its filename-escaped form
-
-    Any '-' characters are currently replaced with '_'.
-    """
-    return name.replace('-', '_')

spaces/Billyosoro/ESRGAN/realesrgan/models/__init__.py

@@ -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/CAMP-ViL/Xplainer/descriptors.py

@@ -1,204 +0,0 @@
-disease_descriptors_chexpert = {
-    "No Finding": [
-        "Clear lung fields",
-        "Normal heart size and shape",
-        "No Abnormal fluid buildup",
-        "No Visible tumors or masses",
-        "No Signs of bone fractures or dislocations"
-    ],
-    "Enlarged Cardiomediastinum": [
-        "Increased width of the heart shadow",
-        "Widened mediastinum",
-        "Abnormal contour of the heart border",
-        "Fluid or air within the pericardium",
-        "Mass within the mediastinum",
-    ],
-    "Cardiomegaly": [
-        "Increased size of the heart shadow",
-        "Enlargement of the heart silhouette",
-        "Increased diameter of the heart border",
-        "Increased cardiothoracic ratio",
-    ],
-    "Lung Opacity": [
-        "Increased density in the lung field",
-        "Whitish or grayish area in the lung field",
-        "Obscured or blurred margins of the lung field",
-        "Loss of normal lung markings within the opacity",
-        "Air bronchograms within the opacity",
-        "Fluid levels within the opacity",
-        "Silhouette sign loss with adjacent structures",
-
-    ],
-    "Lung Lesion": [
-        "Consolidation of lung tissue",
-        "Pleural effusion",
-        "Cavities or abscesses in the lung",
-        "Abnormal opacity or shadow in the lung",
-        "Irregular or blurred margins of the lung",
-
-    ],
-    "Edema": [
-        "Blurry vascular markings in the lungs",
-        "Enlarged heart",
-        "Kerley B lines",
-        "Increased interstitial markings in the lungs",
-        "Widening of interstitial spaces",
-    ],
-    "Consolidation": [
-        "Loss of lung volume",
-        "Increased density of lung tissue",
-        "Obliteration of the diaphragmatic silhouette",
-        "Presence of opacities",
-    ],
-    "Pneumonia": [
-        "Consolidation of lung tissue",
-        "Air bronchograms",
-        "Cavitation",
-        "Interstitial opacities",
-    ],
-    "Atelectasis": [
-        "Increased opacity",
-        "Volume loss of the affected lung region",
-        "Blunting of the costophrenic angle",
-        "Shifting of the mediastinum",
-      ],
-      "Pneumothorax": [
-        "Tracheal deviation",
-        "Deep sulcus sign",
-        "Increased radiolucency",
-        "Flattening of the hemidiaphragm",
-        "Absence of lung markings",
-        "Shifting of the mediastinum"
-      ],
-      "Pleural Effusion": [
-        "Blunting of costophrenic angles",
-        "Opacity in the lower lung fields",
-        "Mediastinal shift",
-        "Reduced lung volume",
-        "Presence of meniscus sign or veil-like appearance"
-      ],
-      "Pleural Other": [
-        "Pleural thickening",
-        "Pleural calcification",
-        "Pleural masses or nodules",
-        "Pleural empyema",
-        "Pleural fibrosis",
-        "Pleural adhesions"
-      ],
-      "Fracture": [
-        "Visible breaks in the continuity of the bone",
-        "Misalignments of bone fragments",
-        "Displacements of bone fragments",
-        "Disruptions of the cortex or outer layer of the bone",
-        "Visible callus or healing tissue",
-        "Fracture lines that are jagged or irregular in shape",
-        "Multiple fracture lines that intersect at different angles"
-      ],
-      "Support Devices": [
-        "Artificial joints or implants",
-        "Pacemakers or cardiac devices",
-        "Stents or other vascular devices",
-        "Prosthetic devices or limbs",
-        "Breast implants",
-        "Radiotherapy markers or seeds"
-      ]
-    }
-
-disease_descriptors_chestxray14 = {
-
-    "No Finding": ["No Finding"],
-    "Cardiomegaly": [
-        "Increased size of the heart shadow",
-        "Enlargement of the heart silhouette",
-        "Increased diameter of the heart border",
-        "Increased cardiothoracic ratio"
-    ],
-    "Edema": [
-        "Blurry vascular markings in the lungs",
-        "Kerley B lines",
-        "Increased interstitial markings in the lungs",
-        "Widening of interstitial spaces"
-    ],
-    "Consolidation": [
-        "Loss of lung volume",
-        "Increased density of lung tissue",
-        "Obliteration of the diaphragmatic silhouette",
-        "Presence of opacities"
-    ],
-    "Pneumonia": [
-        "Consolidation of lung tissue",
-        "Air bronchograms",
-        "Cavitation",
-        "Interstitial opacities"
-    ],
-    "Atelectasis": [
-        "Increased opacity",
-        "Volume loss of the affected lung region",
-        "Displacement of the diaphragm",
-        "Blunting of the costophrenic angle",
-        "Shifting of the mediastinum"
-      ],
-    "Pneumothorax": [
-        "Tracheal deviation",
-        "Deep sulcus sign",
-        "Increased radiolucency",
-        "Flattening of the hemidiaphragm",
-        "Absence of lung markings",
-        "Shifting of the mediastinum"
-      ],
-    "Pleural Effusion": [
-        "Blunting of costophrenic angles",
-        "Opacity in the lower lung fields",
-        "Mediastinal shift",
-        "Reduced lung volume",
-        "Meniscus sign or veil-like appearance"
-      ],
-    "Infiltration": [
-        "Irregular or fuzzy borders around white areas",
-        "Blurring",
-        "Hazy or cloudy areas",
-        "Increased density or opacity of lung tissue",
-        "Air bronchograms",
-      ],
-      "Mass": [
-        "Calcifications or mineralizations",
-        "Shadowing",
-        "Distortion or compression of tissues",
-        "Anomalous structure or irregularity in shape"
-      ],
-      "Nodule": [
-        "Nodular shape that protrudes into a cavity or airway",
-        "Distinct edges or borders",
-        "Calcifications or speckled areas",
-        "Small round oral shaped spots",
-        "White shadows"
-      ],
-      "Emphysema": [
-        "Flattened hemidiaphragm",
-        "Pulmonary bullae",
-        "Hyperlucent lungs",
-        "Horizontalisation of ribs",
-        "Barrel Chest",
-      ],
-      "Fibrosis": [
-        "Reticular shadowing of the lung peripheries",
-        "Volume loss",
-        "Thickened and irregular interstitial markings",
-        "Bronchial dilation",
-        "Shaggy heart borders"
-      ],
-      "Pleural Thickening": [
-        "Thickened pleural line",
-        "Loss of sharpness of the mediastinal border",
-        "Calcifications on the pleura",
-        "Lobulated peripheral shadowing",
-        "Loss of lung volume",
-      ],
-      "Hernia": [
-        "Bulge or swelling in the abdominal wall",
-        "Protrusion of intestine or other abdominal tissue",
-        "Swelling or enlargement of the herniated sac or surrounding tissues",
-        "Retro-cardiac air-fluid level",
-        "Thickening of intestinal folds"
-      ]
-    }

spaces/CVH-vn1210/make_hair/minigpt4/__init__.py

@@ -1,31 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import os
-import sys
-
-from omegaconf import OmegaConf
-
-from minigpt4.common.registry import registry
-
-from minigpt4.datasets.builders import *
-from minigpt4.models import *
-from minigpt4.processors import *
-from minigpt4.tasks import *
-
-
-root_dir = os.path.dirname(os.path.abspath(__file__))
-default_cfg = OmegaConf.load(os.path.join(root_dir, "configs/default.yaml"))
-
-registry.register_path("library_root", root_dir)
-repo_root = os.path.join(root_dir, "..")
-registry.register_path("repo_root", repo_root)
-cache_root = os.path.join(repo_root, default_cfg.env.cache_root)
-registry.register_path("cache_root", cache_root)
-
-registry.register("MAX_INT", sys.maxsize)
-registry.register("SPLIT_NAMES", ["train", "val", "test"])

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/model_zoo/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Model Zoo API for Detectron2: a collection of functions to create common model architectures and
-optionally load pre-trained weights as released in
-`MODEL_ZOO.md <https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md>`_.
-"""
-from .model_zoo import get, get_config_file, get_checkpoint_url
-
-__all__ = ["get_checkpoint_url", "get", "get_config_file"]

spaces/CVPR/LIVE/pybind11/tests/test_enum.cpp

@@ -1,87 +0,0 @@
-/*
-    tests/test_enums.cpp -- enumerations
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-
-TEST_SUBMODULE(enums, m) {
-    // test_unscoped_enum
-    enum UnscopedEnum {
-        EOne = 1,
-        ETwo,
-        EThree
-    };
-    py::enum_<UnscopedEnum>(m, "UnscopedEnum", py::arithmetic(), "An unscoped enumeration")
-        .value("EOne", EOne, "Docstring for EOne")
-        .value("ETwo", ETwo, "Docstring for ETwo")
-        .value("EThree", EThree, "Docstring for EThree")
-        .export_values();
-
-    // test_scoped_enum
-    enum class ScopedEnum {
-        Two = 2,
-        Three
-    };
-    py::enum_<ScopedEnum>(m, "ScopedEnum", py::arithmetic())
-        .value("Two", ScopedEnum::Two)
-        .value("Three", ScopedEnum::Three);
-
-    m.def("test_scoped_enum", [](ScopedEnum z) {
-        return "ScopedEnum::" + std::string(z == ScopedEnum::Two ? "Two" : "Three");
-    });
-
-    // test_binary_operators
-    enum Flags {
-        Read = 4,
-        Write = 2,
-        Execute = 1
-    };
-    py::enum_<Flags>(m, "Flags", py::arithmetic())
-        .value("Read", Flags::Read)
-        .value("Write", Flags::Write)
-        .value("Execute", Flags::Execute)
-        .export_values();
-
-    // test_implicit_conversion
-    class ClassWithUnscopedEnum {
-    public:
-        enum EMode {
-            EFirstMode = 1,
-            ESecondMode
-        };
-
-        static EMode test_function(EMode mode) {
-            return mode;
-        }
-    };
-    py::class_<ClassWithUnscopedEnum> exenum_class(m, "ClassWithUnscopedEnum");
-    exenum_class.def_static("test_function", &ClassWithUnscopedEnum::test_function);
-    py::enum_<ClassWithUnscopedEnum::EMode>(exenum_class, "EMode")
-        .value("EFirstMode", ClassWithUnscopedEnum::EFirstMode)
-        .value("ESecondMode", ClassWithUnscopedEnum::ESecondMode)
-        .export_values();
-
-    // test_enum_to_int
-    m.def("test_enum_to_int", [](int) { });
-    m.def("test_enum_to_uint", [](uint32_t) { });
-    m.def("test_enum_to_long_long", [](long long) { });
-
-    // test_duplicate_enum_name
-    enum SimpleEnum
-    {
-        ONE, TWO, THREE
-    };
-
-    m.def("register_bad_enum", [m]() {
-        py::enum_<SimpleEnum>(m, "SimpleEnum")
-            .value("ONE", SimpleEnum::ONE)          //NOTE: all value function calls are called with the same first parameter value
-            .value("ONE", SimpleEnum::TWO)
-            .value("ONE", SimpleEnum::THREE)
-            .export_values();
-    });
-}

spaces/CVPR/LIVE/thrust/thrust/execution_policy.h

@@ -1,396 +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/execution_policy.h
- *  \brief Thrust execution policies.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/detail/execute_with_allocator.h>
-#include <thrust/detail/seq.h>
-
-//! \cond
-
-// #include the host system's execution_policy header
-#define __THRUST_HOST_SYSTEM_EXECUTION_POLICY_HEADER <__THRUST_HOST_SYSTEM_ROOT/execution_policy.h>
-#include __THRUST_HOST_SYSTEM_EXECUTION_POLICY_HEADER
-#undef __THRUST_HOST_SYSTEM_EXECUTION_POLICY_HEADER
-
-// #include the device system's execution_policy.h header
-#define __THRUST_DEVICE_SYSTEM_EXECUTION_POLICY_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/execution_policy.h>
-#include __THRUST_DEVICE_SYSTEM_EXECUTION_POLICY_HEADER
-#undef __THRUST_DEVICE_SYSTEM_EXECUTION_POLICY_HEADER
-
-//! \endcond
-
-namespace thrust
-{
-
-
-/*! \cond
- */
-
-
-namespace detail
-{
-
-
-typedef thrust::system::__THRUST_HOST_SYSTEM_NAMESPACE::detail::par_t host_t;
-
-
-typedef thrust::system::__THRUST_DEVICE_SYSTEM_NAMESPACE::detail::par_t device_t;
-
-
-} // end detail
-
-
-/*! \endcond
- */
-
-
-/*! \addtogroup execution_policies Parallel Execution Policies
- *  \{
- */
-
-
-// define execution_policy for the purpose of Doxygenating it
-// it is actually defined elsewhere
-#if 0
-/*! \p execution_policy is the base class for all Thrust parallel execution policies
- *  like \p thrust::host, \p thrust::device, and each backend system's tag type.
- *
- *  Custom user-defined backends should derive a policy from this type in order to
- *  interoperate with Thrust algorithm dispatch.
- *
- *  The following code snippet demonstrates how to derive a standalone custom execution policy
- *  from \p thrust::execution_policy to implement a backend which only implements \p for_each:
- *
- *  \code
- *  #include <thrust/execution_policy.h>
- *  #include <iostream>
- *
- *  // define a type derived from thrust::execution_policy to distinguish our custom execution policy:
- *  struct my_policy : thrust::execution_policy<my_policy> {};
- *
- *  // overload for_each on my_policy
- *  template<typename Iterator, typename Function>
- *  Iterator for_each(my_policy, Iterator first, Iterator last, Function f)
- *  {
- *    std::cout << "Hello, world from for_each(my_policy)!" << std::endl;
- *
- *    for(; first < last; ++first)
- *    {
- *      f(*first);
- *    }
- *
- *    return first;
- *  }
- *
- *  struct ignore_argument
- *  {
- *    void operator()(int) {}
- *  };
- *
- *  int main()
- *  {
- *    int data[4];
- *
- *    // dispatch thrust::for_each using our custom policy:
- *    my_policy exec;
- *    thrust::for_each(exec, data, data + 4, ignore_argument());
- *
- *    // can't dispatch thrust::transform because no overload exists for my_policy:
- *    //thrust::transform(exec, data, data, + 4, data, thrust::identity<int>()); // error!
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  \see host_execution_policy
- *  \see device_execution_policy
- */
-template<typename DerivedPolicy>
-struct execution_policy : thrust::detail::execution_policy_base<DerivedPolicy>
-{};
-#endif
-
-
-/*! \p host_execution_policy is the base class for all Thrust parallel execution policies
- *  which are derived from Thrust's default host backend system configured with the \p THRUST_HOST_SYSTEM
- *  macro.
- *
- *  Custom user-defined backends which wish to inherit the functionality of Thrust's host backend system
- *  should derive a policy from this type in order to interoperate with Thrust algorithm dispatch.
- *
- *  The following code snippet demonstrates how to derive a standalone custom execution policy from
- *  \p thrust::host_execution_policy to implement a backend which specializes \p for_each while inheriting
- *  the behavior of every other algorithm from the host system:
- *
- *  \code
- *  #include <thrust/execution_policy.h>
- *  #include <iostream>
- *
- *  // define a type derived from thrust::host_execution_policy to distinguish our custom execution policy:
- *  struct my_policy : thrust::host_execution_policy<my_policy> {};
- *
- *  // overload for_each on my_policy
- *  template<typename Iterator, typename Function>
- *  Iterator for_each(my_policy, Iterator first, Iterator last, Function f)
- *  {
- *    std::cout << "Hello, world from for_each(my_policy)!" << std::endl;
- *
- *    for(; first < last; ++first)
- *    {
- *      f(*first);
- *    }
- *
- *    return first;
- *  }
- *
- *  struct ignore_argument
- *  {
- *    void operator()(int) {}
- *  };
- *
- *  int main()
- *  {
- *    int data[4];
- *
- *    // dispatch thrust::for_each using our custom policy:
- *    my_policy exec;
- *    thrust::for_each(exec, data, data + 4, ignore_argument());
- *
- *    // dispatch thrust::transform whose behavior our policy inherits
- *    thrust::transform(exec, data, data, + 4, data, thrust::identity<int>());
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  \see execution_policy
- *  \see device_execution_policy
- */
-template<typename DerivedPolicy>
-  struct host_execution_policy
-    : thrust::system::__THRUST_HOST_SYSTEM_NAMESPACE::execution_policy<DerivedPolicy>
-{};
-
-
-/*! \p device_execution_policy is the base class for all Thrust parallel execution policies
- *  which are derived from Thrust's default device backend system configured with the \p THRUST_DEVICE_SYSTEM
- *  macro.
- *
- *  Custom user-defined backends which wish to inherit the functionality of Thrust's device backend system
- *  should derive a policy from this type in order to interoperate with Thrust algorithm dispatch.
- *
- *  The following code snippet demonstrates how to derive a standalone custom execution policy from
- *  \p thrust::device_execution_policy to implement a backend which specializes \p for_each while inheriting
- *  the behavior of every other algorithm from the device system:
- *
- *  \code
- *  #include <thrust/execution_policy.h>
- *  #include <iostream>
- *
- *  // define a type derived from thrust::device_execution_policy to distinguish our custom execution policy:
- *  struct my_policy : thrust::device_execution_policy<my_policy> {};
- *
- *  // overload for_each on my_policy
- *  template<typename Iterator, typename Function>
- *  Iterator for_each(my_policy, Iterator first, Iterator last, Function f)
- *  {
- *    std::cout << "Hello, world from for_each(my_policy)!" << std::endl;
- *
- *    for(; first < last; ++first)
- *    {
- *      f(*first);
- *    }
- *
- *    return first;
- *  }
- *
- *  struct ignore_argument
- *  {
- *    void operator()(int) {}
- *  };
- *
- *  int main()
- *  {
- *    int data[4];
- *
- *    // dispatch thrust::for_each using our custom policy:
- *    my_policy exec;
- *    thrust::for_each(exec, data, data + 4, ignore_argument());
- *
- *    // dispatch thrust::transform whose behavior our policy inherits
- *    thrust::transform(exec, data, data, + 4, data, thrust::identity<int>());
- *
- *    return 0;
- *  }
- *  \endcode
- *
- *  \see execution_policy
- *  \see host_execution_policy
- */
-template<typename DerivedPolicy>
-  struct device_execution_policy
-    : thrust::system::__THRUST_DEVICE_SYSTEM_NAMESPACE::execution_policy<DerivedPolicy>
-{};
-
-
-/*! \p thrust::host is the default parallel execution policy associated with Thrust's host backend system
- *  configured by the \p THRUST_HOST_SYSTEM macro.
- *
- *  Instead of relying on implicit algorithm dispatch through iterator system tags, users may directly target
- *  algorithm dispatch at Thrust's host system by providing \p thrust::host as an algorithm parameter.
- *
- *  Explicit dispatch can be useful in avoiding the introduction of data copies into containers such as
- *  \p thrust::host_vector.
- *
- *  Note that even though \p thrust::host targets the host CPU, it is a parallel execution policy. That is,
- *  the order that an algorithm invokes functors or dereferences iterators is not defined.
- *
- *  The type of \p thrust::host is implementation-defined.
- *
- *  The following code snippet demonstrates how to use \p thrust::host to explicitly dispatch an invocation
- *  of \p thrust::for_each to the host backend system:
- *
- *  \code
- *  #include <thrust/for_each.h>
- *  #include <thrust/execution_policy.h>
- *  #include <cstdio>
- *
- *  struct printf_functor
- *  {
- *    __host__ __device__
- *    void operator()(int x)
- *    {
- *      printf("%d\n", x);
- *    }
- *  };
- *  ...
- *  int vec(3);
- *  vec[0] = 0; vec[1] = 1; vec[2] = 2;
- *
- *  thrust::for_each(thrust::host, vec.begin(), vec.end(), printf_functor());
- *
- *  // 0 1 2 is printed to standard output in some unspecified order
- *  \endcode
- *
- *  \see host_execution_policy
- *  \see thrust::device
- */
-static const detail::host_t host;
-
-
-/*! \p thrust::device is the default parallel execution policy associated with Thrust's device backend system
- *  configured by the \p THRUST_DEVICE_SYSTEM macro.
- *
- *  Instead of relying on implicit algorithm dispatch through iterator system tags, users may directly target
- *  algorithm dispatch at Thrust's device system by providing \p thrust::device as an algorithm parameter.
- *
- *  Explicit dispatch can be useful in avoiding the introduction of data copies into containers such as
- *  \p thrust::device_vector or to avoid wrapping e.g. raw pointers allocated by the CUDA API with types
- *  such as \p thrust::device_ptr.
- *
- *  The user must take care to guarantee that the iterators provided to an algorithm are compatible with
- *  the device backend system. For example, raw pointers allocated by <tt>std::malloc</tt> typically
- *  cannot be dereferenced by a GPU. For this reason, raw pointers allocated by host APIs should not be mixed
- *  with a \p thrust::device algorithm invocation when the device backend is CUDA.
- *
- *  The type of \p thrust::device is implementation-defined.
- *
- *  The following code snippet demonstrates how to use \p thrust::device to explicitly dispatch an invocation
- *  of \p thrust::for_each to the device backend system:
- *
- *  \code
- *  #include <thrust/for_each.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  #include <cstdio>
- *
- *  struct printf_functor
- *  {
- *    __host__ __device__
- *    void operator()(int x)
- *    {
- *      printf("%d\n", x);
- *    }
- *  };
- *  ...
- *  thrust::device_vector<int> vec(3);
- *  vec[0] = 0; vec[1] = 1; vec[2] = 2;
- *
- *  thrust::for_each(thrust::device, vec.begin(), vec.end(), printf_functor());
- *
- *  // 0 1 2 is printed to standard output in some unspecified order
- *  \endcode
- *
- *  \see host_execution_policy
- *  \see thrust::device
- */
-THRUST_INLINE_CONSTANT detail::device_t device;
-
-
-// define seq for the purpose of Doxygenating it
-// it is actually defined elsewhere
-#if 0
-/*! \p thrust::seq is an execution policy which requires an algorithm invocation to execute sequentially
- *  in the current thread. It can not be configured by a compile-time macro.
- *
- *  The type of \p thrust::seq is implementation-defined.
- *
- *  The following code snippet demonstrates how to use \p thrust::seq to explicitly execute an invocation
- *  of \p thrust::for_each sequentially:
- *
- *  \code
- *  #include <thrust/for_each.h>
- *  #include <thrust/execution_policy.h>
- *  #include <vector>
- *  #include <cstdio>
- *
- *  struct printf_functor
- *  {
- *    __host__ __device__
- *    void operator()(int x)
- *    {
- *      printf("%d\n", x);
- *    }
- *  };
- *  ...
- *  std::vector<int> vec(3);
- *  vec[0] = 0; vec[1] = 1; vec[2] = 2;
- *
- *  thrust::for_each(thrust::seq, vec.begin(), vec.end(), printf_functor());
- *
- *  // 0 1 2 is printed to standard output in sequential order
- *  \endcode
- *
- *  \see thrust::host
- *  \see thrust::device
- */
-static const detail::seq_t seq;
-#endif
-
-
-/*! \}
- */
-
-
-} // end thrust
-

spaces/CVPR/regionclip-demo/detectron2/data/clip_datasets/clip_img_txt_pair_tsv.py

@@ -1,602 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-from io import BytesIO
-import json
-import logging
-import base64
-import threading
-import random
-import numpy as np
-from typing import Callable, List, Tuple, Union
-from PIL import Image
-from PIL import ImageFile
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-import torch
-import torch.utils.data as data
-from .oscar_tsv import InputExample, convert_example_to_features
-from detectron2.structures.tsv_file import TSVFile, CompositeTSVFile
-from detectron2.data.clip_datasets.clip_prompt_engineering import get_prompt_templates, prompt_engineering
-#import spacy
-
-def pre_fetch(tsv_filename: str):
-    logging.info('Pre-loading %s ...' % tsv_filename)
-    with open(tsv_filename, 'r'):
-        logging.info('Pre-loading %s ended.' % tsv_filename)
-
-class CLIPImgTxtPairTSVDataset(data.Dataset):
-    """
-        This class is intended for encapsulating Image/Text pair data for contrastive learning described in
-        the following paper,
-        "Learning Transferable Visual Models From Natural Language Supervision" (a.k.a CLIP)
-        Specifically, it is used to accomadate the tsv data format from Azure Cognition Service Group.
-    """
-    def __init__(self,
-                 image_tsv_file: Union[str, List[str]],
-                 text_tsv_file: Union[str, List[str]],
-                 transforms: Callable = None,
-                 tokenizer: Callable = None,
-                 seq_len = 0, context_length = 77, target_offset=0, 
-                 args = None, 
-                 dataset_name = "", 
-                 tokenizer_type = "bert",
-                 is_train = True,
-                 map_file = None, 
-                 filtered_datasets = ''):
-        self.args = args
-        self.is_train = is_train    
-        self.dataset_names = dataset_name
-        self.tokenizer_type = tokenizer_type
-        self.target_offset = target_offset
-        self.seq_len = seq_len
-
-        self.transforms = transforms
-        self.tokenizer = tokenizer
-        self._chunk_sizes = None
-        self.context_length = context_length
-        
-        self.prompt_templates = get_prompt_templates() # [:2]
-        self.spacy_nlp = None # spacy.load('en_core_web_sm')
-        
-        self.class_selector = None
-        # self.class_selector = list(self.label2idx.keys()) if self.label2idx else None
-
-        self.label2idx = {}
-        self.idx2label = {}        
-        self.classnames = {}
-        self.dataset_target_offsets = {}; offset = 0
-
-        self.num_classes = sum([len(val) for val in self.classnames.values()])          
-
-        self.filtered_classnames = []
-
-        if isinstance(image_tsv_file, str) and isinstance(text_tsv_file, str):
-            # single tsv file
-            if (
-                os.path.splitext(image_tsv_file)[1].lower() == '.tsv'
-                and os.path.splitext(text_tsv_file)[1].lower() == '.tsv'
-            ):
-                self.image_tsv_file = TSVFile(image_tsv_file, if_generate_lineidx=True)
-                self.text_tsv_file = TSVFile(text_tsv_file, if_generate_lineidx=True)
-            # multiple tsv files specified in a text file
-            elif (
-                os.path.splitext(image_tsv_file)[1].lower() == '.txt'
-                and os.path.splitext(text_tsv_file)[1].lower() == '.txt'
-            ):
-                self.image_tsv_file = CompositeTSVFile(image_tsv_file)
-                self.text_tsv_file = CompositeTSVFile(text_tsv_file)
-                self._chunk_sizes = self.image_tsv_file.get_chunk_size()
-            else:
-                raise ValueError("Invalid input! Please check the tsv filenames.")
-        # multiple tsv files specified in a list
-        elif (
-            isinstance(image_tsv_file, list)
-            and isinstance(text_tsv_file, list)
-        ):
-            assert len(image_tsv_file) == len(text_tsv_file), \
-                "Inconsistent number of Image/Text tsv files!"
-            assert len(image_tsv_file) == len(text_tsv_file), \
-                "Inconsistent number of Image/Text tsv files!"
-            self.image_tsv_path = image_tsv_file
-            self.text_tsv_path = text_tsv_file            
-            self.image_tsv_file = CompositeTSVFile(image_tsv_file, class_selector=self.class_selector)
-            self.text_tsv_file = CompositeTSVFile(text_tsv_file, class_selector=self.class_selector)
-            self._chunk_sizes = self.image_tsv_file.get_chunk_size()
-            self._accumulated_chunk_sizes = np.cumsum(self._chunk_sizes).tolist()
-        else:
-            raise ValueError("Invalid input! Please check the tsv filenames.")
-        
-        assert len(self.image_tsv_file) == len(self.text_tsv_file), \
-            "Inconsistent size of Image/Text ({}/{}) data!".format(
-                len(self.image_tsv_file), len(self.text_tsv_file)
-            )
-    
-    def get_chunk_sizes(self):
-        return self._chunk_sizes
-
-    def get_class_boundaries(self):
-        # The samples of each class are organized class-by-class.
-        # _class_boundaries stores the lower- and upper-bound of each class.
-        return self.image_tsv_file.get_class_boundaries()
-
-    def _load_map(self, map_file: str):
-        if not map_file:
-            return None
-
-        label2idx = {}
-        with open(map_file) as f:
-            for line in f:
-                items = line.strip().split('\t')
-                label2idx[items[0]] = int(items[1])
-
-        return label2idx
-    
-    def _load_darknet_map(self, map_file):
-        if not map_file:
-            return None
-
-        label2idx = {}
-        with open(map_file) as f:
-            linenum = 0
-            for l in f:
-                item = l.strip()
-                label2idx[item] = linenum
-                linenum += 1
-
-        return label2idx    
-
-    def _pre_tokenize(self):
-        """
-        pre-tokenize class names
-        """
-        input_ids_all = []
-        input_masks_all = []
-        segment_ids_all = []
-        for k in range(len(self.classnames["imagenet"])):
-            cur_id = 0; img_id = 0
-            scale = 1.0
-
-            v = self.classnames["imagenet"].label_to_name(k)
-            if isinstance(v, str):
-                vs = [v]
-            elif isinstance(v, list):
-                vs = v
-            t1s = []
-            t2s = []
-            for v in vs:
-                for pt in self.prompt_templates:
-                    t1s.append(prompt_engineering(v, template=pt))
-                    t2s.append("")
-            input_ids = []
-            input_masks = []
-            segment_ids = []
-            is_next_labels = [0] * len(t1s)
-            is_img_matchs = [1] * len(t1s)
-            img_feat_len = 0
-            for t1, t2, is_next_label, is_img_match in zip(t1s, t2s, is_next_labels, is_img_matchs):
-                if self.tokenizer_type == "bert":
-                    # tokenize
-                    tokens_a = self.tokenizer.tokenize(t1)
-                    tokens_b = None
-
-                    # combine to one sample
-                    cur_example = InputExample(guid=cur_id, tokens_a=tokens_a,
-                                            tokens_b=tokens_b, is_next=is_next_label,
-                                            img_id=img_id, is_img_match=is_img_match)
-
-                    # transform sample to features
-                    cur_features = convert_example_to_features(self.args, cur_example,
-                                                            self.seq_len, self.tokenizer,
-                                                            img_feat_len)                                                        
-
-                    input_ids.append(torch.tensor(cur_features.input_ids, dtype=torch.long))
-                    input_masks.append(torch.tensor(cur_features.input_mask, dtype=torch.long))
-                    segment_ids.append(torch.tensor(cur_features.segment_ids, dtype=torch.long))
-
-                elif self.tokenizer_type == "bpe":
-                    tokens_a = t1; tokens_b = None
-                    # combine to one sample
-                    cur_example = InputExample(guid=cur_id, tokens_a=tokens_a,
-                                            tokens_b=tokens_b, is_next=is_next_label,
-                                            img_id=img_id, is_img_match=is_img_match)
-
-                    # transform sample to features
-                    cur_features = convert_example_to_features_bpe(self.args, cur_example,
-                                                            self.seq_len, self.tokenizer,
-                                                            img_feat_len)                                                        
-
-                    input_ids.append(torch.tensor(cur_features.input_ids, dtype=torch.long))
-                    input_masks.append(torch.tensor(cur_features.input_mask, dtype=torch.long))
-                    segment_ids.append(torch.tensor(cur_features.segment_ids, dtype=torch.long))
-
-                else:
-                    raise NotImplementedError
-            input_ids_all.append(torch.stack(input_ids, 0))
-            input_masks_all.append(torch.stack(input_masks, 0))
-            segment_ids_all.append(torch.stack(segment_ids, 0))
-
-        self.input_ids_all_classes = torch.stack(input_ids_all, 0)
-        self.input_mask_all_classes = torch.stack(input_masks_all, 0)
-        self.segment_ids_all_classes = torch.stack(segment_ids_all, 0)
-
-    def _online_tokenize(self, text):
-
-        # random select a prompt template
-        temp_idx = np.random.randint(len(self.prompt_templates))
-        pt = self.prompt_templates[temp_idx]
-
-        names = text.split(";")
-        num_names = np.random.randint(len(names)) + 1
-        names_sampled = random.sample(names, num_names)
-        text = ", ".join(names_sampled)    
-
-        t1 = prompt_engineering(text, template=pt)
-
-        cur_id = 0; img_id = 0; scale = 1.0
-        is_next_label = 0; is_img_match = 1
-        img_feat_len = 0
-
-        if self.tokenizer_type == "bert":
-            # tokenize
-            tokens_a = self.tokenizer.tokenize(t1)
-            tokens_b = None
-
-            # combine to one sample
-            cur_example = InputExample(guid=cur_id, tokens_a=tokens_a,
-                                    tokens_b=tokens_b, is_next=is_next_label,
-                                    img_id=img_id, is_img_match=is_img_match)
-
-            # transform sample to features
-            cur_features = convert_example_to_features(self.args, cur_example,
-                                                    self.context_length, self.tokenizer,
-                                                    img_feat_len)                                                        
-
-
-        elif self.tokenizer_type == "bpe":
-            tokens_a = t1; tokens_b = None
-            # combine to one sample
-            cur_example = InputExample(guid=cur_id, tokens_a=tokens_a,
-                                    tokens_b=tokens_b, is_next=is_next_label,
-                                    img_id=img_id, is_img_match=is_img_match)
-
-            # transform sample to features
-            cur_features = convert_example_to_features_bpe(self.args, cur_example,
-                                                    self.context_length, self.tokenizer,
-                                                    img_feat_len)                                                              
-
-        return torch.tensor(cur_features.input_ids, dtype=torch.long), \
-                torch.tensor(cur_features.input_mask, dtype=torch.long), \
-                torch.tensor(cur_features.segment_ids, dtype=torch.long)
-
-    def get_dataset_name(self, index):
-        """
-        get dataset name according to index
-        """
-        assert index < self._accumulated_chunk_sizes[-1], "index must in the range of accumulated data size"
-        for k, boundary in enumerate(self._accumulated_chunk_sizes):
-            if index < boundary:
-                return self.dataset_names[k], k
-
-    def get_target_offset(self, dataset_name):
-        return self.dataset_target_offsets[dataset_name]
-
-    def get_img_label_pair(self, items_image, index):             
-        dataset_name, chunk_id = self.get_dataset_name(index)
-        target_offset = self.get_target_offset(dataset_name)
-        _, target, img = self._decode_data(items_image, dataset_name)   
-
-        if self.transforms:
-            img = self.transforms(img)
-
-        if target == -1:
-            input_ids, input_mask, segment_ids = \
-                self._online_tokenize("uncovered image")                    
-        else:
-            classname = self.classnames[dataset_name].labels2names[self.idx2label[dataset_name][target]]
-            if classname in self.filtered_classnames:
-                # we filter these classnames for training
-                target = -1
-                input_ids, input_mask, segment_ids = \
-                    self._online_tokenize("uncovered image")                  
-            else:            
-                input_ids, input_mask, segment_ids = \
-                    self._online_tokenize(classname)
-                target += target_offset
-        return img, \
-            input_ids, \
-            input_mask, \
-            segment_ids, \
-            torch.LongTensor([target]), \
-            dataset_name                          
-        
-    def get_img_txt_pair(self, items_image, items_text, index):
-        dataset_name, chunk_id = self.get_dataset_name(index)
-        assert items_text[0] == items_image[0], \
-            'keys do not match for image ({}) and text ({}) for {} at chunk {}-{}'.format(
-                len(items_text[0]), len(items_image[0]), dataset_name, chunk_id, self.image_tsv_path[chunk_id]
-            )
-
-        img = self._decode_image(items_image, dataset_name)
-        #     print("index {}, chunk id {}, name {}".format(index, chunk_id, self.image_tsv_path[chunk_id]))
-        #     raise TypeError("cannot decode current item") 
-        img_width, img_height = img.size  # img_height, img_width = np.array(img).shape
-
-        txts = self._decode_text(items_text)
-        if self.spacy_nlp is not None:
-            np_input_ids, np_input_masks, np_segment_ids = self.create_phrase_text(txts)
-
-        if self.transforms:
-            img = self.transforms(img)
-
-        if isinstance(txts, str):
-            input_ids, input_masks, segment_ids = \
-                convert_txt_to_tokens_bpe(txts, self.tokenizer, self.context_length)
-            all_str2id_links = []
-        elif isinstance(txts, list):
-            input_ids = []
-            input_masks = []
-            segment_ids = []
-            all_str2id_links = []
-            for txt in txts:
-                input_id, input_mask, segment_id, str2id_links = \
-                    convert_txt_to_tokens_bpe(txt, self.tokenizer, self.context_length, return_link=True)
-                input_ids += input_id          
-                input_masks += input_mask
-                segment_ids += segment_id
-                all_str2id_links += [str2id_links]
-        scale = 1.0
-        img_id = 0
-        
-        if self.spacy_nlp is not None:
-            return img, \
-                    torch.tensor(input_ids).long().view(-1), \
-                    torch.tensor(input_masks).long().view(-1), \
-                    torch.tensor(segment_ids).long().view(-1), \
-                    torch.LongTensor([1e5]), \
-                    dataset_name, \
-                    torch.tensor(np_input_ids).long().view(-1), \
-                    torch.tensor(np_input_masks).long().view(-1), \
-                    torch.tensor(np_segment_ids).long().view(-1)
-        else:
-            return img, \
-                    torch.tensor(input_ids).long().view(-1), \
-                    torch.tensor(input_masks).long().view(-1), \
-                    torch.tensor(segment_ids).long().view(-1), \
-                    torch.LongTensor([1e5]), \
-                    (dataset_name, items_text[0], (img_height, img_width), all_str2id_links)  # dataset name, image id, image height&width, links bet string and tokenized texts
-
-    def create_phrase_text(self, txt_list):
-        """ Use NLP tool to detect noun phrases in captions, fill each identified phrase into a random prompt to create a sentence,
-            and convert each sentence to bpe tokens
-        """
-        if isinstance(txt_list, str):
-            txt_list = [txt_list]
-        # detect noun phrase
-        noun_phrase = []
-        for txt in txt_list:
-            doc = self.spacy_nlp(txt.lower())
-            this_text = [nc.text for nc in doc.noun_chunks]
-            this_text = [nc.replace('a ', '').replace('the ', '') for nc in this_text]
-            noun_phrase.extend(this_text)
-        noun_phrase = list(set(noun_phrase))
-        # fill each phrase into a random prompt
-        text_list = []
-        pts = random.sample(self.prompt_templates, len(noun_phrase))
-        for i, np in enumerate(noun_phrase):
-            text_list.append(prompt_engineering(np, pts[i]))
-        # convert string into bpe tokens
-        input_ids = []
-        input_masks = []
-        segment_ids = []
-        for txt in text_list:
-            input_id, input_mask, segment_id = \
-                convert_txt_to_tokens_bpe(txt, self.tokenizer, self.context_length)
-            input_ids += input_id          
-            input_masks += input_mask
-            segment_ids += segment_id
-        return input_ids, input_masks, segment_ids
-
-    def __getitem__(self, index: Union[int, Tuple[int, int]]):
-        if isinstance(index, tuple):
-            items_image = self.image_tsv_file[index[0]]
-            items_text = self.text_tsv_file[index[0]]
-            if index[1] >= 0:
-                tsv_filename = self.image_tsv_file.file_list[index[1]]
-
-                # Python threads are not truly parallel. Spawn a new process instead.
-                # logging.info('Pre-loading %s ...' % tsv_filename)
-                # os.system('cat ' + tsv_filename + ' > /dev/null &')
-                x = threading.Thread(
-                   target=pre_fetch, args=(tsv_filename,), daemon=True
-                )
-                x.start()
-            curr_index = index[0]
-        else:
-            items_image = self.image_tsv_file[index]
-            items_text = self.text_tsv_file[index]
-            curr_index = index
-        
-        # NOTE: since we duplicate image tsv to text tsv for image-label data,
-        # we can determine whether the current instance is an image-label pair or 
-        # a image-text pair data based on whether items_image is identical to items_text or not.
-        if items_image == items_text:
-            return self.get_img_label_pair(items_image, curr_index)
-        else:
-            return self.get_img_txt_pair(items_image, items_text, curr_index)
-            
-    def _decode_image(self, items: Tuple[str, str], dataset_name=""):
-        key = items[0]
-        image = Image.open(BytesIO(base64.b64decode(items[1]))).convert('RGB')
-        return image
-
-    def _decode_text(self, items: Tuple[str, Union[str, dict]]):
-        key = items[0]
-        text = ''
-        if isinstance(items[1], str):
-            try:
-                str_dict = json.loads(items[1])
-                # in this dict, it may contain either "tags" or "captions" or both
-                keys = [key for key in str_dict.keys()]
-                selected_key = random.sample(keys, 1)[0]
-                if selected_key == "captions":
-                    # if this is a caption, we sample a caption
-                    captions = str_dict[selected_key]
-                    text = captions[:5]
-                    # text = random.sample(captions, 1)[0]
-                elif selected_key == "tags":
-                    # for tags, we randomly disorder it
-                    tags = str_dict[selected_key]
-                    tag_words = tags.split(' ')
-                    random.shuffle(tag_words)
-                    tags_shuffled = " ".join(tag_words)
-                    # add prompt template
-                    pt = random.sample(self.prompt_templates, 1)[0]
-                    text = prompt_engineering(tags_shuffled, pt)
-            except:
-                text = items[1]
-        elif isinstance(items[1], dict):
-            assert 'captions' in items[1], '"captions" does not in {}'.format(items[1])
-            captions = items[1]['captions']
-            if isinstance(captions, list):
-                text = random.choice(captions)
-            elif isinstance(captions, str):
-                text = captions
-            else:
-                raise ValueError('captions should be str or list')
-
-        return text
-
-    def _decode_data(self, items, dataset_name):
-        key = items[0]
-        label = self._get_label(items[1], dataset_name)
-        try:
-            image = Image.open(BytesIO(base64.b64decode(items[2])))
-        except:
-            return None
-
-        return key, label, image.convert('RGB')
-
-    def _get_label(self, item, dataset_name):
-        if not self.label2idx[dataset_name]:
-            return int(item)
-
-        if item in self.label2idx[dataset_name]:
-            return self.label2idx[dataset_name][item]
-
-        label = json.loads(item)[0]['class']
-        if label in self.label2idx[dataset_name]:
-            return self.label2idx[dataset_name][label]
-        else:
-            return -1
-
-    def __len__(self):
-        return len(self.image_tsv_file)
-
-def convert_txt_to_tokens_bpe(text, tokenizer, context_length, return_link=False):
-
-    sot_token = tokenizer.encoder["<|startoftext|>"]
-    eot_token = tokenizer.encoder["<|endoftext|>"]
-    if return_link:
-        bpe_tokens, str2id_links = tokenizer.encode(text, return_link=return_link)
-        str2id_links = [["<|startoftext|>", [sot_token]]] + str2id_links + [["<|endoftext|>", [eot_token]]]
-    else:
-        bpe_tokens = tokenizer.encode(text, return_link=return_link)
-    input_ids = [sot_token] + bpe_tokens + [eot_token]
-
-    if len(input_ids) > context_length:
-        input_ids = input_ids[:context_length]
-    segment_ids = [0] * len(input_ids)
-    lm_label_ids = [-1] * len(input_ids)
-
-    # The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
-    input_mask = [1] * len(input_ids)
-
-    # Zero-pad up to the sequence length.
-    while len(input_ids) < context_length:
-        input_ids.append(0)
-        input_mask.append(0)
-        segment_ids.append(0)
-        lm_label_ids.append(-1)
-
-    assert len(input_ids) == context_length
-    assert len(input_mask) == context_length
-    assert len(segment_ids) == context_length
-    assert len(lm_label_ids) == context_length    
-    
-    if return_link:
-        return input_ids, input_mask, segment_ids, str2id_links
-    return input_ids, input_mask, segment_ids
-
-def convert_example_to_features_bpe(args, example, max_seq_length, tokenizer,
-                                img_feat_len, context_length=77):
-    """
-    Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
-    IDs, LM labels, input_mask, CLS and SEP tokens etc.
-    :param args: parameter settings
-    :param img_feat_len: lens of actual img features
-    :param example: InputExample, containing sentence input as strings and is_next label
-    :param max_seq_length: int, maximum length of sequence.
-    :param tokenizer: Tokenizer
-    :return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
-    """
-    # we do not consider tokens_b for now in original CLIP
-    text = example.tokens_a
-    assert isinstance(text, str)
-
-    sot_token = tokenizer.encoder["<|startoftext|>"]
-    eot_token = tokenizer.encoder["<|endoftext|>"]
-    input_ids = [sot_token] + tokenizer.encode(text) + [eot_token]
-
-    if len(input_ids) > context_length:
-        input_ids = input_ids[:context_length]
-    segment_ids = [0] * len(input_ids)
-    lm_label_ids = [-1] * len(input_ids)
-
-    # The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
-    input_mask = [1] * len(input_ids)
-
-    # Zero-pad up to the sequence length.
-    while len(input_ids) < context_length:
-        input_ids.append(0)
-        input_mask.append(0)
-        segment_ids.append(0)
-        lm_label_ids.append(-1)
-
-    assert len(input_ids) == context_length
-    assert len(input_mask) == context_length
-    assert len(segment_ids) == context_length
-    assert len(lm_label_ids) == context_length
-
-    if example.guid < 1:
-        logging.info("*** Example ***")
-        logging.info("guid: %s" % example.guid)
-        logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
-        logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
-        logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
-        logging.info("LM label: %s " % lm_label_ids)
-        logging.info("Is next sentence label: %s " % example.is_next)
-
-    features = InputFeatures(input_ids=input_ids,
-                             input_mask=input_mask,
-                             segment_ids=segment_ids,
-                             lm_label_ids=lm_label_ids,
-                             is_next=example.is_next,
-                             img_feat_len=img_feat_len,
-                             is_img_match=example.is_img_match)
-    return features
-
-class InputFeatures(object):
-    """A single set of features of data."""
-
-    def __init__(self, input_ids, input_mask, segment_ids, is_next,
-                 lm_label_ids, img_feat_len, is_img_match):
-        self.input_ids = input_ids
-        self.input_mask = input_mask
-        self.segment_ids = segment_ids
-        self.is_next = is_next
-        self.lm_label_ids = lm_label_ids
-
-        self.img_feat_len = img_feat_len
-        self.is_img_match = is_img_match    

spaces/CXD200/QSign/unidbg-fetch-qsign/bin/unidbg-fetch-qsign.bat

@@ -1,89 +0,0 @@
-@rem
-@rem Copyright 2015 the original author or authors.
-@rem
-@rem Licensed under the Apache License, Version 2.0 (the "License");
-@rem you may not use this file except in compliance with the License.
-@rem You may obtain a copy of the License at
-@rem
-@rem      https://www.apache.org/licenses/LICENSE-2.0
-@rem
-@rem Unless required by applicable law or agreed to in writing, software
-@rem distributed under the License is distributed on an "AS IS" BASIS,
-@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-@rem See the License for the specific language governing permissions and
-@rem limitations under the License.
-@rem
-
-@if "%DEBUG%" == "" @echo off
-@rem ##########################################################################
-@rem
-@rem  unidbg-fetch-qsign startup script for Windows
-@rem
-@rem ##########################################################################
-
-@rem Set local scope for the variables with windows NT shell
-if "%OS%"=="Windows_NT" setlocal
-
-set DIRNAME=%~dp0
-if "%DIRNAME%" == "" set DIRNAME=.
-set APP_BASE_NAME=%~n0
-set APP_HOME=%DIRNAME%..
-
-@rem Resolve any "." and ".." in APP_HOME to make it shorter.
-for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
-
-@rem Add default JVM options here. You can also use JAVA_OPTS and UNIDBG_FETCH_QSIGN_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
-
-@rem Find java.exe
-if defined JAVA_HOME goto findJavaFromJavaHome
-
-set JAVA_EXE=java.exe
-%JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:findJavaFromJavaHome
-set JAVA_HOME=%JAVA_HOME:"=%
-set JAVA_EXE=%JAVA_HOME%/bin/java.exe
-
-if exist "%JAVA_EXE%" goto execute
-
-echo.
-echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
-echo.
-echo Please set the JAVA_HOME variable in your environment to match the
-echo location of your Java installation.
-
-goto fail
-
-:execute
-@rem Setup the command line
-
-set CLASSPATH=%APP_HOME%\lib\unidbg-fetch-qsign-1.1.0.jar;%APP_HOME%\lib\unidbg-fix.jar;%APP_HOME%\lib\ktor-server-content-negotiation-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-json-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-netty-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-host-common-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-server-core-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-kotlinx-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-serialization-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-events-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-websockets-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-cio-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-http-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-network-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-utils-jvm-2.3.1.jar;%APP_HOME%\lib\ktor-io-jvm-2.3.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk8-1.8.22.jar;%APP_HOME%\lib\kotlinx-serialization-json-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-protobuf-jvm-1.5.1.jar;%APP_HOME%\lib\kotlinx-serialization-core-jvm-1.5.1.jar;%APP_HOME%\lib\logback-classic-1.2.11.jar;%APP_HOME%\lib\kotlinx-coroutines-jdk8-1.7.1.jar;%APP_HOME%\lib\kotlinx-coroutines-core-jvm-1.7.1.jar;%APP_HOME%\lib\kotlin-stdlib-jdk7-1.8.22.jar;%APP_HOME%\lib\kotlin-reflect-1.8.10.jar;%APP_HOME%\lib\kotlin-stdlib-1.8.22.jar;%APP_HOME%\lib\slf4j-api-1.7.36.jar;%APP_HOME%\lib\kotlin-stdlib-common-1.8.22.jar;%APP_HOME%\lib\config-1.4.2.jar;%APP_HOME%\lib\jansi-2.4.0.jar;%APP_HOME%\lib\netty-codec-http2-4.1.92.Final.jar;%APP_HOME%\lib\alpn-api-1.1.3.v20160715.jar;%APP_HOME%\lib\netty-transport-native-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-epoll-4.1.92.Final.jar;%APP_HOME%\lib\logback-core-1.2.11.jar;%APP_HOME%\lib\annotations-23.0.0.jar;%APP_HOME%\lib\netty-codec-http-4.1.92.Final.jar;%APP_HOME%\lib\netty-handler-4.1.92.Final.jar;%APP_HOME%\lib\netty-codec-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-kqueue-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-classes-epoll-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-native-unix-common-4.1.92.Final.jar;%APP_HOME%\lib\netty-transport-4.1.92.Final.jar;%APP_HOME%\lib\netty-buffer-4.1.92.Final.jar;%APP_HOME%\lib\netty-resolver-4.1.92.Final.jar;%APP_HOME%\lib\netty-common-4.1.92.Final.jar
-
-
-@rem Execute unidbg-fetch-qsign
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %UNIDBG_FETCH_QSIGN_OPTS%  -classpath "%CLASSPATH%" MainKt %*
-
-:end
-@rem End local scope for the variables with windows NT shell
-if "%ERRORLEVEL%"=="0" goto mainEnd
-
-:fail
-rem Set variable UNIDBG_FETCH_QSIGN_EXIT_CONSOLE if you need the _script_ return code instead of
-rem the _cmd.exe /c_ return code!
-if  not "" == "%UNIDBG_FETCH_QSIGN_EXIT_CONSOLE%" exit 1
-exit /b 1
-
-:mainEnd
-if "%OS%"=="Windows_NT" endlocal
-
-:omega

spaces/CofAI/chat/g4f/Provider/Providers/Fakeopen.py

@@ -1,54 +0,0 @@
-import os  
-import json
-import requests  
-from typing import Dict, get_type_hints  
-  
-url = 'https://ai.fakeopen.com/v1/'  
-model = [  
-    'gpt-3.5-turbo', 
-    'gpt-3.5-turbo-0613',
-    'gpt-3.5-turbo-16k', 
-    'gpt-3.5-turbo-16k-0613', 
-]  
-
-supports_stream = True  
-needs_auth = False  
-  
-  
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):  
-  
-    headers = {  
-        'Content-Type': 'application/json',  
-        'accept': 'text/event-stream',  
-        'Cache-Control': 'no-cache',  
-        'Proxy-Connection': 'keep-alive',  
-        'Authorization': f"Bearer {os.environ.get('FAKE_OPEN_KEY', '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')}",  
-    }  
-  
-    json_data = {  
-        'messages': messages,  
-        'temperature': 1.0,  
-        'model': model,  
-        'stream': stream,  
-    }  
-  
-    response = requests.post(  
-        'https://ai.fakeopen.com/v1/chat/completions', headers=headers, json=json_data, stream=True  
-    )  
-  
-    for token in response.iter_lines():  
-        decoded = token.decode('utf-8')  
-        if decoded == '[DONE]':  
-            break  
-        if decoded.startswith('data: '):  
-            data_str = decoded.replace('data: ', '')  
-            if data_str != '[DONE]':  
-                data = json.loads(data_str)  
-                if 'choices' in data and 'delta' in data['choices'][0] and 'content' in data['choices'][0]['delta']:  
-                    yield data['choices'][0]['delta']['content']  
-
-
-  
-  
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' +  '(%s)' % ', '.join(  
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])  

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/voc.py

@@ -1,134 +0,0 @@
-import os
-
-import torch
-import torch.utils.data
-from PIL import Image
-import sys
-
-if sys.version_info[0] == 2:
-    import xml.etree.cElementTree as ET
-else:
-    import xml.etree.ElementTree as ET
-
-
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-
-
-class PascalVOCDataset(torch.utils.data.Dataset):
-
-    CLASSES = (
-        "__background__ ",
-        "aeroplane",
-        "bicycle",
-        "bird",
-        "boat",
-        "bottle",
-        "bus",
-        "car",
-        "cat",
-        "chair",
-        "cow",
-        "diningtable",
-        "dog",
-        "horse",
-        "motorbike",
-        "person",
-        "pottedplant",
-        "sheep",
-        "sofa",
-        "train",
-        "tvmonitor",
-    )
-
-    def __init__(self, data_dir, split, use_difficult=False, transforms=None):
-        self.root = data_dir
-        self.image_set = split
-        self.keep_difficult = use_difficult
-        self.transforms = transforms
-
-        self._annopath = os.path.join(self.root, "Annotations", "%s.xml")
-        self._imgpath = os.path.join(self.root, "JPEGImages", "%s.jpg")
-        self._imgsetpath = os.path.join(self.root, "ImageSets", "Main", "%s.txt")
-
-        with open(self._imgsetpath % self.image_set) as f:
-            self.ids = f.readlines()
-        self.ids = [x.strip("\n") for x in self.ids]
-        self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
-
-        cls = PascalVOCDataset.CLASSES
-        self.class_to_ind = dict(zip(cls, range(len(cls))))
-
-    def __getitem__(self, index):
-        img_id = self.ids[index]
-        img = Image.open(self._imgpath % img_id).convert("RGB")
-
-        target = self.get_groundtruth(index)
-        target = target.clip_to_image(remove_empty=True)
-
-        if self.transforms is not None:
-            img, target = self.transforms(img, target)
-
-        return img, target, index
-
-    def __len__(self):
-        return len(self.ids)
-
-    def get_groundtruth(self, index):
-        img_id = self.ids[index]
-        anno = ET.parse(self._annopath % img_id).getroot()
-        anno = self._preprocess_annotation(anno)
-
-        height, width = anno["im_info"]
-        target = BoxList(anno["boxes"], (width, height), mode="xyxy")
-        target.add_field("labels", anno["labels"])
-        target.add_field("difficult", anno["difficult"])
-        return target
-
-    def _preprocess_annotation(self, target):
-        boxes = []
-        gt_classes = []
-        difficult_boxes = []
-        TO_REMOVE = 1
-        
-        for obj in target.iter("object"):
-            difficult = int(obj.find("difficult").text) == 1
-            if not self.keep_difficult and difficult:
-                continue
-            name = obj.find("name").text.lower().strip()
-            bb = obj.find("bndbox")
-            # Make pixel indexes 0-based
-            # Refer to "https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/pascal_voc.py#L208-L211"
-            box = [
-                bb.find("xmin").text, 
-                bb.find("ymin").text, 
-                bb.find("xmax").text, 
-                bb.find("ymax").text,
-            ]
-            bndbox = tuple(
-                map(lambda x: x - TO_REMOVE, list(map(int, box)))
-            )
-
-            boxes.append(bndbox)
-            gt_classes.append(self.class_to_ind[name])
-            difficult_boxes.append(difficult)
-
-        size = target.find("size")
-        im_info = tuple(map(int, (size.find("height").text, size.find("width").text)))
-
-        res = {
-            "boxes": torch.tensor(boxes, dtype=torch.float32),
-            "labels": torch.tensor(gt_classes),
-            "difficult": torch.tensor(difficult_boxes),
-            "im_info": im_info,
-        }
-        return res
-
-    def get_img_info(self, index):
-        img_id = self.ids[index]
-        anno = ET.parse(self._annopath % img_id).getroot()
-        size = anno.find("size")
-        im_info = tuple(map(int, (size.find("height").text, size.find("width").text)))
-        return {"height": im_info[0], "width": im_info[1]}
-
-    def map_class_id_to_class_name(self, class_id):
-        return PascalVOCDataset.CLASSES[class_id]

spaces/Cyril666/ContourNet-ABI/modules/resnet.py

@@ -1,104 +0,0 @@
-import math
-
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.model_zoo as model_zoo
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv1x1(inplanes, planes)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes, stride)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(self, block, layers):
-        self.inplanes = 32
-        super(ResNet, self).__init__()
-        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1,
-                               bias=False)
-        self.bn1 = nn.BatchNorm2d(32)
-        self.relu = nn.ReLU(inplace=True)
-
-        self.layer1 = self._make_layer(block, 32, layers[0], stride=2)
-        self.layer2 = self._make_layer(block, 64, layers[1], stride=1)
-        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 256, layers[3], stride=1)
-        self.layer5 = self._make_layer(block, 512, layers[4], stride=1)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.layer5(x)
-        return x
-
-
-def resnet45():
-    return ResNet(BasicBlock, [3, 4, 6, 6, 3])

spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/processors/__init__.py

@@ -1,38 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from video_llama.processors.base_processor import BaseProcessor
-from video_llama.processors.blip_processors import (
-    Blip2ImageTrainProcessor,
-    Blip2ImageEvalProcessor,
-    BlipCaptionProcessor,
-)
-from video_llama.processors.video_processor import (
-    AlproVideoTrainProcessor,
-    AlproVideoEvalProcessor
-)
-from video_llama.common.registry import registry
-
-__all__ = [
-    "BaseProcessor",
-    "Blip2ImageTrainProcessor",
-    "Blip2ImageEvalProcessor",
-    "BlipCaptionProcessor",
-    "AlproVideoTrainProcessor",
-    "AlproVideoEvalProcessor",
-]
-
-
-def load_processor(name, cfg=None):
-    """
-    Example
-
-    >>> processor = load_processor("alpro_video_train", cfg=None)
-    """
-    processor = registry.get_processor_class(name).from_config(cfg)
-
-    return processor