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-<h1>Bosch ESI Tronic Keygen 1Q.2013.rar: What Is It and How to Use It?</h1>
- <p>If you are a professional mechanic or a car enthusiast, you probably know about Bosch ESI Tronic, a comprehensive software for diagnosing, repairing, and servicing various vehicles. But do you know how to get a valid license for using this software without paying a subscription fee? In this article, we will explain what Bosch ESI Tronic Keygen 1Q.2013.rar is, how to download it, how to generate valid keys with it, and how to use it with Bosch ESI Tronic.</p>
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- <p>Bosch ESI Tronic is a software that provides access to a database of technical information, wiring diagrams, service manuals, troubleshooting guides, spare parts catalogs, and more for over 150,000 vehicles from different manufacturers. It also allows you to connect your PC or laptop with a compatible diagnostic device, such as Bosch KTS series, and perform various tests and functions on your vehicle's electronic systems.</p>
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- <p>A keygen is a software that generates serial numbers or activation codes for another software. You need a keygen for Bosch ESI Tronic because the original software requires a license that can only be obtained by paying a subscription fee to Bosch or an authorized dealer. By using a keygen, you can bypass this requirement and use Bosch ESI Tronic for free.</p>
- <p>The 1Q.2013 version is one of the latest versions of Bosch ESI Tronic that was released in early 2013. It contains updated information and features for vehicles up to model year 2012. It also includes some archive CDs that contain older data for vehicles from previous years.</p>
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- <p>To download Bosch ESI Tronic Keygen 1Q.2013.rar, you need to find a reliable source that provides the link and the password for the file. One such source is MHH AUTO forum, where you can find a thread dedicated to this topic. You need to be an active member of this forum with at least 100 posts to get access to the password.</p>
- <p>Once you have the link and the password, you need to use JDownloader and Winrar to download and extract the file. JDownloader is a free download manager that can handle multiple links and resume interrupted downloads. Winrar is a file compression tool that can open rar files.</p>
- <p>The file size of Bosch ESI Tronic Keygen 1Q.2013.rar is about 33.5 GB, so make sure you have enough space on your hard drive before downloading it. The file also has a 12% recovery record in case of any corruption during the download process.</p>
- <p>After downloading and extracting the file, you will find several files inside it: - **ESI_3Q_2013_DVD_U.iso**: This is the main installation DVD for Bosch ESI Tronic. - **ESI_3Q_2013_DVD_U1.iso**: This is an additional DVD that contains more data for some vehicles. - **ESI_1Q_2013_DVD_1.iso**: This is another installation DVD that contains data for older vehicles. - **ESI_1Q_2013_DVD_2.iso**: This is another installation DVD that contains data for older vehicles. - **ESI_1Q_2013_DVD_3.iso**: This is another installation DVD that contains data for older vehicles. - **ESI_Archive_CD_C.iso**: This is an archive CD that contains data for commercial vehicles from previous years. - **ESI_Archive_CD_K_W.iso**: This is an archive CD that contains data for passenger cars from previous years. - **EIS_3_2013.txt**: This is a text file that contains instructions on how to install Bosch ESI Tronic. - **KG_Patch_1Q_2013.rar**: This is a rar file that contains the keygen and patch for generating valid keys.</p>
- <h2>How to Generate Valid Keys with Bosch ESI Tronic Keygen 1Q.2013.rar</h2>
- <p>To generate valid keys with Bosch ESI Tronic Keygen 1Q.2013.rar, you need to follow these steps: - Extract KG_Patch_1Q_2013.rar with Winrar and open the folder inside it. - Run "ESI KG v10.exe" as administrator. - Enter your hardware ID (HW-ID) in the first box. You can find your HW-ID by running "HW-ID.exe" in the same folder or by opening "ESI[tronic]-Info.exe" in your installed folder of Bosch ESI Tronic. - Select your options in the second box according to your needs. You can choose between "All Modules" or "Only Diagnosis", "All Languages" or "Only English", "All Brands" or "Only One Brand", etc. - Click on "Generate" button and copy the generated keys in the third box. - Run "Patch.exe" as administrator and click on "Patch" button. - Paste the generated keys into the appropriate fields in your installed folder of Bosch ESI Tronic.</p>
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- <p>Bosch ESI Tronic Keygen 1Q.2013.rar is a useful tool for anyone who wants to use Bosch ESI Tronic software without paying a subscription fee. It allows you to generate valid keys for activating all modules and functions of this comprehensive software for diagnosing, repairing, and servicing various vehicles.</p>
- <p>However, before using this tool, you should be aware of some tips and warnings: - Make sure you download Bosch ESI Tronic Keygen 1Q.2013.rar from a trusted source and scan it for viruses or malware before opening it. - Make sure you have enough space on your hard drive and a stable internet connection before downloading and extracting the file. - Make sure you follow the instructions carefully and enter the correct hardware ID and options when generating the keys. - Make sure you backup your original files before applying the patch and keep a copy of the generated keys in case you need to reinstall the software. - Make sure you use Bosch ESI Tronic Keygen 1Q.2013.rar only for personal or educational purposes and not for commercial or illegal activities. You are responsible for any consequences that may arise from using this tool. - Make sure you update your Bosch ESI Tronic software regularly to get the latest information and features for your vehicles. <h2>FAQs</h2>
- <p>Here are some frequently asked questions and answers about Bosch ESI Tronic Keygen 1Q.2013.rar:</p>
- - Q: What are some common problems and solutions when using Bosch ESI Tronic Keygen 1Q.2013.rar? - A: Some common problems and solutions are:   - The keygen does not work or shows an error message. Solution: Try to run the keygen as administrator, disable your antivirus or firewall temporarily, or download a different version of the keygen from another source.   - The patch does not work or shows an error message. Solution: Try to run the patch as administrator, disable your antivirus or firewall temporarily, or download a different version of the patch from another source.   - The generated keys do not work or show an invalid license message. Solution: Try to generate new keys with different options, make sure you enter the correct hardware ID and paste the keys in the right fields, or contact Bosch for technical support.   - The ESI Tronic program does not work or shows an error message. Solution: Try to reinstall the program, update the program to the latest version, check your diagnostic device connection and compatibility, or contact Bosch for technical support. - Q: What are some alternative sources or versions of Bosch ESI Tronic Keygen? - A: Some alternative sources or versions of Bosch ESI Tronic Keygen are:   - AutoProfessionals forum: This is another online forum where you can find links and passwords for different versions of Bosch ESI Tronic Keygen, such as 2013/1, 2014/1, 2015/1, etc.   - BOSCH ESI[tronic] 2.0: This is a newer version of Bosch ESI Tronic software that has a different interface and features than the previous versions. It also requires a different keygen and activation method than the previous versions.   - BOSCH ESI[tronic] Online: This is an online version of Bosch ESI Tronic software that does not require any installation or activation. It allows you to access the database and functions of Bosch ESI Tronic via a web browser. However, it requires a subscription fee and an internet connection to use. - Q: What are some compatible devices and vehicles for using Bosch ESI Tronic? - A: Some compatible devices and vehicles for using Bosch ESI Tronic are:   - Devices: Bosch KTS series (such as KTS 520, KTS 540, KTS 570, KTS 650, KTS 670), Bosch DCU series (such as DCU 100, DCU 130), Bosch MTS series (such as MTS 3100, MTS 6513), etc.   - Vehicles: Over 150,000 vehicles from different manufacturers (such as Audi, BMW, Ford, Honda, Mercedes-Benz, Toyota, Volkswagen, etc) covering various systems (such as engine, transmission, ABS, airbag, immobilizer, etc). - Q: How often does Bosch release new versions of ESI Tronic and how to update them? - A: Bosch usually releases new versions of ESI Tronic every quarter (four times a year). You can update your ESI Tronic software by downloading and installing the new DVDs or CDs from a trusted source. You may also need to generate new keys with a new keygen for each new version. - Q: How to contact Bosch for technical support or feedback? - A: You can contact Bosch for technical support or feedback by visiting their official website and choosing your region and country. You can also find their contact details on their website or on your diagnostic device manual. <p>I hope this article has been helpful and informative for you. If you have any questions or comments, please feel free to leave them below. Thank you for reading.</p>
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- <p>Stars are the currency that you need to unlock new crime scenes, interrogate suspects, analyze evidence, and arrest the killers. You can earn stars by completing crime scenes and mini-games. Energy is the resource that you need to play crime scenes and mini-games. You can replenish energy by waiting, watching ads, or buying it with coins or cash. Coins are the currency that you can use to buy energy, hints, boosters, and other items. You can earn coins by completing cases, achievements, and daily rewards. Cash is the premium currency that you can use to buy coins, stars, energy, and other items. You can earn cash by leveling up, completing achievements, and buying it with real money.</p>
- <h2>What is Criminal Case Travel in Time Mod APK?</h2>
- <h3>A modified version of the original game</h3>
- <p>Criminal Case Travel in Time Mod APK is a modified version of the original game that has been altered by some third-party developers. Mod APK stands for modified application package, which is a file format that contains the installation and configuration data of an Android app. By modifying the original app's code, mod apk developers can add or remove features, change the graphics, unlock the content, and alter the gameplay.</p>
- <h3>A way to get unlimited stars and energy</h3>
- <p>One of the main features of Criminal Case Travel in Time Mod APK is that it gives you unlimited stars and energy. This means you can play as much as you want without having to wait or watch ads. You can also unlock all the crime scenes, interrogate all the suspects, analyze all the evidence, and arrest all the killers without any hassle. You can enjoy the game without any limitations or interruptions.</p>
- <h3>A way to enjoy the game without ads or restrictions</h3>
- <p>Another feature of Criminal Case Travel in Time Mod APK is that it removes all the ads and restrictions from the game. This means you can play without any annoying pop-ups or banners that distract you from your investigation. You can also access all the content and features of the game without having to pay for anything. You can enjoy the game without any costs or risks.</p>
- <h2>How to download and install Criminal Case Travel in Time Mod APK?</h2>
- <h3>The steps to follow</h3>
- <p>If you want to download and install Criminal Case Travel in Time Mod APK, you need to follow these steps:</p>
- <ol>
-<li>First, you need to uninstall the original game from your device if you have it installed.</li>
-<li>Second, you need to find a reliable source that provides the mod apk file. You can search online for websites or blogs that offer mod apk downloads. For example, you can visit [HappyMod](^1^), a website that claims to provide 100% working mods for various games and apps.</li>
-<li>Third, you need to download the mod apk file from the source. You need to make sure that the file is compatible with your device's specifications and operating system. You also need to check that the file is safe and virus-free.</li>
-<li>Fourth, you need to enable the installation of apps from unknown sources on your device. You can do this by going to your device's settings > security > unknown sources > allow.</li>
-<li>Fifth, you need to locate the mod apk file on your device's storage and tap on it to start the installation process. You need to follow the instructions on the screen and grant the necessary permissions for the app.</li>
-<li>Sixth, you need to wait for the installation to finish and then launch the app from your device's menu.</li>
-</ol>
- <p>Congratulations! You have successfully downloaded and installed Criminal Case Travel in Time Mod APK on your device. You can now enjoy playing the game with unlimited stars and energy.</p> <h3>The precautions to take</h3>
- <p>While downloading and installing Criminal Case Travel in Time Mod APK may seem tempting, you need to be careful and aware of the potential risks and consequences. Here are some precautions that you need to take before and after using the mod apk:</p>
- <ul>
-<li>First, you need to make sure that you have a backup of your original game data and progress. You can do this by syncing your game account with Facebook or Google Play. This way, you can restore your game data if something goes wrong with the mod apk or if you want to switch back to the original game.</li>
-<li>Second, you need to make sure that you have a reliable antivirus or anti-malware software on your device. You need to scan the mod apk file before and after installing it to check for any viruses or malware that may harm your device or compromise your privacy.</li>
-<li>Third, you need to make sure that you use the mod apk at your own risk and discretion. You need to understand that using the mod apk may violate the terms and conditions of the original game and its developers. You may also face legal issues or penalties if you are caught using the mod apk. You may also lose your game account or access to the game if the developers detect or ban the mod apk.</li>
-</ul>
- <p>Therefore, you need to weigh the pros and cons of using Criminal Case Travel in Time Mod APK before deciding to use it. You need to be responsible and respectful of the original game and its developers.</p>
- <h3>The benefits and risks of using mod apk</h3>
- <p>As we have mentioned, using Criminal Case Travel in Time Mod APK has its benefits and risks. Here are some of them:</p>
- <table>
-<tr>
-<th>Benefits</th>
-<th>Risks</th>
-</tr>
-<tr>
-<td>You can enjoy unlimited stars and energy.</td>
-<td>You may lose your game data or progress.</td>
-</tr>
-<tr>
-<td>You can enjoy the game without ads or restrictions.</td>
-<td>You may get viruses or malware on your device.</td>
-</tr>
-<tr>
-<td>You can access all the content and features of the game.</td>
-<td>You may violate the terms and conditions of the game.</td>
-</tr>
-<tr>
-<td>You can have more fun and excitement playing the game.</td>
-<td>You may face legal issues or penalties for using the mod apk.</td>
-</tr>
-<tr>
-<td>You can compete with your friends and other players more easily.</td>
-<td>You may lose your game account or access to the game.</td>
-</tr>
-</table>
- <p>As you can see, using Criminal Case Travel in Time Mod APK has its advantages and disadvantages. You need to decide for yourself whether you want to use it or not.</p>
- <h2>How to play Criminal Case Travel in Time?</h2>
- <h3>The gameplay features and mechanics</h3>
- <p>Criminal Case Travel in Time is a game that combines hidden object, adventure, and detective genres. The gameplay features and mechanics are similar to other games in the Criminal Case series. Here are some of them:</p>
- <ul>
-<li>You can join a team of time traveling detectives who solve murder cases in different historical periods. You can meet various characters from history, such as Cleopatra, Joan of Arc, Abraham Lincoln, Jack the Ripper, and more.</li>
-<li>You can investigate crime scenes by finding clues and objects hidden in various locations. You can use hints, boosters, and other items to help you with your investigation. You can also play mini-games, such as puzzles, matching, memory, and more.</li>
-<li>You can interrogate suspects by asking them questions and observing their reactions. You can also analyze evidence by sending it to the lab or examining it yourself. You can use logic, deduction, and intuition to find the killer among the suspects.</li>
-<li>You can arrest the killer by presenting the evidence that proves their guilt. You can also choose how to deal with them, whether by sending them to jail, rehab, asylum, or death penalty. You can also earn rewards, such as coins, stars, energy, cash, clothes, accessories, pets, and more.</li>
-<li>You can travel through history by using a time machine that is powered by time crystals. You can collect time crystals by completing cases, achievements, daily rewards, and more. You can also buy time crystals with real money.</li>
-<li>You can play with your friends and other players by connecting your game account with Facebook or Google Play. You can invite your friends to join your team, send and receive gifts, chat with them, and compete with them on leaderboards and tournaments.</li>
-</ul>
- <p>Criminal Case Travel in Time is a game that offers a lot of gameplay features and mechanics that will keep you entertained and engaged for hours.</p> from you.</li>
-<li>You can have fun and excitement playing the game and traveling through history. You can see how the game graphics and sound effects change according to the historical period. You can also enjoy the game's humor and references to history and pop culture.</li>
-</ul>
- <p>Criminal Case Travel in Time is a game that offers a lot of challenges and fun of traveling through history. You can have a unique and immersive experience of being a time traveling detective.</p>
- <h2>Conclusion</h2>
- <p>Criminal Case Travel in Time is a hidden object adventure game with a time travel twist. You can join a team of time traveling detectives who solve murder cases in different historical periods. You can investigate crime scenes, interrogate suspects, analyze evidence, and catch the killers in this captivating game.</p>
- <p>If you want to enjoy the game without any limitations, you can download and install Criminal Case Travel in Time Mod APK, a modified version of the original game that gives you unlimited stars and energy. You can also enjoy the game without ads or restrictions. However, you need to be careful and aware of the potential risks and consequences of using the mod apk.</p>
- <p>If you want to play the game and travel through history, you need to follow some tips and tricks that can help you with your gameplay. You can also have fun and excitement playing the game and traveling through history. You can explore different historical periods, meet different historical figures, solve different historical crimes, and discover some secrets and surprises that history has hidden from you.</p>
- <p>Criminal Case Travel in Time is a game that combines hidden object, adventure, and detective genres. It is a game that offers a lot of gameplay features and mechanics, benefits and risks, challenges and fun. It is a game that will keep you entertained and engaged for hours.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Criminal Case Travel in Time and Criminal Case Travel in Time Mod APK:</p>
- <h3>Q: Is Criminal Case Travel in Time Mod APK safe to use?</h3>
-<p>A: Criminal Case Travel in Time Mod APK is not an official version of the game. It is a modified version that has been altered by some third-party developers. Therefore, it may not be safe to use. It may contain viruses or malware that may harm your device or compromise your privacy. It may also violate the terms and conditions of the original game and its developers. It may also cause legal issues or penalties if you are caught using it. Therefore, you need to use it at your own risk and discretion.</p>
- <h3>Q: How can I update Criminal Case Travel in Time Mod APK?</h3>
-<p>A: Criminal Case Travel in Time Mod APK may not be compatible with the latest version of the original game. It may not work properly or crash if the original game updates its features or content. Therefore, you need to update the mod apk as well. You can do this by downloading and installing the latest version of the mod apk from the same source that you used before. However, you need to make sure that the source is reliable and safe.</p>
- <h3>Q: Can I play Criminal Case Travel in Time Mod APK offline?</h3>
-<p>A: Criminal Case Travel in Time Mod APK may not work offline. It may require an internet connection to access some features or content of the game. For example, you may need an internet connection to sync your game data with Facebook or Google Play, to play with your friends or other players, to watch ads or buy items, or to travel through history. Therefore, you need to have a stable internet connection to play the mod apk.</p>
- <h3>Q: Can I play Criminal Case Travel in Time Mod APK on PC or Mac?</h3>
-<p>A: Criminal Case Travel in Time Mod APK is designed for Android devices only. It may not work on PC or Mac devices. However, you may be able to play it on PC or Mac devices by using an Android emulator. An Android emulator is a software that allows you to run Android apps on PC or Mac devices. For example, you can use [BlueStacks], a popular Android emulator for PC and Mac devices. However, you need to make sure that the emulator is compatible with your device's specifications and operating system.</p>
- <h3>Q: Where can I find more information about Criminal Case Travel in Time?</h3>
-<p>A: If you want to find more information about Criminal Case Travel in Time, you can visit the official website of the game at [https://www.criminalcase.com/]. You can also visit the official Facebook page of the game at [https://www.facebook.com/CriminalCaseTravelInTime/]. You can also join the official fan group of the game at [https://www.facebook.com/groups/CCTITfans/]. You can also watch the official YouTube channel of the game at [https://www.youtube.com/channel/UC0XZ6ZL8__oLcaZ7otz5xKA]. You can also read the official wiki of the game at [https://criminal-case-official-fanfiction.fandom.com/wiki/Criminal_Case:_Travel_in_Time]. You can also contact the official support team of the game at [https://support.prettysimplegames.com/hc/en-us/requests/new].</p>
- <p>I hope this article has helped you learn more about Criminal Case Travel in Time and Criminal Case Travel in Time Mod APK. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/FIFA World Cup 2022 in FIFA Mobile Mod APK - Enjoy Authentic Stadiums and Commentary.md

@@ -1,116 +0,0 @@
-
-<h1>APK FIFA Mobile Mod: What You Need to Know</h1>
-<p>If you are a fan of soccer games, you might have heard of <strong>APK FIFA Mobile Mod</strong>, a modified version of the popular <strong>FIFA Mobile</strong> game by EA Sports. This mod gives you access to some amazing features and benefits that can enhance your gaming experience and make you feel like a soccer legend. In this article, we will tell you what APK FIFA Mobile Mod is, what it can do for you, and how you can download and install it on your device.</p>
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- <h2>Introduction</h2>
-<p><strong>FIFA Mobile</strong> is one of the most popular soccer games on mobile devices, with over 100 million downloads on Google Play. It lets you build your ultimate team of soccer stars from over 15,000 authentic players, 600+ teams, and 30+ leagues. You can also compete against other players in various modes, including Head-to-Head, VS Attack, Manager Mode, and the exclusive <strong>FIFA World Cup 2022</strong> mode.</p>
-<p>However, if you want to unlock all the features and content of the game, you might need to spend a lot of time and money on it. That's where <strong>APK FIFA Mobile Mod</strong> comes in handy. This mod is a modified version of the original game that gives you some advantages that can make your gaming experience more enjoyable and rewarding. Some of these advantages are:</p>
-<ul>
-<li><strong>Unlocked All</strong>: You can access all the players, teams, kits, stadiums, and modes in the game without any restrictions.</li>
-<li><strong>Unlimited Money</strong>: You can get unlimited coins and gems to spend on upgrades, transfers, and packs.</li>
-<li><strong>Menu Mod</strong>: You can access a menu mod that gives you more control and customization over the game.</li>
-</ul>
-<p>To use APK FIFA Mobile Mod, you need to download and install it on your device. Don't worry, we will show you how to do that in the next section.</p>
- <h2>Features and Benefits of APK FIFA Mobile Mod</h2>
-<p>Now that you know what APK FIFA Mobile Mod is, let's take a closer look at its features and benefits. Here are some of the things that this mod can do for you:</p> <h3>Unlocked All</h3>
-<p>One of the most appealing features of APK FIFA Mobile Mod is that it unlocks all the players, teams, kits, stadiums, and modes in the game. This means that you can choose from any of the 15,000+ players, 600+ teams, and 30+ leagues in the game, without having to unlock them by playing or paying. You can also use any of the kits and stadiums that are available in the game, including the exclusive ones that are only accessible through special events or packs. Moreover, you can play any of the modes in the game, including the FIFA World Cup 2022 mode, which lets you experience the thrill of the biggest soccer tournament in the world.</p>
-<p>By unlocking all the features and content of the game, APK FIFA Mobile Mod gives you more options and flexibility to build your ultimate team and play the game as you like. You can create your dream team with your favorite players, customize their appearance and skills, and challenge other players in various modes. You can also enjoy the FIFA World Cup 2022 mode, which lets you choose your national team, qualify for the tournament, and compete for the trophy.</p>
- <h3>Unlimited Money</h3>
-<p>Another feature of APK FIFA Mobile Mod is that it gives you unlimited coins and gems to spend on upgrades, transfers, and packs. Coins and gems are the main currencies in the game, which you can use to improve your team's performance and unlock more rewards. However, earning coins and gems in the game can be time-consuming and costly, as you need to play matches, complete tasks, or buy them with real money.</p>
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-<p>With APK FIFA Mobile Mod, you don't have to worry about running out of coins and gems. You can get as many coins and gems as you want, without having to spend any money or effort. You can use them to buy players from the transfer market, upgrade your team's attributes and chemistry, and open packs that contain rare and exclusive items. You can also use them to buy stamina, energy, and other resources that can help you play more matches and earn more rewards.</p>
- <h3>Menu Mod</h3>
-<p>The last feature of APK FIFA Mobile Mod is that it lets you access a menu mod that gives you more control and customization over the game. The menu mod is a hidden feature that you can activate by tapping on a button on the screen. The menu mod allows you to adjust some settings and parameters of the game, such as:</p>
-<ul>
-<li><strong>Game Speed</strong>: You can change the speed of the game from normal to fast or slow.</li>
-<li><strong>Freeze Players</strong>: You can freeze your opponents' players or goalkeepers, making them unable to move or react.</li>
-<li><strong>Perfect Skills</strong>: You can enable perfect skills for your players, making them perform flawless dribbles, passes, shots, tackles, etc.</li>
-<li><strong>And More</strong>: You can also enable other features such as unlimited stamina, no ads, no root required, etc.</li>
-</ul>
-<p>The menu mod gives you more power and fun over the game. You can use it to experiment with different settings and scenarios, or to make the game easier or harder for yourself. You can also use it to prank your friends or opponents by freezing their players or making them miss their shots.</p>
- <h2>How to Download and Install APK FIFA Mobile Mod</h2>
-<p>If you are interested in trying APK FIFA Mobile Mod, you need to download and install it on your device. Here are some of the requirements and steps that you need to follow:</p>
- <h3>Requirements</h3>
-<p>Before you download and install APK FIFA Mobile Mod, you need to make sure that your device meets some minimum requirements. These are:</p>
-<ul>
-<li><strong>Android Version</strong>: Your device must have Android 4.4 or higher.</li>
-<li><strong>Storage Space</strong>: Your device must have at least 100 MB of free storage space.</li>
-<li><strong>Internet Connection</strong>: Your device must have a stable internet connection.</li>
-<li><strong>Risks and Precautions</strong>: You also need to be aware of some risks and precautions of using APK FIFA Mobile Mod. These are:</li>
-    <ul>
-    <li><strong>Risks</strong>: APK FIFA Mobile Mod is not an official version of FIFA Mobile. It is a modified version that may contain viruses or malware that can harm your device or data. It may also violate some terms and conditions of EA Sports or Google Play. It may also not work properly with some devices or updates.</li>
-    <li><strong>Precautions</strong>: To avoid or minimize these risks, you should only download APK FIFA Mobile Mod from a reliable source that has positive feedback and reviews. You should also scan the file with an antivirus program before installing it. You should also backup your device data and uninstall the original FIFA Mobile game before installing APK FIFA Mobile Mod. You should also use APK FIFA Mobile Mod at your own risk and discretion, as we are not responsible for any damages or consequences that may arise from using it.</li>
-    </ul>
-</ul>
- <h3>Steps</h3>
-<p>Once you have checked the requirements and taken the precautions, you can follow these steps to download and install APK FIFA Mobile Mod on your device:</p>
-<ol>
-<li><strong>Find a reliable source</strong>: You need to find a website or a platform that offers APK FIFA Mobile Mod for download. You can search for it on Google or use one of the links that we have provided below. Make sure that the source is trustworthy and has positive feedback and reviews from other users.</li>
-<li><strong>Enable unknown sources</strong>: You need to enable unknown sources on your device settings. This will allow you to install apps that are not from Google Play. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li><strong>Install APK FIFA Mobile Mod</strong>: You need to download the APK file of APK FIFA Mobile Mod from the source that you have chosen. Once the download is complete, locate the file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.</li>
-<li><strong>Launch and enjoy APK FIFA Mobile Mod</strong>: You need to launch APK FIFA Mobile Mod on your device and grant it the necessary permissions. You can then enjoy all the features and benefits of APK FIFA Mobile Mod and play the game as you like.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>In conclusion, APK FIFA Mobile Mod is a modified version of FIFA Mobile that gives you some amazing features and benefits that can make your gaming experience more enjoyable and rewarding. You can unlock all the players, teams, kits, stadiums, and modes in the game, get unlimited coins and gems to spend on upgrades, transfers, and packs, and access a menu mod that gives you more control and customization over the game. To use APK FIFA Mobile Mod, you need to download and install it on your device, following some requirements and steps that we have explained in this article.</p>
-<p>If you are a fan of soccer games, you should definitely give APK FIFA Mobile Mod a try. It can make you feel like a soccer legend and have more fun with FIFA Mobile. However, you should also be aware of some risks and precautions of using APK FIFA Mobile Mod, as it is not an official version of the game and may contain viruses or malware that can harm your device or data. You should also use APK FIFA Mobile Mod at your own risk and discretion, as we are not responsible for any damages or consequences that may arise from using it.</p>
-<p>We hope that this article has been helpful and informative for you. If you have any questions or feedback about APK FIFA Mobile Mod, feel free to leave a comment below or contact us through our website. We would love to hear from you and help you with any issues or concerns that you may have. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
-<h3>Is APK FIFA Mobile Mod safe to use?</h3>
-<p>APK FIFA Mobile Mod is not an official version of FIFA Mobile. It is a modified version that may contain viruses or malware that can harm your device or data. It may also violate some terms and conditions of EA Sports or Google Play. Therefore, it is not completely safe to use APK FIFA Mobile Mod. However, you can minimize the risks by downloading APK FIFA Mobile Mod from a reliable source, scanning it with an antivirus program, backing up your device data, uninstalling the original game, and using it at your own risk and discretion.</p>
- <h3>Is APK FIFA Mobile Mod compatible with the latest version of FIFA Mobile?</h3>
-<p>APK FIFA Mobile Mod is usually updated to match the latest version of FIFA Mobile. However, there may be some delays or glitches in the updates due to technical issues or changes in the original game. Therefore, it is possible that APK FIFA Mobile Mod may not work properly with the latest version of FIFA Mobile at some point. In that case, you can wait for a new update of APK FIFA Mobile Mod or revert to an older version of FIFA Mobile.</p>
- <h3>Can I play online with APK FIFA Mobile Mod?</h3>
-<p>APK FIFA Mobile Mod allows you to play online with other players in various modes, such as Head-to-Head, VS Attack, Manager Mode, and FIFA World Cup 2022 mode. However, you may face some problems or limitations when playing online with APK FIFA Mobile Mod. For example, you may not be able to connect to some servers or matches, you may encounter some errors or bugs, you may face some unfair or unbalanced opponents, or you may get banned or suspended by EA Sports for using a modded version of the game. Therefore, you should be careful and cautious when playing online with APK FIFA Mobile Mod.</p>
- <h3>Will I get banned for using APK FIFA Mobile Mod?</h3>
-<p>APK FIFA Mobile Mod is not an official version of FIFA Mobile. It is a modified version that may violate some terms and conditions of EA Sports or Google Play. Therefore, it is possible that you may get banned or suspended by EA Sports for using APK FIFA Mobile Mod. This may happen if EA Sports detects that you are using a modded version of the game, if you abuse or exploit some features of the mod, or if you receive too many reports or complaints from other players. Therefore, you should use APK FIFA Mobile Mod at your own risk and discretion, and avoid doing anything that may get you banned or suspended.</p>
- <h3>How can I update APK FIFA Mobile Mod?</h3>
-<p>APK FIFA Mobile Mod is usually updated to match the latest version of FIFA Mobile. However, there may be some delays or glitches in the updates due to technical issues or changes in the original game. Therefore, you need to check regularly for new updates of APK FIFA Mobile Mod from the source that you have downloaded it from. You can also follow some social media pages or groups that provide updates and news about APK FIFA Mobile Mod. Once you find a new update of APK FIFA Mobile Mod, you need to download and install it on your device, following the same steps that we have explained in this article.</p> 401be4b1e0<br />
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spaces/232labs/VToonify/vtoonify/model/stylegan/op_gpu/upfirdn2d.cpp

@@ -1,31 +0,0 @@
-#include <ATen/ATen.h>
-#include <torch/extension.h>
-
-torch::Tensor upfirdn2d_op(const torch::Tensor &input,
-                           const torch::Tensor &kernel, int up_x, int up_y,
-                           int down_x, int down_y, int pad_x0, int pad_x1,
-                           int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x)                                                          \
-  TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x)                                                    \
-  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x)                                                         \
-  CHECK_CUDA(x);                                                               \
-  CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor &input, const torch::Tensor &kernel,
-                        int up_x, int up_y, int down_x, int down_y, int pad_x0,
-                        int pad_x1, int pad_y0, int pad_y1) {
-  CHECK_INPUT(input);
-  CHECK_INPUT(kernel);
-
-  at::DeviceGuard guard(input.device());
-
-  return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
-                      pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/timm_model.py

@@ -1,106 +0,0 @@
-""" timm model adapter
-
-Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.
-"""
-from collections import OrderedDict
-
-import torch.nn as nn
-
-try:
-    import timm
-    from timm.models.layers import Mlp, to_2tuple
-    from timm.models.layers.attention_pool2d import RotAttentionPool2d
-    from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d
-except ImportError as e:
-    timm = None
-
-from .utils import freeze_batch_norm_2d
-
-
-class TimmModel(nn.Module):
-    """ timm model adapter
-    # FIXME this adapter is a work in progress, may change in ways that break weight compat
-    """
-
-    def __init__(
-            self,
-            model_name,
-            embed_dim,
-            image_size=224,
-            pool='avg',
-            proj='linear',
-            drop=0.,
-            pretrained=False):
-        super().__init__()
-        if timm is None:
-            raise RuntimeError("Please `pip install timm` to use timm models.")
-
-        self.image_size = to_2tuple(image_size)
-        self.trunk = timm.create_model(model_name, pretrained=pretrained)
-        feat_size = self.trunk.default_cfg.get('pool_size', None)
-        feature_ndim = 1 if not feat_size else 2
-        if pool in ('abs_attn', 'rot_attn'):
-            assert feature_ndim == 2
-            # if attn pooling used, remove both classifier and default pool
-            self.trunk.reset_classifier(0, global_pool='')
-        else:
-            # reset global pool if pool config set, otherwise leave as network default
-            reset_kwargs = dict(global_pool=pool) if pool else {}
-            self.trunk.reset_classifier(0, **reset_kwargs)
-        prev_chs = self.trunk.num_features
-
-        head_layers = OrderedDict()
-        if pool == 'abs_attn':
-            head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)
-            prev_chs = embed_dim
-        elif pool == 'rot_attn':
-            head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)
-            prev_chs = embed_dim
-        else:
-            assert proj, 'projection layer needed if non-attention pooling is used.'
-
-        # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used
-        if proj == 'linear':
-            head_layers['drop'] = nn.Dropout(drop)
-            head_layers['proj'] = nn.Linear(prev_chs, embed_dim)
-        elif proj == 'mlp':
-            head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=drop)
-
-        self.head = nn.Sequential(head_layers)
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        """ lock modules
-        Args:
-            unlocked_groups (int): leave last n layer groups unlocked (default: 0)
-        """
-        if not unlocked_groups:
-            # lock full model
-            for param in self.trunk.parameters():
-                param.requires_grad = False
-            if freeze_bn_stats:
-                freeze_batch_norm_2d(self.trunk)
-        else:
-            # NOTE: partial freeze requires latest timm (master) branch and is subject to change
-            try:
-                # FIXME import here until API stable and in an official release
-                from timm.models.helpers import group_parameters, group_modules
-            except ImportError:
-                raise RuntimeError(
-                    'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')
-            matcher = self.trunk.group_matcher()
-            gparams = group_parameters(self.trunk, matcher)
-            max_layer_id = max(gparams.keys())
-            max_layer_id = max_layer_id - unlocked_groups
-            for group_idx in range(max_layer_id + 1):
-                group = gparams[group_idx]
-                for param in group:
-                    self.trunk.get_parameter(param).requires_grad = False
-            if freeze_bn_stats:
-                gmodules = group_modules(self.trunk, matcher, reverse=True)
-                gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}
-                freeze_batch_norm_2d(self.trunk, gmodules)
-
-    def forward(self, x):
-        x = self.trunk(x)
-        x = self.head(x)
-        return x

spaces/AIGuardians/SummarizeWikipediaDocument/tester.py

@@ -1,21 +0,0 @@
-import wikipedia
-
-def search_wiki(text):
-    article_list = wikipedia.search(text)
-    wikipedia.page(article_list[0])
-
-
-def get_wiki(search_term):
-    return wikipedia.page(search_term)
-
-
-
-# src = search_wiki('spacex')
-get = get_wiki('spacex')
-# print(src)
-print(get)
-print(wikipedia.summary("Python Programming Language"))
-x = search_wiki('spacex')
-
-print('done')
-

spaces/Adapter/T2I-Adapter/ldm/modules/diffusionmodules/openaimodel.py

@@ -1,798 +0,0 @@
-from abc import abstractmethod
-import math
-import torch
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-from ldm.util import exists
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, context=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
-    def forward(self,x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None,
-        disable_middle_self_attn=False,
-        use_linear_in_transformer=False,
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
-            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
-                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
-                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
-                  f"attention will still not be set.")
-
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            if isinstance(self.num_classes, int):
-                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-            elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
-                self.label_emb = nn.Linear(1, time_embed_dim)
-            else:
-                raise ValueError()
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
-                            )
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(self.num_res_blocks[level] + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads_upsample,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
-                            )
-                        )
-                if level and i == self.num_res_blocks[level]:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None, features_adapter=None, append_to_context=None, **kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-
-        if append_to_context is not None:
-            context = torch.cat([context, append_to_context], dim=1)
-
-        adapter_idx = 0
-        for id, module in enumerate(self.input_blocks):
-            h = module(h, emb, context)
-            if ((id+1)%3 == 0) and features_adapter is not None:
-                h = h + features_adapter[adapter_idx]
-                adapter_idx += 1
-            hs.append(h)
-        if features_adapter is not None:
-            assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
-
-        h = self.middle_block(h, emb, context)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/Reset.js

@@ -1,16 +0,0 @@
-/* 
-1. Destroy all chess
-2. Fill chess
-3. Break match3
-*/
-
-var Reset = function() {
-    // Destroy all chess
-    this.board.removeAllChess();
-    // Fill chess (with initial symbol map)
-    this.fill(this.initSymbolsMap);
-    // Break match3
-    this.breakMatch3();
-}
-
-export default Reset;

spaces/Ahmedmewloud/Depplearnig/traduction.py

@@ -1,713 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Traduction.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1qOS7cqek1bQPypxFqx-9G1ApPANNHL2X
-"""
-
-!pip install "tensorflow-text>=2.11"
-!pip install einops
-
-from google.colab import drive
-drive.mount('/content/drive')
-
-import numpy as np
-
-import typing
-from typing import Any, Tuple
-
-
-
-
-
-import numpy as np
-
-import typing
-from typing import Any, Tuple
-
-import tensorflow as tf
-
-import tensorflow_text as tf_text
-import einops
-import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
-
-import tensorflow as tf
-
-
-#import tensorflow_text as tf_text
-
-class ShapeChecker():
-  def __init__(self):
-    # Keep a cache of every axis-name seen
-    self.shapes = {}
-
-  def __call__(self, tensor, names, broadcast=False):
-    if not tf.executing_eagerly():
-      return
-
-    parsed = einops.parse_shape(tensor, names)
-
-    for name, new_dim in parsed.items():
-      old_dim = self.shapes.get(name, None)
-      
-      if (broadcast and new_dim == 1):
-        continue
-
-      if old_dim is None:
-        # If the axis name is new, add its length to the cache.
-        self.shapes[name] = new_dim
-        continue
-
-      if new_dim != old_dim:
-        raise ValueError(f"Shape mismatch for dimension: '{name}'\n"
-                         f"    found: {new_dim}\n"
-                         f"    expected: {old_dim}\n")
-
-"""pour les donnees nous utilisons une api par Anki """
-
-# le telechargement du donnees de training
-import os
-
-if not os.path.isfile('/content/fra.txt'):
-    !wget http://www.manythings.org/anki/fra-eng.zip -P /content
-    !unzip /content/fra-eng.zip -d /content
-else:
-    print('File already downloaded and extracted.')
-
-path_to_file ='/content/fra.txt'
-
-
-
-from pathlib import Path
-import numpy as np
-
-"""la fonction load_data(path) une fonction qui retourne un array numpy tel un paire ( pahrse en fr == > phrase en eng )"""
-
-def load_data(path):
-  path = Path(path)
-  text = path.read_text(encoding='utf-8')
-
-  lines = text.splitlines()
-  pairs = [line.split('\t') for line in lines]
-  # print(pairs[2])
-  context = np.array([pairs[index][1] for index in range(len(pairs))])
-  target = np.array([pairs[index][0] for index in range(len(pairs))])
-
-  return target, context
-
-"""un test d'affichage"""
-
-# targ, inp = load_data(path_to_file)
-target_raw, context_raw = load_data(path_to_file)
-
-print(len(context_raw),len(target_raw))
-for i in range(100):
-  print(context_raw[i]+'\t')
-  print(target_raw[i]+'\n')
-
-BUFFER_SIZE = len(context_raw)
-BATCH_SIZE = 64
-
-is_train = np.random.uniform(size=(len(target_raw),)) < 0.8
-
-train_raw = (
-    tf.data.Dataset
-    .from_tensor_slices((context_raw[is_train], target_raw[is_train]))
-    .shuffle(BUFFER_SIZE)
-    .batch(BATCH_SIZE))
-val_raw = (
-    tf.data.Dataset
-    .from_tensor_slices((context_raw[~is_train], target_raw[~is_train]))
-    .shuffle(BUFFER_SIZE)
-    .batch(BATCH_SIZE))
-
-for example_context_strings, example_target_strings in train_raw.take(1):
-  print(example_context_strings[:5])
-  print()
-  print(example_target_strings[:5])
-  break
-
-example_text = tf.constant('Salut Prenez vos jambes à vos cous !')
-
-print(example_text.numpy())
-print(tf_text.normalize_utf8(example_text, 'NFKD').numpy())
-
-#La normalisation
-def tf_lower_and_split_punct(text):
-  # Split accecented characters.
-  text = tf_text.normalize_utf8(text, 'NFKD')
-  text = tf.strings.lower(text)
-  # Keep space, a to z, and select punctuation.
-  text = tf.strings.regex_replace(text, '[^ a-z.?!,¿]', '')
-  # Add spaces around punctuation.
-  text = tf.strings.regex_replace(text, '[.?!,¿]', r' \0 ')
-  # Strip whitespace.
-  text = tf.strings.strip(text)
-
-  text = tf.strings.join(['[START]', text, '[END]'], separator=' ')
-  return text
-
-# Avent la normalisation
-print(example_text.numpy().decode())
-#Apres la normalisation
-print(tf_lower_and_split_punct(example_text).numpy().decode())
-
-#Vectorisation de texte
-max_vocab_size = 5000
-
-input_text_processor = tf.keras.layers.TextVectorization(
-    standardize=tf_lower_and_split_punct,
-    max_tokens=max_vocab_size)
-
-max_vocab_size = 5000
-
-context_text_processor = tf.keras.layers.TextVectorization(
-    standardize=tf_lower_and_split_punct,
-    max_tokens=max_vocab_size,
-    ragged=True)
-
-context_text_processor.adapt(train_raw.map(lambda context, target: context))
-
-# Here are the first 10 words from the vocabulary:
-context_text_processor.get_vocabulary()[:10]
-
-target_text_processor = tf.keras.layers.TextVectorization(
-    standardize=tf_lower_and_split_punct,
-    max_tokens=max_vocab_size,
-    ragged=True)
-
-target_text_processor.adapt(train_raw.map(lambda context, target: target))
-target_text_processor.get_vocabulary()[:10]
-
-example_tokens = context_text_processor(example_context_strings)
-example_tokens[:3, :]
-
-context_vocab = np.array(context_text_processor.get_vocabulary())
-tokens = context_vocab[example_tokens[0].numpy()]
-' '.join(tokens)
-
-plt.subplot(1, 2, 1)
-plt.pcolormesh(example_tokens.to_tensor())
-plt.title('Token IDs')
-
-plt.subplot(1, 2, 2)
-plt.pcolormesh(example_tokens.to_tensor() != 0)
-plt.title('Mask')
-
-def process_text(context, target):
-  context = context_text_processor(context).to_tensor()
-  target = target_text_processor(target)
-  targ_in = target[:,:-1].to_tensor()
-  targ_out = target[:,1:].to_tensor()
-  return (context, targ_in), targ_out
-
-
-train_ds = train_raw.map(process_text, tf.data.AUTOTUNE)
-val_ds = val_raw.map(process_text, tf.data.AUTOTUNE)
-
-for (ex_context_tok, ex_tar_in), ex_tar_out in train_ds.take(1):
-  print(ex_context_tok[0, :10].numpy()) 
-  print()
-  print(ex_tar_in[0, :10].numpy()) 
-  print(ex_tar_out[0, :10].numpy())
-
-UNITS = 256
-
-
-
-"""Fin 21114
-
-# **Encoder/decoder**
-
-**Avant d'entrer dans le détail, nous définissons des constantes pour le modèle :**
-"""
-
-UNITS = 256
-
-"""Un RNN bidirectionnel
-
-**L'** encodeur
-"""
-
-class Encoder(tf.keras.layers.Layer):
-  def __init__(self, text_processor, units):
-    super(Encoder, self).__init__()
-    self.text_processor = text_processor
-    self.vocab_size = text_processor.vocabulary_size()
-    self.units = units
-    
-    # The embedding layer converts tokens to vectors
-    self.embedding = tf.keras.layers.Embedding(self.vocab_size, units,
-                                               mask_zero=True)
-
-    # The RNN layer processes those vectors sequentially.
-    self.rnn = tf.keras.layers.Bidirectional(
-        merge_mode='sum',
-        layer=tf.keras.layers.GRU(units,
-                            # Return the sequence and state
-                            return_sequences=True,
-                            recurrent_initializer='glorot_uniform'))
-
-  def call(self, x):
-    shape_checker = ShapeChecker()
-    shape_checker(x, 'batch s')
-
-    # 2. The embedding layer looks up the embedding vector for each token.
-    x = self.embedding(x)
-    shape_checker(x, 'batch s units')
-
-    # 3. The GRU processes the sequence of embeddings.
-    x = self.rnn(x)
-    shape_checker(x, 'batch s units')
-
-    # 4. Returns the new sequence of embeddings.
-    return x
-
-  def convert_input(self, texts):
-    texts = tf.convert_to_tensor(texts)
-    if len(texts.shape) == 0:
-      texts = tf.convert_to_tensor(texts)[tf.newaxis]
-    context = self.text_processor(texts).to_tensor()
-    context = self(context)
-    return context
-
-# Encode the input sequence.
-encoder = Encoder(context_text_processor, UNITS)
-ex_context = encoder(ex_context_tok)
-
-print(f'Context tokens, shape (batch, s): {ex_context_tok.shape}')
-print(f'Encoder output, shape (batch, s, units): {ex_context.shape}')
-
-"""
-
-La couche d'**attention**"""
-
-class CrossAttention(tf.keras.layers.Layer):
-  def __init__(self, units, **kwargs):
-    super().__init__()
-    self.mha = tf.keras.layers.MultiHeadAttention(key_dim=units, num_heads=1, **kwargs)
-    self.layernorm = tf.keras.layers.LayerNormalization()
-    self.add = tf.keras.layers.Add()
-
-  def call(self, x, context):
-    shape_checker = ShapeChecker()
- 
-    shape_checker(x, 'batch t units')
-    shape_checker(context, 'batch s units')
-
-    attn_output, attn_scores = self.mha(
-        query=x,
-        value=context,
-        return_attention_scores=True)
-    
-    shape_checker(x, 'batch t units')
-    shape_checker(attn_scores, 'batch heads t s')
-    
-    # Cache the attention scores for plotting later.
-    attn_scores = tf.reduce_mean(attn_scores, axis=1)
-    shape_checker(attn_scores, 'batch t s')
-    self.last_attention_weights = attn_scores
-
-    x = self.add([x, attn_output])
-    x = self.layernorm(x)
-
-    return x
-
-attention_layer = CrossAttention(UNITS)
-
-# Attend to the encoded tokens
-embed = tf.keras.layers.Embedding(target_text_processor.vocabulary_size(),
-                                  output_dim=UNITS, mask_zero=True)
-ex_tar_embed = embed(ex_tar_in)
-
-result = attention_layer(ex_tar_embed, ex_context)
-
-print(f'Context sequence, shape (batch, s, units): {ex_context.shape}')
-print(f'Target sequence, shape (batch, t, units): {ex_tar_embed.shape}')
-print(f'Attention result, shape (batch, t, units): {result.shape}')
-print(f'Attention weights, shape (batch, t, s):    {attention_layer.last_attention_weights.shape}')
-
-attention_layer.last_attention_weights[0].numpy().sum(axis=-1)
-
-attention_weights = attention_layer.last_attention_weights
-mask=(ex_context_tok != 0).numpy()
-
-plt.subplot(1, 2, 1)
-plt.pcolormesh(mask*attention_weights[:, 0, :])
-plt.title('Attention weights')
-
-plt.subplot(1, 2, 2)
-plt.pcolormesh(mask)
-plt.title('Mask');
-
-"""Un RNN unidirectionnel
-
-le **Décodeur**
-"""
-
-class Decoder(tf.keras.layers.Layer):
-  @classmethod
-  def add_method(cls, fun):
-    setattr(cls, fun.__name__, fun)
-    return fun
-
-  def __init__(self, text_processor, units):
-    super(Decoder, self).__init__()
-    self.text_processor = text_processor
-    self.vocab_size = text_processor.vocabulary_size()
-    self.word_to_id = tf.keras.layers.StringLookup(
-        vocabulary=text_processor.get_vocabulary(),
-        mask_token='', oov_token='[UNK]')
-    self.id_to_word = tf.keras.layers.StringLookup(
-        vocabulary=text_processor.get_vocabulary(),
-        mask_token='', oov_token='[UNK]',
-        invert=True)
-    self.start_token = self.word_to_id('[START]')
-    self.end_token = self.word_to_id('[END]')
-
-    self.units = units
-
-
-    # 1. The embedding layer converts token IDs to vectors
-    self.embedding = tf.keras.layers.Embedding(self.vocab_size,
-                                               units, mask_zero=True)
-
-    # 2. The RNN keeps track of what's been generated so far.
-    self.rnn = tf.keras.layers.GRU(units,
-                                   return_sequences=True,
-                                   return_state=True,
-                                   recurrent_initializer='glorot_uniform')
-
-    # 3. The RNN output will be the query for the attention layer.
-    self.attention = CrossAttention(units)
-
-    # 4. This fully connected layer produces the logits for each
-    # output token.
-    self.output_layer = tf.keras.layers.Dense(self.vocab_size)
-
-"""**Training**"""
-
-@Decoder.add_method
-def call(self,
-         context, x,
-         state=None,
-         return_state=False):  
-  shape_checker = ShapeChecker()
-  shape_checker(x, 'batch t')
-  shape_checker(context, 'batch s units')
-
-  # 1. Lookup the embeddings
-  x = self.embedding(x)
-  shape_checker(x, 'batch t units')
-
-  # 2. Process the target sequence.
-  x, state = self.rnn(x, initial_state=state)
-  shape_checker(x, 'batch t units')
-
-  # 3. Use the RNN output as the query for the attention over the context.
-  x = self.attention(x, context)
-  self.last_attention_weights = self.attention.last_attention_weights
-  shape_checker(x, 'batch t units')
-  shape_checker(self.last_attention_weights, 'batch t s')
-
-  # Step 4. Generate logit predictions for the next token.
-  logits = self.output_layer(x)
-  shape_checker(logits, 'batch t target_vocab_size')
-
-  if return_state:
-    return logits, state
-  else:
-    return logits
-
-decoder = Decoder(target_text_processor, UNITS)
-
-logits = decoder(ex_context, ex_tar_in)
-
-print(f'encoder output shape: (batch, s, units) {ex_context.shape}')
-print(f'input target tokens shape: (batch, t) {ex_tar_in.shape}')
-print(f'logits shape shape: (batch, target_vocabulary_size) {logits.shape}')
-
-"""**Inference**"""
-
-@Decoder.add_method
-def get_initial_state(self, context):
-  batch_size = tf.shape(context)[0]
-  start_tokens = tf.fill([batch_size, 1], self.start_token)
-  done = tf.zeros([batch_size, 1], dtype=tf.bool)
-  embedded = self.embedding(start_tokens)
-  return start_tokens, done, self.rnn.get_initial_state(embedded)[0]
-
-@Decoder.add_method
-def tokens_to_text(self, tokens):
-  words = self.id_to_word(tokens)
-  result = tf.strings.reduce_join(words, axis=-1, separator=' ')
-  result = tf.strings.regex_replace(result, '^ *\[START\] *', '')
-  result = tf.strings.regex_replace(result, ' *\[END\] *$', '')
-  return result
-
-@Decoder.add_method
-def get_next_token(self, context, next_token, done, state, temperature = 0.0):
-  logits, state = self(
-    context, next_token,
-    state = state,
-    return_state=True) 
-  
-  if temperature == 0.0:
-    next_token = tf.argmax(logits, axis=-1)
-  else:
-    logits = logits[:, -1, :]/temperature
-    next_token = tf.random.categorical(logits, num_samples=1)
-
-  # If a sequence produces an `end_token`, set it `done`
-  done = done | (next_token == self.end_token)
-  # Once a sequence is done it only produces 0-padding.
-  next_token = tf.where(done, tf.constant(0, dtype=tf.int64), next_token)
-  
-  return next_token, done, state
-
-# Setup the loop variables.
-next_token, done, state = decoder.get_initial_state(ex_context)
-tokens = []
-
-for n in range(10):
-  # Run one step.
-  next_token, done, state = decoder.get_next_token(
-      ex_context, next_token, done, state, temperature=1.0)
-  # Add the token to the output.
-  tokens.append(next_token)
-
-# Stack all the tokens together.
-tokens = tf.concat(tokens, axis=-1) # (batch, t)
-
-# Convert the tokens back to a a string
-result = decoder.tokens_to_text(tokens)
-result[:3].numpy()
-
-"""### Fin 21196"""
-
-class Translator(tf.keras.Model):
-  @classmethod
-  def add_method(cls, fun):
-    setattr(cls, fun.__name__, fun)
-    return fun
-
-  def __init__(self, units,
-               context_text_processor,
-               target_text_processor):
-    super().__init__()
-    # Build the encoder and decoder
-    encoder = Encoder(context_text_processor, units)
-    decoder = Decoder(target_text_processor, units)
-
-    self.encoder = encoder
-    self.decoder = decoder
-
-  def call(self, inputs):
-    context, x = inputs
-    context = self.encoder(context)
-    logits = self.decoder(context, x)
-
-    #TODO(b/250038731): remove this
-    try:
-      # Delete the keras mask, so keras doesn't scale the loss+accuracy. 
-      del logits._keras_mask
-    except AttributeError:
-      pass
-
-    return logits
-
-"""necessite clarification"""
-
-model = Translator(UNITS, context_text_processor, target_text_processor)
-
-logits = model((ex_context_tok, ex_tar_in))
-
-print(f'Context tokens, shape: (batch, s, units) {ex_context_tok.shape}')
-print(f'Target tokens, shape: (batch, t) {ex_tar_in.shape}')
-print(f'logits, shape: (batch, t, target_vocabulary_size) {logits.shape}')
-
-def masked_loss(y_true, y_pred):
-    # Calculate the loss for each item in the batch.
-    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
-        from_logits=True, reduction='none')
-    loss = loss_fn(y_true, y_pred)
-
-    # Mask off the losses on padding.
-    mask = tf.cast(y_true != 0, loss.dtype)
-    loss *= mask
-
-    # Return the total.
-    return tf.reduce_sum(loss)/tf.reduce_sum(mask)
-
-def masked_acc(y_true, y_pred):
-    # Calculate the loss for each item in the batch.
-    y_pred = tf.argmax(y_pred, axis=-1)
-    y_pred = tf.cast(y_pred, y_true.dtype)
-    
-    match = tf.cast(y_true == y_pred, tf.float32)
-    mask = tf.cast(y_true != 0, tf.float32)
-    
-    return tf.reduce_sum(match)/tf.reduce_sum(mask)
-
-"""compilation du modele"""
-
-model.compile(optimizer='adam',
-              loss=masked_loss, 
-              metrics=[masked_acc, masked_loss])
-
-"""clalcule metric"""
-
-vocab_size = 1.0 * target_text_processor.vocabulary_size()
-
-{"expected_loss": tf.math.log(vocab_size).numpy(),
- "expected_acc": 1/vocab_size}
-
-"""evalution du modele"""
-
-model.evaluate(val_ds, steps=20, return_dict=True)
-
-import os
-
-# Vérifier si un fichier de sauvegarde existe
-if not os.path.exists('model_weights.h5'):
-    # Le fichier de sauvegarde n'existe pas, exécuter l'entraînement
-    history = model.fit(
-        train_ds.repeat(), 
-        epochs=100,
-        steps_per_epoch=100,
-        validation_data=val_ds,
-        validation_steps=20,
-        callbacks=[
-            tf.keras.callbacks.EarlyStopping(patience=3)])
-    
-    # Sauvegarder les poids du modèle
-    model.save_weights('model_weights.h5')
-else:
-    # Le fichier de sauvegarde existe, on passe à l'étape suivante
-    print("Le modèle a déjà été entraîné. Passer à l'étape suivante.")
-#history = model.fit(
- #   train_ds.repeat(), 
-  #  epochs=100,
-   # steps_per_epoch = 100,
-    #validation_data=val_ds,
-    #validation_steps = 20,
-    #callbacks=[
-     #   tf.keras.callbacks.EarlyStopping(patience=3)])
-
-plt.plot(history.history['loss'], label='loss')
-plt.plot(history.history['val_loss'], label='val_loss')
-plt.ylim([0, max(plt.ylim())])
-plt.xlabel('Epoch #')
-plt.ylabel('CE/token')
-plt.legend()
-
-plt.plot(history.history['masked_acc'], label='accuracy')
-plt.plot(history.history['val_masked_acc'], label='val_accuracy')
-plt.ylim([0, max(plt.ylim())])
-plt.xlabel('Epoch #')
-plt.ylabel('CE/token')
-plt.legend()
-
-"""ici la translation des texts """
-
-#@title
-@Translator.add_method
-def translate(self,
-              texts, *,
-              max_length=50,
-              temperature=0.0):
-  # Process the input texts
-  context = self.encoder.convert_input(texts)
-  batch_size = tf.shape(texts)[0]
-
-  # Setup the loop inputs
-  tokens = []
-  attention_weights = []
-  next_token, done, state = self.decoder.get_initial_state(context)
-
-  for _ in range(max_length):
-    # Generate the next token
-    next_token, done, state = self.decoder.get_next_token(
-        context, next_token, done,  state, temperature)
-        
-    # Collect the generated tokens
-    tokens.append(next_token)
-    attention_weights.append(self.decoder.last_attention_weights)
-    
-    if tf.executing_eagerly() and tf.reduce_all(done):
-      break
-
-  # Stack the lists of tokens and attention weights.
-  tokens = tf.concat(tokens, axis=-1)   # t*[(batch 1)] -> (batch, t)
-  self.last_attention_weights = tf.concat(attention_weights, axis=1)  # t*[(batch 1 s)] -> (batch, t s)
-
-  result = self.decoder.tokens_to_text(tokens)
-  return result
-
-"""test du translate"""
-
-result = model.translate(['tu est dans la maison']) # Are you still home
-result[0].numpy().decode()
-
-#@title
-@Translator.add_method
-def plot_attention(self, text, **kwargs):
-  assert isinstance(text, str)
-  output = self.translate([text], **kwargs)
-  output = output[0].numpy().decode()
-
-  attention = self.last_attention_weights[0]
-
-  context = tf_lower_and_split_punct(text)
-  context = context.numpy().decode().split()
-
-  output = tf_lower_and_split_punct(output)
-  output = output.numpy().decode().split()[1:]
-
-  fig = plt.figure(figsize=(10, 10))
-  ax = fig.add_subplot(1, 1, 1)
-
-  ax.matshow(attention, cmap='viridis', vmin=0.0)
-
-  fontdict = {'fontsize': 14}
-
-  ax.set_xticklabels([''] + context, fontdict=fontdict, rotation=90)
-  ax.set_yticklabels([''] + output, fontdict=fontdict)
-
-  ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
-  ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
-
-  ax.set_xlabel('Input text')
-  ax.set_ylabel('Output text')
-
-"""quelques test"""
-
-# Commented out IPython magic to ensure Python compatibility.
-# %%time
-# # This is my life.
-# model.plot_attention('A partir de ces tableaux de chaînes ')
-#
-
-# Commented out IPython magic to ensure Python compatibility.
-# %%time
-#  # Try to find out.'
-# model.plot_attention('nous sommes des etudiants d''école polytechnique')
-
-"""fin 21199
-
-"""
-
-!pip install gradio
-
-import gradio as gr
-
-def translate_text(text):
-    result = model.translate([text])
-    translated_text = result[0].numpy().decode()
-    return translated_text
-
-iface = gr.Interface(fn=translate_text, inputs="text", outputs="text", title="Translation App")
-iface.launch(debug=True, share=True)

spaces/Aityz/Aityz_Model_Eli5/README.md

@@ -1,13 +0,0 @@
----
-title: Aityz Model Eli5
-emoji: 💻
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Aityz/Aityz_Model_Eli5/app.py

@@ -1,20 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer
-from transformers import AutoModelForCausalLM
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained("Aityz/Aityz_model_eli5")
-
-model = AutoModelForCausalLM.from_pretrained("Aityz/Aityz_model_eli5")
-
-# maxtokens = int(input('What would you like the max tokens to be (default: 100) '))
-
-def aityz(input, maxtokens):
-    prompt = input
-    inputs = tokenizer(prompt, return_tensors="pt").input_ids
-    outputs = model.generate(inputs, max_new_tokens=maxtokens, do_sample=True, top_k=50, top_p=0.95)
-    output = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-    outputstr = ''.join(output)
-    return(outputstr)
-demo = gr.Interface(fn=aityz, inputs=["textbox", gr.Slider(1, 1000, value=100)], outputs="textbox")
-demo.launch() # enable share=True for Non Hugging Face Spaces Usage.........

spaces/AlexWang/lama/bin/paper_runfiles/generate_val_test.sh

@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-
-# !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst
-
-# paths to data are valid for mml7
-PLACES_ROOT="/data/inpainting/Places365"
-OUT_DIR="/data/inpainting/paper_data/Places365_val_test"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in test_large_30k  # val_large
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512
-    do
-        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-
-    for conf in segm_256 segm_512
-    do
-        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done

spaces/Alican/pixera/options/base_options.py

@@ -1,141 +0,0 @@
-import argparse
-import os
-from util import util
-import torch
-import models
-import data
-
-
-class BaseOptions():
-    """This class defines options used during both training and test time.
-
-    It also implements several helper functions such as parsing, printing, and saving the options.
-    It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
-    """
-
-    def __init__(self):
-        """Reset the class; indicates the class hasn't been initailized"""
-        self.initialized = False
-
-    def initialize(self, parser):
-        """Define the common options that are used in both training and test.
-        
-        """
-        # basic parameters
-        parser.add_argument('--dataroot', required=False, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
-        parser.add_argument('--name', type=str, default='pixera_CYCLEGAN', help='name of the experiment. It decides where to store samples and models')
-        parser.add_argument('--gpu_ids', type=str, default='-1', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
-        parser.add_argument('--checkpoints_dir', type=str, default='./models', help='models are saved here')
-        # model parameters
-        parser.add_argument('--model', type=str, default='test', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]')
-        parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
-        parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
-        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
-        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
-        parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
-        parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]')
-        parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
-        parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
-        parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
-        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
-        parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
-        # dataset parameters
-        parser.add_argument('--dataset_mode', type=str, default='single', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
-        parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
-        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
-        parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
-        parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
-        parser.add_argument('--load_size', type=int, default=512, help='scale images to this size')
-        parser.add_argument('--crop_size', type=int, default=512, help='then crop to this size')
-        parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
-        parser.add_argument('--preprocess', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
-        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
-        parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
-        # additional parameters
-        parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
-        parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
-        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
-        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
-        # wandb parameters
-        parser.add_argument('--use_wandb', action='store_true', help='if specified, then init wandb logging')
-        parser.add_argument('--wandb_project_name', type=str, default='CycleGAN-and-pix2pix', help='specify wandb project name')
-        self.initialized = True
-        return parser
-
-    def gather_options(self):
-        """Initialize our parser with basic options(only once).
-        Add additional model-specific and dataset-specific options.
-        These options are defined in the <modify_commandline_options> function
-        in model and dataset classes.
-        """
-        if not self.initialized:  # check if it has been initialized
-            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-            parser = self.initialize(parser)
-
-        # get the basic options
-        opt, _ = parser.parse_known_args()
-
-        # modify model-related parser options
-        model_name = opt.model
-        model_option_setter = models.get_option_setter(model_name)
-        parser = model_option_setter(parser, self.isTrain)
-        opt, _ = parser.parse_known_args()  # parse again with new defaults
-
-        # modify dataset-related parser options
-        dataset_name = opt.dataset_mode
-        dataset_option_setter = data.get_option_setter(dataset_name)
-        parser = dataset_option_setter(parser, self.isTrain)
-
-        # save and return the parser
-        self.parser = parser
-        return parser.parse_args()
-
-    def print_options(self, opt):
-        """Print and save options
-
-        It will print both current options and default values(if different).
-        It will save options into a text file / [checkpoints_dir] / opt.txt
-        """
-        message = ''
-        message += '----------------- Options ---------------\n'
-        for k, v in sorted(vars(opt).items()):
-            comment = ''
-            default = self.parser.get_default(k)
-            if v != default:
-                comment = '\t[default: %s]' % str(default)
-            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
-        message += '----------------- End -------------------'
-        print(message)
-
-        # save to the disk
-        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
-        util.mkdirs(expr_dir)
-        file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
-        with open(file_name, 'wt') as opt_file:
-            opt_file.write(message)
-            opt_file.write('\n')
-
-    def parse(self):
-        """Parse our options, create checkpoints directory suffix, and set up gpu device."""
-        opt = self.gather_options()
-        opt.isTrain = self.isTrain   # train or test
-
-        # process opt.suffix
-        if opt.suffix:
-            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
-            opt.name = opt.name + suffix
-
-        self.print_options(opt)
-
-        # set gpu ids
-        str_ids = opt.gpu_ids.split(',')
-        opt.gpu_ids = []
-        for str_id in str_ids:
-            id = int(str_id)
-            if id >= 0:
-                opt.gpu_ids.append(id)
-        if len(opt.gpu_ids) > 0:
-            torch.cuda.set_device(opt.gpu_ids[0])
-
-        self.opt = opt
-        return self.opt

spaces/Amrrs/DragGan-Inversion/stylegan_human/utils/data_utils.py

@@ -1,37 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-
-import os
-
-from PIL import Image
-
-IMG_EXTENSIONS = [
-    '.jpg', '.JPG', '.jpeg', '.JPEG',
-    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff'
-]
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def tensor2im(var):
-    # var shape: (3, H, W)
-    var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy()
-    var = ((var + 1) / 2)
-    var[var < 0] = 0
-    var[var > 1] = 1
-    var = var * 255
-    return Image.fromarray(var.astype('uint8'))
-
-
-def make_dataset(dir):
-    images = []
-    assert os.path.isdir(dir), '%s is not a valid directory' % dir
-    for root, _, fnames in sorted(os.walk(dir)):
-        for fname in fnames:
-            if is_image_file(fname):
-                path = os.path.join(root, fname)
-                fname = fname.split('.')[0]
-                images.append((fname, path))
-    return images

spaces/Andy1621/uniformer_image_detection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py

@@ -1,16 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        plugins=[
-            dict(
-                cfg=dict(
-                    type='GeneralizedAttention',
-                    spatial_range=-1,
-                    num_heads=8,
-                    attention_type='0010',
-                    kv_stride=2),
-                stages=(False, False, True, True),
-                position='after_conv2')
-        ],
-        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py

@@ -1,4 +0,0 @@
-_base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet101_caffe',
-    backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_detection/exp/mask_rcnn_3x_ms_hybrid_base/config.py

@@ -1,82 +0,0 @@
-_base_ = [
-    '../../configs/_base_/models/mask_rcnn_uniformer_fpn.py',
-    '../../configs/_base_/datasets/coco_instance.py',
-    '../../configs/_base_/schedules/schedule_1x.py', 
-    '../../configs/_base_/default_runtime.py'
-]
-
-model = dict(
-    backbone=dict(
-        embed_dim=[64, 128, 320, 512],
-        layers=[5, 8, 20, 7],
-        head_dim=64,
-        drop_path_rate=0.3,
-        use_checkpoint=True,
-        checkpoint_num=[0, 0, 20, 0],
-        windows=False,
-        hybrid=True,
-        window_size=14
-    ),
-    neck=dict(in_channels=[64, 128, 320, 512]))
-
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='AutoAugment',
-         policies=[
-             [
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
-                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
-                                 (736, 1333), (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True)
-             ],
-             [
-                 dict(type='Resize',
-                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
-                      multiscale_mode='value',
-                      keep_ratio=True),
-                 dict(type='RandomCrop',
-                      crop_type='absolute_range',
-                      crop_size=(384, 600),
-                      allow_negative_crop=True),
-                 dict(type='Resize',
-                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
-                                 (576, 1333), (608, 1333), (640, 1333),
-                                 (672, 1333), (704, 1333), (736, 1333),
-                                 (768, 1333), (800, 1333)],
-                      multiscale_mode='value',
-                      override=True,
-                      keep_ratio=True)
-             ]
-         ]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[27, 33])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
-    type="DistOptimizerHook",
-    update_interval=1,
-    grad_clip=None,
-    coalesce=True,
-    bucket_size_mb=-1,
-    use_fp16=True,
-)

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/samplers/pseudo_sampler.py

@@ -1,41 +0,0 @@
-import torch
-
-from ..builder import BBOX_SAMPLERS
-from .base_sampler import BaseSampler
-from .sampling_result import SamplingResult
-
-
-@BBOX_SAMPLERS.register_module()
-class PseudoSampler(BaseSampler):
-    """A pseudo sampler that does not do sampling actually."""
-
-    def __init__(self, **kwargs):
-        pass
-
-    def _sample_pos(self, **kwargs):
-        """Sample positive samples."""
-        raise NotImplementedError
-
-    def _sample_neg(self, **kwargs):
-        """Sample negative samples."""
-        raise NotImplementedError
-
-    def sample(self, assign_result, bboxes, gt_bboxes, **kwargs):
-        """Directly returns the positive and negative indices  of samples.
-
-        Args:
-            assign_result (:obj:`AssignResult`): Assigned results
-            bboxes (torch.Tensor): Bounding boxes
-            gt_bboxes (torch.Tensor): Ground truth boxes
-
-        Returns:
-            :obj:`SamplingResult`: sampler results
-        """
-        pos_inds = torch.nonzero(
-            assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique()
-        neg_inds = torch.nonzero(
-            assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique()
-        gt_flags = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8)
-        sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
-                                         assign_result, gt_flags)
-        return sampling_result

spaces/AnnonSubmission/xai-cl/ssl_models/simclr2.py

@@ -1,214 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-"""
-from https://github.com/Separius/SimCLRv2-Pytorch
-"""
-
-BATCH_NORM_EPSILON = 1e-5
-BATCH_NORM_DECAY = 0.9  # == pytorch's default value as well
-
-class BatchNormRelu(nn.Sequential):
-    
-    def __init__(self, num_channels, relu=True):
-        super().__init__(nn.BatchNorm2d(num_channels, eps=BATCH_NORM_EPSILON), 
-                         nn.ReLU() if relu else nn.Identity())
-
-
-def conv(in_channels, out_channels, kernel_size=3, stride=1, bias=False):
-    return nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,
-                     stride=stride, padding=(kernel_size - 1) // 2, bias=bias)
-
-
-class SelectiveKernel(nn.Module):
-    
-    def __init__(self, in_channels, out_channels, stride, sk_ratio, min_dim=32):
-        super().__init__()
-        assert sk_ratio > 0.0
-        self.main_conv = nn.Sequential(conv(in_channels, 2 * out_channels, stride=stride),
-                                       BatchNormRelu(2 * out_channels))
-        mid_dim = max(int(out_channels * sk_ratio), min_dim)
-        self.mixing_conv = nn.Sequential(conv(out_channels, mid_dim, kernel_size=1), 
-                                         BatchNormRelu(mid_dim),
-                                         conv(mid_dim, 2 * out_channels, kernel_size=1))
-
-    def forward(self, x):
-        x = self.main_conv(x)
-        x = torch.stack(torch.chunk(x, 2, dim=1), dim=0)  # 2, B, C, H, W
-        g = x.sum(dim=0).mean(dim=[2, 3], keepdim=True)
-        m = self.mixing_conv(g)
-        m = torch.stack(torch.chunk(m, 2, dim=1), dim=0)  # 2, B, C, 1, 1
-        return (x * F.softmax(m, dim=0)).sum(dim=0)
-
-
-class Projection(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, sk_ratio=0):
-        super().__init__()
-        if sk_ratio > 0:
-            self.shortcut = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)),
-                                          nn.AvgPool2d(kernel_size=2, stride=stride, padding=0),
-                                          conv(in_channels, out_channels, kernel_size=1))
-        else:
-            self.shortcut = conv(in_channels, out_channels, kernel_size=1, stride=stride)
-            
-        self.bn = BatchNormRelu(out_channels, relu=False)
-
-    def forward(self, x):
-        return self.bn(self.shortcut(x))
-
-
-class BottleneckBlock(nn.Module):
-    expansion = 4
-
-    def __init__(self, in_channels, out_channels, stride, sk_ratio=0, use_projection=False):
-        super().__init__()
-        if use_projection:
-            self.projection = Projection(in_channels, out_channels * 4, stride, sk_ratio)
-        else:
-            self.projection = nn.Identity()
-            
-        ops = [conv(in_channels, out_channels, kernel_size=1), BatchNormRelu(out_channels)]
-        if sk_ratio > 0:
-            ops.append(SelectiveKernel(out_channels, out_channels, stride, sk_ratio))
-        else:
-            ops.append(conv(out_channels, out_channels, stride=stride))
-            ops.append(BatchNormRelu(out_channels))
-            
-        ops.append(conv(out_channels, out_channels * 4, kernel_size=1))
-        ops.append(BatchNormRelu(out_channels * 4, relu=False))
-        self.net = nn.Sequential(*ops)
-
-    def forward(self, x):
-        shortcut = self.projection(x)
-        return F.relu(shortcut + self.net(x))
-
-
-class Blocks(nn.Module):
-    def __init__(self, num_blocks, in_channels, out_channels, stride, sk_ratio=0):
-        super().__init__()
-        self.blocks = nn.ModuleList([BottleneckBlock(in_channels, out_channels, stride, sk_ratio, True)])
-        self.channels_out = out_channels * BottleneckBlock.expansion
-        for _ in range(num_blocks - 1):
-            self.blocks.append(BottleneckBlock(self.channels_out, out_channels, 1, sk_ratio))
-
-    def forward(self, x):
-        for b in self.blocks:
-            x = b(x)
-        return x
-
-
-class Stem(nn.Sequential):
-    def __init__(self, sk_ratio, width_multiplier):
-        ops = []
-        channels = 64 * width_multiplier // 2
-        if sk_ratio > 0:
-            ops.append(conv(3, channels, stride=2))
-            ops.append(BatchNormRelu(channels))
-            ops.append(conv(channels, channels))
-            ops.append(BatchNormRelu(channels))
-            ops.append(conv(channels, channels * 2))
-        else:
-            ops.append(conv(3, channels * 2, kernel_size=7, stride=2))
-        ops.append(BatchNormRelu(channels * 2))
-        ops.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
-        super().__init__(*ops)
-
-
-class ResNet(nn.Module):
-    def __init__(self, layers, width_multiplier, sk_ratio):
-        super().__init__()
-        ops = [Stem(sk_ratio, width_multiplier)]
-        channels_in = 64 * width_multiplier
-        ops.append(Blocks(layers[0], channels_in, 64 * width_multiplier, 1, sk_ratio))
-        channels_in = ops[-1].channels_out
-        ops.append(Blocks(layers[1], channels_in, 128 * width_multiplier, 2, sk_ratio))
-        channels_in = ops[-1].channels_out
-        ops.append(Blocks(layers[2], channels_in, 256 * width_multiplier, 2, sk_ratio))
-        channels_in = ops[-1].channels_out
-        ops.append(Blocks(layers[3], channels_in, 512 * width_multiplier, 2, sk_ratio))
-        channels_in = ops[-1].channels_out
-        self.channels_out = channels_in
-        self.net = nn.Sequential(*ops)
-        self.fc = nn.Linear(channels_in, 1000)
-
-    def forward(self, x, apply_fc=False):
-        h = self.net(x).mean(dim=[2, 3])
-        if apply_fc:
-            h = self.fc(h)
-        return h
-
-
-class ContrastiveHead(nn.Module):
-    def __init__(self, channels_in, out_dim=128, num_layers=3):
-        super().__init__()
-        self.layers = nn.ModuleList()
-        for i in range(num_layers):
-            if i != num_layers - 1:
-                dim, relu = channels_in, True
-            else:
-                dim, relu = out_dim, False
-            self.layers.append(nn.Linear(channels_in, dim, bias=False))
-            bn = nn.BatchNorm1d(dim, eps=BATCH_NORM_EPSILON, affine=True)
-            if i == num_layers - 1:
-                nn.init.zeros_(bn.bias)
-            self.layers.append(bn)
-            if relu:
-                self.layers.append(nn.ReLU())
-
-    def forward(self, x):
-        for b in self.layers:
-            x = b(x)
-        return x
-
-
-def get_resnet(depth=50, width_multiplier=1, sk_ratio=0):  # sk_ratio=0.0625 is recommended
-    layers = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], 200: [3, 24, 36, 3]}[depth]
-    resnet = ResNet(layers, width_multiplier, sk_ratio)
-    return resnet, ContrastiveHead(resnet.channels_out)
-
-
-def name_to_params(checkpoint):
-    sk_ratio = 0.0625 if '_sk1' in checkpoint else 0
-    if 'r50_' in checkpoint:
-        depth = 50
-    elif 'r101_' in checkpoint:
-        depth = 101
-    elif 'r152_' in checkpoint:
-        depth = 152
-    else:
-        raise NotImplementedError
-
-    if '_1x_' in checkpoint:
-        width = 1
-    elif '_2x_' in checkpoint:
-        width = 2
-    elif '_3x_' in checkpoint:
-        width = 3
-    else:
-        raise NotImplementedError
-
-    return depth, width, sk_ratio
-
-class SimCLRv2(nn.Module):
-    def __init__(self, model, head):
-        super(SimCLRv2, self).__init__()
-        
-        self.encoder = model
-        self.contrastive_head = head
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.contrastive_head(x)
-        return x
-    
-def get_simclr2_model(ckpt_path):
-    depth, width, sk_ratio = name_to_params(ckpt_path)
-    model, head = get_resnet(depth, width, sk_ratio)
-    checkpoint = torch.load('pretrained_models/simclr2_models/' + ckpt_path)
-    model.load_state_dict(checkpoint['resnet'])
-    head.load_state_dict(checkpoint['head'])
-    del model.fc
-    simclr2 = SimCLRv2(model, head)
-    return simclr2.to(device)

spaces/Annotation-AI/segment-similarthings/app.py

@@ -1,17 +0,0 @@
-import os
-
-
-github_user = os.environ.get("GITHUB_USER")
-github_token = os.environ.get("GITHUB_TOKEN")
-
-repo_name = "annotation-ai/mlwiz-technical-demo"
-
-os.system(f"export GITHUB_USER={github_user}")
-os.system(f"export GITHUB_TOKEN={github_token}")
-os.system(f"git clone https://{github_user}:{github_token}@github.com/{repo_name}")
-
-cwd0 = os.getcwd()
-cwd1 = os.path.join(cwd0, "mlwiz-technical-demo/sam")
-os.chdir(cwd1)
-os.system("pip install -r requirements.txt")
-os.system("python app_segment_similarthings.py")

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/options/base_options.py

@@ -1,134 +0,0 @@
-import argparse
-import os
-import torch
-import model
-from util import util
-
-
-class BaseOptions():
-    def __init__(self):
-        self.parser = argparse.ArgumentParser()
-        self.initialized = False
-
-    def initialize(self, parser):
-        # base define
-        parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment.')
-        parser.add_argument('--model', type=str, default='tc', help='name of the model type. [pluralistic]')
-        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are save here')
-        parser.add_argument('--which_iter', type=int, default='0', help='which iterations to load')
-        parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load')
-        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0, 1, 2 use -1 for CPU')
-        # data define
-        parser.add_argument('--mask_type', type=int, default=[0,1,3], help='0:center,1:regular,2:irregular,3:external')
-        parser.add_argument('--img_file', type=str, default='/data/dataset/train', help='training and testing dataset')
-        parser.add_argument('--mask_file', type=str, default='none', help='load test mask')
-        parser.add_argument('--img_nc', type=int, default=3, help='# of image channels')
-        parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='preprocessing image at load time')
-        parser.add_argument('--load_size', type=int, default=542, help='scale examples to this size')
-        parser.add_argument('--fine_size', type=int, default=512, help='then crop to this size')
-        parser.add_argument('--fixed_size', type=int, default=256, help='fixed the image size in S1 with transformer')
-        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the image')
-        parser.add_argument('--data_powers', type=int, default=5, help='# times of the scale to 2 times')
-        parser.add_argument('--reverse_mask', action='store_true', help='if specified, random reverse the mask region')
-        parser.add_argument('--batch_size', type=int, default=8, help='input batch size')
-        parser.add_argument('--nThreads', type=int, default=8, help='# threads for loading data')
-        parser.add_argument('--no_shuffle', action='store_true', help='if true, takes examples serial')
-        # display parameter define
-        parser.add_argument('--display_winsize', type=int, default=256, help='display window size')
-        parser.add_argument('--display_id', type=int, default=None, help='display id of the web')
-        parser.add_argument('--display_server', type=str, default="http://localhost", help='server of the web display')
-        parser.add_argument('--display_env', type=str, default='main', help='display name (default is "main")')
-        parser.add_argument('--display_port', type=int, default=8092, help='port of the web display')
-        parser.add_argument('--display_single_pane_ncols', type=int, default=0, help='if positive, display all examples in a single visidom web panel')
-        # Encoder-Decoder define
-        parser.add_argument('--ngf', type=int, default=32, help='# of gen filters in the last conv layer')
-        parser.add_argument('--ndf', type=int, default=32, help='# of dis filters in the first conv layer')
-        parser.add_argument('--num_res_blocks', type=int, default=2, help='# of residual block in the encoder and decoder layer')
-        parser.add_argument('--netD', type=str, default='style', help='specify discriminator architecture ')
-        parser.add_argument('--netG', type=str, default='diff', help='specify decoder architecture')
-        parser.add_argument('--netE', type=str, default='diff', help='specify encoder architecture')
-        parser.add_argument('--kernel_G', type=int, default=3, help='kernel size for the decoder')
-        parser.add_argument('--kernel_E', type=int, default=1, help='kernel size for the encoder')
-        parser.add_argument('--add_noise', action='store_true', help='if true, add noise to the decoder')
-        parser.add_argument('--attn_E', action='store_true', help='if true, use attention in the encoder')
-        parser.add_argument('--attn_G', action='store_true', help='if true, use attention in the decoder')
-        parser.add_argument('--attn_D', action='store_true', help='if true, use attention in the decoder')
-        parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
-        parser.add_argument('--n_layers_G', type=int, default=4, help='# of down sample layers in the Encoder and Decoder')
-        parser.add_argument('--norm', type=str, default='pixel', help='instance normalization or batch normalization [instance | batch | pixel | none]')
-        parser.add_argument('--activation', type=str, default='leakyrelu', help='activation layer [relu | gelu | leakyrelu | none]')
-        parser.add_argument('--init_type', type=str, default='kaiming', help='network initialization [normal | xavier | kaiming | orthogonal]')
-        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
-        parser.add_argument('--lipip_path', type=str, default='./model/lpips/vgg.pth', help='the pretrained LIPPS model')
-        # Transformer define
-        parser.add_argument('--netT', type=str, default='original', help='specify transformer architecture')
-        parser.add_argument('--embed_dim', type=int, default=512, help='the numbers of embedding dimension')
-        parser.add_argument('--dropout', type=float, default=0., help='the dropout probability in transformer')
-        parser.add_argument('--kernel_T', type=int, default=1, help='kernel size for the transformer projection')
-        parser.add_argument('--n_encoders', type=int, default=12, help='the numbers of encoder in transformer')
-        parser.add_argument('--n_decoders', type=int, default=0, help='the numbers of decoder in transformer')
-        parser.add_argument('--embed_type', type=str, default='learned', choices=['learned', 'sine'])
-        parser.add_argument('--top_k', type=int, default=10, help='sample the results on top k value')
-        # VQ define
-        parser.add_argument('--num_embeds', type=int, default=1024, help='the numbers of words for image')
-        parser.add_argument('--use_pos_G', action='store_true', help='if true, position embedding in G')
-        parser.add_argument('--word_size', type=int, default=16, help='the numbers of word for each image')
-        self.initialized = True
-        return parser
-
-    def gather_options(self):
-        """Add additional model-specific options"""
-        if not self.initialized:
-            parser = self.initialize(self.parser)
-
-        # get basic options
-        opt, _ = parser.parse_known_args()
-
-        # modify the options for different models
-        model_option_set = model.get_option_setter(opt.model)
-        parser = model_option_set(parser, self.isTrain)
-        opt = parser.parse_args()
-
-        return opt
-
-    def parse(self):
-        """Parse the options"""
-        opt = self.gather_options()
-        opt.isTrain = self.isTrain
-
-        self.print_options(opt)
-
-        # set gpu ids
-        str_ids = opt.gpu_ids.split(',')
-        opt.gpu_ids = []
-        for str_id in str_ids:
-            id = int(str_id)
-            if id >= 0:
-                opt.gpu_ids.append(id)
-        if len(opt.gpu_ids):
-            torch.cuda.set_device(opt.gpu_ids[0])
-
-        self.opt = opt
-
-        return self.opt
-
-    @staticmethod
-    def print_options(opt):
-        """print and save options"""
-        print('--------------Options--------------')
-        for k, v in sorted(vars(opt).items()):
-            print('%s: %s' % (str(k), str(v)))
-        print('----------------End----------------')
-
-        # save to the disk
-        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
-        util.mkdirs(expr_dir)
-        if opt.isTrain:
-            file_name = os.path.join(expr_dir, 'train_opt.txt')
-        else:
-            file_name = os.path.join(expr_dir, 'test_opt.txt')
-        with open(file_name, 'wt') as opt_file:
-            opt_file.write('--------------Options--------------\n')
-            for k, v in sorted(vars(opt).items()):
-                opt_file.write('%s: %s\n' % (str(k), str(v)))
-            opt_file.write('----------------End----------------\n')

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/handlers/pickle_handler.py

@@ -1,28 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import pickle
-
-from .base import BaseFileHandler
-
-
-class PickleHandler(BaseFileHandler):
-
-    str_like = False
-
-    def load_from_fileobj(self, file, **kwargs):
-        return pickle.load(file, **kwargs)
-
-    def load_from_path(self, filepath, **kwargs):
-        return super(PickleHandler, self).load_from_path(
-            filepath, mode='rb', **kwargs)
-
-    def dump_to_str(self, obj, **kwargs):
-        kwargs.setdefault('protocol', 2)
-        return pickle.dumps(obj, **kwargs)
-
-    def dump_to_fileobj(self, obj, file, **kwargs):
-        kwargs.setdefault('protocol', 2)
-        pickle.dump(obj, file, **kwargs)
-
-    def dump_to_path(self, obj, filepath, **kwargs):
-        super(PickleHandler, self).dump_to_path(
-            obj, filepath, mode='wb', **kwargs)

spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/datasets/cocogrounding_eval.py

@@ -1,269 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO. Midified by Shilong Liu.
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-COCO evaluator that works in distributed mode.
-
-Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
-The difference is that there is less copy-pasting from pycocotools
-in the end of the file, as python3 can suppress prints with contextlib
-"""
-import contextlib
-import copy
-import os
-
-import numpy as np
-import pycocotools.mask as mask_util
-import torch
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
-from groundingdino.util.misc import all_gather
-
-
-class CocoGroundingEvaluator(object):
-    def __init__(self, coco_gt, iou_types, useCats=True):
-        assert isinstance(iou_types, (list, tuple))
-        coco_gt = copy.deepcopy(coco_gt)
-        self.coco_gt = coco_gt
-
-        self.iou_types = iou_types
-        self.coco_eval = {}
-        for iou_type in iou_types:
-            self.coco_eval[iou_type] = COCOeval(coco_gt, iouType=iou_type)
-            self.coco_eval[iou_type].useCats = useCats
-
-        self.img_ids = []
-        self.eval_imgs = {k: [] for k in iou_types}
-        self.useCats = useCats
-
-    def update(self, predictions):
-        img_ids = list(np.unique(list(predictions.keys())))
-        self.img_ids.extend(img_ids)
-
-        for iou_type in self.iou_types:
-            results = self.prepare(predictions, iou_type)
-
-            # suppress pycocotools prints
-            with open(os.devnull, "w") as devnull:
-                with contextlib.redirect_stdout(devnull):
-                    coco_dt = COCO.loadRes(self.coco_gt, results) if results else COCO()
-
-            coco_eval = self.coco_eval[iou_type]
-
-            coco_eval.cocoDt = coco_dt
-            coco_eval.params.imgIds = list(img_ids)
-            coco_eval.params.useCats = self.useCats
-            img_ids, eval_imgs = evaluate(coco_eval)
-
-            self.eval_imgs[iou_type].append(eval_imgs)
-
-    def synchronize_between_processes(self):
-        for iou_type in self.iou_types:
-            self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)
-            create_common_coco_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type])
-
-    def accumulate(self):
-        for coco_eval in self.coco_eval.values():
-            coco_eval.accumulate()
-
-    def summarize(self):
-        for iou_type, coco_eval in self.coco_eval.items():
-            print("IoU metric: {}".format(iou_type))
-            coco_eval.summarize()
-
-    def prepare(self, predictions, iou_type):
-        if iou_type == "bbox":
-            return self.prepare_for_coco_detection(predictions)
-        elif iou_type == "segm":
-            return self.prepare_for_coco_segmentation(predictions)
-        elif iou_type == "keypoints":
-            return self.prepare_for_coco_keypoint(predictions)
-        else:
-            raise ValueError("Unknown iou type {}".format(iou_type))
-
-    def prepare_for_coco_detection(self, predictions):
-        coco_results = []
-        for original_id, prediction in predictions.items():
-            if len(prediction) == 0:
-                continue
-
-            boxes = prediction["boxes"]
-            boxes = convert_to_xywh(boxes).tolist()
-            scores = prediction["scores"].tolist()
-            labels = prediction["labels"].tolist()
-
-            coco_results.extend(
-                [
-                    {
-                        "image_id": original_id,
-                        "category_id": labels[k],
-                        "bbox": box,
-                        "score": scores[k],
-                    }
-                    for k, box in enumerate(boxes)
-                ]
-            )
-        return coco_results
-
-    def prepare_for_coco_segmentation(self, predictions):
-        coco_results = []
-        for original_id, prediction in predictions.items():
-            if len(prediction) == 0:
-                continue
-
-            scores = prediction["scores"]
-            labels = prediction["labels"]
-            masks = prediction["masks"]
-
-            masks = masks > 0.5
-
-            scores = prediction["scores"].tolist()
-            labels = prediction["labels"].tolist()
-
-            rles = [
-                mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0] 
-                for mask in masks
-            ]
-            for rle in rles:
-                rle["counts"] = rle["counts"].decode("utf-8")
-
-            coco_results.extend(
-                [
-                    {
-                        "image_id": original_id,
-                        "category_id": labels[k],
-                        "segmentation": rle,
-                        "score": scores[k],
-                    }
-                    for k, rle in enumerate(rles)
-                ]
-            )
-        return coco_results
-
-    def prepare_for_coco_keypoint(self, predictions):
-        coco_results = []
-        for original_id, prediction in predictions.items():
-            if len(prediction) == 0:
-                continue
-
-            boxes = prediction["boxes"]
-            boxes = convert_to_xywh(boxes).tolist()
-            scores = prediction["scores"].tolist()
-            labels = prediction["labels"].tolist()
-            keypoints = prediction["keypoints"]
-            keypoints = keypoints.flatten(start_dim=1).tolist()
-
-            coco_results.extend(
-                [
-                    {
-                        "image_id": original_id,
-                        "category_id": labels[k],
-                        "keypoints": keypoint,
-                        "score": scores[k],
-                    }
-                    for k, keypoint in enumerate(keypoints)
-                ]
-            )
-        return coco_results
-
-
-def convert_to_xywh(boxes):
-    xmin, ymin, xmax, ymax = boxes.unbind(1)
-    return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
-
-
-def merge(img_ids, eval_imgs):
-    all_img_ids = all_gather(img_ids)
-    all_eval_imgs = all_gather(eval_imgs)
-
-    merged_img_ids = []
-    for p in all_img_ids:
-        merged_img_ids.extend(p)
-
-    merged_eval_imgs = []
-    for p in all_eval_imgs:
-        merged_eval_imgs.append(p)
-
-    merged_img_ids = np.array(merged_img_ids)
-    merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)
-
-    # keep only unique (and in sorted order) images
-    merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)
-    merged_eval_imgs = merged_eval_imgs[..., idx]
-
-    return merged_img_ids, merged_eval_imgs
-
-
-def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
-    img_ids, eval_imgs = merge(img_ids, eval_imgs)
-    img_ids = list(img_ids)
-    eval_imgs = list(eval_imgs.flatten())
-
-    coco_eval.evalImgs = eval_imgs
-    coco_eval.params.imgIds = img_ids
-    coco_eval._paramsEval = copy.deepcopy(coco_eval.params)
-
-
-#################################################################
-# From pycocotools, just removed the prints and fixed
-# a Python3 bug about unicode not defined
-#################################################################
-
-
-def evaluate(self):
-    """
-    Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
-    :return: None
-    """
-    # tic = time.time()
-    # print('Running per image evaluation...')
-    p = self.params
-    # add backward compatibility if useSegm is specified in params
-    if p.useSegm is not None:
-        p.iouType = "segm" if p.useSegm == 1 else "bbox"
-        print("useSegm (deprecated) is not None. Running {} evaluation".format(p.iouType))
-    # print('Evaluate annotation type *{}*'.format(p.iouType))
-    p.imgIds = list(np.unique(p.imgIds))
-    if p.useCats:
-        p.catIds = list(np.unique(p.catIds))
-    p.maxDets = sorted(p.maxDets)
-    self.params = p
-
-    self._prepare()
-    # loop through images, area range, max detection number
-    catIds = p.catIds if p.useCats else [-1]
-
-    if p.iouType == "segm" or p.iouType == "bbox":
-        computeIoU = self.computeIoU
-    elif p.iouType == "keypoints":
-        computeIoU = self.computeOks
-    self.ious = {
-        (imgId, catId): computeIoU(imgId, catId) 
-        for imgId in p.imgIds 
-        for catId in catIds}
-
-    evaluateImg = self.evaluateImg
-    maxDet = p.maxDets[-1]
-    evalImgs = [
-        evaluateImg(imgId, catId, areaRng, maxDet) 
-        for catId in catIds 
-        for areaRng in p.areaRng 
-        for imgId in p.imgIds
-    ]
-    # this is NOT in the pycocotools code, but could be done outside
-    evalImgs = np.asarray(evalImgs).reshape(len(catIds), len(p.areaRng), len(p.imgIds))
-    self._paramsEval = copy.deepcopy(self.params)
-    # toc = time.time()
-    # print('DONE (t={:0.2f}s).'.format(toc-tic))
-    return p.imgIds, evalImgs
-
-
-#################################################################
-# end of straight copy from pycocotools, just removing the prints
-#################################################################

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/lazyconfig_train_net.py

@@ -1,131 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Training script using the new "LazyConfig" python config files.
-
-This scripts reads a given python config file and runs the training or evaluation.
-It can be used to train any models or dataset as long as they can be
-instantiated by the recursive construction defined in the given config file.
-
-Besides lazy construction of models, dataloader, etc., this scripts expects a
-few common configuration parameters currently defined in "configs/common/train.py".
-To add more complicated training logic, you can easily add other configs
-in the config file and implement a new train_net.py to handle them.
-"""
-import logging
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import LazyConfig, instantiate
-from detectron2.engine import (
-    AMPTrainer,
-    SimpleTrainer,
-    default_argument_parser,
-    default_setup,
-    default_writers,
-    hooks,
-    launch,
-)
-from detectron2.engine.defaults import create_ddp_model
-from detectron2.evaluation import inference_on_dataset, print_csv_format
-from detectron2.utils import comm
-
-logger = logging.getLogger("detectron2")
-
-
-def do_test(cfg, model):
-    if "evaluator" in cfg.dataloader:
-        ret = inference_on_dataset(
-            model, instantiate(cfg.dataloader.test), instantiate(cfg.dataloader.evaluator)
-        )
-        print_csv_format(ret)
-        return ret
-
-
-def do_train(args, cfg):
-    """
-    Args:
-        cfg: an object with the following attributes:
-            model: instantiate to a module
-            dataloader.{train,test}: instantiate to dataloaders
-            dataloader.evaluator: instantiate to evaluator for test set
-            optimizer: instantaite to an optimizer
-            lr_multiplier: instantiate to a fvcore scheduler
-            train: other misc config defined in `configs/common/train.py`, including:
-                output_dir (str)
-                init_checkpoint (str)
-                amp.enabled (bool)
-                max_iter (int)
-                eval_period, log_period (int)
-                device (str)
-                checkpointer (dict)
-                ddp (dict)
-    """
-    model = instantiate(cfg.model)
-    logger = logging.getLogger("detectron2")
-    logger.info("Model:\n{}".format(model))
-    model.to(cfg.train.device)
-
-    cfg.optimizer.params.model = model
-    optim = instantiate(cfg.optimizer)
-
-    train_loader = instantiate(cfg.dataloader.train)
-
-    model = create_ddp_model(model, **cfg.train.ddp)
-    trainer = (AMPTrainer if cfg.train.amp.enabled else SimpleTrainer)(model, train_loader, optim)
-    checkpointer = DetectionCheckpointer(
-        model,
-        cfg.train.output_dir,
-        trainer=trainer,
-    )
-    trainer.register_hooks(
-        [
-            hooks.IterationTimer(),
-            hooks.LRScheduler(scheduler=instantiate(cfg.lr_multiplier)),
-            hooks.PeriodicCheckpointer(checkpointer, **cfg.train.checkpointer)
-            if comm.is_main_process()
-            else None,
-            hooks.EvalHook(cfg.train.eval_period, lambda: do_test(cfg, model)),
-            hooks.PeriodicWriter(
-                default_writers(cfg.train.output_dir, cfg.train.max_iter),
-                period=cfg.train.log_period,
-            )
-            if comm.is_main_process()
-            else None,
-        ]
-    )
-
-    checkpointer.resume_or_load(cfg.train.init_checkpoint, resume=args.resume)
-    if args.resume and checkpointer.has_checkpoint():
-        # The checkpoint stores the training iteration that just finished, thus we start
-        # at the next iteration
-        start_iter = trainer.iter + 1
-    else:
-        start_iter = 0
-    trainer.train(start_iter, cfg.train.max_iter)
-
-
-def main(args):
-    cfg = LazyConfig.load(args.config_file)
-    cfg = LazyConfig.apply_overrides(cfg, args.opts)
-    default_setup(cfg, args)
-
-    if args.eval_only:
-        model = instantiate(cfg.model)
-        model.to(cfg.train.device)
-        model = create_ddp_model(model)
-        DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
-        print(do_test(cfg, model))
-    else:
-        do_train(args, cfg)
-
-
-if __name__ == "__main__":
-    args = default_argument_parser().parse_args()
-    launch(
-        main,
-        args.num_gpus,
-        num_machines=args.num_machines,
-        machine_rank=args.machine_rank,
-        dist_url=args.dist_url,
-        args=(args,),
-    )

spaces/Benson/text-generation/Examples/Descargar Ganador Eleven 2020 Apk.md

@@ -1,67 +0,0 @@
-
-<h1>Descargar Ganar Once 2020 APK para Android</h1>
-<p>Si eres un fan de los juegos de fútbol, es posible que hayas oído hablar de Winning Eleven, uno de los juegos de fútbol más populares y realistas para dispositivos Android. Winning Eleven 2020 es la última versión de este juego, que ofrece muchas nuevas características y mejoras. En este artículo, te diremos qué es Winning Eleven 2020, cuáles son sus características, cómo descargarlo e instalarlo en tu dispositivo Android y cuáles son sus pros y contras. También responderemos algunas preguntas frecuentes sobre este juego. </p>
-<h2>descargar ganador eleven 2020 apk</h2><br /><p><b><b>Download</b> &#10004; <a href="https://bltlly.com/2v6MoF">https://bltlly.com/2v6MoF</a></b></p><br /><br />
- <h2>¿Qué está ganando Eleven 2020? </h2>
-<p>Winning Eleven 2020, también conocido como WE 2020, es un juego de fútbol desarrollado por AndroKim. Se basa en la serie Pro Evolution Soccer (PES), que es uno de los juegos de fútbol más populares del mundo. Winning Eleven 2020 no es un juego oficial de Konami, el desarrollador de PES, sino una versión modificada que ha sido modificada por los fans para incluir a los últimos jugadores, equipos, kits, estadios y ligas. También tiene algunas características exclusivas que no están disponibles en el juego original de PES. </p>
- <h3>Características de ganar once 2020</h3>
-<p>Ganar Eleven 2020 tiene muchas características que lo convierten en uno de los mejores juegos de fútbol para dispositivos Android. Estos son algunos de ellos:</p>
- <h4>Gráficos y sonido de alta calidad</h4>
-<p>El juego tiene gráficos y sonidos de alta calidad que te hacen sentir como si estuvieras viendo un partido de fútbol real. Los jugadores, estadios, multitudes y animaciones son muy realistas y detallados. El juego también tiene comentarios en diferentes idiomas, como inglés, español, francés y árabe.</p>
- <h4>Juego realista y física</h4>
-
- <h4>Varios modos y equipos</h4>
-<p>El juego tiene varios modos y equipos que te dan muchas opciones para jugar. Puedes jugar en diferentes modos, como exhibición, liga, copa, liga principal, multijugador en línea y entrenamiento. También puedes elegir entre más de 200 equipos de diferentes países y ligas, como Inglaterra, España, Italia, Alemania, Francia, Brasil, Argentina, Japón, Corea, China y más. También puedes crear tu propio equipo y personalizar su nombre, logo, kit, jugadores y formación. </p>
- <h4>Opciones y ajustes personalizables</h4>
-<p>El juego tiene opciones y ajustes personalizables que te permiten ajustar el juego según tus preferencias. Puede cambiar el nivel de dificultad, el ángulo de la cámara, el esquema de control, el volumen de sonido, el idioma y más. También puedes editar los nombres, rostros, peinados, accesorios, botas y estadísticas de los jugadores. </p>
- <h3>Cómo descargar e instalar Ganar Eleven 2020 APK? </h3>
-<p>Si desea descargar e instalar Winning Eleven 2020 APK en su dispositivo Android, es necesario seguir estos pasos:</p>
-<p></p>
- <h4>Paso 1: Habilitar fuentes desconocidas</h4>
-<p>Dado que Winning Eleven 2020 APK no está disponible en la Google Play Store o cualquier otra tienda de aplicaciones oficial, es necesario habilitar fuentes desconocidas en su dispositivo para que pueda instalar aplicaciones de fuentes de terceros. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </p>
- <h4>Paso 2: Descargar el archivo APK</h4>
-<p>Siguiente, es necesario descargar el archivo APK de Winning Eleven 2020 de una fuente confiable. Puede <p>Siguiente, es necesario descargar el archivo APK de Winning Eleven 2020 de una fuente confiable. Puede utilizar este enlace para descargar el archivo APK, que es de aproximadamente 150 MB de tamaño. Asegúrate de tener suficiente espacio de almacenamiento en tu dispositivo antes de descargar el archivo. </p>
- <h4>Paso 3: Instalar el archivo APK</h4>
-
- <h4>Paso 4: Iniciar el juego y disfrutar de</h4>
-<p>Una vez que se hace la instalación, puede iniciar el juego y disfrutar jugando Winning Eleven 2020 en su dispositivo Android. Verás el icono del juego en la pantalla de inicio o en el cajón de la aplicación. Toca en él y comienza a jugar. También puedes iniciar sesión con tu cuenta de Google Play Games para guardar tu progreso y logros. </p>
- <h3> Pros y contras de ganar once 2020 APK</h3>
-<p>Ganar Once 2020 APK tiene muchos pros y contras que usted debe considerar antes de descargar y jugar. Aquí están algunos de ellos:</p>
- <h4>Pros</h4>
-<ul>
-<li>El juego es gratis para descargar y jugar, a diferencia del juego oficial PES que requiere una suscripción o compras en la aplicación. </li>
-<li>El juego tiene muchas características y opciones que lo hacen más divertido y realista que el juego original de PES. </li>
-<li>El juego ha actualizado jugadores, equipos, kits, estadios y ligas que reflejan la temporada actual de fútbol. </li>
-<li>El juego tiene un modo multijugador en línea que te permite jugar con o contra otros jugadores de todo el mundo. </li>
-</ul>
- <h4>Contras</h4>
-<ul>
-<li>El juego no es un juego oficial de Konami, por lo que puede tener algunos errores, fallas o errores que afectan su rendimiento o compatibilidad. </li>
-<li>El juego puede no funcionar en algunos dispositivos o versiones de Android, especialmente los más antiguos. </li>
-<li>El juego puede requerir mucho espacio de almacenamiento y datos de Internet para descargar y jugar. </li>
-<li>El juego puede tener algunos anuncios o ventanas emergentes que pueden interrumpir su juego o experiencia. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre ganar once 2020 APK:</p>
- <ol>
-<li><b>Está ganando once 2020 APK seguro para descargar e instalar? </b></li>
-<p>Sí, Ganar Once 2020 APK es seguro para descargar e instalar, siempre y cuando se utiliza una fuente confiable como la que proporcionamos en este artículo. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo o datos. </p>
- <li><b>Está ganando once 2020 APK legal para descargar y jugar? </b></li>
-<p>Sí, Ganar Once 2020 APK es legal para descargar y jugar, siempre y cuando no lo utilice para fines ilegales o poco éticos. Sin embargo, usted debe respetar los derechos de propiedad intelectual de Konami, el desarrollador de PES, y no reclamar o distribuir Winning Eleven 2020 APK como su propio trabajo. </p>
- <li><b>¿Cómo puedo actualizar Ganando Once 2020 APK? </b></li>
-<p>Para actualizar Winning Eleven 2020 APK, es necesario descargar e instalar la última versión del archivo APK de una fuente confiable como la que proporcionamos en este artículo. También debe eliminar la versión anterior del archivo APK de su dispositivo antes de instalar el nuevo. </p>
- <li><b>¿Cómo puedo arreglar Ganar Once 2020 APK errores o problemas? </b></li>
-<p>Si encuentra algún error o problema al descargar, instalar o jugar Winning Eleven 2020 APK, puede probar algunas de estas soluciones:</p>
-<ul>
-<li>Compruebe su conexión a Internet y asegúrese de que es estable y rápido. </li>
-<li>Borrar la caché y los datos del juego desde Configuración > Aplicaciones > Ganar Once 2020 > Almacenamiento > Borrar caché/ datos. </li> <li>Reinicie su dispositivo e intente iniciar el juego de nuevo. </li>
-<li>Desinstalar y volver a instalar el juego desde una fuente confiable como la que proporcionamos en este artículo. </li>
-<li>Póngase en contacto con el desarrollador de Winning Eleven 2020 APK a través de su sitio web o canales de medios sociales y reportar el problema. </li>
-</ul>
-
-<p>Sí, puedes jugar Ganando Once 2020 APK sin conexión a Internet. Sin embargo, no podrás acceder a algunas funciones, como el modo multijugador en línea, las actualizaciones o los anuncios. También necesitará una conexión a Internet para descargar e instalar el juego inicialmente. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Bonp/B/Dockerfile

@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]

spaces/BrunoHempel775/Byzu/README.md

@@ -1,13 +0,0 @@
----
-title: Byzu
-emoji: 🦀
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/checkpoint/detection_checkpoint.py

@@ -1,59 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import pickle
-from fvcore.common.checkpoint import Checkpointer
-from fvcore.common.file_io import PathManager
-
-import detectron2.utils.comm as comm
-
-from .c2_model_loading import align_and_update_state_dicts
-
-
-class DetectionCheckpointer(Checkpointer):
-    """
-    Same as :class:`Checkpointer`, but is able to handle models in detectron & detectron2
-    model zoo, and apply conversions for legacy models.
-    """
-
-    def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
-        is_main_process = comm.is_main_process()
-        super().__init__(
-            model,
-            save_dir,
-            save_to_disk=is_main_process if save_to_disk is None else save_to_disk,
-            **checkpointables,
-        )
-
-    def _load_file(self, filename):
-        if filename.endswith(".pkl"):
-            with PathManager.open(filename, "rb") as f:
-                data = pickle.load(f, encoding="latin1")
-            if "model" in data and "__author__" in data:
-                # file is in Detectron2 model zoo format
-                self.logger.info("Reading a file from '{}'".format(data["__author__"]))
-                return data
-            else:
-                # assume file is from Caffe2 / Detectron1 model zoo
-                if "blobs" in data:
-                    # Detection models have "blobs", but ImageNet models don't
-                    data = data["blobs"]
-                data = {k: v for k, v in data.items() if not k.endswith("_momentum")}
-                return {"model": data, "__author__": "Caffe2", "matching_heuristics": True}
-
-        loaded = super()._load_file(filename)  # load native pth checkpoint
-        if "model" not in loaded:
-            loaded = {"model": loaded}
-        return loaded
-
-    def _load_model(self, checkpoint):
-        if checkpoint.get("matching_heuristics", False):
-            self._convert_ndarray_to_tensor(checkpoint["model"])
-            # convert weights by name-matching heuristics
-            model_state_dict = self.model.state_dict()
-            align_and_update_state_dicts(
-                model_state_dict,
-                checkpoint["model"],
-                c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
-            )
-            checkpoint["model"] = model_state_dict
-        # for non-caffe2 models, use standard ways to load it
-        super()._load_model(checkpoint)

spaces/CVPR/LIVE/thrust/thrust/async/for_each.h

@@ -1,119 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a for_each 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 async/for_each.h
- *  \brief Functions for asynchronously iterating over the elements of a range.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp14_required.h>
-
-#if THRUST_CPP_DIALECT >= 2014
-
-#include <thrust/detail/static_assert.h>
-#include <thrust/detail/select_system.h>
-#include <thrust/type_traits/remove_cvref.h>
-#include <thrust/system/detail/adl/async/for_each.h>
-
-#include <thrust/event.h>
-
-namespace thrust
-{
-
-namespace async
-{
-
-namespace unimplemented
-{
-
-template <
-  typename DerivedPolicy
-, typename ForwardIt, typename Sentinel, typename UnaryFunction
->
-__host__
-event<DerivedPolicy>
-async_for_each(
-  thrust::execution_policy<DerivedPolicy>&, ForwardIt, Sentinel, UnaryFunction
-)
-{
-  THRUST_STATIC_ASSERT_MSG(
-    (thrust::detail::depend_on_instantiation<ForwardIt, false>::value)
-  , "this algorithm is not implemented for the specified system"
-  );
-  return {};
-} 
-
-} // namespace unimplemented
-
-namespace for_each_detail
-{
-    
-using thrust::async::unimplemented::async_for_each;
-
-struct for_each_fn final
-{
-  template <
-    typename DerivedPolicy
-  , typename ForwardIt, typename Sentinel, typename UnaryFunction
-  >
-  __host__
-  static auto call(
-    thrust::detail::execution_policy_base<DerivedPolicy> const& exec
-  , ForwardIt&& first, Sentinel&& last
-  , UnaryFunction&& f 
-  )
-  // ADL dispatch.
-  THRUST_RETURNS(
-    async_for_each(
-      thrust::detail::derived_cast(thrust::detail::strip_const(exec))
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(f)
-    )
-  )
-
-  template <typename ForwardIt, typename Sentinel, typename UnaryFunction>
-  __host__
-  static auto call(ForwardIt&& first, Sentinel&& last, UnaryFunction&& f) 
-  THRUST_RETURNS(
-    for_each_fn::call(
-      thrust::detail::select_system(
-        typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
-      )
-    , THRUST_FWD(first), THRUST_FWD(last)
-    , THRUST_FWD(f)
-    )
-  )
-
-  template <typename... Args>
-  THRUST_NODISCARD __host__
-  auto operator()(Args&&... args) const
-  THRUST_RETURNS(
-    call(THRUST_FWD(args)...)
-  )
-};
-
-} // namespace for_each_detail
-
-THRUST_INLINE_CONSTANT for_each_detail::for_each_fn for_each{};
-
-} // namespace async
-
-} // end namespace thrust
-
-#endif
-

spaces/CVPR/LIVE/thrust/thrust/detail/temporary_array.h

@@ -1,181 +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 temporary_array.h
- *  \brief Container-like class temporary storage inside algorithms.
- */
-
-#pragma once
-
-namespace thrust
-{
-namespace detail
-{
-
-// Forward declare temporary_array, as it's used by the CUDA copy backend, which
-// is included in contiguous_storage's definition.
-template<typename T, typename System>
-  class temporary_array;
-
-} // end detail
-} // end thrust
-
-#include <thrust/detail/config.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/iterator/detail/tagged_iterator.h>
-#include <thrust/detail/contiguous_storage.h>
-#include <thrust/detail/allocator/temporary_allocator.h>
-#include <thrust/detail/allocator/no_throw_allocator.h>
-#include <thrust/detail/memory_wrapper.h>
-
-namespace thrust
-{
-namespace detail
-{
-
-
-template<typename T, typename System>
-  class temporary_array
-    : public contiguous_storage<
-               T,
-               no_throw_allocator<
-                 temporary_allocator<T,System>
-               >
-             >
-{
-  private:
-    typedef contiguous_storage<
-      T,
-      no_throw_allocator<
-        temporary_allocator<T,System>
-      >
-    > super_t;
-
-    // to help out the constructor
-    typedef no_throw_allocator<temporary_allocator<T,System> > alloc_type;
-
-  public:
-    typedef typename super_t::size_type size_type;
-
-    __host__ __device__
-    temporary_array(thrust::execution_policy<System> &system);
-
-    __host__ __device__
-    temporary_array(thrust::execution_policy<System> &system, size_type n);
-
-    // provide a kill-switch to explicitly avoid initialization
-    __host__ __device__
-    temporary_array(int uninit, thrust::execution_policy<System> &system, size_type n);
-
-    template<typename InputIterator>
-    __host__ __device__
-    temporary_array(thrust::execution_policy<System> &system,
-                    InputIterator first,
-                    size_type n);
-
-    template<typename InputIterator, typename InputSystem>
-    __host__ __device__
-    temporary_array(thrust::execution_policy<System> &system,
-                    thrust::execution_policy<InputSystem> &input_system,
-                    InputIterator first,
-                    size_type n);
-
-    template<typename InputIterator>
-    __host__ __device__
-    temporary_array(thrust::execution_policy<System> &system,
-                    InputIterator first,
-                    InputIterator last);
-
-    template<typename InputSystem, typename InputIterator>
-    __host__ __device__
-    temporary_array(thrust::execution_policy<System> &system,
-                    thrust::execution_policy<InputSystem> &input_system,
-                    InputIterator first,
-                    InputIterator last);
-
-    __host__ __device__
-    ~temporary_array();
-}; // end temporary_array
-
-
-// XXX eliminate this when we do ranges for real
-template<typename Iterator, typename System>
-  class tagged_iterator_range
-{
-  public:
-    typedef thrust::detail::tagged_iterator<Iterator,System> iterator;
-
-    template<typename Ignored1, typename Ignored2>
-    tagged_iterator_range(const Ignored1 &, const Ignored2 &, Iterator first, Iterator last)
-      : m_begin(first),
-        m_end(last)
-    {}
-
-    iterator begin(void) const { return m_begin; }
-    iterator end(void) const { return m_end; }
-
-  private:
-    iterator m_begin, m_end;
-};
-
-
-// if FromSystem is convertible to ToSystem, then just make a shallow
-// copy of the range. else, use a temporary_array
-// note that the resulting iterator is explicitly tagged with ToSystem either way
-template<typename Iterator, typename FromSystem, typename ToSystem>
-  struct move_to_system_base
-    : public eval_if<
-        is_convertible<
-          FromSystem,
-          ToSystem
-        >::value,
-        identity_<
-          tagged_iterator_range<Iterator,ToSystem>
-        >,
-        identity_<
-          temporary_array<
-            typename thrust::iterator_value<Iterator>::type,
-            ToSystem
-          >
-        >
-      >
-{};
-
-
-template<typename Iterator, typename FromSystem, typename ToSystem>
-  class move_to_system
-    : public move_to_system_base<
-        Iterator,
-        FromSystem,
-        ToSystem
-      >::type
-{
-  typedef typename move_to_system_base<Iterator,FromSystem,ToSystem>::type super_t;
-
-  public:
-    move_to_system(thrust::execution_policy<FromSystem> &from_system,
-                   thrust::execution_policy<ToSystem> &to_system,
-                   Iterator first,
-                   Iterator last)
-      : super_t(to_system, from_system, first, last) {}
-};
-
-
-} // end detail
-} // end thrust
-
-#include <thrust/detail/temporary_array.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/per_device_resource.h

@@ -1,47 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/system/detail/generic/tag.h>
-#include <thrust/mr/memory_resource.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename MR, typename DerivedPolicy>
-__host__
-MR * get_per_device_resource(thrust::detail::execution_policy_base<DerivedPolicy>&)
-{
-    return mr::get_global_resource<MR>();
-}
-
-
-} // end generic
-} // end detail
-} // end system
-} // end thrust
-

spaces/CVPR/lama-example/bin/filter_sharded_dataset.py

@@ -1,69 +0,0 @@
-#!/usr/bin/env python3
-
-
-import math
-import os
-import random
-
-import braceexpand
-import webdataset as wds
-
-DEFAULT_CATS_FILE = os.path.join(os.path.dirname(__file__), '..', 'configs', 'places2-categories_157.txt')
-
-def is_good_key(key, cats):
-    return any(c in key for c in cats)
-
-
-def main(args):
-    if args.categories == 'nofilter':
-        good_categories = None
-    else:
-        with open(args.categories, 'r') as f:
-            good_categories = set(line.strip().split(' ')[0] for line in f if line.strip())
-
-    all_input_files = list(braceexpand.braceexpand(args.infile))
-    chunk_size = int(math.ceil(len(all_input_files) / args.n_read_streams))
-
-    input_iterators = [iter(wds.Dataset(all_input_files[start : start + chunk_size]).shuffle(args.shuffle_buffer))
-                       for start in range(0, len(all_input_files), chunk_size)]
-    output_datasets = [wds.ShardWriter(args.outpattern.format(i)) for i in range(args.n_write_streams)]
-
-    good_readers = list(range(len(input_iterators)))
-    step_i = 0
-    good_samples = 0
-    bad_samples = 0
-    while len(good_readers) > 0:
-        if step_i % args.print_freq == 0:
-            print(f'Iterations done {step_i}; readers alive {good_readers}; good samples {good_samples}; bad samples {bad_samples}')
-
-        step_i += 1
-
-        ri = random.choice(good_readers)
-        try:
-            sample = next(input_iterators[ri])
-        except StopIteration:
-            good_readers = list(set(good_readers) - {ri})
-            continue
-
-        if good_categories is not None and not is_good_key(sample['__key__'], good_categories):
-            bad_samples += 1
-            continue
-
-        wi = random.randint(0, args.n_write_streams - 1)
-        output_datasets[wi].write(sample)
-        good_samples += 1
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('--categories', type=str, default=DEFAULT_CATS_FILE)
-    aparser.add_argument('--shuffle-buffer', type=int, default=10000)
-    aparser.add_argument('--n-read-streams', type=int, default=10)
-    aparser.add_argument('--n-write-streams', type=int, default=10)
-    aparser.add_argument('--print-freq', type=int, default=1000)
-    aparser.add_argument('infile', type=str)
-    aparser.add_argument('outpattern', type=str)
-
-    main(aparser.parse_args())

spaces/CVPR/regionclip-demo/detectron2/config/compat.py

@@ -1,229 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Backward compatibility of configs.
-
-Instructions to bump version:
-+ It's not needed to bump version if new keys are added.
-  It's only needed when backward-incompatible changes happen
-  (i.e., some existing keys disappear, or the meaning of a key changes)
-+ To bump version, do the following:
-    1. Increment _C.VERSION in defaults.py
-    2. Add a converter in this file.
-
-      Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X,
-      and a function "downgrade" which in-place downgrades config from X to X-1
-
-      In each function, VERSION is left unchanged.
-
-      Each converter assumes that its input has the relevant keys
-      (i.e., the input is not a partial config).
-    3. Run the tests (test_config.py) to make sure the upgrade & downgrade
-       functions are consistent.
-"""
-
-import logging
-from typing import List, Optional, Tuple
-
-from .config import CfgNode as CN
-from .defaults import _C
-
-__all__ = ["upgrade_config", "downgrade_config"]
-
-
-def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN:
-    """
-    Upgrade a config from its current version to a newer version.
-
-    Args:
-        cfg (CfgNode):
-        to_version (int): defaults to the latest version.
-    """
-    cfg = cfg.clone()
-    if to_version is None:
-        to_version = _C.VERSION
-
-    assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format(
-        cfg.VERSION, to_version
-    )
-    for k in range(cfg.VERSION, to_version):
-        converter = globals()["ConverterV" + str(k + 1)]
-        converter.upgrade(cfg)
-        cfg.VERSION = k + 1
-    return cfg
-
-
-def downgrade_config(cfg: CN, to_version: int) -> CN:
-    """
-    Downgrade a config from its current version to an older version.
-
-    Args:
-        cfg (CfgNode):
-        to_version (int):
-
-    Note:
-        A general downgrade of arbitrary configs is not always possible due to the
-        different functionalities in different versions.
-        The purpose of downgrade is only to recover the defaults in old versions,
-        allowing it to load an old partial yaml config.
-        Therefore, the implementation only needs to fill in the default values
-        in the old version when a general downgrade is not possible.
-    """
-    cfg = cfg.clone()
-    assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format(
-        cfg.VERSION, to_version
-    )
-    for k in range(cfg.VERSION, to_version, -1):
-        converter = globals()["ConverterV" + str(k)]
-        converter.downgrade(cfg)
-        cfg.VERSION = k - 1
-    return cfg
-
-
-def guess_version(cfg: CN, filename: str) -> int:
-    """
-    Guess the version of a partial config where the VERSION field is not specified.
-    Returns the version, or the latest if cannot make a guess.
-
-    This makes it easier for users to migrate.
-    """
-    logger = logging.getLogger(__name__)
-
-    def _has(name: str) -> bool:
-        cur = cfg
-        for n in name.split("."):
-            if n not in cur:
-                return False
-            cur = cur[n]
-        return True
-
-    # Most users' partial configs have "MODEL.WEIGHT", so guess on it
-    ret = None
-    if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"):
-        ret = 1
-
-    if ret is not None:
-        logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret))
-    else:
-        ret = _C.VERSION
-        logger.warning(
-            "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format(
-                filename, ret
-            )
-        )
-    return ret
-
-
-def _rename(cfg: CN, old: str, new: str) -> None:
-    old_keys = old.split(".")
-    new_keys = new.split(".")
-
-    def _set(key_seq: List[str], val: str) -> None:
-        cur = cfg
-        for k in key_seq[:-1]:
-            if k not in cur:
-                cur[k] = CN()
-            cur = cur[k]
-        cur[key_seq[-1]] = val
-
-    def _get(key_seq: List[str]) -> CN:
-        cur = cfg
-        for k in key_seq:
-            cur = cur[k]
-        return cur
-
-    def _del(key_seq: List[str]) -> None:
-        cur = cfg
-        for k in key_seq[:-1]:
-            cur = cur[k]
-        del cur[key_seq[-1]]
-        if len(cur) == 0 and len(key_seq) > 1:
-            _del(key_seq[:-1])
-
-    _set(new_keys, _get(old_keys))
-    _del(old_keys)
-
-
-class _RenameConverter:
-    """
-    A converter that handles simple rename.
-    """
-
-    RENAME: List[Tuple[str, str]] = []  # list of tuples of (old name, new name)
-
-    @classmethod
-    def upgrade(cls, cfg: CN) -> None:
-        for old, new in cls.RENAME:
-            _rename(cfg, old, new)
-
-    @classmethod
-    def downgrade(cls, cfg: CN) -> None:
-        for old, new in cls.RENAME[::-1]:
-            _rename(cfg, new, old)
-
-
-class ConverterV1(_RenameConverter):
-    RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")]
-
-
-class ConverterV2(_RenameConverter):
-    """
-    A large bulk of rename, before public release.
-    """
-
-    RENAME = [
-        ("MODEL.WEIGHT", "MODEL.WEIGHTS"),
-        ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"),
-        ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD",
-            "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH",
-        ),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT",
-            "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT",
-        ),
-        (
-            "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD",
-            "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH",
-        ),
-        ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"),
-        ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"),
-        ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"),
-        ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"),
-        ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"),
-        ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"),
-        ("TEST.AUG_ON", "TEST.AUG.ENABLED"),
-        ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"),
-        ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"),
-        ("TEST.AUG_FLIP", "TEST.AUG.FLIP"),
-    ]
-
-    @classmethod
-    def upgrade(cls, cfg: CN) -> None:
-        super().upgrade(cfg)
-
-        if cfg.MODEL.META_ARCHITECTURE == "RetinaNet":
-            _rename(
-                cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS"
-            )
-            _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
-            del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"]
-            del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"]
-        else:
-            _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS")
-            _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
-            del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"]
-            del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"]
-        del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"]
-
-    @classmethod
-    def downgrade(cls, cfg: CN) -> None:
-        super().downgrade(cfg)
-
-        _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS")
-        _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES")
-        cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS
-        cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES
-        cfg.MODEL.RETINANET.ANCHOR_STRIDES = []  # this is not used anywhere in any version

spaces/CVPR/v-doc_abstractive_mac/README.md

@@ -1,11 +0,0 @@
----
-title: VDoc-mac
-emoji: 🙆‍♂️
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.15
-python_version: 3.7.11
-app_file: interface.py
-pinned: false
----

spaces/ChallengeHub/Chinese-LangChain/corpus/zh_wikipedia/wiki_process.py

@@ -1,46 +0,0 @@
-#!/usr/bin/env python
-# -*- coding:utf-8 _*-
-"""
-@author:quincy qiang
-@license: Apache Licence
-@file: wiki_process.py
-@time: 2023/04/19
-@contact: [email protected]
-@software: PyCharm
-@description: https://blog.csdn.net/weixin_40871455/article/details/88822290
-"""
-import logging
-import sys
-from gensim.corpora import WikiCorpus
-
-logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s', level=logging.INFO)
-'''
-    extract data from wiki dumps(*articles.xml.bz2) by gensim.
-    @2019-3-26
-'''
-
-
-def help():
-    print("Usage: python wikipro.py zhwiki-20190320-pages-articles-multistream.xml.bz2 wiki.zh.txt")
-
-
-if __name__ == '__main__':
-    if len(sys.argv) < 3:
-        help()
-        sys.exit(1)
-    logging.info("running %s" % ' '.join(sys.argv))
-    inp, outp = sys.argv[1:3]
-    i = 0
-
-    output = open(outp, 'w', encoding='utf8')
-    wiki = WikiCorpus(inp, dictionary={})
-    for text in wiki.get_texts():
-        output.write(" ".join(text) + "\n")
-        i = i + 1
-        if (i % 10000 == 0):
-            logging.info("Save " + str(i) + " articles")
-    output.close()
-    logging.info("Finished saved " + str(i) + "articles")
-
-    # 命令行下运行
-    # python wikipro.py cache/zh_wikipedia/zhwiki-latest-pages-articles.xml.bz2 wiki.zh.txt

spaces/Choisuren/AnimeGANv3/app.py

@@ -1,67 +0,0 @@
-import os
-import cv2
-import gradio as gr
-import AnimeGANv3_src
-
-
-os.makedirs('output', exist_ok=True)
-
-
-def inference(img_path, Style, if_face=None):
-    print(img_path, Style, if_face)
-    try:
-        img = cv2.imread(img_path)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        if Style == "AnimeGANv3_Arcane":
-            f = "A"
-        elif Style == "AnimeGANv3_Trump v1.0":
-            f = "T"
-        elif Style == "AnimeGANv3_Shinkai":
-            f = "S"
-        elif Style == "AnimeGANv3_PortraitSketch":
-            f = "P"
-        elif Style == "AnimeGANv3_Hayao":
-            f = "H"
-        elif Style == "AnimeGANv3_Disney v1.0":
-            f = "D"
-        elif Style == "AnimeGANv3_JP_face v1.0":
-            f = "J"
-        else:
-            f = "U"
-
-        try:
-            det_face = True if if_face=="Yes" else False
-            output = AnimeGANv3_src.Convert(img, f, det_face)
-            save_path = f"output/out.{img_path.rsplit('.')[-1]}"
-            cv2.imwrite(save_path, output[:, :, ::-1])
-            return output, save_path
-        except RuntimeError as error:
-            print('Error', error)
-    except Exception as error:
-        print('global exception', error)
-        return None, None
-
-
-
-gr.Interface(
-    inference, [
-        gr.inputs.Image(type="filepath", label="Input"),
-        gr.Dropdown([
-            'AnimeGANv3_Hayao',
-            'AnimeGANv3_Shinkai',
-            'AnimeGANv3_Arcane',
-            'AnimeGANv3_USA',
-            'AnimeGANv3_Trump v1.0',
-            'AnimeGANv3_Disney v1.0',
-            'AnimeGANv3_PortraitSketch',
-            'AnimeGANv3_JP_face v1.0',
-        ], 
-            type="value",
-            value='AnimeGANv3_Hayao',
-            label='AnimeGANv3 Style'),
-        gr.inputs.Radio(['Yes', 'No'], type="value", default='No', label='Extract face'),
-    ], [
-        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
-        gr.outputs.File(label="Download the output image")
-    ],
-    allow_flagging="never").launch(enable_queue=True)

spaces/ClassCat/Medical-Image-Classification-with-MONAI/app.py

@@ -1,68 +0,0 @@
-
-
-import torch
-from monai.networks.nets import DenseNet121
-
-import gradio as gr
-
-#from PIL import Image
-
-model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=6)
-model.load_state_dict(torch.load('weights/mednist_model.pth', map_location=torch.device('cpu')))
-
-from monai.transforms import (
-    EnsureChannelFirst,
-    Compose,
-    LoadImage,
-    ScaleIntensity,
-)
-
-test_transforms = Compose(
-        [LoadImage(image_only=True), EnsureChannelFirst(), ScaleIntensity()]
-    )
-
-class_names = [
-    'AbdomenCT', 'BreastMRI', 'CXR', 'ChestCT', 'Hand', 'HeadCT'
-]
-
-import os, glob
-
-#examples_dir = './samples'
-#example_files = glob.glob(os.path.join(examples_dir, '*.jpg'))
-
-def classify_image(image_filepath):
-    input = test_transforms(image_filepath)
-
-    model.eval()
-    with torch.no_grad():
-        pred = model(input.unsqueeze(dim=0))
-    
-    prob = torch.nn.functional.softmax(pred[0], dim=0)
- 
-    confidences = {class_names[i]: float(prob[i]) for i in range(6)} 
-    print(confidences)
-
-    return confidences
-
-
-with gr.Blocks(title="Medical Image Classification with MONAI - ClassCat",
-            css=".gradio-container {background:mintcream;}"
-        ) as demo:
-    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">Medical Image Classification with MONAI</div>""")
-
-    with gr.Row(): 
-        input_image = gr.Image(type="filepath", image_mode="L", shape=(64, 64))        
-        output_label=gr.Label(label="Probabilities", num_top_classes=3)
-
-    send_btn = gr.Button("Infer")
-    send_btn.click(fn=classify_image, inputs=input_image, outputs=output_label)
-
-    with gr.Row():
-        gr.Examples(['./samples/mednist_AbdomenCT00.png'], label='Sample images : AbdomenCT', inputs=input_image)
-        gr.Examples(['./samples/mednist_CXR02.png'], label='CXR', inputs=input_image)
-        gr.Examples(['./samples/mednist_ChestCT08.png'], label='ChestCT', inputs=input_image)
-        gr.Examples(['./samples/mednist_Hand01.png'], label='Hand', inputs=input_image)
-        gr.Examples(['./samples/mednist_HeadCT07.png'], label='HeadCT', inputs=input_image)
-
-#demo.queue(concurrency_count=3)
-demo.launch(debug=True)

spaces/CofAI/chat.b4/client/css/checkbox.css

@@ -1,59 +0,0 @@
-.checkbox input {
-	height: 0;
-	width: 0;
-	display: none;
-}
-
-.checkbox span {
-	font-size: 0.875rem;
-	color: var(--colour-3);
-	margin-left: 4px;
-}
-
-.checkbox label:after {
-	content: "";
-	position: absolute;
-	top: 50%;
-	transform: translateY(-50%);
-	left: 5px;
-	width: 20px;
-	height: 20px;
-	background: var(--blur-border);
-	border-radius: 90px;
-	transition: 0.33s;
-}
-
-.checkbox input + label:after,
-.checkbox input:checked + label {
-	background: var(--colour-3);
-}
-
-.checkbox input + label,
-.checkbox input:checked + label:after {
-	background: var(--blur-border);
-}
-
-.checkbox input:checked + label:after {
-	left: calc(100% - 5px - 20px);
-}
-
-@media screen and (max-width: 990px) {
-	.checkbox span {
-		font-size: 0.75rem;
-	}
-
-	.checkbox label {
-		width: 25px;
-		height: 15px;
-	}
-
-	.checkbox label:after {
-		left: 2px;
-		width: 10px;
-		height: 10px;
-	}
-
-	.checkbox input:checked + label:after {
-		left: calc(100% - 2px - 10px);
-	}
-}

spaces/Cong723/gpt-academic-public/crazy_functions/解析JupyterNotebook.py

@@ -1,145 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-fast_debug = True
-
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(
-            enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-                    file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(
-                        self.file_paths[index] + f".part-{j}.txt")
-
-
-
-def parseNotebook(filename, enable_markdown=1):
-    import json
-
-    CodeBlocks = []
-    with open(filename, 'r', encoding='utf-8', errors='replace') as f:
-        notebook = json.load(f)
-    for cell in notebook['cells']:
-        if cell['cell_type'] == 'code' and cell['source']:
-            # remove blank lines
-            cell['source'] = [line for line in cell['source'] if line.strip()
-                              != '']
-            CodeBlocks.append("".join(cell['source']))
-        elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
-            cell['source'] = [line for line in cell['source'] if line.strip()
-                              != '']
-            CodeBlocks.append("Markdown:"+"".join(cell['source']))
-
-    Code = ""
-    for idx, code in enumerate(CodeBlocks):
-        Code += f"This is {idx+1}th code block: \n"
-        Code += code+"\n"
-
-    return Code 
-
-
-def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    enable_markdown = plugin_kwargs.get("advanced_arg", "1")
-    try:
-        enable_markdown = int(enable_markdown)
-    except ValueError:
-        enable_markdown = 1
-
-    pfg = PaperFileGroup()
-
-    for fp in file_manifest:
-        file_content = parseNotebook(fp, enable_markdown=enable_markdown)
-        pfg.file_paths.append(fp)
-        pfg.file_contents.append(file_content)
-
-    #  <-------- 拆分过长的IPynb文件 ---------->
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-    inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
-                    r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
-                    r"Start a new line for a block and block num use Chinese." +
-                    f"\n\n{frag}" for frag in pfg.sp_file_contents]
-    inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
-    sys_prompt_array = ["You are a professional programmer."] * n_split
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len=80
-    )
-
-    #  <-------- 整理结果，退出 ---------->
-    block_result = "  \n".join(gpt_response_collection)
-    chatbot.append(("解析的结果如下", block_result))
-    history.extend(["解析的结果如下", block_result])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    #  <-------- 写入文件，退出 ---------->
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-@CatchException
-def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    chatbot.append([
-        "函数插件功能？",
-        "对IPynb文件进行解析。Contributor: codycjy."])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    history = []    # 清空历史
-    import glob
-    import os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    if txt.endswith('.ipynb'):
-        file_manifest = [txt]
-    else:
-        file_manifest = [f for f in glob.glob(
-            f'{project_folder}/**/*.ipynb', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-        return
-    yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )

spaces/Cong723/gpt-academic-public/docs/README_JP.md

@@ -1,302 +0,0 @@
-> **Note**
->
-> このReadmeファイルは、このプロジェクトのmarkdown翻訳プラグインによって自動的に生成されたもので、100%正確ではない可能性があります。
->
-
-# <img src="logo.png" width="40" > ChatGPT 学術最適化
-
-**このプロジェクトが好きだったら、スターをつけてください。もし、より使いやすい学術用のショートカットキーまたはファンクションプラグインを発明した場合は、issueを発行するかpull requestを作成してください。また、このプロジェクト自体によって翻訳されたREADMEは[英語説明書|](docs/README_EN.md)[日本語説明書|](docs/README_JP.md)[ロシア語説明書|](docs/README_RS.md)[フランス語説明書](docs/README_FR.md)もあります。**
-
-> **注意事項**
->
-> 1. **赤色**のラベルが付いているファンクションプラグイン（ボタン）のみファイルを読み込めます。一部のプラグインはプラグインエリアのドロップダウンメニューにあります。新しいプラグインのPRを歓迎いたします！
->
-> 2. このプロジェクトの各ファイルの機能は`self_analysis.md`（自己解析レポート）で詳しく説明されています。バージョンが追加されると、関連するファンクションプラグインをクリックして、GPTを呼び出して自己解析レポートを再生成することができます。一般的な質問は`wiki`にまとめられています。(`https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98`)
-
-
-<div align="center">
-    
-機能 | 説明
---- | ---
-ワンクリック整形 | 論文の文法エラーを一括で正確に修正できます。
-ワンクリック日英翻訳 | 日英翻訳には、ワンクリックで対応できます。
-ワンクリックコード説明 | コードの正しい表示と説明が可能です。
-[カスタムショートカットキー](https://www.bilibili.com/video/BV14s4y1E7jN) | カスタムショートカットキーをサポートします。
-[プロキシサーバーの設定](https://www.bilibili.com/video/BV1rc411W7Dr) | プロキシサーバーの設定をサポートします。
-モジュラーデザイン | カスタム高階関数プラグインと[関数プラグイン]、プラグイン[ホット更新]のサポートが可能です。詳細は[こちら](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[自己プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン][ワンクリック理解](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)このプロジェクトのソースコード
-[プログラム解析機能](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン] ワンクリックで別のPython/C/C++/Java/Lua/...プロジェクトツリーを解析できます。
-論文読解 | [関数プラグイン] LaTeX論文の全文をワンクリックで解読し、要約を生成します。
-LaTeX全文翻訳、整形 | [関数プラグイン] ワンクリックでLaTeX論文を翻訳または整形できます。
-注釈生成 | [関数プラグイン] ワンクリックで関数の注釈を大量に生成できます。
-チャット分析レポート生成 | [関数プラグイン] 実行後、まとめレポートを自動生成します。
-[arxivヘルパー](https://www.bilibili.com/video/BV1LM4y1279X) | [関数プラグイン] 入力したarxivの記事URLで要約をワンクリック翻訳+PDFダウンロードができます。
-[PDF論文全文翻訳機能](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] PDF論文タイトルと要約を抽出し、全文を翻訳します（マルチスレッド）。
-[Google Scholar Integratorヘルパー](https://www.bilibili.com/video/BV19L411U7ia) | [関数プラグイン] 任意のGoogle Scholar検索ページURLを指定すると、gptが興味深い記事を選択します。
-数式/画像/テーブル表示 | 数式のTex形式とレンダリング形式を同時に表示できます。数式、コードのハイライトをサポートしています。
-マルチスレッド関数プラグインサポート | ChatGPTをマルチスレッドで呼び出すことができ、大量のテキストやプログラムを簡単に処理できます。
-ダークグラジオ[テーマ](https://github.com/binary-husky/chatgpt_academic/issues/173)の起動 | 「/?__dark-theme=true」というURLをブラウザに追加することで、ダークテーマに切り替えることができます。
-[多数のLLMモデル](https://www.bilibili.com/video/BV1wT411p7yf)をサポート、[API2D](https://api2d.com/)インターフェースをサポート | GPT3.5、GPT4、[清華ChatGLM](https://github.com/THUDM/ChatGLM-6B)による同時サポートは、と���も素晴らしいですね！
-huggingface免科学上网[オンライン版](https://huggingface.co/spaces/qingxu98/gpt-academic) | huggingfaceにログイン後、[このスペース](https://huggingface.co/spaces/qingxu98/gpt-academic)をコピーしてください。
-...... | ......
-
-
-</div>
-
-
-- 新しいインターフェース（config.pyのLAYOUTオプションを変更するだけで、「左右レイアウト」と「上下レイアウト」を切り替えることができます）
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>
-
-
-- すべてのボタンは、functional.pyを読み込んで動的に生成されます。カスタム機能を自由に追加して、クリップボードを解放します
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
-</div>
-
-- 色を修正/修正
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
-</div>
-
-- 出力に数式が含まれている場合、TeX形式とレンダリング形式の両方が表示され、コピーと読み取りが容易になります
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
-- プロジェクトのコードを見るのが面倒？chatgptに整備されたプロジェクトを直接与えましょう
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
-</div>
-
-- 多数の大規模言語モデルの混合呼び出し(ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
-
-多数の大規模言語モデルの混合呼び出し[huggingfaceテスト版](https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta)(huggigface版はchatglmをサポートしていません)
-
-
----
-
-## インストール-方法1：直接運転 (Windows、LinuxまたはMacOS)
-
-1. プロジェクトをダウンロードします。
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. API_KEYとプロキシ設定を構成する
-
-`config.py`で、海外のProxyとOpenAI API KEYを構成して説明します。
-```
-1.あなたが中国にいる場合、OpenAI APIをスムーズに使用するには海外プロキシを設定する必要があります。構成の詳細については、config.py（1.その中のUSE_PROXYをTrueに変更し、2.手順に従ってプロキシを変更する）を詳細に読んでください。
-2. OpenAI API KEYを構成する。OpenAIのウェブサイトでAPI KEYを取得してください。一旦API KEYを手に入れると、config.pyファイルで設定するだけです。
-3.プロキシネットワークに関連する問題(ネットワークタイムアウト、プロキシが動作しない）をhttps://github.com/binary-husky/chatgpt_academic/issues/1にまとめました。
-```
-(P.S. プログラム実行時にconfig.pyの隣にconfig_private.pyという名前のプライバシー設定ファイルを作成し、同じ名前の設定を上書きするconfig_private.pyが存在するかどうかを優先的に確認します。そのため、私たちの構成読み取りロジックを理解できる場合は、config.pyの隣にconfig_private.pyという名前の新しい設定ファイルを作成し、その中のconfig.pyから設定を移動してください。config_private.pyはgitで保守されていないため、プライバシー情報をより安全にすることができます。)
-
-3. 依存関係をインストールします。
-```sh
-# 選択肢があります。
-python -m pip install -r requirements.txt
-
-
-# (選択肢2) もしAnacondaを使用する場合、手順は同様です：
-# (選択肢2.1) conda create -n gptac_venv python=3.11
-# (選択肢2.2) conda activate gptac_venv
-# (選択肢2.3) python -m pip install -r requirements.txt
-
-# 注: 公式のpipソースまたはAlibabaのpipソースを使用してください。 別のpipソース（例：一部の大学のpip）は問題が発生する可能性があります。 一時的なソースの切り替え方法： 
-# python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-```
-
-もしあなたが清華ChatGLMをサポートする必要がある場合、さらに多くの依存関係をインストールする必要があります（Pythonに慣れない方やコンピューターの設定が十分でない方は、試みないことをお勧めします）：
-```sh
-python -m pip install -r request_llm/requirements_chatglm.txt
-```
-
-4. 実行
-```sh
-python main.py
-```
-
-5. 関数プラグインのテスト
-```
-- Pythonプロジェクト分析のテスト
-    入力欄に `./crazy_functions/test_project/python/dqn` と入力し、「Pythonプロジェクト全体の解析」をクリックします。
-- 自己コード解読のテスト
-    「[マルチスレッドデモ] このプロジェクト自体を解析します（ソースを翻訳して解読します）」をクリックします。
-- 実験的な機能テンプレート関数のテスト（GPTが「今日の歴史」に何が起こったかを回答することが求められます）。この関数をテンプレートとして使用して、より複雑な機能を実装できます。
-    「[関数プラグインテンプレートデモ] 今日の歴史」をクリックします。
-- 関数プラグインエリアのドロップダウンメニューには他にも選択肢があります。
-```
-
-## インストール方法2：Dockerを使用する（Linux）
-
-1. ChatGPTのみ（大多数の人にお勧めです）
-``` sh
-# プロジェクトのダウンロード
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-# 海外プロキシとOpenAI API KEYの設定
-config.pyを任意のテキストエディタで編集する
-# インストール
-docker build -t gpt-academic .
-# 実行
-docker run --rm -it --net=host gpt-academic
-
-# 関数プラグインのテスト
-## 関数プラグインテンプレート関数のテスト（GPTが「今日の歴史」に何が起こったかを回答することが求められます）。この関数をテンプレートとして使用して、より複雑な機能を実装できます。
-「[関数プラグインテンプレートデモ] 今日の歴史」をクリックします。
-## Latexプロジェクトの要約を書くテスト
-入力欄に./crazy_functions/test_project/latex/attentionと入力し、「テックス論文を読んで要約を書く」をクリックします。
-## Pythonプロジェクト分析のテスト
-入力欄に./crazy_functions/test_project/python/dqnと入力し、[Pythonプロジェクトの全解析]をクリックします。
-
-関数プラグインエリアのドロップダウンメニューには他にも選択肢があります。
-```
-
-2. ChatGPT + ChatGLM（Dockerに非常に詳しい人+十分なコンピューター設定が必要）
-
-
-
-```sh
-# Dockerfileの編集
-cd docs && nano Dockerfile+ChatGLM
-# ビルド方法
-docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
-# 実行方法 (1) 直接実行: 
-docker run --rm -it --net=host --gpus=all gpt-academic
-# 実行方法 (2) コンテナに入って調整する:
-docker run --rm -it --net=host --gpus=all gpt-academic bash
-```
-
-## インストール方法3：その他のデプロイ方法
-
-1. クラウドサーバーデプロイ
-[デプロイwiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-2. WSL2を使用 (Windows Subsystem for Linux)
-[デプロイwiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-
-## インストール-プロキシ設定
-1. 通常の方法
-[プロキシを設定する](https://github.com/binary-husky/chatgpt_academic/issues/1)
-
-2. 初心者向けチュートリアル
-[初心者向けチュートリアル](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
-
-
----
-
-## カスタムボタンの追加(学術ショートカットキー)
-
-`core_functional.py`を任意のテキストエディタで開き、以下のエントリーを追加し、プログラムを再起動してください。(ボタンが追加されて表示される場合、前置詞と後置詞はホット編集がサポートされているため、プログラムを再起動せずに即座に有効になります。)
-
-例:
-```
-"超级英译中": {
-    # 前置詞 - あなたの要求を説明するために使用されます。翻訳、コードの説明、編集など。
-    "Prefix": "以下のコンテンツを中国語に翻訳して、マークダウンテーブルを使用して専門用語を説明してください。\n\n", 
-    
-    # 後置詞 - プレフィックスと共に使用すると、入力内容を引用符で囲むことができます。
-    "Suffix": "",
-},
-```
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
-
----
-
-## いくつかの機能の例
-
-### 画像表示：
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/228737599-bf0a9d9c-1808-4f43-ae15-dfcc7af0f295.png" width="800" >
-</div>
-
-
-### プログラムが自己解析できる場合：
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936618-9b487e4b-ab5b-4b6e-84c6-16942102e917.png" width="800" >
-</div>
-
-### 他のPython/Cppプロジェクトの解析：
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="800" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="800" >
-</div>
-
-### Latex論文の一括読解と要約生成
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227504406-86ab97cd-f208-41c3-8e4a-7000e51cf980.png" width="800" >
-</div>
-
-### 自動報告生成
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
-</div>
-
-### モジュール化された機能デザイン
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-
-### ソースコードの英語翻訳
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229720562-fe6c3508-6142-4635-a83d-21eb3669baee.png" height="400" >
-</div>
-
-## Todo およびバージョン計画:
-- version 3.2+ (todo): 関数プラグインがより多くのパラメーターインターフェースをサポートするようになります。
-- version 3.1: 複数のgptモデルを同時にクエリし、api2dをサポートし、複数のapikeyの負荷分散をサポートします。
-- version 3.0: chatglmおよび他の小型llmのサポート
-- version 2.6: プラグイン構造を再構成し、相互作用性を高め、より多くのプラグインを追加しました。
-- version 2.5: 自己更新。総括的な大規模プロジェクトのソースコードをまとめた場合、テキストが長すぎる、トークンがオーバーフローする問題を解決します。
-- version 2.4: (1)PDF全文翻訳機能を追加。(2)入力エリアの位置を切り替える機能を追加。(3)垂直レイアウトオプションを追加。(4)マルチスレッド関数プラグインの最適化。
-- version 2.3: 多スレッドの相互作用性を向上させました。
-- version 2.2: 関数プラグインでホットリロードをサポート
-- version 2.1: 折りたたみ式レイアウト
-- version 2.0: モジュール化された関数プラグインを導入
-- version 1.0: 基本機能
-
-## 参考および学習
-
-
-以下は中国語のマークダウンファイルです。日本語に翻訳してください。既存のマークダウンコマンドを変更しないでください：
-
-```
-多くの優秀なプロジェクトの設計を参考にしています。主なものは以下の通りです：
-
-# 参考プロジェクト1：ChuanhuChatGPTから多くのテクニックを借用
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# 参考プロジェクト2：清華ChatGLM-6B：
-https://github.com/THUDM/ChatGLM-6B
-```
-

spaces/CorvaeOboro/gen_ability_icon/torch_utils/ops/bias_act.cpp

@@ -1,99 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-
-static bool has_same_layout(torch::Tensor x, torch::Tensor y)
-{
-    if (x.dim() != y.dim())
-        return false;
-    for (int64_t i = 0; i < x.dim(); i++)
-    {
-        if (x.size(i) != y.size(i))
-            return false;
-        if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
-            return false;
-    }
-    return true;
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
-    TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
-    TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
-    TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(b.dim() == 1, "b must have rank 1");
-    TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
-    TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
-    TORCH_CHECK(grad >= 0, "grad must be non-negative");
-
-    // Validate layout.
-    TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
-    TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
-    TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
-    TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
-    TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    torch::Tensor y = torch::empty_like(x);
-    TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
-
-    // Initialize CUDA kernel parameters.
-    bias_act_kernel_params p;
-    p.x     = x.data_ptr();
-    p.b     = (b.numel()) ? b.data_ptr() : NULL;
-    p.xref  = (xref.numel()) ? xref.data_ptr() : NULL;
-    p.yref  = (yref.numel()) ? yref.data_ptr() : NULL;
-    p.dy    = (dy.numel()) ? dy.data_ptr() : NULL;
-    p.y     = y.data_ptr();
-    p.grad  = grad;
-    p.act   = act;
-    p.alpha = alpha;
-    p.gain  = gain;
-    p.clamp = clamp;
-    p.sizeX = (int)x.numel();
-    p.sizeB = (int)b.numel();
-    p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
-
-    // Choose CUDA kernel.
-    void* kernel;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        kernel = choose_bias_act_kernel<scalar_t>(p);
-    });
-    TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
-
-    // Launch CUDA kernel.
-    p.loopX = 4;
-    int blockSize = 4 * 32;
-    int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("bias_act", &bias_act);
-}
-
-//------------------------------------------------------------------------

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/ldm/modules/midas/midas/vit.py

@@ -1,491 +0,0 @@
-import torch
-import torch.nn as nn
-import timm
-import types
-import math
-import torch.nn.functional as F
-
-
-class Slice(nn.Module):
-    def __init__(self, start_index=1):
-        super(Slice, self).__init__()
-        self.start_index = start_index
-
-    def forward(self, x):
-        return x[:, self.start_index :]
-
-
-class AddReadout(nn.Module):
-    def __init__(self, start_index=1):
-        super(AddReadout, self).__init__()
-        self.start_index = start_index
-
-    def forward(self, x):
-        if self.start_index == 2:
-            readout = (x[:, 0] + x[:, 1]) / 2
-        else:
-            readout = x[:, 0]
-        return x[:, self.start_index :] + readout.unsqueeze(1)
-
-
-class ProjectReadout(nn.Module):
-    def __init__(self, in_features, start_index=1):
-        super(ProjectReadout, self).__init__()
-        self.start_index = start_index
-
-        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
-
-    def forward(self, x):
-        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
-        features = torch.cat((x[:, self.start_index :], readout), -1)
-
-        return self.project(features)
-
-
-class Transpose(nn.Module):
-    def __init__(self, dim0, dim1):
-        super(Transpose, self).__init__()
-        self.dim0 = dim0
-        self.dim1 = dim1
-
-    def forward(self, x):
-        x = x.transpose(self.dim0, self.dim1)
-        return x
-
-
-def forward_vit(pretrained, x):
-    b, c, h, w = x.shape
-
-    glob = pretrained.model.forward_flex(x)
-
-    layer_1 = pretrained.activations["1"]
-    layer_2 = pretrained.activations["2"]
-    layer_3 = pretrained.activations["3"]
-    layer_4 = pretrained.activations["4"]
-
-    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
-    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
-    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
-    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
-
-    unflatten = nn.Sequential(
-        nn.Unflatten(
-            2,
-            torch.Size(
-                [
-                    h // pretrained.model.patch_size[1],
-                    w // pretrained.model.patch_size[0],
-                ]
-            ),
-        )
-    )
-
-    if layer_1.ndim == 3:
-        layer_1 = unflatten(layer_1)
-    if layer_2.ndim == 3:
-        layer_2 = unflatten(layer_2)
-    if layer_3.ndim == 3:
-        layer_3 = unflatten(layer_3)
-    if layer_4.ndim == 3:
-        layer_4 = unflatten(layer_4)
-
-    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
-    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
-    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
-    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
-
-    return layer_1, layer_2, layer_3, layer_4
-
-
-def _resize_pos_embed(self, posemb, gs_h, gs_w):
-    posemb_tok, posemb_grid = (
-        posemb[:, : self.start_index],
-        posemb[0, self.start_index :],
-    )
-
-    gs_old = int(math.sqrt(len(posemb_grid)))
-
-    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
-    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
-    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
-
-    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-
-    return posemb
-
-
-def forward_flex(self, x):
-    b, c, h, w = x.shape
-
-    pos_embed = self._resize_pos_embed(
-        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
-    )
-
-    B = x.shape[0]
-
-    if hasattr(self.patch_embed, "backbone"):
-        x = self.patch_embed.backbone(x)
-        if isinstance(x, (list, tuple)):
-            x = x[-1]  # last feature if backbone outputs list/tuple of features
-
-    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
-
-    if getattr(self, "dist_token", None) is not None:
-        cls_tokens = self.cls_token.expand(
-            B, -1, -1
-        )  # stole cls_tokens impl from Phil Wang, thanks
-        dist_token = self.dist_token.expand(B, -1, -1)
-        x = torch.cat((cls_tokens, dist_token, x), dim=1)
-    else:
-        cls_tokens = self.cls_token.expand(
-            B, -1, -1
-        )  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-
-    x = x + pos_embed
-    x = self.pos_drop(x)
-
-    for blk in self.blocks:
-        x = blk(x)
-
-    x = self.norm(x)
-
-    return x
-
-
-activations = {}
-
-
-def get_activation(name):
-    def hook(model, input, output):
-        activations[name] = output
-
-    return hook
-
-
-def get_readout_oper(vit_features, features, use_readout, start_index=1):
-    if use_readout == "ignore":
-        readout_oper = [Slice(start_index)] * len(features)
-    elif use_readout == "add":
-        readout_oper = [AddReadout(start_index)] * len(features)
-    elif use_readout == "project":
-        readout_oper = [
-            ProjectReadout(vit_features, start_index) for out_feat in features
-        ]
-    else:
-        assert (
-            False
-        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
-
-    return readout_oper
-
-
-def _make_vit_b16_backbone(
-    model,
-    features=[96, 192, 384, 768],
-    size=[384, 384],
-    hooks=[2, 5, 8, 11],
-    vit_features=768,
-    use_readout="ignore",
-    start_index=1,
-):
-    pretrained = nn.Module()
-
-    pretrained.model = model
-    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
-    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
-    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
-    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
-    pretrained.activations = activations
-
-    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
-    # 32, 48, 136, 384
-    pretrained.act_postprocess1 = nn.Sequential(
-        readout_oper[0],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[0],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.ConvTranspose2d(
-            in_channels=features[0],
-            out_channels=features[0],
-            kernel_size=4,
-            stride=4,
-            padding=0,
-            bias=True,
-            dilation=1,
-            groups=1,
-        ),
-    )
-
-    pretrained.act_postprocess2 = nn.Sequential(
-        readout_oper[1],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[1],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.ConvTranspose2d(
-            in_channels=features[1],
-            out_channels=features[1],
-            kernel_size=2,
-            stride=2,
-            padding=0,
-            bias=True,
-            dilation=1,
-            groups=1,
-        ),
-    )
-
-    pretrained.act_postprocess3 = nn.Sequential(
-        readout_oper[2],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[2],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-    )
-
-    pretrained.act_postprocess4 = nn.Sequential(
-        readout_oper[3],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[3],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.Conv2d(
-            in_channels=features[3],
-            out_channels=features[3],
-            kernel_size=3,
-            stride=2,
-            padding=1,
-        ),
-    )
-
-    pretrained.model.start_index = start_index
-    pretrained.model.patch_size = [16, 16]
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-    pretrained.model._resize_pos_embed = types.MethodType(
-        _resize_pos_embed, pretrained.model
-    )
-
-    return pretrained
-
-
-def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
-
-    hooks = [5, 11, 17, 23] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model,
-        features=[256, 512, 1024, 1024],
-        hooks=hooks,
-        vit_features=1024,
-        use_readout=use_readout,
-    )
-
-
-def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
-    )
-
-
-def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
-    )
-
-
-def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
-    model = timm.create_model(
-        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
-    )
-
-    hooks = [2, 5, 8, 11] if hooks == None else hooks
-    return _make_vit_b16_backbone(
-        model,
-        features=[96, 192, 384, 768],
-        hooks=hooks,
-        use_readout=use_readout,
-        start_index=2,
-    )
-
-
-def _make_vit_b_rn50_backbone(
-    model,
-    features=[256, 512, 768, 768],
-    size=[384, 384],
-    hooks=[0, 1, 8, 11],
-    vit_features=768,
-    use_vit_only=False,
-    use_readout="ignore",
-    start_index=1,
-):
-    pretrained = nn.Module()
-
-    pretrained.model = model
-
-    if use_vit_only == True:
-        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
-        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
-    else:
-        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
-            get_activation("1")
-        )
-        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
-            get_activation("2")
-        )
-
-    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
-    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
-
-    pretrained.activations = activations
-
-    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
-    if use_vit_only == True:
-        pretrained.act_postprocess1 = nn.Sequential(
-            readout_oper[0],
-            Transpose(1, 2),
-            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-            nn.Conv2d(
-                in_channels=vit_features,
-                out_channels=features[0],
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            ),
-            nn.ConvTranspose2d(
-                in_channels=features[0],
-                out_channels=features[0],
-                kernel_size=4,
-                stride=4,
-                padding=0,
-                bias=True,
-                dilation=1,
-                groups=1,
-            ),
-        )
-
-        pretrained.act_postprocess2 = nn.Sequential(
-            readout_oper[1],
-            Transpose(1, 2),
-            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-            nn.Conv2d(
-                in_channels=vit_features,
-                out_channels=features[1],
-                kernel_size=1,
-                stride=1,
-                padding=0,
-            ),
-            nn.ConvTranspose2d(
-                in_channels=features[1],
-                out_channels=features[1],
-                kernel_size=2,
-                stride=2,
-                padding=0,
-                bias=True,
-                dilation=1,
-                groups=1,
-            ),
-        )
-    else:
-        pretrained.act_postprocess1 = nn.Sequential(
-            nn.Identity(), nn.Identity(), nn.Identity()
-        )
-        pretrained.act_postprocess2 = nn.Sequential(
-            nn.Identity(), nn.Identity(), nn.Identity()
-        )
-
-    pretrained.act_postprocess3 = nn.Sequential(
-        readout_oper[2],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[2],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-    )
-
-    pretrained.act_postprocess4 = nn.Sequential(
-        readout_oper[3],
-        Transpose(1, 2),
-        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
-        nn.Conv2d(
-            in_channels=vit_features,
-            out_channels=features[3],
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        ),
-        nn.Conv2d(
-            in_channels=features[3],
-            out_channels=features[3],
-            kernel_size=3,
-            stride=2,
-            padding=1,
-        ),
-    )
-
-    pretrained.model.start_index = start_index
-    pretrained.model.patch_size = [16, 16]
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-
-    # We inject this function into the VisionTransformer instances so that
-    # we can use it with interpolated position embeddings without modifying the library source.
-    pretrained.model._resize_pos_embed = types.MethodType(
-        _resize_pos_embed, pretrained.model
-    )
-
-    return pretrained
-
-
-def _make_pretrained_vitb_rn50_384(
-    pretrained, use_readout="ignore", hooks=None, use_vit_only=False
-):
-    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
-
-    hooks = [0, 1, 8, 11] if hooks == None else hooks
-    return _make_vit_b_rn50_backbone(
-        model,
-        features=[256, 512, 768, 768],
-        size=[384, 384],
-        hooks=hooks,
-        use_vit_only=use_vit_only,
-        use_readout=use_readout,
-    )

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/utils/_fixes.py

@@ -1,77 +0,0 @@
-# JSONDecodeError was introduced in requests=2.27 released in 2022.
-# This allows us to support older requests for users
-# More information: https://github.com/psf/requests/pull/5856
-try:
-    from requests import JSONDecodeError  # type: ignore  # noqa: F401
-except ImportError:
-    try:
-        from simplejson import JSONDecodeError  # type: ignore # noqa: F401
-    except ImportError:
-        from json import JSONDecodeError  # type: ignore  # noqa: F401
-
-import contextlib
-import os
-import shutil
-import stat
-import tempfile
-from functools import partial
-from pathlib import Path
-from typing import Callable, Generator, Optional, Union
-
-import yaml
-
-
-# Wrap `yaml.dump` to set `allow_unicode=True` by default.
-#
-# Example:
-# ```py
-# >>> yaml.dump({"emoji": "👀", "some unicode": "日本か"})
-# 'emoji: "\\U0001F440"\nsome unicode: "\\u65E5\\u672C\\u304B"\n'
-#
-# >>> yaml_dump({"emoji": "👀", "some unicode": "日本か"})
-# 'emoji: "👀"\nsome unicode: "日本か"\n'
-# ```
-yaml_dump: Callable[..., str] = partial(yaml.dump, stream=None, allow_unicode=True)  # type: ignore
-
-
-@contextlib.contextmanager
-def SoftTemporaryDirectory(
-    suffix: Optional[str] = None,
-    prefix: Optional[str] = None,
-    dir: Optional[Union[Path, str]] = None,
-    **kwargs,
-) -> Generator[str, None, None]:
-    """
-    Context manager to create a temporary directory and safely delete it.
-
-    If tmp directory cannot be deleted normally, we set the WRITE permission and retry.
-    If cleanup still fails, we give up but don't raise an exception. This is equivalent
-    to  `tempfile.TemporaryDirectory(..., ignore_cleanup_errors=True)` introduced in
-    Python 3.10.
-
-    See https://www.scivision.dev/python-tempfile-permission-error-windows/.
-    """
-    tmpdir = tempfile.TemporaryDirectory(prefix=prefix, suffix=suffix, dir=dir, **kwargs)
-    yield tmpdir.name
-
-    try:
-        # First once with normal cleanup
-        shutil.rmtree(tmpdir.name)
-    except Exception:
-        # If failed, try to set write permission and retry
-        try:
-            shutil.rmtree(tmpdir.name, onerror=_set_write_permission_and_retry)
-        except Exception:
-            pass
-
-    # And finally, cleanup the tmpdir.
-    # If it fails again, give up but do not throw error
-    try:
-        tmpdir.cleanup()
-    except Exception:
-        pass
-
-
-def _set_write_permission_and_retry(func, path, excinfo):
-    os.chmod(path, stat.S_IWRITE)
-    func(path)

spaces/DeclK/pose/main.py

@@ -1,96 +0,0 @@
-# Inference 2 videos and use dtw to match the pose keypoints.
-from tools.inferencer import PoseInferencerV2
-from tools.dtw import DTWForKeypoints
-from tools.visualizer import FastVisualizer
-from argparse import ArgumentParser
-from tools.utils import convert_video_to_playable_mp4
-from tqdm import tqdm
-import mmengine
-import numpy as np
-import mmcv
-import cv2
-
-def parse_args():
-    parser = ArgumentParser()
-    parser.add_argument('--config', type=str, default='configs/mark2.py')
-    parser.add_argument('--video1', type=str, default='assets/tennis1.mp4')
-    parser.add_argument('--video2', type=str, default='assets/tennis2.mp4')
-    return parser.parse_args()
-
-def concat(img1, img2, height=1080):
-    h1, w1, _ = img1.shape
-    h2, w2, _ = img2.shape
-
-    # Calculate the scaling factor for each image
-    scale1 = height / img1.shape[0]
-    scale2 = height / img2.shape[0]
-
-    # Resize the images
-    img1 = cv2.resize(img1, (int(w1*scale1), int(h1*scale1)))
-    img2 = cv2.resize(img2, (int(w2*scale2), int(h2*scale2)))
-
-    # Concatenate the images horizontally
-    image = cv2.hconcat([img1, img2])
-    return image
-
-def draw(vis: FastVisualizer, img, keypoint, box, oks, oks_unnorm, draw_score_bar=True):
-    vis.set_image(img)
-    vis.draw_non_transparent_area(box)
-    if draw_score_bar:
-        vis.draw_score_bar(oks)
-    vis.draw_human_keypoints(keypoint, oks_unnorm)
-    return vis.get_image()
-
-def main(cfg):
-    # build PoseInferencerV2
-    pose_inferencer = PoseInferencerV2(
-                        cfg.det_cfg,
-                        cfg.pose_cfg,
-                        device='cpu')
-    
-    v1 = mmcv.VideoReader(cfg.video1)
-    v2 = mmcv.VideoReader(cfg.video2)
-    video_writer = None
-
-    all_det1, all_pose1 = pose_inferencer.inference_video(cfg.video1)
-    all_det2, all_pose2 = pose_inferencer.inference_video(cfg.video2)
-
-    keypoints1 = np.stack([p.keypoints[0] for p in all_pose1])  # forced the first pred
-    keypoints2 = np.stack([p.keypoints[0] for p in all_pose2])
-    boxes1 = np.stack([d.bboxes[0] for d in all_det1])
-    boxes2 = np.stack([d.bboxes[0] for d in all_det2])
-
-    dtw_path, oks, oks_unnorm = DTWForKeypoints(keypoints1, keypoints2).get_dtw_path()
-
-    vis = FastVisualizer()
-    
-    for i, j in tqdm(dtw_path): 
-        frame1 = v1[i]
-        frame2 = v2[j]
-
-        frame1_ = draw(vis, frame1.copy(), keypoints1[i], boxes1[i],
-                       oks[i, j], oks_unnorm[i, j])
-        frame2_ = draw(vis, frame2.copy(), keypoints2[j], boxes2[j],
-                       oks[i, j], oks_unnorm[i, j], draw_score_bar=False)
-        # concate two frames
-        frame = concat(frame1_, frame2_)
-        # draw logo
-        vis.set_image(frame)
-        frame = vis.draw_logo().get_image()
-        # write video
-        w, h = frame.shape[1], frame.shape[0]
-        if video_writer is None:
-            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-            video_writer = cv2.VideoWriter('dtw_compare.mp4', 
-                                            fourcc, v1.fps, (w, h))
-        video_writer.write(frame)
-    video_writer.release()
-    convert_video_to_playable_mp4('dtw_compare.mp4')
-
-if __name__ == '__main__':
-    args = parse_args()
-    cfg = mmengine.Config.fromfile(args.config)
-    cfg.video1 = args.video1
-    cfg.video2 = args.video2
-
-    main(cfg)

spaces/Demosthene-OR/avr23-cds-translation/style.css

@@ -1,113 +0,0 @@
-/*
-h1,
-h2,
-h3,
-h4 {
-  color: #000000;
-}
-*/
-
-/* La ligne suivante est nécessaire à cause du module streamlit_option_menu qui "casse" les CSS suivants */
-@media (prefers-color-scheme: dark) {
-    .st-cc {
-        color: #fff!important; /* Couleur du texte en mode sombre */
-	}
-	.st-cg:hover {
-		color: rgb(255, 75, 75)!important; /* Couleur du texte en mode sombre */
-    }
-}
-
-p {
-    margin-bottom:0.1rem;
-}
-
-code {
-  color: #1ec3bc;
-}
-
-#MainMenu {
-  display: none;
-}
-
-div[data-testid="stDecoration"] {
-  display: none;
-}
-
-footer {
-  display: none;
-}
-
-/* Radio buttons */
-
-.st-cc {
-  color: black;
-  font-weight: 500;
-}
-
-/* Sidebar */
-
-.css-1544g2n {
-  padding-top: 1rem;
-}
-
-.css-10oheav {
-  padding-top: 3rem;
-}
-
-.css-ue6h4q {
-  min-height: 0.5rem;
-}
-
-section[data-testid="stSidebar"] > div {
-  background-color: #10b8dd;
-  padding-top: 1rem;
-  padding-left: 1.5rem;
-}
-
-section[data-testid="stSidebar"] button[title="View fullscreen"] {
-  display: none;
-}
-
-section[data-testid="stSidebar"] button[kind="icon"] {
-  display: none;
-}
-
-section[data-testid="stSidebar"] .st-bk {
-  background-color: #10b8dd;
-}
-
-section[data-testid="stSidebar"] .st-c0 {
-/*  background-color: #10b8dd; */
-}
-
-section[data-testid="stSidebar"] hr {
-  margin-top: 30px;
-  border-color: white;
-  width: 50px;
-}
-
-section[data-testid="stSidebar"] h2 {
-  color: white;
-}
-
-/* Images */
-
-button[title="View fullscreen"] {
-  display: none;
-}
-
-/* hr */
-
-hr {
-  width: 200px;
-  border-width: 5px;
-  border-color: #10b8dd;
-  margin-top: 0px;
-}
-
-/* First Page */
-
-section[tabindex="0"] .block-container {
-  padding-top: 0px;
-  padding-bottom: 0px;
-}

spaces/Detomo/ai-avatar-frontend/src/converter.js

@@ -1,97 +0,0 @@
-import {
-	AnimationClip,
-	BooleanKeyframeTrack,
-	ColorKeyframeTrack,
-	NumberKeyframeTrack,
-	Vector3,
-	VectorKeyframeTrack
-} from 'three';
-
-var fps = 60
-
-function modifiedKey(key) {
-
-  if (["eyeLookDownLeft", "eyeLookDownRight", "eyeLookInLeft", "eyeLookInRight", "eyeLookOutLeft", "eyeLookOutRight", "eyeLookUpLeft", "eyeLookUpRight"].includes(key)) {
-    return key
-  }
-
-  if (key.endsWith("Right")) {
-    return key.replace("Right", "_R");
-  }
-  if (key.endsWith("Left")) {
-    return key.replace("Left", "_L");
-  }
-  return key;
-}
-
-function createAnimation (recordedData, morphTargetDictionary, bodyPart) {
-
-  // console.log("----morphTargetDictionary", morphTargetDictionary)
-
-  if (recordedData.length != 0) {
-    let animation = []
-    for (let i = 0; i < Object.keys(morphTargetDictionary).length; i++) {
-      animation.push([])
-    }
-    let time = []
-    let finishedFrames = 0
-    recordedData.forEach((d, i) => {
-        Object.entries(d.blendshapes).forEach(([key, value]) => {
-
-          if (! (modifiedKey(key) in morphTargetDictionary)) {return};
-          
-          if (key == 'mouthShrugUpper') {
-            value += 0.4;
-          }
-
-          animation[morphTargetDictionary[modifiedKey(key)]].push(value)
-        });
-        time.push(finishedFrames / fps)
-        finishedFrames++
-
-    })
-
-    // console.log("-----animation", animation);
-
-    let tracks = []
-
-    let flag = false;
-    //create morph animation
-    Object.entries(recordedData[0].blendshapes).forEach(([key, value]) => {
-
-      if (! (modifiedKey(key) in morphTargetDictionary)) {return};
-
-      let i = morphTargetDictionary[modifiedKey(key)]
-      
-      // if (bodyPart === "HG_TeethLower") {
-
-      //       if (flag === true)
-      //         return;
-            
-      //       if(key === 'jawOpen') {
-      //         let track2 = new NumberKeyframeTrack(`HG_TeethLower.morphTargetInfluences[${i}]`, time, animation[i])
-      //         tracks.push(track2)
-      //         flag = true
-      //       }
-      // } else {
-        let track = new NumberKeyframeTrack(`${bodyPart}.morphTargetInfluences[${i}]`, time, animation[i])
-
-        tracks.push(track)
-  
-      // }
-
-      
-      // if (key === "jawOpen") {
-      //   let track2 = new NumberKeyframeTrack(`HG_TeethLower.morphTargetInfluences[${i}]`, time, animation[i])
-      //   tracks.push(track2)
-      //   console.log("----jawOpen Track", track2);
-      // }
-    });
-
-    const clip = new AnimationClip('animation', -1, tracks);
-    return clip
-  }
-  return null
-}
-
-export default createAnimation;

spaces/Dogge/bigscience-bloomz-7b1/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/bigscience/bloomz-7b1").launch()