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spaces/1acneusushi/gradio-2dmoleculeeditor/data/AutoCAD OEM 2017 Crack Universal Product Key Free !!EXCLUSIVE!!.md

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-<p>If you are looking for a powerful, customizable, and scalable CAD platform that can help you create, edit, and share your designs, then you might be interested in AutoCAD OEM 2017. This is a software development toolkit that allows you to build your own branded applications based on the core functionality of AutoCAD, the world's leading CAD software. However, to use AutoCAD OEM 2017, you need a valid product key that can activate the software. And since this is not a cheap product, you might be tempted to look for a crack or a universal product key that can bypass the activation process. In this article, we will show you how to download, install, crack, and activate AutoCAD OEM 2017 for free using X-force 2017, a keygen tool that can generate product keys for all Autodesk products.</p>
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-<p>In conclusion, AutoCAD OEM 2017 is a powerful, customizable, and scalable CAD platform that allows you to create your own branded applications based on the core functionality of AutoCAD. However, to use AutoCAD OEM 2017, you need a valid product key that can activate the software. And since this is not a cheap product, you might be tempted to look for a crack or a universal product key that can bypass the activation process. In this article, we showed you how to download, install, crack, and activate AutoCAD OEM 2017 for free using X-force 2017, a keygen tool that can generate product keys for all Autodesk products. By using this method, you can enjoy several benefits, such as access to full features and updates, saving money and time, and avoiding malware and viruses. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cimatron E11 Download Crack Software.md

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-<p>If you are looking for a powerful CAD/CAM software for tooling and manufacturing, you might have heard of Cimatron E11. This software is designed to help you create high-quality tools of any complexity or size, as well as optimize your CNC machining processes. However, you might also be wondering how to download Cimatron E11 crack software for free, without paying for a license or subscription. In this article, we will explain what Cimatron E11 is, why people want to download crack software, what are the risks of doing so, how to download it safely, and what are the alternatives to downloading crack software.</p>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Ontrack Disk Manager 10.46 ISO and Overcome BIOS Limitations on Your Hard Drive.md

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-<li>A CD/DVD burner software or a USB creator software that can burn or write ISO files.</li>
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- <p>To download Ontrack Disk Manager 10.46 ISO, follow these steps:</p>
- <ol>
-<li>Open your web browser and go to this link: <a href="https://archive.org/details/OnTrackDiskManager_201801">https://archive.org/details/OnTrackDiskManager_201801</a>.</li>
-<li>Click on the "DOWNLOAD OPTIONS" section and select "ISO IMAGE".</li>
-<li>Click on the "OnTrack_Disk_Manager_10.46.iso" file and save it to your computer.</li>
-<li>The download may take some time depending on your internet speed and the size of the file (about 688 MB).</li>
-</ol>
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- <p>After downloading Ontrack Disk Manager 10.46 ISO, you need to burn it to a CD or USB drive and boot from it to run the software. Here are the steps for doing that:</p>
- <h3>How to Burn Ontrack Disk Manager 10.46 ISO to a CD or USB Drive</h3>
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- <ol>
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-<li>Open the CD/DVD burner software or the USB creator software and select the option to burn or write an ISO file.</li>
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-<li>After booting from the CD or USB drive where you burned or wrote the OnTrack_Disk_Manager_10.46.iso file , you will see a welcome screen with some options . Choose "Start Program" . </li>
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-<li>You will see a list of all the hard drives detected by the software . Choose the one that you want to partition and format . </li>
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-<li>After making the changes that you want , click on "Apply" to confirm them . The software will ask you to reboot your computer to complete the process . </li>
-<li>After rebooting your computer , you will see your new partitions and file systems on your hard drive . </li>
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- <p>Here are some tips and tricks for using Ontrack Disk Manager 10.46 ISO effectively:</p>
- <h3>How to Backup and Restore a Hard Drive with Ontrack Disk Manager 10.46 ISO</h3>
- <p>To backup and restore a hard drive with Ontrack Disk Manager 10.46 ISO , follow these steps:</p>
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-<li>Boot from the CD or USB drive where you burned or wrote the OnTrack_Disk_Manager_10.46.iso file . </li>
-<li>From the main menu , choose "Backup/Restore" . </li>
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-<li>After making your choices , click on "Start" to begin the backup or restore process . The software will show you a progress bar and some details about the process . Wait until it is completed . </li>
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- <p>To repair a damaged or corrupted hard drive with Ontrack Disk Manager 10.46 ISO , follow these steps:</p>
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-<li>Boot from the CD or USB drive where you burned or wrote the OnTrack_Disk_Manager_10.46.iso file . </li>
-<li>From the main menu , choose "Disk Utilities" . </li>
-<li>You will see a list of all the hard drives detected by the software . Choose the one that you want to repair . </li>
-<li>You will see a graphical representation of the hard drive with its current partitions . You can use the mouse or keyboard to select any partition that you want to repair . You can also use the "Select All" option to select all partitions on the hard drive . </li>
-<li>Click on "Repair" to start the repair process . The software will scan and fix any errors or bad sectors on the selected partitions . It will also try to recover any lost or deleted data on them . The software will show you a progress bar and some details about the process . Wait until it is completed . </li>
-</ol>
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- <p>To erase a hard drive with Ontrack Disk Manager 10.46 ISO , follow these steps:</p>
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-<li>Boot from the CD or USB drive where you burned or wrote the OnTrack_Disk_Manager_10.46.iso file . </li>
-<li>From the main menu , choose "Disk Utilities" . </li>
-<li>You will see a list of all the hard drives detected by the software . Choose the one that you want to erase . </li>
-the "Select All" option to select all partitions on the hard drive . </li>
-<li>Click on "Erase" to start the erase process . The software will ask you to confirm your choice and warn you that all data on the selected partitions will be permanently deleted . Click on "Yes" to proceed . </li>
-<li>The software will overwrite all data on the selected partitions with zeros or random data , depending on the level of security that you choose . You can choose from three levels of security : "Quick" , "Normal" , or "Secure" . The higher the level of security , the longer the erase process will take . The software will show you a progress bar and some details about the process . Wait until it is completed . </li>
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- <p>Ontrack Disk Manager 10.46 ISO is a powerful and easy-to-use software that can help you manage your hard drives in various ways . You can use it to partition , format , backup , restore , repair , and erase your hard drives in a few simple steps . You can also use it to troubleshoot and fix various disk-related problems . You can download it for free from this link: <a href="https://archive.org/details/OnTrackDiskManager_201801">https://archive.org/details/OnTrackDiskManager_201801</a> and burn it to a CD or USB drive to run it without installing it on your computer . We hope this article has helped you learn more about Ontrack Disk Manager 10.46 ISO and how to use it effectively . If you have any questions or feedback , please feel free to leave a comment below . </p>
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- <p>Here are some frequently asked questions about Ontrack Disk Manager 10.46 ISO :</p>
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-<li>Q: Is Ontrack Disk Manager 10.46 ISO compatible with Windows 10?</li>
-<li>A: Yes, Ontrack Disk Manager 10.46 ISO is compatible with Windows 10 and other versions of Windows, such as Windows 8, Windows 7, Windows Vista, and Windows XP.</li>
-<li>Q: Can I use Ontrack Disk Manager 10.46 ISO to clone a hard drive?</li>
-<li>A: Yes, you can use Ontrack Disk Manager 10.46 ISO to clone a hard drive by using the "Copy" option in the "Disk Utilities" section. You can copy a partition or an entire hard drive to another hard drive.</li>
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-<li>A: Yes, you can use Ontrack Disk Manager 10.46 ISO to recover deleted files by using the "Repair" option in the "Disk Utilities" section. The software will try to recover any lost or deleted data on the selected partitions.</li>
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-<li>A: Yes, you can use Ontrack Disk Manager 10.46 ISO to create bootable disks by using the "Create Bootable Disk" option in the main menu. You can create bootable disks for different operating systems, such as DOS, Windows, Linux, etc.</li>
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-<li>A: Yes, you can use Ontrack Disk Manager 10.46 ISO to resize partitions by using the "Resize" option in the "Disk Utilities" section. You can increase or decrease the size of any partition on your hard drive.</li>
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- <p>If you are looking for a sweet and addictive mobile game that combines action, strategy, and city-building, you might want to check out Cookie Run: Kingdom. This game is the latest installment in the Cookie Run series by Devsisters, and it has become a huge hit since its global launch in January 2021. In this article, we will tell you everything you need to know about Cookie Run: Kingdom, including what it is, what are its features, where and how to download it, what are some tips and tricks for beginners, and what are some pros and cons of playing it.</p>
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- <p>Cookie Run: Kingdom is a game that mixes real-time battle strategy and city-building, with a wide cast of unique cookies and a customizable kingdom. It tells the story of cookies who create a kingdom of their own to call home. Throughout their adventures, they explore other ancient kingdoms, battle fierce adversaries of the darkness, and unravel the mysteries of the ancient heroes who disappeared from the world.</p>
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- <p>To download Cookie Run: Kingdom from the App Store or Google Play Store, follow these steps:</p>
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- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>- Cute and colorful graphics</td>
-<td>- Requires internet connection</td>
-</tr>
-<tr>
-<td>- Engaging and diverse gameplay</td>
-<td>- Can be repetitive or grindy</td>
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- <p>Cookie Run: Kingdom is a game that can be compared to other games in the action-strategy-city-building genre, such as Clash of Clans, Rise of Kingdoms, or Lords Mobile. However, Cookie Run: Kingdom has its own unique charm and style that sets it apart from other games. It has a more whimsical and lighthearted tone, a more casual and accessible gameplay, a more diverse and customizable content, and a more loyal and friendly community.</p>
- <h2>Conclusion</h2>
- <p>Cookie Run: Kingdom is a game that offers a lot of fun and variety for players who love action, strategy, city-building, and cookies. It has an intriguing RPG story mode, a player-versus-player (PvP) battle mode, and many ways to export the goods that you're making in your kingdom. It also has adorable graphics, catchy music, and charming voice acting. The game is free to play, but it also offers optional in-app purchases for players who want to enhance their experience.</p>
- <p>If you are interested in playing Cookie Run: Kingdom, you can download it from the App Store or Google Play Store depending on your device. The game requires iOS 13.0 or later or Android 4.4 or later to run. The game also supports iPadOS 13.0 or later and macOS 11.0 or later with Apple M1 chip or later.</p>
- <p>We hope this article has helped you learn more about Cookie Run: Kingdom and how to download it. If you have any questions or feedback about the game or the article, feel free to leave a comment below. Happy gaming!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Cookie Run: Kingdom:</p>
- <ol>
-<li><b>How do I get more cookies in Cookie Run: Kingdom?</b></li>
-<p>You can get more cookies in Cookie Run: Kingdom by completing quests, clearing areas on the world map, opening chests or gacha boxes, participating in events or promotions, joining a guild or inviting friends, or buying them with crystals.</p>
- <li><b>How do I upgrade my kingdom in Cookie Run: Kingdom?</b></li>
-<p>You can upgrade your kingdom in Cookie Run: Kingdom by building and upgrading various buildings and decorations. You can use coins and materials to construct and improve your buildings. You can also use crystals to speed up the process or buy more slots. You can also unlock new areas and features by increasing your kingdom level and population.</p>
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- <h2>Why should you play Getting Over It with Bennett Foddy?</h2>
-<p>Now that you know what this game is and how it works, you might be wondering: why should I play this game? What's the point? What's the fun? What's the reward? Well, there are many reasons why you should play this game, depending on what kind of person you are and what kind of experience you are looking for. Here are some of them:</p>
- <h3>The challenge and the reward</h3>
-<p>If you are a person who loves a good challenge and a sense of accomplishment, this game is for you. This game is one of the hardest games ever made, and beating it is a feat that only a few people in the world have achieved. This game will push you to your limits and beyond, and it will make you feel every emotion possible along the way. This game will make you suffer, but it will also make you grow. This game will make you hate it, but it will also make you love it. This game will make you cry, but it will also make you smile. This game will make you quit, but it will also make you come back. This game will make you lose everything, but it will also make you gain something priceless: <strong>the satisfaction of overcoming yourself</strong>.</p>
- <h3>The humor and the references</h3>
-<p>If you are a person who loves a good laugh and a dose of culture, this game is for you. This game is full of humor and references that will tickle your funny bone and stimulate your brain. The game is full of jokes and puns that are related to the gameplay and the theme of frustration. The game is full of references and homages to other games, movies, books, songs, art, history, philosophy, and more. The game is full of surprises and secrets that will reward your curiosity and exploration. The game is full of irony and sarcasm that will make you laugh at yourself and the world. The game is full of wisdom and insight that will make you think and learn. The game is full of fun and entertainment that will make you enjoy and appreciate.</p>
- <h3>The exploration and the secrets</h3>
-<p>If you are a person who loves to discover new things and uncover hidden mysteries, this game is for you. This game is not just a linear climb up a mountain. It is also a nonlinear journey through a rich and diverse world that is full of secrets and Easter eggs. The game has many paths, branches, shortcuts, detours, loops, dead ends, and hidden areas that you can explore and find. The game has many objects, items, characters, sounds, music, and dialogues that you can interact with and learn from. The game has many secrets, puzzles, codes, clues, hints, messages, and meanings that you can uncover and decipher. The game has many layers, dimensions, levels, modes, endings, and outcomes that you can experience and achieve.</p>
- <h3>The community and the speedruns</h3>
-<p>If you are a person who loves to share your experiences and compete with others, this game is for you. This game has a huge and active community of players who are passionate about this game and who support each other through their struggles and successes. You can join this community online through various platforms like YouTube, Twitch, Discord, Reddit, Steam, etc., where you can watch, chat, stream, comment, like, subscribe, follow, donate, etc., with other players who are playing this game or who have played this game before. You can also participate in this community offline through various events like conventions, meetups, workshops, etc., where you can meet, talk, play, learn, teach, etc., with other players who are interested in this game or who are experts in this game.</p>
- <p>One of the most popular ways to enjoy this game with the community is to do <strong>speedruns</strong>, which are attempts to complete the game as fast as possible using various techniques and strategies. Speedruns are a form of art and sport that showcase the skill and creativity of the players who perform them. Speedruns are also a form of entertainment and education that inspire and teach the viewers who watch them. Speedruns are also a form of challenge and competition that motivate and reward the participants who achieve them.</p>
- <p>There are many categories of speedruns for this game, such as <strong>Any%</strong>, which is the fastest way to complete the game by any means necessary, <strong>Glitchless</strong>, which is the fastest way to complete the game without using any glitches or exploits, <strong>Space%</strong>, which is the fastest way to launch yourself into space and escape the game world, and more. The current world record for Any% is 56.717 seconds by Lumord, for Glitchless is 1 minute 13.2 seconds by Blastbolt, and for Space% is 1 minute 8.7 seconds by Hitachihex. You can watch these amazing speedruns on YouTube and learn from their strategies and skills. You can also try to beat their times and submit your own speedruns to Speedrun.com, where you can compare your results with other players and see your ranking on the leaderboard.</p>
- <h2>How to get Getting Over It with Bennett Foddy for free?</h2>
-<p>Now that you know why you should play this game, you might be wondering: how can I get this game for free? Well, there are a few ways to do that, but they are not all legal, safe, or ethical. So before you decide to download this game for free, you should be aware of the risks and the precautions that you should take. Here are some of the options that you have:</p>
- <h3>The official platforms and prices</h3>
-<p>The game is officially available on several platforms, such as Steam, Humble Bundle, Epic Games Store, itch.io, Google Play Store, and Apple App Store. The game costs $7.99 on Steam, Humble Bundle, Epic Games Store, and itch.io, $4.99 on Google Play Store, and $3.99 on Apple App Store. However, sometimes the game goes on sale or is offered for free on some of these platforms. For example, in December 2020, the game was free on Epic Games Store for a limited time. So if you want to get this game for free legally and ethically, you should keep an eye on these platforms and wait for a good deal or a giveaway.</p>
- <h3>The alternative platforms and sources</h3>
-<p>The game is also available on some alternative platforms and sources that are not official or authorized by the developer. These include torrent sites, file-sharing sites, modded APK sites, emulator sites, etc. These platforms and sources allow you to download the game for free without paying anything or going through any verification process. However, these platforms and sources are also illegal, unsafe, and unethical. They violate the intellectual property rights of the developer and the publisher of the game. They expose you to the risk of malware, viruses, spyware, adware, etc., that can harm your device or your data. They also deprive the developer and the publisher of the revenue that they deserve for their hard work and creativity.</p>
- <h3>The risks and the precautions</h3>
-<p>If you decide to download this game for free from an alternative platform or source, you should be aware of the risks and the precautions that you should take. Here are some of them: - The risks:   - You could get sued or fined for piracy or copyright infringement.   - You could get infected with malware or viruses that could damage your device or steal your data.   - You could get banned or suspended from the official platforms or services that you use to play the game.   - You could miss out on the updates, patches, bug fixes, features, content, etc., that the developer provides for the game.   - You could have a poor or incomplete gaming experience due to glitches, errors, crashes, etc., that are not fixed or optimized by the developer. - The precautions:   - You should use a VPN or a proxy to hide your IP address and location from the authorities or the hackers.   - You should use an antivirus or a firewall to protect your device and your data from malware or viruses.   - You should backup your device and your data regularly in case of any damage or loss.   - You should check the reviews, ratings, comments, feedback, etc., of the platform or source that you use to download the game to make sure it is reliable and trustworthy.   - You should scan the file or the app that you download for any malware or viruses before installing or running it. <h2>How to play Getting Over It with Bennett Foddy better?</h2>
-<p>Finally, if you have managed to get this game for free (or paid for it) and want to play it better, here are some tips and tricks that can help you improve your skills and performance. These tips and tricks are based on my own experience and research, as well as the advice and guidance of other players who have mastered this game. Here they are:</p>
- <h3>The tips and tricks for beginners</h3>
-<p>If you are new to this game or have not played it much, here are some tips and tricks that can help you get started and make some progress:</p>
- - Practice. This is the most important tip for this game. The only way to get better at this game is to practice a lot. The more you play, the more you learn, the more you improve. Practice makes perfect, or at least better. - Be patient. This is another crucial tip for this game. This game is not meant to be easy or fast. It is meant to be hard and slow. It will take you a long time to beat this game, if ever. So don't rush, don't panic, don't give up. Be patient with yourself and with the game. - Be calm. This is also a vital tip for this game. This game is designed to frustrate you and make you angry. It will test your nerves and your emotions. So don't let it get to you. Be calm and composed. Breathe deeply, relax your muscles, clear your mind. Don't let the game control you, control yourself. - Be positive. This is also a helpful tip for this game. This game is full of negativity and pessimism. It will make you feel bad and hopeless. So don't let it affect you. Be positive and optimistic. Focus on the good things, not the bad things. Celebrate your achievements, not your failures. Enjoy the journey, not the destination. - Be creative. This is also a fun tip for this game. This game is full of possibilities and opportunities. It will let you explore and experiment with different ways of playing and moving. So don't be afraid to try new things and be creative. Use your imagination and your intuition. Find your own style and your own solutions. <h3>The advanced techniques for experts</h3>
-<p>If you are already familiar with this game or have played it a lot, here are some advanced techniques that can help you play faster and better:</p>
- - Pogoing. This is a technique that involves using the hammer as a spring to bounce yourself up in the air. This technique can help you gain height and speed quickly and easily. To do this technique, you need to swing the hammer downwards behind you while lifting yourself up with the pot, then release the hammer when it hits the ground to launch yourself up in the air. - Flying. This is a technique that involves using the hammer as a propeller to fly yourself across long distances. This technique can help you skip large sections of the mountain and save time and effort. To do this technique, you need to swing the hammer in circles around you while moving yourself forward with the pot, then adjust the angle and direction of the hammer to steer yourself in the air. - Hooking. This is a technique that involves using the hammer as a hook to grab onto objects and pull yourself towards them. This technique can help you climb steep slopes and overcome tricky obstacles. To do this technique, you need to swing the hammer towards an object that you want to hook onto, then hold the hammer when it touches the object to attach yourself to it, then pull the hammer towards you while pushing yourself forward with the pot to move yourself closer to the object. - Flipping. This is a technique that involves using the hammer as a lever to flip yourself over objects or gaps. This technique can help you avoid falling or getting stuck in certain situations. To do this technique, you need to swing the hammer upwards in front of you while lowering yourself down with the pot, then release the hammer when it reaches the top of its arc to flip yourself over in the air, then catch yourself with the hammer on the other side of the object or gap. - Sliding. This is a technique that involves using the hammer as a brake to slide down slopes or surfaces. This technique can help you control your speed and direction when descending or moving horizontally. To do this technique, you need to swing the hammer downwards in front of you while moving yourself down or sideways with the pot, then hold the hammer when it touches the ground or the surface to slow yourself down and steer yourself. <h3>The resources and guides for learning</h3>
-<p>If you want to learn more about these techniques and other aspects of this game, there are many resources and guides that you can use to improve your knowledge and skills. Here are some of them:</p>
- - The official website of the game, where you can find information about the game, the developer, the platforms, the updates, etc. - The official wiki of the game, where you can find information about the gameplay, the controls, the narration, the references, etc. - The official subreddit of the game, where you can find discussions, questions, answers, tips, tricks, videos, memes, fan art, etc., related to the game. - The official YouTube channel of the developer, where you can find videos of him playing and talking about his games, including this one. - The unofficial YouTube channels of other players, where you can find videos of them playing and speedrunning this game, as well as tutorials and guides on how to play better. - The unofficial Discord server of the game, where you can chat and voice chat with other players who are playing or have played this game. - The unofficial Steam community hub of the game, where you can find reviews, ratings, comments, feedback, screenshots, videos, etc., related to the game. <h2>Conclusion</h2>
-<p>Getting Over It with Bennett Foddy is a game that is not for everyone. It is a game that will make you love it or hate it. It is a game that will make you happy or sad. It is a game that will make you laugh or cry. It is a game that will make you think or feel. It is a game that will make you succeed or fail. It is a game that will make you get over it or not.</p>
- <p>But whatever your reaction or outcome is, this game is worth trying. It is a game that will challenge you and reward you. It is a game that will entertain you and educate you. It is a game that will surprise you and inspire you. It is a game that will change you and stay with you.</p>
- <p>So if you are interested in this game, I hope this article has helped you learn more about it and how to get it for free and play it better. If not, I hope this article has at least made you curious and amused. Either way, I thank you for reading this article and I wish you all the best in your gaming adventures.</p>
- <p>Now go ahead and try Getting Over It with Bennett Foddy for yourself. And remember: don't hate the player, don't hate the game, just hate yourself.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Getting Over It with Bennett Foddy:</p>
- <h3>Q: Is there an end to this game?</h3>
-<p>A: Yes, there is an end to this game. There is a final obstacle at the top of the mountain that marks the end of the game. However, reaching the end of the game is not easy, and it requires a lot of skill and luck. Also, the end of the game is not the same for everyone, as it depends on your choices and actions. There are different endings that you can get, depending on what you do at the end of the game. Some endings are more satisfying than others, some endings are more secret than others, and some endings are more meta than others. I won't spoil them for you, but I will say that they are worth seeing for yourself.</p>
- <h3>Q: What happens if you fall down the mountain?</h3>
-<p>A: If you fall down the mountain, you will lose some or all of your progress, depending on how far you fall and where you land. You will also hear Bennett Foddy's voice commenting on your fall and giving you some words of encouragement or discouragement. Sometimes, he will also play some music or sound effects to accompany your fall. The music and sound effects are usually related to the theme or the mood of your fall, and they can be either soothing or annoying. You can mute the music and sound effects if you want, but you can't mute Bennett Foddy's voice.</p>
- <h3>Q: How long does it take to beat this game?</h3>
-<p>A: The time it takes to beat this game varies from person to person, depending on their skill level, experience, strategy, luck, etc. Some people can beat this game in less than a minute, some people can beat this game in a few hours, some people can beat this game in a few days, some people can beat this game in a few weeks, some people can beat this game in a few months, some people can beat this game in a few years, and some people can never beat this game at all. The average time it takes to beat this game is around 5 hours, but that doesn't mean that you will beat this game in 5 hours. You might beat this game faster or slower than that, or you might not beat this game at all.</p>
- <h3>Q: Is this game based on a true story?</h3>
-<p>A: No, this game is not based on a true story. This game is a fictional work of art that is inspired by other works of art and culture. However, some elements of this game are based on or related to real-life facts or events. For example, the main character of this game, Diogenes, is named after a famous Greek philosopher who lived in a barrel and rejected social norms. The hammer that he uses is a reference to Thor's hammer from Norse mythology and Marvel comics. The pot that he sits in is a reference to a Chinese legend about a man who was trapped in a bronze pot by his enemies. The mountain that he climbs is a collage of various objects and scenes from different games, movies, books, songs, art, history, philosophy, and more. The narration that he hears is a mix of original and quoted texts from various sources and authors. The game itself is a homage to Sexy Hiking, a 2002 B-Game classic by Jazzuo. So while this game is not based on a true story, it is based on a lot of true stories.</p>
- <h3>Q: Is this game a joke or a serious game?</h3>
-<p>A: This game is both a joke and a serious game. It is a joke because it is full of humor and absurdity that makes fun of itself and other games. It is also a serious game because it is full of meaning and depth that explores various themes and topics related to gaming and life. This game is a paradox and a contradiction that defies easy categorization and interpretation. It is a game that makes you laugh and cry, think and feel, love and hate, succeed and fail, get over it or not.</p> 197e85843d<br />
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-<h1>Dinosaur Hunter 3D Game Download: A Guide for Dino Lovers</h1>
-<p>Do you love dinosaurs? Do you want to experience the thrill of hunting them in a realistic 3D environment? If yes, then you should try Dinosaur Hunter 3D, one of the best dinosaur hunting games available on Android and iOS devices. In this article, we will tell you everything you need to know about this amazing game, including how to download and install it, how to play it, and some tips and tricks to help you become a master dinosaur hunter.</p>
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-<p>Dinosaurs are fascinating creatures that have captivated the imagination of many people for centuries. They were the dominant animals on Earth for millions of years, until they went extinct about 65 million years ago. However, thanks to modern technology, we can now bring them back to life in the form of video games. One of these games is Dinosaur Hunter 3D, a hunting simulation game that lets you explore different environments and hunt down various types of dinosaurs.</p>
-<h3>What is Dinosaur Hunter 3D?</h3>
-<p>Dinosaur Hunter 3D is a free-to-play game developed by ZG Games. It is a realistic and immersive hunting game that features stunning graphics, realistic sounds, and smooth controls. You can choose from different modes, such as survival, campaign, or free hunt, and hunt in different environments, such as jungle, desert, or snow. You can also select from a wide range of weapons and equipment, such as rifles, shotguns, bows, grenades, night vision goggles, camouflage, and more. You can hunt different kinds of dinosaurs, such as T-Rex, Velociraptor, Triceratops, Spinosaurus, and more. You can also collect coins and trophies for your achievements and use them to upgrade your weapons and equipment.</p>
-<h3>Why should you play Dinosaur Hunter 3D?</h3>
-<p>Dinosaur Hunter 3D is a game that will appeal to anyone who loves dinosaurs, hunting, or adventure. It is a game that will challenge your skills, test your reflexes, and stimulate your senses. It is a game that will make you feel like you are in a real dinosaur world, where you have to survive and hunt these majestic beasts. It is a game that will provide you with hours of fun and entertainment. Here are some of the reasons why you should play Dinosaur Hunter 3D:</p>
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-<h2>How to download and install Dinosaur Hunter 3D?</h2>
-<p>Dinosaur Hunter 3D is available for both Android and iOS devices. You can download and install it easily by following these simple steps:</p>
-<h3>For Android devices</h3>
-<h4>Step 1: Go to Google Play Store</h4>
-<p>Open the Google Play Store app on your Android device and make sure you are signed in with your Google account.</p>
-<h4>Step 2: Search for Dinosaur Hunter 3D</h4>
-<p>Type "Dinosaur Hunter 3D" in the search bar and tap on the game icon that appears in the results.</p>
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-<h4>Step 3: Tap on Install and wait for the download to finish</h4>
-<p>Tap on the green Install button and accept the permissions required by the game. Wait for the download to finish, which may take a few minutes depending on your internet speed and device storage.</p>
-<h4>Step 4: Open the game and enjoy hunting dinosaurs</h4>
-<p>Once the installation is complete, you can open the game by tapping on the Open button or by finding it in your app drawer. You can now start playing Dinosaur Hunter 3D and have fun hunting dinosaurs.</p>
-<h3>For iOS devices</h3>
-<h4>Step 1: Go to App Store</h4>
-<p>Open the App Store app on your iOS device and make sure you are signed in with your Apple ID.</p>
-<h4>Step 2: Search for Dinosaur Hunter 3D</h4>
-<p>Type "Dinosaur Hunter 3D" in the search bar and tap on the game icon that appears in the results.</p>
-<h4>Step 3: Tap on Get and wait for the download to finish</h4>
-<p>Tap on the blue Get button and enter your Apple ID password or use Touch ID or Face ID to confirm. Wait for the download to finish, which may take a few minutes depending on your internet speed and device storage.</p>
-<h4>Step 4: Open the game and enjoy hunting dinosaurs</h4>
-<p>Once the installation is complete, you can open the game by tapping on the Open button or by finding it in your home screen. You can now start playing Dinosaur Hunter 3D and have fun hunting dinosaurs.</p>
-<h2>How to play Dinosaur Hunter 3D?</h2>
-<p>Dinosaur Hunter 3D is a simple and intuitive game that anyone can play. Here are some of the basic steps to play the game:</p>
-<h3>Choose your mode and environment</h3>
-<p>When you launch the game, you will see three options: Survival, Campaign, and Free Hunt. You can choose any of them depending on your preference and mood. Survival mode is where you have to survive as long as possible against waves of dinosaurs. Campaign mode is where you have to complete different missions and objectives in various environments. Free Hunt mode is where you can hunt any dinosaur you want without any restrictions or goals. You can also choose from different environments, such as jungle, desert, or snow, each with its own challenges and scenery.</p>
-<h3>Select your weapon and equipment</h3>
-<p>Before you start hunting, you have to select your weapon and equipment from the inventory. You can choose from a variety of weapons, such as rifles, shotguns, bows, grenades, etc., each with its own advantages and disadvantages. You can also choose from different equipment, such as night vision goggles, camouflage, medkits, etc., each with its own uses and benefits. You can upgrade your weapons and equipment using coins that you earn from hunting dinosaurs.</p>
-<h3>Hunt down different types of dinosaurs</h3>
-<p>Once you are ready, you can start hunting dinosaurs in your chosen mode and environment. You will see a radar on the top left corner of your screen that shows you the location of nearby dinosaurs. You can also use binoculars to zoom in and spot them from a distance. You have to aim carefully and shoot them before they notice you or run away. You can also use grenades or other explosives to cause more damage or lure them into traps. You have to be careful not to get too close to them or they will attack you back. You can use medkits to heal yourself if you get injured.</p>
-<h3>Earn coins and trophies for your achievements</h3>
-<p>As you hunt dinosaurs, you will earn coins and trophies for your achievements. Coins are used to upgrade your weapons and equipment, while trophies are used to unlock new modes and environments. You can also compare your scores and achievements with other players on the leaderboard and challenge your friends to beat your records. You can also share your hunting screenshots and videos on social media and show off your skills.</p>
-<h2>Tips and tricks for playing Dinosaur Hunter 3D</h2>
-<p>Dinosaur Hunter 3D is a game that requires strategy, skill, and patience. Here are some tips and tricks that will help you improve your game and become a better dinosaur hunter:</p>
-<h3>Use the radar to locate your prey</h3>
-<p>The radar is a very useful tool that shows you the direction and distance of the nearest dinosaurs. You can use it to plan your approach and avoid wasting time and ammo. You can also use it to avoid dangerous dinosaurs that are too big or too fast for you to handle.</p>
-<h3>Aim for the head or the heart for a quick kill</h3>
-<p>The best way to kill a dinosaur is to aim for its vital organs, such as the head or the heart. This will cause more damage and make them die faster. You can also use the binoculars to zoom in and see where these organs are located on different dinosaurs. However, be careful not to miss or hit the wrong spot, as this will alert them and make them run away or attack you.</p>
-<h3>Avoid getting too close to the dinosaurs or they will attack you</h3>
-<p>Dinosaurs are not friendly creatures and they will not hesitate to attack you if they sense your presence. You have to keep a safe distance from them and use your weapons wisely. If you get too close, they will charge at you, bite you, or stomp on you, causing you to lose health or die. You can use grenades or other explosives to create some distance or distract them, but be careful not to hurt yourself in the process.</p>
-<h3>Upgrade your weapons and equipment to improve your performance</h3>
-<p>As you progress in the game, you will face more challenging dinosaurs that require more powerful weapons and equipment. You can use the coins that you earn from hunting dinosaurs to upgrade your weapons and equipment in the inventory. You can increase their damage, accuracy, range, capacity, reload speed, etc. You can also buy new weapons and equipment that suit your style and preference.</p>
-<h2>Conclusion</h2>
-<p>Dinosaur Hunter 3D is a game that will satisfy your curiosity and passion for dinosaurs. It is a game that will let you experience the thrill and excitement of hunting these ancient creatures in a realistic 3D environment. It is a game that will challenge your skills, test your reflexes, and stimulate your senses. It is a game that will provide you with hours of fun and entertainment. If you are looking for a game that combines adventure, action, and simulation, then Dinosaur Hunter 3D is the game for you. Download it now and start hunting dinosaurs!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Dinosaur Hunter 3D:</p>
-<ul>
-<li><b>Q: Is Dinosaur Hunter 3D free to play?</b></li>
-<li>A: Yes, Dinosaur Hunter 3D is free to download and play on both Android and iOS devices. However, it contains ads and in-app purchases that can enhance your gaming experience.</li>
-<li><b>Q: How many dinosaurs are there in Dinosaur Hunter 3D?</b></li>
-<li>A: There are more than 20 different types of dinosaurs in Dinosaur Hunter 3D, each with its own appearance, behavior, and difficulty level. Some of them are T-Rex, Velociraptor, Triceratops, Spinosaurus, Brachiosaurus, etc.</li>
-<li><b>Q: How many modes and environments are there in Dinosaur Hunter 3D?</b></li>
-<li>A: There are three modes in Dinosaur Hunter 3D: Survival, Campaign, and Free Hunt. Survival mode is where you have to survive as long as possible against waves of dinosaurs. Campaign mode is where you have to complete different missions and objectives in various environments. Free Hunt mode is where you can hunt any dinosaur you want without any restrictions or goals. There are also three environments in Dinosaur Hunter 3D: Jungle, Desert, and Snow. Each environment has its own challenges and scenery.</li>
-<li><b>Q: How do I upgrade my weapons and equipment in Dinosaur Hunter 3D?</b></li>
-<li>A: You can upgrade your weapons and equipment in the inventory using coins that you earn from hunting dinosaurs. You can increase their damage, accuracy, range, capacity, reload speed, etc. You can also buy new weapons and equipment that suit your style and preference.</li>
-<li><b>Q: How do I share my hunting screenshots and videos on social media?</b></li>
-<li>A: You can share your hunting screenshots and videos on social media by tapping on the Share button on the top right corner of your screen. You can choose from different platforms, such as Facebook, Twitter, Instagram, etc. You can also add captions and hashtags to your posts and tag your friends. You can also invite your friends to play Dinosaur Hunter 3D and challenge them to beat your scores and achievements.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/2ndelement/voicevox/test/test_word_types.py

@@ -1,9 +0,0 @@
-from unittest import TestCase
-
-from voicevox_engine.model import WordTypes
-from voicevox_engine.part_of_speech_data import part_of_speech_data
-
-
-class TestWordTypes(TestCase):
-    def test_word_types(self):
-        self.assertCountEqual(list(WordTypes), list(part_of_speech_data.keys()))

spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/__init__.py

@@ -1,2 +0,0 @@
-from .visualization_utils import show_bboxes
-from .detector import detect_faces

spaces/AUBADA-ALARABI/poetry20233/app.py

@@ -1,53 +0,0 @@
-import gc 
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
-           ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
-            ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
-              ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
-                 ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
-                   ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
-                    ,1.0, 50, 1.0, 1.0, 114 ],['.'
-                       ,1.0, 50, 1.0, 1.0, 114]]
-                     
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND 
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed 
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long) 
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) 
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
-    if not int(seed) >= 0: seed=114
-    set_seed(seed)
-    gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
-              min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
-               num_beams=5, num_return_sequences=1)[0]["generated_text"]
-    poetry =""
-    for line in gen.split('.')[:-1]:
-        poetry += line #+ "\n"
-    return poetry
-poetry = gr.Interface(fn=sayPoetry, 
-                    inputs=[
-                        gr.Textbox(label="Enter short prompt or select from examples:"), 
-                        gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control  temperature'),
-                        gr.Slider(25, 100, step=1,value=50, label='control  top k'),
-                        gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control  top p'),
-                        gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control  penalty'),
-                        gr.Number(value=139750, precision=0, label='Seed'),
-                    ], 
-                outputs=[gr.Textbox(label="Generated Poetry:")], 
-                    
-                allow_flagging='never',
-                title='Arabic Poetry Generation Demo (updated Jan. 2023)',
-                description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
-                examples=samples,
-                cache_examples=False,
-                article = notes)
-poetry.launch() # show_error = True, debug=True

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

@@ -1,106 +0,0 @@
-from __future__ import annotations
-
-import hashlib
-import time
-import uuid
-import json
-from datetime import datetime
-from aiohttp import ClientSession
-
-from ..typing import SHA256, AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-
-
-class Ails(AsyncGeneratorProvider):
-    url: str              = "https://ai.ls"
-    working               = True
-    supports_gpt_35_turbo = True
-
-    @staticmethod
-    async def create_async_generator(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator:
-        headers = {
-            "authority": "api.caipacity.com",
-            "accept": "*/*",
-            "accept-language": "en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3",
-            "authorization": "Bearer free",
-            "client-id": str(uuid.uuid4()),
-            "client-v": "0.1.278",
-            "content-type": "application/json",
-            "origin": "https://ai.ls",
-            "referer": "https://ai.ls/",
-            "sec-ch-ua": '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"Windows"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "cross-site",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36",
-            "from-url": "https://ai.ls/?chat=1"
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            timestamp = _format_timestamp(int(time.time() * 1000))
-            json_data = {
-                "model": "gpt-3.5-turbo",
-                "temperature": kwargs.get("temperature", 0.6),
-                "stream": True,
-                "messages": messages,
-                "d": datetime.now().strftime("%Y-%m-%d"),
-                "t": timestamp,
-                "s": _hash({"t": timestamp, "m": messages[-1]["content"]}),
-            }
-            async with session.post(
-                "https://api.caipacity.com/v1/chat/completions",
-                proxy=proxy,
-                json=json_data
-            ) as response:
-                response.raise_for_status()
-                start = "data: "
-                async for line in response.content:
-                    line = line.decode('utf-8')
-                    if line.startswith(start) and line != "data: [DONE]":
-                        line = line[len(start):-1]
-                        line = json.loads(line)
-                        token = line["choices"][0]["delta"].get("content")
-                        if token:
-                            if "ai.ls" in token or "ai.ci" in token:
-                                raise Exception("Response Error: " + token)
-                            yield token
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("temperature", "float"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"
-
-
-def _hash(json_data: dict[str, str]) -> SHA256:
-    base_string: str = "%s:%s:%s:%s" % (
-        json_data["t"],
-        json_data["m"],
-        "WI,2rU#_r:r~aF4aJ36[.Z(/8Rv93Rf",
-        len(json_data["m"]),
-    )
-
-    return SHA256(hashlib.sha256(base_string.encode()).hexdigest())
-
-
-def _format_timestamp(timestamp: int) -> str:
-    e = timestamp
-    n = e % 10
-    r = n + 1 if n % 2 == 0 else n
-    return str(e - n + r)

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

@@ -1,7 +0,0 @@
-// import * as Phaser from 'phaser';
-import Press from "./Press";
-
-export default function (
-    gameObject: Phaser.GameObjects.GameObject | Phaser.Scene,
-    config?: Press.IConfig
-): Press;

spaces/AlStable/AlPrompt/README.md

@@ -1,12 +0,0 @@
----
-title: Al prompt
-emoji: 🤗
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Alpaca233/SadTalker/src/face3d/util/load_mats.py

@@ -1,120 +0,0 @@
-"""This script is to load 3D face model for Deep3DFaceRecon_pytorch
-"""
-
-import numpy as np
-from PIL import Image
-from scipy.io import loadmat, savemat
-from array import array
-import os.path as osp
-
-# load expression basis
-def LoadExpBasis(bfm_folder='BFM'):
-    n_vertex = 53215
-    Expbin = open(osp.join(bfm_folder, 'Exp_Pca.bin'), 'rb')
-    exp_dim = array('i')
-    exp_dim.fromfile(Expbin, 1)
-    expMU = array('f')
-    expPC = array('f')
-    expMU.fromfile(Expbin, 3*n_vertex)
-    expPC.fromfile(Expbin, 3*exp_dim[0]*n_vertex)
-    Expbin.close()
-
-    expPC = np.array(expPC)
-    expPC = np.reshape(expPC, [exp_dim[0], -1])
-    expPC = np.transpose(expPC)
-
-    expEV = np.loadtxt(osp.join(bfm_folder, 'std_exp.txt'))
-
-    return expPC, expEV
-
-
-# transfer original BFM09 to our face model
-def transferBFM09(bfm_folder='BFM'):
-    print('Transfer BFM09 to BFM_model_front......')
-    original_BFM = loadmat(osp.join(bfm_folder, '01_MorphableModel.mat'))
-    shapePC = original_BFM['shapePC']  # shape basis
-    shapeEV = original_BFM['shapeEV']  # corresponding eigen value
-    shapeMU = original_BFM['shapeMU']  # mean face
-    texPC = original_BFM['texPC']  # texture basis
-    texEV = original_BFM['texEV']  # eigen value
-    texMU = original_BFM['texMU']  # mean texture
-
-    expPC, expEV = LoadExpBasis(bfm_folder)
-
-    # transfer BFM09 to our face model
-
-    idBase = shapePC*np.reshape(shapeEV, [-1, 199])
-    idBase = idBase/1e5  # unify the scale to decimeter
-    idBase = idBase[:, :80]  # use only first 80 basis
-
-    exBase = expPC*np.reshape(expEV, [-1, 79])
-    exBase = exBase/1e5  # unify the scale to decimeter
-    exBase = exBase[:, :64]  # use only first 64 basis
-
-    texBase = texPC*np.reshape(texEV, [-1, 199])
-    texBase = texBase[:, :80]  # use only first 80 basis
-
-    # our face model is cropped along face landmarks and contains only 35709 vertex.
-    # original BFM09 contains 53490 vertex, and expression basis provided by Guo et al. contains 53215 vertex.
-    # thus we select corresponding vertex to get our face model.
-
-    index_exp = loadmat(osp.join(bfm_folder, 'BFM_front_idx.mat'))
-    index_exp = index_exp['idx'].astype(np.int32) - 1  # starts from 0 (to 53215)
-
-    index_shape = loadmat(osp.join(bfm_folder, 'BFM_exp_idx.mat'))
-    index_shape = index_shape['trimIndex'].astype(
-        np.int32) - 1  # starts from 0 (to 53490)
-    index_shape = index_shape[index_exp]
-
-    idBase = np.reshape(idBase, [-1, 3, 80])
-    idBase = idBase[index_shape, :, :]
-    idBase = np.reshape(idBase, [-1, 80])
-
-    texBase = np.reshape(texBase, [-1, 3, 80])
-    texBase = texBase[index_shape, :, :]
-    texBase = np.reshape(texBase, [-1, 80])
-
-    exBase = np.reshape(exBase, [-1, 3, 64])
-    exBase = exBase[index_exp, :, :]
-    exBase = np.reshape(exBase, [-1, 64])
-
-    meanshape = np.reshape(shapeMU, [-1, 3])/1e5
-    meanshape = meanshape[index_shape, :]
-    meanshape = np.reshape(meanshape, [1, -1])
-
-    meantex = np.reshape(texMU, [-1, 3])
-    meantex = meantex[index_shape, :]
-    meantex = np.reshape(meantex, [1, -1])
-
-    # other info contains triangles, region used for computing photometric loss,
-    # region used for skin texture regularization, and 68 landmarks index etc.
-    other_info = loadmat(osp.join(bfm_folder, 'facemodel_info.mat'))
-    frontmask2_idx = other_info['frontmask2_idx']
-    skinmask = other_info['skinmask']
-    keypoints = other_info['keypoints']
-    point_buf = other_info['point_buf']
-    tri = other_info['tri']
-    tri_mask2 = other_info['tri_mask2']
-
-    # save our face model
-    savemat(osp.join(bfm_folder, 'BFM_model_front.mat'), {'meanshape': meanshape, 'meantex': meantex, 'idBase': idBase, 'exBase': exBase, 'texBase': texBase,
-            'tri': tri, 'point_buf': point_buf, 'tri_mask2': tri_mask2, 'keypoints': keypoints, 'frontmask2_idx': frontmask2_idx, 'skinmask': skinmask})
-
-
-# load landmarks for standard face, which is used for image preprocessing
-def load_lm3d(bfm_folder):
-
-    Lm3D = loadmat(osp.join(bfm_folder, 'similarity_Lm3D_all.mat'))
-    Lm3D = Lm3D['lm']
-
-    # calculate 5 facial landmarks using 68 landmarks
-    lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
-    Lm3D = np.stack([Lm3D[lm_idx[0], :], np.mean(Lm3D[lm_idx[[1, 2]], :], 0), np.mean(
-        Lm3D[lm_idx[[3, 4]], :], 0), Lm3D[lm_idx[5], :], Lm3D[lm_idx[6], :]], axis=0)
-    Lm3D = Lm3D[[1, 2, 0, 3, 4], :]
-
-    return Lm3D
-
-
-if __name__ == '__main__':
-    transferBFM09()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/test_examples.py

@@ -1,1422 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc..
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import logging
-import os
-import shutil
-import subprocess
-import sys
-import tempfile
-import unittest
-from typing import List
-
-import torch
-from accelerate.utils import write_basic_config
-
-from diffusers import DiffusionPipeline, UNet2DConditionModel
-
-
-logging.basicConfig(level=logging.DEBUG)
-
-logger = logging.getLogger()
-
-
-# These utils relate to ensuring the right error message is received when running scripts
-class SubprocessCallException(Exception):
-    pass
-
-
-def run_command(command: List[str], return_stdout=False):
-    """
-    Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture
-    if an error occurred while running `command`
-    """
-    try:
-        output = subprocess.check_output(command, stderr=subprocess.STDOUT)
-        if return_stdout:
-            if hasattr(output, "decode"):
-                output = output.decode("utf-8")
-            return output
-    except subprocess.CalledProcessError as e:
-        raise SubprocessCallException(
-            f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
-        ) from e
-
-
-stream_handler = logging.StreamHandler(sys.stdout)
-logger.addHandler(stream_handler)
-
-
-class ExamplesTestsAccelerate(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        super().setUpClass()
-        cls._tmpdir = tempfile.mkdtemp()
-        cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")
-
-        write_basic_config(save_location=cls.configPath)
-        cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]
-
-    @classmethod
-    def tearDownClass(cls):
-        super().tearDownClass()
-        shutil.rmtree(cls._tmpdir)
-
-    def test_train_unconditional(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 2
-                --num_epochs 1
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                """.split()
-
-            run_command(self._launch_args + test_args, return_stdout=True)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_textual_inversion(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "learned_embeds.bin")))
-
-    def test_dreambooth(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_dreambooth_if(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --pre_compute_text_embeddings
-                --tokenizer_max_length=77
-                --text_encoder_use_attention_mask
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_dreambooth_checkpointing(self):
-        instance_prompt = "photo"
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 5, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4
-
-            initial_run_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt {instance_prompt}
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 5
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            # check can run the original fully trained output pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(instance_prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
-
-            # check can run an intermediate checkpoint
-            unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
-            pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
-            pipe(instance_prompt, num_inference_steps=2)
-
-            # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
-            shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
-
-            # Run training script for 7 total steps resuming from checkpoint 4
-
-            resume_run_args = f"""
-                examples/dreambooth/train_dreambooth.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt {instance_prompt}
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-4
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check can run new fully trained pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(instance_prompt, num_inference_steps=2)
-
-            # check old checkpoints do not exist
-            self.assertFalse(os.path.isdir(os.path.join(tmpdir, "checkpoint-2")))
-
-            # check new checkpoints exist
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-4")))
-            self.assertTrue(os.path.isdir(os.path.join(tmpdir, "checkpoint-6")))
-
-    def test_dreambooth_lora(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.bin")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = torch.load(os.path.join(tmpdir, "pytorch_lora_weights.bin"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` in their names.
-            starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_unet)
-
-    def test_dreambooth_lora_with_text_encoder(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --train_text_encoder
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.bin")))
-
-            # check `text_encoder` is present at all.
-            lora_state_dict = torch.load(os.path.join(tmpdir, "pytorch_lora_weights.bin"))
-            keys = lora_state_dict.keys()
-            is_text_encoder_present = any(k.startswith("text_encoder") for k in keys)
-            self.assertTrue(is_text_encoder_present)
-
-            # the names of the keys of the state dict should either start with `unet`
-            # or `text_encoder`.
-            is_correct_naming = all(k.startswith("unet") or k.startswith("text_encoder") for k in keys)
-            self.assertTrue(is_correct_naming)
-
-    def test_dreambooth_lora_if_model(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-if-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --pre_compute_text_embeddings
-                --tokenizer_max_length=77
-                --text_encoder_use_attention_mask
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.bin")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = torch.load(os.path.join(tmpdir, "pytorch_lora_weights.bin"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` in their names.
-            starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_unet)
-
-    def test_dreambooth_lora_sdxl(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.bin")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = torch.load(os.path.join(tmpdir, "pytorch_lora_weights.bin"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` in their names.
-            starts_with_unet = all(key.startswith("unet") for key in lora_state_dict.keys())
-            self.assertTrue(starts_with_unet)
-
-    def test_dreambooth_lora_sdxl_with_text_encoder(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/dreambooth/train_dreambooth_lora_sdxl.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-xl-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt photo
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --train_text_encoder
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.bin")))
-
-            # make sure the state_dict has the correct naming in the parameters.
-            lora_state_dict = torch.load(os.path.join(tmpdir, "pytorch_lora_weights.bin"))
-            is_lora = all("lora" in k for k in lora_state_dict.keys())
-            self.assertTrue(is_lora)
-
-            # when not training the text encoder, all the parameters in the state dict should start
-            # with `"unet"` or `"text_encoder"` or `"text_encoder_2"` in their names.
-            keys = lora_state_dict.keys()
-            starts_with_unet = all(
-                k.startswith("unet") or k.startswith("text_encoder") or k.startswith("text_encoder_2") for k in keys
-            )
-            self.assertTrue(starts_with_unet)
-
-    def test_custom_diffusion(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/custom_diffusion/train_custom_diffusion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --instance_data_dir docs/source/en/imgs
-                --instance_prompt <new1>
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 1.0e-05
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --modifier_token <new1>
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_custom_diffusion_weights.bin")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "<new1>.bin")))
-
-    def test_text_to_image(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 2
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                """.split()
-
-            run_command(self._launch_args + test_args)
-            # save_pretrained smoke test
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-
-    def test_text_to_image_checkpointing(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 5, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 5
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4"},
-            )
-
-            # check can run an intermediate checkpoint
-            unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
-            pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
-            shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
-
-            # Run training script for 7 total steps resuming from checkpoint 4
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-4
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check can run new fully trained pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {
-                    # no checkpoint-2 -> check old checkpoints do not exist
-                    # check new checkpoints exist
-                    "checkpoint-4",
-                    "checkpoint-6",
-                },
-            )
-
-    def test_text_to_image_checkpointing_use_ema(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 5, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 5
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --use_ema
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4"},
-            )
-
-            # check can run an intermediate checkpoint
-            unet = UNet2DConditionModel.from_pretrained(tmpdir, subfolder="checkpoint-2/unet")
-            pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, unet=unet, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # Remove checkpoint 2 so that we can check only later checkpoints exist after resuming
-            shutil.rmtree(os.path.join(tmpdir, "checkpoint-2"))
-
-            # Run training script for 7 total steps resuming from checkpoint 4
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-4
-                --use_ema
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check can run new fully trained pipeline
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {
-                    # no checkpoint-2 -> check old checkpoints do not exist
-                    # check new checkpoints exist
-                    "checkpoint-4",
-                    "checkpoint-6",
-                },
-            )
-
-    def test_text_to_image_checkpointing_checkpoints_total_limit(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
-            # Should create checkpoints at steps 2, 4, 6
-            # with checkpoint at step 2 deleted
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_text_to_image_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 9, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4, 6, 8
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 9
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            # resume and we should try to checkpoint at 10, where we'll have to remove
-            # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 11
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-8
-                --checkpoints_total_limit=3
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, safety_checker=None)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-    def test_text_to_image_lora_checkpointing_checkpoints_total_limit(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 7, checkpointing_steps == 2, checkpoints_total_limit == 2
-            # Should create checkpoints at steps 2, 4, 6
-            # with checkpoint at step 2 deleted
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 7
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --seed=0
-                --num_validation_images=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
-            )
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_text_to_image_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        pretrained_model_name_or_path = "hf-internal-testing/tiny-stable-diffusion-pipe"
-        prompt = "a prompt"
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # Run training script with checkpointing
-            # max_train_steps == 9, checkpointing_steps == 2
-            # Should create checkpoints at steps 2, 4, 6, 8
-
-            initial_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 9
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --seed=0
-                --num_validation_images=0
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
-            )
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            # resume and we should try to checkpoint at 10, where we'll have to remove
-            # checkpoint-2 and checkpoint-4 instead of just a single previous checkpoint
-
-            resume_run_args = f"""
-                examples/text_to_image/train_text_to_image_lora.py
-                --pretrained_model_name_or_path {pretrained_model_name_or_path}
-                --dataset_name hf-internal-testing/dummy_image_text_data
-                --resolution 64
-                --center_crop
-                --random_flip
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 11
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=2
-                --resume_from_checkpoint=checkpoint-8
-                --checkpoints_total_limit=3
-                --seed=0
-                --num_validation_images=0
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            pipe = DiffusionPipeline.from_pretrained(
-                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None
-            )
-            pipe.load_lora_weights(tmpdir)
-            pipe(prompt, num_inference_steps=2)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-    def test_unconditional_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            initial_run_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 1
-                --num_epochs 1
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                # checkpoint-2 should have been deleted
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_unconditional_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            initial_run_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 1
-                --num_epochs 1
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                --checkpointing_steps=1
-                """.split()
-
-            run_command(self._launch_args + initial_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-1", "checkpoint-2", "checkpoint-3", "checkpoint-4", "checkpoint-5", "checkpoint-6"},
-            )
-
-            resume_run_args = f"""
-                examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name hf-internal-testing/dummy_image_class_data
-                --model_config_name_or_path diffusers/ddpm_dummy
-                --resolution 64
-                --output_dir {tmpdir}
-                --train_batch_size 1
-                --num_epochs 2
-                --gradient_accumulation_steps 1
-                --ddpm_num_inference_steps 2
-                --learning_rate 1e-3
-                --lr_warmup_steps 5
-                --resume_from_checkpoint=checkpoint-6
-                --checkpointing_steps=2
-                --checkpoints_total_limit=3
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-8", "checkpoint-10", "checkpoint-12"},
-            )
-
-    def test_textual_inversion_checkpointing(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 3
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=1
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-3"},
-            )
-
-    def test_textual_inversion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 3
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=1
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-1", "checkpoint-2", "checkpoint-3"},
-            )
-
-            resume_run_args = f"""
-                examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
-                --train_data_dir docs/source/en/imgs
-                --learnable_property object
-                --placeholder_token <cat-toy>
-                --initializer_token a
-                --validation_prompt <cat-toy>
-                --validation_steps 1
-                --save_steps 1
-                --num_vectors 2
-                --resolution 64
-                --train_batch_size 1
-                --gradient_accumulation_steps 1
-                --max_train_steps 4
-                --learning_rate 5.0e-04
-                --scale_lr
-                --lr_scheduler constant
-                --lr_warmup_steps 0
-                --output_dir {tmpdir}
-                --checkpointing_steps=1
-                --resume_from_checkpoint=checkpoint-3
-                --checkpoints_total_limit=2
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-3", "checkpoint-4"},
-            )
-
-    def test_instruct_pix2pix_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/instruct_pix2pix/train_instruct_pix2pix.py
-                --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name=hf-internal-testing/instructpix2pix-10-samples
-                --resolution=64
-                --random_flip
-                --train_batch_size=1
-                --max_train_steps=7
-                --checkpointing_steps=2
-                --checkpoints_total_limit=2
-                --output_dir {tmpdir}
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-                examples/instruct_pix2pix/train_instruct_pix2pix.py
-                --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name=hf-internal-testing/instructpix2pix-10-samples
-                --resolution=64
-                --random_flip
-                --train_batch_size=1
-                --max_train_steps=9
-                --checkpointing_steps=2
-                --output_dir {tmpdir}
-                --seed=0
-                """.split()
-
-            run_command(self._launch_args + test_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-                examples/instruct_pix2pix/train_instruct_pix2pix.py
-                --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-                --dataset_name=hf-internal-testing/instructpix2pix-10-samples
-                --resolution=64
-                --random_flip
-                --train_batch_size=1
-                --max_train_steps=11
-                --checkpointing_steps=2
-                --output_dir {tmpdir}
-                --seed=0
-                --resume_from_checkpoint=checkpoint-8
-                --checkpoints_total_limit=3
-                """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            # check checkpoint directories exist
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-    def test_dreambooth_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_dreambooth_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/dreambooth/train_dreambooth.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-    def test_dreambooth_lora_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth_lora.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_dreambooth_lora_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/dreambooth/train_dreambooth_lora.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/dreambooth/train_dreambooth_lora.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=prompt
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )
-
-    def test_controlnet_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/controlnet/train_controlnet.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_controlnet_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/controlnet/train_controlnet.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/controlnet/train_controlnet.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-8", "checkpoint-10", "checkpoint-12"},
-            )
-
-    def test_controlnet_sdxl(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/controlnet/train_controlnet_sdxl.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
-            --dataset_name=hf-internal-testing/fill10
-            --output_dir={tmpdir}
-            --resolution=64
-            --train_batch_size=1
-            --gradient_accumulation_steps=1
-            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet-sdxl
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.bin")))
-
-    def test_custom_diffusion_checkpointing_checkpoints_total_limit(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/custom_diffusion/train_custom_diffusion.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=<new1>
-            --resolution=64
-            --train_batch_size=1
-            --modifier_token=<new1>
-            --dataloader_num_workers=0
-            --max_train_steps=6
-            --checkpoints_total_limit=2
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-4", "checkpoint-6"},
-            )
-
-    def test_custom_diffusion_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            test_args = f"""
-            examples/custom_diffusion/train_custom_diffusion.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=<new1>
-            --resolution=64
-            --train_batch_size=1
-            --modifier_token=<new1>
-            --dataloader_num_workers=0
-            --max_train_steps=9
-            --checkpointing_steps=2
-            """.split()
-
-            run_command(self._launch_args + test_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
-            )
-
-            resume_run_args = f"""
-            examples/custom_diffusion/train_custom_diffusion.py
-            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-pipe
-            --instance_data_dir=docs/source/en/imgs
-            --output_dir={tmpdir}
-            --instance_prompt=<new1>
-            --resolution=64
-            --train_batch_size=1
-            --modifier_token=<new1>
-            --dataloader_num_workers=0
-            --max_train_steps=11
-            --checkpointing_steps=2
-            --resume_from_checkpoint=checkpoint-8
-            --checkpoints_total_limit=3
-            """.split()
-
-            run_command(self._launch_args + resume_run_args)
-
-            self.assertEqual(
-                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
-                {"checkpoint-6", "checkpoint-8", "checkpoint-10"},
-            )

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/README.md

@@ -1,3 +0,0 @@
-# Schedulers
-
-For more information on the schedulers, please refer to the [docs](https://huggingface.co/docs/diffusers/api/schedulers/overview).

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/trident_faster_rcnn.py

@@ -1,66 +0,0 @@
-from ..builder import DETECTORS
-from .faster_rcnn import FasterRCNN
-
-
-@DETECTORS.register_module()
-class TridentFasterRCNN(FasterRCNN):
-    """Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_"""
-
-    def __init__(self,
-                 backbone,
-                 rpn_head,
-                 roi_head,
-                 train_cfg,
-                 test_cfg,
-                 neck=None,
-                 pretrained=None):
-
-        super(TridentFasterRCNN, self).__init__(
-            backbone=backbone,
-            neck=neck,
-            rpn_head=rpn_head,
-            roi_head=roi_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained)
-        assert self.backbone.num_branch == self.roi_head.num_branch
-        assert self.backbone.test_branch_idx == self.roi_head.test_branch_idx
-        self.num_branch = self.backbone.num_branch
-        self.test_branch_idx = self.backbone.test_branch_idx
-
-    def simple_test(self, img, img_metas, proposals=None, rescale=False):
-        """Test without augmentation."""
-        assert self.with_bbox, 'Bbox head must be implemented.'
-        x = self.extract_feat(img)
-        if proposals is None:
-            num_branch = (self.num_branch if self.test_branch_idx == -1 else 1)
-            trident_img_metas = img_metas * num_branch
-            proposal_list = self.rpn_head.simple_test_rpn(x, trident_img_metas)
-        else:
-            proposal_list = proposals
-
-        return self.roi_head.simple_test(
-            x, proposal_list, trident_img_metas, rescale=rescale)
-
-    def aug_test(self, imgs, img_metas, rescale=False):
-        """Test with augmentations.
-
-        If rescale is False, then returned bboxes and masks will fit the scale
-        of imgs[0].
-        """
-        x = self.extract_feats(imgs)
-        num_branch = (self.num_branch if self.test_branch_idx == -1 else 1)
-        trident_img_metas = [img_metas * num_branch for img_metas in img_metas]
-        proposal_list = self.rpn_head.aug_test_rpn(x, trident_img_metas)
-        return self.roi_head.aug_test(
-            x, proposal_list, img_metas, rescale=rescale)
-
-    def forward_train(self, img, img_metas, gt_bboxes, gt_labels, **kwargs):
-        """make copies of img and gts to fit multi-branch."""
-        trident_gt_bboxes = tuple(gt_bboxes * self.num_branch)
-        trident_gt_labels = tuple(gt_labels * self.num_branch)
-        trident_img_metas = tuple(img_metas * self.num_branch)
-
-        return super(TridentFasterRCNN,
-                     self).forward_train(img, trident_img_metas,
-                                         trident_gt_bboxes, trident_gt_labels)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/css/chat_style-cai-chat.css

@@ -1,59 +0,0 @@
-.message {
-    display: grid;
-    grid-template-columns: 60px minmax(0, 1fr);
-    padding-bottom: 25px;
-    font-size: 15px;
-    font-family: 'Noto Sans', Helvetica, Arial, sans-serif;
-    line-height: 23px !important;
-}
-
-.circle-you {
-    width: 50px;
-    height: 50px;
-    background-color: rgb(238, 78, 59);
-    border-radius: 50%;
-}
-
-.circle-bot {
-    width: 50px;
-    height: 50px;
-    background-color: rgb(59, 78, 244);
-    border-radius: 50%;
-}
-
-.circle-bot img,
-.circle-you img {
-    border-radius: 50%;
-    width: 100%;
-    height: 100%;
-    object-fit: cover;
-}
-
-.text p {
-    margin-top: 5px;
-}
-
-.username {
-    font-weight: bold;
-}
-
-.message-body img {
-    max-width: 300px;
-    max-height: 300px;
-    border-radius: 20px;
-}
-
-.message-body p {
-    margin-bottom: 0 !important;
-    font-size: 15px !important;
-    line-height: 23px !important;
-}
-
-.dark .message-body p em {
-    color: rgb(138, 138, 138) !important;
-}
-
-.message-body p em {
-    color: rgb(110, 110, 110) !important;
-    font-weight: 500;
-}

spaces/Anustup/NS_AI_LABS/tests/vad_test.py

@@ -1,66 +0,0 @@
-import pprint
-import unittest
-import numpy as np
-import sys
-
-sys.path.append('../NS_AI_LABS')
-
-from src.vad import AbstractTranscription, VadSileroTranscription
-
-class TestVad(unittest.TestCase):
-    def __init__(self, *args, **kwargs):
-        super(TestVad, self).__init__(*args, **kwargs)
-        self.transcribe_calls = []
-
-    def test_transcript(self):
-        mock = MockVadTranscription()
-
-        self.transcribe_calls.clear()
-        result = mock.transcribe("mock", lambda segment : self.transcribe_segments(segment))
-
-        self.assertListEqual(self.transcribe_calls, [ 
-            [30, 30],
-            [100, 100]
-        ])
-
-        self.assertListEqual(result['segments'],
-            [{'end': 50.0, 'start': 40.0, 'text': 'Hello world '},
-            {'end': 120.0, 'start': 110.0, 'text': 'Hello world '}]
-        )
-
-    def transcribe_segments(self, segment):
-        self.transcribe_calls.append(segment.tolist())
-
-        # Dummy text
-        return {
-            'text': "Hello world ",
-            'segments': [
-                {
-                    "start": 10.0,
-                    "end": 20.0,
-                    "text": "Hello world "
-                }   
-            ],
-            'language': ""
-        }
-
-class MockVadTranscription(AbstractTranscription):
-    def __init__(self):
-        super().__init__()
-
-    def get_audio_segment(self, str, start_time: str = None, duration: str = None):
-        start_time_seconds = float(start_time.removesuffix("s"))
-        duration_seconds = float(duration.removesuffix("s"))
-
-        # For mocking, this just returns a simple numppy array
-        return np.array([start_time_seconds, duration_seconds], dtype=np.float64)
-
-    def get_transcribe_timestamps(self, audio: str):
-        result = []
-
-        result.append( {  'start': 30, 'end': 60 } )
-        result.append( {  'start': 100, 'end': 200 } )
-        return result
-
-if __name__ == '__main__':
-    unittest.main()

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/stable_diffusion_video/image_generation.py

@@ -1,363 +0,0 @@
-import json
-import math
-import random
-import time
-from pathlib import Path
-from uuid import uuid4
-
-import torch
-from diffusers import __version__ as diffusers_version
-from huggingface_hub import CommitOperationAdd, create_commit, create_repo
-
-from .upsampling import RealESRGANModel
-from .utils import pad_along_axis
-
-
-def get_all_files(root: Path):
-    dirs = [root]
-    while len(dirs) > 0:
-        dir = dirs.pop()
-        for candidate in dir.iterdir():
-            if candidate.is_file():
-                yield candidate
-            if candidate.is_dir():
-                dirs.append(candidate)
-
-
-def get_groups_of_n(n: int, iterator):
-    assert n > 1
-    buffer = []
-    for elt in iterator:
-        if len(buffer) == n:
-            yield buffer
-            buffer = []
-        buffer.append(elt)
-    if len(buffer) != 0:
-        yield buffer
-
-
-def upload_folder_chunked(
-    repo_id: str,
-    upload_dir: Path,
-    n: int = 100,
-    private: bool = False,
-    create_pr: bool = False,
-):
-    """Upload a folder to the Hugging Face Hub in chunks of n files at a time.
-    Args:
-        repo_id (str): The repo id to upload to.
-        upload_dir (Path): The directory to upload.
-        n (int, *optional*, defaults to 100): The number of files to upload at a time.
-        private (bool, *optional*): Whether to upload the repo as private.
-        create_pr (bool, *optional*): Whether to create a PR after uploading instead of commiting directly.
-    """
-
-    url = create_repo(repo_id, exist_ok=True, private=private, repo_type="dataset")
-    print(f"Uploading files to: {url}")
-
-    root = Path(upload_dir)
-    if not root.exists():
-        raise ValueError(f"Upload directory {root} does not exist.")
-
-    for i, file_paths in enumerate(get_groups_of_n(n, get_all_files(root))):
-        print(f"Committing {file_paths}")
-        operations = [
-            CommitOperationAdd(
-                path_in_repo=f"{file_path.parent.name}/{file_path.name}",
-                path_or_fileobj=str(file_path),
-            )
-            for file_path in file_paths
-        ]
-        create_commit(
-            repo_id=repo_id,
-            operations=operations,
-            commit_message=f"Upload part {i}",
-            repo_type="dataset",
-            create_pr=create_pr,
-        )
-
-
-def generate_input_batches(pipeline, prompts, seeds, batch_size, height, width):
-    if len(prompts) != len(seeds):
-        raise ValueError("Number of prompts and seeds must be equal.")
-
-    embeds_batch, noise_batch = None, None
-    batch_idx = 0
-    for i, (prompt, seed) in enumerate(zip(prompts, seeds)):
-        embeds = pipeline.embed_text(prompt)
-        noise = torch.randn(
-            (1, pipeline.unet.in_channels, height // 8, width // 8),
-            device=pipeline.device,
-            generator=torch.Generator(device="cpu" if pipeline.device.type == "mps" else pipeline.device).manual_seed(
-                seed
-            ),
-        )
-        embeds_batch = embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
-        noise_batch = noise if noise_batch is None else torch.cat([noise_batch, noise])
-        batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == len(prompts)
-        if not batch_is_ready:
-            continue
-        yield batch_idx, embeds_batch.type(torch.cuda.HalfTensor), noise_batch.type(torch.cuda.HalfTensor)
-        batch_idx += 1
-        del embeds_batch, noise_batch
-        torch.cuda.empty_cache()
-        embeds_batch, noise_batch = None, None
-
-
-def generate_images(
-    pipeline,
-    prompt,
-    batch_size=1,
-    num_batches=1,
-    seeds=None,
-    num_inference_steps=50,
-    guidance_scale=7.5,
-    output_dir="./images",
-    image_file_ext=".jpg",
-    upsample=False,
-    height=512,
-    width=512,
-    eta=0.0,
-    push_to_hub=False,
-    repo_id=None,
-    private=False,
-    create_pr=False,
-    name=None,
-):
-    """Generate images using the StableDiffusion pipeline.
-    Args:
-        pipeline (StableDiffusionWalkPipeline): The StableDiffusion pipeline instance.
-        prompt (str): The prompt to use for the image generation.
-        batch_size (int, *optional*, defaults to 1): The batch size to use for image generation.
-        num_batches (int, *optional*, defaults to 1): The number of batches to generate.
-        seeds (list[int], *optional*): The seeds to use for the image generation.
-        num_inference_steps (int, *optional*, defaults to 50): The number of inference steps to take.
-        guidance_scale (float, *optional*, defaults to 7.5): The guidance scale to use for image generation.
-        output_dir (str, *optional*, defaults to "./images"): The output directory to save the images to.
-        image_file_ext (str, *optional*, defaults to '.jpg'): The image file extension to use.
-        upsample (bool, *optional*, defaults to False): Whether to upsample the images.
-        height (int, *optional*, defaults to 512): The height of the images to generate.
-        width (int, *optional*, defaults to 512): The width of the images to generate.
-        eta (float, *optional*, defaults to 0.0): The eta parameter to use for image generation.
-        push_to_hub (bool, *optional*, defaults to False): Whether to push the generated images to the Hugging Face Hub.
-        repo_id (str, *optional*): The repo id to push the images to.
-        private (bool, *optional*): Whether to push the repo as private.
-        create_pr (bool, *optional*): Whether to create a PR after pushing instead of commiting directly.
-        name (str, *optional*, defaults to current timestamp str): The name of the sub-directory of
-            output_dir to save the images to.
-    """
-    if push_to_hub:
-        if repo_id is None:
-            raise ValueError("Must provide repo_id if push_to_hub is True.")
-
-    name = name or time.strftime("%Y%m%d-%H%M%S")
-    save_path = Path(output_dir) / name
-    save_path.mkdir(exist_ok=False, parents=True)
-    prompt_config_path = save_path / "prompt_config.json"
-
-    num_images = batch_size * num_batches
-    seeds = seeds or [random.choice(list(range(0, 9999999))) for _ in range(num_images)]
-    if len(seeds) != num_images:
-        raise ValueError("Number of seeds must be equal to batch_size * num_batches.")
-
-    if upsample:
-        if getattr(pipeline, "upsampler", None) is None:
-            pipeline.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
-        pipeline.upsampler.to(pipeline.device)
-
-    cfg = dict(
-        prompt=prompt,
-        guidance_scale=guidance_scale,
-        eta=eta,
-        num_inference_steps=num_inference_steps,
-        upsample=upsample,
-        height=height,
-        width=width,
-        scheduler=dict(pipeline.scheduler.config),
-        tiled=pipeline.tiled,
-        diffusers_version=diffusers_version,
-        device_name=torch.cuda.get_device_name(0) if torch.cuda.is_available() else "unknown",
-    )
-    prompt_config_path.write_text(json.dumps(cfg, indent=2, sort_keys=False))
-
-    frame_index = 0
-    frame_filepaths = []
-    for batch_idx, embeds, noise in generate_input_batches(
-        pipeline, [prompt] * num_images, seeds, batch_size, height, width
-    ):
-        print(f"Generating batch {batch_idx}")
-
-        outputs = pipeline(
-            text_embeddings=embeds,
-            latents=noise,
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
-            eta=eta,
-            height=height,
-            width=width,
-            output_type="pil" if not upsample else "numpy",
-        )["images"]
-        if upsample:
-            images = []
-            for output in outputs:
-                images.append(pipeline.upsampler(output))
-        else:
-            images = outputs
-
-        for image in images:
-            frame_filepath = save_path / f"{seeds[frame_index]}{image_file_ext}"
-            image.save(frame_filepath)
-            frame_filepaths.append(str(frame_filepath))
-            frame_index += 1
-
-    return frame_filepaths
-
-    if push_to_hub:
-        upload_folder_chunked(repo_id, save_path, private=private, create_pr=create_pr)
-
-
-def generate_images_flax(
-    pipeline,
-    params,
-    prompt,
-    batch_size=1,
-    num_batches=1,
-    seeds=None,
-    num_inference_steps=50,
-    guidance_scale=7.5,
-    output_dir="./images",
-    image_file_ext=".jpg",
-    upsample=False,
-    height=512,
-    width=512,
-    push_to_hub=False,
-    repo_id=None,
-    private=False,
-    create_pr=False,
-    name=None,
-):
-    import jax
-    from flax.training.common_utils import shard
-
-    """Generate images using the StableDiffusion pipeline.
-    Args:
-        pipeline (StableDiffusionWalkPipeline): The StableDiffusion pipeline instance.
-        params (`Union[Dict, FrozenDict]`): The model parameters.
-        prompt (str): The prompt to use for the image generation.
-        batch_size (int, *optional*, defaults to 1): The batch size to use for image generation.
-        num_batches (int, *optional*, defaults to 1): The number of batches to generate.
-        seeds (int, *optional*): The seed to use for the image generation.
-        num_inference_steps (int, *optional*, defaults to 50): The number of inference steps to take.
-        guidance_scale (float, *optional*, defaults to 7.5): The guidance scale to use for image generation.
-        output_dir (str, *optional*, defaults to "./images"): The output directory to save the images to.
-        image_file_ext (str, *optional*, defaults to '.jpg'): The image file extension to use.
-        upsample (bool, *optional*, defaults to False): Whether to upsample the images.
-        height (int, *optional*, defaults to 512): The height of the images to generate.
-        width (int, *optional*, defaults to 512): The width of the images to generate.
-        push_to_hub (bool, *optional*, defaults to False): Whether to push the generated images to the Hugging Face Hub.
-        repo_id (str, *optional*): The repo id to push the images to.
-        private (bool, *optional*): Whether to push the repo as private.
-        create_pr (bool, *optional*): Whether to create a PR after pushing instead of commiting directly.
-        name (str, *optional*, defaults to current timestamp str): The name of the sub-directory of
-            output_dir to save the images to.
-    """
-    if push_to_hub:
-        if repo_id is None:
-            raise ValueError("Must provide repo_id if push_to_hub is True.")
-
-    name = name or time.strftime("%Y%m%d-%H%M%S")
-    save_path = Path(output_dir) / name
-    save_path.mkdir(exist_ok=False, parents=True)
-    prompt_config_path = save_path / "prompt_config.json"
-
-    num_images = batch_size * num_batches
-    seeds = seeds or random.choice(list(range(0, 9999999)))
-    prng_seed = jax.random.PRNGKey(seeds)
-
-    if upsample:
-        if getattr(pipeline, "upsampler", None) is None:
-            pipeline.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
-            if not torch.cuda.is_available():
-                print("Upsampling is recommended to be done on a GPU, as it is very slow on CPU")
-            else:
-                pipeline.upsampler = pipeline.upsampler.cuda()
-
-    cfg = dict(
-        prompt=prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        upsample=upsample,
-        height=height,
-        width=width,
-        scheduler=dict(pipeline.scheduler.config),
-        # tiled=pipeline.tiled,
-        diffusers_version=diffusers_version,
-        device_name=torch.cuda.get_device_name(0) if torch.cuda.is_available() else "unknown",
-    )
-    prompt_config_path.write_text(json.dumps(cfg, indent=2, sort_keys=False))
-
-    NUM_TPU_CORES = jax.device_count()
-    jit = True  # force jit, assume params are already sharded
-    batch_size_total = NUM_TPU_CORES * batch_size if jit else batch_size
-
-    def generate_input_batches(prompts, batch_size):
-        prompt_batch = None
-        for batch_idx in range(math.ceil(len(prompts) / batch_size)):
-            prompt_batch = prompts[batch_idx * batch_size : (batch_idx + 1) * batch_size]
-            yield batch_idx, prompt_batch
-
-    frame_index = 0
-    frame_filepaths = []
-    for batch_idx, prompt_batch in generate_input_batches([prompt] * num_images, batch_size_total):
-        # This batch size correspond to each TPU core, so we are generating batch_size * NUM_TPU_CORES images
-        print(f"Generating batches: {batch_idx*NUM_TPU_CORES} - {min((batch_idx+1)*NUM_TPU_CORES, num_batches)}")
-        prompt_ids_batch = pipeline.prepare_inputs(prompt_batch)
-        prng_seed_batch = prng_seed
-
-        if jit:
-            padded = False
-            # Check if len of prompt_batch is multiple of NUM_TPU_CORES, if not pad its ids
-            if len(prompt_batch) % NUM_TPU_CORES != 0:
-                padded = True
-                pad_size = NUM_TPU_CORES - (len(prompt_batch) % NUM_TPU_CORES)
-                # Pad embeds_batch and noise_batch with zeros in batch dimension
-                prompt_ids_batch = pad_along_axis(prompt_ids_batch, pad_size, axis=0)
-
-            prompt_ids_batch = shard(prompt_ids_batch)
-            prng_seed_batch = jax.random.split(prng_seed, jax.device_count())
-
-        outputs = pipeline(
-            params,
-            prng_seed=prng_seed_batch,
-            prompt_ids=prompt_ids_batch,
-            height=height,
-            width=width,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            output_type="pil" if not upsample else "numpy",
-            jit=jit,
-        )["images"]
-
-        if jit:
-            # check if we padded and remove that padding from outputs
-            if padded:
-                outputs = outputs[:-pad_size]
-
-        if upsample:
-            images = []
-            for output in outputs:
-                images.append(pipeline.upsampler(output))
-        else:
-            images = outputs
-
-        for image in images:
-            uuid = str(uuid4())
-            frame_filepath = save_path / f"{uuid}{image_file_ext}"
-            image.save(frame_filepath)
-            frame_filepaths.append(str(frame_filepath))
-            frame_index += 1
-
-    return frame_filepaths
-
-    if push_to_hub:
-        upload_folder_chunked(repo_id, save_path, private=private, create_pr=create_pr)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/tests/isatty_test.py

@@ -1,57 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import sys
-from unittest import TestCase, main
-
-from ..ansitowin32 import StreamWrapper, AnsiToWin32
-from .utils import pycharm, replace_by, replace_original_by, StreamTTY, StreamNonTTY
-
-
-def is_a_tty(stream):
-    return StreamWrapper(stream, None).isatty()
-
-class IsattyTest(TestCase):
-
-    def test_TTY(self):
-        tty = StreamTTY()
-        self.assertTrue(is_a_tty(tty))
-        with pycharm():
-            self.assertTrue(is_a_tty(tty))
-
-    def test_nonTTY(self):
-        non_tty = StreamNonTTY()
-        self.assertFalse(is_a_tty(non_tty))
-        with pycharm():
-            self.assertFalse(is_a_tty(non_tty))
-
-    def test_withPycharm(self):
-        with pycharm():
-            self.assertTrue(is_a_tty(sys.stderr))
-            self.assertTrue(is_a_tty(sys.stdout))
-
-    def test_withPycharmTTYOverride(self):
-        tty = StreamTTY()
-        with pycharm(), replace_by(tty):
-            self.assertTrue(is_a_tty(tty))
-
-    def test_withPycharmNonTTYOverride(self):
-        non_tty = StreamNonTTY()
-        with pycharm(), replace_by(non_tty):
-            self.assertFalse(is_a_tty(non_tty))
-
-    def test_withPycharmNoneOverride(self):
-        with pycharm():
-            with replace_by(None), replace_original_by(None):
-                self.assertFalse(is_a_tty(None))
-                self.assertFalse(is_a_tty(StreamNonTTY()))
-                self.assertTrue(is_a_tty(StreamTTY()))
-
-    def test_withPycharmStreamWrapped(self):
-        with pycharm():
-            self.assertTrue(AnsiToWin32(StreamTTY()).stream.isatty())
-            self.assertFalse(AnsiToWin32(StreamNonTTY()).stream.isatty())
-            self.assertTrue(AnsiToWin32(sys.stdout).stream.isatty())
-            self.assertTrue(AnsiToWin32(sys.stderr).stream.isatty())
-
-
-if __name__ == '__main__':
-    main()

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

@@ -1,1286 +0,0 @@
-"""distutils.dist
-
-Provides the Distribution class, which represents the module distribution
-being built/installed/distributed.
-"""
-
-import sys
-import os
-import re
-import pathlib
-import contextlib
-from email import message_from_file
-
-try:
-    import warnings
-except ImportError:
-    warnings = None
-
-from distutils.errors import (
-    DistutilsOptionError,
-    DistutilsModuleError,
-    DistutilsArgError,
-    DistutilsClassError,
-)
-from distutils.fancy_getopt import FancyGetopt, translate_longopt
-from distutils.util import check_environ, strtobool, rfc822_escape
-from distutils import log
-from distutils.debug import DEBUG
-
-# Regex to define acceptable Distutils command names.  This is not *quite*
-# the same as a Python NAME -- I don't allow leading underscores.  The fact
-# that they're very similar is no coincidence; the default naming scheme is
-# to look for a Python module named after the command.
-command_re = re.compile(r'^[a-zA-Z]([a-zA-Z0-9_]*)$')
-
-
-def _ensure_list(value, fieldname):
-    if isinstance(value, str):
-        # a string containing comma separated values is okay.  It will
-        # be converted to a list by Distribution.finalize_options().
-        pass
-    elif not isinstance(value, list):
-        # passing a tuple or an iterator perhaps, warn and convert
-        typename = type(value).__name__
-        msg = "Warning: '{fieldname}' should be a list, got type '{typename}'"
-        msg = msg.format(**locals())
-        log.log(log.WARN, msg)
-        value = list(value)
-    return value
-
-
-class Distribution:
-    """The core of the Distutils.  Most of the work hiding behind 'setup'
-    is really done within a Distribution instance, which farms the work out
-    to the Distutils commands specified on the command line.
-
-    Setup scripts will almost never instantiate Distribution directly,
-    unless the 'setup()' function is totally inadequate to their needs.
-    However, it is conceivable that a setup script might wish to subclass
-    Distribution for some specialized purpose, and then pass the subclass
-    to 'setup()' as the 'distclass' keyword argument.  If so, it is
-    necessary to respect the expectations that 'setup' has of Distribution.
-    See the code for 'setup()', in core.py, for details.
-    """
-
-    # 'global_options' describes the command-line options that may be
-    # supplied to the setup script prior to any actual commands.
-    # Eg. "./setup.py -n" or "./setup.py --quiet" both take advantage of
-    # these global options.  This list should be kept to a bare minimum,
-    # since every global option is also valid as a command option -- and we
-    # don't want to pollute the commands with too many options that they
-    # have minimal control over.
-    # The fourth entry for verbose means that it can be repeated.
-    global_options = [
-        ('verbose', 'v', "run verbosely (default)", 1),
-        ('quiet', 'q', "run quietly (turns verbosity off)"),
-        ('dry-run', 'n', "don't actually do anything"),
-        ('help', 'h', "show detailed help message"),
-        ('no-user-cfg', None, 'ignore pydistutils.cfg in your home directory'),
-    ]
-
-    # 'common_usage' is a short (2-3 line) string describing the common
-    # usage of the setup script.
-    common_usage = """\
-Common commands: (see '--help-commands' for more)
-
-  setup.py build      will build the package underneath 'build/'
-  setup.py install    will install the package
-"""
-
-    # options that are not propagated to the commands
-    display_options = [
-        ('help-commands', None, "list all available commands"),
-        ('name', None, "print package name"),
-        ('version', 'V', "print package version"),
-        ('fullname', None, "print <package name>-<version>"),
-        ('author', None, "print the author's name"),
-        ('author-email', None, "print the author's email address"),
-        ('maintainer', None, "print the maintainer's name"),
-        ('maintainer-email', None, "print the maintainer's email address"),
-        ('contact', None, "print the maintainer's name if known, else the author's"),
-        (
-            'contact-email',
-            None,
-            "print the maintainer's email address if known, else the author's",
-        ),
-        ('url', None, "print the URL for this package"),
-        ('license', None, "print the license of the package"),
-        ('licence', None, "alias for --license"),
-        ('description', None, "print the package description"),
-        ('long-description', None, "print the long package description"),
-        ('platforms', None, "print the list of platforms"),
-        ('classifiers', None, "print the list of classifiers"),
-        ('keywords', None, "print the list of keywords"),
-        ('provides', None, "print the list of packages/modules provided"),
-        ('requires', None, "print the list of packages/modules required"),
-        ('obsoletes', None, "print the list of packages/modules made obsolete"),
-    ]
-    display_option_names = [translate_longopt(x[0]) for x in display_options]
-
-    # negative options are options that exclude other options
-    negative_opt = {'quiet': 'verbose'}
-
-    # -- Creation/initialization methods -------------------------------
-
-    def __init__(self, attrs=None):  # noqa: C901
-        """Construct a new Distribution instance: initialize all the
-        attributes of a Distribution, and then use 'attrs' (a dictionary
-        mapping attribute names to values) to assign some of those
-        attributes their "real" values.  (Any attributes not mentioned in
-        'attrs' will be assigned to some null value: 0, None, an empty list
-        or dictionary, etc.)  Most importantly, initialize the
-        'command_obj' attribute to the empty dictionary; this will be
-        filled in with real command objects by 'parse_command_line()'.
-        """
-
-        # Default values for our command-line options
-        self.verbose = 1
-        self.dry_run = 0
-        self.help = 0
-        for attr in self.display_option_names:
-            setattr(self, attr, 0)
-
-        # Store the distribution meta-data (name, version, author, and so
-        # forth) in a separate object -- we're getting to have enough
-        # information here (and enough command-line options) that it's
-        # worth it.  Also delegate 'get_XXX()' methods to the 'metadata'
-        # object in a sneaky and underhanded (but efficient!) way.
-        self.metadata = DistributionMetadata()
-        for basename in self.metadata._METHOD_BASENAMES:
-            method_name = "get_" + basename
-            setattr(self, method_name, getattr(self.metadata, method_name))
-
-        # 'cmdclass' maps command names to class objects, so we
-        # can 1) quickly figure out which class to instantiate when
-        # we need to create a new command object, and 2) have a way
-        # for the setup script to override command classes
-        self.cmdclass = {}
-
-        # 'command_packages' is a list of packages in which commands
-        # are searched for.  The factory for command 'foo' is expected
-        # to be named 'foo' in the module 'foo' in one of the packages
-        # named here.  This list is searched from the left; an error
-        # is raised if no named package provides the command being
-        # searched for.  (Always access using get_command_packages().)
-        self.command_packages = None
-
-        # 'script_name' and 'script_args' are usually set to sys.argv[0]
-        # and sys.argv[1:], but they can be overridden when the caller is
-        # not necessarily a setup script run from the command-line.
-        self.script_name = None
-        self.script_args = None
-
-        # 'command_options' is where we store command options between
-        # parsing them (from config files, the command-line, etc.) and when
-        # they are actually needed -- ie. when the command in question is
-        # instantiated.  It is a dictionary of dictionaries of 2-tuples:
-        #   command_options = { command_name : { option : (source, value) } }
-        self.command_options = {}
-
-        # 'dist_files' is the list of (command, pyversion, file) that
-        # have been created by any dist commands run so far. This is
-        # filled regardless of whether the run is dry or not. pyversion
-        # gives sysconfig.get_python_version() if the dist file is
-        # specific to a Python version, 'any' if it is good for all
-        # Python versions on the target platform, and '' for a source
-        # file. pyversion should not be used to specify minimum or
-        # maximum required Python versions; use the metainfo for that
-        # instead.
-        self.dist_files = []
-
-        # These options are really the business of various commands, rather
-        # than of the Distribution itself.  We provide aliases for them in
-        # Distribution as a convenience to the developer.
-        self.packages = None
-        self.package_data = {}
-        self.package_dir = None
-        self.py_modules = None
-        self.libraries = None
-        self.headers = None
-        self.ext_modules = None
-        self.ext_package = None
-        self.include_dirs = None
-        self.extra_path = None
-        self.scripts = None
-        self.data_files = None
-        self.password = ''
-
-        # And now initialize bookkeeping stuff that can't be supplied by
-        # the caller at all.  'command_obj' maps command names to
-        # Command instances -- that's how we enforce that every command
-        # class is a singleton.
-        self.command_obj = {}
-
-        # 'have_run' maps command names to boolean values; it keeps track
-        # of whether we have actually run a particular command, to make it
-        # cheap to "run" a command whenever we think we might need to -- if
-        # it's already been done, no need for expensive filesystem
-        # operations, we just check the 'have_run' dictionary and carry on.
-        # It's only safe to query 'have_run' for a command class that has
-        # been instantiated -- a false value will be inserted when the
-        # command object is created, and replaced with a true value when
-        # the command is successfully run.  Thus it's probably best to use
-        # '.get()' rather than a straight lookup.
-        self.have_run = {}
-
-        # Now we'll use the attrs dictionary (ultimately, keyword args from
-        # the setup script) to possibly override any or all of these
-        # distribution options.
-
-        if attrs:
-            # Pull out the set of command options and work on them
-            # specifically.  Note that this order guarantees that aliased
-            # command options will override any supplied redundantly
-            # through the general options dictionary.
-            options = attrs.get('options')
-            if options is not None:
-                del attrs['options']
-                for (command, cmd_options) in options.items():
-                    opt_dict = self.get_option_dict(command)
-                    for (opt, val) in cmd_options.items():
-                        opt_dict[opt] = ("setup script", val)
-
-            if 'licence' in attrs:
-                attrs['license'] = attrs['licence']
-                del attrs['licence']
-                msg = "'licence' distribution option is deprecated; use 'license'"
-                if warnings is not None:
-                    warnings.warn(msg)
-                else:
-                    sys.stderr.write(msg + "\n")
-
-            # Now work on the rest of the attributes.  Any attribute that's
-            # not already defined is invalid!
-            for (key, val) in attrs.items():
-                if hasattr(self.metadata, "set_" + key):
-                    getattr(self.metadata, "set_" + key)(val)
-                elif hasattr(self.metadata, key):
-                    setattr(self.metadata, key, val)
-                elif hasattr(self, key):
-                    setattr(self, key, val)
-                else:
-                    msg = "Unknown distribution option: %s" % repr(key)
-                    warnings.warn(msg)
-
-        # no-user-cfg is handled before other command line args
-        # because other args override the config files, and this
-        # one is needed before we can load the config files.
-        # If attrs['script_args'] wasn't passed, assume false.
-        #
-        # This also make sure we just look at the global options
-        self.want_user_cfg = True
-
-        if self.script_args is not None:
-            for arg in self.script_args:
-                if not arg.startswith('-'):
-                    break
-                if arg == '--no-user-cfg':
-                    self.want_user_cfg = False
-                    break
-
-        self.finalize_options()
-
-    def get_option_dict(self, command):
-        """Get the option dictionary for a given command.  If that
-        command's option dictionary hasn't been created yet, then create it
-        and return the new dictionary; otherwise, return the existing
-        option dictionary.
-        """
-        dict = self.command_options.get(command)
-        if dict is None:
-            dict = self.command_options[command] = {}
-        return dict
-
-    def dump_option_dicts(self, header=None, commands=None, indent=""):
-        from pprint import pformat
-
-        if commands is None:  # dump all command option dicts
-            commands = sorted(self.command_options.keys())
-
-        if header is not None:
-            self.announce(indent + header)
-            indent = indent + "  "
-
-        if not commands:
-            self.announce(indent + "no commands known yet")
-            return
-
-        for cmd_name in commands:
-            opt_dict = self.command_options.get(cmd_name)
-            if opt_dict is None:
-                self.announce(indent + "no option dict for '%s' command" % cmd_name)
-            else:
-                self.announce(indent + "option dict for '%s' command:" % cmd_name)
-                out = pformat(opt_dict)
-                for line in out.split('\n'):
-                    self.announce(indent + "  " + line)
-
-    # -- Config file finding/parsing methods ---------------------------
-
-    def find_config_files(self):
-        """Find as many configuration files as should be processed for this
-        platform, and return a list of filenames in the order in which they
-        should be parsed.  The filenames returned are guaranteed to exist
-        (modulo nasty race conditions).
-
-        There are multiple possible config files:
-        - distutils.cfg in the Distutils installation directory (i.e.
-          where the top-level Distutils __inst__.py file lives)
-        - a file in the user's home directory named .pydistutils.cfg
-          on Unix and pydistutils.cfg on Windows/Mac; may be disabled
-          with the ``--no-user-cfg`` option
-        - setup.cfg in the current directory
-        - a file named by an environment variable
-        """
-        check_environ()
-        files = [str(path) for path in self._gen_paths() if os.path.isfile(path)]
-
-        if DEBUG:
-            self.announce("using config files: %s" % ', '.join(files))
-
-        return files
-
-    def _gen_paths(self):
-        # The system-wide Distutils config file
-        sys_dir = pathlib.Path(sys.modules['distutils'].__file__).parent
-        yield sys_dir / "distutils.cfg"
-
-        # The per-user config file
-        prefix = '.' * (os.name == 'posix')
-        filename = prefix + 'pydistutils.cfg'
-        if self.want_user_cfg:
-            yield pathlib.Path('~').expanduser() / filename
-
-        # All platforms support local setup.cfg
-        yield pathlib.Path('setup.cfg')
-
-        # Additional config indicated in the environment
-        with contextlib.suppress(TypeError):
-            yield pathlib.Path(os.getenv("DIST_EXTRA_CONFIG"))
-
-    def parse_config_files(self, filenames=None):  # noqa: C901
-        from configparser import ConfigParser
-
-        # Ignore install directory options if we have a venv
-        if sys.prefix != sys.base_prefix:
-            ignore_options = [
-                'install-base',
-                'install-platbase',
-                'install-lib',
-                'install-platlib',
-                'install-purelib',
-                'install-headers',
-                'install-scripts',
-                'install-data',
-                'prefix',
-                'exec-prefix',
-                'home',
-                'user',
-                'root',
-            ]
-        else:
-            ignore_options = []
-
-        ignore_options = frozenset(ignore_options)
-
-        if filenames is None:
-            filenames = self.find_config_files()
-
-        if DEBUG:
-            self.announce("Distribution.parse_config_files():")
-
-        parser = ConfigParser()
-        for filename in filenames:
-            if DEBUG:
-                self.announce("  reading %s" % filename)
-            parser.read(filename)
-            for section in parser.sections():
-                options = parser.options(section)
-                opt_dict = self.get_option_dict(section)
-
-                for opt in options:
-                    if opt != '__name__' and opt not in ignore_options:
-                        val = parser.get(section, opt)
-                        opt = opt.replace('-', '_')
-                        opt_dict[opt] = (filename, val)
-
-            # Make the ConfigParser forget everything (so we retain
-            # the original filenames that options come from)
-            parser.__init__()
-
-        # If there was a "global" section in the config file, use it
-        # to set Distribution options.
-
-        if 'global' in self.command_options:
-            for (opt, (src, val)) in self.command_options['global'].items():
-                alias = self.negative_opt.get(opt)
-                try:
-                    if alias:
-                        setattr(self, alias, not strtobool(val))
-                    elif opt in ('verbose', 'dry_run'):  # ugh!
-                        setattr(self, opt, strtobool(val))
-                    else:
-                        setattr(self, opt, val)
-                except ValueError as msg:
-                    raise DistutilsOptionError(msg)
-
-    # -- Command-line parsing methods ----------------------------------
-
-    def parse_command_line(self):
-        """Parse the setup script's command line, taken from the
-        'script_args' instance attribute (which defaults to 'sys.argv[1:]'
-        -- see 'setup()' in core.py).  This list is first processed for
-        "global options" -- options that set attributes of the Distribution
-        instance.  Then, it is alternately scanned for Distutils commands
-        and options for that command.  Each new command terminates the
-        options for the previous command.  The allowed options for a
-        command are determined by the 'user_options' attribute of the
-        command class -- thus, we have to be able to load command classes
-        in order to parse the command line.  Any error in that 'options'
-        attribute raises DistutilsGetoptError; any error on the
-        command-line raises DistutilsArgError.  If no Distutils commands
-        were found on the command line, raises DistutilsArgError.  Return
-        true if command-line was successfully parsed and we should carry
-        on with executing commands; false if no errors but we shouldn't
-        execute commands (currently, this only happens if user asks for
-        help).
-        """
-        #
-        # We now have enough information to show the Macintosh dialog
-        # that allows the user to interactively specify the "command line".
-        #
-        toplevel_options = self._get_toplevel_options()
-
-        # We have to parse the command line a bit at a time -- global
-        # options, then the first command, then its options, and so on --
-        # because each command will be handled by a different class, and
-        # the options that are valid for a particular class aren't known
-        # until we have loaded the command class, which doesn't happen
-        # until we know what the command is.
-
-        self.commands = []
-        parser = FancyGetopt(toplevel_options + self.display_options)
-        parser.set_negative_aliases(self.negative_opt)
-        parser.set_aliases({'licence': 'license'})
-        args = parser.getopt(args=self.script_args, object=self)
-        option_order = parser.get_option_order()
-        log.set_verbosity(self.verbose)
-
-        # for display options we return immediately
-        if self.handle_display_options(option_order):
-            return
-        while args:
-            args = self._parse_command_opts(parser, args)
-            if args is None:  # user asked for help (and got it)
-                return
-
-        # Handle the cases of --help as a "global" option, ie.
-        # "setup.py --help" and "setup.py --help command ...".  For the
-        # former, we show global options (--verbose, --dry-run, etc.)
-        # and display-only options (--name, --version, etc.); for the
-        # latter, we omit the display-only options and show help for
-        # each command listed on the command line.
-        if self.help:
-            self._show_help(
-                parser, display_options=len(self.commands) == 0, commands=self.commands
-            )
-            return
-
-        # Oops, no commands found -- an end-user error
-        if not self.commands:
-            raise DistutilsArgError("no commands supplied")
-
-        # All is well: return true
-        return True
-
-    def _get_toplevel_options(self):
-        """Return the non-display options recognized at the top level.
-
-        This includes options that are recognized *only* at the top
-        level as well as options recognized for commands.
-        """
-        return self.global_options + [
-            (
-                "command-packages=",
-                None,
-                "list of packages that provide distutils commands",
-            ),
-        ]
-
-    def _parse_command_opts(self, parser, args):  # noqa: C901
-        """Parse the command-line options for a single command.
-        'parser' must be a FancyGetopt instance; 'args' must be the list
-        of arguments, starting with the current command (whose options
-        we are about to parse).  Returns a new version of 'args' with
-        the next command at the front of the list; will be the empty
-        list if there are no more commands on the command line.  Returns
-        None if the user asked for help on this command.
-        """
-        # late import because of mutual dependence between these modules
-        from distutils.cmd import Command
-
-        # Pull the current command from the head of the command line
-        command = args[0]
-        if not command_re.match(command):
-            raise SystemExit("invalid command name '%s'" % command)
-        self.commands.append(command)
-
-        # Dig up the command class that implements this command, so we
-        # 1) know that it's a valid command, and 2) know which options
-        # it takes.
-        try:
-            cmd_class = self.get_command_class(command)
-        except DistutilsModuleError as msg:
-            raise DistutilsArgError(msg)
-
-        # Require that the command class be derived from Command -- want
-        # to be sure that the basic "command" interface is implemented.
-        if not issubclass(cmd_class, Command):
-            raise DistutilsClassError(
-                "command class %s must subclass Command" % cmd_class
-            )
-
-        # Also make sure that the command object provides a list of its
-        # known options.
-        if not (
-            hasattr(cmd_class, 'user_options')
-            and isinstance(cmd_class.user_options, list)
-        ):
-            msg = (
-                "command class %s must provide "
-                "'user_options' attribute (a list of tuples)"
-            )
-            raise DistutilsClassError(msg % cmd_class)
-
-        # If the command class has a list of negative alias options,
-        # merge it in with the global negative aliases.
-        negative_opt = self.negative_opt
-        if hasattr(cmd_class, 'negative_opt'):
-            negative_opt = negative_opt.copy()
-            negative_opt.update(cmd_class.negative_opt)
-
-        # Check for help_options in command class.  They have a different
-        # format (tuple of four) so we need to preprocess them here.
-        if hasattr(cmd_class, 'help_options') and isinstance(
-            cmd_class.help_options, list
-        ):
-            help_options = fix_help_options(cmd_class.help_options)
-        else:
-            help_options = []
-
-        # All commands support the global options too, just by adding
-        # in 'global_options'.
-        parser.set_option_table(
-            self.global_options + cmd_class.user_options + help_options
-        )
-        parser.set_negative_aliases(negative_opt)
-        (args, opts) = parser.getopt(args[1:])
-        if hasattr(opts, 'help') and opts.help:
-            self._show_help(parser, display_options=0, commands=[cmd_class])
-            return
-
-        if hasattr(cmd_class, 'help_options') and isinstance(
-            cmd_class.help_options, list
-        ):
-            help_option_found = 0
-            for (help_option, short, desc, func) in cmd_class.help_options:
-                if hasattr(opts, parser.get_attr_name(help_option)):
-                    help_option_found = 1
-                    if callable(func):
-                        func()
-                    else:
-                        raise DistutilsClassError(
-                            "invalid help function %r for help option '%s': "
-                            "must be a callable object (function, etc.)"
-                            % (func, help_option)
-                        )
-
-            if help_option_found:
-                return
-
-        # Put the options from the command-line into their official
-        # holding pen, the 'command_options' dictionary.
-        opt_dict = self.get_option_dict(command)
-        for (name, value) in vars(opts).items():
-            opt_dict[name] = ("command line", value)
-
-        return args
-
-    def finalize_options(self):
-        """Set final values for all the options on the Distribution
-        instance, analogous to the .finalize_options() method of Command
-        objects.
-        """
-        for attr in ('keywords', 'platforms'):
-            value = getattr(self.metadata, attr)
-            if value is None:
-                continue
-            if isinstance(value, str):
-                value = [elm.strip() for elm in value.split(',')]
-                setattr(self.metadata, attr, value)
-
-    def _show_help(self, parser, global_options=1, display_options=1, commands=[]):
-        """Show help for the setup script command-line in the form of
-        several lists of command-line options.  'parser' should be a
-        FancyGetopt instance; do not expect it to be returned in the
-        same state, as its option table will be reset to make it
-        generate the correct help text.
-
-        If 'global_options' is true, lists the global options:
-        --verbose, --dry-run, etc.  If 'display_options' is true, lists
-        the "display-only" options: --name, --version, etc.  Finally,
-        lists per-command help for every command name or command class
-        in 'commands'.
-        """
-        # late import because of mutual dependence between these modules
-        from distutils.core import gen_usage
-        from distutils.cmd import Command
-
-        if global_options:
-            if display_options:
-                options = self._get_toplevel_options()
-            else:
-                options = self.global_options
-            parser.set_option_table(options)
-            parser.print_help(self.common_usage + "\nGlobal options:")
-            print('')
-
-        if display_options:
-            parser.set_option_table(self.display_options)
-            parser.print_help(
-                "Information display options (just display "
-                + "information, ignore any commands)"
-            )
-            print('')
-
-        for command in self.commands:
-            if isinstance(command, type) and issubclass(command, Command):
-                klass = command
-            else:
-                klass = self.get_command_class(command)
-            if hasattr(klass, 'help_options') and isinstance(klass.help_options, list):
-                parser.set_option_table(
-                    klass.user_options + fix_help_options(klass.help_options)
-                )
-            else:
-                parser.set_option_table(klass.user_options)
-            parser.print_help("Options for '%s' command:" % klass.__name__)
-            print('')
-
-        print(gen_usage(self.script_name))
-
-    def handle_display_options(self, option_order):
-        """If there were any non-global "display-only" options
-        (--help-commands or the metadata display options) on the command
-        line, display the requested info and return true; else return
-        false.
-        """
-        from distutils.core import gen_usage
-
-        # User just wants a list of commands -- we'll print it out and stop
-        # processing now (ie. if they ran "setup --help-commands foo bar",
-        # we ignore "foo bar").
-        if self.help_commands:
-            self.print_commands()
-            print('')
-            print(gen_usage(self.script_name))
-            return 1
-
-        # If user supplied any of the "display metadata" options, then
-        # display that metadata in the order in which the user supplied the
-        # metadata options.
-        any_display_options = 0
-        is_display_option = {}
-        for option in self.display_options:
-            is_display_option[option[0]] = 1
-
-        for (opt, val) in option_order:
-            if val and is_display_option.get(opt):
-                opt = translate_longopt(opt)
-                value = getattr(self.metadata, "get_" + opt)()
-                if opt in ['keywords', 'platforms']:
-                    print(','.join(value))
-                elif opt in ('classifiers', 'provides', 'requires', 'obsoletes'):
-                    print('\n'.join(value))
-                else:
-                    print(value)
-                any_display_options = 1
-
-        return any_display_options
-
-    def print_command_list(self, commands, header, max_length):
-        """Print a subset of the list of all commands -- used by
-        'print_commands()'.
-        """
-        print(header + ":")
-
-        for cmd in commands:
-            klass = self.cmdclass.get(cmd)
-            if not klass:
-                klass = self.get_command_class(cmd)
-            try:
-                description = klass.description
-            except AttributeError:
-                description = "(no description available)"
-
-            print("  %-*s  %s" % (max_length, cmd, description))
-
-    def print_commands(self):
-        """Print out a help message listing all available commands with a
-        description of each.  The list is divided into "standard commands"
-        (listed in distutils.command.__all__) and "extra commands"
-        (mentioned in self.cmdclass, but not a standard command).  The
-        descriptions come from the command class attribute
-        'description'.
-        """
-        import distutils.command
-
-        std_commands = distutils.command.__all__
-        is_std = {}
-        for cmd in std_commands:
-            is_std[cmd] = 1
-
-        extra_commands = []
-        for cmd in self.cmdclass.keys():
-            if not is_std.get(cmd):
-                extra_commands.append(cmd)
-
-        max_length = 0
-        for cmd in std_commands + extra_commands:
-            if len(cmd) > max_length:
-                max_length = len(cmd)
-
-        self.print_command_list(std_commands, "Standard commands", max_length)
-        if extra_commands:
-            print()
-            self.print_command_list(extra_commands, "Extra commands", max_length)
-
-    def get_command_list(self):
-        """Get a list of (command, description) tuples.
-        The list is divided into "standard commands" (listed in
-        distutils.command.__all__) and "extra commands" (mentioned in
-        self.cmdclass, but not a standard command).  The descriptions come
-        from the command class attribute 'description'.
-        """
-        # Currently this is only used on Mac OS, for the Mac-only GUI
-        # Distutils interface (by Jack Jansen)
-        import distutils.command
-
-        std_commands = distutils.command.__all__
-        is_std = {}
-        for cmd in std_commands:
-            is_std[cmd] = 1
-
-        extra_commands = []
-        for cmd in self.cmdclass.keys():
-            if not is_std.get(cmd):
-                extra_commands.append(cmd)
-
-        rv = []
-        for cmd in std_commands + extra_commands:
-            klass = self.cmdclass.get(cmd)
-            if not klass:
-                klass = self.get_command_class(cmd)
-            try:
-                description = klass.description
-            except AttributeError:
-                description = "(no description available)"
-            rv.append((cmd, description))
-        return rv
-
-    # -- Command class/object methods ----------------------------------
-
-    def get_command_packages(self):
-        """Return a list of packages from which commands are loaded."""
-        pkgs = self.command_packages
-        if not isinstance(pkgs, list):
-            if pkgs is None:
-                pkgs = ''
-            pkgs = [pkg.strip() for pkg in pkgs.split(',') if pkg != '']
-            if "distutils.command" not in pkgs:
-                pkgs.insert(0, "distutils.command")
-            self.command_packages = pkgs
-        return pkgs
-
-    def get_command_class(self, command):
-        """Return the class that implements the Distutils command named by
-        'command'.  First we check the 'cmdclass' dictionary; if the
-        command is mentioned there, we fetch the class object from the
-        dictionary and return it.  Otherwise we load the command module
-        ("distutils.command." + command) and fetch the command class from
-        the module.  The loaded class is also stored in 'cmdclass'
-        to speed future calls to 'get_command_class()'.
-
-        Raises DistutilsModuleError if the expected module could not be
-        found, or if that module does not define the expected class.
-        """
-        klass = self.cmdclass.get(command)
-        if klass:
-            return klass
-
-        for pkgname in self.get_command_packages():
-            module_name = "{}.{}".format(pkgname, command)
-            klass_name = command
-
-            try:
-                __import__(module_name)
-                module = sys.modules[module_name]
-            except ImportError:
-                continue
-
-            try:
-                klass = getattr(module, klass_name)
-            except AttributeError:
-                raise DistutilsModuleError(
-                    "invalid command '%s' (no class '%s' in module '%s')"
-                    % (command, klass_name, module_name)
-                )
-
-            self.cmdclass[command] = klass
-            return klass
-
-        raise DistutilsModuleError("invalid command '%s'" % command)
-
-    def get_command_obj(self, command, create=1):
-        """Return the command object for 'command'.  Normally this object
-        is cached on a previous call to 'get_command_obj()'; if no command
-        object for 'command' is in the cache, then we either create and
-        return it (if 'create' is true) or return None.
-        """
-        cmd_obj = self.command_obj.get(command)
-        if not cmd_obj and create:
-            if DEBUG:
-                self.announce(
-                    "Distribution.get_command_obj(): "
-                    "creating '%s' command object" % command
-                )
-
-            klass = self.get_command_class(command)
-            cmd_obj = self.command_obj[command] = klass(self)
-            self.have_run[command] = 0
-
-            # Set any options that were supplied in config files
-            # or on the command line.  (NB. support for error
-            # reporting is lame here: any errors aren't reported
-            # until 'finalize_options()' is called, which means
-            # we won't report the source of the error.)
-            options = self.command_options.get(command)
-            if options:
-                self._set_command_options(cmd_obj, options)
-
-        return cmd_obj
-
-    def _set_command_options(self, command_obj, option_dict=None):  # noqa: C901
-        """Set the options for 'command_obj' from 'option_dict'.  Basically
-        this means copying elements of a dictionary ('option_dict') to
-        attributes of an instance ('command').
-
-        'command_obj' must be a Command instance.  If 'option_dict' is not
-        supplied, uses the standard option dictionary for this command
-        (from 'self.command_options').
-        """
-        command_name = command_obj.get_command_name()
-        if option_dict is None:
-            option_dict = self.get_option_dict(command_name)
-
-        if DEBUG:
-            self.announce("  setting options for '%s' command:" % command_name)
-        for (option, (source, value)) in option_dict.items():
-            if DEBUG:
-                self.announce("    {} = {} (from {})".format(option, value, source))
-            try:
-                bool_opts = [translate_longopt(o) for o in command_obj.boolean_options]
-            except AttributeError:
-                bool_opts = []
-            try:
-                neg_opt = command_obj.negative_opt
-            except AttributeError:
-                neg_opt = {}
-
-            try:
-                is_string = isinstance(value, str)
-                if option in neg_opt and is_string:
-                    setattr(command_obj, neg_opt[option], not strtobool(value))
-                elif option in bool_opts and is_string:
-                    setattr(command_obj, option, strtobool(value))
-                elif hasattr(command_obj, option):
-                    setattr(command_obj, option, value)
-                else:
-                    raise DistutilsOptionError(
-                        "error in %s: command '%s' has no such option '%s'"
-                        % (source, command_name, option)
-                    )
-            except ValueError as msg:
-                raise DistutilsOptionError(msg)
-
-    def reinitialize_command(self, command, reinit_subcommands=0):
-        """Reinitializes a command to the state it was in when first
-        returned by 'get_command_obj()': ie., initialized but not yet
-        finalized.  This provides the opportunity to sneak option
-        values in programmatically, overriding or supplementing
-        user-supplied values from the config files and command line.
-        You'll have to re-finalize the command object (by calling
-        'finalize_options()' or 'ensure_finalized()') before using it for
-        real.
-
-        'command' should be a command name (string) or command object.  If
-        'reinit_subcommands' is true, also reinitializes the command's
-        sub-commands, as declared by the 'sub_commands' class attribute (if
-        it has one).  See the "install" command for an example.  Only
-        reinitializes the sub-commands that actually matter, ie. those
-        whose test predicates return true.
-
-        Returns the reinitialized command object.
-        """
-        from distutils.cmd import Command
-
-        if not isinstance(command, Command):
-            command_name = command
-            command = self.get_command_obj(command_name)
-        else:
-            command_name = command.get_command_name()
-
-        if not command.finalized:
-            return command
-        command.initialize_options()
-        command.finalized = 0
-        self.have_run[command_name] = 0
-        self._set_command_options(command)
-
-        if reinit_subcommands:
-            for sub in command.get_sub_commands():
-                self.reinitialize_command(sub, reinit_subcommands)
-
-        return command
-
-    # -- Methods that operate on the Distribution ----------------------
-
-    def announce(self, msg, level=log.INFO):
-        log.log(level, msg)
-
-    def run_commands(self):
-        """Run each command that was seen on the setup script command line.
-        Uses the list of commands found and cache of command objects
-        created by 'get_command_obj()'.
-        """
-        for cmd in self.commands:
-            self.run_command(cmd)
-
-    # -- Methods that operate on its Commands --------------------------
-
-    def run_command(self, command):
-        """Do whatever it takes to run a command (including nothing at all,
-        if the command has already been run).  Specifically: if we have
-        already created and run the command named by 'command', return
-        silently without doing anything.  If the command named by 'command'
-        doesn't even have a command object yet, create one.  Then invoke
-        'run()' on that command object (or an existing one).
-        """
-        # Already been here, done that? then return silently.
-        if self.have_run.get(command):
-            return
-
-        log.info("running %s", command)
-        cmd_obj = self.get_command_obj(command)
-        cmd_obj.ensure_finalized()
-        cmd_obj.run()
-        self.have_run[command] = 1
-
-    # -- Distribution query methods ------------------------------------
-
-    def has_pure_modules(self):
-        return len(self.packages or self.py_modules or []) > 0
-
-    def has_ext_modules(self):
-        return self.ext_modules and len(self.ext_modules) > 0
-
-    def has_c_libraries(self):
-        return self.libraries and len(self.libraries) > 0
-
-    def has_modules(self):
-        return self.has_pure_modules() or self.has_ext_modules()
-
-    def has_headers(self):
-        return self.headers and len(self.headers) > 0
-
-    def has_scripts(self):
-        return self.scripts and len(self.scripts) > 0
-
-    def has_data_files(self):
-        return self.data_files and len(self.data_files) > 0
-
-    def is_pure(self):
-        return (
-            self.has_pure_modules()
-            and not self.has_ext_modules()
-            and not self.has_c_libraries()
-        )
-
-    # -- Metadata query methods ----------------------------------------
-
-    # If you're looking for 'get_name()', 'get_version()', and so forth,
-    # they are defined in a sneaky way: the constructor binds self.get_XXX
-    # to self.metadata.get_XXX.  The actual code is in the
-    # DistributionMetadata class, below.
-
-
-class DistributionMetadata:
-    """Dummy class to hold the distribution meta-data: name, version,
-    author, and so forth.
-    """
-
-    _METHOD_BASENAMES = (
-        "name",
-        "version",
-        "author",
-        "author_email",
-        "maintainer",
-        "maintainer_email",
-        "url",
-        "license",
-        "description",
-        "long_description",
-        "keywords",
-        "platforms",
-        "fullname",
-        "contact",
-        "contact_email",
-        "classifiers",
-        "download_url",
-        # PEP 314
-        "provides",
-        "requires",
-        "obsoletes",
-    )
-
-    def __init__(self, path=None):
-        if path is not None:
-            self.read_pkg_file(open(path))
-        else:
-            self.name = None
-            self.version = None
-            self.author = None
-            self.author_email = None
-            self.maintainer = None
-            self.maintainer_email = None
-            self.url = None
-            self.license = None
-            self.description = None
-            self.long_description = None
-            self.keywords = None
-            self.platforms = None
-            self.classifiers = None
-            self.download_url = None
-            # PEP 314
-            self.provides = None
-            self.requires = None
-            self.obsoletes = None
-
-    def read_pkg_file(self, file):
-        """Reads the metadata values from a file object."""
-        msg = message_from_file(file)
-
-        def _read_field(name):
-            value = msg[name]
-            if value and value != "UNKNOWN":
-                return value
-
-        def _read_list(name):
-            values = msg.get_all(name, None)
-            if values == []:
-                return None
-            return values
-
-        metadata_version = msg['metadata-version']
-        self.name = _read_field('name')
-        self.version = _read_field('version')
-        self.description = _read_field('summary')
-        # we are filling author only.
-        self.author = _read_field('author')
-        self.maintainer = None
-        self.author_email = _read_field('author-email')
-        self.maintainer_email = None
-        self.url = _read_field('home-page')
-        self.license = _read_field('license')
-
-        if 'download-url' in msg:
-            self.download_url = _read_field('download-url')
-        else:
-            self.download_url = None
-
-        self.long_description = _read_field('description')
-        self.description = _read_field('summary')
-
-        if 'keywords' in msg:
-            self.keywords = _read_field('keywords').split(',')
-
-        self.platforms = _read_list('platform')
-        self.classifiers = _read_list('classifier')
-
-        # PEP 314 - these fields only exist in 1.1
-        if metadata_version == '1.1':
-            self.requires = _read_list('requires')
-            self.provides = _read_list('provides')
-            self.obsoletes = _read_list('obsoletes')
-        else:
-            self.requires = None
-            self.provides = None
-            self.obsoletes = None
-
-    def write_pkg_info(self, base_dir):
-        """Write the PKG-INFO file into the release tree."""
-        with open(
-            os.path.join(base_dir, 'PKG-INFO'), 'w', encoding='UTF-8'
-        ) as pkg_info:
-            self.write_pkg_file(pkg_info)
-
-    def write_pkg_file(self, file):
-        """Write the PKG-INFO format data to a file object."""
-        version = '1.0'
-        if (
-            self.provides
-            or self.requires
-            or self.obsoletes
-            or self.classifiers
-            or self.download_url
-        ):
-            version = '1.1'
-
-        # required fields
-        file.write('Metadata-Version: %s\n' % version)
-        file.write('Name: %s\n' % self.get_name())
-        file.write('Version: %s\n' % self.get_version())
-
-        def maybe_write(header, val):
-            if val:
-                file.write(f"{header}: {val}\n")
-
-        # optional fields
-        maybe_write("Summary", self.get_description())
-        maybe_write("Home-page", self.get_url())
-        maybe_write("Author", self.get_contact())
-        maybe_write("Author-email", self.get_contact_email())
-        maybe_write("License", self.get_license())
-        maybe_write("Download-URL", self.download_url)
-        maybe_write("Description", rfc822_escape(self.get_long_description() or ""))
-        maybe_write("Keywords", ",".join(self.get_keywords()))
-
-        self._write_list(file, 'Platform', self.get_platforms())
-        self._write_list(file, 'Classifier', self.get_classifiers())
-
-        # PEP 314
-        self._write_list(file, 'Requires', self.get_requires())
-        self._write_list(file, 'Provides', self.get_provides())
-        self._write_list(file, 'Obsoletes', self.get_obsoletes())
-
-    def _write_list(self, file, name, values):
-        values = values or []
-        for value in values:
-            file.write('{}: {}\n'.format(name, value))
-
-    # -- Metadata query methods ----------------------------------------
-
-    def get_name(self):
-        return self.name or "UNKNOWN"
-
-    def get_version(self):
-        return self.version or "0.0.0"
-
-    def get_fullname(self):
-        return "{}-{}".format(self.get_name(), self.get_version())
-
-    def get_author(self):
-        return self.author
-
-    def get_author_email(self):
-        return self.author_email
-
-    def get_maintainer(self):
-        return self.maintainer
-
-    def get_maintainer_email(self):
-        return self.maintainer_email
-
-    def get_contact(self):
-        return self.maintainer or self.author
-
-    def get_contact_email(self):
-        return self.maintainer_email or self.author_email
-
-    def get_url(self):
-        return self.url
-
-    def get_license(self):
-        return self.license
-
-    get_licence = get_license
-
-    def get_description(self):
-        return self.description
-
-    def get_long_description(self):
-        return self.long_description
-
-    def get_keywords(self):
-        return self.keywords or []
-
-    def set_keywords(self, value):
-        self.keywords = _ensure_list(value, 'keywords')
-
-    def get_platforms(self):
-        return self.platforms
-
-    def set_platforms(self, value):
-        self.platforms = _ensure_list(value, 'platforms')
-
-    def get_classifiers(self):
-        return self.classifiers or []
-
-    def set_classifiers(self, value):
-        self.classifiers = _ensure_list(value, 'classifiers')
-
-    def get_download_url(self):
-        return self.download_url
-
-    # PEP 314
-    def get_requires(self):
-        return self.requires or []
-
-    def set_requires(self, value):
-        import distutils.versionpredicate
-
-        for v in value:
-            distutils.versionpredicate.VersionPredicate(v)
-        self.requires = list(value)
-
-    def get_provides(self):
-        return self.provides or []
-
-    def set_provides(self, value):
-        value = [v.strip() for v in value]
-        for v in value:
-            import distutils.versionpredicate
-
-            distutils.versionpredicate.split_provision(v)
-        self.provides = value
-
-    def get_obsoletes(self):
-        return self.obsoletes or []
-
-    def set_obsoletes(self, value):
-        import distutils.versionpredicate
-
-        for v in value:
-            distutils.versionpredicate.VersionPredicate(v)
-        self.obsoletes = list(value)
-
-
-def fix_help_options(options):
-    """Convert a 4-tuple 'help_options' list as found in various command
-    classes to the 3-tuple form required by FancyGetopt.
-    """
-    new_options = []
-    for help_tuple in options:
-        new_options.append(help_tuple[0:3])
-    return new_options

spaces/Awesimo/jojogan/op/fused_bias_act.cpp

@@ -1,21 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale);
-
-#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 fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
-    int act, int grad, float alpha, float scale) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(bias);
-
-    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}

spaces/Banbri/zcvzcv/src/components/ui/alert.tsx

@@ -1,59 +0,0 @@
-import * as React from "react"
-import { cva, type VariantProps } from "class-variance-authority"
-
-import { cn } from "@/lib/utils"
-
-const alertVariants = cva(
-  "relative w-full rounded-lg border border-stone-200 p-4 [&:has(svg)]:pl-11 [&>svg+div]:translate-y-[-3px] [&>svg]:absolute [&>svg]:left-4 [&>svg]:top-4 [&>svg]:text-stone-950 dark:border-stone-800 dark:[&>svg]:text-stone-50",
-  {
-    variants: {
-      variant: {
-        default: "bg-white text-stone-950 dark:bg-stone-950 dark:text-stone-50",
-        destructive:
-          "border-red-500/50 text-red-500 dark:border-red-500 [&>svg]:text-red-500 dark:border-red-900/50 dark:text-red-900 dark:dark:border-red-900 dark:[&>svg]:text-red-900",
-      },
-    },
-    defaultVariants: {
-      variant: "default",
-    },
-  }
-)
-
-const Alert = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement> & VariantProps<typeof alertVariants>
->(({ className, variant, ...props }, ref) => (
-  <div
-    ref={ref}
-    role="alert"
-    className={cn(alertVariants({ variant }), className)}
-    {...props}
-  />
-))
-Alert.displayName = "Alert"
-
-const AlertTitle = React.forwardRef<
-  HTMLParagraphElement,
-  React.HTMLAttributes<HTMLHeadingElement>
->(({ className, ...props }, ref) => (
-  <h5
-    ref={ref}
-    className={cn("mb-1 font-medium leading-none tracking-tight", className)}
-    {...props}
-  />
-))
-AlertTitle.displayName = "AlertTitle"
-
-const AlertDescription = React.forwardRef<
-  HTMLParagraphElement,
-  React.HTMLAttributes<HTMLParagraphElement>
->(({ className, ...props }, ref) => (
-  <div
-    ref={ref}
-    className={cn("text-sm [&_p]:leading-relaxed", className)}
-    {...props}
-  />
-))
-AlertDescription.displayName = "AlertDescription"
-
-export { Alert, AlertTitle, AlertDescription }

spaces/Benson/text-generation/Examples/Android Mini Block Craft Apk.md

@@ -1,90 +0,0 @@
-<br />
-<h1>Mini bloque de arte APK: Un juego de caja de arena para Android</h1>
-<p>Si usted está buscando un juego de sandbox divertido y creativo para su dispositivo Android, es posible que desee echa un vistazo a Mini Block Craft APK. Este juego te permite crear, explorar y sobrevivir en un mundo abierto estilo pixel. Puedes construir tu propia casa, luchar contra monstruos o simplemente disfrutar del paisaje. En este artículo, le diremos todo lo que necesita saber sobre Mini Block Craft APK, incluyendo sus características, cómo descargarlo e instalarlo, sus pros y contras, y algunas alternativas. </p>
- <h2>¿Qué es Mini Block Craft APK? </h2>
-<p>Mini Block Craft APK es un juego para Android desarrollado por Build Block Studio. Está inspirado en el popular juego Minecraft, pero tiene sus propias características únicas y estilo. El juego es gratis para descargar y jugar, pero contiene anuncios y compras en la aplicación. Puedes jugar el juego sin conexión o en línea con otros jugadores. </p>
-<h2>android mini block craft apk</h2><br /><p><b><b>DOWNLOAD</b> >>> <a href="https://bltlly.com/2v6LtV">https://bltlly.com/2v6LtV</a></b></p><br /><br />
- <h3>Características de Mini Block Craft APK</h3>
-<p>Mini Block Craft APK tiene muchas características que lo hacen un juego divertido y adictivo. Aquí están algunos de ellos:</p>
- <h4>Gráficos de estilo de píxeles</h4>
-<p>El juego tiene unos gráficos estilo pixel que le dan una sensación retro y nostálgica. El juego también tiene un ciclo de día y noche, efectos meteorológicos y sombras realistas. Los gráficos son simples pero coloridos y encantadores. </p>
- <h4>Modos creativos y de supervivencia</h4>
-<p>El juego tiene dos modos: creativo y supervivencia. En el modo creativo, tienes recursos ilimitados y puedes construir lo que quieras sin restricciones. También puede volar alrededor del mapa y explorar diferentes biomas. En el modo de supervivencia, tienes que reunir recursos, crear herramientas y armas, y luchar contra los enemigos. También tienes que controlar tu hambre, salud y resistencia. </p>
- <h4>Construye la casa de tus sueños o explora el mapa</h4>
-
- <h4>Lucha contra monstruos y zombies</h4>
-<p>El juego no se trata solo de construir y explorar. También tiene un sistema de combate que te permite luchar contra varios enemigos, como monstruos, zombis, arañas, esqueletos y más. Puedes usar diferentes armas, como espadas, arcos, hachas y armas. También puedes crear armaduras y pociones para protegerte. </p>
- <h3> ¿Cómo descargar e instalar Mini Block Craft APK? </h3>
-<p>Si desea jugar Mini bloque arte APK en su dispositivo Android, es necesario descargar e instalarlo primero. Estos son los pasos para hacer eso:</p>
- <h4> Requisitos para Mini bloque de arte APK</h4>
-<p>Antes de descargar e instalar el juego, asegúrese de que su dispositivo cumple con los siguientes requisitos:</p>
-<p></p>
-<ul>
-<li>Versión de Android 4.1 o superior</li>
-<li>Al menos 28 MB de espacio de almacenamiento libre</li>
-<li>Una conexión a Internet estable (opcional)</li>
-</ul>
- <h4> Pasos para descargar e instalar Mini Block Craft APK</h4>
-<p>Siga estos pasos para descargar e instalar el juego:</p>
-<ol>
-<li>Ir a [este enlace]( 1 ) para descargar la última versión de Mini Block Craft APK.</li>
-<li>Una vez completada la descarga, abra la aplicación de administrador de archivos en su dispositivo y localice el archivo descargado. </li>
-<li>Toque en el archivo y permita la instalación desde fuentes desconocidas si se le solicita. </li>
-<li> <li>Siga las instrucciones en pantalla para completar el proceso de instalación. </li>
-<li>Una vez que se hace la instalación, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio. </li>
-</ol>
- <h3>Pros y contras de Mini Block Craft APK</h3>
-<p>Mini Block Craft APK es un juego divertido y creativo, pero también tiene algunos inconvenientes. Estos son algunos de los pros y los contras del juego:</p>
- <h4> Pros de Mini bloque de arte APK</h4>
-<ul>
-<li>El juego es gratis para descargar y jugar. </li>
-<li>El juego tiene unos gráficos estilo píxel que son atractivos y nostálgicos. </li>
-<li>El juego tiene modos creativos y de supervivencia que ofrecen diferentes desafíos y experiencias. </li>
-
-<li>El juego tiene un modo multijugador en línea que te permite jugar con otros jugadores de todo el mundo. </li>
-</ul>
- <h4> Contras de Mini Block Craft APK</h4>
-<ul>
-<li>El juego contiene anuncios y compras en la aplicación que pueden ser molestos y caros. </li>
-<li>El juego puede ser lento y con errores en algunos dispositivos. </li>
-<li> El juego puede ser repetitivo y aburrido después de un tiempo. </li>
-<li>El juego tiene un sonido y música de baja calidad. </li>
-<li> El juego tiene una interfaz de usuario y controles pobres. </li>
-</ul>
- <h3>Alternativas a Mini bloque de arte APK</h3>
-<p>Si usted está buscando algunas alternativas a Mini Block Craft APK, puede probar estos juegos:</p>
- <h4>Edición de bolsillo de Minecraft</h4>
-<p>Minecraft Pocket Edition es la versión móvil oficial del famoso juego de sandbox Minecraft. Tiene las mismas características y jugabilidad que el juego original, pero está optimizado para dispositivos móviles. Puede crear, explorar y sobrevivir en un mundo generado al azar, o unirse a servidores en línea y jugar con otros jugadores. El juego cuesta $6.99 para descargar, pero ofrece actualizaciones regulares y nuevo contenido. </p>
- <h4>Roblox</h4>
-<p>Roblox es una plataforma de juego online multijugador masivo que te permite crear y jugar varios juegos. Puedes elegir entre millones de juegos creados por otros usuarios, o hacer los tuyos usando Roblox Studio. También puedes personalizar tu avatar, chatear con otros jugadores y unirte a grupos. El juego es gratis para descargar y jugar, pero tiene una moneda virtual llamada Robux que se puede usar para comprar artículos y acceder a funciones premium. </p>
- <h4>Terraria</h4>
-<p>Terraria es un juego de sandbox en 2D que combina elementos de acción, aventura y juegos de rol. Puedes explorar, construir, crear, luchar y sobrevivir en un mundo generado por procedimientos. También puedes jugar con hasta 7 amigos online o localmente. El juego cuesta $4.99 para descargar, pero tiene mucho contenido y actualizaciones. </p>
- <h2>Conclusión</h2>
-
- <p>Esperamos que este artículo le ayudó a aprender más acerca de Mini Block Craft APK. Si usted tiene alguna pregunta o retroalimentación, no dude en dejar un comentario a continuación. ¡Gracias por leer! </p>
- <h3>Preguntas frecuentes</h3>
-<ul>
-<li><b> ¿Es seguro descargar Mini Block Craft APK? </b></li>
-<p>Sí, Mini Block Craft APK es seguro de descargar desde [este enlace]. Sin embargo, siempre debe tener cuidado al descargar archivos de fuentes desconocidas y escanearlos en busca de virus antes de instalarlos. </p>
- <li><b> ¿Puedo jugar Mini Block Craft APK en PC? </b></li>
-<p>No, Mini Block Craft APK solo está disponible para dispositivos Android. Sin embargo, puede utilizar un emulador de Android en su PC para ejecutar el juego. Algunos emuladores de Android populares son BlueStacks, NoxPlayer y LDPlayer.</p>
- <li><b> ¿Cómo actualizo Mini Block Craft APK? </b></li>
-<p>Para actualizar Mini Block Craft APK, es necesario descargar la última versión del juego desde [este enlace] e instalarlo sobre el existente. Alternativamente, puede habilitar actualizaciones automáticas en la configuración de su dispositivo o usar una herramienta de actualización de aplicaciones. </p>
- <li><b> ¿Cómo puedo desinstalar Mini Block Craft APK? </b></li>
-<p>Para desinstalar Mini Block Craft APK, es necesario ir a la configuración de su dispositivo > aplicaciones > Mini Block Craft > desinstalar. Alternativamente, puedes arrastrar <p>el icono de la aplicación a la papelera en la pantalla de inicio o en el cajón de la aplicación. También es posible que tenga que eliminar el archivo APK de su aplicación de administrador de archivos. </p>
- <li><b> ¿Cuáles son algunos consejos y trucos para jugar Mini Block Craft APK? </b></li>
-<p>Aquí hay algunos consejos y trucos para jugar Mini Block Craft APK:</p>
-<ul>
-<li>Utilice el mapa y la brújula para navegar por el mundo y encontrar su hogar. </li>
-<li>Recoge tantos recursos como puedas y guárdalos en cofres. </li>
-<li>Crea una cama y duerme por la noche para evitar monstruos y zombis. </li>
-<li>Utilice antorchas y linternas para iluminar su casa y sus alrededores. </li>
-<li>Utiliza cercas y puertas para proteger tu casa de los enemigos. </li>
-<li>Usa diferentes herramientas y armas para diferentes tareas y enemigos. </li>
-
-<li>Explora el mapa y encuentra tesoros y secretos ocultos. </li>
-<li>Juega en línea con otros jugadores y chatea con ellos usando la función de chat. </li>
-¡Diviértete y sé creativo! </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/docstring.py

@@ -1,77 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from botocore.docs.docstring import LazyLoadedDocstring
-
-from boto3.docs.action import document_action, document_load_reload_action
-from boto3.docs.attr import (
-    document_attribute,
-    document_identifier,
-    document_reference,
-)
-from boto3.docs.collection import (
-    document_batch_action,
-    document_collection_method,
-    document_collection_object,
-)
-from boto3.docs.subresource import document_sub_resource
-from boto3.docs.waiter import document_resource_waiter
-
-
-class ActionDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_action(*args, **kwargs)
-
-
-class LoadReloadDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_load_reload_action(*args, **kwargs)
-
-
-class SubResourceDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_sub_resource(*args, **kwargs)
-
-
-class AttributeDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_attribute(*args, **kwargs)
-
-
-class IdentifierDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_identifier(*args, **kwargs)
-
-
-class ReferenceDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_reference(*args, **kwargs)
-
-
-class CollectionDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_collection_object(*args, **kwargs)
-
-
-class CollectionMethodDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_collection_method(*args, **kwargs)
-
-
-class BatchActionDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_batch_action(*args, **kwargs)
-
-
-class ResourceWaiterDocstring(LazyLoadedDocstring):
-    def _write_docstring(self, *args, **kwargs):
-        document_resource_waiter(*args, **kwargs)

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/exceptions.py

@@ -1,126 +0,0 @@
-# Copyright 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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.
-
-# All exceptions in this class should subclass from Boto3Error.
-import botocore.exceptions
-
-
-# All exceptions should subclass from Boto3Error in this module.
-class Boto3Error(Exception):
-    """Base class for all Boto3 errors."""
-
-
-class ResourceLoadException(Boto3Error):
-    pass
-
-
-# NOTE: This doesn't appear to be used anywhere.
-# It's probably safe to remove this.
-class NoVersionFound(Boto3Error):
-    pass
-
-
-# We're subclassing from botocore.exceptions.DataNotFoundError
-# to keep backwards compatibility with anyone that was catching
-# this low level Botocore error before this exception was
-# introduced in boto3.
-# Same thing for ResourceNotExistsError below.
-class UnknownAPIVersionError(
-    Boto3Error, botocore.exceptions.DataNotFoundError
-):
-    def __init__(self, service_name, bad_api_version, available_api_versions):
-        msg = (
-            f"The '{service_name}' resource does not an API version of: {bad_api_version}\n"
-            f"Valid API versions are: {available_api_versions}"
-        )
-        # Not using super because we don't want the DataNotFoundError
-        # to be called, it has a different __init__ signature.
-        Boto3Error.__init__(self, msg)
-
-
-class ResourceNotExistsError(
-    Boto3Error, botocore.exceptions.DataNotFoundError
-):
-    """Raised when you attempt to create a resource that does not exist."""
-
-    def __init__(self, service_name, available_services, has_low_level_client):
-        msg = (
-            "The '{}' resource does not exist.\n"
-            "The available resources are:\n"
-            "   - {}\n".format(
-                service_name, '\n   - '.join(available_services)
-            )
-        )
-        if has_low_level_client:
-            msg = (
-                f"{msg}\nConsider using a boto3.client('{service_name}') "
-                f"instead of a resource for '{service_name}'"
-            )
-        # Not using super because we don't want the DataNotFoundError
-        # to be called, it has a different __init__ signature.
-        Boto3Error.__init__(self, msg)
-
-
-class RetriesExceededError(Boto3Error):
-    def __init__(self, last_exception, msg='Max Retries Exceeded'):
-        super().__init__(msg)
-        self.last_exception = last_exception
-
-
-class S3TransferFailedError(Boto3Error):
-    pass
-
-
-class S3UploadFailedError(Boto3Error):
-    pass
-
-
-class DynamoDBOperationNotSupportedError(Boto3Error):
-    """Raised for operations that are not supported for an operand."""
-
-    def __init__(self, operation, value):
-        msg = (
-            f'{operation} operation cannot be applied to value {value} of type '
-            f'{type(value)} directly. Must use AttributeBase object methods '
-            f'(i.e. Attr().eq()). to generate ConditionBase instances first.'
-        )
-        Exception.__init__(self, msg)
-
-
-# FIXME: Backward compatibility
-DynanmoDBOperationNotSupportedError = DynamoDBOperationNotSupportedError
-
-
-class DynamoDBNeedsConditionError(Boto3Error):
-    """Raised when input is not a condition"""
-
-    def __init__(self, value):
-        msg = (
-            f'Expecting a ConditionBase object. Got {value} of type {type(value)}. '
-            f'Use AttributeBase object methods (i.e. Attr().eq()). to '
-            f'generate ConditionBase instances.'
-        )
-        Exception.__init__(self, msg)
-
-
-class DynamoDBNeedsKeyConditionError(Boto3Error):
-    pass
-
-
-class PythonDeprecationWarning(Warning):
-    """
-    Python version being used is scheduled to become unsupported
-    in an future release. See warning for specifics.
-    """
-
-    pass

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/structures.py

@@ -1,99 +0,0 @@
-"""
-requests.structures
-~~~~~~~~~~~~~~~~~~~
-
-Data structures that power Requests.
-"""
-
-from collections import OrderedDict
-
-from .compat import Mapping, MutableMapping
-
-
-class CaseInsensitiveDict(MutableMapping):
-    """A case-insensitive ``dict``-like object.
-
-    Implements all methods and operations of
-    ``MutableMapping`` as well as dict's ``copy``. Also
-    provides ``lower_items``.
-
-    All keys are expected to be strings. The structure remembers the
-    case of the last key to be set, and ``iter(instance)``,
-    ``keys()``, ``items()``, ``iterkeys()``, and ``iteritems()``
-    will contain case-sensitive keys. However, querying and contains
-    testing is case insensitive::
-
-        cid = CaseInsensitiveDict()
-        cid['Accept'] = 'application/json'
-        cid['aCCEPT'] == 'application/json'  # True
-        list(cid) == ['Accept']  # True
-
-    For example, ``headers['content-encoding']`` will return the
-    value of a ``'Content-Encoding'`` response header, regardless
-    of how the header name was originally stored.
-
-    If the constructor, ``.update``, or equality comparison
-    operations are given keys that have equal ``.lower()``s, the
-    behavior is undefined.
-    """
-
-    def __init__(self, data=None, **kwargs):
-        self._store = OrderedDict()
-        if data is None:
-            data = {}
-        self.update(data, **kwargs)
-
-    def __setitem__(self, key, value):
-        # Use the lowercased key for lookups, but store the actual
-        # key alongside the value.
-        self._store[key.lower()] = (key, value)
-
-    def __getitem__(self, key):
-        return self._store[key.lower()][1]
-
-    def __delitem__(self, key):
-        del self._store[key.lower()]
-
-    def __iter__(self):
-        return (casedkey for casedkey, mappedvalue in self._store.values())
-
-    def __len__(self):
-        return len(self._store)
-
-    def lower_items(self):
-        """Like iteritems(), but with all lowercase keys."""
-        return ((lowerkey, keyval[1]) for (lowerkey, keyval) in self._store.items())
-
-    def __eq__(self, other):
-        if isinstance(other, Mapping):
-            other = CaseInsensitiveDict(other)
-        else:
-            return NotImplemented
-        # Compare insensitively
-        return dict(self.lower_items()) == dict(other.lower_items())
-
-    # Copy is required
-    def copy(self):
-        return CaseInsensitiveDict(self._store.values())
-
-    def __repr__(self):
-        return str(dict(self.items()))
-
-
-class LookupDict(dict):
-    """Dictionary lookup object."""
-
-    def __init__(self, name=None):
-        self.name = name
-        super().__init__()
-
-    def __repr__(self):
-        return f"<lookup '{self.name}'>"
-
-    def __getitem__(self, key):
-        # We allow fall-through here, so values default to None
-
-        return self.__dict__.get(key, None)
-
-    def get(self, key, default=None):
-        return self.__dict__.get(key, default)

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/request.py

@@ -1,170 +0,0 @@
-from __future__ import absolute_import
-
-from .filepost import encode_multipart_formdata
-from .packages.six.moves.urllib.parse import urlencode
-
-__all__ = ["RequestMethods"]
-
-
-class RequestMethods(object):
-    """
-    Convenience mixin for classes who implement a :meth:`urlopen` method, such
-    as :class:`urllib3.HTTPConnectionPool` and
-    :class:`urllib3.PoolManager`.
-
-    Provides behavior for making common types of HTTP request methods and
-    decides which type of request field encoding to use.
-
-    Specifically,
-
-    :meth:`.request_encode_url` is for sending requests whose fields are
-    encoded in the URL (such as GET, HEAD, DELETE).
-
-    :meth:`.request_encode_body` is for sending requests whose fields are
-    encoded in the *body* of the request using multipart or www-form-urlencoded
-    (such as for POST, PUT, PATCH).
-
-    :meth:`.request` is for making any kind of request, it will look up the
-    appropriate encoding format and use one of the above two methods to make
-    the request.
-
-    Initializer parameters:
-
-    :param headers:
-        Headers to include with all requests, unless other headers are given
-        explicitly.
-    """
-
-    _encode_url_methods = {"DELETE", "GET", "HEAD", "OPTIONS"}
-
-    def __init__(self, headers=None):
-        self.headers = headers or {}
-
-    def urlopen(
-        self,
-        method,
-        url,
-        body=None,
-        headers=None,
-        encode_multipart=True,
-        multipart_boundary=None,
-        **kw
-    ):  # Abstract
-        raise NotImplementedError(
-            "Classes extending RequestMethods must implement "
-            "their own ``urlopen`` method."
-        )
-
-    def request(self, method, url, fields=None, headers=None, **urlopen_kw):
-        """
-        Make a request using :meth:`urlopen` with the appropriate encoding of
-        ``fields`` based on the ``method`` used.
-
-        This is a convenience method that requires the least amount of manual
-        effort. It can be used in most situations, while still having the
-        option to drop down to more specific methods when necessary, such as
-        :meth:`request_encode_url`, :meth:`request_encode_body`,
-        or even the lowest level :meth:`urlopen`.
-        """
-        method = method.upper()
-
-        urlopen_kw["request_url"] = url
-
-        if method in self._encode_url_methods:
-            return self.request_encode_url(
-                method, url, fields=fields, headers=headers, **urlopen_kw
-            )
-        else:
-            return self.request_encode_body(
-                method, url, fields=fields, headers=headers, **urlopen_kw
-            )
-
-    def request_encode_url(self, method, url, fields=None, headers=None, **urlopen_kw):
-        """
-        Make a request using :meth:`urlopen` with the ``fields`` encoded in
-        the url. This is useful for request methods like GET, HEAD, DELETE, etc.
-        """
-        if headers is None:
-            headers = self.headers
-
-        extra_kw = {"headers": headers}
-        extra_kw.update(urlopen_kw)
-
-        if fields:
-            url += "?" + urlencode(fields)
-
-        return self.urlopen(method, url, **extra_kw)
-
-    def request_encode_body(
-        self,
-        method,
-        url,
-        fields=None,
-        headers=None,
-        encode_multipart=True,
-        multipart_boundary=None,
-        **urlopen_kw
-    ):
-        """
-        Make a request using :meth:`urlopen` with the ``fields`` encoded in
-        the body. This is useful for request methods like POST, PUT, PATCH, etc.
-
-        When ``encode_multipart=True`` (default), then
-        :func:`urllib3.encode_multipart_formdata` is used to encode
-        the payload with the appropriate content type. Otherwise
-        :func:`urllib.parse.urlencode` is used with the
-        'application/x-www-form-urlencoded' content type.
-
-        Multipart encoding must be used when posting files, and it's reasonably
-        safe to use it in other times too. However, it may break request
-        signing, such as with OAuth.
-
-        Supports an optional ``fields`` parameter of key/value strings AND
-        key/filetuple. A filetuple is a (filename, data, MIME type) tuple where
-        the MIME type is optional. For example::
-
-            fields = {
-                'foo': 'bar',
-                'fakefile': ('foofile.txt', 'contents of foofile'),
-                'realfile': ('barfile.txt', open('realfile').read()),
-                'typedfile': ('bazfile.bin', open('bazfile').read(),
-                              'image/jpeg'),
-                'nonamefile': 'contents of nonamefile field',
-            }
-
-        When uploading a file, providing a filename (the first parameter of the
-        tuple) is optional but recommended to best mimic behavior of browsers.
-
-        Note that if ``headers`` are supplied, the 'Content-Type' header will
-        be overwritten because it depends on the dynamic random boundary string
-        which is used to compose the body of the request. The random boundary
-        string can be explicitly set with the ``multipart_boundary`` parameter.
-        """
-        if headers is None:
-            headers = self.headers
-
-        extra_kw = {"headers": {}}
-
-        if fields:
-            if "body" in urlopen_kw:
-                raise TypeError(
-                    "request got values for both 'fields' and 'body', can only specify one."
-                )
-
-            if encode_multipart:
-                body, content_type = encode_multipart_formdata(
-                    fields, boundary=multipart_boundary
-                )
-            else:
-                body, content_type = (
-                    urlencode(fields),
-                    "application/x-www-form-urlencoded",
-                )
-
-            extra_kw["body"] = body
-            extra_kw["headers"] = {"Content-Type": content_type}
-
-        extra_kw["headers"].update(headers)
-        extra_kw.update(urlopen_kw)
-
-        return self.urlopen(method, url, **extra_kw)

spaces/CVH-vn1210/make_hair/minigpt4/common/logger.py

@@ -1,195 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import datetime
-import logging
-import time
-from collections import defaultdict, deque
-
-import torch
-import torch.distributed as dist
-
-from minigpt4.common import dist_utils
-
-
-class SmoothedValue(object):
-    """Track a series of values and provide access to smoothed values over a
-    window or the global series average.
-    """
-
-    def __init__(self, window_size=20, fmt=None):
-        if fmt is None:
-            fmt = "{median:.4f} ({global_avg:.4f})"
-        self.deque = deque(maxlen=window_size)
-        self.total = 0.0
-        self.count = 0
-        self.fmt = fmt
-
-    def update(self, value, n=1):
-        self.deque.append(value)
-        self.count += n
-        self.total += value * n
-
-    def synchronize_between_processes(self):
-        """
-        Warning: does not synchronize the deque!
-        """
-        if not dist_utils.is_dist_avail_and_initialized():
-            return
-        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
-        dist.barrier()
-        dist.all_reduce(t)
-        t = t.tolist()
-        self.count = int(t[0])
-        self.total = t[1]
-
-    @property
-    def median(self):
-        d = torch.tensor(list(self.deque))
-        return d.median().item()
-
-    @property
-    def avg(self):
-        d = torch.tensor(list(self.deque), dtype=torch.float32)
-        return d.mean().item()
-
-    @property
-    def global_avg(self):
-        return self.total / self.count
-
-    @property
-    def max(self):
-        return max(self.deque)
-
-    @property
-    def value(self):
-        return self.deque[-1]
-
-    def __str__(self):
-        return self.fmt.format(
-            median=self.median,
-            avg=self.avg,
-            global_avg=self.global_avg,
-            max=self.max,
-            value=self.value,
-        )
-
-
-class MetricLogger(object):
-    def __init__(self, delimiter="\t"):
-        self.meters = defaultdict(SmoothedValue)
-        self.delimiter = delimiter
-
-    def update(self, **kwargs):
-        for k, v in kwargs.items():
-            if isinstance(v, torch.Tensor):
-                v = v.item()
-            assert isinstance(v, (float, int))
-            self.meters[k].update(v)
-
-    def __getattr__(self, attr):
-        if attr in self.meters:
-            return self.meters[attr]
-        if attr in self.__dict__:
-            return self.__dict__[attr]
-        raise AttributeError(
-            "'{}' object has no attribute '{}'".format(type(self).__name__, attr)
-        )
-
-    def __str__(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            loss_str.append("{}: {}".format(name, str(meter)))
-        return self.delimiter.join(loss_str)
-
-    def global_avg(self):
-        loss_str = []
-        for name, meter in self.meters.items():
-            loss_str.append("{}: {:.4f}".format(name, meter.global_avg))
-        return self.delimiter.join(loss_str)
-
-    def synchronize_between_processes(self):
-        for meter in self.meters.values():
-            meter.synchronize_between_processes()
-
-    def add_meter(self, name, meter):
-        self.meters[name] = meter
-
-    def log_every(self, iterable, print_freq, header=None):
-        i = 0
-        if not header:
-            header = ""
-        start_time = time.time()
-        end = time.time()
-        iter_time = SmoothedValue(fmt="{avg:.4f}")
-        data_time = SmoothedValue(fmt="{avg:.4f}")
-        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
-        log_msg = [
-            header,
-            "[{0" + space_fmt + "}/{1}]",
-            "eta: {eta}",
-            "{meters}",
-            "time: {time}",
-            "data: {data}",
-        ]
-        if torch.cuda.is_available():
-            log_msg.append("max mem: {memory:.0f}")
-        log_msg = self.delimiter.join(log_msg)
-        MB = 1024.0 * 1024.0
-        for obj in iterable:
-            data_time.update(time.time() - end)
-            yield obj
-            iter_time.update(time.time() - end)
-            if i % print_freq == 0 or i == len(iterable) - 1:
-                eta_seconds = iter_time.global_avg * (len(iterable) - i)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-                if torch.cuda.is_available():
-                    print(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                            memory=torch.cuda.max_memory_allocated() / MB,
-                        )
-                    )
-                else:
-                    print(
-                        log_msg.format(
-                            i,
-                            len(iterable),
-                            eta=eta_string,
-                            meters=str(self),
-                            time=str(iter_time),
-                            data=str(data_time),
-                        )
-                    )
-            i += 1
-            end = time.time()
-        total_time = time.time() - start_time
-        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-        print(
-            "{} Total time: {} ({:.4f} s / it)".format(
-                header, total_time_str, total_time / len(iterable)
-            )
-        )
-
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def setup_logger():
-    logging.basicConfig(
-        level=logging.INFO if dist_utils.is_main_process() else logging.WARN,
-        format="%(asctime)s [%(levelname)s] %(message)s",
-        handlers=[logging.StreamHandler()],
-    )

spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/tools/process.py

@@ -1,18 +0,0 @@
-# Process data
-import argparse
-from compute_softscore import compute_softscore
-from create_dictionary import create_dictionary
-from detection_features_converter import detection_features_converter 
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', type=str, default='../data/')
-    parser.add_argument('--ver', type=str, default='clean', help='version of the VQAv2 dataset to process. "clean" for the original data. default: clean')
-    parser.add_argument('--detector', type=str, default='R-50')
-    parser.add_argument('--feat', type=int, default=1024, help='feature size')
-    parser.add_argument('--nb', type=int, default=36)
-    parser.add_argument('--emb_dim', type=int, default=300)
-    args = parser.parse_args()
-    create_dictionary(args.dataroot, args.emb_dim)
-    compute_softscore(args.dataroot, args.ver)
-    detection_features_converter(args.dataroot, args.ver, args.detector, args.feat, args.nb)

spaces/CVPR/GFPGAN-example/gfpgan/utils.py

@@ -1,130 +0,0 @@
-import cv2
-import os
-import torch
-from basicsr.utils import img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-from torchvision.transforms.functional import normalize
-
-from gfpgan.archs.gfpganv1_arch import GFPGANv1
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class GFPGANer():
-    """Helper for restoration with GFPGAN.
-
-    It will detect and crop faces, and then resize the faces to 512x512.
-    GFPGAN is used to restored the resized faces.
-    The background is upsampled with the bg_upsampler.
-    Finally, the faces will be pasted back to the upsample background image.
-
-    Args:
-        model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
-        upscale (float): The upscale of the final output. Default: 2.
-        arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        bg_upsampler (nn.Module): The upsampler for the background. Default: None.
-    """
-
-    def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None):
-        self.upscale = upscale
-        self.bg_upsampler = bg_upsampler
-
-        # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        # initialize the GFP-GAN
-        if arch == 'clean':
-            self.gfpgan = GFPGANv1Clean(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        else:
-            self.gfpgan = GFPGANv1(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=True,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        # initialize face helper
-        self.face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model='retinaface_resnet50',
-            save_ext='png',
-            device=self.device)
-
-        if model_path.startswith('https://'):
-            model_path = load_file_from_url(
-                url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None)
-        loadnet = torch.load(model_path)
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
-        self.gfpgan.eval()
-        self.gfpgan = self.gfpgan.to(self.device)
-
-    @torch.no_grad()
-    def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True):
-        self.face_helper.clean_all()
-
-        if has_aligned:  # the inputs are already aligned
-            img = cv2.resize(img, (512, 512))
-            self.face_helper.cropped_faces = [img]
-        else:
-            self.face_helper.read_image(img)
-            # get face landmarks for each face
-            self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5)
-            # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels
-            # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations.
-            # align and warp each face
-            self.face_helper.align_warp_face()
-
-        # face restoration
-        for cropped_face in self.face_helper.cropped_faces:
-            # prepare data
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
-
-            try:
-                output = self.gfpgan(cropped_face_t, return_rgb=False)[0]
-                # convert to image
-                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
-            except RuntimeError as error:
-                print(f'\tFailed inference for GFPGAN: {error}.')
-                restored_face = cropped_face
-
-            restored_face = restored_face.astype('uint8')
-            self.face_helper.add_restored_face(restored_face)
-
-        if not has_aligned and paste_back:
-            # upsample the background
-            if self.bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
-            else:
-                bg_img = None
-
-            self.face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
-        else:
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, None

spaces/CVPR/LIVE/thrust/README.md

@@ -1,161 +0,0 @@
-Thrust: Code at the speed of light
-==================================
-
-Thrust is a C++ parallel programming library which resembles the C++ Standard
-Library. Thrust's **high-level** interface greatly enhances
-programmer **productivity** while enabling performance portability between
-GPUs and multicore CPUs. **Interoperability** with established technologies
-(such as CUDA, TBB, and OpenMP) facilitates integration with existing
-software. Develop **high-performance** applications rapidly with Thrust!
-
-Thrust is included in the NVIDIA HPC SDK and the CUDA Toolkit.
-
-Refer to the [Quick Start Guide](http://github.com/thrust/thrust/wiki/Quick-Start-Guide) page for further information and examples.
-
-Examples
---------
-
-Thrust is best explained through examples. The following source code
-generates random numbers serially and then transfers them to a parallel
-device where they are sorted.
-
-```c++
-#include <thrust/host_vector.h>
-#include <thrust/device_vector.h>
-#include <thrust/generate.h>
-#include <thrust/sort.h>
-#include <thrust/copy.h>
-#include <algorithm>
-#include <cstdlib>
-
-int main(void)
-{
-  // generate 32M random numbers serially
-  thrust::host_vector<int> h_vec(32 << 20);
-  std::generate(h_vec.begin(), h_vec.end(), rand);
-
-  // transfer data to the device
-  thrust::device_vector<int> d_vec = h_vec;
-
-  // sort data on the device (846M keys per second on GeForce GTX 480)
-  thrust::sort(d_vec.begin(), d_vec.end());
-
-  // transfer data back to host
-  thrust::copy(d_vec.begin(), d_vec.end(), h_vec.begin());
-
-  return 0;
-}
-```
-
-This code sample computes the sum of 100 random numbers in parallel:
-
-```c++
-#include <thrust/host_vector.h>
-#include <thrust/device_vector.h>
-#include <thrust/generate.h>
-#include <thrust/reduce.h>
-#include <thrust/functional.h>
-#include <algorithm>
-#include <cstdlib>
-
-int main(void)
-{
-  // generate random data serially
-  thrust::host_vector<int> h_vec(100);
-  std::generate(h_vec.begin(), h_vec.end(), rand);
-
-  // transfer to device and compute sum
-  thrust::device_vector<int> d_vec = h_vec;
-  int x = thrust::reduce(d_vec.begin(), d_vec.end(), 0, thrust::plus<int>());
-  return 0;
-}
-```
-
-Releases
---------
-
-Thrust is distributed with the NVIDIA HPC SDK and the CUDA Toolkit in addition
-to GitHub.
-
-See the [changelog](CHANGELOG.md) for details about specific releases.
-
-| Thrust Release    | Included In                             |
-| ----------------- | --------------------------------------- |
-| 1.9.10-1          | NVIDIA HPC SDK 20.7 & CUDA Toolkit 11.1 |
-| 1.9.10            | NVIDIA HPC SDK 20.5                     |
-| 1.9.9             | CUDA Toolkit 11.0                       |
-| 1.9.8-1           | NVIDIA HPC SDK 20.3                     |
-| 1.9.8             | CUDA Toolkit 11.0 Early Access          |
-| 1.9.7-1           | CUDA Toolkit 10.2 for Tegra             |
-| 1.9.7             | CUDA Toolkit 10.2                       |
-| 1.9.6-1           | NVIDIA HPC SDK 20.3                     |
-| 1.9.6             | CUDA Toolkit 10.1 Update 2              |
-| 1.9.5             | CUDA Toolkit 10.1 Update 1              |
-| 1.9.4             | CUDA Toolkit 10.1                       |
-| 1.9.3             | CUDA Toolkit 10.0                       |
-| 1.9.2             | CUDA Toolkit 9.2                        |
-| 1.9.1-2           | CUDA Toolkit 9.1                        |
-| 1.9.0-5           | CUDA Toolkit 9.0                        |
-| 1.8.3             | CUDA Toolkit 8.0                        |
-| 1.8.2             | CUDA Toolkit 7.5                        |
-| 1.8.1             | CUDA Toolkit 7.0                        |
-| 1.8.0             |                                         |
-| 1.7.2             | CUDA Toolkit 6.5                        |
-| 1.7.1             | CUDA Toolkit 6.0                        |
-| 1.7.0             | CUDA Toolkit 5.5                        |
-| 1.6.0             |                                         |
-| 1.5.3             | CUDA Toolkit 5.0                        |
-| 1.5.2             | CUDA Toolkit 4.2                        |
-| 1.5.1             | CUDA Toolkit 4.1                        |
-| 1.5.0             |                                         |
-| 1.4.0             | CUDA Toolkit 4.0                        |
-| 1.3.0             |                                         |
-| 1.2.1             |                                         |
-| 1.2.0             |                                         |
-| 1.1.1             |                                         |
-| 1.1.0             |                                         |
-| 1.0.0             |                                         |
-
-Adding Thrust To A CMake Project
---------------------------------
-
-Since Thrust is a header library, there is no need to build or install Thrust
-to use it. The `thrust` directory contains a complete, ready-to-use Thrust
-package upon checkout.
-
-We provide CMake configuration files that make it easy to include Thrust
-from other CMake projects. See the [CMake README](thrust/cmake/README.md)
-for details.
-
-Development Process
--------------------
-
-Thrust uses the [CMake build system](https://cmake.org/) to build unit tests,
-examples, and header tests. To build Thrust as a developer, the following
-recipe should be followed:
-
-```
-# Clone Thrust and CUB repos recursively:
-git clone --recursive https://github.com/thrust/thrust.git
-cd thrust
-
-# Create build directory:
-mkdir build
-cd build
-
-# Configure -- use one of the following:
-cmake ..   # Command line interface.
-ccmake ..  # ncurses GUI (Linux only)
-cmake-gui  # Graphical UI, set source/build directories in the app
-
-# Build:
-cmake --build . -j <num jobs>   # invokes make (or ninja, etc)
-
-# Run tests and examples:
-ctest
-```
-
-By default, a serial `CPP` host system, `CUDA` accelerated device system, and
-C++14 standard are used. This can be changed in CMake. More information on
-configuring your Thrust build and creating a pull request can be found in
-[CONTRIBUTING.md](CONTRIBUTING.md).

spaces/CVPR/LIVE/thrust/thrust/detail/functional/operators/operator_adaptors.h

@@ -1,137 +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/functional/argument.h>
-#include <thrust/detail/type_deduction.h>
-#include <thrust/tuple.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/type_traits/void_t.h>
-
-#include <type_traits>
-
-namespace thrust
-{
-namespace detail
-{
-namespace functional
-{
-
-// Adapts a transparent unary functor from functional.h (e.g. thrust::negate<>)
-// into the Eval interface.
-template <typename UnaryFunctor>
-struct transparent_unary_operator
-{
-  template <typename>
-  using operator_type = UnaryFunctor;
-
-  template <typename Env>
-  using argument =
-  typename thrust::detail::eval_if<
-    thrust::tuple_size<Env>::value != 1,
-    thrust::detail::identity_<thrust::null_type>,
-    thrust::detail::functional::argument_helper<0, Env>
-  >::type;
-
-  template <typename Env>
-  struct result_type_impl
-  {
-    using type = decltype(
-      std::declval<UnaryFunctor>()(std::declval<argument<Env>>()));
-  };
-
-  template <typename Env>
-  using result_type =
-  typename thrust::detail::eval_if<
-    std::is_same<thrust::null_type, argument<Env>>::value,
-    thrust::detail::identity_<thrust::null_type>,
-    result_type_impl<Env>
-  >::type;
-
-  template <typename Env>
-  struct result
-  {
-    using op_type = UnaryFunctor;
-    using type = result_type<Env>;
-  };
-
-  template <typename Env>
-  __host__ __device__
-  result_type<Env> eval(Env&& e) const
-  THRUST_RETURNS(UnaryFunctor{}(thrust::get<0>(THRUST_FWD(e))))
-};
-
-
-// Adapts a transparent binary functor from functional.h (e.g. thrust::less<>)
-// into the Eval interface.
-template <typename BinaryFunctor>
-struct transparent_binary_operator
-{
-  template <typename>
-  using operator_type = BinaryFunctor;
-
-  template <typename Env>
-  using first_argument =
-    typename thrust::detail::eval_if<
-      thrust::tuple_size<Env>::value != 2,
-      thrust::detail::identity_<thrust::null_type>,
-      thrust::detail::functional::argument_helper<0, Env>
-    >::type;
-
-  template <typename Env>
-  using second_argument =
-    typename thrust::detail::eval_if<
-      thrust::tuple_size<Env>::value != 2,
-      thrust::detail::identity_<thrust::null_type>,
-      thrust::detail::functional::argument_helper<1, Env>
-    >::type;
-
-  template <typename Env>
-  struct result_type_impl
-  {
-    using type = decltype(
-      std::declval<BinaryFunctor>()(std::declval<first_argument<Env>>(),
-                                    std::declval<second_argument<Env>>()));
-  };
-
-  template <typename Env>
-  using result_type =
-    typename thrust::detail::eval_if<
-      (std::is_same<thrust::null_type, first_argument<Env>>::value ||
-       std::is_same<thrust::null_type, second_argument<Env>>::value),
-      thrust::detail::identity_<thrust::null_type>,
-      result_type_impl<Env>
-    >::type;
-
-  template <typename Env>
-  struct result
-  {
-    using op_type = BinaryFunctor;
-    using type = result_type<Env>;
-  };
-
-  template <typename Env>
-  __host__ __device__
-  result_type<Env> eval(Env&& e) const
-  THRUST_RETURNS(BinaryFunctor{}(thrust::get<0>(e), thrust::get<1>(e)))
-};
-
-} // end functional
-} // end detail
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/reverse.h

@@ -1,215 +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 reverse.h
- *  \brief Reverses the order of a range
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup reordering
- *  \ingroup algorithms
- */
-
-
-/*! \p reverse reverses a range. That is: for every <tt>i</tt> such that
- *  <tt>0 <= i <= (last - first) / 2</tt>, it exchanges <tt>*(first + i)</tt>
- *  and <tt>*(last - (i + 1))</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the range to reverse.
- *  \param last The end of the range to reverse.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam BidirectionalIterator is a model of <a href="http://www.sgi.com/tech/stl/BidirectionalIterator.html">Bidirectional Iterator</a> and
- *          \p BidirectionalIterator is mutable.
- *
- *  The following code snippet demonstrates how to use \p reverse to reverse a
- *  \p device_vector of integers using the \p thrust::device execution policy for
- *  parallelization:
- *
- *  \code
- *  #include <thrust/reverse.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 6;
- *  int data[N] = {0, 1, 2, 3, 4, 5};
- *  thrust::device_vector<int> v(data, data + N);
- *  thrust::reverse(thrust::device, v.begin(), v.end());
- *  // v is now {5, 4, 3, 2, 1, 0}
- *  \endcode
- *  
- *  \see http://www.sgi.com/tech/stl/reverse.html
- *  \see \p reverse_copy
- *  \see \p reverse_iterator
- */
-template<typename DerivedPolicy, typename BidirectionalIterator>
-__host__ __device__
-  void reverse(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-               BidirectionalIterator first,
-               BidirectionalIterator last);
-
-
-/*! \p reverse reverses a range. That is: for every <tt>i</tt> such that
- *  <tt>0 <= i <= (last - first) / 2</tt>, it exchanges <tt>*(first + i)</tt>
- *  and <tt>*(last - (i + 1))</tt>.
- *
- *  \param first The beginning of the range to reverse.
- *  \param last The end of the range to reverse.
- *
- *  \tparam BidirectionalIterator is a model of <a href="http://www.sgi.com/tech/stl/BidirectionalIterator.html">Bidirectional Iterator</a> and
- *          \p BidirectionalIterator is mutable.
- *
- *  The following code snippet demonstrates how to use \p reverse to reverse a
- *  \p device_vector of integers.
- *
- *  \code
- *  #include <thrust/reverse.h>
- *  ...
- *  const int N = 6;
- *  int data[N] = {0, 1, 2, 3, 4, 5};
- *  thrust::device_vector<int> v(data, data + N);
- *  thrust::reverse(v.begin(), v.end());
- *  // v is now {5, 4, 3, 2, 1, 0}
- *  \endcode
- *  
- *  \see http://www.sgi.com/tech/stl/reverse.html
- *  \see \p reverse_copy
- *  \see \p reverse_iterator
- */
-template<typename BidirectionalIterator>
-  void reverse(BidirectionalIterator first,
-               BidirectionalIterator last);
-
-
-/*! \p reverse_copy differs from \p reverse only in that the reversed range
- *  is written to a different output range, rather than inplace.
- *
- *  \p reverse_copy copies elements from the range <tt>[first, last)</tt> to the
- *  range <tt>[result, result + (last - first))</tt> such that the copy is a 
- *  reverse of the original range. Specifically: for every <tt>i</tt> such that
- *  <tt>0 <= i < (last - first)</tt>, \p reverse_copy performs the assignment
- *  <tt>*(result + (last - first) - i) = *(first + i)</tt>.
- *
- *  The return value is <tt>result + (last - first))</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the range to reverse.
- *  \param last The end of the range to reverse.
- *  \param result The beginning of the output range.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam BidirectionalIterator is a model of <a href="http://www.sgi.com/tech/stl/BidirectionalIterator.html">Bidirectional Iterator</a>,
- *          and \p BidirectionalIterator's \p value_type is convertible to \p OutputIterator's \p value_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *
- *  \pre The range <tt>[first, last)</tt> and the range <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p reverse_copy to reverse
- *  an input \p device_vector of integers to an output \p device_vector using the \p thrust::device
- *  execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/reverse.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 6;
- *  int data[N] = {0, 1, 2, 3, 4, 5};
- *  thrust::device_vector<int> input(data, data + N);
- *  thrust::device_vector<int> output(N);
- *  thrust::reverse_copy(thrust::device, v.begin(), v.end(), output.begin());
- *  // input is still {0, 1, 2, 3, 4, 5}
- *  // output is now  {5, 4, 3, 2, 1, 0}
- *  \endcode
- *  
- *  \see http://www.sgi.com/tech/stl/reverse_copy.html
- *  \see \p reverse
- *  \see \p reverse_iterator
- */
-template<typename DerivedPolicy, typename BidirectionalIterator, typename OutputIterator>
-__host__ __device__
-  OutputIterator reverse_copy(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                              BidirectionalIterator first,
-                              BidirectionalIterator last,
-                              OutputIterator result);
-
-
-/*! \p reverse_copy differs from \p reverse only in that the reversed range
- *  is written to a different output range, rather than inplace.
- *
- *  \p reverse_copy copies elements from the range <tt>[first, last)</tt> to the
- *  range <tt>[result, result + (last - first))</tt> such that the copy is a 
- *  reverse of the original range. Specifically: for every <tt>i</tt> such that
- *  <tt>0 <= i < (last - first)</tt>, \p reverse_copy performs the assignment
- *  <tt>*(result + (last - first) - i) = *(first + i)</tt>.
- *
- *  The return value is <tt>result + (last - first))</tt>.
- *
- *  \param first The beginning of the range to reverse.
- *  \param last The end of the range to reverse.
- *  \param result The beginning of the output range.
- *
- *  \tparam BidirectionalIterator is a model of <a href="http://www.sgi.com/tech/stl/BidirectionalIterator.html">Bidirectional Iterator</a>,
- *          and \p BidirectionalIterator's \p value_type is convertible to \p OutputIterator's \p value_type.
- *  \tparam OutputIterator is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a>.
- *
- *  \pre The range <tt>[first, last)</tt> and the range <tt>[result, result + (last - first))</tt> shall not overlap.
- *
- *  The following code snippet demonstrates how to use \p reverse_copy to reverse
- *  an input \p device_vector of integers to an output \p device_vector.
- *
- *  \code
- *  #include <thrust/reverse.h>
- *  ...
- *  const int N = 6;
- *  int data[N] = {0, 1, 2, 3, 4, 5};
- *  thrust::device_vector<int> input(data, data + N);
- *  thrust::device_vector<int> output(N);
- *  thrust::reverse_copy(v.begin(), v.end(), output.begin());
- *  // input is still {0, 1, 2, 3, 4, 5}
- *  // output is now  {5, 4, 3, 2, 1, 0}
- *  \endcode
- *  
- *  \see http://www.sgi.com/tech/stl/reverse_copy.html
- *  \see \p reverse
- *  \see \p reverse_iterator
- */
-template<typename BidirectionalIterator, typename OutputIterator>
-  OutputIterator reverse_copy(BidirectionalIterator first,
-                              BidirectionalIterator last,
-                              OutputIterator result);
-
-
-/*! \} // end reordering
- */
-
-
-} // end thrust
-
-#include <thrust/detail/reverse.inl>
-

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

@@ -1,22 +0,0 @@
-/*
- *  Copyright 2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special per device resource functions
-

spaces/CVPR/LIVE/thrust/thrust/system/system_error.h

@@ -1,179 +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 system/system_error.h
- *  \brief An exception object used to report error conditions that have an
- *         associated error code
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <stdexcept>
-#include <string>
-
-#include <thrust/system/error_code.h>
-
-namespace thrust
-{
-
-namespace system
-{
-
-// [19.5.5] Class system_error
-
-// [19.5.5.1] Class system_error overview
-
-/*! \addtogroup system_diagnostics System Diagnostics
- *  \ingroup system
- *  \{
- */
-
-/*! \brief The class \p system_error describes an exception object used to report error
- *  conditions that have an associated \p error_code. Such error conditions typically
- *  originate from the operating system or other low-level application program interfaces.
- *
- *  Thrust uses \p system_error to report the error codes returned from device backends
- *  such as the CUDA runtime.
- *
- *  The following code listing demonstrates how to catch a \p system_error to recover
- *  from an error.
- *
- *  \code
- *
- *  #include <thrust/device_vector.h>
- *  #include <thrust/system.h>
- *  #include <thrust/sort.h>
- *
- *  void terminate_gracefully(void)
- *  {
- *    // application-specific termination code here
- *    ...
- *  }
- *
- *  int main(void)
- *  {
- *    try
- *    {
- *      thrust::device_vector<float> vec;
- *      thrust::sort(vec.begin(), vec.end());
- *    }
- *    catch(thrust::system_error e)
- *    {
- *      std::cerr << "Error inside sort: " << e.what() << std::endl;
- *      terminate_gracefully();
- *    }
- *
- *    return 0;
- *  }
- *
- *  \endcode
- *
- *  \note If an error represents an out-of-memory condition, implementations are encouraged
- *  to throw an exception object of type \p std::bad_alloc rather than \p system_error.
- */
-class system_error
-  : public std::runtime_error
-{
-  public:
-    // [19.5.5.2] Class system_error members
-    
-    /*! Constructs an object of class \p system_error.
-     *  \param ec The value returned by \p code().
-     *  \param what_arg A string to include in the result returned by \p what().
-     *  \post <tt>code() == ec</tt>.
-     *  \post <tt>std::string(what()).find(what_arg) != string::npos</tt>.
-     */
-    inline system_error(error_code ec, const std::string &what_arg);
-
-    /*! Constructs an object of class \p system_error.
-     *  \param ec The value returned by \p code().
-     *  \param what_arg A string to include in the result returned by \p what().
-     *  \post <tt>code() == ec</tt>.
-     *  \post <tt>std::string(what()).find(what_arg) != string::npos</tt>.
-     */
-    inline system_error(error_code ec, const char *what_arg);
-
-    /*! Constructs an object of class \p system_error.
-     *  \param ec The value returned by \p code().
-     *  \post <tt>code() == ec</tt>.
-     */
-    inline system_error(error_code ec);
-
-    /*! Constructs an object of class \p system_error.
-     *  \param ev The error value used to create an \p error_code.
-     *  \param ecat The \p error_category used to create an \p error_code.
-     *  \param what_arg A string to include in the result returned by \p what().
-     *  \post <tt>code() == error_code(ev, ecat)</tt>.
-     *  \post <tt>std::string(what()).find(what_arg) != string::npos</tt>.
-     */
-    inline system_error(int ev, const error_category &ecat, const std::string &what_arg);
-
-    /*! Constructs an object of class \p system_error.
-     *  \param ev The error value used to create an \p error_code.
-     *  \param ecat The \p error_category used to create an \p error_code.
-     *  \param what_arg A string to include in the result returned by \p what().
-     *  \post <tt>code() == error_code(ev, ecat)</tt>.
-     *  \post <tt>std::string(what()).find(what_arg) != string::npos</tt>.
-     */
-    inline system_error(int ev, const error_category &ecat, const char *what_arg);
-
-    /*! Constructs an object of class \p system_error.
-     *  \param ev The error value used to create an \p error_code.
-     *  \param ecat The \p error_category used to create an \p error_code.
-     *  \post <tt>code() == error_code(ev, ecat)</tt>.
-     */
-    inline system_error(int ev, const error_category &ecat);
-
-    /*! Destructor does not throw.
-     */
-    inline virtual ~system_error(void) throw () {};
-    
-    /*! Returns an object encoding the error.
-     *  \return <tt>ec</tt> or <tt>error_code(ev, ecat)</tt>, from the
-     *          constructor, as appropriate.
-     */
-    inline const error_code &code(void) const throw();
-
-    /*! Returns a human-readable string indicating the nature of the error.
-     *  \return a string incorporating <tt>code().message()</tt> and the
-     *          arguments supplied in the constructor.
-     */
-    inline const char *what(void) const throw();
-
-    /*! \cond
-     */
-  private:
-    error_code          m_error_code;
-    mutable std::string m_what;
-
-    /*! \endcond
-     */
-}; // end system_error
-
-} // end system
-
-/*! \} // end system_diagnostics
- */
-
-// import names into thrust::
-using system::system_error;
-
-} // end thrust
-
-#include <thrust/system/detail/system_error.inl>
-

spaces/CVPR/unicl-zero-shot-img-recog/app.py

@@ -1,163 +0,0 @@
-import argparse
-import requests
-import gradio as gr
-import numpy as np
-import cv2
-import torch
-import torch.nn as nn
-from PIL import Image
-from pathlib import Path
-from torchvision import transforms
-from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from timm.data import create_transform
-from config import get_config
-from model import build_model
-
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-def parse_option():
-    parser = argparse.ArgumentParser('UniCL demo script', add_help=False)
-    parser.add_argument('--cfg', type=str, default="configs/unicl_swin_base.yaml", metavar="FILE", help='path to config file', )
-    args, unparsed = parser.parse_known_args()
-
-    config = get_config(args)
-
-    return args, config
-
-def build_transforms(img_size, center_crop=True):
-    t = [transforms.ToPILImage()]
-    if center_crop:
-        size = int((256 / 224) * img_size)
-        t.append(
-            transforms.Resize(size)
-        )
-        t.append(
-            transforms.CenterCrop(img_size)    
-        )
-    else:
-        t.append(
-            transforms.Resize(img_size)
-        )        
-    t.append(transforms.ToTensor())
-    t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
-    return transforms.Compose(t)
-
-def build_transforms4display(img_size, center_crop=True):
-    t = [transforms.ToPILImage()]
-    if center_crop:
-        size = int((256 / 224) * img_size)
-        t.append(
-            transforms.Resize(size)
-        )
-        t.append(
-            transforms.CenterCrop(img_size)    
-        )
-    else:
-        t.append(
-            transforms.Resize(img_size)
-        )  
-    t.append(transforms.ToTensor())
-    return transforms.Compose(t)
-
-args, config = parse_option()
-
-'''
-build model
-'''
-model = build_model(config)
-
-url = './in21k_yfcc14m_gcc15m_swin_base.pth'
-checkpoint = torch.load(url, map_location="cpu")
-model.load_state_dict(checkpoint["model"])
-model.eval()
-
-'''
-build data transform
-'''
-eval_transforms = build_transforms(224, center_crop=True)
-display_transforms = build_transforms4display(224, center_crop=True)
-
-'''
-build upsampler
-'''
-# upsampler = nn.Upsample(scale_factor=16, mode='bilinear')
-
-'''
-borrow code from here: https://github.com/jacobgil/pytorch-grad-cam/blob/master/pytorch_grad_cam/utils/image.py
-'''
-def show_cam_on_image(img: np.ndarray,
-                      mask: np.ndarray,
-                      use_rgb: bool = False,
-                      colormap: int = cv2.COLORMAP_JET) -> np.ndarray:
-    """ This function overlays the cam mask on the image as an heatmap.
-    By default the heatmap is in BGR format.
-    :param img: The base image in RGB or BGR format.
-    :param mask: The cam mask.
-    :param use_rgb: Whether to use an RGB or BGR heatmap, this should be set to True if 'img' is in RGB format.
-    :param colormap: The OpenCV colormap to be used.
-    :returns: The default image with the cam overlay.
-    """
-    heatmap = cv2.applyColorMap(np.uint8(255 * mask), colormap)
-    if use_rgb:
-        heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
-    heatmap = np.float32(heatmap) / 255
-
-    if np.max(img) > 1:
-        raise Exception(
-            "The input image should np.float32 in the range [0, 1]")
-
-    cam = 0.7*heatmap + 0.3*img
-    # cam = cam / np.max(cam)
-    return np.uint8(255 * cam)
-
-def recognize_image(image, texts):
-    img_t = eval_transforms(image) 
-    img_d = display_transforms(image).permute(1, 2, 0).numpy()
-
-    text_embeddings = model.get_text_embeddings(texts.split(';'))
-
-    # compute output
-    feat_img, feat_map, H, W = model.encode_image(img_t.unsqueeze(0), output_map=True)
-    output = model.logit_scale.exp() * feat_img @ text_embeddings.t()
-    prediction = output.softmax(-1).flatten()
-
-    # generate feat map given the top matched texts
-    output_map = (feat_map * text_embeddings[prediction.argmax()].unsqueeze(-1)).sum(1).softmax(-1)
-    output_map = output_map.view(1, 1, H, W)
-
-    output_map = nn.Upsample(size=img_t.shape[1:], mode='bilinear')(output_map)
-    output_map = output_map.squeeze(1).detach().permute(1, 2, 0).numpy()
-    output_map = (output_map - output_map.min()) / (output_map.max() - output_map.min())
-    heatmap = show_cam_on_image(img_d, output_map, use_rgb=True)
-    
-    show_img = np.concatenate((np.uint8(255 * img_d), heatmap), 1)
-    return {texts.split(';')[i]: float(prediction[i]) for i in range(len(texts.split(';')))}, Image.fromarray(show_img)
-
-
-image = gr.inputs.Image()
-label = gr.outputs.Label(num_top_classes=100)
-
-description = "UniCL for Zero-shot Image Recognition. Given an image, our model maps it to an arbitary text in a candidate pool."
-gr.Interface(
-    description=description,
-    fn=recognize_image,
-    inputs=["image", "text"],
-    outputs=[                          
-        label, 
-        gr.outputs.Image(
-        type="pil",
-        label="crop input/heat map"),                
-    ],
-    examples=[
-    ["./elephants.png", "an elephant; an elephant walking in the river; four elephants walking in the river"], 
-    ["./apple_with_ipod.jpg", "an ipod; an apple with a write note 'ipod'; an apple with a write note 'iphone'"], 
-    ["./apple_with_ipod.jpg", "a write note 'ipod'; a write note 'ipad'; a write note 'iphone'"],
-    ["./crowd2.jpg", "a street; a street with a woman walking in the middle; a street with a man walking in the  middle"],
-    ["./donut.png", "a bread; a donut; some donuts"],    
-    ["./horse.png", "an image of horse; an image of cow; an image of dog"],
-    ["./dog_and_cat.jfif", "a dog; a cat; dog and cat"],
-    ],
-    article=Path("docs/intro.md").read_text()    
-).launch()

spaces/Chintan-Donda/KKMS-KSSW-HF/src/web_crawler.py

@@ -1,58 +0,0 @@
-import requests
-from bs4 import BeautifulSoup as bs
-
-
-class LOAD_ONLINE_PDF_IPM_PACKAGES:
-    def __init__(self):
-        self.base_url = 'https://ppqs.gov.in/ipm-packages'
-        
-        self.ipm_packages = []
-        self.pdfs_urls = []
-        
-        self.headers = {
-            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
-        }
-    
-    
-    def _get_ipm_packages_name_list(self):
-        """
-            Parse HTML page to get the names of each IPM Package
-        """
-        
-        response = requests.get(
-            self.base_url,
-            headers=self.headers,
-        )
-
-        soup = bs(response.text, 'html.parser')
-        packages = soup.findAll('span', {'class': 'field-content region-name'}, limit=None)
-        for package in packages:
-            self.ipm_packages.append(package.a['href'].split('/')[-1])
-       
-    
-    def get_ipm_packages_pdfs_list(self):
-        """
-            Parse HTML page to get the PDF URLs of each IPM Package
-        """
-        self._get_ipm_packages_name_list()
-
-        for ip in self.ipm_packages:
-            source_url = f'{self.base_url}/{ip}'
-            print(f'Loading PDFs from: {source_url}')
-            
-            response = requests.get(
-                source_url,
-                headers=self.headers,
-            )
-
-            soup = bs(response.text, 'html.parser')
-            urls = soup.findAll('td', {'class': 'views-field views-field-php'}, limit=None)
-            for url in urls:
-                self.pdfs_urls.append(url.a['href'])
-
-
-def get_ipm_packages_pdfs_urls():
-    pdf = LOAD_ONLINE_PDF_IPM_PACKAGES()
-    pdf.get_ipm_packages_pdfs_list()
-    print('Total pdfs:', len(pdf.pdfs_urls))
-    return pdf.pdfs_urls

spaces/CikeyQI/meme-api/meme_generator/manager.py

@@ -1,104 +0,0 @@
-import importlib
-import importlib.util
-import pkgutil
-from pathlib import Path
-from typing import Dict, List, Optional, Union
-
-from .config import meme_config
-from .exception import NoSuchMeme
-from .log import logger
-from .meme import Meme, MemeArgsType, MemeFunction, MemeParamsType
-
-_memes: Dict[str, Meme] = {}
-
-
-def path_to_module_name(path: Path) -> str:
-    rel_path = path.resolve().relative_to(Path.cwd().resolve())
-    if rel_path.stem == "__init__":
-        return ".".join(rel_path.parts[:-1])
-    else:
-        return ".".join(rel_path.parts[:-1] + (rel_path.stem,))
-
-
-def load_meme(module_path: Union[str, Path]):
-    module_name = (
-        path_to_module_name(module_path)
-        if isinstance(module_path, Path)
-        else module_path
-    )
-    try:
-        importlib.import_module(module_name)
-    except Exception as e:
-        logger.opt(colors=True, exception=e).error(f"Failed to import {module_path}!")
-
-
-def load_memes(dir_path: Union[str, Path]):
-    if isinstance(dir_path, Path):
-        dir_path = str(dir_path.resolve())
-
-    for module_info in pkgutil.iter_modules([dir_path]):
-        if module_info.name.startswith("_"):
-            continue
-        if not (
-            module_spec := module_info.module_finder.find_spec(module_info.name, None)
-        ):
-            continue
-        if not (module_path := module_spec.origin):
-            continue
-        if not (module_loader := module_spec.loader):
-            continue
-        try:
-            module = importlib.util.module_from_spec(module_spec)
-            module_loader.exec_module(module)
-        except Exception as e:
-            logger.opt(colors=True, exception=e).error(
-                f"Failed to import {module_path}!"
-            )
-
-
-def add_meme(
-    key: str,
-    function: MemeFunction,
-    *,
-    min_images: int = 0,
-    max_images: int = 0,
-    min_texts: int = 0,
-    max_texts: int = 0,
-    default_texts: List[str] = [],
-    args_type: Optional[MemeArgsType] = None,
-    keywords: List[str] = [],
-    patterns: List[str] = [],
-):
-    if key in _memes:
-        logger.warning(f'Meme with key "{key}" already exists!')
-        return
-
-    if key in meme_config.meme.meme_disabled_list:
-        logger.warning(f'The key "{key}" is in the disabled list!')
-        return
-
-    meme = Meme(
-        key,
-        function,
-        MemeParamsType(
-            min_images, max_images, min_texts, max_texts, default_texts, args_type
-        ),
-        keywords=keywords,
-        patterns=patterns,
-    )
-
-    _memes[key] = meme
-
-
-def get_meme(key: str) -> Meme:
-    if key not in _memes:
-        raise NoSuchMeme(key)
-    return _memes[key]
-
-
-def get_memes() -> List[Meme]:
-    return list(_memes.values())
-
-
-def get_meme_keys() -> List[str]:
-    return list(_memes.keys())

spaces/Cong723/gpt-academic-public/crazy_functions/test_project/cpp/longcode/jpge.cpp

@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <[email protected]>
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include <stdlib.h>
-#include <string.h>
-#if PLATFORM_WINDOWS
-#include <malloc.h>
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES]  =
-{
-  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
-  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
-  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
-  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
-  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
-  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
-  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
-  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
-  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
-  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-
-// Low-level helper functions.
-template <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
-  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS  13
-#define ROW_BITS    2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
-  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
-  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
-  int32 u1 = DCT_MUL(t12 + t13, 4433); \
-  s2 = u1 + DCT_MUL(t13, 6270); \
-  s6 = u1 + DCT_MUL(t12, -15137); \
-  u1 = t4 + t7; \
-  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
-  int32 z5 = DCT_MUL(u3 + u4, 9633); \
-  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
-  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
-  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
-  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
-  u3 += z5; u4 += z5; \
-  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
-  int32 c, *q = p;
-  for (c = 7; c >= 0; c--, q += 8)
-  {
-    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
-    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
-  }
-  for (q = p, c = 7; c >= 0; c--, q++)
-  {
-    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
-    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
-  }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
-  const uint cMaxPasses = 4;
-  uint32 hist[256 * cMaxPasses]; clear_obj(hist);
-  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
-  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
-  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
-  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
-  {
-    const uint32* pHist = &hist[pass << 8];
-    uint offsets[256], cur_ofs = 0;
-    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
-    for (uint i = 0; i < num_syms; i++)
-      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
-    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
-  }
-  return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, [email protected], Jyrki Katajainen, [email protected], November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
-  int root, leaf, next, avbl, used, dpth;
-  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
-  A[0].m_key += A[1].m_key; root = 0; leaf = 2;
-  for (next=1; next < n-1; next++)
-  {
-    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;
-    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;
-  }
-  A[n-2].m_key = 0;
-  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
-  avbl = 1; used = dpth = 0; root = n-2; next = n-1;
-  while (avbl>0)
-  {
-    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
-    while (avbl>used) { A[next--].m_key = dpth; avbl--; }
-    avbl = 2*used; dpth++; used = 0;
-  }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
-  if (code_list_len <= 1) return;
-
-  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
-  uint32 total = 0;
-  for (int i = max_code_size; i > 0; i--)
-    total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
-  while (total != (1UL << max_code_size))
-  {
-    pNum_codes[max_code_size]--;
-    for (int i = max_code_size - 1; i > 0; i--)
-    {
-      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
-    }
-    total--;
-  }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
-  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
-  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's
-  int num_used_syms = 1;
-  const uint32 *pSym_count = &m_huff_count[table_num][0];
-  for (int i = 0; i < table_len; i++)
-    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
-  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
-  calculate_minimum_redundancy(pSyms, num_used_syms);
-
-  // Count the # of symbols of each code size.
-  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
-  for (int i = 0; i < num_used_syms; i++)
-    num_codes[pSyms[i].m_key]++;
-
-  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
-  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
-  // Compute m_huff_bits array, which contains the # of symbols per code size.
-  clear_obj(m_huff_bits[table_num]);
-  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
-    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);
-
-  // Remove the dummy symbol added above, which must be in largest bucket.
-  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
-  {
-    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
-  }
-
-  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
-  for (int i = num_used_syms - 1; i >= 1; i--)
-    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
-  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
-  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
-  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
-  emit_marker(M_APP0);
-  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
-  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
-  emit_byte(0);
-  emit_byte(1);      /* Major version */
-  emit_byte(1);      /* Minor version */
-  emit_byte(0);      /* Density unit */
-  emit_word(1);
-  emit_word(1);
-  emit_byte(0);      /* No thumbnail image */
-  emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
-  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
-  {
-    emit_marker(M_DQT);
-    emit_word(64 + 1 + 2);
-    emit_byte(static_cast<uint8>(i));
-    for (int j = 0; j < 64; j++)
-      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));
-  }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
-  emit_marker(M_SOF0);                           /* baseline */
-  emit_word(3 * m_num_components + 2 + 5 + 1);
-  emit_byte(8);                                  /* precision */
-  emit_word(m_image_y);
-  emit_word(m_image_x);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */
-    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */
-    emit_byte(i > 0);                                   /* quant. table num */
-  }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
-  emit_marker(M_DHT);
-
-  int length = 0;
-  for (int i = 1; i <= 16; i++)
-    length += bits[i];
-
-  emit_word(length + 2 + 1 + 16);
-  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));
-
-  for (int i = 1; i <= 16; i++)
-    emit_byte(bits[i]);
-
-  for (int i = 0; i < length; i++)
-    emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
-  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
-  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
-  if (m_num_components == 3)
-  {
-    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
-    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
-  }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
-  emit_marker(M_SOS);
-  emit_word(2 * m_num_components + 2 + 1 + 3);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));
-    if (i == 0)
-      emit_byte((0 << 4) + 0);
-    else
-      emit_byte((1 << 4) + 1);
-  }
-  emit_byte(0);     /* spectral selection */
-  emit_byte(63);
-  emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
-  emit_marker(M_SOI);
-  emit_jfif_app0();
-  emit_dqt();
-  emit_sof();
-  emit_dhts();
-  emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
-  int i, l, last_p, si;
-  uint8 huff_size[257];
-  uint huff_code[257];
-  uint code;
-
-  int p = 0;
-  for (l = 1; l <= 16; l++)
-    for (i = 1; i <= bits[l]; i++)
-      huff_size[p++] = (char)l;
-
-  huff_size[p] = 0; last_p = p; // write sentinel
-
-  code = 0; si = huff_size[0]; p = 0;
-
-  while (huff_size[p])
-  {
-    while (huff_size[p] == si)
-      huff_code[p++] = code++;
-    code <<= 1;
-    si++;
-  }
-
-  memset(codes, 0, sizeof(codes[0])*256);
-  memset(code_sizes, 0, sizeof(code_sizes[0])*256);
-  for (p = 0; p < last_p; p++)
-  {
-    codes[val[p]]      = huff_code[p];
-    code_sizes[val[p]] = huff_size[p];
-  }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
-  int32 q;
-  if (m_params.m_quality < 50)
-    q = 5000 / m_params.m_quality;
-  else
-    q = 200 - m_params.m_quality * 2;
-  for (int i = 0; i < 64; i++)
-  {
-    int32 j = *pSrc++; j = (j * q + 50L) / 100L;
-    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
-  }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
-  m_bit_buffer = 0; m_bits_in = 0;
-  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
-  m_mcu_y_ofs = 0;
-  m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
-  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
-  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
-  if (m_num_components > 1)
-  {
-    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
-    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
-  }
-  first_pass_init();
-  emit_markers();
-  m_pass_num = 2;
-  return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
-  m_num_components = 3;
-  switch (m_params.m_subsampling)
-  {
-    case Y_ONLY:
-    {
-      m_num_components = 1;
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H1V1:
-    {
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H2V1:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 8;
-      break;
-    }
-    case H2V2:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 16;
-    }
-  }
-
-  m_image_x        = p_x_res; m_image_y = p_y_res;
-  m_image_bpp      = src_channels;
-  m_image_bpl      = m_image_x * src_channels;
-  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
-  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
-  m_image_bpl_xlt  = m_image_x * m_num_components;
-  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;
-  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;
-
-  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
-  for (int i = 1; i < m_mcu_y; i++)
-    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
-  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
-  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-  m_pOut_buf = m_out_buf;
-
-  if (m_params.m_two_pass_flag)
-  {
-    clear_obj(m_huff_count);
-    first_pass_init();
-  }
-  else
-  {
-    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
-    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
-    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
-    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
-    if (!second_pass_init()) return false;   // in effect, skip over the first pass
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[i] + x;
-    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
-    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (8 * 3)) + c;
-  y <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[y + i] + x;
-    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
-    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
-  uint8 *pSrc1, *pSrc2;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  int a = 0, b = 2;
-  for (int i = 0; i < 16; i += 2, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pSrc2 = m_mcu_lines[i + 1] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
-    int temp = a; a = b; b = temp;
-  }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
-  uint8 *pSrc1;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
-  }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
-  int32 *q = m_quantization_tables[component_num > 0];
-  int16 *pDst = m_coefficient_array;
-  for (int i = 0; i < 64; i++)
-  {
-    sample_array_t j = m_sample_array[s_zag[i]];
-    if (j < 0)
-    {
-      if ((j = -j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>(-(j / *q));
-    }
-    else
-    {
-      if ((j = j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>((j / *q));
-    }
-    q++;
-  }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
-  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
-    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
-  m_pOut_buf = m_out_buf;
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
-  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
-  while (m_bits_in >= 8)
-  {
-    uint8 c;
-    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
-    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
-    if (c == 0xFF) JPGE_PUT_BYTE(0);
-    m_bit_buffer <<= 8;
-    m_bits_in -= 8;
-  }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
-  if (component_num >= 3) return; // just to shut up static analysis
-  int i, run_len, nbits, temp1;
-  int16 *src = m_coefficient_array;
-  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
-  temp1 = src[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = src[0];
-  if (temp1 < 0) temp1 = -temp1;
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  dc_count[nbits]++;
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        ac_count[0xF0]++;
-        run_len -= 16;
-      }
-      if (temp1 < 0) temp1 = -temp1;
-      nbits = 1;
-      while (temp1 >>= 1) nbits++;
-      ac_count[(run_len << 4) + nbits]++;
-      run_len = 0;
-    }
-  }
-  if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
-  int i, j, run_len, nbits, temp1, temp2;
-  int16 *pSrc = m_coefficient_array;
-  uint *codes[2];
-  uint8 *code_sizes[2];
-
-  if (component_num == 0)
-  {
-    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
-    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
-  }
-  else
-  {
-    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
-    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
-  }
-
-  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = pSrc[0];
-
-  if (temp1 < 0)
-  {
-    temp1 = -temp1; temp2--;
-  }
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  put_bits(codes[0][nbits], code_sizes[0][nbits]);
-  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
-        run_len -= 16;
-      }
-      if ((temp2 = temp1) < 0)
-      {
-        temp1 = -temp1;
-        temp2--;
-      }
-      nbits = 1;
-      while (temp1 >>= 1)
-        nbits++;
-      j = (run_len << 4) + nbits;
-      put_bits(codes[1][j], code_sizes[1][j]);
-      put_bits(temp2 & ((1 << nbits) - 1), nbits);
-      run_len = 0;
-    }
-  }
-  if (run_len)
-    put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
-  DCT2D(m_sample_array);
-  load_quantized_coefficients(component_num);
-  if (m_pass_num == 1)
-    code_coefficients_pass_one(component_num);
-  else
-    code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
-  if (m_num_components == 1)
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8_grey(i); code_block(0);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
-      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
-    }
-  }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
-  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
-  if (m_num_components > 1)
-  {
-    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
-  }
-  return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
-  put_bits(0x7F, 7);
-  flush_output_buffer();
-  emit_marker(M_EOI);
-  m_pass_num++; // purposely bump up m_pass_num, for debugging
-  return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
-  if (m_mcu_y_ofs)
-  {
-    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
-    {
-      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
-        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
-    }
-
-    process_mcu_row();
-  }
-
-  if (m_pass_num == 1)
-    return terminate_pass_one();
-  else
-    return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
-  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);
-
-  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
-  if (m_num_components == 1)
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_Y(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_Y(pDst, Psrc, m_image_x);
-    else
-      memcpy(pDst, Psrc, m_image_x);
-  }
-  else
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_YCC(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_YCC(pDst, Psrc, m_image_x);
-    else
-      Y_to_YCC(pDst, Psrc, m_image_x);
-  }
-
-  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
-  if (m_num_components == 1)
-    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
-  else
-  {
-    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
-    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
-    for (int i = m_image_x; i < m_image_x_mcu; i++)
-    {
-      *q++ = y; *q++ = cb; *q++ = cr;
-    }
-  }
-
-  if (++m_mcu_y_ofs == m_mcu_y)
-  {
-    process_mcu_row();
-    m_mcu_y_ofs = 0;
-  }
-}
-
-void jpeg_encoder::clear()
-{
-  m_mcu_lines[0] = NULL;
-  m_pass_num = 0;
-  m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
-  clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
-  deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
-  deinit();
-  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
-  m_pStream = pStream;
-  m_params = comp_params;
-  return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
-  jpge_free(m_mcu_lines[0]);
-  clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
-  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
-  if (m_all_stream_writes_succeeded)
-  {
-    if (!pScanline)
-    {
-      if (!process_end_of_image()) return false;
-    }
-    else
-    {
-      load_mcu(pScanline);
-    }
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include <stdio.h>
-
-class cfile_stream : public output_stream
-{
-   cfile_stream(const cfile_stream &);
-   cfile_stream &operator= (const cfile_stream &);
-
-   FILE* m_pFile;
-   bool m_bStatus;
-
-public:
-   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
-   virtual ~cfile_stream()
-   {
-      close();
-   }
-
-   bool open(const char *pFilename)
-   {
-      close();
-#if defined(_MSC_VER)
-      if (fopen_s(&m_pFile, pFilename, "wb") != 0)
-	  {
-		  return false;
-	  }
-#else
-      m_pFile = fopen(pFilename, "wb");
-#endif
-      m_bStatus = (m_pFile != NULL);
-      return m_bStatus;
-   }
-
-   bool close()
-   {
-      if (m_pFile)
-      {
-         if (fclose(m_pFile) == EOF)
-         {
-            m_bStatus = false;
-         }
-         m_pFile = NULL;
-      }
-      return m_bStatus;
-   }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
-      return m_bStatus;
-   }
-
-   uint get_size() const
-   {
-      return m_pFile ? ftell(m_pFile) : 0;
-   }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-  cfile_stream dst_stream;
-  if (!dst_stream.open(pFilename))
-    return false;
-
-  jpge::jpeg_encoder dst_image;
-  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-    return false;
-
-  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-  {
-    for (int64_t i = 0; i < height; i++)
-    {
-		// i, width, and num_channels are all 64bit
-       const uint8* pBuf = pImage_data + i * width * num_channels;
-       if (!dst_image.process_scanline(pBuf))
-          return false;
-    }
-    if (!dst_image.process_scanline(NULL))
-       return false;
-  }
-
-  dst_image.deinit();
-
-  return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
-   memory_stream(const memory_stream &);
-   memory_stream &operator= (const memory_stream &);
-
-   uint8 *m_pBuf;
-   uint64_t m_buf_size, m_buf_ofs;
-
-public:
-   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
-   virtual ~memory_stream() { }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      uint64_t buf_remaining = m_buf_size - m_buf_ofs;
-      if ((uint64_t)len > buf_remaining)
-         return false;
-      memcpy(m_pBuf + m_buf_ofs, pBuf, len);
-      m_buf_ofs += len;
-      return true;
-   }
-
-   uint64_t get_size() const
-   {
-      return m_buf_ofs;
-   }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-   if ((!pDstBuf) || (!buf_size))
-      return false;
-
-   memory_stream dst_stream(pDstBuf, buf_size);
-
-   buf_size = 0;
-
-   jpge::jpeg_encoder dst_image;
-   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-      return false;
-
-   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-   {
-     for (int64_t i = 0; i < height; i++)
-     {
-        const uint8* pScanline = pImage_data + i * width * num_channels;
-        if (!dst_image.process_scanline(pScanline))
-           return false;
-     }
-     if (!dst_image.process_scanline(NULL))
-        return false;
-   }
-
-   dst_image.deinit();
-
-   buf_size = dst_stream.get_size();
-   return true;
-}
-
-} // namespace jpge

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/click/decorators.py

@@ -1,565 +0,0 @@
-import inspect
-import types
-import typing as t
-from functools import update_wrapper
-from gettext import gettext as _
-
-from .core import Argument
-from .core import Command
-from .core import Context
-from .core import Group
-from .core import Option
-from .core import Parameter
-from .globals import get_current_context
-from .utils import echo
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-
-    P = te.ParamSpec("P")
-
-R = t.TypeVar("R")
-T = t.TypeVar("T")
-_AnyCallable = t.Callable[..., t.Any]
-FC = t.TypeVar("FC", bound=t.Union[_AnyCallable, Command])
-
-
-def pass_context(f: "t.Callable[te.Concatenate[Context, P], R]") -> "t.Callable[P, R]":
-    """Marks a callback as wanting to receive the current context
-    object as first argument.
-    """
-
-    def new_func(*args: "P.args", **kwargs: "P.kwargs") -> "R":
-        return f(get_current_context(), *args, **kwargs)
-
-    return update_wrapper(new_func, f)
-
-
-def pass_obj(f: "t.Callable[te.Concatenate[t.Any, P], R]") -> "t.Callable[P, R]":
-    """Similar to :func:`pass_context`, but only pass the object on the
-    context onwards (:attr:`Context.obj`).  This is useful if that object
-    represents the state of a nested system.
-    """
-
-    def new_func(*args: "P.args", **kwargs: "P.kwargs") -> "R":
-        return f(get_current_context().obj, *args, **kwargs)
-
-    return update_wrapper(new_func, f)
-
-
-def make_pass_decorator(
-    object_type: t.Type[T], ensure: bool = False
-) -> t.Callable[["t.Callable[te.Concatenate[T, P], R]"], "t.Callable[P, R]"]:
-    """Given an object type this creates a decorator that will work
-    similar to :func:`pass_obj` but instead of passing the object of the
-    current context, it will find the innermost context of type
-    :func:`object_type`.
-
-    This generates a decorator that works roughly like this::
-
-        from functools import update_wrapper
-
-        def decorator(f):
-            @pass_context
-            def new_func(ctx, *args, **kwargs):
-                obj = ctx.find_object(object_type)
-                return ctx.invoke(f, obj, *args, **kwargs)
-            return update_wrapper(new_func, f)
-        return decorator
-
-    :param object_type: the type of the object to pass.
-    :param ensure: if set to `True`, a new object will be created and
-                   remembered on the context if it's not there yet.
-    """
-
-    def decorator(f: "t.Callable[te.Concatenate[T, P], R]") -> "t.Callable[P, R]":
-        def new_func(*args: "P.args", **kwargs: "P.kwargs") -> "R":
-            ctx = get_current_context()
-
-            obj: t.Optional[T]
-            if ensure:
-                obj = ctx.ensure_object(object_type)
-            else:
-                obj = ctx.find_object(object_type)
-
-            if obj is None:
-                raise RuntimeError(
-                    "Managed to invoke callback without a context"
-                    f" object of type {object_type.__name__!r}"
-                    " existing."
-                )
-
-            return ctx.invoke(f, obj, *args, **kwargs)
-
-        return update_wrapper(new_func, f)
-
-    return decorator  # type: ignore[return-value]
-
-
-def pass_meta_key(
-    key: str, *, doc_description: t.Optional[str] = None
-) -> "t.Callable[[t.Callable[te.Concatenate[t.Any, P], R]], t.Callable[P, R]]":
-    """Create a decorator that passes a key from
-    :attr:`click.Context.meta` as the first argument to the decorated
-    function.
-
-    :param key: Key in ``Context.meta`` to pass.
-    :param doc_description: Description of the object being passed,
-        inserted into the decorator's docstring. Defaults to "the 'key'
-        key from Context.meta".
-
-    .. versionadded:: 8.0
-    """
-
-    def decorator(f: "t.Callable[te.Concatenate[t.Any, P], R]") -> "t.Callable[P, R]":
-        def new_func(*args: "P.args", **kwargs: "P.kwargs") -> R:
-            ctx = get_current_context()
-            obj = ctx.meta[key]
-            return ctx.invoke(f, obj, *args, **kwargs)
-
-        return update_wrapper(new_func, f)
-
-    if doc_description is None:
-        doc_description = f"the {key!r} key from :attr:`click.Context.meta`"
-
-    decorator.__doc__ = (
-        f"Decorator that passes {doc_description} as the first argument"
-        " to the decorated function."
-    )
-    return decorator  # type: ignore[return-value]
-
-
-CmdType = t.TypeVar("CmdType", bound=Command)
-
-
-# variant: no call, directly as decorator for a function.
-@t.overload
-def command(name: _AnyCallable) -> Command:
-    ...
-
-
-# variant: with positional name and with positional or keyword cls argument:
-# @command(namearg, CommandCls, ...) or @command(namearg, cls=CommandCls, ...)
-@t.overload
-def command(
-    name: t.Optional[str],
-    cls: t.Type[CmdType],
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], CmdType]:
-    ...
-
-
-# variant: name omitted, cls _must_ be a keyword argument, @command(cls=CommandCls, ...)
-@t.overload
-def command(
-    name: None = None,
-    *,
-    cls: t.Type[CmdType],
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], CmdType]:
-    ...
-
-
-# variant: with optional string name, no cls argument provided.
-@t.overload
-def command(
-    name: t.Optional[str] = ..., cls: None = None, **attrs: t.Any
-) -> t.Callable[[_AnyCallable], Command]:
-    ...
-
-
-def command(
-    name: t.Union[t.Optional[str], _AnyCallable] = None,
-    cls: t.Optional[t.Type[CmdType]] = None,
-    **attrs: t.Any,
-) -> t.Union[Command, t.Callable[[_AnyCallable], t.Union[Command, CmdType]]]:
-    r"""Creates a new :class:`Command` and uses the decorated function as
-    callback.  This will also automatically attach all decorated
-    :func:`option`\s and :func:`argument`\s as parameters to the command.
-
-    The name of the command defaults to the name of the function with
-    underscores replaced by dashes.  If you want to change that, you can
-    pass the intended name as the first argument.
-
-    All keyword arguments are forwarded to the underlying command class.
-    For the ``params`` argument, any decorated params are appended to
-    the end of the list.
-
-    Once decorated the function turns into a :class:`Command` instance
-    that can be invoked as a command line utility or be attached to a
-    command :class:`Group`.
-
-    :param name: the name of the command.  This defaults to the function
-                 name with underscores replaced by dashes.
-    :param cls: the command class to instantiate.  This defaults to
-                :class:`Command`.
-
-    .. versionchanged:: 8.1
-        This decorator can be applied without parentheses.
-
-    .. versionchanged:: 8.1
-        The ``params`` argument can be used. Decorated params are
-        appended to the end of the list.
-    """
-
-    func: t.Optional[t.Callable[[_AnyCallable], t.Any]] = None
-
-    if callable(name):
-        func = name
-        name = None
-        assert cls is None, "Use 'command(cls=cls)(callable)' to specify a class."
-        assert not attrs, "Use 'command(**kwargs)(callable)' to provide arguments."
-
-    if cls is None:
-        cls = t.cast(t.Type[CmdType], Command)
-
-    def decorator(f: _AnyCallable) -> CmdType:
-        if isinstance(f, Command):
-            raise TypeError("Attempted to convert a callback into a command twice.")
-
-        attr_params = attrs.pop("params", None)
-        params = attr_params if attr_params is not None else []
-
-        try:
-            decorator_params = f.__click_params__  # type: ignore
-        except AttributeError:
-            pass
-        else:
-            del f.__click_params__  # type: ignore
-            params.extend(reversed(decorator_params))
-
-        if attrs.get("help") is None:
-            attrs["help"] = f.__doc__
-
-        if t.TYPE_CHECKING:
-            assert cls is not None
-            assert not callable(name)
-
-        cmd = cls(
-            name=name or f.__name__.lower().replace("_", "-"),
-            callback=f,
-            params=params,
-            **attrs,
-        )
-        cmd.__doc__ = f.__doc__
-        return cmd
-
-    if func is not None:
-        return decorator(func)
-
-    return decorator
-
-
-GrpType = t.TypeVar("GrpType", bound=Group)
-
-
-# variant: no call, directly as decorator for a function.
-@t.overload
-def group(name: _AnyCallable) -> Group:
-    ...
-
-
-# variant: with positional name and with positional or keyword cls argument:
-# @group(namearg, GroupCls, ...) or @group(namearg, cls=GroupCls, ...)
-@t.overload
-def group(
-    name: t.Optional[str],
-    cls: t.Type[GrpType],
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], GrpType]:
-    ...
-
-
-# variant: name omitted, cls _must_ be a keyword argument, @group(cmd=GroupCls, ...)
-# The _correct_ way to spell this overload is to use keyword-only argument syntax:
-# def group(*, cls: t.Type[GrpType], **attrs: t.Any) -> ...
-# However, mypy thinks this doesn't fit the overloaded function. Pyright does
-# accept that spelling, and the following work-around makes pyright issue a
-# warning that GrpType could be left unsolved, but mypy sees it as fine. *shrug*
-@t.overload
-def group(
-    name: None = None,
-    cls: t.Type[GrpType] = ...,
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], GrpType]:
-    ...
-
-
-# variant: with optional string name, no cls argument provided.
-@t.overload
-def group(
-    name: t.Optional[str] = ..., cls: None = None, **attrs: t.Any
-) -> t.Callable[[_AnyCallable], Group]:
-    ...
-
-
-def group(
-    name: t.Union[str, _AnyCallable, None] = None,
-    cls: t.Optional[t.Type[GrpType]] = None,
-    **attrs: t.Any,
-) -> t.Union[Group, t.Callable[[_AnyCallable], t.Union[Group, GrpType]]]:
-    """Creates a new :class:`Group` with a function as callback.  This
-    works otherwise the same as :func:`command` just that the `cls`
-    parameter is set to :class:`Group`.
-
-    .. versionchanged:: 8.1
-        This decorator can be applied without parentheses.
-    """
-    if cls is None:
-        cls = t.cast(t.Type[GrpType], Group)
-
-    if callable(name):
-        return command(cls=cls, **attrs)(name)
-
-    return command(name, cls, **attrs)
-
-
-def _param_memo(f: t.Callable[..., t.Any], param: Parameter) -> None:
-    if isinstance(f, Command):
-        f.params.append(param)
-    else:
-        if not hasattr(f, "__click_params__"):
-            f.__click_params__ = []  # type: ignore
-
-        f.__click_params__.append(param)  # type: ignore
-
-
-def argument(
-    *param_decls: str, cls: t.Optional[t.Type[Argument]] = None, **attrs: t.Any
-) -> t.Callable[[FC], FC]:
-    """Attaches an argument to the command.  All positional arguments are
-    passed as parameter declarations to :class:`Argument`; all keyword
-    arguments are forwarded unchanged (except ``cls``).
-    This is equivalent to creating an :class:`Argument` instance manually
-    and attaching it to the :attr:`Command.params` list.
-
-    For the default argument class, refer to :class:`Argument` and
-    :class:`Parameter` for descriptions of parameters.
-
-    :param cls: the argument class to instantiate.  This defaults to
-                :class:`Argument`.
-    :param param_decls: Passed as positional arguments to the constructor of
-        ``cls``.
-    :param attrs: Passed as keyword arguments to the constructor of ``cls``.
-    """
-    if cls is None:
-        cls = Argument
-
-    def decorator(f: FC) -> FC:
-        _param_memo(f, cls(param_decls, **attrs))
-        return f
-
-    return decorator
-
-
-def option(
-    *param_decls: str, cls: t.Optional[t.Type[Option]] = None, **attrs: t.Any
-) -> t.Callable[[FC], FC]:
-    """Attaches an option to the command.  All positional arguments are
-    passed as parameter declarations to :class:`Option`; all keyword
-    arguments are forwarded unchanged (except ``cls``).
-    This is equivalent to creating an :class:`Option` instance manually
-    and attaching it to the :attr:`Command.params` list.
-
-    For the default option class, refer to :class:`Option` and
-    :class:`Parameter` for descriptions of parameters.
-
-    :param cls: the option class to instantiate.  This defaults to
-                :class:`Option`.
-    :param param_decls: Passed as positional arguments to the constructor of
-        ``cls``.
-    :param attrs: Passed as keyword arguments to the constructor of ``cls``.
-    """
-    if cls is None:
-        cls = Option
-
-    def decorator(f: FC) -> FC:
-        _param_memo(f, cls(param_decls, **attrs))
-        return f
-
-    return decorator
-
-
-def confirmation_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--yes`` option which shows a prompt before continuing if
-    not passed. If the prompt is declined, the program will exit.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--yes"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value:
-            ctx.abort()
-
-    if not param_decls:
-        param_decls = ("--yes",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("callback", callback)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("prompt", "Do you want to continue?")
-    kwargs.setdefault("help", "Confirm the action without prompting.")
-    return option(*param_decls, **kwargs)
-
-
-def password_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--password`` option which prompts for a password, hiding
-    input and asking to enter the value again for confirmation.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--password"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-    if not param_decls:
-        param_decls = ("--password",)
-
-    kwargs.setdefault("prompt", True)
-    kwargs.setdefault("confirmation_prompt", True)
-    kwargs.setdefault("hide_input", True)
-    return option(*param_decls, **kwargs)
-
-
-def version_option(
-    version: t.Optional[str] = None,
-    *param_decls: str,
-    package_name: t.Optional[str] = None,
-    prog_name: t.Optional[str] = None,
-    message: t.Optional[str] = None,
-    **kwargs: t.Any,
-) -> t.Callable[[FC], FC]:
-    """Add a ``--version`` option which immediately prints the version
-    number and exits the program.
-
-    If ``version`` is not provided, Click will try to detect it using
-    :func:`importlib.metadata.version` to get the version for the
-    ``package_name``. On Python < 3.8, the ``importlib_metadata``
-    backport must be installed.
-
-    If ``package_name`` is not provided, Click will try to detect it by
-    inspecting the stack frames. This will be used to detect the
-    version, so it must match the name of the installed package.
-
-    :param version: The version number to show. If not provided, Click
-        will try to detect it.
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--version"``.
-    :param package_name: The package name to detect the version from. If
-        not provided, Click will try to detect it.
-    :param prog_name: The name of the CLI to show in the message. If not
-        provided, it will be detected from the command.
-    :param message: The message to show. The values ``%(prog)s``,
-        ``%(package)s``, and ``%(version)s`` are available. Defaults to
-        ``"%(prog)s, version %(version)s"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    :raise RuntimeError: ``version`` could not be detected.
-
-    .. versionchanged:: 8.0
-        Add the ``package_name`` parameter, and the ``%(package)s``
-        value for messages.
-
-    .. versionchanged:: 8.0
-        Use :mod:`importlib.metadata` instead of ``pkg_resources``. The
-        version is detected based on the package name, not the entry
-        point name. The Python package name must match the installed
-        package name, or be passed with ``package_name=``.
-    """
-    if message is None:
-        message = _("%(prog)s, version %(version)s")
-
-    if version is None and package_name is None:
-        frame = inspect.currentframe()
-        f_back = frame.f_back if frame is not None else None
-        f_globals = f_back.f_globals if f_back is not None else None
-        # break reference cycle
-        # https://docs.python.org/3/library/inspect.html#the-interpreter-stack
-        del frame
-
-        if f_globals is not None:
-            package_name = f_globals.get("__name__")
-
-            if package_name == "__main__":
-                package_name = f_globals.get("__package__")
-
-            if package_name:
-                package_name = package_name.partition(".")[0]
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value or ctx.resilient_parsing:
-            return
-
-        nonlocal prog_name
-        nonlocal version
-
-        if prog_name is None:
-            prog_name = ctx.find_root().info_name
-
-        if version is None and package_name is not None:
-            metadata: t.Optional[types.ModuleType]
-
-            try:
-                from importlib import metadata  # type: ignore
-            except ImportError:
-                # Python < 3.8
-                import importlib_metadata as metadata  # type: ignore
-
-            try:
-                version = metadata.version(package_name)  # type: ignore
-            except metadata.PackageNotFoundError:  # type: ignore
-                raise RuntimeError(
-                    f"{package_name!r} is not installed. Try passing"
-                    " 'package_name' instead."
-                ) from None
-
-        if version is None:
-            raise RuntimeError(
-                f"Could not determine the version for {package_name!r} automatically."
-            )
-
-        echo(
-            message % {"prog": prog_name, "package": package_name, "version": version},
-            color=ctx.color,
-        )
-        ctx.exit()
-
-    if not param_decls:
-        param_decls = ("--version",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("is_eager", True)
-    kwargs.setdefault("help", _("Show the version and exit."))
-    kwargs["callback"] = callback
-    return option(*param_decls, **kwargs)
-
-
-def help_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--help`` option which immediately prints the help page
-    and exits the program.
-
-    This is usually unnecessary, as the ``--help`` option is added to
-    each command automatically unless ``add_help_option=False`` is
-    passed.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--help"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value or ctx.resilient_parsing:
-            return
-
-        echo(ctx.get_help(), color=ctx.color)
-        ctx.exit()
-
-    if not param_decls:
-        param_decls = ("--help",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("is_eager", True)
-    kwargs.setdefault("help", _("Show this message and exit."))
-    kwargs["callback"] = callback
-    return option(*param_decls, **kwargs)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/implementations/memory.py

@@ -1,293 +0,0 @@
-from __future__ import absolute_import, annotations, division, print_function
-
-import logging
-from datetime import datetime
-from errno import ENOTEMPTY
-from io import BytesIO
-from typing import Any, ClassVar
-
-from fsspec import AbstractFileSystem
-
-logger = logging.Logger("fsspec.memoryfs")
-
-
-class MemoryFileSystem(AbstractFileSystem):
-    """A filesystem based on a dict of BytesIO objects
-
-    This is a global filesystem so instances of this class all point to the same
-    in memory filesystem.
-    """
-
-    store: ClassVar[dict[str, Any]] = {}  # global, do not overwrite!
-    pseudo_dirs = [""]  # global, do not overwrite!
-    protocol = "memory"
-    root_marker = "/"
-
-    @classmethod
-    def _strip_protocol(cls, path):
-        if path.startswith("memory://"):
-            path = path[len("memory://") :]
-        if "::" in path or "://" in path:
-            return path.rstrip("/")
-        path = path.lstrip("/").rstrip("/")
-        return "/" + path if path else ""
-
-    def ls(self, path, detail=True, **kwargs):
-        path = self._strip_protocol(path)
-        if path in self.store:
-            # there is a key with this exact name
-            if not detail:
-                return [path]
-            return [
-                {
-                    "name": path,
-                    "size": self.store[path].size,
-                    "type": "file",
-                    "created": self.store[path].created.timestamp(),
-                }
-            ]
-        paths = set()
-        starter = path + "/"
-        out = []
-        for p2 in tuple(self.store):
-            if p2.startswith(starter):
-                if "/" not in p2[len(starter) :]:
-                    # exact child
-                    out.append(
-                        {
-                            "name": p2,
-                            "size": self.store[p2].size,
-                            "type": "file",
-                            "created": self.store[p2].created.timestamp(),
-                        }
-                    )
-                elif len(p2) > len(starter):
-                    # implied child directory
-                    ppath = starter + p2[len(starter) :].split("/", 1)[0]
-                    if ppath not in paths:
-                        out = out or []
-                        out.append(
-                            {
-                                "name": ppath,
-                                "size": 0,
-                                "type": "directory",
-                            }
-                        )
-                        paths.add(ppath)
-        for p2 in self.pseudo_dirs:
-            if p2.startswith(starter):
-                if "/" not in p2[len(starter) :]:
-                    # exact child pdir
-                    if p2 not in paths:
-                        out.append({"name": p2, "size": 0, "type": "directory"})
-                        paths.add(p2)
-                else:
-                    # directory implied by deeper pdir
-                    ppath = starter + p2[len(starter) :].split("/", 1)[0]
-                    if ppath not in paths:
-                        out.append({"name": ppath, "size": 0, "type": "directory"})
-                        paths.add(ppath)
-        if not out:
-            if path in self.pseudo_dirs:
-                # empty dir
-                return []
-            raise FileNotFoundError(path)
-        if detail:
-            return out
-        return sorted([f["name"] for f in out])
-
-    def mkdir(self, path, create_parents=True, **kwargs):
-        path = self._strip_protocol(path)
-        if path in self.store or path in self.pseudo_dirs:
-            raise FileExistsError(path)
-        if self._parent(path).strip("/") and self.isfile(self._parent(path)):
-            raise NotADirectoryError(self._parent(path))
-        if create_parents and self._parent(path).strip("/"):
-            try:
-                self.mkdir(self._parent(path), create_parents, **kwargs)
-            except FileExistsError:
-                pass
-        if path and path not in self.pseudo_dirs:
-            self.pseudo_dirs.append(path)
-
-    def makedirs(self, path, exist_ok=False):
-        try:
-            self.mkdir(path, create_parents=True)
-        except FileExistsError:
-            if not exist_ok:
-                raise
-
-    def pipe_file(self, path, value, **kwargs):
-        """Set the bytes of given file
-
-        Avoids copies of the data if possible
-        """
-        self.open(path, "wb", data=value)
-
-    def rmdir(self, path):
-        path = self._strip_protocol(path)
-        if path == "":
-            # silently avoid deleting FS root
-            return
-        if path in self.pseudo_dirs:
-            if not self.ls(path):
-                self.pseudo_dirs.remove(path)
-            else:
-                raise OSError(ENOTEMPTY, "Directory not empty", path)
-        else:
-            raise FileNotFoundError(path)
-
-    def exists(self, path, **kwargs):
-        path = self._strip_protocol(path)
-        return path in self.store or path in self.pseudo_dirs
-
-    def info(self, path, **kwargs):
-        path = self._strip_protocol(path)
-        if path in self.pseudo_dirs or any(
-            p.startswith(path + "/") for p in list(self.store) + self.pseudo_dirs
-        ):
-            return {
-                "name": path,
-                "size": 0,
-                "type": "directory",
-            }
-        elif path in self.store:
-            filelike = self.store[path]
-            return {
-                "name": path,
-                "size": filelike.size,
-                "type": "file",
-                "created": getattr(filelike, "created", None),
-            }
-        else:
-            raise FileNotFoundError(path)
-
-    def _open(
-        self,
-        path,
-        mode="rb",
-        block_size=None,
-        autocommit=True,
-        cache_options=None,
-        **kwargs,
-    ):
-        path = self._strip_protocol(path)
-        if path in self.pseudo_dirs:
-            raise IsADirectoryError(path)
-        parent = path
-        while len(parent) > 1:
-            parent = self._parent(parent)
-            if self.isfile(parent):
-                raise FileExistsError(parent)
-        if mode in ["rb", "ab", "rb+"]:
-            if path in self.store:
-                f = self.store[path]
-                if mode == "ab":
-                    # position at the end of file
-                    f.seek(0, 2)
-                else:
-                    # position at the beginning of file
-                    f.seek(0)
-                return f
-            else:
-                raise FileNotFoundError(path)
-        if mode == "wb":
-            m = MemoryFile(self, path, kwargs.get("data"))
-            if not self._intrans:
-                m.commit()
-            return m
-
-    def cp_file(self, path1, path2, **kwargs):
-        path1 = self._strip_protocol(path1)
-        path2 = self._strip_protocol(path2)
-        if self.isfile(path1):
-            self.store[path2] = MemoryFile(
-                self, path2, self.store[path1].getvalue()
-            )  # implicit copy
-        elif self.isdir(path1):
-            if path2 not in self.pseudo_dirs:
-                self.pseudo_dirs.append(path2)
-        else:
-            raise FileNotFoundError(path1)
-
-    def cat_file(self, path, start=None, end=None, **kwargs):
-        path = self._strip_protocol(path)
-        try:
-            return bytes(self.store[path].getbuffer()[start:end])
-        except KeyError:
-            raise FileNotFoundError(path)
-
-    def _rm(self, path):
-        path = self._strip_protocol(path)
-        try:
-            del self.store[path]
-        except KeyError as e:
-            raise FileNotFoundError(path) from e
-
-    def modified(self, path):
-        path = self._strip_protocol(path)
-        try:
-            return self.store[path].modified
-        except KeyError:
-            raise FileNotFoundError(path)
-
-    def created(self, path):
-        path = self._strip_protocol(path)
-        try:
-            return self.store[path].created
-        except KeyError:
-            raise FileNotFoundError(path)
-
-    def rm(self, path, recursive=False, maxdepth=None):
-        if isinstance(path, str):
-            path = self._strip_protocol(path)
-        else:
-            path = [self._strip_protocol(p) for p in path]
-        paths = self.expand_path(path, recursive=recursive, maxdepth=maxdepth)
-        for p in reversed(paths):
-            # If the expanded path doesn't exist, it is only because the expanded
-            # path was a directory that does not exist in self.pseudo_dirs. This
-            # is possible if you directly create files without making the
-            # directories first.
-            if not self.exists(p):
-                continue
-            if self.isfile(p):
-                self.rm_file(p)
-            else:
-                self.rmdir(p)
-
-
-class MemoryFile(BytesIO):
-    """A BytesIO which can't close and works as a context manager
-
-    Can initialise with data. Each path should only be active once at any moment.
-
-    No need to provide fs, path if auto-committing (default)
-    """
-
-    def __init__(self, fs=None, path=None, data=None):
-        logger.debug("open file %s", path)
-        self.fs = fs
-        self.path = path
-        self.created = datetime.utcnow()
-        self.modified = datetime.utcnow()
-        if data:
-            super().__init__(data)
-            self.seek(0)
-
-    @property
-    def size(self):
-        return self.getbuffer().nbytes
-
-    def __enter__(self):
-        return self
-
-    def close(self):
-        pass
-
-    def discard(self):
-        pass
-
-    def commit(self):
-        self.fs.store[self.path] = self
-        self.modified = datetime.utcnow()

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

@@ -1,99 +0,0 @@
-# flake8: noqa
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License
-
-from . import tqdm as _tqdm  # _tqdm is the module
-from ._cache_assets import cached_assets_path
-from ._cache_manager import (
-    CachedFileInfo,
-    CachedRepoInfo,
-    CachedRevisionInfo,
-    CacheNotFound,
-    CorruptedCacheException,
-    DeleteCacheStrategy,
-    HFCacheInfo,
-    scan_cache_dir,
-)
-from ._chunk_utils import chunk_iterable
-from ._datetime import parse_datetime
-from ._errors import (
-    BadRequestError,
-    EntryNotFoundError,
-    GatedRepoError,
-    HfHubHTTPError,
-    LocalEntryNotFoundError,
-    RepositoryNotFoundError,
-    RevisionNotFoundError,
-    hf_raise_for_status,
-)
-from ._fixes import SoftTemporaryDirectory, yaml_dump
-from ._git_credential import list_credential_helpers, set_git_credential, unset_git_credential
-from ._headers import build_hf_headers, get_token_to_send, LocalTokenNotFoundError
-from ._hf_folder import HfFolder
-from ._http import configure_http_backend, get_session, http_backoff
-from ._pagination import paginate
-from ._paths import filter_repo_objects, IGNORE_GIT_FOLDER_PATTERNS
-from ._experimental import experimental
-from ._runtime import (
-    dump_environment_info,
-    get_aiohttp_version,
-    get_fastai_version,
-    get_fastcore_version,
-    get_gradio_version,
-    get_graphviz_version,
-    get_hf_hub_version,
-    get_hf_transfer_version,
-    get_jinja_version,
-    get_numpy_version,
-    get_pillow_version,
-    get_pydantic_version,
-    get_pydot_version,
-    get_python_version,
-    get_tensorboard_version,
-    get_tf_version,
-    get_torch_version,
-    is_aiohttp_available,
-    is_fastai_available,
-    is_fastcore_available,
-    is_numpy_available,
-    is_google_colab,
-    is_gradio_available,
-    is_graphviz_available,
-    is_hf_transfer_available,
-    is_jinja_available,
-    is_notebook,
-    is_pillow_available,
-    is_pydantic_available,
-    is_pydot_available,
-    is_tensorboard_available,
-    is_tf_available,
-    is_torch_available,
-)
-from ._subprocess import capture_output, run_interactive_subprocess, run_subprocess
-from ._validators import (
-    HFValidationError,
-    smoothly_deprecate_use_auth_token,
-    validate_hf_hub_args,
-    validate_repo_id,
-)
-from .tqdm import (
-    are_progress_bars_disabled,
-    disable_progress_bars,
-    enable_progress_bars,
-    tqdm,
-    tqdm_stream_file,
-)
-from ._telemetry import send_telemetry

spaces/DorisB/streamlit-app/pages/02_Recommendation_system.py

@@ -1,433 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from PIL import Image
-import matplotlib.pyplot as plt
-import glob
-from scipy.sparse import csr_matrix
-from sklearn.neighbors import NearestNeighbors
-import pickle
-import io
-import streamlit.components.v1 as stc
-
-
-
-
-
-
-def main():
-
-    st.set_page_config(layout="wide", initial_sidebar_state='expanded')
-    st.image("images/logo-recom2.png", width=100)
-    with open('style.css') as f:
-        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
-
-    hide_menu = """
-    <style>
-    #MainMenu {
-    visibility:visible;
-    }
-
-    footer{
-        visibility:visible;
-    }
-    footer:after {
-        content: 'Recom © 2022 - Doris BAILLARD';
-        display: block;
-        position: relative;
-        color:blue;
-
-    }
-    </style>
-    """
-
-    st.markdown(hide_menu, unsafe_allow_html=True)
-
-
-    sidebar_header = '''This is a demo of Recom solution version 1.0.0. This demo gathers the main options. Please give it a try :'''
-    
-    page_options = ["Recommendations base on reviews",
-                    "Recommendations based on product similarity",
-                    "Generate email"]
-    
-    st.sidebar.info(sidebar_header)
-
-
-    
-    page_selection = st.sidebar.radio("Try", page_options)
-
-
-    #########################################################################################
-    if page_selection == "Recommendations base on reviews":
-        pid_to_idx = pd.read_pickle("data/pid_to_idx.pkl")
-        idx_to_pid = pd.read_pickle("data/idx_to_pid.pkl")
-        
-        
-        products = pd.read_pickle("data/products.pkl") 
-        lightfm_similarity = pd.read_pickle("data/lightfm-similarity.pkl") 
-        items_pivot=pd.read_pickle("data/items_pivot.pkl")
-        items_sparse = csr_matrix(items_pivot)
-        model = NearestNeighbors(algorithm="brute")
-        model.fit(items_sparse)
-
-
-        def get_product_name(pid, product):
-            try:
-                name = products.loc[products.product_ids == pid].titles.values[0]
-            except:
-                name = "Unknown"
-            return name
-
-        def get_product_id(name):
-            try:
-                product_id = products.loc[products.titles == name].product_ids.values[0]
-            except:
-                product_id = "Unknown"
-            return product_id
-
-
-        def get_sim_scores(pid):
-            idx = pid_to_idx[pid]
-            sims = lightfm_similarity[idx]
-            return sims
-
-
-        def get_ranked_recos(sims):
-            recos = []
-        
-            for idx in np.argsort(-sims):
-                pid = idx_to_pid[idx]
-                name = get_product_name(pid, products)
-                score = sims[idx]
-                recos.append((name,pid))
-            return recos
-
-
-
-
-        st.markdown('<p style="color:darkblue;font-size:160%">Products Recommender System</p>',  unsafe_allow_html=True)
-        st.markdown('This option will allow you to publish on your site recommendations of products "appreciated by other customers". These recommendations are based on the reviews of other customers. ')
-        product_list = products['titles'].values
-        selected_product = st.selectbox(
-        "Type or select a product from the dropdown",
-        product_list
-    )
-        product_id = get_product_id(selected_product)
-        sims = get_sim_scores(product_id)
-        result = get_ranked_recos(sims)[:5]
-
-        recommendation_button = st.button('Show Recommendation')
-        if recommendation_button:
-            product_id = get_product_id(selected_product)
-            sims = get_sim_scores(product_id)
-            result = get_ranked_recos(sims)[:5]
-    
-
-            with st.form("reco1"):
-                cols = st.columns((1, 3))
-                cols[0].image('images/'+result[0][1]+'.jpg', width=200)
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID: </p>',  unsafe_allow_html=True)
-                cols[1].markdown(result[0])
-                cols[1].markdown("Url : yoursiteurl")
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                with cols[1]:
-                    submitted = st.form_submit_button('Deploy')
-                    submitted_all = st.form_submit_button('Deploy All')
-
-                
-
-                
-            with st.form("reco2"):
-                cols2 = st.columns((1, 3))
-                cols2[0].image('images/'+result[1][1]+'.jpg', width=200)
-                cols2[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols2[1].text(result[1])
-                cols2[1].markdown("Url : yoursiteurl")
-                cols2[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols2[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols2[1].form_submit_button('Deploy')
-                cols2[1].form_submit_button('Deploy All')
-
-
-
-            with st.form("reco3"):
-                cols3= st.columns((1, 3))
-                cols3[0].image('images/'+result[2][1]+'.jpg', width=200)
-                cols3[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols3[1].text(result[2])
-                cols3[1].markdown("Url : yoursiteurl")
-                cols3[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols3[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols3[1].form_submit_button('Deploy')
-                cols3[1].form_submit_button('Deploy All')
-
-
-            with st.form("reco4"):
-                cols4 = st.columns((1, 3))
-                cols4[0].image('images/'+result[3][1]+'.jpg', width=200)
-                cols4[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols4[1].text(result[3])
-                cols4[1].markdown("Url : yoursiteurl")
-                cols4[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols4[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols4[1].form_submit_button('Deploy')
-                cols4[1].form_submit_button('Deploy All')
-
-
-
-            with st.form("reco5"):        
-                cols5 = st.columns((1, 3))
-                cols5[0].image('images/'+result[4][1]+'.jpg', width=200)
-                cols5[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols5[1].text(result[4])
-                cols5[1].markdown("Url : yoursiteurl")
-                cols5[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols5[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols5[1].form_submit_button('Deploy')
-                cols5[1].form_submit_button('Deploy All')
-
-                st.markdown("<hr/>", unsafe_allow_html=True)
-                st.success("Successfuly Deployed !")
-                st.success("output 'Deploy' :")
-                st.write("Clients also liked : ")
-                st.image('images/'+result[4][1]+'.jpg', width=150)
-                st.markdown(result[0][0])
-
-                st.markdown("<hr/>", unsafe_allow_html=True)
-                st.success("Successfuly Deployed !")
-                st.success("output 'Deploy All' :")
-
-                st.write("Clients also liked : ")
-                cols_deploy = st.columns(5)           
-                cols_deploy[0].image('images/'+result[0][1]+'.jpg', width=150)
-                cols_deploy[0].markdown(result[0][0])
-                cols_deploy[1].image('images/'+result[1][1]+'.jpg', width=150)
-                cols_deploy[1].markdown(result[1][0])
-                cols_deploy[2].image('images/'+result[2][1]+'.jpg', width=150)
-                cols_deploy[2].markdown(result[2][0])
-                cols_deploy[3].image('images/'+result[3][1]+'.jpg', width=150)
-                cols_deploy[3].markdown(result[3][0])
-                cols_deploy[4].image('images/'+result[4][1]+'.jpg', width=150)
-                cols_deploy[4].markdown(result[4][0])
-
-#########################################################################################
-
-
-    if page_selection == "Recommendations based on product similarity": 
-        products = pd.read_pickle("data/products.pkl") 
-        feature_list = np.array(pickle.load(open('data/embeddings.pkl','rb')))
-        filenames = pickle.load(open('data/filenamesdf.pkl','rb'))
-
-
-        def recommend(features,feature_list):
-            neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-            neighbors.fit(feature_list)
-
-            distances, indices = neighbors.kneighbors([features])
-
-            return indices
-
-        def reco2(indices):
-            for i in range(len(indices)):
-                cols = st.columns((1, 3))
-                cols[1].markdown(filenames.index[indices[i]])
-                cols[1].markdown(filenames.titles[indices[i]])
-                cols[0].image(filenames.image_path[indices[i]].tolist())
-
-
-        st.markdown("<p style='color:darkblue;font-size:160%'>Recommender System - image similarity</p>", unsafe_allow_html=True)
-        st.markdown('This feature will enable you to publish on your site recommendations of "similar products". These recommendations are based on similarities between product images.')
-        product_list = filenames['titles'].values
-        
-        selected_product = st.selectbox("Type or select a product from the dropdown",product_list)
-        
-        if st.button('Recommendation'):
-            
-            id = np.where(selected_product == product_list)
-            id2 = int(id[0])
-
-            result= recommend(feature_list[id2],feature_list)
-            results = list(result)
-
-            with st.form("reco1"):
-                cols = st.columns((1, 3))
-                cols[0].image(filenames.image_path[results[0]].tolist()[0], width=200)
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols[1].markdown(filenames.titles[results[0]].tolist()[0])
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols[1].form_submit_button('Deploy')
-
-            with st.form("reco2"):
-                cols = st.columns((1, 3))
-                cols[0].image(filenames.image_path[results[0]].tolist()[1], width=200)
-                cols[1].markdown('<h4 style="color:#3498db;">Product Name and ID:</h2>', unsafe_allow_html=True)
-                cols[1].markdown(filenames.titles[results[0]].tolist()[1])
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols[1].form_submit_button('Deploy')
-
-
-            with st.form("reco3"):
-                cols = st.columns((1, 3))
-                cols[0].image(filenames.image_path[results[0]].tolist()[2], width=200)
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols[1].markdown(filenames.titles[results[0]].tolist()[2])
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols[1].form_submit_button('Deploy')
-
-            with st.form("reco4"):
-                cols = st.columns((1, 3))
-                cols[0].image(filenames.image_path[results[0]].tolist()[3], width=200)
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols[1].markdown(filenames.titles[results[0]].tolist()[3])
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols[1].form_submit_button('Deploy')
-
-            with st.form("reco5"):
-                cols = st.columns((1, 3))
-                cols[0].image(filenames.image_path[results[0]].tolist()[4], width=200)
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Product Name and ID:</p>', unsafe_allow_html=True)
-                cols[1].markdown(filenames.titles[results[0]].tolist()[4])
-                cols[1].markdown('<p style="color:#3498db;font-size:160%">Description :</p>', unsafe_allow_html=True)
-                cols[1].text('Lorem ipsum dolor sit amet, consectetur adipiscing elit,\nsed do eiusmod tempor incididunt \nut labore et dolore magna aliqua.\nUt enim ad minim veniam, quis nostrud exercitation ullamco.')
-                cols[1].form_submit_button('Deploy')
-                cols[1].form_submit_button('Deploy All')
-
-                st.markdown("<hr/>", unsafe_allow_html=True)
-                st.success("Successfuly Deployed !")
-                st.success("output 'Deploy' :")
-                st.write("Similar products : ")
-                st.image(filenames.image_path[results[0]].tolist()[0], width=150)
-                st.markdown(filenames.titles[results[0]].tolist()[4])
-
-                st.markdown("<hr/>", unsafe_allow_html=True)
-                st.success("Successfuly Deployed !")
-                st.success("output 'Deploy All' :")
-                st.write("Similar products : ")
-                cols_deploy = st.columns(5)           
-                cols_deploy[0].image(filenames.image_path[results[0]].tolist()[0], width=150)
-                cols_deploy[0].markdown(filenames.titles[results[0]].tolist()[0])
-                cols_deploy[1].image(filenames.image_path[results[0]].tolist()[1], width=150)
-                cols_deploy[1].markdown(filenames.titles[results[0]].tolist()[1])
-                cols_deploy[2].image(filenames.image_path[results[0]].tolist()[2], width=150)
-                cols_deploy[2].markdown(filenames.titles[results[0]].tolist()[2])
-                cols_deploy[3].image(filenames.image_path[results[0]].tolist()[3], width=150)
-                cols_deploy[3].markdown(filenames.titles[results[0]].tolist()[3])
-                cols_deploy[4].image(filenames.image_path[results[0]].tolist()[4], width=150)
-                cols_deploy[4].markdown(filenames.titles[results[0]].tolist()[4])
-
-
-#########################################################################################
-
-
-    if page_selection == "Generate email": 
-        user_cart = pd.read_pickle('data/user_cart.pkl')
-        name_list = user_cart['user_name'].values
-        feature_list = np.array(pickle.load(open('data/embeddings.pkl','rb')))
-        filenames = pickle.load(open('data/filenamesdf.pkl','rb'))
-
-
-        def recommend(features,feature_list):
-            neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-            neighbors.fit(feature_list)
-
-            distances, indices = neighbors.kneighbors([features])
-
-            return indices
-
-        def reco2(indices):
-            for i in range(len(indices)):
-                cols = st.columns((1, 3))
-                cols[1].markdown(filenames.index[indices[i]])
-                cols[1].markdown(filenames.titles[indices[i]])
-                cols[0].image(filenames.image_path[indices[i]].tolist())
-        
-        st.markdown("<p style='color:darkblue;font-size:160%'>Mail Generator</p>", unsafe_allow_html=True)
-        st.markdown('With this feature you will be able to generate automatic emails to ask for feedback from old and new customers.\nThis email will include a request for feedback on the last product purchased, \nas well as product recommendations.', unsafe_allow_html=True)
-
-        selected_user = st.selectbox("Type or select a user from the dropdown",name_list)
-    
-
-        if st.button('generate mailing'):
-
-            name = np.where(selected_user == user_cart['user_name'].values)
-            name_id = int(name[0])
-            reco_mailing2 = recommend(feature_list[name_id],feature_list)
-            reco_mailing2 = list(reco_mailing2)           
-
-            st.markdown('>To: '+ selected_user+'', unsafe_allow_html=True)
-            st.markdown("<hr/>", unsafe_allow_html=True)
-
-            st.markdown("<p style='color:Black;font-size:160%'>Hello "+ selected_user+' :) !</p', unsafe_allow_html=True)
-
-
-            st.markdown('We are constantly striving to improve, and we’d love to hear from you about the following your last command:')
-
-            st.markdown('<p style="font-size:120%">Rate your purchased product:</p>', unsafe_allow_html=True)
-            #[a scale from 1 to 5]
-            cols= st.columns((3))
-            cols[0].empty()
-            cols[1].markdown(user_cart.titles[name_id])
-            cols[1].image(user_cart.image_path[name_id], width=200)
-            cols[1].markdown(":star::star::star::star::star:")
-
-            cols[1].button('Rate this article on company.com')
-            cols[2].empty()
-            
-            st.text('\n\n')           
-              
-
-            
-            st.markdown('Your feedback helps us improve and reach more great customers like you.')
-
-           
-            st.text('\n\n')           
-            st.markdown("<hr/>", unsafe_allow_html=True)
-
-            st.markdown('<p style="font-size:120%">Discover products in the same category: </p>', unsafe_allow_html=True)
-            st.text('\n\n')
-            cols_mail = st.columns((7))
-            cols_mail[0].empty()
-            cols_mail[1].image(filenames.image_path[reco_mailing2[0]].tolist()[0], width=100)
-            cols_mail[1].markdown(filenames.titles[reco_mailing2[0]].tolist()[0])
-            cols_mail[1].markdown(":star::star::star::star::star:")
-            cols_mail[2].image(filenames.image_path[reco_mailing2[0]].tolist()[1], width=100)
-            cols_mail[2].markdown(filenames.titles[reco_mailing2[0]].tolist()[1])
-            cols_mail[2].markdown(":star::star::star::star:")
-            cols_mail[3].image(filenames.image_path[reco_mailing2[0]].tolist()[2], width=100)
-            cols_mail[3].markdown(filenames.titles[reco_mailing2[0]].tolist()[2])
-            cols_mail[3].markdown(":star::star::star::star::star:")
-            cols_mail[4].image(filenames.image_path[reco_mailing2[0]].tolist()[3], width=100)
-            cols_mail[4].markdown(filenames.titles[reco_mailing2[0]].tolist()[3])
-            cols_mail[4].markdown(":star::star::star::star:")
-            cols_mail[5].image(filenames.image_path[reco_mailing2[0]].tolist()[4], width=100)
-            cols_mail[5].markdown(filenames.titles[reco_mailing2[0]].tolist()[4])
-            cols_mail[5].markdown(":star::star::star::star::star:")
-            cols_mail[6].empty()
-
-            st.text('\n\n')
-            st.markdown("<hr/>", unsafe_allow_html=True)
-            st.markdown('<p style="font-size:120%">Discover our new products: </p>', unsafe_allow_html=True)
-            st.text('\n\n')
-            cols_mail2 = st.columns((4))
-          
-            cols_mail2[0].image('images/velo.jpg', width=200)
-            cols_mail2[1].image('images/sac.jpg', width=200)
-            cols_mail2[2].image('images/shoes.jpg', width=200)
-            cols_mail2[3].image('images/survet.jpg', width=200)
-            
-            st.text('\n\n\n\n')
-            st.markdown("<hr/>", unsafe_allow_html=True)
-
-            st.markdown('Always yours,')
-            st.markdown('[company] team')
-
-
-
-
-if __name__ == '__main__':
-    main()

spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/persistence.py

@@ -1,262 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# 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.
-
-"""Facilities for pickling Python code alongside other data.
-
-The pickled code is automatically imported into a separate Python module
-during unpickling. This way, any previously exported pickles will remain
-usable even if the original code is no longer available, or if the current
-version of the code is not consistent with what was originally pickled."""
-
-import sys
-import pickle
-import io
-import inspect
-import copy
-import uuid
-import types
-import dnnlib
-
-# ----------------------------------------------------------------------------
-
-_version = 6         # internal version number
-_decorators = set()     # {decorator_class, ...}
-_import_hooks = []        # [hook_function, ...]
-_module_to_src_dict = dict()    # {module: src, ...}
-_src_to_module_dict = dict()    # {src: module, ...}
-
-# ----------------------------------------------------------------------------
-
-
-def persistent_class(orig_class):
-    r"""Class decorator that extends a given class to save its source code
-    when pickled.
-
-    Example:
-
-        from torch_utils import persistence
-
-        @persistence.persistent_class
-        class MyNetwork(torch.nn.Module):
-            def __init__(self, num_inputs, num_outputs):
-                super().__init__()
-                self.fc = MyLayer(num_inputs, num_outputs)
-                ...
-
-        @persistence.persistent_class
-        class MyLayer(torch.nn.Module):
-            ...
-
-    When pickled, any instance of `MyNetwork` and `MyLayer` will save its
-    source code alongside other internal state (e.g., parameters, buffers,
-    and submodules). This way, any previously exported pickle will remain
-    usable even if the class definitions have been modified or are no
-    longer available.
-
-    The decorator saves the source code of the entire Python module
-    containing the decorated class. It does *not* save the source code of
-    any imported modules. Thus, the imported modules must be available
-    during unpickling, also including `torch_utils.persistence` itself.
-
-    It is ok to call functions defined in the same module from the
-    decorated class. However, if the decorated class depends on other
-    classes defined in the same module, they must be decorated as well.
-    This is illustrated in the above example in the case of `MyLayer`.
-
-    It is also possible to employ the decorator just-in-time before
-    calling the constructor. For example:
-
-        cls = MyLayer
-        if want_to_make_it_persistent:
-            cls = persistence.persistent_class(cls)
-        layer = cls(num_inputs, num_outputs)
-
-    As an additional feature, the decorator also keeps track of the
-    arguments that were used to construct each instance of the decorated
-    class. The arguments can be queried via `obj.init_args` and
-    `obj.init_kwargs`, and they are automatically pickled alongside other
-    object state. A typical use case is to first unpickle a previous
-    instance of a persistent class, and then upgrade it to use the latest
-    version of the source code:
-
-        with open('old_pickle.pkl', 'rb') as f:
-            old_net = pickle.load(f)
-        new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
-        misc.copy_params_and_buffers(old_net, new_net, require_all=True)
-    """
-    assert isinstance(orig_class, type)
-    if is_persistent(orig_class):
-        return orig_class
-
-    assert orig_class.__module__ in sys.modules
-    orig_module = sys.modules[orig_class.__module__]
-    orig_module_src = _module_to_src(orig_module)
-
-    class Decorator(orig_class):
-        _orig_module_src = orig_module_src
-        _orig_class_name = orig_class.__name__
-
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            self._init_args = copy.deepcopy(args)
-            self._init_kwargs = copy.deepcopy(kwargs)
-            assert orig_class.__name__ in orig_module.__dict__
-            _check_pickleable(self.__reduce__())
-
-        @property
-        def init_args(self):
-            return copy.deepcopy(self._init_args)
-
-        @property
-        def init_kwargs(self):
-            return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
-
-        def __reduce__(self):
-            fields = list(super().__reduce__())
-            fields += [None] * max(3 - len(fields), 0)
-            if fields[0] is not _reconstruct_persistent_obj:
-                meta = dict(type='class', version=_version, module_src=self._orig_module_src,
-                            class_name=self._orig_class_name, state=fields[2])
-                fields[0] = _reconstruct_persistent_obj  # reconstruct func
-                fields[1] = (meta,)  # reconstruct args
-                fields[2] = None  # state dict
-            return tuple(fields)
-
-    Decorator.__name__ = orig_class.__name__
-    _decorators.add(Decorator)
-    return Decorator
-
-# ----------------------------------------------------------------------------
-
-
-def is_persistent(obj):
-    r"""Test whether the given object or class is persistent, i.e.,
-    whether it will save its source code when pickled.
-    """
-    try:
-        if obj in _decorators:
-            return True
-    except TypeError:
-        pass
-    return type(obj) in _decorators  # pylint: disable=unidiomatic-typecheck
-
-# ----------------------------------------------------------------------------
-
-
-def import_hook(hook):
-    r"""Register an import hook that is called whenever a persistent object
-    is being unpickled. A typical use case is to patch the pickled source
-    code to avoid errors and inconsistencies when the API of some imported
-    module has changed.
-
-    The hook should have the following signature:
-
-        hook(meta) -> modified meta
-
-    `meta` is an instance of `dnnlib.EasyDict` with the following fields:
-
-        type:       Type of the persistent object, e.g. `'class'`.
-        version:    Internal version number of `torch_utils.persistence`.
-        module_src  Original source code of the Python module.
-        class_name: Class name in the original Python module.
-        state:      Internal state of the object.
-
-    Example:
-
-        @persistence.import_hook
-        def wreck_my_network(meta):
-            if meta.class_name == 'MyNetwork':
-                print('MyNetwork is being imported. I will wreck it!')
-                meta.module_src = meta.module_src.replace("True", "False")
-            return meta
-    """
-    assert callable(hook)
-    _import_hooks.append(hook)
-
-# ----------------------------------------------------------------------------
-
-
-def _reconstruct_persistent_obj(meta):
-    r"""Hook that is called internally by the `pickle` module to unpickle
-    a persistent object.
-    """
-    meta = dnnlib.EasyDict(meta)
-    meta.state = dnnlib.EasyDict(meta.state)
-    for hook in _import_hooks:
-        meta = hook(meta)
-        assert meta is not None
-
-    assert meta.version == _version
-    module = _src_to_module(meta.module_src)
-
-    assert meta.type == 'class'
-    orig_class = module.__dict__[meta.class_name]
-    decorator_class = persistent_class(orig_class)
-    obj = decorator_class.__new__(decorator_class)
-
-    setstate = getattr(obj, '__setstate__', None)
-    if callable(setstate):
-        setstate(meta.state)  # pylint: disable=not-callable
-    else:
-        obj.__dict__.update(meta.state)
-    return obj
-
-# ----------------------------------------------------------------------------
-
-
-def _module_to_src(module):
-    r"""Query the source code of a given Python module.
-    """
-    src = _module_to_src_dict.get(module, None)
-    if src is None:
-        src = inspect.getsource(module)
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-    return src
-
-
-def _src_to_module(src):
-    r"""Get or create a Python module for the given source code.
-    """
-    module = _src_to_module_dict.get(src, None)
-    if module is None:
-        module_name = "_imported_module_" + uuid.uuid4().hex
-        module = types.ModuleType(module_name)
-        sys.modules[module_name] = module
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-        exec(src, module.__dict__)  # pylint: disable=exec-used
-    return module
-
-# ----------------------------------------------------------------------------
-
-
-def _check_pickleable(obj):
-    r"""Check that the given object is pickleable, raising an exception if
-    it is not. This function is expected to be considerably more efficient
-    than actually pickling the object.
-    """
-    def recurse(obj):
-        if isinstance(obj, (list, tuple, set)):
-            return [recurse(x) for x in obj]
-        if isinstance(obj, dict):
-            return [[recurse(x), recurse(y)] for x, y in obj.items()]
-        if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
-            return None  # Python primitive types are pickleable.
-        if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']:
-            return None  # NumPy arrays and PyTorch tensors are pickleable.
-        if is_persistent(obj):
-            # Persistent objects are pickleable, by virtue of the constructor check.
-            return None
-        return obj
-    with io.BytesIO() as f:
-        pickle.dump(recurse(obj), f)
-
-# ----------------------------------------------------------------------------

spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/include/kalmanFilter.h

@@ -1,31 +0,0 @@
-#pragma once
-
-#include "dataType.h"
-
-namespace byte_kalman
-{
-	class KalmanFilter
-	{
-	public:
-		static const double chi2inv95[10];
-		KalmanFilter();
-		KAL_DATA initiate(const DETECTBOX& measurement);
-		void predict(KAL_MEAN& mean, KAL_COVA& covariance);
-		KAL_HDATA project(const KAL_MEAN& mean, const KAL_COVA& covariance);
-		KAL_DATA update(const KAL_MEAN& mean,
-			const KAL_COVA& covariance,
-			const DETECTBOX& measurement);
-
-		Eigen::Matrix<float, 1, -1> gating_distance(
-			const KAL_MEAN& mean,
-			const KAL_COVA& covariance,
-			const std::vector<DETECTBOX>& measurements,
-			bool only_position = false);
-
-	private:
-		Eigen::Matrix<float, 8, 8, Eigen::RowMajor> _motion_mat;
-		Eigen::Matrix<float, 4, 8, Eigen::RowMajor> _update_mat;
-		float _std_weight_position;
-		float _std_weight_velocity;
-	};
-}

spaces/ECCV2022/bytetrack/tutorials/ctracker/generate_half_csv.py

@@ -1,37 +0,0 @@
-import os
-import numpy as np
-prefix_dir = 'MOT17/'
-root_dir = 'train/'
-result_csv = 'train_half_annots.csv'
-train_half_set = {2: 301, 4: 526, 5:419, 9:263, 10:328, 11:451, 13:376}
-fout = open(result_csv, 'w')
-
-for data_name in sorted(os.listdir(prefix_dir + root_dir)):
-	print(data_name)
-	gt_path = os.path.join(prefix_dir, root_dir, data_name, 'gt', 'gt.txt')
-	# print(gt_path)
-	data_raw = np.loadtxt(gt_path, delimiter=',', dtype='float', usecols=(0,1,2,3,4,5,6,7,8))
-
-	data_sort = data_raw[np.lexsort(data_raw[:,::-1].T)]
-	visible_raw = data_sort[:,8]
-	# print(data_sort)
-	# print(data_sort[-1, 0])
-	img_num = data_sort[-1, 0]
-
-	# print(data_sort.shape[0])
-	box_num = data_sort.shape[0]
-
-	person_box_num = np.sum(data_sort[:,6] == 1)
-	# print(person_box_num)
-# 	import ipdb; ipdb.set_trace()
-	for i in range(box_num):
-		c = int(data_sort[i, 6])
-		v = visible_raw[i]
-		img_index = int(data_sort[i, 0])
-		if c == 1 and v > 0.1 and img_index < train_half_set[int(data_name[-2:])]:
-			img_index = int(data_sort[i, 0])
-			img_name = data_name + '/img1/' + str(img_index).zfill(6) + '.jpg'
-			print(root_dir + img_name + ', ' + str(int(data_sort[i, 1])) + ', ' + str(data_sort[i, 2]) + ', ' + str(data_sort[i, 3]) + ', ' + str(data_sort[i, 2] + data_sort[i, 4]) + ', ' + str(data_sort[i, 3] + data_sort[i, 5]) + ', person\n')
-			fout.write(root_dir + img_name + ', ' + str(int(data_sort[i, 1])) + ', ' + str(data_sort[i, 2]) + ', ' + str(data_sort[i, 3]) + ', ' + str(data_sort[i, 2] + data_sort[i, 4]) + ', ' + str(data_sort[i, 3] + data_sort[i, 5]) + ', person\n')
-
-fout.close()

spaces/Eddevs/brian-challenge/app.py

@@ -1,98 +0,0 @@
-import streamlit as st
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-
-from fill_in_summary import FillInSummary
-from paraphrase import PegasusParaphraser
-import question_gen as q
-
-
-default_text = "Apple was founded as Apple Computer Company on April 1, 1976, by Steve Jobs, Steve Wozniak and Ronald " \
-               "Wayne to develop and sell Wozniak's Apple I personal computer. It was incorporated by Jobs and " \
-               "Wozniak as Apple Computer, Inc. in 1977 and the company's next computer, the Apple II became a best " \
-               "seller. Apple went public in 1980, to instant financial success. The company went onto develop new " \
-               "computers featuring innovative graphical user interfaces, including the original Macintosh, " \
-               "announced in a critically acclaimed advertisement, '1984', directed by Ridley Scott. By 1985, " \
-               "the high cost of its products and power struggles between executives caused problems. Wozniak stepped " \
-               "back from Apple amicably, while Jobs resigned to found NeXT, taking some Apple employees with him. "
-
-default_text2 = "The board of directors instructed Sculley to contain Jobs and his ability to launch expensive forays " \
-                "into untested products "
-
-
-
-st.set_page_config(layout="centered")
-
-st.header('Question Gen/Paraph/Summarizer by Ed-Devs')
-
-st.write('The Goal of this Space is to help educators lower recognisability of the assessment questions, and enable students achieve higher learning goals, and enable students to achieve higher learning goals by doing so.')
-
-"""
-* You can Generate Question by inputing Context 🤩 
-* Paraphrase the Questions you already have 😮
-* Summarise Section(s) of Course Materials to Generate Question by inputing that as Context 🤯 
-* Remove Entities From Context and Generate Fill in the Blank Questions 🤓
-"""
-
-st.write('You can select the options from below')
-
-
-select = st.selectbox('Type', ['Question Generator', 'Paraphrasing', 'Summarization', 'Fill in the blank'])
-
-if select == "Question Generator":
-    with st.form("question_gen"):
-        left_column, right_column = st.columns(2)
-        num_seq = left_column.slider('Question Count', 0, 10, 3)
-        beams = right_column.slider('Beams', 0, 10, 5)
-        max_length = st.slider('Max Length', 0, 1024, 300)
-        text_input = st.text_area("Input Text", value=default_text)
-
-        submitted = st.form_submit_button("Generate")
-        if submitted:
-            with st.spinner('Wait for it...'):
-                question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
-                question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')
-
-                result = q.get_question(text_input, "", question_model, question_tokenizer, num_seq, beams, max_length)
-            st.write(result)
-
-
-elif select == "Summarization":
-    with st.form("summarization"):
-        text_input = st.text_area("Input Text", value=default_text)
-
-        submitted = st.form_submit_button("Generate")
-
-        if submitted:
-            with st.spinner('Wait for it...'):
-                result = FillInSummary().summarize(text_input)
-            st.write(text_input)
-
-
-elif select == "Fill in the blank":
-    with st.form("fill_in_the_blank"):
-        text_input = st.text_area("Input Text", value=default_text)
-
-        submitted = st.form_submit_button("Generate")
-
-        if submitted:
-            with st.spinner('Wait for it...'):
-                fill = FillInSummary()
-                result = fill.summarize(text_input)
-                result = fill.blank_ne_out(result)
-            st.write(result)
-
-
-elif select == "Paraphrasing":
-    with st.form("paraphrasing"):
-        left_column, right_column = st.columns(2)
-        count = left_column.slider('Count', 0, 10, 3)
-        temperature = right_column.slider('Temperature', 0.0, 10.0, 1.5)
-        text_input = st.text_area("Input Text", value=default_text2)
-
-        submitted = st.form_submit_button("Generate")
-
-        if submitted:
-            with st.spinner('Wait for it...'):
-                paraphrase_model = PegasusParaphraser(num_return_sequences=count, temperature=temperature)
-                result = paraphrase_model.paraphrase(text_input)
-            st.write(result)